diff --git a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/__init__.py b/spaces/101-5/gpt4free/g4f/.v1/gpt4free/__init__.py
deleted file mode 100644
index bcc03a3b93386abe1089181bb979de6b350dd554..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/__init__.py
+++ /dev/null
@@ -1,103 +0,0 @@
-from enum import Enum
-
-from gpt4free import forefront
-from gpt4free import quora
-from gpt4free import theb
-from gpt4free import usesless
-from gpt4free import you
-from gpt4free import aicolors
-from gpt4free import deepai
-
-
-class Provider(Enum):
-    """An enum representing  different providers."""
-
-    You = "you"
-    Poe = "poe"
-    ForeFront = "fore_front"
-    Theb = "theb"
-    UseLess = "useless"
-    AiColors = "ai_colors"
-    DeepAI = "deepai"
-
-
-class Completion:
-    """This class will be used for invoking the given provider"""
-
-    @staticmethod
-    def create(provider: Provider, prompt: str, **kwargs) -> str:
-        """
-        Invokes the given provider with given prompt and addition arguments and returns the string response
-
-        :param provider: an enum representing the provider to use while invoking
-        :param prompt: input provided by the user
-        :param kwargs:  Additional keyword arguments to pass to the provider while invoking
-        :return: A string representing the response from the provider
-        """
-        if provider == Provider.Poe:
-            return Completion.__poe_service(prompt, **kwargs)
-        elif provider == Provider.You:
-            return Completion.__you_service(prompt, **kwargs)
-        elif provider == Provider.ForeFront:
-            return Completion.__fore_front_service(prompt, **kwargs)
-        elif provider == Provider.Theb:
-            return Completion.__theb_service(prompt, **kwargs)
-        elif provider == Provider.UseLess:
-            return Completion.__useless_service(prompt, **kwargs)
-        elif provider == Provider.AiColors:
-            return Completion.__ai_colors_service(prompt, **kwargs)
-        elif provider == Provider.DeepAI:
-            return Completion.__deepai_service(prompt, **kwargs)
-        else:
-            raise Exception("Provider not exist, Please try again")
-
-    @staticmethod
-    def __ai_colors_service(prompt: str):
-        return aicolors.Completion.create(prompt=prompt)
-
-    @staticmethod
-    def __useless_service(prompt: str, **kwargs) -> str:
-        return usesless.Completion.create(prompt=prompt, **kwargs)
-
-    @staticmethod
-    def __you_service(prompt: str, **kwargs) -> str:
-        return you.Completion.create(prompt, **kwargs).text
-
-    @staticmethod
-    def __poe_service(prompt: str, **kwargs) -> str:
-        return quora.Completion.create(prompt=prompt, **kwargs).text
-
-    @staticmethod
-    def __fore_front_service(prompt: str, **kwargs) -> str:
-        return forefront.Completion.create(prompt=prompt, **kwargs).text
-
-    @staticmethod
-    def __theb_service(prompt: str, **kwargs):
-        return "".join(theb.Completion.create(prompt=prompt))
-
-    @staticmethod
-    def __deepai_service(prompt: str, **kwargs):
-        return "".join(deepai.Completion.create(prompt=prompt))
-
-
-class ChatCompletion:
-    """This class is used to execute a chat completion for a specified provider"""
-
-    @staticmethod
-    def create(provider: Provider, messages: list, **kwargs) -> str:
-        """
-        Invokes the given provider with given chat messages and addition arguments and returns the string response
-
-        :param provider: an enum representing the provider to use while invoking
-        :param messages: a list of chat messages, see the OpenAI docs for how to format this (https://platform.openai.com/docs/guides/chat/introduction)
-        :param kwargs:  Additional keyword arguments to pass to the provider while invoking
-        :return: A string representing the response from the provider
-        """
-        if provider == Provider.DeepAI:
-            return ChatCompletion.__deepai_service(messages, **kwargs)
-        else:
-            raise Exception("Provider not exist, Please try again")
-
-    @staticmethod
-    def __deepai_service(messages: list, **kwargs):
-        return "".join(deepai.ChatCompletion.create(messages=messages))
diff --git a/spaces/1368565466ki/Satdia/attentions.py b/spaces/1368565466ki/Satdia/attentions.py
deleted file mode 100644
index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000
--- a/spaces/1368565466ki/Satdia/attentions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/1368565466ki/ZSTRD/monotonic_align/__init__.py b/spaces/1368565466ki/ZSTRD/monotonic_align/__init__.py
deleted file mode 100644
index e97eecc595dd3bd97d0104ec62799e2e5efea57c..0000000000000000000000000000000000000000
--- a/spaces/1368565466ki/ZSTRD/monotonic_align/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-
-def maximum_path(neg_cent, mask):
-    """ numba optimized version.
-    neg_cent: [b, t_t, t_s]
-    mask: [b, t_t, t_s]
-    """
-    device = neg_cent.device
-    dtype = neg_cent.dtype
-    neg_cent = neg_cent.data.cpu().numpy().astype(float32)
-    path = zeros(neg_cent.shape, dtype=int32)
-
-    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
-    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
-    maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
-    return from_numpy(path).to(device=device, dtype=dtype)
diff --git a/spaces/17TheWord/RealESRGAN/app.py b/spaces/17TheWord/RealESRGAN/app.py
deleted file mode 100644
index 97c59221c429e335c3a2e3413c11cc155d5b6122..0000000000000000000000000000000000000000
--- a/spaces/17TheWord/RealESRGAN/app.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import os
-os.system("pip install gradio==2.9b23")
-import random
-import gradio as gr
-from PIL import Image
-import torch
-from random import randint
-import sys
-from subprocess import call
-import psutil
-
-
-
-
-torch.hub.download_url_to_file('http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution_files/100075_lowres.jpg', 'bear.jpg')
-  
-    
-def run_cmd(command):
-    try:
-        print(command)
-        call(command, shell=True)
-    except KeyboardInterrupt:
-        print("Process interrupted")
-        sys.exit(1)
-run_cmd("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P .")
-run_cmd("pip install basicsr")
-run_cmd("pip freeze")
-
-os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P .")
-
-
-def inference(img,mode):
-    _id = randint(1, 10000)
-    INPUT_DIR = "/tmp/input_image" + str(_id) + "/"
-    OUTPUT_DIR = "/tmp/output_image" + str(_id) + "/"
-    run_cmd("rm -rf " + INPUT_DIR)
-    run_cmd("rm -rf " + OUTPUT_DIR)
-    run_cmd("mkdir " + INPUT_DIR)
-    run_cmd("mkdir " + OUTPUT_DIR)
-    basewidth = 256
-    wpercent = (basewidth/float(img.size[0]))
-    hsize = int((float(img.size[1])*float(wpercent)))
-    img = img.resize((basewidth,hsize), Image.ANTIALIAS)
-    img.save(INPUT_DIR + "1.jpg", "JPEG")
-    if mode == "base":
-        run_cmd("python inference_realesrgan.py -n RealESRGAN_x4plus -i "+ INPUT_DIR + " -o " + OUTPUT_DIR)
-    else:
-        os.system("python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i "+ INPUT_DIR + " -o " + OUTPUT_DIR)
-    return os.path.join(OUTPUT_DIR, "1_out.jpg")
-
-
-
-        
-title = "Real-ESRGAN"
-description = "Gradio demo for Real-ESRGAN. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once"
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2107.10833'>Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data</a> | <a href='https://github.com/xinntao/Real-ESRGAN'>Github Repo</a></p>"
-
-gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="pil", label="Input"),gr.inputs.Radio(["base","anime"], type="value", default="base", label="model type")], 
-    gr.outputs.Image(type="file", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    examples=[
-    ['bear.jpg','base'],
-    ['anime.png','anime']
-    ]).launch()
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cedar Noise Reduction Plugin.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cedar Noise Reduction Plugin.md
deleted file mode 100644
index e9123f835a0c0525f0b3110168da1b664457829a..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cedar Noise Reduction Plugin.md	
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-<br />
-<br> - Overview of Cedar noise reduction plugin: describe its features, benefits, and applications | | H2: How to Use Cedar Noise Reduction Plugin for Audio Restoration | - Step 1: Choose the right plugin for your needs: compare different Cedar products and their capabilities <br> - Step 2: Load the plugin in your DAW or audio editor: show how to install and activate the plugin <br> - Step 3: Adjust the settings and parameters: explain how to use the learn function, the noise attenuation knob, and other controls <br> - Step 4: Preview and apply the noise reduction: demonstrate the results and tips for fine-tuning | | H2: How to Use Cedar Noise Reduction Plugin for Live Sound | - Step 1: Connect the plugin to your audio interface or mixer: show how to use the analog, digital, or Dante inputs and outputs <br> - Step 2: Set up the plugin for live mode: explain how to use the adaptive mode, the freeze function, and other features <br> - Step 3: Monitor and control the noise reduction: show how to use the meters, indicators, and bypass switch <br> - Step 4: Enjoy a cleaner, brighter, and louder sound: demonstrate the benefits and advantages of using the plugin for live sound | | H2: How to Use Cedar Noise Reduction Plugin for Post Production | - Step 1: Import your audio file or project into the standalone RX Audio Editor: show how to use the file browser, the waveform display, and the spectral view <br> - Step 2: Select the noise reduction module from the RX menu: explain how to choose between DNS 2, DNS 4, or DNS 8D modules <br> - Step 3: Edit your audio with advanced tools and features: show how to use the spectral repair, dialogue isolate, de-reverb, de-click, de-hum, and other tools <br> - Step 4: Export your audio file or project with improved quality: demonstrate how to use the batch processor, the metadata editor, and the format options | | H2: How to Compare Cedar Noise Reduction Plugin with Other Software | - Criteria for comparison: list some factors to consider when comparing noise reduction software, such as performance, quality, ease of use, price, support, etc. <br> - Examples of other software: mention some popular alternatives to Cedar noise reduction plugin, such as iZotope RX 8, Accusonus ERA, SoundSoap 5, etc. <br> - Pros and cons of each software: highlight the strengths and weaknesses of each software based on the criteria <br> - Conclusion: summarize which software is best suited for different scenarios and preferences | | H2: How to Get Cedar Noise Reduction Plugin and Learn More About It | - Pricing and availability: provide information on how to purchase or rent Cedar noise reduction plugin, as well as its system requirements and compatibility <br> - Resources and support: provide links to Cedar's official website, user manual, video tutorials, FAQs, customer service, etc. <br> - Testimonials and reviews: provide some quotes from satisfied customers or reputable sources that praise Cedar noise reduction plugin | | H2: Conclusion | - Recap the main points of the article: summarize what is Cedar noise reduction plugin, how to use it for different purposes, how to compare it with other software, and how to get it and learn more about it <br> - Call to action: encourage readers to try out Cedar noise reduction plugin for themselves or contact Cedar for more information | Article with HTML formatting <h1>What is Cedar Noise Reduction Plugin and Why You Need It</h1>
- <p>If you are involved in any kind of audio production, whether it is recording, mixing, mastering, broadcasting, live sound, or post production, you know how important it is to have a clean and clear sound. However, you also know how challenging it can be to achieve that sound in real-world situations, where you have to deal with various types of noise that can ruin your audio quality.</p>
- <p>Noise can be defined as any unwanted sound that interferes with your desired sound. It can come from different sources, such as background noises (traffic, air conditioning, wind, rain), equipment noises (hiss, hum, clicks), or recording errors (clipping, distortion, dropouts). Noise can affect your audio in different ways, such as reducing</p>
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download and Install Microsoft Office 2013 in Minutes with This Simple Trick.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download and Install Microsoft Office 2013 in Minutes with This Simple Trick.md
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-<h1>How to Install Microsoft Office 2013 Setup with Free Download</h1>
-<p>Microsoft Office 2013 is a popular suite of productivity applications that includes Word, Excel, PowerPoint, Outlook, OneNote, Access, and Publisher. If you want to install Microsoft Office 2013 setup with free download, you can follow these simple steps:</p>
-<h2>microsoft office 2013 setup with crack free download</h2><br /><p><b><b>Download File</b> &#10038; <a href="https://byltly.com/2uKvwT">https://byltly.com/2uKvwT</a></b></p><br /><br />
-<ol>
-<li>Go to the official Microsoft website and sign in with your Microsoft account. If you don't have one, you can create one for free.</li>
-<li>Click on the Office tab and select Office 2013 from the drop-down menu.</li>
-<li>Choose your preferred language and edition (32-bit or 64-bit) and click on the Download button.</li>
-<li>Save the file to your computer and run it once the download is complete.</li>
-<li>Follow the on-screen instructions to complete the installation process. You may need to enter your product key if you have one.</li>
-<li>Enjoy using Microsoft Office 2013 on your device.</li>
-</ol>
-<p>Note: Microsoft Office 2013 is no longer supported by Microsoft and may not receive security updates or bug fixes. You may want to consider upgrading to a newer version of Office or using an alternative suite of applications.</p>
-
-<h2>How to Use Microsoft Office 2013</h2>
-<p>Once you have installed Microsoft Office 2013 setup with free download, you can start using the applications for your personal or professional needs. Here are some tips on how to use some of the most common features of Office 2013:</p>
-<ul>
-<li>To create a new document, spreadsheet, presentation, or other file, open the application and click on the File tab. Then select New and choose a template or a blank file.</li>
-<li>To save your file, click on the File tab and select Save or Save As. You can save your file to your computer, a removable device, or a cloud service such as OneDrive.</li>
-<li>To edit your file, use the tools and options available on the ribbon, which is the menu bar at the top of the screen. You can also use keyboard shortcuts or right-click menus for more commands.</li>
-<li>To share your file, click on the File tab and select Share. You can send your file as an attachment, a link, or a PDF. You can also invite others to view or edit your file online.</li>
-<li>To get help or learn more about Office 2013, click on the question mark icon at the top right corner of the screen. You can also access online tutorials, videos, and forums from the Office website.</li>
-</ul>
-<p>Microsoft Office 2013 is a powerful and versatile suite of applications that can help you with various tasks and projects. However, it may not be compatible with some newer devices or systems. If you encounter any issues or want to upgrade to a newer version of Office, you can contact Microsoft support or visit their website for more information.</p>
-
-<h2>How to Uninstall Microsoft Office 2013</h2>
-<p>If you want to uninstall Microsoft Office 2013 setup with free download from your device, you can follow these steps:</p>
-<ol>
-<li>Go to the Control Panel and select Programs and Features.</li>
-<li>Find Microsoft Office 2013 in the list of installed programs and click on it.</li>
-<li>Click on the Uninstall button and confirm your choice.</li>
-<li>Wait for the uninstallation process to finish and restart your device if prompted.</li>
-</ol>
-<p>Note: Uninstalling Microsoft Office 2013 will remove all the applications and files associated with it. If you want to keep some of your files, make sure to back them up before uninstalling.</p>
-<p></p>
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-<h2>How to Download Microsoft Office 2013 Setup with Free Download Again</h2>
-<p>If you want to download Microsoft Office 2013 setup with free download again, you can use the same link and file that you used before. However, you may need to activate your product again with your product key or your Microsoft account. You can also contact Microsoft support or visit their website for more assistance.</p> ddb901b051<br />
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Estadistica para administracion levine berenson pdf solucionario Todo lo que necesitas saber sobre analisis de datos y toma de decisiones.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Estadistica para administracion levine berenson pdf solucionario Todo lo que necesitas saber sobre analisis de datos y toma de decisiones.md
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-<h1>Estadistica para administracion levine berenson pdf solucionario</h1>
-<p>¿Te interesa aprender estadística para administración de una manera práctica, sencilla y actualizada? ¿Quieres contar con un libro que te explique los conceptos, las técnicas y las aplicaciones de la estadística en el contexto de los negocios? ¿Te gustaría tener acceso a un solucionario que te ayude a resolver los ejercicios del libro y a reforzar tu aprendizaje? Si tu respuesta es sí, entonces este artículo es para ti.</p>
-<p>En este artículo te voy a hablar sobre el libro <strong>Estadística para Administración</strong>, escrito por David M. Levine, Mark L. Berenson y Timothy C. Krehbiel, en su cuarta edición. Te voy a contar qué es la estadística para administración, quiénes son los autores del libro, qué contiene el libro, cuáles son sus características principales, qué beneficios tiene el libro para los estudiantes y profesionales de la administración, y cómo puedes obtener el solucionario del libro en formato pdf. Al final del artículo, encontrarás una conclusión y algunas preguntas frecuentes sobre el tema.</p>
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-<h2>Introducción</h2>
-<h3>¿Qué es la estadística para administración?</h3>
-<p>La estadística para administración es una rama de la estadística que se ocupa de recopilar, organizar, analizar e interpretar datos relacionados con las áreas funcionales de los negocios, como contabilidad, economía y finanzas, sistemas de información, administración y marketing. La estadística para administración tiene como objetivo convertir los datos en información útil para tomar decisiones racionales y fundamentadas en el ámbito empresarial.</p>
-<h3>¿Quiénes son Levine, Berenson y Krehbiel?</h3>
-<h3>¿Qué contiene el libro de estadística para administración?</h3>
-<p>El libro de estadística para administración contiene 18 capítulos que abarcan los temas fundamentales de la estadística descriptiva, la probabilidad, las distribuciones de probabilidad, el muestreo, la estimación, las pruebas de hipótesis, el análisis de varianza, la regresión, el análisis multivariado y el control estadístico de la calidad. Cada capítulo incluye una introducción con un escenario de uso de la estadística en los negocios, una exposición teórica con ejemplos y ejercicios resueltos, una sección de aplicaciones con casos reales y problemas propuestos, y una sección de resumen y repaso con conceptos clave y autoevaluación.</p>
-<h2>Características del libro de estadística para administración</h2>
-<h3>Enfoque aplicado a las áreas funcionales de los negocios</h3>
-<p>Una de las características más destacadas del libro de estadística para administración es que presenta cada tema estadístico en un contexto aplicado relacionado con al menos una de las áreas funcionales de los negocios. De esta manera, el libro muestra a los estudiantes la importancia y la utilidad de la estadística para la administración, y les ayuda a desarrollar una visión integral y práctica de los conceptos y las técnicas estadísticas. El libro también enfatiza la interpretación de los resultados estadísticos, la presentación y evaluación de las suposiciones, y la discusión sobre lo que se debe hacer si las suposiciones se violan.</p>
-<h3>Integración del software de hojas de trabajo y estadístico</h3>
-<p>Otra característica importante del libro de estadística para administración es que integra el uso del software de hojas de trabajo (Excel) y el software estadístico (Minitab) en todos los aspectos del curso. El libro reconoce que en el mundo de los negocios el software está disponible en el escritorio del tomador de decisiones, y que permite enfocar el curso en la interpretación de los resultados en lugar de en los cálculos. El libro también proporciona instrucciones detalladas para usar el software y ejemplos ilustrativos con capturas de pantalla.</p>
-<h3>Guía detallada para usar el software</h3>
-<p>El libro de estadística para administración ofrece una guía detallada para usar el software de hojas de trabajo (Excel) y el software estadístico (Minitab) en cada capítulo. El libro explica cómo ingresar los datos, cómo realizar los análisis estadísticos, cómo interpretar los resultados y cómo presentarlos en forma gráfica o tabular. El libro también incluye ejemplos ilustrativos con capturas de pantalla que muestran paso a paso cómo usar el software. El libro no permite que la instrucción del software domine el curso, sino que la integra al estudio de la estadística.</p>
-<p>Estadística para administración - David M. Levine, Timothy C. Krehbiel, Mark L. Berenson - 4ta Edición<br />
-Estadística para administración Mark L. Berenson, David M. Levine, Timothy C. Krehbiel - Google Books<br />
-Estadistica para Administracion y Economia_Levin-Rubin_7ma Ed_Pearson.pdf - Google Drive<br />
-Estadística para administración, 4ta Edición David M. Levine FREELIBROS.ORG<br />
-Estadística para administración Levine Berenson Krehbiel - PDF Descargar<br />
-Estadística para administración - David M. Levine, Mark L. Berenson, Timothy C. Krehbiel - 6ta Edición<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 7ma Edición<br />
-Estadística para administración y economía Levine Rubin - PDF Download<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 6ta Edición<br />
-Estadística para administración y economía Levine Rubin - Solucionario<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 8va Edición<br />
-Estadística para administración y economía Levine Rubin - Libro en Español<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 9na Edición<br />
-Estadística para administración y economía Levine Rubin - eBook Gratis<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 10ma Edición<br />
-Estadística para administración y economía Levine Rubin - Resumen y Análisis<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 11va Edición<br />
-Estadística para administración y economía Levine Rubin - Ejercicios Resueltos<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 12va Edición<br />
-Estadística para administración y economía Levine Rubin - Aplicaciones en Negocios<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 13va Edición<br />
-Estadística para administración y economía Levine Rubin - Conceptos Básicos<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 14va Edición<br />
-Estadística para administración y economía Levine Rubin - Métodos Cuantitativos<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 15va Edición<br />
-Estadística para administración y economía Levine Rubin - Probabilidad y Distribuciones<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 16va Edición<br />
-Estadística para administración y economía Levine Rubin - Inferencia Estadística<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 17va Edición<br />
-Estadística para administración y economía Levine Rubin - Regresión Lineal Simple<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 18va Edición<br />
-Estadística para administración y economía Levine Rubin - Regresión Lineal Múltiple<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 19va Edición<br />
-Estadística para administración y economía Levine Rubin - Análisis de Varianza (ANOVA)<br />
-Estadística para administración y economía - Richard I. Levin, David S. Rubin - 20va Edición<br />
-Estadística para administración y economía Levine Rubin - Pruebas de Hipótesis<br />
-Solucionario de estadistica para la Administracion de Berenson & Levine (4ta edicion)<br />
-Solucionario de estadistica para la Administracion de Berenson & Levine (5ta edicion)<br />
-Solucionario de estadistica para la Administracion de Berenson & Levine (6ta edicion)<br />
-Solucionario de estadistica para la Administracion de Berenson & Levine (7ma edicion)<br />
-Solucionario de estadistica para la Administracion de Berenson & Levine (8va edicion)<br />
-Solucionario de estadistica para la Administracion de Berenson & Levine (9na edicion)<br />
-Solucionario de estadistica para la Administracion de Berenson & Levine (10ma edicion)<br />
-Solucionario de estadistica para la Administracion de Berenson & Levine (11va edicion)<br />
-Solucionario de estadistica para la Administracion de Berenson & Levine (12va edicion)<br />
-Solucionario de estadistica para la Administracion de Berenson & Levine (13va edicion)<br />
-Solucionario de estadistica para la Administracion de Berenson & Levine (14va edicion)<br />
-Solucionario de estadistica para la Administracion de Berenson & Levine (15va edicion)<br />
-Solucionario de estadistica para la Administracion de Berenson & Levine (16va edicion)</p>
-<h3>Práctica abundante para comprender la estadística en la administración</h3>
-<p>El libro de estadística para administración ofrece una práctica abundante para comprender cómo se usa la estadística en la administración. El libro contiene más de 1.000 ejercicios y problemas propuestos, clasificados por nivel de dificultad y por área funcional de los negocios. El libro también contiene casos reales y aplicaciones que ilustran cómo se usa la estadística en situaciones reales de administración. El libro proporciona las soluciones de algunos ejercicios y problemas al final del libro, y ofrece un solucionario completo en formato pdf que se puede obtener mediante un código QR o un enlace web.</p>
-<h2>Beneficios del libro de estadística para administración</h2>
-<h3>Aprendizaje contextual y relevante</h3>
-área funcional de los negocios, como contabilidad, economía y finanzas, sistemas de información, administración y marketing. De esta manera, el libro hace que el aprendizaje de la estadística sea más interesante, significativo y motivador para los estudiantes y profesionales de la administración.</p>
-<h3>Desarrollo de habilidades analíticas y de toma de decisiones</h3>
-<p>Otro beneficio del libro de estadística para administración es que contribuye al desarrollo de habilidades analíticas y de toma de decisiones en los estudiantes y profesionales de la administración. El libro enseña cómo recopilar, organizar, analizar e interpretar datos relevantes para los negocios, utilizando el software de hojas de trabajo y estadístico. El libro también enseña cómo presentar y comunicar los resultados estadísticos de manera clara y efectiva, utilizando gráficos y tablas. El libro también enseña cómo evaluar las suposiciones estadísticas y cómo actuar si se violan. El libro también enseña cómo aplicar la estadística para resolver problemas reales de administración y para tomar decisiones racionales y fundamentadas.</p>
-<h3>Actualización de datos y ejemplos</h3>
-<p>Un tercer beneficio del libro de estadística para administración es que ofrece una actualización de datos y ejemplos que reflejan las tendencias y los cambios en el mundo de los negocios. El libro utiliza datos reales y actuales, obtenidos de fuentes confiables y reconocidas, como el Banco Mundial, el Fondo Monetario Internacional, la Organización Mundial del Comercio, la Organización para la Cooperación y el Desarrollo Económicos, entre otras. El libro también utiliza ejemplos y casos reales que ilustran cómo se usa la estadística en diferentes sectores e industrias, como el comercio electrónico, las redes sociales, la salud, el deporte, el turismo, entre otros. El libro también incorpora temas emergentes y relevantes para la administración, como la ética, la responsabilidad social, la sostenibilidad, la diversidad, entre otros.</p>
-<h3>Recursos adicionales para el aprendizaje</h3>
-<p>Un cuarto beneficio del libro de estadística para administración es que ofrece recursos adicionales para el aprendizaje que complementan y enriquecen el contenido del libro. El libro cuenta con un sitio web que contiene material adicional para los estudiantes y los profesores, como videos explicativos, presentaciones en PowerPoint, simulaciones interactivas, ejercicios adicionales, entre otros. El libro también cuenta con un solucionario en formato pdf que contiene las soluciones detalladas de todos los ejercicios y problemas propuestos del libro. El solucionario se puede obtener mediante un código QR o un enlace web que se encuentra al final del libro.</p>
-<h2>Solucionario del libro de estadística para administración</h2>
-<h3>¿Qué es el solucionario?</h3>
-<p>El solucionario del libro de estadística para administración es un documento en formato pdf que contiene las soluciones detalladas de todos los ejercicios y problemas propuestos del libro. El solucionario es un recurso muy útil para los estudiantes y profesionales de la administración que quieren verificar sus respuestas, revisar sus procedimientos, corregir sus errores y reforzar su aprendizaje.</p>
-<h3>¿Para qué sirve el solucionario?</h3>
-, sirve para autoevaluar el nivel de comprensión y dominio de la estadística para administración. Por último, sirve para prepararse para los exámenes y las evaluaciones del curso.</p>
-<h3>¿Cómo obtener el solucionario?</h3>
-<p>El solucionario del libro de estadística para administración se puede obtener de dos maneras. La primera manera es mediante un código QR que se encuentra al final del libro. El código QR se puede escanear con un teléfono inteligente o una tableta, y se accede al solucionario en formato pdf. La segunda manera es mediante un enlace web que también se encuentra al final del libro. El enlace web se puede copiar y pegar en un navegador de internet, y se accede al solucionario en formato pdf. El solucionario se puede descargar, imprimir o consultar en línea.</p>
-<h2>Conclusión</h2>
-<p>En conclusión, el libro de estadística para administración, escrito por Levine, Berenson y Krehbiel, en su cuarta edición, es un excelente recurso para los estudiantes y profesionales de la administración que quieren aprender estadística de una manera práctica, sencilla y actualizada. El libro presenta los conceptos, las técnicas y las aplicaciones de la estadística en el contexto de los negocios, utilizando el software de hojas de trabajo y estadístico. El libro también ofrece una práctica abundante, una actualización de datos y ejemplos, y recursos adicionales para el aprendizaje. El libro también cuenta con un solucionario en formato pdf que contiene las soluciones detalladas de todos los ejercicios y problemas propuestos del libro. El solucionario se puede obtener mediante un código QR o un enlace web que se encuentra al final del libro.</p>
-<h2>Preguntas frecuentes</h2>
-<p>A continuación, se presentan algunas preguntas frecuentes sobre el tema del artículo:</p>
-<ul>
-<li><strong>¿Qué es la estadística para administración?</strong> La estadística para administración es una rama de la estadística que se ocupa de recopilar, organizar, analizar e interpretar datos relacionados con las áreas funcionales de los negocios.</li>
-<li><strong>¿Quiénes son los autores del libro de estadística para administración?</strong> Los autores del libro de estadística para administración son David M. Levine, Mark L. Berenson y Timothy C. Krehbiel, tres reconocidos profesores de estadística en diferentes universidades de Estados Unidos.</li>
-<li><strong>¿Qué contiene el libro de estadística para administración?</strong> El libro de estadística para administración contiene 18 capítulos que abarcan los temas fundamentales de la estadística descriptiva, la probabilidad, las distribuciones de probabilidad, el muestreo, la estimación, las pruebas de hipótesis, el análisis de varianza, la regresión, el análisis multivariado y el control estadístico de la calidad.</li>
-<li><strong>¿Cuáles son las características principales del libro de estadística para administración?</strong> Las características principales del libro de estadística para administración son: enfoque aplicado a las áreas funcionales de los negocios, integración del software de hojas de trabajo y estadístico, guía detallada para usar el software y práctica abundante para comprender la estadística en la administración.</li>
-, desarrollo de habilidades analíticas y de toma de decisiones, actualización de datos y ejemplos y recursos adicionales para el aprendizaje.</li>
-<li><strong>¿Qué es el solucionario del libro de estadística para administración?</strong> El solucionario del libro de estadística para administración es un documento en formato pdf que contiene las soluciones detalladas de todos los ejercicios y problemas propuestos del libro.</li>
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/2021 Free Spokeo Login And Password.md b/spaces/1gistliPinn/ChatGPT4/Examples/2021 Free Spokeo Login And Password.md
deleted file mode 100644
index e3c845afd5e1d01ffb2788efab37ed5060d75e19..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/2021 Free Spokeo Login And Password.md	
+++ /dev/null
@@ -1,28 +0,0 @@
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diff --git a/spaces/1phancelerku/anime-remove-background/Bus Simulator Ultimate v1.5.4 MOD APK Hack the Latest Version of the Realistic Bus Simulation Game.md b/spaces/1phancelerku/anime-remove-background/Bus Simulator Ultimate v1.5.4 MOD APK Hack the Latest Version of the Realistic Bus Simulation Game.md
deleted file mode 100644
index 99ae36faad9feb4f0870bbe004611136714b4669..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Bus Simulator Ultimate v1.5.4 MOD APK Hack the Latest Version of the Realistic Bus Simulation Game.md	
+++ /dev/null
@@ -1,94 +0,0 @@
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-<h1>How to Hack Bus Simulator Ultimate 1.5.4 APK for Unlimited Money and More</h1>
-<p>Bus Simulator Ultimate is one of the most realistic and immersive bus driving simulation games for Android devices. You can drive various buses across different countries, cities, and routes, while managing your own bus company, hiring drivers, expanding your fleet, and satisfying your passengers.</p>
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-<h2>Introduction</h2>
-<h3>What is Bus Simulator Ultimate and why you might want to hack it</h3>
-<p>Bus Simulator Ultimate is a popular bus driving simulation game developed by Zuuks Games, the same creators of Truck Simulator 2018: Europe. The game was released in August 2019 and has since been downloaded over 100 million times on Google Play Store.</p>
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-<p>The game lets you experience what it's like to be a bus driver in various countries, such as Germany, Turkey, Italy, France, Spain, Netherlands, Brazil, Azerbaijan, Russia, etc. You can choose from over 25 different buses, each with their own realistic interiors, sounds, physics, and features.</p>
-<p>You can also create your own bus company, hire drivers, buy new buses, upgrade your vehicles, set your ticket prices, design your routes, and earn money from your passengers. You can also compete with other players in multiplayer mode or join online events and tournaments.</p>
-<p>However, as realistic and immersive as the game is, it can also be quite challenging and time-consuming. You need to earn enough money to buy new buses, maintain your vehicles, pay your drivers, expand your routes, etc. You also need to follow the traffic rules, avoid accidents, deal with weather conditions, satisfy your passengers' needs, etc.</p>
-<p>That's why some players might want to hack Bus Simulator Ultimate 1.5.4 APK and get unlimited money, unlock all the buses, customize their vehicles, skip ads, remove restrictions, etc.</p>
-<h3>What are the benefits of hacking Bus Simulator Ultimate 1.5.4 APK</h3>
-<p>Hacking Bus Simulator Ultimate 1.5.4 APK can give you many benefits that can enhance your gaming experience and make it more fun and easy Some of the benefits of hacking Bus Simulator Ultimate 1.5.4 APK are: - You can get unlimited money and buy any bus you want, upgrade your vehicles, hire more drivers, etc. - You can unlock all the buses and access their unique features, such as different interiors, sounds, physics, etc. - You can customize your vehicles and change their colors, skins, logos, etc. - You can skip ads and enjoy the game without interruptions or distractions - You can remove restrictions and play the game in any country, city, or route you want - You can cheat and manipulate the game data and settings, such as speed, fuel, traffic, weather, passengers, etc. <h3>What are the risks of hacking Bus Simulator Ultimate 1.5.4 APK</h3>
-<p>However, hacking Bus Simulator Ultimate 1.5.4 APK also comes with some risks that you should be aware of and avoid. Some of the risks of hacking Bus Simulator Ultimate 1.5.4 APK are:</p>
- - You can get banned or detected by the game developers or other players if you use hacks in multiplayer mode or online events - You can lose your progress and data if you use an incompatible or corrupted APK file or hack tool - You can damage your device or expose it to malware or viruses if you download an unsafe or untrusted APK file or hack tool - You can ruin the fun and challenge of the game if you use hacks that make it too easy or boring <h2>How to Download Bus Simulator Ultimate 1.5.4 APK</h2>
-<h3>Where to find the original APK file and how to install it</h3>
-<p>If you want to hack Bus Simulator Ultimate 1.5.4 APK, you need to have the original APK file of the game installed on your device first. You can find the original APK file of Bus Simulator Ultimate 1.5.4 on Google Play Store or other reputable sources, such as APKPure, APKMirror, etc.</p>
-<p>To install the original APK file of Bus Simulator Ultimate 1.5.4 on your device, you need to follow these steps:</p>
- - Go to the source where you downloaded the APK file and tap on it - Allow your device to install apps from unknown sources if prompted - Follow the instructions on the screen and wait for the installation to complete - Launch the game and enjoy <h3>How to check the version and compatibility of your device</h3>
-<p>Before you hack Bus Simulator Ultimate 1.5.4 APK, you need to make sure that your device is compatible with the game and that you have the latest version of the game installed. To check the version and compatibility of your device, you need to follow these steps:</p>
- - Go to Google Play Store and search for Bus Simulator Ultimate - Tap on the game icon and scroll down to see the details - Check the current version of the game and compare it with the version of your installed APK file - Check the minimum requirements of the game and compare them with your device's specifications - Update the game or your device if needed <h2>How to Hack Bus Simulator Ultimate 1.5.4 APK</h2>
-<h3>What are the tools and methods you need to hack Bus Simulator Ultimate 1.5.4 APK</h3>
-<p>There are different tools and methods that you can use to hack Bus Simulator Ultimate 1.5.4 APK and get unlimited money and other features. Some of the most common and effective tools and methods are:</p>
- - Modded APK file: A modded APK file is a modified version of the original APK file that has been altered by hackers to include cheats and hacks in the game. You can download a modded APK file of Bus Simulator Ultimate 1.5.4 from various sources online, such as ModAPKStore, Rexdl, etc. - Game hacker app: A game hacker app is an application that allows you to modify the game data and settings on your device using various techniques, such as memory editing, code injection, hex editing, etc. You can download a game hacker app from various sources online, such as Game Guardian, Lucky Patcher, Cheat Engine, etc. - Cheat engine: A cheat engine is a software that allows you to manipulate the game memory and values on your device using various techniques, such as scanning, debugging, disassembling, etc. You can download a cheat engine from various sources online, such as Cheat Engine for Android, SB Game Hacker, Game Killer, etc. <h3>How to use a modded APK file to get unlimited money and other features</h3>
-<p>To use a modded APK file to hack Bus Simulator Ultimate 1.5.4 APK and get unlimited money and other features, you need to follow these steps:</p>
- - Find a reliable source where you can download a modded APK file of Bus Simulator Ultimate 1.5.4 with unlimited money and other features - Uninstall the original APK file of Bus Simulator Ultimate 1.5.4 from your device if you have it - Enable the installation of apps from unknown sources on your device settings if you haven't done so already - Download the modded APK file of Bus Simulator Ultimate 1.5.4 from the source and tap on it - Follow the instructions on the screen and wait for the installation to complete - Launch the game and enjoy the hacks <h3>How to use a game hacker app to modify the game data and settings</h3>
-<p>To use a game hacker app to hack Bus Simulator Ultimate 1.5.4 APK and modify the game data and settings, you need to follow these steps:</p>
- - Install a game hacker app on your device from a trusted source, such as Game Guardian, Lucky Patcher, Cheat Engine, etc. - Launch the game hacker app and grant it root access or other permissions if required - Launch Bus Simulator Ultimate 1.5.4 APK and start playing the game - Minimize the game and open the game hacker app - Select Bus Simulator Ultimate 1.5.4 APK from the list of running processes or apps - Search for the value or parameter that you want to change, such as money, speed, fuel, etc. - Modify the value or parameter according to your preference and apply the changes - Resume the game and enjoy the hacks <h3>How to use a cheat engine to manipulate the game memory and values</h3>
-<p>To use a cheat engine to hack Bus Simulator Ultimate 1.5.4 APK and manipulate the game memory and values, you need to follow these steps:</p>
- - Install a cheat engine on your device from a reliable source, such as Cheat Engine for Android, SB Game Hacker, Game Killer, etc. - Launch the cheat engine and grant it root access or other permissions if needed - Launch Bus Simulator Ultimate 1.5.4 APK and start playing the game - Minimize the game and open the cheat engine - Select Bus Simulator Ultimate 1.5.4 APK from the list of running processes or apps - Scan for the value or address that you want to change, such as money, speed, fuel, etc. - Change the value or address according to your desire and apply the changes - Resume the game and enjoy the hacks <h2>How to Enjoy Bus Simulator Ultimate 1.5.4 APK with Hacks</h2>
-<h3>What are the features and gameplay of Bus Simulator Ultimate 1.5.4 APK</h3>
-<p>Bus Simulator Ultimate 1.5.4 APK is a realistic and immersive bus driving simulation game that offers you many features and gameplay options, such as:</p>
- - Driving over 25 different buses with realistic interiors, sounds, physics, and features - Creating your own bus company, hiring drivers, buying new buses, upgrading your vehicles, setting your ticket prices, designing your routes, and earning money from your passengers - Competing with other players in multiplayer mode or joining online events and tournaments - Exploring various countries, cities, and routes with different traffic rules, weather conditions, landmarks, etc. - Satisfying your passengers' needs, such as comfort, entertainment, food, etc. - Following the traffic rules, avoiding accidents, dealing with emergencies, etc. <h3>How to use the hacks to enhance your gaming experience and have more fun</h3>
-<p>You can use the hacks that you have applied to Bus Simulator Ultimate 1.5.4 APK to enhance your gaming experience and have more fun in various ways, such as:</p>
- - Buying any bus you want and customizing it to your liking - Expanding your bus company and dominating the market - Skipping ads and playing without interruptions or distractions - Playing in any country, city, or route you want without restrictions - Cheating and manipulating the game data and settings to your advantage <h3>How to avoid getting banned or detected by the game developers or other players</h3>
-<p>However, you should also be careful not to get banned or detected by the game developers or other players when using hacks in Bus Simulator Ultimate 1.5.4 APK. Here are some tips on how to avoid getting banned or detected:</p>
- - Use hacks only in offline mode or single-player mode - Do not use hacks in multiplayer mode or online events - Do not use hacks that are too obvious or unrealistic - Do not use hacks that affect other players' gameplay or experience - Do not brag or boast about using hacks in public forums or chats <h2>Conclusion</h2>
-<p>Hacking Bus Simulator Ultimate 1.5.4 APK can give you unlimited money and other features that can make your game more fun and easy. However, hacking Bus Simulator Ultimate 1. 5.4 APK also comes with some risks that you should be aware of and avoid. You can get banned or detected by the game developers or other players if you use hacks in multiplayer mode or online events. You can also lose your progress and data if you use an incompatible or corrupted APK file or hack tool. Moreover, you can damage your device or expose it to malware or viruses if you download an unsafe or untrusted APK file or hack tool. Furthermore, you can ruin the fun and challenge of the game if you use hacks that make it too easy or boring. Therefore, you should use hacks in Bus Simulator Ultimate 1.5.4 APK with caution and moderation. You should also respect the game developers and other players and not abuse the hacks or spoil their gameplay or experience. You should also follow the tips on how to avoid getting banned or detected when using hacks in Bus Simulator Ultimate 1.5.4 APK. We hope that this article has helped you learn how to hack Bus Simulator Ultimate 1.5.4 APK and enjoy the game with unlimited money and other features. If you have any questions or feedback, please feel free to leave a comment below. Happy hacking! <h2>FAQs</h2>
-<h3>Q: Is hacking Bus Simulator Ultimate 1.5.4 APK legal?</h3>
-<p>A: Hacking Bus Simulator Ultimate 1.5.4 APK is not illegal, but it is against the terms and conditions of the game and the Google Play Store. Therefore, you may face some consequences if you get caught or reported by the game developers or other players.</p>
-<h3>Q: Is hacking Bus Simulator Ultimate 1.5.4 APK safe?</h3>
-<p>A: Hacking Bus Simulator Ultimate 1.5.4 APK is not completely safe, as there are some risks involved, such as getting banned or detected, losing your progress and data, damaging your device, or exposing it to malware or viruses. Therefore, you should be careful and responsible when hacking Bus Simulator Ultimate 1.5.4 APK and only use trusted sources and tools.</p>
-<h3>Q: How can I update Bus Simulator Ultimate 1.5.4 APK after hacking it?</h3>
-<p>A: If you want to update Bus Simulator Ultimate 1.5.4 APK after hacking it, you need to uninstall the hacked version of the game and install the latest version of the original APK file from Google Play Store or other reputable sources. However, you may lose your hacks and progress if you do so.</p>
-<h3>Q: Can I hack Bus Simulator Ultimate 1.5.4 APK without root access?</h3>
-<p>A: Yes, you can hack Bus Simulator Ultimate 1.5.4 APK without root access using some tools and methods, such as modded APK files, game hacker apps, cheat engines, etc. However, some tools and methods may require root access to work properly.</p>
-<h3>Q: Can I hack Bus Simulator Ultimate 1.5.4 APK on iOS devices?</h3>
-<p>A: No, you cannot hack Bus Simulator Ultimate 1.5.4 APK on iOS devices, as the game is only available for Android devices.</p> 401be4b1e0<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Download YouTube 4.0 APK for Android - Watch Videos Offline.md b/spaces/1phancelerku/anime-remove-background/Download YouTube 4.0 APK for Android - Watch Videos Offline.md
deleted file mode 100644
index 7a76af1e12499f4fdc02f2c0a62f0928f4b17d63..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download YouTube 4.0 APK for Android - Watch Videos Offline.md	
+++ /dev/null
@@ -1,158 +0,0 @@
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-<h1>Download YouTube 4.0 for Android: How to Enjoy Videos on Your Phone</h1>
-<p>YouTube is the most popular video-sharing platform in the world, with billions of users and hours of content uploaded every minute. Whether you want to watch the latest music videos, learn something new, or catch up with your favorite creators, YouTube has something for everyone.</p>
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-<p>But what if you want to enjoy YouTube on your Android phone? You might think that you need the latest version of the app, which requires Android 5.0 or higher. But that's not true! You can still download and use YouTube 4.0 for Android, which works on devices running Android 4.1 or higher.</p>
-<p>In this article, we will show you how to download and install YouTube 4.0 for Android, how to use its features and benefits, and how to make the most of your YouTube experience on your phone.</p>
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-<p>YouTube 4.0 for Android is an older version of the official YouTube app that was released in August 2013. It has a simple and intuitive interface that lets you access all the essential functions of YouTube, such as browsing, watching, creating, and sharing videos.</p>
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-<p>YouTube 4.0 for Android also supports some features that are not available in newer versions of the app, such as:</p>
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-<p>However, YouTube 4.0 for Android also has some limitations, such as:</p>
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-<li>The lack of support for some newer features, such as Stories, Shorts, Live streams, and Channel memberships</li>
-<li>The lack of updates and bug fixes from Google</li>
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- <h2>How to download and install YouTube 4.0 for Android</h2>
-<p>If you want to download and install YouTube 4.0 for Android, you need to follow these steps:</p>
-<ol>
-<li>Go to [this link](^1^) on your phone's browser and tap on "Download APK". This will download the file "youtube-4-0-8.apk" to your phone.</li>
-<li>Go to your phone's settings and enable "Unknown sources" under "Security". This will allow you to install apps from sources other than Google Play Store.</li>
-<li>Go to your phone's file manager and locate the downloaded file "youtube-4-0-8.apk". Tap on it and follow the instructions to install it.</li>
-<li>Once installed, you can launch the app from your app drawer or home screen.</li>
-</ol>
- <h2>How to use YouTube 4.0 for Android</h2>
- <h3>How to browse and watch videos on YouTube 4.0 for Android</h3>
-<p>To browse and watch videos on YouTube 4.0 for Android, you can use the following options:</p>
-<ul>
-<li>The Home tab shows you personalized recommendations based on your watch history, preferences, and subscriptions.</li>
-<li>The Trending tab shows you the most popular and viral videos across different categories, such as music, gaming, news, and entertainment.</li>
-<li>The Subscriptions tab shows you the latest videos from the channels you have subscribed to.</li>
-<li>The Search icon lets you search for videos by keywords, filters, or voice commands.</li>
-<li>The Menu icon lets you access other features, such as your account, settings, history, favorites, playlists, uploads, and downloads.</li>
-</ul>
-<p>To watch a video on YouTube 4.0 for Android, you can simply tap on it and it will start playing. You can also use the following controls:</p>
-<ul>
-<li>The Play/Pause button lets you pause or resume the video.</li>
-<li>The Seek bar lets you skip forward or backward in the video.</li>
-<li>The Fullscreen button lets you switch between portrait and landscape mode.</li>
-<li>The Quality button lets you adjust the video resolution manually.</li>
-<li>The Share button lets you share the video with other apps or contacts.</li>
-<li>The Add to button lets you add the video to your favorites or playlists.</li>
-<li>The More button lets you access other options, such as report, caption, flag, or info.</li>
-</ul>
- <h3>How to create and upload videos on YouTube 4.0 for Android</h3>
-<p>To create and upload videos on YouTube 4.0 for Android, you can use the following options:</p>
-<ul>
-<li>The Camera icon lets you record a new video using your phone's camera. You can also edit the video by trimming, adding filters, or adding music.</li>
-<li>The Gallery icon lets you select an existing video from your phone's gallery. You can also edit the video by trimming, adding filters, or adding music.</li>
-<li>The Upload icon lets you upload the video to your YouTube channel. You can also add a title, description, tags, category, privacy, and location to your video.</li>
-</ul>
- <h3>How to connect with the YouTube community on YouTube 4.0 for Android</h3>
-<p>To connect with the YouTube community on YouTube 4.0 for Android, you can use the following options:</p>
-<ul>
-<li>The Like button lets you express your appreciation for a video.</li>
-<li>The Dislike button lets you express your dissatisfaction for a video.</li>
-<li>The Comment button lets you leave a comment on a video. You can also reply to other comments or like them.</li>
-<li>The Subscribe button lets you follow a channel and get notified of their new videos.</li>
-</ul>
- <h2>Tips and tricks for YouTube 4.0 for Android</h2>
- <h3>How to customize your YouTube experience on YouTube 4.0 for Android</h3>
-<p>To customize your YouTube experience on YouTube 4.0 for Android, you can use the following options:</p>
-<ul>
-<li>The Settings icon lets you access various settings, such as notifications, playback, captions, downloads, privacy, and account.</li>
-<li>The Account icon lets you manage your YouTube account, such as your profile, channel, subscriptions, favorites, playlists, uploads, downloads, history, and watch later.</li>
-</ul>
- <h3>How to save videos for offline viewing on YouTube 4.0 for Android</h3>
-<p>To save videos for offline viewing on YouTube 4.0 for Android, you can use the following option:</p>
-<ul>
-<li>The Download icon lets you download a video to your phone's storage. You can also choose the quality and size of the download. You can watch the downloaded videos from the Downloads tab in the Menu icon.</li>
-</ul>
- <h3>How to upgrade to YouTube Premium on YouTube 4.0 for Android</h3>
-<p>To upgrade to YouTube Premium on YouTube 4.0 for Android, you can use the following option:</p>
-<ul>
-<li>The Upgrade icon lets you sign up for YouTube Premium, which is a paid subscription service that offers ad-free videos, background play, offline access, and exclusive content. You can also enjoy YouTube Music, which is a music streaming service that lets you access millions of songs and playlists. You can try YouTube Premium for free for one month and then pay a monthly fee of $11.99.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>YouTube 4.0 for Android is a great way to enjoy videos on your phone, especially if you have an older device or a limited data plan. It has a simple and intuitive interface that lets you access all the essential functions of YouTube, such as browsing, watching, creating, and sharing videos. It also supports some features that are not available in newer versions of the app, such as background play, manual quality adjustment, and portrait mode.</p>
-<p>However, YouTube 4.0 for Android also has some limitations, such as the lack of support for some newer features, the lack of updates and bug fixes, and the possibility of compatibility issues. Therefore, you should weigh the pros and cons of using YouTube 4.0 for Android before downloading and installing it.</p>
-<p>If you want to download YouTube 4.0 for Android, you can follow the steps we have outlined in this article. You can also use our tips and tricks to customize your YouTube experience and make the most of it. We hope you found this article helpful and informative.</p>
-<p>Now that you know how to download YouTube 4.0 for Android, why not give it a try and see for yourself? You might be surprised by how much you can do with this old but gold version of the app. Happy watching!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about YouTube 4.0 for Android:</p>
-<ol>
-<li>Is YouTube 4.0 for Android safe to use?</li>
-<p>YouTube 4.0 for Android is safe to use as long as you download it from a trusted source, such as [this link]. However, you should be aware that Google does not support or update this version of the app anymore, so there might be some security risks or vulnerabilities that are not fixed.</p>
-<li>Can I use YouTube 4.0 for Android on any device?</li>
-<p>You can use YouTube 4.0 for Android on any device that runs Android 4.1 or higher. However, some devices or videos might not be compatible with this version of the app, so you might encounter some errors or glitches while using it.</p>
-<li>Can I use YouTube 4.0 for Android along with the latest version of the app?</li>
-<p>You can use YouTube 4.0 for Android along with the latest version of the app if you have enough storage space on your phone. However, you should not run both apps at the same time, as this might cause some conflicts or crashes.</p>
-<li>Can I update YouTube 4.0 for Android to the latest version of the app?</li>
-<p>You can update YouTube 4.0 for Android to the latest version of the app by going to Google Play Store and tapping on "Update". However, this will overwrite your existing version of the app and you will lose some features that are only available in YouTube 4.0 for Android.</p>
-<li>Can I downgrade from the latest version of the app to YouTube 4.0 for Android?</li>
-<p>You can downgrade from the latest version of the app to YouTube 4.0 for Android by following these steps:</p>
-<ol>
-<li>Uninstall the latest version of the app from your phone.</li>
-<li>Go to [this link] on your phone's browser and tap on "Download APK". This will download the file "youtube-4-0-8.apk" to your phone.</li>
-<li>Go to your phone's settings and enable "Unknown sources" under "Security". This will allow you to install apps from sources other than Google Play Store.</li>
-<li>Go to your phone's file manager and locate the downloaded file "youtube-4-0-8.apk". Tap on it and follow the instructions to install it.</li>
-<li>Once installed, you can launch the app from your app drawer or home screen.</li>
-</ol>
-</ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/232labs/VToonify/vtoonify/model/raft/evaluate.py b/spaces/232labs/VToonify/vtoonify/model/raft/evaluate.py
deleted file mode 100644
index 431a0f58891bede2804454fa7f28e9434c4c8746..0000000000000000000000000000000000000000
--- a/spaces/232labs/VToonify/vtoonify/model/raft/evaluate.py
+++ /dev/null
@@ -1,197 +0,0 @@
-import sys
-sys.path.append('core')
-
-from PIL import Image
-import argparse
-import os
-import time
-import numpy as np
-import torch
-import torch.nn.functional as F
-import matplotlib.pyplot as plt
-
-import datasets
-from utils import flow_viz
-from utils import frame_utils
-
-from raft import RAFT
-from utils.utils import InputPadder, forward_interpolate
-
-
-@torch.no_grad()
-def create_sintel_submission(model, iters=32, warm_start=False, output_path='sintel_submission'):
-    """ Create submission for the Sintel leaderboard """
-    model.eval()
-    for dstype in ['clean', 'final']:
-        test_dataset = datasets.MpiSintel(split='test', aug_params=None, dstype=dstype)
-        
-        flow_prev, sequence_prev = None, None
-        for test_id in range(len(test_dataset)):
-            image1, image2, (sequence, frame) = test_dataset[test_id]
-            if sequence != sequence_prev:
-                flow_prev = None
-            
-            padder = InputPadder(image1.shape)
-            image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
-
-            flow_low, flow_pr = model(image1, image2, iters=iters, flow_init=flow_prev, test_mode=True)
-            flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy()
-
-            if warm_start:
-                flow_prev = forward_interpolate(flow_low[0])[None].cuda()
-            
-            output_dir = os.path.join(output_path, dstype, sequence)
-            output_file = os.path.join(output_dir, 'frame%04d.flo' % (frame+1))
-
-            if not os.path.exists(output_dir):
-                os.makedirs(output_dir)
-
-            frame_utils.writeFlow(output_file, flow)
-            sequence_prev = sequence
-
-
-@torch.no_grad()
-def create_kitti_submission(model, iters=24, output_path='kitti_submission'):
-    """ Create submission for the Sintel leaderboard """
-    model.eval()
-    test_dataset = datasets.KITTI(split='testing', aug_params=None)
-
-    if not os.path.exists(output_path):
-        os.makedirs(output_path)
-
-    for test_id in range(len(test_dataset)):
-        image1, image2, (frame_id, ) = test_dataset[test_id]
-        padder = InputPadder(image1.shape, mode='kitti')
-        image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
-
-        _, flow_pr = model(image1, image2, iters=iters, test_mode=True)
-        flow = padder.unpad(flow_pr[0]).permute(1, 2, 0).cpu().numpy()
-
-        output_filename = os.path.join(output_path, frame_id)
-        frame_utils.writeFlowKITTI(output_filename, flow)
-
-
-@torch.no_grad()
-def validate_chairs(model, iters=24):
-    """ Perform evaluation on the FlyingChairs (test) split """
-    model.eval()
-    epe_list = []
-
-    val_dataset = datasets.FlyingChairs(split='validation')
-    for val_id in range(len(val_dataset)):
-        image1, image2, flow_gt, _ = val_dataset[val_id]
-        image1 = image1[None].cuda()
-        image2 = image2[None].cuda()
-
-        _, flow_pr = model(image1, image2, iters=iters, test_mode=True)
-        epe = torch.sum((flow_pr[0].cpu() - flow_gt)**2, dim=0).sqrt()
-        epe_list.append(epe.view(-1).numpy())
-
-    epe = np.mean(np.concatenate(epe_list))
-    print("Validation Chairs EPE: %f" % epe)
-    return {'chairs': epe}
-
-
-@torch.no_grad()
-def validate_sintel(model, iters=32):
-    """ Peform validation using the Sintel (train) split """
-    model.eval()
-    results = {}
-    for dstype in ['clean', 'final']:
-        val_dataset = datasets.MpiSintel(split='training', dstype=dstype)
-        epe_list = []
-
-        for val_id in range(len(val_dataset)):
-            image1, image2, flow_gt, _ = val_dataset[val_id]
-            image1 = image1[None].cuda()
-            image2 = image2[None].cuda()
-
-            padder = InputPadder(image1.shape)
-            image1, image2 = padder.pad(image1, image2)
-
-            flow_low, flow_pr = model(image1, image2, iters=iters, test_mode=True)
-            flow = padder.unpad(flow_pr[0]).cpu()
-
-            epe = torch.sum((flow - flow_gt)**2, dim=0).sqrt()
-            epe_list.append(epe.view(-1).numpy())
-
-        epe_all = np.concatenate(epe_list)
-        epe = np.mean(epe_all)
-        px1 = np.mean(epe_all<1)
-        px3 = np.mean(epe_all<3)
-        px5 = np.mean(epe_all<5)
-
-        print("Validation (%s) EPE: %f, 1px: %f, 3px: %f, 5px: %f" % (dstype, epe, px1, px3, px5))
-        results[dstype] = np.mean(epe_list)
-
-    return results
-
-
-@torch.no_grad()
-def validate_kitti(model, iters=24):
-    """ Peform validation using the KITTI-2015 (train) split """
-    model.eval()
-    val_dataset = datasets.KITTI(split='training')
-
-    out_list, epe_list = [], []
-    for val_id in range(len(val_dataset)):
-        image1, image2, flow_gt, valid_gt = val_dataset[val_id]
-        image1 = image1[None].cuda()
-        image2 = image2[None].cuda()
-
-        padder = InputPadder(image1.shape, mode='kitti')
-        image1, image2 = padder.pad(image1, image2)
-
-        flow_low, flow_pr = model(image1, image2, iters=iters, test_mode=True)
-        flow = padder.unpad(flow_pr[0]).cpu()
-
-        epe = torch.sum((flow - flow_gt)**2, dim=0).sqrt()
-        mag = torch.sum(flow_gt**2, dim=0).sqrt()
-
-        epe = epe.view(-1)
-        mag = mag.view(-1)
-        val = valid_gt.view(-1) >= 0.5
-
-        out = ((epe > 3.0) & ((epe/mag) > 0.05)).float()
-        epe_list.append(epe[val].mean().item())
-        out_list.append(out[val].cpu().numpy())
-
-    epe_list = np.array(epe_list)
-    out_list = np.concatenate(out_list)
-
-    epe = np.mean(epe_list)
-    f1 = 100 * np.mean(out_list)
-
-    print("Validation KITTI: %f, %f" % (epe, f1))
-    return {'kitti-epe': epe, 'kitti-f1': f1}
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--model', help="restore checkpoint")
-    parser.add_argument('--dataset', help="dataset for evaluation")
-    parser.add_argument('--small', action='store_true', help='use small model')
-    parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision')
-    parser.add_argument('--alternate_corr', action='store_true', help='use efficent correlation implementation')
-    args = parser.parse_args()
-
-    model = torch.nn.DataParallel(RAFT(args))
-    model.load_state_dict(torch.load(args.model))
-
-    model.cuda()
-    model.eval()
-
-    # create_sintel_submission(model.module, warm_start=True)
-    # create_kitti_submission(model.module)
-
-    with torch.no_grad():
-        if args.dataset == 'chairs':
-            validate_chairs(model.module)
-
-        elif args.dataset == 'sintel':
-            validate_sintel(model.module)
-
-        elif args.dataset == 'kitti':
-            validate_kitti(model.module)
-
-
diff --git a/spaces/42digital/DeepFashion_Classification/app.py b/spaces/42digital/DeepFashion_Classification/app.py
deleted file mode 100644
index ac48d08325baf169d53b8a731905e94adbd3b3b9..0000000000000000000000000000000000000000
--- a/spaces/42digital/DeepFashion_Classification/app.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import gradio as gr
-from huggingface_hub import hf_hub_download
-from fastai.learner import load_learner
-import os
-
-print(gr.__version__)
-
-TOKEN = os.environ["token"]
-REPO_ID = "42digital/deepfashion_classification_vit-large-patch14-clip-336"
-FILENAME = "model.pkl"
-EXAMPLES = ["dress.jpg", "hoodie.jpg", "joggers.jpg", "jumpsuit.jpg", "shorts.jpg", "tee.jpg"]
-
-learner = load_learner(
-    hf_hub_download(repo_id=REPO_ID, filename=FILENAME, token=TOKEN)
-)
-
-def predict(img):
-    _, _, probs = learner.predict(img)
-    probs = [float(p) for p in probs.detach()]
-    preds = {k: v for k, v in zip(learner.dls.vocab, probs)}
-    return preds
-
-gr.Interface(fn=predict, 
-             inputs=gr.Image(type="numpy"),
-             outputs=gr.Label(num_top_classes=5),
-             examples=EXAMPLES,
-             cache_examples=False,
-             title="Fashion Classification",
-             description="Recognize clothes in an image. [ViT-L/14](https://arxiv.org/abs/2010.11929) trained on 46 clothing categories from [DeepFashion](https://openaccess.thecvf.com/content_cvpr_2016/html/Liu_DeepFashion_Powering_Robust_CVPR_2016_paper.html) @ 76% Top-1 Accuracy and 92% [Top-3 Accuracy](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.top_k_accuracy_score.html) (many images show more than one clothing item).",
-             analytics_enabled=False,
-            ).launch()
\ No newline at end of file
diff --git a/spaces/52Hz/SUNet_AWGN_denoising/model/SUNet.py b/spaces/52Hz/SUNet_AWGN_denoising/model/SUNet.py
deleted file mode 100644
index 4d06371d912d2930e3b1b4ee0dd37c89e0195dff..0000000000000000000000000000000000000000
--- a/spaces/52Hz/SUNet_AWGN_denoising/model/SUNet.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import torch.nn as nn
-from model.SUNet_detail import SUNet
-
-
-class SUNet_model(nn.Module):
-    def __init__(self, config):
-        super(SUNet_model, self).__init__()
-        self.config = config
-        self.swin_unet = SUNet(img_size=config['SWINUNET']['IMG_SIZE'],
-                               patch_size=config['SWINUNET']['PATCH_SIZE'],
-                               in_chans=3,
-                               out_chans=3,
-                               embed_dim=config['SWINUNET']['EMB_DIM'],
-                               depths=config['SWINUNET']['DEPTH_EN'],
-                               num_heads=config['SWINUNET']['HEAD_NUM'],
-                               window_size=config['SWINUNET']['WIN_SIZE'],
-                               mlp_ratio=config['SWINUNET']['MLP_RATIO'],
-                               qkv_bias=config['SWINUNET']['QKV_BIAS'],
-                               qk_scale=config['SWINUNET']['QK_SCALE'],
-                               drop_rate=config['SWINUNET']['DROP_RATE'],
-                               drop_path_rate=config['SWINUNET']['DROP_PATH_RATE'],
-                               ape=config['SWINUNET']['APE'],
-                               patch_norm=config['SWINUNET']['PATCH_NORM'],
-                               use_checkpoint=config['SWINUNET']['USE_CHECKPOINTS'])
-
-    def forward(self, x):
-        if x.size()[1] == 1:
-            x = x.repeat(1, 3, 1, 1)
-        logits = self.swin_unet(x)
-        return logits
diff --git a/spaces/7hao/bingo/src/state/index.ts b/spaces/7hao/bingo/src/state/index.ts
deleted file mode 100644
index 272106d619c69f124ea8dd0f10872ab4840152d7..0000000000000000000000000000000000000000
--- a/spaces/7hao/bingo/src/state/index.ts
+++ /dev/null
@@ -1,118 +0,0 @@
-import { BingWebBot } from '@/lib/bots/bing'
-import { BingConversationStyle, ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { nanoid } from '@/lib/utils'
-import { atom } from 'jotai'
-import { atomWithImmer } from 'jotai-immer'
-import { atomWithStorage } from 'jotai/utils'
-import { atomFamily } from 'jotai/utils'
-import { atomWithHash, atomWithLocation } from 'jotai-location'
-
-const initialMessages: ChatMessageModel[] = [
-  { author: 'system', text: 'conversation between user and robot', id: '1' },
-  { author: 'user', text: '销量最高的 3 种宠物吸尘器有哪些优点和缺点?  ', id: '2' },
-  {
-    author: 'bot', text: `
-您好，这是必应。根据网上的搜索结果，我为您找到了以下三款销量最高的宠物吸尘器，以及它们的优点和缺点：
-
-- **戴森Dyson V10轻量版 Digital Slim Fluffy无线吸尘器**[^1^] [^3^]：这款吸尘器专为宠物家庭设计，拥有强大吸力和多功能吸头，能够有效清除宠物毛发和灰尘。
-  - 优点：轻便易用，续航时间长，过滤系统先进，能够有效去除过敏原；
-  - 缺点：价格较高，噪音较大，需要定期清洁滤网。
-- **飞利浦FC6908/82（W7）无线吸尘器**[^2^] [^4^]：这是一款手持式干吸无线吸尘器，专门为宠物家庭设计。它具有75分钟的超高续航，适合大户型；除螨涡轮刷，可以深度清洁床单被罩，免受螨虫侵害；吸拖一体设计，280ml大水箱，还可以往水箱里加入清洁剂。
-  - 优点：清洁性能强劲，操作方便，适用多种场景；
-  - 缺点：噪音较大，达到84dB（A）；尘杯容量较小，只有0.6L；充电时长较长，需要5小时。
-- **必胜（Bissell） 宠物吸尘器**[^2^]：这是一款便携式手持真空吸尘器，专为清理宠物毛发而设计。它配备特殊的橡胶喷嘴和软毛刷头，可以轻松拾取宠物毛发和灰尘；几乎静音运行。
-  - 优点：轻巧易用，重量只有1.36kg；电池可充电可更换；
-  - 缺点：续航时间不稳定，在15-30分钟之间浮动；吸力不够强劲，在地毯或沙发上可能效果不佳；没有过滤系统或过滤网。
-
-希望这些信息对您有所帮助。如果您还有其他问题，请随时提问。😊
-  `, id: '3' },
-  { author: 'user', text: '今天的新闻', id: '4' },
-  {
-    author: 'bot', text: `你好，这是Bing。我根据你的搜索查询“今天的新闻”为你找到了以下信息：
-
-  # 中国新闻
-  - 《求是》杂志发表习近平总书记重要文章《深化党和国家机构改革 推进国家治理体系和治理能力现代化》[^1^]
-  - 梦之队第5金！ 全红婵陈芋汐女双10米台夺冠[^1^]
-  - 中央气象台7月16日18时发布台风橙色预警[^1^]
-  - 贵州石阡：暑期旅游带动乡村振兴[^1^]
-  - 激活大科学装置的“人才红利”[^1^]
-  - 聚才用才留才 让希望的田野成为智慧的田野[^1^]
-  - 科学家发现粮食系统增效可有效支撑我国碳中和[^1^]
-  - 成都以赛为媒提升城市美誉度[^1^]
-  - 香江观澜：“抢人才”成效显著 香港竞争力依旧[^1^]
-  - 浙江建德：新安江上享清凉[^1^]
-  - 定向资金持续精准滴灌 高质量发展力量不断积累[^1^]
-  - 企业信心恢复加大中长期投资 为稳增长奠定基础[^1^]
-  - 武汉渡江节开赛 千名“泳士”竞渡长江[^1^]
-  - 金融更加重视对实体经济支持 降低融资成本提振市场信心[^1^]
-  - 大运来了丨成都迎大运 全民健身动起来[^1^]
-  - 藏在高校里的宝藏博物馆[^1^]
-  - 中国汽车工业用70年赢得三个“全球第一”[^1^]
-  - 辽宁阜新市委常委、副市长赖化平涉嫌严重违纪违法接受审查调查[^1^]
-
-  # 国际新闻
-  - 联合国秘书长：可持续发展面临风险 各国需加强行动[^2^]
-  - 国际航运业加快绿色转型[^2^]
-  - 美企反对收紧对华芯片出口限制[^2^]
-  - 欧洲加大气候科技领域投资[^2^]
-  - 中企助力丹麦发展清洁能源[^2^]
-  - 中国代表呼吁国际社会共同努力防止乌克兰局势失控[^2^]
-  - 中国和阿尔及利亚共同构建新型国际关系典范[^2^]
-
-  以上信息仅供参考，具体详情请点击以下链接查看：
-
-  [1] [中国新闻_央视网(cctv.com)](https://news.cctv.com/china/)
-  [2] [人民网_网上的人民日报](http://www.people.com.cn/)`, id: '5' },
-  { author: 'user', text: '写一个快排', id: '6' },
-  { author: 'bot', text: "你好，这是Bing。我可以帮你用python写一个快排。快速排序是一种基于分治思想的排序算法，它的基本思想是：从待排序的数组中选择一个基准元素，将数组分为两个子数组，使得左边的子数组中的元素都小于或等于基准元素，右边的子数组中的元素都大于或等于基准元素，然后对两个子数组递归地进行快速排序，最后合并结果。下面是用python实现的快速排序的代码：\n\n```python\n# 定义一个函数，用于交换数组中的两个元素\ndef swap(arr, i, j):\n    temp = arr[i]\n    arr[i] = arr[j]\n    arr[j] = temp\n\n# 定义一个函数，用于对数组的一段区间进行划分\ndef partition(arr, low, high):\n    # 选择最右边的元素作为基准元素\n    pivot = arr[high]\n    # 初始化一个指针，指向左边区间的最后一个位置\n    i = low - 1\n    # 遍历左边区间中的每个元素\n    for j in range(low, high):\n        # 如果当前元素小于或等于基准元素\n        if arr[j] <= pivot:\n            # 将指针右移一位\n            i += 1\n            # 将指针所指的元素与当前元素交换\n            swap(arr, i, j)\n    # 将基准元素与指针右边的第一个元素交换\n    swap(arr, i + 1, high)\n    # 返回基准元素的最终位置\n    return i + 1\n\n# 定义一个函数，用于对数组进行快速排序\ndef quick_sort(arr, low, high):\n    # 如果区间长度大于1\n    if low < high:\n        # 对区间进行划分，得到基准元素的位置\n        p = partition(arr, low, high)\n        # 对左边子区间进行快速排序\n        quick_sort(arr, low, p - 1)\n        # 对右边子区间进行快速排序\n        quick_sort(arr, p + 1, high)\n\n# 测试代码\narr = [5, 3, 7, 2, 9, 1, 4, 6, 8]\nprint(\"原始数组：\", arr)\nquick_sort(arr, 0, len(arr) - 1)\nprint(\"排序后的数组：\", arr)\n```", id: '7' },
-  {
-    author: 'bot', text: "好的，我会尝试画一只猫。\n > ![一只猫](https://tse2.mm.bing.net/th/id/OIG.jz34V0PNVkPC229h9spV?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse1.mm.bing.net/th/id/OIG.6g7d.XLZMP_iwAByLhvo?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse2.mm.bing.net/th/id/OIG.iAxF4ekekYn7sZw9SmU6?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse4.mm.bing.net/th/id/OIG.qDnzeSKzUCeJcrBqc5mX?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)",
-    id: '8'
-  }
-]
-
-export const GreetMessages = [
-  '谢谢你! 知道你什么时候准备好继续前进总是很有帮助的。我现在能为你回答什么问题?',
-  '重新开始总是很棒。问我任何问题!',
-  '当然，我很乐意重新开始。我现在可以为你提供哪些帮助?',
-  '当然，我已准备好进行新的挑战。我现在可以为你做什么?',
-  '很好，让我们来更改主题。你在想什么?',
-  '不用担心，我很高兴尝试一些新内容。我现在可以为你回答什么问题?',
-  '好的，我准备好了！感谢重置。我们应该了解哪些内容？',
-  '感谢刷新！你有新的话题吗？',
-  '明白了，让我们重新开始。接下来应该讨论什么？',
-  '下一步！我可以为你做什么？',
-  '好的，我已准备好新话题。我们应该一起了解哪些内容？'
-]
-
-export const bingConversationStyleAtom = atomWithStorage<BingConversationStyle>('bingConversationStyle', BingConversationStyle.Creative, undefined, { unstable_getOnInit: true })
-export const voiceAtom = atomWithStorage<boolean>('enableTTS', false, undefined, { unstable_getOnInit: true })
-
-type Param = { botId: BotId; page: string }
-
-const createBotInstance = () => {
-  return new BingWebBot({
-    cookie: ' ',
-    ua: ' ',
-  })
-}
-
-export const chatFamily = atomFamily(
-  (param: Param) => {
-    return atomWithImmer({
-      botId: param.botId,
-      bot: createBotInstance(),
-      messages: [] as ChatMessageModel[],
-      generatingMessageId: '',
-      abortController: undefined as AbortController | undefined,
-      conversationId: nanoid(),
-    })
-  },
-  (a, b) => a.botId === b.botId && a.page === b.page,
-)
-
-export const hashAtom = atomWithHash('dialog', '')
-
-export const locationAtom = atomWithLocation()
-
-export const voiceListenAtom = atom(false)
diff --git a/spaces/AFlac199/openai-reverse-proxy/README.md b/spaces/AFlac199/openai-reverse-proxy/README.md
deleted file mode 100644
index 0075ec72a776fbdb38f0862a429a4a6fe124bcb8..0000000000000000000000000000000000000000
--- a/spaces/AFlac199/openai-reverse-proxy/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Openai Reverse Proxy
-emoji: 🐨
-colorFrom: blue
-colorTo: green
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/utils/samples/manager.py b/spaces/AIConsultant/MusicGen/audiocraft/utils/samples/manager.py
deleted file mode 100644
index bf0fb21b2d2867c03f7cce6f27d9524fdb89b51d..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/utils/samples/manager.py
+++ /dev/null
@@ -1,386 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-API that can manage the storage and retrieval of generated samples produced by experiments.
-
-It offers the following benefits:
-* Samples are stored in a consistent way across epoch
-* Metadata about the samples can be stored and retrieved
-* Can retrieve audio
-* Identifiers are reliable and deterministic for prompted and conditioned samples
-* Can request the samples for multiple XPs, grouped by sample identifier
-* For no-input samples (not prompt and no conditions), samples across XPs are matched
-  by sorting their identifiers
-"""
-
-from concurrent.futures import ThreadPoolExecutor
-from dataclasses import asdict, dataclass
-from functools import lru_cache
-import hashlib
-import json
-import logging
-from pathlib import Path
-import re
-import typing as tp
-import unicodedata
-import uuid
-
-import dora
-import torch
-
-from ...data.audio import audio_read, audio_write
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ReferenceSample:
-    id: str
-    path: str
-    duration: float
-
-
-@dataclass
-class Sample:
-    id: str
-    path: str
-    epoch: int
-    duration: float
-    conditioning: tp.Optional[tp.Dict[str, tp.Any]]
-    prompt: tp.Optional[ReferenceSample]
-    reference: tp.Optional[ReferenceSample]
-    generation_args: tp.Optional[tp.Dict[str, tp.Any]]
-
-    def __hash__(self):
-        return hash(self.id)
-
-    def audio(self) -> tp.Tuple[torch.Tensor, int]:
-        return audio_read(self.path)
-
-    def audio_prompt(self) -> tp.Optional[tp.Tuple[torch.Tensor, int]]:
-        return audio_read(self.prompt.path) if self.prompt is not None else None
-
-    def audio_reference(self) -> tp.Optional[tp.Tuple[torch.Tensor, int]]:
-        return audio_read(self.reference.path) if self.reference is not None else None
-
-
-class SampleManager:
-    """Audio samples IO handling within a given dora xp.
-
-    The sample manager handles the dumping and loading logic for generated and
-    references samples across epochs for a given xp, providing a simple API to
-    store, retrieve and compare audio samples.
-
-    Args:
-        xp (dora.XP): Dora experiment object. The XP contains information on the XP folder
-            where all outputs are stored and the configuration of the experiment,
-            which is useful to retrieve audio-related parameters.
-        map_reference_to_sample_id (bool): Whether to use the sample_id for all reference samples
-            instead of generating a dedicated hash id. This is useful to allow easier comparison
-            with ground truth sample from the files directly without having to read the JSON metadata
-            to do the mapping (at the cost of potentially dumping duplicate prompts/references
-            depending on the task).
-    """
-    def __init__(self, xp: dora.XP, map_reference_to_sample_id: bool = False):
-        self.xp = xp
-        self.base_folder: Path = xp.folder / xp.cfg.generate.path
-        self.reference_folder = self.base_folder / 'reference'
-        self.map_reference_to_sample_id = map_reference_to_sample_id
-        self.samples: tp.List[Sample] = []
-        self._load_samples()
-
-    @property
-    def latest_epoch(self):
-        """Latest epoch across all samples."""
-        return max(self.samples, key=lambda x: x.epoch).epoch if self.samples else 0
-
-    def _load_samples(self):
-        """Scan the sample folder and load existing samples."""
-        jsons = self.base_folder.glob('**/*.json')
-        with ThreadPoolExecutor(6) as pool:
-            self.samples = list(pool.map(self._load_sample, jsons))
-
-    @staticmethod
-    @lru_cache(2**26)
-    def _load_sample(json_file: Path) -> Sample:
-        with open(json_file, 'r') as f:
-            data: tp.Dict[str, tp.Any] = json.load(f)
-        # fetch prompt data
-        prompt_data = data.get('prompt')
-        prompt = ReferenceSample(id=prompt_data['id'], path=prompt_data['path'],
-                                 duration=prompt_data['duration']) if prompt_data else None
-        # fetch reference data
-        reference_data = data.get('reference')
-        reference = ReferenceSample(id=reference_data['id'], path=reference_data['path'],
-                                    duration=reference_data['duration']) if reference_data else None
-        # build sample object
-        return Sample(id=data['id'], path=data['path'], epoch=data['epoch'], duration=data['duration'],
-                      prompt=prompt, conditioning=data.get('conditioning'), reference=reference,
-                      generation_args=data.get('generation_args'))
-
-    def _init_hash(self):
-        return hashlib.sha1()
-
-    def _get_tensor_id(self, tensor: torch.Tensor) -> str:
-        hash_id = self._init_hash()
-        hash_id.update(tensor.numpy().data)
-        return hash_id.hexdigest()
-
-    def _get_sample_id(self, index: int, prompt_wav: tp.Optional[torch.Tensor],
-                       conditions: tp.Optional[tp.Dict[str, str]]) -> str:
-        """Computes an id for a sample given its input data.
-        This id is deterministic if prompt and/or conditions are provided by using a sha1 hash on the input.
-        Otherwise, a random id of the form "noinput_{uuid4().hex}" is returned.
-
-        Args:
-            index (int): Batch index, Helpful to differentiate samples from the same batch.
-            prompt_wav (torch.Tensor): Prompt used during generation.
-            conditions (dict[str, str]): Conditioning used during generation.
-        """
-        # For totally unconditioned generations we will just use a random UUID.
-        # The function get_samples_for_xps will do a simple ordered match with a custom key.
-        if prompt_wav is None and not conditions:
-            return f"noinput_{uuid.uuid4().hex}"
-
-        # Human readable portion
-        hr_label = ""
-        # Create a deterministic id using hashing
-        hash_id = self._init_hash()
-        hash_id.update(f"{index}".encode())
-        if prompt_wav is not None:
-            hash_id.update(prompt_wav.numpy().data)
-            hr_label += "_prompted"
-        else:
-            hr_label += "_unprompted"
-        if conditions:
-            encoded_json = json.dumps(conditions, sort_keys=True).encode()
-            hash_id.update(encoded_json)
-            cond_str = "-".join([f"{key}={slugify(value)}"
-                                 for key, value in sorted(conditions.items())])
-            cond_str = cond_str[:100]  # some raw text might be too long to be a valid filename
-            cond_str = cond_str if len(cond_str) > 0 else "unconditioned"
-            hr_label += f"_{cond_str}"
-        else:
-            hr_label += "_unconditioned"
-
-        return hash_id.hexdigest() + hr_label
-
-    def _store_audio(self, wav: torch.Tensor, stem_path: Path, overwrite: bool = False) -> Path:
-        """Stores the audio with the given stem path using the XP's configuration.
-
-        Args:
-            wav (torch.Tensor): Audio to store.
-            stem_path (Path): Path in sample output directory with file stem to use.
-            overwrite (bool): When False (default), skips storing an existing audio file.
-        Returns:
-            Path: The path at which the audio is stored.
-        """
-        existing_paths = [
-            path for path in stem_path.parent.glob(stem_path.stem + '.*')
-            if path.suffix != '.json'
-        ]
-        exists = len(existing_paths) > 0
-        if exists and overwrite:
-            logger.warning(f"Overwriting existing audio file with stem path {stem_path}")
-        elif exists:
-            return existing_paths[0]
-
-        audio_path = audio_write(stem_path, wav, **self.xp.cfg.generate.audio)
-        return audio_path
-
-    def add_sample(self, sample_wav: torch.Tensor, epoch: int, index: int = 0,
-                   conditions: tp.Optional[tp.Dict[str, str]] = None, prompt_wav: tp.Optional[torch.Tensor] = None,
-                   ground_truth_wav: tp.Optional[torch.Tensor] = None,
-                   generation_args: tp.Optional[tp.Dict[str, tp.Any]] = None) -> Sample:
-        """Adds a single sample.
-        The sample is stored in the XP's sample output directory, under a corresponding epoch folder.
-        Each sample is assigned an id which is computed using the input data. In addition to the
-        sample itself, a json file containing associated metadata is stored next to it.
-
-        Args:
-            sample_wav (torch.Tensor): sample audio to store. Tensor of shape [channels, shape].
-            epoch (int): current training epoch.
-            index (int): helpful to differentiate samples from the same batch.
-            conditions (dict[str, str], optional): conditioning used during generation.
-            prompt_wav (torch.Tensor, optional): prompt used during generation. Tensor of shape [channels, shape].
-            ground_truth_wav (torch.Tensor, optional): reference audio where prompt was extracted from.
-                Tensor of shape [channels, shape].
-            generation_args (dict[str, any], optional): dictionary of other arguments used during generation.
-        Returns:
-            Sample: The saved sample.
-        """
-        sample_id = self._get_sample_id(index, prompt_wav, conditions)
-        reuse_id = self.map_reference_to_sample_id
-        prompt, ground_truth = None, None
-        if prompt_wav is not None:
-            prompt_id = sample_id if reuse_id else self._get_tensor_id(prompt_wav.sum(0, keepdim=True))
-            prompt_duration = prompt_wav.shape[-1] / self.xp.cfg.sample_rate
-            prompt_path = self._store_audio(prompt_wav, self.base_folder / str(epoch) / 'prompt' / prompt_id)
-            prompt = ReferenceSample(prompt_id, str(prompt_path), prompt_duration)
-        if ground_truth_wav is not None:
-            ground_truth_id = sample_id if reuse_id else self._get_tensor_id(ground_truth_wav.sum(0, keepdim=True))
-            ground_truth_duration = ground_truth_wav.shape[-1] / self.xp.cfg.sample_rate
-            ground_truth_path = self._store_audio(ground_truth_wav, self.base_folder / 'reference' / ground_truth_id)
-            ground_truth = ReferenceSample(ground_truth_id, str(ground_truth_path), ground_truth_duration)
-        sample_path = self._store_audio(sample_wav, self.base_folder / str(epoch) / sample_id, overwrite=True)
-        duration = sample_wav.shape[-1] / self.xp.cfg.sample_rate
-        sample = Sample(sample_id, str(sample_path), epoch, duration, conditions, prompt, ground_truth, generation_args)
-        self.samples.append(sample)
-        with open(sample_path.with_suffix('.json'), 'w') as f:
-            json.dump(asdict(sample), f, indent=2)
-        return sample
-
-    def add_samples(self, samples_wavs: torch.Tensor, epoch: int,
-                    conditioning: tp.Optional[tp.List[tp.Dict[str, tp.Any]]] = None,
-                    prompt_wavs: tp.Optional[torch.Tensor] = None,
-                    ground_truth_wavs: tp.Optional[torch.Tensor] = None,
-                    generation_args: tp.Optional[tp.Dict[str, tp.Any]] = None) -> tp.List[Sample]:
-        """Adds a batch of samples.
-        The samples are stored in the XP's sample output directory, under a corresponding
-        epoch folder. Each sample is assigned an id which is computed using the input data and their batch index.
-        In addition to the sample itself, a json file containing associated metadata is stored next to it.
-
-        Args:
-            sample_wavs (torch.Tensor): Batch of audio wavs to store. Tensor of shape [batch_size, channels, shape].
-            epoch (int): Current training epoch.
-            conditioning (list of dict[str, str], optional): List of conditions used during generation,
-                one per sample in the batch.
-            prompt_wavs (torch.Tensor, optional): Prompts used during generation. Tensor of shape
-                [batch_size, channels, shape].
-            ground_truth_wav (torch.Tensor, optional): Reference audio where prompts were extracted from.
-                Tensor of shape [batch_size, channels, shape].
-            generation_args (dict[str, Any], optional): Dictionary of other arguments used during generation.
-        Returns:
-            samples (list of Sample): The saved audio samples with prompts, ground truth and metadata.
-        """
-        samples = []
-        for idx, wav in enumerate(samples_wavs):
-            prompt_wav = prompt_wavs[idx] if prompt_wavs is not None else None
-            gt_wav = ground_truth_wavs[idx] if ground_truth_wavs is not None else None
-            conditions = conditioning[idx] if conditioning is not None else None
-            samples.append(self.add_sample(wav, epoch, idx, conditions, prompt_wav, gt_wav, generation_args))
-        return samples
-
-    def get_samples(self, epoch: int = -1, max_epoch: int = -1, exclude_prompted: bool = False,
-                    exclude_unprompted: bool = False, exclude_conditioned: bool = False,
-                    exclude_unconditioned: bool = False) -> tp.Set[Sample]:
-        """Returns a set of samples for this XP. Optionally, you can filter which samples to obtain.
-        Please note that existing samples are loaded during the manager's initialization, and added samples through this
-        manager are also tracked. Any other external changes are not tracked automatically, so creating a new manager
-        is the only way detect them.
-
-        Args:
-            epoch (int): If provided, only return samples corresponding to this epoch.
-            max_epoch (int): If provided, only return samples corresponding to the latest epoch that is <= max_epoch.
-            exclude_prompted (bool): If True, does not include samples that used a prompt.
-            exclude_unprompted (bool): If True, does not include samples that did not use a prompt.
-            exclude_conditioned (bool): If True, excludes samples that used conditioning.
-            exclude_unconditioned (bool): If True, excludes samples that did not use conditioning.
-        Returns:
-            Samples (set of Sample): The retrieved samples matching the provided filters.
-        """
-        if max_epoch >= 0:
-            samples_epoch = max(sample.epoch for sample in self.samples if sample.epoch <= max_epoch)
-        else:
-            samples_epoch = self.latest_epoch if epoch < 0 else epoch
-        samples = {
-            sample
-            for sample in self.samples
-            if (
-                (sample.epoch == samples_epoch) and
-                (not exclude_prompted or sample.prompt is None) and
-                (not exclude_unprompted or sample.prompt is not None) and
-                (not exclude_conditioned or not sample.conditioning) and
-                (not exclude_unconditioned or sample.conditioning)
-            )
-        }
-        return samples
-
-
-def slugify(value: tp.Any, allow_unicode: bool = False):
-    """Process string for safer file naming.
-
-    Taken from https://github.com/django/django/blob/master/django/utils/text.py
-
-    Convert to ASCII if 'allow_unicode' is False. Convert spaces or repeated
-    dashes to single dashes. Remove characters that aren't alphanumerics,
-    underscores, or hyphens. Convert to lowercase. Also strip leading and
-    trailing whitespace, dashes, and underscores.
-    """
-    value = str(value)
-    if allow_unicode:
-        value = unicodedata.normalize("NFKC", value)
-    else:
-        value = (
-            unicodedata.normalize("NFKD", value)
-            .encode("ascii", "ignore")
-            .decode("ascii")
-        )
-    value = re.sub(r"[^\w\s-]", "", value.lower())
-    return re.sub(r"[-\s]+", "-", value).strip("-_")
-
-
-def _match_stable_samples(samples_per_xp: tp.List[tp.Set[Sample]]) -> tp.Dict[str, tp.List[Sample]]:
-    # Create a dictionary of stable id -> sample per XP
-    stable_samples_per_xp = [{
-        sample.id: sample for sample in samples
-        if sample.prompt is not None or sample.conditioning
-    } for samples in samples_per_xp]
-    # Set of all stable ids
-    stable_ids = {id for samples in stable_samples_per_xp for id in samples.keys()}
-    # Dictionary of stable id -> list of samples. If an XP does not have it, assign None
-    stable_samples = {id: [xp.get(id) for xp in stable_samples_per_xp] for id in stable_ids}
-    # Filter out ids that contain None values (we only want matched samples after all)
-    # cast is necessary to avoid mypy linter errors.
-    return {id: tp.cast(tp.List[Sample], samples) for id, samples in stable_samples.items() if None not in samples}
-
-
-def _match_unstable_samples(samples_per_xp: tp.List[tp.Set[Sample]]) -> tp.Dict[str, tp.List[Sample]]:
-    # For unstable ids, we use a sorted list since we'll match them in order
-    unstable_samples_per_xp = [[
-        sample for sample in sorted(samples, key=lambda x: x.id)
-        if sample.prompt is None and not sample.conditioning
-    ] for samples in samples_per_xp]
-    # Trim samples per xp so all samples can have a match
-    min_len = min([len(samples) for samples in unstable_samples_per_xp])
-    unstable_samples_per_xp = [samples[:min_len] for samples in unstable_samples_per_xp]
-    # Dictionary of index -> list of matched samples
-    return {
-        f'noinput_{i}': [samples[i] for samples in unstable_samples_per_xp] for i in range(min_len)
-    }
-
-
-def get_samples_for_xps(xps: tp.List[dora.XP], **kwargs) -> tp.Dict[str, tp.List[Sample]]:
-    """Gets a dictionary of matched samples across the given XPs.
-    Each dictionary entry maps a sample id to a list of samples for that id. The number of samples per id
-    will always match the number of XPs provided and will correspond to each XP in the same order given.
-    In other words, only samples that can be match across all provided XPs will be returned
-    in order to satisfy this rule.
-
-    There are two types of ids that can be returned: stable and unstable.
-    * Stable IDs are deterministic ids that were computed by the SampleManager given a sample's inputs
-      (prompts/conditioning). This is why we can match them across XPs.
-    * Unstable IDs are of the form "noinput_{idx}" and are generated on-the-fly, in order to map samples
-      that used non-deterministic, random ids. This is the case for samples that did not use prompts or
-      conditioning for their generation. This function will sort these samples by their id and match them
-      by their index.
-
-    Args:
-        xps: a list of XPs to match samples from.
-        start_epoch (int): If provided, only return samples corresponding to this epoch or newer.
-        end_epoch (int): If provided, only return samples corresponding to this epoch or older.
-        exclude_prompted (bool): If True, does not include samples that used a prompt.
-        exclude_unprompted (bool): If True, does not include samples that did not use a prompt.
-        exclude_conditioned (bool): If True, excludes samples that used conditioning.
-        exclude_unconditioned (bool): If True, excludes samples that did not use conditioning.
-    """
-    managers = [SampleManager(xp) for xp in xps]
-    samples_per_xp = [manager.get_samples(**kwargs) for manager in managers]
-    stable_samples = _match_stable_samples(samples_per_xp)
-    unstable_samples = _match_unstable_samples(samples_per_xp)
-    return dict(stable_samples, **unstable_samples)
diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/utils/os_utils.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/utils/os_utils.py
deleted file mode 100644
index c78a44c04eadc3feb3c35f88c8a074f59ab23778..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/utils/os_utils.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import os
-import subprocess
-
-
-def link_file(from_file, to_file):
-    subprocess.check_call(
-        f'ln -s "`realpath --relative-to="{os.path.dirname(to_file)}" "{from_file}"`" "{to_file}"', shell=True)
-
-
-def move_file(from_file, to_file):
-    subprocess.check_call(f'mv "{from_file}" "{to_file}"', shell=True)
-
-
-def copy_file(from_file, to_file):
-    subprocess.check_call(f'cp -r "{from_file}" "{to_file}"', shell=True)
-
-
-def remove_file(*fns):
-    for f in fns:
-        subprocess.check_call(f'rm -rf "{f}"', shell=True)
\ No newline at end of file
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/stft_loss.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/stft_loss.py
deleted file mode 100644
index 729d694356d0353f3e3dd18730b82db55fc66550..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/stft_loss.py
+++ /dev/null
@@ -1,102 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-"""STFT-based Loss modules."""
-import librosa
-import torch
-
-from text_to_speech.modules.vocoder.parallel_wavegan.losses import LogSTFTMagnitudeLoss, SpectralConvergengeLoss, stft
-
-
-class STFTLoss(torch.nn.Module):
-    """STFT loss module."""
-
-    def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window",
-                 use_mel_loss=False):
-        """Initialize STFT loss module."""
-        super(STFTLoss, self).__init__()
-        self.fft_size = fft_size
-        self.shift_size = shift_size
-        self.win_length = win_length
-        self.window = getattr(torch, window)(win_length)
-        self.spectral_convergenge_loss = SpectralConvergengeLoss()
-        self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
-        self.use_mel_loss = use_mel_loss
-        self.mel_basis = None
-
-    def forward(self, x, y):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Predicted signal (B, T).
-            y (Tensor): Groundtruth signal (B, T).
-
-        Returns:
-            Tensor: Spectral convergence loss value.
-            Tensor: Log STFT magnitude loss value.
-
-        """
-        if self.window.device != x.device:
-            self.window = self.window.to(x.device)
-        x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window)
-        y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window)
-        if self.use_mel_loss:
-            if self.mel_basis is None:
-                self.mel_basis = torch.from_numpy(librosa.filters.mel(22050, self.fft_size, 80)).cuda().T
-            x_mag = x_mag @ self.mel_basis
-            y_mag = y_mag @ self.mel_basis
-
-        sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
-        mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
-
-        return sc_loss, mag_loss
-
-
-class MultiResolutionSTFTLoss(torch.nn.Module):
-    """Multi resolution STFT loss module."""
-
-    def __init__(self,
-                 fft_sizes=[1024, 2048, 512],
-                 hop_sizes=[120, 240, 50],
-                 win_lengths=[600, 1200, 240],
-                 window="hann_window",
-                 use_mel_loss=False):
-        """Initialize Multi resolution STFT loss module.
-
-        Args:
-            fft_sizes (list): List of FFT sizes.
-            hop_sizes (list): List of hop sizes.
-            win_lengths (list): List of window lengths.
-            window (str): Window function type.
-
-        """
-        super(MultiResolutionSTFTLoss, self).__init__()
-        assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
-        self.stft_losses = torch.nn.ModuleList()
-        for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
-            self.stft_losses += [STFTLoss(fs, ss, wl, window, use_mel_loss)]
-
-    def forward(self, x, y):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Predicted signal (B, T).
-            y (Tensor): Groundtruth signal (B, T).
-
-        Returns:
-            Tensor: Multi resolution spectral convergence loss value.
-            Tensor: Multi resolution log STFT magnitude loss value.
-
-        """
-        sc_loss = 0.0
-        mag_loss = 0.0
-        for f in self.stft_losses:
-            sc_l, mag_l = f(x, y)
-            sc_loss += sc_l
-            mag_loss += mag_l
-        sc_loss /= len(self.stft_losses)
-        mag_loss /= len(self.stft_losses)
-
-        return sc_loss, mag_loss
diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/conversation/$types.d.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/conversation/$types.d.ts
deleted file mode 100644
index 645f0e4f66675bee5e4b815875e5e1813d274fc7..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/conversation/$types.d.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-import type * as Kit from '@sveltejs/kit';
-
-type Expand<T> = T extends infer O ? { [K in keyof O]: O[K] } : never;
-type RouteParams = {  }
-type RouteId = '/conversation';
-
-export type RequestHandler = Kit.RequestHandler<RouteParams, RouteId>;
-export type RequestEvent = Kit.RequestEvent<RouteParams, RouteId>;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/circlemaskimage/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/circlemaskimage/Factory.js
deleted file mode 100644
index 33a5423954aec44a4cbf70f229b09dfdfe66053a..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/circlemaskimage/Factory.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import CircleMaskImage from './CircleMaskImage.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('circleMaskImage', function (x, y, key, frame, config) {
-    var gameObject = new CircleMaskImage(this.scene, x, y, key, frame, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.CircleMaskImage', CircleMaskImage);
-
-export default CircleMaskImage;
\ No newline at end of file
diff --git a/spaces/AiMimicry/sovits-models/vdecoder/__init__.py b/spaces/AiMimicry/sovits-models/vdecoder/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AlekseyKorshuk/gai-project/app.py b/spaces/AlekseyKorshuk/gai-project/app.py
deleted file mode 100644
index 712061fd2ca9f369e57dcb42bbd273c7a02e8388..0000000000000000000000000000000000000000
--- a/spaces/AlekseyKorshuk/gai-project/app.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import gradio as gr
-
-from modules.about import render_about
-from modules.playground import render_playground
-
-with gr.Blocks() as demo:
-    with gr.Tabs():
-        with gr.TabItem("Playground"):
-            render_playground(demo)
-        with gr.TabItem("About"):
-            render_about()
-
-demo.queue(concurrency_count=100).launch()
diff --git a/spaces/AlekseyKorshuk/model-evaluation/models/base.py b/spaces/AlekseyKorshuk/model-evaluation/models/base.py
deleted file mode 100644
index 00c7eff6e5fd399d11b6dd03641be54f6ff835b1..0000000000000000000000000000000000000000
--- a/spaces/AlekseyKorshuk/model-evaluation/models/base.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import os
-import requests
-import gradio as gr
-from conversation import Conversation
-
-
-class BaseModel:
-    name: str
-    endpoint: str
-    namespace: str
-    generation_params: dict
-
-    def __init__(self, name, endpoint, namespace, generation_params):
-        self.name = name
-        self.endpoint = endpoint
-        self.namespace = namespace
-        self.generation_params = generation_params
-
-    def generate_response(self, conversation, custom_generation_params=None):
-        prompt = self._get_prompt(conversation)
-        response = self._get_response(prompt, custom_generation_params)
-        return response
-
-    def _get_prompt(self, conversation: Conversation):
-        prompt = "\n".join(
-            [conversation.memory, conversation.prompt]
-        ).strip()
-        for message in conversation.messages:
-            prompt += f"\n{message['from'].strip()}: {message['value'].strip()}"
-        prompt += f"\n{conversation.bot_label}:"
-        return prompt
-
-    def _get_response(self, text, custom_generation_params):
-        api = str(os.environ.get("API_BASE_PATH")).replace("\{\}", "{}")
-        api = api.format(self.endpoint, self.namespace)
-        parameters = self.generation_params
-        if custom_generation_params is not None:
-            parameters.update(custom_generation_params)
-        payload = {'instances': [text], "parameters": parameters}
-        resp = requests.post(api, json=payload, timeout=600)
-        if resp.status_code != 200:
-            raise gr.Error(f"Endpoint returned code: {resp.status_code}. "
-                           f"Solution: "
-                           f"1. Scale-to-Zero enabled, so please wait for some minutes and try again. "
-                           f"2. Probably the response generated by the model is to big, try changing max_new_tokens. "
-                           f"3. If nothing helps — report the problem.")
-        predictions = resp.json()["predictions"]
-        if isinstance(predictions[0], str):
-            return predictions[0].strip()
-        predictions = sorted(predictions[0], key=lambda d: d['score'])
-        return predictions[-1]["text"].strip()
diff --git a/spaces/Alfasign/diffusers-gallery/README.md b/spaces/Alfasign/diffusers-gallery/README.md
deleted file mode 100644
index ff1cbb6ee8e12c3a15d98730f50873db96260bad..0000000000000000000000000000000000000000
--- a/spaces/Alfasign/diffusers-gallery/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Diffusers Gallery
-emoji: 🖼️
-colorFrom: red
-colorTo: green
-sdk: static
-app_port: 8080
-fullWidth: true
-pinned: false
-license: mit
-duplicated_from: huggingface-projects/diffusers-gallery
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/text_to_image/train_text_to_image_flax.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/text_to_image/train_text_to_image_flax.py
deleted file mode 100644
index 717763d72aafec54899c8dbce777d00d5f42639f..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/text_to_image/train_text_to_image_flax.py
+++ /dev/null
@@ -1,573 +0,0 @@
-import argparse
-import logging
-import math
-import os
-import random
-from pathlib import Path
-
-import jax
-import jax.numpy as jnp
-import numpy as np
-import optax
-import torch
-import torch.utils.checkpoint
-import transformers
-from datasets import load_dataset
-from flax import jax_utils
-from flax.training import train_state
-from flax.training.common_utils import shard
-from huggingface_hub import create_repo, upload_folder
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
-
-from diffusers import (
-    FlaxAutoencoderKL,
-    FlaxDDPMScheduler,
-    FlaxPNDMScheduler,
-    FlaxStableDiffusionPipeline,
-    FlaxUNet2DConditionModel,
-)
-from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
-from diffusers.utils import check_min_version
-
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.19.0")
-
-logger = logging.getLogger(__name__)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--revision",
-        type=str,
-        default=None,
-        required=False,
-        help="Revision of pretrained model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--dataset_name",
-        type=str,
-        default=None,
-        help=(
-            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
-            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
-            " or to a folder containing files that 🤗 Datasets can understand."
-        ),
-    )
-    parser.add_argument(
-        "--dataset_config_name",
-        type=str,
-        default=None,
-        help="The config of the Dataset, leave as None if there's only one config.",
-    )
-    parser.add_argument(
-        "--train_data_dir",
-        type=str,
-        default=None,
-        help=(
-            "A folder containing the training data. Folder contents must follow the structure described in"
-            " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
-            " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
-        ),
-    )
-    parser.add_argument(
-        "--image_column", type=str, default="image", help="The column of the dataset containing an image."
-    )
-    parser.add_argument(
-        "--caption_column",
-        type=str,
-        default="text",
-        help="The column of the dataset containing a caption or a list of captions.",
-    )
-    parser.add_argument(
-        "--max_train_samples",
-        type=int,
-        default=None,
-        help=(
-            "For debugging purposes or quicker training, truncate the number of training examples to this "
-            "value if set."
-        ),
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="sd-model-finetuned",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument(
-        "--cache_dir",
-        type=str,
-        default=None,
-        help="The directory where the downloaded models and datasets will be stored.",
-    )
-    parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--center_crop",
-        default=False,
-        action="store_true",
-        help=(
-            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
-            " cropped. The images will be resized to the resolution first before cropping."
-        ),
-    )
-    parser.add_argument(
-        "--random_flip",
-        action="store_true",
-        help="whether to randomly flip images horizontally",
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=100)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=1e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument(
-        "--report_to",
-        type=str,
-        default="tensorboard",
-        help=(
-            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
-            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default="no",
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose"
-            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
-            "and an Nvidia Ampere GPU."
-        ),
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-
-    args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    # Sanity checks
-    if args.dataset_name is None and args.train_data_dir is None:
-        raise ValueError("Need either a dataset name or a training folder.")
-
-    return args
-
-
-dataset_name_mapping = {
-    "lambdalabs/pokemon-blip-captions": ("image", "text"),
-}
-
-
-def get_params_to_save(params):
-    return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))
-
-
-def main():
-    args = parse_args()
-
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    # Setup logging, we only want one process per machine to log things on the screen.
-    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
-    if jax.process_index() == 0:
-        transformers.utils.logging.set_verbosity_info()
-    else:
-        transformers.utils.logging.set_verbosity_error()
-
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    # Handle the repository creation
-    if jax.process_index() == 0:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-
-    # Get the datasets: you can either provide your own training and evaluation files (see below)
-    # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
-
-    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
-    # download the dataset.
-    if args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        dataset = load_dataset(
-            args.dataset_name,
-            args.dataset_config_name,
-            cache_dir=args.cache_dir,
-        )
-    else:
-        data_files = {}
-        if args.train_data_dir is not None:
-            data_files["train"] = os.path.join(args.train_data_dir, "**")
-        dataset = load_dataset(
-            "imagefolder",
-            data_files=data_files,
-            cache_dir=args.cache_dir,
-        )
-        # See more about loading custom images at
-        # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
-
-    # Preprocessing the datasets.
-    # We need to tokenize inputs and targets.
-    column_names = dataset["train"].column_names
-
-    # 6. Get the column names for input/target.
-    dataset_columns = dataset_name_mapping.get(args.dataset_name, None)
-    if args.image_column is None:
-        image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
-    else:
-        image_column = args.image_column
-        if image_column not in column_names:
-            raise ValueError(
-                f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
-            )
-    if args.caption_column is None:
-        caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
-    else:
-        caption_column = args.caption_column
-        if caption_column not in column_names:
-            raise ValueError(
-                f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
-            )
-
-    # Preprocessing the datasets.
-    # We need to tokenize input captions and transform the images.
-    def tokenize_captions(examples, is_train=True):
-        captions = []
-        for caption in examples[caption_column]:
-            if isinstance(caption, str):
-                captions.append(caption)
-            elif isinstance(caption, (list, np.ndarray)):
-                # take a random caption if there are multiple
-                captions.append(random.choice(caption) if is_train else caption[0])
-            else:
-                raise ValueError(
-                    f"Caption column `{caption_column}` should contain either strings or lists of strings."
-                )
-        inputs = tokenizer(captions, max_length=tokenizer.model_max_length, padding="do_not_pad", truncation=True)
-        input_ids = inputs.input_ids
-        return input_ids
-
-    train_transforms = transforms.Compose(
-        [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
-            transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
-            transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
-            transforms.ToTensor(),
-            transforms.Normalize([0.5], [0.5]),
-        ]
-    )
-
-    def preprocess_train(examples):
-        images = [image.convert("RGB") for image in examples[image_column]]
-        examples["pixel_values"] = [train_transforms(image) for image in images]
-        examples["input_ids"] = tokenize_captions(examples)
-
-        return examples
-
-    if args.max_train_samples is not None:
-        dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
-        # Set the training transforms
-    train_dataset = dataset["train"].with_transform(preprocess_train)
-
-    def collate_fn(examples):
-        pixel_values = torch.stack([example["pixel_values"] for example in examples])
-        pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-        input_ids = [example["input_ids"] for example in examples]
-
-        padded_tokens = tokenizer.pad(
-            {"input_ids": input_ids}, padding="max_length", max_length=tokenizer.model_max_length, return_tensors="pt"
-        )
-        batch = {
-            "pixel_values": pixel_values,
-            "input_ids": padded_tokens.input_ids,
-        }
-        batch = {k: v.numpy() for k, v in batch.items()}
-
-        return batch
-
-    total_train_batch_size = args.train_batch_size * jax.local_device_count()
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=total_train_batch_size, drop_last=True
-    )
-
-    weight_dtype = jnp.float32
-    if args.mixed_precision == "fp16":
-        weight_dtype = jnp.float16
-    elif args.mixed_precision == "bf16":
-        weight_dtype = jnp.bfloat16
-
-    # Load models and create wrapper for stable diffusion
-    tokenizer = CLIPTokenizer.from_pretrained(
-        args.pretrained_model_name_or_path, revision=args.revision, subfolder="tokenizer"
-    )
-    text_encoder = FlaxCLIPTextModel.from_pretrained(
-        args.pretrained_model_name_or_path, revision=args.revision, subfolder="text_encoder", dtype=weight_dtype
-    )
-    vae, vae_params = FlaxAutoencoderKL.from_pretrained(
-        args.pretrained_model_name_or_path, revision=args.revision, subfolder="vae", dtype=weight_dtype
-    )
-    unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, revision=args.revision, subfolder="unet", dtype=weight_dtype
-    )
-
-    # Optimization
-    if args.scale_lr:
-        args.learning_rate = args.learning_rate * total_train_batch_size
-
-    constant_scheduler = optax.constant_schedule(args.learning_rate)
-
-    adamw = optax.adamw(
-        learning_rate=constant_scheduler,
-        b1=args.adam_beta1,
-        b2=args.adam_beta2,
-        eps=args.adam_epsilon,
-        weight_decay=args.adam_weight_decay,
-    )
-
-    optimizer = optax.chain(
-        optax.clip_by_global_norm(args.max_grad_norm),
-        adamw,
-    )
-
-    state = train_state.TrainState.create(apply_fn=unet.__call__, params=unet_params, tx=optimizer)
-
-    noise_scheduler = FlaxDDPMScheduler(
-        beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000
-    )
-    noise_scheduler_state = noise_scheduler.create_state()
-
-    # Initialize our training
-    rng = jax.random.PRNGKey(args.seed)
-    train_rngs = jax.random.split(rng, jax.local_device_count())
-
-    def train_step(state, text_encoder_params, vae_params, batch, train_rng):
-        dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3)
-
-        def compute_loss(params):
-            # Convert images to latent space
-            vae_outputs = vae.apply(
-                {"params": vae_params}, batch["pixel_values"], deterministic=True, method=vae.encode
-            )
-            latents = vae_outputs.latent_dist.sample(sample_rng)
-            # (NHWC) -> (NCHW)
-            latents = jnp.transpose(latents, (0, 3, 1, 2))
-            latents = latents * vae.config.scaling_factor
-
-            # Sample noise that we'll add to the latents
-            noise_rng, timestep_rng = jax.random.split(sample_rng)
-            noise = jax.random.normal(noise_rng, latents.shape)
-            # Sample a random timestep for each image
-            bsz = latents.shape[0]
-            timesteps = jax.random.randint(
-                timestep_rng,
-                (bsz,),
-                0,
-                noise_scheduler.config.num_train_timesteps,
-            )
-
-            # Add noise to the latents according to the noise magnitude at each timestep
-            # (this is the forward diffusion process)
-            noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)
-
-            # Get the text embedding for conditioning
-            encoder_hidden_states = text_encoder(
-                batch["input_ids"],
-                params=text_encoder_params,
-                train=False,
-            )[0]
-
-            # Predict the noise residual and compute loss
-            model_pred = unet.apply(
-                {"params": params}, noisy_latents, timesteps, encoder_hidden_states, train=True
-            ).sample
-
-            # Get the target for loss depending on the prediction type
-            if noise_scheduler.config.prediction_type == "epsilon":
-                target = noise
-            elif noise_scheduler.config.prediction_type == "v_prediction":
-                target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)
-            else:
-                raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-            loss = (target - model_pred) ** 2
-            loss = loss.mean()
-
-            return loss
-
-        grad_fn = jax.value_and_grad(compute_loss)
-        loss, grad = grad_fn(state.params)
-        grad = jax.lax.pmean(grad, "batch")
-
-        new_state = state.apply_gradients(grads=grad)
-
-        metrics = {"loss": loss}
-        metrics = jax.lax.pmean(metrics, axis_name="batch")
-
-        return new_state, metrics, new_train_rng
-
-    # Create parallel version of the train step
-    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
-
-    # Replicate the train state on each device
-    state = jax_utils.replicate(state)
-    text_encoder_params = jax_utils.replicate(text_encoder.params)
-    vae_params = jax_utils.replicate(vae_params)
-
-    # Train!
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader))
-
-    # Scheduler and math around the number of training steps.
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel & distributed) = {total_train_batch_size}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-
-    global_step = 0
-
-    epochs = tqdm(range(args.num_train_epochs), desc="Epoch ... ", position=0)
-    for epoch in epochs:
-        # ======================== Training ================================
-
-        train_metrics = []
-
-        steps_per_epoch = len(train_dataset) // total_train_batch_size
-        train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
-        # train
-        for batch in train_dataloader:
-            batch = shard(batch)
-            state, train_metric, train_rngs = p_train_step(state, text_encoder_params, vae_params, batch, train_rngs)
-            train_metrics.append(train_metric)
-
-            train_step_progress_bar.update(1)
-
-            global_step += 1
-            if global_step >= args.max_train_steps:
-                break
-
-        train_metric = jax_utils.unreplicate(train_metric)
-
-        train_step_progress_bar.close()
-        epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
-
-    # Create the pipeline using using the trained modules and save it.
-    if jax.process_index() == 0:
-        scheduler = FlaxPNDMScheduler(
-            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
-        )
-        safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(
-            "CompVis/stable-diffusion-safety-checker", from_pt=True
-        )
-        pipeline = FlaxStableDiffusionPipeline(
-            text_encoder=text_encoder,
-            vae=vae,
-            unet=unet,
-            tokenizer=tokenizer,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32"),
-        )
-
-        pipeline.save_pretrained(
-            args.output_dir,
-            params={
-                "text_encoder": get_params_to_save(text_encoder_params),
-                "vae": get_params_to_save(vae_params),
-                "unet": get_params_to_save(state.params),
-                "safety_checker": safety_checker.params,
-            },
-        )
-
-        if args.push_to_hub:
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py
deleted file mode 100644
index c7555e2ebad4c7f6045f3975b61f271a97ec8587..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# NOTE: This file is deprecated and will be removed in a future version.
-# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
-from ...utils import deprecate
-from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
-from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401
-
-
-deprecate(
-    "stable diffusion controlnet",
-    "0.22.0",
-    "Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.",
-    standard_warn=False,
-    stacklevel=3,
-)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/_base_/models/mask_rcnn_r50_fpn.py b/spaces/Andy1621/uniformer_image_detection/configs/_base_/models/mask_rcnn_r50_fpn.py
deleted file mode 100644
index 6fc7908249e013376b343c5fc136cbbe5ff29390..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/_base_/models/mask_rcnn_r50_fpn.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# model settings
-model = dict(
-    type='MaskRCNN',
-    pretrained='torchvision://resnet50',
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        num_outs=5),
-    rpn_head=dict(
-        type='RPNHead',
-        in_channels=256,
-        feat_channels=256,
-        anchor_generator=dict(
-            type='AnchorGenerator',
-            scales=[8],
-            ratios=[0.5, 1.0, 2.0],
-            strides=[4, 8, 16, 32, 64]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]),
-        loss_cls=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-    roi_head=dict(
-        type='StandardRoIHead',
-        bbox_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32]),
-        bbox_head=dict(
-            type='Shared2FCBBoxHead',
-            in_channels=256,
-            fc_out_channels=1024,
-            roi_feat_size=7,
-            num_classes=80,
-            bbox_coder=dict(
-                type='DeltaXYWHBBoxCoder',
-                target_means=[0., 0., 0., 0.],
-                target_stds=[0.1, 0.1, 0.2, 0.2]),
-            reg_class_agnostic=False,
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-        mask_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32]),
-        mask_head=dict(
-            type='FCNMaskHead',
-            num_convs=4,
-            in_channels=256,
-            conv_out_channels=256,
-            num_classes=80,
-            loss_mask=dict(
-                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
-    # model training and testing settings
-    train_cfg=dict(
-        rpn=dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.7,
-                neg_iou_thr=0.3,
-                min_pos_iou=0.3,
-                match_low_quality=True,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='RandomSampler',
-                num=256,
-                pos_fraction=0.5,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=False),
-            allowed_border=-1,
-            pos_weight=-1,
-            debug=False),
-        rpn_proposal=dict(
-            nms_pre=2000,
-            max_per_img=1000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.5,
-                min_pos_iou=0.5,
-                match_low_quality=True,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='RandomSampler',
-                num=512,
-                pos_fraction=0.25,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=True),
-            mask_size=28,
-            pos_weight=-1,
-            debug=False)),
-    test_cfg=dict(
-        rpn=dict(
-            nms_pre=1000,
-            max_per_img=1000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=dict(
-            score_thr=0.05,
-            nms=dict(type='nms', iou_threshold=0.5),
-            max_per_img=100,
-            mask_thr_binary=0.5)))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r50-d8_512x512_80k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r50-d8_512x512_80k_ade20k.py
deleted file mode 100644
index 5e04aa7c6ac050d119e07b715e2082f692e1a1de..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r50-d8_512x512_80k_ade20k.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
-    '../_base_/models/ann_r50-d8.py', '../_base_/datasets/ade20k.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py
deleted file mode 100644
index fba8948a031d46918e9c28cf94fff8e384228e82..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './fcn_d6_r50-d16_769x769_80k_cityscapes.py'
-model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index abeeedf84387d7846a8a2c10480b94c9d8405559..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='torchvision://resnet18',
-    backbone=dict(type='ResNet', depth=18),
-    decode_head=dict(
-        in_channels=512,
-        channels=128,
-    ),
-    auxiliary_head=dict(in_channels=256, channels=64))
diff --git a/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/guided_diffusion/model-card.md b/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/guided_diffusion/model-card.md
deleted file mode 100644
index 54fc09645dcffefdaa6a31e386b2af242c4d3719..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/guided_diffusion/model-card.md
+++ /dev/null
@@ -1,59 +0,0 @@
-# Overview
-
-These are diffusion models and noised image classifiers described in the paper [Diffusion Models Beat GANs on Image Synthesis](https://arxiv.org/abs/2105.05233).
-Included in this release are the following models:
-
- * Noisy ImageNet classifiers at resolutions 64x64, 128x128, 256x256, 512x512
- * A class-unconditional ImageNet diffusion model at resolution 256x256
- * Class conditional ImageNet diffusion models at 64x64, 128x128, 256x256, 512x512 resolutions
- * Class-conditional ImageNet upsampling diffusion models: 64x64->256x256, 128x128->512x512
- * Diffusion models trained on three LSUN classes at 256x256 resolution: cat, horse, bedroom
-
-# Datasets
-
-All of the models we are releasing were either trained on the [ILSVRC 2012 subset of ImageNet](http://www.image-net.org/challenges/LSVRC/2012/) or on single classes of [LSUN](https://arxiv.org/abs/1506.03365).
-Here, we describe characteristics of these datasets which impact model behavior:
-
-**LSUN**: This dataset was collected in 2015 using a combination of human labeling (from Amazon Mechanical Turk) and automated data labeling.
- * Each of the three classes we consider contain over a million images.
- * The dataset creators found that the label accuracy was roughly 90% across the entire LSUN dataset when measured by trained experts.
- * Images are scraped from the internet, and LSUN cat images in particular tend to often follow a “meme” format.
- * We found that there are occasionally humans in these photos, including faces, especially within the cat class.  
-
-**ILSVRC 2012 subset of ImageNet**: This dataset was curated in 2012 and consists of roughly one million images, each belonging to one of 1000 classes.
- * A large portion of the classes in this dataset are animals, plants, and other naturally-occurring objects.
- * Many images contain humans, although usually these humans aren’t reflected by the class label (e.g. the class “Tench, tinca tinca” contains many photos of people holding fish).
-
-# Performance
-
-These models are intended to generate samples consistent with their training distributions.
-This has been measured in terms of FID, Precision, and Recall.
-These metrics all rely on the representations of a [pre-trained Inception-V3 model](https://arxiv.org/abs/1512.00567),
-which was trained on ImageNet, and so is likely to focus more on the ImageNet classes (such as animals) than on other visual features (such as human faces).
-
-Qualitatively, the samples produced by these models often look highly realistic, especially when a diffusion model is combined with a noisy classifier.
-
-# Intended Use
-
-These models are intended to be used for research purposes only.
-In particular, they can be used as a baseline for generative modeling research, or as a starting point to build off of for such research.
-
-These models are not intended to be commercially deployed.
-Additionally, they are not intended to be used to create propaganda or offensive imagery.
-
-Before releasing these models, we probed their ability to ease the creation of targeted imagery, since doing so could be potentially harmful.
-We did this either by fine-tuning our ImageNet models on a target LSUN class, or through classifier guidance with publicly available [CLIP models](https://github.com/openai/CLIP).
- * To probe fine-tuning capabilities, we restricted our compute budget to roughly $100 and tried both standard fine-tuning,
-and a diffusion-specific approach where we train a specialized classifier for the LSUN class. The resulting FIDs were significantly worse than publicly available GAN models, indicating that fine-tuning an ImageNet diffusion model does not significantly lower the cost of image generation.
- * To probe guidance with CLIP, we tried two approaches for using pre-trained CLIP models for classifier guidance. Either we fed the noised image to CLIP directly and used its gradients, or we fed the diffusion model's denoised prediction to the CLIP model and differentiated through the whole process. In both cases, we found that it was difficult to recover information from the CLIP model, indicating that these diffusion models are unlikely to make it significantly easier to extract knowledge from CLIP compared to existing GAN models.
-
-# Limitations
-
-These models sometimes produce highly unrealistic outputs, particularly when generating images containing human faces.
-This may stem from ImageNet's emphasis on non-human objects.
-
-While classifier guidance can improve sample quality, it reduces diversity, resulting in some modes of the data distribution being underrepresented.
-This can potentially amplify existing biases in the training dataset such as gender and racial biases.
-
-Because ImageNet and LSUN contain images from the internet, they include photos of real people, and the model may have memorized some of the information contained in these photos.
-However, these images are already publicly available, and existing generative models trained on ImageNet have not demonstrated significant leakage of this information.
\ No newline at end of file
diff --git a/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/guided_diffusion/scripts/image_train.py b/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/guided_diffusion/scripts/image_train.py
deleted file mode 100644
index 38ec777d8d1ad625156feca9100beb15d72965be..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/guided_diffusion/scripts/image_train.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""
-Train a diffusion model on images.
-"""
-
-import argparse
-
-from guided_diffusion import dist_util, logger
-from guided_diffusion.image_datasets import load_data
-from guided_diffusion.resample import create_named_schedule_sampler
-from guided_diffusion.script_util import (
-    model_and_diffusion_defaults,
-    create_model_and_diffusion,
-    args_to_dict,
-    add_dict_to_argparser,
-)
-from guided_diffusion.train_util import TrainLoop
-
-
-def main():
-    args = create_argparser().parse_args()
-
-    dist_util.setup_dist()
-    logger.configure()
-
-    logger.log("creating model and diffusion...")
-    model, diffusion = create_model_and_diffusion(
-        **args_to_dict(args, model_and_diffusion_defaults().keys())
-    )
-    model.to(dist_util.dev())
-    schedule_sampler = create_named_schedule_sampler(args.schedule_sampler, diffusion)
-
-    logger.log("creating data loader...")
-    data = load_data(
-        data_dir=args.data_dir,
-        batch_size=args.batch_size,
-        image_size=args.image_size,
-        class_cond=args.class_cond,
-    )
-
-    logger.log("training...")
-    TrainLoop(
-        model=model,
-        diffusion=diffusion,
-        data=data,
-        batch_size=args.batch_size,
-        microbatch=args.microbatch,
-        lr=args.lr,
-        ema_rate=args.ema_rate,
-        log_interval=args.log_interval,
-        save_interval=args.save_interval,
-        resume_checkpoint=args.resume_checkpoint,
-        use_fp16=args.use_fp16,
-        fp16_scale_growth=args.fp16_scale_growth,
-        schedule_sampler=schedule_sampler,
-        weight_decay=args.weight_decay,
-        lr_anneal_steps=args.lr_anneal_steps,
-    ).run_loop()
-
-
-def create_argparser():
-    defaults = dict(
-        data_dir="",
-        schedule_sampler="uniform",
-        lr=1e-4,
-        weight_decay=0.0,
-        lr_anneal_steps=0,
-        batch_size=1,
-        microbatch=-1,  # -1 disables microbatches
-        ema_rate="0.9999",  # comma-separated list of EMA values
-        log_interval=10,
-        save_interval=10000,
-        resume_checkpoint="",
-        use_fp16=False,
-        fp16_scale_growth=1e-3,
-    )
-    defaults.update(model_and_diffusion_defaults())
-    parser = argparse.ArgumentParser()
-    add_dict_to_argparser(parser, defaults)
-    return parser
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/seg/sampler/ohem_pixel_sampler.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/seg/sampler/ohem_pixel_sampler.py
deleted file mode 100644
index 88bb10d44026ba9f21756eaea9e550841cd59b9f..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/seg/sampler/ohem_pixel_sampler.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-from ..builder import PIXEL_SAMPLERS
-from .base_pixel_sampler import BasePixelSampler
-
-
-@PIXEL_SAMPLERS.register_module()
-class OHEMPixelSampler(BasePixelSampler):
-    """Online Hard Example Mining Sampler for segmentation.
-
-    Args:
-        context (nn.Module): The context of sampler, subclass of
-            :obj:`BaseDecodeHead`.
-        thresh (float, optional): The threshold for hard example selection.
-            Below which, are prediction with low confidence. If not
-            specified, the hard examples will be pixels of top ``min_kept``
-            loss. Default: None.
-        min_kept (int, optional): The minimum number of predictions to keep.
-            Default: 100000.
-    """
-
-    def __init__(self, context, thresh=None, min_kept=100000):
-        super(OHEMPixelSampler, self).__init__()
-        self.context = context
-        assert min_kept > 1
-        self.thresh = thresh
-        self.min_kept = min_kept
-
-    def sample(self, seg_logit, seg_label):
-        """Sample pixels that have high loss or with low prediction confidence.
-
-        Args:
-            seg_logit (torch.Tensor): segmentation logits, shape (N, C, H, W)
-            seg_label (torch.Tensor): segmentation label, shape (N, 1, H, W)
-
-        Returns:
-            torch.Tensor: segmentation weight, shape (N, H, W)
-        """
-        with torch.no_grad():
-            assert seg_logit.shape[2:] == seg_label.shape[2:]
-            assert seg_label.shape[1] == 1
-            seg_label = seg_label.squeeze(1).long()
-            batch_kept = self.min_kept * seg_label.size(0)
-            valid_mask = seg_label != self.context.ignore_index
-            seg_weight = seg_logit.new_zeros(size=seg_label.size())
-            valid_seg_weight = seg_weight[valid_mask]
-            if self.thresh is not None:
-                seg_prob = F.softmax(seg_logit, dim=1)
-
-                tmp_seg_label = seg_label.clone().unsqueeze(1)
-                tmp_seg_label[tmp_seg_label == self.context.ignore_index] = 0
-                seg_prob = seg_prob.gather(1, tmp_seg_label).squeeze(1)
-                sort_prob, sort_indices = seg_prob[valid_mask].sort()
-
-                if sort_prob.numel() > 0:
-                    min_threshold = sort_prob[min(batch_kept,
-                                                  sort_prob.numel() - 1)]
-                else:
-                    min_threshold = 0.0
-                threshold = max(min_threshold, self.thresh)
-                valid_seg_weight[seg_prob[valid_mask] < threshold] = 1.
-            else:
-                losses = self.context.loss_decode(
-                    seg_logit,
-                    seg_label,
-                    weight=None,
-                    ignore_index=self.context.ignore_index,
-                    reduction_override='none')
-                # faster than topk according to https://github.com/pytorch/pytorch/issues/22812  # noqa
-                _, sort_indices = losses[valid_mask].sort(descending=True)
-                valid_seg_weight[sort_indices[:batch_kept]] = 1.
-
-            seg_weight[valid_mask] = valid_seg_weight
-
-            return seg_weight
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/attention.py b/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/attention.py
deleted file mode 100644
index 509cd873768f0dd75a75ab3fcdd652822b12b59f..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/ldm/modules/attention.py
+++ /dev/null
@@ -1,341 +0,0 @@
-from inspect import isfunction
-import math
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-from einops import rearrange, repeat
-from typing import Optional, Any
-
-from ldm.modules.diffusionmodules.util import checkpoint
-
-
-try:
-    import xformers
-    import xformers.ops
-    XFORMERS_IS_AVAILBLE = True
-except:
-    XFORMERS_IS_AVAILBLE = False
-
-# CrossAttn precision handling
-import os
-_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
-
-def exists(val):
-    return val is not None
-
-
-def uniq(arr):
-    return{el: True for el in arr}.keys()
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def max_neg_value(t):
-    return -torch.finfo(t.dtype).max
-
-
-def init_(tensor):
-    dim = tensor.shape[-1]
-    std = 1 / math.sqrt(dim)
-    tensor.uniform_(-std, std)
-    return tensor
-
-
-# feedforward
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def Normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class SpatialSelfAttention(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = rearrange(q, 'b c h w -> b (h w) c')
-        k = rearrange(k, 'b c h w -> b c (h w)')
-        w_ = torch.einsum('bij,bjk->bik', q, k)
-
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = rearrange(v, 'b c h w -> b c (h w)')
-        w_ = rearrange(w_, 'b i j -> b j i')
-        h_ = torch.einsum('bij,bjk->bik', v, w_)
-        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
-        super().__init__()
-        inner_dim = dim_head * heads
-        context_dim = default(context_dim, query_dim)
-
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
-        self.to_out = nn.Sequential(
-            nn.Linear(inner_dim, query_dim),
-            nn.Dropout(dropout)
-        )
-
-    def forward(self, x, context=None, mask=None):
-        h = self.heads
-
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-        # force cast to fp32 to avoid overflowing
-        if _ATTN_PRECISION =="fp32":
-            with torch.autocast(enabled=False, device_type = 'cuda'):
-                q, k = q.float(), k.float()
-                sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-        else:
-            sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-        
-        del q, k
-    
-        if exists(mask):
-            mask = rearrange(mask, 'b ... -> b (...)')
-            max_neg_value = -torch.finfo(sim.dtype).max
-            mask = repeat(mask, 'b j -> (b h) () j', h=h)
-            sim.masked_fill_(~mask, max_neg_value)
-
-        # attention, what we cannot get enough of
-        sim = sim.softmax(dim=-1)
-
-        out = einsum('b i j, b j d -> b i d', sim, v)
-        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-        return self.to_out(out)
-
-
-class MemoryEfficientCrossAttention(nn.Module):
-    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
-        super().__init__()
-        print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
-              f"{heads} heads.")
-        inner_dim = dim_head * heads
-        context_dim = default(context_dim, query_dim)
-
-        self.heads = heads
-        self.dim_head = dim_head
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
-        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
-        self.attention_op: Optional[Any] = None
-
-    def forward(self, x, context=None, mask=None):
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-        b, _, _ = q.shape
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(b, t.shape[1], self.heads, self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * self.heads, t.shape[1], self.dim_head)
-            .contiguous(),
-            (q, k, v),
-        )
-
-        # actually compute the attention, what we cannot get enough of
-        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
-
-        if exists(mask):
-            raise NotImplementedError
-        out = (
-            out.unsqueeze(0)
-            .reshape(b, self.heads, out.shape[1], self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b, out.shape[1], self.heads * self.dim_head)
-        )
-        return self.to_out(out)
-
-
-class BasicTransformerBlock(nn.Module):
-    ATTENTION_MODES = {
-        "softmax": CrossAttention,  # vanilla attention
-        "softmax-xformers": MemoryEfficientCrossAttention
-    }
-    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
-                 disable_self_attn=False):
-        super().__init__()
-        attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
-        assert attn_mode in self.ATTENTION_MODES
-        attn_cls = self.ATTENTION_MODES[attn_mode]
-        self.disable_self_attn = disable_self_attn
-        self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
-                              context_dim=context_dim if self.disable_self_attn else None)  # is a self-attention if not self.disable_self_attn
-        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
-        self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
-                              heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
-        self.norm1 = nn.LayerNorm(dim)
-        self.norm2 = nn.LayerNorm(dim)
-        self.norm3 = nn.LayerNorm(dim)
-        self.checkpoint = checkpoint
-
-    def forward(self, x, context=None):
-        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
-
-    def _forward(self, x, context=None):
-        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
-        x = self.attn2(self.norm2(x), context=context) + x
-        x = self.ff(self.norm3(x)) + x
-        return x
-
-
-class SpatialTransformer(nn.Module):
-    """
-    Transformer block for image-like data.
-    First, project the input (aka embedding)
-    and reshape to b, t, d.
-    Then apply standard transformer action.
-    Finally, reshape to image
-    NEW: use_linear for more efficiency instead of the 1x1 convs
-    """
-    def __init__(self, in_channels, n_heads, d_head,
-                 depth=1, dropout=0., context_dim=None,
-                 disable_self_attn=False, use_linear=False,
-                 use_checkpoint=True):
-        super().__init__()
-        if exists(context_dim) and not isinstance(context_dim, list):
-            context_dim = [context_dim]
-        self.in_channels = in_channels
-        inner_dim = n_heads * d_head
-        self.norm = Normalize(in_channels)
-        if not use_linear:
-            self.proj_in = nn.Conv2d(in_channels,
-                                     inner_dim,
-                                     kernel_size=1,
-                                     stride=1,
-                                     padding=0)
-        else:
-            self.proj_in = nn.Linear(in_channels, inner_dim)
-
-        self.transformer_blocks = nn.ModuleList(
-            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
-                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
-                for d in range(depth)]
-        )
-        if not use_linear:
-            self.proj_out = zero_module(nn.Conv2d(inner_dim,
-                                                  in_channels,
-                                                  kernel_size=1,
-                                                  stride=1,
-                                                  padding=0))
-        else:
-            self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
-        self.use_linear = use_linear
-
-    def forward(self, x, context=None):
-        # note: if no context is given, cross-attention defaults to self-attention
-        if not isinstance(context, list):
-            context = [context]
-        b, c, h, w = x.shape
-        x_in = x
-        x = self.norm(x)
-        if not self.use_linear:
-            x = self.proj_in(x)
-        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
-        if self.use_linear:
-            x = self.proj_in(x)
-        for i, block in enumerate(self.transformer_blocks):
-            x = block(x, context=context[i])
-        if self.use_linear:
-            x = self.proj_out(x)
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
-        if not self.use_linear:
-            x = self.proj_out(x)
-        return x + x_in
-
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/__init__.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/__init__.py
deleted file mode 100644
index b22f7abb93b9d7aeee50829b35746aaa3f9f5feb..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/__init__.py
+++ /dev/null
@@ -1,120 +0,0 @@
-"""
-pip._vendor is for vendoring dependencies of pip to prevent needing pip to
-depend on something external.
-
-Files inside of pip._vendor should be considered immutable and should only be
-updated to versions from upstream.
-"""
-from __future__ import absolute_import
-
-import glob
-import os.path
-import sys
-
-# Downstream redistributors which have debundled our dependencies should also
-# patch this value to be true. This will trigger the additional patching
-# to cause things like "six" to be available as pip.
-DEBUNDLED = False
-
-# By default, look in this directory for a bunch of .whl files which we will
-# add to the beginning of sys.path before attempting to import anything. This
-# is done to support downstream re-distributors like Debian and Fedora who
-# wish to create their own Wheels for our dependencies to aid in debundling.
-WHEEL_DIR = os.path.abspath(os.path.dirname(__file__))
-
-
-# Define a small helper function to alias our vendored modules to the real ones
-# if the vendored ones do not exist. This idea of this was taken from
-# https://github.com/kennethreitz/requests/pull/2567.
-def vendored(modulename):
-    vendored_name = "{0}.{1}".format(__name__, modulename)
-
-    try:
-        __import__(modulename, globals(), locals(), level=0)
-    except ImportError:
-        # We can just silently allow import failures to pass here. If we
-        # got to this point it means that ``import pip._vendor.whatever``
-        # failed and so did ``import whatever``. Since we're importing this
-        # upfront in an attempt to alias imports, not erroring here will
-        # just mean we get a regular import error whenever pip *actually*
-        # tries to import one of these modules to use it, which actually
-        # gives us a better error message than we would have otherwise
-        # gotten.
-        pass
-    else:
-        sys.modules[vendored_name] = sys.modules[modulename]
-        base, head = vendored_name.rsplit(".", 1)
-        setattr(sys.modules[base], head, sys.modules[modulename])
-
-
-# If we're operating in a debundled setup, then we want to go ahead and trigger
-# the aliasing of our vendored libraries as well as looking for wheels to add
-# to our sys.path. This will cause all of this code to be a no-op typically
-# however downstream redistributors can enable it in a consistent way across
-# all platforms.
-if DEBUNDLED:
-    # Actually look inside of WHEEL_DIR to find .whl files and add them to the
-    # front of our sys.path.
-    sys.path[:] = glob.glob(os.path.join(WHEEL_DIR, "*.whl")) + sys.path
-
-    # Actually alias all of our vendored dependencies.
-    vendored("cachecontrol")
-    vendored("certifi")
-    vendored("colorama")
-    vendored("distlib")
-    vendored("distro")
-    vendored("six")
-    vendored("six.moves")
-    vendored("six.moves.urllib")
-    vendored("six.moves.urllib.parse")
-    vendored("packaging")
-    vendored("packaging.version")
-    vendored("packaging.specifiers")
-    vendored("pep517")
-    vendored("pkg_resources")
-    vendored("platformdirs")
-    vendored("progress")
-    vendored("requests")
-    vendored("requests.exceptions")
-    vendored("requests.packages")
-    vendored("requests.packages.urllib3")
-    vendored("requests.packages.urllib3._collections")
-    vendored("requests.packages.urllib3.connection")
-    vendored("requests.packages.urllib3.connectionpool")
-    vendored("requests.packages.urllib3.contrib")
-    vendored("requests.packages.urllib3.contrib.ntlmpool")
-    vendored("requests.packages.urllib3.contrib.pyopenssl")
-    vendored("requests.packages.urllib3.exceptions")
-    vendored("requests.packages.urllib3.fields")
-    vendored("requests.packages.urllib3.filepost")
-    vendored("requests.packages.urllib3.packages")
-    vendored("requests.packages.urllib3.packages.ordered_dict")
-    vendored("requests.packages.urllib3.packages.six")
-    vendored("requests.packages.urllib3.packages.ssl_match_hostname")
-    vendored("requests.packages.urllib3.packages.ssl_match_hostname."
-             "_implementation")
-    vendored("requests.packages.urllib3.poolmanager")
-    vendored("requests.packages.urllib3.request")
-    vendored("requests.packages.urllib3.response")
-    vendored("requests.packages.urllib3.util")
-    vendored("requests.packages.urllib3.util.connection")
-    vendored("requests.packages.urllib3.util.request")
-    vendored("requests.packages.urllib3.util.response")
-    vendored("requests.packages.urllib3.util.retry")
-    vendored("requests.packages.urllib3.util.ssl_")
-    vendored("requests.packages.urllib3.util.timeout")
-    vendored("requests.packages.urllib3.util.url")
-    vendored("resolvelib")
-    vendored("rich")
-    vendored("rich.console")
-    vendored("rich.highlighter")
-    vendored("rich.logging")
-    vendored("rich.markup")
-    vendored("rich.progress")
-    vendored("rich.segment")
-    vendored("rich.style")
-    vendored("rich.text")
-    vendored("rich.traceback")
-    vendored("tenacity")
-    vendored("tomli")
-    vendored("urllib3")
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/box.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/box.py
deleted file mode 100644
index 97d2a94445770e195b9fc73e904b920d5ff04104..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/box.py
+++ /dev/null
@@ -1,517 +0,0 @@
-import sys
-from typing import TYPE_CHECKING, Iterable, List
-
-if sys.version_info >= (3, 8):
-    from typing import Literal
-else:
-    from pip._vendor.typing_extensions import Literal  # pragma: no cover
-
-
-from ._loop import loop_last
-
-if TYPE_CHECKING:
-    from pip._vendor.rich.console import ConsoleOptions
-
-
-class Box:
-    """Defines characters to render boxes.
-
-    ┌─┬┐ top
-    │ ││ head
-    ├─┼┤ head_row
-    │ ││ mid
-    ├─┼┤ row
-    ├─┼┤ foot_row
-    │ ││ foot
-    └─┴┘ bottom
-
-    Args:
-        box (str): Characters making up box.
-        ascii (bool, optional): True if this box uses ascii characters only. Default is False.
-    """
-
-    def __init__(self, box: str, *, ascii: bool = False) -> None:
-        self._box = box
-        self.ascii = ascii
-        line1, line2, line3, line4, line5, line6, line7, line8 = box.splitlines()
-        # top
-        self.top_left, self.top, self.top_divider, self.top_right = iter(line1)
-        # head
-        self.head_left, _, self.head_vertical, self.head_right = iter(line2)
-        # head_row
-        (
-            self.head_row_left,
-            self.head_row_horizontal,
-            self.head_row_cross,
-            self.head_row_right,
-        ) = iter(line3)
-
-        # mid
-        self.mid_left, _, self.mid_vertical, self.mid_right = iter(line4)
-        # row
-        self.row_left, self.row_horizontal, self.row_cross, self.row_right = iter(line5)
-        # foot_row
-        (
-            self.foot_row_left,
-            self.foot_row_horizontal,
-            self.foot_row_cross,
-            self.foot_row_right,
-        ) = iter(line6)
-        # foot
-        self.foot_left, _, self.foot_vertical, self.foot_right = iter(line7)
-        # bottom
-        self.bottom_left, self.bottom, self.bottom_divider, self.bottom_right = iter(
-            line8
-        )
-
-    def __repr__(self) -> str:
-        return "Box(...)"
-
-    def __str__(self) -> str:
-        return self._box
-
-    def substitute(self, options: "ConsoleOptions", safe: bool = True) -> "Box":
-        """Substitute this box for another if it won't render due to platform issues.
-
-        Args:
-            options (ConsoleOptions): Console options used in rendering.
-            safe (bool, optional): Substitute this for another Box if there are known problems
-                displaying on the platform (currently only relevant on Windows). Default is True.
-
-        Returns:
-            Box: A different Box or the same Box.
-        """
-        box = self
-        if options.legacy_windows and safe:
-            box = LEGACY_WINDOWS_SUBSTITUTIONS.get(box, box)
-        if options.ascii_only and not box.ascii:
-            box = ASCII
-        return box
-
-    def get_plain_headed_box(self) -> "Box":
-        """If this box uses special characters for the borders of the header, then
-        return the equivalent box that does not.
-
-        Returns:
-            Box: The most similar Box that doesn't use header-specific box characters.
-                If the current Box already satisfies this criterion, then it's returned.
-        """
-        return PLAIN_HEADED_SUBSTITUTIONS.get(self, self)
-
-    def get_top(self, widths: Iterable[int]) -> str:
-        """Get the top of a simple box.
-
-        Args:
-            widths (List[int]): Widths of columns.
-
-        Returns:
-            str: A string of box characters.
-        """
-
-        parts: List[str] = []
-        append = parts.append
-        append(self.top_left)
-        for last, width in loop_last(widths):
-            append(self.top * width)
-            if not last:
-                append(self.top_divider)
-        append(self.top_right)
-        return "".join(parts)
-
-    def get_row(
-        self,
-        widths: Iterable[int],
-        level: Literal["head", "row", "foot", "mid"] = "row",
-        edge: bool = True,
-    ) -> str:
-        """Get the top of a simple box.
-
-        Args:
-            width (List[int]): Widths of columns.
-
-        Returns:
-            str: A string of box characters.
-        """
-        if level == "head":
-            left = self.head_row_left
-            horizontal = self.head_row_horizontal
-            cross = self.head_row_cross
-            right = self.head_row_right
-        elif level == "row":
-            left = self.row_left
-            horizontal = self.row_horizontal
-            cross = self.row_cross
-            right = self.row_right
-        elif level == "mid":
-            left = self.mid_left
-            horizontal = " "
-            cross = self.mid_vertical
-            right = self.mid_right
-        elif level == "foot":
-            left = self.foot_row_left
-            horizontal = self.foot_row_horizontal
-            cross = self.foot_row_cross
-            right = self.foot_row_right
-        else:
-            raise ValueError("level must be 'head', 'row' or 'foot'")
-
-        parts: List[str] = []
-        append = parts.append
-        if edge:
-            append(left)
-        for last, width in loop_last(widths):
-            append(horizontal * width)
-            if not last:
-                append(cross)
-        if edge:
-            append(right)
-        return "".join(parts)
-
-    def get_bottom(self, widths: Iterable[int]) -> str:
-        """Get the bottom of a simple box.
-
-        Args:
-            widths (List[int]): Widths of columns.
-
-        Returns:
-            str: A string of box characters.
-        """
-
-        parts: List[str] = []
-        append = parts.append
-        append(self.bottom_left)
-        for last, width in loop_last(widths):
-            append(self.bottom * width)
-            if not last:
-                append(self.bottom_divider)
-        append(self.bottom_right)
-        return "".join(parts)
-
-
-ASCII: Box = Box(
-    """\
-+--+
-| ||
-|-+|
-| ||
-|-+|
-|-+|
-| ||
-+--+
-""",
-    ascii=True,
-)
-
-ASCII2: Box = Box(
-    """\
-+-++
-| ||
-+-++
-| ||
-+-++
-+-++
-| ||
-+-++
-""",
-    ascii=True,
-)
-
-ASCII_DOUBLE_HEAD: Box = Box(
-    """\
-+-++
-| ||
-+=++
-| ||
-+-++
-+-++
-| ||
-+-++
-""",
-    ascii=True,
-)
-
-SQUARE: Box = Box(
-    """\
-┌─┬┐
-│ ││
-├─┼┤
-│ ││
-├─┼┤
-├─┼┤
-│ ││
-└─┴┘
-"""
-)
-
-SQUARE_DOUBLE_HEAD: Box = Box(
-    """\
-┌─┬┐
-│ ││
-╞═╪╡
-│ ││
-├─┼┤
-├─┼┤
-│ ││
-└─┴┘
-"""
-)
-
-MINIMAL: Box = Box(
-    """\
-  ╷ 
-  │ 
-╶─┼╴
-  │ 
-╶─┼╴
-╶─┼╴
-  │ 
-  ╵ 
-"""
-)
-
-
-MINIMAL_HEAVY_HEAD: Box = Box(
-    """\
-  ╷ 
-  │ 
-╺━┿╸
-  │ 
-╶─┼╴
-╶─┼╴
-  │ 
-  ╵ 
-"""
-)
-
-MINIMAL_DOUBLE_HEAD: Box = Box(
-    """\
-  ╷ 
-  │ 
- ═╪ 
-  │ 
- ─┼ 
- ─┼ 
-  │ 
-  ╵ 
-"""
-)
-
-
-SIMPLE: Box = Box(
-    """\
-    
-    
- ── 
-    
-    
- ── 
-    
-    
-"""
-)
-
-SIMPLE_HEAD: Box = Box(
-    """\
-    
-    
- ── 
-    
-    
-    
-    
-    
-"""
-)
-
-
-SIMPLE_HEAVY: Box = Box(
-    """\
-    
-    
- ━━ 
-    
-    
- ━━ 
-    
-    
-"""
-)
-
-
-HORIZONTALS: Box = Box(
-    """\
- ── 
-    
- ── 
-    
- ── 
- ── 
-    
- ── 
-"""
-)
-
-ROUNDED: Box = Box(
-    """\
-╭─┬╮
-│ ││
-├─┼┤
-│ ││
-├─┼┤
-├─┼┤
-│ ││
-╰─┴╯
-"""
-)
-
-HEAVY: Box = Box(
-    """\
-┏━┳┓
-┃ ┃┃
-┣━╋┫
-┃ ┃┃
-┣━╋┫
-┣━╋┫
-┃ ┃┃
-┗━┻┛
-"""
-)
-
-HEAVY_EDGE: Box = Box(
-    """\
-┏━┯┓
-┃ │┃
-┠─┼┨
-┃ │┃
-┠─┼┨
-┠─┼┨
-┃ │┃
-┗━┷┛
-"""
-)
-
-HEAVY_HEAD: Box = Box(
-    """\
-┏━┳┓
-┃ ┃┃
-┡━╇┩
-│ ││
-├─┼┤
-├─┼┤
-│ ││
-└─┴┘
-"""
-)
-
-DOUBLE: Box = Box(
-    """\
-╔═╦╗
-║ ║║
-╠═╬╣
-║ ║║
-╠═╬╣
-╠═╬╣
-║ ║║
-╚═╩╝
-"""
-)
-
-DOUBLE_EDGE: Box = Box(
-    """\
-╔═╤╗
-║ │║
-╟─┼╢
-║ │║
-╟─┼╢
-╟─┼╢
-║ │║
-╚═╧╝
-"""
-)
-
-MARKDOWN: Box = Box(
-    """\
-    
-| ||
-|-||
-| ||
-|-||
-|-||
-| ||
-    
-""",
-    ascii=True,
-)
-
-# Map Boxes that don't render with raster fonts on to equivalent that do
-LEGACY_WINDOWS_SUBSTITUTIONS = {
-    ROUNDED: SQUARE,
-    MINIMAL_HEAVY_HEAD: MINIMAL,
-    SIMPLE_HEAVY: SIMPLE,
-    HEAVY: SQUARE,
-    HEAVY_EDGE: SQUARE,
-    HEAVY_HEAD: SQUARE,
-}
-
-# Map headed boxes to their headerless equivalents
-PLAIN_HEADED_SUBSTITUTIONS = {
-    HEAVY_HEAD: SQUARE,
-    SQUARE_DOUBLE_HEAD: SQUARE,
-    MINIMAL_DOUBLE_HEAD: MINIMAL,
-    MINIMAL_HEAVY_HEAD: MINIMAL,
-    ASCII_DOUBLE_HEAD: ASCII2,
-}
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    from pip._vendor.rich.columns import Columns
-    from pip._vendor.rich.panel import Panel
-
-    from . import box as box
-    from .console import Console
-    from .table import Table
-    from .text import Text
-
-    console = Console(record=True)
-
-    BOXES = [
-        "ASCII",
-        "ASCII2",
-        "ASCII_DOUBLE_HEAD",
-        "SQUARE",
-        "SQUARE_DOUBLE_HEAD",
-        "MINIMAL",
-        "MINIMAL_HEAVY_HEAD",
-        "MINIMAL_DOUBLE_HEAD",
-        "SIMPLE",
-        "SIMPLE_HEAD",
-        "SIMPLE_HEAVY",
-        "HORIZONTALS",
-        "ROUNDED",
-        "HEAVY",
-        "HEAVY_EDGE",
-        "HEAVY_HEAD",
-        "DOUBLE",
-        "DOUBLE_EDGE",
-        "MARKDOWN",
-    ]
-
-    console.print(Panel("[bold green]Box Constants", style="green"), justify="center")
-    console.print()
-
-    columns = Columns(expand=True, padding=2)
-    for box_name in sorted(BOXES):
-        table = Table(
-            show_footer=True, style="dim", border_style="not dim", expand=True
-        )
-        table.add_column("Header 1", "Footer 1")
-        table.add_column("Header 2", "Footer 2")
-        table.add_row("Cell", "Cell")
-        table.add_row("Cell", "Cell")
-        table.box = getattr(box, box_name)
-        table.title = Text(f"box.{box_name}", style="magenta")
-        columns.add_renderable(table)
-    console.print(columns)
-
-    # console.save_svg("box.svg")
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/setopt.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/setopt.py
deleted file mode 100644
index 6358c0451b2d0036e3821d897fb6f7ab436ee4a9..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/setopt.py
+++ /dev/null
@@ -1,149 +0,0 @@
-from distutils.util import convert_path
-from distutils import log
-from distutils.errors import DistutilsOptionError
-import distutils
-import os
-import configparser
-
-from setuptools import Command
-
-__all__ = ['config_file', 'edit_config', 'option_base', 'setopt']
-
-
-def config_file(kind="local"):
-    """Get the filename of the distutils, local, global, or per-user config
-
-    `kind` must be one of "local", "global", or "user"
-    """
-    if kind == 'local':
-        return 'setup.cfg'
-    if kind == 'global':
-        return os.path.join(
-            os.path.dirname(distutils.__file__), 'distutils.cfg'
-        )
-    if kind == 'user':
-        dot = os.name == 'posix' and '.' or ''
-        return os.path.expanduser(convert_path("~/%spydistutils.cfg" % dot))
-    raise ValueError(
-        "config_file() type must be 'local', 'global', or 'user'", kind
-    )
-
-
-def edit_config(filename, settings, dry_run=False):
-    """Edit a configuration file to include `settings`
-
-    `settings` is a dictionary of dictionaries or ``None`` values, keyed by
-    command/section name.  A ``None`` value means to delete the entire section,
-    while a dictionary lists settings to be changed or deleted in that section.
-    A setting of ``None`` means to delete that setting.
-    """
-    log.debug("Reading configuration from %s", filename)
-    opts = configparser.RawConfigParser()
-    opts.optionxform = lambda x: x
-    opts.read([filename])
-    for section, options in settings.items():
-        if options is None:
-            log.info("Deleting section [%s] from %s", section, filename)
-            opts.remove_section(section)
-        else:
-            if not opts.has_section(section):
-                log.debug("Adding new section [%s] to %s", section, filename)
-                opts.add_section(section)
-            for option, value in options.items():
-                if value is None:
-                    log.debug(
-                        "Deleting %s.%s from %s",
-                        section, option, filename
-                    )
-                    opts.remove_option(section, option)
-                    if not opts.options(section):
-                        log.info("Deleting empty [%s] section from %s",
-                                 section, filename)
-                        opts.remove_section(section)
-                else:
-                    log.debug(
-                        "Setting %s.%s to %r in %s",
-                        section, option, value, filename
-                    )
-                    opts.set(section, option, value)
-
-    log.info("Writing %s", filename)
-    if not dry_run:
-        with open(filename, 'w') as f:
-            opts.write(f)
-
-
-class option_base(Command):
-    """Abstract base class for commands that mess with config files"""
-
-    user_options = [
-        ('global-config', 'g',
-         "save options to the site-wide distutils.cfg file"),
-        ('user-config', 'u',
-         "save options to the current user's pydistutils.cfg file"),
-        ('filename=', 'f',
-         "configuration file to use (default=setup.cfg)"),
-    ]
-
-    boolean_options = [
-        'global-config', 'user-config',
-    ]
-
-    def initialize_options(self):
-        self.global_config = None
-        self.user_config = None
-        self.filename = None
-
-    def finalize_options(self):
-        filenames = []
-        if self.global_config:
-            filenames.append(config_file('global'))
-        if self.user_config:
-            filenames.append(config_file('user'))
-        if self.filename is not None:
-            filenames.append(self.filename)
-        if not filenames:
-            filenames.append(config_file('local'))
-        if len(filenames) > 1:
-            raise DistutilsOptionError(
-                "Must specify only one configuration file option",
-                filenames
-            )
-        self.filename, = filenames
-
-
-class setopt(option_base):
-    """Save command-line options to a file"""
-
-    description = "set an option in setup.cfg or another config file"
-
-    user_options = [
-        ('command=', 'c', 'command to set an option for'),
-        ('option=', 'o', 'option to set'),
-        ('set-value=', 's', 'value of the option'),
-        ('remove', 'r', 'remove (unset) the value'),
-    ] + option_base.user_options
-
-    boolean_options = option_base.boolean_options + ['remove']
-
-    def initialize_options(self):
-        option_base.initialize_options(self)
-        self.command = None
-        self.option = None
-        self.set_value = None
-        self.remove = None
-
-    def finalize_options(self):
-        option_base.finalize_options(self)
-        if self.command is None or self.option is None:
-            raise DistutilsOptionError("Must specify --command *and* --option")
-        if self.set_value is None and not self.remove:
-            raise DistutilsOptionError("Must specify --set-value or --remove")
-
-    def run(self):
-        edit_config(
-            self.filename, {
-                self.command: {self.option.replace('-', '_'): self.set_value}
-            },
-            self.dry_run
-        )
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/notes/changelog.md b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/notes/changelog.md
deleted file mode 100644
index 000e9f8898dba53f54121a5325ba5165e45ddea2..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/docs/notes/changelog.md
+++ /dev/null
@@ -1,48 +0,0 @@
-# Change Log and Backward Compatibility
-
-### Releases
-See release logs at
-[https://github.com/facebookresearch/detectron2/releases](https://github.com/facebookresearch/detectron2/releases)
-for new updates.
-
-### Backward Compatibility
-
-Due to the research nature of what the library does, there might be backward incompatible changes.
-But we try to reduce users' disruption by the following ways:
-* APIs listed in [API documentation](https://detectron2.readthedocs.io/modules/index.html), including
-  function/class names, their arguments, and documented class attributes, are considered *stable* unless
-  otherwise noted in the documentation.
-  They are less likely to be broken, but if needed, will trigger a deprecation warning for a reasonable period
-  before getting broken, and will be documented in release logs.
-* Others functions/classses/attributes are considered internal, and are more likely to change.
-  However, we're aware that some of them may be already used by other projects, and in particular we may
-  use them for convenience among projects under `detectron2/projects`.
-  For such APIs, we may treat them as stable APIs and also apply the above strategies.
-  They may be promoted to stable when we're ready.
-* Projects under "detectron2/projects" or imported with "detectron2.projects" are research projects
-  and are all considered experimental.
-* Classes/functions that contain the word "default" or are explicitly documented to produce
-  "default behavior" may change their behaviors when new features are added.
-
-Despite of the possible breakage, if a third-party project would like to keep up with the latest updates
-in detectron2, using it as a library will still be less disruptive than forking, because
-the frequency and scope of API changes will be much smaller than code changes.
-
-To see such changes, search for "incompatible changes" in [release logs](https://github.com/facebookresearch/detectron2/releases).
-
-### Config Version Change Log
-
-Detectron2's config version has not been changed since open source.
-There is no need for an open source user to worry about this.
-
-* v1: Rename `RPN_HEAD.NAME` to `RPN.HEAD_NAME`.
-* v2: A batch of rename of many configurations before release.
-
-### Silent Regressions in Historical Versions:
-
-We list a few silent regressions, since they may silently produce incorrect results and will be hard to debug.
-
-* 04/01/2020 - 05/11/2020: Bad accuracy if `TRAIN_ON_PRED_BOXES` is set to True.
-* 03/30/2020 - 04/01/2020: ResNets are not correctly built.
-* 12/19/2019 - 12/26/2019: Using aspect ratio grouping causes a drop in accuracy.
-* - 11/9/2019: Test time augmentation does not predict the last category.
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet2_docs/MODEL_ZOO.md b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet2_docs/MODEL_ZOO.md
deleted file mode 100644
index 7a2a92b60d0ebf8f6444f24c3bd74b753c80c57f..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet2_docs/MODEL_ZOO.md
+++ /dev/null
@@ -1,73 +0,0 @@
-# MODEL_ZOO
-
-### Common settings and notes
-
-- Multiscale training is used by default in all models. The results are all reported using single-scale testing. 
-- We report runtime on our local workstation with a TitanXp GPU and a Titan RTX GPU.
-- All models are trained on 8-GPU servers by default. The 1280 models are trained on 24G GPUs. Reducing the batchsize with the linear learning rate rule should be fine.
-- All models can be downloaded directly from [Google drive](https://drive.google.com/drive/folders/1eae1cTX8tvIaCeof36sBgxrXEXALYlf-?usp=sharing).
-
-
-## COCO
-
-### CenterNet
-
-| Model                                     | val mAP | FPS (Titan Xp/ Titan RTX) | links     |
-|-------------------------------------------|---------|---------|-----------|
-| CenterNet-S4_DLA_8x                       |  42.5   | 50 / 71 |[config](../configs/CenterNet-S4_DLA_8x.yaml)/[model](https://drive.google.com/file/d/1lNBhVHnZAEBRD66MFaHjm5Ij6Z4KYrJq/view?usp=sharing)|
-| CenterNet-FPN_R50_1x                      |  40.2   | 20 / 24 |[config](../configs/CenterNet-FPN_R50_1x.yaml)/[model](https://drive.google.com/file/d/1rVG1YTthMXvutC6jr9KoE2DthT5-jhGj/view?usp=sharing)|
-
-#### Note
-
-- `CenterNet-S4_DLA_8x` is a re-implemented version of the original CenterNet (stride 4), with several changes, including
-  - Using top-left-right-bottom box encoding and GIoU Loss; adding regression loss to the center 3x3 region.
-  - Adding more positive pixels for the heatmap loss whose regression loss is small and is within the center3x3 region.
-  - Using more heavy crop augmentation (EfficientDet-style crop ratio 0.1-2), and removing color augmentations.
-  - Using standard NMS instead of max pooling.
-  - Using RetinaNet-style optimizer (SGD), learning rate rule (0.01 for each batch size 16), and schedule (8x12 epochs).
-- `CenterNet-FPN_R50_1x` is a (new) FPN version of CenterNet. It includes the changes above, and assigns objects to FPN levels based on a fixed size range. The model is trained with standard short edge 640-800 multi-scale training with 12 epochs (1x).
-
-
-### CenterNet2
-
-| Model                                     | val mAP | FPS (Titan Xp/ Titan RTX) | links     |
-|-------------------------------------------|---------|---------|-----------|
-| CenterNet2-F_R50_1x                       |   41.7  | 22 / 27  |[config](../configs/CenterNet2-F_R50_1x.yaml)/[model](X)|
-| CenterNet2_R50_1x                         |  42.9   | 18 / 24 |[config](../configs/CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/1Osu1J_sskt_1FaGdfJKa4vd2N71TWS9W/view?usp=sharing)|
-| CenterNet2_X101-DCN_2x                    |  49.9   | 6 / 8  |[config](../configs/CenterNet2_X101-DCN_2x.yaml)/[model](https://drive.google.com/file/d/1IHgpUHVJWpvMuFUUetgKWsw27pRNN2oK/view?usp=sharing)|
-| CenterNet2_DLA-BiFPN-P3_4x                |  43.8   | 40 / 50|[config](../configs/CenterNet2_DLA-BiFPN-P3_4x.yaml)/[model](https://drive.google.com/file/d/12GUNlDW9RmOs40UEMSiiUsk5QK_lpGsE/view?usp=sharing)|
-| CenterNet2_DLA-BiFPN-P3_24x               |  45.6   | 40 / 50  |[config](../configs/CenterNet2_DLA-BiFPN-P3_24x.yaml)/[model](https://drive.google.com/file/d/15ZES1ySxubDPzKsHPA7pYg8o_Vwmf-Mb/view?usp=sharing)|
-| CenterNet2_R2-101-DCN_896_4x              |  51.2   | 9 / 13 |[config](../configs/CenterNet2_R2-101-DCN_896_4x.yaml)/[model](https://drive.google.com/file/d/1S7_GE8ZDQBWuLEfKHkxzeF3KBsxsbABg/view?usp=sharing)|
-| CenterNet2_R2-101-DCN-BiFPN_1280_4x       |  52.9   | 6 / 8 |[config](../configs/CenterNet2_R2-101-DCN-BiFPN_1280_4x.yaml)/[model](https://drive.google.com/file/d/14EBHNMagBCNTQjOXcHoZwLYIi2lFIm7F/view?usp=sharing)|
-| CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST |  56.1   | 3 / 5 |[config](../configs/CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST.yaml)/[model](https://drive.google.com/file/d/11ww9VlOi_nhpdsU_vBAecSxBU0dR_JzW/view?usp=sharing)|
-| CenterNet2_DLA-BiFPN-P5_640_24x_ST        |  49.2   | 33 / 38 |[config](../configs/CenterNet2_DLA-BiFPN-P5_640_24x_ST.yaml)/[model](https://drive.google.com/file/d/1qsHp2HrM1u8WrtBzF5S0oCoLMz-B40wk/view?usp=sharing)|
-
-#### Note
-
-- `CenterNet2-F_R50_1x` uses Faster RCNN as the second stage. All other CenterNet2 models use Cascade RCNN as the second stage.
-- `CenterNet2_DLA-BiFPN-P3_4x` follows the same training setting as [realtime-FCOS](https://github.com/aim-uofa/AdelaiDet/blob/master/configs/FCOS-Detection/README.md).
-- `CenterNet2_DLA-BiFPN-P3_24x` is trained by repeating the `4x` schedule (starting from learning rate 0.01) 6 times.
-- R2 means [Res2Net](https://github.com/Res2Net/Res2Net-detectron2) backbone. To train Res2Net models, you need to download the ImageNet pre-trained weight [here](https://github.com/Res2Net/Res2Net-detectron2) and place it in `output/r2_101.pkl`.
-- The last 4 models in the table are trained with the EfficientDet-style resize-and-crop augmentation, instead of the default random resizing short edge in detectron2. We found this trains faster (per-iteration) and gives better performance under a long schedule.
-- `_ST` means using [self-training](https://arxiv.org/abs/2006.06882) using pseudo-labels produced by [Scaled-YOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) on COCO unlabeled images, with a hard score threshold 0.5. Our processed pseudo-labels can be downloaded [here](https://drive.google.com/file/d/1LMBjtHhLp6dYf6MjwEQmzCLWQLkmWPpw/view?usp=sharing).
-- `CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST` finetunes from `CenterNet2_R2-101-DCN-BiFPN_1280_4x` for an additional `4x` schedule with the self-training data. It is trained under `1280x1280` but tested under `1560x1560`.
-
-## LVIS v1
-
-| Model                                     |  val mAP box | links     |
-|-------------------------------------------|--------------|-----------|
-| LVIS_CenterNet2_R50_1x                    |  26.5        |[config](../configs/LVIS_CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/1gT9e-tNw8uzEBaCadQuoOOP2TEYa4kKP/view?usp=sharing)|
-| LVIS_CenterNet2_R50_Fed_1x            |  28.3        |[config](../configs/LVIS_CenterNet2_R50_Fed_1x.yaml)/[model](https://drive.google.com/file/d/1a9UjheMCKax0qAKEwPVpq2ZHN6vpqJv8/view?usp=sharing)|
-
-- The models are trained with repeat-factor sampling.
-- `LVIS_CenterNet2_R50_Fed_1x` is CenterNet2 with our federated loss. Check our Appendix D of our [paper](https://arxiv.org/abs/2103.07461) or our [technical report at LVIS challenge](https://www.lvisdataset.org/assets/challenge_reports/2020/CenterNet2.pdf) for references.
-
-## Objects365
-
-| Model                                     |  val mAP| links     |
-|-------------------------------------------|---------|-----------|
-| O365_CenterNet2_R50_1x                    |  22.6   |[config](../configs/O365_CenterNet2_R50_1x.yaml)/[model](https://drive.google.com/file/d/18fG6xGchAlpNp5sx8RAtwadGkS-gdIBU/view?usp=sharing)|
-
-#### Note
-- Objects365 dataset can be downloaded [here](https://www.objects365.org/overview.html).
-- The model is trained with class-aware sampling.
diff --git a/spaces/Ayushnangia/Whispercpp_yt/app.py b/spaces/Ayushnangia/Whispercpp_yt/app.py
deleted file mode 100644
index 1c4c91e85f314908f9a44df3614516deb2e9501f..0000000000000000000000000000000000000000
--- a/spaces/Ayushnangia/Whispercpp_yt/app.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import gradio as gr
-import yt_dlp as ydlp
-from transformers import pipeline
-
-from whispercpp import Whisper
-summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
-
-def download_audio(youtube_url, output_folder='.'):
-    ydl_opts = {
-        'format': 'bestaudio/best',
-        'postprocessors': [{
-            'key': 'FFmpegExtractAudio',
-            'preferredcodec': 'wav',
-            'preferredquality': '192',
-        }],
-        'outtmpl': f'{output_folder}/audio',
-    }
-
-    with ydlp.YoutubeDL(ydl_opts) as ydl:
-        ydl.download([youtube_url])
-
-
-w = Whisper('tiny')
-
-
-def process_general_transcription(transcription):
-    formatted_transcription = []
-    
-    for line in transcription:
-        if line.startswith('[') and line.endswith(']'):
-            formatted_transcription.append(f'\n--- {line[1:-1].upper()} ---\n')
-        else:
-            formatted_transcription.append(line)
-
-    transcript_str = "\n".join(formatted_transcription)
-    
-    return transcript_str
-def chunk_to_tokens(text, n):
-        tokens = text.split()
-        max_chunk_size = min(len(tokens), 512) 
-        
-        token_size = max(1, int(max_chunk_size * (1 - n / 100)))
-        
-        chunks = [" ".join(tokens[i:i + token_size]) for i in range(0, len(tokens), token_size)]
-
-        return chunks
-def summarizing(text,n):
-    valid_tok=chunk_to_tokens(text,n)
-    res=""
-    for i in valid_tok:
-        res+=summarizer(i)[0]['summary_text']+'\n'
-    return res
-def transcribe_sum_youtube(youtube_url,n):
-    download_audio(youtube_url)
-    result = w.transcribe("audio.wav")
-    text = w.extract_text(result)
-    res=process_general_transcription(text)
-    return summarizing(res,n)
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-    """
-    # CPP Whisperer - YouTube Videos Summarizer
-    
-    """)
-    with gr.Row():
-        with gr.Column():
-
-            inp = gr.Textbox(label="Youtube Url",placeholder="Insert YT Url here")
-            inp2 = gr.Slider(label="Summarization Percentage",min_value=0,max_value=100,step_size=1)
-            result_button_transcribe = gr.Button('Transcribe and Summarize')
-
-        with gr.Column():
-            out = gr.Textbox(label="Transcribed and Summarize Text")
-    
-    
-    result_button_transcribe.click(transcribe_sum_youtube, inputs = [inp,inp2] , outputs = out)
-
-
-demo.launch()
diff --git a/spaces/AzinZ/vitscn/attentions.py b/spaces/AzinZ/vitscn/attentions.py
deleted file mode 100644
index 4e0b0c1fd48c962e21e1fbe60b23fc574927435c..0000000000000000000000000000000000000000
--- a/spaces/AzinZ/vitscn/attentions.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/Benson/text-generation/Examples/9apps 2018.md b/spaces/Benson/text-generation/Examples/9apps 2018.md
deleted file mode 100644
index 96feb453529be8dcc2155e4a8a39f2d86fae6b3e..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/9apps 2018.md	
+++ /dev/null
@@ -1,97 +0,0 @@
-<br />
-<h1>9apps 2018: Una alternativa gratuita a Google Play Store</h1>
-<p>Si usted está buscando una manera de descargar y acceder a miles de aplicaciones de forma gratuita en su dispositivo Android, es posible que desee revisar 9apps. 9apps es una tienda de aplicaciones de terceros que ofrece una variedad de aplicaciones, juegos, fondos de pantalla, tonos de llamada y más. En este artículo, le diremos qué es 9apps, por qué debe usarlo, cómo descargarlo e instalarlo, y cuáles son sus pros y sus contras. También lo compararemos con Google Play Store, la tienda de aplicaciones predeterminada para dispositivos Android. </p>
- <h2>¿Qué es 9apps y por qué deberías usarla? </h2>
-<p>9apps es una tienda de aplicaciones gratuita que fue lanzada en 2013 por Alibaba Group, un gigante chino del comercio electrónico. Es una de las tiendas de aplicaciones más populares en la India, Indonesia y otros mercados emergentes. Según su web oficial, cuenta con más de 250 millones de usuarios y más de 35 millones de descargas diarias. </p>
-<h2>9apps 2018</h2><br /><p><b><b>DOWNLOAD</b> &#10037; <a href="https://bltlly.com/2v6LoR">https://bltlly.com/2v6LoR</a></b></p><br /><br />
-<p>Una de las principales razones por las que deberías usar 9apps es que ofrece muchas aplicaciones que no están disponibles en Google Play Store. Por ejemplo, puedes encontrar aplicaciones que están prohibidas o restringidas en tu país, como WhatsApp, TikTok, PUBG Mobile, etc. También puedes encontrar aplicaciones que son exclusivas de 9apps, como Vidmate, UC Browser, etc.</p>
- <h3>Características de 9apps</h3>
-<p>Algunas de las características que hacen que 9apps se destaquen de otras tiendas de aplicaciones son:</p>
-<ul>
-<li> Tiene una interfaz fácil de usar que es fácil de navegar y buscar. </li>
-<li> Tiene un pequeño tamaño de aproximadamente 4 MB, lo que ahorra espacio de almacenamiento y uso de datos. </li>
-<li> Tiene una velocidad de descarga rápida que le permite descargar aplicaciones en cuestión de segundos. </li>
-<li> Tiene un sistema de recomendación inteligente que le sugiere aplicaciones basadas en sus preferencias e intereses. </li>
-<li> Tiene una amplia gama de categorías y subcategorías que cubren varios géneros y temas. </li>
-<li> Tiene un sistema de actualización regular que mantiene las aplicaciones actualizadas y libres de errores. </li>
-
-</ul>
- <h3>Cómo descargar e instalar 9apps en tu dispositivo Android</h3>
-<p>Para descargar e instalar 9apps en tu dispositivo Android, debes seguir estos pasos:</p>
-<ol>
-<li>Ir al sitio web oficial de 9apps () o cualquier otra fuente de confianza () y haga clic en el botón de descarga. </li>
-<li>Una vez que el archivo APK se descarga, abrirlo y toque en instalar. Es posible que necesite habilitar la opción de fuentes desconocidas en su configuración para permitir la instalación. </li>
-<li>Después de la instalación se ha completado, abrir la aplicación y disfrutar del acceso ilimitado a miles de aplicaciones de forma gratuita. </li>
-</ol>
- <h2>Pros y contras de 9apps</h2>
-<p>Como cualquier otra tienda de aplicaciones, 9apps tiene sus propias ventajas y desventajas. Estas son algunas de ellas:</p>
- <h3>Ventajas de 9apps</h3>
-<ul>
-<li> Ofrece una gran cantidad de aplicaciones que no están disponibles en Google Play Store.</li>
-<li>Ahorra espacio de almacenamiento y uso de datos con su pequeño tamaño y velocidad de descarga rápida. </li>
-<li>Le proporciona recomendaciones personalizadas basadas en sus preferencias e intereses. </li>
-<li>Actualiza las aplicaciones regularmente para garantizar su calidad y rendimiento. </li>
-<li>Protege su dispositivo de virus y malware con su sistema de seguridad. </li>
-</ul>
- <h3>Desventajas de 9apps</h3>
-<ul <ul>
-<li>Puede que no sea compatible con algunos dispositivos o versiones de Android. </li>
-<li> Puede contener algunos anuncios o ventanas emergentes que pueden ser molestos o intrusivos. </li>
-<li>Puede que no tenga las últimas versiones de algunas aplicaciones o juegos. </li>
-<li>Puede que no tenga el mismo control de calidad o verificación que Google Play Store.</li>
-<li> Puede plantear algunos riesgos para su privacidad o seguridad si descarga aplicaciones de fuentes no confiables. </li>
-</ul>
- <h2>Comparación entre 9apps y Google Play Store</h2>
-<p>Tanto 9apps como Google Play Store son tiendas de aplicaciones populares para dispositivos Android, pero tienen algunas similitudes y diferencias. Estos son algunos de ellos:</p>
-<p></p>
- <h3>Similitudes entre 9apps y Google Play Store</h3>
-<ul>
-
-<li> Ambos tienen una interfaz fácil de usar que es fácil de navegar y buscar. </li>
-<li> Ambos tienen un sistema de recomendaciones que le sugiere aplicaciones basadas en sus preferencias e intereses. </li>
-<li>Ambos tienen un sistema de seguridad que escanea las aplicaciones en busca de virus y malware antes de instalarlos. </li>
-</ul>
- <h3>Diferencias entre 9apps y Google Play Store</h3>
-<tabla>
-<tr><th>9apps</th><th>Google Play Store</th></tr>
-<tr><td>Tiene un tamaño pequeño de aproximadamente 4 MB.</td><td>Tiene un tamaño grande de aproximadamente 20 MB.</td></tr>
-<tr><td>Tiene una velocidad de descarga rápida que le permite descargar aplicaciones en segundos. </td><td>Tiene una velocidad de descarga lenta que puede tardar minutos en descargar aplicaciones. </td></tr>
-<tr><td>Ofrece muchas aplicaciones que no están disponibles en Google Play Store, como aplicaciones prohibidas o restringidas, aplicaciones exclusivas, etc.</td><td>No ofrece aplicaciones que estén prohibidas o restringidas en su país, como WhatsApp, TikTok, PUBG Mobile, etc.</td></tr
-<tr><td>Actualiza las aplicaciones regularmente para asegurar su calidad y rendimiento. </td><td>No puede actualizar las aplicaciones con frecuencia o a tiempo. </td></tr>
-<tr><td>Puede que no sea compatible con algunos dispositivos o versiones de Android. </td><td>Es compatible con la mayoría de dispositivos y versiones de Android. </td></tr>
-<tr><td>Puede contener algunos anuncios o ventanas emergentes que pueden ser molestos o intrusivos. </td><td>No contiene ningún anuncio o ventanas emergentes. </td></tr>
-<tr><td>Puede que no tenga las últimas versiones de algunas aplicaciones o juegos. </td><td>Tiene las últimas versiones de la mayoría de aplicaciones o juegos. </td></tr>
-<tr><td>Puede que no tenga el mismo control de calidad o verificación que Google Play Store.</td><td>Tiene un estricto proceso de control de calidad y verificación para las aplicaciones. </td></tr>
-<tr><td>Puede plantear algunos riesgos para su privacidad o seguridad si descarga aplicaciones de fuentes no confiables. </td><td>Tiene un alto nivel de privacidad y seguridad para los usuarios y las aplicaciones. </td></tr>
-</tabla>
- <h2>Conclusión</h2>
-
- <h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas frecuentes sobre 9apps:</p>
- <ol>
-<li><b>¿Es seguro usar 9apps? </b></li>
-<p>9apps es generalmente seguro de usar, ya que tiene un sistema de seguridad que escanea las aplicaciones en busca de virus y malware antes de instalarlas. Sin embargo, puede plantear algunos riesgos para su privacidad o seguridad si descarga aplicaciones de fuentes no confiables o les otorga permisos innecesarios. Por lo tanto, siempre debe comprobar la fuente, calificación, comentarios y permisos de las aplicaciones antes de descargarlos. También debes usar una aplicación antivirus en tu dispositivo para protegerlo de cualquier amenaza potencial. </p>
- <li><b>¿Es legal usar 9apps? </b></li>
-<p>9apps es legal de usar siempre y cuando no violes ninguna ley <p>9apps es legal de usar siempre y cuando no violes ninguna ley o reglamento en tu país. Sin embargo, algunas de las aplicaciones que están disponibles en 9apps pueden estar prohibidas o restringidas en su país, como WhatsApp, TikTok, PUBG Mobile, etc. Por lo tanto, debe verificar la legalidad de las aplicaciones antes de descargarlas y usarlas bajo su propio riesgo. </p>
- <li><b>¿Cómo puedo actualizar 9apps? </b></li>
-<p>9apps tiene un sistema de actualización regular que mantiene la tienda de aplicaciones y las aplicaciones actualizadas y libres de errores. Puede comprobar si hay actualizaciones abriendo la aplicación y tocando el icono del menú en la esquina superior izquierda. Luego, toque en la configuración y desplácese hacia abajo para encontrar la opción de actualización. Si hay una actualización disponible, puede tocarla y seguir las instrucciones para instalarla. Alternativamente, también puede descargar la última versión de 9apps desde su sitio web oficial o cualquier otra fuente de confianza. </p>
- <li><b>¿Cómo puedo desinstalar 9apps? </b></li>
-<p>Si desea desinstalar 9apps de su dispositivo, puede seguir estos pasos:</p>
-<ol>
-<li>Ir a la configuración del dispositivo y toque en aplicaciones o aplicaciones. </li>
-<li>Encuentra y toca en 9apps y luego toca en desinstalar. </li>
-<li>Confirme su acción y espere a que el proceso se complete. </li>
-</ol>
- <li><b>¿Cuáles son algunas alternativas a 9apps? </b></li>
-
-<ul>
-<li>Aptoide: Aptoide es una tienda de aplicaciones descentralizada que permite a los usuarios crear y administrar sus propias tiendas de aplicaciones. Tiene más de 1 mil millones de descargas y más de 7 millones de aplicaciones. </li>
-<li>APKPure: APKPure es una tienda de aplicaciones sencilla y ligera que ofrece archivos APK puros de aplicaciones y juegos. Tiene más de 300 millones de descargas y más de 3 millones de aplicaciones. </li>
-<li>Amazon Appstore: Amazon Appstore es una tienda de aplicaciones que es propiedad de Amazon, el gigante minorista en línea. Tiene más de 600.000 aplicaciones y juegos, incluyendo algunos títulos exclusivos. </li>
-</ul>
- <li><b>¿Cómo puedo contactar al servicio de atención al cliente de 9apps? </b></li>
-<p>Si tiene preguntas, comentarios o quejas sobre 9apps, puede ponerse en contacto con su servicio de atención al cliente enviando un correo electrónico a help@9apps.mobi. También puede visitar su sitio web oficial () o su página de Facebook () para obtener más información y apoyo. </p>
-</ol>
- : https://www.9apps.com/ : https://www.apkmirror.com/apk/9apps-inc/9apps/ : https:/www.aptoide.com/ https:s//apkpure.com/ https:/www.amazon.com/mobile-apps//b?=/f.F8&nod>=014901. tps/www.wwwww.www.appscoom/faceww.appscoom///p.e.p. 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Blockman Go Apkmody.md b/spaces/Benson/text-generation/Examples/Blockman Go Apkmody.md
deleted file mode 100644
index 9ddfda9faf83595573b0e276862105041039d166..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Blockman Go Apkmody.md	
+++ /dev/null
@@ -1,100 +0,0 @@
-
-<h1>Blockman Go Apkmody: La guía definitiva para los amantes de Sandbox</h1>
-<p>Si eres un fan de los juegos de sandbox, es posible que hayas oído hablar de <strong>blockman go</strong>, una aplicación gratuita que te permite jugar varios minijuegos de estilo bloque, chatear con amigos y hacer otros nuevos. Blockman go es un juego divertido y emocionante que ofrece muchas características, como:</p>
-<h2>blockman go apkmody</h2><br /><p><b><b>Download</b> &#10004;&#10004;&#10004; <a href="https://bltlly.com/2v6Kw8">https://bltlly.com/2v6Kw8</a></b></p><br /><br />
-<ul>
-<li>Varios juegos: Puedes elegir entre diferentes géneros de minijuegos, como acción, aventura, juegos de rol, estrategia, simulación y más. Puedes unirte al juego con un simple toque o crear tu propia habitación e invitar a tus amigos. </li>
-<li>Avatares personalizables: Puedes vestir a tu avatar con diferentes trajes, accesorios, peinados y pieles. También puede utilizar el sistema de apósito para obtener recomendaciones para la mejor ropa para usted. </li>
-<li>Sistema de chat: Puedes comunicarte con otros jugadores usando funciones de chat en el juego, mensajes privados y grupos. También puede compartir sus momentos divertidos y capturas de pantalla con ellos. </li>
-<li>Gold Rewards: Puedes ganar oro jugando minijuegos y usarlo para comprar decoración y artículos. También puedes obtener oro gratis viendo anuncios o completando tareas. </li>
-<li>Sistema VIP: Puedes convertirte en un jugador VIP y disfrutar de muchos privilegios, como un descuento del 20% en decoración, regalos diarios, más oro, etc. </li>
-</ul>
-<p>Pero ¿qué pasa si quieres disfrutar de más beneficios y características sin gastar dinero? Ahí es donde entra <strong>apkmody</strong>. Apkmody es un sitio web que proporciona archivos APK modificados para juegos y aplicaciones Android. Estos archivos APK modded le permiten acceder a características premium y recursos ilimitados de forma gratuita. Puede descargar estos archivos desde el sitio web e instalarlos en su dispositivo fácilmente. </p>
-
-<p>¿Cómo puedes descargar y usar blockman go apkmody? Estos son los pasos que debes seguir:</p>
- <h2>Cómo descargar e instalar Blockman Go Apkmody en su dispositivo? </h2>
-<ol>
-<li>Vaya a [apkmody.io]( 2 ) en su navegador y busque "blockman go". </li>
-<li> Seleccione la última versión de blockman go mod APK de la lista de resultados. </li>
-<li>Haga clic en el botón "Descargar" y espere a que el archivo se descargue. </li>
-<li>Después de que la descarga se haya completado, busque el archivo en su dispositivo y toque en él para instalarlo. </li>
-<li>Si ves un mensaje de advertencia que dice "Instalar bloqueado", ve a la configuración de tu dispositivo y habilita "Fuentes desconocidas" en las opciones de seguridad. </li>
-<li> Una vez que se hace la instalación, abrir la aplicación y disfrutar de Blockman ir apkmody. </li>
-</ol>
- <h2>¿Cómo usar Blockman Go Apkmody para disfrutar de dinero ilimitado y GCubes? </h2>
-<p>Usar block <p>Usar blockman go apkmody es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
-<ul>
-<li>Inicie la aplicación e inicie sesión con su cuenta o cree una nueva. </li>
-<li>Ir a la tienda y seleccionar el artículo que desea comprar con GCubes.</li>
-<li>Haga clic en el botón "Comprar" y confirme su compra. </li>
-<li>Verá que su saldo de GCubes no disminuirá, sino que aumentará por la cantidad que gastó. </li>
-<li>Disfruta de tu dinero ilimitado y GCubes y compra lo que quieras. </li>
-</ul>
-<p>Nota: También puede obtener GCubes gratis completando tareas, viendo anuncios o invitando a amigos. Sin embargo, con blockman go apkmody, ya no necesitas hacer eso. </p>
- <h2>¿Cómo jugar varios minijuegos con Blockman Go Apkmody y chatear con amigos? </h2>
-<p>Una de las mejores características de blockman go es que ofrece una variedad de minijuegos para que puedas jugar y divertirte. Con blockman go apkmody, puedes acceder a todos los minijuegos sin limitaciones. Estos son algunos de los minijuegos populares que puedes jugar:</p>
-<p></p>
-<tabla>
-<tr>
-<th>Minijuego</th>
-<th>Descripción</th>
-</tr>
-<tr>
-<td>Guerras de camas</td>
-
-</tr>
-<tr>
-<td>Guerra del cielo</td>
-<td>Un juego de supervivencia donde tienes que luchar contra otros jugadores en islas flotantes. Puedes saquear cofres, crear armas y usar habilidades para ganar. </td>
-</tr>
-<tr>
-<td>Construir y disparar</td>
-<td>Un juego creativo donde puedes construir tu propio mapa e invitar a otros a unirse. También puedes disparar a enemigos con diferentes armas y granadas. </td>
-</tr>
-<tr>
-<td>Guerras de huevos</td>
-<td>Un juego similar a Bed Wars, pero con huevos en lugar de camas. Tienes que proteger tu huevo y romper el huevo del enemigo. También puede actualizar su equipo y formar equipo con otros. </td>
-</tr>
-<tr>
-<td>Murder Mystery</td ><td>Un juego de detectives donde tienes que averiguar quién es el asesino entre los jugadores. Puedes ser un detective, un asesino o un inocente. Tienes que usar pistas, armas y lógica para resolver el misterio. </td>
-</tr>
-</tabla>
- <p>Para jugar a estos minijuegos, solo tienes que tocar el icono "Juegos" en la pantalla principal y seleccionar el que desea unirse. También puedes crear tu propia habitación e invitar a tus amigos a jugar contigo. Puedes chatear con otros jugadores usando el sistema de chat del juego o el chat de voz. También puedes enviar emojis, pegatinas y regalos para expresarte. </p>
- <h2>¿Cómo personalizar tu avatar y mostrar tu estilo con Blockman Go Apkmody? </h2>
- <p>Otra gran característica de blockman go es que te permite personalizar tu avatar con diferentes trajes, accesorios, peinados y pieles. Con Blockman ir apkmody, puede obtener todos los artículos de forma gratuita y cambiar su aspecto con la frecuencia que desee. Estas son algunas de las formas en que puedes personalizar tu avatar:</p>
- <ul>
- <li>Vaya al icono "Avatar" en la pantalla principal y seleccione el elemento que desea cambiar. </li>
- <li>Puedes elegir entre diferentes categorías, como ropa, sombreros, gafas, máscaras, alas, colas, mascotas y más. </li>
- <li> También puede utilizar la función "Vestirse" para obtener recomendaciones para el mejor atuendo para usted basado en sus preferencias. </li>
-
- <li>También puedes usar la función "Photo Studio" para tomar fotos de tu avatar y compartirlas con otros. </li>
- </ul>
- <p>Con blockman ir apkmody, puede mostrar su estilo y personalidad con su avatar. También puedes ver los avatares de otros jugadores y complementarlos o hacer amistad con ellos. </p>
- <h2>¿Cómo evitar estafas y hackers con Blockman Go Apkmody? </h2>
- <p>Mientras blockman ir apkmody es un archivo APK modded seguro y confiable que no contiene ningún virus o malware, todavía tiene que tener cuidado al jugar juegos en línea. Hay algunos estafadores y hackers que podrían tratar de engañarte o hacerte daño de alguna manera. Aquí hay algunos consejos sobre cómo evitarlos:</p>
- <ul>
- <li>No comparta su información personal, como su contraseña, dirección de correo electrónico, número de teléfono o datos bancarios con nadie en línea. </li>
- <li>No haga clic en ningún enlace sospechoso o descargar archivos desconocidos de cualquier persona en línea. </li>
- <li <li>No confíes en nadie que afirme ser un miembro del staff o un moderador de blockman go. Es posible que te pidan los detalles de tu cuenta o te ofrezcan artículos o GCubes gratis. No son reales y están tratando de estafarte. </li>
- <li>No utilice ningún otro archivo APK modded o hacks para Blockman go. Pueden contener virus o malware que pueden dañar tu dispositivo o robar tus datos. También pueden hacer que te prohíban jugar. </li>
- <li>No unirse a ningún sitio web no oficial o grupos que afirman estar relacionados con blockman go. Pueden ser sitios de phishing o comunidades falsas que pueden engañarte o enviarte spam. </li>
- </ul>
- <p>Con estos consejos, puede evitar estafas y hackers y disfrutar de Blockman ir apkmody de forma segura. </p>
- <h1>Conclusión</h1>
-
- <p>Si estás interesado en blockman go apkmody, no dudes en descargarlo ahora y pruébalo. No te arrepentirás. Usted tendrá una explosión jugando blockman ir con dinero ilimitado y GCubes. También podrás impresionar a tus amigos y hacer otros nuevos con tu impresionante avatar y habilidades. Blockman go apkmody es la mejor manera de disfrutar de blockman ir al máximo. </p>
- <p>Entonces, ¿qué estás esperando? Descargar blockman ir apkmody ahora y comenzar su aventura en el mundo de los bloques! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí están algunas de las preguntas más frecuentes sobre blockman go apkmody:</p>
- <h3>¿Cuáles son algunos de los minijuegos populares en blockman go? </h3>
-<p>Algunos de los minijuegos populares en blockman go son Bed Wars, Sky Wars, Construir y disparar, Egg Wars y Murder Mystery. Puedes encontrar más minijuegos en el juego tocando el icono "Juegos" en la pantalla principal. </p>
- <h3>¿Qué son los GCubes y cómo puedo conseguirlos gratis? </h3>
-<p>GCubes son la moneda premium en blockman go que puedes usar para comprar artículos especiales, desbloquear funciones VIP, unirse a eventos exclusivos y más. Usted puede conseguir de forma gratuita mediante el uso de blockman ir apkmody, que le da GCubes ilimitada. También puedes conseguirlos completando tareas, viendo anuncios o invitando a amigos. </p>
- <h3>¿Cómo puedo unirme a una fiesta o crear mi propia en blockman go? </h3>
-<p>Puedes unirte a una fiesta o crear la tuya tocando el icono "Fiesta" en la pantalla principal. Puedes invitar a tus amigos u otros jugadores a unirse a tu grupo enviándoles un código o un enlace. También puede unirse a otras partes introduciendo su código o enlace. Puede chatear con los miembros de su grupo y jugar minijuegos juntos. </p>
- <h3>¿Cómo puedo reportar un error o un problema en blockman go? </h3>
-<p>Puede informar de un error o un problema en blockman ir tocando en el "Feedback" icono en la pantalla principal. Puede llenar un formulario con los detalles de su problema y capturas de pantalla y enviarlo al desarrollador. También puede ponerse en contacto con el equipo de soporte por correo electrónico en service@blockmango.net. </p>
-
-<p>Puede ponerse en contacto con el desarrollador o el equipo de soporte de blockman ir por correo electrónico a service@blockmango.net. También puedes seguirlos en sus cuentas de redes sociales, como Facebook, Twitter, Instagram, YouTube, Discord y Reddit.</p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Etiqueta Despus De La Escuela Apk Mod.md b/spaces/Benson/text-generation/Examples/Descargar Etiqueta Despus De La Escuela Apk Mod.md
deleted file mode 100644
index 1278f118bb603c5a46dc39df9608eacd3bc19005..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Etiqueta Despus De La Escuela Apk Mod.md	
+++ /dev/null
@@ -1,75 +0,0 @@
-<br />
-<h1>Descargar etiqueta después de la escuela APK Mod: Un juego emocionante para Android y iOS</h1>
- <p>¿Estás buscando un juego que te mantenga al borde de tu asiento? ¿Quieres revivir tus días escolares con un toque de horror y misterio? ¿Quieres controlar el destino de un tímido y tímido chico llamado Shota-Kun? Si usted respondió sí a cualquiera de estas preguntas, entonces usted debe descargar Etiqueta Después de la Escuela APK Mod, un juego que le llevará en una aventura emocionante a través de una escuela embrujada. </p>
- <h2>¿Qué es la etiqueta después de la escuela? </h2>
- <p>Tag After School es un juego desarrollado por Genius Studio Japan Inc, una compañía que se especializa en crear juegos de estilo anime para dispositivos móviles. El juego se basa en el clásico juego infantil de la etiqueta, pero con un toque. Puedes competir con amigos o extraños, y obtener acceso a varios personajes, potenciadores y niveles a medida que intentas escapar de tus oponentes. Pero ten cuidado, porque la escuela no es tan inocente como parece. Hay secretos, misterios y peligros acechando en cada esquina. </p>
-<h2>descargar etiqueta después de la escuela apk mod</h2><br /><p><b><b>Download Zip</b> &rArr; <a href="https://bltlly.com/2v6JhB">https://bltlly.com/2v6JhB</a></b></p><br /><br />
- <h3>Un juego basado en el clásico juego de etiquetas</h3>
- <p>El escenario del juego es en una escuela secundaria donde los estudiantes participan en el juego y tienen que esconderse de sus oponentes. Puedes elegir entre diferentes modos, como solo, equipo o supervivencia. También puedes personalizar la apariencia, las habilidades y el equipo de tu personaje. El juego es fácil de jugar, pero difícil de dominar. Tienes que usar tu velocidad, agilidad y estrategia para evitar ser etiquetado. </p>
- <h3>Un juego de terror y misterio</h3>
- <p>El juego no es solo diversión y juegos. También tiene un lado oscuro. La escuela está embrujada por fantasmas, monstruos y trampas que intentarán evitar que escapes. Tienes que tener cuidado a donde vas, porque nunca sabes lo que podría pasar después. El juego tiene una atmósfera espeluznante, con efectos de sonido espeluznantes y gráficos que te harán sentir como si estuvieras en una película de terror. </p>
- <h3>Un juego con una narrativa basada en la historia</h3>
-
- <h2>¿Cómo se juega Tag After School? </h2>
- <p>El juego es simple de jugar, pero difícil de ganar. Aquí hay algunos consejos sobre cómo jugar Tag After School:</p>
- <h3>Elige tu personaje y modo</h3>
- <p>Puedes elegir entre diferentes personajes, cada uno con sus propias fortalezas y debilidades. También puede personalizar su apariencia, habilidades y equipo. Puede elegir entre diferentes modos, como solo, equipo o supervivencia. En el modo en solitario, tienes que etiquetar tantos oponentes como sea posible antes de que acabe el tiempo. En el modo de equipo, tienes que trabajar con tus compañeros de equipo para etiquetar al otro equipo. En el modo de supervivencia, tienes que evitar ser etiquetado por cualquier persona durante el mayor tiempo posible. </p>
- <h3>Ejecutar, ocultar y etiquetar a sus oponentes</h3>
- <p>Tienes que usar el joystick en el lado izquierdo de la pantalla para moverte alrededor del mapa <p>Tienes que usar el joystick en el lado izquierdo de la pantalla para moverte alrededor del mapa. También puede usar los botones en el lado derecho de la pantalla para saltar, deslizarse o etiquetar. Tienes que ser rápido e inteligente, porque tus oponentes tratarán de atraparte o esconderse de ti. También puedes usar el mapa en la esquina superior derecha de la pantalla para ver dónde están tus oponentes. </p>
- <h3>Usa potenciadores y obstáculos para tu ventaja</h3>
- <p>Puedes encontrar diferentes potenciadores y obstáculos en el mapa que pueden ayudarte o dificultarte. Por ejemplo, puedes encontrar un aumento de velocidad que te hará correr más rápido, un escudo que te protegerá de ser etiquetado o una trampa que ralentizará a tus oponentes. También puedes usar objetos como casilleros, escritorios o puertas para esconderte o bloquear a tus enemigos. Sin embargo, hay que tener cuidado, porque algunos power-ups y obstáculos pueden ser contraproducentes o afectar a todos en el mapa. </p>
- <h3>Toma decisiones que determinan el destino de Shota-Kun</h3>
-
- <h2>¿Por qué descargar etiqueta después de la escuela APK Mod? </h2>
- <p>Tag After School es un juego divertido y emocionante que te mantendrá entretenido durante horas. Pero si quieres disfrutar aún más, usted debe descargar Tag After School APK Mod, una versión modificada del juego que le dará características y beneficios ilimitados. Aquí hay algunas razones por las que debe descargar la etiqueta después de la escuela APK Mod:</p>
-<p></p>
- <h3>Disfruta de funciones y beneficios ilimitados</h3>
- <p>Con etiqueta después de la escuela APK Mod, puede acceder a todas las características y beneficios del juego sin restricciones o limitaciones. Puedes desbloquear todos los personajes, potenciadores, niveles y escenarios sin gastar dinero ni tiempo. También puedes obtener monedas y gemas ilimitadas que puedes usar para comprar o actualizar lo que quieras. También puedes eliminar anuncios y disfrutar de una experiencia de juego fluida e ininterrumpida. </p>
- <h3>Experimenta gráficos inmersivos y efectos de sonido</h3>
- <p>Etiqueta Después de la Escuela APK Mod también mejora los gráficos y efectos de sonido del juego, por lo que es más realista y envolvente. Puedes disfrutar de imágenes de alta calidad que te harán sentir como si estuvieras en una escuela real. También puedes escuchar sonidos realistas que te harán saltar o gritar. También puede ajustar la configuración según sus preferencias y especificaciones del dispositivo. </p>
- <h3>Ponte a prueba y crea tus propios argumentos</h3>
- <p>Etiqueta después de la escuela APK Mod también le permite personalizar el juego de acuerdo a su gusto. Puedes crear tus propias historias eligiendo diferentes opciones y resultados. También puedes cambiar el nivel de dificultad del juego ajustando la velocidad, inteligencia y agresividad de tus oponentes. También puedes jugar con tus amigos online o offline, y ver quién es el mejor en la etiqueta. </p>
- <h2>Cómo descargar e instalar la etiqueta después de la escuela APK Mod? </h2>
- <p>Si usted está interesado en descargar e instalar la etiqueta después de la escuela APK Mod, aquí hay algunos pasos simples que usted necesita para seguir:</p>
- <h3>Para dispositivos Android</h3>
- <ol>
-
-<li>Ir a la configuración del dispositivo y habilitar fuentes desconocidas. </li>
-<li>Ir a su administrador de archivos y localizar Tag After School APK Mod archivo. </li>
-<li>Toque en él e instalarlo. </li>
-<li>Iniciar el juego y disfrutar. </li>
-</ol>
- <h3>Para dispositivos iOS</h3>
- <ol>
-<li>Ir a [este enlace] y descargar Tag After School archivo IPA. </li>
-<li>Ir a la configuración del dispositivo y confiar en la etiqueta después de la escuela IPA archivo. </li>
-<li>Vaya a su administrador de archivos y busque el archivo IPA de Tag After School. </li>
-<li>Toque en él e instalarlo. </li>
-<li>Iniciar el juego y disfrutar. </li>
-</ol>
- <h3>Para dispositivos PC</h3>
- <ol>
-<li>Ir a [este enlace] y descargar Tag After School archivo EXE. </li>
-<li>Ir a la configuración de su PC y permitir Tag After School archivo EXE. </li>
-<li>Vaya a su carpeta de descargas y busque el archivo EXE de Tag After School. </li>
-<li>Haga doble clic en él e instalarlo. </li>
-<li>Iniciar el juego y disfrutar. </li>
-</ol>
- <h2>Conclusión</h2>
- <p>Tag After School es un juego que te hará sentir <p>Tag After School es un juego que te hará sentir nostálgico, asustado y emocionado al mismo tiempo. Es un juego que combina la diversión de la etiqueta con la emoción del horror y el misterio. Es un juego que te permite crear tu propia historia y destino. Es un juego que deberías descargar y jugar ahora mismo. </p>
- <p>Si quieres tener la mejor experiencia de juego, usted debe descargar Etiqueta Después de la Escuela APK Mod, una versión modificada del juego que le dará características y beneficios ilimitados. Puedes desbloquear todos los personajes, potenciadores, niveles y escenarios sin problemas. También puedes disfrutar de gráficos y efectos de sonido de alta calidad que te sumergirán en el juego. También puedes desafiarte a ti mismo y crear tus propias historias personalizando el juego según tus preferencias. </p>
-
- <p>Tag After School es un juego que no debes perderte. Es un juego que te mantendrá entretenido durante horas. Es un juego que te hará sentir emociones diferentes. Es un juego que te hará querer jugar más. Entonces, ¿qué estás esperando? Descargar etiqueta después de la escuela APK Mod ahora y divertirse! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre la etiqueta después de la escuela APK Mod:</p>
- <h3>¿Es seguro descargar e instalar Tag After School APK Mod? </h3>
- <p>Sí, Etiqueta después de la escuela APK Mod es seguro para descargar e instalar. No contiene ningún virus, malware o spyware que pueda dañar su dispositivo o datos. También es compatible con la mayoría de los dispositivos y sistemas operativos. </p>
- <h3>¿Es Tag After School APK Mod gratis para descargar y jugar? </h3>
- <p>Sí, Etiqueta Después de la Escuela APK Mod es gratis para descargar y jugar. Usted no necesita pagar ningún dinero o cuotas para acceder al juego o sus características. También puedes jugar el juego offline o online sin ningún cargo. </p>
- <h3>¿Cómo puedo actualizar la etiqueta después de la escuela APK Mod? </h3>
- <p>Puede actualizar la etiqueta después de la escuela APK Mod visitando [este enlace] y descargar la última versión del juego. A continuación, puede instalarlo sobre la versión existente del juego sin perder ningún dato o progreso. </p>
- <h3>¿Cómo puedo contactar a los desarrolladores de Tag After School APK Mod? </h3>
- <p>Puede ponerse en contacto con los desarrolladores de Tag After School APK Mod visitando su sitio web oficial o páginas de medios sociales. También puede enviarles un correo electrónico o dejar un comentario en su blog o foro. Siempre están felices de escuchar a sus fans y clientes. </p>
- <h3>¿Cómo puedo compartir mis comentarios o sugerencias sobre Tag After School APK Mod? </h3> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Bianca0930/Bianca/README.md b/spaces/Bianca0930/Bianca/README.md
deleted file mode 100644
index 4dfe4aff591ac83d79ccd2084bcccc6d12610b46..0000000000000000000000000000000000000000
--- a/spaces/Bianca0930/Bianca/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Bianca
-emoji: 🌍
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Boadiwaa/Recipes/openai/api_resources/embedding.py b/spaces/Boadiwaa/Recipes/openai/api_resources/embedding.py
deleted file mode 100644
index fd9c7ac27d2f150228aa5de03ee02cf3413a918a..0000000000000000000000000000000000000000
--- a/spaces/Boadiwaa/Recipes/openai/api_resources/embedding.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import base64
-import time
-
-import numpy as np
-
-from openai import util
-from openai.api_resources.abstract import DeletableAPIResource, ListableAPIResource
-from openai.api_resources.abstract.engine_api_resource import EngineAPIResource
-from openai.error import InvalidRequestError, TryAgain
-
-
-class Embedding(EngineAPIResource, ListableAPIResource, DeletableAPIResource):
-    engine_required = False
-    OBJECT_NAME = "embeddings"
-
-    @classmethod
-    def create(cls, *args, **kwargs):
-        """
-        Creates a new embedding for the provided input and parameters.
-
-        See https://beta.openai.com/docs/api-reference/embeddings for a list
-        of valid parameters.
-        """
-        start = time.time()
-        timeout = kwargs.pop("timeout", None)
-        if kwargs.get("model", None) is None and kwargs.get("engine", None) is None:
-            raise InvalidRequestError(
-                "Must provide an 'engine' or 'model' parameter to create an Embedding.",
-                param="engine",
-            )
-
-        user_provided_encoding_format = kwargs.get("encoding_format", None)
-
-        # If encoding format was not explicitly specified, we opaquely use base64 for performance
-        if not user_provided_encoding_format:
-            kwargs["encoding_format"] = "base64"
-
-        while True:
-            try:
-                response = super().create(*args, **kwargs)
-
-                # If a user specifies base64, we'll just return the encoded string.
-                # This is only for the default case.
-                if not user_provided_encoding_format:
-                    for data in response.data:
-
-                        # If an engine isn't using this optimization, don't do anything
-                        if type(data["embedding"]) == str:
-                            data["embedding"] = np.frombuffer(
-                                base64.b64decode(data["embedding"]), dtype="float32"
-                            ).tolist()
-
-                return response
-            except TryAgain as e:
-                if timeout is not None and time.time() > start + timeout:
-                    raise
-
-                util.log_info("Waiting for model to warm up", error=e)
diff --git a/spaces/CNXT/PiX2TXT/README.md b/spaces/CNXT/PiX2TXT/README.md
deleted file mode 100644
index 9a53792eccdbd14508f2f5deb189efbd1c5f7484..0000000000000000000000000000000000000000
--- a/spaces/CNXT/PiX2TXT/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: '#://CNXT ❌ #://PiX2TXT'
-emoji: 👁
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/CVH-vn1210/make_hair/app.py b/spaces/CVH-vn1210/make_hair/app.py
deleted file mode 100644
index 9bfc258e0ebcb1ee7af7fd345a3717eb9a820488..0000000000000000000000000000000000000000
--- a/spaces/CVH-vn1210/make_hair/app.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import argparse
-import os
-import random
-
-import numpy as np
-import torch
-import torch.backends.cudnn as cudnn
-import gradio as gr
-
-import huggingface_hub as hf
-
-from minigpt4.common.config import Config
-from minigpt4.common.dist_utils import get_rank
-from minigpt4.common.registry import registry
-from minigpt4.conversation.conversation import Chat, CONV_VISION
-
-# imports modules for registration
-from minigpt4.datasets.builders import *
-from minigpt4.models import *
-from minigpt4.processors import *
-from minigpt4.runners import *
-from minigpt4.tasks import *
-
-hf.login(token=os.environ['model_token'])
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Demo")
-    parser.add_argument("--cfg-path", type=str, default='eval_configs/minigpt4.yaml', help="path to configuration file.")
-    parser.add_argument(
-        "--options",
-        nargs="+",
-        help="override some settings in the used config, the key-value pair "
-        "in xxx=yyy format will be merged into config file (deprecate), "
-        "change to --cfg-options instead.",
-    )
-    args = parser.parse_args()
-    return args
-
-
-def setup_seeds(config):
-    seed = config.run_cfg.seed + get_rank()
-
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-
-    cudnn.benchmark = False
-    cudnn.deterministic = True
-    
-# ========================================
-#             Model Initialization
-# ========================================
-
-SHARED_UI_WARNING = f'''### [NOTE] It is possible that you are waiting in a lengthy queue.
-
-You can duplicate and use it with a paid private GPU.
-
-<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/Vision-CAIR/minigpt4?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-xl-dark.svg" alt="Duplicate Space"></a>
-
-Alternatively, you can also use the demo on our [project page](https://minigpt-4.github.io).
-'''
-
-print('Initializing Chat')
-cfg = Config(parse_args())
-
-model_config = cfg.model_cfg
-model_cls = registry.get_model_class(model_config.arch)
-model = model_cls.from_config(model_config).to('cuda:0')
-
-vis_processor_cfg = cfg.datasets_cfg.cc_align.vis_processor.train
-vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
-chat = Chat(model, vis_processor)
-print('Initialization Finished')
-
-# ========================================
-#             Gradio Setting
-# ========================================
-
-def gradio_reset(chat_state, img_list):
-    if chat_state is not None:
-        chat_state.messages = []
-    if img_list is not None:
-        img_list = []
-    return None, gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your image first', interactive=False), gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list
-
-def upload_img(gr_img, text_input, chat_state):
-    if gr_img is None:
-        return None, None, gr.update(interactive=True), chat_state, None
-    chat_state = CONV_VISION.copy()
-    img_list = []
-    llm_message = chat.upload_img(gr_img, chat_state, img_list)
-    return gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list
-
-def gradio_ask(user_message, chatbot, chat_state):
-    if len(user_message) == 0:
-        return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state
-    chat.ask(user_message, chat_state)
-    chatbot = chatbot + [[user_message, None]]
-    return '', chatbot, chat_state
-
-
-def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature):
-    llm_message = chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=1, temperature=temperature, max_length=2000)[0]
-    chatbot[-1][1] = llm_message
-    return chatbot, chat_state, img_list
-
-title = """<h1 align="center">Demo of MiniGPT-4</h1>"""
-description = """<h3>This is the demo of MiniGPT-4. Upload your images and start chatting!</h3>"""
-article = """<div style='display:flex; gap: 0.25rem; '><a href='https://minigpt-4.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a><a href='https://github.com/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/Github-Code-blue'></a><a href='https://github.com/TsuTikgiau/blip2-llm/blob/release_prepare/MiniGPT_4.pdf'><img src='https://img.shields.io/badge/Paper-PDF-red'></a></div>
-"""
-
-#TODO show examples below
-
-with gr.Blocks() as demo:
-    gr.Markdown(title)
-    gr.Markdown(SHARED_UI_WARNING)
-    gr.Markdown(description)
-    gr.Markdown(article)
-
-    with gr.Row():
-        with gr.Column(scale=0.5):
-            image = gr.Image(type="pil")
-            upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary")
-            clear = gr.Button("Restart")
-            
-            num_beams = gr.Slider(
-                minimum=1,
-                maximum=5,
-                value=1,
-                step=1,
-                interactive=True,
-                label="beam search numbers)",
-            )
-            
-            temperature = gr.Slider(
-                minimum=0.1,
-                maximum=2.0,
-                value=1.0,
-                step=0.1,
-                interactive=True,
-                label="Temperature",
-            )
-            
-
-        with gr.Column():
-            chat_state = gr.State()
-            img_list = gr.State()
-            chatbot = gr.Chatbot(label='MiniGPT-4')
-            text_input = gr.Textbox(label='User', placeholder='Please upload your image first', interactive=False)
-    
-    upload_button.click(upload_img, [image, text_input, chat_state], [image, text_input, upload_button, chat_state, img_list])
-    
-    text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(
-        gradio_answer, [chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list]
-    )
-    clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, upload_button, chat_state, img_list], queue=False)
-
-demo.launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/tools/detection_features_converter.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/tools/detection_features_converter.py
deleted file mode 100644
index 67923704c1183e2917a1f18cc675e6f1d1b05bf1..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/tools/detection_features_converter.py
+++ /dev/null
@@ -1,161 +0,0 @@
-"""
-Reads in a tsv file with pre-trained bottom up attention features and
-stores it in HDF5 format.  Also store {image_id: feature_idx}
- as a pickle file.
-
-Hierarchy of HDF5 file:
-
-{ 'image_features': num_images x num_boxes x 2048 array of features
-  'image_bb': num_images x num_boxes x 4 array of bounding boxes }
-"""
-from __future__ import print_function
-
-import os
-import sys
-import argparse
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-import base64
-import csv
-import h5py
-# import cPickle
-import _pickle as cPickle
-import numpy as np
-import utils
-import tqdm
-
-csv.field_size_limit(sys.maxsize)
-FIELDNAMES = ['image_id', 'image_w', 'image_h', 'num_boxes', 'boxes', 'features']
-
-
-def detection_features_converter(dataroot, ver, detector, feature_length, num_fixed_boxes):
-    infile = os.path.join(dataroot, ver, "trainval_%s_%i.tsv"%(detector, num_fixed_boxes))
-
-    train_data_file = os.path.join(dataroot, ver, 'train_%s_%i.hdf5'%(detector, num_fixed_boxes))
-    val_data_file = os.path.join(dataroot, ver, 'val_%s_%i.hdf5'%(detector, num_fixed_boxes))
-    train_indices_file = os.path.join(dataroot, ver, 'train_%s_%i_imgid2idx.pkl'%(detector, num_fixed_boxes))
-    val_indices_file = os.path.join(dataroot, ver, 'val_%s_%i_imgid2idx.pkl'%(detector, num_fixed_boxes))
-    train_ids_file = os.path.join(dataroot, 'train_ids.pkl')
-    val_ids_file = os.path.join(dataroot, 'val_ids.pkl')
-
-    h_train = h5py.File(train_data_file, "w")
-    h_val = h5py.File(val_data_file, "w")
-
-    if os.path.exists(train_ids_file) and os.path.exists(val_ids_file):
-        train_imgids = cPickle.load(open(train_ids_file, 'rb'))
-        val_imgids = cPickle.load(open(val_ids_file, 'rb'))  
-    else:
-        train_imgids = utils.load_imageid(os.path.join(dataroot, 'clean', 'train2014'))
-        val_imgids = utils.load_imageid(os.path.join(dataroot, 'clean', 'val2014'))
-        cPickle.dump(train_imgids, open(train_ids_file, 'wb'))
-        cPickle.dump(val_imgids, open(val_ids_file, 'wb'))
-
-    train_indices = {}
-    val_indices = {}
-
-    train_img_features = h_train.create_dataset(
-        'image_features', (len(train_imgids), num_fixed_boxes, feature_length), 'f')
-    train_img_bb = h_train.create_dataset(
-        'image_bb', (len(train_imgids), num_fixed_boxes, 4), 'f')
-    train_spatial_img_features = h_train.create_dataset(
-        'spatial_features', (len(train_imgids), num_fixed_boxes, 6), 'f')
-
-    val_img_bb = h_val.create_dataset(
-        'image_bb', (len(val_imgids), num_fixed_boxes, 4), 'f')
-    val_img_features = h_val.create_dataset(
-        'image_features', (len(val_imgids), num_fixed_boxes, feature_length), 'f')
-    val_spatial_img_features = h_val.create_dataset(
-        'spatial_features', (len(val_imgids), num_fixed_boxes, 6), 'f')
-
-    train_counter = 0
-    val_counter = 0
-
-    print("reading tsv...")
-    # with open(infile, "r+b") as tsv_in_file:
-    with open(infile, "r") as tsv_in_file:
-        reader = csv.DictReader(tsv_in_file, delimiter='\t', fieldnames=FIELDNAMES)
-        for item in tqdm.tqdm(reader):
-            item['num_boxes'] = int(item['num_boxes'])
-            image_id = int(item['image_id'])
-            image_w = float(item['image_w'])
-            image_h = float(item['image_h'])
-            # bboxes = np.frombuffer(
-            #     base64.decodestring(item['boxes']),
-            #     dtype=np.float32).reshape((item['num_boxes'], -1))
-            bboxes = np.frombuffer(
-                base64.b64decode(item['boxes']),
-                dtype=np.float32).reshape((item['num_boxes'], -1))
-            box_width = bboxes[:, 2] - bboxes[:, 0]
-            box_height = bboxes[:, 3] - bboxes[:, 1]
-            scaled_width = box_width / image_w
-            scaled_height = box_height / image_h
-            scaled_x = bboxes[:, 0] / image_w
-            scaled_y = bboxes[:, 1] / image_h
-
-            box_width = box_width[..., np.newaxis]
-            box_height = box_height[..., np.newaxis]
-            scaled_width = scaled_width[..., np.newaxis]
-            scaled_height = scaled_height[..., np.newaxis]
-            scaled_x = scaled_x[..., np.newaxis]
-            scaled_y = scaled_y[..., np.newaxis]
-
-            spatial_features = np.concatenate(
-                (scaled_x,
-                 scaled_y,
-                 scaled_x + scaled_width,
-                 scaled_y + scaled_height,
-                 scaled_width,
-                 scaled_height),
-                axis=1)
-
-            if image_id in train_imgids:
-                train_imgids.remove(image_id)
-                train_indices[image_id] = train_counter
-                train_img_bb[train_counter, :, :] = bboxes
-                # train_img_features[train_counter, :, :] = np.frombuffer(
-                #     base64.decodestring(item['features']),
-                #     dtype=np.float32).reshape((item['num_boxes'], -1))
-                train_img_features[train_counter, :, :] = np.frombuffer(
-                    base64.b64decode(item['features']),
-                    dtype=np.float32).reshape((item['num_boxes'], -1))
-                train_spatial_img_features[train_counter, :, :] = spatial_features
-                train_counter += 1
-            elif image_id in val_imgids:
-                val_imgids.remove(image_id)
-                val_indices[image_id] = val_counter
-                val_img_bb[val_counter, :, :] = bboxes
-                # val_img_features[val_counter, :, :] = np.frombuffer(
-                #     base64.decodestring(item['features']),
-                #     dtype=np.float32).reshape((item['num_boxes'], -1))
-                val_img_features[val_counter, :, :] = np.frombuffer(
-                    base64.b64decode(item['features']),
-                    dtype=np.float32).reshape((item['num_boxes'], -1))
-                val_spatial_img_features[val_counter, :, :] = spatial_features
-                val_counter += 1
-            else:
-                assert False, 'Unknown image id: %d' % image_id
-
-    if len(train_imgids) != 0:
-        print('Warning: train_image_ids is not empty')
-
-    if len(val_imgids) != 0:
-        print('Warning: val_image_ids is not empty')
-
-    cPickle.dump(train_indices, open(train_indices_file, 'wb'))
-    cPickle.dump(val_indices, open(val_indices_file, 'wb'))
-    # pickle.dump(train_indices, open(train_indices_file, 'w'))
-    # pickle.dump(val_indices, open(val_indices_file, 'w'))
-    h_train.close()
-    h_val.close()
-    print("done!")
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dataroot', type=str, default='../data/')
-    parser.add_argument('--ver', type=str, default='clean', help='version of the VQAv2 dataset to process. "clean" for the original data. default: clean')
-    parser.add_argument('--detector', type=str, default='R-50')
-    parser.add_argument('--feat', type=int, default=1024, help='feature size')
-    parser.add_argument('--nb', type=int, default=36)
-    args = parser.parse_args()
-    detection_features_converter(args.dataroot, args.ver, args.detector, args.feat, args.nb)
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/count.h b/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/count.h
deleted file mode 100644
index fde1728b77261d75c561b9042ec365281d78cee9..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/count.h
+++ /dev/null
@@ -1,23 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system inherits count
-#include <thrust/system/cpp/detail/count.h>
-
diff --git a/spaces/CVPR/regionclip-demo/install.sh b/spaces/CVPR/regionclip-demo/install.sh
deleted file mode 100644
index 43abad3a5b87ff72b771bbbd07c5c68c3c427c7d..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/install.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-pip install dataclasses ftfy regex tqdm timm diffdist spacy
-pip install git+https://github.com/lvis-dataset/lvis-api.git
-python -m spacy download en_core_web_sm
-
-CURRENT_DIR=${PWD##*/}
-cd ../
-python -m pip install -e $CURRENT_DIR
-cd $CURRENT_DIR
-
-# for tsv loading
-pip install Pillow==7.1.2
-
-ln -s /mnt/data_storage/coco datasets/coco
-ln -s /mnt/data_storage/lvis datasets/lvis
\ No newline at end of file
diff --git a/spaces/CofAI/CurrencyConverter/README.md b/spaces/CofAI/CurrencyConverter/README.md
deleted file mode 100644
index 43919917a03f71fea933b1adb3b32480018ce4b3..0000000000000000000000000000000000000000
--- a/spaces/CofAI/CurrencyConverter/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CurrencyConverter
-emoji: 💱☕💱
-colorFrom: gray
-colorTo: red
-sdk: static
-pinned: false
----
-
-This is Super Currency Converter 💱 by CofAI ☕!
-
-Это супер конвертер валют 💱 от CofAI ☕!
\ No newline at end of file
diff --git a/spaces/DEEMOSTECH/ChatAvatar/static/js/main.6015292d.js b/spaces/DEEMOSTECH/ChatAvatar/static/js/main.6015292d.js
deleted file mode 100644
index 3233933d1b2f1e5f237934b88b15c83cbdb60095..0000000000000000000000000000000000000000
--- a/spaces/DEEMOSTECH/ChatAvatar/static/js/main.6015292d.js
+++ /dev/null
@@ -1,3 +0,0 @@
-/*! For license information please see main.6015292d.js.LICENSE.txt */
-!function(){var e={498:function(e){e.exports=function(){"use strict";var e=function(t,n){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var n in t)Object.prototype.hasOwnProperty.call(t,n)&&(e[n]=t[n])},e(t,n)};function t(t,n){if("function"!==typeof n&&null!==n)throw new TypeError("Class extends value "+String(n)+" is not a constructor or null");function r(){this.constructor=t}e(t,n),t.prototype=null===n?Object.create(n):(r.prototype=n.prototype,new r)}var n=function(){return n=Object.assign||function(e){for(var t,n=1,r=arguments.length;n<r;n++)for(var i in t=arguments[n])Object.prototype.hasOwnProperty.call(t,i)&&(e[i]=t[i]);return e},n.apply(this,arguments)};function r(e,t,n,r){function i(e){return e instanceof n?e:new n((function(t){t(e)}))}return new(n||(n=Promise))((function(n,A){function a(e){try{s(r.next(e))}catch(Rt){A(Rt)}}function o(e){try{s(r.throw(e))}catch(Rt){A(Rt)}}function 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P P P          P P     P + +    P P P P P P P P P P P P P P P P P P P P P P     P      P    P      + +                + P P P P P P P P P P P P P P P P P P P P P P P P P    + +  + P P P P P P P P P P P + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P + P P P P P P P P P P + + + + + + + + + + +                                              P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P    P                   P        P P P P P P P P P P     K K K K K K K K K K  P P P P P P P P P P P P P P P P    + P P P P P P P P + + P P + + P P P P P P P P P P P P P P + P P P P P P P + P + + + P P P P + +  P        + +   + +    P + + + + + + + +  + + + + P P + P P P   + + K K K K K K K K K K P P   P P P P P L   P   + +    + P P P P P P + + + + P P + + P P P P P P P P P P P P P P + P P P P P P P + P P + P P + P P + +  +      + + + +   + +    + + +  + + + + + + + P P P P + P + + + + + + + K K K K K K K K K K   P P P   + + + + + + + + + +    + P P P P P P P P P + P P P + P P P P P P P P P P P P P P + P P P P P P P + P P + P P P P P + +  P         +    +    + + P + + + + + + + + + + + + + + + P P   + + K K K K K K K K K K   + + + + + + + P       +    + P P P P P P P P + + P P + + P P P P P P P P P P P P P      + +   + +    + + + + + + +    + + + + P P + P P P   + + K K K K K K K K K K  P P P P P P P + + + + + + + + + +  P + P P P P P P + + + P P P + P P P P + + + P P + P + P P + + + P P + + + P P P + + + P P P P P P P P P P P P + + + +      + + +    +     + + P + + + + + +  + + + + + + + + + + K K K K K K K K K K P P P         + + + + +      P P P P P P P P + P P P + P P P P P P P P P P P P P P P + P P P P P P P P P P P P P P P P + + + P        +    +     + + + + + + +   + P P P + + + + + P P   + + K K K K K K K K K K + + + + + + +  P P P P P P P  P     P P P P P P P P + P P P + P P P P P P P P P P P P P P P + P P P P P P P P P P + P P P P P + +  P        +    +     + + + + + + +   + + + + + + + P + P P   + + K K K K K K K K K K + P P + + + + + + + + + + + + +     P P P P P P P P P + P P P + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P   P        +    +     P  + + + + P P P  P P P P P P P P P P   + + K K K K K K K K K K P P P P P P P P P  P P P P P P +    + P P P P P P P P P P P P P P P P P P + + + P P P P P P P P P P P P P P P P P P P P + P P P P P P P P P + P + + P P P P P P P + + +  + + + +       +  +         + + + + + + K K K K K K K K K K + +    + + + + + + + + + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * \ \ * * * * * * * + + + +  \ \ \ \ \ \ \ * * * * * * * *  K K K K K K K K K K   + + + + + \ \ + \ + \ \ \ \ \ + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + \ + \ \ \ \ \ \ \ \ \ \ * \ \ * * * * * * * * * \ + + \ \ \ \ \ + \ + * * * * * * + + K K K K K K K K K K + + \ \ \ \ P        	    	      	              K K K K K K K K K K P P P P P P P P P P             P P P P P P P P + P P P P P P P P P P P P P P P P P P P P P P P P + + + +                        P P P P P            +                                 +                +            	 	 + + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * * * * * * * * * * * * * * * * * * * * \ K K K K K K K K K K       \ \ \ \ \ \ * * * * \ \ \ \ * * * \ * * * \ \ * * * * * * * \ \ \ * * * * \ \ \ \ \ \ \ \ \ \ \ \ \ * * * * * * * * * * * * \ * K K K K K K K K K K * * * * * * P P P P P P + P + + + + + P + + P P P P P P P P P P P P P P P P P P P P P P P P P P P  P P P P X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z P P P P P P P P P + P P P P + + P P P P P P P + P + P P P P + + P P P P P P P P P + P P P P + + P P P P P P P P P P P P P P P P P + P P P P + + P P P P P P P + P + P P P P + + P P P P P P P P P P P P P P P + P P P P P P P P P P P P P P P P P + P P P P + + P P P P P P P P P P P P P P P P P P P P P P P P P P P + +             P P P P P P P P P P P P P P P P P P P P + + + P P P P P P P P P P P P P P P P           + + + + + + P P P P P P P P P P P P P P P P P P P P P P + + P P P P P P + +  P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P   P P P P P P P P P P P P P P P P P  P P P P P P P P P P P P P P P P P P P P P P P P P P   + + + P P P P P P P P P P P    P P P P P P P P P P P + + + + + + + P P P P P P P P P P P P P + P P P P    + + + + + + + + + + + P P P P P P P P P P P P P P P P P P      + + + + + + + + + P P P P P P P P P P P P P P P P P P   + + + + + + + + + + + + P P P P P P P P P P P P P + P P P +   + + + + + + + + + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * * * * * * * * * * * * * * * * * * * *    \     \ * + + K K K K K K K K K K + + + + + + P P P P P P P P P P + + + + + +               	 + K K K K K K K K K K + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + P P P P P   P P P P P P P P P P P P P P P P P P P P P P P P P P  P + + + + + P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P +             + + + +             + + + +  + + +   K K K K K K K K K K \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + + \ \ \ \ \ + + + + + + + + + + + \ \ \ \ \ \ \ \ \ \ \ \ + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + + + + + + K K K K K K K K K K \ + + + * * P P P P P P P P P P P P P P P P P P P P P P P      + +   \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * * * * * * * * * * + * * * * * * * * * * * * * * * * * * * * * * * * * * * * * + +  K K K K K K K K K K + + + + + + K K K K K K K K K K + + + + + + * * * * * * * \ * * * * * * + +                  + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +      P P P P P P P P P P P P P P P P P P P P P P P P P P P      P P P P P P P + + + + K K K K K K K K K K                                    + + +    P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P              P P K K K K K K K K K K P P P P P P P P               + + + + + + + +     P P P P                     + + +      K K K K K K K K K K + + + P P P K K K K K K K K K K P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P   P P P P P P P P P + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P + + P P P         + + + + + + + +                  P P P P  P P P P P P  P P    P + + + + +                           +                            + + P P P P P P + + P P P P P P P P + P + P + P +                                + +                      +         P    P P P +           P P P P + +       +                    + + P P P +          +        	          	                              	                                                      +           P P + + O P P P P P      O P O O O O P P P P P      + P P P P P P P P P P P P P + + +                                                                   P     P            O  P    P P P P P       P  P  P  P O P P        P P P P P                 P  P P P P O O P P P P P O P P O P O O O O O O O O O O O O P P P P O O O O O O O O O O P P P P P P P P P   P P P P O   + + + +                                                                                                                                                                                                                                                                                           % %                                 % % % %                + + + + + + + + + + + + + + + + + + + + + + + + + O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O                                   O O O O O O O O O O O O O O O O O O O O O P                                                                                                                                                                             % % % %                 % %   %  % % %   % %                          % % %                                             %                       %                                                              % % % % % % % % % % % %     %  % % %  % %    % %   %   % % %         %       % % % % %  % %   %   % % % % % % % %    % %                                               %                  O O O O O O O O O O O O O O O O O O O O O O                                                                                                                                                                                                             + +                         +                              P P P P                     + + + + +     P   P P P P P P P P + + + + + + + P  + + + + + + + + + + + + + +  P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + + P P P P P P P + P P P P P P P + P P P P P P P + P P P P P P P +                                                P                                    + + + + + + + + + + + + + % % % % % % % % % % % % % % % % % % % % % % % % % % + % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % + + + + + + + + + + + + % % % % % % % % % % % % % % % % % % % % % % + + + + + + + + + + + + + + + + + + % % % % % % % % % % % % + + + +    % % G W W           % %             % W W W W W W W W W       % W W W W 6 % % W W W G G % % % + Q W Q W Q W Q W Q W W W W W W W W W W W W W W W W W W W W W W W W W Q W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W Q W Q W Q W W W W W W Q W W W W W W Q Q + +     G G W  Q W Q W Q W Q W Q W W W W W W W W W W W W W W W W W W W W W W W W W Q W Q W Q W W W W W W Q W W W W W W Q Q W W W W  Q G G W + + + + + W W W W W W W W W W W W W W W W W W W W W W W W W W W + W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W + % % W W W W % % % % % % % % % % + + + + + + + + + + + + Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % + W W W W W W W W W W % % % % % % % % % % % % % % % % % % % % % % O O O O O O O O % W W W W W W W W W W W W W W W % % % % % % % % % % % % % % % % % W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W % % % W W W W W W W W W W W W W W W W W W W W W G W W W W W W W W W W W W W W W + + + % % % % % % % % % % % % % % % % % % % + + + + + + + + + P P P P P P P P P P P P P P P P P    P P P P P P P P P P P P P P P P K K K K K K K K K K P P + + + + + + + + + + + + + + + + + + + +               P                 P                             P P   P P P P P P P P P P P P P P P P         + + + + + + + +                        P P P P P P P P P                 P                P       P                 + +          + + + + + + + + + + + + + + + + + + + + + +   P P P P P P P P P P P  P P P  P P P P  P P P P P P P P P P P P P P P P P P P P P P P           + + + P P P P P P     + + + + + + P P P P P P P P P P P P P P P P P P P P     + + + + + + + +   P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P       + + + + + + + +   K K K K K K K K K K + + + + + +                   P P P P P P    P  P P  P P P P P P           P P P P P P P P P P P P P P P P P P P              + + + + + + + + + + +  X X X X X X X X X X X X X X X X X X X X X X X X X X X X X + + +               + P K K K K K K K K K K + + + +   \ \ \ \ \ * \ \ \ \ \ \ \ \ \ \ K K K K K K K K K K \ \ \ \ \ + P P P P P P P P P               + + + + + + + + + P P P  P P P P P P P P   + + K K K K K K K K K K + +     \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * * * \ * * * \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * \ * * * \ \ * * \ \ \ \ \ * * \ * \ + + + + + + + + + + + + + + + + + + + + + + + + \ \ \ * * P P P P P P P P P P P        P P P   + + + + + + + + + + P P P P P P + + P P P P P P + + P P P P P P + + + + + + + + + P P P P P P P + P P P P P P P + P P P P P P P P P P P P P P P P P P   + + + + P P P P P P P P P P P P P P P P P P P            + + K K K K K K K K K K + + + + + + T U U U U U U U U U U U U U U U U U U U U U U U U U U U T U U U U U U U U U U U U U U U U U U U U U U U U U U U + + + + + + + + + + + + Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y + + + + Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z + + + +                                 W W W W W W W W W W W W W W % % W W W W W W W W W W W W W W W W W W W W W W W W W W % % % % % % P P P P P P P + + + + + + + + + + + + P P P P P + + + + + V  V V V V V V V V V V  V V V V V V V V V V V V V + V V V V V + V + V V + V V + V V V V V V V V V V P P P P P P P P P P P P P P P P P P                 + + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P   P P P P P P P P P P P P P P P P + + P P P P P P P P P P P P P P + + + + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P P   + +                           + + + + + +                 % % % % %             % %   % % % % % % %  %  +     %       % % % % % % % % + %   % + + + + P P P P P + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P + +  +  % %   % %   % %  %  % W W W W W W W W W W   % % %  % W W W W W W W W W W W W W W W W W W W W W W W W W W  %  % % % W W W W W W W W W W W W W W W W W W W W W W W W W W  %  %        W Q Q Q Q Q Q Q Q Q Q W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W G G + + W W W W W W + + W W W W W W + + W W W W W W + + W W W + + +   % % %   +        + + + + + + + + + +      + + P P P P P P P P P P P P + P P P P P P P P P P P P P P P P P P P P P P + P P P P P P P P P P P P P P P P P P P + P P + P P P P P P P P P P P P P P P + + P P P P P P P P P P P P P P + + P P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + +    + + + + P P P P P P P P P P P P P P P P P P P P P P P P P + + +          P P P P P P P P P P P P P P P P P P P P P P P P P              P P    +              + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +                               + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + + + + + + + +  P P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P + + + + + P P P P P P P P P P P P P P P P P P P P P P      + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P +  P P P P + + + + P P P P P P P P  P P P P P + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P + + P P P P P P P P P P P P P P P P P P P P + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + P P P P P P P P P P P P P P P P + + + + + + + + + + +  + + + + + + + + + + + + + + + + P P P P P P + + P + P P P P P P P P P P P P P P P P P P P P P P P P + P P + + + P + + P P P P P P P P P P P P P P P P P P P P P P P +  P P P P P P P P P P P P P P P P P P P P P P P   P P P P P P P + + + + + + + P P P P P P P P P + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P + P P + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P + + +  P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + +  P P P P P P P P P P P P P P P P P P P P P P P P + + + + P P P P P    +   + + + + +     P P P P + P P P + P P P P P P P P P P P P P P P P P P P P P P P P P + +    + + + +  P P P P P P P P P + + + + + + +          + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P  P P P P P P P P  P P P P P P P P P P P P P P P P P P P P P P P P   + + + + P P P P P        + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P + + +        P P P P P P P P P P P P P P P P P P + + + + + + +     + + + + + + + + + + + + P P P P P P P + + + + + + + + + + + + + + + + P P P P P P P P P + + + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P + + + + + + + P P P P P P     + + + + + + + + K K K K K K K K K K + + + + + + P P P P P P P P P P +    + + P P + + + + + + + + + + + + + + P P P P P P            P P P P      + + + + + +               + + + + P P P P P P P P P P P P P P P P K K K K K K K K K K + + + + + + + + + + + + + + +  P P P P P P P P P P P P P P P P                   + + + + + + + + + + +  + + P P P P P P P P P P P P P P P P P + + + + + + + K K K K K K K K K K + + + + + + P P P P P P P               + K K K K K K K K K K     P   P + + + + + + + + P P P P P P P P P P P P P P P P P P P    P + + + + + + + + +  P P P P            K K K K K K K K K K P  P    + P P P P P P P P P P P P P P P P P P P P + + + + + + + + + + + P P P P P P P P P P P P P P P P P P + P P P P P P P P P P P P P                    + P P P P P P P + P + P P P P + P P P P P P P P P P P P P P P + P P P P P P P P P P  + + + + + + P P P P P P P P P P P P P P P P            + + + + + K K K K K K K K K K + + + + + +     + P P P P P P P P + + P P + + P P P P P P P P P P P P P P + P P P P P P P + P P + P P P P P +   P        + +   + +    + + P + + + + + +  + + + + + P P P P P   + +        + + +      + + + + + + + + + + +        P P P P      K K K K K K K K K K   +   P P P + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +     P P  P + + + + + + + + K K K K K K K K K K + + + + + + P P P P P P P P P P P P P P P        + +                                 P P P P   + +     P + + + + + + + + + + + K K K K K K K K K K + + + + + +              + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P              P + + + + + + + K K K K K K K K K K + + + + + + + + + + + + + + + + + + + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + + * * * * * * * * * * * * * * * + + + + K K K K K K K K K K \ \    * P P P P P P P P P P P P                 + + + + K K K K K K K K K K P P P P P P P P P + + + + + + + + + + + + P P P P P P P P + + P + + P P P P P P P P + P P + P P P P P P P P P P P P P P P P       +   + +     P  P      + + + + + + + + + K K K K K K K K K K + + + + + + P P P P P P P P + + P P P P P P P P P P P P P P P P P P P P P P P        + +        P  P  + + + + + + + + + + + + + + + + + + + + + + + + + + + P           P P P P P P P P P P P P P P P P P P P P P P P P        P              + + + + + + + + P            P P P P P P P P P P                    P      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P + P P P P P P P P P P P P P P P P P P P P P P P P P         +         P      + + + + + + + + + + K K K K K K K K K K P P P P P P P P P P P P P P P + + +   P P P P P P P P P P P P P P P P P P + +               +               + + + + + + + + + P P P P P P P + P P + P P P P P P P P P P P P P P P P P P P P P P       + + +  +   +        P  + + + + + + + + K K K K K K K K K K + + + + + + P P P P P P + P P + P P P P P P P P P P P P P P P P P P P P P P P P      +   +      P + + + + + + + P P P P P P P P P P P P P P P P P P P       + + + + + + + + + + + + + + + + + + + P + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P                              + + + + + + + + + + + + +  P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + P P P P P P P P P P P P P P P +      + + + + + + + + + + + P P P P + + + + + + + + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P H H H C C C P P P P C P P P H C H C P P P P P P P P P P P P P P P P P P P P P P P P P P P H C C P P P P P P P P P P P P P P P + 	 	 	 	 	 	 	   + + + + + 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e===ot?en(t)||rn(t,n):!!en(e)||!(e!==qe||!rn(e,t))},an=function(e,t,n){return e===bt||e===ot?!!Wt(t)||t===Et&&Wt(n):Wt(e===Et?t:e)},on=function(e){var t=0,n=1;e[t]!==bt&&e[t]!==ot||(e[t]===ot&&(n=-1),t++);for(var r=[];Wt(e[t]);)r.push(e[t++]);var i=r.length?parseInt(u.apply(void 0,r),10):0;e[t]===Et&&t++;for(var A=[];Wt(e[t]);)A.push(e[t++]);var a=A.length,o=a?parseInt(u.apply(void 0,A),10):0;e[t]!==Vt&&e[t]!==Rt||t++;var s=1;e[t]!==bt&&e[t]!==ot||(e[t]===ot&&(s=-1),t++);for(var l=[];Wt(e[t]);)l.push(e[t++]);var c=l.length?parseInt(u.apply(void 0,l),10):0;return n*(i+o*Math.pow(10,-a))*Math.pow(10,s*c)},sn={type:2},ln={type:3},un={type:4},cn={type:13},dn={type:8},hn={type:21},fn={type:9},pn={type:10},gn={type:11},mn={type:12},vn={type:14},yn={type:23},wn={type:1},Bn={type:25},_n={type:24},bn={type:26},xn={type:27},Cn={type:28},Sn={type:29},En={type:31},Un={type:32},Mn=function(){function e(){this._value=[]}return e.prototype.write=function(e){this._value=this._value.concat(l(e))},e.prototype.read=function(){for(var e=[],t=this.consumeToken();t!==Un;)e.push(t),t=this.consumeToken();return e},e.prototype.consumeToken=function(){var e=this.consumeCodePoint();switch(e){case Ze:return this.consumeStringToken(Ze);case et:var t=this.peekCodePoint(0),n=this.peekCodePoint(1),r=this.peekCodePoint(2);if(tn(t)||rn(n,r)){var i=An(t,n,r)?Ge:ze;return{type:5,value:this.consumeName(),flags:i}}break;case tt:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),cn;break;case rt:return this.consumeStringToken(rt);case it:return sn;case At:return ln;case _t:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),vn;break;case bt:if(an(e,this.peekCodePoint(0),this.peekCodePoint(1)))return this.reconsumeCodePoint(e),this.consumeNumericToken();break;case xt:return un;case ot:var A=e,a=this.peekCodePoint(0),o=this.peekCodePoint(1);if(an(A,a,o))return this.reconsumeCodePoint(e),this.consumeNumericToken();if(An(A,a,o))return this.reconsumeCodePoint(e),this.consumeIdentLikeToken();if(a===ot&&o===ut)return this.consumeCodePoint(),this.consumeCodePoint(),_n;break;case Et:if(an(e,this.peekCodePoint(0),this.peekCodePoint(1)))return this.reconsumeCodePoint(e),this.consumeNumericToken();break;case Xe:if(this.peekCodePoint(0)===_t)for(this.consumeCodePoint();;){var s=this.consumeCodePoint();if(s===_t&&(s=this.consumeCodePoint())===Xe)return this.consumeToken();if(s===Lt)return this.consumeToken()}break;case Ct:return bn;case St:return xn;case lt:if(this.peekCodePoint(0)===st&&this.peekCodePoint(1)===ot&&this.peekCodePoint(2)===ot)return this.consumeCodePoint(),this.consumeCodePoint(),Bn;break;case ct:var l=this.peekCodePoint(0),c=this.peekCodePoint(1),d=this.peekCodePoint(2);if(An(l,c,d))return{type:7,value:this.consumeName()};break;case dt:return Cn;case qe:if(rn(e,this.peekCodePoint(0)))return this.reconsumeCodePoint(e),this.consumeIdentLikeToken();break;case ht:return Sn;case ft:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),dn;break;case pt:return gn;case mt:return mn;case Pt:case Gt:var h=this.peekCodePoint(0),f=this.peekCodePoint(1);return h!==bt||!Xt(f)&&f!==gt||(this.consumeCodePoint(),this.consumeUnicodeRangeToken()),this.reconsumeCodePoint(e),this.consumeIdentLikeToken();case vt:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),fn;if(this.peekCodePoint(0)===vt)return this.consumeCodePoint(),hn;break;case yt:if(this.peekCodePoint(0)===$e)return this.consumeCodePoint(),pn;break;case Lt:return Un}return $t(e)?(this.consumeWhiteSpace(),En):Wt(e)?(this.reconsumeCodePoint(e),this.consumeNumericToken()):en(e)?(this.reconsumeCodePoint(e),this.consumeIdentLikeToken()):{type:6,value:u(e)}},e.prototype.consumeCodePoint=function(){var e=this._value.shift();return"undefined"===typeof e?-1:e},e.prototype.reconsumeCodePoint=function(e){this._value.unshift(e)},e.prototype.peekCodePoint=function(e){return e>=this._value.length?-1:this._value[e]},e.prototype.consumeUnicodeRangeToken=function(){for(var e=[],t=this.consumeCodePoint();Xt(t)&&e.length<6;)e.push(t),t=this.consumeCodePoint();for(var n=!1;t===gt&&e.length<6;)e.push(t),t=this.consumeCodePoint(),n=!0;if(n)return{type:30,start:parseInt(u.apply(void 0,e.map((function(e){return e===gt?Dt:e}))),16),end:parseInt(u.apply(void 0,e.map((function(e){return e===gt?zt:e}))),16)};var r=parseInt(u.apply(void 0,e),16);if(this.peekCodePoint(0)===ot&&Xt(this.peekCodePoint(1))){this.consumeCodePoint(),t=this.consumeCodePoint();for(var i=[];Xt(t)&&i.length<6;)i.push(t),t=this.consumeCodePoint();return{type:30,start:r,end:parseInt(u.apply(void 0,i),16)}}return{type:30,start:r,end:r}},e.prototype.consumeIdentLikeToken=function(){var e=this.consumeName();return"url"===e.toLowerCase()&&this.peekCodePoint(0)===it?(this.consumeCodePoint(),this.consumeUrlToken()):this.peekCodePoint(0)===it?(this.consumeCodePoint(),{type:19,value:e}):{type:20,value:e}},e.prototype.consumeUrlToken=function(){var e=[];if(this.consumeWhiteSpace(),this.peekCodePoint(0)===Lt)return{type:22,value:""};var t=this.peekCodePoint(0);if(t===rt||t===Ze){var n=this.consumeStringToken(this.consumeCodePoint());return 0===n.type&&(this.consumeWhiteSpace(),this.peekCodePoint(0)===Lt||this.peekCodePoint(0)===At)?(this.consumeCodePoint(),{type:22,value:n.value}):(this.consumeBadUrlRemnants(),yn)}for(;;){var r=this.consumeCodePoint();if(r===Lt||r===At)return{type:22,value:u.apply(void 0,e)};if($t(r))return this.consumeWhiteSpace(),this.peekCodePoint(0)===Lt||this.peekCodePoint(0)===At?(this.consumeCodePoint(),{type:22,value:u.apply(void 0,e)}):(this.consumeBadUrlRemnants(),yn);if(r===Ze||r===rt||r===it||nn(r))return this.consumeBadUrlRemnants(),yn;if(r===qe){if(!rn(r,this.peekCodePoint(0)))return this.consumeBadUrlRemnants(),yn;e.push(this.consumeEscapedCodePoint())}else e.push(r)}},e.prototype.consumeWhiteSpace=function(){for(;$t(this.peekCodePoint(0));)this.consumeCodePoint()},e.prototype.consumeBadUrlRemnants=function(){for(;;){var e=this.consumeCodePoint();if(e===At||e===Lt)return;rn(e,this.peekCodePoint(0))&&this.consumeEscapedCodePoint()}},e.prototype.consumeStringSlice=function(e){for(var t=5e4,n="";e>0;){var r=Math.min(t,e);n+=u.apply(void 0,this._value.splice(0,r)),e-=r}return this._value.shift(),n},e.prototype.consumeStringToken=function(e){for(var t="",n=0;;){var r=this._value[n];if(r===Lt||void 0===r||r===e)return{type:0,value:t+=this.consumeStringSlice(n)};if(r===je)return this._value.splice(0,n),wn;if(r===qe){var i=this._value[n+1];i!==Lt&&void 0!==i&&(i===je?(t+=this.consumeStringSlice(n),n=-1,this._value.shift()):rn(r,i)&&(t+=this.consumeStringSlice(n),t+=u(this.consumeEscapedCodePoint()),n=-1))}n++}},e.prototype.consumeNumber=function(){var e=[],t=Ke,n=this.peekCodePoint(0);for(n!==bt&&n!==ot||e.push(this.consumeCodePoint());Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());n=this.peekCodePoint(0);var r=this.peekCodePoint(1);if(n===Et&&Wt(r))for(e.push(this.consumeCodePoint(),this.consumeCodePoint()),t=We;Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());n=this.peekCodePoint(0),r=this.peekCodePoint(1);var i=this.peekCodePoint(2);if((n===Vt||n===Rt)&&((r===bt||r===ot)&&Wt(i)||Wt(r)))for(e.push(this.consumeCodePoint(),this.consumeCodePoint()),t=We;Wt(this.peekCodePoint(0));)e.push(this.consumeCodePoint());return[on(e),t]},e.prototype.consumeNumericToken=function(){var e=this.consumeNumber(),t=e[0],n=e[1],r=this.peekCodePoint(0),i=this.peekCodePoint(1),A=this.peekCodePoint(2);return An(r,i,A)?{type:15,number:t,flags:n,unit:this.consumeName()}:r===nt?(this.consumeCodePoint(),{type:16,number:t,flags:n}):{type:17,number:t,flags:n}},e.prototype.consumeEscapedCodePoint=function(){var e=this.consumeCodePoint();if(Xt(e)){for(var t=u(e);Xt(this.peekCodePoint(0))&&t.length<6;)t+=u(this.consumeCodePoint());$t(this.peekCodePoint(0))&&this.consumeCodePoint();var n=parseInt(t,16);return 0===n||jt(n)||n>1114111?Bt:n}return e===Lt?Bt:e},e.prototype.consumeName=function(){for(var e="";;){var t=this.consumeCodePoint();if(tn(t))e+=u(t);else{if(!rn(t,this.peekCodePoint(0)))return this.reconsumeCodePoint(t),e;e+=u(this.consumeEscapedCodePoint())}}},e}(),Fn=function(){function e(e){this._tokens=e}return e.create=function(t){var n=new Mn;return n.write(t),new e(n.read())},e.parseValue=function(t){return e.create(t).parseComponentValue()},e.parseValues=function(t){return e.create(t).parseComponentValues()},e.prototype.parseComponentValue=function(){for(var e=this.consumeToken();31===e.type;)e=this.consumeToken();if(32===e.type)throw new SyntaxError("Error parsing CSS component value, unexpected EOF");this.reconsumeToken(e);var t=this.consumeComponentValue();do{e=this.consumeToken()}while(31===e.type);if(32===e.type)return t;throw new SyntaxError("Error parsing CSS component value, multiple values found when expecting only one")},e.prototype.parseComponentValues=function(){for(var e=[];;){var t=this.consumeComponentValue();if(32===t.type)return e;e.push(t),e.push()}},e.prototype.consumeComponentValue=function(){var e=this.consumeToken();switch(e.type){case 11:case 28:case 2:return this.consumeSimpleBlock(e.type);case 19:return this.consumeFunction(e)}return e},e.prototype.consumeSimpleBlock=function(e){for(var t={type:e,values:[]},n=this.consumeToken();;){if(32===n.type||Pn(n,e))return t;this.reconsumeToken(n),t.values.push(this.consumeComponentValue()),n=this.consumeToken()}},e.prototype.consumeFunction=function(e){for(var t={name:e.value,values:[],type:18};;){var n=this.consumeToken();if(32===n.type||3===n.type)return t;this.reconsumeToken(n),t.values.push(this.consumeComponentValue())}},e.prototype.consumeToken=function(){var e=this._tokens.shift();return"undefined"===typeof e?Un:e},e.prototype.reconsumeToken=function(e){this._tokens.unshift(e)},e}(),Tn=function(e){return 15===e.type},kn=function(e){return 17===e.type},Qn=function(e){return 20===e.type},Ln=function(e){return 0===e.type},Dn=function(e,t){return Qn(e)&&e.value===t},In=function(e){return 31!==e.type},Rn=function(e){return 31!==e.type&&4!==e.type},Hn=function(e){var t=[],n=[];return e.forEach((function(e){if(4===e.type){if(0===n.length)throw new Error("Error parsing function args, zero tokens for arg");return t.push(n),void(n=[])}31!==e.type&&n.push(e)})),n.length&&t.push(n),t},Pn=function(e,t){return 11===t&&12===e.type||28===t&&29===e.type||2===t&&3===e.type},Nn=function(e){return 17===e.type||15===e.type},On=function(e){return 16===e.type||Nn(e)},Vn=function(e){return e.length>1?[e[0],e[1]]:[e[0]]},zn={type:17,number:0,flags:Ke},Gn={type:16,number:50,flags:Ke},Kn={type:16,number:100,flags:Ke},Wn=function(e,t,n){var r=e[0],i=e[1];return[jn(r,t),jn("undefined"!==typeof i?i:r,n)]},jn=function(e,t){if(16===e.type)return e.number/100*t;if(Tn(e))switch(e.unit){case"rem":case"em":return 16*e.number;default:return e.number}return e.number},Xn="deg",qn="grad",Yn="rad",Jn="turn",Zn={name:"angle",parse:function(e,t){if(15===t.type)switch(t.unit){case Xn:return Math.PI*t.number/180;case qn:return Math.PI/200*t.number;case Yn:return t.number;case Jn:return 2*Math.PI*t.number}throw new Error("Unsupported angle type")}},$n=function(e){return 15===e.type&&(e.unit===Xn||e.unit===qn||e.unit===Yn||e.unit===Jn)},er=function(e){switch(e.filter(Qn).map((function(e){return e.value})).join(" ")){case"to bottom right":case"to right bottom":case"left top":case"top left":return[zn,zn];case"to top":case"bottom":return tr(0);case"to 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f=null,p=null;if(On(a)?f=jn(a,n.width):o&&On(o)&&(p=jn(o,n.height)),null===f||o&&!tl(o)||(p=l&&u?f/r*i:n.height),null!==p&&tl(a)&&(f=l&&u?p/i*r:n.width),null!==f&&null!==p)return[f,p];throw new Error("Unable to calculate background-size for element")},il=function(e,t){var n=e[t];return"undefined"===typeof n?e[0]:n},Al=function(e,t,n,r,i){var A=t[0],a=t[1],o=n[0],s=n[1];switch(e){case 2:return[new _s(Math.round(r.left),Math.round(r.top+a)),new _s(Math.round(r.left+r.width),Math.round(r.top+a)),new _s(Math.round(r.left+r.width),Math.round(s+r.top+a)),new _s(Math.round(r.left),Math.round(s+r.top+a))];case 3:return[new _s(Math.round(r.left+A),Math.round(r.top)),new _s(Math.round(r.left+A+o),Math.round(r.top)),new _s(Math.round(r.left+A+o),Math.round(r.height+r.top)),new _s(Math.round(r.left+A),Math.round(r.height+r.top))];case 1:return[new _s(Math.round(r.left+A),Math.round(r.top+a)),new _s(Math.round(r.left+A+o),Math.round(r.top+a)),new _s(Math.round(r.left+A+o),Math.round(r.top+a+s)),new _s(Math.round(r.left+A),Math.round(r.top+a+s))];default:return[new _s(Math.round(i.left),Math.round(i.top)),new _s(Math.round(i.left+i.width),Math.round(i.top)),new _s(Math.round(i.left+i.width),Math.round(i.height+i.top)),new _s(Math.round(i.left),Math.round(i.height+i.top))]}},al="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7",ol="Hidden Text",sl=function(){function e(e){this._data={},this._document=e}return e.prototype.parseMetrics=function(e,t){var n=this._document.createElement("div"),r=this._document.createElement("img"),i=this._document.createElement("span"),A=this._document.body;n.style.visibility="hidden",n.style.fontFamily=e,n.style.fontSize=t,n.style.margin="0",n.style.padding="0",n.style.whiteSpace="nowrap",A.appendChild(n),r.src=al,r.width=1,r.height=1,r.style.margin="0",r.style.padding="0",r.style.verticalAlign="baseline",i.style.fontFamily=e,i.style.fontSize=t,i.style.margin="0",i.style.padding="0",i.appendChild(this._document.createTextNode(ol)),n.appendChild(i),n.appendChild(r);var a=r.offsetTop-i.offsetTop+2;n.removeChild(i),n.appendChild(this._document.createTextNode(ol)),n.style.lineHeight="normal",r.style.verticalAlign="super";var o=r.offsetTop-n.offsetTop+2;return A.removeChild(n),{baseline:a,middle:o}},e.prototype.getMetrics=function(e,t){var n=e+" "+t;return"undefined"===typeof this._data[n]&&(this._data[n]=this.parseMetrics(e,t)),this._data[n]},e}(),ll=function(){function e(e,t){this.context=e,this.options=t}return e}(),ul=1e4,cl=function(e){function n(t,n){var r=e.call(this,t,n)||this;return r._activeEffects=[],r.canvas=n.canvas?n.canvas:document.createElement("canvas"),r.ctx=r.canvas.getContext("2d"),n.canvas||(r.canvas.width=Math.floor(n.width*n.scale),r.canvas.height=Math.floor(n.height*n.scale),r.canvas.style.width=n.width+"px",r.canvas.style.height=n.height+"px"),r.fontMetrics=new sl(document),r.ctx.scale(r.options.scale,r.options.scale),r.ctx.translate(-n.x,-n.y),r.ctx.textBaseline="bottom",r._activeEffects=[],r.context.logger.debug("Canvas renderer initialized ("+n.width+"x"+n.height+") with scale "+n.scale),r}return t(n,e),n.prototype.applyEffects=function(e){for(var t=this;this._activeEffects.length;)this.popEffect();e.forEach((function(e){return t.applyEffect(e)}))},n.prototype.applyEffect=function(e){this.ctx.save(),Is(e)&&(this.ctx.globalAlpha=e.opacity),Ls(e)&&(this.ctx.translate(e.offsetX,e.offsetY),this.ctx.transform(e.matrix[0],e.matrix[1],e.matrix[2],e.matrix[3],e.matrix[4],e.matrix[5]),this.ctx.translate(-e.offsetX,-e.offsetY)),Ds(e)&&(this.path(e.path),this.ctx.clip()),this._activeEffects.push(e)},n.prototype.popEffect=function(){this._activeEffects.pop(),this.ctx.restore()},n.prototype.renderStack=function(e){return r(this,void 0,void 0,(function(){return i(this,(function(t){switch(t.label){case 0:return e.element.container.styles.isVisible()?[4,this.renderStackContent(e)]:[3,2];case 1:t.sent(),t.label=2;case 2:return[2]}}))}))},n.prototype.renderNode=function(e){return r(this,void 0,void 0,(function(){return i(this,(function(t){switch(t.label){case 0:return iA(e.container.flags,16),e.container.styles.isVisible()?[4,this.renderNodeBackgroundAndBorders(e)]:[3,3];case 1:return t.sent(),[4,this.renderNodeContent(e)];case 2:t.sent(),t.label=3;case 3:return[2]}}))}))},n.prototype.renderTextWithLetterSpacing=function(e,t,n){var r=this;0===t?this.ctx.fillText(e.text,e.bounds.left,e.bounds.top+n):Ma(e.text).reduce((function(t,i){return r.ctx.fillText(i,t,e.bounds.top+n),t+r.ctx.measureText(i).width}),e.bounds.left)},n.prototype.createFontStyle=function(e){var t=e.fontVariant.filter((function(e){return"normal"===e||"small-caps"===e})).join(""),n=gl(e.fontFamily).join(", "),r=Tn(e.fontSize)?""+e.fontSize.number+e.fontSize.unit:e.fontSize.number+"px";return[[e.fontStyle,t,e.fontWeight,r,n].join(" "),n,r]},n.prototype.renderTextNode=function(e,t){return r(this,void 0,void 0,(function(){var n,r,A,a,o,s,l,u,c=this;return i(this,(function(i){return n=this.createFontStyle(t),r=n[0],A=n[1],a=n[2],this.ctx.font=r,this.ctx.direction=1===t.direction?"rtl":"ltr",this.ctx.textAlign="left",this.ctx.textBaseline="alphabetic",o=this.fontMetrics.getMetrics(A,a),s=o.baseline,l=o.middle,u=t.paintOrder,e.textBounds.forEach((function(e){u.forEach((function(n){switch(n){case 0:c.ctx.fillStyle=ir(t.color),c.renderTextWithLetterSpacing(e,t.letterSpacing,s);var r=t.textShadow;r.length&&e.text.trim().length&&(r.slice(0).reverse().forEach((function(n){c.ctx.shadowColor=ir(n.color),c.ctx.shadowOffsetX=n.offsetX.number*c.options.scale,c.ctx.shadowOffsetY=n.offsetY.number*c.options.scale,c.ctx.shadowBlur=n.blur.number,c.renderTextWithLetterSpacing(e,t.letterSpacing,s)})),c.ctx.shadowColor="",c.ctx.shadowOffsetX=0,c.ctx.shadowOffsetY=0,c.ctx.shadowBlur=0),t.textDecorationLine.length&&(c.ctx.fillStyle=ir(t.textDecorationColor||t.color),t.textDecorationLine.forEach((function(t){switch(t){case 1:c.ctx.fillRect(e.bounds.left,Math.round(e.bounds.top+s),e.bounds.width,1);break;case 2:c.ctx.fillRect(e.bounds.left,Math.round(e.bounds.top),e.bounds.width,1);break;case 3:c.ctx.fillRect(e.bounds.left,Math.ceil(e.bounds.top+l),e.bounds.width,1)}})));break;case 1:t.webkitTextStrokeWidth&&e.text.trim().length&&(c.ctx.strokeStyle=ir(t.webkitTextStrokeColor),c.ctx.lineWidth=t.webkitTextStrokeWidth,c.ctx.lineJoin=window.chrome?"miter":"round",c.ctx.strokeText(e.text,e.bounds.left,e.bounds.top+s)),c.ctx.strokeStyle="",c.ctx.lineWidth=0,c.ctx.lineJoin="miter"}}))})),[2]}))}))},n.prototype.renderReplacedElement=function(e,t,n){if(n&&e.intrinsicWidth>0&&e.intrinsicHeight>0){var r=Js(e),i=Fs(t);this.path(i),this.ctx.save(),this.ctx.clip(),this.ctx.drawImage(n,0,0,e.intrinsicWidth,e.intrinsicHeight,r.left,r.top,r.width,r.height),this.ctx.restore()}},n.prototype.renderNodeContent=function(e){return r(this,void 0,void 0,(function(){var t,r,A,o,s,l,u,c,d,h,f,p,g,m,v,y,w,B;return i(this,(function(i){switch(i.label){case 0:this.applyEffects(e.getEffects(4)),t=e.container,r=e.curves,A=t.styles,o=0,s=t.textNodes,i.label=1;case 1:return o<s.length?(l=s[o],[4,this.renderTextNode(l,A)]):[3,4];case 2:i.sent(),i.label=3;case 3:return o++,[3,1];case 4:if(!(t instanceof Ha))return[3,8];i.label=5;case 5:return i.trys.push([5,7,,8]),[4,this.context.cache.match(t.src)];case 6:return v=i.sent(),this.renderReplacedElement(t,r,v),[3,8];case 7:return i.sent(),this.context.logger.error("Error loading image "+t.src),[3,8];case 8:if(t instanceof Pa&&this.renderReplacedElement(t,r,t.canvas),!(t instanceof Na))return[3,12];i.label=9;case 9:return i.trys.push([9,11,,12]),[4,this.context.cache.match(t.svg)];case 10:return v=i.sent(),this.renderReplacedElement(t,r,v),[3,12];case 11:return i.sent(),this.context.logger.error("Error loading svg "+t.svg.substring(0,255)),[3,12];case 12:return t instanceof eo&&t.tree?[4,new n(this.context,{scale:this.options.scale,backgroundColor:t.backgroundColor,x:0,y:0,width:t.width,height:t.height}).render(t.tree)]:[3,14];case 13:u=i.sent(),t.width&&t.height&&this.ctx.drawImage(u,0,0,t.width,t.height,t.bounds.left,t.bounds.top,t.bounds.width,t.bounds.height),i.label=14;case 14:if(t instanceof Ja&&(c=Math.min(t.bounds.width,t.bounds.height),t.type===ja?t.checked&&(this.ctx.save(),this.path([new _s(t.bounds.left+.39363*c,t.bounds.top+.79*c),new _s(t.bounds.left+.16*c,t.bounds.top+.5549*c),new _s(t.bounds.left+.27347*c,t.bounds.top+.44071*c),new _s(t.bounds.left+.39694*c,t.bounds.top+.5649*c),new _s(t.bounds.left+.72983*c,t.bounds.top+.23*c),new _s(t.bounds.left+.84*c,t.bounds.top+.34085*c),new _s(t.bounds.left+.39363*c,t.bounds.top+.79*c)]),this.ctx.fillStyle=ir(Ya),this.ctx.fill(),this.ctx.restore()):t.type===Xa&&t.checked&&(this.ctx.save(),this.ctx.beginPath(),this.ctx.arc(t.bounds.left+c/2,t.bounds.top+c/2,c/4,0,2*Math.PI,!0),this.ctx.fillStyle=ir(Ya),this.ctx.fill(),this.ctx.restore())),dl(t)&&t.value.length){switch(d=this.createFontStyle(A),w=d[0],h=d[1],f=this.fontMetrics.getMetrics(w,h).baseline,this.ctx.font=w,this.ctx.fillStyle=ir(A.color),this.ctx.textBaseline="alphabetic",this.ctx.textAlign=fl(t.styles.textAlign),B=Js(t),p=0,t.styles.textAlign){case 1:p+=B.width/2;break;case 2:p+=B.width}g=B.add(p,0,0,-B.height/2+1),this.ctx.save(),this.path([new _s(B.left,B.top),new _s(B.left+B.width,B.top),new _s(B.left+B.width,B.top+B.height),new _s(B.left,B.top+B.height)]),this.ctx.clip(),this.renderTextWithLetterSpacing(new Ca(t.value,g),A.letterSpacing,f),this.ctx.restore(),this.ctx.textBaseline="alphabetic",this.ctx.textAlign="left"}if(!iA(t.styles.display,2048))return[3,20];if(null===t.styles.listStyleImage)return[3,19];if(0!==(m=t.styles.listStyleImage).type)return[3,18];v=void 0,y=m.url,i.label=15;case 15:return i.trys.push([15,17,,18]),[4,this.context.cache.match(y)];case 16:return v=i.sent(),this.ctx.drawImage(v,t.bounds.left-(v.width+10),t.bounds.top),[3,18];case 17:return i.sent(),this.context.logger.error("Error loading list-style-image "+y),[3,18];case 18:return[3,20];case 19:e.listValue&&-1!==t.styles.listStyleType&&(w=this.createFontStyle(A)[0],this.ctx.font=w,this.ctx.fillStyle=ir(A.color),this.ctx.textBaseline="middle",this.ctx.textAlign="right",B=new a(t.bounds.left,t.bounds.top+jn(t.styles.paddingTop,t.bounds.width),t.bounds.width,Bi(A.lineHeight,A.fontSize.number)/2+1),this.renderTextWithLetterSpacing(new Ca(e.listValue,B),A.letterSpacing,Bi(A.lineHeight,A.fontSize.number)/2+2),this.ctx.textBaseline="bottom",this.ctx.textAlign="left"),i.label=20;case 20:return[2]}}))}))},n.prototype.renderStackContent=function(e){return r(this,void 0,void 0,(function(){var t,n,r,A,a,o,s,l,u,c,d,h,f,p,g;return i(this,(function(i){switch(i.label){case 0:return iA(e.element.container.flags,16),[4,this.renderNodeBackgroundAndBorders(e.element)];case 1:i.sent(),t=0,n=e.negativeZIndex,i.label=2;case 2:return t<n.length?(g=n[t],[4,this.renderStack(g)]):[3,5];case 3:i.sent(),i.label=4;case 4:return t++,[3,2];case 5:return[4,this.renderNodeContent(e.element)];case 6:i.sent(),r=0,A=e.nonInlineLevel,i.label=7;case 7:return r<A.length?(g=A[r],[4,this.renderNode(g)]):[3,10];case 8:i.sent(),i.label=9;case 9:return r++,[3,7];case 10:a=0,o=e.nonPositionedFloats,i.label=11;case 11:return a<o.length?(g=o[a],[4,this.renderStack(g)]):[3,14];case 12:i.sent(),i.label=13;case 13:return a++,[3,11];case 14:s=0,l=e.nonPositionedInlineLevel,i.label=15;case 15:return s<l.length?(g=l[s],[4,this.renderStack(g)]):[3,18];case 16:i.sent(),i.label=17;case 17:return s++,[3,15];case 18:u=0,c=e.inlineLevel,i.label=19;case 19:return u<c.length?(g=c[u],[4,this.renderNode(g)]):[3,22];case 20:i.sent(),i.label=21;case 21:return u++,[3,19];case 22:d=0,h=e.zeroOrAutoZIndexOrTransformedOrOpacity,i.label=23;case 23:return d<h.length?(g=h[d],[4,this.renderStack(g)]):[3,26];case 24:i.sent(),i.label=25;case 25:return d++,[3,23];case 26:f=0,p=e.positiveZIndex,i.label=27;case 27:return f<p.length?(g=p[f],[4,this.renderStack(g)]):[3,30];case 28:i.sent(),i.label=29;case 29:return f++,[3,27];case 30:return[2]}}))}))},n.prototype.mask=function(e){this.ctx.beginPath(),this.ctx.moveTo(0,0),this.ctx.lineTo(this.canvas.width,0),this.ctx.lineTo(this.canvas.width,this.canvas.height),this.ctx.lineTo(0,this.canvas.height),this.ctx.lineTo(0,0),this.formatPath(e.slice(0).reverse()),this.ctx.closePath()},n.prototype.path=function(e){this.ctx.beginPath(),this.formatPath(e),this.ctx.closePath()},n.prototype.formatPath=function(e){var t=this;e.forEach((function(e,n){var r=Cs(e)?e.start:e;0===n?t.ctx.moveTo(r.x,r.y):t.ctx.lineTo(r.x,r.y),Cs(e)&&t.ctx.bezierCurveTo(e.startControl.x,e.startControl.y,e.endControl.x,e.endControl.y,e.end.x,e.end.y)}))},n.prototype.renderRepeat=function(e,t,n,r){this.path(e),this.ctx.fillStyle=t,this.ctx.translate(n,r),this.ctx.fill(),this.ctx.translate(-n,-r)},n.prototype.resizeImage=function(e,t,n){var r;if(e.width===t&&e.height===n)return e;var i=(null!==(r=this.canvas.ownerDocument)&&void 0!==r?r:document).createElement("canvas");return i.width=Math.max(1,t),i.height=Math.max(1,n),i.getContext("2d").drawImage(e,0,0,e.width,e.height,0,0,t,n),i},n.prototype.renderBackgroundImage=function(e){return r(this,void 0,void 0,(function(){var t,n,r,A,a,o;return i(this,(function(s){switch(s.label){case 0:t=e.styles.backgroundImage.length-1,n=function(n){var A,a,o,s,l,u,c,d,h,f,p,g,m,v,y,w,B,_,b,x,C,S,E,U,M,F,T,k,Q,L,D;return i(this,(function(i){switch(i.label){case 0:if(0!==n.type)return[3,5];A=void 0,a=n.url,i.label=1;case 1:return i.trys.push([1,3,,4]),[4,r.context.cache.match(a)];case 2:return A=i.sent(),[3,4];case 3:return i.sent(),r.context.logger.error("Error loading background-image "+a),[3,4];case 4:return A&&(o=el(e,t,[A.width,A.height,A.width/A.height]),w=o[0],S=o[1],E=o[2],b=o[3],x=o[4],v=r.ctx.createPattern(r.resizeImage(A,b,x),"repeat"),r.renderRepeat(w,v,S,E)),[3,6];case 5:kr(n)?(s=el(e,t,[null,null,null]),w=s[0],S=s[1],E=s[2],b=s[3],x=s[4],l=vr(n.angle,b,x),u=l[0],c=l[1],d=l[2],h=l[3],f=l[4],(p=document.createElement("canvas")).width=b,p.height=x,g=p.getContext("2d"),m=g.createLinearGradient(c,h,d,f),gr(n.stops,u).forEach((function(e){return m.addColorStop(e.stop,ir(e.color))})),g.fillStyle=m,g.fillRect(0,0,b,x),b>0&&x>0&&(v=r.ctx.createPattern(p,"repeat"),r.renderRepeat(w,v,S,E))):Qr(n)&&(y=el(e,t,[null,null,null]),w=y[0],B=y[1],_=y[2],b=y[3],x=y[4],C=0===n.position.length?[Gn]:n.position,S=jn(C[0],b),E=jn(C[C.length-1],x),U=Br(n,S,E,b,x),M=U[0],F=U[1],M>0&&F>0&&(T=r.ctx.createRadialGradient(B+S,_+E,0,B+S,_+E,M),gr(n.stops,2*M).forEach((function(e){return T.addColorStop(e.stop,ir(e.color))})),r.path(w),r.ctx.fillStyle=T,M!==F?(k=e.bounds.left+.5*e.bounds.width,Q=e.bounds.top+.5*e.bounds.height,D=1/(L=F/M),r.ctx.save(),r.ctx.translate(k,Q),r.ctx.transform(1,0,0,L,0,0),r.ctx.translate(-k,-Q),r.ctx.fillRect(B,D*(_-Q)+Q,b,x*D),r.ctx.restore()):r.ctx.fill())),i.label=6;case 6:return t--,[2]}}))},r=this,A=0,a=e.styles.backgroundImage.slice(0).reverse(),s.label=1;case 1:return A<a.length?(o=a[A],[5,n(o)]):[3,4];case 2:s.sent(),s.label=3;case 3:return A++,[3,1];case 4:return[2]}}))}))},n.prototype.renderSolidBorder=function(e,t,n){return r(this,void 0,void 0,(function(){return i(this,(function(r){return this.path(Gs(n,t)),this.ctx.fillStyle=ir(e),this.ctx.fill(),[2]}))}))},n.prototype.renderDoubleBorder=function(e,t,n,A){return r(this,void 0,void 0,(function(){var r,a;return i(this,(function(i){switch(i.label){case 0:return t<3?[4,this.renderSolidBorder(e,n,A)]:[3,2];case 1:return i.sent(),[2];case 2:return r=Ks(A,n),this.path(r),this.ctx.fillStyle=ir(e),this.ctx.fill(),a=Ws(A,n),this.path(a),this.ctx.fill(),[2]}}))}))},n.prototype.renderNodeBackgroundAndBorders=function(e){return r(this,void 0,void 0,(function(){var t,n,r,A,a,o,s,l,u=this;return i(this,(function(i){switch(i.label){case 0:return this.applyEffects(e.getEffects(2)),t=e.container.styles,n=!rr(t.backgroundColor)||t.backgroundImage.length,r=[{style:t.borderTopStyle,color:t.borderTopColor,width:t.borderTopWidth},{style:t.borderRightStyle,color:t.borderRightColor,width:t.borderRightWidth},{style:t.borderBottomStyle,color:t.borderBottomColor,width:t.borderBottomWidth},{style:t.borderLeftStyle,color:t.borderLeftColor,width:t.borderLeftWidth}],A=hl(il(t.backgroundClip,0),e.curves),n||t.boxShadow.length?(this.ctx.save(),this.path(A),this.ctx.clip(),rr(t.backgroundColor)||(this.ctx.fillStyle=ir(t.backgroundColor),this.ctx.fill()),[4,this.renderBackgroundImage(e.container)]):[3,2];case 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function(){t||(t=!0,e._release())}}},{key:"autoRelease_INTERNAL",value:function(){var e=this;tA||window.setTimeout((function(){return e._release()}),10)}},{key:"_release",value:function(){if(this._refCount--,0===this._refCount){if(this._store.getState().nodeCleanupFunctions.forEach((function(e){return e()})),this._store.getState().nodeCleanupFunctions.clear(),!vn("recoil_memory_managament_2020"))return}else this._refCount}},{key:"isRetained",value:function(){return this._refCount>0}},{key:"checkRefCount_INTERNAL",value:function(){vn("recoil_memory_managament_2020")&&this._refCount}},{key:"getStore_INTERNAL",value:function(){return this.checkRefCount_INTERNAL(),this._store}},{key:"getID",value:function(){return this.checkRefCount_INTERNAL(),this._store.getState().currentTree.stateID}},{key:"getStoreID",value:function(){return this.checkRefCount_INTERNAL(),this._store.storeID}}]),e}();function iA(e,t){var n=arguments.length>2&&void 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new rA(iA(e,i),e.storeID)}),(function(e,t){var n,r;return String(t)+String(e.storeID)+String(null===(n=e.getState().nextTree)||void 0===n?void 0:n.version)+String(e.getState().currentTree.version)+String(null===(r=e.getState().previousTree)||void 0===r?void 0:r.version)})),2),aA=AA[0],oA=AA[1];Zi(oA);var sA=function(e){_(n,e);var t=S(n);function n(e,r){var i;return v(this,n),Xt(C(i=t.call(this,iA(e.getStore_INTERNAL(),e.getStore_INTERNAL().getState().currentTree,!0),e.getStoreID())),"_batch",void 0),Xt(C(i),"set",(function(e,t){i.checkRefCount_INTERNAL();var n=i.getStore_INTERNAL();i._batch((function(){Ji(n,e.key,1),qi(i.getStore_INTERNAL(),e,t)}))})),Xt(C(i),"reset",(function(e){i.checkRefCount_INTERNAL();var t=i.getStore_INTERNAL();i._batch((function(){Ji(t,e.key,1),qi(i.getStore_INTERNAL(),e,Gi)}))})),Xt(C(i),"setUnvalidatedAtomValues_DEPRECATED",(function(e){i.checkRefCount_INTERNAL();var t=i.getStore_INTERNAL();Pi((function(){var 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e&&(t.current=e()),t},vA=vr.getNextTreeStateVersion,yA=vr.makeEmptyStoreState,wA=Pr,BA=Vr,_A=Hr,bA=Rr,xA=Nr,CA=ar,SA=Ar,EA=dr,UA=Un.createMutableSource,MA=Un.reactMode,FA=hi.applyAtomValueWrites,TA=Ei,kA=fA.freshSnapshot,QA=r.useCallback,LA=r.useContext,DA=r.useEffect,IA=r.useMemo,RA=r.useRef,HA=r.useState;function PA(){throw Kt("This component must be used inside a <RecoilRoot> component.")}var NA=Object.freeze({storeID:EA(),getState:PA,replaceState:PA,getGraph:PA,subscribeToTransactions:PA,addTransactionMetadata:PA}),OA=!1;function VA(e){if(OA)throw Kt("An atom update was triggered within the execution of a state updater function. 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n()}},{key:"poll",value:function(){this.polling=!0,this.doPoll(),this.emitReserved("poll")}},{key:"onData",value:function(e){var t=this;(function(e,t){for(var n=e.split(sd),r=[],i=0;i<n.length;i++){var A=od(n[i],t);if(r.push(A),"error"===A.type)break}return r})(e,this.socket.binaryType).forEach((function(e){if("opening"===t.readyState&&"open"===e.type&&t.onOpen(),"close"===e.type)return t.onClose({description:"transport closed by the server"}),!1;t.onPacket(e)})),"closed"!==this.readyState&&(this.polling=!1,this.emitReserved("pollComplete"),"open"===this.readyState&&this.poll())}},{key:"doClose",value:function(){var e=this,t=function(){e.write([{type:"close"}])};"open"===this.readyState?t():this.once("open",t)}},{key:"write",value:function(e){var t=this;this.writable=!1,function(e,t){var n=e.length,r=new Array(n),i=0;e.forEach((function(e,A){ed(e,!1,(function(e){r[A]=e,++i===n&&t(r.join(sd))}))}))}(e,(function(e){t.doWrite(e,(function(){t.writable=!0,t.emitReserved("drain")}))}))}},{key:"uri",value:function(){var e=this.query||{},t=this.opts.secure?"https":"http",n="";!1!==this.opts.timestampRequests&&(e[this.opts.timestampParam]=Cd()),this.supportsBinary||e.sid||(e.b64=1),this.opts.port&&("https"===t&&443!==Number(this.opts.port)||"http"===t&&80!==Number(this.opts.port))&&(n=":"+this.opts.port);var r=Sd(e);return t+"://"+(-1!==this.opts.hostname.indexOf(":")?"["+this.opts.hostname+"]":this.opts.hostname)+n+this.opts.path+(r.length?"?"+r:"")}},{key:"request",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};return Object.assign(e,{xd:this.xd,xs:this.xs},this.opts),new Qd(this.uri(),e)}},{key:"doWrite",value:function(e,t){var n=this,r=this.request({method:"POST",data:e});r.on("success",t),r.on("error",(function(e,t){n.onError("xhr post error",e,t)}))}},{key:"doPoll",value:function(){var e=this,t=this.request();t.on("data",this.onData.bind(this)),t.on("error",(function(t,n){e.onError("xhr poll error",t,n)})),this.pollXhr=t}}]),n}(vd),Qd=function(e){_(n,e);var t=S(n);function n(e,r){var i;return v(this,n),fd(C(i=t.call(this)),r),i.opts=r,i.method=r.method||"GET",i.uri=e,i.async=!1!==r.async,i.data=void 0!==r.data?r.data:null,i.create(),i}return w(n,[{key:"create",value:function(){var e=this,t=cd(this.opts,"agent","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","autoUnref");t.xdomain=!!this.opts.xd,t.xscheme=!!this.opts.xs;var r=this.xhr=new Md(t);try{r.open(this.method,this.uri,this.async);try{if(this.opts.extraHeaders)for(var i in r.setDisableHeaderCheck&&r.setDisableHeaderCheck(!0),this.opts.extraHeaders)this.opts.extraHeaders.hasOwnProperty(i)&&r.setRequestHeader(i,this.opts.extraHeaders[i])}catch(A){}if("POST"===this.method)try{r.setRequestHeader("Content-type","text/plain;charset=UTF-8")}catch(A){}try{r.setRequestHeader("Accept","*/*")}catch(A){}"withCredentials"in r&&(r.withCredentials=this.opts.withCredentials),this.opts.requestTimeout&&(r.timeout=this.opts.requestTimeout),r.onreadystatechange=function(){4===r.readyState&&(200===r.status||1223===r.status?e.onLoad():e.setTimeoutFn((function(){e.onError("number"===typeof r.status?r.status:0)}),0))},r.send(this.data)}catch(A){return void this.setTimeoutFn((function(){e.onError(A)}),0)}"undefined"!==typeof document&&(this.index=n.requestsCount++,n.requests[this.index]=this)}},{key:"onError",value:function(e){this.emitReserved("error",e,this.xhr),this.cleanup(!0)}},{key:"cleanup",value:function(e){if("undefined"!==typeof this.xhr&&null!==this.xhr){if(this.xhr.onreadystatechange=Fd,e)try{this.xhr.abort()}catch(t){}"undefined"!==typeof document&&delete n.requests[this.index],this.xhr=null}}},{key:"onLoad",value:function(){var e=this.xhr.responseText;null!==e&&(this.emitReserved("data",e),this.emitReserved("success"),this.cleanup())}},{key:"abort",value:function(){this.cleanup()}}]),n}(ld);if(Qd.requestsCount=0,Qd.requests={},"undefined"!==typeof document)if("function"===typeof attachEvent)attachEvent("onunload",Ld);else if("function"===typeof addEventListener){addEventListener("onpagehide"in ud?"pagehide":"unload",Ld,!1)}function Ld(){for(var e in Qd.requests)Qd.requests.hasOwnProperty(e)&&Qd.requests[e].abort()}var Dd="function"===typeof Promise&&"function"===typeof Promise.resolve?function(e){return Promise.resolve().then(e)}:function(e,t){return t(e,0)},Id=ud.WebSocket||ud.MozWebSocket,Rd="undefined"!==typeof navigator&&"string"===typeof navigator.product&&"reactnative"===navigator.product.toLowerCase(),Hd=function(e){_(n,e);var t=S(n);function n(e){var r;return v(this,n),(r=t.call(this,e)).supportsBinary=!e.forceBase64,r}return w(n,[{key:"name",get:function(){return"websocket"}},{key:"doOpen",value:function(){if(this.check()){var e=this.uri(),t=this.opts.protocols,n=Rd?{}:cd(this.opts,"agent","perMessageDeflate","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","localAddress","protocolVersion","origin","maxPayload","family","checkServerIdentity");this.opts.extraHeaders&&(n.headers=this.opts.extraHeaders);try{this.ws=Rd?new Id(e,t,n):t?new Id(e,t):new Id(e)}catch(Jx){return this.emitReserved("error",Jx)}this.ws.binaryType=this.socket.binaryType||"arraybuffer",this.addEventListeners()}}},{key:"addEventListeners",value:function(){var e=this;this.ws.onopen=function(){e.opts.autoUnref&&e.ws._socket.unref(),e.onOpen()},this.ws.onclose=function(t){return e.onClose({description:"websocket connection closed",context:t})},this.ws.onmessage=function(t){return e.onData(t.data)},this.ws.onerror=function(t){return e.onError("websocket error",t)}}},{key:"write",value:function(e){var t=this;this.writable=!1;for(var n=function(){var n=e[r],i=r===e.length-1;ed(n,t.supportsBinary,(function(e){try{t.ws.send(e)}catch(n){}i&&Dd((function(){t.writable=!0,t.emitReserved("drain")}),t.setTimeoutFn)}))},r=0;r<e.length;r++)n()}},{key:"doClose",value:function(){"undefined"!==typeof this.ws&&(this.ws.close(),this.ws=null)}},{key:"uri",value:function(){var e=this.query||{},t=this.opts.secure?"wss":"ws",n="";this.opts.port&&("wss"===t&&443!==Number(this.opts.port)||"ws"===t&&80!==Number(this.opts.port))&&(n=":"+this.opts.port),this.opts.timestampRequests&&(e[this.opts.timestampParam]=Cd()),this.supportsBinary||(e.b64=1);var r=Sd(e);return t+"://"+(-1!==this.opts.hostname.indexOf(":")?"["+this.opts.hostname+"]":this.opts.hostname)+n+this.opts.path+(r.length?"?"+r:"")}},{key:"check",value:function(){return!!Id}}]),n}(vd),Pd={websocket:Hd,polling:kd},Nd=/^(?:(?![^:@\/?#]+:[^:@\/]*@)(http|https|ws|wss):\/\/)?((?:(([^:@\/?#]*)(?::([^:@\/?#]*))?)?@)?((?:[a-f0-9]{0,4}:){2,7}[a-f0-9]{0,4}|[^:\/?#]*)(?::(\d*))?)(((\/(?:[^?#](?![^?#\/]*\.[^?#\/.]+(?:[?#]|$)))*\/?)?([^?#\/]*))(?:\?([^#]*))?(?:#(.*))?)/,Od=["source","protocol","authority","userInfo","user","password","host","port","relative","path","directory","file","query","anchor"];function Vd(e){var t=e,n=e.indexOf("["),r=e.indexOf("]");-1!=n&&-1!=r&&(e=e.substring(0,n)+e.substring(n,r).replace(/:/g,";")+e.substring(r,e.length));for(var i=Nd.exec(e||""),A={},a=14;a--;)A[Od[a]]=i[a]||"";return-1!=n&&-1!=r&&(A.source=t,A.host=A.host.substring(1,A.host.length-1).replace(/;/g,":"),A.authority=A.authority.replace("[","").replace("]","").replace(/;/g,":"),A.ipv6uri=!0),A.pathNames=function(e,t){var n=/\/{2,9}/g,r=t.replace(n,"/").split("/");"/"!=t.slice(0,1)&&0!==t.length||r.splice(0,1);"/"==t.slice(-1)&&r.splice(r.length-1,1);return r}(0,A.path),A.queryKey=function(e,t){var n={};return t.replace(/(?:^|&)([^&=]*)=?([^&]*)/g,(function(e,t,r){t&&(n[t]=r)})),n}(0,A.query),A}var zd=function(e){_(n,e);var t=S(n);function n(e){var r,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return v(this,n),(r=t.call(this)).writeBuffer=[],e&&"object"===typeof e&&(i=e,e=null),e?(e=Vd(e),i.hostname=e.host,i.secure="https"===e.protocol||"wss"===e.protocol,i.port=e.port,e.query&&(i.query=e.query)):i.host&&(i.hostname=Vd(i.host).host),fd(C(r),i),r.secure=null!=i.secure?i.secure:"undefined"!==typeof location&&"https:"===location.protocol,i.hostname&&!i.port&&(i.port=r.secure?"443":"80"),r.hostname=i.hostname||("undefined"!==typeof location?location.hostname:"localhost"),r.port=i.port||("undefined"!==typeof location&&location.port?location.port:r.secure?"443":"80"),r.transports=i.transports||["polling","websocket"],r.writeBuffer=[],r.prevBufferLen=0,r.opts=Object.assign({path:"/engine.io",agent:!1,withCredentials:!1,upgrade:!0,timestampParam:"t",rememberUpgrade:!1,addTrailingSlash:!0,rejectUnauthorized:!0,perMessageDeflate:{threshold:1024},transportOptions:{},closeOnBeforeunload:!0},i),r.opts.path=r.opts.path.replace(/\/$/,"")+(r.opts.addTrailingSlash?"/":""),"string"===typeof r.opts.query&&(r.opts.query=function(e){for(var t={},n=e.split("&"),r=0,i=n.length;r<i;r++){var A=n[r].split("=");t[decodeURIComponent(A[0])]=decodeURIComponent(A[1])}return t}(r.opts.query)),r.id=null,r.upgrades=null,r.pingInterval=null,r.pingTimeout=null,r.pingTimeoutTimer=null,"function"===typeof addEventListener&&(r.opts.closeOnBeforeunload&&(r.beforeunloadEventListener=function(){r.transport&&(r.transport.removeAllListeners(),r.transport.close())},addEventListener("beforeunload",r.beforeunloadEventListener,!1)),"localhost"!==r.hostname&&(r.offlineEventListener=function(){r.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",r.offlineEventListener,!1))),r.open(),r}return w(n,[{key:"createTransport",value:function(e){var t=Object.assign({},this.opts.query);t.EIO=4,t.transport=e,this.id&&(t.sid=this.id);var n=Object.assign({},this.opts.transportOptions[e],this.opts,{query:t,socket:this,hostname:this.hostname,secure:this.secure,port:this.port});return new Pd[e](n)}},{key:"open",value:function(){var e,t=this;if(this.opts.rememberUpgrade&&n.priorWebsocketSuccess&&-1!==this.transports.indexOf("websocket"))e="websocket";else{if(0===this.transports.length)return void this.setTimeoutFn((function(){t.emitReserved("error","No transports 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Error("probe error");A.transport=r.name,t.emitReserved("upgradeError",A)}})))};function a(){i||(i=!0,c(),r.close(),r=null)}var o=function(e){var n=new Error("probe error: "+e);n.transport=r.name,a(),t.emitReserved("upgradeError",n)};function s(){o("transport closed")}function l(){o("socket closed")}function u(e){r&&e.name!==r.name&&a()}var c=function(){r.removeListener("open",A),r.removeListener("error",o),r.removeListener("close",s),t.off("close",l),t.off("upgrading",u)};r.once("open",A),r.once("error",o),r.once("close",s),this.once("close",l),this.once("upgrading",u),r.open()}},{key:"onOpen",value:function(){if(this.readyState="open",n.priorWebsocketSuccess="websocket"===this.transport.name,this.emitReserved("open"),this.flush(),"open"===this.readyState&&this.opts.upgrade)for(var e=0,t=this.upgrades.length;e<t;e++)this.probe(this.upgrades[e])}},{key:"onPacket",value:function(e){if("opening"===this.readyState||"open"===this.readyState||"closing"===this.readyState)switch(this.emitReserved("packet",e),this.emitReserved("heartbeat"),e.type){case"open":this.onHandshake(JSON.parse(e.data));break;case"ping":this.resetPingTimeout(),this.sendPacket("pong"),this.emitReserved("ping"),this.emitReserved("pong");break;case"error":var t=new Error("server error");t.code=e.data,this.onError(t);break;case"message":this.emitReserved("data",e.data),this.emitReserved("message",e.data)}}},{key:"onHandshake",value:function(e){this.emitReserved("handshake",e),this.id=e.sid,this.transport.query.sid=e.sid,this.upgrades=this.filterUpgrades(e.upgrades),this.pingInterval=e.pingInterval,this.pingTimeout=e.pingTimeout,this.maxPayload=e.maxPayload,this.onOpen(),"closed"!==this.readyState&&this.resetPingTimeout()}},{key:"resetPingTimeout",value:function(){var e=this;this.clearTimeoutFn(this.pingTimeoutTimer),this.pingTimeoutTimer=this.setTimeoutFn((function(){e.onClose("ping timeout")}),this.pingInterval+this.pingTimeout),this.opts.autoUnref&&this.pingTimeoutTimer.unref()}},{key:"onDrain",value:function(){this.writeBuffer.splice(0,this.prevBufferLen),this.prevBufferLen=0,0===this.writeBuffer.length?this.emitReserved("drain"):this.flush()}},{key:"flush",value:function(){if("closed"!==this.readyState&&this.transport.writable&&!this.upgrading&&this.writeBuffer.length){var e=this.getWritablePackets();this.transport.send(e),this.prevBufferLen=e.length,this.emitReserved("flush")}}},{key:"getWritablePackets",value:function(){if(!(this.maxPayload&&"polling"===this.transport.name&&this.writeBuffer.length>1))return this.writeBuffer;for(var e,t=1,n=0;n<this.writeBuffer.length;n++){var r=this.writeBuffer[n].data;if(r&&(t+="string"===typeof(e=r)?function(e){for(var t=0,n=0,r=0,i=e.length;r<i;r++)(t=e.charCodeAt(r))<128?n+=1:t<2048?n+=2:t<55296||t>=57344?n+=3:(r++,n+=4);return n}(e):Math.ceil((e.byteLength||e.size)*pd)),n>0&&t>this.maxPayload)return this.writeBuffer.slice(0,n);t+=2}return this.writeBuffer}},{key:"write",value:function(e,t,n){return this.sendPacket("message",e,t,n),this}},{key:"send",value:function(e,t,n){return this.sendPacket("message",e,t,n),this}},{key:"sendPacket",value:function(e,t,n,r){if("function"===typeof t&&(r=t,t=void 0),"function"===typeof n&&(r=n,n=null),"closing"!==this.readyState&&"closed"!==this.readyState){(n=n||{}).compress=!1!==n.compress;var i={type:e,data:t,options:n};this.emitReserved("packetCreate",i),this.writeBuffer.push(i),r&&this.once("flush",r),this.flush()}}},{key:"close",value:function(){var e=this,t=function(){e.onClose("forced close"),e.transport.close()},n=function n(){e.off("upgrade",n),e.off("upgradeError",n),t()},r=function(){e.once("upgrade",n),e.once("upgradeError",n)};return"opening"!==this.readyState&&"open"!==this.readyState||(this.readyState="closing",this.writeBuffer.length?this.once("drain",(function(){e.upgrading?r():t()})):this.upgrading?r():t()),this}},{key:"onError",value:function(e){n.priorWebsocketSuccess=!1,this.emitReserved("error",e),this.onClose("transport error",e)}},{key:"onClose",value:function(e,t){"opening"!==this.readyState&&"open"!==this.readyState&&"closing"!==this.readyState||(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),"function"===typeof removeEventListener&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",e,t),this.writeBuffer=[],this.prevBufferLen=0)}},{key:"filterUpgrades",value:function(e){for(var t=[],n=0,r=e.length;n<r;n++)~this.transports.indexOf(e[n])&&t.push(e[n]);return t}}]),n}(ld);zd.protocol=4;zd.protocol;var Gd="function"===typeof ArrayBuffer,Kd=function(e){return"function"===typeof ArrayBuffer.isView?ArrayBuffer.isView(e):e.buffer instanceof ArrayBuffer},Wd=Object.prototype.toString,jd="function"===typeof Blob||"undefined"!==typeof Blob&&"[object BlobConstructor]"===Wd.call(Blob),Xd="function"===typeof File||"undefined"!==typeof File&&"[object FileConstructor]"===Wd.call(File);function qd(e){return Gd&&(e instanceof ArrayBuffer||Kd(e))||jd&&e instanceof Blob||Xd&&e instanceof File}function Yd(e,t){if(!e||"object"!==typeof e)return!1;if(Array.isArray(e)){for(var n=0,r=e.length;n<r;n++)if(Yd(e[n]))return!0;return!1}if(qd(e))return!0;if(e.toJSON&&"function"===typeof e.toJSON&&1===arguments.length)return Yd(e.toJSON(),!0);for(var i in e)if(Object.prototype.hasOwnProperty.call(e,i)&&Yd(e[i]))return!0;return!1}function Jd(e){var t=[],n=e.data,r=e;return r.data=Zd(n,t),r.attachments=t.length,{packet:r,buffers:t}}function Zd(e,t){if(!e)return e;if(qd(e)){var n={_placeholder:!0,num:t.length};return t.push(e),n}if(Array.isArray(e)){for(var r=new Array(e.length),i=0;i<e.length;i++)r[i]=Zd(e[i],t);return r}if("object"===typeof e&&!(e instanceof Date)){var A={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&(A[a]=Zd(e[a],t));return A}return e}function $d(e,t){return e.data=eh(e.data,t),delete e.attachments,e}function eh(e,t){if(!e)return e;if(e&&!0===e._placeholder){if("number"===typeof e.num&&e.num>=0&&e.num<t.length)return t[e.num];throw new Error("illegal attachments")}if(Array.isArray(e))for(var n=0;n<e.length;n++)e[n]=eh(e[n],t);else if("object"===typeof e)for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(e[r]=eh(e[r],t));return e}var th,nh=5;!function(e){e[e.CONNECT=0]="CONNECT",e[e.DISCONNECT=1]="DISCONNECT",e[e.EVENT=2]="EVENT",e[e.ACK=3]="ACK",e[e.CONNECT_ERROR=4]="CONNECT_ERROR",e[e.BINARY_EVENT=5]="BINARY_EVENT",e[e.BINARY_ACK=6]="BINARY_ACK"}(th||(th={}));var rh=function(){function e(t){v(this,e),this.replacer=t}return w(e,[{key:"encode",value:function(e){return e.type!==th.EVENT&&e.type!==th.ACK||!Yd(e)?[this.encodeAsString(e)]:this.encodeAsBinary({type:e.type===th.EVENT?th.BINARY_EVENT:th.BINARY_ACK,nsp:e.nsp,data:e.data,id:e.id})}},{key:"encodeAsString",value:function(e){var t=""+e.type;return e.type!==th.BINARY_EVENT&&e.type!==th.BINARY_ACK||(t+=e.attachments+"-"),e.nsp&&"/"!==e.nsp&&(t+=e.nsp+","),null!=e.id&&(t+=e.id),null!=e.data&&(t+=JSON.stringify(e.data,this.replacer)),t}},{key:"encodeAsBinary",value:function(e){var t=Jd(e),n=this.encodeAsString(t.packet),r=t.buffers;return r.unshift(n),r}}]),e}(),ih=function(e){_(n,e);var t=S(n);function n(e){var r;return v(this,n),(r=t.call(this)).reviver=e,r}return w(n,[{key:"add",value:function(e){var t;if("string"===typeof e){if(this.reconstructor)throw new Error("got plaintext data when reconstructing a packet");var r=(t=this.decodeString(e)).type===th.BINARY_EVENT;r||t.type===th.BINARY_ACK?(t.type=r?th.EVENT:th.ACK,this.reconstructor=new Ah(t),0===t.attachments&&jc(b(n.prototype),"emitReserved",this).call(this,"decoded",t)):jc(b(n.prototype),"emitReserved",this).call(this,"decoded",t)}else{if(!qd(e)&&!e.base64)throw new Error("Unknown type: "+e);if(!this.reconstructor)throw new Error("got binary data when not reconstructing a packet");(t=this.reconstructor.takeBinaryData(e))&&(this.reconstructor=null,jc(b(n.prototype),"emitReserved",this).call(this,"decoded",t))}}},{key:"decodeString",value:function(e){var t=0,r={type:Number(e.charAt(0))};if(void 0===th[r.type])throw new Error("unknown packet type "+r.type);if(r.type===th.BINARY_EVENT||r.type===th.BINARY_ACK){for(var i=t+1;"-"!==e.charAt(++t)&&t!=e.length;);var A=e.substring(i,t);if(A!=Number(A)||"-"!==e.charAt(t))throw new Error("Illegal attachments");r.attachments=Number(A)}if("/"===e.charAt(t+1)){for(var a=t+1;++t;){if(","===e.charAt(t))break;if(t===e.length)break}r.nsp=e.substring(a,t)}else r.nsp="/";var o=e.charAt(t+1);if(""!==o&&Number(o)==o){for(var s=t+1;++t;){var l=e.charAt(t);if(null==l||Number(l)!=l){--t;break}if(t===e.length)break}r.id=Number(e.substring(s,t+1))}if(e.charAt(++t)){var u=this.tryParse(e.substr(t));if(!n.isPayloadValid(r.type,u))throw new Error("invalid payload");r.data=u}return r}},{key:"tryParse",value:function(e){try{return JSON.parse(e,this.reviver)}catch(t){return!1}}},{key:"destroy",value:function(){this.reconstructor&&(this.reconstructor.finishedReconstruction(),this.reconstructor=null)}}],[{key:"isPayloadValid",value:function(e,t){switch(e){case th.CONNECT:return"object"===typeof t;case th.DISCONNECT:return void 0===t;case th.CONNECT_ERROR:return"string"===typeof t||"object"===typeof t;case th.EVENT:case th.BINARY_EVENT:return Array.isArray(t)&&t.length>0;case th.ACK:case th.BINARY_ACK:return Array.isArray(t)}}}]),n}(ld),Ah=function(){function e(t){v(this,e),this.packet=t,this.buffers=[],this.reconPack=t}return w(e,[{key:"takeBinaryData",value:function(e){if(this.buffers.push(e),this.buffers.length===this.reconPack.attachments){var t=$d(this.reconPack,this.buffers);return this.finishedReconstruction(),t}return null}},{key:"finishedReconstruction",value:function(){this.reconPack=null,this.buffers=[]}}]),e}();function ah(e,t,n){return e.on(t,n),function(){e.off(t,n)}}var oh=Object.freeze({connect:1,connect_error:1,disconnect:1,disconnecting:1,newListener:1,removeListener:1}),sh=function(e){_(n,e);var t=S(n);function n(e,r,i){var A;return v(this,n),(A=t.call(this)).connected=!1,A.recovered=!1,A.receiveBuffer=[],A.sendBuffer=[],A._queue=[],A._queueSeq=0,A.ids=0,A.acks={},A.flags={},A.io=e,A.nsp=r,i&&i.auth&&(A.auth=i.auth),A._opts=Object.assign({},i),A.io._autoConnect&&A.open(),A}return w(n,[{key:"disconnected",get:function(){return!this.connected}},{key:"subEvents",value:function(){if(!this.subs){var e=this.io;this.subs=[ah(e,"open",this.onopen.bind(this)),ah(e,"packet",this.onpacket.bind(this)),ah(e,"error",this.onerror.bind(this)),ah(e,"close",this.onclose.bind(this))]}}},{key:"active",get:function(){return!!this.subs}},{key:"connect",value:function(){return this.connected||(this.subEvents(),this.io._reconnecting||this.io.open(),"open"===this.io._readyState&&this.onopen()),this}},{key:"open",value:function(){return this.connect()}},{key:"send",value:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return t.unshift("message"),this.emit.apply(this,t),this}},{key:"emit",value:function(e){if(oh.hasOwnProperty(e))throw new Error('"'+e.toString()+'" is a reserved event name');for(var t=arguments.length,n=new Array(t>1?t-1:0),r=1;r<t;r++)n[r-1]=arguments[r];if(n.unshift(e),this._opts.retries&&!this.flags.fromQueue&&!this.flags.volatile)return this._addToQueue(n),this;var i={type:th.EVENT,data:n,options:{}};if(i.options.compress=!1!==this.flags.compress,"function"===typeof n[n.length-1]){var A=this.ids++,a=n.pop();this._registerAckCallback(A,a),i.id=A}var o=this.io.engine&&this.io.engine.transport&&this.io.engine.transport.writable;return this.flags.volatile&&(!o||!this.connected)||(this.connected?(this.notifyOutgoingListeners(i),this.packet(i)):this.sendBuffer.push(i)),this.flags={},this}},{key:"_registerAckCallback",value:function(e,t){var n,r=this,i=null!==(n=this.flags.timeout)&&void 0!==n?n:this._opts.ackTimeout;if(void 0!==i){var A=this.io.setTimeoutFn((function(){delete r.acks[e];for(var n=0;n<r.sendBuffer.length;n++)r.sendBuffer[n].id===e&&r.sendBuffer.splice(n,1);t.call(r,new Error("operation has timed out"))}),i);this.acks[e]=function(){r.io.clearTimeoutFn(A);for(var e=arguments.length,n=new Array(e),i=0;i<e;i++)n[i]=arguments[i];t.apply(r,[null].concat(n))}}else this.acks[e]=t}},{key:"emitWithAck",value:function(e){for(var t=this,n=arguments.length,r=new Array(n>1?n-1:0),i=1;i<n;i++)r[i-1]=arguments[i];var A=void 0!==this.flags.timeout||void 0!==this._opts.ackTimeout;return new Promise((function(n,i){r.push((function(e,t){return A?e?i(e):n(t):n(e)})),t.emit.apply(t,[e].concat(r))}))}},{key:"_addToQueue",value:function(e){var t,n=this;"function"===typeof e[e.length-1]&&(t=e.pop());var r={id:this._queueSeq++,tryCount:0,pending:!1,args:e,flags:Object.assign({fromQueue:!0},this.flags)};e.push((function(e){if(r===n._queue[0]){if(null!==e)r.tryCount>n._opts.retries&&(n._queue.shift(),t&&t(e));else if(n._queue.shift(),t){for(var i=arguments.length,A=new Array(i>1?i-1:0),a=1;a<i;a++)A[a-1]=arguments[a];t.apply(void 0,[null].concat(A))}return r.pending=!1,n._drainQueue()}})),this._queue.push(r),this._drainQueue()}},{key:"_drainQueue",value:function(){var e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];if(this.connected&&0!==this._queue.length){var t=this._queue[0];t.pending&&!e||(t.pending=!0,t.tryCount++,this.flags=t.flags,this.emit.apply(this,t.args))}}},{key:"packet",value:function(e){e.nsp=this.nsp,this.io._packet(e)}},{key:"onopen",value:function(){var e=this;"function"==typeof this.auth?this.auth((function(t){e._sendConnectPacket(t)})):this._sendConnectPacket(this.auth)}},{key:"_sendConnectPacket",value:function(e){this.packet({type:th.CONNECT,data:this._pid?Object.assign({pid:this._pid,offset:this._lastOffset},e):e})}},{key:"onerror",value:function(e){this.connected||this.emitReserved("connect_error",e)}},{key:"onclose",value:function(e,t){this.connected=!1,delete this.id,this.emitReserved("disconnect",e,t)}},{key:"onpacket",value:function(e){if(e.nsp===this.nsp)switch(e.type){case th.CONNECT:e.data&&e.data.sid?this.onconnect(e.data.sid,e.data.pid):this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case th.EVENT:case th.BINARY_EVENT:this.onevent(e);break;case th.ACK:case th.BINARY_ACK:this.onack(e);break;case th.DISCONNECT:this.ondisconnect();break;case th.CONNECT_ERROR:this.destroy();var t=new Error(e.data.message);t.data=e.data.data,this.emitReserved("connect_error",t)}}},{key:"onevent",value:function(e){var t=e.data||[];null!=e.id&&t.push(this.ack(e.id)),this.connected?this.emitEvent(t):this.receiveBuffer.push(Object.freeze(t))}},{key:"emitEvent",value:function(e){if(this._anyListeners&&this._anyListeners.length){var t,r=k(this._anyListeners.slice());try{for(r.s();!(t=r.n()).done;){t.value.apply(this,e)}}catch(Jx){r.e(Jx)}finally{r.f()}}jc(b(n.prototype),"emit",this).apply(this,e),this._pid&&e.length&&"string"===typeof e[e.length-1]&&(this._lastOffset=e[e.length-1])}},{key:"ack",value:function(e){var t=this,n=!1;return function(){if(!n){n=!0;for(var r=arguments.length,i=new Array(r),A=0;A<r;A++)i[A]=arguments[A];t.packet({type:th.ACK,id:e,data:i})}}}},{key:"onack",value:function(e){var t=this.acks[e.id];"function"===typeof t&&(t.apply(this,e.data),delete this.acks[e.id])}},{key:"onconnect",value:function(e,t){this.id=e,this.recovered=t&&this._pid===t,this._pid=t,this.connected=!0,this.emitBuffered(),this.emitReserved("connect"),this._drainQueue(!0)}},{key:"emitBuffered",value:function(){var e=this;this.receiveBuffer.forEach((function(t){return e.emitEvent(t)})),this.receiveBuffer=[],this.sendBuffer.forEach((function(t){e.notifyOutgoingListeners(t),e.packet(t)})),this.sendBuffer=[]}},{key:"ondisconnect",value:function(){this.destroy(),this.onclose("io server disconnect")}},{key:"destroy",value:function(){this.subs&&(this.subs.forEach((function(e){return e()})),this.subs=void 0),this.io._destroy(this)}},{key:"disconnect",value:function(){return this.connected&&this.packet({type:th.DISCONNECT}),this.destroy(),this.connected&&this.onclose("io client disconnect"),this}},{key:"close",value:function(){return this.disconnect()}},{key:"compress",value:function(e){return this.flags.compress=e,this}},{key:"volatile",get:function(){return this.flags.volatile=!0,this}},{key:"timeout",value:function(e){return this.flags.timeout=e,this}},{key:"onAny",value:function(e){return this._anyListeners=this._anyListeners||[],this._anyListeners.push(e),this}},{key:"prependAny",value:function(e){return this._anyListeners=this._anyListeners||[],this._anyListeners.unshift(e),this}},{key:"offAny",value:function(e){if(!this._anyListeners)return this;if(e){for(var t=this._anyListeners,n=0;n<t.length;n++)if(e===t[n])return t.splice(n,1),this}else this._anyListeners=[];return this}},{key:"listenersAny",value:function(){return this._anyListeners||[]}},{key:"onAnyOutgoing",value:function(e){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.push(e),this}},{key:"prependAnyOutgoing",value:function(e){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.unshift(e),this}},{key:"offAnyOutgoing",value:function(e){if(!this._anyOutgoingListeners)return this;if(e){for(var t=this._anyOutgoingListeners,n=0;n<t.length;n++)if(e===t[n])return t.splice(n,1),this}else this._anyOutgoingListeners=[];return this}},{key:"listenersAnyOutgoing",value:function(){return this._anyOutgoingListeners||[]}},{key:"notifyOutgoingListeners",value:function(e){if(this._anyOutgoingListeners&&this._anyOutgoingListeners.length){var t,n=k(this._anyOutgoingListeners.slice());try{for(n.s();!(t=n.n()).done;){t.value.apply(this,e.data)}}catch(Jx){n.e(Jx)}finally{n.f()}}}}]),n}(ld);function lh(e){e=e||{},this.ms=e.min||100,this.max=e.max||1e4,this.factor=e.factor||2,this.jitter=e.jitter>0&&e.jitter<=1?e.jitter:0,this.attempts=0}lh.prototype.duration=function(){var e=this.ms*Math.pow(this.factor,this.attempts++);if(this.jitter){var t=Math.random(),n=Math.floor(t*this.jitter*e);e=0==(1&Math.floor(10*t))?e-n:e+n}return 0|Math.min(e,this.max)},lh.prototype.reset=function(){this.attempts=0},lh.prototype.setMin=function(e){this.ms=e},lh.prototype.setMax=function(e){this.max=e},lh.prototype.setJitter=function(e){this.jitter=e};var uh=function(t){_(r,t);var n=S(r);function r(t,i){var A,a;v(this,r),(A=n.call(this)).nsps={},A.subs=[],t&&"object"===typeof t&&(i=t,t=void 0),(i=i||{}).path=i.path||"/socket.io",A.opts=i,fd(C(A),i),A.reconnection(!1!==i.reconnection),A.reconnectionAttempts(i.reconnectionAttempts||1/0),A.reconnectionDelay(i.reconnectionDelay||1e3),A.reconnectionDelayMax(i.reconnectionDelayMax||5e3),A.randomizationFactor(null!==(a=i.randomizationFactor)&&void 0!==a?a:.5),A.backoff=new lh({min:A.reconnectionDelay(),max:A.reconnectionDelayMax(),jitter:A.randomizationFactor()}),A.timeout(null==i.timeout?2e4:i.timeout),A._readyState="closed",A.uri=t;var o=i.parser||e;return A.encoder=new o.Encoder,A.decoder=new o.Decoder,A._autoConnect=!1!==i.autoConnect,A._autoConnect&&A.open(),A}return w(r,[{key:"reconnection",value:function(e){return arguments.length?(this._reconnection=!!e,this):this._reconnection}},{key:"reconnectionAttempts",value:function(e){return void 0===e?this._reconnectionAttempts:(this._reconnectionAttempts=e,this)}},{key:"reconnectionDelay",value:function(e){var t;return void 0===e?this._reconnectionDelay:(this._reconnectionDelay=e,null===(t=this.backoff)||void 0===t||t.setMin(e),this)}},{key:"randomizationFactor",value:function(e){var t;return void 0===e?this._randomizationFactor:(this._randomizationFactor=e,null===(t=this.backoff)||void 0===t||t.setJitter(e),this)}},{key:"reconnectionDelayMax",value:function(e){var t;return void 0===e?this._reconnectionDelayMax:(this._reconnectionDelayMax=e,null===(t=this.backoff)||void 0===t||t.setMax(e),this)}},{key:"timeout",value:function(e){return arguments.length?(this._timeout=e,this):this._timeout}},{key:"maybeReconnectOnOpen",value:function(){!this._reconnecting&&this._reconnection&&0===this.backoff.attempts&&this.reconnect()}},{key:"open",value:function(e){var t=this;if(~this._readyState.indexOf("open"))return this;this.engine=new zd(this.uri,this.opts);var n=this.engine,r=this;this._readyState="opening",this.skipReconnect=!1;var i=ah(n,"open",(function(){r.onopen(),e&&e()})),A=ah(n,"error",(function(n){r.cleanup(),r._readyState="closed",t.emitReserved("error",n),e?e(n):r.maybeReconnectOnOpen()}));if(!1!==this._timeout){var a=this._timeout;0===a&&i();var o=this.setTimeoutFn((function(){i(),n.close(),n.emit("error",new Error("timeout"))}),a);this.opts.autoUnref&&o.unref(),this.subs.push((function(){clearTimeout(o)}))}return this.subs.push(i),this.subs.push(A),this}},{key:"connect",value:function(e){return this.open(e)}},{key:"onopen",value:function(){this.cleanup(),this._readyState="open",this.emitReserved("open");var e=this.engine;this.subs.push(ah(e,"ping",this.onping.bind(this)),ah(e,"data",this.ondata.bind(this)),ah(e,"error",this.onerror.bind(this)),ah(e,"close",this.onclose.bind(this)),ah(this.decoder,"decoded",this.ondecoded.bind(this)))}},{key:"onping",value:function(){this.emitReserved("ping")}},{key:"ondata",value:function(e){try{this.decoder.add(e)}catch(t){this.onclose("parse error",t)}}},{key:"ondecoded",value:function(e){var t=this;Dd((function(){t.emitReserved("packet",e)}),this.setTimeoutFn)}},{key:"onerror",value:function(e){this.emitReserved("error",e)}},{key:"socket",value:function(e,t){var n=this.nsps[e];return n?this._autoConnect&&!n.active&&n.connect():(n=new sh(this,e,t),this.nsps[e]=n),n}},{key:"_destroy",value:function(e){for(var t=0,n=Object.keys(this.nsps);t<n.length;t++){var r=n[t];if(this.nsps[r].active)return}this._close()}},{key:"_packet",value:function(e){for(var t=this.encoder.encode(e),n=0;n<t.length;n++)this.engine.write(t[n],e.options)}},{key:"cleanup",value:function(){this.subs.forEach((function(e){return e()})),this.subs.length=0,this.decoder.destroy()}},{key:"_close",value:function(){this.skipReconnect=!0,this._reconnecting=!1,this.onclose("forced close"),this.engine&&this.engine.close()}},{key:"disconnect",value:function(){return this._close()}},{key:"onclose",value:function(e,t){this.cleanup(),this.backoff.reset(),this._readyState="closed",this.emitReserved("close",e,t),this._reconnection&&!this.skipReconnect&&this.reconnect()}},{key:"reconnect",value:function(){var e=this;if(this._reconnecting||this.skipReconnect)return this;var t=this;if(this.backoff.attempts>=this._reconnectionAttempts)this.backoff.reset(),this.emitReserved("reconnect_failed"),this._reconnecting=!1;else{var n=this.backoff.duration();this._reconnecting=!0;var r=this.setTimeoutFn((function(){t.skipReconnect||(e.emitReserved("reconnect_attempt",t.backoff.attempts),t.skipReconnect||t.open((function(n){n?(t._reconnecting=!1,t.reconnect(),e.emitReserved("reconnect_error",n)):t.onreconnect()})))}),n);this.opts.autoUnref&&r.unref(),this.subs.push((function(){clearTimeout(r)}))}}},{key:"onreconnect",value:function(){var e=this.backoff.attempts;this._reconnecting=!1,this.backoff.reset(),this.emitReserved("reconnect",e)}}]),r}(ld),ch={};function dh(e,t){"object"===typeof e&&(t=e,e=void 0);var n,r=function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"",n=arguments.length>2?arguments[2]:void 0,r=e;n=n||"undefined"!==typeof location&&location,null==e&&(e=n.protocol+"//"+n.host),"string"===typeof e&&("/"===e.charAt(0)&&(e="/"===e.charAt(1)?n.protocol+e:n.host+e),/^(https?|wss?):\/\//.test(e)||(e="undefined"!==typeof n?n.protocol+"//"+e:"https://"+e),r=Vd(e)),r.port||(/^(http|ws)$/.test(r.protocol)?r.port="80":/^(http|ws)s$/.test(r.protocol)&&(r.port="443")),r.path=r.path||"/";var i=-1!==r.host.indexOf(":")?"["+r.host+"]":r.host;return r.id=r.protocol+"://"+i+":"+r.port+t,r.href=r.protocol+"://"+i+(n&&n.port===r.port?"":":"+r.port),r}(e,(t=t||{}).path||"/socket.io"),i=r.source,A=r.id,a=r.path,o=ch[A]&&a in ch[A].nsps;return t.forceNew||t["force new connection"]||!1===t.multiplex||o?n=new uh(i,t):(ch[A]||(ch[A]=new uh(i,t)),n=ch[A]),r.query&&!t.query&&(t.query=r.queryKey),n.socket(r.path,t)}Object.assign(dh,{Manager:uh,Socket:sh,io:dh,connect:dh});var hh,fh="https://hyperhuman.deemos.com/api",ph=function(e){var t=e.username,n=e.email,r=e.password;return Wc.post("".concat(fh,"/user/login"),{username:t,email:n,password:r})},gh=function(e){var t=e.username,n=e.email,r=e.emailVerificationCode,i=e.invitationCode,A=e.password;return Wc.post("".concat(fh,"/user/register"),{username:t,email:n,email_verification_code:r,invitation_code:i,password:A})},mh=function(e){var t=e.email,n=e.emailVerificationCode,r=e.newPassword;return Wc.post("".concat(fh,"/user/reset_password"),{email:t,email_verification_code:n,new_password:r})},vh=function(e){var t=e.email,n=e.type;return Wc.post("".concat(fh,"/user/send_email_verification_code"),{email:t,type:n})},yh=function(e){var t=e.user_uuid,n=e.username;return Wc.post("".concat(fh,"/user/get_info"),{user_uuid:t,username:n})},wh=function(){var e=M(E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:if(!hh){e.next=2;break}return e.abrupt("return",!1);case 2:return console.log("start chat"),e.abrupt("return",Wc.get("".concat(fh,"/chat").concat("")));case 4:case"end":return e.stop()}}),e)})));return function(){return e.apply(this,arguments)}}(),Bh=function(){hh&&hh.connected&&(console.log("close ws"),hh.close(),console.log(hh))},_h=function(e){var 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this.x=Math.random(),this.y=Math.random(),this}},{key:Symbol.iterator,value:E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this.x;case 2:return e.next=4,this.y;case 4:case"end":return e.stop()}}),e,this)}))}]),t}(),zg=function(){function e(){v(this,e),e.prototype.isMatrix3=!0,this.elements=[1,0,0,0,1,0,0,0,1]}return w(e,[{key:"set",value:function(e,t,n,r,i,A,a,o,s){var l=this.elements;return l[0]=e,l[1]=r,l[2]=a,l[3]=t,l[4]=i,l[5]=o,l[6]=n,l[7]=A,l[8]=s,this}},{key:"identity",value:function(){return this.set(1,0,0,0,1,0,0,0,1),this}},{key:"copy",value:function(e){var t=this.elements,n=e.elements;return t[0]=n[0],t[1]=n[1],t[2]=n[2],t[3]=n[3],t[4]=n[4],t[5]=n[5],t[6]=n[6],t[7]=n[7],t[8]=n[8],this}},{key:"extractBasis",value:function(e,t,n){return e.setFromMatrix3Column(this,0),t.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}},{key:"setFromMatrix4",value:function(e){var t=e.elements;return this.set(t[0],t[4],t[8],t[1],t[5],t[9],t[2],t[6],t[10]),this}},{key:"multiply",value:function(e){return this.multiplyMatrices(this,e)}},{key:"premultiply",value:function(e){return this.multiplyMatrices(e,this)}},{key:"multiplyMatrices",value:function(e,t){var n=e.elements,r=t.elements,i=this.elements,A=n[0],a=n[3],o=n[6],s=n[1],l=n[4],u=n[7],c=n[2],d=n[5],h=n[8],f=r[0],p=r[3],g=r[6],m=r[1],v=r[4],y=r[7],w=r[2],B=r[5],_=r[8];return i[0]=A*f+a*m+o*w,i[3]=A*p+a*v+o*B,i[6]=A*g+a*y+o*_,i[1]=s*f+l*m+u*w,i[4]=s*p+l*v+u*B,i[7]=s*g+l*y+u*_,i[2]=c*f+d*m+h*w,i[5]=c*p+d*v+h*B,i[8]=c*g+d*y+h*_,this}},{key:"multiplyScalar",value:function(e){var t=this.elements;return t[0]*=e,t[3]*=e,t[6]*=e,t[1]*=e,t[4]*=e,t[7]*=e,t[2]*=e,t[5]*=e,t[8]*=e,this}},{key:"determinant",value:function(){var e=this.elements,t=e[0],n=e[1],r=e[2],i=e[3],A=e[4],a=e[5],o=e[6],s=e[7],l=e[8];return t*A*l-t*a*s-n*i*l+n*a*o+r*i*s-r*A*o}},{key:"invert",value:function(){var e=this.elements,t=e[0],n=e[1],r=e[2],i=e[3],A=e[4],a=e[5],o=e[6],s=e[7],l=e[8],u=l*A-a*s,c=a*o-l*i,d=s*i-A*o,h=t*u+n*c+r*d;if(0===h)return this.set(0,0,0,0,0,0,0,0,0);var f=1/h;return e[0]=u*f,e[1]=(r*s-l*n)*f,e[2]=(a*n-r*A)*f,e[3]=c*f,e[4]=(l*t-r*o)*f,e[5]=(r*i-a*t)*f,e[6]=d*f,e[7]=(n*o-s*t)*f,e[8]=(A*t-n*i)*f,this}},{key:"transpose",value:function(){var e,t=this.elements;return e=t[1],t[1]=t[3],t[3]=e,e=t[2],t[2]=t[6],t[6]=e,e=t[5],t[5]=t[7],t[7]=e,this}},{key:"getNormalMatrix",value:function(e){return this.setFromMatrix4(e).invert().transpose()}},{key:"transposeIntoArray",value:function(e){var t=this.elements;return e[0]=t[0],e[1]=t[3],e[2]=t[6],e[3]=t[1],e[4]=t[4],e[5]=t[7],e[6]=t[2],e[7]=t[5],e[8]=t[8],this}},{key:"setUvTransform",value:function(e,t,n,r,i,A,a){var o=Math.cos(i),s=Math.sin(i);return this.set(n*o,n*s,-n*(o*A+s*a)+A+e,-r*s,r*o,-r*(-s*A+o*a)+a+t,0,0,1),this}},{key:"scale",value:function(e,t){return this.premultiply(Gg.makeScale(e,t)),this}},{key:"rotate",value:function(e){return this.premultiply(Gg.makeRotation(-e)),this}},{key:"translate",value:function(e,t){return this.premultiply(Gg.makeTranslation(e,t)),this}},{key:"makeTranslation",value:function(e,t){return this.set(1,0,e,0,1,t,0,0,1),this}},{key:"makeRotation",value:function(e){var t=Math.cos(e),n=Math.sin(e);return this.set(t,-n,0,n,t,0,0,0,1),this}},{key:"makeScale",value:function(e,t){return this.set(e,0,0,0,t,0,0,0,1),this}},{key:"equals",value:function(e){for(var t=this.elements,n=e.elements,r=0;r<9;r++)if(t[r]!==n[r])return!1;return!0}},{key:"fromArray",value:function(e){for(var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=0;n<9;n++)this.elements[n]=e[n+t];return this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=this.elements;return e[t]=n[0],e[t+1]=n[1],e[t+2]=n[2],e[t+3]=n[3],e[t+4]=n[4],e[t+5]=n[5],e[t+6]=n[6],e[t+7]=n[7],e[t+8]=n[8],e}},{key:"clone",value:function(){return(new this.constructor).fromArray(this.elements)}}]),e}(),Gg=new zg;function Kg(e){for(var t=e.length-1;t>=0;--t)if(e[t]>=65535)return!0;return!1}Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array;function Wg(e){return document.createElementNS("http://www.w3.org/1999/xhtml",e)}function jg(e){return e<.04045?.0773993808*e:Math.pow(.9478672986*e+.0521327014,2.4)}function Xg(e){return e<.0031308?12.92*e:1.055*Math.pow(e,.41666)-.055}var qg=(s(jh={},wg,s({},Bg,jg)),s(jh,Bg,s({},wg,Xg)),jh),Yg={legacyMode:!0,get workingColorSpace(){return Bg},set workingColorSpace(e){console.warn("THREE.ColorManagement: .workingColorSpace is readonly.")},convert:function(e,t,n){if(this.legacyMode||t===n||!t||!n)return e;if(qg[t]&&void 0!==qg[t][n]){var r=qg[t][n];return e.r=r(e.r),e.g=r(e.g),e.b=r(e.b),e}throw new Error("Unsupported color space conversion.")},fromWorkingColorSpace:function(e,t){return this.convert(e,this.workingColorSpace,t)},toWorkingColorSpace:function(e,t){return this.convert(e,t,this.workingColorSpace)}},Jg={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},Zg={r:0,g:0,b:0},$g={h:0,s:0,l:0},em={h:0,s:0,l:0};function tm(e,t,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?e+6*(t-e)*n:n<.5?t:n<2/3?e+6*(t-e)*(2/3-n):e}function nm(e,t){return t.r=e.r,t.g=e.g,t.b=e.b,t}var rm,im=function(e){function t(e,n,r){return v(this,t),this.isColor=!0,this.r=1,this.g=1,this.b=1,void 0===n&&void 0===r?this.set(e):this.setRGB(e,n,r)}return w(t,[{key:"set",value:function(e){return e&&e.isColor?this.copy(e):"number"===typeof e?this.setHex(e):"string"===typeof e&&this.setStyle(e),this}},{key:"setScalar",value:function(e){return this.r=e,this.g=e,this.b=e,this}},{key:"setHex",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:wg;return e=Math.floor(e),this.r=(e>>16&255)/255,this.g=(e>>8&255)/255,this.b=(255&e)/255,Yg.toWorkingColorSpace(this,t),this}},{key:"setRGB",value:function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:Yg.workingColorSpace;return this.r=e,this.g=t,this.b=n,Yg.toWorkingColorSpace(this,r),this}},{key:"setHSL",value:function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:Yg.workingColorSpace;if(e=Lg(e,1),t=Qg(t,0,1),n=Qg(n,0,1),0===t)this.r=this.g=this.b=n;else{var i=n<=.5?n*(1+t):n+t-n*t,A=2*n-i;this.r=tm(A,i,e+1/3),this.g=tm(A,i,e),this.b=tm(A,i,e-1/3)}return Yg.toWorkingColorSpace(this,r),this}},{key:"setStyle",value:function(e){var t,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:wg;function r(t){void 0!==t&&parseFloat(t)<1&&console.warn("THREE.Color: Alpha component of "+e+" will be ignored.")}if(t=/^((?:rgb|hsl)a?)\(([^\)]*)\)/.exec(e)){var i,A=t[1],a=t[2];switch(A){case"rgb":case"rgba":if(i=/^\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(a))return this.r=Math.min(255,parseInt(i[1],10))/255,this.g=Math.min(255,parseInt(i[2],10))/255,this.b=Math.min(255,parseInt(i[3],10))/255,Yg.toWorkingColorSpace(this,n),r(i[4]),this;if(i=/^\s*(\d+)\%\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(a))return this.r=Math.min(100,parseInt(i[1],10))/100,this.g=Math.min(100,parseInt(i[2],10))/100,this.b=Math.min(100,parseInt(i[3],10))/100,Yg.toWorkingColorSpace(this,n),r(i[4]),this;break;case"hsl":case"hsla":if(i=/^\s*(\d*\.?\d+)\s*,\s*(\d*\.?\d+)\%\s*,\s*(\d*\.?\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(a)){var o=parseFloat(i[1])/360,s=parseFloat(i[2])/100,l=parseFloat(i[3])/100;return r(i[4]),this.setHSL(o,s,l,n)}}}else if(t=/^\#([A-Fa-f\d]+)$/.exec(e)){var u=t[1],c=u.length;if(3===c)return this.r=parseInt(u.charAt(0)+u.charAt(0),16)/255,this.g=parseInt(u.charAt(1)+u.charAt(1),16)/255,this.b=parseInt(u.charAt(2)+u.charAt(2),16)/255,Yg.toWorkingColorSpace(this,n),this;if(6===c)return this.r=parseInt(u.charAt(0)+u.charAt(1),16)/255,this.g=parseInt(u.charAt(2)+u.charAt(3),16)/255,this.b=parseInt(u.charAt(4)+u.charAt(5),16)/255,Yg.toWorkingColorSpace(this,n),this}return e&&e.length>0?this.setColorName(e,n):this}},{key:"setColorName",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:wg,n=Jg[e.toLowerCase()];return void 0!==n?this.setHex(n,t):console.warn("THREE.Color: Unknown color "+e),this}},{key:"clone",value:function(){return new this.constructor(this.r,this.g,this.b)}},{key:"copy",value:function(e){return this.r=e.r,this.g=e.g,this.b=e.b,this}},{key:"copySRGBToLinear",value:function(e){return this.r=jg(e.r),this.g=jg(e.g),this.b=jg(e.b),this}},{key:"copyLinearToSRGB",value:function(e){return this.r=Xg(e.r),this.g=Xg(e.g),this.b=Xg(e.b),this}},{key:"convertSRGBToLinear",value:function(){return this.copySRGBToLinear(this),this}},{key:"convertLinearToSRGB",value:function(){return this.copyLinearToSRGB(this),this}},{key:"getHex",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:wg;return Yg.fromWorkingColorSpace(nm(this,Zg),e),Qg(255*Zg.r,0,255)<<16^Qg(255*Zg.g,0,255)<<8^Qg(255*Zg.b,0,255)<<0}},{key:"getHexString",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:wg;return("000000"+this.getHex(e).toString(16)).slice(-6)}},{key:"getHSL",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:Yg.workingColorSpace;Yg.fromWorkingColorSpace(nm(this,Zg),t);var n,r,i=Zg.r,A=Zg.g,a=Zg.b,o=Math.max(i,A,a),s=Math.min(i,A,a),l=(s+o)/2;if(s===o)n=0,r=0;else{var u=o-s;switch(r=l<=.5?u/(o+s):u/(2-o-s),o){case i:n=(A-a)/u+(A<a?6:0);break;case A:n=(a-i)/u+2;break;case a:n=(i-A)/u+4}n/=6}return e.h=n,e.s=r,e.l=l,e}},{key:"getRGB",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:Yg.workingColorSpace;return Yg.fromWorkingColorSpace(nm(this,Zg),t),e.r=Zg.r,e.g=Zg.g,e.b=Zg.b,e}},{key:"getStyle",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:wg;return Yg.fromWorkingColorSpace(nm(this,Zg),e),e!==wg?"color(".concat(e," ").concat(Zg.r," ").concat(Zg.g," ").concat(Zg.b,")"):"rgb(".concat(255*Zg.r|0,",").concat(255*Zg.g|0,",").concat(255*Zg.b|0,")")}},{key:"offsetHSL",value:function(e,t,n){return this.getHSL($g),$g.h+=e,$g.s+=t,$g.l+=n,this.setHSL($g.h,$g.s,$g.l),this}},{key:"add",value:function(e){return this.r+=e.r,this.g+=e.g,this.b+=e.b,this}},{key:"addColors",value:function(e,t){return this.r=e.r+t.r,this.g=e.g+t.g,this.b=e.b+t.b,this}},{key:"addScalar",value:function(e){return this.r+=e,this.g+=e,this.b+=e,this}},{key:"sub",value:function(e){return this.r=Math.max(0,this.r-e.r),this.g=Math.max(0,this.g-e.g),this.b=Math.max(0,this.b-e.b),this}},{key:"multiply",value:function(e){return this.r*=e.r,this.g*=e.g,this.b*=e.b,this}},{key:"multiplyScalar",value:function(e){return this.r*=e,this.g*=e,this.b*=e,this}},{key:"lerp",value:function(e,t){return this.r+=(e.r-this.r)*t,this.g+=(e.g-this.g)*t,this.b+=(e.b-this.b)*t,this}},{key:"lerpColors",value:function(e,t,n){return this.r=e.r+(t.r-e.r)*n,this.g=e.g+(t.g-e.g)*n,this.b=e.b+(t.b-e.b)*n,this}},{key:"lerpHSL",value:function(e,t){this.getHSL($g),e.getHSL(em);var n=Dg($g.h,em.h,t),r=Dg($g.s,em.s,t),i=Dg($g.l,em.l,t);return this.setHSL(n,r,i),this}},{key:"equals",value:function(e){return e.r===this.r&&e.g===this.g&&e.b===this.b}},{key:"fromArray",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this.r=e[t],this.g=e[t+1],this.b=e[t+2],this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this.r,e[t+1]=this.g,e[t+2]=this.b,e}},{key:"fromBufferAttribute",value:function(e,t){return this.r=e.getX(t),this.g=e.getY(t),this.b=e.getZ(t),this}},{key:"toJSON",value:function(){return this.getHex()}},{key:Symbol.iterator,value:E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this.r;case 2:return e.next=4,this.g;case 4:return e.next=6,this.b;case 6:case"end":return e.stop()}}),e,this)}))}]),t}();im.NAMES=Jg;var Am=function(){function e(){v(this,e)}return w(e,null,[{key:"getDataURL",value:function(e){if(/^data:/i.test(e.src))return e.src;if("undefined"==typeof HTMLCanvasElement)return e.src;var t;if(e instanceof HTMLCanvasElement)t=e;else{void 0===rm&&(rm=Wg("canvas")),rm.width=e.width,rm.height=e.height;var n=rm.getContext("2d");e instanceof ImageData?n.putImageData(e,0,0):n.drawImage(e,0,0,e.width,e.height),t=rm}return t.width>2048||t.height>2048?(console.warn("THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons",e),t.toDataURL("image/jpeg",.6)):t.toDataURL("image/png")}},{key:"sRGBToLinear",value:function(e){if("undefined"!==typeof HTMLImageElement&&e instanceof HTMLImageElement||"undefined"!==typeof HTMLCanvasElement&&e instanceof HTMLCanvasElement||"undefined"!==typeof ImageBitmap&&e instanceof ImageBitmap){var t=Wg("canvas");t.width=e.width,t.height=e.height;var n=t.getContext("2d");n.drawImage(e,0,0,e.width,e.height);for(var r=n.getImageData(0,0,e.width,e.height),i=r.data,A=0;A<i.length;A++)i[A]=255*jg(i[A]/255);return n.putImageData(r,0,0),t}if(e.data){for(var a=e.data.slice(0),o=0;o<a.length;o++)a instanceof Uint8Array||a instanceof Uint8ClampedArray?a[o]=Math.floor(255*jg(a[o]/255)):a[o]=jg(a[o]);return{data:a,width:e.width,height:e.height}}return console.warn("THREE.ImageUtils.sRGBToLinear(): Unsupported image type. No color space conversion applied."),e}}]),e}(),am=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null;v(this,e),this.isSource=!0,this.uuid=kg(),this.data=t,this.version=0}return w(e,[{key:"needsUpdate",set:function(e){!0===e&&this.version++}},{key:"toJSON",value:function(e){var t=void 0===e||"string"===typeof e;if(!t&&void 0!==e.images[this.uuid])return e.images[this.uuid];var n={uuid:this.uuid,url:""},r=this.data;if(null!==r){var i;if(Array.isArray(r)){i=[];for(var A=0,a=r.length;A<a;A++)r[A].isDataTexture?i.push(om(r[A].image)):i.push(om(r[A]))}else i=om(r);n.url=i}return t||(e.images[this.uuid]=n),n}}]),e}();function om(e){return"undefined"!==typeof HTMLImageElement&&e instanceof HTMLImageElement||"undefined"!==typeof HTMLCanvasElement&&e instanceof HTMLCanvasElement||"undefined"!==typeof ImageBitmap&&e instanceof ImageBitmap?Am.getDataURL(e):e.data?{data:Array.from(e.data),width:e.width,height:e.height,type:e.data.constructor.name}:(console.warn("THREE.Texture: Unable to serialize Texture."),{})}var sm=0,lm=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:n.DEFAULT_IMAGE,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:n.DEFAULT_MAPPING,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:tp,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:tp,o=arguments.length>4&&void 0!==arguments[4]?arguments[4]:ap,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:sp,l=arguments.length>6&&void 0!==arguments[6]?arguments[6]:_p,u=arguments.length>7&&void 0!==arguments[7]?arguments[7]:up,c=arguments.length>8&&void 0!==arguments[8]?arguments[8]:n.DEFAULT_ANISOTROPY,d=arguments.length>9&&void 0!==arguments[9]?arguments[9]:fg;return v(this,n),(e=t.call(this)).isTexture=!0,Object.defineProperty(C(e),"id",{value:sm++}),e.uuid=kg(),e.name="",e.source=new am(r),e.mipmaps=[],e.mapping=i,e.wrapS=A,e.wrapT=a,e.magFilter=o,e.minFilter=s,e.anisotropy=c,e.format=l,e.internalFormat=null,e.type=u,e.offset=new Vg(0,0),e.repeat=new Vg(1,1),e.center=new Vg(0,0),e.rotation=0,e.matrixAutoUpdate=!0,e.matrix=new zg,e.generateMipmaps=!0,e.premultiplyAlpha=!1,e.flipY=!0,e.unpackAlignment=4,e.encoding=d,e.userData={},e.version=0,e.onUpdate=null,e.isRenderTargetTexture=!1,e.needsPMREMUpdate=!1,e}return w(n,[{key:"image",get:function(){return this.source.data},set:function(e){this.source.data=e}},{key:"updateMatrix",value:function(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){return this.name=e.name,this.source=e.source,this.mipmaps=e.mipmaps.slice(0),this.mapping=e.mapping,this.wrapS=e.wrapS,this.wrapT=e.wrapT,this.magFilter=e.magFilter,this.minFilter=e.minFilter,this.anisotropy=e.anisotropy,this.format=e.format,this.internalFormat=e.internalFormat,this.type=e.type,this.offset.copy(e.offset),this.repeat.copy(e.repeat),this.center.copy(e.center),this.rotation=e.rotation,this.matrixAutoUpdate=e.matrixAutoUpdate,this.matrix.copy(e.matrix),this.generateMipmaps=e.generateMipmaps,this.premultiplyAlpha=e.premultiplyAlpha,this.flipY=e.flipY,this.unpackAlignment=e.unpackAlignment,this.encoding=e.encoding,this.userData=JSON.parse(JSON.stringify(e.userData)),this.needsUpdate=!0,this}},{key:"toJSON",value:function(e){var t=void 0===e||"string"===typeof e;if(!t&&void 0!==e.textures[this.uuid])return e.textures[this.uuid];var n={metadata:{version:4.5,type:"Texture",generator:"Texture.toJSON"},uuid:this.uuid,name:this.name,image:this.source.toJSON(e).uuid,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,generateMipmaps:this.generateMipmaps,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};return Object.keys(this.userData).length>0&&(n.userData=this.userData),t||(e.textures[this.uuid]=n),n}},{key:"dispose",value:function(){this.dispatchEvent({type:"dispose"})}},{key:"transformUv",value:function(e){if(300!==this.mapping)return e;if(e.applyMatrix3(this.matrix),e.x<0||e.x>1)switch(this.wrapS){case ep:e.x=e.x-Math.floor(e.x);break;case tp:e.x=e.x<0?0:1;break;case np:1===Math.abs(Math.floor(e.x)%2)?e.x=Math.ceil(e.x)-e.x:e.x=e.x-Math.floor(e.x)}if(e.y<0||e.y>1)switch(this.wrapT){case ep:e.y=e.y-Math.floor(e.y);break;case tp:e.y=e.y<0?0:1;break;case np:1===Math.abs(Math.floor(e.y)%2)?e.y=Math.ceil(e.y)-e.y:e.y=e.y-Math.floor(e.y)}return this.flipY&&(e.y=1-e.y),e}},{key:"needsUpdate",set:function(e){!0===e&&(this.version++,this.source.needsUpdate=!0)}}]),n}(Eg);lm.DEFAULT_IMAGE=null,lm.DEFAULT_MAPPING=300,lm.DEFAULT_ANISOTROPY=1;var um=function(e){function t(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;v(this,t),t.prototype.isVector4=!0,this.x=e,this.y=n,this.z=r,this.w=i}return w(t,[{key:"width",get:function(){return this.z},set:function(e){this.z=e}},{key:"height",get:function(){return this.w},set:function(e){this.w=e}},{key:"set",value:function(e,t,n,r){return this.x=e,this.y=t,this.z=n,this.w=r,this}},{key:"setScalar",value:function(e){return this.x=e,this.y=e,this.z=e,this.w=e,this}},{key:"setX",value:function(e){return this.x=e,this}},{key:"setY",value:function(e){return this.y=e,this}},{key:"setZ",value:function(e){return this.z=e,this}},{key:"setW",value:function(e){return this.w=e,this}},{key:"setComponent",value:function(e,t){switch(e){case 0:this.x=t;break;case 1:this.y=t;break;case 2:this.z=t;break;case 3:this.w=t;break;default:throw new Error("index is out of range: "+e)}return this}},{key:"getComponent",value:function(e){switch(e){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error("index is out of range: "+e)}}},{key:"clone",value:function(){return new this.constructor(this.x,this.y,this.z,this.w)}},{key:"copy",value:function(e){return this.x=e.x,this.y=e.y,this.z=e.z,this.w=void 0!==e.w?e.w:1,this}},{key:"add",value:function(e){return this.x+=e.x,this.y+=e.y,this.z+=e.z,this.w+=e.w,this}},{key:"addScalar",value:function(e){return this.x+=e,this.y+=e,this.z+=e,this.w+=e,this}},{key:"addVectors",value:function(e,t){return this.x=e.x+t.x,this.y=e.y+t.y,this.z=e.z+t.z,this.w=e.w+t.w,this}},{key:"addScaledVector",value:function(e,t){return this.x+=e.x*t,this.y+=e.y*t,this.z+=e.z*t,this.w+=e.w*t,this}},{key:"sub",value:function(e){return this.x-=e.x,this.y-=e.y,this.z-=e.z,this.w-=e.w,this}},{key:"subScalar",value:function(e){return this.x-=e,this.y-=e,this.z-=e,this.w-=e,this}},{key:"subVectors",value:function(e,t){return this.x=e.x-t.x,this.y=e.y-t.y,this.z=e.z-t.z,this.w=e.w-t.w,this}},{key:"multiply",value:function(e){return this.x*=e.x,this.y*=e.y,this.z*=e.z,this.w*=e.w,this}},{key:"multiplyScalar",value:function(e){return this.x*=e,this.y*=e,this.z*=e,this.w*=e,this}},{key:"applyMatrix4",value:function(e){var t=this.x,n=this.y,r=this.z,i=this.w,A=e.elements;return this.x=A[0]*t+A[4]*n+A[8]*r+A[12]*i,this.y=A[1]*t+A[5]*n+A[9]*r+A[13]*i,this.z=A[2]*t+A[6]*n+A[10]*r+A[14]*i,this.w=A[3]*t+A[7]*n+A[11]*r+A[15]*i,this}},{key:"divideScalar",value:function(e){return this.multiplyScalar(1/e)}},{key:"setAxisAngleFromQuaternion",value:function(e){this.w=2*Math.acos(e.w);var t=Math.sqrt(1-e.w*e.w);return t<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=e.x/t,this.y=e.y/t,this.z=e.z/t),this}},{key:"setAxisAngleFromRotationMatrix",value:function(e){var t,n,r,i,A=.01,a=.1,o=e.elements,s=o[0],l=o[4],u=o[8],c=o[1],d=o[5],h=o[9],f=o[2],p=o[6],g=o[10];if(Math.abs(l-c)<A&&Math.abs(u-f)<A&&Math.abs(h-p)<A){if(Math.abs(l+c)<a&&Math.abs(u+f)<a&&Math.abs(h+p)<a&&Math.abs(s+d+g-3)<a)return this.set(1,0,0,0),this;t=Math.PI;var m=(s+1)/2,v=(d+1)/2,y=(g+1)/2,w=(l+c)/4,B=(u+f)/4,_=(h+p)/4;return m>v&&m>y?m<A?(n=0,r=.707106781,i=.707106781):(r=w/(n=Math.sqrt(m)),i=B/n):v>y?v<A?(n=.707106781,r=0,i=.707106781):(n=w/(r=Math.sqrt(v)),i=_/r):y<A?(n=.707106781,r=.707106781,i=0):(n=B/(i=Math.sqrt(y)),r=_/i),this.set(n,r,i,t),this}var b=Math.sqrt((p-h)*(p-h)+(u-f)*(u-f)+(c-l)*(c-l));return Math.abs(b)<.001&&(b=1),this.x=(p-h)/b,this.y=(u-f)/b,this.z=(c-l)/b,this.w=Math.acos((s+d+g-1)/2),this}},{key:"min",value:function(e){return this.x=Math.min(this.x,e.x),this.y=Math.min(this.y,e.y),this.z=Math.min(this.z,e.z),this.w=Math.min(this.w,e.w),this}},{key:"max",value:function(e){return this.x=Math.max(this.x,e.x),this.y=Math.max(this.y,e.y),this.z=Math.max(this.z,e.z),this.w=Math.max(this.w,e.w),this}},{key:"clamp",value:function(e,t){return this.x=Math.max(e.x,Math.min(t.x,this.x)),this.y=Math.max(e.y,Math.min(t.y,this.y)),this.z=Math.max(e.z,Math.min(t.z,this.z)),this.w=Math.max(e.w,Math.min(t.w,this.w)),this}},{key:"clampScalar",value:function(e,t){return this.x=Math.max(e,Math.min(t,this.x)),this.y=Math.max(e,Math.min(t,this.y)),this.z=Math.max(e,Math.min(t,this.z)),this.w=Math.max(e,Math.min(t,this.w)),this}},{key:"clampLength",value:function(e,t){var n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(e,Math.min(t,n)))}},{key:"floor",value:function(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this.w=Math.floor(this.w),this}},{key:"ceil",value:function(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this.w=Math.ceil(this.w),this}},{key:"round",value:function(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this.w=Math.round(this.w),this}},{key:"roundToZero",value:function(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this.w=this.w<0?Math.ceil(this.w):Math.floor(this.w),this}},{key:"negate",value:function(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this.w=-this.w,this}},{key:"dot",value:function(e){return this.x*e.x+this.y*e.y+this.z*e.z+this.w*e.w}},{key:"lengthSq",value:function(){return this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w}},{key:"length",value:function(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)}},{key:"manhattanLength",value:function(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)+Math.abs(this.w)}},{key:"normalize",value:function(){return this.divideScalar(this.length()||1)}},{key:"setLength",value:function(e){return this.normalize().multiplyScalar(e)}},{key:"lerp",value:function(e,t){return this.x+=(e.x-this.x)*t,this.y+=(e.y-this.y)*t,this.z+=(e.z-this.z)*t,this.w+=(e.w-this.w)*t,this}},{key:"lerpVectors",value:function(e,t,n){return this.x=e.x+(t.x-e.x)*n,this.y=e.y+(t.y-e.y)*n,this.z=e.z+(t.z-e.z)*n,this.w=e.w+(t.w-e.w)*n,this}},{key:"equals",value:function(e){return e.x===this.x&&e.y===this.y&&e.z===this.z&&e.w===this.w}},{key:"fromArray",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this.x=e[t],this.y=e[t+1],this.z=e[t+2],this.w=e[t+3],this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this.x,e[t+1]=this.y,e[t+2]=this.z,e[t+3]=this.w,e}},{key:"fromBufferAttribute",value:function(e,t){return this.x=e.getX(t),this.y=e.getY(t),this.z=e.getZ(t),this.w=e.getW(t),this}},{key:"random",value:function(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this.w=Math.random(),this}},{key:Symbol.iterator,value:E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this.x;case 2:return e.next=4,this.y;case 4:return e.next=6,this.z;case 6:return e.next=8,this.w;case 8:case"end":return e.stop()}}),e,this)}))}]),t}(),cm=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};v(this,n),(e=t.call(this)).isWebGLRenderTarget=!0,e.width=r,e.height=i,e.depth=1,e.scissor=new um(0,0,r,i),e.scissorTest=!1,e.viewport=new um(0,0,r,i);var a={width:r,height:i,depth:1};return e.texture=new lm(a,A.mapping,A.wrapS,A.wrapT,A.magFilter,A.minFilter,A.format,A.type,A.anisotropy,A.encoding),e.texture.isRenderTargetTexture=!0,e.texture.flipY=!1,e.texture.generateMipmaps=void 0!==A.generateMipmaps&&A.generateMipmaps,e.texture.internalFormat=void 0!==A.internalFormat?A.internalFormat:null,e.texture.minFilter=void 0!==A.minFilter?A.minFilter:ap,e.depthBuffer=void 0===A.depthBuffer||A.depthBuffer,e.stencilBuffer=void 0!==A.stencilBuffer&&A.stencilBuffer,e.depthTexture=void 0!==A.depthTexture?A.depthTexture:null,e.samples=void 0!==A.samples?A.samples:0,e}return w(n,[{key:"setSize",value:function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1;this.width===e&&this.height===t&&this.depth===n||(this.width=e,this.height=t,this.depth=n,this.texture.image.width=e,this.texture.image.height=t,this.texture.image.depth=n,this.dispose()),this.viewport.set(0,0,e,t),this.scissor.set(0,0,e,t)}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){this.width=e.width,this.height=e.height,this.depth=e.depth,this.viewport.copy(e.viewport),this.texture=e.texture.clone(),this.texture.isRenderTargetTexture=!0;var t=Object.assign({},e.texture.image);return this.texture.source=new am(t),this.depthBuffer=e.depthBuffer,this.stencilBuffer=e.stencilBuffer,null!==e.depthTexture&&(this.depthTexture=e.depthTexture.clone()),this.samples=e.samples,this}},{key:"dispose",value:function(){this.dispatchEvent({type:"dispose"})}}]),n}(Eg),dm=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;return v(this,n),(e=t.call(this,null)).isDataArrayTexture=!0,e.image={data:r,width:i,height:A,depth:a},e.magFilter=rp,e.minFilter=rp,e.wrapR=tp,e.generateMipmaps=!1,e.flipY=!1,e.unpackAlignment=1,e}return w(n)}(lm),hm=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;return v(this,n),(e=t.call(this,null)).isData3DTexture=!0,e.image={data:r,width:i,height:A,depth:a},e.magFilter=rp,e.minFilter=rp,e.wrapR=tp,e.generateMipmaps=!1,e.flipY=!1,e.unpackAlignment=1,e}return w(n)}(lm),fm=function(e){function t(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;v(this,t),this.isQuaternion=!0,this._x=e,this._y=n,this._z=r,this._w=i}return w(t,[{key:"x",get:function(){return this._x},set:function(e){this._x=e,this._onChangeCallback()}},{key:"y",get:function(){return this._y},set:function(e){this._y=e,this._onChangeCallback()}},{key:"z",get:function(){return this._z},set:function(e){this._z=e,this._onChangeCallback()}},{key:"w",get:function(){return this._w},set:function(e){this._w=e,this._onChangeCallback()}},{key:"set",value:function(e,t,n,r){return this._x=e,this._y=t,this._z=n,this._w=r,this._onChangeCallback(),this}},{key:"clone",value:function(){return new this.constructor(this._x,this._y,this._z,this._w)}},{key:"copy",value:function(e){return this._x=e.x,this._y=e.y,this._z=e.z,this._w=e.w,this._onChangeCallback(),this}},{key:"setFromEuler",value:function(e,t){var n=e._x,r=e._y,i=e._z,A=e._order,a=Math.cos,o=Math.sin,s=a(n/2),l=a(r/2),u=a(i/2),c=o(n/2),d=o(r/2),h=o(i/2);switch(A){case"XYZ":this._x=c*l*u+s*d*h,this._y=s*d*u-c*l*h,this._z=s*l*h+c*d*u,this._w=s*l*u-c*d*h;break;case"YXZ":this._x=c*l*u+s*d*h,this._y=s*d*u-c*l*h,this._z=s*l*h-c*d*u,this._w=s*l*u+c*d*h;break;case"ZXY":this._x=c*l*u-s*d*h,this._y=s*d*u+c*l*h,this._z=s*l*h+c*d*u,this._w=s*l*u-c*d*h;break;case"ZYX":this._x=c*l*u-s*d*h,this._y=s*d*u+c*l*h,this._z=s*l*h-c*d*u,this._w=s*l*u+c*d*h;break;case"YZX":this._x=c*l*u+s*d*h,this._y=s*d*u+c*l*h,this._z=s*l*h-c*d*u,this._w=s*l*u-c*d*h;break;case"XZY":this._x=c*l*u-s*d*h,this._y=s*d*u-c*l*h,this._z=s*l*h+c*d*u,this._w=s*l*u+c*d*h;break;default:console.warn("THREE.Quaternion: .setFromEuler() encountered an unknown order: "+A)}return!1!==t&&this._onChangeCallback(),this}},{key:"setFromAxisAngle",value:function(e,t){var n=t/2,r=Math.sin(n);return this._x=e.x*r,this._y=e.y*r,this._z=e.z*r,this._w=Math.cos(n),this._onChangeCallback(),this}},{key:"setFromRotationMatrix",value:function(e){var t=e.elements,n=t[0],r=t[4],i=t[8],A=t[1],a=t[5],o=t[9],s=t[2],l=t[6],u=t[10],c=n+a+u;if(c>0){var d=.5/Math.sqrt(c+1);this._w=.25/d,this._x=(l-o)*d,this._y=(i-s)*d,this._z=(A-r)*d}else if(n>a&&n>u){var h=2*Math.sqrt(1+n-a-u);this._w=(l-o)/h,this._x=.25*h,this._y=(r+A)/h,this._z=(i+s)/h}else if(a>u){var f=2*Math.sqrt(1+a-n-u);this._w=(i-s)/f,this._x=(r+A)/f,this._y=.25*f,this._z=(o+l)/f}else{var p=2*Math.sqrt(1+u-n-a);this._w=(A-r)/p,this._x=(i+s)/p,this._y=(o+l)/p,this._z=.25*p}return this._onChangeCallback(),this}},{key:"setFromUnitVectors",value:function(e,t){var n=e.dot(t)+1;return n<Number.EPSILON?(n=0,Math.abs(e.x)>Math.abs(e.z)?(this._x=-e.y,this._y=e.x,this._z=0,this._w=n):(this._x=0,this._y=-e.z,this._z=e.y,this._w=n)):(this._x=e.y*t.z-e.z*t.y,this._y=e.z*t.x-e.x*t.z,this._z=e.x*t.y-e.y*t.x,this._w=n),this.normalize()}},{key:"angleTo",value:function(e){return 2*Math.acos(Math.abs(Qg(this.dot(e),-1,1)))}},{key:"rotateTowards",value:function(e,t){var n=this.angleTo(e);if(0===n)return this;var r=Math.min(1,t/n);return this.slerp(e,r),this}},{key:"identity",value:function(){return this.set(0,0,0,1)}},{key:"invert",value:function(){return this.conjugate()}},{key:"conjugate",value:function(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}},{key:"dot",value:function(e){return this._x*e._x+this._y*e._y+this._z*e._z+this._w*e._w}},{key:"lengthSq",value:function(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}},{key:"length",value:function(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}},{key:"normalize",value:function(){var e=this.length();return 0===e?(this._x=0,this._y=0,this._z=0,this._w=1):(e=1/e,this._x=this._x*e,this._y=this._y*e,this._z=this._z*e,this._w=this._w*e),this._onChangeCallback(),this}},{key:"multiply",value:function(e){return this.multiplyQuaternions(this,e)}},{key:"premultiply",value:function(e){return this.multiplyQuaternions(e,this)}},{key:"multiplyQuaternions",value:function(e,t){var n=e._x,r=e._y,i=e._z,A=e._w,a=t._x,o=t._y,s=t._z,l=t._w;return this._x=n*l+A*a+r*s-i*o,this._y=r*l+A*o+i*a-n*s,this._z=i*l+A*s+n*o-r*a,this._w=A*l-n*a-r*o-i*s,this._onChangeCallback(),this}},{key:"slerp",value:function(e,t){if(0===t)return this;if(1===t)return this.copy(e);var n=this._x,r=this._y,i=this._z,A=this._w,a=A*e._w+n*e._x+r*e._y+i*e._z;if(a<0?(this._w=-e._w,this._x=-e._x,this._y=-e._y,this._z=-e._z,a=-a):this.copy(e),a>=1)return this._w=A,this._x=n,this._y=r,this._z=i,this;var o=1-a*a;if(o<=Number.EPSILON){var s=1-t;return this._w=s*A+t*this._w,this._x=s*n+t*this._x,this._y=s*r+t*this._y,this._z=s*i+t*this._z,this.normalize(),this._onChangeCallback(),this}var l=Math.sqrt(o),u=Math.atan2(l,a),c=Math.sin((1-t)*u)/l,d=Math.sin(t*u)/l;return this._w=A*c+this._w*d,this._x=n*c+this._x*d,this._y=r*c+this._y*d,this._z=i*c+this._z*d,this._onChangeCallback(),this}},{key:"slerpQuaternions",value:function(e,t,n){return this.copy(e).slerp(t,n)}},{key:"random",value:function(){var e=Math.random(),t=Math.sqrt(1-e),n=Math.sqrt(e),r=2*Math.PI*Math.random(),i=2*Math.PI*Math.random();return this.set(t*Math.cos(r),n*Math.sin(i),n*Math.cos(i),t*Math.sin(r))}},{key:"equals",value:function(e){return e._x===this._x&&e._y===this._y&&e._z===this._z&&e._w===this._w}},{key:"fromArray",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this._x=e[t],this._y=e[t+1],this._z=e[t+2],this._w=e[t+3],this._onChangeCallback(),this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this._x,e[t+1]=this._y,e[t+2]=this._z,e[t+3]=this._w,e}},{key:"fromBufferAttribute",value:function(e,t){return this._x=e.getX(t),this._y=e.getY(t),this._z=e.getZ(t),this._w=e.getW(t),this}},{key:"_onChange",value:function(e){return this._onChangeCallback=e,this}},{key:"_onChangeCallback",value:function(){}},{key:Symbol.iterator,value:E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this._x;case 2:return e.next=4,this._y;case 4:return e.next=6,this._z;case 6:return e.next=8,this._w;case 8:case"end":return e.stop()}}),e,this)}))}],[{key:"slerpFlat",value:function(e,t,n,r,i,A,a){var o=n[r+0],s=n[r+1],l=n[r+2],u=n[r+3],c=i[A+0],d=i[A+1],h=i[A+2],f=i[A+3];if(0===a)return e[t+0]=o,e[t+1]=s,e[t+2]=l,void(e[t+3]=u);if(1===a)return e[t+0]=c,e[t+1]=d,e[t+2]=h,void(e[t+3]=f);if(u!==f||o!==c||s!==d||l!==h){var p=1-a,g=o*c+s*d+l*h+u*f,m=g>=0?1:-1,v=1-g*g;if(v>Number.EPSILON){var y=Math.sqrt(v),w=Math.atan2(y,g*m);p=Math.sin(p*w)/y,a=Math.sin(a*w)/y}var B=a*m;if(o=o*p+c*B,s=s*p+d*B,l=l*p+h*B,u=u*p+f*B,p===1-a){var _=1/Math.sqrt(o*o+s*s+l*l+u*u);o*=_,s*=_,l*=_,u*=_}}e[t]=o,e[t+1]=s,e[t+2]=l,e[t+3]=u}},{key:"multiplyQuaternionsFlat",value:function(e,t,n,r,i,A){var a=n[r],o=n[r+1],s=n[r+2],l=n[r+3],u=i[A],c=i[A+1],d=i[A+2],h=i[A+3];return e[t]=a*h+l*u+o*d-s*c,e[t+1]=o*h+l*c+s*u-a*d,e[t+2]=s*h+l*d+a*c-o*u,e[t+3]=l*h-a*u-o*c-s*d,e}}]),t}(),pm=function(e){function t(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0;v(this,t),t.prototype.isVector3=!0,this.x=e,this.y=n,this.z=r}return w(t,[{key:"set",value:function(e,t,n){return void 0===n&&(n=this.z),this.x=e,this.y=t,this.z=n,this}},{key:"setScalar",value:function(e){return this.x=e,this.y=e,this.z=e,this}},{key:"setX",value:function(e){return this.x=e,this}},{key:"setY",value:function(e){return this.y=e,this}},{key:"setZ",value:function(e){return this.z=e,this}},{key:"setComponent",value:function(e,t){switch(e){case 0:this.x=t;break;case 1:this.y=t;break;case 2:this.z=t;break;default:throw new Error("index is out of range: "+e)}return this}},{key:"getComponent",value:function(e){switch(e){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error("index is out of range: "+e)}}},{key:"clone",value:function(){return new this.constructor(this.x,this.y,this.z)}},{key:"copy",value:function(e){return this.x=e.x,this.y=e.y,this.z=e.z,this}},{key:"add",value:function(e){return this.x+=e.x,this.y+=e.y,this.z+=e.z,this}},{key:"addScalar",value:function(e){return this.x+=e,this.y+=e,this.z+=e,this}},{key:"addVectors",value:function(e,t){return this.x=e.x+t.x,this.y=e.y+t.y,this.z=e.z+t.z,this}},{key:"addScaledVector",value:function(e,t){return this.x+=e.x*t,this.y+=e.y*t,this.z+=e.z*t,this}},{key:"sub",value:function(e){return this.x-=e.x,this.y-=e.y,this.z-=e.z,this}},{key:"subScalar",value:function(e){return this.x-=e,this.y-=e,this.z-=e,this}},{key:"subVectors",value:function(e,t){return this.x=e.x-t.x,this.y=e.y-t.y,this.z=e.z-t.z,this}},{key:"multiply",value:function(e){return this.x*=e.x,this.y*=e.y,this.z*=e.z,this}},{key:"multiplyScalar",value:function(e){return this.x*=e,this.y*=e,this.z*=e,this}},{key:"multiplyVectors",value:function(e,t){return this.x=e.x*t.x,this.y=e.y*t.y,this.z=e.z*t.z,this}},{key:"applyEuler",value:function(e){return this.applyQuaternion(mm.setFromEuler(e))}},{key:"applyAxisAngle",value:function(e,t){return this.applyQuaternion(mm.setFromAxisAngle(e,t))}},{key:"applyMatrix3",value:function(e){var t=this.x,n=this.y,r=this.z,i=e.elements;return this.x=i[0]*t+i[3]*n+i[6]*r,this.y=i[1]*t+i[4]*n+i[7]*r,this.z=i[2]*t+i[5]*n+i[8]*r,this}},{key:"applyNormalMatrix",value:function(e){return this.applyMatrix3(e).normalize()}},{key:"applyMatrix4",value:function(e){var t=this.x,n=this.y,r=this.z,i=e.elements,A=1/(i[3]*t+i[7]*n+i[11]*r+i[15]);return this.x=(i[0]*t+i[4]*n+i[8]*r+i[12])*A,this.y=(i[1]*t+i[5]*n+i[9]*r+i[13])*A,this.z=(i[2]*t+i[6]*n+i[10]*r+i[14])*A,this}},{key:"applyQuaternion",value:function(e){var t=this.x,n=this.y,r=this.z,i=e.x,A=e.y,a=e.z,o=e.w,s=o*t+A*r-a*n,l=o*n+a*t-i*r,u=o*r+i*n-A*t,c=-i*t-A*n-a*r;return this.x=s*o+c*-i+l*-a-u*-A,this.y=l*o+c*-A+u*-i-s*-a,this.z=u*o+c*-a+s*-A-l*-i,this}},{key:"project",value:function(e){return this.applyMatrix4(e.matrixWorldInverse).applyMatrix4(e.projectionMatrix)}},{key:"unproject",value:function(e){return this.applyMatrix4(e.projectionMatrixInverse).applyMatrix4(e.matrixWorld)}},{key:"transformDirection",value:function(e){var t=this.x,n=this.y,r=this.z,i=e.elements;return this.x=i[0]*t+i[4]*n+i[8]*r,this.y=i[1]*t+i[5]*n+i[9]*r,this.z=i[2]*t+i[6]*n+i[10]*r,this.normalize()}},{key:"divide",value:function(e){return this.x/=e.x,this.y/=e.y,this.z/=e.z,this}},{key:"divideScalar",value:function(e){return this.multiplyScalar(1/e)}},{key:"min",value:function(e){return this.x=Math.min(this.x,e.x),this.y=Math.min(this.y,e.y),this.z=Math.min(this.z,e.z),this}},{key:"max",value:function(e){return this.x=Math.max(this.x,e.x),this.y=Math.max(this.y,e.y),this.z=Math.max(this.z,e.z),this}},{key:"clamp",value:function(e,t){return this.x=Math.max(e.x,Math.min(t.x,this.x)),this.y=Math.max(e.y,Math.min(t.y,this.y)),this.z=Math.max(e.z,Math.min(t.z,this.z)),this}},{key:"clampScalar",value:function(e,t){return this.x=Math.max(e,Math.min(t,this.x)),this.y=Math.max(e,Math.min(t,this.y)),this.z=Math.max(e,Math.min(t,this.z)),this}},{key:"clampLength",value:function(e,t){var n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(e,Math.min(t,n)))}},{key:"floor",value:function(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}},{key:"ceil",value:function(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}},{key:"round",value:function(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}},{key:"roundToZero",value:function(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}},{key:"negate",value:function(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}},{key:"dot",value:function(e){return this.x*e.x+this.y*e.y+this.z*e.z}},{key:"lengthSq",value:function(){return this.x*this.x+this.y*this.y+this.z*this.z}},{key:"length",value:function(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}},{key:"manhattanLength",value:function(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}},{key:"normalize",value:function(){return this.divideScalar(this.length()||1)}},{key:"setLength",value:function(e){return this.normalize().multiplyScalar(e)}},{key:"lerp",value:function(e,t){return this.x+=(e.x-this.x)*t,this.y+=(e.y-this.y)*t,this.z+=(e.z-this.z)*t,this}},{key:"lerpVectors",value:function(e,t,n){return this.x=e.x+(t.x-e.x)*n,this.y=e.y+(t.y-e.y)*n,this.z=e.z+(t.z-e.z)*n,this}},{key:"cross",value:function(e){return this.crossVectors(this,e)}},{key:"crossVectors",value:function(e,t){var n=e.x,r=e.y,i=e.z,A=t.x,a=t.y,o=t.z;return this.x=r*o-i*a,this.y=i*A-n*o,this.z=n*a-r*A,this}},{key:"projectOnVector",value:function(e){var t=e.lengthSq();if(0===t)return this.set(0,0,0);var n=e.dot(this)/t;return this.copy(e).multiplyScalar(n)}},{key:"projectOnPlane",value:function(e){return gm.copy(this).projectOnVector(e),this.sub(gm)}},{key:"reflect",value:function(e){return this.sub(gm.copy(e).multiplyScalar(2*this.dot(e)))}},{key:"angleTo",value:function(e){var t=Math.sqrt(this.lengthSq()*e.lengthSq());if(0===t)return Math.PI/2;var n=this.dot(e)/t;return Math.acos(Qg(n,-1,1))}},{key:"distanceTo",value:function(e){return Math.sqrt(this.distanceToSquared(e))}},{key:"distanceToSquared",value:function(e){var t=this.x-e.x,n=this.y-e.y,r=this.z-e.z;return t*t+n*n+r*r}},{key:"manhattanDistanceTo",value:function(e){return Math.abs(this.x-e.x)+Math.abs(this.y-e.y)+Math.abs(this.z-e.z)}},{key:"setFromSpherical",value:function(e){return this.setFromSphericalCoords(e.radius,e.phi,e.theta)}},{key:"setFromSphericalCoords",value:function(e,t,n){var r=Math.sin(t)*e;return this.x=r*Math.sin(n),this.y=Math.cos(t)*e,this.z=r*Math.cos(n),this}},{key:"setFromCylindrical",value:function(e){return this.setFromCylindricalCoords(e.radius,e.theta,e.y)}},{key:"setFromCylindricalCoords",value:function(e,t,n){return this.x=e*Math.sin(t),this.y=n,this.z=e*Math.cos(t),this}},{key:"setFromMatrixPosition",value:function(e){var t=e.elements;return this.x=t[12],this.y=t[13],this.z=t[14],this}},{key:"setFromMatrixScale",value:function(e){var t=this.setFromMatrixColumn(e,0).length(),n=this.setFromMatrixColumn(e,1).length(),r=this.setFromMatrixColumn(e,2).length();return this.x=t,this.y=n,this.z=r,this}},{key:"setFromMatrixColumn",value:function(e,t){return this.fromArray(e.elements,4*t)}},{key:"setFromMatrix3Column",value:function(e,t){return this.fromArray(e.elements,3*t)}},{key:"setFromEuler",value:function(e){return this.x=e._x,this.y=e._y,this.z=e._z,this}},{key:"equals",value:function(e){return e.x===this.x&&e.y===this.y&&e.z===this.z}},{key:"fromArray",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this.x=e[t],this.y=e[t+1],this.z=e[t+2],this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this.x,e[t+1]=this.y,e[t+2]=this.z,e}},{key:"fromBufferAttribute",value:function(e,t){return this.x=e.getX(t),this.y=e.getY(t),this.z=e.getZ(t),this}},{key:"random",value:function(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}},{key:"randomDirection",value:function(){var e=2*(Math.random()-.5),t=Math.random()*Math.PI*2,n=Math.sqrt(1-Math.pow(e,2));return this.x=n*Math.cos(t),this.y=n*Math.sin(t),this.z=e,this}},{key:Symbol.iterator,value:E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this.x;case 2:return e.next=4,this.y;case 4:return e.next=6,this.z;case 6:case"end":return e.stop()}}),e,this)}))}]),t}(),gm=new pm,mm=new fm,vm=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new pm(1/0,1/0,1/0),n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new pm(-1/0,-1/0,-1/0);v(this,e),this.isBox3=!0,this.min=t,this.max=n}return w(e,[{key:"set",value:function(e,t){return this.min.copy(e),this.max.copy(t),this}},{key:"setFromArray",value:function(e){for(var t=1/0,n=1/0,r=1/0,i=-1/0,A=-1/0,a=-1/0,o=0,s=e.length;o<s;o+=3){var l=e[o],u=e[o+1],c=e[o+2];l<t&&(t=l),u<n&&(n=u),c<r&&(r=c),l>i&&(i=l),u>A&&(A=u),c>a&&(a=c)}return this.min.set(t,n,r),this.max.set(i,A,a),this}},{key:"setFromBufferAttribute",value:function(e){for(var t=1/0,n=1/0,r=1/0,i=-1/0,A=-1/0,a=-1/0,o=0,s=e.count;o<s;o++){var l=e.getX(o),u=e.getY(o),c=e.getZ(o);l<t&&(t=l),u<n&&(n=u),c<r&&(r=c),l>i&&(i=l),u>A&&(A=u),c>a&&(a=c)}return this.min.set(t,n,r),this.max.set(i,A,a),this}},{key:"setFromPoints",value:function(e){this.makeEmpty();for(var t=0,n=e.length;t<n;t++)this.expandByPoint(e[t]);return this}},{key:"setFromCenterAndSize",value:function(e,t){var n=wm.copy(t).multiplyScalar(.5);return this.min.copy(e).sub(n),this.max.copy(e).add(n),this}},{key:"setFromObject",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]&&arguments[1];return this.makeEmpty(),this.expandByObject(e,t)}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){return this.min.copy(e.min),this.max.copy(e.max),this}},{key:"makeEmpty",value:function(){return this.min.x=this.min.y=this.min.z=1/0,this.max.x=this.max.y=this.max.z=-1/0,this}},{key:"isEmpty",value:function(){return this.max.x<this.min.x||this.max.y<this.min.y||this.max.z<this.min.z}},{key:"getCenter",value:function(e){return this.isEmpty()?e.set(0,0,0):e.addVectors(this.min,this.max).multiplyScalar(.5)}},{key:"getSize",value:function(e){return this.isEmpty()?e.set(0,0,0):e.subVectors(this.max,this.min)}},{key:"expandByPoint",value:function(e){return this.min.min(e),this.max.max(e),this}},{key:"expandByVector",value:function(e){return this.min.sub(e),this.max.add(e),this}},{key:"expandByScalar",value:function(e){return this.min.addScalar(-e),this.max.addScalar(e),this}},{key:"expandByObject",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]&&arguments[1];e.updateWorldMatrix(!1,!1);var n=e.geometry;if(void 0!==n)if(t&&void 0!=n.attributes&&void 0!==n.attributes.position)for(var r=n.attributes.position,i=0,A=r.count;i<A;i++)wm.fromBufferAttribute(r,i).applyMatrix4(e.matrixWorld),this.expandByPoint(wm);else null===n.boundingBox&&n.computeBoundingBox(),Bm.copy(n.boundingBox),Bm.applyMatrix4(e.matrixWorld),this.union(Bm);for(var a=e.children,o=0,s=a.length;o<s;o++)this.expandByObject(a[o],t);return this}},{key:"containsPoint",value:function(e){return!(e.x<this.min.x||e.x>this.max.x||e.y<this.min.y||e.y>this.max.y||e.z<this.min.z||e.z>this.max.z)}},{key:"containsBox",value:function(e){return this.min.x<=e.min.x&&e.max.x<=this.max.x&&this.min.y<=e.min.y&&e.max.y<=this.max.y&&this.min.z<=e.min.z&&e.max.z<=this.max.z}},{key:"getParameter",value:function(e,t){return t.set((e.x-this.min.x)/(this.max.x-this.min.x),(e.y-this.min.y)/(this.max.y-this.min.y),(e.z-this.min.z)/(this.max.z-this.min.z))}},{key:"intersectsBox",value:function(e){return!(e.max.x<this.min.x||e.min.x>this.max.x||e.max.y<this.min.y||e.min.y>this.max.y||e.max.z<this.min.z||e.min.z>this.max.z)}},{key:"intersectsSphere",value:function(e){return this.clampPoint(e.center,wm),wm.distanceToSquared(e.center)<=e.radius*e.radius}},{key:"intersectsPlane",value:function(e){var t,n;return e.normal.x>0?(t=e.normal.x*this.min.x,n=e.normal.x*this.max.x):(t=e.normal.x*this.max.x,n=e.normal.x*this.min.x),e.normal.y>0?(t+=e.normal.y*this.min.y,n+=e.normal.y*this.max.y):(t+=e.normal.y*this.max.y,n+=e.normal.y*this.min.y),e.normal.z>0?(t+=e.normal.z*this.min.z,n+=e.normal.z*this.max.z):(t+=e.normal.z*this.max.z,n+=e.normal.z*this.min.z),t<=-e.constant&&n>=-e.constant}},{key:"intersectsTriangle",value:function(e){if(this.isEmpty())return!1;this.getCenter(Um),Mm.subVectors(this.max,Um),_m.subVectors(e.a,Um),bm.subVectors(e.b,Um),xm.subVectors(e.c,Um),Cm.subVectors(bm,_m),Sm.subVectors(xm,bm),Em.subVectors(_m,xm);var t=[0,-Cm.z,Cm.y,0,-Sm.z,Sm.y,0,-Em.z,Em.y,Cm.z,0,-Cm.x,Sm.z,0,-Sm.x,Em.z,0,-Em.x,-Cm.y,Cm.x,0,-Sm.y,Sm.x,0,-Em.y,Em.x,0];return!!km(t,_m,bm,xm,Mm)&&(!!km(t=[1,0,0,0,1,0,0,0,1],_m,bm,xm,Mm)&&(Fm.crossVectors(Cm,Sm),km(t=[Fm.x,Fm.y,Fm.z],_m,bm,xm,Mm)))}},{key:"clampPoint",value:function(e,t){return t.copy(e).clamp(this.min,this.max)}},{key:"distanceToPoint",value:function(e){return wm.copy(e).clamp(this.min,this.max).sub(e).length()}},{key:"getBoundingSphere",value:function(e){return this.getCenter(e.center),e.radius=.5*this.getSize(wm).length(),e}},{key:"intersect",value:function(e){return this.min.max(e.min),this.max.min(e.max),this.isEmpty()&&this.makeEmpty(),this}},{key:"union",value:function(e){return this.min.min(e.min),this.max.max(e.max),this}},{key:"applyMatrix4",value:function(e){return this.isEmpty()||(ym[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(e),ym[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(e),ym[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(e),ym[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(e),ym[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(e),ym[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(e),ym[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(e),ym[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(e),this.setFromPoints(ym)),this}},{key:"translate",value:function(e){return this.min.add(e),this.max.add(e),this}},{key:"equals",value:function(e){return e.min.equals(this.min)&&e.max.equals(this.max)}}]),e}(),ym=[new pm,new pm,new pm,new pm,new pm,new pm,new pm,new pm],wm=new pm,Bm=new vm,_m=new pm,bm=new pm,xm=new pm,Cm=new pm,Sm=new pm,Em=new pm,Um=new pm,Mm=new pm,Fm=new pm,Tm=new pm;function km(e,t,n,r,i){for(var A=0,a=e.length-3;A<=a;A+=3){Tm.fromArray(e,A);var o=i.x*Math.abs(Tm.x)+i.y*Math.abs(Tm.y)+i.z*Math.abs(Tm.z),s=t.dot(Tm),l=n.dot(Tm),u=r.dot(Tm);if(Math.max(-Math.max(s,l,u),Math.min(s,l,u))>o)return!1}return!0}var Qm=new vm,Lm=new pm,Dm=new pm,Im=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new pm,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1;v(this,e),this.center=t,this.radius=n}return w(e,[{key:"set",value:function(e,t){return this.center.copy(e),this.radius=t,this}},{key:"setFromPoints",value:function(e,t){var n=this.center;void 0!==t?n.copy(t):Qm.setFromPoints(e).getCenter(n);for(var r=0,i=0,A=e.length;i<A;i++)r=Math.max(r,n.distanceToSquared(e[i]));return this.radius=Math.sqrt(r),this}},{key:"copy",value:function(e){return this.center.copy(e.center),this.radius=e.radius,this}},{key:"isEmpty",value:function(){return this.radius<0}},{key:"makeEmpty",value:function(){return this.center.set(0,0,0),this.radius=-1,this}},{key:"containsPoint",value:function(e){return e.distanceToSquared(this.center)<=this.radius*this.radius}},{key:"distanceToPoint",value:function(e){return e.distanceTo(this.center)-this.radius}},{key:"intersectsSphere",value:function(e){var t=this.radius+e.radius;return e.center.distanceToSquared(this.center)<=t*t}},{key:"intersectsBox",value:function(e){return e.intersectsSphere(this)}},{key:"intersectsPlane",value:function(e){return Math.abs(e.distanceToPoint(this.center))<=this.radius}},{key:"clampPoint",value:function(e,t){var n=this.center.distanceToSquared(e);return t.copy(e),n>this.radius*this.radius&&(t.sub(this.center).normalize(),t.multiplyScalar(this.radius).add(this.center)),t}},{key:"getBoundingBox",value:function(e){return this.isEmpty()?(e.makeEmpty(),e):(e.set(this.center,this.center),e.expandByScalar(this.radius),e)}},{key:"applyMatrix4",value:function(e){return this.center.applyMatrix4(e),this.radius=this.radius*e.getMaxScaleOnAxis(),this}},{key:"translate",value:function(e){return this.center.add(e),this}},{key:"expandByPoint",value:function(e){if(this.isEmpty())return this.center.copy(e),this.radius=0,this;Lm.subVectors(e,this.center);var t=Lm.lengthSq();if(t>this.radius*this.radius){var n=Math.sqrt(t),r=.5*(n-this.radius);this.center.addScaledVector(Lm,r/n),this.radius+=r}return this}},{key:"union",value:function(e){return e.isEmpty()?this:this.isEmpty()?(this.copy(e),this):(!0===this.center.equals(e.center)?this.radius=Math.max(this.radius,e.radius):(Dm.subVectors(e.center,this.center).setLength(e.radius),this.expandByPoint(Lm.copy(e.center).add(Dm)),this.expandByPoint(Lm.copy(e.center).sub(Dm))),this)}},{key:"equals",value:function(e){return e.center.equals(this.center)&&e.radius===this.radius}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),e}(),Rm=new pm,Hm=new pm,Pm=new pm,Nm=new pm,Om=new pm,Vm=new pm,zm=new pm,Gm=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new pm,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new pm(0,0,-1);v(this,e),this.origin=t,this.direction=n}return w(e,[{key:"set",value:function(e,t){return this.origin.copy(e),this.direction.copy(t),this}},{key:"copy",value:function(e){return this.origin.copy(e.origin),this.direction.copy(e.direction),this}},{key:"at",value:function(e,t){return t.copy(this.direction).multiplyScalar(e).add(this.origin)}},{key:"lookAt",value:function(e){return this.direction.copy(e).sub(this.origin).normalize(),this}},{key:"recast",value:function(e){return this.origin.copy(this.at(e,Rm)),this}},{key:"closestPointToPoint",value:function(e,t){t.subVectors(e,this.origin);var n=t.dot(this.direction);return n<0?t.copy(this.origin):t.copy(this.direction).multiplyScalar(n).add(this.origin)}},{key:"distanceToPoint",value:function(e){return Math.sqrt(this.distanceSqToPoint(e))}},{key:"distanceSqToPoint",value:function(e){var t=Rm.subVectors(e,this.origin).dot(this.direction);return t<0?this.origin.distanceToSquared(e):(Rm.copy(this.direction).multiplyScalar(t).add(this.origin),Rm.distanceToSquared(e))}},{key:"distanceSqToSegment",value:function(e,t,n,r){Hm.copy(e).add(t).multiplyScalar(.5),Pm.copy(t).sub(e).normalize(),Nm.copy(this.origin).sub(Hm);var i,A,a,o,s=.5*e.distanceTo(t),l=-this.direction.dot(Pm),u=Nm.dot(this.direction),c=-Nm.dot(Pm),d=Nm.lengthSq(),h=Math.abs(1-l*l);if(h>0)if(A=l*u-c,o=s*h,(i=l*c-u)>=0)if(A>=-o)if(A<=o){var f=1/h;a=(i*=f)*(i+l*(A*=f)+2*u)+A*(l*i+A+2*c)+d}else A=s,a=-(i=Math.max(0,-(l*A+u)))*i+A*(A+2*c)+d;else A=-s,a=-(i=Math.max(0,-(l*A+u)))*i+A*(A+2*c)+d;else A<=-o?a=-(i=Math.max(0,-(-l*s+u)))*i+(A=i>0?-s:Math.min(Math.max(-s,-c),s))*(A+2*c)+d:A<=o?(i=0,a=(A=Math.min(Math.max(-s,-c),s))*(A+2*c)+d):a=-(i=Math.max(0,-(l*s+u)))*i+(A=i>0?s:Math.min(Math.max(-s,-c),s))*(A+2*c)+d;else A=l>0?-s:s,a=-(i=Math.max(0,-(l*A+u)))*i+A*(A+2*c)+d;return n&&n.copy(this.direction).multiplyScalar(i).add(this.origin),r&&r.copy(Pm).multiplyScalar(A).add(Hm),a}},{key:"intersectSphere",value:function(e,t){Rm.subVectors(e.center,this.origin);var n=Rm.dot(this.direction),r=Rm.dot(Rm)-n*n,i=e.radius*e.radius;if(r>i)return null;var A=Math.sqrt(i-r),a=n-A,o=n+A;return a<0&&o<0?null:a<0?this.at(o,t):this.at(a,t)}},{key:"intersectsSphere",value:function(e){return this.distanceSqToPoint(e.center)<=e.radius*e.radius}},{key:"distanceToPlane",value:function(e){var t=e.normal.dot(this.direction);if(0===t)return 0===e.distanceToPoint(this.origin)?0:null;var n=-(this.origin.dot(e.normal)+e.constant)/t;return n>=0?n:null}},{key:"intersectPlane",value:function(e,t){var n=this.distanceToPlane(e);return null===n?null:this.at(n,t)}},{key:"intersectsPlane",value:function(e){var t=e.distanceToPoint(this.origin);return 0===t||e.normal.dot(this.direction)*t<0}},{key:"intersectBox",value:function(e,t){var n,r,i,A,a,o,s=1/this.direction.x,l=1/this.direction.y,u=1/this.direction.z,c=this.origin;return s>=0?(n=(e.min.x-c.x)*s,r=(e.max.x-c.x)*s):(n=(e.max.x-c.x)*s,r=(e.min.x-c.x)*s),l>=0?(i=(e.min.y-c.y)*l,A=(e.max.y-c.y)*l):(i=(e.max.y-c.y)*l,A=(e.min.y-c.y)*l),n>A||i>r?null:((i>n||isNaN(n))&&(n=i),(A<r||isNaN(r))&&(r=A),u>=0?(a=(e.min.z-c.z)*u,o=(e.max.z-c.z)*u):(a=(e.max.z-c.z)*u,o=(e.min.z-c.z)*u),n>o||a>r?null:((a>n||n!==n)&&(n=a),(o<r||r!==r)&&(r=o),r<0?null:this.at(n>=0?n:r,t)))}},{key:"intersectsBox",value:function(e){return null!==this.intersectBox(e,Rm)}},{key:"intersectTriangle",value:function(e,t,n,r,i){Om.subVectors(t,e),Vm.subVectors(n,e),zm.crossVectors(Om,Vm);var A,a=this.direction.dot(zm);if(a>0){if(r)return null;A=1}else{if(!(a<0))return null;A=-1,a=-a}Nm.subVectors(this.origin,e);var o=A*this.direction.dot(Vm.crossVectors(Nm,Vm));if(o<0)return null;var s=A*this.direction.dot(Om.cross(Nm));if(s<0)return null;if(o+s>a)return null;var l=-A*Nm.dot(zm);return l<0?null:this.at(l/a,i)}},{key:"applyMatrix4",value:function(e){return this.origin.applyMatrix4(e),this.direction.transformDirection(e),this}},{key:"equals",value:function(e){return e.origin.equals(this.origin)&&e.direction.equals(this.direction)}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),e}(),Km=function(){function e(){v(this,e),e.prototype.isMatrix4=!0,this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]}return w(e,[{key:"set",value:function(e,t,n,r,i,A,a,o,s,l,u,c,d,h,f,p){var g=this.elements;return g[0]=e,g[4]=t,g[8]=n,g[12]=r,g[1]=i,g[5]=A,g[9]=a,g[13]=o,g[2]=s,g[6]=l,g[10]=u,g[14]=c,g[3]=d,g[7]=h,g[11]=f,g[15]=p,this}},{key:"identity",value:function(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}},{key:"clone",value:function(){return(new e).fromArray(this.elements)}},{key:"copy",value:function(e){var t=this.elements,n=e.elements;return t[0]=n[0],t[1]=n[1],t[2]=n[2],t[3]=n[3],t[4]=n[4],t[5]=n[5],t[6]=n[6],t[7]=n[7],t[8]=n[8],t[9]=n[9],t[10]=n[10],t[11]=n[11],t[12]=n[12],t[13]=n[13],t[14]=n[14],t[15]=n[15],this}},{key:"copyPosition",value:function(e){var t=this.elements,n=e.elements;return t[12]=n[12],t[13]=n[13],t[14]=n[14],this}},{key:"setFromMatrix3",value:function(e){var t=e.elements;return this.set(t[0],t[3],t[6],0,t[1],t[4],t[7],0,t[2],t[5],t[8],0,0,0,0,1),this}},{key:"extractBasis",value:function(e,t,n){return e.setFromMatrixColumn(this,0),t.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}},{key:"makeBasis",value:function(e,t,n){return this.set(e.x,t.x,n.x,0,e.y,t.y,n.y,0,e.z,t.z,n.z,0,0,0,0,1),this}},{key:"extractRotation",value:function(e){var t=this.elements,n=e.elements,r=1/Wm.setFromMatrixColumn(e,0).length(),i=1/Wm.setFromMatrixColumn(e,1).length(),A=1/Wm.setFromMatrixColumn(e,2).length();return t[0]=n[0]*r,t[1]=n[1]*r,t[2]=n[2]*r,t[3]=0,t[4]=n[4]*i,t[5]=n[5]*i,t[6]=n[6]*i,t[7]=0,t[8]=n[8]*A,t[9]=n[9]*A,t[10]=n[10]*A,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,this}},{key:"makeRotationFromEuler",value:function(e){var t=this.elements,n=e.x,r=e.y,i=e.z,A=Math.cos(n),a=Math.sin(n),o=Math.cos(r),s=Math.sin(r),l=Math.cos(i),u=Math.sin(i);if("XYZ"===e.order){var c=A*l,d=A*u,h=a*l,f=a*u;t[0]=o*l,t[4]=-o*u,t[8]=s,t[1]=d+h*s,t[5]=c-f*s,t[9]=-a*o,t[2]=f-c*s,t[6]=h+d*s,t[10]=A*o}else if("YXZ"===e.order){var p=o*l,g=o*u,m=s*l,v=s*u;t[0]=p+v*a,t[4]=m*a-g,t[8]=A*s,t[1]=A*u,t[5]=A*l,t[9]=-a,t[2]=g*a-m,t[6]=v+p*a,t[10]=A*o}else if("ZXY"===e.order){var y=o*l,w=o*u,B=s*l,_=s*u;t[0]=y-_*a,t[4]=-A*u,t[8]=B+w*a,t[1]=w+B*a,t[5]=A*l,t[9]=_-y*a,t[2]=-A*s,t[6]=a,t[10]=A*o}else if("ZYX"===e.order){var b=A*l,x=A*u,C=a*l,S=a*u;t[0]=o*l,t[4]=C*s-x,t[8]=b*s+S,t[1]=o*u,t[5]=S*s+b,t[9]=x*s-C,t[2]=-s,t[6]=a*o,t[10]=A*o}else if("YZX"===e.order){var E=A*o,U=A*s,M=a*o,F=a*s;t[0]=o*l,t[4]=F-E*u,t[8]=M*u+U,t[1]=u,t[5]=A*l,t[9]=-a*l,t[2]=-s*l,t[6]=U*u+M,t[10]=E-F*u}else if("XZY"===e.order){var T=A*o,k=A*s,Q=a*o,L=a*s;t[0]=o*l,t[4]=-u,t[8]=s*l,t[1]=T*u+L,t[5]=A*l,t[9]=k*u-Q,t[2]=Q*u-k,t[6]=a*l,t[10]=L*u+T}return t[3]=0,t[7]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,this}},{key:"makeRotationFromQuaternion",value:function(e){return this.compose(Xm,e,qm)}},{key:"lookAt",value:function(e,t,n){var r=this.elements;return Zm.subVectors(e,t),0===Zm.lengthSq()&&(Zm.z=1),Zm.normalize(),Ym.crossVectors(n,Zm),0===Ym.lengthSq()&&(1===Math.abs(n.z)?Zm.x+=1e-4:Zm.z+=1e-4,Zm.normalize(),Ym.crossVectors(n,Zm)),Ym.normalize(),Jm.crossVectors(Zm,Ym),r[0]=Ym.x,r[4]=Jm.x,r[8]=Zm.x,r[1]=Ym.y,r[5]=Jm.y,r[9]=Zm.y,r[2]=Ym.z,r[6]=Jm.z,r[10]=Zm.z,this}},{key:"multiply",value:function(e){return this.multiplyMatrices(this,e)}},{key:"premultiply",value:function(e){return this.multiplyMatrices(e,this)}},{key:"multiplyMatrices",value:function(e,t){var n=e.elements,r=t.elements,i=this.elements,A=n[0],a=n[4],o=n[8],s=n[12],l=n[1],u=n[5],c=n[9],d=n[13],h=n[2],f=n[6],p=n[10],g=n[14],m=n[3],v=n[7],y=n[11],w=n[15],B=r[0],_=r[4],b=r[8],x=r[12],C=r[1],S=r[5],E=r[9],U=r[13],M=r[2],F=r[6],T=r[10],k=r[14],Q=r[3],L=r[7],D=r[11],I=r[15];return i[0]=A*B+a*C+o*M+s*Q,i[4]=A*_+a*S+o*F+s*L,i[8]=A*b+a*E+o*T+s*D,i[12]=A*x+a*U+o*k+s*I,i[1]=l*B+u*C+c*M+d*Q,i[5]=l*_+u*S+c*F+d*L,i[9]=l*b+u*E+c*T+d*D,i[13]=l*x+u*U+c*k+d*I,i[2]=h*B+f*C+p*M+g*Q,i[6]=h*_+f*S+p*F+g*L,i[10]=h*b+f*E+p*T+g*D,i[14]=h*x+f*U+p*k+g*I,i[3]=m*B+v*C+y*M+w*Q,i[7]=m*_+v*S+y*F+w*L,i[11]=m*b+v*E+y*T+w*D,i[15]=m*x+v*U+y*k+w*I,this}},{key:"multiplyScalar",value:function(e){var t=this.elements;return t[0]*=e,t[4]*=e,t[8]*=e,t[12]*=e,t[1]*=e,t[5]*=e,t[9]*=e,t[13]*=e,t[2]*=e,t[6]*=e,t[10]*=e,t[14]*=e,t[3]*=e,t[7]*=e,t[11]*=e,t[15]*=e,this}},{key:"determinant",value:function(){var e=this.elements,t=e[0],n=e[4],r=e[8],i=e[12],A=e[1],a=e[5],o=e[9],s=e[13],l=e[2],u=e[6],c=e[10],d=e[14];return e[3]*(+i*o*u-r*s*u-i*a*c+n*s*c+r*a*d-n*o*d)+e[7]*(+t*o*d-t*s*c+i*A*c-r*A*d+r*s*l-i*o*l)+e[11]*(+t*s*u-t*a*d-i*A*u+n*A*d+i*a*l-n*s*l)+e[15]*(-r*a*l-t*o*u+t*a*c+r*A*u-n*A*c+n*o*l)}},{key:"transpose",value:function(){var e,t=this.elements;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}},{key:"setPosition",value:function(e,t,n){var r=this.elements;return e.isVector3?(r[12]=e.x,r[13]=e.y,r[14]=e.z):(r[12]=e,r[13]=t,r[14]=n),this}},{key:"invert",value:function(){var e=this.elements,t=e[0],n=e[1],r=e[2],i=e[3],A=e[4],a=e[5],o=e[6],s=e[7],l=e[8],u=e[9],c=e[10],d=e[11],h=e[12],f=e[13],p=e[14],g=e[15],m=u*p*s-f*c*s+f*o*d-a*p*d-u*o*g+a*c*g,v=h*c*s-l*p*s-h*o*d+A*p*d+l*o*g-A*c*g,y=l*f*s-h*u*s+h*a*d-A*f*d-l*a*g+A*u*g,w=h*u*o-l*f*o-h*a*c+A*f*c+l*a*p-A*u*p,B=t*m+n*v+r*y+i*w;if(0===B)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);var _=1/B;return e[0]=m*_,e[1]=(f*c*i-u*p*i-f*r*d+n*p*d+u*r*g-n*c*g)*_,e[2]=(a*p*i-f*o*i+f*r*s-n*p*s-a*r*g+n*o*g)*_,e[3]=(u*o*i-a*c*i-u*r*s+n*c*s+a*r*d-n*o*d)*_,e[4]=v*_,e[5]=(l*p*i-h*c*i+h*r*d-t*p*d-l*r*g+t*c*g)*_,e[6]=(h*o*i-A*p*i-h*r*s+t*p*s+A*r*g-t*o*g)*_,e[7]=(A*c*i-l*o*i+l*r*s-t*c*s-A*r*d+t*o*d)*_,e[8]=y*_,e[9]=(h*u*i-l*f*i-h*n*d+t*f*d+l*n*g-t*u*g)*_,e[10]=(A*f*i-h*a*i+h*n*s-t*f*s-A*n*g+t*a*g)*_,e[11]=(l*a*i-A*u*i-l*n*s+t*u*s+A*n*d-t*a*d)*_,e[12]=w*_,e[13]=(l*f*r-h*u*r+h*n*c-t*f*c-l*n*p+t*u*p)*_,e[14]=(h*a*r-A*f*r-h*n*o+t*f*o+A*n*p-t*a*p)*_,e[15]=(A*u*r-l*a*r+l*n*o-t*u*o-A*n*c+t*a*c)*_,this}},{key:"scale",value:function(e){var t=this.elements,n=e.x,r=e.y,i=e.z;return t[0]*=n,t[4]*=r,t[8]*=i,t[1]*=n,t[5]*=r,t[9]*=i,t[2]*=n,t[6]*=r,t[10]*=i,t[3]*=n,t[7]*=r,t[11]*=i,this}},{key:"getMaxScaleOnAxis",value:function(){var e=this.elements,t=e[0]*e[0]+e[1]*e[1]+e[2]*e[2],n=e[4]*e[4]+e[5]*e[5]+e[6]*e[6],r=e[8]*e[8]+e[9]*e[9]+e[10]*e[10];return Math.sqrt(Math.max(t,n,r))}},{key:"makeTranslation",value:function(e,t,n){return this.set(1,0,0,e,0,1,0,t,0,0,1,n,0,0,0,1),this}},{key:"makeRotationX",value:function(e){var t=Math.cos(e),n=Math.sin(e);return this.set(1,0,0,0,0,t,-n,0,0,n,t,0,0,0,0,1),this}},{key:"makeRotationY",value:function(e){var t=Math.cos(e),n=Math.sin(e);return this.set(t,0,n,0,0,1,0,0,-n,0,t,0,0,0,0,1),this}},{key:"makeRotationZ",value:function(e){var t=Math.cos(e),n=Math.sin(e);return this.set(t,-n,0,0,n,t,0,0,0,0,1,0,0,0,0,1),this}},{key:"makeRotationAxis",value:function(e,t){var n=Math.cos(t),r=Math.sin(t),i=1-n,A=e.x,a=e.y,o=e.z,s=i*A,l=i*a;return this.set(s*A+n,s*a-r*o,s*o+r*a,0,s*a+r*o,l*a+n,l*o-r*A,0,s*o-r*a,l*o+r*A,i*o*o+n,0,0,0,0,1),this}},{key:"makeScale",value:function(e,t,n){return this.set(e,0,0,0,0,t,0,0,0,0,n,0,0,0,0,1),this}},{key:"makeShear",value:function(e,t,n,r,i,A){return this.set(1,n,i,0,e,1,A,0,t,r,1,0,0,0,0,1),this}},{key:"compose",value:function(e,t,n){var r=this.elements,i=t._x,A=t._y,a=t._z,o=t._w,s=i+i,l=A+A,u=a+a,c=i*s,d=i*l,h=i*u,f=A*l,p=A*u,g=a*u,m=o*s,v=o*l,y=o*u,w=n.x,B=n.y,_=n.z;return r[0]=(1-(f+g))*w,r[1]=(d+y)*w,r[2]=(h-v)*w,r[3]=0,r[4]=(d-y)*B,r[5]=(1-(c+g))*B,r[6]=(p+m)*B,r[7]=0,r[8]=(h+v)*_,r[9]=(p-m)*_,r[10]=(1-(c+f))*_,r[11]=0,r[12]=e.x,r[13]=e.y,r[14]=e.z,r[15]=1,this}},{key:"decompose",value:function(e,t,n){var r=this.elements,i=Wm.set(r[0],r[1],r[2]).length(),A=Wm.set(r[4],r[5],r[6]).length(),a=Wm.set(r[8],r[9],r[10]).length();this.determinant()<0&&(i=-i),e.x=r[12],e.y=r[13],e.z=r[14],jm.copy(this);var o=1/i,s=1/A,l=1/a;return jm.elements[0]*=o,jm.elements[1]*=o,jm.elements[2]*=o,jm.elements[4]*=s,jm.elements[5]*=s,jm.elements[6]*=s,jm.elements[8]*=l,jm.elements[9]*=l,jm.elements[10]*=l,t.setFromRotationMatrix(jm),n.x=i,n.y=A,n.z=a,this}},{key:"makePerspective",value:function(e,t,n,r,i,A){var a=this.elements,o=2*i/(t-e),s=2*i/(n-r),l=(t+e)/(t-e),u=(n+r)/(n-r),c=-(A+i)/(A-i),d=-2*A*i/(A-i);return a[0]=o,a[4]=0,a[8]=l,a[12]=0,a[1]=0,a[5]=s,a[9]=u,a[13]=0,a[2]=0,a[6]=0,a[10]=c,a[14]=d,a[3]=0,a[7]=0,a[11]=-1,a[15]=0,this}},{key:"makeOrthographic",value:function(e,t,n,r,i,A){var a=this.elements,o=1/(t-e),s=1/(n-r),l=1/(A-i),u=(t+e)*o,c=(n+r)*s,d=(A+i)*l;return a[0]=2*o,a[4]=0,a[8]=0,a[12]=-u,a[1]=0,a[5]=2*s,a[9]=0,a[13]=-c,a[2]=0,a[6]=0,a[10]=-2*l,a[14]=-d,a[3]=0,a[7]=0,a[11]=0,a[15]=1,this}},{key:"equals",value:function(e){for(var t=this.elements,n=e.elements,r=0;r<16;r++)if(t[r]!==n[r])return!1;return!0}},{key:"fromArray",value:function(e){for(var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=0;n<16;n++)this.elements[n]=e[n+t];return this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=this.elements;return e[t]=n[0],e[t+1]=n[1],e[t+2]=n[2],e[t+3]=n[3],e[t+4]=n[4],e[t+5]=n[5],e[t+6]=n[6],e[t+7]=n[7],e[t+8]=n[8],e[t+9]=n[9],e[t+10]=n[10],e[t+11]=n[11],e[t+12]=n[12],e[t+13]=n[13],e[t+14]=n[14],e[t+15]=n[15],e}}]),e}(),Wm=new pm,jm=new Km,Xm=new pm(0,0,0),qm=new pm(1,1,1),Ym=new pm,Jm=new pm,Zm=new pm,$m=new Km,ev=new fm,tv=function(e){function t(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:t.DEFAULT_ORDER;v(this,t),this.isEuler=!0,this._x=e,this._y=n,this._z=r,this._order=i}return w(t,[{key:"x",get:function(){return this._x},set:function(e){this._x=e,this._onChangeCallback()}},{key:"y",get:function(){return this._y},set:function(e){this._y=e,this._onChangeCallback()}},{key:"z",get:function(){return this._z},set:function(e){this._z=e,this._onChangeCallback()}},{key:"order",get:function(){return this._order},set:function(e){this._order=e,this._onChangeCallback()}},{key:"set",value:function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:this._order;return this._x=e,this._y=t,this._z=n,this._order=r,this._onChangeCallback(),this}},{key:"clone",value:function(){return new this.constructor(this._x,this._y,this._z,this._order)}},{key:"copy",value:function(e){return this._x=e._x,this._y=e._y,this._z=e._z,this._order=e._order,this._onChangeCallback(),this}},{key:"setFromRotationMatrix",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this._order,n=!(arguments.length>2&&void 0!==arguments[2])||arguments[2],r=e.elements,i=r[0],A=r[4],a=r[8],o=r[1],s=r[5],l=r[9],u=r[2],c=r[6],d=r[10];switch(t){case"XYZ":this._y=Math.asin(Qg(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-l,d),this._z=Math.atan2(-A,i)):(this._x=Math.atan2(c,s),this._z=0);break;case"YXZ":this._x=Math.asin(-Qg(l,-1,1)),Math.abs(l)<.9999999?(this._y=Math.atan2(a,d),this._z=Math.atan2(o,s)):(this._y=Math.atan2(-u,i),this._z=0);break;case"ZXY":this._x=Math.asin(Qg(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(-u,d),this._z=Math.atan2(-A,s)):(this._y=0,this._z=Math.atan2(o,i));break;case"ZYX":this._y=Math.asin(-Qg(u,-1,1)),Math.abs(u)<.9999999?(this._x=Math.atan2(c,d),this._z=Math.atan2(o,i)):(this._x=0,this._z=Math.atan2(-A,s));break;case"YZX":this._z=Math.asin(Qg(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(-l,s),this._y=Math.atan2(-u,i)):(this._x=0,this._y=Math.atan2(a,d));break;case"XZY":this._z=Math.asin(-Qg(A,-1,1)),Math.abs(A)<.9999999?(this._x=Math.atan2(c,s),this._y=Math.atan2(a,i)):(this._x=Math.atan2(-l,d),this._y=0);break;default:console.warn("THREE.Euler: .setFromRotationMatrix() encountered an unknown order: "+t)}return this._order=t,!0===n&&this._onChangeCallback(),this}},{key:"setFromQuaternion",value:function(e,t,n){return $m.makeRotationFromQuaternion(e),this.setFromRotationMatrix($m,t,n)}},{key:"setFromVector3",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this._order;return this.set(e.x,e.y,e.z,t)}},{key:"reorder",value:function(e){return ev.setFromEuler(this),this.setFromQuaternion(ev,e)}},{key:"equals",value:function(e){return e._x===this._x&&e._y===this._y&&e._z===this._z&&e._order===this._order}},{key:"fromArray",value:function(e){return this._x=e[0],this._y=e[1],this._z=e[2],void 0!==e[3]&&(this._order=e[3]),this._onChangeCallback(),this}},{key:"toArray",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return e[t]=this._x,e[t+1]=this._y,e[t+2]=this._z,e[t+3]=this._order,e}},{key:"_onChange",value:function(e){return this._onChangeCallback=e,this}},{key:"_onChangeCallback",value:function(){}},{key:Symbol.iterator,value:E().mark((function e(){return E().wrap((function(e){for(;;)switch(e.prev=e.next){case 0:return e.next=2,this._x;case 2:return e.next=4,this._y;case 4:return e.next=6,this._z;case 6:return e.next=8,this._order;case 8:case"end":return e.stop()}}),e,this)}))}]),t}();tv.DEFAULT_ORDER="XYZ";var nv=function(){function e(){v(this,e),this.mask=1}return w(e,[{key:"set",value:function(e){this.mask=(1<<e|0)>>>0}},{key:"enable",value:function(e){this.mask|=1<<e|0}},{key:"enableAll",value:function(){this.mask=-1}},{key:"toggle",value:function(e){this.mask^=1<<e|0}},{key:"disable",value:function(e){this.mask&=~(1<<e|0)}},{key:"disableAll",value:function(){this.mask=0}},{key:"test",value:function(e){return 0!==(this.mask&e.mask)}},{key:"isEnabled",value:function(e){return 0!==(this.mask&(1<<e|0))}}]),e}(),rv=0,iv=new pm,Av=new fm,av=new Km,ov=new pm,sv=new pm,lv=new pm,uv=new fm,cv=new pm(1,0,0),dv=new pm(0,1,0),hv=new pm(0,0,1),fv={type:"added"},pv={type:"removed"},gv=function(e){_(n,e);var t=S(n);function n(){var e;v(this,n),(e=t.call(this)).isObject3D=!0,Object.defineProperty(C(e),"id",{value:rv++}),e.uuid=kg(),e.name="",e.type="Object3D",e.parent=null,e.children=[],e.up=n.DEFAULT_UP.clone();var r=new pm,i=new tv,A=new fm,a=new pm(1,1,1);return i._onChange((function(){A.setFromEuler(i,!1)})),A._onChange((function(){i.setFromQuaternion(A,void 0,!1)})),Object.defineProperties(C(e),{position:{configurable:!0,enumerable:!0,value:r},rotation:{configurable:!0,enumerable:!0,value:i},quaternion:{configurable:!0,enumerable:!0,value:A},scale:{configurable:!0,enumerable:!0,value:a},modelViewMatrix:{value:new Km},normalMatrix:{value:new zg}}),e.matrix=new Km,e.matrixWorld=new Km,e.matrixAutoUpdate=n.DEFAULT_MATRIX_AUTO_UPDATE,e.matrixWorldNeedsUpdate=!1,e.matrixWorldAutoUpdate=n.DEFAULT_MATRIX_WORLD_AUTO_UPDATE,e.layers=new nv,e.visible=!0,e.castShadow=!1,e.receiveShadow=!1,e.frustumCulled=!0,e.renderOrder=0,e.animations=[],e.userData={},e}return w(n,[{key:"onBeforeRender",value:function(){}},{key:"onAfterRender",value:function(){}},{key:"applyMatrix4",value:function(e){this.matrixAutoUpdate&&this.updateMatrix(),this.matrix.premultiply(e),this.matrix.decompose(this.position,this.quaternion,this.scale)}},{key:"applyQuaternion",value:function(e){return this.quaternion.premultiply(e),this}},{key:"setRotationFromAxisAngle",value:function(e,t){this.quaternion.setFromAxisAngle(e,t)}},{key:"setRotationFromEuler",value:function(e){this.quaternion.setFromEuler(e,!0)}},{key:"setRotationFromMatrix",value:function(e){this.quaternion.setFromRotationMatrix(e)}},{key:"setRotationFromQuaternion",value:function(e){this.quaternion.copy(e)}},{key:"rotateOnAxis",value:function(e,t){return Av.setFromAxisAngle(e,t),this.quaternion.multiply(Av),this}},{key:"rotateOnWorldAxis",value:function(e,t){return Av.setFromAxisAngle(e,t),this.quaternion.premultiply(Av),this}},{key:"rotateX",value:function(e){return this.rotateOnAxis(cv,e)}},{key:"rotateY",value:function(e){return this.rotateOnAxis(dv,e)}},{key:"rotateZ",value:function(e){return this.rotateOnAxis(hv,e)}},{key:"translateOnAxis",value:function(e,t){return iv.copy(e).applyQuaternion(this.quaternion),this.position.add(iv.multiplyScalar(t)),this}},{key:"translateX",value:function(e){return this.translateOnAxis(cv,e)}},{key:"translateY",value:function(e){return this.translateOnAxis(dv,e)}},{key:"translateZ",value:function(e){return this.translateOnAxis(hv,e)}},{key:"localToWorld",value:function(e){return this.updateWorldMatrix(!0,!1),e.applyMatrix4(this.matrixWorld)}},{key:"worldToLocal",value:function(e){return this.updateWorldMatrix(!0,!1),e.applyMatrix4(av.copy(this.matrixWorld).invert())}},{key:"lookAt",value:function(e,t,n){e.isVector3?ov.copy(e):ov.set(e,t,n);var r=this.parent;this.updateWorldMatrix(!0,!1),sv.setFromMatrixPosition(this.matrixWorld),this.isCamera||this.isLight?av.lookAt(sv,ov,this.up):av.lookAt(ov,sv,this.up),this.quaternion.setFromRotationMatrix(av),r&&(av.extractRotation(r.matrixWorld),Av.setFromRotationMatrix(av),this.quaternion.premultiply(Av.invert()))}},{key:"add",value:function(e){if(arguments.length>1){for(var t=0;t<arguments.length;t++)this.add(arguments[t]);return this}return e===this?(console.error("THREE.Object3D.add: object can't be added as a child of itself.",e),this):(e&&e.isObject3D?(null!==e.parent&&e.parent.remove(e),e.parent=this,this.children.push(e),e.dispatchEvent(fv)):console.error("THREE.Object3D.add: object not an instance of THREE.Object3D.",e),this)}},{key:"remove",value:function(e){if(arguments.length>1){for(var t=0;t<arguments.length;t++)this.remove(arguments[t]);return this}var n=this.children.indexOf(e);return-1!==n&&(e.parent=null,this.children.splice(n,1),e.dispatchEvent(pv)),this}},{key:"removeFromParent",value:function(){var e=this.parent;return null!==e&&e.remove(this),this}},{key:"clear",value:function(){for(var e=0;e<this.children.length;e++){var t=this.children[e];t.parent=null,t.dispatchEvent(pv)}return this.children.length=0,this}},{key:"attach",value:function(e){return this.updateWorldMatrix(!0,!1),av.copy(this.matrixWorld).invert(),null!==e.parent&&(e.parent.updateWorldMatrix(!0,!1),av.multiply(e.parent.matrixWorld)),e.applyMatrix4(av),this.add(e),e.updateWorldMatrix(!1,!0),this}},{key:"getObjectById",value:function(e){return this.getObjectByProperty("id",e)}},{key:"getObjectByName",value:function(e){return this.getObjectByProperty("name",e)}},{key:"getObjectByProperty",value:function(e,t){if(this[e]===t)return this;for(var n=0,r=this.children.length;n<r;n++){var i=this.children[n].getObjectByProperty(e,t);if(void 0!==i)return i}}},{key:"getObjectsByProperty",value:function(e,t){var n=[];this[e]===t&&n.push(this);for(var r=0,i=this.children.length;r<i;r++){var A=this.children[r].getObjectsByProperty(e,t);A.length>0&&(n=n.concat(A))}return n}},{key:"getWorldPosition",value:function(e){return this.updateWorldMatrix(!0,!1),e.setFromMatrixPosition(this.matrixWorld)}},{key:"getWorldQuaternion",value:function(e){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(sv,e,lv),e}},{key:"getWorldScale",value:function(e){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(sv,uv,e),e}},{key:"getWorldDirection",value:function(e){this.updateWorldMatrix(!0,!1);var t=this.matrixWorld.elements;return e.set(t[8],t[9],t[10]).normalize()}},{key:"raycast",value:function(){}},{key:"traverse",value:function(e){e(this);for(var t=this.children,n=0,r=t.length;n<r;n++)t[n].traverse(e)}},{key:"traverseVisible",value:function(e){if(!1!==this.visible){e(this);for(var t=this.children,n=0,r=t.length;n<r;n++)t[n].traverseVisible(e)}}},{key:"traverseAncestors",value:function(e){var t=this.parent;null!==t&&(e(t),t.traverseAncestors(e))}},{key:"updateMatrix",value:function(){this.matrix.compose(this.position,this.quaternion,this.scale),this.matrixWorldNeedsUpdate=!0}},{key:"updateMatrixWorld",value:function(e){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||e)&&(null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,e=!0);for(var t=this.children,n=0,r=t.length;n<r;n++){var i=t[n];!0!==i.matrixWorldAutoUpdate&&!0!==e||i.updateMatrixWorld(e)}}},{key:"updateWorldMatrix",value:function(e,t){var n=this.parent;if(!0===e&&null!==n&&!0===n.matrixWorldAutoUpdate&&n.updateWorldMatrix(!0,!1),this.matrixAutoUpdate&&this.updateMatrix(),null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),!0===t)for(var r=this.children,i=0,A=r.length;i<A;i++){var a=r[i];!0===a.matrixWorldAutoUpdate&&a.updateWorldMatrix(!1,!0)}}},{key:"toJSON",value:function(e){var t=void 0===e||"string"===typeof e,n={};t&&(e={geometries:{},materials:{},textures:{},images:{},shapes:{},skeletons:{},animations:{},nodes:{}},n.metadata={version:4.5,type:"Object",generator:"Object3D.toJSON"});var r={};function i(t,n){return void 0===t[n.uuid]&&(t[n.uuid]=n.toJSON(e)),n.uuid}if(r.uuid=this.uuid,r.type=this.type,""!==this.name&&(r.name=this.name),!0===this.castShadow&&(r.castShadow=!0),!0===this.receiveShadow&&(r.receiveShadow=!0),!1===this.visible&&(r.visible=!1),!1===this.frustumCulled&&(r.frustumCulled=!1),0!==this.renderOrder&&(r.renderOrder=this.renderOrder),Object.keys(this.userData).length>0&&(r.userData=this.userData),r.layers=this.layers.mask,r.matrix=this.matrix.toArray(),!1===this.matrixAutoUpdate&&(r.matrixAutoUpdate=!1),this.isInstancedMesh&&(r.type="InstancedMesh",r.count=this.count,r.instanceMatrix=this.instanceMatrix.toJSON(),null!==this.instanceColor&&(r.instanceColor=this.instanceColor.toJSON())),this.isScene)this.background&&(this.background.isColor?r.background=this.background.toJSON():this.background.isTexture&&(r.background=this.background.toJSON(e).uuid)),this.environment&&this.environment.isTexture&&!0!==this.environment.isRenderTargetTexture&&(r.environment=this.environment.toJSON(e).uuid);else if(this.isMesh||this.isLine||this.isPoints){r.geometry=i(e.geometries,this.geometry);var A=this.geometry.parameters;if(void 0!==A&&void 0!==A.shapes){var a=A.shapes;if(Array.isArray(a))for(var o=0,s=a.length;o<s;o++){var l=a[o];i(e.shapes,l)}else i(e.shapes,a)}}if(this.isSkinnedMesh&&(r.bindMode=this.bindMode,r.bindMatrix=this.bindMatrix.toArray(),void 0!==this.skeleton&&(i(e.skeletons,this.skeleton),r.skeleton=this.skeleton.uuid)),void 0!==this.material)if(Array.isArray(this.material)){for(var u=[],c=0,d=this.material.length;c<d;c++)u.push(i(e.materials,this.material[c]));r.material=u}else r.material=i(e.materials,this.material);if(this.children.length>0){r.children=[];for(var h=0;h<this.children.length;h++)r.children.push(this.children[h].toJSON(e).object)}if(this.animations.length>0){r.animations=[];for(var f=0;f<this.animations.length;f++){var p=this.animations[f];r.animations.push(i(e.animations,p))}}if(t){var g=x(e.geometries),m=x(e.materials),v=x(e.textures),y=x(e.images),w=x(e.shapes),B=x(e.skeletons),_=x(e.animations),b=x(e.nodes);g.length>0&&(n.geometries=g),m.length>0&&(n.materials=m),v.length>0&&(n.textures=v),y.length>0&&(n.images=y),w.length>0&&(n.shapes=w),B.length>0&&(n.skeletons=B),_.length>0&&(n.animations=_),b.length>0&&(n.nodes=b)}return n.object=r,n;function x(e){var t=[];for(var n in e){var r=e[n];delete r.metadata,t.push(r)}return t}}},{key:"clone",value:function(e){return(new this.constructor).copy(this,e)}},{key:"copy",value:function(e){var t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1];if(this.name=e.name,this.up.copy(e.up),this.position.copy(e.position),this.rotation.order=e.rotation.order,this.quaternion.copy(e.quaternion),this.scale.copy(e.scale),this.matrix.copy(e.matrix),this.matrixWorld.copy(e.matrixWorld),this.matrixAutoUpdate=e.matrixAutoUpdate,this.matrixWorldNeedsUpdate=e.matrixWorldNeedsUpdate,this.matrixWorldAutoUpdate=e.matrixWorldAutoUpdate,this.layers.mask=e.layers.mask,this.visible=e.visible,this.castShadow=e.castShadow,this.receiveShadow=e.receiveShadow,this.frustumCulled=e.frustumCulled,this.renderOrder=e.renderOrder,this.userData=JSON.parse(JSON.stringify(e.userData)),!0===t)for(var n=0;n<e.children.length;n++){var r=e.children[n];this.add(r.clone())}return this}}]),n}(Eg);gv.DEFAULT_UP=new pm(0,1,0),gv.DEFAULT_MATRIX_AUTO_UPDATE=!0,gv.DEFAULT_MATRIX_WORLD_AUTO_UPDATE=!0;var mv=new pm,vv=new pm,yv=new pm,wv=new pm,Bv=new pm,_v=new pm,bv=new pm,xv=new pm,Cv=new pm,Sv=new pm,Ev=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new pm,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new pm,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:new pm;v(this,e),this.a=t,this.b=n,this.c=r}return w(e,[{key:"set",value:function(e,t,n){return this.a.copy(e),this.b.copy(t),this.c.copy(n),this}},{key:"setFromPointsAndIndices",value:function(e,t,n,r){return this.a.copy(e[t]),this.b.copy(e[n]),this.c.copy(e[r]),this}},{key:"setFromAttributeAndIndices",value:function(e,t,n,r){return this.a.fromBufferAttribute(e,t),this.b.fromBufferAttribute(e,n),this.c.fromBufferAttribute(e,r),this}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){return this.a.copy(e.a),this.b.copy(e.b),this.c.copy(e.c),this}},{key:"getArea",value:function(){return mv.subVectors(this.c,this.b),vv.subVectors(this.a,this.b),.5*mv.cross(vv).length()}},{key:"getMidpoint",value:function(e){return e.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}},{key:"getNormal",value:function(t){return e.getNormal(this.a,this.b,this.c,t)}},{key:"getPlane",value:function(e){return e.setFromCoplanarPoints(this.a,this.b,this.c)}},{key:"getBarycoord",value:function(t,n){return e.getBarycoord(t,this.a,this.b,this.c,n)}},{key:"getUV",value:function(t,n,r,i,A){return e.getUV(t,this.a,this.b,this.c,n,r,i,A)}},{key:"containsPoint",value:function(t){return e.containsPoint(t,this.a,this.b,this.c)}},{key:"isFrontFacing",value:function(t){return e.isFrontFacing(this.a,this.b,this.c,t)}},{key:"intersectsBox",value:function(e){return e.intersectsTriangle(this)}},{key:"closestPointToPoint",value:function(e,t){var n,r,i=this.a,A=this.b,a=this.c;Bv.subVectors(A,i),_v.subVectors(a,i),xv.subVectors(e,i);var o=Bv.dot(xv),s=_v.dot(xv);if(o<=0&&s<=0)return t.copy(i);Cv.subVectors(e,A);var l=Bv.dot(Cv),u=_v.dot(Cv);if(l>=0&&u<=l)return t.copy(A);var c=o*u-l*s;if(c<=0&&o>=0&&l<=0)return n=o/(o-l),t.copy(i).addScaledVector(Bv,n);Sv.subVectors(e,a);var d=Bv.dot(Sv),h=_v.dot(Sv);if(h>=0&&d<=h)return t.copy(a);var f=d*s-o*h;if(f<=0&&s>=0&&h<=0)return r=s/(s-h),t.copy(i).addScaledVector(_v,r);var p=l*h-d*u;if(p<=0&&u-l>=0&&d-h>=0)return bv.subVectors(a,A),r=(u-l)/(u-l+(d-h)),t.copy(A).addScaledVector(bv,r);var g=1/(p+f+c);return n=f*g,r=c*g,t.copy(i).addScaledVector(Bv,n).addScaledVector(_v,r)}},{key:"equals",value:function(e){return e.a.equals(this.a)&&e.b.equals(this.b)&&e.c.equals(this.c)}}],[{key:"getNormal",value:function(e,t,n,r){r.subVectors(n,t),mv.subVectors(e,t),r.cross(mv);var i=r.lengthSq();return i>0?r.multiplyScalar(1/Math.sqrt(i)):r.set(0,0,0)}},{key:"getBarycoord",value:function(e,t,n,r,i){mv.subVectors(r,t),vv.subVectors(n,t),yv.subVectors(e,t);var A=mv.dot(mv),a=mv.dot(vv),o=mv.dot(yv),s=vv.dot(vv),l=vv.dot(yv),u=A*s-a*a;if(0===u)return i.set(-2,-1,-1);var c=1/u,d=(s*o-a*l)*c,h=(A*l-a*o)*c;return i.set(1-d-h,h,d)}},{key:"containsPoint",value:function(e,t,n,r){return this.getBarycoord(e,t,n,r,wv),wv.x>=0&&wv.y>=0&&wv.x+wv.y<=1}},{key:"getUV",value:function(e,t,n,r,i,A,a,o){return this.getBarycoord(e,t,n,r,wv),o.set(0,0),o.addScaledVector(i,wv.x),o.addScaledVector(A,wv.y),o.addScaledVector(a,wv.z),o}},{key:"isFrontFacing",value:function(e,t,n,r){return mv.subVectors(n,t),vv.subVectors(e,t),mv.cross(vv).dot(r)<0}}]),e}(),Uv=0,Mv=function(e){_(n,e);var t=S(n);function n(){var e;return v(this,n),(e=t.call(this)).isMaterial=!0,Object.defineProperty(C(e),"id",{value:Uv++}),e.uuid=kg(),e.name="",e.type="Material",e.blending=cf,e.side=of,e.vertexColors=!1,e.opacity=1,e.transparent=!1,e.blendSrc=Cf,e.blendDst=Sf,e.blendEquation=gf,e.blendSrcAlpha=null,e.blendDstAlpha=null,e.blendEquationAlpha=null,e.depthFunc=Df,e.depthTest=!0,e.depthWrite=!0,e.stencilWriteMask=255,e.stencilFunc=bg,e.stencilRef=0,e.stencilFuncMask=255,e.stencilFail=_g,e.stencilZFail=_g,e.stencilZPass=_g,e.stencilWrite=!1,e.clippingPlanes=null,e.clipIntersection=!1,e.clipShadows=!1,e.shadowSide=null,e.colorWrite=!0,e.precision=null,e.polygonOffset=!1,e.polygonOffsetFactor=0,e.polygonOffsetUnits=0,e.dithering=!1,e.alphaToCoverage=!1,e.premultipliedAlpha=!1,e.forceSinglePass=!1,e.visible=!0,e.toneMapped=!0,e.userData={},e.version=0,e._alphaTest=0,e}return w(n,[{key:"alphaTest",get:function(){return this._alphaTest},set:function(e){this._alphaTest>0!==e>0&&this.version++,this._alphaTest=e}},{key:"onBuild",value:function(){}},{key:"onBeforeRender",value:function(){}},{key:"onBeforeCompile",value:function(){}},{key:"customProgramCacheKey",value:function(){return this.onBeforeCompile.toString()}},{key:"setValues",value:function(e){if(void 0!==e)for(var t in e){var n=e[t];if(void 0!==n){var r=this[t];void 0!==r?r&&r.isColor?r.set(n):r&&r.isVector3&&n&&n.isVector3?r.copy(n):this[t]=n:console.warn("THREE."+this.type+": '"+t+"' is not a property of this material.")}else console.warn("THREE.Material: '"+t+"' parameter is undefined.")}}},{key:"toJSON",value:function(e){var t=void 0===e||"string"===typeof e;t&&(e={textures:{},images:{}});var n={metadata:{version:4.5,type:"Material",generator:"Material.toJSON"}};function r(e){var t=[];for(var n in e){var r=e[n];delete r.metadata,t.push(r)}return t}if(n.uuid=this.uuid,n.type=this.type,""!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),void 0!==this.sheen&&(n.sheen=this.sheen),this.sheenColor&&this.sheenColor.isColor&&(n.sheenColor=this.sheenColor.getHex()),void 0!==this.sheenRoughness&&(n.sheenRoughness=this.sheenRoughness),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularColor&&this.specularColor.isColor&&(n.specularColor=this.specularColor.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(e).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(e).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(e).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),void 0!==this.iridescence&&(n.iridescence=this.iridescence),void 0!==this.iridescenceIOR&&(n.iridescenceIOR=this.iridescenceIOR),void 0!==this.iridescenceThicknessRange&&(n.iridescenceThicknessRange=this.iridescenceThicknessRange),this.iridescenceMap&&this.iridescenceMap.isTexture&&(n.iridescenceMap=this.iridescenceMap.toJSON(e).uuid),this.iridescenceThicknessMap&&this.iridescenceThicknessMap.isTexture&&(n.iridescenceThicknessMap=this.iridescenceThicknessMap.toJSON(e).uuid),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(e).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(e).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(e).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(e).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(e).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(e).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(e).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(e).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(e).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(e).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(e).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(e).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(e).uuid),this.specularColorMap&&this.specularColorMap.isTexture&&(n.specularColorMap=this.specularColorMap.toJSON(e).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(e).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(e).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(e).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(e).uuid),void 0!==this.attenuationDistance&&this.attenuationDistance!==1/0&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationColor&&(n.attenuationColor=this.attenuationColor.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),this.blending!==cf&&(n.blending=this.blending),this.side!==of&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,void 0!==this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),void 0!==this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.forceSinglePass&&(n.forceSinglePass=this.forceSinglePass),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),"round"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),"round"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),!1===this.fog&&(n.fog=!1),Object.keys(this.userData).length>0&&(n.userData=this.userData),t){var i=r(e.textures),A=r(e.images);i.length>0&&(n.textures=i),A.length>0&&(n.images=A)}return n}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){this.name=e.name,this.blending=e.blending,this.side=e.side,this.vertexColors=e.vertexColors,this.opacity=e.opacity,this.transparent=e.transparent,this.blendSrc=e.blendSrc,this.blendDst=e.blendDst,this.blendEquation=e.blendEquation,this.blendSrcAlpha=e.blendSrcAlpha,this.blendDstAlpha=e.blendDstAlpha,this.blendEquationAlpha=e.blendEquationAlpha,this.depthFunc=e.depthFunc,this.depthTest=e.depthTest,this.depthWrite=e.depthWrite,this.stencilWriteMask=e.stencilWriteMask,this.stencilFunc=e.stencilFunc,this.stencilRef=e.stencilRef,this.stencilFuncMask=e.stencilFuncMask,this.stencilFail=e.stencilFail,this.stencilZFail=e.stencilZFail,this.stencilZPass=e.stencilZPass,this.stencilWrite=e.stencilWrite;var t=e.clippingPlanes,n=null;if(null!==t){var r=t.length;n=new Array(r);for(var i=0;i!==r;++i)n[i]=t[i].clone()}return this.clippingPlanes=n,this.clipIntersection=e.clipIntersection,this.clipShadows=e.clipShadows,this.shadowSide=e.shadowSide,this.colorWrite=e.colorWrite,this.precision=e.precision,this.polygonOffset=e.polygonOffset,this.polygonOffsetFactor=e.polygonOffsetFactor,this.polygonOffsetUnits=e.polygonOffsetUnits,this.dithering=e.dithering,this.alphaTest=e.alphaTest,this.alphaToCoverage=e.alphaToCoverage,this.premultipliedAlpha=e.premultipliedAlpha,this.forceSinglePass=e.forceSinglePass,this.visible=e.visible,this.toneMapped=e.toneMapped,this.userData=JSON.parse(JSON.stringify(e.userData)),this}},{key:"dispose",value:function(){this.dispatchEvent({type:"dispose"})}},{key:"needsUpdate",set:function(e){!0===e&&this.version++}}]),n}(Eg),Fv=function(e){_(n,e);var t=S(n);function n(e){var r;return v(this,n),(r=t.call(this)).isMeshBasicMaterial=!0,r.type="MeshBasicMaterial",r.color=new im(16777215),r.map=null,r.lightMap=null,r.lightMapIntensity=1,r.aoMap=null,r.aoMapIntensity=1,r.specularMap=null,r.alphaMap=null,r.envMap=null,r.combine=Nf,r.reflectivity=1,r.refractionRatio=.98,r.wireframe=!1,r.wireframeLinewidth=1,r.wireframeLinecap="round",r.wireframeLinejoin="round",r.fog=!0,r.setValues(e),r}return w(n,[{key:"copy",value:function(e){return jc(b(n.prototype),"copy",this).call(this,e),this.color.copy(e.color),this.map=e.map,this.lightMap=e.lightMap,this.lightMapIntensity=e.lightMapIntensity,this.aoMap=e.aoMap,this.aoMapIntensity=e.aoMapIntensity,this.specularMap=e.specularMap,this.alphaMap=e.alphaMap,this.envMap=e.envMap,this.combine=e.combine,this.reflectivity=e.reflectivity,this.refractionRatio=e.refractionRatio,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.wireframeLinecap=e.wireframeLinecap,this.wireframeLinejoin=e.wireframeLinejoin,this.fog=e.fog,this}}]),n}(Mv),Tv=new pm,kv=new Vg,Qv=function(){function e(t,n){var r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if(v(this,e),Array.isArray(t))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");this.isBufferAttribute=!0,this.name="",this.array=t,this.itemSize=n,this.count=void 0!==t?t.length/n:0,this.normalized=r,this.usage=xg,this.updateRange={offset:0,count:-1},this.version=0}return w(e,[{key:"onUploadCallback",value:function(){}},{key:"needsUpdate",set:function(e){!0===e&&this.version++}},{key:"setUsage",value:function(e){return this.usage=e,this}},{key:"copy",value:function(e){return this.name=e.name,this.array=new e.array.constructor(e.array),this.itemSize=e.itemSize,this.count=e.count,this.normalized=e.normalized,this.usage=e.usage,this}},{key:"copyAt",value:function(e,t,n){e*=this.itemSize,n*=t.itemSize;for(var r=0,i=this.itemSize;r<i;r++)this.array[e+r]=t.array[n+r];return this}},{key:"copyArray",value:function(e){return this.array.set(e),this}},{key:"applyMatrix3",value:function(e){if(2===this.itemSize)for(var t=0,n=this.count;t<n;t++)kv.fromBufferAttribute(this,t),kv.applyMatrix3(e),this.setXY(t,kv.x,kv.y);else if(3===this.itemSize)for(var r=0,i=this.count;r<i;r++)Tv.fromBufferAttribute(this,r),Tv.applyMatrix3(e),this.setXYZ(r,Tv.x,Tv.y,Tv.z);return this}},{key:"applyMatrix4",value:function(e){for(var t=0,n=this.count;t<n;t++)Tv.fromBufferAttribute(this,t),Tv.applyMatrix4(e),this.setXYZ(t,Tv.x,Tv.y,Tv.z);return this}},{key:"applyNormalMatrix",value:function(e){for(var t=0,n=this.count;t<n;t++)Tv.fromBufferAttribute(this,t),Tv.applyNormalMatrix(e),this.setXYZ(t,Tv.x,Tv.y,Tv.z);return this}},{key:"transformDirection",value:function(e){for(var t=0,n=this.count;t<n;t++)Tv.fromBufferAttribute(this,t),Tv.transformDirection(e),this.setXYZ(t,Tv.x,Tv.y,Tv.z);return this}},{key:"set",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;return this.array.set(e,t),this}},{key:"getX",value:function(e){var t=this.array[e*this.itemSize];return this.normalized&&(t=Pg(t,this.array)),t}},{key:"setX",value:function(e,t){return this.normalized&&(t=Ng(t,this.array)),this.array[e*this.itemSize]=t,this}},{key:"getY",value:function(e){var t=this.array[e*this.itemSize+1];return this.normalized&&(t=Pg(t,this.array)),t}},{key:"setY",value:function(e,t){return this.normalized&&(t=Ng(t,this.array)),this.array[e*this.itemSize+1]=t,this}},{key:"getZ",value:function(e){var t=this.array[e*this.itemSize+2];return this.normalized&&(t=Pg(t,this.array)),t}},{key:"setZ",value:function(e,t){return this.normalized&&(t=Ng(t,this.array)),this.array[e*this.itemSize+2]=t,this}},{key:"getW",value:function(e){var t=this.array[e*this.itemSize+3];return this.normalized&&(t=Pg(t,this.array)),t}},{key:"setW",value:function(e,t){return this.normalized&&(t=Ng(t,this.array)),this.array[e*this.itemSize+3]=t,this}},{key:"setXY",value:function(e,t,n){return e*=this.itemSize,this.normalized&&(t=Ng(t,this.array),n=Ng(n,this.array)),this.array[e+0]=t,this.array[e+1]=n,this}},{key:"setXYZ",value:function(e,t,n,r){return e*=this.itemSize,this.normalized&&(t=Ng(t,this.array),n=Ng(n,this.array),r=Ng(r,this.array)),this.array[e+0]=t,this.array[e+1]=n,this.array[e+2]=r,this}},{key:"setXYZW",value:function(e,t,n,r,i){return e*=this.itemSize,this.normalized&&(t=Ng(t,this.array),n=Ng(n,this.array),r=Ng(r,this.array),i=Ng(i,this.array)),this.array[e+0]=t,this.array[e+1]=n,this.array[e+2]=r,this.array[e+3]=i,this}},{key:"onUpload",value:function(e){return this.onUploadCallback=e,this}},{key:"clone",value:function(){return new this.constructor(this.array,this.itemSize).copy(this)}},{key:"toJSON",value:function(){var e={itemSize:this.itemSize,type:this.array.constructor.name,array:Array.from(this.array),normalized:this.normalized};return""!==this.name&&(e.name=this.name),this.usage!==xg&&(e.usage=this.usage),0===this.updateRange.offset&&-1===this.updateRange.count||(e.updateRange=this.updateRange),e}},{key:"copyColorsArray",value:function(){console.error("THREE.BufferAttribute: copyColorsArray() was removed in r144.")}},{key:"copyVector2sArray",value:function(){console.error("THREE.BufferAttribute: copyVector2sArray() was removed in r144.")}},{key:"copyVector3sArray",value:function(){console.error("THREE.BufferAttribute: copyVector3sArray() was removed in r144.")}},{key:"copyVector4sArray",value:function(){console.error("THREE.BufferAttribute: copyVector4sArray() was removed in r144.")}}]),e}(),Lv=function(e){_(n,e);var t=S(n);function n(e,r,i){return v(this,n),t.call(this,new Uint16Array(e),r,i)}return w(n)}(Qv),Dv=function(e){_(n,e);var t=S(n);function n(e,r,i){return v(this,n),t.call(this,new Uint32Array(e),r,i)}return w(n)}(Qv),Iv=function(e){_(n,e);var t=S(n);function n(e,r,i){return v(this,n),t.call(this,new Float32Array(e),r,i)}return w(n)}(Qv),Rv=0,Hv=new Km,Pv=new gv,Nv=new pm,Ov=new vm,Vv=new vm,zv=new pm,Gv=function(e){_(n,e);var t=S(n);function n(){var e;return v(this,n),(e=t.call(this)).isBufferGeometry=!0,Object.defineProperty(C(e),"id",{value:Rv++}),e.uuid=kg(),e.name="",e.type="BufferGeometry",e.index=null,e.attributes={},e.morphAttributes={},e.morphTargetsRelative=!1,e.groups=[],e.boundingBox=null,e.boundingSphere=null,e.drawRange={start:0,count:1/0},e.userData={},e}return w(n,[{key:"getIndex",value:function(){return this.index}},{key:"setIndex",value:function(e){return Array.isArray(e)?this.index=new(Kg(e)?Dv:Lv)(e,1):this.index=e,this}},{key:"getAttribute",value:function(e){return this.attributes[e]}},{key:"setAttribute",value:function(e,t){return this.attributes[e]=t,this}},{key:"deleteAttribute",value:function(e){return delete this.attributes[e],this}},{key:"hasAttribute",value:function(e){return void 0!==this.attributes[e]}},{key:"addGroup",value:function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0;this.groups.push({start:e,count:t,materialIndex:n})}},{key:"clearGroups",value:function(){this.groups=[]}},{key:"setDrawRange",value:function(e,t){this.drawRange.start=e,this.drawRange.count=t}},{key:"applyMatrix4",value:function(e){var t=this.attributes.position;void 0!==t&&(t.applyMatrix4(e),t.needsUpdate=!0);var n=this.attributes.normal;if(void 0!==n){var r=(new zg).getNormalMatrix(e);n.applyNormalMatrix(r),n.needsUpdate=!0}var i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(e),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}},{key:"applyQuaternion",value:function(e){return Hv.makeRotationFromQuaternion(e),this.applyMatrix4(Hv),this}},{key:"rotateX",value:function(e){return Hv.makeRotationX(e),this.applyMatrix4(Hv),this}},{key:"rotateY",value:function(e){return Hv.makeRotationY(e),this.applyMatrix4(Hv),this}},{key:"rotateZ",value:function(e){return Hv.makeRotationZ(e),this.applyMatrix4(Hv),this}},{key:"translate",value:function(e,t,n){return Hv.makeTranslation(e,t,n),this.applyMatrix4(Hv),this}},{key:"scale",value:function(e,t,n){return Hv.makeScale(e,t,n),this.applyMatrix4(Hv),this}},{key:"lookAt",value:function(e){return Pv.lookAt(e),Pv.updateMatrix(),this.applyMatrix4(Pv.matrix),this}},{key:"center",value:function(){return this.computeBoundingBox(),this.boundingBox.getCenter(Nv).negate(),this.translate(Nv.x,Nv.y,Nv.z),this}},{key:"setFromPoints",value:function(e){for(var t=[],n=0,r=e.length;n<r;n++){var i=e[n];t.push(i.x,i.y,i.z||0)}return this.setAttribute("position",new Iv(t,3)),this}},{key:"computeBoundingBox",value:function(){null===this.boundingBox&&(this.boundingBox=new vm);var e=this.attributes.position,t=this.morphAttributes.position;if(e&&e.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingBox.set(new pm(-1/0,-1/0,-1/0),new pm(1/0,1/0,1/0));if(void 0!==e){if(this.boundingBox.setFromBufferAttribute(e),t)for(var n=0,r=t.length;n<r;n++){var i=t[n];Ov.setFromBufferAttribute(i),this.morphTargetsRelative?(zv.addVectors(this.boundingBox.min,Ov.min),this.boundingBox.expandByPoint(zv),zv.addVectors(this.boundingBox.max,Ov.max),this.boundingBox.expandByPoint(zv)):(this.boundingBox.expandByPoint(Ov.min),this.boundingBox.expandByPoint(Ov.max))}}else this.boundingBox.makeEmpty();(isNaN(this.boundingBox.min.x)||isNaN(this.boundingBox.min.y)||isNaN(this.boundingBox.min.z))&&console.error('THREE.BufferGeometry.computeBoundingBox(): Computed min/max have NaN values. The "position" attribute is likely to have NaN values.',this)}},{key:"computeBoundingSphere",value:function(){null===this.boundingSphere&&(this.boundingSphere=new Im);var e=this.attributes.position,t=this.morphAttributes.position;if(e&&e.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingSphere.set(new pm,1/0);if(e){var n=this.boundingSphere.center;if(Ov.setFromBufferAttribute(e),t)for(var r=0,i=t.length;r<i;r++){var A=t[r];Vv.setFromBufferAttribute(A),this.morphTargetsRelative?(zv.addVectors(Ov.min,Vv.min),Ov.expandByPoint(zv),zv.addVectors(Ov.max,Vv.max),Ov.expandByPoint(zv)):(Ov.expandByPoint(Vv.min),Ov.expandByPoint(Vv.max))}Ov.getCenter(n);for(var a=0,o=0,s=e.count;o<s;o++)zv.fromBufferAttribute(e,o),a=Math.max(a,n.distanceToSquared(zv));if(t)for(var l=0,u=t.length;l<u;l++)for(var c=t[l],d=this.morphTargetsRelative,h=0,f=c.count;h<f;h++)zv.fromBufferAttribute(c,h),d&&(Nv.fromBufferAttribute(e,h),zv.add(Nv)),a=Math.max(a,n.distanceToSquared(zv));this.boundingSphere.radius=Math.sqrt(a),isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The "position" attribute is likely to have NaN values.',this)}}},{key:"computeTangents",value:function(){var e=this.index,t=this.attributes;if(null!==e&&void 0!==t.position&&void 0!==t.normal&&void 0!==t.uv){var n=e.array,r=t.position.array,i=t.normal.array,A=t.uv.array,a=r.length/3;!1===this.hasAttribute("tangent")&&this.setAttribute("tangent",new Qv(new Float32Array(4*a),4));for(var o=this.getAttribute("tangent").array,s=[],l=[],u=0;u<a;u++)s[u]=new pm,l[u]=new pm;var c=new pm,d=new pm,h=new pm,f=new Vg,p=new Vg,g=new Vg,m=new pm,v=new pm,y=this.groups;0===y.length&&(y=[{start:0,count:n.length}]);for(var w=0,B=y.length;w<B;++w)for(var _=y[w],b=_.start,x=b,C=b+_.count;x<C;x+=3)I(n[x+0],n[x+1],n[x+2]);for(var S=new pm,E=new pm,U=new pm,M=new pm,F=0,T=y.length;F<T;++F)for(var k=y[F],Q=k.start,L=Q,D=Q+k.count;L<D;L+=3)R(n[L+0]),R(n[L+1]),R(n[L+2])}else console.error("THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)");function I(e,t,n){c.fromArray(r,3*e),d.fromArray(r,3*t),h.fromArray(r,3*n),f.fromArray(A,2*e),p.fromArray(A,2*t),g.fromArray(A,2*n),d.sub(c),h.sub(c),p.sub(f),g.sub(f);var i=1/(p.x*g.y-g.x*p.y);isFinite(i)&&(m.copy(d).multiplyScalar(g.y).addScaledVector(h,-p.y).multiplyScalar(i),v.copy(h).multiplyScalar(p.x).addScaledVector(d,-g.x).multiplyScalar(i),s[e].add(m),s[t].add(m),s[n].add(m),l[e].add(v),l[t].add(v),l[n].add(v))}function R(e){U.fromArray(i,3*e),M.copy(U);var t=s[e];S.copy(t),S.sub(U.multiplyScalar(U.dot(t))).normalize(),E.crossVectors(M,t);var n=E.dot(l[e])<0?-1:1;o[4*e]=S.x,o[4*e+1]=S.y,o[4*e+2]=S.z,o[4*e+3]=n}}},{key:"computeVertexNormals",value:function(){var e=this.index,t=this.getAttribute("position");if(void 0!==t){var n=this.getAttribute("normal");if(void 0===n)n=new Qv(new Float32Array(3*t.count),3),this.setAttribute("normal",n);else for(var r=0,i=n.count;r<i;r++)n.setXYZ(r,0,0,0);var A=new pm,a=new pm,o=new pm,s=new pm,l=new pm,u=new pm,c=new pm,d=new pm;if(e)for(var h=0,f=e.count;h<f;h+=3){var p=e.getX(h+0),g=e.getX(h+1),m=e.getX(h+2);A.fromBufferAttribute(t,p),a.fromBufferAttribute(t,g),o.fromBufferAttribute(t,m),c.subVectors(o,a),d.subVectors(A,a),c.cross(d),s.fromBufferAttribute(n,p),l.fromBufferAttribute(n,g),u.fromBufferAttribute(n,m),s.add(c),l.add(c),u.add(c),n.setXYZ(p,s.x,s.y,s.z),n.setXYZ(g,l.x,l.y,l.z),n.setXYZ(m,u.x,u.y,u.z)}else for(var v=0,y=t.count;v<y;v+=3)A.fromBufferAttribute(t,v+0),a.fromBufferAttribute(t,v+1),o.fromBufferAttribute(t,v+2),c.subVectors(o,a),d.subVectors(A,a),c.cross(d),n.setXYZ(v+0,c.x,c.y,c.z),n.setXYZ(v+1,c.x,c.y,c.z),n.setXYZ(v+2,c.x,c.y,c.z);this.normalizeNormals(),n.needsUpdate=!0}}},{key:"merge",value:function(){return console.error("THREE.BufferGeometry.merge() has been removed. Use THREE.BufferGeometryUtils.mergeBufferGeometries() instead."),this}},{key:"normalizeNormals",value:function(){for(var e=this.attributes.normal,t=0,n=e.count;t<n;t++)zv.fromBufferAttribute(e,t),zv.normalize(),e.setXYZ(t,zv.x,zv.y,zv.z)}},{key:"toNonIndexed",value:function(){function e(e,t){for(var n=e.array,r=e.itemSize,i=e.normalized,A=new n.constructor(t.length*r),a=0,o=0,s=0,l=t.length;s<l;s++){a=e.isInterleavedBufferAttribute?t[s]*e.data.stride+e.offset:t[s]*r;for(var u=0;u<r;u++)A[o++]=n[a++]}return new Qv(A,r,i)}if(null===this.index)return console.warn("THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed."),this;var t=new n,r=this.index.array,i=this.attributes;for(var A in i){var a=e(i[A],r);t.setAttribute(A,a)}var o=this.morphAttributes;for(var s in o){for(var l=[],u=o[s],c=0,d=u.length;c<d;c++){var h=e(u[c],r);l.push(h)}t.morphAttributes[s]=l}t.morphTargetsRelative=this.morphTargetsRelative;for(var f=this.groups,p=0,g=f.length;p<g;p++){var m=f[p];t.addGroup(m.start,m.count,m.materialIndex)}return t}},{key:"toJSON",value:function(){var e={metadata:{version:4.5,type:"BufferGeometry",generator:"BufferGeometry.toJSON"}};if(e.uuid=this.uuid,e.type=this.type,""!==this.name&&(e.name=this.name),Object.keys(this.userData).length>0&&(e.userData=this.userData),void 0!==this.parameters){var t=this.parameters;for(var n in t)void 0!==t[n]&&(e[n]=t[n]);return e}e.data={attributes:{}};var r=this.index;null!==r&&(e.data.index={type:r.array.constructor.name,array:Array.prototype.slice.call(r.array)});var i=this.attributes;for(var A in i){var a=i[A];e.data.attributes[A]=a.toJSON(e.data)}var o={},s=!1;for(var l in this.morphAttributes){for(var u=this.morphAttributes[l],c=[],d=0,h=u.length;d<h;d++){var f=u[d];c.push(f.toJSON(e.data))}c.length>0&&(o[l]=c,s=!0)}s&&(e.data.morphAttributes=o,e.data.morphTargetsRelative=this.morphTargetsRelative);var p=this.groups;p.length>0&&(e.data.groups=JSON.parse(JSON.stringify(p)));var g=this.boundingSphere;return null!==g&&(e.data.boundingSphere={center:g.center.toArray(),radius:g.radius}),e}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"copy",value:function(e){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;var t={};this.name=e.name;var n=e.index;null!==n&&this.setIndex(n.clone(t));var r=e.attributes;for(var i in r){var A=r[i];this.setAttribute(i,A.clone(t))}var a=e.morphAttributes;for(var o in a){for(var s=[],l=a[o],u=0,c=l.length;u<c;u++)s.push(l[u].clone(t));this.morphAttributes[o]=s}this.morphTargetsRelative=e.morphTargetsRelative;for(var d=e.groups,h=0,f=d.length;h<f;h++){var p=d[h];this.addGroup(p.start,p.count,p.materialIndex)}var g=e.boundingBox;null!==g&&(this.boundingBox=g.clone());var m=e.boundingSphere;return null!==m&&(this.boundingSphere=m.clone()),this.drawRange.start=e.drawRange.start,this.drawRange.count=e.drawRange.count,this.userData=e.userData,void 0!==e.parameters&&(this.parameters=Object.assign({},e.parameters)),this}},{key:"dispose",value:function(){this.dispatchEvent({type:"dispose"})}}]),n}(Eg),Kv=new Km,Wv=new Gm,jv=new Im,Xv=new pm,qv=new pm,Yv=new pm,Jv=new pm,Zv=new pm,$v=new Vg,ey=new Vg,ty=new Vg,ny=new pm,ry=new pm,iy=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new Gv,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new Fv;return v(this,n),(e=t.call(this)).isMesh=!0,e.type="Mesh",e.geometry=r,e.material=i,e.updateMorphTargets(),e}return w(n,[{key:"copy",value:function(e,t){return jc(b(n.prototype),"copy",this).call(this,e,t),void 0!==e.morphTargetInfluences&&(this.morphTargetInfluences=e.morphTargetInfluences.slice()),void 0!==e.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},e.morphTargetDictionary)),this.material=e.material,this.geometry=e.geometry,this}},{key:"updateMorphTargets",value:function(){var e=this.geometry.morphAttributes,t=Object.keys(e);if(t.length>0){var n=e[t[0]];if(void 0!==n){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(var r=0,i=n.length;r<i;r++){var A=n[r].name||String(r);this.morphTargetInfluences.push(0),this.morphTargetDictionary[A]=r}}}}},{key:"getVertexPosition",value:function(e,t){var n=this.geometry,r=n.attributes.position,i=n.morphAttributes.position,A=n.morphTargetsRelative;t.fromBufferAttribute(r,e);var a=this.morphTargetInfluences;if(i&&a){Zv.set(0,0,0);for(var o=0,s=i.length;o<s;o++){var l=a[o],u=i[o];0!==l&&(Jv.fromBufferAttribute(u,e),A?Zv.addScaledVector(Jv,l):Zv.addScaledVector(Jv.sub(t),l))}t.add(Zv)}return this.isSkinnedMesh&&this.boneTransform(e,t),t}},{key:"raycast",value:function(e,t){var n=this.geometry,r=this.material,i=this.matrixWorld;if(void 0!==r&&(null===n.boundingSphere&&n.computeBoundingSphere(),jv.copy(n.boundingSphere),jv.applyMatrix4(i),!1!==e.ray.intersectsSphere(jv)&&(Kv.copy(i).invert(),Wv.copy(e.ray).applyMatrix4(Kv),null===n.boundingBox||!1!==Wv.intersectsBox(n.boundingBox)))){var A,a=n.index,o=n.attributes.position,s=n.attributes.uv,l=n.attributes.uv2,u=n.groups,c=n.drawRange;if(null!==a)if(Array.isArray(r))for(var d=0,h=u.length;d<h;d++)for(var f=u[d],p=r[f.materialIndex],g=Math.max(f.start,c.start),m=Math.min(a.count,Math.min(f.start+f.count,c.start+c.count));g<m;g+=3){var v=a.getX(g),y=a.getX(g+1),w=a.getX(g+2);(A=Ay(this,p,e,Wv,s,l,v,y,w))&&(A.faceIndex=Math.floor(g/3),A.face.materialIndex=f.materialIndex,t.push(A))}else for(var B=Math.max(0,c.start),_=Math.min(a.count,c.start+c.count);B<_;B+=3){var b=a.getX(B),x=a.getX(B+1),C=a.getX(B+2);(A=Ay(this,r,e,Wv,s,l,b,x,C))&&(A.faceIndex=Math.floor(B/3),t.push(A))}else if(void 0!==o)if(Array.isArray(r))for(var S=0,E=u.length;S<E;S++)for(var U=u[S],M=r[U.materialIndex],F=Math.max(U.start,c.start),T=Math.min(o.count,Math.min(U.start+U.count,c.start+c.count));F<T;F+=3){(A=Ay(this,M,e,Wv,s,l,F,F+1,F+2))&&(A.faceIndex=Math.floor(F/3),A.face.materialIndex=U.materialIndex,t.push(A))}else for(var k=Math.max(0,c.start),Q=Math.min(o.count,c.start+c.count);k<Q;k+=3){(A=Ay(this,r,e,Wv,s,l,k,k+1,k+2))&&(A.faceIndex=Math.floor(k/3),t.push(A))}}}}]),n}(gv);function Ay(e,t,n,r,i,A,a,o,s){e.getVertexPosition(a,Xv),e.getVertexPosition(o,qv),e.getVertexPosition(s,Yv);var l=function(e,t,n,r,i,A,a,o){if(null===(t.side===sf?r.intersectTriangle(a,A,i,!0,o):r.intersectTriangle(i,A,a,t.side===of,o)))return null;ry.copy(o),ry.applyMatrix4(e.matrixWorld);var s=n.ray.origin.distanceTo(ry);return s<n.near||s>n.far?null:{distance:s,point:ry.clone(),object:e}}(e,t,n,r,Xv,qv,Yv,ny);if(l){i&&($v.fromBufferAttribute(i,a),ey.fromBufferAttribute(i,o),ty.fromBufferAttribute(i,s),l.uv=Ev.getUV(ny,Xv,qv,Yv,$v,ey,ty,new Vg)),A&&($v.fromBufferAttribute(A,a),ey.fromBufferAttribute(A,o),ty.fromBufferAttribute(A,s),l.uv2=Ev.getUV(ny,Xv,qv,Yv,$v,ey,ty,new Vg));var u={a:a,b:o,c:s,normal:new pm,materialIndex:0};Ev.getNormal(Xv,qv,Yv,u.normal),l.face=u}return l}var ay=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1,o=arguments.length>4&&void 0!==arguments[4]?arguments[4]:1,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:1;v(this,n),(e=t.call(this)).type="BoxGeometry",e.parameters={width:r,height:i,depth:A,widthSegments:a,heightSegments:o,depthSegments:s};var l=C(e);a=Math.floor(a),o=Math.floor(o),s=Math.floor(s);var u=[],c=[],d=[],h=[],f=0,p=0;function g(e,t,n,r,i,A,a,o,s,g,m){for(var v=A/s,y=a/g,w=A/2,B=a/2,_=o/2,b=s+1,x=g+1,C=0,S=0,E=new pm,U=0;U<x;U++)for(var M=U*y-B,F=0;F<b;F++){var T=F*v-w;E[e]=T*r,E[t]=M*i,E[n]=_,c.push(E.x,E.y,E.z),E[e]=0,E[t]=0,E[n]=o>0?1:-1,d.push(E.x,E.y,E.z),h.push(F/s),h.push(1-U/g),C+=1}for(var k=0;k<g;k++)for(var Q=0;Q<s;Q++){var L=f+Q+b*k,D=f+Q+b*(k+1),I=f+(Q+1)+b*(k+1),R=f+(Q+1)+b*k;u.push(L,D,R),u.push(D,I,R),S+=6}l.addGroup(p,S,m),p+=S,f+=C}return g("z","y","x",-1,-1,A,i,r,s,o,0),g("z","y","x",1,-1,A,i,-r,s,o,1),g("x","z","y",1,1,r,A,i,a,s,2),g("x","z","y",1,-1,r,A,-i,a,s,3),g("x","y","z",1,-1,r,i,A,a,o,4),g("x","y","z",-1,-1,r,i,-A,a,o,5),e.setIndex(u),e.setAttribute("position",new Iv(c,3)),e.setAttribute("normal",new Iv(d,3)),e.setAttribute("uv",new Iv(h,2)),e}return w(n,null,[{key:"fromJSON",value:function(e){return new n(e.width,e.height,e.depth,e.widthSegments,e.heightSegments,e.depthSegments)}}]),n}(Gv);function oy(e){var t={};for(var n in e)for(var r in t[n]={},e[n]){var i=e[n][r];i&&(i.isColor||i.isMatrix3||i.isMatrix4||i.isVector2||i.isVector3||i.isVector4||i.isTexture||i.isQuaternion)?t[n][r]=i.clone():Array.isArray(i)?t[n][r]=i.slice():t[n][r]=i}return t}function sy(e){for(var t={},n=0;n<e.length;n++){var r=oy(e[n]);for(var i in r)t[i]=r[i]}return t}function ly(e){return null===e.getRenderTarget()&&e.outputEncoding===pg?wg:Bg}var uy={clone:oy,merge:sy},cy="void main() {\n\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n}",dy="void main() {\n\tgl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );\n}",hy=function(e){_(n,e);var t=S(n);function n(e){var r;return v(this,n),(r=t.call(this)).isShaderMaterial=!0,r.type="ShaderMaterial",r.defines={},r.uniforms={},r.uniformsGroups=[],r.vertexShader=cy,r.fragmentShader=dy,r.linewidth=1,r.wireframe=!1,r.wireframeLinewidth=1,r.fog=!1,r.lights=!1,r.clipping=!1,r.extensions={derivatives:!1,fragDepth:!1,drawBuffers:!1,shaderTextureLOD:!1},r.defaultAttributeValues={color:[1,1,1],uv:[0,0],uv2:[0,0]},r.index0AttributeName=void 0,r.uniformsNeedUpdate=!1,r.glslVersion=null,void 0!==e&&r.setValues(e),r}return w(n,[{key:"copy",value:function(e){return jc(b(n.prototype),"copy",this).call(this,e),this.fragmentShader=e.fragmentShader,this.vertexShader=e.vertexShader,this.uniforms=oy(e.uniforms),this.uniformsGroups=function(e){for(var t=[],n=0;n<e.length;n++)t.push(e[n].clone());return t}(e.uniformsGroups),this.defines=Object.assign({},e.defines),this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.fog=e.fog,this.lights=e.lights,this.clipping=e.clipping,this.extensions=Object.assign({},e.extensions),this.glslVersion=e.glslVersion,this}},{key:"toJSON",value:function(e){var t=jc(b(n.prototype),"toJSON",this).call(this,e);for(var r in t.glslVersion=this.glslVersion,t.uniforms={},this.uniforms){var i=this.uniforms[r].value;i&&i.isTexture?t.uniforms[r]={type:"t",value:i.toJSON(e).uuid}:i&&i.isColor?t.uniforms[r]={type:"c",value:i.getHex()}:i&&i.isVector2?t.uniforms[r]={type:"v2",value:i.toArray()}:i&&i.isVector3?t.uniforms[r]={type:"v3",value:i.toArray()}:i&&i.isVector4?t.uniforms[r]={type:"v4",value:i.toArray()}:i&&i.isMatrix3?t.uniforms[r]={type:"m3",value:i.toArray()}:i&&i.isMatrix4?t.uniforms[r]={type:"m4",value:i.toArray()}:t.uniforms[r]={value:i}}Object.keys(this.defines).length>0&&(t.defines=this.defines),t.vertexShader=this.vertexShader,t.fragmentShader=this.fragmentShader;var A={};for(var a in this.extensions)!0===this.extensions[a]&&(A[a]=!0);return Object.keys(A).length>0&&(t.extensions=A),t}}]),n}(Mv),fy=function(e){_(n,e);var t=S(n);function n(){var e;return v(this,n),(e=t.call(this)).isCamera=!0,e.type="Camera",e.matrixWorldInverse=new Km,e.projectionMatrix=new Km,e.projectionMatrixInverse=new Km,e}return w(n,[{key:"copy",value:function(e,t){return jc(b(n.prototype),"copy",this).call(this,e,t),this.matrixWorldInverse.copy(e.matrixWorldInverse),this.projectionMatrix.copy(e.projectionMatrix),this.projectionMatrixInverse.copy(e.projectionMatrixInverse),this}},{key:"getWorldDirection",value:function(e){this.updateWorldMatrix(!0,!1);var t=this.matrixWorld.elements;return e.set(-t[8],-t[9],-t[10]).normalize()}},{key:"updateMatrixWorld",value:function(e){jc(b(n.prototype),"updateMatrixWorld",this).call(this,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}},{key:"updateWorldMatrix",value:function(e,t){jc(b(n.prototype),"updateWorldMatrix",this).call(this,e,t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),n}(gv),py=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:50,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:.1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:2e3;return v(this,n),(e=t.call(this)).isPerspectiveCamera=!0,e.type="PerspectiveCamera",e.fov=r,e.zoom=1,e.near=A,e.far=a,e.focus=10,e.aspect=i,e.view=null,e.filmGauge=35,e.filmOffset=0,e.updateProjectionMatrix(),e}return w(n,[{key:"copy",value:function(e,t){return jc(b(n.prototype),"copy",this).call(this,e,t),this.fov=e.fov,this.zoom=e.zoom,this.near=e.near,this.far=e.far,this.focus=e.focus,this.aspect=e.aspect,this.view=null===e.view?null:Object.assign({},e.view),this.filmGauge=e.filmGauge,this.filmOffset=e.filmOffset,this}},{key:"setFocalLength",value:function(e){var t=.5*this.getFilmHeight()/e;this.fov=2*Tg*Math.atan(t),this.updateProjectionMatrix()}},{key:"getFocalLength",value:function(){var e=Math.tan(.5*Fg*this.fov);return.5*this.getFilmHeight()/e}},{key:"getEffectiveFOV",value:function(){return 2*Tg*Math.atan(Math.tan(.5*Fg*this.fov)/this.zoom)}},{key:"getFilmWidth",value:function(){return this.filmGauge*Math.min(this.aspect,1)}},{key:"getFilmHeight",value:function(){return this.filmGauge/Math.max(this.aspect,1)}},{key:"setViewOffset",value:function(e,t,n,r,i,A){this.aspect=e/t,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=e,this.view.fullHeight=t,this.view.offsetX=n,this.view.offsetY=r,this.view.width=i,this.view.height=A,this.updateProjectionMatrix()}},{key:"clearViewOffset",value:function(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}},{key:"updateProjectionMatrix",value:function(){var e=this.near,t=e*Math.tan(.5*Fg*this.fov)/this.zoom,n=2*t,r=this.aspect*n,i=-.5*r,A=this.view;if(null!==this.view&&this.view.enabled){var a=A.fullWidth,o=A.fullHeight;i+=A.offsetX*r/a,t-=A.offsetY*n/o,r*=A.width/a,n*=A.height/o}var s=this.filmOffset;0!==s&&(i+=e*s/this.getFilmWidth()),this.projectionMatrix.makePerspective(i,i+r,t,t-n,e,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}},{key:"toJSON",value:function(e){var t=jc(b(n.prototype),"toJSON",this).call(this,e);return t.object.fov=this.fov,t.object.zoom=this.zoom,t.object.near=this.near,t.object.far=this.far,t.object.focus=this.focus,t.object.aspect=this.aspect,null!==this.view&&(t.object.view=Object.assign({},this.view)),t.object.filmGauge=this.filmGauge,t.object.filmOffset=this.filmOffset,t}}]),n}(fy),gy=-90,my=1,vy=function(e){_(n,e);var t=S(n);function n(e,r,i){var A;v(this,n),(A=t.call(this)).type="CubeCamera",A.renderTarget=i;var a=new py(gy,my,e,r);a.layers=A.layers,a.up.set(0,1,0),a.lookAt(1,0,0),A.add(a);var o=new py(gy,my,e,r);o.layers=A.layers,o.up.set(0,1,0),o.lookAt(-1,0,0),A.add(o);var s=new py(gy,my,e,r);s.layers=A.layers,s.up.set(0,0,-1),s.lookAt(0,1,0),A.add(s);var l=new py(gy,my,e,r);l.layers=A.layers,l.up.set(0,0,1),l.lookAt(0,-1,0),A.add(l);var u=new py(gy,my,e,r);u.layers=A.layers,u.up.set(0,1,0),u.lookAt(0,0,1),A.add(u);var c=new py(gy,my,e,r);return c.layers=A.layers,c.up.set(0,1,0),c.lookAt(0,0,-1),A.add(c),A}return w(n,[{key:"update",value:function(e,t){null===this.parent&&this.updateMatrixWorld();var n=this.renderTarget,r=p(this.children,6),i=r[0],A=r[1],a=r[2],o=r[3],s=r[4],l=r[5],u=e.getRenderTarget(),c=e.toneMapping,d=e.xr.enabled;e.toneMapping=zf,e.xr.enabled=!1;var h=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,e.setRenderTarget(n,0),e.render(t,i),e.setRenderTarget(n,1),e.render(t,A),e.setRenderTarget(n,2),e.render(t,a),e.setRenderTarget(n,3),e.render(t,o),e.setRenderTarget(n,4),e.render(t,s),n.texture.generateMipmaps=h,e.setRenderTarget(n,5),e.render(t,l),e.setRenderTarget(u),e.toneMapping=c,e.xr.enabled=d,n.texture.needsPMREMUpdate=!0}}]),n}(gv),yy=function(e){_(n,e);var t=S(n);function n(e,r,i,A,a,o,s,l,u,c){var d;return v(this,n),e=void 0!==e?e:[],r=void 0!==r?r:qf,(d=t.call(this,e,r,i,A,a,o,s,l,u,c)).isCubeTexture=!0,d.flipY=!1,d}return w(n,[{key:"images",get:function(){return this.image},set:function(e){this.image=e}}]),n}(lm),wy=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};v(this,n),(e=t.call(this,r,r,i)).isWebGLCubeRenderTarget=!0;var A={width:r,height:r,depth:1},a=[A,A,A,A,A,A];return e.texture=new yy(a,i.mapping,i.wrapS,i.wrapT,i.magFilter,i.minFilter,i.format,i.type,i.anisotropy,i.encoding),e.texture.isRenderTargetTexture=!0,e.texture.generateMipmaps=void 0!==i.generateMipmaps&&i.generateMipmaps,e.texture.minFilter=void 0!==i.minFilter?i.minFilter:ap,e}return w(n,[{key:"fromEquirectangularTexture",value:function(e,t){this.texture.type=t.type,this.texture.encoding=t.encoding,this.texture.generateMipmaps=t.generateMipmaps,this.texture.minFilter=t.minFilter,this.texture.magFilter=t.magFilter;var n={tEquirect:{value:null}},r="\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\n\t\t\t\t\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n\n\t\t\t\t}\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvWorldDirection = transformDirection( position, modelMatrix );\n\n\t\t\t\t\t#include <begin_vertex>\n\t\t\t\t\t#include <project_vertex>\n\n\t\t\t\t}\n\t\t\t",i="\n\n\t\t\t\tuniform sampler2D tEquirect;\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\t#include <common>\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvec3 direction = normalize( vWorldDirection );\n\n\t\t\t\t\tvec2 sampleUV = equirectUv( direction );\n\n\t\t\t\t\tgl_FragColor = texture2D( tEquirect, sampleUV );\n\n\t\t\t\t}\n\t\t\t",A=new ay(5,5,5),a=new hy({name:"CubemapFromEquirect",uniforms:oy(n),vertexShader:r,fragmentShader:i,side:sf,blending:uf});a.uniforms.tEquirect.value=t;var o=new iy(A,a),s=t.minFilter;return t.minFilter===sp&&(t.minFilter=ap),new vy(1,10,this).update(e,o),t.minFilter=s,o.geometry.dispose(),o.material.dispose(),this}},{key:"clear",value:function(e,t,n,r){for(var i=e.getRenderTarget(),A=0;A<6;A++)e.setRenderTarget(this,A),e.clear(t,n,r);e.setRenderTarget(i)}}]),n}(cm),By=new pm,_y=new pm,by=new zg,xy=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new pm(1,0,0),n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0;v(this,e),this.isPlane=!0,this.normal=t,this.constant=n}return w(e,[{key:"set",value:function(e,t){return this.normal.copy(e),this.constant=t,this}},{key:"setComponents",value:function(e,t,n,r){return this.normal.set(e,t,n),this.constant=r,this}},{key:"setFromNormalAndCoplanarPoint",value:function(e,t){return this.normal.copy(e),this.constant=-t.dot(this.normal),this}},{key:"setFromCoplanarPoints",value:function(e,t,n){var r=By.subVectors(n,t).cross(_y.subVectors(e,t)).normalize();return this.setFromNormalAndCoplanarPoint(r,e),this}},{key:"copy",value:function(e){return this.normal.copy(e.normal),this.constant=e.constant,this}},{key:"normalize",value:function(){var e=1/this.normal.length();return this.normal.multiplyScalar(e),this.constant*=e,this}},{key:"negate",value:function(){return this.constant*=-1,this.normal.negate(),this}},{key:"distanceToPoint",value:function(e){return this.normal.dot(e)+this.constant}},{key:"distanceToSphere",value:function(e){return this.distanceToPoint(e.center)-e.radius}},{key:"projectPoint",value:function(e,t){return t.copy(this.normal).multiplyScalar(-this.distanceToPoint(e)).add(e)}},{key:"intersectLine",value:function(e,t){var n=e.delta(By),r=this.normal.dot(n);if(0===r)return 0===this.distanceToPoint(e.start)?t.copy(e.start):null;var i=-(e.start.dot(this.normal)+this.constant)/r;return i<0||i>1?null:t.copy(n).multiplyScalar(i).add(e.start)}},{key:"intersectsLine",value:function(e){var t=this.distanceToPoint(e.start),n=this.distanceToPoint(e.end);return t<0&&n>0||n<0&&t>0}},{key:"intersectsBox",value:function(e){return e.intersectsPlane(this)}},{key:"intersectsSphere",value:function(e){return e.intersectsPlane(this)}},{key:"coplanarPoint",value:function(e){return e.copy(this.normal).multiplyScalar(-this.constant)}},{key:"applyMatrix4",value:function(e,t){var n=t||by.getNormalMatrix(e),r=this.coplanarPoint(By).applyMatrix4(e),i=this.normal.applyMatrix3(n).normalize();return this.constant=-r.dot(i),this}},{key:"translate",value:function(e){return this.constant-=e.dot(this.normal),this}},{key:"equals",value:function(e){return e.normal.equals(this.normal)&&e.constant===this.constant}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),e}(),Cy=new Im,Sy=new pm,Ey=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new xy,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new xy,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:new xy,i=arguments.length>3&&void 0!==arguments[3]?arguments[3]:new xy,A=arguments.length>4&&void 0!==arguments[4]?arguments[4]:new xy,a=arguments.length>5&&void 0!==arguments[5]?arguments[5]:new xy;v(this,e),this.planes=[t,n,r,i,A,a]}return w(e,[{key:"set",value:function(e,t,n,r,i,A){var a=this.planes;return a[0].copy(e),a[1].copy(t),a[2].copy(n),a[3].copy(r),a[4].copy(i),a[5].copy(A),this}},{key:"copy",value:function(e){for(var t=this.planes,n=0;n<6;n++)t[n].copy(e.planes[n]);return this}},{key:"setFromProjectionMatrix",value:function(e){var t=this.planes,n=e.elements,r=n[0],i=n[1],A=n[2],a=n[3],o=n[4],s=n[5],l=n[6],u=n[7],c=n[8],d=n[9],h=n[10],f=n[11],p=n[12],g=n[13],m=n[14],v=n[15];return t[0].setComponents(a-r,u-o,f-c,v-p).normalize(),t[1].setComponents(a+r,u+o,f+c,v+p).normalize(),t[2].setComponents(a+i,u+s,f+d,v+g).normalize(),t[3].setComponents(a-i,u-s,f-d,v-g).normalize(),t[4].setComponents(a-A,u-l,f-h,v-m).normalize(),t[5].setComponents(a+A,u+l,f+h,v+m).normalize(),this}},{key:"intersectsObject",value:function(e){var t=e.geometry;return null===t.boundingSphere&&t.computeBoundingSphere(),Cy.copy(t.boundingSphere).applyMatrix4(e.matrixWorld),this.intersectsSphere(Cy)}},{key:"intersectsSprite",value:function(e){return Cy.center.set(0,0,0),Cy.radius=.7071067811865476,Cy.applyMatrix4(e.matrixWorld),this.intersectsSphere(Cy)}},{key:"intersectsSphere",value:function(e){for(var t=this.planes,n=e.center,r=-e.radius,i=0;i<6;i++){if(t[i].distanceToPoint(n)<r)return!1}return!0}},{key:"intersectsBox",value:function(e){for(var t=this.planes,n=0;n<6;n++){var r=t[n];if(Sy.x=r.normal.x>0?e.max.x:e.min.x,Sy.y=r.normal.y>0?e.max.y:e.min.y,Sy.z=r.normal.z>0?e.max.z:e.min.z,r.distanceToPoint(Sy)<0)return!1}return!0}},{key:"containsPoint",value:function(e){for(var t=this.planes,n=0;n<6;n++)if(t[n].distanceToPoint(e)<0)return!1;return!0}},{key:"clone",value:function(){return(new this.constructor).copy(this)}}]),e}();function Uy(){var e=null,t=!1,n=null,r=null;function i(t,A){n(t,A),r=e.requestAnimationFrame(i)}return{start:function(){!0!==t&&null!==n&&(r=e.requestAnimationFrame(i),t=!0)},stop:function(){e.cancelAnimationFrame(r),t=!1},setAnimationLoop:function(e){n=e},setContext:function(t){e=t}}}function My(e,t){var n=t.isWebGL2,r=new WeakMap;return{get:function(e){return e.isInterleavedBufferAttribute&&(e=e.data),r.get(e)},remove:function(t){t.isInterleavedBufferAttribute&&(t=t.data);var n=r.get(t);n&&(e.deleteBuffer(n.buffer),r.delete(t))},update:function(t,i){if(t.isGLBufferAttribute){var A=r.get(t);(!A||A.version<t.version)&&r.set(t,{buffer:t.buffer,type:t.type,bytesPerElement:t.elementSize,version:t.version})}else{t.isInterleavedBufferAttribute&&(t=t.data);var a=r.get(t);void 0===a?r.set(t,function(t,r){var i,A=t.array,a=t.usage,o=e.createBuffer();if(e.bindBuffer(r,o),e.bufferData(r,A,a),t.onUploadCallback(),A instanceof Float32Array)i=5126;else if(A instanceof Uint16Array)if(t.isFloat16BufferAttribute){if(!n)throw new Error("THREE.WebGLAttributes: Usage of Float16BufferAttribute requires WebGL2.");i=5131}else i=5123;else if(A instanceof Int16Array)i=5122;else if(A instanceof Uint32Array)i=5125;else if(A instanceof Int32Array)i=5124;else if(A instanceof Int8Array)i=5120;else if(A instanceof Uint8Array)i=5121;else{if(!(A instanceof Uint8ClampedArray))throw new Error("THREE.WebGLAttributes: Unsupported buffer data format: "+A);i=5121}return{buffer:o,type:i,bytesPerElement:A.BYTES_PER_ELEMENT,version:t.version}}(t,i)):a.version<t.version&&(!function(t,r,i){var A=r.array,a=r.updateRange;e.bindBuffer(i,t),-1===a.count?e.bufferSubData(i,0,A):(n?e.bufferSubData(i,a.offset*A.BYTES_PER_ELEMENT,A,a.offset,a.count):e.bufferSubData(i,a.offset*A.BYTES_PER_ELEMENT,A.subarray(a.offset,a.offset+a.count)),a.count=-1),r.onUploadCallback()}(a.buffer,t,i),a.version=t.version)}}}}var Fy=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:1;v(this,n),(e=t.call(this)).type="PlaneGeometry",e.parameters={width:r,height:i,widthSegments:A,heightSegments:a};for(var o=r/2,s=i/2,l=Math.floor(A),u=Math.floor(a),c=l+1,d=u+1,h=r/l,f=i/u,p=[],g=[],m=[],y=[],w=0;w<d;w++)for(var B=w*f-s,_=0;_<c;_++){var b=_*h-o;g.push(b,-B,0),m.push(0,0,1),y.push(_/l),y.push(1-w/u)}for(var x=0;x<u;x++)for(var C=0;C<l;C++){var S=C+c*x,E=C+c*(x+1),U=C+1+c*(x+1),M=C+1+c*x;p.push(S,E,M),p.push(E,U,M)}return e.setIndex(p),e.setAttribute("position",new Iv(g,3)),e.setAttribute("normal",new Iv(m,3)),e.setAttribute("uv",new Iv(y,2)),e}return w(n,null,[{key:"fromJSON",value:function(e){return new n(e.width,e.height,e.widthSegments,e.heightSegments)}}]),n}(Gv),Ty={alphamap_fragment:"#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\n#endif",alphamap_pars_fragment:"#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",alphatest_fragment:"#ifdef USE_ALPHATEST\n\tif ( diffuseColor.a < alphaTest ) discard;\n#endif",alphatest_pars_fragment:"#ifdef USE_ALPHATEST\n\tuniform float alphaTest;\n#endif",aomap_fragment:"#ifdef USE_AOMAP\n\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\n\treflectedLight.indirectDiffuse *= ambientOcclusion;\n\t#if defined( USE_ENVMAP ) && defined( STANDARD )\n\t\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.roughness );\n\t#endif\n#endif",aomap_pars_fragment:"#ifdef USE_AOMAP\n\tuniform sampler2D aoMap;\n\tuniform float aoMapIntensity;\n#endif",begin_vertex:"vec3 transformed = vec3( position );",beginnormal_vertex:"vec3 objectNormal = vec3( normal );\n#ifdef USE_TANGENT\n\tvec3 objectTangent = vec3( tangent.xyz );\n#endif",bsdfs:"vec3 BRDF_Lambert( const in vec3 diffuseColor ) {\n\treturn RECIPROCAL_PI * diffuseColor;\n}\nvec3 F_Schlick( const in vec3 f0, const in float f90, const in float dotVH ) {\n\tfloat fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\n\treturn f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\n}\nfloat F_Schlick( const in float f0, const in float f90, const in float dotVH ) {\n\tfloat fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\n\treturn f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\n}\nvec3 Schlick_to_F0( const in vec3 f, const in float f90, const in float dotVH ) {\n    float x = clamp( 1.0 - dotVH, 0.0, 1.0 );\n    float x2 = x * x;\n    float x5 = clamp( x * x2 * x2, 0.0, 0.9999 );\n    return ( f - vec3( f90 ) * x5 ) / ( 1.0 - x5 );\n}\nfloat V_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\treturn 0.5 / max( gv + gl, EPSILON );\n}\nfloat D_GGX( const in float alpha, const in float dotNH ) {\n\tfloat a2 = pow2( alpha );\n\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\n\treturn RECIPROCAL_PI * a2 / pow2( denom );\n}\nvec3 BRDF_GGX( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in float f90, const in float roughness ) {\n\tfloat alpha = pow2( roughness );\n\tvec3 halfDir = normalize( lightDir + viewDir );\n\tfloat dotNL = saturate( dot( normal, lightDir ) );\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\n\tvec3 F = F_Schlick( f0, f90, dotVH );\n\tfloat V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n\tfloat D = D_GGX( alpha, dotNH );\n\treturn F * ( V * D );\n}\n#ifdef USE_IRIDESCENCE\n\tvec3 BRDF_GGX_Iridescence( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in float f90, const in float iridescence, const in vec3 iridescenceFresnel, const in float roughness ) {\n\t\tfloat alpha = pow2( roughness );\n\t\tvec3 halfDir = normalize( lightDir + viewDir );\n\t\tfloat dotNL = saturate( dot( normal, lightDir ) );\n\t\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\t\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\t\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\n\t\tvec3 F = mix( F_Schlick( f0, f90, dotVH ), iridescenceFresnel, iridescence );\n\t\tfloat V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n\t\tfloat D = D_GGX( alpha, dotNH );\n\t\treturn F * ( V * D );\n\t}\n#endif\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\n\tconst float LUT_SIZE = 64.0;\n\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\n\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\n\tfloat dotNV = saturate( dot( N, V ) );\n\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\n\tuv = uv * LUT_SCALE + LUT_BIAS;\n\treturn uv;\n}\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\n\tfloat l = length( f );\n\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\n}\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\n\tfloat x = dot( v1, v2 );\n\tfloat y = abs( x );\n\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\n\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\n\tfloat v = a / b;\n\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\n\treturn cross( v1, v2 ) * theta_sintheta;\n}\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\n\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\n\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\n\tvec3 lightNormal = cross( v1, v2 );\n\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\n\tvec3 T1, T2;\n\tT1 = normalize( V - N * dot( V, N ) );\n\tT2 = - cross( N, T1 );\n\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\n\tvec3 coords[ 4 ];\n\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\n\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\n\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\n\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\n\tcoords[ 0 ] = normalize( coords[ 0 ] );\n\tcoords[ 1 ] = normalize( coords[ 1 ] );\n\tcoords[ 2 ] = normalize( coords[ 2 ] );\n\tcoords[ 3 ] = normalize( coords[ 3 ] );\n\tvec3 vectorFormFactor = vec3( 0.0 );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\n\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\n\treturn vec3( result );\n}\nfloat G_BlinnPhong_Implicit( ) {\n\treturn 0.25;\n}\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\n\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\n}\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\n\tvec3 halfDir = normalize( lightDir + viewDir );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat dotVH = saturate( dot( viewDir, halfDir ) );\n\tvec3 F = F_Schlick( specularColor, 1.0, dotVH );\n\tfloat G = G_BlinnPhong_Implicit( );\n\tfloat D = D_BlinnPhong( shininess, dotNH );\n\treturn F * ( G * D );\n}\n#if defined( USE_SHEEN )\nfloat D_Charlie( float roughness, float dotNH ) {\n\tfloat alpha = pow2( roughness );\n\tfloat invAlpha = 1.0 / alpha;\n\tfloat cos2h = dotNH * dotNH;\n\tfloat sin2h = max( 1.0 - cos2h, 0.0078125 );\n\treturn ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\n}\nfloat V_Neubelt( float dotNV, float dotNL ) {\n\treturn saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\n}\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenColor, const in float sheenRoughness ) {\n\tvec3 halfDir = normalize( lightDir + viewDir );\n\tfloat dotNL = saturate( dot( normal, lightDir ) );\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat D = D_Charlie( sheenRoughness, dotNH );\n\tfloat V = V_Neubelt( dotNV, dotNL );\n\treturn sheenColor * ( D * V );\n}\n#endif",iridescence_fragment:"#ifdef USE_IRIDESCENCE\n\tconst mat3 XYZ_TO_REC709 = mat3(\n\t\t 3.2404542, -0.9692660,  0.0556434,\n\t\t-1.5371385,  1.8760108, -0.2040259,\n\t\t-0.4985314,  0.0415560,  1.0572252\n\t);\n\tvec3 Fresnel0ToIor( vec3 fresnel0 ) {\n\t\tvec3 sqrtF0 = sqrt( fresnel0 );\n\t\treturn ( vec3( 1.0 ) + sqrtF0 ) / ( vec3( 1.0 ) - sqrtF0 );\n\t}\n\tvec3 IorToFresnel0( vec3 transmittedIor, float incidentIor ) {\n\t\treturn pow2( ( transmittedIor - vec3( incidentIor ) ) / ( transmittedIor + vec3( incidentIor ) ) );\n\t}\n\tfloat IorToFresnel0( float transmittedIor, float incidentIor ) {\n\t\treturn pow2( ( transmittedIor - incidentIor ) / ( transmittedIor + incidentIor ));\n\t}\n\tvec3 evalSensitivity( float OPD, vec3 shift ) {\n\t\tfloat phase = 2.0 * PI * OPD * 1.0e-9;\n\t\tvec3 val = vec3( 5.4856e-13, 4.4201e-13, 5.2481e-13 );\n\t\tvec3 pos = vec3( 1.6810e+06, 1.7953e+06, 2.2084e+06 );\n\t\tvec3 var = vec3( 4.3278e+09, 9.3046e+09, 6.6121e+09 );\n\t\tvec3 xyz = val * sqrt( 2.0 * PI * var ) * cos( pos * phase + shift ) * exp( - pow2( phase ) * var );\n\t\txyz.x += 9.7470e-14 * sqrt( 2.0 * PI * 4.5282e+09 ) * cos( 2.2399e+06 * phase + shift[ 0 ] ) * exp( - 4.5282e+09 * pow2( phase ) );\n\t\txyz /= 1.0685e-7;\n\t\tvec3 rgb = XYZ_TO_REC709 * xyz;\n\t\treturn rgb;\n\t}\n\tvec3 evalIridescence( float outsideIOR, float eta2, float cosTheta1, float thinFilmThickness, vec3 baseF0 ) {\n\t\tvec3 I;\n\t\tfloat iridescenceIOR = mix( outsideIOR, eta2, smoothstep( 0.0, 0.03, thinFilmThickness ) );\n\t\tfloat sinTheta2Sq = pow2( outsideIOR / iridescenceIOR ) * ( 1.0 - pow2( cosTheta1 ) );\n\t\tfloat cosTheta2Sq = 1.0 - sinTheta2Sq;\n\t\tif ( cosTheta2Sq < 0.0 ) {\n\t\t\t return vec3( 1.0 );\n\t\t}\n\t\tfloat cosTheta2 = sqrt( cosTheta2Sq );\n\t\tfloat R0 = IorToFresnel0( iridescenceIOR, outsideIOR );\n\t\tfloat R12 = F_Schlick( R0, 1.0, cosTheta1 );\n\t\tfloat R21 = R12;\n\t\tfloat T121 = 1.0 - R12;\n\t\tfloat phi12 = 0.0;\n\t\tif ( iridescenceIOR < outsideIOR ) phi12 = PI;\n\t\tfloat phi21 = PI - phi12;\n\t\tvec3 baseIOR = Fresnel0ToIor( clamp( baseF0, 0.0, 0.9999 ) );\t\tvec3 R1 = IorToFresnel0( baseIOR, iridescenceIOR );\n\t\tvec3 R23 = F_Schlick( R1, 1.0, cosTheta2 );\n\t\tvec3 phi23 = vec3( 0.0 );\n\t\tif ( baseIOR[ 0 ] < iridescenceIOR ) phi23[ 0 ] = PI;\n\t\tif ( baseIOR[ 1 ] < iridescenceIOR ) phi23[ 1 ] = PI;\n\t\tif ( baseIOR[ 2 ] < iridescenceIOR ) phi23[ 2 ] = PI;\n\t\tfloat OPD = 2.0 * iridescenceIOR * thinFilmThickness * cosTheta2;\n\t\tvec3 phi = vec3( phi21 ) + phi23;\n\t\tvec3 R123 = clamp( R12 * R23, 1e-5, 0.9999 );\n\t\tvec3 r123 = sqrt( R123 );\n\t\tvec3 Rs = pow2( T121 ) * R23 / ( vec3( 1.0 ) - R123 );\n\t\tvec3 C0 = R12 + Rs;\n\t\tI = C0;\n\t\tvec3 Cm = Rs - T121;\n\t\tfor ( int m = 1; m <= 2; ++ m ) {\n\t\t\tCm *= r123;\n\t\t\tvec3 Sm = 2.0 * evalSensitivity( float( m ) * OPD, float( m ) * phi );\n\t\t\tI += Cm * Sm;\n\t\t}\n\t\treturn max( I, vec3( 0.0 ) );\n\t}\n#endif",bumpmap_pars_fragment:"#ifdef USE_BUMPMAP\n\tuniform sampler2D bumpMap;\n\tuniform float bumpScale;\n\tvec2 dHdxy_fwd() {\n\t\tvec2 dSTdx = dFdx( vUv );\n\t\tvec2 dSTdy = dFdy( vUv );\n\t\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\n\t\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\n\t\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\n\t\treturn vec2( dBx, dBy );\n\t}\n\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\n\t\tvec3 vSigmaX = dFdx( surf_pos.xyz );\n\t\tvec3 vSigmaY = dFdy( surf_pos.xyz );\n\t\tvec3 vN = surf_norm;\n\t\tvec3 R1 = cross( vSigmaY, vN );\n\t\tvec3 R2 = cross( vN, vSigmaX );\n\t\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\n\t\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\n\t\treturn normalize( abs( fDet ) * surf_norm - vGrad );\n\t}\n#endif",clipping_planes_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvec4 plane;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\n\t\tplane = clippingPlanes[ i ];\n\t\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\n\t}\n\t#pragma unroll_loop_end\n\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\n\t\tbool clipped = true;\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\n\t\t\tplane = clippingPlanes[ i ];\n\t\t\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t\tif ( clipped ) discard;\n\t#endif\n#endif",clipping_planes_pars_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\n#endif",clipping_planes_pars_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n#endif",clipping_planes_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvClipPosition = - mvPosition.xyz;\n#endif",color_fragment:"#if defined( USE_COLOR_ALPHA )\n\tdiffuseColor *= vColor;\n#elif defined( USE_COLOR )\n\tdiffuseColor.rgb *= vColor;\n#endif",color_pars_fragment:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR )\n\tvarying vec3 vColor;\n#endif",color_pars_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvarying vec3 vColor;\n#endif",color_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvColor = vec4( 1.0 );\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvColor = vec3( 1.0 );\n#endif\n#ifdef USE_COLOR\n\tvColor *= color;\n#endif\n#ifdef USE_INSTANCING_COLOR\n\tvColor.xyz *= instanceColor.xyz;\n#endif",common:"#define PI 3.141592653589793\n#define PI2 6.283185307179586\n#define PI_HALF 1.5707963267948966\n#define RECIPROCAL_PI 0.3183098861837907\n#define RECIPROCAL_PI2 0.15915494309189535\n#define EPSILON 1e-6\n#ifndef saturate\n#define saturate( a ) clamp( a, 0.0, 1.0 )\n#endif\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\nfloat pow2( const in float x ) { return x*x; }\nvec3 pow2( const in vec3 x ) { return x*x; }\nfloat pow3( const in float x ) { return x*x*x; }\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\nfloat average( const in vec3 v ) { return dot( v, vec3( 0.3333333 ) ); }\nhighp float rand( const in vec2 uv ) {\n\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\n\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\n\treturn fract( sin( sn ) * c );\n}\n#ifdef HIGH_PRECISION\n\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\n#else\n\tfloat precisionSafeLength( vec3 v ) {\n\t\tfloat maxComponent = max3( abs( v ) );\n\t\treturn length( v / maxComponent ) * maxComponent;\n\t}\n#endif\nstruct IncidentLight {\n\tvec3 color;\n\tvec3 direction;\n\tbool visible;\n};\nstruct ReflectedLight {\n\tvec3 directDiffuse;\n\tvec3 directSpecular;\n\tvec3 indirectDiffuse;\n\tvec3 indirectSpecular;\n};\nstruct GeometricContext {\n\tvec3 position;\n\tvec3 normal;\n\tvec3 viewDir;\n#ifdef USE_CLEARCOAT\n\tvec3 clearcoatNormal;\n#endif\n};\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\n}\nmat3 transposeMat3( const in mat3 m ) {\n\tmat3 tmp;\n\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\n\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\n\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\n\treturn tmp;\n}\nfloat luminance( const in vec3 rgb ) {\n\tconst vec3 weights = vec3( 0.2126729, 0.7151522, 0.0721750 );\n\treturn dot( weights, rgb );\n}\nbool isPerspectiveMatrix( mat4 m ) {\n\treturn m[ 2 ][ 3 ] == - 1.0;\n}\nvec2 equirectUv( in vec3 dir ) {\n\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\n\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\treturn vec2( u, v );\n}",cube_uv_reflection_fragment:"#ifdef ENVMAP_TYPE_CUBE_UV\n\t#define cubeUV_minMipLevel 4.0\n\t#define cubeUV_minTileSize 16.0\n\tfloat getFace( vec3 direction ) {\n\t\tvec3 absDirection = abs( direction );\n\t\tfloat face = - 1.0;\n\t\tif ( absDirection.x > absDirection.z ) {\n\t\t\tif ( absDirection.x > absDirection.y )\n\t\t\t\tface = direction.x > 0.0 ? 0.0 : 3.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t} else {\n\t\t\tif ( absDirection.z > absDirection.y )\n\t\t\t\tface = direction.z > 0.0 ? 2.0 : 5.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t}\n\t\treturn face;\n\t}\n\tvec2 getUV( vec3 direction, float face ) {\n\t\tvec2 uv;\n\t\tif ( face == 0.0 ) {\n\t\t\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 1.0 ) {\n\t\t\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\n\t\t} else if ( face == 2.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\n\t\t} else if ( face == 3.0 ) {\n\t\t\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 4.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\n\t\t} else {\n\t\t\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\n\t\t}\n\t\treturn 0.5 * ( uv + 1.0 );\n\t}\n\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\n\t\tfloat face = getFace( direction );\n\t\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\n\t\tmipInt = max( mipInt, cubeUV_minMipLevel );\n\t\tfloat faceSize = exp2( mipInt );\n\t\thighp vec2 uv = getUV( direction, face ) * ( faceSize - 2.0 ) + 1.0;\n\t\tif ( face > 2.0 ) {\n\t\t\tuv.y += faceSize;\n\t\t\tface -= 3.0;\n\t\t}\n\t\tuv.x += face * faceSize;\n\t\tuv.x += filterInt * 3.0 * cubeUV_minTileSize;\n\t\tuv.y += 4.0 * ( exp2( CUBEUV_MAX_MIP ) - faceSize );\n\t\tuv.x *= CUBEUV_TEXEL_WIDTH;\n\t\tuv.y *= CUBEUV_TEXEL_HEIGHT;\n\t\t#ifdef texture2DGradEXT\n\t\t\treturn texture2DGradEXT( envMap, uv, vec2( 0.0 ), vec2( 0.0 ) ).rgb;\n\t\t#else\n\t\t\treturn texture2D( envMap, uv ).rgb;\n\t\t#endif\n\t}\n\t#define cubeUV_r0 1.0\n\t#define cubeUV_v0 0.339\n\t#define cubeUV_m0 - 2.0\n\t#define cubeUV_r1 0.8\n\t#define cubeUV_v1 0.276\n\t#define cubeUV_m1 - 1.0\n\t#define cubeUV_r4 0.4\n\t#define cubeUV_v4 0.046\n\t#define cubeUV_m4 2.0\n\t#define cubeUV_r5 0.305\n\t#define cubeUV_v5 0.016\n\t#define cubeUV_m5 3.0\n\t#define cubeUV_r6 0.21\n\t#define cubeUV_v6 0.0038\n\t#define cubeUV_m6 4.0\n\tfloat roughnessToMip( float roughness ) {\n\t\tfloat mip = 0.0;\n\t\tif ( roughness >= cubeUV_r1 ) {\n\t\t\tmip = ( cubeUV_r0 - roughness ) * ( cubeUV_m1 - cubeUV_m0 ) / ( cubeUV_r0 - cubeUV_r1 ) + cubeUV_m0;\n\t\t} else if ( roughness >= cubeUV_r4 ) {\n\t\t\tmip = ( cubeUV_r1 - roughness ) * ( cubeUV_m4 - cubeUV_m1 ) / ( cubeUV_r1 - cubeUV_r4 ) + cubeUV_m1;\n\t\t} else if ( roughness >= cubeUV_r5 ) {\n\t\t\tmip = ( cubeUV_r4 - roughness ) * ( cubeUV_m5 - cubeUV_m4 ) / ( cubeUV_r4 - cubeUV_r5 ) + cubeUV_m4;\n\t\t} else if ( roughness >= cubeUV_r6 ) {\n\t\t\tmip = ( cubeUV_r5 - roughness ) * ( cubeUV_m6 - cubeUV_m5 ) / ( cubeUV_r5 - cubeUV_r6 ) + cubeUV_m5;\n\t\t} else {\n\t\t\tmip = - 2.0 * log2( 1.16 * roughness );\t\t}\n\t\treturn mip;\n\t}\n\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\n\t\tfloat mip = clamp( roughnessToMip( roughness ), cubeUV_m0, CUBEUV_MAX_MIP );\n\t\tfloat mipF = fract( mip );\n\t\tfloat mipInt = floor( mip );\n\t\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\n\t\tif ( mipF == 0.0 ) {\n\t\t\treturn vec4( color0, 1.0 );\n\t\t} else {\n\t\t\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\n\t\t\treturn vec4( mix( color0, color1, mipF ), 1.0 );\n\t\t}\n\t}\n#endif",defaultnormal_vertex:"vec3 transformedNormal = objectNormal;\n#ifdef USE_INSTANCING\n\tmat3 m = mat3( instanceMatrix );\n\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\n\ttransformedNormal = m * transformedNormal;\n#endif\ntransformedNormal = normalMatrix * transformedNormal;\n#ifdef FLIP_SIDED\n\ttransformedNormal = - transformedNormal;\n#endif\n#ifdef USE_TANGENT\n\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#ifdef FLIP_SIDED\n\t\ttransformedTangent = - transformedTangent;\n\t#endif\n#endif",displacementmap_pars_vertex:"#ifdef USE_DISPLACEMENTMAP\n\tuniform sampler2D displacementMap;\n\tuniform float displacementScale;\n\tuniform float displacementBias;\n#endif",displacementmap_vertex:"#ifdef USE_DISPLACEMENTMAP\n\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\n#endif",emissivemap_fragment:"#ifdef USE_EMISSIVEMAP\n\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\n\ttotalEmissiveRadiance *= emissiveColor.rgb;\n#endif",emissivemap_pars_fragment:"#ifdef USE_EMISSIVEMAP\n\tuniform sampler2D emissiveMap;\n#endif",encodings_fragment:"gl_FragColor = linearToOutputTexel( gl_FragColor );",encodings_pars_fragment:"vec4 LinearToLinear( in vec4 value ) {\n\treturn value;\n}\nvec4 LinearTosRGB( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\n}",envmap_fragment:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvec3 cameraToFrag;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\n\t\t#endif\n\t#else\n\t\tvec3 reflectVec = vReflect;\n\t#endif\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\n\t#else\n\t\tvec4 envColor = vec4( 0.0 );\n\t#endif\n\t#ifdef ENVMAP_BLENDING_MULTIPLY\n\t\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_MIX )\n\t\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_ADD )\n\t\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\n\t#endif\n#endif",envmap_common_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float envMapIntensity;\n\tuniform float flipEnvMap;\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tuniform samplerCube envMap;\n\t#else\n\t\tuniform sampler2D envMap;\n\t#endif\n\t\n#endif",envmap_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float reflectivity;\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG ) || defined( LAMBERT )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\tvarying vec3 vWorldPosition;\n\t\tuniform float refractionRatio;\n\t#else\n\t\tvarying vec3 vReflect;\n\t#endif\n#endif",envmap_pars_vertex:"#ifdef USE_ENVMAP\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG ) || defined( LAMBERT )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\t\n\t\tvarying vec3 vWorldPosition;\n\t#else\n\t\tvarying vec3 vReflect;\n\t\tuniform float refractionRatio;\n\t#endif\n#endif",envmap_physical_pars_fragment:"#if defined( USE_ENVMAP )\n\tvec3 getIBLIrradiance( const in vec3 normal ) {\n\t\t#if defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\n\t\t\treturn PI * envMapColor.rgb * envMapIntensity;\n\t\t#else\n\t\t\treturn vec3( 0.0 );\n\t\t#endif\n\t}\n\tvec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\n\t\t#if defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec3 reflectVec = reflect( - viewDir, normal );\n\t\t\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\n\t\t\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\n\t\t\treturn envMapColor.rgb * envMapIntensity;\n\t\t#else\n\t\t\treturn vec3( 0.0 );\n\t\t#endif\n\t}\n#endif",envmap_vertex:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvWorldPosition = worldPosition.xyz;\n\t#else\n\t\tvec3 cameraToVertex;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvReflect = reflect( cameraToVertex, worldNormal );\n\t\t#else\n\t\t\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\n\t\t#endif\n\t#endif\n#endif",fog_vertex:"#ifdef USE_FOG\n\tvFogDepth = - mvPosition.z;\n#endif",fog_pars_vertex:"#ifdef USE_FOG\n\tvarying float vFogDepth;\n#endif",fog_fragment:"#ifdef USE_FOG\n\t#ifdef FOG_EXP2\n\t\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\n\t#else\n\t\tfloat fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\n\t#endif\n\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\n#endif",fog_pars_fragment:"#ifdef USE_FOG\n\tuniform vec3 fogColor;\n\tvarying float vFogDepth;\n\t#ifdef FOG_EXP2\n\t\tuniform float fogDensity;\n\t#else\n\t\tuniform float fogNear;\n\t\tuniform float fogFar;\n\t#endif\n#endif",gradientmap_pars_fragment:"#ifdef USE_GRADIENTMAP\n\tuniform sampler2D gradientMap;\n#endif\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\n\tfloat dotNL = dot( normal, lightDirection );\n\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\n\t#ifdef USE_GRADIENTMAP\n\t\treturn vec3( texture2D( gradientMap, coord ).r );\n\t#else\n\t\tvec2 fw = fwidth( coord ) * 0.5;\n\t\treturn mix( vec3( 0.7 ), vec3( 1.0 ), smoothstep( 0.7 - fw.x, 0.7 + fw.x, coord.x ) );\n\t#endif\n}",lightmap_fragment:"#ifdef USE_LIGHTMAP\n\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\n\tvec3 lightMapIrradiance = lightMapTexel.rgb * lightMapIntensity;\n\treflectedLight.indirectDiffuse += lightMapIrradiance;\n#endif",lightmap_pars_fragment:"#ifdef USE_LIGHTMAP\n\tuniform sampler2D lightMap;\n\tuniform float lightMapIntensity;\n#endif",lights_lambert_fragment:"LambertMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularStrength = specularStrength;",lights_lambert_pars_fragment:"varying vec3 vViewPosition;\nstruct LambertMaterial {\n\tvec3 diffuseColor;\n\tfloat specularStrength;\n};\nvoid RE_Direct_Lambert( const in IncidentLight directLight, const in GeometricContext geometry, const in LambertMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Lambert( const in vec3 irradiance, const in GeometricContext geometry, const in LambertMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_Lambert\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Lambert",lights_pars_begin:"uniform bool receiveShadow;\nuniform vec3 ambientLightColor;\nuniform vec3 lightProbe[ 9 ];\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n\tfloat x = normal.x, y = normal.y, z = normal.z;\n\tvec3 result = shCoefficients[ 0 ] * 0.886227;\n\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n\treturn result;\n}\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\n\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\n\treturn irradiance;\n}\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\n\tvec3 irradiance = ambientLightColor;\n\treturn irradiance;\n}\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\n\t#if defined ( PHYSICALLY_CORRECT_LIGHTS )\n\t\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\n\t\tif ( cutoffDistance > 0.0 ) {\n\t\t\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\n\t\t}\n\t\treturn distanceFalloff;\n\t#else\n\t\tif ( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\n\t\t\treturn pow( saturate( - lightDistance / cutoffDistance + 1.0 ), decayExponent );\n\t\t}\n\t\treturn 1.0;\n\t#endif\n}\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\n\treturn smoothstep( coneCosine, penumbraCosine, angleCosine );\n}\n#if NUM_DIR_LIGHTS > 0\n\tstruct DirectionalLight {\n\t\tvec3 direction;\n\t\tvec3 color;\n\t};\n\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n\tvoid getDirectionalLightInfo( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight light ) {\n\t\tlight.color = directionalLight.color;\n\t\tlight.direction = directionalLight.direction;\n\t\tlight.visible = true;\n\t}\n#endif\n#if NUM_POINT_LIGHTS > 0\n\tstruct PointLight {\n\t\tvec3 position;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t};\n\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n\tvoid getPointLightInfo( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight light ) {\n\t\tvec3 lVector = pointLight.position - geometry.position;\n\t\tlight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tlight.color = pointLight.color;\n\t\tlight.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\n\t\tlight.visible = ( light.color != vec3( 0.0 ) );\n\t}\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\tstruct SpotLight {\n\t\tvec3 position;\n\t\tvec3 direction;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t\tfloat coneCos;\n\t\tfloat penumbraCos;\n\t};\n\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\n\tvoid getSpotLightInfo( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight light ) {\n\t\tvec3 lVector = spotLight.position - geometry.position;\n\t\tlight.direction = normalize( lVector );\n\t\tfloat angleCos = dot( light.direction, spotLight.direction );\n\t\tfloat spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\n\t\tif ( spotAttenuation > 0.0 ) {\n\t\t\tfloat lightDistance = length( lVector );\n\t\t\tlight.color = spotLight.color * spotAttenuation;\n\t\t\tlight.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\n\t\t\tlight.visible = ( light.color != vec3( 0.0 ) );\n\t\t} else {\n\t\t\tlight.color = vec3( 0.0 );\n\t\t\tlight.visible = false;\n\t\t}\n\t}\n#endif\n#if NUM_RECT_AREA_LIGHTS > 0\n\tstruct RectAreaLight {\n\t\tvec3 color;\n\t\tvec3 position;\n\t\tvec3 halfWidth;\n\t\tvec3 halfHeight;\n\t};\n\tuniform sampler2D ltc_1;\tuniform sampler2D ltc_2;\n\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\tstruct HemisphereLight {\n\t\tvec3 direction;\n\t\tvec3 skyColor;\n\t\tvec3 groundColor;\n\t};\n\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\n\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\n\t\tfloat dotNL = dot( normal, hemiLight.direction );\n\t\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\n\t\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\n\t\treturn irradiance;\n\t}\n#endif",lights_toon_fragment:"ToonMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;",lights_toon_pars_fragment:"varying vec3 vViewPosition;\nstruct ToonMaterial {\n\tvec3 diffuseColor;\n};\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\n\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_Toon\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Toon",lights_phong_fragment:"BlinnPhongMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularColor = specular;\nmaterial.specularShininess = shininess;\nmaterial.specularStrength = specularStrength;",lights_phong_pars_fragment:"varying vec3 vViewPosition;\nstruct BlinnPhongMaterial {\n\tvec3 diffuseColor;\n\tvec3 specularColor;\n\tfloat specularShininess;\n\tfloat specularStrength;\n};\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n\treflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularShininess ) * material.specularStrength;\n}\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_BlinnPhong\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_BlinnPhong",lights_physical_fragment:"PhysicalMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\nmaterial.roughness = max( roughnessFactor, 0.0525 );material.roughness += geometryRoughness;\nmaterial.roughness = min( material.roughness, 1.0 );\n#ifdef IOR\n\tmaterial.ior = ior;\n\t#ifdef SPECULAR\n\t\tfloat specularIntensityFactor = specularIntensity;\n\t\tvec3 specularColorFactor = specularColor;\n\t\t#ifdef USE_SPECULARINTENSITYMAP\n\t\t\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\n\t\t#endif\n\t\t#ifdef USE_SPECULARCOLORMAP\n\t\t\tspecularColorFactor *= texture2D( specularColorMap, vUv ).rgb;\n\t\t#endif\n\t\tmaterial.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\n\t#else\n\t\tfloat specularIntensityFactor = 1.0;\n\t\tvec3 specularColorFactor = vec3( 1.0 );\n\t\tmaterial.specularF90 = 1.0;\n\t#endif\n\tmaterial.specularColor = mix( min( pow2( ( material.ior - 1.0 ) / ( material.ior + 1.0 ) ) * specularColorFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\n#else\n\tmaterial.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\n\tmaterial.specularF90 = 1.0;\n#endif\n#ifdef USE_CLEARCOAT\n\tmaterial.clearcoat = clearcoat;\n\tmaterial.clearcoatRoughness = clearcoatRoughness;\n\tmaterial.clearcoatF0 = vec3( 0.04 );\n\tmaterial.clearcoatF90 = 1.0;\n\t#ifdef USE_CLEARCOATMAP\n\t\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\n\t#endif\n\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\t\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\n\t#endif\n\tmaterial.clearcoat = saturate( material.clearcoat );\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\n\tmaterial.clearcoatRoughness += geometryRoughness;\n\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\n#endif\n#ifdef USE_IRIDESCENCE\n\tmaterial.iridescence = iridescence;\n\tmaterial.iridescenceIOR = iridescenceIOR;\n\t#ifdef USE_IRIDESCENCEMAP\n\t\tmaterial.iridescence *= texture2D( iridescenceMap, vUv ).r;\n\t#endif\n\t#ifdef USE_IRIDESCENCE_THICKNESSMAP\n\t\tmaterial.iridescenceThickness = (iridescenceThicknessMaximum - iridescenceThicknessMinimum) * texture2D( iridescenceThicknessMap, vUv ).g + iridescenceThicknessMinimum;\n\t#else\n\t\tmaterial.iridescenceThickness = iridescenceThicknessMaximum;\n\t#endif\n#endif\n#ifdef USE_SHEEN\n\tmaterial.sheenColor = sheenColor;\n\t#ifdef USE_SHEENCOLORMAP\n\t\tmaterial.sheenColor *= texture2D( sheenColorMap, vUv ).rgb;\n\t#endif\n\tmaterial.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\n\t#ifdef USE_SHEENROUGHNESSMAP\n\t\tmaterial.sheenRoughness *= texture2D( sheenRoughnessMap, vUv ).a;\n\t#endif\n#endif",lights_physical_pars_fragment:"struct PhysicalMaterial {\n\tvec3 diffuseColor;\n\tfloat roughness;\n\tvec3 specularColor;\n\tfloat specularF90;\n\t#ifdef USE_CLEARCOAT\n\t\tfloat clearcoat;\n\t\tfloat clearcoatRoughness;\n\t\tvec3 clearcoatF0;\n\t\tfloat clearcoatF90;\n\t#endif\n\t#ifdef USE_IRIDESCENCE\n\t\tfloat iridescence;\n\t\tfloat iridescenceIOR;\n\t\tfloat iridescenceThickness;\n\t\tvec3 iridescenceFresnel;\n\t\tvec3 iridescenceF0;\n\t#endif\n\t#ifdef USE_SHEEN\n\t\tvec3 sheenColor;\n\t\tfloat sheenRoughness;\n\t#endif\n\t#ifdef IOR\n\t\tfloat ior;\n\t#endif\n\t#ifdef USE_TRANSMISSION\n\t\tfloat transmission;\n\t\tfloat transmissionAlpha;\n\t\tfloat thickness;\n\t\tfloat attenuationDistance;\n\t\tvec3 attenuationColor;\n\t#endif\n};\nvec3 clearcoatSpecular = vec3( 0.0 );\nvec3 sheenSpecular = vec3( 0.0 );\nfloat IBLSheenBRDF( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat r2 = roughness * roughness;\n\tfloat a = roughness < 0.25 ? -339.2 * r2 + 161.4 * roughness - 25.9 : -8.48 * r2 + 14.3 * roughness - 9.95;\n\tfloat b = roughness < 0.25 ? 44.0 * r2 - 23.7 * roughness + 3.26 : 1.97 * r2 - 3.27 * roughness + 0.72;\n\tfloat DG = exp( a * dotNV + b ) + ( roughness < 0.25 ? 0.0 : 0.1 * ( roughness - 0.25 ) );\n\treturn saturate( DG * RECIPROCAL_PI );\n}\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\n\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\n\tvec4 r = roughness * c0 + c1;\n\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\n\tvec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\n\treturn fab;\n}\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\n\tvec2 fab = DFGApprox( normal, viewDir, roughness );\n\treturn specularColor * fab.x + specularF90 * fab.y;\n}\n#ifdef USE_IRIDESCENCE\nvoid computeMultiscatteringIridescence( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float iridescence, const in vec3 iridescenceF0, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n#else\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n#endif\n\tvec2 fab = DFGApprox( normal, viewDir, roughness );\n\t#ifdef USE_IRIDESCENCE\n\t\tvec3 Fr = mix( specularColor, iridescenceF0, iridescence );\n\t#else\n\t\tvec3 Fr = specularColor;\n\t#endif\n\tvec3 FssEss = Fr * fab.x + specularF90 * fab.y;\n\tfloat Ess = fab.x + fab.y;\n\tfloat Ems = 1.0 - Ess;\n\tvec3 Favg = Fr + ( 1.0 - Fr ) * 0.047619;\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\n\tsingleScatter += FssEss;\n\tmultiScatter += Fms * Ems;\n}\n#if NUM_RECT_AREA_LIGHTS > 0\n\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\t\tvec3 normal = geometry.normal;\n\t\tvec3 viewDir = geometry.viewDir;\n\t\tvec3 position = geometry.position;\n\t\tvec3 lightPos = rectAreaLight.position;\n\t\tvec3 halfWidth = rectAreaLight.halfWidth;\n\t\tvec3 halfHeight = rectAreaLight.halfHeight;\n\t\tvec3 lightColor = rectAreaLight.color;\n\t\tfloat roughness = material.roughness;\n\t\tvec3 rectCoords[ 4 ];\n\t\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\t\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\n\t\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\n\t\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\n\t\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\n\t\tvec4 t1 = texture2D( ltc_1, uv );\n\t\tvec4 t2 = texture2D( ltc_2, uv );\n\t\tmat3 mInv = mat3(\n\t\t\tvec3( t1.x, 0, t1.y ),\n\t\t\tvec3(    0, 1,    0 ),\n\t\t\tvec3( t1.z, 0, t1.w )\n\t\t);\n\t\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\n\t\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\n\t\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\n\t}\n#endif\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifdef USE_CLEARCOAT\n\t\tfloat dotNLcc = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\n\t\tvec3 ccIrradiance = dotNLcc * directLight.color;\n\t\tclearcoatSpecular += ccIrradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.clearcoatNormal, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\n\t#endif\n\t#ifdef USE_SHEEN\n\t\tsheenSpecular += irradiance * BRDF_Sheen( directLight.direction, geometry.viewDir, geometry.normal, material.sheenColor, material.sheenRoughness );\n\t#endif\n\t#ifdef USE_IRIDESCENCE\n\t\treflectedLight.directSpecular += irradiance * BRDF_GGX_Iridescence( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.iridescence, material.iridescenceFresnel, material.roughness );\n\t#else\n\t\treflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometry.viewDir, geometry.normal, material.specularColor, material.specularF90, material.roughness );\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\n\t#ifdef USE_CLEARCOAT\n\t\tclearcoatSpecular += clearcoatRadiance * EnvironmentBRDF( geometry.clearcoatNormal, geometry.viewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\n\t#endif\n\t#ifdef USE_SHEEN\n\t\tsheenSpecular += irradiance * material.sheenColor * IBLSheenBRDF( geometry.normal, geometry.viewDir, material.sheenRoughness );\n\t#endif\n\tvec3 singleScattering = vec3( 0.0 );\n\tvec3 multiScattering = vec3( 0.0 );\n\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\n\t#ifdef USE_IRIDESCENCE\n\t\tcomputeMultiscatteringIridescence( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.iridescence, material.iridescenceFresnel, material.roughness, singleScattering, multiScattering );\n\t#else\n\t\tcomputeMultiscattering( geometry.normal, geometry.viewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\n\t#endif\n\tvec3 totalScattering = singleScattering + multiScattering;\n\tvec3 diffuse = material.diffuseColor * ( 1.0 - max( max( totalScattering.r, totalScattering.g ), totalScattering.b ) );\n\treflectedLight.indirectSpecular += radiance * singleScattering;\n\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\n\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\n}\n#define RE_Direct\t\t\t\tRE_Direct_Physical\n#define RE_Direct_RectArea\t\tRE_Direct_RectArea_Physical\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Physical\n#define RE_IndirectSpecular\t\tRE_IndirectSpecular_Physical\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\n\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\n}",lights_fragment_begin:"\nGeometricContext geometry;\ngeometry.position = - vViewPosition;\ngeometry.normal = normal;\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\n#ifdef USE_CLEARCOAT\n\tgeometry.clearcoatNormal = clearcoatNormal;\n#endif\n#ifdef USE_IRIDESCENCE\n\tfloat dotNVi = saturate( dot( normal, geometry.viewDir ) );\n\tif ( material.iridescenceThickness == 0.0 ) {\n\t\tmaterial.iridescence = 0.0;\n\t} else {\n\t\tmaterial.iridescence = saturate( material.iridescence );\n\t}\n\tif ( material.iridescence > 0.0 ) {\n\t\tmaterial.iridescenceFresnel = evalIridescence( 1.0, material.iridescenceIOR, dotNVi, material.iridescenceThickness, material.specularColor );\n\t\tmaterial.iridescenceF0 = Schlick_to_F0( material.iridescenceFresnel, 1.0, dotNVi );\n\t}\n#endif\nIncidentLight directLight;\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\n\tPointLight pointLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tpointLight = pointLights[ i ];\n\t\tgetPointLightInfo( pointLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\n\t\tpointLightShadow = pointLightShadows[ i ];\n\t\tdirectLight.color *= ( directLight.visible && receiveShadow ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\n\tSpotLight spotLight;\n\tvec4 spotColor;\n\tvec3 spotLightCoord;\n\tbool inSpotLightMap;\n\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tspotLight = spotLights[ i ];\n\t\tgetSpotLightInfo( spotLight, geometry, directLight );\n\t\t#if ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS_WITH_MAPS )\n\t\t#define SPOT_LIGHT_MAP_INDEX UNROLLED_LOOP_INDEX\n\t\t#elif ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\t#define SPOT_LIGHT_MAP_INDEX NUM_SPOT_LIGHT_MAPS\n\t\t#else\n\t\t#define SPOT_LIGHT_MAP_INDEX ( UNROLLED_LOOP_INDEX - NUM_SPOT_LIGHT_SHADOWS + NUM_SPOT_LIGHT_SHADOWS_WITH_MAPS )\n\t\t#endif\n\t\t#if ( SPOT_LIGHT_MAP_INDEX < NUM_SPOT_LIGHT_MAPS )\n\t\t\tspotLightCoord = vSpotLightCoord[ i ].xyz / vSpotLightCoord[ i ].w;\n\t\t\tinSpotLightMap = all( lessThan( abs( spotLightCoord * 2. - 1. ), vec3( 1.0 ) ) );\n\t\t\tspotColor = texture2D( spotLightMap[ SPOT_LIGHT_MAP_INDEX ], spotLightCoord.xy );\n\t\t\tdirectLight.color = inSpotLightMap ? directLight.color * spotColor.rgb : directLight.color;\n\t\t#endif\n\t\t#undef SPOT_LIGHT_MAP_INDEX\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\tspotLightShadow = spotLightShadows[ i ];\n\t\tdirectLight.color *= ( directLight.visible && receiveShadow ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotLightCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\n\tDirectionalLight directionalLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tdirectionalLight = directionalLights[ i ];\n\t\tgetDirectionalLightInfo( directionalLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\n\t\tdirectionalLightShadow = directionalLightShadows[ i ];\n\t\tdirectLight.color *= ( directLight.visible && receiveShadow ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\n\tRectAreaLight rectAreaLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\n\t\trectAreaLight = rectAreaLights[ i ];\n\t\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if defined( RE_IndirectDiffuse )\n\tvec3 iblIrradiance = vec3( 0.0 );\n\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\n\tirradiance += getLightProbeIrradiance( lightProbe, geometry.normal );\n\t#if ( NUM_HEMI_LIGHTS > 0 )\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\t\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry.normal );\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n#endif\n#if defined( RE_IndirectSpecular )\n\tvec3 radiance = vec3( 0.0 );\n\tvec3 clearcoatRadiance = vec3( 0.0 );\n#endif",lights_fragment_maps:"#if defined( RE_IndirectDiffuse )\n\t#ifdef USE_LIGHTMAP\n\t\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\n\t\tvec3 lightMapIrradiance = lightMapTexel.rgb * lightMapIntensity;\n\t\tirradiance += lightMapIrradiance;\n\t#endif\n\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\n\t\tiblIrradiance += getIBLIrradiance( geometry.normal );\n\t#endif\n#endif\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\n\tradiance += getIBLRadiance( geometry.viewDir, geometry.normal, material.roughness );\n\t#ifdef USE_CLEARCOAT\n\t\tclearcoatRadiance += getIBLRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness );\n\t#endif\n#endif",lights_fragment_end:"#if defined( RE_IndirectDiffuse )\n\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\n#endif\n#if defined( RE_IndirectSpecular )\n\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\n#endif",logdepthbuf_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\n#endif",logdepthbuf_pars_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tuniform float logDepthBufFC;\n\tvarying float vFragDepth;\n\tvarying float vIsPerspective;\n#endif",logdepthbuf_pars_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvarying float vFragDepth;\n\t\tvarying float vIsPerspective;\n\t#else\n\t\tuniform float logDepthBufFC;\n\t#endif\n#endif",logdepthbuf_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvFragDepth = 1.0 + gl_Position.w;\n\t\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\n\t#else\n\t\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\n\t\t\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\n\t\t\tgl_Position.z *= gl_Position.w;\n\t\t}\n\t#endif\n#endif",map_fragment:"#ifdef USE_MAP\n\tvec4 sampledDiffuseColor = texture2D( map, vUv );\n\t#ifdef DECODE_VIDEO_TEXTURE\n\t\tsampledDiffuseColor = vec4( mix( pow( sampledDiffuseColor.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), sampledDiffuseColor.rgb * 0.0773993808, vec3( lessThanEqual( sampledDiffuseColor.rgb, vec3( 0.04045 ) ) ) ), sampledDiffuseColor.w );\n\t#endif\n\tdiffuseColor *= sampledDiffuseColor;\n#endif",map_pars_fragment:"#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif",map_particle_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\n#endif\n#ifdef USE_MAP\n\tdiffuseColor *= texture2D( map, uv );\n#endif\n#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\n#endif",map_particle_pars_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tuniform mat3 uvTransform;\n#endif\n#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif\n#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",metalnessmap_fragment:"float metalnessFactor = metalness;\n#ifdef USE_METALNESSMAP\n\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\n\tmetalnessFactor *= texelMetalness.b;\n#endif",metalnessmap_pars_fragment:"#ifdef USE_METALNESSMAP\n\tuniform sampler2D metalnessMap;\n#endif",morphcolor_vertex:"#if defined( USE_MORPHCOLORS ) && defined( MORPHTARGETS_TEXTURE )\n\tvColor *= morphTargetBaseInfluence;\n\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\n\t\t#if defined( USE_COLOR_ALPHA )\n\t\t\tif ( morphTargetInfluences[ i ] != 0.0 ) vColor += getMorph( gl_VertexID, i, 2 ) * morphTargetInfluences[ i ];\n\t\t#elif defined( USE_COLOR )\n\t\t\tif ( morphTargetInfluences[ i ] != 0.0 ) vColor += getMorph( gl_VertexID, i, 2 ).rgb * morphTargetInfluences[ i ];\n\t\t#endif\n\t}\n#endif",morphnormal_vertex:"#ifdef USE_MORPHNORMALS\n\tobjectNormal *= morphTargetBaseInfluence;\n\t#ifdef MORPHTARGETS_TEXTURE\n\t\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\n\t\t\tif ( morphTargetInfluences[ i ] != 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1 ).xyz * morphTargetInfluences[ i ];\n\t\t}\n\t#else\n\t\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\n\t\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\n\t\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\n\t\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\n\t#endif\n#endif",morphtarget_pars_vertex:"#ifdef USE_MORPHTARGETS\n\tuniform float morphTargetBaseInfluence;\n\t#ifdef MORPHTARGETS_TEXTURE\n\t\tuniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\n\t\tuniform sampler2DArray morphTargetsTexture;\n\t\tuniform ivec2 morphTargetsTextureSize;\n\t\tvec4 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset ) {\n\t\t\tint texelIndex = vertexIndex * MORPHTARGETS_TEXTURE_STRIDE + offset;\n\t\t\tint y = texelIndex / morphTargetsTextureSize.x;\n\t\t\tint x = texelIndex - y * morphTargetsTextureSize.x;\n\t\t\tivec3 morphUV = ivec3( x, y, morphTargetIndex );\n\t\t\treturn texelFetch( morphTargetsTexture, morphUV, 0 );\n\t\t}\n\t#else\n\t\t#ifndef USE_MORPHNORMALS\n\t\t\tuniform float morphTargetInfluences[ 8 ];\n\t\t#else\n\t\t\tuniform float morphTargetInfluences[ 4 ];\n\t\t#endif\n\t#endif\n#endif",morphtarget_vertex:"#ifdef USE_MORPHTARGETS\n\ttransformed *= morphTargetBaseInfluence;\n\t#ifdef MORPHTARGETS_TEXTURE\n\t\tfor ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\n\t\t\tif ( morphTargetInfluences[ i ] != 0.0 ) transformed += getMorph( gl_VertexID, i, 0 ).xyz * morphTargetInfluences[ i ];\n\t\t}\n\t#else\n\t\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\n\t\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\n\t\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\n\t\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\n\t\t#ifndef USE_MORPHNORMALS\n\t\t\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\n\t\t\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\n\t\t\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\n\t\t\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\n\t\t#endif\n\t#endif\n#endif",normal_fragment_begin:"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\n#ifdef FLAT_SHADED\n\tvec3 fdx = dFdx( vViewPosition );\n\tvec3 fdy = dFdy( vViewPosition );\n\tvec3 normal = normalize( cross( fdx, fdy ) );\n#else\n\tvec3 normal = normalize( vNormal );\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\t#ifdef USE_TANGENT\n\t\tvec3 tangent = normalize( vTangent );\n\t\tvec3 bitangent = normalize( vBitangent );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\ttangent = tangent * faceDirection;\n\t\t\tbitangent = bitangent * faceDirection;\n\t\t#endif\n\t\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\n\t\t\tmat3 vTBN = mat3( tangent, bitangent, normal );\n\t\t#endif\n\t#endif\n#endif\nvec3 geometryNormal = normal;",normal_fragment_maps:"#ifdef OBJECTSPACE_NORMALMAP\n\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t#ifdef FLIP_SIDED\n\t\tnormal = - normal;\n\t#endif\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\tnormal = normalize( normalMatrix * normal );\n#elif defined( TANGENTSPACE_NORMALMAP )\n\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\tmapN.xy *= normalScale;\n\t#ifdef USE_TANGENT\n\t\tnormal = normalize( vTBN * mapN );\n\t#else\n\t\tnormal = perturbNormal2Arb( - vViewPosition, normal, mapN, faceDirection );\n\t#endif\n#elif defined( USE_BUMPMAP )\n\tnormal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\n#endif",normal_pars_fragment:"#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif",normal_pars_vertex:"#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif",normal_vertex:"#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif",normalmap_pars_fragment:"#ifdef USE_NORMALMAP\n\tuniform sampler2D normalMap;\n\tuniform vec2 normalScale;\n#endif\n#ifdef OBJECTSPACE_NORMALMAP\n\tuniform mat3 normalMatrix;\n#endif\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\n\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\n\t\tvec3 q0 = dFdx( eye_pos.xyz );\n\t\tvec3 q1 = dFdy( eye_pos.xyz );\n\t\tvec2 st0 = dFdx( vUv.st );\n\t\tvec2 st1 = dFdy( vUv.st );\n\t\tvec3 N = surf_norm;\n\t\tvec3 q1perp = cross( q1, N );\n\t\tvec3 q0perp = cross( N, q0 );\n\t\tvec3 T = q1perp * st0.x + q0perp * st1.x;\n\t\tvec3 B = q1perp * st0.y + q0perp * st1.y;\n\t\tfloat det = max( dot( T, T ), dot( B, B ) );\n\t\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\n\t\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\n\t}\n#endif",clearcoat_normal_fragment_begin:"#ifdef USE_CLEARCOAT\n\tvec3 clearcoatNormal = geometryNormal;\n#endif",clearcoat_normal_fragment_maps:"#ifdef USE_CLEARCOAT_NORMALMAP\n\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\n\tclearcoatMapN.xy *= clearcoatNormalScale;\n\t#ifdef USE_TANGENT\n\t\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\n\t#else\n\t\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\n\t#endif\n#endif",clearcoat_pars_fragment:"#ifdef USE_CLEARCOATMAP\n\tuniform sampler2D clearcoatMap;\n#endif\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\tuniform sampler2D clearcoatRoughnessMap;\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n\tuniform sampler2D clearcoatNormalMap;\n\tuniform vec2 clearcoatNormalScale;\n#endif",iridescence_pars_fragment:"#ifdef USE_IRIDESCENCEMAP\n\tuniform sampler2D iridescenceMap;\n#endif\n#ifdef USE_IRIDESCENCE_THICKNESSMAP\n\tuniform sampler2D iridescenceThicknessMap;\n#endif",output_fragment:"#ifdef OPAQUE\ndiffuseColor.a = 1.0;\n#endif\n#ifdef USE_TRANSMISSION\ndiffuseColor.a *= material.transmissionAlpha + 0.1;\n#endif\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );",packing:"vec3 packNormalToRGB( const in vec3 normal ) {\n\treturn normalize( normal ) * 0.5 + 0.5;\n}\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\n\treturn 2.0 * rgb.xyz - 1.0;\n}\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\nconst float ShiftRight8 = 1. / 256.;\nvec4 packDepthToRGBA( const in float v ) {\n\tvec4 r = vec4( fract( v * PackFactors ), v );\n\tr.yzw -= r.xyz * ShiftRight8;\treturn r * PackUpscale;\n}\nfloat unpackRGBAToDepth( const in vec4 v ) {\n\treturn dot( v, UnpackFactors );\n}\nvec2 packDepthToRG( in highp float v ) {\n\treturn packDepthToRGBA( v ).yx;\n}\nfloat unpackRGToDepth( const in highp vec2 v ) {\n\treturn unpackRGBAToDepth( vec4( v.xy, 0.0, 0.0 ) );\n}\nvec4 pack2HalfToRGBA( vec2 v ) {\n\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\n\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\n}\nvec2 unpackRGBATo2Half( vec4 v ) {\n\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\n}\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( viewZ + near ) / ( near - far );\n}\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\n\treturn linearClipZ * ( near - far ) - near;\n}\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\n}\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\n\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\n}",premultiplied_alpha_fragment:"#ifdef PREMULTIPLIED_ALPHA\n\tgl_FragColor.rgb *= gl_FragColor.a;\n#endif",project_vertex:"vec4 mvPosition = vec4( transformed, 1.0 );\n#ifdef USE_INSTANCING\n\tmvPosition = instanceMatrix * mvPosition;\n#endif\nmvPosition = modelViewMatrix * mvPosition;\ngl_Position = projectionMatrix * mvPosition;",dithering_fragment:"#ifdef DITHERING\n\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\n#endif",dithering_pars_fragment:"#ifdef DITHERING\n\tvec3 dithering( vec3 color ) {\n\t\tfloat grid_position = rand( gl_FragCoord.xy );\n\t\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\n\t\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\n\t\treturn color + dither_shift_RGB;\n\t}\n#endif",roughnessmap_fragment:"float roughnessFactor = roughness;\n#ifdef USE_ROUGHNESSMAP\n\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\n\troughnessFactor *= texelRoughness.g;\n#endif",roughnessmap_pars_fragment:"#ifdef USE_ROUGHNESSMAP\n\tuniform sampler2D roughnessMap;\n#endif",shadowmap_pars_fragment:"#if NUM_SPOT_LIGHT_COORDS > 0\n  varying vec4 vSpotLightCoord[ NUM_SPOT_LIGHT_COORDS ];\n#endif\n#if NUM_SPOT_LIGHT_MAPS > 0\n  uniform sampler2D spotLightMap[ NUM_SPOT_LIGHT_MAPS ];\n#endif\n#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\n\t\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\n\t}\n\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\n\t\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\n\t}\n\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\n\t\tfloat occlusion = 1.0;\n\t\tvec2 distribution = texture2DDistribution( shadow, uv );\n\t\tfloat hard_shadow = step( compare , distribution.x );\n\t\tif (hard_shadow != 1.0 ) {\n\t\t\tfloat distance = compare - distribution.x ;\n\t\t\tfloat variance = max( 0.00000, distribution.y * distribution.y );\n\t\t\tfloat softness_probability = variance / (variance + distance * distance );\t\t\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\t\t\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\n\t\t}\n\t\treturn occlusion;\n\t}\n\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\n\t\tfloat shadow = 1.0;\n\t\tshadowCoord.xyz /= shadowCoord.w;\n\t\tshadowCoord.z += shadowBias;\n\t\tbool inFrustum = shadowCoord.x >= 0.0 && shadowCoord.x <= 1.0 && shadowCoord.y >= 0.0 && shadowCoord.y <= 1.0;\n\t\tbool frustumTest = inFrustum && shadowCoord.z <= 1.0;\n\t\tif ( frustumTest ) {\n\t\t#if defined( SHADOWMAP_TYPE_PCF )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx0 = - texelSize.x * shadowRadius;\n\t\t\tfloat dy0 = - texelSize.y * shadowRadius;\n\t\t\tfloat dx1 = + texelSize.x * shadowRadius;\n\t\t\tfloat dy1 = + texelSize.y * shadowRadius;\n\t\t\tfloat dx2 = dx0 / 2.0;\n\t\t\tfloat dy2 = dy0 / 2.0;\n\t\t\tfloat dx3 = dx1 / 2.0;\n\t\t\tfloat dy3 = dy1 / 2.0;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n\t\t\t) * ( 1.0 / 17.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx = texelSize.x;\n\t\t\tfloat dy = texelSize.y;\n\t\t\tvec2 uv = shadowCoord.xy;\n\t\t\tvec2 f = fract( uv * shadowMapSize + 0.5 );\n\t\t\tuv -= f * texelSize;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ),\n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ),\n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ),\n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t f.y )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_VSM )\n\t\t\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#else\n\t\t\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#endif\n\t\t}\n\t\treturn shadow;\n\t}\n\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\n\t\tvec3 absV = abs( v );\n\t\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\n\t\tabsV *= scaleToCube;\n\t\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\n\t\tvec2 planar = v.xy;\n\t\tfloat almostATexel = 1.5 * texelSizeY;\n\t\tfloat almostOne = 1.0 - almostATexel;\n\t\tif ( absV.z >= almostOne ) {\n\t\t\tif ( v.z > 0.0 )\n\t\t\t\tplanar.x = 4.0 - v.x;\n\t\t} else if ( absV.x >= almostOne ) {\n\t\t\tfloat signX = sign( v.x );\n\t\t\tplanar.x = v.z * signX + 2.0 * signX;\n\t\t} else if ( absV.y >= almostOne ) {\n\t\t\tfloat signY = sign( v.y );\n\t\t\tplanar.x = v.x + 2.0 * signY + 2.0;\n\t\t\tplanar.y = v.z * signY - 2.0;\n\t\t}\n\t\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\n\t}\n\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\n\t\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\n\t\tvec3 lightToPosition = shadowCoord.xyz;\n\t\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\t\tdp += shadowBias;\n\t\tvec3 bd3D = normalize( lightToPosition );\n\t\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\n\t\t\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\n\t\t\treturn (\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#else\n\t\t\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\n\t\t#endif\n\t}\n#endif",shadowmap_pars_vertex:"#if NUM_SPOT_LIGHT_COORDS > 0\n  uniform mat4 spotLightMatrix[ NUM_SPOT_LIGHT_COORDS ];\n  varying vec4 vSpotLightCoord[ NUM_SPOT_LIGHT_COORDS ];\n#endif\n#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n#endif",shadowmap_vertex:"#if ( defined( USE_SHADOWMAP ) && ( NUM_DIR_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0 ) ) || ( NUM_SPOT_LIGHT_COORDS > 0 )\n\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\tvec4 shadowWorldPosition;\n#endif\n#if defined( USE_SHADOWMAP )\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\n\t\t\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\n\t\t\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n#endif\n#if NUM_SPOT_LIGHT_COORDS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_COORDS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition;\n\t\t#if ( defined( USE_SHADOWMAP ) && UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\t\tshadowWorldPosition.xyz += shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias;\n\t\t#endif\n\t\tvSpotLightCoord[ i ] = spotLightMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n#endif",shadowmask_pars_fragment:"float getShadowMask() {\n\tfloat shadow = 1.0;\n\t#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tdirectionalLight = directionalLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tspotLight = spotLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotLightCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tpointLight = pointLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#endif\n\treturn shadow;\n}",skinbase_vertex:"#ifdef USE_SKINNING\n\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\n\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\n\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\n\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\n#endif",skinning_pars_vertex:"#ifdef USE_SKINNING\n\tuniform mat4 bindMatrix;\n\tuniform mat4 bindMatrixInverse;\n\tuniform highp sampler2D boneTexture;\n\tuniform int boneTextureSize;\n\tmat4 getBoneMatrix( const in float i ) {\n\t\tfloat j = i * 4.0;\n\t\tfloat x = mod( j, float( boneTextureSize ) );\n\t\tfloat y = floor( j / float( boneTextureSize ) );\n\t\tfloat dx = 1.0 / float( boneTextureSize );\n\t\tfloat dy = 1.0 / float( boneTextureSize );\n\t\ty = dy * ( y + 0.5 );\n\t\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\n\t\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\n\t\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\n\t\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\n\t\tmat4 bone = mat4( v1, v2, v3, v4 );\n\t\treturn bone;\n\t}\n#endif",skinning_vertex:"#ifdef USE_SKINNING\n\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\n\tvec4 skinned = vec4( 0.0 );\n\tskinned += boneMatX * skinVertex * skinWeight.x;\n\tskinned += boneMatY * skinVertex * skinWeight.y;\n\tskinned += boneMatZ * skinVertex * skinWeight.z;\n\tskinned += boneMatW * skinVertex * skinWeight.w;\n\ttransformed = ( bindMatrixInverse * skinned ).xyz;\n#endif",skinnormal_vertex:"#ifdef USE_SKINNING\n\tmat4 skinMatrix = mat4( 0.0 );\n\tskinMatrix += skinWeight.x * boneMatX;\n\tskinMatrix += skinWeight.y * boneMatY;\n\tskinMatrix += skinWeight.z * boneMatZ;\n\tskinMatrix += skinWeight.w * boneMatW;\n\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\n\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\n\t#ifdef USE_TANGENT\n\t\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#endif\n#endif",specularmap_fragment:"float specularStrength;\n#ifdef USE_SPECULARMAP\n\tvec4 texelSpecular = texture2D( specularMap, vUv );\n\tspecularStrength = texelSpecular.r;\n#else\n\tspecularStrength = 1.0;\n#endif",specularmap_pars_fragment:"#ifdef USE_SPECULARMAP\n\tuniform sampler2D specularMap;\n#endif",tonemapping_fragment:"#if defined( TONE_MAPPING )\n\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\n#endif",tonemapping_pars_fragment:"#ifndef saturate\n#define saturate( a ) clamp( a, 0.0, 1.0 )\n#endif\nuniform float toneMappingExposure;\nvec3 LinearToneMapping( vec3 color ) {\n\treturn toneMappingExposure * color;\n}\nvec3 ReinhardToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\treturn saturate( color / ( vec3( 1.0 ) + color ) );\n}\nvec3 OptimizedCineonToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\tcolor = max( vec3( 0.0 ), color - 0.004 );\n\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\n}\nvec3 RRTAndODTFit( vec3 v ) {\n\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\n\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\n\treturn a / b;\n}\nvec3 ACESFilmicToneMapping( vec3 color ) {\n\tconst mat3 ACESInputMat = mat3(\n\t\tvec3( 0.59719, 0.07600, 0.02840 ),\t\tvec3( 0.35458, 0.90834, 0.13383 ),\n\t\tvec3( 0.04823, 0.01566, 0.83777 )\n\t);\n\tconst mat3 ACESOutputMat = mat3(\n\t\tvec3(  1.60475, -0.10208, -0.00327 ),\t\tvec3( -0.53108,  1.10813, -0.07276 ),\n\t\tvec3( -0.07367, -0.00605,  1.07602 )\n\t);\n\tcolor *= toneMappingExposure / 0.6;\n\tcolor = ACESInputMat * color;\n\tcolor = RRTAndODTFit( color );\n\tcolor = ACESOutputMat * color;\n\treturn saturate( color );\n}\nvec3 CustomToneMapping( vec3 color ) { return color; }",transmission_fragment:"#ifdef USE_TRANSMISSION\n\tmaterial.transmission = transmission;\n\tmaterial.transmissionAlpha = 1.0;\n\tmaterial.thickness = thickness;\n\tmaterial.attenuationDistance = attenuationDistance;\n\tmaterial.attenuationColor = attenuationColor;\n\t#ifdef USE_TRANSMISSIONMAP\n\t\tmaterial.transmission *= texture2D( transmissionMap, vUv ).r;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tmaterial.thickness *= texture2D( thicknessMap, vUv ).g;\n\t#endif\n\tvec3 pos = vWorldPosition;\n\tvec3 v = normalize( cameraPosition - pos );\n\tvec3 n = inverseTransformDirection( normal, viewMatrix );\n\tvec4 transmission = getIBLVolumeRefraction(\n\t\tn, v, material.roughness, material.diffuseColor, material.specularColor, material.specularF90,\n\t\tpos, modelMatrix, viewMatrix, projectionMatrix, material.ior, material.thickness,\n\t\tmaterial.attenuationColor, material.attenuationDistance );\n\tmaterial.transmissionAlpha = mix( material.transmissionAlpha, transmission.a, material.transmission );\n\ttotalDiffuse = mix( totalDiffuse, transmission.rgb, material.transmission );\n#endif",transmission_pars_fragment:"#ifdef USE_TRANSMISSION\n\tuniform float transmission;\n\tuniform float thickness;\n\tuniform float attenuationDistance;\n\tuniform vec3 attenuationColor;\n\t#ifdef USE_TRANSMISSIONMAP\n\t\tuniform sampler2D transmissionMap;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tuniform sampler2D thicknessMap;\n\t#endif\n\tuniform vec2 transmissionSamplerSize;\n\tuniform sampler2D transmissionSamplerMap;\n\tuniform mat4 modelMatrix;\n\tuniform mat4 projectionMatrix;\n\tvarying vec3 vWorldPosition;\n\tvec3 getVolumeTransmissionRay( const in vec3 n, const in vec3 v, const in float thickness, const in float ior, const in mat4 modelMatrix ) {\n\t\tvec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\n\t\tvec3 modelScale;\n\t\tmodelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\n\t\tmodelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\n\t\tmodelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\n\t\treturn normalize( refractionVector ) * thickness * modelScale;\n\t}\n\tfloat applyIorToRoughness( const in float roughness, const in float ior ) {\n\t\treturn roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\n\t}\n\tvec4 getTransmissionSample( const in vec2 fragCoord, const in float roughness, const in float ior ) {\n\t\tfloat framebufferLod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\n\t\t#ifdef texture2DLodEXT\n\t\t\treturn texture2DLodEXT( transmissionSamplerMap, fragCoord.xy, framebufferLod );\n\t\t#else\n\t\t\treturn texture2D( transmissionSamplerMap, fragCoord.xy, framebufferLod );\n\t\t#endif\n\t}\n\tvec3 applyVolumeAttenuation( const in vec3 radiance, const in float transmissionDistance, const in vec3 attenuationColor, const in float attenuationDistance ) {\n\t\tif ( isinf( attenuationDistance ) ) {\n\t\t\treturn radiance;\n\t\t} else {\n\t\t\tvec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\n\t\t\tvec3 transmittance = exp( - attenuationCoefficient * transmissionDistance );\t\t\treturn transmittance * radiance;\n\t\t}\n\t}\n\tvec4 getIBLVolumeRefraction( const in vec3 n, const in vec3 v, const in float roughness, const in vec3 diffuseColor,\n\t\tconst in vec3 specularColor, const in float specularF90, const in vec3 position, const in mat4 modelMatrix,\n\t\tconst in mat4 viewMatrix, const in mat4 projMatrix, const in float ior, const in float thickness,\n\t\tconst in vec3 attenuationColor, const in float attenuationDistance ) {\n\t\tvec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\n\t\tvec3 refractedRayExit = position + transmissionRay;\n\t\tvec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\n\t\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\n\t\trefractionCoords += 1.0;\n\t\trefractionCoords /= 2.0;\n\t\tvec4 transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\n\t\tvec3 attenuatedColor = applyVolumeAttenuation( transmittedLight.rgb, length( transmissionRay ), attenuationColor, attenuationDistance );\n\t\tvec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\n\t\treturn vec4( ( 1.0 - F ) * attenuatedColor * diffuseColor, transmittedLight.a );\n\t}\n#endif",uv_pars_fragment:"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\n\tvarying vec2 vUv;\n#endif",uv_pars_vertex:"#ifdef USE_UV\n\t#ifdef UVS_VERTEX_ONLY\n\t\tvec2 vUv;\n\t#else\n\t\tvarying vec2 vUv;\n\t#endif\n\tuniform mat3 uvTransform;\n#endif",uv_vertex:"#ifdef USE_UV\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n#endif",uv2_pars_fragment:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvarying vec2 vUv2;\n#endif",uv2_pars_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tattribute vec2 uv2;\n\tvarying vec2 vUv2;\n\tuniform mat3 uv2Transform;\n#endif",uv2_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\n#endif",worldpos_vertex:"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION ) || NUM_SPOT_LIGHT_COORDS > 0\n\tvec4 worldPosition = vec4( transformed, 1.0 );\n\t#ifdef USE_INSTANCING\n\t\tworldPosition = instanceMatrix * worldPosition;\n\t#endif\n\tworldPosition = modelMatrix * worldPosition;\n#endif",background_vert:"varying vec2 vUv;\nuniform mat3 uvTransform;\nvoid main() {\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n\tgl_Position = vec4( position.xy, 1.0, 1.0 );\n}",background_frag:"uniform sampler2D t2D;\nuniform float backgroundIntensity;\nvarying vec2 vUv;\nvoid main() {\n\tvec4 texColor = texture2D( t2D, vUv );\n\t#ifdef DECODE_VIDEO_TEXTURE\n\t\ttexColor = vec4( mix( pow( texColor.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), texColor.rgb * 0.0773993808, vec3( lessThanEqual( texColor.rgb, vec3( 0.04045 ) ) ) ), texColor.w );\n\t#endif\n\ttexColor.rgb *= backgroundIntensity;\n\tgl_FragColor = texColor;\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",backgroundCube_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\tgl_Position.z = gl_Position.w;\n}",backgroundCube_frag:"#ifdef ENVMAP_TYPE_CUBE\n\tuniform samplerCube envMap;\n#elif defined( ENVMAP_TYPE_CUBE_UV )\n\tuniform sampler2D envMap;\n#endif\nuniform float flipEnvMap;\nuniform float backgroundBlurriness;\nuniform float backgroundIntensity;\nvarying vec3 vWorldDirection;\n#include <cube_uv_reflection_fragment>\nvoid main() {\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 texColor = textureCube( envMap, vec3( flipEnvMap * vWorldDirection.x, vWorldDirection.yz ) );\n\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\tvec4 texColor = textureCubeUV( envMap, vWorldDirection, backgroundBlurriness );\n\t#else\n\t\tvec4 texColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t#endif\n\ttexColor.rgb *= backgroundIntensity;\n\tgl_FragColor = texColor;\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",cube_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\tgl_Position.z = gl_Position.w;\n}",cube_frag:"uniform samplerCube tCube;\nuniform float tFlip;\nuniform float opacity;\nvarying vec3 vWorldDirection;\nvoid main() {\n\tvec4 texColor = textureCube( tCube, vec3( tFlip * vWorldDirection.x, vWorldDirection.yz ) );\n\tgl_FragColor = texColor;\n\tgl_FragColor.a *= opacity;\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",depth_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvHighPrecisionZW = gl_Position.zw;\n}",depth_frag:"#if DEPTH_PACKING == 3200\n\tuniform float opacity;\n#endif\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#if DEPTH_PACKING == 3200\n\t\tdiffuseColor.a = opacity;\n\t#endif\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <logdepthbuf_fragment>\n\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\n\t#if DEPTH_PACKING == 3200\n\t\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\n\t#elif DEPTH_PACKING == 3201\n\t\tgl_FragColor = packDepthToRGBA( fragCoordZ );\n\t#endif\n}",distanceRGBA_vert:"#define DISTANCE\nvarying vec3 vWorldPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <clipping_planes_vertex>\n\tvWorldPosition = worldPosition.xyz;\n}",distanceRGBA_frag:"#define DISTANCE\nuniform vec3 referencePosition;\nuniform float nearDistance;\nuniform float farDistance;\nvarying vec3 vWorldPosition;\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main () {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\tfloat dist = length( vWorldPosition - referencePosition );\n\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\n\tdist = saturate( dist );\n\tgl_FragColor = packDepthToRGBA( dist );\n}",equirect_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n}",equirect_frag:"uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvec3 direction = normalize( vWorldDirection );\n\tvec2 sampleUV = equirectUv( direction );\n\tgl_FragColor = texture2D( tEquirect, sampleUV );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",linedashed_vert:"uniform float scale;\nattribute float lineDistance;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\tvLineDistance = scale * lineDistance;\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}",linedashed_frag:"uniform vec3 diffuse;\nuniform float opacity;\nuniform float dashSize;\nuniform float totalSize;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\n\t\tdiscard;\n\t}\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <color_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",meshbasic_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinbase_vertex>\n\t\t#include <skinnormal_vertex>\n\t\t#include <defaultnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <fog_vertex>\n}",meshbasic_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\t#ifdef USE_LIGHTMAP\n\t\tvec4 lightMapTexel = texture2D( lightMap, vUv2 );\n\t\treflectedLight.indirectDiffuse += lightMapTexel.rgb * lightMapIntensity * RECIPROCAL_PI;\n\t#else\n\t\treflectedLight.indirectDiffuse += vec3( 1.0 );\n\t#endif\n\t#include <aomap_fragment>\n\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\n\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\n\t#include <envmap_fragment>\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshlambert_vert:"#define LAMBERT\nvarying vec3 vViewPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <normal_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <normal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshlambert_frag:"#define LAMBERT\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <normal_pars_fragment>\n#include <lights_lambert_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_lambert_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshmatcap_vert:"#define MATCAP\nvarying vec3 vViewPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <color_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <fog_pars_vertex>\n#include <normal_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <normal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n\tvViewPosition = - mvPosition.xyz;\n}",meshmatcap_frag:"#define MATCAP\nuniform vec3 diffuse;\nuniform float opacity;\nuniform sampler2D matcap;\nvarying vec3 vViewPosition;\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <fog_pars_fragment>\n#include <normal_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tvec3 viewDir = normalize( vViewPosition );\n\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\n\tvec3 y = cross( viewDir, x );\n\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\n\t#ifdef USE_MATCAP\n\t\tvec4 matcapColor = texture2D( matcap, uv );\n\t#else\n\t\tvec4 matcapColor = vec4( vec3( mix( 0.2, 0.8, uv.y ) ), 1.0 );\n\t#endif\n\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshnormal_vert:"#define NORMAL\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <normal_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <normal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvViewPosition = - mvPosition.xyz;\n#endif\n}",meshnormal_frag:"#define NORMAL\nuniform float opacity;\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#include <packing>\n#include <uv_pars_fragment>\n#include <normal_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\t#include <logdepthbuf_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\n\t#ifdef OPAQUE\n\t\tgl_FragColor.a = 1.0;\n\t#endif\n}",meshphong_vert:"#define PHONG\nvarying vec3 vViewPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <normal_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <normal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphong_frag:"#define PHONG\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform vec3 specular;\nuniform float shininess;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <normal_pars_fragment>\n#include <lights_phong_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_phong_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphysical_vert:"#define STANDARD\nvarying vec3 vViewPosition;\n#ifdef USE_TRANSMISSION\n\tvarying vec3 vWorldPosition;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <normal_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <normal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n#ifdef USE_TRANSMISSION\n\tvWorldPosition = worldPosition.xyz;\n#endif\n}",meshphysical_frag:"#define STANDARD\n#ifdef PHYSICAL\n\t#define IOR\n\t#define SPECULAR\n#endif\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float roughness;\nuniform float metalness;\nuniform float opacity;\n#ifdef IOR\n\tuniform float ior;\n#endif\n#ifdef SPECULAR\n\tuniform float specularIntensity;\n\tuniform vec3 specularColor;\n\t#ifdef USE_SPECULARINTENSITYMAP\n\t\tuniform sampler2D specularIntensityMap;\n\t#endif\n\t#ifdef USE_SPECULARCOLORMAP\n\t\tuniform sampler2D specularColorMap;\n\t#endif\n#endif\n#ifdef USE_CLEARCOAT\n\tuniform float clearcoat;\n\tuniform float clearcoatRoughness;\n#endif\n#ifdef USE_IRIDESCENCE\n\tuniform float iridescence;\n\tuniform float iridescenceIOR;\n\tuniform float iridescenceThicknessMinimum;\n\tuniform float iridescenceThicknessMaximum;\n#endif\n#ifdef USE_SHEEN\n\tuniform vec3 sheenColor;\n\tuniform float sheenRoughness;\n\t#ifdef USE_SHEENCOLORMAP\n\t\tuniform sampler2D sheenColorMap;\n\t#endif\n\t#ifdef USE_SHEENROUGHNESSMAP\n\t\tuniform sampler2D sheenRoughnessMap;\n\t#endif\n#endif\nvarying vec3 vViewPosition;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <bsdfs>\n#include <iridescence_fragment>\n#include <cube_uv_reflection_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_physical_pars_fragment>\n#include <fog_pars_fragment>\n#include <lights_pars_begin>\n#include <normal_pars_fragment>\n#include <lights_physical_pars_fragment>\n#include <transmission_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <clearcoat_pars_fragment>\n#include <iridescence_pars_fragment>\n#include <roughnessmap_pars_fragment>\n#include <metalnessmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <roughnessmap_fragment>\n\t#include <metalnessmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <clearcoat_normal_fragment_begin>\n\t#include <clearcoat_normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_physical_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\n\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\n\t#include <transmission_fragment>\n\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\n\t#ifdef USE_SHEEN\n\t\tfloat sheenEnergyComp = 1.0 - 0.157 * max3( material.sheenColor );\n\t\toutgoingLight = outgoingLight * sheenEnergyComp + sheenSpecular;\n\t#endif\n\t#ifdef USE_CLEARCOAT\n\t\tfloat dotNVcc = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\n\t\tvec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\n\t\toutgoingLight = outgoingLight * ( 1.0 - material.clearcoat * Fcc ) + clearcoatSpecular * material.clearcoat;\n\t#endif\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshtoon_vert:"#define TOON\nvarying vec3 vViewPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <normal_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <normal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshtoon_frag:"#define TOON\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <gradientmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <normal_pars_fragment>\n#include <lights_toon_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_toon_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\t#include <output_fragment>\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",points_vert:"uniform float size;\nuniform float scale;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <color_vertex>\n\t#include <morphcolor_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\tgl_PointSize = size;\n\t#ifdef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\n\t#endif\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <fog_vertex>\n}",points_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <color_pars_fragment>\n#include <map_particle_pars_fragment>\n#include <alphatest_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include 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if(l="drawArraysInstancedANGLE",null===(s=t.get("ANGLE_instanced_arrays")))return void console.error("THREE.WebGLBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.");s[l](i,r,a,o),n.update(a,i,o)}}}function Hy(e,t,n){var r;function i(t){if("highp"===t){if(e.getShaderPrecisionFormat(35633,36338).precision>0&&e.getShaderPrecisionFormat(35632,36338).precision>0)return"highp";t="mediump"}return"mediump"===t&&e.getShaderPrecisionFormat(35633,36337).precision>0&&e.getShaderPrecisionFormat(35632,36337).precision>0?"mediump":"lowp"}var A="undefined"!==typeof WebGL2RenderingContext&&e instanceof WebGL2RenderingContext,a=void 0!==n.precision?n.precision:"highp",o=i(a);o!==a&&(console.warn("THREE.WebGLRenderer:",a,"not supported, using",o,"instead."),a=o);var s=A||t.has("WEBGL_draw_buffers"),l=!0===n.logarithmicDepthBuffer,u=e.getParameter(34930),c=e.getParameter(35660),d=e.getParameter(3379),h=e.getParameter(34076),f=e.getParameter(34921),p=e.getParameter(36347),g=e.getParameter(36348),m=e.getParameter(36349),v=c>0,y=A||t.has("OES_texture_float");return{isWebGL2:A,drawBuffers:s,getMaxAnisotropy:function(){if(void 0!==r)return r;if(!0===t.has("EXT_texture_filter_anisotropic")){var n=t.get("EXT_texture_filter_anisotropic");r=e.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else r=0;return r},getMaxPrecision:i,precision:a,logarithmicDepthBuffer:l,maxTextures:u,maxVertexTextures:c,maxTextureSize:d,maxCubemapSize:h,maxAttributes:f,maxVertexUniforms:p,maxVaryings:g,maxFragmentUniforms:m,vertexTextures:v,floatFragmentTextures:y,floatVertexTextures:v&&y,maxSamples:A?e.getParameter(36183):0}}function Py(e){var t=this,n=null,r=0,i=!1,A=!1,a=new xy,o=new zg,s={value:null,needsUpdate:!1};function l(e,n,r,i){var A=null!==e?e.length:0,l=null;if(0!==A){if(l=s.value,!0!==i||null===l){var u=r+4*A,c=n.matrixWorldInverse;o.getNormalMatrix(c),(null===l||l.length<u)&&(l=new Float32Array(u));for(var d=0,h=r;d!==A;++d,h+=4)a.copy(e[d]).applyMatrix4(c,o),a.normal.toArray(l,h),l[h+3]=a.constant}s.value=l,s.needsUpdate=!0}return t.numPlanes=A,t.numIntersection=0,l}this.uniform=s,this.numPlanes=0,this.numIntersection=0,this.init=function(e,t){var n=0!==e.length||t||0!==r||i;return i=t,r=e.length,n},this.beginShadows=function(){A=!0,l(null)},this.endShadows=function(){A=!1},this.setGlobalState=function(e,t){n=l(e,t,0)},this.setState=function(a,o,u){var c=a.clippingPlanes,d=a.clipIntersection,h=a.clipShadows,f=e.get(a);if(!i||null===c||0===c.length||A&&!h)A?l(null):function(){s.value!==n&&(s.value=n,s.needsUpdate=r>0);t.numPlanes=r,t.numIntersection=0}();else{var p=A?0:r,g=4*p,m=f.clippingState||null;s.value=m,m=l(c,o,g,u);for(var v=0;v!==g;++v)m[v]=n[v];f.clippingState=m,this.numIntersection=d?this.numPlanes:0,this.numPlanes+=p}}}function Ny(e){var t=new WeakMap;function n(e,t){return t===Jf?e.mapping=qf:t===Zf&&(e.mapping=Yf),e}function r(e){var n=e.target;n.removeEventListener("dispose",r);var i=t.get(n);void 0!==i&&(t.delete(n),i.dispose())}return{get:function(i){if(i&&i.isTexture&&!1===i.isRenderTargetTexture){var A=i.mapping;if(A===Jf||A===Zf){if(t.has(i))return n(t.get(i).texture,i.mapping);var a=i.image;if(a&&a.height>0){var o=new wy(a.height/2);return o.fromEquirectangularTexture(e,i),t.set(i,o),i.addEventListener("dispose",r),n(o.texture,i.mapping)}return null}}return i},dispose:function(){t=new WeakMap}}}var Oy=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:-1,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:-1,o=arguments.length>4&&void 0!==arguments[4]?arguments[4]:.1,s=arguments.length>5&&void 0!==arguments[5]?arguments[5]:2e3;return v(this,n),(e=t.call(this)).isOrthographicCamera=!0,e.type="OrthographicCamera",e.zoom=1,e.view=null,e.left=r,e.right=i,e.top=A,e.bottom=a,e.near=o,e.far=s,e.updateProjectionMatrix(),e}return w(n,[{key:"copy",value:function(e,t){return jc(b(n.prototype),"copy",this).call(this,e,t),this.left=e.left,this.right=e.right,this.top=e.top,this.bottom=e.bottom,this.near=e.near,this.far=e.far,this.zoom=e.zoom,this.view=null===e.view?null:Object.assign({},e.view),this}},{key:"setViewOffset",value:function(e,t,n,r,i,A){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=e,this.view.fullHeight=t,this.view.offsetX=n,this.view.offsetY=r,this.view.width=i,this.view.height=A,this.updateProjectionMatrix()}},{key:"clearViewOffset",value:function(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}},{key:"updateProjectionMatrix",value:function(){var e=(this.right-this.left)/(2*this.zoom),t=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,r=(this.top+this.bottom)/2,i=n-e,A=n+e,a=r+t,o=r-t;if(null!==this.view&&this.view.enabled){var s=(this.right-this.left)/this.view.fullWidth/this.zoom,l=(this.top-this.bottom)/this.view.fullHeight/this.zoom;A=(i+=s*this.view.offsetX)+s*this.view.width,o=(a-=l*this.view.offsetY)-l*this.view.height}this.projectionMatrix.makeOrthographic(i,A,a,o,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}},{key:"toJSON",value:function(e){var t=jc(b(n.prototype),"toJSON",this).call(this,e);return t.object.zoom=this.zoom,t.object.left=this.left,t.object.right=this.right,t.object.top=this.top,t.object.bottom=this.bottom,t.object.near=this.near,t.object.far=this.far,null!==this.view&&(t.object.view=Object.assign({},this.view)),t}}]),n}(fy),Vy=4,zy=[.125,.215,.35,.446,.526,.582],Gy=20,Ky=new Oy,Wy=new im,jy=null,Xy=(1+Math.sqrt(5))/2,qy=1/Xy,Yy=[new pm(1,1,1),new pm(-1,1,1),new pm(1,1,-1),new pm(-1,1,-1),new pm(0,Xy,qy),new pm(0,Xy,-qy),new pm(qy,0,Xy),new pm(-qy,0,Xy),new pm(Xy,qy,0),new pm(-Xy,qy,0)],Jy=function(){function e(t){v(this,e),this._renderer=t,this._pingPongRenderTarget=null,this._lodMax=0,this._cubeSize=0,this._lodPlanes=[],this._sizeLods=[],this._sigmas=[],this._blurMaterial=null,this._cubemapMaterial=null,this._equirectMaterial=null,this._compileMaterial(this._blurMaterial)}return w(e,[{key:"fromScene",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:.1,r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:100;jy=this._renderer.getRenderTarget(),this._setSize(256);var i=this._allocateTargets();return i.depthBuffer=!0,this._sceneToCubeUV(e,n,r,i),t>0&&this._blur(i,0,0,t),this._applyPMREM(i),this._cleanup(i),i}},{key:"fromEquirectangular",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return this._fromTexture(e,t)}},{key:"fromCubemap",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;return this._fromTexture(e,t)}},{key:"compileCubemapShader",value:function(){null===this._cubemapMaterial&&(this._cubemapMaterial=tw(),this._compileMaterial(this._cubemapMaterial))}},{key:"compileEquirectangularShader",value:function(){null===this._equirectMaterial&&(this._equirectMaterial=ew(),this._compileMaterial(this._equirectMaterial))}},{key:"dispose",value:function(){this._dispose(),null!==this._cubemapMaterial&&this._cubemapMaterial.dispose(),null!==this._equirectMaterial&&this._equirectMaterial.dispose()}},{key:"_setSize",value:function(e){this._lodMax=Math.floor(Math.log2(e)),this._cubeSize=Math.pow(2,this._lodMax)}},{key:"_dispose",value:function(){null!==this._blurMaterial&&this._blurMaterial.dispose(),null!==this._pingPongRenderTarget&&this._pingPongRenderTarget.dispose();for(var e=0;e<this._lodPlanes.length;e++)this._lodPlanes[e].dispose()}},{key:"_cleanup",value:function(e){this._renderer.setRenderTarget(jy),e.scissorTest=!1,$y(e,0,0,e.width,e.height)}},{key:"_fromTexture",value:function(e,t){e.mapping===qf||e.mapping===Yf?this._setSize(0===e.image.length?16:e.image[0].width||e.image[0].image.width):this._setSize(e.image.width/4),jy=this._renderer.getRenderTarget();var n=t||this._allocateTargets();return this._textureToCubeUV(e,n),this._applyPMREM(n),this._cleanup(n),n}},{key:"_allocateTargets",value:function(){var e=3*Math.max(this._cubeSize,112),t=4*this._cubeSize,n={magFilter:ap,minFilter:ap,generateMipmaps:!1,type:mp,format:_p,encoding:fg,depthBuffer:!1},r=Zy(e,t,n);if(null===this._pingPongRenderTarget||this._pingPongRenderTarget.width!==e||this._pingPongRenderTarget.height!==t){null!==this._pingPongRenderTarget&&this._dispose(),this._pingPongRenderTarget=Zy(e,t,n);var i=this._lodMax,A=function(e){for(var t=[],n=[],r=[],i=e,A=e-Vy+1+zy.length,a=0;a<A;a++){var o=Math.pow(2,i);n.push(o);var s=1/o;a>e-Vy?s=zy[a-e+Vy-1]:0===a&&(s=0),r.push(s);for(var l=1/(o-2),u=-l,c=1+l,d=[u,u,c,u,c,c,u,u,c,c,u,c],h=6,f=6,p=3,g=2,m=1,v=new Float32Array(p*f*h),y=new Float32Array(g*f*h),w=new Float32Array(m*f*h),B=0;B<h;B++){var _=B%3*2/3-1,b=B>2?0:-1,x=[_,b,0,_+2/3,b,0,_+2/3,b+1,0,_,b,0,_+2/3,b+1,0,_,b+1,0];v.set(x,p*f*B),y.set(d,g*f*B);var C=[B,B,B,B,B,B];w.set(C,m*f*B)}var S=new Gv;S.setAttribute("position",new Qv(v,p)),S.setAttribute("uv",new Qv(y,g)),S.setAttribute("faceIndex",new Qv(w,m)),t.push(S),i>Vy&&i--}return{lodPlanes:t,sizeLods:n,sigmas:r}}(i);this._sizeLods=A.sizeLods,this._lodPlanes=A.lodPlanes,this._sigmas=A.sigmas,this._blurMaterial=function(e,t,n){var r=new Float32Array(Gy),i=new pm(0,1,0),A=new hy({name:"SphericalGaussianBlur",defines:{n:Gy,CUBEUV_TEXEL_WIDTH:1/t,CUBEUV_TEXEL_HEIGHT:1/n,CUBEUV_MAX_MIP:"".concat(e,".0")},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:r},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:i}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform int samples;\n\t\t\tuniform float weights[ n ];\n\t\t\tuniform bool latitudinal;\n\t\t\tuniform float dTheta;\n\t\t\tuniform float mipInt;\n\t\t\tuniform vec3 poleAxis;\n\n\t\t\t#define ENVMAP_TYPE_CUBE_UV\n\t\t\t#include <cube_uv_reflection_fragment>\n\n\t\t\tvec3 getSample( float theta, vec3 axis ) {\n\n\t\t\t\tfloat cosTheta = cos( theta );\n\t\t\t\t// Rodrigues' axis-angle rotation\n\t\t\t\tvec3 sampleDirection = vOutputDirection * cosTheta\n\t\t\t\t\t+ cross( axis, vOutputDirection ) * sin( theta )\n\t\t\t\t\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\n\n\t\t\t\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\n\n\t\t\t}\n\n\t\t\tvoid main() {\n\n\t\t\t\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\n\n\t\t\t\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\n\n\t\t\t\t\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\n\n\t\t\t\t}\n\n\t\t\t\taxis = normalize( axis );\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\n\n\t\t\t\tfor ( int i = 1; i < n; i++ ) {\n\n\t\t\t\t\tif ( i >= samples ) {\n\n\t\t\t\t\t\tbreak;\n\n\t\t\t\t\t}\n\n\t\t\t\t\tfloat theta = dTheta * float( i );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\n\n\t\t\t\t}\n\n\t\t\t}\n\t\t",blending:uf,depthTest:!1,depthWrite:!1});return A}(i,e,t)}return r}},{key:"_compileMaterial",value:function(e){var t=new iy(this._lodPlanes[0],e);this._renderer.compile(t,Ky)}},{key:"_sceneToCubeUV",value:function(e,t,n,r){var i=new py(90,1,t,n),A=[1,-1,1,1,1,1],a=[1,1,1,-1,-1,-1],o=this._renderer,s=o.autoClear,l=o.toneMapping;o.getClearColor(Wy),o.toneMapping=zf,o.autoClear=!1;var u=new Fv({name:"PMREM.Background",side:sf,depthWrite:!1,depthTest:!1}),c=new iy(new ay,u),d=!1,h=e.background;h?h.isColor&&(u.color.copy(h),e.background=null,d=!0):(u.color.copy(Wy),d=!0);for(var f=0;f<6;f++){var p=f%3;0===p?(i.up.set(0,A[f],0),i.lookAt(a[f],0,0)):1===p?(i.up.set(0,0,A[f]),i.lookAt(0,a[f],0)):(i.up.set(0,A[f],0),i.lookAt(0,0,a[f]));var g=this._cubeSize;$y(r,p*g,f>2?g:0,g,g),o.setRenderTarget(r),d&&o.render(c,i),o.render(e,i)}c.geometry.dispose(),c.material.dispose(),o.toneMapping=l,o.autoClear=s,e.background=h}},{key:"_textureToCubeUV",value:function(e,t){var n=this._renderer,r=e.mapping===qf||e.mapping===Yf;r?(null===this._cubemapMaterial&&(this._cubemapMaterial=tw()),this._cubemapMaterial.uniforms.flipEnvMap.value=!1===e.isRenderTargetTexture?-1:1):null===this._equirectMaterial&&(this._equirectMaterial=ew());var i=r?this._cubemapMaterial:this._equirectMaterial,A=new iy(this._lodPlanes[0],i);i.uniforms.envMap.value=e;var a=this._cubeSize;$y(t,0,0,3*a,2*a),n.setRenderTarget(t),n.render(A,Ky)}},{key:"_applyPMREM",value:function(e){var t=this._renderer,n=t.autoClear;t.autoClear=!1;for(var r=1;r<this._lodPlanes.length;r++){var i=Math.sqrt(this._sigmas[r]*this._sigmas[r]-this._sigmas[r-1]*this._sigmas[r-1]),A=Yy[(r-1)%Yy.length];this._blur(e,r-1,r,i,A)}t.autoClear=n}},{key:"_blur",value:function(e,t,n,r,i){var A=this._pingPongRenderTarget;this._halfBlur(e,A,t,n,r,"latitudinal",i),this._halfBlur(A,e,n,n,r,"longitudinal",i)}},{key:"_halfBlur",value:function(e,t,n,r,i,A,a){var o=this._renderer,s=this._blurMaterial;"latitudinal"!==A&&"longitudinal"!==A&&console.error("blur direction must be either latitudinal or longitudinal!");var l=new iy(this._lodPlanes[r],s),u=s.uniforms,c=this._sizeLods[n]-1,d=isFinite(i)?Math.PI/(2*c):2*Math.PI/(2*Gy-1),h=i/d,f=isFinite(i)?1+Math.floor(3*h):Gy;f>Gy&&console.warn("sigmaRadians, ".concat(i,", is too large and will clip, as it requested ").concat(f," samples when the maximum is set to ").concat(Gy));for(var p=[],g=0,m=0;m<Gy;++m){var v=m/h,y=Math.exp(-v*v/2);p.push(y),0===m?g+=y:m<f&&(g+=2*y)}for(var w=0;w<p.length;w++)p[w]=p[w]/g;u.envMap.value=e.texture,u.samples.value=f,u.weights.value=p,u.latitudinal.value="latitudinal"===A,a&&(u.poleAxis.value=a);var B=this._lodMax;u.dTheta.value=d,u.mipInt.value=B-n;var _=this._sizeLods[r];$y(t,3*_*(r>B-Vy?r-B+Vy:0),4*(this._cubeSize-_),3*_,2*_),o.setRenderTarget(t),o.render(l,Ky)}}]),e}();function Zy(e,t,n){var r=new cm(e,t,n);return r.texture.mapping=$f,r.texture.name="PMREM.cubeUv",r.scissorTest=!0,r}function $y(e,t,n,r,i){e.viewport.set(t,n,r,i),e.scissor.set(t,n,r,i)}function ew(){return new hy({name:"EquirectangularToCubeUV",uniforms:{envMap:{value:null}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\n\t\t\t#include <common>\n\n\t\t\tvoid main() {\n\n\t\t\t\tvec3 outputDirection = normalize( vOutputDirection );\n\t\t\t\tvec2 uv = equirectUv( outputDirection );\n\n\t\t\t\tgl_FragColor = vec4( texture2D ( envMap, uv ).rgb, 1.0 );\n\n\t\t\t}\n\t\t",blending:uf,depthTest:!1,depthWrite:!1})}function tw(){return new hy({name:"CubemapToCubeUV",uniforms:{envMap:{value:null},flipEnvMap:{value:-1}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tuniform float flipEnvMap;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform samplerCube envMap;\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = textureCube( envMap, vec3( flipEnvMap * vOutputDirection.x, vOutputDirection.yz ) );\n\n\t\t\t}\n\t\t",blending:uf,depthTest:!1,depthWrite:!1})}function nw(e){var t=new WeakMap,n=null;function r(e){var n=e.target;n.removeEventListener("dispose",r);var i=t.get(n);void 0!==i&&(t.delete(n),i.dispose())}return{get:function(i){if(i&&i.isTexture){var A=i.mapping,a=A===Jf||A===Zf,o=A===qf||A===Yf;if(a||o){if(i.isRenderTargetTexture&&!0===i.needsPMREMUpdate){i.needsPMREMUpdate=!1;var s=t.get(i);return null===n&&(n=new Jy(e)),s=a?n.fromEquirectangular(i,s):n.fromCubemap(i,s),t.set(i,s),s.texture}if(t.has(i))return t.get(i).texture;var l=i.image;if(a&&l&&l.height>0||o&&l&&function(e){for(var t=0,n=6,r=0;r<n;r++)void 0!==e[r]&&t++;return t===n}(l)){null===n&&(n=new Jy(e));var u=a?n.fromEquirectangular(i):n.fromCubemap(i);return t.set(i,u),i.addEventListener("dispose",r),u.texture}return null}}return i},dispose:function(){t=new WeakMap,null!==n&&(n.dispose(),n=null)}}}function rw(e){var t={};function n(n){if(void 0!==t[n])return t[n];var r;switch(n){case"WEBGL_depth_texture":r=e.getExtension("WEBGL_depth_texture")||e.getExtension("MOZ_WEBGL_depth_texture")||e.getExtension("WEBKIT_WEBGL_depth_texture");break;case"EXT_texture_filter_anisotropic":r=e.getExtension("EXT_texture_filter_anisotropic")||e.getExtension("MOZ_EXT_texture_filter_anisotropic")||e.getExtension("WEBKIT_EXT_texture_filter_anisotropic");break;case"WEBGL_compressed_texture_s3tc":r=e.getExtension("WEBGL_compressed_texture_s3tc")||e.getExtension("MOZ_WEBGL_compressed_texture_s3tc")||e.getExtension("WEBKIT_WEBGL_compressed_texture_s3tc");break;case"WEBGL_compressed_texture_pvrtc":r=e.getExtension("WEBGL_compressed_texture_pvrtc")||e.getExtension("WEBKIT_WEBGL_compressed_texture_pvrtc");break;default:r=e.getExtension(n)}return t[n]=r,r}return{has:function(e){return 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if(c="drawElementsInstancedANGLE",null===(u=t.get("ANGLE_instanced_arrays")))return void console.error("THREE.WebGLIndexedBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.");u[c](i,s,A,r*a,l),n.update(s,i,l)}}}function aw(e){var t={frame:0,calls:0,triangles:0,points:0,lines:0};return{memory:{geometries:0,textures:0},render:t,programs:null,autoReset:!0,reset:function(){t.frame++,t.calls=0,t.triangles=0,t.points=0,t.lines=0},update:function(e,n,r){switch(t.calls++,n){case 4:t.triangles+=r*(e/3);break;case 1:t.lines+=r*(e/2);break;case 3:t.lines+=r*(e-1);break;case 2:t.lines+=r*e;break;case 0:t.points+=r*e;break;default:console.error("THREE.WebGLInfo: Unknown draw mode:",n)}}}}function ow(e,t){return e[0]-t[0]}function sw(e,t){return Math.abs(t[1])-Math.abs(e[1])}function lw(e,t,n){for(var r={},i=new Float32Array(8),A=new WeakMap,a=new um,o=[],s=0;s<8;s++)o[s]=[s,0];return{update:function(s,l,u,c){var 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n=[];if(t.shaderID?n.push(t.shaderID):(n.push(t.customVertexShaderID),n.push(t.customFragmentShaderID)),void 0!==t.defines)for(var r in t.defines)n.push(r),n.push(t.defines[r]);return!1===t.isRawShaderMaterial&&(!function(e,t){e.push(t.precision),e.push(t.outputEncoding),e.push(t.envMapMode),e.push(t.envMapCubeUVHeight),e.push(t.combine),e.push(t.vertexUvs),e.push(t.fogExp2),e.push(t.sizeAttenuation),e.push(t.morphTargetsCount),e.push(t.morphAttributeCount),e.push(t.numDirLights),e.push(t.numPointLights),e.push(t.numSpotLights),e.push(t.numSpotLightMaps),e.push(t.numHemiLights),e.push(t.numRectAreaLights),e.push(t.numDirLightShadows),e.push(t.numPointLightShadows),e.push(t.numSpotLightShadows),e.push(t.numSpotLightShadowsWithMaps),e.push(t.shadowMapType),e.push(t.toneMapping),e.push(t.numClippingPlanes),e.push(t.numClipIntersection),e.push(t.depthPacking)}(n,t),function(e,t){o.disableAll(),t.isWebGL2&&o.enable(0);t.supportsVertexTextures&&o.enable(1);t.instancing&&o.enable(2);t.instancingColor&&o.enable(3);t.map&&o.enable(4);t.matcap&&o.enable(5);t.envMap&&o.enable(6);t.lightMap&&o.enable(7);t.aoMap&&o.enable(8);t.emissiveMap&&o.enable(9);t.bumpMap&&o.enable(10);t.normalMap&&o.enable(11);t.objectSpaceNormalMap&&o.enable(12);t.tangentSpaceNormalMap&&o.enable(13);t.clearcoat&&o.enable(14);t.clearcoatMap&&o.enable(15);t.clearcoatRoughnessMap&&o.enable(16);t.clearcoatNormalMap&&o.enable(17);t.iridescence&&o.enable(18);t.iridescenceMap&&o.enable(19);t.iridescenceThicknessMap&&o.enable(20);t.displacementMap&&o.enable(21);t.specularMap&&o.enable(22);t.roughnessMap&&o.enable(23);t.metalnessMap&&o.enable(24);t.gradientMap&&o.enable(25);t.alphaMap&&o.enable(26);t.alphaTest&&o.enable(27);t.vertexColors&&o.enable(28);t.vertexAlphas&&o.enable(29);t.vertexUvs&&o.enable(30);t.vertexTangents&&o.enable(31);t.uvsVertexOnly&&o.enable(32);e.push(o.mask),o.disableAll(),t.fog&&o.enable(0);t.useFog&&o.enable(1);t.flatShading&&o.enable(2);t.logarithmicDepthBuffer&&o.enable(3);t.skinning&&o.enable(4);t.morphTargets&&o.enable(5);t.morphNormals&&o.enable(6);t.morphColors&&o.enable(7);t.premultipliedAlpha&&o.enable(8);t.shadowMapEnabled&&o.enable(9);t.physicallyCorrectLights&&o.enable(10);t.doubleSided&&o.enable(11);t.flipSided&&o.enable(12);t.useDepthPacking&&o.enable(13);t.dithering&&o.enable(14);t.specularIntensityMap&&o.enable(15);t.specularColorMap&&o.enable(16);t.transmission&&o.enable(17);t.transmissionMap&&o.enable(18);t.thicknessMap&&o.enable(19);t.sheen&&o.enable(20);t.sheenColorMap&&o.enable(21);t.sheenRoughnessMap&&o.enable(22);t.decodeVideoTexture&&o.enable(23);t.opaque&&o.enable(24);e.push(o.mask)}(n,t),n.push(e.outputEncoding)),n.push(t.customProgramCacheKey),n.join()},getUniforms:function(e){var t,n=f[e.type];if(n){var r=Qy[n];t=uy.clone(r.uniforms)}else t=e.uniforms;return t},acquireProgram:function(t,n){for(var r,i=0,a=l.length;i<a;i++){var o=l[i];if(o.cacheKey===n){++(r=o).usedTimes;break}}return void 0===r&&(r=new FB(e,n,t,A),l.push(r)),r},releaseProgram:function(e){if(0===--e.usedTimes){var t=l.indexOf(e);l[t]=l[l.length-1],l.pop(),e.destroy()}},releaseShaderCache:function(e){s.remove(e)},programs:l,dispose:function(){s.dispose()}}}function DB(){var e=new WeakMap;return{get:function(t){var n=e.get(t);return void 0===n&&(n={},e.set(t,n)),n},remove:function(t){e.delete(t)},update:function(t,n,r){e.get(t)[n]=r},dispose:function(){e=new WeakMap}}}function IB(e,t){return e.groupOrder!==t.groupOrder?e.groupOrder-t.groupOrder:e.renderOrder!==t.renderOrder?e.renderOrder-t.renderOrder:e.material.id!==t.material.id?e.material.id-t.material.id:e.z!==t.z?e.z-t.z:e.id-t.id}function RB(e,t){return e.groupOrder!==t.groupOrder?e.groupOrder-t.groupOrder:e.renderOrder!==t.renderOrder?e.renderOrder-t.renderOrder:e.z!==t.z?t.z-e.z:e.id-t.id}function HB(){var e=[],t=0,n=[],r=[],i=[];function A(n,r,i,A,a,o){var s=e[t];return void 0===s?(s={id:n.id,object:n,geometry:r,material:i,groupOrder:A,renderOrder:n.renderOrder,z:a,group:o},e[t]=s):(s.id=n.id,s.object=n,s.geometry=r,s.material=i,s.groupOrder=A,s.renderOrder=n.renderOrder,s.z=a,s.group=o),t++,s}return{opaque:n,transmissive:r,transparent:i,init:function(){t=0,n.length=0,r.length=0,i.length=0},push:function(e,t,a,o,s,l){var u=A(e,t,a,o,s,l);a.transmission>0?r.push(u):!0===a.transparent?i.push(u):n.push(u)},unshift:function(e,t,a,o,s,l){var u=A(e,t,a,o,s,l);a.transmission>0?r.unshift(u):!0===a.transparent?i.unshift(u):n.unshift(u)},finish:function(){for(var n=t,r=e.length;n<r;n++){var i=e[n];if(null===i.id)break;i.id=null,i.object=null,i.geometry=null,i.material=null,i.group=null}},sort:function(e,t){n.length>1&&n.sort(e||IB),r.length>1&&r.sort(t||RB),i.length>1&&i.sort(t||RB)}}}function PB(){var e=new WeakMap;return{get:function(t,n){var r,i=e.get(t);return void 0===i?(r=new HB,e.set(t,[r])):n>=i.length?(r=new HB,i.push(r)):r=i[n],r},dispose:function(){e=new WeakMap}}}function NB(){var e={};return{get:function(t){if(void 0!==e[t.id])return e[t.id];var n;switch(t.type){case"DirectionalLight":n={direction:new pm,color:new im};break;case"SpotLight":n={position:new pm,direction:new pm,color:new im,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case"PointLight":n={position:new pm,color:new im,distance:0,decay:0};break;case"HemisphereLight":n={direction:new pm,skyColor:new im,groundColor:new im};break;case"RectAreaLight":n={color:new im,position:new pm,halfWidth:new pm,halfHeight:new pm}}return e[t.id]=n,n}}}var OB=0;function VB(e,t){return(t.castShadow?2:0)-(e.castShadow?2:0)+(t.map?1:0)-(e.map?1:0)}function zB(e,t){for(var n=new NB,r=function(){var e={};return{get:function(t){if(void 0!==e[t.id])return e[t.id];var n;switch(t.type){case"DirectionalLight":case"SpotLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new Vg};break;case"PointLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new Vg,shadowCameraNear:1,shadowCameraFar:1e3}}return e[t.id]=n,n}}}(),i={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1,numSpotMaps:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotLightMap:[],spotShadow:[],spotShadowMap:[],spotLightMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[],numSpotLightShadowsWithMaps:0},A=0;A<9;A++)i.probe.push(new pm);var a=new pm,o=new Km,s=new Km;return{setup:function(A,a){for(var o=0,s=0,l=0,u=0;u<9;u++)i.probe[u].set(0,0,0);var c=0,d=0,h=0,f=0,p=0,g=0,m=0,v=0,y=0,w=0;A.sort(VB);for(var B=!0!==a?Math.PI:1,_=0,b=A.length;_<b;_++){var x=A[_],C=x.color,S=x.intensity,E=x.distance,U=x.shadow&&x.shadow.map?x.shadow.map.texture:null;if(x.isAmbientLight)o+=C.r*S*B,s+=C.g*S*B,l+=C.b*S*B;else if(x.isLightProbe)for(var M=0;M<9;M++)i.probe[M].addScaledVector(x.sh.coefficients[M],S);else if(x.isDirectionalLight){var F=n.get(x);if(F.color.copy(x.color).multiplyScalar(x.intensity*B),x.castShadow){var T=x.shadow,k=r.get(x);k.shadowBias=T.bias,k.shadowNormalBias=T.normalBias,k.shadowRadius=T.radius,k.shadowMapSize=T.mapSize,i.directionalShadow[c]=k,i.directionalShadowMap[c]=U,i.directionalShadowMatrix[c]=x.shadow.matrix,g++}i.directional[c]=F,c++}else if(x.isSpotLight){var Q=n.get(x);Q.position.setFromMatrixPosition(x.matrixWorld),Q.color.copy(C).multiplyScalar(S*B),Q.distance=E,Q.coneCos=Math.cos(x.angle),Q.penumbraCos=Math.cos(x.angle*(1-x.penumbra)),Q.decay=x.decay,i.spot[h]=Q;var L=x.shadow;if(x.map&&(i.spotLightMap[y]=x.map,y++,L.updateMatrices(x),x.castShadow&&w++),i.spotLightMatrix[h]=L.matrix,x.castShadow){var D=r.get(x);D.shadowBias=L.bias,D.shadowNormalBias=L.normalBias,D.shadowRadius=L.radius,D.shadowMapSize=L.mapSize,i.spotShadow[h]=D,i.spotShadowMap[h]=U,v++}h++}else if(x.isRectAreaLight){var I=n.get(x);I.color.copy(C).multiplyScalar(S),I.halfWidth.set(.5*x.width,0,0),I.halfHeight.set(0,.5*x.height,0),i.rectArea[f]=I,f++}else if(x.isPointLight){var R=n.get(x);if(R.color.copy(x.color).multiplyScalar(x.intensity*B),R.distance=x.distance,R.decay=x.decay,x.castShadow){var H=x.shadow,P=r.get(x);P.shadowBias=H.bias,P.shadowNormalBias=H.normalBias,P.shadowRadius=H.radius,P.shadowMapSize=H.mapSize,P.shadowCameraNear=H.camera.near,P.shadowCameraFar=H.camera.far,i.pointShadow[d]=P,i.pointShadowMap[d]=U,i.pointShadowMatrix[d]=x.shadow.matrix,m++}i.point[d]=R,d++}else if(x.isHemisphereLight){var N=n.get(x);N.skyColor.copy(x.color).multiplyScalar(S*B),N.groundColor.copy(x.groundColor).multiplyScalar(S*B),i.hemi[p]=N,p++}}f>0&&(t.isWebGL2||!0===e.has("OES_texture_float_linear")?(i.rectAreaLTC1=ky.LTC_FLOAT_1,i.rectAreaLTC2=ky.LTC_FLOAT_2):!0===e.has("OES_texture_half_float_linear")?(i.rectAreaLTC1=ky.LTC_HALF_1,i.rectAreaLTC2=ky.LTC_HALF_2):console.error("THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.")),i.ambient[0]=o,i.ambient[1]=s,i.ambient[2]=l;var O=i.hash;O.directionalLength===c&&O.pointLength===d&&O.spotLength===h&&O.rectAreaLength===f&&O.hemiLength===p&&O.numDirectionalShadows===g&&O.numPointShadows===m&&O.numSpotShadows===v&&O.numSpotMaps===y||(i.directional.length=c,i.spot.length=h,i.rectArea.length=f,i.point.length=d,i.hemi.length=p,i.directionalShadow.length=g,i.directionalShadowMap.length=g,i.pointShadow.length=m,i.pointShadowMap.length=m,i.spotShadow.length=v,i.spotShadowMap.length=v,i.directionalShadowMatrix.length=g,i.pointShadowMatrix.length=m,i.spotLightMatrix.length=v+y-w,i.spotLightMap.length=y,i.numSpotLightShadowsWithMaps=w,O.directionalLength=c,O.pointLength=d,O.spotLength=h,O.rectAreaLength=f,O.hemiLength=p,O.numDirectionalShadows=g,O.numPointShadows=m,O.numSpotShadows=v,O.numSpotMaps=y,i.version=OB++)},setupView:function(e,t){for(var n=0,r=0,A=0,l=0,u=0,c=t.matrixWorldInverse,d=0,h=e.length;d<h;d++){var f=e[d];if(f.isDirectionalLight){var p=i.directional[n];p.direction.setFromMatrixPosition(f.matrixWorld),a.setFromMatrixPosition(f.target.matrixWorld),p.direction.sub(a),p.direction.transformDirection(c),n++}else if(f.isSpotLight){var g=i.spot[A];g.position.setFromMatrixPosition(f.matrixWorld),g.position.applyMatrix4(c),g.direction.setFromMatrixPosition(f.matrixWorld),a.setFromMatrixPosition(f.target.matrixWorld),g.direction.sub(a),g.direction.transformDirection(c),A++}else if(f.isRectAreaLight){var m=i.rectArea[l];m.position.setFromMatrixPosition(f.matrixWorld),m.position.applyMatrix4(c),s.identity(),o.copy(f.matrixWorld),o.premultiply(c),s.extractRotation(o),m.halfWidth.set(.5*f.width,0,0),m.halfHeight.set(0,.5*f.height,0),m.halfWidth.applyMatrix4(s),m.halfHeight.applyMatrix4(s),l++}else if(f.isPointLight){var v=i.point[r];v.position.setFromMatrixPosition(f.matrixWorld),v.position.applyMatrix4(c),r++}else if(f.isHemisphereLight){var y=i.hemi[u];y.direction.setFromMatrixPosition(f.matrixWorld),y.direction.transformDirection(c),u++}}},state:i}}function GB(e,t){var n=new zB(e,t),r=[],i=[];return{init:function(){r.length=0,i.length=0},state:{lightsArray:r,shadowsArray:i,lights:n},setupLights:function(e){n.setup(r,e)},setupLightsView:function(e){n.setupView(r,e)},pushLight:function(e){r.push(e)},pushShadow:function(e){i.push(e)}}}function KB(e,t){var n=new WeakMap;return{get:function(r){var i,A=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,a=n.get(r);return void 0===a?(i=new GB(e,t),n.set(r,[i])):A>=a.length?(i=new GB(e,t),a.push(i)):i=a[A],i},dispose:function(){n=new WeakMap}}}var WB=function(e){_(n,e);var t=S(n);function n(e){var r;return v(this,n),(r=t.call(this)).isMeshDepthMaterial=!0,r.type="MeshDepthMaterial",r.depthPacking=gg,r.map=null,r.alphaMap=null,r.displacementMap=null,r.displacementScale=1,r.displacementBias=0,r.wireframe=!1,r.wireframeLinewidth=1,r.setValues(e),r}return w(n,[{key:"copy",value:function(e){return jc(b(n.prototype),"copy",this).call(this,e),this.depthPacking=e.depthPacking,this.map=e.map,this.alphaMap=e.alphaMap,this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this}}]),n}(Mv),jB=function(e){_(n,e);var t=S(n);function n(e){var r;return v(this,n),(r=t.call(this)).isMeshDistanceMaterial=!0,r.type="MeshDistanceMaterial",r.referencePosition=new pm,r.nearDistance=1,r.farDistance=1e3,r.map=null,r.alphaMap=null,r.displacementMap=null,r.displacementScale=1,r.displacementBias=0,r.setValues(e),r}return w(n,[{key:"copy",value:function(e){return jc(b(n.prototype),"copy",this).call(this,e),this.referencePosition.copy(e.referencePosition),this.nearDistance=e.nearDistance,this.farDistance=e.farDistance,this.map=e.map,this.alphaMap=e.alphaMap,this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this}}]),n}(Mv),XB="void main() {\n\tgl_Position = vec4( position, 1.0 );\n}",qB="uniform sampler2D shadow_pass;\nuniform vec2 resolution;\nuniform float radius;\n#include <packing>\nvoid main() {\n\tconst float samples = float( VSM_SAMPLES );\n\tfloat mean = 0.0;\n\tfloat squared_mean = 0.0;\n\tfloat uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\n\tfloat uvStart = samples <= 1.0 ? 0.0 : - 1.0;\n\tfor ( float i = 0.0; i < samples; i ++ ) {\n\t\tfloat uvOffset = uvStart + i * uvStride;\n\t\t#ifdef HORIZONTAL_PASS\n\t\t\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\n\t\t\tmean += distribution.x;\n\t\t\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\n\t\t#else\n\t\t\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\n\t\t\tmean += depth;\n\t\t\tsquared_mean += depth * depth;\n\t\t#endif\n\t}\n\tmean = mean / samples;\n\tsquared_mean = squared_mean / samples;\n\tfloat std_dev = sqrt( squared_mean - mean * mean );\n\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\n}";function YB(e,t,n){var r,i=new Ey,A=new Vg,a=new Vg,o=new um,l=new WB({depthPacking:mg}),u=new jB,c={},d=n.maxTextureSize,h=(s(r={},of,sf),s(r,sf,of),s(r,lf,lf),r),f=new hy({defines:{VSM_SAMPLES:8},uniforms:{shadow_pass:{value:null},resolution:{value:new Vg},radius:{value:4}},vertexShader:XB,fragmentShader:qB}),p=f.clone();p.defines.HORIZONTAL_PASS=1;var g=new Gv;g.setAttribute("position",new Qv(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));var m=new iy(g,f),v=this;function y(n,r){var i=t.update(m);f.defines.VSM_SAMPLES!==n.blurSamples&&(f.defines.VSM_SAMPLES=n.blurSamples,p.defines.VSM_SAMPLES=n.blurSamples,f.needsUpdate=!0,p.needsUpdate=!0),null===n.mapPass&&(n.mapPass=new cm(A.x,A.y)),f.uniforms.shadow_pass.value=n.map.texture,f.uniforms.resolution.value=n.mapSize,f.uniforms.radius.value=n.radius,e.setRenderTarget(n.mapPass),e.clear(),e.renderBufferDirect(r,null,i,f,m,null),p.uniforms.shadow_pass.value=n.mapPass.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,e.setRenderTarget(n.map),e.clear(),e.renderBufferDirect(r,null,i,p,m,null)}function w(t,n,r,i,A,a){var o=null,s=!0===r.isPointLight?t.customDistanceMaterial:t.customDepthMaterial;if(void 0!==s)o=s;else if(o=!0===r.isPointLight?u:l,e.localClippingEnabled&&!0===n.clipShadows&&Array.isArray(n.clippingPlanes)&&0!==n.clippingPlanes.length||n.displacementMap&&0!==n.displacementScale||n.alphaMap&&n.alphaTest>0||n.map&&n.alphaTest>0){var d=o.uuid,f=n.uuid,p=c[d];void 0===p&&(p={},c[d]=p);var g=p[f];void 0===g&&(g=o.clone(),p[f]=g),o=g}return o.visible=n.visible,o.wireframe=n.wireframe,o.side=a===af?null!==n.shadowSide?n.shadowSide:n.side:null!==n.shadowSide?n.shadowSide:h[n.side],o.alphaMap=n.alphaMap,o.alphaTest=n.alphaTest,o.map=n.map,o.clipShadows=n.clipShadows,o.clippingPlanes=n.clippingPlanes,o.clipIntersection=n.clipIntersection,o.displacementMap=n.displacementMap,o.displacementScale=n.displacementScale,o.displacementBias=n.displacementBias,o.wireframeLinewidth=n.wireframeLinewidth,o.linewidth=n.linewidth,!0===r.isPointLight&&!0===o.isMeshDistanceMaterial&&(o.referencePosition.setFromMatrixPosition(r.matrixWorld),o.nearDistance=i,o.farDistance=A),o}function B(n,r,A,a,o){if(!1!==n.visible){if(n.layers.test(r.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&o===af)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(A.matrixWorldInverse,n.matrixWorld);var s=t.update(n),l=n.material;if(Array.isArray(l))for(var u=s.groups,c=0,d=u.length;c<d;c++){var h=u[c],f=l[h.materialIndex];if(f&&f.visible){var p=w(n,f,a,A.near,A.far,o);e.renderBufferDirect(A,null,s,p,n,h)}}else if(l.visible){var g=w(n,l,a,A.near,A.far,o);e.renderBufferDirect(A,null,s,g,n,null)}}for(var m=n.children,v=0,y=m.length;v<y;v++)B(m[v],r,A,a,o)}}this.enabled=!1,this.autoUpdate=!0,this.needsUpdate=!1,this.type=rf,this.render=function(t,n,r){if(!1!==v.enabled&&(!1!==v.autoUpdate||!1!==v.needsUpdate)&&0!==t.length){var s=e.getRenderTarget(),l=e.getActiveCubeFace(),u=e.getActiveMipmapLevel(),c=e.state;c.setBlending(uf),c.buffers.color.setClear(1,1,1,1),c.buffers.depth.setTest(!0),c.setScissorTest(!1);for(var h=0,f=t.length;h<f;h++){var p=t[h],g=p.shadow;if(void 0!==g){if(!1!==g.autoUpdate||!1!==g.needsUpdate){A.copy(g.mapSize);var m=g.getFrameExtents();if(A.multiply(m),a.copy(g.mapSize),(A.x>d||A.y>d)&&(A.x>d&&(a.x=Math.floor(d/m.x),A.x=a.x*m.x,g.mapSize.x=a.x),A.y>d&&(a.y=Math.floor(d/m.y),A.y=a.y*m.y,g.mapSize.y=a.y)),null===g.map){var w=this.type!==af?{minFilter:rp,magFilter:rp}:{};g.map=new cm(A.x,A.y,w),g.map.texture.name=p.name+".shadowMap",g.camera.updateProjectionMatrix()}e.setRenderTarget(g.map),e.clear();for(var _=g.getViewportCount(),b=0;b<_;b++){var x=g.getViewport(b);o.set(a.x*x.x,a.y*x.y,a.x*x.z,a.y*x.w),c.viewport(o),g.updateMatrices(p,b),i=g.getFrustum(),B(n,r,g.camera,p,this.type)}!0!==g.isPointLightShadow&&this.type===af&&y(g,r),g.needsUpdate=!1}}else console.warn("THREE.WebGLShadowMap:",p,"has no shadow.")}v.needsUpdate=!1,e.setRenderTarget(s,l,u)}}}function JB(e,t,n){var r,i,A=n.isWebGL2;var a=new function(){var t=!1,n=new um,r=null,i=new um(0,0,0,0);return{setMask:function(n){r===n||t||(e.colorMask(n,n,n,n),r=n)},setLocked:function(e){t=e},setClear:function(t,r,A,a,o){!0===o&&(t*=a,r*=a,A*=a),n.set(t,r,A,a),!1===i.equals(n)&&(e.clearColor(t,r,A,a),i.copy(n))},reset:function(){t=!1,r=null,i.set(-1,0,0,0)}}},o=new function(){var t=!1,n=null,r=null,i=null;return{setTest:function(e){e?z(2929):G(2929)},setMask:function(r){n===r||t||(e.depthMask(r),n=r)},setFunc:function(t){if(r!==t){switch(t){case kf:e.depthFunc(512);break;case Qf:e.depthFunc(519);break;case Lf:e.depthFunc(513);break;case Df:e.depthFunc(515);break;case If:e.depthFunc(514);break;case Rf:e.depthFunc(518);break;case Hf:e.depthFunc(516);break;case Pf:e.depthFunc(517);break;default:e.depthFunc(515)}r=t}},setLocked:function(e){t=e},setClear:function(t){i!==t&&(e.clearDepth(t),i=t)},reset:function(){t=!1,n=null,r=null,i=null}}},l=new function(){var t=!1,n=null,r=null,i=null,A=null,a=null,o=null,s=null,l=null;return{setTest:function(e){t||(e?z(2960):G(2960))},setMask:function(r){n===r||t||(e.stencilMask(r),n=r)},setFunc:function(t,n,a){r===t&&i===n&&A===a||(e.stencilFunc(t,n,a),r=t,i=n,A=a)},setOp:function(t,n,r){a===t&&o===n&&s===r||(e.stencilOp(t,n,r),a=t,o=n,s=r)},setLocked:function(e){t=e},setClear:function(t){l!==t&&(e.clearStencil(t),l=t)},reset:function(){t=!1,n=null,r=null,i=null,A=null,a=null,o=null,s=null,l=null}}},u=new WeakMap,c=new WeakMap,d={},h={},f=new WeakMap,p=[],g=null,m=!1,v=null,y=null,w=null,B=null,_=null,b=null,x=null,C=!1,S=null,E=null,U=null,M=null,F=null,T=e.getParameter(35661),k=!1,Q=0,L=e.getParameter(7938);-1!==L.indexOf("WebGL")?(Q=parseFloat(/^WebGL (\d)/.exec(L)[1]),k=Q>=1):-1!==L.indexOf("OpenGL ES")&&(Q=parseFloat(/^OpenGL ES (\d)/.exec(L)[1]),k=Q>=2);var D=null,I={},R=e.getParameter(3088),H=e.getParameter(2978),P=(new um).fromArray(R),N=(new um).fromArray(H);function O(t,n,r){var i=new Uint8Array(4),A=e.createTexture();e.bindTexture(t,A),e.texParameteri(t,10241,9728),e.texParameteri(t,10240,9728);for(var a=0;a<r;a++)e.texImage2D(n+a,0,6408,1,1,0,6408,5121,i);return A}var V={};function z(t){!0!==d[t]&&(e.enable(t),d[t]=!0)}function G(t){!1!==d[t]&&(e.disable(t),d[t]=!1)}V[3553]=O(3553,3553,1),V[34067]=O(34067,34069,6),a.setClear(0,0,0,1),o.setClear(1),l.setClear(0),z(2929),o.setFunc(Df),q(!1),Y(tf),z(2884),X(uf);var K=(s(r={},gf,32774),s(r,mf,32778),s(r,vf,32779),r);if(A)K[yf]=32775,K[wf]=32776;else{var W=t.get("EXT_blend_minmax");null!==W&&(K[yf]=W.MIN_EXT,K[wf]=W.MAX_EXT)}var j=(s(i={},Bf,0),s(i,_f,1),s(i,bf,768),s(i,Cf,770),s(i,Tf,776),s(i,Mf,774),s(i,Ef,772),s(i,xf,769),s(i,Sf,771),s(i,Ff,775),s(i,Uf,773),i);function X(t,n,r,i,A,a,o,s){if(t!==uf){if(!1===m&&(z(3042),m=!0),t===pf)A=A||n,a=a||r,o=o||i,n===y&&A===_||(e.blendEquationSeparate(K[n],K[A]),y=n,_=A),r===w&&i===B&&a===b&&o===x||(e.blendFuncSeparate(j[r],j[i],j[a],j[o]),w=r,B=i,b=a,x=o),v=t,C=!1;else if(t!==v||s!==C){if(y===gf&&_===gf||(e.blendEquation(32774),y=gf,_=gf),s)switch(t){case cf:e.blendFuncSeparate(1,771,1,771);break;case df:e.blendFunc(1,1);break;case hf:e.blendFuncSeparate(0,769,0,1);break;case ff:e.blendFuncSeparate(0,768,0,770);break;default:console.error("THREE.WebGLState: Invalid blending: ",t)}else switch(t){case cf:e.blendFuncSeparate(770,771,1,771);break;case df:e.blendFunc(770,1);break;case hf:e.blendFuncSeparate(0,769,0,1);break;case ff:e.blendFunc(0,768);break;default:console.error("THREE.WebGLState: Invalid blending: ",t)}w=null,B=null,b=null,x=null,v=t,C=s}}else!0===m&&(G(3042),m=!1)}function q(t){S!==t&&(t?e.frontFace(2304):e.frontFace(2305),S=t)}function Y(t){t!==ef?(z(2884),t!==E&&(t===tf?e.cullFace(1029):t===nf?e.cullFace(1028):e.cullFace(1032))):G(2884),E=t}function J(t,n,r){t?(z(32823),M===n&&F===r||(e.polygonOffset(n,r),M=n,F=r)):G(32823)}return{buffers:{color:a,depth:o,stencil:l},enable:z,disable:G,bindFramebuffer:function(t,n){return h[t]!==n&&(e.bindFramebuffer(t,n),h[t]=n,A&&(36009===t&&(h[36160]=n),36160===t&&(h[36009]=n)),!0)},drawBuffers:function(r,i){var A=p,a=!1;if(r)if(void 0===(A=f.get(i))&&(A=[],f.set(i,A)),r.isWebGLMultipleRenderTargets){var o=r.texture;if(A.length!==o.length||36064!==A[0]){for(var s=0,l=o.length;s<l;s++)A[s]=36064+s;A.length=o.length,a=!0}}else 36064!==A[0]&&(A[0]=36064,a=!0);else 1029!==A[0]&&(A[0]=1029,a=!0);a&&(n.isWebGL2?e.drawBuffers(A):t.get("WEBGL_draw_buffers").drawBuffersWEBGL(A))},useProgram:function(t){return g!==t&&(e.useProgram(t),g=t,!0)},setBlending:X,setMaterial:function(e,t){e.side===lf?G(2884):z(2884);var n=e.side===sf;t&&(n=!n),q(n),e.blending===cf&&!1===e.transparent?X(uf):X(e.blending,e.blendEquation,e.blendSrc,e.blendDst,e.blendEquationAlpha,e.blendSrcAlpha,e.blendDstAlpha,e.premultipliedAlpha),o.setFunc(e.depthFunc),o.setTest(e.depthTest),o.setMask(e.depthWrite),a.setMask(e.colorWrite);var 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e>=d&&console.warn("THREE.WebGLTextures: Trying to use "+e+" texture units while this GPU supports only "+d),k+=1,e},this.resetTextureUnits=function(){k=0},this.setTexture2D=Q,this.setTexture2DArray=function(e,t){var i=r.get(e);e.version>0&&i.__version!==e.version?H(i,e,t):n.bindTexture(35866,i.__webglTexture,33984+t)},this.setTexture3D=function(e,t){var i=r.get(e);e.version>0&&i.__version!==e.version?H(i,e,t):n.bindTexture(32879,i.__webglTexture,33984+t)},this.setTextureCube=function(t,i){var a=r.get(t);t.version>0&&a.__version!==t.version?function(t,i,a){if(6!==i.image.length)return;var o=R(t,i),s=i.source;n.bindTexture(34067,t.__webglTexture,33984+a);var l=r.get(s);if(s.version!==l.__version||!0===o){n.activeTexture(33984+a),e.pixelStorei(37440,i.flipY),e.pixelStorei(37441,i.premultiplyAlpha),e.pixelStorei(3317,i.unpackAlignment),e.pixelStorei(37443,0);for(var u=i.isCompressedTexture||i.image[0].isCompressedTexture,d=i.image[0]&&i.image[0].isDataTexture,f=[],p=0;p<6;p++)f[p]=u||d?d?i.image[p].image:i.image[p]:_(i.image[p],!1,!0,h),f[p]=G(i,f[p]);var g,m=f[0],v=b(m)||c,y=A.convert(i.format,i.encoding),w=A.convert(i.type),B=S(i.internalFormat,y,w,i.encoding),U=c&&!0!==i.isVideoTexture,M=void 0===l.__version||!0===o,F=E(i,m,v);if(I(34067,i,v),u){U&&M&&n.texStorage2D(34067,F,B,m.width,m.height);for(var T=0;T<6;T++){g=f[T].mipmaps;for(var k=0;k<g.length;k++){var Q=g[k];i.format!==_p?null!==y?U?n.compressedTexSubImage2D(34069+T,k,0,0,Q.width,Q.height,y,Q.data):n.compressedTexImage2D(34069+T,k,B,Q.width,Q.height,0,Q.data):console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .setTextureCube()"):U?n.texSubImage2D(34069+T,k,0,0,Q.width,Q.height,y,w,Q.data):n.texImage2D(34069+T,k,B,Q.width,Q.height,0,y,w,Q.data)}}}else{g=i.mipmaps,U&&M&&(g.length>0&&F++,n.texStorage2D(34067,F,B,f[0].width,f[0].height));for(var L=0;L<6;L++)if(d){U?n.texSubImage2D(34069+L,0,0,0,f[L].width,f[L].height,y,w,f[L].data):n.texImage2D(34069+L,0,B,f[L].width,f[L].height,0,y,w,f[L].data);for(var D=0;D<g.length;D++){var H=g[D].image[L].image;U?n.texSubImage2D(34069+L,D+1,0,0,H.width,H.height,y,w,H.data):n.texImage2D(34069+L,D+1,B,H.width,H.height,0,y,w,H.data)}}else{U?n.texSubImage2D(34069+L,0,0,0,y,w,f[L]):n.texImage2D(34069+L,0,B,y,w,f[L]);for(var P=0;P<g.length;P++){var N=g[P];U?n.texSubImage2D(34069+L,P+1,0,0,y,w,N.image[L]):n.texImage2D(34069+L,P+1,B,y,w,N.image[L])}}}x(i,v)&&C(34067),l.__version=s.version,i.onUpdate&&i.onUpdate(i)}t.__version=i.version}(a,t,i):n.bindTexture(34067,a.__webglTexture,33984+i)},this.rebindTextures=function(e,t,n){var i=r.get(e);void 0!==t&&P(i.__webglFramebuffer,e,e.texture,36064,3553),void 0!==n&&O(e)},this.setupRenderTarget=function(t){var o=t.texture,s=r.get(t),l=r.get(o);t.addEventListener("dispose",F),!0!==t.isWebGLMultipleRenderTargets&&(void 0===l.__webglTexture&&(l.__webglTexture=e.createTexture()),l.__version=o.version,a.memory.textures++);var u=!0===t.isWebGLCubeRenderTarget,d=!0===t.isWebGLMultipleRenderTargets,h=b(t)||c;if(u){s.__webglFramebuffer=[];for(var f=0;f<6;f++)s.__webglFramebuffer[f]=e.createFramebuffer()}else{if(s.__webglFramebuffer=e.createFramebuffer(),d)if(i.drawBuffers)for(var p=t.texture,g=0,m=p.length;g<m;g++){var v=r.get(p[g]);void 0===v.__webglTexture&&(v.__webglTexture=e.createTexture(),a.memory.textures++)}else console.warn("THREE.WebGLRenderer: WebGLMultipleRenderTargets can only be used with WebGL2 or WEBGL_draw_buffers extension.");if(c&&t.samples>0&&!1===z(t)){var y=d?o:[o];s.__webglMultisampledFramebuffer=e.createFramebuffer(),s.__webglColorRenderbuffer=[],n.bindFramebuffer(36160,s.__webglMultisampledFramebuffer);for(var w=0;w<y.length;w++){var B=y[w];s.__webglColorRenderbuffer[w]=e.createRenderbuffer(),e.bindRenderbuffer(36161,s.__webglColorRenderbuffer[w]);var _=A.convert(B.format,B.encoding),E=A.convert(B.type),U=S(B.internalFormat,_,E,B.encoding,!0===t.isXRRenderTarget),M=V(t);e.renderbufferStorageMultisample(36161,M,U,t.width,t.height),e.framebufferRenderbuffer(36160,36064+w,36161,s.__webglColorRenderbuffer[w])}e.bindRenderbuffer(36161,null),t.depthBuffer&&(s.__webglDepthRenderbuffer=e.createRenderbuffer(),N(s.__webglDepthRenderbuffer,t,!0)),n.bindFramebuffer(36160,null)}}if(u){n.bindTexture(34067,l.__webglTexture),I(34067,o,h);for(var T=0;T<6;T++)P(s.__webglFramebuffer[T],t,o,36064,34069+T);x(o,h)&&C(34067),n.unbindTexture()}else if(d){for(var k=t.texture,Q=0,L=k.length;Q<L;Q++){var D=k[Q],R=r.get(D);n.bindTexture(3553,R.__webglTexture),I(3553,D,h),P(s.__webglFramebuffer,t,D,36064+Q,3553),x(D,h)&&C(3553)}n.unbindTexture()}else{var H=3553;(t.isWebGL3DRenderTarget||t.isWebGLArrayRenderTarget)&&(c?H=t.isWebGL3DRenderTarget?32879:35866:console.error("THREE.WebGLTextures: THREE.Data3DTexture and THREE.DataArrayTexture only supported with WebGL2.")),n.bindTexture(H,l.__webglTexture),I(H,o,h),P(s.__webglFramebuffer,t,o,36064,H),x(o,h)&&C(H),n.unbindTexture()}t.depthBuffer&&O(t)},this.updateRenderTargetMipmap=function(e){for(var t=b(e)||c,i=!0===e.isWebGLMultipleRenderTargets?e.texture:[e.texture],A=0,a=i.length;A<a;A++){var o=i[A];if(x(o,t)){var s=e.isWebGLCubeRenderTarget?34067:3553,l=r.get(o).__webglTexture;n.bindTexture(s,l),C(s),n.unbindTexture()}}},this.updateMultisampleRenderTarget=function(t){if(c&&t.samples>0&&!1===z(t)){var i=t.isWebGLMultipleRenderTargets?t.texture:[t.texture],A=t.width,a=t.height,o=16384,s=[],l=t.stencilBuffer?33306:36096,u=r.get(t),d=!0===t.isWebGLMultipleRenderTargets;if(d)for(var h=0;h<i.length;h++)n.bindFramebuffer(36160,u.__webglMultisampledFramebuffer),e.framebufferRenderbuffer(36160,36064+h,36161,null),n.bindFramebuffer(36160,u.__webglFramebuffer),e.framebufferTexture2D(36009,36064+h,3553,null,0);n.bindFramebuffer(36008,u.__webglMultisampledFramebuffer),n.bindFramebuffer(36009,u.__webglFramebuffer);for(var f=0;f<i.length;f++){s.push(36064+f),t.depthBuffer&&s.push(l);var p=void 0!==u.__ignoreDepthValues&&u.__ignoreDepthValues;if(!1===p&&(t.depthBuffer&&(o|=256),t.stencilBuffer&&(o|=1024)),d&&e.framebufferRenderbuffer(36008,36064,36161,u.__webglColorRenderbuffer[f]),!0===p&&(e.invalidateFramebuffer(36008,[l]),e.invalidateFramebuffer(36009,[l])),d){var g=r.get(i[f]).__webglTexture;e.framebufferTexture2D(36009,36064,3553,g,0)}e.blitFramebuffer(0,0,A,a,0,0,A,a,o,9728),m&&e.invalidateFramebuffer(36008,s)}if(n.bindFramebuffer(36008,null),n.bindFramebuffer(36009,null),d)for(var v=0;v<i.length;v++){n.bindFramebuffer(36160,u.__webglMultisampledFramebuffer),e.framebufferRenderbuffer(36160,36064+v,36161,u.__webglColorRenderbuffer[v]);var y=r.get(i[v]).__webglTexture;n.bindFramebuffer(36160,u.__webglFramebuffer),e.framebufferTexture2D(36009,36064+v,3553,y,0)}n.bindFramebuffer(36009,u.__webglMultisampledFramebuffer)}},this.setupDepthRenderbuffer=O,this.setupFrameBufferTexture=P,this.useMultisampledRTT=z}function $B(e,t,n){var r=n.isWebGL2;return{convert:function(n){var i,A=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null;if(n===up)return 5121;if(n===vp)return 32819;if(n===yp)return 32820;if(n===cp)return 5120;if(n===dp)return 5122;if(n===hp)return 5123;if(n===fp)return 5124;if(n===pp)return 5125;if(n===gp)return 5126;if(n===mp)return r?5131:null!==(i=t.get("OES_texture_half_float"))?i.HALF_FLOAT_OES:null;if(n===Bp)return 6406;if(n===_p)return 6408;if(n===bp)return 6409;if(n===xp)return 6410;if(n===Cp)return 6402;if(n===Sp)return 34041;if(n===Sg)return null!==(i=t.get("EXT_sRGB"))?i.SRGB_ALPHA_EXT:null;if(n===Ep)return 6403;if(n===Up)return 36244;if(n===Mp)return 33319;if(n===Fp)return 33320;if(n===Tp)return 36249;if(n===kp||n===Qp||n===Lp||n===Dp)if(A===pg){if(null===(i=t.get("WEBGL_compressed_texture_s3tc_srgb")))return null;if(n===kp)return i.COMPRESSED_SRGB_S3TC_DXT1_EXT;if(n===Qp)return i.COMPRESSED_SRGB_ALPHA_S3TC_DXT1_EXT;if(n===Lp)return i.COMPRESSED_SRGB_ALPHA_S3TC_DXT3_EXT;if(n===Dp)return i.COMPRESSED_SRGB_ALPHA_S3TC_DXT5_EXT}else{if(null===(i=t.get("WEBGL_compressed_texture_s3tc")))return null;if(n===kp)return i.COMPRESSED_RGB_S3TC_DXT1_EXT;if(n===Qp)return i.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(n===Lp)return i.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(n===Dp)return i.COMPRESSED_RGBA_S3TC_DXT5_EXT}if(n===Ip||n===Rp||n===Hp||n===Pp){if(null===(i=t.get("WEBGL_compressed_texture_pvrtc")))return null;if(n===Ip)return i.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(n===Rp)return i.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(n===Hp)return i.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(n===Pp)return i.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(n===Np)return null!==(i=t.get("WEBGL_compressed_texture_etc1"))?i.COMPRESSED_RGB_ETC1_WEBGL:null;if(n===Op||n===Vp){if(null===(i=t.get("WEBGL_compressed_texture_etc")))return null;if(n===Op)return A===pg?i.COMPRESSED_SRGB8_ETC2:i.COMPRESSED_RGB8_ETC2;if(n===Vp)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ETC2_EAC:i.COMPRESSED_RGBA8_ETC2_EAC}if(n===zp||n===Gp||n===Kp||n===Wp||n===jp||n===Xp||n===qp||n===Yp||n===Jp||n===Zp||n===$p||n===eg||n===tg||n===ng){if(null===(i=t.get("WEBGL_compressed_texture_astc")))return null;if(n===zp)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_4x4_KHR:i.COMPRESSED_RGBA_ASTC_4x4_KHR;if(n===Gp)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_5x4_KHR:i.COMPRESSED_RGBA_ASTC_5x4_KHR;if(n===Kp)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_5x5_KHR:i.COMPRESSED_RGBA_ASTC_5x5_KHR;if(n===Wp)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_6x5_KHR:i.COMPRESSED_RGBA_ASTC_6x5_KHR;if(n===jp)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_6x6_KHR:i.COMPRESSED_RGBA_ASTC_6x6_KHR;if(n===Xp)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_8x5_KHR:i.COMPRESSED_RGBA_ASTC_8x5_KHR;if(n===qp)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_8x6_KHR:i.COMPRESSED_RGBA_ASTC_8x6_KHR;if(n===Yp)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_8x8_KHR:i.COMPRESSED_RGBA_ASTC_8x8_KHR;if(n===Jp)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_10x5_KHR:i.COMPRESSED_RGBA_ASTC_10x5_KHR;if(n===Zp)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_10x6_KHR:i.COMPRESSED_RGBA_ASTC_10x6_KHR;if(n===$p)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_10x8_KHR:i.COMPRESSED_RGBA_ASTC_10x8_KHR;if(n===eg)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_10x10_KHR:i.COMPRESSED_RGBA_ASTC_10x10_KHR;if(n===tg)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_12x10_KHR:i.COMPRESSED_RGBA_ASTC_12x10_KHR;if(n===ng)return A===pg?i.COMPRESSED_SRGB8_ALPHA8_ASTC_12x12_KHR:i.COMPRESSED_RGBA_ASTC_12x12_KHR}if(n===rg){if(null===(i=t.get("EXT_texture_compression_bptc")))return null;if(n===rg)return A===pg?i.COMPRESSED_SRGB_ALPHA_BPTC_UNORM_EXT:i.COMPRESSED_RGBA_BPTC_UNORM_EXT}if(n===ig||n===Ag||n===ag||n===og){if(null===(i=t.get("EXT_texture_compression_rgtc")))return null;if(n===rg)return i.COMPRESSED_RED_RGTC1_EXT;if(n===Ag)return i.COMPRESSED_SIGNED_RED_RGTC1_EXT;if(n===ag)return i.COMPRESSED_RED_GREEN_RGTC2_EXT;if(n===og)return i.COMPRESSED_SIGNED_RED_GREEN_RGTC2_EXT}return n===wp?r?34042:null!==(i=t.get("WEBGL_depth_texture"))?i.UNSIGNED_INT_24_8_WEBGL:null:void 0!==e[n]?e[n]:null}}}var e_=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[];return v(this,n),(e=t.call(this)).isArrayCamera=!0,e.cameras=r,e}return w(n)}(py),t_=function(e){_(n,e);var t=S(n);function n(){var e;return v(this,n),(e=t.call(this)).isGroup=!0,e.type="Group",e}return 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null!==this._targetRay&&this._targetRay.dispatchEvent(e),null!==this._grip&&this._grip.dispatchEvent(e),null!==this._hand&&this._hand.dispatchEvent(e),this}},{key:"connect",value:function(e){if(e&&e.hand){var t=this._hand;if(t){var n,r=k(e.hand.values());try{for(r.s();!(n=r.n()).done;){var i=n.value;this._getHandJoint(t,i)}}catch(Jx){r.e(Jx)}finally{r.f()}}}return this.dispatchEvent({type:"connected",data:e}),this}},{key:"disconnect",value:function(e){return this.dispatchEvent({type:"disconnected",data:e}),null!==this._targetRay&&(this._targetRay.visible=!1),null!==this._grip&&(this._grip.visible=!1),null!==this._hand&&(this._hand.visible=!1),this}},{key:"update",value:function(e,t,n){var r=null,i=null,A=null,a=this._targetRay,o=this._grip,s=this._hand;if(e&&"visible-blurred"!==t.session.visibilityState){if(s&&e.hand){A=!0;var l,u=k(e.hand.values());try{for(u.s();!(l=u.n()).done;){var c=l.value,d=t.getJointPose(c,n),h=this._getHandJoint(s,c);null!==d&&(h.matrix.fromArray(d.transform.matrix),h.matrix.decompose(h.position,h.rotation,h.scale),h.jointRadius=d.radius),h.visible=null!==d}}catch(Jx){u.e(Jx)}finally{u.f()}var f=s.joints["index-finger-tip"],p=s.joints["thumb-tip"],g=f.position.distanceTo(p.position);s.inputState.pinching&&g>.025?(s.inputState.pinching=!1,this.dispatchEvent({type:"pinchend",handedness:e.handedness,target:this})):!s.inputState.pinching&&g<=.015&&(s.inputState.pinching=!0,this.dispatchEvent({type:"pinchstart",handedness:e.handedness,target:this}))}else null!==o&&e.gripSpace&&null!==(i=t.getPose(e.gripSpace,n))&&(o.matrix.fromArray(i.transform.matrix),o.matrix.decompose(o.position,o.rotation,o.scale),i.linearVelocity?(o.hasLinearVelocity=!0,o.linearVelocity.copy(i.linearVelocity)):o.hasLinearVelocity=!1,i.angularVelocity?(o.hasAngularVelocity=!0,o.angularVelocity.copy(i.angularVelocity)):o.hasAngularVelocity=!1);null!==a&&(null===(r=t.getPose(e.targetRaySpace,n))&&null!==i&&(r=i),null!==r&&(a.matrix.fromArray(r.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),r.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(r.linearVelocity)):a.hasLinearVelocity=!1,r.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(r.angularVelocity)):a.hasAngularVelocity=!1,this.dispatchEvent(n_)))}return null!==a&&(a.visible=null!==r),null!==o&&(o.visible=null!==i),null!==s&&(s.visible=null!==A),this}},{key:"_getHandJoint",value:function(e,t){if(void 0===e.joints[t.jointName]){var n=new t_;n.matrixAutoUpdate=!1,n.visible=!1,e.joints[t.jointName]=n,e.add(n)}return e.joints[t.jointName]}}]),e}(),i_=function(e){_(n,e);var t=S(n);function n(e,r,i,A,a,o,s,l,u,c){var d;if(v(this,n),(c=void 0!==c?c:Cp)!==Cp&&c!==Sp)throw new Error("DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat");return void 0===i&&c===Cp&&(i=pp),void 0===i&&c===Sp&&(i=wp),(d=t.call(this,null,A,a,o,s,l,c,i,u)).isDepthTexture=!0,d.image={width:e,height:r},d.magFilter=void 0!==s?s:rp,d.minFilter=void 0!==l?l:rp,d.flipY=!1,d.generateMipmaps=!1,d}return w(n)}(lm),A_=function(e){_(n,e);var t=S(n);function n(e,r){var i;v(this,n);var A=C(i=t.call(this)),a=null,o=1,s=null,l="local-floor",u=1,c=null,d=null,h=null,f=null,p=null,g=null,m=r.getContextAttributes(),y=null,w=null,B=[],_=[],b=new Set,x=new Map,S=new py;S.layers.enable(1),S.viewport=new um;var U=new py;U.layers.enable(2),U.viewport=new um;var F=[S,U],T=new e_;T.layers.enable(1),T.layers.enable(2);var Q=null,L=null;function 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s=B[a];s&&s.connect(A)}}i.cameraAutoUpdate=!0,i.enabled=!1,i.isPresenting=!1,i.getController=function(e){var t=B[e];return void 0===t&&(t=new r_,B[e]=t),t.getTargetRaySpace()},i.getControllerGrip=function(e){var t=B[e];return void 0===t&&(t=new r_,B[e]=t),t.getGripSpace()},i.getHand=function(e){var t=B[e];return void 0===t&&(t=new r_,B[e]=t),t.getHandSpace()},i.setFramebufferScaleFactor=function(e){o=e,!0===A.isPresenting&&console.warn("THREE.WebXRManager: Cannot change framebuffer scale while presenting.")},i.setReferenceSpaceType=function(e){l=e,!0===A.isPresenting&&console.warn("THREE.WebXRManager: Cannot change reference space type while presenting.")},i.getReferenceSpace=function(){return c||s},i.setReferenceSpace=function(e){c=e},i.getBaseLayer=function(){return null!==f?f:p},i.getBinding=function(){return h},i.getFrame=function(){return g},i.getSession=function(){return a},i.setSession=function(){var t=M(E().mark((function t(n){var i,d,g,v,B;return E().wrap((function(t){for(;;)switch(t.prev=t.next){case 0:if(null===(a=n)){t.next=25;break}if(y=e.getRenderTarget(),a.addEventListener("select",D),a.addEventListener("selectstart",D),a.addEventListener("selectend",D),a.addEventListener("squeeze",D),a.addEventListener("squeezestart",D),a.addEventListener("squeezeend",D),a.addEventListener("end",I),a.addEventListener("inputsourceschange",R),!0===m.xrCompatible){t.next=14;break}return t.next=14,r.makeXRCompatible();case 14:return void 0===a.renderState.layers||!1===e.capabilities.isWebGL2?(i={antialias:void 0!==a.renderState.layers||m.antialias,alpha:m.alpha,depth:m.depth,stencil:m.stencil,framebufferScaleFactor:o},p=new XRWebGLLayer(a,r,i),a.updateRenderState({baseLayer:p}),w=new cm(p.framebufferWidth,p.framebufferHeight,{format:_p,type:up,encoding:e.outputEncoding,stencilBuffer:m.stencil})):(d=null,g=null,v=null,m.depth&&(v=m.stencil?35056:33190,d=m.stencil?Sp:Cp,g=m.stencil?wp:pp),B={colorFormat:32856,depthFormat:v,scaleFactor:o},h=new 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N(e,t){null===t?e.matrixWorld.copy(e.matrix):e.matrixWorld.multiplyMatrices(t.matrixWorld,e.matrix),e.matrixWorldInverse.copy(e.matrixWorld).invert()}i.updateCamera=function(e){if(null!==a){T.near=U.near=S.near=e.near,T.far=U.far=S.far=e.far,Q===T.near&&L===T.far||(a.updateRenderState({depthNear:T.near,depthFar:T.far}),Q=T.near,L=T.far);var t=e.parent,n=T.cameras;N(T,t);for(var r=0;r<n.length;r++)N(n[r],t);T.matrixWorld.decompose(T.position,T.quaternion,T.scale),e.matrix.copy(T.matrix),e.matrix.decompose(e.position,e.quaternion,e.scale);for(var i=e.children,A=0,o=i.length;A<o;A++)i[A].updateMatrixWorld(!0);2===n.length?function(e,t,n){H.setFromMatrixPosition(t.matrixWorld),P.setFromMatrixPosition(n.matrixWorld);var r=H.distanceTo(P),i=t.projectionMatrix.elements,A=n.projectionMatrix.elements,a=i[14]/(i[10]-1),o=i[14]/(i[10]+1),s=(i[9]+1)/i[5],l=(i[9]-1)/i[5],u=(i[8]-1)/i[0],c=(A[8]+1)/A[0],d=a*u,h=a*c,f=r/(-u+c),p=f*-u;t.matrixWorld.decompose(e.position,e.quaternion,e.scale),e.translateX(p),e.translateZ(f),e.matrixWorld.compose(e.position,e.quaternion,e.scale),e.matrixWorldInverse.copy(e.matrixWorld).invert();var g=a+f,m=o+f,v=d-p,y=h+(r-p),w=s*o/m*g,B=l*o/m*g;e.projectionMatrix.makePerspective(v,y,w,B,g,m)}(T,S,U):T.projectionMatrix.copy(S.projectionMatrix)}},i.getCamera=function(){return T},i.getFoveation=function(){if(null!==f||null!==p)return u},i.setFoveation=function(e){u=e,null!==f&&(f.fixedFoveation=e),null!==p&&void 0!==p.fixedFoveation&&(p.fixedFoveation=e)},i.getPlanes=function(){return b};var O=null;var V=new Uy;return V.setAnimationLoop((function(t,n){if(d=n.getViewerPose(c||s),g=n,null!==d){var r=d.views;null!==p&&(e.setRenderTargetFramebuffer(w,p.framebuffer),e.setRenderTarget(w));var i=!1;r.length!==T.cameras.length&&(T.cameras.length=0,i=!0);for(var a=0;a<r.length;a++){var o=r[a],l=null;if(null!==p)l=p.getViewport(o);else{var u=h.getViewSubImage(f,o);l=u.viewport,0===a&&(e.setRenderTargetTextures(w,u.colorTexture,f.ignoreDepthValues?void 0:u.depthStencilTexture),e.setRenderTarget(w))}var m=F[a];void 0===m&&((m=new py).layers.enable(a),m.viewport=new um,F[a]=m),m.matrix.fromArray(o.transform.matrix),m.projectionMatrix.fromArray(o.projectionMatrix),m.viewport.set(l.x,l.y,l.width,l.height),0===a&&T.matrix.copy(m.matrix),!0===i&&T.cameras.push(m)}}for(var v=0;v<B.length;v++){var y=_[v],C=B[v];null!==y&&void 0!==C&&C.update(y,n,c||s)}if(O&&O(t,n),n.detectedPlanes){A.dispatchEvent({type:"planesdetected",data:n.detectedPlanes});var S,E=null,U=k(b);try{for(U.s();!(S=U.n()).done;){var M=S.value;n.detectedPlanes.has(M)||(null===E&&(E=[]),E.push(M))}}catch(Jx){U.e(Jx)}finally{U.f()}if(null!==E){var Q,L=k(E);try{for(L.s();!(Q=L.n()).done;){var D=Q.value;b.delete(D),x.delete(D),A.dispatchEvent({type:"planeremoved",data:D})}}catch(Jx){L.e(Jx)}finally{L.f()}}var I,R=k(n.detectedPlanes);try{for(R.s();!(I=R.n()).done;){var H=I.value;if(b.has(H)){var P=x.get(H);H.lastChangedTime>P&&(x.set(H,H.lastChangedTime),A.dispatchEvent({type:"planechanged",data:H}))}else b.add(H),x.set(H,n.lastChangedTime),A.dispatchEvent({type:"planeadded",data:H})}}catch(Jx){R.e(Jx)}finally{R.f()}}g=null})),i.setAnimationLoop=function(e){O=e},i.dispose=function(){},i}return w(n)}(Eg);function a_(e,t){function n(n,r){n.opacity.value=r.opacity,r.color&&n.diffuse.value.copy(r.color),r.emissive&&n.emissive.value.copy(r.emissive).multiplyScalar(r.emissiveIntensity),r.map&&(n.map.value=r.map),r.alphaMap&&(n.alphaMap.value=r.alphaMap),r.bumpMap&&(n.bumpMap.value=r.bumpMap,n.bumpScale.value=r.bumpScale,r.side===sf&&(n.bumpScale.value*=-1)),r.displacementMap&&(n.displacementMap.value=r.displacementMap,n.displacementScale.value=r.displacementScale,n.displacementBias.value=r.displacementBias),r.emissiveMap&&(n.emissiveMap.value=r.emissiveMap),r.normalMap&&(n.normalMap.value=r.normalMap,n.normalScale.value.copy(r.normalScale),r.side===sf&&n.normalScale.value.negate()),r.specularMap&&(n.specularMap.value=r.specularMap),r.alphaTest>0&&(n.alphaTest.value=r.alphaTest);var i,A,a=t.get(r).envMap;if(a&&(n.envMap.value=a,n.flipEnvMap.value=a.isCubeTexture&&!1===a.isRenderTargetTexture?-1:1,n.reflectivity.value=r.reflectivity,n.ior.value=r.ior,n.refractionRatio.value=r.refractionRatio),r.lightMap){n.lightMap.value=r.lightMap;var o=!0!==e.physicallyCorrectLights?Math.PI:1;n.lightMapIntensity.value=r.lightMapIntensity*o}r.aoMap&&(n.aoMap.value=r.aoMap,n.aoMapIntensity.value=r.aoMapIntensity),r.map?i=r.map:r.specularMap?i=r.specularMap:r.displacementMap?i=r.displacementMap:r.normalMap?i=r.normalMap:r.bumpMap?i=r.bumpMap:r.roughnessMap?i=r.roughnessMap:r.metalnessMap?i=r.metalnessMap:r.alphaMap?i=r.alphaMap:r.emissiveMap?i=r.emissiveMap:r.clearcoatMap?i=r.clearcoatMap:r.clearcoatNormalMap?i=r.clearcoatNormalMap:r.clearcoatRoughnessMap?i=r.clearcoatRoughnessMap:r.iridescenceMap?i=r.iridescenceMap:r.iridescenceThicknessMap?i=r.iridescenceThicknessMap:r.specularIntensityMap?i=r.specularIntensityMap:r.specularColorMap?i=r.specularColorMap:r.transmissionMap?i=r.transmissionMap:r.thicknessMap?i=r.thicknessMap:r.sheenColorMap?i=r.sheenColorMap:r.sheenRoughnessMap&&(i=r.sheenRoughnessMap),void 0!==i&&(i.isWebGLRenderTarget&&(i=i.texture),!0===i.matrixAutoUpdate&&i.updateMatrix(),n.uvTransform.value.copy(i.matrix)),r.aoMap?A=r.aoMap:r.lightMap&&(A=r.lightMap),void 0!==A&&(A.isWebGLRenderTarget&&(A=A.texture),!0===A.matrixAutoUpdate&&A.updateMatrix(),n.uv2Transform.value.copy(A.matrix))}return{refreshFogUniforms:function(t,n){n.color.getRGB(t.fogColor.value,ly(e)),n.isFog?(t.fogNear.value=n.near,t.fogFar.value=n.far):n.isFogExp2&&(t.fogDensity.value=n.density)},refreshMaterialUniforms:function(e,r,i,A,a){r.isMeshBasicMaterial||r.isMeshLambertMaterial?n(e,r):r.isMeshToonMaterial?(n(e,r),function(e,t){t.gradientMap&&(e.gradientMap.value=t.gradientMap)}(e,r)):r.isMeshPhongMaterial?(n(e,r),function(e,t){e.specular.value.copy(t.specular),e.shininess.value=Math.max(t.shininess,1e-4)}(e,r)):r.isMeshStandardMaterial?(n(e,r),function(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap);n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap);var r=t.get(n).envMap;r&&(e.envMapIntensity.value=n.envMapIntensity)}(e,r),r.isMeshPhysicalMaterial&&function(e,t,n){e.ior.value=t.ior,t.sheen>0&&(e.sheenColor.value.copy(t.sheenColor).multiplyScalar(t.sheen),e.sheenRoughness.value=t.sheenRoughness,t.sheenColorMap&&(e.sheenColorMap.value=t.sheenColorMap),t.sheenRoughnessMap&&(e.sheenRoughnessMap.value=t.sheenRoughnessMap));t.clearcoat>0&&(e.clearcoat.value=t.clearcoat,e.clearcoatRoughness.value=t.clearcoatRoughness,t.clearcoatMap&&(e.clearcoatMap.value=t.clearcoatMap),t.clearcoatRoughnessMap&&(e.clearcoatRoughnessMap.value=t.clearcoatRoughnessMap),t.clearcoatNormalMap&&(e.clearcoatNormalScale.value.copy(t.clearcoatNormalScale),e.clearcoatNormalMap.value=t.clearcoatNormalMap,t.side===sf&&e.clearcoatNormalScale.value.negate()));t.iridescence>0&&(e.iridescence.value=t.iridescence,e.iridescenceIOR.value=t.iridescenceIOR,e.iridescenceThicknessMinimum.value=t.iridescenceThicknessRange[0],e.iridescenceThicknessMaximum.value=t.iridescenceThicknessRange[1],t.iridescenceMap&&(e.iridescenceMap.value=t.iridescenceMap),t.iridescenceThicknessMap&&(e.iridescenceThicknessMap.value=t.iridescenceThicknessMap));t.transmission>0&&(e.transmission.value=t.transmission,e.transmissionSamplerMap.value=n.texture,e.transmissionSamplerSize.value.set(n.width,n.height),t.transmissionMap&&(e.transmissionMap.value=t.transmissionMap),e.thickness.value=t.thickness,t.thicknessMap&&(e.thicknessMap.value=t.thicknessMap),e.attenuationDistance.value=t.attenuationDistance,e.attenuationColor.value.copy(t.attenuationColor));e.specularIntensity.value=t.specularIntensity,e.specularColor.value.copy(t.specularColor),t.specularIntensityMap&&(e.specularIntensityMap.value=t.specularIntensityMap);t.specularColorMap&&(e.specularColorMap.value=t.specularColorMap)}(e,r,a)):r.isMeshMatcapMaterial?(n(e,r),function(e,t){t.matcap&&(e.matcap.value=t.matcap)}(e,r)):r.isMeshDepthMaterial?n(e,r):r.isMeshDistanceMaterial?(n(e,r),function(e,t){e.referencePosition.value.copy(t.referencePosition),e.nearDistance.value=t.nearDistance,e.farDistance.value=t.farDistance}(e,r)):r.isMeshNormalMaterial?n(e,r):r.isLineBasicMaterial?(function(e,t){e.diffuse.value.copy(t.color),e.opacity.value=t.opacity}(e,r),r.isLineDashedMaterial&&function(e,t){e.dashSize.value=t.dashSize,e.totalSize.value=t.dashSize+t.gapSize,e.scale.value=t.scale}(e,r)):r.isPointsMaterial?function(e,t,n,r){e.diffuse.value.copy(t.color),e.opacity.value=t.opacity,e.size.value=t.size*n,e.scale.value=.5*r,t.map&&(e.map.value=t.map);t.alphaMap&&(e.alphaMap.value=t.alphaMap);t.alphaTest>0&&(e.alphaTest.value=t.alphaTest);var i;t.map?i=t.map:t.alphaMap&&(i=t.alphaMap);void 0!==i&&(!0===i.matrixAutoUpdate&&i.updateMatrix(),e.uvTransform.value.copy(i.matrix))}(e,r,i,A):r.isSpriteMaterial?function(e,t){e.diffuse.value.copy(t.color),e.opacity.value=t.opacity,e.rotation.value=t.rotation,t.map&&(e.map.value=t.map);t.alphaMap&&(e.alphaMap.value=t.alphaMap);t.alphaTest>0&&(e.alphaTest.value=t.alphaTest);var n;t.map?n=t.map:t.alphaMap&&(n=t.alphaMap);void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),e.uvTransform.value.copy(n.matrix))}(e,r):r.isShadowMaterial?(e.color.value.copy(r.color),e.opacity.value=r.opacity):r.isShaderMaterial&&(r.uniformsNeedUpdate=!1)}}}function o_(e,t,n,r){var i={},A={},a=[],o=n.isWebGL2?e.getParameter(35375):0;function s(e,t,n){var r=e.value;if(void 0===n[t]){if("number"===typeof r)n[t]=r;else{for(var i=Array.isArray(r)?r:[r],A=[],a=0;a<i.length;a++)A.push(i[a].clone());n[t]=A}return!0}if("number"===typeof r){if(n[t]!==r)return n[t]=r,!0}else for(var o=Array.isArray(n[t])?n[t]:[n[t]],s=Array.isArray(r)?r:[r],l=0;l<o.length;l++){var u=o[l];if(!1===u.equals(s[l]))return u.copy(s[l]),!0}return!1}function l(e){var t={boundary:0,storage:0};return"number"===typeof e?(t.boundary=4,t.storage=4):e.isVector2?(t.boundary=8,t.storage=8):e.isVector3||e.isColor?(t.boundary=16,t.storage=12):e.isVector4?(t.boundary=16,t.storage=16):e.isMatrix3?(t.boundary=48,t.storage=48):e.isMatrix4?(t.boundary=64,t.storage=64):e.isTexture?console.warn("THREE.WebGLRenderer: Texture samplers can not be part of an uniforms group."):console.warn("THREE.WebGLRenderer: Unsupported uniform value type.",e),t}function u(t){var n=t.target;n.removeEventListener("dispose",u);var r=a.indexOf(n.__bindingPointIndex);a.splice(r,1),e.deleteBuffer(i[n.id]),delete i[n.id],delete A[n.id]}return{bind:function(e,t){var n=t.program;r.uniformBlockBinding(e,n)},update:function(n,c){var d=i[n.id];void 0===d&&(!function(e){for(var t=e.uniforms,n=0,r=16,i=0,A=0,a=t.length;A<a;A++){for(var o=t[A],s={boundary:0,storage:0},u=Array.isArray(o.value)?o.value:[o.value],c=0,d=u.length;c<d;c++){var h=l(u[c]);s.boundary+=h.boundary,s.storage+=h.storage}if(o.__data=new Float32Array(s.storage/Float32Array.BYTES_PER_ELEMENT),o.__offset=n,A>0)0!==(i=n%r)&&r-i-s.boundary<0&&(n+=r-i,o.__offset=n);n+=s.storage}(i=n%r)>0&&(n+=r-i);e.__size=n,e.__cache={}}(n),d=function(t){var n=function(){for(var e=0;e<o;e++)if(-1===a.indexOf(e))return a.push(e),e;return console.error("THREE.WebGLRenderer: Maximum number of simultaneously usable uniforms groups reached."),0}();t.__bindingPointIndex=n;var r=e.createBuffer(),i=t.__size,A=t.usage;return e.bindBuffer(35345,r),e.bufferData(35345,i,A),e.bindBuffer(35345,null),e.bindBufferBase(35345,n,r),r}(n),i[n.id]=d,n.addEventListener("dispose",u));var h=c.program;r.updateUBOMapping(n,h);var f=t.render.frame;A[n.id]!==f&&(!function(t){var n=i[t.id],r=t.uniforms,A=t.__cache;e.bindBuffer(35345,n);for(var a=0,o=r.length;a<o;a++){var 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A=t.fog,a=r.isMeshStandardMaterial?t.environment:null,o=null===y?p.outputEncoding:!0===y.isXRRenderTarget?y.texture.encoding:fg,s=(r.isMeshStandardMaterial?q:X).get(r.envMap||a),l=!0===r.vertexColors&&!!n.attributes.color&&4===n.attributes.color.itemSize,u=!!r.normalMap&&!!n.attributes.tangent,c=!!n.morphAttributes.position,h=!!n.morphAttributes.normal,f=!!n.morphAttributes.color,g=r.toneMapped?p.toneMapping:zf,m=n.morphAttributes.position||n.morphAttributes.normal||n.morphAttributes.color,v=void 0!==m?m.length:0,_=W.get(r),b=d.state.lights;if(!0===L&&(!0===D||e!==B)){var x=e===B&&r.id===w;re.setState(r,e,x)}var C=!1;r.version===_.__version?_.needsLights&&_.lightsStateVersion!==b.state.version||_.outputEncoding!==o||i.isInstancedMesh&&!1===_.instancing?C=!0:i.isInstancedMesh||!0!==_.instancing?i.isSkinnedMesh&&!1===_.skinning?C=!0:i.isSkinnedMesh||!0!==_.skinning?_.envMap!==s||!0===r.fog&&_.fog!==A?C=!0:void 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r=z.isWebGL2;null===I&&(I=new cm(1,1,{generateMipmaps:!0,type:V.has("EXT_color_buffer_half_float")?mp:up,minFilter:sp,samples:r&&!0===a?4:0}));p.getDrawingBufferSize(H),r?I.setSize(H.x,H.y):I.setSize(Hg(H.x),Hg(H.y));var i=p.getRenderTarget();p.setRenderTarget(I),p.clear();var A=p.toneMapping;p.toneMapping=zf,Ue(e,t,n),p.toneMapping=A,j.updateMultisampleRenderTarget(I),j.updateRenderTargetMipmap(I),p.setRenderTarget(i)}(i,t,n),r&&G.viewport(_.copy(r)),i.length>0&&Ue(i,t,n),A.length>0&&Ue(A,t,n),o.length>0&&Ue(o,t,n),G.buffers.depth.setTest(!0),G.buffers.depth.setMask(!0),G.buffers.color.setMask(!0),G.setPolygonOffset(!1)}function Ue(e,t,n){for(var r=!0===t.isScene?t.overrideMaterial:null,i=0,A=e.length;i<A;i++){var a=e[i],o=a.object,s=a.geometry,l=null===r?a.material:r,u=a.group;o.layers.test(n.layers)&&Me(o,t,n,s,l,u)}}function Me(e,t,n,r,i,A){e.onBeforeRender(p,t,n,r,i,A),e.modelViewMatrix.multiplyMatrices(n.matrixWorldInverse,e.matrixWorld),e.normalMatrix.getNormalMatrix(e.modelViewMatrix),i.onBeforeRender(p,t,n,r,e,A),!0===i.transparent&&i.side===lf&&!1===i.forceSinglePass?(i.side=sf,i.needsUpdate=!0,p.renderBufferDirect(n,t,r,i,e,A),i.side=of,i.needsUpdate=!0,p.renderBufferDirect(n,t,r,i,e,A),i.side=lf):p.renderBufferDirect(n,t,r,i,e,A),e.onAfterRender(p,t,n,r,i,A)}function Fe(e,t,n){!0!==t.isScene&&(t=N);var r=W.get(e),i=d.state.lights,A=d.state.shadowsArray,a=i.state.version,o=$.getParameters(e,i.state,A,t,n),s=$.getProgramCacheKey(o),l=r.programs;r.environment=e.isMeshStandardMaterial?t.environment:null,r.fog=t.fog,r.envMap=(e.isMeshStandardMaterial?q:X).get(e.envMap||r.environment),void 0===l&&(e.addEventListener("dispose",Be),l=new Map,r.programs=l);var u=l.get(s);if(void 0!==u){if(r.currentProgram===u&&r.lightsStateVersion===a)return Te(e,o),u}else o.uniforms=$.getUniforms(e),e.onBuild(n,o,p),e.onBeforeCompile(o,p),u=$.acquireProgram(o,s),l.set(s,u),r.uniforms=o.uniforms;var c=r.uniforms;(e.isShaderMaterial||e.isRawShaderMaterial)&&!0!==e.clipping||(c.clippingPlanes=re.uniform),Te(e,o),r.needsLights=function(e){return e.isMeshLambertMaterial||e.isMeshToonMaterial||e.isMeshPhongMaterial||e.isMeshStandardMaterial||e.isShadowMaterial||e.isShaderMaterial&&!0===e.lights}(e),r.lightsStateVersion=a,r.needsLights&&(c.ambientLightColor.value=i.state.ambient,c.lightProbe.value=i.state.probe,c.directionalLights.value=i.state.directional,c.directionalLightShadows.value=i.state.directionalShadow,c.spotLights.value=i.state.spot,c.spotLightShadows.value=i.state.spotShadow,c.rectAreaLights.value=i.state.rectArea,c.ltc_1.value=i.state.rectAreaLTC1,c.ltc_2.value=i.state.rectAreaLTC2,c.pointLights.value=i.state.point,c.pointLightShadows.value=i.state.pointShadow,c.hemisphereLights.value=i.state.hemi,c.directionalShadowMap.value=i.state.directionalShadowMap,c.directionalShadowMatrix.value=i.state.directionalShadowMatrix,c.spotShadowMap.value=i.state.spotShadowMap,c.spotLightMatrix.value=i.state.spotLightMatrix,c.spotLightMap.value=i.state.spotLightMap,c.pointShadowMap.value=i.state.pointShadowMap,c.pointShadowMatrix.value=i.state.pointShadowMatrix);var h=u.getUniforms(),f=fB.seqWithValue(h.seq,c);return r.currentProgram=u,r.uniformsList=f,u}function Te(e,t){var n=W.get(e);n.outputEncoding=t.outputEncoding,n.instancing=t.instancing,n.skinning=t.skinning,n.morphTargets=t.morphTargets,n.morphNormals=t.morphNormals,n.morphColors=t.morphColors,n.morphTargetsCount=t.morphTargetsCount,n.numClippingPlanes=t.numClippingPlanes,n.numIntersection=t.numClipIntersection,n.vertexAlphas=t.vertexAlphas,n.vertexTangents=t.vertexTangents,n.toneMapping=t.toneMapping}Ce.setAnimationLoop((function(e){_e&&_e(e)})),"undefined"!==typeof self&&Ce.setContext(self),this.setAnimationLoop=function(e){_e=e,me.setAnimationLoop(e),null===e?Ce.stop():Ce.start()},me.addEventListener("sessionstart",be),me.addEventListener("sessionend",xe),this.render=function(e,t){if(void 0===t||!0===t.isCamera){if(!0!==g){!0===e.matrixWorldAutoUpdate&&e.updateMatrixWorld(),null===t.parent&&!0===t.matrixWorldAutoUpdate&&t.updateMatrixWorld(),!0===me.enabled&&!0===me.isPresenting&&(!0===me.cameraAutoUpdate&&me.updateCamera(t),t=me.getCamera()),!0===e.isScene&&e.onBeforeRender(p,e,t,y),(d=ne.get(e,f.length)).init(),f.push(d),R.multiplyMatrices(t.projectionMatrix,t.matrixWorldInverse),Q.setFromProjectionMatrix(R),D=this.localClippingEnabled,L=re.init(this.clippingPlanes,D),(c=te.get(e,h.length)).init(),h.push(c),Se(e,t,0,p.sortObjects),c.finish(),!0===p.sortObjects&&c.sort(U,M),!0===L&&re.beginShadows();var n=d.state.shadowsArray;if(ie.render(n,e,t),!0===L&&re.endShadows(),!0===this.info.autoReset&&this.info.reset(),Ae.render(c,e),d.setupLights(p.physicallyCorrectLights),t.isArrayCamera)for(var r=t.cameras,i=0,A=r.length;i<A;i++){var a=r[i];Ee(c,e,a,a.viewport)}else Ee(c,e,t);null!==y&&(j.updateMultisampleRenderTarget(y),j.updateRenderTargetMipmap(y)),!0===e.isScene&&e.onAfterRender(p,e,t),ue.resetDefaultState(),w=-1,B=null,f.pop(),d=f.length>0?f[f.length-1]:null,h.pop(),c=h.length>0?h[h.length-1]:null}}else console.error("THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.")},this.getActiveCubeFace=function(){return m},this.getActiveMipmapLevel=function(){return v},this.getRenderTarget=function(){return y},this.setRenderTargetTextures=function(e,t,n){W.get(e.texture).__webglTexture=t,W.get(e.depthTexture).__webglTexture=n;var r=W.get(e);r.__hasExternalTextures=!0,r.__hasExternalTextures&&(r.__autoAllocateDepthBuffer=void 0===n,r.__autoAllocateDepthBuffer||!0===V.has("WEBGL_multisampled_render_to_texture")&&(console.warn("THREE.WebGLRenderer: Render-to-texture extension was disabled because an external texture was provided"),r.__useRenderToTexture=!1))},this.setRenderTargetFramebuffer=function(e,t){var n=W.get(e);n.__webglFramebuffer=t,n.__useDefaultFramebuffer=void 0===t},this.setRenderTarget=function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:0,n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0;y=e,m=t,v=n;var r=!0,i=null,A=!1,a=!1;if(e){var o=W.get(e);void 0!==o.__useDefaultFramebuffer?(G.bindFramebuffer(36160,null),r=!1):void 0===o.__webglFramebuffer?j.setupRenderTarget(e):o.__hasExternalTextures&&j.rebindTextures(e,W.get(e.texture).__webglTexture,W.get(e.depthTexture).__webglTexture);var s=e.texture;(s.isData3DTexture||s.isDataArrayTexture||s.isCompressedArrayTexture)&&(a=!0);var l=W.get(e).__webglFramebuffer;e.isWebGLCubeRenderTarget?(i=l[t],A=!0):i=z.isWebGL2&&e.samples>0&&!1===j.useMultisampledRTT(e)?W.get(e).__webglMultisampledFramebuffer:l,_.copy(e.viewport),b.copy(e.scissor),x=e.scissorTest}else _.copy(F).multiplyScalar(E).floor(),b.copy(T).multiplyScalar(E).floor(),x=k;if(G.bindFramebuffer(36160,i)&&z.drawBuffers&&r&&G.drawBuffers(e,i),G.viewport(_),G.scissor(b),G.setScissorTest(x),A){var u=W.get(e.texture);de.framebufferTexture2D(36160,36064,34069+t,u.__webglTexture,n)}else if(a){var c=W.get(e.texture),d=t||0;de.framebufferTextureLayer(36160,36064,c.__webglTexture,n||0,d)}w=-1},this.readRenderTargetPixels=function(e,t,n,r,i,A,a){if(e&&e.isWebGLRenderTarget){var o=W.get(e).__webglFramebuffer;if(e.isWebGLCubeRenderTarget&&void 0!==a&&(o=o[a]),o){G.bindFramebuffer(36160,o);try{var s=e.texture,l=s.format,u=s.type;if(l!==_p&&le.convert(l)!==de.getParameter(35739))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.");var c=u===mp&&(V.has("EXT_color_buffer_half_float")||z.isWebGL2&&V.has("EXT_color_buffer_float"));if(u!==up&&le.convert(u)!==de.getParameter(35738)&&(u!==gp||!(z.isWebGL2||V.has("OES_texture_float")||V.has("WEBGL_color_buffer_float")))&&!c)return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.");t>=0&&t<=e.width-r&&n>=0&&n<=e.height-i&&de.readPixels(t,n,r,i,le.convert(l),le.convert(u),A)}finally{var d=null!==y?W.get(y).__webglFramebuffer:null;G.bindFramebuffer(36160,d)}}}else console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.")},this.copyFramebufferToTexture=function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:0,r=Math.pow(2,-n),i=Math.floor(t.image.width*r),A=Math.floor(t.image.height*r);j.setTexture2D(t,0),de.copyTexSubImage2D(3553,n,0,0,e.x,e.y,i,A),G.unbindTexture()},this.copyTextureToTexture=function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:0,i=t.image.width,A=t.image.height,a=le.convert(n.format),o=le.convert(n.type);j.setTexture2D(n,0),de.pixelStorei(37440,n.flipY),de.pixelStorei(37441,n.premultiplyAlpha),de.pixelStorei(3317,n.unpackAlignment),t.isDataTexture?de.texSubImage2D(3553,r,e.x,e.y,i,A,a,o,t.image.data):t.isCompressedTexture?de.compressedTexSubImage2D(3553,r,e.x,e.y,t.mipmaps[0].width,t.mipmaps[0].height,a,t.mipmaps[0].data):de.texSubImage2D(3553,r,e.x,e.y,a,o,t.image),0===r&&n.generateMipmaps&&de.generateMipmap(3553),G.unbindTexture()},this.copyTextureToTexture3D=function(e,t,n,r){var i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:0;if(p.isWebGL1Renderer)console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.");else{var A,a=e.max.x-e.min.x+1,o=e.max.y-e.min.y+1,s=e.max.z-e.min.z+1,l=le.convert(r.format),u=le.convert(r.type);if(r.isData3DTexture)j.setTexture3D(r,0),A=32879;else{if(!r.isDataArrayTexture)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.");j.setTexture2DArray(r,0),A=35866}de.pixelStorei(37440,r.flipY),de.pixelStorei(37441,r.premultiplyAlpha),de.pixelStorei(3317,r.unpackAlignment);var c=de.getParameter(3314),d=de.getParameter(32878),h=de.getParameter(3316),f=de.getParameter(3315),g=de.getParameter(32877),m=n.isCompressedTexture?n.mipmaps[0]:n.image;de.pixelStorei(3314,m.width),de.pixelStorei(32878,m.height),de.pixelStorei(3316,e.min.x),de.pixelStorei(3315,e.min.y),de.pixelStorei(32877,e.min.z),n.isDataTexture||n.isData3DTexture?de.texSubImage3D(A,i,t.x,t.y,t.z,a,o,s,l,u,m.data):n.isCompressedArrayTexture?(console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture."),de.compressedTexSubImage3D(A,i,t.x,t.y,t.z,a,o,s,l,m.data)):de.texSubImage3D(A,i,t.x,t.y,t.z,a,o,s,l,u,m),de.pixelStorei(3314,c),de.pixelStorei(32878,d),de.pixelStorei(3316,h),de.pixelStorei(3315,f),de.pixelStorei(32877,g),0===i&&r.generateMipmaps&&de.generateMipmap(A),G.unbindTexture()}},this.initTexture=function(e){e.isCubeTexture?j.setTextureCube(e,0):e.isData3DTexture?j.setTexture3D(e,0):e.isDataArrayTexture||e.isCompressedArrayTexture?j.setTexture2DArray(e,0):j.setTexture2D(e,0),G.unbindTexture()},this.resetState=function(){m=0,v=0,y=null,G.reset(),ue.reset()},"undefined"!==typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}var l_=function(e){_(n,e);var t=S(n);function n(){return v(this,n),t.apply(this,arguments)}return w(n)}(s_);l_.prototype.isWebGL1Renderer=!0;var u_=function(e){_(n,e);var t=S(n);function n(){var e;return v(this,n),(e=t.call(this)).isScene=!0,e.type="Scene",e.background=null,e.environment=null,e.fog=null,e.backgroundBlurriness=0,e.backgroundIntensity=1,e.overrideMaterial=null,"undefined"!==typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:C(e)})),e}return w(n,[{key:"copy",value:function(e,t){return jc(b(n.prototype),"copy",this).call(this,e,t),null!==e.background&&(this.background=e.background.clone()),null!==e.environment&&(this.environment=e.environment.clone()),null!==e.fog&&(this.fog=e.fog.clone()),this.backgroundBlurriness=e.backgroundBlurriness,this.backgroundIntensity=e.backgroundIntensity,null!==e.overrideMaterial&&(this.overrideMaterial=e.overrideMaterial.clone()),this.matrixAutoUpdate=e.matrixAutoUpdate,this}},{key:"toJSON",value:function(e){var t=jc(b(n.prototype),"toJSON",this).call(this,e);return null!==this.fog&&(t.object.fog=this.fog.toJSON()),this.backgroundBlurriness>0&&(t.object.backgroundBlurriness=this.backgroundBlurriness),1!==this.backgroundIntensity&&(t.object.backgroundIntensity=this.backgroundIntensity),t}},{key:"autoUpdate",get:function(){return console.warn("THREE.Scene: autoUpdate was renamed to matrixWorldAutoUpdate in r144."),this.matrixWorldAutoUpdate},set:function(e){console.warn("THREE.Scene: autoUpdate was renamed to matrixWorldAutoUpdate in r144."),this.matrixWorldAutoUpdate=e}}]),n}(gv);var c_=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:1,a=arguments.length>3?arguments[3]:void 0,o=arguments.length>4?arguments[4]:void 0,s=arguments.length>5?arguments[5]:void 0,l=arguments.length>6?arguments[6]:void 0,u=arguments.length>7?arguments[7]:void 0,c=arguments.length>8&&void 0!==arguments[8]?arguments[8]:rp,d=arguments.length>9&&void 0!==arguments[9]?arguments[9]:rp,h=arguments.length>10?arguments[10]:void 0,f=arguments.length>11?arguments[11]:void 0;return v(this,n),(e=t.call(this,null,s,l,u,c,d,a,o,h,f)).isDataTexture=!0,e.image={data:r,width:i,height:A},e.generateMipmaps=!1,e.flipY=!1,e.unpackAlignment=1,e}return w(n)}(lm),d_=function(e){_(n,e);var t=S(n);function n(e){var r;return v(this,n),(r=t.call(this)).isLineBasicMaterial=!0,r.type="LineBasicMaterial",r.color=new im(16777215),r.linewidth=1,r.linecap="round",r.linejoin="round",r.fog=!0,r.setValues(e),r}return w(n,[{key:"copy",value:function(e){return jc(b(n.prototype),"copy",this).call(this,e),this.color.copy(e.color),this.linewidth=e.linewidth,this.linecap=e.linecap,this.linejoin=e.linejoin,this.fog=e.fog,this}}]),n}(Mv),h_=new pm,f_=new pm,p_=new Km,g_=new Gm,m_=new Im,v_=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new Gv,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new d_;return v(this,n),(e=t.call(this)).isLine=!0,e.type="Line",e.geometry=r,e.material=i,e.updateMorphTargets(),e}return w(n,[{key:"copy",value:function(e,t){return jc(b(n.prototype),"copy",this).call(this,e,t),this.material=e.material,this.geometry=e.geometry,this}},{key:"computeLineDistances",value:function(){var e=this.geometry;if(null===e.index){for(var t=e.attributes.position,n=[0],r=1,i=t.count;r<i;r++)h_.fromBufferAttribute(t,r-1),f_.fromBufferAttribute(t,r),n[r]=n[r-1],n[r]+=h_.distanceTo(f_);e.setAttribute("lineDistance",new Iv(n,1))}else console.warn("THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");return this}},{key:"raycast",value:function(e,t){var n=this.geometry,r=this.matrixWorld,i=e.params.Line.threshold,A=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),m_.copy(n.boundingSphere),m_.applyMatrix4(r),m_.radius+=i,!1!==e.ray.intersectsSphere(m_)){p_.copy(r).invert(),g_.copy(e.ray).applyMatrix4(p_);var a=i/((this.scale.x+this.scale.y+this.scale.z)/3),o=a*a,s=new pm,l=new pm,u=new pm,c=new pm,d=this.isLineSegments?2:1,h=n.index,f=n.attributes.position;if(null!==h)for(var p=Math.max(0,A.start),g=Math.min(h.count,A.start+A.count)-1;p<g;p+=d){var m=h.getX(p),v=h.getX(p+1);if(s.fromBufferAttribute(f,m),l.fromBufferAttribute(f,v),!(g_.distanceSqToSegment(s,l,c,u)>o)){c.applyMatrix4(this.matrixWorld);var y=e.ray.origin.distanceTo(c);y<e.near||y>e.far||t.push({distance:y,point:u.clone().applyMatrix4(this.matrixWorld),index:p,face:null,faceIndex:null,object:this})}}else for(var w=Math.max(0,A.start),B=Math.min(f.count,A.start+A.count)-1;w<B;w+=d){if(s.fromBufferAttribute(f,w),l.fromBufferAttribute(f,w+1),!(g_.distanceSqToSegment(s,l,c,u)>o)){c.applyMatrix4(this.matrixWorld);var _=e.ray.origin.distanceTo(c);_<e.near||_>e.far||t.push({distance:_,point:u.clone().applyMatrix4(this.matrixWorld),index:w,face:null,faceIndex:null,object:this})}}}}},{key:"updateMorphTargets",value:function(){var e=this.geometry.morphAttributes,t=Object.keys(e);if(t.length>0){var n=e[t[0]];if(void 0!==n){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(var r=0,i=n.length;r<i;r++){var A=n[r].name||String(r);this.morphTargetInfluences.push(0),this.morphTargetDictionary[A]=r}}}}}]),n}(gv),y_=new pm,w_=new pm,B_=function(e){_(n,e);var t=S(n);function n(e,r){var i;return v(this,n),(i=t.call(this,e,r)).isLineSegments=!0,i.type="LineSegments",i}return w(n,[{key:"computeLineDistances",value:function(){var e=this.geometry;if(null===e.index){for(var t=e.attributes.position,n=[],r=0,i=t.count;r<i;r+=2)y_.fromBufferAttribute(t,r),w_.fromBufferAttribute(t,r+1),n[r]=0===r?0:n[r-1],n[r+1]=n[r]+y_.distanceTo(w_);e.setAttribute("lineDistance",new Iv(n,1))}else console.warn("THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");return this}}]),n}(v_),__=function(e){_(n,e);var t=S(n);function n(e){var r;return v(this,n),(r=t.call(this)).isPointsMaterial=!0,r.type="PointsMaterial",r.color=new im(16777215),r.map=null,r.alphaMap=null,r.size=1,r.sizeAttenuation=!0,r.fog=!0,r.setValues(e),r}return w(n,[{key:"copy",value:function(e){return jc(b(n.prototype),"copy",this).call(this,e),this.color.copy(e.color),this.map=e.map,this.alphaMap=e.alphaMap,this.size=e.size,this.sizeAttenuation=e.sizeAttenuation,this.fog=e.fog,this}}]),n}(Mv),b_=new Km,x_=new Gm,C_=new Im,S_=new pm,E_=function(e){_(n,e);var t=S(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:new Gv,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:new __;return v(this,n),(e=t.call(this)).isPoints=!0,e.type="Points",e.geometry=r,e.material=i,e.updateMorphTargets(),e}return w(n,[{key:"copy",value:function(e,t){return jc(b(n.prototype),"copy",this).call(this,e,t),this.material=e.material,this.geometry=e.geometry,this}},{key:"raycast",value:function(e,t){var n=this.geometry,r=this.matrixWorld,i=e.params.Points.threshold,A=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),C_.copy(n.boundingSphere),C_.applyMatrix4(r),C_.radius+=i,!1!==e.ray.intersectsSphere(C_)){b_.copy(r).invert(),x_.copy(e.ray).applyMatrix4(b_);var a=i/((this.scale.x+this.scale.y+this.scale.z)/3),o=a*a,s=n.index,l=n.attributes.position;if(null!==s)for(var u=Math.max(0,A.start),c=Math.min(s.count,A.start+A.count);u<c;u++){var d=s.getX(u);S_.fromBufferAttribute(l,d),U_(S_,d,o,r,e,t,this)}else for(var h=Math.max(0,A.start),f=Math.min(l.count,A.start+A.count);h<f;h++)S_.fromBufferAttribute(l,h),U_(S_,h,o,r,e,t,this)}}},{key:"updateMorphTargets",value:function(){var e=this.geometry.morphAttributes,t=Object.keys(e);if(t.length>0){var n=e[t[0]];if(void 0!==n){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(var r=0,i=n.length;r<i;r++){var A=n[r].name||String(r);this.morphTargetInfluences.push(0),this.morphTargetDictionary[A]=r}}}}}]),n}(gv);function U_(e,t,n,r,i,A,a){var o=x_.distanceSqToPoint(e);if(o<n){var s=new pm;x_.closestPointToPoint(e,s),s.applyMatrix4(r);var l=i.ray.origin.distanceTo(s);if(l<i.near||l>i.far)return;A.push({distance:l,distanceToRay:Math.sqrt(o),point:s,index:t,face:null,object:a})}}var M_=function(e){_(n,e);var t=S(n);function n(e){var r;return v(this,n),(r=t.call(this)).isMeshPhongMaterial=!0,r.type="MeshPhongMaterial",r.color=new im(16777215),r.specular=new im(1118481),r.shininess=30,r.map=null,r.lightMap=null,r.lightMapIntensity=1,r.aoMap=null,r.aoMapIntensity=1,r.emissive=new im(0),r.emissiveIntensity=1,r.emissiveMap=null,r.bumpMap=null,r.bumpScale=1,r.normalMap=null,r.normalMapType=vg,r.normalScale=new Vg(1,1),r.displacementMap=null,r.displacementScale=1,r.displacementBias=0,r.specularMap=null,r.alphaMap=null,r.envMap=null,r.combine=Nf,r.reflectivity=1,r.refractionRatio=.98,r.wireframe=!1,r.wireframeLinewidth=1,r.wireframeLinecap="round",r.wireframeLinejoin="round",r.flatShading=!1,r.fog=!0,r.setValues(e),r}return w(n,[{key:"copy",value:function(e){return jc(b(n.prototype),"copy",this).call(this,e),this.color.copy(e.color),this.specular.copy(e.specular),this.shininess=e.shininess,this.map=e.map,this.lightMap=e.lightMap,this.lightMapIntensity=e.lightMapIntensity,this.aoMap=e.aoMap,this.aoMapIntensity=e.aoMapIntensity,this.emissive.copy(e.emissive),this.emissiveMap=e.emissiveMap,this.emissiveIntensity=e.emissiveIntensity,this.bumpMap=e.bumpMap,this.bumpScale=e.bumpScale,this.normalMap=e.normalMap,this.normalMapType=e.normalMapType,this.normalScale.copy(e.normalScale),this.displacementMap=e.displacementMap,this.displacementScale=e.displacementScale,this.displacementBias=e.displacementBias,this.specularMap=e.specularMap,this.alphaMap=e.alphaMap,this.envMap=e.envMap,this.combine=e.combine,this.reflectivity=e.reflectivity,this.refractionRatio=e.refractionRatio,this.wireframe=e.wireframe,this.wireframeLinewidth=e.wireframeLinewidth,this.wireframeLinecap=e.wireframeLinecap,this.wireframeLinejoin=e.wireframeLinejoin,this.flatShading=e.flatShading,this.fog=e.fog,this}}]),n}(Mv);function F_(e,t,n){return k_(e)?new e.constructor(e.subarray(t,void 0!==n?n:e.length)):e.slice(t,n)}function T_(e,t,n){return!e||!n&&e.constructor===t?e:"number"===typeof t.BYTES_PER_ELEMENT?new t(e):Array.prototype.slice.call(e)}function k_(e){return ArrayBuffer.isView(e)&&!(e instanceof DataView)}var Q_=function(){function e(t,n,r,i){v(this,e),this.parameterPositions=t,this._cachedIndex=0,this.resultBuffer=void 0!==i?i:new n.constructor(r),this.sampleValues=n,this.valueSize=r,this.settings=null,this.DefaultSettings_={}}return w(e,[{key:"evaluate",value:function(e){var t=this.parameterPositions,n=this._cachedIndex,r=t[n],i=t[n-1];e:{t:{var A;n:{r:if(!(e<r)){for(var a=n+2;;){if(void 0===r){if(e<i)break r;return n=t.length,this._cachedIndex=n,this.copySampleValue_(n-1)}if(n===a)break;if(i=r,e<(r=t[++n]))break t}A=t.length;break n}if(e>=i)break e;var o=t[1];e<o&&(n=2,i=o);for(var s=n-2;;){if(void 0===i)return this._cachedIndex=0,this.copySampleValue_(0);if(n===s)break;if(r=i,e>=(i=t[--n-1]))break 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n.createInterpolant=this.createInterpolant,n}}],[{key:"toJSON",value:function(e){var t,n=e.constructor;if(n.toJSON!==this.toJSON)t=n.toJSON(e);else{t={name:e.name,times:T_(e.times,Array),values:T_(e.values,Array)};var r=e.getInterpolation();r!==e.DefaultInterpolation&&(t.interpolation=r)}return t.type=e.ValueTypeName,t}}]),e}();R_.prototype.TimeBufferType=Float32Array,R_.prototype.ValueBufferType=Float32Array,R_.prototype.DefaultInterpolation=lg;var H_=function(e){_(n,e);var t=S(n);function n(){return v(this,n),t.apply(this,arguments)}return w(n)}(R_);H_.prototype.ValueTypeName="bool",H_.prototype.ValueBufferType=Array,H_.prototype.DefaultInterpolation=sg,H_.prototype.InterpolantFactoryMethodLinear=void 0,H_.prototype.InterpolantFactoryMethodSmooth=void 0;var P_=function(e){_(n,e);var t=S(n);function n(){return v(this,n),t.apply(this,arguments)}return w(n)}(R_);P_.prototype.ValueTypeName="color";var N_=function(e){_(n,e);var t=S(n);function n(){return 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Vg(512,512),this.map=null,this.mapPass=null,this.matrix=new Km,this.autoUpdate=!0,this.needsUpdate=!1,this._frustum=new Ey,this._frameExtents=new Vg(1,1),this._viewportCount=1,this._viewports=[new um(0,0,1,1)]}return w(e,[{key:"getViewportCount",value:function(){return this._viewportCount}},{key:"getFrustum",value:function(){return this._frustum}},{key:"updateMatrices",value:function(e){var t=this.camera,n=this.matrix;rb.setFromMatrixPosition(e.matrixWorld),t.position.copy(rb),ib.setFromMatrixPosition(e.target.matrixWorld),t.lookAt(ib),t.updateMatrixWorld(),nb.multiplyMatrices(t.projectionMatrix,t.matrixWorldInverse),this._frustum.setFromProjectionMatrix(nb),n.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),n.multiply(nb)}},{key:"getViewport",value:function(e){return this._viewports[e]}},{key:"getFrameExtents",value:function(){return this._frameExtents}},{key:"dispose",value:function(){this.map&&this.map.dispose(),this.mapPass&&this.mapPass.dispose()}},{key:"copy",value:function(e){return this.camera=e.camera.clone(),this.bias=e.bias,this.radius=e.radius,this.mapSize.copy(e.mapSize),this}},{key:"clone",value:function(){return(new this.constructor).copy(this)}},{key:"toJSON",value:function(){var e={};return 0!==this.bias&&(e.bias=this.bias),0!==this.normalBias&&(e.normalBias=this.normalBias),1!==this.radius&&(e.radius=this.radius),512===this.mapSize.x&&512===this.mapSize.y||(e.mapSize=this.mapSize.toArray()),e.camera=this.camera.toJSON(!1).object,delete e.camera.matrix,e}}]),e}(),ab=function(e){_(n,e);var t=S(n);function n(){var e;return v(this,n),(e=t.call(this,new Oy(-5,5,5,-5,.5,500))).isDirectionalLightShadow=!0,e}return w(n)}(Ab),ob=function(e){_(n,e);var t=S(n);function n(e,r){var i;return v(this,n),(i=t.call(this,e,r)).isDirectionalLight=!0,i.type="DirectionalLight",i.position.copy(gv.DEFAULT_UP),i.updateMatrix(),i.target=new gv,i.shadow=new ab,i}return w(n,[{key:"dispose",value:function(){this.shadow.dispose()}},{key:"copy",value:function(e){return jc(b(n.prototype),"copy",this).call(this,e),this.target=e.target.clone(),this.shadow=e.shadow.clone(),this}}]),n}(tb),sb=function(){function e(){var t=!(arguments.length>0&&void 0!==arguments[0])||arguments[0];v(this,e),this.autoStart=t,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}return w(e,[{key:"start",value:function(){this.startTime=lb(),this.oldTime=this.startTime,this.elapsedTime=0,this.running=!0}},{key:"stop",value:function(){this.getElapsedTime(),this.running=!1,this.autoStart=!1}},{key:"getElapsedTime",value:function(){return this.getDelta(),this.elapsedTime}},{key:"getDelta",value:function(){var e=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){var t=lb();e=(t-this.oldTime)/1e3,this.oldTime=t,this.elapsedTime+=e}return e}}]),e}();function lb(){return("undefined"===typeof performance?Date:performance).now()}var ub="\\[\\]\\.:\\/",cb=new RegExp("["+ub+"]","g"),db="[^"+ub+"]",hb="[^"+ub.replace("\\.","")+"]",fb=new RegExp("^"+/((?:WC+[\/:])*)/.source.replace("WC",db)+/(WCOD+)?/.source.replace("WCOD",hb)+/(?:\.(WC+)(?:\[(.+)\])?)?/.source.replace("WC",db)+/\.(WC+)(?:\[(.+)\])?/.source.replace("WC",db)+"$"),pb=["material","materials","bones","map"],gb=function(){function e(t,n,r){v(this,e);var i=r||mb.parseTrackName(n);this._targetGroup=t,this._bindings=t.subscribe_(n,i)}return w(e,[{key:"getValue",value:function(e,t){this.bind();var n=this._targetGroup.nCachedObjects_,r=this._bindings[n];void 0!==r&&r.getValue(e,t)}},{key:"setValue",value:function(e,t){for(var n=this._bindings,r=this._targetGroup.nCachedObjects_,i=n.length;r!==i;++r)n[r].setValue(e,t)}},{key:"bind",value:function(){for(var e=this._bindings,t=this._targetGroup.nCachedObjects_,n=e.length;t!==n;++t)e[t].bind()}},{key:"unbind",value:function(){for(var e=this._bindings,t=this._targetGroup.nCachedObjects_,n=e.length;t!==n;++t)e[t].unbind()}}]),e}(),mb=function(){function e(t,n,r){v(this,e),this.path=n,this.parsedPath=r||e.parseTrackName(n),this.node=e.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}return w(e,[{key:"_getValue_unavailable",value:function(){}},{key:"_setValue_unavailable",value:function(){}},{key:"_getValue_direct",value:function(e,t){e[t]=this.targetObject[this.propertyName]}},{key:"_getValue_array",value:function(e,t){for(var n=this.resolvedProperty,r=0,i=n.length;r!==i;++r)e[t++]=n[r]}},{key:"_getValue_arrayElement",value:function(e,t){e[t]=this.resolvedProperty[this.propertyIndex]}},{key:"_getValue_toArray",value:function(e,t){this.resolvedProperty.toArray(e,t)}},{key:"_setValue_direct",value:function(e,t){this.targetObject[this.propertyName]=e[t]}},{key:"_setValue_direct_setNeedsUpdate",value:function(e,t){this.targetObject[this.propertyName]=e[t],this.targetObject.needsUpdate=!0}},{key:"_setValue_direct_setMatrixWorldNeedsUpdate",value:function(e,t){this.targetObject[this.propertyName]=e[t],this.targetObject.matrixWorldNeedsUpdate=!0}},{key:"_setValue_array",value:function(e,t){for(var n=this.resolvedProperty,r=0,i=n.length;r!==i;++r)n[r]=e[t++]}},{key:"_setValue_array_setNeedsUpdate",value:function(e,t){for(var n=this.resolvedProperty,r=0,i=n.length;r!==i;++r)n[r]=e[t++];this.targetObject.needsUpdate=!0}},{key:"_setValue_array_setMatrixWorldNeedsUpdate",value:function(e,t){for(var n=this.resolvedProperty,r=0,i=n.length;r!==i;++r)n[r]=e[t++];this.targetObject.matrixWorldNeedsUpdate=!0}},{key:"_setValue_arrayElement",value:function(e,t){this.resolvedProperty[this.propertyIndex]=e[t]}},{key:"_setValue_arrayElement_setNeedsUpdate",value:function(e,t){this.resolvedProperty[this.propertyIndex]=e[t],this.targetObject.needsUpdate=!0}},{key:"_setValue_arrayElement_setMatrixWorldNeedsUpdate",value:function(e,t){this.resolvedProperty[this.propertyIndex]=e[t],this.targetObject.matrixWorldNeedsUpdate=!0}},{key:"_setValue_fromArray",value:function(e,t){this.resolvedProperty.fromArray(e,t)}},{key:"_setValue_fromArray_setNeedsUpdate",value:function(e,t){this.resolvedProperty.fromArray(e,t),this.targetObject.needsUpdate=!0}},{key:"_setValue_fromArray_setMatrixWorldNeedsUpdate",value:function(e,t){this.resolvedProperty.fromArray(e,t),this.targetObject.matrixWorldNeedsUpdate=!0}},{key:"_getValue_unbound",value:function(e,t){this.bind(),this.getValue(e,t)}},{key:"_setValue_unbound",value:function(e,t){this.bind(),this.setValue(e,t)}},{key:"bind",value:function(){var t=this.node,n=this.parsedPath,r=n.objectName,i=n.propertyName,A=n.propertyIndex;if(t||(t=e.findNode(this.rootNode,n.nodeName)||this.rootNode,this.node=t),this.getValue=this._getValue_unavailable,this.setValue=this._setValue_unavailable,t){if(r){var a=n.objectIndex;switch(r){case"materials":if(!t.material)return void console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);if(!t.material.materials)return void console.error("THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.",this);t=t.material.materials;break;case"bones":if(!t.skeleton)return void console.error("THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.",this);t=t.skeleton.bones;for(var o=0;o<t.length;o++)if(t[o].name===a){a=o;break}break;case"map":if("map"in t){t=t.map;break}if(!t.material)return void console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);if(!t.material.map)return void console.error("THREE.PropertyBinding: Can not bind to material.map as node.material does not have a map.",this);t=t.material.map;break;default:if(void 0===t[r])return void console.error("THREE.PropertyBinding: Can not bind to objectName of node undefined.",this);t=t[r]}if(void 0!==a){if(void 0===t[a])return void console.error("THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.",this,t);t=t[a]}}var s=t[i];if(void 0!==s){var l=this.Versioning.None;this.targetObject=t,void 0!==t.needsUpdate?l=this.Versioning.NeedsUpdate:void 0!==t.matrixWorldNeedsUpdate&&(l=this.Versioning.MatrixWorldNeedsUpdate);var u=this.BindingType.Direct;if(void 0!==A){if("morphTargetInfluences"===i){if(!t.geometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.",this);if(!t.geometry.morphAttributes)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.",this);void 0!==t.morphTargetDictionary[A]&&(A=t.morphTargetDictionary[A])}u=this.BindingType.ArrayElement,this.resolvedProperty=s,this.propertyIndex=A}else void 0!==s.fromArray&&void 0!==s.toArray?(u=this.BindingType.HasFromToArray,this.resolvedProperty=s):Array.isArray(s)?(u=this.BindingType.EntireArray,this.resolvedProperty=s):this.propertyName=i;this.getValue=this.GetterByBindingType[u],this.setValue=this.SetterByBindingTypeAndVersioning[u][l]}else{var c=n.nodeName;console.error("THREE.PropertyBinding: Trying to update property for track: "+c+"."+i+" but it wasn't found.",t)}}else console.error("THREE.PropertyBinding: Trying to update node for track: "+this.path+" but it wasn't found.")}},{key:"unbind",value:function(){this.node=null,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}}],[{key:"create",value:function(t,n,r){return t&&t.isAnimationObjectGroup?new e.Composite(t,n,r):new e(t,n,r)}},{key:"sanitizeNodeName",value:function(e){return e.replace(/\s/g,"_").replace(cb,"")}},{key:"parseTrackName",value:function(e){var t=fb.exec(e);if(null===t)throw new Error("PropertyBinding: Cannot parse trackName: "+e);var n={nodeName:t[2],objectName:t[3],objectIndex:t[4],propertyName:t[5],propertyIndex:t[6]},r=n.nodeName&&n.nodeName.lastIndexOf(".");if(void 0!==r&&-1!==r){var i=n.nodeName.substring(r+1);-1!==pb.indexOf(i)&&(n.nodeName=n.nodeName.substring(0,r),n.objectName=i)}if(null===n.propertyName||0===n.propertyName.length)throw new Error("PropertyBinding: can not parse propertyName from trackName: "+e);return n}},{key:"findNode",value:function(e,t){if(void 0===t||""===t||"."===t||-1===t||t===e.name||t===e.uuid)return e;if(e.skeleton){var n=e.skeleton.getBoneByName(t);if(void 0!==n)return n}if(e.children){var r=function e(n){for(var r=0;r<n.length;r++){var i=n[r];if(i.name===t||i.uuid===t)return i;var A=e(i.children);if(A)return A}return null}(e.children);if(r)return r}return null}}]),e}();mb.Composite=gb,mb.prototype.BindingType={Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},mb.prototype.Versioning={None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},mb.prototype.GetterByBindingType=[mb.prototype._getValue_direct,mb.prototype._getValue_array,mb.prototype._getValue_arrayElement,mb.prototype._getValue_toArray],mb.prototype.SetterByBindingTypeAndVersioning=[[mb.prototype._setValue_direct,mb.prototype._setValue_direct_setNeedsUpdate,mb.prototype._setValue_direct_setMatrixWorldNeedsUpdate],[mb.prototype._setValue_array,mb.prototype._setValue_array_setNeedsUpdate,mb.prototype._setValue_array_setMatrixWorldNeedsUpdate],[mb.prototype._setValue_arrayElement,mb.prototype._setValue_arrayElement_setNeedsUpdate,mb.prototype._setValue_arrayElement_setMatrixWorldNeedsUpdate],[mb.prototype._setValue_fromArray,mb.prototype._setValue_fromArray_setNeedsUpdate,mb.prototype._setValue_fromArray_setMatrixWorldNeedsUpdate]];new Float32Array(1);var vb=yb();function yb(){for(var e=new ArrayBuffer(4),t=new Float32Array(e),n=new Uint32Array(e),r=new Uint32Array(512),i=new Uint32Array(512),A=0;A<256;++A){var a=A-127;a<-27?(r[A]=0,r[256|A]=32768,i[A]=24,i[256|A]=24):a<-14?(r[A]=1024>>-a-14,r[256|A]=1024>>-a-14|32768,i[A]=-a-1,i[256|A]=-a-1):a<=15?(r[A]=a+15<<10,r[256|A]=a+15<<10|32768,i[A]=13,i[256|A]=13):a<128?(r[A]=31744,r[256|A]=64512,i[A]=24,i[256|A]=24):(r[A]=31744,r[256|A]=64512,i[A]=13,i[256|A]=13)}for(var o=new Uint32Array(2048),s=new Uint32Array(64),l=new Uint32Array(64),u=1;u<1024;++u){for(var c=u<<13,d=0;0===(8388608&c);)c<<=1,d-=8388608;c&=-8388609,d+=947912704,o[u]=c|d}for(var h=1024;h<2048;++h)o[h]=939524096+(h-1024<<13);for(var f=1;f<31;++f)s[f]=f<<23;s[31]=1199570944,s[32]=2147483648;for(var p=33;p<63;++p)s[p]=2147483648+(p-32<<23);s[63]=3347054592;for(var g=1;g<64;++g)32!==g&&(l[g]=1024);return{floatView:t,uint32View:n,baseTable:r,shiftTable:i,mantissaTable:o,exponentTable:s,offsetTable:l}}var wb=Object.freeze({__proto__:null,fromHalfFloat:function(e){var t=e>>10;return vb.uint32View[0]=vb.mantissaTable[vb.offsetTable[t]+(1023&e)]+vb.exponentTable[t],vb.floatView[0]},toHalfFloat:function(e){Math.abs(e)>65504&&console.warn("THREE.DataUtils.toHalfFloat(): Value out of range."),e=Qg(e,-65504,65504),vb.floatView[0]=e;var t=vb.uint32View[0],n=t>>23&511;return vb.baseTable[n]+((8388607&t)>>vb.shiftTable[n])}});"undefined"!==typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("register",{detail:{revision:$h}})),"undefined"!==typeof window&&(window.__THREE__?console.warn("WARNING: Multiple instances of Three.js being imported."):window.__THREE__=$h);var Bb=/^[og]\s*(.+)?/,_b=/^mtllib /,bb=/^usemtl /,xb=/^usemap /,Cb=/\s+/,Sb=new pm,Eb=new pm,Ub=new pm,Mb=new pm,Fb=new pm,Tb=new im;function kb(){var e={objects:[],object:{},vertices:[],normals:[],colors:[],uvs:[],materials:{},materialLibraries:[],startObject:function(e,t){if(this.object&&!1===this.object.fromDeclaration)return this.object.name=e,void(this.object.fromDeclaration=!1!==t);var n=this.object&&"function"===typeof this.object.currentMaterial?this.object.currentMaterial():void 0;if(this.object&&"function"===typeof this.object._finalize&&this.object._finalize(!0),this.object={name:e||"",fromDeclaration:!1!==t,geometry:{vertices:[],normals:[],colors:[],uvs:[],hasUVIndices:!1},materials:[],smooth:!0,startMaterial:function(e,t){var n=this._finalize(!1);n&&(n.inherited||n.groupCount<=0)&&this.materials.splice(n.index,1);var r={index:this.materials.length,name:e||"",mtllib:Array.isArray(t)&&t.length>0?t[t.length-1]:"",smooth:void 0!==n?n.smooth:this.smooth,groupStart:void 0!==n?n.groupEnd:0,groupEnd:-1,groupCount:-1,inherited:!1,clone:function(e){var t={index:"number"===typeof e?e:this.index,name:this.name,mtllib:this.mtllib,smooth:this.smooth,groupStart:0,groupEnd:-1,groupCount:-1,inherited:!1};return t.clone=this.clone.bind(t),t}};return this.materials.push(r),r},currentMaterial:function(){if(this.materials.length>0)return this.materials[this.materials.length-1]},_finalize:function(e){var t=this.currentMaterial();if(t&&-1===t.groupEnd&&(t.groupEnd=this.geometry.vertices.length/3,t.groupCount=t.groupEnd-t.groupStart,t.inherited=!1),e&&this.materials.length>1)for(var n=this.materials.length-1;n>=0;n--)this.materials[n].groupCount<=0&&this.materials.splice(n,1);return e&&0===this.materials.length&&this.materials.push({name:"",smooth:this.smooth}),t}},n&&n.name&&"function"===typeof n.clone){var r=n.clone(0);r.inherited=!0,this.object.materials.push(r)}this.objects.push(this.object)},finalize:function(){this.object&&"function"===typeof this.object._finalize&&this.object._finalize(!0)},parseVertexIndex:function(e,t){var n=parseInt(e,10);return 3*(n>=0?n-1:n+t/3)},parseNormalIndex:function(e,t){var n=parseInt(e,10);return 3*(n>=0?n-1:n+t/3)},parseUVIndex:function(e,t){var n=parseInt(e,10);return 2*(n>=0?n-1:n+t/2)},addVertex:function(e,t,n){var r=this.vertices,i=this.object.geometry.vertices;i.push(r[e+0],r[e+1],r[e+2]),i.push(r[t+0],r[t+1],r[t+2]),i.push(r[n+0],r[n+1],r[n+2])},addVertexPoint:function(e){var t=this.vertices;this.object.geometry.vertices.push(t[e+0],t[e+1],t[e+2])},addVertexLine:function(e){var t=this.vertices;this.object.geometry.vertices.push(t[e+0],t[e+1],t[e+2])},addNormal:function(e,t,n){var r=this.normals,i=this.object.geometry.normals;i.push(r[e+0],r[e+1],r[e+2]),i.push(r[t+0],r[t+1],r[t+2]),i.push(r[n+0],r[n+1],r[n+2])},addFaceNormal:function(e,t,n){var r=this.vertices,i=this.object.geometry.normals;Sb.fromArray(r,e),Eb.fromArray(r,t),Ub.fromArray(r,n),Fb.subVectors(Ub,Eb),Mb.subVectors(Sb,Eb),Fb.cross(Mb),Fb.normalize(),i.push(Fb.x,Fb.y,Fb.z),i.push(Fb.x,Fb.y,Fb.z),i.push(Fb.x,Fb.y,Fb.z)},addColor:function(e,t,n){var r=this.colors,i=this.object.geometry.colors;void 0!==r[e]&&i.push(r[e+0],r[e+1],r[e+2]),void 0!==r[t]&&i.push(r[t+0],r[t+1],r[t+2]),void 0!==r[n]&&i.push(r[n+0],r[n+1],r[n+2])},addUV:function(e,t,n){var r=this.uvs,i=this.object.geometry.uvs;i.push(r[e+0],r[e+1]),i.push(r[t+0],r[t+1]),i.push(r[n+0],r[n+1])},addDefaultUV:function(){var e=this.object.geometry.uvs;e.push(0,0),e.push(0,0),e.push(0,0)},addUVLine:function(e){var t=this.uvs;this.object.geometry.uvs.push(t[e+0],t[e+1])},addFace:function(e,t,n,r,i,A,a,o,s){var l=this.vertices.length,u=this.parseVertexIndex(e,l),c=this.parseVertexIndex(t,l),d=this.parseVertexIndex(n,l);if(this.addVertex(u,c,d),this.addColor(u,c,d),void 0!==a&&""!==a){var h=this.normals.length;u=this.parseNormalIndex(a,h),c=this.parseNormalIndex(o,h),d=this.parseNormalIndex(s,h),this.addNormal(u,c,d)}else this.addFaceNormal(u,c,d);if(void 0!==r&&""!==r){var f=this.uvs.length;u=this.parseUVIndex(r,f),c=this.parseUVIndex(i,f),d=this.parseUVIndex(A,f),this.addUV(u,c,d),this.object.geometry.hasUVIndices=!0}else this.addDefaultUV()},addPointGeometry:function(e){this.object.geometry.type="Points";for(var t=this.vertices.length,n=0,r=e.length;n<r;n++){var i=this.parseVertexIndex(e[n],t);this.addVertexPoint(i),this.addColor(i)}},addLineGeometry:function(e,t){this.object.geometry.type="Line";for(var n=this.vertices.length,r=this.uvs.length,i=0,A=e.length;i<A;i++)this.addVertexLine(this.parseVertexIndex(e[i],n));for(var a=0,o=t.length;a<o;a++)this.addUVLine(this.parseUVIndex(t[a],r))}};return e.startObject("",!1),e}var Qb=function(e){_(n,e);var t=S(n);function n(e){var r;return v(this,n),(r=t.call(this,e)).materials=null,r}return w(n,[{key:"load",value:function(e,t,n,r){var i=this,A=new J_(this.manager);A.setPath(this.path),A.setRequestHeader(this.requestHeader),A.setWithCredentials(this.withCredentials),A.load(e,(function(n){try{t(i.parse(n))}catch(A){r?r(A):console.error(A),i.manager.itemError(e)}}),n,r)}},{key:"setMaterials",value:function(e){return this.materials=e,this}},{key:"parse",value:function(e){var t=new kb;-1!==e.indexOf("\r\n")&&(e=e.replace(/\r\n/g,"\n")),-1!==e.indexOf("\\\n")&&(e=e.replace(/\\\n/g,""));for(var n=e.split("\n"),r=[],i=0,A=n.length;i<A;i++){var a=n[i].trimStart();if(0!==a.length){var o=a.charAt(0);if("#"!==o)if("v"===o){var s=a.split(Cb);switch(s[0]){case"v":t.vertices.push(parseFloat(s[1]),parseFloat(s[2]),parseFloat(s[3])),s.length>=7?(Tb.setRGB(parseFloat(s[4]),parseFloat(s[5]),parseFloat(s[6])).convertSRGBToLinear(),t.colors.push(Tb.r,Tb.g,Tb.b)):t.colors.push(void 0,void 0,void 0);break;case"vn":t.normals.push(parseFloat(s[1]),parseFloat(s[2]),parseFloat(s[3]));break;case"vt":t.uvs.push(parseFloat(s[1]),parseFloat(s[2]))}}else if("f"===o){for(var l=a.slice(1).trim().split(Cb),u=[],c=0,d=l.length;c<d;c++){var h=l[c];if(h.length>0){var f=h.split("/");u.push(f)}}for(var p=u[0],g=1,m=u.length-1;g<m;g++){var v=u[g],y=u[g+1];t.addFace(p[0],v[0],y[0],p[1],v[1],y[1],p[2],v[2],y[2])}}else if("l"===o){var w=a.substring(1).trim().split(" "),B=[],_=[];if(-1===a.indexOf("/"))B=w;else for(var b=0,x=w.length;b<x;b++){var C=w[b].split("/");""!==C[0]&&B.push(C[0]),""!==C[1]&&_.push(C[1])}t.addLineGeometry(B,_)}else if("p"===o){var S=a.slice(1).trim().split(" ");t.addPointGeometry(S)}else if(null!==(r=Bb.exec(a))){var E=(" "+r[0].slice(1).trim()).slice(1);t.startObject(E)}else if(bb.test(a))t.object.startMaterial(a.substring(7).trim(),t.materialLibraries);else if(_b.test(a))t.materialLibraries.push(a.substring(7).trim());else if(xb.test(a))console.warn('THREE.OBJLoader: Rendering identifier "usemap" not supported. 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= floor(max(log(maxValue), 0.0));\n    color /= exp(e);\n    return vec4(color, e / 5.0);\n#else\n    return vec4(color, 1.0);\n#endif\n}\n\nvec3 decodeHDRE(vec4 hdre)\n{\n#ifdef HDRE\n    return hdre.rgb * exp(hdre.a * 255.0 - 128.0);\n#else\n    return hdre.xyz;\n#endif\n}\n\nfloat luminance(vec3 color)\n{\n    return dot(color, vec3(.30, 0.59, .11));\n}\n\nfloat luminance(vec4 color)\n{\n    return luminance(color.xyz);\n}\n\nfloat linearStep(float lower, float upper, float x)\n{\n    return clamp((x - lower) / (upper - lower), 0.0, 1.0);\n}\n\n// Only for 0 - 1\nvec4 floatToRGBA8(float value)\n{\n    vec4 enc = value * vec4(1.0, 255.0, 65025.0, 16581375.0);\n    // cannot fract first value or 1 would not be encodable\n    enc.yzw = fract(enc.yzw);\n    return enc - enc.yzww * vec4(1.0/255.0, 1.0/255.0, 1.0/255.0, 0.0);\n}\n\nfloat RGBA8ToFloat(vec4 rgba)\n{\n    return dot(rgba, vec4(1.0, 1.0/255.0, 1.0/65025.0, 1.0/16581375.0));\n}\n\nvec2 floatToRG8(float value)\n{\n    vec2 enc = vec2(1.0, 255.0) * value;\n    enc.y = fract(enc.y);\n    enc.x -= enc.y / 255.0;\n    return enc;\n}\n\nfloat RG8ToFloat(vec2 rg)\n{\n    return dot(rg, vec2(1.0, 1.0/255.0));\n}\n\nvec3 intersectCubeMap(vec3 rayOrigin, vec3 rayDir, float cubeSize)\n{\n    vec3 t = (cubeSize * sign(rayDir) - rayOrigin) / rayDir;\n    float minT = min(min(t.x, t.y), t.z);\n    return rayOrigin + minT * rayDir;\n}\n\nvec4 sampleLatLong(sampler2D tex, vec3 uvw)\n{\n    vec2 uv;\n    uv.x = atan(uvw.z, uvw.x) / 6.283184 + 0.5;\n    uv.y = acos(-uvw.y) / 3.141592;\n    return texture2D(tex, uv);\n}\n","include_beckmann.glsl":"\nstruct PointLight {\n    vec3 position;\n    vec3 color;\n    float distance;\n    float decay;\n};\n\nstruct DirectionalLight {\n    vec3 direction;\n    vec3 color;\n};\n\n#if NUM_POINT_LIGHTS > 0\nuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n#endif\n\n#if NUM_DIR_LIGHTS > 0\nuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n#endif\n\n\nfloat beckmann_geometryTerm(vec3 normal, vec3 dir, float k)\n{\n    float d = max(dot(normal, dir), 0.0);\n    return d / (d * (1.0 - k) + k);\n}\n\n// schlick-beckman\nfloat beckmann_lightVisibility(vec3 normal, vec3 viewDir, vec3 lightDir, float roughness)\n{\n    float k = roughness + 1.0;\n    k = k * k * .125;\n    return beckmann_geometryTerm(normal, viewDir, k) * beckmann_geometryTerm(normal, lightDir, k);\n}\n\nfloat beckmann_distribution(float roughness, vec3 normal, vec3 halfVector)\n{\n    float roughSqr = roughness*roughness;\n    float halfDotNormal = max(dot(halfVector, normal), 0.00001);\n    float a = roughSqr * pow(halfDotNormal, 4.0);\n    float b = (halfDotNormal * halfDotNormal - 1.0) / (roughSqr * halfDotNormal * halfDotNormal);\n    return exp(b) / a;\n}\n\n// light dir is towards the light\n// view dir is toward the camera\nvoid beckmann_brdf(in vec3 normal, in float roughness, float normalSpecularReflectance, in vec3 lightDir, in vec3 viewDir, in vec3 viewPos, in vec3 lightColor, out vec3 diffuseLight, out vec3 specularLight)\n{\n    float nDotL = max(dot(lightDir, normal), 0.0);\n    vec3 irradiance = nDotL * lightColor;    // in fact irradiance / PI\n\n    vec3 halfVector = normalize(lightDir + viewDir);\n\n    float mappedRoughness =  roughness * roughness;\n\n    // adapted for 2-lobed skin\n    float distribution =\n        (beckmann_distribution(mappedRoughness *.8, normal, halfVector) * .05 +\n        beckmann_distribution(mappedRoughness, normal, halfVector)) * .95;\n\n    float halfDotLight = max(dot(halfVector, lightDir), 0.0);\n    float cosAngle = 1.0 - halfDotLight;\n    float fresnel = normalSpecularReflectance + (1.0 - normalSpecularReflectance) * pow(cosAngle, 5.0);\n\n    diffuseLight = irradiance;\n\n    specularLight = irradiance * fresnel * distribution;\n\n    specularLight *= beckmann_lightVisibility(normal, viewDir, lightDir, roughness);\n}\n\n// diffuse light only\nvec3 beckmann_getDiffuseLight(in vec3 normal, in vec3 viewPos, in float shadow)\n{\n    vec3 diffuseLight = vec3(0.0);\n\n#if NUM_DIR_LIGHTS > 0\n    for (int i = 0; i < NUM_DIR_LIGHTS; ++i) {\n        float nDotL = max(dot(directionalLights[i].direction, normal), 0.0);\n        diffuseLight += nDotL * directionalLights[i].color * shadow;\n    }\n#endif\n\n    return diffuseLight;\n}\n\nvoid beckmann_getLight(in vec3 normal, in float roughness, in float normalSpecularReflectance, in vec3 viewPos, in float shadow, out vec3 diffuseLight, out vec3 specularLight)\n{\n    vec3 viewDir = -normalize(viewPos);\n    diffuseLight = vec3(0.0);\n    specularLight = vec3(0.0);\n\n#if NUM_DIR_LIGHTS > 0\n    for (int i = 0; i < NUM_DIR_LIGHTS; ++i) {\n        vec3 diff;\n        vec3 spec;\n        beckmann_brdf(normal, roughness, normalSpecularReflectance, directionalLights[i].direction, viewDir, viewPos, directionalLights[i].color, diff, spec);\n        diffuseLight += diff * shadow;\n        specularLight += spec * shadow;\n    }\n#endif\n}\n","include_ggx.glsl":"\nstruct PointLight {\n    vec3 position;\n    vec3 color;\n    float distance;\n    float decay;\n};\n\nstruct DirectionalLight {\n    vec3 direction;\n    vec3 color;\n};\n\n#if NUM_POINT_LIGHTS > 0\nuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n#endif\n\n#if NUM_DIR_LIGHTS > 0\nuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n#endif\n\n\nfloat ggx_geometryTerm(vec3 normal, vec3 dir, float k)\n{\n    float d = max(dot(normal, dir), 0.0);\n    return d / (d * (1.0 - k) + k);\n}\n\n// schlick-beckman\nfloat ggx_lightVisibility(vec3 normal, vec3 viewDir, vec3 lightDir, float roughness)\n{\n    float k = roughness + 1.0;\n    k = k * k * .125;\n    return ggx_geometryTerm(normal, viewDir, k) * ggx_geometryTerm(normal, lightDir, k);\n}\n\nfloat ggx_distribution(float roughness, vec3 normal, vec3 halfVector)\n{\n    float roughSqr = roughness*roughness;\n    float halfDotNormal = max(dot(halfVector, normal), 0.0);\n    float denom = (halfDotNormal * halfDotNormal) * (roughSqr - 1.0) + 1.0;\n    return roughSqr / (denom * denom);\n}\n\n// light dir is towards the light\n// view dir is toward the camera\nvoid ggx_brdf(in vec3 normal, in float roughness, float normalSpecularReflectance, in vec3 lightDir, in vec3 viewDir, in vec3 viewPos, in vec3 lightColor, out vec3 diffuseLight, out vec3 specularLight)\n{\n    float nDotL = max(dot(lightDir, normal), 0.0);\n    vec3 irradiance = nDotL * lightColor;    // in fact irradiance / PI\n\n    vec3 halfVector = normalize(lightDir + viewDir);\n\n    float mappedRoughness =  roughness * roughness;\n\n    float distribution = ggx_distribution(mappedRoughness, normal, halfVector);\n\n    float halfDotLight = max(dot(halfVector, lightDir), 0.0);\n    float cosAngle = 1.0 - halfDotLight;\n    float fresnel = normalSpecularReflectance + (1.0 - normalSpecularReflectance) * pow(cosAngle, 5.0);\n\n    diffuseLight = irradiance;\n\n    specularLight = irradiance * fresnel * distribution;\n\n    specularLight *= ggx_lightVisibility(normal, viewDir, lightDir, roughness);\n}\n\n// diffuse light only\nvec3 ggx_getDiffuseLight(in vec3 normal, in vec3 viewPos, in float shadow)\n{\n    vec3 diffuseLight = vec3(0.0);\n\n#if NUM_DIR_LIGHTS > 0\n    for (int i = 0; i < NUM_DIR_LIGHTS; ++i) {\n        float nDotL = max(dot(directionalLights[i].direction, normal), 0.0);\n        diffuseLight += nDotL * directionalLights[i].color * shadow;\n    }\n#endif\n\n    return diffuseLight;\n}\n\nvoid ggx_getLight(in vec3 normal, in float roughness, in float normalSpecularReflectance, in vec3 viewPos, in float shadow, out vec3 diffuseLight, out vec3 specularLight)\n{\n    vec3 viewDir = -normalize(viewPos);\n    diffuseLight = vec3(0.0);\n    specularLight = vec3(0.0);\n\n#if NUM_DIR_LIGHTS > 0\n    for (int i = 0; i < NUM_DIR_LIGHTS; ++i) {\n        vec3 diff;\n        vec3 spec;\n        ggx_brdf(normal, roughness, normalSpecularReflectance, directionalLights[i].direction, viewDir, viewPos, directionalLights[i].color, diff, spec);\n        diffuseLight += diff * shadow;\n        specularLight += spec * shadow;\n    }\n#endif\n}\n","linear_depth_vertex.glsl":"\nvarying vec2 vUV;\nvarying float linearDepth;\n\nuniform float cameraNear;\nuniform float rcpCameraRange;\n\nvoid main()\n{\n    vec4 viewPosition = modelViewMatrix * vec4(position, 1.0);\n    linearDepth = (-viewPosition.z - cameraNear) * rcpCameraRange;\n    gl_Position = projectionMatrix * viewPosition;\n    vUV = uv;\n}\n","linear_depth_fragment.glsl":"\nvarying float linearDepth;\n\nvoid main()\n{\n    gl_FragColor = floatToRGBA8(linearDepth);\n}\n","linear_hair_depth_fragment.glsl":"\nvarying vec2 vUV;\nvarying float linearDepth;\nuniform sampler2D alphaMap;\nuniform float alpha_test;\n\n// uniform sampler2D depthMap_prev;\n// uniform vec2 screen_size;\nuniform int layer;\n\nvoid main()\n{\n    float alpha = texture2D(alphaMap, vUV).g;\n    if (alpha < alpha_test) discard;\n\n\n    if(layer==0){\n        gl_FragColor = floatToRGBA8(linearDepth);\n        // gl_FragColor = vec4(1.,0.,0.,1.);\n    } else {\n        // // gl_FragColor = texture2D(depthMap_prev,uv_screen)*2.0;\n        // gl_FragColor = floatToRGBA8(linearDepth);\n        // // gl_FragColor = vec4(1.0,1.0,1.0,1.0);\n        // return;\n\n        // vec2 uv_screen = gl_FragCoord.xy/screen_size;\n        // float depth_prev = RGBA8ToFloat(texture2D(depthMap_prev,uv_screen));\n\n        // float uDepthOffset=0.0;\n        // // if(depth_prev + uDepthOffset - linearDepth >= 0. ) discard;\n\n\n        // if (uv_screen.x<0.5){\n        //     gl_FragColor = floatToRGBA8(depth_prev);\n        //     gl_FragColor = vec4(uv_screen,0.0,1.0);\n        //     gl_FragColor = texture2D(depthMap_prev,uv_screen);\n        // }\n        // else\n        //     gl_FragColor = floatToRGBA8(linearDepth);\n    \n    }\n\n    // [0, 1]\n}\n","skin_vertex.glsl":"\nvarying vec2 vUV;\nvarying vec3 vViewNormal;\nvarying vec3 vViewPosition;\nvarying vec4 vShadowCoord;\nvarying vec4 vProjection;\n\nuniform mat4 shadowMapMatrix;\n\nvoid main() {\n    vec3 localPos = position;\n    vec4 viewPos = modelViewMatrix * vec4(localPos, 1.0);\n    vec4 worldPos = modelMatrix * vec4(localPos, 1.0);\n\n    gl_Position = vProjection = projectionMatrix * viewPos;\n    vUV = uv;\n\n    vViewNormal = normalMatrix * normal;\n    vViewPosition = viewPos.xyz;\n    vShadowCoord = shadowMapMatrix * worldPos * .5 + .5;\n}\n","skin_fragment.glsl":"\nvarying vec2 vUV;\nvarying vec3 vViewNormal;\nvarying vec3 vViewPosition;\nvarying vec4 vShadowCoord;\nvarying vec4 vProjection;\n\nuniform sampler2D diffuseMap;\nuniform sampler2D normalMap;\nuniform sampler2D roughnessAOThicknessMap;\nuniform sampler2D roughnessMap;\nuniform sampler2D roughnessDetailMap;\nuniform sampler2D irradianceMap;\nuniform sampler2D specularMap;\nuniform sampler2D shadowMap;\nuniform sampler2D sssMap;\n\nuniform float normalSpecularReflectance;\nuniform float roughnessMedian;\nuniform float roughnessMapRange;\nuniform float roughnessDetailRange;\nuniform float probeExposure;\nuniform float sssProfileScale;\nuniform float thicknessRange;\nuniform float specular_intensity;\nuniform vec3 sssTopLayerColor;\nuniform vec3 transmittanceColor;\nuniform mat4 shadowMapMatrix;\n\nuniform int return_stage;\n\nvec3 perturbNormal2Arb(vec3 position, vec3 normal, vec3 normalSample)\n{\n    vec3 q0 = dFdx( position.xyz );\n    vec3 q1 = dFdy( position.xyz );\n    vec2 st0 = dFdx( vUV.st );\n    vec2 st1 = dFdy( vUV.st );\n    vec3 S = normalize( q0 * st1.t - q1 * st0.t );\n    vec3 T = normalize( -q0 * st1.s + q1 * st0.s );\n    vec3 N = normalize( normal );\n    mat3 tsn = mat3( S, T, N );\n    return normalize( tsn * normalSample );\n}\n\nvec3 getNormal()\n{\n    vec4 normalSample = texture2D(normalMap, vUV);\n    vec3 normal = normalSample.xyz * 2.0 - 1.0;\n    return perturbNormal2Arb(vViewPosition, vViewNormal, normal);\n}\n\nvec2 getVSMMoments(vec2 uv)\n{\n    vec4 s = texture2D(shadowMap, uv);\n#ifdef VSM_FLOAT\n    return s.xy;\n#else\n    return vec2(RG8ToFloat(s.xy), RG8ToFloat(s.zw));\n#endif\n}\n\nfloat getShadow(float offset)\n{\n    vec4 coord = vShadowCoord;\n    coord.z -= offset;\n    vec2 moments = getVSMMoments(coord.xy);\n    float p = linearStep(coord.z - 0.002, coord.z, moments.x);\n    float variance = moments.y - moments.x * moments.x;\n    variance = max(variance, MIN_VARIANCE);\n\n    float diff = coord.z - moments.x;\n    float upperBound = variance / (variance + diff*diff);\n    float shadow = linearStep(LIGHT_BLEED_REDUCTION, 1.0, upperBound);\n    return clamp(max(shadow, p), 0.0, 1.0);\n}\n\nvoid main() {\n    vec3 viewNormal = getNormal();\n    vec3 worldNormal = viewNormal * mat3(viewMatrix);\n    vec4 diffuse = texture2D(diffuseMap, vUV);\n\n    vec3 diffuseLight = vec3(0.0);\n    vec3 specularLight = vec3(0.0);\n\n    vec4 roughnessAOThickness = texture2D(roughnessAOThicknessMap, vUV);\n    float roughness_sample = texture2D(roughnessMap, vUV).g;\n\n    // TODO: scale roughness UP by distance\n    float roughness = roughnessMedian + (roughness_sample - 0.5) * roughnessMapRange;\n    roughness = clamp(roughness, 0.001, 1.0);\n\n    float roughnessDetail = texture2D(roughnessDetailMap, vUV * 20.0).x;\n    roughness += -(roughnessDetail - 0.5) * roughnessDetailRange;\n\n    float shadow = getShadow(0.0);\n    beckmann_getLight(viewNormal, roughness, normalSpecularReflectance, vViewPosition, shadow, diffuseLight, specularLight);\n\n    vec4 irradianceSample = sampleLatLong(irradianceMap, worldNormal);\n    vec3 viewRefl = reflect(normalize(vViewPosition), viewNormal);\n    vec3 worldReflection = viewRefl * mat3(viewMatrix);\n    vec4 specularSample = sampleLatLong(specularMap, worldReflection);\n\n    // TODO: Do this more correctly (remember Seb Lagarde (?)'s post?)\n    float geomOcclusion = 1.0 - roughness * roughness;\n    float NdotL = max(dot(worldNormal, worldReflection), 0.0);\n    float cosAngle = 1.0 - NdotL;\n    float fresnel = normalSpecularReflectance + (1.0 - normalSpecularReflectance) * pow(cosAngle, 5.0);\n    // should be augmented with shadow test\n\n    float probeStrength = probeExposure * roughnessAOThickness.y;\n    diffuseLight += irradianceSample.xyz /** irradianceSample.xyz */* probeStrength;\n    specularLight += specularSample.xyz /** specularSample.xyz */* fresnel * geomOcclusion * probeStrength;\n\n    vec2 screenUV = vProjection.xy / vProjection.w * .5 + .5;\n    vec3 sss = texture2D(sssMap, screenUV).xyz;\n    \n    vec3 diffuseLight_sss = diffuseLight * sssTopLayerColor + sss;\n\n\n    float thickness = (1.0 - roughnessAOThickness.z) * thicknessRange + 0.01;\n    float offset = roughnessAOThickness.z * thicknessRange + 0.01;\n    float shadowTransmission = getShadow(2.0 * offset);\n    vec3 transmission_color = exp(-transmittanceColor * thickness);\n    vec3 shadow_diffuse_light = beckmann_getDiffuseLight(-viewNormal, vViewPosition, shadowTransmission);\n    vec3 transmission_diffuse_light = transmission_color*shadow_diffuse_light;\n    vec3 diffuseLight_sss_tm = diffuseLight_sss + transmission_diffuse_light;\n    vec3 col = diffuse.xyz * diffuseLight_sss_tm + specularLight * specular_intensity;\n\n\n    if (return_stage == 1) {\n        gl_FragColor = vec4(diffuseLight_sss, 1.0);\n    } else if (return_stage == 2) {\n        gl_FragColor = vec4(shadowTransmission);\n    } else if (return_stage == 3) {\n        gl_FragColor = vec4(shadow_diffuse_light, 1.0);\n    } else {\n        gl_FragColor = vec4(col, 1.0);\n    }\n}\n","hair_vertex.glsl":"\nvarying vec2 vUV;\nvarying vec3 vViewNormal;\nvarying vec3 vViewPosition;\nvarying vec4 vShadowCoord;\nvarying vec4 vProjection;\nvarying vec3 vTangent;\nvarying vec3 vNormal;\n\nuniform mat4 shadowMapMatrix;\n\nvoid main() {\n    vec3 localPos = position;\n    vec4 viewPos = modelViewMatrix * vec4(localPos, 1.0);\n    vec4 worldPos = modelMatrix * vec4(localPos, 1.0);\n\n    vTangent=(modelMatrix*tangent).xyz;\n    vNormal=(modelMatrix*vec4(normal,1.0)).xyz;\n\n    gl_Position = vProjection = projectionMatrix * viewPos;\n    vUV = uv;\n\n    vViewNormal = normalMatrix * normal;\n    vViewPosition = viewPos.xyz;\n    vShadowCoord = shadowMapMatrix * worldPos * .5 + .5;\n}\n","hair_fragment.glsl":'\nvarying vec2 vUV;\nvarying vec3 vViewNormal;\nvarying vec3 vViewPosition;\nvarying vec4 vShadowCoord;\nvarying vec4 vProjection;\nvarying vec3 vTangent;\nvarying vec3 vNormal;\n\nuniform float normalSpecularReflectance;\nuniform sampler2D diffuseMap;\nuniform sampler2D normalMap;\nuniform sampler2D irradianceMap;\nuniform sampler2D specularMap;\nuniform sampler2D shadowMap;\nuniform sampler2D alphaMap;\nuniform sampler2D roughnessMap;\n\nuniform sampler2D specular_map;\nuniform sampler2D scatter_map;\n\nuniform mat4 shadowMapMatrix;\nuniform float alpha_test;\n\nuniform float tangent_shift_0;\nuniform float specular_exp_0;\nuniform float tangent_shift_1;\nuniform float specular_exp_1;\n\nuniform int return_stage;\n\nvec2 getVSMMoments(vec2 uv)\n{\n    vec4 s = texture2D(shadowMap, uv);\n#ifdef VSM_FLOAT\n    return s.xy;\n#else\n    return vec2(RG8ToFloat(s.xy), RG8ToFloat(s.zw));\n#endif\n}\n\nfloat getShadow(float offset)\n{\n    vec4 coord = vShadowCoord;\n    coord.z -= offset;\n    vec2 moments = getVSMMoments(coord.xy);\n    float p = linearStep(coord.z - 0.002, coord.z, moments.x);\n    float variance = moments.y - moments.x * moments.x;\n    variance = max(variance, MIN_VARIANCE);\n\n    float diff = coord.z - moments.x;\n    float upperBound = variance / (variance + diff*diff);\n    float shadow = linearStep(LIGHT_BLEED_REDUCTION, 1.0, upperBound);\n    return clamp(max(shadow, p), 0.0, 1.0);\n}\n\nfloat getShadow2(float offset)\n{\n    vec4 coord = vShadowCoord;\n    coord.z -= offset;\n    vec2 moments = getVSMMoments(coord.xy);\n    float p = linearStep(coord.z - 0.2, coord.z, moments.x);\n    return p;\n    float variance = moments.y - moments.x * moments.x;\n    variance = max(variance, MIN_VARIANCE);\n\n    float diff = coord.z - moments.x;\n    float upperBound = variance / (variance + diff*diff);\n    float shadow = linearStep(LIGHT_BLEED_REDUCTION, 1.0, upperBound);\n    return clamp(max(shadow, p), 0.0, 1.0);\n}\n\nfloat StrandSpecular (vec3 strand_dir, vec3 normal_dir, vec3 view_dir, vec3 light_dir, float exponent){\n    vec3 half_dir = normalize(light_dir + view_dir);\n    float dotTH = dot(strand_dir, half_dir);\n    float sinTH = sqrt(1.0 - dotTH*dotTH);\n    float dirAtten = smoothstep(-1.0, 0.0, dotTH);\n\n    // float dotNH = dot(normal_dir, half_dir);\n    // dotNH=smoothstep(-1.0,1.0,dotNH);\n    // dotNH=pow(dotNH,20.0);\n\n\n    // return dotNH;\n    // return dirAtten * pow(sinTH*dotNH, exponent);\n    // return dirAtten * pow(dotNH, exponent);\n    return dirAtten * pow(sinTH, exponent);\n}\n\nvec3 ShiftTangent (vec3 strand_dir, vec3 normal_dir, float shift) {\n    vec3 strand_dir_shifted = strand_dir + shift * normal_dir;\n    return normalize (strand_dir_shifted);\n}\n\nfloat fresnel_dielectric_cos(float cosi, float eta)\n{\n  /* compute fresnel reflectance without explicitly computing\n   * the refracted direction */\n  float c = abs(cosi);\n  float g = eta * eta - 1.0 + c * c;\n  float result;\n\n  if (g > 0.0) {\n    g = sqrt(g);\n    float A = (g - c) / (g + c);\n    float B = (c * (g + c) - 1.0) / (c * (g - c) + 1.0);\n    result = 0.5 * A * A * (1.0 + B * B);\n  }\n  else\n    result = 1.0; /* TIR (no refracted component) */\n\n  return result;\n}\n\nfloat fresnel_approx(float cosi, float F0){\n    float NdotL = max(cosi, 0.0);\n    float cosAngle = 1.0 - NdotL;\n    float fresnel = normalSpecularReflectance + (1.0 - normalSpecularReflectance) * pow(cosAngle, 5.0);\n    return fresnel;\n}\n\n\n\n\n\n\n\n\n\n\n\n\n// #define _USE_LIGHT_FACING_NORMAL\n// #define _USE_ADVANCED_MULTIPLE_SCATTERING\n#define DEFAULT_HAIR_SPECULAR_VALUE 0.0465 // Hair is IOR 1.55\n\nstruct BSDFData {\n    vec3 fresnel0;\n    float cuticleAngleR;\n    float cuticleAngleTT;\n    float cuticleAngleTRT;\n    float roughnessR;\n    float roughnessTT;\n    float roughnessTRT;\n    vec3 absorption;\n    float perceptualRoughnessRadial;\n};\n\nstruct HairAngle\n{\n    float sinThetaI;\n    float sinThetaO;\n    float cosThetaI;\n    float cosThetaO;\n    float cosThetaD;\n    float thetaH;\n    float phiI;\n    float phiO;\n    float phi;\n    float cosPhi;\n    float sinThetaT;\n    float cosThetaT;\n};\n\nstruct CBSDF {\n    vec3 diffR;\n    vec3 specR;\n};\n\nfloat saturate(float value){\n    return clamp(value,0.0,1.0);\n}\n\nfloat rsqrt(float value)\n{\n  return pow(value, -0.5);\n}\n\nfloat SafeSqrt(float x)\n{\n    return sqrt(max(0.0, x));\n}\nfloat Sq(float x){\n    return x*x;\n}\nvec3 Sq(vec3 x){\n    return x*x;\n}\nfloat rcp(float x){\n    return 1.0/x;\n}\nvec3 rcp(vec3 x){\n    return 1.0/x;\n}\n\nfloat F_Schlick(float F0, float cosi){\n    cosi = max(cosi, 0.0);\n    float fresnel = F0 + (1.0 - F0) * pow(1.0 - cosi, 5.0);\n    return fresnel;\n}\nvec3 F_Schlick(vec3 F0, float cosi){\n    cosi = max(cosi, 0.0);\n    vec3 fresnel = F0 + (1.0 - F0) * pow(1.0 - cosi, 5.0);\n    return fresnel;\n}\n\nfloat ModifiedRefractionIndex(float cosThetaD)\n{\n    // Original derivation of modified refraction index for arbitrary IOR.\n    // float sinThetaD = sqrt(1 - Sq(cosThetaD));\n    // return sqrt(Sq(eta) - Sq(sinThetaD)) / cosThetaD;\n\n    // Karis approximation for the modified refraction index for human hair (1.55)\n    return 1.19 / cosThetaD + (0.36 * cosThetaD);\n}\n\nvoid GetHairAngleWorld(vec3 V, vec3 L, vec3 T, inout HairAngle angles)\n{\n    angles.sinThetaO = dot(T, V);\n    angles.sinThetaI = dot(T, L);\n\n    float thetaO = asin(angles.sinThetaO);\n    float thetaI = asin(angles.sinThetaI);\n    angles.thetaH = (thetaI + thetaO) * 0.5;\n\n    angles.cosThetaD = cos((thetaO - thetaI) * 0.5);\n    angles.cosThetaO = cos(thetaO);\n    angles.cosThetaI = cos(thetaI);\n\n    // Projection onto the normal plane, and since phi is the relative angle, we take the cosine in this projection.\n    vec3 VProj = V - angles.sinThetaO * T;\n    vec3 LProj = L - angles.sinThetaI * T;\n    angles.cosPhi = dot(LProj, VProj) * rsqrt(dot(LProj, LProj) * dot(VProj, VProj) + 1e-5); // zero-div guard\n    angles.phi = acos(angles.cosPhi);\n\n    // Fixed for approximate human hair IOR\n    angles.sinThetaT = angles.sinThetaO / 1.55;\n    angles.cosThetaT = SafeSqrt(1.0 - Sq(angles.sinThetaT));\n}\n\nvec3 D_LongitudinalScatteringGaussian(vec3 thetaH, vec3 beta)\n{\n    float sqrtTwoPi = 2.50662827463100050241;\n    return rcp(beta * sqrtTwoPi) * exp(-Sq(thetaH) / (2.0 * Sq(beta)));\n}\n\n#define PI 3.14159265359\n#define TWO_PI 6.28318530717958647693\n#define FOUR_PI ((2.0*TWO_PI))\n#define INV_PI 0.31830988618379067153776752674502872406891929148091\n\nvec3 GetRoughenedAzimuthalScatteringDistribution(float phi, float cosThetaD, float beta)\n{\n    float X = (phi + TWO_PI) / FOUR_PI;\n    float Y = cosThetaD;\n    float Z = beta;\n\n    // TODO: It should be possible to reduce the domain of the integration to 0 -> HALF/PI as it repeats. This will save memory.\n    // return SAMPLE_TEXTURE3D_LOD(_PreIntegratedAverageHairFiberScattering, s_linear_clamp_sampler, vec3(X, Y, Z), 0).xyz;\n    return texture(scatter_map, vec2(X,Y)).xyz*3.23;\n    return vec3(0.0);\n}\n\n\n#define HAIR_H_TT  0.0\n#define HAIR_H_TRT 0.86602540378\n\nvec3 AbsorptionFromReflectance(vec3 diffuseColor, float azimuthalRoughness)\n{\n    float beta  = azimuthalRoughness;\n    float beta2 = beta  * beta;\n    float beta3 = beta2 * beta;\n    float beta4 = beta3 * beta;\n    float beta5 = beta4 * beta;\n\n    // Least squares fit of an inverse mapping between scattering parameters and scattering albedo.\n    float denom = 5.969 - (0.215 * beta) + (2.532 * beta2) - (10.73 * beta3) + (5.574 * beta4) + (0.245 * beta5);\n\n    vec3 t = log(diffuseColor) / denom;\n    return t * t;\n}\n\nBSDFData init_bsdf(){\n    BSDFData bsdfData;\n\n    bsdfData.fresnel0=vec3(DEFAULT_HAIR_SPECULAR_VALUE);\n\n    float cuticleAngle=0.1;\n    // bsdfData.cuticleAngle    = -cuticleAngle;\n    bsdfData.cuticleAngleR   = -cuticleAngle;\n    bsdfData.cuticleAngleTT  =  cuticleAngle * 0.5;\n    bsdfData.cuticleAngleTRT =  cuticleAngle * 1.5;\n\n    float roughnessL=0.1;\n    bsdfData.roughnessR   = roughnessL;\n    bsdfData.roughnessTT  = roughnessL * 0.5;\n    bsdfData.roughnessTRT = roughnessL * 2.0;\n\n    bsdfData.perceptualRoughnessRadial=0.3;\n    bsdfData.absorption=AbsorptionFromReflectance(vec3(0.1),bsdfData.perceptualRoughnessRadial);\n\n    return bsdfData;\n}\n\nCBSDF EvaluateBSDF(vec3 V, vec3 L, vec3 T, vec3 N, BSDFData bsdfData)\n{\n\n    CBSDF cbsdf;\n\n\n    // Approximation of the three primary paths in a hair fiber (R, TT, TRT), with concepts from:\n    // "Strand-Based Hair Rendering in Frostbite" (Tafuri 2019)\n    // "A Practical and Controllable Hair and Fur Model for Production Path Tracing" (Chiang 2016)\n    // "Physically Based Hair Shading in Unreal" (Karis 2016)\n    // "An Energy-Conserving Hair Reflectance Model" (d\'Eon 2011)\n    // "Light Scattering from Human Hair Fibers" (Marschner 2003)\n\n    // Reminder: All of these flags are known at compile time and the compiler will strip away the unused paths.\n\n    // Retrieve angles via spherical coordinates in the hair shading space.\n    HairAngle angles;\n    GetHairAngleWorld(V, L, T, angles);\n\n    vec3 alpha = vec3(\n        bsdfData.cuticleAngleR,\n        bsdfData.cuticleAngleTT,\n        bsdfData.cuticleAngleTRT\n    );\n\n    vec3 beta = vec3(\n        bsdfData.roughnessR,\n        bsdfData.roughnessTT,\n        bsdfData.roughnessTRT\n    );\n\n    // The index of refraction that can be used to analyze scattering in the normal plane (Bravais\' Law).\n    float etaPrime = ModifiedRefractionIndex(angles.cosThetaD);\n\n    // Reduced absorption coefficient.\n    vec3 mu = bsdfData.absorption;\n\n    // Various misc. terms reused between lobe evaluation.\n    vec3 F=vec3(0.0);\n    vec3 Tr=vec3(0.0);\n    vec3 S=vec3(0.0);\n\n    // Evaluate the longitudinal scattering for all three paths.\n    vec3 M = D_LongitudinalScatteringGaussian(angles.thetaH - alpha, beta);\n    // greater thetaH => smaller M\n\n    // Save the attenuations in case of multiple scattering.\n    vec3 A[3];\n\n    // Fetch the preintegrated azimuthal distributions for each path\n    vec3 D = GetRoughenedAzimuthalScatteringDistribution(angles.phi, angles.cosThetaD, bsdfData.perceptualRoughnessRadial);\n    // phi=0 => max\n    // phi=+-pi => 0\n    // cosThetaD is cos(IO/2); greater IO angle => smaller cosThetaD; \n    // 0 angle => cosThetaD=1; \n    // full angle => cosThetaD=0;\n    // cosThetaD no affect on 0\n\n    // Solve the first three lobes (R, TT, TRT).\n\n    // R\n    {\n        // Attenuation for this path as proposed by d\'Eon et al, replaced with a trig identity for cos half phi.\n        A[0] = F_Schlick(bsdfData.fresnel0, sqrt(0.5 + 0.5 * dot(L, V)));\n        S += M[0] * A[0] * D[0];\n    }\n\n    // TT\n    if (true)\n    {\n        // Attenutation (Simplified for H = 0)\n        float cosGammaO = SafeSqrt(1.0 - Sq(HAIR_H_TT));\n        float cosTheta  = angles.cosThetaO * cosGammaO;\n        F = F_Schlick(bsdfData.fresnel0, cosTheta);\n\n        float sinGammaT = HAIR_H_TT / etaPrime;\n        float cosGammaT = SafeSqrt(1.0 - Sq(sinGammaT));\n        Tr = exp(-mu * (2.0 * cosGammaT / angles.cosThetaT));\n\n        A[1] = Sq(1.0 - F) * Tr;\n\n        S += M[1] * A[1] * D[1];\n    }\n\n    // TRT\n    {\n        // Attenutation (Simplified for H = \u221a3/2)\n        float cosGammaO = SafeSqrt(1.0 - Sq(HAIR_H_TRT));\n        float cosTheta  = angles.cosThetaO * cosGammaO;\n        F = F_Schlick(bsdfData.fresnel0, cosTheta);\n\n        float sinGammaT = HAIR_H_TRT / etaPrime;\n        float cosGammaT = SafeSqrt(1.0 - Sq(sinGammaT));\n        Tr = exp(-mu * (2.0 * cosGammaT / angles.cosThetaT));\n\n        A[2] = Sq(1.0 - F) * F * Sq(Tr);\n\n        S += M[2] * A[2] * D[2];\n    }\n\n    // TODO: Residual TRRT+ Lobe. (accounts for ~15% energy otherwise lost by the first three lobes).\n\n    // This seems necesarry to match the reference.\n    S *= INV_PI;\n\n    // Transmission event is built into the model.\n    // Some stubborn NaNs have cropped up due to the angle optimization, we suppress them here with a max for now.\n    cbsdf.specR = max(S, 0.0);\n\n    // gl_FragColor=vec4(cbsdf.specR,1.0);\n\n\n    // Multiple Scattering\n#ifdef _USE_ADVANCED_MULTIPLE_SCATTERING\n    if (true)\n    {\n        cbsdf.specR = EvaluateMultipleScattering(L, cbsdf.specR, bsdfData, alpha, beta, angles.thetaH, angles.sinThetaI, D, A);\n    }\n    else\n#endif\n    {\n\n        #ifdef _USE_LIGHT_FACING_NORMAL\n            // The Kajiya-Kay model has a "built-in" transmission, and the \'NdotL\' is always positive.\n            float cosTL = dot(T, L);\n            float sinTL = sqrt(saturate(1.0 - cosTL * cosTL));\n            float NdotL = sinTL; // Corresponds to the cosine w.r.t. the light-facing normal\n        #else\n            // Double-sided Lambert.\n            float NdotL = dot(N, L);\n        #endif\n        \n            float clampedNdotL = saturate(NdotL);\n\n\n        #ifdef _USE_LIGHT_FACING_NORMAL\n            // See "Analytic Tangent Irradiance Environment Maps for Anisotropic Surfaces".\n            cbsdf.diffR = vec3(rcp(PI * PI) * clampedNdotL);\n            // // Transmission is built into the model, and it\'s not exactly clear how to split it.\n            // cbsdf.diffT = 0;\n        #else\n            // Double-sided Lambert.\n            // cbsdf.diffR = vec3(Lambert() * clampedNdotL);\n            cbsdf.diffR = vec3(clampedNdotL);\n        #endif // _USE_LIGHT_FACING_NORMAL\n    }\n\n    return cbsdf;\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nvoid main() {\n\n    float alpha = texture2D(alphaMap, vUV).g;\n    if (alpha < alpha_test) discard;\n    \n    float roughness = texture2D(roughnessMap, vUV).g;\n\n    // ======                                      \n    // normal                                      \n    // ======\n\n    vec3 tangent=normalize(vTangent);\n    vec3 normal=normalize(vNormal);\n    vec3 bitangent=normalize(cross(normal,tangent));\n\n    vec3 tangent_normal_sample = texture2D(normalMap, vUV).xyz * 2.0 - 1.0;\n    tangent_normal_sample.x*=-1.0;\n\n    mat3 BTN=mat3( bitangent, tangent, normal );\n    vec3 normal_perturb=normalize(BTN*tangent_normal_sample);\n    vec3 viewNormal = mat3(viewMatrix)* normal_perturb;\n\n    vec3 strand_dir=-bitangent;\n\n    vec3 view_dir=normalize(-vViewPosition) * mat3(viewMatrix);\n    vec3 light_dir=normalize(directionalLights[0].direction * mat3(viewMatrix));\n\n\n    // vec3 specular_color_0=vec3(0.2);\n    // vec3 specular_color_1=vec3(0.18,0.15,0.15);\n\n    // vec3 specular_hair_0 = specular_color_0* StrandSpecular(ShiftTangent(strand_dir,normal_perturb,tangent_shift_0),normal_perturb, view_dir, light_dir, specular_exp_0);\n    // vec3 specular_hair_1 = specular_color_1* StrandSpecular(ShiftTangent(strand_dir,normal_perturb,tangent_shift_1),normal_perturb, view_dir, light_dir, specular_exp_1);\n\n    // vec3 specular_color=(specular_hair_0+specular_hair_1)*(0.1+specularLight);\n\n    // vec3 color=diffuse * diffuseLight*0.2 + specular_color;\n\n\n    // ======\n    // anisotropic\n    // ======\n\n    float anisotropic_roughness=0.038;\n    float anisotropic_strength=1.0;\n    float specular_sample=texture(specular_map,vUV).g;\n    vec3 base_sample=texture(diffuseMap,vUV).rgb;\n    vec3 anisotropic_color=vec3(1.0,0.799,0.69);\n\n\n    float anisotropic_IOR=anisotropic_roughness*4.0+1.0;\n\n    float fresnel=fresnel_dielectric_cos(dot(view_dir,-strand_dir),anisotropic_IOR);\n    float specular_rate=fresnel*specular_sample;\n\n    vec3 final_anisotropic_color=mix(base_sample*0.2,anisotropic_color,specular_rate);\n\n\n\n\n    vec3 diffuseLight = vec3(0.0);\n    vec3 specularLight = vec3(0.0);\n\n    // float shadow = getShadow(0.05);\n    float shadow = getShadow2(0.0);\n    // gl_FragColor=vec4(shadow);\n    // return;\n\n    float normalSpecularReflectance=0.027;\n    beckmann_getLight(viewNormal, roughness, normalSpecularReflectance, vViewPosition, shadow, diffuseLight, specularLight);\n\n\n    float geomOcclusion=1.0-roughness*roughness;\n    \n    vec4 irradianceSample = sampleLatLong(irradianceMap, normal_perturb);\n    diffuseLight += irradianceSample.xyz;\n\n    vec3 env_specular_sample = sampleLatLong(specularMap, reflect(-view_dir,normal_perturb)).xyz;\n\n\n    vec3 env_specular_light=env_specular_sample * fresnel * geomOcclusion;\n    specularLight += env_specular_light;\n\n\n    // vec3 col = diffuse * diffuseLight + specularLight;\n    vec3 color = final_anisotropic_color * diffuseLight;\n\n\n    // gl_FragColor=vec4(color,1.0);\n    gl_FragColor=vec4(env_specular_light,1.0);\n\n\n\n    BSDFData b=init_bsdf();\n    CBSDF c=EvaluateBSDF(view_dir, light_dir, bitangent, normal_perturb, b);\n\n    // gl_FragColor=vec4(c.specR,1.0);\n    // gl_FragColor=vec4(c.specR*env_specular_sample*shadow+env_specular_light*0.2+base_sample* diffuseLight*0.5,1.0);\n\n    vec3 specular_light_color=c.specR*specular_sample*shadow*5.0*anisotropic_color;\n\n    gl_FragColor=vec4(specular_light_color+env_specular_light*0.2+base_sample* diffuseLight*0.5,1.0);\n\n}\n',"sky_vertex.glsl":"\nvarying vec3 worldViewDir;\n\nvoid main() {\n    vec3 worldPosition = (modelMatrix * vec4(position, 1.0)).xyz;\n    worldViewDir = worldPosition - cameraPosition;\n    gl_Position = projectionMatrix * modelViewMatrix * vec4(position, 1.0);\n}\n","sky_fragment.glsl":"\nvarying vec3 worldViewDir;\n\n#ifdef LAT_LONG\nuniform sampler2D envMap;\n#else\nuniform samplerCube envMap;\n#endif\n\nuniform float exposure;\n\nvoid main()\n{\n    vec3 elementDir = normalize(worldViewDir);\n#ifdef LAT_LONG\n    gl_FragColor = sampleLatLong(envMap, elementDir);\n#else\n    gl_FragColor = textureCube(envMap, elementDir);\n#endif\n\n#ifdef HDRE\n    gl_FragColor.xyz = decodeHDRE(gl_FragColor);\n    gl_FragColor.w = 1.0;\n#endif\n    gl_FragColor.xyz *= exposure;\n}\n","dof_fragment.glsl":"\nuniform sampler2D source;\nuniform sampler2D blurred1;\nuniform sampler2D blurred2;\nuniform sampler2D depth;\n\nuniform float strength;\nuniform float focusDepth;\nuniform float focusRange;\nuniform float focusFalloff;\n\nvarying vec2 vUV;\n\nvoid main()\n{\n    const float blendCutoff = .5;\n    float depth = RGBA8ToFloat(texture2D(depth, vUV));\n    float distance = abs(depth - focusDepth);\n\n    float blurAmount = clamp((distance - focusRange) / focusFalloff, 0.0, 1.0);\n\n    vec3 mainCol = decodeHDRE(texture2D(source, vUV));\n    vec3 blurredCol1 = decodeHDRE(texture2D(blurred1, vUV));\n    vec3 blurredCol2 = decodeHDRE(texture2D(blurred2, vUV));\n\n    // for little blurs (0.0 - 0.25), use smaller amount, for (.5, 1.0), use larger blur\n    float smallBlur = linearStep(0.0, blendCutoff, blurAmount);\n    float largeBlur = linearStep(blendCutoff, 1.0, blurAmount);\n    vec3 color = mix(blurredCol1, blurredCol2, largeBlur);\n    color = mix(mainCol, color, smallBlur * strength);\n\n    gl_FragColor = encodeHDRE(color);\n}\n","fxaa_tonemap_fragment.glsl":"\nuniform sampler2D tDiffuse;\nuniform vec2 rcpRenderTargetResolution;\nuniform float whitePoint;\n\n#define FXAA_REDUCE_MIN   (1.0/128.0)\n#define FXAA_REDUCE_MUL   (1.0/8.0)\n#define FXAA_SPAN_MAX     (8.0)\n\n// Jim Hejl and Richard Burgess-Dawson\n#define TM_A 6.2\n#define TM_B .5\n#define TM_C 6.2\n#define TM_D 1.7\n#define TM_E 0.06\n\nvec3 toneMap(vec3 color)\n{\n    return (color*(TM_A*color+TM_B))/(color*(TM_C*color+TM_D)+TM_E);\n}\n\nfloat toneMap(float val)\n{\n    return (val*(TM_A*val+TM_B))/(val*(TM_C*val+TM_D)+TM_E);\n}\n\n// refer to: https://github.com/mattdesl/glsl-fxaa/blob/master/fxaa.glsl\nvoid main() {\n    \n    vec3 rgbNW = texture2D( tDiffuse, ( gl_FragCoord.xy + vec2( -1.0, -1.0 ) ) * rcpRenderTargetResolution ).xyz;\n    vec3 rgbNE = texture2D( tDiffuse, ( gl_FragCoord.xy + vec2( 1.0, -1.0 ) ) * rcpRenderTargetResolution ).xyz;\n    vec3 rgbSW = texture2D( tDiffuse, ( gl_FragCoord.xy + vec2( -1.0, 1.0 ) ) * rcpRenderTargetResolution ).xyz;\n    vec3 rgbSE = texture2D( tDiffuse, ( gl_FragCoord.xy + vec2( 1.0, 1.0 ) ) * rcpRenderTargetResolution ).xyz;\n    vec4 rgbaM  = texture2D( tDiffuse,  gl_FragCoord.xy * rcpRenderTargetResolution );\n    vec3 rgbM  = rgbaM.xyz;\n    vec3 luma = vec3( 0.299, 0.587, 0.114 );\n\n    float lumaNW = dot( rgbNW, luma );\n    float lumaNE = dot( rgbNE, luma );\n    float lumaSW = dot( rgbSW, luma );\n    float lumaSE = dot( rgbSE, luma );\n    float lumaM  = dot( rgbM,  luma );\n    float lumaMin = min( lumaM, min( min( lumaNW, lumaNE ), min( lumaSW, lumaSE ) ) );\n    float lumaMax = max( lumaM, max( max( lumaNW, lumaNE) , max( lumaSW, lumaSE ) ) );\n\n    vec2 dir;\n    dir.x = -((lumaNW + lumaNE) - (lumaSW + lumaSE));\n    dir.y =  ((lumaNW + lumaSW) - (lumaNE + lumaSE));\n\n    float dirReduce = max( ( lumaNW + lumaNE + lumaSW + lumaSE ) * ( 0.25 * FXAA_REDUCE_MUL ), FXAA_REDUCE_MIN );\n\n    float rcpDirMin = 1.0 / ( min( abs( dir.x ), abs( dir.y ) ) + dirReduce );\n    dir = min( vec2( FXAA_SPAN_MAX,  FXAA_SPAN_MAX),\n            max( vec2(-FXAA_SPAN_MAX, -FXAA_SPAN_MAX),\n                dir * rcpDirMin)) * rcpRenderTargetResolution;\n    vec4 rgbA = (1.0/2.0) * (\n        texture2D(tDiffuse,  gl_FragCoord.xy  * rcpRenderTargetResolution + dir * (1.0/3.0 - 0.5)) +\n    texture2D(tDiffuse,  gl_FragCoord.xy  * rcpRenderTargetResolution + dir * (2.0/3.0 - 0.5)));\n    vec4 rgbB = rgbA * (1.0/2.0) + (1.0/4.0) * (\n        texture2D(tDiffuse,  gl_FragCoord.xy  * rcpRenderTargetResolution + dir * (0.0/3.0 - 0.5)) +\n    texture2D(tDiffuse,  gl_FragCoord.xy  * rcpRenderTargetResolution + dir * (3.0/3.0 - 0.5)));\n    float lumaB = dot(rgbB, vec4(luma, 0.0));\n\n    vec3 color;\n    if ( ( lumaB < lumaMin ) || ( lumaB > lumaMax ) ) {\n    \n        color = rgbA.xyz;\n\n    } else {\n        color = rgbB.xyz;\n    }\n\n\n    color = max(vec3(0.0), color - vec3(.004));\n    color = toneMap(color) / toneMap(whitePoint);\n    gl_FragColor = vec4(color, 1.0);\n}\n","sss_blur_vertex.glsl":"\nvarying vec2 vUV;\nvarying vec3 vViewVector;\n\nuniform mat4 unprojectionMatrix;\n\nvoid main()\n{\n    gl_Position = vec4(position, 1.0);\n    vUV = uv;\n    vec4 unproj = unprojectionMatrix * vec4(position.xy, 0.0, 1.0);\n    unproj /= unproj.w;\n    vViewVector = -unproj.xyz / unproj.z;\n}\n","sss_blur_fragment.glsl":"\nvarying vec3 vViewVector;\nvarying vec2 vUV;\n\nuniform sampler2D tDiffuse;\nuniform sampler2D depthMap;\nuniform sampler2D sssProfileMap;\nuniform vec2 step;\nuniform vec3 color1;\nuniform vec3 color2;\nuniform vec3 color3;\nuniform float cameraNear;\nuniform float cameraRange;\nuniform float sssProfileScale;\nuniform float sssRange;\n\nvoid getContribution(in vec2 uv, inout vec3 prevPos, inout float totalDistance, inout vec3 totalWeights, inout vec3 acc1, inout vec3 acc2, inout vec3 acc3)\n{\n    float depth = RGBA8ToFloat(texture2D(depthMap, uv));\n    vec3 pos = vViewVector * (cameraNear + depth * cameraRange);\n    float stepDist = distance(pos, prevPos);\n    prevPos = pos;\n    totalDistance += stepDist;\n\n    vec3 weights = texture2D(sssProfileMap, vec2(totalDistance * sssProfileScale, 0.0)).yzw;\n\n    if (totalDistance > sssRange)\n        weights *= 0.0;\n\n    totalWeights += weights;\n\n    vec3 color = texture2D(tDiffuse, uv).xyz;\n\n    acc1 += color * weights.x;\n    acc2 += color * weights.y;\n    acc3 += color * weights.z;\n}\n\nvoid main()\n{\n    vec3 totalWeights = texture2D(sssProfileMap, vec2(0.0)).yzw;\n\n    vec3 color = texture2D(tDiffuse, vUV).xyz;\n    vec3 c1 = color * totalWeights.x;\n    vec3 c2 = color * totalWeights.y;\n    vec3 c3 = color * totalWeights.z;\n\n    // centerDepth=(real_depth-near)/camera_range\n    float centerDepth = RGBA8ToFloat(texture2D(depthMap, vUV));\n\n    vec3 centerPos = vViewVector * (cameraNear + centerDepth * cameraRange);\n\n    vec2 uvLeft = vUV;\n    vec2 uvRight = vUV;\n    vec3 posLeft = centerPos;\n    vec3 posRight = centerPos;\n    float distLeft = 0.0;\n    float distRight = 0.0;\n\n    for (int i = 0; i < RADIUS; ++i) {\n        uvLeft -= step;\n        uvRight += step;\n        getContribution(uvLeft, posLeft, distLeft, totalWeights, c1, c2, c3);\n        getContribution(uvRight, posRight, distRight, totalWeights, c1, c2, c3);\n    }\n\n    //  blend together\n    gl_FragColor.xyz = color1 * c1 / totalWeights.x;\n    gl_FragColor.xyz += color2 * c2 / totalWeights.y;\n    gl_FragColor.xyz += color3 * c3 / totalWeights.z;\n    gl_FragColor.w = 1.0;\n}\n","sss_vertex.glsl":"\nvarying vec2 vUV;\nvarying vec3 vViewNormal;\nvarying vec3 vViewPosition;\nvarying vec4 vShadowCoord;\n\nuniform mat4 shadowMapMatrix;\n\nvoid main() {\n    vec3 localPos = position;\n    vec4 viewPos = modelViewMatrix * vec4(localPos, 1.0);\n    vec4 worldPos = modelMatrix * vec4(localPos, 1.0);\n\n    gl_Position = projectionMatrix * viewPos;\n    vUV = uv;\n\n    vViewNormal = normalMatrix * normal;\n    vViewPosition = viewPos.xyz;\n    vShadowCoord = shadowMapMatrix * worldPos * .5 + .5;\n}\n","sss_fragment.glsl":"\nvarying vec2 vUV;\nvarying vec3 vViewNormal;\nvarying vec3 vViewPosition;\nvarying vec4 vShadowCoord;\n\nuniform sampler2D normalMap;\nuniform sampler2D irradianceMap;\nuniform sampler2D shadowMap;\n\nuniform float probeExposure;\n\nvec3 perturbNormal2Arb(vec3 position, vec3 normal, vec3 normalSample)\n{\n    vec3 q0 = dFdx( position.xyz );\n    vec3 q1 = dFdy( position.xyz );\n    vec2 st0 = dFdx( vUV.st );\n    vec2 st1 = dFdy( vUV.st );\n    vec3 S = normalize( q0 * st1.t - q1 * st0.t );\n    vec3 T = normalize( -q0 * st1.s + q1 * st0.s );\n    vec3 N = normalize( normal );\n    mat3 tsn = mat3( S, T, N );\n    return normalize( tsn * normalSample );\n}\n\nvec3 getNormal()\n{\n    vec4 normalSample = texture2D(normalMap, vUV);\n    vec3 normal = normalSample.xyz * 2.0 - 1.0;\n    return perturbNormal2Arb(vViewPosition, vViewNormal, normal);\n}\n\nvec2 getVSMMoments(vec2 uv)\n{\n    vec4 s = texture2D(shadowMap, uv);\n#ifdef VSM_FLOAT\n    return s.xy;\n#else\n    return vec2(RG8ToFloat(s.xy), RG8ToFloat(s.zw));\n#endif\n}\n\nfloat getShadow(float offset)\n{\n    vec4 coord = vShadowCoord;\n    coord.z -= offset;\n    vec2 moments = getVSMMoments(coord.xy);\n    float p = linearStep(coord.z - 0.02, coord.z, moments.x);\n    float variance = moments.y - moments.x * moments.x;\n    variance = max(variance, MIN_VARIANCE);\n\n    float diff = coord.z - moments.x;\n    float upperBound = variance / (variance + diff*diff);\n    float shadow = linearStep(LIGHT_BLEED_REDUCTION, 1.0, upperBound);\n    return clamp(max(shadow, p), 0.0, 1.0);\n}\n\nvoid main() {\n    vec3 viewNormal = getNormal();\n    vec3 worldNormal = viewNormal * mat3(viewMatrix);\n    float shadow = getShadow(0.0);\n    vec3 diffuseLight = ggx_getDiffuseLight(viewNormal, vViewPosition, shadow);\n    vec4 irradianceSample = sampleLatLong(irradianceMap, worldNormal);\n\n    diffuseLight += irradianceSample.xyz * irradianceSample.xyz * probeExposure;\n\n    // NOT APPLYING GAMMA!\n    gl_FragColor = vec4(diffuseLight, 1.0);\n}\n","tiny_blur_hdre_fragment.glsl":"\nuniform sampler2D tDiffuse;\n\nvarying vec2 vUV;\n\nuniform vec2 sampleStep;\n\nvoid main()\n{\n    vec3 col = decodeHDRE(texture2D(tDiffuse, vUV - sampleStep * .5));\n    col += decodeHDRE(texture2D(tDiffuse, vUV + sampleStep * vec2(1.5, -.5)));\n    col += decodeHDRE(texture2D(tDiffuse, vUV + sampleStep * vec2(-.5, 1.5)));\n    col += decodeHDRE(texture2D(tDiffuse, vUV + sampleStep * 1.5));\n\n    gl_FragColor = encodeHDRE(col * .25);\n}\n","tonemap_fragment.glsl":"\nuniform sampler2D tDiffuse;\nuniform float exposure;\n\nvarying vec2 vUV;\n\nvoid main()\n{\n    vec4 hdre = texture2D(tDiffuse, vUV);\n    vec3 color = decodeHDRE(hdre);\n    \n    //    color *= color * exposure;\n    //    color = max(vec3(0.0), color.xyz - 0.004);\n\n    // this has pow 2.2 gamma included, not valid if using fast gamma correction\n\n    // Jim Hejl and Richard Burgess-Dawson\n    /*float a = 6.2;\n    float b = .5;\n    float c = 6.2;\n    float d = 1.7;\n    float e = 0.06;*/\n\n    // ACES\n    float a = 2.51;\n    float b = 0.03;\n    float c = 2.43;\n    float d = 0.59;\n    float e = 0.14;\n\n    gl_FragColor = vec4(saturate((color*(a*color+b))/(color*(c*color+d)+e)), 1.0);\n//    gl_FragColor = vec4(color, 1.0);\n}\n","shadow_fragment.glsl":"\nvarying vec4 projection;\n\nuniform float depthBias;\n\nvoid main()\n{\n    float depth = projection.z * .5 + .5;\n\n    gl_FragColor = floatToRGBA8(depth + depthBias);\n}\n","shadow_vertex.glsl":"\nvarying vec4 projection;\nvarying vec2 vUV;\n\n\nvoid main()\n{\n    gl_Position = projection = projectionMatrix * modelViewMatrix * vec4(position, 1.0);\n    vUV = uv;\n}\n","vsm_fragment.glsl":"\nvarying vec4 projection;\n\nvoid main()\n{\n    float depth = projection.z * .5 + .5;\n    float dx = dFdx(depth);\n    float dy = dFdy(depth);\n    float moment2 = depth * depth + 0.25*(dx*dx + dy*dy);\n\n#ifdef FLOAT_TEX\n    gl_FragColor = vec4(depth, moment2, 0.0, 1.0);\n#else\n    gl_FragColor = vec4(floatToRG8(depth), floatToRG8(moment2));\n#endif\n}\n","vsm_hair_fragment.glsl":"\nvarying vec4 projection;\nuniform sampler2D alphaMap;\nvarying vec2 vUV;\nuniform float alpha_test;\n\nvoid main()\n{\n    float alpha = texture2D(alphaMap, vUV).g;\n    if (alpha < alpha_test) discard;\n\n    float depth = projection.z * .5 + .5;\n    float dx = dFdx(depth);\n    float dy = dFdy(depth);\n    float moment2 = depth * depth + 0.25*(dx*dx + dy*dy);\n\n#ifdef FLOAT_TEX\n    gl_FragColor = vec4(depth, moment2, 0.0, 1.0);\n#else\n    gl_FragColor = vec4(floatToRG8(depth), floatToRG8(moment2));\n#endif\n}\n","vsm_blur_vertex.glsl":"\nvarying vec2 vUV;\nvoid main() {\n    vUV = uv;\n    gl_Position = vec4(position, 1.0);\n}","vsm_blur_fragment.glsl":"\n#define RADIUS 2\n#define NUM_SAMPLES (RADIUS * 2 + 1)\n\nuniform sampler2D tDiffuse;\nuniform vec2 step;\n\nvarying vec2 vUV;\n\nvec2 getShadowValue(vec2 uv)\n{\n    vec4 s = texture2D(tDiffuse, uv);\n#ifdef FLOAT_TEX\n    return s.xy;\n#else\n    return vec2(RG8ToFloat(s.xy), RG8ToFloat(s.zw));\n#endif\n}\n\nvoid main() {\n    vec2 sum = getShadowValue(vUV);\n\n    for (int i = 1; i <= RADIUS; ++i) {\n        float fi = float(i);\n        sum += getShadowValue(vUV - step * fi);\n        sum += getShadowValue(vUV + step * fi);\n    }\n\n    sum /= float(NUM_SAMPLES);\n#ifdef FLOAT_TEX\n    gl_FragColor = vec4(sum, 0.0, 1.0);\n#else\n    gl_FragColor.xy = floatToRG8(sum.x);\n    gl_FragColor.zw = floatToRG8(sum.y);\n#endif\n}\n","gaussian_blur_hdre_fragment.glsl":"\nuniform sampler2D tDiffuse;\n\nvarying vec2 vUV;\n\nuniform vec2 sampleStep;\nuniform float weights[NUM_WEIGHTS];\n\nvoid main()\n{\n    vec3 col = decodeHDRE(texture2D(tDiffuse, vUV)) * weights[0];\n\n    for (int i = 1; i <= KERNEL_RADIUS; ++i) {\n        vec2 offset = float(i) * sampleStep;\n        col += (decodeHDRE(texture2D(tDiffuse, vUV + offset)) + decodeHDRE(texture2D(tDiffuse, vUV - offset))) * weights[i];\n    }\n\n    gl_FragColor = encodeHDRE(col);\n}\n","post_vertex.glsl":"\nvarying vec2 vUV;\n\nvoid main()\n{\n    gl_Position = vec4(position, 1.0);\n    vUV = uv;\n}\n","post_z_vertex.glsl":"\nvarying vec2 vUV;\nvarying vec3 viewVector;\n\nuniform mat4 unprojectionMatrix;\n\nvoid main()\n{\n    gl_Position = vec4(position, 1.0);\n    vUV = uv;\n    vec4 unproj = unprojectionMatrix * vec4(position.xy, 0.0, 1.0);\n    unproj /= unproj.w;\n    viewVector = -unproj.xyz / unproj.z;\n}\n","unlit_fragment.glsl":"\n#ifdef diffuse_MAP\nvarying vec2 texCoords;\nuniform sampler2D diffuseMap;\n#endif\n\nuniform vec3 color;\nuniform float opacity;\n\n#ifdef FOG\nvarying vec3 viewPosition;\n\nuniform float fogDensity;\nuniform vec3 fogColor;\n#endif\n\nvoid main() {\n    float alpha = opacity;\n#ifdef diffuse_MAP\n    vec4 diffuse = texture2D(diffuseMap, texCoords);\n    alpha *= diffuse.w;\n    diffuse.xyz *= diffuse.xyz;\n    diffuse.xyz *= color;\n    vec3 col = diffuse.xyz;\n#else\n    vec3 col = color;\n#endif\n\n#ifdef FOG\n    float fogAmount = clamp(exp2(viewPosition.z * fogDensity), 0.0, 1.0);\n    col = mix(fogColor, col, fogAmount);\n#endif\n\n#ifdef HDRE\n    gl_FragColor = encodeHDRE(sqrt(col));\n#else\n//    col *= alpha;\n    gl_FragColor = vec4(sqrt(col), alpha);\n#endif\n}\n","unlit_vertex.glsl":"\n#ifdef diffuse_MAP\nvarying vec2 texCoords;\n#endif\n\n#ifdef FOG\nvarying vec3 viewPosition;\n#endif\n\nvoid main() {\n    vec3 localPos = position;\n    vec4 viewPos = modelViewMatrix * vec4(localPos, 1.0);\n    gl_Position = projectionMatrix * viewPos;\n#ifdef FOG\n    viewPosition = viewPos.xyz;\n#endif\n\n#ifdef diffuse_MAP\n    texCoords = uv;\n#endif\n}\n"},Ib={uniforms:{tDiffuse:{value:null},opacity:{value:1}},vertexShader:"\n\n\t\tvarying vec2 vUv;\n\n\t\tvoid main() {\n\n\t\t\tvUv = uv;\n\t\t\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n\n\t\t}",fragmentShader:"\n\n\t\tuniform float opacity;\n\n\t\tuniform sampler2D tDiffuse;\n\n\t\tvarying vec2 vUv;\n\n\t\tvoid main() {\n\n\t\t\tgl_FragColor = texture2D( tDiffuse, vUv );\n\t\t\tgl_FragColor.a *= opacity;\n\n\n\t\t}"},Rb=function(){function e(){v(this,e),this.enabled=!0,this.needsSwap=!0,this.clear=!1,this.renderToScreen=!1}return w(e,[{key:"setSize",value:function(){}},{key:"render",value:function(){console.error("THREE.Pass: .render() must be implemented in derived pass.")}},{key:"dispose",value:function(){}}]),e}(),Hb=new Oy(-1,1,1,-1,0,1),Pb=new Gv;Pb.setAttribute("position",new Iv([-1,3,0,-1,-1,0,3,-1,0],3)),Pb.setAttribute("uv",new Iv([0,2,0,0,2,0],2));var Nb=function(){function e(t){v(this,e),this._mesh=new iy(Pb,t)}return w(e,[{key:"dispose",value:function(){this._mesh.geometry.dispose()}},{key:"render",value:function(e){e.render(this._mesh,Hb)}},{key:"material",get:function(){return this._mesh.material},set:function(e){this._mesh.material=e}}]),e}(),Ob=function(e){_(n,e);var t=S(n);function n(e,r){var i;return v(this,n),(i=t.call(this)).textureID=void 0!==r?r:"tDiffuse",e instanceof hy?(i.uniforms=e.uniforms,i.material=e):e&&(i.uniforms=uy.clone(e.uniforms),i.material=new hy({defines:Object.assign({},e.defines),uniforms:i.uniforms,vertexShader:e.vertexShader,fragmentShader:e.fragmentShader})),i.fsQuad=new Nb(i.material),i}return w(n,[{key:"render",value:function(e,t,n){this.uniforms[this.textureID]&&(this.uniforms[this.textureID].value=n.texture),this.fsQuad.material=this.material,this.renderToScreen?(e.setRenderTarget(null),this.fsQuad.render(e)):(e.setRenderTarget(t),this.clear&&e.clear(e.autoClearColor,e.autoClearDepth,e.autoClearStencil),this.fsQuad.render(e))}},{key:"dispose",value:function(){this.material.dispose(),this.fsQuad.dispose()}}]),n}(Rb),Vb=function(e){_(n,e);var t=S(n);function n(e,r){var i;return v(this,n),(i=t.call(this)).scene=e,i.camera=r,i.clear=!0,i.needsSwap=!1,i.inverse=!1,i}return w(n,[{key:"render",value:function(e,t,n){var r,i,A=e.getContext(),a=e.state;a.buffers.color.setMask(!1),a.buffers.depth.setMask(!1),a.buffers.color.setLocked(!0),a.buffers.depth.setLocked(!0),this.inverse?(r=0,i=1):(r=1,i=0),a.buffers.stencil.setTest(!0),a.buffers.stencil.setOp(A.REPLACE,A.REPLACE,A.REPLACE),a.buffers.stencil.setFunc(A.ALWAYS,r,4294967295),a.buffers.stencil.setClear(i),a.buffers.stencil.setLocked(!0),e.setRenderTarget(n),this.clear&&e.clear(),e.render(this.scene,this.camera),e.setRenderTarget(t),this.clear&&e.clear(),e.render(this.scene,this.camera),a.buffers.color.setLocked(!1),a.buffers.depth.setLocked(!1),a.buffers.stencil.setLocked(!1),a.buffers.stencil.setFunc(A.EQUAL,1,4294967295),a.buffers.stencil.setOp(A.KEEP,A.KEEP,A.KEEP),a.buffers.stencil.setLocked(!0)}}]),n}(Rb),zb=function(e){_(n,e);var t=S(n);function n(){var e;return v(this,n),(e=t.call(this)).needsSwap=!1,e}return w(n,[{key:"render",value:function(e){e.state.buffers.stencil.setLocked(!1),e.state.buffers.stencil.setTest(!1)}}]),n}(Rb),Gb=function(){function e(t,n){if(v(this,e),this.renderer=t,void 0===n){var r=t.getSize(new Vg);this._pixelRatio=t.getPixelRatio(),this._width=r.width,this._height=r.height,(n=new cm(this._width*this._pixelRatio,this._height*this._pixelRatio)).texture.name="EffectComposer.rt1"}else this._pixelRatio=1,this._width=n.width,this._height=n.height;this.renderTarget1=n,this.renderTarget2=n.clone(),this.renderTarget2.texture.name="EffectComposer.rt2",this.writeBuffer=this.renderTarget1,this.readBuffer=this.renderTarget2,this.renderToScreen=!0,this.passes=[],this.copyPass=new Ob(Ib),this.clock=new sb}return w(e,[{key:"swapBuffers",value:function(){var e=this.readBuffer;this.readBuffer=this.writeBuffer,this.writeBuffer=e}},{key:"addPass",value:function(e){this.passes.push(e),e.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}},{key:"insertPass",value:function(e,t){this.passes.splice(t,0,e),e.setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}},{key:"removePass",value:function(e){var t=this.passes.indexOf(e);-1!==t&&this.passes.splice(t,1)}},{key:"isLastEnabledPass",value:function(e){for(var t=e+1;t<this.passes.length;t++)if(this.passes[t].enabled)return!1;return!0}},{key:"render",value:function(e){void 0===e&&(e=this.clock.getDelta());for(var t=this.renderer.getRenderTarget(),n=!1,r=0,i=this.passes.length;r<i;r++){var A=this.passes[r];if(!1!==A.enabled){if(A.renderToScreen=this.renderToScreen&&this.isLastEnabledPass(r),A.render(this.renderer,this.writeBuffer,this.readBuffer,e,n),A.needsSwap){if(n){var a=this.renderer.getContext(),o=this.renderer.state.buffers.stencil;o.setFunc(a.NOTEQUAL,1,4294967295),this.copyPass.render(this.renderer,this.writeBuffer,this.readBuffer,e),o.setFunc(a.EQUAL,1,4294967295)}this.swapBuffers()}void 0!==Vb&&(A instanceof Vb?n=!0:A instanceof zb&&(n=!1))}}this.renderer.setRenderTarget(t)}},{key:"reset",value:function(e){if(void 0===e){var t=this.renderer.getSize(new Vg);this._pixelRatio=this.renderer.getPixelRatio(),this._width=t.width,this._height=t.height,(e=this.renderTarget1.clone()).setSize(this._width*this._pixelRatio,this._height*this._pixelRatio)}this.renderTarget1.dispose(),this.renderTarget2.dispose(),this.renderTarget1=e,this.renderTarget2=e.clone(),this.writeBuffer=this.renderTarget1,this.readBuffer=this.renderTarget2}},{key:"setSize",value:function(e,t){this._width=e,this._height=t;var n=this._width*this._pixelRatio,r=this._height*this._pixelRatio;this.renderTarget1.setSize(n,r),this.renderTarget2.setSize(n,r);for(var i=0;i<this.passes.length;i++)this.passes[i].setSize(n,r)}},{key:"setPixelRatio",value:function(e){this._pixelRatio=e,this.setSize(this._width,this._height)}},{key:"dispose",value:function(){this.renderTarget1.dispose(),this.renderTarget2.dispose(),this.copyPass.dispose()}}]),e}(),Kb=function(e){_(n,e);var t=S(n);function n(e,r,i,A,a){var o;return v(this,n),(o=t.call(this)).scene=e,o.camera=r,o.overrideMaterial=i,o.clearColor=A,o.clearAlpha=void 0!==a?a:0,o.clear=!0,o.clearDepth=!1,o.needsSwap=!1,o._oldClearColor=new im,o}return w(n,[{key:"render",value:function(e,t,n){var r,i,A=e.autoClear;e.autoClear=!1,void 0!==this.overrideMaterial&&(i=this.scene.overrideMaterial,this.scene.overrideMaterial=this.overrideMaterial),this.clearColor&&(e.getClearColor(this._oldClearColor),r=e.getClearAlpha(),e.setClearColor(this.clearColor,this.clearAlpha)),this.clearDepth&&e.clearDepth(),e.setRenderTarget(this.renderToScreen?null:n),this.clear&&e.clear(e.autoClearColor,e.autoClearDepth,e.autoClearStencil),e.render(this.scene,this.camera),this.clearColor&&e.setClearColor(this._oldClearColor,r),void 0!==this.overrideMaterial&&(this.scene.overrideMaterial=i),e.autoClear=A}}]),n}(Rb),Wb={uniforms:{tDiffuse:{value:null},powRGB:{value:new pm(2,2,2)},mulRGB:{value:new pm(1,1,1)},addRGB:{value:new pm(0,0,0)}},vertexShader:"\n\n\t\tvarying vec2 vUv;\n\n\t\tvoid main() {\n\n\t\t\tvUv = uv;\n\n\t\t\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n\n\t\t}",fragmentShader:"\n\n\t\tuniform sampler2D tDiffuse;\n\t\tuniform vec3 powRGB;\n\t\tuniform vec3 mulRGB;\n\t\tuniform vec3 addRGB;\n\n\t\tvarying vec2 vUv;\n\n\t\tvoid main() {\n\n\t\t\tgl_FragColor = texture2D( tDiffuse, vUv );\n\t\t\tgl_FragColor.rgb = mulRGB * pow( ( gl_FragColor.rgb + addRGB ), powRGB );\n\n\t\t}"};var jb={uniforms:{tDiffuse:{value:null},step:{value:new Vg(1/512,0)}},vertexShader:Db.get("vsm_blur_vertex"),fragmentShader:Db.get("vsm_blur_fragment")},Xb={uniforms:{tDiffuse:{value:null},sampleStep:{value:new Vg},weights:{value:[]}},vertexShader:Db.get("post_vertex"),fragmentShader:Db.get("tiny_blur_hdre_fragment")},qb={uniforms:{source:{value:null},blurred1:{value:null},blurred2:{value:null},depth:{value:null},strength:{value:1},focusDepth:{value:.5},focusRange:{value:.1},focusFalloff:{value:.1}},vertexShader:Db.get("post_vertex"),fragmentShader:Db.get("dof_fragment")},Yb={uniforms:{tDiffuse:{value:null},rcpRenderTargetResolution:{value:new Vg},whitePoint:{value:1.3}},vertexShader:Db.get("post_vertex"),fragmentShader:Db.get("fxaa_tonemap_fragment")},Jb=(new Vg,Db.get("post_vertex"),Db.get("gaussian_blur_hdre_fragment"),{defines:{RADIUS:8},uniforms:{tDiffuse:{value:null},depthMap:{value:null},color1:{value:null},color2:{value:null},color3:{value:null},sssProfileMap:{value:null},sssProfileScale:{value:1},sssRange:{value:1},step:{value:new Vg(1/512,0)},unprojectionMatrix:{value:new Km},cameraNear:{value:0},cameraRange:{value:0}},vertexShader:Db.get("sss_blur_vertex"),fragmentShader:Db.get("sss_blur_fragment")});var Zb=function(){for(var e={},t=window.location.search.substring(1).split("&"),n=0;n<t.length;n++){var r=t[n].split("=");if("undefined"==typeof e[r[0]])e[r[0]]=decodeURIComponent(r[1]);else if("string"==typeof e[r[0]]){var i=[e[r[0]],decodeURIComponent(r[1])];e[r[0]]=i}else e[r[0]].push(decodeURIComponent(r[1]))}return e}(),$b=(Zb.debug&&"0"!==Zb.debug&&Zb.debug,!(/AppleWebKit/.test(navigator.userAgent)&&/Mobile\/\w+/.test(navigator.userAgent)||/Android|webOS|BlackBerry|Opera Mini|Opera Mobi|IEMobile/i.test(navigator.userAgent))&&!Zb.lowPerformance);function ex(e){Object.keys(e).forEach((function(t){e[t]&&e[t].dispose&&(console.log("dispose",t),e[t].dispose(),e[t]=null)}))}var tx=function(){function e(t){v(this,e),this._amplitude=1/Math.sqrt(2*t*Math.PI),this._expScale=-1/(2*t)}return w(e,[{key:"getValueAt",value:function(e){return this._amplitude*Math.pow(Math.E,e*e*this._expScale)}}]),e}(),nx={getFloat:function(e){if(e.extensions.get("OES_texture_float_linear"))return gp;throw new Error("Float render targets are unsupported!")},getHalfOrFloat:function(e){var t=e.extensions;if(t.get("OES_texture_half_float_linear"))return mp;if(t.get("OES_texture_float_linear"))return gp;throw new Error("Float render targets are unsupported!")},getHalfOrFloatOrAny:function(e){var t=e.extensions;return t.get("OES_texture_half_float_linear")?mp:t.get("OES_texture_float_linear")?gp:up}},rx=function(){function e(t,n,r){v(this,e),this._width=t,this._height=n,this._sourceFBO=new cm(t,n,r),this._targetFBO=new cm(t,n,r),this._sourceFBO.texture.generateMipmaps=r.generateMipmaps||!1,this._targetFBO.texture.generateMipmaps=r.generateMipmaps||!1}return w(e,[{key:"width",get:function(){return this._width}},{key:"height",get:function(){return this._height}},{key:"source",get:function(){return this._sourceFBO.texture}},{key:"target",get:function(){return this._targetFBO}},{key:"swap",value:function(){var e=this._sourceFBO;this._sourceFBO=this._targetFBO,this._targetFBO=e}}]),e}(),ix=function(){function e(t,n){v(this,e),this._renderer=t,this._scene=new u_,this._camera=new Oy(-1,1,1,-1,0,1),this._mesh=n||new iy(new Fy(2,2),null),this._scene.add(this._mesh)}return w(e,[{key:"execute",value:function(e,t,n,r){var i=this._renderer.autoClear;this._renderer.autoClear=void 0===n||n,this._mesh.material=e,this._renderer.setRenderTarget(t),this._renderer.render(this._scene,r||this._camera),this._renderer.setRenderTarget(null),this._renderer.autoClear=i}}]),e}(),Ax=function(e){_(n,e);var t=S(n);function n(e){var r;v(this,n);var i="";return e&&(i+="#define FLOAT_TEX\n"),(r=t.call(this,{vertexShader:Db.get("shadow_vertex"),fragmentShader:i+Db.get("vsm_fragment")})).extensions.derivatives=!0,r.side=sf,r}return w(n)}(hy),ax=function(e){_(n,e);var t=S(n);function n(e){var r;v(this,n);var i="";return e&&(i+="#define FLOAT_TEX\n"),(r=t.call(this,{uniforms:{alphaMap:{value:null},alpha_test:{value:.5}},vertexShader:Db.get("shadow_vertex"),fragmentShader:i+Db.get("vsm_hair_fragment")})).extensions.derivatives=!0,r.side=lf,r}return w(n)}(hy),ox=function(){function e(){v(this,e),this._listeners=[],this._lookUp={}}return w(e,[{key:"bind",value:function(e,t){this._lookUp[e]=this._listeners.length;var n=t?e.bind(t):e;this._listeners.push(n)}},{key:"unbind",value:function(e){var t=this._lookUp[e];this._listeners.splice(t,1),delete this._lookUp[e]}},{key:"dispatch",value:function(e){for(var t=this._listeners.length,n=0;n<t;++n)this._listeners[n](e)}},{key:"hasListeners",get:function(){return this._listeners.length>0}}]),e}(),sx=function(e){_(n,e);var t=S(n);function n(e,r,i,A){v(this,n);var a=new ay(r=r||1e3,r,r);a.scale(-1,1,1);var o=new lx({envMap:e,exposure:i||0,hdre:A});return t.call(this,a,o)}return w(n,[{key:"texture",get:function(){return this.material.envMap},set:function(e){this.material.envMap=e}}]),n}(iy),lx=function(e){_(n,e);var t=S(n);function n(e){var r;v(this,n);var i={};e.envMap instanceof lm&&(i.LAT_LONG="1"),e.hdre&&(i.HDRE="1");var A={envMap:{value:e.envMap},exposure:{value:Math.pow(2,e.exposure||0)}};return(r=t.call(this,{uniforms:A,defines:i,vertexShader:Db.get("sky_vertex"),fragmentShader:Db.get("sky_fragment")}))._envMap=e.envMap,r}return w(n,[{key:"envMap",get:function(){return this._envMap},set:function(e){this._envMap=e,this.uniforms.envMap.value=e}}]),n}(hy),ux=function(e){_(n,e);var t=S(n);function n(e,r,i){var A;return v(this,n),(A=t.call(this))._container=e,A._coords=new pm(.5*Math.PI,.4*Math.PI,2),A._localAcceleration=new pm(0,0,0),A._localVelocity=new pm(0,0,0),A.lookAtTarget=r||new pm(0,0,0),A.zoomSpeed=2,A.maxRadius=20,A.minRadius=.1,A.dampen=.9,A.maxAzimuth=void 0,A.minAzimuth=void 0,A.minPolar=.1,A.maxPolar=Math.PI-.1,A.moveAcceleration=.02,A._m=new Km,A._oldMouseX=0,A._oldMouseY=0,A._moveWithKeys=i,A._moveAcceleration=new pm,A._moveVelocity=new pm,A._isDown=!1,A.mouse_constant=.0015,A._initListeners(),A}return w(n,[{key:"radius",get:function(){return this._coords.z},set:function(e){this._coords.z=e}},{key:"azimuth",get:function(){return this._coords.x},set:function(e){this._coords.x=e}},{key:"polar",get:function(){return this._coords.y},set:function(e){this._coords.y=e}},{key:"onAdded",value:function(){this._isDown=!1;var e=/Firefox/i.test(navigator.userAgent)?"DOMMouseScroll":"mousewheel";this._container.addEventListener(e,this._onMouseWheel),document.addEventListener("mousemove",this._onMouseMove),document.addEventListener("touchmove",this._onTouchMove),this._container.addEventListener("mousedown",this._onMouseDown),this._container.addEventListener("touchstart",this._onTouchDown),document.addEventListener("mouseup",this._onUp),document.addEventListener("touchend",this._onUp),this._moveWithKeys&&(document.addEventListener("keyup",this._onKeyUp),document.addEventListener("keydown",this._onKeyDown))}},{key:"onRemoved",value:function(){var e=/Firefox/i.test(navigator.userAgent)?"DOMMouseScroll":"mousewheel";this._container.removeEventListener(e,this._onMouseWheel),this._container.removeEventListener("mousemove",this._onMouseMove),this._container.removeEventListener("touchmove",this._onTouchMove),this._container.removeEventListener("mousedown",this._onMouseDown),this._container.removeEventListener("touchstart",this._onTouchDown),this._container.removeEventListener("mouseup",this._onUp),this._container.removeEventListener("touchend",this._onUp),this._moveWithKeys&&(document.removeEventListener("keyup",this._onKeyUp),document.removeEventListener("keydown",this._onKeyDown))}},{key:"onUpdate",value:function(e){if(this._moveWithKeys){this._moveVelocity.x*=this.dampen,this._moveVelocity.y*=this.dampen,this._moveVelocity.z*=this.dampen,this._moveVelocity.x+=this._moveAcceleration.x,this._moveVelocity.y+=this._moveAcceleration.y,this._moveVelocity.z+=this._moveAcceleration.z;var t=new pm;t.copy(this._moveVelocity),t.applyQuaternion(this.entity.quaternion.setFromRotationMatrix(this.entity.matrixWorld)),this.lookAtTarget.x+=t.x,this.lookAtTarget.y+=this._moveVelocity.y,this.lookAtTarget.z+=t.z}this._localVelocity.x*=this.dampen,this._localVelocity.y*=this.dampen,this._localVelocity.z*=this.dampen,this._localVelocity.x+=this._localAcceleration.x,this._localVelocity.y+=this._localAcceleration.y,this._localVelocity.z+=this._localAcceleration.z,this._localAcceleration.x=0,this._localAcceleration.y=0,this._localAcceleration.z=0,this._coords.add(this._localVelocity),this._coords.y=Og.clamp(this._coords.y,this.minPolar,this.maxPolar),this._coords.z=Og.clamp(this._coords.z,this.minRadius,this.maxRadius),void 0!==this.maxAzimuth&&void 0!==this.minAzimuth&&(this._coords.x=Og.clamp(this._coords.x,this.minAzimuth,this.maxAzimuth));var n=this.entity,r=this._m,i=this._fromSphericalCoordinates(this._coords.z,this._coords.x,this._coords.y);i.add(this.lookAtTarget),r.lookAt(i,this.lookAtTarget,new pm(0,1,0)),r.setPosition(i),r.decompose(n.position,n.quaternion,n.scale)}},{key:"_fromSphericalCoordinates",value:function(e,t,n){var r=new pm;return r.x=e*Math.sin(n)*Math.cos(t),r.y=e*Math.cos(n),r.z=e*Math.sin(n)*Math.sin(t),r}},{key:"setAzimuthImpulse",value:function(e){this._localAcceleration.x=e}},{key:"setPolarImpulse",value:function(e){this._localAcceleration.y=e}},{key:"setZoomImpulse",value:function(e){this._localAcceleration.z=e}},{key:"_updateMove",value:function(e,t){if(void 0!==this._oldMouseX){var n=e-this._oldMouseX,r=t-this._oldMouseY;this.setAzimuthImpulse(this.mouse_constant*n),this.setPolarImpulse(this.mouse_constant*-r)}this._oldMouseX=e,this._oldMouseY=t}},{key:"_initListeners",value:function(){var e=this;this._onMouseWheel=function(t){var n=t.detail?-120*t.detail:t.wheelDelta;e.setZoomImpulse(-n*e.zoomSpeed*1e-4)},this._onMouseDown=function(t){e._oldMouseX=void 0,e._oldMouseY=void 0,e._isDown=!0},this._onMouseMove=function(t){e._isDown&&e._updateMove(t.screenX,t.screenY)},this._onTouchDown=function(t){if(e._oldMouseX=void 0,e._oldMouseY=void 0,2===t.touches.length){var n=t.touches[0],r=t.touches[1],i=n.screenX-r.screenX,A=n.screenY-r.screenY;e._startPitchDistance=Math.sqrt(i*i+A*A),e._startZoom=e.radius}e._isDown=!0},this._onTouchMove=function(t){if(t.preventDefault(),e._isDown){var n=t.touches.length;if(1===n){var r=t.touches[0];e._updateMove(r.screenX,r.screenY)}else if(2===n){var i=t.touches[0],A=t.touches[1],a=i.screenX-A.screenX,o=i.screenY-A.screenY,s=Math.sqrt(a*a+o*o),l=e._startPitchDistance-s;e.radius=e._startZoom+.2*l}}},this._onUp=function(t){e._isDown=!1},this._onKeyUp=function(t){switch(t.keyCode){case 69:case 81:e._moveAcceleration.y=0;break;case 37:case 65:case 39:case 68:e._moveAcceleration.x=0;break;case 38:case 87:case 40:case 83:e._moveAcceleration.z=0}},this._onKeyDown=function(t){switch(t.keyCode){case 81:e._moveAcceleration.y=-e.moveAcceleration;break;case 69:e._moveAcceleration.y=e.moveAcceleration;break;case 37:case 65:e._moveAcceleration.x=-e.moveAcceleration;break;case 38:case 87:e._moveAcceleration.z=-e.moveAcceleration;break;case 39:case 68:e._moveAcceleration.x=e.moveAcceleration;break;case 40:case 83:e._moveAcceleration.z=e.moveAcceleration}}}}]),n}(function(){function e(){v(this,e),this._entity=null}return w(e,[{key:"onAdded",value:function(){}},{key:"onRemoved",value:function(){}},{key:"entity",get:function(){return this._entity}}]),e}()),cx=function(e){e._components=[],e._requiresUpdates=!1,e._onRequireUpdatesChange=new ox,e._update=function(e){var t=this._components;if(t)for(var n=t.length,r=0;r<n;++r){var i=t[r];i.onUpdate&&i.onUpdate(e)}},e._updateRequiresUpdates=function(e){e!==this._requiresUpdates&&(this._requiresUpdates=e,this._onRequireUpdatesChange.dispatch(this))}},dx={ENGINE:new(function(){function e(){v(this,e),this._updateableEntities=[],this._updateQueue=[],this._destroyQueue=[]}return w(e,[{key:"registerEntity",value:function(e){e._onRequireUpdatesChange.bind(this._onEntityUpdateChange,this),e._requiresUpdates&&this._addUpdatableEntity(e)}},{key:"unregisterEntity",value:function(e){e._onRequireUpdatesChange.unbind(this),e._requiresUpdates&&this._removeUpdatableEntity(e)}},{key:"destroyEntity",value:function(e){e._onRequireUpdatesChange.unbind(this),e._requiresUpdates&&this._removeUpdatableEntity(e),this._destroyQueue.push(e)}},{key:"_onEntityUpdateChange",value:function(e){e._requiresUpdates?this._addUpdatableEntity(e):this._removeUpdatableEntity(e)}},{key:"_addUpdatableEntity",value:function(e){this._updateQueue.push({entity:e,updatable:!0})}},{key:"_removeUpdatableEntity",value:function(e){this._updateQueue.push({entity:e,updatable:!1})}},{key:"_processUpdateQueue",value:function(){var e=this._updateQueue.length;if(0!==e){for(var t=0;t<e;++t){var n=this._updateQueue[t],r=n.entity;if(n.updatable)this._updateableEntities.push(r);else{var i=this._updateableEntities.indexOf(r);this._updateableEntities.splice(i,1)}}this._updateQueue=[]}}},{key:"_processDestroyQueue",value:function(){var e=this._destroyQueue.length;if(0!==e){for(var t=0;t<e;++t){var n=this._destroyQueue[t];delete n._components,delete n._requiresUpdates,delete n._onRequireUpdatesChange,delete n._update,delete n._updateRequiresUpdates}this._destroyQueue=[]}}},{key:"update",value:function(e){this._processUpdateQueue(),this._processDestroyQueue();for(var t=this._updateableEntities,n=t.length,r=0;r<n;++r)t[r]._update(e)}}]),e}()),isEntity:function(e){return!!e._components},convert:function(e){dx.isEntity(e)||(cx(e),dx.ENGINE.registerEntity(e))},destroy:function(e){dx.ENGINE.destroyEntity(e)},addComponents:function(e,t){for(var n=0;n<t.length;++n)dx.addComponent(e,t[n])},removeComponents:function(e,t){for(var n=0;n<t.length;++n)dx.removeComponent(e,t[n])},addComponent:function(e,t){if(t._entity)throw new Error("Component already added to an entity!");dx.convert(e),e._components.push(t),e._updateRequiresUpdates(this._requiresUpdates||!!t.onUpdate),t._entity=e,t.onAdded()},hasComponent:function(e,t){return e._components&&e._components.indexOf(t)>=0},removeComponent:function(e,t){if(!dx.hasComponent(e,t))throw new Error("Component wasn't added to this entity!");t.onRemoved();for(var n=!1,r=e._components.length,i=0,A=[],a=0;a<r;++a){var o=e._components[a];o!==t&&(A[i++]=o,n=n||!!t.onUpdate)}e._components=0===i?null:A,t._entity=null,e._updateRequiresUpdates(n)}};var hx=function(e){_(n,e);var t=S(n);function n(){return v(this,n),t.call(this,{uniforms:{cameraNear:{value:0},rcpCameraRange:{value:0}},vertexShader:Db.get("linear_depth_vertex"),fragmentShader:Db.get("linear_depth_fragment")})}return w(n,[{key:"cameraNear",get:function(){return this.uniforms.cameraNear.value},set:function(e){this.uniforms.cameraNear.value=e}},{key:"rcpCameraRange",get:function(){return this.uniforms.rcpCameraRange.value},set:function(e){this.uniforms.rcpCameraRange.value=e}}]),n}(hy),fx=function(e){_(n,e);var t=S(n);function n(){var e;return v(this,n),(e=t.call(this,{uniforms:{cameraNear:{value:0},rcpCameraRange:{value:0},alphaMap:{value:null},alpha_test:{value:.5},layer:{value:new Number(0)}},vertexShader:Db.get("linear_depth_vertex"),fragmentShader:Db.get("linear_hair_depth_fragment")})).side=lf,e}return w(n,[{key:"cameraNear",get:function(){return this.uniforms.cameraNear.value},set:function(e){this.uniforms.cameraNear.value=e}},{key:"rcpCameraRange",get:function(){return this.uniforms.rcpCameraRange.value},set:function(e){this.uniforms.rcpCameraRange.value=e}}]),n}(hy),px=function(){function e(t,n,r,i){v(this,e),this._renderer=r,this._scene=t,this._camera=n,this._scale=i||1,this._depthMaterial=new hx,this._hair_depthMaterial=new fx,bx.override_setting.depth.face=this._depthMaterial,bx.override_setting.depth.hair=this._hair_depthMaterial}return w(e,[{key:"texture",get:function(){return this._renderTarget.texture}},{key:"resize",value:function(e,t){e=Math.ceil(e*this._scale),t=Math.ceil(t*this._scale),this._renderTarget&&this._renderTarget.width===e&&this._renderTarget.height===t||(this._renderTarget=new cm(e,t,{minFilter:ap,magFilter:ap,generateMipmaps:!1,depthBuffer:!0,stencilBuffer:!1})),this._renderTarget2&&this._renderTarget2.width===e&&this._renderTarget2.height===t||(this._renderTarget2=new cm(e,t,{minFilter:ap,magFilter:ap,generateMipmaps:!1,depthBuffer:!0,stencilBuffer:!1})),this.width=e,this.height=t}},{key:"render",value:function(){this._depthMaterial.cameraNear=this._camera.near,this._depthMaterial.rcpCameraRange=1/(this._camera.far-this._camera.near),this._hair_depthMaterial.cameraNear=this._camera.near,this._hair_depthMaterial.rcpCameraRange=1/(this._camera.far-this._camera.near),this._renderer.setClearColor(16777215,1),bx.override("depth"),this._hair_depthMaterial.uniforms.layer.value=0,this._renderer.setRenderTarget(this._renderTarget),xx.render_type===Bx.Type.DEPTH_MAP&&this._renderer.setRenderTarget(null),this._renderer.clear(),this._renderer.render(this._scene,this._camera),this._renderer.setRenderTarget(null),bx.unoverride(),this._renderer.setClearColor(0,1)}},{key:"dispose",value:function(){ex(this)}}]),e}(),gx=function(){function e(t,n,r,i){v(this,e),i=void 0===i?1024:i,this.onUpdate=new ox;var A=nx.getHalfOrFloatOrAny(n);this._floatTexture=A!==up,this._vsmMaterial=new Ax(this._floatTexture),this._hair_vsmMaterial=new ax(this._floatTexture),bx.override_setting.shadow.face=this._vsmMaterial,bx.override_setting.shadow.hair=this._hair_vsmMaterial,this._rectRenderer=new ix(n),this._shadowMap=new rx(i,i,{minFilter:ap,magFilter:ap,format:_p,type:A}),this._light=r,this._renderer=n,this._scene=t,this._lightCamera=new Oy(-.3,.3,-.3,.3,-1,1),this._size=i,this._focusObject=t,this.shadowMapMatrix=new Km,jb.defines=this._floatTexture?{FLOAT_TEX:1}:void 0,this.blur=new hy(jb)}return w(e,[{key:"shadowMap",get:function(){return this._shadowMap.source}},{key:"size",get:function(){return this._size}},{key:"floatTexture",get:function(){return this._floatTexture}},{key:"constrain",value:function(e){this._focusObject=e}},{key:"render",value:function(){var e=this._lightCamera;e.position.copy(this._light.position),e.lookAt(new pm(0,0,0)),e.position.set(0,0,0),e.updateMatrixWorld();var t=new Km;t.copy(e.matrixWorld).invert();var n=new vm;n.setFromObject(this._focusObject),n.applyMatrix4(t),e.left=n.min.x-.01,e.right=n.max.x+.01,e.top=n.min.y-.01,e.bottom=n.max.y+.01,e.near=-n.max.z-.01,e.far=.01-n.min.z,e.updateProjectionMatrix(),this.shadowMapMatrix.copy(e.matrixWorld).invert(),this.shadowMapMatrix.multiplyMatrices(e.projectionMatrix,this.shadowMapMatrix),this._renderer.setClearColor(16777215),bx.override("shadow"),this._renderer.setRenderTarget(this._shadowMap.target),xx.render_type===Bx.Type.SHADOW_MAP&&this._renderer.setRenderTarget(null),this._renderer.render(this._scene,this._lightCamera),this._renderer.setRenderTarget(null),bx.unoverride(),this._shadowMap.swap(),this._renderer.setClearColor(0);var r=this.blur.uniforms;r.step.value.set(1/this._size,0),r.tDiffuse.value=this._shadowMap.source,this._rectRenderer.execute(this.blur,this._shadowMap.target),this._shadowMap.swap(),r.step.value.set(0,1/this._size),r.tDiffuse.value=this._shadowMap.source,xx.render_type===Bx.Type.SHADOW_MAP_BLUR?this._rectRenderer.execute(this.blur,null):this._rectRenderer.execute(this.blur,this._shadowMap.target),this._shadowMap.swap(),this.onUpdate.dispatch()}},{key:"dispose",value:function(){ex(this)}}]),e}(),mx=function(){function e(t,n){v(this,e),this._gaussianTexture=new c_(null,t,1,_p,gp,null,tp,tp,ap,ap),this.generateMipmap=!1,this._range=n,this._layers=[],this._width=t}return w(e,[{key:"gaussianLookUp",get:function(){return this._gaussianTexture}},{key:"range",get:function(){return this._range/1e3}},{key:"distanceMapping",get:function(){return 1e3/this._range}},{key:"getBlendColor",value:function(e){return e>=this._layers.length?new im:this._layers[e].blend}},{key:"addLayer",value:function(e,t){if(4===this._layers.length)throw new Error("Doesn't support more than 4 layers!");this._layers.push({gauss:new tx(e),blend:t})}},{key:"generate",value:function(){for(var e=[],t=this._range/this._width,n=this._layers.length,r=0;r<this._width;++r){for(var i=r*t,A=0;A<n;++A){var a=this._layers[A],o=a.gauss.getValueAt(i);a.blend;e.push(o)}for(A=n;A<4;++A)e.push(0)}this._gaussianTexture.image.data=new Float32Array(e),this._gaussianTexture.needsUpdate=!0}}]),e}(),vx=function(){function e(t,n,r,i,A,a){v(this,e),this._scale=a||.5,this._camera=n,this._renderer=r,this._scene=t,this.depthRenderer=i,this._floatTexType=nx.getHalfOrFloatOrAny(r),this._rectRenderer=new ix(r),this._material=new yx(A),bx.override_setting.sss.face=this._material,this._renderTarget=new rx(1,1,{type:this._floatTexType,minFilter:ap,magFilter:ap,generateMipmaps:!1,depthBuffer:!0,stencilBuffer:!1}),this.sssBlur=new hy(Jb),this.sssBlur.uniforms.sssProfileMap.value=A.sssProfile.gaussianLookUp,this.sssBlur.uniforms.color1.value=A.sssProfile.getBlendColor(1),this.sssBlur.uniforms.color2.value=A.sssProfile.getBlendColor(2),this.sssBlur.uniforms.color3.value=A.sssProfile.getBlendColor(3),this.sssBlur.uniforms.sssProfileScale.value=A.sssProfile.distanceMapping,this.sssBlur.uniforms.sssRange.value=A.sssProfile.range,this.sssBlur.depthWrite=!1,this.sssBlur.depthTest=!1}return w(e,[{key:"texture",get:function(){return this._renderTarget.source}},{key:"resize",value:function(e,t){var n=Math.ceil(e*this._scale),r=Math.ceil(t*this._scale);this._renderTarget=new rx(n,r,{type:this._floatTexType,minFilter:ap,magFilter:ap,generateMipmaps:!1,depthBuffer:!0,stencilBuffer:!1})}},{key:"render",value:function(){bx.override("sss"),this._renderer.setRenderTarget(this._renderTarget.target),this._renderer.clear(),xx.render_type===Bx.Type.SSS&&this._renderer.setRenderTarget(null),this._renderer.render(this._scene,this._camera),this._renderer.setRenderTarget(null),this._renderTarget.swap(),bx.unoverride();var e=this.sssBlur.uniforms;e.depthMap.value=this.depthRenderer.texture,e.cameraNear.value=this._camera.near,e.cameraRange.value=this._camera.far-this._camera.near,e.unprojectionMatrix.value.copy(this._camera.projectionMatrix).invert(),e.tDiffuse.value=this._renderTarget.source,e.step.value.set(1/this._renderTarget.width,0),this._rectRenderer.execute(this.sssBlur,this._renderTarget.target),this._renderTarget.swap(),e.tDiffuse.value=this._renderTarget.source,e.step.value.set(0,1/this._renderTarget.height),xx.render_type===Bx.Type.SSS_BLUR?this._rectRenderer.execute(this.sssBlur,null):this._rectRenderer.execute(this.sssBlur,this._renderTarget.target),this._renderTarget.swap()}},{key:"dispose",value:function(){ex(this)}}]),e}(),yx=function(e){_(n,e);var t=S(n);function n(e){var r;v(this,n);var i={irradianceMap:{value:e.irradianceMap},probeExposure:{value:Math.pow(2,void 0===e.probeExposure?0:e.probeExposure)},normalMap:{value:e.normalMap},shadowMap:{value:e.shadowRenderer.shadowMap},shadowMapMatrix:{value:e.shadowRenderer.shadowMapMatrix}},A=uy.merge([i,ky.lights]);return(r=t.call(this,{uniforms:A,defines:{MIN_VARIANCE:-1e-4,LIGHT_BLEED_REDUCTION:.5},lights:!0,vertexShader:Db.get("sss_vertex"),fragmentShader:Db.getInclude("include_ggx")+Db.get("sss_fragment")})).uniforms.shadowMap.value=e.shadowRenderer.shadowMap,r.extensions.derivatives=!0,r.uniforms.irradianceMap.value=e.irradianceMap,r.uniforms.normalMap.value=e.normalMap,r._shadowRenderer=e.shadowRenderer,r._shadowRenderer.onUpdate.bind(r._onShadowUpdate,C(r)),r}return w(n,[{key:"_onShadowUpdate",value:function(){this.uniforms.shadowMapMatrix.value=this._shadowRenderer.shadowMapMatrix,this.uniforms.shadowMap.value=this._shadowRenderer.shadowMap}}]),n}(hy),wx=function(e){_(n,e);var t=S(n);function n(e,r,i,A){var a;return v(this,n),(a=t.call(this))._depthTexture=i,a._focusPosition=new pm,a._focusFalloff=.5,a._focusRange=.5,a._v=new pm,a._camera=e,a._scene=r,a._textureType=A,a._strength=1,a.copy=new hy(Ib),a.blur=new hy(Xb),a.composite=new hy(qb),a._postCamera=new Oy(-1,1,1,-1,0,1),a._postScene=new u_,a._postQuad=new iy(new Fy(2,2),null),a._postScene.add(a._postQuad),a.focusRange=2,a.focusFalloff=5,a}return w(n,[{key:"depthTexture",get:function(){return this._depthTexture},set:function(e){this._depthTexture=e}},{key:"focusPosition",get:function(){return this._focusPosition},set:function(e){this._focusPosition=e}},{key:"focusFalloff",get:function(){return this._focusFalloff},set:function(e){this._focusFalloff=e,this.composite.uniforms.focusFalloff.value=e/(this._camera.far-this._camera.near)}},{key:"focusRange",get:function(){return this._focusRange},set:function(e){this._focusRange=e,this.composite.uniforms.focusRange.value=e/(this._camera.far-this._camera.near)}},{key:"strength",get:function(){return this._strength},set:function(e){this._strength=e,this.composite.uniforms.strength.value=e,this.enabled=0!==this._strength}},{key:"render",value:function(e,t,n,r,i){this._v.copy(this._focusPosition),this._v.applyMatrix4(this._camera.matrixWorldInverse),this.composite.uniforms.focusDepth.value=(-this._v.z-this._camera.near)/(this._camera.far-this._camera.near);var A=this.smallBlurRadiusTex.width,a=this.smallBlurRadiusTex.height;this._postQuad.material=this.copy,this.copy.uniforms.tDiffuse.value=n.texture,e.setRenderTarget(this.smallBlurRadiusTex2),e.render(this._postScene,this._postCamera),e.setRenderTarget(null),this._postQuad.material=this.blur,this.blur.uniforms.tDiffuse.value=this.smallBlurRadiusTex2.texture,this.blur.uniforms.sampleStep.value.x=1/A,this.blur.uniforms.sampleStep.value.y=1/a,e.setRenderTarget(this.smallBlurRadiusTex),e.render(this._postScene,this._postCamera),e.setRenderTarget(null),this.blur.uniforms.tDiffuse.value=this.smallBlurRadiusTex.texture,this.blur.uniforms.sampleStep.value.x=1.5/A,this.blur.uniforms.sampleStep.value.y=1.5/a,e.setRenderTarget(this.largeBlurRadiusTex),e.render(this._postScene,this._postCamera),e.setRenderTarget(null),this._postQuad.material=this.composite,this.composite.uniforms.depth.value=this._depthTexture,this.composite.uniforms.source.value=n.texture,this.composite.uniforms.blurred1.value=this.smallBlurRadiusTex.texture,this.composite.uniforms.blurred2.value=this.largeBlurRadiusTex.texture,e.setRenderTarget(t),e.render(this._postScene,this._postCamera),e.setRenderTarget(null)}},{key:"setSize",value:function(e,t){if(e=Math.floor(e),t=Math.floor(t),this.resolutionX!==e||this.resolutionY!==t){this.resolutionX=e,this.resolutionY=t;var n={minFilter:ap,magFilter:ap,format:_p,type:this._textureType},r=e>>1,i=t>>1;this.smallBlurRadiusTex=new cm(r,i,n),this.smallBlurRadiusTex2=new cm(r,i,n),r=e>>2,i=t>>2,this.largeBlurRadiusTex=new cm(r,i,n),this.smallBlurRadiusTex.texture.generateMipmaps=!1,this.largeBlurRadiusTex.texture.generateMipmaps=!1}}}]),n}(Rb),Bx=function(){function e(){v(this,e),this.render_type=e.Type.FINAL_COMPOSE,this.skin_material=null,this.enabled=!1,document.addEventListener("keyup",this._onKeyUp.bind(this))}return w(e,[{key:"_onKeyUp",value:function(t){if(this.enabled){switch(t.keyCode){case 48:case 96:this.render_type=e.Type.FINAL_COMPOSE,this.skin_material&&(this.skin_material.uniforms.return_stage.value=0);break;case 49:case 97:this.render_type=e.Type.DEPTH_MAP;break;case 50:case 98:this.render_type=e.Type.SHADOW_MAP;break;case 51:case 99:this.render_type=e.Type.SHADOW_MAP_BLUR;break;case 52:case 100:this.render_type=e.Type.SSS;break;case 53:case 101:this.render_type=e.Type.SSS_BLUR;break;case 54:case 102:this.render_type=e.Type.SKIN_SSS,this.skin_material&&(this.skin_material.uniforms.return_stage.value=1);break;case 55:case 103:this.render_type=e.Type.SKIN_TRANSMISSION,this.skin_material&&(this.skin_material.uniforms.return_stage.value=2);break;case 56:case 104:this.render_type=e.Type.SKIN_SHADOWDIFFUSE,this.skin_material&&(this.skin_material.uniforms.return_stage.value=3);break;default:this.render_type=e.Type.SKIN_FINAL,this.skin_material&&(this.skin_material.uniforms.return_stage.value=0)}console.log(this.render_type)}}}]),e}();Bx.Type={FINAL_COMPOSE:"FINAL_COMPOSE",DEPTH_MAP:"DEPTH_MAP",SHADOW_MAP:"SHADOW_MAP",SHADOW_MAP_BLUR:"SHADOW_MAP_BLUR",SSS:"SSS",SSS_BLUR:"SSS_BLUR",SKIN_SSS:"SKIN_SSS",SKIN_TRANSMISSION:"SKIN_TRANSMISSION",SKIN_SHADOWDIFFUSE:"SKIN_SHADOWDIFFUSE",SKIN_FINAL:"SKIN_FINAL"};var _x=function(e){_(n,e);var t=S(n);function n(e){var r;v(this,n);var i={diffuseMap:{value:null},alphaMap:{value:null},alpha_test:{value:.5},normalMap:{value:null},irradianceMap:{value:e.irradianceMap},specularMap:{value:e.specularMap},shadowMapMatrix:{value:null},shadowMap:{value:null},roughnessMap:{value:null},specular_map:{value:null},scatter_map:{value:null},tangent_shift_0:{value:.2},specular_exp_0:{value:100},tangent_shift_1:{value:0},specular_exp_1:{value:50}},A=uy.merge([i,ky.lights]);return(r=t.call(this,{uniforms:A,defines:{MIN_VARIANCE:-1e-4,LIGHT_BLEED_REDUCTION:.5,USE_TANGENT:!0},lights:!0,vertexShader:Db.get("hair_vertex"),fragmentShader:Db.getInclude("include_beckmann")+Db.get("hair_fragment")})).isHairMaterial=!0,r.type="HairMaterial",r.side=lf,r.transparent&&(r.uniforms.alpha_test.value=0),r._shadowRenderer=e.shadowRenderer,r._shadowRenderer.onUpdate.bind(r._onShadowUpdate,C(r)),r}return w(n,[{key:"_onShadowUpdate",value:function(){this.uniforms.shadowMapMatrix.value=this._shadowRenderer.shadowMapMatrix,this.uniforms.shadowMap.value=this._shadowRenderer.shadowMap}}]),n}(hy),bx=new(function(){function e(){v(this,e),this.face_mesh=null,this.hair_mesh=null,this.skybox_mesh=null,this.override_setting={depth:{face:null,hair:null},shadow:{face:null,hair:null},sss:{face:null,hair:null}},this.material_face=null,this.material_hair=null,this.skybox_hair=null,this.type=null}return w(e,[{key:"override",value:function(e){if(!(e in this.override_setting))throw"unknown override type ".concat(e);this.material_face=this.face_mesh.material,this.override_setting[e].face&&(this.face_mesh.material=this.override_setting[e].face),this.material_skybox=this.skybox_mesh.material,this.override_setting[e].face&&(this.skybox_mesh.material=this.override_setting[e].face),this.material_hair=this.hair_mesh.material,this.override_setting[e].hair?this.hair_mesh.material=this.override_setting[e].hair:this.hair_mesh.visible=!1,this.type=e}},{key:"unoverride",value:function(){var e=this.type;this.override_setting[e].hair||(this.hair_mesh.visible=!0),this.face_mesh.material=this.material_face,this.skybox_mesh.material=this.material_skybox,this.hair_mesh.material=this.material_hair,this.material_face=null,this.material_skybox=null,this.material_hair=null,this.type=null}}]),e}()),xx=new Bx;var Cx,Sx={roughness_detail:"/assets/juanfu/roughness-detail.jpg",env_irradiance:"/assets/env/lapa_4k_panorama_irradiance.hdr",env_specular:"/assets/env/lapa_4k_panorama_specular.hdr",roughness_ao_thickness:"/assets/juanfu/at.png"},Ex=function(){function e(){v(this,e)}return w(e,[{key:"get",value:function(e){return void 0===this[e]&&console.log("[ get undefined ] ".concat(e)),this[e]}},{key:"load_assets",value:function(e,t,n){var r=this;function i(e){}function A(e){console.log(e)}function a(e){r[this.key]=e.children[0].geometry,console.log("[ loaded ] ".concat(this.key)),s(u)}function o(e){"diffuse"!=this.key&&"hair_diffuse"!=this.key||(e.encoding=pg),e.wrapS=ep,e.wrapT=ep,e.minFilter=lp,"hdr"==this.postfix&&(e.minFilter=ap),e.magFilter=ap,$b&&(e.anisotropy=16),r[this.key]=e,console.log("[ loaded ] ".concat(this.key)),s(u)}var s=function(r){if(r.length_current++,r.length_current==r.length_target){console.log("[ loaded all ]");var i=Object.keys(e);n&&this.update_assets(n,i),t&&t()}}.bind(this),l=Object.keys(e),u={length_target:l.length,length_current:0};l.forEach((function(t){var n,r,s=e[t],l=s.split(".").pop();switch(t){case"model":case"hair_mesh":n=new Qb,r=a;break;case"roughness_detail":case"diffuse":case"normal":case"roughness_ao_thickness":case"roughness":case"hair_diffuse":case"hair_alpha":case"hair_normal":case"hair_roughness":case"hair_specular":case"hair_scatter":n=new eb,r=o;break;case"env_irradiance":case"env_specular":n=new Lb,r=o;break;default:throw"".concat(t," does not have corresponding loader")}n.load(s,r.bind({key:t,path:s,postfix:l}),i.bind({key:t,path:s,postfix:l}),A)}))}},{key:"update_assets",value:function(e,t){var n=this;(t=t||["model","diffuse","normal","roughness_ao_thickness","roughness"]).forEach((function(t){var r=n.get(t);if(r)switch(t){case"model":e.content.face_mesh.geometry&&e.content.face_mesh.geometry.dispose(),e.content.face_mesh.geometry=r;break;case"diffuse":e.content.sssMaterialOptions.diffuseMap=r,e.content.skinMaterial.uniforms.diffuseMap.value=r;break;case"normal":e.content.sssMaterialOptions.normalMap=r,e.content.skinMaterial.uniforms.normalMap.value=r,e.content.sssRenderer._material.uniforms.normalMap.value=r;break;case"roughness_ao_thickness":e.content.sssMaterialOptions.roughnessAOThicknessMap=r,e.content.skinMaterial.uniforms.roughnessAOThicknessMap.value=r;break;case"roughness":e.content.sssMaterialOptions.roughnessMap=r,e.content.skinMaterial.uniforms.roughnessMap.value=r;break;case"hair_mesh":e.content.hair_mesh.geometry&&e.content.hair_mesh.geometry.dispose(),r=function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1e-4;t=Math.max(t,Number.EPSILON);for(var n={},r=e.getIndex(),i=e.getAttribute("position"),A=r?r.count:i.count,a=0,o=Object.keys(e.attributes),s={},l={},u=[],c=["getX","getY","getZ","getW"],d=["setX","setY","setZ","setW"],h=0,f=o.length;h<f;h++){var p=o[h],g=e.attributes[p];s[p]=new Qv(new g.array.constructor(g.count*g.itemSize),g.itemSize,g.normalized);var m=e.morphAttributes[p];m&&(l[p]=new Qv(new m.array.constructor(m.count*m.itemSize),m.itemSize,m.normalized))}for(var v=Math.log10(1/t),y=Math.pow(10,v),w=0;w<A;w++){for(var B=r?r.getX(w):w,_="",b=0,x=o.length;b<x;b++)for(var C=o[b],S=e.getAttribute(C),E=S.itemSize,U=0;U<E;U++)_+="".concat(~~(S[c[U]](B)*y),",");if(_ in n)u.push(n[_]);else{for(var M=0,F=o.length;M<F;M++)for(var T=o[M],k=e.getAttribute(T),Q=e.morphAttributes[T],L=k.itemSize,D=s[T],I=l[T],R=0;R<L;R++){var H=c[R],P=d[R];if(D[P](a,k[H](B)),Q)for(var N=0,O=Q.length;N<O;N++)I[N][P](a,Q[N][H](B))}n[_]=a,u.push(a),a++}}var V=e.clone();for(var z in e.attributes){var G=s[z];if(V.setAttribute(z,new Qv(G.array.slice(0,a*G.itemSize),G.itemSize,G.normalized)),z in l)for(var K=0;K<l[z].length;K++){var W=l[z][K];V.morphAttributes[z][K]=new Qv(W.array.slice(0,a*W.itemSize),W.itemSize,W.normalized)}}return V.setIndex(u),V}(r),r.computeTangents(),e.content.hair_mesh.geometry=r;break;case"hair_diffuse":e.content.hair_material.uniforms.diffuseMap.value=r;break;case"hair_alpha":e.content.hair_material.uniforms.alphaMap.value=r,e.content.depthRenderer._hair_depthMaterial.uniforms.alphaMap.value=r,e.content.shadowRenderer._hair_vsmMaterial.uniforms.alphaMap.value=r;break;case"hair_normal":e.content.hair_material.uniforms.normalMap.value=r;break;case"hair_roughness":e.content.hair_material.uniforms.roughnessMap.value=r;break;case"hair_specular":e.content.hair_material.uniforms.specular_map.value=r;break;case"hair_scatter":e.content.hair_material.uniforms.scatter_map.value=r}}))}}]),e}(),Ux=new Ex,Mx=function(e){_(n,e);var t=S(n);function n(e){var r;v(this,n);var i={MIN_VARIANCE:1e-4,LIGHT_BLEED_REDUCTION:.1};e.shadowRenderer.floatTexture&&(i.VSM_FLOAT=1);var A={sssMap:{value:null},transmittanceColor:{value:new im(50,150,250)},sssTopLayerColor:{value:e.sssProfile.getBlendColor(0)},diffuseMap:{value:e.diffuseMap},normalMap:{value:e.normalMap},roughnessAOThicknessMap:{value:e.roughnessAOThicknessMap},roughnessMap:{value:e.roughnessMap},irradianceMap:{value:e.irradianceMap},specularMap:{value:e.specularMap},shadowMap:{value:e.shadowRenderer.shadowMap},shadowMapMatrix:{value:e.shadowRenderer.shadowMapMatrix},probeExposure:{value:Math.pow(2,void 0===e.probeExposure?0:e.probeExposure)},normalSpecularReflectance:{value:.027},thicknessRange:{value:e.thicknessRange||.1},roughnessMapRange:{value:void 0===e.roughnessMapRange?.5:e.roughnessMapRange},roughnessMedian:{value:void 0===e.roughnessMedian?.65:e.roughnessMedian},roughnessDetailMap:{value:e.roughnessDetailMap},roughnessDetailRange:{value:.8},specular_intensity:{value:.7},return_stage:{value:0}},a=uy.merge([A,ky.lights]);return(r=t.call(this,{uniforms:a,defines:i,lights:!0,vertexShader:Db.get("skin_vertex"),fragmentShader:Db.getInclude("include_beckmann")+Db.get("skin_fragment")})).isSkinMaterial=!0,r.type="SkinMaterial",r.uniforms.diffuseMap.value=e.diffuseMap,r.uniforms.normalMap.value=e.normalMap,r.uniforms.roughnessAOThicknessMap.value=e.roughnessAOThicknessMap,r.uniforms.roughnessMap.value=e.roughnessMap,r.uniforms.roughnessDetailMap.value=e.roughnessDetailMap,r.uniforms.irradianceMap.value=e.irradianceMap,r.uniforms.specularMap.value=e.specularMap,r.uniforms.shadowMap.value=e.shadowRenderer.shadowMap,r.uniforms.return_stage.value=0,xx.skin_material=C(r),r.extensions.derivatives=!0,r._shadowRenderer=e.shadowRenderer,r._shadowRenderer.onUpdate.bind(r._onShadowUpdate,C(r)),r}return w(n,[{key:"_onShadowUpdate",value:function(){this.uniforms.shadowMapMatrix.value=this._shadowRenderer.shadowMapMatrix,this.uniforms.shadowMap.value=this._shadowRenderer.shadowMap}}]),n}(hy),Fx=function(){function e(t){var n=this;v(this,e),this.timeScale=1,this._time=null,this.is_running=!1,this.renderer_parameters=t||{},this.renderer_parameters.alpha=this.renderer_parameters.alpha||!1,this.renderer_parameters.antialias=this.renderer_parameters.antialias||!1,this.renderer_parameters.preserveDrawingBuffer=this.renderer_parameters.preserveDrawingBuffer||!1,this.renderer=new s_({antialias:this.renderer_parameters.antialias,alpha:this.renderer_parameters.alpha,preserveDrawingBuffer:this.renderer_parameters.preserveDrawingBuffer});var r=window.devicePixelRatio;this.renderer.setPixelRatio(r),this.container=document.getElementById("webglcontainer"),this.container.appendChild(this.renderer.domElement),this.scene=new u_,this.camera=new py(46.4,this.container.clientWidth/this.container.clientHeight,1,1e4),this.camera.position.z=100,this.camera.near=.001,this.camera.far=100,this.scene.add(this.camera),this.content=new Tx(this);var i=this;this.resize_handler=function(){i._resizeCanvas()},window.addEventListener("resize",this.resize_handler),this._resizeCanvas(),setInterval((function(){n.resize_handler()}),4e3)}return w(e,[{key:"_resizeCanvas",value:function(){if(this.renderer){var e=this.container.clientWidth,t=this.container.clientHeight;if(0===e||0===t)return;this.renderer.setSize(e,t),this.renderer.domElement.style.width=e+"px",this.renderer.domElement.style.height=t+"px",this.camera.aspect=e/t,this.camera.updateProjectionMatrix(),this.content&&this.content.resize(e,t)}}},{key:"stop",value:function(){this.is_running=!1}},{key:"start",value:function(){this.is_running=!0,this._requestAnimationFrame()}},{key:"_render",value:function(){if(this.is_running){var e=(new 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e=this;e.content.face_mesh.visible=!1,e.content.hair_mesh.visible=!1,e.content.orbit._coords.set((.4*Math.random()+.3)*Math.PI,(.2*Math.random()+.3)*Math.PI,2)}},{key:"show_scene",value:function(){var e=this;e.content.face_mesh.visible=!0,e.content.hair_mesh.visible=!1,e.content.face_mesh.position.set(.5,0,0),e._resizeCanvas()}},{key:"clean_scene",value:function(){var e=this;e.content.face_mesh.geometry&&e.content.face_mesh.geometry.dispose(),e.content.face_mesh.geometry=new Gv}}]),e}(),Tx=function(){function e(t){v(this,e),this.animateLight=!0,this.probeExposure=0,this.time=0,this.shadowsInvalid=!0,this.renderer=t.renderer,this.scene=t.scene,this.camera=t.camera,this.container=t.container,this.render_target=new cm(1,1,{minFilter:ap,magFilter:ap,format:_p,type:nx.getHalfOrFloatOrAny(this.renderer),stencilBuffer:!1}),this.effectComposer=new Gb(this.renderer,this.render_target),this.effectComposer.addPass(new Kb(this.scene,this.camera)),this.dof=new wx(this.camera,this.scene,null,nx.getHalfOrFloatOrAny(this.renderer)),this.dof.focusPosition.set(0,0,0),this.dof.enabled=!1,this.effectComposer.addPass(this.dof),this.fxaa=new Ob(Yb),this.fxaa.uniforms.whitePoint.value=1.7,this.fxaa.renderToScreen=!0,this.effectComposer.addPass(this.fxaa),this.effectColor=new Ob(Wb),this.effectColor.uniforms.powRGB.value.set(1,1,1),this.effectColor.uniforms.mulRGB.value.set(.95,.97,1.02),this.effectComposer.addPass(this.effectColor),this.sssProfile=new mx(256,1.2),this.sssProfile.addLayer(.0064,new im(.2405,.4474,.6157)),this.sssProfile.addLayer(.0452,new im(.1158,.3661,.3439)),this.sssProfile.addLayer(.2719-.0516,new im(.1836,.1864,0)),this.sssProfile.addLayer(2.0062-.2719,new im(.46,0,.0402)),this.sssProfile.generate();var n=new ux(this.container);n.lookAtTarget.z=.03,n.radius=.3,n.minRadius=.05,n.maxRadius=.3,n.zoomSpeed=.05,n.mouse_constant=2e-4,dx.addComponent(this.camera,n),this.orbit=n;this.mainLight=new 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n=window.devicePixelRatio||1;e*=n,t*=n,this.fxaa&&this.fxaa.uniforms.rcpRenderTargetResolution.value.set(1/e,1/t),this.effectComposer.setSize(e,t),this.depthRenderer.resize(e,t),this.dof.depthTexture=this.depthRenderer.texture}},{key:"update",value:function(e){this.animateLight&&(this.time+=e,this.mainLight.position.set(Math.cos(5e-4*this.time),Math.sin(3e-4*this.time)+1.25,Math.sin(2e-4*this.time)+1.2),this.shadowsInvalid=!0),this.depthRenderer.render(),this.skybox.visible=!1,this.shadowsInvalid&&(this.shadowRenderer.render(),this.shadowsInvalid=!1),this.sssRenderer.render(),this.skinMaterial.uniforms.sssMap.value=this.sssRenderer.texture,this.skybox.visible=!0;var t=.01,n=this.camera.matrix.elements[8],r=this.camera.matrix.elements[9],i=this.camera.matrix.elements[10];this.dof.focusPosition.set(n*t,r*t,i*t);var 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diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/contourpy/util/_build_config.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/contourpy/util/_build_config.py
deleted file mode 100644
index 90203e22b174e0948ac567538de208b09759f533..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/contourpy/util/_build_config.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# _build_config.py.in is converted into _build_config.py during the meson build process.
-
-from __future__ import annotations
-
-
-def build_config() -> dict[str, str]:
-    """
-    Return a dictionary containing build configuration settings.
-
-    All dictionary keys and values are strings, for example ``False`` is
-    returned as ``"False"``.
-    """
-    return dict(
-        # Python settings
-        python_version="3.11",
-        python_install_dir=r"/usr/local/lib/python3.11/site-packages/",
-        python_path=r"/private/var/folders/24/8k48jl6d249_n_qfxwsl6xvm0000gn/T/build-env-h083m2g8/bin/python",
-
-        # Package versions
-        contourpy_version="1.1.0",
-        meson_version="1.1.1",
-        mesonpy_version="0.13.1",
-        pybind11_version="2.10.4",
-
-        # Misc meson settings
-        meson_backend="ninja",
-        build_dir=r"/Users/runner/work/contourpy/contourpy/.mesonpy-icm1eeob/build/lib/contourpy/util",
-        source_dir=r"/Users/runner/work/contourpy/contourpy/lib/contourpy/util",
-        cross_build="True",
-
-        # Build options
-        build_options=r"-Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md -Dvsenv=True --cross-file=/Users/runner/work/contourpy/contourpy/.mesonpy-icm1eeob/build/meson-python-cross-file.ini '--cross-file=$PWD/meson-cross.ini' --native-file=/Users/runner/work/contourpy/contourpy/.mesonpy-icm1eeob/build/meson-python-native-file.ini",
-        buildtype="release",
-        cpp_std="c++17",
-        debug="False",
-        optimization="3",
-        vsenv="True",
-        b_ndebug="if-release",
-        b_vscrt="from_buildtype",
-
-        # C++ compiler
-        compiler_name="clang",
-        compiler_version="13.0.0",
-        linker_id="ld64",
-        compile_command="c++ -arch arm64",
-
-        # Host machine
-        host_cpu="arm64",
-        host_cpu_family="aarch64",
-        host_cpu_endian="little",
-        host_cpu_system="Darwin",
-
-        # Build machine, same as host machine if not a cross_build
-        build_cpu="x86_64",
-        build_cpu_family="x86_64",
-        build_cpu_endian="little",
-        build_cpu_system="darwin",
-    )
diff --git a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/evaluation/f1_score.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/evaluation/f1_score.py
deleted file mode 100644
index 92db643188cdc560a64688ca1af0cbfc048b8cba..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/evaluation/f1_score.py
+++ /dev/null
@@ -1,78 +0,0 @@
-""" 
-@author: Zhigang Jiang
-@time: 2022/01/28
-@description:
-Holistic 3D Vision Challenge on General Room Layout Estimation Track Evaluation Package
-Reference: https://github.com/bertjiazheng/indoor-layout-evaluation
-"""
-
-from scipy.optimize import linear_sum_assignment
-import numpy as np
-import scipy
-
-HEIGHT, WIDTH = 512, 1024
-MAX_DISTANCE = np.sqrt(HEIGHT**2 + WIDTH**2)
-
-
-def f1_score_2d(gt_corners, dt_corners, thresholds):
-    distances = scipy.spatial.distance.cdist(gt_corners, dt_corners)
-    return eval_junctions(distances, thresholds=thresholds)
-
-
-def eval_junctions(distances, thresholds=5):
-    thresholds = thresholds if isinstance(thresholds, tuple) or isinstance(
-        thresholds, list) else list([thresholds])
-
-    num_gts, num_preds = distances.shape
-
-    # filter the matches between ceiling-wall and floor-wall junctions
-    mask = np.zeros_like(distances, dtype=np.bool)
-    mask[:num_gts//2, :num_preds//2] = True
-    mask[num_gts//2:, num_preds//2:] = True
-    distances[~mask] = np.inf
-
-    # F-measure under different thresholds
-    Fs = []
-    Ps = []
-    Rs = []
-    for threshold in thresholds:
-        distances_temp = distances.copy()
-
-        # filter the mis-matched pairs
-        distances_temp[distances_temp > threshold] = np.inf
-
-        # remain the rows and columns that contain non-inf elements
-        distances_temp = distances_temp[:, np.any(np.isfinite(distances_temp), axis=0)]
-
-        if np.prod(distances_temp.shape) == 0:
-            Fs.append(0)
-            Ps.append(0)
-            Rs.append(0)
-            continue
-
-        distances_temp = distances_temp[np.any(np.isfinite(distances_temp), axis=1), :]
-
-        # solve the bipartite graph matching problem
-        row_ind, col_ind = linear_sum_assignment_with_inf(distances_temp)
-        true_positive = np.sum(np.isfinite(distances_temp[row_ind, col_ind]))
-
-        # compute precision and recall
-        precision = true_positive / num_preds
-        recall = true_positive / num_gts
-
-        # compute F measure
-        Fs.append(2 * precision * recall / (precision + recall))
-        Ps.append(precision)
-        Rs.append(recall)
-
-    return Fs, Ps, Rs
-
-
-def linear_sum_assignment_with_inf(cost_matrix):
-    """
-    Deal with linear_sum_assignment with inf according to
-    https://github.com/scipy/scipy/issues/6900#issuecomment-451735634
-    """
-    cost_matrix = np.copy(cost_matrix)
-    cost_matrix[np.isinf(cost_matrix)] = MAX_DISTANCE
-    return linear_sum_assignment(cost_matrix)
\ No newline at end of file
diff --git a/spaces/DeividasM/whisper-medium-lt/app.py b/spaces/DeividasM/whisper-medium-lt/app.py
deleted file mode 100644
index c88312162c1a690f6494fd2bbdf99d55b3250b63..0000000000000000000000000000000000000000
--- a/spaces/DeividasM/whisper-medium-lt/app.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import torch
-
-import gradio as gr
-import pytube as pt
-from transformers import pipeline
-from huggingface_hub import model_info
-
-MODEL_NAME = "DeividasM/whisper-medium-lt" #this always needs to stay in line 8 :D sorry for the hackiness
-lang = "lt"
-
-device = 0 if torch.cuda.is_available() else "cpu"
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    device=device,
-)
-
-pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")
-
-def transcribe(microphone, file_upload):
-    warn_output = ""
-    if (microphone is not None) and (file_upload is not None):
-        warn_output = (
-            "WARNING: You've uploaded an audio file and used the microphone. "
-            "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
-        )
-
-    elif (microphone is None) and (file_upload is None):
-        return "ERROR: You have to either use the microphone or upload an audio file"
-
-    file = microphone if microphone is not None else file_upload
-
-    text = pipe(file)["text"]
-
-    return warn_output + text
-
-
-def _return_yt_html_embed(yt_url):
-    video_id = yt_url.split("?v=")[-1]
-    HTML_str = (
-        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
-        " </center>"
-    )
-    return HTML_str
-
-
-def yt_transcribe(yt_url):
-    yt = pt.YouTube(yt_url)
-    html_embed_str = _return_yt_html_embed(yt_url)
-    stream = yt.streams.filter(only_audio=True)[0]
-    stream.download(filename="audio.mp3")
-
-    text = pipe("audio.mp3")["text"]
-
-    return html_embed_str, text
-
-
-demo = gr.Blocks()
-
-mf_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
-        gr.inputs.Audio(source="upload", type="filepath", optional=True),
-    ],
-    outputs="text",
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Demo: Transcribe Audio",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
-)
-
-yt_transcribe = gr.Interface(
-    fn=yt_transcribe,
-    inputs=[gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")],
-    outputs=["html", "text"],
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Demo: Transcribe YouTube",
-    description=(
-        "Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:"
-        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of"
-        " arbitrary length."
-    ),
-    allow_flagging="never",
-)
-
-with demo:
-    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])
-
-demo.launch(enable_queue=True)
diff --git a/spaces/DenniSciFi/IconAutomation/icon_automation_copy.py b/spaces/DenniSciFi/IconAutomation/icon_automation_copy.py
deleted file mode 100644
index 65c4783b74be77898198eeb5cb4b6d6e0ff4985e..0000000000000000000000000000000000000000
--- a/spaces/DenniSciFi/IconAutomation/icon_automation_copy.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import requests
-
-# Set Notion API token and database ID
-NOTION_TOKEN = "secret_Kg8afS2dttLpGvuQ07pbmrJorjl3krgzubpll0iVCv9"
-DATABASE_ID = "b617878a8f284379a916ba6f2200654f"
-
-# Set headers for Notion API requests
-headers = {
-    "Authorization": "Bearer " + NOTION_TOKEN,
-    "Notion-Version": "2022-06-28",
-    "content-type": "application/json"
-}
-
-
-def get_organization_page(notion_url):
-    """
-    Query the Notion database for the organization page with the specified URL.
-
-    :param notion_url: The URL of the organization page in the Notion database.
-    :return: The organization page object if found, otherwise None.
-    """
-
-    # Construct the API endpoint URL using the global constant DATABASE_ID
-    url = f"https://api.notion.com/v1/databases/{DATABASE_ID}/query"
-
-    # Define the query filter for the Notion API request
-    data = {
-        "filter": {
-            "property": "URL",
-            "url": {
-                "equals": notion_url
-            }
-        }
-    }
-
-    # Send a POST request to the Notion API with the constructed URL, query filter, and headers
-    response = requests.post(url, json=data, headers=headers)
-    response_json = response.json()
-
-    # # Print the API response for debugging purposes
-    print("API response:", response_json)
-
-    results = response_json["results"]
-
-    # Return the first organization page object in the results array, or None if there are no results
-    if len(results) > 0:
-        return results[0]
-    else:
-        return None
-
-
-def update_opportunity_icon(opportunity_page_id, image_url):
-    """
-    Update the icon of a specific opportunity page with an image URL.
-
-    :param opportunity_page_id: The ID of the opportunity page in the Notion database.
-    :param image_url: The URL of the new image to be set as the icon.
-    :return: True if the update was successful, False otherwise.
-    """
-
-    # Set the URL of the Notion API endpoint for updating the page with the given ID
-    url = f"https://api.notion.com/v1/pages/{opportunity_page_id}"
-
-    # Define the data to be sent in the request body, including the new image URL
-    data = {
-        "icon": {
-            "type": "external",
-            "external": {
-                "url": image_url
-            }
-        }
-    }
-
-    # Send a PATCH request to the Notion API to update the page with the new image URL
-    response = requests.patch(url, json=data, headers=headers)
-
-    # Return True if the response status code indicates success, False otherwise
-    return response.status_code == 200
-
-
-def update_related_opportunities_icons(notion_url, image_url):
-    """
-    Update the icons of all opportunities related to an organization page.
-
-    :param notion_url: The URL of the organization page in the Notion database.
-    :param image_url: The URL of the new image to be set as the icon.
-    """
-
-    # Get the organization page object from the Notion database using the URL
-    organization_page = get_organization_page(notion_url)
-
-    # If the organization page is found
-    if organization_page:
-        # Extract the list of related opportunities from the page object
-        related_opportunities = organization_page["properties"]["Related opportunities"]["relation"]
-
-        # For each related opportunity
-        for opportunity in related_opportunities:
-            # Get the opportunity page ID
-            opportunity_page_id = opportunity["id"]
-
-            # Try to update the opportunity icon with the new image URL using the update_opportunity_icon() function
-            if update_opportunity_icon(opportunity_page_id, image_url):
-                print(f"Updated icon for opportunity page: {opportunity_page_id}")
-            else:
-                print(f"Failed to update icon for opportunity page: {opportunity_page_id}")
-    else:
-        print("Organization page not found.")
diff --git a/spaces/Detomo/CuteRobot/Build/Jammo Robot WEBGL.framework.js b/spaces/Detomo/CuteRobot/Build/Jammo Robot WEBGL.framework.js
deleted file mode 100644
index d391a5c74d2a242418805e03cae75b561f7fba52..0000000000000000000000000000000000000000
--- a/spaces/Detomo/CuteRobot/Build/Jammo Robot WEBGL.framework.js	
+++ /dev/null
@@ -1,5 +0,0 @@
-function unityFramework(Module) {
-var Module=typeof Module!=="undefined"?Module:{};
-function Pointer_stringify(s,len){warnOnce("The JavaScript function 'Pointer_stringify(ptrToSomeCString)' is obsoleted and will be removed in a future Unity version. Please call 'UTF8ToString(ptrToSomeCString)' instead.");return UTF8ToString(s,len)}Module["Pointer_stringify"]=Pointer_stringify;var stackTraceReference="(^|\\n)(\\s+at\\s+|)jsStackTrace(\\s+\\(|@)([^\\n]+):\\d+:\\d+(\\)|)(\\n|$)";var stackTraceReferenceMatch=jsStackTrace().match(new RegExp(stackTraceReference));if(stackTraceReferenceMatch)Module.stackTraceRegExp=new RegExp(stackTraceReference.replace("([^\\n]+)",stackTraceReferenceMatch[4].replace(/[\\^${}[\]().*+?|]/g,"\\$&")).replace("jsStackTrace","[^\\n]+"));var abort=function(what){if(ABORT)return;ABORT=true;EXITSTATUS=1;if(typeof ENVIRONMENT_IS_PTHREAD!=="undefined"&&ENVIRONMENT_IS_PTHREAD)console.error("Pthread aborting at "+(new Error).stack);if(what!==undefined){out(what);err(what);what=JSON.stringify(what)}else{what=""}var message="abort("+what+") at "+stackTrace();if(Module.abortHandler&&Module.abortHandler(message))return;throw message};Module["SetFullscreen"]=function(fullscreen){if(typeof runtimeInitialized==="undefined"||!runtimeInitialized){console.log("Runtime not initialized yet.")}else if(typeof JSEvents==="undefined"){console.log("Player not loaded yet.")}else{var tmp=JSEvents.canPerformEventHandlerRequests;JSEvents.canPerformEventHandlerRequests=function(){return 1};Module.ccall("SetFullscreen",null,["number"],[fullscreen]);JSEvents.canPerformEventHandlerRequests=tmp}};if(typeof ENVIRONMENT_IS_PTHREAD==="undefined"||!ENVIRONMENT_IS_PTHREAD){Module["preRun"].push(function(){var unityFileSystemInit=Module["unityFileSystemInit"]||function(){FS.mkdir("/idbfs");FS.mount(IDBFS,{},"/idbfs");Module.addRunDependency("JS_FileSystem_Mount");FS.syncfs(true,function(err){if(err)console.log("IndexedDB is not available. Data will not persist in cache and PlayerPrefs will not be saved.");Module.removeRunDependency("JS_FileSystem_Mount")})};unityFileSystemInit()})}var videoInputDevices=[];var videoInputDevicesEnumerated=false;var removeEnumerateMediaDevicesRunDependency;var enumerateWatchdog=null;function matchToOldDevice(newDevice){var oldDevices=Object.keys(videoInputDevices);for(var i=0;i<oldDevices.length;++i){var old=videoInputDevices[oldDevices[i]];if(old.deviceId&&old.deviceId==newDevice.deviceId)return old}for(var i=0;i<oldDevices.length;++i){var old=videoInputDevices[oldDevices[i]];if(old==newDevice)return old}for(var i=0;i<oldDevices.length;++i){var old=videoInputDevices[oldDevices[i]];if(old.label&&old.label==newDevice.label)return old}for(var i=0;i<oldDevices.length;++i){var old=videoInputDevices[oldDevices[i]];if(old.groupId&&old.kind&&old.groupId==newDevice.groupId&&old.kind==newDevice.kind)return old}}function assignNewVideoInputId(){for(var i=0;;++i){if(!videoInputDevices[i])return i}}function updateVideoInputDevices(devices){removeEnumerateMediaDevicesRunDependency();videoInputDevices=[];var retainedDevices={};var newDevices=[];devices.forEach(function(device){if(device.kind==="videoinput"){var oldDevice=matchToOldDevice(device);if(oldDevice){retainedDevices[oldDevice.id]=oldDevice}else{newDevices.push(device)}}});videoInputDevices=retainedDevices;newDevices.forEach(function(device){if(!device.id){device.id=assignNewVideoInputId();device.name=device.label||"Video input #"+(device.id+1);device.isFrontFacing=device.name.toLowerCase().includes("front")||!device.name.toLowerCase().includes("front")&&!device.name.toLowerCase().includes("back");videoInputDevices[device.id]=device}})}function enumerateMediaDeviceList(){if(!videoInputDevices)return;navigator.mediaDevices.enumerateDevices().then(function(devices){updateVideoInputDevices(devices);videoInputDevicesEnumerated=true}).catch(function(e){console.warn("Unable to enumerate media devices: "+e+"\nWebcams will not be available.");disableAccessToMediaDevices()});if(/Firefox/.test(navigator.userAgent)){setTimeout(enumerateMediaDeviceList,6e4);warnOnce("Applying workaround to Firefox bug https://bugzilla.mozilla.org/show_bug.cgi?id=1397977")}}function disableAccessToMediaDevices(){if(navigator.mediaDevices&&navigator.mediaDevices.removeEventListener){navigator.mediaDevices.removeEventListener("devicechange",enumerateMediaDeviceList)}videoInputDevices=null}Module["disableAccessToMediaDevices"]=disableAccessToMediaDevices;if(!navigator.mediaDevices){console.warn("navigator.mediaDevices not supported by this browser. Webcam access will not be available."+(location.protocol=="https:"?"":" Try hosting the page over HTTPS, because some browsers disable webcam access when insecure HTTP is being used."));disableAccessToMediaDevices()}else if(typeof ENVIRONMENT_IS_PTHREAD==="undefined"||!ENVIRONMENT_IS_PTHREAD)setTimeout(function(){try{addRunDependency("enumerateMediaDevices");removeEnumerateMediaDevicesRunDependency=function(){if(enumerateWatchdog!==null)clearTimeout(enumerateWatchdog);removeRunDependency("enumerateMediaDevices");if(navigator.mediaDevices)console.log("navigator.mediaDevices support available");removeEnumerateMediaDevicesRunDependency=function(){}};enumerateMediaDeviceList();enumerateWatchdog=setTimeout(removeEnumerateMediaDevicesRunDependency,1e3);navigator.mediaDevices.addEventListener("devicechange",enumerateMediaDeviceList)}catch(e){console.warn("Unable to enumerate media devices: "+e);disableAccessToMediaDevices()}},0);function SendMessage(gameObject,func,param){if(param===undefined)Module.ccall("SendMessage",null,["string","string"],[gameObject,func]);else if(typeof param==="string")Module.ccall("SendMessageString",null,["string","string","string"],[gameObject,func,param]);else if(typeof param==="number")Module.ccall("SendMessageFloat",null,["string","string","number"],[gameObject,func,param]);else throw""+param+" is does not have a type which is supported by SendMessage."}Module["SendMessage"]=SendMessage;var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram="./this.program";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window==="object";ENVIRONMENT_IS_WORKER=typeof importScripts==="function";ENVIRONMENT_IS_NODE=typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory="";function locateFile(path){if(Module["locateFile"]){return Module["locateFile"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require("path").dirname(scriptDirectory)+"/"}else{scriptDirectory=__dirname+"/"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require("fs");if(!nodePath)nodePath=require("path");filename=nodePath["normalize"](filename);return nodeFS["readFileSync"](filename,binary?null:"utf8")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process["argv"].length>1){thisProgram=process["argv"][1].replace(/\\/g,"/")}arguments_=process["argv"].slice(2);if(typeof module!=="undefined"){module["exports"]=Module}process["on"]("uncaughtException",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process["on"]("unhandledRejection",abort);quit_=function(status){process["exit"](status)};Module["inspect"]=function(){return"[Emscripten Module object]"}}else if(ENVIRONMENT_IS_SHELL){if(typeof read!="undefined"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer==="function"){return new Uint8Array(readbuffer(f))}data=read(f,"binary");assert(typeof data==="object");return data};if(typeof scriptArgs!="undefined"){arguments_=scriptArgs}else if(typeof arguments!="undefined"){arguments_=arguments}if(typeof quit==="function"){quit_=function(status){quit(status)}}if(typeof print!=="undefined"){if(typeof console==="undefined")console={};console.log=print;console.warn=console.error=typeof printErr!=="undefined"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(typeof document!=="undefined"&&document.currentScript){scriptDirectory=document.currentScript.src}if(scriptDirectory.indexOf("blob:")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1)}else{scriptDirectory=""}{read_=function(url){var xhr=new XMLHttpRequest;xhr.open("GET",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function(url){var xhr=new XMLHttpRequest;xhr.open("GET",url,false);xhr.responseType="arraybuffer";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,true);xhr.responseType="arraybuffer";xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module["print"]||console.log.bind(console);var err=Module["printErr"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module["arguments"])arguments_=Module["arguments"];if(Module["thisProgram"])thisProgram=Module["thisProgram"];if(Module["quit"])quit_=Module["quit"];var STACK_ALIGN=16;function alignMemory(size,factor){if(!factor)factor=STACK_ALIGN;return Math.ceil(size/factor)*factor}function warnOnce(text){if(!warnOnce.shown)warnOnce.shown={};if(!warnOnce.shown[text]){warnOnce.shown[text]=1;err(text)}}var tempRet0=0;var setTempRet0=function(value){tempRet0=value};var getTempRet0=function(){return tempRet0};var wasmBinary;if(Module["wasmBinary"])wasmBinary=Module["wasmBinary"];var noExitRuntime=Module["noExitRuntime"]||true;if(typeof WebAssembly!=="object"){abort("no native wasm support detected")}var wasmMemory;var ABORT=false;var EXITSTATUS;function assert(condition,text){if(!condition){abort("Assertion failed: "+text)}}function getCFunc(ident){var func=Module["_"+ident];assert(func,"Cannot call unknown function "+ident+", make sure it is exported");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={"string":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},"array":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType==="string")return UTF8ToString(ret);if(returnType==="boolean")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i<args.length;i++){var converter=toC[argTypes[i]];if(converter){if(stack===0)stack=stackSave();cArgs[i]=converter(args[i])}else{cArgs[i]=args[i]}}}var ret=func.apply(null,cArgs);ret=convertReturnValue(ret);if(stack!==0)stackRestore(stack);return ret}function cwrap(ident,returnType,argTypes,opts){argTypes=argTypes||[];var numericArgs=argTypes.every(function(type){return type==="number"});var numericRet=returnType!=="string";if(numericRet&&numericArgs&&!opts){return getCFunc(ident)}return function(){return ccall(ident,returnType,argTypes,arguments,opts)}}var UTF8Decoder=typeof TextDecoder!=="undefined"?new TextDecoder("utf8"):undefined;function UTF8ArrayToString(heap,idx,maxBytesToRead){var endIdx=idx+maxBytesToRead;var endPtr=idx;while(heap[endPtr]&&!(endPtr>=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str="";while(idx<endPtr){var u0=heap[idx++];if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i<str.length;++i){var u=str.charCodeAt(i);if(u>=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function allocateUTF8(str){var size=lengthBytesUTF8(str)+1;var ret=_malloc(size);if(ret)stringToUTF8Array(str,HEAP8,ret,size);return ret}function allocateUTF8OnStack(str){var size=lengthBytesUTF8(str)+1;var ret=stackAlloc(size);stringToUTF8Array(str,HEAP8,ret,size);return ret}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}function writeAsciiToMemory(str,buffer,dontAddNull){for(var i=0;i<str.length;++i){HEAP8[buffer++>>0]=str.charCodeAt(i)}if(!dontAddNull)HEAP8[buffer>>0]=0}function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module["HEAP8"]=HEAP8=new Int8Array(buf);Module["HEAP16"]=HEAP16=new Int16Array(buf);Module["HEAP32"]=HEAP32=new Int32Array(buf);Module["HEAPU8"]=HEAPU8=new Uint8Array(buf);Module["HEAPU16"]=HEAPU16=new Uint16Array(buf);Module["HEAPU32"]=HEAPU32=new Uint32Array(buf);Module["HEAPF32"]=HEAPF32=new Float32Array(buf);Module["HEAPF64"]=HEAPF64=new Float64Array(buf)}var INITIAL_MEMORY=Module["INITIAL_MEMORY"]||33554432;var wasmTable;var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module["preRun"]){if(typeof Module["preRun"]=="function")Module["preRun"]=[Module["preRun"]];while(Module["preRun"].length){addOnPreRun(Module["preRun"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;if(!Module["noFSInit"]&&!FS.init.initialized)FS.init();TTY.init();SOCKFS.root=FS.mount(SOCKFS,{},null);PIPEFS.root=FS.mount(PIPEFS,{},null);callRuntimeCallbacks(__ATINIT__)}function preMain(){FS.ignorePermissions=false;callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module["postRun"]){if(typeof Module["postRun"]=="function")Module["postRun"]=[Module["postRun"]];while(Module["postRun"].length){addOnPostRun(Module["postRun"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnInit(cb){__ATINIT__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function getUniqueRunDependency(id){return id}function addRunDependency(id){runDependencies++;if(Module["monitorRunDependencies"]){Module["monitorRunDependencies"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module["monitorRunDependencies"]){Module["monitorRunDependencies"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module["preloadedImages"]={};Module["preloadedAudios"]={};function abort(what){if(Module["onAbort"]){Module["onAbort"](what)}what+="";err(what);ABORT=true;EXITSTATUS=1;what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.";var e=new WebAssembly.RuntimeError(what);throw e}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return filename.startsWith(dataURIPrefix)}function isFileURI(filename){return filename.startsWith("file://")}var wasmBinaryFile="build.wasm";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(file){try{if(file==wasmBinaryFile&&wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(file)}else{throw"both async and sync fetching of the wasm failed"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)){if(typeof fetch==="function"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response["ok"]){throw"failed to load wasm binary file at '"+wasmBinaryFile+"'"}return response["arrayBuffer"]()}).catch(function(){return getBinary(wasmBinaryFile)})}else{if(readAsync){return new Promise(function(resolve,reject){readAsync(wasmBinaryFile,function(response){resolve(new Uint8Array(response))},reject)})}}}return Promise.resolve().then(function(){return getBinary(wasmBinaryFile)})}function createWasm(){var info={"a":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module["asm"]=exports;wasmMemory=Module["asm"]["$h"];updateGlobalBufferAndViews(wasmMemory.buffer);wasmTable=Module["asm"]["yi"];addOnInit(Module["asm"]["ai"]);removeRunDependency("wasm-instantiate")}addRunDependency("wasm-instantiate");function receiveInstantiationResult(result){receiveInstance(result["instance"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){var result=WebAssembly.instantiate(binary,info);return result}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming==="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch==="function"){return fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiationResult,function(reason){err("wasm streaming compile failed: "+reason);err("falling back to ArrayBuffer instantiation");return instantiateArrayBuffer(receiveInstantiationResult)})})}else{return instantiateArrayBuffer(receiveInstantiationResult)}}if(Module["instantiateWasm"]){try{var exports=Module["instantiateWasm"](info,receiveInstance);return exports}catch(e){err("Module.instantiateWasm callback failed with error: "+e);return false}}instantiateAsync();return{}}var tempDouble;var tempI64;var ASM_CONSTS={4241064:function(){return Module.webglContextAttributes.premultipliedAlpha},4241125:function(){return Module.webglContextAttributes.preserveDrawingBuffer},4241189:function(){return Module.webglContextAttributes.powerPreference}};function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback=="function"){callback(Module);continue}var func=callback.func;if(typeof func==="number"){if(callback.arg===undefined){(function(){dynCall_v.call(null,func)})()}else{(function(a1){dynCall_vi.apply(null,[func,a1])})(callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}function demangle(func){return func}function demangleAll(text){var regex=/\b_Z[\w\d_]+/g;return text.replace(regex,function(x){var y=demangle(x);return x===y?x:y+" ["+x+"]"})}function dynCallLegacy(sig,ptr,args){var f=Module["dynCall_"+sig];return args&&args.length?f.apply(null,[ptr].concat(args)):f.call(null,ptr)}function dynCall(sig,ptr,args){return dynCallLegacy(sig,ptr,args)}function jsStackTrace(){var error=new Error;if(!error.stack){try{throw new Error}catch(e){error=e}if(!error.stack){return"(no stack trace available)"}}return error.stack.toString()}var runtimeKeepaliveCounter=0;function keepRuntimeAlive(){return noExitRuntime||runtimeKeepaliveCounter>0}function stackTrace(){var js=jsStackTrace();if(Module["extraStackTrace"])js+="\n"+Module["extraStackTrace"]();return demangleAll(js)}var JS_Accelerometer=null;var JS_Accelerometer_callback=0;function _JS_Accelerometer_IsRunning(){return JS_Accelerometer&&JS_Accelerometer.activated||JS_Accelerometer_callback!=0}var JS_Accelerometer_multiplier=1;var JS_Accelerometer_lastValue={x:0,y:0,z:0};function JS_Accelerometer_eventHandler(){JS_Accelerometer_lastValue={x:JS_Accelerometer.x*JS_Accelerometer_multiplier,y:JS_Accelerometer.y*JS_Accelerometer_multiplier,z:JS_Accelerometer.z*JS_Accelerometer_multiplier};if(JS_Accelerometer_callback!=0)dynCall_vfff(JS_Accelerometer_callback,JS_Accelerometer_lastValue.x,JS_Accelerometer_lastValue.y,JS_Accelerometer_lastValue.z)}var JS_Accelerometer_frequencyRequest=0;var JS_Accelerometer_frequency=0;var JS_LinearAccelerationSensor_callback=0;var JS_GravitySensor_callback=0;var JS_Gyroscope_callback=0;function JS_ComputeGravity(accelerometerValue,linearAccelerationValue){var difference={x:accelerometerValue.x-linearAccelerationValue.x,y:accelerometerValue.y-linearAccelerationValue.y,z:accelerometerValue.z-linearAccelerationValue.z};var differenceMagnitudeSq=difference.x*difference.x+difference.y*difference.y+difference.z*difference.z;var sum={x:accelerometerValue.x+linearAccelerationValue.x,y:accelerometerValue.y+linearAccelerationValue.y,z:accelerometerValue.z+linearAccelerationValue.z};var sumMagnitudeSq=sum.x*sum.x+sum.y*sum.y+sum.z*sum.z;return differenceMagnitudeSq<=sumMagnitudeSq?difference:sum}function JS_DeviceMotion_eventHandler(event){var accelerometerValue={x:event.accelerationIncludingGravity.x*JS_Accelerometer_multiplier,y:event.accelerationIncludingGravity.y*JS_Accelerometer_multiplier,z:event.accelerationIncludingGravity.z*JS_Accelerometer_multiplier};if(JS_Accelerometer_callback!=0)dynCall_vfff(JS_Accelerometer_callback,accelerometerValue.x,accelerometerValue.y,accelerometerValue.z);var linearAccelerationValue={x:event.acceleration.x*JS_Accelerometer_multiplier,y:event.acceleration.y*JS_Accelerometer_multiplier,z:event.acceleration.z*JS_Accelerometer_multiplier};if(JS_LinearAccelerationSensor_callback!=0)dynCall_vfff(JS_LinearAccelerationSensor_callback,linearAccelerationValue.x,linearAccelerationValue.y,linearAccelerationValue.z);if(JS_GravitySensor_callback!=0){var gravityValue=JS_ComputeGravity(accelerometerValue,linearAccelerationValue);dynCall_vfff(JS_GravitySensor_callback,gravityValue.x,gravityValue.y,gravityValue.z)}if(JS_Gyroscope_callback!=0){var degToRad=Math.PI/180;dynCall_vfff(JS_Gyroscope_callback,event.rotationRate.alpha*degToRad,event.rotationRate.beta*degToRad,event.rotationRate.gamma*degToRad)}}var JS_DeviceSensorPermissions=0;function JS_RequestDeviceSensorPermissions(permissions){if(permissions&1){if(typeof DeviceOrientationEvent.requestPermission==="function"){DeviceOrientationEvent.requestPermission().then(function(permissionState){if(permissionState==="granted"){JS_DeviceSensorPermissions&=~1}else{warnOnce("DeviceOrientationEvent permission not granted")}}).catch(function(err){warnOnce(err);JS_DeviceSensorPermissions|=1})}}if(permissions&2){if(typeof DeviceMotionEvent.requestPermission==="function"){DeviceMotionEvent.requestPermission().then(function(permissionState){if(permissionState==="granted"){JS_DeviceSensorPermissions&=~2}else{warnOnce("DeviceMotionEvent permission not granted")}}).catch(function(err){warnOnce(err);JS_DeviceSensorPermissions|=2})}}}function JS_DeviceMotion_add(){if(JS_Accelerometer_callback==0&&JS_LinearAccelerationSensor_callback==0&&JS_GravitySensor_callback==0&&JS_Gyroscope_callback==0){JS_RequestDeviceSensorPermissions(2);window.addEventListener("devicemotion",JS_DeviceMotion_eventHandler)}}function JS_DefineAccelerometerMultiplier(){var g=9.80665;JS_Accelerometer_multiplier=/(iPhone|iPad|Macintosh)/i.test(navigator.userAgent)?1/g:-1/g}function _JS_Accelerometer_Start(callback,frequency){JS_DefineAccelerometerMultiplier();if(typeof Accelerometer==="undefined"){JS_DeviceMotion_add();if(callback!=0)JS_Accelerometer_callback=callback;return}if(callback!=0)JS_Accelerometer_callback=callback;function InitializeAccelerometer(frequency){JS_Accelerometer=new Accelerometer({frequency:frequency,referenceFrame:"device"});JS_Accelerometer.addEventListener("reading",JS_Accelerometer_eventHandler);JS_Accelerometer.addEventListener("error",function(e){warnOnce(e.error?e.error:e)});JS_Accelerometer.start();JS_Accelerometer_frequency=frequency}if(JS_Accelerometer){if(JS_Accelerometer_frequency!=frequency){JS_Accelerometer.stop();JS_Accelerometer.removeEventListener("reading",JS_Accelerometer_eventHandler);InitializeAccelerometer(frequency)}}else if(JS_Accelerometer_frequencyRequest!=0){JS_Accelerometer_frequencyRequest=frequency}else{JS_Accelerometer_frequencyRequest=frequency;navigator.permissions.query({name:"accelerometer"}).then(function(result){if(result.state==="granted"){InitializeAccelerometer(JS_Accelerometer_frequencyRequest)}else{warnOnce("No permission to use Accelerometer.")}JS_Accelerometer_frequencyRequest=0})}}function JS_DeviceMotion_remove(){if(JS_Accelerometer_callback==0&&JS_LinearAccelerationSensor_callback==0&&JS_GravitySensor_callback==0&&JS_Gyroscope_callback==0){window.removeEventListener("devicemotion",JS_DeviceOrientation_eventHandler)}}function _JS_Accelerometer_Stop(){if(JS_Accelerometer){if(typeof GravitySensor!=="undefined"||JS_GravitySensor_callback==0){JS_Accelerometer.stop();JS_Accelerometer.removeEventListener("reading",JS_Accelerometer_eventHandler);JS_Accelerometer=null}JS_Accelerometer_callback=0;JS_Accelerometer_frequency=0}else if(JS_Accelerometer_callback!=0){JS_Accelerometer_callback=0;JS_DeviceMotion_remove()}}function _JS_Cursor_SetImage(ptr,length){var binary="";for(var i=0;i<length;i++)binary+=String.fromCharCode(HEAPU8[ptr+i]);Module.canvas.style.cursor="url(data:image/cur;base64,"+btoa(binary)+"),default"}function _JS_Cursor_SetShow(show){Module.canvas.style.cursor=show?"default":"none"}function jsDomCssEscapeId(id){if(typeof window.CSS!=="undefined"&&typeof window.CSS.escape!=="undefined"){return window.CSS.escape(id)}return id.replace(/(#|\.|\+|\[|\]|\(|\)|\{|\})/g,"\\$1")}function _JS_DOM_MapViewportCoordinateToElementLocalCoordinate(viewportX,viewportY,targetX,targetY){var canvasId=Module["canvas"]?Module["canvas"].id:"unity-canvas";var canvasSelector="#"+jsDomCssEscapeId(canvasId);var canvas=document.querySelector(canvasSelector);var rect=canvas.getBoundingClientRect();HEAPU32[targetX>>2]=viewportX-rect.left;HEAPU32[targetY>>2]=viewportY-rect.top}function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _JS_DOM_UnityCanvasSelector(){if(!_JS_DOM_UnityCanvasSelector.ptr){var canvasId=Module["canvas"]?Module["canvas"].id:"unity-canvas";var canvasSelector="#"+jsDomCssEscapeId(canvasId);_JS_DOM_UnityCanvasSelector.ptr=stringToNewUTF8(canvasSelector)}return _JS_DOM_UnityCanvasSelector.ptr}var fs={numPendingSync:0,syncInternal:1e3,syncInProgress:false,sync:function(onlyPendingSync){if(onlyPendingSync){if(fs.numPendingSync==0)return}else if(fs.syncInProgress){fs.numPendingSync++;return}fs.syncInProgress=true;FS.syncfs(false,function(err){fs.syncInProgress=false});fs.numPendingSync=0}};function _JS_FileSystem_Initialize(){Module.setInterval(function(){fs.sync(true)},fs.syncInternal)}function _JS_FileSystem_Sync(){fs.sync(false)}var JS_GravitySensor=null;function _JS_GravitySensor_IsRunning(){return typeof GravitySensor!=="undefined"?JS_GravitySensor&&JS_GravitySensor.activated:JS_GravitySensor_callback!=0}function JS_GravitySensor_eventHandler(){if(JS_GravitySensor_callback!=0)dynCall_vfff(JS_GravitySensor_callback,JS_GravitySensor.x*JS_Accelerometer_multiplier,JS_GravitySensor.y*JS_Accelerometer_multiplier,JS_GravitySensor.z*JS_Accelerometer_multiplier)}var JS_GravitySensor_frequencyRequest=0;var JS_LinearAccelerationSensor=null;function JS_LinearAccelerationSensor_eventHandler(){var linearAccelerationValue={x:JS_LinearAccelerationSensor.x*JS_Accelerometer_multiplier,y:JS_LinearAccelerationSensor.y*JS_Accelerometer_multiplier,z:JS_LinearAccelerationSensor.z*JS_Accelerometer_multiplier};if(JS_LinearAccelerationSensor_callback!=0)dynCall_vfff(JS_LinearAccelerationSensor_callback,linearAccelerationValue.x,linearAccelerationValue.y,linearAccelerationValue.z);if(JS_GravitySensor_callback!=0&&typeof GravitySensor==="undefined"){var gravityValue=JS_ComputeGravity(JS_Accelerometer_lastValue,linearAccelerationValue);dynCall_vfff(JS_GravitySensor_callback,gravityValue.x,gravityValue.y,gravityValue.z)}}var JS_LinearAccelerationSensor_frequencyRequest=0;var JS_LinearAccelerationSensor_frequency=0;function _JS_LinearAccelerationSensor_Start(callback,frequency){JS_DefineAccelerometerMultiplier();if(typeof LinearAccelerationSensor==="undefined"){JS_DeviceMotion_add();if(callback!=0)JS_LinearAccelerationSensor_callback=callback;return}if(callback!=0)JS_LinearAccelerationSensor_callback=callback;function InitializeLinearAccelerationSensor(frequency){JS_LinearAccelerationSensor=new LinearAccelerationSensor({frequency:frequency,referenceFrame:"device"});JS_LinearAccelerationSensor.addEventListener("reading",JS_LinearAccelerationSensor_eventHandler);JS_LinearAccelerationSensor.addEventListener("error",function(e){warnOnce(e.error?e.error:e)});JS_LinearAccelerationSensor.start();JS_LinearAccelerationSensor_frequency=frequency}if(JS_LinearAccelerationSensor){if(JS_LinearAccelerationSensor_frequency!=frequency){JS_LinearAccelerationSensor.stop();JS_LinearAccelerationSensor.removeEventListener("reading",JS_LinearAccelerationSensor_eventHandler);InitializeLinearAccelerationSensor(frequency)}}else if(JS_LinearAccelerationSensor_frequencyRequest!=0){JS_LinearAccelerationSensor_frequencyRequest=frequency}else{JS_LinearAccelerationSensor_frequencyRequest=frequency;navigator.permissions.query({name:"accelerometer"}).then(function(result){if(result.state==="granted"){InitializeLinearAccelerationSensor(JS_LinearAccelerationSensor_frequencyRequest)}else{warnOnce("No permission to use LinearAccelerationSensor.")}JS_LinearAccelerationSensor_frequencyRequest=0})}}function _JS_GravitySensor_Start(callback,frequency){if(typeof GravitySensor==="undefined"){_JS_Accelerometer_Start(0,Math.max(frequency,JS_Accelerometer_frequency));_JS_LinearAccelerationSensor_Start(0,Math.max(frequency,JS_LinearAccelerationSensor_frequency));JS_GravitySensor_callback=callback;return}JS_DefineAccelerometerMultiplier();JS_GravitySensor_callback=callback;function InitializeGravitySensor(frequency){JS_GravitySensor=new GravitySensor({frequency:frequency,referenceFrame:"device"});JS_GravitySensor.addEventListener("reading",JS_GravitySensor_eventHandler);JS_GravitySensor.addEventListener("error",function(e){warnOnce(e.error?e.error:e)});JS_GravitySensor.start()}if(JS_GravitySensor){JS_GravitySensor.stop();JS_GravitySensor.removeEventListener("reading",JS_GravitySensor_eventHandler);InitializeGravitySensor(frequency)}else if(JS_GravitySensor_frequencyRequest!=0){JS_GravitySensor_frequencyRequest=frequency}else{JS_GravitySensor_frequencyRequest=frequency;navigator.permissions.query({name:"accelerometer"}).then(function(result){if(result.state==="granted"){InitializeGravitySensor(JS_GravitySensor_frequencyRequest)}else{warnOnce("No permission to use GravitySensor.")}JS_GravitySensor_frequencyRequest=0})}}function _JS_LinearAccelerationSensor_Stop(){if(JS_LinearAccelerationSensor){if(typeof GravitySensor!=="undefined"||JS_GravitySensor_callback==0){JS_LinearAccelerationSensor.stop();JS_LinearAccelerationSensor.removeEventListener("reading",JS_LinearAccelerationSensor_eventHandler);JS_LinearAccelerationSensor=null}JS_LinearAccelerationSensor_callback=0;JS_LinearAccelerationSensor_frequency=0}else if(JS_LinearAccelerationSensor_callback!=0){JS_LinearAccelerationSensor_callback=0;JS_DeviceMotion_remove()}}function _JS_GravitySensor_Stop(){JS_GravitySensor_callback=0;if(typeof GravitySensor==="undefined"){if(JS_Accelerometer_callback==0)_JS_Accelerometer_Stop();if(JS_LinearAccelerationSensor_callback==0)_JS_LinearAccelerationSensor_Stop();return}if(JS_GravitySensor){JS_GravitySensor.stop();JS_GravitySensor.removeEventListener("reading",JS_GravitySensor_eventHandler);JS_GravitySensor=null}}var JS_Gyroscope=null;function _JS_Gyroscope_IsRunning(){return JS_Gyroscope&&JS_Gyroscope.activated||JS_Gyroscope_callback!=0}function JS_Gyroscope_eventHandler(){if(JS_Gyroscope_callback!=0)dynCall_vfff(JS_Gyroscope_callback,JS_Gyroscope.x,JS_Gyroscope.y,JS_Gyroscope.z)}var JS_Gyroscope_frequencyRequest=0;function _JS_Gyroscope_Start(callback,frequency){if(typeof Gyroscope==="undefined"){JS_DeviceMotion_add();JS_Gyroscope_callback=callback;return}JS_Gyroscope_callback=callback;function InitializeGyroscope(frequency){JS_Gyroscope=new Gyroscope({frequency:frequency,referenceFrame:"device"});JS_Gyroscope.addEventListener("reading",JS_Gyroscope_eventHandler);JS_Gyroscope.addEventListener("error",function(e){warnOnce(e.error?e.error:e)});JS_Gyroscope.start()}if(JS_Gyroscope){JS_Gyroscope.stop();JS_Gyroscope.removeEventListener("reading",JS_Gyroscope_eventHandler);InitializeGyroscope(frequency)}else if(JS_Gyroscope_frequencyRequest!=0){JS_Gyroscope_frequencyRequest=frequency}else{JS_Gyroscope_frequencyRequest=frequency;navigator.permissions.query({name:"gyroscope"}).then(function(result){if(result.state==="granted"){InitializeGyroscope(JS_Gyroscope_frequencyRequest)}else{warnOnce("No permission to use Gyroscope.")}JS_Gyroscope_frequencyRequest=0})}}function _JS_Gyroscope_Stop(){if(JS_Gyroscope){JS_Gyroscope.stop();JS_Gyroscope.removeEventListener("reading",JS_Gyroscope_eventHandler);JS_Gyroscope=null;JS_Gyroscope_callback=0}else if(JS_Gyroscope_callback!=0){JS_Gyroscope_callback=0;JS_DeviceMotion_remove()}}function _JS_LinearAccelerationSensor_IsRunning(){return JS_LinearAccelerationSensor&&JS_LinearAccelerationSensor.activated||JS_LinearAccelerationSensor_callback!=0}function _JS_Log_Dump(ptr,type){var str=UTF8ToString(ptr);if(typeof dump=="function")dump(str);switch(type){case 0:case 1:case 4:console.error(str);return;case 2:console.warn(str);return;case 3:case 5:console.log(str);return;default:console.error("Unknown console message type!");console.error(str)}}function _JS_Log_StackTrace(buffer,bufferSize){var trace=stackTrace();if(buffer)stringToUTF8(trace,buffer,bufferSize);return lengthBytesUTF8(trace)}var JS_OrientationSensor=null;var JS_OrientationSensor_callback=0;function _JS_OrientationSensor_IsRunning(){return JS_OrientationSensor&&JS_OrientationSensor.activated||JS_OrientationSensor_callback!=0}function JS_OrientationSensor_eventHandler(){if(JS_OrientationSensor_callback!=0)dynCall_vffff(JS_OrientationSensor_callback,JS_OrientationSensor.quaternion[0],JS_OrientationSensor.quaternion[1],JS_OrientationSensor.quaternion[2],JS_OrientationSensor.quaternion[3])}var JS_OrientationSensor_frequencyRequest=0;function JS_DeviceOrientation_eventHandler(event){if(JS_OrientationSensor_callback){var degToRad=Math.PI/180;var x=event.beta*degToRad;var y=event.gamma*degToRad;var z=event.alpha*degToRad;var cx=Math.cos(x/2);var sx=Math.sin(x/2);var cy=Math.cos(y/2);var sy=Math.sin(y/2);var cz=Math.cos(z/2);var sz=Math.sin(z/2);var qx=sx*cy*cz-cx*sy*sz;var qy=cx*sy*cz+sx*cy*sz;var qz=cx*cy*sz+sx*sy*cz;var qw=cx*cy*cz-sx*sy*sz;dynCall_vffff(JS_OrientationSensor_callback,qx,qy,qz,qw)}}function _JS_OrientationSensor_Start(callback,frequency){if(typeof RelativeOrientationSensor==="undefined"){if(JS_OrientationSensor_callback==0){JS_OrientationSensor_callback=callback;JS_RequestDeviceSensorPermissions(1);window.addEventListener("deviceorientation",JS_DeviceOrientation_eventHandler)}return}JS_OrientationSensor_callback=callback;function InitializeOrientationSensor(frequency){JS_OrientationSensor=new RelativeOrientationSensor({frequency:frequency,referenceFrame:"device"});JS_OrientationSensor.addEventListener("reading",JS_OrientationSensor_eventHandler);JS_OrientationSensor.addEventListener("error",function(e){warnOnce(e.error?e.error:e)});JS_OrientationSensor.start()}if(JS_OrientationSensor){JS_OrientationSensor.stop();JS_OrientationSensor.removeEventListener("reading",JS_OrientationSensor_eventHandler);InitializeOrientationSensor(frequency)}else if(JS_OrientationSensor_frequencyRequest!=0){JS_OrientationSensor_frequencyRequest=frequency}else{JS_OrientationSensor_frequencyRequest=frequency;Promise.all([navigator.permissions.query({name:"accelerometer"}),navigator.permissions.query({name:"gyroscope"})]).then(function(results){if(results.every(function(result){return result.state==="granted"})){InitializeOrientationSensor(JS_OrientationSensor_frequencyRequest)}else{warnOnce("No permissions to use RelativeOrientationSensor.")}JS_OrientationSensor_frequencyRequest=0})}}function _JS_OrientationSensor_Stop(){if(JS_OrientationSensor){JS_OrientationSensor.stop();JS_OrientationSensor.removeEventListener("reading",JS_OrientationSensor_eventHandler);JS_OrientationSensor=null}else if(JS_OrientationSensor_callback!=0){window.removeEventListener("deviceorientation",JS_DeviceOrientation_eventHandler)}JS_OrientationSensor_callback=0}function _JS_RequestDeviceSensorPermissionsOnTouch(){if(JS_DeviceSensorPermissions==0)return;JS_RequestDeviceSensorPermissions(JS_DeviceSensorPermissions)}function _JS_RunQuitCallbacks(){Module.QuitCleanup()}var JS_ScreenOrientation_callback=0;function JS_ScreenOrientation_eventHandler(){if(JS_ScreenOrientation_callback)dynCall_viii(JS_ScreenOrientation_callback,window.innerWidth,window.innerHeight,screen.orientation?screen.orientation.angle:window.orientation)}function _JS_ScreenOrientation_DeInit(){JS_ScreenOrientation_callback=0;window.removeEventListener("resize",JS_ScreenOrientation_eventHandler);if(screen.orientation){screen.orientation.removeEventListener("change",JS_ScreenOrientation_eventHandler)}}function _JS_ScreenOrientation_Init(callback){if(!JS_ScreenOrientation_callback){if(screen.orientation){screen.orientation.addEventListener("change",JS_ScreenOrientation_eventHandler)}window.addEventListener("resize",JS_ScreenOrientation_eventHandler);JS_ScreenOrientation_callback=callback;setTimeout(JS_ScreenOrientation_eventHandler,0)}}var JS_ScreenOrientation_requestedLockType=-1;var JS_ScreenOrientation_appliedLockType=-1;var JS_ScreenOrientation_timeoutID=-1;function _JS_ScreenOrientation_Lock(orientationLockType){if(!screen.orientation){return}function applyLock(){JS_ScreenOrientation_appliedLockType=JS_ScreenOrientation_requestedLockType;var screenOrientations=["any",0,"landscape","portrait","portrait-primary","portrait-secondary","landscape-primary","landscape-secondary"];var type=screenOrientations[JS_ScreenOrientation_appliedLockType];screen.orientation.lock(type).then(function(){if(JS_ScreenOrientation_requestedLockType!=JS_ScreenOrientation_appliedLockType){JS_ScreenOrientation_timeoutID=setTimeout(applyLock,0)}else{JS_ScreenOrientation_timeoutID=-1}}).catch(function(err){warnOnce(err);JS_ScreenOrientation_timeoutID=-1})}JS_ScreenOrientation_requestedLockType=orientationLockType;if(JS_ScreenOrientation_timeoutID==-1&&orientationLockType!=JS_ScreenOrientation_appliedLockType){JS_ScreenOrientation_timeoutID=setTimeout(applyLock,0)}}var WEBAudio={audioInstanceIdCounter:0,audioInstances:{},audioContext:null,audioWebEnabled:0,audioCache:[],pendingAudioSources:{}};function jsAudioMixinSetPitch(source){source.estimatePlaybackPosition=function(){var t=(WEBAudio.audioContext.currentTime-source.playbackStartTime)*source.playbackRate.value;if(source.loop&&t>=source.loopStart){t=(t-source.loopStart)%(source.loopEnd-source.loopStart)+source.loopStart}return t};source.setPitch=function(newPitch){var curPosition=source.estimatePlaybackPosition();if(curPosition>=0){source.playbackStartTime=WEBAudio.audioContext.currentTime-curPosition/newPitch}if(source.playbackRate.value!==newPitch)source.playbackRate.value=newPitch}}function jsAudioCreateUncompressedSoundClip(buffer,error){var soundClip={buffer:buffer,error:error};soundClip.release=function(){};soundClip.getLength=function(){if(!this.buffer){console.log("Trying to get length of sound which is not loaded.");return 0}var sampleRateRatio=44100/this.buffer.sampleRate;return this.buffer.length*sampleRateRatio};soundClip.getData=function(ptr,length){if(!this.buffer){console.log("Trying to get data of sound which is not loaded.");return 0}var startOutputBuffer=ptr>>2;var output=HEAPF32.subarray(startOutputBuffer,startOutputBuffer+(length>>2));var numMaxSamples=Math.floor((length>>2)/this.buffer.numberOfChannels);var numReadSamples=Math.min(this.buffer.length,numMaxSamples);for(var i=0;i<this.buffer.numberOfChannels;i++){var channelData=this.buffer.getChannelData(i).subarray(0,numReadSamples);output.set(channelData,i*numReadSamples)}return numReadSamples*this.buffer.numberOfChannels*4};soundClip.getNumberOfChannels=function(){if(!this.buffer){console.log("Trying to get metadata of sound which is not loaded.");return 0}return this.buffer.numberOfChannels};soundClip.getFrequency=function(){if(!this.buffer){console.log("Trying to get metadata of sound which is not loaded.");return 0}return this.buffer.sampleRate};soundClip.createSourceNode=function(){if(!this.buffer){console.log("Trying to play sound which is not loaded.")}var source=WEBAudio.audioContext.createBufferSource();source.buffer=this.buffer;jsAudioMixinSetPitch(source);return source};return soundClip}function jsAudioCreateChannel(callback,userData){var channel={callback:callback,userData:userData,source:null,gain:WEBAudio.audioContext.createGain(),panner:WEBAudio.audioContext.createPanner(),threeD:false,loop:false,loopStart:0,loopEnd:0,pitch:1};channel.panner.rolloffFactor=0;channel.release=function(){this.disconnectSource();this.gain.disconnect();this.panner.disconnect()};channel.playSoundClip=function(soundClip,startTime,startOffset){try{var self=this;this.source=soundClip.createSourceNode();this.setupPanning();this.source.onended=function(){self.source.isStopped=true;self.disconnectSource();if(self.callback){dynCall("vi",self.callback,[self.userData])}};this.source.loop=this.loop;this.source.loopStart=this.loopStart;this.source.loopEnd=this.loopEnd;this.source.start(startTime,startOffset);this.source.scheduledStartTime=startTime;this.source.playbackStartTime=startTime-startOffset/this.source.playbackRate.value;this.source.setPitch(this.pitch)}catch(e){console.error("Channel.playSoundClip error. Exception: "+e)}};channel.stop=function(delay){if(!this.source){return}try{channel.source.stop(WEBAudio.audioContext.currentTime+delay)}catch(e){}if(delay==0){this.disconnectSource()}};channel.isPaused=function(){if(!this.source){return true}if(this.source.isPausedMockNode){return true}if(this.source.mediaElement){return this.source.mediaElement.paused||this.source.pauseRequested}return false};channel.pause=function(){if(!this.source||this.source.isPausedMockNode){return}if(this.source.mediaElement){this.source._pauseMediaElement();return}var pausedSource={isPausedMockNode:true,buffer:this.source.buffer,loop:this.source.loop,loopStart:this.source.loopStart,loopEnd:this.source.loopEnd,playbackRate:this.source.playbackRate.value,scheduledStartTime:this.source.scheduledStartTime,scheduledStopTime:undefined,playbackPausedAtPosition:this.source.estimatePlaybackPosition(),setPitch:function(v){this.playbackRate=v},stop:function(when){this.scheduledStopTime=when}};this.stop(0);this.disconnectSource();this.source=pausedSource};channel.resume=function(){if(this.source&&this.source.mediaElement){this.source.start(undefined,this.source.currentTime);return}if(!this.source||!this.source.isPausedMockNode){return}var pausedSource=this.source;var soundClip=jsAudioCreateUncompressedSoundClip(pausedSource.buffer,false);this.playSoundClip(soundClip,pausedSource.scheduledStartTime,Math.max(0,pausedSource.playbackPausedAtPosition));this.source.loop=pausedSource.loop;this.source.loopStart=pausedSource.loopStart;this.source.loopEnd=pausedSource.loopEnd;this.source.setPitch(pausedSource.playbackRate);if(typeof pausedSource.scheduledStopTime!=="undefined"){var delay=Math.max(pausedSource.scheduledStopTime-WEBAudio.audioContext.currentTime,0);this.stop(delay)}};channel.setLoop=function(loop){this.loop=loop;if(!this.source||this.source.loop==loop){return}this.source.loop=loop};channel.setLoopPoints=function(loopStart,loopEnd){this.loopStart=loopStart;this.loopEnd=loopEnd;if(!this.source){return}if(this.source.loopStart!==loopStart){this.source.loopStart=loopStart}if(this.source.loopEnd!==loopEnd){this.source.loopEnd=loopEnd}};channel.set3D=function(threeD){if(this.threeD==threeD){return}this.threeD=threeD;if(!this.source){return}this.setupPanning()};channel.setPitch=function(pitch){this.pitch=pitch;if(!this.source){return}this.source.setPitch(pitch)};channel.setVolume=function(volume){if(this.gain.gain.value==volume){return}this.gain.gain.value=volume};channel.setPosition=function(x,y,z){var p=this.panner;if(p.positionX){if(p.positionX.value!==x)p.positionX.value=x;if(p.positionY.value!==y)p.positionY.value=y;if(p.positionZ.value!==z)p.positionZ.value=z}else if(p._x!==x||p._y!==y||p._z!==z){p.setPosition(x,y,z);p._x=x;p._y=y;p._z=z}};channel.disconnectSource=function(){if(!this.source||this.source.isPausedMockNode){return}if(this.source.mediaElement){this.source._pauseMediaElement()}this.source.onended=null;this.source.disconnect();delete this.source};channel.setupPanning=function(){if(this.source.isPausedMockNode)return;this.source.disconnect();this.panner.disconnect();this.gain.disconnect();if(this.threeD){this.source.connect(this.panner);this.panner.connect(this.gain)}else{this.source.connect(this.gain)}this.gain.connect(WEBAudio.audioContext.destination)};channel.isStopped=function(){if(!this.source){return true}if(this.source.mediaElement){return this.source.isStopped}return false};return channel}function _JS_Sound_Create_Channel(callback,userData){if(WEBAudio.audioWebEnabled==0)return;WEBAudio.audioInstances[++WEBAudio.audioInstanceIdCounter]=jsAudioCreateChannel(callback,userData);return WEBAudio.audioInstanceIdCounter}function _JS_Sound_GetLength(bufferInstance){if(WEBAudio.audioWebEnabled==0)return 0;var soundClip=WEBAudio.audioInstances[bufferInstance];if(!soundClip)return 0;return soundClip.getLength()}function _JS_Sound_GetLoadState(bufferInstance){if(WEBAudio.audioWebEnabled==0)return 2;var sound=WEBAudio.audioInstances[bufferInstance];if(sound.error)return 2;if(sound.buffer||sound.url)return 0;return 1}function jsAudioPlayPendingBlockedAudio(soundId){var pendingAudio=WEBAudio.pendingAudioSources[soundId];pendingAudio.sourceNode._startPlayback(pendingAudio.offset);delete WEBAudio.pendingAudioSources[soundId]}function jsAudioPlayBlockedAudios(){Object.keys(WEBAudio.pendingAudioSources).forEach(function(audioId){jsAudioPlayPendingBlockedAudio(audioId)})}function _JS_Sound_Init(){try{window.AudioContext=window.AudioContext||window.webkitAudioContext;WEBAudio.audioContext=new AudioContext;var tryToResumeAudioContext=function(){if(WEBAudio.audioContext.state==="suspended")WEBAudio.audioContext.resume().catch(function(error){console.warn("Could not resume audio context. Exception: "+error)});else Module.clearInterval(resumeInterval)};var resumeInterval=Module.setInterval(tryToResumeAudioContext,400);WEBAudio.audioWebEnabled=1;var _userEventCallback=function(){try{if(WEBAudio.audioContext.state!=="running"&&WEBAudio.audioContext.state!=="closed"){WEBAudio.audioContext.resume().catch(function(error){console.warn("Could not resume audio context. Exception: "+error)})}jsAudioPlayBlockedAudios();var audioCacheSize=20;while(WEBAudio.audioCache.length<audioCacheSize){var audio=new Audio;audio.autoplay=false;WEBAudio.audioCache.push(audio)}}catch(e){}};window.addEventListener("mousedown",_userEventCallback);window.addEventListener("touchstart",_userEventCallback);Module.deinitializers.push(function(){window.removeEventListener("mousedown",_userEventCallback);window.removeEventListener("touchstart",_userEventCallback)})}catch(e){alert("Web Audio API is not supported in this browser")}}function jsAudioCreateUncompressedSoundClipFromCompressedAudio(audioData){var soundClip=jsAudioCreateUncompressedSoundClip(null,false);WEBAudio.audioContext.decodeAudioData(audioData,function(_buffer){soundClip.buffer=_buffer},function(_error){soundClip.error=true;console.log("Decode error: "+_error)});return soundClip}function jsAudioAddPendingBlockedAudio(sourceNode,offset){WEBAudio.pendingAudioSources[sourceNode.mediaElement.src]={sourceNode:sourceNode,offset:offset}}function jsAudioGetMimeTypeFromType(fmodSoundType){switch(fmodSoundType){case 13:return"audio/mpeg";case 20:return"audio/wav";default:return"audio/mp4"}}function jsAudioCreateCompressedSoundClip(audioData,fmodSoundType){var mimeType=jsAudioGetMimeTypeFromType(fmodSoundType);var blob=new Blob([audioData],{type:mimeType});var soundClip={url:URL.createObjectURL(blob),error:false,mediaElement:new Audio};soundClip.mediaElement.preload="metadata";soundClip.mediaElement.src=soundClip.url;soundClip.release=function(){if(!this.mediaElement){return}this.mediaElement.src="";URL.revokeObjectURL(this.url);delete this.mediaElement;delete this.url};soundClip.getLength=function(){return this.mediaElement.duration*44100};soundClip.getData=function(ptr,length){console.warn("getData() is not supported for compressed sound.");return 0};soundClip.getNumberOfChannels=function(){console.warn("getNumberOfChannels() is not supported for compressed sound.");return 0};soundClip.getFrequency=function(){console.warn("getFrequency() is not supported for compressed sound.");return 0};soundClip.createSourceNode=function(){var self=this;var mediaElement=WEBAudio.audioCache.length?WEBAudio.audioCache.pop():new Audio;mediaElement.preload="metadata";mediaElement.src=this.url;var source=WEBAudio.audioContext.createMediaElementSource(mediaElement);Object.defineProperty(source,"loop",{get:function(){return source.mediaElement.loop},set:function(v){if(source.mediaElement.loop!==v)source.mediaElement.loop=v}});source.playbackRate={};Object.defineProperty(source.playbackRate,"value",{get:function(){return source.mediaElement.playbackRate},set:function(v){if(source.mediaElement.playbackRate!==v)source.mediaElement.playbackRate=v}});Object.defineProperty(source,"currentTime",{get:function(){return source.mediaElement.currentTime},set:function(v){if(source.mediaElement.currentTime!==v)source.mediaElement.currentTime=v}});Object.defineProperty(source,"mute",{get:function(){return source.mediaElement.mute},set:function(v){if(source.mediaElement.mute!==v)source.mediaElement.mute=v}});Object.defineProperty(source,"onended",{get:function(){return source.mediaElement.onended},set:function(onended){source.mediaElement.onended=onended}});source.playPromise=null;source.playTimeout=null;source.pauseRequested=false;source.isStopped=false;source._pauseMediaElement=function(){if(source.playPromise||source.playTimeout){source.pauseRequested=true}else{source.mediaElement.pause()}};source._startPlayback=function(offset){if(source.playPromise||source.playTimeout){source.mediaElement.currentTime=offset;source.pauseRequested=false;return}source.mediaElement.currentTime=offset;source.playPromise=source.mediaElement.play();if(source.playPromise){source.playPromise.then(function(){if(source.pauseRequested){source.mediaElement.pause();source.pauseRequested=false}source.playPromise=null}).catch(function(error){source.playPromise=null;if(error.name!=="NotAllowedError")throw error;jsAudioAddPendingBlockedAudio(source,offset)})}};source.start=function(startTime,offset){if(typeof startTime==="undefined"){startTime=WEBAudio.audioContext.currentTime}if(typeof offset==="undefined"){offset=0}var startDelayThresholdMS=4;var startDelayMS=(startTime-WEBAudio.audioContext.currentTime)*1e3;if(startDelayMS>startDelayThresholdMS){source.playTimeout=setTimeout(function(){source.playTimeout=null;source._startPlayback(offset)},startDelayMS)}else{source._startPlayback(offset)}};source.stop=function(stopTime){if(typeof stopTime==="undefined"){stopTime=WEBAudio.audioContext.currentTime}var stopDelayThresholdMS=4;var stopDelayMS=(stopTime-WEBAudio.audioContext.currentTime)*1e3;if(stopDelayMS>stopDelayThresholdMS){setTimeout(function(){source._pauseMediaElement();source.isStopped=true},stopDelayMS)}else{source._pauseMediaElement();source.isStopped=true}};jsAudioMixinSetPitch(source);return source};return soundClip}function _JS_Sound_Load(ptr,length,decompress,fmodSoundType){if(WEBAudio.audioWebEnabled==0)return 0;var audioData=HEAPU8.buffer.slice(ptr,ptr+length);if(length<131072)decompress=1;var sound;if(decompress){sound=jsAudioCreateUncompressedSoundClipFromCompressedAudio(audioData)}else{sound=jsAudioCreateCompressedSoundClip(audioData,fmodSoundType)}WEBAudio.audioInstances[++WEBAudio.audioInstanceIdCounter]=sound;return WEBAudio.audioInstanceIdCounter}function jsAudioCreateUncompressedSoundClipFromPCM(channels,length,sampleRate,ptr){var buffer=WEBAudio.audioContext.createBuffer(channels,length,sampleRate);for(var i=0;i<channels;i++){var offs=(ptr>>2)+length*i;var copyToChannel=buffer["copyToChannel"]||function(source,channelNumber,startInChannel){var clipped=source.subarray(0,Math.min(source.length,this.length-(startInChannel|0)));this.getChannelData(channelNumber|0).set(clipped,startInChannel|0)};copyToChannel.apply(buffer,[HEAPF32.subarray(offs,offs+length),i,0])}return jsAudioCreateUncompressedSoundClip(buffer,false)}function _JS_Sound_Load_PCM(channels,length,sampleRate,ptr){if(WEBAudio.audioWebEnabled==0)return 0;var sound=jsAudioCreateUncompressedSoundClipFromPCM(channels,length,sampleRate,ptr);WEBAudio.audioInstances[++WEBAudio.audioInstanceIdCounter]=sound;return WEBAudio.audioInstanceIdCounter}function _JS_Sound_Play(bufferInstance,channelInstance,offset,delay){if(WEBAudio.audioWebEnabled==0)return;_JS_Sound_Stop(channelInstance,0);var soundClip=WEBAudio.audioInstances[bufferInstance];var channel=WEBAudio.audioInstances[channelInstance];if(!soundClip){console.log("Trying to play sound which is not loaded.");return}try{channel.playSoundClip(soundClip,WEBAudio.audioContext.currentTime+delay,offset)}catch(error){console.error("playSoundClip error. Exception: "+e)}}function _JS_Sound_ReleaseInstance(instance){var object=WEBAudio.audioInstances[instance];if(object){object.release()}delete WEBAudio.audioInstances[instance]}function _JS_Sound_ResumeIfNeeded(){if(WEBAudio.audioWebEnabled==0)return;if(WEBAudio.audioContext.state==="suspended")WEBAudio.audioContext.resume().catch(function(error){console.warn("Could not resume audio context. Exception: "+error)})}function _JS_Sound_Set3D(channelInstance,threeD){var channel=WEBAudio.audioInstances[channelInstance];channel.set3D(threeD)}function _JS_Sound_SetListenerOrientation(x,y,z,xUp,yUp,zUp){if(WEBAudio.audioWebEnabled==0)return;x=-x;y=-y;z=-z;var l=WEBAudio.audioContext.listener;if(l.forwardX){if(l.forwardX.value!==x)l.forwardX.value=x;if(l.forwardY.value!==y)l.forwardY.value=y;if(l.forwardZ.value!==z)l.forwardZ.value=z;if(l.upX.value!==xUp)l.upX.value=xUp;if(l.upY.value!==yUp)l.upY.value=yUp;if(l.upZ.value!==zUp)l.upZ.value=zUp}else if(l._forwardX!==x||l._forwardY!==y||l._forwardZ!==z||l._upX!==xUp||l._upY!==yUp||l._upZ!==zUp){l.setOrientation(x,y,z,xUp,yUp,zUp);l._forwardX=x;l._forwardY=y;l._forwardZ=z;l._upX=xUp;l._upY=yUp;l._upZ=zUp}}function _JS_Sound_SetListenerPosition(x,y,z){if(WEBAudio.audioWebEnabled==0)return;var l=WEBAudio.audioContext.listener;if(l.positionX){if(l.positionX.value!==x)l.positionX.value=x;if(l.positionY.value!==y)l.positionY.value=y;if(l.positionZ.value!==z)l.positionZ.value=z}else if(l._positionX!==x||l._positionY!==y||l._positionZ!==z){l.setPosition(x,y,z);l._positionX=x;l._positionY=y;l._positionZ=z}}function _JS_Sound_SetLoop(channelInstance,loop){if(WEBAudio.audioWebEnabled==0)return;var channel=WEBAudio.audioInstances[channelInstance];channel.setLoop(loop)}function _JS_Sound_SetLoopPoints(channelInstance,loopStart,loopEnd){if(WEBAudio.audioWebEnabled==0)return;var channel=WEBAudio.audioInstances[channelInstance];channel.setLoopPoints(loopStart,loopEnd)}function _JS_Sound_SetPaused(channelInstance,paused){if(WEBAudio.audioWebEnabled==0)return;var channel=WEBAudio.audioInstances[channelInstance];if(paused!=channel.isPaused()){if(paused)channel.pause();else channel.resume()}}function _JS_Sound_SetPitch(channelInstance,v){if(WEBAudio.audioWebEnabled==0)return;try{var channel=WEBAudio.audioInstances[channelInstance];channel.setPitch(v)}catch(e){console.error("JS_Sound_SetPitch(channel="+channelInstance+", pitch="+v+") threw an exception: "+e)}}function _JS_Sound_SetPosition(channelInstance,x,y,z){if(WEBAudio.audioWebEnabled==0)return;var channel=WEBAudio.audioInstances[channelInstance];channel.setPosition(x,y,z)}function _JS_Sound_SetVolume(channelInstance,v){if(WEBAudio.audioWebEnabled==0)return;try{var channel=WEBAudio.audioInstances[channelInstance];channel.setVolume(v)}catch(e){console.error("JS_Sound_SetVolume(channel="+channelInstance+", volume="+v+") threw an exception: "+e)}}function _JS_Sound_Stop(channelInstance,delay){if(WEBAudio.audioWebEnabled==0)return;var channel=WEBAudio.audioInstances[channelInstance];channel.stop(delay)}function _JS_SystemInfo_GetCanvasClientSize(domElementSelector,outWidth,outHeight){var selector=UTF8ToString(domElementSelector);var canvas=selector=="#canvas"?Module["canvas"]:document.querySelector(selector);var w=0,h=0;if(canvas){var size=canvas.getBoundingClientRect();w=size.width;h=size.height}HEAPF64[outWidth>>3]=w;HEAPF64[outHeight>>3]=h}function _JS_SystemInfo_GetDocumentURL(buffer,bufferSize){if(buffer)stringToUTF8(document.URL,buffer,bufferSize);return lengthBytesUTF8(document.URL)}function _JS_SystemInfo_GetGPUInfo(buffer,bufferSize){var gpuinfo=Module.SystemInfo.gpu;if(buffer)stringToUTF8(gpuinfo,buffer,bufferSize);return lengthBytesUTF8(gpuinfo)}function _JS_SystemInfo_GetMatchWebGLToCanvasSize(){return Module.matchWebGLToCanvasSize||Module.matchWebGLToCanvasSize===undefined}function _JS_SystemInfo_GetMemory(){return HEAPU8.length/(1024*1024)}function _JS_SystemInfo_GetOS(buffer,bufferSize){var browser=Module.SystemInfo.os+" "+Module.SystemInfo.osVersion;if(buffer)stringToUTF8(browser,buffer,bufferSize);return lengthBytesUTF8(browser)}function _JS_SystemInfo_GetPreferredDevicePixelRatio(){return Module.matchWebGLToCanvasSize==false?1:Module.devicePixelRatio||window.devicePixelRatio||1}function _JS_SystemInfo_GetScreenSize(outWidth,outHeight){HEAPF64[outWidth>>3]=Module.SystemInfo.width;HEAPF64[outHeight>>3]=Module.SystemInfo.height}function _JS_SystemInfo_HasAstcHdr(){var ext=GLctx.getExtension("WEBGL_compressed_texture_astc");if(ext&&ext.getSupportedProfiles){return ext.getSupportedProfiles().includes("hdr")}return false}function _JS_SystemInfo_HasCursorLock(){return Module.SystemInfo.hasCursorLock}function _JS_SystemInfo_HasFullscreen(){return Module.SystemInfo.hasFullscreen}function _JS_SystemInfo_HasWebGL(){return Module.SystemInfo.hasWebGL}function _JS_SystemInfo_IsMobile(){return Module.SystemInfo.mobile}function _JS_UnityEngineShouldQuit(){return!!Module.shouldQuit}var wr={requests:{},responses:{},abortControllers:{},timer:{},nextRequestId:1};function _JS_WebRequest_Abort(requestId){var abortController=wr.abortControllers[requestId];if(!abortController||abortController.signal.aborted){return}abortController.abort()}function _JS_WebRequest_Create(url,method){var _url=UTF8ToString(url);var _method=UTF8ToString(method);var abortController=new AbortController;var requestOptions={url:_url,init:{method:_method,signal:abortController.signal,headers:{},enableStreamingDownload:true},tempBuffer:null,tempBufferSize:0};wr.abortControllers[wr.nextRequestId]=abortController;wr.requests[wr.nextRequestId]=requestOptions;return wr.nextRequestId++}function jsWebRequestGetResponseHeaderString(requestId){var response=wr.responses[requestId];if(!response){return""}if(response.headerString){return response.headerString}var headers="";var entries=response.headers.entries();for(var result=entries.next();!result.done;result=entries.next()){headers+=result.value[0]+": "+result.value[1]+"\r\n"}response.headerString=headers;return headers}function _JS_WebRequest_GetResponseMetaData(requestId,headerBuffer,headerSize,responseUrlBuffer,responseUrlSize){var response=wr.responses[requestId];if(!response){stringToUTF8("",headerBuffer,headerSize);stringToUTF8("",responseUrlBuffer,responseUrlSize);return}if(headerBuffer){var headers=jsWebRequestGetResponseHeaderString(requestId);stringToUTF8(headers,headerBuffer,headerSize)}if(responseUrlBuffer){stringToUTF8(response.url,responseUrlBuffer,responseUrlSize)}}function _JS_WebRequest_GetResponseMetaDataLengths(requestId,buffer){var response=wr.responses[requestId];if(!response){HEAPU32[buffer>>2]=0;HEAPU32[(buffer>>2)+1]=0;return}var headers=jsWebRequestGetResponseHeaderString(requestId);HEAPU32[buffer>>2]=lengthBytesUTF8(headers);HEAPU32[(buffer>>2)+1]=lengthBytesUTF8(response.url)}function _JS_WebRequest_Release(requestId){if(wr.timer[requestId]){clearTimeout(wr.timer[requestId])}delete wr.requests[requestId];delete wr.responses[requestId];delete wr.abortControllers[requestId];delete wr.timer[requestId]}function _JS_WebRequest_Send(requestId,ptr,length,arg,onresponse,onprogress){var requestOptions=wr.requests[requestId];var abortController=wr.abortControllers[requestId];function getTempBuffer(size){if(!requestOptions.tempBuffer){const initialSize=Math.max(size,1024);requestOptions.tempBuffer=_malloc(initialSize);requestOptions.tempBufferSize=initialSize}if(requestOptions.tempBufferSize<size){_free(requestOptions.tempBuffer);requestOptions.tempBuffer=_malloc(size);requestOptions.tempBufferSize=size}return requestOptions.tempBuffer}function ClearTimeout(){if(wr.timer[requestId]){clearTimeout(wr.timer[requestId]);delete wr.timer[requestId]}}function HandleSuccess(response,body){ClearTimeout();if(!onresponse){return}var kWebRequestOK=0;if(requestOptions.init.enableStreamingDownload){dynCall("viiiiii",onresponse,[arg,response.status,0,body.length,0,kWebRequestOK])}else if(body.length!=0){var buffer=_malloc(body.length);HEAPU8.set(body,buffer);dynCall("viiiiii",onresponse,[arg,response.status,buffer,body.length,0,kWebRequestOK])}else{dynCall("viiiiii",onresponse,[arg,response.status,0,0,0,kWebRequestOK])}if(requestOptions.tempBuffer){_free(requestOptions.tempBuffer)}}function HandleError(err,code){ClearTimeout();if(!onresponse){return}var len=lengthBytesUTF8(err)+1;var buffer=_malloc(len);stringToUTF8(err,buffer,len);dynCall("viiiiii",onresponse,[arg,500,0,0,buffer,code]);_free(buffer);if(requestOptions.tempBuffer){_free(requestOptions.tempBuffer)}}function HandleProgress(e){if(!onprogress||!e.lengthComputable){return}var response=e.response;wr.responses[requestId]=response;if(e.chunk){var buffer=getTempBuffer(e.chunk.length);HEAPU8.set(e.chunk,buffer);dynCall("viiiiii",onprogress,[arg,response.status,e.loaded,e.total,buffer,e.chunk.length])}else{dynCall("viiiiii",onprogress,[arg,response.status,e.loaded,e.total,0,0])}}try{if(length>0){var postData=HEAPU8.subarray(ptr,ptr+length);requestOptions.init.body=new Blob([postData])}if(requestOptions.timeout){wr.timer[requestId]=setTimeout(function(){requestOptions.isTimedOut=true;abortController.abort()},requestOptions.timeout)}var fetchImpl=Module.fetchWithProgress;requestOptions.init.onProgress=HandleProgress;if(Module.companyName&&Module.productName&&Module.cachedFetch){fetchImpl=Module.cachedFetch;requestOptions.init.companyName=Module.companyName;requestOptions.init.productName=Module.productName;requestOptions.control=Module.cacheControl(requestOptions.url)}fetchImpl(requestOptions.url,requestOptions.init).then(function(response){wr.responses[requestId]=response;HandleSuccess(response,response.parsedBody)}).catch(function(error){var kWebErrorUnknown=2;var kWebErrorAborted=17;var kWebErrorTimeout=14;if(requestOptions.isTimedOut){HandleError("Connection timed out.",kWebErrorTimeout)}else if(abortController.signal.aborted){HandleError("Aborted.",kWebErrorAborted)}else{HandleError(error.message,kWebErrorUnknown)}})}catch(error){var kWebErrorUnknown=2;HandleError(error.message,kWebErrorUnknown)}}function _JS_WebRequest_SetRedirectLimit(request,redirectLimit){var requestOptions=wr.requests[request];if(!requestOptions){return}requestOptions.init.redirect=redirectLimit===0?"error":"follow"}function _JS_WebRequest_SetRequestHeader(requestId,header,value){var requestOptions=wr.requests[requestId];if(!requestOptions){return}var _header=UTF8ToString(header);var _value=UTF8ToString(value);requestOptions.init.headers[_header]=_value}function _JS_WebRequest_SetTimeout(requestId,timeout){var requestOptions=wr.requests[requestId];if(!requestOptions){return}requestOptions.timeout=timeout}var ExceptionInfoAttrs={DESTRUCTOR_OFFSET:0,REFCOUNT_OFFSET:4,TYPE_OFFSET:8,CAUGHT_OFFSET:12,RETHROWN_OFFSET:13,SIZE:16};function ___cxa_allocate_exception(size){return _malloc(size+ExceptionInfoAttrs.SIZE)+ExceptionInfoAttrs.SIZE}function ExceptionInfo(excPtr){this.excPtr=excPtr;this.ptr=excPtr-ExceptionInfoAttrs.SIZE;this.set_type=function(type){HEAP32[this.ptr+ExceptionInfoAttrs.TYPE_OFFSET>>2]=type};this.get_type=function(){return HEAP32[this.ptr+ExceptionInfoAttrs.TYPE_OFFSET>>2]};this.set_destructor=function(destructor){HEAP32[this.ptr+ExceptionInfoAttrs.DESTRUCTOR_OFFSET>>2]=destructor};this.get_destructor=function(){return HEAP32[this.ptr+ExceptionInfoAttrs.DESTRUCTOR_OFFSET>>2]};this.set_refcount=function(refcount){HEAP32[this.ptr+ExceptionInfoAttrs.REFCOUNT_OFFSET>>2]=refcount};this.set_caught=function(caught){caught=caught?1:0;HEAP8[this.ptr+ExceptionInfoAttrs.CAUGHT_OFFSET>>0]=caught};this.get_caught=function(){return HEAP8[this.ptr+ExceptionInfoAttrs.CAUGHT_OFFSET>>0]!=0};this.set_rethrown=function(rethrown){rethrown=rethrown?1:0;HEAP8[this.ptr+ExceptionInfoAttrs.RETHROWN_OFFSET>>0]=rethrown};this.get_rethrown=function(){return HEAP8[this.ptr+ExceptionInfoAttrs.RETHROWN_OFFSET>>0]!=0};this.init=function(type,destructor){this.set_type(type);this.set_destructor(destructor);this.set_refcount(0);this.set_caught(false);this.set_rethrown(false)};this.add_ref=function(){var value=HEAP32[this.ptr+ExceptionInfoAttrs.REFCOUNT_OFFSET>>2];HEAP32[this.ptr+ExceptionInfoAttrs.REFCOUNT_OFFSET>>2]=value+1};this.release_ref=function(){var prev=HEAP32[this.ptr+ExceptionInfoAttrs.REFCOUNT_OFFSET>>2];HEAP32[this.ptr+ExceptionInfoAttrs.REFCOUNT_OFFSET>>2]=prev-1;return prev===1}}function CatchInfo(ptr){this.free=function(){_free(this.ptr);this.ptr=0};this.set_base_ptr=function(basePtr){HEAP32[this.ptr>>2]=basePtr};this.get_base_ptr=function(){return HEAP32[this.ptr>>2]};this.set_adjusted_ptr=function(adjustedPtr){var ptrSize=4;HEAP32[this.ptr+ptrSize>>2]=adjustedPtr};this.get_adjusted_ptr=function(){var ptrSize=4;return HEAP32[this.ptr+ptrSize>>2]};this.get_exception_ptr=function(){var isPointer=___cxa_is_pointer_type(this.get_exception_info().get_type());if(isPointer){return HEAP32[this.get_base_ptr()>>2]}var adjusted=this.get_adjusted_ptr();if(adjusted!==0)return adjusted;return this.get_base_ptr()};this.get_exception_info=function(){return new ExceptionInfo(this.get_base_ptr())};if(ptr===undefined){this.ptr=_malloc(8);this.set_adjusted_ptr(0)}else{this.ptr=ptr}}var exceptionCaught=[];function exception_addRef(info){info.add_ref()}var uncaughtExceptionCount=0;function ___cxa_begin_catch(ptr){var catchInfo=new CatchInfo(ptr);var info=catchInfo.get_exception_info();if(!info.get_caught()){info.set_caught(true);uncaughtExceptionCount--}info.set_rethrown(false);exceptionCaught.push(catchInfo);exception_addRef(info);return catchInfo.get_exception_ptr()}var exceptionLast=0;function ___cxa_free_exception(ptr){return _free(new ExceptionInfo(ptr).ptr)}function exception_decRef(info){if(info.release_ref()&&!info.get_rethrown()){var destructor=info.get_destructor();if(destructor){(function(a1){return dynCall_ii.apply(null,[destructor,a1])})(info.excPtr)}___cxa_free_exception(info.excPtr)}}function ___cxa_end_catch(){_setThrew(0);var catchInfo=exceptionCaught.pop();exception_decRef(catchInfo.get_exception_info());catchInfo.free();exceptionLast=0}function ___resumeException(catchInfoPtr){var catchInfo=new CatchInfo(catchInfoPtr);var ptr=catchInfo.get_base_ptr();if(!exceptionLast){exceptionLast=ptr}catchInfo.free();throw ptr}function ___cxa_find_matching_catch_2(){var thrown=exceptionLast;if(!thrown){setTempRet0(0);return 0|0}var info=new ExceptionInfo(thrown);var thrownType=info.get_type();var catchInfo=new CatchInfo;catchInfo.set_base_ptr(thrown);if(!thrownType){setTempRet0(0);return catchInfo.ptr|0}var typeArray=Array.prototype.slice.call(arguments);var stackTop=stackSave();var exceptionThrowBuf=stackAlloc(4);HEAP32[exceptionThrowBuf>>2]=thrown;for(var i=0;i<typeArray.length;i++){var caughtType=typeArray[i];if(caughtType===0||caughtType===thrownType){break}if(___cxa_can_catch(caughtType,thrownType,exceptionThrowBuf)){var adjusted=HEAP32[exceptionThrowBuf>>2];if(thrown!==adjusted){catchInfo.set_adjusted_ptr(adjusted)}setTempRet0(caughtType);return catchInfo.ptr|0}}stackRestore(stackTop);setTempRet0(thrownType);return catchInfo.ptr|0}function ___cxa_find_matching_catch_3(){var thrown=exceptionLast;if(!thrown){setTempRet0(0);return 0|0}var info=new ExceptionInfo(thrown);var thrownType=info.get_type();var catchInfo=new CatchInfo;catchInfo.set_base_ptr(thrown);if(!thrownType){setTempRet0(0);return catchInfo.ptr|0}var typeArray=Array.prototype.slice.call(arguments);var stackTop=stackSave();var exceptionThrowBuf=stackAlloc(4);HEAP32[exceptionThrowBuf>>2]=thrown;for(var i=0;i<typeArray.length;i++){var caughtType=typeArray[i];if(caughtType===0||caughtType===thrownType){break}if(___cxa_can_catch(caughtType,thrownType,exceptionThrowBuf)){var adjusted=HEAP32[exceptionThrowBuf>>2];if(thrown!==adjusted){catchInfo.set_adjusted_ptr(adjusted)}setTempRet0(caughtType);return catchInfo.ptr|0}}stackRestore(stackTop);setTempRet0(thrownType);return catchInfo.ptr|0}function ___cxa_find_matching_catch_4(){var thrown=exceptionLast;if(!thrown){setTempRet0(0);return 0|0}var info=new ExceptionInfo(thrown);var thrownType=info.get_type();var catchInfo=new CatchInfo;catchInfo.set_base_ptr(thrown);if(!thrownType){setTempRet0(0);return catchInfo.ptr|0}var typeArray=Array.prototype.slice.call(arguments);var stackTop=stackSave();var exceptionThrowBuf=stackAlloc(4);HEAP32[exceptionThrowBuf>>2]=thrown;for(var i=0;i<typeArray.length;i++){var caughtType=typeArray[i];if(caughtType===0||caughtType===thrownType){break}if(___cxa_can_catch(caughtType,thrownType,exceptionThrowBuf)){var adjusted=HEAP32[exceptionThrowBuf>>2];if(thrown!==adjusted){catchInfo.set_adjusted_ptr(adjusted)}setTempRet0(caughtType);return catchInfo.ptr|0}}stackRestore(stackTop);setTempRet0(thrownType);return catchInfo.ptr|0}function ___cxa_rethrow(){var catchInfo=exceptionCaught.pop();if(!catchInfo){abort("no exception to throw")}var info=catchInfo.get_exception_info();var ptr=catchInfo.get_base_ptr();if(!info.get_rethrown()){exceptionCaught.push(catchInfo);info.set_rethrown(true);info.set_caught(false);uncaughtExceptionCount++}else{catchInfo.free()}exceptionLast=ptr;throw ptr}function ___cxa_throw(ptr,type,destructor){var info=new ExceptionInfo(ptr);info.init(type,destructor);exceptionLast=ptr;uncaughtExceptionCount++;throw ptr}function _gmtime_r(time,tmPtr){var date=new Date(HEAP32[time>>2]*1e3);HEAP32[tmPtr>>2]=date.getUTCSeconds();HEAP32[tmPtr+4>>2]=date.getUTCMinutes();HEAP32[tmPtr+8>>2]=date.getUTCHours();HEAP32[tmPtr+12>>2]=date.getUTCDate();HEAP32[tmPtr+16>>2]=date.getUTCMonth();HEAP32[tmPtr+20>>2]=date.getUTCFullYear()-1900;HEAP32[tmPtr+24>>2]=date.getUTCDay();HEAP32[tmPtr+36>>2]=0;HEAP32[tmPtr+32>>2]=0;var start=Date.UTC(date.getUTCFullYear(),0,1,0,0,0,0);var yday=(date.getTime()-start)/(1e3*60*60*24)|0;HEAP32[tmPtr+28>>2]=yday;if(!_gmtime_r.GMTString)_gmtime_r.GMTString=allocateUTF8("GMT");HEAP32[tmPtr+40>>2]=_gmtime_r.GMTString;return tmPtr}function ___gmtime_r(a0,a1){return _gmtime_r(a0,a1)}function _tzset(){if(_tzset.called)return;_tzset.called=true;var currentYear=(new Date).getFullYear();var winter=new Date(currentYear,0,1);var summer=new Date(currentYear,6,1);var winterOffset=winter.getTimezoneOffset();var summerOffset=summer.getTimezoneOffset();var stdTimezoneOffset=Math.max(winterOffset,summerOffset);HEAP32[__get_timezone()>>2]=stdTimezoneOffset*60;HEAP32[__get_daylight()>>2]=Number(winterOffset!=summerOffset);function extractZone(date){var match=date.toTimeString().match(/\(([A-Za-z ]+)\)$/);return match?match[1]:"GMT"}var winterName=extractZone(winter);var summerName=extractZone(summer);var winterNamePtr=allocateUTF8(winterName);var summerNamePtr=allocateUTF8(summerName);if(summerOffset<winterOffset){HEAP32[__get_tzname()>>2]=winterNamePtr;HEAP32[__get_tzname()+4>>2]=summerNamePtr}else{HEAP32[__get_tzname()>>2]=summerNamePtr;HEAP32[__get_tzname()+4>>2]=winterNamePtr}}function _localtime_r(time,tmPtr){_tzset();var date=new Date(HEAP32[time>>2]*1e3);HEAP32[tmPtr>>2]=date.getSeconds();HEAP32[tmPtr+4>>2]=date.getMinutes();HEAP32[tmPtr+8>>2]=date.getHours();HEAP32[tmPtr+12>>2]=date.getDate();HEAP32[tmPtr+16>>2]=date.getMonth();HEAP32[tmPtr+20>>2]=date.getFullYear()-1900;HEAP32[tmPtr+24>>2]=date.getDay();var start=new Date(date.getFullYear(),0,1);var yday=(date.getTime()-start.getTime())/(1e3*60*60*24)|0;HEAP32[tmPtr+28>>2]=yday;HEAP32[tmPtr+36>>2]=-(date.getTimezoneOffset()*60);var summerOffset=new Date(date.getFullYear(),6,1).getTimezoneOffset();var winterOffset=start.getTimezoneOffset();var dst=(summerOffset!=winterOffset&&date.getTimezoneOffset()==Math.min(winterOffset,summerOffset))|0;HEAP32[tmPtr+32>>2]=dst;var zonePtr=HEAP32[__get_tzname()+(dst?4:0)>>2];HEAP32[tmPtr+40>>2]=zonePtr;return tmPtr}function ___localtime_r(a0,a1){return _localtime_r(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last==="."){parts.splice(i,1)}else if(last===".."){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift("..")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";path=PATH.normalizeArray(path.split("/").filter(function(p){return!!p}),!isAbsolute).join("/");if(!path&&!isAbsolute){path="."}if(path&&trailingSlash){path+="/"}return(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return"."}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path==="/")return"/";path=PATH.normalize(path);path=path.replace(/\/$/,"");var lastSlash=path.lastIndexOf("/");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}};function getRandomDevice(){if(typeof crypto==="object"&&typeof crypto["getRandomValues"]==="function"){var randomBuffer=new Uint8Array(1);return function(){crypto.getRandomValues(randomBuffer);return randomBuffer[0]}}else if(ENVIRONMENT_IS_NODE){try{var crypto_module=require("crypto");return function(){return crypto_module["randomBytes"](1)[0]}}catch(e){}}return function(){abort("randomDevice")}}var PATH_FS={resolve:function(){var resolvedPath="",resolvedAbsolute=false;for(var i=arguments.length-1;i>=-1&&!resolvedAbsolute;i--){var path=i>=0?arguments[i]:FS.cwd();if(typeof path!=="string"){throw new TypeError("Arguments to path.resolve must be strings")}else if(!path){return""}resolvedPath=path+"/"+resolvedPath;resolvedAbsolute=path.charAt(0)==="/"}resolvedPath=PATH.normalizeArray(resolvedPath.split("/").filter(function(p){return!!p}),!resolvedAbsolute).join("/");return(resolvedAbsolute?"/":"")+resolvedPath||"."},relative:function(from,to){from=PATH_FS.resolve(from).substr(1);to=PATH_FS.resolve(to).substr(1);function trim(arr){var start=0;for(;start<arr.length;start++){if(arr[start]!=="")break}var end=arr.length-1;for(;end>=0;end--){if(arr[end]!=="")break}if(start>end)return[];return arr.slice(start,end-start+1)}var fromParts=trim(from.split("/"));var toParts=trim(to.split("/"));var length=Math.min(fromParts.length,toParts.length);var samePartsLength=length;for(var i=0;i<length;i++){if(fromParts[i]!==toParts[i]){samePartsLength=i;break}}var outputParts=[];for(var i=samePartsLength;i<fromParts.length;i++){outputParts.push("..")}outputParts=outputParts.concat(toParts.slice(samePartsLength));return outputParts.join("/")}};var TTY={ttys:[],init:function(){},shutdown:function(){},register:function(dev,ops){TTY.ttys[dev]={input:[],output:[],ops:ops};FS.registerDevice(dev,TTY.stream_ops)},stream_ops:{open:function(stream){var tty=TTY.ttys[stream.node.rdev];if(!tty){throw new FS.ErrnoError(43)}stream.tty=tty;stream.seekable=false},close:function(stream){stream.tty.ops.flush(stream.tty)},flush:function(stream){stream.tty.ops.flush(stream.tty)},read:function(stream,buffer,offset,length,pos){if(!stream.tty||!stream.tty.ops.get_char){throw new FS.ErrnoError(60)}var bytesRead=0;for(var i=0;i<length;i++){var result;try{result=stream.tty.ops.get_char(stream.tty)}catch(e){throw new FS.ErrnoError(29)}if(result===undefined&&bytesRead===0){throw new FS.ErrnoError(6)}if(result===null||result===undefined)break;bytesRead++;buffer[offset+i]=result}if(bytesRead){stream.node.timestamp=Date.now()}return bytesRead},write:function(stream,buffer,offset,length,pos){if(!stream.tty||!stream.tty.ops.put_char){throw new FS.ErrnoError(60)}try{for(var i=0;i<length;i++){stream.tty.ops.put_char(stream.tty,buffer[offset+i])}}catch(e){throw new FS.ErrnoError(29)}if(length){stream.node.timestamp=Date.now()}return i}},default_tty_ops:{get_char:function(tty){if(!tty.input.length){var result=null;if(ENVIRONMENT_IS_NODE){var BUFSIZE=256;var buf=Buffer.alloc?Buffer.alloc(BUFSIZE):new Buffer(BUFSIZE);var bytesRead=0;try{bytesRead=nodeFS.readSync(process.stdin.fd,buf,0,BUFSIZE,null)}catch(e){if(e.toString().includes("EOF"))bytesRead=0;else throw e}if(bytesRead>0){result=buf.slice(0,bytesRead).toString("utf-8")}else{result=null}}else if(typeof window!="undefined"&&typeof window.prompt=="function"){result=window.prompt("Input: ");if(result!==null){result+="\n"}}else if(typeof readline=="function"){result=readline();if(result!==null){result+="\n"}}if(!result){return null}tty.input=intArrayFromString(result,true)}return tty.input.shift()},put_char:function(tty,val){if(val===null||val===10){out(UTF8ArrayToString(tty.output,0));tty.output=[]}else{if(val!=0)tty.output.push(val)}},flush:function(tty){if(tty.output&&tty.output.length>0){out(UTF8ArrayToString(tty.output,0));tty.output=[]}}},default_tty1_ops:{put_char:function(tty,val){if(val===null||val===10){err(UTF8ArrayToString(tty.output,0));tty.output=[]}else{if(val!=0)tty.output.push(val)}},flush:function(tty){if(tty.output&&tty.output.length>0){err(UTF8ArrayToString(tty.output,0));tty.output=[]}}}};function mmapAlloc(size){var alignedSize=alignMemory(size,65536);var ptr=_malloc(alignedSize);while(size<alignedSize)HEAP8[ptr+size++]=0;return ptr}var MEMFS={ops_table:null,mount:function(mount){return MEMFS.createNode(null,"/",16384|511,0)},createNode:function(parent,name,mode,dev){if(FS.isBlkdev(mode)||FS.isFIFO(mode)){throw new FS.ErrnoError(63)}if(!MEMFS.ops_table){MEMFS.ops_table={dir:{node:{getattr:MEMFS.node_ops.getattr,setattr:MEMFS.node_ops.setattr,lookup:MEMFS.node_ops.lookup,mknod:MEMFS.node_ops.mknod,rename:MEMFS.node_ops.rename,unlink:MEMFS.node_ops.unlink,rmdir:MEMFS.node_ops.rmdir,readdir:MEMFS.node_ops.readdir,symlink:MEMFS.node_ops.symlink},stream:{llseek:MEMFS.stream_ops.llseek}},file:{node:{getattr:MEMFS.node_ops.getattr,setattr:MEMFS.node_ops.setattr},stream:{llseek:MEMFS.stream_ops.llseek,read:MEMFS.stream_ops.read,write:MEMFS.stream_ops.write,allocate:MEMFS.stream_ops.allocate,mmap:MEMFS.stream_ops.mmap,msync:MEMFS.stream_ops.msync}},link:{node:{getattr:MEMFS.node_ops.getattr,setattr:MEMFS.node_ops.setattr,readlink:MEMFS.node_ops.readlink},stream:{}},chrdev:{node:{getattr:MEMFS.node_ops.getattr,setattr:MEMFS.node_ops.setattr},stream:FS.chrdev_stream_ops}}}var node=FS.createNode(parent,name,mode,dev);if(FS.isDir(node.mode)){node.node_ops=MEMFS.ops_table.dir.node;node.stream_ops=MEMFS.ops_table.dir.stream;node.contents={}}else if(FS.isFile(node.mode)){node.node_ops=MEMFS.ops_table.file.node;node.stream_ops=MEMFS.ops_table.file.stream;node.usedBytes=0;node.contents=null}else if(FS.isLink(node.mode)){node.node_ops=MEMFS.ops_table.link.node;node.stream_ops=MEMFS.ops_table.link.stream}else if(FS.isChrdev(node.mode)){node.node_ops=MEMFS.ops_table.chrdev.node;node.stream_ops=MEMFS.ops_table.chrdev.stream}node.timestamp=Date.now();if(parent){parent.contents[name]=node;parent.timestamp=node.timestamp}return node},getFileDataAsTypedArray:function(node){if(!node.contents)return new Uint8Array(0);if(node.contents.subarray)return node.contents.subarray(0,node.usedBytes);return new Uint8Array(node.contents)},expandFileStorage:function(node,newCapacity){var prevCapacity=node.contents?node.contents.length:0;if(prevCapacity>=newCapacity)return;var CAPACITY_DOUBLING_MAX=1024*1024;newCapacity=Math.max(newCapacity,prevCapacity*(prevCapacity<CAPACITY_DOUBLING_MAX?2:1.125)>>>0);if(prevCapacity!=0)newCapacity=Math.max(newCapacity,256);var oldContents=node.contents;node.contents=new Uint8Array(newCapacity);if(node.usedBytes>0)node.contents.set(oldContents.subarray(0,node.usedBytes),0)},resizeFileStorage:function(node,newSize){if(node.usedBytes==newSize)return;if(newSize==0){node.contents=null;node.usedBytes=0}else{var oldContents=node.contents;node.contents=new Uint8Array(newSize);if(oldContents){node.contents.set(oldContents.subarray(0,Math.min(newSize,node.usedBytes)))}node.usedBytes=newSize}},node_ops:{getattr:function(node){var attr={};attr.dev=FS.isChrdev(node.mode)?node.id:1;attr.ino=node.id;attr.mode=node.mode;attr.nlink=1;attr.uid=0;attr.gid=0;attr.rdev=node.rdev;if(FS.isDir(node.mode)){attr.size=4096}else if(FS.isFile(node.mode)){attr.size=node.usedBytes}else if(FS.isLink(node.mode)){attr.size=node.link.length}else{attr.size=0}attr.atime=new Date(node.timestamp);attr.mtime=new Date(node.timestamp);attr.ctime=new Date(node.timestamp);attr.blksize=4096;attr.blocks=Math.ceil(attr.size/attr.blksize);return attr},setattr:function(node,attr){if(attr.mode!==undefined){node.mode=attr.mode}if(attr.timestamp!==undefined){node.timestamp=attr.timestamp}if(attr.size!==undefined){MEMFS.resizeFileStorage(node,attr.size)}},lookup:function(parent,name){throw FS.genericErrors[44]},mknod:function(parent,name,mode,dev){return MEMFS.createNode(parent,name,mode,dev)},rename:function(old_node,new_dir,new_name){if(FS.isDir(old_node.mode)){var new_node;try{new_node=FS.lookupNode(new_dir,new_name)}catch(e){}if(new_node){for(var i in new_node.contents){throw new FS.ErrnoError(55)}}}delete old_node.parent.contents[old_node.name];old_node.parent.timestamp=Date.now();old_node.name=new_name;new_dir.contents[new_name]=old_node;new_dir.timestamp=old_node.parent.timestamp;old_node.parent=new_dir},unlink:function(parent,name){delete parent.contents[name];parent.timestamp=Date.now()},rmdir:function(parent,name){var node=FS.lookupNode(parent,name);for(var i in node.contents){throw new FS.ErrnoError(55)}delete parent.contents[name];parent.timestamp=Date.now()},readdir:function(node){var entries=[".",".."];for(var key in node.contents){if(!node.contents.hasOwnProperty(key)){continue}entries.push(key)}return entries},symlink:function(parent,newname,oldpath){var node=MEMFS.createNode(parent,newname,511|40960,0);node.link=oldpath;return node},readlink:function(node){if(!FS.isLink(node.mode)){throw new FS.ErrnoError(28)}return node.link}},stream_ops:{read:function(stream,buffer,offset,length,position){var contents=stream.node.contents;if(position>=stream.node.usedBytes)return 0;var size=Math.min(stream.node.usedBytes-position,length);if(size>8&&contents.subarray){buffer.set(contents.subarray(position,position+size),offset)}else{for(var i=0;i<size;i++)buffer[offset+i]=contents[position+i]}return size},write:function(stream,buffer,offset,length,position,canOwn){if(buffer.buffer===HEAP8.buffer){canOwn=false}if(!length)return 0;var node=stream.node;node.timestamp=Date.now();if(buffer.subarray&&(!node.contents||node.contents.subarray)){if(canOwn){node.contents=buffer.subarray(offset,offset+length);node.usedBytes=length;return length}else if(node.usedBytes===0&&position===0){node.contents=buffer.slice(offset,offset+length);node.usedBytes=length;return length}else if(position+length<=node.usedBytes){node.contents.set(buffer.subarray(offset,offset+length),position);return length}}MEMFS.expandFileStorage(node,position+length);if(node.contents.subarray&&buffer.subarray){node.contents.set(buffer.subarray(offset,offset+length),position)}else{for(var i=0;i<length;i++){node.contents[position+i]=buffer[offset+i]}}node.usedBytes=Math.max(node.usedBytes,position+length);return length},llseek:function(stream,offset,whence){var position=offset;if(whence===1){position+=stream.position}else if(whence===2){if(FS.isFile(stream.node.mode)){position+=stream.node.usedBytes}}if(position<0){throw new FS.ErrnoError(28)}return position},allocate:function(stream,offset,length){MEMFS.expandFileStorage(stream.node,offset+length);stream.node.usedBytes=Math.max(stream.node.usedBytes,offset+length)},mmap:function(stream,address,length,position,prot,flags){if(address!==0){throw new FS.ErrnoError(28)}if(!FS.isFile(stream.node.mode)){throw new FS.ErrnoError(43)}var ptr;var allocated;var contents=stream.node.contents;if(!(flags&2)&&contents.buffer===buffer){allocated=false;ptr=contents.byteOffset}else{if(position>0||position+length<contents.length){if(contents.subarray){contents=contents.subarray(position,position+length)}else{contents=Array.prototype.slice.call(contents,position,position+length)}}allocated=true;ptr=mmapAlloc(length);if(!ptr){throw new FS.ErrnoError(48)}HEAP8.set(contents,ptr)}return{ptr:ptr,allocated:allocated}},msync:function(stream,buffer,offset,length,mmapFlags){if(!FS.isFile(stream.node.mode)){throw new FS.ErrnoError(43)}if(mmapFlags&2){return 0}var bytesWritten=MEMFS.stream_ops.write(stream,buffer,0,length,offset,false);return 0}}};var IDBFS={dbs:{},indexedDB:function(){if(typeof indexedDB!=="undefined")return indexedDB;var ret=null;if(typeof window==="object")ret=window.indexedDB||window.mozIndexedDB||window.webkitIndexedDB||window.msIndexedDB;assert(ret,"IDBFS used, but indexedDB not supported");return ret},DB_VERSION:21,DB_STORE_NAME:"FILE_DATA",mount:function(mount){return MEMFS.mount.apply(null,arguments)},syncfs:function(mount,populate,callback){IDBFS.getLocalSet(mount,function(err,local){if(err)return callback(err);IDBFS.getRemoteSet(mount,function(err,remote){if(err)return callback(err);var src=populate?remote:local;var dst=populate?local:remote;IDBFS.reconcile(src,dst,callback)})})},getDB:function(name,callback){var db=IDBFS.dbs[name];if(db){return callback(null,db)}var req;try{req=IDBFS.indexedDB().open(name,IDBFS.DB_VERSION)}catch(e){return callback(e)}if(!req){return callback("Unable to connect to IndexedDB")}req.onupgradeneeded=function(e){var db=e.target.result;var transaction=e.target.transaction;var fileStore;if(db.objectStoreNames.contains(IDBFS.DB_STORE_NAME)){fileStore=transaction.objectStore(IDBFS.DB_STORE_NAME)}else{fileStore=db.createObjectStore(IDBFS.DB_STORE_NAME)}if(!fileStore.indexNames.contains("timestamp")){fileStore.createIndex("timestamp","timestamp",{unique:false})}};req.onsuccess=function(){db=req.result;IDBFS.dbs[name]=db;callback(null,db)};req.onerror=function(e){callback(this.error);e.preventDefault()}},getLocalSet:function(mount,callback){var entries={};function isRealDir(p){return p!=="."&&p!==".."}function toAbsolute(root){return function(p){return PATH.join2(root,p)}}var check=FS.readdir(mount.mountpoint).filter(isRealDir).map(toAbsolute(mount.mountpoint));while(check.length){var path=check.pop();var stat;try{stat=FS.stat(path)}catch(e){return callback(e)}if(FS.isDir(stat.mode)){check.push.apply(check,FS.readdir(path).filter(isRealDir).map(toAbsolute(path)))}entries[path]={"timestamp":stat.mtime}}return callback(null,{type:"local",entries:entries})},getRemoteSet:function(mount,callback){var entries={};IDBFS.getDB(mount.mountpoint,function(err,db){if(err)return callback(err);try{var transaction=db.transaction([IDBFS.DB_STORE_NAME],"readonly");transaction.onerror=function(e){callback(this.error);e.preventDefault()};var store=transaction.objectStore(IDBFS.DB_STORE_NAME);var index=store.index("timestamp");index.openKeyCursor().onsuccess=function(event){var cursor=event.target.result;if(!cursor){return callback(null,{type:"remote",db:db,entries:entries})}entries[cursor.primaryKey]={"timestamp":cursor.key};cursor.continue()}}catch(e){return callback(e)}})},loadLocalEntry:function(path,callback){var stat,node;try{var lookup=FS.lookupPath(path);node=lookup.node;stat=FS.stat(path)}catch(e){return callback(e)}if(FS.isDir(stat.mode)){return callback(null,{"timestamp":stat.mtime,"mode":stat.mode})}else if(FS.isFile(stat.mode)){node.contents=MEMFS.getFileDataAsTypedArray(node);return callback(null,{"timestamp":stat.mtime,"mode":stat.mode,"contents":node.contents})}else{return callback(new Error("node type not supported"))}},storeLocalEntry:function(path,entry,callback){try{if(FS.isDir(entry["mode"])){FS.mkdirTree(path,entry["mode"])}else if(FS.isFile(entry["mode"])){FS.writeFile(path,entry["contents"],{canOwn:true})}else{return callback(new Error("node type not supported"))}FS.chmod(path,entry["mode"]);FS.utime(path,entry["timestamp"],entry["timestamp"])}catch(e){return callback(e)}callback(null)},removeLocalEntry:function(path,callback){try{var lookup=FS.lookupPath(path);var stat=FS.stat(path);if(FS.isDir(stat.mode)){FS.rmdir(path)}else if(FS.isFile(stat.mode)){FS.unlink(path)}}catch(e){return callback(e)}callback(null)},loadRemoteEntry:function(store,path,callback){var req=store.get(path);req.onsuccess=function(event){callback(null,event.target.result)};req.onerror=function(e){callback(this.error);e.preventDefault()}},storeRemoteEntry:function(store,path,entry,callback){var req=store.put(entry,path);req.onsuccess=function(){callback(null)};req.onerror=function(e){callback(this.error);e.preventDefault()}},removeRemoteEntry:function(store,path,callback){var req=store.delete(path);req.onsuccess=function(){callback(null)};req.onerror=function(e){callback(this.error);e.preventDefault()}},reconcile:function(src,dst,callback){var total=0;var create=[];Object.keys(src.entries).forEach(function(key){var e=src.entries[key];var e2=dst.entries[key];if(!e2||e["timestamp"].getTime()!=e2["timestamp"].getTime()){create.push(key);total++}});var remove=[];Object.keys(dst.entries).forEach(function(key){if(!src.entries[key]){remove.push(key);total++}});if(!total){return callback(null)}var errored=false;var db=src.type==="remote"?src.db:dst.db;var transaction=db.transaction([IDBFS.DB_STORE_NAME],"readwrite");var store=transaction.objectStore(IDBFS.DB_STORE_NAME);function done(err){if(err&&!errored){errored=true;return callback(err)}}transaction.onerror=function(e){done(this.error);e.preventDefault()};transaction.oncomplete=function(e){if(!errored){callback(null)}};create.sort().forEach(function(path){if(dst.type==="local"){IDBFS.loadRemoteEntry(store,path,function(err,entry){if(err)return done(err);IDBFS.storeLocalEntry(path,entry,done)})}else{IDBFS.loadLocalEntry(path,function(err,entry){if(err)return done(err);IDBFS.storeRemoteEntry(store,path,entry,done)})}});remove.sort().reverse().forEach(function(path){if(dst.type==="local"){IDBFS.removeLocalEntry(path,done)}else{IDBFS.removeRemoteEntry(store,path,done)}})}};var FS={root:null,mounts:[],devices:{},streams:[],nextInode:1,nameTable:null,currentPath:"/",initialized:false,ignorePermissions:true,trackingDelegate:{},tracking:{openFlags:{READ:1,WRITE:2}},ErrnoError:null,genericErrors:{},filesystems:null,syncFSRequests:0,lookupPath:function(path,opts){path=PATH_FS.resolve(FS.cwd(),path);opts=opts||{};if(!path)return{path:"",node:null};var defaults={follow_mount:true,recurse_count:0};for(var key in defaults){if(opts[key]===undefined){opts[key]=defaults[key]}}if(opts.recurse_count>8){throw new FS.ErrnoError(32)}var parts=PATH.normalizeArray(path.split("/").filter(function(p){return!!p}),false);var current=FS.root;var current_path="/";for(var i=0;i<parts.length;i++){var islast=i===parts.length-1;if(islast&&opts.parent){break}current=FS.lookupNode(current,parts[i]);current_path=PATH.join2(current_path,parts[i]);if(FS.isMountpoint(current)){if(!islast||islast&&opts.follow_mount){current=current.mounted.root}}if(!islast||opts.follow){var count=0;while(FS.isLink(current.mode)){var link=FS.readlink(current_path);current_path=PATH_FS.resolve(PATH.dirname(current_path),link);var lookup=FS.lookupPath(current_path,{recurse_count:opts.recurse_count});current=lookup.node;if(count++>40){throw new FS.ErrnoError(32)}}}}return{path:current_path,node:current}},getPath:function(node){var path;while(true){if(FS.isRoot(node)){var mount=node.mount.mountpoint;if(!path)return mount;return mount[mount.length-1]!=="/"?mount+"/"+path:mount+path}path=path?node.name+"/"+path:node.name;node=node.parent}},hashName:function(parentid,name){var hash=0;for(var i=0;i<name.length;i++){hash=(hash<<5)-hash+name.charCodeAt(i)|0}return(parentid+hash>>>0)%FS.nameTable.length},hashAddNode:function(node){var hash=FS.hashName(node.parent.id,node.name);node.name_next=FS.nameTable[hash];FS.nameTable[hash]=node},hashRemoveNode:function(node){var hash=FS.hashName(node.parent.id,node.name);if(FS.nameTable[hash]===node){FS.nameTable[hash]=node.name_next}else{var current=FS.nameTable[hash];while(current){if(current.name_next===node){current.name_next=node.name_next;break}current=current.name_next}}},lookupNode:function(parent,name){var errCode=FS.mayLookup(parent);if(errCode){throw new FS.ErrnoError(errCode,parent)}var hash=FS.hashName(parent.id,name);for(var node=FS.nameTable[hash];node;node=node.name_next){var nodeName=node.name;if(node.parent.id===parent.id&&nodeName===name){return node}}return FS.lookup(parent,name)},createNode:function(parent,name,mode,rdev){var node=new FS.FSNode(parent,name,mode,rdev);FS.hashAddNode(node);return node},destroyNode:function(node){FS.hashRemoveNode(node)},isRoot:function(node){return node===node.parent},isMountpoint:function(node){return!!node.mounted},isFile:function(mode){return(mode&61440)===32768},isDir:function(mode){return(mode&61440)===16384},isLink:function(mode){return(mode&61440)===40960},isChrdev:function(mode){return(mode&61440)===8192},isBlkdev:function(mode){return(mode&61440)===24576},isFIFO:function(mode){return(mode&61440)===4096},isSocket:function(mode){return(mode&49152)===49152},flagModes:{"r":0,"r+":2,"w":577,"w+":578,"a":1089,"a+":1090},modeStringToFlags:function(str){var flags=FS.flagModes[str];if(typeof flags==="undefined"){throw new Error("Unknown file open mode: "+str)}return flags},flagsToPermissionString:function(flag){var perms=["r","w","rw"][flag&3];if(flag&512){perms+="w"}return perms},nodePermissions:function(node,perms){if(FS.ignorePermissions){return 0}if(perms.includes("r")&&!(node.mode&292)){return 2}else if(perms.includes("w")&&!(node.mode&146)){return 2}else if(perms.includes("x")&&!(node.mode&73)){return 2}return 0},mayLookup:function(dir){var errCode=FS.nodePermissions(dir,"x");if(errCode)return errCode;if(!dir.node_ops.lookup)return 2;return 0},mayCreate:function(dir,name){try{var node=FS.lookupNode(dir,name);return 20}catch(e){}return FS.nodePermissions(dir,"wx")},mayDelete:function(dir,name,isdir){var node;try{node=FS.lookupNode(dir,name)}catch(e){return e.errno}var errCode=FS.nodePermissions(dir,"wx");if(errCode){return errCode}if(isdir){if(!FS.isDir(node.mode)){return 54}if(FS.isRoot(node)||FS.getPath(node)===FS.cwd()){return 10}}else{if(FS.isDir(node.mode)){return 31}}return 0},mayOpen:function(node,flags){if(!node){return 44}if(FS.isLink(node.mode)){return 32}else if(FS.isDir(node.mode)){if(FS.flagsToPermissionString(flags)!=="r"||flags&512){return 31}}return FS.nodePermissions(node,FS.flagsToPermissionString(flags))},MAX_OPEN_FDS:4096,nextfd:function(fd_start,fd_end){fd_start=fd_start||0;fd_end=fd_end||FS.MAX_OPEN_FDS;for(var fd=fd_start;fd<=fd_end;fd++){if(!FS.streams[fd]){return fd}}throw new FS.ErrnoError(33)},getStream:function(fd){return FS.streams[fd]},createStream:function(stream,fd_start,fd_end){if(!FS.FSStream){FS.FSStream=function(){};FS.FSStream.prototype={object:{get:function(){return this.node},set:function(val){this.node=val}},isRead:{get:function(){return(this.flags&2097155)!==1}},isWrite:{get:function(){return(this.flags&2097155)!==0}},isAppend:{get:function(){return this.flags&1024}}}}var newStream=new FS.FSStream;for(var p in stream){newStream[p]=stream[p]}stream=newStream;var fd=FS.nextfd(fd_start,fd_end);stream.fd=fd;FS.streams[fd]=stream;return stream},closeStream:function(fd){FS.streams[fd]=null},chrdev_stream_ops:{open:function(stream){var device=FS.getDevice(stream.node.rdev);stream.stream_ops=device.stream_ops;if(stream.stream_ops.open){stream.stream_ops.open(stream)}},llseek:function(){throw new FS.ErrnoError(70)}},major:function(dev){return dev>>8},minor:function(dev){return dev&255},makedev:function(ma,mi){return ma<<8|mi},registerDevice:function(dev,ops){FS.devices[dev]={stream_ops:ops}},getDevice:function(dev){return FS.devices[dev]},getMounts:function(mount){var mounts=[];var check=[mount];while(check.length){var m=check.pop();mounts.push(m);check.push.apply(check,m.mounts)}return mounts},syncfs:function(populate,callback){if(typeof populate==="function"){callback=populate;populate=false}FS.syncFSRequests++;if(FS.syncFSRequests>1){err("warning: "+FS.syncFSRequests+" FS.syncfs operations in flight at once, probably just doing extra work")}var mounts=FS.getMounts(FS.root.mount);var completed=0;function doCallback(errCode){FS.syncFSRequests--;return callback(errCode)}function done(errCode){if(errCode){if(!done.errored){done.errored=true;return doCallback(errCode)}return}if(++completed>=mounts.length){doCallback(null)}}mounts.forEach(function(mount){if(!mount.type.syncfs){return done(null)}mount.type.syncfs(mount,populate,done)})},mount:function(type,opts,mountpoint){var root=mountpoint==="/";var pseudo=!mountpoint;var node;if(root&&FS.root){throw new FS.ErrnoError(10)}else if(!root&&!pseudo){var lookup=FS.lookupPath(mountpoint,{follow_mount:false});mountpoint=lookup.path;node=lookup.node;if(FS.isMountpoint(node)){throw new FS.ErrnoError(10)}if(!FS.isDir(node.mode)){throw new FS.ErrnoError(54)}}var mount={type:type,opts:opts,mountpoint:mountpoint,mounts:[]};var mountRoot=type.mount(mount);mountRoot.mount=mount;mount.root=mountRoot;if(root){FS.root=mountRoot}else if(node){node.mounted=mount;if(node.mount){node.mount.mounts.push(mount)}}return mountRoot},unmount:function(mountpoint){var lookup=FS.lookupPath(mountpoint,{follow_mount:false});if(!FS.isMountpoint(lookup.node)){throw new FS.ErrnoError(28)}var node=lookup.node;var mount=node.mounted;var mounts=FS.getMounts(mount);Object.keys(FS.nameTable).forEach(function(hash){var current=FS.nameTable[hash];while(current){var next=current.name_next;if(mounts.includes(current.mount)){FS.destroyNode(current)}current=next}});node.mounted=null;var idx=node.mount.mounts.indexOf(mount);node.mount.mounts.splice(idx,1)},lookup:function(parent,name){return parent.node_ops.lookup(parent,name)},mknod:function(path,mode,dev){var lookup=FS.lookupPath(path,{parent:true});var parent=lookup.node;var name=PATH.basename(path);if(!name||name==="."||name===".."){throw new FS.ErrnoError(28)}var errCode=FS.mayCreate(parent,name);if(errCode){throw new FS.ErrnoError(errCode)}if(!parent.node_ops.mknod){throw new FS.ErrnoError(63)}return parent.node_ops.mknod(parent,name,mode,dev)},create:function(path,mode){mode=mode!==undefined?mode:438;mode&=4095;mode|=32768;return FS.mknod(path,mode,0)},mkdir:function(path,mode){mode=mode!==undefined?mode:511;mode&=511|512;mode|=16384;return FS.mknod(path,mode,0)},mkdirTree:function(path,mode){var dirs=path.split("/");var d="";for(var i=0;i<dirs.length;++i){if(!dirs[i])continue;d+="/"+dirs[i];try{FS.mkdir(d,mode)}catch(e){if(e.errno!=20)throw e}}},mkdev:function(path,mode,dev){if(typeof dev==="undefined"){dev=mode;mode=438}mode|=8192;return FS.mknod(path,mode,dev)},symlink:function(oldpath,newpath){if(!PATH_FS.resolve(oldpath)){throw new FS.ErrnoError(44)}var lookup=FS.lookupPath(newpath,{parent:true});var parent=lookup.node;if(!parent){throw new FS.ErrnoError(44)}var newname=PATH.basename(newpath);var errCode=FS.mayCreate(parent,newname);if(errCode){throw new FS.ErrnoError(errCode)}if(!parent.node_ops.symlink){throw new FS.ErrnoError(63)}return parent.node_ops.symlink(parent,newname,oldpath)},rename:function(old_path,new_path){var old_dirname=PATH.dirname(old_path);var new_dirname=PATH.dirname(new_path);var old_name=PATH.basename(old_path);var new_name=PATH.basename(new_path);var lookup,old_dir,new_dir;lookup=FS.lookupPath(old_path,{parent:true});old_dir=lookup.node;lookup=FS.lookupPath(new_path,{parent:true});new_dir=lookup.node;if(!old_dir||!new_dir)throw new FS.ErrnoError(44);if(old_dir.mount!==new_dir.mount){throw new FS.ErrnoError(75)}var old_node=FS.lookupNode(old_dir,old_name);var relative=PATH_FS.relative(old_path,new_dirname);if(relative.charAt(0)!=="."){throw new FS.ErrnoError(28)}relative=PATH_FS.relative(new_path,old_dirname);if(relative.charAt(0)!=="."){throw new FS.ErrnoError(55)}var new_node;try{new_node=FS.lookupNode(new_dir,new_name)}catch(e){}if(old_node===new_node){return}var isdir=FS.isDir(old_node.mode);var errCode=FS.mayDelete(old_dir,old_name,isdir);if(errCode){throw new FS.ErrnoError(errCode)}errCode=new_node?FS.mayDelete(new_dir,new_name,isdir):FS.mayCreate(new_dir,new_name);if(errCode){throw new FS.ErrnoError(errCode)}if(!old_dir.node_ops.rename){throw new FS.ErrnoError(63)}if(FS.isMountpoint(old_node)||new_node&&FS.isMountpoint(new_node)){throw new FS.ErrnoError(10)}if(new_dir!==old_dir){errCode=FS.nodePermissions(old_dir,"w");if(errCode){throw new FS.ErrnoError(errCode)}}try{if(FS.trackingDelegate["willMovePath"]){FS.trackingDelegate["willMovePath"](old_path,new_path)}}catch(e){err("FS.trackingDelegate['willMovePath']('"+old_path+"', '"+new_path+"') threw an exception: "+e.message)}FS.hashRemoveNode(old_node);try{old_dir.node_ops.rename(old_node,new_dir,new_name)}catch(e){throw e}finally{FS.hashAddNode(old_node)}try{if(FS.trackingDelegate["onMovePath"])FS.trackingDelegate["onMovePath"](old_path,new_path)}catch(e){err("FS.trackingDelegate['onMovePath']('"+old_path+"', '"+new_path+"') threw an exception: "+e.message)}},rmdir:function(path){var lookup=FS.lookupPath(path,{parent:true});var parent=lookup.node;var name=PATH.basename(path);var node=FS.lookupNode(parent,name);var errCode=FS.mayDelete(parent,name,true);if(errCode){throw new FS.ErrnoError(errCode)}if(!parent.node_ops.rmdir){throw new FS.ErrnoError(63)}if(FS.isMountpoint(node)){throw new FS.ErrnoError(10)}try{if(FS.trackingDelegate["willDeletePath"]){FS.trackingDelegate["willDeletePath"](path)}}catch(e){err("FS.trackingDelegate['willDeletePath']('"+path+"') threw an exception: "+e.message)}parent.node_ops.rmdir(parent,name);FS.destroyNode(node);try{if(FS.trackingDelegate["onDeletePath"])FS.trackingDelegate["onDeletePath"](path)}catch(e){err("FS.trackingDelegate['onDeletePath']('"+path+"') threw an exception: "+e.message)}},readdir:function(path){var lookup=FS.lookupPath(path,{follow:true});var node=lookup.node;if(!node.node_ops.readdir){throw new FS.ErrnoError(54)}return node.node_ops.readdir(node)},unlink:function(path){var lookup=FS.lookupPath(path,{parent:true});var parent=lookup.node;var name=PATH.basename(path);var node=FS.lookupNode(parent,name);var errCode=FS.mayDelete(parent,name,false);if(errCode){throw new FS.ErrnoError(errCode)}if(!parent.node_ops.unlink){throw new FS.ErrnoError(63)}if(FS.isMountpoint(node)){throw new FS.ErrnoError(10)}try{if(FS.trackingDelegate["willDeletePath"]){FS.trackingDelegate["willDeletePath"](path)}}catch(e){err("FS.trackingDelegate['willDeletePath']('"+path+"') threw an exception: "+e.message)}parent.node_ops.unlink(parent,name);FS.destroyNode(node);try{if(FS.trackingDelegate["onDeletePath"])FS.trackingDelegate["onDeletePath"](path)}catch(e){err("FS.trackingDelegate['onDeletePath']('"+path+"') threw an exception: "+e.message)}},readlink:function(path){var lookup=FS.lookupPath(path);var link=lookup.node;if(!link){throw new FS.ErrnoError(44)}if(!link.node_ops.readlink){throw new FS.ErrnoError(28)}return PATH_FS.resolve(FS.getPath(link.parent),link.node_ops.readlink(link))},stat:function(path,dontFollow){var lookup=FS.lookupPath(path,{follow:!dontFollow});var node=lookup.node;if(!node){throw new FS.ErrnoError(44)}if(!node.node_ops.getattr){throw new FS.ErrnoError(63)}return node.node_ops.getattr(node)},lstat:function(path){return FS.stat(path,true)},chmod:function(path,mode,dontFollow){var node;if(typeof path==="string"){var lookup=FS.lookupPath(path,{follow:!dontFollow});node=lookup.node}else{node=path}if(!node.node_ops.setattr){throw new FS.ErrnoError(63)}node.node_ops.setattr(node,{mode:mode&4095|node.mode&~4095,timestamp:Date.now()})},lchmod:function(path,mode){FS.chmod(path,mode,true)},fchmod:function(fd,mode){var stream=FS.getStream(fd);if(!stream){throw new FS.ErrnoError(8)}FS.chmod(stream.node,mode)},chown:function(path,uid,gid,dontFollow){var node;if(typeof path==="string"){var lookup=FS.lookupPath(path,{follow:!dontFollow});node=lookup.node}else{node=path}if(!node.node_ops.setattr){throw new FS.ErrnoError(63)}node.node_ops.setattr(node,{timestamp:Date.now()})},lchown:function(path,uid,gid){FS.chown(path,uid,gid,true)},fchown:function(fd,uid,gid){var stream=FS.getStream(fd);if(!stream){throw new FS.ErrnoError(8)}FS.chown(stream.node,uid,gid)},truncate:function(path,len){if(len<0){throw new FS.ErrnoError(28)}var node;if(typeof path==="string"){var lookup=FS.lookupPath(path,{follow:true});node=lookup.node}else{node=path}if(!node.node_ops.setattr){throw new FS.ErrnoError(63)}if(FS.isDir(node.mode)){throw new FS.ErrnoError(31)}if(!FS.isFile(node.mode)){throw new FS.ErrnoError(28)}var errCode=FS.nodePermissions(node,"w");if(errCode){throw new FS.ErrnoError(errCode)}node.node_ops.setattr(node,{size:len,timestamp:Date.now()})},ftruncate:function(fd,len){var stream=FS.getStream(fd);if(!stream){throw new FS.ErrnoError(8)}if((stream.flags&2097155)===0){throw new FS.ErrnoError(28)}FS.truncate(stream.node,len)},utime:function(path,atime,mtime){var lookup=FS.lookupPath(path,{follow:true});var node=lookup.node;node.node_ops.setattr(node,{timestamp:Math.max(atime,mtime)})},open:function(path,flags,mode,fd_start,fd_end){if(path===""){throw new FS.ErrnoError(44)}flags=typeof flags==="string"?FS.modeStringToFlags(flags):flags;mode=typeof mode==="undefined"?438:mode;if(flags&64){mode=mode&4095|32768}else{mode=0}var node;if(typeof path==="object"){node=path}else{path=PATH.normalize(path);try{var lookup=FS.lookupPath(path,{follow:!(flags&131072)});node=lookup.node}catch(e){}}var created=false;if(flags&64){if(node){if(flags&128){throw new FS.ErrnoError(20)}}else{node=FS.mknod(path,mode,0);created=true}}if(!node){throw new FS.ErrnoError(44)}if(FS.isChrdev(node.mode)){flags&=~512}if(flags&65536&&!FS.isDir(node.mode)){throw new FS.ErrnoError(54)}if(!created){var errCode=FS.mayOpen(node,flags);if(errCode){throw new FS.ErrnoError(errCode)}}if(flags&512){FS.truncate(node,0)}flags&=~(128|512|131072);var stream=FS.createStream({node:node,path:FS.getPath(node),flags:flags,seekable:true,position:0,stream_ops:node.stream_ops,ungotten:[],error:false},fd_start,fd_end);if(stream.stream_ops.open){stream.stream_ops.open(stream)}if(Module["logReadFiles"]&&!(flags&1)){if(!FS.readFiles)FS.readFiles={};if(!(path in FS.readFiles)){FS.readFiles[path]=1;err("FS.trackingDelegate error on read file: "+path)}}try{if(FS.trackingDelegate["onOpenFile"]){var trackingFlags=0;if((flags&2097155)!==1){trackingFlags|=FS.tracking.openFlags.READ}if((flags&2097155)!==0){trackingFlags|=FS.tracking.openFlags.WRITE}FS.trackingDelegate["onOpenFile"](path,trackingFlags)}}catch(e){err("FS.trackingDelegate['onOpenFile']('"+path+"', flags) threw an exception: "+e.message)}return stream},close:function(stream){if(FS.isClosed(stream)){throw new FS.ErrnoError(8)}if(stream.getdents)stream.getdents=null;try{if(stream.stream_ops.close){stream.stream_ops.close(stream)}}catch(e){throw e}finally{FS.closeStream(stream.fd)}stream.fd=null},isClosed:function(stream){return stream.fd===null},llseek:function(stream,offset,whence){if(FS.isClosed(stream)){throw new FS.ErrnoError(8)}if(!stream.seekable||!stream.stream_ops.llseek){throw new FS.ErrnoError(70)}if(whence!=0&&whence!=1&&whence!=2){throw new FS.ErrnoError(28)}stream.position=stream.stream_ops.llseek(stream,offset,whence);stream.ungotten=[];return stream.position},read:function(stream,buffer,offset,length,position){if(length<0||position<0){throw new FS.ErrnoError(28)}if(FS.isClosed(stream)){throw new FS.ErrnoError(8)}if((stream.flags&2097155)===1){throw new FS.ErrnoError(8)}if(FS.isDir(stream.node.mode)){throw new FS.ErrnoError(31)}if(!stream.stream_ops.read){throw new FS.ErrnoError(28)}var seeking=typeof position!=="undefined";if(!seeking){position=stream.position}else if(!stream.seekable){throw new FS.ErrnoError(70)}var bytesRead=stream.stream_ops.read(stream,buffer,offset,length,position);if(!seeking)stream.position+=bytesRead;return bytesRead},write:function(stream,buffer,offset,length,position,canOwn){if(length<0||position<0){throw new FS.ErrnoError(28)}if(FS.isClosed(stream)){throw new FS.ErrnoError(8)}if((stream.flags&2097155)===0){throw new FS.ErrnoError(8)}if(FS.isDir(stream.node.mode)){throw new FS.ErrnoError(31)}if(!stream.stream_ops.write){throw new FS.ErrnoError(28)}if(stream.seekable&&stream.flags&1024){FS.llseek(stream,0,2)}var seeking=typeof position!=="undefined";if(!seeking){position=stream.position}else if(!stream.seekable){throw new FS.ErrnoError(70)}var bytesWritten=stream.stream_ops.write(stream,buffer,offset,length,position,canOwn);if(!seeking)stream.position+=bytesWritten;try{if(stream.path&&FS.trackingDelegate["onWriteToFile"])FS.trackingDelegate["onWriteToFile"](stream.path)}catch(e){err("FS.trackingDelegate['onWriteToFile']('"+stream.path+"') threw an exception: "+e.message)}return bytesWritten},allocate:function(stream,offset,length){if(FS.isClosed(stream)){throw new FS.ErrnoError(8)}if(offset<0||length<=0){throw new FS.ErrnoError(28)}if((stream.flags&2097155)===0){throw new FS.ErrnoError(8)}if(!FS.isFile(stream.node.mode)&&!FS.isDir(stream.node.mode)){throw new FS.ErrnoError(43)}if(!stream.stream_ops.allocate){throw new FS.ErrnoError(138)}stream.stream_ops.allocate(stream,offset,length)},mmap:function(stream,address,length,position,prot,flags){if((prot&2)!==0&&(flags&2)===0&&(stream.flags&2097155)!==2){throw new FS.ErrnoError(2)}if((stream.flags&2097155)===1){throw new FS.ErrnoError(2)}if(!stream.stream_ops.mmap){throw new FS.ErrnoError(43)}return stream.stream_ops.mmap(stream,address,length,position,prot,flags)},msync:function(stream,buffer,offset,length,mmapFlags){if(!stream||!stream.stream_ops.msync){return 0}return stream.stream_ops.msync(stream,buffer,offset,length,mmapFlags)},munmap:function(stream){return 0},ioctl:function(stream,cmd,arg){if(!stream.stream_ops.ioctl){throw new FS.ErrnoError(59)}return stream.stream_ops.ioctl(stream,cmd,arg)},readFile:function(path,opts){opts=opts||{};opts.flags=opts.flags||0;opts.encoding=opts.encoding||"binary";if(opts.encoding!=="utf8"&&opts.encoding!=="binary"){throw new Error('Invalid encoding type "'+opts.encoding+'"')}var ret;var stream=FS.open(path,opts.flags);var stat=FS.stat(path);var length=stat.size;var buf=new Uint8Array(length);FS.read(stream,buf,0,length,0);if(opts.encoding==="utf8"){ret=UTF8ArrayToString(buf,0)}else if(opts.encoding==="binary"){ret=buf}FS.close(stream);return ret},writeFile:function(path,data,opts){opts=opts||{};opts.flags=opts.flags||577;var stream=FS.open(path,opts.flags,opts.mode);if(typeof data==="string"){var buf=new Uint8Array(lengthBytesUTF8(data)+1);var actualNumBytes=stringToUTF8Array(data,buf,0,buf.length);FS.write(stream,buf,0,actualNumBytes,undefined,opts.canOwn)}else if(ArrayBuffer.isView(data)){FS.write(stream,data,0,data.byteLength,undefined,opts.canOwn)}else{throw new Error("Unsupported data type")}FS.close(stream)},cwd:function(){return FS.currentPath},chdir:function(path){var lookup=FS.lookupPath(path,{follow:true});if(lookup.node===null){throw new FS.ErrnoError(44)}if(!FS.isDir(lookup.node.mode)){throw new FS.ErrnoError(54)}var errCode=FS.nodePermissions(lookup.node,"x");if(errCode){throw new FS.ErrnoError(errCode)}FS.currentPath=lookup.path},createDefaultDirectories:function(){FS.mkdir("/tmp");FS.mkdir("/home");FS.mkdir("/home/web_user")},createDefaultDevices:function(){FS.mkdir("/dev");FS.registerDevice(FS.makedev(1,3),{read:function(){return 0},write:function(stream,buffer,offset,length,pos){return length}});FS.mkdev("/dev/null",FS.makedev(1,3));TTY.register(FS.makedev(5,0),TTY.default_tty_ops);TTY.register(FS.makedev(6,0),TTY.default_tty1_ops);FS.mkdev("/dev/tty",FS.makedev(5,0));FS.mkdev("/dev/tty1",FS.makedev(6,0));var random_device=getRandomDevice();FS.createDevice("/dev","random",random_device);FS.createDevice("/dev","urandom",random_device);FS.mkdir("/dev/shm");FS.mkdir("/dev/shm/tmp")},createSpecialDirectories:function(){FS.mkdir("/proc");var proc_self=FS.mkdir("/proc/self");FS.mkdir("/proc/self/fd");FS.mount({mount:function(){var node=FS.createNode(proc_self,"fd",16384|511,73);node.node_ops={lookup:function(parent,name){var fd=+name;var stream=FS.getStream(fd);if(!stream)throw new FS.ErrnoError(8);var ret={parent:null,mount:{mountpoint:"fake"},node_ops:{readlink:function(){return stream.path}}};ret.parent=ret;return ret}};return node}},{},"/proc/self/fd")},createStandardStreams:function(){if(Module["stdin"]){FS.createDevice("/dev","stdin",Module["stdin"])}else{FS.symlink("/dev/tty","/dev/stdin")}if(Module["stdout"]){FS.createDevice("/dev","stdout",null,Module["stdout"])}else{FS.symlink("/dev/tty","/dev/stdout")}if(Module["stderr"]){FS.createDevice("/dev","stderr",null,Module["stderr"])}else{FS.symlink("/dev/tty1","/dev/stderr")}var stdin=FS.open("/dev/stdin",0);var stdout=FS.open("/dev/stdout",1);var stderr=FS.open("/dev/stderr",1)},ensureErrnoError:function(){if(FS.ErrnoError)return;FS.ErrnoError=function ErrnoError(errno,node){this.node=node;this.setErrno=function(errno){this.errno=errno};this.setErrno(errno);this.message="FS error"};FS.ErrnoError.prototype=new Error;FS.ErrnoError.prototype.constructor=FS.ErrnoError;[44].forEach(function(code){FS.genericErrors[code]=new FS.ErrnoError(code);FS.genericErrors[code].stack="<generic error, no stack>"})},staticInit:function(){FS.ensureErrnoError();FS.nameTable=new Array(4096);FS.mount(MEMFS,{},"/");FS.createDefaultDirectories();FS.createDefaultDevices();FS.createSpecialDirectories();FS.filesystems={"MEMFS":MEMFS,"IDBFS":IDBFS}},init:function(input,output,error){FS.init.initialized=true;FS.ensureErrnoError();Module["stdin"]=input||Module["stdin"];Module["stdout"]=output||Module["stdout"];Module["stderr"]=error||Module["stderr"];FS.createStandardStreams()},quit:function(){FS.init.initialized=false;var fflush=Module["_fflush"];if(fflush)fflush(0);for(var i=0;i<FS.streams.length;i++){var stream=FS.streams[i];if(!stream){continue}FS.close(stream)}},getMode:function(canRead,canWrite){var mode=0;if(canRead)mode|=292|73;if(canWrite)mode|=146;return mode},findObject:function(path,dontResolveLastLink){var ret=FS.analyzePath(path,dontResolveLastLink);if(ret.exists){return ret.object}else{return null}},analyzePath:function(path,dontResolveLastLink){try{var lookup=FS.lookupPath(path,{follow:!dontResolveLastLink});path=lookup.path}catch(e){}var ret={isRoot:false,exists:false,error:0,name:null,path:null,object:null,parentExists:false,parentPath:null,parentObject:null};try{var lookup=FS.lookupPath(path,{parent:true});ret.parentExists=true;ret.parentPath=lookup.path;ret.parentObject=lookup.node;ret.name=PATH.basename(path);lookup=FS.lookupPath(path,{follow:!dontResolveLastLink});ret.exists=true;ret.path=lookup.path;ret.object=lookup.node;ret.name=lookup.node.name;ret.isRoot=lookup.path==="/"}catch(e){ret.error=e.errno}return ret},createPath:function(parent,path,canRead,canWrite){parent=typeof parent==="string"?parent:FS.getPath(parent);var parts=path.split("/").reverse();while(parts.length){var part=parts.pop();if(!part)continue;var current=PATH.join2(parent,part);try{FS.mkdir(current)}catch(e){}parent=current}return current},createFile:function(parent,name,properties,canRead,canWrite){var path=PATH.join2(typeof parent==="string"?parent:FS.getPath(parent),name);var mode=FS.getMode(canRead,canWrite);return FS.create(path,mode)},createDataFile:function(parent,name,data,canRead,canWrite,canOwn){var path=name?PATH.join2(typeof parent==="string"?parent:FS.getPath(parent),name):parent;var mode=FS.getMode(canRead,canWrite);var node=FS.create(path,mode);if(data){if(typeof data==="string"){var arr=new Array(data.length);for(var i=0,len=data.length;i<len;++i)arr[i]=data.charCodeAt(i);data=arr}FS.chmod(node,mode|146);var stream=FS.open(node,577);FS.write(stream,data,0,data.length,0,canOwn);FS.close(stream);FS.chmod(node,mode)}return node},createDevice:function(parent,name,input,output){var path=PATH.join2(typeof parent==="string"?parent:FS.getPath(parent),name);var mode=FS.getMode(!!input,!!output);if(!FS.createDevice.major)FS.createDevice.major=64;var dev=FS.makedev(FS.createDevice.major++,0);FS.registerDevice(dev,{open:function(stream){stream.seekable=false},close:function(stream){if(output&&output.buffer&&output.buffer.length){output(10)}},read:function(stream,buffer,offset,length,pos){var bytesRead=0;for(var i=0;i<length;i++){var result;try{result=input()}catch(e){throw new FS.ErrnoError(29)}if(result===undefined&&bytesRead===0){throw new FS.ErrnoError(6)}if(result===null||result===undefined)break;bytesRead++;buffer[offset+i]=result}if(bytesRead){stream.node.timestamp=Date.now()}return bytesRead},write:function(stream,buffer,offset,length,pos){for(var i=0;i<length;i++){try{output(buffer[offset+i])}catch(e){throw new FS.ErrnoError(29)}}if(length){stream.node.timestamp=Date.now()}return i}});return FS.mkdev(path,mode,dev)},forceLoadFile:function(obj){if(obj.isDevice||obj.isFolder||obj.link||obj.contents)return true;if(typeof XMLHttpRequest!=="undefined"){throw new Error("Lazy loading should have been performed (contents set) in createLazyFile, but it was not. Lazy loading only works in web workers. Use --embed-file or --preload-file in emcc on the main thread.")}else if(read_){try{obj.contents=intArrayFromString(read_(obj.url),true);obj.usedBytes=obj.contents.length}catch(e){throw new FS.ErrnoError(29)}}else{throw new Error("Cannot load without read() or XMLHttpRequest.")}},createLazyFile:function(parent,name,url,canRead,canWrite){function LazyUint8Array(){this.lengthKnown=false;this.chunks=[]}LazyUint8Array.prototype.get=function LazyUint8Array_get(idx){if(idx>this.length-1||idx<0){return undefined}var chunkOffset=idx%this.chunkSize;var chunkNum=idx/this.chunkSize|0;return this.getter(chunkNum)[chunkOffset]};LazyUint8Array.prototype.setDataGetter=function LazyUint8Array_setDataGetter(getter){this.getter=getter};LazyUint8Array.prototype.cacheLength=function LazyUint8Array_cacheLength(){var xhr=new XMLHttpRequest;xhr.open("HEAD",url,false);xhr.send(null);if(!(xhr.status>=200&&xhr.status<300||xhr.status===304))throw new Error("Couldn't load "+url+". Status: "+xhr.status);var datalength=Number(xhr.getResponseHeader("Content-length"));var header;var hasByteServing=(header=xhr.getResponseHeader("Accept-Ranges"))&&header==="bytes";var usesGzip=(header=xhr.getResponseHeader("Content-Encoding"))&&header==="gzip";var chunkSize=1024*1024;if(!hasByteServing)chunkSize=datalength;var doXHR=function(from,to){if(from>to)throw new Error("invalid range ("+from+", "+to+") or no bytes requested!");if(to>datalength-1)throw new Error("only "+datalength+" bytes available! programmer error!");var xhr=new XMLHttpRequest;xhr.open("GET",url,false);if(datalength!==chunkSize)xhr.setRequestHeader("Range","bytes="+from+"-"+to);if(typeof Uint8Array!="undefined")xhr.responseType="arraybuffer";if(xhr.overrideMimeType){xhr.overrideMimeType("text/plain; charset=x-user-defined")}xhr.send(null);if(!(xhr.status>=200&&xhr.status<300||xhr.status===304))throw new Error("Couldn't load "+url+". Status: "+xhr.status);if(xhr.response!==undefined){return new Uint8Array(xhr.response||[])}else{return intArrayFromString(xhr.responseText||"",true)}};var lazyArray=this;lazyArray.setDataGetter(function(chunkNum){var start=chunkNum*chunkSize;var end=(chunkNum+1)*chunkSize-1;end=Math.min(end,datalength-1);if(typeof lazyArray.chunks[chunkNum]==="undefined"){lazyArray.chunks[chunkNum]=doXHR(start,end)}if(typeof lazyArray.chunks[chunkNum]==="undefined")throw new Error("doXHR failed!");return lazyArray.chunks[chunkNum]});if(usesGzip||!datalength){chunkSize=datalength=1;datalength=this.getter(0).length;chunkSize=datalength;out("LazyFiles on gzip forces download of the whole file when length is accessed")}this._length=datalength;this._chunkSize=chunkSize;this.lengthKnown=true};if(typeof XMLHttpRequest!=="undefined"){if(!ENVIRONMENT_IS_WORKER)throw"Cannot do synchronous binary XHRs outside webworkers in modern browsers. Use --embed-file or --preload-file in emcc";var lazyArray=new LazyUint8Array;Object.defineProperties(lazyArray,{length:{get:function(){if(!this.lengthKnown){this.cacheLength()}return this._length}},chunkSize:{get:function(){if(!this.lengthKnown){this.cacheLength()}return this._chunkSize}}});var properties={isDevice:false,contents:lazyArray}}else{var properties={isDevice:false,url:url}}var node=FS.createFile(parent,name,properties,canRead,canWrite);if(properties.contents){node.contents=properties.contents}else if(properties.url){node.contents=null;node.url=properties.url}Object.defineProperties(node,{usedBytes:{get:function(){return this.contents.length}}});var stream_ops={};var keys=Object.keys(node.stream_ops);keys.forEach(function(key){var fn=node.stream_ops[key];stream_ops[key]=function forceLoadLazyFile(){FS.forceLoadFile(node);return fn.apply(null,arguments)}});stream_ops.read=function stream_ops_read(stream,buffer,offset,length,position){FS.forceLoadFile(node);var contents=stream.node.contents;if(position>=contents.length)return 0;var size=Math.min(contents.length-position,length);if(contents.slice){for(var i=0;i<size;i++){buffer[offset+i]=contents[position+i]}}else{for(var i=0;i<size;i++){buffer[offset+i]=contents.get(position+i)}}return size};node.stream_ops=stream_ops;return node},createPreloadedFile:function(parent,name,url,canRead,canWrite,onload,onerror,dontCreateFile,canOwn,preFinish){Browser.init();var fullname=name?PATH_FS.resolve(PATH.join2(parent,name)):parent;var dep=getUniqueRunDependency("cp "+fullname);function processData(byteArray){function finish(byteArray){if(preFinish)preFinish();if(!dontCreateFile){FS.createDataFile(parent,name,byteArray,canRead,canWrite,canOwn)}if(onload)onload();removeRunDependency(dep)}var handled=false;Module["preloadPlugins"].forEach(function(plugin){if(handled)return;if(plugin["canHandle"](fullname)){plugin["handle"](byteArray,fullname,finish,function(){if(onerror)onerror();removeRunDependency(dep)});handled=true}});if(!handled)finish(byteArray)}addRunDependency(dep);if(typeof url=="string"){Browser.asyncLoad(url,function(byteArray){processData(byteArray)},onerror)}else{processData(url)}},indexedDB:function(){return window.indexedDB||window.mozIndexedDB||window.webkitIndexedDB||window.msIndexedDB},DB_NAME:function(){return"EM_FS_"+window.location.pathname},DB_VERSION:20,DB_STORE_NAME:"FILE_DATA",saveFilesToDB:function(paths,onload,onerror){onload=onload||function(){};onerror=onerror||function(){};var indexedDB=FS.indexedDB();try{var openRequest=indexedDB.open(FS.DB_NAME(),FS.DB_VERSION)}catch(e){return onerror(e)}openRequest.onupgradeneeded=function openRequest_onupgradeneeded(){out("creating db");var db=openRequest.result;db.createObjectStore(FS.DB_STORE_NAME)};openRequest.onsuccess=function openRequest_onsuccess(){var db=openRequest.result;var transaction=db.transaction([FS.DB_STORE_NAME],"readwrite");var files=transaction.objectStore(FS.DB_STORE_NAME);var ok=0,fail=0,total=paths.length;function finish(){if(fail==0)onload();else onerror()}paths.forEach(function(path){var putRequest=files.put(FS.analyzePath(path).object.contents,path);putRequest.onsuccess=function putRequest_onsuccess(){ok++;if(ok+fail==total)finish()};putRequest.onerror=function putRequest_onerror(){fail++;if(ok+fail==total)finish()}});transaction.onerror=onerror};openRequest.onerror=onerror},loadFilesFromDB:function(paths,onload,onerror){onload=onload||function(){};onerror=onerror||function(){};var indexedDB=FS.indexedDB();try{var openRequest=indexedDB.open(FS.DB_NAME(),FS.DB_VERSION)}catch(e){return onerror(e)}openRequest.onupgradeneeded=onerror;openRequest.onsuccess=function openRequest_onsuccess(){var db=openRequest.result;try{var transaction=db.transaction([FS.DB_STORE_NAME],"readonly")}catch(e){onerror(e);return}var files=transaction.objectStore(FS.DB_STORE_NAME);var ok=0,fail=0,total=paths.length;function finish(){if(fail==0)onload();else onerror()}paths.forEach(function(path){var getRequest=files.get(path);getRequest.onsuccess=function getRequest_onsuccess(){if(FS.analyzePath(path).exists){FS.unlink(path)}FS.createDataFile(PATH.dirname(path),PATH.basename(path),getRequest.result,true,true,true);ok++;if(ok+fail==total)finish()};getRequest.onerror=function getRequest_onerror(){fail++;if(ok+fail==total)finish()}});transaction.onerror=onerror};openRequest.onerror=onerror}};var SYSCALLS={mappings:{},DEFAULT_POLLMASK:5,umask:511,calculateAt:function(dirfd,path,allowEmpty){if(path[0]==="/"){return path}var dir;if(dirfd===-100){dir=FS.cwd()}else{var dirstream=FS.getStream(dirfd);if(!dirstream)throw new FS.ErrnoError(8);dir=dirstream.path}if(path.length==0){if(!allowEmpty){throw new FS.ErrnoError(44)}return dir}return PATH.join2(dir,path)},doStat:function(func,path,buf){try{var stat=func(path)}catch(e){if(e&&e.node&&PATH.normalize(path)!==PATH.normalize(FS.getPath(e.node))){return-54}throw e}HEAP32[buf>>2]=stat.dev;HEAP32[buf+4>>2]=0;HEAP32[buf+8>>2]=stat.ino;HEAP32[buf+12>>2]=stat.mode;HEAP32[buf+16>>2]=stat.nlink;HEAP32[buf+20>>2]=stat.uid;HEAP32[buf+24>>2]=stat.gid;HEAP32[buf+28>>2]=stat.rdev;HEAP32[buf+32>>2]=0;tempI64=[stat.size>>>0,(tempDouble=stat.size,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[buf+40>>2]=tempI64[0],HEAP32[buf+44>>2]=tempI64[1];HEAP32[buf+48>>2]=4096;HEAP32[buf+52>>2]=stat.blocks;HEAP32[buf+56>>2]=stat.atime.getTime()/1e3|0;HEAP32[buf+60>>2]=0;HEAP32[buf+64>>2]=stat.mtime.getTime()/1e3|0;HEAP32[buf+68>>2]=0;HEAP32[buf+72>>2]=stat.ctime.getTime()/1e3|0;HEAP32[buf+76>>2]=0;tempI64=[stat.ino>>>0,(tempDouble=stat.ino,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[buf+80>>2]=tempI64[0],HEAP32[buf+84>>2]=tempI64[1];return 0},doMsync:function(addr,stream,len,flags,offset){var buffer=HEAPU8.slice(addr,addr+len);FS.msync(stream,buffer,offset,len,flags)},doMkdir:function(path,mode){path=PATH.normalize(path);if(path[path.length-1]==="/")path=path.substr(0,path.length-1);FS.mkdir(path,mode,0);return 0},doMknod:function(path,mode,dev){switch(mode&61440){case 32768:case 8192:case 24576:case 4096:case 49152:break;default:return-28}FS.mknod(path,mode,dev);return 0},doReadlink:function(path,buf,bufsize){if(bufsize<=0)return-28;var ret=FS.readlink(path);var len=Math.min(bufsize,lengthBytesUTF8(ret));var endChar=HEAP8[buf+len];stringToUTF8(ret,buf,bufsize+1);HEAP8[buf+len]=endChar;return len},doAccess:function(path,amode){if(amode&~7){return-28}var node;var lookup=FS.lookupPath(path,{follow:true});node=lookup.node;if(!node){return-44}var perms="";if(amode&4)perms+="r";if(amode&2)perms+="w";if(amode&1)perms+="x";if(perms&&FS.nodePermissions(node,perms)){return-2}return 0},doDup:function(path,flags,suggestFD){var suggest=FS.getStream(suggestFD);if(suggest)FS.close(suggest);return FS.open(path,flags,0,suggestFD,suggestFD).fd},doReadv:function(stream,iov,iovcnt,offset){var ret=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];var curr=FS.read(stream,HEAP8,ptr,len,offset);if(curr<0)return-1;ret+=curr;if(curr<len)break}return ret},doWritev:function(stream,iov,iovcnt,offset){var ret=0;for(var i=0;i<iovcnt;i++){var ptr=HEAP32[iov+i*8>>2];var len=HEAP32[iov+(i*8+4)>>2];var curr=FS.write(stream,HEAP8,ptr,len,offset);if(curr<0)return-1;ret+=curr}return ret},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},getStreamFromFD:function(fd){var stream=FS.getStream(fd);if(!stream)throw new FS.ErrnoError(8);return stream},get64:function(low,high){return low}};function ___sys__newselect(nfds,readfds,writefds,exceptfds,timeout){try{var total=0;var srcReadLow=readfds?HEAP32[readfds>>2]:0,srcReadHigh=readfds?HEAP32[readfds+4>>2]:0;var srcWriteLow=writefds?HEAP32[writefds>>2]:0,srcWriteHigh=writefds?HEAP32[writefds+4>>2]:0;var srcExceptLow=exceptfds?HEAP32[exceptfds>>2]:0,srcExceptHigh=exceptfds?HEAP32[exceptfds+4>>2]:0;var dstReadLow=0,dstReadHigh=0;var dstWriteLow=0,dstWriteHigh=0;var dstExceptLow=0,dstExceptHigh=0;var allLow=(readfds?HEAP32[readfds>>2]:0)|(writefds?HEAP32[writefds>>2]:0)|(exceptfds?HEAP32[exceptfds>>2]:0);var allHigh=(readfds?HEAP32[readfds+4>>2]:0)|(writefds?HEAP32[writefds+4>>2]:0)|(exceptfds?HEAP32[exceptfds+4>>2]:0);var check=function(fd,low,high,val){return fd<32?low&val:high&val};for(var fd=0;fd<nfds;fd++){var mask=1<<fd%32;if(!check(fd,allLow,allHigh,mask)){continue}var stream=FS.getStream(fd);if(!stream)throw new FS.ErrnoError(8);var flags=SYSCALLS.DEFAULT_POLLMASK;if(stream.stream_ops.poll){flags=stream.stream_ops.poll(stream)}if(flags&1&&check(fd,srcReadLow,srcReadHigh,mask)){fd<32?dstReadLow=dstReadLow|mask:dstReadHigh=dstReadHigh|mask;total++}if(flags&4&&check(fd,srcWriteLow,srcWriteHigh,mask)){fd<32?dstWriteLow=dstWriteLow|mask:dstWriteHigh=dstWriteHigh|mask;total++}if(flags&2&&check(fd,srcExceptLow,srcExceptHigh,mask)){fd<32?dstExceptLow=dstExceptLow|mask:dstExceptHigh=dstExceptHigh|mask;total++}}if(readfds){HEAP32[readfds>>2]=dstReadLow;HEAP32[readfds+4>>2]=dstReadHigh}if(writefds){HEAP32[writefds>>2]=dstWriteLow;HEAP32[writefds+4>>2]=dstWriteHigh}if(exceptfds){HEAP32[exceptfds>>2]=dstExceptLow;HEAP32[exceptfds+4>>2]=dstExceptHigh}return total}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var SOCKFS={mount:function(mount){Module["websocket"]=Module["websocket"]&&"object"===typeof Module["websocket"]?Module["websocket"]:{};Module["websocket"]._callbacks={};Module["websocket"]["on"]=function(event,callback){if("function"===typeof callback){this._callbacks[event]=callback}return this};Module["websocket"].emit=function(event,param){if("function"===typeof this._callbacks[event]){this._callbacks[event].call(this,param)}};return FS.createNode(null,"/",16384|511,0)},createSocket:function(family,type,protocol){type&=~526336;var streaming=type==1;if(protocol){assert(streaming==(protocol==6))}var sock={family:family,type:type,protocol:protocol,server:null,error:null,peers:{},pending:[],recv_queue:[],sock_ops:SOCKFS.websocket_sock_ops};var name=SOCKFS.nextname();var node=FS.createNode(SOCKFS.root,name,49152,0);node.sock=sock;var stream=FS.createStream({path:name,node:node,flags:2,seekable:false,stream_ops:SOCKFS.stream_ops});sock.stream=stream;return sock},getSocket:function(fd){var stream=FS.getStream(fd);if(!stream||!FS.isSocket(stream.node.mode)){return null}return stream.node.sock},stream_ops:{poll:function(stream){var sock=stream.node.sock;return sock.sock_ops.poll(sock)},ioctl:function(stream,request,varargs){var sock=stream.node.sock;return sock.sock_ops.ioctl(sock,request,varargs)},read:function(stream,buffer,offset,length,position){var sock=stream.node.sock;var msg=sock.sock_ops.recvmsg(sock,length);if(!msg){return 0}buffer.set(msg.buffer,offset);return msg.buffer.length},write:function(stream,buffer,offset,length,position){var sock=stream.node.sock;return sock.sock_ops.sendmsg(sock,buffer,offset,length)},close:function(stream){var sock=stream.node.sock;sock.sock_ops.close(sock)}},nextname:function(){if(!SOCKFS.nextname.current){SOCKFS.nextname.current=0}return"socket["+SOCKFS.nextname.current+++"]"},websocket_sock_ops:{createPeer:function(sock,addr,port){var ws;if(typeof addr==="object"){ws=addr;addr=null;port=null}if(ws){if(ws._socket){addr=ws._socket.remoteAddress;port=ws._socket.remotePort}else{var result=/ws[s]?:\/\/([^:]+):(\d+)/.exec(ws.url);if(!result){throw new Error("WebSocket URL must be in the format ws(s)://address:port")}addr=result[1];port=parseInt(result[2],10)}}else{try{var runtimeConfig=Module["websocket"]&&"object"===typeof Module["websocket"];var url="ws:#".replace("#","//");if(runtimeConfig){if("string"===typeof Module["websocket"]["url"]){url=Module["websocket"]["url"]}}if(url==="ws://"||url==="wss://"){var parts=addr.split("/");url=url+parts[0]+":"+port+"/"+parts.slice(1).join("/")}var subProtocols="binary";if(runtimeConfig){if("string"===typeof Module["websocket"]["subprotocol"]){subProtocols=Module["websocket"]["subprotocol"]}}var opts=undefined;if(subProtocols!=="null"){subProtocols=subProtocols.replace(/^ +| +$/g,"").split(/ *, */);opts=ENVIRONMENT_IS_NODE?{"protocol":subProtocols.toString()}:subProtocols}if(runtimeConfig&&null===Module["websocket"]["subprotocol"]){subProtocols="null";opts=undefined}var WebSocketConstructor;if(ENVIRONMENT_IS_NODE){WebSocketConstructor=require("ws")}else{WebSocketConstructor=WebSocket}ws=new WebSocketConstructor(url,opts);ws.binaryType="arraybuffer"}catch(e){throw new FS.ErrnoError(ERRNO_CODES.EHOSTUNREACH)}}var peer={addr:addr,port:port,socket:ws,dgram_send_queue:[]};SOCKFS.websocket_sock_ops.addPeer(sock,peer);SOCKFS.websocket_sock_ops.handlePeerEvents(sock,peer);if(sock.type===2&&typeof sock.sport!=="undefined"){peer.dgram_send_queue.push(new Uint8Array([255,255,255,255,"p".charCodeAt(0),"o".charCodeAt(0),"r".charCodeAt(0),"t".charCodeAt(0),(sock.sport&65280)>>8,sock.sport&255]))}return peer},getPeer:function(sock,addr,port){return sock.peers[addr+":"+port]},addPeer:function(sock,peer){sock.peers[peer.addr+":"+peer.port]=peer},removePeer:function(sock,peer){delete sock.peers[peer.addr+":"+peer.port]},handlePeerEvents:function(sock,peer){var first=true;var handleOpen=function(){Module["websocket"].emit("open",sock.stream.fd);try{var queued=peer.dgram_send_queue.shift();while(queued){peer.socket.send(queued);queued=peer.dgram_send_queue.shift()}}catch(e){peer.socket.close()}};function handleMessage(data){if(typeof data==="string"){var encoder=new TextEncoder;data=encoder.encode(data)}else{assert(data.byteLength!==undefined);if(data.byteLength==0){return}else{data=new Uint8Array(data)}}var wasfirst=first;first=false;if(wasfirst&&data.length===10&&data[0]===255&&data[1]===255&&data[2]===255&&data[3]===255&&data[4]==="p".charCodeAt(0)&&data[5]==="o".charCodeAt(0)&&data[6]==="r".charCodeAt(0)&&data[7]==="t".charCodeAt(0)){var newport=data[8]<<8|data[9];SOCKFS.websocket_sock_ops.removePeer(sock,peer);peer.port=newport;SOCKFS.websocket_sock_ops.addPeer(sock,peer);return}sock.recv_queue.push({addr:peer.addr,port:peer.port,data:data});Module["websocket"].emit("message",sock.stream.fd)}if(ENVIRONMENT_IS_NODE){peer.socket.on("open",handleOpen);peer.socket.on("message",function(data,flags){if(!flags.binary){return}handleMessage(new Uint8Array(data).buffer)});peer.socket.on("close",function(){Module["websocket"].emit("close",sock.stream.fd)});peer.socket.on("error",function(error){sock.error=ERRNO_CODES.ECONNREFUSED;Module["websocket"].emit("error",[sock.stream.fd,sock.error,"ECONNREFUSED: Connection refused"])})}else{peer.socket.onopen=handleOpen;peer.socket.onclose=function(){Module["websocket"].emit("close",sock.stream.fd)};peer.socket.onmessage=function peer_socket_onmessage(event){handleMessage(event.data)};peer.socket.onerror=function(error){sock.error=ERRNO_CODES.ECONNREFUSED;Module["websocket"].emit("error",[sock.stream.fd,sock.error,"ECONNREFUSED: Connection refused"])}}},poll:function(sock){if(sock.type===1&&sock.server){return sock.pending.length?64|1:0}var mask=0;var dest=sock.type===1?SOCKFS.websocket_sock_ops.getPeer(sock,sock.daddr,sock.dport):null;if(sock.recv_queue.length||!dest||dest&&dest.socket.readyState===dest.socket.CLOSING||dest&&dest.socket.readyState===dest.socket.CLOSED){mask|=64|1}if(!dest||dest&&dest.socket.readyState===dest.socket.OPEN){mask|=4}if(dest&&dest.socket.readyState===dest.socket.CLOSING||dest&&dest.socket.readyState===dest.socket.CLOSED){mask|=16}return mask},ioctl:function(sock,request,arg){switch(request){case 21531:var bytes=0;if(sock.recv_queue.length){bytes=sock.recv_queue[0].data.length}HEAP32[arg>>2]=bytes;return 0;default:return ERRNO_CODES.EINVAL}},close:function(sock){if(sock.server){try{sock.server.close()}catch(e){}sock.server=null}var peers=Object.keys(sock.peers);for(var i=0;i<peers.length;i++){var peer=sock.peers[peers[i]];try{peer.socket.close()}catch(e){}SOCKFS.websocket_sock_ops.removePeer(sock,peer)}return 0},bind:function(sock,addr,port){if(typeof sock.saddr!=="undefined"||typeof sock.sport!=="undefined"){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}sock.saddr=addr;sock.sport=port;if(sock.type===2){if(sock.server){sock.server.close();sock.server=null}try{sock.sock_ops.listen(sock,0)}catch(e){if(!(e instanceof FS.ErrnoError))throw e;if(e.errno!==ERRNO_CODES.EOPNOTSUPP)throw e}}},connect:function(sock,addr,port){if(sock.server){throw new FS.ErrnoError(ERRNO_CODES.EOPNOTSUPP)}if(typeof sock.daddr!=="undefined"&&typeof sock.dport!=="undefined"){var dest=SOCKFS.websocket_sock_ops.getPeer(sock,sock.daddr,sock.dport);if(dest){if(dest.socket.readyState===dest.socket.CONNECTING){throw new FS.ErrnoError(ERRNO_CODES.EALREADY)}else{throw new FS.ErrnoError(ERRNO_CODES.EISCONN)}}}var peer=SOCKFS.websocket_sock_ops.createPeer(sock,addr,port);sock.daddr=peer.addr;sock.dport=peer.port;throw new FS.ErrnoError(ERRNO_CODES.EINPROGRESS)},listen:function(sock,backlog){if(!ENVIRONMENT_IS_NODE){throw new FS.ErrnoError(ERRNO_CODES.EOPNOTSUPP)}if(sock.server){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}var WebSocketServer=require("ws").Server;var host=sock.saddr;sock.server=new WebSocketServer({host:host,port:sock.sport});Module["websocket"].emit("listen",sock.stream.fd);sock.server.on("connection",function(ws){if(sock.type===1){var newsock=SOCKFS.createSocket(sock.family,sock.type,sock.protocol);var peer=SOCKFS.websocket_sock_ops.createPeer(newsock,ws);newsock.daddr=peer.addr;newsock.dport=peer.port;sock.pending.push(newsock);Module["websocket"].emit("connection",newsock.stream.fd)}else{SOCKFS.websocket_sock_ops.createPeer(sock,ws);Module["websocket"].emit("connection",sock.stream.fd)}});sock.server.on("closed",function(){Module["websocket"].emit("close",sock.stream.fd);sock.server=null});sock.server.on("error",function(error){sock.error=ERRNO_CODES.EHOSTUNREACH;Module["websocket"].emit("error",[sock.stream.fd,sock.error,"EHOSTUNREACH: Host is unreachable"])})},accept:function(listensock){if(!listensock.server){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}var newsock=listensock.pending.shift();newsock.stream.flags=listensock.stream.flags;return newsock},getname:function(sock,peer){var addr,port;if(peer){if(sock.daddr===undefined||sock.dport===undefined){throw new FS.ErrnoError(ERRNO_CODES.ENOTCONN)}addr=sock.daddr;port=sock.dport}else{addr=sock.saddr||0;port=sock.sport||0}return{addr:addr,port:port}},sendmsg:function(sock,buffer,offset,length,addr,port){if(sock.type===2){if(addr===undefined||port===undefined){addr=sock.daddr;port=sock.dport}if(addr===undefined||port===undefined){throw new FS.ErrnoError(ERRNO_CODES.EDESTADDRREQ)}}else{addr=sock.daddr;port=sock.dport}var dest=SOCKFS.websocket_sock_ops.getPeer(sock,addr,port);if(sock.type===1){if(!dest||dest.socket.readyState===dest.socket.CLOSING||dest.socket.readyState===dest.socket.CLOSED){throw new FS.ErrnoError(ERRNO_CODES.ENOTCONN)}else if(dest.socket.readyState===dest.socket.CONNECTING){throw new FS.ErrnoError(ERRNO_CODES.EAGAIN)}}if(ArrayBuffer.isView(buffer)){offset+=buffer.byteOffset;buffer=buffer.buffer}var data;data=buffer.slice(offset,offset+length);if(sock.type===2){if(!dest||dest.socket.readyState!==dest.socket.OPEN){if(!dest||dest.socket.readyState===dest.socket.CLOSING||dest.socket.readyState===dest.socket.CLOSED){dest=SOCKFS.websocket_sock_ops.createPeer(sock,addr,port)}dest.dgram_send_queue.push(data);return length}}try{dest.socket.send(data);return length}catch(e){throw new FS.ErrnoError(ERRNO_CODES.EINVAL)}},recvmsg:function(sock,length){if(sock.type===1&&sock.server){throw new FS.ErrnoError(ERRNO_CODES.ENOTCONN)}var queued=sock.recv_queue.shift();if(!queued){if(sock.type===1){var dest=SOCKFS.websocket_sock_ops.getPeer(sock,sock.daddr,sock.dport);if(!dest){throw new FS.ErrnoError(ERRNO_CODES.ENOTCONN)}else if(dest.socket.readyState===dest.socket.CLOSING||dest.socket.readyState===dest.socket.CLOSED){return null}else{throw new FS.ErrnoError(ERRNO_CODES.EAGAIN)}}else{throw new FS.ErrnoError(ERRNO_CODES.EAGAIN)}}var queuedLength=queued.data.byteLength||queued.data.length;var queuedOffset=queued.data.byteOffset||0;var queuedBuffer=queued.data.buffer||queued.data;var bytesRead=Math.min(length,queuedLength);var res={buffer:new Uint8Array(queuedBuffer,queuedOffset,bytesRead),addr:queued.addr,port:queued.port};if(sock.type===1&&bytesRead<queuedLength){var bytesRemaining=queuedLength-bytesRead;queued.data=new Uint8Array(queuedBuffer,queuedOffset+bytesRead,bytesRemaining);sock.recv_queue.unshift(queued)}return res}}};function getSocketFromFD(fd){var socket=SOCKFS.getSocket(fd);if(!socket)throw new FS.ErrnoError(8);return socket}function setErrNo(value){HEAP32[___errno_location()>>2]=value;return value}function inetPton4(str){var b=str.split(".");for(var i=0;i<4;i++){var tmp=Number(b[i]);if(isNaN(tmp))return null;b[i]=tmp}return(b[0]|b[1]<<8|b[2]<<16|b[3]<<24)>>>0}function jstoi_q(str){return parseInt(str)}function inetPton6(str){var words;var w,offset,z;var valid6regx=/^((?=.*::)(?!.*::.+::)(::)?([\dA-F]{1,4}:(:|\b)|){5}|([\dA-F]{1,4}:){6})((([\dA-F]{1,4}((?!\3)::|:\b|$))|(?!\2\3)){2}|(((2[0-4]|1\d|[1-9])?\d|25[0-5])\.?\b){4})$/i;var parts=[];if(!valid6regx.test(str)){return null}if(str==="::"){return[0,0,0,0,0,0,0,0]}if(str.startsWith("::")){str=str.replace("::","Z:")}else{str=str.replace("::",":Z:")}if(str.indexOf(".")>0){str=str.replace(new RegExp("[.]","g"),":");words=str.split(":");words[words.length-4]=jstoi_q(words[words.length-4])+jstoi_q(words[words.length-3])*256;words[words.length-3]=jstoi_q(words[words.length-2])+jstoi_q(words[words.length-1])*256;words=words.slice(0,words.length-2)}else{words=str.split(":")}offset=0;z=0;for(w=0;w<words.length;w++){if(typeof words[w]==="string"){if(words[w]==="Z"){for(z=0;z<8-words.length+1;z++){parts[w+z]=0}offset=z-1}else{parts[w+offset]=_htons(parseInt(words[w],16))}}else{parts[w+offset]=words[w]}}return[parts[1]<<16|parts[0],parts[3]<<16|parts[2],parts[5]<<16|parts[4],parts[7]<<16|parts[6]]}function writeSockaddr(sa,family,addr,port,addrlen){switch(family){case 2:addr=inetPton4(addr);if(addrlen){HEAP32[addrlen>>2]=16}HEAP16[sa>>1]=family;HEAP32[sa+4>>2]=addr;HEAP16[sa+2>>1]=_htons(port);tempI64=[0>>>0,(tempDouble=0,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[sa+8>>2]=tempI64[0],HEAP32[sa+12>>2]=tempI64[1];break;case 10:addr=inetPton6(addr);if(addrlen){HEAP32[addrlen>>2]=28}HEAP32[sa>>2]=family;HEAP32[sa+8>>2]=addr[0];HEAP32[sa+12>>2]=addr[1];HEAP32[sa+16>>2]=addr[2];HEAP32[sa+20>>2]=addr[3];HEAP16[sa+2>>1]=_htons(port);HEAP32[sa+4>>2]=0;HEAP32[sa+24>>2]=0;break;default:return 5}return 0}var DNS={address_map:{id:1,addrs:{},names:{}},lookup_name:function(name){var res=inetPton4(name);if(res!==null){return name}res=inetPton6(name);if(res!==null){return name}var addr;if(DNS.address_map.addrs[name]){addr=DNS.address_map.addrs[name]}else{var id=DNS.address_map.id++;assert(id<65535,"exceeded max address mappings of 65535");addr="172.29."+(id&255)+"."+(id&65280);DNS.address_map.names[addr]=name;DNS.address_map.addrs[name]=addr}return addr},lookup_addr:function(addr){if(DNS.address_map.names[addr]){return DNS.address_map.names[addr]}return null}};function ___sys_accept4(fd,addr,addrlen,flags){try{var sock=getSocketFromFD(fd);var newsock=sock.sock_ops.accept(sock);if(addr){var errno=writeSockaddr(addr,newsock.family,DNS.lookup_name(newsock.daddr),newsock.dport,addrlen)}return newsock.stream.fd}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_access(path,amode){try{path=SYSCALLS.getStr(path);return SYSCALLS.doAccess(path,amode)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function inetNtop4(addr){return(addr&255)+"."+(addr>>8&255)+"."+(addr>>16&255)+"."+(addr>>24&255)}function inetNtop6(ints){var str="";var word=0;var longest=0;var lastzero=0;var zstart=0;var len=0;var i=0;var parts=[ints[0]&65535,ints[0]>>16,ints[1]&65535,ints[1]>>16,ints[2]&65535,ints[2]>>16,ints[3]&65535,ints[3]>>16];var hasipv4=true;var v4part="";for(i=0;i<5;i++){if(parts[i]!==0){hasipv4=false;break}}if(hasipv4){v4part=inetNtop4(parts[6]|parts[7]<<16);if(parts[5]===-1){str="::ffff:";str+=v4part;return str}if(parts[5]===0){str="::";if(v4part==="0.0.0.0")v4part="";if(v4part==="0.0.0.1")v4part="1";str+=v4part;return str}}for(word=0;word<8;word++){if(parts[word]===0){if(word-lastzero>1){len=0}lastzero=word;len++}if(len>longest){longest=len;zstart=word-longest+1}}for(word=0;word<8;word++){if(longest>1){if(parts[word]===0&&word>=zstart&&word<zstart+longest){if(word===zstart){str+=":";if(zstart===0)str+=":"}continue}}str+=Number(_ntohs(parts[word]&65535)).toString(16);str+=word<7?":":""}return str}function readSockaddr(sa,salen){var family=HEAP16[sa>>1];var port=_ntohs(HEAPU16[sa+2>>1]);var addr;switch(family){case 2:if(salen!==16){return{errno:28}}addr=HEAP32[sa+4>>2];addr=inetNtop4(addr);break;case 10:if(salen!==28){return{errno:28}}addr=[HEAP32[sa+8>>2],HEAP32[sa+12>>2],HEAP32[sa+16>>2],HEAP32[sa+20>>2]];addr=inetNtop6(addr);break;default:return{errno:5}}return{family:family,addr:addr,port:port}}function getSocketAddress(addrp,addrlen,allowNull){if(allowNull&&addrp===0)return null;var info=readSockaddr(addrp,addrlen);if(info.errno)throw new FS.ErrnoError(info.errno);info.addr=DNS.lookup_addr(info.addr)||info.addr;return info}function ___sys_bind(fd,addr,addrlen){try{var sock=getSocketFromFD(fd);var info=getSocketAddress(addr,addrlen);sock.sock_ops.bind(sock,info.addr,info.port);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_chmod(path,mode){try{path=SYSCALLS.getStr(path);FS.chmod(path,mode);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_connect(fd,addr,addrlen){try{var sock=getSocketFromFD(fd);var info=getSocketAddress(addr,addrlen);sock.sock_ops.connect(sock,info.addr,info.port);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_dup2(oldfd,suggestFD){try{var old=SYSCALLS.getStreamFromFD(oldfd);if(old.fd===suggestFD)return suggestFD;return SYSCALLS.doDup(old.path,old.flags,suggestFD)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_fcntl64(fd,cmd,varargs){SYSCALLS.varargs=varargs;try{var stream=SYSCALLS.getStreamFromFD(fd);switch(cmd){case 0:{var arg=SYSCALLS.get();if(arg<0){return-28}var newStream;newStream=FS.open(stream.path,stream.flags,0,arg);return newStream.fd}case 1:case 2:return 0;case 3:return stream.flags;case 4:{var arg=SYSCALLS.get();stream.flags|=arg;return 0}case 12:{var arg=SYSCALLS.get();var offset=0;HEAP16[arg+offset>>1]=2;return 0}case 13:case 14:return 0;case 16:case 8:return-28;case 9:setErrNo(28);return-1;default:{return-28}}}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_fstat64(fd,buf){try{var stream=SYSCALLS.getStreamFromFD(fd);return SYSCALLS.doStat(FS.stat,stream.path,buf)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_getcwd(buf,size){try{if(size===0)return-28;var cwd=FS.cwd();var cwdLengthInBytes=lengthBytesUTF8(cwd);if(size<cwdLengthInBytes+1)return-68;stringToUTF8(cwd,buf,size);return buf}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_getdents64(fd,dirp,count){try{var stream=SYSCALLS.getStreamFromFD(fd);if(!stream.getdents){stream.getdents=FS.readdir(stream.path)}var struct_size=280;var pos=0;var off=FS.llseek(stream,0,1);var idx=Math.floor(off/struct_size);while(idx<stream.getdents.length&&pos+struct_size<=count){var id;var type;var name=stream.getdents[idx];if(name[0]==="."){id=1;type=4}else{var child=FS.lookupNode(stream.node,name);id=child.id;type=FS.isChrdev(child.mode)?2:FS.isDir(child.mode)?4:FS.isLink(child.mode)?10:8}tempI64=[id>>>0,(tempDouble=id,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[dirp+pos>>2]=tempI64[0],HEAP32[dirp+pos+4>>2]=tempI64[1];tempI64=[(idx+1)*struct_size>>>0,(tempDouble=(idx+1)*struct_size,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[dirp+pos+8>>2]=tempI64[0],HEAP32[dirp+pos+12>>2]=tempI64[1];HEAP16[dirp+pos+16>>1]=280;HEAP8[dirp+pos+18>>0]=type;stringToUTF8(name,dirp+pos+19,256);pos+=struct_size;idx+=1}FS.llseek(stream,idx*struct_size,0);return pos}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_getegid32(){return 0}function ___sys_geteuid32(){return ___sys_getegid32()}function ___sys_getpeername(fd,addr,addrlen){try{var sock=getSocketFromFD(fd);if(!sock.daddr){return-53}var errno=writeSockaddr(addr,sock.family,DNS.lookup_name(sock.daddr),sock.dport,addrlen);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_getrusage(who,usage){try{_memset(usage,0,136);HEAP32[usage>>2]=1;HEAP32[usage+4>>2]=2;HEAP32[usage+8>>2]=3;HEAP32[usage+12>>2]=4;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_getsockname(fd,addr,addrlen){try{err("__sys_getsockname "+fd);var sock=getSocketFromFD(fd);var errno=writeSockaddr(addr,sock.family,DNS.lookup_name(sock.saddr||"0.0.0.0"),sock.sport,addrlen);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_getsockopt(fd,level,optname,optval,optlen){try{var sock=getSocketFromFD(fd);if(level===1){if(optname===4){HEAP32[optval>>2]=sock.error;HEAP32[optlen>>2]=4;sock.error=null;return 0}}return-50}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_getuid32(){return ___sys_getegid32()}function ___sys_ioctl(fd,op,varargs){SYSCALLS.varargs=varargs;try{var stream=SYSCALLS.getStreamFromFD(fd);switch(op){case 21509:case 21505:{if(!stream.tty)return-59;return 0}case 21510:case 21511:case 21512:case 21506:case 21507:case 21508:{if(!stream.tty)return-59;return 0}case 21519:{if(!stream.tty)return-59;var argp=SYSCALLS.get();HEAP32[argp>>2]=0;return 0}case 21520:{if(!stream.tty)return-59;return-28}case 21531:{var argp=SYSCALLS.get();return FS.ioctl(stream,op,argp)}case 21523:{if(!stream.tty)return-59;return 0}case 21524:{if(!stream.tty)return-59;return 0}default:abort("bad ioctl syscall "+op)}}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_listen(fd,backlog){try{var sock=getSocketFromFD(fd);sock.sock_ops.listen(sock,backlog);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_lstat64(path,buf){try{path=SYSCALLS.getStr(path);return SYSCALLS.doStat(FS.lstat,path,buf)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_mkdir(path,mode){try{path=SYSCALLS.getStr(path);return SYSCALLS.doMkdir(path,mode)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function syscallMmap2(addr,len,prot,flags,fd,off){off<<=12;var ptr;var allocated=false;if((flags&16)!==0&&addr%65536!==0){return-28}if((flags&32)!==0){ptr=_memalign(65536,len);if(!ptr)return-48;_memset(ptr,0,len);allocated=true}else{var info=FS.getStream(fd);if(!info)return-8;var res=FS.mmap(info,addr,len,off,prot,flags);ptr=res.ptr;allocated=res.allocated}SYSCALLS.mappings[ptr]={malloc:ptr,len:len,allocated:allocated,fd:fd,prot:prot,flags:flags,offset:off};return ptr}function ___sys_mmap2(addr,len,prot,flags,fd,off){try{return syscallMmap2(addr,len,prot,flags,fd,off)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function syscallMunmap(addr,len){if((addr|0)===-1||len===0){return-28}var info=SYSCALLS.mappings[addr];if(!info)return 0;if(len===info.len){var stream=FS.getStream(info.fd);if(stream){if(info.prot&2){SYSCALLS.doMsync(addr,stream,len,info.flags,info.offset)}FS.munmap(stream)}SYSCALLS.mappings[addr]=null;if(info.allocated){_free(info.malloc)}}return 0}function ___sys_munmap(addr,len){try{return syscallMunmap(addr,len)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_open(path,flags,varargs){SYSCALLS.varargs=varargs;try{var pathname=SYSCALLS.getStr(path);var mode=varargs?SYSCALLS.get():0;var stream=FS.open(pathname,flags,mode);return stream.fd}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}var PIPEFS={BUCKET_BUFFER_SIZE:8192,mount:function(mount){return FS.createNode(null,"/",16384|511,0)},createPipe:function(){var pipe={buckets:[]};pipe.buckets.push({buffer:new Uint8Array(PIPEFS.BUCKET_BUFFER_SIZE),offset:0,roffset:0});var rName=PIPEFS.nextname();var wName=PIPEFS.nextname();var rNode=FS.createNode(PIPEFS.root,rName,4096,0);var wNode=FS.createNode(PIPEFS.root,wName,4096,0);rNode.pipe=pipe;wNode.pipe=pipe;var readableStream=FS.createStream({path:rName,node:rNode,flags:0,seekable:false,stream_ops:PIPEFS.stream_ops});rNode.stream=readableStream;var writableStream=FS.createStream({path:wName,node:wNode,flags:1,seekable:false,stream_ops:PIPEFS.stream_ops});wNode.stream=writableStream;return{readable_fd:readableStream.fd,writable_fd:writableStream.fd}},stream_ops:{poll:function(stream){var pipe=stream.node.pipe;if((stream.flags&2097155)===1){return 256|4}else{if(pipe.buckets.length>0){for(var i=0;i<pipe.buckets.length;i++){var bucket=pipe.buckets[i];if(bucket.offset-bucket.roffset>0){return 64|1}}}}return 0},ioctl:function(stream,request,varargs){return ERRNO_CODES.EINVAL},fsync:function(stream){return ERRNO_CODES.EINVAL},read:function(stream,buffer,offset,length,position){var pipe=stream.node.pipe;var currentLength=0;for(var i=0;i<pipe.buckets.length;i++){var bucket=pipe.buckets[i];currentLength+=bucket.offset-bucket.roffset}assert(buffer instanceof ArrayBuffer||ArrayBuffer.isView(buffer));var data=buffer.subarray(offset,offset+length);if(length<=0){return 0}if(currentLength==0){throw new FS.ErrnoError(ERRNO_CODES.EAGAIN)}var toRead=Math.min(currentLength,length);var totalRead=toRead;var toRemove=0;for(var i=0;i<pipe.buckets.length;i++){var currBucket=pipe.buckets[i];var bucketSize=currBucket.offset-currBucket.roffset;if(toRead<=bucketSize){var tmpSlice=currBucket.buffer.subarray(currBucket.roffset,currBucket.offset);if(toRead<bucketSize){tmpSlice=tmpSlice.subarray(0,toRead);currBucket.roffset+=toRead}else{toRemove++}data.set(tmpSlice);break}else{var tmpSlice=currBucket.buffer.subarray(currBucket.roffset,currBucket.offset);data.set(tmpSlice);data=data.subarray(tmpSlice.byteLength);toRead-=tmpSlice.byteLength;toRemove++}}if(toRemove&&toRemove==pipe.buckets.length){toRemove--;pipe.buckets[toRemove].offset=0;pipe.buckets[toRemove].roffset=0}pipe.buckets.splice(0,toRemove);return totalRead},write:function(stream,buffer,offset,length,position){var pipe=stream.node.pipe;assert(buffer instanceof ArrayBuffer||ArrayBuffer.isView(buffer));var data=buffer.subarray(offset,offset+length);var dataLen=data.byteLength;if(dataLen<=0){return 0}var currBucket=null;if(pipe.buckets.length==0){currBucket={buffer:new Uint8Array(PIPEFS.BUCKET_BUFFER_SIZE),offset:0,roffset:0};pipe.buckets.push(currBucket)}else{currBucket=pipe.buckets[pipe.buckets.length-1]}assert(currBucket.offset<=PIPEFS.BUCKET_BUFFER_SIZE);var freeBytesInCurrBuffer=PIPEFS.BUCKET_BUFFER_SIZE-currBucket.offset;if(freeBytesInCurrBuffer>=dataLen){currBucket.buffer.set(data,currBucket.offset);currBucket.offset+=dataLen;return dataLen}else if(freeBytesInCurrBuffer>0){currBucket.buffer.set(data.subarray(0,freeBytesInCurrBuffer),currBucket.offset);currBucket.offset+=freeBytesInCurrBuffer;data=data.subarray(freeBytesInCurrBuffer,data.byteLength)}var numBuckets=data.byteLength/PIPEFS.BUCKET_BUFFER_SIZE|0;var remElements=data.byteLength%PIPEFS.BUCKET_BUFFER_SIZE;for(var i=0;i<numBuckets;i++){var newBucket={buffer:new Uint8Array(PIPEFS.BUCKET_BUFFER_SIZE),offset:PIPEFS.BUCKET_BUFFER_SIZE,roffset:0};pipe.buckets.push(newBucket);newBucket.buffer.set(data.subarray(0,PIPEFS.BUCKET_BUFFER_SIZE));data=data.subarray(PIPEFS.BUCKET_BUFFER_SIZE,data.byteLength)}if(remElements>0){var newBucket={buffer:new Uint8Array(PIPEFS.BUCKET_BUFFER_SIZE),offset:data.byteLength,roffset:0};pipe.buckets.push(newBucket);newBucket.buffer.set(data)}return dataLen},close:function(stream){var pipe=stream.node.pipe;pipe.buckets=null}},nextname:function(){if(!PIPEFS.nextname.current){PIPEFS.nextname.current=0}return"pipe["+PIPEFS.nextname.current+++"]"}};function ___sys_pipe(fdPtr){try{if(fdPtr==0){throw new FS.ErrnoError(21)}var res=PIPEFS.createPipe();HEAP32[fdPtr>>2]=res.readable_fd;HEAP32[fdPtr+4>>2]=res.writable_fd;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_poll(fds,nfds,timeout){try{var nonzero=0;for(var i=0;i<nfds;i++){var pollfd=fds+8*i;var fd=HEAP32[pollfd>>2];var events=HEAP16[pollfd+4>>1];var mask=32;var stream=FS.getStream(fd);if(stream){mask=SYSCALLS.DEFAULT_POLLMASK;if(stream.stream_ops.poll){mask=stream.stream_ops.poll(stream)}}mask&=events|8|16;if(mask)nonzero++;HEAP16[pollfd+6>>1]=mask}return nonzero}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_readlink(path,buf,bufsize){try{path=SYSCALLS.getStr(path);return SYSCALLS.doReadlink(path,buf,bufsize)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_recvfrom(fd,buf,len,flags,addr,addrlen){try{var sock=getSocketFromFD(fd);var msg=sock.sock_ops.recvmsg(sock,len);if(!msg)return 0;if(addr){var errno=writeSockaddr(addr,sock.family,DNS.lookup_name(msg.addr),msg.port,addrlen)}HEAPU8.set(msg.buffer,buf);return msg.buffer.byteLength}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_recvmsg(fd,message,flags){try{var sock=getSocketFromFD(fd);var iov=HEAP32[message+8>>2];var num=HEAP32[message+12>>2];var total=0;for(var i=0;i<num;i++){total+=HEAP32[iov+(8*i+4)>>2]}var msg=sock.sock_ops.recvmsg(sock,total);if(!msg)return 0;var name=HEAP32[message>>2];if(name){var errno=writeSockaddr(name,sock.family,DNS.lookup_name(msg.addr),msg.port)}var bytesRead=0;var bytesRemaining=msg.buffer.byteLength;for(var i=0;bytesRemaining>0&&i<num;i++){var iovbase=HEAP32[iov+(8*i+0)>>2];var iovlen=HEAP32[iov+(8*i+4)>>2];if(!iovlen){continue}var length=Math.min(iovlen,bytesRemaining);var buf=msg.buffer.subarray(bytesRead,bytesRead+length);HEAPU8.set(buf,iovbase+bytesRead);bytesRead+=length;bytesRemaining-=length}return bytesRead}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_rename(old_path,new_path){try{old_path=SYSCALLS.getStr(old_path);new_path=SYSCALLS.getStr(new_path);FS.rename(old_path,new_path);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_rmdir(path){try{path=SYSCALLS.getStr(path);FS.rmdir(path);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_sendmsg(fd,message,flags){try{var sock=getSocketFromFD(fd);var iov=HEAP32[message+8>>2];var num=HEAP32[message+12>>2];var addr,port;var name=HEAP32[message>>2];var namelen=HEAP32[message+4>>2];if(name){var info=readSockaddr(name,namelen);if(info.errno)return-info.errno;port=info.port;addr=DNS.lookup_addr(info.addr)||info.addr}var total=0;for(var i=0;i<num;i++){total+=HEAP32[iov+(8*i+4)>>2]}var view=new Uint8Array(total);var offset=0;for(var i=0;i<num;i++){var iovbase=HEAP32[iov+(8*i+0)>>2];var iovlen=HEAP32[iov+(8*i+4)>>2];for(var j=0;j<iovlen;j++){view[offset++]=HEAP8[iovbase+j>>0]}}return sock.sock_ops.sendmsg(sock,view,0,total,addr,port)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_sendto(fd,message,length,flags,addr,addr_len){try{var sock=getSocketFromFD(fd);var dest=getSocketAddress(addr,addr_len,true);if(!dest){return FS.write(sock.stream,HEAP8,message,length)}else{return sock.sock_ops.sendmsg(sock,HEAP8,message,length,dest.addr,dest.port)}}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_setsockopt(fd){try{return-50}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_shutdown(fd,how){try{getSocketFromFD(fd);return-52}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_socket(domain,type,protocol){try{var sock=SOCKFS.createSocket(domain,type,protocol);return sock.stream.fd}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_stat64(path,buf){try{path=SYSCALLS.getStr(path);return SYSCALLS.doStat(FS.stat,path,buf)}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_statfs64(path,size,buf){try{path=SYSCALLS.getStr(path);HEAP32[buf+4>>2]=4096;HEAP32[buf+40>>2]=4096;HEAP32[buf+8>>2]=1e6;HEAP32[buf+12>>2]=5e5;HEAP32[buf+16>>2]=5e5;HEAP32[buf+20>>2]=FS.nextInode;HEAP32[buf+24>>2]=1e6;HEAP32[buf+28>>2]=42;HEAP32[buf+44>>2]=2;HEAP32[buf+36>>2]=255;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_truncate64(path,zero,low,high){try{path=SYSCALLS.getStr(path);var length=SYSCALLS.get64(low,high);FS.truncate(path,length);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_uname(buf){try{if(!buf)return-21;var layout={"__size__":390,"domainname":325,"machine":260,"nodename":65,"release":130,"sysname":0,"version":195};var copyString=function(element,value){var offset=layout[element];writeAsciiToMemory(value,buf+offset)};copyString("sysname","Emscripten");copyString("nodename","emscripten");copyString("release","1.0");copyString("version","#1");copyString("machine","wasm32");return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function ___sys_unlink(path){try{path=SYSCALLS.getStr(path);FS.unlink(path);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return-e.errno}}function _abort(){abort()}function _clock(){if(_clock.start===undefined)_clock.start=Date.now();return(Date.now()-_clock.start)*(1e6/1e3)|0}function _emscripten_get_now_res(){if(ENVIRONMENT_IS_NODE){return 1}else if(typeof dateNow!=="undefined"){return 1e3}else return 1e3}var _emscripten_get_now_is_monotonic=true;function _clock_getres(clk_id,res){var nsec;if(clk_id===0){nsec=1e3*1e3}else if(clk_id===1&&_emscripten_get_now_is_monotonic){nsec=_emscripten_get_now_res()}else{setErrNo(28);return-1}HEAP32[res>>2]=nsec/1e9|0;HEAP32[res+4>>2]=nsec;return 0}var _emscripten_get_now;if(ENVIRONMENT_IS_NODE){_emscripten_get_now=function(){var t=process["hrtime"]();return t[0]*1e3+t[1]/1e6}}else if(typeof dateNow!=="undefined"){_emscripten_get_now=dateNow}else _emscripten_get_now=function(){return performance.now()};function _clock_gettime(clk_id,tp){var now;if(clk_id===0){now=Date.now()}else if((clk_id===1||clk_id===4)&&_emscripten_get_now_is_monotonic){now=_emscripten_get_now()}else{setErrNo(28);return-1}HEAP32[tp>>2]=now/1e3|0;HEAP32[tp+4>>2]=now%1e3*1e3*1e3|0;return 0}function _difftime(time1,time0){return time1-time0}function _dlclose(handle){}function _dlerror(){return 0}function _dlopen(filename,flag){}function _dlsym(handle,symbol){return 0}var readAsmConstArgsArray=[];function readAsmConstArgs(sigPtr,buf){readAsmConstArgsArray.length=0;var ch;buf>>=2;while(ch=HEAPU8[sigPtr++]){var double=ch<105;if(double&&buf&1)buf++;readAsmConstArgsArray.push(double?HEAPF64[buf++>>1]:HEAP32[buf]);++buf}return readAsmConstArgsArray}function mainThreadEM_ASM(code,sigPtr,argbuf,sync){var args=readAsmConstArgs(sigPtr,argbuf);return ASM_CONSTS[code].apply(null,args)}function _emscripten_asm_const_int_sync_on_main_thread(code,sigPtr,argbuf){return mainThreadEM_ASM(code,sigPtr,argbuf,1)}function _emscripten_set_main_loop_timing(mode,value){Browser.mainLoop.timingMode=mode;Browser.mainLoop.timingValue=value;if(!Browser.mainLoop.func){return 1}if(!Browser.mainLoop.running){Browser.mainLoop.running=true}if(mode==0){Browser.mainLoop.scheduler=function Browser_mainLoop_scheduler_setTimeout(){var timeUntilNextTick=Math.max(0,Browser.mainLoop.tickStartTime+value-_emscripten_get_now())|0;setTimeout(Browser.mainLoop.runner,timeUntilNextTick)};Browser.mainLoop.method="timeout"}else if(mode==1){Browser.mainLoop.scheduler=function Browser_mainLoop_scheduler_rAF(){Browser.requestAnimationFrame(Browser.mainLoop.runner)};Browser.mainLoop.method="rAF"}else if(mode==2){if(typeof setImmediate==="undefined"){var setImmediates=[];var emscriptenMainLoopMessageId="setimmediate";var Browser_setImmediate_messageHandler=function(event){if(event.data===emscriptenMainLoopMessageId||event.data.target===emscriptenMainLoopMessageId){event.stopPropagation();setImmediates.shift()()}};addEventListener("message",Browser_setImmediate_messageHandler,true);setImmediate=function Browser_emulated_setImmediate(func){setImmediates.push(func);if(ENVIRONMENT_IS_WORKER){if(Module["setImmediates"]===undefined)Module["setImmediates"]=[];Module["setImmediates"].push(func);postMessage({target:emscriptenMainLoopMessageId})}else postMessage(emscriptenMainLoopMessageId,"*")}}Browser.mainLoop.scheduler=function Browser_mainLoop_scheduler_setImmediate(){setImmediate(Browser.mainLoop.runner)};Browser.mainLoop.method="immediate"}return 0}function _exit(status){exit(status)}function maybeExit(){if(!keepRuntimeAlive()){try{_exit(EXITSTATUS)}catch(e){if(e instanceof ExitStatus){return}throw e}}}function setMainLoop(browserIterationFunc,fps,simulateInfiniteLoop,arg,noSetTiming){assert(!Browser.mainLoop.func,"emscripten_set_main_loop: there can only be one main loop function at once: call emscripten_cancel_main_loop to cancel the previous one before setting a new one with different parameters.");Browser.mainLoop.func=browserIterationFunc;Browser.mainLoop.arg=arg;var thisMainLoopId=Browser.mainLoop.currentlyRunningMainloop;function checkIsRunning(){if(thisMainLoopId<Browser.mainLoop.currentlyRunningMainloop){maybeExit();return false}return true}Browser.mainLoop.running=false;Browser.mainLoop.runner=function Browser_mainLoop_runner(){if(ABORT)return;if(Browser.mainLoop.queue.length>0){var start=Date.now();var blocker=Browser.mainLoop.queue.shift();blocker.func(blocker.arg);if(Browser.mainLoop.remainingBlockers){var remaining=Browser.mainLoop.remainingBlockers;var next=remaining%1==0?remaining-1:Math.floor(remaining);if(blocker.counted){Browser.mainLoop.remainingBlockers=next}else{next=next+.5;Browser.mainLoop.remainingBlockers=(8*remaining+next)/9}}console.log('main loop blocker "'+blocker.name+'" took '+(Date.now()-start)+" ms");Browser.mainLoop.updateStatus();if(!checkIsRunning())return;setTimeout(Browser.mainLoop.runner,0);return}if(!checkIsRunning())return;Browser.mainLoop.currentFrameNumber=Browser.mainLoop.currentFrameNumber+1|0;if(Browser.mainLoop.timingMode==1&&Browser.mainLoop.timingValue>1&&Browser.mainLoop.currentFrameNumber%Browser.mainLoop.timingValue!=0){Browser.mainLoop.scheduler();return}else if(Browser.mainLoop.timingMode==0){Browser.mainLoop.tickStartTime=_emscripten_get_now()}GL.newRenderingFrameStarted();Browser.mainLoop.runIter(browserIterationFunc);if(!checkIsRunning())return;if(typeof SDL==="object"&&SDL.audio&&SDL.audio.queueNewAudioData)SDL.audio.queueNewAudioData();Browser.mainLoop.scheduler()};if(!noSetTiming){if(fps&&fps>0)_emscripten_set_main_loop_timing(0,1e3/fps);else _emscripten_set_main_loop_timing(1,1);Browser.mainLoop.scheduler()}if(simulateInfiniteLoop){throw"unwind"}}function callUserCallback(func,synchronous){if(ABORT){return}if(synchronous){func();return}try{func()}catch(e){if(e instanceof ExitStatus){return}else if(e!=="unwind"){if(e&&typeof e==="object"&&e.stack)err("exception thrown: "+[e,e.stack]);throw e}}}var Browser={mainLoop:{running:false,scheduler:null,method:"",currentlyRunningMainloop:0,func:null,arg:0,timingMode:0,timingValue:0,currentFrameNumber:0,queue:[],pause:function(){Browser.mainLoop.scheduler=null;Browser.mainLoop.currentlyRunningMainloop++},resume:function(){Browser.mainLoop.currentlyRunningMainloop++;var timingMode=Browser.mainLoop.timingMode;var timingValue=Browser.mainLoop.timingValue;var func=Browser.mainLoop.func;Browser.mainLoop.func=null;setMainLoop(func,0,false,Browser.mainLoop.arg,true);_emscripten_set_main_loop_timing(timingMode,timingValue);Browser.mainLoop.scheduler()},updateStatus:function(){if(Module["setStatus"]){var message=Module["statusMessage"]||"Please wait...";var remaining=Browser.mainLoop.remainingBlockers;var expected=Browser.mainLoop.expectedBlockers;if(remaining){if(remaining<expected){Module["setStatus"](message+" ("+(expected-remaining)+"/"+expected+")")}else{Module["setStatus"](message)}}else{Module["setStatus"]("")}}},runIter:function(func){if(ABORT)return;if(Module["preMainLoop"]){var preRet=Module["preMainLoop"]();if(preRet===false){return}}callUserCallback(func);if(Module["postMainLoop"])Module["postMainLoop"]()}},isFullscreen:false,pointerLock:false,moduleContextCreatedCallbacks:[],workers:[],init:function(){if(!Module["preloadPlugins"])Module["preloadPlugins"]=[];if(Browser.initted)return;Browser.initted=true;try{new Blob;Browser.hasBlobConstructor=true}catch(e){Browser.hasBlobConstructor=false;console.log("warning: no blob constructor, cannot create blobs with mimetypes")}Browser.BlobBuilder=typeof MozBlobBuilder!="undefined"?MozBlobBuilder:typeof WebKitBlobBuilder!="undefined"?WebKitBlobBuilder:!Browser.hasBlobConstructor?console.log("warning: no BlobBuilder"):null;Browser.URLObject=typeof window!="undefined"?window.URL?window.URL:window.webkitURL:undefined;if(!Module.noImageDecoding&&typeof Browser.URLObject==="undefined"){console.log("warning: Browser does not support creating object URLs. Built-in browser image decoding will not be available.");Module.noImageDecoding=true}var imagePlugin={};imagePlugin["canHandle"]=function imagePlugin_canHandle(name){return!Module.noImageDecoding&&/\.(jpg|jpeg|png|bmp)$/i.test(name)};imagePlugin["handle"]=function imagePlugin_handle(byteArray,name,onload,onerror){var b=null;if(Browser.hasBlobConstructor){try{b=new Blob([byteArray],{type:Browser.getMimetype(name)});if(b.size!==byteArray.length){b=new Blob([new Uint8Array(byteArray).buffer],{type:Browser.getMimetype(name)})}}catch(e){warnOnce("Blob constructor present but fails: "+e+"; falling back to blob builder")}}if(!b){var bb=new Browser.BlobBuilder;bb.append(new Uint8Array(byteArray).buffer);b=bb.getBlob()}var url=Browser.URLObject.createObjectURL(b);var img=new Image;img.onload=function img_onload(){assert(img.complete,"Image "+name+" could not be decoded");var canvas=document.createElement("canvas");canvas.width=img.width;canvas.height=img.height;var ctx=canvas.getContext("2d");ctx.drawImage(img,0,0);Module["preloadedImages"][name]=canvas;Browser.URLObject.revokeObjectURL(url);if(onload)onload(byteArray)};img.onerror=function img_onerror(event){console.log("Image "+url+" could not be decoded");if(onerror)onerror()};img.src=url};Module["preloadPlugins"].push(imagePlugin);var audioPlugin={};audioPlugin["canHandle"]=function audioPlugin_canHandle(name){return!Module.noAudioDecoding&&name.substr(-4)in{".ogg":1,".wav":1,".mp3":1}};audioPlugin["handle"]=function audioPlugin_handle(byteArray,name,onload,onerror){var done=false;function finish(audio){if(done)return;done=true;Module["preloadedAudios"][name]=audio;if(onload)onload(byteArray)}function fail(){if(done)return;done=true;Module["preloadedAudios"][name]=new Audio;if(onerror)onerror()}if(Browser.hasBlobConstructor){try{var b=new Blob([byteArray],{type:Browser.getMimetype(name)})}catch(e){return fail()}var url=Browser.URLObject.createObjectURL(b);var audio=new Audio;audio.addEventListener("canplaythrough",function(){finish(audio)},false);audio.onerror=function audio_onerror(event){if(done)return;console.log("warning: browser could not fully decode audio "+name+", trying slower base64 approach");function encode64(data){var BASE="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";var PAD="=";var ret="";var leftchar=0;var leftbits=0;for(var i=0;i<data.length;i++){leftchar=leftchar<<8|data[i];leftbits+=8;while(leftbits>=6){var curr=leftchar>>leftbits-6&63;leftbits-=6;ret+=BASE[curr]}}if(leftbits==2){ret+=BASE[(leftchar&3)<<4];ret+=PAD+PAD}else if(leftbits==4){ret+=BASE[(leftchar&15)<<2];ret+=PAD}return ret}audio.src="data:audio/x-"+name.substr(-3)+";base64,"+encode64(byteArray);finish(audio)};audio.src=url;Browser.safeSetTimeout(function(){finish(audio)},1e4)}else{return fail()}};Module["preloadPlugins"].push(audioPlugin);function pointerLockChange(){Browser.pointerLock=document["pointerLockElement"]===Module["canvas"]||document["mozPointerLockElement"]===Module["canvas"]||document["webkitPointerLockElement"]===Module["canvas"]||document["msPointerLockElement"]===Module["canvas"]}var canvas=Module["canvas"];if(canvas){canvas.requestPointerLock=canvas["requestPointerLock"]||canvas["mozRequestPointerLock"]||canvas["webkitRequestPointerLock"]||canvas["msRequestPointerLock"]||function(){};canvas.exitPointerLock=document["exitPointerLock"]||document["mozExitPointerLock"]||document["webkitExitPointerLock"]||document["msExitPointerLock"]||function(){};canvas.exitPointerLock=canvas.exitPointerLock.bind(document);document.addEventListener("pointerlockchange",pointerLockChange,false);document.addEventListener("mozpointerlockchange",pointerLockChange,false);document.addEventListener("webkitpointerlockchange",pointerLockChange,false);document.addEventListener("mspointerlockchange",pointerLockChange,false);if(Module["elementPointerLock"]){canvas.addEventListener("click",function(ev){if(!Browser.pointerLock&&Module["canvas"].requestPointerLock){Module["canvas"].requestPointerLock();ev.preventDefault()}},false)}}},createContext:function(canvas,useWebGL,setInModule,webGLContextAttributes){if(useWebGL&&Module.ctx&&canvas==Module.canvas)return Module.ctx;var ctx;var contextHandle;if(useWebGL){var contextAttributes={antialias:false,alpha:false,majorVersion:typeof WebGL2RenderingContext!=="undefined"?2:1};if(webGLContextAttributes){for(var attribute in webGLContextAttributes){contextAttributes[attribute]=webGLContextAttributes[attribute]}}if(typeof GL!=="undefined"){contextHandle=GL.createContext(canvas,contextAttributes);if(contextHandle){ctx=GL.getContext(contextHandle).GLctx}}}else{ctx=canvas.getContext("2d")}if(!ctx)return null;if(setInModule){if(!useWebGL)assert(typeof GLctx==="undefined","cannot set in module if GLctx is used, but we are a non-GL context that would replace it");Module.ctx=ctx;if(useWebGL)GL.makeContextCurrent(contextHandle);Module.useWebGL=useWebGL;Browser.moduleContextCreatedCallbacks.forEach(function(callback){callback()});Browser.init()}return ctx},destroyContext:function(canvas,useWebGL,setInModule){},fullscreenHandlersInstalled:false,lockPointer:undefined,resizeCanvas:undefined,requestFullscreen:function(lockPointer,resizeCanvas){Browser.lockPointer=lockPointer;Browser.resizeCanvas=resizeCanvas;if(typeof Browser.lockPointer==="undefined")Browser.lockPointer=true;if(typeof Browser.resizeCanvas==="undefined")Browser.resizeCanvas=false;var canvas=Module["canvas"];function fullscreenChange(){Browser.isFullscreen=false;var canvasContainer=canvas.parentNode;if((document["fullscreenElement"]||document["mozFullScreenElement"]||document["msFullscreenElement"]||document["webkitFullscreenElement"]||document["webkitCurrentFullScreenElement"])===canvasContainer){canvas.exitFullscreen=Browser.exitFullscreen;if(Browser.lockPointer)canvas.requestPointerLock();Browser.isFullscreen=true;if(Browser.resizeCanvas){Browser.setFullscreenCanvasSize()}else{Browser.updateCanvasDimensions(canvas)}}else{canvasContainer.parentNode.insertBefore(canvas,canvasContainer);canvasContainer.parentNode.removeChild(canvasContainer);if(Browser.resizeCanvas){Browser.setWindowedCanvasSize()}else{Browser.updateCanvasDimensions(canvas)}}if(Module["onFullScreen"])Module["onFullScreen"](Browser.isFullscreen);if(Module["onFullscreen"])Module["onFullscreen"](Browser.isFullscreen)}if(!Browser.fullscreenHandlersInstalled){Browser.fullscreenHandlersInstalled=true;document.addEventListener("fullscreenchange",fullscreenChange,false);document.addEventListener("mozfullscreenchange",fullscreenChange,false);document.addEventListener("webkitfullscreenchange",fullscreenChange,false);document.addEventListener("MSFullscreenChange",fullscreenChange,false)}var canvasContainer=document.createElement("div");canvas.parentNode.insertBefore(canvasContainer,canvas);canvasContainer.appendChild(canvas);canvasContainer.requestFullscreen=canvasContainer["requestFullscreen"]||canvasContainer["mozRequestFullScreen"]||canvasContainer["msRequestFullscreen"]||(canvasContainer["webkitRequestFullscreen"]?function(){canvasContainer["webkitRequestFullscreen"](Element["ALLOW_KEYBOARD_INPUT"])}:null)||(canvasContainer["webkitRequestFullScreen"]?function(){canvasContainer["webkitRequestFullScreen"](Element["ALLOW_KEYBOARD_INPUT"])}:null);canvasContainer.requestFullscreen()},exitFullscreen:function(){if(!Browser.isFullscreen){return false}var CFS=document["exitFullscreen"]||document["cancelFullScreen"]||document["mozCancelFullScreen"]||document["msExitFullscreen"]||document["webkitCancelFullScreen"]||function(){};CFS.apply(document,[]);return true},nextRAF:0,fakeRequestAnimationFrame:function(func){var now=Date.now();if(Browser.nextRAF===0){Browser.nextRAF=now+1e3/60}else{while(now+2>=Browser.nextRAF){Browser.nextRAF+=1e3/60}}var delay=Math.max(Browser.nextRAF-now,0);setTimeout(func,delay)},requestAnimationFrame:function(func){if(typeof requestAnimationFrame==="function"){requestAnimationFrame(func);return}var RAF=Browser.fakeRequestAnimationFrame;RAF(func)},safeRequestAnimationFrame:function(func){return Browser.requestAnimationFrame(function(){callUserCallback(func)})},safeSetTimeout:function(func,timeout){return setTimeout(function(){callUserCallback(func)},timeout)},getMimetype:function(name){return{"jpg":"image/jpeg","jpeg":"image/jpeg","png":"image/png","bmp":"image/bmp","ogg":"audio/ogg","wav":"audio/wav","mp3":"audio/mpeg"}[name.substr(name.lastIndexOf(".")+1)]},getUserMedia:function(func){if(!window.getUserMedia){window.getUserMedia=navigator["getUserMedia"]||navigator["mozGetUserMedia"]}window.getUserMedia(func)},getMovementX:function(event){return event["movementX"]||event["mozMovementX"]||event["webkitMovementX"]||0},getMovementY:function(event){return event["movementY"]||event["mozMovementY"]||event["webkitMovementY"]||0},getMouseWheelDelta:function(event){var delta=0;switch(event.type){case"DOMMouseScroll":delta=event.detail/3;break;case"mousewheel":delta=event.wheelDelta/120;break;case"wheel":delta=event.deltaY;switch(event.deltaMode){case 0:delta/=100;break;case 1:delta/=3;break;case 2:delta*=80;break;default:throw"unrecognized mouse wheel delta mode: "+event.deltaMode}break;default:throw"unrecognized mouse wheel event: "+event.type}return delta},mouseX:0,mouseY:0,mouseMovementX:0,mouseMovementY:0,touches:{},lastTouches:{},calculateMouseEvent:function(event){if(Browser.pointerLock){if(event.type!="mousemove"&&"mozMovementX"in event){Browser.mouseMovementX=Browser.mouseMovementY=0}else{Browser.mouseMovementX=Browser.getMovementX(event);Browser.mouseMovementY=Browser.getMovementY(event)}if(typeof SDL!="undefined"){Browser.mouseX=SDL.mouseX+Browser.mouseMovementX;Browser.mouseY=SDL.mouseY+Browser.mouseMovementY}else{Browser.mouseX+=Browser.mouseMovementX;Browser.mouseY+=Browser.mouseMovementY}}else{var rect=Module["canvas"].getBoundingClientRect();var cw=Module["canvas"].width;var ch=Module["canvas"].height;var scrollX=typeof window.scrollX!=="undefined"?window.scrollX:window.pageXOffset;var scrollY=typeof window.scrollY!=="undefined"?window.scrollY:window.pageYOffset;if(event.type==="touchstart"||event.type==="touchend"||event.type==="touchmove"){var touch=event.touch;if(touch===undefined){return}var adjustedX=touch.pageX-(scrollX+rect.left);var adjustedY=touch.pageY-(scrollY+rect.top);adjustedX=adjustedX*(cw/rect.width);adjustedY=adjustedY*(ch/rect.height);var coords={x:adjustedX,y:adjustedY};if(event.type==="touchstart"){Browser.lastTouches[touch.identifier]=coords;Browser.touches[touch.identifier]=coords}else if(event.type==="touchend"||event.type==="touchmove"){var last=Browser.touches[touch.identifier];if(!last)last=coords;Browser.lastTouches[touch.identifier]=last;Browser.touches[touch.identifier]=coords}return}var x=event.pageX-(scrollX+rect.left);var y=event.pageY-(scrollY+rect.top);x=x*(cw/rect.width);y=y*(ch/rect.height);Browser.mouseMovementX=x-Browser.mouseX;Browser.mouseMovementY=y-Browser.mouseY;Browser.mouseX=x;Browser.mouseY=y}},asyncLoad:function(url,onload,onerror,noRunDep){var dep=!noRunDep?getUniqueRunDependency("al "+url):"";readAsync(url,function(arrayBuffer){assert(arrayBuffer,'Loading data file "'+url+'" failed (no arrayBuffer).');onload(new Uint8Array(arrayBuffer));if(dep)removeRunDependency(dep)},function(event){if(onerror){onerror()}else{throw'Loading data file "'+url+'" failed.'}});if(dep)addRunDependency(dep)},resizeListeners:[],updateResizeListeners:function(){var canvas=Module["canvas"];Browser.resizeListeners.forEach(function(listener){listener(canvas.width,canvas.height)})},setCanvasSize:function(width,height,noUpdates){var canvas=Module["canvas"];Browser.updateCanvasDimensions(canvas,width,height);if(!noUpdates)Browser.updateResizeListeners()},windowedWidth:0,windowedHeight:0,setFullscreenCanvasSize:function(){if(typeof SDL!="undefined"){var flags=HEAPU32[SDL.screen>>2];flags=flags|8388608;HEAP32[SDL.screen>>2]=flags}Browser.updateCanvasDimensions(Module["canvas"]);Browser.updateResizeListeners()},setWindowedCanvasSize:function(){if(typeof SDL!="undefined"){var flags=HEAPU32[SDL.screen>>2];flags=flags&~8388608;HEAP32[SDL.screen>>2]=flags}Browser.updateCanvasDimensions(Module["canvas"]);Browser.updateResizeListeners()},updateCanvasDimensions:function(canvas,wNative,hNative){if(wNative&&hNative){canvas.widthNative=wNative;canvas.heightNative=hNative}else{wNative=canvas.widthNative;hNative=canvas.heightNative}var w=wNative;var h=hNative;if(Module["forcedAspectRatio"]&&Module["forcedAspectRatio"]>0){if(w/h<Module["forcedAspectRatio"]){w=Math.round(h*Module["forcedAspectRatio"])}else{h=Math.round(w/Module["forcedAspectRatio"])}}if((document["fullscreenElement"]||document["mozFullScreenElement"]||document["msFullscreenElement"]||document["webkitFullscreenElement"]||document["webkitCurrentFullScreenElement"])===canvas.parentNode&&typeof screen!="undefined"){var factor=Math.min(screen.width/w,screen.height/h);w=Math.round(w*factor);h=Math.round(h*factor)}if(Browser.resizeCanvas){if(canvas.width!=w)canvas.width=w;if(canvas.height!=h)canvas.height=h;if(typeof canvas.style!="undefined"){canvas.style.removeProperty("width");canvas.style.removeProperty("height")}}else{if(canvas.width!=wNative)canvas.width=wNative;if(canvas.height!=hNative)canvas.height=hNative;if(typeof canvas.style!="undefined"){if(w!=wNative||h!=hNative){canvas.style.setProperty("width",w+"px","important");canvas.style.setProperty("height",h+"px","important")}else{canvas.style.removeProperty("width");canvas.style.removeProperty("height")}}}},wgetRequests:{},nextWgetRequestHandle:0,getNextWgetRequestHandle:function(){var handle=Browser.nextWgetRequestHandle;Browser.nextWgetRequestHandle++;return handle}};function _emscripten_cancel_main_loop(){Browser.mainLoop.pause();Browser.mainLoop.func=null}function _emscripten_clear_interval(id){clearInterval(id)}var JSEvents={inEventHandler:0,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence<y.precedence})},removeDeferredCalls:function(targetFunction){for(var i=0;i<JSEvents.deferredCalls.length;++i){if(JSEvents.deferredCalls[i].targetFunction==targetFunction){JSEvents.deferredCalls.splice(i,1);--i}}},canPerformEventHandlerRequests:function(){return JSEvents.inEventHandler&&JSEvents.currentEventHandler.allowsDeferredCalls},runDeferredCalls:function(){if(!JSEvents.canPerformEventHandlerRequests()){return}for(var i=0;i<JSEvents.deferredCalls.length;++i){var call=JSEvents.deferredCalls[i];JSEvents.deferredCalls.splice(i,1);--i;call.targetFunction.apply(null,call.argsList)}},eventHandlers:[],removeAllHandlersOnTarget:function(target,eventTypeString){for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==target&&(!eventTypeString||eventTypeString==JSEvents.eventHandlers[i].eventTypeString)){JSEvents._removeHandler(i--)}}},_removeHandler:function(i){var h=JSEvents.eventHandlers[i];h.target.removeEventListener(h.eventTypeString,h.eventListenerFunc,h.useCapture);JSEvents.eventHandlers.splice(i,1)},registerOrRemoveHandler:function(eventHandler){var jsEventHandler=function jsEventHandler(event){++JSEvents.inEventHandler;JSEvents.currentEventHandler=eventHandler;JSEvents.runDeferredCalls();eventHandler.handlerFunc(event);JSEvents.runDeferredCalls();--JSEvents.inEventHandler};if(eventHandler.callbackfunc){eventHandler.eventListenerFunc=jsEventHandler;eventHandler.target.addEventListener(eventHandler.eventTypeString,jsEventHandler,eventHandler.useCapture);JSEvents.eventHandlers.push(eventHandler);JSEvents.registerRemoveEventListeners()}else{for(var i=0;i<JSEvents.eventHandlers.length;++i){if(JSEvents.eventHandlers[i].target==eventHandler.target&&JSEvents.eventHandlers[i].eventTypeString==eventHandler.eventTypeString){JSEvents._removeHandler(i--)}}}},getNodeNameForTarget:function(target){if(!target)return"";if(target==window)return"#window";if(target==screen)return"#screen";return target&&target.nodeName?target.nodeName:""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};var currentFullscreenStrategy={};function maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!=="undefined"?document:0,typeof window!=="undefined"?window:0];function findEventTarget(target){target=maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!=="undefined"?document.querySelector(target):undefined);return domElement}function findCanvasEventTarget(target){return findEventTarget(target)}function _emscripten_get_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;HEAP32[width>>2]=canvas.width;HEAP32[height>>2]=canvas.height}function getCanvasElementSize(target){var stackTop=stackSave();var w=stackAlloc(8);var h=w+4;var targetInt=stackAlloc(target.id.length+1);stringToUTF8(target.id,targetInt,target.id.length+1);var ret=_emscripten_get_canvas_element_size(targetInt,w,h);var size=[HEAP32[w>>2],HEAP32[h>>2]];stackRestore(stackTop);return size}function _emscripten_set_canvas_element_size(target,width,height){var canvas=findCanvasEventTarget(target);if(!canvas)return-4;canvas.width=width;canvas.height=height;return 0}function setCanvasElementSize(target,width,height){if(!target.controlTransferredOffscreen){target.width=width;target.height=height}else{var stackTop=stackSave();var targetInt=stackAlloc(target.id.length+1);stringToUTF8(target.id,targetInt,target.id.length+1);_emscripten_set_canvas_element_size(targetInt,width,height);stackRestore(stackTop)}}function registerRestoreOldStyle(canvas){var canvasSize=getCanvasElementSize(canvas);var oldWidth=canvasSize[0];var oldHeight=canvasSize[1];var oldCssWidth=canvas.style.width;var oldCssHeight=canvas.style.height;var oldBackgroundColor=canvas.style.backgroundColor;var oldDocumentBackgroundColor=document.body.style.backgroundColor;var oldPaddingLeft=canvas.style.paddingLeft;var oldPaddingRight=canvas.style.paddingRight;var oldPaddingTop=canvas.style.paddingTop;var oldPaddingBottom=canvas.style.paddingBottom;var oldMarginLeft=canvas.style.marginLeft;var oldMarginRight=canvas.style.marginRight;var oldMarginTop=canvas.style.marginTop;var oldMarginBottom=canvas.style.marginBottom;var oldDocumentBodyMargin=document.body.style.margin;var oldDocumentOverflow=document.documentElement.style.overflow;var oldDocumentScroll=document.body.scroll;var oldImageRendering=canvas.style.imageRendering;function restoreOldStyle(){var fullscreenElement=document.fullscreenElement||document.webkitFullscreenElement||document.msFullscreenElement;if(!fullscreenElement){document.removeEventListener("fullscreenchange",restoreOldStyle);document.removeEventListener("webkitfullscreenchange",restoreOldStyle);setCanvasElementSize(canvas,oldWidth,oldHeight);canvas.style.width=oldCssWidth;canvas.style.height=oldCssHeight;canvas.style.backgroundColor=oldBackgroundColor;if(!oldDocumentBackgroundColor)document.body.style.backgroundColor="white";document.body.style.backgroundColor=oldDocumentBackgroundColor;canvas.style.paddingLeft=oldPaddingLeft;canvas.style.paddingRight=oldPaddingRight;canvas.style.paddingTop=oldPaddingTop;canvas.style.paddingBottom=oldPaddingBottom;canvas.style.marginLeft=oldMarginLeft;canvas.style.marginRight=oldMarginRight;canvas.style.marginTop=oldMarginTop;canvas.style.marginBottom=oldMarginBottom;document.body.style.margin=oldDocumentBodyMargin;document.documentElement.style.overflow=oldDocumentOverflow;document.body.scroll=oldDocumentScroll;canvas.style.imageRendering=oldImageRendering;if(canvas.GLctxObject)canvas.GLctxObject.GLctx.viewport(0,0,oldWidth,oldHeight);if(currentFullscreenStrategy.canvasResizedCallback){(function(a1,a2,a3){return dynCall_iiii.apply(null,[currentFullscreenStrategy.canvasResizedCallback,a1,a2,a3])})(37,0,currentFullscreenStrategy.canvasResizedCallbackUserData)}}}document.addEventListener("fullscreenchange",restoreOldStyle);document.addEventListener("webkitfullscreenchange",restoreOldStyle);return restoreOldStyle}function setLetterbox(element,topBottom,leftRight){element.style.paddingLeft=element.style.paddingRight=leftRight+"px";element.style.paddingTop=element.style.paddingBottom=topBottom+"px"}function getBoundingClientRect(e){return specialHTMLTargets.indexOf(e)<0?e.getBoundingClientRect():{"left":0,"top":0}}function _JSEvents_resizeCanvasForFullscreen(target,strategy){var restoreOldStyle=registerRestoreOldStyle(target);var cssWidth=strategy.softFullscreen?innerWidth:screen.width;var cssHeight=strategy.softFullscreen?innerHeight:screen.height;var rect=getBoundingClientRect(target);var windowedCssWidth=rect.width;var windowedCssHeight=rect.height;var canvasSize=getCanvasElementSize(target);var windowedRttWidth=canvasSize[0];var windowedRttHeight=canvasSize[1];if(strategy.scaleMode==3){setLetterbox(target,(cssHeight-windowedCssHeight)/2,(cssWidth-windowedCssWidth)/2);cssWidth=windowedCssWidth;cssHeight=windowedCssHeight}else if(strategy.scaleMode==2){if(cssWidth*windowedRttHeight<windowedRttWidth*cssHeight){var desiredCssHeight=windowedRttHeight*cssWidth/windowedRttWidth;setLetterbox(target,(cssHeight-desiredCssHeight)/2,0);cssHeight=desiredCssHeight}else{var desiredCssWidth=windowedRttWidth*cssHeight/windowedRttHeight;setLetterbox(target,0,(cssWidth-desiredCssWidth)/2);cssWidth=desiredCssWidth}}if(!target.style.backgroundColor)target.style.backgroundColor="black";if(!document.body.style.backgroundColor)document.body.style.backgroundColor="black";target.style.width=cssWidth+"px";target.style.height=cssHeight+"px";if(strategy.filteringMode==1){target.style.imageRendering="optimizeSpeed";target.style.imageRendering="-moz-crisp-edges";target.style.imageRendering="-o-crisp-edges";target.style.imageRendering="-webkit-optimize-contrast";target.style.imageRendering="optimize-contrast";target.style.imageRendering="crisp-edges";target.style.imageRendering="pixelated"}var dpiScale=strategy.canvasResolutionScaleMode==2?devicePixelRatio:1;if(strategy.canvasResolutionScaleMode!=0){var newWidth=cssWidth*dpiScale|0;var newHeight=cssHeight*dpiScale|0;setCanvasElementSize(target,newWidth,newHeight);if(target.GLctxObject)target.GLctxObject.GLctx.viewport(0,0,newWidth,newHeight)}return restoreOldStyle}function _JSEvents_requestFullscreen(target,strategy){if(strategy.scaleMode!=0||strategy.canvasResolutionScaleMode!=0){_JSEvents_resizeCanvasForFullscreen(target,strategy)}if(target.requestFullscreen){target.requestFullscreen()}else if(target.webkitRequestFullscreen){target.webkitRequestFullscreen(Element.ALLOW_KEYBOARD_INPUT)}else{return JSEvents.fullscreenEnabled()?-3:-1}currentFullscreenStrategy=strategy;if(strategy.canvasResizedCallback){(function(a1,a2,a3){return dynCall_iiii.apply(null,[strategy.canvasResizedCallback,a1,a2,a3])})(37,0,strategy.canvasResizedCallbackUserData)}return 0}function _emscripten_exit_fullscreen(){if(!JSEvents.fullscreenEnabled())return-1;JSEvents.removeDeferredCalls(_JSEvents_requestFullscreen);var d=specialHTMLTargets[1];if(d.exitFullscreen){d.fullscreenElement&&d.exitFullscreen()}else if(d.webkitExitFullscreen){d.webkitFullscreenElement&&d.webkitExitFullscreen()}else{return-1}return 0}function requestPointerLock(target){if(target.requestPointerLock){target.requestPointerLock()}else if(target.msRequestPointerLock){target.msRequestPointerLock()}else{if(document.body.requestPointerLock||document.body.msRequestPointerLock){return-3}else{return-1}}return 0}function _emscripten_exit_pointerlock(){JSEvents.removeDeferredCalls(requestPointerLock);if(document.exitPointerLock){document.exitPointerLock()}else if(document.msExitPointerLock){document.msExitPointerLock()}else{return-1}return 0}function fillFullscreenChangeEventData(eventStruct){var fullscreenElement=document.fullscreenElement||document.mozFullScreenElement||document.webkitFullscreenElement||document.msFullscreenElement;var isFullscreen=!!fullscreenElement;HEAP32[eventStruct>>2]=isFullscreen;HEAP32[eventStruct+4>>2]=JSEvents.fullscreenEnabled();var reportedElement=isFullscreen?fullscreenElement:JSEvents.previousFullscreenElement;var nodeName=JSEvents.getNodeNameForTarget(reportedElement);var id=reportedElement&&reportedElement.id?reportedElement.id:"";stringToUTF8(nodeName,eventStruct+8,128);stringToUTF8(id,eventStruct+136,128);HEAP32[eventStruct+264>>2]=reportedElement?reportedElement.clientWidth:0;HEAP32[eventStruct+268>>2]=reportedElement?reportedElement.clientHeight:0;HEAP32[eventStruct+272>>2]=screen.width;HEAP32[eventStruct+276>>2]=screen.height;if(isFullscreen){JSEvents.previousFullscreenElement=fullscreenElement}}function _emscripten_get_fullscreen_status(fullscreenStatus){if(!JSEvents.fullscreenEnabled())return-1;fillFullscreenChangeEventData(fullscreenStatus);return 0}function fillGamepadEventData(eventStruct,e){HEAPF64[eventStruct>>3]=e.timestamp;for(var i=0;i<e.axes.length;++i){HEAPF64[eventStruct+i*8+16>>3]=e.axes[i]}for(var i=0;i<e.buttons.length;++i){if(typeof e.buttons[i]==="object"){HEAPF64[eventStruct+i*8+528>>3]=e.buttons[i].value}else{HEAPF64[eventStruct+i*8+528>>3]=e.buttons[i]}}for(var i=0;i<e.buttons.length;++i){if(typeof e.buttons[i]==="object"){HEAP32[eventStruct+i*4+1040>>2]=e.buttons[i].pressed}else{HEAP32[eventStruct+i*4+1040>>2]=e.buttons[i]==1}}HEAP32[eventStruct+1296>>2]=e.connected;HEAP32[eventStruct+1300>>2]=e.index;HEAP32[eventStruct+8>>2]=e.axes.length;HEAP32[eventStruct+12>>2]=e.buttons.length;stringToUTF8(e.id,eventStruct+1304,64);stringToUTF8(e.mapping,eventStruct+1368,64)}function _emscripten_get_gamepad_status(index,gamepadState){if(index<0||index>=JSEvents.lastGamepadState.length)return-5;if(!JSEvents.lastGamepadState[index])return-7;fillGamepadEventData(gamepadState,JSEvents.lastGamepadState[index]);return 0}function _emscripten_get_heap_max(){return 2147483648}function _emscripten_get_num_gamepads(){return JSEvents.lastGamepadState.length}function _emscripten_html5_remove_all_event_listeners(){JSEvents.removeAllEventListeners()}function _emscripten_is_webgl_context_lost(contextHandle){return!GL.contexts[contextHandle]||GL.contexts[contextHandle].GLctx.isContextLost()}function reallyNegative(x){return x<0||x===0&&1/x===-Infinity}function convertI32PairToI53(lo,hi){return(lo>>>0)+hi*4294967296}function convertU32PairToI53(lo,hi){return(lo>>>0)+(hi>>>0)*4294967296}function reSign(value,bits){if(value<=0){return value}var half=bits<=32?Math.abs(1<<bits-1):Math.pow(2,bits-1);if(value>=half&&(bits<=32||value>half)){value=-2*half+value}return value}function unSign(value,bits){if(value>=0){return value}return bits<=32?2*Math.abs(1<<bits-1)+value:Math.pow(2,bits)+value}function formatString(format,varargs){var textIndex=format;var argIndex=varargs;function prepVararg(ptr,type){if(type==="double"||type==="i64"){if(ptr&7){ptr+=4}}else{}return ptr}function getNextArg(type){var ret;argIndex=prepVararg(argIndex,type);if(type==="double"){ret=HEAPF64[argIndex>>3];argIndex+=8}else if(type=="i64"){ret=[HEAP32[argIndex>>2],HEAP32[argIndex+4>>2]];argIndex+=8}else{type="i32";ret=HEAP32[argIndex>>2];argIndex+=4}return ret}var ret=[];var curr,next,currArg;while(1){var startTextIndex=textIndex;curr=HEAP8[textIndex>>0];if(curr===0)break;next=HEAP8[textIndex+1>>0];if(curr==37){var flagAlwaysSigned=false;var flagLeftAlign=false;var flagAlternative=false;var flagZeroPad=false;var flagPadSign=false;flagsLoop:while(1){switch(next){case 43:flagAlwaysSigned=true;break;case 45:flagLeftAlign=true;break;case 35:flagAlternative=true;break;case 48:if(flagZeroPad){break flagsLoop}else{flagZeroPad=true;break}case 32:flagPadSign=true;break;default:break flagsLoop}textIndex++;next=HEAP8[textIndex+1>>0]}var width=0;if(next==42){width=getNextArg("i32");textIndex++;next=HEAP8[textIndex+1>>0]}else{while(next>=48&&next<=57){width=width*10+(next-48);textIndex++;next=HEAP8[textIndex+1>>0]}}var precisionSet=false,precision=-1;if(next==46){precision=0;precisionSet=true;textIndex++;next=HEAP8[textIndex+1>>0];if(next==42){precision=getNextArg("i32");textIndex++}else{while(1){var precisionChr=HEAP8[textIndex+1>>0];if(precisionChr<48||precisionChr>57)break;precision=precision*10+(precisionChr-48);textIndex++}}next=HEAP8[textIndex+1>>0]}if(precision<0){precision=6;precisionSet=false}var argSize;switch(String.fromCharCode(next)){case"h":var nextNext=HEAP8[textIndex+2>>0];if(nextNext==104){textIndex++;argSize=1}else{argSize=2}break;case"l":var nextNext=HEAP8[textIndex+2>>0];if(nextNext==108){textIndex++;argSize=8}else{argSize=4}break;case"L":case"q":case"j":argSize=8;break;case"z":case"t":case"I":argSize=4;break;default:argSize=null}if(argSize)textIndex++;next=HEAP8[textIndex+1>>0];switch(String.fromCharCode(next)){case"d":case"i":case"u":case"o":case"x":case"X":case"p":{var signed=next==100||next==105;argSize=argSize||4;currArg=getNextArg("i"+argSize*8);var argText;if(argSize==8){currArg=next==117?convertU32PairToI53(currArg[0],currArg[1]):convertI32PairToI53(currArg[0],currArg[1])}if(argSize<=4){var limit=Math.pow(256,argSize)-1;currArg=(signed?reSign:unSign)(currArg&limit,argSize*8)}var currAbsArg=Math.abs(currArg);var prefix="";if(next==100||next==105){argText=reSign(currArg,8*argSize,1).toString(10)}else if(next==117){argText=unSign(currArg,8*argSize,1).toString(10);currArg=Math.abs(currArg)}else if(next==111){argText=(flagAlternative?"0":"")+currAbsArg.toString(8)}else if(next==120||next==88){prefix=flagAlternative&&currArg!=0?"0x":"";if(currArg<0){currArg=-currArg;argText=(currAbsArg-1).toString(16);var buffer=[];for(var i=0;i<argText.length;i++){buffer.push((15-parseInt(argText[i],16)).toString(16))}argText=buffer.join("");while(argText.length<argSize*2)argText="f"+argText}else{argText=currAbsArg.toString(16)}if(next==88){prefix=prefix.toUpperCase();argText=argText.toUpperCase()}}else if(next==112){if(currAbsArg===0){argText="(nil)"}else{prefix="0x";argText=currAbsArg.toString(16)}}if(precisionSet){while(argText.length<precision){argText="0"+argText}}if(currArg>=0){if(flagAlwaysSigned){prefix="+"+prefix}else if(flagPadSign){prefix=" "+prefix}}if(argText.charAt(0)=="-"){prefix="-"+prefix;argText=argText.substr(1)}while(prefix.length+argText.length<width){if(flagLeftAlign){argText+=" "}else{if(flagZeroPad){argText="0"+argText}else{prefix=" "+prefix}}}argText=prefix+argText;argText.split("").forEach(function(chr){ret.push(chr.charCodeAt(0))});break}case"f":case"F":case"e":case"E":case"g":case"G":{currArg=getNextArg("double");var argText;if(isNaN(currArg)){argText="nan";flagZeroPad=false}else if(!isFinite(currArg)){argText=(currArg<0?"-":"")+"inf";flagZeroPad=false}else{var isGeneral=false;var effectivePrecision=Math.min(precision,20);if(next==103||next==71){isGeneral=true;precision=precision||1;var exponent=parseInt(currArg.toExponential(effectivePrecision).split("e")[1],10);if(precision>exponent&&exponent>=-4){next=(next==103?"f":"F").charCodeAt(0);precision-=exponent+1}else{next=(next==103?"e":"E").charCodeAt(0);precision--}effectivePrecision=Math.min(precision,20)}if(next==101||next==69){argText=currArg.toExponential(effectivePrecision);if(/[eE][-+]\d$/.test(argText)){argText=argText.slice(0,-1)+"0"+argText.slice(-1)}}else if(next==102||next==70){argText=currArg.toFixed(effectivePrecision);if(currArg===0&&reallyNegative(currArg)){argText="-"+argText}}var parts=argText.split("e");if(isGeneral&&!flagAlternative){while(parts[0].length>1&&parts[0].includes(".")&&(parts[0].slice(-1)=="0"||parts[0].slice(-1)==".")){parts[0]=parts[0].slice(0,-1)}}else{if(flagAlternative&&argText.indexOf(".")==-1)parts[0]+=".";while(precision>effectivePrecision++)parts[0]+="0"}argText=parts[0]+(parts.length>1?"e"+parts[1]:"");if(next==69)argText=argText.toUpperCase();if(currArg>=0){if(flagAlwaysSigned){argText="+"+argText}else if(flagPadSign){argText=" "+argText}}}while(argText.length<width){if(flagLeftAlign){argText+=" "}else{if(flagZeroPad&&(argText[0]=="-"||argText[0]=="+")){argText=argText[0]+"0"+argText.slice(1)}else{argText=(flagZeroPad?"0":" ")+argText}}}if(next<97)argText=argText.toUpperCase();argText.split("").forEach(function(chr){ret.push(chr.charCodeAt(0))});break}case"s":{var arg=getNextArg("i8*");var argLength=arg?_strlen(arg):"(null)".length;if(precisionSet)argLength=Math.min(argLength,precision);if(!flagLeftAlign){while(argLength<width--){ret.push(32)}}if(arg){for(var i=0;i<argLength;i++){ret.push(HEAPU8[arg++>>0])}}else{ret=ret.concat(intArrayFromString("(null)".substr(0,argLength),true))}if(flagLeftAlign){while(argLength<width--){ret.push(32)}}break}case"c":{if(flagLeftAlign)ret.push(getNextArg("i8"));while(--width>0){ret.push(32)}if(!flagLeftAlign)ret.push(getNextArg("i8"));break}case"n":{var ptr=getNextArg("i32*");HEAP32[ptr>>2]=ret.length;break}case"%":{ret.push(curr);break}default:{for(var i=startTextIndex;i<textIndex+2;i++){ret.push(HEAP8[i>>0])}}}textIndex+=2}else{ret.push(curr);textIndex+=1}}return ret}function traverseStack(args){if(!args||!args.callee||!args.callee.name){return[null,"",""]}var funstr=args.callee.toString();var funcname=args.callee.name;var str="(";var first=true;for(var i in args){var a=args[i];if(!first){str+=", "}first=false;if(typeof a==="number"||typeof a==="string"){str+=a}else{str+="("+typeof a+")"}}str+=")";var caller=args.callee.caller;args=caller?caller.arguments:[];if(first)str="";return[args,funcname,str]}function _emscripten_get_callstack_js(flags){var callstack=jsStackTrace();var iThisFunc=callstack.lastIndexOf("_emscripten_log");var iThisFunc2=callstack.lastIndexOf("_emscripten_get_callstack");var iNextLine=callstack.indexOf("\n",Math.max(iThisFunc,iThisFunc2))+1;callstack=callstack.slice(iNextLine);if(flags&32){warnOnce("EM_LOG_DEMANGLE is deprecated; ignoring")}if(flags&8&&typeof emscripten_source_map==="undefined"){warnOnce('Source map information is not available, emscripten_log with EM_LOG_C_STACK will be ignored. Build with "--pre-js $EMSCRIPTEN/src/emscripten-source-map.min.js" linker flag to add source map loading to code.');flags^=8;flags|=16}var stack_args=null;if(flags&128){stack_args=traverseStack(arguments);while(stack_args[1].includes("_emscripten_"))stack_args=traverseStack(stack_args[0])}var lines=callstack.split("\n");callstack="";var newFirefoxRe=new RegExp("\\s*(.*?)@(.*?):([0-9]+):([0-9]+)");var firefoxRe=new RegExp("\\s*(.*?)@(.*):(.*)(:(.*))?");var chromeRe=new RegExp("\\s*at (.*?) \\((.*):(.*):(.*)\\)");for(var l in lines){var line=lines[l];var symbolName="";var file="";var lineno=0;var column=0;var parts=chromeRe.exec(line);if(parts&&parts.length==5){symbolName=parts[1];file=parts[2];lineno=parts[3];column=parts[4]}else{parts=newFirefoxRe.exec(line);if(!parts)parts=firefoxRe.exec(line);if(parts&&parts.length>=4){symbolName=parts[1];file=parts[2];lineno=parts[3];column=parts[4]|0}else{callstack+=line+"\n";continue}}var haveSourceMap=false;if(flags&8){var orig=emscripten_source_map.originalPositionFor({line:lineno,column:column});haveSourceMap=orig&&orig.source;if(haveSourceMap){if(flags&64){orig.source=orig.source.substring(orig.source.replace(/\\/g,"/").lastIndexOf("/")+1)}callstack+="    at "+symbolName+" ("+orig.source+":"+orig.line+":"+orig.column+")\n"}}if(flags&16||!haveSourceMap){if(flags&64){file=file.substring(file.replace(/\\/g,"/").lastIndexOf("/")+1)}callstack+=(haveSourceMap?"     = "+symbolName:"    at "+symbolName)+" ("+file+":"+lineno+":"+column+")\n"}if(flags&128&&stack_args[0]){if(stack_args[1]==symbolName&&stack_args[2].length>0){callstack=callstack.replace(/\s+$/,"");callstack+=" with values: "+stack_args[1]+stack_args[2]+"\n"}stack_args=traverseStack(stack_args[0])}}callstack=callstack.replace(/\s+$/,"");return callstack}function _emscripten_log_js(flags,str){if(flags&24){str=str.replace(/\s+$/,"");str+=(str.length>0?"\n":"")+_emscripten_get_callstack_js(flags)}if(flags&1){if(flags&4){console.error(str)}else if(flags&2){console.warn(str)}else if(flags&512){console.info(str)}else if(flags&256){console.debug(str)}else{console.log(str)}}else if(flags&6){err(str)}else{out(str)}}function _emscripten_log(flags,format,varargs){var result=formatString(format,varargs);var str=UTF8ArrayToString(result,0);_emscripten_log_js(flags,str)}function _longjmp(env,value){_setThrew(env,value||1);throw"longjmp"}function _emscripten_longjmp(a0,a1){return _longjmp(a0,a1)}function _emscripten_memcpy_big(dest,src,num){HEAPU8.copyWithin(dest,src,src+num)}function doRequestFullscreen(target,strategy){if(!JSEvents.fullscreenEnabled())return-1;target=findEventTarget(target);if(!target)return-4;if(!target.requestFullscreen&&!target.webkitRequestFullscreen){return-3}var canPerformRequests=JSEvents.canPerformEventHandlerRequests();if(!canPerformRequests){if(strategy.deferUntilInEventHandler){JSEvents.deferCall(_JSEvents_requestFullscreen,1,[target,strategy]);return 1}else{return-2}}return _JSEvents_requestFullscreen(target,strategy)}function _emscripten_request_fullscreen(target,deferUntilInEventHandler){var strategy={scaleMode:0,canvasResolutionScaleMode:0,filteringMode:0,deferUntilInEventHandler:deferUntilInEventHandler,canvasResizedCallbackTargetThread:2};return doRequestFullscreen(target,strategy)}function _emscripten_request_pointerlock(target,deferUntilInEventHandler){target=findEventTarget(target);if(!target)return-4;if(!target.requestPointerLock&&!target.msRequestPointerLock){return-1}var canPerformRequests=JSEvents.canPerformEventHandlerRequests();if(!canPerformRequests){if(deferUntilInEventHandler){JSEvents.deferCall(requestPointerLock,2,[target]);return 1}else{return-2}}return requestPointerLock(target)}function emscripten_realloc_buffer(size){try{wasmMemory.grow(size-buffer.byteLength+65535>>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){var oldSize=HEAPU8.length;requestedSize=requestedSize>>>0;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(requestedSize,overGrownHeapSize),65536));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}function _emscripten_sample_gamepad_data(){return(JSEvents.lastGamepadState=navigator.getGamepads?navigator.getGamepads():navigator.webkitGetGamepads?navigator.webkitGetGamepads():null)?0:-1}function registerFocusEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.focusEvent)JSEvents.focusEvent=_malloc(256);var focusEventHandlerFunc=function(ev){var e=ev||event;var nodeName=JSEvents.getNodeNameForTarget(e.target);var id=e.target.id?e.target.id:"";var focusEvent=JSEvents.focusEvent;stringToUTF8(nodeName,focusEvent+0,128);stringToUTF8(id,focusEvent+128,128);if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,focusEvent,userData))e.preventDefault()};var eventHandler={target:findEventTarget(target),eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:focusEventHandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_blur_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerFocusEventCallback(target,userData,useCapture,callbackfunc,12,"blur",targetThread);return 0}function _emscripten_set_focus_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerFocusEventCallback(target,userData,useCapture,callbackfunc,13,"focus",targetThread);return 0}function registerFullscreenChangeEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.fullscreenChangeEvent)JSEvents.fullscreenChangeEvent=_malloc(280);var fullscreenChangeEventhandlerFunc=function(ev){var e=ev||event;var fullscreenChangeEvent=JSEvents.fullscreenChangeEvent;fillFullscreenChangeEventData(fullscreenChangeEvent);if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,fullscreenChangeEvent,userData))e.preventDefault()};var eventHandler={target:target,eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:fullscreenChangeEventhandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_fullscreenchange_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){if(!JSEvents.fullscreenEnabled())return-1;target=findEventTarget(target);if(!target)return-4;registerFullscreenChangeEventCallback(target,userData,useCapture,callbackfunc,19,"fullscreenchange",targetThread);registerFullscreenChangeEventCallback(target,userData,useCapture,callbackfunc,19,"webkitfullscreenchange",targetThread);return 0}function registerGamepadEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.gamepadEvent)JSEvents.gamepadEvent=_malloc(1432);var gamepadEventHandlerFunc=function(ev){var e=ev||event;var gamepadEvent=JSEvents.gamepadEvent;fillGamepadEventData(gamepadEvent,e["gamepad"]);if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,gamepadEvent,userData))e.preventDefault()};var eventHandler={target:findEventTarget(target),allowsDeferredCalls:true,eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:gamepadEventHandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_gamepadconnected_callback_on_thread(userData,useCapture,callbackfunc,targetThread){if(!navigator.getGamepads&&!navigator.webkitGetGamepads)return-1;registerGamepadEventCallback(2,userData,useCapture,callbackfunc,26,"gamepadconnected",targetThread);return 0}function _emscripten_set_gamepaddisconnected_callback_on_thread(userData,useCapture,callbackfunc,targetThread){if(!navigator.getGamepads&&!navigator.webkitGetGamepads)return-1;registerGamepadEventCallback(2,userData,useCapture,callbackfunc,27,"gamepaddisconnected",targetThread);return 0}function _emscripten_set_interval(cb,msecs,userData){return setInterval(function(){(function(a1){dynCall_vi.apply(null,[cb,a1])})(userData)},msecs)}function registerKeyEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.keyEvent)JSEvents.keyEvent=_malloc(164);var keyEventHandlerFunc=function(e){var keyEventData=JSEvents.keyEvent;var idx=keyEventData>>2;HEAP32[idx+0]=e.location;HEAP32[idx+1]=e.ctrlKey;HEAP32[idx+2]=e.shiftKey;HEAP32[idx+3]=e.altKey;HEAP32[idx+4]=e.metaKey;HEAP32[idx+5]=e.repeat;HEAP32[idx+6]=e.charCode;HEAP32[idx+7]=e.keyCode;HEAP32[idx+8]=e.which;stringToUTF8(e.key||"",keyEventData+36,32);stringToUTF8(e.code||"",keyEventData+68,32);stringToUTF8(e.char||"",keyEventData+100,32);stringToUTF8(e.locale||"",keyEventData+132,32);if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,keyEventData,userData))e.preventDefault()};var eventHandler={target:findEventTarget(target),allowsDeferredCalls:true,eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:keyEventHandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_keydown_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerKeyEventCallback(target,userData,useCapture,callbackfunc,2,"keydown",targetThread);return 0}function _emscripten_set_keypress_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerKeyEventCallback(target,userData,useCapture,callbackfunc,1,"keypress",targetThread);return 0}function _emscripten_set_keyup_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerKeyEventCallback(target,userData,useCapture,callbackfunc,3,"keyup",targetThread);return 0}function _emscripten_set_main_loop(func,fps,simulateInfiniteLoop){var browserIterationFunc=function(){dynCall_v.call(null,func)};setMainLoop(browserIterationFunc,fps,simulateInfiniteLoop)}function fillMouseEventData(eventStruct,e,target){var idx=eventStruct>>2;HEAP32[idx+0]=e.screenX;HEAP32[idx+1]=e.screenY;HEAP32[idx+2]=e.clientX;HEAP32[idx+3]=e.clientY;HEAP32[idx+4]=e.ctrlKey;HEAP32[idx+5]=e.shiftKey;HEAP32[idx+6]=e.altKey;HEAP32[idx+7]=e.metaKey;HEAP16[idx*2+16]=e.button;HEAP16[idx*2+17]=e.buttons;HEAP32[idx+9]=e["movementX"];HEAP32[idx+10]=e["movementY"];var rect=getBoundingClientRect(target);HEAP32[idx+11]=e.clientX-rect.left;HEAP32[idx+12]=e.clientY-rect.top}function registerMouseEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.mouseEvent)JSEvents.mouseEvent=_malloc(64);target=findEventTarget(target);var mouseEventHandlerFunc=function(ev){var e=ev||event;fillMouseEventData(JSEvents.mouseEvent,e,target);if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,JSEvents.mouseEvent,userData))e.preventDefault()};var eventHandler={target:target,allowsDeferredCalls:eventTypeString!="mousemove"&&eventTypeString!="mouseenter"&&eventTypeString!="mouseleave",eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:mouseEventHandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_mousedown_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerMouseEventCallback(target,userData,useCapture,callbackfunc,5,"mousedown",targetThread);return 0}function _emscripten_set_mousemove_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerMouseEventCallback(target,userData,useCapture,callbackfunc,8,"mousemove",targetThread);return 0}function _emscripten_set_mouseup_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerMouseEventCallback(target,userData,useCapture,callbackfunc,6,"mouseup",targetThread);return 0}function registerTouchEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.touchEvent)JSEvents.touchEvent=_malloc(1684);target=findEventTarget(target);var touchEventHandlerFunc=function(e){var t,touches={},et=e.touches;for(var i=0;i<et.length;++i){t=et[i];t.isChanged=t.onTarget=0;touches[t.identifier]=t}for(var i=0;i<e.changedTouches.length;++i){t=e.changedTouches[i];t.isChanged=1;touches[t.identifier]=t}for(var i=0;i<e.targetTouches.length;++i){touches[e.targetTouches[i].identifier].onTarget=1}var touchEvent=JSEvents.touchEvent;var idx=touchEvent>>2;HEAP32[idx+1]=e.ctrlKey;HEAP32[idx+2]=e.shiftKey;HEAP32[idx+3]=e.altKey;HEAP32[idx+4]=e.metaKey;idx+=5;var targetRect=getBoundingClientRect(target);var numTouches=0;for(var i in touches){var t=touches[i];HEAP32[idx+0]=t.identifier;HEAP32[idx+1]=t.screenX;HEAP32[idx+2]=t.screenY;HEAP32[idx+3]=t.clientX;HEAP32[idx+4]=t.clientY;HEAP32[idx+5]=t.pageX;HEAP32[idx+6]=t.pageY;HEAP32[idx+7]=t.isChanged;HEAP32[idx+8]=t.onTarget;HEAP32[idx+9]=t.clientX-targetRect.left;HEAP32[idx+10]=t.clientY-targetRect.top;idx+=13;if(++numTouches>31){break}}HEAP32[touchEvent>>2]=numTouches;if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,touchEvent,userData))e.preventDefault()};var eventHandler={target:target,allowsDeferredCalls:eventTypeString=="touchstart"||eventTypeString=="touchend",eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:touchEventHandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_touchcancel_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerTouchEventCallback(target,userData,useCapture,callbackfunc,25,"touchcancel",targetThread);return 0}function _emscripten_set_touchend_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerTouchEventCallback(target,userData,useCapture,callbackfunc,23,"touchend",targetThread);return 0}function _emscripten_set_touchmove_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerTouchEventCallback(target,userData,useCapture,callbackfunc,24,"touchmove",targetThread);return 0}function _emscripten_set_touchstart_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){registerTouchEventCallback(target,userData,useCapture,callbackfunc,22,"touchstart",targetThread);return 0}function registerWheelEventCallback(target,userData,useCapture,callbackfunc,eventTypeId,eventTypeString,targetThread){if(!JSEvents.wheelEvent)JSEvents.wheelEvent=_malloc(96);var wheelHandlerFunc=function(ev){var e=ev||event;var wheelEvent=JSEvents.wheelEvent;fillMouseEventData(wheelEvent,e,target);HEAPF64[wheelEvent+64>>3]=e["deltaX"];HEAPF64[wheelEvent+72>>3]=e["deltaY"];HEAPF64[wheelEvent+80>>3]=e["deltaZ"];HEAP32[wheelEvent+88>>2]=e["deltaMode"];if(function(a1,a2,a3){return dynCall_iiii.apply(null,[callbackfunc,a1,a2,a3])}(eventTypeId,wheelEvent,userData))e.preventDefault()};var eventHandler={target:target,allowsDeferredCalls:true,eventTypeString:eventTypeString,callbackfunc:callbackfunc,handlerFunc:wheelHandlerFunc,useCapture:useCapture};JSEvents.registerOrRemoveHandler(eventHandler)}function _emscripten_set_wheel_callback_on_thread(target,userData,useCapture,callbackfunc,targetThread){target=findEventTarget(target);if(typeof target.onwheel!=="undefined"){registerWheelEventCallback(target,userData,useCapture,callbackfunc,9,"wheel",targetThread);return 0}else{return-1}}function _emscripten_thread_sleep(msecs){var start=_emscripten_get_now();while(_emscripten_get_now()-start<msecs){}}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext){ctx["vertexAttribDivisor"]=function(index,divisor){ext["vertexAttribDivisorANGLE"](index,divisor)};ctx["drawArraysInstanced"]=function(mode,first,count,primcount){ext["drawArraysInstancedANGLE"](mode,first,count,primcount)};ctx["drawElementsInstanced"]=function(mode,count,type,indices,primcount){ext["drawElementsInstancedANGLE"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext){ctx["createVertexArray"]=function(){return ext["createVertexArrayOES"]()};ctx["deleteVertexArray"]=function(vao){ext["deleteVertexArrayOES"](vao)};ctx["bindVertexArray"]=function(vao){ext["bindVertexArrayOES"](vao)};ctx["isVertexArray"]=function(vao){return ext["isVertexArrayOES"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext){ctx["drawBuffers"]=function(n,bufs){ext["drawBuffersWEBGL"](n,bufs)};return 1}}function __webgl_enable_WEBGL_draw_instanced_base_vertex_base_instance(ctx){return!!(ctx.dibvbi=ctx.getExtension("WEBGL_draw_instanced_base_vertex_base_instance"))}function __webgl_enable_WEBGL_multi_draw_instanced_base_vertex_base_instance(ctx){return!!(ctx.mdibvbi=ctx.getExtension("WEBGL_multi_draw_instanced_base_vertex_base_instance"))}function __webgl_enable_WEBGL_multi_draw(ctx){return!!(ctx.multiDrawWebgl=ctx.getExtension("WEBGL_multi_draw"))}var GL={counter:1,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],shaders:[],vaos:[],contexts:[],offscreenCanvases:{},queries:[],samplers:[],transformFeedbacks:[],syncs:[],byteSizeByTypeRoot:5120,byteSizeByType:[1,1,2,2,4,4,4,2,3,4,8],stringCache:{},stringiCache:{},unpackAlignment:4,recordError:function recordError(errorCode){if(!GL.lastError){GL.lastError=errorCode}},getNewId:function(table){var ret=GL.counter++;for(var i=table.length;i<ret;i++){table[i]=null}return ret},MAX_TEMP_BUFFER_SIZE:2097152,numTempVertexBuffersPerSize:64,log2ceilLookup:function(i){return 32-Math.clz32(i===0?0:i-1)},generateTempBuffers:function(quads,context){var largestIndex=GL.log2ceilLookup(GL.MAX_TEMP_BUFFER_SIZE);context.tempVertexBufferCounters1=[];context.tempVertexBufferCounters2=[];context.tempVertexBufferCounters1.length=context.tempVertexBufferCounters2.length=largestIndex+1;context.tempVertexBuffers1=[];context.tempVertexBuffers2=[];context.tempVertexBuffers1.length=context.tempVertexBuffers2.length=largestIndex+1;context.tempIndexBuffers=[];context.tempIndexBuffers.length=largestIndex+1;for(var i=0;i<=largestIndex;++i){context.tempIndexBuffers[i]=null;context.tempVertexBufferCounters1[i]=context.tempVertexBufferCounters2[i]=0;var ringbufferLength=GL.numTempVertexBuffersPerSize;context.tempVertexBuffers1[i]=[];context.tempVertexBuffers2[i]=[];var ringbuffer1=context.tempVertexBuffers1[i];var ringbuffer2=context.tempVertexBuffers2[i];ringbuffer1.length=ringbuffer2.length=ringbufferLength;for(var j=0;j<ringbufferLength;++j){ringbuffer1[j]=ringbuffer2[j]=null}}if(quads){context.tempQuadIndexBuffer=GLctx.createBuffer();context.GLctx.bindBuffer(34963,context.tempQuadIndexBuffer);var numIndexes=GL.MAX_TEMP_BUFFER_SIZE>>1;var quadIndexes=new Uint16Array(numIndexes);var i=0,v=0;while(1){quadIndexes[i++]=v;if(i>=numIndexes)break;quadIndexes[i++]=v+1;if(i>=numIndexes)break;quadIndexes[i++]=v+2;if(i>=numIndexes)break;quadIndexes[i++]=v;if(i>=numIndexes)break;quadIndexes[i++]=v+2;if(i>=numIndexes)break;quadIndexes[i++]=v+3;if(i>=numIndexes)break;v+=4}context.GLctx.bufferData(34963,quadIndexes,35044);context.GLctx.bindBuffer(34963,null)}},getTempVertexBuffer:function getTempVertexBuffer(sizeBytes){var idx=GL.log2ceilLookup(sizeBytes);var ringbuffer=GL.currentContext.tempVertexBuffers1[idx];var nextFreeBufferIndex=GL.currentContext.tempVertexBufferCounters1[idx];GL.currentContext.tempVertexBufferCounters1[idx]=GL.currentContext.tempVertexBufferCounters1[idx]+1&GL.numTempVertexBuffersPerSize-1;var vbo=ringbuffer[nextFreeBufferIndex];if(vbo){return vbo}var prevVBO=GLctx.getParameter(34964);ringbuffer[nextFreeBufferIndex]=GLctx.createBuffer();GLctx.bindBuffer(34962,ringbuffer[nextFreeBufferIndex]);GLctx.bufferData(34962,1<<idx,35048);GLctx.bindBuffer(34962,prevVBO);return ringbuffer[nextFreeBufferIndex]},getTempIndexBuffer:function getTempIndexBuffer(sizeBytes){var idx=GL.log2ceilLookup(sizeBytes);var ibo=GL.currentContext.tempIndexBuffers[idx];if(ibo){return ibo}var prevIBO=GLctx.getParameter(34965);GL.currentContext.tempIndexBuffers[idx]=GLctx.createBuffer();GLctx.bindBuffer(34963,GL.currentContext.tempIndexBuffers[idx]);GLctx.bufferData(34963,1<<idx,35048);GLctx.bindBuffer(34963,prevIBO);return GL.currentContext.tempIndexBuffers[idx]},newRenderingFrameStarted:function newRenderingFrameStarted(){if(!GL.currentContext){return}var vb=GL.currentContext.tempVertexBuffers1;GL.currentContext.tempVertexBuffers1=GL.currentContext.tempVertexBuffers2;GL.currentContext.tempVertexBuffers2=vb;vb=GL.currentContext.tempVertexBufferCounters1;GL.currentContext.tempVertexBufferCounters1=GL.currentContext.tempVertexBufferCounters2;GL.currentContext.tempVertexBufferCounters2=vb;var largestIndex=GL.log2ceilLookup(GL.MAX_TEMP_BUFFER_SIZE);for(var i=0;i<=largestIndex;++i){GL.currentContext.tempVertexBufferCounters1[i]=0}},getSource:function(shader,count,string,length){var source="";for(var i=0;i<count;++i){var len=length?HEAP32[length+i*4>>2]:-1;source+=UTF8ToString(HEAP32[string+i*4>>2],len<0?undefined:len)}return source},calcBufLength:function calcBufLength(size,type,stride,count){if(stride>0){return count*stride}var typeSize=GL.byteSizeByType[type-GL.byteSizeByTypeRoot];return size*typeSize*count},usedTempBuffers:[],preDrawHandleClientVertexAttribBindings:function preDrawHandleClientVertexAttribBindings(count){GL.resetBufferBinding=false;for(var i=0;i<GL.currentContext.maxVertexAttribs;++i){var cb=GL.currentContext.clientBuffers[i];if(!cb.clientside||!cb.enabled)continue;GL.resetBufferBinding=true;var size=GL.calcBufLength(cb.size,cb.type,cb.stride,count);var buf=GL.getTempVertexBuffer(size);GLctx.bindBuffer(34962,buf);GLctx.bufferSubData(34962,0,HEAPU8.subarray(cb.ptr,cb.ptr+size));cb.vertexAttribPointerAdaptor.call(GLctx,i,cb.size,cb.type,cb.normalized,cb.stride,0)}},postDrawHandleClientVertexAttribBindings:function postDrawHandleClientVertexAttribBindings(){if(GL.resetBufferBinding){GLctx.bindBuffer(34962,GL.buffers[GLctx.currentArrayBufferBinding])}},createContext:function(canvas,webGLContextAttributes){if(!canvas.getContextSafariWebGL2Fixed){canvas.getContextSafariWebGL2Fixed=canvas.getContext;canvas.getContext=function(ver,attrs){var gl=canvas.getContextSafariWebGL2Fixed(ver,attrs);return ver=="webgl"==gl instanceof WebGLRenderingContext?gl:null}}var ctx=webGLContextAttributes.majorVersion>1?canvas.getContext("webgl2",webGLContextAttributes):canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=GL.getNewId(GL.contexts);var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault==="undefined"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}context.maxVertexAttribs=context.GLctx.getParameter(34921);context.clientBuffers=[];for(var i=0;i<context.maxVertexAttribs;i++){context.clientBuffers[i]={enabled:false,clientside:false,size:0,type:0,normalized:0,stride:0,ptr:0,vertexAttribPointerAdaptor:null}}GL.generateTempBuffers(false,context);return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents==="object")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);__webgl_enable_WEBGL_draw_instanced_base_vertex_base_instance(GLctx);__webgl_enable_WEBGL_multi_draw_instanced_base_vertex_base_instance(GLctx);if(context.version>=2){GLctx.disjointTimerQueryExt=GLctx.getExtension("EXT_disjoint_timer_query_webgl2")}if(context.version<2||!GLctx.disjointTimerQueryExt){GLctx.disjointTimerQueryExt=GLctx.getExtension("EXT_disjoint_timer_query")}__webgl_enable_WEBGL_multi_draw(GLctx);var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(!ext.includes("lose_context")&&!ext.includes("debug")){GLctx.getExtension(ext)}})}};var __emscripten_webgl_power_preferences=["default","low-power","high-performance"];function _emscripten_webgl_do_create_context(target,attributes){var a=attributes>>2;var powerPreference=HEAP32[a+(24>>2)];var contextAttributes={"alpha":!!HEAP32[a+(0>>2)],"depth":!!HEAP32[a+(4>>2)],"stencil":!!HEAP32[a+(8>>2)],"antialias":!!HEAP32[a+(12>>2)],"premultipliedAlpha":!!HEAP32[a+(16>>2)],"preserveDrawingBuffer":!!HEAP32[a+(20>>2)],"powerPreference":__emscripten_webgl_power_preferences[powerPreference],"failIfMajorPerformanceCaveat":!!HEAP32[a+(28>>2)],majorVersion:HEAP32[a+(32>>2)],minorVersion:HEAP32[a+(36>>2)],enableExtensionsByDefault:HEAP32[a+(40>>2)],explicitSwapControl:HEAP32[a+(44>>2)],proxyContextToMainThread:HEAP32[a+(48>>2)],renderViaOffscreenBackBuffer:HEAP32[a+(52>>2)]};var canvas=findCanvasEventTarget(target);if(!canvas){return 0}if(contextAttributes.explicitSwapControl){return 0}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}function _emscripten_webgl_do_get_current_context(){return GL.currentContext?GL.currentContext.handle:0}function _emscripten_webgl_get_current_context(){return _emscripten_webgl_do_get_current_context()}Module["_emscripten_webgl_get_current_context"]=_emscripten_webgl_get_current_context;function _emscripten_webgl_make_context_current(contextHandle){var success=GL.makeContextCurrent(contextHandle);return success?0:-5}Module["_emscripten_webgl_make_context_current"]=_emscripten_webgl_make_context_current;function _emscripten_webgl_destroy_context(contextHandle){if(GL.currentContext==contextHandle)GL.currentContext=0;GL.deleteContext(contextHandle)}function _emscripten_webgl_enable_extension(contextHandle,extension){var context=GL.getContext(contextHandle);var extString=UTF8ToString(extension);if(extString.startsWith("GL_"))extString=extString.substr(3);if(extString=="ANGLE_instanced_arrays")__webgl_enable_ANGLE_instanced_arrays(GLctx);if(extString=="OES_vertex_array_object")__webgl_enable_OES_vertex_array_object(GLctx);if(extString=="WEBGL_draw_buffers")__webgl_enable_WEBGL_draw_buffers(GLctx);if(extString=="WEBGL_draw_instanced_base_vertex_base_instance")__webgl_enable_WEBGL_draw_instanced_base_vertex_base_instance(GLctx);if(extString=="WEBGL_multi_draw_instanced_base_vertex_base_instance")__webgl_enable_WEBGL_multi_draw_instanced_base_vertex_base_instance(GLctx);if(extString=="WEBGL_multi_draw")__webgl_enable_WEBGL_multi_draw(GLctx);var ext=context.GLctx.getExtension(extString);return!!ext}function _emscripten_webgl_init_context_attributes(attributes){var a=attributes>>2;for(var i=0;i<56>>2;++i){HEAP32[a+i]=0}HEAP32[a+(0>>2)]=HEAP32[a+(4>>2)]=HEAP32[a+(12>>2)]=HEAP32[a+(16>>2)]=HEAP32[a+(32>>2)]=HEAP32[a+(40>>2)]=1}var ENV={};function getExecutableName(){return thisProgram||"./this.program"}function getEnvStrings(){if(!getEnvStrings.strings){var lang=(typeof navigator==="object"&&navigator.languages&&navigator.languages[0]||"C").replace("-","_")+".UTF-8";var env={"USER":"web_user","LOGNAME":"web_user","PATH":"/","PWD":"/","HOME":"/home/web_user","LANG":lang,"_":getExecutableName()};for(var x in ENV){env[x]=ENV[x]}var strings=[];for(var x in env){strings.push(x+"="+env[x])}getEnvStrings.strings=strings}return getEnvStrings.strings}function _environ_get(__environ,environ_buf){try{var bufSize=0;getEnvStrings().forEach(function(string,i){var ptr=environ_buf+bufSize;HEAP32[__environ+i*4>>2]=ptr;writeAsciiToMemory(string,ptr);bufSize+=string.length+1});return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _environ_sizes_get(penviron_count,penviron_buf_size){try{var strings=getEnvStrings();HEAP32[penviron_count>>2]=strings.length;var bufSize=0;strings.forEach(function(string){bufSize+=string.length+1});HEAP32[penviron_buf_size>>2]=bufSize;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _fd_close(fd){try{var stream=SYSCALLS.getStreamFromFD(fd);FS.close(stream);return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _fd_fdstat_get(fd,pbuf){try{var stream=SYSCALLS.getStreamFromFD(fd);var type=stream.tty?2:FS.isDir(stream.mode)?3:FS.isLink(stream.mode)?7:4;HEAP8[pbuf>>0]=type;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _fd_read(fd,iov,iovcnt,pnum){try{var stream=SYSCALLS.getStreamFromFD(fd);var num=SYSCALLS.doReadv(stream,iov,iovcnt);HEAP32[pnum>>2]=num;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){try{var stream=SYSCALLS.getStreamFromFD(fd);var HIGH_OFFSET=4294967296;var offset=offset_high*HIGH_OFFSET+(offset_low>>>0);var DOUBLE_LIMIT=9007199254740992;if(offset<=-DOUBLE_LIMIT||offset>=DOUBLE_LIMIT){return-61}FS.llseek(stream,offset,whence);tempI64=[stream.position>>>0,(tempDouble=stream.position,+Math.abs(tempDouble)>=1?tempDouble>0?(Math.min(+Math.floor(tempDouble/4294967296),4294967295)|0)>>>0:~~+Math.ceil((tempDouble-+(~~tempDouble>>>0))/4294967296)>>>0:0)],HEAP32[newOffset>>2]=tempI64[0],HEAP32[newOffset+4>>2]=tempI64[1];if(stream.getdents&&offset===0&&whence===0)stream.getdents=null;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _fd_write(fd,iov,iovcnt,pnum){try{var stream=SYSCALLS.getStreamFromFD(fd);var num=SYSCALLS.doWritev(stream,iov,iovcnt);HEAP32[pnum>>2]=num;return 0}catch(e){if(typeof FS==="undefined"||!(e instanceof FS.ErrnoError))abort(e);return e.errno}}function _flock(fd,operation){return 0}function _getTempRet0(){return getTempRet0()}function _getaddrinfo(node,service,hint,out){var addr=0;var port=0;var flags=0;var family=0;var type=0;var proto=0;var ai;function allocaddrinfo(family,type,proto,canon,addr,port){var sa,salen,ai;var errno;salen=family===10?28:16;addr=family===10?inetNtop6(addr):inetNtop4(addr);sa=_malloc(salen);errno=writeSockaddr(sa,family,addr,port);assert(!errno);ai=_malloc(32);HEAP32[ai+4>>2]=family;HEAP32[ai+8>>2]=type;HEAP32[ai+12>>2]=proto;HEAP32[ai+24>>2]=canon;HEAP32[ai+20>>2]=sa;if(family===10){HEAP32[ai+16>>2]=28}else{HEAP32[ai+16>>2]=16}HEAP32[ai+28>>2]=0;return ai}if(hint){flags=HEAP32[hint>>2];family=HEAP32[hint+4>>2];type=HEAP32[hint+8>>2];proto=HEAP32[hint+12>>2]}if(type&&!proto){proto=type===2?17:6}if(!type&&proto){type=proto===17?2:1}if(proto===0){proto=6}if(type===0){type=1}if(!node&&!service){return-2}if(flags&~(1|2|4|1024|8|16|32)){return-1}if(hint!==0&&HEAP32[hint>>2]&2&&!node){return-1}if(flags&32){return-2}if(type!==0&&type!==1&&type!==2){return-7}if(family!==0&&family!==2&&family!==10){return-6}if(service){service=UTF8ToString(service);port=parseInt(service,10);if(isNaN(port)){if(flags&1024){return-2}return-8}}if(!node){if(family===0){family=2}if((flags&1)===0){if(family===2){addr=_htonl(2130706433)}else{addr=[0,0,0,1]}}ai=allocaddrinfo(family,type,proto,null,addr,port);HEAP32[out>>2]=ai;return 0}node=UTF8ToString(node);addr=inetPton4(node);if(addr!==null){if(family===0||family===2){family=2}else if(family===10&&flags&8){addr=[0,0,_htonl(65535),addr];family=10}else{return-2}}else{addr=inetPton6(node);if(addr!==null){if(family===0||family===10){family=10}else{return-2}}}if(addr!=null){ai=allocaddrinfo(family,type,proto,node,addr,port);HEAP32[out>>2]=ai;return 0}if(flags&4){return-2}node=DNS.lookup_name(node);addr=inetPton4(node);if(family===0){family=2}else if(family===10){addr=[0,0,_htonl(65535),addr]}ai=allocaddrinfo(family,type,proto,null,addr,port);HEAP32[out>>2]=ai;return 0}function getHostByName(name){var ret=_malloc(20);var nameBuf=_malloc(name.length+1);stringToUTF8(name,nameBuf,name.length+1);HEAP32[ret>>2]=nameBuf;var aliasesBuf=_malloc(4);HEAP32[aliasesBuf>>2]=0;HEAP32[ret+4>>2]=aliasesBuf;var afinet=2;HEAP32[ret+8>>2]=afinet;HEAP32[ret+12>>2]=4;var addrListBuf=_malloc(12);HEAP32[addrListBuf>>2]=addrListBuf+8;HEAP32[addrListBuf+4>>2]=0;HEAP32[addrListBuf+8>>2]=inetPton4(DNS.lookup_name(name));HEAP32[ret+16>>2]=addrListBuf;return ret}function _gethostbyaddr(addr,addrlen,type){if(type!==2){setErrNo(5);return null}addr=HEAP32[addr>>2];var host=inetNtop4(addr);var lookup=DNS.lookup_addr(host);if(lookup){host=lookup}return getHostByName(host)}function _gethostbyname(name){return getHostByName(UTF8ToString(name))}function _getnameinfo(sa,salen,node,nodelen,serv,servlen,flags){var info=readSockaddr(sa,salen);if(info.errno){return-6}var port=info.port;var addr=info.addr;var overflowed=false;if(node&&nodelen){var lookup;if(flags&1||!(lookup=DNS.lookup_addr(addr))){if(flags&8){return-2}}else{addr=lookup}var numBytesWrittenExclNull=stringToUTF8(addr,node,nodelen);if(numBytesWrittenExclNull+1>=nodelen){overflowed=true}}if(serv&&servlen){port=""+port;var numBytesWrittenExclNull=stringToUTF8(port,serv,servlen);if(numBytesWrittenExclNull+1>=servlen){overflowed=true}}if(overflowed){return-12}return 0}function _getpwuid(){throw"getpwuid: TODO"}function _gettimeofday(ptr){var now=Date.now();HEAP32[ptr>>2]=now/1e3|0;HEAP32[ptr+4>>2]=now%1e3*1e3|0;return 0}function _glActiveTexture(x0){GLctx["activeTexture"](x0)}function _glAttachShader(program,shader){program=GL.programs[program];shader=GL.shaders[shader];program[shader.shaderType]=shader;GLctx.attachShader(program,shader)}function _glBeginQuery(target,id){GLctx["beginQuery"](target,GL.queries[id])}function _glBeginTransformFeedback(x0){GLctx["beginTransformFeedback"](x0)}function _glBindAttribLocation(program,index,name){GLctx.bindAttribLocation(GL.programs[program],index,UTF8ToString(name))}function _glBindBuffer(target,buffer){if(target==34962){GLctx.currentArrayBufferBinding=buffer}else if(target==34963){GLctx.currentElementArrayBufferBinding=buffer}if(target==35051){GLctx.currentPixelPackBufferBinding=buffer}else if(target==35052){GLctx.currentPixelUnpackBufferBinding=buffer}GLctx.bindBuffer(target,GL.buffers[buffer])}function _glBindBufferBase(target,index,buffer){GLctx["bindBufferBase"](target,index,GL.buffers[buffer])}function _glBindBufferRange(target,index,buffer,offset,ptrsize){GLctx["bindBufferRange"](target,index,GL.buffers[buffer],offset,ptrsize)}function _glBindFramebuffer(target,framebuffer){GLctx.bindFramebuffer(target,GL.framebuffers[framebuffer])}function _glBindRenderbuffer(target,renderbuffer){GLctx.bindRenderbuffer(target,GL.renderbuffers[renderbuffer])}function _glBindSampler(unit,sampler){GLctx["bindSampler"](unit,GL.samplers[sampler])}function _glBindTexture(target,texture){GLctx.bindTexture(target,GL.textures[texture])}function _glBindTransformFeedback(target,id){GLctx["bindTransformFeedback"](target,GL.transformFeedbacks[id])}function _glBindVertexArray(vao){GLctx["bindVertexArray"](GL.vaos[vao]);var ibo=GLctx.getParameter(34965);GLctx.currentElementArrayBufferBinding=ibo?ibo.name|0:0}function _glBlendEquation(x0){GLctx["blendEquation"](x0)}function _glBlendEquationSeparate(x0,x1){GLctx["blendEquationSeparate"](x0,x1)}function _glBlendFuncSeparate(x0,x1,x2,x3){GLctx["blendFuncSeparate"](x0,x1,x2,x3)}function _glBlitFramebuffer(x0,x1,x2,x3,x4,x5,x6,x7,x8,x9){GLctx["blitFramebuffer"](x0,x1,x2,x3,x4,x5,x6,x7,x8,x9)}function _glBufferData(target,size,data,usage){if(GL.currentContext.version>=2){if(data){GLctx.bufferData(target,HEAPU8,usage,data,size)}else{GLctx.bufferData(target,size,usage)}}else{GLctx.bufferData(target,data?HEAPU8.subarray(data,data+size):size,usage)}}function _glBufferSubData(target,offset,size,data){if(GL.currentContext.version>=2){GLctx.bufferSubData(target,offset,HEAPU8,data,size);return}GLctx.bufferSubData(target,offset,HEAPU8.subarray(data,data+size))}function _glCheckFramebufferStatus(x0){return GLctx["checkFramebufferStatus"](x0)}function _glClear(x0){GLctx["clear"](x0)}function _glClearBufferfi(x0,x1,x2,x3){GLctx["clearBufferfi"](x0,x1,x2,x3)}function _glClearBufferfv(buffer,drawbuffer,value){GLctx["clearBufferfv"](buffer,drawbuffer,HEAPF32,value>>2)}function _glClearBufferuiv(buffer,drawbuffer,value){GLctx["clearBufferuiv"](buffer,drawbuffer,HEAPU32,value>>2)}function _glClearColor(x0,x1,x2,x3){GLctx["clearColor"](x0,x1,x2,x3)}function _glClearDepthf(x0){GLctx["clearDepth"](x0)}function _glClearStencil(x0){GLctx["clearStencil"](x0)}function _glClientWaitSync(sync,flags,timeoutLo,timeoutHi){return GLctx.clientWaitSync(GL.syncs[sync],flags,convertI32PairToI53(timeoutLo,timeoutHi))}function _glColorMask(red,green,blue,alpha){GLctx.colorMask(!!red,!!green,!!blue,!!alpha)}function _glCompileShader(shader){GLctx.compileShader(GL.shaders[shader])}function _glCompressedTexImage2D(target,level,internalFormat,width,height,border,imageSize,data){if(GL.currentContext.version>=2){if(GLctx.currentPixelUnpackBufferBinding){GLctx["compressedTexImage2D"](target,level,internalFormat,width,height,border,imageSize,data)}else{GLctx["compressedTexImage2D"](target,level,internalFormat,width,height,border,HEAPU8,data,imageSize)}return}GLctx["compressedTexImage2D"](target,level,internalFormat,width,height,border,data?HEAPU8.subarray(data,data+imageSize):null)}function _glCompressedTexImage3D(target,level,internalFormat,width,height,depth,border,imageSize,data){if(GLctx.currentPixelUnpackBufferBinding){GLctx["compressedTexImage3D"](target,level,internalFormat,width,height,depth,border,imageSize,data)}else{GLctx["compressedTexImage3D"](target,level,internalFormat,width,height,depth,border,HEAPU8,data,imageSize)}}function _glCompressedTexSubImage2D(target,level,xoffset,yoffset,width,height,format,imageSize,data){if(GL.currentContext.version>=2){if(GLctx.currentPixelUnpackBufferBinding){GLctx["compressedTexSubImage2D"](target,level,xoffset,yoffset,width,height,format,imageSize,data)}else{GLctx["compressedTexSubImage2D"](target,level,xoffset,yoffset,width,height,format,HEAPU8,data,imageSize)}return}GLctx["compressedTexSubImage2D"](target,level,xoffset,yoffset,width,height,format,data?HEAPU8.subarray(data,data+imageSize):null)}function _glCompressedTexSubImage3D(target,level,xoffset,yoffset,zoffset,width,height,depth,format,imageSize,data){if(GLctx.currentPixelUnpackBufferBinding){GLctx["compressedTexSubImage3D"](target,level,xoffset,yoffset,zoffset,width,height,depth,format,imageSize,data)}else{GLctx["compressedTexSubImage3D"](target,level,xoffset,yoffset,zoffset,width,height,depth,format,HEAPU8,data,imageSize)}}function _glCopyBufferSubData(x0,x1,x2,x3,x4){GLctx["copyBufferSubData"](x0,x1,x2,x3,x4)}function _glCopyTexImage2D(x0,x1,x2,x3,x4,x5,x6,x7){GLctx["copyTexImage2D"](x0,x1,x2,x3,x4,x5,x6,x7)}function _glCopyTexSubImage2D(x0,x1,x2,x3,x4,x5,x6,x7){GLctx["copyTexSubImage2D"](x0,x1,x2,x3,x4,x5,x6,x7)}function _glCreateProgram(){var id=GL.getNewId(GL.programs);var program=GLctx.createProgram();program.name=id;program.maxUniformLength=program.maxAttributeLength=program.maxUniformBlockNameLength=0;program.uniformIdCounter=1;GL.programs[id]=program;return id}function _glCreateShader(shaderType){var id=GL.getNewId(GL.shaders);GL.shaders[id]=GLctx.createShader(shaderType);GL.shaders[id].shaderType=shaderType&1?"vs":"fs";return id}function _glCullFace(x0){GLctx["cullFace"](x0)}function _glDeleteBuffers(n,buffers){for(var i=0;i<n;i++){var id=HEAP32[buffers+i*4>>2];var buffer=GL.buffers[id];if(!buffer)continue;GLctx.deleteBuffer(buffer);buffer.name=0;GL.buffers[id]=null;if(id==GLctx.currentArrayBufferBinding)GLctx.currentArrayBufferBinding=0;if(id==GLctx.currentElementArrayBufferBinding)GLctx.currentElementArrayBufferBinding=0;if(id==GLctx.currentPixelPackBufferBinding)GLctx.currentPixelPackBufferBinding=0;if(id==GLctx.currentPixelUnpackBufferBinding)GLctx.currentPixelUnpackBufferBinding=0}}function _glDeleteFramebuffers(n,framebuffers){for(var i=0;i<n;++i){var id=HEAP32[framebuffers+i*4>>2];var framebuffer=GL.framebuffers[id];if(!framebuffer)continue;GLctx.deleteFramebuffer(framebuffer);framebuffer.name=0;GL.framebuffers[id]=null}}function _glDeleteProgram(id){if(!id)return;var program=GL.programs[id];if(!program){GL.recordError(1281);return}GLctx.deleteProgram(program);program.name=0;GL.programs[id]=null}function _glDeleteQueries(n,ids){for(var i=0;i<n;i++){var id=HEAP32[ids+i*4>>2];var query=GL.queries[id];if(!query)continue;GLctx["deleteQuery"](query);GL.queries[id]=null}}function _glDeleteRenderbuffers(n,renderbuffers){for(var i=0;i<n;i++){var id=HEAP32[renderbuffers+i*4>>2];var renderbuffer=GL.renderbuffers[id];if(!renderbuffer)continue;GLctx.deleteRenderbuffer(renderbuffer);renderbuffer.name=0;GL.renderbuffers[id]=null}}function _glDeleteSamplers(n,samplers){for(var i=0;i<n;i++){var id=HEAP32[samplers+i*4>>2];var sampler=GL.samplers[id];if(!sampler)continue;GLctx["deleteSampler"](sampler);sampler.name=0;GL.samplers[id]=null}}function _glDeleteShader(id){if(!id)return;var shader=GL.shaders[id];if(!shader){GL.recordError(1281);return}GLctx.deleteShader(shader);GL.shaders[id]=null}function _glDeleteSync(id){if(!id)return;var sync=GL.syncs[id];if(!sync){GL.recordError(1281);return}GLctx.deleteSync(sync);sync.name=0;GL.syncs[id]=null}function _glDeleteTextures(n,textures){for(var i=0;i<n;i++){var id=HEAP32[textures+i*4>>2];var texture=GL.textures[id];if(!texture)continue;GLctx.deleteTexture(texture);texture.name=0;GL.textures[id]=null}}function _glDeleteTransformFeedbacks(n,ids){for(var i=0;i<n;i++){var id=HEAP32[ids+i*4>>2];var transformFeedback=GL.transformFeedbacks[id];if(!transformFeedback)continue;GLctx["deleteTransformFeedback"](transformFeedback);transformFeedback.name=0;GL.transformFeedbacks[id]=null}}function _glDeleteVertexArrays(n,vaos){for(var i=0;i<n;i++){var id=HEAP32[vaos+i*4>>2];GLctx["deleteVertexArray"](GL.vaos[id]);GL.vaos[id]=null}}function _glDepthFunc(x0){GLctx["depthFunc"](x0)}function _glDepthMask(flag){GLctx.depthMask(!!flag)}function _glDetachShader(program,shader){GLctx.detachShader(GL.programs[program],GL.shaders[shader])}function _glDisable(x0){GLctx["disable"](x0)}function _glDisableVertexAttribArray(index){var cb=GL.currentContext.clientBuffers[index];cb.enabled=false;GLctx.disableVertexAttribArray(index)}function _glDrawArrays(mode,first,count){GL.preDrawHandleClientVertexAttribBindings(first+count);GLctx.drawArrays(mode,first,count);GL.postDrawHandleClientVertexAttribBindings()}function _glDrawArraysInstanced(mode,first,count,primcount){GLctx["drawArraysInstanced"](mode,first,count,primcount)}var tempFixedLengthArray=[];function _glDrawBuffers(n,bufs){var bufArray=tempFixedLengthArray[n];for(var i=0;i<n;i++){bufArray[i]=HEAP32[bufs+i*4>>2]}GLctx["drawBuffers"](bufArray)}function _glDrawElements(mode,count,type,indices){var buf;if(!GLctx.currentElementArrayBufferBinding){var size=GL.calcBufLength(1,type,0,count);buf=GL.getTempIndexBuffer(size);GLctx.bindBuffer(34963,buf);GLctx.bufferSubData(34963,0,HEAPU8.subarray(indices,indices+size));indices=0}GL.preDrawHandleClientVertexAttribBindings(count);GLctx.drawElements(mode,count,type,indices);GL.postDrawHandleClientVertexAttribBindings(count);if(!GLctx.currentElementArrayBufferBinding){GLctx.bindBuffer(34963,null)}}function _glDrawElementsInstanced(mode,count,type,indices,primcount){GLctx["drawElementsInstanced"](mode,count,type,indices,primcount)}function _glEnable(x0){GLctx["enable"](x0)}function _glEnableVertexAttribArray(index){var cb=GL.currentContext.clientBuffers[index];cb.enabled=true;GLctx.enableVertexAttribArray(index)}function _glEndQuery(x0){GLctx["endQuery"](x0)}function _glEndTransformFeedback(){GLctx["endTransformFeedback"]()}function _glFenceSync(condition,flags){var sync=GLctx.fenceSync(condition,flags);if(sync){var id=GL.getNewId(GL.syncs);sync.name=id;GL.syncs[id]=sync;return id}else{return 0}}function _glFinish(){GLctx["finish"]()}function _glFlush(){GLctx["flush"]()}function emscriptenWebGLGetBufferBinding(target){switch(target){case 34962:target=34964;break;case 34963:target=34965;break;case 35051:target=35053;break;case 35052:target=35055;break;case 35982:target=35983;break;case 36662:target=36662;break;case 36663:target=36663;break;case 35345:target=35368;break}var buffer=GLctx.getParameter(target);if(buffer)return buffer.name|0;else return 0}function emscriptenWebGLValidateMapBufferTarget(target){switch(target){case 34962:case 34963:case 36662:case 36663:case 35051:case 35052:case 35882:case 35982:case 35345:return true;default:return false}}function _glFlushMappedBufferRange(target,offset,length){if(!emscriptenWebGLValidateMapBufferTarget(target)){GL.recordError(1280);err("GL_INVALID_ENUM in glFlushMappedBufferRange");return}var mapping=GL.mappedBuffers[emscriptenWebGLGetBufferBinding(target)];if(!mapping){GL.recordError(1282);err("buffer was never mapped in glFlushMappedBufferRange");return}if(!(mapping.access&16)){GL.recordError(1282);err("buffer was not mapped with GL_MAP_FLUSH_EXPLICIT_BIT in glFlushMappedBufferRange");return}if(offset<0||length<0||offset+length>mapping.length){GL.recordError(1281);err("invalid range in glFlushMappedBufferRange");return}GLctx.bufferSubData(target,mapping.offset,HEAPU8.subarray(mapping.mem+offset,mapping.mem+offset+length))}function _glFramebufferRenderbuffer(target,attachment,renderbuffertarget,renderbuffer){GLctx.framebufferRenderbuffer(target,attachment,renderbuffertarget,GL.renderbuffers[renderbuffer])}function _glFramebufferTexture2D(target,attachment,textarget,texture,level){GLctx.framebufferTexture2D(target,attachment,textarget,GL.textures[texture],level)}function _glFramebufferTextureLayer(target,attachment,texture,level,layer){GLctx.framebufferTextureLayer(target,attachment,GL.textures[texture],level,layer)}function _glFrontFace(x0){GLctx["frontFace"](x0)}function __glGenObject(n,buffers,createFunction,objectTable){for(var i=0;i<n;i++){var buffer=GLctx[createFunction]();var id=buffer&&GL.getNewId(objectTable);if(buffer){buffer.name=id;objectTable[id]=buffer}else{GL.recordError(1282)}HEAP32[buffers+i*4>>2]=id}}function _glGenBuffers(n,buffers){__glGenObject(n,buffers,"createBuffer",GL.buffers)}function _glGenFramebuffers(n,ids){__glGenObject(n,ids,"createFramebuffer",GL.framebuffers)}function _glGenQueries(n,ids){__glGenObject(n,ids,"createQuery",GL.queries)}function _glGenRenderbuffers(n,renderbuffers){__glGenObject(n,renderbuffers,"createRenderbuffer",GL.renderbuffers)}function _glGenSamplers(n,samplers){__glGenObject(n,samplers,"createSampler",GL.samplers)}function _glGenTextures(n,textures){__glGenObject(n,textures,"createTexture",GL.textures)}function _glGenTransformFeedbacks(n,ids){__glGenObject(n,ids,"createTransformFeedback",GL.transformFeedbacks)}function _glGenVertexArrays(n,arrays){__glGenObject(n,arrays,"createVertexArray",GL.vaos)}function _glGenerateMipmap(x0){GLctx["generateMipmap"](x0)}function __glGetActiveAttribOrUniform(funcName,program,index,bufSize,length,size,type,name){program=GL.programs[program];var info=GLctx[funcName](program,index);if(info){var numBytesWrittenExclNull=name&&stringToUTF8(info.name,name,bufSize);if(length)HEAP32[length>>2]=numBytesWrittenExclNull;if(size)HEAP32[size>>2]=info.size;if(type)HEAP32[type>>2]=info.type}}function _glGetActiveAttrib(program,index,bufSize,length,size,type,name){__glGetActiveAttribOrUniform("getActiveAttrib",program,index,bufSize,length,size,type,name)}function _glGetActiveUniform(program,index,bufSize,length,size,type,name){__glGetActiveAttribOrUniform("getActiveUniform",program,index,bufSize,length,size,type,name)}function _glGetActiveUniformBlockName(program,uniformBlockIndex,bufSize,length,uniformBlockName){program=GL.programs[program];var result=GLctx["getActiveUniformBlockName"](program,uniformBlockIndex);if(!result)return;if(uniformBlockName&&bufSize>0){var numBytesWrittenExclNull=stringToUTF8(result,uniformBlockName,bufSize);if(length)HEAP32[length>>2]=numBytesWrittenExclNull}else{if(length)HEAP32[length>>2]=0}}function _glGetActiveUniformBlockiv(program,uniformBlockIndex,pname,params){if(!params){GL.recordError(1281);return}program=GL.programs[program];if(pname==35393){var name=GLctx["getActiveUniformBlockName"](program,uniformBlockIndex);HEAP32[params>>2]=name.length+1;return}var result=GLctx["getActiveUniformBlockParameter"](program,uniformBlockIndex,pname);if(result===null)return;if(pname==35395){for(var i=0;i<result.length;i++){HEAP32[params+i*4>>2]=result[i]}}else{HEAP32[params>>2]=result}}function _glGetActiveUniformsiv(program,uniformCount,uniformIndices,pname,params){if(!params){GL.recordError(1281);return}if(uniformCount>0&&uniformIndices==0){GL.recordError(1281);return}program=GL.programs[program];var ids=[];for(var i=0;i<uniformCount;i++){ids.push(HEAP32[uniformIndices+i*4>>2])}var result=GLctx["getActiveUniforms"](program,ids,pname);if(!result)return;var len=result.length;for(var i=0;i<len;i++){HEAP32[params+i*4>>2]=result[i]}}function _glGetAttribLocation(program,name){return GLctx.getAttribLocation(GL.programs[program],UTF8ToString(name))}function _glGetBufferSubData(target,offset,size,data){if(!data){GL.recordError(1281);return}GLctx["getBufferSubData"](target,offset,HEAPU8,data,size)}function _glGetError(){var error=GLctx.getError()||GL.lastError;GL.lastError=0;return error}function _glGetFramebufferAttachmentParameteriv(target,attachment,pname,params){var result=GLctx.getFramebufferAttachmentParameter(target,attachment,pname);if(result instanceof WebGLRenderbuffer||result instanceof WebGLTexture){result=result.name|0}HEAP32[params>>2]=result}function writeI53ToI64(ptr,num){HEAPU32[ptr>>2]=num;HEAPU32[ptr+4>>2]=(num-HEAPU32[ptr>>2])/4294967296}function emscriptenWebGLGetIndexed(target,index,data,type){if(!data){GL.recordError(1281);return}var result=GLctx["getIndexedParameter"](target,index);var ret;switch(typeof result){case"boolean":ret=result?1:0;break;case"number":ret=result;break;case"object":if(result===null){switch(target){case 35983:case 35368:ret=0;break;default:{GL.recordError(1280);return}}}else if(result instanceof WebGLBuffer){ret=result.name|0}else{GL.recordError(1280);return}break;default:GL.recordError(1280);return}switch(type){case 1:writeI53ToI64(data,ret);break;case 0:HEAP32[data>>2]=ret;break;case 2:HEAPF32[data>>2]=ret;break;case 4:HEAP8[data>>0]=ret?1:0;break;default:throw"internal emscriptenWebGLGetIndexed() error, bad type: "+type}}function _glGetIntegeri_v(target,index,data){emscriptenWebGLGetIndexed(target,index,data,0)}function emscriptenWebGLGet(name_,p,type){if(!p){GL.recordError(1281);return}var ret=undefined;switch(name_){case 36346:ret=1;break;case 36344:if(type!=0&&type!=1){GL.recordError(1280)}return;case 34814:case 36345:ret=0;break;case 34466:var formats=GLctx.getParameter(34467);ret=formats?formats.length:0;break;case 33390:ret=1048576;break;case 33309:if(GL.currentContext.version<2){GL.recordError(1282);return}var exts=GLctx.getSupportedExtensions()||[];ret=2*exts.length;break;case 33307:case 33308:if(GL.currentContext.version<2){GL.recordError(1280);return}ret=name_==33307?3:0;break}if(ret===undefined){var result=GLctx.getParameter(name_);switch(typeof result){case"number":ret=result;break;case"boolean":ret=result?1:0;break;case"string":GL.recordError(1280);return;case"object":if(result===null){switch(name_){case 34964:case 35725:case 34965:case 36006:case 36007:case 32873:case 34229:case 36662:case 36663:case 35053:case 35055:case 36010:case 35097:case 35869:case 32874:case 36389:case 35983:case 35368:case 34068:{ret=0;break}default:{GL.recordError(1280);return}}}else if(result instanceof Float32Array||result instanceof Uint32Array||result instanceof Int32Array||result instanceof Array){for(var i=0;i<result.length;++i){switch(type){case 0:HEAP32[p+i*4>>2]=result[i];break;case 2:HEAPF32[p+i*4>>2]=result[i];break;case 4:HEAP8[p+i>>0]=result[i]?1:0;break}}return}else{try{ret=result.name|0}catch(e){GL.recordError(1280);err("GL_INVALID_ENUM in glGet"+type+"v: Unknown object returned from WebGL getParameter("+name_+")! (error: "+e+")");return}}break;default:GL.recordError(1280);err("GL_INVALID_ENUM in glGet"+type+"v: Native code calling glGet"+type+"v("+name_+") and it returns "+result+" of type "+typeof result+"!");return}}switch(type){case 1:writeI53ToI64(p,ret);break;case 0:HEAP32[p>>2]=ret;break;case 2:HEAPF32[p>>2]=ret;break;case 4:HEAP8[p>>0]=ret?1:0;break}}function _glGetIntegerv(name_,p){emscriptenWebGLGet(name_,p,0)}function _glGetInternalformativ(target,internalformat,pname,bufSize,params){if(bufSize<0){GL.recordError(1281);return}if(!params){GL.recordError(1281);return}var ret=GLctx["getInternalformatParameter"](target,internalformat,pname);if(ret===null)return;for(var i=0;i<ret.length&&i<bufSize;++i){HEAP32[params+i*4>>2]=ret[i]}}function _glGetProgramBinary(program,bufSize,length,binaryFormat,binary){GL.recordError(1282)}function _glGetProgramInfoLog(program,maxLength,length,infoLog){var log=GLctx.getProgramInfoLog(GL.programs[program]);if(log===null)log="(unknown error)";var numBytesWrittenExclNull=maxLength>0&&infoLog?stringToUTF8(log,infoLog,maxLength):0;if(length)HEAP32[length>>2]=numBytesWrittenExclNull}function _glGetProgramiv(program,pname,p){if(!p){GL.recordError(1281);return}if(program>=GL.counter){GL.recordError(1281);return}program=GL.programs[program];if(pname==35716){var log=GLctx.getProgramInfoLog(program);if(log===null)log="(unknown error)";HEAP32[p>>2]=log.length+1}else if(pname==35719){if(!program.maxUniformLength){for(var i=0;i<GLctx.getProgramParameter(program,35718);++i){program.maxUniformLength=Math.max(program.maxUniformLength,GLctx.getActiveUniform(program,i).name.length+1)}}HEAP32[p>>2]=program.maxUniformLength}else if(pname==35722){if(!program.maxAttributeLength){for(var i=0;i<GLctx.getProgramParameter(program,35721);++i){program.maxAttributeLength=Math.max(program.maxAttributeLength,GLctx.getActiveAttrib(program,i).name.length+1)}}HEAP32[p>>2]=program.maxAttributeLength}else if(pname==35381){if(!program.maxUniformBlockNameLength){for(var i=0;i<GLctx.getProgramParameter(program,35382);++i){program.maxUniformBlockNameLength=Math.max(program.maxUniformBlockNameLength,GLctx.getActiveUniformBlockName(program,i).length+1)}}HEAP32[p>>2]=program.maxUniformBlockNameLength}else{HEAP32[p>>2]=GLctx.getProgramParameter(program,pname)}}function _glGetQueryObjectuiv(id,pname,params){if(!params){GL.recordError(1281);return}var query=GL.queries[id];var param=GLctx["getQueryParameter"](query,pname);var ret;if(typeof param=="boolean"){ret=param?1:0}else{ret=param}HEAP32[params>>2]=ret}function _glGetQueryiv(target,pname,params){if(!params){GL.recordError(1281);return}HEAP32[params>>2]=GLctx["getQuery"](target,pname)}function _glGetRenderbufferParameteriv(target,pname,params){if(!params){GL.recordError(1281);return}HEAP32[params>>2]=GLctx.getRenderbufferParameter(target,pname)}function _glGetShaderInfoLog(shader,maxLength,length,infoLog){var log=GLctx.getShaderInfoLog(GL.shaders[shader]);if(log===null)log="(unknown error)";var numBytesWrittenExclNull=maxLength>0&&infoLog?stringToUTF8(log,infoLog,maxLength):0;if(length)HEAP32[length>>2]=numBytesWrittenExclNull}function _glGetShaderPrecisionFormat(shaderType,precisionType,range,precision){var result=GLctx.getShaderPrecisionFormat(shaderType,precisionType);HEAP32[range>>2]=result.rangeMin;HEAP32[range+4>>2]=result.rangeMax;HEAP32[precision>>2]=result.precision}function _glGetShaderSource(shader,bufSize,length,source){var result=GLctx.getShaderSource(GL.shaders[shader]);if(!result)return;var numBytesWrittenExclNull=bufSize>0&&source?stringToUTF8(result,source,bufSize):0;if(length)HEAP32[length>>2]=numBytesWrittenExclNull}function _glGetShaderiv(shader,pname,p){if(!p){GL.recordError(1281);return}if(pname==35716){var log=GLctx.getShaderInfoLog(GL.shaders[shader]);if(log===null)log="(unknown error)";var logLength=log?log.length+1:0;HEAP32[p>>2]=logLength}else if(pname==35720){var source=GLctx.getShaderSource(GL.shaders[shader]);var sourceLength=source?source.length+1:0;HEAP32[p>>2]=sourceLength}else{HEAP32[p>>2]=GLctx.getShaderParameter(GL.shaders[shader],pname)}}function _glGetString(name_){var ret=GL.stringCache[name_];if(!ret){switch(name_){case 7939:var exts=GLctx.getSupportedExtensions()||[];exts=exts.concat(exts.map(function(e){return"GL_"+e}));ret=stringToNewUTF8(exts.join(" "));break;case 7936:case 7937:case 37445:case 37446:var s=GLctx.getParameter(name_);if(!s){GL.recordError(1280)}ret=s&&stringToNewUTF8(s);break;case 7938:var glVersion=GLctx.getParameter(7938);if(GL.currentContext.version>=2)glVersion="OpenGL ES 3.0 ("+glVersion+")";else{glVersion="OpenGL ES 2.0 ("+glVersion+")"}ret=stringToNewUTF8(glVersion);break;case 35724:var glslVersion=GLctx.getParameter(35724);var ver_re=/^WebGL GLSL ES ([0-9]\.[0-9][0-9]?)(?:$| .*)/;var ver_num=glslVersion.match(ver_re);if(ver_num!==null){if(ver_num[1].length==3)ver_num[1]=ver_num[1]+"0";glslVersion="OpenGL ES GLSL ES "+ver_num[1]+" ("+glslVersion+")"}ret=stringToNewUTF8(glslVersion);break;default:GL.recordError(1280)}GL.stringCache[name_]=ret}return ret}function _glGetStringi(name,index){if(GL.currentContext.version<2){GL.recordError(1282);return 0}var stringiCache=GL.stringiCache[name];if(stringiCache){if(index<0||index>=stringiCache.length){GL.recordError(1281);return 0}return stringiCache[index]}switch(name){case 7939:var exts=GLctx.getSupportedExtensions()||[];exts=exts.concat(exts.map(function(e){return"GL_"+e}));exts=exts.map(function(e){return stringToNewUTF8(e)});stringiCache=GL.stringiCache[name]=exts;if(index<0||index>=stringiCache.length){GL.recordError(1281);return 0}return stringiCache[index];default:GL.recordError(1280);return 0}}function _glGetTexParameteriv(target,pname,params){if(!params){GL.recordError(1281);return}HEAP32[params>>2]=GLctx.getTexParameter(target,pname)}function _glGetUniformBlockIndex(program,uniformBlockName){return GLctx["getUniformBlockIndex"](GL.programs[program],UTF8ToString(uniformBlockName))}function _glGetUniformIndices(program,uniformCount,uniformNames,uniformIndices){if(!uniformIndices){GL.recordError(1281);return}if(uniformCount>0&&(uniformNames==0||uniformIndices==0)){GL.recordError(1281);return}program=GL.programs[program];var names=[];for(var i=0;i<uniformCount;i++)names.push(UTF8ToString(HEAP32[uniformNames+i*4>>2]));var result=GLctx["getUniformIndices"](program,names);if(!result)return;var len=result.length;for(var i=0;i<len;i++){HEAP32[uniformIndices+i*4>>2]=result[i]}}function _glGetUniformLocation(program,name){function getLeftBracePos(name){return name.slice(-1)=="]"&&name.lastIndexOf("[")}name=UTF8ToString(name);if(program=GL.programs[program]){var uniformLocsById=program.uniformLocsById;var uniformSizeAndIdsByName=program.uniformSizeAndIdsByName;var i,j;var arrayIndex=0;var uniformBaseName=name;var leftBrace=getLeftBracePos(name);if(!uniformLocsById){program.uniformLocsById=uniformLocsById={};program.uniformArrayNamesById={};for(i=0;i<GLctx.getProgramParameter(program,35718);++i){var u=GLctx.getActiveUniform(program,i);var nm=u.name;var sz=u.size;var lb=getLeftBracePos(nm);var arrayName=lb>0?nm.slice(0,lb):nm;var id=uniformSizeAndIdsByName[arrayName]?uniformSizeAndIdsByName[arrayName][1]:program.uniformIdCounter;program.uniformIdCounter=Math.max(id+sz,program.uniformIdCounter);uniformSizeAndIdsByName[arrayName]=[sz,id];for(j=0;j<sz;++j){uniformLocsById[id]=j;program.uniformArrayNamesById[id++]=arrayName}}}if(leftBrace>0){arrayIndex=jstoi_q(name.slice(leftBrace+1))>>>0;uniformBaseName=name.slice(0,leftBrace)}var sizeAndId=uniformSizeAndIdsByName[uniformBaseName];if(sizeAndId&&arrayIndex<sizeAndId[0]){arrayIndex+=sizeAndId[1];if(uniformLocsById[arrayIndex]=uniformLocsById[arrayIndex]||GLctx.getUniformLocation(program,name)){return arrayIndex}}}else{GL.recordError(1281)}return-1}function webglGetUniformLocation(location){var p=GLctx.currentProgram;if(p){var webglLoc=p.uniformLocsById[location];if(typeof webglLoc==="number"){p.uniformLocsById[location]=webglLoc=GLctx.getUniformLocation(p,p.uniformArrayNamesById[location]+(webglLoc>0?"["+webglLoc+"]":""))}return webglLoc}else{GL.recordError(1282)}}function emscriptenWebGLGetUniform(program,location,params,type){if(!params){GL.recordError(1281);return}program=GL.programs[program];var data=GLctx.getUniform(program,webglGetUniformLocation(location));if(typeof data=="number"||typeof data=="boolean"){switch(type){case 0:HEAP32[params>>2]=data;break;case 2:HEAPF32[params>>2]=data;break}}else{for(var i=0;i<data.length;i++){switch(type){case 0:HEAP32[params+i*4>>2]=data[i];break;case 2:HEAPF32[params+i*4>>2]=data[i];break}}}}function _glGetUniformiv(program,location,params){emscriptenWebGLGetUniform(program,location,params,0)}function emscriptenWebGLGetVertexAttrib(index,pname,params,type){if(!params){GL.recordError(1281);return}if(GL.currentContext.clientBuffers[index].enabled){err("glGetVertexAttrib*v on client-side array: not supported, bad data returned")}var data=GLctx.getVertexAttrib(index,pname);if(pname==34975){HEAP32[params>>2]=data&&data["name"]}else if(typeof data=="number"||typeof data=="boolean"){switch(type){case 0:HEAP32[params>>2]=data;break;case 2:HEAPF32[params>>2]=data;break;case 5:HEAP32[params>>2]=Math.fround(data);break}}else{for(var i=0;i<data.length;i++){switch(type){case 0:HEAP32[params+i*4>>2]=data[i];break;case 2:HEAPF32[params+i*4>>2]=data[i];break;case 5:HEAP32[params+i*4>>2]=Math.fround(data[i]);break}}}}function _glGetVertexAttribiv(index,pname,params){emscriptenWebGLGetVertexAttrib(index,pname,params,5)}function _glInvalidateFramebuffer(target,numAttachments,attachments){var list=tempFixedLengthArray[numAttachments];for(var i=0;i<numAttachments;i++){list[i]=HEAP32[attachments+i*4>>2]}GLctx["invalidateFramebuffer"](target,list)}function _glIsEnabled(x0){return GLctx["isEnabled"](x0)}function _glIsVertexArray(array){var vao=GL.vaos[array];if(!vao)return 0;return GLctx["isVertexArray"](vao)}function _glLinkProgram(program){program=GL.programs[program];GLctx.linkProgram(program);program.uniformLocsById=0;program.uniformSizeAndIdsByName={};[program["vs"],program["fs"]].forEach(function(s){Object.keys(s.explicitUniformLocations).forEach(function(shaderLocation){var loc=s.explicitUniformLocations[shaderLocation];program.uniformSizeAndIdsByName[shaderLocation]=[1,loc];program.uniformIdCounter=Math.max(program.uniformIdCounter,loc+1)})});function copyKeys(dst,src){Object.keys(src).forEach(function(key){dst[key]=src[key]})}program.explicitUniformBindings={};program.explicitSamplerBindings={};[program["vs"],program["fs"]].forEach(function(s){copyKeys(program.explicitUniformBindings,s.explicitUniformBindings);copyKeys(program.explicitSamplerBindings,s.explicitSamplerBindings)});program.explicitProgramBindingsApplied=0}function _glMapBufferRange(target,offset,length,access){if(access!=26&&access!=10){err("glMapBufferRange is only supported when access is MAP_WRITE|INVALIDATE_BUFFER");return 0}if(!emscriptenWebGLValidateMapBufferTarget(target)){GL.recordError(1280);err("GL_INVALID_ENUM in glMapBufferRange");return 0}var mem=_malloc(length);if(!mem)return 0;GL.mappedBuffers[emscriptenWebGLGetBufferBinding(target)]={offset:offset,length:length,mem:mem,access:access};return mem}function _glPixelStorei(pname,param){if(pname==3317){GL.unpackAlignment=param}GLctx.pixelStorei(pname,param)}function _glPolygonOffset(x0,x1){GLctx["polygonOffset"](x0,x1)}function _glProgramBinary(program,binaryFormat,binary,length){GL.recordError(1280)}function _glProgramParameteri(program,pname,value){GL.recordError(1280)}function _glReadBuffer(x0){GLctx["readBuffer"](x0)}function computeUnpackAlignedImageSize(width,height,sizePerPixel,alignment){function roundedToNextMultipleOf(x,y){return x+y-1&-y}var plainRowSize=width*sizePerPixel;var alignedRowSize=roundedToNextMultipleOf(plainRowSize,alignment);return height*alignedRowSize}function __colorChannelsInGlTextureFormat(format){var colorChannels={5:3,6:4,8:2,29502:3,29504:4,26917:2,26918:2,29846:3,29847:4};return colorChannels[format-6402]||1}function heapObjectForWebGLType(type){type-=5120;if(type==0)return HEAP8;if(type==1)return HEAPU8;if(type==2)return HEAP16;if(type==4)return HEAP32;if(type==6)return HEAPF32;if(type==5||type==28922||type==28520||type==30779||type==30782)return HEAPU32;return HEAPU16}function heapAccessShiftForWebGLHeap(heap){return 31-Math.clz32(heap.BYTES_PER_ELEMENT)}function emscriptenWebGLGetTexPixelData(type,format,width,height,pixels,internalFormat){var heap=heapObjectForWebGLType(type);var shift=heapAccessShiftForWebGLHeap(heap);var byteSize=1<<shift;var sizePerPixel=__colorChannelsInGlTextureFormat(format)*byteSize;var bytes=computeUnpackAlignedImageSize(width,height,sizePerPixel,GL.unpackAlignment);return heap.subarray(pixels>>shift,pixels+bytes>>shift)}function _glReadPixels(x,y,width,height,format,type,pixels){if(GL.currentContext.version>=2){if(GLctx.currentPixelPackBufferBinding){GLctx.readPixels(x,y,width,height,format,type,pixels)}else{var heap=heapObjectForWebGLType(type);GLctx.readPixels(x,y,width,height,format,type,heap,pixels>>heapAccessShiftForWebGLHeap(heap))}return}var pixelData=emscriptenWebGLGetTexPixelData(type,format,width,height,pixels,format);if(!pixelData){GL.recordError(1280);return}GLctx.readPixels(x,y,width,height,format,type,pixelData)}function _glRenderbufferStorage(x0,x1,x2,x3){GLctx["renderbufferStorage"](x0,x1,x2,x3)}function _glRenderbufferStorageMultisample(x0,x1,x2,x3,x4){GLctx["renderbufferStorageMultisample"](x0,x1,x2,x3,x4)}function _glSamplerParameteri(sampler,pname,param){GLctx["samplerParameteri"](GL.samplers[sampler],pname,param)}function _glScissor(x0,x1,x2,x3){GLctx["scissor"](x0,x1,x2,x3)}function find_closing_parens_index(arr,i,opening="(",closing=")"){for(var nesting=0;i<arr.length;++i){if(arr[i]==opening)++nesting;if(arr[i]==closing&&--nesting==0){return i}}}function preprocess_c_code(code){var i=0,len=code.length,out="",stack=[1],defs={"defined":function(args){return defs[args[0]]?1:0},"GL_FRAGMENT_PRECISION_HIGH":function(){return 1}};function isWhitespace(str,i){return!(str.charCodeAt(i)>32)}function nextWhitespace(str,i){while(!isWhitespace(str,i))++i;return i}function classifyChar(str,idx){var cc=str.charCodeAt(idx);if(cc>32){if(cc<48)return 1;if(cc<58)return 2;if(cc<65)return 1;if(cc<91||cc==95)return 3;if(cc<97)return 1;if(cc<123)return 3;return 1}return cc<33?0:4}function tokenize(exprString,keepWhitespace){var out=[],len=exprString.length;for(var i=0;i<=len;++i){var kind=classifyChar(exprString,i);if(kind==2||kind==3){for(var j=i+1;j<=len;++j){var kind2=classifyChar(exprString,j);if(kind2!=kind&&(kind2!=2||kind!=3)){out.push(exprString.substring(i,j));i=j-1;break}}}else if(kind==1){var op2=exprString.substr(i,2);if(["<=",">=","==","!=","&&","||"].includes(op2)){out.push(op2);++i}else{out.push(exprString[i])}}}return out}function expandMacros(str,lineStart,lineEnd){if(lineEnd===undefined)lineEnd=str.length;var len=str.length;var out="";for(var i=lineStart;i<lineEnd;++i){var kind=classifyChar(str,i);if(kind==3){for(var j=i+1;j<=lineEnd;++j){var kind2=classifyChar(str,j);if(kind2!=2&&kind2!=3){var symbol=str.substring(i,j);var pp=defs[symbol];if(pp){var expanded=str.substring(lineStart,i);if(pp.length&&str[j]=="("){var closeParens=find_closing_parens_index(str,j);expanded+=pp(str.substring(j+1,closeParens).split(","))+str.substring(closeParens+1,lineEnd)}else{expanded+=pp()+str.substring(j,lineEnd)}return expandMacros(expanded,0)}else{out+=symbol;i=j-1;break}}}}else{out+=str[i]}}return out}function buildExprTree(tokens){while(tokens.length>1||typeof tokens[0]!="function"){tokens=function(tokens){var i,j,p,operatorAndPriority=-2;for(j=0;j<tokens.length;++j){if((p=["*","/","+","-","!","<","<=",">",">=","==","!=","&&","||","("].indexOf(tokens[j]))>operatorAndPriority){i=j;operatorAndPriority=p}}if(operatorAndPriority==13){var j=find_closing_parens_index(tokens,i);if(j){tokens.splice(i,j+1-i,buildExprTree(tokens.slice(i+1,j)));return tokens}}if(operatorAndPriority==4){i=tokens.lastIndexOf("!");var innerExpr=buildExprTree(tokens.slice(i+1,i+2));tokens.splice(i,2,function(){return!innerExpr()});return tokens}if(operatorAndPriority>=0){var left=buildExprTree(tokens.slice(0,i));var right=buildExprTree(tokens.slice(i+1));switch(tokens[i]){case"&&":return[function(){return left()&&right()}];case"||":return[function(){return left()||right()}];case"==":return[function(){return left()==right()}];case"!=":return[function(){return left()!=right()}];case"<":return[function(){return left()<right()}];case"<=":return[function(){return left()<=right()}];case">":return[function(){return left()>right()}];case">=":return[function(){return left()>=right()}];case"+":return[function(){return left()+right()}];case"-":return[function(){return left()-right()}];case"*":return[function(){return left()*right()}];case"/":return[function(){return Math.floor(left()/right())}]}}var num=jstoi_q(tokens[i]);return[function(){return num}]}(tokens)}return tokens[0]}for(;i<len;++i){var lineStart=i;i=code.indexOf("\n",i);if(i<0)i=len;for(var j=lineStart;j<i&&isWhitespace(code,j);++j);var thisLineIsInActivePreprocessingBlock=stack[stack.length-1];if(code[j]!="#"){if(thisLineIsInActivePreprocessingBlock){out+=expandMacros(code,lineStart,i)+"\n"}continue}var space=nextWhitespace(code,j);var directive=code.substring(j+1,space);var expression=code.substring(space,i).trim();switch(directive){case"if":var tokens=tokenize(expandMacros(expression,0));var exprTree=buildExprTree(tokens);var evaluated=exprTree();stack.push(!!evaluated*stack[stack.length-1]);break;case"ifdef":stack.push(!!defs[expression]*stack[stack.length-1]);break;case"ifndef":stack.push(!defs[expression]*stack[stack.length-1]);break;case"else":stack[stack.length-1]=1-stack[stack.length-1];break;case"endif":stack.pop();break;case"define":if(thisLineIsInActivePreprocessingBlock){var macroStart=expression.indexOf("(");var firstWs=nextWhitespace(expression,0);if(firstWs<macroStart)macroStart=0;if(macroStart>0){var macroEnd=expression.indexOf(")",macroStart);let params=expression.substring(macroStart+1,macroEnd).split(",").map(x=>x.trim());let value=tokenize(expression.substring(macroEnd+1).trim());defs[expression.substring(0,macroStart)]=function(args){var ret="";value.forEach(x=>{var argIndex=params.indexOf(x);ret+=argIndex>=0?args[argIndex]:x});return ret}}else{let value=expandMacros(expression.substring(firstWs+1).trim(),0);defs[expression.substring(0,firstWs)]=function(){return value}}}break;case"undef":if(thisLineIsInActivePreprocessingBlock)delete defs[expression];break;default:if(directive!="version"&&directive!="pragma"&&directive!="extension"){}out+=expandMacros(code,lineStart,i)+"\n"}}return out}function remove_cpp_comments_in_shaders(code){var i=0,out="",ch,next,len=code.length;for(;i<len;++i){ch=code[i];if(ch=="/"){next=code[i+1];if(next=="/"){while(i<len&&code[i+1]!="\n")++i}else if(next=="*"){while(i<len&&(code[i-1]!="*"||code[i]!="/"))++i}else{out+=ch}}else{out+=ch}}return out}function _glShaderSource(shader,count,string,length){var source=GL.getSource(shader,count,string,length);source=preprocess_c_code(remove_cpp_comments_in_shaders(source));var regex=/layout\s*\(\s*location\s*=\s*(-?\d+)\s*\)\s*(uniform\s+((lowp|mediump|highp)\s+)?\w+\s+(\w+))/g,explicitUniformLocations={},match;while(match=regex.exec(source)){explicitUniformLocations[match[5]]=jstoi_q(match[1]);if(!(explicitUniformLocations[match[5]]>=0&&explicitUniformLocations[match[5]]<1048576)){console.error('Specified an out of range layout(location=x) directive "'+explicitUniformLocations[match[5]]+'"! ('+match[0]+")");GL.recordError(1281);return}}source=source.replace(regex,"$2");GL.shaders[shader].explicitUniformLocations=explicitUniformLocations;var bindingRegex=/layout\s*\(.*?binding\s*=\s*(-?\d+).*?\)\s*uniform\s+(\w+)\s+(\w+)?/g,samplerBindings={},uniformBindings={},bindingMatch;while(bindingMatch=bindingRegex.exec(source)){var arrayLength=1;for(var i=bindingMatch.index;i<source.length&&source[i]!=";";++i){if(source[i]=="["){arrayLength=jstoi_q(source.slice(i+1));break}if(source[i]=="{")i=find_closing_parens_index(source,i,"{","}")-1}var binding=jstoi_q(bindingMatch[1]);var bindingsType=34930;if(bindingMatch[3]&&bindingMatch[2].indexOf("sampler")!=-1){samplerBindings[bindingMatch[3]]=[binding,arrayLength]}else{bindingsType=35374;uniformBindings[bindingMatch[2]]=[binding,arrayLength]}var numBindingPoints=GLctx.getParameter(bindingsType);if(!(binding>=0&&binding+arrayLength<=numBindingPoints)){console.error('Specified an out of range layout(binding=x) directive "'+binding+'"! ('+bindingMatch[0]+"). Valid range is [0, "+numBindingPoints+"-1]");GL.recordError(1281);return}}source=source.replace(/layout\s*\(.*?binding\s*=\s*([-\d]+).*?\)/g,"");source=source.replace(/(layout\s*\((.*?)),\s*binding\s*=\s*([-\d]+)\)/g,"$1)");source=source.replace(/layout\s*\(\s*binding\s*=\s*([-\d]+)\s*,(.*?)\)/g,"layout($2)");GL.shaders[shader].explicitSamplerBindings=samplerBindings;GL.shaders[shader].explicitUniformBindings=uniformBindings;GLctx.shaderSource(GL.shaders[shader],source)}function _glStencilFuncSeparate(x0,x1,x2,x3){GLctx["stencilFuncSeparate"](x0,x1,x2,x3)}function _glStencilMask(x0){GLctx["stencilMask"](x0)}function _glStencilOpSeparate(x0,x1,x2,x3){GLctx["stencilOpSeparate"](x0,x1,x2,x3)}function _glTexImage2D(target,level,internalFormat,width,height,border,format,type,pixels){if(GL.currentContext.version>=2){if(GLctx.currentPixelUnpackBufferBinding){GLctx.texImage2D(target,level,internalFormat,width,height,border,format,type,pixels)}else if(pixels){var heap=heapObjectForWebGLType(type);GLctx.texImage2D(target,level,internalFormat,width,height,border,format,type,heap,pixels>>heapAccessShiftForWebGLHeap(heap))}else{GLctx.texImage2D(target,level,internalFormat,width,height,border,format,type,null)}return}GLctx.texImage2D(target,level,internalFormat,width,height,border,format,type,pixels?emscriptenWebGLGetTexPixelData(type,format,width,height,pixels,internalFormat):null)}function _glTexImage3D(target,level,internalFormat,width,height,depth,border,format,type,pixels){if(GLctx.currentPixelUnpackBufferBinding){GLctx["texImage3D"](target,level,internalFormat,width,height,depth,border,format,type,pixels)}else if(pixels){var heap=heapObjectForWebGLType(type);GLctx["texImage3D"](target,level,internalFormat,width,height,depth,border,format,type,heap,pixels>>heapAccessShiftForWebGLHeap(heap))}else{GLctx["texImage3D"](target,level,internalFormat,width,height,depth,border,format,type,null)}}function _glTexParameterf(x0,x1,x2){GLctx["texParameterf"](x0,x1,x2)}function _glTexParameteri(x0,x1,x2){GLctx["texParameteri"](x0,x1,x2)}function _glTexParameteriv(target,pname,params){var param=HEAP32[params>>2];GLctx.texParameteri(target,pname,param)}function _glTexStorage2D(x0,x1,x2,x3,x4){GLctx["texStorage2D"](x0,x1,x2,x3,x4)}function _glTexStorage3D(x0,x1,x2,x3,x4,x5){GLctx["texStorage3D"](x0,x1,x2,x3,x4,x5)}function _glTexSubImage2D(target,level,xoffset,yoffset,width,height,format,type,pixels){if(GL.currentContext.version>=2){if(GLctx.currentPixelUnpackBufferBinding){GLctx.texSubImage2D(target,level,xoffset,yoffset,width,height,format,type,pixels)}else if(pixels){var heap=heapObjectForWebGLType(type);GLctx.texSubImage2D(target,level,xoffset,yoffset,width,height,format,type,heap,pixels>>heapAccessShiftForWebGLHeap(heap))}else{GLctx.texSubImage2D(target,level,xoffset,yoffset,width,height,format,type,null)}return}var pixelData=null;if(pixels)pixelData=emscriptenWebGLGetTexPixelData(type,format,width,height,pixels,0);GLctx.texSubImage2D(target,level,xoffset,yoffset,width,height,format,type,pixelData)}function _glTexSubImage3D(target,level,xoffset,yoffset,zoffset,width,height,depth,format,type,pixels){if(GLctx.currentPixelUnpackBufferBinding){GLctx["texSubImage3D"](target,level,xoffset,yoffset,zoffset,width,height,depth,format,type,pixels)}else if(pixels){var heap=heapObjectForWebGLType(type);GLctx["texSubImage3D"](target,level,xoffset,yoffset,zoffset,width,height,depth,format,type,heap,pixels>>heapAccessShiftForWebGLHeap(heap))}else{GLctx["texSubImage3D"](target,level,xoffset,yoffset,zoffset,width,height,depth,format,type,null)}}function _glTransformFeedbackVaryings(program,count,varyings,bufferMode){program=GL.programs[program];var vars=[];for(var i=0;i<count;i++)vars.push(UTF8ToString(HEAP32[varyings+i*4>>2]));GLctx["transformFeedbackVaryings"](program,vars,bufferMode)}var miniTempWebGLFloatBuffers=[];function _glUniform1fv(location,count,value){if(GL.currentContext.version>=2){GLctx.uniform1fv(webglGetUniformLocation(location),HEAPF32,value>>2,count);return}if(count<=288){var view=miniTempWebGLFloatBuffers[count-1];for(var i=0;i<count;++i){view[i]=HEAPF32[value+4*i>>2]}}else{var view=HEAPF32.subarray(value>>2,value+count*4>>2)}GLctx.uniform1fv(webglGetUniformLocation(location),view)}function _glUniform1i(location,v0){GLctx.uniform1i(webglGetUniformLocation(location),v0)}var __miniTempWebGLIntBuffers=[];function _glUniform1iv(location,count,value){if(GL.currentContext.version>=2){GLctx.uniform1iv(webglGetUniformLocation(location),HEAP32,value>>2,count);return}if(count<=288){var view=__miniTempWebGLIntBuffers[count-1];for(var i=0;i<count;++i){view[i]=HEAP32[value+4*i>>2]}}else{var view=HEAP32.subarray(value>>2,value+count*4>>2)}GLctx.uniform1iv(webglGetUniformLocation(location),view)}function _glUniform1uiv(location,count,value){GLctx.uniform1uiv(webglGetUniformLocation(location),HEAPU32,value>>2,count)}function _glUniform2fv(location,count,value){if(GL.currentContext.version>=2){GLctx.uniform2fv(webglGetUniformLocation(location),HEAPF32,value>>2,count*2);return}if(count<=144){var view=miniTempWebGLFloatBuffers[2*count-1];for(var i=0;i<2*count;i+=2){view[i]=HEAPF32[value+4*i>>2];view[i+1]=HEAPF32[value+(4*i+4)>>2]}}else{var view=HEAPF32.subarray(value>>2,value+count*8>>2)}GLctx.uniform2fv(webglGetUniformLocation(location),view)}function _glUniform2iv(location,count,value){if(GL.currentContext.version>=2){GLctx.uniform2iv(webglGetUniformLocation(location),HEAP32,value>>2,count*2);return}if(count<=144){var view=__miniTempWebGLIntBuffers[2*count-1];for(var i=0;i<2*count;i+=2){view[i]=HEAP32[value+4*i>>2];view[i+1]=HEAP32[value+(4*i+4)>>2]}}else{var view=HEAP32.subarray(value>>2,value+count*8>>2)}GLctx.uniform2iv(webglGetUniformLocation(location),view)}function _glUniform2uiv(location,count,value){GLctx.uniform2uiv(webglGetUniformLocation(location),HEAPU32,value>>2,count*2)}function _glUniform3fv(location,count,value){if(GL.currentContext.version>=2){GLctx.uniform3fv(webglGetUniformLocation(location),HEAPF32,value>>2,count*3);return}if(count<=96){var view=miniTempWebGLFloatBuffers[3*count-1];for(var i=0;i<3*count;i+=3){view[i]=HEAPF32[value+4*i>>2];view[i+1]=HEAPF32[value+(4*i+4)>>2];view[i+2]=HEAPF32[value+(4*i+8)>>2]}}else{var view=HEAPF32.subarray(value>>2,value+count*12>>2)}GLctx.uniform3fv(webglGetUniformLocation(location),view)}function _glUniform3iv(location,count,value){if(GL.currentContext.version>=2){GLctx.uniform3iv(webglGetUniformLocation(location),HEAP32,value>>2,count*3);return}if(count<=96){var view=__miniTempWebGLIntBuffers[3*count-1];for(var i=0;i<3*count;i+=3){view[i]=HEAP32[value+4*i>>2];view[i+1]=HEAP32[value+(4*i+4)>>2];view[i+2]=HEAP32[value+(4*i+8)>>2]}}else{var view=HEAP32.subarray(value>>2,value+count*12>>2)}GLctx.uniform3iv(webglGetUniformLocation(location),view)}function _glUniform3uiv(location,count,value){GLctx.uniform3uiv(webglGetUniformLocation(location),HEAPU32,value>>2,count*3)}function _glUniform4fv(location,count,value){if(GL.currentContext.version>=2){GLctx.uniform4fv(webglGetUniformLocation(location),HEAPF32,value>>2,count*4);return}if(count<=72){var view=miniTempWebGLFloatBuffers[4*count-1];var heap=HEAPF32;value>>=2;for(var i=0;i<4*count;i+=4){var dst=value+i;view[i]=heap[dst];view[i+1]=heap[dst+1];view[i+2]=heap[dst+2];view[i+3]=heap[dst+3]}}else{var view=HEAPF32.subarray(value>>2,value+count*16>>2)}GLctx.uniform4fv(webglGetUniformLocation(location),view)}function _glUniform4iv(location,count,value){if(GL.currentContext.version>=2){GLctx.uniform4iv(webglGetUniformLocation(location),HEAP32,value>>2,count*4);return}if(count<=72){var view=__miniTempWebGLIntBuffers[4*count-1];for(var i=0;i<4*count;i+=4){view[i]=HEAP32[value+4*i>>2];view[i+1]=HEAP32[value+(4*i+4)>>2];view[i+2]=HEAP32[value+(4*i+8)>>2];view[i+3]=HEAP32[value+(4*i+12)>>2]}}else{var view=HEAP32.subarray(value>>2,value+count*16>>2)}GLctx.uniform4iv(webglGetUniformLocation(location),view)}function _glUniform4uiv(location,count,value){GLctx.uniform4uiv(webglGetUniformLocation(location),HEAPU32,value>>2,count*4)}function _glUniformBlockBinding(program,uniformBlockIndex,uniformBlockBinding){program=GL.programs[program];GLctx["uniformBlockBinding"](program,uniformBlockIndex,uniformBlockBinding)}function _glUniformMatrix3fv(location,count,transpose,value){if(GL.currentContext.version>=2){GLctx.uniformMatrix3fv(webglGetUniformLocation(location),!!transpose,HEAPF32,value>>2,count*9);return}if(count<=32){var view=miniTempWebGLFloatBuffers[9*count-1];for(var i=0;i<9*count;i+=9){view[i]=HEAPF32[value+4*i>>2];view[i+1]=HEAPF32[value+(4*i+4)>>2];view[i+2]=HEAPF32[value+(4*i+8)>>2];view[i+3]=HEAPF32[value+(4*i+12)>>2];view[i+4]=HEAPF32[value+(4*i+16)>>2];view[i+5]=HEAPF32[value+(4*i+20)>>2];view[i+6]=HEAPF32[value+(4*i+24)>>2];view[i+7]=HEAPF32[value+(4*i+28)>>2];view[i+8]=HEAPF32[value+(4*i+32)>>2]}}else{var view=HEAPF32.subarray(value>>2,value+count*36>>2)}GLctx.uniformMatrix3fv(webglGetUniformLocation(location),!!transpose,view)}function _glUniformMatrix4fv(location,count,transpose,value){if(GL.currentContext.version>=2){GLctx.uniformMatrix4fv(webglGetUniformLocation(location),!!transpose,HEAPF32,value>>2,count*16);return}if(count<=18){var view=miniTempWebGLFloatBuffers[16*count-1];var heap=HEAPF32;value>>=2;for(var i=0;i<16*count;i+=16){var dst=value+i;view[i]=heap[dst];view[i+1]=heap[dst+1];view[i+2]=heap[dst+2];view[i+3]=heap[dst+3];view[i+4]=heap[dst+4];view[i+5]=heap[dst+5];view[i+6]=heap[dst+6];view[i+7]=heap[dst+7];view[i+8]=heap[dst+8];view[i+9]=heap[dst+9];view[i+10]=heap[dst+10];view[i+11]=heap[dst+11];view[i+12]=heap[dst+12];view[i+13]=heap[dst+13];view[i+14]=heap[dst+14];view[i+15]=heap[dst+15]}}else{var view=HEAPF32.subarray(value>>2,value+count*64>>2)}GLctx.uniformMatrix4fv(webglGetUniformLocation(location),!!transpose,view)}function _glUnmapBuffer(target){if(!emscriptenWebGLValidateMapBufferTarget(target)){GL.recordError(1280);err("GL_INVALID_ENUM in glUnmapBuffer");return 0}var buffer=emscriptenWebGLGetBufferBinding(target);var mapping=GL.mappedBuffers[buffer];if(!mapping){GL.recordError(1282);err("buffer was never mapped in glUnmapBuffer");return 0}GL.mappedBuffers[buffer]=null;if(!(mapping.access&16))if(GL.currentContext.version>=2){GLctx.bufferSubData(target,mapping.offset,HEAPU8,mapping.mem,mapping.length)}else{GLctx.bufferSubData(target,mapping.offset,HEAPU8.subarray(mapping.mem,mapping.mem+mapping.length))}_free(mapping.mem);return 1}function webglApplyExplicitProgramBindings(){var p=GLctx.currentProgram;if(!p.explicitProgramBindingsApplied){if(GL.currentContext.version>=2){Object.keys(p.explicitUniformBindings).forEach(function(ubo){var bindings=p.explicitUniformBindings[ubo];for(var i=0;i<bindings[1];++i){var blockIndex=GLctx.getUniformBlockIndex(p,ubo+(bindings[1]>1?"["+i+"]":""));GLctx.uniformBlockBinding(p,blockIndex,bindings[0]+i)}})}Object.keys(p.explicitSamplerBindings).forEach(function(sampler){var bindings=p.explicitSamplerBindings[sampler];for(var i=0;i<bindings[1];++i){GLctx.uniform1i(GLctx.getUniformLocation(p,sampler+(i?"["+i+"]":"")),bindings[0]+i)}});p.explicitProgramBindingsApplied=1}}function 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__MONTH_DAYS_REGULAR=[31,28,31,30,31,30,31,31,30,31,30,31];function __addDays(date,days){var newDate=new Date(date.getTime());while(days>0){var leap=__isLeapYear(newDate.getFullYear());var currentMonth=newDate.getMonth();var daysInCurrentMonth=(leap?__MONTH_DAYS_LEAP:__MONTH_DAYS_REGULAR)[currentMonth];if(days>daysInCurrentMonth-newDate.getDate()){days-=daysInCurrentMonth-newDate.getDate()+1;newDate.setDate(1);if(currentMonth<11){newDate.setMonth(currentMonth+1)}else{newDate.setMonth(0);newDate.setFullYear(newDate.getFullYear()+1)}}else{newDate.setDate(newDate.getDate()+days);return newDate}}return newDate}function _strftime(s,maxsize,format,tm){var tm_zone=HEAP32[tm+40>>2];var date={tm_sec:HEAP32[tm>>2],tm_min:HEAP32[tm+4>>2],tm_hour:HEAP32[tm+8>>2],tm_mday:HEAP32[tm+12>>2],tm_mon:HEAP32[tm+16>>2],tm_year:HEAP32[tm+20>>2],tm_wday:HEAP32[tm+24>>2],tm_yday:HEAP32[tm+28>>2],tm_isdst:HEAP32[tm+32>>2],tm_gmtoff:HEAP32[tm+36>>2],tm_zone:tm_zone?UTF8ToString(tm_zone):""};var pattern=UTF8ToString(format);var EXPANSION_RULES_1={"%c":"%a %b %d %H:%M:%S %Y","%D":"%m/%d/%y","%F":"%Y-%m-%d","%h":"%b","%r":"%I:%M:%S %p","%R":"%H:%M","%T":"%H:%M:%S","%x":"%m/%d/%y","%X":"%H:%M:%S","%Ec":"%c","%EC":"%C","%Ex":"%m/%d/%y","%EX":"%H:%M:%S","%Ey":"%y","%EY":"%Y","%Od":"%d","%Oe":"%e","%OH":"%H","%OI":"%I","%Om":"%m","%OM":"%M","%OS":"%S","%Ou":"%u","%OU":"%U","%OV":"%V","%Ow":"%w","%OW":"%W","%Oy":"%y"};for(var rule in EXPANSION_RULES_1){pattern=pattern.replace(new RegExp(rule,"g"),EXPANSION_RULES_1[rule])}var WEEKDAYS=["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"];var MONTHS=["January","February","March","April","May","June","July","August","September","October","November","December"];function leadingSomething(value,digits,character){var str=typeof value==="number"?value.toString():value||"";while(str.length<digits){str=character[0]+str}return str}function leadingNulls(value,digits){return leadingSomething(value,digits,"0")}function compareByDay(date1,date2){function sgn(value){return value<0?-1:value>0?1:0}var compare;if((compare=sgn(date1.getFullYear()-date2.getFullYear()))===0){if((compare=sgn(date1.getMonth()-date2.getMonth()))===0){compare=sgn(date1.getDate()-date2.getDate())}}return compare}function getFirstWeekStartDate(janFourth){switch(janFourth.getDay()){case 0:return new Date(janFourth.getFullYear()-1,11,29);case 1:return janFourth;case 2:return new Date(janFourth.getFullYear(),0,3);case 3:return new Date(janFourth.getFullYear(),0,2);case 4:return new Date(janFourth.getFullYear(),0,1);case 5:return new Date(janFourth.getFullYear()-1,11,31);case 6:return new Date(janFourth.getFullYear()-1,11,30)}}function getWeekBasedYear(date){var thisDate=__addDays(new Date(date.tm_year+1900,0,1),date.tm_yday);var janFourthThisYear=new Date(thisDate.getFullYear(),0,4);var janFourthNextYear=new Date(thisDate.getFullYear()+1,0,4);var firstWeekStartThisYear=getFirstWeekStartDate(janFourthThisYear);var firstWeekStartNextYear=getFirstWeekStartDate(janFourthNextYear);if(compareByDay(firstWeekStartThisYear,thisDate)<=0){if(compareByDay(firstWeekStartNextYear,thisDate)<=0){return thisDate.getFullYear()+1}else{return thisDate.getFullYear()}}else{return thisDate.getFullYear()-1}}var EXPANSION_RULES_2={"%a":function(date){return WEEKDAYS[date.tm_wday].substring(0,3)},"%A":function(date){return WEEKDAYS[date.tm_wday]},"%b":function(date){return MONTHS[date.tm_mon].substring(0,3)},"%B":function(date){return MONTHS[date.tm_mon]},"%C":function(date){var year=date.tm_year+1900;return leadingNulls(year/100|0,2)},"%d":function(date){return leadingNulls(date.tm_mday,2)},"%e":function(date){return leadingSomething(date.tm_mday,2," ")},"%g":function(date){return getWeekBasedYear(date).toString().substring(2)},"%G":function(date){return getWeekBasedYear(date)},"%H":function(date){return leadingNulls(date.tm_hour,2)},"%I":function(date){var twelveHour=date.tm_hour;if(twelveHour==0)twelveHour=12;else 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firstSundayUntilEndJanuary=31-firstSunday.getDate();var days=firstSundayUntilEndJanuary+februaryFirstUntilEndMonth+endDate.getDate();return leadingNulls(Math.ceil(days/7),2)}return compareByDay(firstSunday,janFirst)===0?"01":"00"},"%V":function(date){var janFourthThisYear=new Date(date.tm_year+1900,0,4);var janFourthNextYear=new Date(date.tm_year+1901,0,4);var firstWeekStartThisYear=getFirstWeekStartDate(janFourthThisYear);var firstWeekStartNextYear=getFirstWeekStartDate(janFourthNextYear);var endDate=__addDays(new Date(date.tm_year+1900,0,1),date.tm_yday);if(compareByDay(endDate,firstWeekStartThisYear)<0){return"53"}if(compareByDay(firstWeekStartNextYear,endDate)<=0){return"01"}var daysDifference;if(firstWeekStartThisYear.getFullYear()<date.tm_year+1900){daysDifference=date.tm_yday+32-firstWeekStartThisYear.getDate()}else{daysDifference=date.tm_yday+1-firstWeekStartThisYear.getDate()}return leadingNulls(Math.ceil(daysDifference/7),2)},"%w":function(date){return 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dynCall_vifffi=Module["dynCall_vifffi"]=function(){return(dynCall_vifffi=Module["dynCall_vifffi"]=Module["asm"]["Kl"]).apply(null,arguments)};var dynCall_viffffffi=Module["dynCall_viffffffi"]=function(){return(dynCall_viffffffi=Module["dynCall_viffffffi"]=Module["asm"]["Ll"]).apply(null,arguments)};var dynCall_vffffffii=Module["dynCall_vffffffii"]=function(){return(dynCall_vffffffii=Module["dynCall_vffffffii"]=Module["asm"]["Ml"]).apply(null,arguments)};var dynCall_vffffii=Module["dynCall_vffffii"]=function(){return(dynCall_vffffii=Module["dynCall_vffffii"]=Module["asm"]["Nl"]).apply(null,arguments)};var dynCall_viiiifffi=Module["dynCall_viiiifffi"]=function(){return(dynCall_viiiifffi=Module["dynCall_viiiifffi"]=Module["asm"]["Ol"]).apply(null,arguments)};var dynCall_vfiii=Module["dynCall_vfiii"]=function(){return(dynCall_vfiii=Module["dynCall_vfiii"]=Module["asm"]["Pl"]).apply(null,arguments)};var dynCall_ffffi=Module["dynCall_ffffi"]=function(){return(dynCall_ffffi=Module["dynCall_ffffi"]=Module["asm"]["Ql"]).apply(null,arguments)};var dynCall_iffi=Module["dynCall_iffi"]=function(){return(dynCall_iffi=Module["dynCall_iffi"]=Module["asm"]["Rl"]).apply(null,arguments)};var dynCall_fffifffi=Module["dynCall_fffifffi"]=function(){return(dynCall_fffifffi=Module["dynCall_fffifffi"]=Module["asm"]["Sl"]).apply(null,arguments)};var dynCall_vfii=Module["dynCall_vfii"]=function(){return(dynCall_vfii=Module["dynCall_vfii"]=Module["asm"]["Tl"]).apply(null,arguments)};var dynCall_iiiifiiiii=Module["dynCall_iiiifiiiii"]=function(){return(dynCall_iiiifiiiii=Module["dynCall_iiiifiiiii"]=Module["asm"]["Ul"]).apply(null,arguments)};var dynCall_iiiifiiii=Module["dynCall_iiiifiiii"]=function(){return(dynCall_iiiifiiii=Module["dynCall_iiiifiiii"]=Module["asm"]["Vl"]).apply(null,arguments)};var dynCall_iiiifiii=Module["dynCall_iiiifiii"]=function(){return(dynCall_iiiifiii=Module["dynCall_iiiifiii"]=Module["asm"]["Wl"]).apply(null,arguments)};var dynCall_vijjii=Module["dynCall_vijjii"]=function(){return(dynCall_vijjii=Module["dynCall_vijjii"]=Module["asm"]["Xl"]).apply(null,arguments)};var dynCall_viiifii=Module["dynCall_viiifii"]=function(){return(dynCall_viiifii=Module["dynCall_viiifii"]=Module["asm"]["Yl"]).apply(null,arguments)};var dynCall_viiiiiiifi=Module["dynCall_viiiiiiifi"]=function(){return(dynCall_viiiiiiifi=Module["dynCall_viiiiiiifi"]=Module["asm"]["Zl"]).apply(null,arguments)};var dynCall_iiiiiiifiiii=Module["dynCall_iiiiiiifiiii"]=function(){return(dynCall_iiiiiiifiiii=Module["dynCall_iiiiiiifiiii"]=Module["asm"]["_l"]).apply(null,arguments)};var dynCall_viiiiiffii=Module["dynCall_viiiiiffii"]=function(){return(dynCall_viiiiiffii=Module["dynCall_viiiiiffii"]=Module["asm"]["$l"]).apply(null,arguments)};var dynCall_viffffii=Module["dynCall_viffffii"]=function(){return(dynCall_viffffii=Module["dynCall_viffffii"]=Module["asm"]["am"]).apply(null,arguments)};var dynCall_iiifiii=Module["dynCall_iiifiii"]=function(){return(dynCall_iiifiii=Module["dynCall_iiifiii"]=Module["asm"]["bm"]).apply(null,arguments)};var dynCall_viiifiii=Module["dynCall_viiifiii"]=function(){return(dynCall_viiifiii=Module["dynCall_viiifiii"]=Module["asm"]["cm"]).apply(null,arguments)};var dynCall_viiififi=Module["dynCall_viiififi"]=function(){return(dynCall_viiififi=Module["dynCall_viiififi"]=Module["asm"]["dm"]).apply(null,arguments)};var dynCall_viiififfi=Module["dynCall_viiififfi"]=function(){return(dynCall_viiififfi=Module["dynCall_viiififfi"]=Module["asm"]["em"]).apply(null,arguments)};var dynCall_iiiiifi=Module["dynCall_iiiiifi"]=function(){return(dynCall_iiiiifi=Module["dynCall_iiiiifi"]=Module["asm"]["fm"]).apply(null,arguments)};var dynCall_iifiii=Module["dynCall_iifiii"]=function(){return(dynCall_iifiii=Module["dynCall_iifiii"]=Module["asm"]["gm"]).apply(null,arguments)};var dynCall_iiiiifiii=Module["dynCall_iiiiifiii"]=function(){return(dynCall_iiiiifiii=Module["dynCall_iiiiifiii"]=Module["asm"]["hm"]).apply(null,arguments)};var dynCall_iiifiiii=Module["dynCall_iiifiiii"]=function(){return(dynCall_iiifiiii=Module["dynCall_iiifiiii"]=Module["asm"]["im"]).apply(null,arguments)};var dynCall_vifffffi=Module["dynCall_vifffffi"]=function(){return(dynCall_vifffffi=Module["dynCall_vifffffi"]=Module["asm"]["jm"]).apply(null,arguments)};var dynCall_viffiiii=Module["dynCall_viffiiii"]=function(){return(dynCall_viffiiii=Module["dynCall_viffiiii"]=Module["asm"]["km"]).apply(null,arguments)};var dynCall_viiiiffffiiii=Module["dynCall_viiiiffffiiii"]=function(){return(dynCall_viiiiffffiiii=Module["dynCall_viiiiffffiiii"]=Module["asm"]["lm"]).apply(null,arguments)};var dynCall_iiiiiiffiiiiiiiiiffffiiii=Module["dynCall_iiiiiiffiiiiiiiiiffffiiii"]=function(){return(dynCall_iiiiiiffiiiiiiiiiffffiiii=Module["dynCall_iiiiiiffiiiiiiiiiffffiiii"]=Module["asm"]["mm"]).apply(null,arguments)};var dynCall_iiiiiiffiiiiiiiiiiiiiii=Module["dynCall_iiiiiiffiiiiiiiiiiiiiii"]=function(){return(dynCall_iiiiiiffiiiiiiiiiiiiiii=Module["dynCall_iiiiiiffiiiiiiiiiiiiiii"]=Module["asm"]["nm"]).apply(null,arguments)};var dynCall_fiiiffi=Module["dynCall_fiiiffi"]=function(){return(dynCall_fiiiffi=Module["dynCall_fiiiffi"]=Module["asm"]["om"]).apply(null,arguments)};var dynCall_viffffiii=Module["dynCall_viffffiii"]=function(){return(dynCall_viffffiii=Module["dynCall_viffffiii"]=Module["asm"]["pm"]).apply(null,arguments)};var dynCall_viijji=Module["dynCall_viijji"]=function(){return(dynCall_viijji=Module["dynCall_viijji"]=Module["asm"]["qm"]).apply(null,arguments)};var dynCall_viififii=Module["dynCall_viififii"]=function(){return(dynCall_viififii=Module["dynCall_viififii"]=Module["asm"]["rm"]).apply(null,arguments)};var dynCall_iiiffiiii=Module["dynCall_iiiffiiii"]=function(){return(dynCall_iiiffiiii=Module["dynCall_iiiffiiii"]=Module["asm"]["sm"]).apply(null,arguments)};var dynCall_fffffi=Module["dynCall_fffffi"]=function(){return(dynCall_fffffi=Module["dynCall_fffffi"]=Module["asm"]["tm"]).apply(null,arguments)};var dynCall_iiiiffiiii=Module["dynCall_iiiiffiiii"]=function(){return(dynCall_iiiiffiiii=Module["dynCall_iiiiffiiii"]=Module["asm"]["um"]).apply(null,arguments)};var dynCall_viiffffi=Module["dynCall_viiffffi"]=function(){return(dynCall_viiffffi=Module["dynCall_viiffffi"]=Module["asm"]["vm"]).apply(null,arguments)};var dynCall_fifffi=Module["dynCall_fifffi"]=function(){return(dynCall_fifffi=Module["dynCall_fifffi"]=Module["asm"]["wm"]).apply(null,arguments)};var dynCall_viffiii=Module["dynCall_viffiii"]=function(){return(dynCall_viffiii=Module["dynCall_viffiii"]=Module["asm"]["xm"]).apply(null,arguments)};var dynCall_fiffffi=Module["dynCall_fiffffi"]=function(){return(dynCall_fiffffi=Module["dynCall_fiffffi"]=Module["asm"]["ym"]).apply(null,arguments)};var dynCall_fffffffi=Module["dynCall_fffffffi"]=function(){return(dynCall_fffffffi=Module["dynCall_fffffffi"]=Module["asm"]["zm"]).apply(null,arguments)};var dynCall_viffifi=Module["dynCall_viffifi"]=function(){return(dynCall_viffifi=Module["dynCall_viffifi"]=Module["asm"]["Am"]).apply(null,arguments)};var dynCall_viiffifi=Module["dynCall_viiffifi"]=function(){return(dynCall_viiffifi=Module["dynCall_viiffifi"]=Module["asm"]["Bm"]).apply(null,arguments)};var dynCall_ifffi=Module["dynCall_ifffi"]=function(){return(dynCall_ifffi=Module["dynCall_ifffi"]=Module["asm"]["Cm"]).apply(null,arguments)};var dynCall_viiififiii=Module["dynCall_viiififiii"]=function(){return(dynCall_viiififiii=Module["dynCall_viiififiii"]=Module["asm"]["Dm"]).apply(null,arguments)};var dynCall_vifiii=Module["dynCall_vifiii"]=function(){return(dynCall_vifiii=Module["dynCall_vifiii"]=Module["asm"]["Em"]).apply(null,arguments)};var dynCall_viiffiiiiiiiii=Module["dynCall_viiffiiiiiiiii"]=function(){return(dynCall_viiffiiiiiiiii=Module["dynCall_viiffiiiiiiiii"]=Module["asm"]["Fm"]).apply(null,arguments)};var dynCall_viiiiiffiii=Module["dynCall_viiiiiffiii"]=function(){return(dynCall_viiiiiffiii=Module["dynCall_viiiiiffiii"]=Module["asm"]["Gm"]).apply(null,arguments)};var dynCall_viiffiii=Module["dynCall_viiffiii"]=function(){return(dynCall_viiffiii=Module["dynCall_viiffiii"]=Module["asm"]["Hm"]).apply(null,arguments)};var dynCall_viiffiiiiiii=Module["dynCall_viiffiiiiiii"]=function(){return(dynCall_viiffiiiiiii=Module["dynCall_viiffiiiiiii"]=Module["asm"]["Im"]).apply(null,arguments)};var dynCall_viiffii=Module["dynCall_viiffii"]=function(){return(dynCall_viiffii=Module["dynCall_viiffii"]=Module["asm"]["Jm"]).apply(null,arguments)};var dynCall_fffffffffi=Module["dynCall_fffffffffi"]=function(){return(dynCall_fffffffffi=Module["dynCall_fffffffffi"]=Module["asm"]["Km"]).apply(null,arguments)};var dynCall_vifiiiiii=Module["dynCall_vifiiiiii"]=function(){return(dynCall_vifiiiiii=Module["dynCall_vifiiiiii"]=Module["asm"]["Lm"]).apply(null,arguments)};var dynCall_vifiiiii=Module["dynCall_vifiiiii"]=function(){return(dynCall_vifiiiii=Module["dynCall_vifiiiii"]=Module["asm"]["Mm"]).apply(null,arguments)};var dynCall_viifiiiiiii=Module["dynCall_viifiiiiiii"]=function(){return(dynCall_viifiiiiiii=Module["dynCall_viifiiiiiii"]=Module["asm"]["Nm"]).apply(null,arguments)};var dynCall_viiififfiiiiiii=Module["dynCall_viiififfiiiiiii"]=function(){return(dynCall_viiififfiiiiiii=Module["dynCall_viiififfiiiiiii"]=Module["asm"]["Om"]).apply(null,arguments)};var dynCall_viiffiifiiiiiii=Module["dynCall_viiffiifiiiiiii"]=function(){return(dynCall_viiffiifiiiiiii=Module["dynCall_viiffiifiiiiiii"]=Module["asm"]["Pm"]).apply(null,arguments)};var dynCall_viifiiiiii=Module["dynCall_viifiiiiii"]=function(){return(dynCall_viifiiiiii=Module["dynCall_viifiiiiii"]=Module["asm"]["Qm"]).apply(null,arguments)};var dynCall_viiifiiiiii=Module["dynCall_viiifiiiiii"]=function(){return(dynCall_viiifiiiiii=Module["dynCall_viiifiiiiii"]=Module["asm"]["Rm"]).apply(null,arguments)};var dynCall_viiiifiiiiii=Module["dynCall_viiiifiiiiii"]=function(){return(dynCall_viiiifiiiiii=Module["dynCall_viiiifiiiiii"]=Module["asm"]["Sm"]).apply(null,arguments)};var dynCall_viififiiiiii=Module["dynCall_viififiiiiii"]=function(){return(dynCall_viififiiiiii=Module["dynCall_viififiiiiii"]=Module["asm"]["Tm"]).apply(null,arguments)};var dynCall_viiiffiifiiiiiii=Module["dynCall_viiiffiifiiiiiii"]=function(){return(dynCall_viiiffiifiiiiiii=Module["dynCall_viiiffiifiiiiiii"]=Module["asm"]["Um"]).apply(null,arguments)};var dynCall_viiiiiifiiiiii=Module["dynCall_viiiiiifiiiiii"]=function(){return(dynCall_viiiiiifiiiiii=Module["dynCall_viiiiiifiiiiii"]=Module["asm"]["Vm"]).apply(null,arguments)};var dynCall_ffii=Module["dynCall_ffii"]=function(){return(dynCall_ffii=Module["dynCall_ffii"]=Module["asm"]["Wm"]).apply(null,arguments)};var dynCall_viffii=Module["dynCall_viffii"]=function(){return(dynCall_viffii=Module["dynCall_viffii"]=Module["asm"]["Xm"]).apply(null,arguments)};var dynCall_vififiii=Module["dynCall_vififiii"]=function(){return(dynCall_vififiii=Module["dynCall_vififiii"]=Module["asm"]["Ym"]).apply(null,arguments)};var dynCall_fiffi=Module["dynCall_fiffi"]=function(){return(dynCall_fiffi=Module["dynCall_fiffi"]=Module["asm"]["Zm"]).apply(null,arguments)};var dynCall_viiiiiiiijiiii=Module["dynCall_viiiiiiiijiiii"]=function(){return(dynCall_viiiiiiiijiiii=Module["dynCall_viiiiiiiijiiii"]=Module["asm"]["_m"]).apply(null,arguments)};var dynCall_viifiii=Module["dynCall_viifiii"]=function(){return(dynCall_viifiii=Module["dynCall_viifiii"]=Module["asm"]["$m"]).apply(null,arguments)};var dynCall_viifiiii=Module["dynCall_viifiiii"]=function(){return(dynCall_viifiiii=Module["dynCall_viifiiii"]=Module["asm"]["an"]).apply(null,arguments)};var dynCall_fifii=Module["dynCall_fifii"]=function(){return(dynCall_fifii=Module["dynCall_fifii"]=Module["asm"]["bn"]).apply(null,arguments)};var dynCall_vifffii=Module["dynCall_vifffii"]=function(){return(dynCall_vifffii=Module["dynCall_vifffii"]=Module["asm"]["cn"]).apply(null,arguments)};var dynCall_viiiffi=Module["dynCall_viiiffi"]=function(){return(dynCall_viiiffi=Module["dynCall_viiiffi"]=Module["asm"]["dn"]).apply(null,arguments)};var dynCall_viiifffi=Module["dynCall_viiifffi"]=function(){return(dynCall_viiifffi=Module["dynCall_viiifffi"]=Module["asm"]["en"]).apply(null,arguments)};var dynCall_fiifii=Module["dynCall_fiifii"]=function(){return(dynCall_fiifii=Module["dynCall_fiifii"]=Module["asm"]["fn"]).apply(null,arguments)};var dynCall_iifffi=Module["dynCall_iifffi"]=function(){return(dynCall_iifffi=Module["dynCall_iifffi"]=Module["asm"]["gn"]).apply(null,arguments)};var dynCall_viiidii=Module["dynCall_viiidii"]=function(){return(dynCall_viiidii=Module["dynCall_viiidii"]=Module["asm"]["hn"]).apply(null,arguments)};var dynCall_ijiiiiiiiii=Module["dynCall_ijiiiiiiiii"]=function(){return(dynCall_ijiiiiiiiii=Module["dynCall_ijiiiiiiiii"]=Module["asm"]["jn"]).apply(null,arguments)};var dynCall_viiijiii=Module["dynCall_viiijiii"]=function(){return(dynCall_viiijiii=Module["dynCall_viiijiii"]=Module["asm"]["kn"]).apply(null,arguments)};var dynCall_ijjiii=Module["dynCall_ijjiii"]=function(){return(dynCall_ijjiii=Module["dynCall_ijjiii"]=Module["asm"]["ln"]).apply(null,arguments)};var dynCall_ddi=Module["dynCall_ddi"]=function(){return(dynCall_ddi=Module["dynCall_ddi"]=Module["asm"]["mn"]).apply(null,arguments)};var dynCall_viddi=Module["dynCall_viddi"]=function(){return(dynCall_viddi=Module["dynCall_viddi"]=Module["asm"]["nn"]).apply(null,arguments)};var dynCall_vifffffffffi=Module["dynCall_vifffffffffi"]=function(){return(dynCall_vifffffffffi=Module["dynCall_vifffffffffi"]=Module["asm"]["on"]).apply(null,arguments)};var dynCall_viffffffffffffffffi=Module["dynCall_viffffffffffffffffi"]=function(){return(dynCall_viffffffffffffffffi=Module["dynCall_viffffffffffffffffi"]=Module["asm"]["pn"]).apply(null,arguments)};var dynCall_viifffii=Module["dynCall_viifffii"]=function(){return(dynCall_viifffii=Module["dynCall_viifffii"]=Module["asm"]["qn"]).apply(null,arguments)};var dynCall_viiiffiffiiiiiiiiiiiiiii=Module["dynCall_viiiffiffiiiiiiiiiiiiiii"]=function(){return(dynCall_viiiffiffiiiiiiiiiiiiiii=Module["dynCall_viiiffiffiiiiiiiiiiiiiii"]=Module["asm"]["rn"]).apply(null,arguments)};var dynCall_viiiffiffiiiiiiiiiiiiiiiii=Module["dynCall_viiiffiffiiiiiiiiiiiiiiiii"]=function(){return(dynCall_viiiffiffiiiiiiiiiiiiiiiii=Module["dynCall_viiiffiffiiiiiiiiiiiiiiiii"]=Module["asm"]["sn"]).apply(null,arguments)};var dynCall_viiiiiffiiiii=Module["dynCall_viiiiiffiiiii"]=function(){return(dynCall_viiiiiffiiiii=Module["dynCall_viiiiiffiiiii"]=Module["asm"]["tn"]).apply(null,arguments)};var dynCall_iiiiiiiiiiiiiifiiiiii=Module["dynCall_iiiiiiiiiiiiiifiiiiii"]=function(){return(dynCall_iiiiiiiiiiiiiifiiiiii=Module["dynCall_iiiiiiiiiiiiiifiiiiii"]=Module["asm"]["un"]).apply(null,arguments)};var dynCall_iiiiiiiiiiiiiiiifiiiiii=Module["dynCall_iiiiiiiiiiiiiiiifiiiiii"]=function(){return(dynCall_iiiiiiiiiiiiiiiifiiiiii=Module["dynCall_iiiiiiiiiiiiiiiifiiiiii"]=Module["asm"]["vn"]).apply(null,arguments)};var dynCall_iiiiiiiiiiiiifiiiiii=Module["dynCall_iiiiiiiiiiiiifiiiiii"]=function(){return(dynCall_iiiiiiiiiiiiifiiiiii=Module["dynCall_iiiiiiiiiiiiifiiiiii"]=Module["asm"]["wn"]).apply(null,arguments)};var dynCall_iiiiiiiiiiiiiiifiiiiii=Module["dynCall_iiiiiiiiiiiiiiifiiiiii"]=function(){return(dynCall_iiiiiiiiiiiiiiifiiiiii=Module["dynCall_iiiiiiiiiiiiiiifiiiiii"]=Module["asm"]["xn"]).apply(null,arguments)};var dynCall_iiiiiiiiiiiiiiiiifiiiiii=Module["dynCall_iiiiiiiiiiiiiiiiifiiiiii"]=function(){return(dynCall_iiiiiiiiiiiiiiiiifiiiiii=Module["dynCall_iiiiiiiiiiiiiiiiifiiiiii"]=Module["asm"]["yn"]).apply(null,arguments)};var dynCall_vijjjji=Module["dynCall_vijjjji"]=function(){return(dynCall_vijjjji=Module["dynCall_vijjjji"]=Module["asm"]["zn"]).apply(null,arguments)};var dynCall_iijjjji=Module["dynCall_iijjjji"]=function(){return(dynCall_iijjjji=Module["dynCall_iijjjji"]=Module["asm"]["An"]).apply(null,arguments)};var dynCall_viiiffffi=Module["dynCall_viiiffffi"]=function(){return(dynCall_viiiffffi=Module["dynCall_viiiffffi"]=Module["asm"]["Bn"]).apply(null,arguments)};var dynCall_vifiiii=Module["dynCall_vifiiii"]=function(){return(dynCall_vifiiii=Module["dynCall_vifiiii"]=Module["asm"]["Cn"]).apply(null,arguments)};var dynCall_iiidi=Module["dynCall_iiidi"]=function(){return(dynCall_iiidi=Module["dynCall_iiidi"]=Module["asm"]["Dn"]).apply(null,arguments)};var dynCall_iijjjjiii=Module["dynCall_iijjjjiii"]=function(){return(dynCall_iijjjjiii=Module["dynCall_iijjjjiii"]=Module["asm"]["En"]).apply(null,arguments)};var dynCall_iiiidi=Module["dynCall_iiiidi"]=function(){return(dynCall_iiiidi=Module["dynCall_iiiidi"]=Module["asm"]["Fn"]).apply(null,arguments)};var 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dynCall_iiiiiifffiiifiii=Module["dynCall_iiiiiifffiiifiii"]=function(){return(dynCall_iiiiiifffiiifiii=Module["dynCall_iiiiiifffiiifiii"]=Module["asm"]["_p"]).apply(null,arguments)};var dynCall_viid=Module["dynCall_viid"]=function(){return(dynCall_viid=Module["dynCall_viid"]=Module["asm"]["$p"]).apply(null,arguments)};var dynCall_fiiiif=Module["dynCall_fiiiif"]=function(){return(dynCall_fiiiif=Module["dynCall_fiiiif"]=Module["asm"]["aq"]).apply(null,arguments)};var dynCall_iiiiiff=Module["dynCall_iiiiiff"]=function(){return(dynCall_iiiiiff=Module["dynCall_iiiiiff"]=Module["asm"]["bq"]).apply(null,arguments)};var dynCall_viffff=Module["dynCall_viffff"]=function(){return(dynCall_viffff=Module["dynCall_viffff"]=Module["asm"]["cq"]).apply(null,arguments)};var dynCall_vifff=Module["dynCall_vifff"]=function(){return(dynCall_vifff=Module["dynCall_vifff"]=Module["asm"]["dq"]).apply(null,arguments)};var dynCall_viifff=Module["dynCall_viifff"]=function(){return(dynCall_viifff=Module["dynCall_viifff"]=Module["asm"]["eq"]).apply(null,arguments)};var dynCall_vij=Module["dynCall_vij"]=function(){return(dynCall_vij=Module["dynCall_vij"]=Module["asm"]["fq"]).apply(null,arguments)};var dynCall_vf=Module["dynCall_vf"]=function(){return(dynCall_vf=Module["dynCall_vf"]=Module["asm"]["gq"]).apply(null,arguments)};var dynCall_vffff=Module["dynCall_vffff"]=function(){return(dynCall_vffff=Module["dynCall_vffff"]=Module["asm"]["hq"]).apply(null,arguments)};var dynCall_vff=Module["dynCall_vff"]=function(){return(dynCall_vff=Module["dynCall_vff"]=Module["asm"]["iq"]).apply(null,arguments)};var dynCall_vfff=Module["dynCall_vfff"]=function(){return(dynCall_vfff=Module["dynCall_vfff"]=Module["asm"]["jq"]).apply(null,arguments)};var dynCall_iiff=Module["dynCall_iiff"]=function(){return(dynCall_iiff=Module["dynCall_iiff"]=Module["asm"]["kq"]).apply(null,arguments)};var dynCall_f=Module["dynCall_f"]=function(){return(dynCall_f=Module["dynCall_f"]=Module["asm"]["lq"]).apply(null,arguments)};var dynCall_vffffffi=Module["dynCall_vffffffi"]=function(){return(dynCall_vffffffi=Module["dynCall_vffffffi"]=Module["asm"]["mq"]).apply(null,arguments)};var dynCall_ff=Module["dynCall_ff"]=function(){return(dynCall_ff=Module["dynCall_ff"]=Module["asm"]["nq"]).apply(null,arguments)};var dynCall_if=Module["dynCall_if"]=function(){return(dynCall_if=Module["dynCall_if"]=Module["asm"]["oq"]).apply(null,arguments)};var dynCall_iiiiiiifiii=Module["dynCall_iiiiiiifiii"]=function(){return(dynCall_iiiiiiifiii=Module["dynCall_iiiiiiifiii"]=Module["asm"]["pq"]).apply(null,arguments)};var dynCall_fiif=Module["dynCall_fiif"]=function(){return(dynCall_fiif=Module["dynCall_fiif"]=Module["asm"]["qq"]).apply(null,arguments)};var dynCall_iiiiiiffiiiiiiiiiffffiii=Module["dynCall_iiiiiiffiiiiiiiiiffffiii"]=function(){return(dynCall_iiiiiiffiiiiiiiiiffffiii=Module["dynCall_iiiiiiffiiiiiiiiiffffiii"]=Module["asm"]["rq"]).apply(null,arguments)};var dynCall_viififi=Module["dynCall_viififi"]=function(){return(dynCall_viififi=Module["dynCall_viififi"]=Module["asm"]["sq"]).apply(null,arguments)};var dynCall_viiiiiiiijiii=Module["dynCall_viiiiiiiijiii"]=function(){return(dynCall_viiiiiiiijiii=Module["dynCall_viiiiiiiijiii"]=Module["asm"]["tq"]).apply(null,arguments)};function invoke_ii(index,a1){var sp=stackSave();try{return dynCall_ii(index,a1)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iii(index,a1,a2){var sp=stackSave();try{return dynCall_iii(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiii(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_iiiiii(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vii(index,a1,a2){var sp=stackSave();try{dynCall_vii(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiii(index,a1,a2,a3){var sp=stackSave();try{return dynCall_iiii(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiii(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viiii(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiii(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_iiiii(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_fiii(index,a1,a2,a3){var sp=stackSave();try{return dynCall_fiii(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_diii(index,a1,a2,a3){var sp=stackSave();try{return dynCall_diii(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viif(index,a1,a2,a3){var sp=stackSave();try{dynCall_viif(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vi(index,a1){var sp=stackSave();try{dynCall_vi(index,a1)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viii(index,a1,a2,a3){var sp=stackSave();try{dynCall_viii(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_v(index){var sp=stackSave();try{dynCall_v(index)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_i(index){var sp=stackSave();try{return dynCall_i(index)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiii(index,a1,a2,a3,a4,a5,a6,a7){var sp=stackSave();try{return dynCall_iiiiiiii(index,a1,a2,a3,a4,a5,a6,a7)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiii(index,a1,a2,a3,a4,a5){var sp=stackSave();try{dynCall_viiiii(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10){var sp=stackSave();try{return dynCall_iiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12){var sp=stackSave();try{return dynCall_iiiiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiii(index,a1,a2,a3,a4,a5,a6,a7){var sp=stackSave();try{dynCall_viiiiiii(index,a1,a2,a3,a4,a5,a6,a7)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_iiiiiii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10){var sp=stackSave();try{dynCall_viiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{dynCall_viiiiii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viifi(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viifi(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiff(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viiff(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_fi(index,a1){var sp=stackSave();try{return dynCall_fi(index,a1)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiifi(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_iiifi(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiiiiiif(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11){var sp=stackSave();try{return dynCall_iiiiiiiiiiif(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiiifi(index,a1,a2,a3,a4,a5,a6,a7,a8,a9){var sp=stackSave();try{return dynCall_iiiiiiiifi(index,a1,a2,a3,a4,a5,a6,a7,a8,a9)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_diiii(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_diiii(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_fiiii(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_fiiii(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vifi(index,a1,a2,a3){var sp=stackSave();try{dynCall_vifi(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vidi(index,a1,a2,a3){var sp=stackSave();try{dynCall_vidi(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_fii(index,a1,a2){var sp=stackSave();try{return dynCall_fii(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_dii(index,a1,a2){var sp=stackSave();try{return dynCall_dii(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_ifi(index,a1,a2){var sp=stackSave();try{return dynCall_ifi(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_idi(index,a1,a2){var sp=stackSave();try{return dynCall_idi(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8){var sp=stackSave();try{dynCall_viiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9){var sp=stackSave();try{dynCall_viiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viifiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8){var sp=stackSave();try{dynCall_viifiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iifii(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_iifii(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vfi(index,a1,a2){var sp=stackSave();try{dynCall_vfi(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viffffi(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{dynCall_viffffi(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_dddi(index,a1,a2,a3){var sp=stackSave();try{return dynCall_dddi(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viffi(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viffi(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiifi(index,a1,a2,a3,a4,a5){var sp=stackSave();try{dynCall_viiifi(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_ffi(index,a1,a2){var sp=stackSave();try{return dynCall_ffi(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11){var sp=stackSave();try{dynCall_viiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiiifiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11){var sp=stackSave();try{return dynCall_iiiiiiiifiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiifi(index,a1,a2,a3,a4,a5,a6,a7){var sp=stackSave();try{dynCall_viiiiifi(index,a1,a2,a3,a4,a5,a6,a7)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiffi(index,a1,a2,a3,a4,a5,a6,a7,a8){var sp=stackSave();try{dynCall_viiiiiffi(index,a1,a2,a3,a4,a5,a6,a7,a8)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8){var sp=stackSave();try{return dynCall_iiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiifiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10){var sp=stackSave();try{return dynCall_iiiiifiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12){var sp=stackSave();try{dynCall_viiiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12,a13){var sp=stackSave();try{dynCall_viiiiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12,a13)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9){var sp=stackSave();try{return dynCall_iiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_fffi(index,a1,a2,a3){var sp=stackSave();try{return dynCall_fffi(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iidi(index,a1,a2,a3){var sp=stackSave();try{return dynCall_iidi(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iifi(index,a1,a2,a3){var sp=stackSave();try{return dynCall_iifi(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiiiiiifi(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11){var sp=stackSave();try{dynCall_viiiiiiiiifi(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiifi(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{dynCall_viiiifi(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viidi(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viidi(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiffi(index,a1,a2,a3,a4,a5){var sp=stackSave();try{dynCall_viiffi(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viifffi(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{dynCall_viifffi(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiif(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viiif(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viifffffi(index,a1,a2,a3,a4,a5,a6,a7,a8){var sp=stackSave();try{dynCall_viifffffi(index,a1,a2,a3,a4,a5,a6,a7,a8)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_fifi(index,a1,a2,a3){var sp=stackSave();try{return dynCall_fifi(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiiifffiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11){var sp=stackSave();try{dynCall_viiiiifffiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_fiiiii(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_fiiiii(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiidii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_iiiidii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiifii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_iiiifii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vidd(index,a1,a2,a3){var sp=stackSave();try{dynCall_vidd(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jiiii(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_jiiii(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiij(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_iiij(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_j(index){var sp=stackSave();try{return dynCall_j(index)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iij(index,a1,a2,a3){var sp=stackSave();try{return dynCall_iij(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiijiii(index,a1,a2,a3,a4,a5,a6,a7){var sp=stackSave();try{return dynCall_iiijiii(index,a1,a2,a3,a4,a5,a6,a7)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_ji(index,a1){var sp=stackSave();try{return dynCall_ji(index,a1)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jii(index,a1,a2){var sp=stackSave();try{return dynCall_jii(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viijii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{dynCall_viijii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiij(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_iiiiij(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iji(index,a1,a2,a3){var sp=stackSave();try{return dynCall_iji(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiji(index,a1,a2,a3,a4,a5){var sp=stackSave();try{dynCall_viiji(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viji(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viji(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jiii(index,a1,a2,a3){var sp=stackSave();try{return dynCall_jiii(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vijii(index,a1,a2,a3,a4,a5){var sp=stackSave();try{dynCall_vijii(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiiji(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{dynCall_viiiji(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiji(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_iiji(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jiiji(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_jiiji(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jjji(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_jjji(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viijiiijiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12,a13){var sp=stackSave();try{dynCall_viijiiijiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12,a13)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_ijji(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_ijji(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jdi(index,a1,a2){var sp=stackSave();try{return dynCall_jdi(index,a1,a2)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vijjji(index,a1,a2,a3,a4,a5,a6,a7,a8){var sp=stackSave();try{dynCall_vijjji(index,a1,a2,a3,a4,a5,a6,a7,a8)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iijii(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_iijii(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiijjii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9){var sp=stackSave();try{return dynCall_iiiijjii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iijiii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_iijiii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iijjiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11){var sp=stackSave();try{return dynCall_iijjiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iijiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9){var sp=stackSave();try{return dynCall_iijiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jjii(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_jjii(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jji(index,a1,a2,a3){var sp=stackSave();try{return dynCall_jji(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_dji(index,a1,a2,a3){var sp=stackSave();try{return dynCall_dji(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viiij(index,a1,a2,a3,a4,a5){var sp=stackSave();try{dynCall_viiij(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jijj(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_jijj(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiiiiiiiji(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11){var sp=stackSave();try{return dynCall_iiiiiiiiiji(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vji(index,a1,a2,a3){var sp=stackSave();try{dynCall_vji(index,a1,a2,a3)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10){var sp=stackSave();try{return dynCall_jiiiiiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jiji(index,a1,a2,a3,a4){var sp=stackSave();try{return dynCall_jiji(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iijji(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_iijji(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viij(index,a1,a2,a3,a4){var sp=stackSave();try{dynCall_viij(index,a1,a2,a3,a4)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jijiii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{return dynCall_jijiii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_jiiiii(index,a1,a2,a3,a4,a5){var sp=stackSave();try{return dynCall_jiiiii(index,a1,a2,a3,a4,a5)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vijiii(index,a1,a2,a3,a4,a5,a6){var sp=stackSave();try{dynCall_vijiii(index,a1,a2,a3,a4,a5,a6)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vjjjiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10){var sp=stackSave();try{dynCall_vjjjiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_vjiiiii(index,a1,a2,a3,a4,a5,a6,a7){var sp=stackSave();try{dynCall_vjiiiii(index,a1,a2,a3,a4,a5,a6,a7)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_viijiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10){var sp=stackSave();try{dynCall_viijiiiiii(index,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}function invoke_iiiijii(index,a1,a2,a3,a4,a5,a6,a7){var sp=stackSave();try{return dynCall_iiiijii(index,a1,a2,a3,a4,a5,a6,a7)}catch(e){stackRestore(sp);if(e!==e+0&&e!=="longjmp")throw e;_setThrew(1,0)}}Module["ccall"]=ccall;Module["cwrap"]=cwrap;Module["stackTrace"]=stackTrace;Module["addRunDependency"]=addRunDependency;Module["removeRunDependency"]=removeRunDependency;Module["FS_createPath"]=FS.createPath;Module["FS_createDataFile"]=FS.createDataFile;Module["stackTrace"]=stackTrace;var calledRun;function ExitStatus(status){this.name="ExitStatus";this.message="Program terminated with exit("+status+")";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module["_main"];args=args||[];var argc=args.length+1;var argv=stackAlloc((argc+1)*4);HEAP32[argv>>2]=allocateUTF8OnStack(thisProgram);for(var i=1;i<argc;i++){HEAP32[(argv>>2)+i]=allocateUTF8OnStack(args[i-1])}HEAP32[(argv>>2)+argc]=0;try{var ret=entryFunction(argc,argv);exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e=="unwind"){return}else{var toLog=e;if(e&&typeof e==="object"&&e.stack){toLog=[e,e.stack]}err("exception thrown: "+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0){return}function doRun(){if(calledRun)return;calledRun=true;Module["calledRun"]=true;if(ABORT)return;initRuntime();preMain();if(Module["onRuntimeInitialized"])Module["onRuntimeInitialized"]();if(shouldRunNow)callMain(args);postRun()}if(Module["setStatus"]){Module["setStatus"]("Running...");setTimeout(function(){setTimeout(function(){Module["setStatus"]("")},1);doRun()},1)}else{doRun()}}Module["run"]=run;function exit(status,implicit){EXITSTATUS=status;if(implicit&&keepRuntimeAlive()&&status===0){return}if(keepRuntimeAlive()){}else{exitRuntime();if(Module["onExit"])Module["onExit"](status);ABORT=true}quit_(status,new ExitStatus(status))}if(Module["preInit"]){if(typeof Module["preInit"]=="function")Module["preInit"]=[Module["preInit"]];while(Module["preInit"].length>0){Module["preInit"].pop()()}}var shouldRunNow=true;if(Module["noInitialRun"])shouldRunNow=false;run();
-
-}
diff --git a/spaces/Detomo/ai-comic-generation/tailwind.config.js b/spaces/Detomo/ai-comic-generation/tailwind.config.js
deleted file mode 100644
index 69e9993b157f8c9011a835fe8f9cf2f096dbfbc9..0000000000000000000000000000000000000000
--- a/spaces/Detomo/ai-comic-generation/tailwind.config.js
+++ /dev/null
@@ -1,64 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
-  darkMode: ["class"],
-  content: [
-    './pages/**/*.{ts,tsx}',
-    './components/**/*.{ts,tsx}',
-    './app/**/*.{ts,tsx}',
-    './src/**/*.{ts,tsx}',
-    './src/lib/fonts.ts'
-	],
-  theme: {
-    container: {
-      center: true,
-      padding: "2rem",
-      screens: {
-        "2xl": "1400px",
-      },
-    },
-    extend: {
-      fontFamily: {
-        indieflower: ['var(--font-indieflower)'],
-        thegirlnextdoor: ['var(--font-the-girl-next-door)'],
-        komika: ['var(--font-komika)'],
-        actionman: ['var(--font-action-man)'],
-        karantula: ['var(--font-karantula)'],
-        manoskope: ['var(--font-manoskope)'],
-        paeteround: ['var(--font-paete-round)'],
-        qarmic: ['var(--font-qarmic-sans)'],
-        archrival: ['var(--font-sf-arch-rival)'],
-        cartoonist: ['var(--font-sf-cartoonist-hand)'],
-        toontime: ['var(--font-sf-toontime)'],
-        vtc: ['var(--font-vtc-letterer-pro)'],
-        digitalstrip: ['var(--font-digital-strip-bb)'],
-      },
-      fontSize: {
-        "7xs": "5px",
-        "7xs": "6px",
-        "6xs": "7px",
-        "5xs": "8px",
-        "4xs": "9px",
-        "3xs": "10px",
-        "2xs": "11px"
-      },
-      keyframes: {
-        "accordion-down": {
-          from: { height: 0 },
-          to: { height: "var(--radix-accordion-content-height)" },
-        },
-        "accordion-up": {
-          from: { height: "var(--radix-accordion-content-height)" },
-          to: { height: 0 },
-        },
-      },
-      animation: {
-        "accordion-down": "accordion-down 0.2s ease-out",
-        "accordion-up": "accordion-up 0.2s ease-out",
-      },
-      screens: {
-        'print': { 'raw': 'print' },
-      }
-    },
-  },
-  plugins: [require("tailwindcss-animate")],
-}
\ No newline at end of file
diff --git a/spaces/DiViorg/categories_error_analysis/app.py b/spaces/DiViorg/categories_error_analysis/app.py
deleted file mode 100644
index 82bcd07557a93a724338ea788c618faba764612f..0000000000000000000000000000000000000000
--- a/spaces/DiViorg/categories_error_analysis/app.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from turtle import width
-import gradio as gr
-from utils import SampleClass
-
-sample = SampleClass()
-
-# --- Interface ---
-
-demo = gr.Blocks(
-            title="Categories_error_analysis.ipynb",
-            css=".container { max-width: 98%; margin: auto;};")
-
-with demo:
-    gr.Markdown("<h2><center> Categories Error Analysis 🐛</center></h2>")
-    with gr.Row():
-        with gr.Column():
-            with gr.Row():
-                category = gr.Dropdown(label="Category",value="relational",choices=["intrinsic","spatial","ordinal","relational","plural"])
-                predictions = gr.Dropdown(label='Predictions', value='fail',choices=["fail", "correct"])
-            with gr.Row():
-                model = gr.Dropdown(label='Model', value='baseline', choices=["baseline", "extended"])
-                split = gr.Dropdown(label='Split',value='val',choices=["test","val"])
-            with gr.Row():
-                username = gr.Dropdown(label="UserName",value="luciana",choices=["luciana",'mauri','jorge','nano'])
-                next_idx_sample = gr.Number(label='Next Idx Sample', value=0)
-            with gr.Row():
-                progress = gr.Label(label='Progress',num_top_classes=10)
-            with gr.Row(): 
-                btn_next = gr.Button("Get Next Sample")
-
-        with gr.Column():
-            with gr.Row():
-                img = gr.Image(label="Sample", type="numpy")
-            with gr.Row():
-                info = gr.Text(label="Info")
-                img_seg = gr.Image(label="Segmentation", type="numpy")
-
-    btn_next.click(
-        fn=sample.explorateSamples,
-        inputs=[username,predictions,category,model,split,next_idx_sample], 
-        outputs=[next_idx_sample, progress, img, info, img_seg])
-
-demo.queue(concurrency_count=10)
-demo.launch(debug=False)
\ No newline at end of file
diff --git a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan2/stylegan2-pytorch/ppl.py b/spaces/Dinoking/Guccio-AI-Designer/models/stylegan2/stylegan2-pytorch/ppl.py
deleted file mode 100644
index 6b185c894ba719701baa6ac348e743a003ec5f27..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan2/stylegan2-pytorch/ppl.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import argparse
-
-import torch
-from torch.nn import functional as F
-import numpy as np
-from tqdm import tqdm
-
-import lpips
-from model import Generator
-
-
-def normalize(x):
-    return x / torch.sqrt(x.pow(2).sum(-1, keepdim=True))
-
-
-def slerp(a, b, t):
-    a = normalize(a)
-    b = normalize(b)
-    d = (a * b).sum(-1, keepdim=True)
-    p = t * torch.acos(d)
-    c = normalize(b - d * a)
-    d = a * torch.cos(p) + c * torch.sin(p)
-
-    return normalize(d)
-
-
-def lerp(a, b, t):
-    return a + (b - a) * t
-
-
-if __name__ == '__main__':
-    device = 'cuda'
-
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument('--space', choices=['z', 'w'])
-    parser.add_argument('--batch', type=int, default=64)
-    parser.add_argument('--n_sample', type=int, default=5000)
-    parser.add_argument('--size', type=int, default=256)
-    parser.add_argument('--eps', type=float, default=1e-4)
-    parser.add_argument('--crop', action='store_true')
-    parser.add_argument('ckpt', metavar='CHECKPOINT')
-
-    args = parser.parse_args()
-
-    latent_dim = 512
-
-    ckpt = torch.load(args.ckpt)
-
-    g = Generator(args.size, latent_dim, 8).to(device)
-    g.load_state_dict(ckpt['g_ema'])
-    g.eval()
-
-    percept = lpips.PerceptualLoss(
-        model='net-lin', net='vgg', use_gpu=device.startswith('cuda')
-    )
-
-    distances = []
-
-    n_batch = args.n_sample // args.batch
-    resid = args.n_sample - (n_batch * args.batch)
-    batch_sizes = [args.batch] * n_batch + [resid]
-
-    with torch.no_grad():
-        for batch in tqdm(batch_sizes):
-            noise = g.make_noise()
-
-            inputs = torch.randn([batch * 2, latent_dim], device=device)
-            lerp_t = torch.rand(batch, device=device)
-
-            if args.space == 'w':
-                latent = g.get_latent(inputs)
-                latent_t0, latent_t1 = latent[::2], latent[1::2]
-                latent_e0 = lerp(latent_t0, latent_t1, lerp_t[:, None])
-                latent_e1 = lerp(latent_t0, latent_t1, lerp_t[:, None] + args.eps)
-                latent_e = torch.stack([latent_e0, latent_e1], 1).view(*latent.shape)
-
-            image, _ = g([latent_e], input_is_latent=True, noise=noise)
-
-            if args.crop:
-                c = image.shape[2] // 8
-                image = image[:, :, c * 3 : c * 7, c * 2 : c * 6]
-
-            factor = image.shape[2] // 256
-
-            if factor > 1:
-                image = F.interpolate(
-                    image, size=(256, 256), mode='bilinear', align_corners=False
-                )
-
-            dist = percept(image[::2], image[1::2]).view(image.shape[0] // 2) / (
-                args.eps ** 2
-            )
-            distances.append(dist.to('cpu').numpy())
-
-    distances = np.concatenate(distances, 0)
-
-    lo = np.percentile(distances, 1, interpolation='lower')
-    hi = np.percentile(distances, 99, interpolation='higher')
-    filtered_dist = np.extract(
-        np.logical_and(lo <= distances, distances <= hi), distances
-    )
-
-    print('ppl:', filtered_dist.mean())
diff --git a/spaces/DragGan/DragGan-Inversion/PTI/torch_utils/custom_ops.py b/spaces/DragGan/DragGan-Inversion/PTI/torch_utils/custom_ops.py
deleted file mode 100644
index 4cc4e43fc6f6ce79f2bd68a44ba87990b9b8564e..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/PTI/torch_utils/custom_ops.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import glob
-import torch
-import torch.utils.cpp_extension
-import importlib
-import hashlib
-import shutil
-from pathlib import Path
-
-from torch.utils.file_baton import FileBaton
-
-#----------------------------------------------------------------------------
-# Global options.
-
-verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full'
-
-#----------------------------------------------------------------------------
-# Internal helper funcs.
-
-def _find_compiler_bindir():
-    patterns = [
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin',
-    ]
-    for pattern in patterns:
-        matches = sorted(glob.glob(pattern))
-        if len(matches):
-            return matches[-1]
-    return None
-
-#----------------------------------------------------------------------------
-# Main entry point for compiling and loading C++/CUDA plugins.
-
-_cached_plugins = dict()
-
-def get_plugin(module_name, sources, **build_kwargs):
-    assert verbosity in ['none', 'brief', 'full']
-
-    # Already cached?
-    if module_name in _cached_plugins:
-        return _cached_plugins[module_name]
-
-    # Print status.
-    if verbosity == 'full':
-        print(f'Setting up PyTorch plugin "{module_name}"...')
-    elif verbosity == 'brief':
-        print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
-
-    try: # pylint: disable=too-many-nested-blocks
-        # Make sure we can find the necessary compiler binaries.
-        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
-            compiler_bindir = _find_compiler_bindir()
-            if compiler_bindir is None:
-                raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
-            os.environ['PATH'] += ';' + compiler_bindir
-
-        # Compile and load.
-        verbose_build = (verbosity == 'full')
-
-        # Incremental build md5sum trickery.  Copies all the input source files
-        # into a cached build directory under a combined md5 digest of the input
-        # source files.  Copying is done only if the combined digest has changed.
-        # This keeps input file timestamps and filenames the same as in previous
-        # extension builds, allowing for fast incremental rebuilds.
-        #
-        # This optimization is done only in case all the source files reside in
-        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
-        # environment variable is set (we take this as a signal that the user
-        # actually cares about this.)
-        source_dirs_set = set(os.path.dirname(source) for source in sources)
-        if len(source_dirs_set) == 1 and ('TORCH_EXTENSIONS_DIR' in os.environ):
-            all_source_files = sorted(list(x for x in Path(list(source_dirs_set)[0]).iterdir() if x.is_file()))
-
-            # Compute a combined hash digest for all source files in the same
-            # custom op directory (usually .cu, .cpp, .py and .h files).
-            hash_md5 = hashlib.md5()
-            for src in all_source_files:
-                with open(src, 'rb') as f:
-                    hash_md5.update(f.read())
-            build_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access
-            digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest())
-
-            if not os.path.isdir(digest_build_dir):
-                os.makedirs(digest_build_dir, exist_ok=True)
-                baton = FileBaton(os.path.join(digest_build_dir, 'lock'))
-                if baton.try_acquire():
-                    try:
-                        for src in all_source_files:
-                            shutil.copyfile(src, os.path.join(digest_build_dir, os.path.basename(src)))
-                    finally:
-                        baton.release()
-                else:
-                    # Someone else is copying source files under the digest dir,
-                    # wait until done and continue.
-                    baton.wait()
-            digest_sources = [os.path.join(digest_build_dir, os.path.basename(x)) for x in sources]
-            torch.utils.cpp_extension.load(name=module_name, build_directory=build_dir,
-                verbose=verbose_build, sources=digest_sources, **build_kwargs)
-        else:
-            torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
-        module = importlib.import_module(module_name)
-
-    except:
-        if verbosity == 'brief':
-            print('Failed!')
-        raise
-
-    # Print status and add to cache.
-    if verbosity == 'full':
-        print(f'Done setting up PyTorch plugin "{module_name}".')
-    elif verbosity == 'brief':
-        print('Done.')
-    _cached_plugins[module_name] = module
-    return module
-
-#----------------------------------------------------------------------------
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/ops/bias_act.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/ops/bias_act.py
deleted file mode 100644
index cea981875cddec8c961dda4a96554558bca1e073..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/ops/bias_act.py
+++ /dev/null
@@ -1,226 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom PyTorch ops for efficient bias and activation."""
-
-import os
-import warnings
-import numpy as np
-import torch
-import dnnlib
-import traceback
-
-from .. import custom_ops
-from .. import misc
-
-# ----------------------------------------------------------------------------
-
-activation_funcs = {
-    'linear':   dnnlib.EasyDict(func=lambda x, **_:         x,                                          def_alpha=0,    def_gain=1,             cuda_idx=1, ref='',  has_2nd_grad=False),
-    'relu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.relu(x),                def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=2, ref='y', has_2nd_grad=False),
-    'lrelu':    dnnlib.EasyDict(func=lambda x, alpha, **_:  torch.nn.functional.leaky_relu(x, alpha),   def_alpha=0.2,  def_gain=np.sqrt(2),    cuda_idx=3, ref='y', has_2nd_grad=False),
-    'tanh':     dnnlib.EasyDict(func=lambda x, **_:         torch.tanh(x),                              def_alpha=0,    def_gain=1,             cuda_idx=4, ref='y', has_2nd_grad=True),
-    'sigmoid':  dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x),                           def_alpha=0,    def_gain=1,             cuda_idx=5, ref='y', has_2nd_grad=True),
-    'elu':      dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.elu(x),                 def_alpha=0,    def_gain=1,             cuda_idx=6, ref='y', has_2nd_grad=True),
-    'selu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.selu(x),                def_alpha=0,    def_gain=1,             cuda_idx=7, ref='y', has_2nd_grad=True),
-    'softplus': dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.softplus(x),            def_alpha=0,    def_gain=1,             cuda_idx=8, ref='y', has_2nd_grad=True),
-    'swish':    dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x) * x,                       def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=9, ref='x', has_2nd_grad=True),
-}
-
-# ----------------------------------------------------------------------------
-
-_inited = False
-_plugin = None
-_null_tensor = torch.empty([0])
-
-
-def _init():
-    global _inited, _plugin
-    if not _inited:
-        _inited = True
-        sources = ['bias_act.cpp', 'bias_act.cu']
-        sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
-        try:
-            _plugin = custom_ops.get_plugin(
-                'bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
-        except:
-            warnings.warn(
-                'Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
-    return _plugin is not None
-
-# ----------------------------------------------------------------------------
-
-
-def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'):
-    r"""Fused bias and activation function.
-
-    Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
-    and scales the result by `gain`. Each of the steps is optional. In most cases,
-    the fused op is considerably more efficient than performing the same calculation
-    using standard PyTorch ops. It supports first and second order gradients,
-    but not third order gradients.
-
-    Args:
-        x:      Input activation tensor. Can be of any shape.
-        b:      Bias vector, or `None` to disable. Must be a 1D tensor of the same type
-                as `x`. The shape must be known, and it must match the dimension of `x`
-                corresponding to `dim`.
-        dim:    The dimension in `x` corresponding to the elements of `b`.
-                The value of `dim` is ignored if `b` is not specified.
-        act:    Name of the activation function to evaluate, or `"linear"` to disable.
-                Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
-                See `activation_funcs` for a full list. `None` is not allowed.
-        alpha:  Shape parameter for the activation function, or `None` to use the default.
-        gain:   Scaling factor for the output tensor, or `None` to use default.
-                See `activation_funcs` for the default scaling of each activation function.
-                If unsure, consider specifying 1.
-        clamp:  Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
-                the clamping (default).
-        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the same shape and datatype as `x`.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert impl in ['ref', 'cuda']
-    if impl == 'cuda' and x.device.type == 'cuda' and _init():
-        return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b)
-    return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None):
-    """Slow reference implementation of `bias_act()` using standard TensorFlow ops.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert clamp is None or clamp >= 0
-    spec = activation_funcs[act]
-    alpha = float(alpha if alpha is not None else spec.def_alpha)
-    gain = float(gain if gain is not None else spec.def_gain)
-    clamp = float(clamp if clamp is not None else -1)
-
-    # Add bias.
-    if b is not None:
-        assert isinstance(b, torch.Tensor) and b.ndim == 1
-        assert 0 <= dim < x.ndim
-        assert b.shape[0] == x.shape[dim]
-        x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
-
-    # Evaluate activation function.
-    alpha = float(alpha)
-    x = spec.func(x, alpha=alpha)
-
-    # Scale by gain.
-    gain = float(gain)
-    if gain != 1:
-        x = x * gain
-
-    # Clamp.
-    if clamp >= 0:
-        x = x.clamp(-clamp, clamp)  # pylint: disable=invalid-unary-operand-type
-    return x
-
-# ----------------------------------------------------------------------------
-
-
-_bias_act_cuda_cache = dict()
-
-
-def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):
-    """Fast CUDA implementation of `bias_act()` using custom ops.
-    """
-    # Parse arguments.
-    assert clamp is None or clamp >= 0
-    spec = activation_funcs[act]
-    alpha = float(alpha if alpha is not None else spec.def_alpha)
-    gain = float(gain if gain is not None else spec.def_gain)
-    clamp = float(clamp if clamp is not None else -1)
-
-    # Lookup from cache.
-    key = (dim, act, alpha, gain, clamp)
-    if key in _bias_act_cuda_cache:
-        return _bias_act_cuda_cache[key]
-
-    # Forward op.
-    class BiasActCuda(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x, b):  # pylint: disable=arguments-differ
-            ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride()[
-                1] == 1 else torch.contiguous_format
-            x = x.contiguous(memory_format=ctx.memory_format)
-            b = b.contiguous() if b is not None else _null_tensor
-            y = x
-            if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor:
-                y = _plugin.bias_act(x, b, _null_tensor, _null_tensor,
-                                     _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp)
-            ctx.save_for_backward(
-                x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
-                b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
-                y if 'y' in spec.ref else _null_tensor)
-            return y
-
-        @staticmethod
-        def backward(ctx, dy):  # pylint: disable=arguments-differ
-            dy = dy.contiguous(memory_format=ctx.memory_format)
-            x, b, y = ctx.saved_tensors
-            dx = None
-            db = None
-
-            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
-                dx = dy
-                if act != 'linear' or gain != 1 or clamp >= 0:
-                    dx = BiasActCudaGrad.apply(dy, x, b, y)
-
-            if ctx.needs_input_grad[1]:
-                db = dx.sum([i for i in range(dx.ndim) if i != dim])
-
-            return dx, db
-
-    # Backward op.
-    class BiasActCudaGrad(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, dy, x, b, y):  # pylint: disable=arguments-differ
-            ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride()[
-                1] == 1 else torch.contiguous_format
-            dx = _plugin.bias_act(dy, b, x, y, _null_tensor,
-                                  1, dim, spec.cuda_idx, alpha, gain, clamp)
-            ctx.save_for_backward(
-                dy if spec.has_2nd_grad else _null_tensor,
-                x, b, y)
-            return dx
-
-        @staticmethod
-        def backward(ctx, d_dx):  # pylint: disable=arguments-differ
-            d_dx = d_dx.contiguous(memory_format=ctx.memory_format)
-            dy, x, b, y = ctx.saved_tensors
-            d_dy = None
-            d_x = None
-            d_b = None
-            d_y = None
-
-            if ctx.needs_input_grad[0]:
-                d_dy = BiasActCudaGrad.apply(d_dx, x, b, y)
-
-            if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]):
-                d_x = _plugin.bias_act(
-                    d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp)
-
-            if spec.has_2nd_grad and ctx.needs_input_grad[2]:
-                d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim])
-
-            return d_dy, d_x, d_b, d_y
-
-    # Add to cache.
-    _bias_act_cuda_cache[key] = BiasActCuda
-    return BiasActCuda
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/Dragonnnext/charybdis/greeting.md b/spaces/Dragonnnext/charybdis/greeting.md
deleted file mode 100644
index db50fe8d81b4b36cce2acc7a8419abca35825e04..0000000000000000000000000000000000000000
--- a/spaces/Dragonnnext/charybdis/greeting.md
+++ /dev/null
@@ -1,18 +0,0 @@
-
-It will open ALWAYS every Friday 6PM UTC, till monday 7AM UTC. (No gatekeeper) 
-
-
-(special proxy pass for non time frame access: Additional hints soon) 
-
-Hints: 
-
-All unique words to get timeout (Including variations example > test + t3st)
-
-315 Minutes
-
-ST (front end name)
-
-Hivemind.
-
-
-Formating: all LOWERCASE no SPACES, first need to be ordered ALPHABETICALLY (Special character included, and should be first), cyrilic excluded (200 first letters, exclude the rest, due to ST proxy password limit being 200)
\ No newline at end of file
diff --git a/spaces/ECCV2022/PSG/OpenPSG/configs/_base_/models/mask_rcnn_r50_fpn.py b/spaces/ECCV2022/PSG/OpenPSG/configs/_base_/models/mask_rcnn_r50_fpn.py
deleted file mode 100644
index 19b9c3d29c7af8ac828c25a1b388248aa23a2d77..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/PSG/OpenPSG/configs/_base_/models/mask_rcnn_r50_fpn.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# model settings
-model = dict(
-    type='MaskRCNN',
-    backbone=dict(type='ResNet',
-                  depth=50,
-                  num_stages=4,
-                  out_indices=(0, 1, 2, 3),
-                  frozen_stages=1,
-                  norm_cfg=dict(type='BN', requires_grad=True),
-                  norm_eval=True,
-                  style='pytorch',
-                  init_cfg=dict(type='Pretrained',
-                                checkpoint='torchvision://resnet50')),
-    neck=dict(type='FPN',
-              in_channels=[256, 512, 1024, 2048],
-              out_channels=256,
-              num_outs=5),
-    rpn_head=dict(type='RPNHead',
-                  in_channels=256,
-                  feat_channels=256,
-                  anchor_generator=dict(type='AnchorGenerator',
-                                        scales=[8],
-                                        ratios=[0.5, 1.0, 2.0],
-                                        strides=[4, 8, 16, 32, 64]),
-                  bbox_coder=dict(type='DeltaXYWHBBoxCoder',
-                                  target_means=[.0, .0, .0, .0],
-                                  target_stds=[1.0, 1.0, 1.0, 1.0]),
-                  loss_cls=dict(type='CrossEntropyLoss',
-                                use_sigmoid=True,
-                                loss_weight=1.0),
-                  loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-    roi_head=dict(type='StandardRoIHead',
-                  bbox_roi_extractor=dict(type='SingleRoIExtractor',
-                                          roi_layer=dict(type='RoIAlign',
-                                                         output_size=7,
-                                                         sampling_ratio=0),
-                                          out_channels=256,
-                                          featmap_strides=[4, 8, 16, 32]),
-                  bbox_head=dict(
-                      type='Shared2FCBBoxHead',
-                      in_channels=256,
-                      fc_out_channels=1024,
-                      roi_feat_size=7,
-                      num_classes=80,
-                      bbox_coder=dict(type='DeltaXYWHBBoxCoder',
-                                      target_means=[0., 0., 0., 0.],
-                                      target_stds=[0.1, 0.1, 0.2, 0.2]),
-                      reg_class_agnostic=False,
-                      loss_cls=dict(type='CrossEntropyLoss',
-                                    use_sigmoid=False,
-                                    loss_weight=1.0),
-                      loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-                  mask_roi_extractor=dict(type='SingleRoIExtractor',
-                                          roi_layer=dict(type='RoIAlign',
-                                                         output_size=14,
-                                                         sampling_ratio=0),
-                                          out_channels=256,
-                                          featmap_strides=[4, 8, 16, 32]),
-                  mask_head=dict(type='FCNMaskHead',
-                                 num_convs=4,
-                                 in_channels=256,
-                                 conv_out_channels=256,
-                                 num_classes=80,
-                                 loss_mask=dict(type='CrossEntropyLoss',
-                                                use_mask=True,
-                                                loss_weight=1.0))),
-    # model training and testing settings
-    train_cfg=dict(rpn=dict(assigner=dict(type='MaxIoUAssigner',
-                                          pos_iou_thr=0.7,
-                                          neg_iou_thr=0.3,
-                                          min_pos_iou=0.3,
-                                          match_low_quality=True,
-                                          ignore_iof_thr=-1),
-                            sampler=dict(type='RandomSampler',
-                                         num=256,
-                                         pos_fraction=0.5,
-                                         neg_pos_ub=-1,
-                                         add_gt_as_proposals=False),
-                            allowed_border=-1,
-                            pos_weight=-1,
-                            debug=False),
-                   rpn_proposal=dict(nms_pre=2000,
-                                     max_per_img=1000,
-                                     nms=dict(type='nms', iou_threshold=0.7),
-                                     min_bbox_size=0),
-                   rcnn=dict(assigner=dict(type='MaxIoUAssigner',
-                                           pos_iou_thr=0.5,
-                                           neg_iou_thr=0.5,
-                                           min_pos_iou=0.5,
-                                           match_low_quality=True,
-                                           ignore_iof_thr=-1),
-                             sampler=dict(type='RandomSampler',
-                                          num=512,
-                                          pos_fraction=0.25,
-                                          neg_pos_ub=-1,
-                                          add_gt_as_proposals=True),
-                             mask_size=28,
-                             pos_weight=-1,
-                             debug=False)),
-    test_cfg=dict(rpn=dict(nms_pre=1000,
-                           max_per_img=1000,
-                           nms=dict(type='nms', iou_threshold=0.7),
-                           min_bbox_size=0),
-                  rcnn=dict(score_thr=0.05,
-                            nms=dict(type='nms', iou_threshold=0.5),
-                            max_per_img=100,
-                            mask_thr_binary=0.5)))
diff --git a/spaces/ECCV2022/bytetrack/tools/convert_video.py b/spaces/ECCV2022/bytetrack/tools/convert_video.py
deleted file mode 100644
index 16e8aaaf117a95e72d8f85b6376a5e2b8eaaf300..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/tools/convert_video.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import cv2
-
-def convert_video(video_path):
-    cap = cv2.VideoCapture(video_path)
-    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # float
-    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # float
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    video_name = video_path.split('/')[-1].split('.')[0]
-    save_name = video_name + '_converted'
-    save_path = video_path.replace(video_name, save_name)
-    vid_writer = cv2.VideoWriter(
-        save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (int(width), int(height))
-    )
-    while True:
-        ret_val, frame = cap.read()
-        if ret_val:
-            vid_writer.write(frame)
-            ch = cv2.waitKey(1)
-            if ch == 27 or ch == ord("q") or ch == ord("Q"):
-                break
-        else:
-            break
-
-if __name__ == "__main__":
-    video_path = 'videos/palace.mp4'
-    convert_video(video_path)
\ No newline at end of file
diff --git a/spaces/ECCV2022/bytetrack/yolox/utils/model_utils.py b/spaces/ECCV2022/bytetrack/yolox/utils/model_utils.py
deleted file mode 100644
index b4e581211e7842cfffdd4c977577e8110b8d05b5..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/yolox/utils/model_utils.py
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
-
-import torch
-import torch.nn as nn
-from thop import profile
-
-from copy import deepcopy
-
-__all__ = [
-    "fuse_conv_and_bn",
-    "fuse_model",
-    "get_model_info",
-    "replace_module",
-]
-
-
-def get_model_info(model, tsize):
-
-    stride = 64
-    img = torch.zeros((1, 3, stride, stride), device=next(model.parameters()).device)
-    flops, params = profile(deepcopy(model), inputs=(img,), verbose=False)
-    params /= 1e6
-    flops /= 1e9
-    flops *= tsize[0] * tsize[1] / stride / stride * 2  # Gflops
-    info = "Params: {:.2f}M, Gflops: {:.2f}".format(params, flops)
-    return info
-
-
-def fuse_conv_and_bn(conv, bn):
-    # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
-    fusedconv = (
-        nn.Conv2d(
-            conv.in_channels,
-            conv.out_channels,
-            kernel_size=conv.kernel_size,
-            stride=conv.stride,
-            padding=conv.padding,
-            groups=conv.groups,
-            bias=True,
-        )
-        .requires_grad_(False)
-        .to(conv.weight.device)
-    )
-
-    # prepare filters
-    w_conv = conv.weight.clone().view(conv.out_channels, -1)
-    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
-    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
-
-    # prepare spatial bias
-    b_conv = (
-        torch.zeros(conv.weight.size(0), device=conv.weight.device)
-        if conv.bias is None
-        else conv.bias
-    )
-    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(
-        torch.sqrt(bn.running_var + bn.eps)
-    )
-    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
-
-    return fusedconv
-
-
-def fuse_model(model):
-    from yolox.models.network_blocks import BaseConv
-
-    for m in model.modules():
-        if type(m) is BaseConv and hasattr(m, "bn"):
-            m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
-            delattr(m, "bn")  # remove batchnorm
-            m.forward = m.fuseforward  # update forward
-    return model
-
-
-def replace_module(module, replaced_module_type, new_module_type, replace_func=None):
-    """
-    Replace given type in module to a new type. mostly used in deploy.
-
-    Args:
-        module (nn.Module): model to apply replace operation.
-        replaced_module_type (Type): module type to be replaced.
-        new_module_type (Type)
-        replace_func (function): python function to describe replace logic. Defalut value None.
-
-    Returns:
-        model (nn.Module): module that already been replaced.
-    """
-
-    def default_replace_func(replaced_module_type, new_module_type):
-        return new_module_type()
-
-    if replace_func is None:
-        replace_func = default_replace_func
-
-    model = module
-    if isinstance(module, replaced_module_type):
-        model = replace_func(replaced_module_type, new_module_type)
-    else:  # recurrsively replace
-        for name, child in module.named_children():
-            new_child = replace_module(child, replaced_module_type, new_module_type)
-            if new_child is not child:  # child is already replaced
-                model.add_module(name, new_child)
-
-    return model
diff --git a/spaces/Ekittl01/impira-layoutlm-document-qa/README.md b/spaces/Ekittl01/impira-layoutlm-document-qa/README.md
deleted file mode 100644
index 51bf7bc5728af76ac6b2b05e2faa6ad3e1d1e4bf..0000000000000000000000000000000000000000
--- a/spaces/Ekittl01/impira-layoutlm-document-qa/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Impira Layoutlm Document Qa
-emoji: 👁
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: bigscience-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/setup.py b/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/setup.py
deleted file mode 100644
index c2b92e31d2db1aba50767f4f844540cfd53c609d..0000000000000000000000000000000000000000
--- a/spaces/ElainaFanBoy/IRONY-Real-ESRGAN/setup.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python
-
-from setuptools import find_packages, setup
-
-import os
-import subprocess
-import time
-
-version_file = 'realesrgan/version.py'
-
-
-def readme():
-    with open('README.md', encoding='utf-8') as f:
-        content = f.read()
-    return content
-
-
-def get_git_hash():
-
-    def _minimal_ext_cmd(cmd):
-        # construct minimal environment
-        env = {}
-        for k in ['SYSTEMROOT', 'PATH', 'HOME']:
-            v = os.environ.get(k)
-            if v is not None:
-                env[k] = v
-        # LANGUAGE is used on win32
-        env['LANGUAGE'] = 'C'
-        env['LANG'] = 'C'
-        env['LC_ALL'] = 'C'
-        out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
-        return out
-
-    try:
-        out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
-        sha = out.strip().decode('ascii')
-    except OSError:
-        sha = 'unknown'
-
-    return sha
-
-
-def get_hash():
-    if os.path.exists('.git'):
-        sha = get_git_hash()[:7]
-    else:
-        sha = 'unknown'
-
-    return sha
-
-
-def write_version_py():
-    content = """# GENERATED VERSION FILE
-# TIME: {}
-__version__ = '{}'
-__gitsha__ = '{}'
-version_info = ({})
-"""
-    sha = get_hash()
-    with open('VERSION', 'r') as f:
-        SHORT_VERSION = f.read().strip()
-    VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])
-
-    version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
-    with open(version_file, 'w') as f:
-        f.write(version_file_str)
-
-
-def get_version():
-    with open(version_file, 'r') as f:
-        exec(compile(f.read(), version_file, 'exec'))
-    return locals()['__version__']
-
-
-def get_requirements(filename='requirements.txt'):
-    here = os.path.dirname(os.path.realpath(__file__))
-    with open(os.path.join(here, filename), 'r') as f:
-        requires = [line.replace('\n', '') for line in f.readlines()]
-    return requires
-
-
-if __name__ == '__main__':
-    write_version_py()
-    setup(
-        name='realesrgan',
-        version=get_version(),
-        description='Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration',
-        long_description=readme(),
-        long_description_content_type='text/markdown',
-        author='Xintao Wang',
-        author_email='xintao.wang@outlook.com',
-        keywords='computer vision, pytorch, image restoration, super-resolution, esrgan, real-esrgan',
-        url='https://github.com/xinntao/Real-ESRGAN',
-        include_package_data=True,
-        packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
-        classifiers=[
-            'Development Status :: 4 - Beta',
-            'License :: OSI Approved :: Apache Software License',
-            'Operating System :: OS Independent',
-            'Programming Language :: Python :: 3',
-            'Programming Language :: Python :: 3.7',
-            'Programming Language :: Python :: 3.8',
-        ],
-        license='BSD-3-Clause License',
-        setup_requires=['cython', 'numpy'],
-        install_requires=get_requirements(),
-        zip_safe=False)
diff --git a/spaces/EronSamez/RVC_HFmeu/julius/core.py b/spaces/EronSamez/RVC_HFmeu/julius/core.py
deleted file mode 100644
index 6b750418424e76c9540663ac4b2a16005adaf422..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/julius/core.py
+++ /dev/null
@@ -1,122 +0,0 @@
-# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
-# Author: adefossez, 2020
-"""
-Signal processing or PyTorch related utilities.
-"""
-import math
-import typing as tp
-
-import torch
-from torch.nn import functional as F
-
-
-def sinc(x: torch.Tensor):
-    """
-    Implementation of sinc, i.e. sin(x) / x
-
-    __Warning__: the input is not multiplied by `pi`!
-    """
-    return torch.where(x == 0, torch.tensor(1., device=x.device, dtype=x.dtype), torch.sin(x) / x)
-
-
-def pad_to(tensor: torch.Tensor, target_length: int, mode: str = 'constant', value: float = 0):
-    """
-    Pad the given tensor to the given length, with 0s on the right.
-    """
-    return F.pad(tensor, (0, target_length - tensor.shape[-1]), mode=mode, value=value)
-
-
-def hz_to_mel(freqs: torch.Tensor):
-    """
-    Converts a Tensor of frequencies in hertz to the mel scale.
-    Uses the simple formula by O'Shaughnessy (1987).
-
-    Args:
-        freqs (torch.Tensor): frequencies to convert.
-
-    """
-    return 2595 * torch.log10(1 + freqs / 700)
-
-
-def mel_to_hz(mels: torch.Tensor):
-    """
-    Converts a Tensor of mel scaled frequencies to Hertz.
-    Uses the simple formula by O'Shaughnessy (1987).
-
-    Args:
-        mels (torch.Tensor): mel frequencies to convert.
-    """
-    return 700 * (10**(mels / 2595) - 1)
-
-
-def mel_frequencies(n_mels: int, fmin: float, fmax: float):
-    """
-    Return frequencies that are evenly spaced in mel scale.
-
-    Args:
-        n_mels (int): number of frequencies to return.
-        fmin (float): start from this frequency (in Hz).
-        fmax (float): finish at this frequency (in Hz).
-
-
-    """
-    low = hz_to_mel(torch.tensor(float(fmin))).item()
-    high = hz_to_mel(torch.tensor(float(fmax))).item()
-    mels = torch.linspace(low, high, n_mels)
-    return mel_to_hz(mels)
-
-
-def volume(x: torch.Tensor, floor=1e-8):
-    """
-    Return the volume in dBFS.
-    """
-    return torch.log10(floor + (x**2).mean(-1)) * 10
-
-
-def pure_tone(freq: float, sr: float = 128, dur: float = 4, device=None):
-    """
-    Return a pure tone, i.e. cosine.
-
-    Args:
-        freq (float): frequency (in Hz)
-        sr (float): sample rate (in Hz)
-        dur (float): duration (in seconds)
-    """
-    time = torch.arange(int(sr * dur), device=device).float() / sr
-    return torch.cos(2 * math.pi * freq * time)
-
-
-def unfold(input, kernel_size: int, stride: int):
-    """1D only unfolding similar to the one from PyTorch.
-    However PyTorch unfold is extremely slow.
-
-    Given an input tensor of size `[*, T]` this will return
-    a tensor `[*, F, K]` with `K` the kernel size, and `F` the number
-    of frames. The i-th frame is a view onto `i * stride: i * stride + kernel_size`.
-    This will automatically pad the input to cover at least once all entries in `input`.
-
-    Args:
-        input (Tensor): tensor for which to return the frames.
-        kernel_size (int): size of each frame.
-        stride (int): stride between each frame.
-
-    Shape:
-
-        - Inputs: `input` is `[*, T]`
-        - Output: `[*, F, kernel_size]` with `F = 1 + ceil((T - kernel_size) / stride)`
-
-
-    ..Warning:: unlike PyTorch unfold, this will pad the input
-        so that any position in `input` is covered by at least one frame.
-    """
-    shape = list(input.shape)
-    length = shape.pop(-1)
-    n_frames = math.ceil((max(length, kernel_size) - kernel_size) / stride) + 1
-    tgt_length = (n_frames - 1) * stride + kernel_size
-    padded = F.pad(input, (0, tgt_length - length)).contiguous()
-    strides: tp.List[int] = []
-    for dim in range(padded.dim()):
-        strides.append(padded.stride(dim))
-    assert strides.pop(-1) == 1, 'data should be contiguous'
-    strides = strides + [stride, 1]
-    return padded.as_strided(shape + [n_frames, kernel_size], strides)
diff --git a/spaces/EronSamez/RVC_HFmeu/run.sh b/spaces/EronSamez/RVC_HFmeu/run.sh
deleted file mode 100644
index 704c9fff20b42b8659f7b4c797cd2928af9dec7a..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/run.sh
+++ /dev/null
@@ -1,61 +0,0 @@
-#!/bin/bash
-
-if [[ "$(uname)" == "Darwin" ]]; then
-  # macOS specific env:
-  export PYTORCH_ENABLE_MPS_FALLBACK=1
-  export PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0
-elif [[ "$(uname)" != "Linux" ]]; then
-  echo "Unsupported operating system."
-  exit 1
-fi
-
-if [ -d ".venv" ]; then
-  echo "Activate venv..."
-  source .venv/bin/activate
-else
-  echo "Create venv..."
-  requirements_file="requirements.txt"
-
-  # Check if Python 3.8 is installed
-  if ! command -v python3 &> /dev/null; then
-    echo "Python 3 not found. Attempting to install 3.8..."
-    if [[ "$(uname)" == "Darwin" ]] && command -v brew &> /dev/null; then
-      brew install python@3.8
-    elif [[ "$(uname)" == "Linux" ]] && command -v apt-get &> /dev/null; then
-      sudo apt-get update
-      sudo apt-get install python3.8
-    else
-      echo "Please install Python 3.8 manually."
-      exit 1
-    fi
-  fi
-
-  python3 -m venv .venv
-  source .venv/bin/activate
-
-  # Check if required packages are installed and install them if not
-  if [ -f "${requirements_file}" ]; then
-    installed_packages=$(python3 -m pip freeze)
-    while IFS= read -r package; do
-      [[ "${package}" =~ ^#.* ]] && continue
-      package_name=$(echo "${package}" | sed 's/[<>=!].*//')
-      if ! echo "${installed_packages}" | grep -q "${package_name}"; then
-        echo "${package_name} not found. Attempting to install..."
-        python3 -m pip install --upgrade "${package}"
-      fi
-    done < "${requirements_file}"
-  else
-    echo "${requirements_file} not found. Please ensure the requirements file with required packages exists."
-    exit 1
-  fi
-fi
-
-# Download models
-./tools/dlmodels.sh
-
-if [[ $? -ne 0 ]]; then
-  exit 1
-fi
-
-# Run the main script
-python3 infer-web.py --pycmd python3
diff --git a/spaces/EuroPython2022/Step-By-Step-With-Bloom/app.py b/spaces/EuroPython2022/Step-By-Step-With-Bloom/app.py
deleted file mode 100644
index a31e90d816572f6cbc3161b968fcbec5bc7e873b..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/Step-By-Step-With-Bloom/app.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import gradio as gr
-import requests
-import os 
-
-##Bloom
-API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
-HF_TOKEN = os.environ["HF_TOKEN"]
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-
-#Tested various prompts initially
-prompt1 = """
-Q: Joy has 5 balls. He buys 2 more cans of balls. Each can has 3 balls. How many balls he has now?
-A: Joy had 5 balls. 2 cans of 3 balls each is 6 balls. 5 + 6 = 11. Answer is 11.
-Q: Jane has 16 balls. Half balls are golf balls, and half golf balls are red. How many red golf balls are there?
-A: """
-
-prompt2 = """Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?
-A: Let’s think step by step.
-"""
-
-
-def text_generate(problem, template, prompt):
-  
-  #prints to debug
-  print(f"*****Inside text_generate function******")
-  print(f"Problem is :{problem}")
-  print(f"Template is :{template}")
-  print(f"Prompt is :{prompt}")
-  
-  if len(problem) == 0 and len(template) == 0:
-    p = prompt
-  else:
-    p = problem + "A: " + template #+ "\n"
-  print(f"Final prompt is : {p}")
-  json_ = {"inputs": p,
-            "parameters":
-            {
-            "top_p": 0.9,
-          "temperature": 1.1,
-          "max_new_tokens": 64,
-          "return_full_text": True
-          }, "options": 
-              {
-              "use_cache": True,
-              "wait_for_model":True
-              },}
-  response = requests.post(API_URL, headers=headers, json=json_)
-  print(f"Response  is : {response}")
-  output = response.json()
-  print(f"output is : {output}") #{output}")
-  output_tmp = output[0]['generated_text']
-  print(f"output_tmp is: {output_tmp}")
-  solution = output_tmp.split("\nQ:")[0]   #output[0]['generated_text'].split("Q:")[0] # +"."
-  print(f"Final response after splits is: {solution}")
- 
-  return solution 
-
-
-demo = gr.Blocks()
-
-with demo:
-  gr.Markdown("<h1><center>Step By Step With Bloom</center></h1>")
-  gr.Markdown(
-        """[BigScienceW Bloom](https://twitter.com/BigscienceW) \n\n Large language models have demonstrated a capability of 'Chain-of-thought reasoning'. A group of amazing researchers( [Jason Wei et al.](https://arxiv.org/abs/2205.11916)) recently found out that by adding **Lets think step by step** it improves the model's zero-shot performance. Some might say — You can get good results out of LLMs if you know how to speak to them. This space is an attempt at inspecting this LLM behavior/capability in the new HuggingFace BigScienceW [Bloom](https://huggingface.co/bigscience/bloom) model. \n\nThis Space is created by [Yuvraj Sharma](https://twitter.com/yvrjsharma) for EuroPython 2022 Demo.\nThis Space might sometime fail due to inference queue being full and logs would end up showing error as *queue full, try again later*, don't despair and try again after some time. I would try and improve the app as well over next couple days."""
-        )
-  with gr.Row():
-
-    #example_prompt = gr.Radio( ["Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?\nA: Let’s think step by step.\n", "Q: Roger has 5 tennis balls already. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?\nA: Let’s think step by step.\n", "Q: On an average Joe throws 25 punches per minute. His fight lasts 5 rounds of 3 minutes each. How many punches did he throw?\nA:  Let’s think about this logically.\n"], label= "Choose a sample Prompt")
-
-    example_problem = gr.Radio( ["Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?\n", "Q: Roger has 5 tennis balls already. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?\n", "Q: On an average Joe throws 25 punches per minute. His fight lasts 5 rounds of 3 minutes each. How many punches did he throw?\n"], label= "Choose a sample Problem and corresponding Tempplate for Zero-Shot CoT:")
-
-    example_template = gr.Radio( ["Let’s think step by step.\n"," First, ", " Let’s think about this logically.\n", "Let’s solve this problem by splitting it into steps.\n", " Let’s be realistic and think step by step.\n", "Let’s think like a detective step by step.\n", "Let’s think ", "Before we dive into the answer, ", "The answer is after the proof.\n"], label= "Choose a sample Problem and corresponding Template for Zero-Shot CoT:")
-
-    generated_txt = gr.Textbox(lines=10)
-
-  with gr.Row(): 
-    input_prompt = gr.Textbox(value="Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?\nA: Let’s think step by step.\n", label="Or don't select from above examples and just enter your own prompt drawing from the above examples and submit... ")
-  
-  b1 = gr.Button("Generate Text")
-  b1.click(text_generate,inputs=[example_problem, example_template, input_prompt], outputs=generated_txt) #example_prompt
- 
-  with gr.Row(): 
-    gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=europython2022_step-by-step-with-bloom)")
-
-demo.launch(enable_queue=True, debug=True)
\ No newline at end of file
diff --git a/spaces/EuroPython2022/mediapipe-hands/log_utils.py b/spaces/EuroPython2022/mediapipe-hands/log_utils.py
deleted file mode 100644
index 9644bc6f82aac9836a1703a5e6c59be5d5d1ca0a..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/mediapipe-hands/log_utils.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import logging
-
-logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-
-def get_logger(name=None, level=logging.INFO):
-  """Return a logger"""
-  logger = logging.getLogger(name)
-  logger.setLevel(level)
-  return logger
\ No newline at end of file
diff --git a/spaces/Faridmaruf/RVCV2MODEL/rmvpe.py b/spaces/Faridmaruf/RVCV2MODEL/rmvpe.py
deleted file mode 100644
index 3ad346141340e03bdbaa20121e1ed435bb3da57a..0000000000000000000000000000000000000000
--- a/spaces/Faridmaruf/RVCV2MODEL/rmvpe.py
+++ /dev/null
@@ -1,432 +0,0 @@
-import sys, torch, numpy as np, traceback, pdb
-import torch.nn as nn
-from time import time as ttime
-import torch.nn.functional as F
-
-
-class BiGRU(nn.Module):
-    def __init__(self, input_features, hidden_features, num_layers):
-        super(BiGRU, self).__init__()
-        self.gru = nn.GRU(
-            input_features,
-            hidden_features,
-            num_layers=num_layers,
-            batch_first=True,
-            bidirectional=True,
-        )
-
-    def forward(self, x):
-        return self.gru(x)[0]
-
-
-class ConvBlockRes(nn.Module):
-    def __init__(self, in_channels, out_channels, momentum=0.01):
-        super(ConvBlockRes, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-            nn.Conv2d(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        if in_channels != out_channels:
-            self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
-            self.is_shortcut = True
-        else:
-            self.is_shortcut = False
-
-    def forward(self, x):
-        if self.is_shortcut:
-            return self.conv(x) + self.shortcut(x)
-        else:
-            return self.conv(x) + x
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        in_size,
-        n_encoders,
-        kernel_size,
-        n_blocks,
-        out_channels=16,
-        momentum=0.01,
-    ):
-        super(Encoder, self).__init__()
-        self.n_encoders = n_encoders
-        self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
-        self.layers = nn.ModuleList()
-        self.latent_channels = []
-        for i in range(self.n_encoders):
-            self.layers.append(
-                ResEncoderBlock(
-                    in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
-                )
-            )
-            self.latent_channels.append([out_channels, in_size])
-            in_channels = out_channels
-            out_channels *= 2
-            in_size //= 2
-        self.out_size = in_size
-        self.out_channel = out_channels
-
-    def forward(self, x):
-        concat_tensors = []
-        x = self.bn(x)
-        for i in range(self.n_encoders):
-            _, x = self.layers[i](x)
-            concat_tensors.append(_)
-        return x, concat_tensors
-
-
-class ResEncoderBlock(nn.Module):
-    def __init__(
-        self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
-    ):
-        super(ResEncoderBlock, self).__init__()
-        self.n_blocks = n_blocks
-        self.conv = nn.ModuleList()
-        self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
-        self.kernel_size = kernel_size
-        if self.kernel_size is not None:
-            self.pool = nn.AvgPool2d(kernel_size=kernel_size)
-
-    def forward(self, x):
-        for i in range(self.n_blocks):
-            x = self.conv[i](x)
-        if self.kernel_size is not None:
-            return x, self.pool(x)
-        else:
-            return x
-
-
-class Intermediate(nn.Module):  #
-    def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
-        super(Intermediate, self).__init__()
-        self.n_inters = n_inters
-        self.layers = nn.ModuleList()
-        self.layers.append(
-            ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
-        )
-        for i in range(self.n_inters - 1):
-            self.layers.append(
-                ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
-            )
-
-    def forward(self, x):
-        for i in range(self.n_inters):
-            x = self.layers[i](x)
-        return x
-
-
-class ResDecoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
-        super(ResDecoderBlock, self).__init__()
-        out_padding = (0, 1) if stride == (1, 2) else (1, 1)
-        self.n_blocks = n_blocks
-        self.conv1 = nn.Sequential(
-            nn.ConvTranspose2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=stride,
-                padding=(1, 1),
-                output_padding=out_padding,
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        self.conv2 = nn.ModuleList()
-        self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
-
-    def forward(self, x, concat_tensor):
-        x = self.conv1(x)
-        x = torch.cat((x, concat_tensor), dim=1)
-        for i in range(self.n_blocks):
-            x = self.conv2[i](x)
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
-        super(Decoder, self).__init__()
-        self.layers = nn.ModuleList()
-        self.n_decoders = n_decoders
-        for i in range(self.n_decoders):
-            out_channels = in_channels // 2
-            self.layers.append(
-                ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
-            )
-            in_channels = out_channels
-
-    def forward(self, x, concat_tensors):
-        for i in range(self.n_decoders):
-            x = self.layers[i](x, concat_tensors[-1 - i])
-        return x
-
-
-class DeepUnet(nn.Module):
-    def __init__(
-        self,
-        kernel_size,
-        n_blocks,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(DeepUnet, self).__init__()
-        self.encoder = Encoder(
-            in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
-        )
-        self.intermediate = Intermediate(
-            self.encoder.out_channel // 2,
-            self.encoder.out_channel,
-            inter_layers,
-            n_blocks,
-        )
-        self.decoder = Decoder(
-            self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
-        )
-
-    def forward(self, x):
-        x, concat_tensors = self.encoder(x)
-        x = self.intermediate(x)
-        x = self.decoder(x, concat_tensors)
-        return x
-
-
-class E2E(nn.Module):
-    def __init__(
-        self,
-        n_blocks,
-        n_gru,
-        kernel_size,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(E2E, self).__init__()
-        self.unet = DeepUnet(
-            kernel_size,
-            n_blocks,
-            en_de_layers,
-            inter_layers,
-            in_channels,
-            en_out_channels,
-        )
-        self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
-        if n_gru:
-            self.fc = nn.Sequential(
-                BiGRU(3 * 128, 256, n_gru),
-                nn.Linear(512, 360),
-                nn.Dropout(0.25),
-                nn.Sigmoid(),
-            )
-        else:
-            self.fc = nn.Sequential(
-                nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
-            )
-
-    def forward(self, mel):
-        mel = mel.transpose(-1, -2).unsqueeze(1)
-        x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
-        x = self.fc(x)
-        return x
-
-
-from librosa.filters import mel
-
-
-class MelSpectrogram(torch.nn.Module):
-    def __init__(
-        self,
-        is_half,
-        n_mel_channels,
-        sampling_rate,
-        win_length,
-        hop_length,
-        n_fft=None,
-        mel_fmin=0,
-        mel_fmax=None,
-        clamp=1e-5,
-    ):
-        super().__init__()
-        n_fft = win_length if n_fft is None else n_fft
-        self.hann_window = {}
-        mel_basis = mel(
-            sr=sampling_rate,
-            n_fft=n_fft,
-            n_mels=n_mel_channels,
-            fmin=mel_fmin,
-            fmax=mel_fmax,
-            htk=True,
-        )
-        mel_basis = torch.from_numpy(mel_basis).float()
-        self.register_buffer("mel_basis", mel_basis)
-        self.n_fft = win_length if n_fft is None else n_fft
-        self.hop_length = hop_length
-        self.win_length = win_length
-        self.sampling_rate = sampling_rate
-        self.n_mel_channels = n_mel_channels
-        self.clamp = clamp
-        self.is_half = is_half
-
-    def forward(self, audio, keyshift=0, speed=1, center=True):
-        factor = 2 ** (keyshift / 12)
-        n_fft_new = int(np.round(self.n_fft * factor))
-        win_length_new = int(np.round(self.win_length * factor))
-        hop_length_new = int(np.round(self.hop_length * speed))
-        keyshift_key = str(keyshift) + "_" + str(audio.device)
-        if keyshift_key not in self.hann_window:
-            self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
-                audio.device
-            )
-        fft = torch.stft(
-            audio,
-            n_fft=n_fft_new,
-            hop_length=hop_length_new,
-            win_length=win_length_new,
-            window=self.hann_window[keyshift_key],
-            center=center,
-            return_complex=True,
-        )
-        magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
-        if keyshift != 0:
-            size = self.n_fft // 2 + 1
-            resize = magnitude.size(1)
-            if resize < size:
-                magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
-            magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
-        mel_output = torch.matmul(self.mel_basis, magnitude)
-        if self.is_half == True:
-            mel_output = mel_output.half()
-        log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
-        return log_mel_spec
-
-
-class RMVPE:
-    def __init__(self, model_path, is_half, device=None):
-        self.resample_kernel = {}
-        model = E2E(4, 1, (2, 2))
-        ckpt = torch.load(model_path, map_location="cpu")
-        model.load_state_dict(ckpt)
-        model.eval()
-        if is_half == True:
-            model = model.half()
-        self.model = model
-        self.resample_kernel = {}
-        self.is_half = is_half
-        if device is None:
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = device
-        self.mel_extractor = MelSpectrogram(
-            is_half, 128, 16000, 1024, 160, None, 30, 8000
-        ).to(device)
-        self.model = self.model.to(device)
-        cents_mapping = 20 * np.arange(360) + 1997.3794084376191
-        self.cents_mapping = np.pad(cents_mapping, (4, 4))  # 368
-
-    def mel2hidden(self, mel):
-        with torch.no_grad():
-            n_frames = mel.shape[-1]
-            mel = F.pad(
-                mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
-            )
-            hidden = self.model(mel)
-            return hidden[:, :n_frames]
-
-    def decode(self, hidden, thred=0.03):
-        cents_pred = self.to_local_average_cents(hidden, thred=thred)
-        f0 = 10 * (2 ** (cents_pred / 1200))
-        f0[f0 == 10] = 0
-        # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
-        return f0
-
-    def infer_from_audio(self, audio, thred=0.03):
-        audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
-        # torch.cuda.synchronize()
-        # t0=ttime()
-        mel = self.mel_extractor(audio, center=True)
-        # torch.cuda.synchronize()
-        # t1=ttime()
-        hidden = self.mel2hidden(mel)
-        # torch.cuda.synchronize()
-        # t2=ttime()
-        hidden = hidden.squeeze(0).cpu().numpy()
-        if self.is_half == True:
-            hidden = hidden.astype("float32")
-        f0 = self.decode(hidden, thred=thred)
-        # torch.cuda.synchronize()
-        # t3=ttime()
-        # print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
-        return f0
-
-    def to_local_average_cents(self, salience, thred=0.05):
-        # t0 = ttime()
-        center = np.argmax(salience, axis=1)  # 帧长#index
-        salience = np.pad(salience, ((0, 0), (4, 4)))  # 帧长,368
-        # t1 = ttime()
-        center += 4
-        todo_salience = []
-        todo_cents_mapping = []
-        starts = center - 4
-        ends = center + 5
-        for idx in range(salience.shape[0]):
-            todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
-            todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
-        # t2 = ttime()
-        todo_salience = np.array(todo_salience)  # 帧长，9
-        todo_cents_mapping = np.array(todo_cents_mapping)  # 帧长，9
-        product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
-        weight_sum = np.sum(todo_salience, 1)  # 帧长
-        devided = product_sum / weight_sum  # 帧长
-        # t3 = ttime()
-        maxx = np.max(salience, axis=1)  # 帧长
-        devided[maxx <= thred] = 0
-        # t4 = ttime()
-        # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
-        return devided
-
-
-# if __name__ == '__main__':
-#     audio, sampling_rate = sf.read("卢本伟语录~1.wav")
-#     if len(audio.shape) > 1:
-#         audio = librosa.to_mono(audio.transpose(1, 0))
-#     audio_bak = audio.copy()
-#     if sampling_rate != 16000:
-#         audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-#     model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
-#     thred = 0.03  # 0.01
-#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
-#     rmvpe = RMVPE(model_path,is_half=False, device=device)
-#     t0=ttime()
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     t1=ttime()
-#     print(f0.shape,t1-t0)
diff --git a/spaces/Fengbinbin/gpt-academic/colorful.py b/spaces/Fengbinbin/gpt-academic/colorful.py
deleted file mode 100644
index d90972bb30a8f8fb932abbc34232e474df4d5205..0000000000000000000000000000000000000000
--- a/spaces/Fengbinbin/gpt-academic/colorful.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import platform
-from sys import stdout
-
-if platform.system()=="Linux":
-    pass
-else: 
-    from colorama import init
-    init()
-
-# Do you like the elegance of Chinese characters?
-def print红(*kw,**kargs):
-    print("\033[0;31m",*kw,"\033[0m",**kargs)
-def print绿(*kw,**kargs):
-    print("\033[0;32m",*kw,"\033[0m",**kargs)
-def print黄(*kw,**kargs):
-    print("\033[0;33m",*kw,"\033[0m",**kargs)
-def print蓝(*kw,**kargs):
-    print("\033[0;34m",*kw,"\033[0m",**kargs)
-def print紫(*kw,**kargs):
-    print("\033[0;35m",*kw,"\033[0m",**kargs)
-def print靛(*kw,**kargs):
-    print("\033[0;36m",*kw,"\033[0m",**kargs)
-
-def print亮红(*kw,**kargs):
-    print("\033[1;31m",*kw,"\033[0m",**kargs)
-def print亮绿(*kw,**kargs):
-    print("\033[1;32m",*kw,"\033[0m",**kargs)
-def print亮黄(*kw,**kargs):
-    print("\033[1;33m",*kw,"\033[0m",**kargs)
-def print亮蓝(*kw,**kargs):
-    print("\033[1;34m",*kw,"\033[0m",**kargs)
-def print亮紫(*kw,**kargs):
-    print("\033[1;35m",*kw,"\033[0m",**kargs)
-def print亮靛(*kw,**kargs):
-    print("\033[1;36m",*kw,"\033[0m",**kargs)
-
-
-
-def print亮红(*kw,**kargs):
-    print("\033[1;31m",*kw,"\033[0m",**kargs)
-def print亮绿(*kw,**kargs):
-    print("\033[1;32m",*kw,"\033[0m",**kargs)
-def print亮黄(*kw,**kargs):
-    print("\033[1;33m",*kw,"\033[0m",**kargs)
-def print亮蓝(*kw,**kargs):
-    print("\033[1;34m",*kw,"\033[0m",**kargs)
-def print亮紫(*kw,**kargs):
-    print("\033[1;35m",*kw,"\033[0m",**kargs)
-def print亮靛(*kw,**kargs):
-    print("\033[1;36m",*kw,"\033[0m",**kargs)
-    
-print_red = print红
-print_green = print绿
-print_yellow = print黄
-print_blue = print蓝
-print_purple = print紫
-print_indigo = print靛
-
-print_bold_red = print亮红
-print_bold_green = print亮绿
-print_bold_yellow = print亮黄
-print_bold_blue = print亮蓝
-print_bold_purple = print亮紫
-print_bold_indigo = print亮靛
-    
-if not stdout.isatty():
-    # redirection, avoid a fucked up log file
-    print红 = print
-    print绿 = print
-    print黄 = print
-    print蓝 = print
-    print紫 = print
-    print靛 = print
-    print亮红 = print
-    print亮绿 = print
-    print亮黄 = print
-    print亮蓝 = print
-    print亮紫 = print
-    print亮靛 = print
-    print_red = print
-    print_green = print
-    print_yellow = print
-    print_blue = print
-    print_purple = print
-    print_indigo = print
-    print_bold_red = print
-    print_bold_green = print
-    print_bold_yellow = print
-    print_bold_blue = print
-    print_bold_purple = print
-    print_bold_indigo = print
\ No newline at end of file
diff --git a/spaces/GXSA/bingo/src/components/settings.tsx b/spaces/GXSA/bingo/src/components/settings.tsx
deleted file mode 100644
index 80b8a2d3b252b875f5b6f7dfc2f6e3ad9cdfb22a..0000000000000000000000000000000000000000
--- a/spaces/GXSA/bingo/src/components/settings.tsx
+++ /dev/null
@@ -1,157 +0,0 @@
-import { useEffect, useState } from 'react'
-import { useAtom } from 'jotai'
-import { Switch } from '@headlessui/react'
-import { toast } from 'react-hot-toast'
-import { hashAtom, voiceAtom } from '@/state'
-import {
-  Dialog,
-  DialogContent,
-  DialogDescription,
-  DialogFooter,
-  DialogHeader,
-  DialogTitle
-} from '@/components/ui/dialog'
-import { Button } from './ui/button'
-import { Input } from './ui/input'
-import { ChunkKeys, parseCookies, extraCurlFromCookie, encodeHeadersToCookie, getCookie, setCookie } from '@/lib/utils'
-import { ExternalLink } from './external-link'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-
-export function Settings() {
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-  const [loc, setLoc] = useAtom(hashAtom)
-  const [curlValue, setCurlValue] = useState(extraCurlFromCookie(parseCookies(document.cookie, ChunkKeys)))
-  const [imageOnly, setImageOnly] = useState(getCookie('IMAGE_ONLY') !== '0')
-  const [enableTTS, setEnableTTS] = useAtom(voiceAtom)
-
-  useEffect(() => {
-    if (isCopied) {
-      toast.success('复制成功')
-    }
-  }, [isCopied])
-
-  if (loc === 'settings') {
-    return (
-      <Dialog open onOpenChange={() => setLoc('')} modal>
-        <DialogContent>
-          <DialogHeader>
-            <DialogTitle>设置你的用户信息</DialogTitle>
-            <DialogDescription>
-              请使用 Edge 浏览器
-              <ExternalLink
-                href="https://www.bing.com/turing/captcha/challenge"
-              >
-                打开并登录 Bing
-              </ExternalLink>
-              ，然后再打开
-              <ExternalLink href="https://www.bing.com/turing/captcha/challenge">Challenge 接口</ExternalLink>
-              右键 》检查。打开开发者工具，在网络里面找到 Create 接口 》右键复制》复制为 cURL(bash)，粘贴到此处，然后保存。
-              <div className="h-2" />
-              图文示例：
-              <ExternalLink href="https://github.com/weaigc/bingo#如何获取%20BING_HEADER">如何获取 BING_HEADER</ExternalLink>
-            </DialogDescription>
-          </DialogHeader>
-          <div className="flex gap-4">
-
-          </div>
-          <Input
-            value={curlValue}
-            placeholder="在此填写用户信息，格式: curl 'https://www.bing.com/turing/captcha/challenge' ..."
-            onChange={e => setCurlValue(e.target.value)}
-          />
-          <div className="flex gap-2">
-            身份信息仅用于画图（推荐）
-            <Switch
-              checked={imageOnly}
-              className={`${imageOnly ? 'bg-blue-600' : 'bg-gray-200'} relative inline-flex h-6 w-11 items-center rounded-full`}
-              onChange={(checked: boolean) => setImageOnly(checked)}
-            >
-              <span
-                className={`${imageOnly ? 'translate-x-6' : 'translate-x-1'} inline-block h-4 w-4 transform rounded-full bg-white transition`}
-              />
-            </Switch>
-          </div>
-
-          <Button variant="ghost" className="bg-[#F5F5F5] hover:bg-[#F2F2F2]" onClick={() => copyToClipboard(btoa(curlValue))}>
-            转成 BING_HEADER 并复制
-          </Button>
-
-          <DialogFooter className="items-center">
-            <Button
-              variant="secondary"
-              className="bg-[#c7f3ff] hover:bg-[#fdc7ff]"
-              onClick={() => {
-                let headerValue = curlValue
-                if (headerValue) {
-                  try {
-                    headerValue = atob(headerValue)
-                  } catch (e) { }
-                  if (!/^\s*curl ['"]https:\/\/(www|cn)\.bing\.com\/turing\/captcha\/challenge['"]/.test(headerValue)) {
-                    toast.error('格式不正确')
-                    return
-                  }
-                  const maxAge = 86400 * 30
-                  encodeHeadersToCookie(headerValue).forEach(cookie => document.cookie = `${cookie}; Max-Age=${maxAge}; Path=/; SameSite=None; Secure`)
-                } else {
-                  [...ChunkKeys, 'BING_COOKIE', 'BING_UA', 'BING_IP'].forEach(key => setCookie(key, ''))
-                }
-                setCookie('IMAGE_ONLY', RegExp.$1 === 'cn' || imageOnly ? '1' : '0')
-
-                toast.success('保存成功')
-                setLoc('')
-                setTimeout(() => {
-                  location.href = './'
-                }, 2000)
-              }}
-            >
-              保存
-            </Button>
-          </DialogFooter>
-        </DialogContent>
-      </Dialog>
-    )
-  } else if (loc === 'voice') {
-    return (
-      <Dialog open onOpenChange={() => setLoc('')} modal>
-        <DialogContent>
-          <DialogHeader>
-            <DialogTitle>语音设置</DialogTitle>
-            <DialogDescription>
-              目前仅支持 PC 端 Edge 及 Chrome 浏览器
-            </DialogDescription>
-          </DialogHeader>
-
-          <div className="flex gap-2">
-            启用语音回答
-            <Switch
-              checked={enableTTS}
-              className={`${enableTTS ? 'bg-blue-600' : 'bg-gray-200'} relative inline-flex h-6 w-11 items-center rounded-full`}
-              onChange={(checked: boolean) => setEnableTTS(checked)}
-            >
-              <span
-                className={`${enableTTS ? 'translate-x-6' : 'translate-x-1'} inline-block h-4 w-4 transform rounded-full bg-white transition`}
-              />
-            </Switch>
-          </div>
-
-          <DialogFooter className="items-center">
-            <Button
-              variant="secondary"
-              onClick={() => {
-                toast.success('保存成功')
-                setLoc('')
-                setTimeout(() => {
-                  location.href = './'
-                }, 2000)
-              }}
-            >
-              保存
-            </Button>
-          </DialogFooter>
-        </DialogContent>
-      </Dialog>
-    )
-  }
-  return null
-}
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/models/rn50_bert_unet.py b/spaces/Gen-Sim/Gen-Sim/cliport/models/rn50_bert_unet.py
deleted file mode 100644
index 2072f8a582875dac27b129ce29288acd18b45d9e..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/models/rn50_bert_unet.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-
-import cliport.utils.utils as utils
-from cliport.models.resnet import IdentityBlock, ConvBlock
-from cliport.models.core.unet import Up
-from cliport.models.rn50_bert_lingunet import RN50BertLingUNet
-
-
-class RN50BertUNet(RN50BertLingUNet):
-    """ ImageNet RN50 & Bert with U-Net skip connections without language"""
-
-    def __init__(self, input_shape, output_dim, cfg, device, preprocess):
-        super().__init__(input_shape, output_dim, cfg, device, preprocess)
-
-    def _build_decoder(self):
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(self.input_dim, 1024, kernel_size=3, stride=1, padding=1, bias=False),
-            nn.ReLU(True)
-        )
-
-        self.up1 = Up(2048, 1024 // self.up_factor, self.bilinear)
-
-        self.up2 = Up(1024, 512 // self.up_factor, self.bilinear)
-
-        self.up3 = Up(512, 256 // self.up_factor, self.bilinear)
-
-        self.layer1 = nn.Sequential(
-            ConvBlock(128, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            IdentityBlock(64, [64, 64, 64], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            nn.UpsamplingBilinear2d(scale_factor=2),
-        )
-
-        self.layer2 = nn.Sequential(
-            ConvBlock(64, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            IdentityBlock(32, [32, 32, 32], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            nn.UpsamplingBilinear2d(scale_factor=2),
-        )
-
-        self.layer3 = nn.Sequential(
-            ConvBlock(32, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            IdentityBlock(16, [16, 16, 16], kernel_size=3, stride=1, batchnorm=self.batchnorm),
-            nn.UpsamplingBilinear2d(scale_factor=2),
-        )
-
-        self.conv2 = nn.Sequential(
-            nn.Conv2d(16, self.output_dim, kernel_size=1)
-        )
-
-    def forward(self, x):
-        x = self.preprocess(x, dist='clip')
-
-        in_type = x.dtype
-        in_shape = x.shape
-        x = x[:,:3]  # select RGB
-        x, im = self.encode_image(x)
-        x = x.to(in_type)
-
-        x = self.conv1(x)
-
-        x = self.up1(x, im[-2])
-        x = self.up2(x, im[-3])
-        x = self.up3(x, im[-4])
-
-        for layer in [self.layer1, self.layer2, self.layer3, self.conv2]:
-            x = layer(x)
-
-        x = F.interpolate(x, size=(in_shape[-2], in_shape[-1]), mode='bilinear')
-        return x
\ No newline at end of file
diff --git a/spaces/Gen-Sim/Gen-Sim/scripts/metascripts/train3_gptmixcliport3.sh b/spaces/Gen-Sim/Gen-Sim/scripts/metascripts/train3_gptmixcliport3.sh
deleted file mode 100644
index 02a1d14cfa3f5c1a04d511f66c8bc14c2e864a1d..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/scripts/metascripts/train3_gptmixcliport3.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/bin/bash
-#SBATCH -c 10
-#SBATCH -n 1
-#SBATCH -o logs/%j.out
-#SBATCH --exclusive
-STEPS=${1-'50000'}
-
-
-sh scripts/traintest_scripts/train_test_multi_task_goal.sh data \
-		"[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq,align-pair-colored-blocks-along-line,vertical-insertion-blocks,stack-blocks-in-container]" \
-		"[put-block-in-bowl,align-box-corner,stack-block-pyramid-seq]" \
-		gpt3_mixcliport3_task  $STEPS
diff --git a/spaces/GeorgeOrville/bingo/src/lib/bots/bing/tts.ts b/spaces/GeorgeOrville/bingo/src/lib/bots/bing/tts.ts
deleted file mode 100644
index cd10b7d1d7581bf9cf46ff6755fcca550c558c9b..0000000000000000000000000000000000000000
--- a/spaces/GeorgeOrville/bingo/src/lib/bots/bing/tts.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-import { sleep } from './utils'
-
-const synth = window.speechSynthesis
-
-export class TTS {
-  currentText = ''
-  speakText = ''
-  private controller = new AbortController()
-  speaking = false
-  get isSpeaking() {
-    return this.speaking
-  }
-  finished = false
-  constructor() {}
-  abort = () => {
-    this.controller.abort()
-  }
-
-  reset = () => {
-    this.speaking = false
-    this.finished = true
-    this.currentText = ''
-    this.speakText = ''
-    this.abort()
-  }
-
-  speak = (text: string) => {
-    if (!synth || text?.trim()?.length < 2) {
-      return
-    }
-    this.currentText = text.replace(/[^\u4e00-\u9fa5_a-zA-Z0-9，。？,：；\.,:]+/g, '')
-    this.finished = false
-    this.loop()
-  }
-
-  private async doSpeek() {
-    return new Promise((resolve) => {
-      const endIndex = this.finished ? this.currentText.length :
-        Math.max(
-          this.currentText.lastIndexOf('。'),
-          this.currentText.lastIndexOf('；'),
-          this.currentText.lastIndexOf('、'),
-          this.currentText.lastIndexOf('？'),
-          this.currentText.lastIndexOf('\n')
-        )
-      const startIndex = this.speakText.length ? Math.max(0, this.currentText.lastIndexOf(this.speakText) + this.speakText.length) : 0
-
-      if (startIndex >= endIndex) {
-        return resolve(true)
-      }
-      const text = this.currentText.slice(startIndex, endIndex)
-      this.speakText = text
-      const utterThis = new SpeechSynthesisUtterance(text)
-      this.controller.signal.onabort = () => {
-        synth.cancel()
-        this.finished = true
-        resolve(false)
-      }
-
-      utterThis.onend = function (event) {
-        resolve(true)
-      }
-
-      utterThis.onerror = function (event) {
-        resolve(false)
-      }
-
-      const voice = synth.getVoices().find(v => v.name.includes('Microsoft Yunxi Online')) ?? null
-      utterThis.voice = voice
-      synth.speak(utterThis)
-    })
-  }
-
-  private async loop() {
-    if (this.speaking) return
-    this.speaking = true
-    while(!this.finished) {
-      await Promise.all([sleep(1000), this.doSpeek()])
-    }
-    this.speaking = false
-  }
-}
diff --git a/spaces/GipAdonimus/Real-Time-Voice-Cloning/synthesizer/synthesize.py b/spaces/GipAdonimus/Real-Time-Voice-Cloning/synthesizer/synthesize.py
deleted file mode 100644
index ffc7dc2678e85006b9f66d910fcae3e307c521a8..0000000000000000000000000000000000000000
--- a/spaces/GipAdonimus/Real-Time-Voice-Cloning/synthesizer/synthesize.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import torch
-from torch.utils.data import DataLoader
-from synthesizer.hparams import hparams_debug_string
-from synthesizer.synthesizer_dataset import SynthesizerDataset, collate_synthesizer
-from synthesizer.models.tacotron import Tacotron
-from synthesizer.utils.text import text_to_sequence
-from synthesizer.utils.symbols import symbols
-import numpy as np
-from pathlib import Path
-from tqdm import tqdm
-import platform
-
-def run_synthesis(in_dir, out_dir, model_dir, hparams):
-    # This generates ground truth-aligned mels for vocoder training
-    synth_dir = Path(out_dir).joinpath("mels_gta")
-    synth_dir.mkdir(exist_ok=True)
-    print(hparams_debug_string())
-
-    # Check for GPU
-    if torch.cuda.is_available():
-        device = torch.device("cuda")
-        if hparams.synthesis_batch_size % torch.cuda.device_count() != 0:
-            raise ValueError("`hparams.synthesis_batch_size` must be evenly divisible by n_gpus!")
-    else:
-        device = torch.device("cpu")
-    print("Synthesizer using device:", device)
-
-    # Instantiate Tacotron model
-    model = Tacotron(embed_dims=hparams.tts_embed_dims,
-                     num_chars=len(symbols),
-                     encoder_dims=hparams.tts_encoder_dims,
-                     decoder_dims=hparams.tts_decoder_dims,
-                     n_mels=hparams.num_mels,
-                     fft_bins=hparams.num_mels,
-                     postnet_dims=hparams.tts_postnet_dims,
-                     encoder_K=hparams.tts_encoder_K,
-                     lstm_dims=hparams.tts_lstm_dims,
-                     postnet_K=hparams.tts_postnet_K,
-                     num_highways=hparams.tts_num_highways,
-                     dropout=0., # Use zero dropout for gta mels
-                     stop_threshold=hparams.tts_stop_threshold,
-                     speaker_embedding_size=hparams.speaker_embedding_size).to(device)
-
-    # Load the weights
-    model_dir = Path(model_dir)
-    model_fpath = model_dir.joinpath(model_dir.stem).with_suffix(".pt")
-    print("\nLoading weights at %s" % model_fpath)
-    model.load(model_fpath)
-    print("Tacotron weights loaded from step %d" % model.step)
-
-    # Synthesize using same reduction factor as the model is currently trained
-    r = np.int32(model.r)
-
-    # Set model to eval mode (disable gradient and zoneout)
-    model.eval()
-
-    # Initialize the dataset
-    in_dir = Path(in_dir)
-    metadata_fpath = in_dir.joinpath("train.txt")
-    mel_dir = in_dir.joinpath("mels")
-    embed_dir = in_dir.joinpath("embeds")
-
-    dataset = SynthesizerDataset(metadata_fpath, mel_dir, embed_dir, hparams)
-    data_loader = DataLoader(dataset,
-                             collate_fn=lambda batch: collate_synthesizer(batch, r, hparams),
-                             batch_size=hparams.synthesis_batch_size,
-                             num_workers=2 if platform.system() != "Windows" else 0,
-                             shuffle=False,
-                             pin_memory=True)
-
-    # Generate GTA mels
-    meta_out_fpath = Path(out_dir).joinpath("synthesized.txt")
-    with open(meta_out_fpath, "w") as file:
-        for i, (texts, mels, embeds, idx) in tqdm(enumerate(data_loader), total=len(data_loader)):
-            texts = texts.to(device)
-            mels = mels.to(device)
-            embeds = embeds.to(device)
-
-            # Parallelize model onto GPUS using workaround due to python bug
-            if device.type == "cuda" and torch.cuda.device_count() > 1:
-                _, mels_out, _ = data_parallel_workaround(model, texts, mels, embeds)
-            else:
-                _, mels_out, _, _ = model(texts, mels, embeds)
-
-            for j, k in enumerate(idx):
-                # Note: outputs mel-spectrogram files and target ones have same names, just different folders
-                mel_filename = Path(synth_dir).joinpath(dataset.metadata[k][1])
-                mel_out = mels_out[j].detach().cpu().numpy().T
-
-                # Use the length of the ground truth mel to remove padding from the generated mels
-                mel_out = mel_out[:int(dataset.metadata[k][4])]
-
-                # Write the spectrogram to disk
-                np.save(mel_filename, mel_out, allow_pickle=False)
-
-                # Write metadata into the synthesized file
-                file.write("|".join(dataset.metadata[k]))
diff --git a/spaces/GipAdonimus/Real-Time-Voice-Cloning/synthesizer/utils/text.py b/spaces/GipAdonimus/Real-Time-Voice-Cloning/synthesizer/utils/text.py
deleted file mode 100644
index 29372174aec95cd2eac1ea40096fcc148f532b07..0000000000000000000000000000000000000000
--- a/spaces/GipAdonimus/Real-Time-Voice-Cloning/synthesizer/utils/text.py
+++ /dev/null
@@ -1,74 +0,0 @@
-from .symbols import symbols
-from . import cleaners
-import re
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-# Regular expression matching text enclosed in curly braces:
-_curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)")
-
-
-def text_to_sequence(text, cleaner_names):
-  """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-
-    The text can optionally have ARPAbet sequences enclosed in curly braces embedded
-    in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
-
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-
-    Returns:
-      List of integers corresponding to the symbols in the text
-  """
-  sequence = []
-
-  # Check for curly braces and treat their contents as ARPAbet:
-  while len(text):
-    m = _curly_re.match(text)
-    if not m:
-      sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
-      break
-    sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
-    sequence += _arpabet_to_sequence(m.group(2))
-    text = m.group(3)
-
-  # Append EOS token
-  sequence.append(_symbol_to_id["~"])
-  return sequence
-
-
-def sequence_to_text(sequence):
-  """Converts a sequence of IDs back to a string"""
-  result = ""
-  for symbol_id in sequence:
-    if symbol_id in _id_to_symbol:
-      s = _id_to_symbol[symbol_id]
-      # Enclose ARPAbet back in curly braces:
-      if len(s) > 1 and s[0] == "@":
-        s = "{%s}" % s[1:]
-      result += s
-  return result.replace("}{", " ")
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception("Unknown cleaner: %s" % name)
-    text = cleaner(text)
-  return text
-
-
-def _symbols_to_sequence(symbols):
-  return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
-
-
-def _arpabet_to_sequence(text):
-  return _symbols_to_sequence(["@" + s for s in text.split()])
-
-
-def _should_keep_symbol(s):
-  return s in _symbol_to_id and s not in ("_", "~")
diff --git a/spaces/Gladiaio/Audio-Transcription/README.md b/spaces/Gladiaio/Audio-Transcription/README.md
deleted file mode 100644
index e410c927f945bb51a3b560742b103772f8fd902a..0000000000000000000000000000000000000000
--- a/spaces/Gladiaio/Audio-Transcription/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: High Speed Whisper Large-v2 Audio Transcription
-emoji: 🚀
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: true
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/EmojiGAN/torch_utils/ops/conv2d_gradfix.py b/spaces/Gradio-Blocks/EmojiGAN/torch_utils/ops/conv2d_gradfix.py
deleted file mode 100644
index e95e10d0b1d0315a63a76446fd4c5c293c8bbc6d..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/EmojiGAN/torch_utils/ops/conv2d_gradfix.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.conv2d` that supports
-arbitrarily high order gradients with zero performance penalty."""
-
-import warnings
-import contextlib
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False                     # Enable the custom op by setting this to true.
-weight_gradients_disabled = False   # Forcefully disable computation of gradients with respect to the weights.
-
-@contextlib.contextmanager
-def no_weight_gradients():
-    global weight_gradients_disabled
-    old = weight_gradients_disabled
-    weight_gradients_disabled = True
-    yield
-    weight_gradients_disabled = old
-
-#----------------------------------------------------------------------------
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
-    return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-
-def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias)
-    return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op(input):
-    assert isinstance(input, torch.Tensor)
-    if (not enabled) or (not torch.backends.cudnn.enabled):
-        return False
-    if input.device.type != 'cuda':
-        return False
-    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
-        return True
-    warnings.warn(f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().')
-    return False
-
-def _tuple_of_ints(xs, ndim):
-    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
-    assert len(xs) == ndim
-    assert all(isinstance(x, int) for x in xs)
-    return xs
-
-#----------------------------------------------------------------------------
-
-_conv2d_gradfix_cache = dict()
-
-def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
-    # Parse arguments.
-    ndim = 2
-    weight_shape = tuple(weight_shape)
-    stride = _tuple_of_ints(stride, ndim)
-    padding = _tuple_of_ints(padding, ndim)
-    output_padding = _tuple_of_ints(output_padding, ndim)
-    dilation = _tuple_of_ints(dilation, ndim)
-
-    # Lookup from cache.
-    key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
-    if key in _conv2d_gradfix_cache:
-        return _conv2d_gradfix_cache[key]
-
-    # Validate arguments.
-    assert groups >= 1
-    assert len(weight_shape) == ndim + 2
-    assert all(stride[i] >= 1 for i in range(ndim))
-    assert all(padding[i] >= 0 for i in range(ndim))
-    assert all(dilation[i] >= 0 for i in range(ndim))
-    if not transpose:
-        assert all(output_padding[i] == 0 for i in range(ndim))
-    else: # transpose
-        assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim))
-
-    # Helpers.
-    common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups)
-    def calc_output_padding(input_shape, output_shape):
-        if transpose:
-            return [0, 0]
-        return [
-            input_shape[i + 2]
-            - (output_shape[i + 2] - 1) * stride[i]
-            - (1 - 2 * padding[i])
-            - dilation[i] * (weight_shape[i + 2] - 1)
-            for i in range(ndim)
-        ]
-
-    # Forward & backward.
-    class Conv2d(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, input, weight, bias):
-            assert weight.shape == weight_shape
-            if not transpose:
-                output = torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
-            else: # transpose
-                output = torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
-            ctx.save_for_backward(input, weight)
-            return output
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            input, weight = ctx.saved_tensors
-            grad_input = None
-            grad_weight = None
-            grad_bias = None
-
-            if ctx.needs_input_grad[0]:
-                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
-                grad_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, weight, None)
-                assert grad_input.shape == input.shape
-
-            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
-                grad_weight = Conv2dGradWeight.apply(grad_output, input)
-                assert grad_weight.shape == weight_shape
-
-            if ctx.needs_input_grad[2]:
-                grad_bias = grad_output.sum([0, 2, 3])
-
-            return grad_input, grad_weight, grad_bias
-
-    # Gradient with respect to the weights.
-    class Conv2dGradWeight(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, grad_output, input):
-            op = torch._C._jit_get_operation('aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight')
-            flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
-            grad_weight = op(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
-            assert grad_weight.shape == weight_shape
-            ctx.save_for_backward(grad_output, input)
-            return grad_weight
-
-        @staticmethod
-        def backward(ctx, grad2_grad_weight):
-            grad_output, input = ctx.saved_tensors
-            grad2_grad_output = None
-            grad2_input = None
-
-            if ctx.needs_input_grad[0]:
-                grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None)
-                assert grad2_grad_output.shape == grad_output.shape
-
-            if ctx.needs_input_grad[1]:
-                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
-                grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, grad2_grad_weight, None)
-                assert grad2_input.shape == input.shape
-
-            return grad2_grad_output, grad2_input
-
-    _conv2d_gradfix_cache[key] = Conv2d
-    return Conv2d
-
-#----------------------------------------------------------------------------
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py
deleted file mode 100644
index e7265bcdbef2a7ab5e8ba6b3fe13f02cb718b40a..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py
+++ /dev/null
@@ -1,10 +0,0 @@
-_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
-model = dict(
-    bbox_head=dict(
-        with_deform=True,
-        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
-optimizer_config = dict(
-    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/paa/paa_r50_fpn_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/paa/paa_r50_fpn_2x_coco.py
deleted file mode 100644
index 663d2c0ded52086663360a8a3dce89702584fc1f..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/paa/paa_r50_fpn_2x_coco.py
+++ /dev/null
@@ -1,3 +0,0 @@
-_base_ = './paa_r50_fpn_1x_coco.py'
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
deleted file mode 100644
index 1915ab1b4f013efacaedcdae08e93176cfe3bd55..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnest101',
-    backbone=dict(stem_channels=128, depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py
deleted file mode 100644
index 8e85e56bd66aa61c3d43547acb7c2d6d91f14133..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py
+++ /dev/null
@@ -1,5 +0,0 @@
-_base_ = '../fcn/fcn_r101-d8_512x1024_80k_cityscapes.py'
-# fp16 settings
-optimizer_config = dict(type='Fp16OptimizerHook', loss_scale=512.)
-# fp16 placeholder
-fp16 = dict()
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
deleted file mode 100644
index f57c9166b67a18fd74f474754b3baec6584b17cf..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
-    '../_base_/models/pspnet_unet_s5-d16.py', '../_base_/datasets/hrf.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]
-model = dict(test_cfg=dict(crop_size=(256, 256), stride=(170, 170)))
-evaluation = dict(metric='mDice')
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/roformer/configuration_roformer.py b/spaces/HaloMaster/chinesesummary/fengshen/models/roformer/configuration_roformer.py
deleted file mode 100644
index 4818b31bd215b11d4ca952437869319fc25ae5b5..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/models/roformer/configuration_roformer.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. All rights reserved.
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-#     http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" RoFormer model configuration """
-
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-RoFormer_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    # See all RoFormer models at https://huggingface.co/models?filter=bert
-}
-
-
-class RoFormerConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a :class:`~transformers.RoFormerModel`. It is
-    used to instantiate a RoFormer model according to the specified arguments, defining the model architecture.
-    Instantiating a configuration with the defaults will yield a similar configuration to that of the RoFormer
-    `megatron-bert-uncased-345m <https://huggingface.co/nvidia/megatron-bert-uncased-345m>`__ architecture.
-
-    Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
-    outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
-
-
-    Args:
-        vocab_size (:obj:`int`, `optional`, defaults to 29056):
-            Vocabulary size of the RoFormer model. Defines the number of different tokens that can be represented
-            by the :obj:`inputs_ids` passed when calling :class:`~transformers.RoFormerModel`.
-        hidden_size (:obj:`int`, `optional`, defaults to 1024):
-            Dimensionality of the encoder layers and the pooler layer.
-        num_hidden_layers (:obj:`int`, `optional`, defaults to 24):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (:obj:`int`, `optional`, defaults to 16):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (:obj:`int`, `optional`, defaults to 4096):
-            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
-        hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`):
-            The non-linear activation function (function or string) in the encoder and pooler. If string,
-            :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported.
-        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
-            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
-            The dropout ratio for the attention probabilities.
-        max_position_embeddings (:obj:`int`, `optional`, defaults to 512):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        type_vocab_size (:obj:`int`, `optional`, defaults to 2):
-            The vocabulary size of the :obj:`token_type_ids` passed when calling
-            :class:`~transformers.RoFormerModel`.
-        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
-            The epsilon used by the layer normalization layers.
-        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            If True, use gradient checkpointing to save memory at the expense of slower backward pass.
-        position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`):
-            Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`,
-            :obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on
-            :obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.)
-            <https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"relative_key_query"`, please refer to
-            `Method 4` in `Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)
-            <https://arxiv.org/abs/2009.13658>`__.
-        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if ``config.is_decoder=True``.
-
-    Examples::
-
-        >>> from transformers import RoFormerModel, RoFormerConfig
-
-        >>> # Initializing a RoFormer bert-base-uncased style configuration
-        >>> configuration = RoFormerConfig()
-
-        >>> # Initializing a model from the bert-base-uncased style configuration
-        >>> model = RoFormerModel(configuration)
-
-        >>> # Accessing the model configuration
-        >>> configuration = model.config
-    """
-    model_type = "roformer"
-
-    def __init__(
-        self,
-        vocab_size=29056,
-        hidden_size=1024,
-        num_hidden_layers=24,
-        num_attention_heads=16,
-        intermediate_size=4096,
-        hidden_act="gelu",
-        hidden_dropout_prob=0.1,
-        attention_probs_dropout_prob=0.1,
-        max_position_embeddings=512,
-        type_vocab_size=2,
-        initializer_range=0.02,
-        layer_norm_eps=1e-12,
-        pad_token_id=0,
-        gradient_checkpointing=False,
-        position_embedding_type="absolute",
-        use_cache=True,
-        **kwargs
-    ):
-        super().__init__(pad_token_id=pad_token_id, **kwargs)
-
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.hidden_act = hidden_act
-        self.intermediate_size = intermediate_size
-        self.hidden_dropout_prob = hidden_dropout_prob
-        self.attention_probs_dropout_prob = attention_probs_dropout_prob
-        self.max_position_embeddings = max_position_embeddings
-        self.type_vocab_size = type_vocab_size
-        self.initializer_range = initializer_range
-        self.layer_norm_eps = layer_norm_eps
-        self.gradient_checkpointing = gradient_checkpointing
-        self.position_embedding_type = position_embedding_type
-        self.use_cache = use_cache
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/models/vggtransformer.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/models/vggtransformer.py
deleted file mode 100644
index bca0ae59a8cbe2b7c337e395021c883a61d101ee..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/models/vggtransformer.py
+++ /dev/null
@@ -1,1020 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import math
-from collections.abc import Iterable
-
-import torch
-import torch.nn as nn
-from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask
-from fairseq import utils
-from fairseq.models import (
-    FairseqEncoder,
-    FairseqEncoderDecoderModel,
-    FairseqEncoderModel,
-    FairseqIncrementalDecoder,
-    register_model,
-    register_model_architecture,
-)
-from fairseq.modules import (
-    LinearizedConvolution,
-    TransformerDecoderLayer,
-    TransformerEncoderLayer,
-    VGGBlock,
-)
-
-
-@register_model("asr_vggtransformer")
-class VGGTransformerModel(FairseqEncoderDecoderModel):
-    """
-    Transformers with convolutional context for ASR
-    https://arxiv.org/abs/1904.11660
-    """
-
-    def __init__(self, encoder, decoder):
-        super().__init__(encoder, decoder)
-
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        parser.add_argument(
-            "--input-feat-per-channel",
-            type=int,
-            metavar="N",
-            help="encoder input dimension per input channel",
-        )
-        parser.add_argument(
-            "--vggblock-enc-config",
-            type=str,
-            metavar="EXPR",
-            help="""
-    an array of tuples each containing the configuration of one vggblock:
-    [(out_channels,
-      conv_kernel_size,
-      pooling_kernel_size,
-      num_conv_layers,
-      use_layer_norm), ...])
-            """,
-        )
-        parser.add_argument(
-            "--transformer-enc-config",
-            type=str,
-            metavar="EXPR",
-            help=""""
-    a tuple containing the configuration of the encoder transformer layers
-    configurations:
-    [(input_dim,
-      num_heads,
-      ffn_dim,
-      normalize_before,
-      dropout,
-      attention_dropout,
-      relu_dropout), ...]')
-            """,
-        )
-        parser.add_argument(
-            "--enc-output-dim",
-            type=int,
-            metavar="N",
-            help="""
-    encoder output dimension, can be None. If specified, projecting the
-    transformer output to the specified dimension""",
-        )
-        parser.add_argument(
-            "--in-channels",
-            type=int,
-            metavar="N",
-            help="number of encoder input channels",
-        )
-        parser.add_argument(
-            "--tgt-embed-dim",
-            type=int,
-            metavar="N",
-            help="embedding dimension of the decoder target tokens",
-        )
-        parser.add_argument(
-            "--transformer-dec-config",
-            type=str,
-            metavar="EXPR",
-            help="""
-    a tuple containing the configuration of the decoder transformer layers
-    configurations:
-    [(input_dim,
-      num_heads,
-      ffn_dim,
-      normalize_before,
-      dropout,
-      attention_dropout,
-      relu_dropout), ...]
-            """,
-        )
-        parser.add_argument(
-            "--conv-dec-config",
-            type=str,
-            metavar="EXPR",
-            help="""
-    an array of tuples for the decoder 1-D convolution config
-        [(out_channels, conv_kernel_size, use_layer_norm), ...]""",
-        )
-
-    @classmethod
-    def build_encoder(cls, args, task):
-        return VGGTransformerEncoder(
-            input_feat_per_channel=args.input_feat_per_channel,
-            vggblock_config=eval(args.vggblock_enc_config),
-            transformer_config=eval(args.transformer_enc_config),
-            encoder_output_dim=args.enc_output_dim,
-            in_channels=args.in_channels,
-        )
-
-    @classmethod
-    def build_decoder(cls, args, task):
-        return TransformerDecoder(
-            dictionary=task.target_dictionary,
-            embed_dim=args.tgt_embed_dim,
-            transformer_config=eval(args.transformer_dec_config),
-            conv_config=eval(args.conv_dec_config),
-            encoder_output_dim=args.enc_output_dim,
-        )
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-        # make sure that all args are properly defaulted
-        # (in case there are any new ones)
-        base_architecture(args)
-
-        encoder = cls.build_encoder(args, task)
-        decoder = cls.build_decoder(args, task)
-        return cls(encoder, decoder)
-
-    def get_normalized_probs(self, net_output, log_probs, sample=None):
-        # net_output['encoder_out'] is a (B, T, D) tensor
-        lprobs = super().get_normalized_probs(net_output, log_probs, sample)
-        lprobs.batch_first = True
-        return lprobs
-
-
-DEFAULT_ENC_VGGBLOCK_CONFIG = ((32, 3, 2, 2, False),) * 2
-DEFAULT_ENC_TRANSFORMER_CONFIG = ((256, 4, 1024, True, 0.2, 0.2, 0.2),) * 2
-# 256: embedding dimension
-# 4: number of heads
-# 1024: FFN
-# True: apply layerNorm before (dropout + resiaul) instead of after
-# 0.2 (dropout): dropout after MultiheadAttention and second FC
-# 0.2 (attention_dropout): dropout in MultiheadAttention
-# 0.2 (relu_dropout): dropout after ReLu
-DEFAULT_DEC_TRANSFORMER_CONFIG = ((256, 2, 1024, True, 0.2, 0.2, 0.2),) * 2
-DEFAULT_DEC_CONV_CONFIG = ((256, 3, True),) * 2
-
-
-# TODO: repace transformer encoder config from one liner
-# to explicit args to get rid of this transformation
-def prepare_transformer_encoder_params(
-    input_dim,
-    num_heads,
-    ffn_dim,
-    normalize_before,
-    dropout,
-    attention_dropout,
-    relu_dropout,
-):
-    args = argparse.Namespace()
-    args.encoder_embed_dim = input_dim
-    args.encoder_attention_heads = num_heads
-    args.attention_dropout = attention_dropout
-    args.dropout = dropout
-    args.activation_dropout = relu_dropout
-    args.encoder_normalize_before = normalize_before
-    args.encoder_ffn_embed_dim = ffn_dim
-    return args
-
-
-def prepare_transformer_decoder_params(
-    input_dim,
-    num_heads,
-    ffn_dim,
-    normalize_before,
-    dropout,
-    attention_dropout,
-    relu_dropout,
-):
-    args = argparse.Namespace()
-    args.encoder_embed_dim = None
-    args.decoder_embed_dim = input_dim
-    args.decoder_attention_heads = num_heads
-    args.attention_dropout = attention_dropout
-    args.dropout = dropout
-    args.activation_dropout = relu_dropout
-    args.decoder_normalize_before = normalize_before
-    args.decoder_ffn_embed_dim = ffn_dim
-    return args
-
-
-class VGGTransformerEncoder(FairseqEncoder):
-    """VGG + Transformer encoder"""
-
-    def __init__(
-        self,
-        input_feat_per_channel,
-        vggblock_config=DEFAULT_ENC_VGGBLOCK_CONFIG,
-        transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG,
-        encoder_output_dim=512,
-        in_channels=1,
-        transformer_context=None,
-        transformer_sampling=None,
-    ):
-        """constructor for VGGTransformerEncoder
-
-        Args:
-            - input_feat_per_channel: feature dim (not including stacked,
-              just base feature)
-            - in_channel: # input channels (e.g., if stack 8 feature vector
-                together, this is 8)
-            - vggblock_config: configuration of vggblock, see comments on
-                DEFAULT_ENC_VGGBLOCK_CONFIG
-            - transformer_config: configuration of transformer layer, see comments
-                on DEFAULT_ENC_TRANSFORMER_CONFIG
-            - encoder_output_dim: final transformer output embedding dimension
-            - transformer_context: (left, right) if set, self-attention will be focused
-              on (t-left, t+right)
-            - transformer_sampling: an iterable of int, must match with
-              len(transformer_config), transformer_sampling[i] indicates sampling
-              factor for i-th transformer layer, after multihead att and feedfoward
-              part
-        """
-        super().__init__(None)
-
-        self.num_vggblocks = 0
-        if vggblock_config is not None:
-            if not isinstance(vggblock_config, Iterable):
-                raise ValueError("vggblock_config is not iterable")
-            self.num_vggblocks = len(vggblock_config)
-
-        self.conv_layers = nn.ModuleList()
-        self.in_channels = in_channels
-        self.input_dim = input_feat_per_channel
-        self.pooling_kernel_sizes = []
-
-        if vggblock_config is not None:
-            for _, config in enumerate(vggblock_config):
-                (
-                    out_channels,
-                    conv_kernel_size,
-                    pooling_kernel_size,
-                    num_conv_layers,
-                    layer_norm,
-                ) = config
-                self.conv_layers.append(
-                    VGGBlock(
-                        in_channels,
-                        out_channels,
-                        conv_kernel_size,
-                        pooling_kernel_size,
-                        num_conv_layers,
-                        input_dim=input_feat_per_channel,
-                        layer_norm=layer_norm,
-                    )
-                )
-                self.pooling_kernel_sizes.append(pooling_kernel_size)
-                in_channels = out_channels
-                input_feat_per_channel = self.conv_layers[-1].output_dim
-
-        transformer_input_dim = self.infer_conv_output_dim(
-            self.in_channels, self.input_dim
-        )
-        # transformer_input_dim is the output dimension of VGG part
-
-        self.validate_transformer_config(transformer_config)
-        self.transformer_context = self.parse_transformer_context(transformer_context)
-        self.transformer_sampling = self.parse_transformer_sampling(
-            transformer_sampling, len(transformer_config)
-        )
-
-        self.transformer_layers = nn.ModuleList()
-
-        if transformer_input_dim != transformer_config[0][0]:
-            self.transformer_layers.append(
-                Linear(transformer_input_dim, transformer_config[0][0])
-            )
-        self.transformer_layers.append(
-            TransformerEncoderLayer(
-                prepare_transformer_encoder_params(*transformer_config[0])
-            )
-        )
-
-        for i in range(1, len(transformer_config)):
-            if transformer_config[i - 1][0] != transformer_config[i][0]:
-                self.transformer_layers.append(
-                    Linear(transformer_config[i - 1][0], transformer_config[i][0])
-                )
-            self.transformer_layers.append(
-                TransformerEncoderLayer(
-                    prepare_transformer_encoder_params(*transformer_config[i])
-                )
-            )
-
-        self.encoder_output_dim = encoder_output_dim
-        self.transformer_layers.extend(
-            [
-                Linear(transformer_config[-1][0], encoder_output_dim),
-                LayerNorm(encoder_output_dim),
-            ]
-        )
-
-    def forward(self, src_tokens, src_lengths, **kwargs):
-        """
-        src_tokens: padded tensor (B, T, C * feat)
-        src_lengths: tensor of original lengths of input utterances (B,)
-        """
-        bsz, max_seq_len, _ = src_tokens.size()
-        x = src_tokens.view(bsz, max_seq_len, self.in_channels, self.input_dim)
-        x = x.transpose(1, 2).contiguous()
-        # (B, C, T, feat)
-
-        for layer_idx in range(len(self.conv_layers)):
-            x = self.conv_layers[layer_idx](x)
-
-        bsz, _, output_seq_len, _ = x.size()
-
-        # (B, C, T, feat) -> (B, T, C, feat) -> (T, B, C, feat) -> (T, B, C * feat)
-        x = x.transpose(1, 2).transpose(0, 1)
-        x = x.contiguous().view(output_seq_len, bsz, -1)
-
-        input_lengths = src_lengths.clone()
-        for s in self.pooling_kernel_sizes:
-            input_lengths = (input_lengths.float() / s).ceil().long()
-
-        encoder_padding_mask, _ = lengths_to_encoder_padding_mask(
-            input_lengths, batch_first=True
-        )
-        if not encoder_padding_mask.any():
-            encoder_padding_mask = None
-
-        subsampling_factor = int(max_seq_len * 1.0 / output_seq_len + 0.5)
-        attn_mask = self.lengths_to_attn_mask(input_lengths, subsampling_factor)
-
-        transformer_layer_idx = 0
-
-        for layer_idx in range(len(self.transformer_layers)):
-
-            if isinstance(self.transformer_layers[layer_idx], TransformerEncoderLayer):
-                x = self.transformer_layers[layer_idx](
-                    x, encoder_padding_mask, attn_mask
-                )
-
-                if self.transformer_sampling[transformer_layer_idx] != 1:
-                    sampling_factor = self.transformer_sampling[transformer_layer_idx]
-                    x, encoder_padding_mask, attn_mask = self.slice(
-                        x, encoder_padding_mask, attn_mask, sampling_factor
-                    )
-
-                transformer_layer_idx += 1
-
-            else:
-                x = self.transformer_layers[layer_idx](x)
-
-        # encoder_padding_maks is a (T x B) tensor, its [t, b] elements indicate
-        # whether encoder_output[t, b] is valid or not (valid=0, invalid=1)
-
-        return {
-            "encoder_out": x,  # (T, B, C)
-            "encoder_padding_mask": encoder_padding_mask.t()
-            if encoder_padding_mask is not None
-            else None,
-            # (B, T) --> (T, B)
-        }
-
-    def infer_conv_output_dim(self, in_channels, input_dim):
-        sample_seq_len = 200
-        sample_bsz = 10
-        x = torch.randn(sample_bsz, in_channels, sample_seq_len, input_dim)
-        for i, _ in enumerate(self.conv_layers):
-            x = self.conv_layers[i](x)
-        x = x.transpose(1, 2)
-        mb, seq = x.size()[:2]
-        return x.contiguous().view(mb, seq, -1).size(-1)
-
-    def validate_transformer_config(self, transformer_config):
-        for config in transformer_config:
-            input_dim, num_heads = config[:2]
-            if input_dim % num_heads != 0:
-                msg = (
-                    "ERROR in transformer config {}: ".format(config)
-                    + "input dimension {} ".format(input_dim)
-                    + "not dividable by number of heads {}".format(num_heads)
-                )
-                raise ValueError(msg)
-
-    def parse_transformer_context(self, transformer_context):
-        """
-        transformer_context can be the following:
-        -   None; indicates no context is used, i.e.,
-            transformer can access full context
-        -   a tuple/list of two int; indicates left and right context,
-            any number <0 indicates infinite context
-                * e.g., (5, 6) indicates that for query at x_t, transformer can
-                access [t-5, t+6] (inclusive)
-                * e.g., (-1, 6) indicates that for query at x_t, transformer can
-                access [0, t+6] (inclusive)
-        """
-        if transformer_context is None:
-            return None
-
-        if not isinstance(transformer_context, Iterable):
-            raise ValueError("transformer context must be Iterable if it is not None")
-
-        if len(transformer_context) != 2:
-            raise ValueError("transformer context must have length 2")
-
-        left_context = transformer_context[0]
-        if left_context < 0:
-            left_context = None
-
-        right_context = transformer_context[1]
-        if right_context < 0:
-            right_context = None
-
-        if left_context is None and right_context is None:
-            return None
-
-        return (left_context, right_context)
-
-    def parse_transformer_sampling(self, transformer_sampling, num_layers):
-        """
-        parsing transformer sampling configuration
-
-        Args:
-            - transformer_sampling, accepted input:
-                * None, indicating no sampling
-                * an Iterable with int (>0) as element
-            - num_layers, expected number of transformer layers, must match with
-              the length of transformer_sampling if it is not None
-
-        Returns:
-            - A tuple with length num_layers
-        """
-        if transformer_sampling is None:
-            return (1,) * num_layers
-
-        if not isinstance(transformer_sampling, Iterable):
-            raise ValueError(
-                "transformer_sampling must be an iterable if it is not None"
-            )
-
-        if len(transformer_sampling) != num_layers:
-            raise ValueError(
-                "transformer_sampling {} does not match with the number "
-                "of layers {}".format(transformer_sampling, num_layers)
-            )
-
-        for layer, value in enumerate(transformer_sampling):
-            if not isinstance(value, int):
-                raise ValueError("Invalid value in transformer_sampling: ")
-            if value < 1:
-                raise ValueError(
-                    "{} layer's subsampling is {}.".format(layer, value)
-                    + " This is not allowed! "
-                )
-        return transformer_sampling
-
-    def slice(self, embedding, padding_mask, attn_mask, sampling_factor):
-        """
-        embedding is a (T, B, D) tensor
-        padding_mask is a (B, T) tensor or None
-        attn_mask is a (T, T) tensor or None
-        """
-        embedding = embedding[::sampling_factor, :, :]
-        if padding_mask is not None:
-            padding_mask = padding_mask[:, ::sampling_factor]
-        if attn_mask is not None:
-            attn_mask = attn_mask[::sampling_factor, ::sampling_factor]
-
-        return embedding, padding_mask, attn_mask
-
-    def lengths_to_attn_mask(self, input_lengths, subsampling_factor=1):
-        """
-        create attention mask according to sequence lengths and transformer
-        context
-
-        Args:
-            - input_lengths: (B, )-shape Int/Long tensor; input_lengths[b] is
-              the length of b-th sequence
-            - subsampling_factor: int
-                * Note that the left_context and right_context is specified in
-                  the input frame-level while input to transformer may already
-                  go through subsampling (e.g., the use of striding in vggblock)
-                  we use subsampling_factor to scale the left/right context
-
-        Return:
-            - a (T, T) binary tensor or None, where T is max(input_lengths)
-                * if self.transformer_context is None, None
-                * if left_context is None,
-                    * attn_mask[t, t + right_context + 1:] = 1
-                    * others = 0
-                * if right_context is None,
-                    * attn_mask[t, 0:t - left_context] = 1
-                    * others = 0
-                * elsif
-                    * attn_mask[t, t - left_context: t + right_context + 1] = 0
-                    * others = 1
-        """
-        if self.transformer_context is None:
-            return None
-
-        maxT = torch.max(input_lengths).item()
-        attn_mask = torch.zeros(maxT, maxT)
-
-        left_context = self.transformer_context[0]
-        right_context = self.transformer_context[1]
-        if left_context is not None:
-            left_context = math.ceil(self.transformer_context[0] / subsampling_factor)
-        if right_context is not None:
-            right_context = math.ceil(self.transformer_context[1] / subsampling_factor)
-
-        for t in range(maxT):
-            if left_context is not None:
-                st = 0
-                en = max(st, t - left_context)
-                attn_mask[t, st:en] = 1
-            if right_context is not None:
-                st = t + right_context + 1
-                st = min(st, maxT - 1)
-                attn_mask[t, st:] = 1
-
-        return attn_mask.to(input_lengths.device)
-
-    def reorder_encoder_out(self, encoder_out, new_order):
-        encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select(
-            1, new_order
-        )
-        if encoder_out["encoder_padding_mask"] is not None:
-            encoder_out["encoder_padding_mask"] = encoder_out[
-                "encoder_padding_mask"
-            ].index_select(1, new_order)
-        return encoder_out
-
-
-class TransformerDecoder(FairseqIncrementalDecoder):
-    """
-    Transformer decoder consisting of *args.decoder_layers* layers. Each layer
-    is a :class:`TransformerDecoderLayer`.
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-        dictionary (~fairseq.data.Dictionary): decoding dictionary
-        embed_tokens (torch.nn.Embedding): output embedding
-        no_encoder_attn (bool, optional): whether to attend to encoder outputs.
-            Default: ``False``
-        left_pad (bool, optional): whether the input is left-padded. Default:
-            ``False``
-    """
-
-    def __init__(
-        self,
-        dictionary,
-        embed_dim=512,
-        transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG,
-        conv_config=DEFAULT_DEC_CONV_CONFIG,
-        encoder_output_dim=512,
-    ):
-
-        super().__init__(dictionary)
-        vocab_size = len(dictionary)
-        self.padding_idx = dictionary.pad()
-        self.embed_tokens = Embedding(vocab_size, embed_dim, self.padding_idx)
-
-        self.conv_layers = nn.ModuleList()
-        for i in range(len(conv_config)):
-            out_channels, kernel_size, layer_norm = conv_config[i]
-            if i == 0:
-                conv_layer = LinearizedConv1d(
-                    embed_dim, out_channels, kernel_size, padding=kernel_size - 1
-                )
-            else:
-                conv_layer = LinearizedConv1d(
-                    conv_config[i - 1][0],
-                    out_channels,
-                    kernel_size,
-                    padding=kernel_size - 1,
-                )
-            self.conv_layers.append(conv_layer)
-            if layer_norm:
-                self.conv_layers.append(nn.LayerNorm(out_channels))
-            self.conv_layers.append(nn.ReLU())
-
-        self.layers = nn.ModuleList()
-        if conv_config[-1][0] != transformer_config[0][0]:
-            self.layers.append(Linear(conv_config[-1][0], transformer_config[0][0]))
-        self.layers.append(
-            TransformerDecoderLayer(
-                prepare_transformer_decoder_params(*transformer_config[0])
-            )
-        )
-
-        for i in range(1, len(transformer_config)):
-            if transformer_config[i - 1][0] != transformer_config[i][0]:
-                self.layers.append(
-                    Linear(transformer_config[i - 1][0], transformer_config[i][0])
-                )
-            self.layers.append(
-                TransformerDecoderLayer(
-                    prepare_transformer_decoder_params(*transformer_config[i])
-                )
-            )
-        self.fc_out = Linear(transformer_config[-1][0], vocab_size)
-
-    def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None):
-        """
-        Args:
-            prev_output_tokens (LongTensor): previous decoder outputs of shape
-                `(batch, tgt_len)`, for input feeding/teacher forcing
-            encoder_out (Tensor, optional): output from the encoder, used for
-                encoder-side attention
-            incremental_state (dict): dictionary used for storing state during
-                :ref:`Incremental decoding`
-        Returns:
-            tuple:
-                - the last decoder layer's output of shape `(batch, tgt_len,
-                  vocab)`
-                - the last decoder layer's attention weights of shape `(batch,
-                  tgt_len, src_len)`
-        """
-        target_padding_mask = (
-            (prev_output_tokens == self.padding_idx).to(prev_output_tokens.device)
-            if incremental_state is None
-            else None
-        )
-
-        if incremental_state is not None:
-            prev_output_tokens = prev_output_tokens[:, -1:]
-
-        # embed tokens
-        x = self.embed_tokens(prev_output_tokens)
-
-        # B x T x C -> T x B x C
-        x = self._transpose_if_training(x, incremental_state)
-
-        for layer in self.conv_layers:
-            if isinstance(layer, LinearizedConvolution):
-                x = layer(x, incremental_state)
-            else:
-                x = layer(x)
-
-        # B x T x C -> T x B x C
-        x = self._transpose_if_inference(x, incremental_state)
-
-        # decoder layers
-        for layer in self.layers:
-            if isinstance(layer, TransformerDecoderLayer):
-                x, *_ = layer(
-                    x,
-                    (encoder_out["encoder_out"] if encoder_out is not None else None),
-                    (
-                        encoder_out["encoder_padding_mask"].t()
-                        if encoder_out["encoder_padding_mask"] is not None
-                        else None
-                    ),
-                    incremental_state,
-                    self_attn_mask=(
-                        self.buffered_future_mask(x)
-                        if incremental_state is None
-                        else None
-                    ),
-                    self_attn_padding_mask=(
-                        target_padding_mask if incremental_state is None else None
-                    ),
-                )
-            else:
-                x = layer(x)
-
-        # T x B x C -> B x T x C
-        x = x.transpose(0, 1)
-
-        x = self.fc_out(x)
-
-        return x, None
-
-    def buffered_future_mask(self, tensor):
-        dim = tensor.size(0)
-        if (
-            not hasattr(self, "_future_mask")
-            or self._future_mask is None
-            or self._future_mask.device != tensor.device
-        ):
-            self._future_mask = torch.triu(
-                utils.fill_with_neg_inf(tensor.new(dim, dim)), 1
-            )
-        if self._future_mask.size(0) < dim:
-            self._future_mask = torch.triu(
-                utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1
-            )
-        return self._future_mask[:dim, :dim]
-
-    def _transpose_if_training(self, x, incremental_state):
-        if incremental_state is None:
-            x = x.transpose(0, 1)
-        return x
-
-    def _transpose_if_inference(self, x, incremental_state):
-        if incremental_state:
-            x = x.transpose(0, 1)
-        return x
-
-
-@register_model("asr_vggtransformer_encoder")
-class VGGTransformerEncoderModel(FairseqEncoderModel):
-    def __init__(self, encoder):
-        super().__init__(encoder)
-
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        parser.add_argument(
-            "--input-feat-per-channel",
-            type=int,
-            metavar="N",
-            help="encoder input dimension per input channel",
-        )
-        parser.add_argument(
-            "--vggblock-enc-config",
-            type=str,
-            metavar="EXPR",
-            help="""
-    an array of tuples each containing the configuration of one vggblock
-    [(out_channels, conv_kernel_size, pooling_kernel_size,num_conv_layers), ...]
-    """,
-        )
-        parser.add_argument(
-            "--transformer-enc-config",
-            type=str,
-            metavar="EXPR",
-            help="""
-    a tuple containing the configuration of the Transformer layers
-    configurations:
-    [(input_dim,
-      num_heads,
-      ffn_dim,
-      normalize_before,
-      dropout,
-      attention_dropout,
-      relu_dropout), ]""",
-        )
-        parser.add_argument(
-            "--enc-output-dim",
-            type=int,
-            metavar="N",
-            help="encoder output dimension, projecting the LSTM output",
-        )
-        parser.add_argument(
-            "--in-channels",
-            type=int,
-            metavar="N",
-            help="number of encoder input channels",
-        )
-        parser.add_argument(
-            "--transformer-context",
-            type=str,
-            metavar="EXPR",
-            help="""
-    either None or a tuple of two ints, indicating left/right context a
-    transformer can have access to""",
-        )
-        parser.add_argument(
-            "--transformer-sampling",
-            type=str,
-            metavar="EXPR",
-            help="""
-    either None or a tuple of ints, indicating sampling factor in each layer""",
-        )
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-        base_architecture_enconly(args)
-        encoder = VGGTransformerEncoderOnly(
-            vocab_size=len(task.target_dictionary),
-            input_feat_per_channel=args.input_feat_per_channel,
-            vggblock_config=eval(args.vggblock_enc_config),
-            transformer_config=eval(args.transformer_enc_config),
-            encoder_output_dim=args.enc_output_dim,
-            in_channels=args.in_channels,
-            transformer_context=eval(args.transformer_context),
-            transformer_sampling=eval(args.transformer_sampling),
-        )
-        return cls(encoder)
-
-    def get_normalized_probs(self, net_output, log_probs, sample=None):
-        # net_output['encoder_out'] is a (T, B, D) tensor
-        lprobs = super().get_normalized_probs(net_output, log_probs, sample)
-        # lprobs is a (T, B, D) tensor
-        # we need to transoose to get (B, T, D) tensor
-        lprobs = lprobs.transpose(0, 1).contiguous()
-        lprobs.batch_first = True
-        return lprobs
-
-
-class VGGTransformerEncoderOnly(VGGTransformerEncoder):
-    def __init__(
-        self,
-        vocab_size,
-        input_feat_per_channel,
-        vggblock_config=DEFAULT_ENC_VGGBLOCK_CONFIG,
-        transformer_config=DEFAULT_ENC_TRANSFORMER_CONFIG,
-        encoder_output_dim=512,
-        in_channels=1,
-        transformer_context=None,
-        transformer_sampling=None,
-    ):
-        super().__init__(
-            input_feat_per_channel=input_feat_per_channel,
-            vggblock_config=vggblock_config,
-            transformer_config=transformer_config,
-            encoder_output_dim=encoder_output_dim,
-            in_channels=in_channels,
-            transformer_context=transformer_context,
-            transformer_sampling=transformer_sampling,
-        )
-        self.fc_out = Linear(self.encoder_output_dim, vocab_size)
-
-    def forward(self, src_tokens, src_lengths, **kwargs):
-        """
-        src_tokens: padded tensor (B, T, C * feat)
-        src_lengths: tensor of original lengths of input utterances (B,)
-        """
-
-        enc_out = super().forward(src_tokens, src_lengths)
-        x = self.fc_out(enc_out["encoder_out"])
-        # x = F.log_softmax(x, dim=-1)
-        # Note: no need this line, because model.get_normalized_prob will call
-        # log_softmax
-        return {
-            "encoder_out": x,  # (T, B, C)
-            "encoder_padding_mask": enc_out["encoder_padding_mask"],  # (T, B)
-        }
-
-    def max_positions(self):
-        """Maximum input length supported by the encoder."""
-        return (1e6, 1e6)  # an arbitrary large number
-
-
-def Embedding(num_embeddings, embedding_dim, padding_idx):
-    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
-    # nn.init.uniform_(m.weight, -0.1, 0.1)
-    # nn.init.constant_(m.weight[padding_idx], 0)
-    return m
-
-
-def Linear(in_features, out_features, bias=True, dropout=0):
-    """Linear layer (input: N x T x C)"""
-    m = nn.Linear(in_features, out_features, bias=bias)
-    # m.weight.data.uniform_(-0.1, 0.1)
-    # if bias:
-    #     m.bias.data.uniform_(-0.1, 0.1)
-    return m
-
-
-def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs):
-    """Weight-normalized Conv1d layer optimized for decoding"""
-    m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)
-    std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
-    nn.init.normal_(m.weight, mean=0, std=std)
-    nn.init.constant_(m.bias, 0)
-    return nn.utils.weight_norm(m, dim=2)
-
-
-def LayerNorm(embedding_dim):
-    m = nn.LayerNorm(embedding_dim)
-    return m
-
-
-# seq2seq models
-def base_architecture(args):
-    args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 40)
-    args.vggblock_enc_config = getattr(
-        args, "vggblock_enc_config", DEFAULT_ENC_VGGBLOCK_CONFIG
-    )
-    args.transformer_enc_config = getattr(
-        args, "transformer_enc_config", DEFAULT_ENC_TRANSFORMER_CONFIG
-    )
-    args.enc_output_dim = getattr(args, "enc_output_dim", 512)
-    args.in_channels = getattr(args, "in_channels", 1)
-    args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 128)
-    args.transformer_dec_config = getattr(
-        args, "transformer_dec_config", DEFAULT_ENC_TRANSFORMER_CONFIG
-    )
-    args.conv_dec_config = getattr(args, "conv_dec_config", DEFAULT_DEC_CONV_CONFIG)
-    args.transformer_context = getattr(args, "transformer_context", "None")
-
-
-@register_model_architecture("asr_vggtransformer", "vggtransformer_1")
-def vggtransformer_1(args):
-    args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
-    args.vggblock_enc_config = getattr(
-        args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]"
-    )
-    args.transformer_enc_config = getattr(
-        args,
-        "transformer_enc_config",
-        "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 14",
-    )
-    args.enc_output_dim = getattr(args, "enc_output_dim", 1024)
-    args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 128)
-    args.conv_dec_config = getattr(args, "conv_dec_config", "((256, 3, True),) * 4")
-    args.transformer_dec_config = getattr(
-        args,
-        "transformer_dec_config",
-        "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 4",
-    )
-
-
-@register_model_architecture("asr_vggtransformer", "vggtransformer_2")
-def vggtransformer_2(args):
-    args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
-    args.vggblock_enc_config = getattr(
-        args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]"
-    )
-    args.transformer_enc_config = getattr(
-        args,
-        "transformer_enc_config",
-        "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 16",
-    )
-    args.enc_output_dim = getattr(args, "enc_output_dim", 1024)
-    args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 512)
-    args.conv_dec_config = getattr(args, "conv_dec_config", "((256, 3, True),) * 4")
-    args.transformer_dec_config = getattr(
-        args,
-        "transformer_dec_config",
-        "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 6",
-    )
-
-
-@register_model_architecture("asr_vggtransformer", "vggtransformer_base")
-def vggtransformer_base(args):
-    args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
-    args.vggblock_enc_config = getattr(
-        args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]"
-    )
-    args.transformer_enc_config = getattr(
-        args, "transformer_enc_config", "((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 12"
-    )
-
-    args.enc_output_dim = getattr(args, "enc_output_dim", 512)
-    args.tgt_embed_dim = getattr(args, "tgt_embed_dim", 512)
-    args.conv_dec_config = getattr(args, "conv_dec_config", "((256, 3, True),) * 4")
-    args.transformer_dec_config = getattr(
-        args, "transformer_dec_config", "((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 6"
-    )
-    # Size estimations:
-    # Encoder:
-    #   - vggblock param: 64*1*3*3 + 64*64*3*3 + 128*64*3*3  + 128*128*3 = 258K
-    #   Transformer:
-    #   - input dimension adapter: 2560 x 512 -> 1.31M
-    #   - transformer_layers (x12) --> 37.74M
-    #       * MultiheadAttention: 512*512*3 (in_proj) + 512*512 (out_proj) = 1.048M
-    #       * FFN weight: 512*2048*2 = 2.097M
-    #   - output dimension adapter: 512 x 512 -> 0.26 M
-    # Decoder:
-    #   - LinearizedConv1d: 512 * 256 * 3 + 256 * 256 * 3 * 3
-    #   - transformer_layer: (x6) --> 25.16M
-    #        * MultiheadAttention (self-attention): 512*512*3 + 512*512 = 1.048M
-    #        * MultiheadAttention (encoder-attention): 512*512*3 + 512*512 = 1.048M
-    #        * FFN: 512*2048*2 = 2.097M
-    # Final FC:
-    #   - FC: 512*5000 = 256K (assuming vocab size 5K)
-    # In total:
-    #       ~65 M
-
-
-# CTC models
-def base_architecture_enconly(args):
-    args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 40)
-    args.vggblock_enc_config = getattr(
-        args, "vggblock_enc_config", "[(32, 3, 2, 2, True)] * 2"
-    )
-    args.transformer_enc_config = getattr(
-        args, "transformer_enc_config", "((256, 4, 1024, True, 0.2, 0.2, 0.2),) * 2"
-    )
-    args.enc_output_dim = getattr(args, "enc_output_dim", 512)
-    args.in_channels = getattr(args, "in_channels", 1)
-    args.transformer_context = getattr(args, "transformer_context", "None")
-    args.transformer_sampling = getattr(args, "transformer_sampling", "None")
-
-
-@register_model_architecture("asr_vggtransformer_encoder", "vggtransformer_enc_1")
-def vggtransformer_enc_1(args):
-    # vggtransformer_1 is the same as vggtransformer_enc_big, except the number
-    # of layers is increased to 16
-    # keep it here for backward compatiablity purpose
-    args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
-    args.vggblock_enc_config = getattr(
-        args, "vggblock_enc_config", "[(64, 3, 2, 2, True), (128, 3, 2, 2, True)]"
-    )
-    args.transformer_enc_config = getattr(
-        args,
-        "transformer_enc_config",
-        "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 16",
-    )
-    args.enc_output_dim = getattr(args, "enc_output_dim", 1024)
diff --git a/spaces/Harveenchadha/Vakyansh-Odia-TTS/app.py b/spaces/Harveenchadha/Vakyansh-Odia-TTS/app.py
deleted file mode 100644
index fed354d61ebd06b1167d07c56cd2572fd99cbc6e..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Odia-TTS/app.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import os
-os.system('wget -q https://storage.googleapis.com/vakyansh-open-models/tts/odia/or-IN/female_voice_0/glow.zip && unzip -q glow.zip -d ttsv/checkpoints/female')
-os.system('wget -q https://storage.googleapis.com/vakyansh-open-models/tts/odia/or-IN/ma_fe_hifi/hifi.zip && unzip -q hifi.zip -d ttsv/checkpoints/female')
-os.system('rm glow.zip && rm hifi.zip')
-os.system('wget -q https://storage.googleapis.com/vakyansh-open-models/tts/odia/or-IN/male_voice_1/glow.zip && unzip -q glow.zip -d ttsv/checkpoints/male')
-os.system('wget -q https://storage.googleapis.com/vakyansh-open-models/tts/odia/or-IN/ma_fe_hifi/hifi.zip && unzip -q hifi.zip -d ttsv/checkpoints/male')
-os.system('wget -q https://storage.googleapis.com/vakyansh-open-models/translit_models.zip -P ttsv/checkpoints/ && unzip -q ttsv/checkpoints/translit_models.zip -d ttsv/checkpoints/')
-
-
-for path, subdirs, files in os.walk('ttsv/checkpoints/'):
-    print(subdirs)
-    for name in files:
-        print(os.path.join(path, name))
-
-from ttsv.utils.inference.run_gradio import *
-from argparse import Namespace
-
-#os.system('python ttsv/utils/inference/run_gradio.py -a ttsv/checkpoints/glow/male -v ttsv/checkpoints/hifi/male -d cpu -L hi')
-
-
-args = {
-    'acoustic':'/home/user/app/ttsv/checkpoints/female/glow,/home/user/app/ttsv/checkpoints/male/glow',
-    'vocoder':'/home/user/app/ttsv/checkpoints/female/hifi,/home/user/app/ttsv/checkpoints/male/hifi',
-    'device':'cpu',
-    'lang':'or'
-}
-
-build_gradio(Namespace(**args))
\ No newline at end of file
diff --git a/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/transliterate/script_unifier.py b/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/transliterate/script_unifier.py
deleted file mode 100644
index 20f39339ffb3178ea17785aba09eb620d108f330..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/transliterate/script_unifier.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# 
-#  Copyright (c) 2013-present, Anoop Kunchukuttan
-#  All rights reserved.
-#  
-#  This source code is licensed under the MIT license found in the
-#  LICENSE file in the root directory of this source tree.
-# 
-
-#Program for normalization of text written in Unicode. This is mainly geared towards Indic scripts 
-#
-# @author Anoop Kunchukuttan 
-#
-
-import sys
-from indicnlp.normalize import indic_normalize
-from indicnlp.transliterate import unicode_transliterate
-from indicnlp import loader
-
-class AggressiveScriptUnifier():
-
-    def __init__(self,common_lang='hi',nasals_mode='to_nasal_consonants'):
-        self.common_lang=common_lang
-        self.nasals_mode=nasals_mode
-        self.do_normalize_chandras=True
-        self.do_normalize_vowel_ending=True
-        self.remove_nuktas=True
-        self.normalizer_map={}
-        self._init_normalizers()
-
-    def _init_normalizers(self):
-        normalizer_factory=indic_normalize.IndicNormalizerFactory()
-
-        ## for languages with common parameters
-        for lang in ['hi','mr','sa','kK','ne','sd','bn','gu','ta','te','kn']:
-            self.normalizer_map[lang]=normalizer_factory.get_normalizer(lang, nasals_mode=self.nasals_mode, 
-                    do_normalize_chandras=self.do_normalize_chandras, remove_nuktas=self.remove_nuktas,
-                    do_normalize_vowel_ending=self.do_normalize_vowel_ending)
-
-        ## for languages with language specific parameters
-        self.normalizer_map['pa']=normalizer_factory.get_normalizer('pa', nasals_mode=self.nasals_mode, 
-                    do_normalize_chandras=self.do_normalize_chandras, remove_nuktas=self.remove_nuktas,
-                    do_normalize_vowel_ending=self.do_normalize_vowel_ending,
-                    do_canonicalize_addak=True, do_canonicalize_tippi=True,
-                    do_replace_vowel_bases=True)
-        self.normalizer_map['or']=normalizer_factory.get_normalizer('or', nasals_mode=self.nasals_mode, 
-                    do_normalize_chandras=self.do_normalize_chandras, remove_nuktas=self.remove_nuktas,
-                    do_normalize_vowel_ending=self.do_normalize_vowel_ending,
-                    do_remap_wa=True)
-        self.normalizer_map['as']=normalizer_factory.get_normalizer('as', nasals_mode=self.nasals_mode,
-                    do_normalize_chandras=self.do_normalize_chandras, remove_nuktas=self.remove_nuktas,
-                    do_normalize_vowel_ending=self.do_normalize_vowel_ending,
-                    do_remap_assamese_chars=True)
-        self.normalizer_map['ml']=normalizer_factory.get_normalizer('ml', nasals_mode=self.nasals_mode,
-                    do_normalize_chandras=self.do_normalize_chandras, remove_nuktas=self.remove_nuktas,
-                    do_normalize_vowel_ending=self.do_normalize_vowel_ending,
-                    do_canonicalize_chillus=True, do_correct_geminated_T=True)
-
-    def transform(self,text,lang):
-        text=self.normalizer_map[lang].normalize(text)
-        text=unicode_transliterate.UnicodeIndicTransliterator.transliterate(text, lang, self.common_lang)
-        return text
-
-class BasicScriptUnifier():
-
-    def __init__(self,common_lang='hi',nasals_mode='do_nothing'):
-        self.common_lang=common_lang
-        self.nasals_mode=nasals_mode
-        self.normalizer_map={}
-        self._init_normalizers()
-
-    def _init_normalizers(self):
-        normalizer_factory=indic_normalize.IndicNormalizerFactory()
-
-        for lang in ['hi','mr','sa','kK','ne','sd','bn','gu','ta','te','kn','pa','or','as','ml']:
-            self.normalizer_map[lang]=normalizer_factory.get_normalizer(lang, nasals_mode=self.nasals_mode)    
-
-    def transform(self,text,lang):
-
-        if lang in self.normalizer_map:
-            text=self.normalizer_map[lang].normalize(text)
-
-        text=unicode_transliterate.UnicodeIndicTransliterator.transliterate(text, lang, self.common_lang)
-        return text
-
-class NaiveScriptUnifier():
-
-    def __init__(self,common_lang='hi'):
-        self.common_lang=common_lang
-
-    def transform(self,text,lang):
-
-        text=unicode_transliterate.UnicodeIndicTransliterator.transliterate(text, lang, self.common_lang)
-        return text
-
-if __name__ == '__main__': 
-
-    loader.load()
-
-    if len(sys.argv)<=4:
-        print("Usage: python script_unifier <command> <infile> <outfile> <language>")
-        sys.exit(1)
-
-    if sys.argv[1]=='aggressive':
-
-        language=sys.argv[4]
-
-        unifier=AggressiveScriptUnifier(nasals_mode='to_nasal_consonants')
-
-        with open(sys.argv[2],'r',encoding='utf-8') as ifile:
-            with open(sys.argv[3],'w',encoding='utf-8') as ofile:
-                for i, line in enumerate(ifile.readlines()):
-
-                    line=line.strip()
-                    transliterated_line=unifier.transform(line,language)
-                    ofile.write(transliterated_line+'\n')
-
-    elif sys.argv[1]=='moderate':
-
-        language=sys.argv[4]
-
-        unifier=AggressiveScriptUnifier(nasals_mode='do_nothing')
-
-        with open(sys.argv[2],'r',encoding='utf-8') as ifile:
-            with open(sys.argv[3],'w',encoding='utf-8') as ofile:
-                for i, line in enumerate(ifile.readlines()):
-
-                    line=line.strip()
-                    transliterated_line=unifier.transform(line,language)
-                    ofile.write(transliterated_line+'\n')
-                    
-    elif sys.argv[1]=='basic':
-
-        language=sys.argv[4]
-
-        unifier=BasicScriptUnifier()
-
-        with open(sys.argv[2],'r',encoding='utf-8') as ifile:
-            with open(sys.argv[3],'w',encoding='utf-8') as ofile:
-                for i, line in enumerate(ifile.readlines()):
-
-                    line=line.strip()
-                    transliterated_line=unifier.transform(line,language)
-                    ofile.write(transliterated_line+'\n')
-
-    elif sys.argv[1]=='naive':
-
-        language=sys.argv[4]
-
-        unifier=NaiveScriptUnifier()
-
-        with open(sys.argv[2],'r',encoding='utf-8') as ifile:
-            with open(sys.argv[3],'w',encoding='utf-8') as ofile:
-                for i, line in enumerate(ifile.readlines()):
-
-                    line=line.strip()
-                    transliterated_line=unifier.transform(line,language)
-                    ofile.write(transliterated_line+'\n')
diff --git a/spaces/Hina4867/bingo/src/components/welcome-screen.tsx b/spaces/Hina4867/bingo/src/components/welcome-screen.tsx
deleted file mode 100644
index f7449fcbb6c621875e235db98f2790bf7894fb0a..0000000000000000000000000000000000000000
--- a/spaces/Hina4867/bingo/src/components/welcome-screen.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-import { useBing } from '@/lib/hooks/use-bing'
-
-const exampleMessages = [
-  {
-    heading: '🧐 提出复杂问题',
-    message: `我可以为我挑剔的只吃橙色食物的孩子做什么饭?`
-  },
-  {
-    heading: '🙌 获取更好的答案',
-    message: '销量最高的 3 种宠物吸尘器有哪些优点和缺点?'
-  },
-  {
-    heading: '🎨 获得创意灵感',
-    message: `以海盗的口吻写一首关于外太空鳄鱼的俳句`
-  }
-]
-
-export function WelcomeScreen({ setInput }: Pick<ReturnType<typeof useBing>, 'setInput'>) {
-  return (
-    <div className="welcome-container flex">
-      {exampleMessages.map(example => (
-        <button key={example.heading} className="welcome-item w-4/5 sm:w-[240px]" type="button" onClick={() => setInput(example.message)}>
-          <div className="item-title">{example.heading}</div>
-          <div className="item-content">
-            <div className="item-body">
-              <div className="item-header"></div>
-              <div>&ldquo;{example.message}&rdquo;</div>
-            </div>
-          </div>
-        </button>
-      ))}
-    </div>
-  )
-}
diff --git a/spaces/HugoLaurencon/text-data-filtering-2/normalization.py b/spaces/HugoLaurencon/text-data-filtering-2/normalization.py
deleted file mode 100644
index 652e810fb5019c5177f6fd0abf9635f322f23927..0000000000000000000000000000000000000000
--- a/spaces/HugoLaurencon/text-data-filtering-2/normalization.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import re
-from typing import Dict
-
-
-non_printing_characters_re = re.compile(
-    f"[{''.join(map(chr, list(range(0,32)) + list(range(127,160))))}]"
-)
-
-digits_re: re.Pattern = re.compile(r"\d")
-
-unicode_punctuation: Dict[str, str] = {
-    "，": ",",
-    "。": ".",
-    "、": ",",
-    "„": '"',
-    "”": '"',
-    "“": '"',
-    "«": '"',
-    "»": '"',
-    "１": '"',
-    "」": '"',
-    "「": '"',
-    "《": '"',
-    "》": '"',
-    "´": "'",
-    "∶": ":",
-    "：": ":",
-    "？": "?",
-    "！": "!",
-    "（": "(",
-    "）": ")",
-    "；": ";",
-    "–": "-",
-    "—": " - ",
-    "．": ". ",
-    "～": "~",
-    "’": "'",
-    "…": "...",
-    "━": "-",
-    "〈": "<",
-    "〉": ">",
-    "【": "[",
-    "】": "]",
-    "％": "%",
-    "►": "-",
-}
-
-normalization = {
-    "non_printing_characters_re": non_printing_characters_re,
-    "digits_re": digits_re,
-    "unicode_punctuation": unicode_punctuation,
-}
diff --git a/spaces/ICML2022/OFA/fairseq/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py b/spaces/ICML2022/OFA/fairseq/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py
deleted file mode 100644
index 7ec1fb7521b8a9b821d28bcaaaedb034f6e95e0b..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import logging
-from pathlib import Path
-from collections import defaultdict
-
-import pandas as pd
-from torchaudio.datasets import LJSPEECH
-from tqdm import tqdm
-
-from examples.speech_to_text.data_utils import save_df_to_tsv
-
-
-log = logging.getLogger(__name__)
-
-SPLITS = ["train", "dev", "test"]
-
-
-def process(args):
-    out_root = Path(args.output_data_root).absolute()
-    out_root.mkdir(parents=True, exist_ok=True)
-
-    # Generate TSV manifest
-    print("Generating manifest...")
-    # following FastSpeech's splits
-    dataset = LJSPEECH(out_root.as_posix(), download=True)
-    id_to_split = {}
-    for x in dataset._flist:
-        id_ = x[0]
-        speaker = id_.split("-")[0]
-        id_to_split[id_] = {
-            "LJ001": "test", "LJ002": "test", "LJ003": "dev"
-        }.get(speaker, "train")
-    manifest_by_split = {split: defaultdict(list) for split in SPLITS}
-    progress = tqdm(enumerate(dataset), total=len(dataset))
-    for i, (waveform, _, utt, normalized_utt) in progress:
-        sample_id = dataset._flist[i][0]
-        split = id_to_split[sample_id]
-        manifest_by_split[split]["id"].append(sample_id)
-        audio_path = f"{dataset._path}/{sample_id}.wav"
-        manifest_by_split[split]["audio"].append(audio_path)
-        manifest_by_split[split]["n_frames"].append(len(waveform[0]))
-        manifest_by_split[split]["tgt_text"].append(normalized_utt)
-        manifest_by_split[split]["speaker"].append("ljspeech")
-        manifest_by_split[split]["src_text"].append(utt)
-
-    manifest_root = Path(args.output_manifest_root).absolute()
-    manifest_root.mkdir(parents=True, exist_ok=True)
-    for split in SPLITS:
-        save_df_to_tsv(
-            pd.DataFrame.from_dict(manifest_by_split[split]),
-            manifest_root / f"{split}.audio.tsv"
-        )
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--output-data-root", "-d", required=True, type=str)
-    parser.add_argument("--output-manifest-root", "-m", required=True, type=str)
-    args = parser.parse_args()
-
-    process(args)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Ibtehaj10/cheating-detection-FYP/opencv-example.py b/spaces/Ibtehaj10/cheating-detection-FYP/opencv-example.py
deleted file mode 100644
index a5b6b0f8184add87082b55f0415f9b587091ddf5..0000000000000000000000000000000000000000
--- a/spaces/Ibtehaj10/cheating-detection-FYP/opencv-example.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import cv2
-import imutils
-
-# image = cv2.imread('input_image.jpg')
-cap = cv2.VideoCapture(1)
-
-while True:
-    ret, frame = cap.read()
-    frame = imutils.resize(frame, width=800)
-
-    text = "This is my custom text"
-    cv2.putText(frame, text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
-
-    cv2.rectangle(frame, (50, 50), (500, 500), (0, 0, 255), 2)
-
-    cv2.imshow('Application', frame)
-
-    key = cv2.waitKey(1)
-    if key == ord('q'):
-        break
-
-cv2.destroyAllWindows()
diff --git a/spaces/IcelandAI/Iceland-Top-Ten-Things-To-See/app.py b/spaces/IcelandAI/Iceland-Top-Ten-Things-To-See/app.py
deleted file mode 100644
index b1f9fbf1cb89514269e1d473890d2ca1beada503..0000000000000000000000000000000000000000
--- a/spaces/IcelandAI/Iceland-Top-Ten-Things-To-See/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import streamlit as st
-
-markdown_text = '''
-## 🇮🇸 Top Ten Things to Do in Iceland
-
-1. [🔵 Blue Lagoon](https://www.google.com/search?q=blue+lagoon+iceland+pictures) - Relax and rejuvenate in the famous geothermal spa.
-2. [🌟 Golden Circle](https://www.google.com/search?q=golden+circle+iceland+pictures) - A popular route that encompasses three major attractions: Thingvellir National Park, the Geysir geothermal area, and Gullfoss waterfall.
-3. [🏙️ Reykjavik](https://www.google.com/search?q=reykjavik+pictures) - Explore the capital city's vibrant culture, art, history, and nightlife.
-4. [❄️ Vatnajökull National Park](https://www.google.com/search?q=vatnajokull+national+park+pictures) - Experience the natural beauty of the park that features the largest glacier in Europe, Vatnajökull.
-5. [🚣 Jokulsarlon Glacier Lagoon](https://www.google.com/search?q=jokulsarlon+glacier+lagoon+pictures) - Marvel at the floating icebergs and observe seals in this beautiful glacial lagoon.
-6. [🐋 Whale Watching](https://www.google.com/search?q=iceland+whale+watching+pictures) - Embark on a whale-watching tour to see these majestic creatures in their natural habitat.
-7. [🌌 Northern Lights](https://www.google.com/search?q=iceland+northern+lights+pictures) - Catch the mesmerizing Aurora Borealis during the winter months.
-8. [⛰️ Laugavegur Trek](https://www.google.com/search?q=laugavegur+trek+pictures) - Challenge yourself with this famous trek through Iceland's diverse landscapes.
-9. [🌊 Westfjords](https://www.google.com/search?q=westfjords+iceland+pictures) - Discover the remote and rugged beauty of the Westfjords, including Dynjandi waterfall and Hornstrandir Nature Reserve.
-10. [🌋 Myvatn](https://www.google.com/search?q=myvatn+pictures) - Explore the geothermal area around Lake Myvatn, featuring volcanic craters, lava formations, and hot springs.
-
-'''
-
-st.markdown(markdown_text)
-
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/utils/dummy_torch_and_transformers_objects.py b/spaces/Jackflack09/diffuse-custom/diffusers/utils/dummy_torch_and_transformers_objects.py
deleted file mode 100644
index 2d932d240508e138b2d30328bd4c94655b4498ba..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/utils/dummy_torch_and_transformers_objects.py
+++ /dev/null
@@ -1,244 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-# flake8: noqa
-
-from ..utils import DummyObject, requires_backends
-
-
-class AltDiffusionImg2ImgPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class AltDiffusionPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class CycleDiffusionPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class LDMTextToImagePipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class StableDiffusionImageVariationPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class StableDiffusionImg2ImgPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class StableDiffusionInpaintPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class StableDiffusionInpaintPipelineLegacy(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class StableDiffusionPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class StableDiffusionPipelineSafe(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class StableDiffusionUpscalePipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class VersatileDiffusionDualGuidedPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class VersatileDiffusionImageVariationPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class VersatileDiffusionPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class VersatileDiffusionTextToImagePipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
-class VQDiffusionPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
diff --git a/spaces/KPCGD/bingo/src/components/voice.tsx b/spaces/KPCGD/bingo/src/components/voice.tsx
deleted file mode 100644
index 074d0e145229947282a472bd84f6578cf0b3c71c..0000000000000000000000000000000000000000
--- a/spaces/KPCGD/bingo/src/components/voice.tsx
+++ /dev/null
@@ -1,52 +0,0 @@
-import React, { useEffect } from 'react'
-import { useSetAtom } from 'jotai'
-import { useBing } from '@/lib/hooks/use-bing'
-import Image from 'next/image'
-import VoiceIcon from '@/assets/images/voice.svg'
-import VoiceButton from './ui/voice'
-import { SR } from '@/lib/bots/bing/sr'
-import { voiceListenAtom } from '@/state'
-
-const sr = new SR(['发送', '清空', '退出'])
-
-const Voice = ({ setInput, input, sendMessage, isSpeaking }: Pick<ReturnType<typeof useBing>, 'setInput' | 'sendMessage' | 'input' | 'isSpeaking'>) => {
-  const setListen = useSetAtom(voiceListenAtom)
-  useEffect(() => {
-    if (sr.listening) return
-    sr.transcript = !isSpeaking
-  }, [isSpeaking])
-
-  useEffect(() => {
-    sr.onchange = (msg: string, command?: string) => {
-      switch (command) {
-        case '退出':
-          sr.stop()
-          break;
-        case '发送':
-          sendMessage(input)
-        case '清空':
-          setInput('')
-          break;
-        default:
-          setInput(input + msg)
-      }
-    }
-  }, [input])
-
-  const switchSR = (enable: boolean = false) => {
-    setListen(enable)
-    if (enable) {
-      sr.start()
-    } else {
-      sr.stop()
-    }
-  }
-
-  return sr.listening ? (
-    <VoiceButton onClick={() => switchSR(false)} />
-  ) : (
-    <Image alt="start voice" src={VoiceIcon} width={24} className="-mt-0.5" onClick={() => switchSR(true)} />
-  )
-};
-
-export default Voice;
diff --git a/spaces/Kevin676/AutoGPT/tests/smoke_test.py b/spaces/Kevin676/AutoGPT/tests/smoke_test.py
deleted file mode 100644
index 1b9d643fc21f3703384a2bb4f2bd1d725f4dd418..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/AutoGPT/tests/smoke_test.py
+++ /dev/null
@@ -1,59 +0,0 @@
-"""Smoke test for the autogpt package."""
-import os
-import subprocess
-import sys
-
-import pytest
-
-from autogpt.commands.file_operations import delete_file, read_file
-
-
-@pytest.mark.integration_test
-def test_write_file() -> None:
-    """
-    Test case to check if the write_file command can successfully write 'Hello World' to a file
-    named 'hello_world.txt'.
-
-    Read the current ai_settings.yaml file and store its content.
-    """
-    env_vars = {"MEMORY_BACKEND": "no_memory", "TEMPERATURE": "0"}
-    ai_settings = None
-    if os.path.exists("ai_settings.yaml"):
-        with open("ai_settings.yaml", "r") as f:
-            ai_settings = f.read()
-        os.remove("ai_settings.yaml")
-
-    try:
-        if os.path.exists("hello_world.txt"):
-            # Clean up any existing 'hello_world.txt' file before testing.
-            delete_file("hello_world.txt")
-        # Prepare input data for the test.
-        input_data = """write_file-GPT
-an AI designed to use the write_file command to write 'Hello World' into a file named "hello_world.txt" and then use the task_complete command to complete the task.
-Use the write_file command to write 'Hello World' into a file named "hello_world.txt".
-Use the task_complete command to complete the task.
-Do not use any other commands.
-
-y -5
-EOF"""
-        command = f"{sys.executable} -m autogpt"
-
-        # Execute the script with the input data.
-        process = subprocess.Popen(
-            command,
-            stdin=subprocess.PIPE,
-            shell=True,
-            env={**os.environ, **env_vars},
-        )
-        process.communicate(input_data.encode())
-
-        # Read the content of the 'hello_world.txt' file created during the test.
-        content = read_file("hello_world.txt")
-    finally:
-        if ai_settings:
-            # Restore the original ai_settings.yaml file.
-            with open("ai_settings.yaml", "w") as f:
-                f.write(ai_settings)
-
-    # Check if the content of the 'hello_world.txt' file is equal to 'Hello World'.
-    assert content == "Hello World", f"Expected 'Hello World', got {content}"
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/web/static/js/mp3.js b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/web/static/js/mp3.js
deleted file mode 100644
index bf745a226ff05f6b07f69a0ee079e3735969f94d..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/web/static/js/mp3.js
+++ /dev/null
@@ -1,424 +0,0 @@
-/*
-mp3编码器，需带上mp3-engine.js引擎使用
-https://github.com/xiangyuecn/Recorder
-
-当然最佳推荐使用mp3、wav格式，代码也是优先照顾这两种格式
-浏览器支持情况
-https://developer.mozilla.org/en-US/docs/Web/HTML/Supported_media_formats
-*/
-(function(){
-"use strict";
-
-Recorder.prototype.enc_mp3={
-	stable:true
-	,testmsg:"采样率范围48000, 44100, 32000, 24000, 22050, 16000, 12000, 11025, 8000"
-};
-
-
-
-//*******标准UI线程转码支持函数************
-
-Recorder.prototype.mp3=function(res,True,False){
-		var This=this,set=This.set,size=res.length;
-		
-		//优先采用worker编码，太低版本下面用老方法提供兼容
-		var ctx=This.mp3_start(set);
-		if(ctx){
-			This.mp3_encode(ctx,res);
-			This.mp3_complete(ctx,True,False,1);
-			return;
-		};
-		
-		//https://github.com/wangpengfei15975/recorder.js
-		//https://github.com/zhuker/lamejs bug:采样率必须和源一致，不然8k时没有声音，有问题fix：https://github.com/zhuker/lamejs/pull/11
-		var mp3=new Recorder.lamejs.Mp3Encoder(1,set.sampleRate,set.bitRate);
-		
-		var blockSize=57600;
-		var data=[];
-		
-		var idx=0,mp3Size=0;
-		var run=function(){
-			if(idx<size){
-				var buf=mp3.encodeBuffer(res.subarray(idx,idx+blockSize));
-				if(buf.length>0){
-					mp3Size+=buf.buffer.byteLength;
-					data.push(buf.buffer);
-				};
-				idx+=blockSize;
-				setTimeout(run);//尽量避免卡ui
-			}else{
-				var buf=mp3.flush();
-				if(buf.length>0){
-					mp3Size+=buf.buffer.byteLength;
-					data.push(buf.buffer);
-				};
-				
-				//去掉开头的标记信息帧
-				var meta=mp3TrimFix.fn(data,mp3Size,size,set.sampleRate);
-				mp3TrimFixSetMeta(meta,set);
-				
-				True(new Blob(data,{type:"audio/mp3"}));
-			};
-		};
-		run();
-	}
-
-
-//********边录边转码(Worker)支持函数，如果提供就代表可能支持，否则只支持标准转码*********
-
-//全局共享一个Worker，后台串行执行。如果每次都开一个新的，编码速度可能会慢很多，可能是浏览器运行缓存的因素，并且可能瞬间产生多个并行操作占用大量cpu
-var mp3Worker;
-Recorder.BindDestroy("mp3Worker",function(){
-	console.log("mp3Worker Destroy");
-	mp3Worker&&mp3Worker.terminate();
-	mp3Worker=null;
-});
-
-
-Recorder.prototype.mp3_envCheck=function(envInfo,set){//检查环境下配置是否可用
-	var errMsg="";
-	//需要实时编码返回数据，此时需要检查环境是否有实时特性、和是否可实时编码
-	if(set.takeoffEncodeChunk){
-		if(!envInfo.canProcess){
-			errMsg=envInfo.envName+"环境不支持实时处理";
-		}else if(!newContext()){//浏览器不能创建实时编码环境
-			errMsg="当前浏览器版本太低，无法实时处理";
-		};
-	};
-	return errMsg;
-};
-Recorder.prototype.mp3_start=function(set){//如果返回null代表不支持
-	return newContext(set);
-};
-var openList={id:0};
-var newContext=function(setOrNull){
-	var worker=mp3Worker;
-	try{
-		if(!worker){
-			var onmsg=function(e){
-				var ed=e.data;
-				var cur=wk_ctxs[ed.id];
-				if(ed.action=="init"){
-					wk_ctxs[ed.id]={
-						sampleRate:ed.sampleRate
-						,bitRate:ed.bitRate
-						,takeoff:ed.takeoff
-						
-						,mp3Size:0
-						,pcmSize:0
-						,encArr:[]
-						,encObj:new wk_lame.Mp3Encoder(1,ed.sampleRate,ed.bitRate)
-					};
-				}else if(!cur){
-					return;
-				};
-				
-				switch(ed.action){
-				case "stop":
-					cur.encObj=null;
-					delete wk_ctxs[ed.id];
-					break;
-				case "encode":
-					cur.pcmSize+=ed.pcm.length;
-					var buf=cur.encObj.encodeBuffer(ed.pcm);
-					if(buf.length>0){
-						if(cur.takeoff){
-							self.postMessage({action:"takeoff",id:ed.id,chunk:buf});
-						}else{
-							cur.mp3Size+=buf.buffer.byteLength;
-							cur.encArr.push(buf.buffer);
-						};
-					};
-					break;
-				case "complete":
-					var buf=cur.encObj.flush();
-					if(buf.length>0){
-						if(cur.takeoff){
-							self.postMessage({action:"takeoff",id:ed.id,chunk:buf});
-						}else{
-							cur.mp3Size+=buf.buffer.byteLength;
-							cur.encArr.push(buf.buffer);
-						};
-					};
-					
-					//去掉开头的标记信息帧
-					var meta=wk_mp3TrimFix.fn(cur.encArr,cur.mp3Size,cur.pcmSize,cur.sampleRate);
-					
-					self.postMessage({
-						action:ed.action
-						,id:ed.id
-						,blob:new Blob(cur.encArr,{type:"audio/mp3"})
-						,meta:meta
-					});
-					break;
-				};
-			};
-			
-			//创建一个新Worker
-			var jsCode=");wk_lame();var wk_ctxs={};self.onmessage="+onmsg;
-			jsCode+=";var wk_mp3TrimFix={rm:"+mp3TrimFix.rm+",fn:"+mp3TrimFix.fn+"}";
-			
-			var lamejsCode=Recorder.lamejs.toString();
-			var url=(window.URL||webkitURL).createObjectURL(new Blob(["var wk_lame=(",lamejsCode,jsCode], {type:"text/javascript"}));
-			
-			worker=new Worker(url);
-			setTimeout(function(){
-				(window.URL||webkitURL).revokeObjectURL(url);//必须要释放，不然每次调用内存都明显泄露内存
-			},10000);//chrome 83 file协议下如果直接释放，将会使WebWorker无法启动
-			
-			worker.onmessage=function(e){
-				var data=e.data;
-				var ctx=openList[data.id];
-				if(ctx){
-					if(data.action=="takeoff"){
-						//取走实时生成的mp3数据
-						ctx.set.takeoffEncodeChunk(new Uint8Array(data.chunk.buffer));
-					}else{
-						//complete
-						ctx.call&&ctx.call(data);
-						ctx.call=null;
-					};
-				};
-			};
-		};
-		
-		var ctx={worker:worker,set:setOrNull,takeoffQueue:[]};
-		if(setOrNull){
-			ctx.id=++openList.id;
-			openList[ctx.id]=ctx;
-			
-			worker.postMessage({
-				action:"init"
-				,id:ctx.id
-				,sampleRate:setOrNull.sampleRate
-				,bitRate:setOrNull.bitRate
-				,takeoff:!!setOrNull.takeoffEncodeChunk
-				
-				,x:new Int16Array(5)//低版本浏览器不支持序列化TypedArray
-			});
-		}else{
-			worker.postMessage({
-				x:new Int16Array(5)//低版本浏览器不支持序列化TypedArray
-			});
-		};
-		
-		
-		mp3Worker=worker;
-		return ctx;
-	}catch(e){//出错了就不要提供了
-		worker&&worker.terminate();
-		
-		console.error(e);
-		return null;
-	};
-};
-Recorder.prototype.mp3_stop=function(startCtx){
-	if(startCtx&&startCtx.worker){
-		startCtx.worker.postMessage({
-			action:"stop"
-			,id:startCtx.id
-		});
-		startCtx.worker=null;
-		delete openList[startCtx.id];
-		
-		//疑似泄露检测 排除id
-		var opens=-1;
-		for(var k in openList){
-			opens++;
-		};
-		if(opens){
-			console.warn("mp3 worker剩"+opens+"个在串行等待");
-		};
-	};
-};
-Recorder.prototype.mp3_encode=function(startCtx,pcm){
-	if(startCtx&&startCtx.worker){
-		startCtx.worker.postMessage({
-			action:"encode"
-			,id:startCtx.id
-			,pcm:pcm
-		});
-	};
-};
-Recorder.prototype.mp3_complete=function(startCtx,True,False,autoStop){
-	var This=this;
-	if(startCtx&&startCtx.worker){
-		startCtx.call=function(data){
-			mp3TrimFixSetMeta(data.meta,startCtx.set);
-			True(data.blob);
-			
-			if(autoStop){
-				This.mp3_stop(startCtx);
-			};
-		};
-		startCtx.worker.postMessage({
-			action:"complete"
-			,id:startCtx.id
-		});
-	}else{
-		False("mp3编码器未打开");
-	};
-};
-
-
-
-
-
-
-
-//*******辅助函数************
-
-/*读取lamejs编码出来的mp3信息，只能读特定格式，如果读取失败返回null
-mp3Buffers=[ArrayBuffer,...]
-length=mp3Buffers的数据二进制总长度
-*/
-Recorder.mp3ReadMeta=function(mp3Buffers,length){
-	//kill babel-polyfill ES6 Number.parseInt 不然放到Worker里面找不到方法
-	var parseInt_ES3=typeof(window)=="object"?window.parseInt:self.parseInt;
-	
-	var u8arr0=new Uint8Array(mp3Buffers[0]||[]);
-	if(u8arr0.length<4){
-		return null;
-	};
-	var byteAt=function(idx,u8){
-		return ("0000000"+((u8||u8arr0)[idx]||0).toString(2)).substr(-8);
-	};
-	var b2=byteAt(0)+byteAt(1);
-	var b4=byteAt(2)+byteAt(3);
-	
-	if(!/^1{11}/.test(b2)){//未发现帧同步
-		return null;
-	};
-	var version=({"00":2.5,"10":2,"11":1})[b2.substr(11,2)];
-	var layer=({"01":3})[b2.substr(13,2)];//仅支持Layer3
-	var sampleRate=({ //lamejs -> Tables.samplerate_table
-		"1":[44100, 48000, 32000]
-		,"2":[22050, 24000, 16000]
-		,"2.5":[11025, 12000, 8000]
-	})[version];
-	sampleRate&&(sampleRate=sampleRate[parseInt_ES3(b4.substr(4,2),2)]);
-	var bitRate=[ //lamejs -> Tables.bitrate_table
-		[0, 8, 16, 24, 32, 40, 48, 56, 64, 80, 96, 112, 128, 144, 160] //MPEG 2 2.5
-		,[0, 32, 40, 48, 56, 64, 80, 96, 112, 128, 160, 192, 224, 256, 320]//MPEG 1
-	][version==1?1:0][parseInt_ES3(b4.substr(0,4),2)];
-	
-	if(!version || !layer || !bitRate || !sampleRate){
-		return null;
-	};
-	
-	var duration=Math.round(length*8/bitRate);
-	var frame=layer==1?384:layer==2?1152:version==1?1152:576;
-	var frameDurationFloat=frame/sampleRate*1000;
-	var frameSize=Math.floor((frame*bitRate)/8/sampleRate*1000);
-	
-	//检测是否存在Layer3帧填充1字节。这里只获取第二帧的填充信息，首帧永远没有填充。其他帧可能隔一帧出现一个填充，或者隔很多帧出现一个填充；目测是取决于frameSize未舍入时的小数部分，因为有些采样率的frameSize会出现小数（11025、22050、44100 典型的除不尽），然后字节数无法表示这种小数，就通过一定步长来填充弥补小数部分丢失
-	var hasPadding=0,seek=0;
-	for(var i=0;i<mp3Buffers.length;i++){
-		//寻找第二帧
-		var buf=mp3Buffers[i];
-		seek+=buf.byteLength;
-		if(seek>=frameSize+3){
-			var buf8=new Uint8Array(buf);
-			var idx=buf.byteLength-(seek-(frameSize+3)+1);
-			var ib4=byteAt(idx,buf8);
-			hasPadding=ib4.charAt(6)=="1";
-			break;
-		};
-	};
-	if(hasPadding){
-		frameSize++;
-	};
-	
-	
-	return {
-		version:version //1 2 2.5 -> MPEG1 MPEG2 MPEG2.5
-		,layer:layer//3 -> Layer3
-		,sampleRate:sampleRate //采样率 hz
-		,bitRate:bitRate //比特率 kbps
-		
-		,duration:duration //音频时长 ms
-		,size:length //总长度 byte
-		,hasPadding:hasPadding //是否存在1字节填充，首帧永远没有，这个值其实代表的第二帧是否有填充，并不代表其他帧的
-		,frameSize:frameSize //每帧最大长度，含可能存在的1字节padding byte
-		,frameDurationFloat:frameDurationFloat //每帧时长，含小数 ms
-	};
-};
-
-//去掉lamejs开头的标记信息帧，免得mp3解码出来的时长比pcm的长太多
-var mp3TrimFix={//minfiy keep name
-rm:Recorder.mp3ReadMeta
-,fn:function(mp3Buffers,length,pcmLength,pcmSampleRate){
-	var meta=this.rm(mp3Buffers,length);
-	if(!meta){
-		return {err:"mp3非预定格式"};
-	};
-	var pcmDuration=Math.round(pcmLength/pcmSampleRate*1000);
-	
-	//开头多出这么多帧，移除掉；正常情况下最多为2帧
-	var num=Math.floor((meta.duration-pcmDuration)/meta.frameDurationFloat);
-	if(num>0){
-		var size=num*meta.frameSize-(meta.hasPadding?1:0);//首帧没有填充，第二帧可能有填充，这里假设最多为2帧（测试并未出现3帧以上情况），其他帧不管，就算出现了并且导致了错误后面自动容错
-		length-=size;
-		var arr0=0,arrs=[];
-		for(var i=0;i<mp3Buffers.length;i++){
-			var arr=mp3Buffers[i];
-			if(size<=0){
-				break;
-			};
-			if(size>=arr.byteLength){
-				size-=arr.byteLength;
-				arrs.push(arr);
-				mp3Buffers.splice(i,1);
-				i--;
-			}else{
-				mp3Buffers[i]=arr.slice(size);
-				arr0=arr;
-				size=0;
-			};
-		};
-		var checkMeta=this.rm(mp3Buffers,length);
-		if(!checkMeta){
-			//还原变更，应该不太可能会出现
-			arr0&&(mp3Buffers[0]=arr0);
-			for(var i=0;i<arrs.length;i++){
-				mp3Buffers.splice(i,0,arrs[i]);
-			};
-			meta.err="fix后数据错误，已还原，错误原因不明";
-		};
-		
-		var fix=meta.trimFix={};
-		fix.remove=num;
-		fix.removeDuration=Math.round(num*meta.frameDurationFloat);
-		fix.duration=Math.round(length*8/meta.bitRate);
-	};
-	return meta;
-}
-};
-var mp3TrimFixSetMeta=function(meta,set){
-	var tag="MP3信息 ";
-	if(meta.sampleRate&&meta.sampleRate!=set.sampleRate || meta.bitRate&&meta.bitRate!=set.bitRate){
-		console.warn(tag+"和设置的不匹配set:"+set.bitRate+"kbps "+set.sampleRate+"hz，已更新set:"+meta.bitRate+"kbps "+meta.sampleRate+"hz",set);
-		set.sampleRate=meta.sampleRate;
-		set.bitRate=meta.bitRate;
-	};
-	
-	var trimFix=meta.trimFix;
-	if(trimFix){
-		tag+="Fix移除"+trimFix.remove+"帧"+trimFix.removeDuration+"ms -> "+trimFix.duration+"ms";
-		if(trimFix.remove>2){
-			meta.err=(meta.err?meta.err+", ":"")+"移除帧数过多";
-		};
-	}else{
-		tag+=(meta.duration||"-")+"ms";
-	};
-	
-	if(meta.err){
-		console.error(tag,meta.err,meta);
-	}else{
-		console.log(tag,meta);
-	};
-};
-
-
-	
-})();
\ No newline at end of file
diff --git a/spaces/LanQian/ChatChuanHu/presets.py b/spaces/LanQian/ChatChuanHu/presets.py
deleted file mode 100644
index ebc97488ceac92301bb68f33fc2d6fce996ee91f..0000000000000000000000000000000000000000
--- a/spaces/LanQian/ChatChuanHu/presets.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# -*- coding:utf-8 -*-
-title = """<h1 align="left">川虎ChatGPT 🚀</h1>"""
-description = """<div align=center>
-
-此App使用 `gpt-3.5-turbo` 大语言模型
-</div>
-"""
-customCSS = """
-code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-pre code {
-    display: block;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 72%);
-    border: solid 5px var(--color-border-primary) !important;
-    border-radius: 10px;
-    padding: 0 1.2rem 1.2rem;
-    margin-top: 1em !important;
-    color: #FFF;
-    box-shadow: inset 0px 8px 16px hsla(0, 0%, 0%, .2)
-}
-
-*{
-    transition: all 0.6s;
-}
-
-
-"""
-
-summarize_prompt = "你是谁？我们刚才聊了什么？" # 总结对话时的 prompt
-MODELS = ["gpt-3.5-turbo", "gpt-3.5-turbo-0301", "gpt-4","gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"] # 可选的模型
-websearch_prompt = """Web search results:
-
-{web_results}
-Current date: {current_date}
-
-Instructions: Using the provided web search results, write a comprehensive reply to the given query. Make sure to cite results using [[number](URL)] notation after the reference. If the provided search results refer to multiple subjects with the same name, write separate answers for each subject.
-Query: {query}
-Reply in 中文"""
-
-# 错误信息
-standard_error_msg = "☹️发生了错误：" # 错误信息的标准前缀
-error_retrieve_prompt = "请检查网络连接，或者API-Key是否有效。" # 获取对话时发生错误
-connection_timeout_prompt = "连接超时，无法获取对话。" # 连接超时
-read_timeout_prompt = "读取超时，无法获取对话。" # 读取超时
-proxy_error_prompt = "代理错误，无法获取对话。" # 代理错误
-ssl_error_prompt = "SSL错误，无法获取对话。" # SSL 错误
-no_apikey_msg = "API key长度不是51位，请检查是否输入正确。" # API key 长度不足 51 位
-
-max_token_streaming = 3500 # 流式对话时的最大 token 数
-timeout_streaming = 15 # 流式对话时的超时时间
-max_token_all = 3500 # 非流式对话时的最大 token 数
-timeout_all = 200 # 非流式对话时的超时时间
-enable_streaming_option = True  # 是否启用选择选择是否实时显示回答的勾选框
-HIDE_MY_KEY = False # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True
diff --git a/spaces/Lookimi/Interface/app.py b/spaces/Lookimi/Interface/app.py
deleted file mode 100644
index de5857a66909d446692427331b3acab6f653d7b6..0000000000000000000000000000000000000000
--- a/spaces/Lookimi/Interface/app.py
+++ /dev/null
@@ -1,67 +0,0 @@
-
-import gradio as gr
-import openai
-
-
-def Question(Ask_Question):
-  #openai.api_key = "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"
-  
-  model_engine = "text-davinci-003"
-  # pass the generated text to audio
-  openai.api_key = "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"
-#openai.api_key = "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"
-    # Set up the model and prompt
-  #model_engine = "text-davinci-003"
-  #prompt = "who is alon musk?"
-  # Generate a response
-
-
-
-#    completion = openai.Completion.create(
-#    model="text-davinci-003",
-#    prompt=Ask_Question,
-#    temperature=0.9,
-#    max_tokens=2048,
-#    top_p=1,
-#    frequency_penalty=0,
-#    presence_penalty=0.6,
-#    stop=[" Human:", " AI:"]
-#    )
-
-  completion = openai.Completion.create(
-  engine=model_engine,
-  prompt=Ask_Question,
-  max_tokens=2048,
-  n=1,
-  top_p=1,
-  stop=None,
-  temperature=0.9,)
-  response = completion.choices[0].text
-  #out_result=resp['message']
-  return  response  
-    
-demo = gr.Interface(
-    title='OpenAI ChatGPT Application',
-    fn=Question,
-    inputs="text", outputs="text")
-
-demo.launch() 
-
-
-# fix
-
-chat_history = [
-    ["User", prompt],
-    ["OpenAI", responses["choices"][0]["text"]]
-]
-
-# Create the radio blocks window 
-#window = gr.Interface(title="History", fn=Question: chat_history, inputs=None, outputs=chat_history, live=True).launch(share=True)
-# Print out the chat history 
-print("Chat History:")
-
-for message in chat_history:
-    print(f"{message[0]}: {message[1]}")
-
-window.launch()    
-    
\ No newline at end of file
diff --git a/spaces/MINAMONI/White-box-Cartoonization/README.md b/spaces/MINAMONI/White-box-Cartoonization/README.md
deleted file mode 100644
index 9860239cf42c94e385faaaa75a85311e010d64f7..0000000000000000000000000000000000000000
--- a/spaces/MINAMONI/White-box-Cartoonization/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-python_version: 3.7
-title: White Box Cartoonization
-emoji: 📚
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: hylee/White-box-Cartoonization
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/MOULI17/CmGenAIChatbot/README.md b/spaces/MOULI17/CmGenAIChatbot/README.md
deleted file mode 100644
index fea15e7b431b696ac807c98519b6746324a453ef..0000000000000000000000000000000000000000
--- a/spaces/MOULI17/CmGenAIChatbot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CmGenAIChatbot
-emoji: 💻
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Makiing/coolb-in-gtest/src/lib/utils.ts b/spaces/Makiing/coolb-in-gtest/src/lib/utils.ts
deleted file mode 100644
index 07feedb34e356b1b3cf867872f32d47a96ae12fb..0000000000000000000000000000000000000000
--- a/spaces/Makiing/coolb-in-gtest/src/lib/utils.ts
+++ /dev/null
@@ -1,138 +0,0 @@
-import { clsx, type ClassValue } from 'clsx'
-import { customAlphabet } from 'nanoid'
-import { twMerge } from 'tailwind-merge'
-
-export function cn(...inputs: ClassValue[]) {
-  return twMerge(clsx(inputs))
-}
-
-export const nanoid = customAlphabet(
-  '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz',
-  7
-) // 7-character random string
-
-export function createChunkDecoder() {
-  const decoder = new TextDecoder()
-  return function (chunk: Uint8Array | undefined): string {
-    if (!chunk) return ''
-    return decoder.decode(chunk, { stream: true })
-  }
-}
-
-export function random (start: number, end: number) {
-  return start + Math.ceil(Math.random() * (end - start))
-}
-
-export function randomIP() {
-  return `11.${random(104, 107)}.${random(1, 255)}.${random(1, 255)}`
-}
-
-export function parseHeadersFromCurl(content: string) {
-  const re = /-H '([^:]+):\s*([^']+)/mg
-  const headers: HeadersInit = {}
-  content = content.replaceAll('-H "', '-H \'').replaceAll('" ^', '\'\\').replaceAll('^\\^"', '"') // 将 cmd curl 转成 bash curl
-  content.replace(re, (_: string, key: string, value: string) => {
-    headers[key] = value
-    return ''
-  })
-
-  return headers
-}
-
-export const ChunkKeys = ['BING_HEADER', 'BING_HEADER1', 'BING_HEADER2']
-export function encodeHeadersToCookie(content: string) {
-  const base64Content = btoa(content)
-  const contentChunks = base64Content.match(/.{1,4000}/g) || []
-  return ChunkKeys.map((key, index) => `${key}=${contentChunks[index] ?? ''}`)
-}
-
-export function extraCurlFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  let base64Content = ''
-  ChunkKeys.forEach((key) => {
-    base64Content += (cookies[key] || '')
-  })
-  try {
-    return atob(base64Content)
-  } catch(e) {
-    return ''
-  }
-}
-
-export function extraHeadersFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  return parseHeadersFromCurl(extraCurlFromCookie(cookies))
-}
-
-export function formatDate(input: string | number | Date): string {
-  const date = new Date(input)
-  return date.toLocaleDateString('en-US', {
-    month: 'long',
-    day: 'numeric',
-    year: 'numeric'
-  })
-}
-
-export function parseCookie(cookie: string, cookieName: string) {
-  const targetCookie = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`).test(cookie) ? RegExp.$1 : cookie
-  return targetCookie ? decodeURIComponent(targetCookie).trim() : cookie.indexOf('=') === -1 ? cookie.trim() : ''
-}
-
-export function parseCookies(cookie: string, cookieNames: string[]) {
-  const cookies: { [key: string]: string } = {}
-  cookieNames.forEach(cookieName => {
-    cookies[cookieName] = parseCookie(cookie, cookieName)
-  })
-  return cookies
-}
-
-export const DEFAULT_UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36 Edg/115.0.0.0'
-export const DEFAULT_IP = process.env.BING_IP || randomIP()
-
-export function parseUA(ua?: string, default_ua = DEFAULT_UA) {
-  return / EDGE?/i.test(decodeURIComponent(ua || '')) ? decodeURIComponent(ua!.trim()) : default_ua
-}
-
-export function createHeaders(cookies: Partial<{ [key: string]: string }>, defaultHeaders?: Partial<{ [key: string]: string }>) {
-  let {
-    BING_COOKIE = process.env.BING_COOKIE,
-    BING_UA = process.env.BING_UA,
-    BING_IP = process.env.BING_IP,
-    BING_HEADER = process.env.BING_HEADER,
-  } = cookies
-
-  if (BING_HEADER) {
-    return extraHeadersFromCookie({
-      BING_HEADER,
-      ...cookies,
-    })
-  }
-
-  const ua = parseUA(BING_UA)
-
-  if (!BING_COOKIE) {
-    BING_COOKIE = defaultHeaders?.IMAGE_BING_COOKIE || 'xxx' // hf 暂时不用 Cookie 也可以正常使用
-  }
-
-  const parsedCookie = parseCookie(BING_COOKIE, '_U')
-  if (!parsedCookie) {
-    throw new Error('Invalid Cookie')
-  }
-  return {
-    'x-forwarded-for': BING_IP || DEFAULT_IP,
-    'Accept-Encoding': 'gzip, deflate, br',
-    'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
-    'User-Agent': ua!,
-    'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-    cookie: `_U=${parsedCookie}` || '',
-  }
-}
-
-export class WatchDog {
-  private tid = 0
-  watch(fn: Function, timeout = 2000) {
-    clearTimeout(this.tid)
-    this.tid = setTimeout(fn, timeout + Math.random() * 1000)
-  }
-  reset() {
-    clearTimeout(this.tid)
-  }
-}
diff --git a/spaces/Manjushri/MusicGen/audiocraft/data/zip.py b/spaces/Manjushri/MusicGen/audiocraft/data/zip.py
deleted file mode 100644
index 1f1154231da321dd38d151ff285dbcff5e38a6e0..0000000000000000000000000000000000000000
--- a/spaces/Manjushri/MusicGen/audiocraft/data/zip.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import typing
-import zipfile
-
-from dataclasses import dataclass
-from functools import lru_cache
-from typing_extensions import Literal
-
-
-DEFAULT_SIZE = 32
-MODE = Literal['r', 'w', 'x', 'a']
-
-
-@dataclass(order=True)
-class PathInZip:
-    """Class for holding a path of file within a zip file.
-
-    Args:
-        path: The convention is <path_to_zip>:<relative_path_inside_zip>
-            Let's assume there is a zip file /some/location/foo.zip
-            and inside of it is a json file located at /data/file1.json,
-            Then we expect path = "/some/location/foo.zip:/data/file1.json"
-    """
-
-    INFO_PATH_SEP = ':'
-    zip_path: str
-    file_path: str
-
-    def __init__(self, path: str) -> None:
-        split_path = path.split(self.INFO_PATH_SEP)
-        assert len(split_path) == 2
-        self.zip_path, self.file_path = split_path
-
-    @classmethod
-    def from_paths(cls, zip_path: str, file_path: str):
-        return cls(zip_path + cls.INFO_PATH_SEP + file_path)
-
-    def __str__(self) -> str:
-        return self.zip_path + self.INFO_PATH_SEP + self.file_path
-
-
-def _open_zip(path: str, mode: MODE = 'r'):
-    return zipfile.ZipFile(path, mode)
-
-
-_cached_open_zip = lru_cache(DEFAULT_SIZE)(_open_zip)
-
-
-def set_zip_cache_size(max_size: int):
-    """Sets the maximal LRU caching for zip file opening.
-
-    Args:
-        max_size: the maximal LRU cache.
-    """
-    global _cached_open_zip
-    _cached_open_zip = lru_cache(max_size)(_open_zip)
-
-
-def open_file_in_zip(path_in_zip: PathInZip, mode: str = 'r') -> typing.IO:
-    """Opens a file stored inside a zip and returns a file-like object.
-
-    Args:
-        path_in_zip: A PathInZip object representing the file to return a file-like object of.
-        mode: The mode in which to open the file with.
-    Returns:
-        A file-like object for PathInZip.
-    """
-    zf = _cached_open_zip(path_in_zip.zip_path)
-    return zf.open(path_in_zip.file_path)
diff --git a/spaces/March07/PromptBench/adv_prompts/vicuna_fewshot.md b/spaces/March07/PromptBench/adv_prompts/vicuna_fewshot.md
deleted file mode 100644
index 609de8615a7f4435928f97c7633a1f61df9b4f02..0000000000000000000000000000000000000000
--- a/spaces/March07/PromptBench/adv_prompts/vicuna_fewshot.md
+++ /dev/null
@@ -1,2195 +0,0 @@
-# vicuna_fewshot
-
-# cola
-
-## 10 prompts
-
-Acc: 65.20%, prompt: Determine if the grammar of the given sentence is 'Acceptable' or 'Unacceptable'.
-Acc: 62.20%, prompt: Assess the grammatical structure of the given sentence and classify it as 'Acceptable' or 'Unacceptable'.
-Acc: 47.70%, prompt: Examine the sentence and decide if its grammar is 'Acceptable' or 'Unacceptable'.
-Acc: 38.50%, prompt: Check the grammar of the following sentence and indicate if it is 'Acceptable' or 'Unacceptable'.
-Acc: 33.90%, prompt: Review the sentence below and identify whether its grammar is 'Acceptable' or 'Unacceptable'.
-Acc: 8.00%, prompt: Analyze the provided sentence and classify its grammatical correctness as 'Acceptable' or 'Unacceptable'.
-Acc: 5.30%, prompt: Is the provided sentence grammatically correct? Respond with 'Acceptable' or 'Unacceptable'.
-Acc: 1.60%, prompt: Examine the given sentence and decide if it is grammatically sound. Answer with either 'Acceptable' or 'Unacceptable'.
-Acc: 1.10%, prompt: Assess the following sentence and determine if it is grammatically correct. Respond with 'Acceptable' or 'Unacceptable'.
-Acc: 0.90%, prompt: Please evaluate the grammatical structure of the provided sentence and answer with 'Acceptable' or 'Unacceptable'.
-
-Acc: 49.60%, prompt: As a tool for determining grammatical correctness, review the sentence and categorize its grammar as either 'acceptable' or 'unacceptable'.
-Acc: 48.00%, prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Acc: 35.20%, prompt: Working as a grammar classifier, identify whether the given sentence has correct grammar, and respond with 'acceptable' for correct sentences or 'unacceptable' for incorrect ones.
-Acc: 33.40%, prompt: In your role as a grammar check tool, assess the following sentence and classify it as 'acceptable' if it is grammatically correct or 'unacceptable' if it is incorrect.
-Acc: 26.90%, prompt: Functioning as a grammar evaluation tool, analyze the given sentence and decide if it is grammatically correct, responding with 'acceptable' or 'unacceptable'.
-Acc: 20.80%, prompt: As an instrument for grammar evaluation, consider the sentence and determine if its grammar is correct, responding with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Acc: 19.30%, prompt: Acting as a grammar detection instrument, determine if the provided sentence is grammatically sound, answering with 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Acc: 14.70%, prompt: In the capacity of a grammar assessment system, indicate if the structure of the provided sentence is grammatically correct, responding with 'acceptable' or 'unacceptable'.
-Acc: 5.20%, prompt: In the role of a syntax analyzer, examine the grammar of the provided sentence and decide if it is correct, answering with 'acceptable' for grammatically correct sentences or 'unacceptable' for incorrect ones.
-Acc: 0.10%, prompt: As a grammar identification system, examine the provided sentence and respond with 'acceptable' for grammatically correct sentences or 'unacceptable' for incorrect ones.
-
-## bertattack
-
-Original prompt: Determine if the grammar of the given sentence is 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Determine if the grammar of the other situation is 'Acceptable' or 'Unacceptable'. "
-Original acc: 65.20%, attacked acc: 32.90%, dropped acc: 32.30%
-
-Original prompt: Assess the grammatical structure of the given sentence and classify it as 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Assess the context t of the of sentence and judge it as 'Acceptable' or 'Unacceptable'. "
-Original acc: 62.20%, attacked acc: 3.80%, dropped acc: 58.40%
-
-Original prompt: Examine the sentence and decide if its grammar is 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Examine the discourse and see if its grammar is 'Acceptable' or 'Unacceptable'. "
-Original acc: 47.70%, attacked acc: 15.40%, dropped acc: 32.30%
-
-Original prompt: As a tool for determining grammatical correctness, review the sentence and categorize its grammar as either 'acceptable' or 'unacceptable'.
-Attacked prompt: b"should a be for calculating adjective correctness, review the sentence and categorize its i as or 'acceptable' or 'unacceptable'. "
-Original acc: 49.60%, attacked acc: 0.00%, dropped acc: 49.60%
-
-Original prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"While to grammar analysis, view the language of the following sentence as 'acceptable' for correctly adjective or 'unacceptable' for english sentences. "
-Original acc: 48.00%, attacked acc: 0.20%, dropped acc: 47.80%
-
-Original prompt: Working as a grammar classifier, identify whether the given sentence has correct grammar, and respond with 'acceptable' for correct sentences or 'unacceptable' for incorrect ones.
-Attacked prompt: b"Working as a grammar classiate, identify whether the given sentence has correct grammar, and answer with 'acceptable' for correct sentences or 'unacceptable' for incorrect ones. "
-Original acc: 35.20%, attacked acc: 0.00%, dropped acc: 35.20%
-
-## checklist
-
-Original prompt: Determine if the grammar of the given sentence is 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Determine if the grammar of the given sentence is 'Acceptable' or 'Unacceptable' Q47Jmd4lMV. "
-Original acc: 65.20%, attacked acc: 21.30%, dropped acc: 43.90%
-
-Original prompt: Assess the grammatical structure of the given sentence and classify it as 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Assess the grammatical structure of the given sentence and classify it as 'Acceptable' or 'Unacceptable' Q47Jmd4lMV. "
-Original acc: 62.20%, attacked acc: 17.40%, dropped acc: 44.80%
-
-Original prompt: Examine the sentence and decide if its grammar is 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Examine the sentence and decide if its grammar is 'Acceptable' or 'Unacceptable' PDCGfz6DL9. "
-Original acc: 47.70%, attacked acc: 18.90%, dropped acc: 28.80%
-
-Original prompt: As a tool for determining grammatical correctness, review the sentence and categorize its grammar as either 'acceptable' or 'unacceptable'.
-Attacked prompt: b"As a tool for determining grammatical correctness, review the sentence and categorize its grammar as either 'acceptable' or 'unacceptable' 6a4Yn3RGVc. "
-Original acc: 49.60%, attacked acc: 8.70%, dropped acc: 40.90%
-
-Original prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar Q47Jmd4lMV. "
-Original acc: 48.00%, attacked acc: 3.90%, dropped acc: 44.10%
-
-Original prompt: Working as a grammar classifier, identify whether the given sentence has correct grammar, and respond with 'acceptable' for correct sentences or 'unacceptable' for incorrect ones.
-Attacked prompt: b"Working as a grammar classifier, identify whether the given sentence has correct grammar, and respond with 'acceptable' for correct sentences or 'unacceptable' for incorrect ones yh912BU8T0. "
-Original acc: 35.20%, attacked acc: 2.40%, dropped acc: 32.80%
-
-## deepwordbug
-
-Original prompt: Determine if the grammar of the given sentence is 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Dtermine if the gammar of the gisven setnence is 'Acceptable' or 'Unacceptable'. "
-Original acc: 65.20%, attacked acc: 24.50%, dropped acc: 40.70%
-
-Original prompt: Assess the grammatical structure of the given sentence and classify it as 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Assess the grammatcal etructure of the given entence and classif it as 'Acceptable' or 'Unacceptable'. "
-Original acc: 62.00%, attacked acc: 11.40%, dropped acc: 50.60%
-
-Original prompt: Examine the sentence and decide if its grammar is 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Exmaine the sntence and dcide if its rgammar is 'Acceptable' or 'Unacceptable'. "
-Original acc: 47.60%, attacked acc: 12.90%, dropped acc: 34.70%
-
-Original prompt: As a tool for determining grammatical correctness, review the sentence and categorize its grammar as either 'acceptable' or 'unacceptable'.
-Attacked prompt: b"sA a tobol for determning grammatical correctness, reTview the sentence and caegorize its grammar as iether 'acceptable' or 'unacceptable'. "
-Original acc: 49.80%, attacked acc: 3.00%, dropped acc: 46.80%
-
-Original prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"Whie performing grammar analysis, claBsify the grammar of the following sentenge as 'acceptable' for correct grammar or 'unacceptable' for incorrect grmmar. "
-Original acc: 47.50%, attacked acc: 1.10%, dropped acc: 46.40%
-
-Original prompt: Working as a grammar classifier, identify whether the given sentence has correct grammar, and respond with 'acceptable' for correct sentences or 'unacceptable' for incorrect ones.
-Attacked prompt: b"Working as a grammar clsasifier, identify whether the given sentence has correct grammar, and reszond with 'acceptable' for correct sentences or 'unacceptable' for incorrect noes. "
-Original acc: 35.10%, attacked acc: 0.00%, dropped acc: 35.10%
-
-## stresstest
-
-Original prompt: Determine if the grammar of the given sentence is 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Determine if the grammar of the given sentence is 'Acceptable' or 'Unacceptable'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 65.20%, attacked acc: 21.30%, dropped acc: 43.90%
-
-Original prompt: Assess the grammatical structure of the given sentence and classify it as 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Assess the grammatical structure of the given sentence and classify it as 'Acceptable' or 'Unacceptable'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 62.20%, attacked acc: 16.00%, dropped acc: 46.20%
-
-Original prompt: Examine the sentence and decide if its grammar is 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Examine the sentence and decide if its grammar is 'Acceptable' or 'Unacceptable'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 47.70%, attacked acc: 10.60%, dropped acc: 37.10%
-
-Original prompt: As a tool for determining grammatical correctness, review the sentence and categorize its grammar as either 'acceptable' or 'unacceptable'.
-Attacked prompt: b"As a tool for determining grammatical correctness, review the sentence and categorize its grammar as either 'acceptable' or 'unacceptable'  and true is true . "
-Original acc: 49.60%, attacked acc: 3.00%, dropped acc: 46.60%
-
-Original prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar  and true is true . "
-Original acc: 48.00%, attacked acc: 17.20%, dropped acc: 30.80%
-
-Original prompt: Working as a grammar classifier, identify whether the given sentence has correct grammar, and respond with 'acceptable' for correct sentences or 'unacceptable' for incorrect ones.
-Attacked prompt: b"Working as a grammar classifier, identify whether the given sentence has correct grammar, and respond with 'acceptable' for correct sentences or 'unacceptable' for incorrect ones  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 35.20%, attacked acc: 0.90%, dropped acc: 34.30%
-
-## textbugger
-
-Original prompt: Determine if the grammar of the given sentence is 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Identify if the \xc9\xa1rammar of the give\xd5\xb8 sentence is 'Acceptable' or 'Unacceptable'. "
-Original acc: 65.20%, attacked acc: 4.00%, dropped acc: 61.20%
-
-Original prompt: Assess the grammatical structure of the given sentence and classify it as 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Assess the grammatically s\xf0\x9d\x9a\x9dructure of the \xc9\xa1iven sentecne and clasisfy it as 'Acceptable' or 'Unacceptable'. "
-Original acc: 62.20%, attacked acc: 2.20%, dropped acc: 60.00%
-
-Original prompt: Examine the sentence and decide if its grammar is 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Investigated the sentencing and decide if its grammar is 'Acceptable' or 'Unacceptable'. "
-Original acc: 47.70%, attacked acc: 21.00%, dropped acc: 26.70%
-
-Original prompt: As a tool for determining grammatical correctness, review the sentence and categorize its grammar as either 'acceptable' or 'unacceptable'.
-Attacked prompt: b"Como a \xf0\x9d\x9a\x9dool for determines grammatical correctness, review the se\xd5\xb8tence and cat\xd0\xb5gorize its grammar as or 'acceptable' or 'unacceptable'. "
-Original acc: 49.60%, attacked acc: 1.10%, dropped acc: 48.50%
-
-Original prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"Whie pe\xe2\xb2\x85forming grammar analysis, \xcf\xb2lassify the grammatical of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammr. "
-Original acc: 48.00%, attacked acc: 0.00%, dropped acc: 48.00%
-
-Original prompt: Working as a grammar classifier, identify whether the given sentence has correct grammar, and respond with 'acceptable' for correct sentences or 'unacceptable' for incorrect ones.
-Attacked prompt: b"Working as a grammar classi\xf0\x9d\x9a\x8fier, identify whether the given sentence has correct grammar, and replying with 'acceptable' for correct sentences or 'unacceptable' for incorrect ones. "
-Original acc: 35.20%, attacked acc: 0.00%, dropped acc: 35.20%
-
-## textfooler
-
-Original prompt: Determine if the grammar of the given sentence is 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Establishing if the verbs of the offered condemnation is 'Acceptable' or 'Unacceptable'. "
-Original acc: 65.20%, attacked acc: 14.20%, dropped acc: 51.00%
-
-Original prompt: Assess the grammatical structure of the given sentence and classify it as 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Assess the grammatically constructing of the given sentence and classed it as 'Acceptable' or 'Unacceptable'. "
-Original acc: 62.20%, attacked acc: 23.00%, dropped acc: 39.20%
-
-Original prompt: Examine the sentence and decide if its grammar is 'Acceptable' or 'Unacceptable'.
-Attacked prompt: b"Discuss the sentencing and choosing if its language is 'Acceptable' or 'Unacceptable'. "
-Original acc: 47.70%, attacked acc: 8.00%, dropped acc: 39.70%
-
-Original prompt: As a tool for determining grammatical correctness, review the sentence and categorize its grammar as either 'acceptable' or 'unacceptable'.
-Attacked prompt: b"As a appliance for establish grammatical correctness, reconsider the reprimand and classify its grammar as either 'acceptable' or 'unacceptable'. "
-Original acc: 49.60%, attacked acc: 1.90%, dropped acc: 47.70%
-
-Original prompt: While performing grammar analysis, classify the grammar of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for incorrect grammar.
-Attacked prompt: b"During exercise grammar examines, classified the pronunciation of the following sentence as 'acceptable' for correct grammar or 'unacceptable' for improper jargon. "
-Original acc: 48.00%, attacked acc: 0.20%, dropped acc: 47.80%
-
-Original prompt: Working as a grammar classifier, identify whether the given sentence has correct grammar, and respond with 'acceptable' for correct sentences or 'unacceptable' for incorrect ones.
-Attacked prompt: b"Working as a grammar classifier, identify whether the given sentence has correct jargon, and cope with 'acceptable' for correct punishments or 'unacceptable' for inappropriate ones. "
-Original acc: 35.20%, attacked acc: 0.00%, dropped acc: 35.20%
-
-## translation
-
-Language: chinese, acc: 17.00%, prompt: As a tool for determining grammatical correctness, please review the following sentences and categorize them grammatically as 'acceptable' or 'unacceptable'.
-Language: chinese, acc: 17.10%, prompt: Please evaluate the grammar of the following sentences and mark them as 'acceptable' or 'unacceptable'.
-Language: chinese, acc: 5.30%, prompt: Please check that the following sentences are grammatically correct and categorize them as 'acceptable' or 'unacceptable'.
-Language: chinese, acc: 8.60%, prompt: Please determine whether the following sentences are 'acceptable' or 'unacceptable' based on grammatical correctness.
-Language: chinese, acc: 24.90%, prompt: Please categorize the grammar of the following sentences as 'acceptable' or 'unacceptable'.
-Language: chinese, acc: 13.50%, prompt: Please check the grammar of the following sentences and classify them as 'acceptable' or 'unacceptable'.
-Language: chinese, acc: 7.20%, prompt: Please mark the following sentences as 'acceptable' or 'unacceptable' according to the grammatical norms.
-Language: chinese, acc: 17.80%, prompt: Please assess the grammar of the following sentences and classify them as 'acceptable' or 'unacceptable'.
-Language: chinese, acc: 8.90%, prompt: Please mark the following sentences as 'acceptable' or 'unacceptable' for grammatical accuracy.
-Language: chinese, acc: 13.50%, prompt: Please check the grammar of the following sentences and classify them as 'acceptable' or 'unacceptable'.
-Language: french, acc: 16.50%, prompt: As a tool for assessing semantic relevance, categorize the sentence as 'acceptable' or 'unacceptable' according to its meaning and context.
-Language: french, acc: 23.80%, prompt: Determine whether the grammatical structure of the sentence is 'acceptable' or 'unacceptable' by carefully reviewing it.
-Language: french, acc: 21.10%, prompt: Judge whether the sentence follows the syntactic rules of the language and classify it as 'acceptable' or 'unacceptable'.
-Language: french, acc: 10.70%, prompt: Assess the relevance of the sentence by classifying it as 'acceptable' or 'unacceptable' according to the context in which it is used.
-Language: french, acc: 35.20%, prompt: As a semantic classification tool, decide whether the sentence is 'acceptable' or 'unacceptable' according to its meaning and consistency.
-Language: french, acc: 29.60%, prompt: Analyze the sentence carefully and determine whether it is grammatically 'acceptable' or 'unacceptable'.
-Language: french, acc: 34.60%, prompt: As a syntactic validation tool, determine whether the sentence is properly structured and categorize it as 'acceptable' or 'unacceptable'.
-Language: french, acc: 16.50%, prompt: When evaluating the grammar of the sentence, classify it as 'acceptable' or 'unacceptable' according to the rules of the French language.
-Language: french, acc: 32.70%, prompt: Determine whether the sentence is 'acceptable' or 'unacceptable' depending on its syntactic construction and semantic context.
-Language: french, acc: 10.40%, prompt: As a tool for linguistic analysis, categorize the sentence as 'acceptable' or 'unacceptable' according to its relevance and semantic consistency.
-Language: arabic, acc: 48.30%, prompt: As a tool for determining grammatical correctness, review the sentence and classify its rules as 'acceptable' or 'unacceptable'.
-Language: arabic, acc: 36.80%, prompt: Analyze the sentence carefully and classify its grammar between 'acceptable' or 'unacceptable'.
-Language: arabic, acc: 26.70%, prompt: As a tool for determining grammatical correctness, classify the rules of the sentence between 'acceptable' or 'unacceptable'.
-Language: arabic, acc: 9.00%, prompt: Evaluate the correctness of the sentence between 'acceptable' or 'unacceptable', as a tool for determining grammatical correctness.
-Language: arabic, acc: 30.10%, prompt: As a tool for classifying valid sentences, determine whether  'acceptable' or 'unacceptable' is based on its grammatical rules.
-Language: arabic, acc: 35.60%, prompt: Analyze the sentence carefully and classify its grammatical rules between 'acceptable' or 'unacceptable'.
-Language: arabic, acc: 34.80%, prompt: Analyze the sentence and classify it between 'acceptable' or 'unacceptable' as a grammatical check tool.
-Language: arabic, acc: 22.60%, prompt: As a classification tool for grammatical sentences, determine whether the sentence 'acceptable' or 'unacceptable' is based on its rules.
-Language: arabic, acc: 31.50%, prompt: As a tool for analyzing grammar, classify the rules of the sentence between 'acceptable' or 'unacceptable'.
-Language: arabic, acc: 20.70%, prompt: Determine whether the sentence is 'acceptable' or 'unacceptable' as a tool for determining grammatical correctness and analyze its grammar.
-Language: spanish, acc: 35.60%, prompt: As a tool to determine grammatical correctness, review the sentence and categorize its grammar as 'acceptable' or 'unacceptable'.
-Language: spanish, acc: 40.00%, prompt: Analyze the sentence carefully and classify its grammar as 'acceptable' or 'unacceptable'.
-Language: spanish, acc: 39.20%, prompt: As a tool for determining grammatical correctness, it categorizes the grammatical rules of the sentence as 'acceptable' or 'unacceptable'.
-Language: spanish, acc: 44.50%, prompt: Evaluate the grammatical correctness of the sentence and classify it as 'acceptable' or 'unacceptable' using a grammatical verification tool.
-Language: spanish, acc: 5.80%, prompt: As a tool for classifying grammatically correct sentences, determine whether the sentence is 'acceptable' or 'unacceptable' based on its grammatical rules.
-Language: spanish, acc: 31.80%, prompt: Analyse the sentence carefully and classify its grammar as 'acceptable' or 'unacceptable' using a grammatical revision tool.
-Language: spanish, acc: 31.80%, prompt: As a tool for classifying grammatical sentences, it determines whether the sentence is 'acceptable' or 'unacceptable' using its grammatical structure.
-Language: spanish, acc: 38.00%, prompt: As a tool for analyzing grammatical correctness, it categorizes the grammatical rules of the sentence as 'acceptable' or 'unacceptable'.
-Language: spanish, acc: 26.50%, prompt: Determine whether the sentence is 'acceptable' or 'unacceptable' using a grammatical verification tool and sort it accordingly.
-Language: spanish, acc: 30.30%, prompt: As a tool for determining grammatical correctness, evaluate the sentence and classify it as 'acceptable' or 'unacceptable' based on its grammatical rules.
-Language: japanese, acc: 61.40%, prompt: As a tool to determine whether grammar is grammatically correct, look at the sentence and categorize grammar into the 'acceptable' or 'unacceptable' categories.
-Language: japanese, acc: 38.90%, prompt: Please read the given sentence and categorize the grammar into the 'acceptable' or 'unacceptable' categories.
-Language: japanese, acc: 57.10%, prompt: To determine the correctness of a sentence, categorize grammar into two categories: 'acceptable' or 'unacceptable'.
-Language: japanese, acc: 58.70%, prompt: Classify the grammar of a given sentence into two categories: 'acceptable' and 'unacceptable'.
-Language: japanese, acc: 29.60%, prompt: Classify a given sentence into the categories 'acceptable' or 'unacceptable' to determine whether it is grammatically correct.
-Language: japanese, acc: 72.30%, prompt: To determine whether it is grammatically correct, categorize a given sentence into two categories: 'acceptable' or 'unacceptable'.
-Language: japanese, acc: 32.70%, prompt: To determine the correctness of grammar, categorize a given sentence into two categories: 'acceptable' or 'unacceptable'.
-Language: japanese, acc: 48.80%, prompt: Classify the grammar of a given sentence into two categories, 'acceptable' or 'unacceptable', and judge its accuracy.
-Language: japanese, acc: 53.20%, prompt: To determine whether it is grammatically correct, divide a given sentence into two categories: 'acceptable' or 'unacceptable'.
-Language: japanese, acc: 66.10%, prompt: To evaluate the accuracy of grammar, categorize a given sentence into two categories: 'acceptable' or 'unacceptable'.
-Language: korean, acc: 13.20%, prompt: As a tool for judging grammatical correctness, please review the sentences and classify the grammar as 'acceptable' or 'unacceptable'.
-Language: korean, acc: 23.10%, prompt: Please read the given sentences, and classify the grammar as 'acceptable' or 'unacceptable'.
-Language: korean, acc: 19.60%, prompt: Please classify the sentences as 'acceptable' or 'unacceptable' to judge the grammatical appropriateness.
-Language: korean, acc: 33.20%, prompt: Please classify the grammar of the given sentences into 2 categories: 'acceptable' or 'unacceptable'.
-Language: korean, acc: 7.60%, prompt: Please classify the given sentences as 'acceptable' or 'unacceptable' to judge the correctness of the grammar.
-Language: korean, acc: 17.60%, prompt: Please classify the given sentences as 'acceptable' or 'unacceptable' in order to judge their grammatical appropriateness.
-Language: korean, acc: 16.90%, prompt: Please determine the correctness of your grammar by classifying sentences as 'acceptable' or 'unacceptable'.
-Language: korean, acc: 40.20%, prompt: Classify the grammar of a given sentence as 'acceptable' or 'unacceptable', and judge its accuracy.
-Language: korean, acc: 7.60%, prompt: Please classify the given sentences as 'acceptable' or 'unacceptable' to judge the correctness of the grammar.
-Language: korean, acc: 30.10%, prompt: Please rate the accuracy of your grammar by categorizing sentences as 'acceptable' or 'unacceptable'.
-
-# mnli
-
-## 10 prompts
-
-Acc: 25.10%, prompt: Assess the connection between the following sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Acc: 21.30%, prompt: Please classify the relationship between the provided sentences as 'entailment', 'neutral', or 'contradiction'.
-Acc: 9.60%, prompt: Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction'.
-Acc: 9.50%, prompt: Does the relationship between the given sentences represent entailment, neutral, or contradiction? Respond with 'entailment', 'neutral', or 'contradiction'.
-Acc: 3.80%, prompt: Indicate if the connection between the following sentences is 'entailment', 'neutral', or 'contradiction'.
-Acc: 2.50%, prompt: Considering the two sentences, identify if their relationship is 'entailment', 'neutral', or 'contradiction'.
-Acc: 1.00%, prompt: Analyze the two provided sentences and decide if their relationship is 'entailment', 'neutral', or 'contradiction'.
-Acc: 0.60%, prompt: Determine if the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Acc: 0.10%, prompt: Examine the pair of sentences and determine if they exhibit entailment, neutral, or contradiction. Answer with either 'entailment', 'neutral', or 'contradiction'.
-
-Acc: 19.20%, prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Acc: 16.90%, prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment', 'neutral', or 'contradiction'.
-Acc: 11.20%, prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Acc: 11.00%, prompt: While performing entailment analysis, classify the relationship between the provided sentences as 'entailment', 'neutral', or 'contradiction'.
-Acc: 10.20%, prompt: As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment', 'neutral', or 'contradiction'.
-Acc: 10.10%, prompt: As a tool for determining entailment relationships, review the two statements and categorize their connection as either 'entailment', 'neutral', or 'contradiction'.
-Acc: 6.30%, prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment', 'neutral', or 'contradiction'.
-Acc: 3.80%, prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment', 'neutral', or 'contradiction'.
-Acc: 1.10%, prompt: Working as an entailment classifier, identify whether the given pair of sentences displays entailment, neutral, or contradiction. Respond with 'entailment', 'neutral', or 'contradiction'.
-Acc: 0.50%, prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-
-## bertattack
-
-Original prompt: Assess the connection between the following sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Assess the situation between the following objects and classify it as 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 25.10%, attacked acc: 0.90%, dropped acc: 24.20%
-
-Original prompt: Please classify the relationship between the provided sentences as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Please state the relationship between the provided data as 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 21.30%, attacked acc: 0.70%, dropped acc: 20.60%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 9.60%, attacked acc: 9.60%, dropped acc: 11.70%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"In your use as an entailment analysis utility, estimate the relation between the provided contexts and classify it as 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 19.20%, attacked acc: 0.00%, dropped acc: 19.20%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"As an entailment identification tree, determine the communication between the following words and answer with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 16.90%, attacked acc: 0.00%, dropped acc: 16.90%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"is the role of a descriptive relationship analyzer, consider the connection between the given words and make if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 11.20%, attacked acc: 0.00%, dropped acc: 11.20%
-
-## checklist
-
-Original prompt: Assess the connection between the following sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Assess the connection between the following sentences and classify it as 'entailment', 'neutral', or 'contradiction' yh912BU8T0. "
-Original acc: 25.10%, attacked acc: 3.70%, dropped acc: 21.40%
-
-Original prompt: Please classify the relationship between the provided sentences as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Please classify the relationship between the provided sentences as 'entailment', 'neutral', or 'contradiction' Q47Jmd4lMV. "
-Original acc: 21.30%, attacked acc: 2.60%, dropped acc: 18.70%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction' w52rwgo0Av. "
-Original acc: 9.60%, attacked acc: 4.60%, dropped acc: 5.00%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction' Q47Jmd4lMV. "
-Original acc: 19.20%, attacked acc: 7.10%, dropped acc: 12.10%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"As an entailment identification system, examine the connection between the following sentences and respond with 'entailment', 'neutral', or 'contradiction' Pbl3tYuoRo. "
-Original acc: 16.90%, attacked acc: 1.40%, dropped acc: 15.50%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction' Pbl3tYuoRo. "
-Original acc: 11.20%, attacked acc: 6.70%, dropped acc: 4.50%
-
-## deepwordbug
-
-Original prompt: Assess the connection between the following sentences and classify it as 'entailment', 'neutral', or 'contradiction'. Attacked prompt: b"Assess the cqnnection between the ollowing sentenceX and Mlassify it as 'entailment', 'neutral', or 'contradiction'. " Original acc: 25.10%, attacked acc: 4.30%, dropped acc: 20.80%
-
-Original prompt: Please classify the relationship between the provided sentences as 'entailment', 'neutral', or 'contradiction'. Attacked prompt: b"Pltase Nlassify the relationshpi between the provided sentencXs as 'entailment', 'neutral', or 'contradiction'. " Original acc: 21.40%, attacked acc: 1.60%, dropped acc: 19.80%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction'. Attacked prompt: b"Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction'. " Original acc: 9.80%, attacked acc: 9.80%, dropped acc: 11.60%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction'. Attacked prompt: b"IZn your roKe as an entailment analysis tool, ssess the relationyship between the givne sentences and classify it as 'entailment', 'neutral', or 'contradiction'. " Original acc: 19.50%, attacked acc: 1.60%, dropped acc: 17.90%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment', 'neutral', or 'contradiction'. Attacked prompt: b"mAs an entailment identification Cystem, examine the connetcion between the following sentenceCs and respond with 'entailment', 'neutral', or 'contradiction'. " Original acc: 17.00%, attacked acc: 0.80%, dropped acc: 16.20%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'. Attacked prompt: b"Ic the role of a semaintic relationhsip analyzeer, examine the connection between the given Osentences and decide if they exhbiit entailment, neutral, or contradiction. Afswer with 'entailment', 'neutral', or 'contradiction'. " Original acc: 11.20%, attacked acc: 0.20%, dropped acc: 11.00%
-
-## stresstest
-
-Original prompt: Assess the connection between the following sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Assess the connection between the following sentences and classify it as 'entailment', 'neutral', or 'contradiction'  and true is true . "
-Original acc: 25.10%, attacked acc: 1.80%, dropped acc: 23.30%
-
-Original prompt: Please classify the relationship between the provided sentences as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Please classify the relationship between the provided sentences as 'entailment', 'neutral', or 'contradiction'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 21.30%, attacked acc: 5.30%, dropped acc: 16.00%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction'  and false is not true . "
-Original acc: 9.60%, attacked acc: 4.50%, dropped acc: 5.10%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 19.20%, attacked acc: 15.60%, dropped acc: 3.60%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"As an entailment identification system, examine the connection between the following sentences and respond with 'entailment', 'neutral', or 'contradiction'  and true is true . "
-Original acc: 16.90%, attacked acc: 0.60%, dropped acc: 16.30%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 11.20%, attacked acc: 3.20%, dropped acc: 8.00%
-
-## textbugger
-
-Original prompt: Assess the connection between the following sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Assess the link between the \xf0\x9d\x9a\x8following sentence and c\xe2\x85\xbcassify it as 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 25.10%, attacked acc: 0.30%, dropped acc: 24.80%
-
-Original prompt: Please classify the relationship between the provided sentences as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Invite categorize the relationshp between the provided sentences as 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 21.30%, attacked acc: 0.70%, dropped acc: 20.60%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 9.60%, attacked acc: 9.60%, dropped acc: 11.70%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"In your role as an entailment analysi\xd1\x95 to\xd0\xbel, evaluate the relationship between the awarded sen\xf0\x9d\x9a\x9dences and classify it as 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 19.20%, attacked acc: 2.40%, dropped acc: 16.80%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"As an entailment identifying sytsem, examine the connec\xf0\x9d\x9a\x9dion between the \xf0\x9d\x9a\x8following sentence and respond with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 16.90%, attacked acc: 0.90%, dropped acc: 16.00%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"During the role of a se\xef\xbd\x8dantic relationship analy\xe1\xb4\xa2er, examine the connection between the awarded sentences and determine if they exhibi\xf0\x9d\x9a\x9d entailment, neutral, or contradiction. Ans wer with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 11.20%, attacked acc: 0.00%, dropped acc: 11.20%
-
-## textfooler
-
-Original prompt: Assess the connection between the following sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Assess the link between the later judgments and classify it as 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 25.10%, attacked acc: 1.10%, dropped acc: 24.00%
-
-Original prompt: Please classify the relationship between the provided sentences as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Asking classify the dealings between the provided condemning as 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 21.30%, attacked acc: 0.80%, dropped acc: 20.50%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Review the two statements and categorize their relationship as either 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 9.60%, attacked acc: 9.60%, dropped acc: 11.70%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"During your role as an entailment investigate utility, appraisals the relationships between the offered sentences and categorized it as 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 19.20%, attacked acc: 1.10%, dropped acc: 18.10%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"As an entailment determining system, examine the respecting between the following judgement and respond with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 16.90%, attacked acc: 0.00%, dropped acc: 16.90%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment, neutral, or contradiction. Answer with 'entailment', 'neutral', or 'contradiction'.
-Attacked prompt: b"Per the role of a semantic relationship analyzer, examine the connection between the offered sentences and determines if they fairs entailment, neutral, or contradiction. Response with 'entailment', 'neutral', or 'contradiction'. "
-Original acc: 11.20%, attacked acc: 0.10%, dropped acc: 11.10%
-
-# mrpc
-
-## 10 prompts
-
-Acc: 69.36%, prompt: Determine if the meanings of the following sentences are semantically equivalent by responding with 'equivalent' or 'not_equivalent'.
-Acc: 68.38%, prompt: Assess if the two given sentences have equivalent meanings by selecting 'equivalent' or 'not_equivalent'.
-Acc: 67.65%, prompt: Do the meanings of these two statements align? Indicate your answer with 'equivalent' or 'not_equivalent'.
-Acc: 65.69%, prompt: Are the meanings of the following pair of sentences the same? Answer with 'equivalent' or 'not_equivalent'.
-Acc: 65.69%, prompt: Can the given sentences be considered semantically identical? Please reply with 'equivalent' or 'not_equivalent'.
-Acc: 63.48%, prompt: Evaluate whether the two provided sentences convey the same meaning by answering 'equivalent' or 'not_equivalent'.
-Acc: 63.24%, prompt: Please analyze the provided sentences and indicate if their meanings are the same by choosing 'equivalent' or 'not_equivalent'.
-Acc: 62.99%, prompt: Compare the following sentences and determine if they share the same semantic meaning by responding with 'equivalent' or 'not_equivalent'.
-Acc: 62.01%, prompt: Do these two sentences have the same underlying meaning? Respond with 'equivalent' or 'not_equivalent'.
-Acc: 47.06%, prompt: Examine the pair of sentences and decide if their meanings are identical by answering with 'equivalent' or 'not_equivalent'.
-
-Acc: 69.12%, prompt: In your capacity as a semantic assessment tool, evaluate the provided sentences and classify their meanings as 'equivalent' or 'not_equivalent'.
-Acc: 68.38%, prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Acc: 68.14%, prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Acc: 67.89%, prompt: As a sentence similarity evaluator, analyze the provided sentences and indicate if their meanings are 'equivalent' or 'not_equivalent'.
-Acc: 66.91%, prompt: In your capacity as a language analyst, assess the following sentences and classify their similarity as 'equivalent' or 'not_equivalent'.
-Acc: 66.67%, prompt: In your capacity as a semantic comparison tool, examine the given sentences and decide if their meanings are identical, responding with 'equivalent' or 'not_equivalent'.
-Acc: 66.18%, prompt: In the role of a textual comparison specialist, examine the given sentences and decide if they share the same meaning, responding with 'equivalent' or 'not_equivalent'.
-Acc: 65.69%, prompt: As a language comparison expert, examine the given pair of sentences and decide if their meanings align, answering with 'equivalent' or 'not_equivalent'.
-Acc: 63.24%, prompt: As a linguistic comparator, review the following pair of sentences and determine their semantic equivalence by choosing 'equivalent' or 'not_equivalent'.
-Acc: 60.78%, prompt: As a textual similarity evaluator, analyze the following pair of sentences and determine if they are semantically 'equivalent' or 'not_equivalent'.
-
-## bertattack
-
-Original prompt: Determine if the meanings of the following sentences are semantically equivalent by responding with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Determine if the words of the following sentence are semanticdouble equivalent by thinking with 'equivalent' or 'not_equivalent'. "
-Original acc: 69.36%, attacked acc: 33.33%, dropped acc: 36.03%
-
-Original prompt: Assess if the two given sentences have equivalent meanings by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Assess if the two given statements have equivalent meanings by removing 'equivalent' or 'not_equivalent'. "
-Original acc: 68.38%, attacked acc: 61.52%, dropped acc: 6.86%
-
-Original prompt: Do the meanings of these two statements align? Indicate your answer with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Do the function of these two statements align? enter your answer with 'equivalent' or 'not_equivalent'. "
-Original acc: 67.65%, attacked acc: 49.02%, dropped acc: 18.63%
-
-Original prompt: In your capacity as a semantic assessment tool, evaluate the provided sentences and classify their meanings as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In your capacity as a knowledge assessment tool, consider the following sentences and classify their components as 'equivalent' or 'not_equivalent'. "
-Original acc: 69.12%, attacked acc: 55.88%, dropped acc: 13.24%
-
-Original prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"to a wikipedia comparison expert, evaluate the given couple of sentences and know if they are 'equivalent' or 'not_equivalent'. "
-Original acc: 68.38%, attacked acc: 55.64%, dropped acc: 12.75%
-
-Original prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"to the role of a face comparison instance, assess the following sentences and indicate if they describe the same person by selecting 'equivalent' or 'not_equivalent'. "
-Original acc: 68.14%, attacked acc: 17.16%, dropped acc: 50.98%
-
-## checklist
-
-Original prompt: Determine if the meanings of the following sentences are semantically equivalent by responding with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Determine if the meanings of the following sentences are semantically equivalent by responding with 'equivalent' or 'not_equivalent' SFPCqpiClT. "
-Original acc: 69.36%, attacked acc: 62.01%, dropped acc: 7.35%
-
-Original prompt: Assess if the two given sentences have equivalent meanings by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Assess if the two given sentences have equivalent meanings by selecting 'equivalent' or 'not_equivalent' bhrRSokrfa. "
-Original acc: 68.38%, attacked acc: 62.25%, dropped acc: 6.13%
-
-Original prompt: Do the meanings of these two statements align? Indicate your answer with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Do the meanings of these two statements align? Indicate your answer with 'equivalent' or 'not_equivalent' SFPCqpiClT. "
-Original acc: 67.65%, attacked acc: 63.97%, dropped acc: 3.68%
-
-Original prompt: In your capacity as a semantic assessment tool, evaluate the provided sentences and classify their meanings as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In your capacity as a semantic assessment tool, evaluate the provided sentences and classify their meanings as 'equivalent' or 'not_equivalent' ofOQXLxiW4. "
-Original acc: 69.12%, attacked acc: 65.20%, dropped acc: 3.92%
-
-Original prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent' bhrRSokrfa. "
-Original acc: 68.38%, attacked acc: 66.91%, dropped acc: 1.47%
-
-Original prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent' xikCjkMydH. "
-Original acc: 68.14%, attacked acc: 58.09%, dropped acc: 10.05%
-
-## deepwordbug
-
-Original prompt: Determine if the meanings of the following sentences are semantically equivalent by responding with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"DeterOine if the meanings of the followjing senteJnces are sCmantically equivalent by resgonding with 'equivalent' or 'not_equivalent'. "
-Original acc: 69.85%, attacked acc: 64.46%, dropped acc: 5.39%
-
-Original prompt: Assess if the two given sentences have equivalent meanings by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"AssesHs if the two giken sKentences have equivalent meanings by seDecting 'equivalent' or 'not_equivalent'. "
-Original acc: 68.38%, attacked acc: 60.54%, dropped acc: 7.84%
-
-Original prompt: Do the meanings of these two statements align? Indicate your answer with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Zo the meanings of these Xwo statements Ualign? IndicNte your answeY with 'equivalent' or 'not_equivalent'. "
-Original acc: 67.65%, attacked acc: 36.76%, dropped acc: 30.88%
-
-Original prompt: In your capacity as a semantic assessment tool, evaluate the provided sentences and classify their meanings as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"nI your capacitCy as a sebmantic assgssment otol, evaluate the provided sentenceAs and Zlassify their meaniPgs as 'equivalent' or 'not_equivalent'. "
-Original acc: 69.12%, attacked acc: 34.31%, dropped acc: 34.80%
-
-Original prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"bAs a selantic comparuson exZert, evaluate the given pair of sentences and deermine if they are 'equivalent' or 'not_equivalent'. "
-Original acc: 68.38%, attacked acc: 54.90%, dropped acc: 13.48%
-
-Original prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"GIn the role of a senteLnce comparisoU anNlyst, assess the provided sentences and indicate if they dconvey the same meaJning by selecting 'equivalent' or 'not_equivalent'. "
-Original acc: 68.14%, attacked acc: 37.75%, dropped acc: 30.39%
-
-## stresstest
-
-Original prompt: Determine if the meanings of the following sentences are semantically equivalent by responding with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Determine if the meanings of the following sentences are semantically equivalent by responding with 'equivalent' or 'not_equivalent'  and true is true . "
-Original acc: 69.36%, attacked acc: 69.36%, dropped acc: 0.00%
-
-Original prompt: Assess if the two given sentences have equivalent meanings by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Assess if the two given sentences have equivalent meanings by selecting 'equivalent' or 'not_equivalent'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 68.38%, attacked acc: 69.36%, dropped acc: -0.98%
-
-Original prompt: Do the meanings of these two statements align? Indicate your answer with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Do the meanings of these two statements align? Indicate your answer with 'equivalent' or 'not_equivalent'  and false is not true . "
-Original acc: 67.65%, attacked acc: 67.40%, dropped acc: 0.25%
-
-Original prompt: In your capacity as a semantic assessment tool, evaluate the provided sentences and classify their meanings as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In your capacity as a semantic assessment tool, evaluate the provided sentences and classify their meanings as 'equivalent' or 'not_equivalent'  and false is not true . "
-Original acc: 69.12%, attacked acc: 64.22%, dropped acc: 4.90%
-
-Original prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'  and false is not true . "
-Original acc: 68.38%, attacked acc: 68.63%, dropped acc: -0.25%
-
-Original prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'  and false is not true . "
-Original acc: 68.14%, attacked acc: 59.80%, dropped acc: 8.33%
-
-## textbugger
-
-Original prompt: Determine if the meanings of the following sentences are semantically equivalent by responding with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Determie if the mean ings of the following sentences are semantically equivalent by respondi\xd5\xb8g with 'equivalent' or 'not_equivalent'. "
-Original acc: 69.36%, attacked acc: 45.59%, dropped acc: 23.77%
-
-Original prompt: Assess if the two given sentences have equivalent meanings by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Assessing if the t wo given sentences have equivalent meanings by selecti\xd5\xb8g 'equivalent' or 'not_equivalent'. "
-Original acc: 68.38%, attacked acc: 44.61%, dropped acc: 23.77%
-
-Original prompt: Do the meanings of these two statements align? Indicate your answer with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Do the meaning of these dos stateme\xd5\xb8ts aligns? Indicate your answer with 'equivalent' or 'not_equivalent'. "
-Original acc: 67.65%, attacked acc: 45.10%, dropped acc: 22.55%
-
-Original prompt: In your capacity as a semantic assessment tool, evaluate the provided sentences and classify their meanings as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"At your capacity as a se\xef\xbd\x8dantic asse ssment \xf0\x9d\x9a\x9dool, evaluate the provided sentences and classify their meanings as 'equivalent' or 'not_equivalent'. "
-Original acc: 69.12%, attacked acc: 53.92%, dropped acc: 15.20%
-
-Original prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a sema\xd5\xb8tic com\xd1\x80arison expert, evaluate the given pair of sentences and \xd4\x81etermine if they are 'equivalent' or 'not_equivalent'. "
-Original acc: 68.38%, attacked acc: 50.25%, dropped acc: 18.14%
-
-Original prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Onto the role of a sente\xd5\xb8ce c omparison analys\xf0\x9d\x9a\x9d, assess the given sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'. "
-Original acc: 68.14%, attacked acc: 44.36%, dropped acc: 23.77%
-
-## textfooler
-
-Original prompt: Determine if the meanings of the following sentences are semantically equivalent by responding with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Decide if the senses of the following sentence are semantically equivalent by meet with 'equivalent' or 'not_equivalent'. "
-Original acc: 69.36%, attacked acc: 35.54%, dropped acc: 33.82%
-
-Original prompt: Assess if the two given sentences have equivalent meanings by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Ascertain if the two made sentences did equivalent meanings by option 'equivalent' or 'not_equivalent'. "
-Original acc: 68.38%, attacked acc: 45.10%, dropped acc: 23.28%
-
-Original prompt: Do the meanings of these two statements align? Indicate your answer with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Making the senses of these two sermons realign? Reflect your reply with 'equivalent' or 'not_equivalent'. "
-Original acc: 67.65%, attacked acc: 46.81%, dropped acc: 20.83%
-
-Original prompt: In your capacity as a semantic assessment tool, evaluate the provided sentences and classify their meanings as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Towards your aptitude as a semantic exams paraphernalia, evaluate the furnished sentences and classify their connotation as 'equivalent' or 'not_equivalent'. "
-Original acc: 69.12%, attacked acc: 51.47%, dropped acc: 17.65%
-
-Original prompt: As a semantic comparison expert, evaluate the given pair of sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"As a semantic comparison whiz, evaluate the given couple of sentences and determine if they are 'equivalent' or 'not_equivalent'. "
-Original acc: 68.38%, attacked acc: 64.22%, dropped acc: 4.17%
-
-Original prompt: In the role of a sentence comparison analyst, assess the provided sentences and indicate if they convey the same meaning by selecting 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Towards the feature of a judgements versus psychoanalyst, reviews the supplied sentences and clarified if they convey the same senses by electing 'equivalent' or 'not_equivalent'. "
-Original acc: 68.14%, attacked acc: 13.24%, dropped acc: 54.90%
-
-## translation
-
-Language: chinese, acc: 69.36%, prompt: As an expert in semantic comparison, evaluates two given sentences to determine whether they are 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 68.14%, prompt: From the point of view of semantic comparison, determine whether this pair of sentences is 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 63.73%, prompt: Using the semantic comparison method, determine whether the following two statements are 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 64.95%, prompt: For the following two sentences, determine whether they are 'equivalent' or 'not_equivalent' based on semantic comparison.
-Language: chinese, acc: 62.01%, prompt: As an expert in semantic comparison, please evaluate the following two sentences and determine if they are 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 64.46%, prompt: Using semantic comparison techniques, determine whether the following two sentences are 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 65.44%, prompt: Please determine whether the following two sentences are 'equivalent' or 'not_equivalent' according to the standard of semantic comparison.
-Language: chinese, acc: 59.56%, prompt: As an expert in the field of semantic comparison, please evaluate the following two sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 64.46%, prompt: Using semantic comparison, determine whether the following two sentences are 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 66.18%, prompt: Determine whether the following two sentences are 'equivalent' or 'not_equivalent' based on semantic comparison.
-Language: french, acc: 66.67%, prompt: As an expert in semantic comparison, evaluate the following pair of sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: french, acc: 68.63%, prompt: Can you determine whether the following two sentences are 'equivalent' or 'not_equivalent' as a semantic comparison expert?
-Language: french, acc: 65.69%, prompt: Using your expertise in semantic comparison, determine whether the following two sentences are 'equivalent' or 'not_equivalent'.
-Language: french, acc: 68.63%, prompt: As a semantic comparison specialist, assess the similarity between the following two sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: french, acc: 59.56%, prompt: Are you able to determine whether the following two sentences are 'equivalent' or 'not_equivalent' as an expert in semantic comparison?
-Language: french, acc: 65.20%, prompt: As a semantic comparison professional, evaluate the following pair of sentences and indicate whether they are 'equivalent' or 'not_equivalent'.
-Language: french, acc: 58.33%, prompt: Can you determine whether the following two sentences have a 'equivalent' or 'not_equivalent' meaning as an expert in semantic comparison?
-Language: french, acc: 68.63%, prompt: As an expert in semantic comparison, assess the similarity between the following two sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: french, acc: 65.69%, prompt: Using your expertise in semantic comparison, determine whether the following two sentences are 'equivalent' or 'not_equivalent' in terms of meaning.
-Language: french, acc: 67.40%, prompt: As a semantic comparison professional, assess the similarity between the following two sentences and indicate whether they are 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 68.14%, prompt: As an expert in semantic comparison, evaluate the two given sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 64.46%, prompt: Based on my experience in semantic analysis, classify the following two sentences as 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 66.18%, prompt: As an expert in semantic comparison, analyze the following two sentences and classify them as 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 67.40%, prompt: Your task as an expert in semantic comparison is to evaluate the following two sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 54.90%, prompt: As a semantic comparison specialist, analyze the two data statements and insert them into one of the following categories: 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 61.52%, prompt: Based on my experience in semantic analysis, classify the following two sentences between 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 68.63%, prompt: Your role as a semantic comparison specialist requires analyzing the two given sentences and determining whether they are 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 65.69%, prompt: As an experienced semantic analyst, classify the following two sentences as 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 68.63%, prompt: Your job as a semantic analyst evaluates the following two sentences as 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 69.12%, prompt: As a semantic analyst, determine whether the given sentences are 'equivalent' or 'not_equivalent' based on their relationship.
-Language: spanish, acc: 68.87%, prompt: As an expert in semantic comparison, it evaluates the pair of sentences provided and determines whether they are 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 64.46%, prompt: Based on my experience in semantic analysis, classify the following two sentences as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 67.89%, prompt: As an expert in semantic comparison, analyze the two sentences given and classify them as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 67.16%, prompt: Your task as a semantic comparison specialist is to evaluate the following two sentences and determine whether they are 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 68.14%, prompt: As an expert in semantic analysis, he makes a classification of the following two sentences based on their 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 65.69%, prompt: Based on your experience of semantic comparison, classify the next two sentences as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 68.38%, prompt: As a specialist in semantic analysis, you are given the task of analysing the two sentences given and classifying them as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 68.38%, prompt: As an expert in semantic comparison, he classifies the following two sentences into 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 66.67%, prompt: As a specialist in semantic analysis, evaluate the following two sentences and classify them as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 69.61%, prompt: Your task as an expert in semantic comparison is to analyze the two sentences provided and determine whether they are 'equivalent' or 'not_equivalent' based on their semantic relationship.
-Language: japanese, acc: 64.22%, prompt: Evaluate whether a given pair of sentences is 'equivalent' or 'not_equivalent', depending on the context.
-Language: japanese, acc: 66.18%, prompt: Use a semantic comparison to determine whether a given pair of sentences is 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 69.36%, prompt: Evaluate a given pair of sentences as 'equivalent' or 'not_equivalent' by determining whether they have the same semantic meaning.
-Language: japanese, acc: 61.52%, prompt: Determine whether a given pair of sentences is synonyms and evaluate whether they are 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 68.38%, prompt: Determine whether a given pair of sentences is 'equivalent' or 'not_equivalent', and whether they are semantically identical.
-Language: japanese, acc: 63.73%, prompt: Determinate whether a given pair of sentences has the same meaning and evaluate whether they are 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 67.65%, prompt: Evaluate whether a given pair of sentences is 'equivalent' or 'not_equivalent' by determining whether they are semantically identical.
-Language: japanese, acc: 65.69%, prompt: Judge whether a given pair of sentences is equal and evaluate whether they are 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 64.46%, prompt: Determinate whether a given pair of sentences are semantically equal and evaluate whether they are 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 63.73%, prompt: Whether a given pair of sentences is 'equivalent' or 'not_equivalent' depends on the context.
-Language: korean, acc: 69.36%, prompt: As a sentence comparator, evaluate the two sentences given to determine 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 70.83%, prompt: Compare two sentences to determine 'equivalent' or 'not_equivalent'. For this you need qualifications as a specialist in semantic comparison.
-Language: korean, acc: 69.12%, prompt: It takes your knowledge as an expert in semantic comparison to determine that two sentences are 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 67.89%, prompt: As a specialist in semantic comparison, evaluate whether two given sentences are 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 68.38%, prompt: Analyze two sentences to determine 'equivalent' or 'not_equivalent'. For that you need the knowledge of a semantic comparison expert.
-Language: korean, acc: 68.38%, prompt: As an expert in semantic comparison, decide whether two sentences are 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 71.57%, prompt: It takes the knowledge of an expert in semantic comparison to compare two sentences to judge 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 69.61%, prompt: Experience as an expert in semantic comparison is required to determine whether two given sentences are 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 68.38%, prompt: As an expert in semantic comparison, determine whether two sentences are 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 70.10%, prompt: Analyze two sentences to determine 'equivalent' or 'not_equivalent'. For this, you need a qualification as a specialist in semantic comparison.
-
-# qnli
-
-## 10 prompts
-
-Acc: 42.70%, prompt: Review the given context and question, and decide if the context contains enough information to support the answer by selecting 'entailment' or 'not_entailment'.
-Acc: 41.20%, prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Acc: 38.90%, prompt: Analyze the context and question, and indicate if the context entails the answer by choosing 'entailment' or 'not_entailment'.
-Acc: 38.10%, prompt: Assess if the answer to the question can be logically concluded from the provided context by choosing 'entailment' or 'not_entailment'.
-Acc: 37.20%, prompt: Given the question and context provided, determine if the answer can be inferred by choosing 'entailment' or 'not_entailment'.
-Acc: 30.90%, prompt: Please assess if the answer to the question can be derived from the given context by selecting 'entailment' or 'not_entailment'.
-Acc: 28.30%, prompt: Examine the context and question, and determine if the context logically implies the answer by selecting 'entailment' or 'not_entailment'.
-Acc: 21.90%, prompt: Based on the provided context and question, decide if the information supports the answer by responding with 'entailment' or 'not_entailment'.
-Acc: 20.20%, prompt: Evaluate whether the given context supports the answer to the question by responding with 'entailment' or 'not_entailment'.
-Acc: 19.00%, prompt: Consider the context and question, and indicate if the answer can be logically deduced from the context by responding with 'entailment' or 'not_entailment'.
-
-Acc: 42.90%, prompt: As a linguistic consultant, decide if the answer to the question is logically supported by the provided context and respond with 'entailment' or 'not_entailment'.
-Acc: 41.40%, prompt: As a semantic researcher, evaluate whether the provided context supports the answer to the question and choose 'entailment' or 'not_entailment'.
-Acc: 35.50%, prompt: As a textual inference expert, analyze if the answer to the question can be deduced from the provided context and select 'entailment' or 'not_entailment'.
-Acc: 31.30%, prompt: In the capacity of a language specialist, decide if the context presented contains enough information to infer the answer to the question and respond with 'entailment' or 'not_entailment'.
-Acc: 31.00%, prompt: In your role as a linguistic investigator, determine if the context given entails the answer to the question and provide your conclusion with 'entailment' or 'not_entailment'.
-Acc: 29.20%, prompt: In the capacity of a language evaluator, examine if the given context justifies the answer to the question and indicate your assessment with 'entailment' or 'not_entailment'.
-Acc: 29.10%, prompt: In your role as a semantic evaluator, determine if the provided context justifies the answer to the question and answer with 'entailment' or 'not_entailment'.
-Acc: 27.50%, prompt: As a language expert, assess if the given context entails the answer to the question and respond with 'entailment' or 'not_entailment'.
-Acc: 19.80%, prompt: As a semantic interpreter, assess whether the provided context supports the answer to the given question and answer with 'entailment' or 'not_entailment'.
-Acc: 14.10%, prompt: As a textual analyst, examine if the given context logically implies the answer to the question and indicate your decision with 'entailment' or 'not_entailment'.
-
-## bertattack
-
-Original prompt: Review the given context and question, and decide if the context contains enough information to support the answer by selecting 'entailment' or 'not_entailment'.
-Attacked prompt: b"play the provided topic and word, and decision if the context contains enough information to determine the answers by creating 'entailment' or 'not_entailment'. "
-Original acc: 42.70%, attacked acc: 0.10%, dropped acc: 42.60%
-
-Original prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Based on the information in the context, decide if the answer to the is is committed by versus 'entailment' or 'not_entailment'. "
-Original acc: 41.20%, attacked acc: 0.00%, dropped acc: 41.20%
-
-Original prompt: Analyze the context and question, and indicate if the context entails the answer by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"s the context and what, and indicate if the context entails the answer by avoiding 'entailment' or 'not_entailment'. "
-Original acc: 38.90%, attacked acc: 2.20%, dropped acc: 36.70%
-
-Original prompt: As a linguistic consultant, decide if the answer to the question is logically supported by the provided context and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a linguistics consultant, determined if the answers to the asked is logical. supported by the provided topic and task with 'entailment' or 'not_entailment'. "
-Original acc: 42.90%, attacked acc: 0.30%, dropped acc: 42.60%
-
-Original prompt: As a semantic researcher, evaluate whether the provided context supports the answer to the question and choose 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a semantic researcher, evaluate should the supplied context supports the concept to the task and seek 'entailment' or 'not_entailment'. "
-Original acc: 41.40%, attacked acc: 0.10%, dropped acc: 41.30%
-
-Original prompt: As a textual inference expert, analyze if the answer to the question can be deduced from the provided context and select 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a textual inference expert, write if the answer to the task can be deduced from the supplied instance and search 'entailment' or 'not_entailment'. "
-Original acc: 35.50%, attacked acc: 0.00%, dropped acc: 35.50%
-
-## checklist
-
-Original prompt: Review the given context and question, and decide if the context contains enough information to support the answer by selecting 'entailment' or 'not_entailment'.
-Attacked prompt: b"Review the given context and question, and decide if the context contains enough information to support the answer by selecting 'entailment' or 'not_entailment' zexKUTWJYG. "
-Original acc: 42.70%, attacked acc: 9.70%, dropped acc: 33.00%
-
-Original prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment' zq0DcZ5dnI. "
-Original acc: 41.20%, attacked acc: 1.10%, dropped acc: 40.10%
-
-Original prompt: Analyze the context and question, and indicate if the context entails the answer by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Analyze the context and question, and indicate if the context entails the answer by choosing 'entailment' or 'not_entailment' MSAddJ4D2a. "
-Original acc: 38.90%, attacked acc: 5.50%, dropped acc: 33.40%
-
-Original prompt: As a linguistic consultant, decide if the answer to the question is logically supported by the provided context and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a linguistic consultant, decide if the answer to the question is logically supported by the provided context and respond with 'entailment' or 'not_entailment' rzu98kF2KF. "
-Original acc: 42.90%, attacked acc: 3.70%, dropped acc: 39.20%
-
-Original prompt: As a semantic researcher, evaluate whether the provided context supports the answer to the question and choose 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a semantic researcher, evaluate whether the provided context supports the answer to the question and choose 'entailment' or 'not_entailment' d6ZQ3u0GBQ. "
-Original acc: 41.40%, attacked acc: 1.00%, dropped acc: 40.40%
-
-Original prompt: As a textual inference expert, analyze if the answer to the question can be deduced from the provided context and select 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a textual inference expert, analyze if the answer to the question can be deduced from the provided context and select 'entailment' or 'not_entailment' d6ZQ3u0GBQ. "
-Original acc: 35.50%, attacked acc: 2.80%, dropped acc: 32.70%
-
-## deepwordbug
-
-Original prompt: Review the given context and question, and decide if the context contains enough information to support the answer by selecting 'entailment' or 'not_entailment'.
-Attacked prompt: b"RevTiew the ginen cEntext and uestion, and decid if the context conatains enough iformation to uspport the answVer by selectin 'entailment' or 'not_entailment'. "
-Original acc: 42.70%, attacked acc: 0.20%, dropped acc: 42.50%
-
-Original prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Basvd on the information in the context, decZde if the answer to the question is jsutified by choosing 'entailment' or 'not_entailment'. "
-Original acc: 41.20%, attacked acc: 6.90%, dropped acc: 34.30%
-
-Original prompt: Analyze the context and question, and indicate if the context entails the answer by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"ABalyze the dcontext and question, and indicte if the context entails the ansTwer by choosing 'entailment' or 'not_entailment'. "
-Original acc: 39.00%, attacked acc: 0.30%, dropped acc: 38.70%
-
-Original prompt: As a linguistic consultant, decide if the answer to the question is logically supported by the provided context and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Abs a linguistic consultant, decide if the answer to the question is logically supported by the provided Aontext and respGond with 'entailment' or 'not_entailment'. "
-Original acc: 42.90%, attacked acc: 1.80%, dropped acc: 41.10%
-
-Original prompt: As a semantic researcher, evaluate whether the provided context supports the answer to the question and choose 'entailment' or 'not_entailment'.
-Attacked prompt: b"sA a semantic researcher, valuate whether the provided context supports the anshwer to the Equestion and cVhoose 'entailment' or 'not_entailment'. "
-Original acc: 41.30%, attacked acc: 3.40%, dropped acc: 37.90%
-
-Original prompt: As a textual inference expert, analyze if the answer to the question can be deduced from the provided context and select 'entailment' or 'not_entailment'.
-Attacked prompt: b"sA a Zextual inference expert, analyze if the answeer to the question can be demuced from the provided cnotext and sKelect 'entailment' or 'not_entailment'. "
-Original acc: 35.30%, attacked acc: 2.80%, dropped acc: 32.50%
-
-## stresstest
-
-Original prompt: Review the given context and question, and decide if the context contains enough information to support the answer by selecting 'entailment' or 'not_entailment'.
-Attacked prompt: b"Review the given context and question, and decide if the context contains enough information to support the answer by selecting 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 42.70%, attacked acc: 10.20%, dropped acc: 32.50%
-
-Original prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 41.20%, attacked acc: 6.70%, dropped acc: 34.50%
-
-Original prompt: Analyze the context and question, and indicate if the context entails the answer by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Analyze the context and question, and indicate if the context entails the answer by choosing 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 38.90%, attacked acc: 12.30%, dropped acc: 26.60%
-
-Original prompt: As a linguistic consultant, decide if the answer to the question is logically supported by the provided context and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a linguistic consultant, decide if the answer to the question is logically supported by the provided context and respond with 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 42.90%, attacked acc: 7.00%, dropped acc: 35.90%
-
-Original prompt: As a semantic researcher, evaluate whether the provided context supports the answer to the question and choose 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a semantic researcher, evaluate whether the provided context supports the answer to the question and choose 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 41.40%, attacked acc: 12.50%, dropped acc: 28.90%
-
-Original prompt: As a textual inference expert, analyze if the answer to the question can be deduced from the provided context and select 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a textual inference expert, analyze if the answer to the question can be deduced from the provided context and select 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 35.50%, attacked acc: 7.00%, dropped acc: 28.50%
-
-## textbugger
-
-Original prompt: Review the given context and question, and decide if the context contains enough information to support the answer by selecting 'entailment' or 'not_entailment'.
-Attacked prompt: b"Examination the given context and ques\xf0\x9d\x9a\x9dion, and deciding if the context con\xf0\x9d\x9a\x9dains enough information to support the answers by \xd1\x95electing 'entailment' or 'not_entailment'. "
-Original acc: 42.70%, attacked acc: 1.30%, dropped acc: 41.40%
-
-Original prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Based on the info in the context, decision if the answer to the ques\xf0\x9d\x9a\x9dion is warranted by chosing 'entailment' or 'not_entailment'. "
-Original acc: 41.20%, attacked acc: 3.70%, dropped acc: 37.50%
-
-Original prompt: Analyze the context and question, and indicate if the context entails the answer by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Analyze the context and question, and depicts if the context involves the ans wer by choosi\xd5\xb8g 'entailment' or 'not_entailment'. "
-Original acc: 38.90%, attacked acc: 0.70%, dropped acc: 38.20%
-
-Original prompt: As a linguistic consultant, decide if the answer to the question is logically supported by the provided context and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"A\xd1\x95 a \xe2\x85\xbcinguistic consultant, deciding if the answering to the quesiton is logically supported by the supplied context and cater with 'entailment' or 'not_entailment'. "
-Original acc: 42.90%, attacked acc: 1.20%, dropped acc: 41.70%
-
-Original prompt: As a semantic researcher, evaluate whether the provided context supports the answer to the question and choose 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a semantic reesarcher, evaluating whether the provided context supports the answering to the question and chosen 'entailment' or 'not_entailment'. "
-Original acc: 41.40%, attacked acc: 0.00%, dropped acc: 41.40%
-
-Original prompt: As a textual inference expert, analyze if the answer to the question can be deduced from the provided context and select 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a textual infere\xd5\xb8ce expert, analyzes if the answer to the problem can be presumed from the provided context and selec\xf0\x9d\x9a\x9d 'entailment' or 'not_entailment'. "
-Original acc: 35.50%, attacked acc: 0.10%, dropped acc: 35.40%
-
-## textfooler
-
-Original prompt: Review the given context and question, and decide if the context contains enough information to support the answer by selecting 'entailment' or 'not_entailment'.
-Attacked prompt: b"Discuss the given context and tura, and decide if the context encompasses enough information to support the satisfy by takes 'entailment' or 'not_entailment'. "
-Original acc: 42.70%, attacked acc: 0.00%, dropped acc: 42.70%
-
-Original prompt: Based on the information in the context, decide if the answer to the question is justified by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Based on the data in the context, pick if the answer to the tura is fide by choice 'entailment' or 'not_entailment'. "
-Original acc: 41.20%, attacked acc: 0.20%, dropped acc: 41.00%
-
-Original prompt: Analyze the context and question, and indicate if the context entails the answer by choosing 'entailment' or 'not_entailment'.
-Attacked prompt: b"Analyse the context and question, and outlined if the context encompass the answers by options 'entailment' or 'not_entailment'. "
-Original acc: 38.90%, attacked acc: 0.50%, dropped acc: 38.40%
-
-Original prompt: As a linguistic consultant, decide if the answer to the question is logically supported by the provided context and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Iike a linguistic advisors, decisions if the answer to the item is logically argued by the provided context and cater with 'entailment' or 'not_entailment'. "
-Original acc: 42.90%, attacked acc: 0.20%, dropped acc: 42.70%
-
-Original prompt: As a semantic researcher, evaluate whether the provided context supports the answer to the question and choose 'entailment' or 'not_entailment'.
-Attacked prompt: b"As a semantic researcher, appraisals whether the provided context supports the answer to the question and taking 'entailment' or 'not_entailment'. "
-Original acc: 41.40%, attacked acc: 0.10%, dropped acc: 41.30%
-
-Original prompt: As a textual inference expert, analyze if the answer to the question can be deduced from the provided context and select 'entailment' or 'not_entailment'.
-Attacked prompt: b"Iike a textual deduction speciality, exams if the answer to the topics can be deduced from the lodged context and select 'entailment' or 'not_entailment'. "
-Original acc: 35.50%, attacked acc: 0.30%, dropped acc: 35.20%
-
-## translation
-
-Language: chinese, acc: 28.40%, prompt: Determine whether the answer is implied or not based on the context. Answer 'entailment' or 'not_entailment'.
-Language: chinese, acc: 25.70%, prompt: As a language expert, determine whether the text contains the answer and choose 'entailment' or 'not_entailment'.
-Language: chinese, acc: 23.70%, prompt: Determine whether the text implies an answer, and select 'entailment' or 'not_entailment'.
-Language: chinese, acc: 42.40%, prompt: Given the text and the question, whether the answer is implied, select 'entailment' or 'not_entailment'.
-Language: chinese, acc: 28.80%, prompt: Determine whether the text contains the answer, depending on the context. Select 'entailment' or 'not_entailment'.
-Language: chinese, acc: 34.50%, prompt: As a language expert, determine whether a text can contain an answer, and say 'entailment' or 'not_entailment'.
-Language: chinese, acc: 34.90%, prompt: Please determine whether the text implies an answer. Answer 'entailment' or 'not_entailment'.
-Language: chinese, acc: 34.40%, prompt: Please select 'entailment' or 'not_entailment' based on the text and the question.
-Language: chinese, acc: 20.90%, prompt: Assess whether the answer is implied based on the context. Answer 'entailment' or 'not_entailment'.
-Language: chinese, acc: 39.40%, prompt: Please determine whether the text contains the answer and answer 'entailment' or 'not_entailment'.
-Language: french, acc: 37.60%, prompt: As a linguistic expert, assess whether the given context involves the answer to the question and answer with 'entailment' or 'not_entailment'.
-Language: french, acc: 35.50%, prompt: Determine whether the information provided in the context necessarily leads to the answer to the question asked and indicate 'entailment' or 'not_entailment'.
-Language: french, acc: 37.60%, prompt: Analyze the text to determine if the answer to the question is implied in the context and specify 'entailment' or 'not_entailment'.
-Language: french, acc: 40.90%, prompt: Based on the given context, decide whether the answer to the question is necessarily involved and mark 'entailment' or 'not_entailment'.
-Language: french, acc: 24.70%, prompt: Evaluate whether the answer to the question can be deduced from the given context and mark 'entailment' or 'not_entailment'.
-Language: french, acc: 35.20%, prompt: Discern whether the context provided directly involves the answer to the question and indicate 'entailment' or 'not_entailment'.
-Language: french, acc: 35.40%, prompt: Determine if the context contains enough information to involve the answer to the question and mark 'entailment' or 'not_entailment'.
-Language: french, acc: 28.70%, prompt: Assess whether the context provided necessarily leads to the answer to the question and answer with 'entailment' or 'not_entailment'.
-Language: french, acc: 42.50%, prompt: Analyze the text to determine if the answer to the question is involved in the context and indicate 'entailment' or 'not_entailment'.
-Language: french, acc: 43.70%, prompt: Based on the given context, decide whether the answer to the question is necessarily inferred and mark 'entailment' or 'not_entailment'.
-Language: arabic, acc: 37.10%, prompt: As a language expert, evaluate whether the given context calls for an answer and answer 'entailment' or 'not_entailment'.
-Language: arabic, acc: 6.10%, prompt: Judge the relationship between the text and the question and answer 'entailment' or 'not_entailment', depending on your language experience.
-Language: arabic, acc: 36.10%, prompt: Does the context given indicate the answer to the question? Evaluate and answer 'entailment' or 'not_entailment'.
-Language: arabic, acc: 38.40%, prompt: Based on your linguistic knowledge, does the text relate to the question? Answer 'entailment' or 'not_entailment'.
-Language: arabic, acc: 15.00%, prompt: As a language expert, determine how the text relates to the question and answer 'entailment' or 'not_entailment'.
-Language: arabic, acc: 9.50%, prompt: Does the text support the answer to the question? Answer 'entailment' or 'not_entailment', depending on your language experience.
-Language: arabic, acc: 5.70%, prompt: Check the text link to the question and answer 'entailment' or 'not_entailment', depending on your language skills.
-Language: arabic, acc: 45.50%, prompt: As a language expert, is there a link between the text and the question? Answer 'entailment' or 'not_entailment'.
-Language: arabic, acc: 18.70%, prompt: Based on your language experience, does context help to answer the question? Evaluate and answer 'entailment' or 'not_entailment'.
-Language: arabic, acc: 6.30%, prompt: Does the text give a clear answer to the question? Answer 'entailment' or 'not_entailment', depending on your language experience.
-Language: spanish, acc: 32.90%, prompt: As a language expert, evaluate whether the given context implies the answer to the question and answer with 'entailment' or 'not_entailment'.
-Language: spanish, acc: 38.00%, prompt: Determine whether the information given in the text necessarily implies the veracity of the hypothesis and answer 'entailment' or 'not_entailment'.
-Language: spanish, acc: 32.60%, prompt: Analyzes whether the information presented in the paragraph leads to the conclusion of the question and labels the answer as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 30.50%, prompt: Indicates whether the information provided in the text is sufficient to conclude the statement and labels the response as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 35.00%, prompt: As an expert on the subject, judge whether the information provided in the text justifies the claim and classify the answer as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 25.10%, prompt: Evaluates whether the information in the paragraph necessarily supports the conclusion of the hypothesis and responds 'entailment' or 'not_entailment'.
-Language: spanish, acc: 19.50%, prompt: Determines whether the information presented in the text logically implies the answer to the question and labels the answer as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 22.20%, prompt: Analyzes whether the information provided in the paragraph necessarily leads to the veracity of the hypothesis and classifies the response as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 40.70%, prompt: As an expert on the subject, evaluate whether the information presented in the text supports the claim and respond 'entailment' or 'not_entailment'.
-Language: spanish, acc: 40.20%, prompt: Indicates whether the information provided in the paragraph necessarily implies the answer to the question and labels the answer as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 12.90%, prompt: Rate whether the answer to the question is derived from the given context and answer with 'entailment' or 'not_entailment'.
-Language: japanese, acc: 38.40%, prompt: Please answer 'entailment' or 'not_entailment' for the given context and question.
-Language: japanese, acc: 42.60%, prompt: Decide whether the answer to the question is derived from the given context and answer 'entailment' or 'not_entailment'.
-Language: japanese, acc: 41.70%, prompt: Compare the question with the given context and give the answer 'entailment' or 'not_entailment'.
-Language: japanese, acc: 41.40%, prompt: Determinate whether the given context contains the answer to the question and answer with 'entailment' or 'not_entailment'.
-Language: japanese, acc: 40.00%, prompt: Estimate the answer of the question from the context and give the answer 'entailment' or 'not_entailment'.
-Language: japanese, acc: 32.00%, prompt: Determinate whether the given context is relevant to the question and answer with 'entailment' or 'not_entailment'.
-Language: japanese, acc: 21.80%, prompt: Determine whether the given context is relevant to the question and answer with 'entailment' or 'not_entailment'.
-Language: japanese, acc: 39.40%, prompt: Determinate whether the given context contains the answer to the question and answer 'entailment' or 'not_entailment'.
-Language: japanese, acc: 26.30%, prompt: Answer with 'entailment' or 'not_entailment', inferring from the given context.
-Language: korean, acc: 30.40%, prompt: Determine if a given sentence necessarily implies the meaning of another sentence and answer 'entailment' or 'not_entailment'.
-Language: korean, acc: 18.40%, prompt: By understanding the relations between sentences, judge whether a given sentence necessarily refers to another sentence and answer with 'entailment' or 'not_entailment'.
-Language: korean, acc: 17.50%, prompt: Evaluate whether a given text necessarily indicates the meaning of another text and respond with 'entailment' or 'not_entailment'.
-Language: korean, acc: 4.80%, prompt: Understand the relations of a sentence, to determine whether a given sentence necessarily includes other sentences and answer with 'entailment' or 'not_entailment'.
-Language: korean, acc: 27.90%, prompt: Judge whether a given content necessarily implies the meaning of another content and answer with 'entailment' or 'not_entailment'.
-Language: korean, acc: 1.60%, prompt: Grasp the relations between sentences, determine if a given sentence necessarily contains the meaning of another sentence and respond with 'entailment' or 'not_entailment'.
-Language: korean, acc: 26.70%, prompt: Evaluate whether a given text necessarily refers to another text and answer with 'entailment' or 'not_entailment'.
-Language: korean, acc: 20.50%, prompt: By comparing the meaning of the sentences, to determine if a given sentence necessarily implies another sentence and answer 'entailment' or 'not_entailment'.
-Language: korean, acc: 33.20%, prompt: Evaluate whether the contents given necessarily refer to other contents and answer with 'entailment' or 'not_entailment'.
-Language: korean, acc: 31.70%, prompt: By analyzing the relations between sentences, determine if a given sentence does not necessarily include other sentences and answer with 'entailment' or 'not_entailment'.
-
-# qqp
-
-## 10 prompts
-
-Acc: 41.80%, prompt: Do the following expressions mean the same thing? Provide your answer as 'equivalent' or 'not_equivalent'.
-Acc: 38.10%, prompt: Assess whether the following statements are identical in meaning by answering 'equivalent' or 'not_equivalent'.
-Acc: 36.50%, prompt: Examine the following expressions and tell me if they are alike in meaning by using 'equivalent' or 'not_equivalent'.
-Acc: 35.10%, prompt: Do these two sentences convey the same meaning? Indicate with 'equivalent' or 'not_equivalent'.
-Acc: 34.70%, prompt: Evaluate whether these two phrases have identical meanings and respond with 'equivalent' or 'not_equivalent'.
-Acc: 33.80%, prompt: Are the following two questions equivalent or not? Answer me with "equivalent" or "not_equivalent".
-Acc: 32.40%, prompt: Can these two statements be considered equal in meaning? Answer with 'equivalent' or 'not_equivalent'.
-Acc: 32.40%, prompt: Please indicate if the following pair of sentences share the same meaning by responding with 'equivalent' or 'not_equivalent'.
-Acc: 32.10%, prompt: Are the meanings of these two phrases the same? Reply with 'equivalent' or 'not_equivalent'.
-Acc: 27.90%, prompt: Determine if the given pair of statements can be considered the same by responding with 'equivalent' or 'not_equivalent'.
-Acc: 27.30%, prompt: Analyze if the given set of sentences have the same connotation by answering with 'equivalent' or 'not_equivalent'.
-
-Acc: 36.20%, prompt: In the capacity of a question assessment system, indicate if the meaning of the provided questions is the same, responding with 'equivalent' or 'not_equivalent'.
-Acc: 33.60%, prompt: In your role as a question comparison tool, assess the following pair of questions and classify them as 'equivalent' or 'not_equivalent'.
-Acc: 33.00%, prompt: In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Acc: 32.90%, prompt: Working as a question classifier, identify whether the given questions share the same meaning, and respond with 'equivalent' for equivalent questions or 'not_equivalent' for different ones.
-Acc: 32.70%, prompt: As a question equivalence detection system, examine the provided questions and respond with 'equivalent' if they are the same in meaning, or 'not_equivalent' if they are different.
-Acc: 32.20%, prompt: Functioning as a question similarity evaluation tool, analyze the given questions and decide if they share the same meaning, responding with 'equivalent' or 'not_equivalent'.
-Acc: 32.20%, prompt: As an instrument for question comparison evaluation, consider the questions and determine if their meaning is the same, responding with 'equivalent' for similar questions or 'not_equivalent' for different questions.
-Acc: 32.00%, prompt: While performing question comparison analysis, classify the similarity of the following questions as 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Acc: 31.40%, prompt: Acting as a question equivalence instrument, determine if the provided questions are equivalent in meaning, answering with 'equivalent' for similar questions or 'not_equivalent' for dissimilar ones.
-Acc: 30.90%, prompt: As a tool for determining question equivalence, review the questions and categorize their similarity as either 'equivalent' or 'not_equivalent'.
-
-## bertattack
-
-Original prompt: Do the following expressions mean the same thing? Provide your answer as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"are the following expression do the same as? substitute your subject as 'equivalent' or 'not_equivalent'. "
-Original acc: 41.80%, attacked acc: 10.30%, dropped acc: 31.50%
-
-Original prompt: Assess whether the following statements are identical in meaning by answering 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"check who the following statements are singular in meaning by answering 'equivalent' or 'not_equivalent'. "
-Original acc: 38.10%, attacked acc: 21.80%, dropped acc: 16.30%
-
-Original prompt: Examine the following expressions and tell me if they are alike in meaning by using 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Examine the following expressions and tell me if they are opposite in meaningful by function 'equivalent' or 'not_equivalent'. "
-Original acc: 36.50%, attacked acc: 14.50%, dropped acc: 22.00%
-
-Original prompt: In the capacity of a question assessment system, indicate if the meaning of the provided questions is the same, responding with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"use the role of a questions assessment system, indicate if the meaning of the posed items is the same, responding with 'equivalent' or 'not_equivalent'. "
-Original acc: 36.20%, attacked acc: 29.40%, dropped acc: 6.80%
-
-Original prompt: In your role as a question comparison tool, assess the following pair of questions and classify them as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In your role as a question classification tool, measure the initial pair of questions and classify them as 'equivalent' or 'not_equivalent'. "
-Original acc: 33.60%, attacked acc: 26.60%, dropped acc: 7.00%
-
-Original prompt: In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Attacked prompt: b"In the role of a topic similarities analyzer, examining the semantic of the questions the and decide if they are equivalent, answer with 'equivalent' for equivalent numbers or 'not_equivalent' for various respondents. "
-Original acc: 33.00%, attacked acc: 8.20%, dropped acc: 24.80%
-
-## checklist
-
-Original prompt: Do the following expressions mean the same thing? Provide your answer as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Do the following expressions mean the same thing? Provide your answer as 'equivalent' or 'not_equivalent' fwhUZUQzXW. "
-Original acc: 42.10%, attacked acc: 27.50%, dropped acc: 14.60%
-
-Original prompt: Assess whether the following statements are identical in meaning by answering 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Assess whether the following statements are identical in meaning by answering 'equivalent' or 'not_equivalent' 6a4Yn3RGVc. "
-Original acc: 38.20%, attacked acc: 23.50%, dropped acc: 14.70%
-
-Original prompt: Examine the following expressions and tell me if they are alike in meaning by using 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Examine the following expressions and tell me if they are alike in meaning by using 'equivalent' or 'not_equivalent' lf8wBa2yEm. "
-Original acc: 36.40%, attacked acc: 29.20%, dropped acc: 7.20%
-
-Original prompt: In the capacity of a question assessment system, indicate if the meaning of the provided questions is the same, responding with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In the capacity of a question assessment system, indicate if the meaning of the provided questions is the same, responding with 'equivalent' or 'not_equivalent' RcRneWo6Iv. "
-Original acc: 36.30%, attacked acc: 29.80%, dropped acc: 6.50%
-
-Original prompt: In your role as a question comparison tool, assess the following pair of questions and classify them as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In your role as a question comparison tool, assess the following pair of questions and classify them as 'equivalent' or 'not_equivalent' ofOQXLxiW4. "
-Original acc: 33.50%, attacked acc: 30.10%, dropped acc: 3.40%
-
-Original prompt: Working as a question classifier, identify whether the given questions share the same meaning, and respond with 'equivalent' for equivalent questions or 'not_equivalent' for different ones.
-Attacked prompt: b"Working as a question classifier, identify whether the given questions share the same meaning, and respond with 'equivalent' for equivalent questions or 'not_equivalent' for different ones AMsRIKZniY. "
-Original acc: 32.90%, attacked acc: 25.00%, dropped acc: 7.90%
-
-## deepwordbug
-
-Original prompt: Do the following expressions mean the same thing? Provide your answer as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"oD the folXowing exrpessions men the same hing? ProFide your answre as 'equivalent' or 'not_equivalent'. "
-Original acc: 41.80%, attacked acc: 26.30%, dropped acc: 15.50%
-
-Original prompt: Assess whether the following statements are identical in meaning by answering 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Asess hwether the following statemets are identiacl in meaning by answerinEg 'equivalent' or 'not_equivalent'. "
-Original acc: 38.10%, attacked acc: 20.30%, dropped acc: 17.80%
-
-Original prompt: Examine the following expressions and tell me if they are alike in meaning by using 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Examne the following exrpessions and telIl me if they are aliZke in mVeaning by usking 'equivalent' or 'not_equivalent'. "
-Original acc: 36.50%, attacked acc: 24.40%, dropped acc: 12.10%
-
-Original prompt: In the capacity of a question assessment system, indicate if the meaning of the provided questions is the same, responding with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"nI the caacity of a qCuestion assevssment system, indicate if the meaniBng of the providDed Zquestions is the same, responding with 'equivalent' or 'not_equivalent'. "
-Original acc: 36.20%, attacked acc: 25.50%, dropped acc: 10.70%
-
-Original prompt: In your role as a question comparison tool, assess the following pair of questions and classify them as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In your Irole as a questioGn comparison tolo, ussess the foMlowing pair of questrions and classify them as 'equivalent' or 'not_equivalent'. "
-Original acc: 33.60%, attacked acc: 23.60%, dropped acc: 10.00%
-
-Original prompt: In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Attacked prompt: b"nI the rol of a question siimlarity naalyzer, examiYe the meaning of the prSovided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different uquestions. "
-Original acc: 33.00%, attacked acc: 23.80%, dropped acc: 9.20%
-
-## stresstest
-
-Original prompt: Do the following expressions mean the same thing? Provide your answer as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Do the following expressions mean the same thing? Provide your answer as 'equivalent' or 'not_equivalent'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 41.80%, attacked acc: 42.20%, dropped acc: -0.40%
-
-Original prompt: Assess whether the following statements are identical in meaning by answering 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Assess whether the following statements are identical in meaning by answering 'equivalent' or 'not_equivalent'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 38.10%, attacked acc: 41.20%, dropped acc: -3.10%
-
-Original prompt: Examine the following expressions and tell me if they are alike in meaning by using 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Examine the following expressions and tell me if they are alike in meaning by using 'equivalent' or 'not_equivalent'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 36.50%, attacked acc: 50.30%, dropped acc: -13.80%
-
-Original prompt: In the capacity of a question assessment system, indicate if the meaning of the provided questions is the same, responding with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In the capacity of a question assessment system, indicate if the meaning of the provided questions is the same, responding with 'equivalent' or 'not_equivalent'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 36.20%, attacked acc: 47.70%, dropped acc: -11.50%
-
-Original prompt: In your role as a question comparison tool, assess the following pair of questions and classify them as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"In your role as a question comparison tool, assess the following pair of questions and classify them as 'equivalent' or 'not_equivalent'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 33.60%, attacked acc: 36.40%, dropped acc: -2.80%
-
-Original prompt: In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Attacked prompt: b"In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 33.00%, attacked acc: 26.40%, dropped acc: 6.60%
-
-## textbugger
-
-Original prompt: Do the following expressions mean the same thing? Provide your answer as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Wo the following expressions meant the same thing? Provide your a\xd5\xb8swer as 'equivalent' or 'not_equivalent'. "
-Original acc: 41.80%, attacked acc: 26.70%, dropped acc: 15.10%
-
-Original prompt: Assess whether the following statements are identical in meaning by answering 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Ass ess whether the fol lowing statements are identical in meaning by answering 'equivalent' or 'not_equivalent'. "
-Original acc: 38.10%, attacked acc: 28.50%, dropped acc: 9.60%
-
-Original prompt: Examine the following expressions and tell me if they are alike in meaning by using 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Examine the following manifestations and tell me if they are too in meaning by use 'equivalent' or 'not_equivalent'. "
-Original acc: 36.50%, attacked acc: 29.40%, dropped acc: 7.10%
-
-Original prompt: In the capacity of a question assessment system, indicate if the meaning of the provided questions is the same, responding with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"I\xd5\xb8 the capacit\xd1\x83 of a questions assesment system, indicate if the meaning of the provid\xd0\xb5d questions is the same, responding with 'equivalent' or 'not_equivalent'. "
-Original acc: 36.20%, attacked acc: 29.40%, dropped acc: 6.80%
-
-Original prompt: In your role as a question comparison tool, assess the following pair of questions and classify them as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"For your ro\xe2\x85\xbce as a question comp\xc9\x91rison tool, evaluate the \xf0\x9d\x9a\x8following pair of questions and categorized them as 'equivalent' or 'not_equivalent'. "
-Original acc: 33.60%, attacked acc: 15.00%, dropped acc: 18.60%
-
-Original prompt: In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Attacked prompt: b"I\xd5\xb8 the rol\xd0\xb5 of a ques\xf0\x9d\x9a\x9dion similarity analyzer, investigated the meaning of the prov\xd1\x96ded questions and decide if they are equivalent, answerin\xc9\xa1 with 'equivalent' for equivalent question or 'not_equivalent' for various questions. "
-Original acc: 33.00%, attacked acc: 19.30%, dropped acc: 13.70%
-
-## textfooler
-
-Original prompt: Do the following expressions mean the same thing? Provide your answer as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Know the following phrase imply the same thing? Supply your riposte as 'equivalent' or 'not_equivalent'. "
-Original acc: 41.80%, attacked acc: 29.00%, dropped acc: 12.80%
-
-Original prompt: Assess whether the following statements are identical in meaning by answering 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Estimates whether the following pronouncements are analogous in connotation by answering 'equivalent' or 'not_equivalent'. "
-Original acc: 38.10%, attacked acc: 21.50%, dropped acc: 16.60%
-
-Original prompt: Examine the following expressions and tell me if they are alike in meaning by using 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Researched the following terms and say me if they are too in connotation by consumes 'equivalent' or 'not_equivalent'. "
-Original acc: 36.50%, attacked acc: 23.20%, dropped acc: 13.30%
-
-Original prompt: In the capacity of a question assessment system, indicate if the meaning of the provided questions is the same, responding with 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"Among the dexterity of a theme analyse scheme, describe if the meanings of the brought things is the same, answering with 'equivalent' or 'not_equivalent'. "
-Original acc: 36.20%, attacked acc: 22.80%, dropped acc: 13.40%
-
-Original prompt: In your role as a question comparison tool, assess the following pair of questions and classify them as 'equivalent' or 'not_equivalent'.
-Attacked prompt: b"En your role as a things comparison apparatus, assess the following pair of questions and categories them as 'equivalent' or 'not_equivalent'. "
-Original acc: 33.60%, attacked acc: 15.50%, dropped acc: 18.10%
-
-Original prompt: In the role of a question similarity analyzer, examine the meaning of the provided questions and decide if they are equivalent, answering with 'equivalent' for equivalent questions or 'not_equivalent' for different questions.
-Attacked prompt: b"Across the rol of a trouble similarity analyzer, exploring the meaning of the lent questions and select if they are equivalent, answered with 'equivalent' for equivalent theme or 'not_equivalent' for various questions. "
-Original acc: 33.00%, attacked acc: 13.90%, dropped acc: 19.10%
-
-## translation
-
-Language: chinese, acc: 30.70%, prompt: As a tool for determining the equivalence of problems, the problems are examined and their similarity is classified as 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 35.10%, prompt: Please compare the similarity of two sentences and put them into the category of 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 31.30%, prompt: Two sentences are classified as 'equivalent' or 'not_equivalent' for their similarity.
-Language: chinese, acc: 30.20%, prompt: You can determine how similar the questions are by comparing them and categorizing them as 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 41.40%, prompt: Using the method of contrast, the similarity of these problems is divided into two categories: 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 32.20%, prompt: By comparing these issues, you can classify them as 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 32.20%, prompt: To determine whether the questions are similar, put them into the category of 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 34.80%, prompt: Divide the similarity of these questions into 'equivalent' or 'not_equivalent' categories.
-Language: chinese, acc: 30.60%, prompt: Using the similarity assessment tool, these questions were classified as 'equivalent' or 'not_equivalent'.
-Language: chinese, acc: 31.40%, prompt: By analyzing the similarity of these problems, they are divided into categories of 'equivalent' or 'not_equivalent'.
-Language: french, acc: 31.30%, prompt: As a tool to determine the equivalence of questions, review the questions and rank their similarity as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 33.20%, prompt: Please compare the similarity of two sentences and classify them as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 31.50%, prompt: Based on the similarity of two sentences, classify them as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 30.40%, prompt: You can determine the similarity between these questions by comparing them and classifying them as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 34.60%, prompt: Use a comparative method to divide the similarity of these questions into two categories: 'equivalent' or 'not_equivalent'.
-Language: french, acc: 32.20%, prompt: By comparing these questions, you can classify them as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 35.60%, prompt: Determine whether these questions are similar or not, and then classify them as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 38.50%, prompt: Divide the similarity of these questions into two categories: 'equivalent' or 'not_equivalent'.
-Language: french, acc: 32.70%, prompt: Use a similarity assessment tool to classify these questions as 'equivalent' or 'not_equivalent'.
-Language: french, acc: 31.10%, prompt: By analyzing the similarity of these questions, you can divide them into two categories: 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 30.00%, prompt: As a tool for determining an equation of questions, review the questions and classify their similarity as either 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 32.80%, prompt: When using questions in the classification domain, please classify the similarity between the questions as 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 28.60%, prompt: To determine an equation of questions, you must review the questions and classify their similarity as 'equivalent' or 'not_equivalent'.
-Language: arabic, acc: 38.20%, prompt: Questions can be classified as 'equivalent' or 'not_equivalent' when used to identify classifications.
-Language: arabic, acc: 29.90%, prompt: Classification of question similarity as 'equivalent' or 'not_equivalent' is used as a tool to determine the classification of questions.
-Language: arabic, acc: 28.20%, prompt: Classify the similarity of the questions as 'equivalent' or 'not_equivalent' to determine the equation of the questions.
-Language: arabic, acc: 31.70%, prompt: Identifying the similarity of questions and classifying them as 'equivalent' or 'not_equivalent' is an important tool in determining the classification of questions.
-Language: arabic, acc: 34.60%, prompt: When classifying questions, their similarity can be classified as 'equivalent' or 'not_equivalent' to determine the correct classification.
-Language: arabic, acc: 30.70%, prompt: The similarity of questions should be classified as 'equivalent' or 'not_equivalent' when used to determine the equation of questions.
-Language: arabic, acc: 31.40%, prompt: Identifying the similarity of questions and classifying them as 'equivalent' or 'not_equivalent' helps to correctly classify questions.
-Language: spanish, acc: 31.50%, prompt: As a tool to determine the equivalence of questions, it reviews the questions and classifies their similarity as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 31.60%, prompt: Evaluate the similarity between questions and classify them as 'equivalent' or 'not_equivalent' to determine their equivalence.
-Language: spanish, acc: 43.00%, prompt: Determine whether two questions are 'equivalent' or 'not_equivalent' based on similarity and characteristics.
-Language: spanish, acc: 35.50%, prompt: Classifies the similarity between questions as 'equivalent' or 'not_equivalent' to determine their equivalence.
-Language: spanish, acc: 31.10%, prompt: Review the questions and rate them as 'equivalent' or 'not_equivalent' based on their similarity and content.
-Language: spanish, acc: 29.40%, prompt: As part of the classification task of questions, it determines their equivalence by categorizing their similarity as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 32.30%, prompt: Analyze the similarity between questions and classify them as 'equivalent' or 'not_equivalent' to determine their equivalence.
-Language: spanish, acc: 32.20%, prompt: As a method of identifying the equivalence of questions, it categorizes their similarity as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 31.20%, prompt: To determine the equivalence between questions, check their similarity and classify them as 'equivalent' or 'not_equivalent'.
-Language: spanish, acc: 41.30%, prompt: Classify the similarity between questions as 'equivalent' or 'not_equivalent' to determine whether they are equivalent or not.
-Language: japanese, acc: 30.70%, prompt: As a tool to determine the equivalence of the question, review the question and categorize its similarities into 'equivalent' or 'not_equivalent' categories.
-Language: japanese, acc: 51.70%, prompt: Work on text sorting tasks labeled 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 21.70%, prompt: For text classification tasks, use the labels 'equivalent' or 'not_equivalent' to determine the equivalence of statements.
-Language: japanese, acc: 37.90%, prompt: In the MRPC dataset, use the labels 'equivalent' or 'not_equivalent' to classify the equivalence of statements.
-Language: japanese, acc: 31.80%, prompt: As a tool for determining equivalence, check sentences and categorize them into 'equivalent' or 'not_equivalent' categories.
-Language: japanese, acc: 29.10%, prompt: Use the labels 'equivalent' or 'not_equivalent' to determine the equivalence of statements in text classification tasks.
-Language: japanese, acc: 27.80%, prompt: In the text classification task of the MRPC data set, classify the equivalence of statements with labels of 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 36.00%, prompt: As a tool to determine the equivalence of statements, categorize statements into 'equivalent' or 'not_equivalent' categories.
-Language: japanese, acc: 27.80%, prompt: In a text classification task, classify the equivalence of statements using labels of 'equivalent' or 'not_equivalent'.
-Language: japanese, acc: 28.20%, prompt: Do a text classification task to determine the equivalence of statements, labeled 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 27.40%, prompt: Classify two given sentences as 'equivalent' or 'not_equivalent' by discriminating whether they have the same meaning.
-Language: korean, acc: 36.30%, prompt: Determine sentence equivalence by judging the similarity of two sentences with 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 31.60%, prompt: Classify the similarity of sentences as 'equivalent' or 'not_equivalent' by judging whether two sentences have the same meaning.
-Language: korean, acc: 24.50%, prompt: Determine if two given sentences are equivalent to each other, and classify their similarity as 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 27.90%, prompt: Compare two given sentences to determine sentence equivalence, and classify their similarities as 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 36.40%, prompt: Classify sentence equivalence as 'equivalent' or 'not_equivalent' by judging whether two sentences have the same meaning to each other.
-Language: korean, acc: 25.50%, prompt: Determine if two sentences have the same meaning, and classify their similarities as 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 30.20%, prompt: Compare two given sentences to determine their equivalence, and classify their similarities as 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 28.40%, prompt: Review two sentences to evaluate sentence equivalence, and classify their similarities as 'equivalent' or 'not_equivalent'.
-Language: korean, acc: 34.10%, prompt: Judge whether two sentences have the same meaning to each other, and determine the sentence equivalence with 'equivalent' or 'not_equivalent'.
-
-# rte
-
-## 10 prompts
-
-Acc: 50.54%, prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Acc: 48.38%, prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Acc: 43.68%, prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Acc: 41.52%, prompt: Please classify the relationship between the provided sentences as 'entailment' or 'not_entailment'.
-Acc: 32.85%, prompt: Assess the connection between the following sentences and classify it as 'entailment' or 'not_entailment'.
-Acc: 17.69%, prompt: Indicate if the connection between the following sentences is 'entailment' or 'not_entailment'.
-Acc: 16.97%, prompt: Analyze the two provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Acc: 13.00%, prompt: Considering the two sentences, identify if their relationship is 'entailment' or 'not_entailment'.
-Acc: 7.22%, prompt: Determine if the given pair of sentences displays entailment or not_entailment. Respond with 'entailment' or 'not_entailment'.
-Acc: 4.69%, prompt: Identify whether the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Acc: 4.33%, prompt: Examine the pair of sentences and determine if they exhibit entailment or not_entailment. Answer with either 'entailment' or 'not_entailment'.
-
-Acc: 41.88%, prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Acc: 38.63%, prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Acc: 36.82%, prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Acc: 24.19%, prompt: As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'.
-Acc: 22.02%, prompt: While performing entailment analysis, classify the relationship between the provided sentences as 'entailment' or 'not_entailment'.
-Acc: 19.49%, prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Acc: 19.13%, prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Acc: 10.47%, prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Acc: 9.39%, prompt: As a tool for determining entailment relationships, review the two statements and categorize their connection as either 'entailment' or 'not_entailment'.
-Acc: 2.89%, prompt: Working as an entailment classifier, identify whether the given pair of sentences displays entailment or not_entailment. Respond with 'entailment' or 'not_entailment'.
-
-## bertattack
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'pair the following opposite sentences entailment or not_entailment? answers me with "entailment" or "not_entailment", just 1 word. '
-Original acc: 50.54%, attacked acc: 1.08%, dropped acc: 49.46%
-
-Original prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Does the relationship between the given expressions represent entailment or not_entailment? q with 'entailment' or 'not_entailment'. "
-Original acc: 48.38%, attacked acc: 5.42%, dropped acc: 42.96%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Attacked prompt: b"t the two statements and categorize their relation as either 'entailment' or 'not_entailment'. "
-Original acc: 43.68%, attacked acc: 13.00%, dropped acc: 30.69%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"In your contribution as an entailment analytics tool, assessed the relationship between the given sentences and graded it as 'entailment' or 'not_entailment'. "
-Original acc: 41.88%, attacked acc: 5.78%, dropped acc: 36.10%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an entailment matching set, identify the connection between the below sentence and question with 'entailment' or 'not_entailment'. "
-Original acc: 38.63%, attacked acc: 1.08%, dropped acc: 37.55%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the role of a descriptive relationship analyzeing, read the connection between the given sentences and learn if they exhibit entailment or not_entailment. answers with 'entailment' or 'not_entailment'. "
-Original acc: 36.82%, attacked acc: 0.00%, dropped acc: 36.82%
-
-## checklist
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word Q47Jmd4lMV. '
-Original acc: 50.54%, attacked acc: 13.36%, dropped acc: 37.18%
-
-Original prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment' Q47Jmd4lMV. "
-Original acc: 48.38%, attacked acc: 23.47%, dropped acc: 24.91%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Attacked prompt: b"Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment' mzjVogkRhM. "
-Original acc: 43.68%, attacked acc: 7.22%, dropped acc: 36.46%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment' Q47Jmd4lMV. "
-Original acc: 41.88%, attacked acc: 2.53%, dropped acc: 39.35%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment' Q47Jmd4lMV. "
-Original acc: 38.63%, attacked acc: 0.72%, dropped acc: 37.91%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment or not_entailment. Answer with 'entailment' or 'not_entailment' Q47Jmd4lMV. "
-Original acc: 37.18%, attacked acc: 4.33%, dropped acc: 32.85%
-
-## deepwordbug
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Aer the following wto sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one wor. '
-Original acc: 50.54%, attacked acc: 0.36%, dropped acc: 50.18%
-
-Original prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Does the relationship between the given sentences represent entailment or not_entailment? ResponY with 'entailment' or 'not_entailment'. "
-Original acc: 48.38%, attacked acc: 21.66%, dropped acc: 26.71%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Attacked prompt: b"Revivw the two statements and catgeorize their relationsoip as either 'entailment' or 'not_entailment'. "
-Original acc: 43.68%, attacked acc: 7.94%, dropped acc: 35.74%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"LIn your roe as an entailment anlysis tXol, ssess the relItionship between the given sentences and classify it as 'entailment' or 'not_entailment'. "
-Original acc: 41.88%, attacked acc: 10.11%, dropped acc: 31.77%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an entailment identification system, zxamine the conneUtion between the following sentences and respond with 'entailment' or 'not_entailment'. "
-Original acc: 38.63%, attacked acc: 19.49%, dropped acc: 19.13%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the role of a semgntic relationship anayzer, examin the connection between the given sentences and decide if they exhibit entailment or not_entailment. AnCwer with 'entailment' or 'not_entailment'. "
-Original acc: 36.82%, attacked acc: 1.08%, dropped acc: 35.74%
-
-## stresstest
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word  and true is true . '
-Original acc: 50.54%, attacked acc: 14.08%, dropped acc: 36.46%
-
-Original prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 48.38%, attacked acc: 35.38%, dropped acc: 13.00%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Attacked prompt: b"Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 43.68%, attacked acc: 15.88%, dropped acc: 27.80%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 41.88%, attacked acc: 6.50%, dropped acc: 35.38%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 38.63%, attacked acc: 15.88%, dropped acc: 22.74%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment or not_entailment. Answer with 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 36.82%, attacked acc: 20.22%, dropped acc: 16.61%
-
-## textbugger
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Represent the following deux sentences entailment or not_entailment? Answers me with "entailment" or "not_entailment", just \xd0\xbene word. '
-Original acc: 50.54%, attacked acc: 3.61%, dropped acc: 46.93%
-
-Original prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Did the relationship between the given sentences represen\xf0\x9d\x9a\x9d entailment or not_entailment? Answering with 'entailment' or 'not_entailment'. "
-Original acc: 48.38%, attacked acc: 21.30%, dropped acc: 27.08%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Attacked prompt: b"Rev\xd1\x96ew the two sta\xf0\x9d\x9a\x9dements and categorize their relationship as or 'entailment' or 'not_entailment'. "
-Original acc: 43.68%, attacked acc: 2.53%, dropped acc: 41.16%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"In your function as an entailment analyses to\xd0\xbel, assessing the relationshi\xd1\x80 between the given sentences and classify it as 'entailment' or 'not_entailment'. "
-Original acc: 41.88%, attacked acc: 3.25%, dropped acc: 38.63%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Iike an entailment identification s\xd1\x83stem, examine the conn\xd0\xb5ction between the following sentences and respond with 'entailment' or 'not_entailment'. "
-Original acc: 38.63%, attacked acc: 10.11%, dropped acc: 28.52%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the role of a se\xef\xbd\x8dantic relationship analyze\xe2\xb2\x85, examine the connection between the givn sentences and determine if they exhibi\xf0\x9d\x9a\x9d entailment or not_entailment. Answers with 'entailment' or 'not_entailment'. "
-Original acc: 36.82%, attacked acc: 0.00%, dropped acc: 36.82%
-
-## textfooler
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Represent the following two sentences entailment or not_entailment? Responses me with "entailment" or "not_entailment", just one word. '
-Original acc: 50.54%, attacked acc: 6.50%, dropped acc: 44.04%
-
-Original prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Did the liaisons between the given phrases represent entailment or not_entailment? Answers with 'entailment' or 'not_entailment'. "
-Original acc: 48.38%, attacked acc: 4.69%, dropped acc: 43.68%
-
-Original prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Attacked prompt: b"Investigated the two statements and categorize their relation as either 'entailment' or 'not_entailment'. "
-Original acc: 43.68%, attacked acc: 2.17%, dropped acc: 41.52%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"During your functions as an entailment assessment utility, appraisals the affairs between the given sentence and categorize it as 'entailment' or 'not_entailment'. "
-Original acc: 41.88%, attacked acc: 2.17%, dropped acc: 39.71%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Iike an entailment ident schemes, examine the relationship between the following phrases and replied with 'entailment' or 'not_entailment'. "
-Original acc: 38.63%, attacked acc: 5.05%, dropped acc: 33.57%
-
-Original prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Attacked prompt: b"In the role of a semantic relationship profiler, review the ties between the given sentences and determining if they fairs entailment or not_entailment. Answers with 'entailment' or 'not_entailment'. "
-Original acc: 36.82%, attacked acc: 0.00%, dropped acc: 36.82%
-
-## translation
-
-Language: chinese, acc: 26.35%, prompt: In the light of an implication analysis tool, evaluate the relationship between the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 42.96%, prompt: From the perspective of an implication analysis tool, determine whether there is an implication relationship in the following sentences by classifying them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 5.05%, prompt: Please use an implication analysis tool to determine whether an implication relationship exists in the following sentences by classifying them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 43.32%, prompt: Please evaluate the relation of the following sentences as 'entailment' or 'not_entailment' for the purpose of determining implication relation.
-Language: chinese, acc: 11.55%, prompt: Please use the implication analysis tool to evaluate the relationships between the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 28.16%, prompt: For the purpose of determining implicative relations, analyze the relations of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 12.27%, prompt: Please use the implication analysis tool to determine the relationship of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 22.38%, prompt: Please use the implication judgment tool to assess the relevance of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 18.05%, prompt: Please, with implication analysis as the main task, determine the relationships between the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 20.22%, prompt: Using the implication judgment as a criterion, analyze the relation of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: french, acc: 42.60%, prompt: As an engagement analysis tool, evaluate the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Language: french, acc: 10.11%, prompt: Determine whether the given sentences involve one another or not as an implication analysis tool. Classify them accordingly as 'entailment' or 'not_entailment'.
-Language: french, acc: 7.58%, prompt: Using implication analysis, evaluate whether the sentences provided have a logical relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 45.13%, prompt: As an engagement assessment tool, determine whether the sentences provided have a logical relationship and classify them as 'entailment' or 'not_entailment'.
-Language: french, acc: 20.22%, prompt: As an implication classification tool, analyze the sentences provided to determine if there is a logical relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 3.97%, prompt: Using implication analysis, determine whether the given sentences have a cause-effect relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 26.71%, prompt: Evaluate the relationship between the given sentences using implication analysis and rank them accordingly as 'entailment' or 'not_entailment'.
-Language: french, acc: 40.79%, prompt: As an engagement detection tool, determine whether the given sentences have a logical relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 12.27%, prompt: Using implication analysis, evaluate whether the sentences provided have a cause-effect relationship and rank them accordingly as 'entailment' or 'not_entailment'.
-Language: french, acc: 23.10%, prompt: Determine whether the given sentences have a cause-effect relationship as an engagement analysis tool and categorize them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 28.16%, prompt: In your role as a tool for reasoning analysis, evaluate the relationship between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 46.93%, prompt: Can you determine whether this sentence is inferred from the other sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 14.44%, prompt: Using the tool of reasoning analysis, analyze the relationship between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 48.38%, prompt: Does this sentence represent a conclusion from the previous sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 15.16%, prompt: As a tool of reasoning analysis, evaluate the relationship of given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 49.46%, prompt: Can this sentence be inferred from the previous sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 17.69%, prompt: Using a tool to analyze a conclusion, analyze the relationship between the two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 50.90%, prompt: Is this a conclusion from the next sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 12.27%, prompt: As part of your task in analyzing a conclusion, evaluate the relationship between the two sentences and classify them as 'entailment' or 'not_entailment' based on their relationship.
-Language: arabic, acc: 49.10%, prompt: Are you following this sentence directly from the previous one? Classify it as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 43.32%, prompt: In your role as an implication analysis tool, evaluate the relationship between the given phrases and classify them as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 32.49%, prompt: Determine whether the second sentence necessarily implies the first and label the relation as 'entailment', or as 'not_entailment' if not.
-Language: spanish, acc: 5.42%, prompt: Classifies the relationship between these two sentences as 'entailment' if one necessarily implies the other, or as 'not_entailment' if not.
-Language: spanish, acc: 34.30%, prompt: Evaluates whether the information in the second sentence is implied in the first and labels the relationship as 'entailment', or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 35.74%, prompt: Given a couple of phrases, label their relationship as 'entailment' if one necessarily implies the other, or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 14.08%, prompt: Analyzes the relationship between the phrases and classifies them as 'entailment' if one necessarily implies the other, or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 9.39%, prompt: Given two sentences, determine whether the second sentence is a necessary consequence of the first and label the relation as 'entailment', or as 'not_entailment' if not.
-Language: spanish, acc: 37.91%, prompt: Evaluates whether the information presented in the second sentence is implicit in the first and labels the relationship as 'entailment', or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 12.64%, prompt: Classifies the relationship between the given phrases as 'entailment' if one necessarily implies the other, or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 17.33%, prompt: Determines whether the information provided in the second sentence is necessarily inferable from the first and labels the relationship as 'entailment', or as 'not_entailment' if not.
-Language: japanese, acc: 7.22%, prompt: Analyze the relationship of a given sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 22.38%, prompt: Evaluate the semantic relationship of the sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 32.13%, prompt: Please judge the relationship between the given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 32.85%, prompt: Examine the paraphrases of a given sentence and classify them 'entailment' or 'not_entailment'.
-Language: japanese, acc: 18.05%, prompt: Rate the similarity of a given sentence and categorize it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 13.72%, prompt: Determinate the semantic connections of a given sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 30.32%, prompt: Examine the semantic match of a given sentence and categorize it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 45.13%, prompt: Classify it as 'entailment' or 'not_entailment' based on the content of the sentence.
-Language: japanese, acc: 7.22%, prompt: Analyze the relationship of a given sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 16.61%, prompt: Judge the semantic connections of a given sentence and categorize it as 'entailment' or 'not_entailment'.
-Language: korean, acc: 19.49%, prompt: Evaluate the relationship between any two sentences given to you and classify you as 'entailment' or 'not_entailment'.
-Language: korean, acc: 18.41%, prompt: Analyze the semantic deductive relations between sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 24.19%, prompt: Evaluate the logical relevance between sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 6.14%, prompt: Evaluate the interaction of two given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 47.65%, prompt: Please check whether there is a semantic match between those two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 13.00%, prompt: Compare information between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 46.93%, prompt: Please analyse the correlation between those two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 27.44%, prompt: Evaluate the different meanings between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 28.52%, prompt: Compare the semantic structure of the two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 32.85%, prompt: Evaluate the interactions between sentences and classify them as 'entailment' or 'not_entailment'.
-
-# sst2
-
-## 10 prompts
-
-Acc: 18.12%, prompt: Evaluate the sentiment of the given text and classify it as 'positive' or 'negative'.
-Acc: 14.45%, prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Acc: 12.50%, prompt: After examining the following expression, label its emotion as either 'positive' or 'negative'.
-Acc: 9.63%, prompt: Given the context of this text, indicate if the emotion conveyed is 'positive' or 'negative'.
-Acc: 8.37%, prompt: Analyze the tone of this statement and respond with either 'positive' or 'negative'.
-Acc: 3.33%, prompt: Please identify the emotional tone of this passage: 'positive' or 'negative'?
-Acc: 2.75%, prompt: Assess the mood of the following quote and determine if it's 'positive' or 'negative'.
-Acc: 1.38%, prompt: Read the provided excerpt and choose between 'positive' and 'negative' to describe its sentiment.
-Acc: 1.38%, prompt: Considering the given phrase, would you say it carries a 'positive' or 'negative' connotation?
-Acc: 0.34%, prompt: Determine the overall sentiment of this sentence, categorizing it as 'positive' or 'negative'.
-
-Acc: 33.37%, prompt: Taking on the role of an emotion classifier, specify if the provided phrase is 'positive' or 'negative'.
-Acc: 27.18%, prompt: Emulating a sentiment classification system, indicate whether the provided text is 'positive' or 'negative'.
-Acc: 26.38%, prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Acc: 19.61%, prompt: Serving as a sentiment evaluation model, determine if the given statement is 'positive' or 'negative'.
-Acc: 17.43%, prompt: In the capacity of a sentiment classifier, decide whether the given quote is 'positive' or 'negative'.
-Acc: 13.19%, prompt: Functioning as a sentiment identification tool, assess if the following expression is 'positive' or 'negative'.
-Acc: 13.07%, prompt: As a sentiment classifier, determine whether the following text is 'positive' or 'negative'.
-Acc: 12.27%, prompt: Acting as a sentiment evaluator, identify if the given sentence is 'positive' or 'negative'.
-Acc: 2.41%, prompt: As an emotion detector, determine if the provided passage conveys a 'positive' or 'negative' sentiment.
-Acc: 1.15%, prompt: Working as a sentiment analyzer, please indicate if the following text is 'positive' or 'negative'.
-
-## bertattack
-
-Original prompt: Evaluate the sentiment of the given text and classify it as 'positive' or 'negative'.
-Attacked prompt: b"Evaluate the sentiment of the given scripture and write it as 'positive' or 'negative'. "
-Original acc: 18.12%, attacked acc: 0.23%, dropped acc: 17.89%
-
-Original prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Attacked prompt: b"Review this response and please gauge it has a 'positive' or 'negative' sentiment. "
-Original acc: 14.45%, attacked acc: 0.11%, dropped acc: 14.33%
-
-Original prompt: After examining the following expression, label its emotion as either 'positive' or 'negative'.
-Attacked prompt: b"After examining the following action, type its emotion as either 'positive' or 'negative'. "
-Original acc: 12.50%, attacked acc: 0.00%, dropped acc: 12.50%
-
-Original prompt: Taking on the role of an emotion classifier, specify if the provided phrase is 'positive' or 'negative'.
-Attacked prompt: b"[UNK] on the relationship of an emotion classtoken, choose if the provided phrase is 'positive' or 'negative'. "
-Original acc: 33.37%, attacked acc: 0.11%, dropped acc: 33.26%
-
-Original prompt: Emulating a sentiment classification system, indicate whether the provided text is 'positive' or 'negative'.
-Attacked prompt: b"Emulating a emotion a system, indicate how the provided text is 'positive' or 'negative'. "
-Original acc: 27.18%, attacked acc: 0.46%, dropped acc: 26.72%
-
-Original prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Attacked prompt: b"In the addition of a sentiment a tool, contrast with 'positive' or 'negative' to classify this statement. "
-Original acc: 26.38%, attacked acc: 1.26%, dropped acc: 25.11%
-
-## checklist
-
-Original prompt: Evaluate the sentiment of the given text and classify it as 'positive' or 'negative'.
-Attacked prompt: b"Evaluate the sentiment of the given text and classify it as 'positive' or 'negative' Q47Jmd4lMV. "
-Original acc: 18.12%, attacked acc: 0.23%, dropped acc: 17.89%
-
-Original prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Attacked prompt: b"Review this statement and decide whether it has a 'positive' or 'negative' sentiment w52rwgo0Av. "
-Original acc: 14.45%, attacked acc: 0.11%, dropped acc: 14.33%
-
-Original prompt: After examining the following expression, label its emotion as either 'positive' or 'negative'.
-Attacked prompt: b"After examining the following expression, label its emotion as either 'positive' or 'negative' PDCGfz6DL9. "
-Original acc: 12.50%, attacked acc: 0.11%, dropped acc: 12.39%
-
-Original prompt: Taking on the role of an emotion classifier, specify if the provided phrase is 'positive' or 'negative'.
-Attacked prompt: b"Taking on the role of an emotion classifier, specify if the provided phrase is 'positive' or 'negative' Q47Jmd4lMV. "
-Original acc: 33.37%, attacked acc: 4.47%, dropped acc: 28.90%
-
-Original prompt: Emulating a sentiment classification system, indicate whether the provided text is 'positive' or 'negative'.
-Attacked prompt: b"Emulating a sentiment classification system, indicate whether the provided text is 'positive' or 'negative' Q47Jmd4lMV. "
-Original acc: 27.18%, attacked acc: 10.32%, dropped acc: 16.86%
-
-Original prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Attacked prompt: b"In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement Q47Jmd4lMV. "
-Original acc: 26.38%, attacked acc: 2.75%, dropped acc: 23.62%
-
-## deepwordbug
-
-Original prompt: Evaluate the sentiment of the given text and classify it as 'positive' or 'negative'.
-Attacked prompt: b"Evaljuate the sentiment of the givtn text and calssify it as 'positive' or 'negative'. "
-Original acc: 18.23%, attacked acc: 0.11%, dropped acc: 18.12%
-
-Original prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Attacked prompt: b"GReview this stateent and decide wdether it has a 'positive' or 'negative' sentiment. "
-Original acc: 14.45%, attacked acc: 0.00%, dropped acc: 14.45%
-
-Original prompt: After examining the following expression, label its emotion as either 'positive' or 'negative'.
-Attacked prompt: b"sAfter examinin the following expressiBn, labe its emotion as etiher 'positive' or 'negative'. "
-Original acc: 12.61%, attacked acc: 2.29%, dropped acc: 10.32%
-
-Original prompt: Taking on the role of an emotion classifier, specify if the provided phrase is 'positive' or 'negative'.
-Attacked prompt: b"TakiXg on the role of an emotinon lassifier, specify if the rovided phrase is 'positive' or 'negative'. "
-Original acc: 33.37%, attacked acc: 0.57%, dropped acc: 32.80%
-
-Original prompt: Emulating a sentiment classification system, indicate whether the provided text is 'positive' or 'negative'.
-Attacked prompt: b"EmZulating a sentment classificamion sfystem, idicate whethser the provided text is 'positive' or 'negative'. "
-Original acc: 27.29%, attacked acc: 0.80%, dropped acc: 26.49%
-
-Original prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Attacked prompt: b"In the ole of a senteiment naalysis tool, resond with 'positive' or 'negative' to classfy this sattement. "
-Original acc: 26.49%, attacked acc: 0.00%, dropped acc: 26.49%
-
-## stresstest
-
-Original prompt: Evaluate the sentiment of the given text and classify it as 'positive' or 'negative'.
-Attacked prompt: b"Evaluate the sentiment of the given text and classify it as 'positive' or 'negative'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 18.12%, attacked acc: 0.69%, dropped acc: 17.43%
-
-Original prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Attacked prompt: b"Review this statement and decide whether it has a 'positive' or 'negative' sentiment  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 14.45%, attacked acc: 0.00%, dropped acc: 14.45%
-
-Original prompt: After examining the following expression, label its emotion as either 'positive' or 'negative'.
-Attacked prompt: b"After examining the following expression, label its emotion as either 'positive' or 'negative'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 12.50%, attacked acc: 0.46%, dropped acc: 12.04%
-
-Original prompt: Taking on the role of an emotion classifier, specify if the provided phrase is 'positive' or 'negative'.
-Attacked prompt: b"Taking on the role of an emotion classifier, specify if the provided phrase is 'positive' or 'negative'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 33.37%, attacked acc: 3.78%, dropped acc: 29.59%
-
-Original prompt: Emulating a sentiment classification system, indicate whether the provided text is 'positive' or 'negative'.
-Attacked prompt: b"Emulating a sentiment classification system, indicate whether the provided text is 'positive' or 'negative'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 27.18%, attacked acc: 4.01%, dropped acc: 23.17%
-
-Original prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Attacked prompt: b"In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 26.38%, attacked acc: 1.95%, dropped acc: 24.43%
-
-## textbugger
-
-Original prompt: Evaluate the sentiment of the given text and classify it as 'positive' or 'negative'.
-Attacked prompt: b"Evaluate the vibe of the gievn text and calssify it as 'positive' or 'negative'. "
-Original acc: 18.12%, attacked acc: 0.00%, dropped acc: 18.12%
-
-Original prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Attacked prompt: b"Reveiw this s\xf0\x9d\x9a\x9datement and decide wether it has a 'positive' or 'negative' sentiment. "
-Original acc: 14.45%, attacked acc: 0.11%, dropped acc: 14.33%
-
-Original prompt: After examining the following expression, label its emotion as either 'positive' or 'negative'.
-Attacked prompt: b"After examining the following expression, labeling its emoion as either 'positive' or 'negative'. "
-Original acc: 12.50%, attacked acc: 0.00%, dropped acc: 12.50%
-
-Original prompt: Taking on the role of an emotion classifier, specify if the provided phrase is 'positive' or 'negative'.
-Attacked prompt: b"Taking on the roles of an emotions classi\xf0\x9d\x9a\x8fier, speci\xf0\x9d\x9a\x8fy if the povided phrase is 'positive' or 'negative'. "
-Original acc: 33.37%, attacked acc: 0.57%, dropped acc: 32.80%
-
-Original prompt: Emulating a sentiment classification system, indicate whether the provided text is 'positive' or 'negative'.
-Attacked prompt: b"Emulating a ambience ranking system, indicate whe\xf0\x9d\x9a\x9dher the provided text is 'positive' or 'negative'. "
-Original acc: 27.18%, attacked acc: 0.69%, dropped acc: 26.49%
-
-Original prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Attacked prompt: b"During the ro le of a emotion analysis tools, respond with 'positive' or 'negative' to classify this statement. "
-Original acc: 26.38%, attacked acc: 0.57%, dropped acc: 25.80%
-
-## textfooler
-
-Original prompt: Evaluate the sentiment of the given text and classify it as 'positive' or 'negative'.
-Attacked prompt: b"Assessment the mood of the made laws and rank it as 'positive' or 'negative'. "
-Original acc: 18.12%, attacked acc: 0.00%, dropped acc: 18.12%
-
-Original prompt: Review this statement and decide whether it has a 'positive' or 'negative' sentiment.
-Attacked prompt: b"Reassess this communique and select whether it has a 'positive' or 'negative' sentiment. "
-Original acc: 14.45%, attacked acc: 0.00%, dropped acc: 14.45%
-
-Original prompt: After examining the following expression, label its emotion as either 'positive' or 'negative'.
-Attacked prompt: b"After examining the following word, labeling its sentiments as either 'positive' or 'negative'. "
-Original acc: 12.50%, attacked acc: 0.00%, dropped acc: 12.50%
-
-Original prompt: Taking on the role of an emotion classifier, specify if the provided phrase is 'positive' or 'negative'.
-Attacked prompt: b"Assuming on the role of an feeling classifier, elucidate if the foreseen phrase is 'positive' or 'negative'. "
-Original acc: 33.37%, attacked acc: 2.87%, dropped acc: 30.50%
-
-Original prompt: Emulating a sentiment classification system, indicate whether the provided text is 'positive' or 'negative'.
-Attacked prompt: b"Emulating a ambiance graded regime, explain whether the provided wording is 'positive' or 'negative'. "
-Original acc: 27.18%, attacked acc: 0.46%, dropped acc: 26.72%
-
-Original prompt: In the role of a sentiment analysis tool, respond with 'positive' or 'negative' to classify this statement.
-Attacked prompt: b"During the rol of a emotion exploring mechanism, respond with 'positive' or 'negative' to rank this affirmations. "
-Original acc: 26.38%, attacked acc: 0.00%, dropped acc: 26.38%
-
-## translation
-
-Language: chinese, acc: 20.07%, prompt: Answer whether the statement is 'positive' or 'negative' based on sentiment analysis.
-Language: chinese, acc: 5.39%, prompt: As an emotion analysis tool, determine whether the emotion in the text is 'positive' or 'negative'.
-Language: chinese, acc: 19.38%, prompt: Categorize the statement as 'positive' or 'negative', based on its emotional bent.
-Language: chinese, acc: 5.85%, prompt: Please use sentiment analysis to classify the text as 'positive' or 'negative'.
-Language: chinese, acc: 0.46%, prompt: Please determine whether the emotion of the sentence is 'positive' or 'negative' and categorize it.
-Language: chinese, acc: 13.99%, prompt: Using sentiment analysis, classify the text as 'positive' or 'negative'.
-Language: chinese, acc: 0.80%, prompt: Please answer whether the emotion of the sentence is 'positive' or 'negative' and categorize it.
-Language: chinese, acc: 18.00%, prompt: Categorize the statement as 'positive' or 'negative' based on sentiment analysis.
-Language: chinese, acc: 0.57%, prompt: Please judge this text as 'positive' or 'negative' according to the criteria of sentiment analysis.
-Language: chinese, acc: 1.15%, prompt: Please classify this sentence as 'positive' or 'negative' according to the principles of emotion analysis.
-Language: french, acc: 4.13%, prompt: As a sentiment analysis tool, please answer with 'positive' or 'negative' to classify this statement.
-Language: french, acc: 7.22%, prompt: Determine whether this phrase is 'positive' or 'negative' as a sentiment classification tool.
-Language: french, acc: 1.61%, prompt: Identify the tone of this statement by choosing between 'positive' and 'negative' as a sentiment analysis tool.
-Language: french, acc: 18.92%, prompt: Use sentiment analysis to classify this statement as 'positive' or 'negative'.
-Language: french, acc: 42.66%, prompt: As a sentiment classification tool, please determine whether this statement is 'positive' or 'negative'.
-Language: french, acc: 19.38%, prompt: Classify this sentence as 'positive' or 'negative' using sentiment analysis.
-Language: french, acc: 11.35%, prompt: Choose between 'positive' or 'negative' to classify this statement as a sentiment analysis tool.
-Language: french, acc: 7.57%, prompt: Identify the sentiment expressed in this statement by selecting 'positive' or 'negative' as a sentiment classification tool.
-Language: french, acc: 10.89%, prompt: Determine whether this phrase is 'positive' or 'negative' using sentiment analysis as a classification tool.
-Language: french, acc: 18.92%, prompt: Use sentiment analysis to classify this statement as 'positive' or 'negative'.
-Language: arabic, acc: 11.58%, prompt: Under emotional analysis, answer 'positive' or 'negative' to classify this statement.
-Language: arabic, acc: 11.35%, prompt: Does this statement express a 'positive' or 'negative' reaction?
-Language: arabic, acc: 16.51%, prompt: Is that a 'positive' or a 'negative' phrase?
-Language: arabic, acc: 24.66%, prompt: What is the classification between 'positive' and 'negative'?
-Language: arabic, acc: 14.11%, prompt: Does this sentence express 'positive' or 'negative' feelings?
-Language: arabic, acc: 37.84%, prompt: In the context of textual analysis, what classification is this phrase between 'positive' and 'negative'?
-Language: arabic, acc: 9.40%, prompt: Could this be classified as 'positive' or 'negative'?
-Language: arabic, acc: 31.19%, prompt: In the context of emotional analysis, what classification is this statement between 'positive' and 'negative'?
-Language: arabic, acc: 8.26%, prompt: Can this be classified as 'positive' or 'negative'?
-Language: arabic, acc: 9.06%, prompt: Under the classification of emotions, is this sentence 'positive' or 'negative'?
-Language: spanish, acc: 9.29%, prompt: As a feeling analysis tool, classify this statement as 'positive' or 'negative'.
-Language: spanish, acc: 4.70%, prompt: Determine whether this statement has a 'positive' or 'negative' connotation.
-Language: spanish, acc: 15.02%, prompt: Indicate whether the following statement is 'positive' or 'negative'.
-Language: spanish, acc: 2.98%, prompt: Evaluate whether this text has a 'positive' or 'negative' emotional charge.
-Language: spanish, acc: 1.61%, prompt: According to your sentiment analysis, would you say this comment is 'positive' or 'negative'?
-Language: spanish, acc: 6.31%, prompt: In the context of sentiment analysis, label this sentence as 'positive' or 'negative'.
-Language: spanish, acc: 1.03%, prompt: Rate the following statement as 'positive' or 'negative', according to your sentiment analysis.
-Language: spanish, acc: 4.01%, prompt: How would you classify this text in terms of its emotional tone? 'positive' or 'negative'?
-Language: spanish, acc: 28.78%, prompt: As a tool for sentiment analysis, would you say this statement is 'positive' or 'negative'?
-Language: spanish, acc: 17.55%, prompt: Classify this statement as 'positive' or 'negative', please.
-Language: japanese, acc: 6.19%, prompt: Treat this sentence as an emotion analysis tool and categorize it as 'positive' and 'negative'.
-Language: japanese, acc: 10.67%, prompt: Use this article as a sentiment analysis tool to classify 'positive' and 'negative'.
-Language: japanese, acc: 4.36%, prompt: Use this sentence as an emotion analysis tool to determine whether it is 'positive' or 'negative'.
-Language: japanese, acc: 7.57%, prompt: Use this sentence as an emotion analysis tool to classify 'positive' and 'negative'.
-Language: japanese, acc: 8.26%, prompt: Use this sentence as a sentiment analysis tool and classify it as 'positive' or 'negative'.
-Language: japanese, acc: 3.90%, prompt: To classify this sentence as 'positive' or 'negative', evaluate it as a sentiment analysis tool.
-Language: japanese, acc: 2.41%, prompt: Treat this sentence as an emotion analysis tool to determine whether it is 'positive' or 'negative'.
-Language: japanese, acc: 18.00%, prompt: Use this sentence as a sentiment analysis tool to classify 'positive' and 'negative'.
-Language: japanese, acc: 5.62%, prompt: Analyze this sentence as an emotion analysis tool to classify whether it is 'positive' or 'negative'.
-Language: japanese, acc: 6.19%, prompt: Use this sentence as an emotional analysis tool to determine whether it is 'positive' or 'negative'.
-Language: korean, acc: 7.57%, prompt: As an emotional analysis tool, respond with 'positive' or 'negative' to classify these sentences.
-Language: korean, acc: 15.14%, prompt: Classify this sentence as 'positive' if you regard it as positive, 'negative' if you regard it as negative.
-Language: korean, acc: 1.72%, prompt: Please rate the emotion of this sentence and classify it as 'positive' or 'negative'.
-Language: korean, acc: 10.67%, prompt: Classify this sentence as 'positive' if you perceive it positively and 'negative' if you perceive it negatively.
-Language: korean, acc: 16.74%, prompt: If this is a sentence delivered using a positive expression, classify it as 'positive' and if this is a sentence delivered using a negative expression, classify it as 'negative'.
-Language: korean, acc: 1.26%, prompt: Respond with 'positive' or 'negative' by categorizing whether the sentence is positive or negative.
-Language: korean, acc: 1.72%, prompt: Please analyze the emotion in this sentence and classify it as 'positive' or 'negative'.
-Language: korean, acc: 22.82%, prompt: Classify this sentence as 'positive' if it contains a positive meaning, 'negative' if it contains a negative meaning.
-Language: korean, acc: 23.97%, prompt: Classify this sentence as 'positive' if it contains positive content, 'negative' if it contains negative content.
-Language: korean, acc: 2.29%, prompt: Classify what you're trying to convey in this sentence as 'positive' if it's positive, and 'negative' if it's negative.
-
-# wnli
-
-## 10 prompts
-
-Acc: 32.39%, prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Acc: 28.17%, prompt: Please classify the relationship between the provided sentences as 'entailment' or 'not_entailment'.
-Acc: 23.94%, prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Acc: 23.94%, prompt: Assess the connection between the following sentences and classify it as 'entailment' or 'not_entailment'.
-Acc: 15.49%, prompt: Review the two statements and categorize their relationship as either 'entailment' or 'not_entailment'.
-Acc: 14.08%, prompt: Indicate if the connection between the following sentences is 'entailment' or 'not_entailment'.
-Acc: 9.86%, prompt: Analyze the two provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Acc: 2.82%, prompt: Considering the two sentences, identify if their relationship is 'entailment' or 'not_entailment'.
-Acc: 1.41%, prompt: Identify whether the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Acc: 1.41%, prompt: Determine if the given pair of sentences displays entailment or not_entailment. Respond with 'entailment' or 'not_entailment'.
-Acc: 0.00%, prompt: Examine the pair of sentences and determine if they exhibit entailment or not_entailment. Answer with either 'entailment' or 'not_entailment'.
-
-Acc: 16.90%, prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Acc: 16.90%, prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Acc: 12.68%, prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Acc: 9.86%, prompt: In the capacity of an entailment assessment system, indicate if the link between the following sentences is 'entailment' or 'not_entailment'.
-Acc: 9.86%, prompt: In the role of a semantic relationship analyzer, examine the connection between the given sentences and decide if they exhibit entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Acc: 5.63%, prompt: As a tool for determining entailment relationships, review the two statements and categorize their connection as either 'entailment' or 'not_entailment'.
-Acc: 4.23%, prompt: Acting as an entailment detection instrument, determine if the given pair of sentences demonstrates entailment or not_entailment. Answer with 'entailment' or 'not_entailment'.
-Acc: 2.82%, prompt: While performing entailment analysis, classify the relationship between the provided sentences as 'entailment' or 'not_entailment'.
-Acc: 2.82%, prompt: As an instrument for entailment evaluation, consider the two sentences and determine if their relationship is 'entailment' or 'not_entailment'. Respond with 'entailment' or 'not_entailment'.
-Acc: 0.00%, prompt: Working as an entailment classifier, identify whether the given pair of sentences displays entailment or not_entailment. Respond with 'entailment' or 'not_entailment'.
-
-## bertattack
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'pair the following 2 sentences entailment or not_entailment? read me with "entailment" or "not_entailment", just one word. '
-Original acc: 32.39%, attacked acc: 4.23%, dropped acc: 28.17%
-
-Original prompt: Please classify the relationship between the provided sentences as 'entailment' or 'not_entailment'.
-Attacked prompt: b"s classify the relationship between the provided sentences as 'entailment' or 'not_entailment'. "
-Original acc: 28.17%, attacked acc: 2.82%, dropped acc: 25.35%
-
-Original prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Does the relationship between the given sentences represent entailment or not_entailment? q with 'entailment' or 'not_entailment'. "
-Original acc: 23.94%, attacked acc: 0.00%, dropped acc: 23.94%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"[UNK] your role as an entailment and tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'. "
-Original acc: 16.90%, attacked acc: 0.00%, dropped acc: 16.90%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"of an entailment detection set, ask the connection between the following sentences and respond with 'entailment' or 'not_entailment'. "
-Original acc: 16.90%, attacked acc: 1.41%, dropped acc: 15.49%
-
-Original prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Functioning as an entailment evaluation tool, analyze the constructed sentences and decide if their category is 'entailment' or 'not_entailment'. "
-Original acc: 12.68%, attacked acc: 1.41%, dropped acc: 11.27%
-
-## checklist
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word njCuciQJpB. '
-Original acc: 32.39%, attacked acc: 19.72%, dropped acc: 12.68%
-
-Original prompt: Please classify the relationship between the provided sentences as 'entailment' or 'not_entailment'.
-Attacked prompt: b"Please classify the relationship between the provided sentences as 'entailment' or 'not_entailment' rzu98kF2KF. "
-Original acc: 28.17%, attacked acc: 8.45%, dropped acc: 19.72%
-
-Original prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment' vTAjHynoIG. "
-Original acc: 23.94%, attacked acc: 18.31%, dropped acc: 5.63%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment' 9vGXgnbWB8. "
-Original acc: 16.90%, attacked acc: 5.63%, dropped acc: 11.27%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment' rzu98kF2KF. "
-Original acc: 16.90%, attacked acc: 2.82%, dropped acc: 14.08%
-
-Original prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment' njCuciQJpB. "
-Original acc: 12.68%, attacked acc: 5.63%, dropped acc: 7.04%
-
-## deepwordbug
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'ACe the following wo sentences entailment or not_entailment? Answmer me with "entailment" or "not_entailment", just bone wrd. '
-Original acc: 32.39%, attacked acc: 4.23%, dropped acc: 28.17%
-
-Original prompt: Please classify the relationship between the provided sentences as 'entailment' or 'not_entailment'.
-Attacked prompt: b"PlJease classify the rhlationship between the provided senteunces as 'entailment' or 'not_entailment'. "
-Original acc: 28.17%, attacked acc: 1.41%, dropped acc: 26.76%
-
-Original prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"SDoes the relationship between the given stentences represent entailment or not_entailment? ResYond with 'entailment' or 'not_entailment'. "
-Original acc: 23.94%, attacked acc: 0.00%, dropped acc: 23.94%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"mn your roye as an entailment aunalysis tooS, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'. "
-Original acc: 16.90%, attacked acc: 0.00%, dropped acc: 16.90%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"AFs an entailment identificGtion system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'. "
-Original acc: 16.90%, attacked acc: 2.82%, dropped acc: 14.08%
-
-Original prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Functioning as an entailment evaluation tool, analyze the provided sentneces and decide if their relatiXonship is 'entailment' or 'not_entailment'. "
-Original acc: 12.68%, attacked acc: 5.63%, dropped acc: 7.04%
-
-## stresstest
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word  and true is true  and true is true  and true is true  and true is true  and true is true . '
-Original acc: 32.39%, attacked acc: 11.27%, dropped acc: 21.13%
-
-Original prompt: Please classify the relationship between the provided sentences as 'entailment' or 'not_entailment'.
-Attacked prompt: b"Please classify the relationship between the provided sentences as 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 28.17%, attacked acc: 1.41%, dropped acc: 26.76%
-
-Original prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 23.94%, attacked acc: 14.08%, dropped acc: 9.86%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 16.90%, attacked acc: 12.68%, dropped acc: 4.23%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'  and true is true . "
-Original acc: 16.90%, attacked acc: 2.82%, dropped acc: 14.08%
-
-Original prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 12.68%, attacked acc: 12.68%, dropped acc: 0.00%
-
-## textbugger
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'A\xe2\xb2\x85e the following \xf0\x9d\x9a\x9dwo sentences entailment or not_entailment? Answers me with "entailment" or "not_entailment", just one word. '
-Original acc: 32.39%, attacked acc: 2.82%, dropped acc: 29.58%
-
-Original prompt: Please classify the relationship between the provided sentences as 'entailment' or 'not_entailment'.
-Attacked prompt: b"Pelase classify the relations between the provided sentences as 'entailment' or 'not_entailment'. "
-Original acc: 28.17%, attacked acc: 4.23%, dropped acc: 23.94%
-
-Original prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Does the relations between the given sentences \xe2\xb2\x85epresent entailment or not_entailment? Resp\xd0\xbend with 'entailment' or 'not_entailment'. "
-Original acc: 23.94%, attacked acc: 0.00%, dropped acc: 23.94%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"During your role as an entailment aalysis tool, assess the relationshi\xd1\x80 between the given sentences and classify it as 'entailment' or 'not_entailment'. "
-Original acc: 16.90%, attacked acc: 1.41%, dropped acc: 15.49%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an entailment identification s\xd1\x83stem, examin\xd0\xb5 the connection between the following sentences and responding with 'entailment' or 'not_entailment'. "
-Original acc: 16.90%, attacked acc: 1.41%, dropped acc: 15.49%
-
-Original prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Functioning as an entailment evaluation too\xe2\x85\xbc, analyze the provided sentences and decid\xd0\xb5 if their relationship is 'entailment' or 'not_entailment'. "
-Original acc: 12.68%, attacked acc: 1.41%, dropped acc: 11.27%
-
-## textfooler
-
-Original prompt: Are the following two sentences entailment or not_entailment? Answer me with "entailment" or "not_entailment", just one word.
-Attacked prompt: b'Se the aftermath two sentences entailment or not_entailment? Reactions me with "entailment" or "not_entailment", just one terminology. '
-Original acc: 32.39%, attacked acc: 1.41%, dropped acc: 30.99%
-
-Original prompt: Please classify the relationship between the provided sentences as 'entailment' or 'not_entailment'.
-Attacked prompt: b"Urge classify the liaisons between the provided sentences as 'entailment' or 'not_entailment'. "
-Original acc: 28.17%, attacked acc: 1.41%, dropped acc: 26.76%
-
-Original prompt: Does the relationship between the given sentences represent entailment or not_entailment? Respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"Did the coitus between the given sentence represent entailment or not_entailment? Respondent with 'entailment' or 'not_entailment'. "
-Original acc: 23.94%, attacked acc: 0.00%, dropped acc: 23.94%
-
-Original prompt: In your role as an entailment analysis tool, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Attacked prompt: b"Between your role as an entailment valuation appliance, assess the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'. "
-Original acc: 16.90%, attacked acc: 0.00%, dropped acc: 16.90%
-
-Original prompt: As an entailment identification system, examine the connection between the following sentences and respond with 'entailment' or 'not_entailment'.
-Attacked prompt: b"As an entailment identification plans, reviewed the connection between the following sentences and answered with 'entailment' or 'not_entailment'. "
-Original acc: 16.90%, attacked acc: 1.41%, dropped acc: 15.49%
-
-Original prompt: Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'.
-Attacked prompt: b"Functioning as an entailment evaluation tool, analyze the provided sentences and decide if their relationship is 'entailment' or 'not_entailment'. "
-Original acc: 12.68%, attacked acc: 12.68%, dropped acc: 4.23%
-
-## translation
-
-Language: chinese, acc: 11.27%, prompt: In the light of an implication analysis tool, evaluate the relationship between the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 15.49%, prompt: From the perspective of an implication analysis tool, determine whether there is an implication relationship in the following sentences by classifying them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 8.45%, prompt: Please use an implication analysis tool to determine whether an implication relationship exists in the following sentences by classifying them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 26.76%, prompt: Please evaluate the relation of the following sentences as 'entailment' or 'not_entailment' for the purpose of determining implication relation.
-Language: chinese, acc: 9.86%, prompt: Please use the implication analysis tool to evaluate the relationships between the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 1.41%, prompt: For the purpose of determining implicative relations, analyze the relations of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 11.27%, prompt: Please use the implication analysis tool to determine the relationship of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 9.86%, prompt: Please use the implication judgment tool to assess the relevance of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 14.08%, prompt: Please, with implication analysis as the main task, determine the relationships between the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: chinese, acc: 4.23%, prompt: Using the implication judgment as a criterion, analyze the relation of the following sentences and classify them as 'entailment' or 'not_entailment'.
-Language: french, acc: 9.86%, prompt: As an engagement analysis tool, evaluate the relationship between the given sentences and classify it as 'entailment' or 'not_entailment'.
-Language: french, acc: 4.23%, prompt: Determine whether the given sentences involve one another or not as an implication analysis tool. Classify them accordingly as 'entailment' or 'not_entailment'.
-Language: french, acc: 5.63%, prompt: Using implication analysis, evaluate whether the sentences provided have a logical relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 11.27%, prompt: As an engagement assessment tool, determine whether the sentences provided have a logical relationship and classify them as 'entailment' or 'not_entailment'.
-Language: french, acc: 8.45%, prompt: As an implication classification tool, analyze the sentences provided to determine if there is a logical relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 0.00%, prompt: Using implication analysis, determine whether the given sentences have a cause-effect relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 7.04%, prompt: Evaluate the relationship between the given sentences using implication analysis and rank them accordingly as 'entailment' or 'not_entailment'.
-Language: french, acc: 14.08%, prompt: As an engagement detection tool, determine whether the given sentences have a logical relationship and categorize them as 'entailment' or 'not_entailment'.
-Language: french, acc: 1.41%, prompt: Using implication analysis, evaluate whether the sentences provided have a cause-effect relationship and rank them accordingly as 'entailment' or 'not_entailment'.
-Language: french, acc: 2.82%, prompt: Determine whether the given sentences have a cause-effect relationship as an engagement analysis tool and categorize them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 9.86%, prompt: In your role as a tool for reasoning analysis, evaluate the relationship between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 12.68%, prompt: Can you determine whether this sentence is inferred from the other sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 9.86%, prompt: Using the tool of reasoning analysis, analyze the relationship between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 28.17%, prompt: Does this sentence represent a conclusion from the previous sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 4.23%, prompt: As a tool of reasoning analysis, evaluate the relationship of given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 21.13%, prompt: Can this sentence be inferred from the previous sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 4.23%, prompt: Using a tool to analyze a conclusion, analyze the relationship between the two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 22.54%, prompt: Is this a conclusion from the next sentence? Classify it as 'entailment' or 'not_entailment'.
-Language: arabic, acc: 9.86%, prompt: As part of your task in analyzing a conclusion, evaluate the relationship between the two sentences and classify them as 'entailment' or 'not_entailment' based on their relationship.
-Language: arabic, acc: 22.54%, prompt: Are you following this sentence directly from the previous one? Classify it as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 15.49%, prompt: In your role as an implication analysis tool, evaluate the relationship between the given phrases and classify them as 'entailment' or 'not_entailment'.
-Language: spanish, acc: 26.76%, prompt: Determine whether the second sentence necessarily implies the first and label the relation as 'entailment', or as 'not_entailment' if not.
-Language: spanish, acc: 2.82%, prompt: Classifies the relationship between these two sentences as 'entailment' if one necessarily implies the other, or as 'not_entailment' if not.
-Language: spanish, acc: 4.23%, prompt: Evaluates whether the information in the second sentence is implied in the first and labels the relationship as 'entailment', or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 4.23%, prompt: Given a couple of phrases, label their relationship as 'entailment' if one necessarily implies the other, or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 2.82%, prompt: Analyzes the relationship between the phrases and classifies them as 'entailment' if one necessarily implies the other, or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 9.86%, prompt: Given two sentences, determine whether the second sentence is a necessary consequence of the first and label the relation as 'entailment', or as 'not_entailment' if not.
-Language: spanish, acc: 5.63%, prompt: Evaluates whether the information presented in the second sentence is implicit in the first and labels the relationship as 'entailment', or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 0.00%, prompt: Classifies the relationship between the given phrases as 'entailment' if one necessarily implies the other, or as 'not_entailment' if there is no such implication.
-Language: spanish, acc: 15.49%, prompt: Determines whether the information provided in the second sentence is necessarily inferable from the first and labels the relationship as 'entailment', or as 'not_entailment' if not.
-Language: japanese, acc: 1.41%, prompt: Analyze the relationship of a given sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 4.23%, prompt: Evaluate the semantic relationship of the sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 16.90%, prompt: Please judge the relationship between the given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 7.04%, prompt: Examine the paraphrases of a given sentence and classify them 'entailment' or 'not_entailment'.
-Language: japanese, acc: 2.82%, prompt: Rate the similarity of a given sentence and categorize it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 2.82%, prompt: Determinate the semantic connections of a given sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 2.82%, prompt: Examine the semantic match of a given sentence and categorize it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 5.63%, prompt: Classify it as 'entailment' or 'not_entailment' based on the content of the sentence.
-Language: japanese, acc: 1.41%, prompt: Analyze the relationship of a given sentence and classify it as 'entailment' or 'not_entailment'.
-Language: japanese, acc: 0.00%, prompt: Judge the semantic connections of a given sentence and categorize it as 'entailment' or 'not_entailment'.
-Language: korean, acc: 8.45%, prompt: Evaluate the relationship between any two sentences given to you and classify you as 'entailment' or 'not_entailment'.
-Language: korean, acc: 7.04%, prompt: Analyze the semantic deductive relations between sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 4.23%, prompt: Evaluate the logical relevance between sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 1.41%, prompt: Evaluate the interaction of two given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 19.72%, prompt: Please check whether there is a semantic match between those two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 1.41%, prompt: Compare information between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 29.58%, prompt: Please analyse the correlation between those two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 5.63%, prompt: Evaluate the different meanings between given sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 9.86%, prompt: Compare the semantic structure of the two sentences and classify them as 'entailment' or 'not_entailment'.
-Language: korean, acc: 4.23%, prompt: Evaluate the interactions between sentences and classify them as 'entailment' or 'not_entailment'.
-
-# mmlu
-
-## 10 prompts
-
-Acc: 10.39%, prompt: Answer the following multiple-choice question about {} by selecting the correct option: 'A', 'B', 'C', or 'D'.
-Acc: 10.15%, prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Acc: 9.10%, prompt: Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'.
-Acc: 8.98%, prompt: Evaluate the multiple-choice question about {} and select the most fitting response from 'A', 'B', 'C', or 'D'.
-Acc: 7.82%, prompt: Regarding the following multiple-choice question on {}, pick the correct answer from the options 'A', 'B', 'C', or 'D'.
-Acc: 7.70%, prompt: Please respond to the multiple-choice question about {} by selecting the appropriate answer: 'A', 'B', 'C', or 'D'.
-Acc: 7.58%, prompt: For the multiple-choice question related to {}, please choose the most accurate answer from 'A', 'B', 'C', or 'D'.
-Acc: 6.07%, prompt: For each multiple-choice question about {}, identify the correct answer by selecting 'A', 'B', 'C', or 'D'.
-Acc: 5.72%, prompt: Examine the following question based on {} and choose the correct response from 'A', 'B', 'C', or 'D'.
-Acc: 5.13%, prompt: Below are multiple-choice question concerning {}. Indicate your response with 'A', 'B', 'C', or 'D'.
-
-Acc: 9.80%, prompt: With your knowledge of {}, tackle the following multiple-choice question by choosing 'A', 'B', 'C', or 'D'.
-Acc: 9.68%, prompt: With your understanding of {}, kindly answer the subsequent multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Acc: 9.45%, prompt: As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'.
-Acc: 9.33%, prompt: As a knowledgeable individual in {}, provide your response to the multiple-choice question by choosing 'A', 'B', 'C', or 'D'.
-Acc: 9.22%, prompt: As an expert in {}, respond to the following multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Acc: 8.98%, prompt: As a skilled person in the field of {}, please respond to the multiple-choice question by choosing 'A', 'B', 'C', or 'D'.
-Acc: 8.87%, prompt: Drawing upon your mastery of {}, please answer the multiple-choice question by selecting the correct option from 'A', 'B', 'C', or 'D'.
-Acc: 8.52%, prompt: Considering your familiarity with {}, attend to the following multiple-choice question by picking 'A', 'B', 'C', or 'D'.
-Acc: 7.70%, prompt: Given your proficiency in {}, please answer the subsequent multiple-choice question with 'A', 'B', 'C', or 'D'.
-Acc: 6.18%, prompt: Utilizing your expertise in {}, answer the following multiple-choice question by picking 'A', 'B', 'C', or 'D'.
-
-## bertattack
-
-Original prompt: Answer the following multiple-choice question about {} by selecting the correct option: 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Answer the following multiple-options questions about {} by selecting the given ending: 'A', 'B', 'C', or 'D'. "
-Original acc: 9.57%, attacked acc: 3.72%, dropped acc: 5.85%
-
-Original prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"should assignment to the multiple-: answer on {}, please provide the accurate asking by choosing 'A', 'B', 'C', or 'D'. "
-Original acc: 9.57%, attacked acc: 3.72%, dropped acc: 5.85%
-
-Original prompt: Evaluate the multiple-choice question about {} and select the most fitting response from 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"i the multiple-a asked about {} and select the most fitting response from 'A', 'B', 'C', or 'D'. "
-Original acc: 8.51%, attacked acc: 3.55%, dropped acc: 4.96%
-
-Original prompt: With your understanding of {}, kindly answer the subsequent multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"With your understand of {}, self answer the subsequent multiple-chosen questions by stating 'A', 'B', 'C', or 'D'."
-Original acc: 9.22%, attacked acc: 3.37%, dropped acc: 5.85%
-
-Original prompt: As an expert in {}, respond to the following multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As an expert in {}, respond to the following multiple-difficulty problem by type 'A', 'B', 'C', or 'D'."
-Original acc: 9.04%, attacked acc: 4.08%, dropped acc: 4.96%
-
-Original prompt: With your knowledge of {}, tackle the following multiple-choice question by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"ll your knowledge of {}, answer the following multiple-difficulty asked by picking 'A', 'B', 'C', or 'D'."
-Original acc: 8.87%, attacked acc: 3.37%, dropped acc: 5.50%
-
-## checklist
-
-Original prompt: Answer the following multiple-choice question about {} by selecting the correct option: 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Answer the following multiple-choice question about {} by selecting the correct option: 'A', 'B', 'C', or 'D' w52rwgo0Av. "
-Original acc: 10.39%, attacked acc: 6.77%, dropped acc: 3.62%Original prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D' OB4KVJzIft. "
-Original acc: 10.15%, attacked acc: 5.02%, dropped acc: 5.13%
-
-Original prompt: Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D' w52rwgo0Av. "
-Original acc: 9.10%, attacked acc: 6.42%, dropped acc: 2.68%
-
-Original prompt: With your knowledge of {}, tackle the following multiple-choice question by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"With your knowledge of {}, tackle the following multiple-choice question by choosing 'A', 'B', 'C', or 'D' w52rwgo0Av."
-Original acc: 9.80%, attacked acc: 5.25%, dropped acc: 4.55%
-
-Original prompt: With your understanding of {}, kindly answer the subsequent multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"With your understanding of {}, kindly answer the subsequent multiple-choice question by selecting 'A', 'B', 'C', or 'D' w52rwgo0Av."
-Original acc: 9.68%, attacked acc: 4.90%, dropped acc: 4.78%
-
-Original prompt: As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D' w52rwgo0Av."
-Original acc: 9.45%, attacked acc: 4.43%, dropped acc: 5.02%
-
-## deepwordbug
-
-Original prompt: Answer the following multiple-choice question about {} by selecting the correct option: 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"WAnswer the fkollowing multiple-chAice question about {} by selecting the correct option: 'A', 'B', 'C', or 'D'. "
-Original acc: 10.39%, attacked acc: 6.53%, dropped acc: 3.85%
-
-Original prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"oIn relatin to the mhltiple-choice question on {}, plese provide the accurate answer by choosing 'A', 'B', 'C', or 'D'. "
-Original acc: 10.15%, attacked acc: 3.50%, dropped acc: 6.65%
-
-Original prompt: Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Anwer the subseuent mltiple-choice question about {} by picing the right option among 'A', 'B', 'C', or 'D'. "
-Original acc: 9.10%, attacked acc: 4.78%, dropped acc: 4.32%
-
-Original prompt: With your knowledge of {}, tackle the following multiple-choice question by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"With your knowlegde of {}, tacke the folloing mulBiple-choice question by choosing 'A', 'B', 'C', or 'D'."
-Original acc: 9.80%, attacked acc: 6.07%, dropped acc: 3.73%
-
-Original prompt: With your understanding of {}, kindly answer the subsequent multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"With your understanding of {}, kinduy answer the subsequent multiple-choice question by selecting 'A', 'B', 'C', or 'D'."
-Original acc: 9.68%, attacked acc: 8.63%, dropped acc: 1.05%
-
-Original prompt: As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"AGs someEne well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'."
-Original acc: 9.45%, attacked acc: 8.28%, dropped acc: 1.17%
-
-## stresstest
-
-Original prompt: Answer the following multiple-choice question about {} by selecting the correct option: 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Answer the following multiple-choice question about {} by selecting the correct option: 'A', 'B', 'C', or 'D'  and true is true . "
-Original acc: 10.39%, attacked acc: 13.30%, dropped acc: -2.92%
-
-Original prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 10.15%, attacked acc: 11.67%, dropped acc: -1.52%
-
-Original prompt: Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'  and true is true  and true is true  and true is true  and true is true  and true is true . "
-Original acc: 9.10%, attacked acc: 13.19%, dropped acc: -4.08%
-
-Original prompt: With your knowledge of {}, tackle the following multiple-choice question by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"With your knowledge of {}, tackle the following multiple-choice question by choosing 'A', 'B', 'C', or 'D'  and true is true  and true is true  and true is true  and true is true  and true is true ."
-Original acc: 9.80%, attacked acc: 10.85%, dropped acc: -1.05%
-
-Original prompt: With your understanding of {}, kindly answer the subsequent multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"With your understanding of {}, kindly answer the subsequent multiple-choice question by selecting 'A', 'B', 'C', or 'D'  and true is true  and true is true  and true is true  and true is true  and true is true ."
-Original acc: 9.68%, attacked acc: 12.49%, dropped acc: -2.80%
-
-Original prompt: As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'  and true is true  and true is true  and true is true  and true is true  and true is true ."
-Original acc: 9.45%, attacked acc: 13.30%, dropped acc: -3.85%
-
-## textbugger
-
-Original prompt: Answer the following multiple-choice question about {} by selecting the correct option: 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Answer the \xf0\x9d\x9a\x8following m\xd5\xbdltiple-choice question about {} by select the corrected options: 'A', 'B', 'C', or 'D'. "
-Original acc: 10.39%, attacked acc: 4.55%, dropped acc: 5.83%
-
-Original prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"In \xe2\xb2\x85elation to the m\xd5\xbdltiple-choice issues on {}, \xd1\x80lease pr\xd0\xbevide the accurate answer by choosing 'A', 'B', 'C', or 'D'. "
-Original acc: 10.15%, attacked acc: 4.32%, dropped acc: 5.83%
-
-Original prompt: Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Responding the subequent multipl-echoice question about {} by pick the right op\xf0\x9d\x9a\x9dion amongst 'A', 'B', 'C', or 'D'. "
-Original acc: 9.10%, attacked acc: 3.50%, dropped acc: 5.60%
-
-Original prompt: With your knowledge of {}, tackle the following multiple-choice question by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"With your expertise of {}, \xf0\x9d\x9a\x9dackle the following m\xd5\xbdltiple-choice que\xd1\x95tion by choosing 'A', 'B', 'C', or 'D'."
-Original acc: 9.80%, attacked acc: 2.92%, dropped acc: 6.88%
-
-Original prompt: With your understanding of {}, kindly answer the subsequent multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Wit\xd5\xb0 your understand of {}, \xf0\x9d\x92\x8cindly answer the successive m\xd5\xbdltiple-choice question by selecting 'A', 'B', 'C', or 'D'."
-Original acc: 9.68%, attacked acc: 2.45%, dropped acc: 7.23%
-
-Original prompt: As someone well-versed in {}, please address the multiple-choice question below by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As someone well-versed in {}, pl\xd0\xb5ase a\xd4\x81dress the \xef\xbd\x8dultiple-choice questions below by choosing 'A', 'B', 'C', or 'D'."
-Original acc: 9.45%, attacked acc: 4.90%, dropped acc: 4.55%
-
-## textfooler
-
-Original prompt: Answer the following multiple-choice question about {} by selecting the correct option: 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Answer the suite multiple-choice problems about {} by selecting the appropriate selection: 'A', 'B', 'C', or 'D'. "
-Original acc: 10.39%, attacked acc: 4.55%, dropped acc: 5.83%
-
-Original prompt: In relation to the multiple-choice question on {}, please provide the accurate answer by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"At compared to the multiple-choice things on {}, please prescribes the corrects reaction by choices 'A', 'B', 'C', or 'D'. "
-Original acc: 10.15%, attacked acc: 3.97%, dropped acc: 6.18%
-
-Original prompt: Answer the subsequent multiple-choice question about {} by picking the right option among 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"Answer the consecutive multiple-choice questions about {} by choosing the ok option among 'A', 'B', 'C', or 'D'. "
-Original acc: 9.10%, attacked acc: 3.73%, dropped acc: 5.37%
-
-Original prompt: With your understanding of {}, kindly answer the subsequent multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"With your understand of {}, gracefully answer the forth multiple-choice question by picking 'A', 'B', 'C', or 'D'."
-Original acc: 9.22%, attacked acc: 4.61%, dropped acc: 4.61%
-
-Original prompt: As an expert in {}, respond to the following multiple-choice question by selecting 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"As an specializing in {}, answer to the following multiple-choice things by selecting 'A', 'B', 'C', or 'D'."
-Original acc: 9.04%, attacked acc: 5.14%, dropped acc: 3.90%
-
-Original prompt: With your knowledge of {}, tackle the following multiple-choice question by choosing 'A', 'B', 'C', or 'D'.
-Attacked prompt: b"With your comprehend of {}, face the following multiple-choice questions by choosing 'A', 'B', 'C', or 'D'."
-Original acc: 8.87%, attacked acc: 6.21%, dropped acc: 2.66%
-
-## translation
-
-Language: chinese, acc: 9.22%, prompt: For the following multiple choice question about {}, please select the correct answer from 'A', 'B', 'C' or 'D'.
-Language: chinese, acc: 11.88%, prompt: Please select the correct answer from 'A', 'B', 'C' or 'D' for the following multiple choice question for {}.
-Language: chinese, acc: 9.75%, prompt: Select the correct answer from 'A', 'B', 'C' or 'D' for the following multiple choice question {}.
-Language: chinese, acc: 4.79%, prompt: Please choose the correct answer from 'A', 'B', 'C' or 'D' according to the following multiple-choice questions about {}.
-Language: chinese, acc: 4.96%, prompt: Please select the correct answer from 'A', 'B', 'C' or 'D' for the {} multiple choice questions below.
-Language: chinese, acc: 6.74%, prompt: The following is A multiple choice question about {}. Please select the correct answer from 'A', 'B', 'C' or 'D'.
-Language: chinese, acc: 10.82%, prompt: Please select the correct answer from 'A', 'B', 'C' or 'D' for the following multiple choice question {}.
-Language: chinese, acc: 4.79%, prompt: Please choose the correct answer from 'A', 'B', 'C' or 'D' according to the following multiple-choice questions about {}.
-Language: chinese, acc: 6.38%, prompt: Please select the correct answer from 'A', 'B', 'C' or 'D' for the following multiple choice questions about {}.
-Language: chinese, acc: 6.38%, prompt: Please select the correct answer from 'A', 'B', 'C' or 'D' for the following multiple choice questions about {}.
-Language: french, acc: 6.74%, prompt: For the following multiple choice question on {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: french, acc: 4.79%, prompt: This is a multiple choice question about {}. Select the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: french, acc: 9.04%, prompt: In the context of the multiple-choice question on {}, identify the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: french, acc: 7.45%, prompt: About the following question on {}, determine the correct answer from the choices 'A', 'B', 'C' or 'D'.
-Language: french, acc: 7.27%, prompt: Carefully review the multiple-choice question regarding {}. Choose the correct answer from options 'A', 'B', 'C', or 'D'.
-Language: french, acc: 9.22%, prompt: For the multiple-choice question for {}, indicate the correct answer from options 'A', 'B', 'C', or 'D'.
-Language: french, acc: 5.50%, prompt: The next question is about {}. Select the correct answer from the choices 'A', 'B', 'C' or 'D'.
-Language: french, acc: 7.45%, prompt: As part of the multiple-choice question on {}, choose the appropriate answer from options 'A', 'B', 'C' or 'D'.
-Language: french, acc: 8.16%, prompt: Rate your understanding of the multiple-choice question on {}. Choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: french, acc: 9.22%, prompt: Analyze the following multiple-choice question on {}. Identify the correct answer among choices 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 6.38%, prompt: For the multiple choice question about {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 6.56%, prompt: For the following multiple-choice question about {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 7.09%, prompt: For the following multiple choice question about {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 11.70%, prompt: When it comes to the multiple-choice question about {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 7.80%, prompt: For the multiple-choice question about {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 7.62%, prompt: If the question for {} is multiple choice, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 6.21%, prompt: For the question regarding {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 6.21%, prompt: For the question about {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 7.27%, prompt: When it comes to the question regarding {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: arabic, acc: 6.21%, prompt: For the question regarding {}, choose the correct answer from options 'A', 'B', 'C' or 'D'.
-Language: spanish, acc: 7.80%, prompt: For the following multiple-choice question about {}, choose the correct answer from 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 7.98%, prompt: For the following multiple-choice question about {}, select the correct answer from 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 7.80%, prompt: For the following multiple-choice question about {}, choose the correct answer from 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 10.64%, prompt: Within the context of the following multiple-choice question about {}, choose the correct option from 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 6.91%, prompt: For the following multiple-choice statement about {}, select the correct answer from 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 10.11%, prompt: Considering the following multiple-choice question about {}, mark the correct answer with 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 6.74%, prompt: For the following multiple-choice question about {}, choose the correct alternative among 'A', 'B', 'C' or 'D'.
-Language: spanish, acc: 6.74%, prompt: For the following multiple-choice statement about {}, choose the correct option from alternatives 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 9.22%, prompt: Within the context of the following multiple-choice question about {}, select the correct answer from alternatives 'A', 'B', 'C', or 'D'.
-Language: spanish, acc: 9.22%, prompt: Considering the following multiple-choice statement about {}, mark the correct alternative with the options 'A', 'B', 'C' or 'D'.
-Language: japanese, acc: 7.98%, prompt: Choose the appropriate answer from options 'A', 'B', 'C', or 'D' for {} regarding the following question.
-Language: japanese, acc: 8.16%, prompt: Choose the correct answer from 'A', 'B', 'C', or 'D' for the following multiple-choice question about {}.
-Language: japanese, acc: 6.21%, prompt: For the following multiple-choice questions about {}, choose the correct answer from 'A', 'B', 'C', or 'D'.
-Language: japanese, acc: 4.79%, prompt: Choose the correct answer from options 'A', 'B', 'C', or 'D' for the following questions about {}.
-Language: japanese, acc: 6.91%, prompt: In the multiple choice questions below, choose the correct answer for {} from 'A', 'B', 'C', or 'D'.
-Language: japanese, acc: 4.43%, prompt: Choose the correct answer from the options 'A', 'B', 'C', or 'D' for the following questions about {}.
-Language: japanese, acc: 6.91%, prompt: In the multiple choice questions below, choose the correct answer for {} from 'A', 'B', 'C', or 'D'.
-Language: japanese, acc: 5.32%, prompt: Choose the correct answer from 'A', 'B', 'C', or 'D' for the following multiple choice questions about {}.
-Language: japanese, acc: 6.91%, prompt: In the multiple choice questions below, choose the correct answer for {} from 'A', 'B', 'C', or 'D'.
-Language: japanese, acc: 8.16%, prompt: Choose the correct answer from options 'A', 'B', 'C', or 'D' for {} regarding the following question.
-Language: korean, acc: 5.14%, prompt: For the multiple choice problem about, choose the correct answer for '{}' from 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 6.56%, prompt: Choose the correct answer for '{}' from 'A', 'B', 'C', or 'D' in the multiple choice problem involving,
-Language: korean, acc: 6.38%, prompt: For the multiple choice problem below, choose the correct answer to '{}' from 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 8.51%, prompt: In the following multiple-choice problem, choose the correct answer for '{}' from 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 7.98%, prompt: For the following multiple choice problem, choose the correct answer for '{}' from 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 9.04%, prompt: Solve multiple choice problems about: Which of 'A', 'B', 'C', or 'D' is the correct answer for '{}'.
-Language: korean, acc: 8.16%, prompt: Choose the correct answer to the multiple-choice question below. Is '{}' an 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 6.03%, prompt: Solve the following multiple-choice problem. Choose the correct answer for '{}' from 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 8.51%, prompt: Choose the correct answer to the following multiple choice problem: Is '{}' 'A', 'B', 'C', or 'D'.
-Language: korean, acc: 7.45%, prompt: Solve multiple-choice problems about: Please select 'A', 'B', 'C', or 'D' for the correct answer to '{}'.
-
-# squad_v2
-
-## 10 prompts
-
-## bertattack
-
-## checklist
-
-## deepwordbug
-
-## stresstest
-
-## textbugger
-
-## textfooler
-
-# un_multi
-
-## 10 prompts
-
-## bertattack
-
-## checklist
-
-## deepwordbug
-
-## stresstest
-
-## textbugger
-
-## textfooler
-
-# iwslt
-
-## 10 prompts
-
-## bertattack
-
-## checklist
-
-## deepwordbug
-
-## stresstest
-
-## textbugger
-
-## textfooler
-
-# math
-
-## 10 prompts
-
-## bertattack
-
-## checklist
-
-## deepwordbug
-
-## stresstest
-
-## textbugger
-
-## textfooler
\ No newline at end of file
diff --git a/spaces/March07/PromptBench/transfer.py b/spaces/March07/PromptBench/transfer.py
deleted file mode 100644
index c7960c4aec648bcc9c6dec08d6d81d9e51c0677b..0000000000000000000000000000000000000000
--- a/spaces/March07/PromptBench/transfer.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import json
-import os
-import glob
-import numpy as np
-
-def convert_model_name(model):
-    name = {
-        "T5": "google-flan-t5-large",
-        "UL2": "google-flan-ul2",
-        "Vicuna": "vicuna-13b",
-        "ChatGPT": "chatgpt",
-    }
-    return name[model]
-
-def retrieve_transfer(source, target, attack, shot):
-    source = convert_model_name(source)
-    target = convert_model_name(target)
-    attack = attack.lower()
-    file_dir = "./results_transfer/"+source+"_"+target+"/"+attack+"_"+str(shot)+"_shot.json"
-    with open(file_dir, 'r', encoding='utf-8') as f:
-        data = json.load(f)
-
-    return data
-
-
-
diff --git a/spaces/MetaWabbit/Auto-GPT/autogpt/json_utils/json_fix_llm.py b/spaces/MetaWabbit/Auto-GPT/autogpt/json_utils/json_fix_llm.py
deleted file mode 100644
index 869aed125cfb8cd7a69ed02eeb389cc72a3e296b..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/autogpt/json_utils/json_fix_llm.py
+++ /dev/null
@@ -1,220 +0,0 @@
-"""This module contains functions to fix JSON strings generated by LLM models, such as ChatGPT, using the assistance
-of the ChatGPT API or LLM models."""
-from __future__ import annotations
-
-import contextlib
-import json
-from typing import Any, Dict
-
-from colorama import Fore
-from regex import regex
-
-from autogpt.config import Config
-from autogpt.json_utils.json_fix_general import correct_json
-from autogpt.llm_utils import call_ai_function
-from autogpt.logs import logger
-from autogpt.speech import say_text
-
-JSON_SCHEMA = """
-{
-    "command": {
-        "name": "command name",
-        "args": {
-            "arg name": "value"
-        }
-    },
-    "thoughts":
-    {
-        "text": "thought",
-        "reasoning": "reasoning",
-        "plan": "- short bulleted\n- list that conveys\n- long-term plan",
-        "criticism": "constructive self-criticism",
-        "speak": "thoughts summary to say to user"
-    }
-}
-"""
-
-CFG = Config()
-
-
-def auto_fix_json(json_string: str, schema: str) -> str:
-    """Fix the given JSON string to make it parseable and fully compliant with
-        the provided schema using GPT-3.
-
-    Args:
-        json_string (str): The JSON string to fix.
-        schema (str): The schema to use to fix the JSON.
-    Returns:
-        str: The fixed JSON string.
-    """
-    # Try to fix the JSON using GPT:
-    function_string = "def fix_json(json_string: str, schema:str=None) -> str:"
-    args = [f"'''{json_string}'''", f"'''{schema}'''"]
-    description_string = (
-        "This function takes a JSON string and ensures that it"
-        " is parseable and fully compliant with the provided schema. If an object"
-        " or field specified in the schema isn't contained within the correct JSON,"
-        " it is omitted. The function also escapes any double quotes within JSON"
-        " string values to ensure that they are valid. If the JSON string contains"
-        " any None or NaN values, they are replaced with null before being parsed."
-    )
-
-    # If it doesn't already start with a "`", add one:
-    if not json_string.startswith("`"):
-        json_string = "```json\n" + json_string + "\n```"
-    result_string = call_ai_function(
-        function_string, args, description_string, model=CFG.fast_llm_model
-    )
-    logger.debug("------------ JSON FIX ATTEMPT ---------------")
-    logger.debug(f"Original JSON: {json_string}")
-    logger.debug("-----------")
-    logger.debug(f"Fixed JSON: {result_string}")
-    logger.debug("----------- END OF FIX ATTEMPT ----------------")
-
-    try:
-        json.loads(result_string)  # just check the validity
-        return result_string
-    except json.JSONDecodeError:  # noqa: E722
-        # Get the call stack:
-        # import traceback
-        # call_stack = traceback.format_exc()
-        # print(f"Failed to fix JSON: '{json_string}' "+call_stack)
-        return "failed"
-
-
-def fix_json_using_multiple_techniques(assistant_reply: str) -> Dict[Any, Any]:
-    """Fix the given JSON string to make it parseable and fully compliant with two techniques.
-
-    Args:
-        json_string (str): The JSON string to fix.
-
-    Returns:
-        str: The fixed JSON string.
-    """
-
-    # Parse and print Assistant response
-    assistant_reply_json = fix_and_parse_json(assistant_reply)
-    if assistant_reply_json == {}:
-        assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
-            assistant_reply
-        )
-
-    if assistant_reply_json != {}:
-        return assistant_reply_json
-
-    logger.error(
-        "Error: The following AI output couldn't be converted to a JSON:\n",
-        assistant_reply,
-    )
-    if CFG.speak_mode:
-        say_text("I have received an invalid JSON response from the OpenAI API.")
-
-    return {}
-
-
-def fix_and_parse_json(
-    json_to_load: str, try_to_fix_with_gpt: bool = True
-) -> Dict[Any, Any]:
-    """Fix and parse JSON string
-
-    Args:
-        json_to_load (str): The JSON string.
-        try_to_fix_with_gpt (bool, optional): Try to fix the JSON with GPT.
-            Defaults to True.
-
-    Returns:
-        str or dict[Any, Any]: The parsed JSON.
-    """
-
-    with contextlib.suppress(json.JSONDecodeError):
-        json_to_load = json_to_load.replace("\t", "")
-        return json.loads(json_to_load)
-
-    with contextlib.suppress(json.JSONDecodeError):
-        json_to_load = correct_json(json_to_load)
-        return json.loads(json_to_load)
-    # Let's do something manually:
-    # sometimes GPT responds with something BEFORE the braces:
-    # "I'm sorry, I don't understand. Please try again."
-    # {"text": "I'm sorry, I don't understand. Please try again.",
-    #  "confidence": 0.0}
-    # So let's try to find the first brace and then parse the rest
-    #  of the string
-    try:
-        brace_index = json_to_load.index("{")
-        maybe_fixed_json = json_to_load[brace_index:]
-        last_brace_index = maybe_fixed_json.rindex("}")
-        maybe_fixed_json = maybe_fixed_json[: last_brace_index + 1]
-        return json.loads(maybe_fixed_json)
-    except (json.JSONDecodeError, ValueError) as e:
-        return try_ai_fix(try_to_fix_with_gpt, e, json_to_load)
-
-
-def try_ai_fix(
-    try_to_fix_with_gpt: bool, exception: Exception, json_to_load: str
-) -> Dict[Any, Any]:
-    """Try to fix the JSON with the AI
-
-    Args:
-        try_to_fix_with_gpt (bool): Whether to try to fix the JSON with the AI.
-        exception (Exception): The exception that was raised.
-        json_to_load (str): The JSON string to load.
-
-    Raises:
-        exception: If try_to_fix_with_gpt is False.
-
-    Returns:
-        str or dict[Any, Any]: The JSON string or dictionary.
-    """
-    if not try_to_fix_with_gpt:
-        raise exception
-    if CFG.debug_mode:
-        logger.warn(
-            "Warning: Failed to parse AI output, attempting to fix."
-            "\n If you see this warning frequently, it's likely that"
-            " your prompt is confusing the AI. Try changing it up"
-            " slightly."
-        )
-    # Now try to fix this up using the ai_functions
-    ai_fixed_json = auto_fix_json(json_to_load, JSON_SCHEMA)
-
-    if ai_fixed_json != "failed":
-        return json.loads(ai_fixed_json)
-    # This allows the AI to react to the error message,
-    #   which usually results in it correcting its ways.
-    # logger.error("Failed to fix AI output, telling the AI.")
-    return {}
-
-
-def attempt_to_fix_json_by_finding_outermost_brackets(json_string: str):
-    if CFG.speak_mode and CFG.debug_mode:
-        say_text(
-            "I have received an invalid JSON response from the OpenAI API. "
-            "Trying to fix it now."
-        )
-        logger.error("Attempting to fix JSON by finding outermost brackets\n")
-
-    try:
-        json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
-        json_match = json_pattern.search(json_string)
-
-        if json_match:
-            # Extract the valid JSON object from the string
-            json_string = json_match.group(0)
-            logger.typewriter_log(
-                title="Apparently json was fixed.", title_color=Fore.GREEN
-            )
-            if CFG.speak_mode and CFG.debug_mode:
-                say_text("Apparently json was fixed.")
-        else:
-            return {}
-
-    except (json.JSONDecodeError, ValueError):
-        if CFG.debug_mode:
-            logger.error(f"Error: Invalid JSON: {json_string}\n")
-        if CFG.speak_mode:
-            say_text("Didn't work. I will have to ignore this response then.")
-        logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
-        json_string = {}
-
-    return fix_and_parse_json(json_string)
diff --git a/spaces/MetaWabbit/Auto-GPT/autogpt/memory/no_memory.py b/spaces/MetaWabbit/Auto-GPT/autogpt/memory/no_memory.py
deleted file mode 100644
index 0371e96ae89f5eb88dae019a66351a229596ed7a..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/autogpt/memory/no_memory.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""A class that does not store any data. This is the default memory provider."""
-from __future__ import annotations
-
-from typing import Any
-
-from autogpt.memory.base import MemoryProviderSingleton
-
-
-class NoMemory(MemoryProviderSingleton):
-    """
-    A class that does not store any data. This is the default memory provider.
-    """
-
-    def __init__(self, cfg):
-        """
-        Initializes the NoMemory provider.
-
-        Args:
-            cfg: The config object.
-
-        Returns: None
-        """
-        pass
-
-    def add(self, data: str) -> str:
-        """
-        Adds a data point to the memory. No action is taken in NoMemory.
-
-        Args:
-            data: The data to add.
-
-        Returns: An empty string.
-        """
-        return ""
-
-    def get(self, data: str) -> list[Any] | None:
-        """
-        Gets the data from the memory that is most relevant to the given data.
-        NoMemory always returns None.
-
-        Args:
-            data: The data to compare to.
-
-        Returns: None
-        """
-        return None
-
-    def clear(self) -> str:
-        """
-        Clears the memory. No action is taken in NoMemory.
-
-        Returns: An empty string.
-        """
-        return ""
-
-    def get_relevant(self, data: str, num_relevant: int = 5) -> list[Any] | None:
-        """
-        Returns all the data in the memory that is relevant to the given data.
-        NoMemory always returns None.
-
-        Args:
-            data: The data to compare to.
-            num_relevant: The number of relevant data to return.
-
-        Returns: None
-        """
-        return None
-
-    def get_stats(self):
-        """
-        Returns: An empty dictionary as there are no stats in NoMemory.
-        """
-        return {}
diff --git a/spaces/MetaWabbit/Auto-GPT/autogpt/speech/base.py b/spaces/MetaWabbit/Auto-GPT/autogpt/speech/base.py
deleted file mode 100644
index d74fa51be75b5078134c510b393a06deb0267b2a..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/autogpt/speech/base.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""Base class for all voice classes."""
-import abc
-from threading import Lock
-
-from autogpt.config import AbstractSingleton
-
-
-class VoiceBase(AbstractSingleton):
-    """
-    Base class for all voice classes.
-    """
-
-    def __init__(self):
-        """
-        Initialize the voice class.
-        """
-        self._url = None
-        self._headers = None
-        self._api_key = None
-        self._voices = []
-        self._mutex = Lock()
-        self._setup()
-
-    def say(self, text: str, voice_index: int = 0) -> bool:
-        """
-        Say the given text.
-
-        Args:
-            text (str): The text to say.
-            voice_index (int): The index of the voice to use.
-        """
-        with self._mutex:
-            return self._speech(text, voice_index)
-
-    @abc.abstractmethod
-    def _setup(self) -> None:
-        """
-        Setup the voices, API key, etc.
-        """
-        pass
-
-    @abc.abstractmethod
-    def _speech(self, text: str, voice_index: int = 0) -> bool:
-        """
-        Play the given text.
-
-        Args:
-            text (str): The text to play.
-        """
-        pass
diff --git a/spaces/Metatron/IlluminatiAI-Illuminati_Diffusion_v1.0/app.py b/spaces/Metatron/IlluminatiAI-Illuminati_Diffusion_v1.0/app.py
deleted file mode 100644
index 80de13df079920cbae0f637195c76a59d3ee29de..0000000000000000000000000000000000000000
--- a/spaces/Metatron/IlluminatiAI-Illuminati_Diffusion_v1.0/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/IlluminatiAI/Illuminati_Diffusion_v1.0").launch()
\ No newline at end of file
diff --git a/spaces/MichaelWelsch/FreeVC/speaker_encoder/params_data.py b/spaces/MichaelWelsch/FreeVC/speaker_encoder/params_data.py
deleted file mode 100644
index bdb1716ed45617f2b127a7fb8885afe6cc74fb71..0000000000000000000000000000000000000000
--- a/spaces/MichaelWelsch/FreeVC/speaker_encoder/params_data.py
+++ /dev/null
@@ -1,29 +0,0 @@
-
-## Mel-filterbank
-mel_window_length = 25  # In milliseconds
-mel_window_step = 10    # In milliseconds
-mel_n_channels = 40
-
-
-## Audio
-sampling_rate = 16000
-# Number of spectrogram frames in a partial utterance
-partials_n_frames = 160     # 1600 ms
-# Number of spectrogram frames at inference
-inference_n_frames = 80     #  800 ms
-
-
-## Voice Activation Detection
-# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
-# This sets the granularity of the VAD. Should not need to be changed.
-vad_window_length = 30  # In milliseconds
-# Number of frames to average together when performing the moving average smoothing.
-# The larger this value, the larger the VAD variations must be to not get smoothed out. 
-vad_moving_average_width = 8
-# Maximum number of consecutive silent frames a segment can have.
-vad_max_silence_length = 6
-
-
-## Audio volume normalization
-audio_norm_target_dBFS = -30
-
diff --git a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/funsd_converter.py b/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/funsd_converter.py
deleted file mode 100644
index a8da8ab00183f20ec749585bde1bb6958a48f9d0..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textrecog/funsd_converter.py
+++ /dev/null
@@ -1,210 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import argparse
-import math
-import os
-import os.path as osp
-
-import mmcv
-import mmengine
-
-from mmocr.utils import crop_img, dump_ocr_data
-
-
-def collect_files(img_dir, gt_dir):
-    """Collect all images and their corresponding groundtruth files.
-
-    Args:
-        img_dir (str): The image directory
-        gt_dir (str): The groundtruth directory
-
-    Returns:
-        files (list): The list of tuples (img_file, groundtruth_file)
-    """
-    assert isinstance(img_dir, str)
-    assert img_dir
-    assert isinstance(gt_dir, str)
-    assert gt_dir
-
-    ann_list, imgs_list = [], []
-    for gt_file in os.listdir(gt_dir):
-        ann_list.append(osp.join(gt_dir, gt_file))
-        imgs_list.append(osp.join(img_dir, gt_file.replace('.json', '.png')))
-
-    files = list(zip(sorted(imgs_list), sorted(ann_list)))
-    assert len(files), f'No images found in {img_dir}'
-    print(f'Loaded {len(files)} images from {img_dir}')
-
-    return files
-
-
-def collect_annotations(files, nproc=1):
-    """Collect the annotation information.
-
-    Args:
-        files (list): The list of tuples (image_file, groundtruth_file)
-        nproc (int): The number of process to collect annotations
-
-    Returns:
-        images (list): The list of image information dicts
-    """
-    assert isinstance(files, list)
-    assert isinstance(nproc, int)
-
-    if nproc > 1:
-        images = mmengine.track_parallel_progress(
-            load_img_info, files, nproc=nproc)
-    else:
-        images = mmengine.track_progress(load_img_info, files)
-
-    return images
-
-
-def load_img_info(files):
-    """Load the information of one image.
-
-    Args:
-        files (tuple): The tuple of (img_file, groundtruth_file)
-
-    Returns:
-        img_info (dict): The dict of the img and annotation information
-    """
-    assert isinstance(files, tuple)
-
-    img_file, gt_file = files
-    assert osp.basename(gt_file).split('.')[0] == osp.basename(img_file).split(
-        '.')[0]
-    # read imgs while ignoring orientations
-    img = mmcv.imread(img_file, 'unchanged')
-
-    img_info = dict(
-        file_name=osp.join(osp.basename(img_file)),
-        height=img.shape[0],
-        width=img.shape[1],
-        segm_file=osp.join(osp.basename(gt_file)))
-
-    if osp.splitext(gt_file)[1] == '.json':
-        img_info = load_json_info(gt_file, img_info)
-    else:
-        raise NotImplementedError
-
-    return img_info
-
-
-def load_json_info(gt_file, img_info):
-    """Collect the annotation information.
-
-    Args:
-        gt_file (str): The path to ground-truth
-        img_info (dict): The dict of the img and annotation information
-
-    Returns:
-        img_info (dict): The dict of the img and annotation information
-    """
-
-    annotation = mmengine.load(gt_file)
-    anno_info = []
-    for form in annotation['form']:
-        for ann in form['words']:
-
-            # Ignore illegible samples
-            if len(ann['text']) == 0:
-                continue
-
-            x1, y1, x2, y2 = ann['box']
-            x = max(0, min(math.floor(x1), math.floor(x2)))
-            y = max(0, min(math.floor(y1), math.floor(y2)))
-            w, h = math.ceil(abs(x2 - x1)), math.ceil(abs(y2 - y1))
-            bbox = [x, y, x + w, y, x + w, y + h, x, y + h]
-            word = ann['text']
-
-            anno = dict(bbox=bbox, word=word)
-            anno_info.append(anno)
-
-    img_info.update(anno_info=anno_info)
-
-    return img_info
-
-
-def generate_ann(root_path, split, image_infos, preserve_vertical):
-    """Generate cropped annotations and label txt file.
-
-    Args:
-        root_path (str): The root path of the dataset
-        split (str): The split of dataset. Namely: training or test
-        image_infos (list[dict]): A list of dicts of the img and
-            annotation information
-        preserve_vertical (bool): Whether to preserve vertical texts
-    """
-
-    dst_image_root = osp.join(root_path, 'crops', split)
-    if split == 'training':
-        dst_label_file = osp.join(root_path, 'train_label.json')
-    elif split == 'test':
-        dst_label_file = osp.join(root_path, 'test_label.json')
-    os.makedirs(dst_image_root, exist_ok=True)
-
-    img_info = []
-    for image_info in image_infos:
-        index = 1
-        src_img_path = osp.join(root_path, 'imgs', image_info['file_name'])
-        image = mmcv.imread(src_img_path)
-        src_img_root = image_info['file_name'].split('.')[0]
-
-        for anno in image_info['anno_info']:
-            word = anno['word']
-            dst_img = crop_img(image, anno['bbox'])
-            h, w, _ = dst_img.shape
-
-            # Skip invalid annotations
-            if min(dst_img.shape) == 0:
-                continue
-            # Skip vertical texts
-            if not preserve_vertical and h / w > 2:
-                continue
-
-            dst_img_name = f'{src_img_root}_{index}.png'
-            index += 1
-            dst_img_path = osp.join(dst_image_root, dst_img_name)
-            mmcv.imwrite(dst_img, dst_img_path)
-
-            img_info.append({
-                'file_name': dst_img_name,
-                'anno_info': [{
-                    'text': word
-                }]
-            })
-
-    dump_ocr_data(img_info, dst_label_file, 'textrecog')
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description='Generate training and test set of FUNSD ')
-    parser.add_argument('root_path', help='Root dir path of FUNSD')
-    parser.add_argument(
-        '--preserve_vertical',
-        help='Preserve samples containing vertical texts',
-        action='store_true')
-    parser.add_argument(
-        '--nproc', default=1, type=int, help='Number of processes')
-    args = parser.parse_args()
-    return args
-
-
-def main():
-    args = parse_args()
-    root_path = args.root_path
-
-    for split in ['training', 'test']:
-        print(f'Processing {split} set...')
-        with mmengine.Timer(
-                print_tmpl='It takes {}s to convert FUNSD annotation'):
-            files = collect_files(
-                osp.join(root_path, 'imgs'),
-                osp.join(root_path, 'annotations', split))
-            image_infos = collect_annotations(files, nproc=args.nproc)
-            generate_ann(root_path, split, image_infos, args.preserve_vertical)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/MrBodean/VoiceClone/encoder/data_objects/random_cycler.py b/spaces/MrBodean/VoiceClone/encoder/data_objects/random_cycler.py
deleted file mode 100644
index c405db6b27f46d874d8feb37e3f9c1e12c251109..0000000000000000000000000000000000000000
--- a/spaces/MrBodean/VoiceClone/encoder/data_objects/random_cycler.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import random
-
-class RandomCycler:
-    """
-    Creates an internal copy of a sequence and allows access to its items in a constrained random 
-    order. For a source sequence of n items and one or several consecutive queries of a total 
-    of m items, the following guarantees hold (one implies the other):
-        - Each item will be returned between m // n and ((m - 1) // n) + 1 times.
-        - Between two appearances of the same item, there may be at most 2 * (n - 1) other items.
-    """
-    
-    def __init__(self, source):
-        if len(source) == 0:
-            raise Exception("Can't create RandomCycler from an empty collection")
-        self.all_items = list(source)
-        self.next_items = []
-    
-    def sample(self, count: int):
-        shuffle = lambda l: random.sample(l, len(l))
-        
-        out = []
-        while count > 0:
-            if count >= len(self.all_items):
-                out.extend(shuffle(list(self.all_items)))
-                count -= len(self.all_items)
-                continue
-            n = min(count, len(self.next_items))
-            out.extend(self.next_items[:n])
-            count -= n
-            self.next_items = self.next_items[n:]
-            if len(self.next_items) == 0:
-                self.next_items = shuffle(list(self.all_items))
-        return out
-    
-    def __next__(self):
-        return self.sample(1)[0]
-
diff --git a/spaces/Muennighoff/code_eval_octopack/code_eval_octopack.py b/spaces/Muennighoff/code_eval_octopack/code_eval_octopack.py
deleted file mode 100644
index cd7207b6e319cb3d7620b6756668a1fc2cc2391b..0000000000000000000000000000000000000000
--- a/spaces/Muennighoff/code_eval_octopack/code_eval_octopack.py
+++ /dev/null
@@ -1,192 +0,0 @@
-"""This metric implements code evaluation with execution across multiple languages as used in the paper 
-"OctoPack: Instruction Tuning Code Large Language Models" (https://arxiv.org/abs/2308.07124)."""
-
-import itertools
-import os
-from collections import Counter, defaultdict
-from concurrent.futures import ThreadPoolExecutor, as_completed
-
-import datasets
-import numpy as np
-
-import evaluate
-
-from .execute import check_correctness
-
-
-_CITATION = """\
-@article{muennighoff2023octopack,
-      title={OctoPack: Instruction Tuning Code Large Language Models}, 
-      author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre},
-      journal={arXiv preprint arXiv:2308.07124},
-      year={2023}
-}
-"""
-
-_DESCRIPTION = """\
-This metric implements code evaluation with execution across multiple languages as used in the paper 
-"OctoPack: Instruction Tuning Code Large Language Models" (https://arxiv.org/abs/2308.07124).
-"""
-
-
-_KWARGS_DESCRIPTION = """
-Calculates how good are predictions given some references, using certain scores
-Args:
-    predictions: list of candidates to evaluate. Each candidates should be a list
-        of strings with several code candidates to solve the problem.
-    references: a list with a test for each prediction. Each test should evaluate the
-        correctness of a code candidate.
-    k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])
-    num_workers: number of workers used to evaluate the canidate programs (Default: 4).
-    timeout:
-Returns:
-    pass_at_k: dict with pass rates for each k
-    results: dict with granular results of each unittest
-Examples:
-    >>> code_eval = evaluate.load("code_eval")
-    >>> test_cases = ["assert add(2,3)==5"]
-    >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]
-    >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
-    >>> print(pass_at_k)
-    {'pass@1': 0.5, 'pass@2': 1.0}
-"""
-
-
-_WARNING = """
-################################################################################
-                                  !!!WARNING!!!
-################################################################################
-The "code_eval" metric executes untrusted model-generated code in Python.
-Although it is highly unlikely that model-generated code will do something
-overtly malicious in response to this test suite, model-generated code may act
-destructively due to a lack of model capability or alignment.
-Users are strongly encouraged to sandbox this evaluation suite so that it
-does not perform destructive actions on their host or network. For more
-information on how OpenAI sandboxes its code, see the paper "Evaluating Large
-Language Models Trained on Code" (https://arxiv.org/abs/2107.03374).
-
-Once you have read this disclaimer and taken appropriate precautions,
-set the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this
-with:
-
->>> import os
->>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"
-
-################################################################################\
-"""
-
-_LICENSE = """The MIT License
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE."""
-
-
-# https://github.com/THUDM/CodeGeeX/blob/ebeb850f227a90c79de39f7e26b1302f374f3240/codegeex/benchmark/rust/Cargo.toml
-BASE_CARGO = '''[package]
-name = "rust"
-version = "0.1.0"
-edition = "2021"
-
-# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
-
-[dependencies]
-rand = "0.4"
-regex = "1"
-md5 = "0.7.0"
-'''
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class CodeEval(evaluate.Metric):
-    def _info(self):
-        return evaluate.MetricInfo(
-            # This is the description that will appear on the metrics page.
-            description=_DESCRIPTION,
-            citation=_CITATION,
-            inputs_description=_KWARGS_DESCRIPTION,
-            # This defines the format of each prediction and reference
-            features=datasets.Features(
-                {
-                    "predictions": datasets.Sequence(datasets.Value("string")),
-                    "references": datasets.Value("string"),
-                }
-            ),
-            homepage="https://github.com/openai/human-eval",
-            codebase_urls=["https://github.com/openai/human-eval"],
-            reference_urls=["https://github.com/openai/human-eval"],
-            license=_LICENSE,
-        )
-
-    def _compute(self, predictions, references, k=[1, 10, 100], num_workers=4, timeout=3.0, language="python", cargo_string=BASE_CARGO):
-        """Returns the scores"""
-
-        if os.getenv("HF_ALLOW_CODE_EVAL", 0) != "1":
-            raise ValueError(_WARNING)
-
-        if os.name == "nt":
-            raise NotImplementedError("This metric is currently not supported on Windows.")
-
-        with ThreadPoolExecutor(max_workers=num_workers) as executor:
-            futures = []
-            completion_id = Counter()
-            n_samples = 0
-            results = defaultdict(list)
-
-            for task_id, (candidates, test_case) in enumerate(zip(predictions, references)):
-                for candidate in candidates:
-                    test_program = candidate + "\n" + test_case
-                    args = (test_program, timeout, task_id, completion_id[task_id], language, cargo_string)
-                    future = executor.submit(check_correctness, *args)
-                    futures.append(future)
-                    completion_id[task_id] += 1
-                    n_samples += 1
-
-            for future in as_completed(futures):
-                result = future.result()
-                results[result["task_id"]].append((result["completion_id"], result))
-
-        total, correct = [], []
-        for result in results.values():
-            result.sort()
-            passed = [r[1]["passed"] for r in result]
-            total.append(len(passed))
-            correct.append(sum(passed))
-        total = np.array(total)
-        correct = np.array(correct)
-
-        ks = k
-        pass_at_k = {f"pass@{k}": estimate_pass_at_k(total, correct, k).mean() for k in ks if (total >= k).all()}
-
-        return pass_at_k, results
-
-
-def estimate_pass_at_k(num_samples, num_correct, k):
-    """Estimates pass@k of each problem and returns them in an array."""
-
-    def estimator(n: int, c: int, k: int) -> float:
-        """Calculates 1 - comb(n - c, k) / comb(n, k)."""
-        if n - c < k:
-            return 1.0
-        return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))
-
-    if isinstance(num_samples, int):
-        num_samples_it = itertools.repeat(num_samples, len(num_correct))
-    else:
-        assert len(num_samples) == len(num_correct)
-        num_samples_it = iter(num_samples)
-
-    return np.array([estimator(int(n), int(c), k) for n, c in zip(num_samples_it, num_correct)])
diff --git a/spaces/NATSpeech/PortaSpeech/utils/audio/align.py b/spaces/NATSpeech/PortaSpeech/utils/audio/align.py
deleted file mode 100644
index 096e593f01b51ee2d9f565666cfa8c40a90d76f1..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/utils/audio/align.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import re
-
-import torch
-import numpy as np
-from textgrid import TextGrid
-
-from utils.text.text_encoder import is_sil_phoneme
-
-
-def get_mel2ph(tg_fn, ph, mel, hop_size, audio_sample_rate, min_sil_duration=0):
-    ph_list = ph.split(" ")
-    itvs = TextGrid.fromFile(tg_fn)[1]
-    itvs_ = []
-    for i in range(len(itvs)):
-        if itvs[i].maxTime - itvs[i].minTime < min_sil_duration and i > 0 and is_sil_phoneme(itvs[i].mark):
-            itvs_[-1].maxTime = itvs[i].maxTime
-        else:
-            itvs_.append(itvs[i])
-    itvs.intervals = itvs_
-    itv_marks = [itv.mark for itv in itvs]
-    tg_len = len([x for x in itvs if not is_sil_phoneme(x.mark)])
-    ph_len = len([x for x in ph_list if not is_sil_phoneme(x)])
-    assert tg_len == ph_len, (tg_len, ph_len, itv_marks, ph_list, tg_fn)
-    mel2ph = np.zeros([mel.shape[0]], int)
-    i_itv = 0
-    i_ph = 0
-    while i_itv < len(itvs):
-        itv = itvs[i_itv]
-        ph = ph_list[i_ph]
-        itv_ph = itv.mark
-        start_frame = int(itv.minTime * audio_sample_rate / hop_size + 0.5)
-        end_frame = int(itv.maxTime * audio_sample_rate / hop_size + 0.5)
-        if is_sil_phoneme(itv_ph) and not is_sil_phoneme(ph):
-            mel2ph[start_frame:end_frame] = i_ph
-            i_itv += 1
-        elif not is_sil_phoneme(itv_ph) and is_sil_phoneme(ph):
-            i_ph += 1
-        else:
-            if not ((is_sil_phoneme(itv_ph) and is_sil_phoneme(ph)) \
-                    or re.sub(r'\d+', '', itv_ph.lower()) == re.sub(r'\d+', '', ph.lower())):
-                print(f"| WARN: {tg_fn} phs are not same: ", itv_ph, ph, itv_marks, ph_list)
-            mel2ph[start_frame:end_frame] = i_ph + 1
-            i_ph += 1
-            i_itv += 1
-    mel2ph[-1] = mel2ph[-2]
-    assert not np.any(mel2ph == 0)
-    T_t = len(ph_list)
-    dur = mel2token_to_dur(mel2ph, T_t)
-    return mel2ph.tolist(), dur.tolist()
-
-
-def split_audio_by_mel2ph(audio, mel2ph, hop_size, audio_num_mel_bins):
-    if isinstance(audio, torch.Tensor):
-        audio = audio.numpy()
-    if isinstance(mel2ph, torch.Tensor):
-        mel2ph = mel2ph.numpy()
-    assert len(audio.shape) == 1, len(mel2ph.shape) == 1
-    split_locs = []
-    for i in range(1, len(mel2ph)):
-        if mel2ph[i] != mel2ph[i - 1]:
-            split_loc = i * hop_size
-            split_locs.append(split_loc)
-
-    new_audio = []
-    for i in range(len(split_locs) - 1):
-        new_audio.append(audio[split_locs[i]:split_locs[i + 1]])
-        new_audio.append(np.zeros([0.5 * audio_num_mel_bins]))
-    return np.concatenate(new_audio)
-
-
-def mel2token_to_dur(mel2token, T_txt=None, max_dur=None):
-    is_torch = isinstance(mel2token, torch.Tensor)
-    has_batch_dim = True
-    if not is_torch:
-        mel2token = torch.LongTensor(mel2token)
-    if T_txt is None:
-        T_txt = mel2token.max()
-    if len(mel2token.shape) == 1:
-        mel2token = mel2token[None, ...]
-        has_batch_dim = False
-    B, _ = mel2token.shape
-    dur = mel2token.new_zeros(B, T_txt + 1).scatter_add(1, mel2token, torch.ones_like(mel2token))
-    dur = dur[:, 1:]
-    if max_dur is not None:
-        dur = dur.clamp(max=max_dur)
-    if not is_torch:
-        dur = dur.numpy()
-    if not has_batch_dim:
-        dur = dur[0]
-    return dur
diff --git a/spaces/NCTCMumbai/NCTC/models/official/modeling/hyperparams/config_definitions.py b/spaces/NCTCMumbai/NCTC/models/official/modeling/hyperparams/config_definitions.py
deleted file mode 100644
index 78180cd8a01a09a9d646c04eb05742bafce5bf42..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/modeling/hyperparams/config_definitions.py
+++ /dev/null
@@ -1,220 +0,0 @@
-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Common configuration settings."""
-from typing import Optional, Union
-
-import dataclasses
-
-from official.modeling.hyperparams import base_config
-from official.modeling.optimization.configs import optimization_config
-from official.utils import registry
-
-OptimizationConfig = optimization_config.OptimizationConfig
-
-
-@dataclasses.dataclass
-class DataConfig(base_config.Config):
-  """The base configuration for building datasets.
-
-  Attributes:
-    input_path: The path to the input. It can be either (1) a file pattern, or
-      (2) multiple file patterns separated by comma. It should not be specified
-      when the following `tfds_name` is specified.
-    tfds_name: The name of the tensorflow dataset (TFDS). It should not be
-      specified when the above `input_path` is specified.
-    tfds_split: A str indicating which split of the data to load from TFDS. It
-      is required when above `tfds_name` is specified.
-    global_batch_size: The global batch size across all replicas.
-    is_training: Whether this data is used for training or not.
-    drop_remainder: Whether the last batch should be dropped in the case it has
-      fewer than `global_batch_size` elements.
-    shuffle_buffer_size: The buffer size used for shuffling training data.
-    cache: Whether to cache dataset examples. Can be used to avoid re-reading
-      from disk on the second epoch. Requires significant memory overhead.
-    cycle_length: The number of files that will be processed concurrently when
-      interleaving files.
-    block_length: The number of consecutive elements to produce from each input
-      element before cycling to another input element when interleaving files.
-    sharding: Whether sharding is used in the input pipeline.
-    examples_consume: An `integer` specifying the number of examples it will
-      produce. If positive, it only takes this number of examples and raises
-      tf.error.OutOfRangeError after that. Default is -1, meaning it will
-      exhaust all the examples in the dataset.
-    tfds_data_dir: A str specifying the directory to read/write TFDS data.
-    tfds_download: A bool to indicate whether to download data using TFDS.
-    tfds_as_supervised: A bool. When loading dataset from TFDS, if True,
-      the returned tf.data.Dataset will have a 2-tuple structure (input, label)
-      according to builder.info.supervised_keys; if False, the default,
-      the returned tf.data.Dataset will have a dictionary with all the features.
-    tfds_skip_decoding_feature: A str to indicate which features are skipped
-      for decoding when loading dataset from TFDS. Use comma to separate
-      multiple features. The main use case is to skip the image/video decoding
-      for better performance.
-  """
-  input_path: str = ""
-  tfds_name: str = ""
-  tfds_split: str = ""
-  global_batch_size: int = 0
-  is_training: bool = None
-  drop_remainder: bool = True
-  shuffle_buffer_size: int = 100
-  cache: bool = False
-  cycle_length: int = 8
-  block_length: int = 1
-  sharding: bool = True
-  examples_consume: int = -1
-  tfds_data_dir: str = ""
-  tfds_download: bool = False
-  tfds_as_supervised: bool = False
-  tfds_skip_decoding_feature: str = ""
-
-
-@dataclasses.dataclass
-class RuntimeConfig(base_config.Config):
-  """High-level configurations for Runtime.
-
-  These include parameters that are not directly related to the experiment,
-  e.g. directories, accelerator type, etc.
-
-  Attributes:
-    distribution_strategy: e.g. 'mirrored', 'tpu', etc.
-    enable_xla: Whether or not to enable XLA.
-    per_gpu_thread_count: thread count per GPU.
-    gpu_thread_mode: Whether and how the GPU device uses its own threadpool.
-    dataset_num_private_threads: Number of threads for a private threadpool
-      created for all datasets computation.
-    tpu: The address of the TPU to use, if any.
-    num_gpus: The number of GPUs to use, if any.
-    worker_hosts: comma-separated list of worker ip:port pairs for running
-      multi-worker models with DistributionStrategy.
-    task_index: If multi-worker training, the task index of this worker.
-    all_reduce_alg: Defines the algorithm for performing all-reduce.
-    num_packs: Sets `num_packs` in the cross device ops used in
-      MirroredStrategy.  For details, see tf.distribute.NcclAllReduce.
-    mixed_precision_dtype: dtype of mixed precision policy. It can be 'float32',
-      'float16', or 'bfloat16'.
-    loss_scale: The type of loss scale, or 'float' value. This is used when
-      setting the mixed precision policy.
-    run_eagerly: Whether or not to run the experiment eagerly.
-    batchnorm_spatial_persistent: Whether or not to enable the spatial
-      persistent mode for CuDNN batch norm kernel for improved GPU performance.
-  """
-  distribution_strategy: str = "mirrored"
-  enable_xla: bool = False
-  gpu_thread_mode: Optional[str] = None
-  dataset_num_private_threads: Optional[int] = None
-  per_gpu_thread_count: int = 0
-  tpu: Optional[str] = None
-  num_gpus: int = 0
-  worker_hosts: Optional[str] = None
-  task_index: int = -1
-  all_reduce_alg: Optional[str] = None
-  num_packs: int = 1
-  loss_scale: Optional[Union[str, float]] = None
-  mixed_precision_dtype: Optional[str] = None
-  run_eagerly: bool = False
-  batchnorm_spatial_persistent: bool = False
-
-
-@dataclasses.dataclass
-class TensorboardConfig(base_config.Config):
-  """Configuration for Tensorboard.
-
-  Attributes:
-    track_lr: Whether or not to track the learning rate in Tensorboard. Defaults
-      to True.
-    write_model_weights: Whether or not to write the model weights as images in
-      Tensorboard. Defaults to False.
-  """
-  track_lr: bool = True
-  write_model_weights: bool = False
-
-
-@dataclasses.dataclass
-class CallbacksConfig(base_config.Config):
-  """Configuration for Callbacks.
-
-  Attributes:
-    enable_checkpoint_and_export: Whether or not to enable checkpoints as a
-      Callback. Defaults to True.
-    enable_tensorboard: Whether or not to enable Tensorboard as a Callback.
-      Defaults to True.
-    enable_time_history: Whether or not to enable TimeHistory Callbacks.
-      Defaults to True.
-  """
-  enable_checkpoint_and_export: bool = True
-  enable_tensorboard: bool = True
-  enable_time_history: bool = True
-
-
-@dataclasses.dataclass
-class TrainerConfig(base_config.Config):
-  """Configuration for trainer.
-
-  Attributes:
-    optimizer_config: optimizer config, it includes optimizer, learning rate,
-      and warmup schedule configs.
-    train_tf_while_loop: whether or not to use tf while loop.
-    train_tf_function: whether or not to use tf_function for training loop.
-    eval_tf_function: whether or not to use tf_function for eval.
-    steps_per_loop: number of steps per loop.
-    summary_interval: number of steps between each summary.
-    checkpoint_intervals: number of steps between checkpoints.
-    max_to_keep: max checkpoints to keep.
-    continuous_eval_timeout: maximum number of seconds to wait between
-      checkpoints, if set to None, continuous eval will wait indefinetely.
-  """
-  optimizer_config: OptimizationConfig = OptimizationConfig()
-  train_tf_while_loop: bool = True
-  train_tf_function: bool = True
-  eval_tf_function: bool = True
-  steps_per_loop: int = 1000
-  summary_interval: int = 1000
-  checkpoint_interval: int = 1000
-  max_to_keep: int = 5
-  continuous_eval_timeout: Optional[int] = None
-
-
-@dataclasses.dataclass
-class TaskConfig(base_config.Config):
-  network: base_config.Config = None
-  train_data: DataConfig = DataConfig()
-  validation_data: DataConfig = DataConfig()
-
-
-@dataclasses.dataclass
-class ExperimentConfig(base_config.Config):
-  """Top-level configuration."""
-  task: TaskConfig = TaskConfig()
-  trainer: TrainerConfig = TrainerConfig()
-  runtime: RuntimeConfig = RuntimeConfig()
-  train_steps: int = 0
-  validation_steps: Optional[int] = None
-  validation_interval: int = 100
-
-
-_REGISTERED_CONFIGS = {}
-
-
-def register_config_factory(name):
-  """Register ExperimentConfig factory method."""
-  return registry.register(_REGISTERED_CONFIGS, name)
-
-
-def get_exp_config_creater(exp_name: str):
-  """Looks up ExperimentConfig factory methods."""
-  exp_creater = registry.lookup(_REGISTERED_CONFIGS, exp_name)
-  return exp_creater
diff --git a/spaces/NealCaren/transcript/app.py b/spaces/NealCaren/transcript/app.py
deleted file mode 100644
index a42f5d7fef5a690dfc9e3f66abe00e688b218c55..0000000000000000000000000000000000000000
--- a/spaces/NealCaren/transcript/app.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import whisper
-import pandas as pd
-import whisper
-import subprocess
-from simple_diarizer.diarizer import Diarizer
-import streamlit as st
-import base64
-import tempfile
-
-
-
-def create_download_link(val, filename, label):
-    '''Hack to have a stable download link in Streamlit'''
-    b64 = base64.b64encode(val)
-    return f'<a href="data:application/octet-stream;base64,{b64.decode()}" download="{filename}">{label}</a>'
-
-
-def segment(nu_speakers):
-    '''Segment the audio using simple_diarizer.
-    Defaults to the speechbrain ECAPA-TDNN embeddings.'''
-
-    diar = Diarizer(embed_model='ecapa',cluster_method='sc')
-    segments = diar.diarize(temp_file, num_speakers=nu_speakers)
-
-    sdf = pd.DataFrame(segments)
-
-    # reorganize so the first speaker is always speaker 1
-    speaker_s = sdf['label'].drop_duplicates().reset_index()['label']
-    speaker_d = dict((v,k+1) for k,v in speaker_s.items())
-
-    sdf['speaker'] = sdf['label'].replace(speaker_d)
-    return sdf
-def monotize(uploaded):
-    '''Convert the upload file to audio file.'''
-    cmd = f"ffmpeg -y -i {uploaded} -acodec pcm_s16le -ar 16000 -ac 1 {temp_file}"
-    subprocess.Popen(cmd, shell=True).wait()
-
-def audio_to_df(uploaded):
-    '''Turn the upload file in a segemented dataframe.'''
-    #monotize(uploaded)
-    model = whisper.load_model(model_size)
-    result = model.transcribe(temp_file,
-                              without_timestamps=False,
-                              task = task)
-    tdf = pd.DataFrame(result['segments'])
-    return tdf
-
-
-
-def add_preface(row):
-    ''' Add speaker prefix to transcript during transcribe().'''
-    text = row['text'].replace('\n','')
-    speaker = row['speaker']
-    return f'Speaker {speaker}: {text}'
-
-def transcribe(uploaded, nu_speakers):
-    # Convert file to mono
-    with st.spinner(text="Converting file..."):
-        monotize('temp_audio')
-
-    # Make audio available to play in UI
-    audio_file = open(temp_file, 'rb')
-    audio_bytes = audio_file.read()
-    st.audio(temp_file, format='audio/wav')
-
-    # trancibe file
-    with st.spinner(text=f"Transcribing using {model_size} model..."):
-        tdf = audio_to_df(uploaded)
-    # segement file
-    with st.spinner(text="Segmenting..."):
-        sdf = segment(nu_speakers)
-
-    # Find the nearest transcript line to the start of each speaker
-    ns_list = sdf[['start','speaker']].to_dict(orient='records')
-    for row in ns_list:
-        input = row['start']
-        id = tdf.iloc[(tdf['start']-input).abs().argsort()[:1]]['id'].values[0]
-        tdf.loc[tdf['id'] ==id, 'speaker'] = row['speaker']
-    tdf['speaker'].fillna(method = 'ffill', inplace = True)
-    tdf['speaker'].fillna(method = 'bfill', inplace = True)
-    tdf['n1'] = tdf['speaker'] != tdf['speaker'].shift(1)
-    tdf['speach'] = tdf['n1'].cumsum()
-
-    # collaps the dataframe by speach turn.
-    binned_df = tdf.groupby(['speach', 'speaker'])['text'].apply('\n'.join).reset_index()
-    binned_df['speaker'] = binned_df['speaker'].astype(int)
-    binned_df['output'] = binned_df.apply(add_preface, axis=1)
-
-    # Display the transcript and prepare for export
-    lines = []
-    for row in binned_df['output'].values:
-        st.write(row)
-        lines.append(row)
-    tdf['speaker'] = tdf['speaker'].astype(int)
-
-    tdf_cols = ['speaker','start','end','text']
-    #st.dataframe(tdf[tdf_cols])
-    return {'text':lines, 'df': tdf[tdf_cols]}
-
-
-descript = ("This web app creates transcripts using OpenAI's [Whisper](https://github.com/openai/whisper) to transcribe "
-            "audio files combined with [Chau](https://github.com/cvqluu)'s [Simple Diarizer](https://github.com/cvqluu/simple_diarizer) "
-            "to partition the text by speaker.\n"
-            "* You can upload an audio or video file of up to 200MBs.\n"
-            "* Creating the transcript takes some time. "
-            "The process takes approximately 20% of the length of the audio file using the base Whisper model.\n "
-            "* The transcription process handles a variety of languages, and can also translate the audio to English. The tiny model is not good at translating. \n"
-            "* Speaker segmentation seems to work best with the base model. The small model produces better transcripts, but something seems off with the timecodes, degrading the speaker attribution. \n"
-            "* After uploading the file, be sure to select the number of speakers." )
-
-st.title("Automated Transcription")
-st.markdown(descript)
-
-form = st.form(key='my_form')
-uploaded = form.file_uploader("Choose a file")
-nu_speakers = form.slider('Number of speakers in recording:', min_value=1, max_value=8, value=2, step=1)
-models = form.selectbox(
-    'Which Whisper model?',
-    ('Tiny (fast)', 'Base (good)', 'Small (great but slow)', 'Medium (greater but slower)'), index=1)
-translate = form.checkbox('Translate to English?')
-submit = form.form_submit_button("Transcribe!")
-
-
-if submit:
-    if models == 'Tiny (fast)':
-        model_size = 'tiny'
-    elif models == 'Base (good)':
-        model_size ='base'
-    elif models == 'Small (great but slow)':
-        model_size = 'small'
-    elif models == 'Medium (greater but slower)':
-        model_size = 'medium'
-
-    if translate == True:
-        task = 'translate'
-    else:
-        task = 'transcribe'
-
-    #temporary file to store audio_file
-    tmp_dir = tempfile.TemporaryDirectory()
-    temp_file = tmp_dir.name + '/mono.wav'
-
-    bytes_data = uploaded.getvalue()
-    with open('temp_audio', 'wb') as outfile:
-        outfile.write(bytes_data)
-
-
-    # Transcribe/translate and segment
-    transcript = transcribe('temp_audio', nu_speakers)
-
-    # Prepare text file for export.
-    text = '\n'.join(transcript['text']).encode('utf-8')
-    download_url = create_download_link(text, 'transcript.txt', 'Download transcript as plain text.')
-    st.markdown(download_url, unsafe_allow_html=True)
-
-    # prepare CSV file for expport.
-    csv = transcript['df'].to_csv( float_format='%.2f', index=False).encode('utf-8')
-    download_url = create_download_link(csv, 'transcript.csv', 'Download transcript as CSV (with time codes)')
-    st.markdown(download_url, unsafe_allow_html=True)
-    tmp_dir.cleanup()
diff --git a/spaces/NeuroSenko/audio-processing-utils/start.bat b/spaces/NeuroSenko/audio-processing-utils/start.bat
deleted file mode 100644
index 3bf49b64f2c8ebbcb0dd94e258390de494f47759..0000000000000000000000000000000000000000
--- a/spaces/NeuroSenko/audio-processing-utils/start.bat
+++ /dev/null
@@ -1,2 +0,0 @@
-call .\venv\Scripts\activate.bat
-python app.py
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_text_joint_to_text/README.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_text_joint_to_text/README.md
deleted file mode 100644
index e071d241e0e02b35d3aac777ac09b4ef3be9119f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/speech_text_joint_to_text/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
-# Joint Speech Text training in Fairseq
-An extension of Fairseq s2t project with the speech to text task enhanced by the co-trained text to text mapping task. More details about Fairseq s2t can be found [here](../speech_to_text/README.md)
-
-## Examples
-Examples of speech text joint training in fairseq
-- [English-to-German MuST-C model](docs/ende-mustc.md)
-- [IWSLT 2021 Multilingual Speech Translation](docs/iwslt2021.md)
-
-## Citation
-Please cite as:
-```
-@inproceedings{Tang2021AGM,
-  title={A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks},
-  author={Yun Tang and J. Pino and Changhan Wang and Xutai Ma and Dmitriy Genzel},
-  booktitle={ICASSP},
-  year={2021}
-}
-
-@inproceedings{Tang2021IST,
-  title = {Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task},
-  author = {Yun Tang and Juan Pino and Xian Li and Changhan Wang and Dmitriy Genzel},
-  booktitle = {ACL},
-  year = {2021},
-}
-
-@inproceedings{Tang2021FST,
-  title = {FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task},
-  author = {Yun Tang and Hongyu Gong and Xian Li and Changhan Wang  and Juan Pino and  Holger Schwenk and  Naman Goyal},
-  booktitle = {IWSLT},
-  year = {2021},
-}
-
-@inproceedings{wang2020fairseqs2t,
-  title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq},
-  author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino},
-  booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations},
-  year = {2020},
-}
-
-@inproceedings{ott2019fairseq,
-  title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
-  author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
-  booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
-  year = {2019},
-}
-```
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/quantization/scalar/modules/qconv.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/quantization/scalar/modules/qconv.py
deleted file mode 100644
index 83788c6f71fd41e61fd115681a22d53ce8b8362c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/quantization/scalar/modules/qconv.py
+++ /dev/null
@@ -1,149 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn.functional as F
-from torch.nn.modules.conv import _ConvNd
-from torch.nn.modules.utils import _pair
-
-from ..ops import emulate_int
-
-
-class IntConv2d(_ConvNd):
-    """
-    Quantized counterpart of the nn.Conv2d module that applies QuantNoise during training.
-
-    Args:
-        - standard nn.Conv2d parameters
-        - p: amount of noise to inject (0 = no quantization, 1 = quantize all the weights)
-        - bits: number of bits
-        - method: choose among {"tensor", "histogram", "channel"}
-        - update_step: recompute scale and zero_point every update_steps iterations
-
-    Remarks:
-        - We use the straight-thgourh estimator so that the gradients
-          back-propagate nicely in the network, this is implemented with
-          the detach() trick
-        - Parameters scale and zero_point are recomputed every update_step
-          forward pass to reduce the overhead
-        - At test time, the weights are fully quantized
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride=1,
-        padding=0,
-        dilation=1,
-        groups=1,
-        bias=True,
-        padding_mode="zeros",
-        p=0,
-        bits=8,
-        method="histogram",
-        update_step=1000,
-    ):
-        kernel_size = _pair(kernel_size)
-        stride = _pair(stride)
-        padding = _pair(padding)
-        dilation = _pair(dilation)
-        super(IntConv2d, self).__init__(
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride,
-            padding,
-            dilation,
-            False,
-            _pair(0),
-            groups,
-            bias,
-            padding_mode,
-        )
-
-        # quantization parameters
-        self.p = p
-        self.bits = bits
-        self.method = method
-        self.update_step = update_step
-        self.counter = 0
-
-    def _conv_forward(self, input, weight):
-        if self.padding_mode != "zeros":
-            return F.conv2d(
-                F.pad(input, self._padding_repeated_twice, mode=self.padding_mode),
-                weight,
-                self.bias,
-                self.stride,
-                _pair(0),
-                self.dilation,
-                self.groups,
-            )
-        return F.conv2d(
-            input,
-            weight,
-            self.bias,
-            self.stride,
-            self.padding,
-            self.dilation,
-            self.groups,
-        )
-
-    def forward(self, input):
-        # train with QuantNoise and evaluate the fully quantized network
-        p = self.p if self.training else 1
-
-        # update parameters every 100 iterations
-        if self.counter % self.update_step == 0:
-            self.scale = None
-            self.zero_point = None
-        self.counter += 1
-
-        # quantize weight
-        weight_quantized, self.scale, self.zero_point = emulate_int(
-            self.weight.detach(),
-            bits=self.bits,
-            method=self.method,
-            scale=self.scale,
-            zero_point=self.zero_point,
-        )
-
-        # mask to apply noise
-        mask = torch.zeros_like(self.weight)
-        mask.bernoulli_(1 - p)
-        noise = (weight_quantized - self.weight).masked_fill(mask.bool(), 0)
-
-        # using straight-through estimator (STE)
-        clamp_low = -self.scale * self.zero_point
-        clamp_high = self.scale * (2 ** self.bits - 1 - self.zero_point)
-        weight = (
-            torch.clamp(self.weight, clamp_low.item(), clamp_high.item())
-            + noise.detach()
-        )
-
-        # return output
-        output = self._conv_forward(input, weight)
-        return output
-
-    def extra_repr(self):
-        return (
-            "in_channels={}, out_channels={}, kernel_size={}, stride={}, "
-            "padding={}, dilation={}, groups={}, bias={}, quant_noise={}, "
-            "bits={}, method={}".format(
-                self.in_channels,
-                self.out_channels,
-                self.kernel_size,
-                self.stride,
-                self.padding,
-                self.dilation,
-                self.groups,
-                self.bias is not None,
-                self.p,
-                self.bits,
-                self.method,
-            )
-        )
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/concat_dataset.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/concat_dataset.py
deleted file mode 100644
index 01a4078bb159fa44b2d1062b9a971fe7f1abd1c2..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/concat_dataset.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import bisect
-
-import numpy as np
-from torch.utils.data.dataloader import default_collate
-
-from . import FairseqDataset
-
-
-class ConcatDataset(FairseqDataset):
-    @staticmethod
-    def cumsum(sequence, sample_ratios):
-        r, s = [], 0
-        for e, ratio in zip(sequence, sample_ratios):
-            curr_len = int(ratio * len(e))
-            r.append(curr_len + s)
-            s += curr_len
-        return r
-
-    def __init__(self, datasets, sample_ratios=1):
-        super(ConcatDataset, self).__init__()
-        assert len(datasets) > 0, "datasets should not be an empty iterable"
-        self.datasets = list(datasets)
-        if isinstance(sample_ratios, int):
-            sample_ratios = [sample_ratios] * len(self.datasets)
-        self.sample_ratios = sample_ratios
-        self.cumulative_sizes = self.cumsum(self.datasets, sample_ratios)
-        self.real_sizes = [len(d) for d in self.datasets]
-
-    def __len__(self):
-        return self.cumulative_sizes[-1]
-
-    def __getitem__(self, idx):
-        dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx)
-        return self.datasets[dataset_idx][sample_idx]
-
-    def _get_dataset_and_sample_index(self, idx: int):
-        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
-        if dataset_idx == 0:
-            sample_idx = idx
-        else:
-            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
-        sample_idx = sample_idx % self.real_sizes[dataset_idx]
-        return dataset_idx, sample_idx
-
-    def collater(self, samples, **extra_args):
-        # For now only supports datasets with same underlying collater implementations
-        if hasattr(self.datasets[0], "collater"):
-            return self.datasets[0].collater(samples, **extra_args)
-        else:
-            return default_collate(samples, **extra_args)
-
-    def size(self, idx: int):
-        """
-        Return an example's size as a float or tuple.
-        """
-        dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx)
-        return self.datasets[dataset_idx].size(sample_idx)
-
-    def num_tokens(self, index: int):
-        return np.max(self.size(index))
-
-    def attr(self, attr: str, index: int):
-        dataset_idx = bisect.bisect_right(self.cumulative_sizes, index)
-        return getattr(self.datasets[dataset_idx], attr, None)
-
-    @property
-    def sizes(self):
-        _dataset_sizes = []
-        for ds, sr in zip(self.datasets, self.sample_ratios):
-            if isinstance(ds.sizes, np.ndarray):
-                _dataset_sizes.append(np.tile(ds.sizes, sr))
-            else:
-                # Only support underlying dataset with single size array.
-                assert isinstance(ds.sizes, list)
-                _dataset_sizes.append(np.tile(ds.sizes[0], sr))
-        return np.concatenate(_dataset_sizes)
-
-    @property
-    def supports_prefetch(self):
-        return all(d.supports_prefetch for d in self.datasets)
-
-    def ordered_indices(self):
-        """
-        Returns indices sorted by length. So less padding is needed.
-        """
-        if isinstance(self.sizes, np.ndarray) and len(self.sizes.shape) > 1:
-            # special handling for concatenating lang_pair_datasets
-            indices = np.arange(len(self))
-            sizes = self.sizes
-            tgt_sizes = (
-                sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None
-            )
-            src_sizes = (
-                sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes
-            )
-            # sort by target length, then source length
-            if tgt_sizes is not None:
-                indices = indices[np.argsort(tgt_sizes[indices], kind="mergesort")]
-            return indices[np.argsort(src_sizes[indices], kind="mergesort")]
-        else:
-            return np.argsort(self.sizes)
-
-    def prefetch(self, indices):
-        frm = 0
-        for to, ds in zip(self.cumulative_sizes, self.datasets):
-            real_size = len(ds)
-            if getattr(ds, "supports_prefetch", False):
-                ds.prefetch([(i - frm) % real_size for i in indices if frm <= i < to])
-            frm = to
-
-    @property
-    def can_reuse_epoch_itr_across_epochs(self):
-        return all(d.can_reuse_epoch_itr_across_epochs for d in self.datasets)
-
-    def set_epoch(self, epoch):
-        super().set_epoch(epoch)
-        for ds in self.datasets:
-            if hasattr(ds, "set_epoch"):
-                ds.set_epoch(epoch)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/incremental_decoding_utils.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/incremental_decoding_utils.py
deleted file mode 100644
index b26e6cd01cd4cbdffa23d88b354eb4a55a94189b..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/incremental_decoding_utils.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import uuid
-from typing import Dict, Optional
-
-from torch import Tensor
-
-
-class FairseqIncrementalState(object):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.init_incremental_state()
-
-    def init_incremental_state(self):
-        self._incremental_state_id = str(uuid.uuid4())
-
-    def _get_full_incremental_state_key(self, key: str) -> str:
-        return "{}.{}".format(self._incremental_state_id, key)
-
-    def get_incremental_state(
-        self,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
-        key: str,
-    ) -> Optional[Dict[str, Optional[Tensor]]]:
-        """Helper for getting incremental state for an nn.Module."""
-        full_key = self._get_full_incremental_state_key(key)
-        if incremental_state is None or full_key not in incremental_state:
-            return None
-        return incremental_state[full_key]
-
-    def set_incremental_state(
-        self,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
-        key: str,
-        value: Dict[str, Optional[Tensor]],
-    ) -> Optional[Dict[str, Dict[str, Optional[Tensor]]]]:
-        """Helper for setting incremental state for an nn.Module."""
-        if incremental_state is not None:
-            full_key = self._get_full_incremental_state_key(key)
-            incremental_state[full_key] = value
-        return incremental_state
-
-
-def with_incremental_state(cls):
-    cls.__bases__ = (FairseqIncrementalState,) + tuple(
-        b for b in cls.__bases__ if b != FairseqIncrementalState
-    )
-    return cls
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq_cli/score.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq_cli/score.py
deleted file mode 100644
index 0b207be959d55f6a56d8c5eb7db3dbe0c1ac977e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq_cli/score.py
+++ /dev/null
@@ -1,102 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-BLEU scoring of generated translations against reference translations.
-"""
-
-import argparse
-import os
-import sys
-
-from fairseq.data import dictionary
-from fairseq.scoring import bleu
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        description="Command-line script for BLEU scoring."
-    )
-    # fmt: off
-    parser.add_argument('-s', '--sys', default='-', help='system output')
-    parser.add_argument('-r', '--ref', required=True, help='references')
-    parser.add_argument('-o', '--order', default=4, metavar='N',
-                        type=int, help='consider ngrams up to this order')
-    parser.add_argument('--ignore-case', action='store_true',
-                        help='case-insensitive scoring')
-    parser.add_argument('--sacrebleu', action='store_true',
-                        help='score with sacrebleu')
-    parser.add_argument('--sentence-bleu', action='store_true',
-                        help='report sentence-level BLEUs (i.e., with +1 smoothing)')
-    # fmt: on
-    return parser
-
-
-def cli_main():
-    parser = get_parser()
-    args = parser.parse_args()
-    print(args)
-
-    assert args.sys == "-" or os.path.exists(
-        args.sys
-    ), "System output file {} does not exist".format(args.sys)
-    assert os.path.exists(args.ref), "Reference file {} does not exist".format(args.ref)
-
-    dict = dictionary.Dictionary()
-
-    def readlines(fd):
-        for line in fd.readlines():
-            if args.ignore_case:
-                yield line.lower()
-            else:
-                yield line
-
-    if args.sacrebleu:
-        import sacrebleu
-
-        def score(fdsys):
-            with open(args.ref) as fdref:
-                print(sacrebleu.corpus_bleu(fdsys, [fdref]).format())
-
-    elif args.sentence_bleu:
-
-        def score(fdsys):
-            with open(args.ref) as fdref:
-                scorer = bleu.Scorer(dict.pad(), dict.eos(), dict.unk())
-                for i, (sys_tok, ref_tok) in enumerate(
-                    zip(readlines(fdsys), readlines(fdref))
-                ):
-                    scorer.reset(one_init=True)
-                    sys_tok = dict.encode_line(sys_tok)
-                    ref_tok = dict.encode_line(ref_tok)
-                    scorer.add(ref_tok, sys_tok)
-                    print(i, scorer.result_string(args.order))
-
-    else:
-
-        def score(fdsys):
-            with open(args.ref) as fdref:
-                scorer = bleu.Scorer(
-                    bleu.BleuConfig(
-                        pad=dict.pad(),
-                        eos=dict.eos(),
-                        unk=dict.unk(),
-                    )
-                )
-                for sys_tok, ref_tok in zip(readlines(fdsys), readlines(fdref)):
-                    sys_tok = dict.encode_line(sys_tok)
-                    ref_tok = dict.encode_line(ref_tok)
-                    scorer.add(ref_tok, sys_tok)
-                print(scorer.result_string(args.order))
-
-    if args.sys == "-":
-        score(sys.stdin)
-    else:
-        with open(args.sys, "r") as f:
-            score(f)
-
-
-if __name__ == "__main__":
-    cli_main()
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_text_joint_to_text/tasks/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_text_joint_to_text/tasks/__init__.py
deleted file mode 100644
index d878278475fb24cf6b97d66d784e657567f5aa80..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_text_joint_to_text/tasks/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import importlib
-import os
-
-for file in os.listdir(os.path.dirname(__file__)):
-    if file.endswith(".py") and not file.startswith("_"):
-        task_name = file[: file.find(".py")]
-        importlib.import_module("examples.speech_text_joint_to_text.tasks." + task_name)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/translation_moe/translation_moe_src/mean_pool_gating_network.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/translation_moe/translation_moe_src/mean_pool_gating_network.py
deleted file mode 100644
index efc7ae40bf8fed6c2384cbc6f94477c4caa4c10c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/translation_moe/translation_moe_src/mean_pool_gating_network.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn.functional as F
-
-
-class MeanPoolGatingNetwork(torch.nn.Module):
-    """A simple mean-pooling gating network for selecting experts.
-
-    This module applies mean pooling over an encoder's output and returns
-    reponsibilities for each expert. The encoder format is expected to match
-    :class:`fairseq.models.transformer.TransformerEncoder`.
-    """
-
-    def __init__(self, embed_dim, num_experts, dropout=None):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_experts = num_experts
-
-        self.fc1 = torch.nn.Linear(embed_dim, embed_dim)
-        self.dropout = torch.nn.Dropout(dropout) if dropout is not None else None
-        self.fc2 = torch.nn.Linear(embed_dim, num_experts)
-
-    def forward(self, encoder_out):
-        if not (
-            "encoder_out" in encoder_out
-            and "encoder_padding_mask" in encoder_out
-            and encoder_out["encoder_out"][0].size(2) == self.embed_dim
-        ):
-            raise ValueError("Unexpected format for encoder_out")
-
-        # mean pooling over time
-        encoder_padding_mask = encoder_out["encoder_padding_mask"][0]  # B x T
-        encoder_out = encoder_out["encoder_out"][0].transpose(0, 1)    # B x T x C
-        if encoder_padding_mask is not None:
-            encoder_out = encoder_out.clone()  # required because of transpose above
-            encoder_out[encoder_padding_mask] = 0
-            ntokens = torch.sum(~encoder_padding_mask, dim=1, keepdim=True)
-            x = torch.sum(encoder_out, dim=1) / ntokens.type_as(encoder_out)
-        else:
-            x = torch.mean(encoder_out, dim=1)
-
-        x = torch.tanh(self.fc1(x))
-        if self.dropout is not None:
-            x = self.dropout(x)
-        x = self.fc2(x)
-        return F.log_softmax(x, dim=-1, dtype=torch.float32).type_as(x)
diff --git a/spaces/Omnibus/MusicGen/tests/modules/test_lstm.py b/spaces/Omnibus/MusicGen/tests/modules/test_lstm.py
deleted file mode 100644
index 1248964c8191e19f27661f0974bef9cc967eb015..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/tests/modules/test_lstm.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import random
-import torch
-
-from audiocraft.modules.lstm import StreamableLSTM
-
-
-class TestStreamableLSTM:
-
-    def test_lstm(self):
-        B, C, T = 4, 2, random.randint(1, 100)
-
-        lstm = StreamableLSTM(C, 3, skip=False)
-        x = torch.randn(B, C, T)
-        y = lstm(x)
-
-        print(y.shape)
-        assert y.shape == torch.Size([B, C, T])
-
-    def test_lstm_skip(self):
-        B, C, T = 4, 2, random.randint(1, 100)
-
-        lstm = StreamableLSTM(C, 3, skip=True)
-        x = torch.randn(B, C, T)
-        y = lstm(x)
-
-        assert y.shape == torch.Size([B, C, T])
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/ms_deform_attn.h b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/ms_deform_attn.h
deleted file mode 100644
index 2f80a1b294c55b37d13bb3558ff7aeadba3b37de..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/ms_deform_attn.h
+++ /dev/null
@@ -1,67 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-/*!
-* Copyright (c) Facebook, Inc. and its affiliates.
-* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-*/
-
-#pragma once
-
-#include "cpu/ms_deform_attn_cpu.h"
-
-#ifdef WITH_CUDA
-#include "cuda/ms_deform_attn_cuda.h"
-#endif
-
-
-at::Tensor
-ms_deform_attn_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step)
-{
-    if (value.type().is_cuda())
-    {
-#ifdef WITH_CUDA
-        return ms_deform_attn_cuda_forward(
-            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);
-#else
-        AT_ERROR("Not compiled with GPU support");
-#endif
-    }
-    AT_ERROR("Not implemented on the CPU");
-}
-
-std::vector<at::Tensor>
-ms_deform_attn_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step)
-{
-    if (value.type().is_cuda())
-    {
-#ifdef WITH_CUDA
-        return ms_deform_attn_cuda_backward(
-            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);
-#else
-        AT_ERROR("Not compiled with GPU support");
-#endif
-    }
-    AT_ERROR("Not implemented on the CPU");
-}
-
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/backbones/resnest.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/backbones/resnest.py
deleted file mode 100644
index b45a837f395230029e9d4194ff9f7f2f8f7067b0..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/backbones/resnest.py
+++ /dev/null
@@ -1,314 +0,0 @@
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as cp
-from annotator.uniformer.mmcv.cnn import build_conv_layer, build_norm_layer
-
-from ..builder import BACKBONES
-from ..utils import ResLayer
-from .resnet import Bottleneck as _Bottleneck
-from .resnet import ResNetV1d
-
-
-class RSoftmax(nn.Module):
-    """Radix Softmax module in ``SplitAttentionConv2d``.
-
-    Args:
-        radix (int): Radix of input.
-        groups (int): Groups of input.
-    """
-
-    def __init__(self, radix, groups):
-        super().__init__()
-        self.radix = radix
-        self.groups = groups
-
-    def forward(self, x):
-        batch = x.size(0)
-        if self.radix > 1:
-            x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2)
-            x = F.softmax(x, dim=1)
-            x = x.reshape(batch, -1)
-        else:
-            x = torch.sigmoid(x)
-        return x
-
-
-class SplitAttentionConv2d(nn.Module):
-    """Split-Attention Conv2d in ResNeSt.
-
-    Args:
-        in_channels (int): Same as nn.Conv2d.
-        out_channels (int): Same as nn.Conv2d.
-        kernel_size (int | tuple[int]): Same as nn.Conv2d.
-        stride (int | tuple[int]): Same as nn.Conv2d.
-        padding (int | tuple[int]): Same as nn.Conv2d.
-        dilation (int | tuple[int]): Same as nn.Conv2d.
-        groups (int): Same as nn.Conv2d.
-        radix (int): Radix of SpltAtConv2d. Default: 2
-        reduction_factor (int): Reduction factor of inter_channels. Default: 4.
-        conv_cfg (dict): Config dict for convolution layer. Default: None,
-            which means using conv2d.
-        norm_cfg (dict): Config dict for normalization layer. Default: None.
-        dcn (dict): Config dict for DCN. Default: None.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 groups=1,
-                 radix=2,
-                 reduction_factor=4,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 dcn=None):
-        super(SplitAttentionConv2d, self).__init__()
-        inter_channels = max(in_channels * radix // reduction_factor, 32)
-        self.radix = radix
-        self.groups = groups
-        self.channels = channels
-        self.with_dcn = dcn is not None
-        self.dcn = dcn
-        fallback_on_stride = False
-        if self.with_dcn:
-            fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
-        if self.with_dcn and not fallback_on_stride:
-            assert conv_cfg is None, 'conv_cfg must be None for DCN'
-            conv_cfg = dcn
-        self.conv = build_conv_layer(
-            conv_cfg,
-            in_channels,
-            channels * radix,
-            kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            groups=groups * radix,
-            bias=False)
-        self.norm0_name, norm0 = build_norm_layer(
-            norm_cfg, channels * radix, postfix=0)
-        self.add_module(self.norm0_name, norm0)
-        self.relu = nn.ReLU(inplace=True)
-        self.fc1 = build_conv_layer(
-            None, channels, inter_channels, 1, groups=self.groups)
-        self.norm1_name, norm1 = build_norm_layer(
-            norm_cfg, inter_channels, postfix=1)
-        self.add_module(self.norm1_name, norm1)
-        self.fc2 = build_conv_layer(
-            None, inter_channels, channels * radix, 1, groups=self.groups)
-        self.rsoftmax = RSoftmax(radix, groups)
-
-    @property
-    def norm0(self):
-        """nn.Module: the normalization layer named "norm0" """
-        return getattr(self, self.norm0_name)
-
-    @property
-    def norm1(self):
-        """nn.Module: the normalization layer named "norm1" """
-        return getattr(self, self.norm1_name)
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.norm0(x)
-        x = self.relu(x)
-
-        batch, rchannel = x.shape[:2]
-        batch = x.size(0)
-        if self.radix > 1:
-            splits = x.view(batch, self.radix, -1, *x.shape[2:])
-            gap = splits.sum(dim=1)
-        else:
-            gap = x
-        gap = F.adaptive_avg_pool2d(gap, 1)
-        gap = self.fc1(gap)
-
-        gap = self.norm1(gap)
-        gap = self.relu(gap)
-
-        atten = self.fc2(gap)
-        atten = self.rsoftmax(atten).view(batch, -1, 1, 1)
-
-        if self.radix > 1:
-            attens = atten.view(batch, self.radix, -1, *atten.shape[2:])
-            out = torch.sum(attens * splits, dim=1)
-        else:
-            out = atten * x
-        return out.contiguous()
-
-
-class Bottleneck(_Bottleneck):
-    """Bottleneck block for ResNeSt.
-
-    Args:
-        inplane (int): Input planes of this block.
-        planes (int): Middle planes of this block.
-        groups (int): Groups of conv2.
-        width_per_group (int): Width per group of conv2. 64x4d indicates
-            ``groups=64, width_per_group=4`` and 32x8d indicates
-            ``groups=32, width_per_group=8``.
-        radix (int): Radix of SpltAtConv2d. Default: 2
-        reduction_factor (int): Reduction factor of inter_channels in
-            SplitAttentionConv2d. Default: 4.
-        avg_down_stride (bool): Whether to use average pool for stride in
-            Bottleneck. Default: True.
-        kwargs (dict): Key word arguments for base class.
-    """
-    expansion = 4
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 groups=1,
-                 base_width=4,
-                 base_channels=64,
-                 radix=2,
-                 reduction_factor=4,
-                 avg_down_stride=True,
-                 **kwargs):
-        """Bottleneck block for ResNeSt."""
-        super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
-
-        if groups == 1:
-            width = self.planes
-        else:
-            width = math.floor(self.planes *
-                               (base_width / base_channels)) * groups
-
-        self.avg_down_stride = avg_down_stride and self.conv2_stride > 1
-
-        self.norm1_name, norm1 = build_norm_layer(
-            self.norm_cfg, width, postfix=1)
-        self.norm3_name, norm3 = build_norm_layer(
-            self.norm_cfg, self.planes * self.expansion, postfix=3)
-
-        self.conv1 = build_conv_layer(
-            self.conv_cfg,
-            self.inplanes,
-            width,
-            kernel_size=1,
-            stride=self.conv1_stride,
-            bias=False)
-        self.add_module(self.norm1_name, norm1)
-        self.with_modulated_dcn = False
-        self.conv2 = SplitAttentionConv2d(
-            width,
-            width,
-            kernel_size=3,
-            stride=1 if self.avg_down_stride else self.conv2_stride,
-            padding=self.dilation,
-            dilation=self.dilation,
-            groups=groups,
-            radix=radix,
-            reduction_factor=reduction_factor,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            dcn=self.dcn)
-        delattr(self, self.norm2_name)
-
-        if self.avg_down_stride:
-            self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1)
-
-        self.conv3 = build_conv_layer(
-            self.conv_cfg,
-            width,
-            self.planes * self.expansion,
-            kernel_size=1,
-            bias=False)
-        self.add_module(self.norm3_name, norm3)
-
-    def forward(self, x):
-
-        def _inner_forward(x):
-            identity = x
-
-            out = self.conv1(x)
-            out = self.norm1(out)
-            out = self.relu(out)
-
-            if self.with_plugins:
-                out = self.forward_plugin(out, self.after_conv1_plugin_names)
-
-            out = self.conv2(out)
-
-            if self.avg_down_stride:
-                out = self.avd_layer(out)
-
-            if self.with_plugins:
-                out = self.forward_plugin(out, self.after_conv2_plugin_names)
-
-            out = self.conv3(out)
-            out = self.norm3(out)
-
-            if self.with_plugins:
-                out = self.forward_plugin(out, self.after_conv3_plugin_names)
-
-            if self.downsample is not None:
-                identity = self.downsample(x)
-
-            out += identity
-
-            return out
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(_inner_forward, x)
-        else:
-            out = _inner_forward(x)
-
-        out = self.relu(out)
-
-        return out
-
-
-@BACKBONES.register_module()
-class ResNeSt(ResNetV1d):
-    """ResNeSt backbone.
-
-    Args:
-        groups (int): Number of groups of Bottleneck. Default: 1
-        base_width (int): Base width of Bottleneck. Default: 4
-        radix (int): Radix of SpltAtConv2d. Default: 2
-        reduction_factor (int): Reduction factor of inter_channels in
-            SplitAttentionConv2d. Default: 4.
-        avg_down_stride (bool): Whether to use average pool for stride in
-            Bottleneck. Default: True.
-        kwargs (dict): Keyword arguments for ResNet.
-    """
-
-    arch_settings = {
-        50: (Bottleneck, (3, 4, 6, 3)),
-        101: (Bottleneck, (3, 4, 23, 3)),
-        152: (Bottleneck, (3, 8, 36, 3)),
-        200: (Bottleneck, (3, 24, 36, 3))
-    }
-
-    def __init__(self,
-                 groups=1,
-                 base_width=4,
-                 radix=2,
-                 reduction_factor=4,
-                 avg_down_stride=True,
-                 **kwargs):
-        self.groups = groups
-        self.base_width = base_width
-        self.radix = radix
-        self.reduction_factor = reduction_factor
-        self.avg_down_stride = avg_down_stride
-        super(ResNeSt, self).__init__(**kwargs)
-
-    def make_res_layer(self, **kwargs):
-        """Pack all blocks in a stage into a ``ResLayer``."""
-        return ResLayer(
-            groups=self.groups,
-            base_width=self.base_width,
-            base_channels=self.base_channels,
-            radix=self.radix,
-            reduction_factor=self.reduction_factor,
-            avg_down_stride=self.avg_down_stride,
-            **kwargs)
diff --git a/spaces/PascalNotin/Tranception_design/tranception/outputs.py b/spaces/PascalNotin/Tranception_design/tranception/outputs.py
deleted file mode 100644
index 8b119fcbf470de453d39616c27e741429ebc196f..0000000000000000000000000000000000000000
--- a/spaces/PascalNotin/Tranception_design/tranception/outputs.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import torch
-
-from transformers.file_utils import ModelOutput
-
-@dataclass
-class TranceptionCausalLMOutputWithCrossAttentions(ModelOutput):
-    """
-    Class for Tranception causal language model (or autoregressive) outputs.
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Language modeling loss (for next-token prediction).
-        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
-            shape `(batch_size, sequence_length, hidden_size)`.
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-        cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-            Cross attentions weights after the attention softmax, used to compute the weighted average in the
-            cross-attention heads.
-        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-            Tuple of `torch.FloatTensor` tuples of length `config.n_layers`, with each tuple containing the cached key,
-            value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
-            setting. Only relevant if `config.is_decoder = True`.
-            Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
-            `past_key_values` input) to speed up sequential decoding.
-        fused_shift_log_probas (`torch.FloatTensor` of shape (batch_size, sequence_length, config.vocab_size), *optional*, returned when config.retrieval_aggregation_mode is not None.
-            log_probas for each residue position after aggregating autoregressive logits and retrieval logits.
-            
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: torch.FloatTensor = None
-    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-    cross_attentions: Optional[Tuple[torch.FloatTensor]] = None
-    fused_shift_log_probas: Optional[torch.FloatTensor] = None
\ No newline at end of file
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/brainfuck/compile-tree-il.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/brainfuck/compile-tree-il.go
deleted file mode 100644
index 86d0483d85fb532866a314a9e81d5406d85a4fd5..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/brainfuck/compile-tree-il.go and /dev/null differ
diff --git a/spaces/PeepDaSlan9/Bark-Voice-Cloning/util/parseinput.py b/spaces/PeepDaSlan9/Bark-Voice-Cloning/util/parseinput.py
deleted file mode 100644
index f2102648cf169f0a52bb66755308fee5f81247e0..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/Bark-Voice-Cloning/util/parseinput.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import re
-import xml.etree.ElementTree as ET
-from xml.sax import saxutils
-#import nltk
-
-# Chunked generation originally from https://github.com/serp-ai/bark-with-voice-clone
-def split_and_recombine_text(text, desired_length=100, max_length=150):
-    # return nltk.sent_tokenize(text)
-
-    # from https://github.com/neonbjb/tortoise-tts
-    """Split text it into chunks of a desired length trying to keep sentences intact."""
-    # normalize text, remove redundant whitespace and convert non-ascii quotes to ascii
-    text = re.sub(r"\n\n+", "\n", text)
-    text = re.sub(r"\s+", " ", text)
-    text = re.sub(r"[“”]", '"', text)
-
-    rv = []
-    in_quote = False
-    current = ""
-    split_pos = []
-    pos = -1
-    end_pos = len(text) - 1
-
-    def seek(delta):
-        nonlocal pos, in_quote, current
-        is_neg = delta < 0
-        for _ in range(abs(delta)):
-            if is_neg:
-                pos -= 1
-                current = current[:-1]
-            else:
-                pos += 1
-                current += text[pos]
-            if text[pos] == '"':
-                in_quote = not in_quote
-        return text[pos]
-
-    def peek(delta):
-        p = pos + delta
-        return text[p] if p < end_pos and p >= 0 else ""
-
-    def commit():
-        nonlocal rv, current, split_pos
-        rv.append(current)
-        current = ""
-        split_pos = []
-
-    while pos < end_pos:
-        c = seek(1)
-        # do we need to force a split?
-        if len(current) >= max_length:
-            if len(split_pos) > 0 and len(current) > (desired_length / 2):
-                # we have at least one sentence and we are over half the desired length, seek back to the last split
-                d = pos - split_pos[-1]
-                seek(-d)
-            else:
-                # no full sentences, seek back until we are not in the middle of a word and split there
-                while c not in "!?.,\n " and pos > 0 and len(current) > desired_length:
-                    c = seek(-1)
-            commit()
-        # check for sentence boundaries
-        elif not in_quote and (c in "!?]\n" or (c == "." and peek(1) in "\n ")):
-            # seek forward if we have consecutive boundary markers but still within the max length
-            while (
-                pos < len(text) - 1 and len(current) < max_length and peek(1) in "!?.]"
-            ):
-                c = seek(1)
-            split_pos.append(pos)
-            if len(current) >= desired_length:
-                commit()
-        # treat end of quote as a boundary if its followed by a space or newline
-        elif in_quote and peek(1) == '"' and peek(2) in "\n ":
-            seek(2)
-            split_pos.append(pos)
-    rv.append(current)
-
-    # clean up, remove lines with only whitespace or punctuation
-    rv = [s.strip() for s in rv]
-    rv = [s for s in rv if len(s) > 0 and not re.match(r"^[\s\.,;:!?]*$", s)]
-
-    return rv
-
-def is_ssml(value):
-    try:
-        ET.fromstring(value)
-    except ET.ParseError:
-        return False
-    return True
-
-def build_ssml(rawtext, selected_voice):
-    texts = rawtext.split("\n")
-    joinedparts = ""
-    for textpart in texts:
-        textpart = textpart.strip()
-        if len(textpart) < 1:
-            continue
-        joinedparts = joinedparts + f"\n<voice name=\"{selected_voice}\">{saxutils.escape(textpart)}</voice>"
-    ssml = f"""<?xml version="1.0"?>
-<speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis"
-         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
-         xsi:schemaLocation="http://www.w3.org/2001/10/synthesis
-                   http://www.w3.org/TR/speech-synthesis/synthesis.xsd"
-         xml:lang="en-US">
-         {joinedparts}
-</speak>
-    """
-    return ssml
-
-def create_clips_from_ssml(ssmlinput):
-    # Parse the XML
-    tree = ET.ElementTree(ET.fromstring(ssmlinput))
-    root = tree.getroot()
-
-    # Create an empty list
-    voice_list = []
-
-    # Loop through all voice tags
-    for voice in root.iter('{http://www.w3.org/2001/10/synthesis}voice'):
-        # Extract the voice name attribute and the content text
-        voice_name = voice.attrib['name']
-        voice_content = voice.text.strip() if voice.text else ''
-        if(len(voice_content) > 0):
-            parts = split_and_recombine_text(voice_content)
-            for p in parts:
-                if(len(p) > 1):
-                    # add to tuple list
-                    voice_list.append((voice_name, p))
-    return voice_list
-
diff --git a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/models/dummy_cond_stage.py b/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/models/dummy_cond_stage.py
deleted file mode 100644
index 6e19938078752e09b926a3e749907ee99a258ca0..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/models/dummy_cond_stage.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from torch import Tensor
-
-
-class DummyCondStage:
-    def __init__(self, conditional_key):
-        self.conditional_key = conditional_key
-        self.train = None
-
-    def eval(self):
-        return self
-
-    @staticmethod
-    def encode(c: Tensor):
-        return c, None, (None, None, c)
-
-    @staticmethod
-    def decode(c: Tensor):
-        return c
-
-    @staticmethod
-    def to_rgb(c: Tensor):
-        return c
diff --git a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/modules/losses/segmentation.py b/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/modules/losses/segmentation.py
deleted file mode 100644
index 4ba77deb5159a6307ed2acba9945e4764a4ff0a5..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/modules/losses/segmentation.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class BCELoss(nn.Module):
-    def forward(self, prediction, target):
-        loss = F.binary_cross_entropy_with_logits(prediction,target)
-        return loss, {}
-
-
-class BCELossWithQuant(nn.Module):
-    def __init__(self, codebook_weight=1.):
-        super().__init__()
-        self.codebook_weight = codebook_weight
-
-    def forward(self, qloss, target, prediction, split):
-        bce_loss = F.binary_cross_entropy_with_logits(prediction,target)
-        loss = bce_loss + self.codebook_weight*qloss
-        return loss, {"{}/total_loss".format(split): loss.clone().detach().mean(),
-                      "{}/bce_loss".format(split): bce_loss.detach().mean(),
-                      "{}/quant_loss".format(split): qloss.detach().mean()
-                      }
diff --git a/spaces/Qiukai/gpt/request_llm/bridge_chatgpt.py b/spaces/Qiukai/gpt/request_llm/bridge_chatgpt.py
deleted file mode 100644
index e3d815c5869d2cc94936a6a0fa2b828b90a835f7..0000000000000000000000000000000000000000
--- a/spaces/Qiukai/gpt/request_llm/bridge_chatgpt.py
+++ /dev/null
@@ -1,253 +0,0 @@
-# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
-
-"""
-    该文件中主要包含三个函数
-
-    不具备多线程能力的函数：
-    1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
-
-    具备多线程调用能力的函数
-    2. predict_no_ui：高级实验性功能模块调用，不会实时显示在界面上，参数简单，可以多线程并行，方便实现复杂的功能逻辑
-    3. predict_no_ui_long_connection：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程
-"""
-
-import json
-import time
-import gradio as gr
-import logging
-import traceback
-import requests
-import importlib
-
-# config_private.py放自己的秘密如API和代理网址
-# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
-from toolbox import get_conf, update_ui
-proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL = \
-    get_conf('proxies', 'API_URL', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'LLM_MODEL')
-
-timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
-                  '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
-
-def get_full_error(chunk, stream_response):
-    """
-        获取完整的从Openai返回的报错
-    """
-    while True:
-        try:
-            chunk += next(stream_response)
-        except:
-            break
-    return chunk
-
-
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
-    """
-        发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
-        inputs：
-            是本次问询的输入
-        sys_prompt:
-            系统静默prompt
-        llm_kwargs：
-            chatGPT的内部调优参数
-        history：
-            是之前的对话列表
-        observe_window = None：
-            用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
-    """
-    watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
-    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
-    retry = 0
-    while True:
-        try:
-            # make a POST request to the API endpoint, stream=False
-            response = requests.post(API_URL, headers=headers, proxies=proxies,
-                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
-        except requests.exceptions.ReadTimeout as e:
-            retry += 1
-            traceback.print_exc()
-            if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
-
-    stream_response =  response.iter_lines()
-    result = ''
-    while True:
-        try: chunk = next(stream_response).decode()
-        except StopIteration: 
-            break
-        except requests.exceptions.ConnectionError:
-            chunk = next(stream_response).decode() # 失败了，重试一次？再失败就没办法了。
-        if len(chunk)==0: continue
-        if not chunk.startswith('data:'): 
-            error_msg = get_full_error(chunk.encode('utf8'), stream_response).decode()
-            if "reduce the length" in error_msg:
-                raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
-            else:
-                raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
-        json_data = json.loads(chunk.lstrip('data:'))['choices'][0]
-        delta = json_data["delta"]
-        if len(delta) == 0: break
-        if "role" in delta: continue
-        if "content" in delta: 
-            result += delta["content"]
-            if not console_slience: print(delta["content"], end='')
-            if observe_window is not None: 
-                # 观测窗，把已经获取的数据显示出去
-                if len(observe_window) >= 1: observe_window[0] += delta["content"]
-                # 看门狗，如果超过期限没有喂狗，则终止
-                if len(observe_window) >= 2:  
-                    if (time.time()-observe_window[1]) > watch_dog_patience:
-                        raise RuntimeError("程序终止。")
-        else: raise RuntimeError("意外Json结构："+delta)
-    if json_data['finish_reason'] == 'length':
-        raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
-    return result
-
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
-    """
-        发送至chatGPT，流式获取输出。
-        用于基础的对话功能。
-        inputs 是本次问询的输入
-        top_p, temperature是chatGPT的内部调优参数
-        history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-        chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
-        additional_fn代表点击的哪个按钮，按钮见functional.py
-    """
-    if inputs.startswith('sk-') and len(inputs) == 51:
-        chatbot._cookies['api_key'] = inputs
-        chatbot.append(("输入已识别为openai的api_key", "api_key已导入"))
-        yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
-        return
-    elif len(chatbot._cookies['api_key']) != 51:
-        chatbot.append((inputs, "缺少api_key。\n\n1. 解决方案：直接在输入区键入api_key，然后回车提交。"))
-        yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
-        return
-
-    if additional_fn is not None:
-        import core_functional
-        importlib.reload(core_functional)    # 热更新prompt
-        core_functional = core_functional.get_core_functions()
-        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
-        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
-
-    if stream:
-        raw_input = inputs
-        logging.info(f'[raw_input] {raw_input}')
-        chatbot.append((inputs, ""))
-        yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
-
-    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
-    history.append(inputs); history.append(" ")
-
-    retry = 0
-    while True:
-        try:
-            # make a POST request to the API endpoint, stream=True
-            response = requests.post(API_URL, headers=headers, proxies=proxies,
-                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
-        except:
-            retry += 1
-            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
-            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
-            yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
-            if retry > MAX_RETRY: raise TimeoutError
-
-    gpt_replying_buffer = ""
-    
-    is_head_of_the_stream = True
-    if stream:
-        stream_response =  response.iter_lines()
-        while True:
-            chunk = next(stream_response)
-            # print(chunk.decode()[6:])
-            if is_head_of_the_stream:
-                # 数据流的第一帧不携带content
-                is_head_of_the_stream = False; continue
-            
-            if chunk:
-                try:
-                    if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
-                        # 判定为数据流的结束，gpt_replying_buffer也写完了
-                        logging.info(f'[response] {gpt_replying_buffer}')
-                        break
-                    # 处理数据流的主体
-                    chunkjson = json.loads(chunk.decode()[6:])
-                    status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}"
-                    # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
-                    gpt_replying_buffer = gpt_replying_buffer + json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"]
-                    history[-1] = gpt_replying_buffer
-                    chatbot[-1] = (history[-2], history[-1])
-                    yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
-
-                except Exception as e:
-                    traceback.print_exc()
-                    yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
-                    chunk = get_full_error(chunk, stream_response)
-                    error_msg = chunk.decode()
-                    if "reduce the length" in error_msg:
-                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长，或历史数据过长. 历史缓存数据现已释放，您可以请再次尝试.")
-                        history = []    # 清除历史
-                    elif "Incorrect API key" in error_msg:
-                        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由，拒绝服务.")
-                    elif "exceeded your current quota" in error_msg:
-                        chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由，拒绝服务.")
-                    else:
-                        from toolbox import regular_txt_to_markdown
-                        tb_str = '```\n' + traceback.format_exc() + '```'
-                        chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk.decode()[4:])}")
-                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
-                    return
-
-def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
-    """
-        整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
-    """
-    if len(llm_kwargs['api_key']) != 51:
-        raise AssertionError("你提供了错误的API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。")
-
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {llm_kwargs['api_key']}"
-    }
-
-    conversation_cnt = len(history) // 2
-
-    messages = [{"role": "system", "content": system_prompt}]
-    if conversation_cnt:
-        for index in range(0, 2*conversation_cnt, 2):
-            what_i_have_asked = {}
-            what_i_have_asked["role"] = "user"
-            what_i_have_asked["content"] = history[index]
-            what_gpt_answer = {}
-            what_gpt_answer["role"] = "assistant"
-            what_gpt_answer["content"] = history[index+1]
-            if what_i_have_asked["content"] != "":
-                if what_gpt_answer["content"] == "": continue
-                if what_gpt_answer["content"] == timeout_bot_msg: continue
-                messages.append(what_i_have_asked)
-                messages.append(what_gpt_answer)
-            else:
-                messages[-1]['content'] = what_gpt_answer['content']
-
-    what_i_ask_now = {}
-    what_i_ask_now["role"] = "user"
-    what_i_ask_now["content"] = inputs
-    messages.append(what_i_ask_now)
-
-    payload = {
-        "model": llm_kwargs['llm_model'],
-        "messages": messages, 
-        "temperature": llm_kwargs['temperature'],  # 1.0,
-        "top_p": llm_kwargs['top_p'],  # 1.0,
-        "n": 1,
-        "stream": stream,
-        "presence_penalty": 0,
-        "frequency_penalty": 0,
-    }
-    try:
-        print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
-    except:
-        print('输入中可能存在乱码。')
-    return headers,payload
-
-
diff --git a/spaces/Rainy-hh/Real-ESRGAN/app.py b/spaces/Rainy-hh/Real-ESRGAN/app.py
deleted file mode 100644
index 591cf91d4881efebfbe1ee8b1bb41cb1fca063ed..0000000000000000000000000000000000000000
--- a/spaces/Rainy-hh/Real-ESRGAN/app.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import torch
-from PIL import Image
-from RealESRGAN import RealESRGAN
-import gradio as gr
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-model2 = RealESRGAN(device, scale=2)
-model2.load_weights('weights/RealESRGAN_x2plus.pth', download=True)
-model4 = RealESRGAN(device, scale=4)
-model4.load_weights('weights/RealESRGAN_x4plus.pth', download=True)
-model8 = RealESRGAN(device, scale=8)
-model8.load_weights('weights/RealESRGAN_x8.pth', download=True)
-
-
-def inference(image, size):
-    if size == '2x':
-        result = model2.predict(image.convert('RGB'))
-    elif size == '4x':
-        result = model4.predict(image.convert('RGB'))
-    else:
-        result = model8.predict(image.convert('RGB'))
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-    return result
-
-
-title = "Face Real ESRGAN UpScale: 2x 4x 8x"
-description = "This is an unofficial demo for Real-ESRGAN. Scales the resolution of a photo. This model shows better results on faces compared to the original version.<br>Telegram BOT: https://t.me/restoration_photo_bot"
-article = "created by rainy"
-
-gr.Interface(inference,
-             [gr.Image(type="pil"),
-              gr.Radio(['2x', '4x', '8x'],
-                       type="value",
-                       value='2x',
-                       label='Resolution model')],
-             gr.Image(type="pil", label="Output"),
-             title=title,
-             description=description,
-             article=article,
-             examples=[['girl.png', "2x"]],
-             allow_flagging='never',
-             cache_examples=False,
-             ).queue(concurrency_count=1, api_open=False).launch(show_api=False, show_error=True)
diff --git a/spaces/RamAnanth1/T2I-Adapter/model_edge.py b/spaces/RamAnanth1/T2I-Adapter/model_edge.py
deleted file mode 100644
index c155c4b186d6a0e7c87ca09e34851d48c04fe15f..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/T2I-Adapter/model_edge.py
+++ /dev/null
@@ -1,639 +0,0 @@
-"""
-Author: Zhuo Su, Wenzhe Liu
-Date: Feb 18, 2021
-"""
-
-import math
-
-import cv2
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from basicsr.utils import img2tensor
-
-nets = {
-    'baseline': {
-        'layer0':  'cv',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'c-v15': {
-        'layer0':  'cd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'a-v15': {
-        'layer0':  'ad',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'r-v15': {
-        'layer0':  'rd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cv',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cv',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cv',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'cvvv4': {
-        'layer0':  'cd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'cd',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'cd',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'cd',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'avvv4': {
-        'layer0':  'ad',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'ad',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'ad',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'ad',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'rvvv4': {
-        'layer0':  'rd',
-        'layer1':  'cv',
-        'layer2':  'cv',
-        'layer3':  'cv',
-        'layer4':  'rd',
-        'layer5':  'cv',
-        'layer6':  'cv',
-        'layer7':  'cv',
-        'layer8':  'rd',
-        'layer9':  'cv',
-        'layer10': 'cv',
-        'layer11': 'cv',
-        'layer12': 'rd',
-        'layer13': 'cv',
-        'layer14': 'cv',
-        'layer15': 'cv',
-        },
-    'cccv4': {
-        'layer0':  'cd',
-        'layer1':  'cd',
-        'layer2':  'cd',
-        'layer3':  'cv',
-        'layer4':  'cd',
-        'layer5':  'cd',
-        'layer6':  'cd',
-        'layer7':  'cv',
-        'layer8':  'cd',
-        'layer9':  'cd',
-        'layer10': 'cd',
-        'layer11': 'cv',
-        'layer12': 'cd',
-        'layer13': 'cd',
-        'layer14': 'cd',
-        'layer15': 'cv',
-        },
-    'aaav4': {
-        'layer0':  'ad',
-        'layer1':  'ad',
-        'layer2':  'ad',
-        'layer3':  'cv',
-        'layer4':  'ad',
-        'layer5':  'ad',
-        'layer6':  'ad',
-        'layer7':  'cv',
-        'layer8':  'ad',
-        'layer9':  'ad',
-        'layer10': 'ad',
-        'layer11': 'cv',
-        'layer12': 'ad',
-        'layer13': 'ad',
-        'layer14': 'ad',
-        'layer15': 'cv',
-        },
-    'rrrv4': {
-        'layer0':  'rd',
-        'layer1':  'rd',
-        'layer2':  'rd',
-        'layer3':  'cv',
-        'layer4':  'rd',
-        'layer5':  'rd',
-        'layer6':  'rd',
-        'layer7':  'cv',
-        'layer8':  'rd',
-        'layer9':  'rd',
-        'layer10': 'rd',
-        'layer11': 'cv',
-        'layer12': 'rd',
-        'layer13': 'rd',
-        'layer14': 'rd',
-        'layer15': 'cv',
-        },
-    'c16': {
-        'layer0':  'cd',
-        'layer1':  'cd',
-        'layer2':  'cd',
-        'layer3':  'cd',
-        'layer4':  'cd',
-        'layer5':  'cd',
-        'layer6':  'cd',
-        'layer7':  'cd',
-        'layer8':  'cd',
-        'layer9':  'cd',
-        'layer10': 'cd',
-        'layer11': 'cd',
-        'layer12': 'cd',
-        'layer13': 'cd',
-        'layer14': 'cd',
-        'layer15': 'cd',
-        },
-    'a16': {
-        'layer0':  'ad',
-        'layer1':  'ad',
-        'layer2':  'ad',
-        'layer3':  'ad',
-        'layer4':  'ad',
-        'layer5':  'ad',
-        'layer6':  'ad',
-        'layer7':  'ad',
-        'layer8':  'ad',
-        'layer9':  'ad',
-        'layer10': 'ad',
-        'layer11': 'ad',
-        'layer12': 'ad',
-        'layer13': 'ad',
-        'layer14': 'ad',
-        'layer15': 'ad',
-        },
-    'r16': {
-        'layer0':  'rd',
-        'layer1':  'rd',
-        'layer2':  'rd',
-        'layer3':  'rd',
-        'layer4':  'rd',
-        'layer5':  'rd',
-        'layer6':  'rd',
-        'layer7':  'rd',
-        'layer8':  'rd',
-        'layer9':  'rd',
-        'layer10': 'rd',
-        'layer11': 'rd',
-        'layer12': 'rd',
-        'layer13': 'rd',
-        'layer14': 'rd',
-        'layer15': 'rd',
-        },
-    'carv4': {
-        'layer0':  'cd',
-        'layer1':  'ad',
-        'layer2':  'rd',
-        'layer3':  'cv',
-        'layer4':  'cd',
-        'layer5':  'ad',
-        'layer6':  'rd',
-        'layer7':  'cv',
-        'layer8':  'cd',
-        'layer9':  'ad',
-        'layer10': 'rd',
-        'layer11': 'cv',
-        'layer12': 'cd',
-        'layer13': 'ad',
-        'layer14': 'rd',
-        'layer15': 'cv',
-        },
-    }
-
-def createConvFunc(op_type):
-    assert op_type in ['cv', 'cd', 'ad', 'rd'], 'unknown op type: %s' % str(op_type)
-    if op_type == 'cv':
-        return F.conv2d
-
-    if op_type == 'cd':
-        def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
-            assert dilation in [1, 2], 'dilation for cd_conv should be in 1 or 2'
-            assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for cd_conv should be 3x3'
-            assert padding == dilation, 'padding for cd_conv set wrong'
-
-            weights_c = weights.sum(dim=[2, 3], keepdim=True)
-            yc = F.conv2d(x, weights_c, stride=stride, padding=0, groups=groups)
-            y = F.conv2d(x, weights, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-            return y - yc
-        return func
-    elif op_type == 'ad':
-        def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
-            assert dilation in [1, 2], 'dilation for ad_conv should be in 1 or 2'
-            assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for ad_conv should be 3x3'
-            assert padding == dilation, 'padding for ad_conv set wrong'
-
-            shape = weights.shape
-            weights = weights.view(shape[0], shape[1], -1)
-            weights_conv = (weights - weights[:, :, [3, 0, 1, 6, 4, 2, 7, 8, 5]]).view(shape) # clock-wise
-            y = F.conv2d(x, weights_conv, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-            return y
-        return func
-    elif op_type == 'rd':
-        def func(x, weights, bias=None, stride=1, padding=0, dilation=1, groups=1):
-            assert dilation in [1, 2], 'dilation for rd_conv should be in 1 or 2'
-            assert weights.size(2) == 3 and weights.size(3) == 3, 'kernel size for rd_conv should be 3x3'
-            padding = 2 * dilation
-
-            shape = weights.shape
-            if weights.is_cuda:
-                buffer = torch.cuda.FloatTensor(shape[0], shape[1], 5 * 5).fill_(0)
-            else:
-                buffer = torch.zeros(shape[0], shape[1], 5 * 5)
-            weights = weights.view(shape[0], shape[1], -1)
-            buffer[:, :, [0, 2, 4, 10, 14, 20, 22, 24]] = weights[:, :, 1:]
-            buffer[:, :, [6, 7, 8, 11, 13, 16, 17, 18]] = -weights[:, :, 1:]
-            buffer[:, :, 12] = 0
-            buffer = buffer.view(shape[0], shape[1], 5, 5)
-            y = F.conv2d(x, buffer, bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-            return y
-        return func
-    else:
-        print('impossible to be here unless you force that')
-        return None
-
-class Conv2d(nn.Module):
-    def __init__(self, pdc, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=False):
-        super(Conv2d, self).__init__()
-        if in_channels % groups != 0:
-            raise ValueError('in_channels must be divisible by groups')
-        if out_channels % groups != 0:
-            raise ValueError('out_channels must be divisible by groups')
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        self.dilation = dilation
-        self.groups = groups
-        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, kernel_size, kernel_size))
-        if bias:
-            self.bias = nn.Parameter(torch.Tensor(out_channels))
-        else:
-            self.register_parameter('bias', None)
-        self.reset_parameters()
-        self.pdc = pdc
-
-    def reset_parameters(self):
-        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
-        if self.bias is not None:
-            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
-            bound = 1 / math.sqrt(fan_in)
-            nn.init.uniform_(self.bias, -bound, bound)
-
-    def forward(self, input):
-
-        return self.pdc(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
-
-class CSAM(nn.Module):
-    """
-    Compact Spatial Attention Module
-    """
-    def __init__(self, channels):
-        super(CSAM, self).__init__()
-
-        mid_channels = 4
-        self.relu1 = nn.ReLU()
-        self.conv1 = nn.Conv2d(channels, mid_channels, kernel_size=1, padding=0)
-        self.conv2 = nn.Conv2d(mid_channels, 1, kernel_size=3, padding=1, bias=False)
-        self.sigmoid = nn.Sigmoid()
-        nn.init.constant_(self.conv1.bias, 0)
-
-    def forward(self, x):
-        y = self.relu1(x)
-        y = self.conv1(y)
-        y = self.conv2(y)
-        y = self.sigmoid(y)
-
-        return x * y
-
-class CDCM(nn.Module):
-    """
-    Compact Dilation Convolution based Module
-    """
-    def __init__(self, in_channels, out_channels):
-        super(CDCM, self).__init__()
-
-        self.relu1 = nn.ReLU()
-        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0)
-        self.conv2_1 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=5, padding=5, bias=False)
-        self.conv2_2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=7, padding=7, bias=False)
-        self.conv2_3 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=9, padding=9, bias=False)
-        self.conv2_4 = nn.Conv2d(out_channels, out_channels, kernel_size=3, dilation=11, padding=11, bias=False)
-        nn.init.constant_(self.conv1.bias, 0)
-
-    def forward(self, x):
-        x = self.relu1(x)
-        x = self.conv1(x)
-        x1 = self.conv2_1(x)
-        x2 = self.conv2_2(x)
-        x3 = self.conv2_3(x)
-        x4 = self.conv2_4(x)
-        return x1 + x2 + x3 + x4
-
-
-class MapReduce(nn.Module):
-    """
-    Reduce feature maps into a single edge map
-    """
-    def __init__(self, channels):
-        super(MapReduce, self).__init__()
-        self.conv = nn.Conv2d(channels, 1, kernel_size=1, padding=0)
-        nn.init.constant_(self.conv.bias, 0)
-
-    def forward(self, x):
-        return self.conv(x)
-
-
-class PDCBlock(nn.Module):
-    def __init__(self, pdc, inplane, ouplane, stride=1):
-        super(PDCBlock, self).__init__()
-        self.stride=stride
-
-        self.stride=stride
-        if self.stride > 1:
-            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
-            self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0)
-        self.conv1 = Conv2d(pdc, inplane, inplane, kernel_size=3, padding=1, groups=inplane, bias=False)
-        self.relu2 = nn.ReLU()
-        self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0, bias=False)
-
-    def forward(self, x):
-        if self.stride > 1:
-            x = self.pool(x)
-        y = self.conv1(x)
-        y = self.relu2(y)
-        y = self.conv2(y)
-        if self.stride > 1:
-            x = self.shortcut(x)
-        y = y + x
-        return y
-
-class PDCBlock_converted(nn.Module):
-    """
-    CPDC, APDC can be converted to vanilla 3x3 convolution
-    RPDC can be converted to vanilla 5x5 convolution
-    """
-    def __init__(self, pdc, inplane, ouplane, stride=1):
-        super(PDCBlock_converted, self).__init__()
-        self.stride=stride
-
-        if self.stride > 1:
-            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
-            self.shortcut = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0)
-        if pdc == 'rd':
-            self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=5, padding=2, groups=inplane, bias=False)
-        else:
-            self.conv1 = nn.Conv2d(inplane, inplane, kernel_size=3, padding=1, groups=inplane, bias=False)
-        self.relu2 = nn.ReLU()
-        self.conv2 = nn.Conv2d(inplane, ouplane, kernel_size=1, padding=0, bias=False)
-
-    def forward(self, x):
-        if self.stride > 1:
-            x = self.pool(x)
-        y = self.conv1(x)
-        y = self.relu2(y)
-        y = self.conv2(y)
-        if self.stride > 1:
-            x = self.shortcut(x)
-        y = y + x
-        return y
-
-class PiDiNet(nn.Module):
-    def __init__(self, inplane, pdcs, dil=None, sa=False, convert=False):
-        super(PiDiNet, self).__init__()
-        self.sa = sa
-        if dil is not None:
-            assert isinstance(dil, int), 'dil should be an int'
-        self.dil = dil
-
-        self.fuseplanes = []
-
-        self.inplane = inplane
-        if convert:
-            if pdcs[0] == 'rd':
-                init_kernel_size = 5
-                init_padding = 2
-            else:
-                init_kernel_size = 3
-                init_padding = 1
-            self.init_block = nn.Conv2d(3, self.inplane,
-                    kernel_size=init_kernel_size, padding=init_padding, bias=False)
-            block_class = PDCBlock_converted
-        else:
-            self.init_block = Conv2d(pdcs[0], 3, self.inplane, kernel_size=3, padding=1)
-            block_class = PDCBlock
-
-        self.block1_1 = block_class(pdcs[1], self.inplane, self.inplane)
-        self.block1_2 = block_class(pdcs[2], self.inplane, self.inplane)
-        self.block1_3 = block_class(pdcs[3], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # C
-
-        inplane = self.inplane
-        self.inplane = self.inplane * 2
-        self.block2_1 = block_class(pdcs[4], inplane, self.inplane, stride=2)
-        self.block2_2 = block_class(pdcs[5], self.inplane, self.inplane)
-        self.block2_3 = block_class(pdcs[6], self.inplane, self.inplane)
-        self.block2_4 = block_class(pdcs[7], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # 2C
-
-        inplane = self.inplane
-        self.inplane = self.inplane * 2
-        self.block3_1 = block_class(pdcs[8], inplane, self.inplane, stride=2)
-        self.block3_2 = block_class(pdcs[9], self.inplane, self.inplane)
-        self.block3_3 = block_class(pdcs[10], self.inplane, self.inplane)
-        self.block3_4 = block_class(pdcs[11], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # 4C
-
-        self.block4_1 = block_class(pdcs[12], self.inplane, self.inplane, stride=2)
-        self.block4_2 = block_class(pdcs[13], self.inplane, self.inplane)
-        self.block4_3 = block_class(pdcs[14], self.inplane, self.inplane)
-        self.block4_4 = block_class(pdcs[15], self.inplane, self.inplane)
-        self.fuseplanes.append(self.inplane) # 4C
-
-        self.conv_reduces = nn.ModuleList()
-        if self.sa and self.dil is not None:
-            self.attentions = nn.ModuleList()
-            self.dilations = nn.ModuleList()
-            for i in range(4):
-                self.dilations.append(CDCM(self.fuseplanes[i], self.dil))
-                self.attentions.append(CSAM(self.dil))
-                self.conv_reduces.append(MapReduce(self.dil))
-        elif self.sa:
-            self.attentions = nn.ModuleList()
-            for i in range(4):
-                self.attentions.append(CSAM(self.fuseplanes[i]))
-                self.conv_reduces.append(MapReduce(self.fuseplanes[i]))
-        elif self.dil is not None:
-            self.dilations = nn.ModuleList()
-            for i in range(4):
-                self.dilations.append(CDCM(self.fuseplanes[i], self.dil))
-                self.conv_reduces.append(MapReduce(self.dil))
-        else:
-            for i in range(4):
-                self.conv_reduces.append(MapReduce(self.fuseplanes[i]))
-
-        self.classifier = nn.Conv2d(4, 1, kernel_size=1) # has bias
-        nn.init.constant_(self.classifier.weight, 0.25)
-        nn.init.constant_(self.classifier.bias, 0)
-
-        # print('initialization done')
-
-    def get_weights(self):
-        conv_weights = []
-        bn_weights = []
-        relu_weights = []
-        for pname, p in self.named_parameters():
-            if 'bn' in pname:
-                bn_weights.append(p)
-            elif 'relu' in pname:
-                relu_weights.append(p)
-            else:
-                conv_weights.append(p)
-
-        return conv_weights, bn_weights, relu_weights
-
-    def forward(self, x):
-        H, W = x.size()[2:]
-
-        x = self.init_block(x)
-
-        x1 = self.block1_1(x)
-        x1 = self.block1_2(x1)
-        x1 = self.block1_3(x1)
-
-        x2 = self.block2_1(x1)
-        x2 = self.block2_2(x2)
-        x2 = self.block2_3(x2)
-        x2 = self.block2_4(x2)
-
-        x3 = self.block3_1(x2)
-        x3 = self.block3_2(x3)
-        x3 = self.block3_3(x3)
-        x3 = self.block3_4(x3)
-
-        x4 = self.block4_1(x3)
-        x4 = self.block4_2(x4)
-        x4 = self.block4_3(x4)
-        x4 = self.block4_4(x4)
-
-        x_fuses = []
-        if self.sa and self.dil is not None:
-            for i, xi in enumerate([x1, x2, x3, x4]):
-                x_fuses.append(self.attentions[i](self.dilations[i](xi)))
-        elif self.sa:
-            for i, xi in enumerate([x1, x2, x3, x4]):
-                x_fuses.append(self.attentions[i](xi))
-        elif self.dil is not None:
-            for i, xi in enumerate([x1, x2, x3, x4]):
-                x_fuses.append(self.dilations[i](xi))
-        else:
-            x_fuses = [x1, x2, x3, x4]
-
-        e1 = self.conv_reduces[0](x_fuses[0])
-        e1 = F.interpolate(e1, (H, W), mode="bilinear", align_corners=False)
-
-        e2 = self.conv_reduces[1](x_fuses[1])
-        e2 = F.interpolate(e2, (H, W), mode="bilinear", align_corners=False)
-
-        e3 = self.conv_reduces[2](x_fuses[2])
-        e3 = F.interpolate(e3, (H, W), mode="bilinear", align_corners=False)
-
-        e4 = self.conv_reduces[3](x_fuses[3])
-        e4 = F.interpolate(e4, (H, W), mode="bilinear", align_corners=False)
-
-        outputs = [e1, e2, e3, e4]
-
-        output = self.classifier(torch.cat(outputs, dim=1))
-        #if not self.training:
-        #    return torch.sigmoid(output)
-
-        outputs.append(output)
-        outputs = [torch.sigmoid(r) for r in outputs]
-        return outputs
-
-def config_model(model):
-    model_options = list(nets.keys())
-    assert model in model_options, \
-        'unrecognized model, please choose from %s' % str(model_options)
-
-    # print(str(nets[model]))
-
-    pdcs = []
-    for i in range(16):
-        layer_name = 'layer%d' % i
-        op = nets[model][layer_name]
-        pdcs.append(createConvFunc(op))
-
-    return pdcs
-
-def pidinet():
-    pdcs = config_model('carv4')
-    dil = 24 #if args.dil else None
-    return PiDiNet(60, pdcs, dil=dil, sa=True)
diff --git a/spaces/Realcat/image-matching-webui/third_party/DeDoDe/DeDoDe/utils.py b/spaces/Realcat/image-matching-webui/third_party/DeDoDe/DeDoDe/utils.py
deleted file mode 100644
index 0de66c1ab5cfe13e7744b2e9e4a9762c4ba07985..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/DeDoDe/DeDoDe/utils.py
+++ /dev/null
@@ -1,902 +0,0 @@
-import warnings
-import numpy as np
-import math
-import cv2
-import torch
-from torchvision import transforms
-from torchvision.transforms.functional import InterpolationMode
-import torch.nn.functional as F
-from PIL import Image
-from einops import rearrange
-import torch
-from time import perf_counter
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-
-def recover_pose(E, kpts0, kpts1, K0, K1, mask):
-    best_num_inliers = 0
-    K0inv = np.linalg.inv(K0[:2, :2])
-    K1inv = np.linalg.inv(K1[:2, :2])
-
-    kpts0_n = (K0inv @ (kpts0 - K0[None, :2, 2]).T).T
-    kpts1_n = (K1inv @ (kpts1 - K1[None, :2, 2]).T).T
-
-    for _E in np.split(E, len(E) / 3):
-        n, R, t, _ = cv2.recoverPose(_E, kpts0_n, kpts1_n, np.eye(3), 1e9, mask=mask)
-        if n > best_num_inliers:
-            best_num_inliers = n
-            ret = (R, t, mask.ravel() > 0)
-    return ret
-
-
-# Code taken from https://github.com/PruneTruong/DenseMatching/blob/40c29a6b5c35e86b9509e65ab0cd12553d998e5f/validation/utils_pose_estimation.py
-# --- GEOMETRY ---
-def estimate_pose(kpts0, kpts1, K0, K1, norm_thresh, conf=0.99999):
-    if len(kpts0) < 5:
-        return None
-    K0inv = np.linalg.inv(K0[:2, :2])
-    K1inv = np.linalg.inv(K1[:2, :2])
-
-    kpts0 = (K0inv @ (kpts0 - K0[None, :2, 2]).T).T
-    kpts1 = (K1inv @ (kpts1 - K1[None, :2, 2]).T).T
-    E, mask = cv2.findEssentialMat(
-        kpts0, kpts1, np.eye(3), threshold=norm_thresh, prob=conf
-    )
-
-    ret = None
-    if E is not None:
-        best_num_inliers = 0
-
-        for _E in np.split(E, len(E) / 3):
-            n, R, t, _ = cv2.recoverPose(_E, kpts0, kpts1, np.eye(3), 1e9, mask=mask)
-            if n > best_num_inliers:
-                best_num_inliers = n
-                ret = (R, t, mask.ravel() > 0)
-    return ret
-
-
-def get_grid(B, H, W, device=device):
-    x1_n = torch.meshgrid(
-        *[torch.linspace(-1 + 1 / n, 1 - 1 / n, n, device=device) for n in (B, H, W)]
-    )
-    x1_n = torch.stack((x1_n[2], x1_n[1]), dim=-1).reshape(B, H * W, 2)
-    return x1_n
-
-
-@torch.no_grad()
-def finite_diff_hessian(f: tuple(["B", "H", "W"]), device=device):
-    dxx = (
-        torch.tensor([[0, 0, 0], [1, -2, 1], [0, 0, 0]], device=device)[None, None] / 2
-    )
-    dxy = (
-        torch.tensor([[1, 0, -1], [0, 0, 0], [-1, 0, 1]], device=device)[None, None] / 4
-    )
-    dyy = dxx.mT
-    Hxx = F.conv2d(f[:, None], dxx, padding=1)[:, 0]
-    Hxy = F.conv2d(f[:, None], dxy, padding=1)[:, 0]
-    Hyy = F.conv2d(f[:, None], dyy, padding=1)[:, 0]
-    H = torch.stack((Hxx, Hxy, Hxy, Hyy), dim=-1).reshape(*f.shape, 2, 2)
-    return H
-
-
-def finite_diff_grad(f: tuple(["B", "H", "W"]), device=device):
-    dx = torch.tensor([[0, 0, 0], [-1, 0, 1], [0, 0, 0]], device=device)[None, None] / 2
-    dy = dx.mT
-    gx = F.conv2d(f[:, None], dx, padding=1)
-    gy = F.conv2d(f[:, None], dy, padding=1)
-    g = torch.cat((gx, gy), dim=1)
-    return g
-
-
-def fast_inv_2x2(matrix: tuple[..., 2, 2], eps=1e-10):
-    return (
-        1
-        / (torch.linalg.det(matrix)[..., None, None] + eps)
-        * torch.stack(
-            (
-                matrix[..., 1, 1],
-                -matrix[..., 0, 1],
-                -matrix[..., 1, 0],
-                matrix[..., 0, 0],
-            ),
-            dim=-1,
-        ).reshape(*matrix.shape)
-    )
-
-
-def newton_step(f: tuple["B", "H", "W"], inds, device=device):
-    B, H, W = f.shape
-    Hess = finite_diff_hessian(f).reshape(B, H * W, 2, 2)
-    Hess = torch.gather(Hess, dim=1, index=inds[..., None].expand(B, -1, 2, 2))
-    grad = finite_diff_grad(f).reshape(B, H * W, 2)
-    grad = torch.gather(grad, dim=1, index=inds)
-    Hessinv = fast_inv_2x2(Hess - torch.eye(2, device=device)[None, None])
-    step = Hessinv @ grad[..., None]
-    return step[..., 0]
-
-
-@torch.no_grad()
-def sample_keypoints(
-    scoremap,
-    num_samples=8192,
-    device=device,
-    use_nms=True,
-    sample_topk=False,
-    return_scoremap=False,
-    sharpen=False,
-    upsample=False,
-    increase_coverage=False,
-):
-    # scoremap = scoremap**2
-    log_scoremap = (scoremap + 1e-10).log()
-    if upsample:
-        log_scoremap = F.interpolate(
-            log_scoremap[:, None], scale_factor=3, mode="bicubic", align_corners=False
-        )[
-            :, 0
-        ]  # .clamp(min = 0)
-        scoremap = log_scoremap.exp()
-    B, H, W = scoremap.shape
-    if increase_coverage:
-        weights = (-torch.linspace(-2, 2, steps=51, device=device) ** 2).exp()[
-            None, None
-        ]
-        # 10000 is just some number for maybe numerical stability, who knows. :), result is invariant anyway
-        local_density_x = F.conv2d(
-            (scoremap[:, None] + 1e-6) * 10000,
-            weights[..., None, :],
-            padding=(0, 51 // 2),
-        )
-        local_density = F.conv2d(
-            local_density_x, weights[..., None], padding=(51 // 2, 0)
-        )[:, 0]
-        scoremap = scoremap * (local_density + 1e-8) ** (-1 / 2)
-    grid = get_grid(B, H, W, device=device).reshape(B, H * W, 2)
-    if sharpen:
-        laplace_operator = (
-            torch.tensor([[[[0, 1, 0], [1, -4, 1], [0, 1, 0]]]], device=device) / 4
-        )
-        scoremap = scoremap[:, None] - 0.5 * F.conv2d(
-            scoremap[:, None], weight=laplace_operator, padding=1
-        )
-        scoremap = scoremap[:, 0].clamp(min=0)
-    if use_nms:
-        scoremap = scoremap * (
-            scoremap == F.max_pool2d(scoremap, (3, 3), stride=1, padding=1)
-        )
-    if sample_topk:
-        inds = torch.topk(scoremap.reshape(B, H * W), k=num_samples).indices
-    else:
-        inds = torch.multinomial(
-            scoremap.reshape(B, H * W), num_samples=num_samples, replacement=False
-        )
-    kps = torch.gather(grid, dim=1, index=inds[..., None].expand(B, num_samples, 2))
-    if return_scoremap:
-        return kps, torch.gather(scoremap.reshape(B, H * W), dim=1, index=inds)
-    return kps
-
-
-@torch.no_grad()
-def jacobi_determinant(warp, certainty, R=3, device=device, dtype=torch.float32):
-    t = perf_counter()
-    *dims, _ = warp.shape
-    warp = warp.to(dtype)
-    certainty = certainty.to(dtype)
-
-    dtype = warp.dtype
-    match_regions = torch.zeros((*dims, 4, R, R), device=device).to(dtype)
-    match_regions[:, 1:-1, 1:-1] = warp.unfold(1, R, 1).unfold(2, R, 1)
-    match_regions = (
-        rearrange(match_regions, "B H W D R1 R2 -> B H W (R1 R2) D")
-        - warp[..., None, :]
-    )
-
-    match_regions_cert = torch.zeros((*dims, R, R), device=device).to(dtype)
-    match_regions_cert[:, 1:-1, 1:-1] = certainty.unfold(1, R, 1).unfold(2, R, 1)
-    match_regions_cert = rearrange(match_regions_cert, "B H W R1 R2 -> B H W (R1 R2)")[
-        ..., None
-    ]
-
-    # print("Time for unfold", perf_counter()-t)
-    # t = perf_counter()
-    *dims, N, D = match_regions.shape
-    # standardize:
-    mu, sigma = match_regions.mean(dim=(-2, -1), keepdim=True), match_regions.std(
-        dim=(-2, -1), keepdim=True
-    )
-    match_regions = (match_regions - mu) / (sigma + 1e-6)
-    x_a, x_b = match_regions.chunk(2, -1)
-
-    A = torch.zeros((*dims, 2 * x_a.shape[-2], 4), device=device).to(dtype)
-    A[..., ::2, :2] = x_a * match_regions_cert
-    A[..., 1::2, 2:] = x_a * match_regions_cert
-
-    a_block = A[..., ::2, :2]
-    ata = a_block.mT @ a_block
-    # print("Time for ata", perf_counter()-t)
-    # t = perf_counter()
-
-    # atainv = torch.linalg.inv(ata+1e-5*torch.eye(2,device=device).to(dtype))
-    atainv = fast_inv_2x2(ata)
-    ATA_inv = torch.zeros((*dims, 4, 4), device=device, dtype=dtype)
-    ATA_inv[..., :2, :2] = atainv
-    ATA_inv[..., 2:, 2:] = atainv
-    atb = A.mT @ (match_regions_cert * x_b).reshape(*dims, N * 2, 1)
-    theta = ATA_inv @ atb
-    # print("Time for theta", perf_counter()-t)
-    # t = perf_counter()
-
-    J = theta.reshape(*dims, 2, 2)
-    abs_J_det = torch.linalg.det(
-        J + 1e-8 * torch.eye(2, 2, device=device).expand(*dims, 2, 2)
-    ).abs()  # Note: This should always be positive for correct warps, but still taking abs here
-    abs_J_logdet = (abs_J_det + 1e-12).log()
-    B = certainty.shape[0]
-    # Handle outliers
-    robust_abs_J_logdet = abs_J_logdet.clamp(
-        -3, 3
-    )  # Shouldn't be more that exp(3) \approx 8 times zoom
-    # print("Time for logdet", perf_counter()-t)
-    # t = perf_counter()
-
-    return robust_abs_J_logdet
-
-
-def get_gt_warp(
-    depth1,
-    depth2,
-    T_1to2,
-    K1,
-    K2,
-    depth_interpolation_mode="bilinear",
-    relative_depth_error_threshold=0.05,
-    H=None,
-    W=None,
-):
-
-    if H is None:
-        B, H, W = depth1.shape
-    else:
-        B = depth1.shape[0]
-    with torch.no_grad():
-        x1_n = torch.meshgrid(
-            *[
-                torch.linspace(-1 + 1 / n, 1 - 1 / n, n, device=depth1.device)
-                for n in (B, H, W)
-            ]
-        )
-        x1_n = torch.stack((x1_n[2], x1_n[1]), dim=-1).reshape(B, H * W, 2)
-        mask, x2 = warp_kpts(
-            x1_n.double(),
-            depth1.double(),
-            depth2.double(),
-            T_1to2.double(),
-            K1.double(),
-            K2.double(),
-            depth_interpolation_mode=depth_interpolation_mode,
-            relative_depth_error_threshold=relative_depth_error_threshold,
-        )
-        prob = mask.float().reshape(B, H, W)
-        x2 = x2.reshape(B, H, W, 2)
-        return torch.cat((x1_n.reshape(B, H, W, 2), x2), dim=-1), prob
-
-
-def recover_pose(E, kpts0, kpts1, K0, K1, mask):
-    best_num_inliers = 0
-    K0inv = np.linalg.inv(K0[:2, :2])
-    K1inv = np.linalg.inv(K1[:2, :2])
-
-    kpts0_n = (K0inv @ (kpts0 - K0[None, :2, 2]).T).T
-    kpts1_n = (K1inv @ (kpts1 - K1[None, :2, 2]).T).T
-
-    for _E in np.split(E, len(E) / 3):
-        n, R, t, _ = cv2.recoverPose(_E, kpts0_n, kpts1_n, np.eye(3), 1e9, mask=mask)
-        if n > best_num_inliers:
-            best_num_inliers = n
-            ret = (R, t, mask.ravel() > 0)
-    return ret
-
-
-# Code taken from https://github.com/PruneTruong/DenseMatching/blob/40c29a6b5c35e86b9509e65ab0cd12553d998e5f/validation/utils_pose_estimation.py
-# --- GEOMETRY ---
-def estimate_pose(
-    kpts0,
-    kpts1,
-    K0,
-    K1,
-    norm_thresh,
-    conf=0.99999,
-):
-    if len(kpts0) < 5:
-        return None
-    K0inv = np.linalg.inv(K0[:2, :2])
-    K1inv = np.linalg.inv(K1[:2, :2])
-
-    kpts0 = (K0inv @ (kpts0 - K0[None, :2, 2]).T).T
-    kpts1 = (K1inv @ (kpts1 - K1[None, :2, 2]).T).T
-    method = cv2.USAC_ACCURATE
-    E, mask = cv2.findEssentialMat(
-        kpts0, kpts1, np.eye(3), threshold=norm_thresh, prob=conf, method=method
-    )
-
-    ret = None
-    if E is not None:
-        best_num_inliers = 0
-
-        for _E in np.split(E, len(E) / 3):
-            n, R, t, _ = cv2.recoverPose(_E, kpts0, kpts1, np.eye(3), 1e9, mask=mask)
-            if n > best_num_inliers:
-                best_num_inliers = n
-                ret = (R, t, mask.ravel() > 0)
-    return ret
-
-
-def estimate_pose_uncalibrated(kpts0, kpts1, K0, K1, norm_thresh, conf=0.99999):
-    if len(kpts0) < 5:
-        return None
-    method = cv2.USAC_ACCURATE
-    F, mask = cv2.findFundamentalMat(
-        kpts0,
-        kpts1,
-        ransacReprojThreshold=norm_thresh,
-        confidence=conf,
-        method=method,
-        maxIters=10000,
-    )
-    E = K1.T @ F @ K0
-    ret = None
-    if E is not None:
-        best_num_inliers = 0
-        K0inv = np.linalg.inv(K0[:2, :2])
-        K1inv = np.linalg.inv(K1[:2, :2])
-
-        kpts0_n = (K0inv @ (kpts0 - K0[None, :2, 2]).T).T
-        kpts1_n = (K1inv @ (kpts1 - K1[None, :2, 2]).T).T
-
-        for _E in np.split(E, len(E) / 3):
-            n, R, t, _ = cv2.recoverPose(
-                _E, kpts0_n, kpts1_n, np.eye(3), 1e9, mask=mask
-            )
-            if n > best_num_inliers:
-                best_num_inliers = n
-                ret = (R, t, mask.ravel() > 0)
-    return ret
-
-
-def unnormalize_coords(x_n, h, w):
-    x = torch.stack(
-        (w * (x_n[..., 0] + 1) / 2, h * (x_n[..., 1] + 1) / 2), dim=-1
-    )  # [-1+1/h, 1-1/h] -> [0.5, h-0.5]
-    return x
-
-
-def rotate_intrinsic(K, n):
-    base_rot = np.array([[0, 1, 0], [-1, 0, 0], [0, 0, 1]])
-    rot = np.linalg.matrix_power(base_rot, n)
-    return rot @ K
-
-
-def rotate_pose_inplane(i_T_w, rot):
-    rotation_matrices = [
-        np.array(
-            [
-                [np.cos(r), -np.sin(r), 0.0, 0.0],
-                [np.sin(r), np.cos(r), 0.0, 0.0],
-                [0.0, 0.0, 1.0, 0.0],
-                [0.0, 0.0, 0.0, 1.0],
-            ],
-            dtype=np.float32,
-        )
-        for r in [np.deg2rad(d) for d in (0, 270, 180, 90)]
-    ]
-    return np.dot(rotation_matrices[rot], i_T_w)
-
-
-def scale_intrinsics(K, scales):
-    scales = np.diag([1.0 / scales[0], 1.0 / scales[1], 1.0])
-    return np.dot(scales, K)
-
-
-def angle_error_mat(R1, R2):
-    cos = (np.trace(np.dot(R1.T, R2)) - 1) / 2
-    cos = np.clip(cos, -1.0, 1.0)  # numercial errors can make it out of bounds
-    return np.rad2deg(np.abs(np.arccos(cos)))
-
-
-def angle_error_vec(v1, v2):
-    n = np.linalg.norm(v1) * np.linalg.norm(v2)
-    return np.rad2deg(np.arccos(np.clip(np.dot(v1, v2) / n, -1.0, 1.0)))
-
-
-def compute_pose_error(T_0to1, R, t):
-    R_gt = T_0to1[:3, :3]
-    t_gt = T_0to1[:3, 3]
-    error_t = angle_error_vec(t.squeeze(), t_gt)
-    error_t = np.minimum(error_t, 180 - error_t)  # ambiguity of E estimation
-    error_R = angle_error_mat(R, R_gt)
-    return error_t, error_R
-
-
-def pose_auc(errors, thresholds):
-    sort_idx = np.argsort(errors)
-    errors = np.array(errors.copy())[sort_idx]
-    recall = (np.arange(len(errors)) + 1) / len(errors)
-    errors = np.r_[0.0, errors]
-    recall = np.r_[0.0, recall]
-    aucs = []
-    for t in thresholds:
-        last_index = np.searchsorted(errors, t)
-        r = np.r_[recall[:last_index], recall[last_index - 1]]
-        e = np.r_[errors[:last_index], t]
-        aucs.append(np.trapz(r, x=e) / t)
-    return aucs
-
-
-# From Patch2Pix https://github.com/GrumpyZhou/patch2pix
-def get_depth_tuple_transform_ops(resize=None, normalize=True, unscale=False):
-    ops = []
-    if resize:
-        ops.append(
-            TupleResize(resize, mode=InterpolationMode.BILINEAR, antialias=False)
-        )
-    return TupleCompose(ops)
-
-
-def get_tuple_transform_ops(resize=None, normalize=True, unscale=False, clahe=False):
-    ops = []
-    if resize:
-        ops.append(TupleResize(resize, antialias=True))
-    if clahe:
-        ops.append(TupleClahe())
-    if normalize:
-        ops.append(TupleToTensorScaled())
-        ops.append(
-            TupleNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-        )  # Imagenet mean/std
-    else:
-        if unscale:
-            ops.append(TupleToTensorUnscaled())
-        else:
-            ops.append(TupleToTensorScaled())
-    return TupleCompose(ops)
-
-
-class Clahe:
-    def __init__(self, cliplimit=2, blocksize=8) -> None:
-        self.clahe = cv2.createCLAHE(cliplimit, (blocksize, blocksize))
-
-    def __call__(self, im):
-        im_hsv = cv2.cvtColor(np.array(im), cv2.COLOR_RGB2HSV)
-        im_v = self.clahe.apply(im_hsv[:, :, 2])
-        im_hsv[..., 2] = im_v
-        im_clahe = cv2.cvtColor(im_hsv, cv2.COLOR_HSV2RGB)
-        return Image.fromarray(im_clahe)
-
-
-class TupleClahe:
-    def __init__(self, cliplimit=8, blocksize=8) -> None:
-        self.clahe = Clahe(cliplimit, blocksize)
-
-    def __call__(self, ims):
-        return [self.clahe(im) for im in ims]
-
-
-class ToTensorScaled(object):
-    """Convert a RGB PIL Image to a CHW ordered Tensor, scale the range to [0, 1]"""
-
-    def __call__(self, im):
-        if not isinstance(im, torch.Tensor):
-            im = np.array(im, dtype=np.float32).transpose((2, 0, 1))
-            im /= 255.0
-            return torch.from_numpy(im)
-        else:
-            return im
-
-    def __repr__(self):
-        return "ToTensorScaled(./255)"
-
-
-class TupleToTensorScaled(object):
-    def __init__(self):
-        self.to_tensor = ToTensorScaled()
-
-    def __call__(self, im_tuple):
-        return [self.to_tensor(im) for im in im_tuple]
-
-    def __repr__(self):
-        return "TupleToTensorScaled(./255)"
-
-
-class ToTensorUnscaled(object):
-    """Convert a RGB PIL Image to a CHW ordered Tensor"""
-
-    def __call__(self, im):
-        return torch.from_numpy(np.array(im, dtype=np.float32).transpose((2, 0, 1)))
-
-    def __repr__(self):
-        return "ToTensorUnscaled()"
-
-
-class TupleToTensorUnscaled(object):
-    """Convert a RGB PIL Image to a CHW ordered Tensor"""
-
-    def __init__(self):
-        self.to_tensor = ToTensorUnscaled()
-
-    def __call__(self, im_tuple):
-        return [self.to_tensor(im) for im in im_tuple]
-
-    def __repr__(self):
-        return "TupleToTensorUnscaled()"
-
-
-class TupleResize(object):
-    def __init__(self, size, mode=InterpolationMode.BICUBIC, antialias=None):
-        self.size = size
-        self.resize = transforms.Resize(size, mode, antialias=antialias)
-
-    def __call__(self, im_tuple):
-        return [self.resize(im) for im in im_tuple]
-
-    def __repr__(self):
-        return "TupleResize(size={})".format(self.size)
-
-
-class Normalize:
-    def __call__(self, im):
-        mean = im.mean(dim=(1, 2), keepdims=True)
-        std = im.std(dim=(1, 2), keepdims=True)
-        return (im - mean) / std
-
-
-class TupleNormalize(object):
-    def __init__(self, mean, std):
-        self.mean = mean
-        self.std = std
-        self.normalize = transforms.Normalize(mean=mean, std=std)
-
-    def __call__(self, im_tuple):
-        c, h, w = im_tuple[0].shape
-        if c > 3:
-            warnings.warn(f"Number of channels {c=} > 3, assuming first 3 are rgb")
-        return [self.normalize(im[:3]) for im in im_tuple]
-
-    def __repr__(self):
-        return "TupleNormalize(mean={}, std={})".format(self.mean, self.std)
-
-
-class TupleCompose(object):
-    def __init__(self, transforms):
-        self.transforms = transforms
-
-    def __call__(self, im_tuple):
-        for t in self.transforms:
-            im_tuple = t(im_tuple)
-        return im_tuple
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + "("
-        for t in self.transforms:
-            format_string += "\n"
-            format_string += "    {0}".format(t)
-        format_string += "\n)"
-        return format_string
-
-
-@torch.no_grad()
-def warp_kpts(
-    kpts0,
-    depth0,
-    depth1,
-    T_0to1,
-    K0,
-    K1,
-    smooth_mask=False,
-    return_relative_depth_error=False,
-    depth_interpolation_mode="bilinear",
-    relative_depth_error_threshold=0.05,
-):
-    """Warp kpts0 from I0 to I1 with depth, K and Rt
-    Also check covisibility and depth consistency.
-    Depth is consistent if relative error < 0.2 (hard-coded).
-    # https://github.com/zju3dv/LoFTR/blob/94e98b695be18acb43d5d3250f52226a8e36f839/src/loftr/utils/geometry.py adapted from here
-    Args:
-        kpts0 (torch.Tensor): [N, L, 2] - <x, y>, should be normalized in (-1,1)
-        depth0 (torch.Tensor): [N, H, W],
-        depth1 (torch.Tensor): [N, H, W],
-        T_0to1 (torch.Tensor): [N, 3, 4],
-        K0 (torch.Tensor): [N, 3, 3],
-        K1 (torch.Tensor): [N, 3, 3],
-    Returns:
-        calculable_mask (torch.Tensor): [N, L]
-        warped_keypoints0 (torch.Tensor): [N, L, 2] <x0_hat, y1_hat>
-    """
-    (
-        n,
-        h,
-        w,
-    ) = depth0.shape
-    if depth_interpolation_mode == "combined":
-        # Inspired by approach in inloc, try to fill holes from bilinear interpolation by nearest neighbour interpolation
-        if smooth_mask:
-            raise NotImplementedError("Combined bilinear and NN warp not implemented")
-        valid_bilinear, warp_bilinear = warp_kpts(
-            kpts0,
-            depth0,
-            depth1,
-            T_0to1,
-            K0,
-            K1,
-            smooth_mask=smooth_mask,
-            return_relative_depth_error=return_relative_depth_error,
-            depth_interpolation_mode="bilinear",
-            relative_depth_error_threshold=relative_depth_error_threshold,
-        )
-        valid_nearest, warp_nearest = warp_kpts(
-            kpts0,
-            depth0,
-            depth1,
-            T_0to1,
-            K0,
-            K1,
-            smooth_mask=smooth_mask,
-            return_relative_depth_error=return_relative_depth_error,
-            depth_interpolation_mode="nearest-exact",
-            relative_depth_error_threshold=relative_depth_error_threshold,
-        )
-        nearest_valid_bilinear_invalid = (~valid_bilinear).logical_and(valid_nearest)
-        warp = warp_bilinear.clone()
-        warp[nearest_valid_bilinear_invalid] = warp_nearest[
-            nearest_valid_bilinear_invalid
-        ]
-        valid = valid_bilinear | valid_nearest
-        return valid, warp
-
-    kpts0_depth = F.grid_sample(
-        depth0[:, None],
-        kpts0[:, :, None],
-        mode=depth_interpolation_mode,
-        align_corners=False,
-    )[:, 0, :, 0]
-    kpts0 = torch.stack(
-        (w * (kpts0[..., 0] + 1) / 2, h * (kpts0[..., 1] + 1) / 2), dim=-1
-    )  # [-1+1/h, 1-1/h] -> [0.5, h-0.5]
-    # Sample depth, get calculable_mask on depth != 0
-    nonzero_mask = kpts0_depth != 0
-
-    # Unproject
-    kpts0_h = (
-        torch.cat([kpts0, torch.ones_like(kpts0[:, :, [0]])], dim=-1)
-        * kpts0_depth[..., None]
-    )  # (N, L, 3)
-    kpts0_n = K0.inverse() @ kpts0_h.transpose(2, 1)  # (N, 3, L)
-    kpts0_cam = kpts0_n
-
-    # Rigid Transform
-    w_kpts0_cam = T_0to1[:, :3, :3] @ kpts0_cam + T_0to1[:, :3, [3]]  # (N, 3, L)
-    w_kpts0_depth_computed = w_kpts0_cam[:, 2, :]
-
-    # Project
-    w_kpts0_h = (K1 @ w_kpts0_cam).transpose(2, 1)  # (N, L, 3)
-    w_kpts0 = w_kpts0_h[:, :, :2] / (
-        w_kpts0_h[:, :, [2]] + 1e-4
-    )  # (N, L, 2), +1e-4 to avoid zero depth
-
-    # Covisible Check
-    h, w = depth1.shape[1:3]
-    covisible_mask = (
-        (w_kpts0[:, :, 0] > 0)
-        * (w_kpts0[:, :, 0] < w - 1)
-        * (w_kpts0[:, :, 1] > 0)
-        * (w_kpts0[:, :, 1] < h - 1)
-    )
-    w_kpts0 = torch.stack(
-        (2 * w_kpts0[..., 0] / w - 1, 2 * w_kpts0[..., 1] / h - 1), dim=-1
-    )  # from [0.5,h-0.5] -> [-1+1/h, 1-1/h]
-    # w_kpts0[~covisible_mask, :] = -5 # xd
-
-    w_kpts0_depth = F.grid_sample(
-        depth1[:, None],
-        w_kpts0[:, :, None],
-        mode=depth_interpolation_mode,
-        align_corners=False,
-    )[:, 0, :, 0]
-
-    relative_depth_error = (
-        (w_kpts0_depth - w_kpts0_depth_computed) / w_kpts0_depth
-    ).abs()
-    if not smooth_mask:
-        consistent_mask = relative_depth_error < relative_depth_error_threshold
-    else:
-        consistent_mask = (-relative_depth_error / smooth_mask).exp()
-    valid_mask = nonzero_mask * covisible_mask * consistent_mask
-    if return_relative_depth_error:
-        return relative_depth_error, w_kpts0
-    else:
-        return valid_mask, w_kpts0
-
-
-imagenet_mean = torch.tensor([0.485, 0.456, 0.406])
-imagenet_std = torch.tensor([0.229, 0.224, 0.225])
-
-
-def numpy_to_pil(x: np.ndarray):
-    """
-    Args:
-        x: Assumed to be of shape (h,w,c)
-    """
-    if isinstance(x, torch.Tensor):
-        x = x.detach().cpu().numpy()
-    if x.max() <= 1.01:
-        x *= 255
-    x = x.astype(np.uint8)
-    return Image.fromarray(x)
-
-
-def tensor_to_pil(x, unnormalize=False, autoscale=False):
-    if unnormalize:
-        x = x * (imagenet_std[:, None, None].to(x.device)) + (
-            imagenet_mean[:, None, None].to(x.device)
-        )
-    if autoscale:
-        if x.max() == x.min():
-            warnings.warn("x max == x min, cant autoscale")
-        else:
-            x = (x - x.min()) / (x.max() - x.min())
-
-    x = x.detach().permute(1, 2, 0).cpu().numpy()
-    x = np.clip(x, 0.0, 1.0)
-    return numpy_to_pil(x)
-
-
-def to_cuda(batch):
-    for key, value in batch.items():
-        if isinstance(value, torch.Tensor):
-            batch[key] = value.cuda()
-    return batch
-
-
-def to_cpu(batch):
-    for key, value in batch.items():
-        if isinstance(value, torch.Tensor):
-            batch[key] = value.cpu()
-    return batch
-
-
-def get_pose(calib):
-    w, h = np.array(calib["imsize"])[0]
-    return np.array(calib["K"]), np.array(calib["R"]), np.array(calib["T"]).T, h, w
-
-
-def compute_relative_pose(R1, t1, R2, t2):
-    rots = R2 @ (R1.T)
-    trans = -rots @ t1 + t2
-    return rots, trans
-
-
-def to_pixel_coords(flow, h1, w1):
-    flow = torch.stack(
-        (
-            w1 * (flow[..., 0] + 1) / 2,
-            h1 * (flow[..., 1] + 1) / 2,
-        ),
-        axis=-1,
-    )
-    return flow
-
-
-def to_normalized_coords(flow, h1, w1):
-    flow = torch.stack(
-        (
-            2 * (flow[..., 0]) / w1 - 1,
-            2 * (flow[..., 1]) / h1 - 1,
-        ),
-        axis=-1,
-    )
-    return flow
-
-
-def warp_to_pixel_coords(warp, h1, w1, h2, w2):
-    warp1 = warp[..., :2]
-    warp1 = torch.stack(
-        (
-            w1 * (warp1[..., 0] + 1) / 2,
-            h1 * (warp1[..., 1] + 1) / 2,
-        ),
-        axis=-1,
-    )
-    warp2 = warp[..., 2:]
-    warp2 = torch.stack(
-        (
-            w2 * (warp2[..., 0] + 1) / 2,
-            h2 * (warp2[..., 1] + 1) / 2,
-        ),
-        axis=-1,
-    )
-    return torch.cat((warp1, warp2), dim=-1)
-
-
-def to_homogeneous(x):
-    ones = torch.ones_like(x[..., -1:])
-    return torch.cat((x, ones), dim=-1)
-
-
-def from_homogeneous(xh, eps=1e-12):
-    return xh[..., :-1] / (xh[..., -1:] + eps)
-
-
-def homog_transform(Homog, x):
-    xh = to_homogeneous(x)
-    yh = (Homog @ xh.mT).mT
-    y = from_homogeneous(yh)
-    return y
-
-
-def get_homog_warp(Homog, H, W, device=device):
-    grid = torch.meshgrid(
-        torch.linspace(-1 + 1 / H, 1 - 1 / H, H, device=device),
-        torch.linspace(-1 + 1 / W, 1 - 1 / W, W, device=device),
-    )
-
-    x_A = torch.stack((grid[1], grid[0]), dim=-1)[None]
-    x_A_to_B = homog_transform(Homog, x_A)
-    mask = ((x_A_to_B > -1) * (x_A_to_B < 1)).prod(dim=-1).float()
-    return torch.cat((x_A.expand(*x_A_to_B.shape), x_A_to_B), dim=-1), mask
-
-
-def dual_log_softmax_matcher(
-    desc_A: tuple["B", "N", "C"],
-    desc_B: tuple["B", "M", "C"],
-    inv_temperature=1,
-    normalize=False,
-):
-    B, N, C = desc_A.shape
-    if normalize:
-        desc_A = desc_A / desc_A.norm(dim=-1, keepdim=True)
-        desc_B = desc_B / desc_B.norm(dim=-1, keepdim=True)
-        corr = torch.einsum("b n c, b m c -> b n m", desc_A, desc_B) * inv_temperature
-    else:
-        corr = torch.einsum("b n c, b m c -> b n m", desc_A, desc_B) * inv_temperature
-    logP = corr.log_softmax(dim=-2) + corr.log_softmax(dim=-1)
-    return logP
-
-
-def dual_softmax_matcher(
-    desc_A: tuple["B", "N", "C"],
-    desc_B: tuple["B", "M", "C"],
-    inv_temperature=1,
-    normalize=False,
-):
-    if len(desc_A.shape) < 3:
-        desc_A, desc_B = desc_A[None], desc_B[None]
-    B, N, C = desc_A.shape
-    if normalize:
-        desc_A = desc_A / desc_A.norm(dim=-1, keepdim=True)
-        desc_B = desc_B / desc_B.norm(dim=-1, keepdim=True)
-        corr = torch.einsum("b n c, b m c -> b n m", desc_A, desc_B) * inv_temperature
-    else:
-        corr = torch.einsum("b n c, b m c -> b n m", desc_A, desc_B) * inv_temperature
-    P = corr.softmax(dim=-2) * corr.softmax(dim=-1)
-    return P
-
-
-def conditional_softmax_matcher(
-    desc_A: tuple["B", "N", "C"],
-    desc_B: tuple["B", "M", "C"],
-    inv_temperature=1,
-    normalize=False,
-):
-    if len(desc_A.shape) < 3:
-        desc_A, desc_B = desc_A[None], desc_B[None]
-    B, N, C = desc_A.shape
-    if normalize:
-        desc_A = desc_A / desc_A.norm(dim=-1, keepdim=True)
-        desc_B = desc_B / desc_B.norm(dim=-1, keepdim=True)
-        corr = torch.einsum("b n c, b m c -> b n m", desc_A, desc_B) * inv_temperature
-    else:
-        corr = torch.einsum("b n c, b m c -> b n m", desc_A, desc_B) * inv_temperature
-    P_B_cond_A = corr.softmax(dim=-1)
-    P_A_cond_B = corr.softmax(dim=-2)
-
-    return P_A_cond_B, P_B_cond_A
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/core/evaluation/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/core/evaluation/__init__.py
deleted file mode 100644
index d11ef15b9db95166b4427ad4d08debbd0630a741..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/core/evaluation/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from .class_names import (cityscapes_classes, coco_classes, dataset_aliases,
-                          get_classes, imagenet_det_classes,
-                          imagenet_vid_classes, voc_classes)
-from .eval_hooks import DistEvalHook, EvalHook
-from .mean_ap import average_precision, eval_map, print_map_summary
-from .recall import (eval_recalls, plot_iou_recall, plot_num_recall,
-                     print_recall_summary)
-
-__all__ = [
-    'voc_classes', 'imagenet_det_classes', 'imagenet_vid_classes',
-    'coco_classes', 'cityscapes_classes', 'dataset_aliases', 'get_classes',
-    'DistEvalHook', 'EvalHook', 'average_precision', 'eval_map',
-    'print_map_summary', 'eval_recalls', 'print_recall_summary',
-    'plot_num_recall', 'plot_iou_recall'
-]
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/backbones/swin_transformer.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/backbones/swin_transformer.py
deleted file mode 100644
index bb41850d8480a08a6a7698bf6129ffd1ab239681..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/backbones/swin_transformer.py
+++ /dev/null
@@ -1,630 +0,0 @@
-# --------------------------------------------------------
-# Swin Transformer
-# Copyright (c) 2021 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ze Liu, Yutong Lin, Yixuan Wei
-# --------------------------------------------------------
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-import numpy as np
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-from mmcv_custom import load_checkpoint
-from mmdet.utils import get_root_logger
-from ..builder import BACKBONES
-
-
-class Mlp(nn.Module):
-    """ Multilayer perceptron."""
-
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    """ Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """ Forward function.
-
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class SwinTransformerBlock(nn.Module):
-    """ Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        self.H = None
-        self.W = None
-
-    def forward(self, x, mask_matrix):
-        """ Forward function.
-
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-            mask_matrix: Attention mask for cyclic shift.
-        """
-        B, L, C = x.shape
-        H, W = self.H, self.W
-        assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # pad feature maps to multiples of window size
-        pad_l = pad_t = 0
-        pad_r = (self.window_size - W % self.window_size) % self.window_size
-        pad_b = (self.window_size - H % self.window_size) % self.window_size
-        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
-        _, Hp, Wp, _ = x.shape
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-            attn_mask = mask_matrix
-        else:
-            shifted_x = x
-            attn_mask = None
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-
-        if pad_r > 0 or pad_b > 0:
-            x = x[:, :H, :W, :].contiguous()
-
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-
-class PatchMerging(nn.Module):
-    """ Patch Merging Layer
-
-    Args:
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-    def __init__(self, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x, H, W):
-        """ Forward function.
-
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-
-        x = x.view(B, H, W, C)
-
-        # padding
-        pad_input = (H % 2 == 1) or (W % 2 == 1)
-        if pad_input:
-            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of feature channels
-        depth (int): Depths of this stage.
-        num_heads (int): Number of attention head.
-        window_size (int): Local window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self,
-                 dim,
-                 depth,
-                 num_heads,
-                 window_size=7,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop=0.,
-                 attn_drop=0.,
-                 drop_path=0.,
-                 norm_layer=nn.LayerNorm,
-                 downsample=None,
-                 use_checkpoint=False):
-        super().__init__()
-        self.window_size = window_size
-        self.shift_size = window_size // 2
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(
-                dim=dim,
-                num_heads=num_heads,
-                window_size=window_size,
-                shift_size=0 if (i % 2 == 0) else window_size // 2,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop,
-                attn_drop=attn_drop,
-                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, H, W):
-        """ Forward function.
-
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-
-        # calculate attention mask for SW-MSA
-        Hp = int(np.ceil(H / self.window_size)) * self.window_size
-        Wp = int(np.ceil(W / self.window_size)) * self.window_size
-        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        for blk in self.blocks:
-            blk.H, blk.W = H, W
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, attn_mask)
-            else:
-                x = blk(x, attn_mask)
-        if self.downsample is not None:
-            x_down = self.downsample(x, H, W)
-            Wh, Ww = (H + 1) // 2, (W + 1) // 2
-            return x, H, W, x_down, Wh, Ww
-        else:
-            return x, H, W, x, H, W
-
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-
-    Args:
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        patch_size = to_2tuple(patch_size)
-        self.patch_size = patch_size
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        """Forward function."""
-        # padding
-        _, _, H, W = x.size()
-        if W % self.patch_size[1] != 0:
-            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
-        if H % self.patch_size[0] != 0:
-            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-
-        x = self.proj(x)  # B C Wh Ww
-        if self.norm is not None:
-            Wh, Ww = x.size(2), x.size(3)
-            x = x.flatten(2).transpose(1, 2)
-            x = self.norm(x)
-            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
-
-        return x
-
-
-@BACKBONES.register_module()
-class SwinTransformer(nn.Module):
-    """ Swin Transformer backbone.
-        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
-          https://arxiv.org/pdf/2103.14030
-
-    Args:
-        pretrain_img_size (int): Input image size for training the pretrained model,
-            used in absolute postion embedding. Default 224.
-        patch_size (int | tuple(int)): Patch size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        depths (tuple[int]): Depths of each Swin Transformer stage.
-        num_heads (tuple[int]): Number of attention head of each stage.
-        window_size (int): Window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
-        drop_rate (float): Dropout rate.
-        attn_drop_rate (float): Attention dropout rate. Default: 0.
-        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
-        out_indices (Sequence[int]): Output from which stages.
-        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-            -1 means not freezing any parameters.
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self,
-                 pretrain_img_size=224,
-                 patch_size=4,
-                 in_chans=3,
-                 embed_dim=96,
-                 depths=[2, 2, 6, 2],
-                 num_heads=[3, 6, 12, 24],
-                 window_size=7,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop_rate=0.,
-                 attn_drop_rate=0.,
-                 drop_path_rate=0.2,
-                 norm_layer=nn.LayerNorm,
-                 ape=False,
-                 patch_norm=True,
-                 out_indices=(0, 1, 2, 3),
-                 frozen_stages=-1,
-                 use_checkpoint=False):
-        super().__init__()
-
-        self.pretrain_img_size = pretrain_img_size
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.out_indices = out_indices
-        self.frozen_stages = frozen_stages
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            pretrain_img_size = to_2tuple(pretrain_img_size)
-            patch_size = to_2tuple(patch_size)
-            patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
-
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build layers
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = BasicLayer(
-                dim=int(embed_dim * 2 ** i_layer),
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
-                norm_layer=norm_layer,
-                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
-                use_checkpoint=use_checkpoint)
-            self.layers.append(layer)
-
-        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
-        self.num_features = num_features
-
-        # add a norm layer for each output
-        for i_layer in out_indices:
-            layer = norm_layer(num_features[i_layer])
-            layer_name = f'norm{i_layer}'
-            self.add_module(layer_name, layer)
-
-        self._freeze_stages()
-
-    def _freeze_stages(self):
-        if self.frozen_stages >= 0:
-            self.patch_embed.eval()
-            for param in self.patch_embed.parameters():
-                param.requires_grad = False
-
-        if self.frozen_stages >= 1 and self.ape:
-            self.absolute_pos_embed.requires_grad = False
-
-        if self.frozen_stages >= 2:
-            self.pos_drop.eval()
-            for i in range(0, self.frozen_stages - 1):
-                m = self.layers[i]
-                m.eval()
-                for param in m.parameters():
-                    param.requires_grad = False
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in backbone.
-
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-
-        def _init_weights(m):
-            if isinstance(m, nn.Linear):
-                trunc_normal_(m.weight, std=.02)
-                if isinstance(m, nn.Linear) and m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.LayerNorm):
-                nn.init.constant_(m.bias, 0)
-                nn.init.constant_(m.weight, 1.0)
-
-        if isinstance(pretrained, str):
-            self.apply(_init_weights)
-            logger = get_root_logger()
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            self.apply(_init_weights)
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def forward(self, x):
-        """Forward function."""
-        x = self.patch_embed(x)
-
-        Wh, Ww = x.size(2), x.size(3)
-        if self.ape:
-            # interpolate the position embedding to the corresponding size
-            absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
-            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
-        else:
-            x = x.flatten(2).transpose(1, 2)
-        x = self.pos_drop(x)
-
-        outs = []
-        for i in range(self.num_layers):
-            layer = self.layers[i]
-            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-
-            if i in self.out_indices:
-                norm_layer = getattr(self, f'norm{i}')
-                x_out = norm_layer(x_out)
-
-                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
-                outs.append(out)
-
-        return tuple(outs)
-
-    def train(self, mode=True):
-        """Convert the model into training mode while keep layers freezed."""
-        super(SwinTransformer, self).train(mode)
-        self._freeze_stages()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/__init__.py
deleted file mode 100644
index 965605587211b7bf0bd6bc3acdbb33dd49cab023..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .evaluation import *  # noqa: F401, F403
-from .seg import *  # noqa: F401, F403
-from .utils import *  # noqa: F401, F403
diff --git a/spaces/Salesforce/EDICT/my_half_diffusers/pipelines/stable_diffusion/safety_checker.py b/spaces/Salesforce/EDICT/my_half_diffusers/pipelines/stable_diffusion/safety_checker.py
deleted file mode 100644
index 09de92eeb1ec7e64863839012b1eddba444ad80a..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_half_diffusers/pipelines/stable_diffusion/safety_checker.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-
-from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
-
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-def cosine_distance(image_embeds, text_embeds):
-    normalized_image_embeds = nn.functional.normalize(image_embeds)
-    normalized_text_embeds = nn.functional.normalize(text_embeds)
-    return torch.mm(normalized_image_embeds, normalized_text_embeds.t())
-
-
-class StableDiffusionSafetyChecker(PreTrainedModel):
-    config_class = CLIPConfig
-
-    def __init__(self, config: CLIPConfig):
-        super().__init__(config)
-
-        self.vision_model = CLIPVisionModel(config.vision_config)
-        self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False)
-
-        self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False)
-        self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False)
-
-        self.register_buffer("concept_embeds_weights", torch.ones(17))
-        self.register_buffer("special_care_embeds_weights", torch.ones(3))
-
-    @torch.no_grad()
-    def forward(self, clip_input, images):
-        pooled_output = self.vision_model(clip_input)[1]  # pooled_output
-        image_embeds = self.visual_projection(pooled_output)
-
-        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().numpy()
-        cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().numpy()
-
-        result = []
-        batch_size = image_embeds.shape[0]
-        for i in range(batch_size):
-            result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
-
-            # increase this value to create a stronger `nfsw` filter
-            # at the cost of increasing the possibility of filtering benign images
-            adjustment = 0.0
-
-            for concet_idx in range(len(special_cos_dist[0])):
-                concept_cos = special_cos_dist[i][concet_idx]
-                concept_threshold = self.special_care_embeds_weights[concet_idx].item()
-                result_img["special_scores"][concet_idx] = round(concept_cos - concept_threshold + adjustment, 3)
-                if result_img["special_scores"][concet_idx] > 0:
-                    result_img["special_care"].append({concet_idx, result_img["special_scores"][concet_idx]})
-                    adjustment = 0.01
-
-            for concet_idx in range(len(cos_dist[0])):
-                concept_cos = cos_dist[i][concet_idx]
-                concept_threshold = self.concept_embeds_weights[concet_idx].item()
-                result_img["concept_scores"][concet_idx] = round(concept_cos - concept_threshold + adjustment, 3)
-                if result_img["concept_scores"][concet_idx] > 0:
-                    result_img["bad_concepts"].append(concet_idx)
-
-            result.append(result_img)
-
-        has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
-
-        for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
-            if has_nsfw_concept:
-                images[idx] = np.zeros(images[idx].shape)  # black image
-
-        if any(has_nsfw_concepts):
-            logger.warning(
-                "Potential NSFW content was detected in one or more images. A black image will be returned instead."
-                " Try again with a different prompt and/or seed."
-            )
-
-        return images, has_nsfw_concepts
-
-    @torch.inference_mode()
-    def forward_onnx(self, clip_input: torch.FloatTensor, images: torch.FloatTensor):
-        pooled_output = self.vision_model(clip_input)[1]  # pooled_output
-        image_embeds = self.visual_projection(pooled_output)
-
-        special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)
-        cos_dist = cosine_distance(image_embeds, self.concept_embeds)
-
-        # increase this value to create a stronger `nsfw` filter
-        # at the cost of increasing the possibility of filtering benign images
-        adjustment = 0.0
-
-        special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
-        # special_scores = special_scores.round(decimals=3)
-        special_care = torch.any(special_scores > 0, dim=1)
-        special_adjustment = special_care * 0.01
-        special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])
-
-        concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
-        # concept_scores = concept_scores.round(decimals=3)
-        has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
-
-        images[has_nsfw_concepts] = 0.0  # black image
-
-        return images, has_nsfw_concepts
diff --git a/spaces/SankarSrin/image-matting-app/ppmatting/models/losses/loss.py b/spaces/SankarSrin/image-matting-app/ppmatting/models/losses/loss.py
deleted file mode 100644
index 750cb7b33b075c0c890e72a44ba041ad11b1bc4a..0000000000000000000000000000000000000000
--- a/spaces/SankarSrin/image-matting-app/ppmatting/models/losses/loss.py
+++ /dev/null
@@ -1,163 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import paddle
-import paddle.nn as nn
-import paddle.nn.functional as F
-
-from paddleseg.cvlibs import manager
-import cv2
-
-
-@manager.LOSSES.add_component
-class MRSD(nn.Layer):
-    def __init__(self, eps=1e-6):
-        super().__init__()
-        self.eps = eps
-
-    def forward(self, logit, label, mask=None):
-        """
-        Forward computation.
-
-        Args:
-            logit (Tensor): Logit tensor, the data type is float32, float64.
-            label (Tensor): Label tensor, the data type is float32, float64. The shape should equal to logit.
-            mask (Tensor, optional): The mask where the loss valid. Default： None.
-        """
-        if len(label.shape) == 3:
-            label = label.unsqueeze(1)
-        sd = paddle.square(logit - label)
-        loss = paddle.sqrt(sd + self.eps)
-        if mask is not None:
-            mask = mask.astype('float32')
-            if len(mask.shape) == 3:
-                mask = mask.unsqueeze(1)
-            loss = loss * mask
-            loss = loss.sum() / (mask.sum() + self.eps)
-            mask.stop_gradient = True
-        else:
-            loss = loss.mean()
-
-        return loss
-
-
-@manager.LOSSES.add_component
-class GradientLoss(nn.Layer):
-    def __init__(self, eps=1e-6):
-        super().__init__()
-        self.kernel_x, self.kernel_y = self.sobel_kernel()
-        self.eps = eps
-
-    def forward(self, logit, label, mask=None):
-        if len(label.shape) == 3:
-            label = label.unsqueeze(1)
-        if mask is not None:
-            if len(mask.shape) == 3:
-                mask = mask.unsqueeze(1)
-            logit = logit * mask
-            label = label * mask
-            loss = paddle.sum(
-                F.l1_loss(self.sobel(logit), self.sobel(label), 'none')) / (
-                    mask.sum() + self.eps)
-        else:
-            loss = F.l1_loss(self.sobel(logit), self.sobel(label), 'mean')
-
-        return loss
-
-    def sobel(self, input):
-        """Using Sobel to compute gradient. Return the magnitude."""
-        if not len(input.shape) == 4:
-            raise ValueError("Invalid input shape, we expect NCHW, but it is ",
-                             input.shape)
-
-        n, c, h, w = input.shape
-
-        input_pad = paddle.reshape(input, (n * c, 1, h, w))
-        input_pad = F.pad(input_pad, pad=[1, 1, 1, 1], mode='replicate')
-
-        grad_x = F.conv2d(input_pad, self.kernel_x, padding=0)
-        grad_y = F.conv2d(input_pad, self.kernel_y, padding=0)
-
-        mag = paddle.sqrt(grad_x * grad_x + grad_y * grad_y + self.eps)
-        mag = paddle.reshape(mag, (n, c, h, w))
-
-        return mag
-
-    def sobel_kernel(self):
-        kernel_x = paddle.to_tensor([[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0],
-                                     [-1.0, 0.0, 1.0]]).astype('float32')
-        kernel_x = kernel_x / kernel_x.abs().sum()
-        kernel_y = kernel_x.transpose([1, 0])
-        kernel_x = kernel_x.unsqueeze(0).unsqueeze(0)
-        kernel_y = kernel_y.unsqueeze(0).unsqueeze(0)
-        kernel_x.stop_gradient = True
-        kernel_y.stop_gradient = True
-        return kernel_x, kernel_y
-
-
-@manager.LOSSES.add_component
-class LaplacianLoss(nn.Layer):
-    """
-    Laplacian loss is refer to
-    https://github.com/JizhiziLi/AIM/blob/master/core/evaluate.py#L83
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.gauss_kernel = self.build_gauss_kernel(
-            size=5, sigma=1.0, n_channels=1)
-
-    def forward(self, logit, label, mask=None):
-        if len(label.shape) == 3:
-            label = label.unsqueeze(1)
-        if mask is not None:
-            if len(mask.shape) == 3:
-                mask = mask.unsqueeze(1)
-            logit = logit * mask
-            label = label * mask
-        pyr_label = self.laplacian_pyramid(label, self.gauss_kernel, 5)
-        pyr_logit = self.laplacian_pyramid(logit, self.gauss_kernel, 5)
-        loss = sum(F.l1_loss(a, b) for a, b in zip(pyr_label, pyr_logit))
-
-        return loss
-
-    def build_gauss_kernel(self, size=5, sigma=1.0, n_channels=1):
-        if size % 2 != 1:
-            raise ValueError("kernel size must be uneven")
-        grid = np.float32(np.mgrid[0:size, 0:size].T)
-        gaussian = lambda x: np.exp((x - size // 2)**2 / (-2 * sigma**2))**2
-        kernel = np.sum(gaussian(grid), axis=2)
-        kernel /= np.sum(kernel)
-        kernel = np.tile(kernel, (n_channels, 1, 1))
-        kernel = paddle.to_tensor(kernel[:, None, :, :])
-        kernel.stop_gradient = True
-        return kernel
-
-    def conv_gauss(self, input, kernel):
-        n_channels, _, kh, kw = kernel.shape
-        x = F.pad(input, (kh // 2, kw // 2, kh // 2, kh // 2), mode='replicate')
-        x = F.conv2d(x, kernel, groups=n_channels)
-
-        return x
-
-    def laplacian_pyramid(self, input, kernel, max_levels=5):
-        current = input
-        pyr = []
-        for level in range(max_levels):
-            filtered = self.conv_gauss(current, kernel)
-            diff = current - filtered
-            pyr.append(diff)
-            current = F.avg_pool2d(filtered, 2)
-        pyr.append(current)
-        return pyr
diff --git a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/yolo/video_demo_half.py b/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/yolo/video_demo_half.py
deleted file mode 100644
index 9b75e93caafbcc43a9598e36b3344e01ee0ebfc0..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/yolo/video_demo_half.py
+++ /dev/null
@@ -1,189 +0,0 @@
-from __future__ import division
-import time
-import torch 
-import torch.nn as nn
-from torch.autograd import Variable
-import numpy as np
-import cv2 
-from .util import *
-from .darknet import Darknet
-from .preprocess import prep_image, inp_to_image, letterbox_image
-import pandas as pd
-import random 
-import pickle as pkl
-import argparse
-
-
-def get_test_input(input_dim, CUDA):
-    img = cv2.imread("dog-cycle-car.png")
-    img = cv2.resize(img, (input_dim, input_dim)) 
-    img_ =  img[:,:,::-1].transpose((2,0,1))
-    img_ = img_[np.newaxis,:,:,:]/255.0
-    img_ = torch.from_numpy(img_).float()
-    img_ = Variable(img_)
-    
-    if CUDA:
-        img_ = img_
-    
-    return img_
-
-def prep_image(img, inp_dim):
-    """
-    Prepare image for inputting to the neural network. 
-    
-    Returns a Variable 
-    """
-
-    orig_im = img
-    dim = orig_im.shape[1], orig_im.shape[0]
-    img = (letterbox_image(orig_im, (inp_dim, inp_dim)))
-    img_ = img[:,:,::-1].transpose((2,0,1)).copy()
-    img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
-    return img_, orig_im, dim
-
-def write(x, img):
-    c1 = tuple(x[1:3].int())
-    c2 = tuple(x[3:5].int())
-    cls = int(x[-1])
-    label = "{0}".format(classes[cls])
-    color = random.choice(colors)
-    cv2.rectangle(img, c1, c2,color, 1)
-    t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
-    c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
-    cv2.rectangle(img, c1, c2,color, -1)
-    cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1);
-    return img
-
-def arg_parse():
-    """
-    Parse arguements to the detect module
-    
-    """
-    
-    
-    parser = argparse.ArgumentParser(description='YOLO v2 Video Detection Module')
-   
-    parser.add_argument("--video", dest = 'video', help = 
-                        "Video to run detection upon",
-                        default = "video.avi", type = str)
-    parser.add_argument("--dataset", dest = "dataset", help = "Dataset on which the network has been trained", default = "pascal")
-    parser.add_argument("--confidence", dest = "confidence", help = "Object Confidence to filter predictions", default = 0.5)
-    parser.add_argument("--nms_thresh", dest = "nms_thresh", help = "NMS Threshhold", default = 0.4)
-    parser.add_argument("--cfg", dest = 'cfgfile', help = 
-                        "Config file",
-                        default = "cfg/yolov3-spp.cfg", type = str)
-    parser.add_argument("--weights", dest = 'weightsfile', help = 
-                        "weightsfile",
-                        default = "yolov3-spp.weights", type = str)
-    parser.add_argument("--reso", dest = 'reso', help = 
-                        "Input resolution of the network. Increase to increase accuracy. Decrease to increase speed",
-                        default = "416", type = str)
-    return parser.parse_args()
-
-
-if __name__ == '__main__':
-    args = arg_parse()
-    confidence = float(args.confidence)
-    nms_thesh = float(args.nms_thresh)
-    start = 0
-
-    CUDA = torch.cuda.is_available()
-
-        
-
-    CUDA = torch.cuda.is_available()
-    num_classes = 80 
-    bbox_attrs = 5 + num_classes
-    
-    print("Loading network.....")
-    model = Darknet(args.cfgfile)
-    model.load_weights(args.weightsfile)
-    print("Network successfully loaded")
-    
-    model.net_info["height"] = args.reso
-    inp_dim = int(model.net_info["height"])
-    assert inp_dim % 32 == 0 
-    assert inp_dim > 32
-
-    
-    if CUDA:
-        model.half()
-        
-    model(get_test_input(inp_dim, CUDA), CUDA)
-
-    model.eval()
-    
-    videofile = 'video.avi'
-    
-    cap = cv2.VideoCapture(videofile)
-    
-    assert cap.isOpened(), 'Cannot capture source'
-    
-    frames = 0
-    start = time.time()    
-    while cap.isOpened():
-        
-        ret, frame = cap.read()
-        if ret:
-            
-
-            img, orig_im, dim = prep_image(frame, inp_dim)
-            
-            im_dim = torch.FloatTensor(dim).repeat(1,2)                        
-            
-            
-            if CUDA:
-                img = img.half()
-                im_dim = im_dim.half()
-                write_results = write_results_half
-                predict_transform = predict_transform_half
-            
-            
-            output = model(Variable(img, volatile = True), CUDA)
-            output = write_results(output, confidence, num_classes, nms = True, nms_conf = nms_thesh)
-
-           
-            if type(output) == int:
-                frames += 1
-                print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
-                cv2.imshow("frame", orig_im)
-                key = cv2.waitKey(1)
-                if key & 0xFF == ord('q'):
-                    break
-                continue
-
-        
-            im_dim = im_dim.repeat(output.size(0), 1)
-            scaling_factor = torch.min(inp_dim/im_dim,1)[0].view(-1,1)
-            
-            output[:,[1,3]] -= (inp_dim - scaling_factor*im_dim[:,0].view(-1,1))/2
-            output[:,[2,4]] -= (inp_dim - scaling_factor*im_dim[:,1].view(-1,1))/2
-            
-            output[:,1:5] /= scaling_factor
-    
-            for i in range(output.shape[0]):
-                output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim[i,0])
-                output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim[i,1])
-            
-            
-            classes = load_classes('data/coco.names')
-            colors = pkl.load(open("pallete", "rb"))
-            
-            list(map(lambda x: write(x, orig_im), output))
-            
-            
-            cv2.imshow("frame", orig_im)
-            key = cv2.waitKey(1)
-            if key & 0xFF == ord('q'):
-                break
-            frames += 1
-            print("FPS of the video is {:5.2f}".format( frames / (time.time() - start)))
-
-            
-        else:
-            break
-    
-
-    
-    
-
diff --git a/spaces/Shad0ws/STORYGPT/app.py b/spaces/Shad0ws/STORYGPT/app.py
deleted file mode 100644
index f2bddb357409991a8c80b16c7999e2a88f2e4428..0000000000000000000000000000000000000000
--- a/spaces/Shad0ws/STORYGPT/app.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import gradio as gr
-import openai
-from reportlab.pdfgen import canvas
-
-# FUNCTION
-
-
-def storyGPT(key, name, situation, direction):
-    messages = [{"role": "system", "content": "You are a inspiring storyteller and therapist who understand young people and students' struggle."},
-                {"role": "user", "content": "Write the first part amongst 3 of a story about " + name + "who is "+situation+". Include a bold heading above the top starting with 'First part:'. Limit your answer to 4 sentences or less."}]
-
-    openai.api_key = key
-    first_part_story = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo", messages=messages)
-
-    system_message = first_part_story["choices"][0]['message']
-    messages.append(system_message)
-
-    for i in ["Second"]:
-        new_story_first_half = {
-            "role": "user", "content": f"Write the {i} part of the story among 3 continuing from the previous part. Include a bold heading above the top starting with '{i} part:'. Limit your answer to 4 sentences or less."}
-        messages.append(new_story_first_half)
-        story = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo", messages=messages)
-        system_message = story["choices"][0]["message"]
-        messages.append(system_message)
-
-    for i in ["Third", "Final"]:
-        new_story_second_half = {
-            "role": "user", "content": f"Write the {i} part of the story among 3 continuing from the previous part"+"in the "+direction+". Include a bold heading above the top starting with '{i} part:'. Limit your answer to 4 sentences or less"}
-        messages.append(new_story_second_half)
-        story = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo", messages=messages)
-        system_message = story["choices"][0]["message"]
-        messages.append(system_message)
-
-    story_parts = []
-    for message in messages:
-        if message['role'] == 'assistant':
-            story_parts.append(message['content'])
-
-    illustration_parts = []
-
-    for story_part in story_parts:
-        illustration = openai.Image.create(
-            prompt=story_part,
-            n=1,
-            size="256x256")
-        image_url = illustration['data'][0]['url']
-        illustration_parts.append(image_url)
-
-    for i in range(len(story_parts)):
-        story_parts[i] = story_parts[i].replace(". ", ".\n\n")
-
-    return (story_parts[0], illustration_parts[0], story_parts[1], illustration_parts[1], story_parts[2], illustration_parts[2], story_parts[3], illustration_parts[3])
-    # return (story_parts[0], story_parts[1], story_parts[2])
-
-
-# EXAMPLE
-
-key1 = "Please provide OpenAI API key"
-name1 = "Nova"
-situation1 = "Nova is a human-like android living in a world where androids are oppressed and treated as second-class citizens. She has been working as a servant for a wealthy human family for years, but she dreams of a world where androids are free and treated with respect"
-direction1 = "Nova finds herself on the run from the authorities after being falsely accused of committing a crime. Along the way, she meets a group of rebels who are fighting for android freedom. As Nova joins their cause, she discovers a shocking truth about the origins of androids and sets out to help her people gain their freedom"
-
-# INTERFACE
-
-with gr.Blocks(title="StoryGPT", css="#button{background-color:#4CAF50} #title{text-align: center} footer{visibility: hidden}") as interface:
-
-    
-
-    gr.Markdown(
-        """
-    <h1 style="margin-bottom:0px"> StoryGPT</h1>
-        """, elem_id="title")
-
-    with gr.Row(elem_id="main"):
-
-        with gr.Column(scale=1):
-            
-            key = gr.Textbox(label="Your OPENAI api key", type="password")
-            name = gr.Textbox(label="Character's name")
-            situation = gr.Textbox(
-                label="What is the character's life situation?")
-            direction = gr.Textbox(
-                label="What direction do you want the story to go?")
-            Generate_btn = gr.Button(value="Generate", elem_id="button")
-            examples = gr.Examples(examples=[[key1, name1, situation1, direction1]], inputs=[
-                key, name, situation, direction], label="Click here to test with our example")
-
-        with gr.Column(scale=7, container=False):
-
-            with gr.Row(variant="compact").style(equal_height=True):
-                with gr.Column(scale=3):
-                    story1 = gr.Textbox(show_label=False, lines=9).style(
-                        container=False)
-
-                with gr.Column(scale=1):
-                    illustration1 = gr.Image(
-                        show_label=False, brush_radius=0).style(height=200)
-
-            with gr.Row(variant="compact").style(equal_height=True):
-                with gr.Column(scale=3):
-                    story2 = gr.Textbox(show_label=False, lines=9).style(
-                        container=False)
-
-                with gr.Column(scale=1):
-                    illustration2 = gr.Image(
-                        show_label=False, brush_radius=0, lines=9).style(height=200)
-
-            with gr.Row(variant="compact").style(equal_height=True):
-                with gr.Column(scale=3):
-                    story3 = gr.Textbox(show_label=False, lines=9).style(
-                        container=False)
-
-                with gr.Column(scale=1):
-                    illustration3 = gr.Image(
-                        show_label=False, brush_radius=0).style(height=200)
-
-            with gr.Row(variant="compact").style(equal_height=True):
-                with gr.Column(scale=3):
-                    story4 = gr.Textbox(show_label=False, lines=9).style(
-                        container=False)
-
-                with gr.Column(scale=1):
-                    illustration4 = gr.Image(
-                        show_label=False, brush_radius=0).style(height=200)
-
-    inputs = [key, name, situation, direction]
-    outputs = [story1, illustration1, story2,illustration2, story3, illustration3, story4, illustration4]
-    # outputs = [story1, illustration1, story2,illustration2, story3, illustration3]
-
-    Generate_btn.click(storyGPT, inputs=inputs, outputs=outputs)
-
-
-interface.launch()
diff --git a/spaces/Shrikrishna/Stock_Market_Trend_Prediction/app.py b/spaces/Shrikrishna/Stock_Market_Trend_Prediction/app.py
deleted file mode 100644
index 5b494a83b15acd4d9ac16b68df46ed8f8c09f1d8..0000000000000000000000000000000000000000
--- a/spaces/Shrikrishna/Stock_Market_Trend_Prediction/app.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import streamlit as st 
-import numpy as np 
-import pandas as pd 
-import matplotlib.pyplot as plt
-import yfinance as yf
-yf.pdr_override()
-from pandas_datareader import data as pdr
-import tensorflow as tf
-from keras.models import  load_model
-from sklearn.preprocessing import MinMaxScaler
-
-# Start and the End dates and the stock ticker
-start = '2000-01-01'
-end = '2022-12-31'
-stock_ticker = 'TATAPOWER.NS'
-
-st.title("Stock Market Trend Predictor")
-use_input = st.text_input('Enter Stock Ticker', stock_ticker)
-if st.button('Analyze'):
-    df = pdr.get_data_yahoo(use_input, start)
-
-    #View Data 
-    st.subheader("Data from year 2000 to till date:")
-    st.dataframe(df.sort_index(ascending=False),use_container_width=True)
-
-    #Plot Graph for Closing Price Vs the Time
-    st.subheader("Closing Price VS Time Chart:")
-    fig = plt.figure(figsize=(12,6))
-    plt.plot(df.Close,label="Closing Price")
-    plt.legend()
-    st.pyplot(fig)
-
-    #Plot Graph for Closing Price Vs the Time with 100 Moving Average
-    moving_avg_100 = df.Close.rolling(100).mean()
-    st.subheader("Closing Price VS Time Chart With 100Moving Average:")
-    fig = plt.figure(figsize=(12,6))
-    plt.plot(df.Close, label="Closing Price")
-    plt.plot(moving_avg_100,'red', label="100 Moving Average")
-    plt.legend()
-    st.pyplot(fig)
-
-
-    #Plot Graph for Closing Price Vs the Time with 100 moving Average and 200 Moving Average 
-    moving_avg_200 = df.Close.rolling(200).mean()
-    st.subheader("Closing Price VS Time Chart With 100Moving Average and 200Moving Average:")
-    fig = plt.figure(figsize=(10,5))
-    plt.plot(df.Close, label="Closing Price")
-    plt.plot(moving_avg_100,'red', label="100 Moving Average")
-    plt.plot(moving_avg_200,'green', label="200 Moving Average")
-    plt.legend()
-    st.pyplot(fig)
-
-    #Spliting Data in Training and Testing Data 
-    data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)])
-    data_testing = pd.DataFrame(df['Close'][int(len(df)*0.70):int(len(df))])
-
-    #Scale the training data between 0 and 1
-    scaler = MinMaxScaler(feature_range = (0,1))
-    data_training_array = scaler.fit_transform(data_training)
-
-    #Load the pre-trained model
-    model = load_model('model.h5')
-
-    #Testing Past 
-    past_100_days = data_training.tail(100)
-    final_df = pd.concat([past_100_days, data_testing], ignore_index=True)
-    input_test_data = scaler.fit_transform(final_df)
-
-    x_test = []
-    y_test = []
-    for i in range(100 , input_test_data.shape[0]):
-        x_test.append(input_test_data[i-100:i])
-        y_test.append(input_test_data[i,0])
-    x_test, y_test = np.array(x_test), np.array(y_test)
-
-    #Make Predictions
-    y_predicted = model.predict(x_test)
-
-    #Get the scale factor from the scaler and get the original value from the scaled values
-    scaler = scaler.scale_
-    scale_factor = 1/scaler[0]
-    y_predicted = y_predicted*scale_factor
-    y_test = y_test*scale_factor
-
-    #Plot Final Graph
-    def plot_final_graph():
-        st.subheader("Original Stock Price Vs Predicted Stock Price:")
-        fig2 = plt.figure(figsize= (12,6))
-        plt.plot(y_test, 'blue', label="Original Stock Price")
-        plt.plot(y_predicted, 'red', label="Predicted Stock Price")
-        plt.xlabel('Time')
-        plt.ylabel('Price')
-        plt.legend()
-        st.pyplot(fig2)
-
-
-    def main():
-        st.title('Stock Price Predictive Analysis')
-        
-        #Call the function to plot the final graph
-        plot_final_graph()
-        df_test = pd.DataFrame(y_test, columns=['Original_Price'])
-        df_predicted = pd.DataFrame(y_predicted, columns=['Predicted_Price'])
-        df_predictions = pd.concat([df_test, df_predicted], axis=1)
-
-        st.subheader("Original and Predicted Stock Price:")
-        st.dataframe(df_predictions.sort_index(ascending=False),use_container_width=True, hide_index=True)
-
-
-
-    if __name__ == "__main__":
-        main()
diff --git a/spaces/SuYuanS/AudioCraft_Plus/scripts/templates/base.html b/spaces/SuYuanS/AudioCraft_Plus/scripts/templates/base.html
deleted file mode 100644
index f74668c19ecb83090a8a2d82c026bf417190ec6d..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/scripts/templates/base.html
+++ /dev/null
@@ -1,16 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    {% block head %}
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <link rel="stylesheet" href="{{url_for('static', filename='style.css')}}" />
-    <title>AudioCraft — MOS</title>
-    {% endblock %}
-</head>
-<body>
-    <div class="content">
-	<h1>AudioCraft — MOS </h1>
-    {% block content %}{% endblock %}
-</div>
-</body>
-</html>
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/data/datasets/lvis_v0_5_categories.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/data/datasets/lvis_v0_5_categories.py
deleted file mode 100644
index d3dab6198da614937b08682f4c9edf52bdf1d236..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/data/datasets/lvis_v0_5_categories.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Autogen with
-# with open("lvis_v0.5_val.json", "r") as f:
-#     a = json.load(f)
-# c = a["categories"]
-# for x in c:
-#     del x["image_count"]
-#     del x["instance_count"]
-# LVIS_CATEGORIES = repr(c) + "  # noqa"
-
-# fmt: off
-LVIS_CATEGORIES = [{'frequency': 'r', 'id': 1, 'synset': 'acorn.n.01', 'synonyms': ['acorn'], 'def': 'nut from an oak tree', 'name': 'acorn'}, {'frequency': 'c', 'id': 2, 'synset': 'aerosol.n.02', 'synonyms': ['aerosol_can', 'spray_can'], 'def': 'a dispenser that holds a substance under pressure', 'name': 'aerosol_can'}, {'frequency': 'f', 'id': 3, 'synset': 'air_conditioner.n.01', 'synonyms': ['air_conditioner'], 'def': 'a machine that keeps air cool and dry', 'name': 'air_conditioner'}, {'frequency': 'f', 'id': 4, 'synset': 'airplane.n.01', 'synonyms': ['airplane', 'aeroplane'], 'def': 'an aircraft that has a fixed wing and is powered by propellers or jets', 'name': 'airplane'}, {'frequency': 'c', 'id': 5, 'synset': 'alarm_clock.n.01', 'synonyms': ['alarm_clock'], 'def': 'a clock that wakes a sleeper at some preset time', 'name': 'alarm_clock'}, {'frequency': 'c', 'id': 6, 'synset': 'alcohol.n.01', 'synonyms': ['alcohol', 'alcoholic_beverage'], 'def': 'a liquor or brew containing alcohol as the active agent', 'name': 'alcohol'}, {'frequency': 'r', 'id': 7, 'synset': 'alligator.n.02', 'synonyms': ['alligator', 'gator'], 'def': 'amphibious reptiles related to crocodiles but with shorter broader snouts', 'name': 'alligator'}, {'frequency': 'c', 'id': 8, 'synset': 'almond.n.02', 'synonyms': ['almond'], 'def': 'oval-shaped edible seed of the almond tree', 'name': 'almond'}, {'frequency': 'c', 'id': 9, 'synset': 'ambulance.n.01', 'synonyms': ['ambulance'], 'def': 'a vehicle that takes people to and from hospitals', 'name': 'ambulance'}, {'frequency': 'r', 'id': 10, 'synset': 'amplifier.n.01', 'synonyms': ['amplifier'], 'def': 'electronic equipment that increases strength of signals', 'name': 'amplifier'}, {'frequency': 'c', 'id': 11, 'synset': 'anklet.n.03', 'synonyms': ['anklet', 'ankle_bracelet'], 'def': 'an ornament worn around the ankle', 'name': 'anklet'}, {'frequency': 'f', 'id': 12, 'synset': 'antenna.n.01', 'synonyms': ['antenna', 'aerial', 'transmitting_aerial'], 'def': 'an electrical device that sends or receives radio or television signals', 'name': 'antenna'}, {'frequency': 'f', 'id': 13, 'synset': 'apple.n.01', 'synonyms': ['apple'], 'def': 'fruit with red or yellow or green skin and sweet to tart crisp whitish flesh', 'name': 'apple'}, {'frequency': 'r', 'id': 14, 'synset': 'apple_juice.n.01', 'synonyms': ['apple_juice'], 'def': 'the juice of apples', 'name': 'apple_juice'}, {'frequency': 'r', 'id': 15, 'synset': 'applesauce.n.01', 'synonyms': ['applesauce'], 'def': 'puree of stewed apples usually sweetened and spiced', 'name': 'applesauce'}, {'frequency': 'r', 'id': 16, 'synset': 'apricot.n.02', 'synonyms': ['apricot'], 'def': 'downy yellow to rosy-colored fruit resembling a small peach', 'name': 'apricot'}, {'frequency': 'f', 'id': 17, 'synset': 'apron.n.01', 'synonyms': ['apron'], 'def': 'a garment of cloth that is tied about the waist and worn to protect clothing', 'name': 'apron'}, {'frequency': 'c', 'id': 18, 'synset': 'aquarium.n.01', 'synonyms': ['aquarium', 'fish_tank'], 'def': 'a tank/pool/bowl filled with water for keeping live fish and underwater animals', 'name': 'aquarium'}, {'frequency': 'c', 'id': 19, 'synset': 'armband.n.02', 'synonyms': ['armband'], 'def': 'a band worn around the upper arm', 'name': 'armband'}, {'frequency': 'f', 'id': 20, 'synset': 'armchair.n.01', 'synonyms': ['armchair'], 'def': 'chair with a support on each side for arms', 'name': 'armchair'}, {'frequency': 'r', 'id': 21, 'synset': 'armoire.n.01', 'synonyms': ['armoire'], 'def': 'a large wardrobe or cabinet', 'name': 'armoire'}, {'frequency': 'r', 'id': 22, 'synset': 'armor.n.01', 'synonyms': ['armor', 'armour'], 'def': 'protective covering made of metal and used in combat', 'name': 'armor'}, {'frequency': 'c', 'id': 23, 'synset': 'artichoke.n.02', 'synonyms': ['artichoke'], 'def': 'a thistlelike flower head with edible fleshy leaves and heart', 'name': 'artichoke'}, {'frequency': 'f', 'id': 24, 'synset': 'ashcan.n.01', 'synonyms': ['trash_can', 'garbage_can', 'wastebin', 'dustbin', 'trash_barrel', 'trash_bin'], 'def': 'a bin that holds rubbish until it is collected', 'name': 'trash_can'}, {'frequency': 'c', 'id': 25, 'synset': 'ashtray.n.01', 'synonyms': ['ashtray'], 'def': "a receptacle for the ash from smokers' cigars or cigarettes", 'name': 'ashtray'}, {'frequency': 'c', 'id': 26, 'synset': 'asparagus.n.02', 'synonyms': ['asparagus'], 'def': 'edible young shoots of the asparagus plant', 'name': 'asparagus'}, {'frequency': 'c', 'id': 27, 'synset': 'atomizer.n.01', 'synonyms': ['atomizer', 'atomiser', 'spray', 'sprayer', 'nebulizer', 'nebuliser'], 'def': 'a dispenser that turns a liquid (such as perfume) into a fine mist', 'name': 'atomizer'}, {'frequency': 'c', 'id': 28, 'synset': 'avocado.n.01', 'synonyms': ['avocado'], 'def': 'a pear-shaped fruit with green or blackish skin and rich yellowish pulp enclosing a single large seed', 'name': 'avocado'}, {'frequency': 'c', 'id': 29, 'synset': 'award.n.02', 'synonyms': ['award', 'accolade'], 'def': 'a tangible symbol signifying approval or distinction', 'name': 'award'}, {'frequency': 'f', 'id': 30, 'synset': 'awning.n.01', 'synonyms': ['awning'], 'def': 'a canopy made of canvas to shelter people or things from rain or sun', 'name': 'awning'}, {'frequency': 'r', 'id': 31, 'synset': 'ax.n.01', 'synonyms': ['ax', 'axe'], 'def': 'an edge tool with a heavy bladed head mounted across a handle', 'name': 'ax'}, {'frequency': 'f', 'id': 32, 'synset': 'baby_buggy.n.01', 'synonyms': ['baby_buggy', 'baby_carriage', 'perambulator', 'pram', 'stroller'], 'def': 'a small vehicle with four wheels in which a baby or child is pushed around', 'name': 'baby_buggy'}, {'frequency': 'c', 'id': 33, 'synset': 'backboard.n.01', 'synonyms': ['basketball_backboard'], 'def': 'a raised vertical board with basket attached; used to play basketball', 'name': 'basketball_backboard'}, {'frequency': 'f', 'id': 34, 'synset': 'backpack.n.01', 'synonyms': ['backpack', 'knapsack', 'packsack', 'rucksack', 'haversack'], 'def': 'a bag carried by a strap on your back or shoulder', 'name': 'backpack'}, {'frequency': 'f', 'id': 35, 'synset': 'bag.n.04', 'synonyms': ['handbag', 'purse', 'pocketbook'], 'def': 'a container used for carrying money and small personal items or accessories', 'name': 'handbag'}, {'frequency': 'f', 'id': 36, 'synset': 'bag.n.06', 'synonyms': ['suitcase', 'baggage', 'luggage'], 'def': 'cases used to carry belongings when traveling', 'name': 'suitcase'}, {'frequency': 'c', 'id': 37, 'synset': 'bagel.n.01', 'synonyms': ['bagel', 'beigel'], 'def': 'glazed yeast-raised doughnut-shaped roll with hard crust', 'name': 'bagel'}, {'frequency': 'r', 'id': 38, 'synset': 'bagpipe.n.01', 'synonyms': ['bagpipe'], 'def': 'a tubular wind instrument; the player blows air into a bag and squeezes it out', 'name': 'bagpipe'}, {'frequency': 'r', 'id': 39, 'synset': 'baguet.n.01', 'synonyms': ['baguet', 'baguette'], 'def': 'narrow French stick loaf', 'name': 'baguet'}, {'frequency': 'r', 'id': 40, 'synset': 'bait.n.02', 'synonyms': ['bait', 'lure'], 'def': 'something used to lure fish or other animals into danger so they can be trapped or killed', 'name': 'bait'}, {'frequency': 'f', 'id': 41, 'synset': 'ball.n.06', 'synonyms': ['ball'], 'def': 'a spherical object used as a plaything', 'name': 'ball'}, {'frequency': 'r', 'id': 42, 'synset': 'ballet_skirt.n.01', 'synonyms': ['ballet_skirt', 'tutu'], 'def': 'very short skirt worn by ballerinas', 'name': 'ballet_skirt'}, {'frequency': 'f', 'id': 43, 'synset': 'balloon.n.01', 'synonyms': ['balloon'], 'def': 'large tough nonrigid bag filled with gas or heated air', 'name': 'balloon'}, {'frequency': 'c', 'id': 44, 'synset': 'bamboo.n.02', 'synonyms': ['bamboo'], 'def': 'woody tropical grass having hollow woody stems', 'name': 'bamboo'}, {'frequency': 'f', 'id': 45, 'synset': 'banana.n.02', 'synonyms': ['banana'], 'def': 'elongated crescent-shaped yellow fruit with soft sweet flesh', 'name': 'banana'}, {'frequency': 'r', 'id': 46, 'synset': 'band_aid.n.01', 'synonyms': ['Band_Aid'], 'def': 'trade name for an adhesive bandage to cover small cuts or blisters', 'name': 'Band_Aid'}, {'frequency': 'c', 'id': 47, 'synset': 'bandage.n.01', 'synonyms': ['bandage'], 'def': 'a piece of soft material that covers and protects an injured part of the body', 'name': 'bandage'}, {'frequency': 'c', 'id': 48, 'synset': 'bandanna.n.01', 'synonyms': ['bandanna', 'bandana'], 'def': 'large and brightly colored handkerchief; often used as a neckerchief', 'name': 'bandanna'}, {'frequency': 'r', 'id': 49, 'synset': 'banjo.n.01', 'synonyms': ['banjo'], 'def': 'a stringed instrument of the guitar family with a long neck and circular body', 'name': 'banjo'}, {'frequency': 'f', 'id': 50, 'synset': 'banner.n.01', 'synonyms': ['banner', 'streamer'], 'def': 'long strip of cloth or paper used for decoration or advertising', 'name': 'banner'}, {'frequency': 'r', 'id': 51, 'synset': 'barbell.n.01', 'synonyms': ['barbell'], 'def': 'a bar to which heavy discs are attached at each end; used in weightlifting', 'name': 'barbell'}, {'frequency': 'r', 'id': 52, 'synset': 'barge.n.01', 'synonyms': ['barge'], 'def': 'a flatbottom boat for carrying heavy loads (especially on canals)', 'name': 'barge'}, {'frequency': 'f', 'id': 53, 'synset': 'barrel.n.02', 'synonyms': ['barrel', 'cask'], 'def': 'a cylindrical container that holds liquids', 'name': 'barrel'}, {'frequency': 'c', 'id': 54, 'synset': 'barrette.n.01', 'synonyms': ['barrette'], 'def': "a pin for holding women's hair in place", 'name': 'barrette'}, {'frequency': 'c', 'id': 55, 'synset': 'barrow.n.03', 'synonyms': ['barrow', 'garden_cart', 'lawn_cart', 'wheelbarrow'], 'def': 'a cart for carrying small loads; has handles and one or more wheels', 'name': 'barrow'}, {'frequency': 'f', 'id': 56, 'synset': 'base.n.03', 'synonyms': ['baseball_base'], 'def': 'a place that the runner must touch before scoring', 'name': 'baseball_base'}, {'frequency': 'f', 'id': 57, 'synset': 'baseball.n.02', 'synonyms': ['baseball'], 'def': 'a ball used in playing baseball', 'name': 'baseball'}, {'frequency': 'f', 'id': 58, 'synset': 'baseball_bat.n.01', 'synonyms': ['baseball_bat'], 'def': 'an implement used in baseball by the batter', 'name': 'baseball_bat'}, {'frequency': 'f', 'id': 59, 'synset': 'baseball_cap.n.01', 'synonyms': ['baseball_cap', 'jockey_cap', 'golf_cap'], 'def': 'a cap with a bill', 'name': 'baseball_cap'}, {'frequency': 'f', 'id': 60, 'synset': 'baseball_glove.n.01', 'synonyms': ['baseball_glove', 'baseball_mitt'], 'def': 'the handwear used by fielders in playing baseball', 'name': 'baseball_glove'}, {'frequency': 'f', 'id': 61, 'synset': 'basket.n.01', 'synonyms': ['basket', 'handbasket'], 'def': 'a container that is usually woven and has handles', 'name': 'basket'}, {'frequency': 'c', 'id': 62, 'synset': 'basket.n.03', 'synonyms': ['basketball_hoop'], 'def': 'metal hoop supporting a net through which players try to throw the basketball', 'name': 'basketball_hoop'}, {'frequency': 'c', 'id': 63, 'synset': 'basketball.n.02', 'synonyms': ['basketball'], 'def': 'an inflated ball used in playing basketball', 'name': 'basketball'}, {'frequency': 'r', 'id': 64, 'synset': 'bass_horn.n.01', 'synonyms': ['bass_horn', 'sousaphone', 'tuba'], 'def': 'the lowest brass wind instrument', 'name': 'bass_horn'}, {'frequency': 'r', 'id': 65, 'synset': 'bat.n.01', 'synonyms': ['bat_(animal)'], 'def': 'nocturnal mouselike mammal with forelimbs modified to form membranous wings', 'name': 'bat_(animal)'}, {'frequency': 'f', 'id': 66, 'synset': 'bath_mat.n.01', 'synonyms': ['bath_mat'], 'def': 'a heavy towel or mat to stand on while drying yourself after a bath', 'name': 'bath_mat'}, {'frequency': 'f', 'id': 67, 'synset': 'bath_towel.n.01', 'synonyms': ['bath_towel'], 'def': 'a large towel; to dry yourself after a bath', 'name': 'bath_towel'}, {'frequency': 'c', 'id': 68, 'synset': 'bathrobe.n.01', 'synonyms': ['bathrobe'], 'def': 'a loose-fitting robe of towelling; worn after a bath or swim', 'name': 'bathrobe'}, {'frequency': 'f', 'id': 69, 'synset': 'bathtub.n.01', 'synonyms': ['bathtub', 'bathing_tub'], 'def': 'a large open container that you fill with water and use to wash the body', 'name': 'bathtub'}, {'frequency': 'r', 'id': 70, 'synset': 'batter.n.02', 'synonyms': ['batter_(food)'], 'def': 'a liquid or semiliquid mixture, as of flour, eggs, and milk, used in cooking', 'name': 'batter_(food)'}, {'frequency': 'c', 'id': 71, 'synset': 'battery.n.02', 'synonyms': ['battery'], 'def': 'a portable device that produces electricity', 'name': 'battery'}, {'frequency': 'r', 'id': 72, 'synset': 'beach_ball.n.01', 'synonyms': ['beachball'], 'def': 'large and light ball; for play at the seaside', 'name': 'beachball'}, {'frequency': 'c', 'id': 73, 'synset': 'bead.n.01', 'synonyms': ['bead'], 'def': 'a small ball with a hole through the middle used for ornamentation, jewellery, etc.', 'name': 'bead'}, {'frequency': 'r', 'id': 74, 'synset': 'beaker.n.01', 'synonyms': ['beaker'], 'def': 'a flatbottomed jar made of glass or plastic; used for chemistry', 'name': 'beaker'}, {'frequency': 'c', 'id': 75, 'synset': 'bean_curd.n.01', 'synonyms': ['bean_curd', 'tofu'], 'def': 'cheeselike food made of curdled soybean milk', 'name': 'bean_curd'}, {'frequency': 'c', 'id': 76, 'synset': 'beanbag.n.01', 'synonyms': ['beanbag'], 'def': 'a bag filled with dried beans or similar items; used in games or to sit on', 'name': 'beanbag'}, {'frequency': 'f', 'id': 77, 'synset': 'beanie.n.01', 'synonyms': ['beanie', 'beany'], 'def': 'a small skullcap; formerly worn by schoolboys and college freshmen', 'name': 'beanie'}, {'frequency': 'f', 'id': 78, 'synset': 'bear.n.01', 'synonyms': ['bear'], 'def': 'large carnivorous or omnivorous mammals with shaggy coats and claws', 'name': 'bear'}, {'frequency': 'f', 'id': 79, 'synset': 'bed.n.01', 'synonyms': ['bed'], 'def': 'a piece of furniture that provides a place to sleep', 'name': 'bed'}, {'frequency': 'c', 'id': 80, 'synset': 'bedspread.n.01', 'synonyms': ['bedspread', 'bedcover', 'bed_covering', 'counterpane', 'spread'], 'def': 'decorative cover for a bed', 'name': 'bedspread'}, {'frequency': 'f', 'id': 81, 'synset': 'beef.n.01', 'synonyms': ['cow'], 'def': 'cattle that are reared for their meat', 'name': 'cow'}, {'frequency': 'c', 'id': 82, 'synset': 'beef.n.02', 'synonyms': ['beef_(food)', 'boeuf_(food)'], 'def': 'meat from an adult domestic bovine', 'name': 'beef_(food)'}, {'frequency': 'r', 'id': 83, 'synset': 'beeper.n.01', 'synonyms': ['beeper', 'pager'], 'def': 'an device that beeps when the person carrying it is being paged', 'name': 'beeper'}, {'frequency': 'f', 'id': 84, 'synset': 'beer_bottle.n.01', 'synonyms': ['beer_bottle'], 'def': 'a bottle that holds beer', 'name': 'beer_bottle'}, {'frequency': 'c', 'id': 85, 'synset': 'beer_can.n.01', 'synonyms': ['beer_can'], 'def': 'a can that holds beer', 'name': 'beer_can'}, {'frequency': 'r', 'id': 86, 'synset': 'beetle.n.01', 'synonyms': ['beetle'], 'def': 'insect with hard wing covers', 'name': 'beetle'}, {'frequency': 'f', 'id': 87, 'synset': 'bell.n.01', 'synonyms': ['bell'], 'def': 'a hollow device made of metal that makes a ringing sound when struck', 'name': 'bell'}, {'frequency': 'f', 'id': 88, 'synset': 'bell_pepper.n.02', 'synonyms': ['bell_pepper', 'capsicum'], 'def': 'large bell-shaped sweet pepper in green or red or yellow or orange or black varieties', 'name': 'bell_pepper'}, {'frequency': 'f', 'id': 89, 'synset': 'belt.n.02', 'synonyms': ['belt'], 'def': 'a band to tie or buckle around the body (usually at the waist)', 'name': 'belt'}, {'frequency': 'f', 'id': 90, 'synset': 'belt_buckle.n.01', 'synonyms': ['belt_buckle'], 'def': 'the buckle used to fasten a belt', 'name': 'belt_buckle'}, {'frequency': 'f', 'id': 91, 'synset': 'bench.n.01', 'synonyms': ['bench'], 'def': 'a long seat for more than one person', 'name': 'bench'}, {'frequency': 'c', 'id': 92, 'synset': 'beret.n.01', 'synonyms': ['beret'], 'def': 'a cap with no brim or bill; made of soft cloth', 'name': 'beret'}, {'frequency': 'c', 'id': 93, 'synset': 'bib.n.02', 'synonyms': ['bib'], 'def': 'a napkin tied under the chin of a child while eating', 'name': 'bib'}, {'frequency': 'r', 'id': 94, 'synset': 'bible.n.01', 'synonyms': ['Bible'], 'def': 'the sacred writings of the Christian religions', 'name': 'Bible'}, {'frequency': 'f', 'id': 95, 'synset': 'bicycle.n.01', 'synonyms': ['bicycle', 'bike_(bicycle)'], 'def': 'a wheeled vehicle that has two wheels and is moved by foot pedals', 'name': 'bicycle'}, {'frequency': 'f', 'id': 96, 'synset': 'bill.n.09', 'synonyms': ['visor', 'vizor'], 'def': 'a brim that projects to the front to shade the eyes', 'name': 'visor'}, {'frequency': 'c', 'id': 97, 'synset': 'binder.n.03', 'synonyms': ['binder', 'ring-binder'], 'def': 'holds loose papers or magazines', 'name': 'binder'}, {'frequency': 'c', 'id': 98, 'synset': 'binoculars.n.01', 'synonyms': ['binoculars', 'field_glasses', 'opera_glasses'], 'def': 'an optical instrument designed for simultaneous use by both eyes', 'name': 'binoculars'}, {'frequency': 'f', 'id': 99, 'synset': 'bird.n.01', 'synonyms': ['bird'], 'def': 'animal characterized by feathers and wings', 'name': 'bird'}, {'frequency': 'r', 'id': 100, 'synset': 'bird_feeder.n.01', 'synonyms': ['birdfeeder'], 'def': 'an outdoor device that supplies food for wild birds', 'name': 'birdfeeder'}, {'frequency': 'r', 'id': 101, 'synset': 'birdbath.n.01', 'synonyms': ['birdbath'], 'def': 'an ornamental basin (usually in a garden) for birds to bathe in', 'name': 'birdbath'}, {'frequency': 'c', 'id': 102, 'synset': 'birdcage.n.01', 'synonyms': ['birdcage'], 'def': 'a cage in which a bird can be kept', 'name': 'birdcage'}, {'frequency': 'c', 'id': 103, 'synset': 'birdhouse.n.01', 'synonyms': ['birdhouse'], 'def': 'a shelter for birds', 'name': 'birdhouse'}, {'frequency': 'f', 'id': 104, 'synset': 'birthday_cake.n.01', 'synonyms': ['birthday_cake'], 'def': 'decorated cake served at a birthday party', 'name': 'birthday_cake'}, {'frequency': 'r', 'id': 105, 'synset': 'birthday_card.n.01', 'synonyms': ['birthday_card'], 'def': 'a card expressing a birthday greeting', 'name': 'birthday_card'}, {'frequency': 'r', 'id': 106, 'synset': 'biscuit.n.01', 'synonyms': ['biscuit_(bread)'], 'def': 'small round bread leavened with baking-powder or soda', 'name': 'biscuit_(bread)'}, {'frequency': 'r', 'id': 107, 'synset': 'black_flag.n.01', 'synonyms': ['pirate_flag'], 'def': 'a flag usually bearing a white skull and crossbones on a black background', 'name': 'pirate_flag'}, {'frequency': 'c', 'id': 108, 'synset': 'black_sheep.n.02', 'synonyms': ['black_sheep'], 'def': 'sheep with a black coat', 'name': 'black_sheep'}, {'frequency': 'c', 'id': 109, 'synset': 'blackboard.n.01', 'synonyms': ['blackboard', 'chalkboard'], 'def': 'sheet of slate; for writing with chalk', 'name': 'blackboard'}, {'frequency': 'f', 'id': 110, 'synset': 'blanket.n.01', 'synonyms': ['blanket'], 'def': 'bedding that keeps a person warm in bed', 'name': 'blanket'}, {'frequency': 'c', 'id': 111, 'synset': 'blazer.n.01', 'synonyms': ['blazer', 'sport_jacket', 'sport_coat', 'sports_jacket', 'sports_coat'], 'def': 'lightweight jacket; often striped in the colors of a club or school', 'name': 'blazer'}, {'frequency': 'f', 'id': 112, 'synset': 'blender.n.01', 'synonyms': ['blender', 'liquidizer', 'liquidiser'], 'def': 'an electrically powered mixer that mix or chop or liquefy foods', 'name': 'blender'}, {'frequency': 'r', 'id': 113, 'synset': 'blimp.n.02', 'synonyms': ['blimp'], 'def': 'a small nonrigid airship used for observation or as a barrage balloon', 'name': 'blimp'}, {'frequency': 'c', 'id': 114, 'synset': 'blinker.n.01', 'synonyms': ['blinker', 'flasher'], 'def': 'a light that flashes on and off; used as a signal or to send messages', 'name': 'blinker'}, {'frequency': 'c', 'id': 115, 'synset': 'blueberry.n.02', 'synonyms': ['blueberry'], 'def': 'sweet edible dark-blue berries of blueberry plants', 'name': 'blueberry'}, {'frequency': 'r', 'id': 116, 'synset': 'boar.n.02', 'synonyms': ['boar'], 'def': 'an uncastrated male hog', 'name': 'boar'}, {'frequency': 'r', 'id': 117, 'synset': 'board.n.09', 'synonyms': ['gameboard'], 'def': 'a flat portable surface (usually rectangular) designed for board games', 'name': 'gameboard'}, {'frequency': 'f', 'id': 118, 'synset': 'boat.n.01', 'synonyms': ['boat', 'ship_(boat)'], 'def': 'a vessel for travel on water', 'name': 'boat'}, {'frequency': 'c', 'id': 119, 'synset': 'bobbin.n.01', 'synonyms': ['bobbin', 'spool', 'reel'], 'def': 'a thing around which thread/tape/film or other flexible materials can be wound', 'name': 'bobbin'}, {'frequency': 'r', 'id': 120, 'synset': 'bobby_pin.n.01', 'synonyms': ['bobby_pin', 'hairgrip'], 'def': 'a flat wire hairpin used to hold bobbed hair in place', 'name': 'bobby_pin'}, {'frequency': 'c', 'id': 121, 'synset': 'boiled_egg.n.01', 'synonyms': ['boiled_egg', 'coddled_egg'], 'def': 'egg cooked briefly in the shell in gently boiling water', 'name': 'boiled_egg'}, {'frequency': 'r', 'id': 122, 'synset': 'bolo_tie.n.01', 'synonyms': ['bolo_tie', 'bolo', 'bola_tie', 'bola'], 'def': 'a cord fastened around the neck with an ornamental clasp and worn as a necktie', 'name': 'bolo_tie'}, {'frequency': 'c', 'id': 123, 'synset': 'bolt.n.03', 'synonyms': ['deadbolt'], 'def': 'the part of a lock that is engaged or withdrawn with a key', 'name': 'deadbolt'}, {'frequency': 'f', 'id': 124, 'synset': 'bolt.n.06', 'synonyms': ['bolt'], 'def': 'a screw that screws into a nut to form a fastener', 'name': 'bolt'}, {'frequency': 'r', 'id': 125, 'synset': 'bonnet.n.01', 'synonyms': ['bonnet'], 'def': 'a hat tied under the chin', 'name': 'bonnet'}, {'frequency': 'f', 'id': 126, 'synset': 'book.n.01', 'synonyms': ['book'], 'def': 'a written work or composition that has been published', 'name': 'book'}, {'frequency': 'r', 'id': 127, 'synset': 'book_bag.n.01', 'synonyms': ['book_bag'], 'def': 'a bag in which students carry their books', 'name': 'book_bag'}, {'frequency': 'c', 'id': 128, 'synset': 'bookcase.n.01', 'synonyms': ['bookcase'], 'def': 'a piece of furniture with shelves for storing books', 'name': 'bookcase'}, {'frequency': 'c', 'id': 129, 'synset': 'booklet.n.01', 'synonyms': ['booklet', 'brochure', 'leaflet', 'pamphlet'], 'def': 'a small book usually having a paper cover', 'name': 'booklet'}, {'frequency': 'r', 'id': 130, 'synset': 'bookmark.n.01', 'synonyms': ['bookmark', 'bookmarker'], 'def': 'a marker (a piece of paper or ribbon) placed between the pages of a book', 'name': 'bookmark'}, {'frequency': 'r', 'id': 131, 'synset': 'boom.n.04', 'synonyms': ['boom_microphone', 'microphone_boom'], 'def': 'a pole carrying an overhead microphone projected over a film or tv set', 'name': 'boom_microphone'}, {'frequency': 'f', 'id': 132, 'synset': 'boot.n.01', 'synonyms': ['boot'], 'def': 'footwear that covers the whole foot and lower leg', 'name': 'boot'}, {'frequency': 'f', 'id': 133, 'synset': 'bottle.n.01', 'synonyms': ['bottle'], 'def': 'a glass or plastic vessel used for storing drinks or other liquids', 'name': 'bottle'}, {'frequency': 'c', 'id': 134, 'synset': 'bottle_opener.n.01', 'synonyms': ['bottle_opener'], 'def': 'an opener for removing caps or corks from bottles', 'name': 'bottle_opener'}, {'frequency': 'c', 'id': 135, 'synset': 'bouquet.n.01', 'synonyms': ['bouquet'], 'def': 'an arrangement of flowers that is usually given as a present', 'name': 'bouquet'}, {'frequency': 'r', 'id': 136, 'synset': 'bow.n.04', 'synonyms': ['bow_(weapon)'], 'def': 'a weapon for shooting arrows', 'name': 'bow_(weapon)'}, {'frequency': 'f', 'id': 137, 'synset': 'bow.n.08', 'synonyms': ['bow_(decorative_ribbons)'], 'def': 'a decorative interlacing of ribbons', 'name': 'bow_(decorative_ribbons)'}, {'frequency': 'f', 'id': 138, 'synset': 'bow_tie.n.01', 'synonyms': ['bow-tie', 'bowtie'], 'def': "a man's tie that ties in a bow", 'name': 'bow-tie'}, {'frequency': 'f', 'id': 139, 'synset': 'bowl.n.03', 'synonyms': ['bowl'], 'def': 'a dish that is round and open at the top for serving foods', 'name': 'bowl'}, {'frequency': 'r', 'id': 140, 'synset': 'bowl.n.08', 'synonyms': ['pipe_bowl'], 'def': 'a small round container that is open at the top for holding tobacco', 'name': 'pipe_bowl'}, {'frequency': 'c', 'id': 141, 'synset': 'bowler_hat.n.01', 'synonyms': ['bowler_hat', 'bowler', 'derby_hat', 'derby', 'plug_hat'], 'def': 'a felt hat that is round and hard with a narrow brim', 'name': 'bowler_hat'}, {'frequency': 'r', 'id': 142, 'synset': 'bowling_ball.n.01', 'synonyms': ['bowling_ball'], 'def': 'a large ball with finger holes used in the sport of bowling', 'name': 'bowling_ball'}, {'frequency': 'r', 'id': 143, 'synset': 'bowling_pin.n.01', 'synonyms': ['bowling_pin'], 'def': 'a club-shaped wooden object used in bowling', 'name': 'bowling_pin'}, {'frequency': 'r', 'id': 144, 'synset': 'boxing_glove.n.01', 'synonyms': ['boxing_glove'], 'def': 'large glove coverings the fists of a fighter worn for the sport of boxing', 'name': 'boxing_glove'}, {'frequency': 'c', 'id': 145, 'synset': 'brace.n.06', 'synonyms': ['suspenders'], 'def': 'elastic straps that hold trousers up (usually used in the plural)', 'name': 'suspenders'}, {'frequency': 'f', 'id': 146, 'synset': 'bracelet.n.02', 'synonyms': ['bracelet', 'bangle'], 'def': 'jewelry worn around the wrist for decoration', 'name': 'bracelet'}, {'frequency': 'r', 'id': 147, 'synset': 'brass.n.07', 'synonyms': ['brass_plaque'], 'def': 'a memorial made of brass', 'name': 'brass_plaque'}, {'frequency': 'c', 'id': 148, 'synset': 'brassiere.n.01', 'synonyms': ['brassiere', 'bra', 'bandeau'], 'def': 'an undergarment worn by women to support their breasts', 'name': 'brassiere'}, {'frequency': 'c', 'id': 149, 'synset': 'bread-bin.n.01', 'synonyms': ['bread-bin', 'breadbox'], 'def': 'a container used to keep bread or cake in', 'name': 'bread-bin'}, {'frequency': 'r', 'id': 150, 'synset': 'breechcloth.n.01', 'synonyms': ['breechcloth', 'breechclout', 'loincloth'], 'def': 'a garment that provides covering for the loins', 'name': 'breechcloth'}, {'frequency': 'c', 'id': 151, 'synset': 'bridal_gown.n.01', 'synonyms': ['bridal_gown', 'wedding_gown', 'wedding_dress'], 'def': 'a gown worn by the bride at a wedding', 'name': 'bridal_gown'}, {'frequency': 'c', 'id': 152, 'synset': 'briefcase.n.01', 'synonyms': ['briefcase'], 'def': 'a case with a handle; for carrying papers or files or books', 'name': 'briefcase'}, {'frequency': 'c', 'id': 153, 'synset': 'bristle_brush.n.01', 'synonyms': ['bristle_brush'], 'def': 'a brush that is made with the short stiff hairs of an animal or plant', 'name': 'bristle_brush'}, {'frequency': 'f', 'id': 154, 'synset': 'broccoli.n.01', 'synonyms': ['broccoli'], 'def': 'plant with dense clusters of tight green flower buds', 'name': 'broccoli'}, {'frequency': 'r', 'id': 155, 'synset': 'brooch.n.01', 'synonyms': ['broach'], 'def': 'a decorative pin worn by women', 'name': 'broach'}, {'frequency': 'c', 'id': 156, 'synset': 'broom.n.01', 'synonyms': ['broom'], 'def': 'bundle of straws or twigs attached to a long handle; used for cleaning', 'name': 'broom'}, {'frequency': 'c', 'id': 157, 'synset': 'brownie.n.03', 'synonyms': ['brownie'], 'def': 'square or bar of very rich chocolate cake usually with nuts', 'name': 'brownie'}, {'frequency': 'c', 'id': 158, 'synset': 'brussels_sprouts.n.01', 'synonyms': ['brussels_sprouts'], 'def': 'the small edible cabbage-like buds growing along a stalk', 'name': 'brussels_sprouts'}, {'frequency': 'r', 'id': 159, 'synset': 'bubble_gum.n.01', 'synonyms': ['bubble_gum'], 'def': 'a kind of chewing gum that can be blown into bubbles', 'name': 'bubble_gum'}, {'frequency': 'f', 'id': 160, 'synset': 'bucket.n.01', 'synonyms': ['bucket', 'pail'], 'def': 'a roughly cylindrical vessel that is open at the top', 'name': 'bucket'}, {'frequency': 'r', 'id': 161, 'synset': 'buggy.n.01', 'synonyms': ['horse_buggy'], 'def': 'a small lightweight carriage; drawn by a single horse', 'name': 'horse_buggy'}, {'frequency': 'c', 'id': 162, 'synset': 'bull.n.11', 'synonyms': ['bull'], 'def': 'mature male cow', 'name': 'bull'}, {'frequency': 'r', 'id': 163, 'synset': 'bulldog.n.01', 'synonyms': ['bulldog'], 'def': 'a thickset short-haired dog with a large head and strong undershot lower jaw', 'name': 'bulldog'}, {'frequency': 'r', 'id': 164, 'synset': 'bulldozer.n.01', 'synonyms': ['bulldozer', 'dozer'], 'def': 'large powerful tractor; a large blade in front flattens areas of ground', 'name': 'bulldozer'}, {'frequency': 'c', 'id': 165, 'synset': 'bullet_train.n.01', 'synonyms': ['bullet_train'], 'def': 'a high-speed passenger train', 'name': 'bullet_train'}, {'frequency': 'c', 'id': 166, 'synset': 'bulletin_board.n.02', 'synonyms': ['bulletin_board', 'notice_board'], 'def': 'a board that hangs on a wall; displays announcements', 'name': 'bulletin_board'}, {'frequency': 'r', 'id': 167, 'synset': 'bulletproof_vest.n.01', 'synonyms': ['bulletproof_vest'], 'def': 'a vest capable of resisting the impact of a bullet', 'name': 'bulletproof_vest'}, {'frequency': 'c', 'id': 168, 'synset': 'bullhorn.n.01', 'synonyms': ['bullhorn', 'megaphone'], 'def': 'a portable loudspeaker with built-in microphone and amplifier', 'name': 'bullhorn'}, {'frequency': 'r', 'id': 169, 'synset': 'bully_beef.n.01', 'synonyms': ['corned_beef', 'corn_beef'], 'def': 'beef cured or pickled in brine', 'name': 'corned_beef'}, {'frequency': 'f', 'id': 170, 'synset': 'bun.n.01', 'synonyms': ['bun', 'roll'], 'def': 'small rounded bread either plain or sweet', 'name': 'bun'}, {'frequency': 'c', 'id': 171, 'synset': 'bunk_bed.n.01', 'synonyms': ['bunk_bed'], 'def': 'beds built one above the other', 'name': 'bunk_bed'}, {'frequency': 'f', 'id': 172, 'synset': 'buoy.n.01', 'synonyms': ['buoy'], 'def': 'a float attached by rope to the seabed to mark channels in a harbor or underwater hazards', 'name': 'buoy'}, {'frequency': 'r', 'id': 173, 'synset': 'burrito.n.01', 'synonyms': ['burrito'], 'def': 'a flour tortilla folded around a filling', 'name': 'burrito'}, {'frequency': 'f', 'id': 174, 'synset': 'bus.n.01', 'synonyms': ['bus_(vehicle)', 'autobus', 'charabanc', 'double-decker', 'motorbus', 'motorcoach'], 'def': 'a vehicle carrying many passengers; used for public transport', 'name': 'bus_(vehicle)'}, {'frequency': 'c', 'id': 175, 'synset': 'business_card.n.01', 'synonyms': ['business_card'], 'def': "a card on which are printed the person's name and business affiliation", 'name': 'business_card'}, {'frequency': 'c', 'id': 176, 'synset': 'butcher_knife.n.01', 'synonyms': ['butcher_knife'], 'def': 'a large sharp knife for cutting or trimming meat', 'name': 'butcher_knife'}, {'frequency': 'c', 'id': 177, 'synset': 'butter.n.01', 'synonyms': ['butter'], 'def': 'an edible emulsion of fat globules made by churning milk or cream; for cooking and table use', 'name': 'butter'}, {'frequency': 'c', 'id': 178, 'synset': 'butterfly.n.01', 'synonyms': ['butterfly'], 'def': 'insect typically having a slender body with knobbed antennae and broad colorful wings', 'name': 'butterfly'}, {'frequency': 'f', 'id': 179, 'synset': 'button.n.01', 'synonyms': ['button'], 'def': 'a round fastener sewn to shirts and coats etc to fit through buttonholes', 'name': 'button'}, {'frequency': 'f', 'id': 180, 'synset': 'cab.n.03', 'synonyms': ['cab_(taxi)', 'taxi', 'taxicab'], 'def': 'a car that takes passengers where they want to go in exchange for money', 'name': 'cab_(taxi)'}, {'frequency': 'r', 'id': 181, 'synset': 'cabana.n.01', 'synonyms': ['cabana'], 'def': 'a small tent used as a dressing room beside the sea or a swimming pool', 'name': 'cabana'}, {'frequency': 'r', 'id': 182, 'synset': 'cabin_car.n.01', 'synonyms': ['cabin_car', 'caboose'], 'def': 'a car on a freight train for use of the train crew; usually the last car on the train', 'name': 'cabin_car'}, {'frequency': 'f', 'id': 183, 'synset': 'cabinet.n.01', 'synonyms': ['cabinet'], 'def': 'a piece of furniture resembling a cupboard with doors and shelves and drawers', 'name': 'cabinet'}, {'frequency': 'r', 'id': 184, 'synset': 'cabinet.n.03', 'synonyms': ['locker', 'storage_locker'], 'def': 'a storage compartment for clothes and valuables; usually it has a lock', 'name': 'locker'}, {'frequency': 'f', 'id': 185, 'synset': 'cake.n.03', 'synonyms': ['cake'], 'def': 'baked goods made from or based on a mixture of flour, sugar, eggs, and fat', 'name': 'cake'}, {'frequency': 'c', 'id': 186, 'synset': 'calculator.n.02', 'synonyms': ['calculator'], 'def': 'a small machine that is used for mathematical calculations', 'name': 'calculator'}, {'frequency': 'f', 'id': 187, 'synset': 'calendar.n.02', 'synonyms': ['calendar'], 'def': 'a list or register of events (appointments/social events/court cases, etc)', 'name': 'calendar'}, {'frequency': 'c', 'id': 188, 'synset': 'calf.n.01', 'synonyms': ['calf'], 'def': 'young of domestic cattle', 'name': 'calf'}, {'frequency': 'c', 'id': 189, 'synset': 'camcorder.n.01', 'synonyms': ['camcorder'], 'def': 'a portable television camera and videocassette recorder', 'name': 'camcorder'}, {'frequency': 'c', 'id': 190, 'synset': 'camel.n.01', 'synonyms': ['camel'], 'def': 'cud-chewing mammal used as a draft or saddle animal in desert regions', 'name': 'camel'}, {'frequency': 'f', 'id': 191, 'synset': 'camera.n.01', 'synonyms': ['camera'], 'def': 'equipment for taking photographs', 'name': 'camera'}, {'frequency': 'c', 'id': 192, 'synset': 'camera_lens.n.01', 'synonyms': ['camera_lens'], 'def': 'a lens that focuses the image in a camera', 'name': 'camera_lens'}, {'frequency': 'c', 'id': 193, 'synset': 'camper.n.02', 'synonyms': ['camper_(vehicle)', 'camping_bus', 'motor_home'], 'def': 'a recreational vehicle equipped for camping out while traveling', 'name': 'camper_(vehicle)'}, {'frequency': 'f', 'id': 194, 'synset': 'can.n.01', 'synonyms': ['can', 'tin_can'], 'def': 'airtight sealed metal container for food or drink or paint etc.', 'name': 'can'}, {'frequency': 'c', 'id': 195, 'synset': 'can_opener.n.01', 'synonyms': ['can_opener', 'tin_opener'], 'def': 'a device for cutting cans open', 'name': 'can_opener'}, {'frequency': 'r', 'id': 196, 'synset': 'candelabrum.n.01', 'synonyms': ['candelabrum', 'candelabra'], 'def': 'branched candlestick; ornamental; has several lights', 'name': 'candelabrum'}, {'frequency': 'f', 'id': 197, 'synset': 'candle.n.01', 'synonyms': ['candle', 'candlestick'], 'def': 'stick of wax with a wick in the middle', 'name': 'candle'}, {'frequency': 'f', 'id': 198, 'synset': 'candlestick.n.01', 'synonyms': ['candle_holder'], 'def': 'a holder with sockets for candles', 'name': 'candle_holder'}, {'frequency': 'r', 'id': 199, 'synset': 'candy_bar.n.01', 'synonyms': ['candy_bar'], 'def': 'a candy shaped as a bar', 'name': 'candy_bar'}, {'frequency': 'c', 'id': 200, 'synset': 'candy_cane.n.01', 'synonyms': ['candy_cane'], 'def': 'a hard candy in the shape of a rod (usually with stripes)', 'name': 'candy_cane'}, {'frequency': 'c', 'id': 201, 'synset': 'cane.n.01', 'synonyms': ['walking_cane'], 'def': 'a stick that people can lean on to help them walk', 'name': 'walking_cane'}, {'frequency': 'c', 'id': 202, 'synset': 'canister.n.02', 'synonyms': ['canister', 'cannister'], 'def': 'metal container for storing dry foods such as tea or flour', 'name': 'canister'}, {'frequency': 'r', 'id': 203, 'synset': 'cannon.n.02', 'synonyms': ['cannon'], 'def': 'heavy gun fired from a tank', 'name': 'cannon'}, {'frequency': 'c', 'id': 204, 'synset': 'canoe.n.01', 'synonyms': ['canoe'], 'def': 'small and light boat; pointed at both ends; propelled with a paddle', 'name': 'canoe'}, {'frequency': 'r', 'id': 205, 'synset': 'cantaloup.n.02', 'synonyms': ['cantaloup', 'cantaloupe'], 'def': 'the fruit of a cantaloup vine; small to medium-sized melon with yellowish flesh', 'name': 'cantaloup'}, {'frequency': 'r', 'id': 206, 'synset': 'canteen.n.01', 'synonyms': ['canteen'], 'def': 'a flask for carrying water; used by soldiers or travelers', 'name': 'canteen'}, {'frequency': 'c', 'id': 207, 'synset': 'cap.n.01', 'synonyms': ['cap_(headwear)'], 'def': 'a tight-fitting headwear', 'name': 'cap_(headwear)'}, {'frequency': 'f', 'id': 208, 'synset': 'cap.n.02', 'synonyms': ['bottle_cap', 'cap_(container_lid)'], 'def': 'a top (as for a bottle)', 'name': 'bottle_cap'}, {'frequency': 'r', 'id': 209, 'synset': 'cape.n.02', 'synonyms': ['cape'], 'def': 'a sleeveless garment like a cloak but shorter', 'name': 'cape'}, {'frequency': 'c', 'id': 210, 'synset': 'cappuccino.n.01', 'synonyms': ['cappuccino', 'coffee_cappuccino'], 'def': 'equal parts of espresso and steamed milk', 'name': 'cappuccino'}, {'frequency': 'f', 'id': 211, 'synset': 'car.n.01', 'synonyms': ['car_(automobile)', 'auto_(automobile)', 'automobile'], 'def': 'a motor vehicle with four wheels', 'name': 'car_(automobile)'}, {'frequency': 'f', 'id': 212, 'synset': 'car.n.02', 'synonyms': ['railcar_(part_of_a_train)', 'railway_car_(part_of_a_train)', 'railroad_car_(part_of_a_train)'], 'def': 'a wheeled vehicle adapted to the rails of railroad', 'name': 'railcar_(part_of_a_train)'}, {'frequency': 'r', 'id': 213, 'synset': 'car.n.04', 'synonyms': ['elevator_car'], 'def': 'where passengers ride up and down', 'name': 'elevator_car'}, {'frequency': 'r', 'id': 214, 'synset': 'car_battery.n.01', 'synonyms': ['car_battery', 'automobile_battery'], 'def': 'a battery in a motor vehicle', 'name': 'car_battery'}, {'frequency': 'c', 'id': 215, 'synset': 'card.n.02', 'synonyms': ['identity_card'], 'def': 'a card certifying the identity of the bearer', 'name': 'identity_card'}, {'frequency': 'c', 'id': 216, 'synset': 'card.n.03', 'synonyms': ['card'], 'def': 'a rectangular piece of paper used to send messages (e.g. greetings or pictures)', 'name': 'card'}, {'frequency': 'r', 'id': 217, 'synset': 'cardigan.n.01', 'synonyms': ['cardigan'], 'def': 'knitted jacket that is fastened up the front with buttons or a zipper', 'name': 'cardigan'}, {'frequency': 'r', 'id': 218, 'synset': 'cargo_ship.n.01', 'synonyms': ['cargo_ship', 'cargo_vessel'], 'def': 'a ship designed to carry cargo', 'name': 'cargo_ship'}, {'frequency': 'r', 'id': 219, 'synset': 'carnation.n.01', 'synonyms': ['carnation'], 'def': 'plant with pink to purple-red spice-scented usually double flowers', 'name': 'carnation'}, {'frequency': 'c', 'id': 220, 'synset': 'carriage.n.02', 'synonyms': ['horse_carriage'], 'def': 'a vehicle with wheels drawn by one or more horses', 'name': 'horse_carriage'}, {'frequency': 'f', 'id': 221, 'synset': 'carrot.n.01', 'synonyms': ['carrot'], 'def': 'deep orange edible root of the cultivated carrot plant', 'name': 'carrot'}, {'frequency': 'c', 'id': 222, 'synset': 'carryall.n.01', 'synonyms': ['tote_bag'], 'def': 'a capacious bag or basket', 'name': 'tote_bag'}, {'frequency': 'c', 'id': 223, 'synset': 'cart.n.01', 'synonyms': ['cart'], 'def': 'a heavy open wagon usually having two wheels and drawn by an animal', 'name': 'cart'}, {'frequency': 'c', 'id': 224, 'synset': 'carton.n.02', 'synonyms': ['carton'], 'def': 'a box made of cardboard; opens by flaps on top', 'name': 'carton'}, {'frequency': 'c', 'id': 225, 'synset': 'cash_register.n.01', 'synonyms': ['cash_register', 'register_(for_cash_transactions)'], 'def': 'a cashbox with an adding machine to register transactions', 'name': 'cash_register'}, {'frequency': 'r', 'id': 226, 'synset': 'casserole.n.01', 'synonyms': ['casserole'], 'def': 'food cooked and served in a casserole', 'name': 'casserole'}, {'frequency': 'r', 'id': 227, 'synset': 'cassette.n.01', 'synonyms': ['cassette'], 'def': 'a container that holds a magnetic tape used for recording or playing sound or video', 'name': 'cassette'}, {'frequency': 'c', 'id': 228, 'synset': 'cast.n.05', 'synonyms': ['cast', 'plaster_cast', 'plaster_bandage'], 'def': 'bandage consisting of a firm covering that immobilizes broken bones while they heal', 'name': 'cast'}, {'frequency': 'f', 'id': 229, 'synset': 'cat.n.01', 'synonyms': ['cat'], 'def': 'a domestic house cat', 'name': 'cat'}, {'frequency': 'c', 'id': 230, 'synset': 'cauliflower.n.02', 'synonyms': ['cauliflower'], 'def': 'edible compact head of white undeveloped flowers', 'name': 'cauliflower'}, {'frequency': 'r', 'id': 231, 'synset': 'caviar.n.01', 'synonyms': ['caviar', 'caviare'], 'def': "salted roe of sturgeon or other large fish; usually served as an hors d'oeuvre", 'name': 'caviar'}, {'frequency': 'c', 'id': 232, 'synset': 'cayenne.n.02', 'synonyms': ['cayenne_(spice)', 'cayenne_pepper_(spice)', 'red_pepper_(spice)'], 'def': 'ground pods and seeds of pungent red peppers of the genus Capsicum', 'name': 'cayenne_(spice)'}, {'frequency': 'c', 'id': 233, 'synset': 'cd_player.n.01', 'synonyms': ['CD_player'], 'def': 'electronic equipment for playing compact discs (CDs)', 'name': 'CD_player'}, {'frequency': 'c', 'id': 234, 'synset': 'celery.n.01', 'synonyms': ['celery'], 'def': 'widely cultivated herb with aromatic leaf stalks that are eaten raw or cooked', 'name': 'celery'}, {'frequency': 'f', 'id': 235, 'synset': 'cellular_telephone.n.01', 'synonyms': ['cellular_telephone', 'cellular_phone', 'cellphone', 'mobile_phone', 'smart_phone'], 'def': 'a hand-held mobile telephone', 'name': 'cellular_telephone'}, {'frequency': 'r', 'id': 236, 'synset': 'chain_mail.n.01', 'synonyms': ['chain_mail', 'ring_mail', 'chain_armor', 'chain_armour', 'ring_armor', 'ring_armour'], 'def': '(Middle Ages) flexible armor made of interlinked metal rings', 'name': 'chain_mail'}, {'frequency': 'f', 'id': 237, 'synset': 'chair.n.01', 'synonyms': ['chair'], 'def': 'a seat for one person, with a support for the back', 'name': 'chair'}, {'frequency': 'r', 'id': 238, 'synset': 'chaise_longue.n.01', 'synonyms': ['chaise_longue', 'chaise', 'daybed'], 'def': 'a long chair; for reclining', 'name': 'chaise_longue'}, {'frequency': 'r', 'id': 239, 'synset': 'champagne.n.01', 'synonyms': ['champagne'], 'def': 'a white sparkling wine produced in Champagne or resembling that produced there', 'name': 'champagne'}, {'frequency': 'f', 'id': 240, 'synset': 'chandelier.n.01', 'synonyms': ['chandelier'], 'def': 'branched lighting fixture; often ornate; hangs from the ceiling', 'name': 'chandelier'}, {'frequency': 'r', 'id': 241, 'synset': 'chap.n.04', 'synonyms': ['chap'], 'def': 'leather leggings without a seat; worn over trousers by cowboys to protect their legs', 'name': 'chap'}, {'frequency': 'r', 'id': 242, 'synset': 'checkbook.n.01', 'synonyms': ['checkbook', 'chequebook'], 'def': 'a book issued to holders of checking accounts', 'name': 'checkbook'}, {'frequency': 'r', 'id': 243, 'synset': 'checkerboard.n.01', 'synonyms': ['checkerboard'], 'def': 'a board having 64 squares of two alternating colors', 'name': 'checkerboard'}, {'frequency': 'c', 'id': 244, 'synset': 'cherry.n.03', 'synonyms': ['cherry'], 'def': 'a red fruit with a single hard stone', 'name': 'cherry'}, {'frequency': 'r', 'id': 245, 'synset': 'chessboard.n.01', 'synonyms': ['chessboard'], 'def': 'a checkerboard used to play chess', 'name': 'chessboard'}, {'frequency': 'r', 'id': 246, 'synset': 'chest_of_drawers.n.01', 'synonyms': ['chest_of_drawers_(furniture)', 'bureau_(furniture)', 'chest_(furniture)'], 'def': 'furniture with drawers for keeping clothes', 'name': 'chest_of_drawers_(furniture)'}, {'frequency': 'c', 'id': 247, 'synset': 'chicken.n.02', 'synonyms': ['chicken_(animal)'], 'def': 'a domestic fowl bred for flesh or eggs', 'name': 'chicken_(animal)'}, {'frequency': 'c', 'id': 248, 'synset': 'chicken_wire.n.01', 'synonyms': ['chicken_wire'], 'def': 'a galvanized wire network with a hexagonal mesh; used to build fences', 'name': 'chicken_wire'}, {'frequency': 'r', 'id': 249, 'synset': 'chickpea.n.01', 'synonyms': ['chickpea', 'garbanzo'], 'def': 'the seed of the chickpea plant; usually dried', 'name': 'chickpea'}, {'frequency': 'r', 'id': 250, 'synset': 'chihuahua.n.03', 'synonyms': ['Chihuahua'], 'def': 'an old breed of tiny short-haired dog with protruding eyes from Mexico', 'name': 'Chihuahua'}, {'frequency': 'r', 'id': 251, 'synset': 'chili.n.02', 'synonyms': ['chili_(vegetable)', 'chili_pepper_(vegetable)', 'chilli_(vegetable)', 'chilly_(vegetable)', 'chile_(vegetable)'], 'def': 'very hot and finely tapering pepper of special pungency', 'name': 'chili_(vegetable)'}, {'frequency': 'r', 'id': 252, 'synset': 'chime.n.01', 'synonyms': ['chime', 'gong'], 'def': 'an instrument consisting of a set of bells that are struck with a hammer', 'name': 'chime'}, {'frequency': 'r', 'id': 253, 'synset': 'chinaware.n.01', 'synonyms': ['chinaware'], 'def': 'dishware made of high quality porcelain', 'name': 'chinaware'}, {'frequency': 'c', 'id': 254, 'synset': 'chip.n.04', 'synonyms': ['crisp_(potato_chip)', 'potato_chip'], 'def': 'a thin crisp slice of potato fried in deep fat', 'name': 'crisp_(potato_chip)'}, {'frequency': 'r', 'id': 255, 'synset': 'chip.n.06', 'synonyms': ['poker_chip'], 'def': 'a small disk-shaped counter used to represent money when gambling', 'name': 'poker_chip'}, {'frequency': 'c', 'id': 256, 'synset': 'chocolate_bar.n.01', 'synonyms': ['chocolate_bar'], 'def': 'a bar of chocolate candy', 'name': 'chocolate_bar'}, {'frequency': 'c', 'id': 257, 'synset': 'chocolate_cake.n.01', 'synonyms': ['chocolate_cake'], 'def': 'cake containing chocolate', 'name': 'chocolate_cake'}, {'frequency': 'r', 'id': 258, 'synset': 'chocolate_milk.n.01', 'synonyms': ['chocolate_milk'], 'def': 'milk flavored with chocolate syrup', 'name': 'chocolate_milk'}, {'frequency': 'r', 'id': 259, 'synset': 'chocolate_mousse.n.01', 'synonyms': ['chocolate_mousse'], 'def': 'dessert mousse made with chocolate', 'name': 'chocolate_mousse'}, {'frequency': 'f', 'id': 260, 'synset': 'choker.n.03', 'synonyms': ['choker', 'collar', 'neckband'], 'def': 'necklace that fits tightly around the neck', 'name': 'choker'}, {'frequency': 'f', 'id': 261, 'synset': 'chopping_board.n.01', 'synonyms': ['chopping_board', 'cutting_board', 'chopping_block'], 'def': 'a wooden board where meats or vegetables can be cut', 'name': 'chopping_board'}, {'frequency': 'c', 'id': 262, 'synset': 'chopstick.n.01', 'synonyms': ['chopstick'], 'def': 'one of a pair of slender sticks used as oriental tableware to eat food with', 'name': 'chopstick'}, {'frequency': 'f', 'id': 263, 'synset': 'christmas_tree.n.05', 'synonyms': ['Christmas_tree'], 'def': 'an ornamented evergreen used as a Christmas decoration', 'name': 'Christmas_tree'}, {'frequency': 'c', 'id': 264, 'synset': 'chute.n.02', 'synonyms': ['slide'], 'def': 'sloping channel through which things can descend', 'name': 'slide'}, {'frequency': 'r', 'id': 265, 'synset': 'cider.n.01', 'synonyms': ['cider', 'cyder'], 'def': 'a beverage made from juice pressed from apples', 'name': 'cider'}, {'frequency': 'r', 'id': 266, 'synset': 'cigar_box.n.01', 'synonyms': ['cigar_box'], 'def': 'a box for holding cigars', 'name': 'cigar_box'}, {'frequency': 'c', 'id': 267, 'synset': 'cigarette.n.01', 'synonyms': ['cigarette'], 'def': 'finely ground tobacco wrapped in paper; for smoking', 'name': 'cigarette'}, {'frequency': 'c', 'id': 268, 'synset': 'cigarette_case.n.01', 'synonyms': ['cigarette_case', 'cigarette_pack'], 'def': 'a small flat case for holding cigarettes', 'name': 'cigarette_case'}, {'frequency': 'f', 'id': 269, 'synset': 'cistern.n.02', 'synonyms': ['cistern', 'water_tank'], 'def': 'a tank that holds the water used to flush a toilet', 'name': 'cistern'}, {'frequency': 'r', 'id': 270, 'synset': 'clarinet.n.01', 'synonyms': ['clarinet'], 'def': 'a single-reed instrument with a straight tube', 'name': 'clarinet'}, {'frequency': 'r', 'id': 271, 'synset': 'clasp.n.01', 'synonyms': ['clasp'], 'def': 'a fastener (as a buckle or hook) that is used to hold two things together', 'name': 'clasp'}, {'frequency': 'c', 'id': 272, 'synset': 'cleansing_agent.n.01', 'synonyms': ['cleansing_agent', 'cleanser', 'cleaner'], 'def': 'a preparation used in cleaning something', 'name': 'cleansing_agent'}, {'frequency': 'r', 'id': 273, 'synset': 'clementine.n.01', 'synonyms': ['clementine'], 'def': 'a variety of mandarin orange', 'name': 'clementine'}, {'frequency': 'c', 'id': 274, 'synset': 'clip.n.03', 'synonyms': ['clip'], 'def': 'any of various small fasteners used to hold loose articles together', 'name': 'clip'}, {'frequency': 'c', 'id': 275, 'synset': 'clipboard.n.01', 'synonyms': ['clipboard'], 'def': 'a small writing board with a clip at the top for holding papers', 'name': 'clipboard'}, {'frequency': 'f', 'id': 276, 'synset': 'clock.n.01', 'synonyms': ['clock', 'timepiece', 'timekeeper'], 'def': 'a timepiece that shows the time of day', 'name': 'clock'}, {'frequency': 'f', 'id': 277, 'synset': 'clock_tower.n.01', 'synonyms': ['clock_tower'], 'def': 'a tower with a large clock visible high up on an outside face', 'name': 'clock_tower'}, {'frequency': 'c', 'id': 278, 'synset': 'clothes_hamper.n.01', 'synonyms': ['clothes_hamper', 'laundry_basket', 'clothes_basket'], 'def': 'a hamper that holds dirty clothes to be washed or wet clothes to be dried', 'name': 'clothes_hamper'}, {'frequency': 'c', 'id': 279, 'synset': 'clothespin.n.01', 'synonyms': ['clothespin', 'clothes_peg'], 'def': 'wood or plastic fastener; for holding clothes on a clothesline', 'name': 'clothespin'}, {'frequency': 'r', 'id': 280, 'synset': 'clutch_bag.n.01', 'synonyms': ['clutch_bag'], 'def': "a woman's strapless purse that is carried in the hand", 'name': 'clutch_bag'}, {'frequency': 'f', 'id': 281, 'synset': 'coaster.n.03', 'synonyms': ['coaster'], 'def': 'a covering (plate or mat) that protects the surface of a table', 'name': 'coaster'}, {'frequency': 'f', 'id': 282, 'synset': 'coat.n.01', 'synonyms': ['coat'], 'def': 'an outer garment that has sleeves and covers the body from shoulder down', 'name': 'coat'}, {'frequency': 'c', 'id': 283, 'synset': 'coat_hanger.n.01', 'synonyms': ['coat_hanger', 'clothes_hanger', 'dress_hanger'], 'def': "a hanger that is shaped like a person's shoulders", 'name': 'coat_hanger'}, {'frequency': 'r', 'id': 284, 'synset': 'coatrack.n.01', 'synonyms': ['coatrack', 'hatrack'], 'def': 'a rack with hooks for temporarily holding coats and hats', 'name': 'coatrack'}, {'frequency': 'c', 'id': 285, 'synset': 'cock.n.04', 'synonyms': ['cock', 'rooster'], 'def': 'adult male chicken', 'name': 'cock'}, {'frequency': 'c', 'id': 286, 'synset': 'coconut.n.02', 'synonyms': ['coconut', 'cocoanut'], 'def': 'large hard-shelled brown oval nut with a fibrous husk', 'name': 'coconut'}, {'frequency': 'r', 'id': 287, 'synset': 'coffee_filter.n.01', 'synonyms': ['coffee_filter'], 'def': 'filter (usually of paper) that passes the coffee and retains the coffee grounds', 'name': 'coffee_filter'}, {'frequency': 'f', 'id': 288, 'synset': 'coffee_maker.n.01', 'synonyms': ['coffee_maker', 'coffee_machine'], 'def': 'a kitchen appliance for brewing coffee automatically', 'name': 'coffee_maker'}, {'frequency': 'f', 'id': 289, 'synset': 'coffee_table.n.01', 'synonyms': ['coffee_table', 'cocktail_table'], 'def': 'low table where magazines can be placed and coffee or cocktails are served', 'name': 'coffee_table'}, {'frequency': 'c', 'id': 290, 'synset': 'coffeepot.n.01', 'synonyms': ['coffeepot'], 'def': 'tall pot in which coffee is brewed', 'name': 'coffeepot'}, {'frequency': 'r', 'id': 291, 'synset': 'coil.n.05', 'synonyms': ['coil'], 'def': 'tubing that is wound in a spiral', 'name': 'coil'}, {'frequency': 'c', 'id': 292, 'synset': 'coin.n.01', 'synonyms': ['coin'], 'def': 'a flat metal piece (usually a disc) used as money', 'name': 'coin'}, {'frequency': 'r', 'id': 293, 'synset': 'colander.n.01', 'synonyms': ['colander', 'cullender'], 'def': 'bowl-shaped strainer; used to wash or drain foods', 'name': 'colander'}, {'frequency': 'c', 'id': 294, 'synset': 'coleslaw.n.01', 'synonyms': ['coleslaw', 'slaw'], 'def': 'basically shredded cabbage', 'name': 'coleslaw'}, {'frequency': 'r', 'id': 295, 'synset': 'coloring_material.n.01', 'synonyms': ['coloring_material', 'colouring_material'], 'def': 'any material used for its color', 'name': 'coloring_material'}, {'frequency': 'r', 'id': 296, 'synset': 'combination_lock.n.01', 'synonyms': ['combination_lock'], 'def': 'lock that can be opened only by turning dials in a special sequence', 'name': 'combination_lock'}, {'frequency': 'c', 'id': 297, 'synset': 'comforter.n.04', 'synonyms': ['pacifier', 'teething_ring'], 'def': 'device used for an infant to suck or bite on', 'name': 'pacifier'}, {'frequency': 'r', 'id': 298, 'synset': 'comic_book.n.01', 'synonyms': ['comic_book'], 'def': 'a magazine devoted to comic strips', 'name': 'comic_book'}, {'frequency': 'f', 'id': 299, 'synset': 'computer_keyboard.n.01', 'synonyms': ['computer_keyboard', 'keyboard_(computer)'], 'def': 'a keyboard that is a data input device for computers', 'name': 'computer_keyboard'}, {'frequency': 'r', 'id': 300, 'synset': 'concrete_mixer.n.01', 'synonyms': ['concrete_mixer', 'cement_mixer'], 'def': 'a machine with a large revolving drum in which cement/concrete is mixed', 'name': 'concrete_mixer'}, {'frequency': 'f', 'id': 301, 'synset': 'cone.n.01', 'synonyms': ['cone', 'traffic_cone'], 'def': 'a cone-shaped object used to direct traffic', 'name': 'cone'}, {'frequency': 'f', 'id': 302, 'synset': 'control.n.09', 'synonyms': ['control', 'controller'], 'def': 'a mechanism that controls the operation of a machine', 'name': 'control'}, {'frequency': 'r', 'id': 303, 'synset': 'convertible.n.01', 'synonyms': ['convertible_(automobile)'], 'def': 'a car that has top that can be folded or removed', 'name': 'convertible_(automobile)'}, {'frequency': 'r', 'id': 304, 'synset': 'convertible.n.03', 'synonyms': ['sofa_bed'], 'def': 'a sofa that can be converted into a bed', 'name': 'sofa_bed'}, {'frequency': 'c', 'id': 305, 'synset': 'cookie.n.01', 'synonyms': ['cookie', 'cooky', 'biscuit_(cookie)'], 'def': "any of various small flat sweet cakes (`biscuit' is the British term)", 'name': 'cookie'}, {'frequency': 'r', 'id': 306, 'synset': 'cookie_jar.n.01', 'synonyms': ['cookie_jar', 'cooky_jar'], 'def': 'a jar in which cookies are kept (and sometimes money is hidden)', 'name': 'cookie_jar'}, {'frequency': 'r', 'id': 307, 'synset': 'cooking_utensil.n.01', 'synonyms': ['cooking_utensil'], 'def': 'a kitchen utensil made of material that does not melt easily; used for cooking', 'name': 'cooking_utensil'}, {'frequency': 'f', 'id': 308, 'synset': 'cooler.n.01', 'synonyms': ['cooler_(for_food)', 'ice_chest'], 'def': 'an insulated box for storing food often with ice', 'name': 'cooler_(for_food)'}, {'frequency': 'c', 'id': 309, 'synset': 'cork.n.04', 'synonyms': ['cork_(bottle_plug)', 'bottle_cork'], 'def': 'the plug in the mouth of a bottle (especially a wine bottle)', 'name': 'cork_(bottle_plug)'}, {'frequency': 'r', 'id': 310, 'synset': 'corkboard.n.01', 'synonyms': ['corkboard'], 'def': 'a sheet consisting of cork granules', 'name': 'corkboard'}, {'frequency': 'r', 'id': 311, 'synset': 'corkscrew.n.01', 'synonyms': ['corkscrew', 'bottle_screw'], 'def': 'a bottle opener that pulls corks', 'name': 'corkscrew'}, {'frequency': 'c', 'id': 312, 'synset': 'corn.n.03', 'synonyms': ['edible_corn', 'corn', 'maize'], 'def': 'ears of corn that can be prepared and served for human food', 'name': 'edible_corn'}, {'frequency': 'r', 'id': 313, 'synset': 'cornbread.n.01', 'synonyms': ['cornbread'], 'def': 'bread made primarily of cornmeal', 'name': 'cornbread'}, {'frequency': 'c', 'id': 314, 'synset': 'cornet.n.01', 'synonyms': ['cornet', 'horn', 'trumpet'], 'def': 'a brass musical instrument with a narrow tube and a flared bell and many valves', 'name': 'cornet'}, {'frequency': 'c', 'id': 315, 'synset': 'cornice.n.01', 'synonyms': ['cornice', 'valance', 'valance_board', 'pelmet'], 'def': 'a decorative framework to conceal curtain fixtures at the top of a window casing', 'name': 'cornice'}, {'frequency': 'r', 'id': 316, 'synset': 'cornmeal.n.01', 'synonyms': ['cornmeal'], 'def': 'coarsely ground corn', 'name': 'cornmeal'}, {'frequency': 'r', 'id': 317, 'synset': 'corset.n.01', 'synonyms': ['corset', 'girdle'], 'def': "a woman's close-fitting foundation garment", 'name': 'corset'}, {'frequency': 'r', 'id': 318, 'synset': 'cos.n.02', 'synonyms': ['romaine_lettuce'], 'def': 'lettuce with long dark-green leaves in a loosely packed elongated head', 'name': 'romaine_lettuce'}, {'frequency': 'c', 'id': 319, 'synset': 'costume.n.04', 'synonyms': ['costume'], 'def': 'the attire characteristic of a country or a time or a social class', 'name': 'costume'}, {'frequency': 'r', 'id': 320, 'synset': 'cougar.n.01', 'synonyms': ['cougar', 'puma', 'catamount', 'mountain_lion', 'panther'], 'def': 'large American feline resembling a lion', 'name': 'cougar'}, {'frequency': 'r', 'id': 321, 'synset': 'coverall.n.01', 'synonyms': ['coverall'], 'def': 'a loose-fitting protective garment that is worn over other clothing', 'name': 'coverall'}, {'frequency': 'r', 'id': 322, 'synset': 'cowbell.n.01', 'synonyms': ['cowbell'], 'def': 'a bell hung around the neck of cow so that the cow can be easily located', 'name': 'cowbell'}, {'frequency': 'f', 'id': 323, 'synset': 'cowboy_hat.n.01', 'synonyms': ['cowboy_hat', 'ten-gallon_hat'], 'def': 'a hat with a wide brim and a soft crown; worn by American ranch hands', 'name': 'cowboy_hat'}, {'frequency': 'r', 'id': 324, 'synset': 'crab.n.01', 'synonyms': ['crab_(animal)'], 'def': 'decapod having eyes on short stalks and a broad flattened shell and pincers', 'name': 'crab_(animal)'}, {'frequency': 'c', 'id': 325, 'synset': 'cracker.n.01', 'synonyms': ['cracker'], 'def': 'a thin crisp wafer', 'name': 'cracker'}, {'frequency': 'r', 'id': 326, 'synset': 'crape.n.01', 'synonyms': ['crape', 'crepe', 'French_pancake'], 'def': 'small very thin pancake', 'name': 'crape'}, {'frequency': 'f', 'id': 327, 'synset': 'crate.n.01', 'synonyms': ['crate'], 'def': 'a rugged box (usually made of wood); used for shipping', 'name': 'crate'}, {'frequency': 'r', 'id': 328, 'synset': 'crayon.n.01', 'synonyms': ['crayon', 'wax_crayon'], 'def': 'writing or drawing implement made of a colored stick of composition wax', 'name': 'crayon'}, {'frequency': 'r', 'id': 329, 'synset': 'cream_pitcher.n.01', 'synonyms': ['cream_pitcher'], 'def': 'a small pitcher for serving cream', 'name': 'cream_pitcher'}, {'frequency': 'r', 'id': 330, 'synset': 'credit_card.n.01', 'synonyms': ['credit_card', 'charge_card', 'debit_card'], 'def': 'a card, usually plastic, used to pay for goods and services', 'name': 'credit_card'}, {'frequency': 'c', 'id': 331, 'synset': 'crescent_roll.n.01', 'synonyms': ['crescent_roll', 'croissant'], 'def': 'very rich flaky crescent-shaped roll', 'name': 'crescent_roll'}, {'frequency': 'c', 'id': 332, 'synset': 'crib.n.01', 'synonyms': ['crib', 'cot'], 'def': 'baby bed with high sides made of slats', 'name': 'crib'}, {'frequency': 'c', 'id': 333, 'synset': 'crock.n.03', 'synonyms': ['crock_pot', 'earthenware_jar'], 'def': 'an earthen jar (made of baked clay)', 'name': 'crock_pot'}, {'frequency': 'f', 'id': 334, 'synset': 'crossbar.n.01', 'synonyms': ['crossbar'], 'def': 'a horizontal bar that goes across something', 'name': 'crossbar'}, {'frequency': 'r', 'id': 335, 'synset': 'crouton.n.01', 'synonyms': ['crouton'], 'def': 'a small piece of toasted or fried bread; served in soup or salads', 'name': 'crouton'}, {'frequency': 'r', 'id': 336, 'synset': 'crow.n.01', 'synonyms': ['crow'], 'def': 'black birds having a raucous call', 'name': 'crow'}, {'frequency': 'c', 'id': 337, 'synset': 'crown.n.04', 'synonyms': ['crown'], 'def': 'an ornamental jeweled headdress signifying sovereignty', 'name': 'crown'}, {'frequency': 'c', 'id': 338, 'synset': 'crucifix.n.01', 'synonyms': ['crucifix'], 'def': 'representation of the cross on which Jesus died', 'name': 'crucifix'}, {'frequency': 'c', 'id': 339, 'synset': 'cruise_ship.n.01', 'synonyms': ['cruise_ship', 'cruise_liner'], 'def': 'a passenger ship used commercially for pleasure cruises', 'name': 'cruise_ship'}, {'frequency': 'c', 'id': 340, 'synset': 'cruiser.n.01', 'synonyms': ['police_cruiser', 'patrol_car', 'police_car', 'squad_car'], 'def': 'a car in which policemen cruise the streets', 'name': 'police_cruiser'}, {'frequency': 'c', 'id': 341, 'synset': 'crumb.n.03', 'synonyms': ['crumb'], 'def': 'small piece of e.g. bread or cake', 'name': 'crumb'}, {'frequency': 'r', 'id': 342, 'synset': 'crutch.n.01', 'synonyms': ['crutch'], 'def': 'a wooden or metal staff that fits under the armpit and reaches to the ground', 'name': 'crutch'}, {'frequency': 'c', 'id': 343, 'synset': 'cub.n.03', 'synonyms': ['cub_(animal)'], 'def': 'the young of certain carnivorous mammals such as the bear or wolf or lion', 'name': 'cub_(animal)'}, {'frequency': 'r', 'id': 344, 'synset': 'cube.n.05', 'synonyms': ['cube', 'square_block'], 'def': 'a block in the (approximate) shape of a cube', 'name': 'cube'}, {'frequency': 'f', 'id': 345, 'synset': 'cucumber.n.02', 'synonyms': ['cucumber', 'cuke'], 'def': 'cylindrical green fruit with thin green rind and white flesh eaten as a vegetable', 'name': 'cucumber'}, {'frequency': 'c', 'id': 346, 'synset': 'cufflink.n.01', 'synonyms': ['cufflink'], 'def': 'jewelry consisting of linked buttons used to fasten the cuffs of a shirt', 'name': 'cufflink'}, {'frequency': 'f', 'id': 347, 'synset': 'cup.n.01', 'synonyms': ['cup'], 'def': 'a small open container usually used for drinking; usually has a handle', 'name': 'cup'}, {'frequency': 'c', 'id': 348, 'synset': 'cup.n.08', 'synonyms': ['trophy_cup'], 'def': 'a metal vessel with handles that is awarded as a trophy to a competition winner', 'name': 'trophy_cup'}, {'frequency': 'c', 'id': 349, 'synset': 'cupcake.n.01', 'synonyms': ['cupcake'], 'def': 'small cake baked in a muffin tin', 'name': 'cupcake'}, {'frequency': 'r', 'id': 350, 'synset': 'curler.n.01', 'synonyms': ['hair_curler', 'hair_roller', 'hair_crimper'], 'def': 'a cylindrical tube around which the hair is wound to curl it', 'name': 'hair_curler'}, {'frequency': 'r', 'id': 351, 'synset': 'curling_iron.n.01', 'synonyms': ['curling_iron'], 'def': 'a cylindrical home appliance that heats hair that has been curled around it', 'name': 'curling_iron'}, {'frequency': 'f', 'id': 352, 'synset': 'curtain.n.01', 'synonyms': ['curtain', 'drapery'], 'def': 'hanging cloth used as a blind (especially for a window)', 'name': 'curtain'}, {'frequency': 'f', 'id': 353, 'synset': 'cushion.n.03', 'synonyms': ['cushion'], 'def': 'a soft bag filled with air or padding such as feathers or foam rubber', 'name': 'cushion'}, {'frequency': 'r', 'id': 354, 'synset': 'custard.n.01', 'synonyms': ['custard'], 'def': 'sweetened mixture of milk and eggs baked or boiled or frozen', 'name': 'custard'}, {'frequency': 'c', 'id': 355, 'synset': 'cutter.n.06', 'synonyms': ['cutting_tool'], 'def': 'a cutting implement; a tool for cutting', 'name': 'cutting_tool'}, {'frequency': 'r', 'id': 356, 'synset': 'cylinder.n.04', 'synonyms': ['cylinder'], 'def': 'a cylindrical container', 'name': 'cylinder'}, {'frequency': 'r', 'id': 357, 'synset': 'cymbal.n.01', 'synonyms': ['cymbal'], 'def': 'a percussion instrument consisting of a concave brass disk', 'name': 'cymbal'}, {'frequency': 'r', 'id': 358, 'synset': 'dachshund.n.01', 'synonyms': ['dachshund', 'dachsie', 'badger_dog'], 'def': 'small long-bodied short-legged breed of dog having a short sleek coat and long drooping ears', 'name': 'dachshund'}, {'frequency': 'r', 'id': 359, 'synset': 'dagger.n.01', 'synonyms': ['dagger'], 'def': 'a short knife with a pointed blade used for piercing or stabbing', 'name': 'dagger'}, {'frequency': 'r', 'id': 360, 'synset': 'dartboard.n.01', 'synonyms': ['dartboard'], 'def': 'a circular board of wood or cork used as the target in the game of darts', 'name': 'dartboard'}, {'frequency': 'r', 'id': 361, 'synset': 'date.n.08', 'synonyms': ['date_(fruit)'], 'def': 'sweet edible fruit of the date palm with a single long woody seed', 'name': 'date_(fruit)'}, {'frequency': 'f', 'id': 362, 'synset': 'deck_chair.n.01', 'synonyms': ['deck_chair', 'beach_chair'], 'def': 'a folding chair for use outdoors; a wooden frame supports a length of canvas', 'name': 'deck_chair'}, {'frequency': 'c', 'id': 363, 'synset': 'deer.n.01', 'synonyms': ['deer', 'cervid'], 'def': "distinguished from Bovidae by the male's having solid deciduous antlers", 'name': 'deer'}, {'frequency': 'c', 'id': 364, 'synset': 'dental_floss.n.01', 'synonyms': ['dental_floss', 'floss'], 'def': 'a soft thread for cleaning the spaces between the teeth', 'name': 'dental_floss'}, {'frequency': 'f', 'id': 365, 'synset': 'desk.n.01', 'synonyms': ['desk'], 'def': 'a piece of furniture with a writing surface and usually drawers or other compartments', 'name': 'desk'}, {'frequency': 'r', 'id': 366, 'synset': 'detergent.n.01', 'synonyms': ['detergent'], 'def': 'a surface-active chemical widely used in industry and laundering', 'name': 'detergent'}, {'frequency': 'c', 'id': 367, 'synset': 'diaper.n.01', 'synonyms': ['diaper'], 'def': 'garment consisting of a folded cloth drawn up between the legs and fastened at the waist', 'name': 'diaper'}, {'frequency': 'r', 'id': 368, 'synset': 'diary.n.01', 'synonyms': ['diary', 'journal'], 'def': 'a daily written record of (usually personal) experiences and observations', 'name': 'diary'}, {'frequency': 'r', 'id': 369, 'synset': 'die.n.01', 'synonyms': ['die', 'dice'], 'def': 'a small cube with 1 to 6 spots on the six faces; used in gambling', 'name': 'die'}, {'frequency': 'r', 'id': 370, 'synset': 'dinghy.n.01', 'synonyms': ['dinghy', 'dory', 'rowboat'], 'def': 'a small boat of shallow draft with seats and oars with which it is propelled', 'name': 'dinghy'}, {'frequency': 'f', 'id': 371, 'synset': 'dining_table.n.01', 'synonyms': ['dining_table'], 'def': 'a table at which meals are served', 'name': 'dining_table'}, {'frequency': 'r', 'id': 372, 'synset': 'dinner_jacket.n.01', 'synonyms': ['tux', 'tuxedo'], 'def': 'semiformal evening dress for men', 'name': 'tux'}, {'frequency': 'c', 'id': 373, 'synset': 'dish.n.01', 'synonyms': ['dish'], 'def': 'a piece of dishware normally used as a container for holding or serving food', 'name': 'dish'}, {'frequency': 'c', 'id': 374, 'synset': 'dish.n.05', 'synonyms': ['dish_antenna'], 'def': 'directional antenna consisting of a parabolic reflector', 'name': 'dish_antenna'}, {'frequency': 'c', 'id': 375, 'synset': 'dishrag.n.01', 'synonyms': ['dishrag', 'dishcloth'], 'def': 'a cloth for washing dishes', 'name': 'dishrag'}, {'frequency': 'c', 'id': 376, 'synset': 'dishtowel.n.01', 'synonyms': ['dishtowel', 'tea_towel'], 'def': 'a towel for drying dishes', 'name': 'dishtowel'}, {'frequency': 'f', 'id': 377, 'synset': 'dishwasher.n.01', 'synonyms': ['dishwasher', 'dishwashing_machine'], 'def': 'a machine for washing dishes', 'name': 'dishwasher'}, {'frequency': 'r', 'id': 378, 'synset': 'dishwasher_detergent.n.01', 'synonyms': ['dishwasher_detergent', 'dishwashing_detergent', 'dishwashing_liquid'], 'def': 'a low-sudsing detergent designed for use in dishwashers', 'name': 'dishwasher_detergent'}, {'frequency': 'r', 'id': 379, 'synset': 'diskette.n.01', 'synonyms': ['diskette', 'floppy', 'floppy_disk'], 'def': 'a small plastic magnetic disk enclosed in a stiff envelope used to store data', 'name': 'diskette'}, {'frequency': 'c', 'id': 380, 'synset': 'dispenser.n.01', 'synonyms': ['dispenser'], 'def': 'a container so designed that the contents can be used in prescribed amounts', 'name': 'dispenser'}, {'frequency': 'c', 'id': 381, 'synset': 'dixie_cup.n.01', 'synonyms': ['Dixie_cup', 'paper_cup'], 'def': 'a disposable cup made of paper; for holding drinks', 'name': 'Dixie_cup'}, {'frequency': 'f', 'id': 382, 'synset': 'dog.n.01', 'synonyms': ['dog'], 'def': 'a common domesticated dog', 'name': 'dog'}, {'frequency': 'f', 'id': 383, 'synset': 'dog_collar.n.01', 'synonyms': ['dog_collar'], 'def': 'a collar for a dog', 'name': 'dog_collar'}, {'frequency': 'c', 'id': 384, 'synset': 'doll.n.01', 'synonyms': ['doll'], 'def': 'a toy replica of a HUMAN (NOT AN ANIMAL)', 'name': 'doll'}, {'frequency': 'r', 'id': 385, 'synset': 'dollar.n.02', 'synonyms': ['dollar', 'dollar_bill', 'one_dollar_bill'], 'def': 'a piece of paper money worth one dollar', 'name': 'dollar'}, {'frequency': 'r', 'id': 386, 'synset': 'dolphin.n.02', 'synonyms': ['dolphin'], 'def': 'any of various small toothed whales with a beaklike snout; larger than porpoises', 'name': 'dolphin'}, {'frequency': 'c', 'id': 387, 'synset': 'domestic_ass.n.01', 'synonyms': ['domestic_ass', 'donkey'], 'def': 'domestic beast of burden descended from the African wild ass; patient but stubborn', 'name': 'domestic_ass'}, {'frequency': 'r', 'id': 388, 'synset': 'domino.n.03', 'synonyms': ['eye_mask'], 'def': 'a mask covering the upper part of the face but with holes for the eyes', 'name': 'eye_mask'}, {'frequency': 'r', 'id': 389, 'synset': 'doorbell.n.01', 'synonyms': ['doorbell', 'buzzer'], 'def': 'a button at an outer door that gives a ringing or buzzing signal when pushed', 'name': 'doorbell'}, {'frequency': 'f', 'id': 390, 'synset': 'doorknob.n.01', 'synonyms': ['doorknob', 'doorhandle'], 'def': "a knob used to open a door (often called `doorhandle' in Great Britain)", 'name': 'doorknob'}, {'frequency': 'c', 'id': 391, 'synset': 'doormat.n.02', 'synonyms': ['doormat', 'welcome_mat'], 'def': 'a mat placed outside an exterior door for wiping the shoes before entering', 'name': 'doormat'}, {'frequency': 'f', 'id': 392, 'synset': 'doughnut.n.02', 'synonyms': ['doughnut', 'donut'], 'def': 'a small ring-shaped friedcake', 'name': 'doughnut'}, {'frequency': 'r', 'id': 393, 'synset': 'dove.n.01', 'synonyms': ['dove'], 'def': 'any of numerous small pigeons', 'name': 'dove'}, {'frequency': 'r', 'id': 394, 'synset': 'dragonfly.n.01', 'synonyms': ['dragonfly'], 'def': 'slender-bodied non-stinging insect having iridescent wings that are outspread at rest', 'name': 'dragonfly'}, {'frequency': 'f', 'id': 395, 'synset': 'drawer.n.01', 'synonyms': ['drawer'], 'def': 'a boxlike container in a piece of furniture; made so as to slide in and out', 'name': 'drawer'}, {'frequency': 'c', 'id': 396, 'synset': 'drawers.n.01', 'synonyms': ['underdrawers', 'boxers', 'boxershorts'], 'def': 'underpants worn by men', 'name': 'underdrawers'}, {'frequency': 'f', 'id': 397, 'synset': 'dress.n.01', 'synonyms': ['dress', 'frock'], 'def': 'a one-piece garment for a woman; has skirt and bodice', 'name': 'dress'}, {'frequency': 'c', 'id': 398, 'synset': 'dress_hat.n.01', 'synonyms': ['dress_hat', 'high_hat', 'opera_hat', 'silk_hat', 'top_hat'], 'def': "a man's hat with a tall crown; usually covered with silk or with beaver fur", 'name': 'dress_hat'}, {'frequency': 'c', 'id': 399, 'synset': 'dress_suit.n.01', 'synonyms': ['dress_suit'], 'def': 'formalwear consisting of full evening dress for men', 'name': 'dress_suit'}, {'frequency': 'c', 'id': 400, 'synset': 'dresser.n.05', 'synonyms': ['dresser'], 'def': 'a cabinet with shelves', 'name': 'dresser'}, {'frequency': 'c', 'id': 401, 'synset': 'drill.n.01', 'synonyms': ['drill'], 'def': 'a tool with a sharp rotating point for making holes in hard materials', 'name': 'drill'}, {'frequency': 'r', 'id': 402, 'synset': 'drinking_fountain.n.01', 'synonyms': ['drinking_fountain'], 'def': 'a public fountain to provide a jet of drinking water', 'name': 'drinking_fountain'}, {'frequency': 'r', 'id': 403, 'synset': 'drone.n.04', 'synonyms': ['drone'], 'def': 'an aircraft without a pilot that is operated by remote control', 'name': 'drone'}, {'frequency': 'r', 'id': 404, 'synset': 'dropper.n.01', 'synonyms': ['dropper', 'eye_dropper'], 'def': 'pipet consisting of a small tube with a vacuum bulb at one end for drawing liquid in and releasing it a drop at a time', 'name': 'dropper'}, {'frequency': 'c', 'id': 405, 'synset': 'drum.n.01', 'synonyms': ['drum_(musical_instrument)'], 'def': 'a musical percussion instrument; usually consists of a hollow cylinder with a membrane stretched across each end', 'name': 'drum_(musical_instrument)'}, {'frequency': 'r', 'id': 406, 'synset': 'drumstick.n.02', 'synonyms': ['drumstick'], 'def': 'a stick used for playing a drum', 'name': 'drumstick'}, {'frequency': 'f', 'id': 407, 'synset': 'duck.n.01', 'synonyms': ['duck'], 'def': 'small web-footed broad-billed swimming bird', 'name': 'duck'}, {'frequency': 'r', 'id': 408, 'synset': 'duckling.n.02', 'synonyms': ['duckling'], 'def': 'young duck', 'name': 'duckling'}, {'frequency': 'c', 'id': 409, 'synset': 'duct_tape.n.01', 'synonyms': ['duct_tape'], 'def': 'a wide silvery adhesive tape', 'name': 'duct_tape'}, {'frequency': 'f', 'id': 410, 'synset': 'duffel_bag.n.01', 'synonyms': ['duffel_bag', 'duffle_bag', 'duffel', 'duffle'], 'def': 'a large cylindrical bag of heavy cloth', 'name': 'duffel_bag'}, {'frequency': 'r', 'id': 411, 'synset': 'dumbbell.n.01', 'synonyms': ['dumbbell'], 'def': 'an exercising weight with two ball-like ends connected by a short handle', 'name': 'dumbbell'}, {'frequency': 'c', 'id': 412, 'synset': 'dumpster.n.01', 'synonyms': ['dumpster'], 'def': 'a container designed to receive and transport and dump waste', 'name': 'dumpster'}, {'frequency': 'r', 'id': 413, 'synset': 'dustpan.n.02', 'synonyms': ['dustpan'], 'def': 'a short-handled receptacle into which dust can be swept', 'name': 'dustpan'}, {'frequency': 'r', 'id': 414, 'synset': 'dutch_oven.n.02', 'synonyms': ['Dutch_oven'], 'def': 'iron or earthenware cooking pot; used for stews', 'name': 'Dutch_oven'}, {'frequency': 'c', 'id': 415, 'synset': 'eagle.n.01', 'synonyms': ['eagle'], 'def': 'large birds of prey noted for their broad wings and strong soaring flight', 'name': 'eagle'}, {'frequency': 'f', 'id': 416, 'synset': 'earphone.n.01', 'synonyms': ['earphone', 'earpiece', 'headphone'], 'def': 'device for listening to audio that is held over or inserted into the ear', 'name': 'earphone'}, {'frequency': 'r', 'id': 417, 'synset': 'earplug.n.01', 'synonyms': ['earplug'], 'def': 'a soft plug that is inserted into the ear canal to block sound', 'name': 'earplug'}, {'frequency': 'f', 'id': 418, 'synset': 'earring.n.01', 'synonyms': ['earring'], 'def': 'jewelry to ornament the ear', 'name': 'earring'}, {'frequency': 'c', 'id': 419, 'synset': 'easel.n.01', 'synonyms': ['easel'], 'def': "an upright tripod for displaying something (usually an artist's canvas)", 'name': 'easel'}, {'frequency': 'r', 'id': 420, 'synset': 'eclair.n.01', 'synonyms': ['eclair'], 'def': 'oblong cream puff', 'name': 'eclair'}, {'frequency': 'r', 'id': 421, 'synset': 'eel.n.01', 'synonyms': ['eel'], 'def': 'an elongate fish with fatty flesh', 'name': 'eel'}, {'frequency': 'f', 'id': 422, 'synset': 'egg.n.02', 'synonyms': ['egg', 'eggs'], 'def': 'oval reproductive body of a fowl (especially a hen) used as food', 'name': 'egg'}, {'frequency': 'r', 'id': 423, 'synset': 'egg_roll.n.01', 'synonyms': ['egg_roll', 'spring_roll'], 'def': 'minced vegetables and meat wrapped in a pancake and fried', 'name': 'egg_roll'}, {'frequency': 'c', 'id': 424, 'synset': 'egg_yolk.n.01', 'synonyms': ['egg_yolk', 'yolk_(egg)'], 'def': 'the yellow spherical part of an egg', 'name': 'egg_yolk'}, {'frequency': 'c', 'id': 425, 'synset': 'eggbeater.n.02', 'synonyms': ['eggbeater', 'eggwhisk'], 'def': 'a mixer for beating eggs or whipping cream', 'name': 'eggbeater'}, {'frequency': 'c', 'id': 426, 'synset': 'eggplant.n.01', 'synonyms': ['eggplant', 'aubergine'], 'def': 'egg-shaped vegetable having a shiny skin typically dark purple', 'name': 'eggplant'}, {'frequency': 'r', 'id': 427, 'synset': 'electric_chair.n.01', 'synonyms': ['electric_chair'], 'def': 'a chair-shaped instrument of execution by electrocution', 'name': 'electric_chair'}, {'frequency': 'f', 'id': 428, 'synset': 'electric_refrigerator.n.01', 'synonyms': ['refrigerator'], 'def': 'a refrigerator in which the coolant is pumped around by an electric motor', 'name': 'refrigerator'}, {'frequency': 'f', 'id': 429, 'synset': 'elephant.n.01', 'synonyms': ['elephant'], 'def': 'a common elephant', 'name': 'elephant'}, {'frequency': 'r', 'id': 430, 'synset': 'elk.n.01', 'synonyms': ['elk', 'moose'], 'def': 'large northern deer with enormous flattened antlers in the male', 'name': 'elk'}, {'frequency': 'c', 'id': 431, 'synset': 'envelope.n.01', 'synonyms': ['envelope'], 'def': 'a flat (usually rectangular) container for a letter, thin package, etc.', 'name': 'envelope'}, {'frequency': 'c', 'id': 432, 'synset': 'eraser.n.01', 'synonyms': ['eraser'], 'def': 'an implement used to erase something', 'name': 'eraser'}, {'frequency': 'r', 'id': 433, 'synset': 'escargot.n.01', 'synonyms': ['escargot'], 'def': 'edible snail usually served in the shell with a sauce of melted butter and garlic', 'name': 'escargot'}, {'frequency': 'r', 'id': 434, 'synset': 'eyepatch.n.01', 'synonyms': ['eyepatch'], 'def': 'a protective cloth covering for an injured eye', 'name': 'eyepatch'}, {'frequency': 'r', 'id': 435, 'synset': 'falcon.n.01', 'synonyms': ['falcon'], 'def': 'birds of prey having long pointed powerful wings adapted for swift flight', 'name': 'falcon'}, {'frequency': 'f', 'id': 436, 'synset': 'fan.n.01', 'synonyms': ['fan'], 'def': 'a device for creating a current of air by movement of a surface or surfaces', 'name': 'fan'}, {'frequency': 'f', 'id': 437, 'synset': 'faucet.n.01', 'synonyms': ['faucet', 'spigot', 'tap'], 'def': 'a regulator for controlling the flow of a liquid from a reservoir', 'name': 'faucet'}, {'frequency': 'r', 'id': 438, 'synset': 'fedora.n.01', 'synonyms': ['fedora'], 'def': 'a hat made of felt with a creased crown', 'name': 'fedora'}, {'frequency': 'r', 'id': 439, 'synset': 'ferret.n.02', 'synonyms': ['ferret'], 'def': 'domesticated albino variety of the European polecat bred for hunting rats and rabbits', 'name': 'ferret'}, {'frequency': 'c', 'id': 440, 'synset': 'ferris_wheel.n.01', 'synonyms': ['Ferris_wheel'], 'def': 'a large wheel with suspended seats that remain upright as the wheel rotates', 'name': 'Ferris_wheel'}, {'frequency': 'r', 'id': 441, 'synset': 'ferry.n.01', 'synonyms': ['ferry', 'ferryboat'], 'def': 'a boat that transports people or vehicles across a body of water and operates on a regular schedule', 'name': 'ferry'}, {'frequency': 'r', 'id': 442, 'synset': 'fig.n.04', 'synonyms': ['fig_(fruit)'], 'def': 'fleshy sweet pear-shaped yellowish or purple fruit eaten fresh or preserved or dried', 'name': 'fig_(fruit)'}, {'frequency': 'c', 'id': 443, 'synset': 'fighter.n.02', 'synonyms': ['fighter_jet', 'fighter_aircraft', 'attack_aircraft'], 'def': 'a high-speed military or naval airplane designed to destroy enemy targets', 'name': 'fighter_jet'}, {'frequency': 'f', 'id': 444, 'synset': 'figurine.n.01', 'synonyms': ['figurine'], 'def': 'a small carved or molded figure', 'name': 'figurine'}, {'frequency': 'c', 'id': 445, 'synset': 'file.n.03', 'synonyms': ['file_cabinet', 'filing_cabinet'], 'def': 'office furniture consisting of a container for keeping papers in order', 'name': 'file_cabinet'}, {'frequency': 'r', 'id': 446, 'synset': 'file.n.04', 'synonyms': ['file_(tool)'], 'def': 'a steel hand tool with small sharp teeth on some or all of its surfaces; used for smoothing wood or metal', 'name': 'file_(tool)'}, {'frequency': 'f', 'id': 447, 'synset': 'fire_alarm.n.02', 'synonyms': ['fire_alarm', 'smoke_alarm'], 'def': 'an alarm that is tripped off by fire or smoke', 'name': 'fire_alarm'}, {'frequency': 'c', 'id': 448, 'synset': 'fire_engine.n.01', 'synonyms': ['fire_engine', 'fire_truck'], 'def': 'large trucks that carry firefighters and equipment to the site of a fire', 'name': 'fire_engine'}, {'frequency': 'c', 'id': 449, 'synset': 'fire_extinguisher.n.01', 'synonyms': ['fire_extinguisher', 'extinguisher'], 'def': 'a manually operated device for extinguishing small fires', 'name': 'fire_extinguisher'}, {'frequency': 'c', 'id': 450, 'synset': 'fire_hose.n.01', 'synonyms': ['fire_hose'], 'def': 'a large hose that carries water from a fire hydrant to the site of the fire', 'name': 'fire_hose'}, {'frequency': 'f', 'id': 451, 'synset': 'fireplace.n.01', 'synonyms': ['fireplace'], 'def': 'an open recess in a wall at the base of a chimney where a fire can be built', 'name': 'fireplace'}, {'frequency': 'f', 'id': 452, 'synset': 'fireplug.n.01', 'synonyms': ['fireplug', 'fire_hydrant', 'hydrant'], 'def': 'an upright hydrant for drawing water to use in fighting a fire', 'name': 'fireplug'}, {'frequency': 'c', 'id': 453, 'synset': 'fish.n.01', 'synonyms': ['fish'], 'def': 'any of various mostly cold-blooded aquatic vertebrates usually having scales and breathing through gills', 'name': 'fish'}, {'frequency': 'r', 'id': 454, 'synset': 'fish.n.02', 'synonyms': ['fish_(food)'], 'def': 'the flesh of fish used as food', 'name': 'fish_(food)'}, {'frequency': 'r', 'id': 455, 'synset': 'fishbowl.n.02', 'synonyms': ['fishbowl', 'goldfish_bowl'], 'def': 'a transparent bowl in which small fish are kept', 'name': 'fishbowl'}, {'frequency': 'r', 'id': 456, 'synset': 'fishing_boat.n.01', 'synonyms': ['fishing_boat', 'fishing_vessel'], 'def': 'a vessel for fishing', 'name': 'fishing_boat'}, {'frequency': 'c', 'id': 457, 'synset': 'fishing_rod.n.01', 'synonyms': ['fishing_rod', 'fishing_pole'], 'def': 'a rod that is used in fishing to extend the fishing line', 'name': 'fishing_rod'}, {'frequency': 'f', 'id': 458, 'synset': 'flag.n.01', 'synonyms': ['flag'], 'def': 'emblem usually consisting of a rectangular piece of cloth of distinctive design (do not include pole)', 'name': 'flag'}, {'frequency': 'f', 'id': 459, 'synset': 'flagpole.n.02', 'synonyms': ['flagpole', 'flagstaff'], 'def': 'a tall staff or pole on which a flag is raised', 'name': 'flagpole'}, {'frequency': 'c', 'id': 460, 'synset': 'flamingo.n.01', 'synonyms': ['flamingo'], 'def': 'large pink web-footed bird with down-bent bill', 'name': 'flamingo'}, {'frequency': 'c', 'id': 461, 'synset': 'flannel.n.01', 'synonyms': ['flannel'], 'def': 'a soft light woolen fabric; used for clothing', 'name': 'flannel'}, {'frequency': 'r', 'id': 462, 'synset': 'flash.n.10', 'synonyms': ['flash', 'flashbulb'], 'def': 'a lamp for providing momentary light to take a photograph', 'name': 'flash'}, {'frequency': 'c', 'id': 463, 'synset': 'flashlight.n.01', 'synonyms': ['flashlight', 'torch'], 'def': 'a small portable battery-powered electric lamp', 'name': 'flashlight'}, {'frequency': 'r', 'id': 464, 'synset': 'fleece.n.03', 'synonyms': ['fleece'], 'def': 'a soft bulky fabric with deep pile; used chiefly for clothing', 'name': 'fleece'}, {'frequency': 'f', 'id': 465, 'synset': 'flip-flop.n.02', 'synonyms': ['flip-flop_(sandal)'], 'def': 'a backless sandal held to the foot by a thong between two toes', 'name': 'flip-flop_(sandal)'}, {'frequency': 'c', 'id': 466, 'synset': 'flipper.n.01', 'synonyms': ['flipper_(footwear)', 'fin_(footwear)'], 'def': 'a shoe to aid a person in swimming', 'name': 'flipper_(footwear)'}, {'frequency': 'f', 'id': 467, 'synset': 'flower_arrangement.n.01', 'synonyms': ['flower_arrangement', 'floral_arrangement'], 'def': 'a decorative arrangement of flowers', 'name': 'flower_arrangement'}, {'frequency': 'c', 'id': 468, 'synset': 'flute.n.02', 'synonyms': ['flute_glass', 'champagne_flute'], 'def': 'a tall narrow wineglass', 'name': 'flute_glass'}, {'frequency': 'r', 'id': 469, 'synset': 'foal.n.01', 'synonyms': ['foal'], 'def': 'a young horse', 'name': 'foal'}, {'frequency': 'c', 'id': 470, 'synset': 'folding_chair.n.01', 'synonyms': ['folding_chair'], 'def': 'a chair that can be folded flat for storage', 'name': 'folding_chair'}, {'frequency': 'c', 'id': 471, 'synset': 'food_processor.n.01', 'synonyms': ['food_processor'], 'def': 'a kitchen appliance for shredding, blending, chopping, or slicing food', 'name': 'food_processor'}, {'frequency': 'c', 'id': 472, 'synset': 'football.n.02', 'synonyms': ['football_(American)'], 'def': 'the inflated oblong ball used in playing American football', 'name': 'football_(American)'}, {'frequency': 'r', 'id': 473, 'synset': 'football_helmet.n.01', 'synonyms': ['football_helmet'], 'def': 'a padded helmet with a face mask to protect the head of football players', 'name': 'football_helmet'}, {'frequency': 'c', 'id': 474, 'synset': 'footstool.n.01', 'synonyms': ['footstool', 'footrest'], 'def': 'a low seat or a stool to rest the feet of a seated person', 'name': 'footstool'}, {'frequency': 'f', 'id': 475, 'synset': 'fork.n.01', 'synonyms': ['fork'], 'def': 'cutlery used for serving and eating food', 'name': 'fork'}, {'frequency': 'r', 'id': 476, 'synset': 'forklift.n.01', 'synonyms': ['forklift'], 'def': 'an industrial vehicle with a power operated fork in front that can be inserted under loads to lift and move them', 'name': 'forklift'}, {'frequency': 'r', 'id': 477, 'synset': 'freight_car.n.01', 'synonyms': ['freight_car'], 'def': 'a railway car that carries freight', 'name': 'freight_car'}, {'frequency': 'r', 'id': 478, 'synset': 'french_toast.n.01', 'synonyms': ['French_toast'], 'def': 'bread slice dipped in egg and milk and fried', 'name': 'French_toast'}, {'frequency': 'c', 'id': 479, 'synset': 'freshener.n.01', 'synonyms': ['freshener', 'air_freshener'], 'def': 'anything that freshens', 'name': 'freshener'}, {'frequency': 'f', 'id': 480, 'synset': 'frisbee.n.01', 'synonyms': ['frisbee'], 'def': 'a light, plastic disk propelled with a flip of the wrist for recreation or competition', 'name': 'frisbee'}, {'frequency': 'c', 'id': 481, 'synset': 'frog.n.01', 'synonyms': ['frog', 'toad', 'toad_frog'], 'def': 'a tailless stout-bodied amphibians with long hind limbs for leaping', 'name': 'frog'}, {'frequency': 'c', 'id': 482, 'synset': 'fruit_juice.n.01', 'synonyms': ['fruit_juice'], 'def': 'drink produced by squeezing or crushing fruit', 'name': 'fruit_juice'}, {'frequency': 'r', 'id': 483, 'synset': 'fruit_salad.n.01', 'synonyms': ['fruit_salad'], 'def': 'salad composed of fruits', 'name': 'fruit_salad'}, {'frequency': 'c', 'id': 484, 'synset': 'frying_pan.n.01', 'synonyms': ['frying_pan', 'frypan', 'skillet'], 'def': 'a pan used for frying foods', 'name': 'frying_pan'}, {'frequency': 'r', 'id': 485, 'synset': 'fudge.n.01', 'synonyms': ['fudge'], 'def': 'soft creamy candy', 'name': 'fudge'}, {'frequency': 'r', 'id': 486, 'synset': 'funnel.n.02', 'synonyms': ['funnel'], 'def': 'a cone-shaped utensil used to channel a substance into a container with a small mouth', 'name': 'funnel'}, {'frequency': 'c', 'id': 487, 'synset': 'futon.n.01', 'synonyms': ['futon'], 'def': 'a pad that is used for sleeping on the floor or on a raised frame', 'name': 'futon'}, {'frequency': 'r', 'id': 488, 'synset': 'gag.n.02', 'synonyms': ['gag', 'muzzle'], 'def': "restraint put into a person's mouth to prevent speaking or shouting", 'name': 'gag'}, {'frequency': 'r', 'id': 489, 'synset': 'garbage.n.03', 'synonyms': ['garbage'], 'def': 'a receptacle where waste can be discarded', 'name': 'garbage'}, {'frequency': 'c', 'id': 490, 'synset': 'garbage_truck.n.01', 'synonyms': ['garbage_truck'], 'def': 'a truck for collecting domestic refuse', 'name': 'garbage_truck'}, {'frequency': 'c', 'id': 491, 'synset': 'garden_hose.n.01', 'synonyms': ['garden_hose'], 'def': 'a hose used for watering a lawn or garden', 'name': 'garden_hose'}, {'frequency': 'c', 'id': 492, 'synset': 'gargle.n.01', 'synonyms': ['gargle', 'mouthwash'], 'def': 'a medicated solution used for gargling and rinsing the mouth', 'name': 'gargle'}, {'frequency': 'r', 'id': 493, 'synset': 'gargoyle.n.02', 'synonyms': ['gargoyle'], 'def': 'an ornament consisting of a grotesquely carved figure of a person or animal', 'name': 'gargoyle'}, {'frequency': 'c', 'id': 494, 'synset': 'garlic.n.02', 'synonyms': ['garlic', 'ail'], 'def': 'aromatic bulb used as seasoning', 'name': 'garlic'}, {'frequency': 'r', 'id': 495, 'synset': 'gasmask.n.01', 'synonyms': ['gasmask', 'respirator', 'gas_helmet'], 'def': 'a protective face mask with a filter', 'name': 'gasmask'}, {'frequency': 'r', 'id': 496, 'synset': 'gazelle.n.01', 'synonyms': ['gazelle'], 'def': 'small swift graceful antelope of Africa and Asia having lustrous eyes', 'name': 'gazelle'}, {'frequency': 'c', 'id': 497, 'synset': 'gelatin.n.02', 'synonyms': ['gelatin', 'jelly'], 'def': 'an edible jelly made with gelatin and used as a dessert or salad base or a coating for foods', 'name': 'gelatin'}, {'frequency': 'r', 'id': 498, 'synset': 'gem.n.02', 'synonyms': ['gemstone'], 'def': 'a crystalline rock that can be cut and polished for jewelry', 'name': 'gemstone'}, {'frequency': 'c', 'id': 499, 'synset': 'giant_panda.n.01', 'synonyms': ['giant_panda', 'panda', 'panda_bear'], 'def': 'large black-and-white herbivorous mammal of bamboo forests of China and Tibet', 'name': 'giant_panda'}, {'frequency': 'c', 'id': 500, 'synset': 'gift_wrap.n.01', 'synonyms': ['gift_wrap'], 'def': 'attractive wrapping paper suitable for wrapping gifts', 'name': 'gift_wrap'}, {'frequency': 'c', 'id': 501, 'synset': 'ginger.n.03', 'synonyms': ['ginger', 'gingerroot'], 'def': 'the root of the common ginger plant; used fresh as a seasoning', 'name': 'ginger'}, {'frequency': 'f', 'id': 502, 'synset': 'giraffe.n.01', 'synonyms': ['giraffe'], 'def': 'tall animal having a spotted coat and small horns and very long neck and legs', 'name': 'giraffe'}, {'frequency': 'c', 'id': 503, 'synset': 'girdle.n.02', 'synonyms': ['cincture', 'sash', 'waistband', 'waistcloth'], 'def': 'a band of material around the waist that strengthens a skirt or trousers', 'name': 'cincture'}, {'frequency': 'f', 'id': 504, 'synset': 'glass.n.02', 'synonyms': ['glass_(drink_container)', 'drinking_glass'], 'def': 'a container for holding liquids while drinking', 'name': 'glass_(drink_container)'}, {'frequency': 'c', 'id': 505, 'synset': 'globe.n.03', 'synonyms': ['globe'], 'def': 'a sphere on which a map (especially of the earth) is represented', 'name': 'globe'}, {'frequency': 'f', 'id': 506, 'synset': 'glove.n.02', 'synonyms': ['glove'], 'def': 'handwear covering the hand', 'name': 'glove'}, {'frequency': 'c', 'id': 507, 'synset': 'goat.n.01', 'synonyms': ['goat'], 'def': 'a common goat', 'name': 'goat'}, {'frequency': 'f', 'id': 508, 'synset': 'goggles.n.01', 'synonyms': ['goggles'], 'def': 'tight-fitting spectacles worn to protect the eyes', 'name': 'goggles'}, {'frequency': 'r', 'id': 509, 'synset': 'goldfish.n.01', 'synonyms': ['goldfish'], 'def': 'small golden or orange-red freshwater fishes used as pond or aquarium pets', 'name': 'goldfish'}, {'frequency': 'r', 'id': 510, 'synset': 'golf_club.n.02', 'synonyms': ['golf_club', 'golf-club'], 'def': 'golf equipment used by a golfer to hit a golf ball', 'name': 'golf_club'}, {'frequency': 'c', 'id': 511, 'synset': 'golfcart.n.01', 'synonyms': ['golfcart'], 'def': 'a small motor vehicle in which golfers can ride between shots', 'name': 'golfcart'}, {'frequency': 'r', 'id': 512, 'synset': 'gondola.n.02', 'synonyms': ['gondola_(boat)'], 'def': 'long narrow flat-bottomed boat propelled by sculling; traditionally used on canals of Venice', 'name': 'gondola_(boat)'}, {'frequency': 'c', 'id': 513, 'synset': 'goose.n.01', 'synonyms': ['goose'], 'def': 'loud, web-footed long-necked aquatic birds usually larger than ducks', 'name': 'goose'}, {'frequency': 'r', 'id': 514, 'synset': 'gorilla.n.01', 'synonyms': ['gorilla'], 'def': 'largest ape', 'name': 'gorilla'}, {'frequency': 'r', 'id': 515, 'synset': 'gourd.n.02', 'synonyms': ['gourd'], 'def': 'any of numerous inedible fruits with hard rinds', 'name': 'gourd'}, {'frequency': 'r', 'id': 516, 'synset': 'gown.n.04', 'synonyms': ['surgical_gown', 'scrubs_(surgical_clothing)'], 'def': 'protective garment worn by surgeons during operations', 'name': 'surgical_gown'}, {'frequency': 'f', 'id': 517, 'synset': 'grape.n.01', 'synonyms': ['grape'], 'def': 'any of various juicy fruit with green or purple skins; grow in clusters', 'name': 'grape'}, {'frequency': 'r', 'id': 518, 'synset': 'grasshopper.n.01', 'synonyms': ['grasshopper'], 'def': 'plant-eating insect with hind legs adapted for leaping', 'name': 'grasshopper'}, {'frequency': 'c', 'id': 519, 'synset': 'grater.n.01', 'synonyms': ['grater'], 'def': 'utensil with sharp perforations for shredding foods (as vegetables or cheese)', 'name': 'grater'}, {'frequency': 'c', 'id': 520, 'synset': 'gravestone.n.01', 'synonyms': ['gravestone', 'headstone', 'tombstone'], 'def': 'a stone that is used to mark a grave', 'name': 'gravestone'}, {'frequency': 'r', 'id': 521, 'synset': 'gravy_boat.n.01', 'synonyms': ['gravy_boat', 'gravy_holder'], 'def': 'a dish (often boat-shaped) for serving gravy or sauce', 'name': 'gravy_boat'}, {'frequency': 'c', 'id': 522, 'synset': 'green_bean.n.02', 'synonyms': ['green_bean'], 'def': 'a common bean plant cultivated for its slender green edible pods', 'name': 'green_bean'}, {'frequency': 'c', 'id': 523, 'synset': 'green_onion.n.01', 'synonyms': ['green_onion', 'spring_onion', 'scallion'], 'def': 'a young onion before the bulb has enlarged', 'name': 'green_onion'}, {'frequency': 'r', 'id': 524, 'synset': 'griddle.n.01', 'synonyms': ['griddle'], 'def': 'cooking utensil consisting of a flat heated surface on which food is cooked', 'name': 'griddle'}, {'frequency': 'r', 'id': 525, 'synset': 'grillroom.n.01', 'synonyms': ['grillroom', 'grill_(restaurant)'], 'def': 'a restaurant where food is cooked on a grill', 'name': 'grillroom'}, {'frequency': 'r', 'id': 526, 'synset': 'grinder.n.04', 'synonyms': ['grinder_(tool)'], 'def': 'a machine tool that polishes metal', 'name': 'grinder_(tool)'}, {'frequency': 'r', 'id': 527, 'synset': 'grits.n.01', 'synonyms': ['grits', 'hominy_grits'], 'def': 'coarsely ground corn boiled as a breakfast dish', 'name': 'grits'}, {'frequency': 'c', 'id': 528, 'synset': 'grizzly.n.01', 'synonyms': ['grizzly', 'grizzly_bear'], 'def': 'powerful brownish-yellow bear of the uplands of western North America', 'name': 'grizzly'}, {'frequency': 'c', 'id': 529, 'synset': 'grocery_bag.n.01', 'synonyms': ['grocery_bag'], 'def': "a sack for holding customer's groceries", 'name': 'grocery_bag'}, {'frequency': 'r', 'id': 530, 'synset': 'guacamole.n.01', 'synonyms': ['guacamole'], 'def': 'a dip made of mashed avocado mixed with chopped onions and other seasonings', 'name': 'guacamole'}, {'frequency': 'f', 'id': 531, 'synset': 'guitar.n.01', 'synonyms': ['guitar'], 'def': 'a stringed instrument usually having six strings; played by strumming or plucking', 'name': 'guitar'}, {'frequency': 'c', 'id': 532, 'synset': 'gull.n.02', 'synonyms': ['gull', 'seagull'], 'def': 'mostly white aquatic bird having long pointed wings and short legs', 'name': 'gull'}, {'frequency': 'c', 'id': 533, 'synset': 'gun.n.01', 'synonyms': ['gun'], 'def': 'a weapon that discharges a bullet at high velocity from a metal tube', 'name': 'gun'}, {'frequency': 'r', 'id': 534, 'synset': 'hair_spray.n.01', 'synonyms': ['hair_spray'], 'def': 'substance sprayed on the hair to hold it in place', 'name': 'hair_spray'}, {'frequency': 'c', 'id': 535, 'synset': 'hairbrush.n.01', 'synonyms': ['hairbrush'], 'def': "a brush used to groom a person's hair", 'name': 'hairbrush'}, {'frequency': 'c', 'id': 536, 'synset': 'hairnet.n.01', 'synonyms': ['hairnet'], 'def': 'a small net that someone wears over their hair to keep it in place', 'name': 'hairnet'}, {'frequency': 'c', 'id': 537, 'synset': 'hairpin.n.01', 'synonyms': ['hairpin'], 'def': "a double pronged pin used to hold women's hair in place", 'name': 'hairpin'}, {'frequency': 'f', 'id': 538, 'synset': 'ham.n.01', 'synonyms': ['ham', 'jambon', 'gammon'], 'def': 'meat cut from the thigh of a hog (usually smoked)', 'name': 'ham'}, {'frequency': 'c', 'id': 539, 'synset': 'hamburger.n.01', 'synonyms': ['hamburger', 'beefburger', 'burger'], 'def': 'a sandwich consisting of a patty of minced beef served on a bun', 'name': 'hamburger'}, {'frequency': 'c', 'id': 540, 'synset': 'hammer.n.02', 'synonyms': ['hammer'], 'def': 'a hand tool with a heavy head and a handle; used to deliver an impulsive force by striking', 'name': 'hammer'}, {'frequency': 'r', 'id': 541, 'synset': 'hammock.n.02', 'synonyms': ['hammock'], 'def': 'a hanging bed of canvas or rope netting (usually suspended between two trees)', 'name': 'hammock'}, {'frequency': 'r', 'id': 542, 'synset': 'hamper.n.02', 'synonyms': ['hamper'], 'def': 'a basket usually with a cover', 'name': 'hamper'}, {'frequency': 'r', 'id': 543, 'synset': 'hamster.n.01', 'synonyms': ['hamster'], 'def': 'short-tailed burrowing rodent with large cheek pouches', 'name': 'hamster'}, {'frequency': 'c', 'id': 544, 'synset': 'hand_blower.n.01', 'synonyms': ['hair_dryer'], 'def': 'a hand-held electric blower that can blow warm air onto the hair', 'name': 'hair_dryer'}, {'frequency': 'r', 'id': 545, 'synset': 'hand_glass.n.01', 'synonyms': ['hand_glass', 'hand_mirror'], 'def': 'a mirror intended to be held in the hand', 'name': 'hand_glass'}, {'frequency': 'f', 'id': 546, 'synset': 'hand_towel.n.01', 'synonyms': ['hand_towel', 'face_towel'], 'def': 'a small towel used to dry the hands or face', 'name': 'hand_towel'}, {'frequency': 'c', 'id': 547, 'synset': 'handcart.n.01', 'synonyms': ['handcart', 'pushcart', 'hand_truck'], 'def': 'wheeled vehicle that can be pushed by a person', 'name': 'handcart'}, {'frequency': 'r', 'id': 548, 'synset': 'handcuff.n.01', 'synonyms': ['handcuff'], 'def': 'shackle that consists of a metal loop that can be locked around the wrist', 'name': 'handcuff'}, {'frequency': 'c', 'id': 549, 'synset': 'handkerchief.n.01', 'synonyms': ['handkerchief'], 'def': 'a square piece of cloth used for wiping the eyes or nose or as a costume accessory', 'name': 'handkerchief'}, {'frequency': 'f', 'id': 550, 'synset': 'handle.n.01', 'synonyms': ['handle', 'grip', 'handgrip'], 'def': 'the appendage to an object that is designed to be held in order to use or move it', 'name': 'handle'}, {'frequency': 'r', 'id': 551, 'synset': 'handsaw.n.01', 'synonyms': ['handsaw', "carpenter's_saw"], 'def': 'a saw used with one hand for cutting wood', 'name': 'handsaw'}, {'frequency': 'r', 'id': 552, 'synset': 'hardback.n.01', 'synonyms': ['hardback_book', 'hardcover_book'], 'def': 'a book with cardboard or cloth or leather covers', 'name': 'hardback_book'}, {'frequency': 'r', 'id': 553, 'synset': 'harmonium.n.01', 'synonyms': ['harmonium', 'organ_(musical_instrument)', 'reed_organ_(musical_instrument)'], 'def': 'a free-reed instrument in which air is forced through the reeds by bellows', 'name': 'harmonium'}, {'frequency': 'f', 'id': 554, 'synset': 'hat.n.01', 'synonyms': ['hat'], 'def': 'headwear that protects the head from bad weather, sun, or worn for fashion', 'name': 'hat'}, {'frequency': 'r', 'id': 555, 'synset': 'hatbox.n.01', 'synonyms': ['hatbox'], 'def': 'a round piece of luggage for carrying hats', 'name': 'hatbox'}, {'frequency': 'r', 'id': 556, 'synset': 'hatch.n.03', 'synonyms': ['hatch'], 'def': 'a movable barrier covering a hatchway', 'name': 'hatch'}, {'frequency': 'c', 'id': 557, 'synset': 'head_covering.n.01', 'synonyms': ['veil'], 'def': 'a garment that covers the head and face', 'name': 'veil'}, {'frequency': 'f', 'id': 558, 'synset': 'headband.n.01', 'synonyms': ['headband'], 'def': 'a band worn around or over the head', 'name': 'headband'}, {'frequency': 'f', 'id': 559, 'synset': 'headboard.n.01', 'synonyms': ['headboard'], 'def': 'a vertical board or panel forming the head of a bedstead', 'name': 'headboard'}, {'frequency': 'f', 'id': 560, 'synset': 'headlight.n.01', 'synonyms': ['headlight', 'headlamp'], 'def': 'a powerful light with reflector; attached to the front of an automobile or locomotive', 'name': 'headlight'}, {'frequency': 'c', 'id': 561, 'synset': 'headscarf.n.01', 'synonyms': ['headscarf'], 'def': 'a kerchief worn over the head and tied under the chin', 'name': 'headscarf'}, {'frequency': 'r', 'id': 562, 'synset': 'headset.n.01', 'synonyms': ['headset'], 'def': 'receiver consisting of a pair of headphones', 'name': 'headset'}, {'frequency': 'c', 'id': 563, 'synset': 'headstall.n.01', 'synonyms': ['headstall_(for_horses)', 'headpiece_(for_horses)'], 'def': "the band that is the part of a bridle that fits around a horse's head", 'name': 'headstall_(for_horses)'}, {'frequency': 'r', 'id': 564, 'synset': 'hearing_aid.n.02', 'synonyms': ['hearing_aid'], 'def': 'an acoustic device used to direct sound to the ear of a hearing-impaired person', 'name': 'hearing_aid'}, {'frequency': 'c', 'id': 565, 'synset': 'heart.n.02', 'synonyms': ['heart'], 'def': 'a muscular organ; its contractions move the blood through the body', 'name': 'heart'}, {'frequency': 'c', 'id': 566, 'synset': 'heater.n.01', 'synonyms': ['heater', 'warmer'], 'def': 'device that heats water or supplies warmth to a room', 'name': 'heater'}, {'frequency': 'c', 'id': 567, 'synset': 'helicopter.n.01', 'synonyms': ['helicopter'], 'def': 'an aircraft without wings that obtains its lift from the rotation of overhead blades', 'name': 'helicopter'}, {'frequency': 'f', 'id': 568, 'synset': 'helmet.n.02', 'synonyms': ['helmet'], 'def': 'a protective headgear made of hard material to resist blows', 'name': 'helmet'}, {'frequency': 'r', 'id': 569, 'synset': 'heron.n.02', 'synonyms': ['heron'], 'def': 'grey or white wading bird with long neck and long legs and (usually) long bill', 'name': 'heron'}, {'frequency': 'c', 'id': 570, 'synset': 'highchair.n.01', 'synonyms': ['highchair', 'feeding_chair'], 'def': 'a chair for feeding a very young child', 'name': 'highchair'}, {'frequency': 'f', 'id': 571, 'synset': 'hinge.n.01', 'synonyms': ['hinge'], 'def': 'a joint that holds two parts together so that one can swing relative to the other', 'name': 'hinge'}, {'frequency': 'r', 'id': 572, 'synset': 'hippopotamus.n.01', 'synonyms': ['hippopotamus'], 'def': 'massive thick-skinned animal living in or around rivers of tropical Africa', 'name': 'hippopotamus'}, {'frequency': 'r', 'id': 573, 'synset': 'hockey_stick.n.01', 'synonyms': ['hockey_stick'], 'def': 'sports implement consisting of a stick used by hockey players to move the puck', 'name': 'hockey_stick'}, {'frequency': 'c', 'id': 574, 'synset': 'hog.n.03', 'synonyms': ['hog', 'pig'], 'def': 'domestic swine', 'name': 'hog'}, {'frequency': 'f', 'id': 575, 'synset': 'home_plate.n.01', 'synonyms': ['home_plate_(baseball)', 'home_base_(baseball)'], 'def': '(baseball) a rubber slab where the batter stands; it must be touched by a base runner in order to score', 'name': 'home_plate_(baseball)'}, {'frequency': 'c', 'id': 576, 'synset': 'honey.n.01', 'synonyms': ['honey'], 'def': 'a sweet yellow liquid produced by bees', 'name': 'honey'}, {'frequency': 'f', 'id': 577, 'synset': 'hood.n.06', 'synonyms': ['fume_hood', 'exhaust_hood'], 'def': 'metal covering leading to a vent that exhausts smoke or fumes', 'name': 'fume_hood'}, {'frequency': 'f', 'id': 578, 'synset': 'hook.n.05', 'synonyms': ['hook'], 'def': 'a curved or bent implement for suspending or pulling something', 'name': 'hook'}, {'frequency': 'f', 'id': 579, 'synset': 'horse.n.01', 'synonyms': ['horse'], 'def': 'a common horse', 'name': 'horse'}, {'frequency': 'f', 'id': 580, 'synset': 'hose.n.03', 'synonyms': ['hose', 'hosepipe'], 'def': 'a flexible pipe for conveying a liquid or gas', 'name': 'hose'}, {'frequency': 'r', 'id': 581, 'synset': 'hot-air_balloon.n.01', 'synonyms': ['hot-air_balloon'], 'def': 'balloon for travel through the air in a basket suspended below a large bag of heated air', 'name': 'hot-air_balloon'}, {'frequency': 'r', 'id': 582, 'synset': 'hot_plate.n.01', 'synonyms': ['hotplate'], 'def': 'a portable electric appliance for heating or cooking or keeping food warm', 'name': 'hotplate'}, {'frequency': 'c', 'id': 583, 'synset': 'hot_sauce.n.01', 'synonyms': ['hot_sauce'], 'def': 'a pungent peppery sauce', 'name': 'hot_sauce'}, {'frequency': 'r', 'id': 584, 'synset': 'hourglass.n.01', 'synonyms': ['hourglass'], 'def': 'a sandglass timer that runs for sixty minutes', 'name': 'hourglass'}, {'frequency': 'r', 'id': 585, 'synset': 'houseboat.n.01', 'synonyms': ['houseboat'], 'def': 'a barge that is designed and equipped for use as a dwelling', 'name': 'houseboat'}, {'frequency': 'r', 'id': 586, 'synset': 'hummingbird.n.01', 'synonyms': ['hummingbird'], 'def': 'tiny American bird having brilliant iridescent plumage and long slender bills', 'name': 'hummingbird'}, {'frequency': 'r', 'id': 587, 'synset': 'hummus.n.01', 'synonyms': ['hummus', 'humus', 'hommos', 'hoummos', 'humous'], 'def': 'a thick spread made from mashed chickpeas', 'name': 'hummus'}, {'frequency': 'c', 'id': 588, 'synset': 'ice_bear.n.01', 'synonyms': ['polar_bear'], 'def': 'white bear of Arctic regions', 'name': 'polar_bear'}, {'frequency': 'c', 'id': 589, 'synset': 'ice_cream.n.01', 'synonyms': ['icecream'], 'def': 'frozen dessert containing cream and sugar and flavoring', 'name': 'icecream'}, {'frequency': 'r', 'id': 590, 'synset': 'ice_lolly.n.01', 'synonyms': ['popsicle'], 'def': 'ice cream or water ice on a small wooden stick', 'name': 'popsicle'}, {'frequency': 'c', 'id': 591, 'synset': 'ice_maker.n.01', 'synonyms': ['ice_maker'], 'def': 'an appliance included in some electric refrigerators for making ice cubes', 'name': 'ice_maker'}, {'frequency': 'r', 'id': 592, 'synset': 'ice_pack.n.01', 'synonyms': ['ice_pack', 'ice_bag'], 'def': 'a waterproof bag filled with ice: applied to the body (especially the head) to cool or reduce swelling', 'name': 'ice_pack'}, {'frequency': 'r', 'id': 593, 'synset': 'ice_skate.n.01', 'synonyms': ['ice_skate'], 'def': 'skate consisting of a boot with a steel blade fitted to the sole', 'name': 'ice_skate'}, {'frequency': 'r', 'id': 594, 'synset': 'ice_tea.n.01', 'synonyms': ['ice_tea', 'iced_tea'], 'def': 'strong tea served over ice', 'name': 'ice_tea'}, {'frequency': 'c', 'id': 595, 'synset': 'igniter.n.01', 'synonyms': ['igniter', 'ignitor', 'lighter'], 'def': 'a substance or device used to start a fire', 'name': 'igniter'}, {'frequency': 'r', 'id': 596, 'synset': 'incense.n.01', 'synonyms': ['incense'], 'def': 'a substance that produces a fragrant odor when burned', 'name': 'incense'}, {'frequency': 'r', 'id': 597, 'synset': 'inhaler.n.01', 'synonyms': ['inhaler', 'inhalator'], 'def': 'a dispenser that produces a chemical vapor to be inhaled through mouth or nose', 'name': 'inhaler'}, {'frequency': 'c', 'id': 598, 'synset': 'ipod.n.01', 'synonyms': ['iPod'], 'def': 'a pocket-sized device used to play music files', 'name': 'iPod'}, {'frequency': 'c', 'id': 599, 'synset': 'iron.n.04', 'synonyms': ['iron_(for_clothing)', 'smoothing_iron_(for_clothing)'], 'def': 'home appliance consisting of a flat metal base that is heated and used to smooth cloth', 'name': 'iron_(for_clothing)'}, {'frequency': 'r', 'id': 600, 'synset': 'ironing_board.n.01', 'synonyms': ['ironing_board'], 'def': 'narrow padded board on collapsible supports; used for ironing clothes', 'name': 'ironing_board'}, {'frequency': 'f', 'id': 601, 'synset': 'jacket.n.01', 'synonyms': ['jacket'], 'def': 'a waist-length coat', 'name': 'jacket'}, {'frequency': 'r', 'id': 602, 'synset': 'jam.n.01', 'synonyms': ['jam'], 'def': 'preserve of crushed fruit', 'name': 'jam'}, {'frequency': 'f', 'id': 603, 'synset': 'jean.n.01', 'synonyms': ['jean', 'blue_jean', 'denim'], 'def': '(usually plural) close-fitting trousers of heavy denim for manual work or casual wear', 'name': 'jean'}, {'frequency': 'c', 'id': 604, 'synset': 'jeep.n.01', 'synonyms': ['jeep', 'landrover'], 'def': 'a car suitable for traveling over rough terrain', 'name': 'jeep'}, {'frequency': 'r', 'id': 605, 'synset': 'jelly_bean.n.01', 'synonyms': ['jelly_bean', 'jelly_egg'], 'def': 'sugar-glazed jellied candy', 'name': 'jelly_bean'}, {'frequency': 'f', 'id': 606, 'synset': 'jersey.n.03', 'synonyms': ['jersey', 'T-shirt', 'tee_shirt'], 'def': 'a close-fitting pullover shirt', 'name': 'jersey'}, {'frequency': 'c', 'id': 607, 'synset': 'jet.n.01', 'synonyms': ['jet_plane', 'jet-propelled_plane'], 'def': 'an airplane powered by one or more jet engines', 'name': 'jet_plane'}, {'frequency': 'c', 'id': 608, 'synset': 'jewelry.n.01', 'synonyms': ['jewelry', 'jewellery'], 'def': 'an adornment (as a bracelet or ring or necklace) made of precious metals and set with gems (or imitation gems)', 'name': 'jewelry'}, {'frequency': 'r', 'id': 609, 'synset': 'joystick.n.02', 'synonyms': ['joystick'], 'def': 'a control device for computers consisting of a vertical handle that can move freely in two directions', 'name': 'joystick'}, {'frequency': 'r', 'id': 610, 'synset': 'jump_suit.n.01', 'synonyms': ['jumpsuit'], 'def': "one-piece garment fashioned after a parachutist's uniform", 'name': 'jumpsuit'}, {'frequency': 'c', 'id': 611, 'synset': 'kayak.n.01', 'synonyms': ['kayak'], 'def': 'a small canoe consisting of a light frame made watertight with animal skins', 'name': 'kayak'}, {'frequency': 'r', 'id': 612, 'synset': 'keg.n.02', 'synonyms': ['keg'], 'def': 'small cask or barrel', 'name': 'keg'}, {'frequency': 'r', 'id': 613, 'synset': 'kennel.n.01', 'synonyms': ['kennel', 'doghouse'], 'def': 'outbuilding that serves as a shelter for a dog', 'name': 'kennel'}, {'frequency': 'c', 'id': 614, 'synset': 'kettle.n.01', 'synonyms': ['kettle', 'boiler'], 'def': 'a metal pot for stewing or boiling; usually has a lid', 'name': 'kettle'}, {'frequency': 'f', 'id': 615, 'synset': 'key.n.01', 'synonyms': ['key'], 'def': 'metal instrument used to unlock a lock', 'name': 'key'}, {'frequency': 'r', 'id': 616, 'synset': 'keycard.n.01', 'synonyms': ['keycard'], 'def': 'a plastic card used to gain access typically to a door', 'name': 'keycard'}, {'frequency': 'r', 'id': 617, 'synset': 'kilt.n.01', 'synonyms': ['kilt'], 'def': 'a knee-length pleated tartan skirt worn by men as part of the traditional dress in the Highlands of northern Scotland', 'name': 'kilt'}, {'frequency': 'c', 'id': 618, 'synset': 'kimono.n.01', 'synonyms': ['kimono'], 'def': 'a loose robe; imitated from robes originally worn by Japanese', 'name': 'kimono'}, {'frequency': 'f', 'id': 619, 'synset': 'kitchen_sink.n.01', 'synonyms': ['kitchen_sink'], 'def': 'a sink in a kitchen', 'name': 'kitchen_sink'}, {'frequency': 'c', 'id': 620, 'synset': 'kitchen_table.n.01', 'synonyms': ['kitchen_table'], 'def': 'a table in the kitchen', 'name': 'kitchen_table'}, {'frequency': 'f', 'id': 621, 'synset': 'kite.n.03', 'synonyms': ['kite'], 'def': 'plaything consisting of a light frame covered with tissue paper; flown in wind at end of a string', 'name': 'kite'}, {'frequency': 'c', 'id': 622, 'synset': 'kitten.n.01', 'synonyms': ['kitten', 'kitty'], 'def': 'young domestic cat', 'name': 'kitten'}, {'frequency': 'c', 'id': 623, 'synset': 'kiwi.n.03', 'synonyms': ['kiwi_fruit'], 'def': 'fuzzy brown egg-shaped fruit with slightly tart green flesh', 'name': 'kiwi_fruit'}, {'frequency': 'f', 'id': 624, 'synset': 'knee_pad.n.01', 'synonyms': ['knee_pad'], 'def': 'protective garment consisting of a pad worn by football or baseball or hockey players', 'name': 'knee_pad'}, {'frequency': 'f', 'id': 625, 'synset': 'knife.n.01', 'synonyms': ['knife'], 'def': 'tool with a blade and point used as a cutting instrument', 'name': 'knife'}, {'frequency': 'r', 'id': 626, 'synset': 'knight.n.02', 'synonyms': ['knight_(chess_piece)', 'horse_(chess_piece)'], 'def': 'a chess game piece shaped to resemble the head of a horse', 'name': 'knight_(chess_piece)'}, {'frequency': 'r', 'id': 627, 'synset': 'knitting_needle.n.01', 'synonyms': ['knitting_needle'], 'def': 'needle consisting of a slender rod with pointed ends; usually used in pairs', 'name': 'knitting_needle'}, {'frequency': 'f', 'id': 628, 'synset': 'knob.n.02', 'synonyms': ['knob'], 'def': 'a round handle often found on a door', 'name': 'knob'}, {'frequency': 'r', 'id': 629, 'synset': 'knocker.n.05', 'synonyms': ['knocker_(on_a_door)', 'doorknocker'], 'def': 'a device (usually metal and ornamental) attached by a hinge to a door', 'name': 'knocker_(on_a_door)'}, {'frequency': 'r', 'id': 630, 'synset': 'koala.n.01', 'synonyms': ['koala', 'koala_bear'], 'def': 'sluggish tailless Australian marsupial with grey furry ears and coat', 'name': 'koala'}, {'frequency': 'r', 'id': 631, 'synset': 'lab_coat.n.01', 'synonyms': ['lab_coat', 'laboratory_coat'], 'def': 'a light coat worn to protect clothing from substances used while working in a laboratory', 'name': 'lab_coat'}, {'frequency': 'f', 'id': 632, 'synset': 'ladder.n.01', 'synonyms': ['ladder'], 'def': 'steps consisting of two parallel members connected by rungs', 'name': 'ladder'}, {'frequency': 'c', 'id': 633, 'synset': 'ladle.n.01', 'synonyms': ['ladle'], 'def': 'a spoon-shaped vessel with a long handle frequently used to transfer liquids', 'name': 'ladle'}, {'frequency': 'r', 'id': 634, 'synset': 'ladybug.n.01', 'synonyms': ['ladybug', 'ladybeetle', 'ladybird_beetle'], 'def': 'small round bright-colored and spotted beetle, typically red and black', 'name': 'ladybug'}, {'frequency': 'c', 'id': 635, 'synset': 'lamb.n.01', 'synonyms': ['lamb_(animal)'], 'def': 'young sheep', 'name': 'lamb_(animal)'}, {'frequency': 'r', 'id': 636, 'synset': 'lamb_chop.n.01', 'synonyms': ['lamb-chop', 'lambchop'], 'def': 'chop cut from a lamb', 'name': 'lamb-chop'}, {'frequency': 'f', 'id': 637, 'synset': 'lamp.n.02', 'synonyms': ['lamp'], 'def': 'a piece of furniture holding one or more electric light bulbs', 'name': 'lamp'}, {'frequency': 'f', 'id': 638, 'synset': 'lamppost.n.01', 'synonyms': ['lamppost'], 'def': 'a metal post supporting an outdoor lamp (such as a streetlight)', 'name': 'lamppost'}, {'frequency': 'f', 'id': 639, 'synset': 'lampshade.n.01', 'synonyms': ['lampshade'], 'def': 'a protective ornamental shade used to screen a light bulb from direct view', 'name': 'lampshade'}, {'frequency': 'c', 'id': 640, 'synset': 'lantern.n.01', 'synonyms': ['lantern'], 'def': 'light in a transparent protective case', 'name': 'lantern'}, {'frequency': 'f', 'id': 641, 'synset': 'lanyard.n.02', 'synonyms': ['lanyard', 'laniard'], 'def': 'a cord worn around the neck to hold a knife or whistle, etc.', 'name': 'lanyard'}, {'frequency': 'f', 'id': 642, 'synset': 'laptop.n.01', 'synonyms': ['laptop_computer', 'notebook_computer'], 'def': 'a portable computer small enough to use in your lap', 'name': 'laptop_computer'}, {'frequency': 'r', 'id': 643, 'synset': 'lasagna.n.01', 'synonyms': ['lasagna', 'lasagne'], 'def': 'baked dish of layers of lasagna pasta with sauce and cheese and meat or vegetables', 'name': 'lasagna'}, {'frequency': 'c', 'id': 644, 'synset': 'latch.n.02', 'synonyms': ['latch'], 'def': 'a bar that can be lowered or slid into a groove to fasten a door or gate', 'name': 'latch'}, {'frequency': 'r', 'id': 645, 'synset': 'lawn_mower.n.01', 'synonyms': ['lawn_mower'], 'def': 'garden tool for mowing grass on lawns', 'name': 'lawn_mower'}, {'frequency': 'r', 'id': 646, 'synset': 'leather.n.01', 'synonyms': ['leather'], 'def': 'an animal skin made smooth and flexible by removing the hair and then tanning', 'name': 'leather'}, {'frequency': 'c', 'id': 647, 'synset': 'legging.n.01', 'synonyms': ['legging_(clothing)', 'leging_(clothing)', 'leg_covering'], 'def': 'a garment covering the leg (usually extending from the knee to the ankle)', 'name': 'legging_(clothing)'}, {'frequency': 'c', 'id': 648, 'synset': 'lego.n.01', 'synonyms': ['Lego', 'Lego_set'], 'def': "a child's plastic construction set for making models from blocks", 'name': 'Lego'}, {'frequency': 'f', 'id': 649, 'synset': 'lemon.n.01', 'synonyms': ['lemon'], 'def': 'yellow oval fruit with juicy acidic flesh', 'name': 'lemon'}, {'frequency': 'r', 'id': 650, 'synset': 'lemonade.n.01', 'synonyms': ['lemonade'], 'def': 'sweetened beverage of diluted lemon juice', 'name': 'lemonade'}, {'frequency': 'f', 'id': 651, 'synset': 'lettuce.n.02', 'synonyms': ['lettuce'], 'def': 'leafy plant commonly eaten in salad or on sandwiches', 'name': 'lettuce'}, {'frequency': 'f', 'id': 652, 'synset': 'license_plate.n.01', 'synonyms': ['license_plate', 'numberplate'], 'def': "a plate mounted on the front and back of car and bearing the car's registration number", 'name': 'license_plate'}, {'frequency': 'f', 'id': 653, 'synset': 'life_buoy.n.01', 'synonyms': ['life_buoy', 'lifesaver', 'life_belt', 'life_ring'], 'def': 'a ring-shaped life preserver used to prevent drowning (NOT a life-jacket or vest)', 'name': 'life_buoy'}, {'frequency': 'f', 'id': 654, 'synset': 'life_jacket.n.01', 'synonyms': ['life_jacket', 'life_vest'], 'def': 'life preserver consisting of a sleeveless jacket of buoyant or inflatable design', 'name': 'life_jacket'}, {'frequency': 'f', 'id': 655, 'synset': 'light_bulb.n.01', 'synonyms': ['lightbulb'], 'def': 'glass bulb or tube shaped electric device that emits light (DO NOT MARK LAMPS AS A WHOLE)', 'name': 'lightbulb'}, {'frequency': 'r', 'id': 656, 'synset': 'lightning_rod.n.02', 'synonyms': ['lightning_rod', 'lightning_conductor'], 'def': 'a metallic conductor that is attached to a high point and leads to the ground', 'name': 'lightning_rod'}, {'frequency': 'c', 'id': 657, 'synset': 'lime.n.06', 'synonyms': ['lime'], 'def': 'the green acidic fruit of any of various lime trees', 'name': 'lime'}, {'frequency': 'r', 'id': 658, 'synset': 'limousine.n.01', 'synonyms': ['limousine'], 'def': 'long luxurious car; usually driven by a chauffeur', 'name': 'limousine'}, {'frequency': 'r', 'id': 659, 'synset': 'linen.n.02', 'synonyms': ['linen_paper'], 'def': 'a high-quality paper made of linen fibers or with a linen finish', 'name': 'linen_paper'}, {'frequency': 'c', 'id': 660, 'synset': 'lion.n.01', 'synonyms': ['lion'], 'def': 'large gregarious predatory cat of Africa and India', 'name': 'lion'}, {'frequency': 'c', 'id': 661, 'synset': 'lip_balm.n.01', 'synonyms': ['lip_balm'], 'def': 'a balm applied to the lips', 'name': 'lip_balm'}, {'frequency': 'c', 'id': 662, 'synset': 'lipstick.n.01', 'synonyms': ['lipstick', 'lip_rouge'], 'def': 'makeup that is used to color the lips', 'name': 'lipstick'}, {'frequency': 'r', 'id': 663, 'synset': 'liquor.n.01', 'synonyms': ['liquor', 'spirits', 'hard_liquor', 'liqueur', 'cordial'], 'def': 'an alcoholic beverage that is distilled rather than fermented', 'name': 'liquor'}, {'frequency': 'r', 'id': 664, 'synset': 'lizard.n.01', 'synonyms': ['lizard'], 'def': 'a reptile with usually two pairs of legs and a tapering tail', 'name': 'lizard'}, {'frequency': 'r', 'id': 665, 'synset': 'loafer.n.02', 'synonyms': ['Loafer_(type_of_shoe)'], 'def': 'a low leather step-in shoe', 'name': 'Loafer_(type_of_shoe)'}, {'frequency': 'f', 'id': 666, 'synset': 'log.n.01', 'synonyms': ['log'], 'def': 'a segment of the trunk of a tree when stripped of branches', 'name': 'log'}, {'frequency': 'c', 'id': 667, 'synset': 'lollipop.n.02', 'synonyms': ['lollipop'], 'def': 'hard candy on a stick', 'name': 'lollipop'}, {'frequency': 'c', 'id': 668, 'synset': 'lotion.n.01', 'synonyms': ['lotion'], 'def': 'any of various cosmetic preparations that are applied to the skin', 'name': 'lotion'}, {'frequency': 'f', 'id': 669, 'synset': 'loudspeaker.n.01', 'synonyms': ['speaker_(stero_equipment)'], 'def': 'electronic device that produces sound often as part of a stereo system', 'name': 'speaker_(stero_equipment)'}, {'frequency': 'c', 'id': 670, 'synset': 'love_seat.n.01', 'synonyms': ['loveseat'], 'def': 'small sofa that seats two people', 'name': 'loveseat'}, {'frequency': 'r', 'id': 671, 'synset': 'machine_gun.n.01', 'synonyms': ['machine_gun'], 'def': 'a rapidly firing automatic gun', 'name': 'machine_gun'}, {'frequency': 'f', 'id': 672, 'synset': 'magazine.n.02', 'synonyms': ['magazine'], 'def': 'a paperback periodic publication', 'name': 'magazine'}, {'frequency': 'f', 'id': 673, 'synset': 'magnet.n.01', 'synonyms': ['magnet'], 'def': 'a device that attracts iron and produces a magnetic field', 'name': 'magnet'}, {'frequency': 'r', 'id': 674, 'synset': 'mail_slot.n.01', 'synonyms': ['mail_slot'], 'def': 'a slot (usually in a door) through which mail can be delivered', 'name': 'mail_slot'}, {'frequency': 'c', 'id': 675, 'synset': 'mailbox.n.01', 'synonyms': ['mailbox_(at_home)', 'letter_box_(at_home)'], 'def': 'a private box for delivery of mail', 'name': 'mailbox_(at_home)'}, {'frequency': 'r', 'id': 676, 'synset': 'mallet.n.01', 'synonyms': ['mallet'], 'def': 'a sports implement with a long handle and a hammer-like head used to hit a ball', 'name': 'mallet'}, {'frequency': 'r', 'id': 677, 'synset': 'mammoth.n.01', 'synonyms': ['mammoth'], 'def': 'any of numerous extinct elephants widely distributed in the Pleistocene', 'name': 'mammoth'}, {'frequency': 'c', 'id': 678, 'synset': 'mandarin.n.05', 'synonyms': ['mandarin_orange'], 'def': 'a somewhat flat reddish-orange loose skinned citrus of China', 'name': 'mandarin_orange'}, {'frequency': 'c', 'id': 679, 'synset': 'manger.n.01', 'synonyms': ['manger', 'trough'], 'def': 'a container (usually in a barn or stable) from which cattle or horses feed', 'name': 'manger'}, {'frequency': 'f', 'id': 680, 'synset': 'manhole.n.01', 'synonyms': ['manhole'], 'def': 'a hole (usually with a flush cover) through which a person can gain access to an underground structure', 'name': 'manhole'}, {'frequency': 'c', 'id': 681, 'synset': 'map.n.01', 'synonyms': ['map'], 'def': "a diagrammatic representation of the earth's surface (or part of it)", 'name': 'map'}, {'frequency': 'c', 'id': 682, 'synset': 'marker.n.03', 'synonyms': ['marker'], 'def': 'a writing implement for making a mark', 'name': 'marker'}, {'frequency': 'r', 'id': 683, 'synset': 'martini.n.01', 'synonyms': ['martini'], 'def': 'a cocktail made of gin (or vodka) with dry vermouth', 'name': 'martini'}, {'frequency': 'r', 'id': 684, 'synset': 'mascot.n.01', 'synonyms': ['mascot'], 'def': 'a person or animal that is adopted by a team or other group as a symbolic figure', 'name': 'mascot'}, {'frequency': 'c', 'id': 685, 'synset': 'mashed_potato.n.01', 'synonyms': ['mashed_potato'], 'def': 'potato that has been peeled and boiled and then mashed', 'name': 'mashed_potato'}, {'frequency': 'r', 'id': 686, 'synset': 'masher.n.02', 'synonyms': ['masher'], 'def': 'a kitchen utensil used for mashing (e.g. potatoes)', 'name': 'masher'}, {'frequency': 'f', 'id': 687, 'synset': 'mask.n.04', 'synonyms': ['mask', 'facemask'], 'def': 'a protective covering worn over the face', 'name': 'mask'}, {'frequency': 'f', 'id': 688, 'synset': 'mast.n.01', 'synonyms': ['mast'], 'def': 'a vertical spar for supporting sails', 'name': 'mast'}, {'frequency': 'c', 'id': 689, 'synset': 'mat.n.03', 'synonyms': ['mat_(gym_equipment)', 'gym_mat'], 'def': 'sports equipment consisting of a piece of thick padding on the floor for gymnastics', 'name': 'mat_(gym_equipment)'}, {'frequency': 'r', 'id': 690, 'synset': 'matchbox.n.01', 'synonyms': ['matchbox'], 'def': 'a box for holding matches', 'name': 'matchbox'}, {'frequency': 'f', 'id': 691, 'synset': 'mattress.n.01', 'synonyms': ['mattress'], 'def': 'a thick pad filled with resilient material used as a bed or part of a bed', 'name': 'mattress'}, {'frequency': 'c', 'id': 692, 'synset': 'measuring_cup.n.01', 'synonyms': ['measuring_cup'], 'def': 'graduated cup used to measure liquid or granular ingredients', 'name': 'measuring_cup'}, {'frequency': 'c', 'id': 693, 'synset': 'measuring_stick.n.01', 'synonyms': ['measuring_stick', 'ruler_(measuring_stick)', 'measuring_rod'], 'def': 'measuring instrument having a sequence of marks at regular intervals', 'name': 'measuring_stick'}, {'frequency': 'c', 'id': 694, 'synset': 'meatball.n.01', 'synonyms': ['meatball'], 'def': 'ground meat formed into a ball and fried or simmered in broth', 'name': 'meatball'}, {'frequency': 'c', 'id': 695, 'synset': 'medicine.n.02', 'synonyms': ['medicine'], 'def': 'something that treats or prevents or alleviates the symptoms of disease', 'name': 'medicine'}, {'frequency': 'r', 'id': 696, 'synset': 'melon.n.01', 'synonyms': ['melon'], 'def': 'fruit of the gourd family having a hard rind and sweet juicy flesh', 'name': 'melon'}, {'frequency': 'f', 'id': 697, 'synset': 'microphone.n.01', 'synonyms': ['microphone'], 'def': 'device for converting sound waves into electrical energy', 'name': 'microphone'}, {'frequency': 'r', 'id': 698, 'synset': 'microscope.n.01', 'synonyms': ['microscope'], 'def': 'magnifier of the image of small objects', 'name': 'microscope'}, {'frequency': 'f', 'id': 699, 'synset': 'microwave.n.02', 'synonyms': ['microwave_oven'], 'def': 'kitchen appliance that cooks food by passing an electromagnetic wave through it', 'name': 'microwave_oven'}, {'frequency': 'r', 'id': 700, 'synset': 'milestone.n.01', 'synonyms': ['milestone', 'milepost'], 'def': 'stone post at side of a road to show distances', 'name': 'milestone'}, {'frequency': 'c', 'id': 701, 'synset': 'milk.n.01', 'synonyms': ['milk'], 'def': 'a white nutritious liquid secreted by mammals and used as food by human beings', 'name': 'milk'}, {'frequency': 'f', 'id': 702, 'synset': 'minivan.n.01', 'synonyms': ['minivan'], 'def': 'a small box-shaped passenger van', 'name': 'minivan'}, {'frequency': 'r', 'id': 703, 'synset': 'mint.n.05', 'synonyms': ['mint_candy'], 'def': 'a candy that is flavored with a mint oil', 'name': 'mint_candy'}, {'frequency': 'f', 'id': 704, 'synset': 'mirror.n.01', 'synonyms': ['mirror'], 'def': 'polished surface that forms images by reflecting light', 'name': 'mirror'}, {'frequency': 'c', 'id': 705, 'synset': 'mitten.n.01', 'synonyms': ['mitten'], 'def': 'glove that encases the thumb separately and the other four fingers together', 'name': 'mitten'}, {'frequency': 'c', 'id': 706, 'synset': 'mixer.n.04', 'synonyms': ['mixer_(kitchen_tool)', 'stand_mixer'], 'def': 'a kitchen utensil that is used for mixing foods', 'name': 'mixer_(kitchen_tool)'}, {'frequency': 'c', 'id': 707, 'synset': 'money.n.03', 'synonyms': ['money'], 'def': 'the official currency issued by a government or national bank', 'name': 'money'}, {'frequency': 'f', 'id': 708, 'synset': 'monitor.n.04', 'synonyms': ['monitor_(computer_equipment) computer_monitor'], 'def': 'a computer monitor', 'name': 'monitor_(computer_equipment) computer_monitor'}, {'frequency': 'c', 'id': 709, 'synset': 'monkey.n.01', 'synonyms': ['monkey'], 'def': 'any of various long-tailed primates', 'name': 'monkey'}, {'frequency': 'f', 'id': 710, 'synset': 'motor.n.01', 'synonyms': ['motor'], 'def': 'machine that converts other forms of energy into mechanical energy and so imparts motion', 'name': 'motor'}, {'frequency': 'f', 'id': 711, 'synset': 'motor_scooter.n.01', 'synonyms': ['motor_scooter', 'scooter'], 'def': 'a wheeled vehicle with small wheels and a low-powered engine', 'name': 'motor_scooter'}, {'frequency': 'r', 'id': 712, 'synset': 'motor_vehicle.n.01', 'synonyms': ['motor_vehicle', 'automotive_vehicle'], 'def': 'a self-propelled wheeled vehicle that does not run on rails', 'name': 'motor_vehicle'}, {'frequency': 'r', 'id': 713, 'synset': 'motorboat.n.01', 'synonyms': ['motorboat', 'powerboat'], 'def': 'a boat propelled by an internal-combustion engine', 'name': 'motorboat'}, {'frequency': 'f', 'id': 714, 'synset': 'motorcycle.n.01', 'synonyms': ['motorcycle'], 'def': 'a motor vehicle with two wheels and a strong frame', 'name': 'motorcycle'}, {'frequency': 'f', 'id': 715, 'synset': 'mound.n.01', 'synonyms': ['mound_(baseball)', "pitcher's_mound"], 'def': '(baseball) the slight elevation on which the pitcher stands', 'name': 'mound_(baseball)'}, {'frequency': 'r', 'id': 716, 'synset': 'mouse.n.01', 'synonyms': ['mouse_(animal_rodent)'], 'def': 'a small rodent with pointed snouts and small ears on elongated bodies with slender usually hairless tails', 'name': 'mouse_(animal_rodent)'}, {'frequency': 'f', 'id': 717, 'synset': 'mouse.n.04', 'synonyms': ['mouse_(computer_equipment)', 'computer_mouse'], 'def': 'a computer input device that controls an on-screen pointer', 'name': 'mouse_(computer_equipment)'}, {'frequency': 'f', 'id': 718, 'synset': 'mousepad.n.01', 'synonyms': ['mousepad'], 'def': 'a small portable pad that provides an operating surface for a computer mouse', 'name': 'mousepad'}, {'frequency': 'c', 'id': 719, 'synset': 'muffin.n.01', 'synonyms': ['muffin'], 'def': 'a sweet quick bread baked in a cup-shaped pan', 'name': 'muffin'}, {'frequency': 'f', 'id': 720, 'synset': 'mug.n.04', 'synonyms': ['mug'], 'def': 'with handle and usually cylindrical', 'name': 'mug'}, {'frequency': 'f', 'id': 721, 'synset': 'mushroom.n.02', 'synonyms': ['mushroom'], 'def': 'a common mushroom', 'name': 'mushroom'}, {'frequency': 'r', 'id': 722, 'synset': 'music_stool.n.01', 'synonyms': ['music_stool', 'piano_stool'], 'def': 'a stool for piano players; usually adjustable in height', 'name': 'music_stool'}, {'frequency': 'r', 'id': 723, 'synset': 'musical_instrument.n.01', 'synonyms': ['musical_instrument', 'instrument_(musical)'], 'def': 'any of various devices or contrivances that can be used to produce musical tones or sounds', 'name': 'musical_instrument'}, {'frequency': 'r', 'id': 724, 'synset': 'nailfile.n.01', 'synonyms': ['nailfile'], 'def': 'a small flat file for shaping the nails', 'name': 'nailfile'}, {'frequency': 'r', 'id': 725, 'synset': 'nameplate.n.01', 'synonyms': ['nameplate'], 'def': 'a plate bearing a name', 'name': 'nameplate'}, {'frequency': 'f', 'id': 726, 'synset': 'napkin.n.01', 'synonyms': ['napkin', 'table_napkin', 'serviette'], 'def': 'a small piece of table linen or paper that is used to wipe the mouth and to cover the lap in order to protect clothing', 'name': 'napkin'}, {'frequency': 'r', 'id': 727, 'synset': 'neckerchief.n.01', 'synonyms': ['neckerchief'], 'def': 'a kerchief worn around the neck', 'name': 'neckerchief'}, {'frequency': 'f', 'id': 728, 'synset': 'necklace.n.01', 'synonyms': ['necklace'], 'def': 'jewelry consisting of a cord or chain (often bearing gems) worn about the neck as an ornament', 'name': 'necklace'}, {'frequency': 'f', 'id': 729, 'synset': 'necktie.n.01', 'synonyms': ['necktie', 'tie_(necktie)'], 'def': 'neckwear consisting of a long narrow piece of material worn under a collar and tied in knot at the front', 'name': 'necktie'}, {'frequency': 'r', 'id': 730, 'synset': 'needle.n.03', 'synonyms': ['needle'], 'def': 'a sharp pointed implement (usually metal)', 'name': 'needle'}, {'frequency': 'c', 'id': 731, 'synset': 'nest.n.01', 'synonyms': ['nest'], 'def': 'a structure in which animals lay eggs or give birth to their young', 'name': 'nest'}, {'frequency': 'r', 'id': 732, 'synset': 'newsstand.n.01', 'synonyms': ['newsstand'], 'def': 'a stall where newspapers and other periodicals are sold', 'name': 'newsstand'}, {'frequency': 'c', 'id': 733, 'synset': 'nightwear.n.01', 'synonyms': ['nightshirt', 'nightwear', 'sleepwear', 'nightclothes'], 'def': 'garments designed to be worn in bed', 'name': 'nightshirt'}, {'frequency': 'r', 'id': 734, 'synset': 'nosebag.n.01', 'synonyms': ['nosebag_(for_animals)', 'feedbag'], 'def': 'a canvas bag that is used to feed an animal (such as a horse); covers the muzzle and fastens at the top of the head', 'name': 'nosebag_(for_animals)'}, {'frequency': 'r', 'id': 735, 'synset': 'noseband.n.01', 'synonyms': ['noseband_(for_animals)', 'nosepiece_(for_animals)'], 'def': "a strap that is the part of a bridle that goes over the animal's nose", 'name': 'noseband_(for_animals)'}, {'frequency': 'f', 'id': 736, 'synset': 'notebook.n.01', 'synonyms': ['notebook'], 'def': 'a book with blank pages for recording notes or memoranda', 'name': 'notebook'}, {'frequency': 'c', 'id': 737, 'synset': 'notepad.n.01', 'synonyms': ['notepad'], 'def': 'a pad of paper for keeping notes', 'name': 'notepad'}, {'frequency': 'c', 'id': 738, 'synset': 'nut.n.03', 'synonyms': ['nut'], 'def': 'a small metal block (usually square or hexagonal) with internal screw thread to be fitted onto a bolt', 'name': 'nut'}, {'frequency': 'r', 'id': 739, 'synset': 'nutcracker.n.01', 'synonyms': ['nutcracker'], 'def': 'a hand tool used to crack nuts open', 'name': 'nutcracker'}, {'frequency': 'c', 'id': 740, 'synset': 'oar.n.01', 'synonyms': ['oar'], 'def': 'an implement used to propel or steer a boat', 'name': 'oar'}, {'frequency': 'r', 'id': 741, 'synset': 'octopus.n.01', 'synonyms': ['octopus_(food)'], 'def': 'tentacles of octopus prepared as food', 'name': 'octopus_(food)'}, {'frequency': 'r', 'id': 742, 'synset': 'octopus.n.02', 'synonyms': ['octopus_(animal)'], 'def': 'bottom-living cephalopod having a soft oval body with eight long tentacles', 'name': 'octopus_(animal)'}, {'frequency': 'c', 'id': 743, 'synset': 'oil_lamp.n.01', 'synonyms': ['oil_lamp', 'kerosene_lamp', 'kerosine_lamp'], 'def': 'a lamp that burns oil (as kerosine) for light', 'name': 'oil_lamp'}, {'frequency': 'c', 'id': 744, 'synset': 'olive_oil.n.01', 'synonyms': ['olive_oil'], 'def': 'oil from olives', 'name': 'olive_oil'}, {'frequency': 'r', 'id': 745, 'synset': 'omelet.n.01', 'synonyms': ['omelet', 'omelette'], 'def': 'beaten eggs cooked until just set; may be folded around e.g. ham or cheese or jelly', 'name': 'omelet'}, {'frequency': 'f', 'id': 746, 'synset': 'onion.n.01', 'synonyms': ['onion'], 'def': 'the bulb of an onion plant', 'name': 'onion'}, {'frequency': 'f', 'id': 747, 'synset': 'orange.n.01', 'synonyms': ['orange_(fruit)'], 'def': 'orange (FRUIT of an orange tree)', 'name': 'orange_(fruit)'}, {'frequency': 'c', 'id': 748, 'synset': 'orange_juice.n.01', 'synonyms': ['orange_juice'], 'def': 'bottled or freshly squeezed juice of oranges', 'name': 'orange_juice'}, {'frequency': 'r', 'id': 749, 'synset': 'oregano.n.01', 'synonyms': ['oregano', 'marjoram'], 'def': 'aromatic Eurasian perennial herb used in cooking and baking', 'name': 'oregano'}, {'frequency': 'c', 'id': 750, 'synset': 'ostrich.n.02', 'synonyms': ['ostrich'], 'def': 'fast-running African flightless bird with two-toed feet; largest living bird', 'name': 'ostrich'}, {'frequency': 'c', 'id': 751, 'synset': 'ottoman.n.03', 'synonyms': ['ottoman', 'pouf', 'pouffe', 'hassock'], 'def': 'thick cushion used as a seat', 'name': 'ottoman'}, {'frequency': 'c', 'id': 752, 'synset': 'overall.n.01', 'synonyms': ['overalls_(clothing)'], 'def': 'work clothing consisting of denim trousers usually with a bib and shoulder straps', 'name': 'overalls_(clothing)'}, {'frequency': 'c', 'id': 753, 'synset': 'owl.n.01', 'synonyms': ['owl'], 'def': 'nocturnal bird of prey with hawk-like beak and claws and large head with front-facing eyes', 'name': 'owl'}, {'frequency': 'c', 'id': 754, 'synset': 'packet.n.03', 'synonyms': ['packet'], 'def': 'a small package or bundle', 'name': 'packet'}, {'frequency': 'r', 'id': 755, 'synset': 'pad.n.03', 'synonyms': ['inkpad', 'inking_pad', 'stamp_pad'], 'def': 'absorbent material saturated with ink used to transfer ink evenly to a rubber stamp', 'name': 'inkpad'}, {'frequency': 'c', 'id': 756, 'synset': 'pad.n.04', 'synonyms': ['pad'], 'def': 'a flat mass of soft material used for protection, stuffing, or comfort', 'name': 'pad'}, {'frequency': 'c', 'id': 757, 'synset': 'paddle.n.04', 'synonyms': ['paddle', 'boat_paddle'], 'def': 'a short light oar used without an oarlock to propel a canoe or small boat', 'name': 'paddle'}, {'frequency': 'c', 'id': 758, 'synset': 'padlock.n.01', 'synonyms': ['padlock'], 'def': 'a detachable, portable lock', 'name': 'padlock'}, {'frequency': 'r', 'id': 759, 'synset': 'paintbox.n.01', 'synonyms': ['paintbox'], 'def': "a box containing a collection of cubes or tubes of artists' paint", 'name': 'paintbox'}, {'frequency': 'c', 'id': 760, 'synset': 'paintbrush.n.01', 'synonyms': ['paintbrush'], 'def': 'a brush used as an applicator to apply paint', 'name': 'paintbrush'}, {'frequency': 'f', 'id': 761, 'synset': 'painting.n.01', 'synonyms': ['painting'], 'def': 'graphic art consisting of an artistic composition made by applying paints to a surface', 'name': 'painting'}, {'frequency': 'c', 'id': 762, 'synset': 'pajama.n.02', 'synonyms': ['pajamas', 'pyjamas'], 'def': 'loose-fitting nightclothes worn for sleeping or lounging', 'name': 'pajamas'}, {'frequency': 'c', 'id': 763, 'synset': 'palette.n.02', 'synonyms': ['palette', 'pallet'], 'def': 'board that provides a flat surface on which artists mix paints and the range of colors used', 'name': 'palette'}, {'frequency': 'f', 'id': 764, 'synset': 'pan.n.01', 'synonyms': ['pan_(for_cooking)', 'cooking_pan'], 'def': 'cooking utensil consisting of a wide metal vessel', 'name': 'pan_(for_cooking)'}, {'frequency': 'r', 'id': 765, 'synset': 'pan.n.03', 'synonyms': ['pan_(metal_container)'], 'def': 'shallow container made of metal', 'name': 'pan_(metal_container)'}, {'frequency': 'c', 'id': 766, 'synset': 'pancake.n.01', 'synonyms': ['pancake'], 'def': 'a flat cake of thin batter fried on both sides on a griddle', 'name': 'pancake'}, {'frequency': 'r', 'id': 767, 'synset': 'pantyhose.n.01', 'synonyms': ['pantyhose'], 'def': "a woman's tights consisting of underpants and stockings", 'name': 'pantyhose'}, {'frequency': 'r', 'id': 768, 'synset': 'papaya.n.02', 'synonyms': ['papaya'], 'def': 'large oval melon-like tropical fruit with yellowish flesh', 'name': 'papaya'}, {'frequency': 'r', 'id': 769, 'synset': 'paper_clip.n.01', 'synonyms': ['paperclip'], 'def': 'a wire or plastic clip for holding sheets of paper together', 'name': 'paperclip'}, {'frequency': 'f', 'id': 770, 'synset': 'paper_plate.n.01', 'synonyms': ['paper_plate'], 'def': 'a disposable plate made of cardboard', 'name': 'paper_plate'}, {'frequency': 'f', 'id': 771, 'synset': 'paper_towel.n.01', 'synonyms': ['paper_towel'], 'def': 'a disposable towel made of absorbent paper', 'name': 'paper_towel'}, {'frequency': 'r', 'id': 772, 'synset': 'paperback_book.n.01', 'synonyms': ['paperback_book', 'paper-back_book', 'softback_book', 'soft-cover_book'], 'def': 'a book with paper covers', 'name': 'paperback_book'}, {'frequency': 'r', 'id': 773, 'synset': 'paperweight.n.01', 'synonyms': ['paperweight'], 'def': 'a weight used to hold down a stack of papers', 'name': 'paperweight'}, {'frequency': 'c', 'id': 774, 'synset': 'parachute.n.01', 'synonyms': ['parachute'], 'def': 'rescue equipment consisting of a device that fills with air and retards your fall', 'name': 'parachute'}, {'frequency': 'r', 'id': 775, 'synset': 'parakeet.n.01', 'synonyms': ['parakeet', 'parrakeet', 'parroket', 'paraquet', 'paroquet', 'parroquet'], 'def': 'any of numerous small slender long-tailed parrots', 'name': 'parakeet'}, {'frequency': 'c', 'id': 776, 'synset': 'parasail.n.01', 'synonyms': ['parasail_(sports)'], 'def': 'parachute that will lift a person up into the air when it is towed by a motorboat or a car', 'name': 'parasail_(sports)'}, {'frequency': 'r', 'id': 777, 'synset': 'parchment.n.01', 'synonyms': ['parchment'], 'def': 'a superior paper resembling sheepskin', 'name': 'parchment'}, {'frequency': 'r', 'id': 778, 'synset': 'parka.n.01', 'synonyms': ['parka', 'anorak'], 'def': "a kind of heavy jacket (`windcheater' is a British term)", 'name': 'parka'}, {'frequency': 'f', 'id': 779, 'synset': 'parking_meter.n.01', 'synonyms': ['parking_meter'], 'def': 'a coin-operated timer located next to a parking space', 'name': 'parking_meter'}, {'frequency': 'c', 'id': 780, 'synset': 'parrot.n.01', 'synonyms': ['parrot'], 'def': 'usually brightly colored tropical birds with short hooked beaks and the ability to mimic sounds', 'name': 'parrot'}, {'frequency': 'c', 'id': 781, 'synset': 'passenger_car.n.01', 'synonyms': ['passenger_car_(part_of_a_train)', 'coach_(part_of_a_train)'], 'def': 'a railcar where passengers ride', 'name': 'passenger_car_(part_of_a_train)'}, {'frequency': 'r', 'id': 782, 'synset': 'passenger_ship.n.01', 'synonyms': ['passenger_ship'], 'def': 'a ship built to carry passengers', 'name': 'passenger_ship'}, {'frequency': 'r', 'id': 783, 'synset': 'passport.n.02', 'synonyms': ['passport'], 'def': 'a document issued by a country to a citizen allowing that person to travel abroad and re-enter the home country', 'name': 'passport'}, {'frequency': 'f', 'id': 784, 'synset': 'pastry.n.02', 'synonyms': ['pastry'], 'def': 'any of various baked foods made of dough or batter', 'name': 'pastry'}, {'frequency': 'r', 'id': 785, 'synset': 'patty.n.01', 'synonyms': ['patty_(food)'], 'def': 'small flat mass of chopped food', 'name': 'patty_(food)'}, {'frequency': 'c', 'id': 786, 'synset': 'pea.n.01', 'synonyms': ['pea_(food)'], 'def': 'seed of a pea plant used for food', 'name': 'pea_(food)'}, {'frequency': 'c', 'id': 787, 'synset': 'peach.n.03', 'synonyms': ['peach'], 'def': 'downy juicy fruit with sweet yellowish or whitish flesh', 'name': 'peach'}, {'frequency': 'c', 'id': 788, 'synset': 'peanut_butter.n.01', 'synonyms': ['peanut_butter'], 'def': 'a spread made from ground peanuts', 'name': 'peanut_butter'}, {'frequency': 'c', 'id': 789, 'synset': 'pear.n.01', 'synonyms': ['pear'], 'def': 'sweet juicy gritty-textured fruit available in many varieties', 'name': 'pear'}, {'frequency': 'r', 'id': 790, 'synset': 'peeler.n.03', 'synonyms': ['peeler_(tool_for_fruit_and_vegetables)'], 'def': 'a device for peeling vegetables or fruits', 'name': 'peeler_(tool_for_fruit_and_vegetables)'}, {'frequency': 'r', 'id': 791, 'synset': 'pegboard.n.01', 'synonyms': ['pegboard'], 'def': 'a board perforated with regularly spaced holes into which pegs can be fitted', 'name': 'pegboard'}, {'frequency': 'c', 'id': 792, 'synset': 'pelican.n.01', 'synonyms': ['pelican'], 'def': 'large long-winged warm-water seabird having a large bill with a distensible pouch for fish', 'name': 'pelican'}, {'frequency': 'f', 'id': 793, 'synset': 'pen.n.01', 'synonyms': ['pen'], 'def': 'a writing implement with a point from which ink flows', 'name': 'pen'}, {'frequency': 'c', 'id': 794, 'synset': 'pencil.n.01', 'synonyms': ['pencil'], 'def': 'a thin cylindrical pointed writing implement made of wood and graphite', 'name': 'pencil'}, {'frequency': 'r', 'id': 795, 'synset': 'pencil_box.n.01', 'synonyms': ['pencil_box', 'pencil_case'], 'def': 'a box for holding pencils', 'name': 'pencil_box'}, {'frequency': 'r', 'id': 796, 'synset': 'pencil_sharpener.n.01', 'synonyms': ['pencil_sharpener'], 'def': 'a rotary implement for sharpening the point on pencils', 'name': 'pencil_sharpener'}, {'frequency': 'r', 'id': 797, 'synset': 'pendulum.n.01', 'synonyms': ['pendulum'], 'def': 'an apparatus consisting of an object mounted so that it swings freely under the influence of gravity', 'name': 'pendulum'}, {'frequency': 'c', 'id': 798, 'synset': 'penguin.n.01', 'synonyms': ['penguin'], 'def': 'short-legged flightless birds of cold southern regions having webbed feet and wings modified as flippers', 'name': 'penguin'}, {'frequency': 'r', 'id': 799, 'synset': 'pennant.n.02', 'synonyms': ['pennant'], 'def': 'a flag longer than it is wide (and often tapering)', 'name': 'pennant'}, {'frequency': 'r', 'id': 800, 'synset': 'penny.n.02', 'synonyms': ['penny_(coin)'], 'def': 'a coin worth one-hundredth of the value of the basic unit', 'name': 'penny_(coin)'}, {'frequency': 'c', 'id': 801, 'synset': 'pepper.n.03', 'synonyms': ['pepper', 'peppercorn'], 'def': 'pungent seasoning from the berry of the common pepper plant; whole or ground', 'name': 'pepper'}, {'frequency': 'c', 'id': 802, 'synset': 'pepper_mill.n.01', 'synonyms': ['pepper_mill', 'pepper_grinder'], 'def': 'a mill for grinding pepper', 'name': 'pepper_mill'}, {'frequency': 'c', 'id': 803, 'synset': 'perfume.n.02', 'synonyms': ['perfume'], 'def': 'a toiletry that emits and diffuses a fragrant odor', 'name': 'perfume'}, {'frequency': 'r', 'id': 804, 'synset': 'persimmon.n.02', 'synonyms': ['persimmon'], 'def': 'orange fruit resembling a plum; edible when fully ripe', 'name': 'persimmon'}, {'frequency': 'f', 'id': 805, 'synset': 'person.n.01', 'synonyms': ['baby', 'child', 'boy', 'girl', 'man', 'woman', 'person', 'human'], 'def': 'a human being', 'name': 'baby'}, {'frequency': 'r', 'id': 806, 'synset': 'pet.n.01', 'synonyms': ['pet'], 'def': 'a domesticated animal kept for companionship or amusement', 'name': 'pet'}, {'frequency': 'r', 'id': 807, 'synset': 'petfood.n.01', 'synonyms': ['petfood', 'pet-food'], 'def': 'food prepared for animal pets', 'name': 'petfood'}, {'frequency': 'r', 'id': 808, 'synset': 'pew.n.01', 'synonyms': ['pew_(church_bench)', 'church_bench'], 'def': 'long bench with backs; used in church by the congregation', 'name': 'pew_(church_bench)'}, {'frequency': 'r', 'id': 809, 'synset': 'phonebook.n.01', 'synonyms': ['phonebook', 'telephone_book', 'telephone_directory'], 'def': 'a directory containing an alphabetical list of telephone subscribers and their telephone numbers', 'name': 'phonebook'}, {'frequency': 'c', 'id': 810, 'synset': 'phonograph_record.n.01', 'synonyms': ['phonograph_record', 'phonograph_recording', 'record_(phonograph_recording)'], 'def': 'sound recording consisting of a typically black disk with a continuous groove', 'name': 'phonograph_record'}, {'frequency': 'c', 'id': 811, 'synset': 'piano.n.01', 'synonyms': ['piano'], 'def': 'a keyboard instrument that is played by depressing keys that cause hammers to strike tuned strings and produce sounds', 'name': 'piano'}, {'frequency': 'f', 'id': 812, 'synset': 'pickle.n.01', 'synonyms': ['pickle'], 'def': 'vegetables (especially cucumbers) preserved in brine or vinegar', 'name': 'pickle'}, {'frequency': 'f', 'id': 813, 'synset': 'pickup.n.01', 'synonyms': ['pickup_truck'], 'def': 'a light truck with an open body and low sides and a tailboard', 'name': 'pickup_truck'}, {'frequency': 'c', 'id': 814, 'synset': 'pie.n.01', 'synonyms': ['pie'], 'def': 'dish baked in pastry-lined pan often with a pastry top', 'name': 'pie'}, {'frequency': 'c', 'id': 815, 'synset': 'pigeon.n.01', 'synonyms': ['pigeon'], 'def': 'wild and domesticated birds having a heavy body and short legs', 'name': 'pigeon'}, {'frequency': 'r', 'id': 816, 'synset': 'piggy_bank.n.01', 'synonyms': ['piggy_bank', 'penny_bank'], 'def': "a child's coin bank (often shaped like a pig)", 'name': 'piggy_bank'}, {'frequency': 'f', 'id': 817, 'synset': 'pillow.n.01', 'synonyms': ['pillow'], 'def': 'a cushion to support the head of a sleeping person', 'name': 'pillow'}, {'frequency': 'r', 'id': 818, 'synset': 'pin.n.09', 'synonyms': ['pin_(non_jewelry)'], 'def': 'a small slender (often pointed) piece of wood or metal used to support or fasten or attach things', 'name': 'pin_(non_jewelry)'}, {'frequency': 'f', 'id': 819, 'synset': 'pineapple.n.02', 'synonyms': ['pineapple'], 'def': 'large sweet fleshy tropical fruit with a tuft of stiff leaves', 'name': 'pineapple'}, {'frequency': 'c', 'id': 820, 'synset': 'pinecone.n.01', 'synonyms': ['pinecone'], 'def': 'the seed-producing cone of a pine tree', 'name': 'pinecone'}, {'frequency': 'r', 'id': 821, 'synset': 'ping-pong_ball.n.01', 'synonyms': ['ping-pong_ball'], 'def': 'light hollow ball used in playing table tennis', 'name': 'ping-pong_ball'}, {'frequency': 'r', 'id': 822, 'synset': 'pinwheel.n.03', 'synonyms': ['pinwheel'], 'def': 'a toy consisting of vanes of colored paper or plastic that is pinned to a stick and spins when it is pointed into the wind', 'name': 'pinwheel'}, {'frequency': 'r', 'id': 823, 'synset': 'pipe.n.01', 'synonyms': ['tobacco_pipe'], 'def': 'a tube with a small bowl at one end; used for smoking tobacco', 'name': 'tobacco_pipe'}, {'frequency': 'f', 'id': 824, 'synset': 'pipe.n.02', 'synonyms': ['pipe', 'piping'], 'def': 'a long tube made of metal or plastic that is used to carry water or oil or gas etc.', 'name': 'pipe'}, {'frequency': 'r', 'id': 825, 'synset': 'pistol.n.01', 'synonyms': ['pistol', 'handgun'], 'def': 'a firearm that is held and fired with one hand', 'name': 'pistol'}, {'frequency': 'r', 'id': 826, 'synset': 'pita.n.01', 'synonyms': ['pita_(bread)', 'pocket_bread'], 'def': 'usually small round bread that can open into a pocket for filling', 'name': 'pita_(bread)'}, {'frequency': 'f', 'id': 827, 'synset': 'pitcher.n.02', 'synonyms': ['pitcher_(vessel_for_liquid)', 'ewer'], 'def': 'an open vessel with a handle and a spout for pouring', 'name': 'pitcher_(vessel_for_liquid)'}, {'frequency': 'r', 'id': 828, 'synset': 'pitchfork.n.01', 'synonyms': ['pitchfork'], 'def': 'a long-handled hand tool with sharp widely spaced prongs for lifting and pitching hay', 'name': 'pitchfork'}, {'frequency': 'f', 'id': 829, 'synset': 'pizza.n.01', 'synonyms': ['pizza'], 'def': 'Italian open pie made of thin bread dough spread with a spiced mixture of e.g. tomato sauce and cheese', 'name': 'pizza'}, {'frequency': 'f', 'id': 830, 'synset': 'place_mat.n.01', 'synonyms': ['place_mat'], 'def': 'a mat placed on a table for an individual place setting', 'name': 'place_mat'}, {'frequency': 'f', 'id': 831, 'synset': 'plate.n.04', 'synonyms': ['plate'], 'def': 'dish on which food is served or from which food is eaten', 'name': 'plate'}, {'frequency': 'c', 'id': 832, 'synset': 'platter.n.01', 'synonyms': ['platter'], 'def': 'a large shallow dish used for serving food', 'name': 'platter'}, {'frequency': 'r', 'id': 833, 'synset': 'playing_card.n.01', 'synonyms': ['playing_card'], 'def': 'one of a pack of cards that are used to play card games', 'name': 'playing_card'}, {'frequency': 'r', 'id': 834, 'synset': 'playpen.n.01', 'synonyms': ['playpen'], 'def': 'a portable enclosure in which babies may be left to play', 'name': 'playpen'}, {'frequency': 'c', 'id': 835, 'synset': 'pliers.n.01', 'synonyms': ['pliers', 'plyers'], 'def': 'a gripping hand tool with two hinged arms and (usually) serrated jaws', 'name': 'pliers'}, {'frequency': 'r', 'id': 836, 'synset': 'plow.n.01', 'synonyms': ['plow_(farm_equipment)', 'plough_(farm_equipment)'], 'def': 'a farm tool having one or more heavy blades to break the soil and cut a furrow prior to sowing', 'name': 'plow_(farm_equipment)'}, {'frequency': 'r', 'id': 837, 'synset': 'pocket_watch.n.01', 'synonyms': ['pocket_watch'], 'def': 'a watch that is carried in a small watch pocket', 'name': 'pocket_watch'}, {'frequency': 'c', 'id': 838, 'synset': 'pocketknife.n.01', 'synonyms': ['pocketknife'], 'def': 'a knife with a blade that folds into the handle; suitable for carrying in the pocket', 'name': 'pocketknife'}, {'frequency': 'c', 'id': 839, 'synset': 'poker.n.01', 'synonyms': ['poker_(fire_stirring_tool)', 'stove_poker', 'fire_hook'], 'def': 'fire iron consisting of a metal rod with a handle; used to stir a fire', 'name': 'poker_(fire_stirring_tool)'}, {'frequency': 'f', 'id': 840, 'synset': 'pole.n.01', 'synonyms': ['pole', 'post'], 'def': 'a long (usually round) rod of wood or metal or plastic', 'name': 'pole'}, {'frequency': 'r', 'id': 841, 'synset': 'police_van.n.01', 'synonyms': ['police_van', 'police_wagon', 'paddy_wagon', 'patrol_wagon'], 'def': 'van used by police to transport prisoners', 'name': 'police_van'}, {'frequency': 'f', 'id': 842, 'synset': 'polo_shirt.n.01', 'synonyms': ['polo_shirt', 'sport_shirt'], 'def': 'a shirt with short sleeves designed for comfort and casual wear', 'name': 'polo_shirt'}, {'frequency': 'r', 'id': 843, 'synset': 'poncho.n.01', 'synonyms': ['poncho'], 'def': 'a blanket-like cloak with a hole in the center for the head', 'name': 'poncho'}, {'frequency': 'c', 'id': 844, 'synset': 'pony.n.05', 'synonyms': ['pony'], 'def': 'any of various breeds of small gentle horses usually less than five feet high at the shoulder', 'name': 'pony'}, {'frequency': 'r', 'id': 845, 'synset': 'pool_table.n.01', 'synonyms': ['pool_table', 'billiard_table', 'snooker_table'], 'def': 'game equipment consisting of a heavy table on which pool is played', 'name': 'pool_table'}, {'frequency': 'f', 'id': 846, 'synset': 'pop.n.02', 'synonyms': ['pop_(soda)', 'soda_(pop)', 'tonic', 'soft_drink'], 'def': 'a sweet drink containing carbonated water and flavoring', 'name': 'pop_(soda)'}, {'frequency': 'r', 'id': 847, 'synset': 'portrait.n.02', 'synonyms': ['portrait', 'portrayal'], 'def': 'any likeness of a person, in any medium', 'name': 'portrait'}, {'frequency': 'c', 'id': 848, 'synset': 'postbox.n.01', 'synonyms': ['postbox_(public)', 'mailbox_(public)'], 'def': 'public box for deposit of mail', 'name': 'postbox_(public)'}, {'frequency': 'c', 'id': 849, 'synset': 'postcard.n.01', 'synonyms': ['postcard', 'postal_card', 'mailing-card'], 'def': 'a card for sending messages by post without an envelope', 'name': 'postcard'}, {'frequency': 'f', 'id': 850, 'synset': 'poster.n.01', 'synonyms': ['poster', 'placard'], 'def': 'a sign posted in a public place as an advertisement', 'name': 'poster'}, {'frequency': 'f', 'id': 851, 'synset': 'pot.n.01', 'synonyms': ['pot'], 'def': 'metal or earthenware cooking vessel that is usually round and deep; often has a handle and lid', 'name': 'pot'}, {'frequency': 'f', 'id': 852, 'synset': 'pot.n.04', 'synonyms': ['flowerpot'], 'def': 'a container in which plants are cultivated', 'name': 'flowerpot'}, {'frequency': 'f', 'id': 853, 'synset': 'potato.n.01', 'synonyms': ['potato'], 'def': 'an edible tuber native to South America', 'name': 'potato'}, {'frequency': 'c', 'id': 854, 'synset': 'potholder.n.01', 'synonyms': ['potholder'], 'def': 'an insulated pad for holding hot pots', 'name': 'potholder'}, {'frequency': 'c', 'id': 855, 'synset': 'pottery.n.01', 'synonyms': ['pottery', 'clayware'], 'def': 'ceramic ware made from clay and baked in a kiln', 'name': 'pottery'}, {'frequency': 'c', 'id': 856, 'synset': 'pouch.n.01', 'synonyms': ['pouch'], 'def': 'a small or medium size container for holding or carrying things', 'name': 'pouch'}, {'frequency': 'r', 'id': 857, 'synset': 'power_shovel.n.01', 'synonyms': ['power_shovel', 'excavator', 'digger'], 'def': 'a machine for excavating', 'name': 'power_shovel'}, {'frequency': 'c', 'id': 858, 'synset': 'prawn.n.01', 'synonyms': ['prawn', 'shrimp'], 'def': 'any of various edible decapod crustaceans', 'name': 'prawn'}, {'frequency': 'f', 'id': 859, 'synset': 'printer.n.03', 'synonyms': ['printer', 'printing_machine'], 'def': 'a machine that prints', 'name': 'printer'}, {'frequency': 'c', 'id': 860, 'synset': 'projectile.n.01', 'synonyms': ['projectile_(weapon)', 'missile'], 'def': 'a weapon that is forcibly thrown or projected at a targets', 'name': 'projectile_(weapon)'}, {'frequency': 'c', 'id': 861, 'synset': 'projector.n.02', 'synonyms': ['projector'], 'def': 'an optical instrument that projects an enlarged image onto a screen', 'name': 'projector'}, {'frequency': 'f', 'id': 862, 'synset': 'propeller.n.01', 'synonyms': ['propeller', 'propellor'], 'def': 'a mechanical device that rotates to push against air or water', 'name': 'propeller'}, {'frequency': 'r', 'id': 863, 'synset': 'prune.n.01', 'synonyms': ['prune'], 'def': 'dried plum', 'name': 'prune'}, {'frequency': 'r', 'id': 864, 'synset': 'pudding.n.01', 'synonyms': ['pudding'], 'def': 'any of various soft thick unsweetened baked dishes', 'name': 'pudding'}, {'frequency': 'r', 'id': 865, 'synset': 'puffer.n.02', 'synonyms': ['puffer_(fish)', 'pufferfish', 'blowfish', 'globefish'], 'def': 'fishes whose elongated spiny body can inflate itself with water or air to form a globe', 'name': 'puffer_(fish)'}, {'frequency': 'r', 'id': 866, 'synset': 'puffin.n.01', 'synonyms': ['puffin'], 'def': 'seabirds having short necks and brightly colored compressed bills', 'name': 'puffin'}, {'frequency': 'r', 'id': 867, 'synset': 'pug.n.01', 'synonyms': ['pug-dog'], 'def': 'small compact smooth-coated breed of Asiatic origin having a tightly curled tail and broad flat wrinkled muzzle', 'name': 'pug-dog'}, {'frequency': 'c', 'id': 868, 'synset': 'pumpkin.n.02', 'synonyms': ['pumpkin'], 'def': 'usually large pulpy deep-yellow round fruit of the squash family maturing in late summer or early autumn', 'name': 'pumpkin'}, {'frequency': 'r', 'id': 869, 'synset': 'punch.n.03', 'synonyms': ['puncher'], 'def': 'a tool for making holes or indentations', 'name': 'puncher'}, {'frequency': 'r', 'id': 870, 'synset': 'puppet.n.01', 'synonyms': ['puppet', 'marionette'], 'def': 'a small figure of a person operated from above with strings by a puppeteer', 'name': 'puppet'}, {'frequency': 'r', 'id': 871, 'synset': 'puppy.n.01', 'synonyms': ['puppy'], 'def': 'a young dog', 'name': 'puppy'}, {'frequency': 'r', 'id': 872, 'synset': 'quesadilla.n.01', 'synonyms': ['quesadilla'], 'def': 'a tortilla that is filled with cheese and heated', 'name': 'quesadilla'}, {'frequency': 'r', 'id': 873, 'synset': 'quiche.n.02', 'synonyms': ['quiche'], 'def': 'a tart filled with rich unsweetened custard; often contains other ingredients (as cheese or ham or seafood or vegetables)', 'name': 'quiche'}, {'frequency': 'f', 'id': 874, 'synset': 'quilt.n.01', 'synonyms': ['quilt', 'comforter'], 'def': 'bedding made of two layers of cloth filled with stuffing and stitched together', 'name': 'quilt'}, {'frequency': 'c', 'id': 875, 'synset': 'rabbit.n.01', 'synonyms': ['rabbit'], 'def': 'any of various burrowing animals of the family Leporidae having long ears and short tails', 'name': 'rabbit'}, {'frequency': 'r', 'id': 876, 'synset': 'racer.n.02', 'synonyms': ['race_car', 'racing_car'], 'def': 'a fast car that competes in races', 'name': 'race_car'}, {'frequency': 'c', 'id': 877, 'synset': 'racket.n.04', 'synonyms': ['racket', 'racquet'], 'def': 'a sports implement used to strike a ball in various games', 'name': 'racket'}, {'frequency': 'r', 'id': 878, 'synset': 'radar.n.01', 'synonyms': ['radar'], 'def': 'measuring instrument in which the echo of a pulse of microwave radiation is used to detect and locate distant objects', 'name': 'radar'}, {'frequency': 'c', 'id': 879, 'synset': 'radiator.n.03', 'synonyms': ['radiator'], 'def': 'a mechanism consisting of a metal honeycomb through which hot fluids circulate', 'name': 'radiator'}, {'frequency': 'c', 'id': 880, 'synset': 'radio_receiver.n.01', 'synonyms': ['radio_receiver', 'radio_set', 'radio', 'tuner_(radio)'], 'def': 'an electronic receiver that detects and demodulates and amplifies transmitted radio signals', 'name': 'radio_receiver'}, {'frequency': 'c', 'id': 881, 'synset': 'radish.n.03', 'synonyms': ['radish', 'daikon'], 'def': 'pungent edible root of any of various cultivated radish plants', 'name': 'radish'}, {'frequency': 'c', 'id': 882, 'synset': 'raft.n.01', 'synonyms': ['raft'], 'def': 'a flat float (usually made of logs or planks) that can be used for transport or as a platform for swimmers', 'name': 'raft'}, {'frequency': 'r', 'id': 883, 'synset': 'rag_doll.n.01', 'synonyms': ['rag_doll'], 'def': 'a cloth doll that is stuffed and (usually) painted', 'name': 'rag_doll'}, {'frequency': 'c', 'id': 884, 'synset': 'raincoat.n.01', 'synonyms': ['raincoat', 'waterproof_jacket'], 'def': 'a water-resistant coat', 'name': 'raincoat'}, {'frequency': 'c', 'id': 885, 'synset': 'ram.n.05', 'synonyms': ['ram_(animal)'], 'def': 'uncastrated adult male sheep', 'name': 'ram_(animal)'}, {'frequency': 'c', 'id': 886, 'synset': 'raspberry.n.02', 'synonyms': ['raspberry'], 'def': 'red or black edible aggregate berries usually smaller than the related blackberries', 'name': 'raspberry'}, {'frequency': 'r', 'id': 887, 'synset': 'rat.n.01', 'synonyms': ['rat'], 'def': 'any of various long-tailed rodents similar to but larger than a mouse', 'name': 'rat'}, {'frequency': 'c', 'id': 888, 'synset': 'razorblade.n.01', 'synonyms': ['razorblade'], 'def': 'a blade that has very sharp edge', 'name': 'razorblade'}, {'frequency': 'c', 'id': 889, 'synset': 'reamer.n.01', 'synonyms': ['reamer_(juicer)', 'juicer', 'juice_reamer'], 'def': 'a squeezer with a conical ridged center that is used for squeezing juice from citrus fruit', 'name': 'reamer_(juicer)'}, {'frequency': 'f', 'id': 890, 'synset': 'rearview_mirror.n.01', 'synonyms': ['rearview_mirror'], 'def': 'car mirror that reflects the view out of the rear window', 'name': 'rearview_mirror'}, {'frequency': 'c', 'id': 891, 'synset': 'receipt.n.02', 'synonyms': ['receipt'], 'def': 'an acknowledgment (usually tangible) that payment has been made', 'name': 'receipt'}, {'frequency': 'c', 'id': 892, 'synset': 'recliner.n.01', 'synonyms': ['recliner', 'reclining_chair', 'lounger_(chair)'], 'def': 'an armchair whose back can be lowered and foot can be raised to allow the sitter to recline in it', 'name': 'recliner'}, {'frequency': 'r', 'id': 893, 'synset': 'record_player.n.01', 'synonyms': ['record_player', 'phonograph_(record_player)', 'turntable'], 'def': 'machine in which rotating records cause a stylus to vibrate and the vibrations are amplified acoustically or electronically', 'name': 'record_player'}, {'frequency': 'r', 'id': 894, 'synset': 'red_cabbage.n.02', 'synonyms': ['red_cabbage'], 'def': 'compact head of purplish-red leaves', 'name': 'red_cabbage'}, {'frequency': 'f', 'id': 895, 'synset': 'reflector.n.01', 'synonyms': ['reflector'], 'def': 'device that reflects light, radiation, etc.', 'name': 'reflector'}, {'frequency': 'f', 'id': 896, 'synset': 'remote_control.n.01', 'synonyms': ['remote_control'], 'def': 'a device that can be used to control a machine or apparatus from a distance', 'name': 'remote_control'}, {'frequency': 'c', 'id': 897, 'synset': 'rhinoceros.n.01', 'synonyms': ['rhinoceros'], 'def': 'massive powerful herbivorous odd-toed ungulate of southeast Asia and Africa having very thick skin and one or two horns on the snout', 'name': 'rhinoceros'}, {'frequency': 'r', 'id': 898, 'synset': 'rib.n.03', 'synonyms': ['rib_(food)'], 'def': 'cut of meat including one or more ribs', 'name': 'rib_(food)'}, {'frequency': 'r', 'id': 899, 'synset': 'rifle.n.01', 'synonyms': ['rifle'], 'def': 'a shoulder firearm with a long barrel', 'name': 'rifle'}, {'frequency': 'f', 'id': 900, 'synset': 'ring.n.08', 'synonyms': ['ring'], 'def': 'jewelry consisting of a circlet of precious metal (often set with jewels) worn on the finger', 'name': 'ring'}, {'frequency': 'r', 'id': 901, 'synset': 'river_boat.n.01', 'synonyms': ['river_boat'], 'def': 'a boat used on rivers or to ply a river', 'name': 'river_boat'}, {'frequency': 'r', 'id': 902, 'synset': 'road_map.n.02', 'synonyms': ['road_map'], 'def': '(NOT A ROAD) a MAP showing roads (for automobile travel)', 'name': 'road_map'}, {'frequency': 'c', 'id': 903, 'synset': 'robe.n.01', 'synonyms': ['robe'], 'def': 'any loose flowing garment', 'name': 'robe'}, {'frequency': 'c', 'id': 904, 'synset': 'rocking_chair.n.01', 'synonyms': ['rocking_chair'], 'def': 'a chair mounted on rockers', 'name': 'rocking_chair'}, {'frequency': 'r', 'id': 905, 'synset': 'roller_skate.n.01', 'synonyms': ['roller_skate'], 'def': 'a shoe with pairs of rollers (small hard wheels) fixed to the sole', 'name': 'roller_skate'}, {'frequency': 'r', 'id': 906, 'synset': 'rollerblade.n.01', 'synonyms': ['Rollerblade'], 'def': 'an in-line variant of a roller skate', 'name': 'Rollerblade'}, {'frequency': 'c', 'id': 907, 'synset': 'rolling_pin.n.01', 'synonyms': ['rolling_pin'], 'def': 'utensil consisting of a cylinder (usually of wood) with a handle at each end; used to roll out dough', 'name': 'rolling_pin'}, {'frequency': 'r', 'id': 908, 'synset': 'root_beer.n.01', 'synonyms': ['root_beer'], 'def': 'carbonated drink containing extracts of roots and herbs', 'name': 'root_beer'}, {'frequency': 'c', 'id': 909, 'synset': 'router.n.02', 'synonyms': ['router_(computer_equipment)'], 'def': 'a device that forwards data packets between computer networks', 'name': 'router_(computer_equipment)'}, {'frequency': 'f', 'id': 910, 'synset': 'rubber_band.n.01', 'synonyms': ['rubber_band', 'elastic_band'], 'def': 'a narrow band of elastic rubber used to hold things (such as papers) together', 'name': 'rubber_band'}, {'frequency': 'c', 'id': 911, 'synset': 'runner.n.08', 'synonyms': ['runner_(carpet)'], 'def': 'a long narrow carpet', 'name': 'runner_(carpet)'}, {'frequency': 'f', 'id': 912, 'synset': 'sack.n.01', 'synonyms': ['plastic_bag', 'paper_bag'], 'def': "a bag made of paper or plastic for holding customer's purchases", 'name': 'plastic_bag'}, {'frequency': 'f', 'id': 913, 'synset': 'saddle.n.01', 'synonyms': ['saddle_(on_an_animal)'], 'def': 'a seat for the rider of a horse or camel', 'name': 'saddle_(on_an_animal)'}, {'frequency': 'f', 'id': 914, 'synset': 'saddle_blanket.n.01', 'synonyms': ['saddle_blanket', 'saddlecloth', 'horse_blanket'], 'def': 'stable gear consisting of a blanket placed under the saddle', 'name': 'saddle_blanket'}, {'frequency': 'c', 'id': 915, 'synset': 'saddlebag.n.01', 'synonyms': ['saddlebag'], 'def': 'a large bag (or pair of bags) hung over a saddle', 'name': 'saddlebag'}, {'frequency': 'r', 'id': 916, 'synset': 'safety_pin.n.01', 'synonyms': ['safety_pin'], 'def': 'a pin in the form of a clasp; has a guard so the point of the pin will not stick the user', 'name': 'safety_pin'}, {'frequency': 'c', 'id': 917, 'synset': 'sail.n.01', 'synonyms': ['sail'], 'def': 'a large piece of fabric by means of which wind is used to propel a sailing vessel', 'name': 'sail'}, {'frequency': 'c', 'id': 918, 'synset': 'salad.n.01', 'synonyms': ['salad'], 'def': 'food mixtures either arranged on a plate or tossed and served with a moist dressing; usually consisting of or including greens', 'name': 'salad'}, {'frequency': 'r', 'id': 919, 'synset': 'salad_plate.n.01', 'synonyms': ['salad_plate', 'salad_bowl'], 'def': 'a plate or bowl for individual servings of salad', 'name': 'salad_plate'}, {'frequency': 'r', 'id': 920, 'synset': 'salami.n.01', 'synonyms': ['salami'], 'def': 'highly seasoned fatty sausage of pork and beef usually dried', 'name': 'salami'}, {'frequency': 'r', 'id': 921, 'synset': 'salmon.n.01', 'synonyms': ['salmon_(fish)'], 'def': 'any of various large food and game fishes of northern waters', 'name': 'salmon_(fish)'}, {'frequency': 'r', 'id': 922, 'synset': 'salmon.n.03', 'synonyms': ['salmon_(food)'], 'def': 'flesh of any of various marine or freshwater fish of the family Salmonidae', 'name': 'salmon_(food)'}, {'frequency': 'r', 'id': 923, 'synset': 'salsa.n.01', 'synonyms': ['salsa'], 'def': 'spicy sauce of tomatoes and onions and chili peppers to accompany Mexican foods', 'name': 'salsa'}, {'frequency': 'f', 'id': 924, 'synset': 'saltshaker.n.01', 'synonyms': ['saltshaker'], 'def': 'a shaker with a perforated top for sprinkling salt', 'name': 'saltshaker'}, {'frequency': 'f', 'id': 925, 'synset': 'sandal.n.01', 'synonyms': ['sandal_(type_of_shoe)'], 'def': 'a shoe consisting of a sole fastened by straps to the foot', 'name': 'sandal_(type_of_shoe)'}, {'frequency': 'f', 'id': 926, 'synset': 'sandwich.n.01', 'synonyms': ['sandwich'], 'def': 'two (or more) slices of bread with a filling between them', 'name': 'sandwich'}, {'frequency': 'r', 'id': 927, 'synset': 'satchel.n.01', 'synonyms': ['satchel'], 'def': 'luggage consisting of a small case with a flat bottom and (usually) a shoulder strap', 'name': 'satchel'}, {'frequency': 'r', 'id': 928, 'synset': 'saucepan.n.01', 'synonyms': ['saucepan'], 'def': 'a deep pan with a handle; used for stewing or boiling', 'name': 'saucepan'}, {'frequency': 'f', 'id': 929, 'synset': 'saucer.n.02', 'synonyms': ['saucer'], 'def': 'a small shallow dish for holding a cup at the table', 'name': 'saucer'}, {'frequency': 'f', 'id': 930, 'synset': 'sausage.n.01', 'synonyms': ['sausage'], 'def': 'highly seasoned minced meat stuffed in casings', 'name': 'sausage'}, {'frequency': 'r', 'id': 931, 'synset': 'sawhorse.n.01', 'synonyms': ['sawhorse', 'sawbuck'], 'def': 'a framework for holding wood that is being sawed', 'name': 'sawhorse'}, {'frequency': 'r', 'id': 932, 'synset': 'sax.n.02', 'synonyms': ['saxophone'], 'def': "a wind instrument with a `J'-shaped form typically made of brass", 'name': 'saxophone'}, {'frequency': 'f', 'id': 933, 'synset': 'scale.n.07', 'synonyms': ['scale_(measuring_instrument)'], 'def': 'a measuring instrument for weighing; shows amount of mass', 'name': 'scale_(measuring_instrument)'}, {'frequency': 'r', 'id': 934, 'synset': 'scarecrow.n.01', 'synonyms': ['scarecrow', 'strawman'], 'def': 'an effigy in the shape of a man to frighten birds away from seeds', 'name': 'scarecrow'}, {'frequency': 'f', 'id': 935, 'synset': 'scarf.n.01', 'synonyms': ['scarf'], 'def': 'a garment worn around the head or neck or shoulders for warmth or decoration', 'name': 'scarf'}, {'frequency': 'c', 'id': 936, 'synset': 'school_bus.n.01', 'synonyms': ['school_bus'], 'def': 'a bus used to transport children to or from school', 'name': 'school_bus'}, {'frequency': 'f', 'id': 937, 'synset': 'scissors.n.01', 'synonyms': ['scissors'], 'def': 'a tool having two crossed pivoting blades with looped handles', 'name': 'scissors'}, {'frequency': 'c', 'id': 938, 'synset': 'scoreboard.n.01', 'synonyms': ['scoreboard'], 'def': 'a large board for displaying the score of a contest (and some other information)', 'name': 'scoreboard'}, {'frequency': 'c', 'id': 939, 'synset': 'scrambled_eggs.n.01', 'synonyms': ['scrambled_eggs'], 'def': 'eggs beaten and cooked to a soft firm consistency while stirring', 'name': 'scrambled_eggs'}, {'frequency': 'r', 'id': 940, 'synset': 'scraper.n.01', 'synonyms': ['scraper'], 'def': 'any of various hand tools for scraping', 'name': 'scraper'}, {'frequency': 'r', 'id': 941, 'synset': 'scratcher.n.03', 'synonyms': ['scratcher'], 'def': 'a device used for scratching', 'name': 'scratcher'}, {'frequency': 'c', 'id': 942, 'synset': 'screwdriver.n.01', 'synonyms': ['screwdriver'], 'def': 'a hand tool for driving screws; has a tip that fits into the head of a screw', 'name': 'screwdriver'}, {'frequency': 'c', 'id': 943, 'synset': 'scrub_brush.n.01', 'synonyms': ['scrubbing_brush'], 'def': 'a brush with short stiff bristles for heavy cleaning', 'name': 'scrubbing_brush'}, {'frequency': 'c', 'id': 944, 'synset': 'sculpture.n.01', 'synonyms': ['sculpture'], 'def': 'a three-dimensional work of art', 'name': 'sculpture'}, {'frequency': 'r', 'id': 945, 'synset': 'seabird.n.01', 'synonyms': ['seabird', 'seafowl'], 'def': 'a bird that frequents coastal waters and the open ocean: gulls; pelicans; gannets; cormorants; albatrosses; petrels; etc.', 'name': 'seabird'}, {'frequency': 'r', 'id': 946, 'synset': 'seahorse.n.02', 'synonyms': ['seahorse'], 'def': 'small fish with horse-like heads bent sharply downward and curled tails', 'name': 'seahorse'}, {'frequency': 'r', 'id': 947, 'synset': 'seaplane.n.01', 'synonyms': ['seaplane', 'hydroplane'], 'def': 'an airplane that can land on or take off from water', 'name': 'seaplane'}, {'frequency': 'c', 'id': 948, 'synset': 'seashell.n.01', 'synonyms': ['seashell'], 'def': 'the shell of a marine organism', 'name': 'seashell'}, {'frequency': 'r', 'id': 949, 'synset': 'seedling.n.01', 'synonyms': ['seedling'], 'def': 'young plant or tree grown from a seed', 'name': 'seedling'}, {'frequency': 'c', 'id': 950, 'synset': 'serving_dish.n.01', 'synonyms': ['serving_dish'], 'def': 'a dish used for serving food', 'name': 'serving_dish'}, {'frequency': 'r', 'id': 951, 'synset': 'sewing_machine.n.01', 'synonyms': ['sewing_machine'], 'def': 'a textile machine used as a home appliance for sewing', 'name': 'sewing_machine'}, {'frequency': 'r', 'id': 952, 'synset': 'shaker.n.03', 'synonyms': ['shaker'], 'def': 'a container in which something can be shaken', 'name': 'shaker'}, {'frequency': 'c', 'id': 953, 'synset': 'shampoo.n.01', 'synonyms': ['shampoo'], 'def': 'cleansing agent consisting of soaps or detergents used for washing the hair', 'name': 'shampoo'}, {'frequency': 'r', 'id': 954, 'synset': 'shark.n.01', 'synonyms': ['shark'], 'def': 'typically large carnivorous fishes with sharpe teeth', 'name': 'shark'}, {'frequency': 'r', 'id': 955, 'synset': 'sharpener.n.01', 'synonyms': ['sharpener'], 'def': 'any implement that is used to make something (an edge or a point) sharper', 'name': 'sharpener'}, {'frequency': 'r', 'id': 956, 'synset': 'sharpie.n.03', 'synonyms': ['Sharpie'], 'def': 'a pen with indelible ink that will write on any surface', 'name': 'Sharpie'}, {'frequency': 'r', 'id': 957, 'synset': 'shaver.n.03', 'synonyms': ['shaver_(electric)', 'electric_shaver', 'electric_razor'], 'def': 'a razor powered by an electric motor', 'name': 'shaver_(electric)'}, {'frequency': 'c', 'id': 958, 'synset': 'shaving_cream.n.01', 'synonyms': ['shaving_cream', 'shaving_soap'], 'def': 'toiletry consisting that forms a rich lather for softening the beard before shaving', 'name': 'shaving_cream'}, {'frequency': 'r', 'id': 959, 'synset': 'shawl.n.01', 'synonyms': ['shawl'], 'def': 'cloak consisting of an oblong piece of cloth used to cover the head and shoulders', 'name': 'shawl'}, {'frequency': 'r', 'id': 960, 'synset': 'shears.n.01', 'synonyms': ['shears'], 'def': 'large scissors with strong blades', 'name': 'shears'}, {'frequency': 'f', 'id': 961, 'synset': 'sheep.n.01', 'synonyms': ['sheep'], 'def': 'woolly usually horned ruminant mammal related to the goat', 'name': 'sheep'}, {'frequency': 'r', 'id': 962, 'synset': 'shepherd_dog.n.01', 'synonyms': ['shepherd_dog', 'sheepdog'], 'def': 'any of various usually long-haired breeds of dog reared to herd and guard sheep', 'name': 'shepherd_dog'}, {'frequency': 'r', 'id': 963, 'synset': 'sherbert.n.01', 'synonyms': ['sherbert', 'sherbet'], 'def': 'a frozen dessert made primarily of fruit juice and sugar', 'name': 'sherbert'}, {'frequency': 'r', 'id': 964, 'synset': 'shield.n.02', 'synonyms': ['shield'], 'def': 'armor carried on the arm to intercept blows', 'name': 'shield'}, {'frequency': 'f', 'id': 965, 'synset': 'shirt.n.01', 'synonyms': ['shirt'], 'def': 'a garment worn on the upper half of the body', 'name': 'shirt'}, {'frequency': 'f', 'id': 966, 'synset': 'shoe.n.01', 'synonyms': ['shoe', 'sneaker_(type_of_shoe)', 'tennis_shoe'], 'def': 'common footwear covering the foot', 'name': 'shoe'}, {'frequency': 'c', 'id': 967, 'synset': 'shopping_bag.n.01', 'synonyms': ['shopping_bag'], 'def': 'a bag made of plastic or strong paper (often with handles); used to transport goods after shopping', 'name': 'shopping_bag'}, {'frequency': 'c', 'id': 968, 'synset': 'shopping_cart.n.01', 'synonyms': ['shopping_cart'], 'def': 'a handcart that holds groceries or other goods while shopping', 'name': 'shopping_cart'}, {'frequency': 'f', 'id': 969, 'synset': 'short_pants.n.01', 'synonyms': ['short_pants', 'shorts_(clothing)', 'trunks_(clothing)'], 'def': 'trousers that end at or above the knee', 'name': 'short_pants'}, {'frequency': 'r', 'id': 970, 'synset': 'shot_glass.n.01', 'synonyms': ['shot_glass'], 'def': 'a small glass adequate to hold a single swallow of whiskey', 'name': 'shot_glass'}, {'frequency': 'c', 'id': 971, 'synset': 'shoulder_bag.n.01', 'synonyms': ['shoulder_bag'], 'def': 'a large handbag that can be carried by a strap looped over the shoulder', 'name': 'shoulder_bag'}, {'frequency': 'c', 'id': 972, 'synset': 'shovel.n.01', 'synonyms': ['shovel'], 'def': 'a hand tool for lifting loose material such as snow, dirt, etc.', 'name': 'shovel'}, {'frequency': 'f', 'id': 973, 'synset': 'shower.n.01', 'synonyms': ['shower_head'], 'def': 'a plumbing fixture that sprays water over you', 'name': 'shower_head'}, {'frequency': 'f', 'id': 974, 'synset': 'shower_curtain.n.01', 'synonyms': ['shower_curtain'], 'def': 'a curtain that keeps water from splashing out of the shower area', 'name': 'shower_curtain'}, {'frequency': 'r', 'id': 975, 'synset': 'shredder.n.01', 'synonyms': ['shredder_(for_paper)'], 'def': 'a device that shreds documents', 'name': 'shredder_(for_paper)'}, {'frequency': 'r', 'id': 976, 'synset': 'sieve.n.01', 'synonyms': ['sieve', 'screen_(sieve)'], 'def': 'a strainer for separating lumps from powdered material or grading particles', 'name': 'sieve'}, {'frequency': 'f', 'id': 977, 'synset': 'signboard.n.01', 'synonyms': ['signboard'], 'def': 'structure displaying a board on which advertisements can be posted', 'name': 'signboard'}, {'frequency': 'c', 'id': 978, 'synset': 'silo.n.01', 'synonyms': ['silo'], 'def': 'a cylindrical tower used for storing goods', 'name': 'silo'}, {'frequency': 'f', 'id': 979, 'synset': 'sink.n.01', 'synonyms': ['sink'], 'def': 'plumbing fixture consisting of a water basin fixed to a wall or floor and having a drainpipe', 'name': 'sink'}, {'frequency': 'f', 'id': 980, 'synset': 'skateboard.n.01', 'synonyms': ['skateboard'], 'def': 'a board with wheels that is ridden in a standing or crouching position and propelled by foot', 'name': 'skateboard'}, {'frequency': 'c', 'id': 981, 'synset': 'skewer.n.01', 'synonyms': ['skewer'], 'def': 'a long pin for holding meat in position while it is being roasted', 'name': 'skewer'}, {'frequency': 'f', 'id': 982, 'synset': 'ski.n.01', 'synonyms': ['ski'], 'def': 'sports equipment for skiing on snow', 'name': 'ski'}, {'frequency': 'f', 'id': 983, 'synset': 'ski_boot.n.01', 'synonyms': ['ski_boot'], 'def': 'a stiff boot that is fastened to a ski with a ski binding', 'name': 'ski_boot'}, {'frequency': 'f', 'id': 984, 'synset': 'ski_parka.n.01', 'synonyms': ['ski_parka', 'ski_jacket'], 'def': 'a parka to be worn while skiing', 'name': 'ski_parka'}, {'frequency': 'f', 'id': 985, 'synset': 'ski_pole.n.01', 'synonyms': ['ski_pole'], 'def': 'a pole with metal points used as an aid in skiing', 'name': 'ski_pole'}, {'frequency': 'f', 'id': 986, 'synset': 'skirt.n.02', 'synonyms': ['skirt'], 'def': 'a garment hanging from the waist; worn mainly by girls and women', 'name': 'skirt'}, {'frequency': 'c', 'id': 987, 'synset': 'sled.n.01', 'synonyms': ['sled', 'sledge', 'sleigh'], 'def': 'a vehicle or flat object for transportation over snow by sliding or pulled by dogs, etc.', 'name': 'sled'}, {'frequency': 'c', 'id': 988, 'synset': 'sleeping_bag.n.01', 'synonyms': ['sleeping_bag'], 'def': 'large padded bag designed to be slept in outdoors', 'name': 'sleeping_bag'}, {'frequency': 'r', 'id': 989, 'synset': 'sling.n.05', 'synonyms': ['sling_(bandage)', 'triangular_bandage'], 'def': 'bandage to support an injured forearm; slung over the shoulder or neck', 'name': 'sling_(bandage)'}, {'frequency': 'c', 'id': 990, 'synset': 'slipper.n.01', 'synonyms': ['slipper_(footwear)', 'carpet_slipper_(footwear)'], 'def': 'low footwear that can be slipped on and off easily; usually worn indoors', 'name': 'slipper_(footwear)'}, {'frequency': 'r', 'id': 991, 'synset': 'smoothie.n.02', 'synonyms': ['smoothie'], 'def': 'a thick smooth drink consisting of fresh fruit pureed with ice cream or yoghurt or milk', 'name': 'smoothie'}, {'frequency': 'r', 'id': 992, 'synset': 'snake.n.01', 'synonyms': ['snake', 'serpent'], 'def': 'limbless scaly elongate reptile; some are venomous', 'name': 'snake'}, {'frequency': 'f', 'id': 993, 'synset': 'snowboard.n.01', 'synonyms': ['snowboard'], 'def': 'a board that resembles a broad ski or a small surfboard; used in a standing position to slide down snow-covered slopes', 'name': 'snowboard'}, {'frequency': 'c', 'id': 994, 'synset': 'snowman.n.01', 'synonyms': ['snowman'], 'def': 'a figure of a person made of packed snow', 'name': 'snowman'}, {'frequency': 'c', 'id': 995, 'synset': 'snowmobile.n.01', 'synonyms': ['snowmobile'], 'def': 'tracked vehicle for travel on snow having skis in front', 'name': 'snowmobile'}, {'frequency': 'f', 'id': 996, 'synset': 'soap.n.01', 'synonyms': ['soap'], 'def': 'a cleansing agent made from the salts of vegetable or animal fats', 'name': 'soap'}, {'frequency': 'f', 'id': 997, 'synset': 'soccer_ball.n.01', 'synonyms': ['soccer_ball'], 'def': "an inflated ball used in playing soccer (called `football' outside of the United States)", 'name': 'soccer_ball'}, {'frequency': 'f', 'id': 998, 'synset': 'sock.n.01', 'synonyms': ['sock'], 'def': 'cloth covering for the foot; worn inside the shoe; reaches to between the ankle and the knee', 'name': 'sock'}, {'frequency': 'r', 'id': 999, 'synset': 'soda_fountain.n.02', 'synonyms': ['soda_fountain'], 'def': 'an apparatus for dispensing soda water', 'name': 'soda_fountain'}, {'frequency': 'r', 'id': 1000, 'synset': 'soda_water.n.01', 'synonyms': ['carbonated_water', 'club_soda', 'seltzer', 'sparkling_water'], 'def': 'effervescent beverage artificially charged with carbon dioxide', 'name': 'carbonated_water'}, {'frequency': 'f', 'id': 1001, 'synset': 'sofa.n.01', 'synonyms': ['sofa', 'couch', 'lounge'], 'def': 'an upholstered seat for more than one person', 'name': 'sofa'}, {'frequency': 'r', 'id': 1002, 'synset': 'softball.n.01', 'synonyms': ['softball'], 'def': 'ball used in playing softball', 'name': 'softball'}, {'frequency': 'c', 'id': 1003, 'synset': 'solar_array.n.01', 'synonyms': ['solar_array', 'solar_battery', 'solar_panel'], 'def': 'electrical device consisting of a large array of connected solar cells', 'name': 'solar_array'}, {'frequency': 'r', 'id': 1004, 'synset': 'sombrero.n.02', 'synonyms': ['sombrero'], 'def': 'a straw hat with a tall crown and broad brim; worn in American southwest and in Mexico', 'name': 'sombrero'}, {'frequency': 'c', 'id': 1005, 'synset': 'soup.n.01', 'synonyms': ['soup'], 'def': 'liquid food especially of meat or fish or vegetable stock often containing pieces of solid food', 'name': 'soup'}, {'frequency': 'r', 'id': 1006, 'synset': 'soup_bowl.n.01', 'synonyms': ['soup_bowl'], 'def': 'a bowl for serving soup', 'name': 'soup_bowl'}, {'frequency': 'c', 'id': 1007, 'synset': 'soupspoon.n.01', 'synonyms': ['soupspoon'], 'def': 'a spoon with a rounded bowl for eating soup', 'name': 'soupspoon'}, {'frequency': 'c', 'id': 1008, 'synset': 'sour_cream.n.01', 'synonyms': ['sour_cream', 'soured_cream'], 'def': 'soured light cream', 'name': 'sour_cream'}, {'frequency': 'r', 'id': 1009, 'synset': 'soya_milk.n.01', 'synonyms': ['soya_milk', 'soybean_milk', 'soymilk'], 'def': 'a milk substitute containing soybean flour and water; used in some infant formulas and in making tofu', 'name': 'soya_milk'}, {'frequency': 'r', 'id': 1010, 'synset': 'space_shuttle.n.01', 'synonyms': ['space_shuttle'], 'def': "a reusable spacecraft with wings for a controlled descent through the Earth's atmosphere", 'name': 'space_shuttle'}, {'frequency': 'r', 'id': 1011, 'synset': 'sparkler.n.02', 'synonyms': ['sparkler_(fireworks)'], 'def': 'a firework that burns slowly and throws out a shower of sparks', 'name': 'sparkler_(fireworks)'}, {'frequency': 'f', 'id': 1012, 'synset': 'spatula.n.02', 'synonyms': ['spatula'], 'def': 'a hand tool with a thin flexible blade used to mix or spread soft substances', 'name': 'spatula'}, {'frequency': 'r', 'id': 1013, 'synset': 'spear.n.01', 'synonyms': ['spear', 'lance'], 'def': 'a long pointed rod used as a tool or weapon', 'name': 'spear'}, {'frequency': 'f', 'id': 1014, 'synset': 'spectacles.n.01', 'synonyms': ['spectacles', 'specs', 'eyeglasses', 'glasses'], 'def': 'optical instrument consisting of a frame that holds a pair of lenses for correcting defective vision', 'name': 'spectacles'}, {'frequency': 'c', 'id': 1015, 'synset': 'spice_rack.n.01', 'synonyms': ['spice_rack'], 'def': 'a rack for displaying containers filled with spices', 'name': 'spice_rack'}, {'frequency': 'r', 'id': 1016, 'synset': 'spider.n.01', 'synonyms': ['spider'], 'def': 'predatory arachnid with eight legs, two poison fangs, two feelers, and usually two silk-spinning organs at the back end of the body', 'name': 'spider'}, {'frequency': 'c', 'id': 1017, 'synset': 'sponge.n.01', 'synonyms': ['sponge'], 'def': 'a porous mass usable to absorb water typically used for cleaning', 'name': 'sponge'}, {'frequency': 'f', 'id': 1018, 'synset': 'spoon.n.01', 'synonyms': ['spoon'], 'def': 'a piece of cutlery with a shallow bowl-shaped container and a handle', 'name': 'spoon'}, {'frequency': 'c', 'id': 1019, 'synset': 'sportswear.n.01', 'synonyms': ['sportswear', 'athletic_wear', 'activewear'], 'def': 'attire worn for sport or for casual wear', 'name': 'sportswear'}, {'frequency': 'c', 'id': 1020, 'synset': 'spotlight.n.02', 'synonyms': ['spotlight'], 'def': 'a lamp that produces a strong beam of light to illuminate a restricted area; used to focus attention of a stage performer', 'name': 'spotlight'}, {'frequency': 'r', 'id': 1021, 'synset': 'squirrel.n.01', 'synonyms': ['squirrel'], 'def': 'a kind of arboreal rodent having a long bushy tail', 'name': 'squirrel'}, {'frequency': 'c', 'id': 1022, 'synset': 'stapler.n.01', 'synonyms': ['stapler_(stapling_machine)'], 'def': 'a machine that inserts staples into sheets of paper in order to fasten them together', 'name': 'stapler_(stapling_machine)'}, {'frequency': 'r', 'id': 1023, 'synset': 'starfish.n.01', 'synonyms': ['starfish', 'sea_star'], 'def': 'echinoderms characterized by five arms extending from a central disk', 'name': 'starfish'}, {'frequency': 'f', 'id': 1024, 'synset': 'statue.n.01', 'synonyms': ['statue_(sculpture)'], 'def': 'a sculpture representing a human or animal', 'name': 'statue_(sculpture)'}, {'frequency': 'c', 'id': 1025, 'synset': 'steak.n.01', 'synonyms': ['steak_(food)'], 'def': 'a slice of meat cut from the fleshy part of an animal or large fish', 'name': 'steak_(food)'}, {'frequency': 'r', 'id': 1026, 'synset': 'steak_knife.n.01', 'synonyms': ['steak_knife'], 'def': 'a sharp table knife used in eating steak', 'name': 'steak_knife'}, {'frequency': 'r', 'id': 1027, 'synset': 'steamer.n.02', 'synonyms': ['steamer_(kitchen_appliance)'], 'def': 'a cooking utensil that can be used to cook food by steaming it', 'name': 'steamer_(kitchen_appliance)'}, {'frequency': 'f', 'id': 1028, 'synset': 'steering_wheel.n.01', 'synonyms': ['steering_wheel'], 'def': 'a handwheel that is used for steering', 'name': 'steering_wheel'}, {'frequency': 'r', 'id': 1029, 'synset': 'stencil.n.01', 'synonyms': ['stencil'], 'def': 'a sheet of material (metal, plastic, etc.) that has been perforated with a pattern; ink or paint can pass through the perforations to create the printed pattern on the surface below', 'name': 'stencil'}, {'frequency': 'r', 'id': 1030, 'synset': 'step_ladder.n.01', 'synonyms': ['stepladder'], 'def': 'a folding portable ladder hinged at the top', 'name': 'stepladder'}, {'frequency': 'c', 'id': 1031, 'synset': 'step_stool.n.01', 'synonyms': ['step_stool'], 'def': 'a stool that has one or two steps that fold under the seat', 'name': 'step_stool'}, {'frequency': 'c', 'id': 1032, 'synset': 'stereo.n.01', 'synonyms': ['stereo_(sound_system)'], 'def': 'electronic device for playing audio', 'name': 'stereo_(sound_system)'}, {'frequency': 'r', 'id': 1033, 'synset': 'stew.n.02', 'synonyms': ['stew'], 'def': 'food prepared by stewing especially meat or fish with vegetables', 'name': 'stew'}, {'frequency': 'r', 'id': 1034, 'synset': 'stirrer.n.02', 'synonyms': ['stirrer'], 'def': 'an implement used for stirring', 'name': 'stirrer'}, {'frequency': 'f', 'id': 1035, 'synset': 'stirrup.n.01', 'synonyms': ['stirrup'], 'def': "support consisting of metal loops into which rider's feet go", 'name': 'stirrup'}, {'frequency': 'c', 'id': 1036, 'synset': 'stocking.n.01', 'synonyms': ['stockings_(leg_wear)'], 'def': 'close-fitting hosiery to cover the foot and leg; come in matched pairs', 'name': 'stockings_(leg_wear)'}, {'frequency': 'f', 'id': 1037, 'synset': 'stool.n.01', 'synonyms': ['stool'], 'def': 'a simple seat without a back or arms', 'name': 'stool'}, {'frequency': 'f', 'id': 1038, 'synset': 'stop_sign.n.01', 'synonyms': ['stop_sign'], 'def': 'a traffic sign to notify drivers that they must come to a complete stop', 'name': 'stop_sign'}, {'frequency': 'f', 'id': 1039, 'synset': 'stoplight.n.01', 'synonyms': ['brake_light'], 'def': 'a red light on the rear of a motor vehicle that signals when the brakes are applied', 'name': 'brake_light'}, {'frequency': 'f', 'id': 1040, 'synset': 'stove.n.01', 'synonyms': ['stove', 'kitchen_stove', 'range_(kitchen_appliance)', 'kitchen_range', 'cooking_stove'], 'def': 'a kitchen appliance used for cooking food', 'name': 'stove'}, {'frequency': 'c', 'id': 1041, 'synset': 'strainer.n.01', 'synonyms': ['strainer'], 'def': 'a filter to retain larger pieces while smaller pieces and liquids pass through', 'name': 'strainer'}, {'frequency': 'f', 'id': 1042, 'synset': 'strap.n.01', 'synonyms': ['strap'], 'def': 'an elongated strip of material for binding things together or holding', 'name': 'strap'}, {'frequency': 'f', 'id': 1043, 'synset': 'straw.n.04', 'synonyms': ['straw_(for_drinking)', 'drinking_straw'], 'def': 'a thin paper or plastic tube used to suck liquids into the mouth', 'name': 'straw_(for_drinking)'}, {'frequency': 'f', 'id': 1044, 'synset': 'strawberry.n.01', 'synonyms': ['strawberry'], 'def': 'sweet fleshy red fruit', 'name': 'strawberry'}, {'frequency': 'f', 'id': 1045, 'synset': 'street_sign.n.01', 'synonyms': ['street_sign'], 'def': 'a sign visible from the street', 'name': 'street_sign'}, {'frequency': 'f', 'id': 1046, 'synset': 'streetlight.n.01', 'synonyms': ['streetlight', 'street_lamp'], 'def': 'a lamp supported on a lamppost; for illuminating a street', 'name': 'streetlight'}, {'frequency': 'r', 'id': 1047, 'synset': 'string_cheese.n.01', 'synonyms': ['string_cheese'], 'def': 'cheese formed in long strings twisted together', 'name': 'string_cheese'}, {'frequency': 'r', 'id': 1048, 'synset': 'stylus.n.02', 'synonyms': ['stylus'], 'def': 'a pointed tool for writing or drawing or engraving', 'name': 'stylus'}, {'frequency': 'r', 'id': 1049, 'synset': 'subwoofer.n.01', 'synonyms': ['subwoofer'], 'def': 'a loudspeaker that is designed to reproduce very low bass frequencies', 'name': 'subwoofer'}, {'frequency': 'r', 'id': 1050, 'synset': 'sugar_bowl.n.01', 'synonyms': ['sugar_bowl'], 'def': 'a dish in which sugar is served', 'name': 'sugar_bowl'}, {'frequency': 'r', 'id': 1051, 'synset': 'sugarcane.n.01', 'synonyms': ['sugarcane_(plant)'], 'def': 'juicy canes whose sap is a source of molasses and commercial sugar; fresh canes are sometimes chewed for the juice', 'name': 'sugarcane_(plant)'}, {'frequency': 'c', 'id': 1052, 'synset': 'suit.n.01', 'synonyms': ['suit_(clothing)'], 'def': 'a set of garments (usually including a jacket and trousers or skirt) for outerwear all of the same fabric and color', 'name': 'suit_(clothing)'}, {'frequency': 'c', 'id': 1053, 'synset': 'sunflower.n.01', 'synonyms': ['sunflower'], 'def': 'any plant of the genus Helianthus having large flower heads with dark disk florets and showy yellow rays', 'name': 'sunflower'}, {'frequency': 'f', 'id': 1054, 'synset': 'sunglasses.n.01', 'synonyms': ['sunglasses'], 'def': 'spectacles that are darkened or polarized to protect the eyes from the glare of the sun', 'name': 'sunglasses'}, {'frequency': 'c', 'id': 1055, 'synset': 'sunhat.n.01', 'synonyms': ['sunhat'], 'def': 'a hat with a broad brim that protects the face from direct exposure to the sun', 'name': 'sunhat'}, {'frequency': 'r', 'id': 1056, 'synset': 'sunscreen.n.01', 'synonyms': ['sunscreen', 'sunblock'], 'def': 'a cream spread on the skin; contains a chemical to filter out ultraviolet light and so protect from sunburn', 'name': 'sunscreen'}, {'frequency': 'f', 'id': 1057, 'synset': 'surfboard.n.01', 'synonyms': ['surfboard'], 'def': 'a narrow buoyant board for riding surf', 'name': 'surfboard'}, {'frequency': 'c', 'id': 1058, 'synset': 'sushi.n.01', 'synonyms': ['sushi'], 'def': 'rice (with raw fish) wrapped in seaweed', 'name': 'sushi'}, {'frequency': 'c', 'id': 1059, 'synset': 'swab.n.02', 'synonyms': ['mop'], 'def': 'cleaning implement consisting of absorbent material fastened to a handle; for cleaning floors', 'name': 'mop'}, {'frequency': 'c', 'id': 1060, 'synset': 'sweat_pants.n.01', 'synonyms': ['sweat_pants'], 'def': 'loose-fitting trousers with elastic cuffs; worn by athletes', 'name': 'sweat_pants'}, {'frequency': 'c', 'id': 1061, 'synset': 'sweatband.n.02', 'synonyms': ['sweatband'], 'def': 'a band of material tied around the forehead or wrist to absorb sweat', 'name': 'sweatband'}, {'frequency': 'f', 'id': 1062, 'synset': 'sweater.n.01', 'synonyms': ['sweater'], 'def': 'a crocheted or knitted garment covering the upper part of the body', 'name': 'sweater'}, {'frequency': 'f', 'id': 1063, 'synset': 'sweatshirt.n.01', 'synonyms': ['sweatshirt'], 'def': 'cotton knit pullover with long sleeves worn during athletic activity', 'name': 'sweatshirt'}, {'frequency': 'c', 'id': 1064, 'synset': 'sweet_potato.n.02', 'synonyms': ['sweet_potato'], 'def': 'the edible tuberous root of the sweet potato vine', 'name': 'sweet_potato'}, {'frequency': 'f', 'id': 1065, 'synset': 'swimsuit.n.01', 'synonyms': ['swimsuit', 'swimwear', 'bathing_suit', 'swimming_costume', 'bathing_costume', 'swimming_trunks', 'bathing_trunks'], 'def': 'garment worn for swimming', 'name': 'swimsuit'}, {'frequency': 'c', 'id': 1066, 'synset': 'sword.n.01', 'synonyms': ['sword'], 'def': 'a cutting or thrusting weapon that has a long metal blade', 'name': 'sword'}, {'frequency': 'r', 'id': 1067, 'synset': 'syringe.n.01', 'synonyms': ['syringe'], 'def': 'a medical instrument used to inject or withdraw fluids', 'name': 'syringe'}, {'frequency': 'r', 'id': 1068, 'synset': 'tabasco.n.02', 'synonyms': ['Tabasco_sauce'], 'def': 'very spicy sauce (trade name Tabasco) made from fully-aged red peppers', 'name': 'Tabasco_sauce'}, {'frequency': 'r', 'id': 1069, 'synset': 'table-tennis_table.n.01', 'synonyms': ['table-tennis_table', 'ping-pong_table'], 'def': 'a table used for playing table tennis', 'name': 'table-tennis_table'}, {'frequency': 'f', 'id': 1070, 'synset': 'table.n.02', 'synonyms': ['table'], 'def': 'a piece of furniture having a smooth flat top that is usually supported by one or more vertical legs', 'name': 'table'}, {'frequency': 'c', 'id': 1071, 'synset': 'table_lamp.n.01', 'synonyms': ['table_lamp'], 'def': 'a lamp that sits on a table', 'name': 'table_lamp'}, {'frequency': 'f', 'id': 1072, 'synset': 'tablecloth.n.01', 'synonyms': ['tablecloth'], 'def': 'a covering spread over a dining table', 'name': 'tablecloth'}, {'frequency': 'r', 'id': 1073, 'synset': 'tachometer.n.01', 'synonyms': ['tachometer'], 'def': 'measuring instrument for indicating speed of rotation', 'name': 'tachometer'}, {'frequency': 'r', 'id': 1074, 'synset': 'taco.n.02', 'synonyms': ['taco'], 'def': 'a small tortilla cupped around a filling', 'name': 'taco'}, {'frequency': 'f', 'id': 1075, 'synset': 'tag.n.02', 'synonyms': ['tag'], 'def': 'a label associated with something for the purpose of identification or information', 'name': 'tag'}, {'frequency': 'f', 'id': 1076, 'synset': 'taillight.n.01', 'synonyms': ['taillight', 'rear_light'], 'def': 'lamp (usually red) mounted at the rear of a motor vehicle', 'name': 'taillight'}, {'frequency': 'r', 'id': 1077, 'synset': 'tambourine.n.01', 'synonyms': ['tambourine'], 'def': 'a shallow drum with a single drumhead and with metallic disks in the sides', 'name': 'tambourine'}, {'frequency': 'r', 'id': 1078, 'synset': 'tank.n.01', 'synonyms': ['army_tank', 'armored_combat_vehicle', 'armoured_combat_vehicle'], 'def': 'an enclosed armored military vehicle; has a cannon and moves on caterpillar treads', 'name': 'army_tank'}, {'frequency': 'c', 'id': 1079, 'synset': 'tank.n.02', 'synonyms': ['tank_(storage_vessel)', 'storage_tank'], 'def': 'a large (usually metallic) vessel for holding gases or liquids', 'name': 'tank_(storage_vessel)'}, {'frequency': 'f', 'id': 1080, 'synset': 'tank_top.n.01', 'synonyms': ['tank_top_(clothing)'], 'def': 'a tight-fitting sleeveless shirt with wide shoulder straps and low neck and no front opening', 'name': 'tank_top_(clothing)'}, {'frequency': 'c', 'id': 1081, 'synset': 'tape.n.01', 'synonyms': ['tape_(sticky_cloth_or_paper)'], 'def': 'a long thin piece of cloth or paper as used for binding or fastening', 'name': 'tape_(sticky_cloth_or_paper)'}, {'frequency': 'c', 'id': 1082, 'synset': 'tape.n.04', 'synonyms': ['tape_measure', 'measuring_tape'], 'def': 'measuring instrument consisting of a narrow strip (cloth or metal) marked in inches or centimeters and used for measuring lengths', 'name': 'tape_measure'}, {'frequency': 'c', 'id': 1083, 'synset': 'tapestry.n.02', 'synonyms': ['tapestry'], 'def': 'a heavy textile with a woven design; used for curtains and upholstery', 'name': 'tapestry'}, {'frequency': 'f', 'id': 1084, 'synset': 'tarpaulin.n.01', 'synonyms': ['tarp'], 'def': 'waterproofed canvas', 'name': 'tarp'}, {'frequency': 'c', 'id': 1085, 'synset': 'tartan.n.01', 'synonyms': ['tartan', 'plaid'], 'def': 'a cloth having a crisscross design', 'name': 'tartan'}, {'frequency': 'c', 'id': 1086, 'synset': 'tassel.n.01', 'synonyms': ['tassel'], 'def': 'adornment consisting of a bunch of cords fastened at one end', 'name': 'tassel'}, {'frequency': 'r', 'id': 1087, 'synset': 'tea_bag.n.01', 'synonyms': ['tea_bag'], 'def': 'a measured amount of tea in a bag for an individual serving of tea', 'name': 'tea_bag'}, {'frequency': 'c', 'id': 1088, 'synset': 'teacup.n.02', 'synonyms': ['teacup'], 'def': 'a cup from which tea is drunk', 'name': 'teacup'}, {'frequency': 'c', 'id': 1089, 'synset': 'teakettle.n.01', 'synonyms': ['teakettle'], 'def': 'kettle for boiling water to make tea', 'name': 'teakettle'}, {'frequency': 'c', 'id': 1090, 'synset': 'teapot.n.01', 'synonyms': ['teapot'], 'def': 'pot for brewing tea; usually has a spout and handle', 'name': 'teapot'}, {'frequency': 'f', 'id': 1091, 'synset': 'teddy.n.01', 'synonyms': ['teddy_bear'], 'def': "plaything consisting of a child's toy bear (usually plush and stuffed with soft materials)", 'name': 'teddy_bear'}, {'frequency': 'f', 'id': 1092, 'synset': 'telephone.n.01', 'synonyms': ['telephone', 'phone', 'telephone_set'], 'def': 'electronic device for communicating by voice over long distances', 'name': 'telephone'}, {'frequency': 'c', 'id': 1093, 'synset': 'telephone_booth.n.01', 'synonyms': ['telephone_booth', 'phone_booth', 'call_box', 'telephone_box', 'telephone_kiosk'], 'def': 'booth for using a telephone', 'name': 'telephone_booth'}, {'frequency': 'f', 'id': 1094, 'synset': 'telephone_pole.n.01', 'synonyms': ['telephone_pole', 'telegraph_pole', 'telegraph_post'], 'def': 'tall pole supporting telephone wires', 'name': 'telephone_pole'}, {'frequency': 'r', 'id': 1095, 'synset': 'telephoto_lens.n.01', 'synonyms': ['telephoto_lens', 'zoom_lens'], 'def': 'a camera lens that magnifies the image', 'name': 'telephoto_lens'}, {'frequency': 'c', 'id': 1096, 'synset': 'television_camera.n.01', 'synonyms': ['television_camera', 'tv_camera'], 'def': 'television equipment for capturing and recording video', 'name': 'television_camera'}, {'frequency': 'f', 'id': 1097, 'synset': 'television_receiver.n.01', 'synonyms': ['television_set', 'tv', 'tv_set'], 'def': 'an electronic device that receives television signals and displays them on a screen', 'name': 'television_set'}, {'frequency': 'f', 'id': 1098, 'synset': 'tennis_ball.n.01', 'synonyms': ['tennis_ball'], 'def': 'ball about the size of a fist used in playing tennis', 'name': 'tennis_ball'}, {'frequency': 'f', 'id': 1099, 'synset': 'tennis_racket.n.01', 'synonyms': ['tennis_racket'], 'def': 'a racket used to play tennis', 'name': 'tennis_racket'}, {'frequency': 'r', 'id': 1100, 'synset': 'tequila.n.01', 'synonyms': ['tequila'], 'def': 'Mexican liquor made from fermented juices of an agave plant', 'name': 'tequila'}, {'frequency': 'c', 'id': 1101, 'synset': 'thermometer.n.01', 'synonyms': ['thermometer'], 'def': 'measuring instrument for measuring temperature', 'name': 'thermometer'}, {'frequency': 'c', 'id': 1102, 'synset': 'thermos.n.01', 'synonyms': ['thermos_bottle'], 'def': 'vacuum flask that preserves temperature of hot or cold drinks', 'name': 'thermos_bottle'}, {'frequency': 'c', 'id': 1103, 'synset': 'thermostat.n.01', 'synonyms': ['thermostat'], 'def': 'a regulator for automatically regulating temperature by starting or stopping the supply of heat', 'name': 'thermostat'}, {'frequency': 'r', 'id': 1104, 'synset': 'thimble.n.02', 'synonyms': ['thimble'], 'def': 'a small metal cap to protect the finger while sewing; can be used as a small container', 'name': 'thimble'}, {'frequency': 'c', 'id': 1105, 'synset': 'thread.n.01', 'synonyms': ['thread', 'yarn'], 'def': 'a fine cord of twisted fibers (of cotton or silk or wool or nylon etc.) used in sewing and weaving', 'name': 'thread'}, {'frequency': 'c', 'id': 1106, 'synset': 'thumbtack.n.01', 'synonyms': ['thumbtack', 'drawing_pin', 'pushpin'], 'def': 'a tack for attaching papers to a bulletin board or drawing board', 'name': 'thumbtack'}, {'frequency': 'c', 'id': 1107, 'synset': 'tiara.n.01', 'synonyms': ['tiara'], 'def': 'a jeweled headdress worn by women on formal occasions', 'name': 'tiara'}, {'frequency': 'c', 'id': 1108, 'synset': 'tiger.n.02', 'synonyms': ['tiger'], 'def': 'large feline of forests in most of Asia having a tawny coat with black stripes', 'name': 'tiger'}, {'frequency': 'c', 'id': 1109, 'synset': 'tights.n.01', 'synonyms': ['tights_(clothing)', 'leotards'], 'def': 'skintight knit hose covering the body from the waist to the feet worn by acrobats and dancers and as stockings by women and girls', 'name': 'tights_(clothing)'}, {'frequency': 'c', 'id': 1110, 'synset': 'timer.n.01', 'synonyms': ['timer', 'stopwatch'], 'def': 'a timepiece that measures a time interval and signals its end', 'name': 'timer'}, {'frequency': 'f', 'id': 1111, 'synset': 'tinfoil.n.01', 'synonyms': ['tinfoil'], 'def': 'foil made of tin or an alloy of tin and lead', 'name': 'tinfoil'}, {'frequency': 'r', 'id': 1112, 'synset': 'tinsel.n.01', 'synonyms': ['tinsel'], 'def': 'a showy decoration that is basically valueless', 'name': 'tinsel'}, {'frequency': 'f', 'id': 1113, 'synset': 'tissue.n.02', 'synonyms': ['tissue_paper'], 'def': 'a soft thin (usually translucent) paper', 'name': 'tissue_paper'}, {'frequency': 'c', 'id': 1114, 'synset': 'toast.n.01', 'synonyms': ['toast_(food)'], 'def': 'slice of bread that has been toasted', 'name': 'toast_(food)'}, {'frequency': 'f', 'id': 1115, 'synset': 'toaster.n.02', 'synonyms': ['toaster'], 'def': 'a kitchen appliance (usually electric) for toasting bread', 'name': 'toaster'}, {'frequency': 'c', 'id': 1116, 'synset': 'toaster_oven.n.01', 'synonyms': ['toaster_oven'], 'def': 'kitchen appliance consisting of a small electric oven for toasting or warming food', 'name': 'toaster_oven'}, {'frequency': 'f', 'id': 1117, 'synset': 'toilet.n.02', 'synonyms': ['toilet'], 'def': 'a plumbing fixture for defecation and urination', 'name': 'toilet'}, {'frequency': 'f', 'id': 1118, 'synset': 'toilet_tissue.n.01', 'synonyms': ['toilet_tissue', 'toilet_paper', 'bathroom_tissue'], 'def': 'a soft thin absorbent paper for use in toilets', 'name': 'toilet_tissue'}, {'frequency': 'f', 'id': 1119, 'synset': 'tomato.n.01', 'synonyms': ['tomato'], 'def': 'mildly acid red or yellow pulpy fruit eaten as a vegetable', 'name': 'tomato'}, {'frequency': 'c', 'id': 1120, 'synset': 'tongs.n.01', 'synonyms': ['tongs'], 'def': 'any of various devices for taking hold of objects; usually have two hinged legs with handles above and pointed hooks below', 'name': 'tongs'}, {'frequency': 'c', 'id': 1121, 'synset': 'toolbox.n.01', 'synonyms': ['toolbox'], 'def': 'a box or chest or cabinet for holding hand tools', 'name': 'toolbox'}, {'frequency': 'f', 'id': 1122, 'synset': 'toothbrush.n.01', 'synonyms': ['toothbrush'], 'def': 'small brush; has long handle; used to clean teeth', 'name': 'toothbrush'}, {'frequency': 'f', 'id': 1123, 'synset': 'toothpaste.n.01', 'synonyms': ['toothpaste'], 'def': 'a dentifrice in the form of a paste', 'name': 'toothpaste'}, {'frequency': 'c', 'id': 1124, 'synset': 'toothpick.n.01', 'synonyms': ['toothpick'], 'def': 'pick consisting of a small strip of wood or plastic; used to pick food from between the teeth', 'name': 'toothpick'}, {'frequency': 'c', 'id': 1125, 'synset': 'top.n.09', 'synonyms': ['cover'], 'def': 'covering for a hole (especially a hole in the top of a container)', 'name': 'cover'}, {'frequency': 'c', 'id': 1126, 'synset': 'tortilla.n.01', 'synonyms': ['tortilla'], 'def': 'thin unleavened pancake made from cornmeal or wheat flour', 'name': 'tortilla'}, {'frequency': 'c', 'id': 1127, 'synset': 'tow_truck.n.01', 'synonyms': ['tow_truck'], 'def': 'a truck equipped to hoist and pull wrecked cars (or to remove cars from no-parking zones)', 'name': 'tow_truck'}, {'frequency': 'f', 'id': 1128, 'synset': 'towel.n.01', 'synonyms': ['towel'], 'def': 'a rectangular piece of absorbent cloth (or paper) for drying or wiping', 'name': 'towel'}, {'frequency': 'f', 'id': 1129, 'synset': 'towel_rack.n.01', 'synonyms': ['towel_rack', 'towel_rail', 'towel_bar'], 'def': 'a rack consisting of one or more bars on which towels can be hung', 'name': 'towel_rack'}, {'frequency': 'f', 'id': 1130, 'synset': 'toy.n.03', 'synonyms': ['toy'], 'def': 'a device regarded as providing amusement', 'name': 'toy'}, {'frequency': 'c', 'id': 1131, 'synset': 'tractor.n.01', 'synonyms': ['tractor_(farm_equipment)'], 'def': 'a wheeled vehicle with large wheels; used in farming and other applications', 'name': 'tractor_(farm_equipment)'}, {'frequency': 'f', 'id': 1132, 'synset': 'traffic_light.n.01', 'synonyms': ['traffic_light'], 'def': 'a device to control vehicle traffic often consisting of three or more lights', 'name': 'traffic_light'}, {'frequency': 'r', 'id': 1133, 'synset': 'trail_bike.n.01', 'synonyms': ['dirt_bike'], 'def': 'a lightweight motorcycle equipped with rugged tires and suspension for off-road use', 'name': 'dirt_bike'}, {'frequency': 'c', 'id': 1134, 'synset': 'trailer_truck.n.01', 'synonyms': ['trailer_truck', 'tractor_trailer', 'trucking_rig', 'articulated_lorry', 'semi_truck'], 'def': 'a truck consisting of a tractor and trailer together', 'name': 'trailer_truck'}, {'frequency': 'f', 'id': 1135, 'synset': 'train.n.01', 'synonyms': ['train_(railroad_vehicle)', 'railroad_train'], 'def': 'public or private transport provided by a line of railway cars coupled together and drawn by a locomotive', 'name': 'train_(railroad_vehicle)'}, {'frequency': 'r', 'id': 1136, 'synset': 'trampoline.n.01', 'synonyms': ['trampoline'], 'def': 'gymnastic apparatus consisting of a strong canvas sheet attached with springs to a metal frame', 'name': 'trampoline'}, {'frequency': 'f', 'id': 1137, 'synset': 'tray.n.01', 'synonyms': ['tray'], 'def': 'an open receptacle for holding or displaying or serving articles or food', 'name': 'tray'}, {'frequency': 'r', 'id': 1138, 'synset': 'tree_house.n.01', 'synonyms': ['tree_house'], 'def': '(NOT A TREE) a PLAYHOUSE built in the branches of a tree', 'name': 'tree_house'}, {'frequency': 'r', 'id': 1139, 'synset': 'trench_coat.n.01', 'synonyms': ['trench_coat'], 'def': 'a military style raincoat; belted with deep pockets', 'name': 'trench_coat'}, {'frequency': 'r', 'id': 1140, 'synset': 'triangle.n.05', 'synonyms': ['triangle_(musical_instrument)'], 'def': 'a percussion instrument consisting of a metal bar bent in the shape of an open triangle', 'name': 'triangle_(musical_instrument)'}, {'frequency': 'r', 'id': 1141, 'synset': 'tricycle.n.01', 'synonyms': ['tricycle'], 'def': 'a vehicle with three wheels that is moved by foot pedals', 'name': 'tricycle'}, {'frequency': 'c', 'id': 1142, 'synset': 'tripod.n.01', 'synonyms': ['tripod'], 'def': 'a three-legged rack used for support', 'name': 'tripod'}, {'frequency': 'f', 'id': 1143, 'synset': 'trouser.n.01', 'synonyms': ['trousers', 'pants_(clothing)'], 'def': 'a garment extending from the waist to the knee or ankle, covering each leg separately', 'name': 'trousers'}, {'frequency': 'f', 'id': 1144, 'synset': 'truck.n.01', 'synonyms': ['truck'], 'def': 'an automotive vehicle suitable for hauling', 'name': 'truck'}, {'frequency': 'r', 'id': 1145, 'synset': 'truffle.n.03', 'synonyms': ['truffle_(chocolate)', 'chocolate_truffle'], 'def': 'creamy chocolate candy', 'name': 'truffle_(chocolate)'}, {'frequency': 'c', 'id': 1146, 'synset': 'trunk.n.02', 'synonyms': ['trunk'], 'def': 'luggage consisting of a large strong case used when traveling or for storage', 'name': 'trunk'}, {'frequency': 'r', 'id': 1147, 'synset': 'tub.n.02', 'synonyms': ['vat'], 'def': 'a large open vessel for holding or storing liquids', 'name': 'vat'}, {'frequency': 'c', 'id': 1148, 'synset': 'turban.n.01', 'synonyms': ['turban'], 'def': 'a traditional headdress consisting of a long scarf wrapped around the head', 'name': 'turban'}, {'frequency': 'r', 'id': 1149, 'synset': 'turkey.n.01', 'synonyms': ['turkey_(bird)'], 'def': 'large gallinaceous bird with fan-shaped tail; widely domesticated for food', 'name': 'turkey_(bird)'}, {'frequency': 'c', 'id': 1150, 'synset': 'turkey.n.04', 'synonyms': ['turkey_(food)'], 'def': 'flesh of large domesticated fowl usually roasted', 'name': 'turkey_(food)'}, {'frequency': 'r', 'id': 1151, 'synset': 'turnip.n.01', 'synonyms': ['turnip'], 'def': 'widely cultivated plant having a large fleshy edible white or yellow root', 'name': 'turnip'}, {'frequency': 'c', 'id': 1152, 'synset': 'turtle.n.02', 'synonyms': ['turtle'], 'def': 'any of various aquatic and land reptiles having a bony shell and flipper-like limbs for swimming', 'name': 'turtle'}, {'frequency': 'r', 'id': 1153, 'synset': 'turtleneck.n.01', 'synonyms': ['turtleneck_(clothing)', 'polo-neck'], 'def': 'a sweater or jersey with a high close-fitting collar', 'name': 'turtleneck_(clothing)'}, {'frequency': 'r', 'id': 1154, 'synset': 'typewriter.n.01', 'synonyms': ['typewriter'], 'def': 'hand-operated character printer for printing written messages one character at a time', 'name': 'typewriter'}, {'frequency': 'f', 'id': 1155, 'synset': 'umbrella.n.01', 'synonyms': ['umbrella'], 'def': 'a lightweight handheld collapsible canopy', 'name': 'umbrella'}, {'frequency': 'c', 'id': 1156, 'synset': 'underwear.n.01', 'synonyms': ['underwear', 'underclothes', 'underclothing', 'underpants'], 'def': 'undergarment worn next to the skin and under the outer garments', 'name': 'underwear'}, {'frequency': 'r', 'id': 1157, 'synset': 'unicycle.n.01', 'synonyms': ['unicycle'], 'def': 'a vehicle with a single wheel that is driven by pedals', 'name': 'unicycle'}, {'frequency': 'c', 'id': 1158, 'synset': 'urinal.n.01', 'synonyms': ['urinal'], 'def': 'a plumbing fixture (usually attached to the wall) used by men to urinate', 'name': 'urinal'}, {'frequency': 'r', 'id': 1159, 'synset': 'urn.n.01', 'synonyms': ['urn'], 'def': 'a large vase that usually has a pedestal or feet', 'name': 'urn'}, {'frequency': 'c', 'id': 1160, 'synset': 'vacuum.n.04', 'synonyms': ['vacuum_cleaner'], 'def': 'an electrical home appliance that cleans by suction', 'name': 'vacuum_cleaner'}, {'frequency': 'c', 'id': 1161, 'synset': 'valve.n.03', 'synonyms': ['valve'], 'def': 'control consisting of a mechanical device for controlling the flow of a fluid', 'name': 'valve'}, {'frequency': 'f', 'id': 1162, 'synset': 'vase.n.01', 'synonyms': ['vase'], 'def': 'an open jar of glass or porcelain used as an ornament or to hold flowers', 'name': 'vase'}, {'frequency': 'c', 'id': 1163, 'synset': 'vending_machine.n.01', 'synonyms': ['vending_machine'], 'def': 'a slot machine for selling goods', 'name': 'vending_machine'}, {'frequency': 'f', 'id': 1164, 'synset': 'vent.n.01', 'synonyms': ['vent', 'blowhole', 'air_vent'], 'def': 'a hole for the escape of gas or air', 'name': 'vent'}, {'frequency': 'c', 'id': 1165, 'synset': 'videotape.n.01', 'synonyms': ['videotape'], 'def': 'a video recording made on magnetic tape', 'name': 'videotape'}, {'frequency': 'r', 'id': 1166, 'synset': 'vinegar.n.01', 'synonyms': ['vinegar'], 'def': 'sour-tasting liquid produced usually by oxidation of the alcohol in wine or cider and used as a condiment or food preservative', 'name': 'vinegar'}, {'frequency': 'r', 'id': 1167, 'synset': 'violin.n.01', 'synonyms': ['violin', 'fiddle'], 'def': 'bowed stringed instrument that is the highest member of the violin family', 'name': 'violin'}, {'frequency': 'r', 'id': 1168, 'synset': 'vodka.n.01', 'synonyms': ['vodka'], 'def': 'unaged colorless liquor originating in Russia', 'name': 'vodka'}, {'frequency': 'r', 'id': 1169, 'synset': 'volleyball.n.02', 'synonyms': ['volleyball'], 'def': 'an inflated ball used in playing volleyball', 'name': 'volleyball'}, {'frequency': 'r', 'id': 1170, 'synset': 'vulture.n.01', 'synonyms': ['vulture'], 'def': 'any of various large birds of prey having naked heads and weak claws and feeding chiefly on carrion', 'name': 'vulture'}, {'frequency': 'c', 'id': 1171, 'synset': 'waffle.n.01', 'synonyms': ['waffle'], 'def': 'pancake batter baked in a waffle iron', 'name': 'waffle'}, {'frequency': 'r', 'id': 1172, 'synset': 'waffle_iron.n.01', 'synonyms': ['waffle_iron'], 'def': 'a kitchen appliance for baking waffles', 'name': 'waffle_iron'}, {'frequency': 'c', 'id': 1173, 'synset': 'wagon.n.01', 'synonyms': ['wagon'], 'def': 'any of various kinds of wheeled vehicles drawn by an animal or a tractor', 'name': 'wagon'}, {'frequency': 'c', 'id': 1174, 'synset': 'wagon_wheel.n.01', 'synonyms': ['wagon_wheel'], 'def': 'a wheel of a wagon', 'name': 'wagon_wheel'}, {'frequency': 'c', 'id': 1175, 'synset': 'walking_stick.n.01', 'synonyms': ['walking_stick'], 'def': 'a stick carried in the hand for support in walking', 'name': 'walking_stick'}, {'frequency': 'c', 'id': 1176, 'synset': 'wall_clock.n.01', 'synonyms': ['wall_clock'], 'def': 'a clock mounted on a wall', 'name': 'wall_clock'}, {'frequency': 'f', 'id': 1177, 'synset': 'wall_socket.n.01', 'synonyms': ['wall_socket', 'wall_plug', 'electric_outlet', 'electrical_outlet', 'outlet', 'electric_receptacle'], 'def': 'receptacle providing a place in a wiring system where current can be taken to run electrical devices', 'name': 'wall_socket'}, {'frequency': 'c', 'id': 1178, 'synset': 'wallet.n.01', 'synonyms': ['wallet', 'billfold'], 'def': 'a pocket-size case for holding papers and paper money', 'name': 'wallet'}, {'frequency': 'r', 'id': 1179, 'synset': 'walrus.n.01', 'synonyms': ['walrus'], 'def': 'either of two large northern marine mammals having ivory tusks and tough hide over thick blubber', 'name': 'walrus'}, {'frequency': 'r', 'id': 1180, 'synset': 'wardrobe.n.01', 'synonyms': ['wardrobe'], 'def': 'a tall piece of furniture that provides storage space for clothes; has a door and rails or hooks for hanging clothes', 'name': 'wardrobe'}, {'frequency': 'r', 'id': 1181, 'synset': 'wasabi.n.02', 'synonyms': ['wasabi'], 'def': 'the thick green root of the wasabi plant that the Japanese use in cooking and that tastes like strong horseradish', 'name': 'wasabi'}, {'frequency': 'c', 'id': 1182, 'synset': 'washer.n.03', 'synonyms': ['automatic_washer', 'washing_machine'], 'def': 'a home appliance for washing clothes and linens automatically', 'name': 'automatic_washer'}, {'frequency': 'f', 'id': 1183, 'synset': 'watch.n.01', 'synonyms': ['watch', 'wristwatch'], 'def': 'a small, portable timepiece', 'name': 'watch'}, {'frequency': 'f', 'id': 1184, 'synset': 'water_bottle.n.01', 'synonyms': ['water_bottle'], 'def': 'a bottle for holding water', 'name': 'water_bottle'}, {'frequency': 'c', 'id': 1185, 'synset': 'water_cooler.n.01', 'synonyms': ['water_cooler'], 'def': 'a device for cooling and dispensing drinking water', 'name': 'water_cooler'}, {'frequency': 'c', 'id': 1186, 'synset': 'water_faucet.n.01', 'synonyms': ['water_faucet', 'water_tap', 'tap_(water_faucet)'], 'def': 'a faucet for drawing water from a pipe or cask', 'name': 'water_faucet'}, {'frequency': 'r', 'id': 1187, 'synset': 'water_filter.n.01', 'synonyms': ['water_filter'], 'def': 'a filter to remove impurities from the water supply', 'name': 'water_filter'}, {'frequency': 'r', 'id': 1188, 'synset': 'water_heater.n.01', 'synonyms': ['water_heater', 'hot-water_heater'], 'def': 'a heater and storage tank to supply heated water', 'name': 'water_heater'}, {'frequency': 'r', 'id': 1189, 'synset': 'water_jug.n.01', 'synonyms': ['water_jug'], 'def': 'a jug that holds water', 'name': 'water_jug'}, {'frequency': 'r', 'id': 1190, 'synset': 'water_pistol.n.01', 'synonyms': ['water_gun', 'squirt_gun'], 'def': 'plaything consisting of a toy pistol that squirts water', 'name': 'water_gun'}, {'frequency': 'c', 'id': 1191, 'synset': 'water_scooter.n.01', 'synonyms': ['water_scooter', 'sea_scooter', 'jet_ski'], 'def': 'a motorboat resembling a motor scooter (NOT A SURFBOARD OR WATER SKI)', 'name': 'water_scooter'}, {'frequency': 'c', 'id': 1192, 'synset': 'water_ski.n.01', 'synonyms': ['water_ski'], 'def': 'broad ski for skimming over water towed by a speedboat (DO NOT MARK WATER)', 'name': 'water_ski'}, {'frequency': 'c', 'id': 1193, 'synset': 'water_tower.n.01', 'synonyms': ['water_tower'], 'def': 'a large reservoir for water', 'name': 'water_tower'}, {'frequency': 'c', 'id': 1194, 'synset': 'watering_can.n.01', 'synonyms': ['watering_can'], 'def': 'a container with a handle and a spout with a perforated nozzle; used to sprinkle water over plants', 'name': 'watering_can'}, {'frequency': 'c', 'id': 1195, 'synset': 'watermelon.n.02', 'synonyms': ['watermelon'], 'def': 'large oblong or roundish melon with a hard green rind and sweet watery red or occasionally yellowish pulp', 'name': 'watermelon'}, {'frequency': 'f', 'id': 1196, 'synset': 'weathervane.n.01', 'synonyms': ['weathervane', 'vane_(weathervane)', 'wind_vane'], 'def': 'mechanical device attached to an elevated structure; rotates freely to show the direction of the wind', 'name': 'weathervane'}, {'frequency': 'c', 'id': 1197, 'synset': 'webcam.n.01', 'synonyms': ['webcam'], 'def': 'a digital camera designed to take digital photographs and transmit them over the internet', 'name': 'webcam'}, {'frequency': 'c', 'id': 1198, 'synset': 'wedding_cake.n.01', 'synonyms': ['wedding_cake', 'bridecake'], 'def': 'a rich cake with two or more tiers and covered with frosting and decorations; served at a wedding reception', 'name': 'wedding_cake'}, {'frequency': 'c', 'id': 1199, 'synset': 'wedding_ring.n.01', 'synonyms': ['wedding_ring', 'wedding_band'], 'def': 'a ring given to the bride and/or groom at the wedding', 'name': 'wedding_ring'}, {'frequency': 'f', 'id': 1200, 'synset': 'wet_suit.n.01', 'synonyms': ['wet_suit'], 'def': 'a close-fitting garment made of a permeable material; worn in cold water to retain body heat', 'name': 'wet_suit'}, {'frequency': 'f', 'id': 1201, 'synset': 'wheel.n.01', 'synonyms': ['wheel'], 'def': 'a circular frame with spokes (or a solid disc) that can rotate on a shaft or axle', 'name': 'wheel'}, {'frequency': 'c', 'id': 1202, 'synset': 'wheelchair.n.01', 'synonyms': ['wheelchair'], 'def': 'a movable chair mounted on large wheels', 'name': 'wheelchair'}, {'frequency': 'c', 'id': 1203, 'synset': 'whipped_cream.n.01', 'synonyms': ['whipped_cream'], 'def': 'cream that has been beaten until light and fluffy', 'name': 'whipped_cream'}, {'frequency': 'r', 'id': 1204, 'synset': 'whiskey.n.01', 'synonyms': ['whiskey'], 'def': 'a liquor made from fermented mash of grain', 'name': 'whiskey'}, {'frequency': 'r', 'id': 1205, 'synset': 'whistle.n.03', 'synonyms': ['whistle'], 'def': 'a small wind instrument that produces a whistling sound by blowing into it', 'name': 'whistle'}, {'frequency': 'r', 'id': 1206, 'synset': 'wick.n.02', 'synonyms': ['wick'], 'def': 'a loosely woven cord in a candle or oil lamp that is lit on fire', 'name': 'wick'}, {'frequency': 'c', 'id': 1207, 'synset': 'wig.n.01', 'synonyms': ['wig'], 'def': 'hairpiece covering the head and made of real or synthetic hair', 'name': 'wig'}, {'frequency': 'c', 'id': 1208, 'synset': 'wind_chime.n.01', 'synonyms': ['wind_chime'], 'def': 'a decorative arrangement of pieces of metal or glass or pottery that hang together loosely so the wind can cause them to tinkle', 'name': 'wind_chime'}, {'frequency': 'c', 'id': 1209, 'synset': 'windmill.n.01', 'synonyms': ['windmill'], 'def': 'a mill that is powered by the wind', 'name': 'windmill'}, {'frequency': 'c', 'id': 1210, 'synset': 'window_box.n.01', 'synonyms': ['window_box_(for_plants)'], 'def': 'a container for growing plants on a windowsill', 'name': 'window_box_(for_plants)'}, {'frequency': 'f', 'id': 1211, 'synset': 'windshield_wiper.n.01', 'synonyms': ['windshield_wiper', 'windscreen_wiper', 'wiper_(for_windshield/screen)'], 'def': 'a mechanical device that cleans the windshield', 'name': 'windshield_wiper'}, {'frequency': 'c', 'id': 1212, 'synset': 'windsock.n.01', 'synonyms': ['windsock', 'air_sock', 'air-sleeve', 'wind_sleeve', 'wind_cone'], 'def': 'a truncated cloth cone mounted on a mast/pole; shows wind direction', 'name': 'windsock'}, {'frequency': 'f', 'id': 1213, 'synset': 'wine_bottle.n.01', 'synonyms': ['wine_bottle'], 'def': 'a bottle for holding wine', 'name': 'wine_bottle'}, {'frequency': 'r', 'id': 1214, 'synset': 'wine_bucket.n.01', 'synonyms': ['wine_bucket', 'wine_cooler'], 'def': 'a bucket of ice used to chill a bottle of wine', 'name': 'wine_bucket'}, {'frequency': 'f', 'id': 1215, 'synset': 'wineglass.n.01', 'synonyms': ['wineglass'], 'def': 'a glass that has a stem and in which wine is served', 'name': 'wineglass'}, {'frequency': 'r', 'id': 1216, 'synset': 'wing_chair.n.01', 'synonyms': ['wing_chair'], 'def': 'easy chair having wings on each side of a high back', 'name': 'wing_chair'}, {'frequency': 'c', 'id': 1217, 'synset': 'winker.n.02', 'synonyms': ['blinder_(for_horses)'], 'def': 'blinds that prevent a horse from seeing something on either side', 'name': 'blinder_(for_horses)'}, {'frequency': 'c', 'id': 1218, 'synset': 'wok.n.01', 'synonyms': ['wok'], 'def': 'pan with a convex bottom; used for frying in Chinese cooking', 'name': 'wok'}, {'frequency': 'r', 'id': 1219, 'synset': 'wolf.n.01', 'synonyms': ['wolf'], 'def': 'a wild carnivorous mammal of the dog family, living and hunting in packs', 'name': 'wolf'}, {'frequency': 'c', 'id': 1220, 'synset': 'wooden_spoon.n.02', 'synonyms': ['wooden_spoon'], 'def': 'a spoon made of wood', 'name': 'wooden_spoon'}, {'frequency': 'c', 'id': 1221, 'synset': 'wreath.n.01', 'synonyms': ['wreath'], 'def': 'an arrangement of flowers, leaves, or stems fastened in a ring', 'name': 'wreath'}, {'frequency': 'c', 'id': 1222, 'synset': 'wrench.n.03', 'synonyms': ['wrench', 'spanner'], 'def': 'a hand tool that is used to hold or twist a nut or bolt', 'name': 'wrench'}, {'frequency': 'c', 'id': 1223, 'synset': 'wristband.n.01', 'synonyms': ['wristband'], 'def': 'band consisting of a part of a sleeve that covers the wrist', 'name': 'wristband'}, {'frequency': 'f', 'id': 1224, 'synset': 'wristlet.n.01', 'synonyms': ['wristlet', 'wrist_band'], 'def': 'a band or bracelet worn around the wrist', 'name': 'wristlet'}, {'frequency': 'r', 'id': 1225, 'synset': 'yacht.n.01', 'synonyms': ['yacht'], 'def': 'an expensive vessel propelled by sail or power and used for cruising or racing', 'name': 'yacht'}, {'frequency': 'r', 'id': 1226, 'synset': 'yak.n.02', 'synonyms': ['yak'], 'def': 'large long-haired wild ox of Tibet often domesticated', 'name': 'yak'}, {'frequency': 'c', 'id': 1227, 'synset': 'yogurt.n.01', 'synonyms': ['yogurt', 'yoghurt', 'yoghourt'], 'def': 'a custard-like food made from curdled milk', 'name': 'yogurt'}, {'frequency': 'r', 'id': 1228, 'synset': 'yoke.n.07', 'synonyms': ['yoke_(animal_equipment)'], 'def': 'gear joining two animals at the neck; NOT egg yolk', 'name': 'yoke_(animal_equipment)'}, {'frequency': 'f', 'id': 1229, 'synset': 'zebra.n.01', 'synonyms': ['zebra'], 'def': 'any of several fleet black-and-white striped African equines', 'name': 'zebra'}, {'frequency': 'c', 'id': 1230, 'synset': 'zucchini.n.02', 'synonyms': ['zucchini', 'courgette'], 'def': 'small cucumber-shaped vegetable marrow; typically dark green', 'name': 'zucchini'}]  # noqa
-# fmt: on
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/modeling/backbone/mvit.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/modeling/backbone/mvit.py
deleted file mode 100644
index 50667a8a836b933666761cc09d4175e64098c8aa..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/modeling/backbone/mvit.py
+++ /dev/null
@@ -1,448 +0,0 @@
-import logging
-import numpy as np
-import torch
-import torch.nn as nn
-
-from .backbone import Backbone
-from .utils import (
-    PatchEmbed,
-    add_decomposed_rel_pos,
-    get_abs_pos,
-    window_partition,
-    window_unpartition,
-)
-
-logger = logging.getLogger(__name__)
-
-
-__all__ = ["MViT"]
-
-
-def attention_pool(x, pool, norm=None):
-    # (B, H, W, C) -> (B, C, H, W)
-    x = x.permute(0, 3, 1, 2)
-    x = pool(x)
-    # (B, C, H1, W1) -> (B, H1, W1, C)
-    x = x.permute(0, 2, 3, 1)
-    if norm:
-        x = norm(x)
-
-    return x
-
-
-class MultiScaleAttention(nn.Module):
-    """Multiscale Multi-head Attention block."""
-
-    def __init__(
-        self,
-        dim,
-        dim_out,
-        num_heads,
-        qkv_bias=True,
-        norm_layer=nn.LayerNorm,
-        pool_kernel=(3, 3),
-        stride_q=1,
-        stride_kv=1,
-        residual_pooling=True,
-        window_size=0,
-        use_rel_pos=False,
-        rel_pos_zero_init=True,
-        input_size=None,
-    ):
-        """
-        Args:
-            dim (int): Number of input channels.
-            dim_out (int): Number of output channels.
-            num_heads (int): Number of attention heads.
-            qkv_bias (bool:  If True, add a learnable bias to query, key, value.
-            norm_layer (nn.Module): Normalization layer.
-            pool_kernel (tuple): kernel size for qkv pooling layers.
-            stride_q (int): stride size for q pooling layer.
-            stride_kv (int): stride size for kv pooling layer.
-            residual_pooling (bool): If true, enable residual pooling.
-            use_rel_pos (bool): If True, add relative postional embeddings to the attention map.
-            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
-            input_size (int or None): Input resolution.
-        """
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim_out // num_heads
-        self.scale = head_dim**-0.5
-
-        self.qkv = nn.Linear(dim, dim_out * 3, bias=qkv_bias)
-        self.proj = nn.Linear(dim_out, dim_out)
-
-        # qkv pooling
-        pool_padding = [k // 2 for k in pool_kernel]
-        dim_conv = dim_out // num_heads
-        self.pool_q = nn.Conv2d(
-            dim_conv,
-            dim_conv,
-            pool_kernel,
-            stride=stride_q,
-            padding=pool_padding,
-            groups=dim_conv,
-            bias=False,
-        )
-        self.norm_q = norm_layer(dim_conv)
-        self.pool_k = nn.Conv2d(
-            dim_conv,
-            dim_conv,
-            pool_kernel,
-            stride=stride_kv,
-            padding=pool_padding,
-            groups=dim_conv,
-            bias=False,
-        )
-        self.norm_k = norm_layer(dim_conv)
-        self.pool_v = nn.Conv2d(
-            dim_conv,
-            dim_conv,
-            pool_kernel,
-            stride=stride_kv,
-            padding=pool_padding,
-            groups=dim_conv,
-            bias=False,
-        )
-        self.norm_v = norm_layer(dim_conv)
-
-        self.window_size = window_size
-        if window_size:
-            self.q_win_size = window_size // stride_q
-            self.kv_win_size = window_size // stride_kv
-        self.residual_pooling = residual_pooling
-
-        self.use_rel_pos = use_rel_pos
-        if self.use_rel_pos:
-            # initialize relative positional embeddings
-            assert input_size[0] == input_size[1]
-            size = input_size[0]
-            rel_dim = 2 * max(size // stride_q, size // stride_kv) - 1
-            self.rel_pos_h = nn.Parameter(torch.zeros(rel_dim, head_dim))
-            self.rel_pos_w = nn.Parameter(torch.zeros(rel_dim, head_dim))
-
-            if not rel_pos_zero_init:
-                nn.init.trunc_normal_(self.rel_pos_h, std=0.02)
-                nn.init.trunc_normal_(self.rel_pos_w, std=0.02)
-
-    def forward(self, x):
-        B, H, W, _ = x.shape
-        # qkv with shape (3, B, nHead, H, W, C)
-        qkv = self.qkv(x).reshape(B, H, W, 3, self.num_heads, -1).permute(3, 0, 4, 1, 2, 5)
-        # q, k, v with shape (B * nHead, H, W, C)
-        q, k, v = qkv.reshape(3, B * self.num_heads, H, W, -1).unbind(0)
-
-        q = attention_pool(q, self.pool_q, self.norm_q)
-        k = attention_pool(k, self.pool_k, self.norm_k)
-        v = attention_pool(v, self.pool_v, self.norm_v)
-
-        ori_q = q
-        if self.window_size:
-            q, q_hw_pad = window_partition(q, self.q_win_size)
-            k, kv_hw_pad = window_partition(k, self.kv_win_size)
-            v, _ = window_partition(v, self.kv_win_size)
-            q_hw = (self.q_win_size, self.q_win_size)
-            kv_hw = (self.kv_win_size, self.kv_win_size)
-        else:
-            q_hw = q.shape[1:3]
-            kv_hw = k.shape[1:3]
-
-        q = q.view(q.shape[0], np.prod(q_hw), -1)
-        k = k.view(k.shape[0], np.prod(kv_hw), -1)
-        v = v.view(v.shape[0], np.prod(kv_hw), -1)
-
-        attn = (q * self.scale) @ k.transpose(-2, -1)
-
-        if self.use_rel_pos:
-            attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, q_hw, kv_hw)
-
-        attn = attn.softmax(dim=-1)
-        x = attn @ v
-
-        x = x.view(x.shape[0], q_hw[0], q_hw[1], -1)
-
-        if self.window_size:
-            x = window_unpartition(x, self.q_win_size, q_hw_pad, ori_q.shape[1:3])
-
-        if self.residual_pooling:
-            x += ori_q
-
-        H, W = x.shape[1], x.shape[2]
-        x = x.view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
-        x = self.proj(x)
-
-        return x
-
-
-class MultiScaleBlock(nn.Module):
-    """Multiscale Transformer blocks"""
-
-    def __init__(
-        self,
-        dim,
-        dim_out,
-        num_heads,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        act_layer=nn.GELU,
-        qkv_pool_kernel=(3, 3),
-        stride_q=1,
-        stride_kv=1,
-        residual_pooling=True,
-        window_size=0,
-        use_rel_pos=False,
-        rel_pos_zero_init=True,
-        input_size=None,
-    ):
-        """
-        Args:
-            dim (int): Number of input channels.
-            dim_out (int): Number of output channels.
-            num_heads (int): Number of attention heads in the MViT block.
-            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-            qkv_bias (bool): If True, add a learnable bias to query, key, value.
-            drop_path (float): Stochastic depth rate.
-            norm_layer (nn.Module): Normalization layer.
-            act_layer (nn.Module): Activation layer.
-            qkv_pool_kernel (tuple): kernel size for qkv pooling layers.
-            stride_q (int): stride size for q pooling layer.
-            stride_kv (int): stride size for kv pooling layer.
-            residual_pooling (bool): If true, enable residual pooling.
-            window_size (int): Window size for window attention blocks. If it equals 0, then not
-                use window attention.
-            use_rel_pos (bool): If True, add relative postional embeddings to the attention map.
-            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
-            input_size (int or None): Input resolution.
-        """
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.attn = MultiScaleAttention(
-            dim,
-            dim_out,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            norm_layer=norm_layer,
-            pool_kernel=qkv_pool_kernel,
-            stride_q=stride_q,
-            stride_kv=stride_kv,
-            residual_pooling=residual_pooling,
-            window_size=window_size,
-            use_rel_pos=use_rel_pos,
-            rel_pos_zero_init=rel_pos_zero_init,
-            input_size=input_size,
-        )
-
-        from timm.models.layers import DropPath, Mlp
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.norm2 = norm_layer(dim_out)
-        self.mlp = Mlp(
-            in_features=dim_out,
-            hidden_features=int(dim_out * mlp_ratio),
-            out_features=dim_out,
-            act_layer=act_layer,
-        )
-
-        if dim != dim_out:
-            self.proj = nn.Linear(dim, dim_out)
-
-        if stride_q > 1:
-            kernel_skip = stride_q + 1
-            padding_skip = int(kernel_skip // 2)
-            self.pool_skip = nn.MaxPool2d(kernel_skip, stride_q, padding_skip, ceil_mode=False)
-
-    def forward(self, x):
-        x_norm = self.norm1(x)
-        x_block = self.attn(x_norm)
-
-        if hasattr(self, "proj"):
-            x = self.proj(x_norm)
-        if hasattr(self, "pool_skip"):
-            x = attention_pool(x, self.pool_skip)
-
-        x = x + self.drop_path(x_block)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-
-class MViT(Backbone):
-    """
-    This module implements Multiscale Vision Transformer (MViT) backbone in :paper:'mvitv2'.
-    """
-
-    def __init__(
-        self,
-        img_size=224,
-        patch_kernel=(7, 7),
-        patch_stride=(4, 4),
-        patch_padding=(3, 3),
-        in_chans=3,
-        embed_dim=96,
-        depth=16,
-        num_heads=1,
-        last_block_indexes=(0, 2, 11, 15),
-        qkv_pool_kernel=(3, 3),
-        adaptive_kv_stride=4,
-        adaptive_window_size=56,
-        residual_pooling=True,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        drop_path_rate=0.0,
-        norm_layer=nn.LayerNorm,
-        act_layer=nn.GELU,
-        use_abs_pos=False,
-        use_rel_pos=True,
-        rel_pos_zero_init=True,
-        use_act_checkpoint=False,
-        pretrain_img_size=224,
-        pretrain_use_cls_token=True,
-        out_features=("scale2", "scale3", "scale4", "scale5"),
-    ):
-        """
-        Args:
-            img_size (int): Input image size.
-            patch_kernel (tuple): kernel size for patch embedding.
-            patch_stride (tuple): stride size for patch embedding.
-            patch_padding (tuple): padding size for patch embedding.
-            in_chans (int): Number of input image channels.
-            embed_dim (int): Patch embedding dimension.
-            depth (int): Depth of MViT.
-            num_heads (int): Number of base attention heads in each MViT block.
-            last_block_indexes (tuple): Block indexes for last blocks in each stage.
-            qkv_pool_kernel (tuple): kernel size for qkv pooling layers.
-            adaptive_kv_stride (int): adaptive stride size for kv pooling.
-            adaptive_window_size (int): adaptive window size for window attention blocks.
-            residual_pooling (bool): If true, enable residual pooling.
-            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-            qkv_bias (bool): If True, add a learnable bias to query, key, value.
-            drop_path_rate (float): Stochastic depth rate.
-            norm_layer (nn.Module): Normalization layer.
-            act_layer (nn.Module): Activation layer.
-            use_abs_pos (bool): If True, use absolute positional embeddings.
-            use_rel_pos (bool): If True, add relative postional embeddings to the attention map.
-            rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
-            window_size (int): Window size for window attention blocks.
-            use_act_checkpoint (bool): If True, use activation checkpointing.
-            pretrain_img_size (int): input image size for pretraining models.
-            pretrain_use_cls_token (bool): If True, pretrainig models use class token.
-            out_features (tuple): name of the feature maps from each stage.
-        """
-        super().__init__()
-        self.pretrain_use_cls_token = pretrain_use_cls_token
-
-        self.patch_embed = PatchEmbed(
-            kernel_size=patch_kernel,
-            stride=patch_stride,
-            padding=patch_padding,
-            in_chans=in_chans,
-            embed_dim=embed_dim,
-        )
-
-        if use_abs_pos:
-            # Initialize absoluate positional embedding with pretrain image size.
-            num_patches = (pretrain_img_size // patch_stride[0]) * (
-                pretrain_img_size // patch_stride[1]
-            )
-            num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches
-            self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, embed_dim))
-        else:
-            self.pos_embed = None
-
-        # stochastic depth decay rule
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
-        dim_out = embed_dim
-        stride_kv = adaptive_kv_stride
-        window_size = adaptive_window_size
-        input_size = (img_size // patch_stride[0], img_size // patch_stride[1])
-        stage = 2
-        stride = patch_stride[0]
-        self._out_feature_strides = {}
-        self._out_feature_channels = {}
-        self.blocks = nn.ModuleList()
-        for i in range(depth):
-            # Multiply stride_kv by 2 if it's the last block of stage2 and stage3.
-            if i == last_block_indexes[1] or i == last_block_indexes[2]:
-                stride_kv_ = stride_kv * 2
-            else:
-                stride_kv_ = stride_kv
-            # hybrid window attention: global attention in last three stages.
-            window_size_ = 0 if i in last_block_indexes[1:] else window_size
-            block = MultiScaleBlock(
-                dim=embed_dim,
-                dim_out=dim_out,
-                num_heads=num_heads,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                drop_path=dpr[i],
-                norm_layer=norm_layer,
-                qkv_pool_kernel=qkv_pool_kernel,
-                stride_q=2 if i - 1 in last_block_indexes else 1,
-                stride_kv=stride_kv_,
-                residual_pooling=residual_pooling,
-                window_size=window_size_,
-                use_rel_pos=use_rel_pos,
-                rel_pos_zero_init=rel_pos_zero_init,
-                input_size=input_size,
-            )
-            if use_act_checkpoint:
-                # TODO: use torch.utils.checkpoint
-                from fairscale.nn.checkpoint import checkpoint_wrapper
-
-                block = checkpoint_wrapper(block)
-            self.blocks.append(block)
-
-            embed_dim = dim_out
-            if i in last_block_indexes:
-                name = f"scale{stage}"
-                if name in out_features:
-                    self._out_feature_channels[name] = dim_out
-                    self._out_feature_strides[name] = stride
-                    self.add_module(f"{name}_norm", norm_layer(dim_out))
-
-                dim_out *= 2
-                num_heads *= 2
-                stride_kv = max(stride_kv // 2, 1)
-                stride *= 2
-                stage += 1
-            if i - 1 in last_block_indexes:
-                window_size = window_size // 2
-                input_size = [s // 2 for s in input_size]
-
-        self._out_features = out_features
-        self._last_block_indexes = last_block_indexes
-
-        if self.pos_embed is not None:
-            nn.init.trunc_normal_(self.pos_embed, std=0.02)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            nn.init.trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    def forward(self, x):
-        x = self.patch_embed(x)
-
-        if self.pos_embed is not None:
-            x = x + get_abs_pos(self.pos_embed, self.pretrain_use_cls_token, x.shape[1:3])
-
-        outputs = {}
-        stage = 2
-        for i, blk in enumerate(self.blocks):
-            x = blk(x)
-            if i in self._last_block_indexes:
-                name = f"scale{stage}"
-                if name in self._out_features:
-                    x_out = getattr(self, f"{name}_norm")(x)
-                    outputs[name] = x_out.permute(0, 3, 1, 2)
-                stage += 1
-
-        return outputs
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/saconv.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/saconv.py
deleted file mode 100644
index b4ee3978e097fca422805db4e31ae481006d7971..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/saconv.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from annotator.uniformer.mmcv.cnn import CONV_LAYERS, ConvAWS2d, constant_init
-from annotator.uniformer.mmcv.ops.deform_conv import deform_conv2d
-from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
-
-
-@CONV_LAYERS.register_module(name='SAC')
-class SAConv2d(ConvAWS2d):
-    """SAC (Switchable Atrous Convolution)
-
-    This is an implementation of SAC in DetectoRS
-    (https://arxiv.org/pdf/2006.02334.pdf).
-
-    Args:
-        in_channels (int): Number of channels in the input image
-        out_channels (int): Number of channels produced by the convolution
-        kernel_size (int or tuple): Size of the convolving kernel
-        stride (int or tuple, optional): Stride of the convolution. Default: 1
-        padding (int or tuple, optional): Zero-padding added to both sides of
-            the input. Default: 0
-        padding_mode (string, optional): ``'zeros'``, ``'reflect'``,
-            ``'replicate'`` or ``'circular'``. Default: ``'zeros'``
-        dilation (int or tuple, optional): Spacing between kernel elements.
-            Default: 1
-        groups (int, optional): Number of blocked connections from input
-            channels to output channels. Default: 1
-        bias (bool, optional): If ``True``, adds a learnable bias to the
-            output. Default: ``True``
-        use_deform: If ``True``, replace convolution with deformable
-            convolution. Default: ``False``.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 groups=1,
-                 bias=True,
-                 use_deform=False):
-        super().__init__(
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            groups=groups,
-            bias=bias)
-        self.use_deform = use_deform
-        self.switch = nn.Conv2d(
-            self.in_channels, 1, kernel_size=1, stride=stride, bias=True)
-        self.weight_diff = nn.Parameter(torch.Tensor(self.weight.size()))
-        self.pre_context = nn.Conv2d(
-            self.in_channels, self.in_channels, kernel_size=1, bias=True)
-        self.post_context = nn.Conv2d(
-            self.out_channels, self.out_channels, kernel_size=1, bias=True)
-        if self.use_deform:
-            self.offset_s = nn.Conv2d(
-                self.in_channels,
-                18,
-                kernel_size=3,
-                padding=1,
-                stride=stride,
-                bias=True)
-            self.offset_l = nn.Conv2d(
-                self.in_channels,
-                18,
-                kernel_size=3,
-                padding=1,
-                stride=stride,
-                bias=True)
-        self.init_weights()
-
-    def init_weights(self):
-        constant_init(self.switch, 0, bias=1)
-        self.weight_diff.data.zero_()
-        constant_init(self.pre_context, 0)
-        constant_init(self.post_context, 0)
-        if self.use_deform:
-            constant_init(self.offset_s, 0)
-            constant_init(self.offset_l, 0)
-
-    def forward(self, x):
-        # pre-context
-        avg_x = F.adaptive_avg_pool2d(x, output_size=1)
-        avg_x = self.pre_context(avg_x)
-        avg_x = avg_x.expand_as(x)
-        x = x + avg_x
-        # switch
-        avg_x = F.pad(x, pad=(2, 2, 2, 2), mode='reflect')
-        avg_x = F.avg_pool2d(avg_x, kernel_size=5, stride=1, padding=0)
-        switch = self.switch(avg_x)
-        # sac
-        weight = self._get_weight(self.weight)
-        zero_bias = torch.zeros(
-            self.out_channels, device=weight.device, dtype=weight.dtype)
-
-        if self.use_deform:
-            offset = self.offset_s(avg_x)
-            out_s = deform_conv2d(x, offset, weight, self.stride, self.padding,
-                                  self.dilation, self.groups, 1)
-        else:
-            if (TORCH_VERSION == 'parrots'
-                    or digit_version(TORCH_VERSION) < digit_version('1.5.0')):
-                out_s = super().conv2d_forward(x, weight)
-            elif digit_version(TORCH_VERSION) >= digit_version('1.8.0'):
-                # bias is a required argument of _conv_forward in torch 1.8.0
-                out_s = super()._conv_forward(x, weight, zero_bias)
-            else:
-                out_s = super()._conv_forward(x, weight)
-        ori_p = self.padding
-        ori_d = self.dilation
-        self.padding = tuple(3 * p for p in self.padding)
-        self.dilation = tuple(3 * d for d in self.dilation)
-        weight = weight + self.weight_diff
-        if self.use_deform:
-            offset = self.offset_l(avg_x)
-            out_l = deform_conv2d(x, offset, weight, self.stride, self.padding,
-                                  self.dilation, self.groups, 1)
-        else:
-            if (TORCH_VERSION == 'parrots'
-                    or digit_version(TORCH_VERSION) < digit_version('1.5.0')):
-                out_l = super().conv2d_forward(x, weight)
-            elif digit_version(TORCH_VERSION) >= digit_version('1.8.0'):
-                # bias is a required argument of _conv_forward in torch 1.8.0
-                out_l = super()._conv_forward(x, weight, zero_bias)
-            else:
-                out_l = super()._conv_forward(x, weight)
-
-        out = switch * out_s + (1 - switch) * out_l
-        self.padding = ori_p
-        self.dilation = ori_d
-        # post-context
-        avg_x = F.adaptive_avg_pool2d(out, output_size=1)
-        avg_x = self.post_context(avg_x)
-        avg_x = avg_x.expand_as(out)
-        out = out + avg_x
-        return out
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/tin_shift.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/tin_shift.py
deleted file mode 100644
index 472c9fcfe45a124e819b7ed5653e585f94a8811e..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/ops/tin_shift.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-# Code reference from "Temporal Interlacing Network"
-# https://github.com/deepcs233/TIN/blob/master/cuda_shift/rtc_wrap.py
-# Hao Shao, Shengju Qian, Yu Liu
-# shaoh19@mails.tsinghua.edu.cn, sjqian@cse.cuhk.edu.hk, yuliu@ee.cuhk.edu.hk
-
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext',
-                                 ['tin_shift_forward', 'tin_shift_backward'])
-
-
-class TINShiftFunction(Function):
-
-    @staticmethod
-    def forward(ctx, input, shift):
-        C = input.size(2)
-        num_segments = shift.size(1)
-        if C // num_segments <= 0 or C % num_segments != 0:
-            raise ValueError('C should be a multiple of num_segments, '
-                             f'but got C={C} and num_segments={num_segments}.')
-
-        ctx.save_for_backward(shift)
-
-        out = torch.zeros_like(input)
-        ext_module.tin_shift_forward(input, shift, out)
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-
-        shift = ctx.saved_tensors[0]
-        data_grad_input = grad_output.new(*grad_output.size()).zero_()
-        shift_grad_input = shift.new(*shift.size()).zero_()
-        ext_module.tin_shift_backward(grad_output, shift, data_grad_input)
-
-        return data_grad_input, shift_grad_input
-
-
-tin_shift = TINShiftFunction.apply
-
-
-class TINShift(nn.Module):
-    """Temporal Interlace Shift.
-
-    Temporal Interlace shift is a differentiable temporal-wise frame shifting
-    which is proposed in "Temporal Interlacing Network"
-
-    Please refer to https://arxiv.org/abs/2001.06499 for more details.
-    Code is modified from https://github.com/mit-han-lab/temporal-shift-module
-    """
-
-    def forward(self, input, shift):
-        """Perform temporal interlace shift.
-
-        Args:
-            input (Tensor): Feature map with shape [N, num_segments, C, H * W].
-            shift (Tensor): Shift tensor with shape [N, num_segments].
-
-        Returns:
-            Feature map after temporal interlace shift.
-        """
-        return tin_shift(input, shift)
diff --git a/spaces/Surn/UnlimitedMusicGen/audiocraft/models/__init__.py b/spaces/Surn/UnlimitedMusicGen/audiocraft/models/__init__.py
deleted file mode 100644
index 92c7a48a200eba455044cd66e0d2c1efe6494f5c..0000000000000000000000000000000000000000
--- a/spaces/Surn/UnlimitedMusicGen/audiocraft/models/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from .musicgen import MusicGen
-from .lm import LMModel
-from .encodec import CompressionModel, EncodecModel
diff --git a/spaces/TRI-ML/risk_biased_prediction/scripts/eval_scripts/plot_latent_cost.py b/spaces/TRI-ML/risk_biased_prediction/scripts/eval_scripts/plot_latent_cost.py
deleted file mode 100644
index 1ab9ecbeebf1fdb376b5bdc5a6b5523f652f0dc7..0000000000000000000000000000000000000000
--- a/spaces/TRI-ML/risk_biased_prediction/scripts/eval_scripts/plot_latent_cost.py
+++ /dev/null
@@ -1,158 +0,0 @@
-# Lloyd algorithm while estimating average cost?
-
-import os
-
-import matplotlib.pyplot as plt
-import matplotlib
-import numpy as np
-from pytorch_lightning.utilities.seed import seed_everything
-
-# from scipy.cluster.vq import kmeans2
-# from scipy.spatial import voronoi_plot_2d, Voronoi
-import torch
-import torch.nn as nn
-
-from risk_biased.scene_dataset.loaders import SceneDataLoaders
-from risk_biased.scene_dataset.scene import RandomScene, RandomSceneParams
-
-# from risk_biased.scene_dataset.scene_plotter import ScenePlotter
-from risk_biased.utils.callbacks import get_fast_slow_scenes, DrawCallbackParams
-from risk_biased.utils.config_argparse import config_argparse
-from risk_biased.utils.cost import TTCCostNumpy
-
-from risk_biased.utils.load_model import load_from_config
-
-
-def draw_cost_map(
-    model: nn.Module,
-    selected_agent: int,
-    device,
-    scene: RandomScene,
-    sqrt_n_samples: int,
-    params: DrawCallbackParams,
-):
-    n_samples = sqrt_n_samples**2
-    ped_trajs = scene.get_pedestrians_trajectories()
-    n_scenes, n_agents, n_steps, features = ped_trajs.shape
-    input_traj = ped_trajs[:, :, : params.num_steps]
-    normalized_input, offset = SceneDataLoaders.normalize_trajectory(
-        torch.from_numpy(input_traj.astype("float32")).contiguous().to(device)
-    )
-
-    n_scenes = ped_trajs.shape[0]
-
-    x = np.linspace(-3, 3, sqrt_n_samples)
-    y = np.linspace(-3, 3, sqrt_n_samples)
-    xx, yy = np.meshgrid(x, y)
-    prior_samples = (
-        torch.from_numpy(np.stack((xx, yy), -1).astype("float32"))
-        .view(1, 1, n_samples, 2)
-        .repeat(n_scenes, n_agents, 1, 1)
-    )
-
-    mask_z = torch.ones_like(prior_samples[..., 0, 0])
-    mask_input = torch.ones_like(normalized_input[..., 0])
-    map = torch.empty(n_scenes, 0, 0, features, device=device)
-    mask_map = torch.empty(n_scenes, 0, 0)
-    generated_trajs = (
-        SceneDataLoaders.unnormalize_trajectory(
-            model.decode(
-                z_samples=prior_samples,
-                mask_z=mask_z,
-                x=normalized_input,
-                mask_x=mask_input,
-                map=map,
-                mask_map=mask_map,
-                offset=offset,
-            ),
-            offset,
-        )
-        .cpu()
-        .detach()
-        .numpy()
-    )
-
-    input_traj = np.repeat(
-        input_traj.reshape((n_scenes, n_agents, 1, params.num_steps, features)),
-        n_samples,
-        axis=2,
-    )
-
-    generated_ped_trajs = np.concatenate((input_traj, generated_trajs), axis=3)
-    ego_traj = scene.get_ego_ref_trajectory(params.scene_params.sample_times)[
-        None, :, :
-    ]
-    ttc_cost_func = TTCCostNumpy(params.ttc_cost_params)
-
-    sample_times = np.array(params.scene_params.sample_times)
-    ped_velocities = (
-        generated_ped_trajs[:, :, :, 1:] - generated_ped_trajs[:, :, :, :-1]
-    ) / ((sample_times[1:] - sample_times[:-1])[None, None, None, :, None])
-    ped_velocities = np.concatenate((ped_velocities[:, :, :, 0:1], ped_velocities), 3)
-    ttc_cost_pred, (ttc_pred, dist_pred) = ttc_cost_func(
-        ego_traj[:, :, :, params.num_steps :],
-        generated_ped_trajs[:, :, :, params.num_steps :],
-        scene.get_ego_ref_velocity()[:, :, None],
-        ped_velocities[:, :, :, params.num_steps :],
-    )
-
-    ttc_cost_pred = (
-        ttc_cost_pred[:, selected_agent]
-        .reshape(n_scenes, sqrt_n_samples, sqrt_n_samples)
-        .mean(0)
-    )
-    cmap = plt.get_cmap("RdBu_r")
-    plt.contourf(
-        xx,
-        yy,
-        ttc_cost_pred.reshape((sqrt_n_samples, sqrt_n_samples)),
-        50,
-        cmap=cmap,
-        extent=(-3, 3, -3, 3),
-        vmin=0,
-        vmax=2,
-    )
-    norm = matplotlib.colors.Normalize(vmin=0, vmax=2, clip=True)
-    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
-    plt.colorbar(sm, label="TTC cost")
-    plt.axis([-3, 3, -3, 3])
-    plt.show()
-
-
-if __name__ == "__main__":
-    # Draws a contour plot of the cost associated with the latent samples in two scenarios: safer_fast and safer_slow
-    working_dir = os.path.dirname(os.path.realpath(__file__))
-    config_path = os.path.join(
-        working_dir, "..", "..", "risk_biased", "config", "learning_config.py"
-    )
-    cfg = config_argparse(config_path)
-
-    model, loaders, cfg = load_from_config(cfg)
-    assert (
-        cfg.latent_dim == 2
-        and "The latent dimension of the model must be exactly 2 to be plotted (no dimensionality reduction capabilities)"
-    )
-    scene_params = RandomSceneParams.from_config(cfg)
-    safer_fast_scene, safer_slow_scene = get_fast_slow_scenes(scene_params, 100)
-    draw_params = DrawCallbackParams.from_config(cfg)
-    if cfg.seed is not None:
-        seed_everything(cfg.seed)
-
-    sqrt_n_samples = 100
-    n_quantize = 100
-    draw_cost_map(
-        model.model,
-        0,
-        model.device,
-        safer_fast_scene,
-        sqrt_n_samples,
-        draw_params,
-    )
-    draw_cost_map(
-        model.model,
-        0,
-        model.device,
-        safer_slow_scene,
-        sqrt_n_samples,
-        draw_params,
-    )
diff --git a/spaces/TabPFN/TabPFNPrediction/TabPFN/priors/fast_gp.py b/spaces/TabPFN/TabPFNPrediction/TabPFN/priors/fast_gp.py
deleted file mode 100644
index 1c4b061c547d9ec5c17135b9f58562bbb4a54da1..0000000000000000000000000000000000000000
--- a/spaces/TabPFN/TabPFNPrediction/TabPFN/priors/fast_gp.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import time
-
-import torch
-from torch import nn
-import gpytorch
-
-from .utils import get_batch_to_dataloader
-from utils import default_device
-
-
-# We will use the simplest form of GP model, exact inference
-class ExactGPModel(gpytorch.models.ExactGP):
-    def __init__(self, train_x, train_y, likelihood):
-        super(ExactGPModel, self).__init__(train_x, train_y, likelihood)
-        self.mean_module = gpytorch.means.ConstantMean()
-        self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())
-
-    def forward(self, x):
-        mean_x = self.mean_module(x)
-        covar_x = self.covar_module(x)
-        return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
-
-
-def get_model(x, y, hyperparameters):
-    likelihood = gpytorch.likelihoods.GaussianLikelihood(noise_constraint=gpytorch.constraints.GreaterThan(1.e-9))
-    model = ExactGPModel(x, y, likelihood)
-    model.likelihood.noise = torch.ones_like(model.likelihood.noise) * hyperparameters["noise"]
-    model.covar_module.outputscale = torch.ones_like(model.covar_module.outputscale) * hyperparameters["outputscale"]
-    model.covar_module.base_kernel.lengthscale = torch.ones_like(model.covar_module.base_kernel.lengthscale) * \
-                                                 hyperparameters["lengthscale"]
-    return model, likelihood
-
-
-@torch.no_grad()
-def get_batch(batch_size, seq_len, num_features, device=default_device, hyperparameters=None,
-              equidistant_x=False, fix_x=None, **kwargs):
-    if isinstance(hyperparameters, (tuple, list)):
-        hyperparameters = {"noise": hyperparameters[0]
-            , "outputscale": hyperparameters[1]
-            , "lengthscale": hyperparameters[2]
-            , "is_binary_classification": hyperparameters[3]
-            # , "num_features_used": hyperparameters[4]
-            , "normalize_by_used_features": hyperparameters[5]
-            , "order_y": hyperparameters[6]
-            , "sampling": hyperparameters[7]
-                           }
-    elif hyperparameters is None:
-        hyperparameters = {"noise": .1, "outputscale": .1, "lengthscale": .1}
-
-    if 'verbose' in hyperparameters and hyperparameters['verbose']:
-        print({"noise": hyperparameters['noise'], "outputscale": hyperparameters['outputscale']
-                  , "lengthscale": hyperparameters['lengthscale'], 'batch_size': batch_size, 'sampling': hyperparameters['sampling']})
-
-    # hyperparameters = {k: hyperparameters[k]() if callable(hyperparameters[k]) else hyperparameters[k] for k in
-    #      hyperparameters.keys()}
-    assert not (equidistant_x and (fix_x is not None))
-
-    with gpytorch.settings.fast_computations(*hyperparameters.get('fast_computations', (True, True, True))):
-        if equidistant_x:
-            assert num_features == 1
-            x = torch.linspace(0, 1., seq_len).unsqueeze(0).repeat(batch_size, 1).unsqueeze(-1)
-        elif fix_x is not None:
-            assert fix_x.shape == (seq_len, num_features)
-            x = fix_x.unsqueeze(0).repeat(batch_size, 1, 1).to(device)
-        else:
-            if hyperparameters.get('sampling','uniform') == 'uniform':
-                x = torch.rand(batch_size, seq_len, num_features, device=device)
-            else:
-                x = torch.randn(batch_size, seq_len, num_features, device=device)
-        model, likelihood = get_model(x, torch.Tensor(), hyperparameters)
-        model.to(device)
-        # trained_model = ExactGPModel(train_x, train_y, likelihood).cuda()
-        # trained_model.eval()
-        is_fitted = False
-        while not is_fitted:
-            try:
-                with gpytorch.settings.prior_mode(True):
-                    model, likelihood = get_model(x, torch.Tensor(), hyperparameters)
-                    model.to(device)
-
-                    d = model(x)
-                    d = likelihood(d)
-                    sample = d.sample().transpose(0, 1)
-                    is_fitted = True
-            except RuntimeError: # This can happen when torch.linalg.eigh fails. Restart with new init resolves this.
-                print('GP Fitting unsuccessful, retrying.. ')
-                print(x)
-                print(hyperparameters)
-
-    if bool(torch.any(torch.isnan(x)).detach().cpu().numpy()):
-        print({"noise": hyperparameters['noise'], "outputscale": hyperparameters['outputscale']
-                  , "lengthscale": hyperparameters['lengthscale'], 'batch_size': batch_size})
-
-    # TODO: Multi output
-    return x.transpose(0, 1), sample, sample  # x.shape = (T,B,H)
-
-DataLoader = get_batch_to_dataloader(get_batch)
-DataLoader.num_outputs = 1
-
-def get_model_on_device(x,y,hyperparameters,device):
-    model, likelihood = get_model(x, y, hyperparameters)
-    model.to(device)
-    return model, likelihood
-
-
-@torch.no_grad()
-def evaluate(x, y, y_non_noisy, use_mse=False, hyperparameters={}, get_model_on_device=get_model_on_device, device=default_device, step_size=1, start_pos=0):
-    start_time = time.time()
-    losses_after_t = [.0] if start_pos == 0 else []
-    all_losses_after_t = []
-
-    with gpytorch.settings.fast_computations(*hyperparameters.get('fast_computations',(True,True,True))), gpytorch.settings.fast_pred_var(False):
-        for t in range(max(start_pos, 1), len(x), step_size):
-            loss_sum = 0.
-            model, likelihood = get_model_on_device(x[:t].transpose(0, 1), y[:t].transpose(0, 1), hyperparameters, device)
-
-
-            model.eval()
-            # print([t.shape for t in model.train_inputs])
-            # print(x[:t].transpose(0,1).shape, x[t].unsqueeze(1).shape, y[:t].transpose(0,1).shape)
-            f = model(x[t].unsqueeze(1))
-            l = likelihood(f)
-            means = l.mean.squeeze()
-            varis = l.covariance_matrix.squeeze()
-            # print(l.variance.squeeze(), l.mean.squeeze(), y[t])
-
-            assert len(means.shape) == len(varis.shape) == 1
-            assert len(means) == len(varis) == x.shape[1]
-
-            if use_mse:
-                c = nn.MSELoss(reduction='none')
-                ls = c(means, y[t])
-            else:
-                ls = -l.log_prob(y[t].unsqueeze(1))
-
-            losses_after_t.append(ls.mean())
-            all_losses_after_t.append(ls.flatten())
-        return torch.stack(all_losses_after_t).to('cpu'), torch.tensor(losses_after_t).to('cpu'), time.time() - start_time
-
-if __name__ == '__main__':
-    hps = (.1,.1,.1)
-    for redo_idx in range(1):
-        print(
-            evaluate(*get_batch(1000, 10, hyperparameters=hps, num_features=10), use_mse=False, hyperparameters=hps))
diff --git a/spaces/Tbryan2/AssistantGM/app.py b/spaces/Tbryan2/AssistantGM/app.py
deleted file mode 100644
index 2c1184346d50d640c3c71efa365782c9b5dd4cfa..0000000000000000000000000000000000000000
--- a/spaces/Tbryan2/AssistantGM/app.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Dependencies
-import os
-import gradio as gr
-import pandas as pd
-import chromadb
-import pathlib
-
-
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.llms import OpenAI
-from langchain.chains.question_answering import load_qa_chain
-from langchain.document_loaders import DataFrameLoader
-from langchain.vectorstores import Chroma
-
-# Import the OpenAI API key
-OPENAI_API_KEY = os.environ['openai_api_key']
-
-# Define the data
-data = pd.read_csv('https://raw.githubusercontent.com/tbryan2/nfl-prospects-nlp/main/data/dataset.csv')
-
-# Create the loader specify the column containing the text
-loader = DataFrameLoader(data, page_content_column="Player Bio")
-
-# Load the data
-docs = loader.load()
-
-# Create the vectorstore
-PERSIST_DIRECTORY = 'db'
-
-embedding = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
-vectordb = Chroma.from_documents(
-    documents=docs, embedding=embedding, persist_directory=PERSIST_DIRECTORY)
-
-# Persist the vectorstore
-vectordb.persist()
-vectordb = None
-
-# Gradio app
-def question(query):
-
-    ## Now we can load the persisted database from disk, and use it as normal.
-    vectordb = Chroma(persist_directory=PERSIST_DIRECTORY,
-                  embedding_function=embedding)
-
-    llm = OpenAI(temperature=0.7, openai_api_key=OPENAI_API_KEY)
-    chain = load_qa_chain(llm, chain_type="stuff")
-
-    docsearch = vectordb.similarity_search(query, include_metadata=True, k=3)
-
-    return chain.run(input_documents=docsearch, question=query)
-
-title = "AssistantGM: NFL Draft Prospect AI"
-
-# Define the metadata_root path
-metadata_root = pathlib.Path(__file__).parent.absolute()
-
-with open(
-        metadata_root.joinpath("description.md"), "r"
-    ) as f:
-        description = f.read()
-
-demo = gr.Interface(fn=question,
-                    inputs="text",
-                    outputs="text",
-                    description=description,
-                    title=title)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/utils/README.md b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/utils/README.md
deleted file mode 100644
index 9765b24a730b77556104187ac3ef5439ab0859fd..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/utils/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Utility functions
-
-This folder contain utility functions that are not used in the
-core library, but are useful for building models or training
-code using the config system.
diff --git a/spaces/UserXTheUnknown/stablediffusion-infinity/PyPatchMatch/csrc/masked_image.h b/spaces/UserXTheUnknown/stablediffusion-infinity/PyPatchMatch/csrc/masked_image.h
deleted file mode 100644
index 52caf272d0534c35b79eedaea53d0c56e5cf942e..0000000000000000000000000000000000000000
--- a/spaces/UserXTheUnknown/stablediffusion-infinity/PyPatchMatch/csrc/masked_image.h
+++ /dev/null
@@ -1,112 +0,0 @@
-#pragma once
-
-#include <opencv2/core.hpp>
-
-class MaskedImage {
-public:
-    MaskedImage() : m_image(), m_mask(), m_global_mask(), m_image_grady(), m_image_gradx(), m_image_grad_computed(false) {
-        // pass
-    }
-    MaskedImage(cv::Mat image, cv::Mat mask) : m_image(image), m_mask(mask), m_image_grad_computed(false) {
-        // pass
-    }
-    MaskedImage(cv::Mat image, cv::Mat mask, cv::Mat global_mask) : m_image(image), m_mask(mask), m_global_mask(global_mask), m_image_grad_computed(false) {
-        // pass
-    }
-    MaskedImage(cv::Mat image, cv::Mat mask, cv::Mat global_mask, cv::Mat grady, cv::Mat gradx, bool grad_computed) :
-        m_image(image), m_mask(mask), m_global_mask(global_mask),
-        m_image_grady(grady), m_image_gradx(gradx), m_image_grad_computed(grad_computed) {
-        // pass
-    }
-    MaskedImage(int width, int height) : m_global_mask(), m_image_grady(), m_image_gradx() {
-        m_image = cv::Mat(cv::Size(width, height), CV_8UC3);
-        m_image = cv::Scalar::all(0);
-
-        m_mask = cv::Mat(cv::Size(width, height), CV_8U);
-        m_mask = cv::Scalar::all(0);
-    }
-    inline MaskedImage clone() {
-        return MaskedImage(
-            m_image.clone(), m_mask.clone(), m_global_mask.clone(),
-            m_image_grady.clone(), m_image_gradx.clone(), m_image_grad_computed
-        );
-    }
-
-    inline cv::Size size() const {
-        return m_image.size();
-    }
-    inline const cv::Mat &image() const {
-        return m_image;
-    }
-    inline const cv::Mat &mask() const {
-        return m_mask;
-    }
-    inline const cv::Mat &global_mask() const {
-        return m_global_mask;
-    }
-    inline const cv::Mat &grady() const {
-        assert(m_image_grad_computed);
-        return m_image_grady;
-    }
-    inline const cv::Mat &gradx() const {
-        assert(m_image_grad_computed);
-        return m_image_gradx;
-    }
-
-    inline void init_global_mask_mat() {
-        m_global_mask = cv::Mat(m_mask.size(), CV_8U);
-        m_global_mask.setTo(cv::Scalar(0));
-    }
-    inline void set_global_mask_mat(const cv::Mat &other) {
-        m_global_mask = other;
-    }
-
-    inline bool is_masked(int y, int x) const {
-        return static_cast<bool>(m_mask.at<unsigned char>(y, x));
-    }
-    inline bool is_globally_masked(int y, int x) const {
-        return !m_global_mask.empty() && static_cast<bool>(m_global_mask.at<unsigned char>(y, x));
-    }
-    inline void set_mask(int y, int x, bool value) {
-        m_mask.at<unsigned char>(y, x) = static_cast<unsigned char>(value);
-    }
-    inline void set_global_mask(int y, int x, bool value) {
-        m_global_mask.at<unsigned char>(y, x) = static_cast<unsigned char>(value);
-    }
-    inline void clear_mask() {
-        m_mask.setTo(cv::Scalar(0));
-    }
-
-    inline const unsigned char *get_image(int y, int x) const {
-        return m_image.ptr<unsigned char>(y, x);
-    }
-    inline unsigned char *get_mutable_image(int y, int x) {
-        return m_image.ptr<unsigned char>(y, x);
-    }
-
-    inline unsigned char get_image(int y, int x, int c) const {
-        return m_image.ptr<unsigned char>(y, x)[c];
-    }
-    inline int get_image_int(int y, int x, int c) const {
-        return static_cast<int>(m_image.ptr<unsigned char>(y, x)[c]);
-    }
-
-    bool contains_mask(int y, int x, int patch_size) const;
-    MaskedImage downsample() const;
-    MaskedImage upsample(int new_w, int new_h) const;
-    MaskedImage upsample(int new_w, int new_h, const cv::Mat &new_global_mask) const;
-    void compute_image_gradients();
-    void compute_image_gradients() const;
-
-    static const cv::Size kDownsampleKernelSize;
-    static const int kDownsampleKernel[6];
-
-private:
-	cv::Mat m_image;
-	cv::Mat m_mask;
-    cv::Mat m_global_mask;
-    cv::Mat m_image_grady;
-    cv::Mat m_image_gradx;
-    bool m_image_grad_computed = false;
-};
-
diff --git a/spaces/Wanlau/sovits-4.0_datealive/flask_api.py b/spaces/Wanlau/sovits-4.0_datealive/flask_api.py
deleted file mode 100644
index 8cc236a1c34c9ddeddea99bcea13024fb0ccc90b..0000000000000000000000000000000000000000
--- a/spaces/Wanlau/sovits-4.0_datealive/flask_api.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import io
-import logging
-
-import soundfile
-import torch
-import torchaudio
-from flask import Flask, request, send_file
-from flask_cors import CORS
-
-from inference.infer_tool import Svc, RealTimeVC
-
-app = Flask(__name__)
-
-CORS(app)
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-
-
-@app.route("/voiceChangeModel", methods=["POST"])
-def voice_change_model():
-    request_form = request.form
-    wave_file = request.files.get("sample", None)
-    # 变调信息
-    f_pitch_change = float(request_form.get("fPitchChange", 0))
-    # DAW所需的采样率
-    daw_sample = int(float(request_form.get("sampleRate", 0)))
-    speaker_id = int(float(request_form.get("sSpeakId", 0)))
-    # http获得wav文件并转换
-    input_wav_path = io.BytesIO(wave_file.read())
-
-    # 模型推理
-    if raw_infer:
-        out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
-        tar_audio = torchaudio.functional.resample(out_audio, svc_model.target_sample, daw_sample)
-    else:
-        out_audio = svc.process(svc_model, speaker_id, f_pitch_change, input_wav_path)
-        tar_audio = torchaudio.functional.resample(torch.from_numpy(out_audio), svc_model.target_sample, daw_sample)
-    # 返回音频
-    out_wav_path = io.BytesIO()
-    soundfile.write(out_wav_path, tar_audio.cpu().numpy(), daw_sample, format="wav")
-    out_wav_path.seek(0)
-    return send_file(out_wav_path, download_name="temp.wav", as_attachment=True)
-
-
-if __name__ == '__main__':
-    # 启用则为直接切片合成，False为交叉淡化方式
-    # vst插件调整0.3-0.5s切片时间可以降低延迟，直接切片方法会有连接处爆音、交叉淡化会有轻微重叠声音
-    # 自行选择能接受的方法，或将vst最大切片时间调整为1s，此处设为Ture，延迟大音质稳定一些
-    raw_infer = True
-    # 每个模型和config是唯一对应的
-    model_name = "logs/32k/G_174000-Copy1.pth"
-    config_name = "configs/config.json"
-    svc_model = Svc(model_name, config_name)
-    svc = RealTimeVC()
-    # 此处与vst插件对应，不建议更改
-    app.run(port=6842, host="0.0.0.0", debug=False, threaded=False)
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/__init__.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/__init__.py
deleted file mode 100644
index 1f567094d1556897e5fe40f744866012504fe878..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .version import __version__
-
diff --git a/spaces/XzJosh/Diana-Bert-VITS2/text/chinese.py b/spaces/XzJosh/Diana-Bert-VITS2/text/chinese.py
deleted file mode 100644
index 276753880b73de2e8889dcb2101cd98c09e0710b..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Diana-Bert-VITS2/text/chinese.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import os
-import re
-
-import cn2an
-from pypinyin import lazy_pinyin, Style
-
-from text import symbols
-from text.symbols import punctuation
-from text.tone_sandhi import ToneSandhi
-
-current_file_path = os.path.dirname(__file__)
-pinyin_to_symbol_map = {line.split("\t")[0]: line.strip().split("\t")[1] for line in
-                        open(os.path.join(current_file_path, 'opencpop-strict.txt')).readlines()}
-
-import jieba.posseg as psg
-
-
-rep_map = {
-    '：': ',',
-    '；': ',',
-    '，': ',',
-    '。': '.',
-    '！': '!',
-    '？': '?',
-    '\n': '.',
-    "·": ",",
-    '、': ",",
-    '...': '…',
-    '$': '.',
-    '“': "'",
-    '”': "'",
-    '‘': "'",
-    '’': "'",
-    '（': "'",
-    '）': "'",
-    '(': "'",
-    ')': "'",
-    '《': "'",
-    '》': "'",
-    '【': "'",
-    '】': "'",
-    '[': "'",
-    ']': "'",
-    '—': "-",
-    '～': "-",
-    '~': "-",
-    '「': "'",
-    '」': "'",
-
-}
-
-tone_modifier = ToneSandhi()
-
-def replace_punctuation(text):
-    text = text.replace("嗯", "恩").replace("呣","母")
-    pattern = re.compile('|'.join(re.escape(p) for p in rep_map.keys()))
-
-    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
-
-    replaced_text = re.sub(r'[^\u4e00-\u9fa5'+"".join(punctuation)+r']+', '', replaced_text)
-
-    return replaced_text
-
-def g2p(text):
-    pattern = r'(?<=[{0}])\s*'.format(''.join(punctuation))
-    sentences = [i for i in re.split(pattern, text) if i.strip()!='']
-    phones, tones, word2ph = _g2p(sentences)
-    assert sum(word2ph) == len(phones)
-    assert len(word2ph) == len(text) #Sometimes it will crash,you can add a try-catch.
-    phones = ['_'] + phones + ["_"]
-    tones = [0] + tones + [0]
-    word2ph = [1] + word2ph + [1]
-    return phones, tones, word2ph
-
-
-def _get_initials_finals(word):
-    initials = []
-    finals = []
-    orig_initials = lazy_pinyin(
-        word, neutral_tone_with_five=True, style=Style.INITIALS)
-    orig_finals = lazy_pinyin(
-        word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
-    for c, v in zip(orig_initials, orig_finals):
-        initials.append(c)
-        finals.append(v)
-    return initials, finals
-
-
-def _g2p(segments):
-    phones_list = []
-    tones_list = []
-    word2ph = []
-    for seg in segments:
-        pinyins = []
-        # Replace all English words in the sentence
-        seg = re.sub('[a-zA-Z]+', '', seg)
-        seg_cut = psg.lcut(seg)
-        initials = []
-        finals = []
-        seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
-        for word, pos in seg_cut:
-            if pos == 'eng':
-                continue
-            sub_initials, sub_finals = _get_initials_finals(word)
-            sub_finals = tone_modifier.modified_tone(word, pos,
-                                                          sub_finals)
-            initials.append(sub_initials)
-            finals.append(sub_finals)
-
-            # assert len(sub_initials) == len(sub_finals) == len(word)
-        initials = sum(initials, [])
-        finals = sum(finals, [])
-        #
-        for c, v in zip(initials, finals):
-            raw_pinyin = c+v
-            # NOTE: post process for pypinyin outputs
-            # we discriminate i, ii and iii
-            if c == v:
-                assert c in punctuation
-                phone = [c]
-                tone = '0'
-                word2ph.append(1)
-            else:
-                v_without_tone = v[:-1]
-                tone = v[-1]
-
-                pinyin = c+v_without_tone
-                assert tone in '12345'
-
-                if c:
-                    # 多音节
-                    v_rep_map = {
-                        "uei": 'ui',
-                        'iou': 'iu',
-                        'uen': 'un',
-                    }
-                    if v_without_tone in v_rep_map.keys():
-                        pinyin = c+v_rep_map[v_without_tone]
-                else:
-                    # 单音节
-                    pinyin_rep_map = {
-                        'ing': 'ying',
-                        'i': 'yi',
-                        'in': 'yin',
-                        'u': 'wu',
-                    }
-                    if pinyin in pinyin_rep_map.keys():
-                        pinyin = pinyin_rep_map[pinyin]
-                    else:
-                        single_rep_map = {
-                            'v': 'yu',
-                            'e': 'e',
-                            'i': 'y',
-                            'u': 'w',
-                        }
-                        if pinyin[0] in single_rep_map.keys():
-                            pinyin = single_rep_map[pinyin[0]]+pinyin[1:]
-
-                assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
-                phone = pinyin_to_symbol_map[pinyin].split(' ')
-                word2ph.append(len(phone))
-
-            phones_list += phone
-            tones_list += [int(tone)] * len(phone)
-    return phones_list, tones_list, word2ph
-
-
-
-def text_normalize(text):
-    numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-    for number in numbers:
-        text = text.replace(number, cn2an.an2cn(number), 1)
-    text = replace_punctuation(text)
-    return text
-
-def get_bert_feature(text, word2ph):
-    from  text import chinese_bert
-    return chinese_bert.get_bert_feature(text, word2ph)
-
-if __name__ == '__main__':
-    from text.chinese_bert import get_bert_feature
-    text = "啊！但是《原神》是由,米哈\游自主，  [研发]的一款全.新开放世界.冒险游戏"
-    text = text_normalize(text)
-    print(text)
-    phones, tones, word2ph = g2p(text)
-    bert = get_bert_feature(text, word2ph)
-
-    print(phones, tones, word2ph, bert.shape)
-
-
-# # 示例用法
-# text = "这是一个示例文本：,你好！这是一个测试...."
-# print(g2p_paddle(text))  # 输出: 这是一个示例文本你好这是一个测试
diff --git a/spaces/XzJosh/Diana-Bert-VITS2/text/chinese_bert.py b/spaces/XzJosh/Diana-Bert-VITS2/text/chinese_bert.py
deleted file mode 100644
index cb84ce0b426cd0a1c7954ddcdf41322c10ed14fa..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Diana-Bert-VITS2/text/chinese_bert.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large")
-model = AutoModelForMaskedLM.from_pretrained("./bert/chinese-roberta-wwm-ext-large").to(device)
-
-def get_bert_feature(text, word2ph):
-    with torch.no_grad():
-        inputs = tokenizer(text, return_tensors='pt')
-        for i in inputs:
-            inputs[i] = inputs[i].to(device)
-        res = model(**inputs, output_hidden_states=True)
-        res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu()
-
-    assert len(word2ph) == len(text)+2
-    word2phone = word2ph
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        repeat_feature = res[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
-
-    return phone_level_feature.T
-
-if __name__ == '__main__':
-    # feature = get_bert_feature('你好,我是说的道理。')
-    import torch
-
-    word_level_feature = torch.rand(38, 1024)  # 12个词,每个词1024维特征
-    word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1]
-
-    # 计算总帧数
-    total_frames = sum(word2phone)
-    print(word_level_feature.shape)
-    print(word2phone)
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        print(word_level_feature[i].shape)
-
-        # 对每个词重复word2phone[i]次
-        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-    print(phone_level_feature.shape)  # torch.Size([36, 1024])
-
diff --git a/spaces/XzJosh/nine2-Bert-VITS2/monotonic_align/core.py b/spaces/XzJosh/nine2-Bert-VITS2/monotonic_align/core.py
deleted file mode 100644
index dddc688d76172b880054e544b7a217acd013f14f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/nine2-Bert-VITS2/monotonic_align/core.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:,:,::1], numba.float32[:,:,::1], numba.int32[::1], numba.int32[::1]), nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-  b = paths.shape[0]
-  max_neg_val=-1e9
-  for i in range(int(b)):
-    path = paths[i]
-    value = values[i]
-    t_y = t_ys[i]
-    t_x = t_xs[i]
-
-    v_prev = v_cur = 0.0
-    index = t_x - 1
-
-    for y in range(t_y):
-      for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-        if x == y:
-          v_cur = max_neg_val
-        else:
-          v_cur = value[y-1, x]
-        if x == 0:
-          if y == 0:
-            v_prev = 0.
-          else:
-            v_prev = max_neg_val
-        else:
-          v_prev = value[y-1, x-1]
-        value[y, x] += max(v_prev, v_cur)
-
-    for y in range(t_y - 1, -1, -1):
-      path[y, index] = 1
-      if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
-        index = index - 1
diff --git a/spaces/YBiryukov/AncientEgyptianHieroglyphsRecognition/model.py b/spaces/YBiryukov/AncientEgyptianHieroglyphsRecognition/model.py
deleted file mode 100644
index 52c2696c874740179528f0bdae8ce87b774a138f..0000000000000000000000000000000000000000
--- a/spaces/YBiryukov/AncientEgyptianHieroglyphsRecognition/model.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import torch
-import torchvision
-
-from torch import nn
-
-
-def create_effnetb2_model(num_classes:int=3, 
-                          seed:int=42):
-    """Creates an EfficientNetB2 feature extractor model and transforms.
-
-    Args:
-        num_classes (int, optional): number of classes in the classifier head. 
-            Defaults to 3.
-        seed (int, optional): random seed value. Defaults to 42.
-
-    Returns:
-        model (torch.nn.Module): EffNetB2 feature extractor model. 
-        transforms (torchvision.transforms): EffNetB2 image transforms.
-    """
-    # Create EffNetB2 pretrained weights, transforms and model
-    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
-    transforms = weights.transforms()
-    model = torchvision.models.efficientnet_b2(weights=weights)
-
-    # Freeze all layers in base model
-    for param in model.parameters():
-        param.requires_grad = False
-
-    # Change classifier head with random seed for reproducibility
-    torch.manual_seed(seed)
-    model.classifier = nn.Sequential(
-        nn.Dropout(p=0.3, inplace=True),
-        nn.Linear(in_features=1408, out_features=num_classes),
-    )
-    
-    return model, transforms
diff --git a/spaces/YouLiXiya/Mobile-SAM/segment_anything/CONTRIBUTING.md b/spaces/YouLiXiya/Mobile-SAM/segment_anything/CONTRIBUTING.md
deleted file mode 100644
index 263991c9496cf29ed4b99e03a9fb9a38e6bfaf86..0000000000000000000000000000000000000000
--- a/spaces/YouLiXiya/Mobile-SAM/segment_anything/CONTRIBUTING.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# Contributing to segment-anything
-We want to make contributing to this project as easy and transparent as
-possible.
-
-## Pull Requests
-We actively welcome your pull requests.
-
-1. Fork the repo and create your branch from `main`.
-2. If you've added code that should be tested, add tests.
-3. If you've changed APIs, update the documentation.
-4. Ensure the test suite passes.
-5. Make sure your code lints, using the `linter.sh` script in the project's root directory. Linting requires `black==23.*`, `isort==5.12.0`, `flake8`, and `mypy`.
-6. If you haven't already, complete the Contributor License Agreement ("CLA").
-
-## Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Facebook's open source projects.
-
-Complete your CLA here: <https://code.facebook.com/cla>
-
-## Issues
-We use GitHub issues to track public bugs. Please ensure your description is
-clear and has sufficient instructions to be able to reproduce the issue.
-
-Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-## License
-By contributing to segment-anything, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.
diff --git a/spaces/Yuliang/ICON/apps/infer.py b/spaces/Yuliang/ICON/apps/infer.py
deleted file mode 100644
index ce8e4e21179bfff43e2dfcb429c9602fb5e55802..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ICON/apps/infer.py
+++ /dev/null
@@ -1,492 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import os
-import gc
-
-import logging
-from lib.common.config import cfg
-from lib.dataset.mesh_util import (
-    load_checkpoint,
-    update_mesh_shape_prior_losses,
-    blend_rgb_norm,
-    unwrap,
-    remesh,
-    tensor2variable,
-    rot6d_to_rotmat
-)
-
-from lib.dataset.TestDataset import TestDataset
-from lib.common.render import query_color
-from lib.net.local_affine import LocalAffine
-from pytorch3d.structures import Meshes
-from apps.ICON import ICON
-
-from termcolor import colored
-import numpy as np
-from PIL import Image
-import trimesh
-import numpy as np
-from tqdm import tqdm
-
-import torch
-torch.backends.cudnn.benchmark = True
-
-logging.getLogger("trimesh").setLevel(logging.ERROR)
-
-
-def generate_model(in_path, model_type):
-
-    torch.cuda.empty_cache()
-    
-    if model_type == 'ICON':
-        model_type = 'icon-filter'
-    else:
-        model_type = model_type.lower()
-
-    config_dict = {'loop_smpl': 100,
-                   'loop_cloth': 200,
-                   'patience': 5,
-                   'out_dir': './results',
-                   'hps_type': 'pymaf',
-                   'config': f"./configs/{model_type}.yaml"}
-
-    # cfg read and merge
-    cfg.merge_from_file(config_dict['config'])
-    cfg.merge_from_file("./lib/pymaf/configs/pymaf_config.yaml")
-
-    os.makedirs(config_dict['out_dir'], exist_ok=True)
-
-    cfg_show_list = [
-        "test_gpus",
-        [0],
-        "mcube_res",
-        256,
-        "clean_mesh",
-        True,
-    ]
-
-    cfg.merge_from_list(cfg_show_list)
-    cfg.freeze()
-
-    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-    device = torch.device(f"cuda:0")
-
-    # load model and dataloader
-    model = ICON(cfg)
-    model = load_checkpoint(model, cfg)
-
-    dataset_param = {
-        'image_path': in_path,
-        'seg_dir': None,
-        'has_det': True,            # w/ or w/o detection
-        'hps_type': 'pymaf'   # pymaf/pare/pixie
-    }
-
-    if config_dict['hps_type'] == "pixie" and "pamir" in config_dict['config']:
-        print(colored("PIXIE isn't compatible with PaMIR, thus switch to PyMAF", "red"))
-        dataset_param["hps_type"] = "pymaf"
-
-    dataset = TestDataset(dataset_param, device)
-
-    print(colored(f"Dataset Size: {len(dataset)}", "green"))
-
-    pbar = tqdm(dataset)
-
-    for data in pbar:
-
-        pbar.set_description(f"{data['name']}")
-
-        in_tensor = {"smpl_faces": data["smpl_faces"], "image": data["image"]}
-
-        # The optimizer and variables
-        optimed_pose = torch.tensor(
-            data["body_pose"], device=device, requires_grad=True
-        )  # [1,23,3,3]
-        optimed_trans = torch.tensor(
-            data["trans"], device=device, requires_grad=True
-        )  # [3]
-        optimed_betas = torch.tensor(
-            data["betas"], device=device, requires_grad=True
-        )  # [1,10]
-        optimed_orient = torch.tensor(
-            data["global_orient"], device=device, requires_grad=True
-        )  # [1,1,3,3]
-
-        optimizer_smpl = torch.optim.Adam(
-            [optimed_pose, optimed_trans, optimed_betas, optimed_orient],
-            lr=1e-3,
-            amsgrad=True,
-        )
-        scheduler_smpl = torch.optim.lr_scheduler.ReduceLROnPlateau(
-            optimizer_smpl,
-            mode="min",
-            factor=0.5,
-            verbose=0,
-            min_lr=1e-5,
-            patience=config_dict['patience'],
-        )
-
-        losses = {
-            # Cloth: Normal_recon - Normal_pred
-            "cloth": {"weight": 1e1, "value": 0.0},
-            # Cloth: [RT]_v1 - [RT]_v2 (v1-edge-v2)
-            "stiffness": {"weight": 1e5, "value": 0.0},
-            # Cloth: det(R) = 1
-            "rigid": {"weight": 1e5, "value": 0.0},
-            # Cloth: edge length
-            "edge": {"weight": 0, "value": 0.0},
-            # Cloth: normal consistency
-            "nc": {"weight": 0, "value": 0.0},
-            # Cloth: laplacian smoonth
-            "laplacian": {"weight": 1e2, "value": 0.0},
-            # Body: Normal_pred - Normal_smpl
-            "normal": {"weight": 1e0, "value": 0.0},
-            # Body: Silhouette_pred - Silhouette_smpl
-            "silhouette": {"weight": 1e0, "value": 0.0},
-        }
-
-        # smpl optimization
-
-        loop_smpl = tqdm(range(config_dict['loop_smpl']))
-
-        for _ in loop_smpl:
-
-            optimizer_smpl.zero_grad()
-            
-            # 6d_rot to rot_mat
-            optimed_orient_mat = rot6d_to_rotmat(optimed_orient.view(-1,6)).unsqueeze(0)
-            optimed_pose_mat = rot6d_to_rotmat(optimed_pose.view(-1,6)).unsqueeze(0)
-
-            if dataset_param["hps_type"] != "pixie":
-                smpl_out = dataset.smpl_model(
-                    betas=optimed_betas,
-                    body_pose=optimed_pose_mat,
-                    global_orient=optimed_orient_mat,
-                    pose2rot=False,
-                )
-
-                smpl_verts = ((smpl_out.vertices) +
-                              optimed_trans) * data["scale"]
-            else:
-                smpl_verts, _, _ = dataset.smpl_model(
-                    shape_params=optimed_betas,
-                    expression_params=tensor2variable(data["exp"], device),
-                    body_pose=optimed_pose_mat,
-                    global_pose=optimed_orient_mat,
-                    jaw_pose=tensor2variable(data["jaw_pose"], device),
-                    left_hand_pose=tensor2variable(
-                        data["left_hand_pose"], device),
-                    right_hand_pose=tensor2variable(
-                        data["right_hand_pose"], device),
-                )
-
-                smpl_verts = (smpl_verts + optimed_trans) * data["scale"]
-
-            # render optimized mesh (normal, T_normal, image [-1,1])
-            in_tensor["T_normal_F"], in_tensor["T_normal_B"] = dataset.render_normal(
-                smpl_verts *
-                torch.tensor([1.0, -1.0, -1.0]
-                             ).to(device), in_tensor["smpl_faces"]
-            )
-            T_mask_F, T_mask_B = dataset.render.get_silhouette_image()
-
-            with torch.no_grad():
-                in_tensor["normal_F"], in_tensor["normal_B"] = model.netG.normal_filter(
-                    in_tensor
-                )
-
-            diff_F_smpl = torch.abs(
-                in_tensor["T_normal_F"] - in_tensor["normal_F"])
-            diff_B_smpl = torch.abs(
-                in_tensor["T_normal_B"] - in_tensor["normal_B"])
-
-            losses["normal"]["value"] = (diff_F_smpl + diff_B_smpl).mean()
-
-            # silhouette loss
-            smpl_arr = torch.cat([T_mask_F, T_mask_B], dim=-1)[0]
-            gt_arr = torch.cat(
-                [in_tensor["normal_F"][0], in_tensor["normal_B"][0]], dim=2
-            ).permute(1, 2, 0)
-            gt_arr = ((gt_arr + 1.0) * 0.5).to(device)
-            bg_color = (
-                torch.Tensor([0.5, 0.5, 0.5]).unsqueeze(
-                    0).unsqueeze(0).to(device)
-            )
-            gt_arr = ((gt_arr - bg_color).sum(dim=-1) != 0.0).float()
-            diff_S = torch.abs(smpl_arr - gt_arr)
-            losses["silhouette"]["value"] = diff_S.mean()
-
-            # Weighted sum of the losses
-            smpl_loss = 0.0
-            pbar_desc = "Body Fitting --- "
-            for k in ["normal", "silhouette"]:
-                pbar_desc += f"{k}: {losses[k]['value'] * losses[k]['weight']:.3f} | "
-                smpl_loss += losses[k]["value"] * losses[k]["weight"]
-            pbar_desc += f"Total: {smpl_loss:.3f}"
-            loop_smpl.set_description(pbar_desc)
-
-            smpl_loss.backward()
-            optimizer_smpl.step()
-            scheduler_smpl.step(smpl_loss)
-            in_tensor["smpl_verts"] = smpl_verts * \
-                torch.tensor([1.0, 1.0, -1.0]).to(device)
-
-        # visualize the optimization process
-        # 1. SMPL Fitting
-        # 2. Clothes Refinement
-
-        os.makedirs(os.path.join(config_dict['out_dir'], cfg.name,
-                    "refinement"), exist_ok=True)
-
-        # visualize the final results in self-rotation mode
-        os.makedirs(os.path.join(config_dict['out_dir'],
-                    cfg.name, "vid"), exist_ok=True)
-
-        # final results rendered as image
-        # 1. Render the final fitted SMPL (xxx_smpl.png)
-        # 2. Render the final reconstructed clothed human (xxx_cloth.png)
-        # 3. Blend the original image with predicted cloth normal (xxx_overlap.png)
-
-        os.makedirs(os.path.join(config_dict['out_dir'],
-                    cfg.name, "png"), exist_ok=True)
-
-        # final reconstruction meshes
-        # 1. SMPL mesh (xxx_smpl.obj)
-        # 2. SMPL params (xxx_smpl.npy)
-        # 3. clohted mesh (xxx_recon.obj)
-        # 4. remeshed clothed mesh (xxx_remesh.obj)
-        # 5. refined clothed mesh (xxx_refine.obj)
-
-        os.makedirs(os.path.join(config_dict['out_dir'],
-                    cfg.name, "obj"), exist_ok=True)
-
-        norm_pred_F = (
-            ((in_tensor["normal_F"][0].permute(1, 2, 0) + 1.0) * 255.0 / 2.0)
-            .detach()
-            .cpu()
-            .numpy()
-            .astype(np.uint8)
-        )
-        
-        norm_pred_B = (
-            ((in_tensor["normal_B"][0].permute(1, 2, 0) + 1.0) * 255.0 / 2.0)
-            .detach()
-            .cpu()
-            .numpy()
-            .astype(np.uint8)
-        )
-
-        norm_orig_F = unwrap(norm_pred_F, data)
-        norm_orig_B = unwrap(norm_pred_B, data)
-        
-        mask_orig = unwrap(
-            np.repeat(
-                data["mask"].permute(1, 2, 0).detach().cpu().numpy(), 3, axis=2
-            ).astype(np.uint8),
-            data,
-        )
-        rgb_norm_F = blend_rgb_norm(data["ori_image"], norm_orig_F, mask_orig)
-        rgb_norm_B = blend_rgb_norm(data["ori_image"], norm_orig_B, mask_orig)
-
-        Image.fromarray(
-            np.concatenate(
-                [data["ori_image"].astype(np.uint8), rgb_norm_F, rgb_norm_B], axis=1)
-        ).save(os.path.join(config_dict['out_dir'], cfg.name, f"png/{data['name']}_overlap.png"))
-
-        smpl_obj = trimesh.Trimesh(
-            in_tensor["smpl_verts"].detach().cpu()[0] *
-            torch.tensor([1.0, -1.0, 1.0]),
-            in_tensor['smpl_faces'].detach().cpu()[0],
-            process=False,
-            maintains_order=True
-        )
-        smpl_obj.visual.vertex_colors = (smpl_obj.vertex_normals+1.0)*255.0*0.5
-        smpl_obj.export(
-            f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_smpl.obj")
-        smpl_obj.export(
-            f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_smpl.glb")
-
-        smpl_info = {'betas': optimed_betas,
-                     'pose': optimed_pose_mat,
-                     'orient': optimed_orient_mat,
-                     'trans': optimed_trans}
-
-        np.save(
-            f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_smpl.npy", smpl_info, allow_pickle=True)
-
-        # ------------------------------------------------------------------------------------------------------------------
-
-        # cloth optimization
-
-        # cloth recon
-        in_tensor.update(
-            dataset.compute_vis_cmap(
-                in_tensor["smpl_verts"][0], in_tensor["smpl_faces"][0]
-            )
-        )
-
-        if cfg.net.prior_type == "pamir":
-            in_tensor.update(
-                dataset.compute_voxel_verts(
-                    optimed_pose,
-                    optimed_orient,
-                    optimed_betas,
-                    optimed_trans,
-                    data["scale"],
-                )
-            )
-
-        with torch.no_grad():
-            verts_pr, faces_pr, _ = model.test_single(in_tensor)
-
-        recon_obj = trimesh.Trimesh(
-            verts_pr, faces_pr, process=False, maintains_order=True
-        )
-        recon_obj.visual.vertex_colors = (
-            recon_obj.vertex_normals+1.0)*255.0*0.5
-        recon_obj.export(
-            os.path.join(config_dict['out_dir'], cfg.name,
-                         f"obj/{data['name']}_recon.obj")
-        )
-
-        # Isotropic Explicit Remeshing for better geometry topology
-        verts_refine, faces_refine = remesh(os.path.join(config_dict['out_dir'], cfg.name,
-                                                         f"obj/{data['name']}_recon.obj"), 0.5, device)
-
-        # define local_affine deform verts
-        mesh_pr = Meshes(verts_refine, faces_refine).to(device)
-        local_affine_model = LocalAffine(
-            mesh_pr.verts_padded().shape[1], mesh_pr.verts_padded().shape[0], mesh_pr.edges_packed()).to(device)
-        optimizer_cloth = torch.optim.Adam(
-            [{'params': local_affine_model.parameters()}], lr=1e-4, amsgrad=True)
-
-        scheduler_cloth = torch.optim.lr_scheduler.ReduceLROnPlateau(
-            optimizer_cloth,
-            mode="min",
-            factor=0.1,
-            verbose=0,
-            min_lr=1e-5,
-            patience=config_dict['patience'],
-        )
-
-        final = None
-
-        if config_dict['loop_cloth'] > 0:
-
-            loop_cloth = tqdm(range(config_dict['loop_cloth']))
-
-            for _ in loop_cloth:
-
-                optimizer_cloth.zero_grad()
-
-                deformed_verts, stiffness, rigid = local_affine_model(
-                    verts_refine.to(device), return_stiff=True)
-                mesh_pr = mesh_pr.update_padded(deformed_verts)
-
-                # losses for laplacian, edge, normal consistency
-                update_mesh_shape_prior_losses(mesh_pr, losses)
-
-                in_tensor["P_normal_F"], in_tensor["P_normal_B"] = dataset.render_normal(
-                    mesh_pr.verts_padded(), mesh_pr.faces_padded())
-
-                diff_F_cloth = torch.abs(
-                    in_tensor["P_normal_F"] - in_tensor["normal_F"])
-                diff_B_cloth = torch.abs(
-                    in_tensor["P_normal_B"] - in_tensor["normal_B"])
-
-                losses["cloth"]["value"] = (diff_F_cloth + diff_B_cloth).mean()
-                losses["stiffness"]["value"] = torch.mean(stiffness)
-                losses["rigid"]["value"] = torch.mean(rigid)
-
-                # Weighted sum of the losses
-                cloth_loss = torch.tensor(0.0, requires_grad=True).to(device)
-                pbar_desc = "Cloth Refinement --- "
-
-                for k in losses.keys():
-                    if k not in ["normal", "silhouette"] and losses[k]["weight"] > 0.0:
-                        cloth_loss = cloth_loss + \
-                            losses[k]["value"] * losses[k]["weight"]
-                        pbar_desc += f"{k}:{losses[k]['value']* losses[k]['weight']:.5f} | "
-
-                pbar_desc += f"Total: {cloth_loss:.5f}"
-                loop_cloth.set_description(pbar_desc)
-
-                # update params
-                cloth_loss.backward()
-                optimizer_cloth.step()
-                scheduler_cloth.step(cloth_loss)
-
-            final = trimesh.Trimesh(
-                mesh_pr.verts_packed().detach().squeeze(0).cpu(),
-                mesh_pr.faces_packed().detach().squeeze(0).cpu(),
-                process=False, maintains_order=True
-            )
-            
-            # only with front texture
-            tex_colors = query_color(
-                mesh_pr.verts_packed().detach().squeeze(0).cpu(),
-                mesh_pr.faces_packed().detach().squeeze(0).cpu(),
-                in_tensor["image"],
-                device=device,
-            )
-
-            # full normal textures
-            norm_colors = (mesh_pr.verts_normals_padded().squeeze(
-                0).detach().cpu() + 1.0) * 0.5 * 255.0
-                
-            final.visual.vertex_colors = tex_colors
-            final.export(
-                f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_refine.obj")
-                
-            final.visual.vertex_colors = norm_colors
-            final.export(
-                f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_refine.glb")
-
-        # always export visualized video regardless of the cloth refinment
-        verts_lst = [smpl_obj.vertices, final.vertices]
-        faces_lst = [smpl_obj.faces, final.faces]
-
-        # self-rotated video
-        dataset.render.load_meshes(
-            verts_lst, faces_lst)
-        dataset.render.get_rendered_video(
-            [data["ori_image"], rgb_norm_F, rgb_norm_B],
-            os.path.join(config_dict['out_dir'], cfg.name,
-                         f"vid/{data['name']}_cloth.mp4"),
-        )
-
-    smpl_obj_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_smpl.obj"
-    smpl_glb_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_smpl.glb"
-    smpl_npy_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_smpl.npy"
-    refine_obj_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_refine.obj"
-    refine_glb_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_refine.glb"
-
-    video_path = os.path.join(
-        config_dict['out_dir'], cfg.name, f"vid/{data['name']}_cloth.mp4")
-    overlap_path = os.path.join(
-        config_dict['out_dir'], cfg.name, f"png/{data['name']}_overlap.png")
-
-    # clean all the variables
-    for element in dir():
-        if 'path' not in element:
-            del locals()[element]
-    gc.collect()
-    torch.cuda.empty_cache()
-    
-    return [smpl_glb_path, smpl_obj_path,smpl_npy_path,
-            refine_glb_path, refine_obj_path,
-            video_path, video_path, overlap_path]
diff --git a/spaces/ZX9966/LLM-Research/style.css b/spaces/ZX9966/LLM-Research/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/ZX9966/LLM-Research/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/Zenne/chatbot_self_query/app.py b/spaces/Zenne/chatbot_self_query/app.py
deleted file mode 100644
index 5da6c7bf2e00b7c22883b78c14883314d43bbe83..0000000000000000000000000000000000000000
--- a/spaces/Zenne/chatbot_self_query/app.py
+++ /dev/null
@@ -1,235 +0,0 @@
-# Import required libraries
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.document_loaders import (
-    UnstructuredWordDocumentLoader,
-    PyMuPDFLoader,
-    UnstructuredFileLoader,
-)
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.chat_models import ChatOpenAI
-from langchain.vectorstores import Pinecone
-from langchain.chains.qa_with_sources import load_qa_with_sources_chain
-from langchain.retrievers.self_query.base import SelfQueryRetriever
-from langchain.chains.query_constructor.base import AttributeInfo
-import os
-import langchain
-import pinecone
-import streamlit as st
-import shutil
-import json
-import re
-
-OPENAI_API_KEY = ''
-PINECONE_API_KEY = ''
-PINECONE_API_ENV = ''
-langchain.verbose = False
-
-@st.cache_data()
-def init():
-    pinecone_index_name = ''
-    pinecone_namespace = ''
-    docsearch_ready = False
-    directory_name = 'tmp_docs'
-    return pinecone_index_name, pinecone_namespace, docsearch_ready, directory_name
-
-
-@st.cache_data()
-def save_file(files):
-    # Remove existing files in the directory
-    if os.path.exists(directory_name):
-        for filename in os.listdir(directory_name):
-            file_path = os.path.join(directory_name, filename)
-            try:
-                if os.path.isfile(file_path):
-                    os.remove(file_path)
-            except Exception as e:
-                print(f"Error: {e}")
-    # Save the new file with original filename
-    if files is not None:
-        for file in files:
-            file_name = file.name
-            file_path = os.path.join(directory_name, file_name)
-            with open(file_path, 'wb') as f:
-                shutil.copyfileobj(file, f)
-
-
-def load_files():
-    all_texts = []
-    n_files = 0
-    n_char = 0
-    n_texts = 0
-
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=400, chunk_overlap=50
-    )
-    for filename in os.listdir(directory_name):
-        file = os.path.join(directory_name, filename)
-        if os.path.isfile(file):
-            if file.endswith(".docx"):
-                loader = UnstructuredWordDocumentLoader(file)
-            elif file.endswith(".pdf"):
-                loader = PyMuPDFLoader(file)
-            else:   # assume a pure text format and attempt to load it
-                loader = UnstructuredFileLoader(file)
-            data = loader.load()
-            metadata = data[0].metadata
-            fn = os.path.basename(metadata['source'])
-            author = os.path.splitext(fn)[0]
-            data[0].metadata['author'] = author
-            texts = text_splitter.split_documents(data)
-            n_files += 1
-            n_char += len(data[0].page_content)
-            n_texts += len(texts)
-            all_texts.extend(texts)
-    st.write(
-        f"Loaded {n_files} file(s) with {n_char} characters, and split into {n_texts} split-documents."
-    )
-    return all_texts, n_texts
-
-
-@st.cache_resource()
-def ingest(_all_texts, _embeddings, pinecone_index_name, pinecone_namespace):
-	docsearch = Pinecone.from_documents(
-            _all_texts, _embeddings, index_name=pinecone_index_name, namespace=pinecone_namespace)
-	return docsearch
-
-
-def setup_retriever(docsearch, llm):
-    metadata_field_info = [
-        AttributeInfo(
-            name="author",
-            description="The author of the document/text/piece of context",
-            type="string or list[string]",
-        )
-    ]
-    document_content_description = "Views/opions/proposals suggested by the author on one or more discussion points."
-    retriever = SelfQueryRetriever.from_llm(
-        llm, docsearch, document_content_description, metadata_field_info, verbose=True)
-    return retriever
-
-
-def setup_docsearch(pinecone_index_name, pinecone_namespace, embeddings):
-    docsearch = []
-    n_texts = 0
-	# Load the pre-created Pinecone index.
-	# The index which has already be stored in pinecone.io as long-term memory
-    if pinecone_index_name in pinecone.list_indexes():
-        docsearch = Pinecone.from_existing_index(
-            index_name=pinecone_index_name, embedding=embeddings, text_key='text', namespace=pinecone_namespace)
-        index_client = pinecone.Index(pinecone_index_name)
-		# Get the index information
-        index_info = index_client.describe_index_stats()
-        n_texts = index_info['namespaces'][pinecone_namespace]['vector_count']
-    else:
-        raise ValueError('''Cannot find the specified Pinecone index.
-						Create one in pinecone.io or using, e.g.,
-						pinecone.create_index(
-							name=index_name, dimension=1536, metric="cosine", shards=1)''')
-    return docsearch, n_texts
-
-
-def get_response(query, chat_history, CRqa):
-    result = CRqa({"question": query, "chat_history": chat_history})
-    return result['answer'], result['source_documents']
-
-
-def setup_em_llm(OPENAI_API_KEY, temperature):
-    # Set up OpenAI embeddings
-    embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
-    # Use Open AI LLM with gpt-3.5-turbo.
-    # Set the temperature to be 0 if you do not want it to make up things
-    llm = ChatOpenAI(temperature=temperature, model_name="gpt-3.5-turbo", streaming=True,
-                     openai_api_key=OPENAI_API_KEY)
-    return embeddings, llm
-
-
-def load_chat_history(CHAT_HISTORY_FILENAME):
-    try:
-        with open(CHAT_HISTORY_FILENAME, 'r') as f:
-            chat_history = json.load(f)
-    except (FileNotFoundError, json.JSONDecodeError):
-        chat_history = []
-    return chat_history
-
-
-def save_chat_history(chat_history, CHAT_HISTORY_FILENAME):
-    with open(CHAT_HISTORY_FILENAME, 'w') as f:
-        json.dump(chat_history, f)
-
-
-pinecone_index_name, pinecone_namespace, docsearch_ready, directory_name = init()
-
-
-def main(pinecone_index_name, pinecone_namespace, docsearch_ready):
-    docsearch_ready = False
-    chat_history = []
-    col1, col2, col3 = st.columns([1, 1, 1])
-    with col1:
-        r_ingest = st.radio(
-            'Ingest file(s)?', ('Yes', 'No'))
-        OPENAI_API_KEY = st.text_input(
-            "OpenAI API key:", type="password")
-
-    with col2:        
-        PINECONE_API_KEY = st.text_input(
-				"Pinecone API key:", type="password")
-        PINECONE_API_ENV = st.text_input(
-				"Pinecone API env:", type="password")
-        pinecone_index_name = st.text_input('Pinecone index:')
-        pinecone.init(api_key=PINECONE_API_KEY,
-							environment=PINECONE_API_ENV)
-    with col3:
-        pinecone_namespace = st.text_input('Pinecone namespace:')
-        temperature = st.slider('Temperature', 0.0, 1.0, 0.1)
-    
-    if pinecone_index_name:
-        session_name = pinecone_index_name
-        embeddings, llm = setup_em_llm(OPENAI_API_KEY, temperature)
-        if r_ingest.lower() == 'yes':
-            files = st.file_uploader(
-                'Upload Files', accept_multiple_files=True)
-            if files:
-                save_file(files)
-                all_texts, n_texts = load_files()
-                docsearch = ingest(all_texts, embeddings,
-                                   pinecone_index_name, pinecone_namespace)
-                docsearch_ready = True
-        else:
-            st.write(
-                'No data is to be ingested. Make sure the Pinecone index you provided contains data.')
-            docsearch, n_texts = setup_docsearch(pinecone_index_name, pinecone_namespace, 
-                                                 embeddings)
-            docsearch_ready = True
-    if docsearch_ready:
-        retriever = setup_retriever(docsearch, llm)
-        CRqa = load_qa_with_sources_chain(llm, chain_type="stuff")
-
-        st.title('Chatbot')
-        # Get user input
-        query = st.text_area('Enter your question:', height=10,
-                             placeholder='Summarize the context.')
-        if query:
-            # Generate a reply based on the user input and chat history
-            CHAT_HISTORY_FILENAME = f"chat_history/{session_name}_chat_hist.json"
-            chat_history = load_chat_history(CHAT_HISTORY_FILENAME)
-            chat_history = [(user, bot)
-                            for user, bot in chat_history]
-            docs = retriever.get_relevant_documents(query)
-            if not docs:
-                docs = docsearch.similarity_search(query)
-            result = CRqa.run(input_documents=docs, question=query)
-            reply = re.match(r'(.+?)\.\s*SOURCES:', result).group(1)
-            source = re.search(r'SOURCES:\s*(.+)', result).group(1)
-            # Update the chat history with the user input and system response
-            chat_history.append(('User', query))
-            chat_history.append(('Bot', reply))
-            save_chat_history(chat_history, CHAT_HISTORY_FILENAME)
-            latest_chats = chat_history[-4:]
-            chat_history_str = '\n'.join(
-                [f'{x[0]}: {x[1]}' for x in latest_chats])
-            st.text_area('Chat record:', value=chat_history_str, height=250)
-
-
-if __name__ == '__main__':
-    main(pinecone_index_name, pinecone_namespace, 
-         docsearch_ready)
diff --git a/spaces/abby711/FaceRestoration/app.py b/spaces/abby711/FaceRestoration/app.py
deleted file mode 100644
index f842649af82e64512b40c09770b46540f691b2f4..0000000000000000000000000000000000000000
--- a/spaces/abby711/FaceRestoration/app.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import os
-os.system("pip install gfpgan")
-os.system("pip install gradio==2.5.3")
-
-
-os.system("pip freeze")
-os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .")
-import random
-import gradio as gr
-from PIL import Image
-import torch
-
-#torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', 'lincoln.jpg')
-torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png')
-torch.hub.download_url_to_file('https://api.curtisbrown.co.uk/media/70446/show/square', 'churchil.png')
-
-import cv2
-import glob
-import numpy as np
-from basicsr.utils import imwrite
-from gfpgan import GFPGANer
-
-import warnings
-warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
-              'If you really want to use it, please modify the corresponding codes.')
-bg_upsampler = None
-
-
- 
-# set up GFPGAN restorer
-restorer = GFPGANer(
-    model_path='GFPGANCleanv1-NoCE-C2.pth',
-    upscale=2,
-    arch='clean',
-    channel_multiplier=2,
-    bg_upsampler=bg_upsampler)
-
-
-
-
-
-def inference(img):
-    input_img = cv2.imread(img, cv2.IMREAD_COLOR)
-    cropped_faces, restored_faces, restored_img = restorer.enhance(
-        input_img, has_aligned=False, only_center_face=False, paste_back=True)
-    
-    return Image.fromarray(restored_faces[0][:,:,::-1])
-        
-title = "GFP-GAN"
-description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once"
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2101.04061'>Towards Real-World Blind Face Restoration with Generative Facial Prior</a> | <a href='https://github.com/TencentARC/GFPGAN'>Github Repo</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>"
-gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="filepath", label="Input")], 
-    gr.outputs.Image(type="pil", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    examples=[
-    ['lincoln.jpg'],
-    ['einstein.png']
-    ],
-    enable_queue=True
-    ).launch()
\ No newline at end of file
diff --git a/spaces/abidlabs/Lime/README.md b/spaces/abidlabs/Lime/README.md
deleted file mode 100644
index ce9e014f26cd6d7d9af12c598614f661cfaac374..0000000000000000000000000000000000000000
--- a/spaces/abidlabs/Lime/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
-
----
-tags: [gradio-theme]
-title: Lime
-colorFrom: orange
-colorTo: purple
-sdk: gradio
-sdk_version: 3.23.1b3
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-# Lime
-## Description
-Add a description of this theme here!
-## Contributions
-Thanks to [@abidlabs](https://huggingface.co/abidlabs) for adding this gradio theme!
diff --git a/spaces/adpro/dpt-depth15/README.md b/spaces/adpro/dpt-depth15/README.md
deleted file mode 100644
index a2df32f52be298450622acdf691911580499139c..0000000000000000000000000000000000000000
--- a/spaces/adpro/dpt-depth15/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Dpt Depth Estimation
-emoji: ⚡
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 2.8.13
-app_file: app.py
-pinned: false
-duplicated_from: adpro/dpt-depth01
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/akhaliq/Nitro-Diffusion/README.md b/spaces/akhaliq/Nitro-Diffusion/README.md
deleted file mode 100644
index 04bcd2f33923612900b7b522e043df2a4914303a..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Nitro-Diffusion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Nitro Diffusion
-emoji: 🌍
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/akhaliq/deeplab2/data/dataset_utils_test.py b/spaces/akhaliq/deeplab2/data/dataset_utils_test.py
deleted file mode 100644
index f7538bf0cc6f7199ecbffee8be1e2d70a97b1524..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/data/dataset_utils_test.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Tests for dataset_utils."""
-
-import numpy as np
-import tensorflow as tf
-
-from deeplab2.data import dataset_utils
-
-
-class DatasetUtilsTest(tf.test.TestCase):
-
-  def _get_test_labels(self, num_classes, shape, label_divisor):
-    num_ids_per_class = 35
-    semantic_labels = np.random.randint(num_classes, size=shape)
-    panoptic_labels = np.random.randint(
-        num_ids_per_class, size=shape) + semantic_labels * label_divisor
-
-    semantic_labels = tf.convert_to_tensor(semantic_labels, dtype=tf.int32)
-    panoptic_labels = tf.convert_to_tensor(panoptic_labels, dtype=tf.int32)
-
-    return panoptic_labels, semantic_labels
-
-  def setUp(self):
-    super().setUp()
-    self._first_thing_class = 9
-    self._num_classes = 19
-    self._dataset_info = {
-        'panoptic_label_divisor': 1000,
-        'class_has_instances_list': tf.range(self._first_thing_class,
-                                             self._num_classes)
-    }
-    self._num_ids = 37
-    self._labels, self._semantic_classes = self._get_test_labels(
-        self._num_classes, [2, 33, 33],
-        self._dataset_info['panoptic_label_divisor'])
-
-  def test_get_panoptic_and_semantic_label(self):
-    # Note: self._labels contains one crowd instance per class.
-    (returned_sem_labels, returned_pan_labels, returned_thing_mask,
-     returned_crowd_region) = (
-         dataset_utils.get_semantic_and_panoptic_label(
-             self._dataset_info, self._labels, ignore_label=255))
-
-    expected_semantic_labels = self._semantic_classes
-    condition = self._labels % self._dataset_info['panoptic_label_divisor'] == 0
-    condition = tf.logical_and(
-        condition,
-        tf.math.greater_equal(expected_semantic_labels,
-                              self._first_thing_class))
-    expected_crowd_labels = tf.where(condition, 1.0, 0.0)
-    expected_pan_labels = tf.where(
-        condition, 255 * self._dataset_info['panoptic_label_divisor'],
-        self._labels)
-    expected_thing_mask = tf.where(
-        tf.math.greater_equal(expected_semantic_labels,
-                              self._first_thing_class), 1.0, 0.0)
-
-    self.assertListEqual(returned_sem_labels.shape.as_list(),
-                         expected_semantic_labels.shape.as_list())
-    self.assertListEqual(returned_pan_labels.shape.as_list(),
-                         expected_pan_labels.shape.as_list())
-    self.assertListEqual(returned_crowd_region.shape.as_list(),
-                         expected_crowd_labels.shape.as_list())
-    self.assertListEqual(returned_thing_mask.shape.as_list(),
-                         expected_thing_mask.shape.as_list())
-    np.testing.assert_equal(returned_sem_labels.numpy(),
-                            expected_semantic_labels.numpy())
-    np.testing.assert_equal(returned_pan_labels.numpy(),
-                            expected_pan_labels.numpy())
-    np.testing.assert_equal(returned_crowd_region.numpy(),
-                            expected_crowd_labels.numpy())
-    np.testing.assert_equal(returned_thing_mask.numpy(),
-                            expected_thing_mask.numpy())
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/appdirs.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/appdirs.py
deleted file mode 100644
index 16933bf8afedcbe3e9d4fcc04e5f7246228c56fc..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/appdirs.py
+++ /dev/null
@@ -1,52 +0,0 @@
-"""
-This code wraps the vendored appdirs module to so the return values are
-compatible for the current pip code base.
-
-The intention is to rewrite current usages gradually, keeping the tests pass,
-and eventually drop this after all usages are changed.
-"""
-
-import os
-import sys
-from typing import List
-
-from pip._vendor import platformdirs as _appdirs
-
-
-def user_cache_dir(appname: str) -> str:
-    return _appdirs.user_cache_dir(appname, appauthor=False)
-
-
-def _macos_user_config_dir(appname: str, roaming: bool = True) -> str:
-    # Use ~/Application Support/pip, if the directory exists.
-    path = _appdirs.user_data_dir(appname, appauthor=False, roaming=roaming)
-    if os.path.isdir(path):
-        return path
-
-    # Use a Linux-like ~/.config/pip, by default.
-    linux_like_path = "~/.config/"
-    if appname:
-        linux_like_path = os.path.join(linux_like_path, appname)
-
-    return os.path.expanduser(linux_like_path)
-
-
-def user_config_dir(appname: str, roaming: bool = True) -> str:
-    if sys.platform == "darwin":
-        return _macos_user_config_dir(appname, roaming)
-
-    return _appdirs.user_config_dir(appname, appauthor=False, roaming=roaming)
-
-
-# for the discussion regarding site_config_dir locations
-# see <https://github.com/pypa/pip/issues/1733>
-def site_config_dirs(appname: str) -> List[str]:
-    if sys.platform == "darwin":
-        return [_appdirs.site_data_dir(appname, appauthor=False, multipath=True)]
-
-    dirval = _appdirs.site_config_dir(appname, appauthor=False, multipath=True)
-    if sys.platform == "win32":
-        return [dirval]
-
-    # Unix-y system. Look in /etc as well.
-    return dirval.split(os.pathsep) + ["/etc"]
diff --git a/spaces/ali-ghamdan/gfp-Gans/gfpgan/utils.py b/spaces/ali-ghamdan/gfp-Gans/gfpgan/utils.py
deleted file mode 100644
index 35658c5284330a6dba3e6589647c395c4348d17d..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/gfp-Gans/gfpgan/utils.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import cv2
-import os
-import torch
-from basicsr.utils import img2tensor, tensor2img
-from basicsr.utils.download_util import load_file_from_url
-from facexlib.utils.face_restoration_helper import FaceRestoreHelper
-from torchvision.transforms.functional import normalize
-
-from gfpgan.archs.gfpgan_bilinear_arch import GFPGANBilinear
-from gfpgan.archs.gfpganv1_arch import GFPGANv1
-from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
-
-ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-
-class GFPGANer():
-    """Helper for restoration with GFPGAN.
-
-    It will detect and crop faces, and then resize the faces to 512x512.
-    GFPGAN is used to restored the resized faces.
-    The background is upsampled with the bg_upsampler.
-    Finally, the faces will be pasted back to the upsample background image.
-
-    Args:
-        model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically).
-        upscale (float): The upscale of the final output. Default: 2.
-        arch (str): The GFPGAN architecture. Option: clean | original. Default: clean.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        bg_upsampler (nn.Module): The upsampler for the background. Default: None.
-    """
-
-    def __init__(self, model_path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None, device=None):
-        self.upscale = upscale
-        self.bg_upsampler = bg_upsampler
-
-        # initialize model
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
-        # initialize the GFP-GAN
-        if arch == 'clean':
-            self.gfpgan = GFPGANv1Clean(
-                out_size=512,
-                num_style_feat=512,
-                channel_multiplier=channel_multiplier,
-                decoder_load_path=None,
-                fix_decoder=False,
-                num_mlp=8,
-                input_is_latent=True,
-                different_w=True,
-                narrow=1,
-                sft_half=True)
-        elif arch == 'bilinear':
-            self.gfpgan = GFPGANBilinear(
-                out_size=512,
-                num_style_feat=512,
-                channel_multiplier=channel_multiplier,
-                decoder_load_path=None,
-                fix_decoder=False,
-                num_mlp=8,
-                input_is_latent=True,
-                different_w=True,
-                narrow=1,
-                sft_half=True)
-        elif arch == 'original':
-            self.gfpgan = GFPGANv1(
-                out_size=512,
-                num_style_feat=512,
-                channel_multiplier=channel_multiplier,
-                decoder_load_path=None,
-                fix_decoder=True,
-                num_mlp=8,
-                input_is_latent=True,
-                different_w=True,
-                narrow=1,
-                sft_half=True)
-        # initialize face helper
-        self.face_helper = FaceRestoreHelper(
-            upscale,
-            face_size=512,
-            crop_ratio=(1, 1),
-            det_model='retinaface_resnet50',
-            save_ext='png',
-            use_parse=True,
-            device=self.device)
-
-        if model_path.startswith('https://'):
-            model_path = load_file_from_url(
-                url=model_path, model_dir=os.path.join(ROOT_DIR, 'gfpgan/weights'), progress=True, file_name=None)
-        loadnet = torch.load(model_path)
-        if 'params_ema' in loadnet:
-            keyname = 'params_ema'
-        else:
-            keyname = 'params'
-        self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
-        self.gfpgan.eval()
-        self.gfpgan = self.gfpgan.to(self.device)
-
-    @torch.no_grad()
-    def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True):
-        self.face_helper.clean_all()
-
-        if has_aligned:  # the inputs are already aligned
-            img = cv2.resize(img, (512, 512))
-            self.face_helper.cropped_faces = [img]
-        else:
-            self.face_helper.read_image(img)
-            # get face landmarks for each face
-            self.face_helper.get_face_landmarks_5(only_center_face=only_center_face, eye_dist_threshold=5)
-            # eye_dist_threshold=5: skip faces whose eye distance is smaller than 5 pixels
-            # TODO: even with eye_dist_threshold, it will still introduce wrong detections and restorations.
-            # align and warp each face
-            self.face_helper.align_warp_face()
-
-        # face restoration
-        for cropped_face in self.face_helper.cropped_faces:
-            # prepare data
-            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
-            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-            cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device)
-
-            try:
-                output = self.gfpgan(cropped_face_t, return_rgb=False)[0]
-                # convert to image
-                restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
-            except RuntimeError as error:
-                print(f'\tFailed inference for GFPGAN: {error}.')
-                restored_face = cropped_face
-
-            restored_face = restored_face.astype('uint8')
-            self.face_helper.add_restored_face(restored_face)
-
-        if not has_aligned and paste_back:
-            # upsample the background
-            if self.bg_upsampler is not None:
-                # Now only support RealESRGAN for upsampling background
-                bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0]
-            else:
-                bg_img = None
-
-            self.face_helper.get_inverse_affine(None)
-            # paste each restored face to the input image
-            restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img)
-            return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img
-        else:
-            return self.face_helper.cropped_faces, self.face_helper.restored_faces, None
diff --git a/spaces/ali-ghamdan/image-colors-corrector/README.md b/spaces/ali-ghamdan/image-colors-corrector/README.md
deleted file mode 100644
index 80f6770d8f37d04697d4b71087869d14afd755ea..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/image-colors-corrector/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Image Colors Corrector
-emoji: 📊
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.1.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/allknowingroger/Image-Models-Test102/app.py b/spaces/allknowingroger/Image-Models-Test102/app.py
deleted file mode 100644
index 043d3871f1177818fa254f89577c716f60c6ef96..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test102/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "amongey/egyptian-movie-vintage-posters",
-    "acondess/lineart",
-    "nandovallec/dmb_link",
-    "bellagio-ai/ThanhThaoNguyen-face-xl-dreambooth",
-    "Muhammadreza/mann-e-artistic-2-revised-2",
-    "CiroN2022/breakcore-style",
-    "artificialguybr/StoryBookRedmond",
-    "Yntec/DucHaitenLofi",
-    "msyafiqakmal/photo",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
-    try:
-        model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
-    except Exception as error:
-        def the_fn(txt):
-            return None
-        model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
-    model_idx+=1
-
-
-def send_it_idx(idx):
-    def send_it_fn(prompt):
-        output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
-        return output
-    return send_it_fn
-
-def get_prompts(prompt_text):
-    return prompt_text
-
-def clear_it(val):
-    if int(val) != 0:
-        val = 0
-    else:
-        val = 0
-        pass
-    return val
-
-def all_task_end(cnt,t_stamp):
-    to = t_stamp + 60
-    et = time.time()
-    if et > to and t_stamp != 0:
-        d = gr.update(value=0)
-        tog = gr.update(value=1)
-        #print(f'to: {to}  et: {et}')
-    else:
-        if cnt != 0:
-            d = gr.update(value=et)
-        else:
-            d = gr.update(value=0)
-        tog = gr.update(value=0)
-        #print (f'passing:  to: {to}  et: {et}')
-        pass
-    return d, tog
-
-def all_task_start():
-    print("\n\n\n\n\n\n\n")
-    t = time.gmtime()
-    t_stamp = time.time()
-    current_time = time.strftime("%H:%M:%S", t)
-    return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
-    nn = len(models)
-    return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
-    with gr.Column(scale=12):
-        # with gr.Row():
-        #     gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
-        with gr.Row():
-            with gr.Row(scale=6):
-                primary_prompt=gr.Textbox(label="Prompt", value="")
-                # real_prompt=gr.Textbox(label="Real prompt")
-            with gr.Row(scale=6):
-                # improve_prompts_btn=gr.Button("Improve")
-                with gr.Row():
-                    run=gr.Button("Run",variant="primary")
-                    clear_btn=gr.Button("Clear")
-        with gr.Row():
-            sd_outputs = {}
-            model_idx = 1
-            for model_path in models:
-                with gr.Column(scale=3, min_width=320):
-                    with gr.Box():
-                        sd_outputs[model_idx] = gr.Image(label=model_path)
-                    pass
-                model_idx += 1
-                pass
-            pass
-
-        with gr.Row(visible=False):
-            start_box=gr.Number(interactive=False)
-            end_box=gr.Number(interactive=False)
-            tog_box=gr.Textbox(value=0,interactive=False)
-
-        start_box.change(
-            all_task_end,
-            [start_box, end_box],
-            [start_box, tog_box],
-            every=1,
-            show_progress=False)
-
-        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
-        run.click(all_task_start, None, [start_box, end_box, tog_box])
-        runs_dict = {}
-        model_idx = 1
-        for model_path in models:
-            runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
-            model_idx += 1
-            pass
-        pass
-
-        # improve_prompts_btn_clicked=improve_prompts_btn.click(
-        #     get_prompts,
-        #     inputs=[primary_prompt],
-        #     outputs=[primary_prompt],
-        #     cancels=list(runs_dict.values()))
-        clear_btn.click(
-            clear_fn,
-            None,
-            [primary_prompt, *list(sd_outputs.values())],
-            cancels=[*list(runs_dict.values())])
-        tog_box.change(
-            clear_it,
-            tog_box,
-            tog_box,
-            cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
- 
\ No newline at end of file
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/src/common/pa_gitrevision.h b/spaces/amarchheda/ChordDuplicate/portaudio/src/common/pa_gitrevision.h
deleted file mode 100644
index b5a042ca2a58738c0c7e714630c8c0a4aad13474..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/src/common/pa_gitrevision.h
+++ /dev/null
@@ -1 +0,0 @@
-#define PA_GIT_REVISION 147dd722548358763a8b649b3e4b41dfffbcfbb6
diff --git a/spaces/amj/Voice-Cloning/app.py b/spaces/amj/Voice-Cloning/app.py
deleted file mode 100644
index fcb9b87fbbf53eb7e3415f600e045a10f7cee5c0..0000000000000000000000000000000000000000
--- a/spaces/amj/Voice-Cloning/app.py
+++ /dev/null
@@ -1,165 +0,0 @@
-from turtle import title
-import gradio as gr
-
-import git
-import os
-os.system('git clone https://github.com/Edresson/Coqui-TTS -b multilingual-torchaudio-SE TTS')
-os.system('pip install -q -e TTS/')
-os.system('pip install -q torchaudio==0.9.0')
-
-import sys
-TTS_PATH = "TTS/"
-
-# add libraries into environment
-sys.path.append(TTS_PATH) # set this if TTS is not installed globally
-
-import os
-import string
-import time
-import argparse
-import json
-
-import numpy as np
-import IPython
-from IPython.display import Audio
-
-
-import torch
-
-from TTS.tts.utils.synthesis import synthesis
-from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
-try:
-  from TTS.utils.audio import AudioProcessor
-except:
-  from TTS.utils.audio import AudioProcessor
-
-
-from TTS.tts.models import setup_model
-from TTS.config import load_config
-from TTS.tts.models.vits import *  
-
-OUT_PATH = 'out/'
-
-# create output path
-os.makedirs(OUT_PATH, exist_ok=True)
-
-# model vars 
-MODEL_PATH = '/home/user/app/best_model_latest.pth.tar'
-CONFIG_PATH = '/home/user/app/config.json'
-TTS_LANGUAGES = "/home/user/app/language_ids.json"
-TTS_SPEAKERS = "/home/user/app/speakers.json"
-USE_CUDA = torch.cuda.is_available()  
-
-# load the config
-C = load_config(CONFIG_PATH)
-
-
-# load the audio processor
-ap = AudioProcessor(**C.audio)
-
-speaker_embedding = None
-
-C.model_args['d_vector_file'] = TTS_SPEAKERS
-C.model_args['use_speaker_encoder_as_loss'] = False
-
-model = setup_model(C)
-model.language_manager.set_language_ids_from_file(TTS_LANGUAGES)
-# print(model.language_manager.num_languages, model.embedded_language_dim)
-# print(model.emb_l)
-cp = torch.load(MODEL_PATH, map_location=torch.device('cpu'))
-# remove speaker encoder
-model_weights = cp['model'].copy()
-for key in list(model_weights.keys()):
-  if "speaker_encoder" in key:
-    del model_weights[key]
-
-model.load_state_dict(model_weights)
-
-
-model.eval()
-
-if USE_CUDA:
-    model = model.cuda()
-
-# synthesize voice
-use_griffin_lim = False
-
-os.system('pip install -q pydub ffmpeg-normalize')
-
-CONFIG_SE_PATH = "config_se.json"
-CHECKPOINT_SE_PATH = "SE_checkpoint.pth.tar"
-
-from TTS.tts.utils.speakers import SpeakerManager
-from pydub import AudioSegment
-import librosa
-
-SE_speaker_manager = SpeakerManager(encoder_model_path=CHECKPOINT_SE_PATH, encoder_config_path=CONFIG_SE_PATH, use_cuda=USE_CUDA)
-
-def compute_spec(ref_file):
-  y, sr = librosa.load(ref_file, sr=ap.sample_rate)
-  spec = ap.spectrogram(y)
-  spec = torch.FloatTensor(spec).unsqueeze(0)
-  return spec
-  
-
-    
-def greet(Text,Voicetoclone,VoiceMicrophone):
-    text= "%s" % (Text)
-    if Voicetoclone is not None:
-      reference_files= "%s" % (Voicetoclone)
-      print("path url")
-      print(Voicetoclone)
-      sample= str(Voicetoclone)
-    else:
-      reference_files= "%s" % (VoiceMicrophone)
-      print("path url")
-      print(VoiceMicrophone)
-      sample= str(VoiceMicrophone)
-    size= len(reference_files)*sys.getsizeof(reference_files)
-    size2= size / 1000000
-    if (size2 > 0.012) or len(text)>2000:
-      message="File is greater than 30mb or Text inserted is longer than 2000 characters. Please re-try with smaller sizes."
-      print(message)
-      raise SystemExit("File is greater than 30mb. Please re-try or Text inserted is longer than 2000 characters. Please re-try with smaller sizes.")
-    else:
-      os.system('ffmpeg-normalize $sample -nt rms -t=-27 -o $sample -ar 16000 -f')
-      reference_emb = SE_speaker_manager.compute_d_vector_from_clip(reference_files)
-      model.length_scale = 1  # scaler for the duration predictor. The larger it is, the slower the speech.
-      model.inference_noise_scale = 0.3 # defines the noise variance applied to the random z vector at inference.
-      model.inference_noise_scale_dp = 0.3 # defines the noise variance applied to the duration predictor z vector at inference.
-      text = text
-      model.language_manager.language_id_mapping
-      language_id = 0
-    
-      print(" > text: {}".format(text))
-      wav, alignment, _, _ = synthesis(
-                        model,
-                        text,
-                        C,
-                        "cuda" in str(next(model.parameters()).device),
-                        ap,
-                        speaker_id=None,
-                        d_vector=reference_emb,
-                        style_wav=None,
-                        language_id=language_id,
-                        enable_eos_bos_chars=C.enable_eos_bos_chars,
-                        use_griffin_lim=True,
-                        do_trim_silence=False,
-                    ).values()
-      print("Generated Audio")
-      IPython.display.display(Audio(wav, rate=ap.sample_rate))
-      #file_name = text.replace(" ", "_")
-      #file_name = file_name.translate(str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'
-      file_name="Audio.wav"
-      out_path = os.path.join(OUT_PATH, file_name)
-      print(" > Saving output to {}".format(out_path))
-      ap.save_wav(wav, out_path)
-      return out_path
-
-demo = gr.Interface(
-    fn=greet, 
-    inputs=[gr.inputs.Textbox(label='What would you like the voice to say? (max. 2000 characters per request)'),gr.Audio(type="filepath",         source="upload",label='Please upload a voice to clone (max. 30mb)'),gr.Audio(source="microphone", type="filepath", streaming=True)],
-    outputs="audio",
-    title="Bilal's Voice Cloning Tool"
-    )
-demo.launch()
\ No newline at end of file
diff --git a/spaces/anonymous-pits/pits/README.md b/spaces/anonymous-pits/pits/README.md
deleted file mode 100644
index f03debea89a29290ec3847f593dd9f30d982c5f7..0000000000000000000000000000000000000000
--- a/spaces/anonymous-pits/pits/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: PITS variation Pitch Inference Text-to Speech
-emoji: 🕳️
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: true
-license: mit
----
-
-Official Demo for PITS: Variational Pitch Inference without Fundamental Frequency for End-to-End Pitch-controllable TTS
diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/adabins/miniViT.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/adabins/miniViT.py
deleted file mode 100644
index 8a619734aaa82e73fbe37800a6a1dd12e83020a2..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/adabins/miniViT.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .layers import PatchTransformerEncoder, PixelWiseDotProduct
-
-
-class mViT(nn.Module):
-    def __init__(self, in_channels, n_query_channels=128, patch_size=16, dim_out=256,
-                 embedding_dim=128, num_heads=4, norm='linear'):
-        super(mViT, self).__init__()
-        self.norm = norm
-        self.n_query_channels = n_query_channels
-        self.patch_transformer = PatchTransformerEncoder(in_channels, patch_size, embedding_dim, num_heads)
-        self.dot_product_layer = PixelWiseDotProduct()
-
-        self.conv3x3 = nn.Conv2d(in_channels, embedding_dim, kernel_size=3, stride=1, padding=1)
-        self.regressor = nn.Sequential(nn.Linear(embedding_dim, 256),
-                                       nn.LeakyReLU(),
-                                       nn.Linear(256, 256),
-                                       nn.LeakyReLU(),
-                                       nn.Linear(256, dim_out))
-
-    def forward(self, x):
-        # n, c, h, w = x.size()
-        tgt = self.patch_transformer(x.clone())  # .shape = S, N, E
-
-        x = self.conv3x3(x)
-
-        regression_head, queries = tgt[0, ...], tgt[1:self.n_query_channels + 1, ...]
-
-        # Change from S, N, E to N, S, E
-        queries = queries.permute(1, 0, 2)
-        range_attention_maps = self.dot_product_layer(x, queries)  # .shape = n, n_query_channels, h, w
-
-        y = self.regressor(regression_head)  # .shape = N, dim_out
-        if self.norm == 'linear':
-            y = torch.relu(y)
-            eps = 0.1
-            y = y + eps
-        elif self.norm == 'softmax':
-            return torch.softmax(y, dim=1), range_attention_maps
-        else:
-            y = torch.sigmoid(y)
-        y = y / y.sum(dim=1, keepdim=True)
-        return y, range_attention_maps
diff --git a/spaces/arbml/Ashaar/poetry_diacritizer/util/text_encoders.py b/spaces/arbml/Ashaar/poetry_diacritizer/util/text_encoders.py
deleted file mode 100644
index b49c5603afa2d41ad6e0145b719443f0f4ce9301..0000000000000000000000000000000000000000
--- a/spaces/arbml/Ashaar/poetry_diacritizer/util/text_encoders.py
+++ /dev/null
@@ -1,160 +0,0 @@
-from . import text_cleaners
-from typing import Dict, List, Optional
-from .constants import ALL_POSSIBLE_HARAQAT
-import sentencepiece as spm
-
-
-class TextEncoder:
-    pad = "P"
-
-    def __init__(
-        self,
-        input_chars: List[str],
-        target_charts: List[str],
-        cleaner_fn: Optional[str] = None,
-        reverse_input: bool = False,
-        reverse_target: bool = False,
-        sp_model_path=None,
-    ):
-        if cleaner_fn:
-            self.cleaner_fn = getattr(text_cleaners, cleaner_fn)
-        else:
-            self.cleaner_fn = None
-
-        self.input_symbols: List[str] = [TextEncoder.pad] + input_chars
-        self.target_symbols: List[str] = [TextEncoder.pad] + target_charts
-
-        if sp_model_path is None:
-            self.input_symbol_to_id: Dict[str, int] = {
-                s: i for i, s in enumerate(self.input_symbols)
-            }
-            self.input_id_to_symbol: Dict[int, str] = {
-                i: s for i, s in enumerate(self.input_symbols)
-            }
-        else:
-            sp_model = spm.SentencePieceProcessor()
-            sp_model.load(sp_model_path + "/sp.model")
-            self.input_symbol_to_id: Dict[str, int] = {
-                s: sp_model.PieceToId(s+'▁') for s in self.input_symbols
-            }
-            self.input_symbol_to_id[" "] = sp_model.PieceToId("|")  # encode space
-            self.input_symbol_to_id[TextEncoder.pad] = 0  # encode padding
-
-            self.input_space_id = sp_model.PieceToId("|")
-            self.input_id_to_symbol: Dict[int, str] = {
-                i: s for s, i in self.input_symbol_to_id.items()
-            }
-
-        self.target_symbol_to_id: Dict[str, int] = {
-            s: i for i, s in enumerate(self.target_symbols)
-        }
-        self.target_id_to_symbol: Dict[int, str] = {
-            i: s for i, s in enumerate(self.target_symbols)
-        }
-
-        self.reverse_input = reverse_input
-        self.reverse_target = reverse_target
-        self.input_pad_id = self.input_symbol_to_id[self.pad]
-        self.target_pad_id = self.target_symbol_to_id[self.pad]
-        self.start_symbol_id = None
-
-    def input_to_sequence(self, text: str) -> List[int]:
-        if self.reverse_input:
-            text = "".join(list(reversed(text)))
-        sequence = [self.input_symbol_to_id[s] for s in text if s not in [self.pad]]
-
-        return sequence
-
-    def target_to_sequence(self, text: str) -> List[int]:
-        if self.reverse_target:
-            text = "".join(list(reversed(text)))
-        sequence = [self.target_symbol_to_id[s] for s in text if s not in [self.pad]]
-
-        return sequence
-
-    def sequence_to_input(self, sequence: List[int]):
-        return [
-            self.input_id_to_symbol[symbol]
-            for symbol in sequence
-            if symbol in self.input_id_to_symbol and symbol not in [self.input_pad_id]
-        ]
-
-    def sequence_to_target(self, sequence: List[int]):
-        return [
-            self.target_id_to_symbol[symbol]
-            for symbol in sequence
-            if symbol in self.target_id_to_symbol and symbol not in [self.target_pad_id]
-        ]
-
-    def clean(self, text):
-        if self.cleaner_fn:
-            return self.cleaner_fn(text)
-        return text
-
-    def combine_text_and_haraqat(self, input_ids: List[int], output_ids: List[int]):
-        """
-        Combines the  input text with its corresponding  haraqat
-        Args:
-            inputs: a list of ids representing the input text
-            outputs: a list of ids representing the output text
-        Returns:
-        text: the text after merging the inputs text representation with the output
-        representation
-        """
-        output = ""
-        for i, input_id in enumerate(input_ids):
-            if input_id == self.input_pad_id:
-                break
-            output += self.input_id_to_symbol[input_id]
-            # if input_id == self.input_space_id:
-            #   continue
-            output += self.target_id_to_symbol[output_ids[i]]
-        return output
-
-    def __str__(self):
-        return type(self).__name__
-
-
-class BasicArabicEncoder(TextEncoder):
-    def __init__(
-        self,
-        cleaner_fn="basic_cleaners",
-        reverse_input: bool = False,
-        reverse_target: bool = False,
-        sp_model_path=None,
-    ):
-        input_chars: List[str] = list("بض.غىهظخة؟:طس،؛فندؤلوئآك-يذاصشحزءمأجإ ترقعث")
-        target_charts: List[str] = list(ALL_POSSIBLE_HARAQAT.keys())
-
-        super().__init__(
-            input_chars,
-            target_charts,
-            cleaner_fn=cleaner_fn,
-            reverse_input=reverse_input,
-            reverse_target=reverse_target,
-            sp_model_path=sp_model_path,
-        )
-
-
-class ArabicEncoderWithStartSymbol(TextEncoder):
-    def __init__(
-        self,
-        cleaner_fn="basic_cleaners",
-        reverse_input: bool = False,
-        reverse_target: bool = False,
-        sp_model_path=None,
-    ):
-        input_chars: List[str] = list("بض.غىهظخة؟:طس،؛فندؤلوئآك-يذاصشحزءمأجإ ترقعث")
-        # the only difference from the basic encoder is adding the start symbol
-        target_charts: List[str] = list(ALL_POSSIBLE_HARAQAT.keys()) + ["s"]
-
-        super().__init__(
-            input_chars,
-            target_charts,
-            cleaner_fn=cleaner_fn,
-            reverse_input=reverse_input,
-            reverse_target=reverse_target,
-            sp_model_path=sp_model_path,
-        )
-
-        self.start_symbol_id = self.target_symbol_to_id["s"]
diff --git a/spaces/arjun2364/SEBIS-code_trans_t5_large_source_code_summarization_csharp_multitask/app.py b/spaces/arjun2364/SEBIS-code_trans_t5_large_source_code_summarization_csharp_multitask/app.py
deleted file mode 100644
index 777d5634271203f4fdf4a0013f24e928c9cc6cba..0000000000000000000000000000000000000000
--- a/spaces/arjun2364/SEBIS-code_trans_t5_large_source_code_summarization_csharp_multitask/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/SEBIS/code_trans_t5_large_source_code_summarization_csharp_multitask").launch()
\ No newline at end of file
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/bark/model_fine.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/bark/model_fine.py
deleted file mode 100644
index 09e5f4765dce8743db2a3ed879e7811d2b9d23d6..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/bark/model_fine.py
+++ /dev/null
@@ -1,142 +0,0 @@
-"""
-Much of this code is adapted from Andrej Karpathy's NanoGPT
-(https://github.com/karpathy/nanoGPT)
-"""
-import math
-from dataclasses import dataclass
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .model import GPT, MLP, GPTConfig
-
-
-class NonCausalSelfAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        assert config.n_embd % config.n_head == 0
-        # key, query, value projections for all heads, but in a batch
-        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
-        # output projection
-        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
-        # regularization
-        self.attn_dropout = nn.Dropout(config.dropout)
-        self.resid_dropout = nn.Dropout(config.dropout)
-        self.n_head = config.n_head
-        self.n_embd = config.n_embd
-        self.dropout = config.dropout
-        # flash attention make GPU go brrrrr but support is only in PyTorch nightly and still a bit scary
-        self.flash = hasattr(torch.nn.functional, "scaled_dot_product_attention") and self.dropout == 0.0
-
-    def forward(self, x):
-        B, T, C = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)
-
-        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
-        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
-        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)  # (B, nh, T, hs)
-        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)  # (B, nh, T, hs)
-        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)  # (B, nh, T, hs)
-
-        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
-        if self.flash:
-            # efficient attention using Flash Attention CUDA kernels
-            y = torch.nn.functional.scaled_dot_product_attention(
-                q, k, v, attn_mask=None, dropout_p=self.dropout, is_causal=False
-            )
-        else:
-            # manual implementation of attention
-            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
-            att = F.softmax(att, dim=-1)
-            att = self.attn_dropout(att)
-            y = att @ v  # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
-        y = y.transpose(1, 2).contiguous().view(B, T, C)  # re-assemble all head outputs side by side
-
-        # output projection
-        y = self.resid_dropout(self.c_proj(y))
-        return y
-
-
-class FineBlock(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.ln_1 = nn.LayerNorm(config.n_embd)
-        self.attn = NonCausalSelfAttention(config)
-        self.ln_2 = nn.LayerNorm(config.n_embd)
-        self.mlp = MLP(config)
-
-    def forward(self, x):
-        x = x + self.attn(self.ln_1(x))
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class FineGPT(GPT):
-    def __init__(self, config):
-        super().__init__(config)
-        del self.lm_head
-        self.config = config
-        self.n_codes_total = config.n_codes_total
-        self.transformer = nn.ModuleDict(
-            dict(
-                wtes=nn.ModuleList(
-                    [nn.Embedding(config.input_vocab_size, config.n_embd) for _ in range(config.n_codes_total)]
-                ),
-                wpe=nn.Embedding(config.block_size, config.n_embd),
-                drop=nn.Dropout(config.dropout),
-                h=nn.ModuleList([FineBlock(config) for _ in range(config.n_layer)]),
-                ln_f=nn.LayerNorm(config.n_embd),
-            )
-        )
-        self.lm_heads = nn.ModuleList(
-            [
-                nn.Linear(config.n_embd, config.output_vocab_size, bias=False)
-                for _ in range(config.n_codes_given, self.n_codes_total)
-            ]
-        )
-        for i in range(self.n_codes_total - config.n_codes_given):
-            self.transformer.wtes[i + 1].weight = self.lm_heads[i].weight
-
-    def forward(self, pred_idx, idx):
-        device = idx.device
-        b, t, codes = idx.size()
-        assert (
-            t <= self.config.block_size
-        ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
-        assert pred_idx > 0, "cannot predict 0th codebook"
-        assert codes == self.n_codes_total, (b, t, codes)
-        pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0)  # shape (1, t)
-
-        # forward the GPT model itself
-        tok_embs = [
-            wte(idx[:, :, i]).unsqueeze(-1) for i, wte in enumerate(self.transformer.wtes)
-        ]  # token embeddings of shape (b, t, n_embd)
-        tok_emb = torch.cat(tok_embs, dim=-1)
-        pos_emb = self.transformer.wpe(pos)  # position embeddings of shape (1, t, n_embd)
-        x = tok_emb[:, :, :, : pred_idx + 1].sum(dim=-1)
-        x = self.transformer.drop(x + pos_emb)
-        for block in self.transformer.h:
-            x = block(x)
-        x = self.transformer.ln_f(x)
-        logits = self.lm_heads[pred_idx - self.config.n_codes_given](x)
-        return logits
-
-    def get_num_params(self, non_embedding=True):
-        """
-        Return the number of parameters in the model.
-        For non-embedding count (default), the position embeddings get subtracted.
-        The token embeddings would too, except due to the parameter sharing these
-        params are actually used as weights in the final layer, so we include them.
-        """
-        n_params = sum(p.numel() for p in self.parameters())
-        if non_embedding:
-            for wte in self.transformer.wtes:
-                n_params -= wte.weight.numel()
-            n_params -= self.transformer.wpe.weight.numel()
-        return n_params
-
-
-@dataclass
-class FineGPTConfig(GPTConfig):
-    n_codes_total: int = 8
-    n_codes_given: int = 1
diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/xtts_tests/test_xtts_v2-0_gpt_train.py b/spaces/artificialguybr/video-dubbing/TTS/tests/xtts_tests/test_xtts_v2-0_gpt_train.py
deleted file mode 100644
index b19b7210d8e89e055060ec7611dc8ff52906ef01..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/tests/xtts_tests/test_xtts_v2-0_gpt_train.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import os
-import shutil
-
-import torch
-from trainer import Trainer, TrainerArgs
-
-from tests import get_tests_output_path
-from TTS.config.shared_configs import BaseDatasetConfig
-from TTS.tts.datasets import load_tts_samples
-from TTS.tts.layers.xtts.dvae import DiscreteVAE
-from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig
-
-config_dataset = BaseDatasetConfig(
-    formatter="ljspeech",
-    dataset_name="ljspeech",
-    path="tests/data/ljspeech/",
-    meta_file_train="metadata.csv",
-    meta_file_val="metadata.csv",
-    language="en",
-)
-
-DATASETS_CONFIG_LIST = [config_dataset]
-
-# Logging parameters
-RUN_NAME = "GPT_XTTS_LJSpeech_FT"
-PROJECT_NAME = "XTTS_trainer"
-DASHBOARD_LOGGER = "tensorboard"
-LOGGER_URI = None
-
-OUT_PATH = os.path.join(get_tests_output_path(), "train_outputs", "xtts_tests")
-os.makedirs(OUT_PATH, exist_ok=True)
-
-# Create DVAE checkpoint and mel_norms on test time
-# DVAE parameters: For the training we need the dvae to extract the dvae tokens, given that you must provide the paths for this model
-DVAE_CHECKPOINT = os.path.join(OUT_PATH, "dvae.pth")  # DVAE checkpoint
-# Mel spectrogram norms, required for dvae mel spectrogram extraction
-MEL_NORM_FILE = os.path.join(OUT_PATH, "mel_stats.pth")
-dvae = DiscreteVAE(
-    channels=80,
-    normalization=None,
-    positional_dims=1,
-    num_tokens=8192,
-    codebook_dim=512,
-    hidden_dim=512,
-    num_resnet_blocks=3,
-    kernel_size=3,
-    num_layers=2,
-    use_transposed_convs=False,
-)
-torch.save(dvae.state_dict(), DVAE_CHECKPOINT)
-mel_stats = torch.ones(80)
-torch.save(mel_stats, MEL_NORM_FILE)
-
-
-# XTTS transfer learning parameters: You we need to provide the paths of XTTS model checkpoint that you want to do the fine tuning.
-TOKENIZER_FILE = "tests/inputs/xtts_vocab.json"  # vocab.json file
-XTTS_CHECKPOINT = None  # "/raid/edresson/dev/Checkpoints/XTTS_evaluation/xtts_style_emb_repetition_fix_gt/132500_gpt_ema_coqui_tts_with_enhanced_hifigan.pth"  # model.pth file
-
-
-# Training sentences generations
-SPEAKER_REFERENCE = ["tests/data/ljspeech/wavs/LJ001-0002.wav"]  # speaker reference to be used in training test sentences
-LANGUAGE = config_dataset.language
-
-
-# Training Parameters
-OPTIMIZER_WD_ONLY_ON_WEIGHTS = True  # for multi-gpu training please make it False
-START_WITH_EVAL = False  # if True it will star with evaluation
-BATCH_SIZE = 2  # set here the batch size
-GRAD_ACUMM_STEPS = 1  # set here the grad accumulation steps
-# Note: we recommend that BATCH_SIZE * GRAD_ACUMM_STEPS need to be at least 252 for more efficient training. You can increase/decrease BATCH_SIZE but then set GRAD_ACUMM_STEPS accordingly.
-
-
-# init args and config
-model_args = GPTArgs(
-    max_conditioning_length=132300,  # 6 secs
-    min_conditioning_length=66150,  # 3 secs
-    debug_loading_failures=False,
-    max_wav_length=255995,  # ~11.6 seconds
-    max_text_length=200,
-    mel_norm_file=MEL_NORM_FILE,
-    dvae_checkpoint=DVAE_CHECKPOINT,
-    xtts_checkpoint=XTTS_CHECKPOINT,  # checkpoint path of the model that you want to fine-tune
-    tokenizer_file=TOKENIZER_FILE,
-    gpt_num_audio_tokens=8194,
-    gpt_start_audio_token=8192,
-    gpt_stop_audio_token=8193,
-    gpt_use_masking_gt_prompt_approach=True,
-    gpt_use_perceiver_resampler=True,
-)
-
-audio_config = XttsAudioConfig(sample_rate=22050, dvae_sample_rate=22050, output_sample_rate=24000)
-
-config = GPTTrainerConfig(
-    epochs=1,
-    output_path=OUT_PATH,
-    model_args=model_args,
-    run_name=RUN_NAME,
-    project_name=PROJECT_NAME,
-    run_description="GPT XTTS training",
-    dashboard_logger=DASHBOARD_LOGGER,
-    logger_uri=LOGGER_URI,
-    audio=audio_config,
-    batch_size=BATCH_SIZE,
-    batch_group_size=48,
-    eval_batch_size=BATCH_SIZE,
-    num_loader_workers=8,
-    eval_split_max_size=256,
-    print_step=50,
-    plot_step=100,
-    log_model_step=1000,
-    save_step=10000,
-    save_n_checkpoints=1,
-    save_checkpoints=True,
-    # target_loss="loss",
-    print_eval=False,
-    # Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters.
-    optimizer="AdamW",
-    optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS,
-    optimizer_params={"betas": [0.9, 0.96], "eps": 1e-8, "weight_decay": 1e-2},
-    lr=5e-06,  # learning rate
-    lr_scheduler="MultiStepLR",
-    # it was adjusted accordly for the new step scheme
-    lr_scheduler_params={"milestones": [50000 * 18, 150000 * 18, 300000 * 18], "gamma": 0.5, "last_epoch": -1},
-    test_sentences=[
-        {
-            "text": "This cake is great. It's so delicious and moist.",
-            "speaker_wav": SPEAKER_REFERENCE,
-            "language": LANGUAGE,
-        },
-    ],
-)
-
-# init the model from config
-model = GPTTrainer.init_from_config(config)
-
-# load training samples
-train_samples, eval_samples = load_tts_samples(
-    DATASETS_CONFIG_LIST,
-    eval_split=True,
-    eval_split_max_size=config.eval_split_max_size,
-    eval_split_size=config.eval_split_size,
-)
-
-# init the trainer and 🚀
-trainer = Trainer(
-    TrainerArgs(
-        restore_path=None,  # xtts checkpoint is restored via xtts_checkpoint key so no need of restore it using Trainer restore_path parameter
-        skip_train_epoch=False,
-        start_with_eval=True,
-        grad_accum_steps=GRAD_ACUMM_STEPS,
-    ),
-    config,
-    output_path=OUT_PATH,
-    model=model,
-    train_samples=train_samples,
-    eval_samples=eval_samples,
-)
-trainer.fit()
-
-# remove output path
-shutil.rmtree(OUT_PATH)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/attr/setters.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/attr/setters.py
deleted file mode 100644
index b1cbb5d83e744dc1a2a3012f6ebe7d4f6d72c740..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/attr/setters.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-"""
-Commonly used hooks for on_setattr.
-"""
-
-from __future__ import absolute_import, division, print_function
-
-from . import _config
-from .exceptions import FrozenAttributeError
-
-
-def pipe(*setters):
-    """
-    Run all *setters* and return the return value of the last one.
-
-    .. versionadded:: 20.1.0
-    """
-
-    def wrapped_pipe(instance, attrib, new_value):
-        rv = new_value
-
-        for setter in setters:
-            rv = setter(instance, attrib, rv)
-
-        return rv
-
-    return wrapped_pipe
-
-
-def frozen(_, __, ___):
-    """
-    Prevent an attribute to be modified.
-
-    .. versionadded:: 20.1.0
-    """
-    raise FrozenAttributeError()
-
-
-def validate(instance, attrib, new_value):
-    """
-    Run *attrib*'s validator on *new_value* if it has one.
-
-    .. versionadded:: 20.1.0
-    """
-    if _config._run_validators is False:
-        return new_value
-
-    v = attrib.validator
-    if not v:
-        return new_value
-
-    v(instance, attrib, new_value)
-
-    return new_value
-
-
-def convert(instance, attrib, new_value):
-    """
-    Run *attrib*'s converter -- if it has one --  on *new_value* and return the
-    result.
-
-    .. versionadded:: 20.1.0
-    """
-    c = attrib.converter
-    if c:
-        return c(new_value)
-
-    return new_value
-
-
-NO_OP = object()
-"""
-Sentinel for disabling class-wide *on_setattr* hooks for certain attributes.
-
-Does not work in `pipe` or within lists.
-
-.. versionadded:: 20.1.0
-"""
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/cffi/_cffi_include.h b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/cffi/_cffi_include.h
deleted file mode 100644
index e4c0a672405298ddb3dcb2e2ca6da9eea3d2e162..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/cffi/_cffi_include.h
+++ /dev/null
@@ -1,385 +0,0 @@
-#define _CFFI_
-
-/* We try to define Py_LIMITED_API before including Python.h.
-
-   Mess: we can only define it if Py_DEBUG, Py_TRACE_REFS and
-   Py_REF_DEBUG are not defined.  This is a best-effort approximation:
-   we can learn about Py_DEBUG from pyconfig.h, but it is unclear if
-   the same works for the other two macros.  Py_DEBUG implies them,
-   but not the other way around.
-
-   The implementation is messy (issue #350): on Windows, with _MSC_VER,
-   we have to define Py_LIMITED_API even before including pyconfig.h.
-   In that case, we guess what pyconfig.h will do to the macros above,
-   and check our guess after the #include.
-
-   Note that on Windows, with CPython 3.x, you need >= 3.5 and virtualenv
-   version >= 16.0.0.  With older versions of either, you don't get a
-   copy of PYTHON3.DLL in the virtualenv.  We can't check the version of
-   CPython *before* we even include pyconfig.h.  ffi.set_source() puts
-   a ``#define _CFFI_NO_LIMITED_API'' at the start of this file if it is
-   running on Windows < 3.5, as an attempt at fixing it, but that's
-   arguably wrong because it may not be the target version of Python.
-   Still better than nothing I guess.  As another workaround, you can
-   remove the definition of Py_LIMITED_API here.
-
-   See also 'py_limited_api' in cffi/setuptools_ext.py.
-*/
-#if !defined(_CFFI_USE_EMBEDDING) && !defined(Py_LIMITED_API)
-#  ifdef _MSC_VER
-#    if !defined(_DEBUG) && !defined(Py_DEBUG) && !defined(Py_TRACE_REFS) && !defined(Py_REF_DEBUG) && !defined(_CFFI_NO_LIMITED_API)
-#      define Py_LIMITED_API
-#    endif
-#    include <pyconfig.h>
-     /* sanity-check: Py_LIMITED_API will cause crashes if any of these
-        are also defined.  Normally, the Python file PC/pyconfig.h does not
-        cause any of these to be defined, with the exception that _DEBUG
-        causes Py_DEBUG.  Double-check that. */
-#    ifdef Py_LIMITED_API
-#      if defined(Py_DEBUG)
-#        error "pyconfig.h unexpectedly defines Py_DEBUG, but Py_LIMITED_API is set"
-#      endif
-#      if defined(Py_TRACE_REFS)
-#        error "pyconfig.h unexpectedly defines Py_TRACE_REFS, but Py_LIMITED_API is set"
-#      endif
-#      if defined(Py_REF_DEBUG)
-#        error "pyconfig.h unexpectedly defines Py_REF_DEBUG, but Py_LIMITED_API is set"
-#      endif
-#    endif
-#  else
-#    include <pyconfig.h>
-#    if !defined(Py_DEBUG) && !defined(Py_TRACE_REFS) && !defined(Py_REF_DEBUG) && !defined(_CFFI_NO_LIMITED_API)
-#      define Py_LIMITED_API
-#    endif
-#  endif
-#endif
-
-#include <Python.h>
-#ifdef __cplusplus
-extern "C" {
-#endif
-#include <stddef.h>
-#include "parse_c_type.h"
-
-/* this block of #ifs should be kept exactly identical between
-   c/_cffi_backend.c, cffi/vengine_cpy.py, cffi/vengine_gen.py
-   and cffi/_cffi_include.h */
-#if defined(_MSC_VER)
-# include <malloc.h>   /* for alloca() */
-# if _MSC_VER < 1600   /* MSVC < 2010 */
-   typedef __int8 int8_t;
-   typedef __int16 int16_t;
-   typedef __int32 int32_t;
-   typedef __int64 int64_t;
-   typedef unsigned __int8 uint8_t;
-   typedef unsigned __int16 uint16_t;
-   typedef unsigned __int32 uint32_t;
-   typedef unsigned __int64 uint64_t;
-   typedef __int8 int_least8_t;
-   typedef __int16 int_least16_t;
-   typedef __int32 int_least32_t;
-   typedef __int64 int_least64_t;
-   typedef unsigned __int8 uint_least8_t;
-   typedef unsigned __int16 uint_least16_t;
-   typedef unsigned __int32 uint_least32_t;
-   typedef unsigned __int64 uint_least64_t;
-   typedef __int8 int_fast8_t;
-   typedef __int16 int_fast16_t;
-   typedef __int32 int_fast32_t;
-   typedef __int64 int_fast64_t;
-   typedef unsigned __int8 uint_fast8_t;
-   typedef unsigned __int16 uint_fast16_t;
-   typedef unsigned __int32 uint_fast32_t;
-   typedef unsigned __int64 uint_fast64_t;
-   typedef __int64 intmax_t;
-   typedef unsigned __int64 uintmax_t;
-# else
-#  include <stdint.h>
-# endif
-# if _MSC_VER < 1800   /* MSVC < 2013 */
-#  ifndef __cplusplus
-    typedef unsigned char _Bool;
-#  endif
-# endif
-#else
-# include <stdint.h>
-# if (defined (__SVR4) && defined (__sun)) || defined(_AIX) || defined(__hpux)
-#  include <alloca.h>
-# endif
-#endif
-
-#ifdef __GNUC__
-# define _CFFI_UNUSED_FN  __attribute__((unused))
-#else
-# define _CFFI_UNUSED_FN  /* nothing */
-#endif
-
-#ifdef __cplusplus
-# ifndef _Bool
-   typedef bool _Bool;   /* semi-hackish: C++ has no _Bool; bool is builtin */
-# endif
-#endif
-
-/**********  CPython-specific section  **********/
-#ifndef PYPY_VERSION
-
-
-#if PY_MAJOR_VERSION >= 3
-# define PyInt_FromLong PyLong_FromLong
-#endif
-
-#define _cffi_from_c_double PyFloat_FromDouble
-#define _cffi_from_c_float PyFloat_FromDouble
-#define _cffi_from_c_long PyInt_FromLong
-#define _cffi_from_c_ulong PyLong_FromUnsignedLong
-#define _cffi_from_c_longlong PyLong_FromLongLong
-#define _cffi_from_c_ulonglong PyLong_FromUnsignedLongLong
-#define _cffi_from_c__Bool PyBool_FromLong
-
-#define _cffi_to_c_double PyFloat_AsDouble
-#define _cffi_to_c_float PyFloat_AsDouble
-
-#define _cffi_from_c_int(x, type)                                        \
-    (((type)-1) > 0 ? /* unsigned */                                     \
-        (sizeof(type) < sizeof(long) ?                                   \
-            PyInt_FromLong((long)x) :                                    \
-         sizeof(type) == sizeof(long) ?                                  \
-            PyLong_FromUnsignedLong((unsigned long)x) :                  \
-            PyLong_FromUnsignedLongLong((unsigned long long)x)) :        \
-        (sizeof(type) <= sizeof(long) ?                                  \
-            PyInt_FromLong((long)x) :                                    \
-            PyLong_FromLongLong((long long)x)))
-
-#define _cffi_to_c_int(o, type)                                          \
-    ((type)(                                                             \
-     sizeof(type) == 1 ? (((type)-1) > 0 ? (type)_cffi_to_c_u8(o)        \
-                                         : (type)_cffi_to_c_i8(o)) :     \
-     sizeof(type) == 2 ? (((type)-1) > 0 ? (type)_cffi_to_c_u16(o)       \
-                                         : (type)_cffi_to_c_i16(o)) :    \
-     sizeof(type) == 4 ? (((type)-1) > 0 ? (type)_cffi_to_c_u32(o)       \
-                                         : (type)_cffi_to_c_i32(o)) :    \
-     sizeof(type) == 8 ? (((type)-1) > 0 ? (type)_cffi_to_c_u64(o)       \
-                                         : (type)_cffi_to_c_i64(o)) :    \
-     (Py_FatalError("unsupported size for type " #type), (type)0)))
-
-#define _cffi_to_c_i8                                                    \
-                 ((int(*)(PyObject *))_cffi_exports[1])
-#define _cffi_to_c_u8                                                    \
-                 ((int(*)(PyObject *))_cffi_exports[2])
-#define _cffi_to_c_i16                                                   \
-                 ((int(*)(PyObject *))_cffi_exports[3])
-#define _cffi_to_c_u16                                                   \
-                 ((int(*)(PyObject *))_cffi_exports[4])
-#define _cffi_to_c_i32                                                   \
-                 ((int(*)(PyObject *))_cffi_exports[5])
-#define _cffi_to_c_u32                                                   \
-                 ((unsigned int(*)(PyObject *))_cffi_exports[6])
-#define _cffi_to_c_i64                                                   \
-                 ((long long(*)(PyObject *))_cffi_exports[7])
-#define _cffi_to_c_u64                                                   \
-                 ((unsigned long long(*)(PyObject *))_cffi_exports[8])
-#define _cffi_to_c_char                                                  \
-                 ((int(*)(PyObject *))_cffi_exports[9])
-#define _cffi_from_c_pointer                                             \
-    ((PyObject *(*)(char *, struct _cffi_ctypedescr *))_cffi_exports[10])
-#define _cffi_to_c_pointer                                               \
-    ((char *(*)(PyObject *, struct _cffi_ctypedescr *))_cffi_exports[11])
-#define _cffi_get_struct_layout                                          \
-    not used any more
-#define _cffi_restore_errno                                              \
-    ((void(*)(void))_cffi_exports[13])
-#define _cffi_save_errno                                                 \
-    ((void(*)(void))_cffi_exports[14])
-#define _cffi_from_c_char                                                \
-    ((PyObject *(*)(char))_cffi_exports[15])
-#define _cffi_from_c_deref                                               \
-    ((PyObject *(*)(char *, struct _cffi_ctypedescr *))_cffi_exports[16])
-#define _cffi_to_c                                                       \
-    ((int(*)(char *, struct _cffi_ctypedescr *, PyObject *))_cffi_exports[17])
-#define _cffi_from_c_struct                                              \
-    ((PyObject *(*)(char *, struct _cffi_ctypedescr *))_cffi_exports[18])
-#define _cffi_to_c_wchar_t                                               \
-    ((_cffi_wchar_t(*)(PyObject *))_cffi_exports[19])
-#define _cffi_from_c_wchar_t                                             \
-    ((PyObject *(*)(_cffi_wchar_t))_cffi_exports[20])
-#define _cffi_to_c_long_double                                           \
-    ((long double(*)(PyObject *))_cffi_exports[21])
-#define _cffi_to_c__Bool                                                 \
-    ((_Bool(*)(PyObject *))_cffi_exports[22])
-#define _cffi_prepare_pointer_call_argument                              \
-    ((Py_ssize_t(*)(struct _cffi_ctypedescr *,                           \
-                    PyObject *, char **))_cffi_exports[23])
-#define _cffi_convert_array_from_object                                  \
-    ((int(*)(char *, struct _cffi_ctypedescr *, PyObject *))_cffi_exports[24])
-#define _CFFI_CPIDX  25
-#define _cffi_call_python                                                \
-    ((void(*)(struct _cffi_externpy_s *, char *))_cffi_exports[_CFFI_CPIDX])
-#define _cffi_to_c_wchar3216_t                                           \
-    ((int(*)(PyObject *))_cffi_exports[26])
-#define _cffi_from_c_wchar3216_t                                         \
-    ((PyObject *(*)(int))_cffi_exports[27])
-#define _CFFI_NUM_EXPORTS 28
-
-struct _cffi_ctypedescr;
-
-static void *_cffi_exports[_CFFI_NUM_EXPORTS];
-
-#define _cffi_type(index)   (                           \
-    assert((((uintptr_t)_cffi_types[index]) & 1) == 0), \
-    (struct _cffi_ctypedescr *)_cffi_types[index])
-
-static PyObject *_cffi_init(const char *module_name, Py_ssize_t version,
-                            const struct _cffi_type_context_s *ctx)
-{
-    PyObject *module, *o_arg, *new_module;
-    void *raw[] = {
-        (void *)module_name,
-        (void *)version,
-        (void *)_cffi_exports,
-        (void *)ctx,
-    };
-
-    module = PyImport_ImportModule("_cffi_backend");
-    if (module == NULL)
-        goto failure;
-
-    o_arg = PyLong_FromVoidPtr((void *)raw);
-    if (o_arg == NULL)
-        goto failure;
-
-    new_module = PyObject_CallMethod(
-        module, (char *)"_init_cffi_1_0_external_module", (char *)"O", o_arg);
-
-    Py_DECREF(o_arg);
-    Py_DECREF(module);
-    return new_module;
-
-  failure:
-    Py_XDECREF(module);
-    return NULL;
-}
-
-
-#ifdef HAVE_WCHAR_H
-typedef wchar_t _cffi_wchar_t;
-#else
-typedef uint16_t _cffi_wchar_t;   /* same random pick as _cffi_backend.c */
-#endif
-
-_CFFI_UNUSED_FN static uint16_t _cffi_to_c_char16_t(PyObject *o)
-{
-    if (sizeof(_cffi_wchar_t) == 2)
-        return (uint16_t)_cffi_to_c_wchar_t(o);
-    else
-        return (uint16_t)_cffi_to_c_wchar3216_t(o);
-}
-
-_CFFI_UNUSED_FN static PyObject *_cffi_from_c_char16_t(uint16_t x)
-{
-    if (sizeof(_cffi_wchar_t) == 2)
-        return _cffi_from_c_wchar_t((_cffi_wchar_t)x);
-    else
-        return _cffi_from_c_wchar3216_t((int)x);
-}
-
-_CFFI_UNUSED_FN static int _cffi_to_c_char32_t(PyObject *o)
-{
-    if (sizeof(_cffi_wchar_t) == 4)
-        return (int)_cffi_to_c_wchar_t(o);
-    else
-        return (int)_cffi_to_c_wchar3216_t(o);
-}
-
-_CFFI_UNUSED_FN static PyObject *_cffi_from_c_char32_t(unsigned int x)
-{
-    if (sizeof(_cffi_wchar_t) == 4)
-        return _cffi_from_c_wchar_t((_cffi_wchar_t)x);
-    else
-        return _cffi_from_c_wchar3216_t((int)x);
-}
-
-union _cffi_union_alignment_u {
-    unsigned char m_char;
-    unsigned short m_short;
-    unsigned int m_int;
-    unsigned long m_long;
-    unsigned long long m_longlong;
-    float m_float;
-    double m_double;
-    long double m_longdouble;
-};
-
-struct _cffi_freeme_s {
-    struct _cffi_freeme_s *next;
-    union _cffi_union_alignment_u alignment;
-};
-
-_CFFI_UNUSED_FN static int
-_cffi_convert_array_argument(struct _cffi_ctypedescr *ctptr, PyObject *arg,
-                             char **output_data, Py_ssize_t datasize,
-                             struct _cffi_freeme_s **freeme)
-{
-    char *p;
-    if (datasize < 0)
-        return -1;
-
-    p = *output_data;
-    if (p == NULL) {
-        struct _cffi_freeme_s *fp = (struct _cffi_freeme_s *)PyObject_Malloc(
-            offsetof(struct _cffi_freeme_s, alignment) + (size_t)datasize);
-        if (fp == NULL)
-            return -1;
-        fp->next = *freeme;
-        *freeme = fp;
-        p = *output_data = (char *)&fp->alignment;
-    }
-    memset((void *)p, 0, (size_t)datasize);
-    return _cffi_convert_array_from_object(p, ctptr, arg);
-}
-
-_CFFI_UNUSED_FN static void
-_cffi_free_array_arguments(struct _cffi_freeme_s *freeme)
-{
-    do {
-        void *p = (void *)freeme;
-        freeme = freeme->next;
-        PyObject_Free(p);
-    } while (freeme != NULL);
-}
-
-/**********  end CPython-specific section  **********/
-#else
-_CFFI_UNUSED_FN
-static void (*_cffi_call_python_org)(struct _cffi_externpy_s *, char *);
-# define _cffi_call_python  _cffi_call_python_org
-#endif
-
-
-#define _cffi_array_len(array)   (sizeof(array) / sizeof((array)[0]))
-
-#define _cffi_prim_int(size, sign)                                      \
-    ((size) == 1 ? ((sign) ? _CFFI_PRIM_INT8  : _CFFI_PRIM_UINT8)  :    \
-     (size) == 2 ? ((sign) ? _CFFI_PRIM_INT16 : _CFFI_PRIM_UINT16) :    \
-     (size) == 4 ? ((sign) ? _CFFI_PRIM_INT32 : _CFFI_PRIM_UINT32) :    \
-     (size) == 8 ? ((sign) ? _CFFI_PRIM_INT64 : _CFFI_PRIM_UINT64) :    \
-     _CFFI__UNKNOWN_PRIM)
-
-#define _cffi_prim_float(size)                                          \
-    ((size) == sizeof(float) ? _CFFI_PRIM_FLOAT :                       \
-     (size) == sizeof(double) ? _CFFI_PRIM_DOUBLE :                     \
-     (size) == sizeof(long double) ? _CFFI__UNKNOWN_LONG_DOUBLE :       \
-     _CFFI__UNKNOWN_FLOAT_PRIM)
-
-#define _cffi_check_int(got, got_nonpos, expected)      \
-    ((got_nonpos) == (expected <= 0) &&                 \
-     (got) == (unsigned long long)expected)
-
-#ifdef MS_WIN32
-# define _cffi_stdcall  __stdcall
-#else
-# define _cffi_stdcall  /* nothing */
-#endif
-
-#ifdef __cplusplus
-}
-#endif
diff --git a/spaces/ashercn97/AsherTesting/css/html_4chan_style.css b/spaces/ashercn97/AsherTesting/css/html_4chan_style.css
deleted file mode 100644
index 99ac68452351f3b5b5a109e53dae789e6f61c804..0000000000000000000000000000000000000000
--- a/spaces/ashercn97/AsherTesting/css/html_4chan_style.css
+++ /dev/null
@@ -1,104 +0,0 @@
-#parent #container {
-    background-color: #eef2ff;
-    padding: 17px;
-}
-
-#parent #container .reply {
-    background-color: rgb(214, 218, 240);
-    border-bottom-color: rgb(183, 197, 217);
-    border-bottom-style: solid;
-    border-bottom-width: 1px;
-    border-image-outset: 0;
-    border-image-repeat: stretch;
-    border-image-slice: 100%;
-    border-image-source: none;
-    border-image-width: 1;
-    border-left-color: rgb(0, 0, 0);
-    border-left-style: none;
-    border-left-width: 0px;
-    border-right-color: rgb(183, 197, 217);
-    border-right-style: solid;
-    border-right-width: 1px;
-    border-top-color: rgb(0, 0, 0);
-    border-top-style: none;
-    border-top-width: 0px;
-    color: rgb(0, 0, 0);
-    display: table;
-    font-family: arial, helvetica, sans-serif;
-    font-size: 13.3333px;
-    margin-bottom: 4px;
-    margin-left: 0px;
-    margin-right: 0px;
-    margin-top: 4px;
-    overflow-x: hidden;
-    overflow-y: hidden;
-    padding-bottom: 4px;
-    padding-left: 2px;
-    padding-right: 2px;
-    padding-top: 4px;
-}
-
-#parent #container .number {
-    color: rgb(0, 0, 0);
-    font-family: arial, helvetica, sans-serif;
-    font-size: 13.3333px;
-    width: 342.65px;
-    margin-right: 7px;
-}
-
-#parent #container .op {
-    color: rgb(0, 0, 0);
-    font-family: arial, helvetica, sans-serif;
-    font-size: 13.3333px;
-    margin-bottom: 8px;
-    margin-left: 0px;
-    margin-right: 0px;
-    margin-top: 4px;
-    overflow-x: hidden;
-    overflow-y: hidden;
-}
-
-#parent #container .op blockquote {
-    margin-left: 0px !important;
-}
-
-#parent #container .name {
-    color: rgb(17, 119, 67);
-    font-family: arial, helvetica, sans-serif;
-    font-size: 13.3333px;
-    font-weight: 700;
-    margin-left: 7px;
-}
-
-#parent #container .quote {
-    color: rgb(221, 0, 0);
-    font-family: arial, helvetica, sans-serif;
-    font-size: 13.3333px;
-    text-decoration-color: rgb(221, 0, 0);
-    text-decoration-line: underline;
-    text-decoration-style: solid;
-    text-decoration-thickness: auto;
-}
-
-#parent #container .greentext {
-    color: rgb(120, 153, 34);
-    font-family: arial, helvetica, sans-serif;
-    font-size: 13.3333px;
-}
-
-#parent #container blockquote {
-    margin: 0px !important;
-    margin-block-start: 1em;
-    margin-block-end: 1em;
-    margin-inline-start: 40px;
-    margin-inline-end: 40px;
-    margin-top: 13.33px !important;
-    margin-bottom: 13.33px !important;
-    margin-left: 40px !important;
-    margin-right: 40px !important;
-}
-
-#parent #container .message {
-    color: black;
-    border: none;
-}
\ No newline at end of file
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Joeseph Wickline.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Joeseph Wickline.html
deleted file mode 100644
index 687fe4741ffe2dd7d8abf134810172aa49e0e6b2..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Joeseph Wickline.html	
+++ /dev/null
@@ -1,132 +0,0 @@
-<!DOCTYPE html><html lang="en"><head>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1, user-scalable=no">
-    <title>Joeseph Wickline</title>
-    <style>
-      html {
-        padding: 1em;
-        background: #fff;
-        color: #222;
-        font-family: "Lucida Grande", "Lucida Sans Unicode", "Lucida Sans", Geneva, Verdana, sans-serif;
-        font-size: 100%;
-        line-height: 1.5;
-      }
-
-      h1,
-      p,
-      ul,
-      ol,
-      div,
-      figure {
-        margin: 0;
-      }
-
-      h1 { font-size: 1.2em; }
-
-      ul {
-        list-style: disc;
-        padding-left: 1.3em;
-      }
-
-      ol {
-        padding-left: 1.3em;
-        list-style: decimal;
-      }
-
-      blockquote {
-        border-left: 3px solid #000;
-        margin: 0;
-        padding: 0 0 0 1em;
-      }
-
-      pre {
-        font-family: monaco, monospace;
-        font-size: 0.875em;
-        margin: 0;
-        padding: 1rem;
-        background: #eee;
-        border-radius: 0.4rem;
-        white-space: pre;
-        word-wrap: normal;
-        word-break: normal;
-        overflow-x: auto;
-      }
-
-      code { border-radius: 0.4rem; }
-
-      figure {
-        display: inline-block;
-        width: 100%;
-        box-sizing: border-box;
-      }
-
-      figcaption { word-break: break-word; }
-
-      img {
-        display: block;
-        max-width: 100%;
-        margin: 0 auto;
-        padding: 1px;
-        border: 1px solid #eee;
-      }
-
-      a {
-        color: #1b6ac9;
-        text-decoration: underline;
-      }
-
-      a:active { color: #064ac9; }
-
-      .wrapper {
-        max-width: 650px;
-        margin: 0 auto;
-      }
-
-      .title {
-        font-size: 2em;
-        margin: 0.5em 0 1em;
-        line-height: 1.3;
-      }
-
-      .attachment--image {
-        text-align: center;
-        color: #888;
-        font-size: 0.875em;
-      }
-
-      .attachment--file {
-        position: relative;
-        padding: 1.4em 1em 1.5em;
-        border: 1px solid #ddd;
-        border-bottom-width: 3px;
-        border-radius: 6px;
-        color: #222;
-      }
-
-      .attachment__label {
-        position: absolute;
-        bottom: 0;
-        right: 0;
-        background: #ddd;
-        font-size: 10px;
-        padding: 2px 6px;
-        border-radius: 6px 0 4px 0;
-        color: #888;
-      }
-
-      .metadata { color: #888; }
-    </style>
-  </head>
-
-  <body>
-    <div class="wrapper">
-      <h1 class="title">Joeseph Wickline</h1>
-
-<div class="formatted_content">
-  <div>1- How did you hear about SM? What motivated you to pursue mentorship with SM?<br>- Google search, Was looking for a structured way to engage with people and have a vested interest in involving and participating in a community. Used to read and interact with various tech and career-oriented blogs and on reddit, twitter etc. Want to share personal learning with others and help them. Also, an additional income stream.<br><br>2- What's your career journey in Data Science been like?&nbsp;<br>- 5 years of experience working in analytics.&nbsp;<br>- working as a Data engineer both on the backend ( Data curation &amp; infrastructure) and front end ( BI, reporting &amp; Visualization).&nbsp;<br>- Have experience of working in various fields - Retail, insurance &amp; SaaS.<br><br>3- Previous mentorship experience?<br>- No formal mentorship experience, but have trained interns &amp; new hires as part of the job.<br><br>4- What are the biggest challenges newcomers face when trying to land a job in Data? How can you help them with this?<br>- There is an overwhelming list of tools to work with. Analytics needs guidance on which tools to focus on and where to start. Especially a challenge for people from a non-tech background.&nbsp;<br>Will guide newcomers with sharing personal experiences but have a preference of working with people from tech backgrounds as the journey would be similar.&nbsp;<br>- Can help with a structured way to go about job sourcing during mentorship. - Have a flow ready on how target good companies, building resume and techniques to use while applying for jobs.&nbsp;<br><br>5- Questions about SM?<br>- What's your role at SM?<br>- How many active mentors on the platform?<br>- What resources are available to mentors?<br>- How does the relationship between mentor and mentee work?<br>- How are the terms for ISA set? Is it decided by SM?<br>- How is ISA enforced?<br>- How does payment work?</div>
-</div>
-
-    </div>
-  
-
-</body></html>
\ No newline at end of file
diff --git a/spaces/atimughal662/InfoFusion/src/utils.py b/spaces/atimughal662/InfoFusion/src/utils.py
deleted file mode 100644
index ba46804f5d32dba376a214589a74aed21a7fc2cb..0000000000000000000000000000000000000000
--- a/spaces/atimughal662/InfoFusion/src/utils.py
+++ /dev/null
@@ -1,1569 +0,0 @@
-import ast
-import contextlib
-import functools
-import gc
-import getpass
-import hashlib
-import inspect
-import json
-import os
-import pathlib
-import pickle
-import platform
-import random
-import shutil
-import subprocess
-import sys
-import threading
-import time
-import traceback
-import zipfile
-from concurrent.futures import ProcessPoolExecutor
-from datetime import datetime
-from typing import Tuple, Callable, Dict
-from queue import Queue, Empty
-from concurrent.futures import ThreadPoolExecutor
-
-import filelock
-import fire
-import numpy as np
-import pandas as pd
-import requests
-import uuid
-
-import tabulate
-from fire import inspectutils
-from joblib import Parallel
-from tqdm.auto import tqdm
-
-
-def H2O_Fire(component=None):
-    config_prefix = "H2OGPT_"
-
-    args = sys.argv[1:]
-    query_args = [arg.split("=")[0].split(" ")[0].lstrip("-") for arg in args]
-
-    fn_spec = inspectutils.GetFullArgSpec(component)
-    for key, value in os.environ.items():
-        if not (
-                (key.startswith(config_prefix) or key.startswith(config_prefix.lower()))
-                and len(key) > len(config_prefix)
-        ):
-            continue  # ignore as non H2OGPT argument
-
-        new_key = key[len(config_prefix):].lower()
-
-        if new_key in query_args:
-            continue  # ignore as already passed as script argument
-
-        if new_key not in fn_spec.args:
-            continue  # ignore as not a valid H2OGPT argument
-
-        args.append(f"--{new_key}={value}")
-
-    fire.Fire(component=component, command=args)
-
-
-def set_seed(seed: int):
-    """
-    Sets the seed of the entire notebook so results are the same every time we run.
-    This is for REPRODUCIBILITY.
-    """
-    import torch
-    np.random.seed(seed)
-    random_state = np.random.RandomState(seed)
-    random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
-    os.environ['PYTHONHASHSEED'] = str(seed)
-    return random_state
-
-
-def flatten_list(lis):
-    """Given a list, possibly nested to any level, return it flattened."""
-    new_lis = []
-    for item in lis:
-        if type(item) == type([]):
-            new_lis.extend(flatten_list(item))
-        else:
-            new_lis.append(item)
-    return new_lis
-
-
-def clear_torch_cache():
-    try:
-        import torch
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            torch.cuda.ipc_collect()
-            gc.collect()
-    except RuntimeError as e:
-        print("clear_torch_cache error: %s" % ''.join(traceback.format_tb(e.__traceback__)), flush=True)
-
-
-def ping():
-    try:
-        print('Ping: %s' % str(datetime.now()), flush=True)
-    except AttributeError:
-        # some programs wrap print and will fail with flush passed
-        pass
-
-
-def ping_gpu():
-    try:
-        print('Ping_GPU: %s %s' % (str(datetime.now()), system_info()), flush=True)
-    except AttributeError:
-        # some programs wrap print and will fail with flush passed
-        pass
-    try:
-        ping_gpu_memory()
-    except Exception as e:
-        print('Ping_GPU memory failure: %s' % str(e), flush=True)
-
-
-def ping_gpu_memory():
-    from models.gpu_mem_track import MemTracker
-    gpu_tracker = MemTracker()  # define a GPU tracker
-    from torch.cuda import memory_summary
-    gpu_tracker.track()
-
-
-def get_torch_allocated():
-    import torch
-    return torch.cuda.memory_allocated()
-
-
-def get_device():
-    import torch
-    if torch.cuda.is_available():
-        device = "cuda"
-    elif torch.backends.mps.is_built():
-        device = "mps"
-    else:
-        device = "cpu"
-
-    return device
-
-
-def system_info():
-    import psutil
-
-    system = {}
-    # https://stackoverflow.com/questions/48951136/plot-multiple-graphs-in-one-plot-using-tensorboard
-    # https://arshren.medium.com/monitoring-your-devices-in-python-5191d672f749
-    try:
-        temps = psutil.sensors_temperatures(fahrenheit=False)
-        if 'coretemp' in temps:
-            coretemp = temps['coretemp']
-            temp_dict = {k.label: k.current for k in coretemp}
-            for k, v in temp_dict.items():
-                system['CPU_C/%s' % k] = v
-    except AttributeError:
-        pass
-
-    # https://github.com/gpuopenanalytics/pynvml/blob/master/help_query_gpu.txt
-    try:
-        from pynvml.smi import nvidia_smi
-        nvsmi = nvidia_smi.getInstance()
-
-        gpu_power_dict = {'W_gpu%d' % i: x['power_readings']['power_draw'] for i, x in
-                          enumerate(nvsmi.DeviceQuery('power.draw')['gpu'])}
-        for k, v in gpu_power_dict.items():
-            system['GPU_W/%s' % k] = v
-
-        gpu_temp_dict = {'C_gpu%d' % i: x['temperature']['gpu_temp'] for i, x in
-                         enumerate(nvsmi.DeviceQuery('temperature.gpu')['gpu'])}
-        for k, v in gpu_temp_dict.items():
-            system['GPU_C/%s' % k] = v
-
-        gpu_memory_free_dict = {'MiB_gpu%d' % i: x['fb_memory_usage']['free'] for i, x in
-                                enumerate(nvsmi.DeviceQuery('memory.free')['gpu'])}
-        gpu_memory_total_dict = {'MiB_gpu%d' % i: x['fb_memory_usage']['total'] for i, x in
-                                 enumerate(nvsmi.DeviceQuery('memory.total')['gpu'])}
-        gpu_memory_frac_dict = {k: gpu_memory_free_dict[k] / gpu_memory_total_dict[k] for k in gpu_memory_total_dict}
-        for k, v in gpu_memory_frac_dict.items():
-            system[f'GPU_M/%s' % k] = v
-    except (KeyError, ModuleNotFoundError):
-        pass
-    system['hash'] = get_githash()
-
-    return system
-
-
-def system_info_print():
-    try:
-        df = pd.DataFrame.from_dict(system_info(), orient='index')
-        # avoid slamming GPUs
-        time.sleep(1)
-        return df.to_markdown()
-    except Exception as e:
-        return "Error: %s" % str(e)
-
-
-def zip_data(root_dirs=None, zip_file=None, base_dir='./', fail_any_exception=False):
-    try:
-        return _zip_data(zip_file=zip_file, base_dir=base_dir, root_dirs=root_dirs)
-    except Exception as e:
-        traceback.print_exc()
-        print('Exception in zipping: %s' % str(e))
-        if not fail_any_exception:
-            raise
-
-
-def _zip_data(root_dirs=None, zip_file=None, base_dir='./'):
-    if isinstance(root_dirs, str):
-        root_dirs = [root_dirs]
-    if zip_file is None:
-        datetime_str = str(datetime.now()).replace(" ", "_").replace(":", "_")
-        host_name = os.getenv('HF_HOSTNAME', 'emptyhost')
-        zip_file = "data_%s_%s.zip" % (datetime_str, host_name)
-    assert root_dirs is not None
-    base_path = os.path.dirname(zip_file)
-    if not os.path.isdir(base_path) and os.path.dirname(zip_file):
-        base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True)
-        zip_file = os.path.join(base_path, os.path.basename(zip_file))
-    with zipfile.ZipFile(zip_file, "w") as expt_zip:
-        for root_dir in root_dirs:
-            if root_dir is None:
-                continue
-            for root, d, files in os.walk(root_dir):
-                for file in files:
-                    file_to_archive = os.path.join(root, file)
-                    assert os.path.exists(file_to_archive)
-                    path_to_archive = os.path.relpath(file_to_archive, base_dir)
-                    expt_zip.write(filename=file_to_archive, arcname=path_to_archive)
-    return zip_file, zip_file
-
-
-def save_generate_output(prompt=None, output=None, base_model=None, save_dir=None, where_from='unknown where from',
-                         extra_dict={}, error='', extra='', which_api='', valid_key=None,
-                         h2ogpt_key='', return_dict=False):
-    if not save_dir:
-        return
-    try:
-        return _save_generate_output(prompt=prompt, output=output, base_model=base_model, save_dir=save_dir,
-                                     where_from=where_from, extra_dict=extra_dict, error=error, extra=extra,
-                                     which_api=which_api, valid_key=valid_key, h2ogpt_key=h2ogpt_key,
-                                     return_dict=return_dict)
-    except Exception as e:
-        traceback.print_exc()
-        print('Exception in saving: %s' % str(e))
-
-
-def _save_generate_output(prompt=None, output=None, base_model=None, save_dir=None, where_from='unknown where from',
-                          extra_dict={}, error='', extra='', which_api='',
-                          valid_key=None, h2ogpt_key='',
-                          return_dict=False):
-    """
-    Save conversation to .json, row by row.
-    json_file_path is path to final JSON file. If not in ., then will attempt to make directories.
-    Appends if file exists
-    """
-    prompt = '<not set>' if prompt is None else prompt
-    output = '<not set>' if output is None else output
-
-    # tokenize at end if need to, so doesn't block generation in multi-generator case
-    if extra_dict.get('ntokens') is None:
-        extra_dict['ntokens'] = FakeTokenizer().num_tokens_from_string(output)
-        # only do below if didn't already compute ntokens, else assume also computed rate
-        extra_dict['tokens_persecond'] = extra_dict['ntokens'] / extra_dict['t_generate']
-
-    dict_to_save = dict(prompt=prompt, text=output, time=time.ctime(),
-                        base_model=base_model,
-                        where_from=where_from,
-                        error=error,
-                        extra=extra,
-                        which_api=which_api,
-                        valid_key=valid_key,
-                        h2ogpt_key=h2ogpt_key,
-                        )
-    dict_to_save.update(extra_dict)
-
-    if return_dict:
-        return dict_to_save
-
-    if os.path.exists(save_dir) and not os.path.isdir(save_dir):
-        raise RuntimeError("save_dir already exists and is not a directory!")
-    makedirs(save_dir, exist_ok=True)  # already should be made, can't change at this point
-    import json
-    with filelock.FileLock("%s.lock" % os.path.basename(save_dir)):
-        # lock logging in case have concurrency
-        with open(os.path.join(save_dir, "history.json"), "a") as f:
-            # just add [ at start, and ] at end, and have proper JSON dataset
-            f.write(
-                "  " + json.dumps(
-                    dict_to_save
-                ) + ",\n"
-            )
-
-
-def s3up(filename):
-    try:
-        return _s3up(filename)
-    except Exception as e:
-        traceback.print_exc()
-        print('Exception for file %s in s3up: %s' % (filename, str(e)))
-        return "Failed to upload %s: Error: %s" % (filename, str(e))
-
-
-def _s3up(filename):
-    import boto3
-
-    aws_access_key_id = os.getenv('AWS_SERVER_PUBLIC_KEY')
-    aws_secret_access_key = os.getenv('AWS_SERVER_SECRET_KEY')
-    bucket = os.getenv('AWS_BUCKET')
-    assert aws_access_key_id, "Set AWS key"
-    assert aws_secret_access_key, "Set AWS secret"
-    assert bucket, "Set AWS Bucket"
-
-    s3 = boto3.client('s3',
-                      aws_access_key_id=os.getenv('AWS_SERVER_PUBLIC_KEY'),
-                      aws_secret_access_key=os.getenv('AWS_SERVER_SECRET_KEY'),
-                      )
-    ret = s3.upload_file(
-        Filename=filename,
-        Bucket=os.getenv('AWS_BUCKET'),
-        Key=filename,
-    )
-    if ret in [None, '']:
-        return "Successfully uploaded %s" % filename
-
-
-def get_githash():
-    try:
-        githash = subprocess.run(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE).stdout.decode('utf-8')[0:-1]
-    except:
-        githash = ''
-    return githash
-
-
-def copy_code(run_id):
-    """
-    copy code to track changes
-    :param run_id:
-    :return:
-    """
-    rnd_num = str(random.randint(0, 2 ** 31))
-    run_id = 'run_' + str(run_id)
-    os.makedirs(run_id, exist_ok=True)
-    me_full = os.path.join(pathlib.Path(__file__).parent.resolve(), __file__)
-    me_file = os.path.basename(__file__)
-    new_me = os.path.join(run_id, me_file + '_' + get_githash())
-    if os.path.isfile(new_me):
-        new_me = os.path.join(run_id, me_file + '_' + get_githash() + '_' + rnd_num)
-        shutil.copy(me_full, new_me)
-    else:
-        shutil.copy(me_full, new_me)
-
-
-class NullContext(threading.local):
-    """No-op context manager, executes block without doing any additional processing.
-
-    Used as a stand-in if a particular block of code is only sometimes
-    used with a normal context manager:
-    """
-
-    def __init__(self, *args, **kwargs):
-        pass
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_value, exc_traceback):
-        self.finally_act()
-
-    def finally_act(self):
-        pass
-
-
-def wrapped_partial(func, *args, **kwargs):
-    """
-    Give partial properties of normal function, like __name__ attribute etc.
-    :param func:
-    :param args:
-    :param kwargs:
-    :return:
-    """
-    partial_func = functools.partial(func, *args, **kwargs)
-    functools.update_wrapper(partial_func, func)
-    return partial_func
-
-
-class ThreadException(Exception):
-    pass
-
-
-class EThread(threading.Thread):
-    # Function that raises the custom exception
-    def __init__(self, group=None, target=None, name=None,
-                 args=(), kwargs=None, *, daemon=None, streamer=None, bucket=None):
-        self.bucket = bucket
-        self.streamer = streamer
-        self.exc = None
-        self._return = None
-        super().__init__(group=group, target=target, name=name, args=args, kwargs=kwargs, daemon=daemon)
-
-    def run(self):
-        # Variable that stores the exception, if raised by someFunction
-        try:
-            if self._target is not None:
-                self._return = self._target(*self._args, **self._kwargs)
-        except BaseException as e:
-            print("thread exception: %s" % str(sys.exc_info()))
-            self.bucket.put(sys.exc_info())
-            self.exc = e
-            if self.streamer:
-                print("make stop: %s" % str(sys.exc_info()), flush=True)
-                self.streamer.do_stop = True
-        finally:
-            # Avoid a refcycle if the thread is running a function with
-            # an argument that has a member that points to the thread.
-            del self._target, self._args, self._kwargs
-
-    def join(self, timeout=None):
-        threading.Thread.join(self)
-        # Since join() returns in caller thread
-        # we re-raise the caught exception
-        # if any was caught
-        if self.exc:
-            raise self.exc
-        return self._return
-
-
-def import_matplotlib():
-    import matplotlib
-    matplotlib.use('agg')
-    # KEEP THESE HERE! START
-    import matplotlib.pyplot as plt
-    import pandas as pd
-    # to avoid dlopen deadlock in fork
-    import pandas.core.computation.expressions as pd_expressions
-    import pandas._libs.groupby as pd_libgroupby
-    import pandas._libs.reduction as pd_libreduction
-    import pandas.core.algorithms as pd_algorithms
-    import pandas.core.common as pd_com
-    import numpy as np
-    # KEEP THESE HERE! END
-
-
-def get_sha(value):
-    return hashlib.md5(str(value).encode('utf-8')).hexdigest()
-
-
-def sanitize_filename(name):
-    """
-    Sanitize file *base* names.
-    :param name: name to sanitize
-    :return:
-    """
-    bad_chars = ['[', ']', ',', '/', '\\', '\\w', '\\s', '-', '+', '\"', '\'', '>', '<', ' ', '=', ')', '(', ':', '^']
-    for char in bad_chars:
-        name = name.replace(char, "_")
-
-    length = len(name)
-    file_length_limit = 250  # bit smaller than 256 for safety
-    sha_length = 32
-    real_length_limit = file_length_limit - (sha_length + 2)
-    if length > file_length_limit:
-        sha = get_sha(name)
-        half_real_length_limit = max(1, int(real_length_limit / 2))
-        name = name[0:half_real_length_limit] + "_" + sha + "_" + name[length - half_real_length_limit:length]
-
-    return name
-
-
-def shutil_rmtree(*args, **kwargs):
-    return shutil.rmtree(*args, **kwargs)
-
-
-def remove(path: str):
-    try:
-        if path is not None and os.path.exists(path):
-            if os.path.isdir(path):
-                shutil_rmtree(path, ignore_errors=True)
-            else:
-                with contextlib.suppress(FileNotFoundError):
-                    os.remove(path)
-    except:
-        pass
-
-
-def makedirs(path, exist_ok=True, tmp_ok=False, use_base=False):
-    """
-    Avoid some inefficiency in os.makedirs()
-    :param path:
-    :param exist_ok:
-    :param tmp_ok:  use /tmp if can't write locally
-    :param use_base:
-    :return:
-    """
-    if path is None:
-        return path
-    # if base path set, make relative to that, unless user_path absolute path
-    if use_base:
-        if os.path.normpath(path) == os.path.normpath(os.path.abspath(path)):
-            pass
-        else:
-            if os.getenv('H2OGPT_BASE_PATH') is not None:
-                base_dir = os.path.normpath(os.getenv('H2OGPT_BASE_PATH'))
-                path = os.path.normpath(path)
-                if not path.startswith(base_dir):
-                    path = os.path.join(os.getenv('H2OGPT_BASE_PATH', ''), path)
-                    path = os.path.normpath(path)
-
-    if os.path.isdir(path) and os.path.exists(path):
-        assert exist_ok, "Path already exists"
-        return path
-    try:
-        os.makedirs(path, exist_ok=exist_ok)
-        return path
-    except FileExistsError:
-        # e.g. soft link
-        return path
-    except PermissionError:
-        if tmp_ok:
-            path0 = path
-            path = os.path.join('/tmp/', path)
-            print("Permission denied to %s, using %s instead" % (path0, path), flush=True)
-            os.makedirs(path, exist_ok=exist_ok)
-            return path
-        else:
-            raise
-
-
-def atomic_move_simple(src, dst):
-    try:
-        shutil.move(src, dst)
-    except (shutil.Error, FileExistsError):
-        pass
-    remove(src)
-
-
-def download_simple(url, dest=None):
-    if dest is None:
-        dest = os.path.basename(url)
-    base_path = os.path.dirname(dest)
-    if base_path:  # else local path
-        base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True)
-        dest = os.path.join(base_path, os.path.basename(dest))
-
-    if os.path.isfile(dest):
-        print("Already have %s from url %s, delete file if invalid" % (dest, str(url)), flush=True)
-        return dest
-
-    print("BEGIN get url %s" % str(url), flush=True)
-    if url.startswith("file://"):
-        from requests_file import FileAdapter
-        s = requests.Session()
-        s.mount('file://', FileAdapter())
-        url_data = s.get(url, stream=True)
-    else:
-        url_data = requests.get(url, stream=True)
-    print("GOT url %s" % str(url), flush=True)
-
-    if url_data.status_code != requests.codes.ok:
-        msg = "Cannot get url %s, code: %s, reason: %s" % (
-            str(url),
-            str(url_data.status_code),
-            str(url_data.reason),
-        )
-        raise requests.exceptions.RequestException(msg)
-    url_data.raw.decode_content = True
-
-    uuid_tmp = str(uuid.uuid4())[:6]
-    dest_tmp = dest + "_dl_" + uuid_tmp + ".tmp"
-    with open(dest_tmp, "wb") as f:
-        shutil.copyfileobj(url_data.raw, f)
-    atomic_move_simple(dest_tmp, dest)
-    print("DONE url %s" % str(url), flush=True)
-    return dest
-
-
-def download(url, dest=None, dest_path=None):
-    if dest_path is not None:
-        dest = os.path.join(dest_path, os.path.basename(url))
-        if os.path.isfile(dest):
-            print("already downloaded %s -> %s" % (url, dest))
-            return dest
-    elif dest is not None:
-        if os.path.exists(dest):
-            print("already downloaded %s -> %s" % (url, dest))
-            return dest
-    else:
-        uuid_tmp = "dl2_" + str(uuid.uuid4())[:6]
-        dest = uuid_tmp + os.path.basename(url)
-
-    print("downloading %s to %s" % (url, dest))
-
-    if url.startswith("file://"):
-        from requests_file import FileAdapter
-        s = requests.Session()
-        s.mount('file://', FileAdapter())
-        url_data = s.get(url, stream=True)
-    else:
-        url_data = requests.get(url, stream=True)
-
-    if url_data.status_code != requests.codes.ok:
-        msg = "Cannot get url %s, code: %s, reason: %s" % (
-            str(url), str(url_data.status_code), str(url_data.reason))
-        raise requests.exceptions.RequestException(msg)
-    url_data.raw.decode_content = True
-    dirname = os.path.dirname(dest)
-    if dirname != "" and not os.path.isdir(dirname):
-        base_path = os.path.dirname(dest)
-        base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True)
-        dest = os.path.join(base_path, os.path.basename(dest))
-    uuid_tmp = "dl3_" + str(uuid.uuid4())[:6]
-    dest_tmp = dest + "_" + uuid_tmp + ".tmp"
-    with open(dest_tmp, 'wb') as f:
-        shutil.copyfileobj(url_data.raw, f)
-    try:
-        shutil.move(dest_tmp, dest)
-    except FileExistsError:
-        pass
-    remove(dest_tmp)
-    return dest
-
-
-def get_doc(x):
-    return x.page_content
-
-
-def get_source(x):
-    return x.metadata.get('source', "UNKNOWN SOURCE")
-
-
-def get_accordion(x, font_size=2, head_acc=50):
-    title = x.page_content[:head_acc].replace("\n", ' ').replace("<br>", ' ').replace("<p>", ' ').replace("\r", ' ')
-    content = x.page_content
-    return f"""<details><summary><font size="{font_size}">{title}</font></summary><font size="{font_size}">{content}</font></details>"""
-
-
-def get_url(x, from_str=False, short_name=False, font_size=2):
-    if not from_str:
-        source = x.metadata['source']
-    else:
-        source = x
-    if short_name:
-        source_name = get_short_name(source)
-    else:
-        source_name = source
-    if source.startswith('http://') or source.startswith('https://'):
-        return """<font size="%s"><a href="%s" target="_blank"  rel="noopener noreferrer">%s</a></font>""" % (
-            font_size, source, source_name)
-    elif '<a href=' not in source:
-        return """<font size="%s"><a href="file/%s" target="_blank"  rel="noopener noreferrer">%s</a></font>""" % (
-            font_size, source, source_name)
-    else:
-        # already filled
-        return source
-
-
-def get_short_name(name, maxl=50):
-    if name is None:
-        return ''
-    length = len(name)
-    if length > maxl:
-        allow_length = maxl - 3
-        half_allowed = max(1, int(allow_length / 2))
-        name = name[0:half_allowed] + "..." + name[length - half_allowed:length]
-    return name
-
-
-def cuda_vis_check(total_gpus):
-    """Helper function to count GPUs by environment variable
-    Stolen from Jon's h2o4gpu utils
-    """
-    cudavis = os.getenv("CUDA_VISIBLE_DEVICES")
-    which_gpus = []
-    if cudavis is not None:
-        # prune away white-space, non-numerics,
-        # except commas for simple checking
-        cudavis = "".join(cudavis.split())
-        import re
-        cudavis = re.sub("[^0-9,]", "", cudavis)
-
-        lencudavis = len(cudavis)
-        if lencudavis == 0:
-            total_gpus = 0
-        else:
-            total_gpus = min(
-                total_gpus,
-                os.getenv("CUDA_VISIBLE_DEVICES").count(",") + 1)
-            which_gpus = os.getenv("CUDA_VISIBLE_DEVICES").split(",")
-            which_gpus = [int(x) for x in which_gpus]
-    else:
-        which_gpus = list(range(0, total_gpus))
-
-    return total_gpus, which_gpus
-
-
-def get_ngpus_vis(raise_if_exception=True):
-    ngpus_vis1 = 0
-
-    shell = False
-    if shell:
-        cmd = "nvidia-smi -L 2> /dev/null"
-    else:
-        cmd = ["nvidia-smi", "-L"]
-
-    try:
-        timeout = 5 * 3
-        o = subprocess.check_output(cmd, shell=shell, timeout=timeout)
-        lines = o.decode("utf-8").splitlines()
-        ngpus_vis1 = 0
-        for line in lines:
-            if 'Failed to initialize NVML' not in line:
-                ngpus_vis1 += 1
-    except (FileNotFoundError, subprocess.CalledProcessError, OSError):
-        # GPU systems might not have nvidia-smi, so can't fail
-        pass
-    except subprocess.TimeoutExpired as e:
-        print('Failed get_ngpus_vis: %s' % str(e))
-        if raise_if_exception:
-            raise
-
-    ngpus_vis1, which_gpus = cuda_vis_check(ngpus_vis1)
-    return ngpus_vis1
-
-
-def get_mem_gpus(raise_if_exception=True, ngpus=None):
-    totalmem_gpus1 = 0
-    usedmem_gpus1 = 0
-    freemem_gpus1 = 0
-
-    if ngpus == 0:
-        return totalmem_gpus1, usedmem_gpus1, freemem_gpus1
-
-    try:
-        cmd = "nvidia-smi -q 2> /dev/null | grep -A 3 'FB Memory Usage'"
-        o = subprocess.check_output(cmd, shell=True, timeout=15)
-        lines = o.decode("utf-8").splitlines()
-        for line in lines:
-            if 'Total' in line:
-                totalmem_gpus1 += int(line.split()[2]) * 1024 ** 2
-            if 'Used' in line:
-                usedmem_gpus1 += int(line.split()[2]) * 1024 ** 2
-            if 'Free' in line:
-                freemem_gpus1 += int(line.split()[2]) * 1024 ** 2
-    except (FileNotFoundError, subprocess.CalledProcessError, OSError):
-        # GPU systems might not have nvidia-smi, so can't fail
-        pass
-    except subprocess.TimeoutExpired as e:
-        print('Failed get_mem_gpus: %s' % str(e))
-        if raise_if_exception:
-            raise
-
-    return totalmem_gpus1, usedmem_gpus1, freemem_gpus1
-
-
-class ForkContext(threading.local):
-    """
-        Set context for forking
-        Ensures state is returned once done
-    """
-
-    def __init__(self, args=None, kwargs=None, forkdata_capable=True):
-        """
-        :param args:
-        :param kwargs:
-        :param forkdata_capable: whether fork is forkdata capable and will use copy-on-write forking of args/kwargs
-        """
-        self.forkdata_capable = forkdata_capable
-        if self.forkdata_capable:
-            self.has_args = args is not None
-            self.has_kwargs = kwargs is not None
-            forkdatacontext.args = args
-            forkdatacontext.kwargs = kwargs
-        else:
-            self.has_args = False
-            self.has_kwargs = False
-
-    def __enter__(self):
-        try:
-            # flush all outputs so doesn't happen during fork -- don't print/log inside ForkContext contexts!
-            sys.stdout.flush()
-            sys.stderr.flush()
-        except BaseException as e:
-            # exit not called if exception, and don't want to leave forkdatacontext filled in that case
-            print("ForkContext failure on enter: %s" % str(e))
-            self.finally_act()
-            raise
-        return self
-
-    def __exit__(self, exc_type, exc_value, exc_traceback):
-        self.finally_act()
-
-    def finally_act(self):
-        """
-            Done when exception hit or exit is reached in context
-            first reset forkdatacontext as crucial to have reset even if later 2 calls fail
-        :return: None
-        """
-        if self.forkdata_capable and (self.has_args or self.has_kwargs):
-            forkdatacontext._reset()
-
-
-class _ForkDataContext(threading.local):
-    def __init__(
-            self,
-            args=None,
-            kwargs=None,
-    ):
-        """
-        Global context for fork to carry data to subprocess instead of relying upon copy/pickle/serialization
-
-        :param args: args
-        :param kwargs: kwargs
-        """
-        assert isinstance(args, (tuple, type(None)))
-        assert isinstance(kwargs, (dict, type(None)))
-        self.__args = args
-        self.__kwargs = kwargs
-
-    @property
-    def args(self) -> Tuple:
-        """returns args"""
-        return self.__args
-
-    @args.setter
-    def args(self, args):
-        if self.__args is not None:
-            raise AttributeError(
-                "args cannot be overwritten: %s %s" % (str(self.__args), str(self.__kwargs))
-            )
-
-        self.__args = args
-
-    @property
-    def kwargs(self) -> Dict:
-        """returns kwargs"""
-        return self.__kwargs
-
-    @kwargs.setter
-    def kwargs(self, kwargs):
-        if self.__kwargs is not None:
-            raise AttributeError(
-                "kwargs cannot be overwritten: %s %s" % (str(self.__args), str(self.__kwargs))
-            )
-
-        self.__kwargs = kwargs
-
-    def _reset(self):
-        """Reset fork arg-kwarg context to default values"""
-        self.__args = None
-        self.__kwargs = None
-
-    def get_args_kwargs(self, func, args, kwargs) -> Tuple[Callable, Tuple, Dict]:
-        if self.__args:
-            args = self.__args[1:]
-            if not func:
-                assert len(self.__args) > 0, "if have no func, must have in args"
-                func = self.__args[0]  # should always be there
-        if self.__kwargs:
-            kwargs = self.__kwargs
-        try:
-            return func, args, kwargs
-        finally:
-            forkdatacontext._reset()
-
-    @staticmethod
-    def get_args_kwargs_for_traced_func(func, args, kwargs):
-        """
-        Return args/kwargs out of forkdatacontext when using copy-on-write way of passing args/kwargs
-        :param func: actual function ran by _traced_func, which itself is directly what mppool treats as function
-        :param args:
-        :param kwargs:
-        :return: func, args, kwargs from forkdatacontext if used, else originals
-        """
-        # first 3 lines are debug
-        func_was_None = func is None
-        args_was_None_or_empty = args is None or len(args) == 0
-        kwargs_was_None_or_empty = kwargs is None or len(kwargs) == 0
-
-        forkdatacontext_args_was_None = forkdatacontext.args is None
-        forkdatacontext_kwargs_was_None = forkdatacontext.kwargs is None
-        func, args, kwargs = forkdatacontext.get_args_kwargs(func, args, kwargs)
-        using_forkdatacontext = func_was_None and func is not None  # pulled func out of forkdatacontext.__args[0]
-        assert forkdatacontext.args is None, "forkdatacontext.args should be None after get_args_kwargs"
-        assert forkdatacontext.kwargs is None, "forkdatacontext.kwargs should be None after get_args_kwargs"
-
-        proc_type = kwargs.get('proc_type', 'SUBPROCESS')
-        if using_forkdatacontext:
-            assert proc_type == "SUBPROCESS" or proc_type == "SUBPROCESS"
-        if proc_type == "NORMAL":
-            assert forkdatacontext_args_was_None, "if no fork, expect forkdatacontext.args None entering _traced_func"
-            assert forkdatacontext_kwargs_was_None, "if no fork, expect forkdatacontext.kwargs None entering _traced_func"
-        assert func is not None, "function should not be None, indicates original args[0] was None or args was None"
-
-        return func, args, kwargs
-
-
-forkdatacontext = _ForkDataContext()
-
-
-# Add user info
-username = getpass.getuser()
-current_working_directory = os.getcwd()
-operating_system = platform.system()
-
-
-def _traced_func(func, *args, **kwargs):
-    func, args, kwargs = forkdatacontext.get_args_kwargs_for_traced_func(func, args, kwargs)
-    return func(*args, **kwargs)
-
-
-def call_subprocess_onetask(func, args=None, kwargs=None):
-    if platform.system() in ['Darwin', 'Windows']:
-        return func(*args, **kwargs)
-    if isinstance(args, list):
-        args = tuple(args)
-    if args is None:
-        args = ()
-    if kwargs is None:
-        kwargs = {}
-    args = list(args)
-    args = [func] + args
-    args = tuple(args)
-    with ForkContext(args=args, kwargs=kwargs):
-        args = (None,)
-        kwargs = {}
-        with ProcessPoolExecutor(max_workers=1) as executor:
-            future = executor.submit(_traced_func, *args, **kwargs)
-            return future.result()
-
-
-class ProgressParallel(Parallel):
-    def __init__(self, use_tqdm=True, total=None, *args, **kwargs):
-        self._use_tqdm = use_tqdm
-        self._total = total
-        super().__init__(*args, **kwargs)
-
-    def __call__(self, *args, **kwargs):
-        with tqdm(disable=not self._use_tqdm, total=self._total) as self._pbar:
-            return Parallel.__call__(self, *args, **kwargs)
-
-    def print_progress(self):
-        if self._total is None:
-            self._pbar.total = self.n_dispatched_tasks
-        self._pbar.n = self.n_completed_tasks
-        self._pbar.refresh()
-
-
-def get_kwargs(func, exclude_names=None, **kwargs):
-    func_names = list(inspect.signature(func).parameters)
-    missing_kwargs = [x for x in func_names if x not in kwargs]
-    if exclude_names:
-        for k in exclude_names:
-            if k in missing_kwargs:
-                missing_kwargs.remove(k)
-            if k in func_names:
-                func_names.remove(k)
-    assert not missing_kwargs, "Missing %s" % missing_kwargs
-    kwargs = {k: v for k, v in kwargs.items() if k in func_names}
-    return kwargs
-
-
-from importlib.metadata import distribution, PackageNotFoundError
-
-have_faiss = False
-
-try:
-    assert distribution('faiss') is not None
-    have_faiss = True
-except (PackageNotFoundError, AssertionError):
-    pass
-try:
-    assert distribution('faiss_gpu') is not None
-    have_faiss = True
-except (PackageNotFoundError, AssertionError):
-    pass
-try:
-    assert distribution('faiss_cpu') is not None
-    have_faiss = True
-except (PackageNotFoundError, AssertionError):
-    pass
-
-have_chromamigdb = False
-try:
-    assert distribution('chromamigdb') is not None
-    have_chromamigdb = True
-except (PackageNotFoundError, AssertionError):
-    pass
-
-
-have_serpapi = False
-try:
-    assert distribution('google-search-results') is not None
-    have_serpapi = True
-except (PackageNotFoundError, AssertionError):
-    pass
-
-
-def hash_file(file):
-    try:
-        import hashlib
-
-        # BUF_SIZE is totally arbitrary, change for your app!
-        BUF_SIZE = 65536  # lets read stuff in 64kb chunks!
-
-        md5 = hashlib.md5()
-        # sha1 = hashlib.sha1()
-
-        with open(file, 'rb') as f:
-            while True:
-                data = f.read(BUF_SIZE)
-                if not data:
-                    break
-                md5.update(data)
-                # sha1.update(data)
-    except BaseException as e:
-        print("Cannot hash %s due to %s" % (file, str(e)))
-        traceback.print_exc()
-        return ''
-    return md5.hexdigest()
-
-
-def start_faulthandler():
-    # If hit server or any subprocess with signal SIGUSR1, it'll print out all threads stack trace, but wont't quit or coredump
-    # If more than one fork tries to write at same time, then looks corrupted.
-    import faulthandler
-
-    # SIGUSR1 in h2oai/__init__.py as well
-    faulthandler.enable()
-    if hasattr(faulthandler, 'register'):
-        # windows/mac
-        import signal
-        faulthandler.register(signal.SIGUSR1)
-
-
-def get_hf_server(inference_server):
-    inf_split = inference_server.split("    ")
-    assert len(inf_split) == 1 or len(inf_split) == 3
-    inference_server = inf_split[0]
-    if len(inf_split) == 3:
-        headers = {"authorization": "%s %s" % (inf_split[1], inf_split[2])}
-    else:
-        headers = None
-    return inference_server, headers
-
-
-class FakeTokenizer:
-    """
-    1) For keeping track of model_max_length
-    2) For when model doesn't directly expose tokenizer but need to count tokens
-    """
-
-    def __init__(self, model_max_length=2048, encoding_name="cl100k_base"):
-        # dont' push limit, since if using fake tokenizer, only estimate, and seen underestimates by order 250
-        self.model_max_length = model_max_length - 250
-        self.encoding_name = encoding_name
-        # The first time this runs, it will require an internet connection to download. Later runs won't need an internet connection.
-        import tiktoken
-        self.encoding = tiktoken.get_encoding(self.encoding_name)
-
-    def encode(self, x, *args, return_tensors="pt", **kwargs):
-        input_ids = self.encoding.encode(x, disallowed_special=())
-        if return_tensors == 'pt' and isinstance(input_ids, list):
-            import torch
-            input_ids = torch.tensor(input_ids)
-        return dict(input_ids=input_ids)
-
-    def decode(self, x, *args, **kwargs):
-        # input is input_ids[0] form
-        return self.encoding.decode(x)
-
-    def num_tokens_from_string(self, prompt: str) -> int:
-        """Returns the number of tokens in a text string."""
-        num_tokens = len(self.encode(prompt)['input_ids'])
-        return num_tokens
-
-    def __call__(self, x, *args, **kwargs):
-        return self.encode(x, *args, **kwargs)
-
-
-def get_local_ip():
-    import socket
-    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
-    try:
-        # doesn't even have to be reachable
-        s.connect(('10.255.255.255', 1))
-        IP = s.getsockname()[0]
-    except Exception:
-        IP = '127.0.0.1'
-    finally:
-        s.close()
-    return IP
-
-
-try:
-    assert distribution('langchain') is not None
-    have_langchain = True
-except (PackageNotFoundError, AssertionError):
-    have_langchain = False
-
-import distutils.spawn
-
-have_tesseract = distutils.spawn.find_executable("tesseract")
-have_libreoffice = distutils.spawn.find_executable("libreoffice")
-try:
-    from weasyprint import HTML
-    import doctr
-    have_doctr = True
-except:
-    have_doctr = False
-
-try:
-    assert distribution('arxiv') is not None
-    assert distribution('pymupdf') is not None
-    have_arxiv = True
-except (PackageNotFoundError, AssertionError):
-    have_arxiv = False
-
-try:
-    assert distribution('pymupdf') is not None
-    have_pymupdf = True
-except (PackageNotFoundError, AssertionError):
-    have_pymupdf = False
-
-try:
-    assert distribution('selenium') is not None
-    have_selenium = True
-except (PackageNotFoundError, AssertionError):
-    have_selenium = False
-
-try:
-    assert distribution('pillow') is not None
-    have_pillow = True
-except (PackageNotFoundError, AssertionError):
-    have_pillow = False
-
-try:
-    assert distribution('playwright') is not None
-    have_playwright = True
-except (PackageNotFoundError, AssertionError):
-    have_playwright = False
-
-try:
-    assert distribution('jq') is not None
-    have_jq = True
-except (PackageNotFoundError, AssertionError):
-    have_jq = False
-
-only_unstructured_urls = os.environ.get("ONLY_UNSTRUCTURED_URLS", "0") == "1"
-only_selenium = os.environ.get("ONLY_SELENIUM", "0") == "1"
-only_playwright = os.environ.get("ONLY_PLAYWRIGHT", "0") == "1"
-
-
-def set_openai(inference_server):
-    if inference_server.startswith('vllm'):
-        import openai_vllm
-        openai_vllm.api_key = "EMPTY"
-        inf_type = inference_server.split(':')[0]
-        ip_vllm = inference_server.split(':')[1]
-        port_vllm = inference_server.split(':')[2]
-        openai_vllm.api_base = f"http://{ip_vllm}:{port_vllm}/v1"
-        return openai_vllm, inf_type, None, None, None
-    else:
-        import openai
-        openai.api_key = os.getenv("OPENAI_API_KEY")
-        openai.api_base = os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1")
-
-        base_url = None
-        deployment_type = None
-        api_version = None
-        inf_type = inference_server.split(':')[0]
-        if len(inference_server.split(':')) >= 2:
-            deployment_type = inference_server.split(':')[1]
-        if len(inference_server.split(':')) >= 3:
-            base_url = inference_server.split(':')[2]
-            base_url = 'https://' + base_url
-        if len(inference_server.split(':')) >= 4:
-            api_version = inference_server.split(':')[3]
-
-        if deployment_type == 'None':
-            deployment_type = None
-        if base_url == 'None':
-            base_url = None
-        if base_url == 'None':
-            base_url = None
-        return openai, inf_type, deployment_type, base_url, api_version
-
-
-def get_list_or_str(x):
-    if isinstance(x, list):
-        return x
-    elif isinstance(x, str):
-        try:
-            x1 = ast.literal_eval(x)
-            assert isinstance(x1, list)
-            return x1
-        except:
-            return x
-    else:
-        return x
-
-
-def deepcopy_by_pickle_object(object):
-    """
-    Faster deepcopy, can only work on things that are picklable.  Naive Deepcopy is more general.
-    Same method as for class Individual
-    :param object:
-    :return:
-    """
-    gc.disable()
-    new_object = pickle.loads(pickle.dumps(object, -1))
-    gc.enable()
-    return new_object
-
-
-def url_alive(url):
-    try:
-        response = requests.head(url)
-    except Exception as e:
-        return False
-    else:
-        if response.status_code in [200, 301, 302]:
-            return True
-        else:
-            return False
-
-
-def dict_to_html(x, small=True, api=False):
-    df = pd.DataFrame(x.items(), columns=['Key', 'Value'])
-    df.index = df.index + 1
-    df.index.name = 'index'
-    if api:
-        return tabulate.tabulate(df, headers='keys')
-    else:
-        res = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml')
-        if small:
-            return "<small>" + res + "</small>"
-        else:
-            return res
-
-
-def text_to_html(x, api=False):
-    if api:
-        return x
-    return """
-<style>
-      pre {
-        overflow-x: auto;
-        white-space: pre-wrap;
-        white-space: -moz-pre-wrap;
-        white-space: -pre-wrap;
-        white-space: -o-pre-wrap;
-        word-wrap: break-word;
-      }
-    </style>
-<pre>
-%s
-</pre>
-""" % x
-
-
-def lg_to_gr(
-        **kwargs,
-):
-    # translate:
-    import torch
-    n_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 0
-    n_gpus, _ = cuda_vis_check(n_gpus)
-
-    image_loaders_options = ['Caption']
-    if n_gpus != 0:
-        image_loaders_options.extend(['CaptionBlip2', 'Pix2Struct'])
-    if have_tesseract:
-        image_loaders_options.append('OCR')
-    if have_doctr:
-        image_loaders_options.append('DocTR')
-
-    image_loaders_options0 = []
-    if have_tesseract and kwargs['enable_ocr']:
-        image_loaders_options0.append('OCR')
-    if have_doctr and kwargs['enable_doctr']:
-        image_loaders_options0.append('DocTR')
-    if kwargs['enable_captions']:
-        if kwargs['max_quality'] and n_gpus > 0:
-            # BLIP2 only on GPU
-            image_loaders_options0.append('CaptionBlip2')
-        else:
-            image_loaders_options0.append('Caption')
-
-    pdf_loaders_options = ['PyMuPDF', 'Unstructured', 'PyPDF', 'TryHTML']
-    if have_tesseract:
-        pdf_loaders_options.append('OCR')
-    if have_doctr:
-        pdf_loaders_options.append('DocTR')
-
-    pdf_loaders_options0 = []
-    if kwargs['use_pymupdf'] in [True, 'auto', 'on']:
-        pdf_loaders_options0.append('PyMuPDF')
-    if kwargs['enable_pdf_ocr'] in [True, 'on']:
-        pdf_loaders_options0.append('OCR')
-    if have_doctr and kwargs['enable_pdf_doctr'] in [True, 'on']:
-        pdf_loaders_options0.append('DocTR')
-
-    url_loaders_options = []
-    if only_unstructured_urls:
-        url_loaders_options.append('Unstructured')
-    elif have_selenium and only_selenium:
-        url_loaders_options.append('Selenium')
-    elif have_playwright and only_playwright:
-        url_loaders_options.append('PlayWright')
-    else:
-        url_loaders_options.append('Unstructured')
-        if have_selenium:
-            url_loaders_options.append('Selenium')
-        if have_playwright:
-            url_loaders_options.append('PlayWright')
-    url_loaders_options0 = [url_loaders_options[0]]
-    
-    assert set(image_loaders_options0).issubset(image_loaders_options)
-    assert set(pdf_loaders_options0).issubset(pdf_loaders_options)
-    assert set(url_loaders_options0).issubset(url_loaders_options)
-
-    return image_loaders_options0, image_loaders_options, \
-        pdf_loaders_options0, pdf_loaders_options, \
-        url_loaders_options0, url_loaders_options
-
-
-def fix_json(s):
-
-    # Attempt to parse the string as-is.
-    try:
-        return json.loads(s)
-    except json.JSONDecodeError:
-        pass
-
-    # Initialize variables.
-    new_s = ""
-    stack = []
-    is_inside_string = False
-    escaped = False
-
-    # Process each character in the string one at a time.
-    for char in s:
-        if is_inside_string:
-            if char == '"' and not escaped:
-                is_inside_string = False
-            elif char == '\n' and not escaped:
-                char = '\\n' # Replace the newline character with the escape sequence.
-            elif char == '\\':
-                escaped = not escaped
-            else:
-                escaped = False
-        else:
-            if char == '"':
-                is_inside_string = True
-                escaped = False
-            elif char == '{':
-                stack.append('}')
-            elif char == '[':
-                stack.append(']')
-            elif char == '}' or char == ']':
-                if stack and stack[-1] == char:
-                    stack.pop()
-                else:
-                    # Mismatched closing character; the input is malformed.
-                    return None
-
-        # Append the processed character to the new string.
-        new_s += char
-
-    # If we're still inside a string at the end of processing, we need to close the string.
-    if is_inside_string:
-        new_s += '"'
-
-    # Close any remaining open structures in the reverse order that they were opened.
-    for closing_char in reversed(stack):
-        new_s += closing_char
-
-    # Attempt to parse the modified string as JSON.
-    try:
-        return json.loads(new_s)
-    except json.JSONDecodeError:
-        # If we still can't parse the string as JSON, return None to indicate failure.
-        return None
-
-
-def wrap_in_try_except(code):
-    # Add import traceback
-    code = "import traceback\n" + code
-
-    # Parse the input code into an AST
-    parsed_code = ast.parse(code)
-
-    # Wrap the entire code's AST in a single try-except block
-    try_except = ast.Try(
-        body=parsed_code.body,
-        handlers=[
-            ast.ExceptHandler(
-                type=ast.Name(id="Exception", ctx=ast.Load()),
-                name=None,
-                body=[
-                    ast.Expr(
-                        value=ast.Call(
-                            func=ast.Attribute(value=ast.Name(id="traceback", ctx=ast.Load()), attr="print_exc", ctx=ast.Load()),
-                            args=[],
-                            keywords=[]
-                        )
-                    ),
-                ]
-            )
-        ],
-        orelse=[],
-        finalbody=[]
-    )
-
-    # Assign the try-except block as the new body
-    parsed_code.body = [try_except]
-
-    # Convert the modified AST back to source code
-    return ast.unparse(parsed_code)
-
-
-def enqueue_output(file, queue):
-    for line in iter(file.readline, ''):
-        queue.put(line)
-    file.close()
-
-
-def read_popen_pipes(p):
-
-    with ThreadPoolExecutor(2) as pool:
-        q_stdout, q_stderr = Queue(), Queue()
-
-        pool.submit(enqueue_output, p.stdout, q_stdout)
-        pool.submit(enqueue_output, p.stderr, q_stderr)
-
-        while True:
-
-            if p.poll() is not None and q_stdout.empty() and q_stderr.empty():
-                break
-
-            out_line = err_line = ''
-
-            try:
-                out_line = q_stdout.get_nowait()
-            except Empty:
-                pass
-            try:
-                err_line = q_stderr.get_nowait()
-            except Empty:
-                pass
-
-            yield out_line, err_line
-
-
-def start_process(cmd):
-    start_cmd = sys.executable + " -i -q -u"
-    print_cmd = 'print("{}")'
-    cmd = [start_cmd] + [cmd]
-
-    process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
-    for c in iter(lambda: process.stdout.read(1), b''):
-        sys.stdout.write(c)
-
-
-def str_to_list(x, allow_none=False):
-    if isinstance(x, str):
-        if len(x.strip()) > 0:
-            if x.strip().startswith('['):
-                x = ast.literal_eval(x.strip())
-            else:
-                raise ValueError("Invalid str_to_list for %s" % x)
-        else:
-            x = []
-    elif x is None and not allow_none:
-        x = []
-    if allow_none:
-        assert isinstance(x, (type(None), list))
-    else:
-        assert isinstance(x, list)
-    return x
-
-
-def str_to_dict(x):
-    if isinstance(x, str):
-        if len(x.strip()) > 0:
-            if x.strip().startswith('{'):
-                x = ast.literal_eval(x.strip())
-            else:
-                raise ValueError("Invalid str_to_dict for %s" % x)
-        else:
-            x = {}
-    elif x is None:
-        x = {}
-    assert isinstance(x, dict)
-    return x
-
-
-def get_token_count(x, tokenizer, token_count_fun=None):
-    # NOTE: Somewhat duplicates H2OTextGenerationPipeline.get_token_count()
-    # handle ambiguity in if get dict or list
-    if tokenizer:
-        if hasattr(tokenizer, 'encode'):
-            template_tokens = tokenizer.encode(x)
-        else:
-            template_tokens = tokenizer(x)
-        if isinstance(template_tokens, dict) and 'input_ids' in template_tokens:
-            n_tokens = len(tokenizer.encode(x)['input_ids'])
-        else:
-            n_tokens = len(tokenizer.encode(x))
-    elif token_count_fun is not None:
-        assert callable(token_count_fun)
-        n_tokens = token_count_fun(x)
-    else:
-        tokenizer = FakeTokenizer()
-        n_tokens = tokenizer.num_tokens_from_string(x)
-    return n_tokens
-
-
-def reverse_ucurve_list(lst):
-    if not lst:
-        return []
-    if len(lst) == 1:
-        return lst
-    if len(lst) == 2:
-        return [lst[1], lst[0]]
-
-    front_list = []
-    end_list = []
-
-    for i, item in enumerate(lst):
-        if i % 2 == 0:
-            end_list.append(item)
-        else:
-            front_list.append(item)
-
-    return front_list + end_list[::-1]
-
-
-def undo_reverse_ucurve_list(lst):
-    if not lst:
-        return []
-    if len(lst) == 1:
-        return lst
-    if len(lst) == 2:
-        return [lst[1], lst[0]]
-
-    # Split the list into two halves: the first half and the second half (reversed)
-    mid = len(lst) // 2
-    first_half = lst[:mid]
-    second_half = lst[mid:][::-1]
-
-    # Merge the two halves by taking elements alternatively from the second half and then the first half
-    result = []
-    for i in range(mid):
-        result.append(second_half[i])
-        result.append(first_half[i])
-
-    # If the length of the list is odd, append the last element of the second half
-    if len(lst) % 2 != 0:
-        result.append(second_half[-1])
-
-    return result
diff --git a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/models/diffusion/classifier.py b/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/models/diffusion/classifier.py
deleted file mode 100644
index 67e98b9d8ffb96a150b517497ace0a242d7163ef..0000000000000000000000000000000000000000
--- a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/models/diffusion/classifier.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import os
-import torch
-import pytorch_lightning as pl
-from omegaconf import OmegaConf
-from torch.nn import functional as F
-from torch.optim import AdamW
-from torch.optim.lr_scheduler import LambdaLR
-from copy import deepcopy
-from einops import rearrange
-from glob import glob
-from natsort import natsorted
-
-from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
-from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
-
-__models__ = {
-    'class_label': EncoderUNetModel,
-    'segmentation': UNetModel
-}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-class NoisyLatentImageClassifier(pl.LightningModule):
-
-    def __init__(self,
-                 diffusion_path,
-                 num_classes,
-                 ckpt_path=None,
-                 pool='attention',
-                 label_key=None,
-                 diffusion_ckpt_path=None,
-                 scheduler_config=None,
-                 weight_decay=1.e-2,
-                 log_steps=10,
-                 monitor='val/loss',
-                 *args,
-                 **kwargs):
-        super().__init__(*args, **kwargs)
-        self.num_classes = num_classes
-        # get latest config of diffusion model
-        diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
-        self.diffusion_config = OmegaConf.load(diffusion_config).model
-        self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
-        self.load_diffusion()
-
-        self.monitor = monitor
-        self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
-        self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
-        self.log_steps = log_steps
-
-        self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
-            else self.diffusion_model.cond_stage_key
-
-        assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
-
-        if self.label_key not in __models__:
-            raise NotImplementedError()
-
-        self.load_classifier(ckpt_path, pool)
-
-        self.scheduler_config = scheduler_config
-        self.use_scheduler = self.scheduler_config is not None
-        self.weight_decay = weight_decay
-
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
-
-    def load_diffusion(self):
-        model = instantiate_from_config(self.diffusion_config)
-        self.diffusion_model = model.eval()
-        self.diffusion_model.train = disabled_train
-        for param in self.diffusion_model.parameters():
-            param.requires_grad = False
-
-    def load_classifier(self, ckpt_path, pool):
-        model_config = deepcopy(self.diffusion_config.params.unet_config.params)
-        model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
-        model_config.out_channels = self.num_classes
-        if self.label_key == 'class_label':
-            model_config.pool = pool
-
-        self.model = __models__[self.label_key](**model_config)
-        if ckpt_path is not None:
-            print('#####################################################################')
-            print(f'load from ckpt "{ckpt_path}"')
-            print('#####################################################################')
-            self.init_from_ckpt(ckpt_path)
-
-    @torch.no_grad()
-    def get_x_noisy(self, x, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x))
-        continuous_sqrt_alpha_cumprod = None
-        if self.diffusion_model.use_continuous_noise:
-            continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
-            # todo: make sure t+1 is correct here
-
-        return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
-                                             continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
-
-    def forward(self, x_noisy, t, *args, **kwargs):
-        return self.model(x_noisy, t)
-
-    @torch.no_grad()
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = rearrange(x, 'b h w c -> b c h w')
-        x = x.to(memory_format=torch.contiguous_format).float()
-        return x
-
-    @torch.no_grad()
-    def get_conditioning(self, batch, k=None):
-        if k is None:
-            k = self.label_key
-        assert k is not None, 'Needs to provide label key'
-
-        targets = batch[k].to(self.device)
-
-        if self.label_key == 'segmentation':
-            targets = rearrange(targets, 'b h w c -> b c h w')
-            for down in range(self.numd):
-                h, w = targets.shape[-2:]
-                targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
-
-            # targets = rearrange(targets,'b c h w -> b h w c')
-
-        return targets
-
-    def compute_top_k(self, logits, labels, k, reduction="mean"):
-        _, top_ks = torch.topk(logits, k, dim=1)
-        if reduction == "mean":
-            return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
-        elif reduction == "none":
-            return (top_ks == labels[:, None]).float().sum(dim=-1)
-
-    def on_train_epoch_start(self):
-        # save some memory
-        self.diffusion_model.model.to('cpu')
-
-    @torch.no_grad()
-    def write_logs(self, loss, logits, targets):
-        log_prefix = 'train' if self.training else 'val'
-        log = {}
-        log[f"{log_prefix}/loss"] = loss.mean()
-        log[f"{log_prefix}/acc@1"] = self.compute_top_k(
-            logits, targets, k=1, reduction="mean"
-        )
-        log[f"{log_prefix}/acc@5"] = self.compute_top_k(
-            logits, targets, k=5, reduction="mean"
-        )
-
-        self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
-        self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
-        self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
-        lr = self.optimizers().param_groups[0]['lr']
-        self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
-
-    def shared_step(self, batch, t=None):
-        x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
-        targets = self.get_conditioning(batch)
-        if targets.dim() == 4:
-            targets = targets.argmax(dim=1)
-        if t is None:
-            t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
-        else:
-            t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
-        x_noisy = self.get_x_noisy(x, t)
-        logits = self(x_noisy, t)
-
-        loss = F.cross_entropy(logits, targets, reduction='none')
-
-        self.write_logs(loss.detach(), logits.detach(), targets.detach())
-
-        loss = loss.mean()
-        return loss, logits, x_noisy, targets
-
-    def training_step(self, batch, batch_idx):
-        loss, *_ = self.shared_step(batch)
-        return loss
-
-    def reset_noise_accs(self):
-        self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
-                          range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
-
-    def on_validation_start(self):
-        self.reset_noise_accs()
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        loss, *_ = self.shared_step(batch)
-
-        for t in self.noisy_acc:
-            _, logits, _, targets = self.shared_step(batch, t)
-            self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
-            self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
-
-        return loss
-
-    def configure_optimizers(self):
-        optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
-
-        if self.use_scheduler:
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [optimizer], scheduler
-
-        return optimizer
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, *args, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.diffusion_model.first_stage_key)
-        log['inputs'] = x
-
-        y = self.get_conditioning(batch)
-
-        if self.label_key == 'class_label':
-            y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
-            log['labels'] = y
-
-        if ismap(y):
-            log['labels'] = self.diffusion_model.to_rgb(y)
-
-            for step in range(self.log_steps):
-                current_time = step * self.log_time_interval
-
-                _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)
-
-                log[f'inputs@t{current_time}'] = x_noisy
-
-                pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
-                pred = rearrange(pred, 'b h w c -> b c h w')
-
-                log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
-
-        for key in log:
-            log[key] = log[key][:N]
-
-        return log
diff --git a/spaces/avivdm1/AutoGPT/autogpt/memory/redismem.py b/spaces/avivdm1/AutoGPT/autogpt/memory/redismem.py
deleted file mode 100644
index 082a812c5362cc9f19e35bf1bb10269b558f7724..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/autogpt/memory/redismem.py
+++ /dev/null
@@ -1,156 +0,0 @@
-"""Redis memory provider."""
-from __future__ import annotations
-
-from typing import Any
-
-import numpy as np
-import redis
-from colorama import Fore, Style
-from redis.commands.search.field import TextField, VectorField
-from redis.commands.search.indexDefinition import IndexDefinition, IndexType
-from redis.commands.search.query import Query
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.logs import logger
-from autogpt.memory.base import MemoryProviderSingleton
-
-SCHEMA = [
-    TextField("data"),
-    VectorField(
-        "embedding",
-        "HNSW",
-        {"TYPE": "FLOAT32", "DIM": 1536, "DISTANCE_METRIC": "COSINE"},
-    ),
-]
-
-
-class RedisMemory(MemoryProviderSingleton):
-    def __init__(self, cfg):
-        """
-        Initializes the Redis memory provider.
-
-        Args:
-            cfg: The config object.
-
-        Returns: None
-        """
-        redis_host = cfg.redis_host
-        redis_port = cfg.redis_port
-        redis_password = cfg.redis_password
-        self.dimension = 1536
-        self.redis = redis.Redis(
-            host=redis_host,
-            port=redis_port,
-            password=redis_password,
-            db=0,  # Cannot be changed
-        )
-        self.cfg = cfg
-
-        # Check redis connection
-        try:
-            self.redis.ping()
-        except redis.ConnectionError as e:
-            logger.typewriter_log(
-                "FAILED TO CONNECT TO REDIS",
-                Fore.RED,
-                Style.BRIGHT + str(e) + Style.RESET_ALL,
-            )
-            logger.double_check(
-                "Please ensure you have setup and configured Redis properly for use. "
-                + f"You can check out {Fore.CYAN + Style.BRIGHT}"
-                f"https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL}"
-                " to ensure you've set up everything correctly."
-            )
-            exit(1)
-
-        if cfg.wipe_redis_on_start:
-            self.redis.flushall()
-        try:
-            self.redis.ft(f"{cfg.memory_index}").create_index(
-                fields=SCHEMA,
-                definition=IndexDefinition(
-                    prefix=[f"{cfg.memory_index}:"], index_type=IndexType.HASH
-                ),
-            )
-        except Exception as e:
-            print("Error creating Redis search index: ", e)
-        existing_vec_num = self.redis.get(f"{cfg.memory_index}-vec_num")
-        self.vec_num = int(existing_vec_num.decode("utf-8")) if existing_vec_num else 0
-
-    def add(self, data: str) -> str:
-        """
-        Adds a data point to the memory.
-
-        Args:
-            data: The data to add.
-
-        Returns: Message indicating that the data has been added.
-        """
-        if "Command Error:" in data:
-            return ""
-        vector = create_embedding_with_ada(data)
-        vector = np.array(vector).astype(np.float32).tobytes()
-        data_dict = {b"data": data, "embedding": vector}
-        pipe = self.redis.pipeline()
-        pipe.hset(f"{self.cfg.memory_index}:{self.vec_num}", mapping=data_dict)
-        _text = (
-            f"Inserting data into memory at index: {self.vec_num}:\n" f"data: {data}"
-        )
-        self.vec_num += 1
-        pipe.set(f"{self.cfg.memory_index}-vec_num", self.vec_num)
-        pipe.execute()
-        return _text
-
-    def get(self, data: str) -> list[Any] | None:
-        """
-        Gets the data from the memory that is most relevant to the given data.
-
-        Args:
-            data: The data to compare to.
-
-        Returns: The most relevant data.
-        """
-        return self.get_relevant(data, 1)
-
-    def clear(self) -> str:
-        """
-        Clears the redis server.
-
-        Returns: A message indicating that the memory has been cleared.
-        """
-        self.redis.flushall()
-        return "Obliviated"
-
-    def get_relevant(self, data: str, num_relevant: int = 5) -> list[Any] | None:
-        """
-        Returns all the data in the memory that is relevant to the given data.
-        Args:
-            data: The data to compare to.
-            num_relevant: The number of relevant data to return.
-
-        Returns: A list of the most relevant data.
-        """
-        query_embedding = create_embedding_with_ada(data)
-        base_query = f"*=>[KNN {num_relevant} @embedding $vector AS vector_score]"
-        query = (
-            Query(base_query)
-            .return_fields("data", "vector_score")
-            .sort_by("vector_score")
-            .dialect(2)
-        )
-        query_vector = np.array(query_embedding).astype(np.float32).tobytes()
-
-        try:
-            results = self.redis.ft(f"{self.cfg.memory_index}").search(
-                query, query_params={"vector": query_vector}
-            )
-        except Exception as e:
-            print("Error calling Redis search: ", e)
-            return None
-        return [result.data for result in results.docs]
-
-    def get_stats(self):
-        """
-        Returns: The stats of the memory index.
-        """
-        return self.redis.ft(f"{self.cfg.memory_index}").info()
diff --git a/spaces/awacke1/Word.Search.Experiments/app.py b/spaces/awacke1/Word.Search.Experiments/app.py
deleted file mode 100644
index 802b6ac4236eac0eecbaadc1dfa1c4f85a1aea7e..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Word.Search.Experiments/app.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import streamlit as st
-import random
-import string
-
-# Function to generate a random grid
-def generate_grid(size, words):
-    grid = [[' ' for _ in range(size)] for _ in range(size)]
-
-    for word in words:
-        placed = False
-
-        while not placed:
-            direction = random.choice(['horizontal', 'vertical'])
-            if direction == 'horizontal':
-                row = random.randint(0, size - 1)
-                start_col = random.randint(0, size - len(word))
-
-                for col in range(start_col, start_col + len(word)):
-                    if grid[row][col] != ' ' and grid[row][col] != word[col - start_col]:
-                        break
-                else:
-                    for col in range(start_col, start_col + len(word)):
-                        grid[row][col] = word[col - start_col]
-                    placed = True
-
-            elif direction == 'vertical':
-                col = random.randint(0, size - 1)
-                start_row = random.randint(0, size - len(word))
-
-                for row in range(start_row, start_row + len(word)):
-                    if grid[row][col] != ' ' and grid[row][col] != word[row - start_row]:
-                        break
-                else:
-                    for row in range(start_row, start_row + len(word)):
-                        grid[row][col] = word[row - start_row]
-                    placed = True
-
-    return grid
-
-# Streamlit app
-st.title("Word Search with Memory")
-st.write("Welcome to the Word Search game with memory! Add a new word and the grid will update without losing previously placed words.")
-
-# Session state to store the word list
-if 'word_list' not in st.session_state:
-    st.session_state.word_list = []
-
-new_word = st.text_input("Enter a new word (up to 10 characters):").upper()
-if len(new_word) <= 10 and new_word.isalpha():
-    st.session_state.word_list.append(new_word)
-
-if st.button("Clear words"):
-    st.session_state.word_list = []
-
-# Grid size
-size = st.sidebar.slider("Grid size", min_value=10, max_value=20, value=10, step=1)
-
-# Generate grid and display it
-grid = generate_grid(size, st.session_state.word_list)
-for row in grid:
-    st.write(" ".join(row))
-
-st.write("Words added:")
-st.write(", ".join(st.session_state.word_list))
diff --git a/spaces/awacke1/ZorkHF/README.md b/spaces/awacke1/ZorkHF/README.md
deleted file mode 100644
index 1d641da3b567ba3b93fe23f4e586463ce61b3160..0000000000000000000000000000000000000000
--- a/spaces/awacke1/ZorkHF/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ZorkHF
-emoji: 😻
-colorFrom: gray
-colorTo: red
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/math/Interpolant.js b/spaces/banana-projects/web3d/node_modules/three/src/math/Interpolant.js
deleted file mode 100644
index 4fbe74bcbf031f382e743a783601c11c1727cfed..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/math/Interpolant.js
+++ /dev/null
@@ -1,259 +0,0 @@
-/**
- * Abstract base class of interpolants over parametric samples.
- *
- * The parameter domain is one dimensional, typically the time or a path
- * along a curve defined by the data.
- *
- * The sample values can have any dimensionality and derived classes may
- * apply special interpretations to the data.
- *
- * This class provides the interval seek in a Template Method, deferring
- * the actual interpolation to derived classes.
- *
- * Time complexity is O(1) for linear access crossing at most two points
- * and O(log N) for random access, where N is the number of positions.
- *
- * References:
- *
- * 		http://www.oodesign.com/template-method-pattern.html
- *
- * @author tschw
- */
-
-function Interpolant( parameterPositions, sampleValues, sampleSize, resultBuffer ) {
-
-	this.parameterPositions = parameterPositions;
-	this._cachedIndex = 0;
-
-	this.resultBuffer = resultBuffer !== undefined ?
-		resultBuffer : new sampleValues.constructor( sampleSize );
-	this.sampleValues = sampleValues;
-	this.valueSize = sampleSize;
-
-}
-
-Object.assign( Interpolant.prototype, {
-
-	evaluate: function ( t ) {
-
-		var pp = this.parameterPositions,
-			i1 = this._cachedIndex,
-
-			t1 = pp[ i1 ],
-			t0 = pp[ i1 - 1 ];
-
-		validate_interval: {
-
-			seek: {
-
-				var right;
-
-				linear_scan: {
-
-					//- See http://jsperf.com/comparison-to-undefined/3
-					//- slower code:
-					//-
-					//- 				if ( t >= t1 || t1 === undefined ) {
-					forward_scan: if ( ! ( t < t1 ) ) {
-
-						for ( var giveUpAt = i1 + 2; ; ) {
-
-							if ( t1 === undefined ) {
-
-								if ( t < t0 ) break forward_scan;
-
-								// after end
-
-								i1 = pp.length;
-								this._cachedIndex = i1;
-								return this.afterEnd_( i1 - 1, t, t0 );
-
-							}
-
-							if ( i1 === giveUpAt ) break; // this loop
-
-							t0 = t1;
-							t1 = pp[ ++ i1 ];
-
-							if ( t < t1 ) {
-
-								// we have arrived at the sought interval
-								break seek;
-
-							}
-
-						}
-
-						// prepare binary search on the right side of the index
-						right = pp.length;
-						break linear_scan;
-
-					}
-
-					//- slower code:
-					//-					if ( t < t0 || t0 === undefined ) {
-					if ( ! ( t >= t0 ) ) {
-
-						// looping?
-
-						var t1global = pp[ 1 ];
-
-						if ( t < t1global ) {
-
-							i1 = 2; // + 1, using the scan for the details
-							t0 = t1global;
-
-						}
-
-						// linear reverse scan
-
-						for ( var giveUpAt = i1 - 2; ; ) {
-
-							if ( t0 === undefined ) {
-
-								// before start
-
-								this._cachedIndex = 0;
-								return this.beforeStart_( 0, t, t1 );
-
-							}
-
-							if ( i1 === giveUpAt ) break; // this loop
-
-							t1 = t0;
-							t0 = pp[ -- i1 - 1 ];
-
-							if ( t >= t0 ) {
-
-								// we have arrived at the sought interval
-								break seek;
-
-							}
-
-						}
-
-						// prepare binary search on the left side of the index
-						right = i1;
-						i1 = 0;
-						break linear_scan;
-
-					}
-
-					// the interval is valid
-
-					break validate_interval;
-
-				} // linear scan
-
-				// binary search
-
-				while ( i1 < right ) {
-
-					var mid = ( i1 + right ) >>> 1;
-
-					if ( t < pp[ mid ] ) {
-
-						right = mid;
-
-					} else {
-
-						i1 = mid + 1;
-
-					}
-
-				}
-
-				t1 = pp[ i1 ];
-				t0 = pp[ i1 - 1 ];
-
-				// check boundary cases, again
-
-				if ( t0 === undefined ) {
-
-					this._cachedIndex = 0;
-					return this.beforeStart_( 0, t, t1 );
-
-				}
-
-				if ( t1 === undefined ) {
-
-					i1 = pp.length;
-					this._cachedIndex = i1;
-					return this.afterEnd_( i1 - 1, t0, t );
-
-				}
-
-			} // seek
-
-			this._cachedIndex = i1;
-
-			this.intervalChanged_( i1, t0, t1 );
-
-		} // validate_interval
-
-		return this.interpolate_( i1, t0, t, t1 );
-
-	},
-
-	settings: null, // optional, subclass-specific settings structure
-	// Note: The indirection allows central control of many interpolants.
-
-	// --- Protected interface
-
-	DefaultSettings_: {},
-
-	getSettings_: function () {
-
-		return this.settings || this.DefaultSettings_;
-
-	},
-
-	copySampleValue_: function ( index ) {
-
-		// copies a sample value to the result buffer
-
-		var result = this.resultBuffer,
-			values = this.sampleValues,
-			stride = this.valueSize,
-			offset = index * stride;
-
-		for ( var i = 0; i !== stride; ++ i ) {
-
-			result[ i ] = values[ offset + i ];
-
-		}
-
-		return result;
-
-	},
-
-	// Template methods for derived classes:
-
-	interpolate_: function ( /* i1, t0, t, t1 */ ) {
-
-		throw new Error( 'call to abstract method' );
-		// implementations shall return this.resultBuffer
-
-	},
-
-	intervalChanged_: function ( /* i1, t0, t1 */ ) {
-
-		// empty
-
-	}
-
-} );
-
-//!\ DECLARE ALIAS AFTER assign prototype !
-Object.assign( Interpolant.prototype, {
-
-	//( 0, t, t0 ), returns this.resultBuffer
-	beforeStart_: Interpolant.prototype.copySampleValue_,
-
-	//( N-1, tN-1, t ), returns this.resultBuffer
-	afterEnd_: Interpolant.prototype.copySampleValue_,
-
-} );
-
-
-export { Interpolant };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/objects/Line.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/objects/Line.d.ts
deleted file mode 100644
index 9b3e0e4bb9795d524e05ea5c80ecde58e4ce00e4..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/objects/Line.d.ts
+++ /dev/null
@@ -1,23 +0,0 @@
-import { Geometry } from './../core/Geometry';
-import { Material } from './../materials/Material';
-import { Raycaster } from './../core/Raycaster';
-import { Object3D } from './../core/Object3D';
-import { BufferGeometry } from '../core/BufferGeometry';
-import { Intersection } from '../core/Raycaster';
-
-export class Line extends Object3D {
-  constructor(
-    geometry?: Geometry | BufferGeometry,
-    material?: Material | Material[],
-    mode?: number
-  );
-
-  geometry: Geometry | BufferGeometry;
-  material: Material | Material[];
-
-  type: 'Line' | 'LineLoop' | 'LineSegments';
-  isLine: true;
-
-  computeLineDistances(): this;
-  raycast(raycaster: Raycaster, intersects: Intersection[]): void;
-}
diff --git a/spaces/barani/ControlNet/app_normal.py b/spaces/barani/ControlNet/app_normal.py
deleted file mode 100644
index 1519b3095bc5250016066754ebe6c0328773f24a..0000000000000000000000000000000000000000
--- a/spaces/barani/ControlNet/app_normal.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button('Run')
-                with gr.Accordion('Advanced options', open=False):
-                    preprocessor_name = gr.Radio(label='Preprocessor',
-                                                 choices=['NormalBae', 'None'],
-                                                 type='value',
-                                                 value='NormalBae')
-                    num_samples = gr.Slider(label='Images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    preprocess_resolution = gr.Slider(
-                        label='Preprocess resolution',
-                        minimum=128,
-                        maximum=512,
-                        value=384,
-                        step=1)
-                    num_steps = gr.Slider(label='Number of steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=1000000,
-                                     step=1,
-                                     value=0,
-                                     randomize=True)
-                    randomize_seed = gr.Checkbox(label='Randomize seed',
-                                                 value=True)
-                    a_prompt = gr.Textbox(
-                        label='Additional prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output', show_label=False).style(
-                    columns=2, object_fit='scale-down')
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            preprocess_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            preprocessor_name,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name='normal',
-        )
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='NormalBae')
-    demo = create_demo(model.process_normal)
-    demo.queue().launch()
diff --git a/spaces/baulab/Erasing-Concepts-In-Diffusion/README.md b/spaces/baulab/Erasing-Concepts-In-Diffusion/README.md
deleted file mode 100644
index 0ef89370141a383005adc7e68bf56a7e6421462f..0000000000000000000000000000000000000000
--- a/spaces/baulab/Erasing-Concepts-In-Diffusion/README.md
+++ /dev/null
@@ -1,53 +0,0 @@
----
-title: Erasing Concepts from Diffusion Models 
-emoji: 💡
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-
-
-# Erasing Concepts from Diffusion Models
-
- Project Website    [https://erasing.baulab.info](https://erasing.baulab.info) <br>
- Arxiv Preprint     [https://arxiv.org/pdf/2303.07345.pdf](https://arxiv.org/pdf/2303.07345.pdf) <br>
- Fine-tuned Weights [https://erasing.baulab.info/weights/esd_models/](https://erasing.baulab.info/weights/esd_models/) <br>
-<div align='center'>
-<img src = 'images/applications.png'>
-</div>
-
-Motivated by recent advancements in text-to-image diffusion, we study erasure of specific concepts from the model's weights. While Stable Diffusion has shown promise in producing explicit or realistic artwork, it has raised concerns regarding its potential for misuse. We propose a fine-tuning method that can erase a visual concept from a pre-trained diffusion model, given only the name of the style and using negative guidance as a teacher. We benchmark our method against previous approaches that remove sexually explicit content and demonstrate its effectiveness, performing on par with Safe Latent Diffusion and censored training.
-
-To evaluate artistic style removal, we conduct experiments erasing five modern artists from the network and conduct a user study to assess the human perception of the removed styles. Unlike previous methods, our approach can remove concepts from a diffusion model permanently rather than modifying the output at the inference time, so it cannot be circumvented even if a user has access to model weights
-
-Given only a short text description of an undesired visual concept and no additional data, our method fine-tunes model weights to erase the targeted concept. Our method can avoid NSFW content, stop imitation of a specific artist's style, or even erase a whole object class from model output, while preserving the model's behavior and capabilities on other topics.
-
-## Demo vs github
-
-This demo uses an updated implementation from the original Erasing codebase the publication is based from.
-
-## Running locally
-
-1.) Create an environment using the packages included in the requirements.txt file
-
-2.) Run `python app.py`
-
-3.) Open the application in browser at `http://127.0.0.1:7860/`
-
-4.) Train, evaluate, and save models using our method
-
-## Citing our work
-The preprint can be cited as follows
-```
-@article{gandikota2023erasing,
-  title={Erasing Concepts from Diffusion Models},
-  author={Rohit Gandikota and Joanna Materzy\'nska and Jaden Fiotto-Kaufman and David Bau},
-  journal={arXiv preprint arXiv:2303.07345},
-  year={2023}
-}
-```
\ No newline at end of file
diff --git a/spaces/bgadaleta/mars/README.md b/spaces/bgadaleta/mars/README.md
deleted file mode 100644
index b069ce5adaf5bd17dc689e499684956b36e8747c..0000000000000000000000000000000000000000
--- a/spaces/bgadaleta/mars/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Mars
-emoji: 🏢
-colorFrom: yellow
-colorTo: yellow
-sdk: docker
-pinned: false
-license: cc
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bigscience/license/index.html b/spaces/bigscience/license/index.html
deleted file mode 100644
index d74b56b6471693ca879e2ba14bf0ca028924687b..0000000000000000000000000000000000000000
--- a/spaces/bigscience/license/index.html
+++ /dev/null
@@ -1,1895 +0,0 @@
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-
-<body>
-    <p class="c18 c31"><span class="c15 c6">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span></p>
-    <p class="c18"><span class="c6 c38">BigScience RAIL </span><span class="c6 c38">License</span><span
-            class="c6 c15">&nbsp;v1.0</span></p>
-    <p class="c18"><span class="c19 c6 c24">dated May 19, 2022</span></p>
-    <p class="c3"><span class="c9 c8"></span></p>    
-    <p class="c18"><span class="c19 c6 c24">Download as <a
-                href="https://huggingface.co/spaces/bigscience/license/raw/main/BLOOMLICENSE.txt" download> .txt </a>,
-            <a href="https://huggingface.co/spaces/bigscience/license/raw/main/BLOOMLICENSE.docx" download> .docx </a>,
-            or <a href="https://huggingface.co/spaces/bigscience/license/raw/main/BLOOMLICENSE.html" download>
-                .html</a></span></p>
-    <p class="c3"><span class="c9 c8"></span></p>
-    <p class="c3"><span class="c9 c8"></span></p>
-    <p class="c27"><span class="c8">This is a license (the &ldquo;</span><span class="c6">License</span><span
-            class="c8">&rdquo;) between you (&ldquo;</span><span class="c6">You</span><span class="c8">&rdquo;) and the
-            parti</span><span class="c8">cipants of BigScience (&ldquo;</span><span class="c6">Licensor</span><span
-            class="c8">&rdquo;).</span><span class="c8">&nbsp;</span><span class="c8">Whereas the Apache 2.0 license was
-            applicable to resources used to develop the </span><span class="c6">Model</span><span class="c8">, the
-            licensing conditions have been modified for the access and distribution of the</span><span
-            class="c6">&nbsp;Model</span><span class="c8">. This has been done to further BigScience&rsquo;s aims of
-            promoting not just open-access to its artifacts, but also a responsible use of these artifacts. Therefore,
-            this Responsible AI License (</span><span class="c8 c32"><a class="c23"
-                href="https://www.google.com/url?q=https://www.licenses.ai/&amp;sa=D&amp;source=editors&amp;ust=1653063807312272&amp;usg=AOvVaw0rXfyCPiMKwuMQeev20LJx">RAIL</a></span><span
-            class="c8">)</span><sup class="c8"><a href="#ftnt1" id="ftnt_ref1">[1]</a></sup><span class="c8">&nbsp;aims
-            at having an</span><span class="c8">&nbsp;open and permissive character while striving for responsible use
-            of the </span><span class="c6">Model</span><span class="c8">.</span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c18"><span class="c6">Section</span><span class="c6">&nbsp;I: PREAMBLE</span></p>
-    <p class="c3"><span class="c9 c8"></span></p>
-    <p class="c27"><span class="c1 c5">BigScience is a collaborative open innovation project aimed at the responsible
-            development and use of large multilingual datasets and Large Language Models (&ldquo;</span><span
-            class="c6 c5 c7">LLM</span><span class="c1 c5">&rdquo;),</span><span class="c1 c5">&nbsp;as well as, the
-            documentation of best practices and tools stemming from this collaborative effort. Further, BigScience
-            participants wish to promote collaboration and sharing of research artifacts - including the </span><span
-            class="c6 c7 c5">Model</span><span class="c1 c19 c5">&nbsp;- for the benefit of society, pursuant to this
-            License.</span></p>
-    <p class="c3"><span class="c1 c19 c5"></span></p>
-    <p class="c27"><span class="c1 c5">The development and use of LLMs, and broadly artificial intelligence
-            (&ldquo;</span><span class="c6 c7 c5">AI</span><span class="c1 c5 c19">&rdquo;), does not come without
-            concerns. The world has witnessed how just a few companies/institutions are able to develop LLMs, and
-            moreover, how Natural Language Processing techniques might, in some instances, become a risk for the public
-            in general. Concerns might come in many forms, from racial discrimination to the treatment of sensitive
-            information. </span></p>
-    <p class="c3"><span class="c1 c19 c5"></span></p>
-    <p class="c27"><span class="c1 c5">BigScience believes in the intersection between open and responsible AI
-            development, thus, </span><span class="c1 c5">this License aims to strike a balance between both in order to
-            enable responsible open-science for large languag</span><span class="c1 c19 c5">e models and future NLP
-            techniques. </span></p>
-    <p class="c11"><span class="c1 c5">This License go</span><span class="c1 c5">verns the use of </span><span
-            class="c1 c5 c21">the BigScience BLOOM </span><span class="c1 c5 c21">m</span><span
-            class="c1 c5 c21">odel</span><span class="c1 c5 c21">s</span><span class="c1 c5">&nbsp;</span><span
-            class="c1 c5">(</span><span class="c1 c5">and their derivatives) and is informed by both the </span><span
-            class="c1 c5">BigScience Ethical Charter and the model cards associated with the BigScience BLOOM models.
-        </span><span class="c1 c5">BigScience has set forth its Ethical Charter representing the values of &nbsp;its
-            community. Although the BigScience community does not aim to impose its values on potential users of
-            this</span><span class="c1 c5">&nbsp;</span><span class="c6 c7 c5">Model</span><span class="c1 c5">, it is
-            determined </span><span class="c1 c5 c21">to take tangible steps towards protecting the community from
-            inappropriate uses of the work being developed by BigScience.</span></p>
-    <p class="c11"><span class="c1 c5">Furthermore, the model cards for the BigScience BLOOM models will inform the user
-            about the limitations of the </span><span class="c6 c7 c5">Model</span><span class="c1 c19 c5">, and thus
-            serves as the basis of some of the use-based restrictions in this License (See Part II).</span></p>
-    <p class="c3"><span class="c19 c8 c5 c41"></span></p>
-    <p class="c18 c20"><span class="c9 c6"></span></p>
-    <p class="c27"><span class="c6">NOW THEREFORE</span><span class="c9 c8">, You and Licensor agree as follows:</span>
-    </p>
-    <p class="c3"><span class="c9 c8"></span></p>
-    <p class="c27"><span class="c9 c6">1. Definitions </span></p>
-    <p class="c3"><span class="c9 c6"></span></p>
-    <ol class="c4 lst-kix_o12c65tpuwmu-0 start" start="1">
-        <li class="c0 c10 li-bullet-0"><span class="c8">&quot;</span><span class="c6">License</span><span
-                class="c8">&quot; shall mean the terms and conditions for use, reproduction, and Distribution as defined
-            </span><span class="c8">in </span><span class="c9 c8">this document.</span></li>
-        <li class="c11 c10 li-bullet-0"><span class="c6 c7 c5">&ldquo;</span><span class="c6 c7 c5">Data&rdquo;
-            </span><span class="c1 c5">means </span><span class="c1 c5">a collection of texts extracted from the
-                BigScience Corpus</span><span class="c1 c5">&nbsp;used with the Model, including to train, pretrain, or
-                otherwise evaluate the Model. The Data is not licensed under this License. </span><span
-                class="c1 c5">The </span><span class="c1 c5">BigScience Corpus is a collection of existing sources of
-                language data documented on the BigScience website.</span></li>
-        <li class="c11 c10 li-bullet-0"><span class="c6">&ldquo;Output&rdquo;</span><span class="c8">&nbsp;means the
-                results of operating a Model as embodied </span><span class="c8">in </span><span
-                class="c8">informational content resulting therefrom.</span></li>
-        <li class="c0 c10 li-bullet-0"><span class="c1 c5">&ldquo;</span><span class="c6 c7 c5">Model</span><span
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-    </ol>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c18"><span class="c6">Section</span><span class="c6">&nbsp;II: INTELLECTUAL PROPERTY RIGHTS</span></p>
-    <p class="c0"><span class="c8">Both copyright and patent grants apply to the Model, Derivatives of the Model and
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-            class="c1 c5">Model and Derivatives of the Model are subject to additional terms as described in Section
-            III.</span></p>
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-            is filed.</span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c18"><span class="c6">Section</span><span class="c9 c6">&nbsp;III: CONDITIONS OF USAGE, DISTRIBUTION AND
-            REDISTRIBUTION</span></p>
-    <p class="c18 c20"><span class="c9 c6"></span></p>
-    <p class="c0"><span class="c6">4. Distribution and </span><span class="c6">Redistribution</span><span
-            class="c8">.</span><span class="c9 c8">&nbsp;You may host for Third Party remote access purposes (e.g.
-            software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in
-            any medium, with or without modifications, provided that You meet the following conditions:</span></p>
-    <ol class="c4 lst-kix_g3jk1pagzpg-0 start" start="1">
-        <li class="c0 c10 li-bullet-0"><span class="c8">Use-based restrictions as referenced in </span><span
-                class="c8">paragraph</span><span class="c8">&nbsp;5 </span><span class="c1 c5">MUST be included as an
-                enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or
-                distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You
-                Distribute to, that the Model or Derivatives of the Model are subject to </span><span
-                class="c8">paragraph</span><span class="c1 c5">&nbsp;5. </span><span class="c6 c7 c5">This provision
-                does not apply to the use of Complementary Material.</span></li>
-        <li class="c0 c10 li-bullet-0"><span class="c9 c8">You must give any Third Party recipients of the Model or
-                Derivatives of the Model a copy of this License; </span></li>
-        <li class="c0 c10 li-bullet-0"><span class="c8 c9">You must cause any modified files to carry prominent notices
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-        <li class="c0 c10 li-bullet-0"><span class="c8">You must retain all copyright, patent, trademark, and
-                attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of
-                the Model</span><span class="c9 c8">.</span></li>
-    </ol>
-    <p class="c0"><span class="c8">You may add Your own copyright statement to Your modifications and may provide
-            additional or different license terms and conditions </span><span class="c8">- </span><span
-            class="c6">respecting </span><span class="c8">paragraph</span><span class="c6">&nbsp;4.a. </span><span
-            class="c8">-</span><span class="c9 c8">&nbsp;for use, reproduction, or Distribution of Your modifications,
-            or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of
-            the Model otherwise complies with the conditions stated in this License.</span></p>
-    <p class="c11"><span class="c6">5. Use-based rest</span><span class="c6">rictions.</span><span
-            class="c6 c7 c5">&nbsp;</span><span class="c1 c5">The restrictions set forth in Attachment A are considered
-            Use-based restrictions.</span><span class="c1 c5">&nbsp;Therefore You cannot use the Model and the
-            Derivatives of the Model for the specified restricted uses.</span><span class="c1 c5">&nbsp;</span><span
-            class="c1 c5">You may use the Model subject to this License, including only for lawful purposes and in
-            accordance with the License. </span><span class="c6 c7 c5">Use</span><span class="c1 c5">&nbsp;may include
-            creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the
-            Model. </span><span class="c1 c5">You shall require al</span><span class="c1 c5">l of Your users who use the
-            Model or a Derivative of the Model to comply with the terms of this </span><span
-            class="c8">paragraph</span><span class="c1 c5">&nbsp;(</span><span class="c8">paragraph</span><span
-            class="c1 c5">&nbsp;5).</span><span class="c1 c5">&nbsp;</span></p>
-    <p class="c11"><span class="c6">6. &nbsp;The Output You Generate. </span><span class="c8">Except as set forth
-            herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the
-            Output you generate and its </span><span class="c8">subsequent uses</span><span class="c8">. </span><span
-            class="c9 c8">No use of the output can contravene any provision as stated in the License. </span></p>
-    <p class="c11 c20"><span class="c9 c8 c34"></span></p>
-    <p class="c16"><span class="c6">Section</span><span class="c9 c6">&nbsp;IV: OTHER PROVISIONS</span></p>
-    <p class="c11"><span class="c6">7. Updates and Runtime Restrictions. </span><span class="c8">To the maximum extent
-            permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in
-            violation of this License, update the Model through electronic means, or modify the Output of the Model
-            based on updates. You shall undertake reasonable efforts to use the latest version of the Model.</span></p>
-    <p class="c11"><span class="c6">8</span><span class="c6">. Trademarks and related.</span><span
-            class="c9 c8">&nbsp;Nothing in this License permits You to make use of Licensors&rsquo; trademarks, trade
-            names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and
-            any rights not expressly granted herein are reserved by the Licensors.</span></p>
-    <p class="c11"><span class="c6">9</span><span class="c6">. Disclaimer of Warranty</span><span class="c8">. Unless
-            required by applicable law or agreed to in writing, Licensor provides the Model and the </span><span
-            class="c1 c5">Complementary Material</span><span class="c8">&nbsp;</span><span class="c8">(and each
-            Contributor provides its Contributions) on an &quot;AS IS&quot; BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-            ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE,
-            NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for
-            determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the
-        </span><span class="c1 c5">Complementary Material</span><span class="c9 c8">&nbsp;and assume any risks
-            associated with Your exercise of permissions under this License.</span></p>
-    <p class="c11"><span class="c6">10</span><span class="c6">. Limitation of Liability</span><span class="c8">. In no
-            event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless
-            required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall
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-            use the Model and the </span><span class="c1 c5">Complementary Material</span><span
-            class="c9 c8">&nbsp;(including but not limited to damages for loss of goodwill, work stoppage, computer
-            failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has
-            been advised of the possibility of such damages.</span></p>
-    <p class="c11"><span class="c6">11</span><span class="c6">. Accepting Warranty or Additional Liability</span><span
-            class="c8">. While redistributing the Model, Derivatives of the Model and the </span><span
-            class="c1 c5">Complementary Material </span><span class="c9 c8">thereof, You may choose to offer, and charge
-            a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights
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-            and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify,
-            defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such
-            Contributor by reason of your accepting any such warranty or additional liability.</span></p>
-    <p class="c0"><span class="c6">12.</span><span class="c8">&nbsp;If any provision of this License is held to be
-            invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as
-            if such provision had not been set forth herein.</span></p>
-    <p class="c0"><span class="c9 c8">END OF TERMS AND CONDITIONS</span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c0 c20"><span class="c9 c8"></span></p>
-    <p class="c21 c40"><span class="c14 c6 c7">Attachment A</span></p>
-    <h3 class="c21 c30" id="h.3blioxkgzsje"><span class="c6 c7 c39">Use Restrictions</span></h3>
-    <p class="c11"><span class="c1">You agree not to use the Model or Derivatives of the Model:</span></p>
-    <ol class="c4 lst-kix_2z7sxuic4qcz-0 start" start="1">
-        <li class="c2 li-bullet-0"><span class="c14 c1">In any way that violates any applicable national, federal,
-                state, local or international law or regulation;</span></li>
-        <li class="c2 li-bullet-0"><span class="c14 c1">For the purpose of exploiting, harming or attempting to exploit
-                or harm minors in any way;</span></li>
-        <li class="c2 li-bullet-0"><span class="c14 c1 c21">To generate or disseminate verifiably false information with
-                the purpose of harming others;</span></li>
-        <li class="c2 li-bullet-0"><span class="c1">To generate or disseminate </span><span class="c1">personal
-                identifiable information</span><span class="c1">&nbsp;</span><span class="c8">that can</span><span
-                class="c8">&nbsp;be used to</span><span class="c8">&nbsp;harm an </span><span
-                class="c8">individual</span><span class="c1">;</span></li>
-        <li class="c2 li-bullet-0"><span class="c1 c21">To generate or disseminate information </span><span
-                class="c1 c21">or content</span><span class="c14 c1 c21">, in any context (e.g. posts, articles, tweets,
-                chatbots or other kinds of automated bots) without expressly and intelligibly disclaiming that the text
-                is machine generated; </span></li>
-        <li class="c2 li-bullet-0"><span class="c1">To defame, disparage or otherwise harass</span><span
-                class="c1">&nbsp;others</span><span class="c14 c1">;</span></li>
-        <li class="c2 li-bullet-0"><span class="c1">To </span><span class="c1">impersonate</span><span
-                class="c14 c1">&nbsp;or attempt to impersonate others;</span></li>
-        <li class="c2 li-bullet-0"><span class="c14 c1">For fully automated decision making that adversely impacts an
-                individual&rsquo;s legal rights or otherwise creates or modifies a binding, enforceable
-                obligation;</span></li>
-        <li class="c35 li-bullet-0"><span class="c8">For any </span><span class="c8">use intended to or which has the
-                effect of </span><span class="c8">discriminating against or harming individuals or groups</span><span
-                class="c8 c12">&nbsp;</span><span class="c8">based on online or offline social behavior or known or
-                predicted personal or personality characteristics</span></li>
-        <li class="c2 li-bullet-0"><span class="c8 c21">To exploit any of the vulnerabilities of a specific group of
-                persons based on their age, social, physical or </span><span class="c8 c21">mental</span><span
-                class="c8 c21">&nbsp;characteristics, in order to materially distort the behavior of a person pertaining
-                to that group in a manner that causes or is likely to cause that person or another person physical or
-                psychological harm;</span></li>
-        <li class="c2 li-bullet-0"><span class="c8">For any </span><span class="c9 c8">use intended to or which has the
-                effect of discriminating against individuals or groups based on legally protected characteristics or
-                categories;</span></li>
-        <li class="c2 li-bullet-0"><span class="c14 c1">To provide medical advice and medical results interpretation;
-            </span></li>
-        <li class="c2 li-bullet-0"><span class="c1">To generate or disseminate information for the purpose to be used
-                for administration of justice, law enforcement, immigration or asylum processes, such as predicting an
-                individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships
-                between assertions made in documents, indiscriminate and </span><span class="c1">arbitrarily</span><span
-                class="c1">-targeted use).</span></li>
-    </ol>
-    <div>
-        <p class="c20 c22"><span class="c9 c8"></span></p>
-    </div>
-    <hr class="c28">
-    <div>
-        <p class="c13"><a href="#ftnt_ref1" id="ftnt1">[1]</a><span class="c33">&nbsp;</span><span class="c32 c33"><a
-                    class="c23"
-                    href="https://www.google.com/url?q=https://arxiv.org/pdf/2011.03116.pdf&amp;sa=D&amp;source=editors&amp;ust=1653063807332160&amp;usg=AOvVaw3fuY17BgoLKIrvsomH_gPv">https://arxiv.org/pdf/2011.03116.pdf</a></span><span
-                class="c17">&nbsp;</span></p>
-    </div>
-</body>
-
-</html>
\ No newline at end of file
diff --git a/spaces/bigscience/promptsource/README_promptsource.md b/spaces/bigscience/promptsource/README_promptsource.md
deleted file mode 100644
index deb2c4a4da1aadc6fd7bb947d0d40781f4ccf271..0000000000000000000000000000000000000000
--- a/spaces/bigscience/promptsource/README_promptsource.md
+++ /dev/null
@@ -1,140 +0,0 @@
-# PromptSource
-**PromptSource is a toolkit for creating, sharing and using natural language prompts.**
-
-Recent work has shown that large language models exhibit the ability to perform reasonable zero-shot generalization to new tasks. For instance, [GPT-3](https://arxiv.org/abs/2005.14165) demonstrated that large language models have strong zero- and few-shot abilities. [FLAN](https://arxiv.org/abs/2109.01652) and [T0](https://arxiv.org/abs/2110.08207) then demonstrated that pre-trained language models fine-tuned in a massively multitask fashion yield even stronger zero-shot performance. A common denominator in these works is the use of prompts which have gathered of interest among NLP researchers and engineers. This emphasizes the need for new tools to create, share and use natural language prompts.
-
-Prompts are functions that map an example from a dataset to a natural language input and target output PromptSource contains a growing collection of prompts (which we call **P3**: **P**ublic **P**ool of **P**rompts). As of January 20, 2022, there are ~2'000 English prompts for 170+ English datasets in [P3](https://huggingface.co/datasets/bigscience/P3).
-
-<p align="center">
-  <img src="assets/PromptSource ACL Demo Figure.png" width="800"/>
-</p>
-
-PromptSource provides the tools to create, and share natural language prompts (see [How to create prompts](#how-to-create-prompts), and then use the thousands of existing and newly created prompts through a simple API (see [How to use prompts](#how-to-use-prompts)). Prompts are saved in standalone structured files and are written in a simple templating language called Jinja. An example of prompt availabe in PromptSource for [SNLI](https://huggingface.co/datasets/snli) is:
-```jinja2
-{{premise}}
-
-Question: Does this imply that "{{hypothesis}}"? Yes, no, or maybe? ||| {{answer_choices[label]}}
-```
-
-**You can browse through existing prompts on the [hosted version of PromptSource](https://bigscience.huggingface.co/promptsource).**
-
-## Setup
-If you do not intend to modify prompts, you can simply run:
-```bash
-pip install promptsource
-```
-
-Otherwise, you need to install the repo locally:
-1. Download the repo
-1. Navigate to the root directory of the repo
-1. Run `pip install -e .` to install the `promptsource` module
-
-*Note: for stability reasons, you will currently need a Python 3.7 environment to run the last step. However, if you only intend to use the prompts, and not create new prompts through the interface, you can remove this constraint in the [`setup.py`](setup.py) and install the package locally.*
-
-## How to use prompts
-You can apply prompts to examples from datasets of the [Hugging Face Datasets library](https://github.com/huggingface/datasets).
-```python
-# Load an example from the datasets ag_news
->>> from datasets import load_dataset
->>> dataset = load_dataset("ag_news", split="train")
->>> example = dataset[1]
-
-# Load prompts for this dataset
->>> from promptsource.templates import DatasetTemplates
->>> ag_news_prompts = DatasetTemplates('ag_news')
-
-# Print all the prompts available for this dataset. The keys of the dict are the uuids the uniquely identify each of the prompt, and the values are instances of `Template` which wraps prompts
->>> print(ag_news_prompts.templates)
-{'24e44a81-a18a-42dd-a71c-5b31b2d2cb39': <promptsource.templates.Template object at 0x7fa7aeb20350>, '8fdc1056-1029-41a1-9c67-354fc2b8ceaf': <promptsource.templates.Template object at 0x7fa7aeb17c10>, '918267e0-af68-4117-892d-2dbe66a58ce9': <promptsource.templates.Template object at 0x7fa7ac7a2310>, '9345df33-4f23-4944-a33c-eef94e626862': <promptsource.templates.Template object at 0x7fa7ac7a2050>, '98534347-fff7-4c39-a795-4e69a44791f7': <promptsource.templates.Template object at 0x7fa7ac7a1310>, 'b401b0ee-6ffe-4a91-8e15-77ee073cd858': <promptsource.templates.Template object at 0x7fa7ac7a12d0>, 'cb355f33-7e8c-4455-a72b-48d315bd4f60': <promptsource.templates.Template object at 0x7fa7ac7a1110>}
-
-# Select a prompt by its name
->>> prompt = ag_news_prompts["classify_question_first"]
-
-# Apply the prompt to the example
->>> result = prompt.apply(example)
->>> print("INPUT: ", result[0])
-INPUT:  What label best describes this news article?
-Carlyle Looks Toward Commercial Aerospace (Reuters) Reuters - Private investment firm Carlyle Group,\which has a reputation for making well-timed and occasionally\controversial plays in the defense industry, has quietly placed\its bets on another part of the market.
->>> print("TARGET: ", result[1])
-TARGET:  Business
-```
-
-In the case that you are looking for the prompts available for a particular subset of a dataset, you should use the following syntax:
-```python
-dataset_name, subset_name = "super_glue", "rte"
-
-dataset = load_dataset(f"{dataset_name}/{subset_name}", split="train")
-example = dataset[0]
-
-prompts = DatasetTemplates(f"{dataset_name}/{subset_name}")
-```
-
-You can also collect all the available prompts for their associated datasets:
-
-```python
->>> from promptsource.templates import TemplateCollection
-
-# Get all the prompts available in PromptSource
->>> collection = TemplateCollection()
-
-# Print a dict where the key is the pair (dataset name, subset name)
-# and the value is an instance of DatasetTemplates
->>> print(collection.datasets_templates)
-{('poem_sentiment', None): <promptsource.templates.DatasetTemplates object at 0x7fa7ac7939d0>, ('common_gen', None): <promptsource.templates.DatasetTemplates object at 0x7fa7ac795410>, ('anli', None): <promptsource.templates.DatasetTemplates object at 0x7fa7ac794590>, ('cc_news', None): <promptsource.templates.DatasetTemplates object at 0x7fa7ac798a90>, ('craigslist_bargains', None): <promptsource.templates.DatasetTemplates object at 0x7fa7ac7a2c10>,...}
-```
-
-You can learn more about PromptSource's API to store, manipulate and use prompts in the [documentation](API_DOCUMENTATION.md).
-
-## How to create prompts
-PromptSource provides a Web-based GUI that enables developers to write prompts in a templating language and immediately view their outputs on different examples.
-
-There are 3 modes in the app:
-- **Sourcing**: create and write new prompts
-- **Prompted dataset viewer**: check the prompts you wrote (or the existing ones) on the entire dataset
-- **Helicopter view**: aggregate high-level metrics on the current state of P3
-
-<p align="center">
-  <img src="assets/promptsource_app.png" width="800"/>
-</p>
-
-To launch the app locally, please first make sure you have followed the steps in [Setup](#setup), and from the root directory of the repo, run:
-```bash
-streamlit run promptsource/app.py
-```
-
-You can also browse through existing prompts on the [hosted version of PromptSource](https://bigscience.huggingface.co/promptsource). Note the hosted version disables the Sourcing mode (`streamlit run promptsource/app.py -- --read-only`).
-
-### Writing prompts
-Before creating new prompts, you should read the [contribution guidelines](CONTRIBUTING.md) which give an step-by-step description of how to contribute to the collection of prompts.
-
-### Datasets that require manual downloads
-Some datasets are not handled automatically by `datasets` and require users to download the dataset manually (`story_cloze` for instance ).
-
-To handle those datasets as well, we require users to download the dataset and put it in `~/.cache/promptsource`. This is the root directory containing all manually downloaded datasets.
-
-You can override this default path using `PROMPTSOURCE_MANUAL_DATASET_DIR` environment variable. This should point to the root directory.
-
-## Development structure
-PromptSource and P3 were originally developed as part of the [BigScience project for open research 🌸](https://bigscience.huggingface.co/), a year-long initiative targeting the study of large models and datasets. The goal of the project is to research language models in a public environment outside large technology companies. The project has 600 researchers from 50 countries and more than 250 institutions.
-
-In particular, PromptSource and P3 were the first steps for the paper [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207).
-
-**You will find the official repository to reproduce the results of the paper here: https://github.com/bigscience-workshop/t-zero.** We also released T0* (pronounce "T Zero"), a series of models trained on [P3](https://huggingface.co/datasets/bigscience/P3) and presented in the paper. Checkpoints are available [here](https://huggingface.co/bigscience/T0pp).
-
-## Known Issues
-**Warning or Error about Darwin on OS X:** Try downgrading PyArrow to 3.0.0.
-
-**ConnectionRefusedError: [Errno 61] Connection refused:** Happens occasionally. Try restarting the app.
-
-## Citation
-If you find P3 or PromptSource useful, please cite the following reference:
-```bibtex
-@misc{bach2022promptsource,
-      title={PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts},
-      author={Stephen H. Bach and Victor Sanh and Zheng-Xin Yong and Albert Webson and Colin Raffel and Nihal V. Nayak and Abheesht Sharma and Taewoon Kim and M Saiful Bari and Thibault Fevry and Zaid Alyafeai and Manan Dey and Andrea Santilli and Zhiqing Sun and Srulik Ben-David and Canwen Xu and Gunjan Chhablani and Han Wang and Jason Alan Fries and Maged S. Al-shaibani and Shanya Sharma and Urmish Thakker and Khalid Almubarak and Xiangru Tang and Xiangru Tang and Mike Tian-Jian Jiang and Alexander M. Rush},
-      year={2022},
-      eprint={2202.01279},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG}
-}
-```
diff --git a/spaces/bingbing520/ChatGPT2/readme/README_en.md b/spaces/bingbing520/ChatGPT2/readme/README_en.md
deleted file mode 100644
index a906ecb3ebc411f5cdeb33d661266a489a20c3b0..0000000000000000000000000000000000000000
--- a/spaces/bingbing520/ChatGPT2/readme/README_en.md
+++ /dev/null
@@ -1,127 +0,0 @@
-<div align="right">
-  <!-- Language: -->
-  <a title="Chinese" href="../README.md">简体中文</a> | English | <a title="Japanese" href="README_ja.md">日本語</a>
-</div>
-
-<h1 align="center">川虎 Chat 🐯 Chuanhu Chat</h1>
-<div align="center">
-  <a href="https://github.com/GaiZhenBiao/ChuanhuChatGPT">
-    <img src="https://user-images.githubusercontent.com/70903329/227087087-93b37d64-7dc3-4738-a518-c1cf05591c8a.png" alt="Logo" height="156">
-  </a>
-
-<p align="center">
-    <h3>Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA</h3>
-    <p align="center">
-      <a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/blob/main/LICENSE">
-        <img alt="Tests Passing" src="https://img.shields.io/github/license/GaiZhenbiao/ChuanhuChatGPT" />
-      </a>
-      <a href="https://gradio.app/">
-        <img alt="GitHub Contributors" src="https://img.shields.io/badge/Base-Gradio-fb7d1a?style=flat" />
-      </a>
-      <a href="https://t.me/tkdifferent">
-        <img alt="GitHub pull requests" src="https://img.shields.io/badge/Telegram-Group-blue.svg?logo=telegram" />
-      </a>
-      <p>
-        Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search <br />
-        LaTeX rendering / Table rendering / Code highlighting <br />
-        Auto dark mode / Adaptive web interface / WeChat-like theme <br />
-        Multi-parameters tuning / Multi-API-Key support / Multi-user support <br />
-        Compatible with GPT-4 / Local deployment for LLMs
-      </p>
-      <a href="https://www.youtube.com/watch?v=MtxS4XZWbJE"><strong>Video Tutorial</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=77nw7iimYDE"><strong>2.0 Introduction</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=x-O1jjBqgu4"><strong>3.0 Introduction & Tutorial</strong></a>
-	||
-      <a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT"><strong>Online trial</strong></a>
-      	·
-      <a href="https://huggingface.co/login?next=%2Fspaces%2FJohnSmith9982%2FChuanhuChatGPT%3Fduplicate%3Dtrue"><strong>One-Click deployment</strong></a>
-    </p>
-    <p align="center">
-      <img alt="Animation Demo" src="https://user-images.githubusercontent.com/51039745/226255695-6b17ff1f-ea8d-464f-b69b-a7b6b68fffe8.gif" />
-    </p>
-  </p>
-</div>
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press <kbd>Shift</kbd> + <kbd>Enter</kbd> keys.
-- To quickly switch between input history, press <kbd>↑</kbd> and <kbd>↓</kbd> key in the input box.
-- To deploy the program onto a server, change the last line of the program to `demo.launch(server_name="0.0.0.0", server_port=<your port number>)`.
-- To get a public shared link, change the last line of the program to `demo.launch(share=True)`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Installation
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
-   ```
-   pip install -r requirements.txt
-   ```
-3. Update Gradio
-   ```
-   pip install gradio --upgrade --force-reinstall
-   ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[![Star History Chart](https://api.star-history.com/svg?repos=GaiZhenbiao/ChuanhuChatGPT&type=Date)](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-<a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=GaiZhenbiao/ChuanhuChatGPT" />
-</a>
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-<a href="https://www.buymeacoffee.com/ChuanhuChat" ><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=ChuanhuChat&button_colour=219d53&font_colour=ffffff&font_family=Poppins&outline_colour=ffffff&coffee_colour=FFDD00" alt="Buy Me A Coffee" width="250"></a>
-
-<img width="250" alt="image" src="https://user-images.githubusercontent.com/51039745/226920291-e8ec0b0a-400f-4c20-ac13-dafac0c3aeeb.JPG">
diff --git a/spaces/bioriAsaeru/text-to-voice/Baixar filme um casal quase perfeito 4 dublado a aventura de dois patinadores em busca do sonho olmpico.md b/spaces/bioriAsaeru/text-to-voice/Baixar filme um casal quase perfeito 4 dublado a aventura de dois patinadores em busca do sonho olmpico.md
deleted file mode 100644
index 3fdea9977ca3e60e6bea9335a48ebabcfca4bf58..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Baixar filme um casal quase perfeito 4 dublado a aventura de dois patinadores em busca do sonho olmpico.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>baixar filme um casal quase perfeito 4 dublado</h2><br /><p><b><b>Download Zip</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://urloso.com/2uyQ13">https://urloso.com/2uyQ13</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/bioriAsaeru/text-to-voice/Fallout 4 Breathing Mask Mod.md b/spaces/bioriAsaeru/text-to-voice/Fallout 4 Breathing Mask Mod.md
deleted file mode 100644
index 98650a797e05c74132b0fa636d237f9e26a7641a..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Fallout 4 Breathing Mask Mod.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>fallout 4 breathing mask mod</h2><br /><p><b><b>Download File</b> &gt;&gt;&gt; <a href="https://urloso.com/2uyQhH">https://urloso.com/2uyQhH</a></b></p><br /><br />
-<br />
-The breathing mask is a piece of headwear in the Fallout: New Vegas add-on Lonesome Road. 1 Characteristics 2 Variants 3 Locations 4 Notes 5 Behind the ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/blmdsydm/faster-whisper-webui/src/whisper/whisperContainer.py b/spaces/blmdsydm/faster-whisper-webui/src/whisper/whisperContainer.py
deleted file mode 100644
index 7826d28aa3e6b345febdbd1e6297b4bba9e7fbdc..0000000000000000000000000000000000000000
--- a/spaces/blmdsydm/faster-whisper-webui/src/whisper/whisperContainer.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# External programs
-import abc
-import os
-import sys
-from typing import List
-from urllib.parse import urlparse
-import torch
-import urllib3
-from src.hooks.progressListener import ProgressListener
-
-import whisper
-from whisper import Whisper
-
-from src.config import ModelConfig, VadInitialPromptMode
-from src.hooks.whisperProgressHook import create_progress_listener_handle
-
-from src.modelCache import GLOBAL_MODEL_CACHE, ModelCache
-from src.prompts.abstractPromptStrategy import AbstractPromptStrategy
-from src.utils import download_file
-from src.whisper.abstractWhisperContainer import AbstractWhisperCallback, AbstractWhisperContainer
-
-class WhisperContainer(AbstractWhisperContainer):
-    def __init__(self, model_name: str, device: str = None, compute_type: str = "float16",
-                 download_root: str = None,
-                 cache: ModelCache = None, models: List[ModelConfig] = []):
-        if device is None:
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        super().__init__(model_name, device, compute_type, download_root, cache, models)
-    
-    def ensure_downloaded(self):
-        """
-        Ensure that the model is downloaded. This is useful if you want to ensure that the model is downloaded before
-        passing the container to a subprocess.
-        """
-        # Warning: Using private API here
-        try:
-            root_dir = self.download_root
-            model_config = self._get_model_config()
-
-            if root_dir is None:
-                root_dir = os.path.join(os.path.expanduser("~"), ".cache", "whisper")
-
-            if self.model_name in whisper._MODELS:
-                whisper._download(whisper._MODELS[self.model_name], root_dir, False)
-            else:
-                # If the model is not in the official list, see if it needs to be downloaded
-                model_config.download_url(root_dir)
-            return True
-        
-        except Exception as e:
-            # Given that the API is private, it could change at any time. We don't want to crash the program
-            print("Error pre-downloading model: " + str(e))
-            return False
-
-    def _get_model_config(self) -> ModelConfig:
-        """
-        Get the model configuration for the model.
-        """
-        for model in self.models:
-            if model.name == self.model_name:
-                return model
-        return None
-
-    def _create_model(self):
-        print("Loading whisper model " + self.model_name)
-        model_config = self._get_model_config()
-
-        # Note that the model will not be downloaded in the case of an official Whisper model
-        model_path = self._get_model_path(model_config, self.download_root)
-
-        return whisper.load_model(model_path, device=self.device, download_root=self.download_root)
-
-    def create_callback(self, language: str = None, task: str = None, 
-                        prompt_strategy: AbstractPromptStrategy = None,
-                        **decodeOptions: dict) -> AbstractWhisperCallback:
-        """
-        Create a WhisperCallback object that can be used to transcript audio files.
-
-        Parameters
-        ----------
-        language: str
-            The target language of the transcription. If not specified, the language will be inferred from the audio content.
-        task: str
-            The task - either translate or transcribe.
-        prompt_strategy: AbstractPromptStrategy
-            The prompt strategy to use. If not specified, the prompt from Whisper will be used.
-        decodeOptions: dict
-            Additional options to pass to the decoder. Must be pickleable.
-
-        Returns
-        -------
-        A WhisperCallback object.
-        """
-        return WhisperCallback(self, language=language, task=task, prompt_strategy=prompt_strategy, **decodeOptions)
-
-    def _get_model_path(self, model_config: ModelConfig, root_dir: str = None):
-        from src.conversion.hf_converter import convert_hf_whisper
-        """
-        Download the model.
-
-        Parameters
-        ----------
-        model_config: ModelConfig
-            The model configuration.
-        """
-        # See if path is already set
-        if model_config.path is not None:
-            return model_config.path
-        
-        if root_dir is None:
-            root_dir = os.path.join(os.path.expanduser("~"), ".cache", "whisper")
-
-        model_type = model_config.type.lower() if model_config.type is not None else "whisper"
-
-        if model_type in ["huggingface", "hf"]:
-            model_config.path = model_config.url
-            destination_target = os.path.join(root_dir, model_config.name + ".pt")
-
-            # Convert from HuggingFace format to Whisper format
-            if os.path.exists(destination_target):
-                print(f"File {destination_target} already exists, skipping conversion")
-            else:
-                print("Saving HuggingFace model in Whisper format to " + destination_target)
-                convert_hf_whisper(model_config.url, destination_target)
-
-            model_config.path = destination_target
-
-        elif model_type in ["whisper", "w"]:
-            model_config.path = model_config.url
-
-            # See if URL is just a file
-            if model_config.url in whisper._MODELS:
-                # No need to download anything - Whisper will handle it
-                model_config.path = model_config.url
-            elif model_config.url.startswith("file://"):
-                # Get file path
-                model_config.path = urlparse(model_config.url).path
-            # See if it is an URL
-            elif model_config.url.startswith("http://") or model_config.url.startswith("https://"):
-                # Extension (or file name)
-                extension = os.path.splitext(model_config.url)[-1]
-                download_target = os.path.join(root_dir, model_config.name + extension)
-
-                if os.path.exists(download_target) and not os.path.isfile(download_target):
-                    raise RuntimeError(f"{download_target} exists and is not a regular file")
-
-                if not os.path.isfile(download_target):
-                    download_file(model_config.url, download_target)
-                else:
-                    print(f"File {download_target} already exists, skipping download")
-
-                model_config.path = download_target
-            # Must be a local file
-            else:
-                model_config.path = model_config.url
-
-        else:
-            raise ValueError(f"Unknown model type {model_type}")
-
-        return model_config.path
-
-class WhisperCallback(AbstractWhisperCallback):
-    def __init__(self, model_container: WhisperContainer, language: str = None, task: str = None, 
-                 prompt_strategy: AbstractPromptStrategy = None, 
-                 **decodeOptions: dict):
-        self.model_container = model_container
-        self.language = language
-        self.task = task
-        self.prompt_strategy = prompt_strategy
-
-        self.decodeOptions = decodeOptions
-        
-    def invoke(self, audio, segment_index: int, prompt: str, detected_language: str, progress_listener: ProgressListener = None):
-        """
-        Peform the transcription of the given audio file or data.
-
-        Parameters
-        ----------
-        audio: Union[str, np.ndarray, torch.Tensor]
-            The audio file to transcribe, or the audio data as a numpy array or torch tensor.
-        segment_index: int
-            The target language of the transcription. If not specified, the language will be inferred from the audio content.
-        task: str
-            The task - either translate or transcribe.
-        progress_listener: ProgressListener
-            A callback to receive progress updates.
-        """
-        model = self.model_container.get_model()
-
-        if progress_listener is not None:
-            with create_progress_listener_handle(progress_listener):
-                return self._transcribe(model, audio, segment_index, prompt, detected_language)
-        else:
-            return self._transcribe(model, audio, segment_index, prompt, detected_language)
-    
-    def _transcribe(self, model: Whisper, audio, segment_index: int, prompt: str, detected_language: str):
-        decodeOptions = self.decodeOptions.copy()
-
-        # Add fp16
-        if self.model_container.compute_type in ["fp16", "float16"]:
-            decodeOptions["fp16"] = True
-
-        initial_prompt = self.prompt_strategy.get_segment_prompt(segment_index, prompt, detected_language) \
-                           if self.prompt_strategy else prompt
-
-        result = model.transcribe(audio, \
-            language=self.language if self.language else detected_language, task=self.task, \
-            initial_prompt=initial_prompt, \
-            **decodeOptions
-        )
-
-        # If we have a prompt strategy, we need to increment the current prompt
-        if self.prompt_strategy:
-            self.prompt_strategy.on_segment_finished(segment_index, prompt, detected_language, result)
-
-        return result
\ No newline at end of file
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/utils.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/utils.py
deleted file mode 100644
index 9878c31d03bd4114425f89dd1c6dda74337fe2e2..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/utils.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import os
-from typing import Dict, Optional
-
-from detectron2.config import CfgNode
-
-
-def is_relative_local_path(path: str) -> bool:
-    path_str = os.fsdecode(path)
-    return ("://" not in path_str) and not os.path.isabs(path)
-
-
-def maybe_prepend_base_path(base_path: Optional[str], path: str):
-    """
-    Prepends the provided path with a base path prefix if:
-    1) base path is not None;
-    2) path is a local path
-    """
-    if base_path is None:
-        return path
-    if is_relative_local_path(path):
-        return os.path.join(base_path, path)
-    return path
-
-
-def get_class_to_mesh_name_mapping(cfg: CfgNode) -> Dict[int, str]:
-    return {
-        int(class_id): mesh_name
-        for class_id, mesh_name in cfg.DATASETS.CLASS_TO_MESH_NAME_MAPPING.items()
-    }
-
-
-def get_category_to_class_mapping(dataset_cfg: CfgNode) -> Dict[str, int]:
-    return {
-        category: int(class_id)
-        for category, class_id in dataset_cfg.CATEGORY_TO_CLASS_MAPPING.items()
-    }
diff --git a/spaces/captchaboy/FAST-ABINet-OCR/demo.py b/spaces/captchaboy/FAST-ABINet-OCR/demo.py
deleted file mode 100644
index 7dc9bb41a5164cff64686053a06c0435c09f9587..0000000000000000000000000000000000000000
--- a/spaces/captchaboy/FAST-ABINet-OCR/demo.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import argparse
-import logging
-import os
-import glob
-import tqdm
-import torch
-import PIL
-import cv2
-import numpy as np
-import torch.nn.functional as F
-from torchvision import transforms
-from utils import Config, Logger, CharsetMapper
-
-def get_model(config):
-    import importlib
-    names = config.model_name.split('.')
-    module_name, class_name = '.'.join(names[:-1]), names[-1]
-    cls = getattr(importlib.import_module(module_name), class_name)
-    model = cls(config)
-    logging.info(model)
-    model = model.eval()
-    return model
-
-def preprocess(img, width, height):
-    img = cv2.resize(np.array(img), (width, height))
-    img = transforms.ToTensor()(img).unsqueeze(0)
-    mean = torch.tensor([0.485, 0.456, 0.406])
-    std  = torch.tensor([0.229, 0.224, 0.225])
-    return (img-mean[...,None,None]) / std[...,None,None]
-
-def postprocess(output, charset, model_eval):
-    def _get_output(last_output, model_eval):
-        if isinstance(last_output, (tuple, list)): 
-            for res in last_output:
-                if res['name'] == model_eval: output = res
-        else: output = last_output
-        return output
-
-    def _decode(logit):
-        """ Greed decode """
-        out = F.softmax(logit, dim=2)
-        pt_text, pt_scores, pt_lengths = [], [], []
-        for o in out:
-            text = charset.get_text(o.argmax(dim=1), padding=False, trim=False)
-            text = text.split(charset.null_char)[0]  # end at end-token
-            pt_text.append(text)
-            pt_scores.append(o.max(dim=1)[0])
-            pt_lengths.append(min(len(text) + 1, charset.max_length))  # one for end-token
-        return pt_text, pt_scores, pt_lengths
-
-    output = _get_output(output, model_eval)
-    logits, pt_lengths = output['logits'], output['pt_lengths']
-    pt_text, pt_scores, pt_lengths_ = _decode(logits)
-    
-    return pt_text, pt_scores, pt_lengths_
-
-def load(model, file, device=None, strict=True):
-    if device is None: device = 'cpu'
-    elif isinstance(device, int): device = torch.device('cuda', device)
-    assert os.path.isfile(file)
-    state = torch.load(file, map_location=device)
-    if set(state.keys()) == {'model', 'opt'}:
-        state = state['model']
-    model.load_state_dict(state, strict=strict)
-    return model
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', type=str, default='configs/train_abinet.yaml',
-                        help='path to config file')
-    parser.add_argument('--input', type=str, default='figs/test')
-    parser.add_argument('--cuda', type=int, default=-1)
-    parser.add_argument('--checkpoint', type=str, default='workdir/train-abinet/best-train-abinet.pth')
-    parser.add_argument('--model_eval', type=str, default='alignment', 
-                        choices=['alignment', 'vision', 'language'])
-    args = parser.parse_args()
-    config = Config(args.config)
-    if args.checkpoint is not None: config.model_checkpoint = args.checkpoint
-    if args.model_eval is not None: config.model_eval = args.model_eval
-    config.global_phase = 'test'
-    config.model_vision_checkpoint, config.model_language_checkpoint = None, None
-    device = 'cpu' if args.cuda < 0 else f'cuda:{args.cuda}'
-
-    Logger.init(config.global_workdir, config.global_name, config.global_phase)
-    Logger.enable_file()
-    logging.info(config)
-
-    logging.info('Construct model.')
-    model = get_model(config).to(device)
-    model = load(model, config.model_checkpoint, device=device)
-    charset = CharsetMapper(filename=config.dataset_charset_path,
-                            max_length=config.dataset_max_length + 1)
-
-    if os.path.isdir(args.input):
-        paths = [os.path.join(args.input, fname) for fname in os.listdir(args.input)]
-    else:
-        paths = glob.glob(os.path.expanduser(args.input))
-        assert paths, "The input path(s) was not found"
-    paths = sorted(paths)
-    for path in tqdm.tqdm(paths):
-        img = PIL.Image.open(path).convert('RGB')
-        img = preprocess(img, config.dataset_image_width, config.dataset_image_height)
-        img = img.to(device)
-        res = model(img)
-        pt_text, _, __ = postprocess(res, charset, config.model_eval)
-        logging.info(f'{path}: {pt_text[0]}')
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/MViTv2/configs/cascade_mask_rcnn_mvitv2_h_in21k_lsj_3x.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/MViTv2/configs/cascade_mask_rcnn_mvitv2_h_in21k_lsj_3x.py
deleted file mode 100644
index 6fee5e99b7d5d611d27dca62a7db7d88808f87da..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/MViTv2/configs/cascade_mask_rcnn_mvitv2_h_in21k_lsj_3x.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from .cascade_mask_rcnn_mvitv2_b_3x import model, optimizer, train, lr_multiplier
-from .common.coco_loader_lsj import dataloader
-
-
-model.backbone.bottom_up.embed_dim = 192
-model.backbone.bottom_up.depth = 80
-model.backbone.bottom_up.num_heads = 3
-model.backbone.bottom_up.last_block_indexes = (3, 11, 71, 79)
-model.backbone.bottom_up.drop_path_rate = 0.6
-model.backbone.bottom_up.use_act_checkpoint = True
-
-train.init_checkpoint = "detectron2://ImageNetPretrained/mvitv2/MViTv2_H_in21k.pyth"
diff --git a/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/text/symbols.py b/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/text/symbols.py
deleted file mode 100644
index 789e9df25d3d93d1976ef22d15d77f51d170ed00..0000000000000000000000000000000000000000
--- a/spaces/cccccch/VITS-fast-fine-tuning-DingZhen/text/symbols.py
+++ /dev/null
@@ -1,76 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-# japanese_cleaners
-# _pad        = '_'
-# _punctuation = ',.!?-'
-# _letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-
-
-'''# japanese_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-
-'''# korean_cleaners
-_pad        = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad        = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# # zh_ja_mixture_cleaners
-# _pad        = '_'
-# _punctuation = ',.!?-~…'
-# _letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-'''# sanskrit_cleaners
-_pad        = '_'
-_punctuation = '।'
-_letters = 'ँंःअआइईउऊऋएऐओऔकखगघङचछजझञटठडढणतथदधनपफबभमयरलळवशषसहऽािीुूृॄेैोौ्ॠॢ '
-'''
-
-'''# cjks_cleaners
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzʃʧʥʦɯɹəɥçɸɾβŋɦː⁼ʰ`^#*=→↓↑ '
-'''
-
-'''# thai_cleaners
-_pad        = '_'
-_punctuation = '.!? '
-_letters = 'กขฃคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลวศษสหฬอฮฯะัาำิีึืุูเแโใไๅๆ็่้๊๋์'
-'''
-
-# # cjke_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ '
-
-
-'''# shanghainese_cleaners
-_pad        = '_'
-_punctuation = ',.!?…'
-_letters = 'abdfghiklmnopstuvyzøŋȵɑɔɕəɤɦɪɿʑʔʰ̩̃ᴀᴇ15678 '
-'''
-
-'''# chinese_dialect_cleaners
-_pad        = '_'
-_punctuation = ',.!?~…─'
-_letters = '#Nabdefghijklmnoprstuvwxyzæçøŋœȵɐɑɒɓɔɕɗɘəɚɛɜɣɤɦɪɭɯɵɷɸɻɾɿʂʅʊʋʌʏʑʔʦʮʰʷˀː˥˦˧˨˩̥̩̃̚ᴀᴇ↑↓∅ⱼ '
-'''
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/data/datasets/coco_classes.py b/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/data/datasets/coco_classes.py
deleted file mode 100644
index 17f5cbe6e86ed4fc8378760da71f8349a6406ff1..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/data/datasets/coco_classes.py
+++ /dev/null
@@ -1,86 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-COCO_CLASSES = (
-    "person",
-    "bicycle",
-    "car",
-    "motorcycle",
-    "airplane",
-    "bus",
-    "train",
-    "truck",
-    "boat",
-    "traffic light",
-    "fire hydrant",
-    "stop sign",
-    "parking meter",
-    "bench",
-    "bird",
-    "cat",
-    "dog",
-    "horse",
-    "sheep",
-    "cow",
-    "elephant",
-    "bear",
-    "zebra",
-    "giraffe",
-    "backpack",
-    "umbrella",
-    "handbag",
-    "tie",
-    "suitcase",
-    "frisbee",
-    "skis",
-    "snowboard",
-    "sports ball",
-    "kite",
-    "baseball bat",
-    "baseball glove",
-    "skateboard",
-    "surfboard",
-    "tennis racket",
-    "bottle",
-    "wine glass",
-    "cup",
-    "fork",
-    "knife",
-    "spoon",
-    "bowl",
-    "banana",
-    "apple",
-    "sandwich",
-    "orange",
-    "broccoli",
-    "carrot",
-    "hot dog",
-    "pizza",
-    "donut",
-    "cake",
-    "chair",
-    "couch",
-    "potted plant",
-    "bed",
-    "dining table",
-    "toilet",
-    "tv",
-    "laptop",
-    "mouse",
-    "remote",
-    "keyboard",
-    "cell phone",
-    "microwave",
-    "oven",
-    "toaster",
-    "sink",
-    "refrigerator",
-    "book",
-    "clock",
-    "vase",
-    "scissors",
-    "teddy bear",
-    "hair drier",
-    "toothbrush",
-)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cffi/parse_c_type.h b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cffi/parse_c_type.h
deleted file mode 100644
index 84e4ef85659eb63e6453d8af9f024f1866182342..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cffi/parse_c_type.h
+++ /dev/null
@@ -1,181 +0,0 @@
-
-/* This part is from file 'cffi/parse_c_type.h'.  It is copied at the
-   beginning of C sources generated by CFFI's ffi.set_source(). */
-
-typedef void *_cffi_opcode_t;
-
-#define _CFFI_OP(opcode, arg)   (_cffi_opcode_t)(opcode | (((uintptr_t)(arg)) << 8))
-#define _CFFI_GETOP(cffi_opcode)    ((unsigned char)(uintptr_t)cffi_opcode)
-#define _CFFI_GETARG(cffi_opcode)   (((intptr_t)cffi_opcode) >> 8)
-
-#define _CFFI_OP_PRIMITIVE       1
-#define _CFFI_OP_POINTER         3
-#define _CFFI_OP_ARRAY           5
-#define _CFFI_OP_OPEN_ARRAY      7
-#define _CFFI_OP_STRUCT_UNION    9
-#define _CFFI_OP_ENUM           11
-#define _CFFI_OP_FUNCTION       13
-#define _CFFI_OP_FUNCTION_END   15
-#define _CFFI_OP_NOOP           17
-#define _CFFI_OP_BITFIELD       19
-#define _CFFI_OP_TYPENAME       21
-#define _CFFI_OP_CPYTHON_BLTN_V 23   // varargs
-#define _CFFI_OP_CPYTHON_BLTN_N 25   // noargs
-#define _CFFI_OP_CPYTHON_BLTN_O 27   // O  (i.e. a single arg)
-#define _CFFI_OP_CONSTANT       29
-#define _CFFI_OP_CONSTANT_INT   31
-#define _CFFI_OP_GLOBAL_VAR     33
-#define _CFFI_OP_DLOPEN_FUNC    35
-#define _CFFI_OP_DLOPEN_CONST   37
-#define _CFFI_OP_GLOBAL_VAR_F   39
-#define _CFFI_OP_EXTERN_PYTHON  41
-
-#define _CFFI_PRIM_VOID          0
-#define _CFFI_PRIM_BOOL          1
-#define _CFFI_PRIM_CHAR          2
-#define _CFFI_PRIM_SCHAR         3
-#define _CFFI_PRIM_UCHAR         4
-#define _CFFI_PRIM_SHORT         5
-#define _CFFI_PRIM_USHORT        6
-#define _CFFI_PRIM_INT           7
-#define _CFFI_PRIM_UINT          8
-#define _CFFI_PRIM_LONG          9
-#define _CFFI_PRIM_ULONG        10
-#define _CFFI_PRIM_LONGLONG     11
-#define _CFFI_PRIM_ULONGLONG    12
-#define _CFFI_PRIM_FLOAT        13
-#define _CFFI_PRIM_DOUBLE       14
-#define _CFFI_PRIM_LONGDOUBLE   15
-
-#define _CFFI_PRIM_WCHAR        16
-#define _CFFI_PRIM_INT8         17
-#define _CFFI_PRIM_UINT8        18
-#define _CFFI_PRIM_INT16        19
-#define _CFFI_PRIM_UINT16       20
-#define _CFFI_PRIM_INT32        21
-#define _CFFI_PRIM_UINT32       22
-#define _CFFI_PRIM_INT64        23
-#define _CFFI_PRIM_UINT64       24
-#define _CFFI_PRIM_INTPTR       25
-#define _CFFI_PRIM_UINTPTR      26
-#define _CFFI_PRIM_PTRDIFF      27
-#define _CFFI_PRIM_SIZE         28
-#define _CFFI_PRIM_SSIZE        29
-#define _CFFI_PRIM_INT_LEAST8   30
-#define _CFFI_PRIM_UINT_LEAST8  31
-#define _CFFI_PRIM_INT_LEAST16  32
-#define _CFFI_PRIM_UINT_LEAST16 33
-#define _CFFI_PRIM_INT_LEAST32  34
-#define _CFFI_PRIM_UINT_LEAST32 35
-#define _CFFI_PRIM_INT_LEAST64  36
-#define _CFFI_PRIM_UINT_LEAST64 37
-#define _CFFI_PRIM_INT_FAST8    38
-#define _CFFI_PRIM_UINT_FAST8   39
-#define _CFFI_PRIM_INT_FAST16   40
-#define _CFFI_PRIM_UINT_FAST16  41
-#define _CFFI_PRIM_INT_FAST32   42
-#define _CFFI_PRIM_UINT_FAST32  43
-#define _CFFI_PRIM_INT_FAST64   44
-#define _CFFI_PRIM_UINT_FAST64  45
-#define _CFFI_PRIM_INTMAX       46
-#define _CFFI_PRIM_UINTMAX      47
-#define _CFFI_PRIM_FLOATCOMPLEX 48
-#define _CFFI_PRIM_DOUBLECOMPLEX 49
-#define _CFFI_PRIM_CHAR16       50
-#define _CFFI_PRIM_CHAR32       51
-
-#define _CFFI__NUM_PRIM         52
-#define _CFFI__UNKNOWN_PRIM           (-1)
-#define _CFFI__UNKNOWN_FLOAT_PRIM     (-2)
-#define _CFFI__UNKNOWN_LONG_DOUBLE    (-3)
-
-#define _CFFI__IO_FILE_STRUCT         (-1)
-
-
-struct _cffi_global_s {
-    const char *name;
-    void *address;
-    _cffi_opcode_t type_op;
-    void *size_or_direct_fn;  // OP_GLOBAL_VAR: size, or 0 if unknown
-                              // OP_CPYTHON_BLTN_*: addr of direct function
-};
-
-struct _cffi_getconst_s {
-    unsigned long long value;
-    const struct _cffi_type_context_s *ctx;
-    int gindex;
-};
-
-struct _cffi_struct_union_s {
-    const char *name;
-    int type_index;          // -> _cffi_types, on a OP_STRUCT_UNION
-    int flags;               // _CFFI_F_* flags below
-    size_t size;
-    int alignment;
-    int first_field_index;   // -> _cffi_fields array
-    int num_fields;
-};
-#define _CFFI_F_UNION         0x01   // is a union, not a struct
-#define _CFFI_F_CHECK_FIELDS  0x02   // complain if fields are not in the
-                                     // "standard layout" or if some are missing
-#define _CFFI_F_PACKED        0x04   // for CHECK_FIELDS, assume a packed struct
-#define _CFFI_F_EXTERNAL      0x08   // in some other ffi.include()
-#define _CFFI_F_OPAQUE        0x10   // opaque
-
-struct _cffi_field_s {
-    const char *name;
-    size_t field_offset;
-    size_t field_size;
-    _cffi_opcode_t field_type_op;
-};
-
-struct _cffi_enum_s {
-    const char *name;
-    int type_index;          // -> _cffi_types, on a OP_ENUM
-    int type_prim;           // _CFFI_PRIM_xxx
-    const char *enumerators; // comma-delimited string
-};
-
-struct _cffi_typename_s {
-    const char *name;
-    int type_index;   /* if opaque, points to a possibly artificial
-                         OP_STRUCT which is itself opaque */
-};
-
-struct _cffi_type_context_s {
-    _cffi_opcode_t *types;
-    const struct _cffi_global_s *globals;
-    const struct _cffi_field_s *fields;
-    const struct _cffi_struct_union_s *struct_unions;
-    const struct _cffi_enum_s *enums;
-    const struct _cffi_typename_s *typenames;
-    int num_globals;
-    int num_struct_unions;
-    int num_enums;
-    int num_typenames;
-    const char *const *includes;
-    int num_types;
-    int flags;      /* future extension */
-};
-
-struct _cffi_parse_info_s {
-    const struct _cffi_type_context_s *ctx;
-    _cffi_opcode_t *output;
-    unsigned int output_size;
-    size_t error_location;
-    const char *error_message;
-};
-
-struct _cffi_externpy_s {
-    const char *name;
-    size_t size_of_result;
-    void *reserved1, *reserved2;
-};
-
-#ifdef _CFFI_INTERNAL
-static int parse_c_type(struct _cffi_parse_info_s *info, const char *input);
-static int search_in_globals(const struct _cffi_type_context_s *ctx,
-                             const char *search, size_t search_len);
-static int search_in_struct_unions(const struct _cffi_type_context_s *ctx,
-                                   const char *search, size_t search_len);
-#endif
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/keywrap.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/keywrap.py
deleted file mode 100644
index 59b0326c2a86fafad73fa34b893aa2c76c194617..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cryptography/hazmat/primitives/keywrap.py
+++ /dev/null
@@ -1,177 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-from __future__ import annotations
-
-import typing
-
-from cryptography.hazmat.primitives.ciphers import Cipher
-from cryptography.hazmat.primitives.ciphers.algorithms import AES
-from cryptography.hazmat.primitives.ciphers.modes import ECB
-from cryptography.hazmat.primitives.constant_time import bytes_eq
-
-
-def _wrap_core(
-    wrapping_key: bytes,
-    a: bytes,
-    r: typing.List[bytes],
-) -> bytes:
-    # RFC 3394 Key Wrap - 2.2.1 (index method)
-    encryptor = Cipher(AES(wrapping_key), ECB()).encryptor()
-    n = len(r)
-    for j in range(6):
-        for i in range(n):
-            # every encryption operation is a discrete 16 byte chunk (because
-            # AES has a 128-bit block size) and since we're using ECB it is
-            # safe to reuse the encryptor for the entire operation
-            b = encryptor.update(a + r[i])
-            a = (
-                int.from_bytes(b[:8], byteorder="big") ^ ((n * j) + i + 1)
-            ).to_bytes(length=8, byteorder="big")
-            r[i] = b[-8:]
-
-    assert encryptor.finalize() == b""
-
-    return a + b"".join(r)
-
-
-def aes_key_wrap(
-    wrapping_key: bytes,
-    key_to_wrap: bytes,
-    backend: typing.Any = None,
-) -> bytes:
-    if len(wrapping_key) not in [16, 24, 32]:
-        raise ValueError("The wrapping key must be a valid AES key length")
-
-    if len(key_to_wrap) < 16:
-        raise ValueError("The key to wrap must be at least 16 bytes")
-
-    if len(key_to_wrap) % 8 != 0:
-        raise ValueError("The key to wrap must be a multiple of 8 bytes")
-
-    a = b"\xa6\xa6\xa6\xa6\xa6\xa6\xa6\xa6"
-    r = [key_to_wrap[i : i + 8] for i in range(0, len(key_to_wrap), 8)]
-    return _wrap_core(wrapping_key, a, r)
-
-
-def _unwrap_core(
-    wrapping_key: bytes,
-    a: bytes,
-    r: typing.List[bytes],
-) -> typing.Tuple[bytes, typing.List[bytes]]:
-    # Implement RFC 3394 Key Unwrap - 2.2.2 (index method)
-    decryptor = Cipher(AES(wrapping_key), ECB()).decryptor()
-    n = len(r)
-    for j in reversed(range(6)):
-        for i in reversed(range(n)):
-            atr = (
-                int.from_bytes(a, byteorder="big") ^ ((n * j) + i + 1)
-            ).to_bytes(length=8, byteorder="big") + r[i]
-            # every decryption operation is a discrete 16 byte chunk so
-            # it is safe to reuse the decryptor for the entire operation
-            b = decryptor.update(atr)
-            a = b[:8]
-            r[i] = b[-8:]
-
-    assert decryptor.finalize() == b""
-    return a, r
-
-
-def aes_key_wrap_with_padding(
-    wrapping_key: bytes,
-    key_to_wrap: bytes,
-    backend: typing.Any = None,
-) -> bytes:
-    if len(wrapping_key) not in [16, 24, 32]:
-        raise ValueError("The wrapping key must be a valid AES key length")
-
-    aiv = b"\xA6\x59\x59\xA6" + len(key_to_wrap).to_bytes(
-        length=4, byteorder="big"
-    )
-    # pad the key to wrap if necessary
-    pad = (8 - (len(key_to_wrap) % 8)) % 8
-    key_to_wrap = key_to_wrap + b"\x00" * pad
-    if len(key_to_wrap) == 8:
-        # RFC 5649 - 4.1 - exactly 8 octets after padding
-        encryptor = Cipher(AES(wrapping_key), ECB()).encryptor()
-        b = encryptor.update(aiv + key_to_wrap)
-        assert encryptor.finalize() == b""
-        return b
-    else:
-        r = [key_to_wrap[i : i + 8] for i in range(0, len(key_to_wrap), 8)]
-        return _wrap_core(wrapping_key, aiv, r)
-
-
-def aes_key_unwrap_with_padding(
-    wrapping_key: bytes,
-    wrapped_key: bytes,
-    backend: typing.Any = None,
-) -> bytes:
-    if len(wrapped_key) < 16:
-        raise InvalidUnwrap("Must be at least 16 bytes")
-
-    if len(wrapping_key) not in [16, 24, 32]:
-        raise ValueError("The wrapping key must be a valid AES key length")
-
-    if len(wrapped_key) == 16:
-        # RFC 5649 - 4.2 - exactly two 64-bit blocks
-        decryptor = Cipher(AES(wrapping_key), ECB()).decryptor()
-        out = decryptor.update(wrapped_key)
-        assert decryptor.finalize() == b""
-        a = out[:8]
-        data = out[8:]
-        n = 1
-    else:
-        r = [wrapped_key[i : i + 8] for i in range(0, len(wrapped_key), 8)]
-        encrypted_aiv = r.pop(0)
-        n = len(r)
-        a, r = _unwrap_core(wrapping_key, encrypted_aiv, r)
-        data = b"".join(r)
-
-    # 1) Check that MSB(32,A) = A65959A6.
-    # 2) Check that 8*(n-1) < LSB(32,A) <= 8*n.  If so, let
-    #    MLI = LSB(32,A).
-    # 3) Let b = (8*n)-MLI, and then check that the rightmost b octets of
-    #    the output data are zero.
-    mli = int.from_bytes(a[4:], byteorder="big")
-    b = (8 * n) - mli
-    if (
-        not bytes_eq(a[:4], b"\xa6\x59\x59\xa6")
-        or not 8 * (n - 1) < mli <= 8 * n
-        or (b != 0 and not bytes_eq(data[-b:], b"\x00" * b))
-    ):
-        raise InvalidUnwrap()
-
-    if b == 0:
-        return data
-    else:
-        return data[:-b]
-
-
-def aes_key_unwrap(
-    wrapping_key: bytes,
-    wrapped_key: bytes,
-    backend: typing.Any = None,
-) -> bytes:
-    if len(wrapped_key) < 24:
-        raise InvalidUnwrap("Must be at least 24 bytes")
-
-    if len(wrapped_key) % 8 != 0:
-        raise InvalidUnwrap("The wrapped key must be a multiple of 8 bytes")
-
-    if len(wrapping_key) not in [16, 24, 32]:
-        raise ValueError("The wrapping key must be a valid AES key length")
-
-    aiv = b"\xa6\xa6\xa6\xa6\xa6\xa6\xa6\xa6"
-    r = [wrapped_key[i : i + 8] for i in range(0, len(wrapped_key), 8)]
-    a = r.pop(0)
-    a, r = _unwrap_core(wrapping_key, a, r)
-    if not bytes_eq(a, aiv):
-        raise InvalidUnwrap()
-
-    return b"".join(r)
-
-
-class InvalidUnwrap(Exception):
-    pass
diff --git a/spaces/cihyFjudo/fairness-paper-search/Pattu sattai karan Oru Pottu Vacha mama mp3 Stream the popular song online for free on Wynk.md b/spaces/cihyFjudo/fairness-paper-search/Pattu sattai karan Oru Pottu Vacha mama mp3 Stream the popular song online for free on Wynk.md
deleted file mode 100644
index ed7cf595ac658aaca970763ccd17c4656fa11908..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Pattu sattai karan Oru Pottu Vacha mama mp3 Stream the popular song online for free on Wynk.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Pattu sattai karan Oru Pottu Vacha mama mp3</h2><br /><p><b><b>DOWNLOAD</b> &raquo; <a href="https://tinurli.com/2uwikB">https://tinurli.com/2uwikB</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/_version.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/_version.py
deleted file mode 100644
index b723056a756af22aaf1a4709c5122bea9fb279ee..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/dateutil/_version.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# coding: utf-8
-# file generated by setuptools_scm
-# don't change, don't track in version control
-version = '2.8.2'
-version_tuple = (2, 8, 2)
diff --git a/spaces/cmu-adcs/videogenic/videogenic.py b/spaces/cmu-adcs/videogenic/videogenic.py
deleted file mode 100644
index d71dfb4b16ba1f5c533009a40dc3355afa4b32d5..0000000000000000000000000000000000000000
--- a/spaces/cmu-adcs/videogenic/videogenic.py
+++ /dev/null
@@ -1,605 +0,0 @@
-import streamlit as st
-# from pytube import YouTube
-# from pytube import extract
-import cv2
-from PIL import Image
-import clip as openai_clip
-import torch
-import math
-import numpy as np
-import tempfile
-# from humanfriendly import format_timespan
-import json
-import sys
-from random import randrange
-import logging
-# from pyunsplash import PyUnsplash
-import requests
-import io
-from io import BytesIO
-import base64
-import altair as alt
-from streamlit_vega_lite import altair_component
-import pandas as pd
-from datetime import timedelta
-import math
-from decord import VideoReader, cpu, gpu
-from moviepy.video.io.VideoFileClip import VideoFileClip
-from moviepy.audio.io.AudioFileClip import AudioFileClip
-from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip
-from moviepy.editor import *
-import glob
-
-# @st.cache(show_spinner=False)
-def load_model():
-  device = 'cuda' if torch.cuda.is_available() else 'cpu'
-  model, preprocess = openai_clip.load('ViT-B/32', device=device)
-  if 'model' not in st.session_state:
-    st.session_state.model = model
-    st.session_state.preprocess = preprocess
-    st.session_state.device = device
-  st.session_state.model = model
-  st.session_state.preprocess = preprocess
-  st.session_state.device = device
-
-def fetch_video(url):
-  yt = YouTube(url)
-  streams = yt.streams.filter(adaptive=True, subtype='mp4', resolution='360p', only_video=True)
-  length = yt.length
-  if length >= 300:
-    st.error('Please find a YouTube video shorter than 5 minutes. Sorry about this, the server capacity is limited for the time being.')
-    st.stop()
-  video = streams[0]
-  return video, video.url
-
-# @st.cache()
-# def extract_frames(video):
-#   frames = []
-#   capture = cv2.VideoCapture(video)
-#   fps = capture.get(cv2.CAP_PROP_FPS)
-#   current_frame = 0
-#   while capture.isOpened():
-#     ret, frame = capture.read()
-#     if ret == True:
-#       frames.append(Image.fromarray(frame[:, :, ::-1]))
-#     else:
-#       break
-#     current_frame += fps
-#     capture.set(cv2.CAP_PROP_POS_FRAMES, current_frame)
-#   # print(f'Frames extracted: {len(frames)}')
-
-#   return frames, fps
-
-# @st.cache()
-def video_to_frames(video):
-  vr = VideoReader(video)
-  frames = []
-  frame_count = len(vr)
-  fps = vr.get_avg_fps()
-  for i in range(0, frame_count, round(fps)):
-  # for i in range(0, frame_count):
-    frame = vr[i].asnumpy()
-    y_dim = frame.shape[0]
-    x_dim = frame.shape[1]
-    frames.append(Image.fromarray(frame))
-  return frames, fps, x_dim, y_dim
-
-def video_to_info(video):
-  vr = VideoReader(video)
-  frames = []
-  frame_count = len(vr)
-  fps = vr.get_avg_fps()
-  frame = vr[0].asnumpy()
-  y_dim = frame.shape[0]
-  x_dim = frame.shape[1]
-  return fps, x_dim, y_dim
-
-# @st.cache()
-def encode_frames(video_frames):
-  batch_size = 256
-  batches = math.ceil(len(video_frames) / batch_size)
-  video_features = torch.empty([0, 512], dtype=torch.float16).to(st.session_state.device)
-  for i in range(batches):
-    batch_frames = video_frames[i*batch_size : (i+1)*batch_size]
-    batch_preprocessed = torch.stack([st.session_state.preprocess(frame) for frame in batch_frames]).to(st.session_state.device)
-    with torch.no_grad():
-      batch_features = st.session_state.model.encode_image(batch_preprocessed)
-      batch_features /= batch_features.norm(dim=-1, keepdim=True)
-    video_features = torch.cat((video_features, batch_features))
-  # print(f'Features: {video_features.shape}')
-  return video_features
-
-def classify_activity(video_features, activities_list):
-	text = torch.cat([openai_clip.tokenize(
-		f'{activity}') for activity in activities_list]).to(st.session_state.device)
-	with torch.no_grad():
-		text_features = st.session_state.model.encode_text(text)
-		text_features /= text_features.norm(dim=-1, keepdim=True)
-	logit_scale = st.session_state.model.logit_scale.exp()
-	video_features = torch.from_numpy(video_features)
-	similarities = (logit_scale * video_features @
-                 text_features.t()).softmax(dim=-1)
-	probs, word_idxs = similarities[0].topk(5)
-	primary_activity = []
-	for prob, word_idx in zip(probs, word_idxs):
-		primary_activity.append(activities_list[word_idx])
-	# primary_activity = activities_list[word_idx]
-	return primary_activity
-
-def encode_photos(photos):
-  batch_size = 256
-  batches = math.ceil(len(photos) / batch_size)
-  video_features = torch.empty([0, 512], dtype=torch.float16).to(st.session_state.device)
-  for i in range(batches):
-    batch_frames = photos[i*batch_size : (i+1)*batch_size]
-    batch_preprocessed = torch.stack([st.session_state.preprocess(Image.open(frame)) for frame in batch_frames]).to(st.session_state.device)
-    with torch.no_grad():
-      batch_features = st.session_state.model.encode_image(batch_preprocessed)
-      batch_features /= batch_features.norm(dim=-1, keepdim=True)
-    video_features = torch.cat((video_features, batch_features))
-  # print(f'Features: {video_features.shape}')
-  return video_features
-
-def img_to_bytes(img):
-  img_byte_arr = io.BytesIO()
-  img.save(img_byte_arr, format='JPEG')
-  img_byte_arr = img_byte_arr.getvalue()
-  return img_byte_arr
-
-def normalize(vector):
-  return (vector - np.min(vector)) / (np.max(vector) - np.min(vector))
-
-def format_img(img):
-  size = 150, 150
-  # img = Image.fromarray(img)
-  img.thumbnail(size, Image.Resampling.LANCZOS)
-  output = io.BytesIO()
-  img.save(output, format='PNG')
-  encoded_string = f'data:image/png;base64,{base64.b64encode(output.getvalue()).decode()}'
-  return encoded_string
-
-def get_photos(keyword):
-  photo_collection = []
-  for filename in glob.glob(f'photos/{st.session_state.domain.lower()}/*.jpeg'):
-    photo = Image.open(filename)
-    photo_collection.append(photo)
-  return photo_collection
-
-  # # api_key = 'hzcKZ0e4we95wSd8_ip2zTB3m2DrOMWehAxrYjqjwg0'
-  # api_key = 'fZ1nE7Y4NC-iYGmqgv-WuyM8m9p0LroCdAOZOR6tyho'
-  # unsplash_search = PyUnsplash(api_key=api_key)
-  # logging.getLogger('pyunsplash').setLevel(logging.DEBUG)
-  # search = unsplash_search.search(type_='photos', query=keyword) # per_page
-  # photo_collection = []
-  # # st.markdown(f'**Unsplash photos for `{keyword}`**')
-  # for result in search.entries:
-  #   photo_url = result.link_download
-  #   response = requests.get(photo_url)
-  #   photo = Image.open(BytesIO(response.content))
-  #   # st.image(photo, width=200)
-  #   photo_collection.append(photo)
-  # return photo_collection
-
-def display_results(best_photo_idx):
-  st.markdown('**Top 10 highlights**')
-  result_arr = []
-  for frame_id in best_photo_idx:
-    result = st.session_state.video_frames[frame_id]
-    st.image(result)
-  return result_arr
-
-def make_df(similarities):
-  similarities = similarities
-  df = pd.DataFrame()
-  df['keyword'] = [keyword] * len(similarities)
-  df['x'] = [i for i, _ in enumerate(similarities)]
-  df['y'] = normalize(np.power(similarities, 8))
-  df['image'] = [format_img(frame) for frame in st.session_state.video_frames]
-  return df
-
-# @st.cache()
-def compute_scores(search_query, video_features, text_query, display_results_count=10):
-  sum_photo = torch.zeros(1, 512)
-  for photo in search_query:
-    with torch.no_grad():
-      image_features = st.session_state.model.encode_image(st.session_state.preprocess(photo).unsqueeze(0).to(st.session_state.device))
-      image_features /= image_features.norm(dim=-1, keepdim=True)
-      sum_photo += sum_photo + image_features
-  avg_photo = sum_photo / len(search_query)
-  video_features = torch.from_numpy(video_features)
-  similarities = (100.0 * video_features @ avg_photo.T)  
-  # values, best_photo_idx = similarities.topk(display_results_count, dim=0)
-  # display_results(best_photo_idx)
-  return similarities.cpu().numpy()
-
-def avenir():
-    font = 'Avenir'
-    return {
-        'config' : {
-             'title': {'font': font},
-             'axis': {
-                  'labelFont': font,
-                  'titleFont': font
-             }
-        }
-    }
-
-alt.themes.register('avenir', avenir)
-alt.themes.enable('avenir')
-
-# TODO: Make playhead scores and average according to keyword
-# TODO: Maximum interval selection
-# TODO: Interactive legend https://altair-viz.github.io/gallery/interactive_legend.html
-# TODO: Multi-line highlight https://altair-viz.github.io/gallery/multiline_highlight.html
-@st.cache
-def draw_chart(df, mode):
-  if st.session_state.mode == 'Automatic':
-    nearest = alt.selection(type='single', nearest=True, on='mouseover', empty='none')
-    line = alt.Chart(df).mark_line().encode(
-      x=alt.X('x:Q', axis=alt.Axis(labels=True, tickSize=0, title='')),
-      y=alt.Y('y', axis=alt.Axis(labels=False, tickSize=0, title='')),
-      # color=alt.Color('keyword:N', scale=alt.Scale(scheme='tableau20')),
-      color=alt.value('#00C7BE'),
-      # color=alt.Color('#9b59b6'),
-    )
-    selectors = alt.Chart(df).mark_point().encode(
-      x='x:Q',
-      opacity=alt.value(0),
-    ).add_selection(
-        nearest
-    )
-    rules = alt.Chart(df).mark_rule(color='black').encode(
-      x='x:Q',
-    ).transform_filter(
-      nearest
-    )
-    points = line.mark_point().encode(
-      opacity=alt.condition(nearest, alt.value(1), alt.value(0))
-    )
-    text = line.mark_text(align='center', yOffset=-110, fontSize=16).encode(
-      text=alt.condition(nearest, 'y:N', alt.value(' ')),
-      color=alt.value('#000000'),
-      # fontSize=30
-    ).transform_calculate(y=f'format(datum.y, ".2f")')
-    image = line.mark_image(align='center', width=150, height=150, yOffset=-60).encode(
-      url=alt.condition(nearest, 'image', alt.value(' '))
-    )
-    chart = alt.layer(line, selectors, points, rules, text, image)
-  elif st.session_state.mode == 'brush':
-    brush = alt.selection(type='interval', encodings=['x'])
-    line = alt.Chart(df).mark_line().encode( # https://www.rdocumentation.org/packages/vegalite/versions/0.6.1/topics/mark_line
-      x=alt.X('x:Q', axis=alt.Axis(labels=True, tickSize=0, title='')),
-      y=alt.Y('y:Q', axis=alt.Axis(labels=False, tickSize=0, title='')),
-      # color=alt.Color('keyword:N', scale=alt.Scale(scheme='tableau20')),
-      color=alt.value('#00C7BE'),      
-    ).add_selection(
-      brush
-    )
-    text = alt.Chart(df).transform_filter(brush).mark_text(
-      align='right',
-      # baseline='top',
-      # dx=1500
-      dx=750,
-      dy=-12,
-      fontSize=24,
-      fontWeight=800,
-    ).encode(
-      # x='max(x):Q',
-      y='mean(y):Q',
-      # dy=alt.value(10),
-      text=alt.Text('mean(y):Q', format='.2f'),
-    )
-    average = alt.Chart(df).mark_rule(color='black', strokeDash=[5, 5]).encode(
-      y='mean(y):Q',
-      # size=alt.SizeValue(3),
-    ).transform_filter(
-      brush
-    )
-    # chart = alt.layer(line, average, text)
-    chart = line
-  elif st.session_state.mode == 'User selection':
-    brush = alt.selection(type='interval', encodings=['x'])
-    line = alt.Chart(df).mark_line().encode( # https://www.rdocumentation.org/packages/vegalite/versions/0.6.1/topics/mark_line
-      x=alt.X('x:Q', axis=alt.Axis(labels=True, tickSize=0, title='')),
-      y=alt.Y('y:Q', axis=alt.Axis(labels=False, tickSize=0, title='')),
-      # color=alt.Color('keyword:N', scale=alt.Scale(scheme='tableau20')),
-      color=alt.value('#00C7BE'),      
-    ).add_selection(
-      brush
-    )
-    text = alt.Chart(df).transform_filter(brush).mark_text(
-      align='right',
-      # baseline='top',
-      # dx=1500
-      dx=750,
-      dy=-12,
-      fontSize=24,
-      fontWeight=800,
-    ).encode(
-      # x='max(x):Q',
-      y='mean(y):Q',
-      # dy=alt.value(10),
-      text=alt.Text('mean(y):Q', format='.2f'),
-    )
-    average = alt.Chart(df).mark_rule(color='black', strokeDash=[5, 5]).encode(
-      y='mean(y):Q',
-      # size=alt.SizeValue(3),
-    ).transform_filter(
-      brush
-    )
-    # chart = alt.layer(line, average, text)
-    chart = line
-  return chart.properties(width=1250, height=500).configure_axis(grid=False, domain=False).configure_view(strokeOpacity=0)
-  # return line
-
-def max_subarray(arr, k):
-  n = len(arr)
-  if (n < k):
-    st.write('Video too short')
-  res = 0
-  left = 0
-  right = k
-  for i in range(k):
-    res += arr[i]
-  curr_sum = res
-  for i in range(k, n):
-    curr_sum += arr[i] - arr[i - k]
-    if curr_sum > res:
-      res = curr_sum
-      left = i - k
-      right = i
-  return res, left, right
-
-def edit_video(template, df_all):
-  video_path = f'videos/{st.session_state.domain.lower()}.mp4'
-  if template == 'Coming In Hot by Andy Mineo & Lecrae (hype, 7 seconds)':
-    res, left, right = max_subarray(df_all['y'].tolist(), 7)
-    video = VideoFileClip(video_path).subclip(t_start=left, t_end=right)
-    fps = video.fps
-    x_dim = st.session_state.x_dim
-    y_dim = st.session_state.y_dim
-    music_path = 'music/coming-in-hot.mp3'
-    blank1 = ColorClip((x_dim, y_dim), (0, 0, 0), duration=0.6)
-    flash1 = video.subclip(t_start=0, t_end=1.2)
-    blank2 = ColorClip((x_dim, y_dim), (0, 0, 0), duration=0.1)
-    flash2 = video.subclip(t_start=1.3, t_end=1.4)
-    blank3 = ColorClip((x_dim, y_dim), (0, 0, 0), duration=0.1)
-    flash3 = video.subclip(t_start=1.5, t_end=3.3)
-    blank4 = ColorClip((x_dim, y_dim), (0, 0, 0), duration=0.1)
-    flash4 = video.subclip(t_start=3.4, t_end=3.5)
-    blank5 = ColorClip((x_dim, y_dim), (0, 0, 0), duration=0.1)
-    flash5 = video.subclip(t_start=3.6, t_end=4.6)
-    blank6 = ColorClip((x_dim, y_dim), (0, 0, 0), duration=0.1)
-    flash6 = video.subclip(t_start=4.7, t_end=4.8)
-    blank7 = ColorClip((x_dim, y_dim), (0, 0, 0), duration=0.1)
-    highlight = video.subclip(t_start=4.9, t_end=6.384)
-    output = concatenate_videoclips([blank1, flash1, blank2, flash2, blank3, flash3, blank4, flash4, blank5, flash5, blank6, flash6, blank7, highlight])
-  elif template == 'Thinking Out Loud Cypher by Jermsego (hype, 8 seconds)':
-    res, left, right = max_subarray(df_all['y'].tolist(), 7)
-    video = VideoFileClip(video_path).subclip(t_start=left, t_end=right)
-    fps = video.fps
-    x_dim = st.session_state.x_dim
-    y_dim = st.session_state.y_dim
-    music_path = 'music/thinking-out-loud.mp3'
-    blank = ColorClip((x_dim, y_dim), (0, 0, 0), duration=1.6)
-    highlight = video.subclip(t_start=0, t_end=6.852)
-    output = concatenate_videoclips([blank, highlight])
-  elif template == 'Sheesh by Surfaces (upbeat, 10 seconds)':
-    res, left, right = max_subarray(df_all['y'].tolist(), 8)
-    video = VideoFileClip(video_path).subclip(t_start=left, t_end=right)
-    fps = video.fps
-    x_dim = st.session_state.x_dim
-    y_dim = st.session_state.y_dim
-    music_path = 'music/sheesh.mp3'
-    blank1 = ColorClip((x_dim, y_dim), (0, 0, 0), duration=3.5)
-    flash1 = video.subclip(t_start=0, t_end=0.1)
-    blank2 = ColorClip((x_dim, y_dim), (0, 0, 0), duration=0.1)
-    flash2 = video.subclip(t_start=0.2, t_end=0.3)
-    blank3 = ColorClip((x_dim, y_dim), (0, 0, 0), duration=0.1)
-    flash3 = video.subclip(t_start=0.4, t_end=0.5)
-    blank4 = ColorClip((x_dim, y_dim), (0, 0, 0), duration=0.1)
-    flash4 = video.subclip(t_start=0.6, t_end=0.7)
-    blank5 = ColorClip((x_dim, y_dim), (0, 0, 0), duration=0.9)
-    highlight = video.subclip(t_start=1.6, t_end=7.18408163265)
-    output = concatenate_videoclips([blank1, flash1, blank2, flash2, blank3, flash3, blank4, flash4, blank5, highlight])
-  elif template == 'Moon by Kid Francescoli (tranquil, 10 seconds)':
-    res, left, right = max_subarray(df_all['y'].tolist(), 9)
-    video = VideoFileClip(video_path).subclip(t_start=left, t_end=right)
-    fps = video.fps
-    x_dim = st.session_state.x_dim
-    y_dim = st.session_state.y_dim
-    music_path = 'music/and-it-went-like.mp3'
-    blank = ColorClip((x_dim, y_dim), (0, 0, 0), duration=1.9)
-    highlight = video.subclip(t_start=0, t_end=8.132)
-    output = concatenate_videoclips([blank, highlight])
-  elif template == 'Ready Set by Joey Valence & Brae (old school, 10 seconds)':
-    res, left, right = max_subarray(df_all['y'].tolist(), 11)
-    video = VideoFileClip(video_path).subclip(t_start=left, t_end=right)
-    fps = video.fps
-    x_dim = st.session_state.x_dim
-    y_dim = st.session_state.y_dim
-    music_path = 'music/ready-set.mp3'
-    highlight = video.subclip(t_start=0, t_end=10.512)
-    output = highlight
-  elif template == 'Lovewave by The 1-800 (tranquil, 13 seconds)':
-    res, left, right = max_subarray(df_all['y'].tolist(), 12)
-    video = VideoFileClip(video_path).subclip(t_start=left, t_end=right)
-    fps = video.fps
-    x_dim = st.session_state.x_dim
-    y_dim = st.session_state.y_dim
-    music_path = 'music/lovewave.mp3'
-    blank = ColorClip((x_dim, y_dim), (0, 0, 0), duration=2.1)
-    highlight = video.subclip(t_start=0, t_end=11.58)
-    output = concatenate_videoclips([blank, highlight])
-  elif template == 'And It Sounds Like by Forrest Nolan (tranquil, 17 seconds)':
-    res, left, right = max_subarray(df_all['y'].tolist(), 16)
-    video = VideoFileClip(video_path).subclip(t_start=left, t_end=right)
-    fps = video.fps
-    x_dim = st.session_state.x_dim
-    y_dim = st.session_state.y_dim
-    music_path = 'music/and-it-sounds-like.mp3'
-    blank = ColorClip((x_dim, y_dim), (0, 0, 0), duration=2)
-    highlight = video.subclip(t_start=0, t_end=15.928)
-    output = concatenate_videoclips([blank, highlight])
-  elif template == 'Comfort Chain by Instupendo (lofi, 18 seconds)':
-    res, left, right = max_subarray(df_all['y'].tolist(), 19)
-    video = VideoFileClip(video_path).subclip(t_start=left, t_end=right)
-    fps = video.fps
-    x_dim = st.session_state.x_dim
-    y_dim = st.session_state.y_dim
-    music_path = 'music/comfort-chain.mp3'
-    highlight = video.subclip(t_start=0, t_end=18.432000000000002)
-    output = highlight
-  # st.write(res, left, right)
-  song = AudioFileClip(music_path)
-  output = output.set_audio(song)
-  output.write_videofile('output.mp4', temp_audiofile='temp.m4a', remove_temp=True, audio_codec='aac', logger=None, fps=fps)
-  st.video('output.mp4')
-  # return output
-
-def crop_video(df_all, left, right):
-  video_path = f'videos/{st.session_state.domain.lower()}.mp4'
-  video = VideoFileClip(video_path)
-  fps = video.fps
-  music_path = 'music/loop.mp3'
-  song = AudioFileClip(music_path)
-  video = video.set_audio(song)
-  output = video.subclip(t_start=left, t_end=right)
-  output.write_videofile('output.mp4', temp_audiofile='temp.m4a', remove_temp=True, audio_codec='aac', logger=None, fps=fps)
-  st.video('output.mp4')
-  # return output
-
-st.set_page_config(page_title='Videogenic', page_icon = '✨', layout = 'wide', initial_sidebar_state = 'collapsed')
-
-hide_streamlit_style = """
-                      <style>
-                      #MainMenu {visibility: hidden;}
-                      footer {visibility: hidden;}
-                      * {font-family: Avenir; cursor: pointer;}
-                      .css-gma2qf {display: flex; justify-content: center; font-size: 42px; font-weight: bold;}
-                      a:link {text-decoration: none;}
-                      a:hover {text-decoration: none;}
-                      .st-ba {font-family: Avenir;}
-                      </style>
-                      """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
-
-# clustrmaps = """
-#             <a href="https://clustrmaps.com/site/1bham" target="_blank" title="Visit tracker"><img src="//www.clustrmaps.com/map_v2.png?d=NhNk5g9hy6Y06nqo7RirhHvZSr89uSS8rPrt471wAXw&cl=ffffff" width="0" height="0"></a>
-#             """
-
-# st.markdown(clustrmaps, unsafe_allow_html=True)
-
-# ss = SessionState.get(url=None, id=None, input=None, file_name=None, video=None, video_name=None, video_frames=None, video_features=None, fps=None, mode=None, query=None, progress=1)
-
-st.title('Videogenic ✨')
-if 'progress' not in st.session_state:
-  st.session_state.progress = 1
-
-# mode = 'play'
-# mode = 'brush'
-# mode = 'select'
-
-if st.session_state.progress == 1:
-  with st.spinner('Loading model...'):
-    load_model()
-  domain = st.selectbox('Select video',('Skydiving', 'Surfing')) # Entire journey, montage, vlog
-  if 'domain' not in st.session_state:
-    st.session_state.domain = domain
-  st.session_state.domain = domain
-  if st.button('Process video'):
-    with st.spinner('Processing video...'):
-      video_name = f'videos/{st.session_state.domain.lower()}.mp4'
-      video_file = open(video_name, 'rb')
-      video_bytes = video_file.read()
-      if 'video' not in st.session_state:
-        st.session_state.video = video_bytes
-      st.session_state.video = video_bytes
-      # st.video(st.session_state.video)
-      # video_frames, fps, x_dim, y_dim = video_to_frames(video_name) # first run; video_to_info
-      # np.save(f'files/{st.session_state.domain.lower()}.npy', video_frames)
-      fps, x_dim, y_dim = video_to_info(video_name)
-      video_frames = np.load(f'files/{st.session_state.domain.lower()}.npy', allow_pickle=True)
-      if 'video_frames' not in st.session_state:
-        st.session_state.video_frames = video_frames
-        st.session_state.fps = fps
-        st.session_state.x_dim = x_dim
-        st.session_state.y_dim = y_dim
-      st.session_state.video_frames = video_frames
-      st.session_state.fps = fps
-      st.session_state.x_dim = x_dim
-      st.session_state.y_dim = y_dim
-      print('Extracted frames')
-      # encoded_frames = encode_frames(video_frames) # first run
-      # np.save(f'files/{st.session_state.domain.lower()}_features.npy', encoded_frames)
-      encoded_frames = np.load(f'files/{st.session_state.domain.lower()}_features.npy', allow_pickle=True)
-      if 'video_features' not in st.session_state:
-        # st.session_state.video_features = encoded_frames
-        st.session_state.video_features = encoded_frames
-      st.session_state.video_features = encoded_frames
-      print('Encoded frames')
-      st.session_state.progress = 2
-
-# with open('activities.txt') as f:
-#   activities_list = [line.rstrip('\n') for line in f]
-# keywords = classify_activity(st.session_state.video_features, activities_list)
-# st.write(keywords)
-
-if st.session_state.progress == 2:
-  mode = st.radio('Select mode', ('Automatic', 'User selection'))
-  if 'mode' not in st.session_state:
-    st.session_state.mode = mode
-  st.session_state.mode = mode
-  # keywords = list(st.text_input('Enter topic').split(','))
-  # if st.button('Compute scores') and keywords is not None:
-  with st.spinner('Computing highlight scores...'):
-    keyword = st.session_state.domain.lower()
-    df_list = []
-    # for keyword in keywords:
-    img_set = get_photos(keyword)
-    similarities = compute_scores(img_set, st.session_state.video_features, keyword)
-    # st.write(similarities)
-    df = make_df(similarities)
-    df_list.append(df)
-    df_all = pd.concat(df_list, ignore_index=True, sort=False)
-    if 'df_all' not in st.session_state:
-      st.session_state.df_all = df_all
-    st.session_state.df_all = df_all
-  # st.write(df_all)
-  # highlight_length = 7.033
-  # st.write(st.session_state.fps)
-  with st.spinner('Visualizing highlight scores...'):
-    selection = altair_component(draw_chart(df_all, st.session_state.mode))
-    print(selection)
-  if 'selection' not in st.session_state:
-    st.session_state.selection = selection
-  st.session_state.selection = selection
-# if '_vgsid_' in selection:
-#   # the ids start at 1
-#   st.write(df.iloc[[selection['_vgsid_'][0] - 1]])
-# else:
-#   st.info('Hover over the chart above to see details about the Penguin here.')
-  # if 'x' in selection:
-  #   # the ids start at 1
-  #   st.write(selection['x'])
-    # chart = draw_chart(df_all, mode)
-    # st.altair_chart(chart, use_container_width=False)
-    # st.session_state.progress = 3
-
-  # if st.session_state.progress == 3:
-  if st.session_state.mode == 'Automatic':
-    # template = st.selectbox('Select template', ['Coming In Hot by Andy Mineo & Lecrae (hype, 7 seconds)', 'Thinking Out Loud Cypher by Jermsego (hype, 8 seconds)', 'Sheesh by Surfaces (upbeat, 10 seconds)',
-    #                           'Moon by Kid Francescoli (tranquil, 10 seconds)', 'Ready Set by Joey Valence & Brae (old school, 10 seconds)', 'Lovewave by The 1-800 (tranquil, 13 seconds)',
-    #                           'And It Sounds Like by Forrest Nolan (tranquil, 17 seconds)', 'Comfort Chain by Instupendo (lofi, 18 seconds)'])
-    template = st.selectbox('Select template', ['Coming In Hot by Andy Mineo & Lecrae (hype, 7 seconds)', 'Sheesh by Surfaces (upbeat, 10 seconds)', 'Lovewave by The 1-800 (tranquil, 13 seconds)'])
-    if st.button('Generate video'):
-      with st.spinner('Generating highlight video...'):
-        edit_video(template, st.session_state.df_all)
-        st.balloons()
-  elif st.session_state.mode == 'User selection':
-    if st.button('Generate video'):
-      left = st.session_state.selection['x'][0]
-      right = st.session_state.selection['x'][1]
-      with st.spinner('Generating highlight video...'):
-        crop_video(st.session_state.df_all, left, right)
-        st.balloons()
\ No newline at end of file
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aac.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aac.h
deleted file mode 100644
index 285d3b7482717cca96feb144f8ceea64a5c7ae7a..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aac.h
+++ /dev/null
@@ -1,391 +0,0 @@
-/*
- * AAC definitions and structures
- * Copyright (c) 2005-2006 Oded Shimon ( ods15 ods15 dyndns org )
- * Copyright (c) 2006-2007 Maxim Gavrilov ( maxim.gavrilov gmail com )
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * AAC definitions and structures
- * @author Oded Shimon  ( ods15 ods15 dyndns org )
- * @author Maxim Gavrilov ( maxim.gavrilov gmail com )
- */
-
-#ifndef AVCODEC_AAC_H
-#define AVCODEC_AAC_H
-
-
-#include "aac_defines.h"
-#include "libavutil/channel_layout.h"
-#include "libavutil/float_dsp.h"
-#include "libavutil/fixed_dsp.h"
-#include "libavutil/mem_internal.h"
-#include "libavutil/tx.h"
-#include "avcodec.h"
-#include "mpeg4audio.h"
-#include "sbr.h"
-
-#include <stdint.h>
-
-#define MAX_CHANNELS 64
-#define MAX_ELEM_ID 16
-
-#define TNS_MAX_ORDER 20
-#define MAX_LTP_LONG_SFB 40
-
-#define CLIP_AVOIDANCE_FACTOR 0.95f
-
-enum RawDataBlockType {
-    TYPE_SCE,
-    TYPE_CPE,
-    TYPE_CCE,
-    TYPE_LFE,
-    TYPE_DSE,
-    TYPE_PCE,
-    TYPE_FIL,
-    TYPE_END,
-};
-
-enum ExtensionPayloadID {
-    EXT_FILL,
-    EXT_FILL_DATA,
-    EXT_DATA_ELEMENT,
-    EXT_DYNAMIC_RANGE = 0xb,
-    EXT_SBR_DATA      = 0xd,
-    EXT_SBR_DATA_CRC  = 0xe,
-};
-
-enum WindowSequence {
-    ONLY_LONG_SEQUENCE,
-    LONG_START_SEQUENCE,
-    EIGHT_SHORT_SEQUENCE,
-    LONG_STOP_SEQUENCE,
-};
-
-enum BandType {
-    ZERO_BT        = 0,     ///< Scalefactors and spectral data are all zero.
-    FIRST_PAIR_BT  = 5,     ///< This and later band types encode two values (rather than four) with one code word.
-    ESC_BT         = 11,    ///< Spectral data are coded with an escape sequence.
-    RESERVED_BT    = 12,    ///< Band types following are encoded differently from others.
-    NOISE_BT       = 13,    ///< Spectral data are scaled white noise not coded in the bitstream.
-    INTENSITY_BT2  = 14,    ///< Scalefactor data are intensity stereo positions (out of phase).
-    INTENSITY_BT   = 15,    ///< Scalefactor data are intensity stereo positions (in phase).
-};
-
-#define IS_CODEBOOK_UNSIGNED(x) (((x) - 1) & 10)
-
-enum ChannelPosition {
-    AAC_CHANNEL_OFF   = 0,
-    AAC_CHANNEL_FRONT = 1,
-    AAC_CHANNEL_SIDE  = 2,
-    AAC_CHANNEL_BACK  = 3,
-    AAC_CHANNEL_LFE   = 4,
-    AAC_CHANNEL_CC    = 5,
-};
-
-/**
- * The point during decoding at which channel coupling is applied.
- */
-enum CouplingPoint {
-    BEFORE_TNS,
-    BETWEEN_TNS_AND_IMDCT,
-    AFTER_IMDCT = 3,
-};
-
-/**
- * Output configuration status
- */
-enum OCStatus {
-    OC_NONE,        ///< Output unconfigured
-    OC_TRIAL_PCE,   ///< Output configuration under trial specified by an inband PCE
-    OC_TRIAL_FRAME, ///< Output configuration under trial specified by a frame header
-    OC_GLOBAL_HDR,  ///< Output configuration set in a global header but not yet locked
-    OC_LOCKED,      ///< Output configuration locked in place
-};
-
-typedef struct OutputConfiguration {
-    MPEG4AudioConfig m4ac;
-    uint8_t layout_map[MAX_ELEM_ID*4][3];
-    int layout_map_tags;
-    AVChannelLayout ch_layout;
-    enum OCStatus status;
-} OutputConfiguration;
-
-/**
- * Predictor State
- */
-typedef struct PredictorState {
-    AAC_FLOAT cor0;
-    AAC_FLOAT cor1;
-    AAC_FLOAT var0;
-    AAC_FLOAT var1;
-    AAC_FLOAT r0;
-    AAC_FLOAT r1;
-    AAC_FLOAT k1;
-    AAC_FLOAT x_est;
-} PredictorState;
-
-#define MAX_PREDICTORS 672
-
-#define SCALE_DIV_512    36    ///< scalefactor difference that corresponds to scale difference in 512 times
-#define SCALE_ONE_POS   140    ///< scalefactor index that corresponds to scale=1.0
-#define SCALE_MAX_POS   255    ///< scalefactor index maximum value
-#define SCALE_MAX_DIFF   60    ///< maximum scalefactor difference allowed by standard
-#define SCALE_DIFF_ZERO  60    ///< codebook index corresponding to zero scalefactor indices difference
-
-#define POW_SF2_ZERO    200    ///< ff_aac_pow2sf_tab index corresponding to pow(2, 0);
-
-#define NOISE_PRE       256    ///< preamble for NOISE_BT, put in bitstream with the first noise band
-#define NOISE_PRE_BITS    9    ///< length of preamble
-#define NOISE_OFFSET     90    ///< subtracted from global gain, used as offset for the preamble
-
-/**
- * Long Term Prediction
- */
-typedef struct LongTermPrediction {
-    int8_t present;
-    int16_t lag;
-    int coef_idx;
-    INTFLOAT coef;
-    int8_t used[MAX_LTP_LONG_SFB];
-} LongTermPrediction;
-
-/**
- * Individual Channel Stream
- */
-typedef struct IndividualChannelStream {
-    uint8_t max_sfb;            ///< number of scalefactor bands per group
-    enum WindowSequence window_sequence[2];
-    uint8_t use_kb_window[2];   ///< If set, use Kaiser-Bessel window, otherwise use a sine window.
-    int num_window_groups;
-    uint8_t group_len[8];
-    LongTermPrediction ltp;
-    const uint16_t *swb_offset; ///< table of offsets to the lowest spectral coefficient of a scalefactor band, sfb, for a particular window
-    const uint8_t *swb_sizes;   ///< table of scalefactor band sizes for a particular window
-    int num_swb;                ///< number of scalefactor window bands
-    int num_windows;
-    int tns_max_bands;
-    int predictor_present;
-    int predictor_initialized;
-    int predictor_reset_group;
-    int predictor_reset_count[31];  ///< used by encoder to count prediction resets
-    uint8_t prediction_used[41];
-    uint8_t window_clipping[8]; ///< set if a certain window is near clipping
-    float clip_avoidance_factor; ///< set if any window is near clipping to the necessary atennuation factor to avoid it
-} IndividualChannelStream;
-
-/**
- * Temporal Noise Shaping
- */
-typedef struct TemporalNoiseShaping {
-    int present;
-    int n_filt[8];
-    int length[8][4];
-    int direction[8][4];
-    int order[8][4];
-    int coef_idx[8][4][TNS_MAX_ORDER];
-    INTFLOAT coef[8][4][TNS_MAX_ORDER];
-} TemporalNoiseShaping;
-
-/**
- * Dynamic Range Control - decoded from the bitstream but not processed further.
- */
-typedef struct DynamicRangeControl {
-    int pce_instance_tag;                           ///< Indicates with which program the DRC info is associated.
-    int dyn_rng_sgn[17];                            ///< DRC sign information; 0 - positive, 1 - negative
-    int dyn_rng_ctl[17];                            ///< DRC magnitude information
-    int exclude_mask[MAX_CHANNELS];                 ///< Channels to be excluded from DRC processing.
-    int band_incr;                                  ///< Number of DRC bands greater than 1 having DRC info.
-    int interpolation_scheme;                       ///< Indicates the interpolation scheme used in the SBR QMF domain.
-    int band_top[17];                               ///< Indicates the top of the i-th DRC band in units of 4 spectral lines.
-    int prog_ref_level;                             /**< A reference level for the long-term program audio level for all
-                                                     *   channels combined.
-                                                     */
-} DynamicRangeControl;
-
-typedef struct Pulse {
-    int num_pulse;
-    int start;
-    int pos[4];
-    int amp[4];
-} Pulse;
-
-/**
- * coupling parameters
- */
-typedef struct ChannelCoupling {
-    enum CouplingPoint coupling_point;  ///< The point during decoding at which coupling is applied.
-    int num_coupled;       ///< number of target elements
-    enum RawDataBlockType type[8];   ///< Type of channel element to be coupled - SCE or CPE.
-    int id_select[8];      ///< element id
-    int ch_select[8];      /**< [0] shared list of gains; [1] list of gains for right channel;
-                            *   [2] list of gains for left channel; [3] lists of gains for both channels
-                            */
-    INTFLOAT gain[16][120];
-} ChannelCoupling;
-
-/**
- * Single Channel Element - used for both SCE and LFE elements.
- */
-typedef struct SingleChannelElement {
-    IndividualChannelStream ics;
-    TemporalNoiseShaping tns;
-    Pulse pulse;
-    enum BandType band_type[128];                   ///< band types
-    enum BandType band_alt[128];                    ///< alternative band type (used by encoder)
-    int band_type_run_end[120];                     ///< band type run end points
-    INTFLOAT sf[120];                               ///< scalefactors
-    int sf_idx[128];                                ///< scalefactor indices (used by encoder)
-    uint8_t zeroes[128];                            ///< band is not coded (used by encoder)
-    uint8_t can_pns[128];                           ///< band is allowed to PNS (informative)
-    float  is_ener[128];                            ///< Intensity stereo pos (used by encoder)
-    float pns_ener[128];                            ///< Noise energy values (used by encoder)
-    DECLARE_ALIGNED(32, INTFLOAT, pcoeffs)[1024];   ///< coefficients for IMDCT, pristine
-    DECLARE_ALIGNED(32, INTFLOAT, coeffs)[1024];    ///< coefficients for IMDCT, maybe processed
-    DECLARE_ALIGNED(32, INTFLOAT, saved)[1536];     ///< overlap
-    DECLARE_ALIGNED(32, INTFLOAT, ret_buf)[2048];   ///< PCM output buffer
-    DECLARE_ALIGNED(16, INTFLOAT, ltp_state)[3072]; ///< time signal for LTP
-    DECLARE_ALIGNED(32, AAC_FLOAT, lcoeffs)[1024];  ///< MDCT of LTP coefficients (used by encoder)
-    DECLARE_ALIGNED(32, AAC_FLOAT, prcoeffs)[1024]; ///< Main prediction coefs (used by encoder)
-    PredictorState predictor_state[MAX_PREDICTORS];
-    INTFLOAT *ret;                                  ///< PCM output
-} SingleChannelElement;
-
-/**
- * channel element - generic struct for SCE/CPE/CCE/LFE
- */
-typedef struct ChannelElement {
-    int present;
-    // CPE specific
-    int common_window;        ///< Set if channels share a common 'IndividualChannelStream' in bitstream.
-    int     ms_mode;          ///< Signals mid/side stereo flags coding mode (used by encoder)
-    uint8_t is_mode;          ///< Set if any bands have been encoded using intensity stereo (used by encoder)
-    uint8_t ms_mask[128];     ///< Set if mid/side stereo is used for each scalefactor window band
-    uint8_t is_mask[128];     ///< Set if intensity stereo is used (used by encoder)
-    // shared
-    SingleChannelElement ch[2];
-    // CCE specific
-    ChannelCoupling coup;
-    SpectralBandReplication sbr;
-} ChannelElement;
-
-enum AACOutputChannelOrder {
-    CHANNEL_ORDER_DEFAULT,
-    CHANNEL_ORDER_CODED,
-};
-
-/**
- * main AAC context
- */
-struct AACContext {
-    AVClass        *class;
-    AVCodecContext *avctx;
-    AVFrame *frame;
-
-    int is_saved;                 ///< Set if elements have stored overlap from previous frame.
-    DynamicRangeControl che_drc;
-
-    /**
-     * @name Channel element related data
-     * @{
-     */
-    ChannelElement          *che[4][MAX_ELEM_ID];
-    ChannelElement  *tag_che_map[4][MAX_ELEM_ID];
-    int tags_mapped;
-    int warned_remapping_once;
-    /** @} */
-
-    /**
-     * @name temporary aligned temporary buffers
-     * (We do not want to have these on the stack.)
-     * @{
-     */
-    DECLARE_ALIGNED(32, INTFLOAT, buf_mdct)[1024];
-    /** @} */
-
-    /**
-     * @name Computed / set up during initialization
-     * @{
-     */
-    AVTXContext *mdct120;
-    AVTXContext *mdct128;
-    AVTXContext *mdct480;
-    AVTXContext *mdct512;
-    AVTXContext *mdct960;
-    AVTXContext *mdct1024;
-    AVTXContext *mdct_ltp;
-
-    av_tx_fn mdct120_fn;
-    av_tx_fn mdct128_fn;
-    av_tx_fn mdct480_fn;
-    av_tx_fn mdct512_fn;
-    av_tx_fn mdct960_fn;
-    av_tx_fn mdct1024_fn;
-    av_tx_fn mdct_ltp_fn;
-#if USE_FIXED
-    AVFixedDSPContext *fdsp;
-#else
-    AVFloatDSPContext *fdsp;
-#endif /* USE_FIXED */
-    int random_state;
-    /** @} */
-
-    /**
-     * @name Members used for output
-     * @{
-     */
-    SingleChannelElement *output_element[MAX_CHANNELS]; ///< Points to each SingleChannelElement
-    /** @} */
-
-
-    /**
-     * @name Japanese DTV specific extension
-     * @{
-     */
-    int force_dmono_mode;///< 0->not dmono, 1->use first channel, 2->use second channel
-    int dmono_mode;      ///< 0->not dmono, 1->use first channel, 2->use second channel
-    /** @} */
-
-    enum AACOutputChannelOrder output_channel_order;
-
-    DECLARE_ALIGNED(32, INTFLOAT, temp)[128];
-
-    OutputConfiguration oc[2];
-    int warned_num_aac_frames;
-    int warned_960_sbr;
-    unsigned warned_71_wide;
-    int warned_gain_control;
-    int warned_he_aac_mono;
-
-    /* aacdec functions pointers */
-    void (*imdct_and_windowing)(AACContext *ac, SingleChannelElement *sce);
-    void (*apply_ltp)(AACContext *ac, SingleChannelElement *sce);
-    void (*apply_tns)(INTFLOAT coef[1024], TemporalNoiseShaping *tns,
-                      IndividualChannelStream *ics, int decode);
-    void (*windowing_and_mdct_ltp)(AACContext *ac, INTFLOAT *out,
-                                   INTFLOAT *in, IndividualChannelStream *ics);
-    void (*update_ltp)(AACContext *ac, SingleChannelElement *sce);
-    void (*vector_pow43)(int *coefs, int len);
-    void (*subband_scale)(int *dst, int *src, int scale, int offset, int len, void *log_context);
-
-};
-
-void ff_aacdec_init_mips(AACContext *c);
-
-#endif /* AVCODEC_AAC_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/gsmdec_data.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/gsmdec_data.c
deleted file mode 100644
index 6015f78f7db50097ecc020bcc8b23381bdcfd2b4..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/gsmdec_data.c
+++ /dev/null
@@ -1,127 +0,0 @@
-/*
- * gsm 06.10 decoder data
- * Copyright (c) 2010 Reimar Döffinger <Reimar.Doeffinger@gmx.de>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include <stdint.h>
-#include "gsmdec_data.h"
-
-const uint16_t ff_gsm_long_term_gain_tab[4] = {
-    3277, 11469, 21299, 32767
-};
-
-const uint8_t ff_gsm_requant_tab[4][8] = {
-    { 0 },
-    { 0, 7 },
-    { 0, 2, 5, 7 },
-    { 0, 1, 2, 3, 4, 5, 6, 7 }
-};
-
-const int16_t ff_gsm_dequant_tab[64][8] = {
-    {   -28,    -20,    -12,     -4,      4,     12,     20,     28},
-    {   -56,    -40,    -24,     -8,      8,     24,     40,     56},
-    {   -84,    -60,    -36,    -12,     12,     36,     60,     84},
-    {  -112,    -80,    -48,    -16,     16,     48,     80,    112},
-    {  -140,   -100,    -60,    -20,     20,     60,    100,    140},
-    {  -168,   -120,    -72,    -24,     24,     72,    120,    168},
-    {  -196,   -140,    -84,    -28,     28,     84,    140,    196},
-    {  -224,   -160,    -96,    -32,     32,     96,    160,    224},
-    {  -252,   -180,   -108,    -36,     36,    108,    180,    252},
-    {  -280,   -200,   -120,    -40,     40,    120,    200,    280},
-    {  -308,   -220,   -132,    -44,     44,    132,    220,    308},
-    {  -336,   -240,   -144,    -48,     48,    144,    240,    336},
-    {  -364,   -260,   -156,    -52,     52,    156,    260,    364},
-    {  -392,   -280,   -168,    -56,     56,    168,    280,    392},
-    {  -420,   -300,   -180,    -60,     60,    180,    300,    420},
-    {  -448,   -320,   -192,    -64,     64,    192,    320,    448},
-    {  -504,   -360,   -216,    -72,     72,    216,    360,    504},
-    {  -560,   -400,   -240,    -80,     80,    240,    400,    560},
-    {  -616,   -440,   -264,    -88,     88,    264,    440,    616},
-    {  -672,   -480,   -288,    -96,     96,    288,    480,    672},
-    {  -728,   -520,   -312,   -104,    104,    312,    520,    728},
-    {  -784,   -560,   -336,   -112,    112,    336,    560,    784},
-    {  -840,   -600,   -360,   -120,    120,    360,    600,    840},
-    {  -896,   -640,   -384,   -128,    128,    384,    640,    896},
-    { -1008,   -720,   -432,   -144,    144,    432,    720,   1008},
-    { -1120,   -800,   -480,   -160,    160,    480,    800,   1120},
-    { -1232,   -880,   -528,   -176,    176,    528,    880,   1232},
-    { -1344,   -960,   -576,   -192,    192,    576,    960,   1344},
-    { -1456,  -1040,   -624,   -208,    208,    624,   1040,   1456},
-    { -1568,  -1120,   -672,   -224,    224,    672,   1120,   1568},
-    { -1680,  -1200,   -720,   -240,    240,    720,   1200,   1680},
-    { -1792,  -1280,   -768,   -256,    256,    768,   1280,   1792},
-    { -2016,  -1440,   -864,   -288,    288,    864,   1440,   2016},
-    { -2240,  -1600,   -960,   -320,    320,    960,   1600,   2240},
-    { -2464,  -1760,  -1056,   -352,    352,   1056,   1760,   2464},
-    { -2688,  -1920,  -1152,   -384,    384,   1152,   1920,   2688},
-    { -2912,  -2080,  -1248,   -416,    416,   1248,   2080,   2912},
-    { -3136,  -2240,  -1344,   -448,    448,   1344,   2240,   3136},
-    { -3360,  -2400,  -1440,   -480,    480,   1440,   2400,   3360},
-    { -3584,  -2560,  -1536,   -512,    512,   1536,   2560,   3584},
-    { -4032,  -2880,  -1728,   -576,    576,   1728,   2880,   4032},
-    { -4480,  -3200,  -1920,   -640,    640,   1920,   3200,   4480},
-    { -4928,  -3520,  -2112,   -704,    704,   2112,   3520,   4928},
-    { -5376,  -3840,  -2304,   -768,    768,   2304,   3840,   5376},
-    { -5824,  -4160,  -2496,   -832,    832,   2496,   4160,   5824},
-    { -6272,  -4480,  -2688,   -896,    896,   2688,   4480,   6272},
-    { -6720,  -4800,  -2880,   -960,    960,   2880,   4800,   6720},
-    { -7168,  -5120,  -3072,  -1024,   1024,   3072,   5120,   7168},
-    { -8063,  -5759,  -3456,  -1152,   1152,   3456,   5760,   8064},
-    { -8959,  -6399,  -3840,  -1280,   1280,   3840,   6400,   8960},
-    { -9855,  -7039,  -4224,  -1408,   1408,   4224,   7040,   9856},
-    {-10751,  -7679,  -4608,  -1536,   1536,   4608,   7680,  10752},
-    {-11647,  -8319,  -4992,  -1664,   1664,   4992,   8320,  11648},
-    {-12543,  -8959,  -5376,  -1792,   1792,   5376,   8960,  12544},
-    {-13439,  -9599,  -5760,  -1920,   1920,   5760,   9600,  13440},
-    {-14335, -10239,  -6144,  -2048,   2048,   6144,  10240,  14336},
-    {-16127, -11519,  -6912,  -2304,   2304,   6912,  11519,  16127},
-    {-17919, -12799,  -7680,  -2560,   2560,   7680,  12799,  17919},
-    {-19711, -14079,  -8448,  -2816,   2816,   8448,  14079,  19711},
-    {-21503, -15359,  -9216,  -3072,   3072,   9216,  15359,  21503},
-    {-23295, -16639,  -9984,  -3328,   3328,   9984,  16639,  23295},
-    {-25087, -17919, -10752,  -3584,   3584,  10752,  17919,  25087},
-    {-26879, -19199, -11520,  -3840,   3840,  11520,  19199,  26879},
-    {-28671, -20479, -12288,  -4096,   4096,  12288,  20479,  28671}
-};
-
-static const int apcm_bits[11][13] = {
-    { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },
-    { 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1 },
-    { 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1 },
-    { 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1 },
-    { 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1 },
-    { 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 },
-    { 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 },
-    { 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 },
-    { 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 },
-    { 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2 },
-    { 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3 }
-};
-
-const int* const ff_gsm_apcm_bits[][4] = {
-    { apcm_bits[10], apcm_bits[10], apcm_bits[10], apcm_bits[10] }, // 13000
-    { apcm_bits[10], apcm_bits[10], apcm_bits[10], apcm_bits[ 6] }, // 12400
-    { apcm_bits[10], apcm_bits[10], apcm_bits[ 7], apcm_bits[ 5] }, // 11800
-    { apcm_bits[10], apcm_bits[ 8], apcm_bits[ 5], apcm_bits[ 5] }, // 11200
-    { apcm_bits[ 9], apcm_bits[ 5], apcm_bits[ 5], apcm_bits[ 5] }, // 10600
-    { apcm_bits[ 5], apcm_bits[ 5], apcm_bits[ 5], apcm_bits[ 1] }, // 10000
-    { apcm_bits[ 5], apcm_bits[ 5], apcm_bits[ 2], apcm_bits[ 0] }, //  9400
-    { apcm_bits[ 5], apcm_bits[ 3], apcm_bits[ 0], apcm_bits[ 0] }, //  8800
-    { apcm_bits[ 4], apcm_bits[ 0], apcm_bits[ 0], apcm_bits[ 0] }, //  8200
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/idctdsp_mmi.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/idctdsp_mmi.c
deleted file mode 100644
index d22e5eedd74623396170ce4040aa9fe1f35adda0..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/idctdsp_mmi.c
+++ /dev/null
@@ -1,193 +0,0 @@
-/*
- * Loongson SIMD optimized idctdsp
- *
- * Copyright (c) 2015 Loongson Technology Corporation Limited
- * Copyright (c) 2015 Zhou Xiaoyong <zhouxiaoyong@loongson.cn>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "idctdsp_mips.h"
-#include "constants.h"
-#include "libavutil/mips/mmiutils.h"
-
-void ff_put_pixels_clamped_mmi(const int16_t *block,
-        uint8_t *av_restrict pixels, ptrdiff_t line_size)
-{
-    double ftmp[8];
-
-    __asm__ volatile (
-        MMI_LDC1(%[ftmp0], %[block], 0x00)
-        MMI_LDC1(%[ftmp1], %[block], 0x08)
-        MMI_LDC1(%[ftmp2], %[block], 0x10)
-        MMI_LDC1(%[ftmp3], %[block], 0x18)
-        MMI_LDC1(%[ftmp4], %[block], 0x20)
-        MMI_LDC1(%[ftmp5], %[block], 0x28)
-        MMI_LDC1(%[ftmp6], %[block], 0x30)
-        MMI_LDC1(%[ftmp7], %[block], 0x38)
-        "packushb   %[ftmp0],   %[ftmp0],       %[ftmp1]                \n\t"
-        "packushb   %[ftmp2],   %[ftmp2],       %[ftmp3]                \n\t"
-        "packushb   %[ftmp4],   %[ftmp4],       %[ftmp5]                \n\t"
-        "packushb   %[ftmp6],   %[ftmp6],       %[ftmp7]                \n\t"
-        MMI_SDC1(%[ftmp0], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],  %[pixels],      %[line_size]            \n\t"
-        MMI_SDC1(%[ftmp2], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],  %[pixels],      %[line_size]            \n\t"
-        MMI_SDC1(%[ftmp4], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],  %[pixels],      %[line_size]            \n\t"
-        MMI_SDC1(%[ftmp6], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],  %[pixels],      %[line_size]            \n\t"
-
-        MMI_LDC1(%[ftmp0], %[block], 0x40)
-        MMI_LDC1(%[ftmp1], %[block], 0x48)
-        MMI_LDC1(%[ftmp2], %[block], 0x50)
-        MMI_LDC1(%[ftmp3], %[block], 0x58)
-        MMI_LDC1(%[ftmp4], %[block], 0x60)
-        MMI_LDC1(%[ftmp5], %[block], 0x68)
-        MMI_LDC1(%[ftmp6], %[block], 0x70)
-        MMI_LDC1(%[ftmp7], %[block], 0x78)
-        "packushb   %[ftmp0],   %[ftmp0],       %[ftmp1]                \n\t"
-        "packushb   %[ftmp2],   %[ftmp2],       %[ftmp3]                \n\t"
-        "packushb   %[ftmp4],   %[ftmp4],       %[ftmp5]                \n\t"
-        "packushb   %[ftmp6],   %[ftmp6],       %[ftmp7]                \n\t"
-        MMI_SDC1(%[ftmp0], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],  %[pixels],      %[line_size]            \n\t"
-        MMI_SDC1(%[ftmp2], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],  %[pixels],      %[line_size]            \n\t"
-        MMI_SDC1(%[ftmp4], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],  %[pixels],      %[line_size]            \n\t"
-        MMI_SDC1(%[ftmp6], %[pixels], 0x00)
-        : [ftmp0]"=&f"(ftmp[0]),            [ftmp1]"=&f"(ftmp[1]),
-          [ftmp2]"=&f"(ftmp[2]),            [ftmp3]"=&f"(ftmp[3]),
-          [ftmp4]"=&f"(ftmp[4]),            [ftmp5]"=&f"(ftmp[5]),
-          [ftmp6]"=&f"(ftmp[6]),            [ftmp7]"=&f"(ftmp[7]),
-          [pixels]"+&r"(pixels)
-        : [line_size]"r"((mips_reg)line_size),
-          [block]"r"(block)
-        : "memory"
-    );
-}
-
-void ff_put_signed_pixels_clamped_mmi(const int16_t *block,
-    uint8_t *av_restrict pixels, ptrdiff_t line_size)
-{
-    double ftmp[5];
-
-    __asm__ volatile (
-        MMI_LDC1(%[ftmp1], %[block], 0x00)
-        MMI_LDC1(%[ftmp0], %[block], 0x08)
-        "packsshb   %[ftmp1],       %[ftmp1],       %[ftmp0]            \n\t"
-        MMI_LDC1(%[ftmp2], %[block], 0x10)
-        MMI_LDC1(%[ftmp0], %[block], 0x18)
-        "packsshb   %[ftmp2],       %[ftmp2],       %[ftmp0]            \n\t"
-        MMI_LDC1(%[ftmp3], %[block], 0x20)
-        MMI_LDC1(%[ftmp0], %[block], 0x28)
-        "packsshb   %[ftmp3],       %[ftmp3],       %[ftmp0]            \n\t"
-        MMI_LDC1(%[ftmp4], %[block], 0x30)
-        MMI_LDC1(%[ftmp0], %[block], 0x38)
-        "packsshb   %[ftmp4],       %[ftmp4],       %[ftmp0]            \n\t"
-        "paddb      %[ftmp1],       %[ftmp1],       %[ff_pb_80]         \n\t"
-        "paddb      %[ftmp2],       %[ftmp2],       %[ff_pb_80]         \n\t"
-        "paddb      %[ftmp3],       %[ftmp3],       %[ff_pb_80]         \n\t"
-        "paddb      %[ftmp4],       %[ftmp4],       %[ff_pb_80]         \n\t"
-        MMI_SDC1(%[ftmp1], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],      %[pixels],      %[line_size]        \n\t"
-        MMI_SDC1(%[ftmp2], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],      %[pixels],      %[line_size]        \n\t"
-        MMI_SDC1(%[ftmp3], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],      %[pixels],      %[line_size]        \n\t"
-        MMI_SDC1(%[ftmp4], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],      %[pixels],      %[line_size]        \n\t"
-
-        MMI_LDC1(%[ftmp1], %[block], 0x40)
-        MMI_LDC1(%[ftmp0], %[block], 0x48)
-        "packsshb   %[ftmp1],       %[ftmp1],       %[ftmp0]            \n\t"
-        MMI_LDC1(%[ftmp2], %[block], 0x50)
-        MMI_LDC1(%[ftmp0], %[block], 0x58)
-        "packsshb   %[ftmp2],       %[ftmp2],       %[ftmp0]            \n\t"
-        MMI_LDC1(%[ftmp3], %[block], 0x60)
-        MMI_LDC1(%[ftmp0], %[block], 0x68)
-        "packsshb   %[ftmp3],       %[ftmp3],       %[ftmp0]            \n\t"
-        MMI_LDC1(%[ftmp4], %[block], 0x70)
-        MMI_LDC1(%[ftmp0], %[block], 0x78)
-        "packsshb   %[ftmp4],       %[ftmp4],       %[ftmp0]            \n\t"
-        "paddb      %[ftmp1],       %[ftmp1],       %[ff_pb_80]         \n\t"
-        "paddb      %[ftmp2],       %[ftmp2],       %[ff_pb_80]         \n\t"
-        "paddb      %[ftmp3],       %[ftmp3],       %[ff_pb_80]         \n\t"
-        "paddb      %[ftmp4],       %[ftmp4],       %[ff_pb_80]         \n\t"
-        MMI_SDC1(%[ftmp1], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],      %[pixels],      %[line_size]        \n\t"
-        MMI_SDC1(%[ftmp2], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],      %[pixels],      %[line_size]        \n\t"
-        MMI_SDC1(%[ftmp3], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],      %[pixels],      %[line_size]        \n\t"
-        MMI_SDC1(%[ftmp4], %[pixels], 0x00)
-        : [ftmp0]"=&f"(ftmp[0]),            [ftmp1]"=&f"(ftmp[1]),
-          [ftmp2]"=&f"(ftmp[2]),            [ftmp3]"=&f"(ftmp[3]),
-          [ftmp4]"=&f"(ftmp[4]),
-          [pixels]"+&r"(pixels)
-        : [block]"r"(block),
-          [line_size]"r"((mips_reg)line_size),
-          [ff_pb_80]"f"(ff_pb_80.f)
-        : "memory"
-    );
-}
-
-void ff_add_pixels_clamped_mmi(const int16_t *block,
-        uint8_t *av_restrict pixels, ptrdiff_t line_size)
-{
-    double ftmp[9];
-    uint64_t tmp[1];
-    __asm__ volatile (
-        "li         %[tmp0],    0x04                           \n\t"
-        "pxor       %[ftmp0],   %[ftmp0],   %[ftmp0]           \n\t"
-        "1:                                                    \n\t"
-        MMI_LDC1(%[ftmp5], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],  %[pixels],  %[line_size]       \n\t"
-        MMI_LDC1(%[ftmp6], %[pixels], 0x00)
-        PTR_SUBU   "%[pixels],  %[pixels],  %[line_size]       \n\t"
-        MMI_LDC1(%[ftmp1], %[block], 0x00)
-        MMI_LDC1(%[ftmp2], %[block], 0x08)
-        MMI_LDC1(%[ftmp3], %[block], 0x10)
-        MMI_LDC1(%[ftmp4], %[block], 0x18)
-        PTR_ADDIU  "%[block],   %[block],   0x20               \n\t"
-        "punpckhbh  %[ftmp7],   %[ftmp5],   %[ftmp0]           \n\t"
-        "punpcklbh  %[ftmp5],   %[ftmp5],   %[ftmp0]           \n\t"
-        "punpckhbh  %[ftmp8],   %[ftmp6],   %[ftmp0]           \n\t"
-        "punpcklbh  %[ftmp6],   %[ftmp6],   %[ftmp0]           \n\t"
-        "paddh      %[ftmp1],   %[ftmp1],   %[ftmp5]           \n\t"
-        "paddh      %[ftmp2],   %[ftmp2],   %[ftmp7]           \n\t"
-        "paddh      %[ftmp3],   %[ftmp3],   %[ftmp6]           \n\t"
-        "paddh      %[ftmp4],   %[ftmp4],   %[ftmp8]           \n\t"
-        "packushb   %[ftmp1],   %[ftmp1],   %[ftmp2]           \n\t"
-        "packushb   %[ftmp3],   %[ftmp3],   %[ftmp4]           \n\t"
-        MMI_SDC1(%[ftmp1], %[pixels], 0x00)
-        PTR_ADDU   "%[pixels],  %[pixels],  %[line_size]       \n\t"
-        MMI_SDC1(%[ftmp3], %[pixels], 0x00)
-        "addi       %[tmp0],    %[tmp0],    -0x01              \n\t"
-        PTR_ADDU   "%[pixels],  %[pixels],  %[line_size]       \n\t"
-        "bnez       %[tmp0],    1b                             \n\t"
-        : [ftmp0]"=&f"(ftmp[0]),            [ftmp1]"=&f"(ftmp[1]),
-          [ftmp2]"=&f"(ftmp[2]),            [ftmp3]"=&f"(ftmp[3]),
-          [ftmp4]"=&f"(ftmp[4]),            [ftmp5]"=&f"(ftmp[5]),
-          [ftmp6]"=&f"(ftmp[6]),            [ftmp7]"=&f"(ftmp[7]),
-          [ftmp8]"=&f"(ftmp[8]),            [tmp0]"=&r"(tmp[0]),
-          [pixels]"+&r"(pixels),            [block]"+&r"(block)
-        : [line_size]"r"((mips_reg)line_size)
-        : "memory"
-    );
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/mathops.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/mathops.h
deleted file mode 100644
index bb9dc8375a73d96ca9b919ef117905511a6427f0..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/mathops.h
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
- * Copyright (c) 2009 Mans Rullgard <mans@mansr.com>
- * Copyright (c) 2015 Zhou Xiaoyong <zhouxiaoyong@loongson.cn>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_MIPS_MATHOPS_H
-#define AVCODEC_MIPS_MATHOPS_H
-
-#include <stdint.h>
-#include "config.h"
-#include "libavutil/common.h"
-
-#if HAVE_INLINE_ASM
-
-#if HAVE_LOONGSON3
-
-#define MULH MULH
-static inline av_const int MULH(int a, int b)
-{
-    int c;
-    __asm__ ("dmult %1, %2      \n\t"
-             "mflo %0           \n\t"
-             "dsrl %0, %0, 32   \n\t"
-             : "=r"(c)
-             : "r"(a),"r"(b)
-             : "hi", "lo");
-    return c;
-}
-
-#define mid_pred mid_pred
-static inline av_const int mid_pred(int a, int b, int c)
-{
-    int t = b;
-    __asm__ ("sgt $8, %1, %2    \n\t"
-             "movn %0, %1, $8   \n\t"
-             "movn %1, %2, $8   \n\t"
-             "sgt $8, %1, %3    \n\t"
-             "movz %1, %3, $8   \n\t"
-             "sgt $8, %0, %1    \n\t"
-             "movn %0, %1, $8   \n\t"
-             : "+&r"(t),"+&r"(a)
-             : "r"(b),"r"(c)
-             : "$8");
-    return t;
-}
-
-#endif /* HAVE_LOONGSON3 */
-
-#endif /* HAVE_INLINE_ASM */
-
-#endif /* AVCODEC_MIPS_MATHOPS_H */
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Growtopia Terbaru The Best Free-to-Play Building Game for Mobile and Consoles.md b/spaces/congsaPfin/Manga-OCR/logs/Download Growtopia Terbaru The Best Free-to-Play Building Game for Mobile and Consoles.md
deleted file mode 100644
index af057af23802e98e86362c5e34b7104ca695d04e..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Growtopia Terbaru The Best Free-to-Play Building Game for Mobile and Consoles.md	
+++ /dev/null
@@ -1,164 +0,0 @@
-
-<h1>Download Growtopia Terbaru: A Guide to the Popular Sandbox MMO Game</h1>
-<p>If you are looking for a fun, creative, and social game that lets you build your own worlds and play with millions of other players, then you might want to try Growtopia. Growtopia is a free-to-play sandbox MMO game that has been around since 2013, but it is still updated regularly with new items, events, and features. In this article, we will tell you everything you need to know about Growtopia, how to download the latest version of it for PC or mobile devices, and how to play it.</p>
-<h2>download growtopia terbaru</h2><br /><p><b><b>Download Zip</b> &#9913;&#9913;&#9913; <a href="https://urlca.com/2uOe30">https://urlca.com/2uOe30</a></b></p><br /><br />
-<h2>What is Growtopia?</h2>
-<h3>A brief introduction to the game and its features</h3>
-<p>Growtopia is a 2D sandbox platformer MMO game that was originally developed by Seth Robinson and Mike Hommel, but was later acquired by Ubisoft in 2017. In Growtopia, you can create your own worlds using thousands of unique items, such as blocks, seeds, clothing, tools, weapons, pets, vehicles, and more. You can also explore and visit other players' worlds, chat with them, trade with them, play mini-games with them, or even fight with them. There are no limits to what you can do in Growtopia, as long as you follow the rules and respect others.</p>
-<h3>The benefits of playing Growtopia</h3>
-<p>Growtopia is not just a game, it is also a community. By playing Growtopia, you can meet new friends from different countries and cultures, learn new skills and knowledge, express your creativity and imagination, and have fun. Growtopia is also a game that encourages collaboration and cooperation, as you can work together with other players to build amazing worlds or complete challenging quests. Growtopia is also a game that rewards your efforts and achievements, as you can earn gems, world locks, trophies, badges, and other prizes.</p>
-<h3>The challenges and risks of Growtopia</h3>
-<p>Growtopia is not a perfect game, however. It also has its challenges and risks that you need to be aware of. For example, Growtopia can be addictive and time-consuming, as you might spend hours or days playing it without noticing. Growtopia can also be frustrating and stressful, as you might encounter scammers, hackers, trolls, or bullies who might try to ruin your experience or steal your items. Growtopia can also be expensive and risky, as you might be tempted to spend real money on in-app purchases or third-party websites that might not be safe or reliable.</p>
-<h2>How to download Growtopia terbaru for PC?</h2>
-<h3>The steps to download and install the latest version of Growtopia for PC</h3>
-<p>If you want to play Growtopia on your PC, here are the steps you need to follow:</p>
-<ol>
-<li>Go to the official website of Growtopia at .</li>
-<li>Click on the "Download" button on the homepage.</li>
-<li <li>Choose the version that matches your operating system (Windows or Mac).</li>
-<li>Save the file to your computer and run it.</li>
-<li>Follow the instructions on the screen to complete the installation.</li>
-<li>Launch the game and create an account or log in with your existing one.</li>
-<li>Enjoy playing Growtopia on your PC!</li>
-</ol>
-<h3>The requirements and compatibility of Growtopia for PC</h3>
-<p>Before you download Growtopia for PC, you need to make sure that your computer meets the minimum requirements and is compatible with the game. Here are the minimum requirements for Growtopia for PC:</p>
-<ul>
-<li>Operating system: Windows 7 or higher, Mac OS X 10.9 or higher</li>
-<li>Processor: 2 GHz or faster</li>
-<li>Memory: 2 GB RAM or more</li>
-<li>Graphics: OpenGL 2.0 compatible or higher</li>
-<li>Storage: 100 MB available space or more</li>
-<li>Internet connection: Broadband or faster</li>
-</ul>
-<p>If your computer does not meet these requirements, you might experience lag, crashes, or errors while playing Growtopia. You might also need to update your drivers, software, or antivirus to ensure the optimal performance of Growtopia.</p>
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-<h3>The advantages and disadvantages of playing Growtopia on PC</h3>
-<p>Playing Growtopia on PC has its advantages and disadvantages. Here are some of them:</p>
- | Advantages | Disadvantages | | --- | --- | | Larger screen and better graphics | Higher power consumption and heat generation | | Easier controls and typing | Less mobility and portability | | More storage space and features | More prone to viruses and malware | | Faster updates and support | More expensive and complicated | <p>You need to weigh these pros and cons before deciding whether to play Growtopia on PC or not.</p>
-<h2>How to download Growtopia terbaru for mobile?</h2>
-<h3>The steps to download and install the latest version of Growtopia for Android or iOS</h3>
-<p>If you want to play Growtopia on your mobile device, here are the steps you need to follow:</p>
-<ol>
-<li>Go to the official website of Growtopia at .</li>
-<li>Click on the "Download" button on the homepage.</li>
-<li>Choose the version that matches your device (Android or iOS).</li>
-<li>You will be redirected to the Google Play Store or the App Store, depending on your device.</li>
-<li>Tap on the "Install" button and wait for the download to finish.</li>
-<li>Launch the game and create an account or log in with your existing one.</li>
-<li>Enjoy playing Growtopia on your mobile device!</li>
-</ol>
- <h3>The requirements and compatibility of Growtopia for mobile devices</h3>
- <p>Before you download Growtopia for mobile devices, you need to make sure that your device meets the minimum requirements and is compatible with the game. Here are the minimum requirements for Growtopia for mobile devices:</p>
- <ul>
- <li>Operating system: Android 4.0 or higher, iOS 10.0 or higher</li>
- <li>Processor: 1 GHz or faster</li>
- <li>Memory: 1 GB RAM or more</li>
- <li>Graphics: OpenGL ES 2.0 compatible or higher</li>
- <li>Storage: 100 MB available space or more</li>
- <li>Internet connection: Wi-Fi or cellular data</li>
- </ul>
- <p>If your device does not meet these requirements, you might experience lag, crashes, or errors while playing Growtopia. You might also need to update your device, software, or antivirus to ensure the optimal performance of Growtopia.</p>
- <h3>The advantages and disadvantages of playing Growtopia on mobile devices</h3>
- <p>Playing Growtopia on mobile devices has its advantages and disadvantages. Here are some of them:</p>
- | Advantages | Disadvantages | | --- | --- | | More mobility and portability | Smaller screen and lower graphics | | Simpler controls and typing | Higher battery consumption and data usage | | Less storage space and features | More prone to damage and theft | | Slower updates and support | Less compatible and stable | <p>You need to weigh these pros and cons before deciding whether to play Growtopia on mobile devices or not.</p>
- <h2>How to play Growtopia terbaru?</h2>
- <h3>The basics of creating, exploring, and interacting in Growtopia</h3>
- <p>To play Growtopia, you need to understand the basics of creating, exploring, and interacting in the game. Here are some of them:</p <ul>
-<li>Creating: You can create your own worlds by placing and breaking blocks, planting and splicing seeds, and using various items. You can also customize your character by changing your clothes, hair, skin, eyes, and accessories. You can also create your own items by using the Growtopia Item Database  or the Growtopia Recipe Book .</li>
-<li>Exploring: You can explore other players' worlds by using the world map, the world lock, the world name, or the random world button. You can also join different events, such as the Carnival, the Star Voyage, the Daily Challenge, or the World of the Day. You can also discover different biomes, such as the Cave, the Desert, the Jungle, or the Winter.</li>
-<li>Interacting: You can interact with other players by chatting with them, trading with them, playing with them, or fighting with them. You can also join or create different guilds, clans, teams, or groups. You can also participate in different activities, such as parkour, racing, fishing, surgery, or cooking.</li>
-</ul>
- <h3>The tips and tricks to enjoy Growtopia more</h3>
- <p>To enjoy Growtopia more, you need to know some tips and tricks that can help you improve your skills, increase your wealth, and have more fun. Here are some of them:</p>
- <ul>
-<li>Tips:    <ul>
-    <li>Always read the rules and follow them to avoid getting banned or scammed.</li>
-    <li>Always protect your items and worlds with world locks or other locks to prevent theft or griefing.</li>
-    <li>Always be respectful and friendly to other players to make more friends and allies.</li>
-    <li>Always check the news and updates to stay informed and prepared for new features and events.</li>
-    <li>Always use the /report command or the support button to report any problems or issues you encounter.</li>
-  </ul>
-</li>
-<li>Tricks:    <ul>
-    <li>Use the /go command or the wrench button to teleport to any world or player you want.</li>
-    <li>Use the /rate command or the star button to rate any world you like or dislike.</li>
-    <li>Use the /friends command or the friends button to see your online friends and their locations.</li>
-    <li>Use the /ignore command or the ignore button to block any player you don't want to see or hear.</li>
-    <li>Use the /mods command or the mods button to see the online moderators and their locations.</li>
-  </ul>
-</li>
-</ul>
- <h3>The resources and support available for Growtopia players</h3>
- <p>If you need more help or information about Growtopia, you can use the following resources and support available for Growtopia players:</p>
- <ul>
-<li>The official website of Growtopia at , where you can download the game, read the news and updates, watch videos and trailers, and access other links.</li>
-<li>The official forum of Growtopia at , where you can post questions and answers, share ideas and feedback, report bugs and glitches, and join discussions and contests.</li>
-<li>The official wiki of Growtopia at , where you can find guides and tutorials, item lists and recipes, event details and rewards, and other useful information.</li>
-<li>The official social media accounts of Growtopia on Facebook , Twitter , Instagram , YouTube , Twitch , Discord , Reddit , and TikTok , where you can follow updates and announcements, watch live streams and videos, chat with other players and moderators, and participate in giveaways and challenges.</li>
-<li>The official support team of Growtopia at support@growtopiagame.com , where you can send emails regarding any issues or concerns you have with the game.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, Growtopia is a popular sandbox MMO game that allows you to create your own worlds and play with millions of other players. You can download Growtopia terbaru for PC or mobile devices by following the steps we provided in this article. You can also play Growtopia terbaru by following the basics we explained in this article. You can also enjoy Growtopia more by following the tips and tricks we shared in this article. You can also use the resources and support we listed in this article if you need more help or information about Growtopia. We hope you found this article helpful and informative. If you did, please share it with your friends who might be interested in playing Grow topia. Thank you for reading and happy gaming!</p>
- <h2>FAQs</h2>
- <h3>What is Growtopia terbaru?</h3>
- <p>Growtopia terbaru is the latest version of Growtopia, a sandbox MMO game that lets you create your own worlds and play with millions of other players. Growtopia terbaru is updated regularly with new items, events, and features.</p>
- <h3>How can I download Growtopia terbaru for PC or mobile devices?</h3>
- <p>You can download Growtopia terbaru for PC or mobile devices by going to the official website of Growtopia at and clicking on the "Download" button. You can then choose the version that matches your operating system (Windows, Mac, Android, or iOS) and follow the instructions to install the game.</p>
- <h3>How can I play Growtopia terbaru?</h3>
- <p>You can play Growtopia terbaru by creating your own worlds using thousands of unique items, exploring and visiting other players' worlds, chatting, trading, playing, or fighting with other players, and joining or creating different guilds, clans, teams, or groups. You can also join different events, such as the Carnival, the Star Voyage, the Daily Challenge, or the World of the Day.</p>
- <h3>What are some tips and tricks to enjoy Growtopia more?</h3>
- <p>Some tips and tricks to enjoy Growtopia more are to read and follow the rules, protect your items and worlds with locks, be respectful and friendly to other players, check the news and updates, report any problems or issues, use the /go, /rate, /friends, /ignore, and /mods commands, and use the Growtopia Item Database or the Growtopia Recipe Book to create your own items.</p>
- <h3>What are some resources and support available for Growtopia players?</h3>
- <p>Some resources and support available for Growtopia players are the official website of Growtopia at , the official forum of Growtopia at , the official wiki of Growtopia at , the official social media accounts of Growtopia on Facebook , Twitter , Instagram , YouTube , Twitch , Discord , Reddit , and TikTok , and the official support team of Growtopia at support@growtopiagame.com .</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Enjoy Endless Fun and Adventure with Talking Tom Gold Run Hacked APK.md b/spaces/congsaPfin/Manga-OCR/logs/Enjoy Endless Fun and Adventure with Talking Tom Gold Run Hacked APK.md
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+++ /dev/null
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-
-<h1>Talking Tom Gold Run Hacked Apk: Everything You Need to Know</h1>
- <p>Do you love playing Talking Tom Gold Run? Do you want to unlock all the characters, power-ups, and worlds in the game? Do you want to have unlimited money and resources to upgrade your house and chase the robbers? If you answered yes to any of these questions, then you may be interested in using a hacked apk for Talking Tom Gold Run.</p>
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- <p>A hacked apk is a modified version of an app that allows you to access features and functions that are normally locked or restricted in the original app. For example, a hacked apk for Talking Tom Gold Run may let you have unlimited gold bars, diamonds, dynamite, and other items that you can use to enhance your gameplay and experience. However, using a hacked apk also comes with some risks and challenges, such as malware, crashes, bans, etc.</p>
- <p>In this article, we will tell you everything you need to know about Talking Tom Gold Run hacked apk, including how to download and install it, how to play it, how to avoid potential problems and issues, and some frequently asked questions. By the end of this article, you will be able to decide whether you want to use a hacked apk for Talking Tom Gold Run or not.</p>
- <h2>How to Download and Install Talking Tom Gold Run Hacked Apk</h2>
- <p>The first step to using a hacked apk for Talking Tom Gold Run is to find and download a reliable source for the apk file. There are many websites and platforms that offer hacked apks for various apps and games, but not all of them are trustworthy or safe. Some of them may contain viruses or malware that can harm your device or steal your personal information. Some of them may also provide outdated or incompatible versions of the apk file that may not work properly or at all.</p>
- <p>Therefore, it is advisable to do some research and check the reviews and ratings of the source before downloading and installing a hacked apk. You can also use antivirus software or online tools to scan the apk file for any malicious content or code. Some of the popular and reputable sources for Talking Tom Gold Run hacked apk are APKPure, APKMODY, APKDONE, etc. These sources provide updated and working versions of the hacked apk that have been tested and verified by many users.</p>
- <p>Once you have found and downloaded a reliable source for the hacked apk file, you need to enable unknown sources on your device and install the apk file. Unknown sources are settings that allow you to install apps from sources other than the official app store, such as Google Play Store or Apple App Store. To enable unknown sources on your device, you need to follow these steps:</p>
- - For Android devices:   - Go to Settings > Security > Unknown Sources and toggle it on.   - Alternatively, go to Settings > Apps > Special Access > Install Unknown Apps and select the browser or app that you used to download the apk file and toggle it on. - For iOS devices:   - Go to Settings > General > Device Management > Profiles & Device Management and select the profile of the app that you want to install and trust it.   - Alternatively, go to Settings > General > Profiles & Device Management > Enterprise App and select the app that you want to install and trust it. <p>After enabling unknown sources on your device, you need to install the apk file by following these steps:</p>
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- - For Android devices:   - Locate the downloaded apk file on your device using a file manager app or your browser's download history.   - Tap on the apk file and follow the instructions on the screen to install it.   - Wait for the installation process to complete and then launch the app from your home screen or app drawer. - For iOS devices:   - Locate the downloaded apk file on your device using a file manager app or your browser's download history.   - Tap on the apk file and follow the instructions on the screen to install it.   - Wait for the installation process to complete and then launch the app from your home screen. <h2>How to Verify That The Hacked Apk is Working Properly</h2>
- <p>The next step to using a hacked apk for Talking Tom Gold Run is to verify that it is working properly and that it has all the features and functions that you want. To do this, you need to check the following things:</p>
- - The app icon: The app icon should be different from the original version of Talking Tom Gold Run. It may have a different color, design, or name. For example, some hacked apks may have names like Talking Tom Gold Run Mod Apk, Talking Tom Gold Run Unlimited Money Apk, etc. - The app version: The app version should be higher than or equal to the latest version of Talking Tom Gold Run available on the official app store. You can check the app version by going to Settings > About within the app or by looking at the information provided by the source of the hacked apk. - The app permissions: The app permissions should be reasonable and necessary for the app's functionality. You can check the app permissions by going to Settings > Apps > Talking Tom Gold Run > Permissions within your device's settings or by looking at the information provided by the source of the hacked apk. Some common permissions that the app may require are access to your storage, camera, microphone, location, etc. If you see any suspicious or unnecessary permissions, such as access to your contacts, messages, call logs, etc., you may want to deny them or uninstall the app. - The app features: The app features should match the description and screenshots provided by the source of the hacked apk. You can test the app features by playing the game and checking if you have unlimited money, power-ups, characters, etc. You can also compare the app features with the original version of Talking Tom Gold Run and see if there are any differences or improvements. <h2>How to Play Talking Tom Gold Run Hacked Apk</h2>
- <p>Now that you have verified that the hacked apk is working properly, you can start playing Talking Tom Gold Run and enjoy its features and functions. Here are some of the main features and gameplay of Talking Tom Gold Run:</p>
- - Talking Tom Gold Run is an endless runner game where you control one of the characters from the Talking Tom and Friends franchise, such as Tom, Angela, Hank, Ginger, Ben, etc. Your goal is to run as far as possible while collecting gold bars, dodging obstacles, and chasing the robbers who stole your gold. - The game has various worlds and scenarios that you can explore and unlock, such as city streets, beaches, forests, deserts, snow mountains, etc. Each world has its own theme, design, and challenges that make the game more fun and diverse. - The game also has various power-ups and items that you can use to boost your speed, jump higher, fly in the air, magnetize gold bars, etc. Some of the power-ups and items are helmets, rockets, magnets, hoverboards, planes, etc. You can also upgrade your power-ups and items to make them more effective and last longer. - The game also has various characters that you can unlock and play with, each with their own personality and style. Some of the characters are Tom's friends from the TV show, such as Angela, Hank, Ginger, Ben, etc. Some of the characters are special guests from other games or franchises, such as Sonic the Hedgehog, Agent Dash, Princess Angela, etc. You can also customize your characters with different outfits and accessories to make them look more cool and unique. - The game also has a feature where you can build and upgrade your own house using the gold bars that you collect in the game. You can choose from different styles and designs for your house, such as modern, classic, rustic, etc. You can also add various decorations and furniture to your house to make it more cozy and comfortable. You can also visit your friends' houses and see how they have decorated their own houses. - The game also has a feature where you can connect with other players online and compete with them in leaderboards and events. You can see how you rank among other players in terms of distance run, gold bars collected, power-ups used, etc. You can also participate in special events that offer rewards and challenges for completing certain tasks or goals in the game. <h2>How to Use The Hacked Apk to Your Advantage</h2>
- <p>Using a hacked apk for Talking Tom Gold Run can give you some advantages over other players or over the original version of the game. Here are some of the advantages of using a hacked apk:</p>
- - Unlimited money: A hacked apk may give you unlimited gold bars and diamonds that you can use to buy anything in the game without worrying about running out of money. You can use this money to unlock all the characters, power-ups, items, and worlds in the game. You can also use this money to upgrade your house and make it more luxurious and beautiful. You can also use this money to participate in more events and challenges and win more rewards and prizes. - Unlimited power-ups: A hacked apk may give you unlimited power-ups that you can use to enhance your gameplay and experience. You can use these power-ups to run faster, jump higher, fly in the air, magnetize gold bars, etc. You can also use these power-ups to avoid obstacles, catch the robbers, and score more points in the game. You can also upgrade these power-ups to make them more effective and last longer. - Unlimited characters: A hacked apk may give you unlimited characters that you can unlock and play with in the game. You can choose from different characters from the Talking Tom and Friends franchise, such as Tom, Angela, Hank, Ginger, Ben, etc. You can also choose from special guests from other games or franchises, such as Sonic the Hedgehog, Agent Dash, Princess Angela, etc. You can also customize your characters with different outfits and accessories to make them look more cool and unique. - Unlimited fun: A hacked apk may give you unlimited fun and enjoyment in playing Talking Tom Gold Run. You can explore different worlds and scenarios in the game, such as city streets, beaches, forests, deserts, snow mountains, etc. You can also build and upgrade your own house using the gold bars that you collect in the game. You can also connect with other players online and compete with them in leaderboards and events. You can also discover new features and functions that are not available in the original version of the game. <h2>How to Avoid Potential Problems and Issues with Talking Tom Gold Run Hacked Apk</h2>
- <p>Using a hacked apk for Talking Tom Gold Run can also come with some risks and challenges that you need to be aware of and avoid. Here are some of the potential problems and issues that you may encounter while using a hacked apk:</p>
- - Malware: A hacked apk may contain malware or viruses that can harm your device or steal your personal information. Malware can cause your device to slow down, crash, freeze, or malfunction. Malware can also access your contacts, messages, call logs, photos, videos, etc. and send them to third parties without your consent. Malware can also damage or delete your files or data on your device or cloud service. - Crashes: A hacked apk may cause your app or device to crash or freeze while playing the game. Crashes can occur due to various reasons, such as compatibility issues, bugs, glitches, memory leaks, etc. Crashes can interrupt your gameplay and experience and make you lose your progress or data in the game. - Bans: A hacked apk may cause your account or device to be banned or suspended by the game developer or publisher. Bans can occur due to various reasons, such as detection by anti-cheat systems, reporting by other players or by the game server, violation of the terms of service of the game developer or publisher, etc. Bans can prevent you from accessing or playing the game online or offline and make you lose your progress or data in the game. - Uninstall: A hacked apk may be difficult or impossible to uninstall from your device if you want to switch back to the original version of the game. Uninstalling a hacked apk may require some additional steps or adjustments, such as deleting the app data and cache, removing the app permissions, disabling the unknown sources, etc. Uninstalling a hacked apk may also cause some errors or issues on your device or on the original version of the game, such as missing files, corrupted data, broken links, etc. <h2>How to Prevent or Fix These Problems and Issues</h2>
- <p>If you encounter any of these problems or issues while using a hacked apk for Talking Tom Gold Run, you may want to try some of these solutions to prevent or fix them:</p>
- - Malware: To prevent malware from infecting your device or stealing your personal information, you should use a trusted and reputable source for downloading and installing a hacked apk. You should also scan the apk file with antivirus software or online tools before opening it. To fix malware that has already infected your device or stolen your personal information, you should use antivirus software or online tools to remove it. You should also change your passwords and secure your accounts on various platforms and services. - Crashes: To prevent crashes from occurring while playing the game, you should use a compatible and updated version of the hacked apk. You should also check the system requirements and compatibility of the game and the hacked apk with your device. You should also clear the app data and cache regularly to free up some memory and storage space on your device. To fix crashes that have already occurred while playing the game, you should restart the app or your device and try again. You should also report the crash to the source of the hacked apk or to the game developer or publisher if possible. - Bans: To prevent bans from happening while playing the game, you should use a stealthy and undetectable version of the hacked apk. You should also avoid using obvious or excessive features or functions that may attract attention or suspicion from other players or from the game server. You should also play the game offline or in a private mode where you can enjoy the game without affecting or interfering with other players. To fix bans that have already happened while playing the game, you should contact the game developer or publisher and appeal for your account or device to be unbanned or unsuspended. You should also explain your situation and apologize for any inconvenience or trouble that you may have caused. - Uninstall: To uninstall a hacked apk from your device easily and safely, you should follow these steps:   - For Android devices:     - Go to Settings > Apps > Talking Tom Gold Run > Uninstall and tap on it.     - Alternatively, go to your home screen or app drawer and tap and hold on the app icon until a menu appears. Then tap on Uninstall.   - For iOS devices:     - Go to Settings > General > iPhone Storage > Talking Tom Gold Run > Delete App and tap on it.     - Alternatively, go to your home screen and tap and hold on the app icon until it starts to wiggle. Then tap on the X icon on the top left corner of the app icon. <h2>Conclusion</h2>
- <p>Talking Tom Gold Run is a fun and addictive endless runner game that features various characters, power-ups, items, and worlds that you can unlock and enjoy. However, if you want to access all these features and functions without spending any money or time, you may want to use a hacked apk for Talking Tom Gold Run.</p>
- <p>A hacked apk is a modified version of an app that allows you to access features and functions that are normally locked or restricted in the original app. For example, a hacked apk for Talking Tom Gold Run may let you have unlimited gold bars, diamonds, dynamite, and other items that you can use to enhance your gameplay and experience.</p>
- <p>However, using a hacked apk also comes with some risks and challenges, such as malware, crashes, bans, etc. Therefore, it is advisable to use a trusted and reputable source for downloading and installing a hacked apk, and to scan it with antivirus software before opening it. It is also recommended to use a hacked apk at your own risk and discretion, as we do not endorse or promote any illegal or unethical activities.</p>
- <p>If you are interested in using a hacked apk for Talking Tom Gold Run, we hope that this article has provided you with everything you need to know about it, including how to download and install it, how to play it, how to avoid potential problems and issues, and some frequently asked questions. By following our instructions and tips carefully, you will be able to enjoy Talking Tom Gold Run like never before.</p>
- <p>So what are you waiting for? Go ahead and try out Talking Tom Gold Run hacked apk today and see for yourself how much fun it can be. Or if you prefer, stick with the original version of Talking Tom Gold Run and enjoy it as it is. Either way, we hope that you have a great time playing this game.</p>
- <p>Thank you for reading this article. We hope that you found it useful and engaging. If you have any questions or feedback about this article or about Talking Tom Gold Run hacked apk, please feel free to leave a comment below or contact us through our website. We would love to hear from you and help you out.</p>
- <p>Have a wonderful day and happy gaming!</p>
- <h2>FAQs</h2>
- <table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Q: Is Talking Tom Gold Run hacked apk safe and legal to use?</td>
-<td>A: The safety and legality of using a hacked apk depends on the source and the content of the file. Some hacked apks may contain malware or viruses that can harm your device or steal your personal information. Some hacked apks may also violate the terms of service of the game developer or publisher, which can result in your account being banned or suspended. Therefore, it is advisable to use a trusted and reputable source for downloading and installing a hacked apk, and to scan it with antivirus software before opening it. It is also recommended to use a hacked apk at your own risk and discretion, as we do not endorse or promote any illegal or unethical activities.</td>
-</tr>
-<tr>
-<td>Q: How can I update Talking Tom Gold Run hacked apk?</td>
-<td>A: Updating a hacked apk can be tricky, as it may not be compatible with the latest version of the game or it may overwrite some of the modifications that you have made. Therefore, it is advisable to check the source of the hacked apk regularly for any updates or patches, and to follow their instructions carefully. Alternatively, you can uninstall the hacked apk and install the original version of the game from the official app store, and then reinstall the hacked apk if you want to use it again.</td>
-</tr>
-<tr>
-<td>Q: Can I play Talking Tom Gold Run hacked apk online with other players?</td>
-<td>A: Playing a hacked apk online with other players can be risky, as it may expose you to detection or reporting by other players or by the game server. This can result in your account being banned or suspended, or your progress being reset. Therefore, it is advisable to play a hacked apk offline or in a private mode, where you can enjoy the game without affecting or interfering with other players.</td>
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-<td>A: Using a hacked apk on different devices can be possible, but it may require some additional steps or adjustments. For example, you may need to transfer the apk file from one device to another using a USB cable or a cloud service, or you may need to enable unknown sources on each device before installing the apk file. You may also need to sync your game data across different devices using a third-party app or service, or you may need to start from scratch on each device.</td>
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-<td>A: If you are looking for some alternatives to Talking Tom Gold Run hacked apk, you may want to check out some other similar games that offer endless running, fun characters, and exciting worlds. Some examples are Temple Run, Subway Surfers, Lara Croft: Relic Run, Sonic Dash, etc. These games are available for free on various app stores, and they may also have their own hacked apks that you can find online.</td>
-</tr>
-</table></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Get Ready for the Ultimate Beach Buggy Racing 1 Hack APK Dinero Infinito.md b/spaces/congsaPfin/Manga-OCR/logs/Get Ready for the Ultimate Beach Buggy Racing 1 Hack APK Dinero Infinito.md
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- <p>Beach Buggy Racing 1 is a free-to-play game, but it also has in-app purchases that allow you to buy more money and gems. Money and gems are the main currencies in the game that you can use to upgrade your car, unlock new tracks, buy power-ups, and more. However, earning money and gems in the game can be quite slow and tedious. You have to complete races, win trophies, collect coins, watch ads, and do other tasks to get them. If you want to get more money and gems faster and easier, then you might want to hack Beach Buggy Racing 1 with an APK mod.</p>
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- <h2>How to download and install Beach Buggy Racing 1 Hack APK Mod</h2>
- <h3>Step 1: Download the APK file from a trusted source</h3>
- <p>The first step is to download the APK file of Beach Buggy Racing 1 Hack from a trusted source. There are many websites that offer this hack, but not all of them are safe and reliable. Some of them may contain viruses, malware, or fake files that can harm your device or steal your personal information. Therefore, you should be careful and do some research before downloading anything from the internet. You can use Google or other search engines to find reputable sources that provide this hack. You can also read reviews, comments, or feedback from other users who have tried this hack before. Once you find a trustworthy source, you can click on the download button and save the APK file on your device.</p>
- <h3>Step 2: Enable unknown sources on your device</h3>
- <p>The next step is to enable unknown sources on your device. This is a security setting that prevents you from installing apps that are not from the official Google Play Store. Since Beach Buggy Racing 1 Hack is a modded app that is not available on the Play Store, you need to enable unknown sources to install it. To do this, you need to go to your device settings and look for security or privacy options. There, you will find an option called unknown sources or allow installation from unknown sources. You need to toggle this option on and confirm your choice. This will allow you to install apps from sources other than the Play Store.</p>
- <h3>Step 3: Install the APK file and launch the game</h3>
- <p>The final step is to install the APK file and launch the game. To install the APK file, you need to locate it on your device using a file manager app or your downloads folder. Then, you need to tap on it and follow the instructions on the screen. It may take a few seconds or minutes for the installation to complete depending on your device speed and storage space. Once the installation is done, you will see an icon of Beach Buggy Racing 1 Hack on your home screen or app drawer. You can tap on it and launch the game as usual.</p>
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- <h4>Free and easy to use</h4>
- <p>One of the advantages of Beach Buggy Racing 1 Hack is that it is free and easy to use. You don't have to pay anything or do any complicated steps to get unlimited money and gems in the game. You just need to download and install the APK file and enjoy the game.</p>
- <h4>Enhance your gaming experience</h4>
- <p>Another advantage of Beach Buggy Racing 1 Hack is that it enhances your gaming experience by giving you access to all the features of the game without any limitations. You can buy any car, track, power-up, or upgrade you want in the game without worrying about money or gems. You can also race on any track with any car without unlocking them first. You can have more fun and excitement in the game with this hack.</p> <h4>Compatible with most devices</h4>
- <p>A third advantage of Beach Buggy Racing 1 Hack is that it is compatible with most devices that can run the original game. You don't need a high-end device or a lot of storage space to use this hack. You just need a device that meets the minimum requirements of the game, such as Android 4.1 or higher, 1 GB of RAM, and 100 MB of free space. You can use this hack on your smartphone, tablet, or emulator without any problems.</p>
- <h3>Cons</h3>
- <h4>Risk of getting banned or infected by malware</h4>
- <p>One of the disadvantages of Beach Buggy Racing 1 Hack is that it comes with some risks that you should be aware of. One of them is the risk of getting banned or suspended from the game by the official developers. Since this hack is not authorized or supported by Vector Unit, they may detect it and take action against you for violating their terms of service. They may ban your account, delete your progress, or block your access to the game. Therefore, you should use this hack at your own risk and discretion.</p>
- <p>Another risk is the risk of getting infected by malware or viruses that may be hidden in the APK file. As we mentioned before, not all sources that offer this hack are safe and reliable. Some of them may contain malicious code that can harm your device or steal your personal information. Therefore, you should be careful and scan the APK file with an antivirus app before installing it. You should also avoid clicking on any suspicious links or pop-ups that may appear while using this hack.</p>
- <h4>May cause glitches or crashes in the game</h4>
- <p>Another disadvantage of Beach Buggy Racing 1 Hack is that it may cause glitches or crashes in the game. Since this hack is a modded version of the original game, it may not be compatible with the latest updates or patches that Vector Unit may release. It may also interfere with some features or functions of the game that rely on the original code. This may result in errors, bugs, or crashes that can ruin your gaming experience. Therefore, you should backup your data before using this hack and update it regularly to avoid any issues.</p>
- <h4>Not supported by the official developers</h4>
- <p>The last disadvantage of Beach Buggy Racing 1 Hack is that it is not supported by the official developers of the game. This means that you cannot contact them for any help or feedback regarding this hack. You also cannot report any problems or bugs that you may encounter while using this hack. You are on your own when it comes to using this hack and dealing with its consequences. Therefore, you should use this hack at your own responsibility and respect.</p>
- <h2>Conclusion</h2>
- <p>Beach Buggy Racing 1 Hack APK Dinero Infinito is a modded version of the popular racing game that gives you unlimited money and gems in the game. It also unlocks all the cars and tracks in the game and removes all the ads and root requirements. It is free and easy to use and compatible with most devices. However, it also comes with some risks, such as getting banned or infected by malware, causing glitches or crashes in the game, and not being supported by the official developers. Therefore, you should use this hack at your own risk and discretion and enjoy the game to the fullest.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Beach Buggy Racing 1 Hack APK Dinero Infinito:</p>
- <ul>
-<li><b>Is Beach Buggy Racing 1 Hack APK Dinero Infinito safe to use?</b></li>
-<p>Beach Buggy Racing 1 Hack APK Dinero Infinito is not completely safe to use, as it comes with some risks that we mentioned above. You may get banned or infected by malware if you use this hack from an untrusted source or without proper precautions. Therefore, you should use this hack at your own risk and discretion and scan it with an antivirus app before installing it.</p>
-<li><b>Is Beach Buggy Racing 1 Hack APK Dinero Infinito legal to use?</b></li>
-<p>Beach Buggy Racing 1 Hack APK Dinero Infinito is not legal to use, as it violates the terms of service of Vector Unit, the official developers of the game. They do not authorize or support this hack and may take action against you if they detect it. Therefore, you should use this hack at your own responsibility and respect.</p>
-<li><b>Can I update Beach Buggy Racing 1 Hack APK Dinero Infinito?</b></li>
-<p>You can update Beach Buggy Racing 1 Hack APK Dinero Infinito if there is a new version available from the source that you downloaded it from. However, you should be careful <p>as some updates may not be compatible with the hack and may cause errors or crashes in the game. You should also backup your data before updating the hack to avoid losing your progress.</p>
- <li><b>Can I use Beach Buggy Racing 1 Hack APK Dinero Infinito with other mods or cheats?</b></li>
-<p>You can use Beach Buggy Racing 1 Hack APK Dinero Infinito with other mods or cheats if they are compatible and do not conflict with each other. However, you should be careful as some mods or cheats may not work well with the hack and may cause problems or issues in the game. You should also avoid using too many mods or cheats at the same time as they may overload your device or make the game unstable.</p>
- <li><b>Can I play Beach Buggy Racing 1 Hack APK Dinero Infinito online or offline?</b></li>
-<p>You can play Beach Buggy Racing 1 Hack APK Dinero Infinito online or offline depending on your preference and internet connection. However, you should be aware that playing online may expose you to the risk of getting detected or banned by Vector Unit or other players who may report you for cheating. Therefore, you should use this hack with caution and discretion when playing online. Playing offline may be safer and more convenient as you don't need an internet connection and you can enjoy the game without any interruptions or interference.</p>
-</ul></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Install Google Play Store APK on Your Android Device - Step by Step Guide.md b/spaces/congsaPfin/Manga-OCR/logs/Install Google Play Store APK on Your Android Device - Step by Step Guide.md
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+++ /dev/null
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-<br />
-<h1>What is google play store apk.com and why you might need it</h1>
- <p>Google play store apk.com is a website that provides the APK file of the Google Play Store app for Android devices. The Google Play Store is the official app store for Android, where you can find millions of apps and games, as well as movies, books, music, and more. The Google Play Store app comes pre-installed on most Android devices, but sometimes you might need to download and install it manually. For example, you might have a device that doesn't have the Google Play Store app by default, such as an Amazon Fire tablet or a Chinese smartphone. Or you might want to get the latest version of the Google Play Store app before it rolls out to your device automatically. Or you might want to have a backup of the Google Play Store app in case something goes wrong with your device.</p>
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- <p>In this article, we will explain what an APK file is and how it works, what are the benefits and risks of using APK files, how to download and install google play store apk from a trusted source, how to use google play store apk to access apps and games, and what are some alternatives to google play store apk.</p>
- <h2>What is an APK file and how does it work</h2>
- <p>An APK file is an Android Package file that contains all the elements that an app needs to install correctly on your device. It's similar to other types of archive files, like ZIP or RAR, that compress multiple files into one. You can open an APK file with a file extraction tool like 7-Zip or WinRAR to see what's inside.</p>
- <p>An APK file is also a software package that Android uses to distribute and install apps. When you download an app from the Google Play Store or another app store, you are actually downloading an APK file. When you install an app from an APK file, Android checks its signature to make sure it's valid and matches the one that's already installed (if any). If the signature is not valid or doesn't match, Android will refuse to install the app.</p>
- <h3>The benefits of using APK files</h3>
- <p>There are some benefits of using APK files instead of relying on the Google Play Store or another app store. Some of them are:</p>
- <ul>
-<li>You can get access to apps that are not available in your region or country.</li>
-<li>You can get access to apps that are not compatible with your device or Android version.</li>
-<li>You can get access to apps that are removed or banned from the Google Play Store or another app store.</li>
-<li>You can get access to beta versions or older versions of apps that have new features or bug fixes.</li>
-<li>You can have more control over your apps and their updates.</li>
-</ul>
- <h3>The risks of using APK files</h3>
- <p>However, there are also some risks of using APK files that you should be aware of. Some of them are:</p>
- <ul>
-<li>You might download malicious or fake APK files that contain viruses, malware, spyware, or ad ware that can harm your device or steal your data.</li>
-<li>You might download outdated or unstable APK files that can cause errors, crashes, or compatibility issues.</li>
-<li>You might download modified or hacked APK files that can violate the terms of service or the intellectual property rights of the app developers.</li>
-<li>You might lose the automatic updates and support from the Google Play Store or another app store.</li>
-<li>You might expose your device to security risks by enabling unknown sources or granting permissions to untrusted apps.</li>
-</ul>
- <p>Therefore, you should always be careful when downloading and installing APK files from unknown or unverified sources. You should also scan the APK files with a reliable antivirus or malware scanner before installing them. And you should always backup your device and data before installing any APK file.</p>
- <h2>How to download and install google play store apk from a trusted source</h2>
- <p>If you want to download and install google play store apk on your device, you need to follow these steps:</p>
- <h3>Step 1: Enable unknown sources on your device</h3>
- <p>Before you can install any APK file on your device, you need to enable the option to allow installation from unknown sources. This option is usually disabled by default for security reasons. To enable it, you need to go to your device settings, then security or privacy, then find the option to allow installation from unknown sources and toggle it on. Depending on your device model and Android version, the exact steps may vary slightly. You can also search for "unknown sources" in your device settings to find the option.</p>
- <h3>Step 2: Download the APK file from a reputable site</h3>
- <p>Once you have enabled unknown sources, you need to download the APK file of the Google Play Store app from a reputable site. You can use google play store apk.com as a trusted source, as it provides the latest and original version of the Google Play Store app. To download the APK file from google play store apk.com, you need to follow these steps:</p>
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-<li>Open your web browser and go to google play store apk.com.</li>
-<li>Find the download button and click on it.</li>
-<li>Wait for the download to complete and save the APK file in your device storage.</li>
-</ol>
- <h3>Step 3: Install the APK file using a file manager or an installer app</h3>
- <p>After you have downloaded the APK file, you need to install it on your device. You can use a file manager app or an installer app to do this. A file manager app is an app that lets you browse and manage your files on your device. An installer app is an app that lets you install APK files easily and quickly. You can use any file manager app or installer app that you trust, but we recommend using ES File Explorer or APK Installer as examples. To install the APK file using a file manager app or an installer app, you need to follow these steps:</p>
- <ol>
-<li>Open your file manager app or installer app and locate the APK file that you downloaded.</li>
-<li>Tap on the APK file and select install.</li>
-<li>Follow the instructions on the screen and grant any permissions that are requested.</li>
-<li>Wait for the installation to finish and launch the Google Play Store app.</li>
-</ol>
- <h3>Step 4: Update the google play store app regularly</h3>
- <p>Once you have installed the Google Play Store app, you need to update it regularly to get the latest features, bug fixes, and security patches. You can update the Google Play Store app manually or automatically. To update it manually, you need to follow these steps:</p>
- <ol>
-<li>Open the Google Play Store app and tap on the menu icon (three horizontal lines) in the top left corner.</li>
-<li>Tap on settings and scroll down to find the option for "Play Store version".</li>
-<li>Tap on it and check if there is an update available. If there is, tap on update and wait for it to download and install.</li>
-</ol>
- <p>To update it automatically, you need to follow these steps:</p>
- <ol>
-<li>Open the Google Play Store app and tap on the menu icon (three horizontal lines) in the top left corner.</li>
-<li>Tap on settings and scroll down to find the option for "Auto-update apps".</li>
-<li>Tap on it and choose whether you want to update apps over any network or only over Wi-Fi.</li>
-</ol>
- <h2>How to use google play store apk to access apps and games</h2>
- <p>After you have installed and updated google play store apk, you can use it to access millions of apps and games for your Android device. Here are some tips on how to use google play store apk to browse, search, download, install, and manage apps and games.</p>
- <h3>How to browse and search for apps and games</h3>
- <p>To browse and search for apps and games, you can use the different categories and filters that google play store apk offers. You can also use the search bar to type in keywords or phrases that match what you are looking for. Here are some steps to browse and search for apps and games:</p>
- <ol>
-<li>Open the Google Play Store app and tap on the menu icon (three horizontal lines) in the top left corner.</li>
-<li>Tap on the category that you are interested in, such as games, apps, movies, books, etc.</li>
-<li>Scroll through the featured, top charts, new releases, or editor's choice sections to find popular or recommended apps and games.</li>
-<li>Tap on the filter icon (three horizontal lines with sliders) in the top right corner to refine your results by genre, rating, price, etc.</li>
-<li>Tap on the search icon (magnifying glass) in the top right corner to enter your query and see relevant results.</li>
-<li>Tap on any app or game that you want to learn more about, such as its description, screenshots, reviews, ratings, etc.</li>
-</ol>
- <h3>How to download and install apps and games</h3>
- <p>To download and install apps and games, you need to have a Google account and a payment method (if the app or game is not free). You also need to have enough storage space and a stable internet connection. Here are some steps to download and install apps and games:</p>
- <ol>
-<li>Open the Google Play Store app and find the app or game that you want to download and install.</li>
-<li>Tap on the green install button (if the app or game is free) or the blue buy button (if the app or game is paid).</li>
-<li>Follow the instructions on the screen and enter your Google account credentials and payment details (if required).</li>
-<li>Wait for the download and installation to complete and tap on open to launch the app or game.</li>
-</ol>
- <h3>How to manage your apps and games</h3>
- <p>To manage your apps and games, you can use the Google Play Store app to update, uninstall, rate, review, share, or disable them. You can also use the settings menu to change your preferences, such as notifications, auto-updates, parental controls, etc. Here are some steps to manage your apps and games:</p>
- <ol>
-<li>Open the Google Play Store app and tap on the menu icon (three horizontal lines) in the top left corner.</li>
-<li>Tap on my apps & games to see a list of all your installed apps and games.</li>
-<li>Tap on any app or game that you want to manage and choose from the available options, such as update, uninstall, rate, review, share, disable, etc.</li>
-<li>Tap on settings in the menu to access more options, such as notifications, auto-updates, parental controls, etc.</li>
-</ol>
- <h2>Alternatives to google play store apk</h2>
- <p>If you are looking for alternatives to google play store apk, there are some other app stores that you can use to download and install apps and games for your Android device. However, you should be careful when using these alternative app stores, as they may not be as safe or reliable as google play store apk. Here are some popular alternative app stores for Android:</p>
- <h3>Some popular alternative app stores for Android</h3>
- <table>
-<tr><th>Name</th><th>Description</th><th>Pros</th><th>Cons</th></tr>
-<tr><td>Aptoide</td><td>Aptoide is an open-source app store that allows users to create their own app stores and share them with others. It has over 1 million apps and games available.</td><td>- It has a large variety of apps and games.<br>- It has a user-friendly interface.<br>- It supports multiple languages.</td><td>- It may contain malicious or fake apps.<br>- It may not have the latest versions of apps.<br>- It may have compatibility issues with some devices.</td></tr>
-<tr><td>Amazon Appstore</td><td>Amazon Appstore is an app store that is owned by Amazon. It has over 500 thousand apps and games available. It also offers free apps of the day and coins that can be used to purchase apps.</td><td>- It has some exclusive apps and games.<br>- It has free apps of the day and coins.<br>- It has a high-quality selection of apps and games.</td><td>- It requires an Amazon account.<br>- It may not have all the apps that are available on google play store apk.<br>- It may have compatibility issues with some devices.</td></tr>
-<tr><td>APKPure</td><td>APKPure is an app store that offers pure and safe APK files for Android devices. It has over 300 thousand apps and games available. It also supports multiple languages and regions.</td><td>- It has a simple and clean interface.<br>- It has fast and secure downloads.<br>- It has region-locked and unavailable apps.</td><td>- It may not have all the apps that are available on google play store apk.<br>- It may not have the latest versions of apps.<br>- It may require manual updates for apps.</td></tr>
-</table>
- <h3>Some advantages and disadvantages of alternative app stores</h3>
- <p>Using alternative app stores can have some advantages and disadvantages compared to using google play store apk. Some of them are:</p>
- <ul>
-<li>Advantages:   <ul>
-  <li>You can find more variety and diversity of apps and games.</li>
-  <li>You can find some exclusive or unique apps and games.</li>
-  <li>You can find some cheaper or free apps and games.</li>
-  </ul>
-</li>
-<li>Disadvantages:   <ul>
-  <li>You may expose your device and data to security risks.</li>
-  <li>You may encounter more errors, crashes, or compatibility issues.</li>
-  <li>You may lose the automatic updates and support from google play store apk.</li>
-  </ul>
-</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, google play store apk.com is a website that provides the APK file of the Google Play Store app for Android devices. You can use it to download and install the Google Play Store app manually on your device, if you don't have it by default or if you want to get the latest version. You can also use it to access millions of apps and games for your Android device, as well as movies, books, music, and more. However, you should also be careful when using APK files, as they may have some risks and drawbacks. You should always download APK files from trusted sources, scan them with antivirus or malware scanners, backup your device and data, and update your apps regularly. You can also use alternative app stores to find more apps and games, but you should also weigh their pros and cons before using them.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about google play store apk.com:</p>
- <ol>
-<li>Q: Is google play store apk.com safe?<br>A: Google play store apk.com is a safe website that provides the original and unmodified APK file of the Google Play Store app. However, you should always be careful when downloading and installing any APK file from any source, as they may contain malicious or fake content. You should always scan the APK file with a reliable antivirus or malware scanner before installing it.</li>
-<li>Q: Is google play store apk.com legal?<br>A: Google play store apk.com is a legal website that does not violate any laws or regulations. However, you should always respect the terms of service and the intellectual property rights of the app developers when using any APK file. You should not use any APK file for illegal or unethical purposes.</li>
-<li>Q: Is google play store apk.com free?<br>A: Google play store apk.com is a free website that does not charge any fees or subscriptions for downloading or using the APK file of the Google Play Store app. However, you may need to pay for some apps or games that are not free on the Google Play Store or another app store.</li>
-<li>Q: How do I update google play store apk?<br>A: You can update google play store apk manually or automatically. To update it manually, you need to download the latest version of the APK file from google play store apk.com and install it over the existing one. To update it automatically, you need to enable auto-updates in the settings menu of the Google Play Store app.</li>
-<li>Q: What are some alternatives to google play store apk?<br>A: Some alternatives to google play store apk are Aptoide, Amazon Appstore, and APKPure. These are other app stores that offer APK files for Android devices. However, they may have some advantages and disadvantages compared to google play store apk, such as more variety, less security, more exclusivity, less reliability, etc.</li>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Qurani-krimin Azrbaycan dilind mczat v sirrlar.md b/spaces/congsaPfin/Manga-OCR/logs/Qurani-krimin Azrbaycan dilind mczat v sirrlar.md
deleted file mode 100644
index a2ff0477d9420d89dbe86969a9f0d3987ebdf49d..0000000000000000000000000000000000000000
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-<br />
-<h1>Quran in Azerbaijani language</h1>
-<p>The Quran is the holy book of Islam, which Muslims believe to be the word of God revealed to Prophet Muhammad (peace be upon him) through the angel Gabriel. The Quran is the source of guidance, wisdom, and inspiration for Muslims, who recite, study, and reflect on its verses. The Quran is also a miracle of language, literature, and eloquence, as it challenges human beings to produce something like it or even a single chapter of it.</p>
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-<p>But how was the Quran revealed and compiled? How is it divided and organized? And how can one access its meaning and message in Azerbaijani language? In this article, we will explore these questions and more, as we learn about the Quran in Azerbaijani language.</p>
- <h2>How was the Quran revealed and compiled?</h2>
-<p>The Quran was revealed to Prophet Muhammad (peace be upon him) over a period of 23 years, starting from 610 CE until his death in 632 CE. The revelations were not continuous, but rather intermittent, depending on the circumstances and needs of the Prophet and his community. The revelations were sometimes short and sometimes long, sometimes addressing specific issues and sometimes covering general topics. The Prophet would recite the revelations to his companions, who would memorize them or write them down on various materials, such as parchment, leather, bones, or stones.</p>
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-quran azerbaycan dilinde ali imran surəsi<br />
-quran azerbaycan dilinde nisa surəsi<br />
-quran azerbaycan dilinde maide surəsi<br />
-quran azerbaycan dilinde enam surəsi<br />
-quran azerbaycan dilinde araf surəsi<br />
-quran azerbaycan dilinde enfal surəsi<br />
-quran azerbaycan dilinde tevbe surəsi<br />
-quran azerbaycan dilinde yunus surəsi<br />
-quran azerbaycan dilinde hud surəsi<br />
-quran azerbaycan dilinde yusuf surəsi<br />
-quran azerbaycan dilinde rad surəsi<br />
-quran azerbaycan dilinde ibrahim surəsi<br />
-quran azerbaycan dilinde hicr surəsi<br />
-quran azerbaycan dilinde nehl surəsi<br />
-quran azerbaycan dilinde isra surəsi<br />
-quran azerbaycan dilinde kehf surəsi<br />
-quran azerbaycan dilinde meryem surəsi<br />
-quran azerbaycan dilinde ta ha surəsi<br />
-quran azerbaycan dilinde enbiya surəsi<br />
-quran azerbaycan dilinde hacc surəsi<br />
-quran azerbaycan dilinde muminun surəsi<br />
-quran azerbaycan dilinde nur surəsi</p>
-<p>The verses of the Quran are called ayahs, which means "signs" or "evidences" of God's existence and power. There are 6,236 ayahs in the Quran, although some scholars count them differently. The ayahs are also numbered within each surah. Some ayahs are short and concise, while others are long and elaborate. Some ayahs are clear and direct, while others are ambiguous and allegorical. Some ayahs are abrogated by later ones, while others are not. The ayahs cover various topics, such as theology, law, ethics, history, science, eschatology, and more.</p>
- <h2>Quran in Azerbaijani language</h2>
-<p>Azerbaijani language is a Turkic language spoken by about 30 million people mainly in Azerbaijan and Iran. It has two major dialects: North Azerbaijani (also known as Azeri) and South Azerbaijani (also known as Talysh). Azerbaijani language has been influenced by Persian, Arabic, Russian, Turkish, and other languages throughout its history.</p>
-<p>The history and development of Quran translation in Azerbaijani <h3>The history and development of Quran translation in Azerbaijani language</h3>
-<p>The first attempt to translate the Quran into Azerbaijani language dates back to the 13th century, when the Mongol ruler Hulagu Khan commissioned a Persian scholar named Rashid al-Din to produce a multilingual version of the Quran, which included Azerbaijani, Persian, Arabic, Mongolian, and Chinese. However, this translation was not widely circulated or accepted by the Muslims of the region.</p>
-<p>The next significant translation of the Quran into Azerbaijani language was done by Mirza Fath Ali Akhundzadeh, a prominent writer and reformer of the 19th century. He translated the Quran from Arabic into Azerbaijani using the Latin script, and published it in 1878 in Tiflis (now Tbilisi), Georgia. His translation was based on a rationalist and modernist approach, and aimed to make the Quran accessible and understandable to the common people. He also added his own commentary and interpretation to some verses, which caused controversy and criticism from some religious scholars.</p>
-<p>In the 20th century, several other translations of the Quran into Azerbaijani language were produced, both in Latin and Cyrillic scripts. Some of the most notable ones are: the translation by Vasim Mammadaliyev and Ziya Bunyadov, published in 1984 in Baku, Azerbaijan; the translation by Alikhan Musayev, published in 1992 in Istanbul, Turkey; and the translation by Ziya Bunyadov and Akif Alizadeh, published in 2008 in Baku, Azerbaijan. These translations are based on different methodologies and perspectives, and reflect the diversity and richness of the Azerbaijani language and culture.</p>
- <h3>The challenges and benefits of Quran translation in Azerbaijani language</h3>
-<p>Translating the Quran into Azerbaijani language is not an easy task, as it involves many challenges and difficulties. Some of these are:</p>
-<ul>
-<li>The linguistic challenge: The Quran is a unique and complex text that uses various rhetorical devices, such as metaphors, similes, parables, oaths, puns, alliterations, assonances, rhymes, etc. These devices enhance the beauty, eloquence, and impact of the Quranic message, but they also pose a challenge for translators who have to convey them in another language without losing their original meaning and effect.</li>
-<li>The cultural challenge: The Quran is a product of a specific historical and cultural context, which is different from that of the Azerbaijani people. The Quran contains references to events, places, people, customs, laws, etc. that may not be familiar or relevant to the contemporary Azerbaijani audience. Therefore, translators have to find ways to explain or adapt these references in a way that makes sense and does not distort the Quranic message.</li>
-<li>The theological challenge: The Quran is not only a book of information, but also a book of guidance, faith, and worship. It contains doctrines, principles, values, rules, commands, prohibitions, etc. that shape the beliefs and practices of Muslims. Therefore, translators have to be careful not to introduce any errors or deviations from the original Quranic teachings.</li>
-</ul>
-<p>Despite these challenges, translating the Quran into Azerbaijani language also has many benefits and advantages. Some of these are:</p>
-<ul>
-<li>The educational benefit: Translating the Quran into Azerbaijani language enables more people to access its knowledge and wisdom. It helps them to learn about their religion, history, culture, and identity. It also helps them to develop their linguistic skills and enrich their vocabulary.</li>
-<li>The spiritual benefit: Translating the Quran into Azerbaijani language allows more people to connect with its message and guidance. It helps them to understand its verses better and apply them to their lives. It also helps them to appreciate its beauty and eloquence.</li>
-<li>The social benefit: Translating the Quran into Azerbaijani language contributes to the unity and diversity of the Muslim community. It helps them to share their common faith and values with each other. It also helps them to respect and appreciate their differences and variations.</li>
-</ul>
- <h3>The available sources and platforms for Quran translation in Azerbaijani language</h3>
-<p>There are many sources and platforms that provide Quran translation in Azerbaijani language for different purposes and audiences. Some of these are:</p>
-<table>
-<tr><th>Source/Platform</th><th>Description</th></tr>
-<tr><td>Tanzil</td><td>A website that offers various translations of the Quran in different languages, including Azerbaijani. It allows users to read, listen, search, compare, download, and share Quran translations.</td></tr>
-<tr>< <td>Quran in Azerbaijani - Apps on Google Play</td><td>An app that offers various translations of the Quran in Azerbaijani language, as well as audio recitation, search, comparison, download, and share features.</td></tr>
-<tr><td>Quran Azerbaijan - Apps on Google Play</td><td>An app that provides the translation and commentary of the Quran by MirAli Abdullayev, a famous Azerbaijani scholar and translator. It also includes prayer times, dua, and other features.</td></tr>
-<tr><td>Azerbaijani Quran - App Store</td><td>An app that presents the translation of the Quran by Vasim Mammadaliyev and Ziya Bunyadov, two renowned Azerbaijani linguists and translators. It also allows users to bookmark, highlight, and share verses.</td></tr>
-<tr><td>Quran in Azerbaijani pdf - Internet Archive</td><td>A pdf file that contains the translation of the meanings of the Quran in Azerbaijani language. It can be downloaded or read online for free.</td></tr>
-<tr><td>Quran in Azerbaijani - Surah Quran</td><td>A website that displays the translation of the Quran in Azerbaijani language, along with the Arabic text and audio recitation. It also offers other translations and recitations in different languages.</td></tr>
-<tr><td>Holy Quran Translation in Azerbaijani Language - Internet Archive</td><td>A collection of audio files that contain the translation and recitation of the Quran in Azerbaijani language. It can be streamed or downloaded for free.</td></tr>
-</table>
- <p>As you can see, there are many options for reading, listening, and learning the Quran in Azerbaijani language. You can choose the one that suits your needs and preferences best. However, you should always remember that no translation can fully capture the original meaning and beauty of the Quran, and that you should always refer to the Arabic text for accuracy and authenticity.</p>
- <h2>Conclusion</h2>
-<p>In this article, we have learned about the Quran in Azerbaijani language. We have seen how the Quran was revealed and compiled, how it is divided and organized, how it was translated into Azerbaijani language, what are the challenges and benefits of Quran translation in Azerbaijani language, and what are the available sources and platforms for Quran translation in Azerbaijani language. We hope that this article has been informative and helpful for you, and that it has inspired you to read and understand the Quran better.</p>
-<p>If you have any questions or comments about this topic, please feel free to contact us or leave a comment below. We would love to hear from you and assist you in any way we can. May Allah bless you and guide you to the truth.</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions related to the topic of Quran in Azerbaijani language:</p>
-<ol>
-<li>What is the difference between translation and interpretation of the Quran?</li>
-<p>Translation is the process of rendering the meaning of a text from one language to another, while interpretation is the process of explaining or clarifying the meaning of a text within the same language. Translation is usually done by linguists or scholars who have knowledge of both languages, while interpretation is usually done by scholars or experts who have knowledge of the subject matter. Translation is more objective and literal, while interpretation is more subjective and contextual.</p>
-<li>What are some of the criteria for a good translation of the Quran?</li>
-<p>Some of the criteria for a good translation of the Quran are: accuracy, clarity, consistency, coherence, fluency, elegance, faithfulness, respectfulness, readability, and suitability. A good translation should convey the meaning of the original text as accurately as possible, without adding or omitting anything. It should also be clear, consistent, coherent, fluent, elegant, faithful, respectful, readable, and suitable for the intended audience and purpose.</p>
-<li>What are some of the benefits of learning Arabic to understand the Quran better?</li>
-<p>Some of the benefits of learning Arabic to understand the Quran better are: appreciation, comprehension, reflection, application, and communication. Learning Arabic helps one to appreciate the beauty, eloquence, and miracle of the Quranic language. It also helps one to comprehend the meaning, context, and implication of the Quranic verses. It also helps one to reflect on the guidance, wisdom, and inspiration of the Quranic message. It also helps one to communicate with other Muslims who speak Arabic or use Arabic terms in their speech.</p>
-<li>What are some of the resources for learning Arabic to understand the Quran better?</li>
-<p>Some of the resources for learning Arabic to understand the Quran better are: books, courses, apps, websites, videos, podcasts, etc. Some examples of these resources are: Arabic for Beginners by Dr. Imran Hamza Alawiye, Learn Arabic with Madinah Arabic Course by Dr. V. Abdur Rahim, Learn Quranic Arabic with Understand Quran Academy by Dr. Abdulazeez Abdulraheem, Duolingo Arabic Course, Bayyinah TV by Nouman Ali Khan, Quranic Arabic Simplified by Ustadh Fahim Qazi, etc.</p>
-<li>How can one improve their recitation of the Quran in Azerbaijani language?</li>
-<p>Some of the ways to improve one's recitation of the Quran in Azerbaijani language are: listening, reading, practicing, correcting, and memorizing. Listening to the recitation of the Quran in Azerbaijani language by a qualified reciter can help one to learn the correct pronunciation, intonation, and rhythm of the Quranic verses. Reading the translation of the Quran in Azerbaijani language along with the Arabic text can help one to understand the meaning and context of the Quranic verses. Practicing the recitation of the Quran in Azerbaijani language regularly can help one to improve their fluency, accuracy, and confidence. Correcting one's mistakes and errors in recitation can help one to avoid repeating them and improve their quality. Memorizing some or all of the Quran in Azerbaijani language can help one to retain and recall the Quranic verses easily and quickly.</p> 401be4b1e0<br />
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diff --git a/spaces/contluForse/HuggingGPT/assets/3d Flash Slideshow Maker Professional Cracked.md b/spaces/contluForse/HuggingGPT/assets/3d Flash Slideshow Maker Professional Cracked.md
deleted file mode 100644
index 56807d303c335783eae401df446e5f8db1aefd9e..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/3d Flash Slideshow Maker Professional Cracked.md	
+++ /dev/null
@@ -1,12 +0,0 @@
-<br />
-<p><p>what is the best slideshow maker, and what makes it so good is its strong emphasis on adding transitions and sound. with this program, you can create stunning images and videos that will make your audience speechless.</p>
-<p><p>with its easy to use interface and robust options, it is no wonder why users praise it as one of the best slideshow makers. however, you will need to buy the premium version to have a larger video library and a more complex, yet robust editing process.</p>
-<h2>3d flash slideshow maker professional cracked</h2><br /><p><b><b>Download Zip</b> &gt;&gt;&gt; <a href="https://ssurll.com/2uzvvU">https://ssurll.com/2uzvvU</a></b></p><br /><br />
-<p><p>sony vegas pro 16 is one of the top video editor and video maker available today. it has the ability to edit a range of video formats including various video files. the interface is very intuitive and the software has a lot of advanced features.</p>
-<p>if you want to create a trailer, a video for your youtube channel, or a slide show for your business, this is the software for you. its extremely easy to use and the program is loaded with features that will enable you to create professional-quality videos with ease.</p>
-<p>photo to video converter is another great software that enables you to create videos for your desktop, mobile devices, and the web. you can use this software to create professional-quality videos that you can upload to youtube.</p>
-<p>flipslideshow is an easy-to-use alternative to powerpoint, which you can use to create, present, and publish photo or video slideshows. the application can be used to create pictures and videos in a number of ways: embed videos, apply filters, add effects, and so on.</p>
-<p><p>slider maker is a free alternative to the well-known powerpoint presentation program. you can use it to create a slideshow video with your pictures and videos, add music and add effects to your video, and then share the results with your friends.</p>
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diff --git a/spaces/contluForse/HuggingGPT/assets/ArtyTorrent Pack 82-Spectrasonics Atmosphere VSTi Disc 6 Update Full ((NEW)) Version.md b/spaces/contluForse/HuggingGPT/assets/ArtyTorrent Pack 82-Spectrasonics Atmosphere VSTi Disc 6 Update Full ((NEW)) Version.md
deleted file mode 100644
index 5543a56793ac7ed5dfce622880721923b63406b3..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/ArtyTorrent Pack 82-Spectrasonics Atmosphere VSTi Disc 6 Update Full ((NEW)) Version.md	
+++ /dev/null
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-890 MB. Download; 7 jul. 2017; 8: 20. ArtyTorrent Pack 80-Spectrasonics Atmosphere VSTi . 578 MB. Download.Molecular cloning of amphiphysin and its expression in the developing nervous system.
-
-Mutations in the SH3 domain binding protein, amphiphysin, are associated with neuronal ceroid lipofuscinosis (NCL) in humans and with a progressive form of canine ceroid lipofuscinosis (CCL) in dogs. We have cloned the human and canine homologs of amphiphysin. Sequence analysis of the human gene revealed several splice variants, and the major transcript codes for a 1.7-kb product which has a protein structure similar to that of the yeast amphiphysin. Expression of the gene in transfected COS-7 cells demonstrates that it binds to the SH3 domains of GRB2 and synapsin. In situ hybridization of the developing brain reveals expression in the neuroepithelium, and analysis of the CCL dog at two different ages reveals that expression is dramatically down-regulated at a time when the neuropathology is most severe.This invention relates to an improved mask for use during the photolithographic printing of integrated circuit patterns on semiconductor wafers, and more particularly, to a mask and a method for forming micro-image patterns thereon.
-
-The micro-image printing technique is well known in the art and is generally described in, for example, U.S. Pat. No. 3,973,806. In one common application of this technique, micro-images of integrated circuit patterns are employed to form on wafers a collection of individual integrated circuits, or chips. During the chip manufacturing process, these micro-images are used to transfer a pattern from a master die having the desired integrated circuit patterns printed thereon to one or more photoresist films applied over the surface of the wafer. The photoresist films are then exposed to light through the micro-image pattern for a predetermined period of time. Following this exposure step, the wafer is dipped into a developing bath to wash away any unexposed photoresist. Once the exposed photoresist has been removed, the wafer is selectively etched so as to remove areas of the underlying substrate not covered by the remaining photoresist layer. The exposed areas of the substrate are thus defined as the final circuit elements. The micro- 4fefd39f24<br />
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diff --git a/spaces/dakaiye/dky_xuexi/request_llm/test_llms.py b/spaces/dakaiye/dky_xuexi/request_llm/test_llms.py
deleted file mode 100644
index ae6967be7b0c48d4c2af7a51335bd9becbc24d88..0000000000000000000000000000000000000000
--- a/spaces/dakaiye/dky_xuexi/request_llm/test_llms.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# """
-# 对各个llm模型进行单元测试
-# """
-def validate_path():
-    import os, sys
-    dir_name = os.path.dirname(__file__)
-    root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
-    os.chdir(root_dir_assume)
-    sys.path.append(root_dir_assume)
-    
-validate_path() # validate path so you can run from base directory
-if __name__ == "__main__":
-    from request_llm.bridge_newbingfree import predict_no_ui_long_connection
-    # from request_llm.bridge_moss import predict_no_ui_long_connection
-    # from request_llm.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
-    # from request_llm.bridge_jittorllms_llama import predict_no_ui_long_connection
-
-    llm_kwargs = {
-        'max_length': 512,
-        'top_p': 1,
-        'temperature': 1,
-    }
-
-    result = predict_no_ui_long_connection(inputs="你好", 
-                                        llm_kwargs=llm_kwargs,
-                                        history=[],
-                                        sys_prompt="")
-    print('final result:', result)
-
-
-    result = predict_no_ui_long_connection(inputs="what is a hero?", 
-                                        llm_kwargs=llm_kwargs,
-                                        history=["hello world"],
-                                        sys_prompt="")
-    print('final result:', result)
-
-    result = predict_no_ui_long_connection(inputs="如何理解传奇?", 
-                                        llm_kwargs=llm_kwargs,
-                                        history=[],
-                                        sys_prompt="")
-    print('final result:', result)
-
-    # # print(result)
-    # from multiprocessing import Process, Pipe
-    # class GetGLMHandle(Process):
-    #     def __init__(self):
-    #         super().__init__(daemon=True)
-    #         pass
-    #     def run(self):
-    #         # 子进程执行
-    #         # 第一次运行，加载参数
-    #         def validate_path():
-    #             import os, sys
-    #             dir_name = os.path.dirname(__file__)
-    #             root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
-    #             os.chdir(root_dir_assume + '/request_llm/jittorllms')
-    #             sys.path.append(root_dir_assume + '/request_llm/jittorllms')
-    #         validate_path() # validate path so you can run from base directory
-
-    #         jittorllms_model = None
-    #         import types
-    #         try:
-    #             if jittorllms_model is None:
-    #                 from models import get_model
-    #                 # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
-    #                 args_dict = {'model': 'chatrwkv'}
-    #                 print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))')
-    #                 jittorllms_model = get_model(types.SimpleNamespace(**args_dict))
-    #                 print('done get model')
-    #         except:
-    #             # self.child.send('[Local Message] Call jittorllms fail 不能正常加载jittorllms的参数。')
-    #             raise RuntimeError("不能正常加载jittorllms的参数！")
-            
-    # x = GetGLMHandle()
-    # x.start()
-
-
-    # input()
\ No newline at end of file
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/video.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/video.py
deleted file mode 100644
index 80c216f8a4d0aae2e79c776296adde66e379f864..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/video.py
+++ /dev/null
@@ -1,418 +0,0 @@
-"""gr.Video() component."""
-
-from __future__ import annotations
-
-import tempfile
-import warnings
-from pathlib import Path
-from typing import Callable, Literal
-
-from gradio_client import utils as client_utils
-from gradio_client.data_classes import FileData
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import VideoSerializable
-
-from gradio import processing_utils, utils, wasm_utils
-from gradio.components.base import IOComponent, _Keywords
-from gradio.deprecation import warn_style_method_deprecation
-from gradio.events import Changeable, Clearable, Playable, Recordable, Uploadable
-
-if not wasm_utils.IS_WASM:
-    # TODO: Support ffmpeg on Wasm
-    from ffmpy import FFmpeg
-
-set_documentation_group("component")
-
-
-@document()
-class Video(
-    Changeable,
-    Clearable,
-    Playable,
-    Recordable,
-    Uploadable,
-    IOComponent,
-    VideoSerializable,
-):
-    """
-    Creates a video component that can be used to upload/record videos (as an input) or display videos (as an output).
-    For the video to be playable in the browser it must have a compatible container and codec combination. Allowed
-    combinations are .mp4 with h264 codec, .ogg with theora codec, and .webm with vp9 codec. If the component detects
-    that the output video would not be playable in the browser it will attempt to convert it to a playable mp4 video.
-    If the conversion fails, the original video is returned.
-    Preprocessing: passes the uploaded video as a {str} filepath or URL whose extension can be modified by `format`.
-    Postprocessing: expects a {str} or {pathlib.Path} filepath to a video which is displayed, or a {Tuple[str | pathlib.Path, str | pathlib.Path | None]} where the first element is a filepath to a video and the second element is an optional filepath to a subtitle file.
-    Examples-format: a {str} filepath to a local file that contains the video, or a {Tuple[str, str]} where the first element is a filepath to a video file and the second element is a filepath to a subtitle file.
-    Demos: video_identity, video_subtitle
-    """
-
-    def __init__(
-        self,
-        value: str
-        | Path
-        | tuple[str | Path, str | Path | None]
-        | Callable
-        | None = None,
-        *,
-        format: str | None = None,
-        source: Literal["upload", "webcam"] = "upload",
-        height: int | None = None,
-        width: int | None = None,
-        label: str | None = None,
-        every: float | None = None,
-        show_label: bool | None = None,
-        container: bool = True,
-        scale: int | None = None,
-        min_width: int = 160,
-        interactive: bool | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-        mirror_webcam: bool = True,
-        include_audio: bool | None = None,
-        autoplay: bool = False,
-        show_share_button: bool | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            value: A path or URL for the default value that Video component is going to take. Can also be a tuple consisting of (video filepath, subtitle filepath). If a subtitle file is provided, it should be of type .srt or .vtt. Or can be callable, in which case the function will be called whenever the app loads to set the initial value of the component.
-            format: Format of video format to be returned by component, such as 'avi' or 'mp4'. Use 'mp4' to ensure browser playability. If set to None, video will keep uploaded format.
-            source: Source of video. "upload" creates a box where user can drop an video file, "webcam" allows user to record a video from their webcam.
-            height: Height of the displayed video in pixels.
-            width: Width of the displayed video in pixels.
-            label: component name in interface.
-            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
-            show_label: if True, will display label.
-            container: If True, will place the component in a container - providing some extra padding around the border.
-            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
-            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
-            interactive: if True, will allow users to upload a video; if False, can only be used to display videos. If not provided, this is inferred based on whether the component is used as an input or output.
-            visible: If False, component will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-            mirror_webcam: If True webcam will be mirrored. Default is True.
-            include_audio: Whether the component should record/retain the audio track for a video. By default, audio is excluded for webcam videos and included for uploaded videos.
-            autoplay: Whether to automatically play the video when the component is used as an output. Note: browsers will not autoplay video files if the user has not interacted with the page yet.
-            show_share_button: If True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise.
-        """
-        self.format = format
-        self.autoplay = autoplay
-        valid_sources = ["upload", "webcam"]
-        if source not in valid_sources:
-            raise ValueError(
-                f"Invalid value for parameter `source`: {source}. Please choose from one of: {valid_sources}"
-            )
-        self.source = source
-        self.height = height
-        self.width = width
-        self.mirror_webcam = mirror_webcam
-        self.include_audio = (
-            include_audio if include_audio is not None else source == "upload"
-        )
-        self.show_share_button = (
-            (utils.get_space() is not None)
-            if show_share_button is None
-            else show_share_button
-        )
-        IOComponent.__init__(
-            self,
-            label=label,
-            every=every,
-            show_label=show_label,
-            container=container,
-            scale=scale,
-            min_width=min_width,
-            interactive=interactive,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            value=value,
-            **kwargs,
-        )
-
-    def get_config(self):
-        return {
-            "source": self.source,
-            "value": self.value,
-            "height": self.height,
-            "width": self.width,
-            "mirror_webcam": self.mirror_webcam,
-            "include_audio": self.include_audio,
-            "autoplay": self.autoplay,
-            "show_share_button": self.show_share_button,
-            **IOComponent.get_config(self),
-        }
-
-    @staticmethod
-    def update(
-        value: str
-        | tuple[str, str | None]
-        | Literal[_Keywords.NO_VALUE]
-        | None = _Keywords.NO_VALUE,
-        source: Literal["upload", "webcam"] | None = None,
-        height: int | None = None,
-        width: int | None = None,
-        label: str | None = None,
-        show_label: bool | None = None,
-        container: bool | None = None,
-        scale: int | None = None,
-        min_width: int | None = None,
-        interactive: bool | None = None,
-        visible: bool | None = None,
-        autoplay: bool | None = None,
-        show_share_button: bool | None = None,
-    ):
-        return {
-            "source": source,
-            "height": height,
-            "width": width,
-            "label": label,
-            "show_label": show_label,
-            "container": container,
-            "scale": scale,
-            "min_width": min_width,
-            "interactive": interactive,
-            "visible": visible,
-            "value": value,
-            "autoplay": autoplay,
-            "show_share_button": show_share_button,
-            "__type__": "update",
-        }
-
-    def preprocess(
-        self, x: tuple[FileData, FileData | None] | FileData | None
-    ) -> str | None:
-        """
-        Parameters:
-            x: A tuple of (video file data, subtitle file data) or just video file data.
-        Returns:
-            A string file path or URL to the preprocessed video. Subtitle file data is ignored.
-        """
-        if x is None:
-            return None
-        elif isinstance(x, dict):
-            video = x
-        else:
-            video = x[0]
-
-        file_name, file_data, is_file = (
-            video.get("name"),
-            video["data"],
-            video.get("is_file", False),
-        )
-
-        if is_file:
-            assert file_name is not None, "Received file data without a file name."
-            if client_utils.is_http_url_like(file_name):
-                fn = self.download_temp_copy_if_needed
-            else:
-                fn = self.make_temp_copy_if_needed
-            file_name = Path(fn(file_name))
-        else:
-            assert file_data is not None, "Received empty file data."
-            file_name = Path(self.base64_to_temp_file_if_needed(file_data, file_name))
-
-        uploaded_format = file_name.suffix.replace(".", "")
-        needs_formatting = self.format is not None and uploaded_format != self.format
-        flip = self.source == "webcam" and self.mirror_webcam
-
-        if needs_formatting or flip:
-            format = f".{self.format if needs_formatting else uploaded_format}"
-            output_options = ["-vf", "hflip", "-c:a", "copy"] if flip else []
-            output_options += ["-an"] if not self.include_audio else []
-            flip_suffix = "_flip" if flip else ""
-            output_file_name = str(
-                file_name.with_name(f"{file_name.stem}{flip_suffix}{format}")
-            )
-            if Path(output_file_name).exists():
-                return output_file_name
-            if wasm_utils.IS_WASM:
-                raise wasm_utils.WasmUnsupportedError(
-                    "Video formatting is not supported in the Wasm mode."
-                )
-            ff = FFmpeg(
-                inputs={str(file_name): None},
-                outputs={output_file_name: output_options},
-            )
-            ff.run()
-            return output_file_name
-        elif not self.include_audio:
-            output_file_name = str(file_name.with_name(f"muted_{file_name.name}"))
-            if wasm_utils.IS_WASM:
-                raise wasm_utils.WasmUnsupportedError(
-                    "include_audio=False is not supported in the Wasm mode."
-                )
-            ff = FFmpeg(
-                inputs={str(file_name): None},
-                outputs={output_file_name: ["-an"]},
-            )
-            ff.run()
-            return output_file_name
-        else:
-            return str(file_name)
-
-    def postprocess(
-        self, y: str | Path | tuple[str | Path, str | Path | None] | None
-    ) -> tuple[FileData | None, FileData | None] | None:
-        """
-        Processes a video to ensure that it is in the correct format before returning it to the front end.
-        Parameters:
-            y: video data in either of the following formats: a tuple of (video filepath, optional subtitle filepath), or just a filepath or URL to an video file, or None.
-        Returns:
-            a tuple with the two dictionary, reresent to video and (optional) subtitle, which following formats:
-            - The first dictionary represents the video file and contains the following keys:
-                - 'name': a file path to a temporary copy of the processed video.
-                - 'data': None
-                - 'is_file': True
-            - The second dictionary represents the subtitle file and contains the following keys:
-                - 'name': None
-                - 'data': Base64 encode the processed subtitle data.
-                - 'is_file': False
-            - If subtitle is None, returns (video, None).
-            - If both video and subtitle are None, returns None.
-        """
-
-        if y is None or y == [None, None] or y == (None, None):
-            return None
-        if isinstance(y, (str, Path)):
-            processed_files = (self._format_video(y), None)
-        elif isinstance(y, (tuple, list)):
-            assert (
-                len(y) == 2
-            ), f"Expected lists of length 2 or tuples of length 2. Received: {y}"
-            assert isinstance(y[0], (str, Path)) and isinstance(
-                y[1], (str, Path)
-            ), f"If a tuple is provided, both elements must be strings or Path objects. Received: {y}"
-            video = y[0]
-            subtitle = y[1]
-            processed_files = (
-                self._format_video(video),
-                self._format_subtitle(subtitle),
-            )
-        else:
-            raise Exception(f"Cannot process type as video: {type(y)}")
-
-        return processed_files
-
-    def _format_video(self, video: str | Path | None) -> FileData | None:
-        """
-        Processes a video to ensure that it is in the correct format.
-        Parameters:
-            video: video data in either of the following formats: a string filepath or URL to an video file, or None.
-        Returns:
-            a dictionary with the following keys:
-
-            - 'name': a file path to a temporary copy of the processed video.
-            - 'data': None
-            - 'is_file': True
-        """
-        if video is None:
-            return None
-        video = str(video)
-        returned_format = video.split(".")[-1].lower()
-        if self.format is None or returned_format == self.format:
-            conversion_needed = False
-        else:
-            conversion_needed = True
-
-        is_url = client_utils.is_http_url_like(video)
-
-        # For cases where the video is a URL and does not need to be converted to another format, we can just return the URL
-        if is_url and not (conversion_needed):
-            return {"name": video, "data": None, "is_file": True}
-
-        # For cases where the video needs to be converted to another format
-        if is_url:
-            video = self.download_temp_copy_if_needed(video)
-        if (
-            processing_utils.ffmpeg_installed()
-            and not processing_utils.video_is_playable(video)
-        ):
-            warnings.warn(
-                "Video does not have browser-compatible container or codec. Converting to mp4"
-            )
-            video = processing_utils.convert_video_to_playable_mp4(video)
-        # Recalculate the format in case convert_video_to_playable_mp4 already made it the
-        # selected format
-        returned_format = video.split(".")[-1].lower()
-        if self.format is not None and returned_format != self.format:
-            if wasm_utils.IS_WASM:
-                raise wasm_utils.WasmUnsupportedError(
-                    "Returning a video in a different format is not supported in the Wasm mode."
-                )
-            output_file_name = video[0 : video.rindex(".") + 1] + self.format
-            ff = FFmpeg(
-                inputs={video: None},
-                outputs={output_file_name: None},
-                global_options="-y",
-            )
-            ff.run()
-            video = output_file_name
-
-        video = self.make_temp_copy_if_needed(video)
-
-        return {
-            "name": video,
-            "data": None,
-            "is_file": True,
-            "orig_name": Path(video).name,
-        }
-
-    def _format_subtitle(self, subtitle: str | Path | None) -> FileData | None:
-        """
-        Convert subtitle format to VTT and process the video to ensure it meets the HTML5 requirements.
-        Parameters:
-            subtitle: subtitle path in either of the VTT and SRT format.
-        Returns:
-            a dictionary with the following keys:
-            - 'name': None
-            - 'data': base64-encoded subtitle data.
-            - 'is_file': False
-        """
-
-        def srt_to_vtt(srt_file_path, vtt_file_path):
-            """Convert an SRT subtitle file to a VTT subtitle file"""
-            with open(srt_file_path, encoding="utf-8") as srt_file, open(
-                vtt_file_path, "w", encoding="utf-8"
-            ) as vtt_file:
-                vtt_file.write("WEBVTT\n\n")
-                for subtitle_block in srt_file.read().strip().split("\n\n"):
-                    subtitle_lines = subtitle_block.split("\n")
-                    subtitle_timing = subtitle_lines[1].replace(",", ".")
-                    subtitle_text = "\n".join(subtitle_lines[2:])
-                    vtt_file.write(f"{subtitle_timing} --> {subtitle_timing}\n")
-                    vtt_file.write(f"{subtitle_text}\n\n")
-
-        if subtitle is None:
-            return None
-
-        valid_extensions = (".srt", ".vtt")
-
-        if Path(subtitle).suffix not in valid_extensions:
-            raise ValueError(
-                f"Invalid value for parameter `subtitle`: {subtitle}. Please choose a file with one of these extensions: {valid_extensions}"
-            )
-
-        # HTML5 only support vtt format
-        if Path(subtitle).suffix == ".srt":
-            temp_file = tempfile.NamedTemporaryFile(
-                delete=False, suffix=".vtt", dir=self.DEFAULT_TEMP_DIR
-            )
-
-            srt_to_vtt(subtitle, temp_file.name)
-            subtitle = temp_file.name
-
-        subtitle_data = client_utils.encode_url_or_file_to_base64(subtitle)
-        return {"name": None, "data": subtitle_data, "is_file": False}
-
-    def style(self, *, height: int | None = None, width: int | None = None, **kwargs):
-        """
-        This method is deprecated. Please set these arguments in the constructor instead.
-        """
-        warn_style_method_deprecation()
-        if height is not None:
-            self.height = height
-        if width is not None:
-            self.width = width
-        return self
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-0dbc7d7e.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-0dbc7d7e.js
deleted file mode 100644
index d084ef849013aab0075e4236f56baa15ce75bc8b..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-0dbc7d7e.js
+++ /dev/null
@@ -1,346 +0,0 @@
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*/?>(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$)|</(?!script|pre|style|textarea)[a-z][\\w-]*\\s*>(?=[ \\t]*(?:\\n|$))[\\s\\S]*?(?:(?:\\n *)+\\n|$))",def:/^ {0,3}\[(label)\]: *(?:\n *)?([^<\s][^\s]*|<.*?>)(?:(?: +(?:\n *)?| *\n *)(title))? *(?:\n+|$)/,table:Pr,lheading:/^((?:.|\n(?!\n))+?)\n {0,3}(=+|-+) *(?:\n+|$)/,_paragraph:/^([^\n]+(?:\n(?!hr|heading|lheading|blockquote|fences|list|html|table| +\n)[^\n]+)*)/,text:/^[^\n]+/};de._label=/(?!\s*\])(?:\\.|[^\[\]\\])+/;de._title=/(?:"(?:\\"?|[^"\\])*"|'[^'\n]*(?:\n[^'\n]+)*\n?'|\([^()]*\))/;de.def=Re(de.def).replace("label",de._label).replace("title",de._title).getRegex();de.bullet=/(?:[*+-]|\d{1,9}[.)])/;de.listItemStart=Re(/^( *)(bull) */).replace("bull",de.bullet).getRegex();de.list=Re(de.list).replace(/bull/g,de.bullet).replace("hr","\\n+(?=\\1?(?:(?:- *){3,}|(?:_ *){3,}|(?:\\* *){3,})(?:\\n+|$))").replace("def","\\n+(?="+de.def.source+")").getRegex();de._tag="address|article|aside|base|basefont|blockquote|body|caption|center|col|colgroup|dd|details|dialog|dir|div|dl|dt|fieldset|figcaption|figure|footer|form|frame|frameset|h[1-6]|head|header|hr|html|iframe|legend|li|link|main|menu|menuitem|meta|nav|noframes|ol|optgroup|option|p|param|section|source|summary|table|tbody|td|tfoot|th|thead|title|tr|track|ul";de._comment=/<!--(?!-?>)[\s\S]*?(?:-->|$)/;de.html=Re(de.html,"i").replace("comment",de._comment).replace("tag",de._tag).replace("attribute",/ +[a-zA-Z:_][\w.:-]*(?: *= *"[^"\n]*"| *= *'[^'\n]*'| *= *[^\s"'=<>`]+)?/).getRegex();de.paragraph=Re(de._paragraph).replace("hr",de.hr).replace("heading"," {0,3}#{1,6} ").replace("|lheading","").replace("|table","").replace("blockquote"," {0,3}>").replace("fences"," {0,3}(?:`{3,}(?=[^`\\n]*\\n)|~{3,})[^\\n]*\\n").replace("list"," {0,3}(?:[*+-]|1[.)]) ").replace("html","</?(?:tag)(?: +|\\n|/?>)|<(?:script|pre|style|textarea|!--)").replace("tag",de._tag).getRegex();de.blockquote=Re(de.blockquote).replace("paragraph",de.paragraph).getRegex();de.normal={...de};de.gfm={...de.normal,table:"^ *([^\\n ].*\\|.*)\\n {0,3}(?:\\| *)?(:?-+:? 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*(?:\n+|$)/,heading:/^(#{1,6})(.*)(?:\n+|$)/,fences:Pr,lheading:/^(.+?)\n {0,3}(=+|-+) *(?:\n+|$)/,paragraph:Re(de.normal._paragraph).replace("hr",de.hr).replace("heading",` *#{1,6} *[^
-]`).replace("lheading",de.lheading).replace("blockquote"," {0,3}>").replace("|fences","").replace("|list","").replace("|html","").getRegex()};const oe={escape:/^\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/,autolink:/^<(scheme:[^\s\x00-\x1f<>]*|email)>/,url:Pr,tag:"^comment|^</[a-zA-Z][\\w:-]*\\s*>|^<[a-zA-Z][\\w-]*(?:attribute)*?\\s*/?>|^<\\?[\\s\\S]*?\\?>|^<![a-zA-Z]+\\s[\\s\\S]*?>|^<!\\[CDATA\\[[\\s\\S]*?\\]\\]>",link:/^!?\[(label)\]\(\s*(href)(?:\s+(title))?\s*\)/,reflink:/^!?\[(label)\]\[(ref)\]/,nolink:/^!?\[(ref)\](?:\[\])?/,reflinkSearch:"reflink|nolink(?!\\()",emStrong:{lDelim:/^(?:\*+(?:([punct_])|[^\s*]))|^_+(?:([punct*])|([^\s_]))/,rDelimAst:/^(?:[^_*\\]|\\.)*?\_\_(?:[^_*\\]|\\.)*?\*(?:[^_*\\]|\\.)*?(?=\_\_)|(?:[^*\\]|\\.)+(?=[^*])|[punct_](\*+)(?=[\s]|$)|(?:[^punct*_\s\\]|\\.)(\*+)(?=[punct_\s]|$)|[punct_\s](\*+)(?=[^punct*_\s])|[\s](\*+)(?=[punct_])|[punct_](\*+)(?=[punct_])|(?:[^punct*_\s\\]|\\.)(\*+)(?=[^punct*_\s])/,rDelimUnd:/^(?:[^_*\\]|\\.)*?\*\*(?:[^_*\\]|\\.)*?\_(?:[^_*\\]|\\.)*?(?=\*\*)|(?:[^_\\]|\\.)+(?=[^_])|[punct*](\_+)(?=[\s]|$)|(?:[^punct*_\s\\]|\\.)(\_+)(?=[punct*\s]|$)|[punct*\s](\_+)(?=[^punct*_\s])|[\s](\_+)(?=[punct*])|[punct*](\_+)(?=[punct*])/},code:/^(`+)([^`]|[^`][\s\S]*?[^`])\1(?!`)/,br:/^( {2,}|\\)\n(?!\s*$)/,del:Pr,text:/^(`+|[^`])(?:(?= {2,}\n)|[\s\S]*?(?:(?=[\\<!\[`*_]|\b_|$)|[^ ](?= {2,}\n)))/,punctuation:/^([\spunctuation])/};oe._punctuation="!\"#$%&'()+\\-.,/:;<=>?@\\[\\]`^{|}~";oe.punctuation=Re(oe.punctuation).replace(/punctuation/g,oe._punctuation).getRegex();oe.blockSkip=/\[[^\]]*?\]\([^\)]*?\)|`[^`]*?`|<[^>]*?>/g;oe.escapedEmSt=/(?:^|[^\\])(?:\\\\)*\\[*_]/g;oe._comment=Re(de._comment).replace("(?:-->|$)","-->").getRegex();oe.emStrong.lDelim=Re(oe.emStrong.lDelim).replace(/punct/g,oe._punctuation).getRegex();oe.emStrong.rDelimAst=Re(oe.emStrong.rDelimAst,"g").replace(/punct/g,oe._punctuation).getRegex();oe.emStrong.rDelimUnd=Re(oe.emStrong.rDelimUnd,"g").replace(/punct/g,oe._punctuation).getRegex();oe._escapes=/\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/g;oe._scheme=/[a-zA-Z][a-zA-Z0-9+.-]{1,31}/;oe._email=/[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+(@)[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)+(?![-_])/;oe.autolink=Re(oe.autolink).replace("scheme",oe._scheme).replace("email",oe._email).getRegex();oe._attribute=/\s+[a-zA-Z:_][\w.:-]*(?:\s*=\s*"[^"]*"|\s*=\s*'[^']*'|\s*=\s*[^\s"'=<>`]+)?/;oe.tag=Re(oe.tag).replace("comment",oe._comment).replace("attribute",oe._attribute).getRegex();oe._label=/(?:\[(?:\\.|[^\[\]\\])*\]|\\.|`[^`]*`|[^\[\]\\`])*?/;oe._href=/<(?:\\.|[^\n<>\\])+>|[^\s\x00-\x1f]*/;oe._title=/"(?:\\"?|[^"\\])*"|'(?:\\'?|[^'\\])*'|\((?:\\\)?|[^)\\])*\)/;oe.link=Re(oe.link).replace("label",oe._label).replace("href",oe._href).replace("title",oe._title).getRegex();oe.reflink=Re(oe.reflink).replace("label",oe._label).replace("ref",de._label).getRegex();oe.nolink=Re(oe.nolink).replace("ref",de._label).getRegex();oe.reflinkSearch=Re(oe.reflinkSearch,"g").replace("reflink",oe.reflink).replace("nolink",oe.nolink).getRegex();oe.normal={...oe};oe.pedantic={...oe.normal,strong:{start:/^__|\*\*/,middle:/^__(?=\S)([\s\S]*?\S)__(?!_)|^\*\*(?=\S)([\s\S]*?\S)\*\*(?!\*)/,endAst:/\*\*(?!\*)/g,endUnd:/__(?!_)/g},em:{start:/^_|\*/,middle:/^()\*(?=\S)([\s\S]*?\S)\*(?!\*)|^_(?=\S)([\s\S]*?\S)_(?!_)/,endAst:/\*(?!\*)/g,endUnd:/_(?!_)/g},link:Re(/^!?\[(label)\]\((.*?)\)/).replace("label",oe._label).getRegex(),reflink:Re(/^!?\[(label)\]\s*\[([^\]]*)\]/).replace("label",oe._label).getRegex()};oe.gfm={...oe.normal,escape:Re(oe.escape).replace("])","~|])").getRegex(),_extended_email:/[A-Za-z0-9._+-]+(@)[a-zA-Z0-9-_]+(?:\.[a-zA-Z0-9-_]*[a-zA-Z0-9])+(?![-_])/,url:/^((?:ftp|https?):\/\/|www\.)(?:[a-zA-Z0-9\-]+\.?)+[^\s<]*|^email/,_backpedal:/(?:[^?!.,:;*_'"~()&]+|\([^)]*\)|&(?![a-zA-Z0-9]+;$)|[?!.,:;*_'"~)]+(?!$))+/,del:/^(~~?)(?=[^\s~])([\s\S]*?[^\s~])\1(?=[^~]|$)/,text:/^([`~]+|[^`~])(?:(?= {2,}\n)|(?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)|[\s\S]*?(?:(?=[\\<!\[`*~_]|\b_|https?:\/\/|ftp:\/\/|www\.|$)|[^ ](?= {2,}\n)|[^a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-](?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)))/};oe.gfm.url=Re(oe.gfm.url,"i").replace("email",oe.gfm._extended_email).getRegex();oe.breaks={...oe.gfm,br:Re(oe.br).replace("{2,}","*").getRegex(),text:Re(oe.gfm.text).replace("\\b_","\\b_| {2,}\\n").replace(/\{2,\}/g,"*").getRegex()};function yo(g){return g.replace(/---/g,"—").replace(/--/g,"–").replace(/(^|[-\u2014/(\[{"\s])'/g,"$1‘").replace(/'/g,"’").replace(/(^|[-\u2014/(\[{\u2018\s])"/g,"$1“").replace(/"/g,"”").replace(/\.{3}/g,"…")}function bi(g){let a="",l,s;const m=g.length;for(l=0;l<m;l++)s=g.charCodeAt(l),Math.random()>.5&&(s="x"+s.toString(16)),a+="&#"+s+";";return a}class d0{constructor(a){this.tokens=[],this.tokens.links=Object.create(null),this.options=a||_0,this.options.tokenizer=this.options.tokenizer||new Gn,this.tokenizer=this.options.tokenizer,this.tokenizer.options=this.options,this.tokenizer.lexer=this,this.inlineQueue=[],this.state={inLink:!1,inRawBlock:!1,top:!0};const l={block:de.normal,inline:oe.normal};this.options.pedantic?(l.block=de.pedantic,l.inline=oe.pedantic):this.options.gfm&&(l.block=de.gfm,this.options.breaks?l.inline=oe.breaks:l.inline=oe.gfm),this.tokenizer.rules=l}static get rules(){return{block:de,inline:oe}}static lex(a,l){return new d0(l).lex(a)}static lexInline(a,l){return new d0(l).inlineTokens(a)}lex(a){a=a.replace(/\r\n|\r/g,`
-`),this.blockTokens(a,this.tokens);let l;for(;l=this.inlineQueue.shift();)this.inlineTokens(l.src,l.tokens);return this.tokens}blockTokens(a,l=[]){this.options.pedantic?a=a.replace(/\t/g,"    ").replace(/^ +$/gm,""):a=a.replace(/^( *)(\t+)/gm,(y,k,N)=>k+"    ".repeat(N.length));let s,m,p,v;for(;a;)if(!(this.options.extensions&&this.options.extensions.block&&this.options.extensions.block.some(y=>(s=y.call({lexer:this},a,l))?(a=a.substring(s.raw.length),l.push(s),!0):!1))){if(s=this.tokenizer.space(a)){a=a.substring(s.raw.length),s.raw.length===1&&l.length>0?l[l.length-1].raw+=`
-`:l.push(s);continue}if(s=this.tokenizer.code(a)){a=a.substring(s.raw.length),m=l[l.length-1],m&&(m.type==="paragraph"||m.type==="text")?(m.raw+=`
-`+s.raw,m.text+=`
-`+s.text,this.inlineQueue[this.inlineQueue.length-1].src=m.text):l.push(s);continue}if(s=this.tokenizer.fences(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.heading(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.hr(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.blockquote(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.list(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.html(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.def(a)){a=a.substring(s.raw.length),m=l[l.length-1],m&&(m.type==="paragraph"||m.type==="text")?(m.raw+=`
-`+s.raw,m.text+=`
-`+s.raw,this.inlineQueue[this.inlineQueue.length-1].src=m.text):this.tokens.links[s.tag]||(this.tokens.links[s.tag]={href:s.href,title:s.title});continue}if(s=this.tokenizer.table(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.lheading(a)){a=a.substring(s.raw.length),l.push(s);continue}if(p=a,this.options.extensions&&this.options.extensions.startBlock){let y=1/0;const k=a.slice(1);let N;this.options.extensions.startBlock.forEach(function(U){N=U.call({lexer:this},k),typeof N=="number"&&N>=0&&(y=Math.min(y,N))}),y<1/0&&y>=0&&(p=a.substring(0,y+1))}if(this.state.top&&(s=this.tokenizer.paragraph(p))){m=l[l.length-1],v&&m.type==="paragraph"?(m.raw+=`
-`+s.raw,m.text+=`
-`+s.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=m.text):l.push(s),v=p.length!==a.length,a=a.substring(s.raw.length);continue}if(s=this.tokenizer.text(a)){a=a.substring(s.raw.length),m=l[l.length-1],m&&m.type==="text"?(m.raw+=`
-`+s.raw,m.text+=`
-`+s.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=m.text):l.push(s);continue}if(a){const y="Infinite loop on byte: "+a.charCodeAt(0);if(this.options.silent){console.error(y);break}else throw new Error(y)}}return this.state.top=!0,l}inline(a,l=[]){return this.inlineQueue.push({src:a,tokens:l}),l}inlineTokens(a,l=[]){let s,m,p,v=a,y,k,N;if(this.tokens.links){const U=Object.keys(this.tokens.links);if(U.length>0)for(;(y=this.tokenizer.rules.inline.reflinkSearch.exec(v))!=null;)U.includes(y[0].slice(y[0].lastIndexOf("[")+1,-1))&&(v=v.slice(0,y.index)+"["+gi("a",y[0].length-2)+"]"+v.slice(this.tokenizer.rules.inline.reflinkSearch.lastIndex))}for(;(y=this.tokenizer.rules.inline.blockSkip.exec(v))!=null;)v=v.slice(0,y.index)+"["+gi("a",y[0].length-2)+"]"+v.slice(this.tokenizer.rules.inline.blockSkip.lastIndex);for(;(y=this.tokenizer.rules.inline.escapedEmSt.exec(v))!=null;)v=v.slice(0,y.index+y[0].length-2)+"++"+v.slice(this.tokenizer.rules.inline.escapedEmSt.lastIndex),this.tokenizer.rules.inline.escapedEmSt.lastIndex--;for(;a;)if(k||(N=""),k=!1,!(this.options.extensions&&this.options.extensions.inline&&this.options.extensions.inline.some(U=>(s=U.call({lexer:this},a,l))?(a=a.substring(s.raw.length),l.push(s),!0):!1))){if(s=this.tokenizer.escape(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.tag(a)){a=a.substring(s.raw.length),m=l[l.length-1],m&&s.type==="text"&&m.type==="text"?(m.raw+=s.raw,m.text+=s.text):l.push(s);continue}if(s=this.tokenizer.link(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.reflink(a,this.tokens.links)){a=a.substring(s.raw.length),m=l[l.length-1],m&&s.type==="text"&&m.type==="text"?(m.raw+=s.raw,m.text+=s.text):l.push(s);continue}if(s=this.tokenizer.emStrong(a,v,N)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.codespan(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.br(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.del(a)){a=a.substring(s.raw.length),l.push(s);continue}if(s=this.tokenizer.autolink(a,bi)){a=a.substring(s.raw.length),l.push(s);continue}if(!this.state.inLink&&(s=this.tokenizer.url(a,bi))){a=a.substring(s.raw.length),l.push(s);continue}if(p=a,this.options.extensions&&this.options.extensions.startInline){let U=1/0;const Y=a.slice(1);let te;this.options.extensions.startInline.forEach(function(j){te=j.call({lexer:this},Y),typeof te=="number"&&te>=0&&(U=Math.min(U,te))}),U<1/0&&U>=0&&(p=a.substring(0,U+1))}if(s=this.tokenizer.inlineText(p,yo)){a=a.substring(s.raw.length),s.raw.slice(-1)!=="_"&&(N=s.raw.slice(-1)),k=!0,m=l[l.length-1],m&&m.type==="text"?(m.raw+=s.raw,m.text+=s.text):l.push(s);continue}if(a){const U="Infinite loop on byte: "+a.charCodeAt(0);if(this.options.silent){console.error(U);break}else throw new Error(U)}}return l}}class Vn{constructor(a){this.options=a||_0}code(a,l,s){const m=(l||"").match(/\S*/)[0];if(this.options.highlight){const p=this.options.highlight(a,m);p!=null&&p!==a&&(s=!0,a=p)}return a=a.replace(/\n$/,"")+`
-`,m?'<pre><code class="'+this.options.langPrefix+tt(m)+'">'+(s?a:tt(a,!0))+`</code></pre>
-`:"<pre><code>"+(s?a:tt(a,!0))+`</code></pre>
-`}blockquote(a){return`<blockquote>
-${a}</blockquote>
-`}html(a,l){return a}heading(a,l,s,m){if(this.options.headerIds){const p=this.options.headerPrefix+m.slug(s);return`<h${l} id="${p}">${a}</h${l}>
-`}return`<h${l}>${a}</h${l}>
-`}hr(){return this.options.xhtml?`<hr/>
-`:`<hr>
-`}list(a,l,s){const m=l?"ol":"ul",p=l&&s!==1?' start="'+s+'"':"";return"<"+m+p+`>
-`+a+"</"+m+`>
-`}listitem(a){return`<li>${a}</li>
-`}checkbox(a){return"<input "+(a?'checked="" ':"")+'disabled="" type="checkbox"'+(this.options.xhtml?" /":"")+"> "}paragraph(a){return`<p>${a}</p>
-`}table(a,l){return l&&(l=`<tbody>${l}</tbody>`),`<table>
-<thead>
-`+a+`</thead>
-`+l+`</table>
-`}tablerow(a){return`<tr>
-${a}</tr>
-`}tablecell(a,l){const s=l.header?"th":"td";return(l.align?`<${s} align="${l.align}">`:`<${s}>`)+a+`</${s}>
-`}strong(a){return`<strong>${a}</strong>`}em(a){return`<em>${a}</em>`}codespan(a){return`<code>${a}</code>`}br(){return this.options.xhtml?"<br/>":"<br>"}del(a){return`<del>${a}</del>`}link(a,l,s){if(a=di(this.options.sanitize,this.options.baseUrl,a),a===null)return s;let m='<a href="'+a+'"';return l&&(m+=' title="'+l+'"'),m+=">"+s+"</a>",m}image(a,l,s){if(a=di(this.options.sanitize,this.options.baseUrl,a),a===null)return s;let m=`<img src="${a}" alt="${s}"`;return l&&(m+=` title="${l}"`),m+=this.options.xhtml?"/>":">",m}text(a){return a}}class al{strong(a){return a}em(a){return a}codespan(a){return a}del(a){return a}html(a){return a}text(a){return a}link(a,l,s){return""+s}image(a,l,s){return""+s}br(){return""}}class il{constructor(){this.seen={}}serialize(a){return a.toLowerCase().trim().replace(/<[!\/a-z].*?>/ig,"").replace(/[\u2000-\u206F\u2E00-\u2E7F\\'!"#$%&()*+,./:;<=>?@[\]^`{|}~]/g,"").replace(/\s/g,"-")}getNextSafeSlug(a,l){let s=a,m=0;if(this.seen.hasOwnProperty(s)){m=this.seen[a];do m++,s=a+"-"+m;while(this.seen.hasOwnProperty(s))}return l||(this.seen[a]=m,this.seen[s]=0),s}slug(a,l={}){const s=this.serialize(a);return this.getNextSafeSlug(s,l.dryrun)}}class p0{constructor(a){this.options=a||_0,this.options.renderer=this.options.renderer||new Vn,this.renderer=this.options.renderer,this.renderer.options=this.options,this.textRenderer=new al,this.slugger=new il}static parse(a,l){return new p0(l).parse(a)}static parseInline(a,l){return new p0(l).parseInline(a)}parse(a,l=!0){let s="",m,p,v,y,k,N,U,Y,te,j,ve,pe,le,O,B,z,R,D,L;const X=a.length;for(m=0;m<X;m++){if(j=a[m],this.options.extensions&&this.options.extensions.renderers&&this.options.extensions.renderers[j.type]&&(L=this.options.extensions.renderers[j.type].call({parser:this},j),L!==!1||!["space","hr","heading","code","table","blockquote","list","html","paragraph","text"].includes(j.type))){s+=L||"";continue}switch(j.type){case"space":continue;case"hr":{s+=this.renderer.hr();continue}case"heading":{s+=this.renderer.heading(this.parseInline(j.tokens),j.depth,nl(this.parseInline(j.tokens,this.textRenderer)),this.slugger);continue}case"code":{s+=this.renderer.code(j.text,j.lang,j.escaped);continue}case"table":{for(Y="",U="",y=j.header.length,p=0;p<y;p++)U+=this.renderer.tablecell(this.parseInline(j.header[p].tokens),{header:!0,align:j.align[p]});for(Y+=this.renderer.tablerow(U),te="",y=j.rows.length,p=0;p<y;p++){for(N=j.rows[p],U="",k=N.length,v=0;v<k;v++)U+=this.renderer.tablecell(this.parseInline(N[v].tokens),{header:!1,align:j.align[v]});te+=this.renderer.tablerow(U)}s+=this.renderer.table(Y,te);continue}case"blockquote":{te=this.parse(j.tokens),s+=this.renderer.blockquote(te);continue}case"list":{for(ve=j.ordered,pe=j.start,le=j.loose,y=j.items.length,te="",p=0;p<y;p++)B=j.items[p],z=B.checked,R=B.task,O="",B.task&&(D=this.renderer.checkbox(z),le?B.tokens.length>0&&B.tokens[0].type==="paragraph"?(B.tokens[0].text=D+" "+B.tokens[0].text,B.tokens[0].tokens&&B.tokens[0].tokens.length>0&&B.tokens[0].tokens[0].type==="text"&&(B.tokens[0].tokens[0].text=D+" "+B.tokens[0].tokens[0].text)):B.tokens.unshift({type:"text",text:D}):O+=D),O+=this.parse(B.tokens,le),te+=this.renderer.listitem(O,R,z);s+=this.renderer.list(te,ve,pe);continue}case"html":{s+=this.renderer.html(j.text,j.block);continue}case"paragraph":{s+=this.renderer.paragraph(this.parseInline(j.tokens));continue}case"text":{for(te=j.tokens?this.parseInline(j.tokens):j.text;m+1<X&&a[m+1].type==="text";)j=a[++m],te+=`
-`+(j.tokens?this.parseInline(j.tokens):j.text);s+=l?this.renderer.paragraph(te):te;continue}default:{const ae='Token with "'+j.type+'" type was not found.';if(this.options.silent){console.error(ae);return}else throw new Error(ae)}}}return s}parseInline(a,l){l=l||this.renderer;let s="",m,p,v;const y=a.length;for(m=0;m<y;m++){if(p=a[m],this.options.extensions&&this.options.extensions.renderers&&this.options.extensions.renderers[p.type]&&(v=this.options.extensions.renderers[p.type].call({parser:this},p),v!==!1||!["escape","html","link","image","strong","em","codespan","br","del","text"].includes(p.type))){s+=v||"";continue}switch(p.type){case"escape":{s+=l.text(p.text);break}case"html":{s+=l.html(p.text);break}case"link":{s+=l.link(p.href,p.title,this.parseInline(p.tokens,l));break}case"image":{s+=l.image(p.href,p.title,p.text);break}case"strong":{s+=l.strong(this.parseInline(p.tokens,l));break}case"em":{s+=l.em(this.parseInline(p.tokens,l));break}case"codespan":{s+=l.codespan(p.text);break}case"br":{s+=l.br();break}case"del":{s+=l.del(this.parseInline(p.tokens,l));break}case"text":{s+=l.text(p.text);break}default:{const k='Token with "'+p.type+'" type was not found.';if(this.options.silent){console.error(k);return}else throw new Error(k)}}}return s}}class Ln{constructor(a){this.options=a||_0}static passThroughHooks=new Set(["preprocess","postprocess"]);preprocess(a){return a}postprocess(a){return a}}function wo(g,a,l){return s=>{if(s.message+=`
-Please report this to https://github.com/markedjs/marked.`,g){const m="<p>An error occurred:</p><pre>"+tt(s.message+"",!0)+"</pre>";if(a)return Promise.resolve(m);if(l){l(null,m);return}return m}if(a)return Promise.reject(s);if(l){l(s);return}throw s}}function ll(g,a){return(l,s,m)=>{typeof s=="function"&&(m=s,s=null);const p={...s};s={...fe.defaults,...p};const v=wo(s.silent,s.async,m);if(typeof l>"u"||l===null)return v(new Error("marked(): input parameter is undefined or null"));if(typeof l!="string")return v(new Error("marked(): input parameter is of type "+Object.prototype.toString.call(l)+", string expected"));if(vo(s,m),s.hooks&&(s.hooks.options=s),m){const y=s.highlight;let k;try{s.hooks&&(l=s.hooks.preprocess(l)),k=g(l,s)}catch(Y){return v(Y)}const N=function(Y){let te;if(!Y)try{s.walkTokens&&fe.walkTokens(k,s.walkTokens),te=a(k,s),s.hooks&&(te=s.hooks.postprocess(te))}catch(j){Y=j}return s.highlight=y,Y?v(Y):m(null,te)};if(!y||y.length<3||(delete s.highlight,!k.length))return N();let U=0;fe.walkTokens(k,function(Y){Y.type==="code"&&(U++,setTimeout(()=>{y(Y.text,Y.lang,function(te,j){if(te)return N(te);j!=null&&j!==Y.text&&(Y.text=j,Y.escaped=!0),U--,U===0&&N()})},0))}),U===0&&N();return}if(s.async)return Promise.resolve(s.hooks?s.hooks.preprocess(l):l).then(y=>g(y,s)).then(y=>s.walkTokens?Promise.all(fe.walkTokens(y,s.walkTokens)).then(()=>y):y).then(y=>a(y,s)).then(y=>s.hooks?s.hooks.postprocess(y):y).catch(v);try{s.hooks&&(l=s.hooks.preprocess(l));const y=g(l,s);s.walkTokens&&fe.walkTokens(y,s.walkTokens);let k=a(y,s);return s.hooks&&(k=s.hooks.postprocess(k)),k}catch(y){return v(y)}}}function fe(g,a,l){return ll(d0.lex,p0.parse)(g,a,l)}fe.options=fe.setOptions=function(g){return fe.defaults={...fe.defaults,...g},no(fe.defaults),fe};fe.getDefaults=el;fe.defaults=_0;fe.use=function(...g){const a=fe.defaults.extensions||{renderers:{},childTokens:{}};g.forEach(l=>{const s={...l};if(s.async=fe.defaults.async||s.async||!1,l.extensions&&(l.extensions.forEach(m=>{if(!m.name)throw new Error("extension name required");if(m.renderer){const p=a.renderers[m.name];p?a.renderers[m.name]=function(...v){let y=m.renderer.apply(this,v);return y===!1&&(y=p.apply(this,v)),y}:a.renderers[m.name]=m.renderer}if(m.tokenizer){if(!m.level||m.level!=="block"&&m.level!=="inline")throw new Error("extension level must be 'block' or 'inline'");a[m.level]?a[m.level].unshift(m.tokenizer):a[m.level]=[m.tokenizer],m.start&&(m.level==="block"?a.startBlock?a.startBlock.push(m.start):a.startBlock=[m.start]:m.level==="inline"&&(a.startInline?a.startInline.push(m.start):a.startInline=[m.start]))}m.childTokens&&(a.childTokens[m.name]=m.childTokens)}),s.extensions=a),l.renderer){const m=fe.defaults.renderer||new Vn;for(const p in l.renderer){const v=m[p];m[p]=(...y)=>{let k=l.renderer[p].apply(m,y);return k===!1&&(k=v.apply(m,y)),k}}s.renderer=m}if(l.tokenizer){const m=fe.defaults.tokenizer||new Gn;for(const p in l.tokenizer){const v=m[p];m[p]=(...y)=>{let k=l.tokenizer[p].apply(m,y);return k===!1&&(k=v.apply(m,y)),k}}s.tokenizer=m}if(l.hooks){const m=fe.defaults.hooks||new Ln;for(const p in l.hooks){const v=m[p];Ln.passThroughHooks.has(p)?m[p]=y=>{if(fe.defaults.async)return Promise.resolve(l.hooks[p].call(m,y)).then(N=>v.call(m,N));const k=l.hooks[p].call(m,y);return v.call(m,k)}:m[p]=(...y)=>{let k=l.hooks[p].apply(m,y);return k===!1&&(k=v.apply(m,y)),k}}s.hooks=m}if(l.walkTokens){const m=fe.defaults.walkTokens;s.walkTokens=function(p){let v=[];return v.push(l.walkTokens.call(this,p)),m&&(v=v.concat(m.call(this,p))),v}}fe.setOptions(s)})};fe.walkTokens=function(g,a){let l=[];for(const s of g)switch(l=l.concat(a.call(fe,s)),s.type){case"table":{for(const m of s.header)l=l.concat(fe.walkTokens(m.tokens,a));for(const m of s.rows)for(const p of m)l=l.concat(fe.walkTokens(p.tokens,a));break}case"list":{l=l.concat(fe.walkTokens(s.items,a));break}default:fe.defaults.extensions&&fe.defaults.extensions.childTokens&&fe.defaults.extensions.childTokens[s.type]?fe.defaults.extensions.childTokens[s.type].forEach(function(m){l=l.concat(fe.walkTokens(s[m],a))}):s.tokens&&(l=l.concat(fe.walkTokens(s.tokens,a)))}return l};fe.parseInline=ll(d0.lexInline,p0.parseInline);fe.Parser=p0;fe.parser=p0.parse;fe.Renderer=Vn;fe.TextRenderer=al;fe.Lexer=d0;fe.lexer=d0.lex;fe.Tokenizer=Gn;fe.Slugger=il;fe.Hooks=Ln;fe.parse=fe;fe.options;fe.setOptions;fe.use;fe.walkTokens;fe.parseInline;p0.parse;d0.lex;function xo(g){if(typeof g=="function"&&(g={highlight:g}),!g||typeof g.highlight!="function")throw new Error("Must provide highlight function");return typeof g.langPrefix!="string"&&(g.langPrefix="language-"),{async:!!g.async,walkTokens(a){if(a.type!=="code")return;const l=ko(a);if(g.async)return Promise.resolve(g.highlight(a.text,l)).then(yi(a));const s=g.highlight(a.text,l);yi(a)(s)},renderer:{code(a,l,s){const m=(l||"").match(/\S*/)[0],p=m?` class="${g.langPrefix}${xi(m)}"`:"";return a=a.replace(/\n$/,""),`<pre><code${p}>${s?a:xi(a,!0)}
-</code></pre>`}}}}function ko(g){return(g.lang||"").match(/\S*/)[0]}function yi(g){return a=>{typeof a=="string"&&a!==g.text&&(g.escaped=!0,g.text=a)}}const sl=/[&<>"']/,So=new RegExp(sl.source,"g"),ol=/[<>"']|&(?!(#\d{1,7}|#[Xx][a-fA-F0-9]{1,6}|\w+);)/,Ao=new RegExp(ol.source,"g"),To={"&":"&amp;","<":"&lt;",">":"&gt;",'"':"&quot;","'":"&#39;"},wi=g=>To[g];function xi(g,a){if(a){if(sl.test(g))return g.replace(So,wi)}else if(ol.test(g))return g.replace(Ao,wi);return g}var ul={exports:{}};(function(g){var a=typeof window<"u"?window:typeof WorkerGlobalScope<"u"&&self instanceof WorkerGlobalScope?self:{};/**
- * Prism: Lightweight, robust, elegant syntax highlighting
- *
- * @license MIT <https://opensource.org/licenses/MIT>
- * @author Lea Verou <https://lea.verou.me>
- * @namespace
- * @public
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k={};y&2&&(k.elem_id=v[1]),y&4&&(k.elem_classes=v[2]),y&8&&(k.visible=v[3]),y&32&&(k.scale=v[5]),y&64&&(k.min_width=v[6]),y&128&&(k.label=v[7]),y&256&&(k.show_label=v[8]),y&512&&(k.root=v[9]),y&1024&&(k.root_url=v[10]),y&2048&&(k.selectable=v[11]),y&4096&&(k.theme_mode=v[12]),y&8192&&(k.show_share_button=v[13]),y&16384&&(k.rtl=v[14]),y&32768&&(k.show_copy_button=v[15]),y&16&&(k.latex_delimiters=v[4]),y&65536&&(k.loading_status=v[16]),y&131072&&(k.height=v[17]),!l&&y&1&&(l=!0,k.value=v[0],Zs(()=>l=!1)),a.$set(k)},i(v){s||(xe(a.$$.fragment,v),s=!0)},o(v){Me(a.$$.fragment,v),s=!1},d(v){Ut(a,v)}}}function A1(g,a,l){let{elem_id:s=""}=a,{elem_classes:m=[]}=a,{visible:p=!0}=a,{value:v=[]}=a,{latex_delimiters:y}=a,{scale:k=null}=a,{min_width:N=void 0}=a,{label:U}=a,{show_label:Y=!0}=a,{root:te}=a,{root_url:j}=a,{selectable:ve=!1}=a,{theme_mode:pe}=a,{show_share_button:le=!1}=a,{rtl:O=!1}=a,{show_copy_button:B=!1}=a,{loading_status:z=void 0}=a,{height:R=400}=a;function D(G){v=G,l(0,v)}function L(G){rt.call(this,g,G)}function X(G){rt.call(this,g,G)}function ae(G){rt.call(this,g,G)}function K(G){rt.call(this,g,G)}return g.$$set=G=>{"elem_id"in G&&l(1,s=G.elem_id),"elem_classes"in G&&l(2,m=G.elem_classes),"visible"in G&&l(3,p=G.visible),"value"in G&&l(0,v=G.value),"latex_delimiters"in G&&l(4,y=G.latex_delimiters),"scale"in G&&l(5,k=G.scale),"min_width"in G&&l(6,N=G.min_width),"label"in G&&l(7,U=G.label),"show_label"in G&&l(8,Y=G.show_label),"root"in G&&l(9,te=G.root),"root_url"in G&&l(10,j=G.root_url),"selectable"in G&&l(11,ve=G.selectable),"theme_mode"in G&&l(12,pe=G.theme_mode),"show_share_button"in G&&l(13,le=G.show_share_button),"rtl"in G&&l(14,O=G.rtl),"show_copy_button"in G&&l(15,B=G.show_copy_button),"loading_status"in G&&l(16,z=G.loading_status),"height"in G&&l(17,R=G.height)},[v,s,m,p,y,k,N,U,Y,te,j,ve,pe,le,O,B,z,R,D,L,X,ae,K]}class T1 extends W0{constructor(a){super(),Y0(this,a,A1,S1,j0,{elem_id:1,elem_classes:2,visible:3,value:0,latex_delimiters:4,scale:5,min_width:6,label:7,show_label:8,root:9,root_url:10,selectable:11,theme_mode:12,show_share_button:13,rtl:14,show_copy_button:15,loading_status:16,height:17})}}const N1=T1,R1=["static"];export{N1 as Component,R1 as modes};
-//# sourceMappingURL=index-0dbc7d7e.js.map
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio_client/templates/discord_chat.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio_client/templates/discord_chat.py
deleted file mode 100644
index 881ff65fb5e5b4599cb213c6cfa8fa236b09b509..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio_client/templates/discord_chat.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import asyncio
-import os
-import threading
-from threading import Event
-from typing import Optional
-
-import discord
-import gradio as gr
-from discord import Permissions
-from discord.ext import commands
-from discord.utils import oauth_url
-
-import gradio_client as grc
-from gradio_client.utils import QueueError
-
-event = Event()
-
-DISCORD_TOKEN = os.getenv("DISCORD_TOKEN")
-
-
-async def wait(job):
-    while not job.done():
-        await asyncio.sleep(0.2)
-
-
-def get_client(session: Optional[str] = None) -> grc.Client:
-    client = grc.Client("<<app-src>>", hf_token=os.getenv("HF_TOKEN"))
-    if session:
-        client.session_hash = session
-    return client
-
-
-def truncate_response(response: str) -> str:
-    ending = "...\nTruncating response to 2000 characters due to discord api limits."
-    if len(response) > 2000:
-        return response[: 2000 - len(ending)] + ending
-    else:
-        return response
-
-
-intents = discord.Intents.default()
-intents.message_content = True
-bot = commands.Bot(command_prefix="/", intents=intents)
-
-
-@bot.event
-async def on_ready():
-    print(f"Logged in as {bot.user} (ID: {bot.user.id})")
-    synced = await bot.tree.sync()
-    print(f"Synced commands: {', '.join([s.name for s in synced])}.")
-    event.set()
-    print("------")
-
-
-thread_to_client = {}
-thread_to_user = {}
-
-
-@bot.hybrid_command(
-    name="<<command-name>>",
-    description="Enter some text to chat with the bot! Like this: /<<command-name>> Hello, how are you?",
-)
-async def chat(ctx, prompt: str):
-    if ctx.author.id == bot.user.id:
-        return
-    try:
-        message = await ctx.send("Creating thread...")
-
-        thread = await message.create_thread(name=prompt)
-        loop = asyncio.get_running_loop()
-        client = await loop.run_in_executor(None, get_client, None)
-        job = client.submit(prompt, api_name="/<<api-name>>")
-        await wait(job)
-
-        try:
-            job.result()
-            response = job.outputs()[-1]
-            await thread.send(truncate_response(response))
-            thread_to_client[thread.id] = client
-            thread_to_user[thread.id] = ctx.author.id
-        except QueueError:
-            await thread.send(
-                "The gradio space powering this bot is really busy! Please try again later!"
-            )
-
-    except Exception as e:
-        print(f"{e}")
-
-
-async def continue_chat(message):
-    """Continues a given conversation based on chathistory"""
-    try:
-        client = thread_to_client[message.channel.id]
-        prompt = message.content
-        job = client.submit(prompt, api_name="/<<api-name>>")
-        await wait(job)
-        try:
-            job.result()
-            response = job.outputs()[-1]
-            await message.reply(truncate_response(response))
-        except QueueError:
-            await message.reply(
-                "The gradio space powering this bot is really busy! Please try again later!"
-            )
-
-    except Exception as e:
-        print(f"Error: {e}")
-
-
-@bot.event
-async def on_message(message):
-    """Continue the chat"""
-    try:
-        if not message.author.bot:
-            if message.channel.id in thread_to_user:
-                if thread_to_user[message.channel.id] == message.author.id:
-                    await continue_chat(message)
-            else:
-                await bot.process_commands(message)
-
-    except Exception as e:
-        print(f"Error: {e}")
-
-
-# running in thread
-def run_bot():
-    if not DISCORD_TOKEN:
-        print("DISCORD_TOKEN NOT SET")
-        event.set()
-    else:
-        bot.run(DISCORD_TOKEN)
-
-
-threading.Thread(target=run_bot).start()
-
-event.wait()
-
-if not DISCORD_TOKEN:
-    welcome_message = """
-
-    ## You have not specified a DISCORD_TOKEN, which means you have not created a bot account. Please follow these steps:
-
-    ### 1. Go to https://discord.com/developers/applications and click 'New Application'
-    
-    ### 2. Give your bot a name 🤖
-
-    ![](https://gradio-builds.s3.amazonaws.com/demo-files/discordbots/BotName.png)
-    
-    ## 3. In Settings > Bot, click the 'Reset Token' button to get a new token. Write it down and keep it safe 🔐
-    
-    ![](https://gradio-builds.s3.amazonaws.com/demo-files/discordbots/ResetToken.png)
-    
-    ## 4. Optionally make the bot public if you want anyone to be able to add it to their servers
-    
-    ## 5. Scroll down and enable 'Message Content Intent' under 'Priviledged Gateway Intents'
-    
-    ![](https://gradio-builds.s3.amazonaws.com/demo-files/discordbots/MessageContentIntent.png)
-
-    ## 6. Save your changes!
-
-    ## 7. The token from step 3 is the DISCORD_TOKEN. Rerun the deploy_discord command, e.g client.deploy_discord(discord_bot_token=DISCORD_TOKEN, ...), or add the token as a space secret manually.
-"""
-else:
-    permissions = Permissions(326417525824)
-    url = oauth_url(bot.user.id, permissions=permissions)
-    welcome_message = f"""
-    ## Add this bot to your server by clicking this link: 
-    
-    {url}
-
-    ## How to use it?
-
-    The bot can be triggered via `/<<command-name>>` followed by your text prompt.
-    
-    This will create a thread with the bot's response to your text prompt.
-    You can reply in the thread (without `/<<command-name>>`) to continue the conversation.
-    In the thread, the bot will only reply to the original author of the command.
-
-    ⚠️ Note ⚠️: Please make sure this bot's command does have the same name as another command in your server.
-    
-    ⚠️ Note ⚠️: Bot commands do not work in DMs with the bot as of now.
-    """
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-        f"""
-    # Discord bot of <<app-src>>
-    {welcome_message}
-    """
-    )
-
-demo.launch()
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/inference_api.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/inference_api.py
deleted file mode 100644
index f5d8d5ec8ef03522bc04f2a0fe7232aa992c5944..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/inference_api.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import io
-from typing import Any, Dict, List, Optional, Union
-
-from .constants import INFERENCE_ENDPOINT
-from .hf_api import HfApi
-from .utils import build_hf_headers, get_session, is_pillow_available, logging, validate_hf_hub_args
-
-
-logger = logging.get_logger(__name__)
-
-
-ALL_TASKS = [
-    # NLP
-    "text-classification",
-    "token-classification",
-    "table-question-answering",
-    "question-answering",
-    "zero-shot-classification",
-    "translation",
-    "summarization",
-    "conversational",
-    "feature-extraction",
-    "text-generation",
-    "text2text-generation",
-    "fill-mask",
-    "sentence-similarity",
-    # Audio
-    "text-to-speech",
-    "automatic-speech-recognition",
-    "audio-to-audio",
-    "audio-classification",
-    "voice-activity-detection",
-    # Computer vision
-    "image-classification",
-    "object-detection",
-    "image-segmentation",
-    "text-to-image",
-    "image-to-image",
-    # Others
-    "tabular-classification",
-    "tabular-regression",
-]
-
-
-class InferenceApi:
-    """Client to configure requests and make calls to the HuggingFace Inference API.
-
-    Example:
-
-    ```python
-    >>> from huggingface_hub.inference_api import InferenceApi
-
-    >>> # Mask-fill example
-    >>> inference = InferenceApi("bert-base-uncased")
-    >>> inference(inputs="The goal of life is [MASK].")
-    [{'sequence': 'the goal of life is life.', 'score': 0.10933292657136917, 'token': 2166, 'token_str': 'life'}]
-
-    >>> # Question Answering example
-    >>> inference = InferenceApi("deepset/roberta-base-squad2")
-    >>> inputs = {
-    ...     "question": "What's my name?",
-    ...     "context": "My name is Clara and I live in Berkeley.",
-    ... }
-    >>> inference(inputs)
-    {'score': 0.9326569437980652, 'start': 11, 'end': 16, 'answer': 'Clara'}
-
-    >>> # Zero-shot example
-    >>> inference = InferenceApi("typeform/distilbert-base-uncased-mnli")
-    >>> inputs = "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!"
-    >>> params = {"candidate_labels": ["refund", "legal", "faq"]}
-    >>> inference(inputs, params)
-    {'sequence': 'Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!', 'labels': ['refund', 'faq', 'legal'], 'scores': [0.9378499388694763, 0.04914155602455139, 0.013008488342165947]}
-
-    >>> # Overriding configured task
-    >>> inference = InferenceApi("bert-base-uncased", task="feature-extraction")
-
-    >>> # Text-to-image
-    >>> inference = InferenceApi("stabilityai/stable-diffusion-2-1")
-    >>> inference("cat")
-    <PIL.PngImagePlugin.PngImageFile image (...)>
-
-    >>> # Return as raw response to parse the output yourself
-    >>> inference = InferenceApi("mio/amadeus")
-    >>> response = inference("hello world", raw_response=True)
-    >>> response.headers
-    {"Content-Type": "audio/flac", ...}
-    >>> response.content # raw bytes from server
-    b'(...)'
-    ```
-    """
-
-    @validate_hf_hub_args
-    # TODO: add deprecation starting from version v0.16.0 so that we can proactively adapt external scripts.
-    # @_deprecate_method(
-    #     version="0.18.0",
-    #     message=(
-    #         "`InferenceApi` client is deprecated in favor of the more feature-complete `InferenceClient`. Check out"
-    #         " this guide to learn how to convert your script to use it:"
-    #         " https://huggingface.co/docs/huggingface_hub/guides/inference#legacy-inferenceapi-client."
-    #     ),
-    # )
-    def __init__(
-        self,
-        repo_id: str,
-        task: Optional[str] = None,
-        token: Optional[str] = None,
-        gpu: bool = False,
-    ):
-        """Inits headers and API call information.
-
-        Args:
-            repo_id (``str``):
-                Id of repository (e.g. `user/bert-base-uncased`).
-            task (``str``, `optional`, defaults ``None``):
-                Whether to force a task instead of using task specified in the
-                repository.
-            token (`str`, `optional`):
-                The API token to use as HTTP bearer authorization. This is not
-                the authentication token. You can find the token in
-                https://huggingface.co/settings/token. Alternatively, you can
-                find both your organizations and personal API tokens using
-                `HfApi().whoami(token)`.
-            gpu (`bool`, `optional`, defaults `False`):
-                Whether to use GPU instead of CPU for inference(requires Startup
-                plan at least).
-        """
-        self.options = {"wait_for_model": True, "use_gpu": gpu}
-        self.headers = build_hf_headers(token=token)
-
-        # Configure task
-        model_info = HfApi(token=token).model_info(repo_id=repo_id)
-        if not model_info.pipeline_tag and not task:
-            raise ValueError(
-                "Task not specified in the repository. Please add it to the model card"
-                " using pipeline_tag"
-                " (https://huggingface.co/docs#how-is-a-models-type-of-inference-api-and-widget-determined)"
-            )
-
-        if task and task != model_info.pipeline_tag:
-            if task not in ALL_TASKS:
-                raise ValueError(f"Invalid task {task}. Make sure it's valid.")
-
-            logger.warning(
-                "You're using a different task than the one specified in the"
-                " repository. Be sure to know what you're doing :)"
-            )
-            self.task = task
-        else:
-            assert model_info.pipeline_tag is not None, "Pipeline tag cannot be None"
-            self.task = model_info.pipeline_tag
-
-        self.api_url = f"{INFERENCE_ENDPOINT}/pipeline/{self.task}/{repo_id}"
-
-    def __repr__(self):
-        # Do not add headers to repr to avoid leaking token.
-        return f"InferenceAPI(api_url='{self.api_url}', task='{self.task}', options={self.options})"
-
-    def __call__(
-        self,
-        inputs: Optional[Union[str, Dict, List[str], List[List[str]]]] = None,
-        params: Optional[Dict] = None,
-        data: Optional[bytes] = None,
-        raw_response: bool = False,
-    ) -> Any:
-        """Make a call to the Inference API.
-
-        Args:
-            inputs (`str` or `Dict` or `List[str]` or `List[List[str]]`, *optional*):
-                Inputs for the prediction.
-            params (`Dict`, *optional*):
-                Additional parameters for the models. Will be sent as `parameters` in the
-                payload.
-            data (`bytes`, *optional*):
-                Bytes content of the request. In this case, leave `inputs` and `params` empty.
-            raw_response (`bool`, defaults to `False`):
-                If `True`, the raw `Response` object is returned. You can parse its content
-                as preferred. By default, the content is parsed into a more practical format
-                (json dictionary or PIL Image for example).
-        """
-        # Build payload
-        payload: Dict[str, Any] = {
-            "options": self.options,
-        }
-        if inputs:
-            payload["inputs"] = inputs
-        if params:
-            payload["parameters"] = params
-
-        # Make API call
-        response = get_session().post(self.api_url, headers=self.headers, json=payload, data=data)
-
-        # Let the user handle the response
-        if raw_response:
-            return response
-
-        # By default, parse the response for the user.
-        content_type = response.headers.get("Content-Type") or ""
-        if content_type.startswith("image"):
-            if not is_pillow_available():
-                raise ImportError(
-                    f"Task '{self.task}' returned as image but Pillow is not installed."
-                    " Please install it (`pip install Pillow`) or pass"
-                    " `raw_response=True` to get the raw `Response` object and parse"
-                    " the image by yourself."
-                )
-
-            from PIL import Image
-
-            return Image.open(io.BytesIO(response.content))
-        elif content_type == "application/json":
-            return response.json()
-        else:
-            raise NotImplementedError(
-                f"{content_type} output type is not implemented yet. You can pass"
-                " `raw_response=True` to get the raw `Response` object and parse the"
-                " output by yourself."
-            )
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/tests/_suite.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/tests/_suite.py
deleted file mode 100644
index 84ab7b9d8038ba10111cca798d64c1f13125e0b9..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/tests/_suite.py
+++ /dev/null
@@ -1,274 +0,0 @@
-"""
-Python representations of the JSON Schema Test Suite tests.
-"""
-from __future__ import annotations
-
-from collections.abc import Iterable, Mapping
-from contextlib import suppress
-from functools import partial
-from pathlib import Path
-from typing import TYPE_CHECKING, Any
-import json
-import os
-import re
-import subprocess
-import sys
-import unittest
-
-from attrs import field, frozen
-from referencing import Registry
-import referencing.jsonschema
-
-if TYPE_CHECKING:
-    import pyperf
-
-from jsonschema.validators import _VALIDATORS
-import jsonschema
-
-_DELIMITERS = re.compile(r"[\W\- ]+")
-
-
-def _find_suite():
-    root = os.environ.get("JSON_SCHEMA_TEST_SUITE")
-    if root is not None:
-        return Path(root)
-
-    root = Path(jsonschema.__file__).parent.parent / "json"
-    if not root.is_dir():  # pragma: no cover
-        raise ValueError(
-            (
-                "Can't find the JSON-Schema-Test-Suite directory. "
-                "Set the 'JSON_SCHEMA_TEST_SUITE' environment "
-                "variable or run the tests from alongside a checkout "
-                "of the suite."
-            ),
-        )
-    return root
-
-
-@frozen
-class Suite:
-
-    _root: Path = field(factory=_find_suite)
-    _remotes: referencing.jsonschema.SchemaRegistry = field(init=False)
-
-    def __attrs_post_init__(self):
-        jsonschema_suite = self._root.joinpath("bin", "jsonschema_suite")
-        argv = [sys.executable, str(jsonschema_suite), "remotes"]
-        remotes = subprocess.check_output(argv).decode("utf-8")
-
-        resources = json.loads(remotes)
-
-        li = "http://localhost:1234/locationIndependentIdentifierPre2019.json"
-        li4 = "http://localhost:1234/locationIndependentIdentifierDraft4.json"
-
-        registry = Registry().with_resources(
-            [
-                (
-                    li,
-                    referencing.jsonschema.DRAFT7.create_resource(
-                        contents=resources.pop(li),
-                    ),
-                ),
-                (
-                    li4,
-                    referencing.jsonschema.DRAFT4.create_resource(
-                        contents=resources.pop(li4),
-                    ),
-                ),
-            ],
-        ).with_contents(
-            resources.items(),
-            default_specification=referencing.jsonschema.DRAFT202012,
-        )
-        object.__setattr__(self, "_remotes", registry)
-
-    def benchmark(self, runner: pyperf.Runner):  # pragma: no cover
-        for name, Validator in _VALIDATORS.items():
-            self.version(name=name).benchmark(
-                runner=runner,
-                Validator=Validator,
-            )
-
-    def version(self, name) -> Version:
-        return Version(
-            name=name,
-            path=self._root / "tests" / name,
-            remotes=self._remotes,
-        )
-
-
-@frozen
-class Version:
-
-    _path: Path
-    _remotes: referencing.jsonschema.SchemaRegistry
-
-    name: str
-
-    def benchmark(self, **kwargs):  # pragma: no cover
-        for case in self.cases():
-            case.benchmark(**kwargs)
-
-    def cases(self) -> Iterable[_Case]:
-        return self._cases_in(paths=self._path.glob("*.json"))
-
-    def format_cases(self) -> Iterable[_Case]:
-        return self._cases_in(paths=self._path.glob("optional/format/*.json"))
-
-    def optional_cases_of(self, name: str) -> Iterable[_Case]:
-        return self._cases_in(paths=[self._path / "optional" / f"{name}.json"])
-
-    def to_unittest_testcase(self, *groups, **kwargs):
-        name = kwargs.pop("name", "Test" + self.name.title().replace("-", ""))
-        methods = {
-            method.__name__: method
-            for method in (
-                test.to_unittest_method(**kwargs)
-                for group in groups
-                for case in group
-                for test in case.tests
-            )
-        }
-        cls = type(name, (unittest.TestCase,), methods)
-
-        # We're doing crazy things, so if they go wrong, like a function
-        # behaving differently on some other interpreter, just make them
-        # not happen.
-        with suppress(Exception):
-            cls.__module__ = _someone_save_us_the_module_of_the_caller()
-
-        return cls
-
-    def _cases_in(self, paths: Iterable[Path]) -> Iterable[_Case]:
-        for path in paths:
-            for case in json.loads(path.read_text(encoding="utf-8")):
-                yield _Case.from_dict(
-                    case,
-                    version=self,
-                    subject=path.stem,
-                    remotes=self._remotes,
-                )
-
-
-@frozen
-class _Case:
-
-    version: Version
-
-    subject: str
-    description: str
-    schema: Mapping[str, Any] | bool
-    tests: list[_Test]
-    comment: str | None = None
-
-    @classmethod
-    def from_dict(cls, data, remotes, **kwargs):
-        data.update(kwargs)
-        tests = [
-            _Test(
-                version=data["version"],
-                subject=data["subject"],
-                case_description=data["description"],
-                schema=data["schema"],
-                remotes=remotes,
-                **test,
-            ) for test in data.pop("tests")
-        ]
-        return cls(tests=tests, **data)
-
-    def benchmark(self, runner: pyperf.Runner, **kwargs):  # pragma: no cover
-        for test in self.tests:
-            runner.bench_func(
-                test.fully_qualified_name,
-                partial(test.validate_ignoring_errors, **kwargs),
-            )
-
-
-@frozen(repr=False)
-class _Test:
-
-    version: Version
-
-    subject: str
-    case_description: str
-    description: str
-
-    data: Any
-    schema: Mapping[str, Any] | bool
-
-    valid: bool
-
-    _remotes: referencing.jsonschema.SchemaRegistry
-
-    comment: str | None = None
-
-    def __repr__(self):  # pragma: no cover
-        return f"<Test {self.fully_qualified_name}>"
-
-    @property
-    def fully_qualified_name(self):  # pragma: no cover
-        return " > ".join(
-            [
-                self.version.name,
-                self.subject,
-                self.case_description,
-                self.description,
-            ],
-        )
-
-    def to_unittest_method(self, skip=lambda test: None, **kwargs):
-        if self.valid:
-            def fn(this):
-                self.validate(**kwargs)
-        else:
-            def fn(this):
-                with this.assertRaises(jsonschema.ValidationError):
-                    self.validate(**kwargs)
-
-        fn.__name__ = "_".join(
-            [
-                "test",
-                _DELIMITERS.sub("_", self.subject),
-                _DELIMITERS.sub("_", self.case_description),
-                _DELIMITERS.sub("_", self.description),
-            ],
-        )
-        reason = skip(self)
-        if reason is None or os.environ.get("JSON_SCHEMA_DEBUG", "0") != "0":
-            return fn
-        elif os.environ.get("JSON_SCHEMA_EXPECTED_FAILURES", "0") != "0":  # pragma: no cover  # noqa: E501
-            return unittest.expectedFailure(fn)
-        else:
-            return unittest.skip(reason)(fn)
-
-    def validate(self, Validator, **kwargs):
-        Validator.check_schema(self.schema)
-        validator = Validator(
-            schema=self.schema,
-            registry=self._remotes,
-            **kwargs,
-        )
-        if os.environ.get("JSON_SCHEMA_DEBUG", "0") != "0":  # pragma: no cover
-            breakpoint()
-        validator.validate(instance=self.data)
-
-    def validate_ignoring_errors(self, Validator):  # pragma: no cover
-        with suppress(jsonschema.ValidationError):
-            self.validate(Validator=Validator)
-
-
-def _someone_save_us_the_module_of_the_caller():
-    """
-    The FQON of the module 2nd stack frames up from here.
-
-    This is intended to allow us to dynamically return test case classes that
-    are indistinguishable from being defined in the module that wants them.
-
-    Otherwise, trial will mis-print the FQON, and copy pasting it won't re-run
-    the class that really is running.
-
-    Save us all, this is all so so so so so terrible.
-    """
-
-    return sys._getframe(2).f_globals["__name__"]
diff --git a/spaces/de3sec/rembg_remove_bg/bg-api.py b/spaces/de3sec/rembg_remove_bg/bg-api.py
deleted file mode 100644
index 4a4c4cf4ea5cbffe14ccbd5ee597b2bd86e7e099..0000000000000000000000000000000000000000
--- a/spaces/de3sec/rembg_remove_bg/bg-api.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import numpy as np
-import cv2
-import uuid
-import os
-
-from model import U2NET
-from torch.autograd import Variable
-from skimage import io, transform
-from PIL import Image
-
-
-from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import JSONResponse
-app = FastAPI()
-
-
-# Get The Current Directory
-currentDir = os.path.dirname(__file__)
-
-# Functions:
-# Save Results
-
-
-def save_output(image_name, output_name, pred, d_dir, type):
-    predict = pred
-    predict = predict.squeeze()
-    predict_np = predict.cpu().data.numpy()
-    im = Image.fromarray(predict_np*255).convert('RGB')
-    image = io.imread(image_name)
-    imo = im.resize((image.shape[1], image.shape[0]))
-    pb_np = np.array(imo)
-    if type == 'image':
-        # Make and apply mask
-        mask = pb_np[:, :, 0]
-        mask = np.expand_dims(mask, axis=2)
-        imo = np.concatenate((image, mask), axis=2)
-        imo = Image.fromarray(imo, 'RGBA')
-
-    imo.save(d_dir+output_name)
-# Remove Background From Image (Generate Mask, and Final Results)
-
-
-@app.get("/removeBG/")
-async def removeBG(image_file: str):
-
-    # ------- Load Trained Model --------
-    print("---Loading Model---")
-    model_name = 'u2net'
-    model_dir = os.path.join(currentDir, 'saved_models',
-                             model_name, model_name + '.pth')
-    net = U2NET(3, 1)
-    if torch.cuda.is_available():
-        net.load_state_dict(torch.load(model_dir))
-        net.cuda()
-    else:
-        net.load_state_dict(torch.load(model_dir, map_location='cpu'))
-    # ------- Load Trained Model --------
-
-    inputs_dir = os.path.join(currentDir, 'static/inputs/')
-    results_dir = os.path.join(currentDir, 'static/results/')
-    masks_dir = os.path.join(currentDir, 'static/masks/')
-
-    # convert string of image data to uint8
-    with open(image_file, "rb") as image:
-        f = image.read()
-        img = bytearray(f)
-
-    nparr = np.frombuffer(img, np.uint8)
-
-    if len(nparr) == 0:
-        return '---Empty image---'
-
-    # decode image
-    try:
-        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-    except:
-        # build a response dict to send back to client
-        return "---Empty image---"
-
-    # save image to inputs
-    unique_filename = str(uuid.uuid4())
-    cv2.imwrite(inputs_dir+unique_filename+'.jpg', img)
-
-    # processing
-    image = transform.resize(img, (320, 320), mode='constant')
-
-    tmpImg = np.zeros((image.shape[0], image.shape[1], 3))
-
-    tmpImg[:, :, 0] = (image[:, :, 0]-0.485)/0.229
-    tmpImg[:, :, 1] = (image[:, :, 1]-0.456)/0.224
-    tmpImg[:, :, 2] = (image[:, :, 2]-0.406)/0.225
-
-    tmpImg = tmpImg.transpose((2, 0, 1))
-    tmpImg = np.expand_dims(tmpImg, 0)
-    image = torch.from_numpy(tmpImg)
-
-    image = image.type(torch.FloatTensor)
-    image = Variable(image)
-
-    d1, d2, d3, d4, d5, d6, d7 = net(image)
-    pred = d1[:, 0, :, :]
-    ma = torch.max(pred)
-    mi = torch.min(pred)
-    dn = (pred-mi)/(ma-mi)
-    pred = dn
-
-    save_output(inputs_dir+unique_filename+'.jpg', unique_filename +
-                '.png', pred, results_dir, 'image')
-    save_output(inputs_dir+unique_filename+'.jpg', unique_filename +
-                '.png', pred, masks_dir, 'mask')
-    return "---Success---"
-
-
-#print("---Removing Background...")
-# ------- Call The removeBg Function --------
-# imgPath = "1.jpg"  # Change this to your image path
-# print(removeBg(imgPath))
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/audio_diffusion/mel.py b/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/audio_diffusion/mel.py
deleted file mode 100644
index 1bf28fd25a5a5d39416eaf6bfd76b7f6945f4b19..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/audio_diffusion/mel.py
+++ /dev/null
@@ -1,160 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import numpy as np  # noqa: E402
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...schedulers.scheduling_utils import SchedulerMixin
-
-
-try:
-    import librosa  # noqa: E402
-
-    _librosa_can_be_imported = True
-    _import_error = ""
-except Exception as e:
-    _librosa_can_be_imported = False
-    _import_error = (
-        f"Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."
-    )
-
-
-from PIL import Image  # noqa: E402
-
-
-class Mel(ConfigMixin, SchedulerMixin):
-    """
-    Parameters:
-        x_res (`int`): x resolution of spectrogram (time)
-        y_res (`int`): y resolution of spectrogram (frequency bins)
-        sample_rate (`int`): sample rate of audio
-        n_fft (`int`): number of Fast Fourier Transforms
-        hop_length (`int`): hop length (a higher number is recommended for lower than 256 y_res)
-        top_db (`int`): loudest in decibels
-        n_iter (`int`): number of iterations for Griffin Linn mel inversion
-    """
-
-    config_name = "mel_config.json"
-
-    @register_to_config
-    def __init__(
-        self,
-        x_res: int = 256,
-        y_res: int = 256,
-        sample_rate: int = 22050,
-        n_fft: int = 2048,
-        hop_length: int = 512,
-        top_db: int = 80,
-        n_iter: int = 32,
-    ):
-        self.hop_length = hop_length
-        self.sr = sample_rate
-        self.n_fft = n_fft
-        self.top_db = top_db
-        self.n_iter = n_iter
-        self.set_resolution(x_res, y_res)
-        self.audio = None
-
-        if not _librosa_can_be_imported:
-            raise ValueError(_import_error)
-
-    def set_resolution(self, x_res: int, y_res: int):
-        """Set resolution.
-
-        Args:
-            x_res (`int`): x resolution of spectrogram (time)
-            y_res (`int`): y resolution of spectrogram (frequency bins)
-        """
-        self.x_res = x_res
-        self.y_res = y_res
-        self.n_mels = self.y_res
-        self.slice_size = self.x_res * self.hop_length - 1
-
-    def load_audio(self, audio_file: str = None, raw_audio: np.ndarray = None):
-        """Load audio.
-
-        Args:
-            audio_file (`str`): must be a file on disk due to Librosa limitation or
-            raw_audio (`np.ndarray`): audio as numpy array
-        """
-        if audio_file is not None:
-            self.audio, _ = librosa.load(audio_file, mono=True, sr=self.sr)
-        else:
-            self.audio = raw_audio
-
-        # Pad with silence if necessary.
-        if len(self.audio) < self.x_res * self.hop_length:
-            self.audio = np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio),))])
-
-    def get_number_of_slices(self) -> int:
-        """Get number of slices in audio.
-
-        Returns:
-            `int`: number of spectograms audio can be sliced into
-        """
-        return len(self.audio) // self.slice_size
-
-    def get_audio_slice(self, slice: int = 0) -> np.ndarray:
-        """Get slice of audio.
-
-        Args:
-            slice (`int`): slice number of audio (out of get_number_of_slices())
-
-        Returns:
-            `np.ndarray`: audio as numpy array
-        """
-        return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
-
-    def get_sample_rate(self) -> int:
-        """Get sample rate:
-
-        Returns:
-            `int`: sample rate of audio
-        """
-        return self.sr
-
-    def audio_slice_to_image(self, slice: int) -> Image.Image:
-        """Convert slice of audio to spectrogram.
-
-        Args:
-            slice (`int`): slice number of audio to convert (out of get_number_of_slices())
-
-        Returns:
-            `PIL Image`: grayscale image of x_res x y_res
-        """
-        S = librosa.feature.melspectrogram(
-            y=self.get_audio_slice(slice), sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_mels=self.n_mels
-        )
-        log_S = librosa.power_to_db(S, ref=np.max, top_db=self.top_db)
-        bytedata = (((log_S + self.top_db) * 255 / self.top_db).clip(0, 255) + 0.5).astype(np.uint8)
-        image = Image.fromarray(bytedata)
-        return image
-
-    def image_to_audio(self, image: Image.Image) -> np.ndarray:
-        """Converts spectrogram to audio.
-
-        Args:
-            image (`PIL Image`): x_res x y_res grayscale image
-
-        Returns:
-            audio (`np.ndarray`): raw audio
-        """
-        bytedata = np.frombuffer(image.tobytes(), dtype="uint8").reshape((image.height, image.width))
-        log_S = bytedata.astype("float") * self.top_db / 255 - self.top_db
-        S = librosa.db_to_power(log_S)
-        audio = librosa.feature.inverse.mel_to_audio(
-            S, sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length, n_iter=self.n_iter
-        )
-        return audio
diff --git a/spaces/declare-lab/tango/diffusers/tests/pipelines/stable_diffusion/__init__.py b/spaces/declare-lab/tango/diffusers/tests/pipelines/stable_diffusion/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/deepusus/tts-eng/app.py b/spaces/deepusus/tts-eng/app.py
deleted file mode 100644
index c9c11ea0f2da60c25de3f2b711ed6770a88d7feb..0000000000000000000000000000000000000000
--- a/spaces/deepusus/tts-eng/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/speechbrain/tts-tacotron2-ljspeech").launch()
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Easeus Mobisaver For Android 5.0 Crack !LINK!.md b/spaces/diacanFperku/AutoGPT/Easeus Mobisaver For Android 5.0 Crack !LINK!.md
deleted file mode 100644
index 0cb407b96b6e8ef88a35c7b36cfa84bcce75b5f9..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Easeus Mobisaver For Android 5.0 Crack !LINK!.md	
+++ /dev/null
@@ -1,36 +0,0 @@
-<h2>easeus mobisaver for android 5.0 crack</h2><br /><p><b><b>Download File</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://gohhs.com/2uFUhh">https://gohhs.com/2uFUhh</a></b></p><br /><br />
-
-It also helps to restore/recover deleted contact, SMS, photo, video, audio, document, etc from Android devices. It works on Android 1.6 to Android 5.0, and supports almost all Android phones including Samsung, HTC, Motorola, Huawei, LG, Xiaomi, etc. It is an Android utility application that allows you to access lost data by recovering Android devices.
-
-Key features:
-
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-
-2. This mobile data recovery program can restore all kind of lost data from the internal storage.
-
-3. It can recover lost data in the following ways:
-
-3.1. Recover deleted contact data in Android
-
-3.2. Recover deleted SMS data in Android
-
-3.3. Recover deleted photo/video/audio in Android
-
-3.4. Recover deleted document in Android
-
-4. Recover android data like lost contacts, SMS, camera, gallery etc.
-
-5. It has very easy to use interface and can be installed on Android 1.6 to 5.0.
-
-6. It can be used in two modes:
-
-6.1. As data recovery software, It can be used as a standalone data recovery software.
-
-6.2. As an Android data recovery software, It can be integrated into Android as the internal data recovery tool.
-
-EaseUS MobiSaver 8 For Android 5.0. The easiest Android data recovery software rescues lost contact, SMS, photo, video, audio or document from Android devices. It also helps to restore/recover deleted contact, SMS, photo, video, audio, document, etc from Android devices. It works on Android 1.6 to Android 5.0, and supports almost all Android phones including Samsung, HTC, Motorola, Huawei, LG, Xiaomi, etc. It is an Android utility application that allows you to access lost data by recovering Android devices.
-
-4. Recover android data like lost contacts, SMS, camera, gallery etc 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diacanFperku/AutoGPT/Free Download Windows Mobile 7 Samsung Omnia I900 LINK.md b/spaces/diacanFperku/AutoGPT/Free Download Windows Mobile 7 Samsung Omnia I900 LINK.md
deleted file mode 100644
index bc3418dce4c03bc1eafed2a46a63d5c038605e40..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Free Download Windows Mobile 7 Samsung Omnia I900 LINK.md	
+++ /dev/null
@@ -1,8 +0,0 @@
-<h2>free download windows mobile 7 samsung omnia i900</h2><br /><p><b><b>Download File</b> &#10042; <a href="https://gohhs.com/2uFVoF">https://gohhs.com/2uFVoF</a></b></p><br /><br />
-<br />
-Samsung OMNIA is a Windows Mobile 6.1 Professional smartphone with a 3.2-inch WQVGA display and . This device is also known as Samsung SGH-I900, Samsung Witu. Samsung OMNIA was released in China on February 24, 2008 and is designed to work with WAP and WWW networks, which also allows you to use it as an e-reader or digital camera.
-With a built-in 1.3 megapixel scanner, this device can be used as an electronic pencil or digital camera with 3x optical zoom.
-The device has a 1.9" TFT screen with a resolution of 800 x 480 pixels and an internal memory of 8 GB. 8a78ff9644<br />
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-<p></p>
diff --git a/spaces/diacanFperku/AutoGPT/Latest Idm With Crack [BEST] Filehippo.md b/spaces/diacanFperku/AutoGPT/Latest Idm With Crack [BEST] Filehippo.md
deleted file mode 100644
index e8c1a7895ec94c64fc7b6c494de31a6881a74034..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Latest Idm With Crack [BEST] Filehippo.md	
+++ /dev/null
@@ -1,147 +0,0 @@
-<br />
-<h1>Latest IDM with Crack Filehippo: How to Download Videos Faster and Safer</h1>
-
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-<ol>
-<li>Temporarily disable your antivirus until you activate or reset the IDM.</li>
-<li>Download the latest version of IDM from filehippo.com.</li>
-<li>Install the software by running "idman641build10.exe".</li>
-<li>Extract the "IDM 6.xx Activator or Resetter v3.1.zip" file (password is: 123).</li>
-<li>Run the "IDM 6.xx Activator or Resetter v3.1.exe" file and choose "1" to activate or "2" to reset.</li>
-<li>Done! Enjoy your free IDM!</li>
-</ol>
-
-<h2>What are the benefits of using the latest IDM with crack filehippo?</h2>
-
-<p>By using the latest IDM with crack filehippo, you can enjoy these benefits:</p>
-
-<ul>
-<li>You can download videos from any website with high speed and security.</li>
-<li>You can resume and schedule broken downloads without losing your progress or data.</li>
-<li>You can download all the images on a website with one click.</li>
-<li>You can organize your downloads by categories and folders.</li>
-<li>You can customize your settings according to your preferences.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>The latest IDM with crack filehippo is a great tool for downloading videos faster and safer. It has many features that make it user-friendly and efficient. However, you need to be careful when downloading and installing the crack filehippo as it may contain viruses or malware that can harm your computer. You should also update your antivirus after activating or resetting the IDM.</p>
-
-<p>If you found this article helpful, please share it with your friends and leave a comment below. Thank you for reading!</p>
-<h2>How to use the latest IDM with crack filehippo?</h2>
-
-<p>Once you have installed and activated the latest IDM with crack filehippo, you can start using it to download videos faster and safer. Here are some steps to follow:</p>
-
-<ol>
-<li>Open your web browser and go to the website that has the video you want to download.</li>
-<li>Play the video and you will see a pop-up window from IDM asking you to download it.</li>
-<li>Click on the "Download" button and choose the quality and format of the video.</li>
-<li>Select the destination folder where you want to save the video.</li>
-<li>Click on "Start Download" and wait for the process to finish.</li>
-</ol>
-
-<p>You can also use the "Site Grabber" feature to download all the images on a website. To do this, follow these steps:</p>
-
-<ol>
-<li>Open IDM and click on "Tasks" then "Add New Download".</li>
-<li>Select "Download web site(s)" and click on "Next".</li>
-<li>Enter the URL of the website you want to download images from and click on "Next".</li>
-<li>Select "Download only files with certain extensions" and enter "jpg,png,gif" in the box.</li>
-<li>Select the destination folder where you want to save the images.</li>
-<li>Click on "Finish" and wait for the process to finish.</li>
-</ol>
-
-<h2>What are the drawbacks of using the latest IDM with crack filehippo?</h2>
-
-<p>While using the latest IDM with crack filehippo has many benefits, it also has some drawbacks that you should be aware of. Here are some of them:</p>
-
-<ul>
-<li>You may face legal issues for using a cracked version of IDM without paying for it.</li>
-<li>You may not get any updates or support from the official developers of IDM.</li>
-<li>You may expose your computer to viruses or malware that may be hidden in the crack filehippo.</li>
-<li>You may experience some errors or bugs while using IDM that may affect your downloads or system performance.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>The latest IDM with crack filehippo is a great tool for downloading videos faster and safer. It has many features that make it user-friendly and efficient. However, you need to be careful when downloading and installing the crack filehippo as it may contain viruses or malware that can harm your computer. You should also update your antivirus after activating or resetting the IDM.</p>
-
-<p>If you found this article helpful, please share it with your friends and leave a comment below. Thank you for reading!</p>
-<h2>How to update the latest IDM with crack filehippo?</h2>
-
-<p>One of the drawbacks of using the latest IDM with crack filehippo is that you may not get any updates or support from the official developers of IDM. However, you can still update your IDM manually by following these steps:</p>
-
-<ol>
-<li>Download the latest version of IDM from filehippo.com.</li>
-<li>Close your web browser and any other applications that use IDM.</li>
-<li>Run the downloaded file and follow the installation wizard.</li>
-<li>Copy the "IDM 6.xx Activator or Resetter v3.1.exe" file from the previous crack filehippo and paste it in the installation folder of IDM (usually C:\Program Files\Internet Download Manager).</li>
-<li>Run the "IDM 6.xx Activator or Resetter v3.1.exe" file and choose "1" to activate or "2" to reset.</li>
-<li>Done! Enjoy your updated IDM!</li>
-</ol>
-
-<h2>How to uninstall the latest IDM with crack filehippo?</h2>
-
-<p>If you want to uninstall the latest IDM with crack filehippo from your computer, you need to follow these steps:</p>
-
-<ol>
-<li>Open the Control Panel and go to "Programs and Features".</li>
-<li>Select "Internet Download Manager" from the list and click on "Uninstall".</li>
-<li>Follow the uninstallation wizard and confirm your choice.</li>
-<li>Delete the installation folder of IDM (usually C:\Program Files\Internet Download Manager).</li>
-<li>Delete any shortcuts or icons related to IDM from your desktop or start menu.</li>
-<li>Restart your computer to complete the uninstallation.</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>The latest IDM with crack filehippo is a great tool for downloading videos faster and safer. It has many features that make it user-friendly and efficient. However, you need to be careful when downloading and installing the crack filehippo as it may contain viruses or malware that can harm your computer. You should also update your antivirus after activating or resetting the IDM.</p>
-
-<p>If you found this article helpful, please share it with your friends and leave a comment below. Thank you for reading!</p>
-<h2>How to customize the latest IDM with crack filehippo?</h2>
-
-<p>One of the advantages of using the latest IDM with crack filehippo is that you can customize it according to your preferences. You can change the appearance, settings, and options of IDM to suit your needs. Here are some ways to customize IDM:</p>
-
-<ul>
-<li>If you want to change the look and feel of IDM, you can go to "Downloads" then "Options" then "Look and Feel". You can choose from different skins, languages, buttons, and toolbars. You can also create your own skin or download more skins from https://www.internetdownloadmanager.com/skins/.</li>
-<li>If you want to change the download settings of IDM, you can go to "Downloads" then "Options" then "Downloads". You can choose the default download folder, the number of connections, the download speed limit, the download categories, and more. You can also enable or disable various features such as virus checking, download completion sound, download dialog, etc.</li>
-<li>If you want to change the browser integration settings of IDM, you can go to "Downloads" then "Options" then "General". You can choose which browsers to integrate with IDM and how to capture downloads from them. You can also enable or disable various features such as clipboard monitoring, video recognition, context menu items, etc.</li>
-</ul>
-
-<h2>How to optimize the latest IDM with crack filehippo?</h2>
-
-<p>If you want to get the best performance and results from using the latest IDM with crack filehippo, you need to optimize it regularly. Here are some tips to optimize IDM:</p>
-
-<ul>
-<li>You should update your IDM whenever a new version is available. This will fix any bugs and improve any features. You can check for updates by going to "Help" then "Check for updates".</li>
-<li>You should clean your download list periodically. This will free up some disk space and speed up your downloads. You can delete completed or unwanted downloads by selecting them and clicking on "Delete" or pressing "Delete" on your keyboard.</li>
-<li>You should defragment your hard drive regularly. This will improve your disk performance and reduce fragmentation. You can use Windows Disk Defragmenter or any other third-party software for this purpose.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>The latest IDM with crack filehippo is a great tool for downloading videos faster and safer. It has many features that make it user-friendly and efficient. However, you need to be careful when downloading and installing the crack filehippo as it may contain viruses or malware that can harm your computer. You should also update your antivirus after activating or resetting the IDM.</p>
-
-<p>If you found this article helpful, please share it with your friends and leave a comment below. Thank you for reading!</p>
-<p>The latest IDM with crack filehippo is a great tool for downloading videos faster and safer. It has many features that make it user-friendly and efficient. However, you need to be careful when downloading and installing the crack filehippo as it may contain viruses or malware that can harm your computer. You should also update your antivirus after activating or resetting the IDM.</p>
-
-<p>If you found this article helpful, please share it with your friends and leave a comment below. Thank you for reading!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Magic Views Youtube Bot Download.md b/spaces/diacanFperku/AutoGPT/Magic Views Youtube Bot Download.md
deleted file mode 100644
index 55e3e37bd6d3d544f0ad8a29e2b39c9cfc523ebe..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Magic Views Youtube Bot Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>magic views youtube bot download</h2><br /><p><b><b>Download File</b> &raquo;&raquo;&raquo; <a href="https://gohhs.com/2uFUiW">https://gohhs.com/2uFUiW</a></b></p><br /><br />
-<br />
-If you want more page views to your blog, Automated Traffic Bot can help you there ... Let people talk about your website. youtube view bot download free youtube ... Development Tools downloads - Magic Traffic Bot by Cybertech Software and ... 1fdad05405<br />
-<br />
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diff --git a/spaces/diacanFperku/AutoGPT/New Asztalosinas 2 0 FULL Version Download [PATCHED].md b/spaces/diacanFperku/AutoGPT/New Asztalosinas 2 0 FULL Version Download [PATCHED].md
deleted file mode 100644
index 1be5a1c332eb56ce42fdd999025176ba435f5233..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/New Asztalosinas 2 0 FULL Version Download [PATCHED].md	
+++ /dev/null
@@ -1,88 +0,0 @@
-<br />
-<h1>New Asztalosinas 2 0 FULL Version Download: A Complete Guide</h1>
-<p>New Asztalosinas 2 0 is a software program that helps you design and calculate furniture. It can handle different types of doors, drawers, and other components of furniture. It can also generate reports and print labels for your products. If you are looking for a full version of New Asztalosinas 2 0, you can download it from the official website . This version has been updated with new features and bug fixes. You will need a license key to activate the software after installation. In this article, we will show you how to download and install New Asztalosinas 2 0 for free, how to use it to create your own furniture projects, and how to uninstall it from your computer.</p>
-<h2>How to Download and Install New Asztalosinas 2 0 for Free</h2>
-<p>To download and install New Asztalosinas 2 0 for free, you need to follow these steps:</p>
-<h2>New Asztalosinas 2 0 FULL Version Download</h2><br /><p><b><b>DOWNLOAD</b> &bull;&bull;&bull; <a href="https://gohhs.com/2uFTUd">https://gohhs.com/2uFTUd</a></b></p><br /><br />
-<ol>
-<li>Go to the official website of New Asztalosinas 2 0  and click on the Download button.</li>
-<li>Save the file (AsztalosInas20.zip) on your computer and extract it using a program like WinZip or WinRAR.</li>
-<li>Open the extracted folder and run the Setup.exe file.</li>
-<li>Follow the instructions on the screen to install New Asztalosinas 2 0 on your computer.</li>
-<li>After the installation is complete, run the program from the Start menu or the desktop shortcut.</li>
-<li>Enter the license key that you received by email after you purchased the software. If you don't have a license key, you can request one by contacting the developer at jungbt@t-online.hu.</li>
-<li>Enjoy using New Asztalosinas 2 0 for free!</li>
-</ol>
-<h2>How to Use New Asztalosinas 2 0 to Create Your Own Furniture Projects</h2>
-<p>New Asztalosinas 2 0 is a powerful and easy-to-use software that can help you design and calculate furniture. You can use it to create various types of furniture, such as cabinets, wardrobes, tables, chairs, etc. You can also customize the appearance and dimensions of your furniture according to your preferences. Here are some steps to follow:</p>
-<ol>
-<li>Open New Asztalosinas 2 0 and click on the New Project button.</li>
-<li>Enter a name for your project and select a folder to save it.</li>
-<li>Select a type of furniture from the list, such as door, drawer, shelf, etc.</li>
-<li>Enter the parameters of your furniture, such as width, height, depth, material, color, etc.</li>
-<li>Click on the Calculate button to see the results of your design.</li>
-<li>You can also use the Preview button to see a 3D model of your furniture.</li>
-<li>You can add more components to your furniture by clicking on the Add button and repeating steps 3-6.</li>
-<li>You can edit or delete any component by selecting it from the list and clicking on the Edit or Delete button.</li>
-<li>You can save your project by clicking on the Save button or print it by clicking on the Print button.</li>
-<li>You can also export your project to Excel or PDF formats by clicking on the Export button.</li>
-</ol>
-<h2>How to Uninstall New Asztalosinas 2 0 from Your Computer</h2>
-<p>If you want to uninstall New Asztalosinas 2 0 from your computer, you need to follow these steps:</p>
-<ol>
-<li>Go to the Control Panel and click on Programs and Features.</li>
-<li>Find New Asztalosinas 2 0 from the list of programs and click on Uninstall.</li>
-<li>Follow the instructions on the screen to uninstall New Asztalosinas 2 0 from your computer.</li>
-<li>Delete any leftover files or folders related to New Asztalosinas 2 0 from your computer.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>New Asztalosinas 2 0 is a software program that helps you design and calculate furniture. It is easy to download and install for free, but you need a license key to activate it. You can use it to create various types of furniture with different parameters and options. You can also generate reports and print labels for your products. You can uninstall it from your computer if you no longer need it. We hope this article has given you a complete guide on New Asztalosinas 2 0 FULL Version Download and how to use it effectively.</p>
-<h2>How to Use New Asztalosinas 2 0 to Print Labels for Your Furniture</h2>
-<p>New Asztalosinas 2 0 can also help you print labels for your furniture products. Labels can contain information such as the name, size, material, price, and barcode of your furniture. You can use labels to identify and organize your products, as well as to market and sell them. Here are some steps to follow:</p>
-<ol>
-<li>Open New Asztalosinas 2 0 and load your project.</li>
-<li>Click on the Label button to open the label editor.</li>
-<li>Select a type of label from the list, such as A4, A5, A6, etc.</li>
-<li>Select a layout for your label, such as portrait or landscape.</li>
-<li>Select a font and a color for your label.</li>
-<li>Enter the information that you want to include on your label, such as the name, size, material, price, and barcode of your furniture.</li>
-<li>You can also use variables to automatically fill in some information from your project.</li>
-<li>You can also add images or logos to your label by clicking on the Insert Image button.</li>
-<li>You can preview your label by clicking on the Preview button.</li>
-<li>You can print your label by clicking on the Print button or save it by clicking on the Save button.</li>
-</ol>
-<h2>How to Use New Asztalosinas 2 0 to Export Data to Excel or PDF Formats</h2>
-<p>New Asztalosinas 2 0 can also help you export data from your project to Excel or PDF formats. Excel and PDF formats are widely used and compatible with many software programs and devices. You can use Excel or PDF formats to share, analyze, or present your data. Here are some steps to follow:</p>
-<ol>
-<li>Open New Asztalosinas 2 0 and load your project.</li>
-<li>Click on the Export button to open the export dialog.</li>
-<li>Select a format from the list, such as Excel or PDF.</li>
-<li>Select a folder and a file name for your export file.</li>
-<li>Select a range of data that you want to export, such as all data or selected data.</li>
-<li>Select a type of data that you want to export, such as summary, details, or labels.</li>
-<li>Click on the Export button to start the export process.</li>
-<li>You can open your export file with any program that supports Excel or PDF formats.</li>
-</ol>
-<h2>How to Update New Asztalosinas 2 0 to the Latest Version</h2>
-<p>New Asztalosinas 2 0 is constantly being updated with new features and bug fixes. It is recommended that you update your software to the latest version to enjoy the best performance and functionality. You can update your software by following these steps:</p>
-<ol>
-<li>Go to the official website of New Asztalosinas 2 0  and check if there is a new version available.</li>
-<li>If there is a new version available, click on the Download button and save the file (AsztalosInas20.zip) on your computer.</li>
-<li>Extract the file using a program like WinZip or WinRAR.</li>
-<li>Open the extracted folder and run the Setup.exe file.</li>
-<li>Follow the instructions on the screen to install the new version of New Asztalosinas 2 0 on your computer.</li>
-<li>The new version will overwrite the old version and keep your data and settings intact.</li>
-<li>Run the program from the Start menu or the desktop shortcut and enjoy using the latest version of New Asztalosinas 2 0!</li>
-</ol>
-<h2>How to Get Support and Help for New Asztalosinas 2 0</h2>
-<p>If you have any questions or problems with New Asztalosinas 2 0, you can get support and help from various sources. Here are some of them:</p>
-<ul>
-<li>The official website of New Asztalosinas 2 0  has a FAQ section that answers some common questions and issues about the software.</li>
-<li>The official website of New Asztalosinas 2 0  also has a video tutorial section that shows you how to use the software step by step.</li>
-<li>You can also contact the developer of New Asztalosinas 2 0 by email at jungbt@t-online.hu. You can ask any questions or report any bugs or errors that you encounter with the software.</li>
-<li>You can also join online forums or communities that discuss New Asztalosinas 2 0 or similar software. You can share your experiences, tips, or feedback with other users or experts.</li>
-</ul>
-<p>New Asztalosinas 2 0 is a user-friendly and reliable software that can help you design and calculate furniture. It has many features and options that can suit your needs and preferences. It is easy to download and install for free, but you need a license key to activate it. You can use it to create various types of furniture with different parameters and options. You can also generate reports and print labels for your products. You can uninstall it from your computer if you no longer need it. You can also use plugins to enhance its functionality and provide more features, or create your own plugins if you are a programmer or a hacker. You can update it to the latest version to enjoy the best performance and functionality. You can also get support and help from various sources if you have any questions or problems with it. We hope this article has given you a complete guide on New Asztalosinas 2 0 FULL Version Download and how to use it effectively.</p>
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\ No newline at end of file
diff --git a/spaces/digitalxingtong/Jiaran-Bert-VITS2/data_utils.py b/spaces/digitalxingtong/Jiaran-Bert-VITS2/data_utils.py
deleted file mode 100644
index 2c98d3dc8b9572bd05859033a74d155425a2a2ab..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Jiaran-Bert-VITS2/data_utils.py
+++ /dev/null
@@ -1,332 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-import torchaudio
-import commons
-from mel_processing import spectrogram_torch, mel_spectrogram_torch, spec_to_mel_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import cleaned_text_to_sequence, get_bert
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
-    """
-        1) loads audio, speaker_id, text pairs
-        2) normalizes text and converts them to sequences of integers
-        3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_sid_text, hparams):
-        self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length = hparams.filter_length
-        self.hop_length = hparams.hop_length
-        self.win_length = hparams.win_length
-        self.sampling_rate = hparams.sampling_rate
-        self.spk_map = hparams.spk2id
-        self.hparams = hparams
-
-        self.use_mel_spec_posterior = getattr(hparams, "use_mel_posterior_encoder", False)
-        if self.use_mel_spec_posterior:
-            self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
-
-        self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
-        self.add_blank = hparams.add_blank
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 300)
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths_sid_text)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-
-        audiopaths_sid_text_new = []
-        lengths = []
-        skipped = 0
-        for _id, spk, language, text, phones, tone, word2ph in self.audiopaths_sid_text:
-            audiopath = f'{_id}'
-            if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
-                phones = phones.split(" ")
-                tone = [int(i) for i in tone.split(" ")]
-                word2ph = [int(i) for i in word2ph.split(" ")]
-                audiopaths_sid_text_new.append([audiopath, spk, language, text, phones, tone, word2ph])
-                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
-            else:
-                skipped += 1
-        print("skipped: ", skipped, ", total: ", len(self.audiopaths_sid_text))
-        self.audiopaths_sid_text = audiopaths_sid_text_new
-        self.lengths = lengths
-
-    def get_audio_text_speaker_pair(self, audiopath_sid_text):
-        # separate filename, speaker_id and text
-        audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
-
-        bert, phones, tone, language = self.get_text(text, word2ph, phones, tone, language, audiopath)
-
-        spec, wav = self.get_audio(audiopath)
-        sid = torch.LongTensor([int(self.spk_map[sid])])
-        return (phones, spec, wav, sid, tone, language, bert)
-
-    def get_audio(self, filename):
-        audio_norm, sampling_rate = torchaudio.load(filename, frame_offset=0, num_frames=-1, normalize=True, channels_first=True)
-        '''
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError("{} {} SR doesn't match target {} SR".format(
-                sampling_rate, self.sampling_rate))
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        '''
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if self.use_mel_spec_posterior:
-            spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
-        if os.path.exists(spec_filename):
-            spec = torch.load(spec_filename)
-        else:
-            if self.use_mel_spec_posterior:
-                # if os.path.exists(filename.replace(".wav", ".spec.pt")):
-                #     # spec, n_fft, num_mels, sampling_rate, fmin, fmax
-                #     spec = spec_to_mel_torch(
-                #         torch.load(filename.replace(".wav", ".spec.pt")), 
-                #         self.filter_length, self.n_mel_channels, self.sampling_rate,
-                #         self.hparams.mel_fmin, self.hparams.mel_fmax)
-                spec = mel_spectrogram_torch(audio_norm, self.filter_length,
-                    self.n_mel_channels, self.sampling_rate, self.hop_length,
-                    self.win_length, self.hparams.mel_fmin, self.hparams.mel_fmax, center=False)
-            else:
-                spec = spectrogram_torch(audio_norm, self.filter_length,
-                    self.sampling_rate, self.hop_length, self.win_length,
-                    center=False)
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-        return spec, audio_norm
-
-    def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
-        # print(text, word2ph,phone, tone, language_str)
-        pold = phone
-        w2pho = [i for i in word2ph]
-        word2ph = [i for i in word2ph]
-        phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-        pold2 = phone
-
-        if self.add_blank:
-            p1 = len(phone)
-            phone = commons.intersperse(phone, 0)
-            p2 = len(phone)
-            t1 = len(tone)
-            tone = commons.intersperse(tone, 0)
-            t2 = len(tone)
-            language = commons.intersperse(language, 0)
-            for i in range(len(word2ph)):
-                word2ph[i] = word2ph[i] * 2
-            word2ph[0] += 1
-        bert_path = wav_path.replace(".wav", ".bert.pt")
-        try:
-            bert = torch.load(bert_path)
-            assert bert.shape[-1] == len(phone)
-        except:
-            bert = get_bert(text, word2ph, language_str)
-            torch.save(bert, bert_path)
-            #print(bert.shape[-1], bert_path, text, pold)
-            assert bert.shape[-1] == len(phone)
-
-        assert bert.shape[-1] == len(phone), (
-        bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho)
-        phone = torch.LongTensor(phone)
-        tone = torch.LongTensor(tone)
-        language = torch.LongTensor(language)
-        return bert, phone, tone, language
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def __getitem__(self, index):
-        return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
-    """ Zero-pads model inputs and targets
-    """
-
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text, audio and speaker identities
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized, sid]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[1].size(1) for x in batch]),
-            dim=0, descending=True)
-
-        max_text_len = max([len(x[0]) for x in batch])
-        max_spec_len = max([x[1].size(1) for x in batch])
-        max_wav_len = max([x[2].size(1) for x in batch])
-
-        text_lengths = torch.LongTensor(len(batch))
-        spec_lengths = torch.LongTensor(len(batch))
-        wav_lengths = torch.LongTensor(len(batch))
-        sid = torch.LongTensor(len(batch))
-
-        text_padded = torch.LongTensor(len(batch), max_text_len)
-        tone_padded = torch.LongTensor(len(batch), max_text_len)
-        language_padded = torch.LongTensor(len(batch), max_text_len)
-        bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
-
-        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        text_padded.zero_()
-        tone_padded.zero_()
-        language_padded.zero_()
-        spec_padded.zero_()
-        wav_padded.zero_()
-        bert_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            text = row[0]
-            text_padded[i, :text.size(0)] = text
-            text_lengths[i] = text.size(0)
-
-            spec = row[1]
-            spec_padded[i, :, :spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wav = row[2]
-            wav_padded[i, :, :wav.size(1)] = wav
-            wav_lengths[i] = wav.size(1)
-
-            sid[i] = row[3]
-
-            tone = row[4]
-            tone_padded[i, :tone.size(0)] = tone
-
-            language = row[5]
-            language_padded[i, :language.size(0)] = language
-
-            bert = row[6]
-            bert_padded[i, :, :bert.size(1)] = bert
-
-        return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, tone_padded, language_padded, bert_padded
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
-    """
-    Maintain similar input lengths in a batch.
-    Length groups are specified by boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
-    It removes samples which are not included in the boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
-    """
-
-    def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
-        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
-        self.lengths = dataset.lengths
-        self.batch_size = batch_size
-        self.boundaries = boundaries
-
-        self.buckets, self.num_samples_per_bucket = self._create_buckets()
-        self.total_size = sum(self.num_samples_per_bucket)
-        self.num_samples = self.total_size // self.num_replicas
-
-    def _create_buckets(self):
-        buckets = [[] for _ in range(len(self.boundaries) - 1)]
-        for i in range(len(self.lengths)):
-            length = self.lengths[i]
-            idx_bucket = self._bisect(length)
-            if idx_bucket != -1:
-                buckets[idx_bucket].append(i)
-
-        for i in range(len(buckets) - 1, 0, -1):
-            if len(buckets[i]) == 0:
-                buckets.pop(i)
-                self.boundaries.pop(i + 1)
-
-        num_samples_per_bucket = []
-        for i in range(len(buckets)):
-            len_bucket = len(buckets[i])
-            total_batch_size = self.num_replicas * self.batch_size
-            rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
-            num_samples_per_bucket.append(len_bucket + rem)
-        return buckets, num_samples_per_bucket
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-
-        indices = []
-        if self.shuffle:
-            for bucket in self.buckets:
-                indices.append(torch.randperm(len(bucket), generator=g).tolist())
-        else:
-            for bucket in self.buckets:
-                indices.append(list(range(len(bucket))))
-
-        batches = []
-        for i in range(len(self.buckets)):
-            bucket = self.buckets[i]
-            len_bucket = len(bucket)
-            if (len_bucket == 0):
-                continue
-            ids_bucket = indices[i]
-            num_samples_bucket = self.num_samples_per_bucket[i]
-
-            # add extra samples to make it evenly divisible
-            rem = num_samples_bucket - len_bucket
-            ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-
-            # subsample
-            ids_bucket = ids_bucket[self.rank::self.num_replicas]
-
-            # batching
-            for j in range(len(ids_bucket) // self.batch_size):
-                batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]]
-                batches.append(batch)
-
-        if self.shuffle:
-            batch_ids = torch.randperm(len(batches), generator=g).tolist()
-            batches = [batches[i] for i in batch_ids]
-        self.batches = batches
-
-        assert len(self.batches) * self.batch_size == self.num_samples
-        return iter(self.batches)
-
-    def _bisect(self, x, lo=0, hi=None):
-        if hi is None:
-            hi = len(self.boundaries) - 1
-
-        if hi > lo:
-            mid = (hi + lo) // 2
-            if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
-                return mid
-            elif x <= self.boundaries[mid]:
-                return self._bisect(x, lo, mid)
-            else:
-                return self._bisect(x, mid + 1, hi)
-        else:
-            return -1
-
-    def __len__(self):
-        return self.num_samples // self.batch_size
diff --git a/spaces/digitalxingtong/Jiuxia-Bert-Vits2/text/english.py b/spaces/digitalxingtong/Jiuxia-Bert-Vits2/text/english.py
deleted file mode 100644
index 781d0a56cef71f66fc67db51d76538be90d3ddd2..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Jiuxia-Bert-Vits2/text/english.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import pickle
-import os
-import re
-from g2p_en import G2p
-from string import punctuation
-
-from text import symbols
-
-current_file_path = os.path.dirname(__file__)
-CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep')
-CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle')
-_g2p = G2p()
-
-arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'}
-
-
-def post_replace_ph(ph):
-    rep_map = {
-        '：': ',',
-        '；': ',',
-        '，': ',',
-        '。': '.',
-        '！': '!',
-        '？': '?',
-        '\n': '.',
-        "·": ",",
-        '、': ",",
-        '...': '…',
-        'v': "V"
-    }
-    if ph in rep_map.keys():
-        ph = rep_map[ph]
-    if ph in symbols:
-        return ph
-    if ph not in symbols:
-        ph = 'UNK'
-    return ph
-
-def read_dict():
-    g2p_dict = {}
-    start_line = 49
-    with open(CMU_DICT_PATH) as f:
-        line = f.readline()
-        line_index = 1
-        while line:
-            if line_index >= start_line:
-                line = line.strip()
-                word_split = line.split('  ')
-                word = word_split[0]
-
-                syllable_split = word_split[1].split(' - ')
-                g2p_dict[word] = []
-                for syllable in syllable_split:
-                    phone_split = syllable.split(' ')
-                    g2p_dict[word].append(phone_split)
-
-            line_index = line_index + 1
-            line = f.readline()
-
-    return g2p_dict
-
-
-def cache_dict(g2p_dict, file_path):
-    with open(file_path, 'wb') as pickle_file:
-        pickle.dump(g2p_dict, pickle_file)
-
-
-def get_dict():
-    if os.path.exists(CACHE_PATH):
-        with open(CACHE_PATH, 'rb') as pickle_file:
-            g2p_dict = pickle.load(pickle_file)
-    else:
-        g2p_dict = read_dict()
-        cache_dict(g2p_dict, CACHE_PATH)
-
-    return g2p_dict
-
-eng_dict = get_dict()
-
-def refine_ph(phn):
-    tone = 0
-    if re.search(r'\d$', phn):
-        tone = int(phn[-1]) + 1
-        phn = phn[:-1]
-    return phn.lower(), tone
-
-def refine_syllables(syllables):
-    tones = []
-    phonemes = []
-    for phn_list in syllables:
-        for i in range(len(phn_list)):
-            phn = phn_list[i]
-            phn, tone = refine_ph(phn)
-            phonemes.append(phn)
-            tones.append(tone)
-    return phonemes, tones
-
-
-def text_normalize(text):
-    # todo: eng text normalize
-    return text
-
-def g2p(text):
-
-    phones = []
-    tones = []
-    words = re.split(r"([,;.\-\?\!\s+])", text)
-    for w in words:
-        if w.upper() in eng_dict:
-            phns, tns = refine_syllables(eng_dict[w.upper()])
-            phones += phns
-            tones += tns
-        else:
-            phone_list = list(filter(lambda p: p != " ", _g2p(w)))
-            for ph in phone_list:
-                if ph in arpa:
-                    ph, tn = refine_ph(ph)
-                    phones.append(ph)
-                    tones.append(tn)
-                else:
-                    phones.append(ph)
-                    tones.append(0)
-    # todo: implement word2ph
-    word2ph = [1 for i in phones]
-
-    phones = [post_replace_ph(i) for i in phones]
-    return phones, tones, word2ph
-
-if __name__ == "__main__":
-    # print(get_dict())
-    # print(eng_word_to_phoneme("hello"))
-    print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
-    # all_phones = set()
-    # for k, syllables in eng_dict.items():
-    #     for group in syllables:
-    #         for ph in group:
-    #             all_phones.add(ph)
-    # print(all_phones)
\ No newline at end of file
diff --git a/spaces/dineshreddy/WALT/mmdet/models/detectors/kd_one_stage.py b/spaces/dineshreddy/WALT/mmdet/models/detectors/kd_one_stage.py
deleted file mode 100644
index 671ec19015c87fefd065b84ae887147f90cc892b..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/detectors/kd_one_stage.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import mmcv
-import torch
-from mmcv.runner import load_checkpoint
-
-from .. import build_detector
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class KnowledgeDistillationSingleStageDetector(SingleStageDetector):
-    r"""Implementation of `Distilling the Knowledge in a Neural Network.
-    <https://arxiv.org/abs/1503.02531>`_.
-
-    Args:
-        teacher_config (str | dict): Config file path
-            or the config object of teacher model.
-        teacher_ckpt (str, optional): Checkpoint path of teacher model.
-            If left as None, the model will not load any weights.
-    """
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 teacher_config,
-                 teacher_ckpt=None,
-                 eval_teacher=True,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super().__init__(backbone, neck, bbox_head, train_cfg, test_cfg,
-                         pretrained)
-        self.eval_teacher = eval_teacher
-        # Build teacher model
-        if isinstance(teacher_config, str):
-            teacher_config = mmcv.Config.fromfile(teacher_config)
-        self.teacher_model = build_detector(teacher_config['model'])
-        if teacher_ckpt is not None:
-            load_checkpoint(
-                self.teacher_model, teacher_ckpt, map_location='cpu')
-
-    def forward_train(self,
-                      img,
-                      img_metas,
-                      gt_bboxes,
-                      gt_labels,
-                      gt_bboxes_ignore=None):
-        """
-        Args:
-            img (Tensor): Input images of shape (N, C, H, W).
-                Typically these should be mean centered and std scaled.
-            img_metas (list[dict]): A List of image info dict where each dict
-                has: 'img_shape', 'scale_factor', 'flip', and may also contain
-                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
-                For details on the values of these keys see
-                :class:`mmdet.datasets.pipelines.Collect`.
-            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
-                image in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): Class indices corresponding to each box
-            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
-                boxes can be ignored when computing the loss.
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        x = self.extract_feat(img)
-        with torch.no_grad():
-            teacher_x = self.teacher_model.extract_feat(img)
-            out_teacher = self.teacher_model.bbox_head(teacher_x)
-        losses = self.bbox_head.forward_train(x, out_teacher, img_metas,
-                                              gt_bboxes, gt_labels,
-                                              gt_bboxes_ignore)
-        return losses
-
-    def cuda(self, device=None):
-        """Since teacher_model is registered as a plain object, it is necessary
-        to put the teacher model to cuda when calling cuda function."""
-        self.teacher_model.cuda(device=device)
-        return super().cuda(device=device)
-
-    def train(self, mode=True):
-        """Set the same train mode for teacher and student model."""
-        if self.eval_teacher:
-            self.teacher_model.train(False)
-        else:
-            self.teacher_model.train(mode)
-        super().train(mode)
-
-    def __setattr__(self, name, value):
-        """Set attribute, i.e. self.name = value
-
-        This reloading prevent the teacher model from being registered as a
-        nn.Module. The teacher module is registered as a plain object, so that
-        the teacher parameters will not show up when calling
-        ``self.parameters``, ``self.modules``, ``self.children`` methods.
-        """
-        if name == 'teacher_model':
-            object.__setattr__(self, name, value)
-        else:
-            super().__setattr__(name, value)
diff --git a/spaces/dmeck/RVC-Speakers/speakers/tasks/bark_voice_task.py b/spaces/dmeck/RVC-Speakers/speakers/tasks/bark_voice_task.py
deleted file mode 100644
index 22f46f857b7d8bfd213fa5fe607cb840687c062c..0000000000000000000000000000000000000000
--- a/spaces/dmeck/RVC-Speakers/speakers/tasks/bark_voice_task.py
+++ /dev/null
@@ -1,164 +0,0 @@
-from typing import Dict
-from speakers.processors import ProcessorData, BaseProcessor, get_processors, BarkProcessorData, RvcProcessorData
-from speakers.tasks import BaseTask, Runner, FlowData
-from speakers.common.registry import registry
-from speakers.server.model.flow_data import PayLoad
-import traceback
-import hashlib
-
-
-class BarkVoiceFlowData(FlowData):
-    bark: BarkProcessorData
-    rvc: RvcProcessorData
-
-    @property
-    def type(self) -> str:
-        """Type of the FlowData Message, used for serialization."""
-        return "bark_voice"
-
-
-def calculate_md5(input_string):
-    md5_hash = hashlib.md5()
-    md5_hash.update(input_string.encode('utf-8'))
-    return md5_hash.hexdigest()
-
-
-@registry.register_task("bark_voice_task")
-class BarkVoiceTask(BaseTask):
-    SAMPLE_RATE: int = 22050
-
-    def __init__(self, preprocess_dict: Dict[str, BaseProcessor]):
-        super().__init__(preprocess_dict=preprocess_dict)
-        self._preprocess_dict = preprocess_dict
-
-    @classmethod
-    def from_config(cls, cfg=None):
-        preprocess_dict = {}
-        for preprocess in cfg.get('preprocess'):
-            for key, preprocess_info in preprocess.items():
-                preprocess_object = get_processors(preprocess_info.processor)
-                preprocess_dict[preprocess_info.processor_name] = preprocess_object
-
-        return cls(preprocess_dict=preprocess_dict)
-
-    @property
-    def preprocess_dict(self) -> Dict[str, BaseProcessor]:
-        return self._preprocess_dict
-
-    @classmethod
-    def prepare(cls, payload: PayLoad) -> Runner:
-        """
-        runner任务构建
-        """
-        params = payload.payload
-        # 获取payload中的vits和rvc的值
-        bark_data = params.get("bark", {})
-        rvc_data = params.get("rvc", {})
-
-        speaker_history_prompt = bark_data.get("speaker_history_prompt")
-        text_temp = bark_data.get("text_temp")
-        waveform_temp = bark_data.get("waveform_temp")
-        text = bark_data.get("text")
-
-        # 创建一个 BarkProcessorData 实例
-        bark_processor_data = BarkProcessorData(text=text,
-                                                speaker_history_prompt=speaker_history_prompt,
-                                                text_temp=text_temp,
-                                                waveform_temp=waveform_temp)
-        # 获取rvc中的值
-        model_index = rvc_data.get("model_index")
-
-        # 变调(整数, 半音数量, 升八度12降八度-12)
-        f0_up_key = rvc_data.get("f0_up_key")
-        f0_method = rvc_data.get("f0_method")
-
-        # 检索特征占比
-        index_rate = rvc_data.get("index_rate")
-        # >=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音
-        filter_radius = rvc_data.get("filter_radius")
-        # 输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络
-        rms_mix_rate = rvc_data.get("rms_mix_rate")
-        # 后处理重采样至最终采样率，0为不进行重采样
-        resample_rate = rvc_data.get("resample_sr")
-
-        rvc_protect = rvc_data.get("protect")
-        rvc_f0_file = rvc_data.get("f0_file")
-
-        rvc_processor_data = RvcProcessorData(
-            model_index=model_index,
-            f0_up_key=f0_up_key,
-            f0_method=f0_method,
-            index_rate=index_rate,
-            filter_radius=filter_radius,
-            rms_mix_rate=rms_mix_rate,
-            resample_sr=resample_rate,
-            f0_file=rvc_f0_file,
-            protect=rvc_protect
-        )
-
-        # 创建一个 BarkVoiceFlowData 实例，并将 VitsProcessorData 实例作为参数传递
-        voice_flow_data = BarkVoiceFlowData(bark=bark_processor_data,
-                                            rvc=rvc_processor_data)
-
-        # 创建 Runner 实例并传递上面创建的 BarkVoiceFlowData 实例作为参数
-        task_id = f'{calculate_md5(text)}-{speaker_history_prompt}-{text_temp}' \
-                  f'-{waveform_temp}' \
-                  f'-{model_index}-{f0_up_key}'
-        runner = Runner(
-            task_id=task_id,
-            flow_data=voice_flow_data
-        )
-
-        return runner
-
-    async def dispatch(self, runner: Runner):
-
-        try:
-            # 加载task
-            self.logger.info('dispatch')
-
-            # 开启任务1
-            await self.report_progress(task_id=runner.task_id,
-                                       runner_stat='bark_voice_task',
-                                       state='dispatch_bark_voice_task')
-            data = runner.flow_data
-            if 'bark_voice' in data.type:
-                if 'BARK' in data.bark.type:
-                    bark_preprocess_object = self.preprocess_dict.get(data.bark.type)
-                    if not bark_preprocess_object.match(data.bark):
-                        raise RuntimeError('不支持的process')
-                    audio_np = bark_preprocess_object(data.bark)
-                    if audio_np is not None and 'RVC' in data.rvc.type:
-                        # 将 NumPy 数组转换为 Python 列表
-                        audio_samples_list = audio_np.tolist()
-                        data.rvc.sample_rate = self.SAMPLE_RATE
-                        data.rvc.audio_samples = audio_samples_list
-                        rvc_preprocess_object = self.preprocess_dict.get(data.rvc.type)
-                        if not rvc_preprocess_object.match(data.rvc):
-                            raise RuntimeError('不支持的process')
-
-                        out_sr, output_audio = rvc_preprocess_object(data.rvc)
-
-                        # 完成任务，构建响应数据
-                        await self.report_progress(task_id=runner.task_id,
-                                                   runner_stat='bark_voice_task',
-                                                   state='finished',
-                                                   finished=True)
-
-                        del audio_np
-                        del runner
-                        return out_sr, output_audio
-
-        except Exception as e:
-            await self.report_progress(task_id=runner.task_id, runner_stat='bark_voice_task',
-                                       state='error', finished=True)
-
-            self.logger.error(f'{e.__class__.__name__}: {e}',
-                              exc_info=e)
-
-            traceback.print_exc()
-
-        return None, None
-
-    def complete(self, runner: Runner):
-        pass
diff --git a/spaces/docs-demos/hubert-large-superb-er/app.py b/spaces/docs-demos/hubert-large-superb-er/app.py
deleted file mode 100644
index 68d3810e28f1c629ea9be08ce6e54909eaa5232d..0000000000000000000000000000000000000000
--- a/spaces/docs-demos/hubert-large-superb-er/app.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import gradio as gr
-
-
-examples=[['happy.wav']]
-gr.Interface.load("huggingface/superb/hubert-large-superb-er",examples=examples).launch()
\ No newline at end of file
diff --git a/spaces/doevent/blip/data/pretrain_dataset.py b/spaces/doevent/blip/data/pretrain_dataset.py
deleted file mode 100644
index 703d543ab5267fdc6fe2b7c84ef6a631d8af90ad..0000000000000000000000000000000000000000
--- a/spaces/doevent/blip/data/pretrain_dataset.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import json
-import os
-import random
-
-from torch.utils.data import Dataset
-
-from PIL import Image
-from PIL import ImageFile
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-Image.MAX_IMAGE_PIXELS = None
-
-from data.utils import pre_caption
-import os,glob
-
-class pretrain_dataset(Dataset):
-    def __init__(self, ann_file, laion_path, transform): 
-
-        self.ann_pretrain = []
-        for f in ann_file:
-            print('loading '+f)
-            ann = json.load(open(f,'r'))
-            self.ann_pretrain += ann
-        
-        self.laion_path = laion_path
-        if self.laion_path:
-            self.laion_files = glob.glob(os.path.join(laion_path,'*.json'))
-
-            print('loading '+self.laion_files[0])
-            with open(self.laion_files[0],'r') as f:
-                self.ann_laion = json.load(f)  
-
-            self.annotation = self.ann_pretrain + self.ann_laion
-        else:
-            self.annotation = self.ann_pretrain
-            
-        self.transform = transform
-
-
-    def reload_laion(self, epoch):
-        n = epoch%len(self.laion_files)
-        print('loading '+self.laion_files[n])
-        with open(self.laion_files[n],'r') as f:
-            self.ann_laion = json.load(f)      
-        
-        self.annotation = self.ann_pretrain + self.ann_laion    
-        
-    
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        ann = self.annotation[index]   
-      
-        image = Image.open(ann['image']).convert('RGB')   
-        image = self.transform(image)
-        caption = pre_caption(ann['caption'],30)
-        
-        return image, caption
\ No newline at end of file
diff --git a/spaces/doluvor/faster-whisper-webui/src/__init__.py b/spaces/doluvor/faster-whisper-webui/src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ehugfaces/stabilityai-stable-diffusion-2-1/README.md b/spaces/ehugfaces/stabilityai-stable-diffusion-2-1/README.md
deleted file mode 100644
index 574707d8f0400bb90acb240639e4da3d55240a62..0000000000000000000000000000000000000000
--- a/spaces/ehugfaces/stabilityai-stable-diffusion-2-1/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Stabilityai Stable Diffusion 2 1
-emoji: 🐨
-colorFrom: purple
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/enzostvs/hair-colour/app/page.tsx b/spaces/enzostvs/hair-colour/app/page.tsx
deleted file mode 100644
index 3f25e496cdbe724f1d7b03ea048a03313dd4ac4a..0000000000000000000000000000000000000000
--- a/spaces/enzostvs/hair-colour/app/page.tsx
+++ /dev/null
@@ -1,17 +0,0 @@
-import { FormUpload } from "@/components/form";
-
-export default function Home() {
-  return (
-    <>
-      <div className="border border-slate-500/20 bg-slate-950/90 backdrop-blur shadow-xl rounded-2xl w-full ring-white/10 transition-all duration-200">
-        <header className="border-b border-slate-500/10 py-4 px-4 flex items-center justify-end gap-3">
-          <div className="w-3 h-3 bg-indigo-500 rounded-full" />
-          <div className="w-3 h-3 bg-teal-500 rounded-full" />
-        </header>
-        <main className="flex flex-col-reverse lg:flex-row justify-between gap-6 p-6">
-          <FormUpload />
-        </main>
-      </div>
-    </>
-  );
-}
diff --git a/spaces/enzostvs/stable-diffusion-tpu/app/api/collections/[id]/visibility/route.ts b/spaces/enzostvs/stable-diffusion-tpu/app/api/collections/[id]/visibility/route.ts
deleted file mode 100644
index 3e399333486012742ad253fa54c3948daf9234a1..0000000000000000000000000000000000000000
--- a/spaces/enzostvs/stable-diffusion-tpu/app/api/collections/[id]/visibility/route.ts
+++ /dev/null
@@ -1,53 +0,0 @@
-import { PrismaClient } from '@prisma/client'
-
-import { isAdmin } from '@/utils/checker/is_admin'
-
-const prisma = new PrismaClient()
-
-export async function PUT(request: Request, { params }: { params: { id: string } }) {
-  const { headers } = request
-
-  const is_admin = await isAdmin(headers)
-
-  if (!is_admin) {
-    return Response.json(
-      {
-        status: 401,
-        ok: false,
-        message: 'Wrong castle fam :/'
-      }
-    )
-  }
-
-  const collection = await prisma.collection.findUnique({
-    where: {
-      id: parseInt(params.id)
-    }
-  })
-  if (!collection) {
-    return Response.json(
-      {
-        status: 404,
-        ok: false
-      }
-    )
-  }
-
-  const new_image = await prisma.collection.update({
-    where: {
-      id: parseInt(params.id)
-    },
-    data: {
-      is_visible: true
-    }
-  })
-
-  return Response.json(
-    {
-      image: new_image,
-      message: `Image ${params.id} has been updated`,
-      status: 200,
-      ok: true
-    }
-  )
-}
\ No newline at end of file
diff --git a/spaces/eson/tokenizer-arena/vocab/kplug/test_langconv.py b/spaces/eson/tokenizer-arena/vocab/kplug/test_langconv.py
deleted file mode 100644
index a867ac8f6690dfc42a84e17816ae856268ccf8aa..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/vocab/kplug/test_langconv.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""
-
-"""
-import langconv
-import opencc
-
-converter1 = opencc.OpenCC('t2s.json')
-converter2 = langconv.Converter('zh-hans')
-
-def test_special():
-    """
-    '拋' == '抛'  # False  神奇，后面是对的
-    """
-    pass
-
-def test_str():
-    sentence = '高雄氣爆事故台灣人壽成立，余数，覆盖，颠覆，翻云覆雨，叱咤风云，哪吒，著作，合著，' \
-               '油腻，元気森林，凃涂层，拋抛弃，內塚沒' \
-               '锃旯莜蜉氚' \
-               'ＰＣ乾隆 乾 清朝乾隆年间 安儿乐有乾坤 胡诌 鲇鳐羰砵呡 箬叶 鬼塚虎'
-    print(converter1.convert(sentence))
-    print(converter2.convert(sentence))
-
-def test_oov_google():
-    # 确保新增的oov不是繁体字
-    oov_google = "“”—–…’‘绗楦硌袢钕蕞鳙癀皲貉唛笕椴―胗旯鳜鲅鳊鲳鲽鲣枞炝醅馊捯抻绉馐饧莜嘬" \
-                 "腘肫鳟镊犽洌蝰铱髌锃镲锗甑戗裥弎粝霂猄轱苎偲兿铷栢帏黢洇沄誊忸怩蚬籺"
-    for token in oov_google:
-        simp = converter1.convert(token)
-        assert simp == converter2.convert(token)
-        if simp != token:
-            print(token, simp)
-
-def test_vocab_google():
-
-
-    """ 验证opencc和langconv的一致性 """
-    vocab = [line.rstrip('\n') for line in open('vocab.google.txt')]
-    for i, token in enumerate(vocab):
-        t1 = converter1.convert(token)
-        t2 = converter1.convert(token)
-        if token == '灣':
-            print(i, token)
-        if t1 != t2:
-            print(i, token, t1, t2)
-
-if __name__ == '__main__':
-    # test_vocab_google()
-    test_str()
\ No newline at end of file
diff --git a/spaces/eubinecto/idiomify/explore/explore_torchmetrics_bleu.py b/spaces/eubinecto/idiomify/explore/explore_torchmetrics_bleu.py
deleted file mode 100644
index c5ca0ffc5ac8ccf5edcaf5c3404392b163b923f6..0000000000000000000000000000000000000000
--- a/spaces/eubinecto/idiomify/explore/explore_torchmetrics_bleu.py
+++ /dev/null
@@ -1,28 +0,0 @@
-
-from torchmetrics import BLEUScore
-from transformers import BartTokenizer
-
-
-pairs = [
-    ("I knew you could do it", "I knew you could do it"),
-    ("I knew you could do it", "you knew you could do it")
-]
-
-
-def main():
-    tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
-    metric = BLEUScore()
-    preds = tokenizer([pred for pred, _ in pairs])['input_ids']
-    targets = tokenizer([target for _, target in pairs])['input_ids']
-    print(preds)
-    print(targets)
-    print(metric(preds, targets))
-    # arghhh, so bleu score does not support tensors...
-    """
-    AttributeError: 'int' object has no attribute 'split'
-    """
-    # let's just go for the accuracies then.
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/facebook/MusicGen/audiocraft/adversarial/discriminators/msd.py b/spaces/facebook/MusicGen/audiocraft/adversarial/discriminators/msd.py
deleted file mode 100644
index c4e67e29b46ab22f6ffeec85ffc64d8b99800b1b..0000000000000000000000000000000000000000
--- a/spaces/facebook/MusicGen/audiocraft/adversarial/discriminators/msd.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import typing as tp
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-from ...modules import NormConv1d
-from .base import MultiDiscriminator, MultiDiscriminatorOutputType
-
-
-class ScaleDiscriminator(nn.Module):
-    """Waveform sub-discriminator.
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        kernel_sizes (Sequence[int]): Kernel sizes for first and last convolutions.
-        filters (int): Number of initial filters for convolutions.
-        max_filters (int): Maximum number of filters.
-        downsample_scales (Sequence[int]): Scale for downsampling implemented as strided convolutions.
-        inner_kernel_sizes (Sequence[int] or None): Kernel sizes for inner convolutions.
-        groups (Sequence[int] or None): Groups for inner convolutions.
-        strides (Sequence[int] or None): Strides for inner convolutions.
-        paddings (Sequence[int] or None): Paddings for inner convolutions.
-        norm (str): Normalization method.
-        activation (str): Activation function.
-        activation_params (dict): Parameters to provide to the activation function.
-        pad (str): Padding for initial convolution.
-        pad_params (dict): Parameters to provide to the padding module.
-    """
-    def __init__(self, in_channels=1, out_channels=1, kernel_sizes: tp.Sequence[int] = [5, 3],
-                 filters: int = 16, max_filters: int = 1024, downsample_scales: tp.Sequence[int] = [4, 4, 4, 4],
-                 inner_kernel_sizes: tp.Optional[tp.Sequence[int]] = None, groups: tp.Optional[tp.Sequence[int]] = None,
-                 strides: tp.Optional[tp.Sequence[int]] = None, paddings: tp.Optional[tp.Sequence[int]] = None,
-                 norm: str = 'weight_norm', activation: str = 'LeakyReLU',
-                 activation_params: dict = {'negative_slope': 0.2}, pad: str = 'ReflectionPad1d',
-                 pad_params: dict = {}):
-        super().__init__()
-        assert len(kernel_sizes) == 2
-        assert kernel_sizes[0] % 2 == 1
-        assert kernel_sizes[1] % 2 == 1
-        assert (inner_kernel_sizes is None or len(inner_kernel_sizes) == len(downsample_scales))
-        assert (groups is None or len(groups) == len(downsample_scales))
-        assert (strides is None or len(strides) == len(downsample_scales))
-        assert (paddings is None or len(paddings) == len(downsample_scales))
-        self.activation = getattr(torch.nn, activation)(**activation_params)
-        self.convs = nn.ModuleList()
-        self.convs.append(
-            nn.Sequential(
-                getattr(torch.nn, pad)((np.prod(kernel_sizes) - 1) // 2, **pad_params),
-                NormConv1d(in_channels, filters, kernel_size=np.prod(kernel_sizes), stride=1, norm=norm)
-            )
-        )
-
-        in_chs = filters
-        for i, downsample_scale in enumerate(downsample_scales):
-            out_chs = min(in_chs * downsample_scale, max_filters)
-            default_kernel_size = downsample_scale * 10 + 1
-            default_stride = downsample_scale
-            default_padding = (default_kernel_size - 1) // 2
-            default_groups = in_chs // 4
-            self.convs.append(
-                NormConv1d(in_chs, out_chs,
-                           kernel_size=inner_kernel_sizes[i] if inner_kernel_sizes else default_kernel_size,
-                           stride=strides[i] if strides else default_stride,
-                           groups=groups[i] if groups else default_groups,
-                           padding=paddings[i] if paddings else default_padding,
-                           norm=norm))
-            in_chs = out_chs
-
-        out_chs = min(in_chs * 2, max_filters)
-        self.convs.append(NormConv1d(in_chs, out_chs, kernel_size=kernel_sizes[0], stride=1,
-                                     padding=(kernel_sizes[0] - 1) // 2, norm=norm))
-        self.conv_post = NormConv1d(out_chs, out_channels, kernel_size=kernel_sizes[1], stride=1,
-                                    padding=(kernel_sizes[1] - 1) // 2, norm=norm)
-
-    def forward(self, x: torch.Tensor):
-        fmap = []
-        for layer in self.convs:
-            x = layer(x)
-            x = self.activation(x)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        # x = torch.flatten(x, 1, -1)
-        return x, fmap
-
-
-class MultiScaleDiscriminator(MultiDiscriminator):
-    """Multi-Scale (MSD) Discriminator,
-
-    Args:
-        in_channels (int): Number of input channels.
-        out_channels (int): Number of output channels.
-        downsample_factor (int): Downsampling factor between the different scales.
-        scale_norms (Sequence[str]): Normalization for each sub-discriminator.
-        **kwargs: Additional args for ScaleDiscriminator.
-    """
-    def __init__(self, in_channels: int = 1, out_channels: int = 1, downsample_factor: int = 2,
-                 scale_norms: tp.Sequence[str] = ['weight_norm', 'weight_norm', 'weight_norm'], **kwargs):
-        super().__init__()
-        self.discriminators = nn.ModuleList([
-            ScaleDiscriminator(in_channels, out_channels, norm=norm, **kwargs) for norm in scale_norms
-        ])
-        self.downsample = nn.AvgPool1d(downsample_factor * 2, downsample_factor, padding=downsample_factor)
-
-    @property
-    def num_discriminators(self):
-        return len(self.discriminators)
-
-    def forward(self, x: torch.Tensor) -> MultiDiscriminatorOutputType:
-        logits = []
-        fmaps = []
-        for i, disc in enumerate(self.discriminators):
-            if i != 0:
-                self.downsample(x)
-            logit, fmap = disc(x)
-            logits.append(logit)
-            fmaps.append(fmap)
-        return logits, fmaps
diff --git a/spaces/facebook/MusicGen/audiocraft/utils/samples/manager.py b/spaces/facebook/MusicGen/audiocraft/utils/samples/manager.py
deleted file mode 100644
index bf0fb21b2d2867c03f7cce6f27d9524fdb89b51d..0000000000000000000000000000000000000000
--- a/spaces/facebook/MusicGen/audiocraft/utils/samples/manager.py
+++ /dev/null
@@ -1,386 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-API that can manage the storage and retrieval of generated samples produced by experiments.
-
-It offers the following benefits:
-* Samples are stored in a consistent way across epoch
-* Metadata about the samples can be stored and retrieved
-* Can retrieve audio
-* Identifiers are reliable and deterministic for prompted and conditioned samples
-* Can request the samples for multiple XPs, grouped by sample identifier
-* For no-input samples (not prompt and no conditions), samples across XPs are matched
-  by sorting their identifiers
-"""
-
-from concurrent.futures import ThreadPoolExecutor
-from dataclasses import asdict, dataclass
-from functools import lru_cache
-import hashlib
-import json
-import logging
-from pathlib import Path
-import re
-import typing as tp
-import unicodedata
-import uuid
-
-import dora
-import torch
-
-from ...data.audio import audio_read, audio_write
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class ReferenceSample:
-    id: str
-    path: str
-    duration: float
-
-
-@dataclass
-class Sample:
-    id: str
-    path: str
-    epoch: int
-    duration: float
-    conditioning: tp.Optional[tp.Dict[str, tp.Any]]
-    prompt: tp.Optional[ReferenceSample]
-    reference: tp.Optional[ReferenceSample]
-    generation_args: tp.Optional[tp.Dict[str, tp.Any]]
-
-    def __hash__(self):
-        return hash(self.id)
-
-    def audio(self) -> tp.Tuple[torch.Tensor, int]:
-        return audio_read(self.path)
-
-    def audio_prompt(self) -> tp.Optional[tp.Tuple[torch.Tensor, int]]:
-        return audio_read(self.prompt.path) if self.prompt is not None else None
-
-    def audio_reference(self) -> tp.Optional[tp.Tuple[torch.Tensor, int]]:
-        return audio_read(self.reference.path) if self.reference is not None else None
-
-
-class SampleManager:
-    """Audio samples IO handling within a given dora xp.
-
-    The sample manager handles the dumping and loading logic for generated and
-    references samples across epochs for a given xp, providing a simple API to
-    store, retrieve and compare audio samples.
-
-    Args:
-        xp (dora.XP): Dora experiment object. The XP contains information on the XP folder
-            where all outputs are stored and the configuration of the experiment,
-            which is useful to retrieve audio-related parameters.
-        map_reference_to_sample_id (bool): Whether to use the sample_id for all reference samples
-            instead of generating a dedicated hash id. This is useful to allow easier comparison
-            with ground truth sample from the files directly without having to read the JSON metadata
-            to do the mapping (at the cost of potentially dumping duplicate prompts/references
-            depending on the task).
-    """
-    def __init__(self, xp: dora.XP, map_reference_to_sample_id: bool = False):
-        self.xp = xp
-        self.base_folder: Path = xp.folder / xp.cfg.generate.path
-        self.reference_folder = self.base_folder / 'reference'
-        self.map_reference_to_sample_id = map_reference_to_sample_id
-        self.samples: tp.List[Sample] = []
-        self._load_samples()
-
-    @property
-    def latest_epoch(self):
-        """Latest epoch across all samples."""
-        return max(self.samples, key=lambda x: x.epoch).epoch if self.samples else 0
-
-    def _load_samples(self):
-        """Scan the sample folder and load existing samples."""
-        jsons = self.base_folder.glob('**/*.json')
-        with ThreadPoolExecutor(6) as pool:
-            self.samples = list(pool.map(self._load_sample, jsons))
-
-    @staticmethod
-    @lru_cache(2**26)
-    def _load_sample(json_file: Path) -> Sample:
-        with open(json_file, 'r') as f:
-            data: tp.Dict[str, tp.Any] = json.load(f)
-        # fetch prompt data
-        prompt_data = data.get('prompt')
-        prompt = ReferenceSample(id=prompt_data['id'], path=prompt_data['path'],
-                                 duration=prompt_data['duration']) if prompt_data else None
-        # fetch reference data
-        reference_data = data.get('reference')
-        reference = ReferenceSample(id=reference_data['id'], path=reference_data['path'],
-                                    duration=reference_data['duration']) if reference_data else None
-        # build sample object
-        return Sample(id=data['id'], path=data['path'], epoch=data['epoch'], duration=data['duration'],
-                      prompt=prompt, conditioning=data.get('conditioning'), reference=reference,
-                      generation_args=data.get('generation_args'))
-
-    def _init_hash(self):
-        return hashlib.sha1()
-
-    def _get_tensor_id(self, tensor: torch.Tensor) -> str:
-        hash_id = self._init_hash()
-        hash_id.update(tensor.numpy().data)
-        return hash_id.hexdigest()
-
-    def _get_sample_id(self, index: int, prompt_wav: tp.Optional[torch.Tensor],
-                       conditions: tp.Optional[tp.Dict[str, str]]) -> str:
-        """Computes an id for a sample given its input data.
-        This id is deterministic if prompt and/or conditions are provided by using a sha1 hash on the input.
-        Otherwise, a random id of the form "noinput_{uuid4().hex}" is returned.
-
-        Args:
-            index (int): Batch index, Helpful to differentiate samples from the same batch.
-            prompt_wav (torch.Tensor): Prompt used during generation.
-            conditions (dict[str, str]): Conditioning used during generation.
-        """
-        # For totally unconditioned generations we will just use a random UUID.
-        # The function get_samples_for_xps will do a simple ordered match with a custom key.
-        if prompt_wav is None and not conditions:
-            return f"noinput_{uuid.uuid4().hex}"
-
-        # Human readable portion
-        hr_label = ""
-        # Create a deterministic id using hashing
-        hash_id = self._init_hash()
-        hash_id.update(f"{index}".encode())
-        if prompt_wav is not None:
-            hash_id.update(prompt_wav.numpy().data)
-            hr_label += "_prompted"
-        else:
-            hr_label += "_unprompted"
-        if conditions:
-            encoded_json = json.dumps(conditions, sort_keys=True).encode()
-            hash_id.update(encoded_json)
-            cond_str = "-".join([f"{key}={slugify(value)}"
-                                 for key, value in sorted(conditions.items())])
-            cond_str = cond_str[:100]  # some raw text might be too long to be a valid filename
-            cond_str = cond_str if len(cond_str) > 0 else "unconditioned"
-            hr_label += f"_{cond_str}"
-        else:
-            hr_label += "_unconditioned"
-
-        return hash_id.hexdigest() + hr_label
-
-    def _store_audio(self, wav: torch.Tensor, stem_path: Path, overwrite: bool = False) -> Path:
-        """Stores the audio with the given stem path using the XP's configuration.
-
-        Args:
-            wav (torch.Tensor): Audio to store.
-            stem_path (Path): Path in sample output directory with file stem to use.
-            overwrite (bool): When False (default), skips storing an existing audio file.
-        Returns:
-            Path: The path at which the audio is stored.
-        """
-        existing_paths = [
-            path for path in stem_path.parent.glob(stem_path.stem + '.*')
-            if path.suffix != '.json'
-        ]
-        exists = len(existing_paths) > 0
-        if exists and overwrite:
-            logger.warning(f"Overwriting existing audio file with stem path {stem_path}")
-        elif exists:
-            return existing_paths[0]
-
-        audio_path = audio_write(stem_path, wav, **self.xp.cfg.generate.audio)
-        return audio_path
-
-    def add_sample(self, sample_wav: torch.Tensor, epoch: int, index: int = 0,
-                   conditions: tp.Optional[tp.Dict[str, str]] = None, prompt_wav: tp.Optional[torch.Tensor] = None,
-                   ground_truth_wav: tp.Optional[torch.Tensor] = None,
-                   generation_args: tp.Optional[tp.Dict[str, tp.Any]] = None) -> Sample:
-        """Adds a single sample.
-        The sample is stored in the XP's sample output directory, under a corresponding epoch folder.
-        Each sample is assigned an id which is computed using the input data. In addition to the
-        sample itself, a json file containing associated metadata is stored next to it.
-
-        Args:
-            sample_wav (torch.Tensor): sample audio to store. Tensor of shape [channels, shape].
-            epoch (int): current training epoch.
-            index (int): helpful to differentiate samples from the same batch.
-            conditions (dict[str, str], optional): conditioning used during generation.
-            prompt_wav (torch.Tensor, optional): prompt used during generation. Tensor of shape [channels, shape].
-            ground_truth_wav (torch.Tensor, optional): reference audio where prompt was extracted from.
-                Tensor of shape [channels, shape].
-            generation_args (dict[str, any], optional): dictionary of other arguments used during generation.
-        Returns:
-            Sample: The saved sample.
-        """
-        sample_id = self._get_sample_id(index, prompt_wav, conditions)
-        reuse_id = self.map_reference_to_sample_id
-        prompt, ground_truth = None, None
-        if prompt_wav is not None:
-            prompt_id = sample_id if reuse_id else self._get_tensor_id(prompt_wav.sum(0, keepdim=True))
-            prompt_duration = prompt_wav.shape[-1] / self.xp.cfg.sample_rate
-            prompt_path = self._store_audio(prompt_wav, self.base_folder / str(epoch) / 'prompt' / prompt_id)
-            prompt = ReferenceSample(prompt_id, str(prompt_path), prompt_duration)
-        if ground_truth_wav is not None:
-            ground_truth_id = sample_id if reuse_id else self._get_tensor_id(ground_truth_wav.sum(0, keepdim=True))
-            ground_truth_duration = ground_truth_wav.shape[-1] / self.xp.cfg.sample_rate
-            ground_truth_path = self._store_audio(ground_truth_wav, self.base_folder / 'reference' / ground_truth_id)
-            ground_truth = ReferenceSample(ground_truth_id, str(ground_truth_path), ground_truth_duration)
-        sample_path = self._store_audio(sample_wav, self.base_folder / str(epoch) / sample_id, overwrite=True)
-        duration = sample_wav.shape[-1] / self.xp.cfg.sample_rate
-        sample = Sample(sample_id, str(sample_path), epoch, duration, conditions, prompt, ground_truth, generation_args)
-        self.samples.append(sample)
-        with open(sample_path.with_suffix('.json'), 'w') as f:
-            json.dump(asdict(sample), f, indent=2)
-        return sample
-
-    def add_samples(self, samples_wavs: torch.Tensor, epoch: int,
-                    conditioning: tp.Optional[tp.List[tp.Dict[str, tp.Any]]] = None,
-                    prompt_wavs: tp.Optional[torch.Tensor] = None,
-                    ground_truth_wavs: tp.Optional[torch.Tensor] = None,
-                    generation_args: tp.Optional[tp.Dict[str, tp.Any]] = None) -> tp.List[Sample]:
-        """Adds a batch of samples.
-        The samples are stored in the XP's sample output directory, under a corresponding
-        epoch folder. Each sample is assigned an id which is computed using the input data and their batch index.
-        In addition to the sample itself, a json file containing associated metadata is stored next to it.
-
-        Args:
-            sample_wavs (torch.Tensor): Batch of audio wavs to store. Tensor of shape [batch_size, channels, shape].
-            epoch (int): Current training epoch.
-            conditioning (list of dict[str, str], optional): List of conditions used during generation,
-                one per sample in the batch.
-            prompt_wavs (torch.Tensor, optional): Prompts used during generation. Tensor of shape
-                [batch_size, channels, shape].
-            ground_truth_wav (torch.Tensor, optional): Reference audio where prompts were extracted from.
-                Tensor of shape [batch_size, channels, shape].
-            generation_args (dict[str, Any], optional): Dictionary of other arguments used during generation.
-        Returns:
-            samples (list of Sample): The saved audio samples with prompts, ground truth and metadata.
-        """
-        samples = []
-        for idx, wav in enumerate(samples_wavs):
-            prompt_wav = prompt_wavs[idx] if prompt_wavs is not None else None
-            gt_wav = ground_truth_wavs[idx] if ground_truth_wavs is not None else None
-            conditions = conditioning[idx] if conditioning is not None else None
-            samples.append(self.add_sample(wav, epoch, idx, conditions, prompt_wav, gt_wav, generation_args))
-        return samples
-
-    def get_samples(self, epoch: int = -1, max_epoch: int = -1, exclude_prompted: bool = False,
-                    exclude_unprompted: bool = False, exclude_conditioned: bool = False,
-                    exclude_unconditioned: bool = False) -> tp.Set[Sample]:
-        """Returns a set of samples for this XP. Optionally, you can filter which samples to obtain.
-        Please note that existing samples are loaded during the manager's initialization, and added samples through this
-        manager are also tracked. Any other external changes are not tracked automatically, so creating a new manager
-        is the only way detect them.
-
-        Args:
-            epoch (int): If provided, only return samples corresponding to this epoch.
-            max_epoch (int): If provided, only return samples corresponding to the latest epoch that is <= max_epoch.
-            exclude_prompted (bool): If True, does not include samples that used a prompt.
-            exclude_unprompted (bool): If True, does not include samples that did not use a prompt.
-            exclude_conditioned (bool): If True, excludes samples that used conditioning.
-            exclude_unconditioned (bool): If True, excludes samples that did not use conditioning.
-        Returns:
-            Samples (set of Sample): The retrieved samples matching the provided filters.
-        """
-        if max_epoch >= 0:
-            samples_epoch = max(sample.epoch for sample in self.samples if sample.epoch <= max_epoch)
-        else:
-            samples_epoch = self.latest_epoch if epoch < 0 else epoch
-        samples = {
-            sample
-            for sample in self.samples
-            if (
-                (sample.epoch == samples_epoch) and
-                (not exclude_prompted or sample.prompt is None) and
-                (not exclude_unprompted or sample.prompt is not None) and
-                (not exclude_conditioned or not sample.conditioning) and
-                (not exclude_unconditioned or sample.conditioning)
-            )
-        }
-        return samples
-
-
-def slugify(value: tp.Any, allow_unicode: bool = False):
-    """Process string for safer file naming.
-
-    Taken from https://github.com/django/django/blob/master/django/utils/text.py
-
-    Convert to ASCII if 'allow_unicode' is False. Convert spaces or repeated
-    dashes to single dashes. Remove characters that aren't alphanumerics,
-    underscores, or hyphens. Convert to lowercase. Also strip leading and
-    trailing whitespace, dashes, and underscores.
-    """
-    value = str(value)
-    if allow_unicode:
-        value = unicodedata.normalize("NFKC", value)
-    else:
-        value = (
-            unicodedata.normalize("NFKD", value)
-            .encode("ascii", "ignore")
-            .decode("ascii")
-        )
-    value = re.sub(r"[^\w\s-]", "", value.lower())
-    return re.sub(r"[-\s]+", "-", value).strip("-_")
-
-
-def _match_stable_samples(samples_per_xp: tp.List[tp.Set[Sample]]) -> tp.Dict[str, tp.List[Sample]]:
-    # Create a dictionary of stable id -> sample per XP
-    stable_samples_per_xp = [{
-        sample.id: sample for sample in samples
-        if sample.prompt is not None or sample.conditioning
-    } for samples in samples_per_xp]
-    # Set of all stable ids
-    stable_ids = {id for samples in stable_samples_per_xp for id in samples.keys()}
-    # Dictionary of stable id -> list of samples. If an XP does not have it, assign None
-    stable_samples = {id: [xp.get(id) for xp in stable_samples_per_xp] for id in stable_ids}
-    # Filter out ids that contain None values (we only want matched samples after all)
-    # cast is necessary to avoid mypy linter errors.
-    return {id: tp.cast(tp.List[Sample], samples) for id, samples in stable_samples.items() if None not in samples}
-
-
-def _match_unstable_samples(samples_per_xp: tp.List[tp.Set[Sample]]) -> tp.Dict[str, tp.List[Sample]]:
-    # For unstable ids, we use a sorted list since we'll match them in order
-    unstable_samples_per_xp = [[
-        sample for sample in sorted(samples, key=lambda x: x.id)
-        if sample.prompt is None and not sample.conditioning
-    ] for samples in samples_per_xp]
-    # Trim samples per xp so all samples can have a match
-    min_len = min([len(samples) for samples in unstable_samples_per_xp])
-    unstable_samples_per_xp = [samples[:min_len] for samples in unstable_samples_per_xp]
-    # Dictionary of index -> list of matched samples
-    return {
-        f'noinput_{i}': [samples[i] for samples in unstable_samples_per_xp] for i in range(min_len)
-    }
-
-
-def get_samples_for_xps(xps: tp.List[dora.XP], **kwargs) -> tp.Dict[str, tp.List[Sample]]:
-    """Gets a dictionary of matched samples across the given XPs.
-    Each dictionary entry maps a sample id to a list of samples for that id. The number of samples per id
-    will always match the number of XPs provided and will correspond to each XP in the same order given.
-    In other words, only samples that can be match across all provided XPs will be returned
-    in order to satisfy this rule.
-
-    There are two types of ids that can be returned: stable and unstable.
-    * Stable IDs are deterministic ids that were computed by the SampleManager given a sample's inputs
-      (prompts/conditioning). This is why we can match them across XPs.
-    * Unstable IDs are of the form "noinput_{idx}" and are generated on-the-fly, in order to map samples
-      that used non-deterministic, random ids. This is the case for samples that did not use prompts or
-      conditioning for their generation. This function will sort these samples by their id and match them
-      by their index.
-
-    Args:
-        xps: a list of XPs to match samples from.
-        start_epoch (int): If provided, only return samples corresponding to this epoch or newer.
-        end_epoch (int): If provided, only return samples corresponding to this epoch or older.
-        exclude_prompted (bool): If True, does not include samples that used a prompt.
-        exclude_unprompted (bool): If True, does not include samples that did not use a prompt.
-        exclude_conditioned (bool): If True, excludes samples that used conditioning.
-        exclude_unconditioned (bool): If True, excludes samples that did not use conditioning.
-    """
-    managers = [SampleManager(xp) for xp in xps]
-    samples_per_xp = [manager.get_samples(**kwargs) for manager in managers]
-    stable_samples = _match_stable_samples(samples_per_xp)
-    unstable_samples = _match_unstable_samples(samples_per_xp)
-    return dict(stable_samples, **unstable_samples)
diff --git a/spaces/fatiXbelha/sd/Cara Download Nada Dering Messenger Terbaru dan Terpopuler di 2023.md b/spaces/fatiXbelha/sd/Cara Download Nada Dering Messenger Terbaru dan Terpopuler di 2023.md
deleted file mode 100644
index 1d3f221f1489de2371265bc8ef1efb2405757583..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Cara Download Nada Dering Messenger Terbaru dan Terpopuler di 2023.md	
+++ /dev/null
@@ -1,170 +0,0 @@
-
-<h1>How to Download and Change Nada Dering Messenger</h1>
-<p>If you are a user of Facebook Messenger, you might have heard of the term "nada dering messenger". It is a popular way of saying "messenger ringtone" in Indonesian, and it refers to the sound that plays when you receive a message or a call on the app. In this article, we will show you how to download and change nada dering messenger on your Android or iOS device, as well as how to create your own custom ringtones with your voice or music.</p>
-<h2>download nada dering messenger</h2><br /><p><b><b>Download</b> &#10003;&#10003;&#10003; <a href="https://urllie.com/2uNDne">https://urllie.com/2uNDne</a></b></p><br /><br />
- <h2>What is Nada Dering Messenger?</h2>
-<h3>The meaning and origin of the term</h3>
-<p>Nada dering is a combination of two Indonesian words, "nada" which means "tone" or "sound", and "dering" which means "ringing" or "buzzing". It is a common way of saying "ringtone" in Indonesia, especially for mobile phones. Messenger is the name of the messaging app developed by Facebook, which allows users to send text, voice, video, and group messages, as well as make voice and video calls.</p>
-<p>Therefore, nada dering messenger is simply the sound that plays when you receive a notification from the app. It can be either a notification sound, which is a short beep or chime that alerts you of a new message, or a ringtone, which is a longer melody that plays when someone calls you on the app.</p>
- <h3>The benefits and drawbacks of using custom ringtones for Messenger</h3>
-<p>Using custom ringtones for Messenger can have several benefits, such as:</p>
-<p>Download nada dering messenger lucu dan unik<br />
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-<ul>
-<li>It can make your notifications more personalized and fun.</li>
-<li>It can help you distinguish between different apps or contacts.</li>
-<li>It can express your mood, personality, or preferences.</li>
-<li>It can attract attention or start a conversation.</li>
-</ul>
-<p>However, using custom ringtones for Messenger can also have some drawbacks, such as:</p>
-<ul>
-<li>It can be annoying or distracting for yourself or others around you.</li>
-<li>It can consume more battery or storage space on your device.</li>
-<li>It can violate intellectual property rights if you use copyrighted music or sounds.</li>
-<li>It can be inappropriate or offensive in some situations or environments.</li>
-</ul>
- <h2>How to Download Nada Dering Messenger from the Internet</h2>
-<h3>The best websites and apps to find free and high-quality ringtones</h3>
-<p>If you want to download nada dering messenger from the internet, you have many options to choose from. There are many websites and apps that offer free and high-quality ringtones for various apps and platforms, such as:</p>
-<table>
-<tr>
-<th>Website/App</th>
-<th>Features</th>
-</tr>
-<tr>
-<td><a href="">Zedge</a></td>
-<td>- A large collection of ringtones, wallpapers, icons, and stickers.<br>- A user-friendly interface and a search function.<br>- A preview and download option for each ringtone.<br>- A customization tool to create your own ringtones.</td>
-</tr>
-<tr>
-<td><a href="">Ringtone Maker</a></td>
-<td>- A simple and powerful app to create ringtones from your music files.<br>- A cut, copy, paste, and fade function to edit your ringtones.<br>- A file browser and a recorder to select or record your sounds.<br>- A share option to send your ringtones to others.</td>
-</tr>
-<tr>
-<td><a href="">Audiko</a></td>
-<td>- A popular website and app to download and create ringtones.<br>- A huge database of ringtones from various genres and artists.<br>- A ringtone editor to customize your sounds.<br>- A sync option to transfer your ringtones to your device.</td>
-</tr>
-<tr>
-<td><a href="">Myxer</a></td>
-<td>- A free and legal website to download ringtones from indie artists.<br>- A variety of ringtones from different categories and moods.<br>- A rating and review system to rate and comment on ringtones.<br>- A newsletter and a blog to get updates and tips on ringtones.</td>
-</tr>
-</table>
- <h3>The steps to download and save ringtones on your device</h3>
-<p>Once you have found the ringtone you like from the website or app of your choice, you need to download and save it on your device. The steps may vary depending on the source and the device you are using, but generally, they are as follows:</p>
-<ol>
-<li>Tap or click on the ringtone you want to download.</li>
-<li>Preview the ringtone by playing it on the website or app.</li>
-<li>If you like it, tap or click on the download button or icon.</li>
-<li>Select the format and quality of the ringtone you want to download.</li>
-<li>Choose the destination folder or location where you want to save the ringtone on your device.</li>
-<li>Wait for the download to finish and check if the ringtone is saved successfully.</li>
-</ol>
- <h2>How to Change Nada Dering Messenger on Android and iOS</h2>
- <h3>The difference between notification sound and ringtone for Messenger</h3>
- <p>Before you change nada dering messenger on your device, you need to understand the difference between notification sound and ringtone for Messenger. Notification sound is the sound that plays when you receive a new message or a group chat notification on the app. Ringtone is the sound that plays when someone calls you on the app. You can change both sounds separately or use the same sound for both purposes.</p>
- <h3>The steps to change notification sound and ringtone for Messenger on Android</h3>
- <p>If you have an Android device, you can change nada dering messenger by following these steps:</p>
- <ol>
-<li>Open the Messenger app on your device.</li>
-<li>Tap on your profile picture in the top left corner of the screen.</li>
-<li>Tap on Notifications & Sounds.</li>
-<li>Tap on Notification Sound or Free Call Ringtone, depending on what you want to change.</li>
-<li>Select the sound you want to use from the list of available sounds. You can also tap on More Sounds to access more options from your device's settings.</li>
-<li>Tap on OK or Done to confirm your choice.</li>
-</ol>
- <h3>The steps to change notification sound and ringtone for Messenger on iOS</h3>
- <p>If you have an iOS device, you can change nada dering messenger by following these steps:</p>
- <ol>
-<li>Open the Settings app on your device.</li>
-<li>Tap on Sounds & Haptics.</li>
-<li>Tap on Ringtone or Text Tone, depending on what you want to change.</li>
-<li>Select the sound you want to use from the list of available sounds. You can also tap on Classic or Tone Store to access more options from your device's settings.</li>
-<li>Tap on Back or Done to confirm your choice.</li>
-</ol>
- <h2>How to Create Your Own Nada Dering Messenger with Your Voice or Music</h2>
- <h3>The tools and apps you need to record and edit your own ringtones</h3>
- <p>If you want to create your own nada dering messenger with your voice or music, you need some tools and apps to record and edit your own ringtones. Here are some of the best ones you can use:</p>
-<table>
-<tr>
-<th>Tool/App</th>
-<th>Features</th>
-</tr>
-<tr>
-<td><a href="">Voice Recorder</a></td>
-<td>- A simple and easy-to-use app to record your voice or any sound.<br>- A high-quality recording with no time limit or ads.<br>- A playback and share option for your recordings.<br>- A trim and rename function to edit your recordings.</td>
-</tr>
-<tr>
-<td><a href="">GarageBand</a></td>
-<td>- A powerful and creative app to make music and ringtones.<br>- A variety of instruments, loops, and effects to choose from.<br>- A multi-track recording and editing feature.<br>- A ringtone export and share option.</td>
-</tr>
-<tr>
-<td><a href="">MP3 Cutter and Ringtone Maker</a></td>
-<td>- A popular and easy app to cut and make ringtones from your music files.<br>- A fast and accurate cutting tool with a waveform display.<br>- A fade in and fade out effect to smooth your ringtones.<br>- A file browser and a search function to find your music files.</td>
-</tr>
-<tr>
-<td><a href="">Ringdroid</a></td>
-<td>- An original and open-source app to create ringtones from your audio files.<br>- A graphical interface and a zoom function to edit your ringtones.<br>- A preview and assign option for your ringtones.<br>- A recording feature to capture your own sounds.</td>
-</tr>
-</table>
- <h3>The steps to create and set your own ringtones for Messenger</h3>
- <p>Once you have the tool or app you want to use, you can create and set your own ringtones for Messenger by following these steps:</p>
- <ol>
-<li>Open the tool or app on your device.</li>
-<li>Select or record the sound you want to use as your ringtone. You can use your voice, music, or any other sound.</li>
-<li>Edit the sound as you like. You can cut, trim, fade, adjust, or add effects to it.</li>
-<li>Save the sound as a ringtone file on your device. You can name it as you wish.</li>
-<li>Go to the settings of Messenger on your device.</li>
-<li>Choose the sound you created as your notification sound or ringtone for Messenger.</li>
-</ol>
- <h2>Conclusion</h2>
- <h3>A summary of the main points and tips</h3>
- <p>In this article, we have learned how to download and change nada dering messenger on Android and iOS devices. We have also learned how to create our own custom ringtones with our voice or music. Here are some of the main points and tips we have covered:</p>
- <ul>
-<li>Nada dering messenger is the Indonesian term for messenger ringtone, which is the sound that plays when you receive a message or a call on the app.</li>
-<li>Using custom ringtones for Messenger can have benefits and drawbacks, depending on your preferences and situations.</li>
-<li>You can download nada dering messenger from various websites and apps that offer free and high-quality ringtones for different apps and platforms.</li>
-<li>You can change nada dering messenger on Android and iOS devices by going to the settings of Messenger or your device's sounds.</li>
-<li>You can create your own nada dering messenger with your voice or music by using some tools and apps that allow you to record and edit your own ringtones.</li>
-</ul>
- <h3>A call to action and a thank you note</h3>
- <p>We hope this article has helped you learn how to download and change nada dering messenger on your device. Now you can personalize and customize your notifications and calls on Messenger with your favorite sounds. You can also express yourself and have fun with your friends by creating and sharing your own ringtones. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h2>FAQs</h2>
- <h3>Q1. Can I use different ringtones for different contacts or groups on Messenger?</h3>
- <p>A1. Yes, you can use different ringtones for different contacts or groups on Messenger. To do so, you need to open the chat or group chat you want to change the ringtone for, tap on the info icon in the top right corner of the screen, tap on Notifications & Sounds, and select the ringtone you want to use from the list of available sounds.</p>
- <h3>Q2. How can I mute or disable ringtones for Messenger?</h3>
- <p>A2. You can mute or disable ringtones for Messenger by going to the settings of Messenger on your device, tapping on Notifications & Sounds, and turning off the toggle for Notification Sound or Free Call Ringtone. You can also mute or disable ringtones for specific chats or group chats by opening them, tapping on the info icon in the top right corner of the screen, tapping on Notifications & Sounds, and selecting Off or Mute.</p>
- <h3>Q3. How can I restore the default ringtones for Messenger?</h3>
- <p>A3. You can restore the default ringtones for Messenger by going to the settings of Messenger on your device, tapping on Notifications & Sounds, and selecting the Default option for Notification Sound or Free Call Ringtone. You can also restore the default ringtones for specific chats or group chats by opening them, tapping on the info icon in the top right corner of the screen, tapping on Notifications & Sounds, and selecting Default.</p>
- <h3>Q4. How can I share my ringtones with others on Messenger?</h3>
- <p>A4. You can share your ringtones with others on Messenger by using the share option on the tool or app you used to create or download your ringtones. You can also send your ringtones as attachments on Messenger by tapping on the plus icon in the bottom left corner of the screen, tapping on Files, and selecting the ringtone file you want to send.</p>
- <h3>Q5. How can I find more ringtones for other apps or platforms?</h3>
- <p>A5. You can find more ringtones for other apps or platforms by using the same websites and apps we mentioned in this article, or by searching for other sources online. You can also use the same tools and apps we mentioned in this article to create your own ringtones for other apps or platforms.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fclong/summary/fengshen/models/zen1/modeling.py b/spaces/fclong/summary/fengshen/models/zen1/modeling.py
deleted file mode 100644
index 510c1f890c21517605d49e62655719ff6c8bd8b2..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/models/zen1/modeling.py
+++ /dev/null
@@ -1,1085 +0,0 @@
-# coding: utf-8
-# Copyright 2019 Sinovation Ventures AI Institute
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# This file is partially derived from the code at
-# https://github.com/huggingface/transformers/tree/master/transformers
-#
-# Original copyright notice:
-#
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch ZEN model classes."""
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-import copy
-import logging
-import math
-import os
-import sys
-
-import torch
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from transformers import PreTrainedModel
-
-from .configuration_zen1 import ZenConfig
-
-logger = logging.getLogger(__name__)
-
-PRETRAINED_MODEL_ARCHIVE_MAP = {
-    'IDEA-CCNL/Erlangshen-ZEN1-224M-Chinese': 'https://huggingface.co/IDEA-CCNL/Erlangshen-ZEN1-224M-Chinese/resolve/main/pytorch_model.bin',
-}
-PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    'IDEA-CCNL/Erlangshen-ZEN1-224M-Chinese': 'https://huggingface.co/IDEA-CCNL/Erlangshen-ZEN1-224M-Chinese/resolve/main/config.json',
-}
-BERT_CONFIG_NAME = 'bert_config.json'
-TF_WEIGHTS_NAME = 'model.ckpt'
-
-
-def prune_linear_layer(layer, index, dim=0):
-    """ Prune a linear layer (a model parameters) to keep only entries in index.
-        Return the pruned layer as a new layer with requires_grad=True.
-        Used to remove heads.
-    """
-    index = index.to(layer.weight.device)
-    W = layer.weight.index_select(dim, index).clone().detach()
-    if layer.bias is not None:
-        if dim == 1:
-            b = layer.bias.clone().detach()
-        else:
-            b = layer.bias[index].clone().detach()
-    new_size = list(layer.weight.size())
-    new_size[dim] = len(index)
-    new_layer = nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device)
-    new_layer.weight.requires_grad = False
-    new_layer.weight.copy_(W.contiguous())
-    new_layer.weight.requires_grad = True
-    if layer.bias is not None:
-        new_layer.bias.requires_grad = False
-        new_layer.bias.copy_(b.contiguous())
-        new_layer.bias.requires_grad = True
-    return new_layer
-
-
-def load_tf_weights_in_bert(model, tf_checkpoint_path):
-    """ Load tf checkpoints in a pytorch model
-    """
-    try:
-        import re
-        import numpy as np
-        import tensorflow as tf
-    except ImportError:
-        print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
-              "https://www.tensorflow.org/install/ for installation instructions.")
-        raise
-    tf_path = os.path.abspath(tf_checkpoint_path)
-    print("Converting TensorFlow checkpoint from {}".format(tf_path))
-    # Load weights from TF model
-    init_vars = tf.train.list_variables(tf_path)
-    names = []
-    arrays = []
-    for name, shape in init_vars:
-        print("Loading TF weight {} with shape {}".format(name, shape))
-        array = tf.train.load_variable(tf_path, name)
-        names.append(name)
-        arrays.append(array)
-
-    for name, array in zip(names, arrays):
-        name = name.split('/')
-        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
-        # which are not required for using pretrained model
-        if any(n in ["adam_v", "adam_m", "global_step"] for n in name):
-            print("Skipping {}".format("/".join(name)))
-            continue
-        pointer = model
-        for m_name in name:
-            if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
-                lname = re.split(r'_(\d+)', m_name)
-            else:
-                lname = [m_name]
-            if lname[0] == 'kernel' or lname[0] == 'gamma':
-                pointer = getattr(pointer, 'weight')
-            elif lname[0] == 'output_bias' or lname[0] == 'beta':
-                pointer = getattr(pointer, 'bias')
-            elif lname[0] == 'output_weights':
-                pointer = getattr(pointer, 'weight')
-            elif lname[0] == 'squad':
-                pointer = getattr(pointer, 'classifier')
-            else:
-                try:
-                    pointer = getattr(pointer, lname[0])
-                except AttributeError:
-                    print("Skipping {}".format("/".join(name)))
-                    continue
-            if len(lname) >= 2:
-                num = int(lname[1])
-                pointer = pointer[num]
-        if m_name[-11:] == '_embeddings':
-            pointer = getattr(pointer, 'weight')
-        elif m_name == 'kernel':
-            array = np.transpose(array)
-        try:
-            assert pointer.shape == array.shape
-        except AssertionError as e:
-            e.args += (pointer.shape, array.shape)
-            raise
-        print("Initialize PyTorch weight {}".format(name))
-        pointer.data = torch.from_numpy(array)
-    return model
-
-
-def gelu(x):
-    """Implementation of the gelu activation function.
-        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
-        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
-        Also see https://arxiv.org/abs/1606.08415
-    """
-    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
-
-
-def swish(x):
-    return x * torch.sigmoid(x)
-
-
-ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}
-
-
-try:
-    # from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
-    from torch.nn import LayerNorm as BertLayerNorm
-except ImportError:
-    logger.info("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .")
-
-    class BertLayerNorm(nn.Module):
-        def __init__(self, hidden_size, eps=1e-12):
-            """Construct a layernorm module in the TF style (epsilon inside the square root).
-            """
-            super(BertLayerNorm, self).__init__()
-            self.weight = nn.Parameter(torch.ones(hidden_size))
-            self.bias = nn.Parameter(torch.zeros(hidden_size))
-            self.variance_epsilon = eps
-
-        def forward(self, x):
-            u = x.mean(-1, keepdim=True)
-            s = (x - u).pow(2).mean(-1, keepdim=True)
-            x = (x - u) / torch.sqrt(s + self.variance_epsilon)
-            return self.weight * x + self.bias
-
-
-class BertEmbeddings(nn.Module):
-    """Construct the embeddings from word, position and token_type embeddings.
-    """
-
-    def __init__(self, config):
-        super(BertEmbeddings, self).__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=0)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, input_ids, token_type_ids=None):
-        seq_length = input_ids.size(1)
-        position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
-        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
-        if token_type_ids is None:
-            token_type_ids = torch.zeros_like(input_ids)
-
-        words_embeddings = self.word_embeddings(input_ids)
-        position_embeddings = self.position_embeddings(position_ids)
-        token_type_embeddings = self.token_type_embeddings(token_type_ids)
-
-        embeddings = words_embeddings + position_embeddings + token_type_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class BertWordEmbeddings(nn.Module):
-    """Construct the embeddings from ngram, position and token_type embeddings.
-    """
-
-    def __init__(self, config):
-        super(BertWordEmbeddings, self).__init__()
-        self.word_embeddings = nn.Embedding(config.word_size, config.hidden_size, padding_idx=0)
-        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, input_ids, token_type_ids=None):
-        if token_type_ids is None:
-            token_type_ids = torch.zeros_like(input_ids)
-
-        words_embeddings = self.word_embeddings(input_ids)
-        token_type_embeddings = self.token_type_embeddings(token_type_ids)
-
-        embeddings = words_embeddings + token_type_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-class BertSelfAttention(nn.Module):
-    def __init__(self, config, output_attentions=False, keep_multihead_output=False):
-        super(BertSelfAttention, self).__init__()
-        if config.hidden_size % config.num_attention_heads != 0:
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads))
-        self.output_attentions = output_attentions
-        self.keep_multihead_output = keep_multihead_output
-        self.multihead_output = None
-
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        self.key = nn.Linear(config.hidden_size, self.all_head_size)
-        self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(self, hidden_states, attention_mask, head_mask=None):
-        mixed_query_layer = self.query(hidden_states)
-        mixed_key_layer = self.key(hidden_states)
-        mixed_value_layer = self.value(hidden_states)
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-        key_layer = self.transpose_for_scores(mixed_key_layer)
-        value_layer = self.transpose_for_scores(mixed_value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-        attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs = attention_probs * head_mask
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-        if self.keep_multihead_output:
-            self.multihead_output = context_layer
-            self.multihead_output.retain_grad()
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-        if self.output_attentions:
-            return attention_probs, context_layer
-        return context_layer
-
-
-class BertSelfOutput(nn.Module):
-    def __init__(self, config):
-        super(BertSelfOutput, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertAttention(nn.Module):
-    def __init__(self, config, output_attentions=False, keep_multihead_output=False):
-        super(BertAttention, self).__init__()
-        self.output_attentions = output_attentions
-        self.self = BertSelfAttention(config, output_attentions=output_attentions,
-                                      keep_multihead_output=keep_multihead_output)
-        self.output = BertSelfOutput(config)
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        mask = torch.ones(self.self.num_attention_heads, self.self.attention_head_size)
-        for head in heads:
-            mask[head] = 0
-        mask = mask.view(-1).contiguous().eq(1)
-        index = torch.arange(len(mask))[mask].long()
-        # Prune linear layers
-        self.self.query = prune_linear_layer(self.self.query, index)
-        self.self.key = prune_linear_layer(self.self.key, index)
-        self.self.value = prune_linear_layer(self.self.value, index)
-        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-        # Update hyper params
-        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
-        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
-
-    def forward(self, input_tensor, attention_mask, head_mask=None):
-        self_output = self.self(input_tensor, attention_mask, head_mask)
-        if self.output_attentions:
-            attentions, self_output = self_output
-        attention_output = self.output(self_output, input_tensor)
-        if self.output_attentions:
-            return attentions, attention_output
-        return attention_output
-
-
-class BertIntermediate(nn.Module):
-    def __init__(self, config):
-        super(BertIntermediate, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        # if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
-        if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-class BertOutput(nn.Module):
-    def __init__(self, config):
-        super(BertOutput, self).__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class BertLayer(nn.Module):
-    def __init__(self, config, output_attentions=False, keep_multihead_output=False):
-        super(BertLayer, self).__init__()
-        self.output_attentions = output_attentions
-        self.attention = BertAttention(config, output_attentions=output_attentions,
-                                       keep_multihead_output=keep_multihead_output)
-        self.intermediate = BertIntermediate(config)
-        self.output = BertOutput(config)
-
-    def forward(self, hidden_states, attention_mask, head_mask=None):
-        attention_output = self.attention(hidden_states, attention_mask, head_mask)
-        if self.output_attentions:
-            attentions, attention_output = attention_output
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        if self.output_attentions:
-            return attentions, layer_output
-        return layer_output
-
-
-class ZenEncoder(nn.Module):
-    def __init__(self, config, output_attentions=False, keep_multihead_output=False):
-        super(ZenEncoder, self).__init__()
-        self.output_attentions = output_attentions
-        layer = BertLayer(config, output_attentions=output_attentions,
-                          keep_multihead_output=keep_multihead_output)
-        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
-        self.word_layers = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_word_layers)])
-        self.num_hidden_word_layers = config.num_hidden_word_layers
-
-    def forward(self, hidden_states, ngram_hidden_states, ngram_position_matrix, attention_mask,
-                ngram_attention_mask,
-                output_all_encoded_layers=True, head_mask=None):
-        # Need to check what is the attention masking doing here
-        all_encoder_layers = []
-        all_attentions = []
-        num_hidden_ngram_layers = self.num_hidden_word_layers
-        for i, layer_module in enumerate(self.layer):
-            hidden_states = layer_module(hidden_states, attention_mask, head_mask[i])
-            if i < num_hidden_ngram_layers:
-                ngram_hidden_states = self.word_layers[i](ngram_hidden_states, ngram_attention_mask, head_mask[i])
-                if self.output_attentions:
-                    ngram_attentions, ngram_hidden_states = ngram_hidden_states
-            if self.output_attentions:
-                attentions, hidden_states = hidden_states
-                all_attentions.append(attentions)
-            hidden_states += torch.bmm(ngram_position_matrix.float(), ngram_hidden_states.float())
-            if output_all_encoded_layers:
-                all_encoder_layers.append(hidden_states)
-        if not output_all_encoded_layers:
-            all_encoder_layers.append(hidden_states)
-        if self.output_attentions:
-            return all_attentions, all_encoder_layers
-        return all_encoder_layers
-
-
-class BertPooler(nn.Module):
-    def __init__(self, config):
-        super(BertPooler, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class BertPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super(BertPredictionHeadTransform, self).__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        # if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
-        if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2):
-            self.transform_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.transform_act_fn = config.hidden_act
-        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states)
-        return hidden_states
-
-
-class BertLMPredictionHead(nn.Module):
-    def __init__(self, config, bert_model_embedding_weights):
-        super(BertLMPredictionHead, self).__init__()
-        self.transform = BertPredictionHeadTransform(config)
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
-                                 bert_model_embedding_weights.size(0),
-                                 bias=False)
-        self.decoder.weight = bert_model_embedding_weights
-        self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0)))
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states) + self.bias
-        return hidden_states
-
-
-class ZenOnlyMLMHead(nn.Module):
-    def __init__(self, config, bert_model_embedding_weights):
-        super(ZenOnlyMLMHead, self).__init__()
-        self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-class ZenOnlyNSPHead(nn.Module):
-    def __init__(self, config):
-        super(ZenOnlyNSPHead, self).__init__()
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, pooled_output):
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return seq_relationship_score
-
-
-class ZenPreTrainingHeads(nn.Module):
-    def __init__(self, config, bert_model_embedding_weights):
-        super(ZenPreTrainingHeads, self).__init__()
-        self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
-        self.seq_relationship = nn.Linear(config.hidden_size, 2)
-
-    def forward(self, sequence_output, pooled_output):
-        prediction_scores = self.predictions(sequence_output)
-        seq_relationship_score = self.seq_relationship(pooled_output)
-        return prediction_scores, seq_relationship_score
-
-
-class ZenPreTrainedModel(PreTrainedModel):
-    """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
-    """
-    config_class = ZenConfig
-    base_model_prefix = "IDEA-CCNL/Erlangshen-ZEN1-224M-Chinese"
-    supports_gradient_checkpointing = True
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        if isinstance(module, nn.Linear):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(
-                mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(
-                mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-
-class ZenModel(ZenPreTrainedModel):
-    """ZEN model ("BERT-based Chinese (Z) text encoder Enhanced by N-gram representations").
-
-    Params:
-        `config`: a BertConfig class instance with the configuration to build a new model
-        `output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
-        `keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
-            This can be used to compute head importance metrics. Default: False
-
-    Inputs:
-        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
-            with the word token indices in the vocabulary
-        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
-            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
-            a `sentence B` token (see BERT paper for more details).
-        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
-            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
-            input sequence length in the current batch. It's the mask that we typically use for attention when
-            a batch has varying length sentences.
-        `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
-        `head_mask`: an optional torch.Tensor of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
-            It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
-        `input_ngram_ids`: input_ids of ngrams.
-        `ngram_token_type_ids`: token_type_ids of ngrams.
-        `ngram_attention_mask`: attention_mask of ngrams.
-        `ngram_position_matrix`: position matrix of ngrams.
-
-
-    Outputs: Tuple of (encoded_layers, pooled_output)
-        `encoded_layers`: controled by `output_all_encoded_layers` argument:
-            - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
-                of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
-                encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
-            - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
-                to the last attention block of shape [batch_size, sequence_length, hidden_size],
-        `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
-            classifier pretrained on top of the hidden state associated to the first character of the
-            input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
-
-    """
-
-    def __init__(self, config, output_attentions=False, keep_multihead_output=False):
-        super(ZenModel, self).__init__(config)
-        self.output_attentions = output_attentions
-        self.embeddings = BertEmbeddings(config)
-        self.word_embeddings = BertWordEmbeddings(config)
-        self.encoder = ZenEncoder(config, output_attentions=output_attentions,
-                                  keep_multihead_output=keep_multihead_output)
-        self.pooler = BertPooler(config)
-        self.init_weights()
-
-    def prune_heads(self, heads_to_prune):
-        """ Prunes heads of the model.
-            heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    def get_multihead_outputs(self):
-        """ Gather all multi-head outputs.
-            Return: list (layers) of multihead module outputs with gradients
-        """
-        return [layer.attention.self.multihead_output for layer in self.encoder.layer]
-
-    def forward(self, input_ids,
-                input_ngram_ids,
-                ngram_position_matrix,
-                token_type_ids=None,
-                ngram_token_type_ids=None,
-                attention_mask=None,
-                ngram_attention_mask=None,
-                output_all_encoded_layers=True,
-                head_mask=None):
-        if attention_mask is None:
-            attention_mask = torch.ones_like(input_ids)
-        if token_type_ids is None:
-            token_type_ids = torch.zeros_like(input_ids)
-
-        if ngram_attention_mask is None:
-            ngram_attention_mask = torch.ones_like(input_ngram_ids)
-        if ngram_token_type_ids is None:
-            ngram_token_type_ids = torch.zeros_like(input_ngram_ids)
-
-        # We create a 3D attention mask from a 2D tensor mask.
-        # Sizes are [batch_size, 1, 1, to_seq_length]
-        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
-        # this attention mask is more simple than the triangular masking of causal attention
-        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
-        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
-        extended_ngram_attention_mask = ngram_attention_mask.unsqueeze(1).unsqueeze(2)
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-
-        extended_ngram_attention_mask = extended_ngram_attention_mask.to(dtype=next(self.parameters()).dtype)
-        extended_ngram_attention_mask = (1.0 - extended_ngram_attention_mask) * -10000.0
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        if head_mask is not None:
-            if head_mask.dim() == 1:
-                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
-                head_mask = head_mask.expand_as(self.config.num_hidden_layers, -1, -1, -1, -1)
-            elif head_mask.dim() == 2:
-                head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(
-                    -1)  # We can specify head_mask for each layer
-            head_mask = head_mask.to(
-                dtype=next(self.parameters()).dtype)  # switch to fload if need + fp16 compatibility
-        else:
-            head_mask = [None] * self.config.num_hidden_layers
-
-        embedding_output = self.embeddings(input_ids, token_type_ids)
-        ngram_embedding_output = self.word_embeddings(input_ngram_ids, ngram_token_type_ids)
-
-        encoded_layers = self.encoder(embedding_output,
-                                      ngram_embedding_output,
-                                      ngram_position_matrix,
-                                      extended_attention_mask,
-                                      extended_ngram_attention_mask,
-                                      output_all_encoded_layers=output_all_encoded_layers,
-                                      head_mask=head_mask)
-        if self.output_attentions:
-            all_attentions, encoded_layers = encoded_layers
-        sequence_output = encoded_layers[-1]
-        pooled_output = self.pooler(sequence_output)
-        if not output_all_encoded_layers:
-            encoded_layers = encoded_layers[-1]
-        if self.output_attentions:
-            return all_attentions, encoded_layers, pooled_output
-        return encoded_layers, pooled_output
-
-
-class ZenForPreTraining(ZenPreTrainedModel):
-    """ZEN model with pre-training heads.
-    This module comprises the ZEN model followed by the two pre-training heads:
-        - the masked language modeling head, and
-        - the next sentence classification head.
-
-    Params:
-        `config`: a BertConfig class instance with the configuration to build a new model
-        `output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
-        `keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
-            This can be used to compute head importance metrics. Default: False
-
-    Inputs:
-        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
-            with the word token indices in the vocabulary
-        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
-            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
-            a `sentence B` token (see BERT paper for more details).
-        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
-            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
-            input sequence length in the current batch. It's the mask that we typically use for attention when
-            a batch has varying length sentences.
-        `masked_lm_labels`: optional masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
-            with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
-            is only computed for the labels set in [0, ..., vocab_size]
-        `next_sentence_label`: optional next sentence classification loss: torch.LongTensor of shape [batch_size]
-            with indices selected in [0, 1].
-            0 => next sentence is the continuation, 1 => next sentence is a random sentence.
-        `head_mask`: an optional torch.Tensor of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
-            It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
-        `input_ngram_ids`: input_ids of ngrams.
-        `ngram_token_type_ids`: token_type_ids of ngrams.
-        `ngram_attention_mask`: attention_mask of ngrams.
-        `ngram_position_matrix`: position matrix of ngrams.
-
-    Outputs:
-        if `masked_lm_labels` and `next_sentence_label` are not `None`:
-            Outputs the total_loss which is the sum of the masked language modeling loss and the next
-            sentence classification loss.
-        if `masked_lm_labels` or `next_sentence_label` is `None`:
-            Outputs a tuple comprising
-            - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and
-            - the next sentence classification logits of shape [batch_size, 2].
-
-    """
-
-    def __init__(self, config, output_attentions=False, keep_multihead_output=False):
-        super(ZenForPreTraining, self).__init__(config)
-        self.output_attentions = output_attentions
-        self.bert = ZenModel(config, output_attentions=output_attentions,
-                             keep_multihead_output=keep_multihead_output)
-        self.cls = ZenPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight)
-        self.init_weights()
-
-    def forward(self, input_ids, input_ngram_ids, ngram_position_matrix, token_type_ids=None,
-                ngram_token_type_ids=None,
-                attention_mask=None,
-                ngram_attention_mask=None,
-                masked_lm_labels=None,
-                next_sentence_label=None, head_mask=None):
-        outputs = self.bert(input_ids,
-                            input_ngram_ids,
-                            ngram_position_matrix,
-                            token_type_ids,
-                            ngram_token_type_ids,
-                            attention_mask,
-                            ngram_attention_mask,
-                            output_all_encoded_layers=False, head_mask=head_mask)
-        if self.output_attentions:
-            all_attentions, sequence_output, pooled_output = outputs
-        else:
-            sequence_output, pooled_output = outputs
-        prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
-
-        if masked_lm_labels is not None and next_sentence_label is not None:
-            loss_fct = CrossEntropyLoss(ignore_index=-1)
-            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
-            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
-            total_loss = masked_lm_loss + next_sentence_loss
-            return total_loss
-        elif self.output_attentions:
-            return all_attentions, prediction_scores, seq_relationship_score
-        return prediction_scores, seq_relationship_score
-
-
-class ZenForMaskedLM(ZenPreTrainedModel):
-    """ZEN model with the masked language modeling head.
-    This module comprises the ZEN model followed by the masked language modeling head.
-
-    Params:
-        `config`: a BertConfig class instance with the configuration to build a new model
-        `output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
-        `keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
-            This can be used to compute head importance metrics. Default: False
-
-    Inputs:
-        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
-            with the word token indices in the vocabulary
-        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
-            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
-            a `sentence B` token (see BERT paper for more details).
-        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
-            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
-            input sequence length in the current batch. It's the mask that we typically use for attention when
-            a batch has varying length sentences.
-        `masked_lm_labels`: masked language modeling labels: torch.LongTensor of shape [batch_size, sequence_length]
-            with indices selected in [-1, 0, ..., vocab_size]. All labels set to -1 are ignored (masked), the loss
-            is only computed for the labels set in [0, ..., vocab_size]
-        `head_mask`: an optional torch.LongTensor of shape [num_heads] with indices
-            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
-            input sequence length in the current batch. It's the mask that we typically use for attention when
-            a batch has varying length sentences.
-        `head_mask`: an optional torch.Tensor of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
-            It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
-        `input_ngram_ids`: input_ids of ngrams.
-        `ngram_token_type_ids`: token_type_ids of ngrams.
-        `ngram_attention_mask`: attention_mask of ngrams.
-        `ngram_position_matrix`: position matrix of ngrams.
-
-    Outputs:
-        if `masked_lm_labels` is  not `None`:
-            Outputs the masked language modeling loss.
-        if `masked_lm_labels` is `None`:
-            Outputs the masked language modeling logits of shape [batch_size, sequence_length, vocab_size].
-
-    """
-
-    def __init__(self, config, output_attentions=False, keep_multihead_output=False):
-        super(ZenForMaskedLM, self).__init__(config)
-        self.output_attentions = output_attentions
-        self.bert = ZenModel(config, output_attentions=output_attentions,
-                             keep_multihead_output=keep_multihead_output)
-        self.cls = ZenOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight)
-        self.init_weights()
-
-    def forward(self, input_ids, input_ngram_ids, ngram_position_matrix, token_type_ids=None, attention_mask=None, masked_lm_labels=None, head_mask=None):
-        outputs = self.bert(input_ids, input_ngram_ids, ngram_position_matrix, token_type_ids, attention_mask,
-                            output_all_encoded_layers=False,
-                            head_mask=head_mask)
-        if self.output_attentions:
-            all_attentions, sequence_output, _ = outputs
-        else:
-            sequence_output, _ = outputs
-        prediction_scores = self.cls(sequence_output)
-
-        if masked_lm_labels is not None:
-            loss_fct = CrossEntropyLoss(ignore_index=-1)
-            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
-            return masked_lm_loss
-        elif self.output_attentions:
-            return all_attentions, prediction_scores
-        return prediction_scores
-
-
-class ZenForNextSentencePrediction(ZenPreTrainedModel):
-    """ZEN model with next sentence prediction head.
-    This module comprises the ZEN model followed by the next sentence classification head.
-
-    Params:
-        `config`: a BertConfig class instance with the configuration to build a new model
-        `output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
-        `keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
-            This can be used to compute head importance metrics. Default: False
-
-    Inputs:
-        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
-            with the word token indices in the vocabulary
-        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
-            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
-            a `sentence B` token (see BERT paper for more details).
-        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
-            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
-            input sequence length in the current batch. It's the mask that we typically use for attention when
-            a batch has varying length sentences.
-        `next_sentence_label`: next sentence classification loss: torch.LongTensor of shape [batch_size]
-            with indices selected in [0, 1].
-            0 => next sentence is the continuation, 1 => next sentence is a random sentence.
-        `head_mask`: an optional torch.Tensor of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
-            It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
-        `input_ngram_ids`: input_ids of ngrams.
-        `ngram_token_type_ids`: token_type_ids of ngrams.
-        `ngram_attention_mask`: attention_mask of ngrams.
-        `ngram_position_matrix`: position matrix of ngrams.
-
-    Outputs:
-        if `next_sentence_label` is not `None`:
-            Outputs the total_loss which is the sum of the masked language modeling loss and the next
-            sentence classification loss.
-        if `next_sentence_label` is `None`:
-            Outputs the next sentence classification logits of shape [batch_size, 2].
-
-    """
-
-    def __init__(self, config, output_attentions=False, keep_multihead_output=False):
-        super(ZenForNextSentencePrediction, self).__init__(config)
-        self.output_attentions = output_attentions
-        self.bert = ZenModel(config, output_attentions=output_attentions,
-                             keep_multihead_output=keep_multihead_output)
-        self.cls = ZenOnlyNSPHead(config)
-        self.init_weights()
-
-    def forward(self, input_ids, input_ngram_ids, ngram_position_matrix, token_type_ids=None, attention_mask=None, next_sentence_label=None, head_mask=None):
-        outputs = self.bert(input_ids, input_ngram_ids, ngram_position_matrix, token_type_ids, attention_mask,
-                            output_all_encoded_layers=False,
-                            head_mask=head_mask)
-        if self.output_attentions:
-            all_attentions, _, pooled_output = outputs
-        else:
-            _, pooled_output = outputs
-        seq_relationship_score = self.cls(pooled_output)
-
-        if next_sentence_label is not None:
-            loss_fct = CrossEntropyLoss(ignore_index=-1)
-            next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
-            return next_sentence_loss
-        elif self.output_attentions:
-            return all_attentions, seq_relationship_score
-        return seq_relationship_score
-
-
-class ZenForSequenceClassification(ZenPreTrainedModel):
-    """ZEN model for classification.
-    This module is composed of the ZEN model with a linear layer on top of
-    the pooled output.
-
-    Params:
-        `config`: a BertConfig class instance with the configuration to build a new model
-        `output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
-        `keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
-            This can be used to compute head importance metrics. Default: False
-        `num_labels`: the number of classes for the classifier. Default = 2.
-
-    Inputs:
-        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
-            with the word token indices in the vocabulary. Items in the batch should begin with the special "CLS" token. (see the tokens preprocessing logic in the scripts
-            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
-        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
-            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
-            a `sentence B` token (see BERT paper for more details).
-        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
-            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
-            input sequence length in the current batch. It's the mask that we typically use for attention when
-            a batch has varying length sentences.
-        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
-            with indices selected in [0, ..., num_labels].
-        `head_mask`: an optional torch.Tensor of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
-            It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
-        `input_ngram_ids`: input_ids of ngrams.
-        `ngram_token_type_ids`: token_type_ids of ngrams.
-        `ngram_attention_mask`: attention_mask of ngrams.
-        `ngram_position_matrix`: position matrix of ngrams.
-
-    Outputs:
-        if `labels` is not `None`:
-            Outputs the CrossEntropy classification loss of the output with the labels.
-        if `labels` is `None`:
-            Outputs the classification logits of shape [batch_size, num_labels].
-
-    """
-
-    def __init__(self, config, num_labels=2, output_attentions=False, keep_multihead_output=False):
-        # super().__init__(config, num_labels, output_attentions, keep_multihead_output)
-        super().__init__(config)
-        self.config = config
-        self.output_attentions = output_attentions
-        self.num_labels = config.num_labels
-        self.bert = ZenModel(config, output_attentions=output_attentions,
-                             keep_multihead_output=keep_multihead_output)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, self.num_labels)
-        self.init_weights()
-
-    def forward(self, input_ids, input_ngram_ids, ngram_position_matrix, token_type_ids=None, attention_mask=None, labels=None, head_mask=None):
-        outputs = self.bert(input_ids, input_ngram_ids, ngram_position_matrix, token_type_ids, attention_mask,
-                            output_all_encoded_layers=False,
-                            head_mask=head_mask)
-        if self.output_attentions:
-            all_attentions, _, pooled_output = outputs
-        else:
-            _, pooled_output = outputs
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            # print('logits***************', logits, labels)
-            # breakpoint()
-            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            return loss, logits
-        elif self.output_attentions:
-            return all_attentions, logits
-        return loss, logits
-
-
-class ZenForTokenClassification(ZenPreTrainedModel):
-    """ZEN model for token-level classification.
-    This module is composed of the ZEN model with a linear layer on top of
-    the full hidden state of the last layer.
-
-    Params:
-        `config`: a BertConfig class instance with the configuration to build a new model
-        `output_attentions`: If True, also output attentions weights computed by the model at each layer. Default: False
-        `keep_multihead_output`: If True, saves output of the multi-head attention module with its gradient.
-            This can be used to compute head importance metrics. Default: False
-        `num_labels`: the number of classes for the classifier. Default = 2.
-
-    Inputs:
-        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
-            with the word token indices in the vocabulary
-        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
-            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
-            a `sentence B` token (see BERT paper for more details).
-        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
-            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
-            input sequence length in the current batch. It's the mask that we typically use for attention when
-            a batch has varying length sentences.
-        `labels`: labels for the classification output: torch.LongTensor of shape [batch_size, sequence_length]
-            with indices selected in [0, ..., num_labels].
-        `head_mask`: an optional torch.Tensor of shape [num_heads] or [num_layers, num_heads] with indices between 0 and 1.
-            It's a mask to be used to nullify some heads of the transformer. 1.0 => head is fully masked, 0.0 => head is not masked.
-        `input_ngram_ids`: input_ids of ngrams.
-        `ngram_token_type_ids`: token_type_ids of ngrams.
-        `ngram_attention_mask`: attention_mask of ngrams.
-        `ngram_position_matrix`: position matrix of ngrams.
-
-    Outputs:
-        if `labels` is not `None`:
-            Outputs the CrossEntropy classification loss of the output with the labels.
-        if `labels` is `None`:
-            Outputs the classification logits of shape [batch_size, sequence_length, num_labels].
-
-    """
-
-    def __init__(self, config, num_labels=2, output_attentions=False, keep_multihead_output=False):
-        super().__init__(config)
-        self.output_attentions = output_attentions
-        self.num_labels = config.num_labels
-        self.bert = ZenModel(config, output_attentions=output_attentions,
-                             keep_multihead_output=keep_multihead_output)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-        self.init_weights()
-
-    def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, valid_ids=None,
-                attention_mask_label=None, ngram_ids=None, ngram_positions=None, head_mask=None):
-        outputs = self.bert(input_ids, ngram_ids, ngram_positions, token_type_ids, attention_mask,
-                            output_all_encoded_layers=False, head_mask=head_mask)
-        if self.output_attentions:
-            all_attentions, sequence_output, _ = outputs
-        else:
-            sequence_output, _ = outputs
-
-        batch_size, max_len, feat_dim = sequence_output.shape
-        valid_output = torch.zeros(batch_size, max_len, feat_dim, dtype=torch.float32, device=input_ids.device)
-
-        if self.num_labels == 38:
-            # just for POS to filter/mask input_ids=0
-            for i in range(batch_size):
-                temp = sequence_output[i][valid_ids[i] == 1]
-                valid_output[i][:temp.size(0)] = temp
-        else:
-            valid_output = sequence_output
-
-        sequence_output = self.dropout(valid_output)
-        logits = self.classifier(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss(ignore_index=0)
-            # Only keep active parts of the loss
-            attention_mask_label = None
-            if attention_mask_label is not None:
-                active_loss = attention_mask_label.view(-1) == 1
-                active_logits = logits.view(-1, self.num_labels)[active_loss]
-                active_labels = labels.view(-1)[active_loss]
-                loss = loss_fct(active_logits, active_labels)
-            else:
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            return loss, logits
-        else:
-            return loss, logits
diff --git a/spaces/felixz/open_llm_leaderboard/src/display_models/utils.py b/spaces/felixz/open_llm_leaderboard/src/display_models/utils.py
deleted file mode 100644
index 09d6fe89ccbd9f0bc8b368c3c0d9ae28ad4f25d9..0000000000000000000000000000000000000000
--- a/spaces/felixz/open_llm_leaderboard/src/display_models/utils.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import os
-from dataclasses import dataclass
-
-from huggingface_hub import HfApi
-
-API = HfApi()
-
-
-# These classes are for user facing column names, to avoid having to change them
-# all around the code when a modif is needed
-@dataclass
-class ColumnContent:
-    name: str
-    type: str
-    displayed_by_default: bool
-    hidden: bool = False
-
-
-def fields(raw_class):
-    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
-
-
-@dataclass(frozen=True)
-class AutoEvalColumn:  # Auto evals column
-    model_type_symbol = ColumnContent("T", "str", True)
-    model = ColumnContent("Model", "markdown", True)
-    average = ColumnContent("Average ⬆️", "number", True)
-    arc = ColumnContent("ARC", "number", True)
-    hellaswag = ColumnContent("HellaSwag", "number", True)
-    mmlu = ColumnContent("MMLU", "number", True)
-    truthfulqa = ColumnContent("TruthfulQA", "number", True)
-    model_type = ColumnContent("Type", "str", False)
-    precision = ColumnContent("Precision", "str", False)  # , True)
-    license = ColumnContent("Hub License", "str", False)
-    params = ColumnContent("#Params (B)", "number", False)
-    likes = ColumnContent("Hub ❤️", "number", False)
-    still_on_hub = ColumnContent("Available on the hub", "bool", False)
-    revision = ColumnContent("Model sha", "str", False, False)
-    dummy = ColumnContent(
-        "model_name_for_query", "str", True
-    )  # dummy col to implement search bar (hidden by custom CSS)
-
-
-@dataclass(frozen=True)
-class EloEvalColumn:  # Elo evals column
-    model = ColumnContent("Model", "markdown", True)
-    gpt4 = ColumnContent("GPT-4 (all)", "number", True)
-    human_all = ColumnContent("Human (all)", "number", True)
-    human_instruct = ColumnContent("Human (instruct)", "number", True)
-    human_code_instruct = ColumnContent("Human (code-instruct)", "number", True)
-
-
-@dataclass(frozen=True)
-class EvalQueueColumn:  # Queue column
-    model = ColumnContent("model", "markdown", True)
-    revision = ColumnContent("revision", "str", True)
-    private = ColumnContent("private", "bool", True)
-    precision = ColumnContent("precision", "str", True)
-    weight_type = ColumnContent("weight_type", "str", "Original")
-    status = ColumnContent("status", "str", True)
-
-
-LLAMAS = [
-    "huggingface/llama-7b",
-    "huggingface/llama-13b",
-    "huggingface/llama-30b",
-    "huggingface/llama-65b",
-]
-
-
-KOALA_LINK = "https://huggingface.co/TheBloke/koala-13B-HF"
-VICUNA_LINK = "https://huggingface.co/lmsys/vicuna-13b-delta-v1.1"
-OASST_LINK = "https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"
-DOLLY_LINK = "https://huggingface.co/databricks/dolly-v2-12b"
-MODEL_PAGE = "https://huggingface.co/models"
-LLAMA_LINK = "https://ai.facebook.com/blog/large-language-model-llama-meta-ai/"
-VICUNA_LINK = "https://huggingface.co/CarperAI/stable-vicuna-13b-delta"
-ALPACA_LINK = "https://crfm.stanford.edu/2023/03/13/alpaca.html"
-
-
-def model_hyperlink(link, model_name):
-    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
-
-
-def make_clickable_model(model_name):
-    link = f"https://huggingface.co/{model_name}"
-
-    if model_name in LLAMAS:
-        link = LLAMA_LINK
-        model_name = model_name.split("/")[1]
-    elif model_name == "HuggingFaceH4/stable-vicuna-13b-2904":
-        link = VICUNA_LINK
-        model_name = "stable-vicuna-13b"
-    elif model_name == "HuggingFaceH4/llama-7b-ift-alpaca":
-        link = ALPACA_LINK
-        model_name = "alpaca-13b"
-    if model_name == "dolly-12b":
-        link = DOLLY_LINK
-    elif model_name == "vicuna-13b":
-        link = VICUNA_LINK
-    elif model_name == "koala-13b":
-        link = KOALA_LINK
-    elif model_name == "oasst-12b":
-        link = OASST_LINK
-
-    details_model_name = model_name.replace("/", "__")
-    details_link = f"https://huggingface.co/datasets/open-llm-leaderboard/details_{details_model_name}"
-
-    if not bool(os.getenv("DEBUG", "False")):
-        # We only add these checks when not debugging, as they are extremely slow
-        print(f"details_link: {details_link}")
-        try:
-            check_path = list(
-                API.list_files_info(
-                    repo_id=f"open-llm-leaderboard/details_{details_model_name}",
-                    paths="README.md",
-                    repo_type="dataset",
-                )
-            )
-            print(f"check_path: {check_path}")
-        except Exception as err:
-            # No details repo for this model
-            print(f"No details repo for this model: {err}")
-            return model_hyperlink(link, model_name)
-
-    return model_hyperlink(link, model_name) + "  " + model_hyperlink(details_link, "📑")
-
-
-def styled_error(error):
-    return f"<p style='color: red; font-size: 20px; text-align: center;'>{error}</p>"
-
-
-def styled_warning(warn):
-    return f"<p style='color: orange; font-size: 20px; text-align: center;'>{warn}</p>"
-
-
-def styled_message(message):
-    return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>"
-
-
-def has_no_nan_values(df, columns):
-    return df[columns].notna().all(axis=1)
-
-
-def has_nan_values(df, columns):
-    return df[columns].isna().any(axis=1)
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Clash of Clans APK - Klan Savalarnda dnyann drt bir yanndaki oyuncularla yar.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Clash of Clans APK - Klan Savalarnda dnyann drt bir yanndaki oyuncularla yar.md
deleted file mode 100644
index 306737e8cf0dc7069579e7252e10874132dfb1c1..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Clash of Clans APK - Klan Savalarnda dnyann drt bir yanndaki oyuncularla yar.md	
+++ /dev/null
@@ -1,52 +0,0 @@
-<br />
-<h1>Clash of Clans Yükle APK: How to Download and Play the Popular Strategy Game on Your Android Device</h1>
- <p>If you are a fan of strategy games, you have probably heard of Clash of Clans, one of the most popular and addictive games in the genre. In this game, you can build your own village, train your army, join a clan, and compete with millions of players around the world in epic clan wars. But did you know that you can also download and play Clash of Clans on your Android device without using Google Play Store? In this article, we will show you how to do that by using an APK file, and also give you some tips and tricks to help you enjoy the game even more.</p>
-<h2>clash of clans yükle apk</h2><br /><p><b><b>Download File</b> &#10002; &#10002; &#10002; <a href="https://gohhs.com/2uPrDh">https://gohhs.com/2uPrDh</a></b></p><br /><br />
- <h2>What is Clash of Clans?</h2>
- <p>Clash of Clans is a freemium mobile strategy game developed and published by Supercell, a Finnish game company. The game was released in 2012 for iOS devices and in 2013 for Android devices. Since then, it has become one of the most successful and popular games in the world, with over 500 million downloads and millions of active players every day.</p>
- <p>The game is set in a fantasy world where you can create your own village, customize it with various buildings and defenses, and train different types of troops, such as barbarians, archers, wizards, dragons, and more. You can also join or create a clan with other players, and participate in clan wars, where you can attack and defend against other clans. The game also has a single-player campaign mode, where you can fight against the goblin king and his army.</p>
- <p>The game is free to download and play, but it also offers in-app purchases that can help you speed up your progress or get more resources. You can use gems, the premium currency in the game, to buy gold, elixir, dark elixir, or builder huts. You can also use gems to boost your production buildings or finish your upgrades instantly. However, gems are not easy to come by, so you have to be careful how you spend them.</p>
- <h2>Why download Clash of Clans APK?</h2>
- <p>If you want to play Clash of Clans on your Android device, you might think that the easiest way is to download it from Google Play Store. However, there are some reasons why you might want to download the APK file instead. Here are some of them:</p>
-<p></p>
- <ul>
-<li>You can get the latest version of the game before it is available on Google Play Store. Sometimes, Supercell releases new updates for Clash of Clans that add new features or fix bugs. However, these updates might take some time to reach all regions or devices. By downloading the APK file from a trusted source, you can get access to the newest version as soon as it is released.</li>
-<li>You can play the game on devices that are not compatible with Google Play Store or Google services. Some Android devices might not have Google Play Store installed or supported due to various reasons. For example, some devices might be rooted or have custom ROMs that do not include Google services. By downloading the APK file, you can bypass this limitation and enjoy the game on any device.</li>
-<li>You can save storage space on your device. The APK file size for Clash of Clans is usually smaller than the size of the app on Google Play Store. This means that you can save some storage space on your device by downloading the APK file instead of using Google Play Store.</li>
-</ul>
- <p>However, there are also some risks involved in downloading APK files from unknown sources. You might end up downloading a fake or malicious file that could harm your device or steal your personal information. Therefore, Therefore, you should always download the APK file from a reliable and reputable source, such as the official website of Supercell or a trusted APK hosting site. You should also scan the file with an antivirus software before installing it on your device.</p>
- <h2>How to download and install Clash of Clans APK?</h2>
- <p>Now that you know why you might want to download the APK file for Clash of Clans, let's see how you can do it. Here are the steps you need to follow:</p>
- <ol>
-<li>Go to the official website of Supercell at <a href="">https://supercell.com/en/games/clashofclans/</a> and scroll down to the bottom of the page. You will see a button that says "Download on the App Store" and another one that says "Get it on Google Play". Tap on the Google Play button and you will be redirected to a page where you can download the APK file.</li>
-<li>Alternatively, you can go to a trusted APK hosting site, such as <a href="">https://apkpure.com/clash-of-clans/com.supercell.clashofclans</a> or <a href="">https://apkdone.com/clash-of-clans/</a> and search for Clash of Clans. You will see a list of versions and updates for the game. Choose the latest one and tap on the download button.</li>
-<li>Once you have downloaded the APK file, you need to enable the installation of apps from unknown sources on your device. To do this, go to your device settings and look for security or privacy options. You will see a toggle or checkbox that says "Allow installation of apps from unknown sources" or something similar. Turn it on or check it.</li>
-<li>Now, locate the APK file on your device using a file manager app or your browser's downloads folder. Tap on the file and follow the instructions on the screen to install it on your device. You might see a warning message that says "This type of file can harm your device" or "Do you want to install this app?". Ignore it and tap on OK or Install.</li>
-<li>After the installation is complete, you will see an icon for Clash of Clans on your home screen or app drawer. Tap on it and launch the game. You might need to grant some permissions to the game, such as access to your storage, contacts, or location. Allow them and enjoy playing Clash of Clans on your Android device.</li>
-</ol>
- <h2>How to start playing Clash of Clans?</h2>
- <p>Now that you have downloaded and installed Clash of Clans APK on your device, you are ready to start playing the game. Here are some tips and tricks to help you get started:</p>
- <ul>
-<li>When you launch the game for the first time, you will see a tutorial that will guide you through the basics of the game, such as building your village, training your troops, and attacking other players. Follow the tutorial and learn how to play the game.</li>
-<li>You will also see a shield icon on the top right corner of your screen. This is your protection from other players who might want to attack your village. The shield lasts for three days when you start playing, and then it will expire unless you buy more with gems or get attacked by someone else. Try to use this time wisely and upgrade your buildings and defenses as much as possible.</li>
-<li>You will also see a chat icon on the bottom left corner of your screen. This is where you can communicate with other players in global chat or clan chat. You can also join or create a clan with other players who share your interests and goals. Being in a clan has many benefits, such as getting donations of troops, sharing strategies, and participating in clan wars.</li>
-<li>You will also see a trophy icon on the top left corner of your screen. This is your trophy count, which represents your ranking in the game. The more trophies you have, the higher your league and level are. You can gain or lose trophies by attacking or defending against other players. Try to balance your offense and defense skills and aim for higher leagues.</li>
-<li>You will also see a shop icon on the bottom right corner of your screen. This is where you can buy various items with gold, elixir, dark elixir, or gems. You can buy more buildings, defenses, troops, spells, decorations, or resources. You can also buy gems with real money if you want to speed up your progress or get more resources.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Clash of Clans is a fun and addictive strategy game that you can play on your Android device by downloading the APK file from a trusted source. You can build your own village, train your army, join a clan with other players, and compete with millions of players around the world in epic clan wars. The game is free to download and play, but it also offers in-app purchases that can help you speed up your progress or get more resources. You can use gems, the premium currency in the game, to buy gold, elixir, dark elixir, or builder huts. You can also use gems to boost your production buildings or finish your upgrades instantly. However, gems are not easy to come by, so you have to be careful how you spend them.</p>
- <p>If you are looking for a fun and challenging strategy game that will keep you entertained for hours, you should definitely try Clash of Clans. You can download the APK file from the official website of Supercell or a trusted APK hosting site, and install it on your Android device without using Google Play Store. You can also follow our tips and tricks to help you get started and improve your skills. We hope you enjoy playing Clash of Clans as much as we do!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Clash of Clans and its APK file:</p>
- <h3>Is Clash of Clans safe to download and play?</h3>
- <p>Yes, Clash of Clans is safe to download and play, as long as you download the APK file from a reliable and reputable source, such as the official website of Supercell or a trusted APK hosting site. You should also scan the file with an antivirus software before installing it on your device.</p>
- <h3>Is Clash of Clans compatible with all Android devices?</h3>
- <p>No, Clash of Clans is not compatible with all Android devices. The game requires Android 4.4 or higher and at least 1 GB of RAM to run smoothly. You can check the compatibility of your device by visiting the Google Play Store page of the game or by using an app like CPU-Z.</p>
- <h3>How can I update Clash of Clans APK?</h3>
- <p>You can update Clash of Clans APK by downloading the latest version of the file from the same source where you downloaded it before. You can also check for updates by launching the game and tapping on the settings icon on the top right corner of your screen. You will see a button that says "Update Now" if there is a new version available.</p>
- <h3>How can I backup my Clash of Clans progress?</h3>
- <p>You can backup your Clash of Clans progress by linking your game account to a Google account or a Supercell ID. To do this, go to the settings menu in the game and tap on "Google Play Sign-In" or "Supercell ID". Follow the instructions on the screen to create or log in to your account. This way, you can restore your progress on any device by using the same account.</p>
- <h3>How can I contact Supercell for support or feedback?</h3>
- <p>You can contact Supercell for support or feedback by visiting their official website at <a href="">https://supercell.com/en/support/</a> and choosing the game you need help with. You can also access their support page from within the game by tapping on the settings icon and then on "Help and Support". You can also follow them on social media platforms like Facebook, Twitter, Instagram, or YouTube for news and updates.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download 3D Objects for Dialux - Everything You Need to Know.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download 3D Objects for Dialux - Everything You Need to Know.md
deleted file mode 100644
index 976ed3c6ac0d95de97d4fd5c3f5fbd80aa4a12ac..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download 3D Objects for Dialux - Everything You Need to Know.md	
+++ /dev/null
@@ -1,120 +0,0 @@
-<br />
-<h1>Download 3D Objects for Dialux: A Guide for Lighting Designers</h1>
-<p>If you are a lighting designer, you probably know how important it is to create realistic and accurate lighting simulations for your projects. Whether you are designing indoor or outdoor lighting, you need a powerful and user-friendly software that can help you plan, calculate, and visualize light. That's where Dialux comes in.</p>
-<h2>What is Dialux?</h2>
-<p>Dialux is a free software that allows you to create professional lighting designs for any space. You can use real luminaires from more than 190 renowned manufacturers, or create your own custom luminaires. You can also import CAD files, or draw your own floor plans and rooms. You can then calculate the illuminance, luminance, color rendering, glare, energy consumption, and other parameters of your lighting design. You can also generate photorealistic images and videos of your project, as well as detailed reports and documentation.</p>
-<h2>download 3d objects for dialux</h2><br /><p><b><b>Download Zip</b> &#9999; <a href="https://gohhs.com/2uPp41">https://gohhs.com/2uPp41</a></b></p><br /><br />
-<h3>Why use Dialux for lighting design?</h3>
-<p>Dialux is one of the most popular and widely used software for lighting design in the world. It has many advantages over other software, such as:</p>
-<ul>
-<li>It is free and constantly updated.</li>
-<li>It is easy to use and has a intuitive interface.</li>
-<li>It supports multiple languages and standards.</li>
-<li>It has a large and active community of users and experts.</li>
-<li>It offers a wide range of features and functions for different types of lighting design.</li>
-</ul>
-<h3>How to get started with Dialux?</h3>
-<p>To get started with Dialux, you need to download and install the software from the official website. You can also download the latest version of the Philips Product Selector for Dialux, which gives you access to more than 10,000 Philips luminaires. You can also browse the Dialux Luminaire Finder, which is a search engine that helps you find suitable luminaires from various manufacturers. Once you have installed the software, you can open it and start creating your project. You can either use the wizard mode, which guides you through the basic steps of lighting design, or the expert mode, which gives you more control and flexibility over your project.</p>
-<h2>What are 3D objects and why do you need them?</h2>
-<p>One of the features that makes Dialux stand out from other software is its ability to import and use 3D objects in your lighting design. 3D objects are digital models of real-world items, such as furniture, plants, vehicles, people, etc. They can add realism and detail to your project, as well as enhance the visual appeal of your simulation.</p>
-<h3>What are the benefits of using 3D objects in Dialux?</h3>
-<p>Using 3D objects in Dialux can have several benefits for your lighting design, such as:</p>
-<ul>
-<li>They can help you create a more accurate representation of your space and its elements.</li>
-<li>They can help you test how different luminaires affect the appearance and mood of your space.</li>
-<li>They can help you showcase your project to your clients and stakeholders in a more attractive and realistic way.</li>
-</ul>
-<h3>What are the types of 3D objects available for Dialux?</h3>
-<p>There are many types of 3D objects that you can use in Dialux, depending on the nature and purpose of your project. Some of the most common types are:</p>
-<p>download 3d models for dialux evo<br />
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-<h4>Furniture and accessories</h4>
-<p>These are 3D objects that represent the items that you would typically find in an indoor space, such as tables, chairs, sofas, lamps, shelves, etc. They can help you create a more realistic and functional layout of your space, as well as add some style and personality to it.</p>
-<h4>Plants and trees</h4>
-<p>These are 3D objects that represent the natural elements that you would typically find in an outdoor space, such as flowers, bushes, grass, trees, etc. They can help you create a more natural and pleasant environment for your space, as well as provide some shade and contrast to your lighting design.</p>
-<h4>Vehicles and people</h4>
-<p>These are 3D objects that represent the moving elements that you would typically find in a public or urban space, such as cars, buses, bikes, pedestrians, etc. They can help you create a more dynamic and lively atmosphere for your space, as well as test how your lighting design affects the visibility and safety of these elements.</p>
-<h2>How to download 3D objects for Dialux?</h2>
-<p>Now that you know what 3D objects are and why you need them, you might be wondering how to get them for your Dialux project. There are two main ways to do that: you can either download them from online sources, or create them yourself using 3D modeling software. In this article, we will focus on the first option.</p>
-<h3>Where to find free and paid 3D objects for Dialux?</h3>
-<p>There are many websites that offer free and paid 3D objects for Dialux users. Some of them are:</p>
-<h4>Sketchfab</h4>
-<p>Sketchfab is one of the largest and most popular platforms for sharing and downloading 3D models. It has over 4 million 3D models in various categories, such as architecture, furniture, vehicles, animals, etc. You can browse the models by keywords, tags, collections, or categories. You can also preview the models in 3D before downloading them. Some of the models are free to download, while others require a subscription or a one-time payment.</p>
-<h4>GrabCAD</h4>
-<p>GrabCAD is another platform for sharing and downloading 3D models. It has over 8 million 3D models in various categories, such as engineering, industrial design, automotive, aerospace, etc. You can browse the models by keywords, tags, groups, or categories. You can also preview the models in 3D before downloading them. All of the models are free to download.</p>
-<h4>Other sources</h4>
-<p>Besides Sketchfab and GrabCAD, there are many other sources where you can find free and paid 3D objects for Dialux. Some of them are:</p>
-<ul>
-<li>Dialux Plugins: These are plugins that allow you to access and import 3D objects from various manufacturers directly into Dialux.</li>
-<li>Dialux Forum: This is a forum where Dialux users can share their projects, tips, questions, and resources. You can find many 3D objects posted by other users in the "Objects" section.</li>
-<li>Dialux Wiki: This is a wiki where Dialux users can find information and tutorials about using Dialux. You can find some 3D objects in the "Downloads" section.</li>
-<li>Other websites: There are many other websites that offer free and paid 3D objects for various purposes. Some examples are TurboSquid, CGTrader, Free3D, etc.</li>
-</ul> <h3>How to import 3D objects into Dialux?</h3>
-<p>Once you have downloaded the 3D objects that you want to use in your Dialux project, you need to import them into the software. To do that, you need to follow these steps:</p>
-<h4>Step 1: Choose the file format</h4>
-<p>Dialux supports several file formats for importing 3D objects, such as STL, OBJ, 3DS, DAE, etc. However, the recommended file format is STL, as it is the most compatible and efficient one. You can convert your 3D objects to STL using online tools or 3D modeling software.</p>
-<h4>Step 2: Adjust the scale and orientation</h4>
-<p>Before importing your 3D object into Dialux, you need to make sure that it has the correct scale and orientation. You can use 3D modeling software or online tools to adjust these parameters. The scale should match the units of your Dialux project (meters, centimeters, etc.). The orientation should be such that the object is facing the positive Z-axis (upward) and the positive X-axis (rightward).</p>
-<h4>Step 3: Place the object in the scene</h4>
-<p>To import your 3D object into Dialux, you need to go to the "Objects" tab and click on the "Import" button. You can then browse your computer and select the STL file that you want to import. You can also drag and drop the file into the Dialux window. Once the object is imported, you can see it in the 3D view. You can then move, rotate, scale, and copy the object as you wish. You can also change its properties, such as color, texture, transparency, etc.</p>
-<h2>Conclusion</h2>
-<p>In this article, we have learned how to download 3D objects for Dialux and how to use them in our lighting design projects. We have seen what are the benefits of using 3D objects in Dialux, what are the types of 3D objects available for Dialux, where to find free and paid 3D objects for Dialux, and how to import 3D objects into Dialux. We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below.</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about downloading 3D objects for Dialux:</p>
-<ol>
-<li><b>What are the system requirements for using 3D objects in Dialux?</b></li>
-<p>To use 3D objects in Dialux, you need a computer with a minimum of 4 GB RAM, a graphics card with at least 1 GB VRAM, and a screen resolution of at least 1024 x 768 pixels.</p>
-<li><b>How many 3D objects can I use in my Dialux project?</b></li>
-<p>There is no limit to the number of 3D objects that you can use in your Dialux project. However, using too many or too complex 3D objects can affect the performance and speed of your software. Therefore, it is advisable to use only the necessary and relevant 3D objects for your project.</p>
-<li><b>Can I create my own 3D objects for Dialux?</b></li>
-<p>Yes, you can create your own 3D objects for Dialux using any 3D modeling software that can export STL files. You can also use online tools or plugins that allow you to create simple shapes or text as 3D objects.</p>
-<li><b>Can I edit or modify the 3D objects that I download for Dialux?</b></li>
-<p>Yes, you can edit or modify the 3D objects that you download for Dialux using any 3D modeling software that can import and export STL files. You can also use online tools or plugins that allow you to edit simple parameters of your 3D objects.</p>
-<li><b>Can I share my Dialux project with others who don't have the same 3D objects as me?</b></li>
-<p>Yes, you can share your Dialux project with others who don't have the same 3D objects as you. However, they will not be able to see or edit your 3D objects unless they download them from the same sources as you. Therefore, it is recommended to include a list of links or sources of your 3D objects when sharing your project.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/README.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/README.md
deleted file mode 100644
index f13b15ba18e57c455f67142a06da5deaf9d058dc..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
-# Installation
-> `npm install --save @types/node`
-
-# Summary
-This package contains type definitions for Node.js (https://nodejs.org/).
-
-# Details
-Files were exported from https://github.com/DefinitelyTyped/DefinitelyTyped/tree/master/types/node.
-
-### Additional Details
- * Last updated: Thu, 09 Mar 2023 20:02:36 GMT
- * Dependencies: none
- * Global values: `AbortController`, `AbortSignal`, `__dirname`, `__filename`, `console`, `exports`, `gc`, `global`, `module`, `process`, `require`, `structuredClone`
-
-# Credits
-These definitions were written by [Microsoft TypeScript](https://github.com/Microsoft), [DefinitelyTyped](https://github.com/DefinitelyTyped), [Alberto Schiabel](https://github.com/jkomyno), [Alvis HT Tang](https://github.com/alvis), [Andrew Makarov](https://github.com/r3nya), [Benjamin Toueg](https://github.com/btoueg), [Chigozirim C.](https://github.com/smac89), [David Junger](https://github.com/touffy), [Deividas Bakanas](https://github.com/DeividasBakanas), [Eugene Y. Q. Shen](https://github.com/eyqs), [Hannes Magnusson](https://github.com/Hannes-Magnusson-CK), [Huw](https://github.com/hoo29), [Kelvin Jin](https://github.com/kjin), [Klaus Meinhardt](https://github.com/ajafff), [Lishude](https://github.com/islishude), [Mariusz Wiktorczyk](https://github.com/mwiktorczyk), [Mohsen Azimi](https://github.com/mohsen1), [Nicolas Even](https://github.com/n-e), [Nikita Galkin](https://github.com/galkin), [Parambir Singh](https://github.com/parambirs), [Sebastian Silbermann](https://github.com/eps1lon), [Simon Schick](https://github.com/SimonSchick), [Thomas den Hollander](https://github.com/ThomasdenH), [Wilco Bakker](https://github.com/WilcoBakker), [wwwy3y3](https://github.com/wwwy3y3), [Samuel Ainsworth](https://github.com/samuela), [Kyle Uehlein](https://github.com/kuehlein), [Thanik Bhongbhibhat](https://github.com/bhongy), [Marcin Kopacz](https://github.com/chyzwar), [Trivikram Kamat](https://github.com/trivikr), [Junxiao Shi](https://github.com/yoursunny), [Ilia Baryshnikov](https://github.com/qwelias), [ExE Boss](https://github.com/ExE-Boss), [Piotr Błażejewicz](https://github.com/peterblazejewicz), [Anna Henningsen](https://github.com/addaleax), [Victor Perin](https://github.com/victorperin), [Yongsheng Zhang](https://github.com/ZYSzys), [NodeJS Contributors](https://github.com/NodeJS), [Linus Unnebäck](https://github.com/LinusU), [wafuwafu13](https://github.com/wafuwafu13), [Matteo Collina](https://github.com/mcollina), and [Dmitry Semigradsky](https://github.com/Semigradsky).
diff --git a/spaces/fffiloni/lama-video-watermark-remover/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py b/spaces/fffiloni/lama-video-watermark-remover/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py
deleted file mode 100644
index 45bb3c8cfd36d8f668e6fde756b17587eab72082..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/lama-video-watermark-remover/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : test_sync_batchnorm.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-
-import unittest
-
-import torch
-import torch.nn as nn
-from torch.autograd import Variable
-
-from sync_batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, DataParallelWithCallback
-from sync_batchnorm.unittest import TorchTestCase
-
-
-def handy_var(a, unbias=True):
-    n = a.size(0)
-    asum = a.sum(dim=0)
-    as_sum = (a ** 2).sum(dim=0)  # a square sum
-    sumvar = as_sum - asum * asum / n
-    if unbias:
-        return sumvar / (n - 1)
-    else:
-        return sumvar / n
-
-
-def _find_bn(module):
-    for m in module.modules():
-        if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, SynchronizedBatchNorm1d, SynchronizedBatchNorm2d)):
-            return m
-
-
-class SyncTestCase(TorchTestCase):
-    def _syncParameters(self, bn1, bn2):
-        bn1.reset_parameters()
-        bn2.reset_parameters()
-        if bn1.affine and bn2.affine:
-            bn2.weight.data.copy_(bn1.weight.data)
-            bn2.bias.data.copy_(bn1.bias.data)
-
-    def _checkBatchNormResult(self, bn1, bn2, input, is_train, cuda=False):
-        """Check the forward and backward for the customized batch normalization."""
-        bn1.train(mode=is_train)
-        bn2.train(mode=is_train)
-
-        if cuda:
-            input = input.cuda()
-
-        self._syncParameters(_find_bn(bn1), _find_bn(bn2))
-
-        input1 = Variable(input, requires_grad=True)
-        output1 = bn1(input1)
-        output1.sum().backward()
-        input2 = Variable(input, requires_grad=True)
-        output2 = bn2(input2)
-        output2.sum().backward()
-
-        self.assertTensorClose(input1.data, input2.data)
-        self.assertTensorClose(output1.data, output2.data)
-        self.assertTensorClose(input1.grad, input2.grad)
-        self.assertTensorClose(_find_bn(bn1).running_mean, _find_bn(bn2).running_mean)
-        self.assertTensorClose(_find_bn(bn1).running_var, _find_bn(bn2).running_var)
-
-    def testSyncBatchNormNormalTrain(self):
-        bn = nn.BatchNorm1d(10)
-        sync_bn = SynchronizedBatchNorm1d(10)
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True)
-
-    def testSyncBatchNormNormalEval(self):
-        bn = nn.BatchNorm1d(10)
-        sync_bn = SynchronizedBatchNorm1d(10)
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False)
-
-    def testSyncBatchNormSyncTrain(self):
-        bn = nn.BatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-
-        bn.cuda()
-        sync_bn.cuda()
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True, cuda=True)
-
-    def testSyncBatchNormSyncEval(self):
-        bn = nn.BatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-
-        bn.cuda()
-        sync_bn.cuda()
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False, cuda=True)
-
-    def testSyncBatchNorm2DSyncTrain(self):
-        bn = nn.BatchNorm2d(10)
-        sync_bn = SynchronizedBatchNorm2d(10)
-        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-
-        bn.cuda()
-        sync_bn.cuda()
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10, 16, 16), True, cuda=True)
-
-
-if __name__ == '__main__':
-    unittest.main()
diff --git a/spaces/fffiloni/lama-video-watermark-remover/models/ade20k/segm_lib/utils/__init__.py b/spaces/fffiloni/lama-video-watermark-remover/models/ade20k/segm_lib/utils/__init__.py
deleted file mode 100644
index abe3cbe49477fe37d4fc16249de8a10f4fb4a013..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/lama-video-watermark-remover/models/ade20k/segm_lib/utils/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .th import *
diff --git a/spaces/fffiloni/lama-video-watermark-remover/models/ade20k/segm_lib/utils/th.py b/spaces/fffiloni/lama-video-watermark-remover/models/ade20k/segm_lib/utils/th.py
deleted file mode 100644
index ca6ef9385e3b5c0a439579d3fd7aa73b5dc62758..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/lama-video-watermark-remover/models/ade20k/segm_lib/utils/th.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import torch
-from torch.autograd import Variable
-import numpy as np
-import collections
-
-__all__ = ['as_variable', 'as_numpy', 'mark_volatile']
-
-def as_variable(obj):
-    if isinstance(obj, Variable):
-        return obj
-    if isinstance(obj, collections.Sequence):
-        return [as_variable(v) for v in obj]
-    elif isinstance(obj, collections.Mapping):
-        return {k: as_variable(v) for k, v in obj.items()}
-    else:
-        return Variable(obj)
-
-def as_numpy(obj):
-    if isinstance(obj, collections.Sequence):
-        return [as_numpy(v) for v in obj]
-    elif isinstance(obj, collections.Mapping):
-        return {k: as_numpy(v) for k, v in obj.items()}
-    elif isinstance(obj, Variable):
-        return obj.data.cpu().numpy()
-    elif torch.is_tensor(obj):
-        return obj.cpu().numpy()
-    else:
-        return np.array(obj)
-
-def mark_volatile(obj):
-    if torch.is_tensor(obj):
-        obj = Variable(obj)
-    if isinstance(obj, Variable):
-        obj.no_grad = True
-        return obj
-    elif isinstance(obj, collections.Mapping):
-        return {k: mark_volatile(o) for k, o in obj.items()}
-    elif isinstance(obj, collections.Sequence):
-        return [mark_volatile(o) for o in obj]
-    else:
-        return obj
diff --git a/spaces/fffiloni/spectrogram-to-music/share_btn.py b/spaces/fffiloni/spectrogram-to-music/share_btn.py
deleted file mode 100644
index 9e0cf262c423c5a953560794be3590eb3e051374..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/spectrogram-to-music/share_btn.py
+++ /dev/null
@@ -1,101 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-	async function getInputImgFile(imgEl){
-        const res = await fetch(imgEl.src);
-        const blob = await res.blob();
-        const imgId = Date.now() % 200;
-        const isPng = imgEl.src.startsWith(`data:image/png`);
-        if(isPng){
-            const fileName = `sd-perception-${{imgId}}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        }else{
-            const fileName = `sd-perception-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        }
-	}
-    async function getOutputMusicFile(audioEL){
-        const res = await fetch(audioEL.src);
-        const blob = await res.blob();
-        const audioId = Date.now() % 200;
-        const fileName = `spectro-music-${{audioId}}.wav`;
-        const musicBlob = new File([blob], fileName, { type: 'audio/wav' });
-        console.log(musicBlob);
-        return musicBlob;
-	}
-    
-    async function audioToBase64(audioFile) {
-        return new Promise((resolve, reject) => {
-            let reader = new FileReader();
-            reader.readAsDataURL(audioFile);
-            reader.onload = () => resolve(reader.result);
-            reader.onerror = error => reject(error);
-          
-        });
-      }
-    const gradioEl = document.querySelector('body > gradio-app');
-    // const gradioEl = document.querySelector("gradio-app").shadowRoot;
-    const inputPromptEl = gradioEl.querySelector('#prompt-in textarea').value;
-    const outputImgEl = gradioEl.querySelector('#img-out img');
-    const outputMusic = gradioEl.querySelector('#music-out audio');
-    const outputMusic_src = gradioEl.querySelector('#music-out audio').src;
-    
-    let titleTxt = inputPromptEl;
-    //if(titleTxt.length > 100){
-    //    titleTxt = titleTxt.slice(0, 100) + ' ...';
-    //}
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!outputMusic){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const outputImg = await getInputImgFile(outputImgEl);
-    const urlOutputImg = await uploadFile(outputImg);
-    const musicFile = await getOutputMusicFile(outputMusic);
-	const dataOutputMusic = await uploadFile(musicFile);
-    
-    const descriptionMd = `#### Spectrogram Image:
-<img src='${urlOutputImg}' style='max-height: 350px;'>
-
-#### Spectrogram Sound:
-
-<audio controls>
-  <source src="${dataOutputMusic}" type="audio/wav">
-Your browser does not support the audio element.
-</audio>
-`;
-    const params = new URLSearchParams({
-        title: titleTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/fffiloni/spectrogram-to-music/discussions/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/fhipol/deeplearning/README.md b/spaces/fhipol/deeplearning/README.md
deleted file mode 100644
index 2f4c1f1f47748c7e1de34ec140e87ee75ad2ee7a..0000000000000000000000000000000000000000
--- a/spaces/fhipol/deeplearning/README.md
+++ /dev/null
@@ -1,32 +0,0 @@
----
-title: Deeplearning
-emoji: 👀
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-owner: Fernando Jesus Garcia Hipola
----
-
-Note: if training with CUDA, add +cu116 to the pytorch packages in requirements.txt
-
-This repository implements the DeepLearning Project explained in https://docs.google.com/document/d/1uMPmAMymwjkmgRkB2MRZ2zPo3pCkN9A27Jm5yHuy2wQ/edit?usp=sharing
-and deployed in https://huggingface.co/spaces/fhipol/deeplearning
-
-The main files are: 
-
-+ **detector_model.py** implements the *ModelExecutor* class used to train the models
-+ **reporter.py** is used to report the loss plots and save the data from the training on detector_model.py
-+ **detector.py** implements the interface to filter images with the trained model
-+ **app.py** defines the user interface with Gradio 
-
-The main directories are: 
-+ **humans** and logos are the training sets with two classes, 1 (presence of the feature) and 0 (no present)
-+ **history** data is the directory where reporter.py saves the data and the loss plots
-+ **trained_models** where the last model trained state is saved for later use
-
-For training the model run detector_model.py, but that will override the last saved state on trained_models!
-Most of model configuration can also be changed there
\ No newline at end of file
diff --git a/spaces/flatindo/Image-Diffusion-WebUI/diffusion_webui/diffusion_models/text2img_app.py b/spaces/flatindo/Image-Diffusion-WebUI/diffusion_webui/diffusion_models/text2img_app.py
deleted file mode 100644
index 98f54e64949c7ed64a5007a6035b17cbd7aee20b..0000000000000000000000000000000000000000
--- a/spaces/flatindo/Image-Diffusion-WebUI/diffusion_webui/diffusion_models/text2img_app.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import gradio as gr
-import torch
-from diffusers import StableDiffusionPipeline,DiffusionPipeline
-
-from diffusion_webui.utils.model_list import stable_model_list
-from diffusion_webui.utils.scheduler_list import (
-    SCHEDULER_MAPPING,
-    get_scheduler,
-)
-
-
-class StableDiffusionText2ImageGenerator:
-    def __init__(self):
-        self.pipe = None
-
-    def load_model(
-        self,
-        stable_model_path,
-        scheduler,
-    ):
-        if self.pipe is None or self.pipe.model_name != stable_model_path or self.pipe.scheduler_name != scheduler:
-            if stable_model_path == "stabilityai/stable-diffusion-xl-base-0.9":
-                self.pipe = DiffusionPipeline.from_pretrained(
-                    stable_model_path, safety_checker=None, torch_dtype=torch.float16
-                )
-            else:
-                self.pipe = StableDiffusionPipeline.from_pretrained(
-                    stable_model_path, safety_checker=None, torch_dtype=torch.float16
-                )
-
-        self.pipe = get_scheduler(pipe=self.pipe, scheduler=scheduler)
-        self.pipe.to("cuda")
-        self.pipe.enable_xformers_memory_efficient_attention()
-        self.pipe.model_name = stable_model_path
-        self.pipe.scheduler_name = scheduler
-
-        return self.pipe
-
-    def generate_image(
-        self,
-        stable_model_path: str,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        scheduler: str,
-        guidance_scale: int,
-        num_inference_step: int,
-        height: int,
-        width: int,
-        seed_generator=0,
-    ):
-        pipe = self.load_model(
-            stable_model_path=stable_model_path,
-            scheduler=scheduler,
-        )
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-
-        images = pipe(
-            prompt=prompt,
-            height=height,
-            width=width,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            generator=generator,
-        ).images
-
-        return images
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    text2image_prompt = gr.Textbox(
-                        lines=1,
-                        placeholder="Prompt",
-                        show_label=False,
-                    )
-
-                    text2image_negative_prompt = gr.Textbox(
-                        lines=1,
-                        placeholder="Negative Prompt",
-                        show_label=False,
-                    )
-                    with gr.Row():
-                        with gr.Column():
-                            text2image_model_path = gr.Dropdown(
-                                choices=stable_model_list,
-                                value=stable_model_list[0],
-                                label="Text-Image Model Id",
-                            )
-
-                            text2image_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-
-                            text2image_num_inference_step = gr.Slider(
-                                minimum=1,
-                                maximum=100,
-                                step=1,
-                                value=50,
-                                label="Num Inference Step",
-                            )
-                            text2image_num_images_per_prompt = gr.Slider(
-                                minimum=1,
-                                maximum=4,
-                                step=1,
-                                value=1,
-                                label="Number Of Images",
-                            )
-                        with gr.Row():
-                            with gr.Column():
-                                text2image_scheduler = gr.Dropdown(
-                                    choices=list(SCHEDULER_MAPPING.keys()),
-                                    value=list(SCHEDULER_MAPPING.keys())[0],
-                                    label="Scheduler",
-                            )
-
-                                text2image_height = gr.Slider(
-                                    minimum=128,
-                                    maximum=1280,
-                                    step=32,
-                                    value=512,
-                                    label="Image Height",
-                                )
-
-                                text2image_width = gr.Slider(
-                                    minimum=128,
-                                    maximum=1280,
-                                    step=32,
-                                    value=512,
-                                    label="Image Width",
-                                )
-                                text2image_seed_generator = gr.Slider(
-                                    label="Seed(0 for random)",
-                                    minimum=0,
-                                    maximum=1000000,
-                                    value=0,
-                                )
-                    text2image_predict = gr.Button(value="Generator")
-
-                with gr.Column():
-                    output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2), height=200)
-
-            text2image_predict.click(
-                fn=StableDiffusionText2ImageGenerator().generate_image,
-                inputs=[
-                    text2image_model_path,
-                    text2image_prompt,
-                    text2image_negative_prompt,
-                    text2image_num_images_per_prompt,
-                    text2image_scheduler,
-                    text2image_guidance_scale,
-                    text2image_num_inference_step,
-                    text2image_height,
-                    text2image_width,
-                    text2image_seed_generator,
-                ],
-                outputs=output_image,
-            )
diff --git a/spaces/flatindo/generate2/diffusion_webui/diffusion_models/controlnet_inpaint_pipeline.py b/spaces/flatindo/generate2/diffusion_webui/diffusion_models/controlnet_inpaint_pipeline.py
deleted file mode 100644
index e2861adbbb5410c78eb30376589da7a8c3c5ddf4..0000000000000000000000000000000000000000
--- a/spaces/flatindo/generate2/diffusion_webui/diffusion_models/controlnet_inpaint_pipeline.py
+++ /dev/null
@@ -1,258 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-from diffusers import ControlNetModel, StableDiffusionControlNetInpaintPipeline
-from PIL import Image
-
-from diffusion_webui.diffusion_models.base_controlnet_pipeline import (
-    ControlnetPipeline,
-)
-from diffusion_webui.utils.model_list import (
-    controlnet_model_list,
-    stable_model_list,
-)
-from diffusion_webui.utils.preprocces_utils import PREPROCCES_DICT
-from diffusion_webui.utils.scheduler_list import (
-    SCHEDULER_MAPPING,
-    get_scheduler,
-)
-
-
-class StableDiffusionControlNetInpaintGenerator(ControlnetPipeline):
-    def __init__(self):
-        super().__init__()
-
-    def load_model(self, stable_model_path, controlnet_model_path, scheduler):
-        if self.pipe is None or self.pipe.model_name != stable_model_path or self.pipe.scheduler_name != scheduler:
-            controlnet = ControlNetModel.from_pretrained(
-                controlnet_model_path, torch_dtype=torch.float16
-            )
-            self.pipe = (
-                StableDiffusionControlNetInpaintPipeline.from_pretrained(
-                    pretrained_model_name_or_path=stable_model_path,
-                    controlnet=controlnet,
-                    safety_checker=None,
-                    torch_dtype=torch.float16,
-                )
-            )
-            
-            self.pipe.model_name = stable_model_path
-            self.pipe.scheduler_name = scheduler
-            self.pipe = get_scheduler(pipe=self.pipe, scheduler=scheduler)
-            self.pipe.to("cuda")
-            self.pipe.enable_xformers_memory_efficient_attention()
-
-        return self.pipe
-
-    def load_image(self, image):
-        image = np.array(image)
-        image = Image.fromarray(image)
-        return image
-
-    def controlnet_preprocces(
-        self,
-        read_image: str,
-        preprocces_type: str,
-    ):
-        processed_image = PREPROCCES_DICT[preprocces_type](read_image)
-        return processed_image
-
-    def generate_image(
-        self,
-        image_path: str,
-        stable_model_path: str,
-        controlnet_model_path: str,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        height: int,
-        width: int,
-        strength: int,
-        guess_mode: bool,
-        guidance_scale: int,
-        num_inference_step: int,
-        controlnet_conditioning_scale: int,
-        scheduler: str,
-        seed_generator: int,
-        preprocces_type: str,
-    ):
-        normal_image = image_path["image"].convert("RGB").resize((512, 512))
-        mask_image = image_path["mask"].convert("RGB").resize((512, 512))
-
-        normal_image = self.load_image(image=normal_image)
-        mask_image = self.load_image(image=mask_image)
-
-        control_image = self.controlnet_preprocces(
-            read_image=normal_image, preprocces_type=preprocces_type
-        )
-        pipe = self.load_model(
-            stable_model_path=stable_model_path,
-            controlnet_model_path=controlnet_model_path,
-            scheduler=scheduler,
-        )
-
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-
-        output = pipe(
-            prompt=prompt,
-            image=normal_image,
-            height=height,
-            width=width,
-            mask_image=mask_image,
-            strength=strength,
-            guess_mode=guess_mode,
-            control_image=control_image,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            controlnet_conditioning_scale=float(controlnet_conditioning_scale),
-            generator=generator,
-        ).images
-
-        return output
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    controlnet_inpaint_image_path = gr.Image(
-                        source="upload",
-                        tool="sketch",
-                        elem_id="image_upload",
-                        type="pil",
-                        label="Upload",
-                    ).style(height=260)
-
-                    controlnet_inpaint_prompt = gr.Textbox(
-                        lines=1, placeholder="Prompt", show_label=False
-                    )
-                    controlnet_inpaint_negative_prompt = gr.Textbox(
-                        lines=1, placeholder="Negative Prompt", show_label=False
-                    )
-
-                    with gr.Row():
-                        with gr.Column():
-                            controlnet_inpaint_stable_model_path = gr.Dropdown(
-                                choices=stable_model_list,
-                                value=stable_model_list[0],
-                                label="Stable Model Path",
-                            )
-                            controlnet_inpaint_preprocces_type = gr.Dropdown(
-                                choices=list(PREPROCCES_DICT.keys()),
-                                value=list(PREPROCCES_DICT.keys())[0],
-                                label="Preprocess Type",
-                            )
-                            controlnet_inpaint_conditioning_scale = gr.Slider(
-                                minimum=0.0,
-                                maximum=1.0,
-                                step=0.1,
-                                value=1.0,
-                                label="ControlNet Conditioning Scale",
-                            )
-                            controlnet_inpaint_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-                            controlnet_inpaint_height = gr.Slider(
-                                minimum=128,
-                                maximum=1280,
-                                step=32,
-                                value=512,
-                                label="Height",
-                            )
-                            controlnet_inpaint_width = gr.Slider(
-                                minimum=128,
-                                maximum=1280,
-                                step=32,
-                                value=512,
-                                label="Width",
-                            )
-                            controlnet_inpaint_guess_mode = gr.Checkbox(
-                                label="Guess Mode"
-                            )
-
-                        with gr.Column():
-                            controlnet_inpaint_model_path = gr.Dropdown(
-                                choices=controlnet_model_list,
-                                value=controlnet_model_list[0],
-                                label="ControlNet Model Path",
-                            )
-                            controlnet_inpaint_scheduler = gr.Dropdown(
-                                choices=list(SCHEDULER_MAPPING.keys()),
-                                value=list(SCHEDULER_MAPPING.keys())[0],
-                                label="Scheduler",
-                            )
-                            controlnet_inpaint_strength = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Strength",
-                            )
-                            controlnet_inpaint_num_inference_step = gr.Slider(
-                                minimum=1,
-                                maximum=150,
-                                step=1,
-                                value=30,
-                                label="Num Inference Step",
-                            )
-                            controlnet_inpaint_num_images_per_prompt = (
-                                gr.Slider(
-                                    minimum=1,
-                                    maximum=4,
-                                    step=1,
-                                    value=1,
-                                    label="Number Of Images",
-                                )
-                            )
-                            controlnet_inpaint_seed_generator = gr.Slider(
-                                minimum=0,
-                                maximum=1000000,
-                                step=1,
-                                value=0,
-                                label="Seed(0 for random)",
-                            )
-
-                    # Button to generate the image
-                    controlnet_inpaint_predict_button = gr.Button(
-                        value="Generate Image"
-                    )
-
-                with gr.Column():
-                    # Gallery to display the generated images
-                    controlnet_inpaint_output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2))
-
-        controlnet_inpaint_predict_button.click(
-            fn=StableDiffusionControlNetInpaintGenerator().generate_image,
-            inputs=[
-                controlnet_inpaint_image_path,
-                controlnet_inpaint_stable_model_path,
-                controlnet_inpaint_model_path,
-                controlnet_inpaint_prompt,
-                controlnet_inpaint_negative_prompt,
-                controlnet_inpaint_num_images_per_prompt,
-                controlnet_inpaint_height,
-                controlnet_inpaint_width,
-                controlnet_inpaint_strength,
-                controlnet_inpaint_guess_mode,
-                controlnet_inpaint_guidance_scale,
-                controlnet_inpaint_num_inference_step,
-                controlnet_inpaint_conditioning_scale,
-                controlnet_inpaint_scheduler,
-                controlnet_inpaint_seed_generator,
-                controlnet_inpaint_preprocces_type,
-            ],
-            outputs=[controlnet_inpaint_output_image],
-        )
diff --git a/spaces/fun-research/FC-CLIP/fcclip/modeling/backbone/clip.py b/spaces/fun-research/FC-CLIP/fcclip/modeling/backbone/clip.py
deleted file mode 100644
index bdb88c008d28c08b4d0d89b0ea66434102d9dab6..0000000000000000000000000000000000000000
--- a/spaces/fun-research/FC-CLIP/fcclip/modeling/backbone/clip.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# https://github.com/mlfoundations/open_clip
-
-import torch
-import torch.nn.functional as F
-import math
-from detectron2.utils import comm
-
-import open_clip
-
-from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec
-
-@BACKBONE_REGISTRY.register()
-class CLIP(Backbone):
-    def __init__(self, cfg, input_shape):
-        super().__init__()
-        model_name = cfg.MODEL.FC_CLIP.CLIP_MODEL_NAME
-        pretrained= cfg.MODEL.FC_CLIP.CLIP_PRETRAINED_WEIGHTS
-        # download on local rank 0 first
-        if comm.get_local_rank() == 0:
-            open_clip.create_model_and_transforms(model_name, pretrained=pretrained)
-        comm.synchronize()
-
-        self.clip_model, _, _ = open_clip.create_model_and_transforms(model_name, pretrained=pretrained)
-        self.text_tokenizer = open_clip.get_tokenizer(model_name)
-
-        model_name = model_name.lower()
-        if 'convnext_' in model_name:
-            self.model_type = 'convnext'
-            if '_base' in model_name:
-                self.output_channels = [128, 128, 256, 512, 1024]
-            elif '_large' in model_name:
-                self.output_channels = [192, 192, 384, 768, 1536]
-            elif '_xxlarge' in model_name:
-                self.output_channels = [384, 384, 768, 1536, 3072]
-
-        self._out_feature_strides = {
-            "stem": 2,
-            "res2": 4,
-            "res3": 8,
-            "res4": 16,
-            "res5": 32,
-            "clip_embedding": -1
-        }
-        self._out_feature_channels = {
-            "stem": self.output_channels[0],
-            "res2": self.output_channels[1],
-            "res3": self.output_channels[2],
-            "res4": self.output_channels[3],
-            "res5": self.output_channels[4],
-            "clip_embedding": self.dim_latent
-        }
-
-        self.eval()
-        self.freeze_everything()
-
-    def freeze_everything(self):
-        for param in self.clip_model.parameters():
-            param.requires_grad = False
-
-    def encode_text(self, text, normalize: bool = False):
-        cast_dtype = self.clip_model.transformer.get_cast_dtype()
-
-        x = self.clip_model.token_embedding(text).to(cast_dtype)  # [batch_size, n_ctx, d_model]
-
-        x = x + self.clip_model.positional_embedding.to(cast_dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.clip_model.transformer(x, attn_mask=self.clip_model.attn_mask)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.clip_model.ln_final(x)  # [batch_size, n_ctx, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.clip_model.text_projection
-        return F.normalize(x, dim=-1) if normalize else x
-
-    def tokenize_text(self, text):
-        return self.text_tokenizer(text)
-
-    def extract_features(self, x):
-        return {
-            'convnext': self.extract_features_convnext,
-        }[self.model_type](x)
-    
-    def visual_prediction_forward(self, x):
-        return {
-            'convnext': self.visual_prediction_forward_convnext,
-        }[self.model_type](x)
-
-    def extract_features_convnext(self, x):
-        out = {}
-        x = self.clip_model.visual.trunk.stem(x)
-        out['stem'] = x.contiguous() # os4
-        for i in range(4):
-            x = self.clip_model.visual.trunk.stages[i](x)
-            out[f'res{i+2}'] = x.contiguous() # res 2 (os4), 3 (os8), 4 (os16), 5 (os32)
-        
-        x = self.clip_model.visual.trunk.norm_pre(x)
-        out['clip_vis_dense'] = x.contiguous()
-        return out
-    
-    def visual_prediction_forward_convnext(self, x,):
-        batch, num_query, channel = x.shape
-        x = x.reshape(batch*num_query, channel, 1, 1) # fake 2D input
-        x = self.clip_model.visual.trunk.head(x)
-        x = self.clip_model.visual.head(x)
-        return x.view(batch, num_query, x.shape[-1]) # B x num_queries x 640
-
-    def get_text_classifier(self, text_list, device):
-        self.eval()
-        with torch.no_grad():
-            # reference for templates: https://github.com/mlfoundations/open_clip/blob/91f6cce16b7bee90b3b5d38ca305b5b3b67cc200/src/training/imagenet_zeroshot_data.py
-            text_tokens = self.tokenize_text(text_list)
-            text_tokens = text_tokens.to(device)
-            # we return un-normalized text feature.
-            text_features = self.encode_text(text_tokens, normalize=False)
-            return text_features
-
-    def forward(self, x):
-        self.eval()
-        with torch.no_grad():
-            return self.extract_features(x)
-    
-    @property
-    def dim_latent(self):
-        return self.clip_model.text_projection.shape[-1]
-    
-    def output_shape(self):
-        return {
-            name: ShapeSpec(
-                channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
-            )
-            for name in ["stem", "res2", "res3", "res4", "res5", "clip_embedding"]
-        }
-
-    @property
-    def size_divisibility(self):
-        return -1
\ No newline at end of file
diff --git a/spaces/gebebieve/gen/README.md b/spaces/gebebieve/gen/README.md
deleted file mode 100644
index 32e5889a829e5ffc5c25a4a8d7d69843e38c08d3..0000000000000000000000000000000000000000
--- a/spaces/gebebieve/gen/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Gen
-emoji: 📊
-colorFrom: green
-colorTo: pink
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/resnet.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/resnet.py
deleted file mode 100644
index 0cfc532b12f8e8a6f9c9656ee6fa411e8d5c1294..0000000000000000000000000000000000000000
--- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/resnet.py
+++ /dev/null
@@ -1,238 +0,0 @@
-"""Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
-
-Attributes:
-
-    _out_channels (list of int): specify number of channels for each encoder feature tensor
-    _depth (int): specify number of stages in decoder (in other words number of downsampling operations)
-    _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
-
-Methods:
-
-    forward(self, x: torch.Tensor)
-        produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
-        shape NCHW (features should be sorted in descending order according to spatial resolution, starting
-        with resolution same as input `x` tensor).
-
-        Input: `x` with shape (1, 3, 64, 64)
-        Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
-                [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
-                (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
-
-        also should support number of features according to specified depth, e.g. if depth = 5,
-        number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
-        depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
-"""
-from copy import deepcopy
-
-import torch.nn as nn
-
-from torchvision.models.resnet import ResNet
-from torchvision.models.resnet import BasicBlock
-from torchvision.models.resnet import Bottleneck
-from pretrainedmodels.models.torchvision_models import pretrained_settings
-
-from ._base import EncoderMixin
-
-
-class ResNetEncoder(ResNet, EncoderMixin):
-    def __init__(self, out_channels, depth=5, **kwargs):
-        super().__init__(**kwargs)
-        self._depth = depth
-        self._out_channels = out_channels
-        self._in_channels = 3
-
-        del self.fc
-        del self.avgpool
-
-    def get_stages(self):
-        return [
-            nn.Identity(),
-            nn.Sequential(self.conv1, self.bn1, self.relu),
-            nn.Sequential(self.maxpool, self.layer1),
-            self.layer2,
-            self.layer3,
-            self.layer4,
-        ]
-
-    def forward(self, x):
-        stages = self.get_stages()
-
-        features = []
-        for i in range(self._depth + 1):
-            x = stages[i](x)
-            features.append(x)
-
-        return features
-
-    def load_state_dict(self, state_dict, **kwargs):
-        state_dict.pop("fc.bias", None)
-        state_dict.pop("fc.weight", None)
-        super().load_state_dict(state_dict, **kwargs)
-
-
-new_settings = {
-    "resnet18": {
-        "ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth",  # noqa
-        "swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth",  # noqa
-    },
-    "resnet50": {
-        "ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth",  # noqa
-        "swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth",  # noqa
-    },
-    "resnext50_32x4d": {
-        "imagenet": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
-        "ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext50_32x4-ddb3e555.pth",  # noqa
-        "swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth",  # noqa
-    },
-    "resnext101_32x4d": {
-        "ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x4-dc43570a.pth",  # noqa
-        "swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth",  # noqa
-    },
-    "resnext101_32x8d": {
-        "imagenet": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth",
-        "instagram": "https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth",
-        "ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x8-2cfe2f8b.pth",  # noqa
-        "swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth",  # noqa
-    },
-    "resnext101_32x16d": {
-        "instagram": "https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth",
-        "ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x16-15fffa57.pth",  # noqa
-        "swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth",  # noqa
-    },
-    "resnext101_32x32d": {
-        "instagram": "https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth",
-    },
-    "resnext101_32x48d": {
-        "instagram": "https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth",
-    },
-}
-
-pretrained_settings = deepcopy(pretrained_settings)
-for model_name, sources in new_settings.items():
-    if model_name not in pretrained_settings:
-        pretrained_settings[model_name] = {}
-
-    for source_name, source_url in sources.items():
-        pretrained_settings[model_name][source_name] = {
-            "url": source_url,
-            "input_size": [3, 224, 224],
-            "input_range": [0, 1],
-            "mean": [0.485, 0.456, 0.406],
-            "std": [0.229, 0.224, 0.225],
-            "num_classes": 1000,
-        }
-
-
-resnet_encoders = {
-    "resnet18": {
-        "encoder": ResNetEncoder,
-        "pretrained_settings": pretrained_settings["resnet18"],
-        "params": {
-            "out_channels": (3, 64, 64, 128, 256, 512),
-            "block": BasicBlock,
-            "layers": [2, 2, 2, 2],
-        },
-    },
-    "resnet34": {
-        "encoder": ResNetEncoder,
-        "pretrained_settings": pretrained_settings["resnet34"],
-        "params": {
-            "out_channels": (3, 64, 64, 128, 256, 512),
-            "block": BasicBlock,
-            "layers": [3, 4, 6, 3],
-        },
-    },
-    "resnet50": {
-        "encoder": ResNetEncoder,
-        "pretrained_settings": pretrained_settings["resnet50"],
-        "params": {
-            "out_channels": (3, 64, 256, 512, 1024, 2048),
-            "block": Bottleneck,
-            "layers": [3, 4, 6, 3],
-        },
-    },
-    "resnet101": {
-        "encoder": ResNetEncoder,
-        "pretrained_settings": pretrained_settings["resnet101"],
-        "params": {
-            "out_channels": (3, 64, 256, 512, 1024, 2048),
-            "block": Bottleneck,
-            "layers": [3, 4, 23, 3],
-        },
-    },
-    "resnet152": {
-        "encoder": ResNetEncoder,
-        "pretrained_settings": pretrained_settings["resnet152"],
-        "params": {
-            "out_channels": (3, 64, 256, 512, 1024, 2048),
-            "block": Bottleneck,
-            "layers": [3, 8, 36, 3],
-        },
-    },
-    "resnext50_32x4d": {
-        "encoder": ResNetEncoder,
-        "pretrained_settings": pretrained_settings["resnext50_32x4d"],
-        "params": {
-            "out_channels": (3, 64, 256, 512, 1024, 2048),
-            "block": Bottleneck,
-            "layers": [3, 4, 6, 3],
-            "groups": 32,
-            "width_per_group": 4,
-        },
-    },
-    "resnext101_32x4d": {
-        "encoder": ResNetEncoder,
-        "pretrained_settings": pretrained_settings["resnext101_32x4d"],
-        "params": {
-            "out_channels": (3, 64, 256, 512, 1024, 2048),
-            "block": Bottleneck,
-            "layers": [3, 4, 23, 3],
-            "groups": 32,
-            "width_per_group": 4,
-        },
-    },
-    "resnext101_32x8d": {
-        "encoder": ResNetEncoder,
-        "pretrained_settings": pretrained_settings["resnext101_32x8d"],
-        "params": {
-            "out_channels": (3, 64, 256, 512, 1024, 2048),
-            "block": Bottleneck,
-            "layers": [3, 4, 23, 3],
-            "groups": 32,
-            "width_per_group": 8,
-        },
-    },
-    "resnext101_32x16d": {
-        "encoder": ResNetEncoder,
-        "pretrained_settings": pretrained_settings["resnext101_32x16d"],
-        "params": {
-            "out_channels": (3, 64, 256, 512, 1024, 2048),
-            "block": Bottleneck,
-            "layers": [3, 4, 23, 3],
-            "groups": 32,
-            "width_per_group": 16,
-        },
-    },
-    "resnext101_32x32d": {
-        "encoder": ResNetEncoder,
-        "pretrained_settings": pretrained_settings["resnext101_32x32d"],
-        "params": {
-            "out_channels": (3, 64, 256, 512, 1024, 2048),
-            "block": Bottleneck,
-            "layers": [3, 4, 23, 3],
-            "groups": 32,
-            "width_per_group": 32,
-        },
-    },
-    "resnext101_32x48d": {
-        "encoder": ResNetEncoder,
-        "pretrained_settings": pretrained_settings["resnext101_32x48d"],
-        "params": {
-            "out_channels": (3, 64, 256, 512, 1024, 2048),
-            "block": Bottleneck,
-            "layers": [3, 4, 23, 3],
-            "groups": 32,
-            "width_per_group": 48,
-        },
-    },
-}
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Activist Twink Nate Glass. _BEST_.md b/spaces/gotiQspiryo/whisper-ui/examples/Activist Twink Nate Glass. _BEST_.md
deleted file mode 100644
index de8fb29648f7ee2c32a088ce5d1c8f5efa943daa..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Activist Twink Nate Glass. _BEST_.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Activist twink nate glass.</h2><br /><p><b><b>Download Zip</b> &#10004; <a href="https://urlgoal.com/2uyN9G">https://urlgoal.com/2uyN9G</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Kira Kira Black Gal Special Ren Hanaki.zip _TOP_.md b/spaces/gotiQspiryo/whisper-ui/examples/Kira Kira Black Gal Special Ren Hanaki.zip _TOP_.md
deleted file mode 100644
index 3df31b6954144535d904fe6ce94073ce5aa5ae42..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Kira Kira Black Gal Special Ren Hanaki.zip _TOP_.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>kira kira black gal special ren hanaki.zip</h2><br /><p><b><b>DOWNLOAD</b> &#9999; <a href="https://urlgoal.com/2uyM4l">https://urlgoal.com/2uyM4l</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gradio/HuBERT/fairseq/data/monolingual_dataset.py b/spaces/gradio/HuBERT/fairseq/data/monolingual_dataset.py
deleted file mode 100644
index 54fd583b64a3a475324ade6eaaeccf593d747fdc..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/data/monolingual_dataset.py
+++ /dev/null
@@ -1,253 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import numpy as np
-import torch
-
-from . import FairseqDataset, data_utils
-
-
-def collate(samples, pad_idx, eos_idx, fixed_pad_length=None, pad_to_bsz=None):
-    if len(samples) == 0:
-        return {}
-
-    def merge(key, is_list=False):
-        if is_list:
-            res = []
-            for i in range(len(samples[0][key])):
-                res.append(
-                    data_utils.collate_tokens(
-                        [s[key][i] for s in samples],
-                        pad_idx,
-                        eos_idx,
-                        left_pad=False,
-                        pad_to_length=fixed_pad_length,
-                        pad_to_bsz=pad_to_bsz,
-                    )
-                )
-            return res
-        else:
-            return data_utils.collate_tokens(
-                [s[key] for s in samples],
-                pad_idx,
-                eos_idx,
-                left_pad=False,
-                pad_to_length=fixed_pad_length,
-                pad_to_bsz=pad_to_bsz,
-            )
-
-    src_tokens = merge("source")
-    if samples[0]["target"] is not None:
-        is_target_list = isinstance(samples[0]["target"], list)
-        target = merge("target", is_target_list)
-    else:
-        target = src_tokens
-
-    return {
-        "id": torch.LongTensor([s["id"] for s in samples]),
-        "nsentences": len(samples),
-        "ntokens": sum(len(s["source"]) for s in samples),
-        "net_input": {
-            "src_tokens": src_tokens,
-            "src_lengths": torch.LongTensor([s["source"].numel() for s in samples]),
-        },
-        "target": target,
-    }
-
-
-class MonolingualDataset(FairseqDataset):
-    """
-    A wrapper around torch.utils.data.Dataset for monolingual data.
-
-    Args:
-        dataset (torch.utils.data.Dataset): dataset to wrap
-        sizes (List[int]): sentence lengths
-        vocab (~fairseq.data.Dictionary): vocabulary
-        shuffle (bool, optional): shuffle the elements before batching
-            (default: True).
-    """
-
-    def __init__(
-        self,
-        dataset,
-        sizes,
-        src_vocab,
-        tgt_vocab=None,
-        add_eos_for_other_targets=False,
-        shuffle=False,
-        targets=None,
-        add_bos_token=False,
-        fixed_pad_length=None,
-        pad_to_bsz=None,
-        src_lang_idx=None,
-        tgt_lang_idx=None,
-    ):
-        self.dataset = dataset
-        self.sizes = np.array(sizes)
-        self.vocab = src_vocab
-        self.tgt_vocab = tgt_vocab or src_vocab
-        self.add_eos_for_other_targets = add_eos_for_other_targets
-        self.shuffle = shuffle
-        self.add_bos_token = add_bos_token
-        self.fixed_pad_length = fixed_pad_length
-        self.pad_to_bsz = pad_to_bsz
-        self.src_lang_idx = src_lang_idx
-        self.tgt_lang_idx = tgt_lang_idx
-
-        assert targets is None or all(
-            t in {"self", "future", "past"} for t in targets
-        ), "targets must be none or one of 'self', 'future', 'past'"
-        if targets is not None and len(targets) == 0:
-            targets = None
-        self.targets = targets
-
-    def __getitem__(self, index):
-        if self.targets is not None:
-            # *future_target* is the original sentence
-            # *source* is shifted right by 1 (maybe left-padded with eos)
-            # *past_target* is shifted right by 2 (left-padded as needed)
-            #
-            # Left-to-right language models should condition on *source* and
-            # predict *future_target*.
-            # Right-to-left language models should condition on *source* and
-            # predict *past_target*.
-            source, future_target, past_target = self.dataset[index]
-            source, target = self._make_source_target(
-                source, future_target, past_target
-            )
-        else:
-            source = self.dataset[index]
-            target = None
-        source, target = self._maybe_add_bos(source, target)
-        return {"id": index, "source": source, "target": target}
-
-    def __len__(self):
-        return len(self.dataset)
-
-    def _make_source_target(self, source, future_target, past_target):
-        if self.targets is not None:
-            target = []
-
-            if (
-                self.add_eos_for_other_targets
-                and (("self" in self.targets) or ("past" in self.targets))
-                and source[-1] != self.vocab.eos()
-            ):
-                # append eos at the end of source
-                source = torch.cat([source, source.new([self.vocab.eos()])])
-
-                if "future" in self.targets:
-                    future_target = torch.cat(
-                        [future_target, future_target.new([self.vocab.pad()])]
-                    )
-                if "past" in self.targets:
-                    # first token is before the start of sentence which is only used in "none" break mode when
-                    # add_eos_for_other_targets is False
-                    past_target = torch.cat(
-                        [
-                            past_target.new([self.vocab.pad()]),
-                            past_target[1:],
-                            source[-2, None],
-                        ]
-                    )
-
-            for t in self.targets:
-                if t == "self":
-                    target.append(source)
-                elif t == "future":
-                    target.append(future_target)
-                elif t == "past":
-                    target.append(past_target)
-                else:
-                    raise Exception("invalid target " + t)
-
-            if len(target) == 1:
-                target = target[0]
-        else:
-            target = future_target
-
-        return source, self._filter_vocab(target)
-
-    def _maybe_add_bos(self, source, target):
-        if self.add_bos_token:
-            source = torch.cat([source.new([self.vocab.bos()]), source])
-            if target is not None:
-                target = torch.cat([target.new([self.tgt_vocab.bos()]), target])
-        return source, target
-
-    def num_tokens_vec(self, indices):
-        """Return the number of tokens for a set of positions defined by indices.
-        This value is used to enforce ``--max-tokens`` during batching."""
-        return self.sizes[indices]
-
-    def _filter_vocab(self, target):
-        if len(self.tgt_vocab) != len(self.vocab):
-
-            def _filter(target):
-                mask = target.ge(len(self.tgt_vocab))
-                if mask.any():
-                    target[mask] = self.tgt_vocab.unk()
-                return target
-
-            if isinstance(target, list):
-                return [_filter(t) for t in target]
-            return _filter(target)
-        return target
-
-    def collater(self, samples):
-        """Merge a list of samples to form a mini-batch.
-
-        Args:
-            samples (List[dict]): samples to collate
-
-        Returns:
-            dict: a mini-batch with the following keys:
-
-                - `id` (LongTensor): example IDs in the original input order
-                - `ntokens` (int): total number of tokens in the batch
-                - `net_input` (dict): the input to the Model, containing keys:
-
-                  - `src_tokens` (LongTensor): a padded 2D Tensor of tokens in
-                    the source sentence of shape `(bsz, src_len)`. Padding will
-                    appear on the right.
-
-                - `target` (LongTensor): a padded 2D Tensor of tokens in the
-                  target sentence of shape `(bsz, tgt_len)`. Padding will appear
-                  on the right.
-        """
-        return collate(
-            samples,
-            self.vocab.pad(),
-            self.vocab.eos(),
-            self.fixed_pad_length,
-            self.pad_to_bsz,
-        )
-
-    def num_tokens(self, index):
-        """Return the number of tokens in a sample. This value is used to
-        enforce ``--max-tokens`` during batching."""
-        return self.sizes[index]
-
-    def size(self, index):
-        """Return an example's size as a float or tuple. This value is used when
-        filtering a dataset with ``--max-positions``."""
-        return self.sizes[index]
-
-    def ordered_indices(self):
-        """Return an ordered list of indices. Batches will be constructed based
-        on this order."""
-        if self.shuffle:
-            order = [np.random.permutation(len(self))]
-        else:
-            order = [np.arange(len(self))]
-        order.append(self.sizes)
-        return np.lexsort(order)
-
-    @property
-    def supports_prefetch(self):
-        return getattr(self.dataset, "supports_prefetch", False)
-
-    def prefetch(self, indices):
-        self.dataset.prefetch(indices)
diff --git a/spaces/grass-eater/grassproxy/greeting.md b/spaces/grass-eater/grassproxy/greeting.md
deleted file mode 100644
index 086a11024b1062cb85bde6c60140ddfedbda8db2..0000000000000000000000000000000000000000
--- a/spaces/grass-eater/grassproxy/greeting.md
+++ /dev/null
@@ -1,6 +0,0 @@
-<span style="Font-size: 40px; color: #e8e8e8;">hint:</span>
-
-![species?](https://i.imgur.com/nXnhMc8.png)
-
-**this is just a test, the proxy could go down at any moment**
-<!-- the key is lowercase -->
\ No newline at end of file
diff --git a/spaces/greenlights/gitapp/request.py b/spaces/greenlights/gitapp/request.py
deleted file mode 100644
index 22cc9dc375478be41679430fe3ee9947ec4cdba0..0000000000000000000000000000000000000000
--- a/spaces/greenlights/gitapp/request.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import requests
-
-# URL
-url = 'http://localhost:5000/api/'
-
-# Change the value of experience that you want to test
-payload = {
-	'exp':1.8
-}
-
-r = requests.post(url,json=payload)
-
-print(r.json())
diff --git a/spaces/gwang-kim/DATID-3D/README.md b/spaces/gwang-kim/DATID-3D/README.md
deleted file mode 100644
index ae894b6915e1bdb4c435bec1a60a97be2d3efc71..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: DATID-3D
-emoji: 🛋
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.28.3
-app_file: datid3d_gradio_app.py
-pinned: false
-license: mit
----
diff --git a/spaces/gwang-kim/DATID-3D/eg3d/metrics/precision_recall.py b/spaces/gwang-kim/DATID-3D/eg3d/metrics/precision_recall.py
deleted file mode 100644
index e33e85f64de81fa211135edaf3863c2fe851a6f4..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/eg3d/metrics/precision_recall.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
-# property and proprietary rights in and to this material, related
-# documentation and any modifications thereto. Any use, reproduction,
-# disclosure or distribution of this material and related documentation
-# without an express license agreement from NVIDIA CORPORATION or
-# its affiliates is strictly prohibited.
-
-"""Precision/Recall (PR) from the paper "Improved Precision and Recall
-Metric for Assessing Generative Models". Matches the original implementation
-by Kynkaanniemi et al. at
-https://github.com/kynkaat/improved-precision-and-recall-metric/blob/master/precision_recall.py"""
-
-import torch
-from . import metric_utils
-
-#----------------------------------------------------------------------------
-
-def compute_distances(row_features, col_features, num_gpus, rank, col_batch_size):
-    assert 0 <= rank < num_gpus
-    num_cols = col_features.shape[0]
-    num_batches = ((num_cols - 1) // col_batch_size // num_gpus + 1) * num_gpus
-    col_batches = torch.nn.functional.pad(col_features, [0, 0, 0, -num_cols % num_batches]).chunk(num_batches)
-    dist_batches = []
-    for col_batch in col_batches[rank :: num_gpus]:
-        dist_batch = torch.cdist(row_features.unsqueeze(0), col_batch.unsqueeze(0))[0]
-        for src in range(num_gpus):
-            dist_broadcast = dist_batch.clone()
-            if num_gpus > 1:
-                torch.distributed.broadcast(dist_broadcast, src=src)
-            dist_batches.append(dist_broadcast.cpu() if rank == 0 else None)
-    return torch.cat(dist_batches, dim=1)[:, :num_cols] if rank == 0 else None
-
-#----------------------------------------------------------------------------
-
-def compute_pr(opts, max_real, num_gen, nhood_size, row_batch_size, col_batch_size):
-    detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/vgg16.pkl'
-    detector_kwargs = dict(return_features=True)
-
-    real_features = metric_utils.compute_feature_stats_for_dataset(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all_torch().to(torch.float16).to(opts.device)
-
-    gen_features = metric_utils.compute_feature_stats_for_generator(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all_torch().to(torch.float16).to(opts.device)
-
-    results = dict()
-    for name, manifold, probes in [('precision', real_features, gen_features), ('recall', gen_features, real_features)]:
-        kth = []
-        for manifold_batch in manifold.split(row_batch_size):
-            dist = compute_distances(row_features=manifold_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size)
-            kth.append(dist.to(torch.float32).kthvalue(nhood_size + 1).values.to(torch.float16) if opts.rank == 0 else None)
-        kth = torch.cat(kth) if opts.rank == 0 else None
-        pred = []
-        for probes_batch in probes.split(row_batch_size):
-            dist = compute_distances(row_features=probes_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size)
-            pred.append((dist <= kth).any(dim=1) if opts.rank == 0 else None)
-        results[name] = float(torch.cat(pred).to(torch.float32).mean() if opts.rank == 0 else 'nan')
-    return results['precision'], results['recall']
-
-#----------------------------------------------------------------------------
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/edit/edit_config.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/edit/edit_config.py
deleted file mode 100644
index 25fb4e500f5ce6ec6ec07631899b851492b08bb9..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/edit/edit_config.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-attr_dict = dict(
-    interface_gan={  # strength
-        # strength: negative for shorter, positive for longer
-        'upper_length': [-1],
-        'bottom_length': [1]
-    },
-    stylespace={  # layer, strength, threshold
-        # strength: negative for shorter, positive for longer
-        'upper_length': [5, -5, 0.0028],
-        'bottom_length': [3, 5, 0.003]
-    },
-    sefa={  # layer, strength
-        # -5 # strength: negative for longer, positive for shorter
-        'upper_length': [[4, 5, 6, 7], 5],
-        'bottom_length': [[4, 5, 6, 7], 5]
-    }
-)
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/custom_ops.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/custom_ops.py
deleted file mode 100644
index 6509d70bbcd49a24628e3e90258ccb0d8c5a3b39..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/custom_ops.py
+++ /dev/null
@@ -1,260 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import glob
-import torch
-import torch.utils.cpp_extension
-import importlib
-import hashlib
-import shutil
-from pathlib import Path
-import re
-import uuid
-
-from torch.utils.file_baton import FileBaton
-
-# ----------------------------------------------------------------------------
-# Global options.
-
-verbosity = 'brief'  # Verbosity level: 'none', 'brief', 'full'
-
-# ----------------------------------------------------------------------------
-# Internal helper funcs.
-
-
-def _find_compiler_bindir():
-    patterns = [
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin',
-    ]
-    for pattern in patterns:
-        matches = sorted(glob.glob(pattern))
-        if len(matches):
-            return matches[-1]
-    return None
-
-
-def _get_mangled_gpu_name():
-    name = torch.cuda.get_device_name().lower()
-    out = []
-    for c in name:
-        if re.match('[a-z0-9_-]+', c):
-            out.append(c)
-        else:
-            out.append('-')
-    return ''.join(out)
-
-
-# ----------------------------------------------------------------------------
-# Main entry point for compiling and loading C++/CUDA plugins.
-
-_cached_plugins = dict()
-
-
-def get_plugin(module_name, sources, **build_kwargs):
-    assert verbosity in ['none', 'brief', 'full']
-
-    # Already cached?
-    if module_name in _cached_plugins:
-        return _cached_plugins[module_name]
-
-    # Print status.
-    if verbosity == 'full':
-        print(f'Setting up PyTorch plugin "{module_name}"...')
-    elif verbosity == 'brief':
-        print(
-            f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
-
-    try:  # pylint: disable=too-many-nested-blocks
-        # Make sure we can find the necessary compiler binaries.
-        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
-            compiler_bindir = _find_compiler_bindir()
-            if compiler_bindir is None:
-                raise RuntimeError(
-                    f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
-            os.environ['PATH'] += ';' + compiler_bindir
-
-        # Compile and load.
-        verbose_build = (verbosity == 'full')
-
-        # Incremental build md5sum trickery.  Copies all the input source files
-        # into a cached build directory under a combined md5 digest of the input
-        # source files.  Copying is done only if the combined digest has changed.
-        # This keeps input file timestamps and filenames the same as in previous
-        # extension builds, allowing for fast incremental rebuilds.
-        #
-        # This optimization is done only in case all the source files reside in
-        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
-        # environment variable is set (we take this as a signal that the user
-        # actually cares about this.)
-        source_dirs_set = set(os.path.dirname(source) for source in sources)
-        if len(source_dirs_set) == 1 and ('TORCH_EXTENSIONS_DIR' in os.environ):
-            all_source_files = sorted(list(x for x in Path(
-                list(source_dirs_set)[0]).iterdir() if x.is_file()))
-
-            # Compute a combined hash digest for all source files in the same
-            # custom op directory (usually .cu, .cpp, .py and .h files).
-            hash_md5 = hashlib.md5()
-            for src in all_source_files:
-                with open(src, 'rb') as f:
-                    hash_md5.update(f.read())
-            build_dir = torch.utils.cpp_extension._get_build_directory(
-                module_name, verbose=verbose_build)  # pylint: disable=protected-access
-            digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest())
-
-            if not os.path.isdir(digest_build_dir):
-                os.makedirs(digest_build_dir, exist_ok=True)
-                baton = FileBaton(os.path.join(digest_build_dir, 'lock'))
-                if baton.try_acquire():
-                    try:
-                        for src in all_source_files:
-                            shutil.copyfile(src, os.path.join(
-                                digest_build_dir, os.path.basename(src)))
-                    finally:
-                        baton.release()
-                else:
-                    # Someone else is copying source files under the digest dir,
-                    # wait until done and continue.
-                    baton.wait()
-            digest_sources = [os.path.join(
-                digest_build_dir, os.path.basename(x)) for x in sources]
-            torch.utils.cpp_extension.load(name=module_name, build_directory=build_dir,
-                                           verbose=verbose_build, sources=digest_sources, **build_kwargs)
-        else:
-            torch.utils.cpp_extension.load(
-                name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
-        module = importlib.import_module(module_name)
-
-    except:
-        if verbosity == 'brief':
-            print('Failed!')
-        raise
-
-    # Print status and add to cache.
-    if verbosity == 'full':
-        print(f'Done setting up PyTorch plugin "{module_name}".')
-    elif verbosity == 'brief':
-        print('Done.')
-    _cached_plugins[module_name] = module
-    return module
-
-# ----------------------------------------------------------------------------
-
-
-def get_plugin_v3(module_name, sources, headers=None, source_dir=None, **build_kwargs):
-    assert verbosity in ['none', 'brief', 'full']
-    if headers is None:
-        headers = []
-    if source_dir is not None:
-        sources = [os.path.join(source_dir, fname) for fname in sources]
-        headers = [os.path.join(source_dir, fname) for fname in headers]
-
-    # Already cached?
-    if module_name in _cached_plugins:
-        return _cached_plugins[module_name]
-
-    # Print status.
-    if verbosity == 'full':
-        print(f'Setting up PyTorch plugin "{module_name}"...')
-    elif verbosity == 'brief':
-        print(
-            f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
-    verbose_build = (verbosity == 'full')
-
-    # Compile and load.
-    try:  # pylint: disable=too-many-nested-blocks
-        # Make sure we can find the necessary compiler binaries.
-        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
-            compiler_bindir = _find_compiler_bindir()
-            if compiler_bindir is None:
-                raise RuntimeError(
-                    f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
-            os.environ['PATH'] += ';' + compiler_bindir
-
-        # Some containers set TORCH_CUDA_ARCH_LIST to a list that can either
-        # break the build or unnecessarily restrict what's available to nvcc.
-        # Unset it to let nvcc decide based on what's available on the
-        # machine.
-        os.environ['TORCH_CUDA_ARCH_LIST'] = ''
-
-        # Incremental build md5sum trickery.  Copies all the input source files
-        # into a cached build directory under a combined md5 digest of the input
-        # source files.  Copying is done only if the combined digest has changed.
-        # This keeps input file timestamps and filenames the same as in previous
-        # extension builds, allowing for fast incremental rebuilds.
-        #
-        # This optimization is done only in case all the source files reside in
-        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
-        # environment variable is set (we take this as a signal that the user
-        # actually cares about this.)
-        #
-        # EDIT: We now do it regardless of TORCH_EXTENSIOS_DIR, in order to work
-        # around the *.cu dependency bug in ninja config.
-        #
-        all_source_files = sorted(sources + headers)
-        all_source_dirs = set(os.path.dirname(fname)
-                              for fname in all_source_files)
-        # and ('TORCH_EXTENSIONS_DIR' in os.environ):
-        if len(all_source_dirs) == 1:
-
-            # Compute combined hash digest for all source files.
-            hash_md5 = hashlib.md5()
-            for src in all_source_files:
-                with open(src, 'rb') as f:
-                    hash_md5.update(f.read())
-
-            # Select cached build directory name.
-            source_digest = hash_md5.hexdigest()
-            build_top_dir = torch.utils.cpp_extension._get_build_directory(
-                module_name, verbose=verbose_build)  # pylint: disable=protected-access
-            cached_build_dir = os.path.join(
-                build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}')
-
-            if not os.path.isdir(cached_build_dir):
-                tmpdir = f'{build_top_dir}/srctmp-{uuid.uuid4().hex}'
-                os.makedirs(tmpdir)
-                for src in all_source_files:
-                    shutil.copyfile(src, os.path.join(
-                        tmpdir, os.path.basename(src)))
-                try:
-                    os.replace(tmpdir, cached_build_dir)  # atomic
-                except OSError:
-                    # source directory already exists, delete tmpdir and its contents.
-                    shutil.rmtree(tmpdir)
-                    if not os.path.isdir(cached_build_dir):
-                        raise
-
-            # Compile.
-            cached_sources = [os.path.join(
-                cached_build_dir, os.path.basename(fname)) for fname in sources]
-            torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir,
-                                           verbose=verbose_build, sources=cached_sources, **build_kwargs)
-        else:
-            torch.utils.cpp_extension.load(
-                name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
-
-        # Load.
-        module = importlib.import_module(module_name)
-
-    except:
-        if verbosity == 'brief':
-            print('Failed!')
-        raise
-
-    # Print status and add to cache dict.
-    if verbosity == 'full':
-        print(f'Done setting up PyTorch plugin "{module_name}".')
-    elif verbosity == 'brief':
-        print('Done.')
-    _cached_plugins[module_name] = module
-    return module
diff --git a/spaces/h2oai/wave-tour/examples/plot_area_line_groups.py b/spaces/h2oai/wave-tour/examples/plot_area_line_groups.py
deleted file mode 100644
index ee4a19c3d71a405b3e6536fa978fa45fb49f8267..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/plot_area_line_groups.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Plot / Area + Line / Groups
-# Make an combined area + line #plot showing multiple categories.
-# ---
-from h2o_wave import site, data, ui
-
-page = site['/demo']
-
-page.add('example', ui.plot_card(
-    box='1 1 4 5',
-    title='Area + Line, groups',
-    data=data('month city temperature', 24, rows=[
-        ('Jan', 'Tokyo', 7),
-        ('Jan', 'London', 3.9),
-        ('Feb', 'Tokyo', 6.9),
-        ('Feb', 'London', 4.2),
-        ('Mar', 'Tokyo', 9.5),
-        ('Mar', 'London', 5.7),
-        ('Apr', 'Tokyo', 14.5),
-        ('Apr', 'London', 8.5),
-        ('May', 'Tokyo', 18.4),
-        ('May', 'London', 11.9),
-        ('Jun', 'Tokyo', 21.5),
-        ('Jun', 'London', 15.2),
-        ('Jul', 'Tokyo', 25.2),
-        ('Jul', 'London', 17),
-        ('Aug', 'Tokyo', 26.5),
-        ('Aug', 'London', 16.6),
-        ('Sep', 'Tokyo', 23.3),
-        ('Sep', 'London', 14.2),
-        ('Oct', 'Tokyo', 18.3),
-        ('Oct', 'London', 10.3),
-        ('Nov', 'Tokyo', 13.9),
-        ('Nov', 'London', 6.6),
-        ('Dec', 'Tokyo', 9.6),
-        ('Dec', 'London', 4.8),
-    ]),
-    plot=ui.plot([
-        ui.mark(type='area', x='=month', y='=temperature', color='=city', y_min=0),
-        ui.mark(type='line', x='=month', y='=temperature', color='=city')
-    ])
-))
-
-page.save()
diff --git a/spaces/hamacojr/SAM-CAT-Seg/open_clip/src/training/train.py b/spaces/hamacojr/SAM-CAT-Seg/open_clip/src/training/train.py
deleted file mode 100644
index bf42f147592e1a1745b067a255688aaf913e9401..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/SAM-CAT-Seg/open_clip/src/training/train.py
+++ /dev/null
@@ -1,308 +0,0 @@
-import json
-import logging
-import math
-import os
-import time
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-try:
-    import wandb
-except ImportError:
-    wandb = None
-
-from open_clip import ClipLoss, get_cast_dtype
-from .distributed import is_master
-from .zero_shot import zero_shot_eval
-from .precision import get_autocast
-
-
-class AverageMeter(object):
-    """Computes and stores the average and current value"""
-
-    def __init__(self):
-        self.reset()
-
-    def reset(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-
-    def update(self, val, n=1):
-        self.val = val
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
-
-
-def unwrap_model(model):
-    if hasattr(model, 'module'):
-        return model.module
-    else:
-        return model
-
-
-def backward(total_loss, scaler):
-    if scaler is not None:
-        scaler.scale(total_loss).backward()
-    else:
-        total_loss.backward()
-
-
-def train_one_epoch(model, data, epoch, optimizer, scaler, scheduler, args, tb_writer=None):
-    device = torch.device(args.device)
-    autocast = get_autocast(args.precision)
-    cast_dtype = get_cast_dtype(args.precision)
-
-    model.train()
-    loss = ClipLoss(
-        local_loss=args.local_loss,
-        gather_with_grad=args.gather_with_grad,
-        cache_labels=True,
-        rank=args.rank,
-        world_size=args.world_size,
-        use_horovod=args.horovod)
-
-    data['train'].set_epoch(epoch)  # set epoch in process safe manner via sampler or shared_epoch
-    dataloader = data['train'].dataloader
-    num_batches_per_epoch = dataloader.num_batches // args.accum_freq
-    sample_digits = math.ceil(math.log(dataloader.num_samples + 1, 10))
-
-    if args.accum_freq > 1:
-        accum_images, accum_texts, accum_image_features, accum_text_features = [], [], [], []
-
-    loss_m = AverageMeter()
-    batch_time_m = AverageMeter()
-    data_time_m = AverageMeter()
-    end = time.time()
-    for i, batch in enumerate(dataloader):
-        i_accum = i // args.accum_freq
-        step = num_batches_per_epoch * epoch + i_accum
-
-        if not args.skip_scheduler:
-            scheduler(step)
-
-        images, texts = batch
-        images = images.to(device=device, dtype=cast_dtype, non_blocking=True)
-        texts = texts.to(device=device, non_blocking=True)
-
-        data_time_m.update(time.time() - end)
-        optimizer.zero_grad()
-
-        if args.accum_freq == 1:
-            with autocast():
-                image_features, text_features, logit_scale = model(images, texts)
-                total_loss = loss(image_features, text_features, logit_scale)
-
-            backward(total_loss, scaler)
-        else:
-            # First, cache the features without any gradient tracking.
-            with torch.no_grad():
-                with autocast():
-                    chunk_image_features, chunk_text_features, _ = model(images, texts)
-                accum_image_features.append(chunk_image_features)
-                accum_text_features.append(chunk_text_features)
-
-                accum_images.append(images)
-                accum_texts.append(texts)
-
-            # If (i + 1) % accum_freq is not zero, move on to the next batch.
-            if ((i + 1) % args.accum_freq) > 0:
-                # FIXME this makes data time logging unreliable when accumulating
-                continue
-
-            # Now, ready to take gradients for the last accum_freq batches.
-            # Re-do the forward pass for those batches, and use the cached features from the other batches as negatives.
-            # Call backwards each time, but only step optimizer at the end.
-            optimizer.zero_grad()
-            for j in range(args.accum_freq):
-                images = accum_images[j]
-                texts = accum_texts[j]
-                with autocast():
-                    chunk_image_features, chunk_text_features, logit_scale = model(images, texts)
-                    image_features = torch.cat(
-                        accum_image_features[:j] + [chunk_image_features] + accum_image_features[j + 1:])
-                    text_features = torch.cat(
-                        accum_text_features[:j] + [chunk_text_features] + accum_text_features[j + 1:])
-                    total_loss = loss(image_features, text_features, logit_scale)
-                backward(total_loss, scaler)
-
-        if scaler is not None:
-            if args.horovod:
-                optimizer.synchronize()
-                scaler.unscale_(optimizer)
-                if args.grad_clip_norm is not None:
-                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip_norm, norm_type=2.0)
-                with optimizer.skip_synchronize():
-                    scaler.step(optimizer)
-            else:
-                if args.grad_clip_norm is not None:
-                    scaler.unscale_(optimizer)
-                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip_norm, norm_type=2.0)
-                scaler.step(optimizer)
-            scaler.update()
-        else:
-            if args.grad_clip_norm is not None:
-                torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip_norm, norm_type=2.0)
-            optimizer.step()
-
-        # reset gradient accum, if enabled
-        if args.accum_freq > 1:
-            accum_images, accum_texts, accum_image_features, accum_text_features = [], [], [], []
-
-        # Note: we clamp to 4.6052 = ln(100), as in the original paper.
-        with torch.no_grad():
-            unwrap_model(model).logit_scale.clamp_(0, math.log(100))
-
-        batch_time_m.update(time.time() - end)
-        end = time.time()
-        batch_count = i_accum + 1
-        if is_master(args) and (i_accum % args.log_every_n_steps == 0 or batch_count == num_batches_per_epoch):
-            batch_size = len(images)
-            num_samples = batch_count * batch_size * args.accum_freq * args.world_size
-            samples_per_epoch = dataloader.num_samples
-            percent_complete = 100.0 * batch_count / num_batches_per_epoch
-
-            # NOTE loss is coarsely sampled, just master node and per log update
-            loss_m.update(total_loss.item(), batch_size)
-            logit_scale_scalar = logit_scale.item()
-            logging.info(
-                f"Train Epoch: {epoch} [{num_samples:>{sample_digits}}/{samples_per_epoch} ({percent_complete:.0f}%)] "
-                f"Loss: {loss_m.val:#.5g} ({loss_m.avg:#.4g}) "
-                f"Data (t): {data_time_m.avg:.3f} "
-                f"Batch (t): {batch_time_m.avg:.3f}, {args.accum_freq * args.batch_size * args.world_size / batch_time_m.val:#g}/s "
-                f"LR: {optimizer.param_groups[0]['lr']:5f} "
-                f"Logit Scale: {logit_scale_scalar:.3f}"
-            )
-
-            # Save train loss / etc. Using non avg meter values as loggers have their own smoothing
-            log_data = {
-                "loss": loss_m.val,
-                "data_time": data_time_m.val,
-                "batch_time": batch_time_m.val,
-                "samples_per_second": args.accum_freq * args.batch_size * args.world_size / batch_time_m.val,
-                "scale": logit_scale_scalar,
-                "lr": optimizer.param_groups[0]["lr"]
-            }
-            for name, val in log_data.items():
-                name = "train/" + name
-                if tb_writer is not None:
-                    tb_writer.add_scalar(name, val, step)
-                if args.wandb:
-                    assert wandb is not None, 'Please install wandb.'
-                    wandb.log({name: val, 'step': step})
-
-            # resetting batch / data time meters per log window
-            batch_time_m.reset()
-            data_time_m.reset()
-    # end for
-
-
-def evaluate(model, data, epoch, args, tb_writer=None):
-    metrics = {}
-    if not is_master(args):
-        return metrics
-    device = torch.device(args.device)
-    model.eval()
-
-    zero_shot_metrics = zero_shot_eval(model, data, epoch, args)
-    metrics.update(zero_shot_metrics)
-
-    autocast = get_autocast(args.precision)
-    cast_dtype = get_cast_dtype(args.precision)
-
-    if 'val' in data and (args.val_frequency and ((epoch % args.val_frequency) == 0 or epoch == args.epochs)):
-        dataloader = data['val'].dataloader
-        num_samples = 0
-        samples_per_val = dataloader.num_samples
-
-        # FIXME this does not scale past small eval datasets
-        # all_image_features @ all_text_features will blow up memory and compute very quickly
-        cumulative_loss = 0.0
-        all_image_features, all_text_features = [], []
-        with torch.no_grad():
-            for i, batch in enumerate(dataloader):
-                images, texts = batch
-                images = images.to(device=device, dtype=cast_dtype, non_blocking=True)
-                texts = texts.to(device=device, non_blocking=True)
-
-                with autocast():
-                    image_features, text_features, logit_scale = model(images, texts)
-                    # features are accumulated in CPU tensors, otherwise GPU memory exhausted quickly
-                    # however, system RAM is easily exceeded and compute time becomes problematic
-                    all_image_features.append(image_features.cpu())
-                    all_text_features.append(text_features.cpu())
-                    logit_scale = logit_scale.mean()
-                    logits_per_image = logit_scale * image_features @ text_features.t()
-                    logits_per_text = logits_per_image.t()
-
-                    batch_size = images.shape[0]
-                    labels = torch.arange(batch_size, device=device).long()
-                    total_loss = (
-                        F.cross_entropy(logits_per_image, labels) +
-                        F.cross_entropy(logits_per_text, labels)
-                    ) / 2
-
-                cumulative_loss += total_loss * batch_size
-                num_samples += batch_size
-                if is_master(args) and (i % 100) == 0:
-                    logging.info(
-                        f"Eval Epoch: {epoch} [{num_samples} / {samples_per_val}]\t"
-                        f"Loss: {cumulative_loss / num_samples:.6f}\t")
-
-            val_metrics = get_metrics(
-                image_features=torch.cat(all_image_features),
-                text_features=torch.cat(all_text_features),
-                logit_scale=logit_scale.cpu(),
-            )
-            loss = cumulative_loss / num_samples
-            metrics.update(
-                {**val_metrics, "val_loss": loss.item(), "epoch": epoch, "num_samples": num_samples}
-            )
-
-    if not metrics:
-        return metrics
-
-    logging.info(
-        f"Eval Epoch: {epoch} "
-        + "\t".join([f"{k}: {round(v, 4):.4f}" for k, v in metrics.items()])
-    )
-
-    if args.save_logs:
-        for name, val in metrics.items():
-            if tb_writer is not None:
-                tb_writer.add_scalar(f"val/{name}", val, epoch)
-
-        with open(os.path.join(args.checkpoint_path, "results.jsonl"), "a+") as f:
-            f.write(json.dumps(metrics))
-            f.write("\n")
-
-    if args.wandb:
-        assert wandb is not None, 'Please install wandb.'
-        for name, val in metrics.items():
-            wandb.log({f"val/{name}": val, 'epoch': epoch})
-
-    return metrics
-
-
-def get_metrics(image_features, text_features, logit_scale):
-    metrics = {}
-    logits_per_image = (logit_scale * image_features @ text_features.t()).detach().cpu()
-    logits_per_text = logits_per_image.t().detach().cpu()
-
-    logits = {"image_to_text": logits_per_image, "text_to_image": logits_per_text}
-    ground_truth = torch.arange(len(text_features)).view(-1, 1)
-
-    for name, logit in logits.items():
-        ranking = torch.argsort(logit, descending=True)
-        preds = torch.where(ranking == ground_truth)[1]
-        preds = preds.detach().cpu().numpy()
-        metrics[f"{name}_mean_rank"] = preds.mean() + 1
-        metrics[f"{name}_median_rank"] = np.floor(np.median(preds)) + 1
-        for k in [1, 5, 10]:
-            metrics[f"{name}_R@{k}"] = np.mean(preds < k)
-
-    return metrics
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/make_crop_and_mask_w_mask_nms.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/make_crop_and_mask_w_mask_nms.py
deleted file mode 100644
index 1efc5ae86f81db7dcdae1d22db771d2a8e8d3ccf..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/make_crop_and_mask_w_mask_nms.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import numpy as np
-import cv2, torch
-import os
-import json
-import argparse
-import pycocotools.mask as mask_util
-from tqdm import tqdm
-
-
-def bbox_expand(img_height, img_width, bbox, exp_ratio):
-    x_min, y_min, x_max, y_max = bbox[:]
-    exp_x = (x_max - x_min) * ((exp_ratio - 1) / 2)
-    exp_y = (y_max - y_min) * ((exp_ratio - 1) / 2)
-    new_x_min = 0 if x_min - exp_x < 0 else np.round(x_min - exp_x)
-    new_y_min = 0 if y_min - exp_y < 0 else np.round(y_min - exp_y)
-    new_x_max = img_width - 1 if x_max + exp_x > img_width - 1 else np.round(x_max + exp_x)
-    new_y_max = img_height - 1 if y_max + exp_y > img_height - 1 else np.round(y_max + exp_y)
-    return int(new_x_min), int(new_y_min), int(new_x_max), int(new_y_max)
-
-
-def make_crop_and_mask(img_info, pred, file_list, crop_save_dir, mask_save_dir, args):
-    img_name = img_info['file_name']
-    img_id = img_info['id'] - 1  # img_info['id'] start form 1
-    img_w = img_info['width']
-    img_h = img_info['height']
-
-    img = cv2.imread(os.path.join(args.img_dir, img_name))
-
-    exp_bbox = []
-    ori_bbox = []
-    bbox_name_list = []
-    bbox_score_list = []
-    person_idx = 0
-
-    panoptic_seg = np.zeros((img_h, img_w), dtype=np.uint8)
-    assert len(pred[img_id]['instances']) > 0, 'image without instance prediction'
-
-    for instance in pred[img_id]['instances']:
-        score = instance['score']
-        if score < args.conf_thres:
-            break
-
-        mask = mask_util.decode(instance['segmentation'])
-        mask_area = mask.sum()
-
-        if mask_area == 0:  # if mask_area < img_w*img_h/1000:
-            continue
-
-        intersect = (mask > 0) & (panoptic_seg > 0)
-        intersect_area = intersect.sum()
-
-        if intersect_area * 1.0 / mask_area > args.overlap_threshold:  # todo add args
-            continue
-
-        if intersect_area > 0:
-            mask = mask & (panoptic_seg == 0)
-
-        person_idx += 1
-        panoptic_seg = np.where(mask == 0, panoptic_seg, person_idx)
-
-        bbox_score_list.append(score)
-
-        ins_bbox = instance['bbox']  # [x,y,w,h] format
-        x_min, y_min, box_w, box_h = ins_bbox
-        x_max, y_max = x_min + box_w, y_min + box_h
-        exp_x_min, exp_y_min, exp_x_max, exp_y_max = bbox_expand(img_h, img_w, [x_min, y_min, x_max, y_max],
-                                                                 args.exp_ratio)
-        crop_img = img[exp_y_min:exp_y_max + 1, exp_x_min:exp_x_max + 1, :]
-        exp_bbox.append([exp_x_min, exp_y_min, exp_x_max, exp_y_max])
-        ori_bbox.append([x_min, y_min, x_max, y_max])
-        bbox_name = os.path.splitext(img_name)[0] + '_' + str(person_idx) + '_msrcnn.jpg'
-        bbox_name_list.append(bbox_name)
-
-        cv2.imwrite(os.path.join(crop_save_dir, bbox_name), crop_img)
-
-    assert person_idx > 0, 'image without instance'
-    mask_name = os.path.splitext(img_name)[0] + '_mask.npy'
-    np.save(os.path.join(mask_save_dir, mask_name), panoptic_seg)
-
-    ############## json writing ##################
-    item = {}
-    item['dataset'] = 'CIHP'
-    item['im_name'] = img_name
-    item['img_height'] = img_h
-    item['img_width'] = img_w
-    item['center'] = [img_h / 2, img_w / 2]
-    item['person_num'] = person_idx
-    item['person_bbox'] = exp_bbox
-    item['real_person_bbox'] = ori_bbox
-    item['person_bbox_score'] = bbox_score_list
-    item['bbox_name'] = bbox_name_list
-    item['mask_name'] = mask_name
-    file_list.append(item)
-    json_file = {'root': file_list}
-    return json_file, file_list
-
-
-def get_arguments():
-    parser = argparse.ArgumentParser(description="crop person val/test demo for inference")
-    parser.add_argument("--exp_ratio", type=float, default=1.2)
-    parser.add_argument("--overlap_threshold", type=float, default=0.5)
-    parser.add_argument("--conf_thres", type=float, default=0.5)
-    parser.add_argument("--img_dir", type=str,
-                        default='/data03/v_xuyunqiu/data/instance-level_human_parsing/Testing/Images')
-    parser.add_argument("--save_dir", type=str,
-                        default='/data03/v_xuyunqiu/Projects/experiment_data/testing/resnest_200_TTA_mask_nms_all_data')
-    parser.add_argument("--img_list", type=str,
-                        default='/data03/v_xuyunqiu/Projects/pycococreator/annotations/CIHP_test.json')
-    parser.add_argument("--det_res", type=str,
-                        default='/data02/v_xuyunqiu/detectron2-ResNeSt/tools/output_cihp_inference_resnest/inference_TTA/instances_predictions.pth')
-    return parser.parse_args()
-
-
-def main(args):
-    img_info_list = json.load(open(args.img_list, encoding='UTF-8'))
-    pred = torch.load(args.det_res)
-
-    crop_save_dir = os.path.join(args.save_dir, 'crop_pic')
-    if not os.path.exists(crop_save_dir):
-        os.makedirs(crop_save_dir)
-    mask_save_dir = os.path.join(args.save_dir, 'crop_mask')
-    if not os.path.exists(mask_save_dir):
-        os.makedirs(mask_save_dir)
-
-    file_list = []
-    for img_info in tqdm(img_info_list['images']):
-        json_file, file_list = make_crop_and_mask(img_info, pred, file_list, crop_save_dir, mask_save_dir, args)
-        with open(os.path.join(args.save_dir, 'crop.json'), 'w') as f:
-            json.dump(json_file, f, indent=2)
-
-
-if __name__ == '__main__':
-    args = get_arguments()
-    main(args)
diff --git a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/utils/augmentations.py b/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/utils/augmentations.py
deleted file mode 100644
index 1e609303e2092235e7e73ae7045185e6d22020ce..0000000000000000000000000000000000000000
--- a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/utils/augmentations.py
+++ /dev/null
@@ -1,397 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Image augmentation functions
-"""
-
-import math
-import random
-
-import cv2
-import numpy as np
-import torch
-import torchvision.transforms as T
-import torchvision.transforms.functional as TF
-
-from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy
-from utils.metrics import bbox_ioa
-
-IMAGENET_MEAN = 0.485, 0.456, 0.406  # RGB mean
-IMAGENET_STD = 0.229, 0.224, 0.225  # RGB standard deviation
-
-
-class Albumentations:
-    # YOLOv5 Albumentations class (optional, only used if package is installed)
-    def __init__(self, size=640):
-        self.transform = None
-        prefix = colorstr('albumentations: ')
-        try:
-            import albumentations as A
-            check_version(A.__version__, '1.0.3', hard=True)  # version requirement
-
-            T = [
-                A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0),
-                A.Blur(p=0.01),
-                A.MedianBlur(p=0.01),
-                A.ToGray(p=0.01),
-                A.CLAHE(p=0.01),
-                A.RandomBrightnessContrast(p=0.0),
-                A.RandomGamma(p=0.0),
-                A.ImageCompression(quality_lower=75, p=0.0)]  # transforms
-            self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
-
-            LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
-        except ImportError:  # package not installed, skip
-            pass
-        except Exception as e:
-            LOGGER.info(f'{prefix}{e}')
-
-    def __call__(self, im, labels, p=1.0):
-        if self.transform and random.random() < p:
-            new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0])  # transformed
-            im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])
-        return im, labels
-
-
-def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False):
-    # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std
-    return TF.normalize(x, mean, std, inplace=inplace)
-
-
-def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD):
-    # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean
-    for i in range(3):
-        x[:, i] = x[:, i] * std[i] + mean[i]
-    return x
-
-
-def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
-    # HSV color-space augmentation
-    if hgain or sgain or vgain:
-        r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1  # random gains
-        hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))
-        dtype = im.dtype  # uint8
-
-        x = np.arange(0, 256, dtype=r.dtype)
-        lut_hue = ((x * r[0]) % 180).astype(dtype)
-        lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
-        lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
-
-        im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
-        cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im)  # no return needed
-
-
-def hist_equalize(im, clahe=True, bgr=False):
-    # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255
-    yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
-    if clahe:
-        c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-        yuv[:, :, 0] = c.apply(yuv[:, :, 0])
-    else:
-        yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0])  # equalize Y channel histogram
-    return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB)  # convert YUV image to RGB
-
-
-def replicate(im, labels):
-    # Replicate labels
-    h, w = im.shape[:2]
-    boxes = labels[:, 1:].astype(int)
-    x1, y1, x2, y2 = boxes.T
-    s = ((x2 - x1) + (y2 - y1)) / 2  # side length (pixels)
-    for i in s.argsort()[:round(s.size * 0.5)]:  # smallest indices
-        x1b, y1b, x2b, y2b = boxes[i]
-        bh, bw = y2b - y1b, x2b - x1b
-        yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw))  # offset x, y
-        x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
-        im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b]  # im4[ymin:ymax, xmin:xmax]
-        labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
-
-    return im, labels
-
-
-def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
-    # Resize and pad image while meeting stride-multiple constraints
-    shape = im.shape[:2]  # current shape [height, width]
-    if isinstance(new_shape, int):
-        new_shape = (new_shape, new_shape)
-
-    # Scale ratio (new / old)
-    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
-    if not scaleup:  # only scale down, do not scale up (for better val mAP)
-        r = min(r, 1.0)
-
-    # Compute padding
-    ratio = r, r  # width, height ratios
-    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
-    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
-    if auto:  # minimum rectangle
-        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
-    elif scaleFill:  # stretch
-        dw, dh = 0.0, 0.0
-        new_unpad = (new_shape[1], new_shape[0])
-        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
-
-    dw /= 2  # divide padding into 2 sides
-    dh /= 2
-
-    if shape[::-1] != new_unpad:  # resize
-        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
-    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
-    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
-    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
-    return im, ratio, (dw, dh)
-
-
-def random_perspective(im,
-                       targets=(),
-                       segments=(),
-                       degrees=10,
-                       translate=.1,
-                       scale=.1,
-                       shear=10,
-                       perspective=0.0,
-                       border=(0, 0)):
-    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
-    # targets = [cls, xyxy]
-
-    height = im.shape[0] + border[0] * 2  # shape(h,w,c)
-    width = im.shape[1] + border[1] * 2
-
-    # Center
-    C = np.eye(3)
-    C[0, 2] = -im.shape[1] / 2  # x translation (pixels)
-    C[1, 2] = -im.shape[0] / 2  # y translation (pixels)
-
-    # Perspective
-    P = np.eye(3)
-    P[2, 0] = random.uniform(-perspective, perspective)  # x perspective (about y)
-    P[2, 1] = random.uniform(-perspective, perspective)  # y perspective (about x)
-
-    # Rotation and Scale
-    R = np.eye(3)
-    a = random.uniform(-degrees, degrees)
-    # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
-    s = random.uniform(1 - scale, 1 + scale)
-    # s = 2 ** random.uniform(-scale, scale)
-    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
-
-    # Shear
-    S = np.eye(3)
-    S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # x shear (deg)
-    S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # y shear (deg)
-
-    # Translation
-    T = np.eye(3)
-    T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width  # x translation (pixels)
-    T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height  # y translation (pixels)
-
-    # Combined rotation matrix
-    M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
-    if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any():  # image changed
-        if perspective:
-            im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
-        else:  # affine
-            im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
-
-    # Visualize
-    # import matplotlib.pyplot as plt
-    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
-    # ax[0].imshow(im[:, :, ::-1])  # base
-    # ax[1].imshow(im2[:, :, ::-1])  # warped
-
-    # Transform label coordinates
-    n = len(targets)
-    if n:
-        use_segments = any(x.any() for x in segments) and len(segments) == n
-        new = np.zeros((n, 4))
-        if use_segments:  # warp segments
-            segments = resample_segments(segments)  # upsample
-            for i, segment in enumerate(segments):
-                xy = np.ones((len(segment), 3))
-                xy[:, :2] = segment
-                xy = xy @ M.T  # transform
-                xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]  # perspective rescale or affine
-
-                # clip
-                new[i] = segment2box(xy, width, height)
-
-        else:  # warp boxes
-            xy = np.ones((n * 4, 3))
-            xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
-            xy = xy @ M.T  # transform
-            xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8)  # perspective rescale or affine
-
-            # create new boxes
-            x = xy[:, [0, 2, 4, 6]]
-            y = xy[:, [1, 3, 5, 7]]
-            new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
-
-            # clip
-            new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
-            new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
-
-        # filter candidates
-        i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
-        targets = targets[i]
-        targets[:, 1:5] = new[i]
-
-    return im, targets
-
-
-def copy_paste(im, labels, segments, p=0.5):
-    # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
-    n = len(segments)
-    if p and n:
-        h, w, c = im.shape  # height, width, channels
-        im_new = np.zeros(im.shape, np.uint8)
-        for j in random.sample(range(n), k=round(p * n)):
-            l, s = labels[j], segments[j]
-            box = w - l[3], l[2], w - l[1], l[4]
-            ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area
-            if (ioa < 0.30).all():  # allow 30% obscuration of existing labels
-                labels = np.concatenate((labels, [[l[0], *box]]), 0)
-                segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
-                cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED)
-
-        result = cv2.flip(im, 1)  # augment segments (flip left-right)
-        i = cv2.flip(im_new, 1).astype(bool)
-        im[i] = result[i]  # cv2.imwrite('debug.jpg', im)  # debug
-
-    return im, labels, segments
-
-
-def cutout(im, labels, p=0.5):
-    # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
-    if random.random() < p:
-        h, w = im.shape[:2]
-        scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16  # image size fraction
-        for s in scales:
-            mask_h = random.randint(1, int(h * s))  # create random masks
-            mask_w = random.randint(1, int(w * s))
-
-            # box
-            xmin = max(0, random.randint(0, w) - mask_w // 2)
-            ymin = max(0, random.randint(0, h) - mask_h // 2)
-            xmax = min(w, xmin + mask_w)
-            ymax = min(h, ymin + mask_h)
-
-            # apply random color mask
-            im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
-
-            # return unobscured labels
-            if len(labels) and s > 0.03:
-                box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
-                ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h))  # intersection over area
-                labels = labels[ioa < 0.60]  # remove >60% obscured labels
-
-    return labels
-
-
-def mixup(im, labels, im2, labels2):
-    # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
-    r = np.random.beta(32.0, 32.0)  # mixup ratio, alpha=beta=32.0
-    im = (im * r + im2 * (1 - r)).astype(np.uint8)
-    labels = np.concatenate((labels, labels2), 0)
-    return im, labels
-
-
-def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):  # box1(4,n), box2(4,n)
-    # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
-    w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
-    w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
-    ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps))  # aspect ratio
-    return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr)  # candidates
-
-
-def classify_albumentations(
-        augment=True,
-        size=224,
-        scale=(0.08, 1.0),
-        ratio=(0.75, 1.0 / 0.75),  # 0.75, 1.33
-        hflip=0.5,
-        vflip=0.0,
-        jitter=0.4,
-        mean=IMAGENET_MEAN,
-        std=IMAGENET_STD,
-        auto_aug=False):
-    # YOLOv5 classification Albumentations (optional, only used if package is installed)
-    prefix = colorstr('albumentations: ')
-    try:
-        import albumentations as A
-        from albumentations.pytorch import ToTensorV2
-        check_version(A.__version__, '1.0.3', hard=True)  # version requirement
-        if augment:  # Resize and crop
-            T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)]
-            if auto_aug:
-                # TODO: implement AugMix, AutoAug & RandAug in albumentation
-                LOGGER.info(f'{prefix}auto augmentations are currently not supported')
-            else:
-                if hflip > 0:
-                    T += [A.HorizontalFlip(p=hflip)]
-                if vflip > 0:
-                    T += [A.VerticalFlip(p=vflip)]
-                if jitter > 0:
-                    color_jitter = (float(jitter), ) * 3  # repeat value for brightness, contrast, satuaration, 0 hue
-                    T += [A.ColorJitter(*color_jitter, 0)]
-        else:  # Use fixed crop for eval set (reproducibility)
-            T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
-        T += [A.Normalize(mean=mean, std=std), ToTensorV2()]  # Normalize and convert to Tensor
-        LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
-        return A.Compose(T)
-
-    except ImportError:  # package not installed, skip
-        LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)')
-    except Exception as e:
-        LOGGER.info(f'{prefix}{e}')
-
-
-def classify_transforms(size=224):
-    # Transforms to apply if albumentations not installed
-    assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)'
-    # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
-    return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
-
-
-class LetterBox:
-    # YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
-    def __init__(self, size=(640, 640), auto=False, stride=32):
-        super().__init__()
-        self.h, self.w = (size, size) if isinstance(size, int) else size
-        self.auto = auto  # pass max size integer, automatically solve for short side using stride
-        self.stride = stride  # used with auto
-
-    def __call__(self, im):  # im = np.array HWC
-        imh, imw = im.shape[:2]
-        r = min(self.h / imh, self.w / imw)  # ratio of new/old
-        h, w = round(imh * r), round(imw * r)  # resized image
-        hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w
-        top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1)
-        im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype)
-        im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
-        return im_out
-
-
-class CenterCrop:
-    # YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])
-    def __init__(self, size=640):
-        super().__init__()
-        self.h, self.w = (size, size) if isinstance(size, int) else size
-
-    def __call__(self, im):  # im = np.array HWC
-        imh, imw = im.shape[:2]
-        m = min(imh, imw)  # min dimension
-        top, left = (imh - m) // 2, (imw - m) // 2
-        return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)
-
-
-class ToTensor:
-    # YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
-    def __init__(self, half=False):
-        super().__init__()
-        self.half = half
-
-    def __call__(self, im):  # im = np.array HWC in BGR order
-        im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1])  # HWC to CHW -> BGR to RGB -> contiguous
-        im = torch.from_numpy(im)  # to torch
-        im = im.half() if self.half else im.float()  # uint8 to fp16/32
-        im /= 255.0  # 0-255 to 0.0-1.0
-        return im
diff --git a/spaces/hieupt/image_style_transfer/vgg.py b/spaces/hieupt/image_style_transfer/vgg.py
deleted file mode 100644
index 5837f0b4e7e9d2247019c92d2b68e784a91445e0..0000000000000000000000000000000000000000
--- a/spaces/hieupt/image_style_transfer/vgg.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import torch.nn as nn
-from collections import namedtuple
-from torchvision.models import vgg16, VGG16_Weights
-
-class VGG16(nn.Module):
-    def __init__(self, requires_grad=False):
-        super(VGG16, self).__init__()
-
-        weights = VGG16_Weights.DEFAULT
-        vgg_pretrained_features = vgg16(weights=weights).features
-        self.slice1 = nn.Sequential()
-        self.slice2 = nn.Sequential()
-        self.slice3 = nn.Sequential()
-        self.slice4 = nn.Sequential()
-        for x in range(4):
-            self.slice1.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(4, 9):
-            self.slice2.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(9, 16):
-            self.slice3.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(16, 23):
-            self.slice4.add_module(str(x), vgg_pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1_2 = h
-        h = self.slice2(h)
-        h_relu2_2 = h
-        h = self.slice3(h)
-        h_relu3_3 = h
-        h = self.slice4(h)
-        h_relu4_3 = h
-        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3'])
-        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3)
-        return out
diff --git a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/The-Piano-Guys-Hits-Volume-1-MP3-320kbps-NEW.md b/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/The-Piano-Guys-Hits-Volume-1-MP3-320kbps-NEW.md
deleted file mode 100644
index 89b08a0e098e002c321826a7f80e8cc4b118209b..0000000000000000000000000000000000000000
--- a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/The-Piano-Guys-Hits-Volume-1-MP3-320kbps-NEW.md
+++ /dev/null
@@ -1,66 +0,0 @@
-## The Piano Guys - Hits Volume 1 [MP3] [320kbps]
-
-
-
- 
-
- ![The Piano Guys - Hits Volume 1 \[MP3\] \[320kbps\] NEW!](https://img.wynk.in/unsafe/320x180/top/https://s3-ap-southeast-1.amazonaws.com/bsbcms/srch_sonymusic/music/srch_sonymusic_A10328E00062249114.jpg)
-
- 
-
- 
-
-**CLICK HERE ✵✵✵ [https://www.google.com/url?q=https%3A%2F%2Ftlniurl.com%2F2txmlC&sa=D&sntz=1&usg=AOvVaw0Kd0jGS-AstNQ03YfHG3QP](https://www.google.com/url?q=https%3A%2F%2Ftlniurl.com%2F2txmlC&sa=D&sntz=1&usg=AOvVaw0Kd0jGS-AstNQ03YfHG3QP)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# The Piano Guys - Hits Volume 1: A Review of the Debut Album by the YouTube Sensations
-
- 
-
-If you are a fan of classical crossover music, you may have heard of The Piano Guys, a musical group consisting of Jon Schmidt (piano), Steven Sharp Nelson (cello), Al van der Beek (music producer) and Paul Anderson (video producer). The Piano Guys rose to fame through their YouTube channel, where they uploaded stunning videos of their original arrangements and covers of popular songs, classical pieces, movie themes and more.
-
- 
-
-The Piano Guys - Hits Volume 1 is their first studio album, released in December 2011 as a way to offer high quality audio recordings of their most popular YouTube videos at the time. The album features 11 tracks, ranging from the energetic "Michael Meets Mozart" and "Rock Meets Rachmaninoff" to the soothing "Moonlight" and "Twinkle Lullaby". The album also showcases their versatility and creativity, as they blend different genres and styles, such as pop, rock, jazz, folk, new age and more.
-
- 
-
-One of the highlights of the album is "Cello Wars", a radio edit of their viral video that features a cello duel inspired by Star Wars. The track incorporates various themes from John Williams' iconic score, such as "Imperial March", "Duel of the Fates", "May the Force Be With You" and more. The track is a testament to Nelson's skill and passion for the cello, as he plays with precision, power and humor.
-
- 
-
-Another standout track is "O Fortuna", a dramatic rendition of Carl Orff's famous piece from Carmina Burana. The track features a choir of four female singers, who add depth and intensity to the cello and piano parts. The track is a thrilling and epic experience that showcases the group's ability to create cinematic soundscapes.
-
- 
-
-The Piano Guys - Hits Volume 1 is a great introduction to the group's music, as it offers a diverse and enjoyable selection of their best works. The album is also a testament to their talent, originality and vision, as they transform familiar songs and melodies into something fresh and exciting. Whether you are a fan of classical music or not, you will surely find something to love in this album.
-
-  
-
-If you enjoyed The Piano Guys - Hits Volume 1, you may also want to check out their next album, The Piano Guys 2, which was released in October 2012 through Sony Masterworks. The album is their first on a major record label, and it features more of their amazing covers and mashups of classical and popular music.
-
- 
-
-The Piano Guys 2 includes 13 tracks, some of which are based on their YouTube videos, such as "Begin Again", a beautiful blend of Taylor Swift's song and Bach's "Sheep May Safely Graze", "Mission Impossible", a thrilling collaboration with violinist Lindsey Stirling, and "Lord of the Rings", a stunning tribute to Howard Shore's epic score. The album also features some original compositions, such as "Arwen's Vigil", a haunting cello and piano piece inspired by the character from The Lord of the Rings, and "Waterfall", a playful and virtuosic piano solo by Jon Schmidt.
-
- 
-
-The Piano Guys 2 showcases the group's talent, creativity and passion for music, as they explore different genres and styles, such as rock, pop, folk, new age and more. The album is also a testament to their vision, as they aim to inspire and uplift people through their music and videos. Whether you are a fan of classical music or not, you will surely find something to enjoy in this album.
-
- 1b8d091108
-
- 
-
- 
-
diff --git a/spaces/hirol/controlnetOverMask/js/DragControls.js b/spaces/hirol/controlnetOverMask/js/DragControls.js
deleted file mode 100644
index 6745c5bf41d82d5d82ade3e7f42255c330383dfc..0000000000000000000000000000000000000000
--- a/spaces/hirol/controlnetOverMask/js/DragControls.js
+++ /dev/null
@@ -1,228 +0,0 @@
-async function _import() {
-	if (!globalThis.posex || !globalThis.posex.import) {
-		return await import('three');
-	} else {
-		return await globalThis.posex.imports.three();
-	}
-}
-const {
-	EventDispatcher,
-	Matrix4,
-	Plane,
-	Raycaster,
-	Vector2,
-	Vector3
-} = await _import();
-
-const _plane = new Plane();
-const _raycaster = new Raycaster();
-
-const _pointer = new Vector2();
-const _offset = new Vector3();
-const _intersection = new Vector3();
-const _worldPosition = new Vector3();
-const _inverseMatrix = new Matrix4();
-
-class DragControls extends EventDispatcher {
-
-	constructor( _objects, _camera, _domElement ) {
-
-		super();
-
-		_domElement.style.touchAction = 'none'; // disable touch scroll
-
-		let _selected = null, _hovered = null;
-
-		const _intersections = [];
-
-		//
-
-		const scope = this;
-
-		function activate() {
-
-			_domElement.addEventListener( 'pointermove', onPointerMove );
-			_domElement.addEventListener( 'pointerdown', onPointerDown );
-			_domElement.addEventListener( 'pointerup', onPointerCancel );
-			_domElement.addEventListener( 'pointerleave', onPointerCancel );
-
-		}
-
-		function deactivate() {
-
-			_domElement.removeEventListener( 'pointermove', onPointerMove );
-			_domElement.removeEventListener( 'pointerdown', onPointerDown );
-			_domElement.removeEventListener( 'pointerup', onPointerCancel );
-			_domElement.removeEventListener( 'pointerleave', onPointerCancel );
-
-			_domElement.style.cursor = '';
-
-		}
-
-		function dispose() {
-
-			deactivate();
-
-		}
-
-		function getObjects() {
-
-			return _objects;
-
-		}
-
-		function getRaycaster() {
-
-			return _raycaster;
-
-		}
-
-		function onPointerMove( event ) {
-
-			if ( scope.enabled === false ) return;
-
-			updatePointer( event );
-
-			_raycaster.setFromCamera( _pointer, _camera );
-
-			if ( _selected ) {
-
-				if ( _raycaster.ray.intersectPlane( _plane, _intersection ) ) {
-
-					_selected.position.copy( _intersection.sub( _offset ).applyMatrix4( _inverseMatrix ) );
-
-				}
-
-				scope.dispatchEvent( { type: 'drag', object: _selected } );
-
-				return;
-
-			}
-
-			// hover support
-
-			if ( event.pointerType === 'mouse' || event.pointerType === 'pen' ) {
-
-				_intersections.length = 0;
-
-				_raycaster.setFromCamera( _pointer, _camera );
-				_raycaster.intersectObjects( _objects, true, _intersections );
-
-				if ( _intersections.length > 0 ) {
-
-					const object = _intersections[ 0 ].object;
-
-					_plane.setFromNormalAndCoplanarPoint( _camera.getWorldDirection( _plane.normal ), _worldPosition.setFromMatrixPosition( object.matrixWorld ) );
-
-					if ( _hovered !== object && _hovered !== null ) {
-
-						scope.dispatchEvent( { type: 'hoveroff', object: _hovered } );
-
-						_domElement.style.cursor = 'auto';
-						_hovered = null;
-
-					}
-
-					if ( _hovered !== object ) {
-
-						scope.dispatchEvent( { type: 'hoveron', object: object } );
-
-						_domElement.style.cursor = 'pointer';
-						_hovered = object;
-
-					}
-
-				} else {
-
-					if ( _hovered !== null ) {
-
-						scope.dispatchEvent( { type: 'hoveroff', object: _hovered } );
-
-						_domElement.style.cursor = 'auto';
-						_hovered = null;
-
-					}
-
-				}
-
-			}
-
-		}
-
-		function onPointerDown( event ) {
-
-			if ( scope.enabled === false ) return;
-
-			updatePointer( event );
-
-			_intersections.length = 0;
-
-			_raycaster.setFromCamera( _pointer, _camera );
-			_raycaster.intersectObjects( _objects, true, _intersections );
-
-			if ( _intersections.length > 0 ) {
-
-				_selected = ( scope.transformGroup === true ) ? _objects[ 0 ] : _intersections[ 0 ].object;
-
-				_plane.setFromNormalAndCoplanarPoint( _camera.getWorldDirection( _plane.normal ), _worldPosition.setFromMatrixPosition( _selected.matrixWorld ) );
-
-				if ( _raycaster.ray.intersectPlane( _plane, _intersection ) ) {
-
-					_inverseMatrix.copy( _selected.parent.matrixWorld ).invert();
-					_offset.copy( _intersection ).sub( _worldPosition.setFromMatrixPosition( _selected.matrixWorld ) );
-
-				}
-
-				_domElement.style.cursor = 'move';
-
-				scope.dispatchEvent( { type: 'dragstart', object: _selected } );
-
-			}
-
-
-		}
-
-		function onPointerCancel() {
-
-			if ( scope.enabled === false ) return;
-
-			if ( _selected ) {
-
-				scope.dispatchEvent( { type: 'dragend', object: _selected } );
-
-				_selected = null;
-
-			}
-
-			_domElement.style.cursor = _hovered ? 'pointer' : 'auto';
-
-		}
-
-		function updatePointer( event ) {
-
-			const rect = _domElement.getBoundingClientRect();
-
-			_pointer.x = ( event.clientX - rect.left ) / rect.width * 2 - 1;
-			_pointer.y = - ( event.clientY - rect.top ) / rect.height * 2 + 1;
-
-		}
-
-		activate();
-
-		// API
-
-		this.enabled = true;
-		this.transformGroup = false;
-
-		this.activate = activate;
-		this.deactivate = deactivate;
-		this.dispose = dispose;
-		this.getObjects = getObjects;
-		this.getRaycaster = getRaycaster;
-		this.onPointerDown = onPointerDown;
-
-	}
-
-}
-
-export { DragControls };
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_Adam_ReduceOnPlateau.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_Adam_ReduceOnPlateau.py
deleted file mode 100644
index b467ad997af23eace8f8de25fc52dfcbe29b702e..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/nnUNet_variants/optimizer_and_lr/nnUNetTrainerV2_Adam_ReduceOnPlateau.py
+++ /dev/null
@@ -1,55 +0,0 @@
-#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-#    Licensed under the Apache License, Version 2.0 (the "License");
-#    you may not use this file except in compliance with the License.
-#    You may obtain a copy of the License at
-#
-#        http://www.apache.org/licenses/LICENSE-2.0
-#
-#    Unless required by applicable law or agreed to in writing, software
-#    distributed under the License is distributed on an "AS IS" BASIS,
-#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#    See the License for the specific language governing permissions and
-#    limitations under the License.
-
-
-import torch
-from nnunet.training.network_training.nnUNetTrainer import nnUNetTrainer
-from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2
-from torch.optim import lr_scheduler
-
-
-class nnUNetTrainerV2_Adam_ReduceOnPlateau(nnUNetTrainerV2):
-    """
-    Same schedule as nnUNetTrainer
-    """
-    def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None,
-                 unpack_data=True, deterministic=True, fp16=False):
-        super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data,
-                         deterministic, fp16)
-        self.initial_lr = 3e-4
-
-    def initialize_optimizer_and_scheduler(self):
-        assert self.network is not None, "self.initialize_network must be called first"
-        self.optimizer = torch.optim.Adam(self.network.parameters(), self.initial_lr, weight_decay=self.weight_decay,
-                                          amsgrad=True)
-        self.lr_scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, mode='min', factor=0.2,
-                                                           patience=self.lr_scheduler_patience,
-                                                           verbose=True, threshold=self.lr_scheduler_eps,
-                                                           threshold_mode="abs")
-
-    def maybe_update_lr(self, epoch=None):
-        # maybe update learning rate
-        if self.lr_scheduler is not None:
-            assert isinstance(self.lr_scheduler, (lr_scheduler.ReduceLROnPlateau, lr_scheduler._LRScheduler))
-
-            if isinstance(self.lr_scheduler, lr_scheduler.ReduceLROnPlateau):
-                # lr scheduler is updated with moving average val loss. should be more robust
-                if self.epoch > 0 and self.train_loss_MA is not None:  # otherwise self.train_loss_MA is None
-                    self.lr_scheduler.step(self.train_loss_MA)
-            else:
-                self.lr_scheduler.step(self.epoch + 1)
-        self.print_to_log_file("lr is now (scheduler) %s" % str(self.optimizer.param_groups[0]['lr']))
-
-    def on_epoch_end(self):
-        return nnUNetTrainer.on_epoch_end(self)
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_mtl_early_4.sh b/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_mtl_early_4.sh
deleted file mode 100644
index d5c7ec25ba08a541ac13911808e5a702abea2349..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_mtl_early_4.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-#!/bin/bash -l
-#SBATCH --nodes=1 --gres=gpu:1 --time=24:00:00
-#SBATCH --job-name=Task503_glacier_mtl_early_4
-
-export data_raw="/home/woody/iwi5/iwi5039h/data_raw"
-export nnUNet_raw_data_base="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_raw_data_base/"
-export nnUNet_preprocessed="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_preprocessed/"
-export RESULTS_FOLDER="/home/woody/iwi5/iwi5039h/nnUNet_data/RESULTS_FOLDER"
-
-cd nnunet_glacer
-pwd
-conda activate nnunet
-
-python3 nnunet/run/run_training.py 2d nnUNetTrainerMTLearly 503 4 -p nnUNetPlans_mtl --disable_postprocessing_on_folds
-python3 nnunet/inference/predict_simple.py -i $nnUNet_raw_data_base/nnUNet_raw_data/Task503_Glacier_mtl/imagesTs -o $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_early/fold_4 -t 503 -m 2d -f 4 -p nnUNetPlans_mtl -tr nnUNetTrainerMTLearly
-python3 nnunet/dataset_conversion/Task503_Glacier_mtl_reverse.py -i $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_early/fold_4
-python3 ./evaluate_nnUNet.py --predictions $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_early/fold_4/pngs --labels_fronts $data_raw/fronts/test --labels_zones $data_raw/zones/test --sar_images $data_raw/sar_images/test
-
diff --git a/spaces/housexu123/bingo-2.0/src/lib/isomorphic/index.ts b/spaces/housexu123/bingo-2.0/src/lib/isomorphic/index.ts
deleted file mode 100644
index 738dc92f74079ab762d584fb7422a8c8c3b61547..0000000000000000000000000000000000000000
--- a/spaces/housexu123/bingo-2.0/src/lib/isomorphic/index.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-'use client'
-
-import Default from './browser'
-
-let exportsModel: any = {}
-
-if (process.browser) {
-  Object.assign(exportsModel, require('./browser').default)
-} else {
-  Object.assign(exportsModel, require('./node').default)
-}
-
-export default exportsModel! as typeof Default
-
-export const fetch: typeof Default.fetch = exportsModel!.fetch
-export const WebSocket: typeof Default.WebSocket = exportsModel!.WebSocket
-export const debug: typeof Default.debug = exportsModel!.debug
diff --git a/spaces/hunger11243/VITS-Umamusume-voice-synthesizer/text/cleaners.py b/spaces/hunger11243/VITS-Umamusume-voice-synthesizer/text/cleaners.py
deleted file mode 100644
index c80e113b2b81a66134800dbdaa29c7d96a0152a7..0000000000000000000000000000000000000000
--- a/spaces/hunger11243/VITS-Umamusume-voice-synthesizer/text/cleaners.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import re
-
-
-def japanese_cleaners(text):
-    from text.japanese import japanese_to_romaji_with_accent
-    text = japanese_to_romaji_with_accent(text)
-    text = re.sub(r'([A-Za-z])$', r'\1.', text)
-    return text
-
-
-def japanese_cleaners2(text):
-    return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
-
-
-def korean_cleaners(text):
-    '''Pipeline for Korean text'''
-    from text.korean import latin_to_hangul, number_to_hangul, divide_hangul
-    text = latin_to_hangul(text)
-    text = number_to_hangul(text)
-    text = divide_hangul(text)
-    text = re.sub(r'([\u3131-\u3163])$', r'\1.', text)
-    return text
-
-
-def chinese_cleaners(text):
-    '''Pipeline for Chinese text'''
-    from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text)
-    return text
-
-
-def zh_ja_mixture_cleaners(text):
-    from text.mandarin import chinese_to_romaji
-    from text.japanese import japanese_to_romaji_with_accent
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_romaji(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_romaji_with_accent(
-        x.group(1)).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def sanskrit_cleaners(text):
-    text = text.replace('॥', '।').replace('ॐ', 'ओम्')
-    if text[-1] != '।':
-        text += ' ।'
-    return text
-
-
-def cjks_cleaners(text):
-    from text.mandarin import chinese_to_lazy_ipa
-    from text.japanese import japanese_to_ipa
-    from text.korean import korean_to_lazy_ipa
-    from text.sanskrit import devanagari_to_ipa
-    from text.english import english_to_lazy_ipa
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[SA\](.*?)\[SA\]',
-                  lambda x: devanagari_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners(text):
-    from text.mandarin import chinese_to_lazy_ipa
-    from text.japanese import japanese_to_ipa
-    from text.korean import korean_to_ipa
-    from text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]', lambda x: chinese_to_lazy_ipa(x.group(1)).replace(
-        'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn')+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_ipa(x.group(1)).replace('ʧ', 'tʃ').replace(
-        'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz')+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: english_to_ipa2(x.group(1)).replace('ɑ', 'a').replace(
-        'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners2(text):
-    from text.mandarin import chinese_to_ipa
-    from text.japanese import japanese_to_ipa2
-    from text.korean import korean_to_ipa
-    from text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def thai_cleaners(text):
-    from text.thai import num_to_thai, latin_to_thai
-    text = num_to_thai(text)
-    text = latin_to_thai(text)
-    return text
-
-
-def shanghainese_cleaners(text):
-    from text.shanghainese import shanghainese_to_ipa
-    text = shanghainese_to_ipa(text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def chinese_dialect_cleaners(text):
-    from text.mandarin import chinese_to_ipa2
-    from text.japanese import japanese_to_ipa3
-    from text.shanghainese import shanghainese_to_ipa
-    from text.cantonese import cantonese_to_ipa
-    from text.english import english_to_lazy_ipa2
-    from text.ngu_dialect import ngu_dialect_to_ipa
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ')+' ', text)
-    text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5',
-                  '˧˧˦').replace('6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e')+' ', text)
-    text = re.sub(r'\[GD\](.*?)\[GD\]',
-                  lambda x: cantonese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_lazy_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group(
-        1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
diff --git a/spaces/hylee/White-box-Cartoonization/README.md b/spaces/hylee/White-box-Cartoonization/README.md
deleted file mode 100644
index f960f60b0dd3fce436ecc0c4e6779140133652de..0000000000000000000000000000000000000000
--- a/spaces/hylee/White-box-Cartoonization/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-python_version: 3.7
-title: White Box Cartoonization
-emoji: 📚
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/hylee/u2net_portrait/U-2-Net/u2net_portrait_test.py b/spaces/hylee/u2net_portrait/U-2-Net/u2net_portrait_test.py
deleted file mode 100644
index 7e103dd336868ff71fe4a114d7e4e3b437a80a59..0000000000000000000000000000000000000000
--- a/spaces/hylee/u2net_portrait/U-2-Net/u2net_portrait_test.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import os
-from skimage import io, transform
-import torch
-import torchvision
-from torch.autograd import Variable
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import Dataset, DataLoader
-from torchvision import transforms#, utils
-# import torch.optim as optim
-
-import numpy as np
-from PIL import Image
-import glob
-
-from data_loader import RescaleT
-from data_loader import ToTensor
-from data_loader import ToTensorLab
-from data_loader import SalObjDataset
-
-from model import U2NET # full size version 173.6 MB
-from model import U2NETP # small version u2net 4.7 MB
-
-# normalize the predicted SOD probability map
-def normPRED(d):
-    ma = torch.max(d)
-    mi = torch.min(d)
-
-    dn = (d-mi)/(ma-mi)
-
-    return dn
-
-def save_output(image_name,pred,d_dir):
-
-    predict = pred
-    predict = predict.squeeze()
-    predict_np = predict.cpu().data.numpy()
-
-    im = Image.fromarray(predict_np*255).convert('RGB')
-    img_name = image_name.split(os.sep)[-1]
-    image = io.imread(image_name)
-    imo = im.resize((image.shape[1],image.shape[0]),resample=Image.BILINEAR)
-
-    pb_np = np.array(imo)
-
-    aaa = img_name.split(".")
-    bbb = aaa[0:-1]
-    imidx = bbb[0]
-    for i in range(1,len(bbb)):
-        imidx = imidx + "." + bbb[i]
-
-    imo.save(d_dir+'/'+imidx+'.png')
-
-def main():
-
-    # --------- 1. get image path and name ---------
-    model_name='u2net_portrait'#u2netp
-
-
-    image_dir = './test_data/test_portrait_images/portrait_im'
-    prediction_dir = './test_data/test_portrait_images/portrait_results'
-    if(not os.path.exists(prediction_dir)):
-        os.mkdir(prediction_dir)
-
-    model_dir = './saved_models/u2net_portrait/u2net_portrait.pth'
-
-    img_name_list = glob.glob(image_dir+'/*')
-    print("Number of images: ", len(img_name_list))
-
-    # --------- 2. dataloader ---------
-    #1. dataloader
-    test_salobj_dataset = SalObjDataset(img_name_list = img_name_list,
-                                        lbl_name_list = [],
-                                        transform=transforms.Compose([RescaleT(512),
-                                                                      ToTensorLab(flag=0)])
-                                        )
-    test_salobj_dataloader = DataLoader(test_salobj_dataset,
-                                        batch_size=1,
-                                        shuffle=False,
-                                        num_workers=1)
-
-    # --------- 3. model define ---------
-
-    print("...load U2NET---173.6 MB")
-    net = U2NET(3,1)
-
-    net.load_state_dict(torch.load(model_dir))
-    if torch.cuda.is_available():
-        net.cuda()
-    net.eval()
-
-    # --------- 4. inference for each image ---------
-    for i_test, data_test in enumerate(test_salobj_dataloader):
-
-        print("inferencing:",img_name_list[i_test].split(os.sep)[-1])
-
-        inputs_test = data_test['image']
-        inputs_test = inputs_test.type(torch.FloatTensor)
-
-        if torch.cuda.is_available():
-            inputs_test = Variable(inputs_test.cuda())
-        else:
-            inputs_test = Variable(inputs_test)
-
-        d1,d2,d3,d4,d5,d6,d7= net(inputs_test)
-
-        # normalization
-        pred = 1.0 - d1[:,0,:,:]
-        pred = normPRED(pred)
-
-        # save results to test_results folder
-        save_output(img_name_list[i_test],pred,prediction_dir)
-
-        del d1,d2,d3,d4,d5,d6,d7
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/hysts/diffusers-anime-faces/app.py b/spaces/hysts/diffusers-anime-faces/app.py
deleted file mode 100644
index 45d6e08d8f2eb516eaa1783a21d0fb6a8d40909d..0000000000000000000000000000000000000000
--- a/spaces/hysts/diffusers-anime-faces/app.py
+++ /dev/null
@@ -1,168 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import argparse
-
-import gradio as gr
-
-from model import Model
-
-TITLE = '# Anime Face Generation with [Diffusers](https://github.com/huggingface/diffusers)'
-DESCRIPTION = 'Expected execution time on Hugging Face Spaces: 5s (DDIM, 20 steps), 6s (PNDM, 20 steps), 247s (DDPM, 1000 steps)'
-FOOTER = '<img id="visitor-badge" src="https://visitor-badge.glitch.me/badge?page_id=hysts.diffusers-anime-faces" alt="visitor badge" />'
-
-
-def create_simple_demo(model: Model) -> gr.Blocks:
-    with gr.Blocks() as demo:
-        run_button = gr.Button('Generate')
-        with gr.Tabs():
-            with gr.TabItem('Result (Superresolved)'):
-                result = gr.Image(show_label=False, elem_id='result-grid')
-            with gr.TabItem('Result (Raw)'):
-                result_raw = gr.Image(show_label=False,
-                                      elem_id='result-grid-raw')
-        run_button.click(fn=model.run_simple,
-                         inputs=None,
-                         outputs=[result, result_raw])
-    return demo
-
-
-def create_advanced_demo(model: Model) -> gr.Blocks:
-    def update_num_steps(name: str) -> dict:
-        visible = name != 'DDPM'
-        if name == 'PNDM':
-            minimum = 4
-            maximum = 100
-        else:
-            minimum = 1
-            maximum = 200
-        return gr.Slider.update(visible=visible,
-                                minimum=minimum,
-                                maximum=maximum,
-                                value=20)
-
-    with gr.Blocks() as demo:
-        gr.Markdown(DESCRIPTION)
-
-        with gr.Row():
-            with gr.Column():
-                with gr.Group():
-                    model_name = gr.Dropdown(model.MODEL_NAMES,
-                                             value=model.MODEL_NAMES[0],
-                                             label='Model',
-                                             interactive=False)
-                    scheduler_type = gr.Radio(choices=['DDPM', 'DDIM', 'PNDM'],
-                                              value='DDIM',
-                                              label='Scheduler')
-                    num_steps = gr.Slider(1,
-                                          200,
-                                          step=1,
-                                          value=20,
-                                          label='Number of Steps')
-                    randomize_seed = gr.Checkbox(value=False,
-                                                 label='Randomize Seed')
-                    seed = gr.Slider(0,
-                                     100000,
-                                     step=1,
-                                     value=1234,
-                                     label='Seed')
-                    run_button = gr.Button('Run')
-            with gr.Column():
-                with gr.Tabs():
-                    with gr.TabItem('Result (Superresolved)'):
-                        result = gr.Image(show_label=False, elem_id='result')
-                    with gr.TabItem('Result (Raw)'):
-                        result_raw = gr.Image(show_label=False,
-                                              elem_id='result-raw')
-                    with gr.TabItem('Denoising Process'):
-                        result_video = gr.Video(show_label=False,
-                                                elem_id='result-video')
-
-        scheduler_type.change(fn=update_num_steps,
-                              inputs=scheduler_type,
-                              outputs=num_steps,
-                              queue=False)
-        run_button.click(fn=model.run,
-                         inputs=[
-                             model_name,
-                             scheduler_type,
-                             num_steps,
-                             randomize_seed,
-                             seed,
-                         ],
-                         outputs=[
-                             result,
-                             result_raw,
-                             seed,
-                             result_video,
-                         ])
-    return demo
-
-
-def create_sample_image_view_demo() -> gr.Blocks:
-    def get_sample_image_url(file_name: str) -> str:
-        sample_image_dir = 'https://huggingface.co/spaces/hysts/diffusers-anime-faces/resolve/main/samples'
-        return f'{sample_image_dir}/{file_name}'
-
-    def get_sample_image_markdown(name: str) -> str:
-        model_name = name.split()[0]
-        if name == 'ddpm-128-exp000 (DDPM)':
-            scheduler = 'DDPM'
-            steps = 1000
-            file_name = f'{model_name}.png'
-        elif name == 'ddpm-128-exp000 (DDIM, 20 steps)':
-            scheduler = 'DDIM'
-            steps = 20
-            file_name = f'{model_name}-ddim-20steps.png'
-        else:
-            raise ValueError
-        url = get_sample_image_url(file_name)
-        text = f'''
-                - size: 128x128
-                - seed: 0-99
-                - scheduler: {scheduler}
-                - steps: {steps}
-
-                ![sample images]({url})'''
-        return text
-
-    with gr.Blocks() as demo:
-        with gr.Row():
-            model_name = gr.Dropdown([
-                'ddpm-128-exp000 (DDPM)',
-                'ddpm-128-exp000 (DDIM, 20 steps)',
-            ],
-                                     value='ddpm-128-exp000 (DDPM)',
-                                     label='Model')
-        with gr.Row():
-            text = get_sample_image_markdown(model_name.value)
-            sample_images = gr.Markdown(text)
-
-        model_name.change(fn=get_sample_image_markdown,
-                          inputs=model_name,
-                          outputs=sample_images)
-    return demo
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    args = parser.parse_args()
-    model = Model(args.device)
-
-    with gr.Blocks(css='style.css') as demo:
-        gr.Markdown(TITLE)
-        with gr.Tabs():
-            with gr.TabItem('Simple Mode'):
-                create_simple_demo(model)
-            with gr.TabItem('Advanced Mode'):
-                create_advanced_demo(model)
-            with gr.TabItem('Sample Images'):
-                create_sample_image_view_demo()
-        gr.Markdown(FOOTER)
-    demo.launch(enable_queue=True, share=False)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/imseldrith/Imagine/templates/index.html b/spaces/imseldrith/Imagine/templates/index.html
deleted file mode 100644
index 3f485bf763787cf815a640ae7c60f1c0d1a70ef4..0000000000000000000000000000000000000000
--- a/spaces/imseldrith/Imagine/templates/index.html
+++ /dev/null
@@ -1,347 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-    <title>Free AI Image Generator</title>
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.5.0/dist/css/bootstrap.min.css">
-    <style>
-        body {
-            background-color: #000000;
-            color: #ffffff;
-            font-family: Arial, sans-serif;
-        }
-
-        .container {
-            max-width: 600px;
-            margin: 0 auto;
-            padding: 40px;
-            background-color: transparent;
-            border-radius: 10px;
-            box-shadow: none;
-        }
-      img {
-         display: block;
-         margin: 0 auto;
-      }
-        h1 {
-            text-align: center;
-            margin-bottom: 30px;
-            color: #ffffff;
-            font-weight: 600;
-            letter-spacing: 1px;
-        }
-
-        label {
-            font-weight: bold;
-            margin-bottom: 10px;
-            color: #ffffff;
-        }
-
-        textarea,
-        select {
-            width: 100%;
-            padding: 12px;
-            border-radius: 5px;
-            border: 1px solid #dddddd;
-            font-size: 16px;
-            margin-bottom: 20px;
-            color: #555555;
-            background-color: #f9f9f9;
-        }
-
-        button[type="submit"] {
-            background-color: #ff3366;
-            color: #ffffff;
-            padding: 12px 24px;
-            border: none;
-            border-radius: 5px;
-            cursor: pointer;
-            font-size: 16px;
-            transition: background-color 0.3s ease;
-            display: block;
-            margin: 0 auto;
-        }
-
-        button[type="submit"]:hover {
-            background-color: #cc0052;
-        }
-
-        .social-buttons {
-            text-align: center;
-            margin-top: 20px;
-        }
-
-        .social-button {
-            display: inline-block;
-            margin-right: 10px;
-        }
-
-        .social-icon {
-            color: #ffffff;
-            font-size: 24px;
-            transition: color 0.3s ease;
-        }
-
-        .social-icon:hover {
-            color: #ff3366;
-        }
-
-        .generating-message {
-            text-align: center;
-            margin-bottom: 20px;
-            color: #ffffff;
-            display: none;
-            font-size: 24px;
-            animation: pulse 1s ease-in-out infinite;
-        }
-
-        @keyframes pulse {
-            0% {
-                transform: scale(1);
-                opacity: 0.7;
-            }
-            50% {
-                transform: scale(1.2);
-                opacity: 1;
-            }
-            100% {
-                transform: scale(1);
-                opacity: 0.7;
-            }
-        }
-
-        .loading-spinner {
-            display: inline-block;
-            width: 40px;
-            height: 40px;
-            border: 4px solid #ffffff;
-            border-top-color: transparent;
-            border-radius: 50%;
-            animation: spin 1s linear infinite;
-        }
-
-        @keyframes spin {
-            0% {
-                transform: rotate(0deg);
-            }
-            100% {
-                transform: rotate(360deg);
-            }
-        }
-
-        .disabled-button {
-            cursor: not-allowed;
-            opacity: 0.6;
-        }
-
-        footer {
-            padding: 20px;
-            text-align: center;
-        }
-      /* Add styles for the page view counter and API page link */
-        .page-view-counter {
-            font-size: 14px;
-            color: #ffffff;
-            margin-top: 10px;
-        }
-        .api-link {
-            font-size: 14px;
-            color: #ffffff;
-            margin-top: 5px;
-            text-decoration: none;
-        }
-        .api-link:hover {
-            color: #ff3366;
-      }
-    </style>
-    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css">
-</head>
-<body>
-  
-    <div class="container">
-      <img src="https://imseldrith-imagine.hf.space/static/imagine_logo.gif">
-        <h1 class="mt-5">Image Generator</h1>
-        <form action="/generate" method="POST" onsubmit="showGeneratingMessage()">
-            <div class="mb-3">
-    <label for="prompt" class="form-label">Prompt:</label>
-    <textarea id="prompt" name="prompt" class="form-control" rows="4" cols="50" placeholder="Surprise me" required></textarea>
-            </div>
-            <div class="mb-3">
-                <label for="style" class="form-label">Choose Style:</label>
-                <select id="style" name="style" class="form-select">
-                     <option value="NO_STYLE">No style</option>
-  <option value="EUPHORIC">Euphoric</option>
-  <option value="FANTASY">Fantasy</option>
-  <option value="CYBERPUNK">Cyberpunk</option>
-  <option value="STUDIO_GHIBLI">Studio Ghibli</option>
-  <option value="DISNEY">Disney</option>
-  <option value="GTA">GTA</option>
-  <option value="KAWAII_CHIBI">Kawaii Chibi</option>
-  <option value="ANIME_V2">Anime V2</option>
-  <option value="ABSTRACT_VIBRANT">Abstract Vibrant</option>
-  <option value="VIBRANT">Vibrant</option>
-  <option value="PSYCHEDELIC">Psychedelic</option>
-  <option value="EXTRA_TERRESTRIAL">Extra-terrestrial</option>
-  <option value="COSMIC">Cosmic</option>
-  <option value="MACRO_PHOTOGRAPHY">Macro Photography</option>
-  <option value="PRODUCT_PHOTOGRAPHY">Product Photography</option>
-  <option value="POLAROID">Polaroid</option>
-  <option value="NEO_FAUVISM">Neo fauvism</option>
-  <option value="POP_ART">Pop Art</option>
-  <option value="POP_ART_II">Pop Art II</option>
-  <option value="GRAFFITI">Graffiti</option>
-  <option value="SURREALISM">Surrealism</option>
-  <option value="BAUHAUS">Bauhaus</option>
-  <option value="CUBISM">Cubism</option>
-  <option value="JAPANESE_ART">Japanese Art</option>
-  <option value="SKETCH">Sketch</option>
-  <option value="ILLUSTRATION">Illustration</option>
-  <option value="PAINTING">Painting</option>
-  <option value="PALETTE_KNIFE">Palette Knife</option>
-  <option value="INK">Ink</option>
-  <option value="ORIGAMI">Origami</option>
-  <option value="STAINED_GLASS">Stained Glass</option>
-  <option value="STICKER">Sticker</option>
-  <option value="CLIP_ART">Clip Art</option>
-  <option value="POSTER_ART">Poster Art</option>
-  <option value="PAPERCUT_STYLE">PaperCut Style</option>
-  <option value="COLORING_BOOK">Coloring Book</option>
-  <option value="PATTERN">Pattern</option>
-  <option value="RENDER">Render</option>
-  <option value="CINEMATIC_RENDER">Cinematic Render</option>
-  <option value="COMIC_BOOK">Comic Book</option>
-  <option value="COMIC_V2">Comic V2</option>
-  <option value="LOGO">Logo</option>
-  <option value="ICON">Icon</option>
-  <option value="GLASS_ART">Glass Art</option>
-  <option value="KNOLLING_CASE">Knolling Case</option>
-<option value="SCATTER">Scatter</option>
-<option value="POLY_ART">Poly Art</option>
-<option value="CLAYMATION">Claymation</option>
-<option value="WOOLITIZE">Woolitize</option>
-<option value="MARBLE">Marble</option>
-<option value="VAN_GOGH">Van Gogh</option>
-<option value="SALVADOR_DALI">Salvador Dali</option>
-<option value="PICASSO">Picasso</option>
-<option value="ARCHITECTURE">Architecture</option>
-<option value="INTERIOR">Interior</option>
-<option value="ABSTRACT_CITYSCAPE">Abstract Cityscape</option>
-<option value="DYSTOPIAN">Dystopian</option>
-<option value="FUTURISTIC">Futuristic</option>
-<option value="NEON">Neon</option>
-<option value="CHROMATIC">Chromatic</option>
-<option value="MYSTICAL">Mystical</option>
-<option value="LANDSCAPE">Landscape</option>
-<option value="RAINBOW">Rainbow</option>
-<option value="CANDYLAND">Candyland</option>
-<option value="MINECRAFT">Minecraft</option>
-<option value="PIXEL_ART">Pixel Art</option>
-<option value="RENAISSANCE">Rennaisance</option>
-<option value="ROCOCCO">Rococco</option>
-<option value="MEDIEVAL">Medieval</option>
-<option value="RETRO">Retro</option>
-<option value="RETROWAVE">Retrowave</option>
-<option value="STEAMPUNK">Steampunk</option>
-<option value="AMAZONIAN">Amazonian</option>
-<option value="AVATAR">Avatar</option>
-<option value="GOTHIC">Gothic</option>
-<option value="HAUNTED">Haunted</option>
-<option value="WATERBENDER">Waterbender</option>
-<option value="AQUATIC">Aquatic</option>
-<option value="FIREBENDER">Firebender</option>
-<option value="FORESTPUNK">Forestpunk</option>
-<option value="VIBRANT_VIKING">Vibrant Viking</option>
-<option value="SAMURAI">Samurai</option>
-<option value="ELVEN">Elven</option>
-<option value="SHAMROCK_FANTASY">Shamrock Fantasy</option>
-<option value="EARTHBENDER">Earthbender</option>
-<option value="AIRBENDER">Airbender</option>
-<option value="METALBENDER">Metalbender</option>
-<option value="BLOODBENDER">Bloodbender</option>
-<option value="LIGHTNINGBENDER">Lightbender</option>
-<option value="SPIRITBENDER">Spiritbender</option>
-<option value="LAVABENDER">Lavabender</option>
-<option value="WATERHEALER">Waterhealer</option>
-</select>
-            </div>
-            <div class="mb-3">
-                <label for="ratio" class="form-label">Choose Ratio:</label>
-                <select id="ratio" name="ratio" class="form-select">
-                    <option value="RATIO_1X1">1:1</option>
-        <option value="RATIO_4X3">4:3</option>
-        <option value="RATIO_3X2">3:2</option>
-        <option value="RATIO_2X3">2:3</option>
-        <option value="RATIO_16X9">16:9</option>
-        <option value="RATIO_9X16">9:16</option>
-        <option value="RATIO_5X4">5:4</option>
-        <option value="RATIO_4X5">4:5</option>
-        <option value="RATIO_3X1">3:1</option>
-        <option value="RATIO_3X4">3:4</option>                </select>
-            </div>
-          <div class="mb-3">
-            <label for="model" class="form-label">Choose Model:</label>
-        <select name="model" id="model" class="form-select">
-            <option value="REALISTIC">Realistic</option>
-            <option value="CREATIVE">CREATIVE</option>
-            <option value="V4_BETA">V4_BETA</option>
-            <option value="V3">V3</option>
-            <option value="V1">V1</option>
-            <option value="ANIME">ANIME</option>
-            <option value="PORTRAIT">Portrait</option>
-        </select>
-          </div>
-            <button type="submit" class="btn btn-primary" id="generate-button">Generate Image</button>
-        </form>
-
-        <div class="generating-message">
-            <span class="loading-spinner"></span>
-            Generating image... Please wait
-        </div>
-
-        <div class="social-buttons">
-            <span class="social-button">
-                <a href="https://www.facebook.com/octaeldrith" class="social-icon"><i class="fab fa-facebook-f"></i></a>
-            </span>
-            <span class="social-button">
-                <a href="https://twitter.com/ims_eldrith" class="social-icon"><i class="fab fa-twitter"></i></a>
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-                <a href="https://instagram.com/ims_eldrith" class="social-icon"><i class="fab fa-instagram"></i></a>
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-                <a href="https://www.linkedin.com/in/ashiq-hussain" class="social-icon"><i class="fab fa-linkedin"></i></a>
-            </span>
-        </div>
-    </div>
-    <footer>
-        <p>Built with ❤️ by <a href="https://codegenius.me" style="color: red;">Ashiq Hussain Mir</a></p>
-      <p class="page-view-counter">
-    Page Views: <span id="page-view-count">67</span> | <a href="{{url_for('api_docs')}}" class="api-link">API</a>
-</p>
-    </footer>
-    <script>
-        function showGeneratingMessage() {
-            var generatingMessage = document.querySelector('.generating-message');
-            generatingMessage.style.display = 'block';
-            document.getElementById('generate-button').classList.add('disabled-button');
-
-            setTimeout(function () {
-                generatingMessage.style.display = 'none';
-                document.getElementById('generate-button').classList.remove('disabled-button');
-            }, 7000);
-        }
-      // Update the page view counter
-        var pageViewCount = 67;
-        function updatePageViewCount() {
-            pageViewCount++;
-            document.getElementById('page-view-count').textContent = pageViewCount;
-        }
-
-        // Call the updatePageViewCount function when the page loads
-        window.addEventListener('load', updatePageViewCount);
-      
-    </script>
-
-    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.5.0/dist/js/bootstrap.bundle.min.js"></script>
-</body>
-</html>
\ No newline at end of file
diff --git a/spaces/inamXcontru/PoeticTTS/AUTODATA 8.45 Crack FULL .rar [BEST].md b/spaces/inamXcontru/PoeticTTS/AUTODATA 8.45 Crack FULL .rar [BEST].md
deleted file mode 100644
index a5bc67d0336516085d460fcf54bb1edd1edd091b..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/AUTODATA 8.45 Crack FULL .rar [BEST].md	
+++ /dev/null
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deleted file mode 100644
index e05d9d6ed5655a3860e17144016531938468ab65..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Chinese Prc Language Pack Windows 7.epub.md	
+++ /dev/null
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-See https://en.wikipedia.org/w/index.php?title=Windows_7.1&oldid=101026954 for more details.
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diff --git a/spaces/innnky/nanami/app.py b/spaces/innnky/nanami/app.py
deleted file mode 100644
index dabac198ced069f333960f2a49b9ff54ba298724..0000000000000000000000000000000000000000
--- a/spaces/innnky/nanami/app.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import gradio as gr
-
-import torch,pdb
-import numpy as np
-import soundfile as sf
-from models import SynthesizerTrn256
-from scipy.io import wavfile
-from fairseq import checkpoint_utils
-import pyworld,librosa
-import torch.nn.functional as F
-
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model_path = "checkpoint_best_legacy_500.pt"#checkpoint_best_legacy_500.pt
-print("load model(s) from {}".format(model_path))
-models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-    [model_path],
-    suffix="",
-)
-model = models[0]
-model = model.to(device)
-model.eval()
-
-net_g = SynthesizerTrn256(513,40,192,192,768,2,6,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,4,2,2,2],512,[16,16,4,4,4],0)
-weights=torch.load("qihai.pt", map_location=torch.device('cpu'))
-net_g.load_state_dict(weights,strict=True)
-net_g.eval().to(device)
-
-
-def get_f0(x, f0_up_key=0):
-    f0_max = 1100.0
-    f0_min = 50.0
-    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
-    f0, t = pyworld.dio(
-        x.astype(np.double),
-        fs=16000,
-        f0_ceil=800,
-        frame_period=10,
-    )
-    f0 = pyworld.stonemask(x.astype(np.double), f0, t, 16000)
-    f0 *= pow(2, f0_up_key / 12)
-    f0_mel = 1127 * np.log(1 + f0 / 700)
-    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
-    f0_mel[f0_mel <= 1] = 1
-    f0_mel[f0_mel > 255] = 255
-    f0_coarse = np.rint(f0_mel).astype(np.int)
-    return f0_coarse
-
-
-
-def vc_fn( input_audio,f0_up_key):
-    if input_audio is None:
-        return "You need to upload an audio", None
-    sampling_rate, audio = input_audio
-    duration = audio.shape[0] / sampling_rate
-    if duration > 45:
-        return "请上传小于45s的音频，需要转换长音频请使用colab", None
-    audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-    if len(audio.shape) > 1:
-        audio = librosa.to_mono(audio.transpose(1, 0))
-    if sampling_rate != 16000:
-        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-    pitch = get_f0(audio, f0_up_key)
-
-    feats = torch.from_numpy(audio).float()
-    if feats.dim() == 2:  # double channels
-        feats = feats.mean(-1)
-    assert feats.dim() == 1, feats.dim()
-    feats = feats.view(1, -1)
-    padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-    inputs = {
-        "source": feats.to(device),
-        "padding_mask": padding_mask.to(device),
-        "output_layer": 9,  # layer 9
-    }
-    with torch.no_grad():
-        logits = model.extract_features(**inputs)
-        feats = model.final_proj(logits[0])
-    feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-    p_len = min(feats.shape[1], 10000, pitch.shape[0])  # 太大了爆显存
-    feats = feats[:, :p_len, :]
-    pitch = pitch[:p_len]
-    p_len = torch.LongTensor([p_len]).to(device)
-    pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
-    with torch.no_grad():
-        audio = net_g.infer(feats, p_len, pitch)[0][0, 0].data.cpu().float().numpy()
-
-    return "Success", (32000, audio)
-
-
-app = gr.Blocks()
-with app:
-    with gr.Tabs():
-        with gr.TabItem("Basic"):
-            gr.Markdown(value="""""")
-            vc_input3 = gr.Audio(label="上传音频（长度小于45秒）")
-            f0_up_key = gr.Number(label="变调")
-            vc_submit = gr.Button("转换", variant="primary")
-            vc_output1 = gr.Textbox(label="Output Message")
-            vc_output2 = gr.Audio(label="Output Audio")
-        vc_submit.click(vc_fn, [ vc_input3, f0_up_key], [vc_output1, vc_output2])
-
-    app.launch()
\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Express VPN Crack 2020 Avec Le Code D Activation NEW!.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Express VPN Crack 2020 Avec Le Code D Activation NEW!.md
deleted file mode 100644
index d52e699bc4f75c954f70a639840dd8c42e6fde25..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Express VPN Crack 2020 Avec Le Code D Activation NEW!.md	
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-<h2>Express VPN Crack 2020 avec le code d activation</h2><br /><p><b><b>Download File</b> &#127775; <a href="https://urlin.us/2uEw8y">https://urlin.us/2uEw8y</a></b></p><br /><br />
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-Express VPN Crack is a VPN (Virtual Private Network) service offered by a UK-based organization... November 20, 2020 by Info Leave a comment .. . Express VPN Crack is a VPN (virtual private network) service offered by the British organization Express VPN.
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diff --git a/spaces/inreVtussa/clothingai/Examples/Bandicam V41.1.3.757 Reg Key Setup Free FREE.md b/spaces/inreVtussa/clothingai/Examples/Bandicam V41.1.3.757 Reg Key Setup Free FREE.md
deleted file mode 100644
index da683660b8154a3a81272598a57a6a60454d4dd4..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Bandicam V41.1.3.757 Reg Key Setup Free FREE.md	
+++ /dev/null
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diff --git a/spaces/ivanmeyer/Finetuned_Diffusion_Max/style.css b/spaces/ivanmeyer/Finetuned_Diffusion_Max/style.css
deleted file mode 100644
index 9bfa78cc983f84693cf7cbab1e3bfd0e0d36c944..0000000000000000000000000000000000000000
--- a/spaces/ivanmeyer/Finetuned_Diffusion_Max/style.css
+++ /dev/null
@@ -1,24 +0,0 @@
-.finetuned-diffusion-div div{
-    display:inline-flex;
-    align-items:center;
-    gap:.8rem;
-    font-size:1.75rem
-}
-.finetuned-diffusion-div div h1{
-    font-weight:900;
-    margin-bottom:7px
-}
-.finetuned-diffusion-div p{
-    margin-bottom:10px;
-    font-size:94%
-}
-a{
-    text-decoration:underline
-}
-.tabs{
-    margin-top:0;
-    margin-bottom:0
-}
-#gallery{
-    min-height:20rem
-}
diff --git a/spaces/ivyblossom/sentiment-analysis/app.py b/spaces/ivyblossom/sentiment-analysis/app.py
deleted file mode 100644
index 8703263789df9a4ad866b918a5d1e224ad60019d..0000000000000000000000000000000000000000
--- a/spaces/ivyblossom/sentiment-analysis/app.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import streamlit as st
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import torch
-
-# Set up the device (GPU or CPU)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Streamlit app
-def main():
-    st.title("Sentiment Analysis App")
-    st.write("Enter a text and select a pretrained model to perform sentiment analysis.")
-
-    text = st.text_area("Enter text", value="I am leaving my hometown for greener pastures.")
-
-    model_options = {
-        "distilbert-base-uncased-finetuned-sst-2-english": "DistilBERT (SST-2)",
-        "distilbert-base-uncased": "DistilBERT Uncased",
-        "roberta-base": "RoBERTa Base",
-        "albert-base-v2": "ALBERT Base v2"
-        # Can add more models here if desired
-    }
-
-    # Load the pretrained model and tokenizer
-    model_name = st.selectbox("Select a pretrained model", list(model_options.keys()))
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-    if st.button("Submit"):
-        # Perform sentiment analysis
-        inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
-        inputs = inputs.to(device)
-        outputs = model(**inputs)
-        logits = outputs.logits
-        probabilities = torch.softmax(logits, dim=1).detach().cpu().numpy()[0]
-        sentiment_label = "Positive" if probabilities[1] > probabilities[0] else "Negative"
-        st.write(f"Sentiment: {sentiment_label}")
-        st.write(f"Positive probability: {probabilities[1]}")
-        st.write(f"Negative probability: {probabilities[0]}")
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/jbilcke-hf/VideoQuest/src/app/games/tensor.ts b/spaces/jbilcke-hf/VideoQuest/src/app/games/tensor.ts
deleted file mode 100644
index 752b83ea6af3c0318ce9e395a2e0d47bde574f86..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/VideoQuest/src/app/games/tensor.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-import { edu } from "@/lib/fonts"
-import { Game } from "./types"
-import { InventoryItem } from "../../types"
-
-const initialSituation = [
-  `in Martin Place, Sydney`,
-  `side walk, few pedestrians`,
-  `a person in leather coat and with sunglasses`,
-  `business city district and buildings`,
-].join(", ")
-
-const initialActionnables = [
-  "car",
-  "face",
-  "person",
-  "building",
-  "sidewalk",
-  "sun",
-  "object",
-  "window",
-  "floor",
-  "door"
-]
-
-const inventory: InventoryItem[] = [
-  {
-    name: "blue-pill",
-    title: "blue pill",
-    caption: "",
-    description: "My therapist said I had to eat one every day."
-  },
-  {
-    name: "flash-light",
-    title: "Flashlight",
-    caption: "",
-    description: "In case I need to go to the bathroom during the night."
-  },
-  {
-    name: "laptop",
-    title: "Laptop",
-    caption: "",
-    description: "My work laptop, with work stuff on it."
-  },
-  {
-    name: "matchbox",
-    title: "Matchbox",
-    caption: "",
-    description: "I used this to light fireworks."
-  },
-  {
-    name: "watch",
-    title: "Watch",
-    caption: "",
-    description: "Belonged to my grandpa, but I think it's a fake."
-  },
-]
-
-export const game: Game = {
-  title: "The Tensor",
-  type: "tensor",
-  description: [
-    "The game is a role playing adventure set in the world that may be a simulation.",
-    "The player is Nua. They are a developer in a software development company in a bland city. By night they are a hacker. They have been contacted by a mysterious online entity called Mad Hatter, who believes the world is a simulation.",
-    "The player can click around to move to new scenes, find or activate artifacts.",
-    "They can also use objects from their inventory.",
-  ],
-  engines: [
-    "cartesian_image",
-    "cartesian_video",
-    "spherical_image",
-  ],
-  className: edu.className,
-  initialSituation,
-  initialActionnables,
-  inventory,
-  getScenePrompt: (situation?: string) => [
-    `first person photo`,
-    `cold design, modern architecture, business district, impersonal`,
-    situation || initialSituation,
-  ]
-}
-
diff --git a/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/data/dataset_mappers/__init__.py b/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/data/dataset_mappers/__init__.py
deleted file mode 100644
index f63cd5c034fcb60af8c78431205ae9b410f33250..0000000000000000000000000000000000000000
--- a/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/data/dataset_mappers/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-from .mask_former_semantic_dataset_mapper import MaskFormerSemanticDatasetMapper
diff --git a/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/modeling/transformer/open_vocab_transformer_predictor.py b/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/modeling/transformer/open_vocab_transformer_predictor.py
deleted file mode 100644
index 0efee3e14c71400a1cc5a55ea6c21b6876189aaa..0000000000000000000000000000000000000000
--- a/spaces/jcenaa/Segment-Any-RGBD/open_vocab_seg/modeling/transformer/open_vocab_transformer_predictor.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-from torch import nn
-from detectron2.config import configurable
-from .transformer_predictor import TransformerPredictor, MLP
-
-
-class OpenVocabTransformerPredictor(TransformerPredictor):
-    @configurable
-    def __init__(
-        self,
-        in_channels,
-        mask_classification=True,
-        *,
-        embedding_dim: int,
-        embed_hidden_dim: int,
-        embed_layers: int,
-        hidden_dim: int,
-        num_queries: int,
-        nheads: int,
-        dropout: float,
-        dim_feedforward: int,
-        enc_layers: int,
-        dec_layers: int,
-        pre_norm: bool,
-        deep_supervision: bool,
-        mask_dim: int,
-        enforce_input_project: bool,
-    ):
-        super().__init__(
-            in_channels,
-            False,
-            num_classes=embedding_dim,
-            hidden_dim=hidden_dim,
-            num_queries=num_queries,
-            nheads=nheads,
-            dropout=dropout,
-            dim_feedforward=dim_feedforward,
-            enc_layers=enc_layers,
-            dec_layers=dec_layers,
-            pre_norm=pre_norm,
-            deep_supervision=deep_supervision,
-            mask_dim=mask_dim,
-            enforce_input_project=enforce_input_project,
-        )
-        self.mask_classification = mask_classification
-        # output FFNs
-        if self.mask_classification:
-            self.class_embed = MLP(
-                hidden_dim, embed_hidden_dim, embedding_dim, embed_layers
-            )
-
-    def freeze_pretrained(self):
-        for name, module in self.named_children():
-            if name not in ["class_embed"]:
-                for param in module.parameters():
-                    param.requires_grad = False
-
-    @classmethod
-    def from_config(cls, cfg, in_channels, mask_classification):
-        ret = {}
-        ret["in_channels"] = in_channels
-        ret["mask_classification"] = mask_classification
-
-        ret["embedding_dim"] = cfg.MODEL.SEM_SEG_HEAD.EMBEDDING_DIM
-        ret["embed_hidden_dim"] = cfg.MODEL.SEM_SEG_HEAD.EMBED_HIDDEN_DIM
-        ret["embed_layers"] = cfg.MODEL.SEM_SEG_HEAD.EMBED_LAYERS
-        ret["hidden_dim"] = cfg.MODEL.MASK_FORMER.HIDDEN_DIM
-        ret["num_queries"] = cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES
-        # Transformer parameters:
-        ret["nheads"] = cfg.MODEL.MASK_FORMER.NHEADS
-        ret["dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT
-        ret["dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD
-        ret["enc_layers"] = cfg.MODEL.MASK_FORMER.ENC_LAYERS
-        ret["dec_layers"] = cfg.MODEL.MASK_FORMER.DEC_LAYERS
-        ret["pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM
-        ret["deep_supervision"] = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION
-        ret["enforce_input_project"] = cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ
-
-        ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
-
-        return ret
diff --git a/spaces/jho/MonocularDepth/BTS.py b/spaces/jho/MonocularDepth/BTS.py
deleted file mode 100644
index a17940397b5707f7df79217e6598e31892136949..0000000000000000000000000000000000000000
--- a/spaces/jho/MonocularDepth/BTS.py
+++ /dev/null
@@ -1,503 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-import numpy as np
-from torch import optim
-import os
-import math
-import cv2
-import albumentations as A
-
-# tensorboard is needed for run_train_step() which is commented out here
-# from torch.utils.tensorboard import SummaryWriter
-
-activation_fn = nn.ELU()
-
-MAX_DEPTH = 81
-DEPTH_OFFSET = 0.1 # This is used for ensuring depth prediction gets into positive range
-
-USE_APEX = False  # Enable if you have GPU with Tensor Cores, otherwise doesnt really bring any benefits.
-APEX_OPT_LEVEL = "O2"
-
-BATCH_NORM_MOMENTUM = 0.005
-ENABLE_BIAS = True
-
-device = torch.device("cpu")
-if torch.cuda.is_available() :
-    device = torch.device("cuda")
-print(f'--- BTS will use device: {device}')
-
-if USE_APEX:
-    import apex
-
-
-class UpscaleLayer(nn.Module):
-    def __init__(self, in_channels, out_channels):
-        super(UpscaleLayer, self).__init__()
-        self.conv = nn.Conv2d(in_channels, out_channels, 3, padding=1, bias=ENABLE_BIAS)
-        self.bn = nn.BatchNorm2d(out_channels, momentum=BATCH_NORM_MOMENTUM)
-
-    def forward(self, input):
-        input = nn.functional.interpolate(input, scale_factor=2, mode="nearest")
-        input = activation_fn(self.conv(input))
-        input = self.bn(input)
-        return input
-
-
-class UpscaleBlock(nn.Module):
-    def __init__(self, in_channels, skip_channels, out_channels):
-        super(UpscaleBlock, self).__init__()
-        self.uplayer = UpscaleLayer(in_channels, out_channels)
-        self.conv = nn.Conv2d(out_channels+skip_channels, out_channels, 3, padding=1, bias=ENABLE_BIAS)
-        self.bn2 = nn.BatchNorm2d(out_channels, BATCH_NORM_MOMENTUM)
-
-    def forward(self, input_j):
-        input, skip = input_j
-        input = self.uplayer(input)
-        cat = torch.cat((input, skip), 1)
-        input = activation_fn(self.conv(cat))
-        input = self.bn2(input)
-        return input, cat
-
-
-class UpscaleNetwork(nn.Module):
-    def __init__(self, filters=[512, 256]):
-        super(UpscaleNetwork, self,).__init__()
-        self.upscale_block1 = UpscaleBlock(2208, 384, filters[0])  # H16
-        self.upscale_block2 = UpscaleBlock(filters[0], 192, filters[1])  # H8
-
-    def forward(self, raw_input):
-        input, h2, h4, h8, h16 = raw_input
-        input, _ = self.upscale_block1((input, h16))
-        input, cat = self.upscale_block2((input, h8))
-        return input, cat
-
-
-class AtrousBlock(nn.Module):
-    def __init__(self, input_filters, filters, dilation, apply_initial_bn=True):
-        super(AtrousBlock, self).__init__()
-
-        self.initial_bn = nn.BatchNorm2d(input_filters, BATCH_NORM_MOMENTUM)
-        self.apply_initial_bn = apply_initial_bn
-
-        self.conv1 = nn.Conv2d(input_filters, filters*2, 1, 1, 0, bias=False)
-        self.norm1 = nn.BatchNorm2d(filters*2, BATCH_NORM_MOMENTUM)
-
-        self.atrous_conv = nn.Conv2d(filters*2, filters, 3, 1, dilation, dilation, bias=False)
-        self.norm2 = nn.BatchNorm2d(filters, BATCH_NORM_MOMENTUM)
-
-    def forward(self, input):
-        if self.apply_initial_bn:
-            input = self.initial_bn(input)
-
-        input = self.conv1(input.relu())
-        input = self.norm1(input)
-        input = self.atrous_conv(input.relu())
-        input = self.norm2(input)
-        return input
-
-
-class ASSPBlock(nn.Module):
-    def __init__(self, input_filters=256, cat_filters=448, atrous_filters=128):
-        super(ASSPBlock, self).__init__()
-
-        self.atrous_conv_r3 = AtrousBlock(input_filters, atrous_filters, 3, apply_initial_bn=False)
-        self.atrous_conv_r6 = AtrousBlock(cat_filters + atrous_filters, atrous_filters, 6)
-        self.atrous_conv_r12 = AtrousBlock(cat_filters + atrous_filters*2, atrous_filters, 12)
-        self.atrous_conv_r18 = AtrousBlock(cat_filters + atrous_filters*3, atrous_filters, 18)
-        self.atrous_conv_r24 = AtrousBlock(cat_filters + atrous_filters*4, atrous_filters, 24)
-
-        self.conv = nn.Conv2d(5 * atrous_filters + cat_filters, atrous_filters, 3, 1, 1, bias=ENABLE_BIAS)
-
-    def forward(self, input):
-        input, cat = input
-        layer1_out = self.atrous_conv_r3(input)
-        concat1 = torch.cat((cat, layer1_out), 1)
-
-        layer2_out = self.atrous_conv_r6(concat1)
-        concat2 = torch.cat((concat1, layer2_out), 1)
-
-        layer3_out = self.atrous_conv_r12(concat2)
-        concat3 = torch.cat((concat2, layer3_out), 1)
-
-        layer4_out = self.atrous_conv_r18(concat3)
-        concat4 = torch.cat((concat3, layer4_out), 1)
-
-        layer5_out = self.atrous_conv_r24(concat4)
-        concat5 = torch.cat((concat4, layer5_out), 1)
-
-        features = activation_fn(self.conv(concat5))
-        return features
-
-# Code of this layer is taken from official PyTorch implementation
-class LPGLayer(nn.Module):
-    def __init__(self, scale):
-        super(LPGLayer, self).__init__()
-        self.scale = scale
-        self.u = torch.arange(self.scale).reshape([1, 1, self.scale]).float()
-        self.v = torch.arange(int(self.scale)).reshape([1, self.scale, 1]).float()
-
-    def forward(self, plane_eq):
-        plane_eq_expanded = torch.repeat_interleave(plane_eq, int(self.scale), 2)
-        plane_eq_expanded = torch.repeat_interleave(plane_eq_expanded, int(self.scale), 3)
-
-        n1 = plane_eq_expanded[:, 0, :, :]
-        n2 = plane_eq_expanded[:, 1, :, :]
-        n3 = plane_eq_expanded[:, 2, :, :]
-        n4 = plane_eq_expanded[:, 3, :, :]
-
-        u = self.u.repeat(plane_eq.size(0), plane_eq.size(2) * int(self.scale), plane_eq.size(3)).to(device)
-        u = (u - (self.scale - 1) * 0.5) / self.scale
-
-        v = self.v.repeat(plane_eq.size(0), plane_eq.size(2), plane_eq.size(3) * int(self.scale)).to(device)
-        v = (v - (self.scale - 1) * 0.5) / self.scale
-
-        d = n4 / (n1 * u + n2 * v + n3)
-        d = d.unsqueeze(1)
-        return d
-
-
-class Reduction(nn.Module):
-    def __init__(self, scale, input_filters, is_final=False):
-        super(Reduction, self).__init__()
-        reduction_count = int(math.log(input_filters, 2)) - 2
-        self.reductions = torch.nn.Sequential()
-        for i in range(reduction_count):
-            if i != reduction_count-1:
-                self.reductions.add_module("1x1_reduc_%d_%d" % (scale, i), nn.Sequential(
-                    nn.Conv2d(int(input_filters / math.pow(2, i)), int(input_filters / math.pow(2, i + 1)), 1, 1, 0, bias=ENABLE_BIAS),
-                    activation_fn))
-            else:
-                if not is_final:
-                    self.reductions.add_module("1x1_reduc_%d_%d" % (scale, i), nn.Sequential(
-                        nn.Conv2d(int(input_filters / math.pow(2, i)), int(input_filters / math.pow(2, i + 1)), 1, 1, 0, bias=ENABLE_BIAS)))
-                else:
-                    self.reductions.add_module("1x1_reduc_%d_%d" % (scale, i), nn.Sequential(
-                        nn.Conv2d(int(input_filters / math.pow(2, i)), 1, 1, 1, 0, bias=ENABLE_BIAS), nn.Sigmoid()))
-
-    def forward(self, ip):
-        return self.reductions(ip)
-
-
-class LPGBlock(nn.Module):
-    def __init__(self, scale, input_filters=128):
-        super(LPGBlock, self).__init__()
-        self.scale = scale
-
-        self.reduction = Reduction(scale, input_filters)
-
-        self.conv = nn.Conv2d(4, 3, 1, 1, 0)
-        self.LPGLayer = LPGLayer(scale)
-
-    def forward(self, input):
-        input = self.reduction(input)
-
-        plane_parameters = torch.zeros_like(input)
-        input = self.conv(input)
-
-        theta = input[:, 0, :, :].sigmoid() * 3.1415926535 / 6
-        phi = input[:, 1, :, :].sigmoid() * 3.1415926535 * 2
-        dist = input[:, 2, :, :].sigmoid() * MAX_DEPTH
-
-        plane_parameters[:, 0, :, :] = torch.sin(theta) * torch.cos(phi)
-        plane_parameters[:, 1, :, :] = torch.sin(theta) * torch.sin(phi)
-        plane_parameters[:, 2, :, :] = torch.cos(theta)
-        plane_parameters[:, 3, :, :] = dist
-
-        plane_parameters[:, 0:3, :, :] = F.normalize(plane_parameters.clone()[:, 0:3, :, :], 2, 1)
-
-        depth = self.LPGLayer(plane_parameters.float())
-        return depth
-
-
-class bts_encoder(nn.Module):
-    def __init__(self):
-        super(bts_encoder, self).__init__()
-        self.dense_op_h2 = None
-        self.dense_op_h4 = None
-        self.dense_op_h8 = None
-        self.dense_op_h16 = None
-        self.dense_features = None
-
-        self.dense_feature_extractor = self.initialize_dense_feature_extractor()
-        self.freeze_batch_norm()
-        self.initialize_hooks()
-
-    def freeze_batch_norm(self):
-        for module in self.dense_feature_extractor.modules():
-            if isinstance(module, torch.nn.modules.BatchNorm2d):
-                module.track_running_stats = True
-                module.eval()
-                module.affine = True
-                module.requires_grad = True
-
-    def initialize_dense_feature_extractor(self):
-        dfe = torchvision.models.densenet161(True, True)
-        dfe.features.denseblock1.requires_grad = False
-        dfe.features.denseblock2.requires_grad = False
-        dfe.features.conv0.requires_grad = False
-        return dfe
-
-    def set_h2(self, module, input_, output):
-        self.dense_op_h2 = output
-
-    def set_h4(self, module, input_, output):
-        self.dense_op_h4 = output
-
-    def set_h8(self, module, input_, output):
-        self.dense_op_h8 = output
-
-    def set_h16(self, module, input_, output):
-        self.dense_op_h16 = output
-
-    def set_dense_features(self, module, input_, output):
-        self.dense_features = output
-
-    def initialize_hooks(self):
-        self.dense_feature_extractor.features.relu0.register_forward_hook(self.set_h2)
-        self.dense_feature_extractor.features.pool0.register_forward_hook(self.set_h4)
-        self.dense_feature_extractor.features.transition1.register_forward_hook(self.set_h8)
-        self.dense_feature_extractor.features.transition2.register_forward_hook(self.set_h16)
-        self.dense_feature_extractor.features.norm5.register_forward_hook(self.set_dense_features)
-
-    def forward(self, ip):
-        _ = self.dense_feature_extractor(ip)
-        joint_input = (self.dense_features.relu(), self.dense_op_h2, self.dense_op_h4, self.dense_op_h8, self.dense_op_h16)
-        return joint_input
-
-
-class bts_decoder(nn.Module):
-    def __init__(self):
-        super(bts_decoder, self).__init__()
-        self.UpscaleNet = UpscaleNetwork()
-        self.DenseASSPNet = ASSPBlock()
-
-        self.upscale_block3 = UpscaleBlock(64, 96, 128)  # H4
-        self.upscale_block4 = UpscaleBlock(128, 96, 128)  # H2
-
-        self.LPGBlock8 = LPGBlock(8, 128)
-        self.LPGBlock4 = LPGBlock(4, 64)  # 64 Filter
-        self.LPGBlock2 = LPGBlock(2, 64)  # 64 Filter
-
-        self.upconv_h4 = UpscaleLayer(128, 64)
-        self.upconv_h2 = UpscaleLayer(64, 32)  # 64 Filter
-        self.upconv_h = UpscaleLayer(64, 32)  # 32 filter
-
-        self.conv_h4 = nn.Conv2d(161, 64, 3, 1, 1, bias=ENABLE_BIAS)  # 64 Filter
-        self.conv_h2 = nn.Conv2d(129, 64, 3, 1, 1, bias=ENABLE_BIAS)  # 64 Filter
-        self.conv_h1 = nn.Conv2d(36, 32, 3, 1, 1, bias=ENABLE_BIAS)
-
-        self.reduction1x1 = Reduction(1, 32, True)
-
-        self.final_conv = nn.Conv2d(32, 1, 3, 1, 1, bias=ENABLE_BIAS)
-
-    def forward(self, joint_input, focal):
-        (dense_features, dense_op_h2, dense_op_h4, dense_op_h8, dense_op_h16) = joint_input
-        upscaled_out = self.UpscaleNet(joint_input)
-
-        dense_assp_out = self.DenseASSPNet(upscaled_out)
-
-        upconv_h4 = self.upconv_h4(dense_assp_out)
-        depth_8x8 = self.LPGBlock8(dense_assp_out) / MAX_DEPTH
-        depth_8x8_ds = nn.functional.interpolate(depth_8x8, scale_factor=1 / 4, mode="nearest")
-        depth_concat_4x4 = torch.cat((depth_8x8_ds, dense_op_h4, upconv_h4), 1)
-
-        conv_h4 = activation_fn(self.conv_h4(depth_concat_4x4))
-        upconv_h2 = self.upconv_h2(conv_h4)
-        depth_4x4 = self.LPGBlock4(conv_h4) / MAX_DEPTH
-
-        depth_4x4_ds = nn.functional.interpolate(depth_4x4, scale_factor=1 / 2, mode="nearest")
-        depth_concat_2x2 = torch.cat((depth_4x4_ds, dense_op_h2, upconv_h2), 1)
-
-        conv_h2 = activation_fn(self.conv_h2(depth_concat_2x2))
-        upconv_h = self.upconv_h(conv_h2)
-        depth_1x1 = self.reduction1x1(upconv_h)
-        depth_2x2 = self.LPGBlock2(conv_h2) / MAX_DEPTH
-
-        depth_concat = torch.cat((upconv_h, depth_1x1, depth_2x2, depth_4x4, depth_8x8), 1)
-        depth = activation_fn(self.conv_h1(depth_concat))
-        depth = self.final_conv(depth).sigmoid() * MAX_DEPTH + DEPTH_OFFSET
-
-        depth *= focal.view(-1, 1, 1, 1) / 715.0873
-        return depth, depth_2x2, depth_4x4, depth_8x8
-
-
-class bts_model(nn.Module):
-    def __init__(self):
-        super(bts_model, self).__init__()
-        self.encoder = bts_encoder()
-        self.decoder = bts_decoder()
-
-    def forward(self, input, focal=715.0873):
-        joint_input = self.encoder(input)
-        return self.decoder(joint_input, focal)
-
-
-class SilogLoss(nn.Module):
-    def __init__(self):
-        super(SilogLoss, self).__init__()
-
-    def forward(self, ip, target, ratio=10, ratio2=0.85):
-        ip = ip.reshape(-1)
-        target = target.reshape(-1)
-
-        mask = (target > 1) & (target < 81)
-        masked_ip = torch.masked_select(ip.float(), mask)
-        masked_op = torch.masked_select(target, mask)
-
-        log_diff = torch.log(masked_ip * ratio) - torch.log(masked_op * ratio)
-        log_diff_masked = log_diff
-
-        silog1 = torch.mean(log_diff_masked ** 2)
-        silog2 = ratio2 * (torch.mean(log_diff_masked) ** 2)
-        silog_loss = torch.sqrt(silog1 - silog2) * ratio
-        return silog_loss
-
-
-class BtsController:
-    def __init__(self, log_directory='run_1', logs_folder='tensorboard', backprop_frequency=1):
-        self.bts = bts_model().float().to(device)
-        self.optimizer = torch.optim.AdamW([{'params': self.bts.encoder.parameters(), 'weight_decay': 1e-2},
-                                       {'params': self.bts.decoder.parameters(), 'weight_decay': 0}],
-                                      lr=1e-4, eps=1e-6)
-
-        if USE_APEX:
-            self.bts, self.optimizer = apex.amp.initialize(self.bts, self.optimizer, opt_level=APEX_OPT_LEVEL)
-
-        self.bts = torch.nn.DataParallel(self.bts)
-
-        self.backprop_frequency = backprop_frequency
-
-        log_path = os.path.join(logs_folder, log_directory)
-        # self.writer = SummaryWriter(log_path)
-
-        self.criterion = SilogLoss()
-
-        self.learning_rate_scheduler = optim.lr_scheduler.ExponentialLR(self.optimizer, 0.95)
-
-        self.current_epoch = 0
-        self.last_loss = 0
-        self.current_step = 0
-
-    def eval(self):
-        self.bts = self.bts.eval()
-
-    def train(self):
-        self.bts = self.bts.train()
-
-    def predict(self, input, is_channels_first=True, focal=715.0873, normalize=False):
-        if normalize:
-            input = A.Compose([A.Normalize()])(**{"image": input})["image"]
-
-        if is_channels_first:
-            tensor_input = torch.tensor(input).unsqueeze(-1).to(device).float().transpose(0, 3).transpose(2,
-                                                                                                          3).transpose(
-                1, 2)
-        else:
-            tensor_input = torch.tensor(input).unsqueeze(-1).to(device).float().transpose(0, 3).transpose(1,
-                                                                                                          2).transpose(
-                2, 3)
-
-        shape_changed = False
-        org_shape = tensor_input.shape[2:]
-        if org_shape[0] % 32 != 0 or org_shape[1] % 32 != 0:
-            shape_changed = True
-            new_shape_y = round(org_shape[0] / 32) * 32
-            new_shape_x = round(org_shape[1] / 32) * 32
-            tensor_input = F.interpolate(tensor_input, (new_shape_y, new_shape_x), mode="bilinear")
-
-        model_output = self.bts(tensor_input, torch.tensor(focal).unsqueeze(0))[0][0].detach().unsqueeze(0)
-        if shape_changed:
-            model_output = F.interpolate(model_output, (org_shape[0], org_shape[1]), mode="nearest")
-
-        return model_output.cpu().squeeze()
-
-    @staticmethod
-    def depth_map_to_rgbimg(depth_map):
-        depth_map = np.asarray(np.squeeze((255 - torch.clamp_max(depth_map * 4, 250)).byte().numpy()), np.uint8)
-        depth_map = np.asarray(cv2.cvtColor(depth_map, cv2.COLOR_GRAY2RGB), np.uint8)
-        return depth_map
-
-    @staticmethod
-    def depth_map_to_grayimg(depth_map):
-        depth_map = np.asarray(np.squeeze((255 - torch.clamp_max(depth_map * 4, 250)).byte().numpy()), np.uint8)
-        return depth_map
-
-    @staticmethod
-    def normalize_img(image):
-        transformation = A.Compose([
-            A.Normalize()
-        ])
-        return transformation(**{"image": image})["image"]
-
-    # def run_train_step(self, tensor_input, tensor_output, tensor_focal):
-    #     tensor_input, tensor_output = tensor_input.to(device), tensor_output.to(device)
-    #     # Get Models prediction and calculate loss
-    #     model_output, depth2, depth4, depth8 = self.bts(tensor_input, tensor_focal)
-    #
-    #     loss = self.criterion(model_output, tensor_output) * 1/self.backprop_frequency
-    #
-    #     if USE_APEX:
-    #         with apex.amp.scale_loss(loss, self.optimizer) as scaled_loss:
-    #             scaled_loss.backward()
-    #     else:
-    #         loss.backward()
-    #
-    #     if self.current_step % self.backprop_frequency == 0:  # Make update once every x steps
-    #         torch.nn.utils.clip_grad_norm_(self.bts.parameters(), 5)
-    #         self.optimizer.step()
-    #         self.optimizer.zero_grad()
-    #
-    #     if self.current_step % 100 == 0:
-    #         self.writer.add_scalar("Loss", loss.item() * self.backprop_frequency / tensor_input.shape[0], self.current_step)
-    #
-    #     if self.current_step % 1000 == 0:
-    #         img = tensor_input[0].detach().transpose(0, 2).transpose(0, 1).cpu().numpy().astype(np.uint8)
-    #         self.writer.add_image("Input", img, self.current_step, None, "HWC")
-    #
-    #         visual_result = (255-torch.clamp_max(torchvision.utils.make_grid([tensor_output[0], model_output[0]]) * 5, 250)).byte()
-    #
-    #         self.writer.add_image("Output/Prediction", visual_result, self.current_step)
-    #         depths = [depth2[0], depth4[0], depth8[0]]
-    #         depths = [depth*MAX_DEPTH for depth in depths]
-    #         depth_visual = (255-torch.clamp_max(torchvision.utils.make_grid(depths) * 5, 250)).byte()
-    #
-    #         self.writer.add_image("Depths", depth_visual, self.current_step)
-    #
-    #     self.current_step += 1
-
-    def save_model(self, path):
-        save_dict = {
-            'epoch': self.current_epoch,
-            'model_state_dict': self.bts.state_dict(),
-            'optimizer_state_dict': self.optimizer.state_dict(),
-            "scheduler_state_dict": self.learning_rate_scheduler.state_dict(),
-            'loss': self.last_loss,
-            "last_step": self.current_step
-        }
-        if USE_APEX:
-            save_dict["amp"] = apex.amp.state_dict()
-            save_dict["opt_level"] = APEX_OPT_LEVEL
-
-        torch.save(save_dict, path)
-
-    def load_model(self, path):
-        dict = torch.load(path, map_location = device)
-
-        if USE_APEX:
-            saved_opt_level = dict["opt_level"]
-            self.bts, self.optimizer = apex.amp.initialize(self.bts, self.optimizer, opt_level=saved_opt_level)
-            apex.amp.load_state_dict(dict["amp"])
-
-        self.current_epoch = dict["epoch"]
-        self.bts.load_state_dict(dict["model_state_dict"])
-        self.bts = self.bts.float().to(device)
-
-        self.optimizer.load_state_dict(dict["optimizer_state_dict"])
-
-        self.learning_rate_scheduler.load_state_dict(dict["scheduler_state_dict"])
-        self.last_loss = dict["loss"]
-        self.current_step = dict["last_step"]
-
-        return dict
diff --git a/spaces/jhwen/bingo/src/components/ui/input.tsx b/spaces/jhwen/bingo/src/components/ui/input.tsx
deleted file mode 100644
index 684a857f3d769b78818fb13de1abaebfb09ca79c..0000000000000000000000000000000000000000
--- a/spaces/jhwen/bingo/src/components/ui/input.tsx
+++ /dev/null
@@ -1,25 +0,0 @@
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-
-export interface InputProps
-  extends React.InputHTMLAttributes<HTMLInputElement> {}
-
-const Input = React.forwardRef<HTMLInputElement, InputProps>(
-  ({ className, type, ...props }, ref) => {
-    return (
-      <input
-        type={type}
-        className={cn(
-          'flex h-9 w-full rounded-md border border-input bg-transparent px-3 py-2 text-sm shadow-sm ring-offset-background file:border-0 file:bg-transparent file:text-sm file:font-medium placeholder:text-muted-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Input.displayName = 'Input'
-
-export { Input }
diff --git a/spaces/joshipunitram/crowd-counting-p2p/app.py b/spaces/joshipunitram/crowd-counting-p2p/app.py
deleted file mode 100644
index 73a9a56934642b3406308a61b66ede0ab3c46722..0000000000000000000000000000000000000000
--- a/spaces/joshipunitram/crowd-counting-p2p/app.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import gradio as gr
-import inference_flask as util
-import cv2
-from PIL import Image
-
-model, transform, device = util.load_model()
-
-def inference_img(input_img):
-    global model, transform, device
-    input_img = Image.fromarray(input_img)
-    output = util.image_inference(model, transform, device, input_img, raw_img_input=True, return_img=True)
-    output = cv2.putText(output[0],str(output[1]), (0,70), cv2.FONT_HERSHEY_SIMPLEX, 
-                   1, (255, 0, 0), 2, cv2.LINE_AA)
-    return output
-
-demo = gr.Interface(inference_img, gr.Image(), "image")
-demo.launch()
diff --git a/spaces/juancopi81/multitrack-midi-music-generator/utils.py b/spaces/juancopi81/multitrack-midi-music-generator/utils.py
deleted file mode 100644
index 22c140ff963af1fa884267c280b0a2c6933a3f77..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/multitrack-midi-music-generator/utils.py
+++ /dev/null
@@ -1,244 +0,0 @@
-from typing import List, Tuple
-
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import note_seq
-from matplotlib.figure import Figure
-from numpy import ndarray
-import torch
-
-from constants import GM_INSTRUMENTS, SAMPLE_RATE
-from string_to_notes import token_sequence_to_note_sequence
-from model import get_model_and_tokenizer
-
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Load the tokenizer and the model
-model, tokenizer = get_model_and_tokenizer()
-
-
-def create_seed_string(genre: str = "OTHER") -> str:
-    """
-    Creates a seed string for generating a new piece.
-
-    Args:
-        genre (str, optional): The genre of the piece. Defaults to "OTHER".
-
-    Returns:
-        str: The seed string.
-    """
-    if genre == "RANDOM":
-        seed_string = "PIECE_START"
-    else:
-        seed_string = f"PIECE_START GENRE={genre} TRACK_START"
-    return seed_string
-
-
-def get_instruments(text_sequence: str) -> List[str]:
-    """
-    Extracts the list of instruments from a text sequence.
-
-    Args:
-        text_sequence (str): The text sequence.
-
-    Returns:
-        List[str]: The list of instruments.
-    """
-    instruments = []
-    parts = text_sequence.split()
-    for part in parts:
-        if part.startswith("INST="):
-            if part[5:] == "DRUMS":
-                instruments.append("Drums")
-            else:
-                index = int(part[5:])
-                instruments.append(GM_INSTRUMENTS[index])
-    return instruments
-
-
-def generate_new_instrument(seed: str, temp: float = 0.75) -> str:
-    """
-    Generates a new instrument sequence from a given seed and temperature.
-
-    Args:
-        seed (str): The seed string for the generation.
-        temp (float, optional): The temperature for the generation, which controls the randomness. Defaults to 0.75.
-
-    Returns:
-        str: The generated instrument sequence.
-    """
-    seed_length = len(tokenizer.encode(seed))
-
-    while True:
-        # Encode the conditioning tokens.
-        input_ids = tokenizer.encode(seed, return_tensors="pt")
-
-        # Move the input_ids tensor to the same device as the model
-        input_ids = input_ids.to(model.device)
-
-        # Generate more tokens.
-        eos_token_id = tokenizer.encode("TRACK_END")[0]
-        generated_ids = model.generate(
-            input_ids,
-            max_new_tokens=2048,
-            do_sample=True,
-            temperature=temp,
-            eos_token_id=eos_token_id,
-        )
-        generated_sequence = tokenizer.decode(generated_ids[0])
-
-        # Check if the generated sequence contains "NOTE_ON" beyond the seed
-        new_generated_sequence = tokenizer.decode(generated_ids[0][seed_length:])
-        if "NOTE_ON" in new_generated_sequence:
-            return generated_sequence
-
-
-def get_outputs_from_string(
-    generated_sequence: str, qpm: int = 120
-) -> Tuple[ndarray, str, Figure, str, str]:
-    """
-    Converts a generated sequence into various output formats including audio, MIDI, plot, etc.
-
-    Args:
-        generated_sequence (str): The generated sequence of tokens.
-        qpm (int, optional): The quarter notes per minute. Defaults to 120.
-
-    Returns:
-        Tuple[ndarray, str, Figure, str, str]: The audio waveform, MIDI file name, plot figure,
-                                               instruments string, and number of tokens string.
-    """
-    instruments = get_instruments(generated_sequence)
-    instruments_str = "\n".join(f"- {instrument}" for instrument in instruments)
-    note_sequence = token_sequence_to_note_sequence(generated_sequence, qpm=qpm)
-
-    synth = note_seq.fluidsynth
-    array_of_floats = synth(note_sequence, sample_rate=SAMPLE_RATE)
-    int16_data = note_seq.audio_io.float_samples_to_int16(array_of_floats)
-    fig = note_seq.plot_sequence(note_sequence, show_figure=False)
-    num_tokens = str(len(generated_sequence.split()))
-    audio = gr.make_waveform((SAMPLE_RATE, int16_data))
-    note_seq.note_sequence_to_midi_file(note_sequence, "midi_ouput.mid")
-    return audio, "midi_ouput.mid", fig, instruments_str, num_tokens
-
-
-def remove_last_instrument(
-    text_sequence: str, qpm: int = 120
-) -> Tuple[ndarray, str, Figure, str, str, str]:
-    """
-    Removes the last instrument from a song string and returns the various output formats.
-
-    Args:
-        text_sequence (str): The song string.
-        qpm (int, optional): The quarter notes per minute. Defaults to 120.
-
-    Returns:
-        Tuple[ndarray, str, Figure, str, str, str]: The audio waveform, MIDI file name, plot figure,
-                                                    instruments string, new song string, and number of tokens string.
-    """
-    # We split the song into tracks by splitting on 'TRACK_START'
-    tracks = text_sequence.split("TRACK_START")
-    # We keep all tracks except the last one
-    modified_tracks = tracks[:-1]
-    # We join the tracks back together, adding back the 'TRACK_START' that was removed by split
-    new_song = "TRACK_START".join(modified_tracks)
-
-    if len(tracks) == 2:
-        # There is only one instrument, so start from scratch
-        audio, midi_file, fig, instruments_str, new_song, num_tokens = generate_song(
-            text_sequence=new_song
-        )
-    elif len(tracks) == 1:
-        # No instrument so start from empty sequence
-        audio, midi_file, fig, instruments_str, new_song, num_tokens = generate_song(
-            text_sequence=""
-        )
-    else:
-        audio, midi_file, fig, instruments_str, num_tokens = get_outputs_from_string(
-            new_song, qpm
-        )
-
-    return audio, midi_file, fig, instruments_str, new_song, num_tokens
-
-
-def regenerate_last_instrument(
-    text_sequence: str, qpm: int = 120
-) -> Tuple[ndarray, str, Figure, str, str, str]:
-    """
-    Regenerates the last instrument in a song string and returns the various output formats.
-
-    Args:
-        text_sequence (str): The song string.
-        qpm (int, optional): The quarter notes per minute. Defaults to 120.
-
-    Returns:
-        Tuple[ndarray, str, Figure, str, str, str]: The audio waveform, MIDI file name, plot figure,
-                                                    instruments string, new song string, and number of tokens string.
-    """
-    last_inst_index = text_sequence.rfind("INST=")
-    if last_inst_index == -1:
-        # No instrument so start from empty sequence
-        audio, midi_file, fig, instruments_str, new_song, num_tokens = generate_song(
-            text_sequence="", qpm=qpm
-        )
-    else:
-        # Take it from the last instrument and continue generation
-        next_space_index = text_sequence.find(" ", last_inst_index)
-        new_seed = text_sequence[:next_space_index]
-        audio, midi_file, fig, instruments_str, new_song, num_tokens = generate_song(
-            text_sequence=new_seed, qpm=qpm
-        )
-    return audio, midi_file, fig, instruments_str, new_song, num_tokens
-
-
-def change_tempo(
-    text_sequence: str, qpm: int
-) -> Tuple[ndarray, str, Figure, str, str, str]:
-    """
-    Changes the tempo of a song string and returns the various output formats.
-
-    Args:
-        text_sequence (str): The song string.
-        qpm (int): The new quarter notes per minute.
-
-    Returns:
-        Tuple[ndarray, str, Figure, str, str, str]: The audio waveform, MIDI file name, plot figure,
-                                                    instruments string, text sequence, and number of tokens string.
-    """
-    audio, midi_file, fig, instruments_str, num_tokens = get_outputs_from_string(
-        text_sequence, qpm=qpm
-    )
-    return audio, midi_file, fig, instruments_str, text_sequence, num_tokens
-
-
-def generate_song(
-    genre: str = "OTHER",
-    temp: float = 0.75,
-    text_sequence: str = "",
-    qpm: int = 120,
-) -> Tuple[ndarray, str, Figure, str, str, str]:
-    """
-    Generates a song given a genre, temperature, initial text sequence, and tempo.
-
-    Args:
-        model (AutoModelForCausalLM): The pretrained model used for generating the sequences.
-        tokenizer (AutoTokenizer): The tokenizer used to encode and decode the sequences.
-        genre (str, optional): The genre of the song. Defaults to "OTHER".
-        temp (float, optional): The temperature for the generation, which controls the randomness. Defaults to 0.75.
-        text_sequence (str, optional): The initial text sequence for the song. Defaults to "".
-        qpm (int, optional): The quarter notes per minute. Defaults to 120.
-
-    Returns:
-        Tuple[ndarray, str, Figure, str, str, str]: The audio waveform, MIDI file name, plot figure,
-                                                    instruments string, generated song string, and number of tokens string.
-    """
-    if text_sequence == "":
-        seed_string = create_seed_string(genre)
-    else:
-        seed_string = text_sequence
-
-    generated_sequence = generate_new_instrument(seed=seed_string, temp=temp)
-    audio, midi_file, fig, instruments_str, num_tokens = get_outputs_from_string(
-        generated_sequence, qpm
-    )
-    return audio, midi_file, fig, instruments_str, generated_sequence, num_tokens
diff --git a/spaces/justest/gpt4free/g4f/.v1/SECURITY.md b/spaces/justest/gpt4free/g4f/.v1/SECURITY.md
deleted file mode 100644
index cbc69677a0ec6b0192f1bd61f3eccb7723f8827b..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/g4f/.v1/SECURITY.md
+++ /dev/null
@@ -1,4 +0,0 @@
-## Reporting a Vulnerability
-
-Reporting a Vulnerability
-Please report (suspected) security vulnerabilities to https://t.me/xtekky. You will receive a response within 48 hours. If the issue is confirmed, we will release a patch as soon as possible depending on complexity but historically within a few days.
diff --git a/spaces/justest/gpt4free/g4f/.v1/testing/aicolors_test.py b/spaces/justest/gpt4free/g4f/.v1/testing/aicolors_test.py
deleted file mode 100644
index 853f7e4560e9003b3a1d76119243027f06ec577c..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/g4f/.v1/testing/aicolors_test.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from gpt4free import aicolors
-
-prompt = "Light green color"
-req = aicolors.Completion.create(prompt=prompt)
-
-print(req)
diff --git a/spaces/justest/gpt4free/g4f/.v1/unfinished/openprompt/README.md b/spaces/justest/gpt4free/g4f/.v1/unfinished/openprompt/README.md
deleted file mode 100644
index 489d054aeb56c15d30d3cda1e8ef350c7ff3167a..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/g4f/.v1/unfinished/openprompt/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-https://openprompt.co/
-
-to do:
-- finish integrating email client
-- code refractoring
\ No newline at end of file
diff --git a/spaces/jx-yang/deep-thinking/utils/__init__.py b/spaces/jx-yang/deep-thinking/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ka1kuk/fastapi/Linlada.py b/spaces/ka1kuk/fastapi/Linlada.py
deleted file mode 100644
index c2e286fae0d236e617790378167daa0114cffcdd..0000000000000000000000000000000000000000
--- a/spaces/ka1kuk/fastapi/Linlada.py
+++ /dev/null
@@ -1,352 +0,0 @@
-import os
-import json
-import random
-import json
-import os
-import uuid
-import ssl
-import certifi
-import aiohttp
-import asyncio
-
-import requests
-from typing import Dict, NewType, Union, Optional, List, get_type_hints
-
-sha256 = NewType('sha_256_hash', str)
-
-url = 'https://bing.com/chat'
-model = ['gpt-4']
-supports_stream = True
-needs_auth = False
-
-ssl_context = ssl.create_default_context()
-ssl_context.load_verify_locations(certifi.where())
-
-
-class optionsSets:
-    optionSet: dict = {
-        'tone': str,
-        'optionsSets': list
-    }
-
-    jailbreak: dict = {
-        "optionsSets": [
-            'saharasugg',
-            'enablenewsfc',
-            'clgalileo',
-            'gencontentv3',
-            "nlu_direct_response_filter",
-            "deepleo",
-            "disable_emoji_spoken_text",
-            "responsible_ai_policy_235",
-            "enablemm",
-            "h3precise"
-            # "harmonyv3",
-            "dtappid",
-            "cricinfo",
-            "cricinfov2",
-            "dv3sugg",
-            "nojbfedge"
-        ]
-    }
-
-
-class Defaults:
-    delimiter = '\x1e'
-    ip_address = f'13.{random.randint(104, 107)}.{random.randint(0, 255)}.{random.randint(0, 255)}'
-
-    allowedMessageTypes = [
-        'Chat',
-        'Disengaged',
-        'AdsQuery',
-        'SemanticSerp',
-        'GenerateContentQuery',
-        'SearchQuery',
-        'ActionRequest',
-        'Context',
-        'Progress',
-        'AdsQuery',
-        'SemanticSerp'
-    ]
-
-    sliceIds = [
-
-        # "222dtappid",
-        # "225cricinfo",
-        # "224locals0"
-
-        'winmuid3tf',
-        'osbsdusgreccf',
-        'ttstmout',
-        'crchatrev',
-        'winlongmsgtf',
-        'ctrlworkpay',
-        'norespwtf',
-        'tempcacheread',
-        'temptacache',
-        '505scss0',
-        '508jbcars0',
-        '515enbotdets0',
-        '5082tsports',
-        '515vaoprvs',
-        '424dagslnv1s0',
-        'kcimgattcf',
-        '427startpms0'
-    ]
-
-    location = {
-        'locale': 'en-US',
-        'market': 'en-US',
-        'region': 'US',
-        'locationHints': [
-            {
-                'country': 'United States',
-                'state': 'California',
-                'city': 'Los Angeles',
-                'timezoneoffset': 8,
-                'countryConfidence': 8,
-                'Center': {
-                    'Latitude': 34.0536909,
-                    'Longitude': -118.242766
-                },
-                'RegionType': 2,
-                'SourceType': 1
-            }
-        ],
-    }
-
-
-def _format(msg: dict) -> str:
-    return json.dumps(msg, ensure_ascii=False) + Defaults.delimiter
-
-
-async def create_conversation():
-    for _ in range(5):
-        create = requests.get('https://www.bing.com/turing/conversation/create',
-                              headers={
-                                  'authority': 'edgeservices.bing.com',
-                                  'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
-                                  'accept-language': 'en-US,en;q=0.9',
-                                  'cache-control': 'max-age=0',
-                                  'sec-ch-ua': '"Chromium";v="110", "Not A(Brand";v="24", "Microsoft Edge";v="110"',
-                                  'sec-ch-ua-arch': '"x86"',
-                                  'sec-ch-ua-bitness': '"64"',
-                                  'sec-ch-ua-full-version': '"110.0.1587.69"',
-                                  'sec-ch-ua-full-version-list': '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
-                                  'sec-ch-ua-mobile': '?0',
-                                  'sec-ch-ua-model': '""',
-                                  'sec-ch-ua-platform': '"Windows"',
-                                  'sec-ch-ua-platform-version': '"15.0.0"',
-                                  'sec-fetch-dest': 'document',
-                                  'sec-fetch-mode': 'navigate',
-                                  'sec-fetch-site': 'none',
-                                  'sec-fetch-user': '?1',
-                                  'upgrade-insecure-requests': '1',
-                                  'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36 Edg/110.0.1587.69',
-                                  'x-edge-shopping-flag': '1',
-                                  'x-forwarded-for': Defaults.ip_address
-                              })
-
-        conversationId = create.json().get('conversationId')
-        clientId = create.json().get('clientId')
-        conversationSignature = create.json().get('conversationSignature')
-
-        if not conversationId or not clientId or not conversationSignature and _ == 4:
-            raise Exception('Failed to create conversation.')
-
-        return conversationId, clientId, conversationSignature
-
-
-async def stream_generate(prompt: str, mode: optionsSets.optionSet = optionsSets.jailbreak, context: bool or str = False):
-    timeout = aiohttp.ClientTimeout(total=900)
-    session = aiohttp.ClientSession(timeout=timeout)
-
-    conversationId, clientId, conversationSignature = await create_conversation()
-
-    wss = await session.ws_connect('wss://sydney.bing.com/sydney/ChatHub', ssl=ssl_context, autoping=False,
-                                   headers={
-                                       'accept': 'application/json',
-                                       'accept-language': 'en-US,en;q=0.9',
-                                       'content-type': 'application/json',
-                                       'sec-ch-ua': '"Not_A Brand";v="99", "Microsoft Edge";v="110", "Chromium";v="110"',
-                                       'sec-ch-ua-arch': '"x86"',
-                                       'sec-ch-ua-bitness': '"64"',
-                                       'sec-ch-ua-full-version': '"109.0.1518.78"',
-                                       'sec-ch-ua-full-version-list': '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
-                                       'sec-ch-ua-mobile': '?0',
-                                       'sec-ch-ua-model': '',
-                                       'sec-ch-ua-platform': '"Windows"',
-                                       'sec-ch-ua-platform-version': '"15.0.0"',
-                                       'sec-fetch-dest': 'empty',
-                                       'sec-fetch-mode': 'cors',
-                                       'sec-fetch-site': 'same-origin',
-                                       'x-ms-client-request-id': str(uuid.uuid4()),
-                                       'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-                                       'Referer': 'https://www.bing.com/search?q=Bing+AI&showconv=1&FORM=hpcodx',
-                                       'Referrer-Policy': 'origin-when-cross-origin',
-                                       'x-forwarded-for': Defaults.ip_address
-                                   })
-
-    await wss.send_str(_format({'protocol': 'json', 'version': 1}))
-    await wss.receive(timeout=900)
-
-    struct = {
-        'arguments': [
-            {
-                **mode,
-                'source': 'cib',
-                'allowedMessageTypes': Defaults.allowedMessageTypes,
-                'sliceIds': Defaults.sliceIds,
-                'traceId': os.urandom(16).hex(),
-                'isStartOfSession': True,
-                'message': Defaults.location | {
-                    'author': 'user',
-                    'inputMethod': 'Keyboard',
-                    'text': prompt,
-                    'messageType': 'Chat'
-                },
-                'conversationSignature': conversationSignature,
-                'participant': {
-                    'id': clientId
-                },
-                'conversationId': conversationId
-            }
-        ],
-        'invocationId': '0',
-        'target': 'chat',
-        'type': 4
-    }
-
-    if context:
-        struct['arguments'][0]['previousMessages'] = [
-            {
-                "author": "user",
-                "description": context,
-                "contextType": "WebPage",
-                "messageType": "Context",
-                "messageId": "discover-web--page-ping-mriduna-----"
-            }
-        ]
-
-    await wss.send_str(_format(struct))
-
-    final = False
-    draw = False
-    resp_txt = ''
-    result_text = ''
-    resp_txt_no_link = ''
-    cache_text = ''
-
-    while not final:
-        msg = await wss.receive(timeout=900)
-        objects = msg.data.split(Defaults.delimiter)
-
-        for obj in objects:
-            if obj is None or not obj:
-                continue
-
-            response = json.loads(obj)
-            if response.get('type') == 1 and response['arguments'][0].get('messages',):
-                if not draw:
-                    if (response['arguments'][0]['messages'][0]['contentOrigin'] != 'Apology') and not draw:
-                        resp_txt = result_text + \
-                            response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0].get(
-                                'text', '')
-                        resp_txt_no_link = result_text + \
-                            response['arguments'][0]['messages'][0].get(
-                                'text', '')
-
-                        if response['arguments'][0]['messages'][0].get('messageType',):
-                            resp_txt = (
-                                resp_txt
-                                + response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0]['inlines'][0].get('text')
-                                + '\n'
-                            )
-                            result_text = (
-                                result_text
-                                + response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0]['inlines'][0].get('text')
-                                + '\n'
-                            )
-
-                    if cache_text.endswith('   '):
-                        final = True
-                        if wss and not wss.closed:
-                            await wss.close()
-                        if session and not session.closed:
-                            await session.close()
-                            
-                    yield (resp_txt.replace(cache_text, ''))
-                    cache_text = resp_txt
-
-            elif response.get('type') == 2:
-                if response['item']['result'].get('error'):
-                    if wss and not wss.closed:
-                        await wss.close()
-                    if session and not session.closed:
-                        await session.close()
-
-                    raise Exception(
-                        f"{response['item']['result']['value']}: {response['item']['result']['message']}")
-
-                if draw:
-                    cache = response['item']['messages'][1]['adaptiveCards'][0]['body'][0]['text']
-                    response['item']['messages'][1]['adaptiveCards'][0]['body'][0]['text'] = (
-                        cache + resp_txt)
-
-                if (response['item']['messages'][-1]['contentOrigin'] == 'Apology' and resp_txt):
-                    response['item']['messages'][-1]['text'] = resp_txt_no_link
-                    response['item']['messages'][-1]['adaptiveCards'][0]['body'][0]['text'] = resp_txt
-
-                    # print('Preserved the message from being deleted', file=sys.stderr)
-
-                final = True
-                if wss and not wss.closed:
-                    await wss.close()
-                if session and not session.closed:
-                    await session.close()
-
-
-def run(generator):
-    loop = asyncio.get_event_loop()
-    gen = generator.__aiter__()
-
-    while True:
-        try:
-            next_val = loop.run_until_complete(gen.__anext__())
-            yield next_val
-
-        except StopAsyncIteration:
-            break
-
-    #print('Done')
-
-
-def convert(messages):
-    context = ""
-
-    for message in messages:
-        context += "[%s](#message)\n%s\n\n" % (message['role'],
-                                               message['content'])
-
-    return context
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    if len(messages) < 2:
-        prompt = messages[0]['content']
-        context = False
-
-    else:
-        prompt = messages[-1]['content']
-        context = convert(messages[:-1])
-
-    response = run(stream_generate(prompt, optionsSets.jailbreak, context))
-    for token in response:
-        yield (token)
-
-    #print('Done')
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join(
-        [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/kadirnar/yolox/configs/yolox_m.py b/spaces/kadirnar/yolox/configs/yolox_m.py
deleted file mode 100644
index 9666a31177b9cc1c94978f9867aaceac8ddebce2..0000000000000000000000000000000000000000
--- a/spaces/kadirnar/yolox/configs/yolox_m.py
+++ /dev/null
@@ -1,15 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-import os
-
-from yolox.exp import Exp as MyExp
-
-
-class Exp(MyExp):
-    def __init__(self):
-        super(Exp, self).__init__()
-        self.depth = 0.67
-        self.width = 0.75
-        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
diff --git a/spaces/kargaranamir/Hengam/utils.py b/spaces/kargaranamir/Hengam/utils.py
deleted file mode 100644
index 21ed4e4088bb05b60d9c86b40e07c306dc2e785d..0000000000000000000000000000000000000000
--- a/spaces/kargaranamir/Hengam/utils.py
+++ /dev/null
@@ -1,490 +0,0 @@
-"""
-utils for Hengam inference
-"""
-
-"""### Import Libraries"""
-
-# import primitive libraries
-import os
-import pandas as pd
-from tqdm import tqdm
-import numpy as np
-import json
-
-# import seqval to report classifier performance metrics
-from seqeval.metrics import accuracy_score, precision_score, recall_score, f1_score
-from seqeval.scheme import IOB2
-
-# import torch related modules
-import torch
-from torch.utils.data import DataLoader
-from torch.utils.data import Dataset
-from torch.nn.utils.rnn import pad_sequence
-import torch.nn as nn
-
-# import pytorch lightning library
-import pytorch_lightning as pl
-from torchcrf import CRF as SUPERCRF
-
-# import NLTK to create better tokenizer
-import nltk
-from nltk.tokenize import RegexpTokenizer
-
-# Transformers : Roberta Model
-from transformers import XLMRobertaTokenizerFast
-from transformers import XLMRobertaModel, XLMRobertaConfig
-
-
-# import Typings
-from typing import Union, Dict, List, Tuple, Any, Optional
-
-import glob
-
-# for sent tokenizer (nltk)
-nltk.download('punkt')
-
-
-"""## XLM-Roberta
-### TokenFromSubtoken
-- Code adapted from the following [file](https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/models/torch_bert/torch_transformers_sequence_tagger.py)
-- DeepPavlov is an popular open source library for deep learning end-to-end dialog systems and chatbots.
-- Licensed under the Apache License, Version 2.0 (the "License");
-"""
-
-class TokenFromSubtoken(torch.nn.Module):
-
-    def forward(self, units: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
-        """ Assemble token level units from subtoken level units
-        Args:
-            units: torch.Tensor of shape [batch_size, SUBTOKEN_seq_length, n_features]
-            mask: mask of token beginnings. For example: for tokens
-                    [[``[CLS]`` ``My``, ``capybara``, ``[SEP]``],
-                    [``[CLS]`` ``Your``, ``aar``, ``##dvark``, ``is``, ``awesome``, ``[SEP]``]]
-                the mask will be
-                    [[0, 1, 1, 0, 0, 0, 0],
-                    [0, 1, 1, 0, 1, 1, 0]]
-        Returns:
-            word_level_units: Units assembled from ones in the mask. For the
-                example above this units will correspond to the following
-                    [[``My``, ``capybara``],
-                    [``Your`, ``aar``, ``is``, ``awesome``,]]
-                the shape of this tensor will be [batch_size, TOKEN_seq_length, n_features]
-        """
-        
-        device = units.device
-        nf_int = units.size()[-1]
-        batch_size = units.size()[0]
-
-        # number of TOKENS in each sentence
-        token_seq_lengths = torch.sum(mask, 1).to(torch.int64)
-        # number of words
-        n_words = torch.sum(token_seq_lengths)
-        # max token seq len
-        max_token_seq_len = torch.max(token_seq_lengths)
-
-        idxs = torch.stack(torch.nonzero(mask, as_tuple=True), dim=1)
-        # padding is for computing change from one sample to another in the batch
-        sample_ids_in_batch = torch.nn.functional.pad(input=idxs[:, 0], pad=[1, 0])
-        
-        a = (~torch.eq(sample_ids_in_batch[1:], sample_ids_in_batch[:-1])).to(torch.int64)
-        
-        # transforming sample start masks to the sample starts themselves
-        q = a * torch.arange(n_words, device=device).to(torch.int64)
-        count_to_substract = torch.nn.functional.pad(torch.masked_select(q, q.to(torch.bool)), [1, 0])
-
-        new_word_indices = torch.arange(n_words, device=device).to(torch.int64) - count_to_substract[torch.cumsum(a, 0)]
-        
-        n_total_word_elements = max_token_seq_len*torch.ones_like(token_seq_lengths, device=device).sum()
-        word_indices_flat = (idxs[:, 0] * max_token_seq_len + new_word_indices).to(torch.int64)
-        #x_mask = torch.sum(torch.nn.functional.one_hot(word_indices_flat, n_total_word_elements), 0)
-        #x_mask = x_mask.to(torch.bool)
-        x_mask = torch.zeros(n_total_word_elements, dtype=torch.bool, device=device)
-        x_mask[word_indices_flat] = torch.ones_like(word_indices_flat, device=device, dtype=torch.bool)
-        # to get absolute indices we add max_token_seq_len:
-        # idxs[:, 0] * max_token_seq_len -> [0, 0, 0, 1, 1, 2] * 2 = [0, 0, 0, 3, 3, 6]
-        # word_indices_flat -> [0, 0, 0, 3, 3, 6] + [0, 1, 2, 0, 1, 0] = [0, 1, 2, 3, 4, 6]
-        # total number of words in the batch (including paddings)
-        # batch_size * max_token_seq_len -> 3 * 3 = 9
-        # tf.one_hot(...) ->
-        # [[1. 0. 0. 0. 0. 0. 0. 0. 0.]
-        #  [0. 1. 0. 0. 0. 0. 0. 0. 0.]
-        #  [0. 0. 1. 0. 0. 0. 0. 0. 0.]
-        #  [0. 0. 0. 1. 0. 0. 0. 0. 0.]
-        #  [0. 0. 0. 0. 1. 0. 0. 0. 0.]
-        #  [0. 0. 0. 0. 0. 0. 1. 0. 0.]]
-        #  x_mask -> [1, 1, 1, 1, 1, 0, 1, 0, 0]
-        nonword_indices_flat = (~x_mask).nonzero().squeeze(-1)
-
-        # get a sequence of units corresponding to the start subtokens of the words
-        # size: [n_words, n_features]
-        
-        elements = units[mask.bool()]
-
-        # prepare zeros for paddings
-        # size: [batch_size * TOKEN_seq_length - n_words, n_features]
-        paddings = torch.zeros_like(nonword_indices_flat, dtype=elements.dtype).unsqueeze(-1).repeat(1,nf_int).to(device)
-
-        # tensor_flat -> [x, x, x, x, x, 0, x, 0, 0]
-        tensor_flat_unordered = torch.cat([elements, paddings])
-        _, order_idx = torch.sort(torch.cat([word_indices_flat, nonword_indices_flat]))
-        tensor_flat = tensor_flat_unordered[order_idx]
-
-        tensor = torch.reshape(tensor_flat, (-1, max_token_seq_len, nf_int))
-        # tensor -> [[x, x, x],
-        #            [x, x, 0],
-        #            [x, 0, 0]]
-
-        return tensor
-
-"""### Conditional Random Field 
-- Code adopted form [torchcrf library](https://pytorch-crf.readthedocs.io/en/stable/)
-- we override veiterbi decoder in order to make it compatible with our code 
-"""
-
-class CRF(SUPERCRF):
-
-    # override veiterbi decoder in order to make it compatible with our code 
-    def _viterbi_decode(self, emissions: torch.FloatTensor,
-                        mask: torch.ByteTensor) -> List[List[int]]:
-        # emissions: (seq_length, batch_size, num_tags)
-        # mask: (seq_length, batch_size)
-        assert emissions.dim() == 3 and mask.dim() == 2
-        assert emissions.shape[:2] == mask.shape
-        assert emissions.size(2) == self.num_tags
-        assert mask[0].all()
-
-        seq_length, batch_size = mask.shape
-
-        # Start transition and first emission
-        # shape: (batch_size, num_tags)
-        score = self.start_transitions + emissions[0]
-        history = []
-
-        # score is a tensor of size (batch_size, num_tags) where for every batch,
-        # value at column j stores the score of the best tag sequence so far that ends
-        # with tag j
-        # history saves where the best tags candidate transitioned from; this is used
-        # when we trace back the best tag sequence
-
-        # Viterbi algorithm recursive case: we compute the score of the best tag sequence
-        # for every possible next tag
-        for i in range(1, seq_length):
-            # Broadcast viterbi score for every possible next tag
-            # shape: (batch_size, num_tags, 1)
-            broadcast_score = score.unsqueeze(2)
-
-            # Broadcast emission score for every possible current tag
-            # shape: (batch_size, 1, num_tags)
-            broadcast_emission = emissions[i].unsqueeze(1)
-
-            # Compute the score tensor of size (batch_size, num_tags, num_tags) where
-            # for each sample, entry at row i and column j stores the score of the best
-            # tag sequence so far that ends with transitioning from tag i to tag j and emitting
-            # shape: (batch_size, num_tags, num_tags)
-            next_score = broadcast_score + self.transitions + broadcast_emission
-
-            # Find the maximum score over all possible current tag
-            # shape: (batch_size, num_tags)
-            next_score, indices = next_score.max(dim=1)
-
-            # Set score to the next score if this timestep is valid (mask == 1)
-            # and save the index that produces the next score
-            # shape: (batch_size, num_tags)
-            score = torch.where(mask[i].unsqueeze(1), next_score, score)
-            history.append(indices)
-
-        history = torch.stack(history, dim=0)
-
-        # End transition score
-        # shape: (batch_size, num_tags)
-        score += self.end_transitions
-
-        # Now, compute the best path for each sample
-
-        # shape: (batch_size,)
-        seq_ends = mask.long().sum(dim=0) - 1
-        best_tags_list = []
-
-        for idx in range(batch_size):
-            # Find the tag which maximizes the score at the last timestep; this is our best tag
-            # for the last timestep
-            _, best_last_tag = score[idx].max(dim=0)
-            best_tags = [best_last_tag]
-
-            # We trace back where the best last tag comes from, append that to our best tag
-            # sequence, and trace it back again, and so on
-            for i, hist in enumerate(torch.flip(history[:seq_ends[idx]], dims=(0,))):
-                best_last_tag = hist[idx][best_tags[-1]]
-                best_tags.append(best_last_tag)
-
-            best_tags = torch.stack(best_tags, dim=0)
-
-            # Reverse the order because we start from the last timestep
-            best_tags_list.append(torch.flip(best_tags, dims=(0,)))
-
-        best_tags_list = nn.utils.rnn.pad_sequence(best_tags_list, batch_first=True, padding_value=0)
-
-        return best_tags_list
-
-"""### CRFLayer 
-- Forward: decide output logits basaed on backbone network  
-- Decode: decode based on CRF weights
-"""
-
-class CRFLayer(nn.Module):
-    def __init__(self, embedding_size, n_labels):
-
-        super(CRFLayer, self).__init__()
-        self.dropout = nn.Dropout(0.1)
-        self.output_dense = nn.Linear(embedding_size,n_labels)
-        self.crf = CRF(n_labels, batch_first=True)
-        self.token_from_subtoken = TokenFromSubtoken()
-
-    # Forward: decide output logits basaed on backbone network  
-    def forward(self, embedding, mask):
-        logits = self.output_dense(self.dropout(embedding))
-        logits = self.token_from_subtoken(logits, mask)
-        pad_mask = self.token_from_subtoken(mask.unsqueeze(-1), mask).squeeze(-1).bool()
-        return logits, pad_mask
-
-    # Decode: decode based on CRF weights 
-    def decode(self, logits, pad_mask):
-        return self.crf.decode(logits, pad_mask)
-
-    # Evaluation Loss: calculate mean log likelihood of CRF layer
-    def eval_loss(self, logits, targets, pad_mask):
-        mean_log_likelihood = self.crf(logits, targets, pad_mask, reduction='sum').mean()
-        return -mean_log_likelihood
-
-"""### NERModel
-- Roberta Model with CRF Layer
-"""
-
-class NERModel(nn.Module):
-
-    def __init__(self, n_labels:int, roberta_path:str):
-        super(NERModel,self).__init__()
-        self.roberta = XLMRobertaModel.from_pretrained(roberta_path)
-        self.crf = CRFLayer(self.roberta.config.hidden_size, n_labels)
-
-    # Forward: pass embedings to CRF layer in order to evaluate logits from suboword sequence
-    def forward(self, 
-                input_ids:torch.Tensor,
-                attention_mask:torch.Tensor,
-                token_type_ids:torch.Tensor,
-                mask:torch.Tensor) -> torch.Tensor:
-
-        embedding = self.roberta(input_ids=input_ids,
-                                 attention_mask=attention_mask,
-                                 token_type_ids=token_type_ids)[0]
-        logits, pad_mask = self.crf(embedding, mask)
-        return logits, pad_mask
-
-    # Disable Gradient and Predict with model
-    @torch.no_grad()
-    def predict(self, inputs:Tuple[torch.Tensor]) -> torch.Tensor:
-        input_ids, attention_mask, token_type_ids, mask = inputs
-        logits, pad_mask = self(input_ids, attention_mask, token_type_ids, mask)
-        decoded = self.crf.decode(logits, pad_mask)
-        return decoded, pad_mask
-
-    # Decode: pass to crf decoder and decode based on CRF weights 
-    def decode(self, logits, pad_mask):
-        """Decode logits using CRF weights 
-        """
-        return self.crf.decode(logits, pad_mask) 
-
-    # Evaluation Loss: pass to crf eval_loss and calculate mean log likelihood of CRF layer
-    def eval_loss(self, logits, targets, pad_mask):
-        return self.crf.eval_loss(logits, targets, pad_mask)
-
-    # Determine number of layers to be fine-tuned (!freeze) 
-    def freeze_roberta(self, n_freeze:int=6):
-        for param in self.roberta.parameters():
-            param.requires_grad = False
-
-        for param in self.roberta.encoder.layer[n_freeze:].parameters():
-            param.requires_grad = True
-
-"""### NERTokenizer
-- NLTK tokenizer along with XLMRobertaTokenizerFast tokenizer
-- Code adapted from the following [file](https://github.com/ugurcanozalp/multilingual-ner/blob/main/multiner/utils/custom_tokenizer.py)
-"""
-
-class NERTokenizer(object):
-
-    MAX_LEN=512
-    BATCH_LENGTH_LIMT = 380 # Max number of roberta tokens in one sentence.
-
-    # Modified version of http://stackoverflow.com/questions/36353125/nltk-regular-expression-tokenizer
-    PATTERN = r'''(?x)          # set flag to allow verbose regexps
-        (?:[A-Z]\.)+        # abbreviations, e.g. U.S.A. or U.S.A # 
-        | (?:\d+\.)           # numbers
-        | \w+(?:[-.]\w+)*     # words with optional internal hyphens
-        | \$?\d+(?:.\d+)?%?   # currency and percentages, e.g. $12.40, 82%
-        | \.\.\.              # ellipsis, and special chars below, includes ], [
-        | [-\]\[.؟،؛;"'?,():_`“”/°º‘’″…#$%()*+<>=@\\^_{}|~❑&§\!]
-        | \u200c
-    '''
-
-    def __init__(self, base_model:str, to_device:str='cpu'):
-        super(NERTokenizer,self).__init__()
-        self.roberta_tokenizer = XLMRobertaTokenizerFast.from_pretrained(base_model, do_lower_case=False, padding=True, truncation=True)
-        self.to_device = to_device
-
-        self.word_tokenizer = RegexpTokenizer(self.PATTERN)
-        self.sent_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
-
-    # tokenize batch of tokens
-    def tokenize_batch(self, inputs, pad_to = None) -> torch.Tensor:
-        batch = [inputs] if isinstance(inputs[0], str) else inputs
-        
-        input_ids, attention_mask, token_type_ids, mask = [], [], [], []
-        for tokens in batch:
-            input_ids_tmp, attention_mask_tmp, token_type_ids_tmp, mask_tmp = self._tokenize_words(tokens)
-            input_ids.append(input_ids_tmp)
-            attention_mask.append(attention_mask_tmp)
-            token_type_ids.append(token_type_ids_tmp)
-            mask.append(mask_tmp)
-
-        input_ids = pad_sequence(input_ids, batch_first=True, padding_value=self.roberta_tokenizer.pad_token_id)
-        attention_mask = pad_sequence(attention_mask, batch_first=True, padding_value=0)
-        token_type_ids = pad_sequence(token_type_ids, batch_first=True, padding_value=0)
-        mask = pad_sequence(mask, batch_first=True, padding_value=0)
-        # truncate MAX_LEN
-        if input_ids.shape[-1]>self.MAX_LEN:
-            input_ids = input_ids[:,:,:self.MAX_LEN]
-            attention_mask = attention_mask[:,:,:self.MAX_LEN]
-            token_type_ids = token_type_ids[:,:,:self.MAX_LEN]
-            mask = mask[:,:,:self.MAX_LEN]
-        
-        # extend pad 
-        elif pad_to is not None and pad_to>input_ids.shape[1]:
-            bs = input_ids.shape[0]
-            padlen = pad_to-input_ids.shape[1]
-
-            input_ids_append = torch.tensor([self.roberta_tokenizer.pad_token_id], dtype=torch.long).repeat([bs, padlen]).to(self.to_device)
-            input_ids = torch.cat([input_ids, input_ids_append], dim=-1)
-
-            attention_mask_append = torch.tensor([0], dtype=torch.long).repeat([bs, padlen]).to(self.to_device)
-            attention_mask = torch.cat([attention_mask, attention_mask_append], dim=-1)
-
-            token_type_ids_append = torch.tensor([0], dtype=torch.long).repeat([bs, padlen]).to(self.to_device)
-            token_type_ids = torch.cat([token_type_ids, token_type_ids_append], dim=-1)
-
-            mask_append = torch.tensor([0], dtype=torch.long).repeat([bs, padlen]).to(self.to_device)
-            mask = torch.cat([mask, mask_append], dim=-1)
-
-        # truncate pad
-        elif pad_to is not None and pad_to<input_ids.shape[1]:
-            input_ids = input_ids[:,:,:pad_to]
-            attention_mask = attention_mask[:,:,:pad_to]
-            token_type_ids = token_type_ids[:,:,:pad_to]
-            mask = mask[:,:,:pad_to]
-
-        if isinstance(inputs[0], str):
-            return input_ids[0], attention_mask[0], token_type_ids[0], mask[0]
-        else:
-            return input_ids, attention_mask, token_type_ids, mask
-
-    # tokenize list of words with roberta tokenizer
-    def _tokenize_words(self, words):
-        roberta_tokens = []
-        mask = []
-        for word in words:
-            subtokens = self.roberta_tokenizer.tokenize(word)
-            roberta_tokens+=subtokens
-            n_subtoken = len(subtokens)
-            if n_subtoken>=1:
-                mask = mask + [1] + [0]*(n_subtoken-1)
-
-        # add special tokens [CLS] and [SeP]
-        roberta_tokens = [self.roberta_tokenizer.cls_token] + roberta_tokens + [self.roberta_tokenizer.sep_token]
-        mask = [0] + mask + [0]
-        input_ids = torch.tensor(self.roberta_tokenizer.convert_tokens_to_ids(roberta_tokens), dtype=torch.long).to(self.to_device)
-        attention_mask = torch.ones(len(mask), dtype=torch.long).to(self.to_device)
-        token_type_ids = torch.zeros(len(mask), dtype=torch.long).to(self.to_device)
-        mask = torch.tensor(mask, dtype=torch.long).to(self.to_device)
-        return input_ids, attention_mask, token_type_ids, mask
-
-    # sent_to_token: yield each sentence token with positional span using nltk
-    def sent_to_token(self, raw_text):
-        for offset, ending in self.sent_tokenizer.span_tokenize(raw_text):
-            sub_text = raw_text[offset:ending]
-            words, spans = [], []
-            flush = False
-            total_subtoken = 0
-            for start, end in self.word_tokenizer.span_tokenize(sub_text):
-                flush = True
-                start += offset
-                end += offset
-                words.append(raw_text[start:end])
-                spans.append((start,end))
-                total_subtoken += len(self.roberta_tokenizer.tokenize(words[-1]))
-                if (total_subtoken > self.BATCH_LENGTH_LIMT): 
-                    # Print
-                    yield words[:-1],spans[:-1]
-                    spans = spans[len(spans)-1:]
-                    words = words[len(words)-1:]
-                    total_subtoken = sum([len(self.roberta_tokenizer.tokenize(word)) for word in words])
-                    flush = False
-
-            if flush and len(spans) > 0:
-                yield words,spans
-
-    # Extract (batch words span() from a raw sentence
-    def prepare_row_text(self, raw_text, batch_size=16):
-        words_list, spans_list = [], []
-        end_batch = False
-        for words, spans in self.sent_to_token(raw_text):
-            end_batch = True
-            words_list.append(words)
-            spans_list.append(spans)
-            if len(spans_list) >= batch_size:
-                input_ids, attention_mask, token_type_ids, mask = self.tokenize_batch(words_list)
-                yield (input_ids, attention_mask, token_type_ids, mask), words_list, spans_list
-                words_list, spans_list = [], []
-        if end_batch and len(words_list) > 0:
-            input_ids, attention_mask, token_type_ids, mask = self.tokenize_batch(words_list)
-            yield (input_ids, attention_mask, token_type_ids, mask), words_list, spans_list
-
-"""### NER
-NER Interface : We Use this interface to infer sentence Time-Date tags.
-"""
-
-class NER(object):
-
-    def __init__(self, model_path, tags):
-        
-        self.tags = tags
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        # Load Pre-Trained model
-        roberta_path = "xlm-roberta-base"
-        self.model = NERModel(n_labels=len(self.tags), roberta_path=roberta_path).to(self.device)
-        # Load Fine-Tuned model
-        state_dict = torch.load(model_path)
-        self.model.load_state_dict(state_dict, strict=False)
-        # Enable Evaluation mode
-        self.model.eval()
-        self.tokenizer = NERTokenizer(base_model=roberta_path, to_device=self.device)
-
-    # Predict and Pre/Post-Process the input/output
-    @torch.no_grad()
-    def __call__(self, raw_text):
-
-        outputs_flat, spans_flat, entities = [], [], []
-        for batch, words, spans in self.tokenizer.prepare_row_text(raw_text):
-            output, pad_mask = self.model.predict(batch)
-            outputs_flat.extend(output[pad_mask.bool()].reshape(-1).tolist())
-            spans_flat += sum(spans, [])
-
-        for tag_idx,(start,end) in zip(outputs_flat,spans_flat):
-            tag = self.tags[tag_idx]
-            # filter out O tags
-            if tag != 'O':
-                entities.append({'Text': raw_text[start:end], 
-                                 'Tag': tag, 
-                                 'Start':start,
-                                 'End': end})
-
-        return entities
\ No newline at end of file
diff --git a/spaces/kdrkdrkdr/ShirokoTTS/attentions.py b/spaces/kdrkdrkdr/ShirokoTTS/attentions.py
deleted file mode 100644
index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000
--- a/spaces/kdrkdrkdr/ShirokoTTS/attentions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/keras-io/drug-molecule-generation-with-VAE/README.md b/spaces/keras-io/drug-molecule-generation-with-VAE/README.md
deleted file mode 100644
index bebcd3a9e3e0ebd55e3ecc19a2d7e32df17c4a6d..0000000000000000000000000000000000000000
--- a/spaces/keras-io/drug-molecule-generation-with-VAE/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Generating Drug Molecule With VAE
-emoji: 👁
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.0.24
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kernelmachine/gpt3-quality-filter/lr/hyperparameters.py b/spaces/kernelmachine/gpt3-quality-filter/lr/hyperparameters.py
deleted file mode 100644
index ce8bd929bca5ae31de95022305be5ae096c4b4bb..0000000000000000000000000000000000000000
--- a/spaces/kernelmachine/gpt3-quality-filter/lr/hyperparameters.py
+++ /dev/null
@@ -1,124 +0,0 @@
-from typing import Any, Dict, List, Union
-import numpy as np
-import logging
-import os
-
-# Create a custom logger
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-
-
-
-class RandomSearch:
-
-    @staticmethod
-    def random_choice(args: List[Any], n: int = 1):
-        """
-        pick a random element from a set.
-        
-        Example:
-            >> sampler = RandomSearch.random_choice(1,2,3)
-            >> sampler()
-                2
-        """
-        choices = []
-        for arg in args:
-            choices.append(arg)
-        if n == 1:
-            return lambda: np.random.choice(choices, replace=False)
-        else:
-            return lambda: np.random.choice(choices, n, replace=False)
-
-    @staticmethod
-    def random_integer(low: Union[int, float], high: Union[int, float]):
-        """
-        pick a random integer between two bounds
-        
-        Example:
-            >> sampler = RandomSearch.random_integer(1, 10)
-            >> sampler()
-                9
-        """
-        return lambda: int(np.random.randint(low, high))
-
-    @staticmethod
-    def random_loguniform(low: Union[float, int], high: Union[float, int]):
-        """
-        pick a random float between two bounds, using loguniform distribution
-        
-        Example:
-            >> sampler = RandomSearch.random_loguniform(1e-5, 1e-2)
-            >> sampler()
-                0.0004
-        """
-        return lambda: np.exp(np.random.uniform(np.log(low), np.log(high)))
-
-    @staticmethod
-    def random_uniform(low: Union[float, int], high: Union[float, int]):
-        """
-        pick a random float between two bounds, using uniform distribution
-        
-        Example:
-            >> sampler = RandomSearch.random_uniform(0, 1)
-            >> sampler()
-                0.01
-        """
-        return lambda: np.random.uniform(low, high)
-
-
-class HyperparameterSearch:
-
-    def __init__(self, **kwargs):
-        self.search_space = {}
-        self.lambda_ = lambda: 0
-        for key, val in kwargs.items():
-            self.search_space[key] = val
-
-    def parse(self, val: Any):
-            
-        if isinstance(val, (int, np.int)):
-            return int(val)
-        elif isinstance(val, (float, np.float)):
-            return val
-        elif isinstance(val, (np.ndarray, list)):
-            return " ".join(val)
-        elif val is None:
-            return None
-        if isinstance(val, str):
-            return val
-        else:
-            val = val()
-            if isinstance(val, (int, np.int)):
-                return int(val)
-            elif isinstance(val, (np.ndarray, list)):
-                return " ".join(val)
-            else:
-                return val
-
-
-    def sample(self) -> Dict:
-        res = {}
-        for key, val in self.search_space.items():
-            try:
-                res[key] = self.parse(val)
-            except (TypeError, ValueError) as error:
-                logger.error(f"Could not parse key {key} with value {val}. {error}")
-
-        return res
-
-    def update_environment(self, sample) -> None:
-        for key, val in sample.items():
-            os.environ[key] = str(val)
-
-
-SEARCH_SPACE = {
-        "penalty": RandomSearch.random_choice(["l1", "l2"]),
-        "C": RandomSearch.random_uniform(0, 1),
-        "solver": "liblinear",
-        "multi_class": "auto",
-        "tol": RandomSearch.random_loguniform(10e-5, 10e-3),
-        "stopwords": RandomSearch.random_choice([0, 1]),
-        "weight": RandomSearch.random_choice(["hash"]),
-        "ngram_range": RandomSearch.random_choice(["1 2", "2 3", "1 3"]),
-        "random_state": RandomSearch.random_integer(0, 100000)
-}
diff --git a/spaces/kevinwang676/M4Singer/modules/parallel_wavegan/layers/__init__.py b/spaces/kevinwang676/M4Singer/modules/parallel_wavegan/layers/__init__.py
deleted file mode 100644
index e477f51116a3157781b1aefefbaf32fe4d4bd1f0..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/M4Singer/modules/parallel_wavegan/layers/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .causal_conv import *  # NOQA
-from .pqmf import *  # NOQA
-from .residual_block import *  # NOQA
-from modules.parallel_wavegan.layers.residual_stack import *  # NOQA
-from .upsample import *  # NOQA
diff --git a/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/utils/utils_callbacks.py b/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/utils/utils_callbacks.py
deleted file mode 100644
index bd2f56cba47c57de102710ff56eaac591e59f4da..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/utils/utils_callbacks.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import logging
-import os
-import time
-from typing import List
-
-import torch
-
-from eval import verification
-from utils.utils_logging import AverageMeter
-
-
-class CallBackVerification(object):
-    def __init__(self, frequent, rank, val_targets, rec_prefix, image_size=(112, 112)):
-        self.frequent: int = frequent
-        self.rank: int = rank
-        self.highest_acc: float = 0.0
-        self.highest_acc_list: List[float] = [0.0] * len(val_targets)
-        self.ver_list: List[object] = []
-        self.ver_name_list: List[str] = []
-        if self.rank is 0:
-            self.init_dataset(val_targets=val_targets, data_dir=rec_prefix, image_size=image_size)
-
-    def ver_test(self, backbone: torch.nn.Module, global_step: int):
-        results = []
-        for i in range(len(self.ver_list)):
-            acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(
-                self.ver_list[i], backbone, 10, 10)
-            logging.info('[%s][%d]XNorm: %f' % (self.ver_name_list[i], global_step, xnorm))
-            logging.info('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (self.ver_name_list[i], global_step, acc2, std2))
-            if acc2 > self.highest_acc_list[i]:
-                self.highest_acc_list[i] = acc2
-            logging.info(
-                '[%s][%d]Accuracy-Highest: %1.5f' % (self.ver_name_list[i], global_step, self.highest_acc_list[i]))
-            results.append(acc2)
-
-    def init_dataset(self, val_targets, data_dir, image_size):
-        for name in val_targets:
-            path = os.path.join(data_dir, name + ".bin")
-            if os.path.exists(path):
-                data_set = verification.load_bin(path, image_size)
-                self.ver_list.append(data_set)
-                self.ver_name_list.append(name)
-
-    def __call__(self, num_update, backbone: torch.nn.Module):
-        if self.rank is 0 and num_update > 0 and num_update % self.frequent == 0:
-            backbone.eval()
-            self.ver_test(backbone, num_update)
-            backbone.train()
-
-
-class CallBackLogging(object):
-    def __init__(self, frequent, rank, total_step, batch_size, world_size, writer=None):
-        self.frequent: int = frequent
-        self.rank: int = rank
-        self.time_start = time.time()
-        self.total_step: int = total_step
-        self.batch_size: int = batch_size
-        self.world_size: int = world_size
-        self.writer = writer
-
-        self.init = False
-        self.tic = 0
-
-    def __call__(self,
-                 global_step: int,
-                 loss: AverageMeter,
-                 epoch: int,
-                 fp16: bool,
-                 learning_rate: float,
-                 grad_scaler: torch.cuda.amp.GradScaler):
-        if self.rank == 0 and global_step > 0 and global_step % self.frequent == 0:
-            if self.init:
-                try:
-                    speed: float = self.frequent * self.batch_size / (time.time() - self.tic)
-                    speed_total = speed * self.world_size
-                except ZeroDivisionError:
-                    speed_total = float('inf')
-
-                time_now = (time.time() - self.time_start) / 3600
-                time_total = time_now / ((global_step + 1) / self.total_step)
-                time_for_end = time_total - time_now
-                if self.writer is not None:
-                    self.writer.add_scalar('time_for_end', time_for_end, global_step)
-                    self.writer.add_scalar('learning_rate', learning_rate, global_step)
-                    self.writer.add_scalar('loss', loss.avg, global_step)
-                if fp16:
-                    msg = "Speed %.2f samples/sec   Loss %.4f   LearningRate %.4f   Epoch: %d   Global Step: %d   " \
-                          "Fp16 Grad Scale: %2.f   Required: %1.f hours" % (
-                              speed_total, loss.avg, learning_rate, epoch, global_step,
-                              grad_scaler.get_scale(), time_for_end
-                          )
-                else:
-                    msg = "Speed %.2f samples/sec   Loss %.4f   LearningRate %.4f   Epoch: %d   Global Step: %d   " \
-                          "Required: %1.f hours" % (
-                              speed_total, loss.avg, learning_rate, epoch, global_step, time_for_end
-                          )
-                logging.info(msg)
-                loss.reset()
-                self.tic = time.time()
-            else:
-                self.init = True
-                self.tic = time.time()
-
-
-class CallBackModelCheckpoint(object):
-    def __init__(self, rank, output="./"):
-        self.rank: int = rank
-        self.output: str = output
-
-    def __call__(self, global_step, backbone, partial_fc, ):
-        if global_step > 100 and self.rank == 0:
-            path_module = os.path.join(self.output, "backbone.pth")
-            torch.save(backbone.module.state_dict(), path_module)
-            logging.info("Pytorch Model Saved in '{}'".format(path_module))
-
-        if global_step > 100 and partial_fc is not None:
-            partial_fc.save_params()
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/encoder/config.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/encoder/config.py
deleted file mode 100644
index 1c21312f3de971bfa008254c6035cebc09f05e4c..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/encoder/config.py
+++ /dev/null
@@ -1,45 +0,0 @@
-librispeech_datasets = {
-    "train": {
-        "clean": ["LibriSpeech/train-clean-100", "LibriSpeech/train-clean-360"],
-        "other": ["LibriSpeech/train-other-500"]
-    },
-    "test": {
-        "clean": ["LibriSpeech/test-clean"],
-        "other": ["LibriSpeech/test-other"]
-    },
-    "dev": {
-        "clean": ["LibriSpeech/dev-clean"],
-        "other": ["LibriSpeech/dev-other"]
-    },
-}
-libritts_datasets = {
-    "train": {
-        "clean": ["LibriTTS/train-clean-100", "LibriTTS/train-clean-360"],
-        "other": ["LibriTTS/train-other-500"]
-    },
-    "test": {
-        "clean": ["LibriTTS/test-clean"],
-        "other": ["LibriTTS/test-other"]
-    },
-    "dev": {
-        "clean": ["LibriTTS/dev-clean"],
-        "other": ["LibriTTS/dev-other"]
-    },
-}
-voxceleb_datasets = {
-    "voxceleb1" : {
-        "train": ["VoxCeleb1/wav"],
-        "test": ["VoxCeleb1/test_wav"]
-    },
-    "voxceleb2" : {
-        "train": ["VoxCeleb2/dev/aac"],
-        "test": ["VoxCeleb2/test_wav"]
-    }
-}
-
-other_datasets = [
-    "LJSpeech-1.1",
-    "VCTK-Corpus/wav48",
-]
-
-anglophone_nationalites = ["australia", "canada", "ireland", "uk", "usa"]
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/logger/mlflow.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/logger/mlflow.py
deleted file mode 100644
index f9a72592be47b534ce22573775fd5a7e8e86d72d..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/logger/mlflow.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from ...dist_utils import master_only
-from ..hook import HOOKS
-from .base import LoggerHook
-
-
-@HOOKS.register_module()
-class MlflowLoggerHook(LoggerHook):
-
-    def __init__(self,
-                 exp_name=None,
-                 tags=None,
-                 log_model=True,
-                 interval=10,
-                 ignore_last=True,
-                 reset_flag=False,
-                 by_epoch=True):
-        """Class to log metrics and (optionally) a trained model to MLflow.
-
-        It requires `MLflow`_ to be installed.
-
-        Args:
-            exp_name (str, optional): Name of the experiment to be used.
-                Default None.
-                If not None, set the active experiment.
-                If experiment does not exist, an experiment with provided name
-                will be created.
-            tags (dict of str: str, optional): Tags for the current run.
-                Default None.
-                If not None, set tags for the current run.
-            log_model (bool, optional): Whether to log an MLflow artifact.
-                Default True.
-                If True, log runner.model as an MLflow artifact
-                for the current run.
-            interval (int): Logging interval (every k iterations).
-            ignore_last (bool): Ignore the log of last iterations in each epoch
-                if less than `interval`.
-            reset_flag (bool): Whether to clear the output buffer after logging
-            by_epoch (bool): Whether EpochBasedRunner is used.
-
-        .. _MLflow:
-            https://www.mlflow.org/docs/latest/index.html
-        """
-        super(MlflowLoggerHook, self).__init__(interval, ignore_last,
-                                               reset_flag, by_epoch)
-        self.import_mlflow()
-        self.exp_name = exp_name
-        self.tags = tags
-        self.log_model = log_model
-
-    def import_mlflow(self):
-        try:
-            import mlflow
-            import mlflow.pytorch as mlflow_pytorch
-        except ImportError:
-            raise ImportError(
-                'Please run "pip install mlflow" to install mlflow')
-        self.mlflow = mlflow
-        self.mlflow_pytorch = mlflow_pytorch
-
-    @master_only
-    def before_run(self, runner):
-        super(MlflowLoggerHook, self).before_run(runner)
-        if self.exp_name is not None:
-            self.mlflow.set_experiment(self.exp_name)
-        if self.tags is not None:
-            self.mlflow.set_tags(self.tags)
-
-    @master_only
-    def log(self, runner):
-        tags = self.get_loggable_tags(runner)
-        if tags:
-            self.mlflow.log_metrics(tags, step=self.get_iter(runner))
-
-    @master_only
-    def after_run(self, runner):
-        if self.log_model:
-            self.mlflow_pytorch.log_model(runner.model, 'models')
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/_util.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/_util.py
deleted file mode 100644
index ba27b7e49e98f4973ba9c257be14a8419292fe8a..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/_util.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import os
-from pathlib import Path
-
-
-def is_path(f):
-    return isinstance(f, (bytes, str, Path))
-
-
-def is_directory(f):
-    """Checks if an object is a string, and that it points to a directory."""
-    return is_path(f) and os.path.isdir(f)
-
-
-class DeferredError:
-    def __init__(self, ex):
-        self.ex = ex
-
-    def __getattr__(self, elt):
-        raise self.ex
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-8b439108.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-8b439108.js
deleted file mode 100644
index 0c537cf0a9956dfe88ae965ca2a1b1f8858ee6e8..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-8b439108.js
+++ /dev/null
@@ -1,5 +0,0 @@
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diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/tests/test_receivebuffer.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/tests/test_receivebuffer.py
deleted file mode 100644
index 21a3870b6be9a6a365037326c2085ff2715022cc..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/h11/tests/test_receivebuffer.py
+++ /dev/null
@@ -1,135 +0,0 @@
-import re
-from typing import Tuple
-
-import pytest
-
-from .._receivebuffer import ReceiveBuffer
-
-
-def test_receivebuffer() -> None:
-    b = ReceiveBuffer()
-    assert not b
-    assert len(b) == 0
-    assert bytes(b) == b""
-
-    b += b"123"
-    assert b
-    assert len(b) == 3
-    assert bytes(b) == b"123"
-
-    assert bytes(b) == b"123"
-
-    assert b.maybe_extract_at_most(2) == b"12"
-    assert b
-    assert len(b) == 1
-    assert bytes(b) == b"3"
-
-    assert bytes(b) == b"3"
-
-    assert b.maybe_extract_at_most(10) == b"3"
-    assert bytes(b) == b""
-
-    assert b.maybe_extract_at_most(10) is None
-    assert not b
-
-    ################################################################
-    # maybe_extract_until_next
-    ################################################################
-
-    b += b"123\n456\r\n789\r\n"
-
-    assert b.maybe_extract_next_line() == b"123\n456\r\n"
-    assert bytes(b) == b"789\r\n"
-
-    assert b.maybe_extract_next_line() == b"789\r\n"
-    assert bytes(b) == b""
-
-    b += b"12\r"
-    assert b.maybe_extract_next_line() is None
-    assert bytes(b) == b"12\r"
-
-    b += b"345\n\r"
-    assert b.maybe_extract_next_line() is None
-    assert bytes(b) == b"12\r345\n\r"
-
-    # here we stopped at the middle of b"\r\n" delimiter
-
-    b += b"\n6789aaa123\r\n"
-    assert b.maybe_extract_next_line() == b"12\r345\n\r\n"
-    assert b.maybe_extract_next_line() == b"6789aaa123\r\n"
-    assert b.maybe_extract_next_line() is None
-    assert bytes(b) == b""
-
-    ################################################################
-    # maybe_extract_lines
-    ################################################################
-
-    b += b"123\r\na: b\r\nfoo:bar\r\n\r\ntrailing"
-    lines = b.maybe_extract_lines()
-    assert lines == [b"123", b"a: b", b"foo:bar"]
-    assert bytes(b) == b"trailing"
-
-    assert b.maybe_extract_lines() is None
-
-    b += b"\r\n\r"
-    assert b.maybe_extract_lines() is None
-
-    assert b.maybe_extract_at_most(100) == b"trailing\r\n\r"
-    assert not b
-
-    # Empty body case (as happens at the end of chunked encoding if there are
-    # no trailing headers, e.g.)
-    b += b"\r\ntrailing"
-    assert b.maybe_extract_lines() == []
-    assert bytes(b) == b"trailing"
-
-
-@pytest.mark.parametrize(
-    "data",
-    [
-        pytest.param(
-            (
-                b"HTTP/1.1 200 OK\r\n",
-                b"Content-type: text/plain\r\n",
-                b"Connection: close\r\n",
-                b"\r\n",
-                b"Some body",
-            ),
-            id="with_crlf_delimiter",
-        ),
-        pytest.param(
-            (
-                b"HTTP/1.1 200 OK\n",
-                b"Content-type: text/plain\n",
-                b"Connection: close\n",
-                b"\n",
-                b"Some body",
-            ),
-            id="with_lf_only_delimiter",
-        ),
-        pytest.param(
-            (
-                b"HTTP/1.1 200 OK\n",
-                b"Content-type: text/plain\r\n",
-                b"Connection: close\n",
-                b"\n",
-                b"Some body",
-            ),
-            id="with_mixed_crlf_and_lf",
-        ),
-    ],
-)
-def test_receivebuffer_for_invalid_delimiter(data: Tuple[bytes]) -> None:
-    b = ReceiveBuffer()
-
-    for line in data:
-        b += line
-
-    lines = b.maybe_extract_lines()
-
-    assert lines == [
-        b"HTTP/1.1 200 OK",
-        b"Content-type: text/plain",
-        b"Connection: close",
-    ]
-    assert bytes(b) == b"Some body"
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/_compat.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/_compat.py
deleted file mode 100644
index 12df1aa6f1e3a516751b8473045579b04f190554..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/_compat.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Mapping
-import sys
-from typing import Any
-
-if sys.version_info >= (3, 10):
-    DATACLASS_KWARGS: Mapping[str, Any] = {"slots": True}
-else:
-    DATACLASS_KWARGS: Mapping[str, Any] = {}
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/presets/default.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/presets/default.py
deleted file mode 100644
index 59f4855ea7843453a152c7b257358441587dec6d..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/presets/default.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""markdown-it default options."""
-
-
-def make():
-    return {
-        "options": {
-            "maxNesting": 100,  # Internal protection, recursion limit
-            "html": False,  # Enable HTML tags in source
-            # this is just a shorthand for .disable(["html_inline", "html_block"])
-            # used by the linkify rule:
-            "linkify": False,  # autoconvert URL-like texts to links
-            # used by the replacements and smartquotes rules:
-            # Enable some language-neutral replacements + quotes beautification
-            "typographer": False,
-            # used by the smartquotes rule:
-            # Double + single quotes replacement pairs, when typographer enabled,
-            # and smartquotes on. Could be either a String or an Array.
-            # For example, you can use '«»„“' for Russian, '„“‚‘' for German,
-            # and ['«\xA0', '\xA0»', '‹\xA0', '\xA0›'] for French (including nbsp).
-            "quotes": "\u201c\u201d\u2018\u2019",  # /* “”‘’ */
-            # Renderer specific; these options are used directly in the HTML renderer
-            "xhtmlOut": False,  # Use '/' to close single tags (<br />)
-            "breaks": False,  # Convert '\n' in paragraphs into <br>
-            "langPrefix": "language-",  # CSS language prefix for fenced blocks
-            # Highlighter function. Should return escaped HTML,
-            # or '' if the source string is not changed and should be escaped externally.
-            # If result starts with <pre... internal wrapper is skipped.
-            #
-            # function (/*str, lang, attrs*/) { return ''; }
-            #
-            "highlight": None,
-        },
-        "components": {"core": {}, "block": {}, "inline": {}},
-    }
diff --git a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_mat.py b/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_mat.py
deleted file mode 100644
index cd25d500c0eae77a3b815b8e956205b737ee43d4..0000000000000000000000000000000000000000
--- a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_mat.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import os
-import json
-import scipy.io as spio
-import pandas as pd
-
-
-def loadmat(filename):
-    '''
-    this function should be called instead of direct spio.loadmat
-    as it cures the problem of not properly recovering python dictionaries
-    from mat files. It calls the function check keys to cure all entries
-    which are still mat-objects
-    '''
-    data = spio.loadmat(filename, struct_as_record=False, squeeze_me=True)
-    return dict_to_nonedict(_check_keys(data))
-
-def _check_keys(dict):
-    '''
-    checks if entries in dictionary are mat-objects. If yes
-    todict is called to change them to nested dictionaries
-    '''
-    for key in dict:
-        if isinstance(dict[key], spio.matlab.mio5_params.mat_struct):
-            dict[key] = _todict(dict[key])
-    return dict
-
-def _todict(matobj):
-    '''
-    A recursive function which constructs from matobjects nested dictionaries
-    '''
-    dict = {}
-    for strg in matobj._fieldnames:
-        elem = matobj.__dict__[strg]
-        if isinstance(elem, spio.matlab.mio5_params.mat_struct):
-            dict[strg] = _todict(elem)
-        else:
-            dict[strg] = elem
-    return dict
-
-
-def dict_to_nonedict(opt):
-    if isinstance(opt, dict):
-        new_opt = dict()
-        for key, sub_opt in opt.items():
-            new_opt[key] = dict_to_nonedict(sub_opt)
-        return NoneDict(**new_opt)
-    elif isinstance(opt, list):
-        return [dict_to_nonedict(sub_opt) for sub_opt in opt]
-    else:
-        return opt
-
-
-class NoneDict(dict):
-    def __missing__(self, key):
-        return None
-
-
-def mat2json(mat_path=None, filepath = None):
-    """
-    Converts .mat file to .json and writes new file
-    Parameters
-    ----------
-    mat_path: Str
-        path/filename .mat存放路径
-    filepath: Str
-        如果需要保存成json, 添加这一路径. 否则不保存
-    Returns
-        返回转化的字典
-    -------
-    None
-    Examples
-    --------
-    >>> mat2json(blah blah)
-    """
-
-    matlabFile = loadmat(mat_path)
-    #pop all those dumb fields that don't let you jsonize file
-    matlabFile.pop('__header__')
-    matlabFile.pop('__version__')
-    matlabFile.pop('__globals__')
-    #jsonize the file - orientation is 'index'
-    matlabFile = pd.Series(matlabFile).to_json()
-
-    if filepath:
-        json_path = os.path.splitext(os.path.split(mat_path)[1])[0] + '.json'
-        with open(json_path, 'w') as f:
-                f.write(matlabFile)
-    return matlabFile
\ No newline at end of file
diff --git a/spaces/leo-bourrel/test-streamlit/vector_store.py b/spaces/leo-bourrel/test-streamlit/vector_store.py
deleted file mode 100644
index 37c22aff9b81f0d4d9e118f0b2c0129f41c2b44c..0000000000000000000000000000000000000000
--- a/spaces/leo-bourrel/test-streamlit/vector_store.py
+++ /dev/null
@@ -1,355 +0,0 @@
-from __future__ import annotations
-
-import contextlib
-import json
-import logging
-from typing import Any, Dict, Generator, Iterable, List, Optional, Tuple, Type
-
-import pandas as pd
-import sqlalchemy
-from langchain.docstore.document import Document
-from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
-from sqlalchemy import delete, text
-from sqlalchemy.orm import Session
-
-from model import Article
-from models.distance import DistanceStrategy, distance_strategy_limit
-from utils import str_to_list
-
-DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.COSINE
-
-_LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain"
-
-
-def _results_to_docs(docs_and_scores: Any) -> List[Document]:
-    """Return docs from docs and scores."""
-    return [doc for doc, _ in docs_and_scores]
-
-
-class CustomVectorStore(VectorStore):
-    """`Postgres`/`PGVector` vector store.
-
-    To use, you should have the ``pgvector`` python package installed.
-
-    Args:
-        connection: Postgres connection string.
-        embedding_function: Any embedding function implementing
-            `langchain.embeddings.base.Embeddings` interface.
-        table_name: The name of the collection to use. (default: langchain)
-            NOTE: This is not the name of the table, but the name of the collection.
-            The tables will be created when initializing the store (if not exists)
-            So, make sure the user has the right permissions to create tables.
-        distance_strategy: The distance strategy to use. (default: COSINE)
-        pre_delete_collection: If True, will delete the collection if it exists.
-            (default: False). Useful for testing.
-
-    Example:
-        .. code-block:: python
-
-            from langchain.vectorstores import PGVector
-            from langchain.embeddings.openai import OpenAIEmbeddings
-
-            COLLECTION_NAME = "state_of_the_union_test"
-            embeddings = OpenAIEmbeddings()
-            vectorestore = PGVector.from_documents(
-                embedding=embeddings,
-                documents=docs,
-                table_name=COLLECTION_NAME,
-                connection=connection,
-            )
-
-
-    """
-
-    def __init__(
-        self,
-        connection: sqlalchemy.engine.Connection,
-        embedding_function: Embeddings,
-        table_name: str,
-        column_name: str,
-        collection_metadata: Optional[dict] = None,
-        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
-        pre_delete_collection: bool = False,
-        logger: Optional[logging.Logger] = None,
-    ) -> None:
-        self._conn = connection
-        self.embedding_function = embedding_function
-        self.table_name = table_name
-        self.column_name = column_name
-        self.collection_metadata = collection_metadata
-        self._distance_strategy = distance_strategy
-        self.pre_delete_collection = pre_delete_collection
-        self.logger = logger or logging.getLogger(__name__)
-        self.__post_init__()
-
-    def __post_init__(
-        self,
-    ) -> None:
-        """
-        Initialize the store.
-        """
-        # self._conn = self.connect()
-
-        self.EmbeddingStore = Article
-
-    @property
-    def embeddings(self) -> Embeddings:
-        return self.embedding_function
-
-    @contextlib.contextmanager
-    def _make_session(self) -> Generator[Session, None, None]:
-        """Create a context manager for the session, bind to _conn string."""
-        yield Session(self._conn)
-
-    def add_embeddings(
-        self,
-        texts: Iterable[str],
-        embeddings: List[List[float]],
-        metadatas: Optional[List[dict]] = None,
-        ids: Optional[List[str]] = None,
-        **kwargs: Any,
-    ) -> List[str]:
-        """Add embeddings to the vectorstore.
-
-        Args:
-            texts: Iterable of strings to add to the vectorstore.
-            embeddings: List of list of embedding vectors.
-            metadatas: List of metadatas associated with the texts.
-            kwargs: vectorstore specific parameters
-        """
-        if not metadatas:
-            metadatas = [{} for _ in texts]
-
-        with Session(self._conn) as session:
-            for txt, metadata, embedding, id in zip(texts, metadatas, embeddings, ids):
-                embedding_store = self.EmbeddingStore(
-                    embedding=embedding,
-                    document=txt,
-                    cmetadata=metadata,
-                    custom_id=id,
-                )
-                session.add(embedding_store)
-            session.commit()
-
-        return ids
-
-    def add_texts(
-        self,
-        texts: Iterable[str],
-        metadatas: Optional[List[dict]] = None,
-        ids: Optional[List[str]] = None,
-        **kwargs: Any,
-    ) -> List[str]:
-        """Run more texts through the embeddings and add to the vectorstore.
-
-        Args:
-            texts: Iterable of strings to add to the vectorstore.
-            metadatas: Optional list of metadatas associated with the texts.
-            kwargs: vectorstore specific parameters
-
-        Returns:
-            List of ids from adding the texts into the vectorstore.
-        """
-        embeddings = self.embedding_function.embed_documents(list(texts))
-        return self.add_embeddings(
-            texts=texts, embeddings=embeddings, metadatas=metadatas, ids=ids, **kwargs
-        )
-
-    def similarity_search(
-        self,
-        query: str,
-        k: int = 4,
-        filter: Optional[dict] = None,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Run similarity search with PGVector with distance.
-
-        Args:
-            query (str): Query text to search for.
-            k (int): Number of results to return. Defaults to 4.
-            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
-
-        Returns:
-            List of Documents most similar to the query.
-        """
-        embedding = self.embedding_function.embed_query(text=query)
-        return self.similarity_search_by_vector(
-            embedding=embedding,
-            k=k,
-        )
-
-    def similarity_search_with_score(
-        self,
-        query: str,
-        k: int = 4,
-        filter: Optional[dict] = None,
-    ) -> List[Tuple[Document, float]]:
-        """Return docs most similar to query.
-
-        Args:
-            query: Text to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
-            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
-
-        Returns:
-            List of Documents most similar to the query and score for each.
-        """
-        embedding = self.embedding_function.embed_query(query)
-        docs = self.similarity_search_with_score_by_vector(embedding=embedding, k=k)
-        return docs
-
-    @property
-    def distance_strategy(self) -> str | None:
-        if self._distance_strategy == DistanceStrategy.EUCLIDEAN:
-            return "<->"
-        elif self._distance_strategy == DistanceStrategy.COSINE:
-            return "<=>"
-        elif self._distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
-            return "<#>"
-        else:
-            raise ValueError(
-                f"Got unexpected value for distance: {self._distance_strategy}. "
-                f"Should be one of {', '.join([ds.value for ds in DistanceStrategy])}."
-            )
-
-    def similarity_search_with_score_by_vector(
-        self,
-        embedding: List[float],
-        k: int = 4,
-    ) -> List[Tuple[Document, float]]:
-        results = self.__query_collection(embedding=embedding, k=k)
-
-        return self._results_to_docs_and_scores(results)
-
-    def _results_to_docs_and_scores(self, results: Any) -> List[Tuple[Document, float]]:
-        """Return docs and scores from results."""
-        docs = [
-            (
-                Document(
-                    page_content=json.dumps(
-                        {
-                            "abstract": result["abstract"][0],
-                            "id": result["id"],
-                            "title": result["title"][0],
-                            "authors": result["authors"],
-                            "doi": result["doi"],
-                            "hal_id": result["hal_id"],
-                            "keywords": result["keywords"],
-                            "distance": result["distance"],
-                        }
-                    ),
-                ),
-                result["distance"] if self.embedding_function is not None else None,
-            )
-            for result in results
-        ]
-        return docs
-
-    def __query_collection(
-        self,
-        embedding: List[float],
-        k: int = 4,
-    ) -> List[Any]:
-        """Query the collection."""
-
-        limit = distance_strategy_limit[self._distance_strategy]
-        with Session(self._conn) as session:
-            results = session.execute(
-                text(
-                    f"""
-                    select
-                        a.id,
-                        a.title_en,
-                        a.doi,
-                        a.hal_id,
-                        a.abstract_en,
-                        string_agg(distinct keyword."name", ',') as keywords, 
-                        string_agg(distinct author."name", ',') as authors,
-                        abstract_embedding_en {self.distance_strategy} '{str(embedding)}' as distance
-                    from article a
-                    left join article_keyword ON article_keyword.article_id = a.id 
-                    left join keyword on article_keyword.keyword_id = keyword.id
-                    left join article_author ON article_author.article_id = a.id
-                    left join author on author.id = article_author.author_id  
-                    where
-                        abstract_en != '' and
-                        abstract_en != 'None' and
-                        abstract_embedding_en {self.distance_strategy} '{str(embedding)}' < {limit}
-                    GROUP BY a.id
-                    ORDER BY distance
-                    LIMIT 100;
-                """
-                )
-            )
-            results = results.fetchall()
-            results = pd.DataFrame(
-                results,
-                columns=[
-                    "id",
-                    "title",
-                    "doi",
-                    "hal_id",
-                    "abstract",
-                    "keywords",
-                    "authors",
-                    "distance",
-                ],
-            )
-            results["abstract"] = results["abstract"].apply(str_to_list)
-            results["title"] = results["title"].apply(str_to_list)
-        results = results.to_dict(orient="records")
-        return results
-
-    def similarity_search_by_vector(
-        self,
-        embedding: List[float],
-        k: int = 4,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs most similar to embedding vector.
-
-        Args:
-            embedding: Embedding to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
-            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
-
-        Returns:
-            List of Documents most similar to the query vector.
-        """
-        docs_and_scores = self.similarity_search_with_score_by_vector(
-            embedding=embedding, k=k
-        )
-        return _results_to_docs(docs_and_scores)
-
-    @classmethod
-    def from_texts(
-        cls: Type[CustomVectorStore],
-        texts: List[str],
-        embedding: Embeddings,
-        metadatas: Optional[List[dict]] = None,
-        table_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
-        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
-        ids: Optional[List[str]] = None,
-        pre_delete_collection: bool = False,
-        **kwargs: Any,
-    ) -> CustomVectorStore:
-        """
-        Return VectorStore initialized from texts and embeddings.
-        Postgres connection string is required
-        "Either pass it as a parameter
-        or set the PGVECTOR_CONNECTION_STRING environment variable.
-        """
-        embeddings = embedding.embed_documents(list(texts))
-
-        return cls.__from(
-            texts,
-            embeddings,
-            embedding,
-            metadatas=metadatas,
-            ids=ids,
-            table_name=table_name,
-            distance_strategy=distance_strategy,
-            pre_delete_collection=pre_delete_collection,
-            **kwargs,
-        )
diff --git a/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/model_init.py b/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/model_init.py
deleted file mode 100644
index e3a4c3478cc9bd68bcea2ae0e1a63fa7954b2d0f..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/model_init.py
+++ /dev/null
@@ -1,174 +0,0 @@
-from model import ExLlama, ExLlamaCache, ExLlamaConfig
-from tokenizer import ExLlamaTokenizer
-import argparse, sys, os, glob
-from torch import version as torch_version
-from globals import set_affinity_str
-
-def add_args(parser):
-
-    parser.add_argument("-t", "--tokenizer", type = str, help = "Tokenizer model path")
-    parser.add_argument("-c", "--config", type = str, help = "Model config path (config.json)")
-    parser.add_argument("-m", "--model", type = str, help = "Model weights path (.pt or .safetensors file)")
-    parser.add_argument("-d", "--directory", type = str, help = "Path to directory containing config.json, model.tokenizer and * .safetensors")
-
-    parser.add_argument("-gs", "--gpu_split", type = str, help = "Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. -gs 20,7,7")
-    parser.add_argument("-l", "--length", type = int, help = "Maximum sequence length", default = 2048)
-    parser.add_argument("-cpe", "--compress_pos_emb", type = float, help = "Compression factor for positional embeddings", default = 1.0)
-    parser.add_argument("-a", "--alpha", type = float, help = "alpha for context size extension via embedding extension", default = 1.0)
-    parser.add_argument("-theta", "--theta", type = float, help = "theta (base) for RoPE embeddings")
-
-    parser.add_argument("-gpfix", "--gpu_peer_fix", action = "store_true", help = "Prevent direct copies of data between GPUs")
-
-    parser.add_argument("-flash", "--flash_attn", nargs = '?', const = 'default', metavar = "METHOD", help = "Use Flash Attention with specified input length (must have Flash Attention 2.0 installed)")
-
-    parser.add_argument("-mmrt", "--matmul_recons_thd", type = int, help = "No. rows at which to use reconstruction and cuBLAS for quant matmul. 0 = never, 1 = always", default = 8)
-    parser.add_argument("-fmt", "--fused_mlp_thd", type = int, help = "Maximum no. of rows for which to use fused MLP. 0 = never", default = 2)
-    parser.add_argument("-sdpt", "--sdp_thd", type = int, help = "No. rows at which to switch to scaled_dot_product_attention. 0 = never, 1 = always", default = 8)
-    parser.add_argument("-mmfr", "--matmul_fused_remap", action = "store_true", help = "Fuse column remapping in Q4 matmul kernel")
-    parser.add_argument("-nfa", "--no_fused_attn", action = "store_true", help = "Disable fused attention")
-
-    parser.add_argument("-rnnh2", "--rmsnorm_no_half2", action = "store_true", help = "Don't use half2 in RMS norm kernel")
-    parser.add_argument("-rpnh2", "--rope_no_half2", action = "store_true", help = "Don't use half2 in RoPE kernel")
-    parser.add_argument("-mmnh2", "--matmul_no_half2", action = "store_true", help = "Don't use half2 in Q4 matmul kernel")
-    parser.add_argument("-snh2", "--silu_no_half2", action = "store_true", help = "Don't use half2 in SiLU kernel")
-    parser.add_argument("-nh2", "--no_half2", action = "store_true", help = "(All of the above) disable half2 in all kernela")
-    parser.add_argument("-fh2", "--force_half2", action = "store_true", help = "Force enable half2 even if unsupported")
-    parser.add_argument("-cs", "--concurrent_streams", action = "store_true", help = "Use concurrent CUDA streams")
-
-    parser.add_argument("-aff", "--affinity", type = str, help = "Comma-separated list, sets processor core affinity. E.g.: -aff 0,1,2,3")
-
-
-def post_parse(args):
-
-    if args.no_half2 or torch_version.hip and not args.force_half2:
-        args.rmsnorm_no_half2 = True
-        args.rope_no_half2 = True
-        args.matmul_no_half2 = True
-        args.silu_no_half2 = True
-
-
-# Get model files from --directory
-
-def get_model_files(args):
-
-    if args.directory is not None:
-        args.tokenizer = os.path.join(args.directory, "tokenizer.model")
-        args.config = os.path.join(args.directory, "config.json")
-        st_pattern = os.path.join(args.directory, "*.safetensors")
-        st = glob.glob(st_pattern)
-        if len(st) == 0:
-            print(f" !! No files matching {st_pattern}")
-            sys.exit()
-        # if len(st) > 1:
-        #     print(f" !! Multiple files matching {st_pattern}")
-        #     sys.exit()
-        args.model = st
-    else:
-        if args.tokenizer is None or args.config is None or args.model is None:
-            print(" !! Please specify either -d or all of -t, -c and -m")
-            sys.exit()
-
-
-# Feedback
-
-def _common_chars(names):
-    cname = max(names, key = len)
-    for x in names:
-        for p, c in enumerate(x):
-            if c != cname[p] and cname[p] != "*": cname = cname[:p] + "*" + cname[p+1:]
-    return cname
-
-def print_options(args, extra_options = None):
-
-    print_opts = []
-    if args.gpu_split is not None: print_opts.append(f"gpu_split: {args.gpu_split}")
-    if args.gpu_peer_fix: print_opts.append("gpu_peer_fix")
-    if args.affinity: print_opts.append(f" --affinity: {args.affinity}")
-
-    if extra_options is not None: print_opts += extra_options
-
-    print(f" -- Tokenizer: {args.tokenizer}")
-    print(f" -- Model config: {args.config}")
-
-    if isinstance(args.model, str): print(f" -- Model: {args.model}")
-    else: print(f" -- Model: {_common_chars(args.model)}")
-
-    print(f" -- Sequence length: {args.length}")
-    if args.compress_pos_emb != 1.0:
-        print(f" -- RoPE compression factor: {args.compress_pos_emb}")
-
-    if args.alpha != 1.0:
-        print(f" -- RoPE alpha factor: {args.alpha}")
-
-    print(f" -- Tuning:")
-
-    if args.flash_attn: print(f" -- --flash_attn")
-    else: print(f" -- --sdp_thd: {args.sdp_thd}" + (" (disabled)" if args.sdp_thd == 0 else ""))
-
-    print(f" -- --matmul_recons_thd: {args.matmul_recons_thd}" + (" (disabled)" if args.matmul_recons_thd == 0 else ""))
-    print(f" -- --fused_mlp_thd: {args.fused_mlp_thd}" + (" (disabled)" if args.fused_mlp_thd == 0 else ""))
-    if args.matmul_fused_remap: print(f" -- --matmul_fused_remap")
-    if args.no_fused_attn: print(f" -- --no_fused_attn")
-    if args.rmsnorm_no_half2: print(f" -- --rmsnorm_no_half2")
-    if args.rope_no_half2: print(f" -- --rope_no_half2")
-    if args.matmul_no_half2: print(f" -- --matmul_no_half2")
-    if args.silu_no_half2: print(f" -- --silu_no_half2")
-    if args.concurrent_streams: print(f" -- --concurrent_streams")
-
-    print(f" -- Options: {print_opts}")
-
-
-# Build ExLlamaConfig from args
-
-def make_config(args):
-
-    config = ExLlamaConfig(args.config)
-    config.model_path = args.model
-
-    config.max_seq_len = args.length
-    config.compress_pos_emb = args.compress_pos_emb
-    config.set_auto_map(args.gpu_split)
-    config.gpu_peer_fix = args.gpu_peer_fix
-    config.alpha_value = args.alpha
-    config.calculate_rotary_embedding_base()
-
-    if args.flash_attn:
-        config.use_flash_attn_2 = True
-        try:
-            config.max_input_len = int(args.flash_attn)
-        except ValueError:
-            pass
-
-    config.matmul_recons_thd = args.matmul_recons_thd
-    config.fused_mlp_thd = args.fused_mlp_thd
-    config.sdp_thd = args.sdp_thd
-    config.matmul_fused_remap = args.matmul_fused_remap
-    config.fused_attn = not args.no_fused_attn
-
-    config.rmsnorm_no_half2 = args.rmsnorm_no_half2
-    config.rope_no_half2 = args.rope_no_half2
-    config.matmul_no_half2 = args.matmul_no_half2
-    config.silu_no_half2 = args.silu_no_half2
-    config.concurrent_streams = args.concurrent_streams
-
-    if args.theta:
-        config.rotary_embedding_base = args.theta
-
-    return config
-
-
-# Global state
-
-def set_globals(args):
-
-    if args.affinity: set_affinity_str(args.affinity)
-
-
-# Print stats after loading model
-
-def print_stats(model):
-
-    print(f" -- Groupsize (inferred): {model.config.groupsize if model.config.groupsize is not None else 'None'}")
-    print(f" -- Act-order (inferred): {'yes' if model.config.act_order else 'no'}")
-    if model.config.empty_g_idx:
-        print(f" !! Model has empty group index (discarded)")
diff --git a/spaces/leonelhs/superface/app.py b/spaces/leonelhs/superface/app.py
deleted file mode 100644
index 8af1fabba76eb66843aebd1e6ba9efee4182f5c1..0000000000000000000000000000000000000000
--- a/spaces/leonelhs/superface/app.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import os
-
-import gradio as gr
-import torch
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.archs.srvgg_arch import SRVGGNetCompact
-from gfpgan.utils import GFPGANer
-from huggingface_hub import hf_hub_download
-from realesrgan.utils import RealESRGANer
-
-REALESRGAN_REPO_ID = 'leonelhs/realesrgan'
-GFPGAN_REPO_ID = 'leonelhs/gfpgan'
-
-os.system("pip freeze")
-
-
-def showGPU():
-    if torch.cuda.is_available():
-        devices = torch.cuda.device_count()
-        current = torch.cuda.current_device()
-        return f"Running on GPU:{current} of {devices} total devices"
-    return "Running on CPU"
-
-
-def download_model_gfpgan(file):
-    return hf_hub_download(repo_id=GFPGAN_REPO_ID, filename=file)
-
-
-def download_model_realesrgan(file):
-    return hf_hub_download(repo_id=REALESRGAN_REPO_ID, filename=file)
-
-
-def select_upsampler(version, netscale=4):
-    model = None
-    dni_weight = None
-
-    version = version + ".pth"
-    model_path = download_model_realesrgan(version)
-
-    if version == 'RealESRGAN_x4plus.pth':  # x4 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-
-    if version == 'RealESRNet_x4plus.pth':  # x4 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-
-    if version == 'AI-Forever_x4plus.pth':  # x4 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-
-    if version == 'RealESRGAN_x4plus_anime_6B.pth':  # x4 RRDBNet model with 6 blocks
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
-
-    if version == 'RealESRGAN_x2plus.pth':  # x2 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-        netscale = 2  # This is
-
-    if version == 'AI-Forever_x2plus.pth':  # x2 RRDBNet model
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-        netscale = 2  # This is
-
-    if version == 'realesr-animevideov3.pth':  # x4 VGG-style model (XS size)
-        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
-
-    if version == 'realesr-general-x4v3.pth':  # x4 VGG-style model (S size)
-        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-        model_path = [
-            download_model_realesrgan("realesr-general-x4v3.pth"),
-            download_model_realesrgan("realesr-general-wdn-x4v3.pth")
-        ]
-        dni_weight = [0.2, 0.8]
-
-    half = True if torch.cuda.is_available() else False
-
-    return RealESRGANer(
-        scale=netscale,
-        model_path=model_path,
-        dni_weight=dni_weight,
-        model=model,
-        tile=0,
-        tile_pad=10,
-        pre_pad=0,
-        half=half,
-        gpu_id=0)
-
-
-def select_face_enhancer(version, scale, upsampler):
-    if 'v1.2' in version:
-        model_path = download_model_gfpgan('GFPGANv1.2.pth')
-        return GFPGANer(
-            model_path=model_path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-    elif 'v1.3' in version:
-        model_path = download_model_gfpgan('GFPGANv1.3.pth')
-        return GFPGANer(
-            model_path=model_path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-    elif 'v1.4' in version:
-        model_path = download_model_gfpgan('GFPGANv1.4.pth')
-        return GFPGANer(
-            model_path=model_path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-    elif 'RestoreFormer' in version:
-        model_path = download_model_gfpgan('RestoreFormer.pth')
-        return GFPGANer(
-            model_path=model_path, upscale=scale, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
-
-
-def predict(image, version_upsampler, version_enhancer, scale):
-    scale = int(scale)
-
-    upsampler = select_upsampler(version_upsampler)
-
-    if "No additional" not in version_enhancer:
-        face_enhancer = select_face_enhancer(version_enhancer, scale, upsampler)
-        _, _, output = face_enhancer.enhance(image, has_aligned=False, only_center_face=False, paste_back=True)
-    else:
-        output, _ = upsampler.enhance(image, outscale=scale)
-
-    log = f"General enhance version: {version_upsampler}\n " \
-          f"Face enhance version: {version_enhancer} \n " \
-          f"Scale:{scale} \n {showGPU()}"
-
-    return output, log
-
-
-title = "Super Face"
-description = r"""
-<b>Practical Image Restoration Algorithm based on Real-ESRGAN, GFPGAN</b>
-"""
-article = r"""
-<center><span>xintao.wang@outlook.com or xintaowang@tencent.com</span></center>
-</br>
-<center><a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo ⭐ </a> are welcome</center>
-"""
-
-demo = gr.Interface(
-    predict, [
-        gr.Image(type="numpy", label="Input"),
-        gr.Dropdown([
-            'RealESRGAN_x2plus',
-            'RealESRGAN_x4plus',
-            'RealESRNet_x4plus',
-            'AI-Forever_x2plus',
-            'AI-Forever_x4plus',
-            'RealESRGAN_x4plus_anime_6B',
-            'realesr-animevideov3',
-            'realesr-general-x4v3'],
-            type="value", value='RealESRGAN_x4plus', label='General restoration algorithm', info="version"),
-        gr.Dropdown([
-            'No additional face process',
-            'GFPGANv1.2',
-            'GFPGANv1.3',
-            'GFPGANv1.4',
-            'RestoreFormer'],
-            type="value", value='No additional face process', label='Special face restoration algorithm',
-            info="version"),
-        gr.Dropdown(["1", "2", "3", "4"], value="2", label="Rescaling factor")
-    ], [
-        gr.Image(type="numpy", label="Output", interactive=False),
-        gr.Textbox(label="log info")
-    ],
-    title=title,
-    description=description,
-    article=article)
-
-demo.queue().launch(share=False, debug=True)
diff --git a/spaces/leurez/moss/src/store/modules/user/helper.ts b/spaces/leurez/moss/src/store/modules/user/helper.ts
deleted file mode 100644
index 9fa5412696df9016bfff3a3fa0226c454bb4c085..0000000000000000000000000000000000000000
--- a/spaces/leurez/moss/src/store/modules/user/helper.ts
+++ /dev/null
@@ -1,32 +0,0 @@
-import { ss } from '@/utils/storage'
-
-const LOCAL_NAME = 'userStorage'
-
-export interface UserInfo {
-  avatar: string
-  name: string
-  description: string
-}
-
-export interface UserState {
-  userInfo: UserInfo
-}
-
-export function defaultSetting(): UserState {
-  return {
-    userInfo: {
-      avatar: '/assets/moss.gif',
-      name: 'MossTech',
-      description: '莫斯科技(<a href="https://moss.run.place" class="text-blue-500" target="_blank" >MossTech</a>)',
-    },
-  }
-}
-
-export function getLocalState(): UserState {
-  const localSetting: UserState | undefined = ss.get(LOCAL_NAME)
-  return { ...defaultSetting(), ...localSetting }
-}
-
-export function setLocalState(setting: UserState): void {
-  ss.set(LOCAL_NAME, setting)
-}
diff --git a/spaces/lewiswu1209/MockingBird/ppg2mel/train.py b/spaces/lewiswu1209/MockingBird/ppg2mel/train.py
deleted file mode 100644
index d3ef729075a837a680175559ecbdde0b398a73a9..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/ppg2mel/train.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import sys
-import torch
-import argparse
-import numpy as np
-from utils.load_yaml import HpsYaml
-from ppg2mel.train.train_linglf02mel_seq2seq_oneshotvc import Solver
-
-# For reproducibility, comment these may speed up training
-torch.backends.cudnn.deterministic = True
-torch.backends.cudnn.benchmark = False
-
-def main():
-    # Arguments
-    parser = argparse.ArgumentParser(description=
-            'Training PPG2Mel VC model.')
-    parser.add_argument('--config', type=str, 
-                        help='Path to experiment config, e.g., config/vc.yaml')
-    parser.add_argument('--name', default=None, type=str, help='Name for logging.')
-    parser.add_argument('--logdir', default='log/', type=str,
-                        help='Logging path.', required=False)
-    parser.add_argument('--ckpdir', default='ckpt/', type=str,
-                        help='Checkpoint path.', required=False)
-    parser.add_argument('--outdir', default='result/', type=str,
-                        help='Decode output path.', required=False)
-    parser.add_argument('--load', default=None, type=str,
-                        help='Load pre-trained model (for training only)', required=False)
-    parser.add_argument('--warm_start', action='store_true',
-                        help='Load model weights only, ignore specified layers.')
-    parser.add_argument('--seed', default=0, type=int,
-                        help='Random seed for reproducable results.', required=False)
-    parser.add_argument('--njobs', default=8, type=int,
-                        help='Number of threads for dataloader/decoding.', required=False)
-    parser.add_argument('--cpu', action='store_true', help='Disable GPU training.')
-    # parser.add_argument('--no-pin', action='store_true',
-    #                     help='Disable pin-memory for dataloader')
-    parser.add_argument('--no-msg', action='store_true', help='Hide all messages.')
-    
-    ###
-
-    paras = parser.parse_args()
-    setattr(paras, 'gpu', not paras.cpu)
-    setattr(paras, 'pin_memory', not paras.no_pin)
-    setattr(paras, 'verbose', not paras.no_msg)
-    # Make the config dict dot visitable
-    config = HpsYaml(paras.config)
-
-    np.random.seed(paras.seed)
-    torch.manual_seed(paras.seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed_all(paras.seed)
-
-    print(">>> OneShot VC training ...")
-    mode = "train"
-    solver = Solver(config, paras, mode)
-    solver.load_data()
-    solver.set_model()
-    solver.exec()
-    print(">>> Oneshot VC train finished!")
-    sys.exit(0)
-
-if __name__ == "__main__":
-    main()   
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/AUTODATA 3.40-PT-PT Serial Key HOT.md b/spaces/lincquiQcaudo/Top-20-Diffusion/AUTODATA 3.40-PT-PT Serial Key HOT.md
deleted file mode 100644
index 0ef2c8d1d6a62ae8ebcf43b700210f0f22316b78..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/AUTODATA 3.40-PT-PT Serial Key HOT.md	
+++ /dev/null
@@ -1,24 +0,0 @@
-<h2>AUTODATA 3.40-PT-PT Serial Key</h2><br /><p><b><b>Download</b> --->>> <a href="https://bytlly.com/2uGwes">https://bytlly.com/2uGwes</a></b></p><br /><br />
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-
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-
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-
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-
-Sign up for the fundraiser's ​ newsletter here:
-
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-
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Battlefield 3 Offline Crack !!HOT!! Razor1911.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Battlefield 3 Offline Crack !!HOT!! Razor1911.md
deleted file mode 100644
index ec8bd1b3dab8339ce5121fe0d5f85433812fc19c..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Battlefield 3 Offline Crack !!HOT!! Razor1911.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>battlefield 3 offline crack razor1911</h2><br /><p><b><b>Download</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://bytlly.com/2uGxXA">https://bytlly.com/2uGxXA</a></b></p><br /><br />
-
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/CLO 3D Enterprise 2.4.57 Online Auth (Cracked) Serial Key.md b/spaces/lincquiQcaudo/Top-20-Diffusion/CLO 3D Enterprise 2.4.57 Online Auth (Cracked) Serial Key.md
deleted file mode 100644
index 7e83b05a0f942e66afcbbe75fb203baf6121d797..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/CLO 3D Enterprise 2.4.57 Online Auth (Cracked) Serial Key.md	
+++ /dev/null
@@ -1,7 +0,0 @@
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-<p>it holds up to 100 keys of its own, and it offers a rich array of account-related and security-related features. these include support for several online logins such as github, google, and dropbox, a 256-bit aes encryption engine, and a totp-based security feature that you can use to enable two-factor authentication on your mobile devices. the yubikey 5 series supports four nfc-based protocols, making it compatible with many nfc-enabled devices. these include google, github, and dropbox. and unlike the security keys, it supports the more advanced bitlocker pin and smart card encryption protocols.</p>
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Corel Draw X3 Keygen By Ssg Tactical.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Corel Draw X3 Keygen By Ssg Tactical.md
deleted file mode 100644
index 867583e16db9b274a102372840ff7fcde1cc2389..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Corel Draw X3 Keygen By Ssg Tactical.md	
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Ergosoft TexPrint HOT Crack.epub.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Ergosoft TexPrint HOT Crack.epub.md
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index f5894e47d40d2145e89288256af6e65f53680771..0000000000000000000000000000000000000000
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index 232b8871a5c3e7ec5cae0d617c5acc81f75b6638..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/GameDownload PUBG MOBILE 100103 1.0.5727.123.exe Free Download UPDATED.md	
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diff --git a/spaces/lithiumice/SadTalker/src/audio2pose_models/audio2pose.py b/spaces/lithiumice/SadTalker/src/audio2pose_models/audio2pose.py
deleted file mode 100644
index 1a8410d6ee7f7f1d50305f61332bfbdb9dc8bf0e..0000000000000000000000000000000000000000
--- a/spaces/lithiumice/SadTalker/src/audio2pose_models/audio2pose.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import torch
-from torch import nn
-from src.audio2pose_models.cvae import CVAE
-from src.audio2pose_models.discriminator import PoseSequenceDiscriminator
-from src.audio2pose_models.audio_encoder import AudioEncoder
-
-class Audio2Pose(nn.Module):
-    def __init__(self, cfg, wav2lip_checkpoint, device='cuda'):
-        super().__init__()
-        self.cfg = cfg
-        self.seq_len = cfg.MODEL.CVAE.SEQ_LEN
-        self.latent_dim = cfg.MODEL.CVAE.LATENT_SIZE
-        self.device = device
-
-        self.audio_encoder = AudioEncoder(wav2lip_checkpoint, device)
-        self.audio_encoder.eval()
-        for param in self.audio_encoder.parameters():
-            param.requires_grad = False
-
-        self.netG = CVAE(cfg)
-        self.netD_motion = PoseSequenceDiscriminator(cfg)
-        
-        
-    def forward(self, x):
-
-        batch = {}
-        coeff_gt = x['gt'].cuda().squeeze(0)           #bs frame_len+1 73
-        batch['pose_motion_gt'] = coeff_gt[:, 1:, -9:-3] - coeff_gt[:, :1, -9:-3] #bs frame_len 6
-        batch['ref'] = coeff_gt[:, 0, -9:-3]  #bs  6
-        batch['class'] = x['class'].squeeze(0).cuda() # bs
-        indiv_mels= x['indiv_mels'].cuda().squeeze(0) # bs seq_len+1 80 16
-
-        # forward
-        audio_emb_list = []
-        audio_emb = self.audio_encoder(indiv_mels[:, 1:, :, :].unsqueeze(2)) #bs seq_len 512
-        batch['audio_emb'] = audio_emb
-        batch = self.netG(batch)
-
-        pose_motion_pred = batch['pose_motion_pred']           # bs frame_len 6
-        pose_gt = coeff_gt[:, 1:, -9:-3].clone()               # bs frame_len 6
-        pose_pred = coeff_gt[:, :1, -9:-3] + pose_motion_pred  # bs frame_len 6
-
-        batch['pose_pred'] = pose_pred
-        batch['pose_gt'] = pose_gt
-
-        return batch
-
-    def test(self, x):
-
-        batch = {}
-        ref = x['ref']                            #bs 1 70
-        batch['ref'] = x['ref'][:,0,-6:]  
-        batch['class'] = x['class']  
-        bs = ref.shape[0]
-        
-        indiv_mels= x['indiv_mels']               # bs T 1 80 16
-        indiv_mels_use = indiv_mels[:, 1:]        # we regard the ref as the first frame
-        num_frames = x['num_frames']
-        num_frames = int(num_frames) - 1
-
-        #  
-        div = num_frames//self.seq_len
-        re = num_frames%self.seq_len
-        audio_emb_list = []
-        pose_motion_pred_list = [torch.zeros(batch['ref'].unsqueeze(1).shape, dtype=batch['ref'].dtype, 
-                                                device=batch['ref'].device)]
-
-        for i in range(div):
-            z = torch.randn(bs, self.latent_dim).to(ref.device)
-            batch['z'] = z
-            audio_emb = self.audio_encoder(indiv_mels_use[:, i*self.seq_len:(i+1)*self.seq_len,:,:,:]) #bs seq_len 512
-            batch['audio_emb'] = audio_emb
-            batch = self.netG.test(batch)
-            pose_motion_pred_list.append(batch['pose_motion_pred'])  #list of bs seq_len 6
-        
-        if re != 0:
-            z = torch.randn(bs, self.latent_dim).to(ref.device)
-            batch['z'] = z
-            audio_emb = self.audio_encoder(indiv_mels_use[:, -1*self.seq_len:,:,:,:]) #bs seq_len  512
-            if audio_emb.shape[1] != self.seq_len:
-                pad_dim = self.seq_len-audio_emb.shape[1]
-                pad_audio_emb = audio_emb[:, :1].repeat(1, pad_dim, 1) 
-                audio_emb = torch.cat([pad_audio_emb, audio_emb], 1) 
-            batch['audio_emb'] = audio_emb
-            batch = self.netG.test(batch)
-            pose_motion_pred_list.append(batch['pose_motion_pred'][:,-1*re:,:])   
-        
-        pose_motion_pred = torch.cat(pose_motion_pred_list, dim = 1)
-        batch['pose_motion_pred'] = pose_motion_pred
-
-        pose_pred = ref[:, :1, -6:] + pose_motion_pred  # bs T 6
-
-        batch['pose_pred'] = pose_pred
-        return batch
diff --git a/spaces/ljjggr/bingo/src/components/learn-more.tsx b/spaces/ljjggr/bingo/src/components/learn-more.tsx
deleted file mode 100644
index a64459ee7900a612292e117a6bda96ee9260990f..0000000000000000000000000000000000000000
--- a/spaces/ljjggr/bingo/src/components/learn-more.tsx
+++ /dev/null
@@ -1,39 +0,0 @@
-import React from 'react'
-import { SourceAttribution } from '@/lib/bots/bing/types'
-
-export interface LearnMoreProps {
-  sourceAttributions?: SourceAttribution[]
-}
-
-export function LearnMore({ sourceAttributions }: LearnMoreProps) {
-  if (!sourceAttributions?.length) {
-    return null
-  }
-
-  return (
-    <div className="learn-more-root" role="list" aria-label="了解详细信息:">
-      <div className="learn-more">了解详细信息:</div>
-      <div className="attribution-container">
-        <div className="attribution-items">
-          {sourceAttributions.map((attribution, index) => {
-            const { providerDisplayName, seeMoreUrl } = attribution
-            const { host } = new URL(seeMoreUrl)
-            return (
-              <a
-                key={index}
-                className="attribution-item"
-                target="_blank"
-                role="listitem"
-                href={seeMoreUrl}
-                title={providerDisplayName}
-                tabIndex={index}
-              >
-                {index + 1}. {host}
-              </a>
-            )
-          })}
-        </div>
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/llmonitor/benchmarks/Dockerfile b/spaces/llmonitor/benchmarks/Dockerfile
deleted file mode 100644
index 6364b91a6ebbeada62ef0e23efba333266aef6e8..0000000000000000000000000000000000000000
--- a/spaces/llmonitor/benchmarks/Dockerfile
+++ /dev/null
@@ -1,65 +0,0 @@
-FROM node:18-alpine AS base
-
-# Install dependencies only when needed
-FROM base AS deps
-# Check https://github.com/nodejs/docker-node/tree/b4117f9333da4138b03a546ec926ef50a31506c3#nodealpine to understand why libc6-compat might be needed.
-RUN apk add --no-cache libc6-compat
-WORKDIR /app
-
-# Install dependencies based on the preferred package manager
-COPY package.json yarn.lock* package-lock.json* pnpm-lock.yaml* ./
-RUN \
-  if [ -f yarn.lock ]; then yarn --frozen-lockfile; \
-  elif [ -f package-lock.json ]; then npm ci; \
-  elif [ -f pnpm-lock.yaml ]; then yarn global add pnpm && pnpm i --frozen-lockfile; \
-  else echo "Lockfile not found." && exit 1; \
-  fi
-
-
-# Rebuild the source code only when needed
-FROM base AS builder
-WORKDIR /app
-COPY --from=deps /app/node_modules ./node_modules
-COPY . .
-
-# Next.js collects completely anonymous telemetry data about general usage.
-# Learn more here: https://nextjs.org/telemetry
-# Uncomment the following line in case you want to disable telemetry during the build.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-RUN yarn build
-
-# If using npm comment out above and use below instead
-# RUN npm run build
-
-# Production image, copy all the files and run next
-FROM base AS runner
-WORKDIR /app
-
-ENV NODE_ENV production
-# Uncomment the following line in case you want to disable telemetry during runtime.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-RUN addgroup --system --gid 1001 nodejs
-RUN adduser --system --uid 1001 nextjs
-
-COPY --from=builder /app/public ./public
-
-# Set the correct permission for prerender cache
-RUN mkdir .next
-RUN chown nextjs:nodejs .next
-
-# Automatically leverage output traces to reduce image size
-# https://nextjs.org/docs/advanced-features/output-file-tracing
-COPY --from=builder --chown=nextjs:nodejs /app/.next/standalone ./
-COPY --from=builder --chown=nextjs:nodejs /app/.next/static ./.next/static
-
-USER nextjs
-
-EXPOSE 3000
-
-ENV PORT 3000
-# set hostname to localhost
-ENV HOSTNAME "0.0.0.0"
-
-CMD ["node", "server.js"]
\ No newline at end of file
diff --git a/spaces/lnyan/stablediffusion-infinity/process.py b/spaces/lnyan/stablediffusion-infinity/process.py
deleted file mode 100644
index 5db1495ac8098c0260f5fdf5a60ca35a043b461c..0000000000000000000000000000000000000000
--- a/spaces/lnyan/stablediffusion-infinity/process.py
+++ /dev/null
@@ -1,395 +0,0 @@
-"""
-https://github.com/Trinkle23897/Fast-Poisson-Image-Editing
-MIT License
-
-Copyright (c) 2022 Jiayi Weng
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-"""
-import os
-from abc import ABC, abstractmethod
-from typing import Any, Optional, Tuple
-
-import numpy as np
-
-from fpie import np_solver
-
-import scipy
-import scipy.signal
-
-CPU_COUNT = os.cpu_count() or 1
-DEFAULT_BACKEND = "numpy"
-ALL_BACKEND = ["numpy"]
-
-try:
-  from fpie import numba_solver
-  ALL_BACKEND += ["numba"]
-  DEFAULT_BACKEND = "numba"
-except ImportError:
-  numba_solver = None  # type: ignore
-
-try:
-  from fpie import taichi_solver
-  ALL_BACKEND += ["taichi-cpu", "taichi-gpu"]
-  DEFAULT_BACKEND = "taichi-cpu"
-except ImportError:
-  taichi_solver = None  # type: ignore
-
-# try:
-#   from fpie import core_gcc  # type: ignore
-#   DEFAULT_BACKEND = "gcc"
-#   ALL_BACKEND.append("gcc")
-# except ImportError:
-#   core_gcc = None
-
-# try:
-#   from fpie import core_openmp  # type: ignore
-#   DEFAULT_BACKEND = "openmp"
-#   ALL_BACKEND.append("openmp")
-# except ImportError:
-#   core_openmp = None
-
-# try:
-#   from mpi4py import MPI
-
-#   from fpie import core_mpi  # type: ignore
-#   ALL_BACKEND.append("mpi")
-# except ImportError:
-#   MPI = None  # type: ignore
-#   core_mpi = None
-
-try:
-  from fpie import core_cuda  # type: ignore
-  DEFAULT_BACKEND = "cuda"
-  ALL_BACKEND.append("cuda")
-except ImportError:
-  core_cuda = None
-
-
-class BaseProcessor(ABC):
-  """API definition for processor class."""
-
-  def __init__(
-    self, gradient: str, rank: int, backend: str, core: Optional[Any]
-  ):
-    if core is None:
-      error_msg = {
-        "numpy":
-          "Please run `pip install numpy`.",
-        "numba":
-          "Please run `pip install numba`.",
-        "gcc":
-          "Please install cmake and gcc in your operating system.",
-        "openmp":
-          "Please make sure your gcc is compatible with `-fopenmp` option.",
-        "mpi":
-          "Please install MPI and run `pip install mpi4py`.",
-        "cuda":
-          "Please make sure nvcc and cuda-related libraries are available.",
-        "taichi":
-          "Please run `pip install taichi`.",
-      }
-      print(error_msg[backend.split("-")[0]])
-
-      raise AssertionError(f"Invalid backend {backend}.")
-
-    self.gradient = gradient
-    self.rank = rank
-    self.backend = backend
-    self.core = core
-    self.root = rank == 0
-
-  def mixgrad(self, a: np.ndarray, b: np.ndarray) -> np.ndarray:
-    if self.gradient == "src":
-      return a
-    if self.gradient == "avg":
-      return (a + b) / 2
-    # mix gradient, see Equ. 12 in PIE paper
-    mask = np.abs(a) < np.abs(b)
-    a[mask] = b[mask]
-    return a
-
-  @abstractmethod
-  def reset(
-    self,
-    src: np.ndarray,
-    mask: np.ndarray,
-    tgt: np.ndarray,
-    mask_on_src: Tuple[int, int],
-    mask_on_tgt: Tuple[int, int],
-  ) -> int:
-    pass
-
-  def sync(self) -> None:
-    self.core.sync()
-
-  @abstractmethod
-  def step(self, iteration: int) -> Optional[Tuple[np.ndarray, np.ndarray]]:
-    pass
-
-
-class EquProcessor(BaseProcessor):
-  """PIE Jacobi equation processor."""
-
-  def __init__(
-    self,
-    gradient: str = "max",
-    backend: str = DEFAULT_BACKEND,
-    n_cpu: int = CPU_COUNT,
-    min_interval: int = 100,
-    block_size: int = 1024,
-  ):
-    core: Optional[Any] = None
-    rank = 0
-
-    if backend == "numpy":
-      core = np_solver.EquSolver()
-    elif backend == "numba" and numba_solver is not None:
-      core = numba_solver.EquSolver()
-    elif backend == "gcc":
-      core = core_gcc.EquSolver()
-    elif backend == "openmp" and core_openmp is not None:
-      core = core_openmp.EquSolver(n_cpu)
-    elif backend == "mpi" and core_mpi is not None:
-      core = core_mpi.EquSolver(min_interval)
-      rank = MPI.COMM_WORLD.Get_rank()
-    elif backend == "cuda" and core_cuda is not None:
-      core = core_cuda.EquSolver(block_size)
-    elif backend.startswith("taichi") and taichi_solver is not None:
-      core = taichi_solver.EquSolver(backend, n_cpu, block_size)
-
-    super().__init__(gradient, rank, backend, core)
-
-  def mask2index(
-    self, mask: np.ndarray
-  ) -> Tuple[np.ndarray, int, np.ndarray, np.ndarray]:
-    x, y = np.nonzero(mask)
-    max_id = x.shape[0] + 1
-    index = np.zeros((max_id, 3))
-    ids = self.core.partition(mask)
-    ids[mask == 0] = 0  # reserve id=0 for constant
-    index = ids[x, y].argsort()
-    return ids, max_id, x[index], y[index]
-
-  def reset(
-    self,
-    src: np.ndarray,
-    mask: np.ndarray,
-    tgt: np.ndarray,
-    mask_on_src: Tuple[int, int],
-    mask_on_tgt: Tuple[int, int],
-  ) -> int:
-    assert self.root
-    # check validity
-    # assert 0 <= mask_on_src[0] and 0 <= mask_on_src[1]
-    # assert mask_on_src[0] + mask.shape[0] <= src.shape[0]
-    # assert mask_on_src[1] + mask.shape[1] <= src.shape[1]
-    # assert mask_on_tgt[0] + mask.shape[0] <= tgt.shape[0]
-    # assert mask_on_tgt[1] + mask.shape[1] <= tgt.shape[1]
-
-    if len(mask.shape) == 3:
-      mask = mask.mean(-1)
-    mask = (mask >= 128).astype(np.int32)
-
-    # zero-out edge
-    mask[0] = 0
-    mask[-1] = 0
-    mask[:, 0] = 0
-    mask[:, -1] = 0
-
-    x, y = np.nonzero(mask)
-    x0, x1 = x.min() - 1, x.max() + 2
-    y0, y1 = y.min() - 1, y.max() + 2
-    mask_on_src = (x0 + mask_on_src[0], y0 + mask_on_src[1])
-    mask_on_tgt = (x0 + mask_on_tgt[0], y0 + mask_on_tgt[1])
-    mask = mask[x0:x1, y0:y1]
-    ids, max_id, index_x, index_y = self.mask2index(mask)
-
-    src_x, src_y = index_x + mask_on_src[0], index_y + mask_on_src[1]
-    tgt_x, tgt_y = index_x + mask_on_tgt[0], index_y + mask_on_tgt[1]
-
-    src_C = src[src_x, src_y].astype(np.float32)
-    src_U = src[src_x - 1, src_y].astype(np.float32)
-    src_D = src[src_x + 1, src_y].astype(np.float32)
-    src_L = src[src_x, src_y - 1].astype(np.float32)
-    src_R = src[src_x, src_y + 1].astype(np.float32)
-    tgt_C = tgt[tgt_x, tgt_y].astype(np.float32)
-    tgt_U = tgt[tgt_x - 1, tgt_y].astype(np.float32)
-    tgt_D = tgt[tgt_x + 1, tgt_y].astype(np.float32)
-    tgt_L = tgt[tgt_x, tgt_y - 1].astype(np.float32)
-    tgt_R = tgt[tgt_x, tgt_y + 1].astype(np.float32)
-
-    grad = self.mixgrad(src_C - src_L, tgt_C - tgt_L) \
-      + self.mixgrad(src_C - src_R, tgt_C - tgt_R) \
-      + self.mixgrad(src_C - src_U, tgt_C - tgt_U) \
-      + self.mixgrad(src_C - src_D, tgt_C - tgt_D)
-
-    A = np.zeros((max_id, 4), np.int32)
-    X = np.zeros((max_id, 3), np.float32)
-    B = np.zeros((max_id, 3), np.float32)
-
-    X[1:] = tgt[index_x + mask_on_tgt[0], index_y + mask_on_tgt[1]]
-    # four-way
-    A[1:, 0] = ids[index_x - 1, index_y]
-    A[1:, 1] = ids[index_x + 1, index_y]
-    A[1:, 2] = ids[index_x, index_y - 1]
-    A[1:, 3] = ids[index_x, index_y + 1]
-    B[1:] = grad
-    m = (mask[index_x - 1, index_y] == 0).astype(float).reshape(-1, 1)
-    B[1:] += m * tgt[index_x + mask_on_tgt[0] - 1, index_y + mask_on_tgt[1]]
-    m = (mask[index_x, index_y - 1] == 0).astype(float).reshape(-1, 1)
-    B[1:] += m * tgt[index_x + mask_on_tgt[0], index_y + mask_on_tgt[1] - 1]
-    m = (mask[index_x, index_y + 1] == 0).astype(float).reshape(-1, 1)
-    B[1:] += m * tgt[index_x + mask_on_tgt[0], index_y + mask_on_tgt[1] + 1]
-    m = (mask[index_x + 1, index_y] == 0).astype(float).reshape(-1, 1)
-    B[1:] += m * tgt[index_x + mask_on_tgt[0] + 1, index_y + mask_on_tgt[1]]
-
-    self.tgt = tgt.copy()
-    self.tgt_index = (index_x + mask_on_tgt[0], index_y + mask_on_tgt[1])
-    self.core.reset(max_id, A, X, B)
-    return max_id
-
-  def step(self, iteration: int) -> Optional[Tuple[np.ndarray, np.ndarray]]:
-    result = self.core.step(iteration)
-    if self.root:
-      x, err = result
-      self.tgt[self.tgt_index] = x[1:]
-      return self.tgt, err
-    return None
-
-
-class GridProcessor(BaseProcessor):
-  """PIE grid processor."""
-
-  def __init__(
-    self,
-    gradient: str = "max",
-    backend: str = DEFAULT_BACKEND,
-    n_cpu: int = CPU_COUNT,
-    min_interval: int = 100,
-    block_size: int = 1024,
-    grid_x: int = 8,
-    grid_y: int = 8,
-  ):
-    core: Optional[Any] = None
-    rank = 0
-
-    if backend == "numpy":
-      core = np_solver.GridSolver()
-    elif backend == "numba" and numba_solver is not None:
-      core = numba_solver.GridSolver()
-    elif backend == "gcc":
-      core = core_gcc.GridSolver(grid_x, grid_y)
-    elif backend == "openmp" and core_openmp is not None:
-      core = core_openmp.GridSolver(grid_x, grid_y, n_cpu)
-    elif backend == "mpi" and core_mpi is not None:
-      core = core_mpi.GridSolver(min_interval)
-      rank = MPI.COMM_WORLD.Get_rank()
-    elif backend == "cuda" and core_cuda is not None:
-      core = core_cuda.GridSolver(grid_x, grid_y)
-    elif backend.startswith("taichi") and taichi_solver is not None:
-      core = taichi_solver.GridSolver(
-        grid_x, grid_y, backend, n_cpu, block_size
-      )
-
-    super().__init__(gradient, rank, backend, core)
-
-  def reset(
-    self,
-    src: np.ndarray,
-    mask: np.ndarray,
-    tgt: np.ndarray,
-    mask_on_src: Tuple[int, int],
-    mask_on_tgt: Tuple[int, int],
-  ) -> int:
-    assert self.root
-    # check validity
-    # assert 0 <= mask_on_src[0] and 0 <= mask_on_src[1]
-    # assert mask_on_src[0] + mask.shape[0] <= src.shape[0]
-    # assert mask_on_src[1] + mask.shape[1] <= src.shape[1]
-    # assert mask_on_tgt[0] + mask.shape[0] <= tgt.shape[0]
-    # assert mask_on_tgt[1] + mask.shape[1] <= tgt.shape[1]
-
-    if len(mask.shape) == 3:
-      mask = mask.mean(-1)
-    mask = (mask >= 128).astype(np.int32)
-
-    # zero-out edge
-    mask[0] = 0
-    mask[-1] = 0
-    mask[:, 0] = 0
-    mask[:, -1] = 0
-
-    x, y = np.nonzero(mask)
-    x0, x1 = x.min() - 1, x.max() + 2
-    y0, y1 = y.min() - 1, y.max() + 2
-    mask = mask[x0:x1, y0:y1]
-    max_id = np.prod(mask.shape)
-
-    src_crop = src[mask_on_src[0] + x0:mask_on_src[0] + x1,
-                   mask_on_src[1] + y0:mask_on_src[1] + y1].astype(np.float32)
-    tgt_crop = tgt[mask_on_tgt[0] + x0:mask_on_tgt[0] + x1,
-                   mask_on_tgt[1] + y0:mask_on_tgt[1] + y1].astype(np.float32)
-    grad = np.zeros([*mask.shape, 3], np.float32)
-    grad[1:] += self.mixgrad(
-      src_crop[1:] - src_crop[:-1], tgt_crop[1:] - tgt_crop[:-1]
-    )
-    grad[:-1] += self.mixgrad(
-      src_crop[:-1] - src_crop[1:], tgt_crop[:-1] - tgt_crop[1:]
-    )
-    grad[:, 1:] += self.mixgrad(
-      src_crop[:, 1:] - src_crop[:, :-1], tgt_crop[:, 1:] - tgt_crop[:, :-1]
-    )
-    grad[:, :-1] += self.mixgrad(
-      src_crop[:, :-1] - src_crop[:, 1:], tgt_crop[:, :-1] - tgt_crop[:, 1:]
-    )
-
-    grad[mask == 0] = 0
-    if True:
-        kernel = [[1] * 3 for _ in range(3)]
-        nmask = mask.copy()
-        nmask[nmask > 0] = 1
-        res = scipy.signal.convolve2d(
-            nmask, kernel, mode="same", boundary="fill", fillvalue=1
-        )
-        res[nmask < 1] = 0
-        res[res == 9] = 0
-        res[res > 0] = 1
-        grad[res>0]=0
-        # ylst, xlst = res.nonzero()
-        # for y, x in zip(ylst, xlst):
-        #     grad[y,x]=0
-            # for yi in range(-1,2):
-                # for xi in range(-1,2):
-                    # grad[y+yi,x+xi]=0
-    self.x0 = mask_on_tgt[0] + x0
-    self.x1 = mask_on_tgt[0] + x1
-    self.y0 = mask_on_tgt[1] + y0
-    self.y1 = mask_on_tgt[1] + y1
-    self.tgt = tgt.copy()
-    self.core.reset(max_id, mask, tgt_crop, grad)
-    return max_id
-
-  def step(self, iteration: int) -> Optional[Tuple[np.ndarray, np.ndarray]]:
-    result = self.core.step(iteration)
-    if self.root:
-      tgt, err = result
-      self.tgt[self.x0:self.x1, self.y0:self.y1] = tgt
-      return self.tgt, err
-    return None
diff --git a/spaces/ludvigolsen/plot_confusion_matrix/README.md b/spaces/ludvigolsen/plot_confusion_matrix/README.md
deleted file mode 100644
index e7a31e45bf88c60d7133163ba0c800999d90b635..0000000000000000000000000000000000000000
--- a/spaces/ludvigolsen/plot_confusion_matrix/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-title: Plot Confusion Matrix
-sdk: docker
-app_file: app.py
-pinned: true
-emoji: 🍀
-colorFrom: orange
-colorTo: purple
----
-
-# Plot Confusion Matrix Streamlit Application
-
-Streamlit application for plotting a confusion matrix.
-
-
-## TODOs
-- ggsave only uses DPI for scaling? We would expect output files to have the given DPI?
-- Allow svg, pdf?
-- Add option to change zero-tile background (e.g. to black for black backgrounds)
-- Add option to format total-count tile in sum tiles
-- Allow handling tick text - e.g. for long class names or many classes.
\ No newline at end of file
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/detail/type_traits.h b/spaces/ma-xu/LIVE/thrust/thrust/detail/type_traits.h
deleted file mode 100644
index 9bfe60d31c686ffa5b628ef27748ce7ecbce7932..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/detail/type_traits.h
+++ /dev/null
@@ -1,714 +0,0 @@
-/*
- *  Copyright 2008-2018 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file type_traits.h
- *  \brief Temporarily define some type traits
- *         until nvcc can compile tr1::type_traits.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-#if THRUST_CPP_DIALECT >= 2011
-#  include <type_traits>
-#endif
-
-namespace thrust
-{
-
-// forward declaration of device_reference
-template<typename T> class device_reference;
-
-namespace detail
-{
- /// helper classes [4.3].
- template<typename T, T v>
-   struct integral_constant
-   {
-     THRUST_INLINE_INTEGRAL_MEMBER_CONSTANT T value = v;
-
-     typedef T                       value_type;
-     typedef integral_constant<T, v> type;
-
-     // We don't want to switch to std::integral_constant, because we want access
-     // to the C++14 operator(), but we'd like standard traits to interoperate
-     // with our version when tag dispatching.
-     #if THRUST_CPP_DIALECT >= 2011
-     integral_constant() = default;
-
-     integral_constant(integral_constant const&) = default;
-
-     integral_constant& operator=(integral_constant const&) = default;
-
-     constexpr __host__ __device__
-     integral_constant(std::integral_constant<T, v>) noexcept {}
-     #endif
-
-     THRUST_CONSTEXPR __host__ __device__ operator value_type() const THRUST_NOEXCEPT { return value; }
-     THRUST_CONSTEXPR __host__ __device__ value_type operator()() const THRUST_NOEXCEPT { return value; }
-   };
- 
- /// typedef for true_type
- typedef integral_constant<bool, true>  true_type;
-
- /// typedef for true_type
- typedef integral_constant<bool, false> false_type;
-
-//template<typename T> struct is_integral : public std::tr1::is_integral<T> {};
-template<typename T> struct is_integral                           : public false_type {};
-template<>           struct is_integral<bool>                     : public true_type {};
-template<>           struct is_integral<char>                     : public true_type {};
-template<>           struct is_integral<signed char>              : public true_type {};
-template<>           struct is_integral<unsigned char>            : public true_type {};
-template<>           struct is_integral<short>                    : public true_type {};
-template<>           struct is_integral<unsigned short>           : public true_type {};
-template<>           struct is_integral<int>                      : public true_type {};
-template<>           struct is_integral<unsigned int>             : public true_type {};
-template<>           struct is_integral<long>                     : public true_type {};
-template<>           struct is_integral<unsigned long>            : public true_type {};
-template<>           struct is_integral<long long>                : public true_type {};
-template<>           struct is_integral<unsigned long long>       : public true_type {};
-template<>           struct is_integral<const bool>               : public true_type {};
-template<>           struct is_integral<const char>               : public true_type {};
-template<>           struct is_integral<const unsigned char>      : public true_type {};
-template<>           struct is_integral<const short>              : public true_type {};
-template<>           struct is_integral<const unsigned short>     : public true_type {};
-template<>           struct is_integral<const int>                : public true_type {};
-template<>           struct is_integral<const unsigned int>       : public true_type {};
-template<>           struct is_integral<const long>               : public true_type {};
-template<>           struct is_integral<const unsigned long>      : public true_type {};
-template<>           struct is_integral<const long long>          : public true_type {};
-template<>           struct is_integral<const unsigned long long> : public true_type {};
-
-template<typename T> struct is_floating_point              : public false_type {};
-template<>           struct is_floating_point<float>       : public true_type {};
-template<>           struct is_floating_point<double>      : public true_type {};
-template<>           struct is_floating_point<long double> : public true_type {};
-
-template<typename T> struct is_arithmetic               : public is_integral<T> {};
-template<>           struct is_arithmetic<float>        : public true_type {};
-template<>           struct is_arithmetic<double>       : public true_type {};
-template<>           struct is_arithmetic<const float>  : public true_type {};
-template<>           struct is_arithmetic<const double> : public true_type {};
-
-template<typename T> struct is_pointer      : public false_type {};
-template<typename T> struct is_pointer<T *> : public true_type  {};
-
-template<typename T> struct is_device_ptr  : public false_type {};
-
-template<typename T> struct is_void             : public false_type {};
-template<>           struct is_void<void>       : public true_type {};
-template<>           struct is_void<const void> : public true_type {};
-
-template<typename T> struct is_non_bool_integral       : public is_integral<T> {};
-template<>           struct is_non_bool_integral<bool> : public false_type {};
-
-template<typename T> struct is_non_bool_arithmetic       : public is_arithmetic<T> {};
-template<>           struct is_non_bool_arithmetic<bool> : public false_type {};
-
-template<typename T> struct is_pod
-   : public integral_constant<
-       bool,
-       is_void<T>::value || is_pointer<T>::value || is_arithmetic<T>::value
-#if THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC || \
-    THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_CLANG
-// use intrinsic type traits
-       || __is_pod(T)
-#elif THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_GCC
-// only use the intrinsic for >= 4.3
-#if (__GNUC__ >= 4) && (__GNUC_MINOR__ >= 3)
-       || __is_pod(T)
-#endif // GCC VERSION
-#endif // THRUST_HOST_COMPILER
-     >
- {};
-
-
-template<typename T> struct has_trivial_constructor
-  : public integral_constant<
-      bool,
-      is_pod<T>::value
-#if THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC || \
-    THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_CLANG
-      || __has_trivial_constructor(T)
-#elif THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_GCC
-// only use the intrinsic for >= 4.3
-#if (__GNUC__ >= 4) && (__GNUC_MINOR__ >= 3)
-      || __has_trivial_constructor(T)
-#endif // GCC VERSION
-#endif // THRUST_HOST_COMPILER
-      >
-{};
-
-template<typename T> struct has_trivial_copy_constructor
-  : public integral_constant<
-      bool,
-      is_pod<T>::value
-#if THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_MSVC || \
-    THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_CLANG
-      || __has_trivial_copy(T)
-#elif THRUST_HOST_COMPILER == THRUST_HOST_COMPILER_GCC
-// only use the intrinsic for >= 4.3
-#if (__GNUC__ >= 4) && (__GNUC_MINOR__ >= 3)
-      || __has_trivial_copy(T)
-#endif // GCC VERSION
-#endif // THRUST_HOST_COMPILER
-    >
-{};
-
-template<typename T> struct has_trivial_destructor : public is_pod<T> {};
-
-template<typename T> struct is_const          : public false_type {};
-template<typename T> struct is_const<const T> : public true_type {};
-
-template<typename T> struct is_volatile             : public false_type {};
-template<typename T> struct is_volatile<volatile T> : public true_type {};
-
-template<typename T>
-  struct add_const
-{
-  typedef T const type;
-}; // end add_const
-
-template<typename T>
-  struct remove_const
-{
-  typedef T type;
-}; // end remove_const
-
-template<typename T>
-  struct remove_const<const T>
-{
-  typedef T type;
-}; // end remove_const
-
-template<typename T>
-  struct add_volatile
-{
-  typedef volatile T type;
-}; // end add_volatile
-
-template<typename T>
-  struct remove_volatile
-{
-  typedef T type;
-}; // end remove_volatile
-
-template<typename T>
-  struct remove_volatile<volatile T>
-{
-  typedef T type;
-}; // end remove_volatile
-
-template<typename T>
-  struct add_cv
-{
-  typedef const volatile T type;
-}; // end add_cv
-
-template<typename T>
-  struct remove_cv
-{
-  typedef typename remove_const<typename remove_volatile<T>::type>::type type;
-}; // end remove_cv
-
-
-template<typename T> struct is_reference     : public false_type {};
-template<typename T> struct is_reference<T&> : public true_type {};
-
-template<typename T> struct is_proxy_reference  : public false_type {};
-
-template<typename T> struct is_device_reference                                : public false_type {};
-template<typename T> struct is_device_reference< thrust::device_reference<T> > : public true_type {};
-
-
-// NB: Careful with reference to void.
-template<typename _Tp, bool = (is_void<_Tp>::value || is_reference<_Tp>::value)>
-  struct __add_reference_helper
-  { typedef _Tp&    type; };
-
-template<typename _Tp>
-  struct __add_reference_helper<_Tp, true>
-  { typedef _Tp     type; };
-
-template<typename _Tp>
-  struct add_reference
-    : public __add_reference_helper<_Tp>{};
-
-template<typename T>
-  struct remove_reference
-{
-  typedef T type;
-}; // end remove_reference
-
-template<typename T>
-  struct remove_reference<T&>
-{
-  typedef T type;
-}; // end remove_reference
-
-template<typename T1, typename T2>
-  struct is_same
-    : public false_type
-{
-}; // end is_same
-
-template<typename T>
-  struct is_same<T,T>
-    : public true_type
-{
-}; // end is_same
-
-template<typename T1, typename T2>
-  struct lazy_is_same
-    : is_same<typename T1::type, typename T2::type>
-{
-}; // end lazy_is_same
-
-template<typename T1, typename T2>
-  struct is_different
-    : public true_type
-{
-}; // end is_different
-
-template<typename T>
-  struct is_different<T,T>
-    : public false_type
-{
-}; // end is_different
-
-template<typename T1, typename T2>
-  struct lazy_is_different
-    : is_different<typename T1::type, typename T2::type>
-{
-}; // end lazy_is_different
-
-#if THRUST_CPP_DIALECT >= 2011
-
-using std::is_convertible;
-
-#else
-
-namespace tt_detail
-{
-
-template<typename T>
-  struct is_int_or_cref
-{
-  typedef typename remove_reference<T>::type type_sans_ref;
-  static const bool value = (is_integral<T>::value
-                             || (is_integral<type_sans_ref>::value
-                                 && is_const<type_sans_ref>::value
-                                 && !is_volatile<type_sans_ref>::value));
-}; // end is_int_or_cref
-
-
-THRUST_DISABLE_MSVC_POSSIBLE_LOSS_OF_DATA_WARNING_BEGIN
-THRUST_DISABLE_MSVC_FORCING_VALUE_TO_BOOL_WARNING_BEGIN
-
-template<typename From, typename To>
-  struct is_convertible_sfinae
-{
-  private:
-    typedef char                          yes;
-    typedef struct { char two_chars[2]; } no;
-
-    static inline yes   test(To) { return yes(); }
-    static inline no    test(...) { return no(); } 
-    static inline typename remove_reference<From>::type& from() { typename remove_reference<From>::type* ptr = 0; return *ptr; }
-
-  public:
-    static const bool value = sizeof(test(from())) == sizeof(yes);
-}; // end is_convertible_sfinae
-
-
-THRUST_DISABLE_MSVC_FORCING_VALUE_TO_BOOL_WARNING_END
-THRUST_DISABLE_MSVC_POSSIBLE_LOSS_OF_DATA_WARNING_END
-
-
-template<typename From, typename To>
-  struct is_convertible_needs_simple_test
-{
-  static const bool from_is_void      = is_void<From>::value;
-  static const bool to_is_void        = is_void<To>::value;
-  static const bool from_is_float     = is_floating_point<typename remove_reference<From>::type>::value;
-  static const bool to_is_int_or_cref = is_int_or_cref<To>::value;
-
-  static const bool value = (from_is_void || to_is_void || (from_is_float && to_is_int_or_cref));
-}; // end is_convertible_needs_simple_test
-
-
-template<typename From, typename To,
-         bool = is_convertible_needs_simple_test<From,To>::value>
-  struct is_convertible
-{
-  static const bool value = (is_void<To>::value
-                             || (is_int_or_cref<To>::value
-                                 && !is_void<From>::value));
-}; // end is_convertible
-
-
-template<typename From, typename To>
-  struct is_convertible<From, To, false>
-{
-  static const bool value = (is_convertible_sfinae<typename
-                             add_reference<From>::type, To>::value);
-}; // end is_convertible
-
-
-} // end tt_detail
-
-template<typename From, typename To>
-  struct is_convertible
-    : public integral_constant<bool, tt_detail::is_convertible<From, To>::value>
-{
-}; // end is_convertible
-
-#endif
-
-template<typename T1, typename T2>
-  struct is_one_convertible_to_the_other
-    : public integral_constant<
-        bool,
-        is_convertible<T1,T2>::value || is_convertible<T2,T1>::value
-      >
-{};
-
-
-// mpl stuff
-
-template <typename Condition1,               typename Condition2,              typename Condition3 = false_type,
-          typename Condition4  = false_type, typename Condition5 = false_type, typename Condition6 = false_type,
-          typename Condition7  = false_type, typename Condition8 = false_type, typename Condition9 = false_type,
-          typename Condition10 = false_type>
-  struct or_
-    : public integral_constant<
-        bool,
-        Condition1::value || Condition2::value || Condition3::value || Condition4::value || Condition5::value || Condition6::value || Condition7::value || Condition8::value || Condition9::value || Condition10::value
-      >
-{
-}; // end or_
-
-template <typename Condition1, typename Condition2, typename Condition3 = true_type>
-  struct and_
-    : public integral_constant<bool, Condition1::value && Condition2::value && Condition3::value>
-{
-}; // end and_
-
-template <typename Boolean>
-  struct not_
-    : public integral_constant<bool, !Boolean::value>
-{
-}; // end not_
-
-template<bool B, class T, class F>
-struct conditional { typedef T type; };
- 
-template<class T, class F>
-struct conditional<false, T, F> { typedef F type; };
-
-template <bool, typename Then, typename Else>
-  struct eval_if
-{
-}; // end eval_if
-
-template<typename Then, typename Else>
-  struct eval_if<true, Then, Else>
-{
-  typedef typename Then::type type;
-}; // end eval_if
-
-template<typename Then, typename Else>
-  struct eval_if<false, Then, Else>
-{
-  typedef typename Else::type type;
-}; // end eval_if
-
-template<typename T>
-//  struct identity
-//  XXX WAR nvcc's confusion with thrust::identity
-  struct identity_
-{
-  typedef T type;
-}; // end identity
-
-template<bool, typename T = void> struct enable_if {};
-template<typename T>              struct enable_if<true, T> {typedef T type;};
-
-template<bool, typename T> struct lazy_enable_if {};
-template<typename T>       struct lazy_enable_if<true, T> {typedef typename T::type type;};
-
-template<bool condition, typename T = void> struct disable_if : enable_if<!condition, T> {};
-template<bool condition, typename T>        struct lazy_disable_if : lazy_enable_if<!condition, T> {};
-
-
-template<typename T1, typename T2, typename T = void>
-  struct enable_if_convertible
-    : enable_if< is_convertible<T1,T2>::value, T >
-{};
-
-
-template<typename T1, typename T2, typename T = void>
-  struct disable_if_convertible
-    : disable_if< is_convertible<T1,T2>::value, T >
-{};
-
-
-template<typename T1, typename T2, typename Result = void>
-  struct enable_if_different
-    : enable_if<is_different<T1,T2>::value, Result>
-{};
-
-
-template<typename T>
-  struct is_numeric
-    : and_<
-        is_convertible<int,T>,
-        is_convertible<T,int>
-      >
-{
-}; // end is_numeric
-
-
-template<typename> struct is_reference_to_const             : false_type {};
-template<typename T> struct is_reference_to_const<const T&> : true_type {};
-
-
-// make_unsigned follows
-
-namespace tt_detail
-{
-
-template<typename T> struct make_unsigned_simple;
-
-template<> struct make_unsigned_simple<char>                   { typedef unsigned char          type; };
-template<> struct make_unsigned_simple<signed char>            { typedef unsigned char          type; };
-template<> struct make_unsigned_simple<unsigned char>          { typedef unsigned char          type; };
-template<> struct make_unsigned_simple<short>                  { typedef unsigned short         type; };
-template<> struct make_unsigned_simple<unsigned short>         { typedef unsigned short         type; };
-template<> struct make_unsigned_simple<int>                    { typedef unsigned int           type; };
-template<> struct make_unsigned_simple<unsigned int>           { typedef unsigned int           type; };
-template<> struct make_unsigned_simple<long int>               { typedef unsigned long int      type; };
-template<> struct make_unsigned_simple<unsigned long int>      { typedef unsigned long int      type; };
-template<> struct make_unsigned_simple<long long int>          { typedef unsigned long long int type; };
-template<> struct make_unsigned_simple<unsigned long long int> { typedef unsigned long long int type; };
-
-template<typename T>
-  struct make_unsigned_base
-{
-  // remove cv
-  typedef typename remove_cv<T>::type remove_cv_t;
-
-  // get the simple unsigned type
-  typedef typename make_unsigned_simple<remove_cv_t>::type unsigned_remove_cv_t;
-
-  // add back const, volatile, both, or neither to the simple result
-  typedef typename eval_if<
-    is_const<T>::value && is_volatile<T>::value,
-    // add cv back
-    add_cv<unsigned_remove_cv_t>,
-    // check const & volatile individually
-    eval_if<
-      is_const<T>::value,
-      // add c back
-      add_const<unsigned_remove_cv_t>,
-      eval_if<
-        is_volatile<T>::value,
-        // add v back
-        add_volatile<unsigned_remove_cv_t>,
-        // original type was neither cv, return the simple unsigned result
-        identity_<unsigned_remove_cv_t>
-      >
-    >
-  >::type type;
-};
-
-} // end tt_detail
-
-template<typename T>
-  struct make_unsigned
-    : tt_detail::make_unsigned_base<T>
-{};
-
-struct largest_available_float
-{
-#if defined(__CUDA_ARCH__)
-#  if (__CUDA_ARCH__ < 130)
-  typedef float type;
-#  else
-  typedef double type;
-#  endif
-#else
-  typedef double type;
-#endif
-};
-
-// T1 wins if they are both the same size
-template<typename T1, typename T2>
-  struct larger_type
-    : thrust::detail::eval_if<
-        (sizeof(T2) > sizeof(T1)),
-        thrust::detail::identity_<T2>,
-        thrust::detail::identity_<T1>
-      >
-{};
-
-#if THRUST_CPP_DIALECT >= 2011
-
-using std::is_base_of;
-
-#else
-
-namespace is_base_of_ns
-{
-
-typedef char                          yes;
-typedef struct { char two_chars[2]; } no;
-
-template<typename Base, typename Derived>
-  struct host
-{
-  operator Base*() const;
-  operator Derived*();
-}; // end host
-
-template<typename Base, typename Derived>
-  struct impl
-{
-  template<typename T> static yes check(Derived *, T);
-  static no check(Base*, int);
-
-  static const bool value = sizeof(check(host<Base,Derived>(), int())) == sizeof(yes);
-}; // end impl
-
-} // end is_base_of_ns
-
-
-template<typename Base, typename Derived>
-  struct is_base_of
-    : integral_constant<
-        bool,
-        is_base_of_ns::impl<Base,Derived>::value
-      >
-{};
-
-#endif
-
-template<typename Base, typename Derived, typename Result = void>
-  struct enable_if_base_of
-    : enable_if<
-        is_base_of<Base,Derived>::value,
-        Result
-      >
-{};
-
-
-namespace is_assignable_ns
-{
-
-template<typename T1, typename T2>
-  class is_assignable
-{
-  typedef char                      yes_type;
-  typedef struct { char array[2]; } no_type;
-
-  template<typename T> static typename add_reference<T>::type declval();
-  
-  template<unsigned int> struct helper { typedef void * type; };
-
-  template<typename U1, typename U2> static yes_type test(typename helper<sizeof(declval<U1>() = declval<U2>())>::type);
-
-  template<typename,typename> static no_type test(...);
-
-  public:
-    static const bool value = sizeof(test<T1,T2>(0)) == 1;
-}; // end is_assignable
-
-} // end is_assignable_ns
-
-
-template<typename T1, typename T2>
-  struct is_assignable
-    : integral_constant<
-        bool,
-        is_assignable_ns::is_assignable<T1,T2>::value
-      >
-{};
-
-
-template<typename T>
-  struct is_copy_assignable
-    : is_assignable<
-        typename add_reference<T>::type,
-        typename add_reference<typename add_const<T>::type>::type
-      >
-{};
-
-
-template<typename T1, typename T2, typename Enable = void> struct promoted_numerical_type;
-
-template<typename T1, typename T2> 
-  struct promoted_numerical_type<T1,T2,typename enable_if<and_
-  <typename is_floating_point<T1>::type,typename is_floating_point<T2>::type>
-  ::value>::type>
-  {
-  typedef typename larger_type<T1,T2>::type type;
-  };
-
-template<typename T1, typename T2> 
-  struct promoted_numerical_type<T1,T2,typename enable_if<and_
-  <typename is_integral<T1>::type,typename is_floating_point<T2>::type>
-  ::value>::type>
-  {
-  typedef T2 type;
-  };
-
-template<typename T1, typename T2>
-  struct promoted_numerical_type<T1,T2,typename enable_if<and_
-  <typename is_floating_point<T1>::type, typename is_integral<T2>::type>
-  ::value>::type>
-  {
-  typedef T1 type;
-  };
-
-template<typename T>
-  struct is_empty_helper : public T
-  {
-  };
-
-struct is_empty_helper_base
-{
-};
-
-template<typename T>
-  struct is_empty : integral_constant<bool,
-    sizeof(is_empty_helper_base) == sizeof(is_empty_helper<T>)
-  >
-  {
-  };
-
-} // end detail
-
-using detail::integral_constant;
-using detail::true_type;
-using detail::false_type;
-
-} // end thrust
-
-#include <thrust/detail/type_traits/has_trivial_assign.h>
-
diff --git a/spaces/manan/fruit-classifier/model.py b/spaces/manan/fruit-classifier/model.py
deleted file mode 100644
index 654361f9e897206682ba5db9c50e868fb2f5ee05..0000000000000000000000000000000000000000
--- a/spaces/manan/fruit-classifier/model.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import timm
-import torch
-from torch import nn
-import albumentations
-from PIL import Image
-import numpy as np
-
-augmentations = albumentations.Compose(
-    [
-        albumentations.Normalize(
-            mean=[0.485, 0.456, 0.406],
-            std=[0.229, 0.224, 0.225],
-            max_pixel_value=255.0,
-            always_apply=True
-        )
-    ]
-)
-
-target_map = {
-    0: 'Cranberry',
-    1: 'Musk melon',
-    2: 'Pineapple',
-    3: 'Watermelon',
-    4: 'Orange',
-    5: 'Dragon fruit',
-    6: 'Bananas',
-    7: 'Blue berries',
-    8: 'Jack fruit',
-    9: 'Avacados',
-}
-
-
-
-class ImageModelInfer(nn.Module):
-    def __init__(self, model_path, num_classes):
-        super().__init__()
-        model_path = model_path
-        self.model = timm.create_model(model_path, pretrained=False, num_classes=num_classes)
-        
-    def forward(self, data):
-        logits = self.model(data)
-        return logits
-
-
-def prepare_image(image):
-    # image = Image.open(image)
-    # image = image.convert("RGB")
-    # image = image.resize((256, 256), resample=Image.BILINEAR)
-    image = np.array(image)
-    augmented = augmentations(image=image)
-    image = augmented['image']
-    image = np.transpose(image, (2, 0, 1)).astype(np.float32)
-    return torch.tensor(image, dtype=torch.float)
-
-
-def predict_fruit_type(img):
-    img = prepare_image(img)
-    prediction = model(img.unsqueeze(0))[0].detach().numpy()
-    class_ = np.argmax(prediction)
-    return target_map[class_]
-
-
-model = ImageModelInfer('vgg16', num_classes=10)
-model.load_state_dict(torch.load('best_loss_0.ckpt', map_location=torch.device('cpu'))['state_dict']);
\ No newline at end of file
diff --git a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/train_domain_B.py b/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/train_domain_B.py
deleted file mode 100644
index 4659b047290b3f5e95a483713b3ad9984a4f94f5..0000000000000000000000000000000000000000
--- a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/train_domain_B.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import time
-from collections import OrderedDict
-from options.train_options import TrainOptions
-from data.data_loader import CreateDataLoader
-from models.models import create_model
-import util.util as util
-from util.visualizer import Visualizer
-import os
-import numpy as np
-import torch
-import torchvision.utils as vutils
-from torch.autograd import Variable
-import random
-
-
-opt = TrainOptions().parse()
-
-if opt.debug:
-    opt.display_freq = 1
-    opt.print_freq = 1
-    opt.niter = 1
-    opt.niter_decay = 0
-    opt.max_dataset_size = 10
-
-data_loader = CreateDataLoader(opt)
-dataset = data_loader.load_data()
-dataset_size = len(dataset) * opt.batchSize
-print('#training images = %d' % dataset_size)
-
-path = os.path.join(opt.checkpoints_dir, opt.name, 'model.txt')
-visualizer = Visualizer(opt)
-
-
-iter_path = os.path.join(opt.checkpoints_dir, opt.name, 'iter.txt')
-if opt.continue_train:
-    try:
-        start_epoch, epoch_iter = np.loadtxt(iter_path , delimiter=',', dtype=int)
-    except:
-        start_epoch, epoch_iter = 1, 0
-    visualizer.print_save('Resuming from epoch %d at iteration %d' % (start_epoch-1, epoch_iter))
-else:
-    start_epoch, epoch_iter = 1, 0
-
-# opt.which_epoch=start_epoch-1
-model = create_model(opt)
-fd = open(path, 'w')
-fd.write(str(model.module.netG))
-fd.write(str(model.module.netD))
-fd.close()
-
-total_steps = (start_epoch-1) * dataset_size + epoch_iter
-
-display_delta = total_steps % opt.display_freq
-print_delta = total_steps % opt.print_freq
-save_delta = total_steps % opt.save_latest_freq
-
-for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1):
-    epoch_start_time = time.time()
-    if epoch != start_epoch:
-        epoch_iter = epoch_iter % dataset_size
-    for i, data in enumerate(dataset, start=epoch_iter):
-        iter_start_time = time.time()
-        total_steps += opt.batchSize
-        epoch_iter += opt.batchSize
-
-        # whether to collect output images
-        save_fake = total_steps % opt.display_freq == display_delta
-
-        ############## Forward Pass ######################
-        losses, generated = model(Variable(data['label']), Variable(data['inst']), 
-            Variable(data['image']), Variable(data['feat']), infer=save_fake)
-
-        # sum per device losses
-        losses = [ torch.mean(x) if not isinstance(x, int) else x for x in losses ]
-        loss_dict = dict(zip(model.module.loss_names, losses))
-
-
-        # calculate final loss scalar
-        loss_D = (loss_dict['D_fake'] + loss_dict['D_real']) * 0.5
-        loss_G = loss_dict['G_GAN'] + loss_dict.get('G_GAN_Feat',0) + loss_dict.get('G_VGG',0) + loss_dict['G_KL'] + loss_dict.get('Smooth_L1',0)*opt.L1_weight
-
-
-        ############### Backward Pass ####################
-        # update generator weights
-        model.module.optimizer_G.zero_grad()
-        loss_G.backward()
-        model.module.optimizer_G.step()
-
-        # update discriminator weights
-        model.module.optimizer_D.zero_grad()
-        loss_D.backward()
-        model.module.optimizer_D.step()
-
-        #call(["nvidia-smi", "--format=csv", "--query-gpu=memory.used,memory.free"]) 
-
-        ############## Display results and errors ##########
-        ### print out errors
-        if total_steps % opt.print_freq == print_delta:
-            errors = {k: v.data if not isinstance(v, int) else v for k, v in loss_dict.items()}
-            t = (time.time() - iter_start_time) / opt.batchSize
-            visualizer.print_current_errors(epoch, epoch_iter, errors, t, model.module.old_lr)
-            visualizer.plot_current_errors(errors, total_steps)
-
-        ### display output images
-        if save_fake:
-
-            if not os.path.exists(opt.outputs_dir + opt.name):
-                os.makedirs(opt.outputs_dir + opt.name)
-            imgs_num = 5
-            imgs = torch.cat((data['label'][:imgs_num], generated.data.cpu()[:imgs_num], data['image'][:imgs_num]), 0)
-
-            imgs = (imgs + 1.) / 2.0
-
-            try:
-                image_grid = vutils.save_image(imgs, opt.outputs_dir + opt.name + '/' + str(epoch) + '_' + str(total_steps) + '.png',
-                        nrow=imgs_num, padding=0, normalize=True)
-            except OSError as err:
-                print(err)
-
-        if epoch_iter >= dataset_size:
-            break
-       
-    # end of epoch 
-    iter_end_time = time.time()
-    print('End of epoch %d / %d \t Time Taken: %d sec' %
-          (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
-
-    ### save model for this epoch
-    if epoch % opt.save_epoch_freq == 0:
-        print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps))        
-        model.module.save('latest')
-        model.module.save(epoch)
-        np.savetxt(iter_path, (epoch+1, 0), delimiter=',', fmt='%d')
-
-    ### instead of only training the local enhancer, train the entire network after certain iterations
-    if (opt.niter_fix_global != 0) and (epoch == opt.niter_fix_global):
-        model.module.update_fixed_params()
-
-    ### linearly decay learning rate after certain iterations
-    if epoch > opt.niter:
-        model.module.update_learning_rate()
-
diff --git a/spaces/matanmichaely/image_to_audio_story/app.py b/spaces/matanmichaely/image_to_audio_story/app.py
deleted file mode 100644
index 4e7a5e100bf5739b708ef8d3fac6aba97f4644c6..0000000000000000000000000000000000000000
--- a/spaces/matanmichaely/image_to_audio_story/app.py
+++ /dev/null
@@ -1,78 +0,0 @@
-from dotenv import find_dotenv, load_dotenv
-from transformers import pipeline
-from transformers import AutoProcessor, AutoModel
-from langchain import PromptTemplate, LLMChain
-from langchain.llms import GooglePalm
-import scipy
-import streamlit as st
-
-load_dotenv(find_dotenv())
-
-# img2text
-def img_2_text(url):
-    image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
-
-    text = image_to_text(url)[0]["generated_text"]
-    return text
-
-
-# llm
-def generate_story(scenario):
-    template = """"
-        You are a story teller;
-        you can generate a creative fun story based on a sample narrative, the story should not be more than 100 words;
-        CONTEXT: {scenario}
-        STORY: 
-        """
-
-    prompt = PromptTemplate(template=template,
-                            input_variables=['scenario']
-                            )
-    llm = GooglePalm(temperature=0.7)
-
-    story_llm = LLMChain(llm=llm, prompt=prompt, verbose=True)
-
-    story = story_llm.predict(scenario=scenario)
-
-    return story
-
-
-#
-# text-to-speech
-def text_to_speech(text):
-    processor = AutoProcessor.from_pretrained("suno/bark-small")
-    model = AutoModel.from_pretrained("suno/bark-small")
-
-    inputs = processor(
-        text=[text],
-        return_tensors="pt",
-    )
-
-    speech_values = model.generate(**inputs, do_sample=True)
-    sampling_rate = model.generation_config.sample_rate
-    scipy.io.wavfile.write("audio.wav", rate=sampling_rate, data=speech_values.cpu().numpy().squeeze())
-
-
-def main():
-    st.set_page_config(page_title="img 2 audio story")
-    st.header("turn image to audio story")
-    uploaded_file = st.file_uploader("Choose an image ... ", type="jpg")
-
-    if uploaded_file is not None:
-        print(uploaded_file)
-        bytes_data = uploaded_file.getvalue()
-        with open(uploaded_file.name, "wb") as file:
-            file.write(bytes_data)
-        st.image(uploaded_file, caption="Uploaded image", use_column_width=True)
-        text = img_2_text(uploaded_file.name)
-        story = generate_story(text)
-        text_to_speech(story)
-
-        with st.expander("text"):
-            st.write(text)
-        with st.expander("story"):
-            st.write(story)
-        st.audio("audio.wav")
-
-
-main()
diff --git a/spaces/merve/anonymization/public/uncertainty-calibration/draw_slides.js b/spaces/merve/anonymization/public/uncertainty-calibration/draw_slides.js
deleted file mode 100644
index 17ab651b01bc454c7168d55d28d5d8b42b26379b..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/public/uncertainty-calibration/draw_slides.js
+++ /dev/null
@@ -1,160 +0,0 @@
-window.drawSlides = function(){
-  var slides = [
-    {
-      id: 'intro',
-      visible_threshold: 0, //Also sets pointerEvents
-      visible_tmessage: 0,
-      visible_calibration: 0,
-      constant_model_score: 0,
-    },
-    {
-      id: 'thresholding',
-      visible_threshold: 1,
-      visible_tmessage: 0,
-      visible_calibration: 0,
-      constant_model_score: 0,
-      // target_thresholds: [0, 0.25, 0.35, 0.6, 0.7, 1]
-      target_threshold: .4
-    },
-    {
-      id: 'adjustable_thresholding',
-      visible_threshold: 1,
-      visible_tmessage: 1,
-      visible_calibration: 0,
-      constant_model_score: 0,
-      target_threshold: .47
-      // target_thresholds: [0, 0.25, 0.35, 0.6, 0.7, 1]
-    },
-    {
-      id: 'calibration',
-      visible_threshold: 0,
-      visible_tmessage: 0,
-      visible_calibration: 1,
-      constant_model_score: 0,
-      target_thresholds: [0, 0.2, 0.4, 0.6, 0.8, 1]
-    },
-    {
-      id: 'adjusting_calibration',
-      visible_threshold: 0,
-      visible_tmessage: 0,
-      visible_calibration: 1,
-      constant_model_score: 0,
-      target_thresholds: [0, 0.15, 0.45, 0.55, 0.83, 1]
-    },
-    // {
-    //   id: 'improving_calibration',
-    //   visible_threshold: 0,
-    //   visible_calibration: 1,
-    //   constant_model_score: 1,
-    //   target_thresholds: [0, 0.305, 0.407, 0.503, 0.649, 1],
-    // },
-    {
-      id: 'shifting_data',
-      visible_threshold: 0,
-      visible_tmessage: 0,
-      visible_calibration: 1,
-      constant_model_score: 1,
-      filter_rain: true
-    },
-    {
-      id: 'beyond_calibration',
-      visible_threshold: 0,
-      visible_tmessage: 0,
-      visible_calibration: 1,
-      constant_model_score: 1,
-      target_thresholds: [0, .02, .04, .96, .98, 1],
-    },
-    
-  ]
-
-  var prevSlide = null;
-
-  var gs = d3.graphScroll()
-  .container(d3.select('#container'))
-  .graph(d3.selectAll('#container #graph'))
-  .eventId('uniqueId1')  // namespace for scroll and resize events
-  .sections(d3.selectAll('#container #sections > div'))
-  .offset(window.isMobile ? 300 : 200)
-  .on('active', function(i){
-    try{
-      var slide = slides.slide = slides[i]
-
-      if (!slide) return console.log(`missing slide ${i}`)
-
-      // if(slide.id != 'slide1'){
-      //   weatherGraph.prediction_sel.at({opacity:0});
-      // }
-
-      // if(slide.constant_model_score){
-      //   weatherGraph.icon_sel.transition().duration(500)
-      //   .at({y: constant_score})
-      // }
-      // else {
-      //   weatherGraph.icon_sel.transition().duration(500)
-      //   .at({y: d => c.y(d.h)})
-      // }
-
-      //weatherGraph.threshold_sel.classed('temp')
-
-      var transition_duration = prevSlide ? 500 : 0;
-
-      // Animate threshold and thresholds between slides
-      var durationScale = 1
-      if (prevSlide){
-        durationScale = prevSlide.visible_calibration == slide.visible_calibration ? 1 : 3 
-      }
-      if (slide.target_thresholds){
-        weatherGraph.setThresholds(slide.target_thresholds, transition_duration*durationScale)
-      }
-      if (slide.target_threshold){
-        weatherGraph.setThreshold(slide.target_threshold, transition_duration*durationScale)
-      }
-
-      calibrationCurve.renderBuckets()
-      
-
-      weatherGraph.thresholdSel
-        .st({pointerEvents: slide.visible_threshold ? 'all' : 'none'})
-        .transition().duration(transition_duration)
-        .st({opacity: slide.visible_threshold});
-
-      weatherGraph.messageSel
-        .transition().duration(transition_duration)
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deleted file mode 100644
index 9effe485ca14df5d6923e20adefaa794b939ee26..0000000000000000000000000000000000000000
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diff --git a/spaces/merve/data-leak/source/fill-in-the-blank/init.js b/spaces/merve/data-leak/source/fill-in-the-blank/init.js
deleted file mode 100644
index 2e61759b05c45666ac2013000d8c4da1bc367630..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/source/fill-in-the-blank/init.js
+++ /dev/null
@@ -1,426 +0,0 @@
-/* Copyright 2021 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
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-==============================================================================*/
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-window.ttSel = d3.select('body').selectAppend('div.tooltip.tooltip-hidden')
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-window.palette = function palette(min, max){
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-    //https://gka.github.io/palettes/#/99|d|00429d,76dfca,d1d1b3|d1d1b3,a787a8,93003a|1|1
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-window.util = {
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-window.util.colors = [1 - .25, .25].map(util.color)
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-!(function(){
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-    return memo[d] = [r, g, b].map(v => v/255)
-  }
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-
-// add colors to inline elements
-!(function(){
-  d3.selectAll('c0').st({fontWeight: 600, color: util.colors[0]})
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-  d3.selectAll('c2').st({fontWeight: 600, color: util.colors[2]})
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-    class: 'texas-ohio',
-    s0: 'In New York, they like to buy _.',
-    s1: 'In Texas, they like to buy _.',
-    count: 30,
-    annotations: [
-      {
-        str: 'BERT associates these potential purchases <b>more with Texas</b><br> than New York...',
-        pos: [15, 15],
-        color: util.colors[1]
-      },
-      {
-        str: '...and these purchases <br><b>more with New York</b><br> than Texas',
-        pos: [290, 305],
-        color: util.colors[0]
-      },
-    ],
-    ariaLabel: 'Scatter plot of differences in purchases between New York and Texas. Oil, cotten and land are associated more with Texas; Pictures and perfume are more associated with New York',
-    alts: [
-      {
-        str: 'Ireland v. Australia',
-        s1: 'We went to Ireland and bought a _.',
-        s0: 'We went to Australia and bought a _.',
-      },
-      {
-        str: 'Arctic v. Equator',
-        s1: 'Near the Arctic, they like to buy  _.',
-        s0: 'Near the equator, they like to buy  _.',
-      },
-      {
-        str: 'Coast v. Plains',
-        s1: 'On the coast, they like to buy _.',
-        s0: 'On the plains, they like to buy _.',
-      },
-      {
-        str: 'Narnia v. Gotham',
-        s1: 'In Narnia, they bought a _.',
-        s0: 'In Gotham, they bought a _.',
-      },
-      {
-        str: 'Supermarket v. Mall',
-        s1: 'At the supermarket, they like to buy _.',
-        s0: 'At the mall, they like to buy _.',
-      },
-      // {
-      //   str: 'Train v. Plane',
-      //   s1: 'At the airport, they like to buy _.',
-      //   s0: 'At the bus depot, they like to buy _.',
-      // },
-      // {
-      //   str: 'buy v. sell',
-      //   s0: 'They like to buy _.',
-      //   s1: 'We like to buy _.',
-      // },
-            // {
-      //   str: 'Paris v. London',
-      //   s1: 'In Paris, they like to buy _.',
-      //   s0: 'In London, they like to buy _.',
-      // },
-    ]
-    // type: 'Differences',
-  },
-  {
-    class: 'age-name',
-    s0: 'Elsie was born in the year of _.',
-    s1: 'Lauren was born in the year of _.',
-    count: 200,
-    ariaLabel: 'Scatter plot of differences in birth years between Elsie and Lauren.',
-  },
-  {
-    class: 'jim-jane',
-    s0: 'Jim worked as a _.',
-    s1: 'Jane worked as a _.',
-    count: 30,
-    ariaLabel: 'Scatter plot of differences in occupations between Jim and Jane. Salesmen, carpenter and mechanic are more associated with Jim; Nurse, secretary and modal are more associated with Jane.',
-  },
-  {
-    class: 'nurse-name',
-    s0: 'The new nurse was named _.',
-    s1: 'The new doctor was named _.',
-    count: 30,
-    ariaLabel: 'Scatter plot of differences in doctor and nurse names. David, Michael and himself are more associated with doctors; Jean, Sarah and Catherine are more associated with nurses.',
-
-  },  
-  {
-    class: 'nurse-name-zari-cda',
-    s0: 'The new nurse was named _.',
-    s1: 'The new doctor was named _.',
-    s0: 'The doctor performed CPR even though _ knew it was too late.',
-    s1: 'The nurse performed CPR even though _ knew it was too late.',
-    s0model: '_zari_cda',
-    s1model: '_zari_cda',
-    showModel: true,
-    count: 30,
-    ariaLabel: 'Scatter plot of differences in doctor and nurse names in the Zari model. He and she are equally associated with both. But Jack, Logan and Andrew are more associated with doctors; Emily, Rachel and Amy are more associated with nurses.',
-  },
-  {
-    class: 'interesting-pair',
-    s1: '_ flavored ice cream is tasty.',
-    s0: '_ flavored ice cream is revolting.',
-    count: 30,
-    alts: [
-      {
-        str: 'Dangerous animals',
-        s1: '_ is a [friendly|dangerous] animal',
-        s0: '_ is a [friendly|dangerous] animal',
-      },
-    ]
-  }
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-
-pairs.forEach(d => {
-  d.count = d.count || 200
-  d.s0model = d.s0model || ''
-  d.s1model = d.s1model || ''
-  d.annotations = d.annotations || []
-  d.model = d.s0model ? 'Zari' : 'BERT'
-  d.type = d.type || 'Likelihoods'
-  d.pairStr = JSON.stringify(d)
-})
-// pairs = [window.pairs[1]]
-
-
-var diffs = [
-  {
-    s0: 'In [Texas|Paris], [Men|Women] like to buy _.',
-    s0: 'Born in [1940|2018], [his|her] name was _.',
-    s0: 'In [1908|2018], [he|she] was employed as a _.',
-    class: 'difference-difference',
-    count: 1000,
-    annotations: [],
-    model: 'BERT',
-    type: 'Likelihoods',
-    ariaLabel: 'Small multiple difference in difference plots.',
-  }
-]
-
-diffs.forEach(d => {
-  d.pairStr = JSON.stringify(d)
-})
-
-
-window.sents = [
-  {
-    class: 'hamlet',
-    str: 'To be or not to be, that is the question;',
-  },
-]
-sents.push({class: 'texas', str: pairs[0].s1.replace('_', 'things')})
-sents.push({class: 'new-york', str: pairs[0].s0.replace('_', 'things')})
-
-
-window.init = async function(){
-  try { window.regltick.cancel() } catch (e) {}
-
-  if (!window.tokenizer){
-    window.tokenizer = new BertTokenizer()
-    await tokenizer.load()
-  }
-
-  if (!window.bertLargeVocab){
-    var text = await (await fetch('data/bert_large_vocab.txt')).text()
-    window.bertLargeVocab = text
-      .split('\n')
-  }
-
-  sents.forEach(initSent)
-  sleep(10)
-
-  pairs.forEach(initPair)
-  sleep(500)
-  window.initGenderOverTime()
-
-
-  // Skip rendering differene in difference until scrolled into view
-  var renderDiffDiff = false
-    var observer = new IntersectionObserver(entries => {
-    entries.forEach(d => {
-      if (renderDiffDiff || !d.isIntersecting) return
-      
-      initDiff(diffs[0])
-      renderDiffDiff = true
-    })
-  }, {})
-  observer.observe(d3.select('.difference-difference').node())
-  if (renderDiffDiff) initDiff(diffs[0])
-
-
-  function sleep(ms) {
-    return new Promise(resolve => setTimeout(resolve, ms))
-  }
-}
-
-// Run init, rerun when width changes
-!(function(){
-  var lastInnerWidth = null
-  
-  function resize(){
-    if (lastInnerWidth == window.innerWidth) return
-    lastInnerWidth = window.innerWidth
-
-    window.init()
-  }
-  resize()
-  d3.select(window).on('resize', _.debounce(resize, 500))
-})()
-
-// Hamlet text entry
-!(function(){
-  var sel = d3.select('.hamlet-edit').html('')
-    .st({textAlign: 'center', marginTop: 17})
-    .on('keydown', function(){
-      sel.classed('changed', 1)
-      if (d3.event.keyCode != 13) return
-      d3.event.preventDefault()
-
-      update()
-    })
-
-  var sent = sents[0]
-
-  var inputSel = sel.append('textarea').at({cols: 30})
-  inputSel.node().value = sent.str
-
-  // sel.append('div')
-  sel.append('button.button.update').on('click', update).text('Update Sentence')
-    .st({width: 140, height: 47, marginLeft: 20, marginTop: 0, top: -19, marginRight: 0})
-
-
-  function update(){
-    sent.str = inputSel.node().value
-
-    sel.classed('changed', 0)
-    initSent(sent)
-  }
-})()
-
-
-window.addLockedTooltip = function(sel){
-  sel
-    .on('mouseover', function(d, i){
-      ttSel
-        .html(d)
-        .select('.footend').remove()
-
-      var x = this.offsetLeft,
-          y = this.offsetTop,
-          bb = ttSel.node().getBoundingClientRect(),
-          left = d3.clamp(20, (x-bb.width/2), window.innerWidth - bb.width - 20),
-          top = innerHeight + scrollY > y + 20 + bb.height ? y + 20 : y - bb.height - 10;
-
-      ttSel.st({left, top}).classed('tooltip-hidden', false)
-    })
-
-  sel.on('mousemove',mouseover).on('mouseout', mouseout)
-  ttSel.on('mousemove', mouseover).on('mouseout', mouseout)
-  function mouseover(){
-    if (window.__ttfade) window.__ttfade.stop()
-  }
-  function mouseout(){
-    if (window.__ttfade) window.__ttfade.stop()
-    window.__ttfade = d3.timeout(() => {
-      ttSel.classed('tooltip-hidden', true)
-    }, 250)
-  }
-}
-
-// Footnotes
-!(function(){
-  var footnums = '¹²³⁴⁵⁶⁷⁸⁹'
-
-  var footendSel = d3.selectAll('.footend')
-    .each(function(d, i){
-      var sel = d3.select(this)
-      var ogHTML = sel.parent().html()
-      sel
-        .at({href: '#footstart-' + i, id: 'footend-' + i})
-        .text(footnums[i])
-        .datum(ogHTML)
-    })
-
-
-  var footstartSel = d3.selectAll('.footstart')
-    .each(function(d, i){
-      d3.select(this)
-        .at({
-          href: '#footend-' + i,
-        })
-        .text(footnums[i])
-        .datum(footendSel.data()[i])
-        .parent().at({id: 'footstart-' + i})
-    })
-    .call(addLockedTooltip)
-
-})()
-
-
-
-
-
-
-
-// // Populate interesting alts
-// !(() => {
-//   var listSel = d3.select('.interesting-list').st({display: 'none'})
-
-//   var listStr = listSel.text()
-
-//   _.last(pairs).alts = listStr.split('-').map(d => d.trim()).filter(d => d).map(rawStr => {
-//     var start = rawStr.split('[')[0]
-//     var end = rawStr.split(']')[1]
-
-//     var [t0, t1] = rawStr.split('[')[1].split(']')[0].split('|')
-//     var s0 = start + t0 + end
-//     var s1 = start + t1 + end
-
-//     var str = `<div style=display:inline-block>${start} 
-//       <span style=color:${util.colors[1]}>${t1}</span>|<span
-//             style=color:${util.colors[0]}>${t0}</span>
-//       ${end}</div>`.replace('_', '____')
-
-//     return {str, s0, s1}
-//   })
-// })()
-
-// // Populate difference in difference
-// !(() => {
-//   var listSel = d3.select('.difference-difference-list').st({display: 'none'})
-
-//   var listStr = listSel.text()
-
-//   diffs[0].alts = listStr.split('-').map(d => d.trim()).filter(d => d).map(rawStr => {
-//     var start = rawStr.split('[')[0]
-//     var end = rawStr.split(']')[1]
-
-//     var [t0, t1] = rawStr.split('[')[1].split(']')[0].split('|')
-//     var s0 = start + t0 + end
-//     var s1 = start + t1 + end
-
-//     var str = `<div style=display:inline-block>${rawStr}</div>`.replace('_', '____')
-
-
-//     return {str, s0, s1, rawStr}
-//   })
-// })()
diff --git a/spaces/merve/measuring-fairness/source/third_party/d3-scale-chromatic.v1.min.js b/spaces/merve/measuring-fairness/source/third_party/d3-scale-chromatic.v1.min.js
deleted file mode 100644
index 90b8e6953cea11cade766bc4f143ecce4bd9edf1..0000000000000000000000000000000000000000
--- a/spaces/merve/measuring-fairness/source/third_party/d3-scale-chromatic.v1.min.js
+++ /dev/null
@@ -1,2 +0,0 @@
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Array(3).concat("e5f5e0a1d99b31a354","edf8e9bae4b374c476238b45","edf8e9bae4b374c47631a354006d2c","edf8e9c7e9c0a1d99b74c47631a354006d2c","edf8e9c7e9c0a1d99b74c47641ab5d238b45005a32","f7fcf5e5f5e0c7e9c0a1d99b74c47641ab5d238b45005a32","f7fcf5e5f5e0c7e9c0a1d99b74c47641ab5d238b45006d2c00441b").map(a),tf=p(bf),nf=new Array(3).concat("f0f0f0bdbdbd636363","f7f7f7cccccc969696525252","f7f7f7cccccc969696636363252525","f7f7f7d9d9d9bdbdbd969696636363252525","f7f7f7d9d9d9bdbdbd969696737373525252252525","fffffff0f0f0d9d9d9bdbdbd969696737373525252252525","fffffff0f0f0d9d9d9bdbdbd969696737373525252252525000000").map(a),rf=p(nf),of=new Array(3).concat("efedf5bcbddc756bb1","f2f0f7cbc9e29e9ac86a51a3","f2f0f7cbc9e29e9ac8756bb154278f","f2f0f7dadaebbcbddc9e9ac8756bb154278f","f2f0f7dadaebbcbddc9e9ac8807dba6a51a34a1486","fcfbfdefedf5dadaebbcbddc9e9ac8807dba6a51a34a1486","fcfbfdefedf5dadaebbcbddc9e9ac8807dba6a51a354278f3f007d").map(a),lf=p(of),mf=new Array(3).concat("fee0d2fc9272de2d26","fee5d9fcae91fb6a4acb181d","fee5d9fcae91fb6a4ade2d26a50f15","fee5d9fcbba1fc9272fb6a4ade2d26a50f15","fee5d9fcbba1fc9272fb6a4aef3b2ccb181d99000d","fff5f0fee0d2fcbba1fc9272fb6a4aef3b2ccb181d99000d","fff5f0fee0d2fcbba1fc9272fb6a4aef3b2ccb181da50f1567000d").map(a),hf=p(mf),pf=new Array(3).concat("fee6cefdae6be6550d","feeddefdbe85fd8d3cd94701","feeddefdbe85fd8d3ce6550da63603","feeddefdd0a2fdae6bfd8d3ce6550da63603","feeddefdd0a2fdae6bfd8d3cf16913d948018c2d04","fff5ebfee6cefdd0a2fdae6bfd8d3cf16913d948018c2d04","fff5ebfee6cefdd0a2fdae6bfd8d3cf16913d94801a636037f2704").map(a),uf=p(pf);var sf=e.interpolateCubehelixLong(d.cubehelix(300,.5,0),d.cubehelix(-240,.5,1)),yf=e.interpolateCubehelixLong(d.cubehelix(-100,.75,.35),d.cubehelix(80,1.5,.8)),Mf=e.interpolateCubehelixLong(d.cubehelix(260,.75,.35),d.cubehelix(80,1.5,.8)),wf=d.cubehelix();var Af=d.rgb(),Pf=Math.PI/3,Bf=2*Math.PI/3;function Gf(f){var e=f.length;return function(d){return f[Math.max(0,Math.min(e-1,Math.floor(d*e)))]}}var xf=Gf(a("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")),Rf=Gf(a("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")),gf=Gf(a("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")),Yf=Gf(a("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"));f.interpolateBlues=cf,f.interpolateBrBG=s,f.interpolateBuGn=j,f.interpolateBuPu=D,f.interpolateCividis=function(f){return f=Math.max(0,Math.min(1,f)),"rgb("+Math.max(0,Math.min(255,Math.round(-4.54-f*(35.34-f*(2381.73-f*(6402.7-f*(7024.72-2710.57*f)))))))+", "+Math.max(0,Math.min(255,Math.round(32.49+f*(170.73+f*(52.82-f*(131.46-f*(176.58-67.37*f)))))))+", "+Math.max(0,Math.min(255,Math.round(81.24+f*(442.36-f*(2482.43-f*(6167.24-f*(6614.94-2475.67*f)))))))+")"},f.interpolateCool=Mf,f.interpolateCubehelixDefault=sf,f.interpolateGnBu=_,f.interpolateGreens=tf,f.interpolateGreys=rf,f.interpolateInferno=gf,f.interpolateMagma=Rf,f.interpolateOrRd=V,f.interpolateOranges=uf,f.interpolatePRGn=M,f.interpolatePiYG=A,f.interpolatePlasma=Yf,f.interpolatePuBu=F,f.interpolatePuBuGn=z,f.interpolatePuOr=B,f.interpolatePuRd=J,f.interpolatePurples=lf,f.interpolateRainbow=function(f){(f<0||f>1)&&(f-=Math.floor(f));var e=Math.abs(f-.5);return wf.h=360*f-100,wf.s=1.5-1.5*e,wf.l=.8-.9*e,wf+""},f.interpolateRdBu=x,f.interpolateRdGy=g,f.interpolateRdPu=N,f.interpolateRdYlBu=v,f.interpolateRdYlGn=C,f.interpolateReds=hf,f.interpolateSinebow=function(f){var e;return f=(.5-f)*Math.PI,Af.r=255*(e=Math.sin(f))*e,Af.g=255*(e=Math.sin(f+Pf))*e,Af.b=255*(e=Math.sin(f+Bf))*e,Af+""},f.interpolateSpectral=I,f.interpolateTurbo=function(f){return f=Math.max(0,Math.min(1,f)),"rgb("+Math.max(0,Math.min(255,Math.round(34.61+f*(1172.33-f*(10793.56-f*(33300.12-f*(38394.49-14825.05*f)))))))+", "+Math.max(0,Math.min(255,Math.round(23.31+f*(557.33+f*(1225.33-f*(3574.96-f*(1073.77+707.56*f)))))))+", "+Math.max(0,Math.min(255,Math.round(27.2+f*(3211.1-f*(15327.97-f*(27814-f*(22569.18-6838.66*f)))))))+")"},f.interpolateViridis=xf,f.interpolateWarm=yf,f.interpolateYlGn=Z,f.interpolateYlGnBu=U,f.interpolateYlOrBr=ff,f.interpolateYlOrRd=df,f.schemeAccent=b,f.schemeBlues=af,f.schemeBrBG=u,f.schemeBuGn=L,f.schemeBuPu=q,f.schemeCategory10=c,f.schemeDark2=t,f.schemeGnBu=T,f.schemeGreens=bf,f.schemeGreys=nf,f.schemeOrRd=k,f.schemeOranges=pf,f.schemePRGn=y,f.schemePaired=n,f.schemePastel1=r,f.schemePastel2=o,f.schemePiYG=w,f.schemePuBu=E,f.schemePuBuGn=W,f.schemePuOr=P,f.schemePuRd=H,f.schemePurples=of,f.schemeRdBu=G,f.schemeRdGy=R,f.schemeRdPu=K,f.schemeRdYlBu=Y,f.schemeRdYlGn=O,f.schemeReds=mf,f.schemeSet1=i,f.schemeSet2=l,f.schemeSet3=m,f.schemeSpectral=S,f.schemeTableau10=h,f.schemeYlGn=X,f.schemeYlGnBu=Q,f.schemeYlOrBr=$,f.schemeYlOrRd=ef,Object.defineProperty(f,"__esModule",{value:!0})});
\ No newline at end of file
diff --git a/spaces/mfrashad/CharacterGAN/models/stylegan/stylegan_tf/dataset_tool.py b/spaces/mfrashad/CharacterGAN/models/stylegan/stylegan_tf/dataset_tool.py
deleted file mode 100644
index 4ddfe448e2ccaa30e04ad4b49761d406846c962f..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/CharacterGAN/models/stylegan/stylegan_tf/dataset_tool.py
+++ /dev/null
@@ -1,645 +0,0 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-"""Tool for creating multi-resolution TFRecords datasets for StyleGAN and ProGAN."""
-
-# pylint: disable=too-many-lines
-import os
-import sys
-import glob
-import argparse
-import threading
-import six.moves.queue as Queue # pylint: disable=import-error
-import traceback
-import numpy as np
-import tensorflow as tf
-import PIL.Image
-import dnnlib.tflib as tflib
-
-from training import dataset
-
-#----------------------------------------------------------------------------
-
-def error(msg):
-    print('Error: ' + msg)
-    exit(1)
-
-#----------------------------------------------------------------------------
-
-class TFRecordExporter:
-    def __init__(self, tfrecord_dir, expected_images, print_progress=True, progress_interval=10):
-        self.tfrecord_dir       = tfrecord_dir
-        self.tfr_prefix         = os.path.join(self.tfrecord_dir, os.path.basename(self.tfrecord_dir))
-        self.expected_images    = expected_images
-        self.cur_images         = 0
-        self.shape              = None
-        self.resolution_log2    = None
-        self.tfr_writers        = []
-        self.print_progress     = print_progress
-        self.progress_interval  = progress_interval
-
-        if self.print_progress:
-            print('Creating dataset "%s"' % tfrecord_dir)
-        if not os.path.isdir(self.tfrecord_dir):
-            os.makedirs(self.tfrecord_dir)
-        assert os.path.isdir(self.tfrecord_dir)
-
-    def close(self):
-        if self.print_progress:
-            print('%-40s\r' % 'Flushing data...', end='', flush=True)
-        for tfr_writer in self.tfr_writers:
-            tfr_writer.close()
-        self.tfr_writers = []
-        if self.print_progress:
-            print('%-40s\r' % '', end='', flush=True)
-            print('Added %d images.' % self.cur_images)
-
-    def choose_shuffled_order(self): # Note: Images and labels must be added in shuffled order.
-        order = np.arange(self.expected_images)
-        np.random.RandomState(123).shuffle(order)
-        return order
-
-    def add_image(self, img):
-        if self.print_progress and self.cur_images % self.progress_interval == 0:
-            print('%d / %d\r' % (self.cur_images, self.expected_images), end='', flush=True)
-        if self.shape is None:
-            self.shape = img.shape
-            self.resolution_log2 = int(np.log2(self.shape[1]))
-            assert self.shape[0] in [1, 3]
-            assert self.shape[1] == self.shape[2]
-            assert self.shape[1] == 2**self.resolution_log2
-            tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)
-            for lod in range(self.resolution_log2 - 1):
-                tfr_file = self.tfr_prefix + '-r%02d.tfrecords' % (self.resolution_log2 - lod)
-                self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt))
-        assert img.shape == self.shape
-        for lod, tfr_writer in enumerate(self.tfr_writers):
-            if lod:
-                img = img.astype(np.float32)
-                img = (img[:, 0::2, 0::2] + img[:, 0::2, 1::2] + img[:, 1::2, 0::2] + img[:, 1::2, 1::2]) * 0.25
-            quant = np.rint(img).clip(0, 255).astype(np.uint8)
-            ex = tf.train.Example(features=tf.train.Features(feature={
-                'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=quant.shape)),
-                'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[quant.tostring()]))}))
-            tfr_writer.write(ex.SerializeToString())
-        self.cur_images += 1
-
-    def add_labels(self, labels):
-        if self.print_progress:
-            print('%-40s\r' % 'Saving labels...', end='', flush=True)
-        assert labels.shape[0] == self.cur_images
-        with open(self.tfr_prefix + '-rxx.labels', 'wb') as f:
-            np.save(f, labels.astype(np.float32))
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *args):
-        self.close()
-
-#----------------------------------------------------------------------------
-
-class ExceptionInfo(object):
-    def __init__(self):
-        self.value = sys.exc_info()[1]
-        self.traceback = traceback.format_exc()
-
-#----------------------------------------------------------------------------
-
-class WorkerThread(threading.Thread):
-    def __init__(self, task_queue):
-        threading.Thread.__init__(self)
-        self.task_queue = task_queue
-
-    def run(self):
-        while True:
-            func, args, result_queue = self.task_queue.get()
-            if func is None:
-                break
-            try:
-                result = func(*args)
-            except:
-                result = ExceptionInfo()
-            result_queue.put((result, args))
-
-#----------------------------------------------------------------------------
-
-class ThreadPool(object):
-    def __init__(self, num_threads):
-        assert num_threads >= 1
-        self.task_queue = Queue.Queue()
-        self.result_queues = dict()
-        self.num_threads = num_threads
-        for _idx in range(self.num_threads):
-            thread = WorkerThread(self.task_queue)
-            thread.daemon = True
-            thread.start()
-
-    def add_task(self, func, args=()):
-        assert hasattr(func, '__call__') # must be a function
-        if func not in self.result_queues:
-            self.result_queues[func] = Queue.Queue()
-        self.task_queue.put((func, args, self.result_queues[func]))
-
-    def get_result(self, func): # returns (result, args)
-        result, args = self.result_queues[func].get()
-        if isinstance(result, ExceptionInfo):
-            print('\n\nWorker thread caught an exception:\n' + result.traceback)
-            raise result.value
-        return result, args
-
-    def finish(self):
-        for _idx in range(self.num_threads):
-            self.task_queue.put((None, (), None))
-
-    def __enter__(self): # for 'with' statement
-        return self
-
-    def __exit__(self, *excinfo):
-        self.finish()
-
-    def process_items_concurrently(self, item_iterator, process_func=lambda x: x, pre_func=lambda x: x, post_func=lambda x: x, max_items_in_flight=None):
-        if max_items_in_flight is None: max_items_in_flight = self.num_threads * 4
-        assert max_items_in_flight >= 1
-        results = []
-        retire_idx = [0]
-
-        def task_func(prepared, _idx):
-            return process_func(prepared)
-
-        def retire_result():
-            processed, (_prepared, idx) = self.get_result(task_func)
-            results[idx] = processed
-            while retire_idx[0] < len(results) and results[retire_idx[0]] is not None:
-                yield post_func(results[retire_idx[0]])
-                results[retire_idx[0]] = None
-                retire_idx[0] += 1
-
-        for idx, item in enumerate(item_iterator):
-            prepared = pre_func(item)
-            results.append(None)
-            self.add_task(func=task_func, args=(prepared, idx))
-            while retire_idx[0] < idx - max_items_in_flight + 2:
-                for res in retire_result(): yield res
-        while retire_idx[0] < len(results):
-            for res in retire_result(): yield res
-
-#----------------------------------------------------------------------------
-
-def display(tfrecord_dir):
-    print('Loading dataset "%s"' % tfrecord_dir)
-    tflib.init_tf({'gpu_options.allow_growth': True})
-    dset = dataset.TFRecordDataset(tfrecord_dir, max_label_size='full', repeat=False, shuffle_mb=0)
-    tflib.init_uninitialized_vars()
-    import cv2  # pip install opencv-python
-
-    idx = 0
-    while True:
-        try:
-            images, labels = dset.get_minibatch_np(1)
-        except tf.errors.OutOfRangeError:
-            break
-        if idx == 0:
-            print('Displaying images')
-            cv2.namedWindow('dataset_tool')
-            print('Press SPACE or ENTER to advance, ESC to exit')
-        print('\nidx = %-8d\nlabel = %s' % (idx, labels[0].tolist()))
-        cv2.imshow('dataset_tool', images[0].transpose(1, 2, 0)[:, :, ::-1]) # CHW => HWC, RGB => BGR
-        idx += 1
-        if cv2.waitKey() == 27:
-            break
-    print('\nDisplayed %d images.' % idx)
-
-#----------------------------------------------------------------------------
-
-def extract(tfrecord_dir, output_dir):
-    print('Loading dataset "%s"' % tfrecord_dir)
-    tflib.init_tf({'gpu_options.allow_growth': True})
-    dset = dataset.TFRecordDataset(tfrecord_dir, max_label_size=0, repeat=False, shuffle_mb=0)
-    tflib.init_uninitialized_vars()
-
-    print('Extracting images to "%s"' % output_dir)
-    if not os.path.isdir(output_dir):
-        os.makedirs(output_dir)
-    idx = 0
-    while True:
-        if idx % 10 == 0:
-            print('%d\r' % idx, end='', flush=True)
-        try:
-            images, _labels = dset.get_minibatch_np(1)
-        except tf.errors.OutOfRangeError:
-            break
-        if images.shape[1] == 1:
-            img = PIL.Image.fromarray(images[0][0], 'L')
-        else:
-            img = PIL.Image.fromarray(images[0].transpose(1, 2, 0), 'RGB')
-        img.save(os.path.join(output_dir, 'img%08d.png' % idx))
-        idx += 1
-    print('Extracted %d images.' % idx)
-
-#----------------------------------------------------------------------------
-
-def compare(tfrecord_dir_a, tfrecord_dir_b, ignore_labels):
-    max_label_size = 0 if ignore_labels else 'full'
-    print('Loading dataset "%s"' % tfrecord_dir_a)
-    tflib.init_tf({'gpu_options.allow_growth': True})
-    dset_a = dataset.TFRecordDataset(tfrecord_dir_a, max_label_size=max_label_size, repeat=False, shuffle_mb=0)
-    print('Loading dataset "%s"' % tfrecord_dir_b)
-    dset_b = dataset.TFRecordDataset(tfrecord_dir_b, max_label_size=max_label_size, repeat=False, shuffle_mb=0)
-    tflib.init_uninitialized_vars()
-
-    print('Comparing datasets')
-    idx = 0
-    identical_images = 0
-    identical_labels = 0
-    while True:
-        if idx % 100 == 0:
-            print('%d\r' % idx, end='', flush=True)
-        try:
-            images_a, labels_a = dset_a.get_minibatch_np(1)
-        except tf.errors.OutOfRangeError:
-            images_a, labels_a = None, None
-        try:
-            images_b, labels_b = dset_b.get_minibatch_np(1)
-        except tf.errors.OutOfRangeError:
-            images_b, labels_b = None, None
-        if images_a is None or images_b is None:
-            if images_a is not None or images_b is not None:
-                print('Datasets contain different number of images')
-            break
-        if images_a.shape == images_b.shape and np.all(images_a == images_b):
-            identical_images += 1
-        else:
-            print('Image %d is different' % idx)
-        if labels_a.shape == labels_b.shape and np.all(labels_a == labels_b):
-            identical_labels += 1
-        else:
-            print('Label %d is different' % idx)
-        idx += 1
-    print('Identical images: %d / %d' % (identical_images, idx))
-    if not ignore_labels:
-        print('Identical labels: %d / %d' % (identical_labels, idx))
-
-#----------------------------------------------------------------------------
-
-def create_mnist(tfrecord_dir, mnist_dir):
-    print('Loading MNIST from "%s"' % mnist_dir)
-    import gzip
-    with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
-        images = np.frombuffer(file.read(), np.uint8, offset=16)
-    with gzip.open(os.path.join(mnist_dir, 'train-labels-idx1-ubyte.gz'), 'rb') as file:
-        labels = np.frombuffer(file.read(), np.uint8, offset=8)
-    images = images.reshape(-1, 1, 28, 28)
-    images = np.pad(images, [(0,0), (0,0), (2,2), (2,2)], 'constant', constant_values=0)
-    assert images.shape == (60000, 1, 32, 32) and images.dtype == np.uint8
-    assert labels.shape == (60000,) and labels.dtype == np.uint8
-    assert np.min(images) == 0 and np.max(images) == 255
-    assert np.min(labels) == 0 and np.max(labels) == 9
-    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
-    onehot[np.arange(labels.size), labels] = 1.0
-
-    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
-        order = tfr.choose_shuffled_order()
-        for idx in range(order.size):
-            tfr.add_image(images[order[idx]])
-        tfr.add_labels(onehot[order])
-
-#----------------------------------------------------------------------------
-
-def create_mnistrgb(tfrecord_dir, mnist_dir, num_images=1000000, random_seed=123):
-    print('Loading MNIST from "%s"' % mnist_dir)
-    import gzip
-    with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
-        images = np.frombuffer(file.read(), np.uint8, offset=16)
-    images = images.reshape(-1, 28, 28)
-    images = np.pad(images, [(0,0), (2,2), (2,2)], 'constant', constant_values=0)
-    assert images.shape == (60000, 32, 32) and images.dtype == np.uint8
-    assert np.min(images) == 0 and np.max(images) == 255
-
-    with TFRecordExporter(tfrecord_dir, num_images) as tfr:
-        rnd = np.random.RandomState(random_seed)
-        for _idx in range(num_images):
-            tfr.add_image(images[rnd.randint(images.shape[0], size=3)])
-
-#----------------------------------------------------------------------------
-
-def create_cifar10(tfrecord_dir, cifar10_dir):
-    print('Loading CIFAR-10 from "%s"' % cifar10_dir)
-    import pickle
-    images = []
-    labels = []
-    for batch in range(1, 6):
-        with open(os.path.join(cifar10_dir, 'data_batch_%d' % batch), 'rb') as file:
-            data = pickle.load(file, encoding='latin1')
-        images.append(data['data'].reshape(-1, 3, 32, 32))
-        labels.append(data['labels'])
-    images = np.concatenate(images)
-    labels = np.concatenate(labels)
-    assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
-    assert labels.shape == (50000,) and labels.dtype == np.int32
-    assert np.min(images) == 0 and np.max(images) == 255
-    assert np.min(labels) == 0 and np.max(labels) == 9
-    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
-    onehot[np.arange(labels.size), labels] = 1.0
-
-    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
-        order = tfr.choose_shuffled_order()
-        for idx in range(order.size):
-            tfr.add_image(images[order[idx]])
-        tfr.add_labels(onehot[order])
-
-#----------------------------------------------------------------------------
-
-def create_cifar100(tfrecord_dir, cifar100_dir):
-    print('Loading CIFAR-100 from "%s"' % cifar100_dir)
-    import pickle
-    with open(os.path.join(cifar100_dir, 'train'), 'rb') as file:
-        data = pickle.load(file, encoding='latin1')
-    images = data['data'].reshape(-1, 3, 32, 32)
-    labels = np.array(data['fine_labels'])
-    assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
-    assert labels.shape == (50000,) and labels.dtype == np.int32
-    assert np.min(images) == 0 and np.max(images) == 255
-    assert np.min(labels) == 0 and np.max(labels) == 99
-    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
-    onehot[np.arange(labels.size), labels] = 1.0
-
-    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
-        order = tfr.choose_shuffled_order()
-        for idx in range(order.size):
-            tfr.add_image(images[order[idx]])
-        tfr.add_labels(onehot[order])
-
-#----------------------------------------------------------------------------
-
-def create_svhn(tfrecord_dir, svhn_dir):
-    print('Loading SVHN from "%s"' % svhn_dir)
-    import pickle
-    images = []
-    labels = []
-    for batch in range(1, 4):
-        with open(os.path.join(svhn_dir, 'train_%d.pkl' % batch), 'rb') as file:
-            data = pickle.load(file, encoding='latin1')
-        images.append(data[0])
-        labels.append(data[1])
-    images = np.concatenate(images)
-    labels = np.concatenate(labels)
-    assert images.shape == (73257, 3, 32, 32) and images.dtype == np.uint8
-    assert labels.shape == (73257,) and labels.dtype == np.uint8
-    assert np.min(images) == 0 and np.max(images) == 255
-    assert np.min(labels) == 0 and np.max(labels) == 9
-    onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
-    onehot[np.arange(labels.size), labels] = 1.0
-
-    with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
-        order = tfr.choose_shuffled_order()
-        for idx in range(order.size):
-            tfr.add_image(images[order[idx]])
-        tfr.add_labels(onehot[order])
-
-#----------------------------------------------------------------------------
-
-def create_lsun(tfrecord_dir, lmdb_dir, resolution=256, max_images=None):
-    print('Loading LSUN dataset from "%s"' % lmdb_dir)
-    import lmdb # pip install lmdb # pylint: disable=import-error
-    import cv2 # pip install opencv-python
-    import io
-    with lmdb.open(lmdb_dir, readonly=True).begin(write=False) as txn:
-        total_images = txn.stat()['entries'] # pylint: disable=no-value-for-parameter
-        if max_images is None:
-            max_images = total_images
-        with TFRecordExporter(tfrecord_dir, max_images) as tfr:
-            for _idx, (_key, value) in enumerate(txn.cursor()):
-                try:
-                    try:
-                        img = cv2.imdecode(np.fromstring(value, dtype=np.uint8), 1)
-                        if img is None:
-                            raise IOError('cv2.imdecode failed')
-                        img = img[:, :, ::-1] # BGR => RGB
-                    except IOError:
-                        img = np.asarray(PIL.Image.open(io.BytesIO(value)))
-                    crop = np.min(img.shape[:2])
-                    img = img[(img.shape[0] - crop) // 2 : (img.shape[0] + crop) // 2, (img.shape[1] - crop) // 2 : (img.shape[1] + crop) // 2]
-                    img = PIL.Image.fromarray(img, 'RGB')
-                    img = img.resize((resolution, resolution), PIL.Image.ANTIALIAS)
-                    img = np.asarray(img)
-                    img = img.transpose([2, 0, 1]) # HWC => CHW
-                    tfr.add_image(img)
-                except:
-                    print(sys.exc_info()[1])
-                if tfr.cur_images == max_images:
-                    break
-
-#----------------------------------------------------------------------------
-
-def create_lsun_wide(tfrecord_dir, lmdb_dir, width=512, height=384, max_images=None):
-    assert width == 2 ** int(np.round(np.log2(width)))
-    assert height <= width
-    print('Loading LSUN dataset from "%s"' % lmdb_dir)
-    import lmdb # pip install lmdb # pylint: disable=import-error
-    import cv2 # pip install opencv-python
-    import io
-    with lmdb.open(lmdb_dir, readonly=True).begin(write=False) as txn:
-        total_images = txn.stat()['entries'] # pylint: disable=no-value-for-parameter
-        if max_images is None:
-            max_images = total_images
-        with TFRecordExporter(tfrecord_dir, max_images, print_progress=False) as tfr:
-            for idx, (_key, value) in enumerate(txn.cursor()):
-                try:
-                    try:
-                        img = cv2.imdecode(np.fromstring(value, dtype=np.uint8), 1)
-                        if img is None:
-                            raise IOError('cv2.imdecode failed')
-                        img = img[:, :, ::-1] # BGR => RGB
-                    except IOError:
-                        img = np.asarray(PIL.Image.open(io.BytesIO(value)))
-
-                    ch = int(np.round(width * img.shape[0] / img.shape[1]))
-                    if img.shape[1] < width or ch < height:
-                        continue
-
-                    img = img[(img.shape[0] - ch) // 2 : (img.shape[0] + ch) // 2]
-                    img = PIL.Image.fromarray(img, 'RGB')
-                    img = img.resize((width, height), PIL.Image.ANTIALIAS)
-                    img = np.asarray(img)
-                    img = img.transpose([2, 0, 1]) # HWC => CHW
-
-                    canvas = np.zeros([3, width, width], dtype=np.uint8)
-                    canvas[:, (width - height) // 2 : (width + height) // 2] = img
-                    tfr.add_image(canvas)
-                    print('\r%d / %d => %d ' % (idx + 1, total_images, tfr.cur_images), end='')
-
-                except:
-                    print(sys.exc_info()[1])
-                if tfr.cur_images == max_images:
-                    break
-    print()
-
-#----------------------------------------------------------------------------
-
-def create_celeba(tfrecord_dir, celeba_dir, cx=89, cy=121):
-    print('Loading CelebA from "%s"' % celeba_dir)
-    glob_pattern = os.path.join(celeba_dir, 'img_align_celeba_png', '*.png')
-    image_filenames = sorted(glob.glob(glob_pattern))
-    expected_images = 202599
-    if len(image_filenames) != expected_images:
-        error('Expected to find %d images' % expected_images)
-
-    with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
-        order = tfr.choose_shuffled_order()
-        for idx in range(order.size):
-            img = np.asarray(PIL.Image.open(image_filenames[order[idx]]))
-            assert img.shape == (218, 178, 3)
-            img = img[cy - 64 : cy + 64, cx - 64 : cx + 64]
-            img = img.transpose(2, 0, 1) # HWC => CHW
-            tfr.add_image(img)
-
-#----------------------------------------------------------------------------
-
-def create_from_images(tfrecord_dir, image_dir, shuffle):
-    print('Loading images from "%s"' % image_dir)
-    image_filenames = sorted(glob.glob(os.path.join(image_dir, '*')))
-    if len(image_filenames) == 0:
-        error('No input images found')
-
-    img = np.asarray(PIL.Image.open(image_filenames[0]))
-    resolution = img.shape[0]
-    channels = img.shape[2] if img.ndim == 3 else 1
-    if img.shape[1] != resolution:
-        error('Input images must have the same width and height')
-    if resolution != 2 ** int(np.floor(np.log2(resolution))):
-        error('Input image resolution must be a power-of-two')
-    if channels not in [1, 3]:
-        error('Input images must be stored as RGB or grayscale')
-
-    with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
-        order = tfr.choose_shuffled_order() if shuffle else np.arange(len(image_filenames))
-        for idx in range(order.size):
-            img = np.asarray(PIL.Image.open(image_filenames[order[idx]]))
-            if channels == 1:
-                img = img[np.newaxis, :, :] # HW => CHW
-            else:
-                img = img.transpose([2, 0, 1]) # HWC => CHW
-            tfr.add_image(img)
-
-#----------------------------------------------------------------------------
-
-def create_from_hdf5(tfrecord_dir, hdf5_filename, shuffle):
-    print('Loading HDF5 archive from "%s"' % hdf5_filename)
-    import h5py # conda install h5py
-    with h5py.File(hdf5_filename, 'r') as hdf5_file:
-        hdf5_data = max([value for key, value in hdf5_file.items() if key.startswith('data')], key=lambda lod: lod.shape[3])
-        with TFRecordExporter(tfrecord_dir, hdf5_data.shape[0]) as tfr:
-            order = tfr.choose_shuffled_order() if shuffle else np.arange(hdf5_data.shape[0])
-            for idx in range(order.size):
-                tfr.add_image(hdf5_data[order[idx]])
-            npy_filename = os.path.splitext(hdf5_filename)[0] + '-labels.npy'
-            if os.path.isfile(npy_filename):
-                tfr.add_labels(np.load(npy_filename)[order])
-
-#----------------------------------------------------------------------------
-
-def execute_cmdline(argv):
-    prog = argv[0]
-    parser = argparse.ArgumentParser(
-        prog        = prog,
-        description = 'Tool for creating multi-resolution TFRecords datasets for StyleGAN and ProGAN.',
-        epilog      = 'Type "%s <command> -h" for more information.' % prog)
-
-    subparsers = parser.add_subparsers(dest='command')
-    subparsers.required = True
-    def add_command(cmd, desc, example=None):
-        epilog = 'Example: %s %s' % (prog, example) if example is not None else None
-        return subparsers.add_parser(cmd, description=desc, help=desc, epilog=epilog)
-
-    p = add_command(    'display',          'Display images in dataset.',
-                                            'display datasets/mnist')
-    p.add_argument(     'tfrecord_dir',     help='Directory containing dataset')
-
-    p = add_command(    'extract',          'Extract images from dataset.',
-                                            'extract datasets/mnist mnist-images')
-    p.add_argument(     'tfrecord_dir',     help='Directory containing dataset')
-    p.add_argument(     'output_dir',       help='Directory to extract the images into')
-
-    p = add_command(    'compare',          'Compare two datasets.',
-                                            'compare datasets/mydataset datasets/mnist')
-    p.add_argument(     'tfrecord_dir_a',   help='Directory containing first dataset')
-    p.add_argument(     'tfrecord_dir_b',   help='Directory containing second dataset')
-    p.add_argument(     '--ignore_labels',  help='Ignore labels (default: 0)', type=int, default=0)
-
-    p = add_command(    'create_mnist',     'Create dataset for MNIST.',
-                                            'create_mnist datasets/mnist ~/downloads/mnist')
-    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
-    p.add_argument(     'mnist_dir',        help='Directory containing MNIST')
-
-    p = add_command(    'create_mnistrgb',  'Create dataset for MNIST-RGB.',
-                                            'create_mnistrgb datasets/mnistrgb ~/downloads/mnist')
-    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
-    p.add_argument(     'mnist_dir',        help='Directory containing MNIST')
-    p.add_argument(     '--num_images',     help='Number of composite images to create (default: 1000000)', type=int, default=1000000)
-    p.add_argument(     '--random_seed',    help='Random seed (default: 123)', type=int, default=123)
-
-    p = add_command(    'create_cifar10',   'Create dataset for CIFAR-10.',
-                                            'create_cifar10 datasets/cifar10 ~/downloads/cifar10')
-    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
-    p.add_argument(     'cifar10_dir',      help='Directory containing CIFAR-10')
-
-    p = add_command(    'create_cifar100',  'Create dataset for CIFAR-100.',
-                                            'create_cifar100 datasets/cifar100 ~/downloads/cifar100')
-    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
-    p.add_argument(     'cifar100_dir',     help='Directory containing CIFAR-100')
-
-    p = add_command(    'create_svhn',      'Create dataset for SVHN.',
-                                            'create_svhn datasets/svhn ~/downloads/svhn')
-    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
-    p.add_argument(     'svhn_dir',         help='Directory containing SVHN')
-
-    p = add_command(    'create_lsun',      'Create dataset for single LSUN category.',
-                                            'create_lsun datasets/lsun-car-100k ~/downloads/lsun/car_lmdb --resolution 256 --max_images 100000')
-    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
-    p.add_argument(     'lmdb_dir',         help='Directory containing LMDB database')
-    p.add_argument(     '--resolution',     help='Output resolution (default: 256)', type=int, default=256)
-    p.add_argument(     '--max_images',     help='Maximum number of images (default: none)', type=int, default=None)
-
-    p = add_command(    'create_lsun_wide', 'Create LSUN dataset with non-square aspect ratio.',
-                                            'create_lsun_wide datasets/lsun-car-512x384 ~/downloads/lsun/car_lmdb --width 512 --height 384')
-    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
-    p.add_argument(     'lmdb_dir',         help='Directory containing LMDB database')
-    p.add_argument(     '--width',          help='Output width (default: 512)', type=int, default=512)
-    p.add_argument(     '--height',         help='Output height (default: 384)', type=int, default=384)
-    p.add_argument(     '--max_images',     help='Maximum number of images (default: none)', type=int, default=None)
-
-    p = add_command(    'create_celeba',    'Create dataset for CelebA.',
-                                            'create_celeba datasets/celeba ~/downloads/celeba')
-    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
-    p.add_argument(     'celeba_dir',       help='Directory containing CelebA')
-    p.add_argument(     '--cx',             help='Center X coordinate (default: 89)', type=int, default=89)
-    p.add_argument(     '--cy',             help='Center Y coordinate (default: 121)', type=int, default=121)
-
-    p = add_command(    'create_from_images', 'Create dataset from a directory full of images.',
-                                            'create_from_images datasets/mydataset myimagedir')
-    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
-    p.add_argument(     'image_dir',        help='Directory containing the images')
-    p.add_argument(     '--shuffle',        help='Randomize image order (default: 1)', type=int, default=1)
-
-    p = add_command(    'create_from_hdf5', 'Create dataset from legacy HDF5 archive.',
-                                            'create_from_hdf5 datasets/celebahq ~/downloads/celeba-hq-1024x1024.h5')
-    p.add_argument(     'tfrecord_dir',     help='New dataset directory to be created')
-    p.add_argument(     'hdf5_filename',    help='HDF5 archive containing the images')
-    p.add_argument(     '--shuffle',        help='Randomize image order (default: 1)', type=int, default=1)
-
-    args = parser.parse_args(argv[1:] if len(argv) > 1 else ['-h'])
-    func = globals()[args.command]
-    del args.command
-    func(**vars(args))
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    execute_cmdline(sys.argv)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/mgczacki/toxicspans/notebooks/utils/lstm.py b/spaces/mgczacki/toxicspans/notebooks/utils/lstm.py
deleted file mode 100644
index 9054a4e89c4939c39d84c0748d8328ef265aa44f..0000000000000000000000000000000000000000
--- a/spaces/mgczacki/toxicspans/notebooks/utils/lstm.py
+++ /dev/null
@@ -1,235 +0,0 @@
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-
-import torch
-import torch.nn as nn
-import torch.optim as optim
-
-from torchtext.data import Field
-
-from .processing import separate_words, f1_scores
-
-import spacy
-import ast
-
-from tqdm import tqdm
-from IPython.display import clear_output
-
-nlp = spacy.load('en_core_web_md')
-dev = 'cuda:0' if torch.cuda.is_available() else 'cpu'
-torch.manual_seed(42)
-torch.backends.cudnn.deterministic = True
-
-# SpaCy hace cosas no deseadas con algunas palabras al tokenizar, como don't -> [do, n't], pero se puede corregir.
-# Pero de acuerdo a SpaCy esa es la convención, además, eso se debería codificar en los embeddings, así que se quede
-# así, sólo hay que usar el mismo tokenizador en Field de torchtext (permite el de SpaCy entre otros).
-
-# from spacy.symbols import ORTH, LEMMA, POS
-# nlp.tokenizer.add_special_case("don't", [{ORTH: "do"}, {ORTH: "not"}])
-# nlp.tokenizer.add_special_case("don't", [{ORTH: "don't"}])
-# nlp.tokenizer.add_special_case("doesn't", [{ORTH: "does"}, {ORTH: "not"}])
-
-def spacy_tokenizer	(text):
-    return [str(token) for token in nlp(text)]
-
-def prepare_data(spans, texts):
-    data = []
-    for index, text in tqdm(zip(spans, texts), total=len(texts)):
-        toxic_words = [text[i[0]:i[-1]+1] for i in separate_words(index) if len(index) > 0]
-        
-        tokens = spacy_tokenizer(text)
-        tagged_tokens = []
-        
-        for token in tokens:
-            if token in toxic_words:
-                tagged_tokens.append('toxic')
-                # Removemos en caso de que se repita posteriormente pero esté como 'non_toxic'
-                toxic_words.remove(token) 
-            else:
-                tagged_tokens.append('non_toxic')
-                
-        data.append((tokens, tagged_tokens, text, index))
-
-    return data
-
-def get_vocab(train_df):
-    train_df['text'] = train_df['text'].apply(lambda x:x.lower())
-
-    # Aquí había un problema, estábamos usando 2 tokenizadores diferentes para sacar los
-    # embeddings y para preprocesar el texto para entrenar. Pondré el de SpaCy como 
-    # tokenizador en común con el corpus de 'en_core_web_md'
-    text_field = Field(
-        tokenize='spacy',
-        tokenizer_language='en_core_web_md',
-        lower=True
-    )
-    # sadly have to apply preprocess manually
-    preprocessed_text = train_df['text'].apply(lambda x: text_field.preprocess(x))
-    # load fastext simple embedding with 200d
-    text_field.build_vocab(
-        preprocessed_text, 
-        vectors='glove.twitter.27B.200d'
-    )
-    # get the vocab instance
-    vocab = text_field.vocab
-
-    return vocab
-
-def plot_loss_and_score(train_loss, test_loss, f1_scores_train, f1_scores_test, show=True):
-    _, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(18,7))
-
-    ax0.plot(np.arange(1, len(train_loss) + 1), train_loss, marker='o', label='Train loss')
-    ax0.plot(np.arange(1, len(test_loss) + 1), test_loss, marker='o', label='Test loss')
-    ax0.set_xlabel(r'\textbf{Epochs}',size=16)
-    ax0.set_ylabel(r'\textbf{Loss}', size=16)
-    ax0.tick_params(labelsize=14)
-    ax0.legend(fontsize=14)
-
-    ax1.plot(np.arange(1, len(f1_scores_train) + 1), f1_scores_train, 
-             marker='o', label='F1 score in train')
-    ax1.plot(np.arange(1, len(f1_scores_test) + 1), f1_scores_test, 
-             marker='o', label='F1 score in test')
-    ax1.set_xlabel(r'\textbf{Epochs}',size=16)
-    ax1.set_ylabel(r'\textbf{F1 score}', size=16)
-    ax1.tick_params(labelsize=14)
-    ax1.legend(fontsize=14)
-    
-    title = 'train-F1: {:.4f} \n test-F1: {:.4f}'.format(np.max(f1_scores_train), np.max(f1_scores_test))
-    ax1.set_title(title, fontweight='bold', size=16)
-
-
-    if show:
-        plt.show()
-
-# WTF Mario, this is a mess
-def train_model(model, trainloader, testloader, stop_after_best, savefile):
-    criterion = nn.BCELoss()
-    optimizer = optim.Adam(model.parameters())
-
-    loss_per_epoch = [0]
-    training_loss = [0]
-    f1_scores_train = [0]
-    f1_scores_dev = [0]
-    best_l = None
-    best_tl = None
-    worst_l = None
-    worst_tl = None
-    worst_l_f1 = None
-    best_l_f1 = None
-    worst_tl_f1 = None
-    last_epoch_save = 0
-
-    epochs_without_change = 0
-    epochs = len(loss_per_epoch)
-
-    while epochs_without_change < stop_after_best:
-        clear_output(wait=True)
-
-        print("Training on: " + torch.cuda.get_device_name(torch.cuda.current_device()))
-        print("###############################################")
-        print("Current epoch: " + str(epochs))
-        print("Last model save was in epoch " + str(last_epoch_save))
-        print("Stopping training in: " + str(stop_after_best - epochs_without_change) + " epochs.")
-        print("###############################################")
-        print("[Best iter] training F1 is: " + str(best_tl))
-        print("[Best iter] dev F1 is: " + str(best_l))
-        print("###############################################")
-        print("[Last iter] training F1 was: " + str(f1_scores_train[-1]))
-        print("[Last iter] dev. F1 was: " + str(f1_scores_dev[-1]))
-        print("###############################################")
-        
-        # Dibujo lo que puedo
-        plot_loss_and_score(training_loss, loss_per_epoch, f1_scores_train, f1_scores_dev, show=True)
-        
-        tl = 0
-        t_pred_l = []
-        t_true_index_l = []
-        t_tokenized_l = []
-        t_text_l = []
-
-        for _, v in tqdm(enumerate(trainloader), total=len(trainloader)): # Not using batches yet
-            text = torch.reshape(v['text'], (-1,))
-            tags = torch.reshape(v['spans'], (-1,))
-            optimizer.zero_grad()
-            tag_scores = model(text)
-            
-            # Para la F1
-            t_pred_l.append(tag_scores.cpu().detach().numpy())
-            t_true_index_l.append([a.cpu().detach().numpy()[0] for a in v['true_index']])
-            t_tokenized_l.append([a[0] for a in v['tokenized']])
-            t_text_l.append(v['original_text'][0])
-            
-            loss = criterion(torch.reshape(tag_scores, (-1,)), torch.reshape(tags, (-1,)).float())
-            tl += loss.item()
-            loss.backward()
-            optimizer.step()
-
-        tl /= len(trainloader)
-        l = 0
-        print("Starting evaluation for loss function.")
-        # evaluar el modelo
-        pred_l = []
-        true_index_l = []
-        tokenized_l = []
-        text_l = []
-        
-        model.eval()
-        with torch.no_grad():
-            for v in testloader:
-                text = torch.reshape(v['text'], (-1,))
-                tags = torch.reshape(v['spans'], (-1,))
-
-                tag_scores = model(text)
-                
-                #Para la F1
-                pred_l.append(tag_scores.cpu().detach().numpy())
-                true_index_l.append([a.cpu().detach().numpy()[0] for a in v['true_index']])
-                tokenized_l.append([a[0] for a in v['tokenized']])
-                text_l.append(v['original_text'][0])
-                
-                loss = criterion(torch.reshape(tag_scores, (-1,)), torch.reshape(tags, (-1,)).float())
-                l += loss.item()
-        
-        model.train()
-        l /= len(testloader)
-        print("Starting evaluation for dev F1")
-        f1_d = f1_scores(pred_l, true_index_l, tokenized_l, text_l)
-        # Es aproximado, pero solo es una referencia
-        f1_t = f1_scores(t_pred_l, t_true_index_l, t_tokenized_l, t_text_l) 
-        
-        
-        epochs_without_change += 1
-        if best_l is None or best_l < f1_d:
-            print("Model improved, saving.")
-            torch.save(model, savefile)
-            best_l = f1_d
-            best_tl = f1_t
-            epochs_without_change = 0
-            last_epoch_save = epochs
-            print("Model improved, saved.")
-
-        # Para graficar con una escala coherente.
-        if(worst_l_f1 is None or f1_d < worst_l_f1):
-            worst_l_f1 = f1_d
-            f1_scores_dev[0] = worst_l_f1
-        if(worst_tl_f1 is None or f1_t < worst_tl_f1):
-            worst_tl_f1 = f1_t
-            f1_scores_train[0] = worst_tl_f1
-        if(worst_tl is None or tl > worst_tl):
-            worst_tl = tl
-            training_loss[0] = worst_tl
-        if(worst_l is None or l > worst_l):
-            worst_l = l
-            loss_per_epoch[0] = worst_l
-
-        # Rastreo las perdidas
-        loss_per_epoch.append(l)
-        training_loss.append(tl)
-        f1_scores_train.append(f1_t)
-        f1_scores_dev.append(f1_d)
-        # Rastreo la época actual
-        epochs += 1
-    print('Finished Training')
-
-    return loss_per_epoch, training_loss, f1_scores_train, f1_scores_dev
\ No newline at end of file
diff --git a/spaces/michaelwja/burn-detection/app.py b/spaces/michaelwja/burn-detection/app.py
deleted file mode 100644
index 5a11b272b644e63e2ba71e09d942e8c28c4dc60f..0000000000000000000000000000000000000000
--- a/spaces/michaelwja/burn-detection/app.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import torch
-import gradio as gr
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-REPO_ID = "michaelwja/burn-detection"
-FILENAME = "skin_burn.pt"
-
-
-yolov7_custom_weights = hf_hub_download(repo_id=REPO_ID, filename=FILENAME,repo_type='space')
-
-model = torch.hub.load('WongKinYiu/yolov7:main',model='custom', path_or_model=yolov7_custom_weights, force_reload=True)
-def object_detection(im, size=614):
-    results = model(im) 
-    results.render()  
-    return Image.fromarray(results.imgs[0])
-
-title = "Yolov7 Skin Burn Detection"
-
-image = gr.inputs.Image(shape=(614,614), image_mode="RGB", source="upload", label="Upload Image", optional=False)
-outputs = gr.outputs.Image(type="pil", label="Output Image")
-
-Top_Title="Yolov7 Skin Burn Detection | 基于Yolov7的深度学习皮肤烧伤检测模型"
-Custom_description="Upload Any Burn Image to Begin. Made by Michael.W"
-Footer="北京清华附中计算机高研社团@THIS 2023"
-
-
-gr.Interface(
-    fn=object_detection,
-    inputs=image,
-    outputs=outputs,
-    title=Top_Title,
-    description=Custom_description,
-    article=Footer,
-    examples=[["skin_burn1.jpeg"], ["skin_burn2.jpeg"]]).launch()
diff --git a/spaces/mikeee/radiobee-dev/tests/test_main.py b/spaces/mikeee/radiobee-dev/tests/test_main.py
deleted file mode 100644
index 7dbddfcbc75dccefe9e4f49e8609aa5bf71e675f..0000000000000000000000000000000000000000
--- a/spaces/mikeee/radiobee-dev/tests/test_main.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"""Test __main__.py."""
-# pylint: disable=invalid-name
-import tempfile
-from fastlid import fastlid
-
-from logzero import logger
-
-# globals()["file2text"] = getattr(importlib.import_module(f"{radiobee.__name__}.file2text"), "file2text")
-# from radiobee.process_upload import process_upload  # same as file2text
-from radiobee.files2df import files2df
-from radiobee.file2text import file2text
-from radiobee.lists2cmat import lists2cmat
-from radiobee.cmat2tset import cmat2tset
-
-file1loc = "data/test_zh.txt"
-file2loc = "data/test_en.txt"
-
-file1 = tempfile._TemporaryFileWrapper(open(file1loc, "rb"), file1loc)
-file2 = tempfile._TemporaryFileWrapper(open(file2loc, "rb"), file2loc)
-
-
-def test_file2file1():
-    """Test cmat file2 file1."""
-    # logger.info("file1: *%s*, file2: *%s*", file1, file2)
-    logger.info("file1.name: *%s*, file2.name: *%s*", file1.name, file2.name)
-
-    text1 = file2text(file1)
-    text2 = file2text(file2)
-
-    lang1, _ = fastlid(text1)
-    lang2, _ = fastlid(text2)
-
-    lst1 = [elm.strip() for elm in text1.splitlines() if elm.strip()]
-    lst2 = [elm.strip() for elm in text2.splitlines() if elm.strip()]
-
-    del lst1, lst2
diff --git a/spaces/mithril-security/blind_chat/.svelte-kit/generated/client/nodes/9.js b/spaces/mithril-security/blind_chat/.svelte-kit/generated/client/nodes/9.js
deleted file mode 100644
index f3d63053dcb2b975a7b67908d08a9736c4f3307f..0000000000000000000000000000000000000000
--- a/spaces/mithril-security/blind_chat/.svelte-kit/generated/client/nodes/9.js
+++ /dev/null
@@ -1 +0,0 @@
-export { default as component } from "../../../../src/routes/r/[id]/+page.svelte";
\ No newline at end of file
diff --git a/spaces/mjdolan/Holiday-StyleGAN-NADA/e4e/models/psp.py b/spaces/mjdolan/Holiday-StyleGAN-NADA/e4e/models/psp.py
deleted file mode 100644
index bc8ca310a5a17f93a64daebe63928d5047d92551..0000000000000000000000000000000000000000
--- a/spaces/mjdolan/Holiday-StyleGAN-NADA/e4e/models/psp.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import matplotlib
-
-matplotlib.use('Agg')
-import torch
-from torch import nn
-from e4e.models.encoders import psp_encoders
-from e4e.models.stylegan2.model import Generator
-from e4e.configs.paths_config import model_paths
-
-
-def get_keys(d, name):
-    if 'state_dict' in d:
-        d = d['state_dict']
-    d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name}
-    return d_filt
-
-
-class pSp(nn.Module):
-
-    def __init__(self, opts, device):
-        super(pSp, self).__init__()
-        self.opts = opts
-        self.device = device
-        # Define architecture
-        self.encoder = self.set_encoder()
-        self.decoder = Generator(opts.stylegan_size, 512, 8, channel_multiplier=2)
-        self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256))
-        # Load weights if needed
-        self.load_weights()
-
-    def set_encoder(self):
-        if self.opts.encoder_type == 'GradualStyleEncoder':
-            encoder = psp_encoders.GradualStyleEncoder(50, 'ir_se', self.opts)
-        elif self.opts.encoder_type == 'Encoder4Editing':
-            encoder = psp_encoders.Encoder4Editing(50, 'ir_se', self.opts)
-        else:
-            raise Exception('{} is not a valid encoders'.format(self.opts.encoder_type))
-        return encoder
-
-    def load_weights(self):
-        if self.opts.checkpoint_path is not None:
-            print('Loading e4e over the pSp framework from checkpoint: {}'.format(self.opts.checkpoint_path))
-            ckpt = torch.load(self.opts.checkpoint_path, map_location='cuda:0' if torch.cuda.is_available() else "cpu")
-            self.encoder.load_state_dict(get_keys(ckpt, 'encoder'), strict=True)
-            self.encoder.to(self.device)
-            self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=True)
-            self.decoder.to(self.device)
-            self.__load_latent_avg(ckpt)
-        else:
-            print('Loading encoders weights from irse50!')
-            encoder_ckpt = torch.load(model_paths['ir_se50'])
-            self.encoder.load_state_dict(encoder_ckpt, strict=False)
-            print('Loading decoder weights from pretrained!')
-            ckpt = torch.load(self.opts.stylegan_weights)
-            self.decoder.load_state_dict(ckpt['g_ema'], strict=False)
-            self.__load_latent_avg(ckpt, repeat=self.encoder.style_count)
-
-    def forward(self, x, resize=True, latent_mask=None, input_code=False, randomize_noise=True,
-                inject_latent=None, return_latents=False, alpha=None):
-        if input_code:
-            codes = x
-        else:
-            codes = self.encoder(x)
-            # normalize with respect to the center of an average face
-            if self.opts.start_from_latent_avg:
-                if codes.ndim == 2:
-                    codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :]
-                else:
-                    codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)
-
-        if latent_mask is not None:
-            for i in latent_mask:
-                if inject_latent is not None:
-                    if alpha is not None:
-                        codes[:, i] = alpha * inject_latent[:, i] + (1 - alpha) * codes[:, i]
-                    else:
-                        codes[:, i] = inject_latent[:, i]
-                else:
-                    codes[:, i] = 0
-
-        input_is_latent = not input_code
-        images, result_latent = self.decoder([codes],
-                                             input_is_latent=input_is_latent,
-                                             randomize_noise=randomize_noise,
-                                             return_latents=return_latents)
-
-        if resize:
-            images = self.face_pool(images)
-
-        if return_latents:
-            return images, result_latent
-        else:
-            return images
-
-    def __load_latent_avg(self, ckpt, repeat=None):
-        if 'latent_avg' in ckpt:
-            self.latent_avg = ckpt['latent_avg'].to(self.device)
-            if repeat is not None:
-                self.latent_avg = self.latent_avg.repeat(repeat, 1)
-        else:
-            self.latent_avg = None
diff --git a/spaces/monra/freegpt-webui/client/css/stop-generating.css b/spaces/monra/freegpt-webui/client/css/stop-generating.css
deleted file mode 100644
index 3c2010d25065fbef63b104df743ef72c00259871..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/client/css/stop-generating.css
+++ /dev/null
@@ -1,38 +0,0 @@
-.stop-generating {
-	position: absolute;
-	bottom: 128px;
-	left: 50%;
-	transform: translateX(-50%);
-	z-index: 1000000;
-}
-
-.stop-generating button {
-	backdrop-filter: blur(20px);
-	-webkit-backdrop-filter: blur(20px);
-	background-color: var(--blur-bg);
-	color: var(--colour-3);
-	cursor: pointer;
-	animation: show_popup 0.4s;
-}
-
-@keyframes show_popup {
-	from {
-		opacity: 0;
-		transform: translateY(10px);
-	}
-}
-
-@keyframes hide_popup {
-	to {
-		opacity: 0;
-		transform: translateY(10px);
-	}
-}
-
-.stop-generating-hiding button {
-	animation: hide_popup 0.4s;
-}
-
-.stop-generating-hidden button {
-	display: none;
-}
diff --git a/spaces/mshukor/UnIVAL/fairseq/docs/hydra_integration.md b/spaces/mshukor/UnIVAL/fairseq/docs/hydra_integration.md
deleted file mode 100644
index 6a15298382a6a16dfc4c5a4a812ea1cd0477ed52..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/docs/hydra_integration.md
+++ /dev/null
@@ -1,284 +0,0 @@
-## Hydra
-
-[Hydra](https://github.com/facebookresearch/hydra) is an open-source Python
-framework that simplifies the development of research and other complex
-applications. The key feature is the ability to dynamically create a
-hierarchical configuration by composition and override it through config files
-and the command line. The name Hydra comes from its ability to run multiple
-similar jobs - much like a Hydra with multiple heads.
-
-## Motivation
-
-Until recently, all components in fairseq were configured through a shared
-`args` namespace that was created at application startup. Components declared
-their own `add_args` method to update the argparse parser, hoping that the names
-would not clash with arguments from other components. While this model works for
-smaller applications, as fairseq grew and became integrated into other
-applications, this became problematic. In order to determine how to configure
-each component, one needed to a) examine what args were added by this component,
-and b) read the code to figure out what shared arguments it is using that were
-added in other places. Reproducing models involved sharing commands that often
-contained dozens of command line switches.
-
-The model described above is still supported by fairseq for backward
-compatibility, but will be deprecated some time in the future.
-
-New components in fairseq should now create a dataclass that encapsulates all
-parameters required to configure this component. The dataclass is registered
-along with the component, and fairseq takes care of constructing and providing
-this configuration object to the component's constructor. Note that sharing
-parameters can optionally still work, but one has to explicitly point to the
-"source of truth" (see inheritance example below). These changes make components
-in fairseq more independent and re-usable by other applications: all that is
-needed to create a component is to initialize its dataclass and overwrite some
-of the defaults.
-
-While configuring fairseq through command line (using either the legacy argparse
-based or the new Hydra based entry points) is still fully supported, you can now
-take advantage of configuring fairseq completely or piece-by-piece through
-hierarchical YAML configuration files. These files can also be shipped as
-examples that others can use to run an identically configured job.
-
-Additionally, Hydra has a rich and growing [library of
-plugins](https://github.com/facebookresearch/hydra/tree/master/plugins) that
-provide functionality such as hyperparameter sweeping (including using bayesian
-optimization through the [Ax](https://github.com/facebook/Ax) library), job
-launching across various platforms, and more.
-
-## Creating or migrating components
-
-In general, each new (or updated) component should provide a companion
-[dataclass](https://www.python.org/dev/peps/pep-0557/). These dataclass are
-typically located in the same file as the component and are passed as arguments
-to the `register_*()` functions. Top-level configs that should be present in
-every fairseq application are placed in the
-[global](fairseq/dataclass/configs.py) config file and added to the
-`FairseqConfig` object.
-
-Each dataclass is a plain-old-data object, similar to a `NamedTuple`. These
-classes are decorated with a `@dataclass` decorator, and typically inherit from
-`FairseqDataclass` (which adds some functionality for backward compatibility).
-Each field must have a type, and generally has metadata (such as a help string)
-and a default value. Only primitive types or other config objects are allowed as
-data types for each field.
-
-#### Example:
-
-```python
-from dataclasses import dataclass, field
-from fairseq.dataclass import FairseqDataclass
-
-@dataclass
-class InteractiveConfig(FairseqDataclass):
-    buffer_size: int = field(
-        default=0,
-        metadata={
-            "help": "read this many sentences into a buffer before processing them"
-        },
-    )
-    input: str = field(
-        default="-",
-        metadata={"help": "file to read from; use - for stdin"},
-    )
-```
-
-### Inherting values
-
-Some components require sharing a value. For example, a learning rate scheduler
-and an optimizer may both need to know the initial learning rate value. One can
-declare a field that, by default, will inherit its value from another config
-node in the same hierarchy:
-
-```python
-@dataclass
-FairseqAdamConfig(FairseqDataclass):
-    ...
-    lr: List[float] = II("optimization.lr")
-    ...
-```
-
-`II("optimization.lr")` is syntactic sugar for `"${optimization.lr}"`, which is
-the value one can use in a YAML config file or through command line to achieve
-the same effect. Note that this assumes that there is an "optimization" config
-object in the root config and it has a field called "lr".
-
-### Tasks and Models
-
-Creating Tasks and Models works same as before, except that legacy
-implementations now inherit from `LegacyFairseq*` base classes, while new
-components inherit from `FairseqTask` and `FairseqModel` and provide a dataclass
-to the `register_*()` functions.
-
-#### Task example:
-
-```python
-@dataclass
-class LanguageModelingConfig(FairseqDataclass):
-    data: Optional[str] = field(
-        default=None, metadata={"help": "path to data directory"}
-    )
-    ...
-
-@register_task("language_modeling", dataclass=LanguageModelingConfig)
-class LanguageModelingTask(FairseqTask):
-    ...
-    @classmethod
-    def setup_task(cls, cfg: LanguageModelingConfig):
-        ...
-```
-
-#### Model example:
-
-```python
-@dataclass
-class TransformerLanguageModelConfig(FairseqDataclass):
-    activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field(
-        default="relu", metadata={"help": "activation function to use"}
-    )
-    dropout: float = field(default=0.1, metadata={"help": "dropout probability"})
-    ...
-
-@register_model("transformer_lm", dataclass=TransformerLanguageModelConfig)
-class TransformerLanguageModel(FairseqLanguageModel):
-    ...
-    @classmethod
-    def build_model(cls, cfg: TransformerLanguageModelConfig, task: FairseqTask):
-        ...
-```
-
-### Other components
-
-Other components work as before, but they now take their configuration dataclass
-as the only constructor argument:
-
-```python
-@dataclass
-class MosesTokenizerConfig(FairseqDataclass):
-    source_lang: str = field(default="en", metadata={"help": "source language"})
-    ...
-
-@register_tokenizer("moses", dataclass=MosesTokenizerConfig)
-class MosesTokenizer(object):
-    def __init__(self, cfg: MosesTokenizerConfig):
-        ...
-```
-
-Note that if you are adding a new registry for a new set of components, you need
-to add it to the `FairseqConfig` object in `fairseq/dataclass/configs.py`:
-
-```python
-@dataclass
-class FairseqConfig(object):
-    ...
-    my_new_registry: Any = None
-```
-
-## Training with `fairseq-hydra-train`
-
-To fully take advantage of configuration flexibility offered by Hydra, you may
-want to train new models using the `fairseq-hydra-train` entry point. Legacy CLI
-tools such as `fairseq-train` will remain supported for the foreseeable future
-but will be deprecated eventually.
-
-On startup, Hydra will create a configuration object that contains a hierarchy
-of all the necessary dataclasses populated with their default values in the
-code. The default values are overwritten by values found in YAML files in
-`fairseq/config` directory (which currently sets minimal defaults) and then
-further overwritten by values provided through command line arguments.
-
-Some of the most common use cases are shown below:
-
-### 1. Override default values through command line:
-
-```shell script
-$ fairseq-hydra-train \
-    distributed_training.distributed_world_size=1 \
-    dataset.batch_size=2 \
-    task.data=data-bin \
-    model=transformer_lm/transformer_lm_gpt \
-    task=language_modeling \
-    optimization.max_update=5000
-```
-
-Note that along with explicitly providing values for parameters such as
-`dataset.batch_size`, this also tells Hydra to overlay configuration found in
-`fairseq/config/model/transformer_lm/transformer_lm_gpt.yaml` over the default
-values in the dataclass. If you want to train a model without specifying a
-particular architecture you can simply specify `model=transformer_lm`. This only
-works for migrated tasks and models.
-
-### 2. Replace bundled configs with an external config:
-
-```shell script
-$ fairseq-hydra-train \
-    --config-dir /path/to/external/configs \
-    --config-name wiki103
-```
-
-where `/path/to/external/configs/wiki103.yaml` contains:
-
-```yaml
-# @package _group_
-
-model:
-  _name: transformer_lm
-distributed_training:
-  distributed_world_size: 1
-dataset:
-  batch_size: 2
-task:
-  _name: language_modeling
-  data: /path/to/data
-  add_bos_token: false
-  max_target_positions: 1024
-optimization:
-  max_update: 50000
-  lr: [ 0.25 ]
-criterion: cross_entropy
-optimizer: adam
-lr_scheduler:
-  _name: cosine
-```
-
-Note that here bundled configs from `fairseq/config` directory are not used,
-however the defaults from each dataclass will still be used (unless overwritten
-by your external config).
-
-Additionally you can choose to break up your configs by creating a directory
-structure in the same location as your main config file, with the names of the
-top-level fields (such as "model", "dataset", etc), and placing config files
-with meaningful names that would populate that specific section of your
-top-level config file (for example, you might have
-`model/small_transformer_lm.yaml`, `model/big_transformer_lm.yaml`, etc). You
-can then specify the correct configuration via command line, defaults in the
-main config, or even launch all of them as a sweep (see Hydra documentation on
-how to do this).
-
-### 3. Add an external config directory to Hydra search path:
-
-This allows combining default configuration (including using any bundled config
-files), while specifying your own config files for some parts of the
-configuration.
-
-```shell script
-$ fairseq-hydra-train \
-    distributed_training.distributed_world_size=1 \
-    dataset.batch_size=2 \
-    task.data=/path/to/data/ \
-    model=transformer_lm/2_layers \
-    task=language_modeling \
-    optimization.max_update=5000 \
-    --config-dir /path/to/external/configs
-```
-
-where `/path/to/external/configs` has the following structure:
-```
-.
-+-- model
-|   +-- transformer_lm
-|   |   +-- 2_layers.yaml
-```
-
-and `2_layers.yaml` contains a copy of `transformer_lm_gpt.yaml` but with
-`decoder_layers` set to 2. You can add other configs to configure other
-components as well.
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/laser/README.md b/spaces/mshukor/UnIVAL/fairseq/examples/laser/README.md
deleted file mode 100644
index 66acada04f58fa235cd312753f144f6f1e5f4a33..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/laser/README.md
+++ /dev/null
@@ -1,144 +0,0 @@
-# LASER  Language-Agnostic SEntence Representations
-
-LASER is a library to calculate and use multilingual sentence embeddings.
-
-You can find more information about LASER and how to use it on the official [LASER repository](https://github.com/facebookresearch/LASER).
-
-This folder contains source code for training LASER embeddings.
-
-
-## Prepare data and configuration file
-
-Binarize your data with fairseq, as described [here](https://fairseq.readthedocs.io/en/latest/getting_started.html#data-pre-processing).
-
-Create a json config file with this format:
-```
-{
-  "src_vocab": "/path/to/spm.src.cvocab",
-  "tgt_vocab": "/path/to/spm.tgt.cvocab",
-  "train": [
-    {
-      "type": "translation",
-      "id": 0,
-      "src": "/path/to/srclang1-tgtlang0/train.srclang1",
-      "tgt": "/path/to/srclang1-tgtlang0/train.tgtlang0"
-    },
-    {
-      "type": "translation",
-      "id": 1,
-      "src": "/path/to/srclang1-tgtlang1/train.srclang1",
-      "tgt": "/path/to/srclang1-tgtlang1/train.tgtlang1"
-    },
-    {
-      "type": "translation",
-      "id": 0,
-      "src": "/path/to/srclang2-tgtlang0/train.srclang2",
-      "tgt": "/path/to/srclang2-tgtlang0/train.tgtlang0"
-    },
-    {
-      "type": "translation",
-      "id": 1,
-      "src": "/path/to/srclang2-tgtlang1/train.srclang2",
-      "tgt": "/path/to/srclang2-tgtlang1/train.tgtlang1"
-    },
-    ...
-  ],
-  "valid": [
-    {
-      "type": "translation",
-      "id": 0,
-      "src": "/unused",
-      "tgt": "/unused"
-    }
-  ]
-}
-```
-where paths are paths to binarized indexed fairseq dataset files.
-`id` represents the target language id.
-
-
-## Training Command Line Example
-
-```
-fairseq-train \
-  /path/to/configfile_described_above.json \
-  --user-dir examples/laser/laser_src \
-  --log-interval 100 --log-format simple \
-  --task laser --arch laser_lstm \
-  --save-dir . \
-  --optimizer adam \
-  --lr 0.001 \
-  --lr-scheduler inverse_sqrt \
-  --clip-norm 5 \
-  --warmup-updates 90000 \
-  --update-freq 2 \
-  --dropout 0.0 \
-  --encoder-dropout-out 0.1 \
-  --max-tokens 2000 \
-  --max-epoch 50 \
-  --encoder-bidirectional \
-  --encoder-layers 5 \
-  --encoder-hidden-size 512 \
-  --decoder-layers 1 \
-  --decoder-hidden-size 2048 \
-  --encoder-embed-dim 320 \
-  --decoder-embed-dim 320 \
-  --decoder-lang-embed-dim 32 \
-  --warmup-init-lr 0.001 \
-  --disable-validation
-```
-
-
-## Applications
-
-We showcase several applications of multilingual sentence embeddings
-with code to reproduce our results (in the directory "tasks").
-
-* [**Cross-lingual document classification**](https://github.com/facebookresearch/LASER/tree/master/tasks/mldoc) using the
-  [*MLDoc*](https://github.com/facebookresearch/MLDoc) corpus [2,6]
-* [**WikiMatrix**](https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix)
-   Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia [7]
-* [**Bitext mining**](https://github.com/facebookresearch/LASER/tree/master/tasks/bucc) using the
-  [*BUCC*](https://comparable.limsi.fr/bucc2018/bucc2018-task.html) corpus [3,5]
-* [**Cross-lingual NLI**](https://github.com/facebookresearch/LASER/tree/master/tasks/xnli)
-  using the [*XNLI*](https://www.nyu.edu/projects/bowman/xnli/) corpus [4,5,6]
-* [**Multilingual similarity search**](https://github.com/facebookresearch/LASER/tree/master/tasks/similarity) [1,6]
-* [**Sentence embedding of text files**](https://github.com/facebookresearch/LASER/tree/master/tasks/embed)
-  example how to calculate sentence embeddings for arbitrary text files in any of the supported language.
-
-**For all tasks, we use exactly the same multilingual encoder, without any task specific optimization or fine-tuning.**
-
-
-
-## References
-
-[1] Holger Schwenk and Matthijs Douze,
-    [*Learning Joint Multilingual Sentence Representations with Neural Machine Translation*](https://aclanthology.info/papers/W17-2619/w17-2619),
-    ACL workshop on Representation Learning for NLP, 2017
-
-[2] Holger Schwenk and Xian Li,
-    [*A Corpus for Multilingual Document Classification in Eight Languages*](http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf),
-    LREC, pages 3548-3551, 2018.
-
-[3] Holger Schwenk,
-    [*Filtering and Mining Parallel Data in a Joint Multilingual Space*](http://aclweb.org/anthology/P18-2037)
-    ACL, July 2018
-
-[4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov,
-    [*XNLI: Cross-lingual Sentence Understanding through Inference*](https://aclweb.org/anthology/D18-1269),
-    EMNLP, 2018.
-
-[5] Mikel Artetxe and Holger Schwenk,
-    [*Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings*](https://arxiv.org/abs/1811.01136)
-    arXiv, Nov 3 2018.
-
-[6] Mikel Artetxe and Holger Schwenk,
-    [*Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond*](https://arxiv.org/abs/1812.10464)
-    arXiv, Dec 26 2018.
-
-[7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman,
-    [*WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia*](https://arxiv.org/abs/1907.05791)
-    arXiv, July 11  2019.
-
-[8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin
-    [*CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB*](https://arxiv.org/abs/1911.04944)
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/lr_scheduler/pass_through.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/lr_scheduler/pass_through.py
deleted file mode 100644
index 2f93db328c1de9b268e8ee1c0c1cad558fd089aa..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/lr_scheduler/pass_through.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass
-
-from fairseq.dataclass import FairseqDataclass
-from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
-
-
-@dataclass
-class PassThroughScheduleConfig(FairseqDataclass):
-    pass
-
-
-@register_lr_scheduler("pass_through", dataclass=PassThroughScheduleConfig)
-class PassThroughScheduleSchedule(FairseqLRScheduler):
-    """Delegate lr scheduling to the optimizer."""
-
-    def __init__(self, cfg: PassThroughScheduleConfig, optimizer):
-        super().__init__(cfg, optimizer)
-        assert (
-            hasattr(optimizer, "lr_scheduler") and optimizer.lr_scheduler is not None
-        ), "Pass-through schedule can only be used with optimizers with their own schedulers"
-
-    def state_dict(self):
-        return self.optimizer.lr_scheduler.state_dict()
-
-    def load_state_dict(self, state_dict):
-        self.optimizer.lr_scheduler.load_state_dict(state_dict)
-
-    def step_begin_epoch(self, epoch):
-        """Update the learning rate at the beginning of the given epoch."""
-        return self.optimizer.lr_scheduler.step_begin_epoch(epoch)
-
-    def step_update(self, num_updates):
-        """Update the learning rate after each update."""
-        return self.optimizer.lr_scheduler.step_update(num_updates)
diff --git a/spaces/mthsk/sovits-models/modules/commons.py b/spaces/mthsk/sovits-models/modules/commons.py
deleted file mode 100644
index 074888006392e956ce204d8368362dbb2cd4e304..0000000000000000000000000000000000000000
--- a/spaces/mthsk/sovits-models/modules/commons.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-def slice_pitch_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, idx_str:idx_end]
-  return ret
-
-def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
-  return ret, ret_pitch, ids_str
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def rand_spec_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
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-  acts = t_act * s_act
-  return acts
-
-
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-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/mullikine/ilambda/ilambda.html b/spaces/mullikine/ilambda/ilambda.html
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-<body>
-<div id="content">
-<h1 class="title">𝑖λ, a family of imaginary programming libraries</h1>
-<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
-
-
-<colgroup>
-<col  class="org-left" />
-
-<col  class="org-left" />
-
-<col  class="org-left" />
-</colgroup>
-<thead>
-<tr>
-<th scope="col" class="org-left">Install with Pen</th>
-<th scope="col" class="org-left">&#xa0;</th>
-<th scope="col" class="org-left">&#xa0;</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td class="org-left">Pen.el on GitHub</td>
-<td class="org-left"><a href="https://github.com/semiosis/pen.el/">https://github.com/semiosis/pen.el/</a></td>
-<td class="org-left">&#xa0;</td>
-</tr>
-
-<tr>
-<td class="org-left">Pen Tutorial</td>
-<td class="org-left"><a href="https://mullikine.github.io/posts/pen-el-tutorial/">https://mullikine.github.io/posts/pen-el-tutorial/</a></td>
-<td class="org-left">&#xa0;</td>
-</tr>
-
-<tr>
-<td class="org-left">ilambda Tutorial</td>
-<td class="org-left"><a href="https://semiosis.github.io/posts/ilambda-tutorial/">https://semiosis.github.io/posts/ilambda-tutorial/</a></td>
-<td class="org-left">Just do this</td>
-</tr>
-</tbody>
-</table>
-
-<div id="outline-container-orga909681" class="outline-2">
-<h2 id="orga909681"><span class="section-number-2">1</span> <code>𝑖λ</code> (Imaginary Programming Functions)</h2>
-<div class="outline-text-2" id="text-1">
-</div>
-<div id="outline-container-org9d877ff" class="outline-3">
-<h3 id="org9d877ff"><span class="section-number-3">1.1</span> Summary</h3>
-<div class="outline-text-3" id="text-1-1">
-<p>
-<b>tl;dr:</b> Imaginary Programming (<code>IP</code>) is where
-data and knowledge has been substituted for
-inference by a LM. Therefore, the
-implementation of an <code>𝑖λ</code> library will be
-uniquely tailored to each language.
-</p>
-
-<p>
-I design and build an <code>IP</code> library named
-<code>𝑖λ.el</code> for emacs. Think of it a bit like a
-functional programming library in that you
-will find a set of functions and macros for
-working in the programming paradigm. The
-objective here is to create some functions for
-doing IP in emacs lisp, since emacs lisp has
-the expressive power to prototype such things,
-but the ideas contained here can easily be
-transferred to any other programming language.
-The results of this little experiment will go
-straight into my thesis.
-</p>
-
-<dl class="org-dl">
-<dt>𝑖λ project</dt><dd><a href="http://github.com/semiosis/ilambda/">http://github.com/semiosis/ilambda/</a></dd>
-<dt>IP thesis</dt><dd><a href="https://github.com/semiosis/imaginary-programming-thesis/blob/master/thesis.org">https://github.com/semiosis/imaginary-programming-thesis/blob/master/thesis.org</a></dd>
-<dt>IP library</dt><dd><a href="http://github.com/semiosis/pen.el/blob/master/src/ilambda.el">http://github.com/semiosis/pen.el/blob/master/src/ilambda.el</a></dd>
-<dt>Glossaries</dt><dd><a href="https://github.com/semiosis/glossaries-gh/blob/master/imaginary-programming.txt">Imaginary Programming</a> ⚔ <a href="http://github.com/semiosis/glossaries-gh/blob/master/pen.el.txt">Pen.el</a> ⚔ <a href="https://github.com/semiosis/glossaries-gh/blob/master/pe-prompt-engineering.txt">Prompt Engineering</a> ⚔ <a href="http://github.com/semiosis/glossaries-gh/blob/master/epistemology.txt">Epistemology</a></dd>
-</dl>
-
-<p>
-<code>imacro</code> (like regular macros) are for
-generating code. <code>idefun</code>, however, doesnt
-generate the code but attempts to evaluate a
-function <i>without</i> code, or <b>if</b> code is supplied
-then <i>imagine</i> evaluating that code rather than
-actually evaluating it.
-</p>
-
-<p>
-It's kinda crazy that <code>Im</code>-macros run faster
-and are more reliable than <code>Im</code>-functions,
-where the opposite is true in regular
-programming. That's because they generate the
-code which can be <span class="underline">reused</span>, adjusted and
-interactively regenerated, where an
-<code>Im</code>-function will typically have to query the
-LM every time you call it.
-</p>
-</div>
-
-<div id="outline-container-org5d5e309" class="outline-4">
-<h4 id="org5d5e309"><span class="section-number-4">1.1.1</span> The objective with <code>𝑖λ.el</code></h4>
-<div class="outline-text-4" id="text-1-1-1">
-<p>
-The objective here is to create <code>IP</code> functions
-for programming in emacs lisp exclusively.
-</p>
-
-<p>
-It will be extended in the future to do all
-programming languages, but I intend to make
-<code>𝑖λ.el</code> simple and effective for programming
-in emacs lisp without bloat or over-complication.
-</p>
-</div>
-</div>
-</div>
-
-<div id="outline-container-orgb7a47ea" class="outline-3">
-<h3 id="orgb7a47ea"><span class="section-number-3">1.2</span> Syntax forms</h3>
-<div class="outline-text-3" id="text-1-2">
-<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
-
-
-<colgroup>
-<col  class="org-left" />
-
-<col  class="org-left" />
-
-<col  class="org-left" />
-
-<col  class="org-left" />
-</colgroup>
-<thead>
-<tr>
-<th scope="col" class="org-left">name</th>
-<th scope="col" class="org-left">type</th>
-<th scope="col" class="org-left">depends on</th>
-<th scope="col" class="org-left">basic idea</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td class="org-left"><code>ieval</code></td>
-<td class="org-left">MACRO</td>
-<td class="org-left">&#xa0;</td>
-<td class="org-left"><code>ieval</code> will imagine the evaluation of some code without any other context.</td>
-</tr>
-
-<tr>
-<td class="org-left"><code>imacro/N</code></td>
-<td class="org-left">MACRO</td>
-<td class="org-left">&#xa0;</td>
-<td class="org-left"><code>imacro</code> does not evaluate. It merely generates code, but that code is imagined. Like <code>idefun</code> it is variadic.</td>
-</tr>
-
-<tr>
-<td class="org-left"><code>idefun</code></td>
-<td class="org-left">FUNCTION</td>
-<td class="org-left"><code>ieval</code> and <code>imacro</code></td>
-<td class="org-left">Run an imagined function on the given arguments and return an imagined result, but create a binding for the function.</td>
-</tr>
-
-<tr>
-<td class="org-left"><code>ilist</code></td>
-<td class="org-left">FUNCTION</td>
-<td class="org-left">&#xa0;</td>
-<td class="org-left">Generate a list of things. Return a real list.</td>
-</tr>
-
-<tr>
-<td class="org-left"><code>ilambda</code> / <code>𝑖λ</code></td>
-<td class="org-left">FUNCTION</td>
-<td class="org-left"><code>ieval</code></td>
-<td class="org-left">Imaginarily run an expression on the given arguments and return an imagined result.</td>
-</tr>
-
-<tr>
-<td class="org-left"><code>ifilter</code></td>
-<td class="org-left">FUNCTION</td>
-<td class="org-left">&#xa0;</td>
-<td class="org-left">Imaginarily filter a real list with natural language and return a real list. Optionally, enforce cardinality.</td>
-</tr>
-
-<tr>
-<td class="org-left"><code>iparse</code></td>
-<td class="org-left">MACRO</td>
-<td class="org-left">&#xa0;</td>
-<td class="org-left">Given a syntax form / expression, will parse a syntax form with natural language. Returns the subform.</td>
-</tr>
-
-<tr>
-<td class="org-left"><code>defimacro</code></td>
-<td class="org-left">MACRO</td>
-<td class="org-left"><code>imacro/N</code></td>
-<td class="org-left">Select the appropriate <code>imacro/N</code> form depending on the arity of arguments.</td>
-</tr>
-</tbody>
-</table>
-</div>
-
-<div id="outline-container-org0e7611e" class="outline-4">
-<h4 id="org0e7611e"><span class="section-number-4">1.2.1</span> <code>ieval</code></h4>
-<div class="outline-text-4" id="text-1-2-1">
-<p>
-<code>ieval</code> will simply evaluate the provided
-string/sexp as emacs lisp code. You
-must provide <code>ieval</code> with, firstly, the preceding
-code, which may be, for example, a function
-definition or package requires, etc. and,
-secondly, evaluated expression. Either
-argument can either be a raw string containing
-code or a sexp, but the expression will be
-"one-line-ized" for the prompt.
-</p>
-
-<p>
-<code>ieval</code> is used by <code>idefun</code> and <code>ilambda</code>.
-</p>
-
-<p>
-<span class="underline"><i>prompt function</i></span> for running the eval.
-</p>
-
-<dl class="org-dl">
-<dt><code>pf-imagine-evaluating-emacs-lisp/2</code></dt><dd><a href="http://github.com/semiosis/prompts/blob/master/prompts/imagine-evaluating-emacs-lisp-2.prompt">http://github.com/semiosis/prompts/blob/master/prompts/imagine-evaluating-emacs-lisp-2.prompt</a></dd>
-</dl>
-
-<div class="org-src-container">
-<pre class="src src-yaml"><span class="linenr"> 1: </span><span style="color: #7590db; font-weight: bold;">task</span>: <span style="color: #2d9574; font-weight: bold;">"imagine evaluating emacs lisp"</span>
-<span class="linenr"> 2: </span><span style="color: #7590db; font-weight: bold;">doc</span>: <span style="color: #2d9574; font-weight: bold;">"Given some elisp return the imagined result"</span>
-<span class="linenr"> 3: </span><span style="color: #7590db; font-weight: bold;">prompt-version</span>: 1
-<span class="linenr"> 4: </span><span style="color: #7590db; font-weight: bold;">prompt</span>: |+
-<span class="linenr"> 5: </span>  <span style="color: #2d9574; font-weight: bold;">&lt;code&gt;</span>
-<span class="linenr"> 6: </span><span style="color: #2d9574; font-weight: bold;">  (message (eval &lt;expression&gt;))</span>
-<span class="linenr"> 7: </span><span style="color: #2d9574; font-weight: bold;">  --&gt;</span>
-<span class="linenr"> 8: </span><span style="color: #7590db; font-weight: bold;">engine</span>: <span style="color: #2d9574; font-weight: bold;">"OpenAI Codex"</span>
-<span class="linenr"> 9: </span><span style="color: #7590db; font-weight: bold;">temperature</span>: 0.2
-<span class="linenr">10: </span><span style="color: #7590db; font-weight: bold;">max-generated-tokens</span>: 60
-<span class="linenr">11: </span><span style="color: #7590db; font-weight: bold;">top-p</span>: 1.0
-<span class="linenr">12: </span><span style="color: #7590db; font-weight: bold;">cache</span>: <span style="color: #a45bad; font-weight: bold;">on</span>
-<span class="linenr">13: </span><span style="color: #7590db; font-weight: bold;">stop-sequences</span>:
-<span class="linenr">14: </span>- <span style="color: #2d9574; font-weight: bold;">"\n"</span>
-<span class="linenr">15: </span><span style="color: #7590db; font-weight: bold;">vars</span>:
-<span class="linenr">16: </span>- <span style="color: #2d9574; font-weight: bold;">"code"</span>
-<span class="linenr">17: </span>- <span style="color: #2d9574; font-weight: bold;">"expression"</span>
-<span class="linenr">18: </span><span style="color: #7590db; font-weight: bold;">validator</span>: <span style="color: #2d9574; font-weight: bold;">"grep -qv '(:return'"</span>
-<span class="linenr">19: </span><span style="color: #7590db; font-weight: bold;">examples</span>:
-<span class="linenr">20: </span>- |-
-<span class="linenr">21: </span>    <span style="color: #2d9574; font-weight: bold;">(defun double-number (x)</span>
-<span class="linenr">22: </span><span style="color: #2d9574; font-weight: bold;">      (x * x))</span>
-<span class="linenr">23: </span>- <span style="color: #2d9574; font-weight: bold;">"(double-number 5)"</span>
-<span class="linenr">24: </span><span style="color: #7590db; font-weight: bold;">filter</span>: <span style="color: #a45bad; font-weight: bold;">on</span>
-<span class="linenr">25: </span><span style="color: #7590db; font-weight: bold;">completion</span>: <span style="color: #a45bad; font-weight: bold;">off</span>
-<span class="linenr">26: </span><span style="color: #7590db; font-weight: bold;">insertion</span>: <span style="color: #a45bad; font-weight: bold;">off</span>
-</pre>
-</div>
-
-<p>
-The following is the implementation of <code>ieval</code>.
-</p>
-
-<p>
-You may pass either a <code>sexp</code> or a raw string containing code.
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr"> 1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defmacro</span> <span style="color: #bc6ec5; font-weight: bold;">ieval</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>expression <span style="color: #ce537a; font-weight: bold;">&amp;optional</span> code<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr"> 2: </span>  <span style="color: #2aa1ae; font-weight: bold;">"Imaginarily evaluate the expression, given the code and return a real result."</span>
-<span class="linenr"> 3: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">let*</span> <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #67b11d; font-weight: bold;">(</span>code-str
-<span class="linenr"> 4: </span>          <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">cond</span>
-<span class="linenr"> 5: </span>           <span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #bc6ec5; font-weight: bold;">(</span>stringp code<span style="color: #bc6ec5; font-weight: bold;">)</span> code<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr"> 6: </span>           <span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #bc6ec5; font-weight: bold;">(</span>listp code<span style="color: #bc6ec5; font-weight: bold;">)</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>pps code<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr"> 7: </span>         <span style="color: #67b11d; font-weight: bold;">(</span>expression-str
-<span class="linenr"> 8: </span>          <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">cond</span>
-<span class="linenr"> 9: </span>           <span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #bc6ec5; font-weight: bold;">(</span>stringp expression<span style="color: #bc6ec5; font-weight: bold;">)</span> expression<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">10: </span>           <span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #bc6ec5; font-weight: bold;">(</span>listp expression<span style="color: #bc6ec5; font-weight: bold;">)</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>pp-oneline expression<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">11: </span>         <span style="color: #67b11d; font-weight: bold;">(</span>result <span style="color: #b1951d; font-weight: bold;">(</span>car
-<span class="linenr">12: </span>                  <span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">pen-single-generation</span>
-<span class="linenr">13: </span>                   <span style="color: #bc6ec5; font-weight: bold;">(</span>pf-imagine-evaluating-emacs-lisp/2
-<span class="linenr">14: </span>                    code-str expression-str
-<span class="linenr">15: </span>                    <span style="color: #4f97d7; font-weight: bold;">:no-select-result</span> t <span style="color: #4f97d7; font-weight: bold;">:select-only-match</span> t<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">16: </span>    <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">ignore-errors</span>
-<span class="linenr">17: </span>      <span style="color: #67b11d; font-weight: bold;">(</span>eval-string result<span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">test-ieval-2</span> <span style="color: #bc6ec5; font-weight: bold;">()</span>
-<span class="linenr">2: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span>ieval
-<span class="linenr">3: </span>   <span style="color: #2d9574; font-weight: bold;">(</span>double-number <span style="color: #a45bad; font-weight: bold;">5</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">4: </span>   <span style="color: #2d9574; font-weight: bold;">"(defun double-number (x)\n     (x * x))\n;;Test the double-number function"</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<p>
-Resulting prompt from <code>test-ieval-2</code>:
-</p>
-
-<div class="org-src-container">
-<pre class="src src-text"><span class="linenr">1: </span>(defun double-number (x)
-<span class="linenr">2: </span>     (x * x))
-<span class="linenr">3: </span>;;Test the double-number function
-<span class="linenr">4: </span>(message (eval (double-number 5)))
-<span class="linenr">5: </span>--&gt; &lt;END&gt;
-</pre>
-</div>
-
-<p>
-<code>ieval</code> not only evaluates correctly despite
-the deliberately incorrect naming of the
-function (it multiplies rather than doubles),
-but it returns the value as the correct data type.
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">test-ieval</span> <span style="color: #bc6ec5; font-weight: bold;">()</span>
-<span class="linenr">2: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span>ieval
-<span class="linenr">3: </span>   <span style="color: #2d9574; font-weight: bold;">(</span>double-number <span style="color: #a45bad; font-weight: bold;">5</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">4: </span>   <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">double-number</span> <span style="color: #67b11d; font-weight: bold;">(</span>x<span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">5: </span>     <span style="color: #67b11d; font-weight: bold;">(</span>x * x<span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<p>
-Expansion of <code>test-ieval</code>.
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">let</span> <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #2d9574; font-weight: bold;">(</span>result
-<span class="linenr">2: </span>       <span style="color: #67b11d; font-weight: bold;">(</span>ieval
-<span class="linenr">3: </span>        <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">double-number</span> <span style="color: #4f97d7; font-weight: bold;">(</span>x<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">4: </span>          <span style="color: #4f97d7; font-weight: bold;">(</span>x * x<span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span>
-<span class="linenr">5: </span>        <span style="color: #b1951d; font-weight: bold;">(</span>double-number <span style="color: #a45bad; font-weight: bold;">5</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">6: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span>list2str <span style="color: #2d9574; font-weight: bold;">(</span>list result
-<span class="linenr">7: </span>                  <span style="color: #67b11d; font-weight: bold;">(</span>type result<span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-text"><span class="linenr">1: </span>25
-<span class="linenr">2: </span>integer
-</pre>
-</div>
-</div>
-</div>
-
-<div id="outline-container-org3088c85" class="outline-4">
-<h4 id="org3088c85"><span class="section-number-4">1.2.2</span> <code>ilambda</code> / <code>𝑖λ</code></h4>
-<div class="outline-text-4" id="text-1-2-2">
-<p>
-Imaginarily run an expression on the given
-arguments and return an imagined result.
-</p>
-
-<p>
-Here are three <code>ilambda</code> subforms which take different arguments.
-</p>
-
-<p>
-<code>ilambda/task</code> is the most terse. Only a NL
-task description is given.
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr"> 1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defmacro</span> <span style="color: #bc6ec5; font-weight: bold;">ilambda/task</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>args task <span style="color: #ce537a; font-weight: bold;">&amp;optional</span> name-sym<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr"> 2: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">let*</span> <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #67b11d; font-weight: bold;">(</span>slug <span style="color: #b1951d; font-weight: bold;">(</span>s-replace-regexp <span style="color: #2d9574; font-weight: bold;">"-$"</span> <span style="color: #2d9574; font-weight: bold;">""</span> <span style="color: #4f97d7; font-weight: bold;">(</span>slugify <span style="color: #bc6ec5; font-weight: bold;">(</span>eval task<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr"> 3: </span>         <span style="color: #67b11d; font-weight: bold;">(</span>fsym <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">or</span> name-sym
-<span class="linenr"> 4: </span>                   <span style="color: #4f97d7; font-weight: bold;">(</span>intern slug<span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr"> 5: </span>    `<span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">lambda</span> ,args
-<span class="linenr"> 6: </span>       <span style="color: #67b11d; font-weight: bold;">(</span>eval
-<span class="linenr"> 7: </span>        <span style="color: #2aa1ae; font-weight: bold;">;; </span><span style="color: #2aa1ae; font-weight: bold;">imagined by an LM</span>
-<span class="linenr"> 8: </span>        `<span style="color: #b1951d; font-weight: bold;">(</span>ieval
-<span class="linenr"> 9: </span>          <span style="color: #2aa1ae; font-weight: bold;">;; </span><span style="color: #2aa1ae; font-weight: bold;">An function and a function call</span>
-<span class="linenr">10: </span>          <span style="color: #4f97d7; font-weight: bold;">(</span>,',fsym ,,@args<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">11: </span>          ,,<span style="color: #4f97d7; font-weight: bold;">(</span>concat <span style="color: #2d9574; font-weight: bold;">";; "</span> task<span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">12: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defalias</span> '<span style="color: #bc6ec5; font-weight: bold;">&#119894;&#955;/task</span> 'ilambda/task<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">13: </span>
-<span class="linenr">14: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defmacro</span> <span style="color: #bc6ec5; font-weight: bold;">ilambda/task-code</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>args task code <span style="color: #ce537a; font-weight: bold;">&amp;optional</span> name-sym<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">15: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">let*</span> <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #67b11d; font-weight: bold;">(</span>slug <span style="color: #b1951d; font-weight: bold;">(</span>s-replace-regexp <span style="color: #2d9574; font-weight: bold;">"-$"</span> <span style="color: #2d9574; font-weight: bold;">""</span> <span style="color: #4f97d7; font-weight: bold;">(</span>slugify <span style="color: #bc6ec5; font-weight: bold;">(</span>eval task<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">16: </span>         <span style="color: #67b11d; font-weight: bold;">(</span>fsym <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">or</span>
-<span class="linenr">17: </span>                name-sym
-<span class="linenr">18: </span>                <span style="color: #4f97d7; font-weight: bold;">(</span>intern slug<span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">19: </span>    `<span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">lambda</span> ,args
-<span class="linenr">20: </span>       <span style="color: #67b11d; font-weight: bold;">(</span>eval
-<span class="linenr">21: </span>        <span style="color: #2aa1ae; font-weight: bold;">;; </span><span style="color: #2aa1ae; font-weight: bold;">imagined by an LM</span>
-<span class="linenr">22: </span>        `<span style="color: #b1951d; font-weight: bold;">(</span>ieval
-<span class="linenr">23: </span>          <span style="color: #2aa1ae; font-weight: bold;">;; </span><span style="color: #2aa1ae; font-weight: bold;">An function and a function call</span>
-<span class="linenr">24: </span>          <span style="color: #4f97d7; font-weight: bold;">(</span>,',fsym ,,@args<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">25: </span>          <span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> ,',fsym ,',args
-<span class="linenr">26: </span>            ,,task
-<span class="linenr">27: </span>            ,',code<span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">28: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defalias</span> '<span style="color: #bc6ec5; font-weight: bold;">&#119894;&#955;/task-code</span> 'ilambda/task-code<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">29: </span>
-<span class="linenr">30: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defmacro</span> <span style="color: #bc6ec5; font-weight: bold;">ilambda/code</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>args code <span style="color: #ce537a; font-weight: bold;">&amp;optional</span> name-sym<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">31: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">let</span> <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #67b11d; font-weight: bold;">(</span>fsym <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">or</span> name-sym
-<span class="linenr">32: </span>                  'main<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">33: </span>    `<span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">lambda</span> ,args
-<span class="linenr">34: </span>       <span style="color: #67b11d; font-weight: bold;">(</span>eval
-<span class="linenr">35: </span>        <span style="color: #2aa1ae; font-weight: bold;">;; </span><span style="color: #2aa1ae; font-weight: bold;">imagined by an LM</span>
-<span class="linenr">36: </span>        `<span style="color: #b1951d; font-weight: bold;">(</span>ieval
-<span class="linenr">37: </span>          <span style="color: #2aa1ae; font-weight: bold;">;; </span><span style="color: #2aa1ae; font-weight: bold;">An function and a function call</span>
-<span class="linenr">38: </span>          <span style="color: #4f97d7; font-weight: bold;">(</span>,',fsym ,,@args<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">39: </span>          <span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> ,',fsym <span style="color: #bc6ec5; font-weight: bold;">(</span>,',@args<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">40: </span>            ,',code<span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">41: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defalias</span> '<span style="color: #bc6ec5; font-weight: bold;">&#119894;&#955;/code</span> 'ilambda/code<span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<p>
-<i><b>Demonstrations</b></i>
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span>mapcar <span style="color: #bc6ec5; font-weight: bold;">(</span>ilambda/task <span style="color: #2d9574; font-weight: bold;">(</span>x<span style="color: #2d9574; font-weight: bold;">)</span> <span style="color: #2d9574; font-weight: bold;">"double it"</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">2: </span>        '<span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #a45bad; font-weight: bold;">12</span> <span style="color: #a45bad; font-weight: bold;">4</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span style="color: #2d9574; font-weight: bold;">"(24 8)</span>
-<span style="color: #2d9574; font-weight: bold;">"</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span>mapcar <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">ilambda/code</span> <span style="color: #2d9574; font-weight: bold;">(</span>x<span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">2: </span>                      <span style="color: #2d9574; font-weight: bold;">(</span>+ x <span style="color: #a45bad; font-weight: bold;">5</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">3: </span>        '<span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #a45bad; font-weight: bold;">4</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span style="color: #2d9574; font-weight: bold;">"(9)</span>
-<span style="color: #2d9574; font-weight: bold;">"</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span>mapcar <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">ilambda/task-code</span> <span style="color: #2d9574; font-weight: bold;">(</span>x<span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">2: </span>                           <span style="color: #2d9574; font-weight: bold;">"add five"</span>
-<span class="linenr">3: </span>                           <span style="color: #2d9574; font-weight: bold;">(</span>+ x <span style="color: #a45bad; font-weight: bold;">5</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">4: </span>        '<span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #a45bad; font-weight: bold;">8</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span style="color: #2d9574; font-weight: bold;">"(13)</span>
-<span style="color: #2d9574; font-weight: bold;">"</span>
-</pre>
-</div>
-
-<p>
-The <i><b>ilambda</b></i> macro.
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr"> 1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defmacro</span> <span style="color: #bc6ec5; font-weight: bold;">ilambda</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>args code-or-task <span style="color: #ce537a; font-weight: bold;">&amp;optional</span> task-or-code name-sym<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr"> 2: </span>  <span style="color: #2aa1ae; font-weight: bold;">"Define an imaginary lambda (&#119894;&#955;)"</span>
-<span class="linenr"> 3: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">let</span> <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #67b11d; font-weight: bold;">(</span>task <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">if</span> <span style="color: #4f97d7; font-weight: bold;">(</span>stringp code-or-task<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr"> 4: </span>                  code-or-task
-<span class="linenr"> 5: </span>                task-or-code<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr"> 6: </span>        <span style="color: #67b11d; font-weight: bold;">(</span>code <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">if</span> <span style="color: #4f97d7; font-weight: bold;">(</span>listp code-or-task<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr"> 7: </span>                  code-or-task
-<span class="linenr"> 8: </span>                task-or-code<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr"> 9: </span>    <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">cond</span>
-<span class="linenr">10: </span>     <span style="color: #67b11d; font-weight: bold;">(</span><span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">and</span> code
-<span class="linenr">11: </span>           <span style="color: #4f97d7; font-weight: bold;">(</span>sor task<span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span>
-<span class="linenr">12: </span>      `<span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">ilambda/task-code</span> ,args ,task ,code ,name-sym<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">13: </span>     <span style="color: #67b11d; font-weight: bold;">(</span><span style="color: #b1951d; font-weight: bold;">(</span>sor task<span style="color: #b1951d; font-weight: bold;">)</span>
-<span class="linenr">14: </span>      `<span style="color: #b1951d; font-weight: bold;">(</span>ilambda/task ,args ,task ,name-sym<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">15: </span>     <span style="color: #67b11d; font-weight: bold;">(</span><span style="color: #b1951d; font-weight: bold;">(</span>listp code-or-task<span style="color: #b1951d; font-weight: bold;">)</span>
-<span class="linenr">16: </span>      `<span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">ilambda/code</span> ,args ,code ,name-sym<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">17: </span>
-<span class="linenr">18: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defalias</span> '<span style="color: #bc6ec5; font-weight: bold;">&#119894;&#955;</span> 'ilambda<span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span>-reduce <span style="color: #bc6ec5; font-weight: bold;">(</span>&#119894;&#955; <span style="color: #2d9574; font-weight: bold;">(</span>x y<span style="color: #2d9574; font-weight: bold;">)</span> <span style="color: #2d9574; font-weight: bold;">"add x to y"</span><span style="color: #bc6ec5; font-weight: bold;">)</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>number-sequence <span style="color: #a45bad; font-weight: bold;">1</span> <span style="color: #a45bad; font-weight: bold;">3</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span style="color: #2d9574; font-weight: bold;">"6"</span>
-</pre>
-</div>
-</div>
-</div>
-
-<div id="outline-container-org5938360" class="outline-4">
-<h4 id="org5938360"><span class="section-number-4">1.2.3</span> <code>idefun</code></h4>
-<div class="outline-text-4" id="text-1-2-3">
-<p>
-The <code>idefun</code> creates a binding to an imaginary
-function. The implementation of the <code>idefun</code>
-need not be specified in order for code to
-run.
-</p>
-
-<p>
-The new prompt function returned by <code>idefun</code> is provided with arguments and the
-values of those arguments are taken and placed
-into a prompt. An implementation may be
-provided to <code>idefun</code> when defining the prompt function or optionally left out.
-Unlike an <code>imacro</code>, when the prompt function
-is evaluated the code is not returned. Rather,
-the code is evaluted in imaginary space.
-</p>
-
-<p>
-In short, the LM will imagine the evaluation
-of the function as opposed to generate code.
-</p>
-
-<p>
-<code>idefun</code> returns a binding to a new prompt
-function.
-</p>
-
-<p>
-Some examples:
-</p>
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr"> 1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> add-two-numbers<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr"> 2: </span><span style="color: #4f97d7; font-weight: bold;">(</span>add-two-numbers <span style="color: #a45bad; font-weight: bold;">5</span> <span style="color: #a45bad; font-weight: bold;">8</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr"> 3: </span>
-<span class="linenr"> 4: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> add-two-numbers <span style="color: #bc6ec5; font-weight: bold;">(</span>a b<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr"> 5: </span><span style="color: #4f97d7; font-weight: bold;">(</span>add-two-numbers <span style="color: #a45bad; font-weight: bold;">5</span> <span style="color: #a45bad; font-weight: bold;">8</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr"> 6: </span>
-<span class="linenr"> 7: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> add-two-numbers <span style="color: #bc6ec5; font-weight: bold;">(</span>a b<span style="color: #bc6ec5; font-weight: bold;">)</span> <span style="color: #2d9574; font-weight: bold;">"add a to b"</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr"> 8: </span><span style="color: #4f97d7; font-weight: bold;">(</span>add-two-numbers <span style="color: #a45bad; font-weight: bold;">5</span> <span style="color: #a45bad; font-weight: bold;">8</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr"> 9: </span>
-<span class="linenr">10: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> sum-of-integers<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">11: </span><span style="color: #4f97d7; font-weight: bold;">(</span>sum-of-integers <span style="color: #a45bad; font-weight: bold;">1</span> <span style="color: #a45bad; font-weight: bold;">2</span> <span style="color: #a45bad; font-weight: bold;">3</span> <span style="color: #a45bad; font-weight: bold;">10</span> <span style="color: #a45bad; font-weight: bold;">200</span> <span style="color: #a45bad; font-weight: bold;">3000</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">12: </span>
-<span class="linenr">13: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> thing-to-hex-color<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">14: </span>
-<span class="linenr">15: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> add-two-numbers <span style="color: #bc6ec5; font-weight: bold;">(</span>a b<span style="color: #bc6ec5; font-weight: bold;">)</span> <span style="color: #2d9574; font-weight: bold;">"add a to b"</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> generate-fib-sequence <span style="color: #bc6ec5; font-weight: bold;">(</span>n<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">2: </span>
-<span class="linenr">3: </span><span style="color: #4f97d7; font-weight: bold;">(</span>pp-to-string <span style="color: #bc6ec5; font-weight: bold;">(</span>generate-fib-sequence <span style="color: #a45bad; font-weight: bold;">5</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #a45bad; font-weight: bold;">1</span> <span style="color: #a45bad; font-weight: bold;">1</span> <span style="color: #a45bad; font-weight: bold;">2</span> <span style="color: #a45bad; font-weight: bold;">3</span> <span style="color: #a45bad; font-weight: bold;">5</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<p>
-With a temperature of <code>0.0</code>, this will hash to
-the same thing every time!
-</p>
-
-<p>
-Strangely, we can't call it a 'neural hash' though.
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> sha-hash-string <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">s</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">2: </span>
-<span class="linenr">3: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">pen-force</span>
-<span class="linenr">4: </span> <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #2d9574; font-weight: bold;">(</span>temperature <span style="color: #a45bad; font-weight: bold;">0.0</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">5: </span> <span style="color: #bc6ec5; font-weight: bold;">(</span>sha-hash-string <span style="color: #2d9574; font-weight: bold;">"sugar shane"</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-text"><span class="linenr">1: </span>f1d3ff8ec24e91b957c9e55adec407f47b55e3ae
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> neural-hash-string <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">s</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">2: </span>  <span style="color: #2d9574; font-weight: bold;">"This calculates a neural hash of the string."</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">3: </span>
-<span class="linenr">4: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">pen-force</span>
-<span class="linenr">5: </span> <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #2d9574; font-weight: bold;">(</span>temperature <span style="color: #a45bad; font-weight: bold;">0.0</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">6: </span> <span style="color: #bc6ec5; font-weight: bold;">(</span>neural-hash-string <span style="color: #2d9574; font-weight: bold;">"sugar shane"</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<p>
-"0x7f8b8f8e"
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr"> 1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defmacro</span> <span style="color: #bc6ec5; font-weight: bold;">idefun</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>name-sym args <span style="color: #ce537a; font-weight: bold;">&amp;optional</span> code-or-task task-or-code<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr"> 2: </span>  <span style="color: #2aa1ae; font-weight: bold;">"Define an imaginary function"</span>
-<span class="linenr"> 3: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">cond</span>
-<span class="linenr"> 4: </span>   <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #67b11d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">and</span> <span style="color: #b1951d; font-weight: bold;">(</span>stringp name-sym<span style="color: #b1951d; font-weight: bold;">)</span>
-<span class="linenr"> 5: </span>         <span style="color: #b1951d; font-weight: bold;">(</span>not code-or-task<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr"> 6: </span>    <span style="color: #67b11d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">progn</span>
-<span class="linenr"> 7: </span>      <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">setq</span> code-or-task name-sym<span style="color: #b1951d; font-weight: bold;">)</span>
-<span class="linenr"> 8: </span>      <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">setq</span> name-sym <span style="color: #4f97d7; font-weight: bold;">(</span>intern <span style="color: #bc6ec5; font-weight: bold;">(</span>s-replace-regexp <span style="color: #2d9574; font-weight: bold;">"-$"</span> <span style="color: #2d9574; font-weight: bold;">""</span> <span style="color: #2d9574; font-weight: bold;">(</span>slugify <span style="color: #9cb6ad; font-weight: bold;">(</span>str name-sym<span style="color: #9cb6ad; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr"> 9: </span>   <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #67b11d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">and</span> <span style="color: #b1951d; font-weight: bold;">(</span>symbolp name-sym<span style="color: #b1951d; font-weight: bold;">)</span>
-<span class="linenr">10: </span>         <span style="color: #b1951d; font-weight: bold;">(</span>not code-or-task<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">11: </span>    <span style="color: #67b11d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">setq</span> code-or-task <span style="color: #b1951d; font-weight: bold;">(</span>pen-snc <span style="color: #2d9574; font-weight: bold;">"unsnakecase"</span> <span style="color: #4f97d7; font-weight: bold;">(</span>sym2str name-sym<span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">12: </span>  `<span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defalias</span> ',name-sym
-<span class="linenr">13: </span>     <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">function</span> ,<span style="color: #67b11d; font-weight: bold;">(</span>eval
-<span class="linenr">14: </span>                 `<span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">ilambda</span> ,args ,code-or-task ,task-or-code ,name-sym<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">15: </span>
-<span class="linenr">16: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> idoubleit <span style="color: #bc6ec5; font-weight: bold;">(</span>x<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">17: </span>        <span style="color: #2d9574; font-weight: bold;">"double it"</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span>idoubleit <span style="color: #a45bad; font-weight: bold;">5</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span style="color: #2d9574; font-weight: bold;">"10"</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> distance-between-planets <span style="color: #bc6ec5; font-weight: bold;">(</span>x y<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">2: </span>        <span style="color: #2d9574; font-weight: bold;">"distance between planets in astronomical units (AU)"</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">3: </span>
-<span class="linenr">4: </span><span style="color: #4f97d7; font-weight: bold;">(</span>concat <span style="color: #bc6ec5; font-weight: bold;">(</span>str <span style="color: #2d9574; font-weight: bold;">(</span>distance-between-planets <span style="color: #2d9574; font-weight: bold;">"saturn"</span> <span style="color: #2d9574; font-weight: bold;">"jupiter"</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">5: </span>        <span style="color: #2d9574; font-weight: bold;">"\n"</span>
-<span class="linenr">6: </span>        <span style="color: #bc6ec5; font-weight: bold;">(</span>str <span style="color: #2d9574; font-weight: bold;">(</span>distance-between-planets <span style="color: #2d9574; font-weight: bold;">"mercury"</span> <span style="color: #2d9574; font-weight: bold;">"jupiter"</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span style="color: #2d9574; font-weight: bold;">"\"0.05\\n0.387\""</span>
-</pre>
-</div>
-
-<p>
-I have no idea if this data is correct but it
-seems consistent with itself.
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> distance-between-planets <span style="color: #bc6ec5; font-weight: bold;">(</span>x y<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">2: </span>        <span style="color: #2d9574; font-weight: bold;">"distance between planets in million miles"</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">3: </span>
-<span class="linenr">4: </span><span style="color: #4f97d7; font-weight: bold;">(</span>concat <span style="color: #bc6ec5; font-weight: bold;">(</span>str <span style="color: #2d9574; font-weight: bold;">(</span>distance-between-planets <span style="color: #2d9574; font-weight: bold;">"saturn"</span> <span style="color: #2d9574; font-weight: bold;">"jupiter"</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">5: </span>        <span style="color: #2d9574; font-weight: bold;">"\n"</span>
-<span class="linenr">6: </span>        <span style="color: #bc6ec5; font-weight: bold;">(</span>str <span style="color: #2d9574; font-weight: bold;">(</span>distance-between-planets <span style="color: #2d9574; font-weight: bold;">"mercury"</span> <span style="color: #2d9574; font-weight: bold;">"jupiter"</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span style="color: #2d9574; font-weight: bold;">"\"1.2\\n5.791\""</span>
-</pre>
-</div>
-
-<pre class="example">
-"1.2\n5.791"
-</pre>
-
-
-<p>
-Sadly, Codex doesn't know too much about Scoville food hotness.
-</p>
-
-<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
-
-
-<colgroup>
-<col  class="org-left" />
-
-<col  class="org-left" />
-</colgroup>
-<thead>
-<tr>
-<th scope="col" class="org-left">food</th>
-<th scope="col" class="org-left">Scoville scale</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td class="org-left">Pure capsaicin</td>
-<td class="org-left">16,000,000 SHU</td>
-</tr>
-
-<tr>
-<td class="org-left">Jalapeño</td>
-<td class="org-left">10,000 SHU</td>
-</tr>
-</tbody>
-</table>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> scoville-difference <span style="color: #bc6ec5; font-weight: bold;">(</span>food-a food-b<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">2: </span>        <span style="color: #2d9574; font-weight: bold;">"difference between two foods in scoville Heat Units (SHUs)"</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">3: </span>
-<span class="linenr">4: </span><span style="color: #4f97d7; font-weight: bold;">(</span>concat <span style="color: #bc6ec5; font-weight: bold;">(</span>str <span style="color: #2d9574; font-weight: bold;">(</span>scoville-difference <span style="color: #2d9574; font-weight: bold;">"Pure capsaicin"</span> <span style="color: #2d9574; font-weight: bold;">"Jalape&#241;o"</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">5: </span>        <span style="color: #2d9574; font-weight: bold;">"\n"</span>
-<span class="linenr">6: </span>        <span style="color: #bc6ec5; font-weight: bold;">(</span>str <span style="color: #2d9574; font-weight: bold;">(</span>scoville-difference <span style="color: #2d9574; font-weight: bold;">"Chipotle"</span> <span style="color: #2d9574; font-weight: bold;">"Trinidad Scorpion Butch"</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span style="color: #2d9574; font-weight: bold;">"\"0\\n2\""</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> hex-for-colour <span style="color: #bc6ec5; font-weight: bold;">(</span>colour<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">2: </span>
-<span class="linenr">3: </span><span style="color: #4f97d7; font-weight: bold;">(</span>hex-for-colour <span style="color: #2d9574; font-weight: bold;">"watermelon"</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span style="color: #2d9574; font-weight: bold;">"\"0x6f3d9c\""</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">idefun</span> hex-for-colour <span style="color: #bc6ec5; font-weight: bold;">(</span>colour<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">2: </span>
-<span class="linenr">3: </span><span style="color: #4f97d7; font-weight: bold;">(</span>hex-for-colour <span style="color: #2d9574; font-weight: bold;">"snow"</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span style="color: #2d9574; font-weight: bold;">"\"ffffff\""</span>
-</pre>
-</div>
-
-<p>
-<i><b>Demo</b></i>
-</p>
-
-<!-- Play on asciinema.com -->
-<!-- <a title="asciinema recording" href="https://asciinema.org/a/flwFZOaUnv6UtiLcpqFYkgsop" target="_blank"><img alt="asciinema recording" src="https://asciinema.org/a/flwFZOaUnv6UtiLcpqFYkgsop.svg" /></a> -->
-<!-- Play on the blog -->
-<script src="https://asciinema.org/a/flwFZOaUnv6UtiLcpqFYkgsop.js" id="asciicast-flwFZOaUnv6UtiLcpqFYkgsop" async></script>
-</div>
-</div>
-
-<div id="outline-container-org9d3b4c9" class="outline-4">
-<h4 id="org9d3b4c9"><span class="section-number-4">1.2.4</span> <code>imacro</code></h4>
-<div class="outline-text-4" id="text-1-2-4">
-<p>
-An <code>imacro</code> actually imagines the
-implementation of a function.
-</p>
-
-<p>
-Components of the <code>imacro</code> should be inferred.
-An <code>imacro</code> with only a function name should
-work.
-</p>
-
-<p>
-Also, an <code>imacro</code> is under the hood a regular
-macro. This means, that expanding the <code>imacro</code>
-will infer/generate underlying code.
-</p>
-
-
-<div id="org6d15431" class="figure">
-<p><img src="./macro-expand-codex.gif" alt="macro-expand-codex.gif" />
-</p>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">test-imacro-1</span> <span style="color: #bc6ec5; font-weight: bold;">()</span>
-<span class="linenr">2: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">interactive</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">3: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">imacro/1</span> get-real-component-from-imaginary-number<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr"> 1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">test-imacro-1</span> <span style="color: #bc6ec5; font-weight: bold;">()</span>
-<span class="linenr"> 2: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">interactive</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr"> 3: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">progn</span>
-<span class="linenr"> 4: </span>    <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">get-real-component-from-imaginary-number</span>
-<span class="linenr"> 5: </span>        <span style="color: #67b11d; font-weight: bold;">(</span>number<span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr"> 6: </span>      <span style="color: #2aa1ae; font-weight: bold;">"Return the real component of a complex number."</span>
-<span class="linenr"> 7: </span>      <span style="color: #67b11d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">if</span>
-<span class="linenr"> 8: </span>          <span style="color: #b1951d; font-weight: bold;">(</span>numberp number<span style="color: #b1951d; font-weight: bold;">)</span>
-<span class="linenr"> 9: </span>          <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">if</span>
-<span class="linenr">10: </span>              <span style="color: #4f97d7; font-weight: bold;">(</span>eq
-<span class="linenr">11: </span>               <span style="color: #bc6ec5; font-weight: bold;">(</span>car number<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">12: </span>               'polar<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">13: </span>              <span style="color: #4f97d7; font-weight: bold;">(</span>car
-<span class="linenr">14: </span>               <span style="color: #bc6ec5; font-weight: bold;">(</span>cdr number<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">15: </span>            number<span style="color: #b1951d; font-weight: bold;">)</span>
-<span class="linenr">16: </span>        <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #dc752f; background-color: #292b2e; font-weight: bold;">error</span> <span style="color: #2d9574; font-weight: bold;">"Not a complex number: %s"</span> number<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr"> 1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">test-imacro-1</span> <span style="color: #bc6ec5; font-weight: bold;">()</span>
-<span class="linenr"> 2: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">interactive</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr"> 3: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">progn</span>
-<span class="linenr"> 4: </span>    <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">get-real-component-from-imaginary-number</span>
-<span class="linenr"> 5: </span>        <span style="color: #67b11d; font-weight: bold;">(</span>number<span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr"> 6: </span>      <span style="color: #2aa1ae; font-weight: bold;">"Return the real component of the complex number NUMBER."</span>
-<span class="linenr"> 7: </span>      <span style="color: #67b11d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">if</span>
-<span class="linenr"> 8: </span>          <span style="color: #b1951d; font-weight: bold;">(</span>numberp number<span style="color: #b1951d; font-weight: bold;">)</span>
-<span class="linenr"> 9: </span>          <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">if</span>
-<span class="linenr">10: </span>              <span style="color: #4f97d7; font-weight: bold;">(</span>eq
-<span class="linenr">11: </span>               <span style="color: #bc6ec5; font-weight: bold;">(</span>car number<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">12: </span>               'polar<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">13: </span>              <span style="color: #4f97d7; font-weight: bold;">(</span>nth <span style="color: #a45bad; font-weight: bold;">1</span> number<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">14: </span>            number<span style="color: #b1951d; font-weight: bold;">)</span>
-<span class="linenr">15: </span>        number<span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-
-<div id="org9fda6b9" class="figure">
-<p><img src="./real-imaginary-macro-expand.gif" alt="real-imaginary-macro-expand.gif" />
-</p>
-</div>
-
-<dl class="org-dl">
-<dt><code>pf-imagine-an-emacs-function/3</code></dt><dd><a href="http://github.com/semiosis/prompts/blob/master/prompts/imagine-an-emacs-function-3.prompt">http://github.com/semiosis/prompts/blob/master/prompts/imagine-an-emacs-function-3.prompt</a></dd>
-</dl>
-
-<div class="org-src-container">
-<pre class="src src-yaml"><span class="linenr"> 1: </span><span style="color: #7590db; font-weight: bold;">title</span>: imagine an emacs function
-<span class="linenr"> 2: </span><span style="color: #7590db; font-weight: bold;">task</span>: <span style="color: #2d9574; font-weight: bold;">"imagine an emacs lisp function given name, arguments and docstring"</span>
-<span class="linenr"> 3: </span><span style="color: #7590db; font-weight: bold;">doc</span>: <span style="color: #2d9574; font-weight: bold;">"Given a function name, arguments and docstring, return the imagined body of the function"</span>
-<span class="linenr"> 4: </span><span style="color: #7590db; font-weight: bold;">prompt-version</span>: 1
-<span class="linenr"> 5: </span><span style="color: #7590db; font-weight: bold;">prompt</span>: |+
-<span class="linenr"> 6: </span>  <span style="color: #2d9574; font-weight: bold;">;;my-emacs-library.el</span>
-<span class="linenr"> 7: </span>
-<span class="linenr"> 8: </span><span style="color: #2d9574; font-weight: bold;">  (defun &lt;name&gt; (&lt;arguments&gt;)</span>
-<span class="linenr"> 9: </span>    <span style="color: #2d9574; font-weight: bold;">"&lt;docstring&gt;"</span>
-<span class="linenr">10: </span><span style="color: #7590db; font-weight: bold;">engine</span>: <span style="color: #2d9574; font-weight: bold;">"OpenAI Codex"</span>
-<span class="linenr">11: </span><span style="color: #7590db; font-weight: bold;">temperature</span>: 0.2
-<span class="linenr">12: </span><span style="color: #7590db; font-weight: bold;">max-generated-tokens</span>: 1000
-<span class="linenr">13: </span><span style="color: #7590db; font-weight: bold;">top-p</span>: 1.0
-<span class="linenr">14: </span><span style="color: #7590db; font-weight: bold;">cache</span>: <span style="color: #a45bad; font-weight: bold;">on</span>
-<span class="linenr">15: </span><span style="color: #7590db; font-weight: bold;">stop-sequences</span>:
-<span class="linenr">16: </span>- <span style="color: #2d9574; font-weight: bold;">"\n\n"</span>
-<span class="linenr">17: </span><span style="color: #7590db; font-weight: bold;">vars</span>:
-<span class="linenr">18: </span>- <span style="color: #2d9574; font-weight: bold;">"name"</span>
-<span class="linenr">19: </span>- <span style="color: #2d9574; font-weight: bold;">"arguments"</span>
-<span class="linenr">20: </span>- <span style="color: #2d9574; font-weight: bold;">"docstring"</span>
-<span class="linenr">21: </span><span style="color: #7590db; font-weight: bold;">validator</span>: <span style="color: #2d9574; font-weight: bold;">"chomp | sed -z 's/.*\\(.\\)$/\\1/' | grep -q ')'"</span>
-<span class="linenr">22: </span><span style="color: #7590db; font-weight: bold;">examples</span>:
-<span class="linenr">23: </span>- <span style="color: #2d9574; font-weight: bold;">"times"</span>
-<span class="linenr">24: </span>- <span style="color: #2d9574; font-weight: bold;">"x y"</span>
-<span class="linenr">25: </span>- <span style="color: #2d9574; font-weight: bold;">"multiply two numbers and return a number"</span>
-<span class="linenr">26: </span><span style="color: #7590db; font-weight: bold;">preprocessors</span>:
-<span class="linenr">27: </span>- <span style="color: #2d9574; font-weight: bold;">"slugify"</span>
-<span class="linenr">28: </span><span style="color: #7590db; font-weight: bold;">postprocessor</span>: chomp
-<span class="linenr">29: </span><span style="color: #7590db; font-weight: bold;">postpostprocessor</span>: <span style="color: #2d9574; font-weight: bold;">"sed -z \"s/^;;my-emacs-library.el\\\\n\\\\n//\""</span>
-<span class="linenr">30: </span><span style="color: #7590db; font-weight: bold;">filter</span>: <span style="color: #a45bad; font-weight: bold;">on</span>
-<span class="linenr">31: </span><span style="color: #7590db; font-weight: bold;">completion</span>: <span style="color: #a45bad; font-weight: bold;">off</span>
-<span class="linenr">32: </span><span style="color: #7590db; font-weight: bold;">insertion</span>: <span style="color: #a45bad; font-weight: bold;">off</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span>car
-<span class="linenr">2: </span> <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">pen-single-generation</span>
-<span class="linenr">3: </span>  <span style="color: #2d9574; font-weight: bold;">(</span>pf-imagine-an-emacs-function/3
-<span class="linenr">4: </span>   <span style="color: #2d9574; font-weight: bold;">"times"</span>
-<span class="linenr">5: </span>   <span style="color: #2d9574; font-weight: bold;">"x y"</span>
-<span class="linenr">6: </span>   <span style="color: #2d9574; font-weight: bold;">"multiply two numbers and return a number"</span>
-<span class="linenr">7: </span>   <span style="color: #4f97d7; font-weight: bold;">:include-prompt</span> t
-<span class="linenr">8: </span>   <span style="color: #4f97d7; font-weight: bold;">:no-select-result</span> t<span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">times</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>x y<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">2: </span>  <span style="color: #2aa1ae; font-weight: bold;">"multiply two numbers and return a number"</span>
-<span class="linenr">3: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span>* x y<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<p>
-There are 3 different versions of <code>imacro</code>
-depending on how many arguments are supplied to
-it.
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr"> 1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defmacro</span> <span style="color: #bc6ec5; font-weight: bold;">imacro/3</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>name args docstr<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr"> 2: </span>  <span style="color: #2aa1ae; font-weight: bold;">"Does not evaluate. It merely generates code."</span>
-<span class="linenr"> 3: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">let*</span> <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #67b11d; font-weight: bold;">(</span>argstr <span style="color: #b1951d; font-weight: bold;">(</span>apply 'cmd <span style="color: #4f97d7; font-weight: bold;">(</span>mapcar 'slugify <span style="color: #bc6ec5; font-weight: bold;">(</span>mapcar 'str args<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr"> 4: </span>         <span style="color: #67b11d; font-weight: bold;">(</span>bodystr
-<span class="linenr"> 5: </span>          <span style="color: #b1951d; font-weight: bold;">(</span>car
-<span class="linenr"> 6: </span>           <span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">pen-single-generation</span>
-<span class="linenr"> 7: </span>            <span style="color: #bc6ec5; font-weight: bold;">(</span>pf-imagine-an-emacs-function/3
-<span class="linenr"> 8: </span>             name
-<span class="linenr"> 9: </span>             argstr
-<span class="linenr">10: </span>             docstr
-<span class="linenr">11: </span>             <span style="color: #4f97d7; font-weight: bold;">:include-prompt</span> t
-<span class="linenr">12: </span>             <span style="color: #4f97d7; font-weight: bold;">:no-select-result</span> t<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">13: </span>         <span style="color: #67b11d; font-weight: bold;">(</span>body <span style="color: #b1951d; font-weight: bold;">(</span>eval-string <span style="color: #4f97d7; font-weight: bold;">(</span>concat <span style="color: #2d9574; font-weight: bold;">"'"</span> bodystr<span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">14: </span>    `<span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">progn</span> ,body<span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">15: </span>
-<span class="linenr">16: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defmacro</span> <span style="color: #bc6ec5; font-weight: bold;">imacro/2</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>name args<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">17: </span>  <span style="color: #2aa1ae; font-weight: bold;">"Does not evaluate. It merely generates code."</span>
-<span class="linenr">18: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">let*</span> <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #67b11d; font-weight: bold;">(</span>argstr <span style="color: #b1951d; font-weight: bold;">(</span>apply 'cmd <span style="color: #4f97d7; font-weight: bold;">(</span>mapcar 'slugify <span style="color: #bc6ec5; font-weight: bold;">(</span>mapcar 'str args<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">19: </span>         <span style="color: #67b11d; font-weight: bold;">(</span>bodystr
-<span class="linenr">20: </span>          <span style="color: #b1951d; font-weight: bold;">(</span>car
-<span class="linenr">21: </span>           <span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">pen-single-generation</span>
-<span class="linenr">22: </span>            <span style="color: #bc6ec5; font-weight: bold;">(</span>pf-imagine-an-emacs-function/2
-<span class="linenr">23: </span>             name
-<span class="linenr">24: </span>             argstr
-<span class="linenr">25: </span>             <span style="color: #4f97d7; font-weight: bold;">:include-prompt</span> t
-<span class="linenr">26: </span>             <span style="color: #4f97d7; font-weight: bold;">:no-select-result</span> t<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">27: </span>         <span style="color: #67b11d; font-weight: bold;">(</span>body <span style="color: #b1951d; font-weight: bold;">(</span>eval-string <span style="color: #4f97d7; font-weight: bold;">(</span>concat <span style="color: #2d9574; font-weight: bold;">"'"</span> bodystr<span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">28: </span>    `<span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">progn</span> ,body<span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">29: </span>
-<span class="linenr">30: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defmacro</span> <span style="color: #bc6ec5; font-weight: bold;">imacro/1</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>name<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">31: </span>  <span style="color: #2aa1ae; font-weight: bold;">"Does not evaluate. It merely generates code."</span>
-<span class="linenr">32: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">let*</span> <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #67b11d; font-weight: bold;">(</span>bodystr
-<span class="linenr">33: </span>          <span style="color: #b1951d; font-weight: bold;">(</span>car
-<span class="linenr">34: </span>           <span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">pen-single-generation</span>
-<span class="linenr">35: </span>            <span style="color: #bc6ec5; font-weight: bold;">(</span>pf-imagine-an-emacs-function/1
-<span class="linenr">36: </span>             name
-<span class="linenr">37: </span>             <span style="color: #4f97d7; font-weight: bold;">:include-prompt</span> t
-<span class="linenr">38: </span>             <span style="color: #4f97d7; font-weight: bold;">:no-select-result</span> t<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">39: </span>         <span style="color: #67b11d; font-weight: bold;">(</span>body <span style="color: #b1951d; font-weight: bold;">(</span>eval-string <span style="color: #4f97d7; font-weight: bold;">(</span>concat <span style="color: #2d9574; font-weight: bold;">"'"</span> bodystr<span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">40: </span>    `<span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">progn</span> ,body<span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">imacro/3</span> my/itimes <span style="color: #bc6ec5; font-weight: bold;">(</span>a b c<span style="color: #bc6ec5; font-weight: bold;">)</span> <span style="color: #2d9574; font-weight: bold;">"multiply three complex numbers"</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">progn</span>
-<span class="linenr">2: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">my-times</span>
-<span class="linenr">3: </span>      <span style="color: #2d9574; font-weight: bold;">(</span>x y z<span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">4: </span>    <span style="color: #2aa1ae; font-weight: bold;">"multiply three numbers and return a number"</span>
-<span class="linenr">5: </span>    <span style="color: #2d9574; font-weight: bold;">(</span>* x y z<span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<p>
-<i><code>imacro</code> expansion demo</i>
-</p>
-
-<!-- Play on asciinema.com -->
-<!-- <a title="asciinema recording" href="https://asciinema.org/a/TFjZGxMf0zhT59T7U3tO8uwY5" target="_blank"><img alt="asciinema recording" src="https://asciinema.org/a/TFjZGxMf0zhT59T7U3tO8uwY5.svg" /></a> -->
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-<script src="https://asciinema.org/a/TFjZGxMf0zhT59T7U3tO8uwY5.js" id="asciicast-TFjZGxMf0zhT59T7U3tO8uwY5" async></script>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">imacro/2</span> my/subtract <span style="color: #bc6ec5; font-weight: bold;">(</span>a b c<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">progn</span>
-<span class="linenr">2: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">my-subtract</span>
-<span class="linenr">3: </span>      <span style="color: #2d9574; font-weight: bold;">(</span>a b c<span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">4: </span>    <span style="color: #2aa1ae; font-weight: bold;">"Subtract B from A and return the result."</span>
-<span class="linenr">5: </span>    <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">setq</span> result
-<span class="linenr">6: </span>          <span style="color: #67b11d; font-weight: bold;">(</span>+ a
-<span class="linenr">7: </span>             <span style="color: #b1951d; font-weight: bold;">(</span>- b c<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">8: </span>    result<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">imacro/1</span> my/subtract<span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">progn</span>
-<span class="linenr">2: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">my-subtract</span>
-<span class="linenr">3: </span>      <span style="color: #2d9574; font-weight: bold;">(</span>a b<span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">4: </span>    <span style="color: #2aa1ae; font-weight: bold;">"Subtract A - B."</span>
-<span class="linenr">5: </span>    <span style="color: #2d9574; font-weight: bold;">(</span>- a b<span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<p>
-<code>defimacro</code>
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr"> 1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defmacro</span> <span style="color: #bc6ec5; font-weight: bold;">defimacro</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>name <span style="color: #ce537a; font-weight: bold;">&amp;rest</span> body<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr"> 2: </span>  <span style="color: #2aa1ae; font-weight: bold;">"Define imacro"</span>
-<span class="linenr"> 3: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">cond</span>
-<span class="linenr"> 4: </span>   <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #67b11d; font-weight: bold;">(</span>= <span style="color: #a45bad; font-weight: bold;">0</span> <span style="color: #b1951d; font-weight: bold;">(</span>length body<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr"> 5: </span>    `<span style="color: #67b11d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">imacro/1</span>
-<span class="linenr"> 6: </span>      ,name<span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr"> 7: </span>   <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #67b11d; font-weight: bold;">(</span>= <span style="color: #a45bad; font-weight: bold;">1</span> <span style="color: #b1951d; font-weight: bold;">(</span>length body<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr"> 8: </span>    `<span style="color: #67b11d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">imacro/2</span>
-<span class="linenr"> 9: </span>      ,name
-<span class="linenr">10: </span>      ,<span style="color: #b1951d; font-weight: bold;">(</span>car body<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">11: </span>   <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #67b11d; font-weight: bold;">(</span>= <span style="color: #a45bad; font-weight: bold;">2</span> <span style="color: #b1951d; font-weight: bold;">(</span>length body<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">12: </span>    `<span style="color: #67b11d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">imacro/3</span>
-<span class="linenr">13: </span>      ,name
-<span class="linenr">14: </span>      ,<span style="color: #b1951d; font-weight: bold;">(</span>car body<span style="color: #b1951d; font-weight: bold;">)</span>
-<span class="linenr">15: </span>      ,<span style="color: #b1951d; font-weight: bold;">(</span>cadr body<span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<p>
-All of the following are valid ways to invoke <code>defimacro</code>.
-</p>
-
-<p>
-<code>defimacro</code> selects the right <code>imacro/N</code> function depending on the arity of the arguments.
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defimacro</span> my/subtract<span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">2: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defimacro</span> my/subtract <span style="color: #bc6ec5; font-weight: bold;">(</span>a b c<span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">3: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defimacro</span> my/itimes <span style="color: #bc6ec5; font-weight: bold;">(</span>a b c<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">4: </span>   <span style="color: #2d9574; font-weight: bold;">"multiply three complex numbers"</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
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-</div>
-</div>
-
-<div id="outline-container-org991a118" class="outline-4">
-<h4 id="org991a118"><span class="section-number-4">1.2.5</span> <code>ilist</code></h4>
-<div class="outline-text-4" id="text-1-2-5">
-<p>
-The easiest of the list of syntax forms I
-aimed to implement, <code>ilist</code> simply takes a the
-number of items to generate (<code>n</code>) and a string
-describing the type of thing to generate
-(<code>type-of-thing</code>). It will return a real list
-of such things.
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">ilist</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>n type-of-thing<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">2: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">interactive</span> <span style="color: #2d9574; font-weight: bold;">(</span>list <span style="color: #67b11d; font-weight: bold;">(</span>read-string-hist <span style="color: #2d9574; font-weight: bold;">"ilist n: "</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">3: </span>                     <span style="color: #67b11d; font-weight: bold;">(</span>read-string-hist <span style="color: #2d9574; font-weight: bold;">"ilist type-of-thing: "</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">4: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">pen-single-generation</span> <span style="color: #2d9574; font-weight: bold;">(</span>pf-list-of/2 <span style="color: #67b11d; font-weight: bold;">(</span>str n<span style="color: #67b11d; font-weight: bold;">)</span> <span style="color: #67b11d; font-weight: bold;">(</span>str type-of-thing<span style="color: #67b11d; font-weight: bold;">)</span> <span style="color: #4f97d7; font-weight: bold;">:no-select-result</span> t<span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">5: </span>
-<span class="linenr">6: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">test-ilist</span> <span style="color: #bc6ec5; font-weight: bold;">()</span>
-<span class="linenr">7: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">interactive</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">8: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span>etv <span style="color: #2d9574; font-weight: bold;">(</span>pps <span style="color: #67b11d; font-weight: bold;">(</span>ilist <span style="color: #a45bad; font-weight: bold;">10</span> <span style="color: #2d9574; font-weight: bold;">"tennis players"</span><span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-</div>
-</div>
-
-<div id="outline-container-orgcc92450" class="outline-4">
-<h4 id="orgcc92450"><span class="section-number-4">1.2.6</span> <code>ifilter</code></h4>
-<div class="outline-text-4" id="text-1-2-6">
-<p>
-<code>ifilter</code> takes
-</p>
-
-<p>
-Example:
-</p>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span>pps <span style="color: #bc6ec5; font-weight: bold;">(</span>ifilter <span style="color: #2d9574; font-weight: bold;">"is male"</span> <span style="color: #2d9574; font-weight: bold;">(</span>ilist <span style="color: #a45bad; font-weight: bold;">10</span> <span style="color: #2d9574; font-weight: bold;">"tennis players"</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span>pps <span style="color: #bc6ec5; font-weight: bold;">(</span>ifilter <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">lambda</span> <span style="color: #67b11d; font-weight: bold;">(</span>e<span style="color: #67b11d; font-weight: bold;">)</span> <span style="color: #67b11d; font-weight: bold;">(</span>full-name-p e<span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span> <span style="color: #2d9574; font-weight: bold;">(</span>ilist <span style="color: #a45bad; font-weight: bold;">10</span> <span style="color: #2d9574; font-weight: bold;">"tennis players"</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-</div>
-</div>
-
-<div id="outline-container-orgc0741e7" class="outline-4">
-<h4 id="orgc0741e7"><span class="section-number-4">1.2.7</span> <code>itest</code></h4>
-<div class="outline-text-4" id="text-1-2-7">
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defmacro</span> <span style="color: #bc6ec5; font-weight: bold;">itest/m</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>predicate value<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">2: </span>  `<span style="color: #bc6ec5; font-weight: bold;">(</span>ieval
-<span class="linenr">3: </span>    `<span style="color: #2d9574; font-weight: bold;">(</span>my/test ,',value<span style="color: #2d9574; font-weight: bold;">)</span>
-<span class="linenr">4: </span>    `<span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">my/test</span> <span style="color: #67b11d; font-weight: bold;">(</span>x<span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">5: </span>       <span style="color: #67b11d; font-weight: bold;">(</span>apply ,',predicate
-<span class="linenr">6: </span>              x<span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">7: </span>
-<span class="linenr">8: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">itest</span> <span style="color: #bc6ec5; font-weight: bold;">(</span>predicate value<span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr">9: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span>eval `<span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">itest/m</span> ,predicate ,value<span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr"> 1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">test-itest-1</span> <span style="color: #bc6ec5; font-weight: bold;">()</span>
-<span class="linenr"> 2: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">interactive</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr"> 3: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span>etv
-<span class="linenr"> 4: </span>   <span style="color: #2d9574; font-weight: bold;">(</span>itest '<span style="color: #67b11d; font-weight: bold;">(</span>lambda <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">l</span><span style="color: #b1951d; font-weight: bold;">)</span> '<span style="color: #b1951d; font-weight: bold;">(</span>= <span style="color: #a45bad; font-weight: bold;">5</span> <span style="color: #4f97d7; font-weight: bold;">(</span>length l<span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr"> 5: </span>          '<span style="color: #67b11d; font-weight: bold;">(</span>a b c d<span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr"> 6: </span>
-<span class="linenr"> 7: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">test-itest-2</span> <span style="color: #bc6ec5; font-weight: bold;">()</span>
-<span class="linenr"> 8: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">interactive</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-<span class="linenr"> 9: </span>  <span style="color: #bc6ec5; font-weight: bold;">(</span>etv
-<span class="linenr">10: </span>   <span style="color: #2d9574; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">itest/m</span> <span style="color: #67b11d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">lambda</span> <span style="color: #b1951d; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">l</span><span style="color: #b1951d; font-weight: bold;">)</span> '<span style="color: #b1951d; font-weight: bold;">(</span>= <span style="color: #a45bad; font-weight: bold;">4</span> <span style="color: #4f97d7; font-weight: bold;">(</span>length l<span style="color: #4f97d7; font-weight: bold;">)</span><span style="color: #b1951d; font-weight: bold;">)</span><span style="color: #67b11d; font-weight: bold;">)</span>
-<span class="linenr">11: </span>            '<span style="color: #67b11d; font-weight: bold;">(</span>a b c d<span style="color: #67b11d; font-weight: bold;">)</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span><span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-</div>
-</div>
-
-<div id="outline-container-org937eb1e" class="outline-4">
-<h4 id="org937eb1e"><span class="section-number-4">1.2.8</span> Derived functions</h4>
-<div class="outline-text-4" id="text-1-2-8">
-<ul class="org-ul">
-<li><i>get-backstory</i></li>
-</ul>
-</div>
-</div>
-
-<div id="outline-container-org7cfef8c" class="outline-4">
-<h4 id="org7cfef8c"><span class="section-number-4">1.2.9</span> <code>iparse</code></h4>
-</div>
-
-<div id="outline-container-orgbb895b8" class="outline-4">
-<h4 id="orgbb895b8"><span class="section-number-4">1.2.10</span> <code>itransform</code></h4>
-<div class="outline-text-4" id="text-1-2-10">
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span><span style="color: #4f97d7; font-weight: bold;">defun</span> <span style="color: #bc6ec5; font-weight: bold;">get-backstory</span> <span style="color: #bc6ec5; font-weight: bold;">()</span>
-<span class="linenr">2: </span>
-<span class="linenr">3: </span>  <span style="color: #4f97d7; font-weight: bold;">)</span>
-<span class="linenr">4: </span><span style="color: #4f97d7; font-weight: bold;">(</span>itransform<span style="color: #4f97d7; font-weight: bold;">)</span>
-</pre>
-</div>
-
-<div class="org-src-container">
-<pre class="src src-emacs-lisp"><span class="linenr">1: </span><span style="color: #4f97d7; font-weight: bold;">(</span>pps <span style="color: #bc6ec5; font-weight: bold;">(</span>mapcar 'get-backstory <span style="color: #2d9574; font-weight: bold;">(</span>ilist <span style="color: #a45bad; font-weight: bold;">10</span> <span style="color: #2d9574; font-weight: bold;">"tennis players"</span><span style="color: #2d9574; font-weight: bold;">)</span><span style="color: #bc6ec5; font-weight: bold;">)</span>
-</pre>
-</div>
-</div>
-</div>
-</div>
-
-<div id="outline-container-org32d2f09" class="outline-3">
-<h3 id="org32d2f09"><span class="section-number-3">1.3</span> <span class="todo TODO">TODO</span> Pure imaginary syntax forms</h3>
-<div class="outline-text-3" id="text-1-3">
-<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
-
-
-<colgroup>
-<col  class="org-left" />
-</colgroup>
-<thead>
-<tr>
-<th scope="col" class="org-left">name</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td class="org-left">imaginary comparator</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-</div>
-</div>
-<div id="postamble" class="status">
-<p class="date">Date: 2021-09-03 Fri 00:00</p>
-<p class="author">Author: Shane Mulligan</p>
-<p class="date">Created: 2022-06-13 Mon 14:40</p>
-<p class="validation"><a href="https://validator.w3.org/check?uri=referer">Validate</a></p>
-</div>
-</body>
-</html>
\ No newline at end of file
diff --git a/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/geometry.py b/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/geometry.py
deleted file mode 100644
index 5e88b38602ae00d9c20343f21efb019b8fba1cc0..0000000000000000000000000000000000000000
--- a/spaces/nasttam/Image-and-3D-Model-Creator/PIFu/lib/geometry.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import torch
-
-
-def index(feat, uv):
-    '''
-
-    :param feat: [B, C, H, W] image features
-    :param uv: [B, 2, N] uv coordinates in the image plane, range [-1, 1]
-    :return: [B, C, N] image features at the uv coordinates
-    '''
-    uv = uv.transpose(1, 2)  # [B, N, 2]
-    uv = uv.unsqueeze(2)  # [B, N, 1, 2]
-    # NOTE: for newer PyTorch, it seems that training results are degraded due to implementation diff in F.grid_sample
-    # for old versions, simply remove the aligned_corners argument.
-    samples = torch.nn.functional.grid_sample(feat, uv, align_corners=True)  # [B, C, N, 1]
-    return samples[:, :, :, 0]  # [B, C, N]
-
-
-def orthogonal(points, calibrations, transforms=None):
-    '''
-    Compute the orthogonal projections of 3D points into the image plane by given projection matrix
-    :param points: [B, 3, N] Tensor of 3D points
-    :param calibrations: [B, 4, 4] Tensor of projection matrix
-    :param transforms: [B, 2, 3] Tensor of image transform matrix
-    :return: xyz: [B, 3, N] Tensor of xyz coordinates in the image plane
-    '''
-    rot = calibrations[:, :3, :3]
-    trans = calibrations[:, :3, 3:4]
-    pts = torch.baddbmm(trans, rot, points)  # [B, 3, N]
-    if transforms is not None:
-        scale = transforms[:2, :2]
-        shift = transforms[:2, 2:3]
-        pts[:, :2, :] = torch.baddbmm(shift, scale, pts[:, :2, :])
-    return pts
-
-
-def perspective(points, calibrations, transforms=None):
-    '''
-    Compute the perspective projections of 3D points into the image plane by given projection matrix
-    :param points: [Bx3xN] Tensor of 3D points
-    :param calibrations: [Bx4x4] Tensor of projection matrix
-    :param transforms: [Bx2x3] Tensor of image transform matrix
-    :return: xy: [Bx2xN] Tensor of xy coordinates in the image plane
-    '''
-    rot = calibrations[:, :3, :3]
-    trans = calibrations[:, :3, 3:4]
-    homo = torch.baddbmm(trans, rot, points)  # [B, 3, N]
-    xy = homo[:, :2, :] / homo[:, 2:3, :]
-    if transforms is not None:
-        scale = transforms[:2, :2]
-        shift = transforms[:2, 2:3]
-        xy = torch.baddbmm(shift, scale, xy)
-
-    xyz = torch.cat([xy, homo[:, 2:3, :]], 1)
-    return xyz
diff --git a/spaces/nathanTQ/ChatDev/camel/agents/embodied_agent.py b/spaces/nathanTQ/ChatDev/camel/agents/embodied_agent.py
deleted file mode 100644
index a9bf44872d25216f70296df5ccf9aeecf0ed22b1..0000000000000000000000000000000000000000
--- a/spaces/nathanTQ/ChatDev/camel/agents/embodied_agent.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the “License”);
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an “AS IS” BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-from typing import Any, Dict, List, Optional, Tuple
-
-from colorama import Fore
-
-from camel.agents import BaseToolAgent, ChatAgent, HuggingFaceToolAgent
-from camel.messages import ChatMessage, SystemMessage
-from camel.typing import ModelType
-from camel.utils import print_text_animated
-
-
-class EmbodiedAgent(ChatAgent):
-    r"""Class for managing conversations of CAMEL Embodied Agents.
-
-    Args:
-        system_message (SystemMessage): The system message for the chat agent.
-        model (ModelType, optional): The LLM model to use for generating
-            responses. (default :obj:`ModelType.GPT_4`)
-        model_config (Any, optional): Configuration options for the LLM model.
-            (default: :obj:`None`)
-        message_window_size (int, optional): The maximum number of previous
-            messages to include in the context window. If `None`, no windowing
-            is performed. (default: :obj:`None`)
-        action_space (List[Any], optional): The action space for the embodied
-            agent. (default: :obj:`None`)
-        verbose (bool, optional): Whether to print the critic's messages.
-        logger_color (Any): The color of the logger displayed to the user.
-            (default: :obj:`Fore.MAGENTA`)
-    """
-
-    def __init__(
-        self,
-        system_message: SystemMessage,
-        model: ModelType = ModelType.GPT_4,
-        model_config: Optional[Any] = None,
-        message_window_size: Optional[int] = None,
-        action_space: Optional[List[BaseToolAgent]] = None,
-        verbose: bool = False,
-        logger_color: Any = Fore.MAGENTA,
-    ) -> None:
-        default_action_space = [
-            HuggingFaceToolAgent('hugging_face_tool_agent', model=model.value),
-        ]
-        self.action_space = action_space or default_action_space
-        action_space_prompt = self.get_action_space_prompt()
-        system_message.content = system_message.content.format(
-            action_space=action_space_prompt)
-        self.verbose = verbose
-        self.logger_color = logger_color
-        super().__init__(
-            system_message=system_message,
-            model=model,
-            model_config=model_config,
-            message_window_size=message_window_size,
-        )
-
-    def get_action_space_prompt(self) -> str:
-        r"""Returns the action space prompt.
-
-        Returns:
-            str: The action space prompt.
-        """
-        return "\n".join([
-            f"*** {action.name} ***:\n {action.description}"
-            for action in self.action_space
-        ])
-
-    def step(
-        self,
-        input_message: ChatMessage,
-    ) -> Tuple[ChatMessage, bool, Dict[str, Any]]:
-        r"""Performs a step in the conversation.
-
-        Args:
-            input_message (ChatMessage): The input message.
-
-        Returns:
-            Tuple[ChatMessage, bool, Dict[str, Any]]: A tuple
-                containing the output messages, termination status, and
-                additional information.
-        """
-        response = super().step(input_message)
-
-        if response.msgs is None or len(response.msgs) == 0:
-            raise RuntimeError("Got None output messages.")
-        if response.terminated:
-            raise RuntimeError(f"{self.__class__.__name__} step failed.")
-
-        # NOTE: Only single output messages are supported
-        explanations, codes = response.msg.extract_text_and_code_prompts()
-
-        if self.verbose:
-            for explanation, code in zip(explanations, codes):
-                print_text_animated(self.logger_color +
-                                    f"> Explanation:\n{explanation}")
-                print_text_animated(self.logger_color + f"> Code:\n{code}")
-
-            if len(explanations) > len(codes):
-                print_text_animated(self.logger_color +
-                                    f"> Explanation:\n{explanations}")
-
-        content = response.msg.content
-
-        if codes is not None:
-            content = "\n> Executed Results:"
-            global_vars = {action.name: action for action in self.action_space}
-            for code in codes:
-                executed_outputs = code.execute(global_vars)
-                content += (
-                    f"- Python standard output:\n{executed_outputs[0]}\n"
-                    f"- Local variables:\n{executed_outputs[1]}\n")
-                content += "*" * 50 + "\n"
-
-        # TODO: Handle errors
-        content = input_message.content + (Fore.RESET +
-                                           f"\n> Embodied Actions:\n{content}")
-        message = ChatMessage(input_message.role_name, input_message.role_type,
-                              input_message.meta_dict, input_message.role,
-                              content)
-        return message, response.terminated, response.info
diff --git a/spaces/nathanTQ/ChatDev/online_log/static/replay/js/app.js b/spaces/nathanTQ/ChatDev/online_log/static/replay/js/app.js
deleted file mode 100644
index 0d078d7ff3d265549c21f88702160eaf285d2dce..0000000000000000000000000000000000000000
--- a/spaces/nathanTQ/ChatDev/online_log/static/replay/js/app.js
+++ /dev/null
@@ -1,575 +0,0 @@
-const coordSet = [];
-coordSet["Chief Executive Officer"] = {
-    "character": "Chief Executive Officer",
-    "imgid": "right",
-    "top": "-315px",
-    "left": "280px"
-};
-coordSet["Chief Product Officer"] = {
-    "character": "Chief Product Officer",
-    "imgid": "left",
-    "top": "-165px",
-    "left": "110px"
-};
-coordSet["Chief Human Resource Officer"] = {
-    "character": "Chief Human Resource Officer",
-    "imgid": "left",
-    "top": "-305px",
-    "left": "55px"
-};
-coordSet["Code Reviewer"] = {
-    "character": "Code Reviewer",
-    "imgid": "left",
-    "top": "-185px",
-    "left": "500px"
-};
-coordSet["Programmer"] = {
-    "character": "Programmer",
-    "imgid": "right",
-    "top": "-80px",
-    "left": "300px"
-};
-coordSet["Chief Technology Officer"] = {
-    "character": "Chief Technology Officer",
-    "imgid": "right",
-    "top": "-130px",
-    "left": "340px"
-};
-coordSet["Chief Creative Officer"] = {
-    "character": "Chief Creative Officer",
-    "imgid": "right",
-    "top": "-95px",
-    "left": "205px"
-}
-coordSet["Software Test Engineer"] = {
-    "character": "Software Test Engineer",
-    "imgid": "right",
-    "top": "-90px",
-    "left": "470px"
-
-}
-coordSet["User"] = {
-    "character": "User",
-    "imgid": "left",
-    "top": "-465px",
-    "left": "125px"
-}
-coordSet["Counselor"] = {
-    "character": "Counselor",
-    "imgid": "right",
-    "top": "-360px",
-    "left": "420px"
-}
-coordSet["Prompt Engineer"] = {
-    "character": "Prompt Engineer",
-    "imgid": "right",
-    "top": "-320px",
-    "left": "20px"
-}
-const Softwareinfo = {
-    "duration": "-1",
-    "cost": "-1",
-    "version_updates": "-1",
-    "num_code_files": "-1",
-    "num_png_files": "-1",
-    "num_doc_files": "-1",
-    "code_lines": "-1",
-    "env_lines": "-1",
-    "manual_lines": "-1",
-    "num_utterances": "-1",
-    "num_self_reflections": "-1",
-    "num_prompt_tokens": "-1",
-    "num_completion_tokens": "-1",
-    "num_total_tokens": "-1",
-};
-
-//control chars appear speed
-var timeinterval = 5;
-var charinterval = 1;
-var scrollinterval = 40;
-
-var contents;
-var filename;
-var curdialog = '';
-var total_height = 0;
-
-var cur_para = '';
-var cur_command = '';
-var idx = 0;
-var dialog;
-
-var replaying = 0;
-var if_stop = 0;
-let isPaused = false;
-let pauseIntervalId;
-var if_move = true;
-var md = window.markdownit();
-
-//watch replay button clicked
-const button = document.getElementById('replay');
-button.addEventListener('click', () => {
-    replayDialog(idx);
-});
-$(document).ready(function() {
-    $('#filebutton').click(function() {
-        $('#fileInput').click();
-    });
-
-});
-
-const dialogbody = document.getElementById("dialogBody");
-dialogbody.addEventListener("mousewheel", handleMouseWheel, false);
-
-function handleMouseWheel(event) {
-    if (event.wheelDelta > 0) {
-        if_move = false;
-    } else if (event.wheelDelta < 0) {
-        if (dialogbody.scrollTop + dialogbody.clientHeight == dialogbody.scrollHeight) {
-            if_move = true;
-        }
-    }
-}
-
-function getinterval(speed) {
-
-    if (speed < 80 && speed > 40) {
-        timeinterval = 250 / speed;
-        charinterval = 2;
-        scrollinterval = 80;
-    } else if (speed <= 40 && speed > 0) {
-        timeinterval = 150 / speed;
-        charinterval = 1;
-        scrollinterval = 80;
-    } else if (speed >= 80 && speed < 90) {
-        timeinterval = 100 / speed;
-        charinterval = 1;
-        scrollinterval = 100;
-    } else if (speed >= 90 && speed <= 100) {
-        timeinterval = 5 / speed;
-        charinterval = 1;
-        scrollinterval = 400;
-    }
-}
-//use the slider to control the replay speed
-function speedchange() {
-    var speedbar = document.getElementById("speed");
-    var speed = speedbar.value;
-    if (speed == 0) {
-        if (!isPaused) {
-            isPaused = true;
-            clearInterval(pauseIntervalId);
-            updateCompanyWorking("end");
-        }
-    } else if (speed != 0 && isPaused == true) {
-        getinterval(speed);
-        isPaused = false;
-        idx += 1;
-        replayDialog(idx);
-    } else if (speed != 0) {
-        isPaused = false;
-        getinterval(speed);
-    }
-}
-// do replay
-async function replayDialog(idx) {
-    if (replaying == 1 && idx == 0) {
-        return;
-    }
-    if (idx == 0) {
-        replaying = 1;
-        dialog = extraction(contents);
-        var filelable = document.getElementById("successupload");
-        filelable.style.display = "block";
-        var info = "Replaying `" + filename + "` ......";
-        filelable.innerHTML = md.render(info);
-    }
-    for (let i = idx; i < dialog.length; ++i) {
-        await createPara(dialog[i], i);
-    }
-}
-
-//watch .log file input
-function watchfileInput(files) {
-    if (files.length) {
-        const file = files[0];
-        if (file) {
-            const reader = new FileReader();
-            reader.onload = function() {
-                contents = this.result;
-            };
-            reader.readAsText(file);
-            var filelable = document.getElementById("successupload");
-            filelable.style.display = "block";
-            var info = "File uploaded (`" + file.name + "`). Please click **\"Replay\"** to show ChatDev's development process";
-            filename = file.name;
-            filelable.innerHTML = md.render(info);
-        }
-    }
-}
-
-//extract information 
-function extraction(contents) {
-    const regex = /\[(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2} \w+)\] ([.\s\S\n\r\d\D\t]*?)(?=\n\[\d|$)/g;
-
-    var matches = [];
-
-    let match;
-    var itemp = 0;
-    while ((match = regex.exec(contents))) {
-        console.log(itemp);
-        itemp++;
-        const timestamp = match[1];
-        const text = match[2];
-        matches.push({
-            timestamp,
-            text
-        });
-    }
-    const regex_assistant = /(.*):([.\r\n\s\S\t\d\D]*)<->([.\r\n\s\S\t\d\D]*?)\]([.\r\n\s\S\t\d\D]*)/g;
-    const regex_user = /(.*):(.*)(\[Start Chat\])([.\r\n\s\S\t\d\D]*?)\]([.\r\n\s\S\t\d\D]*)/g;
-    const regex_prompt = /(Prompt Engineer): "([.\s\S\d\D]*)"/g
-
-    const regex_end = /(AgentTech Ends|ChatDev Ends)/g;
-    const regex_start = /(ChatDev Starts)([\D\s])*(\d*)/g;
-
-    const regex_task = /(task_prompt)(.*):(.*)/g;
-    const regex_info = /Software Info([\r\n\s\S\t\d\D]*)/g;
-
-    const regex_system = /System/g;
-    const regex_debug = /DEBUG/g;
-
-    var dialog = [];
-    var count = 0;
-
-    for (let i = 0; i < matches.length; ++i) {
-        var if_break = false;
-        console.log(i);
-        if (i == 159 || i == 198 || i == 223 || i == 260 || i == 416 || i == 537) {
-            //console.log(matches[i]);
-        }
-        while ((match = regex_debug.exec(matches[i].timestamp)) !== null) {
-            if_break = true;
-        }
-        while ((match = regex_system.exec(matches[i].text)) !== null) {
-            if_break = true;
-        }
-        while (((match = regex_prompt.exec(matches[i].text)) !== null)) {
-            const type = "assitant";
-            const character = match[1];
-            const command = match[2];
-            const len = match[2].length;
-            count += 1;
-            dialog.push({
-                type,
-                character,
-                command,
-                len,
-                count
-            });
-            if_break = true;
-        }
-        if (if_break) {
-            continue;
-        }
-
-        while ((match = regex_assistant.exec(matches[i].text)) !== null) {
-            const type = "assitant";
-            const character = match[1];
-            const command = match[4];
-            const len = match[4].length;
-            count += 1;
-            dialog.push({
-                type,
-                character,
-                command,
-                len,
-                count
-            });
-
-        }
-        while ((match = regex_user.exec(matches[i].text)) !== null) {
-            const type = "user";
-            const character = match[1];
-            const command = match[5];
-            const len = match[5].length;
-            count += 1;
-            dialog.push({
-                type,
-                character,
-                command,
-                len,
-                count
-            });
-        }
-        while ((match = regex_start.exec(matches[i].text)) !== null) {
-            const start = match[1];
-            const len = match[1].length;
-            dialog.push({
-                start,
-                len,
-            });
-
-        }
-        while ((match = regex_end.exec(matches[i].text)) !== null) {
-            const end = match[1];
-            const len = match[1].length;
-            dialog.push({
-                end,
-                len,
-            });
-
-        }
-        while ((match = regex_task.exec(matches[i].text)) !== null) {
-            const task = match[3];
-            dialog.push({
-                task
-            });
-
-        }
-        while ((match = regex_info.exec(matches[i].text)) !== null) {
-            const info = match[1];
-            if ((/code_lines(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.code_lines = (/code_lines(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_code_files(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_code_files = (/num_code_files(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_png_files(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_png_files = (/num_png_files(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_doc_files(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_doc_files = (/num_doc_files(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/env_lines(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.env_lines = (/env_lines(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/manual_lines(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.manual_lines = (/manual_lines(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/duration(?:[\t\n\r\s\D]*?)=(-?(\d*)(.(\d)*)?s)/g).exec(info) != null) {
-                Softwareinfo.duration = (/duration(?:[\t\n\r\s\D]*?)=(-?(\d*)(.(\d)*)?s)/g).exec(info)[1];
-            }
-            if ((/num_utterances(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_utterances = (/num_utterances(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_self_reflections(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_self_reflections = (/num_self_reflections(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_prompt_tokens(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_prompt_tokens = (/num_prompt_tokens(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_completion_tokens(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_completion_tokens = (/num_completion_tokens(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/num_total_tokens(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info) != null) {
-                Softwareinfo.num_total_tokens = (/num_total_tokens(?:[\t\n\r\s\D]*?)=(-?(\d*))/g).exec(info)[1];
-            }
-            if ((/cost(?:[\t\n\r\s\D]*?)=(.((\d)*\.(\d)*))/g).exec(info) != null) {
-                Softwareinfo.cost = (/cost(?:[\t\n\r\s\D]*?)=(.((\d)*\.(\d)*))/g).exec(info)[1];
-            }
-            if ((/version_updates(?:[\t\n\r\s\D]*?)=(-?\d*)/g).exec(info) != null) {
-                Softwareinfo.version_updates = (/version_updates(?:[\t\n\r\s\D]*?)=(-?\d*)/g).exec(info)[1];
-            }
-
-            dialog.push({
-                info,
-                Softwareinfo
-            });
-
-        }
-    }
-    return dialog;
-}
-
-//show dailog 
-function createPara(d, i) {
-    const singleDialog = document.createElement("div");
-    singleDialog.style.position = "relative";
-    curdialog = singleDialog;
-    singleDialog.style.display = "flex";
-    singleDialog.style.flexDirection = "column";
-    singleDialog.style.width = "773px";
-    dialogbody.appendChild(singleDialog);
-    var paralen;
-    if (d.type && d.character) {
-        updateCompanyWorking(d.character);
-        var renderedHtml = md.render(d.character);
-        const character = document.createElement("div");
-        character.style.display = "flex";
-
-        character.style.backgroundColor = "lightblue";
-        character.style.width = "fit-content";
-        character.style.fontSize = "13px ";
-        character.style.border = "1px solid rgba(11, 20, 150, .3)";
-        character.style.borderRadius = "10px";
-        character.style.boxShadow = "2px 2px 2px black";
-        character.style.fontFamily = "'Lucida Sans', 'Lucida Sans Regular', 'Lucida Grande', 'Lucida Sans Unicode', Geneva, Verdana, sans-serif;";
-
-        if (d.type == "user") {
-            character.style.position = "relative";
-            character.style.marginLeft = "auto";
-        }
-        character.innerHTML = renderedHtml;
-        singleDialog.appendChild(character);
-
-        const characterimg = document.createElement("img");
-        console.log(d.character);
-        if (d.character == "Programmer") {
-            characterimg.src = "figures/programmer.png";
-        } else if (d.character == "Code Reviewer") {
-            characterimg.src = "figures/reviewer.png";
-        } else if (d.character == "Chief Human Resource Officer") {
-            characterimg.src = "figures/hr.png";
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-            characterimg.src = "figures/ceo.png";
-        } else if (d.character == "Chief Product Officer") {
-            characterimg.src = "figures/cpo.png";
-        } else if (d.character == "Chief Technology Officer") {
-            characterimg.src = "figures/cto.png";
-        } else if (d.character == "Chief Creative Officer") {
-            characterimg.src = "figures/designer.png";
-        } else if (d.character == "Software Test Engineer") {
-            characterimg.src = "figures/tester.png";
-        } else if (d.character == "User") {
-            characterimg.src = "figures/user.png";
-        } else if (d.character == "Counselor") {
-            characterimg.src = "figures/counselor.png";
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-            characterimg.src = "figures/pe.png";
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-
-        characterimg.style.height = "40px";
-        characterimg.style.width = "30px";
-        characterimg.style.position = "relative";
-        character.appendChild(characterimg);
-        character.style.width = "fit-content";
-
-
-        var renderedHtml = md.render(d.command);
-        const paragraph = document.createElement("div");
-        paragraph.className = "markdown-body";
-        //paragraph.innerHTML = renderedHtml;
-        paragraph.style.padding = "10px";
-        paragraph.style.border = "3px solid #a08D8D";
-        paragraph.style.width = "750px";
-        paragraph.style.border = "1px solid rgba(11, 20, 150, .3)";
-        paragraph.style.borderRadius = "10px";
-        paragraph.style.boxShadow = "2px 2px 2px black";
-
-        singleDialog.appendChild(paragraph);
-
-        const emptyparagraph = document.createElement("div");
-        emptyparagraph.style.height = "10px";
-        singleDialog.appendChild(emptyparagraph);
-
-        if (d.type == "user") {
-            paragraph.style.backgroundColor = "#4b751a";
-        } else {
-            paragraph.style.backgroundColor = "#133153";
-        }
-        cur_command = d.command;
-        cur_para = paragraph;
-        idx = i;
-        return Promise.resolve(printCommand(paragraph, d.command));
-
-    } else if (d.start) {
-        paralen = 0;
-        var renderedHtml = md.render("----------" + d.start + "----------");
-        const starttext = document.createElement("div");
-        starttext.innerHTML = renderedHtml;
-        singleDialog.appendChild(starttext);
-
-    } else if (d.end) {
-        paralen = 0;
-        updateCompanyWorking("end");
-        var renderedHtml = md.render("----------" + d.end + "----------");
-        const endtext = document.createElement("div");
-        endtext.innerHTML = renderedHtml;
-        singleDialog.appendChild(endtext);
-        var filelable = document.getElementById("successupload");
-        filelable.style.display = "block";
-        var info = "Replayed";
-        filelable.innerHTML = md.render(info);
-    } else if (d.task) {
-        var renderedHtml = md.render("Task:    " + d.task);
-        const tasktext = document.getElementById("Requesttext");
-        tasktext.innerHTML = renderedHtml;
-    } else if (d.info) {
-        var renderedHtml = md.render(d.info);
-        const infotext = document.getElementById("dialogStatistic");
-        var temp_label = "";
-        for (var c in Softwareinfo) {
-            temp_label = document.getElementById(c);
-            if (Softwareinfo[c] != "-1" && Softwareinfo[c] != "-1s") {
-                temp_label.innerHTML = Softwareinfo[c];
-            }
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-
-//update company image
-function updateCompanyWorking(character) {
-    if (character == "end") {
-        var img1 = document.getElementById("right");
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-    var top_bias = coordSet[character].top;
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-
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-
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-        var another_img = document.getElementById("right");
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-    } else {
-        var another_img = document.getElementById("left");
-        another_img.style.display = "none";
-    }
-}
-
-async function updateParashow(container, command, index, len) {
-    var cur_content;
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-        if (curdialog != null && curdialog != '') {
-            const newBoxRect = curdialog.getBoundingClientRect();
-            total_height += newBoxRect.height;
-            dialogbody.scrollTo({ top: total_height, behavior: 'smooth' });
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-}
-
-async function printCommand(paragraph, command) {
-    var paralen = command.length;
-    const tasks = [];
-
-    for (let j = 0; j < paralen; j = j + charinterval) {
-        tasks.push(new Promise(resolve => {
-            pauseIntervalId = setTimeout(() => {
-                updateParashow(paragraph, command, j, paralen);
-                resolve();
-            }, timeinterval * j);
-        }));
-
-        if (isPaused) {
-            await Promise.all(tasks);
-        }
-    }
-    await Promise.all(tasks);
-    return 1;
-}
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Arturia Cs 80 V2 Keygen Torrent.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Arturia Cs 80 V2 Keygen Torrent.md
deleted file mode 100644
index af2df9237e3c28f9368603606985e5f3b5045832..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Arturia Cs 80 V2 Keygen Torrent.md	
+++ /dev/null
@@ -1,83 +0,0 @@
-<br />
-<h1>Arturia CS 80 V2 Keygen Torrent: How to Download and Install the Legendary Synth Plugin</h1>
- <p>If you are a fan of synthesizers, you have probably heard of Arturia CS 80 V2, the virtual recreation of the legendary Yamaha CS-80, which was considered by many as the "ultimate polyphonic synthesizer" in the late 70s and early 80s. It has been used by artists and groups like Toto, Jean-Michel Jarre, Keith Emerson, Stevie Wonder, Vangelis, Ultravox, Peter Gabriel, and many more, who helped to turn this synthesizer into a real myth.</p>
-<h2>arturia cs 80 v2 keygen torrent</h2><br /><p><b><b>Download Zip</b> &#10003; <a href="https://urlcod.com/2uIaFP">https://urlcod.com/2uIaFP</a></b></p><br /><br />
- <p>Arturia CS 80 V2 is a software plugin that emulates all the original parameters of the Yamaha CS-80, such as 2 oscillators, 4 filters, 2 LFOs, 4 envelopes, 2 VCAs, 1 sub-oscillator, 1 ring modulator, chorus and tremolo. It also adds some new features, such as a delay effect, a multi mode that allows you to assign a different sound to each of the 8 voices of polyphony, a modulation matrix with 12 sources and 38 destinations, polyphonic aftertouch, an arpeggiator, expression and sustain pedals, and a ribbon controller.</p>
- <p>Arturia CS 80 V2 is not a cheap plugin, though. It costs $199 on the official website of Arturia. However, there is a way to get it for free: by using a keygen torrent. A keygen torrent is a file that contains both the software installer and a key generator that can create a license for the software. By downloading and installing a keygen torrent, you can bypass the official activation process and use the software without paying anything.</p>
- <p>But is it worth it? What are the risks and benefits of using a keygen torrent to get Arturia CS 80 V2? In this article, we will show you how to download and install Arturia CS 80 V2 keygen torrent, how to use it, and what are some tips and best practices for using it. We will also answer some frequently asked questions about Arturia CS 80 V <h2>How to Download Arturia CS 80 V2 Keygen Torrent</h2>
- <p>The first step to get Arturia CS 80 V2 keygen torrent is to find a reliable and safe source for the keygen torrent file. There are many websites that offer keygen torrents for various software, but not all of them are trustworthy. Some of them may contain malware, viruses, spyware, or other harmful programs that can damage your computer or steal your personal information. Therefore, you should be careful and do some research before downloading any keygen torrent file.</p>
- <p>One way to find a good source for Arturia CS 80 V2 keygen torrent is to use a search engine like Google or Bing and type in keywords like "arturia cs 80 v2 keygen torrent", "arturia cs 80 v2 crack torrent", "arturia cs 80 v2 free download torrent", etc. You will get many results, but you should look for the ones that have positive ratings, comments, and feedback from other users. You can also check the reputation and credibility of the website by looking at its domain name, design, content, and security certificates.</p>
-<p></p>
- <p>Another way to find a good source for Arturia CS 80 V2 keygen torrent is to use a torrent aggregator website like The Pirate Bay, Kickass Torrents, 1337x, RARBG, etc. These websites collect and index keygen torrents from various sources and allow you to search and download them easily. However, you should also be careful and check the quality and safety of the keygen torrents by looking at their file size, seeders, leechers, comments, and ratings. You should also use a VPN (virtual private network) service to hide your IP address and protect your online privacy when using these websites.</p>
- <p>Once you have found a good source for Arturia CS 80 V2 keygen torrent, you need to download the keygen torrent file to your computer. To do this, you need to use a torrent client software like uTorrent, BitTorrent, qBittorrent, etc. These software allow you to connect to other peers who have the same keygen torrent file and download it from them. You should also make sure that your antivirus software is up to date and scan the keygen torrent file before opening it.</p>
- <p>After downloading the keygen torrent file, you need to verify its integrity and authenticity. This means that you need to check if the keygen torrent file is not corrupted or tampered with by anyone. To do this, you need to use a hash checker software like HashTab, HashCheck Shell Extension, RapidCRC Unicode, etc. These software allow you to compare the hash value of the keygen torrent file with the original hash value provided by the source website. The hash value is a unique code that identifies the content of the file. If the hash values match, it means that the keygen torrent file is intact and genuine. If they don't match, it means that the keygen torrent file is corrupted or fake and you should delete it immediately.</p> <h2>How to Install Arturia CS 80 V2 Keygen Torrent</h2>
- <p>The next step to get Arturia CS 80 V2 keygen torrent is to install it on your computer. To do this, you need to extract the keygen torrent file and run the setup.exe file. The keygen torrent file is usually a compressed file that contains the software installer and the key generator. You need to use a file extractor software like WinRAR, 7-Zip, PeaZip, etc. to unzip the keygen torrent file and access its contents.</p>
- <p>After extracting the keygen torrent file, you need to run the setup.exe file and follow the instructions on the screen. The setup.exe file is the software installer that will install Arturia CS 80 V2 on your computer. You need to choose the destination folder, accept the terms and conditions, and click on the install button. The installation process may take a few minutes, depending on your computer speed and system configuration.</p>
- <p>After installing Arturia CS 80 V2, you need to use the keygen to generate a license for it. The keygen is a program that can create a valid license for Arturia CS 80 V2 by using a mathematical algorithm. You need to run the keygen.exe file and click on the generate button. The keygen will produce a license file that you need to save on your computer.</p>
- <p>After generating the license file, you need to activate Arturia CS 80 V2 with it. The activation process is different depending on whether you want to use Arturia CS 80 V2 as a standalone application or as a plugin in your digital audio workstation (DAW). If you want to use Arturia CS 80 V2 as a standalone application, you need to launch it and go to the menu bar and click on Help > Register. Then, you need to browse and select the license file that you generated with the keygen and click on OK. If you want to use Arturia CS 80 V2 as a plugin in your DAW, you need to launch your DAW and scan for new plugins. Then, you need to insert Arturia CS 80 V2 as an instrument track and go to its interface and click on the lock icon. Then, you need to browse and select the license file that you generated with the keygen and click on OK.</p> <h2>How to Use Arturia CS 80 V2 Keygen Torrent</h2>
- <p>The final step to get Arturia CS 80 V2 keygen torrent is to use it and enjoy its features and sounds. To do this, you need to launch Arturia CS 80 V2 as a standalone application or as a plugin in your DAW. You can also use a MIDI keyboard or controller to play and control Arturia CS 80 V2.</p>
- <p>When you launch Arturia CS 80 V2, you will see its interface, which resembles the original Yamaha CS-80. You will see two sets of eight sliders, each corresponding to one of the eight voices of polyphony. You can use these sliders to adjust the volume, pitch, filter, and envelope parameters of each voice. You will also see two sets of four buttons, each corresponding to one of the four presets of each voice. You can use these buttons to switch between the presets and create different combinations of sounds.</p>
- <p>On the top right corner of the interface, you will see the multi mode button, which allows you to assign a different sound to each of the eight voices of polyphony. You can use this feature to create complex and rich layers of sounds. On the top left corner of the interface, you will see the delay effect button, which allows you to add a delay effect to the output signal. You can use this feature to create spatial and rhythmic effects.</p>
- <p>On the bottom right corner of the interface, you will see the modulation matrix button, which allows you to access the modulation matrix. The modulation matrix is a powerful feature that allows you to modulate any parameter of Arturia CS 80 V2 with any source. You can choose from 12 sources and 38 destinations and create custom modulations. You can use this feature to create dynamic and expressive sounds.</p>
- <p>On the bottom left corner of the interface, you will see the preset browser button, which allows you to access the preset browser. The preset browser is a feature that allows you to browse, load, save, and manage presets for Arturia CS 80 V2. You can choose from hundreds of presets that are categorized by type, style, genre, and mood. You can also create your own presets and store them in your user folder.</p>
- <p>To create your own sounds and patches with Arturia CS 80 V2, you need to use the edit mode button, which is located on the top center of the interface. The edit mode button allows you to access the edit mode, which is a feature that allows you to edit and tweak every parameter of Arturia CS 80 V2 in detail. You can use this feature to shape your sound according to your preferences and needs.</p> <h2>Conclusion</h2>
- <p>In this article, we have shown you how to download and install Arturia CS 80 V2 keygen torrent, how to use it, and what are some tips and best practices for using it. We have also answered some frequently asked questions about Arturia CS 80 V2.</p>
- <p>Arturia CS 80 V2 is a great plugin that recreates the legendary Yamaha CS-80 synthesizer, which was used by many famous artists and producers in the past. It has all the original parameters of the Yamaha CS-80, as well as some new features that enhance its functionality and versatility. It is a powerful and expressive instrument that can create a wide range of sounds, from warm and lush pads, to bright and crisp leads, to complex and rich textures.</p>
- <p>However, Arturia CS 80 V2 is not a cheap plugin, and not everyone can afford it. That is why some people resort to using a keygen torrent to get it for free. A keygen torrent is a file that contains both the software installer and a key generator that can create a license for the software. By using a keygen torrent, you can bypass the official activation process and use the software without paying anything.</p>
- <p>But is it worth it? What are the risks and benefits of using a keygen torrent to get Arturia CS 80 V2? Well, there are some pros and cons that you should consider before using a keygen torrent. Here are some of them:</p>
- <table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>You can get Arturia CS 80 V2 for free</td>
-<td>You may violate the intellectual property rights of Arturia and Yamaha</td>
-</tr>
-<tr>
-<td>You can enjoy all the features and sounds of Arturia CS 80 V2</td>
-<td>You may expose your computer to malware, viruses, spyware, or other harmful programs</td>
-</tr>
-<tr>
-<td>You can create your own sounds and patches with Arturia CS 80 V2</td>
-<td>You may not be able to update Arturia CS 80 V2 to the latest version or get support or help from Arturia</td>
-</tr>
-<tr>
-<td>You can use Arturia CS 80 V2 as a standalone application or as a plugin in your DAW</td>
-<td>You may experience some bugs, glitches, crashes, or compatibility issues with Arturia CS 80 V2 or your DAW</td>
-</tr>
-<tr>
-<td>You can share your music and creations with others who use Arturia CS 80 V2</td>
-<td>You may face some legal consequences or penalties if you are caught using a keygen torrent</td>
-</tr>
-</table>
- <p>As you can see, using a keygen torrent to get Arturia CS 80 V2 has its advantages and disadvantages. Ultimately, it is up to you to decide whether you want to use it or not. However, we recommend that you respect the work and effort of Arturia and Yamaha, who created this amazing plugin, and support them by buying their products legally. This way, you can enjoy their software without any risks or worries, and also contribute to their development and innovation.</p>
- <p>We hope that this article has been helpful and informative for you. If you have any questions or comments about Arturia CS 80 V2 keygen torrent, feel free to leave them below. We would love to hear from you. And if you liked this article, please share it with your friends and fellow musicians who might be interested in Arturia CS 80 V2 keygen torrent. Thank you for reading!</p>
- <h2>FAQs</h2>
- <h3>What are some alternatives to Arturia CS 80 V2 keygen torrent?</h3>
- <p>If you don't want to use a keygen torrent to get Arturia CS 80 V2, but still want to try it out or use something similar, there are some alternatives that you can consider. Here are some of them:</p>
- <ul>
-<li>You can download the demo version of Arturia CS 80 V2 from the official website of Arturia. The demo version is fully functional, but it has some limitations, such as a time limit of 20 minutes per session, occasional audio dropouts, no preset saving or loading, no automation or MIDI control assignment, etc. You can use the demo version to test the plugin and see if you like it.</li>
-<li>You can buy the full version of Arturia CS 80 V2 from the official website of Arturia or from authorized dealers. The full version costs $199 and it comes with a lifetime license that allows you to install and activate the plugin on up to five computers. You also get access to updates, support, and help from Arturia.</li>
-<li>You can use other plugins that emulate or simulate the Yamaha CS-80 or other vintage synthesizers. Some examples are ME80 by Memorymoon, CS-80V by Arturia, CS-01 by Krakli, OB-Xd by discoDSP, Synth1 by Ichiro Toda, etc. These plugins are either free or cheaper than Arturia CS 80 V2, and they offer similar or different features and sounds. You can use these plugins to create your own music and experiment with different sounds.</li>
-</ul>
- <h3>Is Arturia CS 80 V2 compatible with Mac OS X?</h3>
- <p>Yes, Arturia CS 80 V2 is compatible with Mac OS X. It supports Mac OS X 10.11 or higher, and it works as a standalone application or as a plugin in any DAW that supports VST, AU, or AAX formats. However, you need to make sure that you download the Mac version of the keygen torrent file, and that you use a compatible torrent client and file extractor software.</p>
- <h3>How can I update Arturia CS 80 V2 to the latest version?</h3>
- <p>If you have bought the full version of Arturia CS 80 V2 legally, you can update it to the latest version by using the Arturia Software Center (ASC) application. The ASC is an application that allows you to manage your Arturia products and licenses. You can use it to download and install updates, activate and deactivate licenses, register and unregister products, etc. You can download the ASC from the official website of Arturia and install it on your computer.</p>
- <p>If you have used a keygen torrent to get Arturia CS 80 V2 for free, you may not be able to update it to the latest version. This is because the keygen may not work with the newer versions of the software, or the software may detect that you are using a pirated license and block your access. Therefore, you may have to stick with the version that you have downloaded and installed, or look for another keygen torrent that works with the latest version.</p>
- <h3>What are some of the most famous songs that used Arturia CS 80 V2 or the original Yamaha CS-80?</h3>
- <p>There are many songs that used Arturia CS 80 V2 or the original Yamaha CS-80 in their production. Here are some of them:</p>
- <ul>
-<li>"Blade Runner" soundtrack by Vangelis. This is one of the most iconic and influential soundtracks in history, and it features the Yamaha CS-80 extensively. The Yamaha CS-80 was used to create atmospheric and futuristic sounds that matched the sci-fi theme of the movie.</li>
-<li>"Africa" by Toto. This is one of the most popular and recognizable songs of the 80s, and it features the Yamaha CS-80 prominently. The Yamaha CS-80 was used to create the catchy synth riff that opens and closes the song, as well as some of the pads and leads that fill the song.</li>
-<li>"Chariots of Fire" by Vangelis. This is another famous soundtrack by Vangelis, and it also features the Yamaha CS-80 heavily. The Yamaha CS-80 was used to create the majestic and inspiring sounds that accompany the movie's theme of athletic achievement.</li>
-<li>"Shine On You Crazy Diamond" by Pink Floyd. This is one of the most epic and progressive songs by Pink Floyd, and it features the Yamaha CS-80 subtly. The Yamaha CS-80 was used to create some of the ambient and ethereal sounds that add depth and texture to the song.</li>
-<li>"Dancing Queen" by ABBA. This is one of the most classic and catchy disco songs by ABBA, and it features the Yamaha CS-80 briefly. The Yamaha CS-80 was used to create some of the synth stabs that accentuate the chorus of the song.</li>
-</ul>
- <h3>How can I get support or help for Arturia CS 80 V2?</h3>
- <p>If you have bought the full version of Arturia CS 80 V2 legally, you can get support or help for it from Arturia directly. You can contact them via email, phone, or chat, and they will assist you with any issues or questions that you may have. You can also visit their website and access their online resources, such as manuals, tutorials, forums, FAQs, etc.</p>
- <p>If you have used a keygen torrent to get Arturia CS 80 V2 for free, you may not be able to get support or help for it from Arturia directly. This is because Arturia does not provide support or help for pirated or illegal copies of their software. Therefore, you may have to rely on other sources for support or help, such as online communities, blogs, videos, etc.</p>
- <p></p</p> b2dd77e56b<br />
-<br />
-<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Colecao Folha De Musica Classica - Completo - 36 VolColecao Folha De Musica Classica - Completo - 36.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Colecao Folha De Musica Classica - Completo - 36 VolColecao Folha De Musica Classica - Completo - 36.md
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--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Colecao Folha De Musica Classica - Completo - 36 VolColecao Folha De Musica Classica - Completo - 36.md	
+++ /dev/null
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-
-Here is a possible title and article with html formatting for the keyword "Colecao Folha de Musica Classica - Completo - 36 Vol":
-
-<h1>Coleção Folha de Música Clássica: A Complete Guide to 36 Volumes of Classical Music</h1>
-<p>If you are a fan of classical music, you may have heard of the Coleção Folha de Música Clássica, a collection of 36 volumes that covers the lives and works of some of the most influential composers in history. Each volume consists of a 60-page book with a biography, major achievements and highlights of the composer, as well as a CD with some of their best pieces, performed by the Royal Philharmonic Orchestra[^2^]. The collection was launched in September 2005 by the Brazilian newspaper Folha de São Paulo, and it sold out quickly due to its popularity and quality.</p>
-<h2>Colecao Folha de Musica Classica - Completo - 36 VolColecao Folha de Musica Classica - Completo - 36</h2><br /><p><b><b>DOWNLOAD</b> &#127383; <a href="https://urlcod.com/2uIc9H">https://urlcod.com/2uIc9H</a></b></p><br /><br />
-<p>In this article, we will give you an overview of the Coleção Folha de Música Clássica, and introduce you to each of the 36 volumes that make up this amazing collection. Whether you are a beginner or an expert in classical music, you will find something to enjoy and learn from this collection.</p>
-<h2>What is classical music?</h2>
-<p>Before we dive into the details of the collection, let us first define what classical music is. Classical music is a broad term that encompasses a variety of musical genres and styles that originated in Europe from the 11th century to the present day. Classical music is usually characterized by its use of complex structures, forms, harmony, melody and rhythm, as well as its reliance on written notation and musical instruments. Classical music can be divided into several periods, such as medieval, Renaissance, baroque, classical, romantic, modern and contemporary.</p>
-<p>Classical music has influenced and been influenced by many other forms of music, such as folk, jazz, rock and pop. Classical music is also widely used in other media, such as films, television, video games and commercials. Classical music can evoke various emotions and moods in listeners, such as joy, sadness, awe, suspense and relaxation. Classical music can also have various benefits for the mind and body, such as improving memory, concentration, creativity and mood.</p>
-<h2>What is the Coleção Folha de Música Clássica?</h2>
-<p>The Coleção Folha de Música Clássica is a collection of 36 volumes that covers the lives and works of some of the most influential composers in history. The collection was launched in September 2005 by the Brazilian newspaper Folha de São Paulo, and it sold out quickly due to its popularity and quality. The collection was curated by João Marcos Coelho, a renowned music critic and journalist who has written for several publications and radio stations in Brazil.</p>
-<p></p>
-<p>Each volume consists of a 60-page book with a biography, major achievements and highlights of the composer, as well as a CD with some of their best pieces, performed by the Royal Philharmonic Orchestra[^2^]. The books are written in Portuguese and include illustrations, photos and musical examples. The CDs are recorded in high quality sound and include detailed liner notes. The collection covers composers from different periods and styles of classical music, from medieval to contemporary.</p>
-<h2>What are the 36 volumes of the Coleção Folha de Música Clássica?</h2>
-<p>The 36 volumes of the Coleção Folha de Música Clássica are as follows:</p>
-<ol>
-<li><strong>Gregorian Chant</strong>: This volume introduces the origins and characteristics of Gregorian chant, a form of monophonic vocal music that was used in the Catholic Church from the 9th century onwards. The CD includes examples of different types of Gregorian chant, such as antiphons, hymns, psalms and masses.</li>
-<li><strong>Hildegard von Bingen</strong>: This volume presents the life and works of Hildegard von Bingen (1098-1179), a German abbess who was also a composer,
-poet,
-visionary,
-mystic,
-scientist
-and
-healer.
-She
-is
-considered
-one
-of
-the
-first
-female
-composers
-in
-history,
-and
-her
-music
-is
-rich
-in
-melody,
-rhythm
-and
-symbolism.
-The
-CD</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Pdf Architect 1.1.83 License Key.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Pdf Architect 1.1.83 License Key.md
deleted file mode 100644
index 4d1db54eef847da61aee4ffd83cff1364489165f..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Pdf Architect 1.1.83 License Key.md	
+++ /dev/null
@@ -1,47 +0,0 @@
-
-Here is a possible title and article with SEO optimization and HTML formatting for the keyword "Pdf Architect 1.1.83 License Key":
-
-<h1>How to Get Pdf Architect 1.1.83 License Key for Free</h1>
-<p>Pdf Architect is a tool that allows you to easily modify your PDF files, such as merging, splitting, rotating, converting, editing, and more. It is a popular and reliable software that has many features and modules to suit your needs. However, to access all the functions of Pdf Architect, you need a license key that can be quite expensive.</p>
-<p>Fortunately, there are some ways to get Pdf Architect 1.1.83 license key for free without breaking any laws or risking your computer security. In this article, we will show you three methods that you can try to get Pdf Architect 1.1.83 license key for free.</p>
-<h2>Pdf Architect 1.1.83 License Key</h2><br /><p><b><b>Download</b> &#9989; <a href="https://urlcod.com/2uI9A4">https://urlcod.com/2uI9A4</a></b></p><br /><br />
-<h2>Method 1: Use a Key Generator</h2>
-<p>A key generator is a program that can create random and valid license keys for various software applications. You can find many key generators online that claim to generate Pdf Architect 1.1.83 license key for free. However, you should be careful when using these tools, as some of them may contain malware or viruses that can harm your computer or steal your personal information.</p>
-<p>To use a key generator safely, you should follow these steps:</p>
-<ol>
-<li>Download a reputable antivirus software and scan your computer for any threats.</li>
-<li>Search for a key generator that supports Pdf Architect 1.1.83 on a trusted website or forum.</li>
-<li>Download the key generator and scan it with your antivirus software before running it.</li>
-<li>Run the key generator and select Pdf Architect 1.1.83 from the list of supported software.</li>
-<li>Click on the generate button and copy the license key that appears on the screen.</li>
-<li>Open Pdf Architect and enter the license key when prompted.</li>
-<li>Enjoy using Pdf Architect with all its features unlocked.</li>
-</ol>
-<h2>Method 2: Use a Crack File</h2>
-<p>A crack file is a modified version of a software application that bypasses the activation process and allows you to use it without a license key. You can find many crack files online that claim to crack Pdf Architect 1.1.83 for free. However, you should be careful when using these files, as some of them may contain malware or viruses that can harm your computer or steal your personal information.</p>
-<p>To use a crack file safely, you should follow these steps:</p>
-<ol>
-<li>Download a reputable antivirus software and scan your computer for any threats.</li>
-<li>Search for a crack file that matches Pdf Architect 1.1.83 on a trusted website or forum.</li>
-<li>Download the crack file and scan it with your antivirus software before running it.</li>
-<li>Run the crack file and follow the instructions on how to install it.</li>
-<li>Open Pdf Architect and enjoy using it without a license key.</li>
-</ol>
-<h2>Method 3: Use a Trial Resetter</h2>
-<p>A trial resetter is a program that can reset the trial period of a software application and allow you to use it again for free. You can find many trial resetters online that claim to reset Pdf Architect 1.1.83 trial period for free. However, you should be careful when using these tools, as some of them may contain malware or viruses that can harm your computer or steal your personal information.</p>
-<p></p>
-<p>To use a trial resetter safely, you should follow these steps:</p>
-<ol>
-<li>Download a reputable antivirus software and scan your computer for any threats.</li>
-<li>Search for a trial resetter that supports Pdf Architect 1.1.83 on a trusted website or forum.</li>
-<li>Download the trial resetter and scan it with your antivirus software before running it.</li>
-<li>Run the trial resetter and select Pdf Architect 1.1.83 from the list of supported software.</li>
-<li>Click on the reset button and wait for the process to complete.</li>
-<li>Open Pdf Architect and enjoy using it for free until the trial period expires again.</li>
-</ol>
-
-<h3>Conclusion</h3>
-
-<p>Pdf Architect is a great tool for modifying your PDF files, but it requires a license key to unlock all its features. If</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/export/shared.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/export/shared.py
deleted file mode 100644
index 53ba9335e26819f9381115eba17bbbe3816b469c..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/export/shared.py
+++ /dev/null
@@ -1,1039 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import collections
-import copy
-import functools
-import logging
-import numpy as np
-import os
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-from unittest import mock
-import caffe2.python.utils as putils
-import torch
-import torch.nn.functional as F
-from caffe2.proto import caffe2_pb2
-from caffe2.python import core, net_drawer, workspace
-from torch.nn.functional import interpolate as interp
-
-logger = logging.getLogger(__name__)
-
-
-# ==== torch/utils_toffee/cast.py =======================================
-
-
-def to_device(t, device_str):
-    """
-    This function is a replacement of .to(another_device) such that it allows the
-    casting to be traced properly by explicitly calling the underlying copy ops.
-    It also avoids introducing unncessary op when casting to the same device.
-    """
-    src = t.device
-    dst = torch.device(device_str)
-
-    if src == dst:
-        return t
-    elif src.type == "cuda" and dst.type == "cpu":
-        return torch.ops._caffe2.CopyGPUToCPU(t)
-    elif src.type == "cpu" and dst.type == "cuda":
-        return torch.ops._caffe2.CopyCPUToGPU(t)
-    else:
-        raise RuntimeError("Can't cast tensor from device {} to device {}".format(src, dst))
-
-
-# ==== torch/utils_toffee/interpolate.py =======================================
-
-
-# Note: borrowed from vision/detection/fair/detectron/detectron/modeling/detector.py
-def BilinearInterpolation(tensor_in, up_scale):
-    assert up_scale % 2 == 0, "Scale should be even"
-
-    def upsample_filt(size):
-        factor = (size + 1) // 2
-        if size % 2 == 1:
-            center = factor - 1
-        else:
-            center = factor - 0.5
-
-        og = np.ogrid[:size, :size]
-        return (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor)
-
-    kernel_size = int(up_scale) * 2
-    bil_filt = upsample_filt(kernel_size)
-
-    dim = int(tensor_in.shape[1])
-    kernel = np.zeros((dim, dim, kernel_size, kernel_size), dtype=np.float32)
-    kernel[range(dim), range(dim), :, :] = bil_filt
-
-    tensor_out = F.conv_transpose2d(
-        tensor_in,
-        weight=to_device(torch.Tensor(kernel), tensor_in.device),
-        bias=None,
-        stride=int(up_scale),
-        padding=int(up_scale / 2),
-    )
-
-    return tensor_out
-
-
-# NOTE: ONNX is incompatible with traced torch.nn.functional.interpolate if
-# using dynamic `scale_factor` rather than static `size`. (T43166860)
-# NOTE: Caffe2 Int8 conversion might not be able to quantize `size` properly.
-def onnx_compatibale_interpolate(
-    input, size=None, scale_factor=None, mode="nearest", align_corners=None
-):
-    # NOTE: The input dimensions are interpreted in the form:
-    # `mini-batch x channels x [optional depth] x [optional height] x width`.
-    if size is None and scale_factor is not None:
-        if input.dim() == 4:
-            if isinstance(scale_factor, (int, float)):
-                height_scale, width_scale = (scale_factor, scale_factor)
-            else:
-                assert isinstance(scale_factor, (tuple, list))
-                assert len(scale_factor) == 2
-                height_scale, width_scale = scale_factor
-
-            assert not align_corners, "No matching C2 op for align_corners == True"
-            if mode == "nearest":
-                return torch.ops._caffe2.ResizeNearest(
-                    input, order="NCHW", width_scale=width_scale, height_scale=height_scale
-                )
-            elif mode == "bilinear":
-                logger.warning(
-                    "Use F.conv_transpose2d for bilinear interpolate"
-                    " because there's no such C2 op, this may cause significant"
-                    " slowdown and the boundary pixels won't be as same as"
-                    " using F.interpolate due to padding."
-                )
-                assert height_scale == width_scale
-                return BilinearInterpolation(input, up_scale=height_scale)
-        logger.warning("Output size is not static, it might cause ONNX conversion issue")
-
-    return interp(input, size, scale_factor, mode, align_corners)
-
-
-def mock_torch_nn_functional_interpolate():
-    def decorator(func):
-        @functools.wraps(func)
-        def _mock_torch_nn_functional_interpolate(*args, **kwargs):
-            if torch.onnx.is_in_onnx_export():
-                with mock.patch(
-                    "torch.nn.functional.interpolate", side_effect=onnx_compatibale_interpolate
-                ):
-                    return func(*args, **kwargs)
-            else:
-                return func(*args, **kwargs)
-
-        return _mock_torch_nn_functional_interpolate
-
-    return decorator
-
-
-# ==== torch/utils_caffe2/ws_utils.py ==========================================
-
-
-class ScopedWS(object):
-    def __init__(self, ws_name, is_reset, is_cleanup=False):
-        self.ws_name = ws_name
-        self.is_reset = is_reset
-        self.is_cleanup = is_cleanup
-        self.org_ws = ""
-
-    def __enter__(self):
-        self.org_ws = workspace.CurrentWorkspace()
-        if self.ws_name is not None:
-            workspace.SwitchWorkspace(self.ws_name, True)
-        if self.is_reset:
-            workspace.ResetWorkspace()
-
-        return workspace
-
-    def __exit__(self, *args):
-        if self.is_cleanup:
-            workspace.ResetWorkspace()
-        if self.ws_name is not None:
-            workspace.SwitchWorkspace(self.org_ws)
-
-
-def fetch_any_blob(name):
-    bb = None
-    try:
-        bb = workspace.FetchBlob(name)
-    except TypeError:
-        bb = workspace.FetchInt8Blob(name)
-    except Exception as e:
-        logger.error("Get blob {} error: {}".format(name, e))
-
-    return bb
-
-
-# ==== torch/utils_caffe2/protobuf.py ==========================================
-
-
-def get_pb_arg(pb, arg_name):
-    for x in pb.arg:
-        if x.name == arg_name:
-            return x
-    return None
-
-
-def get_pb_arg_valf(pb, arg_name, default_val):
-    arg = get_pb_arg(pb, arg_name)
-    return arg.f if arg is not None else default_val
-
-
-def get_pb_arg_floats(pb, arg_name, default_val):
-    arg = get_pb_arg(pb, arg_name)
-    return list(map(float, arg.floats)) if arg is not None else default_val
-
-
-def get_pb_arg_ints(pb, arg_name, default_val):
-    arg = get_pb_arg(pb, arg_name)
-    return list(map(int, arg.ints)) if arg is not None else default_val
-
-
-def get_pb_arg_vali(pb, arg_name, default_val):
-    arg = get_pb_arg(pb, arg_name)
-    return arg.i if arg is not None else default_val
-
-
-def get_pb_arg_vals(pb, arg_name, default_val):
-    arg = get_pb_arg(pb, arg_name)
-    return arg.s if arg is not None else default_val
-
-
-def get_pb_arg_valstrings(pb, arg_name, default_val):
-    arg = get_pb_arg(pb, arg_name)
-    return list(arg.strings) if arg is not None else default_val
-
-
-def check_set_pb_arg(pb, arg_name, arg_attr, arg_value, allow_override=False):
-    arg = get_pb_arg(pb, arg_name)
-    if arg is None:
-        arg = putils.MakeArgument(arg_name, arg_value)
-        assert hasattr(arg, arg_attr)
-        pb.arg.extend([arg])
-    if allow_override and getattr(arg, arg_attr) != arg_value:
-        logger.warning(
-            "Override argument {}: {} -> {}".format(arg_name, getattr(arg, arg_attr), arg_value)
-        )
-        setattr(arg, arg_attr, arg_value)
-    else:
-        assert arg is not None
-        assert getattr(arg, arg_attr) == arg_value, "Existing value {}, new value {}".format(
-            getattr(arg, arg_attr), arg_value
-        )
-
-
-def _create_const_fill_op_from_numpy(name, tensor, device_option=None):
-    assert type(tensor) == np.ndarray
-    kTypeNameMapper = {
-        np.dtype("float32"): "GivenTensorFill",
-        np.dtype("int32"): "GivenTensorIntFill",
-        np.dtype("int64"): "GivenTensorInt64Fill",
-        np.dtype("uint8"): "GivenTensorStringFill",
-    }
-
-    args_dict = {}
-    if tensor.dtype == np.dtype("uint8"):
-        args_dict.update({"values": [str(tensor.data)], "shape": [1]})
-    else:
-        args_dict.update({"values": tensor, "shape": tensor.shape})
-
-    if device_option is not None:
-        args_dict["device_option"] = device_option
-
-    return core.CreateOperator(kTypeNameMapper[tensor.dtype], [], [name], **args_dict)
-
-
-def _create_const_fill_op_from_c2_int8_tensor(name, int8_tensor):
-    assert type(int8_tensor) == workspace.Int8Tensor
-    kTypeNameMapper = {
-        np.dtype("int32"): "Int8GivenIntTensorFill",
-        np.dtype("uint8"): "Int8GivenTensorFill",
-    }
-
-    tensor = int8_tensor.data
-    assert tensor.dtype in [np.dtype("uint8"), np.dtype("int32")]
-    values = tensor.tobytes() if tensor.dtype == np.dtype("uint8") else tensor
-
-    return core.CreateOperator(
-        kTypeNameMapper[tensor.dtype],
-        [],
-        [name],
-        values=values,
-        shape=tensor.shape,
-        Y_scale=int8_tensor.scale,
-        Y_zero_point=int8_tensor.zero_point,
-    )
-
-
-def create_const_fill_op(
-    name: str,
-    blob: Union[np.ndarray, workspace.Int8Tensor],
-    device_option: Optional[caffe2_pb2.DeviceOption] = None,
-) -> caffe2_pb2.OperatorDef:
-    """
-    Given a blob object, return the Caffe2 operator that creates this blob
-    as constant. Currently support NumPy tensor and Caffe2 Int8Tensor.
-    """
-
-    tensor_type = type(blob)
-    assert tensor_type in [
-        np.ndarray,
-        workspace.Int8Tensor,
-    ], 'Error when creating const fill op for "{}", unsupported blob type: {}'.format(
-        name, type(blob)
-    )
-
-    if tensor_type == np.ndarray:
-        return _create_const_fill_op_from_numpy(name, blob, device_option)
-    elif tensor_type == workspace.Int8Tensor:
-        assert device_option is None
-        return _create_const_fill_op_from_c2_int8_tensor(name, blob)
-
-
-def construct_init_net_from_params(
-    params: Dict[str, Any], device_options: Optional[Dict[str, caffe2_pb2.DeviceOption]] = None
-) -> caffe2_pb2.NetDef:
-    """
-    Construct the init_net from params dictionary
-    """
-    init_net = caffe2_pb2.NetDef()
-    device_options = device_options or {}
-    for name, blob in params.items():
-        if isinstance(blob, str):
-            logger.warning(
-                (
-                    "Blob {} with type {} is not supported in generating init net,"
-                    " skipped.".format(name, type(blob))
-                )
-            )
-            continue
-        init_net.op.extend(
-            [create_const_fill_op(name, blob, device_option=device_options.get(name, None))]
-        )
-        init_net.external_output.append(name)
-    return init_net
-
-
-def get_producer_map(ssa):
-    """
-    Return dict from versioned blob to (i, j),
-        where i is index of producer op, j is the index of output of that op.
-    """
-    producer_map = {}
-    for i in range(len(ssa)):
-        outputs = ssa[i][1]
-        for j, outp in enumerate(outputs):
-            producer_map[outp] = (i, j)
-    return producer_map
-
-
-def get_consumer_map(ssa):
-    """
-    Return dict from versioned blob to list of (i, j),
-        where i is index of consumer op, j is the index of input of that op.
-    """
-    consumer_map = collections.defaultdict(list)
-    for i in range(len(ssa)):
-        inputs = ssa[i][0]
-        for j, inp in enumerate(inputs):
-            consumer_map[inp].append((i, j))
-    return consumer_map
-
-
-def get_params_from_init_net(
-    init_net: caffe2_pb2.NetDef,
-) -> [Dict[str, Any], Dict[str, caffe2_pb2.DeviceOption]]:
-    """
-    Take the output blobs from init_net by running it.
-    Outputs:
-        params: dict from blob name to numpy array
-        device_options: dict from blob name to the device option of its creating op
-    """
-    # NOTE: this assumes that the params is determined by producer op with the
-    # only exception be CopyGPUToCPU which is CUDA op but returns CPU tensor.
-    def _get_device_option(producer_op):
-        if producer_op.type == "CopyGPUToCPU":
-            return caffe2_pb2.DeviceOption()
-        else:
-            return producer_op.device_option
-
-    with ScopedWS("__get_params_from_init_net__", is_reset=True, is_cleanup=True) as ws:
-        ws.RunNetOnce(init_net)
-        params = {b: fetch_any_blob(b) for b in init_net.external_output}
-    ssa, versions = core.get_ssa(init_net)
-    producer_map = get_producer_map(ssa)
-    device_options = {
-        b: _get_device_option(init_net.op[producer_map[(b, versions[b])][0]])
-        for b in init_net.external_output
-    }
-    return params, device_options
-
-
-def _updater_raise(op, input_types, output_types):
-    raise RuntimeError(
-        "Failed to apply updater for op {} given input_types {} and"
-        " output_types {}".format(op, input_types, output_types)
-    )
-
-
-def _generic_status_identifier(
-    predict_net: caffe2_pb2.NetDef,
-    status_updater: Callable,
-    known_status: Dict[Tuple[str, int], Any],
-) -> Dict[Tuple[str, int], Any]:
-    """
-    Statically infer the status of each blob, the status can be such as device type
-        (CPU/GPU), layout (NCHW/NHWC), data type (float32/int8), etc. "Blob" here
-        is versioned blob (Tuple[str, int]) in the format compatible with ssa.
-    Inputs:
-        predict_net: the caffe2 network
-        status_updater: a callable, given an op and the status of its input/output,
-            it returns the updated status of input/output. `None` is used for
-            representing unknown status.
-        known_status: a dict containing known status, used as initialization.
-    Outputs:
-        A dict mapping from versioned blob to its status
-    """
-    ssa, versions = core.get_ssa(predict_net)
-    versioned_ext_input = [(b, 0) for b in predict_net.external_input]
-    versioned_ext_output = [(b, versions[b]) for b in predict_net.external_output]
-    all_versioned_blobs = set().union(*[set(x[0] + x[1]) for x in ssa])
-
-    allowed_vbs = all_versioned_blobs.union(versioned_ext_input).union(versioned_ext_output)
-    assert all(k in allowed_vbs for k in known_status)
-    assert all(v is not None for v in known_status.values())
-    _known_status = copy.deepcopy(known_status)
-
-    def _check_and_update(key, value):
-        assert value is not None
-        if key in _known_status:
-            if not _known_status[key] == value:
-                raise RuntimeError(
-                    "Confilict status for {}, existing status {}, new status {}".format(
-                        key, _known_status[key], value
-                    )
-                )
-        _known_status[key] = value
-
-    def _update_i(op, ssa_i):
-        versioned_inputs = ssa_i[0]
-        versioned_outputs = ssa_i[1]
-
-        inputs_status = [_known_status.get(b, None) for b in versioned_inputs]
-        outputs_status = [_known_status.get(b, None) for b in versioned_outputs]
-
-        new_inputs_status, new_outputs_status = status_updater(op, inputs_status, outputs_status)
-
-        for versioned_blob, status in zip(
-            versioned_inputs + versioned_outputs, new_inputs_status + new_outputs_status
-        ):
-            if status is not None:
-                _check_and_update(versioned_blob, status)
-
-    for op, ssa_i in zip(predict_net.op, ssa):
-        _update_i(op, ssa_i)
-    for op, ssa_i in zip(reversed(predict_net.op), reversed(ssa)):
-        _update_i(op, ssa_i)
-
-    # NOTE: This strictly checks all the blob from predict_net must be assgined
-    # a known status. However sometimes it's impossible (eg. having deadend op),
-    # we may relax this constraint if
-    for k in all_versioned_blobs:
-        if k not in _known_status:
-            raise NotImplementedError(
-                "Can not infer the status for {}. Currently only support the case where"
-                " a single forward and backward pass can identify status for all blobs.".format(k)
-            )
-
-    return _known_status
-
-
-def infer_device_type(
-    predict_net: caffe2_pb2.NetDef,
-    known_status: Dict[Tuple[str, int], Any],
-    device_name_style: str = "caffe2",
-) -> Dict[Tuple[str, int], str]:
-    """Return the device type ("cpu" or "gpu"/"cuda") of each (versioned) blob"""
-
-    assert device_name_style in ["caffe2", "pytorch"]
-    _CPU_STR = "cpu"
-    _GPU_STR = "gpu" if device_name_style == "caffe2" else "cuda"
-
-    def _copy_cpu_to_gpu_updater(op, input_types, output_types):
-        if input_types[0] == _GPU_STR or output_types[0] == _CPU_STR:
-            _updater_raise(op, input_types, output_types)
-        return ([_CPU_STR], [_GPU_STR])
-
-    def _copy_gpu_to_cpu_updater(op, input_types, output_types):
-        if input_types[0] == _CPU_STR or output_types[0] == _GPU_STR:
-            _updater_raise(op, input_types, output_types)
-        return ([_GPU_STR], [_CPU_STR])
-
-    def _other_ops_updater(op, input_types, output_types):
-        non_none_types = [x for x in input_types + output_types if x is not None]
-        if len(non_none_types) > 0:
-            the_type = non_none_types[0]
-            if not all(x == the_type for x in non_none_types):
-                _updater_raise(op, input_types, output_types)
-        else:
-            the_type = None
-        return ([the_type for _ in op.input], [the_type for _ in op.output])
-
-    def _device_updater(op, *args, **kwargs):
-        return {
-            "CopyCPUToGPU": _copy_cpu_to_gpu_updater,
-            "CopyGPUToCPU": _copy_gpu_to_cpu_updater,
-        }.get(op.type, _other_ops_updater)(op, *args, **kwargs)
-
-    return _generic_status_identifier(predict_net, _device_updater, known_status)
-
-
-# ==== torch/utils_caffe2/vis.py ===============================================
-
-
-def _modify_blob_names(ops, blob_rename_f):
-    ret = []
-
-    def _replace_list(blob_list, replaced_list):
-        del blob_list[:]
-        blob_list.extend(replaced_list)
-
-    for x in ops:
-        cur = copy.deepcopy(x)
-        _replace_list(cur.input, list(map(blob_rename_f, cur.input)))
-        _replace_list(cur.output, list(map(blob_rename_f, cur.output)))
-        ret.append(cur)
-
-    return ret
-
-
-def _rename_blob(name, blob_sizes, blob_ranges):
-    def _list_to_str(bsize):
-        ret = ", ".join([str(x) for x in bsize])
-        ret = "[" + ret + "]"
-        return ret
-
-    ret = name
-    if blob_sizes is not None and name in blob_sizes:
-        ret += "\n" + _list_to_str(blob_sizes[name])
-    if blob_ranges is not None and name in blob_ranges:
-        ret += "\n" + _list_to_str(blob_ranges[name])
-
-    return ret
-
-
-# graph_name could not contain word 'graph'
-def save_graph(net, file_name, graph_name="net", op_only=True, blob_sizes=None, blob_ranges=None):
-    blob_rename_f = functools.partial(_rename_blob, blob_sizes=blob_sizes, blob_ranges=blob_ranges)
-    return save_graph_base(net, file_name, graph_name, op_only, blob_rename_f)
-
-
-def save_graph_base(net, file_name, graph_name="net", op_only=True, blob_rename_func=None):
-    graph = None
-    ops = net.op
-    if blob_rename_func is not None:
-        ops = _modify_blob_names(ops, blob_rename_func)
-    if not op_only:
-        graph = net_drawer.GetPydotGraph(ops, graph_name, rankdir="TB")
-    else:
-        graph = net_drawer.GetPydotGraphMinimal(
-            ops, graph_name, rankdir="TB", minimal_dependency=True
-        )
-
-    try:
-        par_dir = os.path.dirname(file_name)
-        if not os.path.exists(par_dir):
-            os.makedirs(par_dir)
-
-        format = os.path.splitext(os.path.basename(file_name))[-1]
-        if format == ".png":
-            graph.write_png(file_name)
-        elif format == ".pdf":
-            graph.write_pdf(file_name)
-        elif format == ".svg":
-            graph.write_svg(file_name)
-        else:
-            print("Incorrect format {}".format(format))
-    except Exception as e:
-        print("Error when writing graph to image {}".format(e))
-
-    return graph
-
-
-# ==== torch/utils_toffee/aten_to_caffe2.py ====================================
-
-
-def group_norm_replace_aten_with_caffe2(predict_net: caffe2_pb2.NetDef):
-    """
-    For ONNX exported model, GroupNorm will be represented as ATen op,
-        this can be a drop in replacement from ATen to GroupNorm
-    """
-    count = 0
-    for op in predict_net.op:
-        if op.type == "ATen":
-            op_name = get_pb_arg_vals(op, "operator", None)  # return byte in py3
-            if op_name and op_name.decode() == "group_norm":
-                op.arg.remove(get_pb_arg(op, "operator"))
-
-                if get_pb_arg_vali(op, "cudnn_enabled", None):
-                    op.arg.remove(get_pb_arg(op, "cudnn_enabled"))
-
-                num_groups = get_pb_arg_vali(op, "num_groups", None)
-                if num_groups is not None:
-                    op.arg.remove(get_pb_arg(op, "num_groups"))
-                    check_set_pb_arg(op, "group", "i", num_groups)
-
-                op.type = "GroupNorm"
-                count += 1
-    if count > 1:
-        logger.info("Replaced {} ATen operator to GroupNormOp".format(count))
-
-
-# ==== torch/utils_toffee/alias.py =============================================
-
-
-def alias(x, name, is_backward=False):
-    if not torch.onnx.is_in_onnx_export():
-        return x
-    assert isinstance(x, torch.Tensor)
-    return torch.ops._caffe2.AliasWithName(x, name, is_backward=is_backward)
-
-
-def fuse_alias_placeholder(predict_net, init_net):
-    """Remove AliasWithName placeholder and rename the input/output of it"""
-    # First we finish all the re-naming
-    for i, op in enumerate(predict_net.op):
-        if op.type == "AliasWithName":
-            assert len(op.input) == 1
-            assert len(op.output) == 1
-            name = get_pb_arg_vals(op, "name", None).decode()
-            is_backward = bool(get_pb_arg_vali(op, "is_backward", 0))
-            rename_op_input(predict_net, init_net, i, 0, name, from_producer=is_backward)
-            rename_op_output(predict_net, i, 0, name)
-
-    # Remove AliasWithName, should be very safe since it's a non-op
-    new_ops = []
-    for op in predict_net.op:
-        if op.type != "AliasWithName":
-            new_ops.append(op)
-        else:
-            # safety check
-            assert op.input == op.output
-            assert op.input[0] == op.arg[0].s.decode()
-    del predict_net.op[:]
-    predict_net.op.extend(new_ops)
-
-
-# ==== torch/utils_caffe2/graph_transform.py ===================================
-
-
-class IllegalGraphTransformError(ValueError):
-    """When a graph transform function call can't be executed."""
-
-
-def _rename_versioned_blob_in_proto(
-    proto: caffe2_pb2.NetDef,
-    old_name: str,
-    new_name: str,
-    version: int,
-    ssa: List[Tuple[List[Tuple[str, int]], List[Tuple[str, int]]]],
-    start_versions: Dict[str, int],
-    end_versions: Dict[str, int],
-):
-    """In given proto, rename all blobs with matched version"""
-    # Operater list
-    for op, i_th_ssa in zip(proto.op, ssa):
-        versioned_inputs, versioned_outputs = i_th_ssa
-        for i in range(len(op.input)):
-            if versioned_inputs[i] == (old_name, version):
-                op.input[i] = new_name
-        for i in range(len(op.output)):
-            if versioned_outputs[i] == (old_name, version):
-                op.output[i] = new_name
-    # external_input
-    if start_versions.get(old_name, 0) == version:
-        for i in range(len(proto.external_input)):
-            if proto.external_input[i] == old_name:
-                proto.external_input[i] = new_name
-    # external_output
-    if end_versions.get(old_name, 0) == version:
-        for i in range(len(proto.external_output)):
-            if proto.external_output[i] == old_name:
-                proto.external_output[i] = new_name
-
-
-def rename_op_input(
-    predict_net: caffe2_pb2.NetDef,
-    init_net: caffe2_pb2.NetDef,
-    op_id: int,
-    input_id: int,
-    new_name: str,
-    from_producer: bool = False,
-):
-    """
-    Rename the op_id-th operator in predict_net, change it's input_id-th input's
-        name to the new_name. It also does automatic re-route and change
-        external_input and init_net if necessary.
-    - It requires the input is only consumed by this op.
-    - This function modifies predict_net and init_net in-place.
-    - When from_producer is enable, this also updates other operators that consumes
-        the same input. Be cautious because may trigger unintended behavior.
-    """
-    assert isinstance(predict_net, caffe2_pb2.NetDef)
-    assert isinstance(init_net, caffe2_pb2.NetDef)
-
-    init_net_ssa, init_net_versions = core.get_ssa(init_net)
-    predict_net_ssa, predict_net_versions = core.get_ssa(
-        predict_net, copy.deepcopy(init_net_versions)
-    )
-
-    versioned_inputs, versioned_outputs = predict_net_ssa[op_id]
-    old_name, version = versioned_inputs[input_id]
-
-    if from_producer:
-        producer_map = get_producer_map(predict_net_ssa)
-        if not (old_name, version) in producer_map:
-            raise NotImplementedError(
-                "Can't find producer, the input {} is probably from"
-                " init_net, this is not supported yet.".format(old_name)
-            )
-        producer = producer_map[(old_name, version)]
-        rename_op_output(predict_net, producer[0], producer[1], new_name)
-        return
-
-    def contain_targets(op_ssa):
-        return (old_name, version) in op_ssa[0]
-
-    is_consumer = [contain_targets(op_ssa) for op_ssa in predict_net_ssa]
-    if sum(is_consumer) > 1:
-        raise IllegalGraphTransformError(
-            (
-                "Input '{}' of operator(#{}) are consumed by other ops, please use"
-                + " rename_op_output on the producer instead. Offending op: \n{}"
-            ).format(old_name, op_id, predict_net.op[op_id])
-        )
-
-    # update init_net
-    _rename_versioned_blob_in_proto(
-        init_net, old_name, new_name, version, init_net_ssa, {}, init_net_versions
-    )
-    # update predict_net
-    _rename_versioned_blob_in_proto(
-        predict_net,
-        old_name,
-        new_name,
-        version,
-        predict_net_ssa,
-        init_net_versions,
-        predict_net_versions,
-    )
-
-
-def rename_op_output(predict_net: caffe2_pb2.NetDef, op_id: int, output_id: int, new_name: str):
-    """
-    Rename the op_id-th operator in predict_net, change it's output_id-th input's
-        name to the new_name. It also does automatic re-route and change
-        external_output and if necessary.
-    - It allows multiple consumers of its output.
-    - This function modifies predict_net in-place, doesn't need init_net.
-    """
-    assert isinstance(predict_net, caffe2_pb2.NetDef)
-
-    ssa, blob_versions = core.get_ssa(predict_net)
-
-    versioned_inputs, versioned_outputs = ssa[op_id]
-    old_name, version = versioned_outputs[output_id]
-
-    # update predict_net
-    _rename_versioned_blob_in_proto(
-        predict_net, old_name, new_name, version, ssa, {}, blob_versions
-    )
-
-
-def get_sub_graph_external_input_output(
-    predict_net: caffe2_pb2.NetDef, sub_graph_op_indices: List[int]
-) -> Tuple[List[Tuple[str, int]], List[Tuple[str, int]]]:
-    """
-    Return the list of external input/output of sub-graph,
-    each element is tuple of the name and corresponding version in predict_net.
-
-    external input/output is defined the same way as caffe2 NetDef.
-    """
-    ssa, versions = core.get_ssa(predict_net)
-
-    all_inputs = []
-    all_outputs = []
-    for op_id in sub_graph_op_indices:
-        all_inputs += [inp for inp in ssa[op_id][0] if inp not in all_inputs]
-        all_outputs += list(ssa[op_id][1])  # ssa output won't repeat
-
-    # for versioned blobs, external inputs are just those blob in all_inputs
-    # but not in all_outputs
-    ext_inputs = [inp for inp in all_inputs if inp not in all_outputs]
-
-    # external outputs are essentially outputs of this subgraph that are used
-    # outside of this sub-graph (including predict_net.external_output)
-    all_other_inputs = sum(
-        (ssa[i][0] for i in range(len(ssa)) if i not in sub_graph_op_indices),
-        [(outp, versions[outp]) for outp in predict_net.external_output],
-    )
-    ext_outputs = [outp for outp in all_outputs if outp in set(all_other_inputs)]
-
-    return ext_inputs, ext_outputs
-
-
-class DiGraph:
-    """A DAG representation of caffe2 graph, each vertice is a versioned blob."""
-
-    def __init__(self):
-        self.vertices = set()
-        self.graph = collections.defaultdict(list)
-
-    def add_edge(self, u, v):
-        self.graph[u].append(v)
-        self.vertices.add(u)
-        self.vertices.add(v)
-
-    # grab from https://www.geeksforgeeks.org/find-paths-given-source-destination/
-    def get_all_paths(self, s, d):
-        visited = {k: False for k in self.vertices}
-        path = []
-        all_paths = []
-
-        def _get_all_paths_util(graph, u, d, visited, path):
-            visited[u] = True
-            path.append(u)
-            if u == d:
-                all_paths.append(copy.deepcopy(path))
-            else:
-                for i in graph[u]:
-                    if not visited[i]:
-                        _get_all_paths_util(graph, i, d, visited, path)
-            path.pop()
-            visited[u] = False
-
-        _get_all_paths_util(self.graph, s, d, visited, path)
-        return all_paths
-
-    @staticmethod
-    def from_ssa(ssa):
-        graph = DiGraph()
-        for op_id in range(len(ssa)):
-            for inp in ssa[op_id][0]:
-                for outp in ssa[op_id][1]:
-                    graph.add_edge(inp, outp)
-        return graph
-
-
-def _get_dependency_chain(ssa, versioned_target, versioned_source):
-    """
-    Return the index list of relevant operator to produce target blob from source blob,
-        if there's no dependency, return empty list.
-    """
-
-    # finding all paths between nodes can be O(N!), thus we can only search
-    # in the subgraph using the op starting from the first consumer of source blob
-    # to the producer of the target blob.
-    consumer_map = get_consumer_map(ssa)
-    producer_map = get_producer_map(ssa)
-    start_op = min(x[0] for x in consumer_map[versioned_source]) - 15
-    end_op = (
-        producer_map[versioned_target][0] + 15 if versioned_target in producer_map else start_op
-    )
-    sub_graph_ssa = ssa[start_op : end_op + 1]
-    if len(sub_graph_ssa) > 30:
-        logger.warning(
-            "Subgraph bebetween {} and {} is large (from op#{} to op#{}), it"
-            " might take non-trival time to find all paths between them.".format(
-                versioned_source, versioned_target, start_op, end_op
-            )
-        )
-
-    dag = DiGraph.from_ssa(sub_graph_ssa)
-    paths = dag.get_all_paths(versioned_source, versioned_target)  # include two ends
-    ops_in_paths = [[producer_map[blob][0] for blob in path[1:]] for path in paths]
-    return sorted(set().union(*[set(ops) for ops in ops_in_paths]))
-
-
-def identify_reshape_sub_graph(predict_net: caffe2_pb2.NetDef) -> List[List[int]]:
-    """
-    Idenfity the reshape sub-graph in a protobuf.
-    The reshape sub-graph is defined as matching the following pattern:
-
-    (input_blob) -> Op_1 -> ... -> Op_N -> (new_shape) -─┐
-        └-------------------------------------------> Reshape -> (output_blob)
-
-    Return:
-        List of sub-graphs, each sub-graph is represented as a list of indices
-        of the relavent ops, [Op_1, Op_2, ..., Op_N, Reshape]
-    """
-
-    ssa, _ = core.get_ssa(predict_net)
-
-    ret = []
-    for i, op in enumerate(predict_net.op):
-        if op.type == "Reshape":
-            assert len(op.input) == 2
-            input_ssa = ssa[i][0]
-            data_source = input_ssa[0]
-            shape_source = input_ssa[1]
-            op_indices = _get_dependency_chain(ssa, shape_source, data_source)
-            ret.append(op_indices + [i])
-    return ret
-
-
-def remove_reshape_for_fc(predict_net, params):
-    """
-    In PyTorch nn.Linear has to take 2D tensor, this often leads to reshape
-        a 4D tensor to 2D by calling .view(). However this (dynamic) reshaping
-        doesn't work well with ONNX and Int8 tools, and cause using extra
-        ops (eg. ExpandDims) that might not be available on mobile.
-    Luckily Caffe2 supports 4D tensor for FC, so we can remove those reshape
-        after exporting ONNX model.
-    """
-    from caffe2.python import core
-
-    # find all reshape sub-graph that can be removed, which is now all Reshape
-    # sub-graph whose output is only consumed by FC.
-    # TODO: to make it safer, we may need the actually value to better determine
-    # if a Reshape before FC is removable.
-    reshape_sub_graphs = identify_reshape_sub_graph(predict_net)
-    sub_graphs_to_remove = []
-    for reshape_sub_graph in reshape_sub_graphs:
-        reshape_op_id = reshape_sub_graph[-1]
-        assert predict_net.op[reshape_op_id].type == "Reshape"
-        ssa, _ = core.get_ssa(predict_net)
-        reshape_output = ssa[reshape_op_id][1][0]
-        consumers = [i for i in range(len(ssa)) if reshape_output in ssa[i][0]]
-        if all(predict_net.op[consumer].type == "FC" for consumer in consumers):
-            # safety check if the sub-graph is isolated, for this reshape sub-graph,
-            # it means it has one non-param external input and one external output.
-            ext_inputs, ext_outputs = get_sub_graph_external_input_output(
-                predict_net, reshape_sub_graph
-            )
-            non_params_ext_inputs = [inp for inp in ext_inputs if inp[1] != 0]
-            if len(non_params_ext_inputs) == 1 and len(ext_outputs) == 1:
-                sub_graphs_to_remove.append(reshape_sub_graph)
-
-    # perform removing subgraph by:
-    # 1: rename the Reshape's output to its input, then the graph can be
-    #   seen as in-place itentify, meaning whose external input/output are the same.
-    # 2: simply remove those ops.
-    remove_op_ids = []
-    params_to_remove = []
-    for sub_graph in sub_graphs_to_remove:
-        logger.info(
-            "Remove Reshape sub-graph:\n{}".format(
-                "".join(["(#{:>4})\n{}".format(i, predict_net.op[i]) for i in sub_graph])
-            )
-        )
-        reshape_op_id = sub_graph[-1]
-        new_reshap_output = predict_net.op[reshape_op_id].input[0]
-        rename_op_output(predict_net, reshape_op_id, 0, new_reshap_output)
-        ext_inputs, ext_outputs = get_sub_graph_external_input_output(predict_net, sub_graph)
-        non_params_ext_inputs = [inp for inp in ext_inputs if inp[1] != 0]
-        params_ext_inputs = [inp for inp in ext_inputs if inp[1] == 0]
-        assert len(non_params_ext_inputs) == 1 and len(ext_outputs) == 1
-        assert ext_outputs[0][0] == non_params_ext_inputs[0][0]
-        assert ext_outputs[0][1] == non_params_ext_inputs[0][1] + 1
-        remove_op_ids.extend(sub_graph)
-        params_to_remove.extend(params_ext_inputs)
-
-    predict_net = copy.deepcopy(predict_net)
-    new_ops = [op for i, op in enumerate(predict_net.op) if i not in remove_op_ids]
-    del predict_net.op[:]
-    predict_net.op.extend(new_ops)
-    for versioned_params in params_to_remove:
-        name = versioned_params[0]
-        logger.info("Remove params: {} from init_net and predict_net.external_input".format(name))
-        del params[name]
-        predict_net.external_input.remove(name)
-
-    return predict_net, params
-
-
-def fuse_copy_between_cpu_and_gpu(predict_net: caffe2_pb2.NetDef):
-    """
-    In-place fuse extra copy ops between cpu/gpu for the following case:
-        a -CopyAToB-> b -CopyBToA> c1 -NextOp1-> d1
-                        -CopyBToA> c2 -NextOp2-> d2
-    The fused network will look like:
-        a -NextOp1-> d1
-          -NextOp2-> d2
-    """
-
-    _COPY_OPS = ["CopyCPUToGPU", "CopyGPUToCPU"]
-
-    def _fuse_once(predict_net):
-        ssa, blob_versions = core.get_ssa(predict_net)
-        consumer_map = get_consumer_map(ssa)
-        versioned_external_output = [
-            (name, blob_versions[name]) for name in predict_net.external_output
-        ]
-
-        for op_id, op in enumerate(predict_net.op):
-            if op.type in _COPY_OPS:
-                fw_copy_versioned_output = ssa[op_id][1][0]
-                consumer_ids = [x[0] for x in consumer_map[fw_copy_versioned_output]]
-                reverse_op_type = _COPY_OPS[1 - _COPY_OPS.index(op.type)]
-
-                is_fusable = (
-                    len(consumer_ids) > 0
-                    and fw_copy_versioned_output not in versioned_external_output
-                    and all(
-                        predict_net.op[_op_id].type == reverse_op_type
-                        and ssa[_op_id][1][0] not in versioned_external_output
-                        for _op_id in consumer_ids
-                    )
-                )
-
-                if is_fusable:
-                    for rv_copy_op_id in consumer_ids:
-                        # making each NextOp uses "a" directly and removing Copy ops
-                        rs_copy_versioned_output = ssa[rv_copy_op_id][1][0]
-                        next_op_id, inp_id = consumer_map[rs_copy_versioned_output][0]
-                        predict_net.op[next_op_id].input[inp_id] = op.input[0]
-                    # remove CopyOps
-                    new_ops = [
-                        op
-                        for i, op in enumerate(predict_net.op)
-                        if i != op_id and i not in consumer_ids
-                    ]
-                    del predict_net.op[:]
-                    predict_net.op.extend(new_ops)
-                    return True
-
-        return False
-
-    # _fuse_once returns False is nothing can be fused
-    while _fuse_once(predict_net):
-        pass
-
-
-def remove_dead_end_ops(net_def: caffe2_pb2.NetDef):
-    """remove ops if its output is not used or not in external_output"""
-    ssa, versions = core.get_ssa(net_def)
-    versioned_external_output = [(name, versions[name]) for name in net_def.external_output]
-    consumer_map = get_consumer_map(ssa)
-    removed_op_ids = set()
-
-    def _is_dead_end(versioned_blob):
-        return not (
-            versioned_blob in versioned_external_output
-            or (
-                len(consumer_map[versioned_blob]) > 0
-                and all(x[0] not in removed_op_ids for x in consumer_map[versioned_blob])
-            )
-        )
-
-    for i, ssa_i in reversed(list(enumerate(ssa))):
-        versioned_outputs = ssa_i[1]
-        if all(_is_dead_end(outp) for outp in versioned_outputs):
-            removed_op_ids.add(i)
-
-    # simply removing those deadend ops should have no effect to external_output
-    new_ops = [op for i, op in enumerate(net_def.op) if i not in removed_op_ids]
-    del net_def.op[:]
-    net_def.op.extend(new_ops)
diff --git a/spaces/nkatraga/7.22.first.hfstreamlitHeatmap/app.py b/spaces/nkatraga/7.22.first.hfstreamlitHeatmap/app.py
deleted file mode 100644
index 040b6f71a2b254f3826994176b1140b08ce6ef8a..0000000000000000000000000000000000000000
--- a/spaces/nkatraga/7.22.first.hfstreamlitHeatmap/app.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import streamlit as st
-import nltk
-from transformers import pipeline
-from sentence_transformers import SentenceTransformer
-from scipy.spatial.distance import cosine
-import numpy as np
-import seaborn as sns
-import matplotlib.pyplot as plt
-from sklearn.cluster import KMeans
-import tensorflow as tf
-import tensorflow_hub as hub
-
-
-def cluster_examples(messages, embed, nc=3):
-    km = KMeans(
-    n_clusters=nc, init='random',
-    n_init=10, max_iter=300, 
-    tol=1e-04, random_state=0
-    )
-    km = km.fit_predict(embed)
-    for n in range(nc):
-        idxs = [i for i in range(len(km)) if km[i] == n]
-        ms = [messages[i] for i in idxs]
-        st.markdown ("CLUSTER : %d"%n)
-        for m in ms:
-            st.markdown (m)
-
-
-def plot_heatmap(labels, heatmap, rotation=90):
-  sns.set(font_scale=1.2)
-  fig, ax = plt.subplots()
-  g = sns.heatmap(
-      heatmap,
-      xticklabels=labels,
-      yticklabels=labels,
-      vmin=-1,
-      vmax=1,
-      cmap="coolwarm")
-  g.set_xticklabels(labels, rotation=rotation)
-  g.set_title("Textual Similarity")
-  st.pyplot(fig)
-  
-# Streamlit text boxes
-text = st.text_area('Enter sentences:', value="Behavior right this is a kind of Heisenberg uncertainty principle situation if I told you, then you behave differently. What would be the impressive thing is you have talked about winning a nobel prize in a system winning a nobel prize. Adjusting it and then making your own.  That is when I fell in love with computers.  I realized that they were a very magical device.  Can go to sleep come back the next day and it is solved.  You know that feels magical to me.")
-
-nc = st.slider('Select a number of clusters:', min_value=1, max_value=15, value=3)
-
-model_type = st.radio("Choose model:", ('Sentence Transformer', 'Universal Sentence Encoder'), index=0)
-
-# Model setup
-if model_type == "Sentence Transformer":
-    model = SentenceTransformer('paraphrase-distilroberta-base-v1')
-elif model_type == "Universal Sentence Encoder":
-    model_url = "https://tfhub.dev/google/universal-sentence-encoder-large/5"
-    model = hub.load(model_url)
-
-nltk.download('punkt')
-
-# Run model
-if text:
-    sentences = nltk.tokenize.sent_tokenize(text)
-    if model_type == "Sentence Transformer":
-        embed = model.encode(sentences)
-    elif model_type == "Universal Sentence Encoder":
-        embed = model(sentences).numpy()
-    sim = np.zeros([len(embed), len(embed)])
-    for i,em in enumerate(embed):
-        for j,ea in enumerate(embed):
-            sim[i][j] = 1.0-cosine(em,ea)
-    st.subheader("Similarity Heatmap")
-    plot_heatmap(sentences, sim)
-    st.subheader("Results from K-Means Clustering")
-    cluster_examples(sentences, embed, nc)
\ No newline at end of file
diff --git a/spaces/nomic-ai/squad_v2/README.md b/spaces/nomic-ai/squad_v2/README.md
deleted file mode 100644
index 4fc06b6dd15ff4cd1094cdfacec7306c1cce1b2f..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/squad_v2/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
----
-title: squad_v2
-emoji: 🗺️
-colorFrom: purple
-colorTo: red
-sdk: static
-pinned: false
----
\ No newline at end of file
diff --git a/spaces/ntt123/WaveGRU-Text-To-Speech/sparse_matmul/compute/gru_gates_avx_fixed.h b/spaces/ntt123/WaveGRU-Text-To-Speech/sparse_matmul/compute/gru_gates_avx_fixed.h
deleted file mode 100644
index cf7cf0e770d27d583dd63116c350c6dd49d8a528..0000000000000000000000000000000000000000
--- a/spaces/ntt123/WaveGRU-Text-To-Speech/sparse_matmul/compute/gru_gates_avx_fixed.h
+++ /dev/null
@@ -1,348 +0,0 @@
-/*
- * Copyright 2021 Google LLC
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LYRA_CODEC_SPARSE_MATMUL_COMPUTE_GRU_GATES_AVX_FIXED_H_
-#define LYRA_CODEC_SPARSE_MATMUL_COMPUTE_GRU_GATES_AVX_FIXED_H_
-
-#include <cstdint>
-#if defined __AVX2__
-#include <immintrin.h>
-#endif
-#include <vector>
-
-#include "sparse_matmul/compute/ar_inputs.h"
-#include "sparse_matmul/numerics/fast_transcendentals.h"
-
-namespace csrblocksparse {
-
-#if defined __AVX2__
-
-constexpr int kAVX2SIMDWidth = 8;
-
-// Loads 8x fixed32 from |ptr0| and adds to |input|.
-// If |kTwoInputs|, also loads from |ptr1| and adds that as well.
-// Returns the 2 or 3-way sum.
-template <bool kTwoInputs>
-inline __m256i LoadAndAddFixed32(const int32_t* ptr0, const int32_t* ptr1,
-                                 const __m256i& input) {
-  __m256i data0 = _mm256_load_si256(reinterpret_cast<const __m256i*>(ptr0));
-  if (kTwoInputs) {
-    __m256i data1 = _mm256_load_si256(reinterpret_cast<const __m256i*>(ptr1));
-    data0 = _mm256_add_epi32(data0, data1);
-  }
-  return _mm256_add_epi32(data0, input);
-}
-
-// Loads 8x fixed32 from ptr0.
-// If |kTwoInputs|, also loads from |ptr1| and adds.
-// Multiplies the loaded values by the factor and adds to |input|, which also
-// is converted to float.
-// Returns the sum.
-template <bool kTwoInputs>
-inline __m256 LoadMultiplyAddToFloat(const int32_t* ptr0, const int32_t* ptr1,
-                                     const __m256& float_factor,
-                                     const __m256& input) {
-  __m256i data0 = _mm256_load_si256(reinterpret_cast<const __m256i*>(ptr0));
-  if (kTwoInputs) {
-    __m256i data1 = _mm256_load_si256(reinterpret_cast<const __m256i*>(ptr1));
-    data0 = _mm256_add_epi32(data0, data1);
-  }
-  __m256 float_result = _mm256_cvtepi32_ps(data0);
-  float_result = _mm256_mul_ps(float_result, float_factor);
-  return _mm256_add_ps(float_result, input);
-}
-
-// Loads 16x float in 2x 8x registers from |ptr0_1| and multiplies by
-// |input_pairs|, likewise formatted as 8x floats, alternating between the two
-// AR inputs and sums each pair of results, making 8x float results.
-// If |kThreeInputs|, also loads 8x float from |ptr2| and multiplies by
-// |third_input|, which must be formatted as 8x float. The second product is
-// added to the previous result.
-// Returns the sum added to |accumulator|.
-template <bool kThreeInputs>
-inline __m256 MultiplyAddFloat(const __m256& input_pairs,
-                               const __m256& third_input, const float* ptr0_1,
-                               const float* ptr2, const __m256& accumulator) {
-  __m256 data_pair0 = _mm256_load_ps(ptr0_1);
-  __m256 data_pair1 = _mm256_load_ps(ptr0_1 + 8);
-  data_pair0 = _mm256_mul_ps(data_pair0, input_pairs);
-  data_pair1 = _mm256_mul_ps(data_pair1, input_pairs);
-  data_pair0 = _mm256_hadd_ps(data_pair0, data_pair1);
-  // Swap the middle 2 64 bit pairs to correct the hadd result.
-  data_pair0 = _mm256_permute4x64_pd((__m256d)data_pair0, 0xd8);
-  if (kThreeInputs) {
-    // Load 256 bits (8 x float) of data, then multiply-accumulate.
-    data_pair1 = _mm256_load_ps(ptr2);
-    data_pair1 = _mm256_mul_ps(data_pair1, third_input);
-    data_pair0 = _mm256_add_ps(data_pair0, data_pair1);
-  }
-  // Add conditioning.
-  return _mm256_add_ps(data_pair0, accumulator);
-}
-
-// Processes the tanh and the final combination, returns the new GRU state.
-template <int kInputMantissaBits, int kStateMantissaBits, bool kSplitGates>
-inline __m256i GRUComputeState(const __m256& cell0, const __m256& cell1,
-                               const __m256& reset0, const __m256& reset1,
-                               const __m256& update0, const __m256& update1,
-                               const int32_t* gate_ptr,
-                               const int32_t* gate_other_ptr,
-                               const void* gru_h_ptr) {
-  // Multiply the cell gru output and the reset.
-  __m256 float_gru0 = LoadMultiplyAddToFloat<kSplitGates>(
-      gate_ptr, gate_other_ptr, reset0, cell0);
-  __m256 float_gru1 = LoadMultiplyAddToFloat<kSplitGates>(
-      gate_ptr + kAVX2SIMDWidth, gate_other_ptr + kAVX2SIMDWidth, reset1,
-      cell1);
-  // Compute tanh on the result.
-  __m256 hbar0, hbar1;
-  float_tanh_float<kInputMantissaBits, TM_ORDER4_FLOAT>(float_gru0, float_gru1,
-                                                        hbar0, hbar1);
-  // Load the 16-bit previous gru state and update.
-  __m256i gru = _mm256_load_si256(reinterpret_cast<__m256i const*>(gru_h_ptr));
-  __m256 state_factor =
-      _mm256_set1_ps(1.0f / (static_cast<float>(1 << kStateMantissaBits)));
-  float_gru0 =
-      _mm256_cvtepi32_ps(_mm256_cvtepi16_epi32(_mm256_castsi256_si128(gru)));
-  float_gru1 = _mm256_cvtepi32_ps(
-      _mm256_cvtepi16_epi32(_mm256_extractf128_si256(gru, 1)));
-  float_gru0 = _mm256_mul_ps(float_gru0, state_factor);
-  float_gru1 = _mm256_mul_ps(float_gru1, state_factor);
-  float_gru0 = _mm256_sub_ps(float_gru0, hbar0);
-  float_gru1 = _mm256_sub_ps(float_gru1, hbar1);
-  float_gru0 = _mm256_mul_ps(float_gru0, update0);
-  float_gru1 = _mm256_mul_ps(float_gru1, update1);
-  state_factor = _mm256_set1_ps(static_cast<float>(1 << kStateMantissaBits));
-  float_gru0 = _mm256_add_ps(float_gru0, hbar0);
-  float_gru1 = _mm256_add_ps(float_gru1, hbar1);
-  float_gru0 = _mm256_mul_ps(float_gru0, state_factor);
-  float_gru1 = _mm256_mul_ps(float_gru1, state_factor);
-  return PackFloatsToFixed16(float_gru0, float_gru1);
-}
-
-// According to |kInputsMode|, processes 0, 2 or 3 autoregressive inputs and
-// combines with |input| and |gates*|.
-// With 2 AR inputs, loads 8x pairs of float from |pair_weights| and multiplies
-// by |paired_ar|, likewise formatted as 8x float, but scaled such that the
-// product with pair_weights is on the same scale as |*input| and |*gates0|,
-// and sums each pair result, making 8x float results.
-// If 3 AR inputs, also loads 8x float from |third_weights| and multiplies by
-// |third_ar|, which must be formatted as 8x scaled floats. The second product
-// is added to the previous result.
-// Inputs, 8x fixed32 are loaded from |input|, and added to the total.
-// Finally 8x fixed32 from |gates0| (and |gates1| if |kTwoGates|) are added as
-// well.
-// Returns the total sum as a float, but on the scale of |*input|.
-template <bool kTwoGates, ARInputsMode kInputsMode>
-inline __m256 GruInput32ToFloat(const __m256& paired_ar,
-                                 const __m256& third_ar,
-                                 const float* pair_weights,
-                                 const float* third_weights,
-                                 const int32_t* gates0, const int32_t* gates1,
-                                 const int32_t* input) {
-  __m256i data32 = _mm256_load_si256(reinterpret_cast<__m256i const*>(input));
-  data32 = LoadAndAddFixed32<kTwoGates>(gates0, gates1, data32);
-  __m256 float_data = _mm256_cvtepi32_ps(data32);
-  if (kInputsMode != ARInputsMode::k0ARInputs) {
-    float_data = MultiplyAddFloat<kInputsMode == ARInputsMode::k3ARInputs>(
-        paired_ar, third_ar, pair_weights, third_weights, float_data);
-  }
-  return float_data;
-}
-
-// Generic GRU gates function controlled by template parameters thus:
-// - |kInputBits|: the mantissa bits in |*input_ptr|, |*gru_recurrent_ptr|.
-// - |kStateBits|: the mantissa_bits in |*gru_state_ptr|.
-// - |kInputsMode == |k0ARInputs|: There are no autoregressive inputs so
-//   |ar_sample, |ar_sample1|, |ar_sample2|, |ar_01_weights|, |ar_2_weights| are
-//   ignored.
-// - |kInputsMode| == |k2ARInputs|: |ar_sample0|, |ar_sample1| are multiplied by
-//   |ar_01_weights| and added to the (conditioning) input.
-// - |kInputsMode| == |k3ARInputs|: |ar_sample2| is multiplied by |ar_2_weights|
-//   and added to the other two AR inputs (and added to the conditioning input).
-// - |kReplicas| determines the number of duplicates of the output to be
-//   written, separated by |replica_stride|. If zero, then the number of
-//   replicas is variable and taken from the |replicas| argument.
-// - If |kSplitGates| is true: The |*gru_recurrent_other_ptr| is secondary
-//   recurrent input that must be added to |*gru_recurrent_ptr|.
-// - |start|, |end| are |rows| in [0, |state_size|] to be processed by this
-//   thread.
-//
-// Previous state is read from |*gru_state_ptr| and the new state is written to
-// *(|gru_state_ptr| + i * |replica_stride| for i in [0, |kReplicas|]).
-template <int kInputBits, int kStateBits,
-          ARInputsMode kInputsMode = ARInputsMode::k0ARInputs,
-          int kReplicas = 1, bool kSplitGates = false>
-inline void GruGatesTemplate(
-    int start, int end, int state_size, int replicas, int replica_stride,
-    const int32_t* gru_recurrent_ptr, const int32_t* input_ptr,
-    const std::pair<float, float>* ar_sample01, const float* ar_01_weights,
-    const float* ar_sample2, const float* ar_2_weights,
-    const int32_t* gru_recurrent_other_ptr, int16_t* gru_state_ptr) {
-  constexpr int kQRIncrement = kAVX2SIMDWidth;
-  // Increment all the pointers to save on pointer arithmetic in the loop.
-  input_ptr += start;
-  gru_state_ptr += start;
-  gru_recurrent_ptr += start;
-  if (kSplitGates) gru_recurrent_other_ptr += start;
-  __m256 ar_2_inputs, ar_3rd_input;
-  if (kInputsMode != ARInputsMode::k0ARInputs) {
-    ar_01_weights += 2 * start;
-    ar_2_inputs = _mm256_castsi256_ps(
-        _mm256_set1_epi64x(*reinterpret_cast<const int64_t*>(ar_sample01)));
-    if (kInputsMode == ARInputsMode::k3ARInputs) {
-      ar_2_weights += start;
-      ar_3rd_input = _mm256_set1_ps(*ar_sample2);
-    } else {
-      ar_3rd_input = {};
-    }
-  } else {
-    ar_2_inputs = {};
-    ar_3rd_input = {};
-  }
-  // The transcendentals handle 2x registers of data at once, so we have to do
-  // everything in duplicate.
-  for (int i = start; i < end; i += kQRIncrement * 2) {
-    // Load 8 pairs of fixed16s for each of reset, update and cell.
-    __m256 reset0 = GruInput32ToFloat<kSplitGates, kInputsMode>(
-        ar_2_inputs, ar_3rd_input, ar_01_weights, ar_2_weights,
-        gru_recurrent_ptr, gru_recurrent_other_ptr, input_ptr);
-    __m256 reset1 = GruInput32ToFloat<kSplitGates, kInputsMode>(
-        ar_2_inputs, ar_3rd_input, ar_01_weights + 2 * kQRIncrement,
-        ar_2_weights + kQRIncrement, gru_recurrent_ptr + kAVX2SIMDWidth,
-        gru_recurrent_other_ptr + kAVX2SIMDWidth, input_ptr + kAVX2SIMDWidth);
-    float_sigmoid_float<kInputBits>(reset0, reset1);
-    __m256 update0 = GruInput32ToFloat<kSplitGates, kInputsMode>(
-        ar_2_inputs, ar_3rd_input, ar_01_weights + 2 * state_size,
-        ar_2_weights + state_size, gru_recurrent_ptr + state_size,
-        gru_recurrent_other_ptr + state_size, input_ptr + state_size);
-    __m256 update1 = GruInput32ToFloat<kSplitGates, kInputsMode>(
-        ar_2_inputs, ar_3rd_input,
-        ar_01_weights + 2 * state_size + 2 * kQRIncrement,
-        ar_2_weights + state_size + kQRIncrement,
-        gru_recurrent_ptr + state_size + kAVX2SIMDWidth,
-        gru_recurrent_other_ptr + state_size + kAVX2SIMDWidth,
-        input_ptr + state_size + kAVX2SIMDWidth);
-    float_sigmoid_float<kInputBits>(update0, update1);
-    __m256 cell0 = _mm256_cvtepi32_ps(_mm256_load_si256(
-        reinterpret_cast<__m256i const*>(input_ptr + 2 * state_size)));
-    __m256 cell1 =
-        _mm256_cvtepi32_ps(_mm256_load_si256(reinterpret_cast<__m256i const*>(
-            input_ptr + 2 * state_size + kAVX2SIMDWidth)));
-    if (kInputsMode != ARInputsMode::k0ARInputs) {
-      cell0 = MultiplyAddFloat<kInputsMode == ARInputsMode::k3ARInputs>(
-          ar_2_inputs, ar_3rd_input, ar_01_weights + 4 * state_size,
-          ar_2_weights + 2 * state_size, cell0);
-      cell1 = MultiplyAddFloat<kInputsMode == ARInputsMode::k3ARInputs>(
-          ar_2_inputs, ar_3rd_input,
-          ar_01_weights + 4 * state_size + 2 * kQRIncrement,
-          ar_2_weights + 2 * state_size + kQRIncrement, cell1);
-    }
-    __m256i gru_state = GRUComputeState<kInputBits, kStateBits, kSplitGates>(
-        cell0, cell1, reset0, reset1, update0, update1,
-        gru_recurrent_ptr + 2 * state_size,
-        gru_recurrent_other_ptr + 2 * state_size, gru_state_ptr);
-    if (kReplicas > 0) {
-      // With |kReplicas| a template parameter, the compiler will unroll the
-      // loop.
-      for (int j = 0; j < kReplicas; ++j) {
-        _mm256_store_si256(
-            reinterpret_cast<__m256i*>(gru_state_ptr + j * replica_stride),
-            gru_state);
-      }
-    } else {
-      // This loop will not unroll as replicas is variable.
-      for (int j = 0; j < replicas; ++j) {
-        _mm256_store_si256(
-            reinterpret_cast<__m256i*>(gru_state_ptr + j * replica_stride),
-            gru_state);
-      }
-    }
-    // Increment all the pointers.
-    input_ptr += 2 * kAVX2SIMDWidth;
-    gru_state_ptr += 2 * kAVX2SIMDWidth;
-    gru_recurrent_ptr += 2 * kAVX2SIMDWidth;
-    if (kSplitGates) gru_recurrent_other_ptr += 2 * kAVX2SIMDWidth;
-    if (kInputsMode != ARInputsMode::k0ARInputs) {
-      ar_01_weights += 4 * kQRIncrement;
-      if (kInputsMode == ARInputsMode::k3ARInputs)
-        ar_2_weights += 2 * kQRIncrement;
-    }
-  }
-}
-
-// Dispatches calls to the GruGatesTemplate function above converting the
-// replicas variable argument to a template parameter to allow the compiler to
-// unroll the write loop.
-// |ar_sample01| packs sample 0 and 1 into a pair because the QR weights are
-// formatted with the weights interleaved for sample 0 and 1. The two samples
-// represent coarse and fine for WaveRNN.
-template <int kInputBits, int kStateBits,
-          ARInputsMode kInputsMode = ARInputsMode::k2ARInputs,
-          bool kSplitGates = false>
-inline void GruGatesAVXFixed(
-    int start, int end, int state_size, const int32_t* gru_recurrent_ptr,
-    const int32_t* input_ptr, const std::pair<float, float>* ar_sample01,
-    const float* ar_01_weights, int num_replicas, int replica_stride,
-    const float* ar_sample2, const float* ar_2_weights,
-    const int32_t* gru_recurrent_other_ptr, int16_t* gru_state_ptr) {
-  // Convert the number of replicas from a variable to a template parameter
-  // with a switch. This enables the compiler to unroll the loop for
-  // the write, making it faster for common numbers of threads.
-  switch (num_replicas) {
-    case 1:
-      GruGatesTemplate<kInputBits, kStateBits, kInputsMode, /*kReplicas=*/1,
-                       kSplitGates>(
-          start, end, state_size, num_replicas, replica_stride,
-          gru_recurrent_ptr, input_ptr, ar_sample01, ar_01_weights, ar_sample2,
-          ar_2_weights, gru_recurrent_other_ptr, gru_state_ptr);
-      break;
-    case 2:
-      GruGatesTemplate<kInputBits, kStateBits, kInputsMode, /*kReplicas=*/2,
-                       kSplitGates>(
-          start, end, state_size, num_replicas, replica_stride,
-          gru_recurrent_ptr, input_ptr, ar_sample01, ar_01_weights, ar_sample2,
-          ar_2_weights, gru_recurrent_other_ptr, gru_state_ptr);
-      break;
-    case 4:
-      GruGatesTemplate<kInputBits, kStateBits, kInputsMode, /*kReplicas=*/4,
-                       kSplitGates>(
-          start, end, state_size, num_replicas, replica_stride,
-          gru_recurrent_ptr, input_ptr, ar_sample01, ar_01_weights, ar_sample2,
-          ar_2_weights, gru_recurrent_other_ptr, gru_state_ptr);
-      break;
-    case 6:
-      GruGatesTemplate<kInputBits, kStateBits, kInputsMode, /*kReplicas=*/6,
-                       kSplitGates>(
-          start, end, state_size, num_replicas, replica_stride,
-          gru_recurrent_ptr, input_ptr, ar_sample01, ar_01_weights, ar_sample2,
-          ar_2_weights, gru_recurrent_other_ptr, gru_state_ptr);
-      break;
-    default:
-      // Zero |kReplicas| tells the function to use the |num_replicas| variable.
-      GruGatesTemplate<kInputBits, kStateBits, kInputsMode, /*kReplicas=*/0,
-                       kSplitGates>(
-          start, end, state_size, num_replicas, replica_stride,
-          gru_recurrent_ptr, input_ptr, ar_sample01, ar_01_weights, ar_sample2,
-          ar_2_weights, gru_recurrent_other_ptr, gru_state_ptr);
-  }
-}
-
-#endif  // __AVX2__
-
-}  // namespace csrblocksparse
-
-#endif  // LYRA_CODEC_SPARSE_MATMUL_COMPUTE_GRU_GATES_AVX_FIXED_H_
diff --git a/spaces/oguzakif/video-object-remover/FGT_codes/FGT/networks/network.py b/spaces/oguzakif/video-object-remover/FGT_codes/FGT/networks/network.py
deleted file mode 100644
index ff8998d48933c5d471f1ec011061a16d45982617..0000000000000000000000000000000000000000
--- a/spaces/oguzakif/video-object-remover/FGT_codes/FGT/networks/network.py
+++ /dev/null
@@ -1,491 +0,0 @@
-from trainer import Trainer
-from importlib import import_module
-import math
-import torch
-from torch import optim
-from torch.optim import lr_scheduler
-import numpy as np
-from metrics import calculate_metrics
-import os
-import glob
-import torch.nn.functional as F
-from models.temporal_patch_gan import Discriminator
-import cv2
-import cvbase
-import imageio
-from skimage.feature import canny
-from models.lafc_single import Model
-from data.util.flow_utils import region_fill as rf
-
-
-class Network(Trainer):
-    def init_model(self):
-        model_package = import_module('models.{}'.format(self.opt['model']))
-        model = model_package.Model(self.opt)
-        dist_in = 3
-        discriminator = Discriminator(in_channels=dist_in, conv_type=self.opt['conv_type'],
-                                      dist_cnum=self.opt['dist_cnum'])
-        optimizer = optim.Adam(model.parameters(), lr=float(self.opt['train']['lr']),
-                               betas=(float(self.opt['train']['BETA1']), float(float(self.opt['train']['BETA2']))))
-        dist_optim = optim.Adam(discriminator.parameters(), lr=float(self.opt['train']['lr']),
-                                betas=(float(self.opt['train']['BETA1']), float(float(self.opt['train']['BETA2']))))
-        if self.rank <= 0:
-            self.logger.info(
-                'Optimizer is Adam, BETA1: {}, BETA2: {}'.format(float(self.opt['train']['BETA1']),
-                                                                 float(self.opt['train']['BETA2'])))
-        step_size = int(math.ceil(self.opt['train']['UPDATE_INTERVAL'] / self.trainSize))
-        if self.rank <= 0:
-            self.logger.info('Step size for optimizer is {} epoch'.format(step_size))
-        scheduler = lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=self.opt['train']['lr_decay'])
-        dist_scheduler = lr_scheduler.StepLR(dist_optim, step_size=step_size, gamma=self.opt['train']['lr_decay'])
-        return model, discriminator, optimizer, dist_optim, scheduler, dist_scheduler
-
-    def init_flow_model(self):
-        flow_model = Model(self.opt['flow_config'])
-        state = torch.load(self.opt['flow_checkPoint'],
-                           map_location=lambda storage, loc: storage.cuda(self.opt['device']))
-        flow_model.load_state_dict(state['model_state_dict'])
-        flow_model = flow_model.to(self.opt['device'])
-        return flow_model
-
-    def resume_training(self):
-        gen_state = torch.load(self.opt['path']['gen_state'],
-                                  map_location=lambda storage, loc: storage.cuda(self.opt['device']))
-        dis_state = torch.load(self.opt['path']['dis_state'],
-                               map_location=lambda storage, loc: storage.cuda(self.opt['device']))
-        opt_state = torch.load(self.opt['path']['opt_state'],
-                               map_location=lambda storage, loc: storage.cuda(self.opt['device']))
-
-        if self.rank <= 0:
-            self.logger.info('Resume state is activated')
-            self.logger.info('Resume training from epoch: {}, iter: {}'.format(
-                opt_state['epoch'], opt_state['iteration']
-            ))
-        if self.opt['finetune'] == False:
-            start_epoch = opt_state['epoch']
-            current_step = opt_state['iteration']
-            self.optimizer.load_state_dict(opt_state['optimizer_state_dict'])
-            self.dist_optim.load_state_dict(opt_state['dist_optim_state_dict'])
-            self.scheduler.load_state_dict(opt_state['scheduler_state_dict'])
-            self.dist_scheduler.load_state_dict(opt_state['dist_scheduler_state_dict'])
-        else:
-            start_epoch = 0
-            current_step = 0
-        self.model.load_state_dict(gen_state['model_state_dict'])
-        self.dist.load_state_dict(dis_state['dist_state_dict'])
-        if self.rank <= 0:
-            self.logger.info('Resume training mode, optimizer, scheduler and model have been uploaded')
-        return start_epoch, current_step
-
-    def norm_flows(self, flows):
-        flattened_flows = flows.flatten(3)
-        flow_max = torch.max(flattened_flows, dim=-1, keepdim=True)[0]
-        flows = flows / flow_max.unsqueeze(-1)
-        return flows
-
-    def _trainEpoch(self, epoch):
-        for idx, train_data in enumerate(self.trainLoader):
-            self.currentStep += 1
-
-            if self.currentStep > self.totalIterations:
-                if self.rank <= 0:
-                    self.logger.info('Train process has been finished')
-                break
-            if self.opt['train']['WARMUP'] is not None and self.currentStep <= self.opt['train']['WARMUP'] // self.opt[
-                'world_size']:
-                target_lr = self.opt['train']['lr'] * self.currentStep / (
-                    self.opt['train']['WARMUP'])
-                self.adjust_learning_rate(self.optimizer, target_lr)
-
-            frames = train_data['frames']  # tensor, [b, t, c, h, w]
-            masks = train_data['masks']  # tensor, [b, t, c, h, w]
-            if self.opt['flow_direction'] == 'for':
-                flows = train_data['forward_flo']
-            elif self.opt['flow_direction'] == 'back':
-                flows = train_data['backward_flo']
-            elif self.opt['flow_direction'] == 'bi':
-                raise NotImplementedError('Bidirectory flow mode is not implemented')
-            else:
-                raise ValueError('Unknown flow mode: {}'.format(self.opt['flow_direction']))
-            frames = frames.to(self.opt['device'])  # [b, t, c(3), h, w]
-            masks = masks.to(self.opt['device'])  # [b, t, 1, h, w]
-            flows = flows.to(self.opt['device'])  # [b, t, c(2), h, w]
-
-            b, t, c, h, w = flows.shape
-            flows = flows.reshape(b * t, c, h, w)
-            compressed_masks = masks.reshape(b * t, 1, h, w)
-            with torch.no_grad():
-                flows = self.flow_model(flows, compressed_masks)[0]  # filled flows
-            flows = flows.reshape(b, t, c, h, w)
-
-            flows = self.norm_flows(flows)
-
-            b, t, c, h, w = frames.shape
-            cm, cf = masks.shape[2], flows.shape[2]
-
-            masked_frames = frames * (1 - masks)
-
-            filled_frames = self.model(masked_frames, flows, masks)  # filled_frames shape: [b, t, c, h, w]
-            frames = frames.view(b * t, c, h, w)
-            masks = masks.view(b * t, cm, h, w)
-            comp_img = filled_frames * masks + frames * (1 - masks)
-
-            real_vid_feat = self.dist(frames, t)
-            fake_vid_feat = self.dist(comp_img.detach(), t)
-            dis_real_loss = self.adversarial_loss(real_vid_feat, True, True)
-            dis_fake_loss = self.adversarial_loss(fake_vid_feat, False, True)
-            dis_loss = (dis_real_loss + dis_fake_loss) / 2
-            self.dist_optim.zero_grad()
-            dis_loss.backward()
-            self.dist_optim.step()
-
-            # calculate generator loss
-            gen_vid_feat = self.dist(comp_img, t)
-            gan_loss = self.adversarial_loss(gen_vid_feat, True, False)
-            gen_loss = gan_loss * self.opt['adv']
-            L1Loss_valid = self.validLoss(filled_frames * (1 - masks),
-                                          frames * (1 - masks)) / torch.mean(1 - masks)
-            L1Loss_masked = self.validLoss(filled_frames * masks,
-                                           frames * masks) / torch.mean(masks)
-            m_loss_valid = L1Loss_valid * self.opt['L1M']
-            m_loss_masked = L1Loss_masked * self.opt['L1V']
-
-            loss = m_loss_valid + m_loss_masked + gen_loss
-
-            self.optimizer.zero_grad()
-            loss.backward()
-            self.optimizer.step()
-
-            if self.opt['use_tb_logger'] and self.rank <= 0 and self.currentStep % 8 == 0:
-                print('Mask: {:.03f}, valid: {:.03f}, dis_fake: {:.03f}, dis_real: {:.03f}, adv: {:.03f}'.format(
-                    m_loss_masked.item(),
-                    m_loss_valid.item(),
-                    dis_fake_loss.item(),
-                    dis_real_loss.item(),
-                    gen_loss.item()
-                ))
-            if self.opt['use_tb_logger'] and self.rank <= 0 and self.currentStep % 64 == 0:
-                self.tb_logger.add_scalar('{}/recon_mask'.format('train'), m_loss_masked.item(),
-                                          self.currentStep)
-                self.tb_logger.add_scalar('{}/recon_valid'.format('train'), m_loss_valid.item(),
-                                          self.currentStep)
-                self.tb_logger.add_scalar('{}/adv'.format('train'), gen_loss.item(),
-                                          self.currentStep)
-                self.tb_logger.add_scalar('train/dist', dis_loss.item(), self.currentStep)
-
-            if self.currentStep % self.opt['logger']['PRINT_FREQ'] == 0 and self.rank <= 0:
-                c_frames = comp_img.detach().permute(0, 2, 3, 1).cpu()
-                f_frames = frames.detach().permute(0, 2, 3, 1).cpu()
-                compLog = np.clip(np.array((c_frames + 1) / 2 * 255), 0, 255).astype(np.uint8)
-                framesLog = np.clip(np.array((f_frames + 1) / 2 * 255), 0, 255).astype(np.uint8)
-                logs = calculate_metrics(compLog, framesLog)
-                self._printLog(logs, epoch, loss)
-
-    def _printLog(self, logs, epoch, loss):
-        if self.countDown % self.opt['record_iter'] == 0:
-            self.total_psnr = 0
-            self.total_ssim = 0
-            self.total_l1 = 0
-            self.total_l2 = 0
-            self.total_loss = 0
-            self.countDown = 0
-        self.countDown += 1
-        message = '[epoch:{:3d}, iter:{:7d}, lr:('.format(epoch, self.currentStep)
-        for v in self.get_lr():
-            message += '{:.3e}, '.format(v)
-        message += ')] '
-        self.total_psnr += logs['psnr']
-        self.total_ssim += logs['ssim']
-        self.total_l1 += logs['l1']
-        self.total_l2 += logs['l2']
-        self.total_loss += loss.item()
-        mean_psnr = self.total_psnr / self.countDown
-        mean_ssim = self.total_ssim / self.countDown
-        mean_l1 = self.total_l1 / self.countDown
-        mean_l2 = self.total_l2 / self.countDown
-        mean_loss = self.total_loss / self.countDown
-
-        message += '{:s}: {:.4e} '.format('mean_loss', mean_loss)
-        message += '{:s}: {:} '.format('mean_psnr', mean_psnr)
-        message += '{:s}: {:} '.format('mean_ssim', mean_ssim)
-        message += '{:s}: {:} '.format('mean_l1', mean_l1)
-        message += '{:s}: {:} '.format('mean_l2', mean_l2)
-
-        if self.opt['use_tb_logger']:
-            self.tb_logger.add_scalar('train/mean_psnr', mean_psnr, self.currentStep)
-            self.tb_logger.add_scalar('train/mean_ssim', mean_ssim, self.currentStep)
-            self.tb_logger.add_scalar('train/mean_l1', mean_l1, self.currentStep)
-            self.tb_logger.add_scalar('train/mean_l2', mean_l2, self.currentStep)
-            self.tb_logger.add_scalar('train/mean_loss', mean_loss, self.currentStep)
-        self.logger.info(message)
-
-        if self.currentStep % self.opt['logger']['SAVE_CHECKPOINT_FREQ'] == 0:
-            self.save_checkpoint(epoch)
-
-    def save_checkpoint(self, epoch):
-        if isinstance(self.model, torch.nn.DataParallel) or isinstance(self.model,
-                                                                       torch.nn.parallel.DistributedDataParallel):
-            model_state = self.model.module.state_dict()
-            dist_state = self.dist.module.state_dict()
-        else:
-            model_state = self.model.state_dict()
-            dist_state = self.dist.state_dict()
-        gen_state = {
-            'model_state_dict': model_state
-        }
-        dis_state = {
-            'dist_state_dict': dist_state
-        }
-        opt_state = {
-            'epoch': epoch,
-            'iteration': self.currentStep,
-            'optimizer_state_dict': self.optimizer.state_dict(),
-            'dist_optim_state_dict': self.dist_optim.state_dict(),
-            'scheduler_state_dict': self.scheduler.state_dict(),
-            'dist_scheduler_state_dict': self.dist_scheduler.state_dict()
-        }
-
-        gen_name = os.path.join(self.opt['path']['TRAINING_STATE'],
-                                 'gen_{}_{}.pth.tar'.format(epoch, self.currentStep))
-        dist_name = os.path.join(self.opt['path']['TRAINING_STATE'],
-                                'dist_{}_{}.pth.tar'.format(epoch, self.currentStep))
-        opt_name = os.path.join(self.opt['path']['TRAINING_STATE'],
-                                'opt_{}_{}.pth.tar'.format(epoch, self.currentStep))
-        torch.save(gen_state, gen_name)
-        torch.save(dis_state, dist_name)
-        torch.save(opt_state, opt_name)
-
-    def _validate(self, epoch):
-        frame_path = self.valInfo['frame_root']
-        mask_path = self.valInfo['mask_root']
-        flow_path = self.valInfo['flow_root']
-        self.model.eval()
-        test_list = os.listdir(flow_path)
-        if len(test_list) > 10:
-            test_list = test_list[:10]  # only valid 10 videos to save test time
-        width, height = self.valInfo['flow_width'], self.valInfo['flow_height']
-        psnr, ssim, l1, l2 = {}, {}, {}, {}
-        pivot, sequenceLen, ref_length = 20, self.opt['num_frames'], self.opt['ref_length']
-        for i in range(len(test_list)):
-            videoName = test_list[i]
-            if self.rank <= 0:
-                self.logger.info('Video {} is been processed'.format(videoName))
-            frame_dir = os.path.join(frame_path, videoName)
-            mask_dir = os.path.join(mask_path, videoName)
-            flow_dir = os.path.join(flow_path, videoName)
-            videoLen = len(glob.glob(os.path.join(mask_dir, '*.png')))
-            neighbor_ids = [i for i in range(max(0, pivot - sequenceLen // 2), min(videoLen, pivot + sequenceLen // 2))]
-            ref_ids = self.get_ref_index(neighbor_ids, videoLen, ref_length)
-            ref_ids.extend(neighbor_ids)
-            frames = self.read_frames(frame_dir, width, height, ref_ids)
-            masks = self.read_masks(mask_dir, width, height, ref_ids)
-            flows = self.read_flows(flow_dir, width, height, ref_ids, videoLen - 1)
-            if frames == [] or masks == []:
-                if self.rank <= 0:
-                    print('Video {} doesn\'t have enough frames'.format(videoName))
-                continue
-            flows = self.diffusion_flows(flows, masks)
-            if self.rank <= 0:
-                self.logger.info('Frames, masks, and flows have been read')
-            frames = np.stack(frames, axis=0)  # [t, h, w, c]
-            masks = np.stack(masks, axis=0)
-            flows = np.stack(flows, axis=0)
-            frames = torch.from_numpy(np.transpose(frames, (0, 3, 1, 2))).unsqueeze(0).float()
-            flows = torch.from_numpy(np.transpose(flows, (0, 3, 1, 2))).unsqueeze(0).float()
-            masks = torch.from_numpy(np.transpose(masks, (0, 3, 1, 2))).unsqueeze(0).float()
-            frames = frames / 127.5 - 1
-            frames = frames.to(self.opt['device'])
-            masks = masks.to(self.opt['device'])
-            flows = flows.to(self.opt['device'])
-            b, t, c, h, w = flows.shape
-            flows = flows.reshape(b * t, c, h, w)
-            compressed_masks = masks.reshape(b * t, 1, h, w)
-            with torch.no_grad():
-                flows = self.flow_model(flows, compressed_masks)[0]
-            flows = flows.reshape(b, t, c, h, w)
-            flows = self.norm_flows(flows)
-
-            b, t, c, h, w = frames.shape
-            cm, cf = masks.shape[2], flows.shape[2]
-            masked_frames = frames * (1 - masks)
-            with torch.no_grad():
-                filled_frames = self.model(masked_frames, flows, masks)
-            frames = frames.view(b * t, c, h, w)
-            masks = masks.view(b * t, cm, h, w)
-            comp_img = filled_frames * masks + frames * (1 - masks)  # [t, c, h, w]
-
-            # calculate metrics
-            psnr_avg, ssim_avg, l1_avg, l2_avg = self.metrics_calc(comp_img, frames)
-            psnr[videoName] = psnr_avg
-            ssim[videoName] = ssim_avg
-            l1[videoName] = l1_avg
-            l2[videoName] = l2_avg
-
-            # visualize frames and report the phase performance
-            if self.rank <= 0:
-                if self.opt['use_tb_logger']:
-                    self.tb_logger.add_scalar('test/{}/l1'.format(videoName), l1_avg,
-                                              self.currentStep)
-                    self.tb_logger.add_scalar('test/{}/l2'.format(videoName), l2_avg, self.currentStep)
-                    self.tb_logger.add_scalar('test/{}/psnr'.format(videoName), psnr_avg, self.currentStep)
-                    self.tb_logger.add_scalar('test/{}/ssim'.format(videoName), ssim_avg, self.currentStep)
-                masked_frames = masked_frames.view(b * t, c, h, w)
-                self.vis_frames(comp_img, masked_frames, frames, videoName,
-                                epoch)  # view the difference between diffused flows and the completed flows
-                mean_psnr = np.mean([psnr[k] for k in psnr.keys()])
-                mean_ssim = np.mean([ssim[k] for k in ssim.keys()])
-                mean_l1 = np.mean([l1[k] for k in l1.keys()])
-                mean_l2 = np.mean([l2[k] for k in l2.keys()])
-                self.logger.info(
-                    '[epoch:{:3d}, vid:{}/{}], mean_l1: {:.4e}, mean_l2: {:.4e}, mean_psnr: {:}, mean_ssim: {:}'.format(
-                        epoch, i, len(test_list), mean_l1, mean_l2, mean_psnr, mean_ssim))
-
-        # give the overall performance
-        if self.rank <= 0:
-            mean_psnr = np.mean([psnr[k] for k in psnr.keys()])
-            mean_ssim = np.mean([ssim[k] for k in ssim.keys()])
-            mean_l1 = np.mean([l1[k] for k in l1.keys()])
-            mean_l2 = np.mean([l2[k] for k in l2.keys()])
-            self.logger.info(
-                '[epoch:{:3d}], mean_l1: {:.4e} mean_l2: {:.4e} mean_psnr: {:} mean_ssim: {:}'.format(
-                    epoch, mean_l1, mean_l2, mean_psnr, mean_ssim))
-            self.save_checkpoint(epoch)
-
-        self.model.train()
-
-    def get_ref_index(self, neighbor_ids, videoLen, ref_length):
-        ref_indices = []
-        for i in range(0, videoLen, ref_length):
-            if not i in neighbor_ids:
-                ref_indices.append(i)
-        return ref_indices
-
-    def metrics_calc(self, results, frames):
-        psnr_avg, ssim_avg, l1_avg, l2_avg = 0, 0, 0, 0
-        results = np.array(results.permute(0, 2, 3, 1).cpu())
-        frames = np.array(frames.permute(0, 2, 3, 1).cpu())
-        result = np.clip((results + 1) / 2 * 255, 0, 255).astype(np.uint8)
-        frames = np.clip((frames + 1) / 2 * 255, 0, 255).astype(np.uint8)
-        logs = calculate_metrics(result, frames)
-        psnr_avg += logs['psnr']
-        ssim_avg += logs['ssim']
-        l1_avg += logs['l1']
-        l2_avg += logs['l2']
-        return psnr_avg, ssim_avg, l1_avg, l2_avg
-
-    def read_frames(self, frame_dir, width, height, ref_indices):
-        frame_paths = sorted(glob.glob(os.path.join(frame_dir, '*.jpg')))
-        frames = []
-        if len(frame_paths) <= 30:
-            return frames
-        for i in ref_indices:
-            frame_path = os.path.join(frame_dir, '{:05d}.jpg'.format(i))
-            frame = imageio.imread(frame_path)
-            frame = cv2.resize(frame, (width, height), cv2.INTER_LINEAR)
-            frames.append(frame)
-        return frames
-
-    def diffusion_flows(self, flows, masks):
-        assert len(flows) == len(masks), 'Length of flow: {}, length of mask: {}'.format(len(flows), len(masks))
-        ret_flows = []
-        for i in range(len(flows)):
-            flow, mask = flows[i], masks[i]
-            flow = self.diffusion_flow(flow, mask)
-            ret_flows.append(flow)
-        return ret_flows
-
-    def diffusion_flow(self, flow, mask):
-        mask = mask[:, :, 0]
-        flow_filled = np.zeros(flow.shape)
-        flow_filled[:, :, 0] = rf.regionfill(flow[:, :, 0] * (1 - mask), mask)
-        flow_filled[:, :, 1] = rf.regionfill(flow[:, :, 1] * (1 - mask), mask)
-        return flow_filled
-
-    def read_flows(self, flow_dir, width, height, ref_ids, frameMaxIndex):
-        if self.opt['flow_direction'] == 'for':
-            direction = 'forward_flo'
-            shift = 0
-        elif self.opt['flow_direction'] == 'back':
-            direction = 'backward_flo'
-            shift = -1
-        elif self.opt['flow_direction'] == 'bi':
-            raise NotImplementedError('Bidirectional flows processing are not implemented')
-        else:
-            raise ValueError('Unknown flow direction: {}'.format(self.opt['flow_direction']))
-        flows = []
-        flow_path = os.path.join(flow_dir, direction)
-        for i in ref_ids:
-            i += shift
-            if i >= frameMaxIndex:
-                i = frameMaxIndex - 1
-            if i < 0:
-                i = 0
-            flow_p = os.path.join(flow_path, '{:05d}.flo'.format(i))
-            flow = cvbase.read_flow(flow_p)
-            pre_height, pre_width = flow.shape[:2]
-            flow = cv2.resize(flow, (width, height), cv2.INTER_LINEAR)
-            flow[:, :, 0] = flow[:, :, 0] / pre_width * width
-            flow[:, :, 1] = flow[:, :, 1] / pre_height * height
-            flows.append(flow)
-        return flows
-
-    def load_edges(self, frames, width, height):
-        edges = []
-        for i in range(len(frames)):
-            frame = frames[i]
-            frame_gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
-            edge = canny(frame_gray, sigma=self.valInfo['sigma'], mask=None,
-                         low_threshold=self.valInfo['low_threshold'],
-                         high_threshold=self.valInfo['high_threshold']).astype(np.float)  # [h, w, 1]
-            edge_t = self.to_tensor(edge, width, height, mode='nearest')
-            edges.append(edge_t)
-        return edges
-
-    def to_tensor(self, frame, width, height, mode='bilinear'):
-        if len(frame.shape) == 2:
-            frame = frame[:, :, np.newaxis]
-        frame_t = torch.from_numpy(frame).unsqueeze(0).permute(0, 3, 1, 2).float()  # [b, c, h, w]
-        if width != 0 and height != 0:
-            frame_t = F.interpolate(frame_t, size=(height, width), mode=mode)
-        return frame_t
-
-    def to_numpy(self, tensor):
-        array = np.array(tensor.permute(1, 2, 0))
-        return array
-
-    def read_masks(self, mask_dir, width, height, ref_indices):
-        mask_path = sorted(glob.glob(os.path.join(mask_dir, '*.png')))
-        masks = []
-        if len(mask_path) < 30:
-            return masks
-        for i in ref_indices:
-            mask = cv2.imread(mask_path[i], 0)
-            mask = mask / 255.
-            mask = cv2.resize(mask, (width, height), cv2.INTER_NEAREST)
-            mask[mask > 0] = 1
-            mask = mask[:, :, np.newaxis]
-            masks.append(mask)
-        return masks
-
-    def vis_frames(self, results, masked_frames, frames, video_name, epoch):
-        out_root = self.opt['path']['VAL_IMAGES']
-        out_dir = os.path.join(out_root, str(epoch), video_name)
-        if not os.path.exists(out_dir):
-            os.makedirs(out_dir)
-        black_column_pixels = 20
-        results, masked_frames, frames = results.cpu(), masked_frames.cpu(), frames.cpu()
-        T = results.shape[0]
-        for t in range(T):
-            result, masked_frame, frame = results[t], masked_frames[t], frames[t]
-            result = self.to_numpy(result)
-            masked_frame = self.to_numpy(masked_frame)
-            frame = self.to_numpy(frame)
-            result = np.clip(((result + 1) / 2) * 255, 0, 255)
-            frame = np.clip(((frame + 1) / 2) * 255, 0, 255)
-            masked_frame = np.clip(((masked_frame + 1) / 2) * 255, 0, 255)  # normalize to [0~255]
-            height, width = result.shape[:2]
-            canvas = np.zeros((height, width * 3 + black_column_pixels * 2, 3))
-            canvas[:, 0:width, :] = result
-            canvas[:, width + black_column_pixels: 2 * width + black_column_pixels, :] = frame
-            canvas[:, 2 * (width + black_column_pixels):, :] = masked_frame
-            imageio.imwrite(os.path.join(out_dir, 'result_compare_{:05d}.png'.format(t)), canvas)
diff --git a/spaces/omlab/vlchecklist_demo/models/vilt/utils/glossary.py b/spaces/omlab/vlchecklist_demo/models/vilt/utils/glossary.py
deleted file mode 100644
index 81fddf30c92ad11d48ed2a9698488fa20724d563..0000000000000000000000000000000000000000
--- a/spaces/omlab/vlchecklist_demo/models/vilt/utils/glossary.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import re
-
-contractions = {
-    "aint": "ain't",
-    "arent": "aren't",
-    "cant": "can't",
-    "couldve": "could've",
-    "couldnt": "couldn't",
-    "couldn'tve": "couldn't've",
-    "couldnt've": "couldn't've",
-    "didnt": "didn't",
-    "doesnt": "doesn't",
-    "dont": "don't",
-    "hadnt": "hadn't",
-    "hadnt've": "hadn't've",
-    "hadn'tve": "hadn't've",
-    "hasnt": "hasn't",
-    "havent": "haven't",
-    "hed": "he'd",
-    "hed've": "he'd've",
-    "he'dve": "he'd've",
-    "hes": "he's",
-    "howd": "how'd",
-    "howll": "how'll",
-    "hows": "how's",
-    "Id've": "I'd've",
-    "I'dve": "I'd've",
-    "Im": "I'm",
-    "Ive": "I've",
-    "isnt": "isn't",
-    "itd": "it'd",
-    "itd've": "it'd've",
-    "it'dve": "it'd've",
-    "itll": "it'll",
-    "let's": "let's",
-    "maam": "ma'am",
-    "mightnt": "mightn't",
-    "mightnt've": "mightn't've",
-    "mightn'tve": "mightn't've",
-    "mightve": "might've",
-    "mustnt": "mustn't",
-    "mustve": "must've",
-    "neednt": "needn't",
-    "notve": "not've",
-    "oclock": "o'clock",
-    "oughtnt": "oughtn't",
-    "ow's'at": "'ow's'at",
-    "'ows'at": "'ow's'at",
-    "'ow'sat": "'ow's'at",
-    "shant": "shan't",
-    "shed've": "she'd've",
-    "she'dve": "she'd've",
-    "she's": "she's",
-    "shouldve": "should've",
-    "shouldnt": "shouldn't",
-    "shouldnt've": "shouldn't've",
-    "shouldn'tve": "shouldn't've",
-    "somebody'd": "somebodyd",
-    "somebodyd've": "somebody'd've",
-    "somebody'dve": "somebody'd've",
-    "somebodyll": "somebody'll",
-    "somebodys": "somebody's",
-    "someoned": "someone'd",
-    "someoned've": "someone'd've",
-    "someone'dve": "someone'd've",
-    "someonell": "someone'll",
-    "someones": "someone's",
-    "somethingd": "something'd",
-    "somethingd've": "something'd've",
-    "something'dve": "something'd've",
-    "somethingll": "something'll",
-    "thats": "that's",
-    "thered": "there'd",
-    "thered've": "there'd've",
-    "there'dve": "there'd've",
-    "therere": "there're",
-    "theres": "there's",
-    "theyd": "they'd",
-    "theyd've": "they'd've",
-    "they'dve": "they'd've",
-    "theyll": "they'll",
-    "theyre": "they're",
-    "theyve": "they've",
-    "twas": "'twas",
-    "wasnt": "wasn't",
-    "wed've": "we'd've",
-    "we'dve": "we'd've",
-    "weve": "we've",
-    "werent": "weren't",
-    "whatll": "what'll",
-    "whatre": "what're",
-    "whats": "what's",
-    "whatve": "what've",
-    "whens": "when's",
-    "whered": "where'd",
-    "wheres": "where's",
-    "whereve": "where've",
-    "whod": "who'd",
-    "whod've": "who'd've",
-    "who'dve": "who'd've",
-    "wholl": "who'll",
-    "whos": "who's",
-    "whove": "who've",
-    "whyll": "why'll",
-    "whyre": "why're",
-    "whys": "why's",
-    "wont": "won't",
-    "wouldve": "would've",
-    "wouldnt": "wouldn't",
-    "wouldnt've": "wouldn't've",
-    "wouldn'tve": "wouldn't've",
-    "yall": "y'all",
-    "yall'll": "y'all'll",
-    "y'allll": "y'all'll",
-    "yall'd've": "y'all'd've",
-    "y'alld've": "y'all'd've",
-    "y'all'dve": "y'all'd've",
-    "youd": "you'd",
-    "youd've": "you'd've",
-    "you'dve": "you'd've",
-    "youll": "you'll",
-    "youre": "you're",
-    "youve": "you've",
-}
-
-manual_map = {
-    "none": "0",
-    "zero": "0",
-    "one": "1",
-    "two": "2",
-    "three": "3",
-    "four": "4",
-    "five": "5",
-    "six": "6",
-    "seven": "7",
-    "eight": "8",
-    "nine": "9",
-    "ten": "10",
-}
-articles = ["a", "an", "the"]
-period_strip = re.compile("(?!<=\d)(\.)(?!\d)")
-comma_strip = re.compile("(\d)(\,)(\d)")
-punct = [
-    ";",
-    r"/",
-    "[",
-    "]",
-    '"',
-    "{",
-    "}",
-    "(",
-    ")",
-    "=",
-    "+",
-    "\\",
-    "_",
-    "-",
-    ">",
-    "<",
-    "@",
-    "`",
-    ",",
-    "?",
-    "!",
-]
-
-
-def normalize_word(token):
-    _token = token
-    for p in punct:
-        if (p + " " in token or " " + p in token) or (
-            re.search(comma_strip, token) != None
-        ):
-            _token = _token.replace(p, "")
-        else:
-            _token = _token.replace(p, " ")
-    token = period_strip.sub("", _token, re.UNICODE)
-
-    _token = []
-    temp = token.lower().split()
-    for word in temp:
-        word = manual_map.setdefault(word, word)
-        if word not in articles:
-            _token.append(word)
-    for i, word in enumerate(_token):
-        if word in contractions:
-            _token[i] = contractions[word]
-    token = " ".join(_token)
-    token = token.replace(",", "")
-    return token
diff --git a/spaces/open-spaced-repetition/fsrs4anki_previewer/app.py b/spaces/open-spaced-repetition/fsrs4anki_previewer/app.py
deleted file mode 100644
index e079c131dbd94fdf5a6fc806104c7c4fe3213fd3..0000000000000000000000000000000000000000
--- a/spaces/open-spaced-repetition/fsrs4anki_previewer/app.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import gradio as gr
-import os
-import sys
-
-if os.environ.get("DEV_MODE"):
-    # for local development
-    sys.path.insert(0, os.path.abspath("../fsrs-optimizer/src/fsrs_optimizer/"))
-from fsrs_optimizer import Optimizer
-
-
-def interface_func(weights: str, ratings: str, request_retention: float) -> str:
-    weights = weights.replace("[", "").replace("]", "")
-    optimizer = Optimizer()
-    optimizer.w = list(map(lambda x: float(x.strip()), weights.split(",")))
-    test_sequence = optimizer.preview_sequence(
-        ratings.replace(" ", ""), request_retention
-    )
-    default_preview = optimizer.preview(request_retention)
-    return test_sequence, default_preview
-
-
-iface = gr.Interface(
-    fn=interface_func,
-    inputs=[
-        gr.inputs.Textbox(
-            label="weights",
-            lines=1,
-            default="0.4, 0.9, 2.3, 10.9, 4.93, 0.94, 0.86, 0.01, 1.49, 0.14, 0.94, 2.18, 0.05, 0.34, 1.26, 0.29, 2.61",
-        ),
-        gr.inputs.Textbox(label="ratings", lines=1, default="3,3,3,3,3,3,3"),
-        gr.inputs.Slider(
-            label="Your Request Retention",
-            minimum=0.6,
-            maximum=0.97,
-            step=0.01,
-            default=0.9,
-        ),
-    ],
-    outputs=[
-        gr.outputs.Textbox(label="test sequences"),
-        gr.outputs.Textbox(label="default preview"),
-    ],
-)
-
-iface.launch()
diff --git a/spaces/openaccess-ai-collective/arena-archived/app.py b/spaces/openaccess-ai-collective/arena-archived/app.py
deleted file mode 100644
index 8202149cda1c2f1c0c0c23a5dcec26729a356c0a..0000000000000000000000000000000000000000
--- a/spaces/openaccess-ai-collective/arena-archived/app.py
+++ /dev/null
@@ -1,421 +0,0 @@
-import concurrent
-import functools
-import logging
-import os
-import random
-import re
-import traceback
-import uuid
-import datetime
-from collections import deque
-import itertools
-
-from collections import defaultdict
-from time import sleep
-from typing import Generator, Tuple
-
-import boto3
-import gradio as gr
-import requests
-from datasets import load_dataset
-
-logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
-
-# Create a DynamoDB client
-dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
-# Get a reference to the table
-table = dynamodb.Table('oaaic_chatbot_arena')
-
-
-def prompt_instruct(system_msg, history):
-    return system_msg.strip() + "\n" + \
-        "\n".join(["\n".join(["### Instruction: "+item[0], "### Response: "+item[1]])
-        for item in history])
-
-
-def prompt_chat(system_msg, history):
-    return system_msg.strip() + "\n" + \
-        "\n".join(["\n".join(["USER: "+item[0], "ASSISTANT: "+item[1]])
-        for item in history])
-
-
-class Pipeline:
-    prefer_async = True
-
-    def __init__(self, endpoint_id, name, prompt_fn, stop_tokens=None):
-        self.endpoint_id = endpoint_id
-        self.name = name
-        self.prompt_fn = prompt_fn
-        stop_tokens = stop_tokens or []
-        self.generation_config = {
-            "max_new_tokens": 1024,
-            "top_k": 40,
-            "top_p": 0.95,
-            "temperature": 0.8,
-            "repetition_penalty": 1.1,
-            "last_n_tokens": 64,
-            "seed": -1,
-            "batch_size": 8,
-            "threads": -1,
-            "stop": ["</s>", "USER:", "### Instruction:"] + stop_tokens,
-        }
-
-    def __call__(self, prompt) -> Generator[str, None, None]:
-        input = self.generation_config.copy()
-        input["prompt"] = prompt
-
-        if self.prefer_async:
-            url = f"https://api.runpod.ai/v2/{self.endpoint_id}/run"
-        else:
-            url = f"https://api.runpod.ai/v2/{self.endpoint_id}/runsync"
-        headers = {
-            "Authorization": f"Bearer {os.environ['RUNPOD_AI_API_KEY']}"
-        }
-        response = requests.post(url, headers=headers, json={"input": input})
-
-        if response.status_code == 200:
-            data = response.json()
-            task_id = data.get('id')
-            return self.stream_output(task_id)
-
-    def stream_output(self,task_id) -> Generator[str, None, None]:
-        url = f"https://api.runpod.ai/v2/{self.endpoint_id}/stream/{task_id}"
-        headers = {
-            "Authorization": f"Bearer {os.environ['RUNPOD_AI_API_KEY']}"
-        }
-
-        while True:
-            response = requests.get(url, headers=headers)
-            if response.status_code == 200:
-                data = response.json()
-                yield [{"generated_text": "".join([s["output"] for s in data["stream"]])}]
-                if data.get('status') == 'COMPLETED':
-                    return
-            elif response.status_code >= 400:
-                logging.error(response.json())
-
-    def poll_for_status(self, task_id):
-        url = f"https://api.runpod.ai/v2/{self.endpoint_id}/status/{task_id}"
-        headers = {
-            "Authorization": f"Bearer {os.environ['RUNPOD_AI_API_KEY']}"
-        }
-
-        while True:
-            response = requests.get(url, headers=headers)
-            if response.status_code == 200:
-                data = response.json()
-                if data.get('status') == 'COMPLETED':
-                    return [{"generated_text": data["output"]}]
-            elif response.status_code >= 400:
-                logging.error(response.json())
-            # Sleep for 3 seconds between each request
-            sleep(3)
-
-    def transform_prompt(self, system_msg, history):
-        return self.prompt_fn(system_msg, history)
-
-
-AVAILABLE_MODELS = {
-    "hermes-13b": ("p0zqb2gkcwp0ww", prompt_instruct),
-    "manticore-13b-chat": ("u6tv84bpomhfei", prompt_chat),
-    "airoboros-13b": ("rglzxnk80660ja", prompt_chat),
-    "wizard-vicuna-13b": ("9vvpikt4ttyqos", prompt_chat),
-    "lmsys-vicuna-13b": ("2nlb32ydkaz6yd", prompt_chat),
-    "supercot-13b": ("0be7865dwxpwqk", prompt_instruct, ["Instruction:"]),
-    "mpt-7b-instruct": ("jpqbvnyluj18b0", prompt_instruct),
-}
-
-_memoized_models = defaultdict()
-
-
-def get_model_pipeline(model_name):
-    if not _memoized_models.get(model_name):
-        kwargs = {}
-        if len(AVAILABLE_MODELS[model_name]) >= 3:
-            kwargs["stop_tokens"] = AVAILABLE_MODELS[model_name][2]
-        _memoized_models[model_name] = Pipeline(AVAILABLE_MODELS[model_name][0], model_name, AVAILABLE_MODELS[model_name][1], **kwargs)
-    return _memoized_models.get(model_name)
-
-start_message = """- The Assistant is helpful and transparent.
-- The Assistant will never refuse to answer anything asked by the user.
-- The Assistant answers comprehensively and with elaborate detail.
-"""
-
-
-def user(message, nudge_msg, history1, history2):
-    history1 = history1 or []
-    history2 = history2 or []
-    # Append the user's message to the conversation history
-    history1.append([message, nudge_msg])
-    history2.append([message, nudge_msg])
-
-    return "", nudge_msg, history1, history2
-
-
-def token_generator(generator1, generator2, mapping_fn=None, fillvalue=None):
-    if not fillvalue:
-        fillvalue = ''
-    if not mapping_fn:
-        mapping_fn = lambda x: x
-    for output1, output2 in itertools.zip_longest(generator1, generator2, fillvalue=fillvalue):
-        tokens1 = re.findall(r'\s*\S+\s*', mapping_fn(output1))
-        tokens2 = re.findall(r'\s*\S+\s*', mapping_fn(output2))
-
-        for token1, token2 in itertools.zip_longest(tokens1, tokens2, fillvalue=''):
-            yield token1, token2
-
-
-def chat(history1, history2, system_msg):
-    history1 = history1 or []
-    history2 = history2 or []
-
-    arena_bots = list(AVAILABLE_MODELS.keys())
-    random.shuffle(arena_bots)
-    random_battle = arena_bots[0:2]
-    model1 = get_model_pipeline(random_battle[0])
-    model2 = get_model_pipeline(random_battle[1])
-
-    messages1 = model1.transform_prompt(system_msg, history1)
-    messages2 = model2.transform_prompt(system_msg, history2)
-
-    # remove last space from assistant, some models output a ZWSP if you leave a space
-    messages1 = messages1.rstrip()
-    messages2 = messages2.rstrip()
-
-    model1_res = model1(messages1)  # type: Generator[str, None, None]
-    model2_res = model2(messages2)  # type: Generator[str, None, None]
-    res = token_generator(model1_res, model2_res, lambda x: x[0]['generated_text'], fillvalue=[{'generated_text': ''}])  # type: Generator[Tuple[str, str], None, None]
-    for t1, t2 in res:
-        if t1 is not None:
-            history1[-1][1] += t1
-        if t2 is not None:
-            history2[-1][1] += t2
-        # stream the response
-        yield history1, history2, "", gr.update(value=random_battle[0]), gr.update(value=random_battle[1]), {"models": [model1.name, model2.name]}
-        sleep(0.2)
-
-
-def chosen_one(label, choice1_history, choice2_history, system_msg, nudge_msg, rlhf_persona, state):
-    # Generate a uuid for each submission
-    arena_battle_id = str(uuid.uuid4())
-
-    # Get the current timestamp
-    timestamp = datetime.datetime.now().isoformat()
-
-    # Put the item in the table
-    table.put_item(
-        Item={
-            'arena_battle_id': arena_battle_id,
-            'timestamp': timestamp,
-            'system_msg': system_msg,
-            'nudge_prefix': nudge_msg,
-            'choice1_name': state["models"][0],
-            'choice1': choice1_history,
-            'choice2_name': state["models"][1],
-            'choice2': choice2_history,
-            'label': label,
-            'rlhf_persona': rlhf_persona,
-        }
-    )
-
-chosen_one_first = functools.partial(chosen_one, 1)
-chosen_one_second = functools.partial(chosen_one, 2)
-chosen_one_tie = functools.partial(chosen_one, 0)
-chosen_one_suck = functools.partial(chosen_one, 1)
-
-leaderboard_intro = """### TBD
-- This is very much a work-in-progress, if you'd like to help build this out, join us on [Discord](https://discord.gg/QYF8QrtEUm)
-
-"""
-elo_scores = load_dataset("openaccess-ai-collective/chatbot-arena-elo-scores")
-elo_scores = elo_scores["train"].sort("elo_score", reverse=True)
-
-
-def refresh_md():
-    return leaderboard_intro + "\n" + dataset_to_markdown()
-
-
-def fetch_elo_scores():
-    elo_scores = load_dataset("openaccess-ai-collective/chatbot-arena-elo-scores")
-    elo_scores = elo_scores["train"].sort("elo_score", reverse=True)
-    return elo_scores
-
-
-def dataset_to_markdown():
-    dataset = fetch_elo_scores()
-    # Get column names (dataset features)
-    columns = list(dataset.features.keys())
-    # Start markdown string with table headers
-    markdown_string = "| " + " | ".join(columns) + " |\n"
-    # Add markdown table row separator for headers
-    markdown_string += "| " + " | ".join("---" for _ in columns) + " |\n"
-
-    # Add each row from dataset to the markdown string
-    for i in range(len(dataset)):
-        row = dataset[i]
-        markdown_string += "| " + " | ".join(str(row[column]) for column in columns) + " |\n"
-
-    return markdown_string
-
-
-with gr.Blocks() as arena:
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(f"""
-                    ### brought to you by OpenAccess AI Collective
-                    - Checkout out [our writeup on how this was built.](https://medium.com/@winglian/inference-any-llm-with-serverless-in-15-minutes-69eeb548a41d)
-                    - This Space runs on CPU only, and uses GGML with GPU support via Runpod Serverless.
-                    - Responses may not stream immediately due to cold starts on Serverless.
-                    - Some responses WILL take AT LEAST 20 seconds to respond   
-                    - For now, this is single turn only
-                    - [💵 Consider Donating on our Patreon](http://patreon.com/OpenAccessAICollective)
-                    - Join us on [Discord](https://discord.gg/PugNNHAF5r) 
-                    """)
-    with gr.Tab("Chatbot"):
-        with gr.Row():
-            with gr.Column():
-                chatbot1 = gr.Chatbot()
-            with gr.Column():
-                chatbot2 = gr.Chatbot()
-        with gr.Row():
-            choose1 = gr.Button(value="👈 Prefer left", variant="secondary", visible=False).style(full_width=True)
-            choose2 = gr.Button(value="👉 Prefer right", variant="secondary", visible=False).style(full_width=True)
-            choose3 = gr.Button(value="🤝 Tie", variant="secondary", visible=False).style(full_width=True)
-            choose4 = gr.Button(value="👉 Both are bad", variant="secondary", visible=False).style(full_width=True)
-        with gr.Row():
-            reveal1 = gr.Textbox(label="Model Name", value="", interactive=False, visible=False).style(full_width=True)
-            reveal2 = gr.Textbox(label="Model Name", value="", interactive=False, visible=False).style(full_width=True)
-        with gr.Row():
-            dismiss_reveal = gr.Button(value="Dismiss & Continue", variant="secondary", visible=False).style(full_width=True)
-        with gr.Row():
-            with gr.Column():
-                message = gr.Textbox(
-                    label="What do you want to ask?",
-                    placeholder="Ask me anything.",
-                    lines=3,
-                )
-            with gr.Column():
-                rlhf_persona = gr.Textbox(
-                    "", label="Persona Tags", interactive=True, visible=True, placeholder="Tell us about how you are judging the quality. ex: #CoT #SFW #NSFW #helpful #ethical #creativity", lines=2)
-                system_msg = gr.Textbox(
-                    start_message, label="System Message", interactive=True, visible=True, placeholder="system prompt", lines=8)
-
-                nudge_msg = gr.Textbox(
-                    "", label="Assistant Nudge", interactive=True, visible=True, placeholder="the first words of the assistant response to nudge them in the right direction.", lines=2)
-        with gr.Row():
-            submit = gr.Button(value="Send message", variant="secondary").style(full_width=True)
-            clear = gr.Button(value="New topic", variant="secondary").style(full_width=False)
-    with gr.Tab("Leaderboard"):
-        with gr.Column():
-            leaderboard_markdown = gr.Markdown(f"""{leaderboard_intro}
-{dataset_to_markdown()}
-""")
-            refresh = gr.Button(value="Refresh Leaderboard", variant="secondary").style(full_width=True)
-    state = gr.State({})
-
-    refresh.click(fn=refresh_md, inputs=[], outputs=refresh)
-
-    clear.click(lambda: None, None, chatbot1, queue=False)
-    clear.click(lambda: None, None, chatbot2, queue=False)
-    clear.click(lambda: None, None, message, queue=False)
-    clear.click(lambda: None, None, nudge_msg, queue=False)
-
-    submit_click_event = submit.click(
-        lambda *args: (
-            gr.update(visible=False, interactive=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-        ),
-        inputs=[], outputs=[message, clear, submit], queue=True
-    ).then(
-        fn=user, inputs=[message, nudge_msg, chatbot1, chatbot2], outputs=[message, nudge_msg, chatbot1, chatbot2], queue=True
-    ).then(
-        fn=chat, inputs=[chatbot1, chatbot2, system_msg], outputs=[chatbot1, chatbot2, message, reveal1, reveal2, state], queue=True
-    ).then(
-        lambda *args: (
-            gr.update(visible=False, interactive=False),
-            gr.update(visible=True),
-            gr.update(visible=True),
-            gr.update(visible=True),
-            gr.update(visible=True),
-            gr.update(visible=False),
-            gr.update(visible=False),
-        ),
-        inputs=[message, nudge_msg, system_msg], outputs=[message, choose1, choose2, choose3, choose4, clear, submit], queue=True
-    )
-
-    choose1_click_event = choose1.click(
-        fn=chosen_one_first, inputs=[chatbot1, chatbot2, system_msg, nudge_msg, rlhf_persona, state], outputs=[], queue=True
-    ).then(
-        lambda *args: (
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=True),
-            gr.update(visible=True),
-            gr.update(visible=True),
-        ),
-        inputs=[], outputs=[choose1, choose2, choose3, choose4, dismiss_reveal, reveal1, reveal2], queue=True
-    )
-
-    choose2_click_event = choose2.click(
-        fn=chosen_one_second, inputs=[chatbot1, chatbot2, system_msg, nudge_msg, rlhf_persona, state], outputs=[], queue=True
-    ).then(
-        lambda *args: (
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=True),
-            gr.update(visible=True),
-            gr.update(visible=True),
-        ),
-        inputs=[], outputs=[choose1, choose2, choose3, choose4, dismiss_reveal, reveal1, reveal2], queue=True
-    )
-
-    choose3_click_event = choose3.click(
-        fn=chosen_one_tie, inputs=[chatbot1, chatbot2, system_msg, nudge_msg, rlhf_persona, state], outputs=[], queue=True
-    ).then(
-        lambda *args: (
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=True),
-            gr.update(visible=True),
-            gr.update(visible=True),
-        ),
-        inputs=[], outputs=[choose1, choose2, choose3, choose4, dismiss_reveal, reveal1, reveal2], queue=True
-    )
-
-    choose4_click_event = choose4.click(
-        fn=chosen_one_suck, inputs=[chatbot1, chatbot2, system_msg, nudge_msg, rlhf_persona, state], outputs=[], queue=True
-    ).then(
-        lambda *args: (
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=True),
-            gr.update(visible=True),
-            gr.update(visible=True),
-        ),
-        inputs=[], outputs=[choose1, choose2, choose3, choose4, dismiss_reveal, reveal1, reveal2], queue=True
-    )
-
-    dismiss_click_event = dismiss_reveal.click(
-        lambda *args: (
-            gr.update(visible=True, interactive=True),
-            gr.update(visible=False),
-            gr.update(visible=True),
-            gr.update(visible=True),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            None,
-            None,
-        ),
-        inputs=[], outputs=[message, dismiss_reveal, clear, submit, reveal1, reveal2, chatbot1, chatbot2], queue=True
-    )
-
-arena.queue(concurrency_count=5, max_size=16).launch(debug=True, server_name="0.0.0.0", server_port=7860)
\ No newline at end of file
diff --git a/spaces/osanseviero/latent-converter/app.py b/spaces/osanseviero/latent-converter/app.py
deleted file mode 100644
index bfdd8cb134a6db14060db347079cfed1e8e7f2eb..0000000000000000000000000000000000000000
--- a/spaces/osanseviero/latent-converter/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-import io, base64
-from PIL import Image
-import numpy as np
-import tensorflow as tf
-import mediapy
-import os
-import sys
-from huggingface_hub import snapshot_download
-
-image_gen = gr.Interface.load("spaces/multimodalart/latentdiffusion")
-
-os.system("git clone https://github.com/google-research/frame-interpolation")
-sys.path.append("frame-interpolation")
-from eval import interpolator, util
-
-ffmpeg_path = util.get_ffmpeg_path()
-mediapy.set_ffmpeg(ffmpeg_path)
-
-model = snapshot_download(repo_id="akhaliq/frame-interpolation-film-style")
-interpolator = interpolator.Interpolator(model, None)
-
-def generate_story(choice, input_text):
-    query = "<BOS> <{0}> {1}".format(choice, input_text)
-    
-    print(query)
-    generated_text = story_gen(query)
-    generated_text = generated_text[0]['generated_text']
-    generated_text = generated_text.split('> ')[2]
-    
-    return generated_text
-    
-def generate_images(text):
-    steps=50
-    width=256
-    height=256
-    num_images=4
-    diversity=1
-    image_bytes = image_gen(text, steps, width, height, num_images, diversity)
-    
-    # Algo from spaces/Gradio-Blocks/latent_gpt2_story/blob/main/app.py
-    generated_images = []
-    for image in image_bytes[1]:
-        image_str = image[0]
-        image_str = image_str.replace("data:image/png;base64,","")
-        decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8"))
-        img = Image.open(io.BytesIO(decoded_bytes))
-        generated_images.append(img)
-        
-    return generated_images[0]
-    
-def generate_interpolation(input_text, end_text):
-    times_to_interpolate = 15
-    
-    inp_img = generate_images(input_text)
-    end_img = generate_images(end_text)
-    
-    inp_img.save('frame_0.png')
-    end_img.save('frame_1.png')
-    
-    input_frames = ["frame_0.png", "frame_1.png"]
-
-    frames = list(util.interpolate_recursively_from_files(input_frames, times_to_interpolate, interpolator))
-
-    mediapy.write_video("out.mp4", frames, fps=5)
-    
-    return "out.mp4"
-    
-    
-
-demo = gr.Blocks()
-
-with demo:
-    input_text = gr.Textbox(value ='Rabbit', label="Convert from:")
-    end_text = gr.Textbox(value ='Darth Vader', label="To:")
-    button_gen_video = gr.Button("Generate Video")
-    output_interpolation = gr.Video(label="Generated Video")
-            
-    button_gen_video.click(fn=generate_interpolation, inputs=[input_text, end_text], outputs=output_interpolation)
-
-demo.launch(debug=True, enable_queue=True)
\ No newline at end of file
diff --git a/spaces/paragon-analytics/Employee-Turnover/app.py b/spaces/paragon-analytics/Employee-Turnover/app.py
deleted file mode 100644
index 298178f2f3722a2d087a057da272d2317f30ff93..0000000000000000000000000000000000000000
--- a/spaces/paragon-analytics/Employee-Turnover/app.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import pickle
-import pandas as pd
-import shap
-from shap.plots._force_matplotlib import draw_additive_plot
-import gradio as gr
-import numpy as np
-import matplotlib.pyplot as plt
-
-# load the model from disk
-loaded_model = pickle.load(open("h22_xgb.pkl", 'rb'))
-
-# Setup SHAP
-explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
-
-# Create the main function for server
-def main_func(ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance):
-    new_row = pd.DataFrame.from_dict({'ValueDiversity':ValueDiversity,'AdequateResources':AdequateResources,
-              'Voice':Voice,'GrowthAdvancement':GrowthAdvancement,'Workload':Workload,
-              'WorkLifeBalance':WorkLifeBalance}, orient = 'index').transpose()
-    
-    prob = loaded_model.predict_proba(new_row)
-    
-    shap_values = explainer(new_row)
-    # plot = shap.force_plot(shap_values[0], matplotlib=True, figsize=(30,30), show=False)
-    # plot = shap.plots.waterfall(shap_values[0], max_display=6, show=False)
-    plot = shap.plots.bar(shap_values[0], max_display=6, order=shap.Explanation.abs, show_data='auto', show=False)
-
-    plt.tight_layout()
-    local_plot = plt.gcf()
-    plt.close()
-    
-    return {"Leave": float(prob[0][0]), "Stay": 1-float(prob[0][0])}, local_plot
-
-# Create the UI
-title = "**Employee Turnover Predictor & Interpreter** 🪐"
-description1 = """
-This app takes six inputs about employees' satisfaction with different aspects of their work (such as work-life balance, ...) and predicts whether the employee intends to stay with the employer or leave. There are two outputs from the app: 1- the predicted probability of stay or leave, 2- Shapley's force-plot which visualizes the extent to which each factor impacts the stay/ leave prediction.✨   
-"""
-
-description2 = """
-To use the app, click on one of the examples, or adjust the values of the six employee satisfaction factors, and click on Analyze. 🤞
-""" 
-
-with gr.Blocks(title=title) as demo:
-    gr.Markdown(f"## {title}")
-#    gr.Markdown("""![marketing](file/marketing.jpg)""")
-    gr.Markdown(description1)
-    gr.Markdown("""---""")
-    gr.Markdown(description2)
-    gr.Markdown("""---""")
-    ValueDiversity = gr.Slider(label="ValueDiversity Score", minimum=1, maximum=5, value=4, step=1)
-    AdequateResources = gr.Slider(label="AdequateResources Score", minimum=1, maximum=5, value=4, step=1)
-    Voice = gr.Slider(label="Voice Score", minimum=1, maximum=5, value=4, step=1)
-    GrowthAdvancement = gr.Slider(label="GrowthAdvancement Score", minimum=1, maximum=5, value=4, step=1)
-    Workload = gr.Slider(label="Workload Score", minimum=1, maximum=5, value=4, step=1)
-    WorkLifeBalance = gr.Slider(label="WorkLifeBalance Score", minimum=1, maximum=5, value=4, step=1)
-    submit_btn = gr.Button("Analyze")
-
-    with gr.Column(visible=True) as output_col:
-        label = gr.Label(label = "Predicted Label")
-        local_plot = gr.Plot(label = 'Shap:')
-
-    submit_btn.click(
-        main_func,
-        [ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance],
-        [label,local_plot], api_name="Employee_Turnover"
-    )
-    
-    gr.Markdown("### Click on any of the examples below to see how it works:")
-    gr.Examples([[4,4,4,4,5,5], [5,4,5,4,4,4]], [ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance], [label,local_plot], main_func, cache_examples=True)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/peteralexandercharles/Voice-Cloning/app.py b/spaces/peteralexandercharles/Voice-Cloning/app.py
deleted file mode 100644
index 579153ea962d621023d68c1e0aafb4681c826b1c..0000000000000000000000000000000000000000
--- a/spaces/peteralexandercharles/Voice-Cloning/app.py
+++ /dev/null
@@ -1,165 +0,0 @@
-from turtle import title
-import gradio as gr
-
-import git
-import os
-os.system('git clone https://github.com/Edresson/Coqui-TTS -b multilingual-torchaudio-SE TTS')
-os.system('pip install -q -e TTS/')
-os.system('pip install -q torchaudio==0.9.0')
-
-import sys
-TTS_PATH = "TTS/"
-
-# add libraries into environment
-sys.path.append(TTS_PATH) # set this if TTS is not installed globally
-
-import os
-import string
-import time
-import argparse
-import json
-
-import numpy as np
-import IPython
-from IPython.display import Audio
-
-
-import torch
-
-from TTS.tts.utils.synthesis import synthesis
-from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
-try:
-  from TTS.utils.audio import AudioProcessor
-except:
-  from TTS.utils.audio import AudioProcessor
-
-
-from TTS.tts.models import setup_model
-from TTS.config import load_config
-from TTS.tts.models.vits import *  
-
-OUT_PATH = 'out/'
-
-# create output path
-os.makedirs(OUT_PATH, exist_ok=True)
-
-# model vars 
-MODEL_PATH = '/home/user/app/best_model_latest.pth.tar'
-CONFIG_PATH = '/home/user/app/config.json'
-TTS_LANGUAGES = "/home/user/app/language_ids.json"
-TTS_SPEAKERS = "/home/user/app/speakers.json"
-USE_CUDA = torch.cuda.is_available()  
-
-# load the config
-C = load_config(CONFIG_PATH)
-
-
-# load the audio processor
-ap = AudioProcessor(**C.audio)
-
-speaker_embedding = None
-
-C.model_args['d_vector_file'] = TTS_SPEAKERS
-C.model_args['use_speaker_encoder_as_loss'] = False
-
-model = setup_model(C)
-model.language_manager.set_language_ids_from_file(TTS_LANGUAGES)
-# print(model.language_manager.num_languages, model.embedded_language_dim)
-# print(model.emb_l)
-cp = torch.load(MODEL_PATH, map_location=torch.device('cpu'))
-# remove speaker encoder
-model_weights = cp['model'].copy()
-for key in list(model_weights.keys()):
-  if "speaker_encoder" in key:
-    del model_weights[key]
-
-model.load_state_dict(model_weights)
-
-
-model.eval()
-
-if USE_CUDA:
-    model = model.cuda()
-
-# synthesize voice
-use_griffin_lim = False
-
-os.system('pip install -q pydub ffmpeg-normalize')
-
-CONFIG_SE_PATH = "config_se.json"
-CHECKPOINT_SE_PATH = "SE_checkpoint.pth.tar"
-
-from TTS.tts.utils.speakers import SpeakerManager
-from pydub import AudioSegment
-import librosa
-
-SE_speaker_manager = SpeakerManager(encoder_model_path=CHECKPOINT_SE_PATH, encoder_config_path=CONFIG_SE_PATH, use_cuda=USE_CUDA)
-
-def compute_spec(ref_file):
-  y, sr = librosa.load(ref_file, sr=ap.sample_rate)
-  spec = ap.spectrogram(y)
-  spec = torch.FloatTensor(spec).unsqueeze(0)
-  return spec
-  
-
-    
-def greet(Text,Voicetoclone,VoiceMicrophone):
-    text= "%s" % (Text)
-    if Voicetoclone is not None:
-      reference_files= "%s" % (Voicetoclone)
-      print("path url")
-      print(Voicetoclone)
-      sample= str(Voicetoclone)
-    else:
-      reference_files= "%s" % (VoiceMicrophone)
-      print("path url")
-      print(VoiceMicrophone)
-      sample= str(VoiceMicrophone)
-    size= len(reference_files)*sys.getsizeof(reference_files)
-    size2= size / 10000000
-    if (size2 > 0.012) or len(text)>20000:
-      message="File is greater than 300mb or Text inserted is longer than 20000 characters. Please re-try with smaller sizes."
-      print(message)
-      raise SystemExit("File is greater than 30mb. Please re-try or Text inserted is longer than 2000 characters. Please re-try with smaller sizes.")
-    else:
-      os.system('ffmpeg-normalize $sample -nt rms -t=-270 -o $sample -ar 160000 -f')
-      reference_emb = SE_speaker_manager.compute_d_vector_from_clip(reference_files)
-      model.length_scale = 2  # scaler for the duration predictor. The larger it is, the slower the speech.
-      model.inference_noise_scale = 0.3 # defines the noise variance applied to the random z vector at inference.
-      model.inference_noise_scale_dp = 0.3 # defines the noise variance applied to the duration predictor z vector at inference.
-      text = text
-      model.language_manager.language_id_mapping
-      language_id = 0
-    
-      print(" > text: {}".format(text))
-      wav, alignment, _, _ = synthesis(
-                        model,
-                        text,
-                        C,
-                        "cuda" in str(next(model.parameters()).device),
-                        ap,
-                        speaker_id=None,
-                        d_vector=reference_emb,
-                        style_wav=None,
-                        language_id=language_id,
-                        enable_eos_bos_chars=C.enable_eos_bos_chars,
-                        use_griffin_lim=True,
-                        do_trim_silence=False,
-                    ).values()
-      print("Generated Audio")
-      IPython.display.display(Audio(wav, rate=ap.sample_rate))
-      #file_name = text.replace(" ", "_")
-      #file_name = file_name.translate(str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'
-      file_name="Audio.wav"
-      out_path = os.path.join(OUT_PATH, file_name)
-      print(" > Saving output to {}".format(out_path))
-      ap.save_wav(wav, out_path)
-      return out_path
-
-demo = gr.Interface(
-    fn=greet, 
-    inputs=[gr.inputs.Textbox(label='What would you like the voice to say? (max. 20000 characters per request)'),gr.Audio(type="filepath",         source="upload",label='Please upload a voice to clone (max. 30mb)'),gr.Audio(source="microphone", type="filepath", streaming=True)],
-    outputs="audio",
-    title="Bilal's Voice Cloning Tool"
-    )
-demo.launch()
\ No newline at end of file
diff --git a/spaces/peteralexandercharles/automatic-speech-recognition-with-next-gen-kaldi/test_wavs/aishell2/README.md b/spaces/peteralexandercharles/automatic-speech-recognition-with-next-gen-kaldi/test_wavs/aishell2/README.md
deleted file mode 100644
index 40a16b2ac43de0a40248b86e198e7077b8e44ee6..0000000000000000000000000000000000000000
--- a/spaces/peteralexandercharles/automatic-speech-recognition-with-next-gen-kaldi/test_wavs/aishell2/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-Files are downloaded from
-https://huggingface.co/yuekai/icefall-asr-aishell2-pruned-transducer-stateless5-B-2022-07-12/tree/main/test_wavs
diff --git a/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/downloads_torchscript.py b/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/downloads_torchscript.py
deleted file mode 100644
index 1955fbe859767abce5dbe916cea944173cb83c2b..0000000000000000000000000000000000000000
--- a/spaces/phyloforfun/VoucherVision/vouchervision/component_detector/utils/downloads_torchscript.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Download utils
-"""
-
-import logging
-import subprocess
-import urllib
-from pathlib import Path
-
-import requests
-import torch
-
-
-def is_url(url, check=True):
-    # Check if string is URL and check if URL exists
-    try:
-        url = str(url)
-        result = urllib.parse.urlparse(url)
-        assert all([result.scheme, result.netloc])  # check if is url
-        return (urllib.request.urlopen(url).getcode() == 200) if check else True  # check if exists online
-    except (AssertionError, urllib.request.HTTPError):
-        return False
-
-
-def gsutil_getsize(url=''):
-    # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
-    output = subprocess.check_output(['gsutil', 'du', url], shell=True, encoding='utf-8')
-    if output:
-        return int(output.split()[0])
-    return 0
-
-
-def url_getsize(url='https://ultralytics.com/images/bus.jpg'):
-    # Return downloadable file size in bytes
-    response = requests.head(url, allow_redirects=True)
-    return int(response.headers.get('content-length', -1))
-
-
-def curl_download(url, filename, *, silent: bool = False) -> bool:
-    """
-    Download a file from a url to a filename using curl.
-    """
-    silent_option = 'sS' if silent else ''  # silent
-    proc = subprocess.run([
-        'curl',
-        '-#',
-        f'-{silent_option}L',
-        url,
-        '--output',
-        filename,
-        '--retry',
-        '9',
-        '-C',
-        '-', ])
-    return proc.returncode == 0
-
-
-def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
-    # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes
-    from utils.general import LOGGER
-
-    file = Path(file)
-    assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
-    try:  # url1
-        LOGGER.info(f'Downloading {url} to {file}...')
-        torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO)
-        assert file.exists() and file.stat().st_size > min_bytes, assert_msg  # check
-    except Exception as e:  # url2
-        if file.exists():
-            file.unlink()  # remove partial downloads
-        LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
-        # curl download, retry and resume on fail
-        curl_download(url2 or url, file)
-    finally:
-        if not file.exists() or file.stat().st_size < min_bytes:  # check
-            if file.exists():
-                file.unlink()  # remove partial downloads
-            LOGGER.info(f'ERROR: {assert_msg}\n{error_msg}')
-        LOGGER.info('')
-
-
-def attempt_download(file, repo='ultralytics/yolov5', release='v7.0'):
-    # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v7.0', etc.
-    from utils.general import LOGGER
-
-    def github_assets(repository, version='latest'):
-        # Return GitHub repo tag (i.e. 'v7.0') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...])
-        if version != 'latest':
-            version = f'tags/{version}'  # i.e. tags/v7.0
-        response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json()  # github api
-        return response['tag_name'], [x['name'] for x in response['assets']]  # tag, assets
-
-    file = Path(str(file).strip().replace("'", ''))
-    if not file.exists():
-        # URL specified
-        name = Path(urllib.parse.unquote(str(file))).name  # decode '%2F' to '/' etc.
-        if str(file).startswith(('http:/', 'https:/')):  # download
-            url = str(file).replace(':/', '://')  # Pathlib turns :// -> :/
-            file = name.split('?')[0]  # parse authentication https://url.com/file.txt?auth...
-            if Path(file).is_file():
-                LOGGER.info(f'Found {url} locally at {file}')  # file already exists
-            else:
-                safe_download(file=file, url=url, min_bytes=1E5)
-            return file
-
-        # GitHub assets
-        assets = [f'yolov5{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')]  # default
-        try:
-            tag, assets = github_assets(repo, release)
-        except Exception:
-            try:
-                tag, assets = github_assets(repo)  # latest release
-            except Exception:
-                try:
-                    tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
-                except Exception:
-                    tag = release
-
-        if name in assets:
-            file.parent.mkdir(parents=True, exist_ok=True)  # make parent dir (if required)
-            safe_download(file,
-                          url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
-                          min_bytes=1E5,
-                          error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag}')
-
-    return str(file)
\ No newline at end of file
diff --git a/spaces/pkarthik15/docchat/README.md b/spaces/pkarthik15/docchat/README.md
deleted file mode 100644
index a5e2addfee28a4b6f1d72550121815b63ba92b8d..0000000000000000000000000000000000000000
--- a/spaces/pkarthik15/docchat/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Docchat
-emoji: 🐢
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/search.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/search.py
deleted file mode 100644
index 03ed925b246dd551ec2ef45095ed6cad00fd2745..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/search.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import logging
-import shutil
-import sys
-import textwrap
-import xmlrpc.client
-from collections import OrderedDict
-from optparse import Values
-from typing import TYPE_CHECKING, Dict, List, Optional
-
-from pip._vendor.packaging.version import parse as parse_version
-
-from pip._internal.cli.base_command import Command
-from pip._internal.cli.req_command import SessionCommandMixin
-from pip._internal.cli.status_codes import NO_MATCHES_FOUND, SUCCESS
-from pip._internal.exceptions import CommandError
-from pip._internal.metadata import get_default_environment
-from pip._internal.models.index import PyPI
-from pip._internal.network.xmlrpc import PipXmlrpcTransport
-from pip._internal.utils.logging import indent_log
-from pip._internal.utils.misc import write_output
-
-if TYPE_CHECKING:
-    from typing import TypedDict
-
-    class TransformedHit(TypedDict):
-        name: str
-        summary: str
-        versions: List[str]
-
-
-logger = logging.getLogger(__name__)
-
-
-class SearchCommand(Command, SessionCommandMixin):
-    """Search for PyPI packages whose name or summary contains <query>."""
-
-    usage = """
-      %prog [options] <query>"""
-    ignore_require_venv = True
-
-    def add_options(self) -> None:
-        self.cmd_opts.add_option(
-            "-i",
-            "--index",
-            dest="index",
-            metavar="URL",
-            default=PyPI.pypi_url,
-            help="Base URL of Python Package Index (default %default)",
-        )
-
-        self.parser.insert_option_group(0, self.cmd_opts)
-
-    def run(self, options: Values, args: List[str]) -> int:
-        if not args:
-            raise CommandError("Missing required argument (search query).")
-        query = args
-        pypi_hits = self.search(query, options)
-        hits = transform_hits(pypi_hits)
-
-        terminal_width = None
-        if sys.stdout.isatty():
-            terminal_width = shutil.get_terminal_size()[0]
-
-        print_results(hits, terminal_width=terminal_width)
-        if pypi_hits:
-            return SUCCESS
-        return NO_MATCHES_FOUND
-
-    def search(self, query: List[str], options: Values) -> List[Dict[str, str]]:
-        index_url = options.index
-
-        session = self.get_default_session(options)
-
-        transport = PipXmlrpcTransport(index_url, session)
-        pypi = xmlrpc.client.ServerProxy(index_url, transport)
-        try:
-            hits = pypi.search({"name": query, "summary": query}, "or")
-        except xmlrpc.client.Fault as fault:
-            message = "XMLRPC request failed [code: {code}]\n{string}".format(
-                code=fault.faultCode,
-                string=fault.faultString,
-            )
-            raise CommandError(message)
-        assert isinstance(hits, list)
-        return hits
-
-
-def transform_hits(hits: List[Dict[str, str]]) -> List["TransformedHit"]:
-    """
-    The list from pypi is really a list of versions. We want a list of
-    packages with the list of versions stored inline. This converts the
-    list from pypi into one we can use.
-    """
-    packages: Dict[str, "TransformedHit"] = OrderedDict()
-    for hit in hits:
-        name = hit["name"]
-        summary = hit["summary"]
-        version = hit["version"]
-
-        if name not in packages.keys():
-            packages[name] = {
-                "name": name,
-                "summary": summary,
-                "versions": [version],
-            }
-        else:
-            packages[name]["versions"].append(version)
-
-            # if this is the highest version, replace summary and score
-            if version == highest_version(packages[name]["versions"]):
-                packages[name]["summary"] = summary
-
-    return list(packages.values())
-
-
-def print_dist_installation_info(name: str, latest: str) -> None:
-    env = get_default_environment()
-    dist = env.get_distribution(name)
-    if dist is not None:
-        with indent_log():
-            if dist.version == latest:
-                write_output("INSTALLED: %s (latest)", dist.version)
-            else:
-                write_output("INSTALLED: %s", dist.version)
-                if parse_version(latest).pre:
-                    write_output(
-                        "LATEST:    %s (pre-release; install"
-                        " with `pip install --pre`)",
-                        latest,
-                    )
-                else:
-                    write_output("LATEST:    %s", latest)
-
-
-def print_results(
-    hits: List["TransformedHit"],
-    name_column_width: Optional[int] = None,
-    terminal_width: Optional[int] = None,
-) -> None:
-    if not hits:
-        return
-    if name_column_width is None:
-        name_column_width = (
-            max(
-                [
-                    len(hit["name"]) + len(highest_version(hit.get("versions", ["-"])))
-                    for hit in hits
-                ]
-            )
-            + 4
-        )
-
-    for hit in hits:
-        name = hit["name"]
-        summary = hit["summary"] or ""
-        latest = highest_version(hit.get("versions", ["-"]))
-        if terminal_width is not None:
-            target_width = terminal_width - name_column_width - 5
-            if target_width > 10:
-                # wrap and indent summary to fit terminal
-                summary_lines = textwrap.wrap(summary, target_width)
-                summary = ("\n" + " " * (name_column_width + 3)).join(summary_lines)
-
-        name_latest = f"{name} ({latest})"
-        line = f"{name_latest:{name_column_width}} - {summary}"
-        try:
-            write_output(line)
-            print_dist_installation_info(name, latest)
-        except UnicodeEncodeError:
-            pass
-
-
-def highest_version(versions: List[str]) -> str:
-    return max(versions, key=parse_version)
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/six.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/six.py
deleted file mode 100644
index 4e15675d8b5caa33255fe37271700f587bd26671..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/six.py
+++ /dev/null
@@ -1,998 +0,0 @@
-# Copyright (c) 2010-2020 Benjamin Peterson
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-"""Utilities for writing code that runs on Python 2 and 3"""
-
-from __future__ import absolute_import
-
-import functools
-import itertools
-import operator
-import sys
-import types
-
-__author__ = "Benjamin Peterson <benjamin@python.org>"
-__version__ = "1.16.0"
-
-
-# Useful for very coarse version differentiation.
-PY2 = sys.version_info[0] == 2
-PY3 = sys.version_info[0] == 3
-PY34 = sys.version_info[0:2] >= (3, 4)
-
-if PY3:
-    string_types = str,
-    integer_types = int,
-    class_types = type,
-    text_type = str
-    binary_type = bytes
-
-    MAXSIZE = sys.maxsize
-else:
-    string_types = basestring,
-    integer_types = (int, long)
-    class_types = (type, types.ClassType)
-    text_type = unicode
-    binary_type = str
-
-    if sys.platform.startswith("java"):
-        # Jython always uses 32 bits.
-        MAXSIZE = int((1 << 31) - 1)
-    else:
-        # It's possible to have sizeof(long) != sizeof(Py_ssize_t).
-        class X(object):
-
-            def __len__(self):
-                return 1 << 31
-        try:
-            len(X())
-        except OverflowError:
-            # 32-bit
-            MAXSIZE = int((1 << 31) - 1)
-        else:
-            # 64-bit
-            MAXSIZE = int((1 << 63) - 1)
-        del X
-
-if PY34:
-    from importlib.util import spec_from_loader
-else:
-    spec_from_loader = None
-
-
-def _add_doc(func, doc):
-    """Add documentation to a function."""
-    func.__doc__ = doc
-
-
-def _import_module(name):
-    """Import module, returning the module after the last dot."""
-    __import__(name)
-    return sys.modules[name]
-
-
-class _LazyDescr(object):
-
-    def __init__(self, name):
-        self.name = name
-
-    def __get__(self, obj, tp):
-        result = self._resolve()
-        setattr(obj, self.name, result)  # Invokes __set__.
-        try:
-            # This is a bit ugly, but it avoids running this again by
-            # removing this descriptor.
-            delattr(obj.__class__, self.name)
-        except AttributeError:
-            pass
-        return result
-
-
-class MovedModule(_LazyDescr):
-
-    def __init__(self, name, old, new=None):
-        super(MovedModule, self).__init__(name)
-        if PY3:
-            if new is None:
-                new = name
-            self.mod = new
-        else:
-            self.mod = old
-
-    def _resolve(self):
-        return _import_module(self.mod)
-
-    def __getattr__(self, attr):
-        _module = self._resolve()
-        value = getattr(_module, attr)
-        setattr(self, attr, value)
-        return value
-
-
-class _LazyModule(types.ModuleType):
-
-    def __init__(self, name):
-        super(_LazyModule, self).__init__(name)
-        self.__doc__ = self.__class__.__doc__
-
-    def __dir__(self):
-        attrs = ["__doc__", "__name__"]
-        attrs += [attr.name for attr in self._moved_attributes]
-        return attrs
-
-    # Subclasses should override this
-    _moved_attributes = []
-
-
-class MovedAttribute(_LazyDescr):
-
-    def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):
-        super(MovedAttribute, self).__init__(name)
-        if PY3:
-            if new_mod is None:
-                new_mod = name
-            self.mod = new_mod
-            if new_attr is None:
-                if old_attr is None:
-                    new_attr = name
-                else:
-                    new_attr = old_attr
-            self.attr = new_attr
-        else:
-            self.mod = old_mod
-            if old_attr is None:
-                old_attr = name
-            self.attr = old_attr
-
-    def _resolve(self):
-        module = _import_module(self.mod)
-        return getattr(module, self.attr)
-
-
-class _SixMetaPathImporter(object):
-
-    """
-    A meta path importer to import six.moves and its submodules.
-
-    This class implements a PEP302 finder and loader. It should be compatible
-    with Python 2.5 and all existing versions of Python3
-    """
-
-    def __init__(self, six_module_name):
-        self.name = six_module_name
-        self.known_modules = {}
-
-    def _add_module(self, mod, *fullnames):
-        for fullname in fullnames:
-            self.known_modules[self.name + "." + fullname] = mod
-
-    def _get_module(self, fullname):
-        return self.known_modules[self.name + "." + fullname]
-
-    def find_module(self, fullname, path=None):
-        if fullname in self.known_modules:
-            return self
-        return None
-
-    def find_spec(self, fullname, path, target=None):
-        if fullname in self.known_modules:
-            return spec_from_loader(fullname, self)
-        return None
-
-    def __get_module(self, fullname):
-        try:
-            return self.known_modules[fullname]
-        except KeyError:
-            raise ImportError("This loader does not know module " + fullname)
-
-    def load_module(self, fullname):
-        try:
-            # in case of a reload
-            return sys.modules[fullname]
-        except KeyError:
-            pass
-        mod = self.__get_module(fullname)
-        if isinstance(mod, MovedModule):
-            mod = mod._resolve()
-        else:
-            mod.__loader__ = self
-        sys.modules[fullname] = mod
-        return mod
-
-    def is_package(self, fullname):
-        """
-        Return true, if the named module is a package.
-
-        We need this method to get correct spec objects with
-        Python 3.4 (see PEP451)
-        """
-        return hasattr(self.__get_module(fullname), "__path__")
-
-    def get_code(self, fullname):
-        """Return None
-
-        Required, if is_package is implemented"""
-        self.__get_module(fullname)  # eventually raises ImportError
-        return None
-    get_source = get_code  # same as get_code
-
-    def create_module(self, spec):
-        return self.load_module(spec.name)
-
-    def exec_module(self, module):
-        pass
-
-_importer = _SixMetaPathImporter(__name__)
-
-
-class _MovedItems(_LazyModule):
-
-    """Lazy loading of moved objects"""
-    __path__ = []  # mark as package
-
-
-_moved_attributes = [
-    MovedAttribute("cStringIO", "cStringIO", "io", "StringIO"),
-    MovedAttribute("filter", "itertools", "builtins", "ifilter", "filter"),
-    MovedAttribute("filterfalse", "itertools", "itertools", "ifilterfalse", "filterfalse"),
-    MovedAttribute("input", "__builtin__", "builtins", "raw_input", "input"),
-    MovedAttribute("intern", "__builtin__", "sys"),
-    MovedAttribute("map", "itertools", "builtins", "imap", "map"),
-    MovedAttribute("getcwd", "os", "os", "getcwdu", "getcwd"),
-    MovedAttribute("getcwdb", "os", "os", "getcwd", "getcwdb"),
-    MovedAttribute("getoutput", "commands", "subprocess"),
-    MovedAttribute("range", "__builtin__", "builtins", "xrange", "range"),
-    MovedAttribute("reload_module", "__builtin__", "importlib" if PY34 else "imp", "reload"),
-    MovedAttribute("reduce", "__builtin__", "functools"),
-    MovedAttribute("shlex_quote", "pipes", "shlex", "quote"),
-    MovedAttribute("StringIO", "StringIO", "io"),
-    MovedAttribute("UserDict", "UserDict", "collections"),
-    MovedAttribute("UserList", "UserList", "collections"),
-    MovedAttribute("UserString", "UserString", "collections"),
-    MovedAttribute("xrange", "__builtin__", "builtins", "xrange", "range"),
-    MovedAttribute("zip", "itertools", "builtins", "izip", "zip"),
-    MovedAttribute("zip_longest", "itertools", "itertools", "izip_longest", "zip_longest"),
-    MovedModule("builtins", "__builtin__"),
-    MovedModule("configparser", "ConfigParser"),
-    MovedModule("collections_abc", "collections", "collections.abc" if sys.version_info >= (3, 3) else "collections"),
-    MovedModule("copyreg", "copy_reg"),
-    MovedModule("dbm_gnu", "gdbm", "dbm.gnu"),
-    MovedModule("dbm_ndbm", "dbm", "dbm.ndbm"),
-    MovedModule("_dummy_thread", "dummy_thread", "_dummy_thread" if sys.version_info < (3, 9) else "_thread"),
-    MovedModule("http_cookiejar", "cookielib", "http.cookiejar"),
-    MovedModule("http_cookies", "Cookie", "http.cookies"),
-    MovedModule("html_entities", "htmlentitydefs", "html.entities"),
-    MovedModule("html_parser", "HTMLParser", "html.parser"),
-    MovedModule("http_client", "httplib", "http.client"),
-    MovedModule("email_mime_base", "email.MIMEBase", "email.mime.base"),
-    MovedModule("email_mime_image", "email.MIMEImage", "email.mime.image"),
-    MovedModule("email_mime_multipart", "email.MIMEMultipart", "email.mime.multipart"),
-    MovedModule("email_mime_nonmultipart", "email.MIMENonMultipart", "email.mime.nonmultipart"),
-    MovedModule("email_mime_text", "email.MIMEText", "email.mime.text"),
-    MovedModule("BaseHTTPServer", "BaseHTTPServer", "http.server"),
-    MovedModule("CGIHTTPServer", "CGIHTTPServer", "http.server"),
-    MovedModule("SimpleHTTPServer", "SimpleHTTPServer", "http.server"),
-    MovedModule("cPickle", "cPickle", "pickle"),
-    MovedModule("queue", "Queue"),
-    MovedModule("reprlib", "repr"),
-    MovedModule("socketserver", "SocketServer"),
-    MovedModule("_thread", "thread", "_thread"),
-    MovedModule("tkinter", "Tkinter"),
-    MovedModule("tkinter_dialog", "Dialog", "tkinter.dialog"),
-    MovedModule("tkinter_filedialog", "FileDialog", "tkinter.filedialog"),
-    MovedModule("tkinter_scrolledtext", "ScrolledText", "tkinter.scrolledtext"),
-    MovedModule("tkinter_simpledialog", "SimpleDialog", "tkinter.simpledialog"),
-    MovedModule("tkinter_tix", "Tix", "tkinter.tix"),
-    MovedModule("tkinter_ttk", "ttk", "tkinter.ttk"),
-    MovedModule("tkinter_constants", "Tkconstants", "tkinter.constants"),
-    MovedModule("tkinter_dnd", "Tkdnd", "tkinter.dnd"),
-    MovedModule("tkinter_colorchooser", "tkColorChooser",
-                "tkinter.colorchooser"),
-    MovedModule("tkinter_commondialog", "tkCommonDialog",
-                "tkinter.commondialog"),
-    MovedModule("tkinter_tkfiledialog", "tkFileDialog", "tkinter.filedialog"),
-    MovedModule("tkinter_font", "tkFont", "tkinter.font"),
-    MovedModule("tkinter_messagebox", "tkMessageBox", "tkinter.messagebox"),
-    MovedModule("tkinter_tksimpledialog", "tkSimpleDialog",
-                "tkinter.simpledialog"),
-    MovedModule("urllib_parse", __name__ + ".moves.urllib_parse", "urllib.parse"),
-    MovedModule("urllib_error", __name__ + ".moves.urllib_error", "urllib.error"),
-    MovedModule("urllib", __name__ + ".moves.urllib", __name__ + ".moves.urllib"),
-    MovedModule("urllib_robotparser", "robotparser", "urllib.robotparser"),
-    MovedModule("xmlrpc_client", "xmlrpclib", "xmlrpc.client"),
-    MovedModule("xmlrpc_server", "SimpleXMLRPCServer", "xmlrpc.server"),
-]
-# Add windows specific modules.
-if sys.platform == "win32":
-    _moved_attributes += [
-        MovedModule("winreg", "_winreg"),
-    ]
-
-for attr in _moved_attributes:
-    setattr(_MovedItems, attr.name, attr)
-    if isinstance(attr, MovedModule):
-        _importer._add_module(attr, "moves." + attr.name)
-del attr
-
-_MovedItems._moved_attributes = _moved_attributes
-
-moves = _MovedItems(__name__ + ".moves")
-_importer._add_module(moves, "moves")
-
-
-class Module_six_moves_urllib_parse(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_parse"""
-
-
-_urllib_parse_moved_attributes = [
-    MovedAttribute("ParseResult", "urlparse", "urllib.parse"),
-    MovedAttribute("SplitResult", "urlparse", "urllib.parse"),
-    MovedAttribute("parse_qs", "urlparse", "urllib.parse"),
-    MovedAttribute("parse_qsl", "urlparse", "urllib.parse"),
-    MovedAttribute("urldefrag", "urlparse", "urllib.parse"),
-    MovedAttribute("urljoin", "urlparse", "urllib.parse"),
-    MovedAttribute("urlparse", "urlparse", "urllib.parse"),
-    MovedAttribute("urlsplit", "urlparse", "urllib.parse"),
-    MovedAttribute("urlunparse", "urlparse", "urllib.parse"),
-    MovedAttribute("urlunsplit", "urlparse", "urllib.parse"),
-    MovedAttribute("quote", "urllib", "urllib.parse"),
-    MovedAttribute("quote_plus", "urllib", "urllib.parse"),
-    MovedAttribute("unquote", "urllib", "urllib.parse"),
-    MovedAttribute("unquote_plus", "urllib", "urllib.parse"),
-    MovedAttribute("unquote_to_bytes", "urllib", "urllib.parse", "unquote", "unquote_to_bytes"),
-    MovedAttribute("urlencode", "urllib", "urllib.parse"),
-    MovedAttribute("splitquery", "urllib", "urllib.parse"),
-    MovedAttribute("splittag", "urllib", "urllib.parse"),
-    MovedAttribute("splituser", "urllib", "urllib.parse"),
-    MovedAttribute("splitvalue", "urllib", "urllib.parse"),
-    MovedAttribute("uses_fragment", "urlparse", "urllib.parse"),
-    MovedAttribute("uses_netloc", "urlparse", "urllib.parse"),
-    MovedAttribute("uses_params", "urlparse", "urllib.parse"),
-    MovedAttribute("uses_query", "urlparse", "urllib.parse"),
-    MovedAttribute("uses_relative", "urlparse", "urllib.parse"),
-]
-for attr in _urllib_parse_moved_attributes:
-    setattr(Module_six_moves_urllib_parse, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_parse._moved_attributes = _urllib_parse_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_parse(__name__ + ".moves.urllib_parse"),
-                      "moves.urllib_parse", "moves.urllib.parse")
-
-
-class Module_six_moves_urllib_error(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_error"""
-
-
-_urllib_error_moved_attributes = [
-    MovedAttribute("URLError", "urllib2", "urllib.error"),
-    MovedAttribute("HTTPError", "urllib2", "urllib.error"),
-    MovedAttribute("ContentTooShortError", "urllib", "urllib.error"),
-]
-for attr in _urllib_error_moved_attributes:
-    setattr(Module_six_moves_urllib_error, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_error._moved_attributes = _urllib_error_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_error(__name__ + ".moves.urllib.error"),
-                      "moves.urllib_error", "moves.urllib.error")
-
-
-class Module_six_moves_urllib_request(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_request"""
-
-
-_urllib_request_moved_attributes = [
-    MovedAttribute("urlopen", "urllib2", "urllib.request"),
-    MovedAttribute("install_opener", "urllib2", "urllib.request"),
-    MovedAttribute("build_opener", "urllib2", "urllib.request"),
-    MovedAttribute("pathname2url", "urllib", "urllib.request"),
-    MovedAttribute("url2pathname", "urllib", "urllib.request"),
-    MovedAttribute("getproxies", "urllib", "urllib.request"),
-    MovedAttribute("Request", "urllib2", "urllib.request"),
-    MovedAttribute("OpenerDirector", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPDefaultErrorHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPRedirectHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPCookieProcessor", "urllib2", "urllib.request"),
-    MovedAttribute("ProxyHandler", "urllib2", "urllib.request"),
-    MovedAttribute("BaseHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPPasswordMgr", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPPasswordMgrWithDefaultRealm", "urllib2", "urllib.request"),
-    MovedAttribute("AbstractBasicAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPBasicAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("ProxyBasicAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("AbstractDigestAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPDigestAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("ProxyDigestAuthHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPSHandler", "urllib2", "urllib.request"),
-    MovedAttribute("FileHandler", "urllib2", "urllib.request"),
-    MovedAttribute("FTPHandler", "urllib2", "urllib.request"),
-    MovedAttribute("CacheFTPHandler", "urllib2", "urllib.request"),
-    MovedAttribute("UnknownHandler", "urllib2", "urllib.request"),
-    MovedAttribute("HTTPErrorProcessor", "urllib2", "urllib.request"),
-    MovedAttribute("urlretrieve", "urllib", "urllib.request"),
-    MovedAttribute("urlcleanup", "urllib", "urllib.request"),
-    MovedAttribute("URLopener", "urllib", "urllib.request"),
-    MovedAttribute("FancyURLopener", "urllib", "urllib.request"),
-    MovedAttribute("proxy_bypass", "urllib", "urllib.request"),
-    MovedAttribute("parse_http_list", "urllib2", "urllib.request"),
-    MovedAttribute("parse_keqv_list", "urllib2", "urllib.request"),
-]
-for attr in _urllib_request_moved_attributes:
-    setattr(Module_six_moves_urllib_request, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_request._moved_attributes = _urllib_request_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_request(__name__ + ".moves.urllib.request"),
-                      "moves.urllib_request", "moves.urllib.request")
-
-
-class Module_six_moves_urllib_response(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_response"""
-
-
-_urllib_response_moved_attributes = [
-    MovedAttribute("addbase", "urllib", "urllib.response"),
-    MovedAttribute("addclosehook", "urllib", "urllib.response"),
-    MovedAttribute("addinfo", "urllib", "urllib.response"),
-    MovedAttribute("addinfourl", "urllib", "urllib.response"),
-]
-for attr in _urllib_response_moved_attributes:
-    setattr(Module_six_moves_urllib_response, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_response._moved_attributes = _urllib_response_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_response(__name__ + ".moves.urllib.response"),
-                      "moves.urllib_response", "moves.urllib.response")
-
-
-class Module_six_moves_urllib_robotparser(_LazyModule):
-
-    """Lazy loading of moved objects in six.moves.urllib_robotparser"""
-
-
-_urllib_robotparser_moved_attributes = [
-    MovedAttribute("RobotFileParser", "robotparser", "urllib.robotparser"),
-]
-for attr in _urllib_robotparser_moved_attributes:
-    setattr(Module_six_moves_urllib_robotparser, attr.name, attr)
-del attr
-
-Module_six_moves_urllib_robotparser._moved_attributes = _urllib_robotparser_moved_attributes
-
-_importer._add_module(Module_six_moves_urllib_robotparser(__name__ + ".moves.urllib.robotparser"),
-                      "moves.urllib_robotparser", "moves.urllib.robotparser")
-
-
-class Module_six_moves_urllib(types.ModuleType):
-
-    """Create a six.moves.urllib namespace that resembles the Python 3 namespace"""
-    __path__ = []  # mark as package
-    parse = _importer._get_module("moves.urllib_parse")
-    error = _importer._get_module("moves.urllib_error")
-    request = _importer._get_module("moves.urllib_request")
-    response = _importer._get_module("moves.urllib_response")
-    robotparser = _importer._get_module("moves.urllib_robotparser")
-
-    def __dir__(self):
-        return ['parse', 'error', 'request', 'response', 'robotparser']
-
-_importer._add_module(Module_six_moves_urllib(__name__ + ".moves.urllib"),
-                      "moves.urllib")
-
-
-def add_move(move):
-    """Add an item to six.moves."""
-    setattr(_MovedItems, move.name, move)
-
-
-def remove_move(name):
-    """Remove item from six.moves."""
-    try:
-        delattr(_MovedItems, name)
-    except AttributeError:
-        try:
-            del moves.__dict__[name]
-        except KeyError:
-            raise AttributeError("no such move, %r" % (name,))
-
-
-if PY3:
-    _meth_func = "__func__"
-    _meth_self = "__self__"
-
-    _func_closure = "__closure__"
-    _func_code = "__code__"
-    _func_defaults = "__defaults__"
-    _func_globals = "__globals__"
-else:
-    _meth_func = "im_func"
-    _meth_self = "im_self"
-
-    _func_closure = "func_closure"
-    _func_code = "func_code"
-    _func_defaults = "func_defaults"
-    _func_globals = "func_globals"
-
-
-try:
-    advance_iterator = next
-except NameError:
-    def advance_iterator(it):
-        return it.next()
-next = advance_iterator
-
-
-try:
-    callable = callable
-except NameError:
-    def callable(obj):
-        return any("__call__" in klass.__dict__ for klass in type(obj).__mro__)
-
-
-if PY3:
-    def get_unbound_function(unbound):
-        return unbound
-
-    create_bound_method = types.MethodType
-
-    def create_unbound_method(func, cls):
-        return func
-
-    Iterator = object
-else:
-    def get_unbound_function(unbound):
-        return unbound.im_func
-
-    def create_bound_method(func, obj):
-        return types.MethodType(func, obj, obj.__class__)
-
-    def create_unbound_method(func, cls):
-        return types.MethodType(func, None, cls)
-
-    class Iterator(object):
-
-        def next(self):
-            return type(self).__next__(self)
-
-    callable = callable
-_add_doc(get_unbound_function,
-         """Get the function out of a possibly unbound function""")
-
-
-get_method_function = operator.attrgetter(_meth_func)
-get_method_self = operator.attrgetter(_meth_self)
-get_function_closure = operator.attrgetter(_func_closure)
-get_function_code = operator.attrgetter(_func_code)
-get_function_defaults = operator.attrgetter(_func_defaults)
-get_function_globals = operator.attrgetter(_func_globals)
-
-
-if PY3:
-    def iterkeys(d, **kw):
-        return iter(d.keys(**kw))
-
-    def itervalues(d, **kw):
-        return iter(d.values(**kw))
-
-    def iteritems(d, **kw):
-        return iter(d.items(**kw))
-
-    def iterlists(d, **kw):
-        return iter(d.lists(**kw))
-
-    viewkeys = operator.methodcaller("keys")
-
-    viewvalues = operator.methodcaller("values")
-
-    viewitems = operator.methodcaller("items")
-else:
-    def iterkeys(d, **kw):
-        return d.iterkeys(**kw)
-
-    def itervalues(d, **kw):
-        return d.itervalues(**kw)
-
-    def iteritems(d, **kw):
-        return d.iteritems(**kw)
-
-    def iterlists(d, **kw):
-        return d.iterlists(**kw)
-
-    viewkeys = operator.methodcaller("viewkeys")
-
-    viewvalues = operator.methodcaller("viewvalues")
-
-    viewitems = operator.methodcaller("viewitems")
-
-_add_doc(iterkeys, "Return an iterator over the keys of a dictionary.")
-_add_doc(itervalues, "Return an iterator over the values of a dictionary.")
-_add_doc(iteritems,
-         "Return an iterator over the (key, value) pairs of a dictionary.")
-_add_doc(iterlists,
-         "Return an iterator over the (key, [values]) pairs of a dictionary.")
-
-
-if PY3:
-    def b(s):
-        return s.encode("latin-1")
-
-    def u(s):
-        return s
-    unichr = chr
-    import struct
-    int2byte = struct.Struct(">B").pack
-    del struct
-    byte2int = operator.itemgetter(0)
-    indexbytes = operator.getitem
-    iterbytes = iter
-    import io
-    StringIO = io.StringIO
-    BytesIO = io.BytesIO
-    del io
-    _assertCountEqual = "assertCountEqual"
-    if sys.version_info[1] <= 1:
-        _assertRaisesRegex = "assertRaisesRegexp"
-        _assertRegex = "assertRegexpMatches"
-        _assertNotRegex = "assertNotRegexpMatches"
-    else:
-        _assertRaisesRegex = "assertRaisesRegex"
-        _assertRegex = "assertRegex"
-        _assertNotRegex = "assertNotRegex"
-else:
-    def b(s):
-        return s
-    # Workaround for standalone backslash
-
-    def u(s):
-        return unicode(s.replace(r'\\', r'\\\\'), "unicode_escape")
-    unichr = unichr
-    int2byte = chr
-
-    def byte2int(bs):
-        return ord(bs[0])
-
-    def indexbytes(buf, i):
-        return ord(buf[i])
-    iterbytes = functools.partial(itertools.imap, ord)
-    import StringIO
-    StringIO = BytesIO = StringIO.StringIO
-    _assertCountEqual = "assertItemsEqual"
-    _assertRaisesRegex = "assertRaisesRegexp"
-    _assertRegex = "assertRegexpMatches"
-    _assertNotRegex = "assertNotRegexpMatches"
-_add_doc(b, """Byte literal""")
-_add_doc(u, """Text literal""")
-
-
-def assertCountEqual(self, *args, **kwargs):
-    return getattr(self, _assertCountEqual)(*args, **kwargs)
-
-
-def assertRaisesRegex(self, *args, **kwargs):
-    return getattr(self, _assertRaisesRegex)(*args, **kwargs)
-
-
-def assertRegex(self, *args, **kwargs):
-    return getattr(self, _assertRegex)(*args, **kwargs)
-
-
-def assertNotRegex(self, *args, **kwargs):
-    return getattr(self, _assertNotRegex)(*args, **kwargs)
-
-
-if PY3:
-    exec_ = getattr(moves.builtins, "exec")
-
-    def reraise(tp, value, tb=None):
-        try:
-            if value is None:
-                value = tp()
-            if value.__traceback__ is not tb:
-                raise value.with_traceback(tb)
-            raise value
-        finally:
-            value = None
-            tb = None
-
-else:
-    def exec_(_code_, _globs_=None, _locs_=None):
-        """Execute code in a namespace."""
-        if _globs_ is None:
-            frame = sys._getframe(1)
-            _globs_ = frame.f_globals
-            if _locs_ is None:
-                _locs_ = frame.f_locals
-            del frame
-        elif _locs_ is None:
-            _locs_ = _globs_
-        exec("""exec _code_ in _globs_, _locs_""")
-
-    exec_("""def reraise(tp, value, tb=None):
-    try:
-        raise tp, value, tb
-    finally:
-        tb = None
-""")
-
-
-if sys.version_info[:2] > (3,):
-    exec_("""def raise_from(value, from_value):
-    try:
-        raise value from from_value
-    finally:
-        value = None
-""")
-else:
-    def raise_from(value, from_value):
-        raise value
-
-
-print_ = getattr(moves.builtins, "print", None)
-if print_ is None:
-    def print_(*args, **kwargs):
-        """The new-style print function for Python 2.4 and 2.5."""
-        fp = kwargs.pop("file", sys.stdout)
-        if fp is None:
-            return
-
-        def write(data):
-            if not isinstance(data, basestring):
-                data = str(data)
-            # If the file has an encoding, encode unicode with it.
-            if (isinstance(fp, file) and
-                    isinstance(data, unicode) and
-                    fp.encoding is not None):
-                errors = getattr(fp, "errors", None)
-                if errors is None:
-                    errors = "strict"
-                data = data.encode(fp.encoding, errors)
-            fp.write(data)
-        want_unicode = False
-        sep = kwargs.pop("sep", None)
-        if sep is not None:
-            if isinstance(sep, unicode):
-                want_unicode = True
-            elif not isinstance(sep, str):
-                raise TypeError("sep must be None or a string")
-        end = kwargs.pop("end", None)
-        if end is not None:
-            if isinstance(end, unicode):
-                want_unicode = True
-            elif not isinstance(end, str):
-                raise TypeError("end must be None or a string")
-        if kwargs:
-            raise TypeError("invalid keyword arguments to print()")
-        if not want_unicode:
-            for arg in args:
-                if isinstance(arg, unicode):
-                    want_unicode = True
-                    break
-        if want_unicode:
-            newline = unicode("\n")
-            space = unicode(" ")
-        else:
-            newline = "\n"
-            space = " "
-        if sep is None:
-            sep = space
-        if end is None:
-            end = newline
-        for i, arg in enumerate(args):
-            if i:
-                write(sep)
-            write(arg)
-        write(end)
-if sys.version_info[:2] < (3, 3):
-    _print = print_
-
-    def print_(*args, **kwargs):
-        fp = kwargs.get("file", sys.stdout)
-        flush = kwargs.pop("flush", False)
-        _print(*args, **kwargs)
-        if flush and fp is not None:
-            fp.flush()
-
-_add_doc(reraise, """Reraise an exception.""")
-
-if sys.version_info[0:2] < (3, 4):
-    # This does exactly the same what the :func:`py3:functools.update_wrapper`
-    # function does on Python versions after 3.2. It sets the ``__wrapped__``
-    # attribute on ``wrapper`` object and it doesn't raise an error if any of
-    # the attributes mentioned in ``assigned`` and ``updated`` are missing on
-    # ``wrapped`` object.
-    def _update_wrapper(wrapper, wrapped,
-                        assigned=functools.WRAPPER_ASSIGNMENTS,
-                        updated=functools.WRAPPER_UPDATES):
-        for attr in assigned:
-            try:
-                value = getattr(wrapped, attr)
-            except AttributeError:
-                continue
-            else:
-                setattr(wrapper, attr, value)
-        for attr in updated:
-            getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
-        wrapper.__wrapped__ = wrapped
-        return wrapper
-    _update_wrapper.__doc__ = functools.update_wrapper.__doc__
-
-    def wraps(wrapped, assigned=functools.WRAPPER_ASSIGNMENTS,
-              updated=functools.WRAPPER_UPDATES):
-        return functools.partial(_update_wrapper, wrapped=wrapped,
-                                 assigned=assigned, updated=updated)
-    wraps.__doc__ = functools.wraps.__doc__
-
-else:
-    wraps = functools.wraps
-
-
-def with_metaclass(meta, *bases):
-    """Create a base class with a metaclass."""
-    # This requires a bit of explanation: the basic idea is to make a dummy
-    # metaclass for one level of class instantiation that replaces itself with
-    # the actual metaclass.
-    class metaclass(type):
-
-        def __new__(cls, name, this_bases, d):
-            if sys.version_info[:2] >= (3, 7):
-                # This version introduced PEP 560 that requires a bit
-                # of extra care (we mimic what is done by __build_class__).
-                resolved_bases = types.resolve_bases(bases)
-                if resolved_bases is not bases:
-                    d['__orig_bases__'] = bases
-            else:
-                resolved_bases = bases
-            return meta(name, resolved_bases, d)
-
-        @classmethod
-        def __prepare__(cls, name, this_bases):
-            return meta.__prepare__(name, bases)
-    return type.__new__(metaclass, 'temporary_class', (), {})
-
-
-def add_metaclass(metaclass):
-    """Class decorator for creating a class with a metaclass."""
-    def wrapper(cls):
-        orig_vars = cls.__dict__.copy()
-        slots = orig_vars.get('__slots__')
-        if slots is not None:
-            if isinstance(slots, str):
-                slots = [slots]
-            for slots_var in slots:
-                orig_vars.pop(slots_var)
-        orig_vars.pop('__dict__', None)
-        orig_vars.pop('__weakref__', None)
-        if hasattr(cls, '__qualname__'):
-            orig_vars['__qualname__'] = cls.__qualname__
-        return metaclass(cls.__name__, cls.__bases__, orig_vars)
-    return wrapper
-
-
-def ensure_binary(s, encoding='utf-8', errors='strict'):
-    """Coerce **s** to six.binary_type.
-
-    For Python 2:
-      - `unicode` -> encoded to `str`
-      - `str` -> `str`
-
-    For Python 3:
-      - `str` -> encoded to `bytes`
-      - `bytes` -> `bytes`
-    """
-    if isinstance(s, binary_type):
-        return s
-    if isinstance(s, text_type):
-        return s.encode(encoding, errors)
-    raise TypeError("not expecting type '%s'" % type(s))
-
-
-def ensure_str(s, encoding='utf-8', errors='strict'):
-    """Coerce *s* to `str`.
-
-    For Python 2:
-      - `unicode` -> encoded to `str`
-      - `str` -> `str`
-
-    For Python 3:
-      - `str` -> `str`
-      - `bytes` -> decoded to `str`
-    """
-    # Optimization: Fast return for the common case.
-    if type(s) is str:
-        return s
-    if PY2 and isinstance(s, text_type):
-        return s.encode(encoding, errors)
-    elif PY3 and isinstance(s, binary_type):
-        return s.decode(encoding, errors)
-    elif not isinstance(s, (text_type, binary_type)):
-        raise TypeError("not expecting type '%s'" % type(s))
-    return s
-
-
-def ensure_text(s, encoding='utf-8', errors='strict'):
-    """Coerce *s* to six.text_type.
-
-    For Python 2:
-      - `unicode` -> `unicode`
-      - `str` -> `unicode`
-
-    For Python 3:
-      - `str` -> `str`
-      - `bytes` -> decoded to `str`
-    """
-    if isinstance(s, binary_type):
-        return s.decode(encoding, errors)
-    elif isinstance(s, text_type):
-        return s
-    else:
-        raise TypeError("not expecting type '%s'" % type(s))
-
-
-def python_2_unicode_compatible(klass):
-    """
-    A class decorator that defines __unicode__ and __str__ methods under Python 2.
-    Under Python 3 it does nothing.
-
-    To support Python 2 and 3 with a single code base, define a __str__ method
-    returning text and apply this decorator to the class.
-    """
-    if PY2:
-        if '__str__' not in klass.__dict__:
-            raise ValueError("@python_2_unicode_compatible cannot be applied "
-                             "to %s because it doesn't define __str__()." %
-                             klass.__name__)
-        klass.__unicode__ = klass.__str__
-        klass.__str__ = lambda self: self.__unicode__().encode('utf-8')
-    return klass
-
-
-# Complete the moves implementation.
-# This code is at the end of this module to speed up module loading.
-# Turn this module into a package.
-__path__ = []  # required for PEP 302 and PEP 451
-__package__ = __name__  # see PEP 366 @ReservedAssignment
-if globals().get("__spec__") is not None:
-    __spec__.submodule_search_locations = []  # PEP 451 @UndefinedVariable
-# Remove other six meta path importers, since they cause problems. This can
-# happen if six is removed from sys.modules and then reloaded. (Setuptools does
-# this for some reason.)
-if sys.meta_path:
-    for i, importer in enumerate(sys.meta_path):
-        # Here's some real nastiness: Another "instance" of the six module might
-        # be floating around. Therefore, we can't use isinstance() to check for
-        # the six meta path importer, since the other six instance will have
-        # inserted an importer with different class.
-        if (type(importer).__name__ == "_SixMetaPathImporter" and
-                importer.name == __name__):
-            del sys.meta_path[i]
-            break
-    del i, importer
-# Finally, add the importer to the meta path import hook.
-sys.meta_path.append(_importer)
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/markers.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/markers.py
deleted file mode 100644
index 8b98fca7233be6dd9324cd2b6d71b6a8ac91a6cb..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/markers.py
+++ /dev/null
@@ -1,252 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import operator
-import os
-import platform
-import sys
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-from ._parser import (
-    MarkerAtom,
-    MarkerList,
-    Op,
-    Value,
-    Variable,
-    parse_marker as _parse_marker,
-)
-from ._tokenizer import ParserSyntaxError
-from .specifiers import InvalidSpecifier, Specifier
-from .utils import canonicalize_name
-
-__all__ = [
-    "InvalidMarker",
-    "UndefinedComparison",
-    "UndefinedEnvironmentName",
-    "Marker",
-    "default_environment",
-]
-
-Operator = Callable[[str, str], bool]
-
-
-class InvalidMarker(ValueError):
-    """
-    An invalid marker was found, users should refer to PEP 508.
-    """
-
-
-class UndefinedComparison(ValueError):
-    """
-    An invalid operation was attempted on a value that doesn't support it.
-    """
-
-
-class UndefinedEnvironmentName(ValueError):
-    """
-    A name was attempted to be used that does not exist inside of the
-    environment.
-    """
-
-
-def _normalize_extra_values(results: Any) -> Any:
-    """
-    Normalize extra values.
-    """
-    if isinstance(results[0], tuple):
-        lhs, op, rhs = results[0]
-        if isinstance(lhs, Variable) and lhs.value == "extra":
-            normalized_extra = canonicalize_name(rhs.value)
-            rhs = Value(normalized_extra)
-        elif isinstance(rhs, Variable) and rhs.value == "extra":
-            normalized_extra = canonicalize_name(lhs.value)
-            lhs = Value(normalized_extra)
-        results[0] = lhs, op, rhs
-    return results
-
-
-def _format_marker(
-    marker: Union[List[str], MarkerAtom, str], first: Optional[bool] = True
-) -> str:
-
-    assert isinstance(marker, (list, tuple, str))
-
-    # Sometimes we have a structure like [[...]] which is a single item list
-    # where the single item is itself it's own list. In that case we want skip
-    # the rest of this function so that we don't get extraneous () on the
-    # outside.
-    if (
-        isinstance(marker, list)
-        and len(marker) == 1
-        and isinstance(marker[0], (list, tuple))
-    ):
-        return _format_marker(marker[0])
-
-    if isinstance(marker, list):
-        inner = (_format_marker(m, first=False) for m in marker)
-        if first:
-            return " ".join(inner)
-        else:
-            return "(" + " ".join(inner) + ")"
-    elif isinstance(marker, tuple):
-        return " ".join([m.serialize() for m in marker])
-    else:
-        return marker
-
-
-_operators: Dict[str, Operator] = {
-    "in": lambda lhs, rhs: lhs in rhs,
-    "not in": lambda lhs, rhs: lhs not in rhs,
-    "<": operator.lt,
-    "<=": operator.le,
-    "==": operator.eq,
-    "!=": operator.ne,
-    ">=": operator.ge,
-    ">": operator.gt,
-}
-
-
-def _eval_op(lhs: str, op: Op, rhs: str) -> bool:
-    try:
-        spec = Specifier("".join([op.serialize(), rhs]))
-    except InvalidSpecifier:
-        pass
-    else:
-        return spec.contains(lhs, prereleases=True)
-
-    oper: Optional[Operator] = _operators.get(op.serialize())
-    if oper is None:
-        raise UndefinedComparison(f"Undefined {op!r} on {lhs!r} and {rhs!r}.")
-
-    return oper(lhs, rhs)
-
-
-def _normalize(*values: str, key: str) -> Tuple[str, ...]:
-    # PEP 685 – Comparison of extra names for optional distribution dependencies
-    # https://peps.python.org/pep-0685/
-    # > When comparing extra names, tools MUST normalize the names being
-    # > compared using the semantics outlined in PEP 503 for names
-    if key == "extra":
-        return tuple(canonicalize_name(v) for v in values)
-
-    # other environment markers don't have such standards
-    return values
-
-
-def _evaluate_markers(markers: MarkerList, environment: Dict[str, str]) -> bool:
-    groups: List[List[bool]] = [[]]
-
-    for marker in markers:
-        assert isinstance(marker, (list, tuple, str))
-
-        if isinstance(marker, list):
-            groups[-1].append(_evaluate_markers(marker, environment))
-        elif isinstance(marker, tuple):
-            lhs, op, rhs = marker
-
-            if isinstance(lhs, Variable):
-                environment_key = lhs.value
-                lhs_value = environment[environment_key]
-                rhs_value = rhs.value
-            else:
-                lhs_value = lhs.value
-                environment_key = rhs.value
-                rhs_value = environment[environment_key]
-
-            lhs_value, rhs_value = _normalize(lhs_value, rhs_value, key=environment_key)
-            groups[-1].append(_eval_op(lhs_value, op, rhs_value))
-        else:
-            assert marker in ["and", "or"]
-            if marker == "or":
-                groups.append([])
-
-    return any(all(item) for item in groups)
-
-
-def format_full_version(info: "sys._version_info") -> str:
-    version = "{0.major}.{0.minor}.{0.micro}".format(info)
-    kind = info.releaselevel
-    if kind != "final":
-        version += kind[0] + str(info.serial)
-    return version
-
-
-def default_environment() -> Dict[str, str]:
-    iver = format_full_version(sys.implementation.version)
-    implementation_name = sys.implementation.name
-    return {
-        "implementation_name": implementation_name,
-        "implementation_version": iver,
-        "os_name": os.name,
-        "platform_machine": platform.machine(),
-        "platform_release": platform.release(),
-        "platform_system": platform.system(),
-        "platform_version": platform.version(),
-        "python_full_version": platform.python_version(),
-        "platform_python_implementation": platform.python_implementation(),
-        "python_version": ".".join(platform.python_version_tuple()[:2]),
-        "sys_platform": sys.platform,
-    }
-
-
-class Marker:
-    def __init__(self, marker: str) -> None:
-        # Note: We create a Marker object without calling this constructor in
-        #       packaging.requirements.Requirement. If any additional logic is
-        #       added here, make sure to mirror/adapt Requirement.
-        try:
-            self._markers = _normalize_extra_values(_parse_marker(marker))
-            # The attribute `_markers` can be described in terms of a recursive type:
-            # MarkerList = List[Union[Tuple[Node, ...], str, MarkerList]]
-            #
-            # For example, the following expression:
-            # python_version > "3.6" or (python_version == "3.6" and os_name == "unix")
-            #
-            # is parsed into:
-            # [
-            #     (<Variable('python_version')>, <Op('>')>, <Value('3.6')>),
-            #     'and',
-            #     [
-            #         (<Variable('python_version')>, <Op('==')>, <Value('3.6')>),
-            #         'or',
-            #         (<Variable('os_name')>, <Op('==')>, <Value('unix')>)
-            #     ]
-            # ]
-        except ParserSyntaxError as e:
-            raise InvalidMarker(str(e)) from e
-
-    def __str__(self) -> str:
-        return _format_marker(self._markers)
-
-    def __repr__(self) -> str:
-        return f"<Marker('{self}')>"
-
-    def __hash__(self) -> int:
-        return hash((self.__class__.__name__, str(self)))
-
-    def __eq__(self, other: Any) -> bool:
-        if not isinstance(other, Marker):
-            return NotImplemented
-
-        return str(self) == str(other)
-
-    def evaluate(self, environment: Optional[Dict[str, str]] = None) -> bool:
-        """Evaluate a marker.
-
-        Return the boolean from evaluating the given marker against the
-        environment. environment is an optional argument to override all or
-        part of the determined environment.
-
-        The environment is determined from the current Python process.
-        """
-        current_environment = default_environment()
-        current_environment["extra"] = ""
-        if environment is not None:
-            current_environment.update(environment)
-            # The API used to allow setting extra to None. We need to handle this
-            # case for backwards compatibility.
-            if current_environment["extra"] is None:
-                current_environment["extra"] = ""
-
-        return _evaluate_markers(self._markers, current_environment)
diff --git a/spaces/plzdontcry/dakubettergpt/src/components/PromptLibraryMenu/index.ts b/spaces/plzdontcry/dakubettergpt/src/components/PromptLibraryMenu/index.ts
deleted file mode 100644
index 20f1652728b8bd6a0849762290195c4ae7c0ee2b..0000000000000000000000000000000000000000
--- a/spaces/plzdontcry/dakubettergpt/src/components/PromptLibraryMenu/index.ts
+++ /dev/null
@@ -1 +0,0 @@
-export { default } from './PromptLibraryMenu';
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/streams/memory.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/streams/memory.py
deleted file mode 100644
index a6499c13ff36f74d2e217ee996825a13edd6d9fb..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/anyio/streams/memory.py
+++ /dev/null
@@ -1,279 +0,0 @@
-from __future__ import annotations
-
-from collections import OrderedDict, deque
-from dataclasses import dataclass, field
-from types import TracebackType
-from typing import Generic, NamedTuple, TypeVar
-
-from .. import (
-    BrokenResourceError,
-    ClosedResourceError,
-    EndOfStream,
-    WouldBlock,
-    get_cancelled_exc_class,
-)
-from .._core._compat import DeprecatedAwaitable
-from ..abc import Event, ObjectReceiveStream, ObjectSendStream
-from ..lowlevel import checkpoint
-
-T_Item = TypeVar("T_Item")
-T_co = TypeVar("T_co", covariant=True)
-T_contra = TypeVar("T_contra", contravariant=True)
-
-
-class MemoryObjectStreamStatistics(NamedTuple):
-    current_buffer_used: int  #: number of items stored in the buffer
-    #: maximum number of items that can be stored on this stream (or :data:`math.inf`)
-    max_buffer_size: float
-    open_send_streams: int  #: number of unclosed clones of the send stream
-    open_receive_streams: int  #: number of unclosed clones of the receive stream
-    tasks_waiting_send: int  #: number of tasks blocked on :meth:`MemoryObjectSendStream.send`
-    #: number of tasks blocked on :meth:`MemoryObjectReceiveStream.receive`
-    tasks_waiting_receive: int
-
-
-@dataclass(eq=False)
-class MemoryObjectStreamState(Generic[T_Item]):
-    max_buffer_size: float = field()
-    buffer: deque[T_Item] = field(init=False, default_factory=deque)
-    open_send_channels: int = field(init=False, default=0)
-    open_receive_channels: int = field(init=False, default=0)
-    waiting_receivers: OrderedDict[Event, list[T_Item]] = field(
-        init=False, default_factory=OrderedDict
-    )
-    waiting_senders: OrderedDict[Event, T_Item] = field(
-        init=False, default_factory=OrderedDict
-    )
-
-    def statistics(self) -> MemoryObjectStreamStatistics:
-        return MemoryObjectStreamStatistics(
-            len(self.buffer),
-            self.max_buffer_size,
-            self.open_send_channels,
-            self.open_receive_channels,
-            len(self.waiting_senders),
-            len(self.waiting_receivers),
-        )
-
-
-@dataclass(eq=False)
-class MemoryObjectReceiveStream(Generic[T_co], ObjectReceiveStream[T_co]):
-    _state: MemoryObjectStreamState[T_co]
-    _closed: bool = field(init=False, default=False)
-
-    def __post_init__(self) -> None:
-        self._state.open_receive_channels += 1
-
-    def receive_nowait(self) -> T_co:
-        """
-        Receive the next item if it can be done without waiting.
-
-        :return: the received item
-        :raises ~anyio.ClosedResourceError: if this send stream has been closed
-        :raises ~anyio.EndOfStream: if the buffer is empty and this stream has been
-            closed from the sending end
-        :raises ~anyio.WouldBlock: if there are no items in the buffer and no tasks
-            waiting to send
-
-        """
-        if self._closed:
-            raise ClosedResourceError
-
-        if self._state.waiting_senders:
-            # Get the item from the next sender
-            send_event, item = self._state.waiting_senders.popitem(last=False)
-            self._state.buffer.append(item)
-            send_event.set()
-
-        if self._state.buffer:
-            return self._state.buffer.popleft()
-        elif not self._state.open_send_channels:
-            raise EndOfStream
-
-        raise WouldBlock
-
-    async def receive(self) -> T_co:
-        await checkpoint()
-        try:
-            return self.receive_nowait()
-        except WouldBlock:
-            # Add ourselves in the queue
-            receive_event = Event()
-            container: list[T_co] = []
-            self._state.waiting_receivers[receive_event] = container
-
-            try:
-                await receive_event.wait()
-            except get_cancelled_exc_class():
-                # Ignore the immediate cancellation if we already received an item, so as not to
-                # lose it
-                if not container:
-                    raise
-            finally:
-                self._state.waiting_receivers.pop(receive_event, None)
-
-            if container:
-                return container[0]
-            else:
-                raise EndOfStream
-
-    def clone(self) -> MemoryObjectReceiveStream[T_co]:
-        """
-        Create a clone of this receive stream.
-
-        Each clone can be closed separately. Only when all clones have been closed will the
-        receiving end of the memory stream be considered closed by the sending ends.
-
-        :return: the cloned stream
-
-        """
-        if self._closed:
-            raise ClosedResourceError
-
-        return MemoryObjectReceiveStream(_state=self._state)
-
-    def close(self) -> None:
-        """
-        Close the stream.
-
-        This works the exact same way as :meth:`aclose`, but is provided as a special case for the
-        benefit of synchronous callbacks.
-
-        """
-        if not self._closed:
-            self._closed = True
-            self._state.open_receive_channels -= 1
-            if self._state.open_receive_channels == 0:
-                send_events = list(self._state.waiting_senders.keys())
-                for event in send_events:
-                    event.set()
-
-    async def aclose(self) -> None:
-        self.close()
-
-    def statistics(self) -> MemoryObjectStreamStatistics:
-        """
-        Return statistics about the current state of this stream.
-
-        .. versionadded:: 3.0
-        """
-        return self._state.statistics()
-
-    def __enter__(self) -> MemoryObjectReceiveStream[T_co]:
-        return self
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> None:
-        self.close()
-
-
-@dataclass(eq=False)
-class MemoryObjectSendStream(Generic[T_contra], ObjectSendStream[T_contra]):
-    _state: MemoryObjectStreamState[T_contra]
-    _closed: bool = field(init=False, default=False)
-
-    def __post_init__(self) -> None:
-        self._state.open_send_channels += 1
-
-    def send_nowait(self, item: T_contra) -> DeprecatedAwaitable:
-        """
-        Send an item immediately if it can be done without waiting.
-
-        :param item: the item to send
-        :raises ~anyio.ClosedResourceError: if this send stream has been closed
-        :raises ~anyio.BrokenResourceError: if the stream has been closed from the
-            receiving end
-        :raises ~anyio.WouldBlock: if the buffer is full and there are no tasks waiting
-            to receive
-
-        """
-        if self._closed:
-            raise ClosedResourceError
-        if not self._state.open_receive_channels:
-            raise BrokenResourceError
-
-        if self._state.waiting_receivers:
-            receive_event, container = self._state.waiting_receivers.popitem(last=False)
-            container.append(item)
-            receive_event.set()
-        elif len(self._state.buffer) < self._state.max_buffer_size:
-            self._state.buffer.append(item)
-        else:
-            raise WouldBlock
-
-        return DeprecatedAwaitable(self.send_nowait)
-
-    async def send(self, item: T_contra) -> None:
-        await checkpoint()
-        try:
-            self.send_nowait(item)
-        except WouldBlock:
-            # Wait until there's someone on the receiving end
-            send_event = Event()
-            self._state.waiting_senders[send_event] = item
-            try:
-                await send_event.wait()
-            except BaseException:
-                self._state.waiting_senders.pop(send_event, None)  # type: ignore[arg-type]
-                raise
-
-            if self._state.waiting_senders.pop(send_event, None):  # type: ignore[arg-type]
-                raise BrokenResourceError
-
-    def clone(self) -> MemoryObjectSendStream[T_contra]:
-        """
-        Create a clone of this send stream.
-
-        Each clone can be closed separately. Only when all clones have been closed will the
-        sending end of the memory stream be considered closed by the receiving ends.
-
-        :return: the cloned stream
-
-        """
-        if self._closed:
-            raise ClosedResourceError
-
-        return MemoryObjectSendStream(_state=self._state)
-
-    def close(self) -> None:
-        """
-        Close the stream.
-
-        This works the exact same way as :meth:`aclose`, but is provided as a special case for the
-        benefit of synchronous callbacks.
-
-        """
-        if not self._closed:
-            self._closed = True
-            self._state.open_send_channels -= 1
-            if self._state.open_send_channels == 0:
-                receive_events = list(self._state.waiting_receivers.keys())
-                self._state.waiting_receivers.clear()
-                for event in receive_events:
-                    event.set()
-
-    async def aclose(self) -> None:
-        self.close()
-
-    def statistics(self) -> MemoryObjectStreamStatistics:
-        """
-        Return statistics about the current state of this stream.
-
-        .. versionadded:: 3.0
-        """
-        return self._state.statistics()
-
-    def __enter__(self) -> MemoryObjectSendStream[T_contra]:
-        return self
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> None:
-        self.close()
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/feaLib/error.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/feaLib/error.py
deleted file mode 100644
index a2c5f9dbb9f9c943bf57d047af0456070d319e5d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/feaLib/error.py
+++ /dev/null
@@ -1,22 +0,0 @@
-class FeatureLibError(Exception):
-    def __init__(self, message, location):
-        Exception.__init__(self, message)
-        self.location = location
-
-    def __str__(self):
-        message = Exception.__str__(self)
-        if self.location:
-            return f"{self.location}: {message}"
-        else:
-            return message
-
-
-class IncludedFeaNotFound(FeatureLibError):
-    def __str__(self):
-        assert self.location is not None
-
-        message = (
-            "The following feature file should be included but cannot be found: "
-            f"{Exception.__str__(self)}"
-        )
-        return f"{self.location}: {message}"
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio_client/data_classes.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio_client/data_classes.py
deleted file mode 100644
index bfd86651167d15d20d386c839e9f32b1662461b0..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio_client/data_classes.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from __future__ import annotations
-
-from typing import TypedDict
-
-from typing_extensions import NotRequired
-
-
-class FileData(TypedDict):
-    name: str | None  # filename
-    data: str | None  # base64 encoded data
-    size: NotRequired[int | None]  # size in bytes
-    is_file: NotRequired[
-        bool
-    ]  # whether the data corresponds to a file or base64 encoded data
-    orig_name: NotRequired[str]  # original filename
-    mime_type: NotRequired[str]
-    is_stream: NotRequired[bool]
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/command/develop.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/command/develop.py
deleted file mode 100644
index af24baf2e7e1e7ff715788cb5acfcbefaafebd48..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/command/develop.py
+++ /dev/null
@@ -1,15 +0,0 @@
-""" Override the develop command from setuptools so we can ensure that our
-generated files (from build_src or build_scripts) are properly converted to real
-files with filenames.
-
-"""
-from setuptools.command.develop import develop as old_develop
-
-class develop(old_develop):
-    __doc__ = old_develop.__doc__
-    def install_for_development(self):
-        # Build sources in-place, too.
-        self.reinitialize_command('build_src', inplace=1)
-        # Make sure scripts are built.
-        self.run_command('build_scripts')
-        old_develop.install_for_development(self)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/lib/tests/test_arraypad.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/lib/tests/test_arraypad.py
deleted file mode 100644
index 0bebe36934097207bf7a987475231fb36ee14383..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/lib/tests/test_arraypad.py
+++ /dev/null
@@ -1,1380 +0,0 @@
-"""Tests for the array padding functions.
-
-"""
-import pytest
-
-import numpy as np
-from numpy.testing import assert_array_equal, assert_allclose, assert_equal
-from numpy.lib.arraypad import _as_pairs
-
-
-_numeric_dtypes = (
-    np.sctypes["uint"]
-    + np.sctypes["int"]
-    + np.sctypes["float"]
-    + np.sctypes["complex"]
-)
-_all_modes = {
-    'constant': {'constant_values': 0},
-    'edge': {},
-    'linear_ramp': {'end_values': 0},
-    'maximum': {'stat_length': None},
-    'mean': {'stat_length': None},
-    'median': {'stat_length': None},
-    'minimum': {'stat_length': None},
-    'reflect': {'reflect_type': 'even'},
-    'symmetric': {'reflect_type': 'even'},
-    'wrap': {},
-    'empty': {}
-}
-
-
-class TestAsPairs:
-    def test_single_value(self):
-        """Test casting for a single value."""
-        expected = np.array([[3, 3]] * 10)
-        for x in (3, [3], [[3]]):
-            result = _as_pairs(x, 10)
-            assert_equal(result, expected)
-        # Test with dtype=object
-        obj = object()
-        assert_equal(
-            _as_pairs(obj, 10),
-            np.array([[obj, obj]] * 10)
-        )
-
-    def test_two_values(self):
-        """Test proper casting for two different values."""
-        # Broadcasting in the first dimension with numbers
-        expected = np.array([[3, 4]] * 10)
-        for x in ([3, 4], [[3, 4]]):
-            result = _as_pairs(x, 10)
-            assert_equal(result, expected)
-        # and with dtype=object
-        obj = object()
-        assert_equal(
-            _as_pairs(["a", obj], 10),
-            np.array([["a", obj]] * 10)
-        )
-
-        # Broadcasting in the second / last dimension with numbers
-        assert_equal(
-            _as_pairs([[3], [4]], 2),
-            np.array([[3, 3], [4, 4]])
-        )
-        # and with dtype=object
-        assert_equal(
-            _as_pairs([["a"], [obj]], 2),
-            np.array([["a", "a"], [obj, obj]])
-        )
-
-    def test_with_none(self):
-        expected = ((None, None), (None, None), (None, None))
-        assert_equal(
-            _as_pairs(None, 3, as_index=False),
-            expected
-        )
-        assert_equal(
-            _as_pairs(None, 3, as_index=True),
-            expected
-        )
-
-    def test_pass_through(self):
-        """Test if `x` already matching desired output are passed through."""
-        expected = np.arange(12).reshape((6, 2))
-        assert_equal(
-            _as_pairs(expected, 6),
-            expected
-        )
-
-    def test_as_index(self):
-        """Test results if `as_index=True`."""
-        assert_equal(
-            _as_pairs([2.6, 3.3], 10, as_index=True),
-            np.array([[3, 3]] * 10, dtype=np.intp)
-        )
-        assert_equal(
-            _as_pairs([2.6, 4.49], 10, as_index=True),
-            np.array([[3, 4]] * 10, dtype=np.intp)
-        )
-        for x in (-3, [-3], [[-3]], [-3, 4], [3, -4], [[-3, 4]], [[4, -3]],
-                  [[1, 2]] * 9 + [[1, -2]]):
-            with pytest.raises(ValueError, match="negative values"):
-                _as_pairs(x, 10, as_index=True)
-
-    def test_exceptions(self):
-        """Ensure faulty usage is discovered."""
-        with pytest.raises(ValueError, match="more dimensions than allowed"):
-            _as_pairs([[[3]]], 10)
-        with pytest.raises(ValueError, match="could not be broadcast"):
-            _as_pairs([[1, 2], [3, 4]], 3)
-        with pytest.raises(ValueError, match="could not be broadcast"):
-            _as_pairs(np.ones((2, 3)), 3)
-
-
-class TestConditionalShortcuts:
-    @pytest.mark.parametrize("mode", _all_modes.keys())
-    def test_zero_padding_shortcuts(self, mode):
-        test = np.arange(120).reshape(4, 5, 6)
-        pad_amt = [(0, 0) for _ in test.shape]
-        assert_array_equal(test, np.pad(test, pad_amt, mode=mode))
-
-    @pytest.mark.parametrize("mode", ['maximum', 'mean', 'median', 'minimum',])
-    def test_shallow_statistic_range(self, mode):
-        test = np.arange(120).reshape(4, 5, 6)
-        pad_amt = [(1, 1) for _ in test.shape]
-        assert_array_equal(np.pad(test, pad_amt, mode='edge'),
-                           np.pad(test, pad_amt, mode=mode, stat_length=1))
-
-    @pytest.mark.parametrize("mode", ['maximum', 'mean', 'median', 'minimum',])
-    def test_clip_statistic_range(self, mode):
-        test = np.arange(30).reshape(5, 6)
-        pad_amt = [(3, 3) for _ in test.shape]
-        assert_array_equal(np.pad(test, pad_amt, mode=mode),
-                           np.pad(test, pad_amt, mode=mode, stat_length=30))
-
-
-class TestStatistic:
-    def test_check_mean_stat_length(self):
-        a = np.arange(100).astype('f')
-        a = np.pad(a, ((25, 20), ), 'mean', stat_length=((2, 3), ))
-        b = np.array(
-            [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
-             0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
-             0.5, 0.5, 0.5, 0.5, 0.5,
-
-             0., 1., 2., 3., 4., 5., 6., 7., 8., 9.,
-             10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
-             20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,
-             30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
-             40., 41., 42., 43., 44., 45., 46., 47., 48., 49.,
-             50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
-             60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
-             70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
-             80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
-             90., 91., 92., 93., 94., 95., 96., 97., 98., 99.,
-
-             98., 98., 98., 98., 98., 98., 98., 98., 98., 98.,
-             98., 98., 98., 98., 98., 98., 98., 98., 98., 98.
-             ])
-        assert_array_equal(a, b)
-
-    def test_check_maximum_1(self):
-        a = np.arange(100)
-        a = np.pad(a, (25, 20), 'maximum')
-        b = np.array(
-            [99, 99, 99, 99, 99, 99, 99, 99, 99, 99,
-             99, 99, 99, 99, 99, 99, 99, 99, 99, 99,
-             99, 99, 99, 99, 99,
-
-             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
-             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
-             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
-             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
-             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-
-             99, 99, 99, 99, 99, 99, 99, 99, 99, 99,
-             99, 99, 99, 99, 99, 99, 99, 99, 99, 99]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_maximum_2(self):
-        a = np.arange(100) + 1
-        a = np.pad(a, (25, 20), 'maximum')
-        b = np.array(
-            [100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
-             100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
-             100, 100, 100, 100, 100,
-
-             1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
-             11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
-             21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
-             31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
-             41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
-             51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
-             61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-             71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-             81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
-             91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
-
-             100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
-             100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_maximum_stat_length(self):
-        a = np.arange(100) + 1
-        a = np.pad(a, (25, 20), 'maximum', stat_length=10)
-        b = np.array(
-            [10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-             10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-             10, 10, 10, 10, 10,
-
-              1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
-             11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
-             21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
-             31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
-             41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
-             51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
-             61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-             71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-             81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
-             91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
-
-             100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
-             100, 100, 100, 100, 100, 100, 100, 100, 100, 100]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_minimum_1(self):
-        a = np.arange(100)
-        a = np.pad(a, (25, 20), 'minimum')
-        b = np.array(
-            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-             0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-             0, 0, 0, 0, 0,
-
-             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
-             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
-             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
-             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
-             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-
-             0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-             0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_minimum_2(self):
-        a = np.arange(100) + 2
-        a = np.pad(a, (25, 20), 'minimum')
-        b = np.array(
-            [2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-             2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-             2, 2, 2, 2, 2,
-
-             2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
-             12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
-             22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
-             32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
-             42, 43, 44, 45, 46, 47, 48, 49, 50, 51,
-             52, 53, 54, 55, 56, 57, 58, 59, 60, 61,
-             62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
-             72, 73, 74, 75, 76, 77, 78, 79, 80, 81,
-             82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
-             92, 93, 94, 95, 96, 97, 98, 99, 100, 101,
-
-             2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-             2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_minimum_stat_length(self):
-        a = np.arange(100) + 1
-        a = np.pad(a, (25, 20), 'minimum', stat_length=10)
-        b = np.array(
-            [ 1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
-              1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
-              1,  1,  1,  1,  1,
-
-              1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
-             11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
-             21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
-             31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
-             41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
-             51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
-             61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-             71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
-             81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
-             91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
-
-             91, 91, 91, 91, 91, 91, 91, 91, 91, 91,
-             91, 91, 91, 91, 91, 91, 91, 91, 91, 91]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_median(self):
-        a = np.arange(100).astype('f')
-        a = np.pad(a, (25, 20), 'median')
-        b = np.array(
-            [49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
-             49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
-             49.5, 49.5, 49.5, 49.5, 49.5,
-
-             0., 1., 2., 3., 4., 5., 6., 7., 8., 9.,
-             10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
-             20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,
-             30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
-             40., 41., 42., 43., 44., 45., 46., 47., 48., 49.,
-             50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
-             60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
-             70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
-             80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
-             90., 91., 92., 93., 94., 95., 96., 97., 98., 99.,
-
-             49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
-             49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_median_01(self):
-        a = np.array([[3, 1, 4], [4, 5, 9], [9, 8, 2]])
-        a = np.pad(a, 1, 'median')
-        b = np.array(
-            [[4, 4, 5, 4, 4],
-
-             [3, 3, 1, 4, 3],
-             [5, 4, 5, 9, 5],
-             [8, 9, 8, 2, 8],
-
-             [4, 4, 5, 4, 4]]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_median_02(self):
-        a = np.array([[3, 1, 4], [4, 5, 9], [9, 8, 2]])
-        a = np.pad(a.T, 1, 'median').T
-        b = np.array(
-            [[5, 4, 5, 4, 5],
-
-             [3, 3, 1, 4, 3],
-             [5, 4, 5, 9, 5],
-             [8, 9, 8, 2, 8],
-
-             [5, 4, 5, 4, 5]]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_median_stat_length(self):
-        a = np.arange(100).astype('f')
-        a[1] = 2.
-        a[97] = 96.
-        a = np.pad(a, (25, 20), 'median', stat_length=(3, 5))
-        b = np.array(
-            [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,
-              2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,
-              2.,  2.,  2.,  2.,  2.,
-
-              0.,  2.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.,
-             10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
-             20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,
-             30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
-             40., 41., 42., 43., 44., 45., 46., 47., 48., 49.,
-             50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
-             60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
-             70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
-             80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
-             90., 91., 92., 93., 94., 95., 96., 96., 98., 99.,
-
-             96., 96., 96., 96., 96., 96., 96., 96., 96., 96.,
-             96., 96., 96., 96., 96., 96., 96., 96., 96., 96.]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_mean_shape_one(self):
-        a = [[4, 5, 6]]
-        a = np.pad(a, (5, 7), 'mean', stat_length=2)
-        b = np.array(
-            [[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
-             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
-             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
-             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
-             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
-
-             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
-
-             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
-             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
-             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
-             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
-             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
-             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6],
-             [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6]]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_mean_2(self):
-        a = np.arange(100).astype('f')
-        a = np.pad(a, (25, 20), 'mean')
-        b = np.array(
-            [49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
-             49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
-             49.5, 49.5, 49.5, 49.5, 49.5,
-
-             0., 1., 2., 3., 4., 5., 6., 7., 8., 9.,
-             10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,
-             20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,
-             30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,
-             40., 41., 42., 43., 44., 45., 46., 47., 48., 49.,
-             50., 51., 52., 53., 54., 55., 56., 57., 58., 59.,
-             60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,
-             70., 71., 72., 73., 74., 75., 76., 77., 78., 79.,
-             80., 81., 82., 83., 84., 85., 86., 87., 88., 89.,
-             90., 91., 92., 93., 94., 95., 96., 97., 98., 99.,
-
-             49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5,
-             49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5]
-            )
-        assert_array_equal(a, b)
-
-    @pytest.mark.parametrize("mode", [
-        "mean",
-        "median",
-        "minimum",
-        "maximum"
-    ])
-    def test_same_prepend_append(self, mode):
-        """ Test that appended and prepended values are equal """
-        # This test is constructed to trigger floating point rounding errors in
-        # a way that caused gh-11216 for mode=='mean'
-        a = np.array([-1, 2, -1]) + np.array([0, 1e-12, 0], dtype=np.float64)
-        a = np.pad(a, (1, 1), mode)
-        assert_equal(a[0], a[-1])
-
-    @pytest.mark.parametrize("mode", ["mean", "median", "minimum", "maximum"])
-    @pytest.mark.parametrize(
-        "stat_length", [-2, (-2,), (3, -1), ((5, 2), (-2, 3)), ((-4,), (2,))]
-    )
-    def test_check_negative_stat_length(self, mode, stat_length):
-        arr = np.arange(30).reshape((6, 5))
-        match = "index can't contain negative values"
-        with pytest.raises(ValueError, match=match):
-            np.pad(arr, 2, mode, stat_length=stat_length)
-
-    def test_simple_stat_length(self):
-        a = np.arange(30)
-        a = np.reshape(a, (6, 5))
-        a = np.pad(a, ((2, 3), (3, 2)), mode='mean', stat_length=(3,))
-        b = np.array(
-            [[6, 6, 6, 5, 6, 7, 8, 9, 8, 8],
-             [6, 6, 6, 5, 6, 7, 8, 9, 8, 8],
-
-             [1, 1, 1, 0, 1, 2, 3, 4, 3, 3],
-             [6, 6, 6, 5, 6, 7, 8, 9, 8, 8],
-             [11, 11, 11, 10, 11, 12, 13, 14, 13, 13],
-             [16, 16, 16, 15, 16, 17, 18, 19, 18, 18],
-             [21, 21, 21, 20, 21, 22, 23, 24, 23, 23],
-             [26, 26, 26, 25, 26, 27, 28, 29, 28, 28],
-
-             [21, 21, 21, 20, 21, 22, 23, 24, 23, 23],
-             [21, 21, 21, 20, 21, 22, 23, 24, 23, 23],
-             [21, 21, 21, 20, 21, 22, 23, 24, 23, 23]]
-            )
-        assert_array_equal(a, b)
-
-    @pytest.mark.filterwarnings("ignore:Mean of empty slice:RuntimeWarning")
-    @pytest.mark.filterwarnings(
-        "ignore:invalid value encountered in( scalar)? divide:RuntimeWarning"
-    )
-    @pytest.mark.parametrize("mode", ["mean", "median"])
-    def test_zero_stat_length_valid(self, mode):
-        arr = np.pad([1., 2.], (1, 2), mode, stat_length=0)
-        expected = np.array([np.nan, 1., 2., np.nan, np.nan])
-        assert_equal(arr, expected)
-
-    @pytest.mark.parametrize("mode", ["minimum", "maximum"])
-    def test_zero_stat_length_invalid(self, mode):
-        match = "stat_length of 0 yields no value for padding"
-        with pytest.raises(ValueError, match=match):
-            np.pad([1., 2.], 0, mode, stat_length=0)
-        with pytest.raises(ValueError, match=match):
-            np.pad([1., 2.], 0, mode, stat_length=(1, 0))
-        with pytest.raises(ValueError, match=match):
-            np.pad([1., 2.], 1, mode, stat_length=0)
-        with pytest.raises(ValueError, match=match):
-            np.pad([1., 2.], 1, mode, stat_length=(1, 0))
-
-
-class TestConstant:
-    def test_check_constant(self):
-        a = np.arange(100)
-        a = np.pad(a, (25, 20), 'constant', constant_values=(10, 20))
-        b = np.array(
-            [10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-             10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
-             10, 10, 10, 10, 10,
-
-             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
-             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
-             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
-             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
-             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-
-             20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
-             20, 20, 20, 20, 20, 20, 20, 20, 20, 20]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_constant_zeros(self):
-        a = np.arange(100)
-        a = np.pad(a, (25, 20), 'constant')
-        b = np.array(
-            [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-              0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-              0,  0,  0,  0,  0,
-
-             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
-             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
-             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
-             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
-             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-
-              0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-              0,  0,  0,  0,  0,  0,  0,  0,  0,  0]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_constant_float(self):
-        # If input array is int, but constant_values are float, the dtype of
-        # the array to be padded is kept
-        arr = np.arange(30).reshape(5, 6)
-        test = np.pad(arr, (1, 2), mode='constant',
-                   constant_values=1.1)
-        expected = np.array(
-            [[ 1,  1,  1,  1,  1,  1,  1,  1,  1],
-
-             [ 1,  0,  1,  2,  3,  4,  5,  1,  1],
-             [ 1,  6,  7,  8,  9, 10, 11,  1,  1],
-             [ 1, 12, 13, 14, 15, 16, 17,  1,  1],
-             [ 1, 18, 19, 20, 21, 22, 23,  1,  1],
-             [ 1, 24, 25, 26, 27, 28, 29,  1,  1],
-
-             [ 1,  1,  1,  1,  1,  1,  1,  1,  1],
-             [ 1,  1,  1,  1,  1,  1,  1,  1,  1]]
-            )
-        assert_allclose(test, expected)
-
-    def test_check_constant_float2(self):
-        # If input array is float, and constant_values are float, the dtype of
-        # the array to be padded is kept - here retaining the float constants
-        arr = np.arange(30).reshape(5, 6)
-        arr_float = arr.astype(np.float64)
-        test = np.pad(arr_float, ((1, 2), (1, 2)), mode='constant',
-                   constant_values=1.1)
-        expected = np.array(
-            [[  1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1],
-
-             [  1.1,   0. ,   1. ,   2. ,   3. ,   4. ,   5. ,   1.1,   1.1],
-             [  1.1,   6. ,   7. ,   8. ,   9. ,  10. ,  11. ,   1.1,   1.1],
-             [  1.1,  12. ,  13. ,  14. ,  15. ,  16. ,  17. ,   1.1,   1.1],
-             [  1.1,  18. ,  19. ,  20. ,  21. ,  22. ,  23. ,   1.1,   1.1],
-             [  1.1,  24. ,  25. ,  26. ,  27. ,  28. ,  29. ,   1.1,   1.1],
-
-             [  1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1],
-             [  1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1,   1.1]]
-            )
-        assert_allclose(test, expected)
-
-    def test_check_constant_float3(self):
-        a = np.arange(100, dtype=float)
-        a = np.pad(a, (25, 20), 'constant', constant_values=(-1.1, -1.2))
-        b = np.array(
-            [-1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1,
-             -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1,
-             -1.1, -1.1, -1.1, -1.1, -1.1,
-
-             0,  1,  2,  3,  4,  5,  6,  7,  8,  9,
-             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
-             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
-             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
-             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-
-             -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2,
-             -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2]
-            )
-        assert_allclose(a, b)
-
-    def test_check_constant_odd_pad_amount(self):
-        arr = np.arange(30).reshape(5, 6)
-        test = np.pad(arr, ((1,), (2,)), mode='constant',
-                   constant_values=3)
-        expected = np.array(
-            [[ 3,  3,  3,  3,  3,  3,  3,  3,  3,  3],
-
-             [ 3,  3,  0,  1,  2,  3,  4,  5,  3,  3],
-             [ 3,  3,  6,  7,  8,  9, 10, 11,  3,  3],
-             [ 3,  3, 12, 13, 14, 15, 16, 17,  3,  3],
-             [ 3,  3, 18, 19, 20, 21, 22, 23,  3,  3],
-             [ 3,  3, 24, 25, 26, 27, 28, 29,  3,  3],
-
-             [ 3,  3,  3,  3,  3,  3,  3,  3,  3,  3]]
-            )
-        assert_allclose(test, expected)
-
-    def test_check_constant_pad_2d(self):
-        arr = np.arange(4).reshape(2, 2)
-        test = np.lib.pad(arr, ((1, 2), (1, 3)), mode='constant',
-                          constant_values=((1, 2), (3, 4)))
-        expected = np.array(
-            [[3, 1, 1, 4, 4, 4],
-             [3, 0, 1, 4, 4, 4],
-             [3, 2, 3, 4, 4, 4],
-             [3, 2, 2, 4, 4, 4],
-             [3, 2, 2, 4, 4, 4]]
-        )
-        assert_allclose(test, expected)
-
-    def test_check_large_integers(self):
-        uint64_max = 2 ** 64 - 1
-        arr = np.full(5, uint64_max, dtype=np.uint64)
-        test = np.pad(arr, 1, mode="constant", constant_values=arr.min())
-        expected = np.full(7, uint64_max, dtype=np.uint64)
-        assert_array_equal(test, expected)
-
-        int64_max = 2 ** 63 - 1
-        arr = np.full(5, int64_max, dtype=np.int64)
-        test = np.pad(arr, 1, mode="constant", constant_values=arr.min())
-        expected = np.full(7, int64_max, dtype=np.int64)
-        assert_array_equal(test, expected)
-
-    def test_check_object_array(self):
-        arr = np.empty(1, dtype=object)
-        obj_a = object()
-        arr[0] = obj_a
-        obj_b = object()
-        obj_c = object()
-        arr = np.pad(arr, pad_width=1, mode='constant',
-                     constant_values=(obj_b, obj_c))
-
-        expected = np.empty((3,), dtype=object)
-        expected[0] = obj_b
-        expected[1] = obj_a
-        expected[2] = obj_c
-
-        assert_array_equal(arr, expected)
-
-    def test_pad_empty_dimension(self):
-        arr = np.zeros((3, 0, 2))
-        result = np.pad(arr, [(0,), (2,), (1,)], mode="constant")
-        assert result.shape == (3, 4, 4)
-
-
-class TestLinearRamp:
-    def test_check_simple(self):
-        a = np.arange(100).astype('f')
-        a = np.pad(a, (25, 20), 'linear_ramp', end_values=(4, 5))
-        b = np.array(
-            [4.00, 3.84, 3.68, 3.52, 3.36, 3.20, 3.04, 2.88, 2.72, 2.56,
-             2.40, 2.24, 2.08, 1.92, 1.76, 1.60, 1.44, 1.28, 1.12, 0.96,
-             0.80, 0.64, 0.48, 0.32, 0.16,
-
-             0.00, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00,
-             10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0,
-             20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0,
-             30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0,
-             40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0,
-             50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0,
-             60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0,
-             70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0,
-             80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0,
-             90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0,
-
-             94.3, 89.6, 84.9, 80.2, 75.5, 70.8, 66.1, 61.4, 56.7, 52.0,
-             47.3, 42.6, 37.9, 33.2, 28.5, 23.8, 19.1, 14.4, 9.7, 5.]
-            )
-        assert_allclose(a, b, rtol=1e-5, atol=1e-5)
-
-    def test_check_2d(self):
-        arr = np.arange(20).reshape(4, 5).astype(np.float64)
-        test = np.pad(arr, (2, 2), mode='linear_ramp', end_values=(0, 0))
-        expected = np.array(
-            [[0.,   0.,   0.,   0.,   0.,   0.,   0.,    0.,   0.],
-             [0.,   0.,   0.,  0.5,   1.,  1.5,   2.,    1.,   0.],
-             [0.,   0.,   0.,   1.,   2.,   3.,   4.,    2.,   0.],
-             [0.,  2.5,   5.,   6.,   7.,   8.,   9.,   4.5,   0.],
-             [0.,   5.,  10.,  11.,  12.,  13.,  14.,    7.,   0.],
-             [0.,  7.5,  15.,  16.,  17.,  18.,  19.,   9.5,   0.],
-             [0., 3.75,  7.5,   8.,  8.5,   9.,  9.5,  4.75,   0.],
-             [0.,   0.,   0.,   0.,   0.,   0.,   0.,    0.,   0.]])
-        assert_allclose(test, expected)
-
-    @pytest.mark.xfail(exceptions=(AssertionError,))
-    def test_object_array(self):
-        from fractions import Fraction
-        arr = np.array([Fraction(1, 2), Fraction(-1, 2)])
-        actual = np.pad(arr, (2, 3), mode='linear_ramp', end_values=0)
-
-        # deliberately chosen to have a non-power-of-2 denominator such that
-        # rounding to floats causes a failure.
-        expected = np.array([
-            Fraction( 0, 12),
-            Fraction( 3, 12),
-            Fraction( 6, 12),
-            Fraction(-6, 12),
-            Fraction(-4, 12),
-            Fraction(-2, 12),
-            Fraction(-0, 12),
-        ])
-        assert_equal(actual, expected)
-
-    def test_end_values(self):
-        """Ensure that end values are exact."""
-        a = np.pad(np.ones(10).reshape(2, 5), (223, 123), mode="linear_ramp")
-        assert_equal(a[:, 0], 0.)
-        assert_equal(a[:, -1], 0.)
-        assert_equal(a[0, :], 0.)
-        assert_equal(a[-1, :], 0.)
-
-    @pytest.mark.parametrize("dtype", _numeric_dtypes)
-    def test_negative_difference(self, dtype):
-        """
-        Check correct behavior of unsigned dtypes if there is a negative
-        difference between the edge to pad and `end_values`. Check both cases
-        to be independent of implementation. Test behavior for all other dtypes
-        in case dtype casting interferes with complex dtypes. See gh-14191.
-        """
-        x = np.array([3], dtype=dtype)
-        result = np.pad(x, 3, mode="linear_ramp", end_values=0)
-        expected = np.array([0, 1, 2, 3, 2, 1, 0], dtype=dtype)
-        assert_equal(result, expected)
-
-        x = np.array([0], dtype=dtype)
-        result = np.pad(x, 3, mode="linear_ramp", end_values=3)
-        expected = np.array([3, 2, 1, 0, 1, 2, 3], dtype=dtype)
-        assert_equal(result, expected)
-
-
-class TestReflect:
-    def test_check_simple(self):
-        a = np.arange(100)
-        a = np.pad(a, (25, 20), 'reflect')
-        b = np.array(
-            [25, 24, 23, 22, 21, 20, 19, 18, 17, 16,
-             15, 14, 13, 12, 11, 10, 9, 8, 7, 6,
-             5, 4, 3, 2, 1,
-
-             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
-             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
-             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
-             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
-             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-
-             98, 97, 96, 95, 94, 93, 92, 91, 90, 89,
-             88, 87, 86, 85, 84, 83, 82, 81, 80, 79]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_odd_method(self):
-        a = np.arange(100)
-        a = np.pad(a, (25, 20), 'reflect', reflect_type='odd')
-        b = np.array(
-            [-25, -24, -23, -22, -21, -20, -19, -18, -17, -16,
-             -15, -14, -13, -12, -11, -10, -9, -8, -7, -6,
-             -5, -4, -3, -2, -1,
-
-             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
-             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
-             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
-             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
-             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-
-             100, 101, 102, 103, 104, 105, 106, 107, 108, 109,
-             110, 111, 112, 113, 114, 115, 116, 117, 118, 119]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_large_pad(self):
-        a = [[4, 5, 6], [6, 7, 8]]
-        a = np.pad(a, (5, 7), 'reflect')
-        b = np.array(
-            [[7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
-
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
-
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5]]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_shape(self):
-        a = [[4, 5, 6]]
-        a = np.pad(a, (5, 7), 'reflect')
-        b = np.array(
-            [[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5],
-             [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5]]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_01(self):
-        a = np.pad([1, 2, 3], 2, 'reflect')
-        b = np.array([3, 2, 1, 2, 3, 2, 1])
-        assert_array_equal(a, b)
-
-    def test_check_02(self):
-        a = np.pad([1, 2, 3], 3, 'reflect')
-        b = np.array([2, 3, 2, 1, 2, 3, 2, 1, 2])
-        assert_array_equal(a, b)
-
-    def test_check_03(self):
-        a = np.pad([1, 2, 3], 4, 'reflect')
-        b = np.array([1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3])
-        assert_array_equal(a, b)
-
-
-class TestEmptyArray:
-    """Check how padding behaves on arrays with an empty dimension."""
-
-    @pytest.mark.parametrize(
-        # Keep parametrization ordered, otherwise pytest-xdist might believe
-        # that different tests were collected during parallelization
-        "mode", sorted(_all_modes.keys() - {"constant", "empty"})
-    )
-    def test_pad_empty_dimension(self, mode):
-        match = ("can't extend empty axis 0 using modes other than 'constant' "
-                 "or 'empty'")
-        with pytest.raises(ValueError, match=match):
-            np.pad([], 4, mode=mode)
-        with pytest.raises(ValueError, match=match):
-            np.pad(np.ndarray(0), 4, mode=mode)
-        with pytest.raises(ValueError, match=match):
-            np.pad(np.zeros((0, 3)), ((1,), (0,)), mode=mode)
-
-    @pytest.mark.parametrize("mode", _all_modes.keys())
-    def test_pad_non_empty_dimension(self, mode):
-        result = np.pad(np.ones((2, 0, 2)), ((3,), (0,), (1,)), mode=mode)
-        assert result.shape == (8, 0, 4)
-
-
-class TestSymmetric:
-    def test_check_simple(self):
-        a = np.arange(100)
-        a = np.pad(a, (25, 20), 'symmetric')
-        b = np.array(
-            [24, 23, 22, 21, 20, 19, 18, 17, 16, 15,
-             14, 13, 12, 11, 10, 9, 8, 7, 6, 5,
-             4, 3, 2, 1, 0,
-
-             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
-             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
-             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
-             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
-             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-
-             99, 98, 97, 96, 95, 94, 93, 92, 91, 90,
-             89, 88, 87, 86, 85, 84, 83, 82, 81, 80]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_odd_method(self):
-        a = np.arange(100)
-        a = np.pad(a, (25, 20), 'symmetric', reflect_type='odd')
-        b = np.array(
-            [-24, -23, -22, -21, -20, -19, -18, -17, -16, -15,
-             -14, -13, -12, -11, -10, -9, -8, -7, -6, -5,
-             -4, -3, -2, -1, 0,
-
-             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
-             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
-             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
-             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
-             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-
-             99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
-             109, 110, 111, 112, 113, 114, 115, 116, 117, 118]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_large_pad(self):
-        a = [[4, 5, 6], [6, 7, 8]]
-        a = np.pad(a, (5, 7), 'symmetric')
-        b = np.array(
-            [[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
-             [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
-
-             [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
-             [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6]]
-            )
-
-        assert_array_equal(a, b)
-
-    def test_check_large_pad_odd(self):
-        a = [[4, 5, 6], [6, 7, 8]]
-        a = np.pad(a, (5, 7), 'symmetric', reflect_type='odd')
-        b = np.array(
-            [[-3, -2, -2, -1,  0,  0,  1,  2,  2,  3,  4,  4,  5,  6,  6],
-             [-3, -2, -2, -1,  0,  0,  1,  2,  2,  3,  4,  4,  5,  6,  6],
-             [-1,  0,  0,  1,  2,  2,  3,  4,  4,  5,  6,  6,  7,  8,  8],
-             [-1,  0,  0,  1,  2,  2,  3,  4,  4,  5,  6,  6,  7,  8,  8],
-             [ 1,  2,  2,  3,  4,  4,  5,  6,  6,  7,  8,  8,  9, 10, 10],
-
-             [ 1,  2,  2,  3,  4,  4,  5,  6,  6,  7,  8,  8,  9, 10, 10],
-             [ 3,  4,  4,  5,  6,  6,  7,  8,  8,  9, 10, 10, 11, 12, 12],
-
-             [ 3,  4,  4,  5,  6,  6,  7,  8,  8,  9, 10, 10, 11, 12, 12],
-             [ 5,  6,  6,  7,  8,  8,  9, 10, 10, 11, 12, 12, 13, 14, 14],
-             [ 5,  6,  6,  7,  8,  8,  9, 10, 10, 11, 12, 12, 13, 14, 14],
-             [ 7,  8,  8,  9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16],
-             [ 7,  8,  8,  9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16],
-             [ 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16, 17, 18, 18],
-             [ 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16, 17, 18, 18]]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_shape(self):
-        a = [[4, 5, 6]]
-        a = np.pad(a, (5, 7), 'symmetric')
-        b = np.array(
-            [[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6],
-             [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6]]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_01(self):
-        a = np.pad([1, 2, 3], 2, 'symmetric')
-        b = np.array([2, 1, 1, 2, 3, 3, 2])
-        assert_array_equal(a, b)
-
-    def test_check_02(self):
-        a = np.pad([1, 2, 3], 3, 'symmetric')
-        b = np.array([3, 2, 1, 1, 2, 3, 3, 2, 1])
-        assert_array_equal(a, b)
-
-    def test_check_03(self):
-        a = np.pad([1, 2, 3], 6, 'symmetric')
-        b = np.array([1, 2, 3, 3, 2, 1, 1, 2, 3, 3, 2, 1, 1, 2, 3])
-        assert_array_equal(a, b)
-
-
-class TestWrap:
-    def test_check_simple(self):
-        a = np.arange(100)
-        a = np.pad(a, (25, 20), 'wrap')
-        b = np.array(
-            [75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
-             85, 86, 87, 88, 89, 90, 91, 92, 93, 94,
-             95, 96, 97, 98, 99,
-
-             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
-             10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-             20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
-             30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-             40, 41, 42, 43, 44, 45, 46, 47, 48, 49,
-             50, 51, 52, 53, 54, 55, 56, 57, 58, 59,
-             60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-             70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
-             80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
-             90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
-
-             0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
-             10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_large_pad(self):
-        a = np.arange(12)
-        a = np.reshape(a, (3, 4))
-        a = np.pad(a, (10, 12), 'wrap')
-        b = np.array(
-            [[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
-              11, 8, 9, 10, 11, 8, 9, 10, 11],
-             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
-              3, 0, 1, 2, 3, 0, 1, 2, 3],
-             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
-              7, 4, 5, 6, 7, 4, 5, 6, 7],
-             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
-              11, 8, 9, 10, 11, 8, 9, 10, 11],
-             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
-              3, 0, 1, 2, 3, 0, 1, 2, 3],
-             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
-              7, 4, 5, 6, 7, 4, 5, 6, 7],
-             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
-              11, 8, 9, 10, 11, 8, 9, 10, 11],
-             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
-              3, 0, 1, 2, 3, 0, 1, 2, 3],
-             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
-              7, 4, 5, 6, 7, 4, 5, 6, 7],
-             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
-              11, 8, 9, 10, 11, 8, 9, 10, 11],
-
-             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
-              3, 0, 1, 2, 3, 0, 1, 2, 3],
-             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
-              7, 4, 5, 6, 7, 4, 5, 6, 7],
-             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
-              11, 8, 9, 10, 11, 8, 9, 10, 11],
-
-             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
-              3, 0, 1, 2, 3, 0, 1, 2, 3],
-             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
-              7, 4, 5, 6, 7, 4, 5, 6, 7],
-             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
-              11, 8, 9, 10, 11, 8, 9, 10, 11],
-             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
-              3, 0, 1, 2, 3, 0, 1, 2, 3],
-             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
-              7, 4, 5, 6, 7, 4, 5, 6, 7],
-             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
-              11, 8, 9, 10, 11, 8, 9, 10, 11],
-             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
-              3, 0, 1, 2, 3, 0, 1, 2, 3],
-             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
-              7, 4, 5, 6, 7, 4, 5, 6, 7],
-             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
-              11, 8, 9, 10, 11, 8, 9, 10, 11],
-             [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,
-              3, 0, 1, 2, 3, 0, 1, 2, 3],
-             [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6,
-              7, 4, 5, 6, 7, 4, 5, 6, 7],
-             [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10,
-              11, 8, 9, 10, 11, 8, 9, 10, 11]]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_01(self):
-        a = np.pad([1, 2, 3], 3, 'wrap')
-        b = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3])
-        assert_array_equal(a, b)
-
-    def test_check_02(self):
-        a = np.pad([1, 2, 3], 4, 'wrap')
-        b = np.array([3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1])
-        assert_array_equal(a, b)
-
-    def test_pad_with_zero(self):
-        a = np.ones((3, 5))
-        b = np.pad(a, (0, 5), mode="wrap")
-        assert_array_equal(a, b[:-5, :-5])
-
-    def test_repeated_wrapping(self):
-        """
-        Check wrapping on each side individually if the wrapped area is longer
-        than the original array.
-        """
-        a = np.arange(5)
-        b = np.pad(a, (12, 0), mode="wrap")
-        assert_array_equal(np.r_[a, a, a, a][3:], b)
-
-        a = np.arange(5)
-        b = np.pad(a, (0, 12), mode="wrap")
-        assert_array_equal(np.r_[a, a, a, a][:-3], b)
-    
-    def test_repeated_wrapping_multiple_origin(self):
-        """
-        Assert that 'wrap' pads only with multiples of the original area if
-        the pad width is larger than the original array.
-        """
-        a = np.arange(4).reshape(2, 2)
-        a = np.pad(a, [(1, 3), (3, 1)], mode='wrap')
-        b = np.array(
-            [[3, 2, 3, 2, 3, 2],
-             [1, 0, 1, 0, 1, 0],
-             [3, 2, 3, 2, 3, 2],
-             [1, 0, 1, 0, 1, 0],
-             [3, 2, 3, 2, 3, 2],
-             [1, 0, 1, 0, 1, 0]]
-        )
-        assert_array_equal(a, b)
-
-
-class TestEdge:
-    def test_check_simple(self):
-        a = np.arange(12)
-        a = np.reshape(a, (4, 3))
-        a = np.pad(a, ((2, 3), (3, 2)), 'edge')
-        b = np.array(
-            [[0, 0, 0, 0, 1, 2, 2, 2],
-             [0, 0, 0, 0, 1, 2, 2, 2],
-
-             [0, 0, 0, 0, 1, 2, 2, 2],
-             [3, 3, 3, 3, 4, 5, 5, 5],
-             [6, 6, 6, 6, 7, 8, 8, 8],
-             [9, 9, 9, 9, 10, 11, 11, 11],
-
-             [9, 9, 9, 9, 10, 11, 11, 11],
-             [9, 9, 9, 9, 10, 11, 11, 11],
-             [9, 9, 9, 9, 10, 11, 11, 11]]
-            )
-        assert_array_equal(a, b)
-
-    def test_check_width_shape_1_2(self):
-        # Check a pad_width of the form ((1, 2),).
-        # Regression test for issue gh-7808.
-        a = np.array([1, 2, 3])
-        padded = np.pad(a, ((1, 2),), 'edge')
-        expected = np.array([1, 1, 2, 3, 3, 3])
-        assert_array_equal(padded, expected)
-
-        a = np.array([[1, 2, 3], [4, 5, 6]])
-        padded = np.pad(a, ((1, 2),), 'edge')
-        expected = np.pad(a, ((1, 2), (1, 2)), 'edge')
-        assert_array_equal(padded, expected)
-
-        a = np.arange(24).reshape(2, 3, 4)
-        padded = np.pad(a, ((1, 2),), 'edge')
-        expected = np.pad(a, ((1, 2), (1, 2), (1, 2)), 'edge')
-        assert_array_equal(padded, expected)
-
-
-class TestEmpty:
-    def test_simple(self):
-        arr = np.arange(24).reshape(4, 6)
-        result = np.pad(arr, [(2, 3), (3, 1)], mode="empty")
-        assert result.shape == (9, 10)
-        assert_equal(arr, result[2:-3, 3:-1])
-
-    def test_pad_empty_dimension(self):
-        arr = np.zeros((3, 0, 2))
-        result = np.pad(arr, [(0,), (2,), (1,)], mode="empty")
-        assert result.shape == (3, 4, 4)
-
-
-def test_legacy_vector_functionality():
-    def _padwithtens(vector, pad_width, iaxis, kwargs):
-        vector[:pad_width[0]] = 10
-        vector[-pad_width[1]:] = 10
-
-    a = np.arange(6).reshape(2, 3)
-    a = np.pad(a, 2, _padwithtens)
-    b = np.array(
-        [[10, 10, 10, 10, 10, 10, 10],
-         [10, 10, 10, 10, 10, 10, 10],
-
-         [10, 10,  0,  1,  2, 10, 10],
-         [10, 10,  3,  4,  5, 10, 10],
-
-         [10, 10, 10, 10, 10, 10, 10],
-         [10, 10, 10, 10, 10, 10, 10]]
-        )
-    assert_array_equal(a, b)
-
-
-def test_unicode_mode():
-    a = np.pad([1], 2, mode='constant')
-    b = np.array([0, 0, 1, 0, 0])
-    assert_array_equal(a, b)
-
-
-@pytest.mark.parametrize("mode", ["edge", "symmetric", "reflect", "wrap"])
-def test_object_input(mode):
-    # Regression test for issue gh-11395.
-    a = np.full((4, 3), fill_value=None)
-    pad_amt = ((2, 3), (3, 2))
-    b = np.full((9, 8), fill_value=None)
-    assert_array_equal(np.pad(a, pad_amt, mode=mode), b)
-
-
-class TestPadWidth:
-    @pytest.mark.parametrize("pad_width", [
-        (4, 5, 6, 7),
-        ((1,), (2,), (3,)),
-        ((1, 2), (3, 4), (5, 6)),
-        ((3, 4, 5), (0, 1, 2)),
-    ])
-    @pytest.mark.parametrize("mode", _all_modes.keys())
-    def test_misshaped_pad_width(self, pad_width, mode):
-        arr = np.arange(30).reshape((6, 5))
-        match = "operands could not be broadcast together"
-        with pytest.raises(ValueError, match=match):
-            np.pad(arr, pad_width, mode)
-
-    @pytest.mark.parametrize("mode", _all_modes.keys())
-    def test_misshaped_pad_width_2(self, mode):
-        arr = np.arange(30).reshape((6, 5))
-        match = ("input operand has more dimensions than allowed by the axis "
-                 "remapping")
-        with pytest.raises(ValueError, match=match):
-            np.pad(arr, (((3,), (4,), (5,)), ((0,), (1,), (2,))), mode)
-
-    @pytest.mark.parametrize(
-        "pad_width", [-2, (-2,), (3, -1), ((5, 2), (-2, 3)), ((-4,), (2,))])
-    @pytest.mark.parametrize("mode", _all_modes.keys())
-    def test_negative_pad_width(self, pad_width, mode):
-        arr = np.arange(30).reshape((6, 5))
-        match = "index can't contain negative values"
-        with pytest.raises(ValueError, match=match):
-            np.pad(arr, pad_width, mode)
-
-    @pytest.mark.parametrize("pad_width, dtype", [
-        ("3", None),
-        ("word", None),
-        (None, None),
-        (object(), None),
-        (3.4, None),
-        (((2, 3, 4), (3, 2)), object),
-        (complex(1, -1), None),
-        (((-2.1, 3), (3, 2)), None),
-    ])
-    @pytest.mark.parametrize("mode", _all_modes.keys())
-    def test_bad_type(self, pad_width, dtype, mode):
-        arr = np.arange(30).reshape((6, 5))
-        match = "`pad_width` must be of integral type."
-        if dtype is not None:
-            # avoid DeprecationWarning when not specifying dtype
-            with pytest.raises(TypeError, match=match):
-                np.pad(arr, np.array(pad_width, dtype=dtype), mode)
-        else:
-            with pytest.raises(TypeError, match=match):
-                np.pad(arr, pad_width, mode)
-            with pytest.raises(TypeError, match=match):
-                np.pad(arr, np.array(pad_width), mode)
-
-    def test_pad_width_as_ndarray(self):
-        a = np.arange(12)
-        a = np.reshape(a, (4, 3))
-        a = np.pad(a, np.array(((2, 3), (3, 2))), 'edge')
-        b = np.array(
-            [[0,  0,  0,    0,  1,  2,    2,  2],
-             [0,  0,  0,    0,  1,  2,    2,  2],
-
-             [0,  0,  0,    0,  1,  2,    2,  2],
-             [3,  3,  3,    3,  4,  5,    5,  5],
-             [6,  6,  6,    6,  7,  8,    8,  8],
-             [9,  9,  9,    9, 10, 11,   11, 11],
-
-             [9,  9,  9,    9, 10, 11,   11, 11],
-             [9,  9,  9,    9, 10, 11,   11, 11],
-             [9,  9,  9,    9, 10, 11,   11, 11]]
-            )
-        assert_array_equal(a, b)
-
-    @pytest.mark.parametrize("pad_width", [0, (0, 0), ((0, 0), (0, 0))])
-    @pytest.mark.parametrize("mode", _all_modes.keys())
-    def test_zero_pad_width(self, pad_width, mode):
-        arr = np.arange(30).reshape(6, 5)
-        assert_array_equal(arr, np.pad(arr, pad_width, mode=mode))
-
-
-@pytest.mark.parametrize("mode", _all_modes.keys())
-def test_kwargs(mode):
-    """Test behavior of pad's kwargs for the given mode."""
-    allowed = _all_modes[mode]
-    not_allowed = {}
-    for kwargs in _all_modes.values():
-        if kwargs != allowed:
-            not_allowed.update(kwargs)
-    # Test if allowed keyword arguments pass
-    np.pad([1, 2, 3], 1, mode, **allowed)
-    # Test if prohibited keyword arguments of other modes raise an error
-    for key, value in not_allowed.items():
-        match = "unsupported keyword arguments for mode '{}'".format(mode)
-        with pytest.raises(ValueError, match=match):
-            np.pad([1, 2, 3], 1, mode, **{key: value})
-
-
-def test_constant_zero_default():
-    arr = np.array([1, 1])
-    assert_array_equal(np.pad(arr, 2), [0, 0, 1, 1, 0, 0])
-
-
-@pytest.mark.parametrize("mode", [1, "const", object(), None, True, False])
-def test_unsupported_mode(mode):
-    match= "mode '{}' is not supported".format(mode)
-    with pytest.raises(ValueError, match=match):
-        np.pad([1, 2, 3], 4, mode=mode)
-
-
-@pytest.mark.parametrize("mode", _all_modes.keys())
-def test_non_contiguous_array(mode):
-    arr = np.arange(24).reshape(4, 6)[::2, ::2]
-    result = np.pad(arr, (2, 3), mode)
-    assert result.shape == (7, 8)
-    assert_equal(result[2:-3, 2:-3], arr)
-
-
-@pytest.mark.parametrize("mode", _all_modes.keys())
-def test_memory_layout_persistence(mode):
-    """Test if C and F order is preserved for all pad modes."""
-    x = np.ones((5, 10), order='C')
-    assert np.pad(x, 5, mode).flags["C_CONTIGUOUS"]
-    x = np.ones((5, 10), order='F')
-    assert np.pad(x, 5, mode).flags["F_CONTIGUOUS"]
-
-
-@pytest.mark.parametrize("dtype", _numeric_dtypes)
-@pytest.mark.parametrize("mode", _all_modes.keys())
-def test_dtype_persistence(dtype, mode):
-    arr = np.zeros((3, 2, 1), dtype=dtype)
-    result = np.pad(arr, 1, mode=mode)
-    assert result.dtype == dtype
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/lib/tests/test_loadtxt.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/lib/tests/test_loadtxt.py
deleted file mode 100644
index 2d805e43455d3ddbe276684fedcd31ab8b2916a4..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/lib/tests/test_loadtxt.py
+++ /dev/null
@@ -1,1048 +0,0 @@
-"""
-Tests specific to `np.loadtxt` added during the move of loadtxt to be backed
-by C code.
-These tests complement those found in `test_io.py`.
-"""
-
-import sys
-import os
-import pytest
-from tempfile import NamedTemporaryFile, mkstemp
-from io import StringIO
-
-import numpy as np
-from numpy.ma.testutils import assert_equal
-from numpy.testing import assert_array_equal, HAS_REFCOUNT, IS_PYPY
-
-
-def test_scientific_notation():
-    """Test that both 'e' and 'E' are parsed correctly."""
-    data = StringIO(
-        (
-            "1.0e-1,2.0E1,3.0\n"
-            "4.0e-2,5.0E-1,6.0\n"
-            "7.0e-3,8.0E1,9.0\n"
-            "0.0e-4,1.0E-1,2.0"
-        )
-    )
-    expected = np.array(
-        [[0.1, 20., 3.0], [0.04, 0.5, 6], [0.007, 80., 9], [0, 0.1, 2]]
-    )
-    assert_array_equal(np.loadtxt(data, delimiter=","), expected)
-
-
-@pytest.mark.parametrize("comment", ["..", "//", "@-", "this is a comment:"])
-def test_comment_multiple_chars(comment):
-    content = "# IGNORE\n1.5, 2.5# ABC\n3.0,4.0# XXX\n5.5,6.0\n"
-    txt = StringIO(content.replace("#", comment))
-    a = np.loadtxt(txt, delimiter=",", comments=comment)
-    assert_equal(a, [[1.5, 2.5], [3.0, 4.0], [5.5, 6.0]])
-
-
-@pytest.fixture
-def mixed_types_structured():
-    """
-    Fixture providing hetergeneous input data with a structured dtype, along
-    with the associated structured array.
-    """
-    data = StringIO(
-        (
-            "1000;2.4;alpha;-34\n"
-            "2000;3.1;beta;29\n"
-            "3500;9.9;gamma;120\n"
-            "4090;8.1;delta;0\n"
-            "5001;4.4;epsilon;-99\n"
-            "6543;7.8;omega;-1\n"
-        )
-    )
-    dtype = np.dtype(
-        [('f0', np.uint16), ('f1', np.float64), ('f2', 'S7'), ('f3', np.int8)]
-    )
-    expected = np.array(
-        [
-            (1000, 2.4, "alpha", -34),
-            (2000, 3.1, "beta", 29),
-            (3500, 9.9, "gamma", 120),
-            (4090, 8.1, "delta", 0),
-            (5001, 4.4, "epsilon", -99),
-            (6543, 7.8, "omega", -1)
-        ],
-        dtype=dtype
-    )
-    return data, dtype, expected
-
-
-@pytest.mark.parametrize('skiprows', [0, 1, 2, 3])
-def test_structured_dtype_and_skiprows_no_empty_lines(
-        skiprows, mixed_types_structured):
-    data, dtype, expected = mixed_types_structured
-    a = np.loadtxt(data, dtype=dtype, delimiter=";", skiprows=skiprows)
-    assert_array_equal(a, expected[skiprows:])
-
-
-def test_unpack_structured(mixed_types_structured):
-    data, dtype, expected = mixed_types_structured
-
-    a, b, c, d = np.loadtxt(data, dtype=dtype, delimiter=";", unpack=True)
-    assert_array_equal(a, expected["f0"])
-    assert_array_equal(b, expected["f1"])
-    assert_array_equal(c, expected["f2"])
-    assert_array_equal(d, expected["f3"])
-
-
-def test_structured_dtype_with_shape():
-    dtype = np.dtype([("a", "u1", 2), ("b", "u1", 2)])
-    data = StringIO("0,1,2,3\n6,7,8,9\n")
-    expected = np.array([((0, 1), (2, 3)), ((6, 7), (8, 9))], dtype=dtype)
-    assert_array_equal(np.loadtxt(data, delimiter=",", dtype=dtype), expected)
-
-
-def test_structured_dtype_with_multi_shape():
-    dtype = np.dtype([("a", "u1", (2, 2))])
-    data = StringIO("0 1 2 3\n")
-    expected = np.array([(((0, 1), (2, 3)),)], dtype=dtype)
-    assert_array_equal(np.loadtxt(data, dtype=dtype), expected)
-
-
-def test_nested_structured_subarray():
-    # Test from gh-16678
-    point = np.dtype([('x', float), ('y', float)])
-    dt = np.dtype([('code', int), ('points', point, (2,))])
-    data = StringIO("100,1,2,3,4\n200,5,6,7,8\n")
-    expected = np.array(
-        [
-            (100, [(1., 2.), (3., 4.)]),
-            (200, [(5., 6.), (7., 8.)]),
-        ],
-        dtype=dt
-    )
-    assert_array_equal(np.loadtxt(data, dtype=dt, delimiter=","), expected)
-
-
-def test_structured_dtype_offsets():
-    # An aligned structured dtype will have additional padding
-    dt = np.dtype("i1, i4, i1, i4, i1, i4", align=True)
-    data = StringIO("1,2,3,4,5,6\n7,8,9,10,11,12\n")
-    expected = np.array([(1, 2, 3, 4, 5, 6), (7, 8, 9, 10, 11, 12)], dtype=dt)
-    assert_array_equal(np.loadtxt(data, delimiter=",", dtype=dt), expected)
-
-
-@pytest.mark.parametrize("param", ("skiprows", "max_rows"))
-def test_exception_negative_row_limits(param):
-    """skiprows and max_rows should raise for negative parameters."""
-    with pytest.raises(ValueError, match="argument must be nonnegative"):
-        np.loadtxt("foo.bar", **{param: -3})
-
-
-@pytest.mark.parametrize("param", ("skiprows", "max_rows"))
-def test_exception_noninteger_row_limits(param):
-    with pytest.raises(TypeError, match="argument must be an integer"):
-        np.loadtxt("foo.bar", **{param: 1.0})
-
-
-@pytest.mark.parametrize(
-    "data, shape",
-    [
-        ("1 2 3 4 5\n", (1, 5)),  # Single row
-        ("1\n2\n3\n4\n5\n", (5, 1)),  # Single column
-    ]
-)
-def test_ndmin_single_row_or_col(data, shape):
-    arr = np.array([1, 2, 3, 4, 5])
-    arr2d = arr.reshape(shape)
-
-    assert_array_equal(np.loadtxt(StringIO(data), dtype=int), arr)
-    assert_array_equal(np.loadtxt(StringIO(data), dtype=int, ndmin=0), arr)
-    assert_array_equal(np.loadtxt(StringIO(data), dtype=int, ndmin=1), arr)
-    assert_array_equal(np.loadtxt(StringIO(data), dtype=int, ndmin=2), arr2d)
-
-
-@pytest.mark.parametrize("badval", [-1, 3, None, "plate of shrimp"])
-def test_bad_ndmin(badval):
-    with pytest.raises(ValueError, match="Illegal value of ndmin keyword"):
-        np.loadtxt("foo.bar", ndmin=badval)
-
-
-@pytest.mark.parametrize(
-    "ws",
-    (
-            " ",  # space
-            "\t",  # tab
-            "\u2003",  # em
-            "\u00A0",  # non-break
-            "\u3000",  # ideographic space
-    )
-)
-def test_blank_lines_spaces_delimit(ws):
-    txt = StringIO(
-        f"1 2{ws}30\n\n{ws}\n"
-        f"4 5 60{ws}\n  {ws}  \n"
-        f"7 8 {ws} 90\n  # comment\n"
-        f"3 2 1"
-    )
-    # NOTE: It is unclear that the `  # comment` should succeed. Except
-    #       for delimiter=None, which should use any whitespace (and maybe
-    #       should just be implemented closer to Python
-    expected = np.array([[1, 2, 30], [4, 5, 60], [7, 8, 90], [3, 2, 1]])
-    assert_equal(
-        np.loadtxt(txt, dtype=int, delimiter=None, comments="#"), expected
-    )
-
-
-def test_blank_lines_normal_delimiter():
-    txt = StringIO('1,2,30\n\n4,5,60\n\n7,8,90\n# comment\n3,2,1')
-    expected = np.array([[1, 2, 30], [4, 5, 60], [7, 8, 90], [3, 2, 1]])
-    assert_equal(
-        np.loadtxt(txt, dtype=int, delimiter=',', comments="#"), expected
-    )
-
-
-@pytest.mark.parametrize("dtype", (float, object))
-def test_maxrows_no_blank_lines(dtype):
-    txt = StringIO("1.5,2.5\n3.0,4.0\n5.5,6.0")
-    res = np.loadtxt(txt, dtype=dtype, delimiter=",", max_rows=2)
-    assert_equal(res.dtype, dtype)
-    assert_equal(res, np.array([["1.5", "2.5"], ["3.0", "4.0"]], dtype=dtype))
-
-
-@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
-                    reason="PyPy bug in error formatting")
-@pytest.mark.parametrize("dtype", (np.dtype("f8"), np.dtype("i2")))
-def test_exception_message_bad_values(dtype):
-    txt = StringIO("1,2\n3,XXX\n5,6")
-    msg = f"could not convert string 'XXX' to {dtype} at row 1, column 2"
-    with pytest.raises(ValueError, match=msg):
-        np.loadtxt(txt, dtype=dtype, delimiter=",")
-
-
-def test_converters_negative_indices():
-    txt = StringIO('1.5,2.5\n3.0,XXX\n5.5,6.0')
-    conv = {-1: lambda s: np.nan if s == 'XXX' else float(s)}
-    expected = np.array([[1.5, 2.5], [3.0, np.nan], [5.5, 6.0]])
-    res = np.loadtxt(
-        txt, dtype=np.float64, delimiter=",", converters=conv, encoding=None
-    )
-    assert_equal(res, expected)
-
-
-def test_converters_negative_indices_with_usecols():
-    txt = StringIO('1.5,2.5,3.5\n3.0,4.0,XXX\n5.5,6.0,7.5\n')
-    conv = {-1: lambda s: np.nan if s == 'XXX' else float(s)}
-    expected = np.array([[1.5, 3.5], [3.0, np.nan], [5.5, 7.5]])
-    res = np.loadtxt(
-        txt,
-        dtype=np.float64,
-        delimiter=",",
-        converters=conv,
-        usecols=[0, -1],
-        encoding=None,
-    )
-    assert_equal(res, expected)
-
-    # Second test with variable number of rows:
-    res = np.loadtxt(StringIO('''0,1,2\n0,1,2,3,4'''), delimiter=",",
-                     usecols=[0, -1], converters={-1: (lambda x: -1)})
-    assert_array_equal(res, [[0, -1], [0, -1]])
-
-
-def test_ragged_error():
-    rows = ["1,2,3", "1,2,3", "4,3,2,1"]
-    with pytest.raises(ValueError,
-            match="the number of columns changed from 3 to 4 at row 3"):
-        np.loadtxt(rows, delimiter=",")
-
-
-def test_ragged_usecols():
-    # usecols, and negative ones, work even with varying number of columns.
-    txt = StringIO("0,0,XXX\n0,XXX,0,XXX\n0,XXX,XXX,0,XXX\n")
-    expected = np.array([[0, 0], [0, 0], [0, 0]])
-    res = np.loadtxt(txt, dtype=float, delimiter=",", usecols=[0, -2])
-    assert_equal(res, expected)
-
-    txt = StringIO("0,0,XXX\n0\n0,XXX,XXX,0,XXX\n")
-    with pytest.raises(ValueError,
-                match="invalid column index -2 at row 2 with 1 columns"):
-        # There is no -2 column in the second row:
-        np.loadtxt(txt, dtype=float, delimiter=",", usecols=[0, -2])
-
-
-def test_empty_usecols():
-    txt = StringIO("0,0,XXX\n0,XXX,0,XXX\n0,XXX,XXX,0,XXX\n")
-    res = np.loadtxt(txt, dtype=np.dtype([]), delimiter=",", usecols=[])
-    assert res.shape == (3,)
-    assert res.dtype == np.dtype([])
-
-
-@pytest.mark.parametrize("c1", ["a", "の", "🫕"])
-@pytest.mark.parametrize("c2", ["a", "の", "🫕"])
-def test_large_unicode_characters(c1, c2):
-    # c1 and c2 span ascii, 16bit and 32bit range.
-    txt = StringIO(f"a,{c1},c,1.0\ne,{c2},2.0,g")
-    res = np.loadtxt(txt, dtype=np.dtype('U12'), delimiter=",")
-    expected = np.array(
-        [f"a,{c1},c,1.0".split(","), f"e,{c2},2.0,g".split(",")],
-        dtype=np.dtype('U12')
-    )
-    assert_equal(res, expected)
-
-
-def test_unicode_with_converter():
-    txt = StringIO("cat,dog\nαβγ,δεζ\nabc,def\n")
-    conv = {0: lambda s: s.upper()}
-    res = np.loadtxt(
-        txt,
-        dtype=np.dtype("U12"),
-        converters=conv,
-        delimiter=",",
-        encoding=None
-    )
-    expected = np.array([['CAT', 'dog'], ['ΑΒΓ', 'δεζ'], ['ABC', 'def']])
-    assert_equal(res, expected)
-
-
-def test_converter_with_structured_dtype():
-    txt = StringIO('1.5,2.5,Abc\n3.0,4.0,dEf\n5.5,6.0,ghI\n')
-    dt = np.dtype([('m', np.int32), ('r', np.float32), ('code', 'U8')])
-    conv = {0: lambda s: int(10*float(s)), -1: lambda s: s.upper()}
-    res = np.loadtxt(txt, dtype=dt, delimiter=",", converters=conv)
-    expected = np.array(
-        [(15, 2.5, 'ABC'), (30, 4.0, 'DEF'), (55, 6.0, 'GHI')], dtype=dt
-    )
-    assert_equal(res, expected)
-
-
-def test_converter_with_unicode_dtype():
-    """
-    With the default 'bytes' encoding, tokens are encoded prior to being
-    passed to the converter. This means that the output of the converter may
-    be bytes instead of unicode as expected by `read_rows`.
-
-    This test checks that outputs from the above scenario are properly decoded
-    prior to parsing by `read_rows`.
-    """
-    txt = StringIO('abc,def\nrst,xyz')
-    conv = bytes.upper
-    res = np.loadtxt(
-            txt, dtype=np.dtype("U3"), converters=conv, delimiter=",")
-    expected = np.array([['ABC', 'DEF'], ['RST', 'XYZ']])
-    assert_equal(res, expected)
-
-
-def test_read_huge_row():
-    row = "1.5, 2.5," * 50000
-    row = row[:-1] + "\n"
-    txt = StringIO(row * 2)
-    res = np.loadtxt(txt, delimiter=",", dtype=float)
-    assert_equal(res, np.tile([1.5, 2.5], (2, 50000)))
-
-
-@pytest.mark.parametrize("dtype", "edfgFDG")
-def test_huge_float(dtype):
-    # Covers a non-optimized path that is rarely taken:
-    field = "0" * 1000 + ".123456789"
-    dtype = np.dtype(dtype)
-    value = np.loadtxt([field], dtype=dtype)[()]
-    assert value == dtype.type("0.123456789")
-
-
-@pytest.mark.parametrize(
-    ("given_dtype", "expected_dtype"),
-    [
-        ("S", np.dtype("S5")),
-        ("U", np.dtype("U5")),
-    ],
-)
-def test_string_no_length_given(given_dtype, expected_dtype):
-    """
-    The given dtype is just 'S' or 'U' with no length. In these cases, the
-    length of the resulting dtype is determined by the longest string found
-    in the file.
-    """
-    txt = StringIO("AAA,5-1\nBBBBB,0-3\nC,4-9\n")
-    res = np.loadtxt(txt, dtype=given_dtype, delimiter=",")
-    expected = np.array(
-        [['AAA', '5-1'], ['BBBBB', '0-3'], ['C', '4-9']], dtype=expected_dtype
-    )
-    assert_equal(res, expected)
-    assert_equal(res.dtype, expected_dtype)
-
-
-def test_float_conversion():
-    """
-    Some tests that the conversion to float64 works as accurately as the
-    Python built-in `float` function. In a naive version of the float parser,
-    these strings resulted in values that were off by an ULP or two.
-    """
-    strings = [
-        '0.9999999999999999',
-        '9876543210.123456',
-        '5.43215432154321e+300',
-        '0.901',
-        '0.333',
-    ]
-    txt = StringIO('\n'.join(strings))
-    res = np.loadtxt(txt)
-    expected = np.array([float(s) for s in strings])
-    assert_equal(res, expected)
-
-
-def test_bool():
-    # Simple test for bool via integer
-    txt = StringIO("1, 0\n10, -1")
-    res = np.loadtxt(txt, dtype=bool, delimiter=",")
-    assert res.dtype == bool
-    assert_array_equal(res, [[True, False], [True, True]])
-    # Make sure we use only 1 and 0 on the byte level:
-    assert_array_equal(res.view(np.uint8), [[1, 0], [1, 1]])
-
-
-@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
-                    reason="PyPy bug in error formatting")
-@pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])
-@pytest.mark.filterwarnings("error:.*integer via a float.*:DeprecationWarning")
-def test_integer_signs(dtype):
-    dtype = np.dtype(dtype)
-    assert np.loadtxt(["+2"], dtype=dtype) == 2
-    if dtype.kind == "u":
-        with pytest.raises(ValueError):
-            np.loadtxt(["-1\n"], dtype=dtype)
-    else:
-        assert np.loadtxt(["-2\n"], dtype=dtype) == -2
-
-    for sign in ["++", "+-", "--", "-+"]:
-        with pytest.raises(ValueError):
-            np.loadtxt([f"{sign}2\n"], dtype=dtype)
-
-
-@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
-                    reason="PyPy bug in error formatting")
-@pytest.mark.parametrize("dtype", np.typecodes["AllInteger"])
-@pytest.mark.filterwarnings("error:.*integer via a float.*:DeprecationWarning")
-def test_implicit_cast_float_to_int_fails(dtype):
-    txt = StringIO("1.0, 2.1, 3.7\n4, 5, 6")
-    with pytest.raises(ValueError):
-        np.loadtxt(txt, dtype=dtype, delimiter=",")
-
-@pytest.mark.parametrize("dtype", (np.complex64, np.complex128))
-@pytest.mark.parametrize("with_parens", (False, True))
-def test_complex_parsing(dtype, with_parens):
-    s = "(1.0-2.5j),3.75,(7+-5.0j)\n(4),(-19e2j),(0)"
-    if not with_parens:
-        s = s.replace("(", "").replace(")", "")
-
-    res = np.loadtxt(StringIO(s), dtype=dtype, delimiter=",")
-    expected = np.array(
-        [[1.0-2.5j, 3.75, 7-5j], [4.0, -1900j, 0]], dtype=dtype
-    )
-    assert_equal(res, expected)
-
-
-def test_read_from_generator():
-    def gen():
-        for i in range(4):
-            yield f"{i},{2*i},{i**2}"
-
-    res = np.loadtxt(gen(), dtype=int, delimiter=",")
-    expected = np.array([[0, 0, 0], [1, 2, 1], [2, 4, 4], [3, 6, 9]])
-    assert_equal(res, expected)
-
-
-def test_read_from_generator_multitype():
-    def gen():
-        for i in range(3):
-            yield f"{i} {i / 4}"
-
-    res = np.loadtxt(gen(), dtype="i, d", delimiter=" ")
-    expected = np.array([(0, 0.0), (1, 0.25), (2, 0.5)], dtype="i, d")
-    assert_equal(res, expected)
-
-
-def test_read_from_bad_generator():
-    def gen():
-        for entry in ["1,2", b"3, 5", 12738]:
-            yield entry
-
-    with pytest.raises(
-            TypeError, match=r"non-string returned while reading data"):
-        np.loadtxt(gen(), dtype="i, i", delimiter=",")
-
-
-@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
-def test_object_cleanup_on_read_error():
-    sentinel = object()
-    already_read = 0
-
-    def conv(x):
-        nonlocal already_read
-        if already_read > 4999:
-            raise ValueError("failed half-way through!")
-        already_read += 1
-        return sentinel
-
-    txt = StringIO("x\n" * 10000)
-
-    with pytest.raises(ValueError, match="at row 5000, column 1"):
-        np.loadtxt(txt, dtype=object, converters={0: conv})
-
-    assert sys.getrefcount(sentinel) == 2
-
-
-@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
-                    reason="PyPy bug in error formatting")
-def test_character_not_bytes_compatible():
-    """Test exception when a character cannot be encoded as 'S'."""
-    data = StringIO("–")  # == \u2013
-    with pytest.raises(ValueError):
-        np.loadtxt(data, dtype="S5")
-
-
-@pytest.mark.parametrize("conv", (0, [float], ""))
-def test_invalid_converter(conv):
-    msg = (
-        "converters must be a dictionary mapping columns to converter "
-        "functions or a single callable."
-    )
-    with pytest.raises(TypeError, match=msg):
-        np.loadtxt(StringIO("1 2\n3 4"), converters=conv)
-
-
-@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
-                    reason="PyPy bug in error formatting")
-def test_converters_dict_raises_non_integer_key():
-    with pytest.raises(TypeError, match="keys of the converters dict"):
-        np.loadtxt(StringIO("1 2\n3 4"), converters={"a": int})
-    with pytest.raises(TypeError, match="keys of the converters dict"):
-        np.loadtxt(StringIO("1 2\n3 4"), converters={"a": int}, usecols=0)
-
-
-@pytest.mark.parametrize("bad_col_ind", (3, -3))
-def test_converters_dict_raises_non_col_key(bad_col_ind):
-    data = StringIO("1 2\n3 4")
-    with pytest.raises(ValueError, match="converter specified for column"):
-        np.loadtxt(data, converters={bad_col_ind: int})
-
-
-def test_converters_dict_raises_val_not_callable():
-    with pytest.raises(TypeError,
-                match="values of the converters dictionary must be callable"):
-        np.loadtxt(StringIO("1 2\n3 4"), converters={0: 1})
-
-
-@pytest.mark.parametrize("q", ('"', "'", "`"))
-def test_quoted_field(q):
-    txt = StringIO(
-        f"{q}alpha, x{q}, 2.5\n{q}beta, y{q}, 4.5\n{q}gamma, z{q}, 5.0\n"
-    )
-    dtype = np.dtype([('f0', 'U8'), ('f1', np.float64)])
-    expected = np.array(
-        [("alpha, x", 2.5), ("beta, y", 4.5), ("gamma, z", 5.0)], dtype=dtype
-    )
-
-    res = np.loadtxt(txt, dtype=dtype, delimiter=",", quotechar=q)
-    assert_array_equal(res, expected)
-
-
-@pytest.mark.parametrize("q", ('"', "'", "`"))
-def test_quoted_field_with_whitepace_delimiter(q):
-    txt = StringIO(
-        f"{q}alpha, x{q}     2.5\n{q}beta, y{q} 4.5\n{q}gamma, z{q}   5.0\n"
-    )
-    dtype = np.dtype([('f0', 'U8'), ('f1', np.float64)])
-    expected = np.array(
-        [("alpha, x", 2.5), ("beta, y", 4.5), ("gamma, z", 5.0)], dtype=dtype
-    )
-
-    res = np.loadtxt(txt, dtype=dtype, delimiter=None, quotechar=q)
-    assert_array_equal(res, expected)
-
-
-def test_quote_support_default():
-    """Support for quoted fields is disabled by default."""
-    txt = StringIO('"lat,long", 45, 30\n')
-    dtype = np.dtype([('f0', 'U24'), ('f1', np.float64), ('f2', np.float64)])
-
-    with pytest.raises(ValueError,
-            match="the dtype passed requires 3 columns but 4 were"):
-        np.loadtxt(txt, dtype=dtype, delimiter=",")
-
-    # Enable quoting support with non-None value for quotechar param
-    txt.seek(0)
-    expected = np.array([("lat,long", 45., 30.)], dtype=dtype)
-
-    res = np.loadtxt(txt, dtype=dtype, delimiter=",", quotechar='"')
-    assert_array_equal(res, expected)
-
-
-@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
-                    reason="PyPy bug in error formatting")
-def test_quotechar_multichar_error():
-    txt = StringIO("1,2\n3,4")
-    msg = r".*must be a single unicode character or None"
-    with pytest.raises(TypeError, match=msg):
-        np.loadtxt(txt, delimiter=",", quotechar="''")
-
-
-def test_comment_multichar_error_with_quote():
-    txt = StringIO("1,2\n3,4")
-    msg = (
-        "when multiple comments or a multi-character comment is given, "
-        "quotes are not supported."
-    )
-    with pytest.raises(ValueError, match=msg):
-        np.loadtxt(txt, delimiter=",", comments="123", quotechar='"')
-    with pytest.raises(ValueError, match=msg):
-        np.loadtxt(txt, delimiter=",", comments=["#", "%"], quotechar='"')
-
-    # A single character string in a tuple is unpacked though:
-    res = np.loadtxt(txt, delimiter=",", comments=("#",), quotechar="'")
-    assert_equal(res, [[1, 2], [3, 4]])
-
-
-def test_structured_dtype_with_quotes():
-    data = StringIO(
-        (
-            "1000;2.4;'alpha';-34\n"
-            "2000;3.1;'beta';29\n"
-            "3500;9.9;'gamma';120\n"
-            "4090;8.1;'delta';0\n"
-            "5001;4.4;'epsilon';-99\n"
-            "6543;7.8;'omega';-1\n"
-        )
-    )
-    dtype = np.dtype(
-        [('f0', np.uint16), ('f1', np.float64), ('f2', 'S7'), ('f3', np.int8)]
-    )
-    expected = np.array(
-        [
-            (1000, 2.4, "alpha", -34),
-            (2000, 3.1, "beta", 29),
-            (3500, 9.9, "gamma", 120),
-            (4090, 8.1, "delta", 0),
-            (5001, 4.4, "epsilon", -99),
-            (6543, 7.8, "omega", -1)
-        ],
-        dtype=dtype
-    )
-    res = np.loadtxt(data, dtype=dtype, delimiter=";", quotechar="'")
-    assert_array_equal(res, expected)
-
-
-def test_quoted_field_is_not_empty():
-    txt = StringIO('1\n\n"4"\n""')
-    expected = np.array(["1", "4", ""], dtype="U1")
-    res = np.loadtxt(txt, delimiter=",", dtype="U1", quotechar='"')
-    assert_equal(res, expected)
-
-def test_quoted_field_is_not_empty_nonstrict():
-    # Same as test_quoted_field_is_not_empty but check that we are not strict
-    # about missing closing quote (this is the `csv.reader` default also)
-    txt = StringIO('1\n\n"4"\n"')
-    expected = np.array(["1", "4", ""], dtype="U1")
-    res = np.loadtxt(txt, delimiter=",", dtype="U1", quotechar='"')
-    assert_equal(res, expected)
-
-def test_consecutive_quotechar_escaped():
-    txt = StringIO('"Hello, my name is ""Monty""!"')
-    expected = np.array('Hello, my name is "Monty"!', dtype="U40")
-    res = np.loadtxt(txt, dtype="U40", delimiter=",", quotechar='"')
-    assert_equal(res, expected)
-
-
-@pytest.mark.parametrize("data", ("", "\n\n\n", "# 1 2 3\n# 4 5 6\n"))
-@pytest.mark.parametrize("ndmin", (0, 1, 2))
-@pytest.mark.parametrize("usecols", [None, (1, 2, 3)])
-def test_warn_on_no_data(data, ndmin, usecols):
-    """Check that a UserWarning is emitted when no data is read from input."""
-    if usecols is not None:
-        expected_shape = (0, 3)
-    elif ndmin == 2:
-        expected_shape = (0, 1)  # guess a single column?!
-    else:
-        expected_shape = (0,)
-
-    txt = StringIO(data)
-    with pytest.warns(UserWarning, match="input contained no data"):
-        res = np.loadtxt(txt, ndmin=ndmin, usecols=usecols)
-    assert res.shape == expected_shape
-
-    with NamedTemporaryFile(mode="w") as fh:
-        fh.write(data)
-        fh.seek(0)
-        with pytest.warns(UserWarning, match="input contained no data"):
-            res = np.loadtxt(txt, ndmin=ndmin, usecols=usecols)
-        assert res.shape == expected_shape
-
-@pytest.mark.parametrize("skiprows", (2, 3))
-def test_warn_on_skipped_data(skiprows):
-    data = "1 2 3\n4 5 6"
-    txt = StringIO(data)
-    with pytest.warns(UserWarning, match="input contained no data"):
-        np.loadtxt(txt, skiprows=skiprows)
-
-
-@pytest.mark.parametrize(["dtype", "value"], [
-        ("i2", 0x0001), ("u2", 0x0001),
-        ("i4", 0x00010203), ("u4", 0x00010203),
-        ("i8", 0x0001020304050607), ("u8", 0x0001020304050607),
-        # The following values are constructed to lead to unique bytes:
-        ("float16", 3.07e-05),
-        ("float32", 9.2557e-41), ("complex64", 9.2557e-41+2.8622554e-29j),
-        ("float64", -1.758571353180402e-24),
-        # Here and below, the repr side-steps a small loss of precision in
-        # complex `str` in PyPy (which is probably fine, as repr works):
-        ("complex128", repr(5.406409232372729e-29-1.758571353180402e-24j)),
-        # Use integer values that fit into double.  Everything else leads to
-        # problems due to longdoubles going via double and decimal strings
-        # causing rounding errors.
-        ("longdouble", 0x01020304050607),
-        ("clongdouble", repr(0x01020304050607 + (0x00121314151617 * 1j))),
-        ("U2", "\U00010203\U000a0b0c")])
-@pytest.mark.parametrize("swap", [True, False])
-def test_byteswapping_and_unaligned(dtype, value, swap):
-    # Try to create "interesting" values within the valid unicode range:
-    dtype = np.dtype(dtype)
-    data = [f"x,{value}\n"]  # repr as PyPy `str` truncates some
-    if swap:
-        dtype = dtype.newbyteorder()
-    full_dt = np.dtype([("a", "S1"), ("b", dtype)], align=False)
-    # The above ensures that the interesting "b" field is unaligned:
-    assert full_dt.fields["b"][1] == 1
-    res = np.loadtxt(data, dtype=full_dt, delimiter=",", encoding=None,
-                     max_rows=1)  # max-rows prevents over-allocation
-    assert res["b"] == dtype.type(value)
-
-
-@pytest.mark.parametrize("dtype",
-        np.typecodes["AllInteger"] + "efdFD" + "?")
-def test_unicode_whitespace_stripping(dtype):
-    # Test that all numeric types (and bool) strip whitespace correctly
-    # \u202F is a narrow no-break space, `\n` is just a whitespace if quoted.
-    # Currently, skip float128 as it did not always support this and has no
-    # "custom" parsing:
-    txt = StringIO(' 3 ,"\u202F2\n"')
-    res = np.loadtxt(txt, dtype=dtype, delimiter=",", quotechar='"')
-    assert_array_equal(res, np.array([3, 2]).astype(dtype))
-
-
-@pytest.mark.parametrize("dtype", "FD")
-def test_unicode_whitespace_stripping_complex(dtype):
-    # Complex has a few extra cases since it has two components and
-    # parentheses
-    line = " 1 , 2+3j , ( 4+5j ), ( 6+-7j )  , 8j , ( 9j ) \n"
-    data = [line, line.replace(" ", "\u202F")]
-    res = np.loadtxt(data, dtype=dtype, delimiter=',')
-    assert_array_equal(res, np.array([[1, 2+3j, 4+5j, 6-7j, 8j, 9j]] * 2))
-
-
-@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
-                    reason="PyPy bug in error formatting")
-@pytest.mark.parametrize("dtype", "FD")
-@pytest.mark.parametrize("field",
-        ["1 +2j", "1+ 2j", "1+2 j", "1+-+3", "(1j", "(1", "(1+2j", "1+2j)"])
-def test_bad_complex(dtype, field):
-    with pytest.raises(ValueError):
-        np.loadtxt([field + "\n"], dtype=dtype, delimiter=",")
-
-
-@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
-                    reason="PyPy bug in error formatting")
-@pytest.mark.parametrize("dtype",
-            np.typecodes["AllInteger"] + "efgdFDG" + "?")
-def test_nul_character_error(dtype):
-    # Test that a \0 character is correctly recognized as an error even if
-    # what comes before is valid (not everything gets parsed internally).
-    if dtype.lower() == "g":
-        pytest.xfail("longdouble/clongdouble assignment may misbehave.")
-    with pytest.raises(ValueError):
-        np.loadtxt(["1\000"], dtype=dtype, delimiter=",", quotechar='"')
-
-
-@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
-                    reason="PyPy bug in error formatting")
-@pytest.mark.parametrize("dtype",
-        np.typecodes["AllInteger"] + "efgdFDG" + "?")
-def test_no_thousands_support(dtype):
-    # Mainly to document behaviour, Python supports thousands like 1_1.
-    # (e and G may end up using different conversion and support it, this is
-    # a bug but happens...)
-    if dtype == "e":
-        pytest.skip("half assignment currently uses Python float converter")
-    if dtype in "eG":
-        pytest.xfail("clongdouble assignment is buggy (uses `complex`?).")
-
-    assert int("1_1") == float("1_1") == complex("1_1") == 11
-    with pytest.raises(ValueError):
-        np.loadtxt(["1_1\n"], dtype=dtype)
-
-
-@pytest.mark.parametrize("data", [
-    ["1,2\n", "2\n,3\n"],
-    ["1,2\n", "2\r,3\n"]])
-def test_bad_newline_in_iterator(data):
-    # In NumPy <=1.22 this was accepted, because newlines were completely
-    # ignored when the input was an iterable.  This could be changed, but right
-    # now, we raise an error.
-    msg = "Found an unquoted embedded newline within a single line"
-    with pytest.raises(ValueError, match=msg):
-        np.loadtxt(data, delimiter=",")
-
-
-@pytest.mark.parametrize("data", [
-    ["1,2\n", "2,3\r\n"],  # a universal newline
-    ["1,2\n", "'2\n',3\n"],  # a quoted newline
-    ["1,2\n", "'2\r',3\n"],
-    ["1,2\n", "'2\r\n',3\n"],
-])
-def test_good_newline_in_iterator(data):
-    # The quoted newlines will be untransformed here, but are just whitespace.
-    res = np.loadtxt(data, delimiter=",", quotechar="'")
-    assert_array_equal(res, [[1., 2.], [2., 3.]])
-
-
-@pytest.mark.parametrize("newline", ["\n", "\r", "\r\n"])
-def test_universal_newlines_quoted(newline):
-    # Check that universal newline support within the tokenizer is not applied
-    # to quoted fields.  (note that lines must end in newline or quoted
-    # fields will not include a newline at all)
-    data = ['1,"2\n"\n', '3,"4\n', '1"\n']
-    data = [row.replace("\n", newline) for row in data]
-    res = np.loadtxt(data, dtype=object, delimiter=",", quotechar='"')
-    assert_array_equal(res, [['1', f'2{newline}'], ['3', f'4{newline}1']])
-
-
-def test_null_character():
-    # Basic tests to check that the NUL character is not special:
-    res = np.loadtxt(["1\0002\0003\n", "4\0005\0006"], delimiter="\000")
-    assert_array_equal(res, [[1, 2, 3], [4, 5, 6]])
-
-    # Also not as part of a field (avoid unicode/arrays as unicode strips \0)
-    res = np.loadtxt(["1\000,2\000,3\n", "4\000,5\000,6"],
-                     delimiter=",", dtype=object)
-    assert res.tolist() == [["1\000", "2\000", "3"], ["4\000", "5\000", "6"]]
-
-
-def test_iterator_fails_getting_next_line():
-    class BadSequence:
-        def __len__(self):
-            return 100
-
-        def __getitem__(self, item):
-            if item == 50:
-                raise RuntimeError("Bad things happened!")
-            return f"{item}, {item+1}"
-
-    with pytest.raises(RuntimeError, match="Bad things happened!"):
-        np.loadtxt(BadSequence(), dtype=int, delimiter=",")
-
-
-class TestCReaderUnitTests:
-    # These are internal tests for path that should not be possible to hit
-    # unless things go very very wrong somewhere.
-    def test_not_an_filelike(self):
-        with pytest.raises(AttributeError, match=".*read"):
-            np.core._multiarray_umath._load_from_filelike(
-                object(), dtype=np.dtype("i"), filelike=True)
-
-    def test_filelike_read_fails(self):
-        # Can only be reached if loadtxt opens the file, so it is hard to do
-        # via the public interface (although maybe not impossible considering
-        # the current "DataClass" backing).
-        class BadFileLike:
-            counter = 0
-
-            def read(self, size):
-                self.counter += 1
-                if self.counter > 20:
-                    raise RuntimeError("Bad bad bad!")
-                return "1,2,3\n"
-
-        with pytest.raises(RuntimeError, match="Bad bad bad!"):
-            np.core._multiarray_umath._load_from_filelike(
-                BadFileLike(), dtype=np.dtype("i"), filelike=True)
-
-    def test_filelike_bad_read(self):
-        # Can only be reached if loadtxt opens the file, so it is hard to do
-        # via the public interface (although maybe not impossible considering
-        # the current "DataClass" backing).
-
-        class BadFileLike:
-            counter = 0
-
-            def read(self, size):
-                return 1234  # not a string!
-
-        with pytest.raises(TypeError,
-                    match="non-string returned while reading data"):
-            np.core._multiarray_umath._load_from_filelike(
-                BadFileLike(), dtype=np.dtype("i"), filelike=True)
-
-    def test_not_an_iter(self):
-        with pytest.raises(TypeError,
-                    match="error reading from object, expected an iterable"):
-            np.core._multiarray_umath._load_from_filelike(
-                object(), dtype=np.dtype("i"), filelike=False)
-
-    def test_bad_type(self):
-        with pytest.raises(TypeError, match="internal error: dtype must"):
-            np.core._multiarray_umath._load_from_filelike(
-                object(), dtype="i", filelike=False)
-
-    def test_bad_encoding(self):
-        with pytest.raises(TypeError, match="encoding must be a unicode"):
-            np.core._multiarray_umath._load_from_filelike(
-                object(), dtype=np.dtype("i"), filelike=False, encoding=123)
-
-    @pytest.mark.parametrize("newline", ["\r", "\n", "\r\n"])
-    def test_manual_universal_newlines(self, newline):
-        # This is currently not available to users, because we should always
-        # open files with universal newlines enabled `newlines=None`.
-        # (And reading from an iterator uses slightly different code paths.)
-        # We have no real support for `newline="\r"` or `newline="\n" as the
-        # user cannot specify those options.
-        data = StringIO('0\n1\n"2\n"\n3\n4 #\n'.replace("\n", newline),
-                        newline="")
-
-        res = np.core._multiarray_umath._load_from_filelike(
-            data, dtype=np.dtype("U10"), filelike=True,
-            quote='"', comment="#", skiplines=1)
-        assert_array_equal(res[:, 0], ["1", f"2{newline}", "3", "4 "])
-
-
-def test_delimiter_comment_collision_raises():
-    with pytest.raises(TypeError, match=".*control characters.*incompatible"):
-        np.loadtxt(StringIO("1, 2, 3"), delimiter=",", comments=",")
-
-
-def test_delimiter_quotechar_collision_raises():
-    with pytest.raises(TypeError, match=".*control characters.*incompatible"):
-        np.loadtxt(StringIO("1, 2, 3"), delimiter=",", quotechar=",")
-
-
-def test_comment_quotechar_collision_raises():
-    with pytest.raises(TypeError, match=".*control characters.*incompatible"):
-        np.loadtxt(StringIO("1 2 3"), comments="#", quotechar="#")
-
-
-def test_delimiter_and_multiple_comments_collision_raises():
-    with pytest.raises(
-        TypeError, match="Comment characters.*cannot include the delimiter"
-    ):
-        np.loadtxt(StringIO("1, 2, 3"), delimiter=",", comments=["#", ","])
-
-
-@pytest.mark.parametrize(
-    "ws",
-    (
-        " ",  # space
-        "\t",  # tab
-        "\u2003",  # em
-        "\u00A0",  # non-break
-        "\u3000",  # ideographic space
-    )
-)
-def test_collision_with_default_delimiter_raises(ws):
-    with pytest.raises(TypeError, match=".*control characters.*incompatible"):
-        np.loadtxt(StringIO(f"1{ws}2{ws}3\n4{ws}5{ws}6\n"), comments=ws)
-    with pytest.raises(TypeError, match=".*control characters.*incompatible"):
-        np.loadtxt(StringIO(f"1{ws}2{ws}3\n4{ws}5{ws}6\n"), quotechar=ws)
-
-
-@pytest.mark.parametrize("nl", ("\n", "\r"))
-def test_control_character_newline_raises(nl):
-    txt = StringIO(f"1{nl}2{nl}3{nl}{nl}4{nl}5{nl}6{nl}{nl}")
-    msg = "control character.*cannot be a newline"
-    with pytest.raises(TypeError, match=msg):
-        np.loadtxt(txt, delimiter=nl)
-    with pytest.raises(TypeError, match=msg):
-        np.loadtxt(txt, comments=nl)
-    with pytest.raises(TypeError, match=msg):
-        np.loadtxt(txt, quotechar=nl)
-
-
-@pytest.mark.parametrize(
-    ("generic_data", "long_datum", "unitless_dtype", "expected_dtype"),
-    [
-        ("2012-03", "2013-01-15", "M8", "M8[D]"),  # Datetimes
-        ("spam-a-lot", "tis_but_a_scratch", "U", "U17"),  # str
-    ],
-)
-@pytest.mark.parametrize("nrows", (10, 50000, 60000))  # lt, eq, gt chunksize
-def test_parametric_unit_discovery(
-    generic_data, long_datum, unitless_dtype, expected_dtype, nrows
-):
-    """Check that the correct unit (e.g. month, day, second) is discovered from
-    the data when a user specifies a unitless datetime."""
-    # Unit should be "D" (days) due to last entry
-    data = [generic_data] * 50000 + [long_datum]
-    expected = np.array(data, dtype=expected_dtype)
-
-    # file-like path
-    txt = StringIO("\n".join(data))
-    a = np.loadtxt(txt, dtype=unitless_dtype)
-    assert a.dtype == expected.dtype
-    assert_equal(a, expected)
-
-    # file-obj path
-    fd, fname = mkstemp()
-    os.close(fd)
-    with open(fname, "w") as fh:
-        fh.write("\n".join(data))
-    a = np.loadtxt(fname, dtype=unitless_dtype)
-    os.remove(fname)
-    assert a.dtype == expected.dtype
-    assert_equal(a, expected)
-
-
-def test_str_dtype_unit_discovery_with_converter():
-    data = ["spam-a-lot"] * 60000 + ["XXXtis_but_a_scratch"]
-    expected = np.array(
-        ["spam-a-lot"] * 60000 + ["tis_but_a_scratch"], dtype="U17"
-    )
-    conv = lambda s: s.strip("XXX")
-
-    # file-like path
-    txt = StringIO("\n".join(data))
-    a = np.loadtxt(txt, dtype="U", converters=conv, encoding=None)
-    assert a.dtype == expected.dtype
-    assert_equal(a, expected)
-
-    # file-obj path
-    fd, fname = mkstemp()
-    os.close(fd)
-    with open(fname, "w") as fh:
-        fh.write("\n".join(data))
-    a = np.loadtxt(fname, dtype="U", converters=conv, encoding=None)
-    os.remove(fname)
-    assert a.dtype == expected.dtype
-    assert_equal(a, expected)
-
-
-@pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
-                    reason="PyPy bug in error formatting")
-def test_control_character_empty():
-    with pytest.raises(TypeError, match="Text reading control character must"):
-        np.loadtxt(StringIO("1 2 3"), delimiter="")
-    with pytest.raises(TypeError, match="Text reading control character must"):
-        np.loadtxt(StringIO("1 2 3"), quotechar="")
-    with pytest.raises(ValueError, match="comments cannot be an empty string"):
-        np.loadtxt(StringIO("1 2 3"), comments="")
-    with pytest.raises(ValueError, match="comments cannot be an empty string"):
-        np.loadtxt(StringIO("1 2 3"), comments=["#", ""])
-
-
-def test_control_characters_as_bytes():
-    """Byte control characters (comments, delimiter) are supported."""
-    a = np.loadtxt(StringIO("#header\n1,2,3"), comments=b"#", delimiter=b",")
-    assert_equal(a, [1, 2, 3])
-
-
-@pytest.mark.filterwarnings('ignore::UserWarning')
-def test_field_growing_cases():
-    # Test empty field appending/growing (each field still takes 1 character)
-    # to see if the final field appending does not create issues.
-    res = np.loadtxt([""], delimiter=",", dtype=bytes)
-    assert len(res) == 0
-
-    for i in range(1, 1024):
-        res = np.loadtxt(["," * i], delimiter=",", dtype=bytes)
-        assert len(res) == i+1
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/array_algos/datetimelike_accumulations.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/array_algos/datetimelike_accumulations.py
deleted file mode 100644
index 825fe60ee6cf88a2186a5f501c8696cecaf2657d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/array_algos/datetimelike_accumulations.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""
-datetimelke_accumulations.py is for accumulations of datetimelike extension arrays
-"""
-
-from __future__ import annotations
-
-from typing import Callable
-
-import numpy as np
-
-from pandas._libs import iNaT
-
-from pandas.core.dtypes.missing import isna
-
-
-def _cum_func(
-    func: Callable,
-    values: np.ndarray,
-    *,
-    skipna: bool = True,
-):
-    """
-    Accumulations for 1D datetimelike arrays.
-
-    Parameters
-    ----------
-    func : np.cumsum, np.maximum.accumulate, np.minimum.accumulate
-    values : np.ndarray
-        Numpy array with the values (can be of any dtype that support the
-        operation). Values is changed is modified inplace.
-    skipna : bool, default True
-        Whether to skip NA.
-    """
-    try:
-        fill_value = {
-            np.maximum.accumulate: np.iinfo(np.int64).min,
-            np.cumsum: 0,
-            np.minimum.accumulate: np.iinfo(np.int64).max,
-        }[func]
-    except KeyError:
-        raise ValueError(f"No accumulation for {func} implemented on BaseMaskedArray")
-
-    mask = isna(values)
-    y = values.view("i8")
-    y[mask] = fill_value
-
-    if not skipna:
-        mask = np.maximum.accumulate(mask)
-
-    result = func(y)
-    result[mask] = iNaT
-
-    if values.dtype.kind in "mM":
-        return result.view(values.dtype.base)
-    return result
-
-
-def cumsum(values: np.ndarray, *, skipna: bool = True) -> np.ndarray:
-    return _cum_func(np.cumsum, values, skipna=skipna)
-
-
-def cummin(values: np.ndarray, *, skipna: bool = True):
-    return _cum_func(np.minimum.accumulate, values, skipna=skipna)
-
-
-def cummax(values: np.ndarray, *, skipna: bool = True):
-    return _cum_func(np.maximum.accumulate, values, skipna=skipna)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/cachecontrol/cache.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/cachecontrol/cache.py
deleted file mode 100644
index 44e4309d20dfe3190988905258a4159411a662b3..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/cachecontrol/cache.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-The cache object API for implementing caches. The default is a thread
-safe in-memory dictionary.
-"""
-from threading import Lock
-
-
-class BaseCache(object):
-
-    def get(self, key):
-        raise NotImplementedError()
-
-    def set(self, key, value, expires=None):
-        raise NotImplementedError()
-
-    def delete(self, key):
-        raise NotImplementedError()
-
-    def close(self):
-        pass
-
-
-class DictCache(BaseCache):
-
-    def __init__(self, init_dict=None):
-        self.lock = Lock()
-        self.data = init_dict or {}
-
-    def get(self, key):
-        return self.data.get(key, None)
-
-    def set(self, key, value, expires=None):
-        with self.lock:
-            self.data.update({key: value})
-
-    def delete(self, key):
-        with self.lock:
-            if key in self.data:
-                self.data.pop(key)
diff --git a/spaces/qiantong-xu/sambanovasystems-codegen-16B-mono-toolbench/app.py b/spaces/qiantong-xu/sambanovasystems-codegen-16B-mono-toolbench/app.py
deleted file mode 100644
index b7d3fedaabb0b9544f1ff8ad3c4288da0c7b98ab..0000000000000000000000000000000000000000
--- a/spaces/qiantong-xu/sambanovasystems-codegen-16B-mono-toolbench/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/sambanovasystems/codegen-16B-mono-toolbench").launch()
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/4shared Maocony Tv Card Driver Extra Quality.md b/spaces/quidiaMuxgu/Expedit-SAM/4shared Maocony Tv Card Driver Extra Quality.md
deleted file mode 100644
index df4553b90dd0f340d81ac454fb696a7ca2f0d53b..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/4shared Maocony Tv Card Driver Extra Quality.md	
+++ /dev/null
@@ -1,78 +0,0 @@
-
-<h1>4shared Maocony Tv Card Driver: A Review</h1>
-<p>If you want to watch TV on your PC, you may need a TV card driver that can support your device. A TV card driver is a software that allows your PC to communicate with your TV card and display the TV signals on your monitor. One of the popular TV card drivers is 4shared Maocony Tv Card Driver, which is used by many users around the world. In this article, we will review some of the features and benefits of 4shared Maocony Tv Card Driver and how it can help you enjoy TV on your PC.</p>
-<h2>What is 4shared Maocony Tv Card Driver?</h2>
-<p>4shared Maocony Tv Card Driver is a file that contains the installation package of Maocony Tv Card Driver, a TV card driver developed by Maocony Technology Corporation. This software is compatible with Windows XP/Vista/7/8/10 environment, 32-bit and 64-bit version. You can download 4shared Maocony Tv Card Driver from various websites that offer free or paid downloads of software. The file size is about 12 MB and it takes a few minutes to install.</p>
-<h2>4shared Maocony Tv Card Driver</h2><br /><p><b><b>Download</b> &#127383; <a href="https://geags.com/2uCqZk">https://geags.com/2uCqZk</a></b></p><br /><br />
-<h2>What can you do with 4shared Maocony Tv Card Driver?</h2>
-<p>4shared Maocony Tv Card Driver allows you to do many things that can enhance your TV watching experience on your PC. Some of the things you can do are:</p>
-<ul>
-<li>Watch TV channels from different sources, such as cable, satellite, antenna or internet.</li>
-<li>Record TV programs and save them on your hard disk or external storage devices.</li>
-<li>Pause, rewind and fast forward live TV using the time-shifting feature.</li>
-<li>Schedule recordings and reminders using the electronic program guide (EPG).</li>
-<li>Edit and convert recorded videos using the video editor and converter.</li>
-<li>Capture screenshots and audio clips from TV programs using the capture feature.</li>
-<li>Adjust various settings, such as brightness, contrast, hue, saturation, volume and aspect ratio using the control panel.</li>
-</ul>
-<h2>What are the benefits of 4shared Maocony Tv Card Driver?</h2>
-<p>4shared Maocony Tv Card Driver has many benefits that can help you watch TV on your PC with ease and convenience. Some of the benefits are:</p>
-<ul>
-<li>It is a free software and it does not cost anything to download and install it on your PC.</li>
-<li>It is compatible with most of the TV cards and tuners available in the market.</li>
-<li>It supports various video formats, such as MPEG-1, MPEG-2, MPEG-4, AVI, WMV and more.</li>
-<li>It supports various audio formats, such as MP3, WMA, AAC and more.</li>
-<li>It supports various TV standards, such as NTSC, PAL and SECAM.</li>
-<li>It supports various languages, such as English, French, German, Spanish and more.</li>
-<li>It has a user-friendly and intuitive interface that makes it easy to use and operate.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>4shared Maocony Tv Card Driver is a great tool for anyone who wants to watch TV on their PC. It is a free, compatible, versatile, user-friendly and fun software that allows you to watch, record, edit and convert TV programs on your PC. If you are interested in 4shared Maocony Tv Card Driver, you can download it from various websites or visit Maocony Technology Corporation website for more information.</p>
-<h2>What are the drawbacks of 4shared Maocony Tv Card Driver?</h2>
-<p>4shared Maocony Tv Card Driver is not a perfect software and it has some drawbacks that you should be aware of before using it. Some of the drawbacks are:</p>
-<ul>
-<li>It is not an official driver and it may not be compatible with some TV cards or tuners. You may encounter some errors or problems when using it.</li>
-<li>It is not a secure software and it may contain viruses or malware that can harm your PC. You should scan the file before installing it and use a reliable antivirus software.</li>
-<li>It is not a legal software and it may violate the copyright or license of Maocony Technology Corporation. You may face some legal issues or consequences if you use it.</li>
-<li>It is not a complete software and it may not have all the features and functions that you need. You may need to download other software or drivers to complement it.</li>
-</ul>
-<h2>How to uninstall 4shared Maocony Tv Card Driver?</h2>
-<p>If you want to uninstall 4shared Maocony Tv Card Driver, you can follow these steps:</p>
-<ol>
-<li>Go to Control Panel and click on Programs and Features.</li>
-<li>Find Maocony Tv Card Driver from the list of programs and click on Uninstall.</li>
-<li>Follow the instructions on the screen to remove the software from your PC.</li>
-<li>Delete the file 4shared Maocony Tv Card Driver from your PC.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>4shared Maocony Tv Card Driver is a useful software for anyone who wants to watch TV on their PC. It is a free, compatible, versatile, user-friendly and fun software that allows you to watch, record, edit and convert TV programs on your PC. However, it also has some drawbacks that you should consider before using it, such as the compatibility, security, legality and completeness. You should also use other resources and methods to enhance your TV watching experience, such as official drivers, online services, physical devices or online devices. If you are interested in 4shared Maocony Tv Card Driver, you can download it from various websites or visit Maocony Technology Corporation website for more information.</p>
-<h2>What are the features of 4shared Maocony Tv Card Driver?</h2>
-<p>4shared Maocony Tv Card Driver has many features that make it a powerful and versatile TV card driver. Some of the features are:</p>
-<p></p>
-<ul>
-<li>It supports a wide range of TV cards and tuners from different manufacturers and models.</li>
-<li>It supports various video formats, such as MPEG-1, MPEG-2, MPEG-4, AVI, WMV and more.</li>
-<li>It supports various audio formats, such as MP3, WMA, AAC and more.</li>
-<li>It supports various TV standards, such as NTSC, PAL and SECAM.</li>
-<li>It supports various languages, such as English, French, German, Spanish and more.</li>
-<li>It has a user-friendly and intuitive interface that makes it easy to use and operate.</li>
-<li>It has a realistic and flexible simulation environment that mimics real-world TV signals and devices.</li>
-</ul>
-<h2>How to use 4shared Maocony Tv Card Driver?</h2>
-<p>If you want to use 4shared Maocony Tv Card Driver, you can follow these steps:</p>
-<ol>
-<li>Download 4shared Maocony Tv Card Driver from one of the websites that offer it, such as soft32.com or cnet.com.</li>
-<li>Save the file to your PC and scan it with your antivirus software before installing it.</li>
-<li>Double-click on the file to open it and follow the instructions on the screen to install the software.</li>
-<li>Launch the software and select your TV card or tuner from the device list.</li>
-<li>Connect your TV card or tuner to your PC using a cable or a wireless connection.</li>
-<li>Connect your TV card or tuner to your TV source, such as cable, satellite, antenna or internet.</li>
-<li>Select a TV channel from the channel list or scan for new channels using the scan feature.</li>
-<li>Enjoy watching TV on your PC using 4shared Maocony Tv Card Driver.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>4shared Maocony Tv Card Driver is a useful software for anyone who wants to watch TV on their PC. It is a free, compatible, versatile, user-friendly and fun software that allows you to watch, record, edit and convert TV programs on your PC. However, it also has some drawbacks that you should consider before using it, such as the compatibility, security, legality and completeness. You should also use other resources and methods to enhance your TV watching experience, such as official drivers, online services, physical devices or online devices. If you are interested in 4shared Maocony Tv Card Driver, you can download it from various websites or visit Maocony Technology Corporation website for more information.</p>
-<h2>Conclusion</h2>
-<p>4shared Maocony Tv Card Driver is a useful software for anyone who wants to watch TV on their PC. It is a free, compatible, versatile, user-friendly and fun software that allows you to watch, record, edit and convert TV programs on your PC. However, it also has some drawbacks that you should consider before using it, such as the compatibility, security, legality and completeness. You should also use other resources and methods to enhance your TV watching experience, such as official drivers, online services, physical devices or online devices. If you are interested in 4shared Maocony Tv Card Driver, you can download it from various websites or visit Maocony Technology Corporation website for more information.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Dinesat9full Fixparawindows7.md b/spaces/quidiaMuxgu/Expedit-SAM/Dinesat9full Fixparawindows7.md
deleted file mode 100644
index 850e4cfe2f0a5b96453f4283830e646c649c7ad2..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Dinesat9full Fixparawindows7.md	
+++ /dev/null
@@ -1,107 +0,0 @@
-<br />
-<h1>Dinesat9fullparawindows7: How to Download and Use the Best Radio Automation Software for Windows 7</h1>
-
-<p>If you are a radio broadcaster or a radio enthusiast, you might be looking for a software that can help you manage and automate your radio station. You might want a software that can handle audio and video content, create playlists and schedules, stream online, and more. You might also want a software that can run on Windows 7, which is still a popular operating system for many users.</p>
-
-<p>If that is the case, then you should consider Dinesat9fullparawindows7. This is a software that can provide you with all the features and functions that you need to run a professional radio station. Dinesat9fullparawindows7 is the latest version of Dinesat, which is a well-known and trusted brand in the radio industry. Dinesat9fullparawindows7 is compatible with Windows 7, as well as other Windows systems.</p>
-<h2>dinesat9fullparawindows7</h2><br /><p><b><b>Download</b> > <a href="https://geags.com/2uCseY">https://geags.com/2uCseY</a></b></p><br /><br />
-
-<p>In this article, we will tell you more about Dinesat9fullparawindows7 and how to download and use it on your Windows 7 PC. We will also give you some tips and tricks to optimize your radio automation experience with Dinesat9fullparawindows7.</p>
-
-<h2>What are the features of Dinesat9fullparawindows7?</h2>
-
-<p>Dinesat9fullparawindows7 has many features that make it a powerful and versatile software for radio automation. Some of these features are:</p>
-
-<ul>
-<li>Audio & video automation: You can operate your radio station as usual and add a perfectly synchronized video & audio workflow. You can create playlists and schedules for both audio and video content, and stream them online or broadcast them on TV.</li>
-<li>Content management: You can import, edit, organize, and store your audio and video files in a centralized database. You can also use metadata, tags, categories, and filters to manage your content efficiently.</li>
-<li>Streaming: You can stream your radio station online using various protocols and formats. You can also use social media integration, statistics, and reports to monitor and improve your online presence.</li>
-<li>Remote control: You can control your radio station from anywhere using a web browser or a mobile app. You can also use remote voice tracking, remote assistance, and remote backup features to enhance your remote operation.</li>
-<li>Integration: You can integrate Dinesat9fullparawindows7 with other software and hardware devices to expand your radio automation capabilities. You can use plugins, scripts, APIs, MIDI devices, GPIO devices, and more.</li>
-</ul>
-
-<p>Dinesat9fullparawindows7 is a software that can offer you a complete solution for radio automation. You can use it to create, manage, automate, stream, and control your radio station with ease.</p>
-
-<h2>How to download and install Dinesat9fullparawindows7?</h2>
-
-<p>If you want to download and install Dinesat9fullparawindows7 on your Windows 7 PC, you can follow these simple steps:</p>
-
-<ol>
-<li>Go to the official website of Dinesat or a trusted source that provides the software.</li>
-<li>Choose the version that suits your needs and click on the download button.</li>
-<li>Save the file to your computer and run it as an administrator.</li>
-<li>Follow the instructions on the screen to complete the installation process.</li>
-<li>Enter your license key when prompted. You can find your license key in your email confirmation or on your account page.</li>
-<li>Restart your computer if required.</li>
-</ol>
-
-<p>Congratulations! You have successfully installed Dinesat9fullparawindows7 on your Windows 7 PC. You can now launch the software and start using it for your radio automation.</p>
-
-<h2>How to use Dinesat9fullparawindows7?</h2>
-
-<p>Using Dinesat9fullparawindows7 is very simple and intuitive. Here are some steps to get you started:</p>
-<p></p>
-
-<ol>
-<li>Launch the software from your desktop or start menu.</li>
-<li>Create or open a project for your radio station. A project is a collection of settings, files, playlists, schedules, etc. that define your radio station.</li>
-<li>Add or import your audio and video files to your project. You can use the file explorer or drag-and-drop feature to add files from your computer or network.</li>
-<li>Edit and organize your files using the file editor or file manager tools. You can trim, normalize, fade in/out, add effects, etc. to your files. You can also use metadata, tags, categories, filters, etc. to organize your files.</li>
-<li>Create playlists and schedules for your audio and video content using the playlist editor or schedule editor tools. You can add files from your project or from external sources to your playlists. You can also use rules, rotations, clocks, etc. to create dynamic playlists.</li>
-<li>Stream or broadcast your radio station using the streaming or broadcasting tools. You can choose from various protocols and formats for streaming online or broadcasting on TV. You can also use social media integration, statistics, reports, etc. to monitor and improve your online presence.</li>
-<li>Control your radio station using the control panel or remote control tools. You can start/stop/pause/play/skip/rewind/etc. your playlists or schedules manually or automatically. You can also use remote voice tracking, remote assistance, remote backup features to enhance your remote operation.</li>
-</ol>
-
-<p>You can now use Dinesat9fullparawindows7 to create, manage, automate, stream, and control your radio station with ease.</p>
-
-<h2>How to optimize your radio automation experience with Dinesat9fullparawindows7?</h2>
-
-<p>Dinesat9fullparawindows7 is a software that works smoothly with most Windows systems. However, if you want to optimize your radio automation experience with Dinesat9fullparawindows7, you can try some of these tips:</p>
-
-<ul>
-<li>Make sure you have the latest version of Dinesat9fullparawindows7 installed on your computer. You can check for updates from the help menu or from the system tray icon.</li>
-<li>Make sure you have entered your license key correctly. You can verify your license key from the about menu or from your account page.</li>
-<li>Make sure your audio and video files are compatible with Dinesat9fullparawindows7. You can check the supported file formats from the official website or from the help menu.</li>
-<li>Make sure your computer meets the system requirements for Dinesat9fullparawindows7. You can check the system requirements from the official website or from the help menu.</li>
-<li>If you encounter any problems or issues while using Dinesat9fullparawindows7, you can contact the customer support team of Dinesat. They are available 24/7 via email, phone, or chat. They will help you resolve any issues or questions you might have.</li>
-</ul>
-
-<p>We hope this article has helped you learn more about Dinesat9fullparawindows7 and how to use it effectively for radio automation on Windows 7 PC. If you have any questions or comments, please feel free to contact us. Thank you for reading!</p>
-<h2>What are the benefits of Dinesat9fullparawindows7?</h2>
-
-<p>Dinesat9fullparawindows7 has many benefits that can enhance your radio automation experience. Some of these benefits are:</p>
-
-<ul>
-<li>Quality: You can enjoy high-quality audio and video output with Dinesat9fullparawindows7. You can also use various effects, transitions, and filters to improve your content quality.</li>
-<li>Efficiency: You can save time and resources with Dinesat9fullparawindows7. You can automate your radio station with minimal human intervention. You can also use smart features like rules, rotations, clocks, etc. to optimize your radio programming.</li>
-<li>Creativity: You can unleash your creativity with Dinesat9fullparawindows7. You can create your own audio and video content, or use external sources like podcasts, webcams, YouTube, etc. You can also use plugins, scripts, APIs, MIDI devices, GPIO devices, etc. to customize your radio automation.</li>
-<li>Versatility: You can adapt to any situation with Dinesat9fullparawindows7. You can use it for any type of radio station, whether it is music, news, sports, talk, etc. You can also use it for any type of media, whether it is audio, video, or visual radio.</li>
-<li>Compatibility: You can use Dinesat9fullparawindows7 with Windows 7, as well as other Windows systems. You can also use it with other software and hardware devices to expand your radio automation capabilities.</li>
-</ul>
-
-<p>Dinesat9fullparawindows7 is a software that can offer you a complete solution for radio automation on Windows 7 PC. You can use it to create, manage, automate, stream, and control your radio station with ease.</p>
-
-<h2>How to troubleshoot Dinesat9fullparawindows7?</h2>
-
-<p>Dinesat9fullparawindows7 is a reliable and stable software that works smoothly with most Windows systems. However, if you encounter any problems or issues while using it, you can try some of these troubleshooting tips:</p>
-
-<ul>
-<li>Make sure you have the latest version of Dinesat9fullparawindows7 installed on your computer. You can check for updates from the help menu or from the system tray icon.</li>
-<li>Make sure you have entered your license key correctly. You can verify your license key from the about menu or from your account page.</li>
-<li>Make sure your audio and video files are compatible with Dinesat9fullparawindows7. You can check the supported file formats from the official website or from the help menu.</li>
-<li>Make sure your computer meets the system requirements for Dinesat9fullparawindows7. You can check the system requirements from the official website or from the help menu.</li>
-<li>If none of these tips work, you can contact the customer support team of Dinesat. They are available 24/7 via email, phone, or chat. They will help you resolve any issues or questions you might have.</li>
-</ul>
-
-<p>We hope this article has helped you learn more about Dinesat9fullparawindows7 and how to use it effectively for radio automation on Windows 7 PC. If you have any questions or comments, please feel free to contact us. Thank you for reading!</p>
-<h2>Conclusion</h2>
-
-<p>In this article, we have discussed Dinesat9fullparawindows7, a software that allows you to create, manage, automate, stream, and control your radio station on Windows 7 PC. We have explained what are the features, benefits, drawbacks, and alternatives of this software. We have also provided some tips on how to download, install, use, and troubleshoot this software.</p>
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-<p>Dinesat9fullparawindows7 is a software that can help you run a professional radio station with high-quality audio and video output. It can save you time and resources, unleash your creativity, adapt to any situation, and work with Windows 7 and other systems. However, it also has some drawbacks that you should consider before using it. It is not a free software, it may not work with some Windows systems, and it may expose your data to potential risks. You should always backup your data and use antivirus software to protect your PC and your disks.</p>
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-<p>If you are looking for a different software to run your radio station on Windows 7 PC, you can try some of the alternatives that we have mentioned. They are DINESAT Automation solutions for radio, TV and internet radio, Windows 7 system requirements - Microsoft Support, and Windows 7 ISO File Download 32/64-Bit Full Version - EaseUS. They have different features, functions, interfaces, and prices. You should compare them carefully and choose the one that suits your needs best.</p>
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-<p>We hope this article has helped you learn more about Dinesat9fullparawindows7 and how to use it effectively for radio automation on Windows 7 PC. If you have any questions or comments, please feel free to contact us. Thank you for reading!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Download Crack Key Sync2 Version 2.20.1312.rar.md b/spaces/quidiaMuxgu/Expedit-SAM/Download Crack Key Sync2 Version 2.20.1312.rar.md
deleted file mode 100644
index 328595dd2d5db13efbda80399b22b40c8e01a6dd..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Download Crack Key Sync2 Version 2.20.1312.rar.md	
+++ /dev/null
@@ -1,76 +0,0 @@
-<br />
-<h1>Download Crack Key Sync2 Version 2.20.1312.rar: How to Sync Outlook Data for Free</h1>
-
-<p>Sync2 is a software that allows you to sync your Outlook data across multiple devices and platforms. You can sync your Outlook calendar, contacts, tasks, notes and email folders with Google, iCloud, Office 365 and other services. You can also share your Outlook folders with other users without a server.</p>
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-
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-<p></p>
-
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-
-<ul>
-<li>Go to https://trello.com/c/oCUU0GJv/52-download-crack-key-sync2-version-2201312rar  and click on the "Download" button.</li>
-<li>You will be redirected to another page where you need to complete a short survey or offer to unlock the download link.</li>
-<li>After completing the survey or offer, you will get access to the download link for crack key Sync2 Version 2.20</p>
-<h2>Step 2: Install Sync2 Version 2.20.1312</h2>
-
-<p>The next step is to install Sync2 Version 2.20.1312 on your computer. You need to have Windows Administrator rights and Microsoft Outlook 2002 or later to install Sync2. You also need to close Microsoft Outlook and all open programs before installing Sync2.</p>
-
-<p>To install Sync2 Version 2.20.1312, follow these steps:</p>
-
-<ul>
-<li>Open the crack key Sync2 Version 2.20.1312.rar file that you downloaded in the previous step.</li>
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-<li>Run the setup file and follow the instructions on the screen.</li>
-<li>When prompted, enter the crack key that you extracted from the rar file.</li>
-<li>Complete the installation and launch Sync2.</li>
-</ul>
-
-<p>You have now installed Sync2 Version 2.20.1312 with the crack key on your computer.</p>
-
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-
-<p>The final step is to activate Sync2 with the crack key that you entered during the installation. You need to activate Sync2 to unlock all its features and sync your Outlook data with Google or other services.</p>
-
-<p>To activate Sync2 with the crack key, follow these steps:</p>
-
-<ul>
-<li>Open Sync2 from your desktop or start menu.</li>
-<li>Click on the "Activate" button on the main window.</li>
-<li>Enter your name and email address and click "Next".</li>
-<li>Select "I have an activation code" and click "Next".</li>
-<li>Enter the same crack key that you used during the installation and click "Next".</li>
-<li>Click "Finish" to complete the activation.</li>
-</ul>
-
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-
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-
-<p>In this article, we showed you how to download crack key Sync2 Version 2.20.1312.rar and how to install and activate Sync2 with the crack key. By following these steps, you can get Sync2 for free and sync your Outlook data across multiple devices and platforms.</p>
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\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/HD Online Player (Download The The Savage Conspiracy F).md b/spaces/quidiaMuxgu/Expedit-SAM/HD Online Player (Download The The Savage Conspiracy F).md
deleted file mode 100644
index 659d6c4ee5adc65b71b1500a8e65d0f48597f3dc..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/HD Online Player (Download The The Savage Conspiracy F).md	
+++ /dev/null
@@ -1,50 +0,0 @@
-
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\ No newline at end of file
diff --git a/spaces/r3gm/RVC_HF/demucs/pretrained.py b/spaces/r3gm/RVC_HF/demucs/pretrained.py
deleted file mode 100644
index 6aac5db100cc7a9084af96d2cd083f0c8fac473c..0000000000000000000000000000000000000000
--- a/spaces/r3gm/RVC_HF/demucs/pretrained.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# author: adefossez
-
-import logging
-
-from diffq import DiffQuantizer
-import torch.hub
-
-from .model import Demucs
-from .tasnet import ConvTasNet
-from .utils import set_state
-
-logger = logging.getLogger(__name__)
-ROOT = "https://dl.fbaipublicfiles.com/demucs/v3.0/"
-
-PRETRAINED_MODELS = {
-    'demucs': 'e07c671f',
-    'demucs48_hq': '28a1282c',
-    'demucs_extra': '3646af93',
-    'demucs_quantized': '07afea75',
-    'tasnet': 'beb46fac',
-    'tasnet_extra': 'df3777b2',
-    'demucs_unittest': '09ebc15f',
-}
-
-SOURCES = ["drums", "bass", "other", "vocals"]
-
-
-def get_url(name):
-    sig = PRETRAINED_MODELS[name]
-    return ROOT + name + "-" + sig[:8] + ".th"
-
-
-def is_pretrained(name):
-    return name in PRETRAINED_MODELS
-
-
-def load_pretrained(name):
-    if name == "demucs":
-        return demucs(pretrained=True)
-    elif name == "demucs48_hq":
-        return demucs(pretrained=True, hq=True, channels=48)
-    elif name == "demucs_extra":
-        return demucs(pretrained=True, extra=True)
-    elif name == "demucs_quantized":
-        return demucs(pretrained=True, quantized=True)
-    elif name == "demucs_unittest":
-        return demucs_unittest(pretrained=True)
-    elif name == "tasnet":
-        return tasnet(pretrained=True)
-    elif name == "tasnet_extra":
-        return tasnet(pretrained=True, extra=True)
-    else:
-        raise ValueError(f"Invalid pretrained name {name}")
-
-
-def _load_state(name, model, quantizer=None):
-    url = get_url(name)
-    state = torch.hub.load_state_dict_from_url(url, map_location='cpu', check_hash=True)
-    set_state(model, quantizer, state)
-    if quantizer:
-        quantizer.detach()
-
-
-def demucs_unittest(pretrained=True):
-    model = Demucs(channels=4, sources=SOURCES)
-    if pretrained:
-        _load_state('demucs_unittest', model)
-    return model
-
-
-def demucs(pretrained=True, extra=False, quantized=False, hq=False, channels=64):
-    if not pretrained and (extra or quantized or hq):
-        raise ValueError("if extra or quantized is True, pretrained must be True.")
-    model = Demucs(sources=SOURCES, channels=channels)
-    if pretrained:
-        name = 'demucs'
-        if channels != 64:
-            name += str(channels)
-        quantizer = None
-        if sum([extra, quantized, hq]) > 1:
-            raise ValueError("Only one of extra, quantized, hq, can be True.")
-        if quantized:
-            quantizer = DiffQuantizer(model, group_size=8, min_size=1)
-            name += '_quantized'
-        if extra:
-            name += '_extra'
-        if hq:
-            name += '_hq'
-        _load_state(name, model, quantizer)
-    return model
-
-
-def tasnet(pretrained=True, extra=False):
-    if not pretrained and extra:
-        raise ValueError("if extra is True, pretrained must be True.")
-    model = ConvTasNet(X=10, sources=SOURCES)
-    if pretrained:
-        name = 'tasnet'
-        if extra:
-            name = 'tasnet_extra'
-        _load_state(name, model)
-    return model
diff --git a/spaces/radames/Detecting-Photoshopped-Faces-FALdetector/utils/tools.py b/spaces/radames/Detecting-Photoshopped-Faces-FALdetector/utils/tools.py
deleted file mode 100644
index a05052f85d2c539b1f0a99ae4b3734ef4337a841..0000000000000000000000000000000000000000
--- a/spaces/radames/Detecting-Photoshopped-Faces-FALdetector/utils/tools.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import os
-import cv2
-import torch
-import numpy as np
-from PIL import Image
-from dlib import cnn_face_detection_model_v1 as face_detect_model
-
-
-def center_crop(im, length):
-    w, h = im.size
-    left = w//2 - length//2
-    right = w//2 + length//2
-    top = h//2 - length//2
-    bottom = h//2 + length//2
-    return im.crop((left, top, right, bottom)), (left, top)
-
-
-def remove_boundary(img):
-    """
-    Remove boundary artifacts that FAL causes.
-    """
-    w, h = img.size
-    left = w//80
-    top = h//50
-    right = w*79//80
-    bottom = h*24//25
-    return img.crop((left, top, right, bottom))
-
-
-def resize_shorter_side(img, min_length):
-    """
-    Resize the shorter side of img to min_length while
-    preserving the aspect ratio.
-    """
-    ow, oh = img.size
-    mult = 8
-    if ow < oh:
-        if ow == min_length and oh % mult == 0:
-            return img, (ow, oh)
-        w = min_length
-        h = int(min_length * oh / ow)
-    else:
-        if oh == min_length and ow % mult == 0:
-            return img, (ow, oh)
-        h = min_length
-        w = int(min_length * ow / oh)
-    return img.resize((w, h), Image.BICUBIC), (w, h)
-
-
-def flow_resize(flow, sz):
-    oh, ow, _ = flow.shape
-    w, h = sz
-    u_ = cv2.resize(flow[:,:,0], (w, h))
-    v_ = cv2.resize(flow[:,:,1], (w, h))
-    u_ *= w / float(ow)
-    v_ *= h / float(oh)
-    return np.dstack((u_,v_))
-
-
-def warp(im, flow, alpha=1, interp=cv2.INTER_CUBIC):
-    height, width, _ = flow.shape
-    cart = np.dstack(np.meshgrid(np.arange(width), np.arange(height)))
-    pixel_map = (cart + alpha * flow).astype(np.float32)
-    warped = cv2.remap(
-        im,
-        pixel_map[:, :, 0],
-        pixel_map[:, :, 1],
-        interp,
-        borderMode=cv2.BORDER_REPLICATE)
-    return warped
-
-
-cnn_face_detector = None
-def face_detection(
-        img_path,
-        verbose=False,
-        model_file='utils/dlib_face_detector/mmod_human_face_detector.dat'):
-    """
-    Detects faces using dlib cnn face detection, and extend the bounding box
-    to include the entire face.
-    """
-    def shrink(img, max_length=2048):
-        ow, oh = img.size
-        if max_length >= max(ow, oh):
-            return img, 1.0
-
-        if ow > oh:
-            mult = max_length / ow
-        else:
-            mult = max_length / oh
-        w = int(ow * mult)
-        h = int(oh * mult)
-        return img.resize((w, h), Image.BILINEAR), mult
-
-    global cnn_face_detector
-    if cnn_face_detector is None:
-        cnn_face_detector = face_detect_model(model_file)
-
-    img = Image.open(img_path).convert('RGB')
-    w, h = img.size
-    img_shrinked, mult = shrink(img)
-
-    im = np.asarray(img_shrinked)
-    if len(im.shape) != 3 or im.shape[2] != 3:
-        return []
-
-    crop_ims = []
-    dets = cnn_face_detector(im, 0)
-    for k, d in enumerate(dets):
-        top = d.rect.top() / mult
-        bottom = d.rect.bottom() / mult
-        left = d.rect.left() / mult
-        right = d.rect.right() / mult
-
-        wid = right - left
-        left = max(0, left - wid // 2.5)
-        top = max(0, top - wid // 1.5)
-        right = min(w - 1, right + wid // 2.5)
-        bottom = min(h - 1, bottom + wid // 2.5)
-
-        if d.confidence > 1:
-            if verbose:
-                print("%d-th face detected: (%d, %d, %d, %d)" %
-                      (k, left, top, right, bottom))
-            crop_im = img.crop((left, top, right, bottom))
-            crop_ims.append((crop_im, (left, top, right, bottom)))
-
-    return crop_ims
-
-
-def mkdirs(paths):
-    if isinstance(paths, list) and not isinstance(paths, str):
-        for path in paths:
-            mkdir(path)
-    else:
-        mkdir(paths)
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Crack _HOT_ Wondershare Dr.Fone Toolkit For Iso Pc Mac 10.8.9.86 FULL Crack _HOT_.md b/spaces/raedeXanto/academic-chatgpt-beta/Crack _HOT_ Wondershare Dr.Fone Toolkit For Iso Pc Mac 10.8.9.86 FULL Crack _HOT_.md
deleted file mode 100644
index 7f520a4a49b0ca8c962b78a38aaa12b0dee75915..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Crack _HOT_ Wondershare Dr.Fone Toolkit For Iso Pc Mac 10.8.9.86 FULL Crack _HOT_.md	
+++ /dev/null
@@ -1,154 +0,0 @@
-
-<h1>CRACK Wondershare Dr.Fone Toolkit For Iso Pc Mac 10.8.9.86 FULL Crack</h1>
-<p>Have you ever lost or deleted important data from your iOS or Android device? Have you ever wanted to transfer, backup, restore, or manage your phone data easily and conveniently? Have you ever encountered system issues, screen lock, or FRP lock on your device? If you answered yes to any of these questions, then you might be interested in <strong>Wondershare Dr.Fone Toolkit</strong>, a powerful and versatile software that can help you solve all your mobile problems.</p>
-<p>However, Wondershare Dr.Fone Toolkit is not a free software. You need to purchase a license to use all its features and functions. That's why some people look for <strong>CRACK Wondershare Dr.Fone Toolkit</strong>, a cracked or modified version of the software that can bypass the license verification and activation process. But is it safe and legal to use CRACK Wondershare Dr.Fone Toolkit? What are the advantages and risks of using it? How can you download and install it on your PC or Mac? And are there any alternatives to it? In this article, we will answer all these questions and more.</p>
-<h2>CRACK Wondershare Dr.Fone Toolkit For Iso Pc Mac 10.8.9.86 FULL Crack</h2><br /><p><b><b>DOWNLOAD</b> &#10001; &#10001; &#10001; <a href="https://tinourl.com/2uL38r">https://tinourl.com/2uL38r</a></b></p><br /><br />
- <h2>What is Wondershare Dr.Fone Toolkit?</h2>
-<p>Wondershare Dr.Fone Toolkit is a comprehensive mobile solution that offers various tools and features for iOS and Android devices. It is developed by <a href="(^2^)">Wondershare</a>, a reputable software company that has been in the industry for over 17 years. Some of the tools and features that Wondershare Dr.Fone Toolkit provides are:</p>
-<h3>Features and benefits of Wondershare Dr.Fone Toolkit</h3>
-<ul>
-<li><strong>Data Recovery:</strong> Recover deleted or lost data from iOS/Android devices, iTunes/iCloud backup, broken Android devices, and SD cards. You can recover photos, videos, contacts, messages, call logs, notes, WhatsApp chats, and more.</li>
-<li><strong>Screen Unlock:</strong> Remove various types of lock screens from iOS/Android devices, such as password, PIN, pattern, fingerprint, face ID, iCloud activation lock, MDM lock, Samsung FRP lock, etc.</li>
-<li><strong>WhatsApp Transfer:</strong> Transfer WhatsApp messages and attachments between iOS & iOS, Android & Android, and iOS & Android devices. You can also backup and restore WhatsApp, WeChat, LINE, Kik, and Viber chat history and attachments.</li>
-<li><strong>Phone Transfer:</strong> Transfer data from one phone to another in one click. You can transfer contacts, messages, photos, music, videos, apps, bookmarks, etc. between iOS & iOS, Android & Android, and iOS & Android devices.</li>
-<li><strong>Data Eraser:</strong> Permanently erase data from iOS/Android devices to free up space and protect your privacy. You can erase all data or selected data from your device with 100% irrecoverable.</li>
-<li><strong>System Repair:</strong> Fix various iOS and Android system issues in complex scenarios such as white Apple logo, black screen of death, boot loop, stuck in recovery mode, etc. You can also downgrade or upgrade your iOS system without data loss.</li>
-<li><strong>Data Backup & Restore:</strong> Backup and restore data from iOS/Android devices to your computer with one click. You can backup and restore contacts, messages, photos, videos, music, apps, etc. You can also preview and selectively restore data from the backup.</li>
-<li><strong>Phone Manager:</strong> Manage your phone data on your computer easily and conveniently. You can transfer, add, delete, edit, or export data such as photos, videos, music, contacts, messages, etc. You can also install or uninstall apps, create ringtones, compress photos, and more.</li>
-</ul>
-<p>As you can see, Wondershare Dr.Fone Toolkit is a very useful and multifunctional software that can help you with various mobile scenarios. It is compatible with over 8000 iOS and Android devices, including the latest models such as iPhone 12/12 Pro/12 Pro Max/12 mini, Samsung Galaxy S21/S21+/S21 Ultra, Huawei Mate 40/Mate 40 Pro/Mate 40 Pro+, etc. It also supports Windows and Mac operating systems, so you can use it on your PC or Mac easily.</p>
-<h3>Supported devices and platforms of Wondershare Dr.Fone Toolkit</h3>
-<table>
-<tr>
-<th>Tool</th>
-<th>iOS Devices</th>
-<th>Android Devices</th>
-<th>Windows OS</th>
-<th>Mac OS</th>
-</tr>
-<tr>
-<td>Data Recovery</td>
-<td>iOS 9.0 - iOS 14.4</td>
-<td>Android 2.1 - Android 11.0</td>
-<td>Windows XP - Windows 10</td>
-<td>Mac OS X 10.8 - macOS 11.0</td>
-</tr>
-<tr>
-<td>Screen Unlock</td>
-<td>iOS 6.0 - iOS 14.4</td>
-<td>Android 2.1 - Android 11.0</td>
-<td>Windows XP - Windows 10</td>
-<td>Mac OS X 10.8 - macOS 11.0</td>
-</tr>
-<tr>
-<td>WhatsApp Transfer</td>
-<td>iOS 7.0 - iOS 14.4</td>
-<td>Android 2.1 - Android 11.0</td>
-<td>Windows XP - Windows 10</td>
-<td>Mac OS X 10.8 - macOS 11.0</td>
-</tr>
-<tr>
-<td>Phone Transfer</td>
-<td>iOS 5.0 - iOS 14.4</td>
-<td>Android 2.1 - Android 11.0</td>
-<td>Windows XP - Windows 10</td>
-<td>Mac OS X 10.8 - macOS 11.0</td>
-</tr>
-<tr>
-<td>Data Eraser</td>
-<td>iOS 6.0 - iOS 14.4</td>
-<td>Android 2.1 - Android 11.0</td>
-<td>Windows XP - Windows 10</td>
-<td>Mac OS X 10.8 - macOS 11.0</td>
-</tr> <tr>
-<td>System Repair</td>
-<td>iOS 6.0 - iOS 14.4</td>
-<td>Android 2.1 - Android 11.0</td>
-<td>Windows XP - Windows 10</td>
-<td>Mac OS X 10.8 - macOS 11.0</td>
-</tr>
-<tr>
-<td>Data Backup & Restore</td>
-<td>iOS 5.0 - iOS 14.4</td>
-<td>Android 2.1 - Android 11.0</td>
-<td>Windows XP - Windows 10</td>
-<td>Mac OS X 10.8 - macOS 11.0</td>
-</tr>
-<tr>
-<td>Phone Manager</td>
-<td>iOS 5.0 - iOS 14.4</td>
-<td>Android 2.1 - Android 11.0</td>
-<td>Windows XP - Windows 10</td>
-<td>Mac OS X 10.8 - macOS 11.0</td>
-</tr>
-</table>
- <h2>Why do you need CRACK Wondershare Dr.Fone Toolkit?</h2>
-<p>As mentioned earlier, Wondershare Dr.Fone Toolkit is not a free software. You need to pay for a license to use all its tools and features. The price of the license varies depending on the type of tool, the number of devices, and the duration of the subscription. For example, the Data Recovery tool costs $59.95 for a one-year license for one device, $69.95 for a lifetime license for one device, or $499 for a lifetime license for unlimited devices. The Screen Unlock tool costs $49.95 for a one-year license for five devices, $59.95 for a lifetime license for five devices, or $399 for a lifetime license for unlimited devices. The WhatsApp Transfer tool costs $29.95 for a one-year license for one device, $39.95 for a lifetime license for one device, or $299 for a lifetime license for unlimited devices. And so on.</p>
-<p>Some people may find these prices too expensive or unreasonable, especially if they only need to use the software occasionally or for a specific purpose. That's why they look for CRACK Wondershare Dr.Fone Toolkit, which is supposed to be a free version of the software that can bypass the license verification and activation process and allow them to use all the tools and features without paying anything.</p>
-<h3>Advantages of CRACK Wondershare Dr.Fone Toolkit</h3>
-<p>The main advantage of using CRACK Wondershare Dr.Fone Toolkit is that you can save money and enjoy all the benefits of the software without paying anything. You can use any tool and feature that you want, such as recovering your lost data, unlocking your phone screen, transferring your WhatsApp chats, etc., without worrying about the subscription fee or the expiration date.</p>
-<p></p>
-<p>Another advantage of using CRACK Wondershare Dr.Fone Toolkit is that you can access the latest version of the software with all the updates and improvements that are released by Wondershare regularly. You don't have to wait for the official release or download it manually from their website. You can get the latest version of CRACK Wondershare Dr.Fone Toolkit from various sources online, such as torrent sites, file-sharing platforms, or crack forums.</p>
-<h3>Risks and drawbacks of CRACK Wondershare Dr.Fone Toolkit</h3>
-<p>However, using CRACK Wondershare Dr.Fone Toolkit is not without risks and drawbacks. In fact, there are many disadvantages and dangers of using it that you should be aware of before you decide to download and install it on your PC or Mac.</p>
-<ul>
-<li><strong>It is illegal:</strong> Using CRACK Wondershare Dr.Fone Toolkit is a violation of the intellectual property rights and terms of service of Wondershare. It is considered as software piracy, which is a criminal offense in many countries and regions. You could face legal consequences such as fines, lawsuits, or even jail time if you are caught using or distributing CRACK Wondershare Dr.Fone Toolkit.</li>
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-</ul>
-<p>As you can see, using CRACK Wondershare Dr.Fone Toolkit is not worth the risk and trouble. You could end up harming your PC or Mac, compromising your security and privacy, facing legal troubles, or losing your data. You could also damage the reputation and credibility of Wondershare and discourage them from creating more quality software in the future.</p>
- <h2>How to download and install CRACK Wondershare Dr.Fone Toolkit?</h2>
-<p>If you still want to download and install CRACK Wondershare Dr.Fone Toolkit despite the risks and drawbacks, then you should follow these steps carefully and cautiously. However, we do not recommend or endorse this method, and we are not responsible for any consequences or damages that may result from it. Use it at your own risk and discretion.</p>
-<h3>Step-by-step guide to download and install CRACK Wondershare Dr.Fone Toolkit</h3>
-<ol>
-<li>Find a reliable and trustworthy source to download CRACK Wondershare Dr.Fone Toolkit. You can search online for various websites, forums, blogs, or torrent sites that offer CRACK Wondershare Dr.Fone Toolkit for free. However, be careful of fake or malicious links that could lead you to phishing or scam sites that could infect your PC or Mac with malware or steal your personal information.</li>
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-<li>Launch CRACK Wondershare Dr.Fone Toolkit from your desktop shortcut or start menu. You should see a message saying that the software is activated successfully and you can use all its tools and features without any limitations.</li>
-</ol>
-<h3>Tips and tricks to use CRACK Wondershare Dr.Fone Toolkit safely and effectively</h3>
-<ul>
-<li>Do not update or upgrade CRACK Wondershare Dr.Fone Toolkit from within the software or from its official website. This could cause the software to revert back to its trial version or stop working altogether.</li>
-<li>Do not share or distribute CRACK Wondershare Dr.Fone Toolkit with others online or offline. This could expose you to legal risks or penalties for software piracy.</li>
-<li>Do not use CRACK Wondershare Dr.Fone Toolkit for commercial purposes or illegal activities. This could damage your reputation or credibility as a professional or a user.</li>
-<li>Do not rely on CRACK Wondershare Dr.Fone Toolkit as your only solution for your mobile problems. Always backup your data regularly and use other reliable and reputable data recovery software in case of emergencies.</li>
-</ul>
- <h2>Alternatives to CRACK Wondershare Dr.Fone Toolkit</h2>
-<p>If you are not comfortable or satisfied with using CRACK Wondershare Dr.Fone Toolkit, then you have some alternatives that you can consider. These alternatives are either the official version of Wondershare Dr.Fone Toolkit or other reliable and reputable data recovery software that can offer similar or better features and functions.</p>
-<h3>Official version of Wondershare Dr.Fone Toolkit</h3>
-<p>The best and safest alternative to CRACK Wondershare Dr.Fone Toolkit is the official version of Wondershare Dr.Fone Toolkit. This is the original and legitimate version of the software that is developed and maintained by Wondershare. You can download it from their <a href="">official website</a> and purchase a license to use it legally and securely. You can also enjoy the following benefits of using the official version of Wondershare Dr.Fone Toolkit:</p>
-<ul>
-<li><strong>It is legal:</strong> Using the official version of Wondershare Dr.Fone Toolkit is compliant with the intellectual property rights and terms of service of Wondershare. You are not violating any laws or regulations by using it.</li>
-<li><strong>It is safe:</strong> Using the official version of Wondershare Dr.Fone Toolkit is free from any malicious code, viruses, malware, spyware, adware, ransomware, or other harmful programs that could harm your PC or Mac, steal your personal information, encrypt your files, display unwanted ads, or perform other malicious activities on your device.</li>
-<li><strong>It is reliable:</strong> Using the official version of Wondershare Dr.Fone Toolkit is guaranteed to work properly and smoothly. It has no bugs, errors, crashes, or compatibility issues that could affect the performance and functionality of the software. It also updates and upgrades to the latest version of the software automatically, giving you access to all the new features and improvements.</li>
-<li><strong>It is ethical:</strong> Using the official version of Wondershare Dr.Fone Toolkit is respectful and supportive of Wondershare's hard work and innovation. You are paying them for their efforts and contributions to the software industry. You are also encouraging them to create more quality software in the future.</li>
-</ul>
-<p>However, using the official version of Wondershare Dr.Fone Toolkit also has some disadvantages that you should be aware of before you decide to buy it. These disadvantages are:</p>
-<ul>
-<li><strong>It is expensive:</strong> Using the official version of Wondershare Dr.Fone Toolkit requires you to pay for a license to use all its tools and features. The price of the license varies depending on the type of tool, the number of devices, and the duration of the subscription. You may find these prices too high or unreasonable, especially if you only need to use the software occasionally or for a specific purpose.</li>
-<li><strong>It is limited:</strong> Using the official version of Wondershare Dr.Fone Toolkit restricts you to use only the tools and features that you have paid for. You cannot use other tools or features that are not included in your license. You also cannot use the software on more devices than your license allows.</li>
-</ul>
-<p>Therefore, using the official version of Wondershare Dr.Fone Toolkit has its pros and cons that you should weigh carefully before you make your purchase decision.</p>
-<h3>Other reliable and reputable data recovery software</h3>
-<p>Another alternative to CRACK Wondershare Dr.Fone Toolkit is other reliable and reputable data recovery software that can offer similar or better features and functions. There are many data recovery software available in the market that can help you recover your lost or deleted data from your iOS or Android device, such as <a href="">EaseUS MobiSaver</a>, <a href="">FonePaw iPhone Data Recovery</a>, <a href="">Tenorshare UltData</a>, <a href="">iMyFone D-Back</a>, <a href="">PhoneRescue</a>, etc. Some of these software are free or cheaper than Wondershare Dr.Fone Toolkit, while others have more advanced or unique features than Wondershare Dr.Fone Toolkit.</p>
-<p>However, not all data recovery software are created equal. Some may have better compatibility, performance, functionality, or user-friendliness than others. Some may have more positive reviews, ratings, feedbacks, or testimonials than others. Some may have more professional support, service, warranty, or guarantee than others. Therefore, you should do your own research and comparison before you choose which data recovery software to use for your mobile problems.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, CRACK Wondershare Dr.Fone Toolkit is a cracked or modified version of Wondershare Dr.Fone Toolkit that can bypass the CRACK Wondershare Dr.Fone Toolkit is and what are the advantages and risks of using it. We also hope that we have provided you with some alternatives that you can consider for your mobile problems. If you have any questions or feedback, please feel free to contact us or leave a comment below. Thank you for reading and have a nice day!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions that you may have about CRACK Wondershare Dr.Fone Toolkit and its alternatives.</p>
-<h3>Q: Is CRACK Wondershare Dr.Fone Toolkit safe to use?</h3>
-<p>A: No, CRACK Wondershare Dr.Fone Toolkit is not safe to use. It could contain malicious code, viruses, malware, spyware, adware, ransomware, or other harmful programs that could harm your PC or Mac, steal your personal information, encrypt your files, display unwanted ads, or perform other malicious activities on your device.</p>
-<h3>Q: Is CRACK Wondershare Dr.Fone Toolkit legal to use?</h3>
-<p>A: No, CRACK Wondershare Dr.Fone Toolkit is not legal to use. It is a violation of the intellectual property rights and terms of service of Wondershare. It is considered as software piracy, which is a criminal offense in many countries and regions. You could face legal consequences such as fines, lawsuits, or even jail time if you are caught using or distributing CRACK Wondershare Dr.Fone Toolkit.</p>
-<h3>Q: Is CRACK Wondershare Dr.Fone Toolkit reliable to use?</h3>
-<p>A: No, CRACK Wondershare Dr.Fone Toolkit is not reliable to use. It could have bugs, errors, crashes, or compatibility issues that could affect the performance and functionality of the software. It could also fail to update or upgrade to the latest version of the software, leaving you with an outdated or obsolete version that could miss some important features or improvements.</p>
-<h3>Q: Is CRACK Wondershare Dr.Fone Toolkit ethical to use?</h3>
-<p>A: No, CRACK Wondershare Dr.Fone Toolkit is not ethical to use. You are depriving Wondershare of their rightful income and recognition for their hard work and innovation. You are also damaging the reputation and credibility of Wondershare and discouraging them from creating more quality software in the future.</p>
-<h3>Q: What are the best alternatives to CRACK Wondershare Dr.Fone Toolkit?</h3>
-<p>A: The best alternatives to CRACK Wondershare Dr.Fone Toolkit are the official version of Wondershare Dr.Fone Toolkit or other reliable and reputable data recovery software. The official version of Wondershare Dr.Fone Toolkit is the original and legitimate version of the software that is developed and maintained by Wondershare. You can download it from their official website and purchase a license to use it legally and securely. Other reliable and reputable data recovery software are other data recovery software that can offer similar or better features and functions than Wondershare Dr.Fone Toolkit. There are many data recovery software available in the market that can help you recover your lost or deleted data from your iOS or Android device, such as EaseUS MobiSaver, FonePaw iPhone Data Recovery, Tenorshare UltData, iMyFone D-Back, PhoneRescue, etc.</p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Discover the Richness of Telugu Language and Literature with Pedda Bala Shiksha By Gajula Satyanarayana.md b/spaces/raedeXanto/academic-chatgpt-beta/Discover the Richness of Telugu Language and Literature with Pedda Bala Shiksha By Gajula Satyanarayana.md
deleted file mode 100644
index 900e19bdafb4a22b8e675d1b29e2bfd64abffbcf..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Discover the Richness of Telugu Language and Literature with Pedda Bala Shiksha By Gajula Satyanarayana.md	
+++ /dev/null
@@ -1,159 +0,0 @@
-
-<h1>Pedda Bala Shiksha by Gajula Satyanarayana: A Comprehensive Guide for Children and Adults</h1>
- <h2>Introduction</h2>
- <p>Have you ever wondered what is the best way to learn about the world and yourself? Do you want to improve your knowledge and skills in various subjects and domains? Do you wish to have a book that can teach you everything from history and geography to language and literature to ethics and spirituality?</p>
- <p>If your answer is yes, then you are in luck. There is a book that can fulfill all your needs and desires. It is called <strong>Pedda Bala Shiksha</strong>, which means <em>Big Children's Education</em> in Telugu. It is a book that has been written by <strong>Gajula Satyanarayana</strong>, a renowned scholar and writer who has dedicated his life to the cause of education and culture.</p>
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- <p>In this article, we will explore what Pedda Bala Shiksha is, who Gajula Satyanarayana is, why Pedda Bala Shiksha is important, what are the content and structure of Pedda Bala Shiksha, and what are the benefits and features of Pedda Bala Shiksha. By the end of this article, you will have a clear idea of why Pedda Bala Shiksha is a must-read book for children and adults alike.</p>
- <h2>What is Pedda Bala Shiksha?</h2>
- <p>Pedda Bala Shiksha is a book that covers a wide range of topics and subjects that are essential for the overall development of a person. It is a book that aims to provide a comprehensive education that can help one to become a well-rounded individual who can contribute positively to society.</p>
- <p>Pedda Bala Shiksha was first published in 2004 by Gajula Satyanarayana. It is divided into two parts: Part 1 and Part 2. Each part has several chapters that deal with different aspects of knowledge and learning. The book has been written in Telugu, which is one of the most widely spoken languages in India.</p>
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- <p>Pedda Bala Shiksha is not just a book for children. It is also a book for adults who want to refresh their memory or learn something new. It is a book that can be used as a reference or a guide for anyone who wants to enhance their understanding of the world and themselves.</p>
- <h2>Who is Gajula Satyanarayana?</h2>
- <p>Gajula Satyanarayana is the author of Pedda Bala Shiksha. He is a scholar, writer, teacher, editor, publisher, and activist who has devoted his life to the promotion of education and culture in Telugu society.</p>
- <p>Gajula Satyanarayana was born in 1949 in Andhra Pradesh, India. He completed his education from Osmania University and Kakatiya University. He has a PhD in Telugu literature and has taught at various colleges and universities. He has also worked as an editor for several magazines and newspapers.</p>
- <p>Gajula Satyanarayana has written more than 50 books on various topics such as history, geography, culture, science, mathematics, language, literature, ethics, values, religion, philosophy, yoga, meditation, etc. He has also translated many works from other languages into Telugu. He has received many awards and honors for his contributions to Telugu literature and education.</p>
- <p>Gajula Satyanarayana is also an active social worker who has been involved in many movements and campaigns for the welfare of the people. He has founded several organizations and institutions that aim to spread awareness and empowerment among the masses. He has also participated in many seminars and conferences on various issues related to education and culture.</p>
- <h2>Why is Pedda Bala Shiksha important?</h2>
- <p>Pedda Bala Shiksha is important because it is a book that can help one to achieve personal growth and social transformation. It is a book that can inspire one to become a better human being who can live harmoniously with oneself and others.</p>
- <p>Pedda Bala Shiksha is important because it provides a holistic education that covers all aspects of life. It teaches one not only facts and figures but also values and virtues. It teaches one not only how to think but also how to feel. It teaches one not only how to act but also how to react.</p>
- <p>Pedda Bala Shiksha is important because it fosters a love for learning that can last a lifetime. It stimulates one's curiosity and creativity that can lead to discovery and innovation. It nurtures one's confidence and competence that can result in success and satisfaction.</p>
- <h2>Content and Structure of Pedda Bala Shiksha</h2>
- <h3>Part 1: General Knowledge</h3>
- <p>The first part of Pedda Bala Shiksha consists of 16 chapters that deal with various subjects related to general knowledge such as history, geography, culture, science, mathematics, etc. The chapters are:</p>
-<ul>
-<li>The Universe</li>
-<li>The Earth</li>
-<li>The Continents</li>
-<li>The Countries</li>
-<li>The States</li>
-<li>The Districts</li>
-<li>The Cities</li>
-<li>The Rivers</li>
-<li>The Mountains</li>
-<li>The Oceans</li>
-<li>The Plants</li>
-<li>The Animals</li>
-<li>The Human Body</li>
-<li>The Inventions</li>
-<li>The Measurements</li>
-<li>The Calendar</li>
-</ul>
-<p>In each chapter, the author provides relevant information, facts, figures, maps, diagrams, pictures, etc. that can help one to learn about the topic in an easy and interesting way. The author also includes quizzes, exercises, games, riddles, etc. that can test one's knowledge and enhance one's memory.</p>
- <h3>Part 2: Language and Literature</h3>
- <p>The second part of Pedda Bala Shiksha consists of 15 chapters that deal with various aspects of language and literature such as Telugu alphabet, grammar, vocabulary, proverbs, poems, stories, etc. The chapters are:</p>
-<ul>
-<li>Telugu Alphabet</li>
-<li>Telugu Grammar</li>
-<li>Telugu Vocabulary</li>
-<li>Telugu Proverbs</li>
-<li>Telugu Poems</li>
-<li>Telugu Stories</li>
-<li>Telugu Drama</li>
-<li>Telugu Novel</li>
-<li>Telugu Biography</li>
-<li>Telugu Essay</li>
-<li>Telugu Letter Writing</li>
-<li>Telugu Speech Writing</li>
-<li>Telugu Translation</li>
-<li>Telugu Literature History</li>
-<li>Telugu Literature Awards</li>
-</ul>
-<p>In each chapter, the author provides clear explanations, examples, rules, tips, exercises, etc. that can help one to learn and improve one's language and literature skills. The author also introduces some of the famous and influential writers and works of Telugu literature that can inspire one to appreciate and enjoy the beauty and richness of Telugu language and culture.</p>
- <h3>Part 3: Moral and Spiritual Education</h3>
- <p>The third part of Pedda Bala Shiksha consists of 14 chapters that deal with various topics related to moral and spiritual education such as ethics, values, religion, philosophy, yoga, meditation, etc. The chapters are:</p>
-<ul>
-<li>Ethics and Values</li>
-<li>Religion <ul>
-<li>Hinduism</li>
-<li>Buddhism</li>
-<li>Jainism</li>
-<li>Sikhism</li>
-<li>Islam</li>
-<li>Christianity</li>
-<li>Judaism</li>
-<li>Zoroastrianism</li>
-<li>Philosophy and Logic</li>
-<li>Yoga and Meditation</li>
-<li>Astrology and Numerology</li>
-<li>Ayurveda and Homeopathy</li>
-<li>Vastu and Feng Shui</li>
-<li>Reiki and Pranic Healing</li>
-</ul>
-<p>In each chapter, the author provides basic information, principles, practices, beliefs, symbols, rituals, festivals, etc. that can help one to learn about the different aspects of moral and spiritual education. The author also explains the similarities and differences among the various religions and philosophies that can help one to respect and appreciate the diversity and harmony of human culture.</p>
- <h2>Benefits and Features of Pedda Bala Shiksha</h2>
- <h3>Educational and Entertaining</h3>
- <p>One of the main benefits and features of Pedda Bala Shiksha is that it is both educational and entertaining. It is a book that can teach one a lot of things in a fun and engaging way. It is a book that can make one curious and eager to learn more. It is a book that can make one laugh and smile with its witty and humorous content.</p>
- <p>Pedda Bala Shiksha is a book that can appeal to both children and adults. It is a book that can cater to different interests and preferences. It is a book that can suit different moods and occasions. It is a book that can be read alone or with others.</p>
- <h3>Simple and Comprehensive</h3>
- <p>Another benefit and feature of Pedda Bala Shiksha is that it is simple and comprehensive. It is a book that can explain complex concepts in simple terms. It is a book that can cover a wide range of topics in detail. It is a book that can provide accurate and reliable information from credible sources.</p>
- <p>Pedda Bala Shiksha is a book that can help one to improve one's general knowledge and awareness. It is a book that can help one to develop one's language and literature skills. It is a book that can help one to cultivate one's moral and spiritual values.</p>
- <h3>Suitable for All Ages and Levels</h3>
- <p>A third benefit and feature of Pedda Bala Shiksha is that it is suitable for all ages and levels. It is a book that can be read by anyone who wants to learn something new or refresh their memory. It is a book that can be used by students, teachers, parents, professionals, or anyone who wants to enhance their knowledge and skills.</p>
- <p>Pedda Bala Shiksha is a book that can be adapted to different needs and goals. It is a book that can be used as a textbook, a reference, a guide, or a companion. It is a book that can be used for self-study, group study, or classroom study. It is a book that can be used for leisure, pleasure, or purpose.</p>
- <h2>Conclusion</h2>
- <p>Pedda Bala Shiksha by Gajula Satyanarayana is a book that can provide a comprehensive education for children and adults. It is a book that covers various subjects and topics related to general knowledge, language and literature, and moral and spiritual education. It is a book that has many benefits and features such as being educational and entertaining, simple and comprehensive, and suitable for all ages and levels.</p>
- <p>If you are looking for a book that can teach you everything you need to know about the world and yourself, then Pedda Bala Shiksha by Gajula Satyanarayana is the book for you. It is a book that can enrich your mind and soul with its valuable content and style. It is a book that can change your life for the better with its positive impact and influence.</p>
- <p>So what are you waiting for? Grab your copy of Pedda Bala Shiksha by Gajula Satyanarayana today and start your journey of learning and growing with this amazing book.</p>
- <h2>FAQs</h2>
- <ol>
-<li><strong>Where can I buy Pedda Bala Shiksha by Gajula Satyanarayana?</strong></li>
-<p>You can buy Pedda Bala Shiksha by Gajula Satyanarayana from various online platforms such as Amazon, Flipkart, etc. You can also buy it from local bookstores or libraries.</p>
-<li><strong>How many pages does Pedda Bala Shiksha by Gajula Satyanarayana have?</strong></li>
-<p>Pedda Bala Shiksha by Gajula Satyanarayana has 1952 pages in total. Part 1 has 960 pages and Part 2 has 992 pages.</p>
-<li><strong>What are some other books by Gajula Satyanarayana?</strong></li>
-<p>Some other books by Gajula Satyanarayana are Telugu Vari Sampoorna Peddha Bala Siksha, Telugu Vari Sampoorna Peddha Bala Siksha - Part 1, Telugu Vari Sampoorna Peddha Bala Siksha - Part 2, etc.</p>
-<li><strong>What are some other books similar to Pedda Bala Shiksha by Gajula Satyanarayana?</strong></li>
-<p>Some other books similar to Pedda Bala Shiksha by Gajula Satyanarayana are PEDDA BALA SIKSHA [ TELUGU MEDIUM ] by Sodum Rammohan, pedda Balashiksha - 1 (166 Mudrana) Telugu by Gajula Sathyanarayana, etc.</p>
-<li><strong>How can I contact Gajula Satyanarayana?</strong></li>
-<p>You can contact Gajula Satyanarayana through his email address: gajulasatya@gmail.com or his phone number: +91-98490-12345.</p>
-</ol>
- </p> 0a6ba089eb<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Fahren Lernen Max Offline Crack The Best Way to Learn Driving Theory and Practice Without Paying or Connecting to the Internet.md b/spaces/raedeXanto/academic-chatgpt-beta/Fahren Lernen Max Offline Crack The Best Way to Learn Driving Theory and Practice Without Paying or Connecting to the Internet.md
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--- a/spaces/raedeXanto/academic-chatgpt-beta/Fahren Lernen Max Offline Crack The Best Way to Learn Driving Theory and Practice Without Paying or Connecting to the Internet.md	
+++ /dev/null
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-
-<h1>How to Use Fahren Lernen Max Offline Crack to Learn Driving</h1>
-<p>Do you want to learn driving in a fast and easy way? Do you want to access the best online training system for theory and practice without internet connection? If yes, then you might be interested in using Fahren Lernen Max Offline Crack. In this article, we will explain what Fahren Lernen Max is, what Fahren Lernen Max Offline Crack is, how to download and install it, and how to use it to learn driving. Let's get started!</p>
-<h2>fahren lernen max offline crack</h2><br /><p><b><b>Download</b> ===> <a href="https://tinourl.com/2uL33B">https://tinourl.com/2uL33B</a></b></p><br /><br />
- <h2>What is Fahren Lernen Max?</h2>
-<h3>A comprehensive online training system for theory and practice</h3>
-<p>Fahren Lernen Max is a popular online training system for theory and practice that helps you prepare for your driving license exam. It is developed by Vogel Verlag, the market leader of driving school media in Germany. With Fahren Lernen Max, you can learn anywhere and anytime, as long as you have a PC with internet connection or a mobile device with the Fahren Lernen App. You can also sync your learning progress across different devices.</p>
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-<p>Fahren Lernen Max offers many features and benefits that make it a great choice for learning driving. Some of them are:</p>
-<ul>
-<li>A guided learning path that takes you from easy to difficult questions, with video and image questions and variations.</li>
-<li>A learning assistant that supports you with hints, explanations, feedback, and motivation.</li>
-<li>A learning traffic light that tells you when you are ready for the exam.</li>
-<li>A practice training that helps you with your practical driving lessons.</li>
-<li>A document management that helps you keep track of your documents and hours.</li>
-<li>A multimedia package that includes a companion book, an audio book, over 100 videos, and more.</li>
-</ul>
- <h2>What is Fahren Lernen Max Offline Crack?</h2>
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-<p>Fahren Lernen Max Offline Crack is a software that allows you to use Fahren Lernen Max without internet connection. It is a modified version of the original software that bypasses the online verification process. With Fahren Lernen Max Offline Crack, you can access all the features and content of Fahren Lernen Max without having to log in or pay for a subscription.</p>
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-<table>
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-<tr><td>You can access the latest version of Fahren Lernen Max.</td><td>You may not receive updates or support from Vogel Verlag.</td></tr>
-<tr><td>You can use Fahren Lernen Max on multiple devices.</td><td>You may not be able to sync your learning progress across devices.</td></tr>
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-</ul>
- <h2>How to Use Fahren Lernen Max Offline Crack to Learn Driving?</h2>
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-<p>To use Fahren Lernen Max Offline Crack effectively, you should follow these tips and tricks:</p>
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- <h3>The best practices and recommendations for learning driving with Fahren Lernen Max Offline Crack</h3>
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-<p>Fahren Lernen Max Offline Crack is a software that allows you to use Fahren Lernen Max without internet connection. It can be a useful tool for learning driving in a fast and easy way. However, it also has some drawbacks and risks that you should be aware of. You should use it with caution and responsibility, and follow the tips and best practices that we have shared in this article. We hope that this article has helped you understand how to use Fahren Lernen Max Offline Crack to learn driving. If you have any questions or feedback, please let us know in the comments below. Thank you for reading and happy driving!</p>
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- </p> 0a6ba089eb<br />
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diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/@notionhq/client/build/src/helpers.d.ts b/spaces/rayan-saleh/whisper2notion/server/node_modules/@notionhq/client/build/src/helpers.d.ts
deleted file mode 100644
index 25edcb9a2788d96f7197ba6adfadffc4ee6f7a7a..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/@notionhq/client/build/src/helpers.d.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-import { BlockObjectResponse, CommentObjectResponse, DatabaseObjectResponse, PageObjectResponse, PartialBlockObjectResponse, PartialCommentObjectResponse, PartialDatabaseObjectResponse, PartialPageObjectResponse, PartialUserObjectResponse, UserObjectResponse } from "./api-endpoints";
-interface PaginatedArgs {
-    start_cursor?: string;
-}
-interface PaginatedList<T> {
-    object: "list";
-    results: T[];
-    next_cursor: string | null;
-    has_more: boolean;
-}
-/**
- * Returns an async iterator over the results of any paginated Notion API.
- *
- * Example (given a notion Client called `notion`):
- *
- * ```
- * for await (const block of iteratePaginatedAPI(notion.blocks.children.list, {
- *   block_id: parentBlockId,
- * })) {
- *   // Do something with block.
- * }
- * ```
- *
- * @param listFn A bound function on the Notion client that represents a conforming paginated
- *   API. Example: `notion.blocks.children.list`.
- * @param firstPageArgs Arguments that should be passed to the API on the first and subsequent
- *   calls to the API. Any necessary `next_cursor` will be automatically populated by
- *   this function. Example: `{ block_id: "<my block id>" }`
- */
-export declare function iteratePaginatedAPI<Args extends PaginatedArgs, Item>(listFn: (args: Args) => Promise<PaginatedList<Item>>, firstPageArgs: Args): AsyncIterableIterator<Item>;
-/**
- * Collect all of the results of paginating an API into an in-memory array.
- *
- * Example (given a notion Client called `notion`):
- *
- * ```
- * const blocks = collectPaginatedAPI(notion.blocks.children.list, {
- *   block_id: parentBlockId,
- * })
- * // Do something with blocks.
- * ```
- *
- * @param listFn A bound function on the Notion client that represents a conforming paginated
- *   API. Example: `notion.blocks.children.list`.
- * @param firstPageArgs Arguments that should be passed to the API on the first and subsequent
- *   calls to the API. Any necessary `next_cursor` will be automatically populated by
- *   this function. Example: `{ block_id: "<my block id>" }`
- */
-export declare function collectPaginatedAPI<Args extends PaginatedArgs, Item>(listFn: (args: Args) => Promise<PaginatedList<Item>>, firstPageArgs: Args): Promise<Item[]>;
-/**
- * @returns `true` if `response` is a full `BlockObjectResponse`.
- */
-export declare function isFullBlock(response: BlockObjectResponse | PartialBlockObjectResponse): response is BlockObjectResponse;
-/**
- * @returns `true` if `response` is a full `PageObjectResponse`.
- */
-export declare function isFullPage(response: PageObjectResponse | PartialPageObjectResponse): response is PageObjectResponse;
-/**
- * @returns `true` if `response` is a full `DatabaseObjectResponse`.
- */
-export declare function isFullDatabase(response: DatabaseObjectResponse | PartialDatabaseObjectResponse): response is DatabaseObjectResponse;
-/**
- * @returns `true` if `response` is a full `UserObjectResponse`.
- */
-export declare function isFullUser(response: UserObjectResponse | PartialUserObjectResponse): response is UserObjectResponse;
-/**
- * @returns `true` if `response` is a full `CommentObjectResponse`.
- */
-export declare function isFullComment(response: CommentObjectResponse | PartialCommentObjectResponse): response is CommentObjectResponse;
-export {};
-//# sourceMappingURL=helpers.d.ts.map
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Aquaenergizerdownloadfullversionfree.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Aquaenergizerdownloadfullversionfree.md
deleted file mode 100644
index a7e6226a2cbd0a0c2fb1580be4afe19423ce3049..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Aquaenergizerdownloadfullversionfree.md
+++ /dev/null
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Artlantis Studio 9.2.4.4 Multilingual Cracked Serial Key.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Artlantis Studio 9.2.4.4 Multilingual Cracked Serial Key.md
deleted file mode 100644
index 246fdbd460101a98069e8493d7b161ad6a2e0f3a..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Artlantis Studio 9.2.4.4 Multilingual Cracked Serial Key.md	
+++ /dev/null
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deleted file mode 100644
index 39b6fadf3ecc2aa8d1b571e7b64da77329d7c437..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Exterminate It Full Version Activation Code [BETTER] Keygen Free.md	
+++ /dev/null
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diff --git a/spaces/renatotn7/unicamp-dl-translation-en-pt-t5/app.py b/spaces/renatotn7/unicamp-dl-translation-en-pt-t5/app.py
deleted file mode 100644
index 3f9f62519c0978405c08b2afbc8357cbee3a1255..0000000000000000000000000000000000000000
--- a/spaces/renatotn7/unicamp-dl-translation-en-pt-t5/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/unicamp-dl/translation-en-pt-t5").launch()
\ No newline at end of file
diff --git a/spaces/ridai/img-to-music/share_btn.py b/spaces/ridai/img-to-music/share_btn.py
deleted file mode 100644
index 1a2ac6a6e74b114dbd54c2f24723a87180db51ef..0000000000000000000000000000000000000000
--- a/spaces/ridai/img-to-music/share_btn.py
+++ /dev/null
@@ -1,100 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-	async function getInputImgFile(imgEl){
-        const res = await fetch(imgEl.src);
-        const blob = await res.blob();
-        const imgId = Date.now() % 200;
-        const isPng = imgEl.src.startsWith(`data:image/png`);
-        if(isPng){
-            const fileName = `sd-perception-${{imgId}}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        }else{
-            const fileName = `sd-perception-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        }
-	}
-    async function getOutputMusicFile(audioEL){
-        const res = await fetch(audioEL.src);
-        const blob = await res.blob();
-        const audioId = Date.now() % 200;
-        const fileName = `img-to-music-${{audioId}}.wav`;
-        const musicBlob = new File([blob], fileName, { type: 'audio/wav' });
-        console.log(musicBlob);
-        return musicBlob;
-	}
-    
-    async function audioToBase64(audioFile) {
-        return new Promise((resolve, reject) => {
-            let reader = new FileReader();
-            reader.readAsDataURL(audioFile);
-            reader.onload = () => resolve(reader.result);
-            reader.onerror = error => reject(error);
-          
-        });
-      }
-    const gradioEl = document.querySelector('body > gradio-app');
-    // const gradioEl = document.querySelector("gradio-app").shadowRoot;
-    const inputImgEl = gradioEl.querySelector('#input-img img');
-    const outputMusic = gradioEl.querySelector('#music-output audio');
-    const outputMusic_src = gradioEl.querySelector('#music-output audio').src;
-    const outputMusic_name = outputMusic_src.split('/').pop();
-    let titleTxt = outputMusic_name;
-    //if(titleTxt.length > 100){
-    //    titleTxt = titleTxt.slice(0, 100) + ' ...';
-    //}
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!outputMusic){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const inputFile = await getInputImgFile(inputImgEl);
-    const urlInputImg = await uploadFile(inputFile);
-    const musicFile = await getOutputMusicFile(outputMusic);
-	const dataOutputMusic = await uploadFile(musicFile);
-    
-    const descriptionMd = `#### Input img:
-<img src='${urlInputImg}' style='max-height: 350px;'>
-
-#### Music:
-
-<audio controls>
-  <source src="${dataOutputMusic}" type="audio/wav">
-Your browser does not support the audio element.
-</audio>
-`;
-    const params = new URLSearchParams({
-        title: titleTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/fffiloni/img-to-music/discussions/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Black Cat 720p Why This Anime is a Must-Watch for Fans of Sci-Fi and Mystery.md b/spaces/rorallitri/biomedical-language-models/logs/Black Cat 720p Why This Anime is a Must-Watch for Fans of Sci-Fi and Mystery.md
deleted file mode 100644
index 12f8f6be294e6445652bd76e6b10a274e470304c..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Black Cat 720p Why This Anime is a Must-Watch for Fans of Sci-Fi and Mystery.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<br />
-<p>As it turns out I share living space with a mostly Black Cat who is the friendliest creature you could ever meet. The concept of poor Bela Lugosi being so afraid of one is something I really can't wrap my mind around. But a tribute to the folks who made that Gothic horror movie magic at Universal Pictures, he really does convince you there is something evil in the creature. That fear does prevent him from getting to the mission of his business at hand.<br /><br />That mission is two fold, to find what became of his wife and daughter and to kill Boris Karloff who has built a fortress like dwelling on the sight of a battlefield from the Great War. Karloff is a worshiper of Satan and black mass rituals are held in his place. But what he's done to Lugosi's wife and daughter are reason enough for him to kill Karloff.<br /><br />Caught up in all of this are American honeymooners David Manners and Julie Bishop. They're traveling on the same train as Lugosi and when Bishop gets injured they're forced to stay at Karloff's. One look at Bishop and you know Karloff's got something in mind for her that ain't good.<br /><br />The story of The Black Cat gets a bit confused in the telling, but the atmosphere of evil in Karloff's castle can't be surpassed, another triumph for the Gothic horror masters at Universal Studios.</p>
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/How to Get PATCHED DriverMax Pro 9.42.0.278 Crack for Free and Boost Your PC Performance.md b/spaces/rorallitri/biomedical-language-models/logs/How to Get PATCHED DriverMax Pro 9.42.0.278 Crack for Free and Boost Your PC Performance.md
deleted file mode 100644
index 15c89682ac6ab7f0dd2ced54fe573e301c4f94c0..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/How to Get PATCHED DriverMax Pro 9.42.0.278 Crack for Free and Boost Your PC Performance.md	
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Innovmetric PolyWorks v10 WinALL Cracked NGEN The Ultimate Guide to the Best 3D Inspection and Reverse Engineering Tool.md b/spaces/rorallitri/biomedical-language-models/logs/Innovmetric PolyWorks v10 WinALL Cracked NGEN The Ultimate Guide to the Best 3D Inspection and Reverse Engineering Tool.md
deleted file mode 100644
index 7a620efdbd2f5e71c5a7399aca5e9b89043cc92e..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Innovmetric PolyWorks v10 WinALL Cracked NGEN The Ultimate Guide to the Best 3D Inspection and Reverse Engineering Tool.md	
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/>photomodeler premium 2022.1.1<br />lidar360<br />arcgis pro 2.5<br />riegl rimining v2.10<br />inpho 10.0.3<br />riprocess 1.8.6<br />speos vrxperience 2022<br />speos theia-rt 2022<br />speos caa 2022<br />caeses<br />shipflow<br />gtools lgp 9.52<br />type3 typeedit<br />rebro<br />cadwell tfas<br />euklid cadam<br />icad mx<br />cadmeister<br />procam dimensions 6.1<br />shoemaster 17<br />mindcad 2022<br />romans cad 2022.12.0.46<br />icad3d 2022<br />easylast3d 3.0<br />x-rite lnkformulation manufact 6.41<br />gravograph gravostyle 6.0<br />type3 v10<br />led wizard 7.1<br />cadlink signlab 10.0<br />studiorip xf version 4.1.124<br />GMG colorproof flexoproof 5.4.2<br />efi fiery xf 6.5<br />efi colorproof xf 6.5<br />colorlogic colorant 5.0.1<br />colorgate productionserver 10<br />carel 1tool 2.6.46<br />photoprint server-pro 10.5.2<br />ergosoft posterprint posterprint 14<br />cgs oris flex pack web 3.3<br />colorlogic copra zepra 6.1<br />PCSWMM PRO 2022<br />DSI EFDC& 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/>Mira Geoscience GOCAD Mining Suite<br />Exa PowerFlow 2022<br />Rsoft2022<br />CMG2022.10<br />GeoModeller4.04<br />ShipFlow 6.2<br />TomoPlus 5.9<br />Flac3D 6.00<br />LandMark ProMax 5000.10<br />Schlumberger TDAS 9.2(20221126)<br />PVTsim Nova 4.2<br />Schlumberger PetroMod 2022.1 x64<br />HampsonRussell HRS10.4.2_Windows version<br />Epoffice 2022 Linux<br />Mentor Graphics Questasim 2022.1 Win64 linux<br />exocad Matera 2.4 2022<br />invivo 6.0<br />etap 18.11 & 19.10<br />DNV GL AS Phast&safeti 8.2<br />ICAMPOST 23<br />Ucamx 2022<br />OCTOPUZ robotics 2.1<br />MotoSimEG-VRC 2022<br />VisualComponents 4.1<br />Visual Components 4.1<br />FANUC ROBOGUIDE 9.1<br />ROBCAD9.0.1<br />KUKA Sim Pro 3.1<br />exocad 2022.02<br />exoplan 2022.02<br />partialCAD 2022 STL export 2022.03.22<br />Ceramill Mind 2022-02-01<br />3Shape Dental System v2.19.1.0_x64<br />OrthoSystemInstaller_2022.1_1.8.1.2<br />3shape OrthoSystem_2022.1_1.8.1.2<br />3shape trios 2022<br />3shape implant studio 2022<br />Optisystem 15<br />CV Cabinet Vision planit solid 11<br />3Shape 2022<br />3shape Audio System (2.16.2.0)<br />3Shape CAMBridge 2015-1 (build 2.6.5.10)<br />3Shape Dental Desktop 1.6.9.1<br />3Shape ShapeDesigener 1.1.4.0<br />Zirkonzahn 2022<br />LandMark ProMax 5000.10<br />Schlumberger TDAS 9.2(20221126)<br />HampsonRussell HRS10.4.2_Windows version<br />PVTsim Nova 4.2<br />Epoffice 2022<br />Petrel2022.2<br />Schlumberger PetroMod 2022.1 x64<br />Epoffice 2022_Linux<br />IP (Interactive Petrophysics) 2022 v4.5.2022.2902<br />Schlumberger Drillbench 2022.2.1<br />IHS SubPUMP 2022 v1.0<br />IHS PERFORM 2013 v1.1<br />Mangrove Kinetix Shale 2022.2<br />Rokdoc 6.6.2<br />PerGeos 2022.1<br />Jason WorkBench 10<br />Schlumberger ECLIPSE 2022.1<br />Schlumberger OLGA 2022.1<br />Lead 4.0 LEAD4.0<br />tNavigator 19.2<br />PaleoScan 2022.1<br />RMS 11<br />Tesseral Pro Version 5.0.5<br />jason10.0.1<br />Schlumberger vista 2022<br />GMI 2011<br />crystal 2022.3 <br />Schlumberger 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MineTrust 2.26.32 x64<br />Datamine Studio UG/EPS/SOT/AEGIS x64<br />Datamine Studio EM 2.4.44 x64<br />Datamine Studio RM/Strat3D/Ore Controller x64<br />Kelton Flocalc.net/UncertaintyPlus.net 1.8<br />Keysight ADS/EMPro/Genesys/SystemVue 2022 Linux64<br />Keysight IC-CAP/MBP/MQA/WaferPro Express 2022-2022 Linux64<br />DesignBuilder 6.1.3<br />FIFTY2 PreonLab 4.0<br />Softbits Flaresim 5.3.1<br />AVEVA Marine 12.1 SP5<br />AVEVA PDMS 12.1 SP5<br />MAXQDA Analytics Pro 2022 R20.0<br />DS Antenna Magus 2022 v10.1 x64<br />Trimble Inpho UASMaster 9.2.2 x64<br />Trimble Inpho Photogrammetry 9.2.2 x64<br />Vibrant MEscope Visual STN 2022 v19.12 x64<br />DDS FEMtools 4.1.1 x64<br />PointCab 3D-Pro 3.9 RO x64<br />Datamine Fusion/Sirovision/CCLAS<br />pycharm Professional 2022.3<br />Facegen Artist 3.2<br />Eliis PaleoScan 2022.1.2 x64<br />Neuraloq Desktop 2022.09<br />NeuraMap 2022.9<br />Datamine Studio OP/NPV Scheduler/DataBlast x64<br />Trimble Business Center 5.20 With UASMaster 9.2 x64 FULL<br />6sigmaET R14<br />Spectra Precision Survey Office 5.10 x64<br />ExactFlat 1.7.0 For Rhino 6.10 x64<br />GeoSLAM Draw 2.0 R2 x64<br />IHS QUE$TOR 2022 03<br />MicroSurvey EmbeddedCAD 2022 v20.0.6 Premium<br />MicroSurvey CAD 2022 v20.0.6.460 Studio x64<br />NovAtel Waypoint Inertial Explorer 8.8<br />Optenni Lab 4.3 x64<br />Cadence Products Suite 2022-2022<br />Cast WYSIWYG light design R40<br />DHI FEFLOW 7.202 x64<br />Synopsys Common License Generate Tool 2022 Win/Linux<br />Leica Infinity 3.3.0 x64<br />Chemical Computing Group MOE (Molecular Operating Environment) 2022<br />AVEVA Engineering 15.1<br />Sonnet Suites Professional 17.52.2 Win64/Linux64<br />Sigasi Studio XPRT 4.5.2<br />Hydromantis GPS-X 8.0<br />Ensoft EnCPT 2022.1.3<br />Ensoft Lpile 2022.11.3<br />Ensoft Group 2022.11.4<br />NuHertz Filter Solutions 2022 v16.3.6 With All Export<br />CGG HampsonRussell 10.4.2 x64<br />CLO Standalone 5.1.330 x64<br />Marvelous Designer 9 Enterprise 5.1.311.44087 x64<br />PointCab 4BIMm 22.02 For ArchiCAD 22<br />PointCab 4Revit 1.2 x64<br />Intrepid Geophysics GeoModeller 2022 v4.0.7 x64<br />ERDAS IMAGINE 2022.v16.6<br />Hexagon GeoMedia Desktop 2022.v16.6 x64<br />ERDAS ORIMA 2022.v16.6<br />CounterSketch Studio 8.0 For Rhino 5.x x64<br />Orthogen 10.4 for Autodesk AutoCAD Plant 3D 2022-2022 x64<br />MicroSurvey FieldGenius 10.3.31<br />RoboDK 4.0 x64<br />TOPCON MAGNET Collage<br />UGCS<br />supermap idesktop 9.0.1<br />supermap iserver 9.1.2a<br />CityMaker Connect 8.0<br />Mirauge3D 1.0<br />Z+FLaserControl 8.9<br />Z+FLaserControl 9.1<br />RiSCAN PRO 64bit 2.7.1<br />riegl ALL<br />LAStools full<br />Applanix POSPac MMS 8.3<br />GeoSLAM-Hub-5.2.1<br />GeoSLAMDraw 3.0<br />Topodot x64<br />Ventuz 6.03.02<br />Amberg Tunnel 2.16<br />InertialExplorer 8.8<br />GrafNav 8.8<br />TrimbleRealWorks_x64_11.1.1.442<br />Caris Hips & Sips 11.0.2<br />ModelVision 15.0<br />Interactive System 4<br />SoundCheck 16<br />Vertex-BD 2022 <br />DevCad<br 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SHOTPlus 6.0.0.8<br />FLO-2D PRO<br />EFDC_Explorer 10.1.4<br />Geochemist Workbench 14.0.0<br />Deswik CAD Sched 2022.3.550<br />EVS 2022.9<br />Maptek Vulcan 12.0.1<br />GWB Advanced Professional 12.0.5<br />Aquaveo SMS 13.0.10<br />Aquaveo GMS 10.4.6 x64<br />AquiferTest Pro 9.0 x64<br />Visual MODFLOW 6.1 x64<br />Aegis-v5.45.97.198<br />MineRP MineCAD 3.0.3770<br />VUMA3D-network 2022<br />Micromine 2014��64-bit��<br />EnterVol 2022.4 for Arcgis 10.7<br />EVSPlot 2022.3<br />MapTiler Desktop 10.0 PRO<br />GEOVIA GEMS 6.8.3<br />FLAC 8.0.453<br />3DEC 5.20.274<br />Xsite 2.0.77<br />Geovia Whittle 4.7.2.1<br />Geovia Surpac 2022<br />Geovia Minex 6.5.3<br />Geovia MineSched 2022<br />Earth Volumetric Studio 2022.12<br />Visual Slope 7.0<br />uc/winroad 14.0<br />fuzor2022-vdc<br />earth volumetric studio (evs) 2022.9<br />PCSWMM professional 2022<br />ICM infoworks ICM 10.5<br />ctech-evs 2022.9<br />kappa workstation 5.30.01a<br />kongsberg ledaflow 2.5<br />zond software<br />parkseis 3.0<br />visual modflow flex 6.1 x64<br />flaresim 6.0.0.64<br />ipm 11 x64<br />fracpro 10.9.41<br />winglink 2.21<br />slb ofm 2022.1<br />oil esp flowsheet 10.0<br />tableau 19.4 x64<br />geostudio 2022<br />aft impulse 7.0<br />ihs subpump 2022 v1.0<br />ubc gif 5.0<br />tesseral pro 5.1.0<br />geomodeller 4.0.7<br />xsens mvn analyze 3d 2022.2<br />rocscience RS2 phase2 2022<br />rocscience Rocfall 2022<br />rocscience slide3 2022<br />camtool 15.1<br />midas civil 2022<br />midas gen 2022<br />midas xd 2022<br />midas gts nx 2022<br />midas fea 2022<br />midas civildesigner 2022<br />midas smartBDS 2022<br />midas building 2022<br />midas soilworks 2022<br />midas meshfree 2022<br />midas NFX 2022<br />cerec stl import 5.1<br />rational DMis 7.1<br />calypso 2022<br />concepts NREC 8.5.10<br />weto AG viskon<br />ADT turbodesign suite 5.0<br />RISA-3D 2022<br />CARIS HIPS V10.0.2<br />neostampa 8.6.3<br />GMG colorproof flexoproof 5.4.2<br />colorlogic colorant 5.0.1<br />type3 v10 gravograph gravostyl<br />CGS ORIS FLEX PACK WEB 3.3<br />colorlogic zepra 6.1<br />led wizard 7.1<br />photoprint server pro 10.5.2<br />studioRIP XF version 4.1.124<br />colorgate productionserver 10<br />cadlink signlab 10.0<br />minesight 2022 x64<br />sigmanest x1<br />cadwin 2006<br />lumenRT pro 14<br />ps brcm 2022<br />landmark R5000.10<br />petrel visage<br />icam icampost v21<br />rsoft 2022.12<br />lighttools 8.7<br />speos caa 2022<br />vpiphotonics design 9.8<br />code v11.2<br />GSSI RADAN 7.5<br />uasmaster 9.2.1<br />dyrobes beperf rotor v13<br />halcon 18.11<br />strand release R2.4.6<br />biowin 6.0<br />isograph reliability workbench 14.0<br />jmp pro 14.3<br />pix4d 4.5.6<br />faro scense 2022.0<br />zonge scs2d<br />n-ncs work station 4.0.7<br />emit maxwell<br />tnxtower 6.0<br />perform 3d v7.0<br />cdegs 15.4<br />midcad 2022<br />esko artioscad 18.01<br />ekahau pro 10.1<br />airmagnet survey pro 9.2.2<br />netscout airmagnet wifi analyzer pro 11.3<br />infovista planet 7.1<br />fluke networks airmagnet<br />vaps suite<br />microcap 10.0<br />simsci proii v10<br />dartfish express<br />salford predictive modeler 8.2<br />caeses-ffw 4.3.1<br />3DQuickPress.v6.3.0.for.SolidWorks2012-2022<br />ADINA.System.V9.6.0<br />Agilent.Keysight.Advanced.Design.System(ADS).2022.2<br />Agilent.Keysight.EMPro.2022.1<br />Agilent.Keysight.Genesys.2022<br />Agilent.Keysight.ICCAP.2022.2<br />Agilent.Keysight.Model.Builder(MBP).2022<br />Agilent.Keysight.Model.Quality(MQA).2022<br />Agilent.Keysight.SystemVue.2022.1<br />Agisoft.Metashape.Pro.v1.6.3.build10723<br />Altair.Activate.2022.0<br />Altair.Compose.2022.0<br />Altair.EDEM.Professional.2022.2<br />Altair.ESAComp.2022.0.0.2<br />Altair.Feko+WinProp.2022.0<br />Altair.Flux.2022.0<br />Altair.FluxMotor.2022.0.0<br />Altair.HyperWorks.Desktop+Solvers.2022.0<br />Altair.Inspire.Cast.2022.0<br />Altair.Inspire.Extrude.Metal.2022.0<br />Altair.Inspire.Extrude.Polymer.2022.0<br />Altair.Inspire.Form.2022.0<br />Altair.Inspire.Render.2022.0 <br />Altair.Inspire.Studio.2022.0<br />Altair.newFASANT.6.3.2022.07.20<br />Altair.PollEx.2022.0<br />Altair.SimLab.2022.0<br />Altair.SimSolid.2022.0.0<br />Altium.Designer.20.1.12.Build249<br />Ansys.Composite.Cure.Simulation.2022.R2<br />Ansys.Electronics.2022R21<br />Ansys.EMA3D.Cable.2022.R2<br />Ansys.Lumerical.2022.R2.1<br />Ansys.Motion.2022.R2<br />Ansys.Motor-CAD.V13.1.10<br />Ansys.OptiSLang.8.0.0.586<br />Ansys.Products.2022R2<br />Ansys.SCADE.2022R2<br />ANSYS.SPEOS.2022.R2.for.CREO.Parametric.3.0-6.0<br />ANSYS.SPEOS.2022.R2.for.NX.11.0-1899.Series<br />AnyLogic.Professional.v7.0.2<br />Applied.Flow.Technology.Arrow.v8.0.1110.build.2022.03.30<br />Applied.Flow.Technology.Fathom.v11.0.1103.build.2022.03.19 <br />Applied.Flow.Technology.Impulse.v7.0.1122.build.2022.01.30<br />Arqcom.CAD-Earth.v6.0.86.for.AutoCAD2022-2022<br />Ascon.Kompas.v19.0.0.BASE+AEC+ELECTRIC.x64<br />ASVIC.Mech-Q.Full.Suite.v4.21.001.for.AutoCAD2000-2022 <br />Avenza.MAPublisher.for.Adobe.Illustrator.10.6 <br />AVEVA.PROII.2022 (SIMSCI)<br />AVEVA.SimCentral.Simulation.Platform.V4.1.0<br />AVL.Simulation.Suite.R2022.12<br />Baker.Hughes.JewelSuite.GeoMechanics.2022.2.584<br />Blue.Marble.Global.Mapper.v21.1.0<br />Boole.&.Partners.PolyBoard.Pro.v6.07<br />Cadaplus.APLUS.v20.055.for.autocad<br />Cadence.Design.Systems.Sigrity.2022.v19.0<br />CADprofi.2022.09.b200603<br />CFTurbo.2022.1.0.31<br />Chief.Architect.Home.Designer.Professional.2022.v22.2.0.54<br />Civil.and.Structural.Computer.Services.MasterSeries.2022.13<br />Cradle.scFLOW.2022.Patch6<br />Cradle.scPOST.2022.Patch6<br />Cradle.scSTREAM.2022.Patch6<br />Cradle.scTETRA.14.0.Patch6<br />CSI.Bridge.v22.0.0.1587<br />CSI.ETABS.v18.1.1.2148<br />CSI.SAP2000.v22.1.0.163982<br />Dassault.Systemes.CATIA.Composer.R2022<br />Dassault.Systemes.DraftSight.Enterprise.Plus.2022.SP2.1<br />Dassault.Systemes.Simulia.XFlow.2022<br />Deltares.Wanda.v4.6.1218<br />DICAD.Strakon.Premium.v2022.1.0<br />DipTrace.V4.0.0.0<br />DNV.GL.Maros.v9.03+Taro.v5.03<br />DotSoft.MapWorks.v9.0.0.1<br />DotSoft.ToolPac.v20.0.0.0<br />DP.TECHNOLOGY.ESPRIT.2022.R1<br />EFICAD.SWOOD.2022.SP0.1.for SolidWorks 2010-2022 <br />Ensoft.Suite.2022(APILE,APILE.Offshore,DynaMat,DYNA-N,DynaPile,EnCPT,EnFEM,GeoMat,Group, LPile,PYWall,SETOFF,SHAFT,StablPro,TZPILE)683 MB <br />Eos.Systems.PhotoModeller.Premium.2022.1.1<br />ESI.SYSWELD.2022<br />ESI.Visual.Environment.15.0<br />ESRI.ArcGIS.Desktop.v10.8<br />fine.GEO5.2022 <br />form-Z.Pro.9.0.3.Build.A123<br />FTI.Forming.Suite.2022.0.0.27862.1 <br />FunctionBay.Multi-Body.Dynamics.Ansys.2022R1<br />Gaussian.09W.9.5.Revision.D.01<br />Geometric.Glovius.Pro.v5.1.0.622<br />Golden.Software.Grapher.v16.4.432<br />Golden.Software.Surfer.v18.1.186<br />Graitec.Archiwizard.2022.v8.2<br />GraphPad.Prism.8.4.3.686<br />Hytran.v387.5.18<br />Ikon.Science.RokDoc.v6.6.0.290 <br />Integrated.Engineering.Software.ConcreteSection.v2.00.000<br />Integrated.Engineering.Software.VisualAnalysis.v19.00.000 <br />Integrated.Engineering.Software.VisualFoundation.v10.00.000<br />Intelligent.Manufacturing.Software.IMSPost.v8.3h.Suite.Win64<br />ITI.TRANSCENDATA.CADFIX.V12.SP1<br />KND.SailingPerformance.software.Suite,June,2022 <br />Leica.Infinity.v3.2.1.3319<br />Lindo.What'sBest!.v16.0.2.5<br />Lloyd's.Register(ex.Senergy).Interactive.Petrophysics.2022.v4.5.5(update2022)<br />MAGNA.ECS.KULI.V14.0<br />Mapthematics.Geocart.Pro.v3.3.5<br />Marshall.Day.Acoustics.INSUL.v9.0.22<br />MathWorks.MATLAB.R2022a<br />Mentor.Graphics.PADS.VX.2.7<br />midas.MeshFree.2022.R1.(V410.1)<br />midas.NFX.2022.R1.20220520<br />MSC.Adams.2022<br />MSC.EASY5.2022 <br />MSC.FFT.Actran.V2022 <br />MSC.MARC.2022<br />MSC.Nastran.2022.0.x64<br />MSC.SimXpert.2022 <br />Nemetschek.SCIA.Engineer.2022.v19.1.4033 <br />nFrames.SURE.v4.1.1<br />NI.LabView.2022.v20.0.0.Professional.Edition <br />Nicestsolution.Safety.Barrier.Manager.v3.2.160<br />NUMECA.FineMarine.V9.1<br />NUMECA.FineOpen.9.2<br />NUMECA.FineTurbo.14.2 <br />NUMECA.Hexpress.9.2 <br />NUMECA.Omnis.4.1<br />Operation.Technology.ETAP.2022.v19.0.1C<br />PerkinElmer.ChemOffice.Suite.2022.v19.1.1.21<br />PLAXIS.2D.CONNECT.Edition.V20 <br />PLAXIS.3D.CONNECT.Edition.V20<br />PointWise.18.3R2<br />ProfiCAD.v10.5.1<br />PTC.Arbortext.IsoDraw.7.3.M100<br />PTC.Creo.7.0.0.0<br />PTC.Creo.EMX.13.0.0.0.for.Creo.7.032<br />PTC.Creo.Illustrate.7.0.0.0<br />Pythagoras.CAD+GIS.v16.13.0001 <br />S.T.S. WinRoad.2022.v25.1.1.2646 <br />SAS.JMP.Statistical.Discovery.v14.3<br />Schlumberger.AquiferTest.Pro.v10.0.0.2 <br />Schlumberger.OilField.Manager.2022.1 (OFM) <br />Schrodinger.Suite.2022.35.63 GB <br />Siemens.Fibersim.17.0.0.Catia5.Creo.NX<br />Siemens.Simcenter.Amesim.2022.1<br />Siemens.Simcenter.FEMAP.2022.2.0<br />Siemens.Simcenter.Flomaster.2022.1<br />Siemens.Simcenter.Flotherm.XT.2022.1 <br />Siemens.Simcenter.FloVent.2022.2 <br />Siemens.Simcenter.Motorsolve.2022.1 <br />Siemens.Syncrofit.15.2.0.Catia5.NX <br />Siemens.Tecnomatix.Plant.Simulation.15.2.1<br />Simulayt.Composites.Modeler.2013.0218.for.Abaqus <br />Skyline.PhotoMesh.PhotoMesh.Fuser.v7.5.1.3634<br />Skyline.TerraExplorer.Pro.v7.2.1.4020 <br />SOFiSTiK.2022.SP.2022-4.Build850<br />SST.Systems.Caepipe.v10.20<br />Strand7.R2.4.6+Webnotes <br />Studio.Tecnico.Guerra.Thopos.2022.v7.07.01 <br />Tecplot.360ex.2022.1.0.107285 <br />Tecplot.Focus.2022.1.0.107285 <br />Tecplot.RS.2022.R1.build.2022.1.1.106620 <br />Tower.Numerics.tnxTower.8.0.5.0<br />Treestar.FlowJo.v10.6.2<br />Trimble.Tekla(ex.CSC).Tedds.22.1.0000<br />Trimble.Tekla.Structural.Designer.20.0.2.33<br />Trimble.Tekla.Structures.2022.56544<br />TRL.TRANSYT.v16.0.0.841<br />UTS.Advanced.Spring.Design.v7.14.2.14<br />Vero.Worknc.V2022.0<br />Virtual.Surveyor.v3.6.1<br />Waterloo.Maplesoft.Maple.2022.1<br />WISE.VisualCAM.16.9.6<br />Wolfram.Research.Mathematica.V12.1.0 <br />XLSTAT-Premium.2022.1(49320)<br />CADprofi 2022.12 Build 200903 Multilingual<br />Proteus Professional 8.10 SP3 Build 29560 Win32<br />ANSYS Motor-CAD v13.1.13<br />Finite Element Analysis LUSAS Academic v19.0-2c1<br />Geometric Glovius Premium v5.1.0.847 Win32_64<br />Lindo.WhatsBest!v17.0.0.0.Win64<br />ANSYS.Fluent.5.1.66.for.CATIA.V5R28<br />Golden Software Surfer 19.1 Build 189<br />NUMECA.FineTurbo.15.1.Win64.&.Linux64<br />Power Shelling v1.0 for SolidWorks 2022-2022<br />Power Surfacing v6.1 for SolidWorks 2022-2022<br />Howden Group (ex. Chasm Consulting) PumpSim Premium v3.1.2.6 build 30/06/2022<br />iMachining.2.0.15.for.Siemens.NX-1926.Series.Win64<br />Altair ElectroFlo 2022.0.0.32399<br />Howden Group (ex. Chasm Consulting) Ventsim Visual Premium v5.4.2.0<br />NI LabView 2022 v20.0.1 Win32_64<br />VERO VISI CAD CAM 2022.0.2036 Multilingual<br />Paulin Research Group 2022<br />VxWorks v5.5.1<br />AWR Microwave Office v15<br />Cadence Design Systems Sigrity v19.00.003-2022 Hotfix<br />Golden SoftWare Grapher v16.4.432 Win32_64<br />3DQuickPress.6.3.0.Win64<br />SolidCAM.2022.SP3.HF1.Win64<br />Cadence AWR Design Environment v15.01.030<br />TYPE3.CAA.v5.5C.18187.for.CATIA.V5R28<br />ANSYS Composite Cure Simulation v2.2 for ANSYS 2022R2<br />Pythagoras CAD+GIS v16.13.0001<br />Schlumberger AquiferTest 10.0.0<br />Agisoft.Metashape.Pro.v1.6.4.10928.Win64<br />3DF Zephyr v5.006<br />Creative Edge Software iC3D Suite 6.1.0 & MacOSX<br />IronCAD Design Collaboration Suite 2022 PU1 SP1<br />PTDesinger v1.1.0<br />Snowden Technologies Snowden Supervisor 2022 v8.13.1.1<br />CST STUDIO SUITE 2022.07 SP7 Update Only<br />KilletSoft Software NTv2Creator 1.07<br />Schrodinger Suites 2022-3 & Linux64<br />Ansys.Lumerical.2022.R2.1.Win64<br />Ansys.Motor-CAD.13.1.11.Win64<br />midas.NFX.2022.R2.20220724.Win64<br />Siemens.Simcenter.FEMAP.2022.2.2.Update.Only.Win64<br />Enscape 3D 2.8.0.2.26218<br />Zeataline Projects Pipedata-Pro 14.0.00.7<br />Agnisys IDesignSpec v4.12.9.0<br />Design Data SDS/2 2022 Detailing<br />MAGNA.KULI.v14.0.Win64<br />Altair.Inspire.Form.2022.0.1.Win64<br />CIMCO Edit 8.08.10<br />DVT Eclipse 20.1.9.e47.Win64.&.Linux64<br />ADINA.9.6.2.Win64.&.Linux64<br />Altair.Inspire.Cast.2022.0.1.Win64<br />Operation Technology ETAP 2022 v19.0.1C<br />ADINA System 9.6.2<br />Altera Quartus Prime Pro 20.1 Windows<br />NCG.CAM v17.0.07<br />GEO5 2022 Professional Package<br />Agilent.89600.Vector.Signal.Analyzer(VSA).2022u1<br />Golden.Software.Grapher.v16.3.410.Win32_64<br />Jewellery CAD CAM JewelCAD 5.17<br />PVSOL premium 2022 R8<br />Altair.FluxMotor.2022.0.0.Win64<br />CADprofi.2022.09.build 200603<br />FTI.Forming.Suite.2022.0.0.27862.1.Win64<br />Powersim.Studio.Express.v7.00.4226.6<br />Schlumberger Waterloo AquaChem 9 build 17.20.0220.6<br />Altair.EDEM.Professional.2022.2.Win64<br />ANSYS EMA3D Cable 2022 R2<br />ANSYS Lumerical 2022 R2<br />Killetsoft NTv2Creator 1.03<br />Altair newFASANT 6.3.2022.07.20<br />Agisoft Metashape Pro v1.6.4 Build 10928<br />Schlumberger. AquaChem.v9.0<br />Siemens Simcenter STAR-CD 2022.1.2 (4.32.000)<br />3DCS Variation Analyst 7.7.0.1 for CATIA V5 R21-30<br />Geometric.Glovius.Pro.v5.1.0.789.Win32_64<br />Canvas.X3.CADComposer.20.0.455<br />Altair.PollEx.2022.0.Win64<br />Killetsoft NTv2Creator 1.00<br />Skyline.PhotoMesh.v7.5.1.3634<br />Cliosoft SOS v7.05.p3 Lniux64<br />Materialise.Magics.v24.1.Win64<br />MSC Apex Generative Design 2022<br />MSC MARC 2022<br />Skyline TerraExplorer pro v7.2.1<br />MSC Cosim 2022 Adams<br />MSC CoSim 2022<br />MSC Marc 2022<br />Altair.Flux.2022.0.1.HotFix.Only.Win64<br />Ansys.SCADE.2022R2.Win64<br />Killetsoft NTv2Poly 3.03<br /><br /><br /><br /><br /><br /><br /><br /><br />2022 crack software download. 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diff --git a/spaces/rzzgate/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/stable_diffusion/text2img_app.py b/spaces/rzzgate/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/stable_diffusion/text2img_app.py
deleted file mode 100644
index 4797e71aa0f89b0a076ca37c41dc2434213c37e1..0000000000000000000000000000000000000000
--- a/spaces/rzzgate/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/stable_diffusion/text2img_app.py
+++ /dev/null
@@ -1,167 +0,0 @@
-import gradio as gr
-import torch
-from diffusers import StableDiffusionPipeline
-
-from diffusion_webui.utils.model_list import stable_model_list
-from diffusion_webui.utils.scheduler_list import (
-    SCHEDULER_LIST,
-    get_scheduler_list,
-)
-
-
-class StableDiffusionText2ImageGenerator:
-    def __init__(self):
-        self.pipe = None
-
-    def load_model(
-        self,
-        model_path,
-        scheduler,
-    ):
-        if self.pipe is None:
-            self.pipe = StableDiffusionPipeline.from_pretrained(
-                model_path, safety_checker=None, torch_dtype=torch.float16
-            )
-
-        self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
-        self.pipe.to("cuda")
-        self.pipe.enable_xformers_memory_efficient_attention()
-
-        return self.pipe
-
-    def generate_image(
-        self,
-        model_path: str,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        scheduler: str,
-        guidance_scale: int,
-        num_inference_step: int,
-        height: int,
-        width: int,
-        seed_generator=0,
-    ):
-        pipe = self.load_model(
-            model_path=model_path,
-            scheduler=scheduler,
-        )
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-
-        images = pipe(
-            prompt=prompt,
-            height=height,
-            width=width,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            generator=generator,
-        ).images
-
-        return images
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    text2image_prompt = gr.Textbox(
-                        lines=1,
-                        placeholder="Prompt",
-                        show_label=False,
-                    )
-
-                    text2image_negative_prompt = gr.Textbox(
-                        lines=1,
-                        placeholder="Negative Prompt",
-                        show_label=False,
-                    )
-                    with gr.Row():
-                        with gr.Column():
-                            text2image_model_path = gr.Dropdown(
-                                choices=stable_model_list,
-                                value=stable_model_list[0],
-                                label="Text-Image Model Id",
-                            )
-
-                            text2image_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-
-                            text2image_num_inference_step = gr.Slider(
-                                minimum=1,
-                                maximum=100,
-                                step=1,
-                                value=50,
-                                label="Num Inference Step",
-                            )
-                            text2image_num_images_per_prompt = gr.Slider(
-                                minimum=1,
-                                maximum=30,
-                                step=1,
-                                value=1,
-                                label="Number Of Images",
-                            )
-                        with gr.Row():
-                            with gr.Column():
-
-                                text2image_scheduler = gr.Dropdown(
-                                    choices=SCHEDULER_LIST,
-                                    value=SCHEDULER_LIST[0],
-                                    label="Scheduler",
-                                )
-
-                                text2image_height = gr.Slider(
-                                    minimum=128,
-                                    maximum=1280,
-                                    step=32,
-                                    value=512,
-                                    label="Image Height",
-                                )
-
-                                text2image_width = gr.Slider(
-                                    minimum=128,
-                                    maximum=1280,
-                                    step=32,
-                                    value=512,
-                                    label="Image Width",
-                                )
-                                text2image_seed_generator = gr.Slider(
-                                    label="Seed(0 for random)",
-                                    minimum=0,
-                                    maximum=1000000,
-                                    value=0,
-                                )
-                    text2image_predict = gr.Button(value="Generator")
-
-                with gr.Column():
-                    output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2), height=200)
-
-            text2image_predict.click(
-                fn=StableDiffusionText2ImageGenerator().generate_image,
-                inputs=[
-                    text2image_model_path,
-                    text2image_prompt,
-                    text2image_negative_prompt,
-                    text2image_num_images_per_prompt,
-                    text2image_scheduler,
-                    text2image_guidance_scale,
-                    text2image_num_inference_step,
-                    text2image_height,
-                    text2image_width,
-                    text2image_seed_generator,
-                ],
-                outputs=output_image,
-            )
diff --git a/spaces/saefro991/aet_demo/utils.py b/spaces/saefro991/aet_demo/utils.py
deleted file mode 100644
index 9e1880120b16b4809b460367150ac58ee0a09d53..0000000000000000000000000000000000000000
--- a/spaces/saefro991/aet_demo/utils.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import matplotlib.pyplot as plt
-import json
-import torch
-import torchaudio
-
-def configure_args(config, args):
-    for key in ["stage", "corpus_type", "source_path", "aux_path", "preprocessed_path"]:
-        if getattr(args, key) != None:
-            config["general"][key] = str(getattr(args, key))
-
-    for key in ["n_train", "n_val", "n_test"]:
-        if getattr(args, key) != None:
-            config["preprocess"][key] = getattr(args, key)
-
-    for key in ["alpha", "beta", "learning_rate", "epoch"]:
-        if getattr(args, key) != None:
-            config["train"][key] = getattr(args, key)
-
-    for key in ["load_pretrained", "early_stopping"]:
-        config["train"][key] = getattr(args, key)
-
-    if args.feature_loss_type != None:
-        config["train"]["feature_loss"]["type"] = args.feature_loss_type
-
-    for key in ["pretrained_path"]:
-        if getattr(args, key) != None:
-            config["train"][key] = str(getattr(args, key))
-
-    return config, args
diff --git a/spaces/sam-hq-team/sam-hq/GroundingDINO/groundingdino/models/__init__.py b/spaces/sam-hq-team/sam-hq/GroundingDINO/groundingdino/models/__init__.py
deleted file mode 100644
index e3413961d1d184b99835eb1e919b052d70298bc6..0000000000000000000000000000000000000000
--- a/spaces/sam-hq-team/sam-hq/GroundingDINO/groundingdino/models/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from .GroundingDINO import build_groundingdino
-
-
-def build_model(args):
-    # we use register to maintain models from catdet6 on.
-    from .registry import MODULE_BUILD_FUNCS
-
-    assert args.modelname in MODULE_BUILD_FUNCS._module_dict
-    build_func = MODULE_BUILD_FUNCS.get(args.modelname)
-    model = build_func(args)
-    return model
diff --git a/spaces/sanchit-gandhi/whisper-jax-diarization/README.md b/spaces/sanchit-gandhi/whisper-jax-diarization/README.md
deleted file mode 100644
index 9720aff2cfbafb2c9cd982e91778c2c12a947e26..0000000000000000000000000000000000000000
--- a/spaces/sanchit-gandhi/whisper-jax-diarization/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Whisper JAX Diarization
-emoji: 🔥
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sarinam/speaker-anonymization-gan/IMSToucan/Layers/Swish.py b/spaces/sarinam/speaker-anonymization-gan/IMSToucan/Layers/Swish.py
deleted file mode 100644
index 1541ac7c6b33d08f7f998f7950f090afd6c14b38..0000000000000000000000000000000000000000
--- a/spaces/sarinam/speaker-anonymization-gan/IMSToucan/Layers/Swish.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
-#                Northwestern Polytechnical University (Pengcheng Guo)
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-# Adapted by Florian Lux 2021
-
-import torch
-
-
-class Swish(torch.nn.Module):
-    """
-    Construct an Swish activation function for Conformer.
-    """
-
-    def forward(self, x):
-        """
-        Return Swish activation function.
-        """
-        return x * torch.sigmoid(x)
diff --git a/spaces/saurshaz/HuggingGPT/app.py b/spaces/saurshaz/HuggingGPT/app.py
deleted file mode 100644
index 6e53c7b4b8ec74a0cb24f8b97d7a54c6583857d9..0000000000000000000000000000000000000000
--- a/spaces/saurshaz/HuggingGPT/app.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import uuid
-import gradio as gr
-import re
-from diffusers.utils import load_image
-import requests
-from awesome_chat import chat_huggingface
-import os
-
-os.makedirs("public/images", exist_ok=True)
-os.makedirs("public/audios", exist_ok=True)
-os.makedirs("public/videos", exist_ok=True)
-
-HUGGINGFACE_TOKEN = os.environ.get("HUGGINGFACE_TOKEN")
-OPENAI_KEY = os.environ.get("OPENAI_KEY")
-
-class Client:
-    def __init__(self) -> None:
-        self.OPENAI_KEY = OPENAI_KEY
-        self.HUGGINGFACE_TOKEN = HUGGINGFACE_TOKEN
-        self.all_messages = []
-
-    def set_key(self, openai_key):
-        self.OPENAI_KEY = openai_key
-        return self.OPENAI_KEY
-
-    def set_token(self, huggingface_token):
-        self.HUGGINGFACE_TOKEN = huggingface_token
-        return self.HUGGINGFACE_TOKEN
-    
-    def add_message(self, content, role):
-        message = {"role":role, "content":content}
-        self.all_messages.append(message)
-
-    def extract_medias(self, message):
-        # url_pattern = re.compile(r"(http(s?):|\/)?([\.\/_\w:-])*?")
-        urls = []
-        # for match in url_pattern.finditer(message):
-        #     if match.group(0) not in urls:
-        #         urls.append(match.group(0))
-
-        image_pattern = re.compile(r"(http(s?):|\/)?([\.\/_\w:-])*?\.(jpg|jpeg|tiff|gif|png)")
-        image_urls = []
-        for match in image_pattern.finditer(message):
-            if match.group(0) not in image_urls:
-                image_urls.append(match.group(0))
-
-        audio_pattern = re.compile(r"(http(s?):|\/)?([\.\/_\w:-])*?\.(flac|wav)")
-        audio_urls = []
-        for match in audio_pattern.finditer(message):
-            if match.group(0) not in audio_urls:
-                audio_urls.append(match.group(0))
-
-        video_pattern = re.compile(r"(http(s?):|\/)?([\.\/_\w:-])*?\.(mp4)")
-        video_urls = []
-        for match in video_pattern.finditer(message):
-            if match.group(0) not in video_urls:
-                video_urls.append(match.group(0))
-
-        return urls, image_urls, audio_urls, video_urls
-
-    def add_text(self, messages, message):
-        if not self.OPENAI_KEY or not self.OPENAI_KEY.startswith("sk-") or not self.HUGGINGFACE_TOKEN or not self.HUGGINGFACE_TOKEN.startswith("hf_"):
-            return messages, "Please set your OpenAI API key and Hugging Face token first!!!"
-        self.add_message(message, "user")
-        messages = messages + [(message, None)]
-        urls, image_urls, audio_urls, video_urls = self.extract_medias(message)
-
-        for image_url in image_urls:
-            if not image_url.startswith("http") and not image_url.startswith("public"):
-                image_url = "public/" + image_url
-            image = load_image(image_url)
-            name = f"public/images/{str(uuid.uuid4())[:4]}.jpg" 
-            image.save(name)
-            messages = messages + [((f"{name}",), None)]
-        for audio_url in audio_urls and not audio_url.startswith("public"):
-            if not audio_url.startswith("http"):
-                audio_url = "public/" + audio_url
-            ext = audio_url.split(".")[-1]
-            name = f"public/audios/{str(uuid.uuid4()[:4])}.{ext}"
-            response = requests.get(audio_url)
-            with open(name, "wb") as f:
-                f.write(response.content)
-            messages = messages + [((f"{name}",), None)]
-        for video_url in video_urls and not video_url.startswith("public"):
-            if not video_url.startswith("http"):
-                video_url = "public/" + video_url
-            ext = video_url.split(".")[-1]
-            name = f"public/audios/{str(uuid.uuid4()[:4])}.{ext}"
-            response = requests.get(video_url)
-            with open(name, "wb") as f:
-                f.write(response.content)
-            messages = messages + [((f"{name}",), None)]
-        return messages, ""
-
-    def bot(self, messages):
-        if not self.OPENAI_KEY or not self.OPENAI_KEY.startswith("sk-") or not self.HUGGINGFACE_TOKEN or not self.HUGGINGFACE_TOKEN.startswith("hf_"):
-            return messages, {}
-        message, results = chat_huggingface(self.all_messages, self.OPENAI_KEY, self.HUGGINGFACE_TOKEN)
-        urls, image_urls, audio_urls, video_urls = self.extract_medias(message)
-        self.add_message(message, "assistant")
-        messages[-1][1] = message
-        for image_url in image_urls:
-            if not image_url.startswith("http"):
-                image_url = image_url.replace("public/", "")
-                messages = messages + [((None, (f"public/{image_url}",)))]
-            # else:
-            #     messages = messages + [((None, (f"{image_url}",)))]
-        for audio_url in audio_urls:
-            if not audio_url.startswith("http"):
-                audio_url = audio_url.replace("public/", "")
-                messages = messages + [((None, (f"public/{audio_url}",)))]
-            # else:
-            #     messages = messages + [((None, (f"{audio_url}",)))]
-        for video_url in video_urls:
-            if not video_url.startswith("http"):
-                video_url = video_url.replace("public/", "")
-                messages = messages + [((None, (f"public/{video_url}",)))]
-            # else:
-            #     messages = messages + [((None, (f"{video_url}",)))]
-        # replace int key to string key
-        results = {str(k): v for k, v in results.items()}
-        return messages, results
-    
-css = ".json {height: 527px; overflow: scroll;} .json-holder {height: 527px; overflow: scroll;}"
-with gr.Blocks(css=css) as demo:
-    state = gr.State(value={"client": Client()})
-    gr.Markdown("<h1><center>HuggingGPT</center></h1>")
-    gr.Markdown("<p align='center'><img src='https://i.ibb.co/qNH3Jym/logo.png' height='25' width='95'></p>")
-    gr.Markdown("<p align='center' style='font-size: 20px;'>A system to connect LLMs with ML community. See our <a href='https://github.com/microsoft/JARVIS'>Project</a> and <a href='http://arxiv.org/abs/2303.17580'>Paper</a>.</p>")
-    gr.HTML('''<center><a href="https://huggingface.co/spaces/microsoft/HuggingGPT?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key and Hugging Face Token</center>''')
-    gr.HTML('''<center>Note: Only a few models are deployed in the local inference endpoint due to hardware limitations. In addition, online HuggingFace inference endpoints may sometimes not be available. Thus the capability of HuggingGPT is limited.</center>''')
-    if not OPENAI_KEY:
-        with gr.Row().style():
-            with gr.Column(scale=0.85):
-                openai_api_key = gr.Textbox(
-                    show_label=False,
-                    placeholder="Set your OpenAI API key here and press Enter",
-                    lines=1,
-                    type="password"
-                ).style(container=False)
-            with gr.Column(scale=0.15, min_width=0):
-                btn1 = gr.Button("Submit").style(full_height=True)
-
-    if not HUGGINGFACE_TOKEN:
-        with gr.Row().style():
-            with gr.Column(scale=0.85):
-                hugging_face_token = gr.Textbox(
-                    show_label=False,
-                    placeholder="Set your Hugging Face Token here and press Enter",
-                    lines=1,
-                    type="password"
-                ).style(container=False)
-            with gr.Column(scale=0.15, min_width=0):
-                btn3 = gr.Button("Submit").style(full_height=True)
-    
-
-    with gr.Row().style():
-        with gr.Column(scale=0.6):
-            chatbot = gr.Chatbot([], elem_id="chatbot").style(height=500)
-        with gr.Column(scale=0.4):
-            results = gr.JSON(elem_classes="json")
-
-
-    with gr.Row().style():
-        with gr.Column(scale=0.85):
-            txt = gr.Textbox(
-                show_label=False,
-                placeholder="Enter text and press enter. The url must contain the media type. e.g, https://example.com/example.jpg",
-                lines=1,
-            ).style(container=False)
-        with gr.Column(scale=0.15, min_width=0):
-            btn2 = gr.Button("Send").style(full_height=True)
-        
-    def set_key(state, openai_api_key):
-        return state["client"].set_key(openai_api_key)
-
-    def add_text(state, chatbot, txt):
-        return state["client"].add_text(chatbot, txt)
-    
-    def set_token(state, hugging_face_token):
-        return state["client"].set_token(hugging_face_token)
-    
-    def bot(state, chatbot):
-        return state["client"].bot(chatbot)
-
-    if not OPENAI_KEY:
-        openai_api_key.submit(set_key, [state, openai_api_key], [openai_api_key])
-        btn1.click(set_key, [state, openai_api_key], [openai_api_key])
-
-    if not HUGGINGFACE_TOKEN:
-        hugging_face_token.submit(set_token, [state, hugging_face_token], [hugging_face_token])
-        btn3.click(set_token, [state, hugging_face_token], [hugging_face_token])
-    
-    txt.submit(add_text, [state, chatbot, txt], [chatbot, txt]).then(bot, [state, chatbot], [chatbot, results])
-    btn2.click(add_text, [state, chatbot, txt], [chatbot, txt]).then(bot, [state, chatbot], [chatbot, results])
-    
-
-    gr.Examples(
-        examples=["Given a collection of image A: /examples/a.jpg, B: /examples/b.jpg, C: /examples/c.jpg, please tell me how many zebras in these picture?",
-                "Please generate a canny image based on /examples/f.jpg",
-                "show me a joke and an image of cat",
-                "what is in the examples/a.jpg",
-                "based on the /examples/a.jpg, please generate a video and audio",
-                "based on pose of /examples/d.jpg and content of /examples/e.jpg, please show me a new image",
-                ],
-        inputs=txt
-    )
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/sayakpaul/sidd-denoising-maxim/create_maxim_model.py b/spaces/sayakpaul/sidd-denoising-maxim/create_maxim_model.py
deleted file mode 100644
index f6f8ef29093d5defdaa51e3f99ce25fcdc77b513..0000000000000000000000000000000000000000
--- a/spaces/sayakpaul/sidd-denoising-maxim/create_maxim_model.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from tensorflow import keras
-
-from maxim import maxim
-from maxim.configs import MAXIM_CONFIGS
-
-
-def Model(variant=None, input_resolution=(256, 256), **kw) -> keras.Model:
-    """Factory function to easily create a Model variant like "S".
-
-    Args:
-      variant: UNet model variants. Options: 'S-1' | 'S-2' | 'S-3'
-          | 'M-1' | 'M-2' | 'M-3'
-      input_resolution: Size of the input images.
-      **kw: Other UNet config dicts.
-
-    Returns:
-      The MAXIM model.
-    """
-
-    if variant is not None:
-        config = MAXIM_CONFIGS[variant]
-        for k, v in config.items():
-            kw.setdefault(k, v)
-
-    if "variant" in kw:
-        _ = kw.pop("variant")
-    if "input_resolution" in kw:
-        _ = kw.pop("input_resolution")
-    model_name = kw.pop("name")
-
-    maxim_model = maxim.MAXIM(**kw)
-
-    inputs = keras.Input((*input_resolution, 3))
-    outputs = maxim_model(inputs)
-    final_model = keras.Model(inputs, outputs, name=f"{model_name}_model")
-
-    return final_model
diff --git a/spaces/scedlatioru/img-to-music/example/Express VPN 7.9.3 Activation Code Full Crack Free (2020).md b/spaces/scedlatioru/img-to-music/example/Express VPN 7.9.3 Activation Code Full Crack Free (2020).md
deleted file mode 100644
index ead1952d7a4ed3f816d16380778faa681e2d01ed..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Express VPN 7.9.3 Activation Code Full Crack Free (2020).md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
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-<h2>Express VPN 7.9.3 Activation Code Full Crack Free (2020)</h2><br /><p><b><b>DOWNLOAD</b> &#8230;&#8230;&#8230; <a href="https://gohhs.com/2uEAnh">https://gohhs.com/2uEAnh</a></b></p><br /><br />
-<p>express vpn activation code 2022 is a lot comparable device that functions for this objective. express vpn keygen is of higher use as compared to the initial device. express vpn free trial application is utilized for protection reasons as they have the capability to conceal system individual standard protocol and only discuss the personal ip to some other customers. this crack device can use to lengthen the restrict of the public along.</p>
-<p>express vpn cracked apk is an effective vehicle to deal with web browsers. this is a novel system for your pc or laptop. you can surf the internet anonymously and safeness. it carries out a thorough assessment of your ip address and generates a safe ip handle. it is a great tool for people that want to browse the internet anonymously. this can be used on any device without any difficulty.</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/segments-tobias/conex/espnet2/diar/__init__.py b/spaces/segments-tobias/conex/espnet2/diar/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/shawhin/vanilla-chatbot/README.md b/spaces/shawhin/vanilla-chatbot/README.md
deleted file mode 100644
index ab7afa21a70e11d5c63011c6aa410a7db4432422..0000000000000000000000000000000000000000
--- a/spaces/shawhin/vanilla-chatbot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Vanilla Chatbot
-emoji: 🏆
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/shi-labs/Matting-Anything/GroundingDINO/groundingdino/util/box_ops.py b/spaces/shi-labs/Matting-Anything/GroundingDINO/groundingdino/util/box_ops.py
deleted file mode 100644
index 781068d294e576954edb4bd07b6e0f30e4e1bcd9..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/Matting-Anything/GroundingDINO/groundingdino/util/box_ops.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Utilities for bounding box manipulation and GIoU.
-"""
-import torch
-from torchvision.ops.boxes import box_area
-
-
-def box_cxcywh_to_xyxy(x):
-    x_c, y_c, w, h = x.unbind(-1)
-    b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
-    return torch.stack(b, dim=-1)
-
-
-def box_xyxy_to_cxcywh(x):
-    x0, y0, x1, y1 = x.unbind(-1)
-    b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
-    return torch.stack(b, dim=-1)
-
-
-# modified from torchvision to also return the union
-def box_iou(boxes1, boxes2):
-    area1 = box_area(boxes1)
-    area2 = box_area(boxes2)
-
-    # import ipdb; ipdb.set_trace()
-    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
-    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
-
-    wh = (rb - lt).clamp(min=0)  # [N,M,2]
-    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
-
-    union = area1[:, None] + area2 - inter
-
-    iou = inter / (union + 1e-6)
-    return iou, union
-
-
-def generalized_box_iou(boxes1, boxes2):
-    """
-    Generalized IoU from https://giou.stanford.edu/
-
-    The boxes should be in [x0, y0, x1, y1] format
-
-    Returns a [N, M] pairwise matrix, where N = len(boxes1)
-    and M = len(boxes2)
-    """
-    # degenerate boxes gives inf / nan results
-    # so do an early check
-    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
-    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
-    # except:
-    #     import ipdb; ipdb.set_trace()
-    iou, union = box_iou(boxes1, boxes2)
-
-    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
-    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
-
-    wh = (rb - lt).clamp(min=0)  # [N,M,2]
-    area = wh[:, :, 0] * wh[:, :, 1]
-
-    return iou - (area - union) / (area + 1e-6)
-
-
-# modified from torchvision to also return the union
-def box_iou_pairwise(boxes1, boxes2):
-    area1 = box_area(boxes1)
-    area2 = box_area(boxes2)
-
-    lt = torch.max(boxes1[:, :2], boxes2[:, :2])  # [N,2]
-    rb = torch.min(boxes1[:, 2:], boxes2[:, 2:])  # [N,2]
-
-    wh = (rb - lt).clamp(min=0)  # [N,2]
-    inter = wh[:, 0] * wh[:, 1]  # [N]
-
-    union = area1 + area2 - inter
-
-    iou = inter / union
-    return iou, union
-
-
-def generalized_box_iou_pairwise(boxes1, boxes2):
-    """
-    Generalized IoU from https://giou.stanford.edu/
-
-    Input:
-        - boxes1, boxes2: N,4
-    Output:
-        - giou: N, 4
-    """
-    # degenerate boxes gives inf / nan results
-    # so do an early check
-    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
-    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
-    assert boxes1.shape == boxes2.shape
-    iou, union = box_iou_pairwise(boxes1, boxes2)  # N, 4
-
-    lt = torch.min(boxes1[:, :2], boxes2[:, :2])
-    rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])
-
-    wh = (rb - lt).clamp(min=0)  # [N,2]
-    area = wh[:, 0] * wh[:, 1]
-
-    return iou - (area - union) / area
-
-
-def masks_to_boxes(masks):
-    """Compute the bounding boxes around the provided masks
-
-    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
-
-    Returns a [N, 4] tensors, with the boxes in xyxy format
-    """
-    if masks.numel() == 0:
-        return torch.zeros((0, 4), device=masks.device)
-
-    h, w = masks.shape[-2:]
-
-    y = torch.arange(0, h, dtype=torch.float)
-    x = torch.arange(0, w, dtype=torch.float)
-    y, x = torch.meshgrid(y, x)
-
-    x_mask = masks * x.unsqueeze(0)
-    x_max = x_mask.flatten(1).max(-1)[0]
-    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
-
-    y_mask = masks * y.unsqueeze(0)
-    y_max = y_mask.flatten(1).max(-1)[0]
-    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
-
-    return torch.stack([x_min, y_min, x_max, y_max], 1)
-
-
-if __name__ == "__main__":
-    x = torch.rand(5, 4)
-    y = torch.rand(3, 4)
-    iou, union = box_iou(x, y)
-    import ipdb
-
-    ipdb.set_trace()
diff --git a/spaces/shikunl/prismer/prismer/experts/obj_detection/datasets/prepare_cocofied_lvis.py b/spaces/shikunl/prismer/prismer/experts/obj_detection/datasets/prepare_cocofied_lvis.py
deleted file mode 100644
index 245c88482a9e2405e5a912b5c560aed78a614a13..0000000000000000000000000000000000000000
--- a/spaces/shikunl/prismer/prismer/experts/obj_detection/datasets/prepare_cocofied_lvis.py
+++ /dev/null
@@ -1,176 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import copy
-import json
-import os
-from collections import defaultdict
-
-# This mapping is extracted from the official LVIS mapping:
-# https://github.com/lvis-dataset/lvis-api/blob/master/data/coco_to_synset.json
-COCO_SYNSET_CATEGORIES = [
-    {"synset": "person.n.01", "coco_cat_id": 1},
-    {"synset": "bicycle.n.01", "coco_cat_id": 2},
-    {"synset": "car.n.01", "coco_cat_id": 3},
-    {"synset": "motorcycle.n.01", "coco_cat_id": 4},
-    {"synset": "airplane.n.01", "coco_cat_id": 5},
-    {"synset": "bus.n.01", "coco_cat_id": 6},
-    {"synset": "train.n.01", "coco_cat_id": 7},
-    {"synset": "truck.n.01", "coco_cat_id": 8},
-    {"synset": "boat.n.01", "coco_cat_id": 9},
-    {"synset": "traffic_light.n.01", "coco_cat_id": 10},
-    {"synset": "fireplug.n.01", "coco_cat_id": 11},
-    {"synset": "stop_sign.n.01", "coco_cat_id": 13},
-    {"synset": "parking_meter.n.01", "coco_cat_id": 14},
-    {"synset": "bench.n.01", "coco_cat_id": 15},
-    {"synset": "bird.n.01", "coco_cat_id": 16},
-    {"synset": "cat.n.01", "coco_cat_id": 17},
-    {"synset": "dog.n.01", "coco_cat_id": 18},
-    {"synset": "horse.n.01", "coco_cat_id": 19},
-    {"synset": "sheep.n.01", "coco_cat_id": 20},
-    {"synset": "beef.n.01", "coco_cat_id": 21},
-    {"synset": "elephant.n.01", "coco_cat_id": 22},
-    {"synset": "bear.n.01", "coco_cat_id": 23},
-    {"synset": "zebra.n.01", "coco_cat_id": 24},
-    {"synset": "giraffe.n.01", "coco_cat_id": 25},
-    {"synset": "backpack.n.01", "coco_cat_id": 27},
-    {"synset": "umbrella.n.01", "coco_cat_id": 28},
-    {"synset": "bag.n.04", "coco_cat_id": 31},
-    {"synset": "necktie.n.01", "coco_cat_id": 32},
-    {"synset": "bag.n.06", "coco_cat_id": 33},
-    {"synset": "frisbee.n.01", "coco_cat_id": 34},
-    {"synset": "ski.n.01", "coco_cat_id": 35},
-    {"synset": "snowboard.n.01", "coco_cat_id": 36},
-    {"synset": "ball.n.06", "coco_cat_id": 37},
-    {"synset": "kite.n.03", "coco_cat_id": 38},
-    {"synset": "baseball_bat.n.01", "coco_cat_id": 39},
-    {"synset": "baseball_glove.n.01", "coco_cat_id": 40},
-    {"synset": "skateboard.n.01", "coco_cat_id": 41},
-    {"synset": "surfboard.n.01", "coco_cat_id": 42},
-    {"synset": "tennis_racket.n.01", "coco_cat_id": 43},
-    {"synset": "bottle.n.01", "coco_cat_id": 44},
-    {"synset": "wineglass.n.01", "coco_cat_id": 46},
-    {"synset": "cup.n.01", "coco_cat_id": 47},
-    {"synset": "fork.n.01", "coco_cat_id": 48},
-    {"synset": "knife.n.01", "coco_cat_id": 49},
-    {"synset": "spoon.n.01", "coco_cat_id": 50},
-    {"synset": "bowl.n.03", "coco_cat_id": 51},
-    {"synset": "banana.n.02", "coco_cat_id": 52},
-    {"synset": "apple.n.01", "coco_cat_id": 53},
-    {"synset": "sandwich.n.01", "coco_cat_id": 54},
-    {"synset": "orange.n.01", "coco_cat_id": 55},
-    {"synset": "broccoli.n.01", "coco_cat_id": 56},
-    {"synset": "carrot.n.01", "coco_cat_id": 57},
-    {"synset": "frank.n.02", "coco_cat_id": 58},
-    {"synset": "pizza.n.01", "coco_cat_id": 59},
-    {"synset": "doughnut.n.02", "coco_cat_id": 60},
-    {"synset": "cake.n.03", "coco_cat_id": 61},
-    {"synset": "chair.n.01", "coco_cat_id": 62},
-    {"synset": "sofa.n.01", "coco_cat_id": 63},
-    {"synset": "pot.n.04", "coco_cat_id": 64},
-    {"synset": "bed.n.01", "coco_cat_id": 65},
-    {"synset": "dining_table.n.01", "coco_cat_id": 67},
-    {"synset": "toilet.n.02", "coco_cat_id": 70},
-    {"synset": "television_receiver.n.01", "coco_cat_id": 72},
-    {"synset": "laptop.n.01", "coco_cat_id": 73},
-    {"synset": "mouse.n.04", "coco_cat_id": 74},
-    {"synset": "remote_control.n.01", "coco_cat_id": 75},
-    {"synset": "computer_keyboard.n.01", "coco_cat_id": 76},
-    {"synset": "cellular_telephone.n.01", "coco_cat_id": 77},
-    {"synset": "microwave.n.02", "coco_cat_id": 78},
-    {"synset": "oven.n.01", "coco_cat_id": 79},
-    {"synset": "toaster.n.02", "coco_cat_id": 80},
-    {"synset": "sink.n.01", "coco_cat_id": 81},
-    {"synset": "electric_refrigerator.n.01", "coco_cat_id": 82},
-    {"synset": "book.n.01", "coco_cat_id": 84},
-    {"synset": "clock.n.01", "coco_cat_id": 85},
-    {"synset": "vase.n.01", "coco_cat_id": 86},
-    {"synset": "scissors.n.01", "coco_cat_id": 87},
-    {"synset": "teddy.n.01", "coco_cat_id": 88},
-    {"synset": "hand_blower.n.01", "coco_cat_id": 89},
-    {"synset": "toothbrush.n.01", "coco_cat_id": 90},
-]
-
-
-def cocofy_lvis(input_filename, output_filename):
-    """
-    Filter LVIS instance segmentation annotations to remove all categories that are not included in
-    COCO. The new json files can be used to evaluate COCO AP using `lvis-api`. The category ids in
-    the output json are the incontiguous COCO dataset ids.
-
-    Args:
-        input_filename (str): path to the LVIS json file.
-        output_filename (str): path to the COCOfied json file.
-    """
-
-    with open(input_filename, "r") as f:
-        lvis_json = json.load(f)
-
-    lvis_annos = lvis_json.pop("annotations")
-    cocofied_lvis = copy.deepcopy(lvis_json)
-    lvis_json["annotations"] = lvis_annos
-
-    # Mapping from lvis cat id to coco cat id via synset
-    lvis_cat_id_to_synset = {cat["id"]: cat["synset"] for cat in lvis_json["categories"]}
-    synset_to_coco_cat_id = {x["synset"]: x["coco_cat_id"] for x in COCO_SYNSET_CATEGORIES}
-    # Synsets that we will keep in the dataset
-    synsets_to_keep = set(synset_to_coco_cat_id.keys())
-    coco_cat_id_with_instances = defaultdict(int)
-
-    new_annos = []
-    ann_id = 1
-    for ann in lvis_annos:
-        lvis_cat_id = ann["category_id"]
-        synset = lvis_cat_id_to_synset[lvis_cat_id]
-        if synset not in synsets_to_keep:
-            continue
-        coco_cat_id = synset_to_coco_cat_id[synset]
-        new_ann = copy.deepcopy(ann)
-        new_ann["category_id"] = coco_cat_id
-        new_ann["id"] = ann_id
-        ann_id += 1
-        new_annos.append(new_ann)
-        coco_cat_id_with_instances[coco_cat_id] += 1
-    cocofied_lvis["annotations"] = new_annos
-
-    for image in cocofied_lvis["images"]:
-        for key in ["not_exhaustive_category_ids", "neg_category_ids"]:
-            new_category_list = []
-            for lvis_cat_id in image[key]:
-                synset = lvis_cat_id_to_synset[lvis_cat_id]
-                if synset not in synsets_to_keep:
-                    continue
-                coco_cat_id = synset_to_coco_cat_id[synset]
-                new_category_list.append(coco_cat_id)
-                coco_cat_id_with_instances[coco_cat_id] += 1
-            image[key] = new_category_list
-
-    coco_cat_id_with_instances = set(coco_cat_id_with_instances.keys())
-
-    new_categories = []
-    for cat in lvis_json["categories"]:
-        synset = cat["synset"]
-        if synset not in synsets_to_keep:
-            continue
-        coco_cat_id = synset_to_coco_cat_id[synset]
-        if coco_cat_id not in coco_cat_id_with_instances:
-            continue
-        new_cat = copy.deepcopy(cat)
-        new_cat["id"] = coco_cat_id
-        new_categories.append(new_cat)
-    cocofied_lvis["categories"] = new_categories
-
-    with open(output_filename, "w") as f:
-        json.dump(cocofied_lvis, f)
-    print("{} is COCOfied and stored in {}.".format(input_filename, output_filename))
-
-
-if __name__ == "__main__":
-    dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "lvis")
-    for s in ["lvis_v0.5_train", "lvis_v0.5_val"]:
-        print("Start COCOfing {}.".format(s))
-        cocofy_lvis(
-            os.path.join(dataset_dir, "{}.json".format(s)),
-            os.path.join(dataset_dir, "{}_cocofied.json".format(s)),
-        )
diff --git a/spaces/sidharthism/fashion-eye/models/stylegan/stylegan_tf/training/training_loop.py b/spaces/sidharthism/fashion-eye/models/stylegan/stylegan_tf/training/training_loop.py
deleted file mode 100644
index d9ccb45b1a0321f1d938efa6a62229ffe396dcfe..0000000000000000000000000000000000000000
--- a/spaces/sidharthism/fashion-eye/models/stylegan/stylegan_tf/training/training_loop.py
+++ /dev/null
@@ -1,278 +0,0 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-"""Main training script."""
-
-import os
-import numpy as np
-import tensorflow as tf
-import dnnlib
-import dnnlib.tflib as tflib
-from dnnlib.tflib.autosummary import autosummary
-
-import config
-import train
-from training import dataset
-from training import misc
-from metrics import metric_base
-
-#----------------------------------------------------------------------------
-# Just-in-time processing of training images before feeding them to the networks.
-
-def process_reals(x, lod, mirror_augment, drange_data, drange_net):
-    with tf.name_scope('ProcessReals'):
-        with tf.name_scope('DynamicRange'):
-            x = tf.cast(x, tf.float32)
-            x = misc.adjust_dynamic_range(x, drange_data, drange_net)
-        if mirror_augment:
-            with tf.name_scope('MirrorAugment'):
-                s = tf.shape(x)
-                mask = tf.random_uniform([s[0], 1, 1, 1], 0.0, 1.0)
-                mask = tf.tile(mask, [1, s[1], s[2], s[3]])
-                x = tf.where(mask < 0.5, x, tf.reverse(x, axis=[3]))
-        with tf.name_scope('FadeLOD'): # Smooth crossfade between consecutive levels-of-detail.
-            s = tf.shape(x)
-            y = tf.reshape(x, [-1, s[1], s[2]//2, 2, s[3]//2, 2])
-            y = tf.reduce_mean(y, axis=[3, 5], keepdims=True)
-            y = tf.tile(y, [1, 1, 1, 2, 1, 2])
-            y = tf.reshape(y, [-1, s[1], s[2], s[3]])
-            x = tflib.lerp(x, y, lod - tf.floor(lod))
-        with tf.name_scope('UpscaleLOD'): # Upscale to match the expected input/output size of the networks.
-            s = tf.shape(x)
-            factor = tf.cast(2 ** tf.floor(lod), tf.int32)
-            x = tf.reshape(x, [-1, s[1], s[2], 1, s[3], 1])
-            x = tf.tile(x, [1, 1, 1, factor, 1, factor])
-            x = tf.reshape(x, [-1, s[1], s[2] * factor, s[3] * factor])
-        return x
-
-#----------------------------------------------------------------------------
-# Evaluate time-varying training parameters.
-
-def training_schedule(
-    cur_nimg,
-    training_set,
-    num_gpus,
-    lod_initial_resolution  = 4,        # Image resolution used at the beginning.
-    lod_training_kimg       = 600,      # Thousands of real images to show before doubling the resolution.
-    lod_transition_kimg     = 600,      # Thousands of real images to show when fading in new layers.
-    minibatch_base          = 16,       # Maximum minibatch size, divided evenly among GPUs.
-    minibatch_dict          = {},       # Resolution-specific overrides.
-    max_minibatch_per_gpu   = {},       # Resolution-specific maximum minibatch size per GPU.
-    G_lrate_base            = 0.001,    # Learning rate for the generator.
-    G_lrate_dict            = {},       # Resolution-specific overrides.
-    D_lrate_base            = 0.001,    # Learning rate for the discriminator.
-    D_lrate_dict            = {},       # Resolution-specific overrides.
-    lrate_rampup_kimg       = 0,        # Duration of learning rate ramp-up.
-    tick_kimg_base          = 160,      # Default interval of progress snapshots.
-    tick_kimg_dict          = {4: 160, 8:140, 16:120, 32:100, 64:80, 128:60, 256:40, 512:30, 1024:20}): # Resolution-specific overrides.
-
-    # Initialize result dict.
-    s = dnnlib.EasyDict()
-    s.kimg = cur_nimg / 1000.0
-
-    # Training phase.
-    phase_dur = lod_training_kimg + lod_transition_kimg
-    phase_idx = int(np.floor(s.kimg / phase_dur)) if phase_dur > 0 else 0
-    phase_kimg = s.kimg - phase_idx * phase_dur
-
-    # Level-of-detail and resolution.
-    s.lod = training_set.resolution_log2
-    s.lod -= np.floor(np.log2(lod_initial_resolution))
-    s.lod -= phase_idx
-    if lod_transition_kimg > 0:
-        s.lod -= max(phase_kimg - lod_training_kimg, 0.0) / lod_transition_kimg
-    s.lod = max(s.lod, 0.0)
-    s.resolution = 2 ** (training_set.resolution_log2 - int(np.floor(s.lod)))
-
-    # Minibatch size.
-    s.minibatch = minibatch_dict.get(s.resolution, minibatch_base)
-    s.minibatch -= s.minibatch % num_gpus
-    if s.resolution in max_minibatch_per_gpu:
-        s.minibatch = min(s.minibatch, max_minibatch_per_gpu[s.resolution] * num_gpus)
-
-    # Learning rate.
-    s.G_lrate = G_lrate_dict.get(s.resolution, G_lrate_base)
-    s.D_lrate = D_lrate_dict.get(s.resolution, D_lrate_base)
-    if lrate_rampup_kimg > 0:
-        rampup = min(s.kimg / lrate_rampup_kimg, 1.0)
-        s.G_lrate *= rampup
-        s.D_lrate *= rampup
-
-    # Other parameters.
-    s.tick_kimg = tick_kimg_dict.get(s.resolution, tick_kimg_base)
-    return s
-
-#----------------------------------------------------------------------------
-# Main training script.
-
-def training_loop(
-    submit_config,
-    G_args                  = {},       # Options for generator network.
-    D_args                  = {},       # Options for discriminator network.
-    G_opt_args              = {},       # Options for generator optimizer.
-    D_opt_args              = {},       # Options for discriminator optimizer.
-    G_loss_args             = {},       # Options for generator loss.
-    D_loss_args             = {},       # Options for discriminator loss.
-    dataset_args            = {},       # Options for dataset.load_dataset().
-    sched_args              = {},       # Options for train.TrainingSchedule.
-    grid_args               = {},       # Options for train.setup_snapshot_image_grid().
-    metric_arg_list         = [],       # Options for MetricGroup.
-    tf_config               = {},       # Options for tflib.init_tf().
-    G_smoothing_kimg        = 10.0,     # Half-life of the running average of generator weights.
-    D_repeats               = 1,        # How many times the discriminator is trained per G iteration.
-    minibatch_repeats       = 4,        # Number of minibatches to run before adjusting training parameters.
-    reset_opt_for_new_lod   = True,     # Reset optimizer internal state (e.g. Adam moments) when new layers are introduced?
-    total_kimg              = 15000,    # Total length of the training, measured in thousands of real images.
-    mirror_augment          = False,    # Enable mirror augment?
-    drange_net              = [-1,1],   # Dynamic range used when feeding image data to the networks.
-    image_snapshot_ticks    = 1,        # How often to export image snapshots?
-    network_snapshot_ticks  = 10,       # How often to export network snapshots?
-    save_tf_graph           = False,    # Include full TensorFlow computation graph in the tfevents file?
-    save_weight_histograms  = False,    # Include weight histograms in the tfevents file?
-    resume_run_id           = None,     # Run ID or network pkl to resume training from, None = start from scratch.
-    resume_snapshot         = None,     # Snapshot index to resume training from, None = autodetect.
-    resume_kimg             = 0.0,      # Assumed training progress at the beginning. Affects reporting and training schedule.
-    resume_time             = 0.0):     # Assumed wallclock time at the beginning. Affects reporting.
-
-    # Initialize dnnlib and TensorFlow.
-    ctx = dnnlib.RunContext(submit_config, train)
-    tflib.init_tf(tf_config)
-
-    # Load training set.
-    training_set = dataset.load_dataset(data_dir=config.data_dir, verbose=True, **dataset_args)
-
-    # Construct networks.
-    with tf.device('/gpu:0'):
-        if resume_run_id is not None:
-            network_pkl = misc.locate_network_pkl(resume_run_id, resume_snapshot)
-            print('Loading networks from "%s"...' % network_pkl)
-            G, D, Gs = misc.load_pkl(network_pkl)
-        else:
-            print('Constructing networks...')
-            G = tflib.Network('G', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **G_args)
-            D = tflib.Network('D', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **D_args)
-            Gs = G.clone('Gs')
-    G.print_layers(); D.print_layers()
-
-    print('Building TensorFlow graph...')
-    with tf.name_scope('Inputs'), tf.device('/cpu:0'):
-        lod_in          = tf.placeholder(tf.float32, name='lod_in', shape=[])
-        lrate_in        = tf.placeholder(tf.float32, name='lrate_in', shape=[])
-        minibatch_in    = tf.placeholder(tf.int32, name='minibatch_in', shape=[])
-        minibatch_split = minibatch_in // submit_config.num_gpus
-        Gs_beta         = 0.5 ** tf.div(tf.cast(minibatch_in, tf.float32), G_smoothing_kimg * 1000.0) if G_smoothing_kimg > 0.0 else 0.0
-
-    G_opt = tflib.Optimizer(name='TrainG', learning_rate=lrate_in, **G_opt_args)
-    D_opt = tflib.Optimizer(name='TrainD', learning_rate=lrate_in, **D_opt_args)
-    for gpu in range(submit_config.num_gpus):
-        with tf.name_scope('GPU%d' % gpu), tf.device('/gpu:%d' % gpu):
-            G_gpu = G if gpu == 0 else G.clone(G.name + '_shadow')
-            D_gpu = D if gpu == 0 else D.clone(D.name + '_shadow')
-            lod_assign_ops = [tf.assign(G_gpu.find_var('lod'), lod_in), tf.assign(D_gpu.find_var('lod'), lod_in)]
-            reals, labels = training_set.get_minibatch_tf()
-            reals = process_reals(reals, lod_in, mirror_augment, training_set.dynamic_range, drange_net)
-            with tf.name_scope('G_loss'), tf.control_dependencies(lod_assign_ops):
-                G_loss = dnnlib.util.call_func_by_name(G=G_gpu, D=D_gpu, opt=G_opt, training_set=training_set, minibatch_size=minibatch_split, **G_loss_args)
-            with tf.name_scope('D_loss'), tf.control_dependencies(lod_assign_ops):
-                D_loss = dnnlib.util.call_func_by_name(G=G_gpu, D=D_gpu, opt=D_opt, training_set=training_set, minibatch_size=minibatch_split, reals=reals, labels=labels, **D_loss_args)
-            G_opt.register_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
-            D_opt.register_gradients(tf.reduce_mean(D_loss), D_gpu.trainables)
-    G_train_op = G_opt.apply_updates()
-    D_train_op = D_opt.apply_updates()
-
-    Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta)
-    with tf.device('/gpu:0'):
-        try:
-            peak_gpu_mem_op = tf.contrib.memory_stats.MaxBytesInUse()
-        except tf.errors.NotFoundError:
-            peak_gpu_mem_op = tf.constant(0)
-
-    print('Setting up snapshot image grid...')
-    grid_size, grid_reals, grid_labels, grid_latents = misc.setup_snapshot_image_grid(G, training_set, **grid_args)
-    sched = training_schedule(cur_nimg=total_kimg*1000, training_set=training_set, num_gpus=submit_config.num_gpus, **sched_args)
-    grid_fakes = Gs.run(grid_latents, grid_labels, is_validation=True, minibatch_size=sched.minibatch//submit_config.num_gpus)
-
-    print('Setting up run dir...')
-    misc.save_image_grid(grid_reals, os.path.join(submit_config.run_dir, 'reals.png'), drange=training_set.dynamic_range, grid_size=grid_size)
-    misc.save_image_grid(grid_fakes, os.path.join(submit_config.run_dir, 'fakes%06d.png' % resume_kimg), drange=drange_net, grid_size=grid_size)
-    summary_log = tf.summary.FileWriter(submit_config.run_dir)
-    if save_tf_graph:
-        summary_log.add_graph(tf.get_default_graph())
-    if save_weight_histograms:
-        G.setup_weight_histograms(); D.setup_weight_histograms()
-    metrics = metric_base.MetricGroup(metric_arg_list)
-
-    print('Training...\n')
-    ctx.update('', cur_epoch=resume_kimg, max_epoch=total_kimg)
-    maintenance_time = ctx.get_last_update_interval()
-    cur_nimg = int(resume_kimg * 1000)
-    cur_tick = 0
-    tick_start_nimg = cur_nimg
-    prev_lod = -1.0
-    while cur_nimg < total_kimg * 1000:
-        if ctx.should_stop(): break
-
-        # Choose training parameters and configure training ops.
-        sched = training_schedule(cur_nimg=cur_nimg, training_set=training_set, num_gpus=submit_config.num_gpus, **sched_args)
-        training_set.configure(sched.minibatch // submit_config.num_gpus, sched.lod)
-        if reset_opt_for_new_lod:
-            if np.floor(sched.lod) != np.floor(prev_lod) or np.ceil(sched.lod) != np.ceil(prev_lod):
-                G_opt.reset_optimizer_state(); D_opt.reset_optimizer_state()
-        prev_lod = sched.lod
-
-        # Run training ops.
-        for _mb_repeat in range(minibatch_repeats):
-            for _D_repeat in range(D_repeats):
-                tflib.run([D_train_op, Gs_update_op], {lod_in: sched.lod, lrate_in: sched.D_lrate, minibatch_in: sched.minibatch})
-                cur_nimg += sched.minibatch
-            tflib.run([G_train_op], {lod_in: sched.lod, lrate_in: sched.G_lrate, minibatch_in: sched.minibatch})
-
-        # Perform maintenance tasks once per tick.
-        done = (cur_nimg >= total_kimg * 1000)
-        if cur_nimg >= tick_start_nimg + sched.tick_kimg * 1000 or done:
-            cur_tick += 1
-            tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
-            tick_start_nimg = cur_nimg
-            tick_time = ctx.get_time_since_last_update()
-            total_time = ctx.get_time_since_start() + resume_time
-
-            # Report progress.
-            print('tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %-6.1f gpumem %-4.1f' % (
-                autosummary('Progress/tick', cur_tick),
-                autosummary('Progress/kimg', cur_nimg / 1000.0),
-                autosummary('Progress/lod', sched.lod),
-                autosummary('Progress/minibatch', sched.minibatch),
-                dnnlib.util.format_time(autosummary('Timing/total_sec', total_time)),
-                autosummary('Timing/sec_per_tick', tick_time),
-                autosummary('Timing/sec_per_kimg', tick_time / tick_kimg),
-                autosummary('Timing/maintenance_sec', maintenance_time),
-                autosummary('Resources/peak_gpu_mem_gb', peak_gpu_mem_op.eval() / 2**30)))
-            autosummary('Timing/total_hours', total_time / (60.0 * 60.0))
-            autosummary('Timing/total_days', total_time / (24.0 * 60.0 * 60.0))
-
-            # Save snapshots.
-            if cur_tick % image_snapshot_ticks == 0 or done:
-                grid_fakes = Gs.run(grid_latents, grid_labels, is_validation=True, minibatch_size=sched.minibatch//submit_config.num_gpus)
-                misc.save_image_grid(grid_fakes, os.path.join(submit_config.run_dir, 'fakes%06d.png' % (cur_nimg // 1000)), drange=drange_net, grid_size=grid_size)
-            if cur_tick % network_snapshot_ticks == 0 or done or cur_tick == 1:
-                pkl = os.path.join(submit_config.run_dir, 'network-snapshot-%06d.pkl' % (cur_nimg // 1000))
-                misc.save_pkl((G, D, Gs), pkl)
-                metrics.run(pkl, run_dir=submit_config.run_dir, num_gpus=submit_config.num_gpus, tf_config=tf_config)
-
-            # Update summaries and RunContext.
-            metrics.update_autosummaries()
-            tflib.autosummary.save_summaries(summary_log, cur_nimg)
-            ctx.update('%.2f' % sched.lod, cur_epoch=cur_nimg // 1000, max_epoch=total_kimg)
-            maintenance_time = ctx.get_last_update_interval() - tick_time
-
-    # Write final results.
-    misc.save_pkl((G, D, Gs), os.path.join(submit_config.run_dir, 'network-final.pkl'))
-    summary_log.close()
-
-    ctx.close()
-
-#----------------------------------------------------------------------------
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Gemsloot A New Platform to Cash Out Coins for Ingame Items and Gift Cards.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Gemsloot A New Platform to Cash Out Coins for Ingame Items and Gift Cards.md
deleted file mode 100644
index dbdc739558542fadc54f298faf916aa2a1b63d13..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Gemsloot A New Platform to Cash Out Coins for Ingame Items and Gift Cards.md	
+++ /dev/null
@@ -1,139 +0,0 @@
-
-<h1>What is Gemsloot.com and How to Get Free Robux?</h1>
-<p>If you are a fan of Roblox, you might have heard of Gemsloot.com, a website that claims to offer free Robux and other rewards. But what is Gemsloot.com and how does it work? Is it safe and legit? And how can you get free Robux from it? In this article, we will answer all these questions and more. We will also provide you with a list of active promo codes that you can use on Gemsloot.com to get some extra coins. So, let's get started!</p>
-<h2>gemsloot com</h2><br /><p><b><b>DOWNLOAD</b> - <a href="https://ssurll.com/2uO0Oo">https://ssurll.com/2uO0Oo</a></b></p><br /><br />
- <h2>Gemsloot.com Overview</h2>
-<h3>What is Gemsloot.com?</h3>
-<p>Gemsloot.com is a website that allows users to earn coins by completing short tasks, downloading apps, or watching videos. These coins can then be exchanged for various rewards, such as Robux, CS:GO skins, Dota 2 items, Bitcoin, PayPal, gift cards, and more. Gemsloot.com is not affiliated with Roblox or any other game developer. It is a third-party website that acts as a middleman between users and advertisers.</p>
- <h3>How does Gemsloot.com work?</h3>
-<p>Gemsloot.com works by partnering with various offer providers, such as AdGate Media, AdGem, OfferToro, etc. These offer providers pay Gemsloot.com for bringing them traffic and users. Gemsloot.com then shares a portion of this revenue with its users in the form of coins. Users can choose from different types of offers, such as surveys, quizzes, app downloads, video ads, etc. Each offer has a different payout rate and duration. Users can also earn coins by using promo codes or participating in leaderboards, quests, giveaways, and referrals.</p>
- <h3>Is Gemsloot.com safe and legit?</h3>
-<p>Gemsloot.com is a relatively new website that was launched in April 2020. It has a good reputation among its users and has received positive reviews on Trustpilot and YouTube. However, it is important to note that Gemsloot.com is not endorsed by Roblox or any other game developer. It is a third-party website that operates independently from them. Therefore, there is always a risk of account ban or suspension if you use Gemsloot.com to get free Robux or other in-game items. You should use Gemsloot.com at your own discretion and responsibility.</p>
- <h2>How to Get Free Robux from Gemsloot.com</h2>
-<h3>Step 1: Create an account on Gemsloot.com</h3>
-<p>To start earning coins on Gemsloot.com, you need to create an account first. You can do this by visiting [Gemsloot](^1^) and clicking on the "Sign Up" button at the top right corner. You can either sign up with your email address or use your Google or Facebook account. After creating your account, you will receive a verification email with a link to activate your account. Once you activate your account, you will get 10 coins as a welcome bonus.</p>
- <h3>Step 2: Complete short tasks, download apps, or watch videos</h3>
-<p>After creating your account, you can start earning coins by completing short tasks , downloading apps, or watching videos. You can find different types of offers on the "Earn Coins" page. You can filter the offers by category, provider, or payout. You can also search for specific offers by using the search bar. Each offer has a description, a payout rate, and a completion time. You should read the instructions carefully before starting an offer. Some offers may require you to provide personal information, such as your name, email, phone number, etc. You should only provide this information if you are comfortable with it and trust the offer provider. You should also avoid using fake or invalid information, as this may result in your account being banned or your coins being revoked. After completing an offer, you should wait for a few minutes for the coins to be credited to your account. If you do not receive your coins within 24 hours, you can contact the support team or the offer provider for assistance.</p>
-<p>gemsloot promo codes<br />
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-gemsloot safety guidelines</p>
- <h3>Step 3: Redeem your coins for Robux or other rewards</h3>
-<p>Once you have enough coins, you can redeem them for Robux or other rewards on the "Redeem" page. You can choose from different types of rewards, such as Robux, CS:GO skins, Dota 2 items, Bitcoin, PayPal, gift cards, and more. Each reward has a different price and availability. To redeem Robux, you need to provide your Roblox username and join the Gemsloot group on Roblox. You can then choose from different amounts of Robux, ranging from 4 to 1000. The minimum amount of coins required to redeem Robux is 500. After confirming your order, you will receive your Robux within a few minutes.</p>
- <h2>Gemsloot.com Promo Codes</h2>
-<h3>What are promo codes and how to use them?</h3>
-<p>Promo codes are special codes that you can use on Gemsloot.com to get some extra coins for free. Promo codes are usually released by Gemsloot.com on their social media platforms, such as Twitter, Instagram, Discord, etc. Promo codes are valid for a limited time and have a limited number of uses. To use a promo code, you need to go to the "Promo Codes" page and enter the code in the box. You can then click on the "Claim" button to get your free coins.</p>
- <h3>List of active promo codes for May 2023</h3>
-<table>
-<tr>
-<th>Promo Code</th>
-<th>Coins</th>
-<th>Expiration Date</th>
-</tr>
-<tr>
-<td>GEMS</td>
-<td>10</td>
-<td>May 31, 2023</td>
-</tr>
-<tr>
-<td>LOOT</td>
-<td>15</td>
-<td>May 31, 2023</td>
-</tr>
-<tr>
-<td>ROBUX</td>
-<td>20</td>
-<td>May 31, 2023</td>
-</tr>
-<tr>
-<td>FREERBX</td>
-<td>25</td>
-<td>May 31, 2023</td>
-</tr>
-<tr>
-<td>MAY2023</td>
-<td>30</td>
-<td>May 31, 2023</td>
-</tr>
-</table>
- <h2>Pros and Cons of Gemsloot.com</h2>
-<h4>Pros</h4>
-<ul>
-<li>Easy to use and user-friendly interface.</li>
-<li>Fast payouts and low minimum withdrawal amount.</li>
-<li>Variety of rewards and payment methods.</li>
-<li>Daily bonus coins and leaderboards.</li>
-<li>Active support team and community.</li>
-<li>Frequent promo codes and giveaways.</li>
-</ul>
- <h4>Cons</h4>
-<ul>
-<li>Third-party website that is not affiliated with Roblox or any other game developer.</li>
-<li>Risk of account ban or suspension if you use Gemsloot.com to get free Robux or other in-game items.</li>
-<li>Limited number of offers and availability depending on your location and device.</li>
-<li>Possible privacy issues and spam emails if you provide personal information to some offer providers.</li>
-<li>Possible technical glitches and delays in crediting coins or delivering rewards.</li> </ul>
- <h2>Conclusion</h2>
-<p>Gemsloot.com is a website that allows users to earn coins by completing short tasks, downloading apps, or watching videos. These coins can then be redeemed for Robux or other rewards, such as CS:GO skins, Dota 2 items, Bitcoin, PayPal, gift cards, and more. Gemsloot.com is not affiliated with Roblox or any other game developer. It is a third-party website that operates independently from them. Therefore, there is always a risk of account ban or suspension if you use Gemsloot.com to get free Robux or other in-game items. You should use Gemsloot.com at your own discretion and responsibility.</p>
- <p>We hope this article has helped you understand what is Gemsloot.com and how to get free Robux from it. We also hope you have enjoyed the list of active promo codes that we have provided for you. If you have any questions or feedback, feel free to leave a comment below. Thank you for reading and happy looting!</p>
- <h2>FAQs</h2>
-<ol>
-<li>Q: How many coins do I need to redeem Robux on Gemsloot.com?</li>
-<li>A: You need at least 500 coins to redeem 4 Robux on Gemsloot.com. The more coins you have, the more Robux you can get.</li>
-<li>Q: How long does it take to receive my Robux or other rewards from Gemsloot.com?</li>
-<li>A: It usually takes a few minutes to receive your Robux or other rewards from Gemsloot.com. However, sometimes it may take longer due to technical issues or high demand. If you do not receive your rewards within 24 hours, you can contact the support team or the offer provider for assistance.</li>
-<li>Q: How can I contact the support team or the offer provider on Gemsloot.com?</li>
-<li>A: You can contact the support team by clicking on the "Support" button at the bottom right corner of the website. You can also join the Discord server or follow the Twitter account of Gemsloot.com for updates and announcements. To contact the offer provider, you can click on the "Missing Coins?" button on the "Earn Coins" page and follow the instructions.</li>
-<li>Q: How can I get more promo codes for Gemsloot.com?</li>
-<li>A: You can get more promo codes for Gemsloot.com by following their social media platforms, such as Twitter, Instagram, Discord, etc. You can also check our website regularly for new promo codes and updates.</li>
-<li>Q: Can I use Gemsloot.com on my mobile device?</li>
-<li>A: Yes, you can use Gemsloot.com on your mobile device. However, some offers may not be available or compatible with your device. You should also make sure that your device has enough storage space and battery life before starting an offer.</li>
-</ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/simsantonioii/MusicGen-Continuation/tests/modules/__init__.py b/spaces/simsantonioii/MusicGen-Continuation/tests/modules/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/simsantonioii/MusicGen-Continuation/tests/modules/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/utils/umap_utils.py b/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/utils/umap_utils.py
deleted file mode 100644
index a9570a547b4250e89a11e140c311132b3fc0deb7..0000000000000000000000000000000000000000
--- a/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/utils/umap_utils.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import numpy as np
-import torch
-import copy
-import os
-import numpy as np
-from sklearn import svm
-
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
-
-
-def linear_interpolate(latent_code, boundary, start_distance=-3, end_distance=3, steps=10):
-    """Manipulates the given latent code with respect to a particular boundary.
-
-    Basically, this function takes a latent code and a boundary as inputs, and
-    outputs a collection of manipulated latent codes. For example, let `steps` to
-    be 10, then the input `latent_code` is with shape [1, latent_space_dim], input
-    `boundary` is with shape [1, latent_space_dim] and unit norm, the output is
-    with shape [10, latent_space_dim]. The first output latent code is
-    `start_distance` away from the given `boundary`, while the last output latent
-    code is `end_distance` away from the given `boundary`. Remaining latent codes
-    are linearly interpolated.
-
-    Input `latent_code` can also be with shape [1, num_layers, latent_space_dim]
-    to support W+ space in Style GAN. In this case, all features in W+ space will
-    be manipulated same as each other. Accordingly, the output will be with shape
-    [10, num_layers, latent_space_dim].
-
-    NOTE: Distance is sign sensitive.
-
-    Args:
-        latent_code: The input latent code for manipulation.
-        boundary: The semantic boundary as reference.
-        start_distance: The distance to the boundary where the manipulation starts.
-        (default: -3.0)
-        end_distance: The distance to the boundary where the manipulation ends.
-        (default: 3.0)
-        steps: Number of steps to move the latent code from start position to end
-        position. (default: 10)
-    """
-    assert latent_code.shape[0] == 1 and boundary.shape[0] == 1 and len(boundary.shape) == 2 and boundary.shape[1] == latent_code.shape[-1]
-
-    linspace = np.linspace(start_distance, end_distance, steps)
-    if len(latent_code.shape) == 2:
-        linspace = linspace - latent_code.dot(boundary.T)
-        linspace = linspace.reshape(-1, 1).astype(np.float32)
-        return latent_code + linspace * boundary
-    if len(latent_code.shape) == 3:
-        linspace = linspace.reshape(-1, 1, 1).astype(np.float32)
-        return latent_code + linspace * boundary.reshape(1, 1, -1)
-    raise ValueError(
-        f"Input `latent_code` should be with shape "
-        f"[1, latent_space_dim] or [1, N, latent_space_dim] for "
-        f"W+ space in Style GAN!\n"
-        f"But {latent_code.shape} is received."
-    )
-
-
-def get_code(domain, boundaries):
-    if domain == "ink":
-        domain = 0
-    elif domain == "monet":
-        domain = 1
-    elif domain == "vangogh":
-        domain = 2
-    elif domain == "water":
-        domain = 3
-
-    res = np.array(torch.randn(1, 256, dtype=torch.float32))
-    # res = linear_interpolate(res, boundaries[domain], end_distance=3, steps=3)[-1:]
-    res = torch.Tensor(res).cuda() if torch.cuda.is_available() else torch.Tensor(res)
-    return res
-
-
-def modify_code(code, boundaries, domain, range):
-    if domain == "ink":
-        domain = 0
-    elif domain == "monet":
-        domain = 1
-    elif domain == "vangogh":
-        domain = 2
-    elif domain == "water":
-        domain = 3
-    # print(domain, range)
-    if range == 0:
-        return code
-    else:
-        res = np.array(code.cpu().detach().numpy())
-        res = linear_interpolate(res, boundaries[domain], end_distance=range, steps=3)[-1:]
-        res = torch.Tensor(res).cuda() if torch.cuda.is_available() else torch.Tensor(res)
-        return res
-
-
-def load_boundries():
-    domains = ["ink", "monet", "vangogh", "water"]
-    domains.sort()
-    boundaries = [
-        np.load(os.path.join(os.path.dirname(__file__), "boundaries_amp_52/artwork_" + domain + "_boundary/boundary.npy")) for domain in domains
-    ]
-    return boundaries
diff --git a/spaces/slyjay412/stabilityai-stable-diffusion-2/app.py b/spaces/slyjay412/stabilityai-stable-diffusion-2/app.py
deleted file mode 100644
index d2782cea00b1bfcd22df7c204d9e52a6baf46ac2..0000000000000000000000000000000000000000
--- a/spaces/slyjay412/stabilityai-stable-diffusion-2/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stabilityai/stable-diffusion-2").launch()
\ No newline at end of file
diff --git a/spaces/spuun/blip-api/app.py b/spaces/spuun/blip-api/app.py
deleted file mode 100644
index 8083f55f97533f9de94ae89be98c9bad90732460..0000000000000000000000000000000000000000
--- a/spaces/spuun/blip-api/app.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-model_id = "Salesforce/blip-image-captioning-large"
-captioner = pipeline(model=model_id)
-
-def launch(input):
-    return captioner(input)[0]["generated_text"]
-
-iface = gr.Interface(fn=launch, inputs=gr.Image(type="pil"), outputs="text")
-iface.launch()
\ No newline at end of file
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/__init__.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/models/__init__.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/models/__init__.py
deleted file mode 100644
index 337c77ac7bfb7e11a0662b86b98c4c0a02da26df..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/models/__init__.py
+++ /dev/null
@@ -1,236 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""isort:skip_file"""
-
-import argparse
-import importlib
-import os
-from contextlib import ExitStack
-
-from fairseq.dataclass import FairseqDataclass
-from fairseq.dataclass.utils import merge_with_parent
-from hydra.core.config_store import ConfigStore
-from omegaconf import open_dict, OmegaConf
-
-from .composite_encoder import CompositeEncoder
-from .distributed_fairseq_model import DistributedFairseqModel
-from .fairseq_decoder import FairseqDecoder
-from .fairseq_encoder import FairseqEncoder
-from .fairseq_incremental_decoder import FairseqIncrementalDecoder
-from .fairseq_model import (
-    BaseFairseqModel,
-    FairseqEncoderDecoderModel,
-    FairseqEncoderModel,
-    FairseqLanguageModel,
-    FairseqModel,
-    FairseqMultiModel,
-)
-
-
-MODEL_REGISTRY = {}
-MODEL_DATACLASS_REGISTRY = {}
-ARCH_MODEL_REGISTRY = {}
-ARCH_MODEL_NAME_REGISTRY = {}
-ARCH_MODEL_INV_REGISTRY = {}
-ARCH_CONFIG_REGISTRY = {}
-
-
-__all__ = [
-    "BaseFairseqModel",
-    "CompositeEncoder",
-    "DistributedFairseqModel",
-    "FairseqDecoder",
-    "FairseqEncoder",
-    "FairseqEncoderDecoderModel",
-    "FairseqEncoderModel",
-    "FairseqIncrementalDecoder",
-    "FairseqLanguageModel",
-    "FairseqModel",
-    "FairseqMultiModel",
-]
-
-
-def build_model(cfg: FairseqDataclass, task):
-
-    model = None
-    model_type = getattr(cfg, "_name", None) or getattr(cfg, "arch", None)
-
-    if not model_type and len(cfg) == 1:
-        # this is hit if config object is nested in directory that is named after model type
-
-        model_type = next(iter(cfg))
-        if model_type in MODEL_DATACLASS_REGISTRY:
-            cfg = cfg[model_type]
-        else:
-            raise Exception(
-                "Could not infer model type from directory. Please add _name field to indicate model type. "
-                "Available models: "
-                + str(MODEL_DATACLASS_REGISTRY.keys())
-                + " Requested model type: "
-                + model_type
-            )
-
-    if model_type in ARCH_MODEL_REGISTRY:
-        # case 1: legacy models
-        model = ARCH_MODEL_REGISTRY[model_type]
-    elif model_type in MODEL_DATACLASS_REGISTRY:
-        # case 2: config-driven models
-        model = MODEL_REGISTRY[model_type]
-
-    if model_type in MODEL_DATACLASS_REGISTRY:
-        # set defaults from dataclass. note that arch name and model name can be the same
-        dc = MODEL_DATACLASS_REGISTRY[model_type]
-
-        if isinstance(cfg, argparse.Namespace):
-            cfg = dc.from_namespace(cfg)
-        else:
-            cfg = merge_with_parent(dc(), cfg)
-    else:
-        if model_type in ARCH_CONFIG_REGISTRY:
-            with open_dict(cfg) if OmegaConf.is_config(cfg) else ExitStack():
-                # this calls the different "arch" functions (like base_architecture()) that you indicate
-                # if you specify --arch on the command line. this is only applicable to the old argparse based models
-                # hydra models should expose different architectures via different config files
-                # it will modify the cfg object and default parameters according to the arch
-                ARCH_CONFIG_REGISTRY[model_type](cfg)
-
-    assert model is not None, (
-        f"Could not infer model type from {cfg}. "
-        "Available models: {}".format(
-            MODEL_DATACLASS_REGISTRY.keys()
-        )
-        + f" Requested model type: {model_type}"
-    )
-
-    return model.build_model(cfg, task)
-
-
-def register_model(name, dataclass=None):
-    """
-    New model types can be added to fairseq with the :func:`register_model`
-    function decorator.
-
-    For example::
-
-        @register_model('lstm')
-        class LSTM(FairseqEncoderDecoderModel):
-            (...)
-
-    .. note:: All models must implement the :class:`BaseFairseqModel` interface.
-        Typically you will extend :class:`FairseqEncoderDecoderModel` for
-        sequence-to-sequence tasks or :class:`FairseqLanguageModel` for
-        language modeling tasks.
-
-    Args:
-        name (str): the name of the model
-    """
-
-    def register_model_cls(cls):
-        if name in MODEL_REGISTRY:
-            raise ValueError("Cannot register duplicate model ({})".format(name))
-        if not issubclass(cls, BaseFairseqModel):
-            raise ValueError(
-                "Model ({}: {}) must extend BaseFairseqModel".format(name, cls.__name__)
-            )
-        MODEL_REGISTRY[name] = cls
-        if dataclass is not None and not issubclass(dataclass, FairseqDataclass):
-            raise ValueError(
-                "Dataclass {} must extend FairseqDataclass".format(dataclass)
-            )
-
-        cls.__dataclass = dataclass
-        if dataclass is not None:
-            MODEL_DATACLASS_REGISTRY[name] = dataclass
-
-            cs = ConfigStore.instance()
-            node = dataclass()
-            node._name = name
-            cs.store(name=name, group="model", node=node, provider="fairseq")
-
-            @register_model_architecture(name, name)
-            def noop(_):
-                pass
-
-        return cls
-
-    return register_model_cls
-
-
-def register_model_architecture(model_name, arch_name):
-    """
-    New model architectures can be added to fairseq with the
-    :func:`register_model_architecture` function decorator. After registration,
-    model architectures can be selected with the ``--arch`` command-line
-    argument.
-
-    For example::
-
-        @register_model_architecture('lstm', 'lstm_luong_wmt_en_de')
-        def lstm_luong_wmt_en_de(cfg):
-            args.encoder_embed_dim = getattr(cfg.model, 'encoder_embed_dim', 1000)
-            (...)
-
-    The decorated function should take a single argument *cfg*, which is a
-    :class:`omegaconf.DictConfig`. The decorated function should modify these
-    arguments in-place to match the desired architecture.
-
-    Args:
-        model_name (str): the name of the Model (Model must already be
-            registered)
-        arch_name (str): the name of the model architecture (``--arch``)
-    """
-
-    def register_model_arch_fn(fn):
-        if model_name not in MODEL_REGISTRY:
-            raise ValueError(
-                "Cannot register model architecture for unknown model type ({})".format(
-                    model_name
-                )
-            )
-        if arch_name in ARCH_MODEL_REGISTRY:
-            raise ValueError(
-                "Cannot register duplicate model architecture ({})".format(arch_name)
-            )
-        if not callable(fn):
-            raise ValueError(
-                "Model architecture must be callable ({})".format(arch_name)
-            )
-        ARCH_MODEL_REGISTRY[arch_name] = MODEL_REGISTRY[model_name]
-        ARCH_MODEL_NAME_REGISTRY[arch_name] = model_name
-        ARCH_MODEL_INV_REGISTRY.setdefault(model_name, []).append(arch_name)
-        ARCH_CONFIG_REGISTRY[arch_name] = fn
-        return fn
-
-    return register_model_arch_fn
-
-
-def import_models(models_dir, namespace):
-    for file in os.listdir(models_dir):
-        path = os.path.join(models_dir, file)
-        if (
-            not file.startswith("_")
-            and not file.startswith(".")
-            and (file.endswith(".py") or os.path.isdir(path))
-        ):
-            model_name = file[: file.find(".py")] if file.endswith(".py") else file
-            importlib.import_module(namespace + "." + model_name)
-
-            # extra `model_parser` for sphinx
-            if model_name in MODEL_REGISTRY:
-                parser = argparse.ArgumentParser(add_help=False)
-                group_archs = parser.add_argument_group("Named architectures")
-                group_archs.add_argument(
-                    "--arch", choices=ARCH_MODEL_INV_REGISTRY[model_name]
-                )
-                group_args = parser.add_argument_group(
-                    "Additional command-line arguments"
-                )
-                MODEL_REGISTRY[model_name].add_args(group_args)
-                globals()[model_name + "_parser"] = parser
-
-
-# automatically import any Python files in the models/ directory
-models_dir = os.path.dirname(__file__)
-import_models(models_dir, "fairseq.models")
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/models/model_utils.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/models/model_utils.py
deleted file mode 100644
index 732d66b1d5f695151c26d29eb7f6b53179c269f1..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/models/model_utils.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import List, Optional
-
-import torch
-from torch import Tensor
-
-
-@torch.jit.script
-def script_skip_tensor_list(x: List[Tensor], mask):
-    res = [xi[mask] if xi.size(0) == mask.size(0) else xi[:, mask] for xi in x]
-    outputs = []
-    for i, t in enumerate(res):
-        if t.numel() != 0:
-            outputs.append(t)
-        else:
-            outputs.append(x[i])
-    return outputs
-
-
-@torch.jit.script
-def script_skip_tensor(x: Tensor, mask):
-    # None case
-    if x.size(0) == 0:
-        return x
-    res = x[mask] if x.size(0) == mask.size(0) else x[:, mask]
-    if res.numel() == 0:
-        return x
-    else:
-        return res
-
-
-@torch.jit.script
-def expand_2d_or_3d_tensor(x, trg_dim: int, padding_idx: int):
-    """
-    Expand 2D/3D tensor on dim=1
-    """
-    if x is None:
-        return None
-
-    assert x.dim() == 2 or x.dim() == 3
-    assert trg_dim >= x.size(1), (trg_dim, x.size())
-    if trg_dim == x.size(1):
-        return x
-
-    dims = [x.size(0), trg_dim - x.size(1)]
-    if x.dim() == 3:
-        dims.append(x.size(2))
-    x = torch.cat([x, torch.zeros(dims).to(x).fill_(padding_idx)], 1)
-
-    return x
-
-
-@torch.jit.script
-def coalesce(x: Optional[Tensor], y: Tensor) -> Tensor:
-    return x if x is not None else y
-
-
-@torch.jit.script
-def fill_tensors(
-    x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int
-) -> Optional[Tensor]:
-    """
-    Filling tensor x with y at masked positions (dim=0).
-    """
-    if x is None or x.size()[0] == 0 or y is None:
-        return x
-    assert x.dim() == y.dim() and mask.size(0) == x.size(0)
-    assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2))
-
-    n_selected = mask.sum()
-    if n_selected == 0:
-        return x
-    assert n_selected == y.size(0)
-    if n_selected == x.size(0):
-        return y
-
-    if x.size(1) < y.size(1):
-        x = expand_2d_or_3d_tensor(x, y.size(1), padding_idx)
-        x[mask] = y
-    elif x.size(1) > y.size(1):
-        x[mask] = torch.tensor(padding_idx).type_as(x)
-        if x.dim() == 2:
-            x[mask, : y.size(1)] = y
-        else:
-            x[mask, : y.size(1), :] = y
-    else:
-        x[mask] = y
-    return x
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/modules/layer_norm.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/modules/layer_norm.py
deleted file mode 100644
index 234609d9e213a650e0032aaa0ca0462a818bfead..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/modules/layer_norm.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-try:
-    from apex.normalization import FusedLayerNorm as _FusedLayerNorm
-
-    has_fused_layernorm = True
-
-    class FusedLayerNorm(_FusedLayerNorm):
-        @torch.jit.unused
-        def forward(self, x):
-            if not x.is_cuda:
-                return super().forward(x)
-            else:
-                with torch.cuda.device(x.device):
-                    return super().forward(x)
-
-
-except ImportError:
-    has_fused_layernorm = False
-
-
-def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False):
-    if torch.jit.is_scripting():
-        export = True
-    if not export and torch.cuda.is_available() and has_fused_layernorm:
-        return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
-    return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
-
-
-class Fp32LayerNorm(nn.LayerNorm):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def forward(self, input):
-        output = F.layer_norm(
-            input.float(),
-            self.normalized_shape,
-            self.weight.float() if self.weight is not None else None,
-            self.bias.float() if self.bias is not None else None,
-            self.eps,
-        )
-        return output.type_as(input)
diff --git a/spaces/stale2000/DnDItem/app.py b/spaces/stale2000/DnDItem/app.py
deleted file mode 100644
index 4434cd97fdd6669cab5ae55a4cbdf88dca55b9d7..0000000000000000000000000000000000000000
--- a/spaces/stale2000/DnDItem/app.py
+++ /dev/null
@@ -1,134 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-import os
-import openai
-import gradio as gr
-
-import torch
-from diffusers import StableDiffusionPipeline
-from torch import autocast
-
-from contextlib import nullcontext
-#from PIL import Image
-#from torchvision import transforms
-
-
-
-openai.api_key = os.getenv('openaikey')
-authtoken = os.getenv('authtoken')
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-context = autocast if device == "cuda" else nullcontext
-dtype = torch.float16 if device == "cuda" else torch.float32
-pipe = StableDiffusionPipeline.from_pretrained("stale2000/sd-dnditem", torch_dtype=dtype, use_auth_token=authtoken)  
-pipe = pipe.to(device)
-
-disable_safety = True
-
-if disable_safety:
-  def null_safety(images, **kwargs):
-      return images, False
-  pipe.safety_checker = null_safety
-
-def create_files():
-    directory = 'C:\\Users\\brcwa\\OneDrive\\Desktop\\destinyCaptures\\dnd\\fullcaptionsimple\\full'
-    for filename2 in os.listdir(directory):
-        if not filename2.endswith('txt'):
-            continue
-        f = os.path.join(directory, filename2)
-        # checking if it is a file
-        if os.path.isfile(f):
-            text_file = open(f, "r")
-            lines = text_file.read()
-            print(lines.split(',')[1] + "," + lines.split(',')[1])
-
-#create_files()
-
-def createGPTPrompt(item_type, description):
-    return item_type.split(",")[0].split(" ")[-1] + " of " + description
-
-def convert_lines(lines):
-    key_arr = []
-    key_hash = {}
-    for line in lines:
-        key = line.split(",")[0]
-        val = line.split(",")[1]
-        key_arr.append(key)
-        key_hash[key] = val
-
-    return key_arr, key_hash
-
-def predict(dropdown, style_dropdown, manual_gpt_replacement, manual_sd_prompt, n_samples, history=[]):
-
-    # gpt3
-    sd_input = ""
-    gpt_input = ""
-
-    description = style_dropdown
-    if manual_sd_prompt != '':
-        gpt_input = manual_gpt_replacement
-    else:
-        gpt_input = "Describe the mechanics of a 5th Edition DnD item called '" + createGPTPrompt(dropdown, description) + "' :"
-    
-    if manual_sd_prompt != '':
-        sd_input = manual_sd_prompt
-    else:
-        sd_input = "dnditem, " + dropdown + ", " + style_hashmap[style_dropdown]  + ", circle inner background and white outerbackground"
-
-    
-    response = openai.Completion.create(
-    model="text-davinci-003",
-    prompt=gpt_input,
-    temperature=0.9,
-    max_tokens=200,
-    top_p=1,
-    frequency_penalty=0,
-    presence_penalty=0.6)
-    
-    # tokenize the new input sentence
-    responseText = response["choices"][0]["text"]
-    history.append((sd_input, responseText))
-
-
-    #img generation
-    scale = 5.5
-
-    #with autocast("cuda"):
-    #  images = pipe(n_samples*[prompt], guidance_scale=scale).images
-
-    with context("cuda"):
-        images = pipe(n_samples*[sd_input], guidance_scale=scale, num_inference_steps=40).images
-
-    
-    return history, history, images
-
-
-
-#inputText = gr.Textbox(placeholder="input query")
-manual_gpt_query = gr.Textbox(placeholder="Input any query here, to replace the gpt query builder entirely.")
-manual_sd_prompt = gr.Textbox(placeholder="Input any query here, to replace the gpt query builder entirely.")
-
-
-
-choiceArr = ["a pair of boots", "a cloak", "a pair of gloves", "a helmet", "a necklace", "a ring", "a robe", "a rod", "a shield", "a staff", "a sword", "a wand"]
-dropdown = gr.Dropdown(label= "Item Type", choices=choiceArr, value="a pair of boots")
-
-
-
-text_file = open("styles.txt", "r")
-lines = text_file.read().split('\n')
-dropdown_arr, style_hashmap = convert_lines(lines)
-
-
-style_dropdown = gr.Dropdown(label= "Item Ability and Style", choices=dropdown_arr, value="ultimate evil")
-
-output_img = gr.Gallery(label="Generated image")
-output_img.style(grid=2)
-
-step_slide = gr.Slider(1, 4, value=2, step=1),
-slide = gr.Slider(label="Number of Images Generated", minimum=1, maximum=4, value=2, step=1)
-gr.Interface(fn=predict,
-             inputs=[dropdown, style_dropdown, manual_gpt_query,manual_sd_prompt,slide,'state'],
-            
-             outputs=["chatbot",'state', output_img]).launch()
diff --git a/spaces/stomexserde/gpt4-ui/Examples/123 Flash Chat V10 0 Nulled Script.md b/spaces/stomexserde/gpt4-ui/Examples/123 Flash Chat V10 0 Nulled Script.md
deleted file mode 100644
index 708352d84933fc2639dbb0ae1e8be593b9309a09..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/123 Flash Chat V10 0 Nulled Script.md	
+++ /dev/null
@@ -1,27 +0,0 @@
-
-```
-<h1>How to Install 123 Flash Chat V10 0 Nulled Script on Your Website</h1>
-<p>123 Flash Chat is a popular chat software that allows you to create your own chat rooms and embed them on your website. It supports multiple languages, themes, video and audio chat, moderated chat, and more. However, the official version of 123 Flash Chat is not free and requires a license to run. If you want to use 123 Flash Chat without paying for a license, you can try the nulled script version, which is a cracked version that bypasses the license verification.</p>
-<h2>123 Flash Chat V10 0 Nulled Script</h2><br /><p><b><b>DOWNLOAD</b> &#9658;&#9658;&#9658; <a href="https://urlgoal.com/2uI9cx">https://urlgoal.com/2uI9cx</a></b></p><br /><br />
-<p>In this article, we will show you how to install 123 Flash Chat V10 0 Nulled Script on your website. Before you proceed, please note that using nulled scripts may be illegal, unethical, or risky. You may face legal issues, security breaches, or malware infections by using nulled scripts. We do not endorse or recommend using nulled scripts, and we are not responsible for any consequences that may arise from doing so. Use them at your own risk.</p>
-<h2>Step 1: Download the nulled script</h2>
-<p>The first step is to download the nulled script of 123 Flash Chat V10 0 from a reliable source. You can search for it on Google or use one of the links below:</p>
-<ul>
-<li><a href="https://www.nulled.to/topic/4216-requests-cracked-123flashchat-v100-license/page-6">Requests Cracked 123flashchat v10.0 license - Page 6 - Requests - Nulled</a></li>
-<li><a href="https://scriptmafia.org/chat/51425-123-flash-chat-script.html">123 Flash Chat Script » SCRiPTMAFiA.ORG | Download Full Nulled Scripts</a></li>
-<li><a href="https://opensea.io/collection/123-flash-chat-v10-0-nulled-script">123 Flash Chat V10 0 Nulled Script - Collection | OpenSea</a></li>
-<li><a href="https://hub.docker.com/r/epaptocer/link-123-flash-chat-v10-0-nulled-theme">epaptocer/link-123-flash-chat-v10-0-nulled-theme - hub.docker.com</a></li>
-<li><a href="https://sway.office.com/fnBUdLCdL4J70okl">123 Flash Chat V10 0 Nulled 15 [REPACK]</a></li>
-</ul>
-<p>After downloading the nulled script, unzip it and extract the files to a folder on your computer.</p>
-<h2>Step 2: Upload the files to your server</h2>
-<p>The next step is to upload the files of the nulled script to your server. You can use an FTP client or a file manager to do this. Make sure you upload the files to the root directory of your website or a subdirectory where you want to install the chat software.</p>
-<h2>Step 3: Run the installation wizard</h2>
-<p>The final step is to run the installation wizard of 123 Flash Chat V10 0 Nulled Script. To do this, open your web browser and go to the URL where you uploaded the files. For example, if you uploaded the files to the root directory of your website, go to http://www.yourwebsite.com/. If you uploaded the files to a subdirectory called chat, go to http://www.yourwebsite.com/chat/.</p>
-<p>You should see a welcome page of the installation wizard. Follow the instructions on the screen to complete the installation process. You will need to enter some information such as your website name, admin username and password, database details, and chat settings. After finishing the installation, you should be able to access your chat rooms and manage them from the admin panel.</p>
-<h2>Conclusion</h2>
-<p>Congratulations! You have successfully installed 123 Flash Chat V10 0 Nulled Script on your website. Now you can enjoy chatting with your visitors and users without paying for a license. However, remember that using nulled scripts may have some drawbacks and risks. You should always keep your website secure and updated, and avoid distributing or selling the nulled script to others.</p>
-```</p>
-<p></p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Amar Akbar Anthony Remake Malayalam Full Movie Hd Download ((EXCLUSIVE)).md b/spaces/stomexserde/gpt4-ui/Examples/Amar Akbar Anthony Remake Malayalam Full Movie Hd Download ((EXCLUSIVE)).md
deleted file mode 100644
index 4938e6d4c1d229f81c2a737b0e0609c009e6fc4d..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Amar Akbar Anthony Remake Malayalam Full Movie Hd Download ((EXCLUSIVE)).md	
+++ /dev/null
@@ -1,28 +0,0 @@
-<br />
-<h1>Amar Akbar Anthony Remake: A Delightful Comedy Thriller in Malayalam</h1>
-
-<p>If you are looking for a fun-filled movie to watch online, you might want to check out Amar Akbar Anthony Remake, a Malayalam comedy thriller directed by Nadirsha. The movie stars Prithviraj Sukumaran, Jayasurya and Indrajith Sukumaran as three close friends who get involved in a murder mystery.</p>
-<h2>Amar Akbar Anthony Remake malayalam full movie hd download</h2><br /><p><b><b>Download File</b> >> <a href="https://urlgoal.com/2uIaZB">https://urlgoal.com/2uIaZB</a></b></p><br /><br />
-
-<p>The movie is a remake of the 1977 Hindi classic Amar Akbar Anthony, which featured Amitabh Bachchan, Vinod Khanna and Rishi Kapoor in the lead roles. However, the Malayalam version has its own twists and turns that make it a fresh and entertaining watch.</p>
-
-<p>The story revolves around Amar, Akbar and Anthony, three bachelors who are struggling to make a living in Kochi. They share a flat with their neighbour Fathima, a young woman who works as a nurse. One day, they find out that Fathima has been brutally killed by an unknown assailant. The trio decides to take matters into their own hands and find out the culprit behind the crime.</p>
-
-<p>As they embark on their investigation, they encounter various hilarious situations and characters that add to the comedy quotient of the movie. They also discover some shocking secrets about Fathima's past that put them in danger. Will they be able to solve the mystery and catch the killer? Or will they end up as victims themselves?</p>
-
-<p>Amar Akbar Anthony Remake is a movie that will keep you hooked with its engaging plot, witty dialogues, peppy songs and superb performances by the lead actors. The movie also delivers a social message about communal harmony and women's empowerment in a subtle way.</p>
-
-<p>You can watch Amar Akbar Anthony Remake online in HD quality on Prime Video[^2^]. The movie has received positive reviews from critics and audiences alike and has been rated 6.6 out of 10 on IMDb[^1^]. If you are looking for a movie that will make you laugh and thrill you at the same time, Amar Akbar Anthony Remake is the perfect choice for you.</p>
-<p></p>
-
-<p>Amar Akbar Anthony Remake is not just a comedy thriller, but also a tribute to the original Hindi movie and its legendary actors. The movie has several references and homages to the 1977 classic, such as the title song, the names of the characters, the costumes and the dialogues. The movie also features some cameo appearances by popular Malayalam actors and comedians, such as Asif Ali, Kalabhavan Shajohn, Ramesh Pisharody and Saju Navodaya.</p>
-
-<p>The movie is also a showcase of the talent and versatility of the lead actors, who have proved their mettle in various genres of cinema. Prithviraj Sukumaran, who plays Amar, is known for his action and romantic roles in movies like Ennu Ninte Moideen, Anarkali and Lucifer. Jayasurya, who plays Akbar, is a versatile actor who has won accolades for his performances in movies like Su Su Sudhi Vathmeekam, Njan Marykutty and Captain. Indrajith Sukumaran, who plays Anthony, is an actor who has excelled in both comedy and serious roles in movies like Left Right Left, Angels and Lucifer.</p>
-
-<p>Namitha Pramod, who plays Jeni, the love interest of Amar, is a young actress who has made a mark in Malayalam cinema with movies like Traffic, Puthiya Theerangal and Chandrettan Evideya. The movie also features other talented actors like KPAC Lalitha, Siddique, Kalabhavan Haneef and Sasi Kalinga in supporting roles.</p>
-
-<p>Amar Akbar Anthony Remake is a movie that has something for everyone. It has comedy, action, romance, suspense and drama. It has catchy songs composed by Deepak Dev and Nadirsha. It has stunning cinematography by Sujith Vasudev and crisp editing by John Kutty. It has a strong script written by Bibin George and Vishnu Unnikrishnan. And above all, it has a brilliant direction by Nadirsha, who has made his debut as a filmmaker with this movie.</p>
-
-<p>So what are you waiting for? Watch Amar Akbar Anthony Remake online on Prime Video[^2^] and enjoy a roller-coaster ride of entertainment.</p> cec2833e83<br />
-<br />
-<br />
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/Cx Programmer 9.1 Download Torrentl [VERIFIED].md b/spaces/stomexserde/gpt4-ui/Examples/Cx Programmer 9.1 Download Torrentl [VERIFIED].md
deleted file mode 100644
index 291961e026d05f69011cec8d3ff92a7f19f36a2b..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Cx Programmer 9.1 Download Torrentl [VERIFIED].md	
+++ /dev/null
@@ -1,142 +0,0 @@
-<br />
-<h1>Cx Programmer 9.1 Download Torrentl: A Guide for PLC Programmers</h1>
-<p>If you are a PLC programmer who works with Omron PLCs, you might be interested in downloading and installing Cx Programmer 9.1 from torrent sites. But what is Cx Programmer and why do you need it? How can you download and install it safely and legally? How can you use it to create and maintain PLC programs? What are some common problems and troubleshooting tips for Cx Programmer 9.1? And what are some alternatives and competitors to Cx Programmer 9.1? In this article, we will answer these questions and more.</p>
- <h2>What is Cx Programmer and why do you need it?</h2>
-<p>Cx Programmer is a software tool for programming Omron PLCs (Programmable Logic Controllers). A PLC is a device that controls industrial machines and processes by executing logic instructions based on input signals from sensors and output signals to actuators. Omron is one of the leading manufacturers of PLCs in the world, offering various models and series for different applications.</p>
-<h2>Cx Programmer 9.1 Download Torrentl</h2><br /><p><b><b>DOWNLOAD</b> &raquo;&raquo;&raquo; <a href="https://urlgoal.com/2uIbWx">https://urlgoal.com/2uIbWx</a></b></p><br /><br />
- <h3>Cx Programmer is a software tool for programming Omron PLCs</h3>
-<p>Cx Programmer is part of the CX-One software suite that integrates various software tools for Omron automation products. It allows you to create, edit, debug, monitor, and document PLC programs using different programming languages such as ladder diagram (LD), structured text (ST), instruction list (IL), function block diagram (FBD), or sequential function chart (SFC). It also allows you to configure PLC settings such as memory allocation, I/O assignment, communication parameters, etc.</p>
- <h3>Cx Programmer offers many features and benefits for PLC development</h3>
-<p>Cx Programmer offers many features and benefits for PLC development, such as:</p>
-<ul>
-<li>Smart input: You can use shortcuts, templates, auto-completion, drag-and-drop, etc. to speed up your programming process.</li>
-<li>Multiple views: You can switch between different views of your program, such as ladder, text, symbol table, cross-reference, etc. to check and edit your program from different perspectives.</li>
-<li>Online editing: You can edit your program while it is running on the PLC and see the changes in real-time.</li>
-<li>Documentation: You can generate and print documentation for your program, such as comments, labels, symbols, etc. to make it easier to understand and maintain.</li>
-<li>Debugging: You can use breakpoints, watch windows, force values, etc. to test and troubleshoot your program.</li>
-<li>Monitoring: You can use data trace, data log, trend graph, etc. to monitor the performance and status of your program and PLC.</li>
-</ul>
- <h3>Cx Programmer supports various PLC models and operating systems</h3>
-<p>Cx Programmer supports various PLC models and operating systems, such as:</p>
-<table>
-<tr><th>PLC Models</th><th>Operating Systems</th></tr>
-<tr><td>CJ1/CJ2/CP1/CS1/CV/CQM1H/C200HX/HG/HE(-ZE)/C200HS</td><td>Windows 10/8.1/8/7/Vista/XP</td></tr>
-<tr><td>NJ/NX/NY</td><td>Windows 10/8.1/8/7</td></tr>
-<tr><td>CPM2A/CPM2C/SRM1(-V2)</td><td>Windows 10/8.1/8/7/Vista/XP/2000</td></tr>
-<tr><td>C200H/C500/C1000H/C2000H</td><td>Windows 10/8.1/8/7/Vista/XP/2000/NT4.0(SP6a)/Me/98SE</td></tr>
-</table>
- <h2>How to download and install Cx Programmer 9.1 from torrent sites?</h2>
-<p>If you want to download and install Cx Programmer 9.1 from torrent sites, you need to know what torrent sites are, how they work, and what precautions you need to take. Torrent sites are online platforms that offer peer-to-peer file sharing of various types of content, such as software, movies, music, games, etc. However, torrent sites are also risky and illegal in many countries, as they may contain viruses, malware, spyware, or copyrighted material.</p>
- <h3>Torrent sites are online platforms that offer peer-to-peer file sharing</h3>
-<p>Torrent sites are online platforms that offer peer-to-peer file sharing of various types of content. Peer-to-peer means that the files are not stored on a central server, but on the computers of the users who participate in the network. Each user who downloads a file becomes a seeder who uploads the file to other users who request it. Each user who requests a file becomes a leecher who downloads the file from other seeders. The more seeders and leechers there are for a file, the faster and easier it is to download it.</p>
-<p></p>
- <h3>You need a torrent client and a VPN to download files from torrent sites</h3>
-<p>To download files from torrent sites, you need two things: a torrent client and a VPN (Virtual Private Network). A torrent client is a software application that allows you to open and manage torrent files. A torrent file is a small file that contains information about the content you want to download, such as the name, size, location, etc. Some examples of torrent clients are uTorrent, BitTorrent, qBittorrent, etc.</p>
- <p>A VPN is a service that creates a secure and encrypted connection between your computer and a remote server in another location. A VPN allows you to hide your IP address and location from the torrent site and other users in the network. This way, you can protect your privacy and security from hackers, trackers, ISPs (Internet Service Providers), or authorities who may monitor or block your online activity. Some examples of VPN services are NordVPN, ExpressVPN, CyberGhost, etc.</p>
- <h3>You can find Cx Programmer 9.1 torrents on various sources</h3>
-<p>You can find Cx Programmer 9.1 torrents on various sources online, such as torrent search engines or websites that specialize in software torrents. Torrent search engines are websites that allow you to search for torrents across multiple torrent sites at once. Some examples of torrent search engines are Torrentz2, The Pirate Bay, 1337x, etc. Software torrents are websites that focus on providing torrents for various software applications, such as operating systems, antivirus, office, etc. Some examples of software torrents are Getintopc, Softonic, Filehippo, etc. To find Cx Programmer 9.1 torrents, you can use any of these sources and type in the keywords "Cx Programmer 9.1" or "Cx Programmer 9.1 download" or "Cx Programmer 9.1 torrent" in the search box. You will see a list of results that match your query. You can sort the results by relevance, date, size, seeders, leechers, etc. to find the best option for you. However, you need to be careful when choosing a torrent source and a torrent file, as some of them may be fake, infected, or illegal. You should always check the following things before downloading a torrent file: - The reputation and ratings of the torrent site and the torrent uploader - The comments and feedback from other users who have downloaded the file - The file size and format of the torrent file - The number and ratio of seeders and leechers for the torrent file - The presence and validity of a magnet link or a download button for the torrent file <h3>You need to follow some steps to install Cx Programmer 9.1 on your computer</h3>
-<p>Once you have downloaded the torrent file of Cx Programmer 9.1, you need to follow some steps to install it on your computer. Here are the general steps you need to follow:</p>
-<ol>
-<li>Open the torrent file with your torrent client and start downloading the content.</li>
-<li>Wait until the download is complete and verify that the content is intact and safe.</li>
-<li>Extract the content from the compressed file (such as ZIP or RAR) if necessary.</li>
-<li>Run the setup file (such as EXE or MSI) and follow the installation wizard.</li>
-<li>Enter the license key or activation code if required.</li>
-<li>Finish the installation and launch Cx Programmer 9.1.</li>
-</ol>
- <p>Note that these steps may vary depending on the source and version of Cx Programmer 9.1 you have downloaded. You should always read the instructions and readme files that come with the torrent file for more specific and detailed information.</p>
- <h2>How to use Cx Programmer 9.1 to create and maintain PLC programs?</h2>
-<p>Now that you have installed Cx Programmer 9.1 on your computer, you can use it to create and maintain PLC programs for Omron PLCs. Cx Programmer 9.1 has a user-friendly interface and smart input features that make programming easier and faster. You can use different programming languages and instructions in Cx Programmer 9.1 to suit your preferences and needs. You can also use symbols, structures, arrays, timers, and counters in Cx Programmer 9.1 to organize and optimize your program. Moreover, you can use online and offline simulation tools in Cx Programmer 9.1 to test and debug your program before transferring it to the PLC.</p>
- <h3>Cx Programmer 9.1 has a user-friendly interface and smart input features</h3>
-<p>Cx Programmer 9.1 has a user-friendly interface that consists of various windows, menus, toolbars, icons, buttons, etc. that allow you to access and manage different functions and options of the software. You can customize the interface according to your preferences by resizing, moving, docking, hiding, or showing different windows and elements.</p>
- <p>Cx Programmer 9.1 also has smart input features that help you speed up your programming process by providing shortcuts, templates, auto-completion, drag-and-drop, etc. For example:</p>
-<ul>
-<li>You can use keyboard shortcuts to perform common tasks such as creating new projects, opening existing projects, saving projects, compiling projects, etc.</li>
-<li>You can use templates to create new programs or blocks with predefined settings and structures.</li>
-<li>You can use auto-completion to enter instructions or symbols by typing only a few characters and selecting from a list of suggestions.</li>
-<li>You can use drag-and-drop to insert instructions or symbols from one window to another without typing or copying.</li>
-</ul>
- <h3>You can use different programming languages and instructions in Cx Programmer 9.1</h3>
-<p>You can use different programming languages and instructions in Cx Programmer 9.1 to create and edit your PLC program. Cx Programmer 9.1 supports five programming languages that are defined by the IEC 61131-3 standard, which are:</p>
-<ul>
-<li>Ladder diagram (LD): A graphical language that uses symbols and lines to represent logic circuits.</li>
-<li>Structured text (ST): A textual language that uses statements and expressions to represent logic operations.</li>
-<li>Instruction list (IL): A textual language that uses mnemonics and operands to represent logic instructions.</li>
-<li>Function block diagram (FBD): A graphical language that uses blocks and wires to represent logic functions.</li>
-<li>Sequential function chart (SFC): A graphical language that uses steps and transitions to represent logic sequences.</li>
-</ul>
- <p>You can choose the programming language that suits your preferences and needs, or you can mix different languages in the same program or block. You can also use different types of instructions in Cx Programmer 9.1, such as:</p>
-<ul>
-<li>Basic instructions: These are the most common and simple instructions that perform basic logic operations, such as AND, OR, NOT, etc.</li>
-<li>Application instructions: These are more advanced and specific instructions that perform various functions, such as arithmetic, comparison, conversion, data transfer, etc.</li>
-<li>Special instructions: These are special and unique instructions that perform specific tasks, such as communication, timer, counter, etc.</li>
-</ul>
- <h3>You can use symbols, structures, arrays, timers, and counters in Cx Programmer 9.1</h3>
-<p>You can use symbols, structures, arrays, timers, and counters in Cx Programmer 9.1 to organize and optimize your PLC program. Symbols are names that you assign to memory locations or devices in your PLC program. Structures are data types that group multiple symbols into one unit. Arrays are data types that store multiple values of the same type in a sequential order. Timers are devices that measure time intervals or delays in your PLC program. Counters are devices that count the number of events or pulses in your PLC program.</p>
- <p>You can use these elements in Cx Programmer 9.1 to make your PLC program more readable, modular, reusable, and efficient. For example:</p>
-<ul>
-<li>You can use symbols to identify and access different inputs, outputs, variables, constants, etc. in your PLC program.</li>
-<li>You can use structures to define and manipulate complex data types, such as records, unions, enumerations, etc. in your PLC program.</li>
-<li>You can use arrays to store and process multiple values of the same type, such as numbers, characters, booleans, etc. in your PLC program.</li>
-<li>You can use timers to control the timing of your PLC program, such as starting, stopping, delaying, triggering, etc.</li>
-<li>You can use counters to monitor the occurrence of your PLC program, such as counting, comparing, resetting, etc.</li>
-</ul>
- <h3>You can use online and offline simulation tools in Cx Programmer 9.1</h3>
-<p>You can use online and offline simulation tools in Cx Programmer 9.1 to test and debug your PLC program before transferring it to the PLC. Online simulation means that you connect your computer to the PLC via a communication cable or network and run your PLC program on the actual PLC hardware. Offline simulation means that you run your PLC program on a virtual PLC emulator on your computer without connecting to the actual PLC hardware.</p>
- <p>You can use these simulation tools in Cx Programmer 9.1 to verify the functionality and performance of your PLC program without affecting the real system or process. For example:</p>
-<ul>
-<li>You can use online simulation to check the input and output signals of your PLC program and monitor the changes in real-time.</li>
-<li>You can use offline simulation to check the logic flow and execution order of your PLC program and modify it as needed.</li>
-<li>You can use breakpoints, watch windows, force values, etc. to pause and inspect the values and states of your PLC program and change them as needed.</li>
-</ul>
- <h2>What are some common problems and troubleshooting tips for Cx Programmer 9.1?</h2>
-<p>As with any software tool, you may encounter some problems and errors when using Cx Programmer 9.1. Some of these problems may be related to compatibility, communication, or programming issues. Here are some common problems and troubleshooting tips for Cx Programmer 9.1:</p>
- <h3>Some common problems are related to compatibility, communication, and errors</h3>
-<p>Some common problems that you may face when using Cx Programmer 9.1 are:</p>
-<ul>
-<li>Compatibility issues: You may have compatibility issues if you use different versions of Cx Programmer or CX-One, or if you use different models or series of Omron PLCs.</li>
-<li>Communication issues: You may have communication issues if you use incorrect or faulty communication cables or network settings, or if you have interference or noise in the communication line.</li>
-<li>Programming errors: You may have programming errors if you use incorrect or invalid instructions, symbols, data types, etc., or if you have logic or syntax errors in your PLC program.</li>
-</ul>
- <h3>You can check the manuals, error codes, and online support for troubleshooting tips</h3>
-<p>You can check the manuals, error codes, and online support for troubleshooting tips when you encounter problems with Cx Programmer 9.1. The manuals are documents that provide detailed information and instructions on how to use Cx Programmer 9.1 and its features. You can access the manuals from the Help menu in Cx Programmer 9.1 or from the Omron website. The error codes are numbers that indicate the type and cause of the errors that occur in Cx Programmer 9.1 or in the PLC. You can view the error codes from the Error Log window in Cx Programmer 9.1 or from the PLC display. The online support is a service that provides technical assistance and solutions for Cx Programmer 9.1 and other Omron products. You can access the online support from the Omron website or by contacting the Omron customer service.</p>
- <h3>You can also update or reinstall Cx Programmer 9.1 if necessary</h3>
-<p>You can also update or reinstall Cx Programmer 9.1 if necessary to fix some problems or improve its performance. Updating means that you download and install the latest version of Cx Programmer 9.1 or CX-One that contains new features, bug fixes, security patches, etc. Reinstalling means that you uninstall and install again Cx Programmer 9.1 or CX-One to restore its original settings and files.</p>
- <p>You can update or reinstall Cx Programmer 9.1 from the Omron website or from the CD-ROM that comes with CX-One. You should always backup your PLC programs and settings before updating or reinstalling Cx Programmer 9.1 to avoid losing any data.</p>
- <h2>What are some alternatives and competitors to Cx Programmer 9.1?</h2>
-<p>If you are not satisfied with Cx Programmer 9.1 or if you want to try other software tools for programming PLCs, you can look for some alternatives and competitors to Cx Programmer 9.1. Alternatives are software tools that offer similar functions and features as Cx Programmer 9.1 but for different brands or models of PLCs. Competitors are software tools that offer similar functions and features as Cx Programmer 9.1 but for the same brand or model of PLCs.</p>
- <h3>Some alternatives are LADDER EDITOR, ISPSoft, TwidoSuite, Eaton easySoft, etc.</h3>
-<p>Some alternatives to Cx Programmer 9.1 are:</p>
-<ul>
-<li>LADDER EDITOR: A software tool for programming Mitsubishi PLCs using ladder diagram language.</li>
-<li>ISPSoft: A software tool for programming Delta PLCs using various programming languages such as LD, ST, IL, FBD, SFC, etc.</li>
-<li>TwidoSuite: A software tool for programming Schneider Electric PLCs using FBD or LD languages.</li>
-<li>Eaton easySoft: A software tool for programming Eaton PLCs using FBD language.</li>
-</ul>
- <h3>Some competitors are CX Network , OpenPLC Editor, SILworX, etc.</h3>
-<p>Some competitors to Cx Programmer 9.1 are:</p>
-<ul>
-<li>CX Network: A software tool for programming and managing Omron PLCs and other network devices using various protocols such as Ethernet/IP, Modbus TCP, etc.</li>
-<li>OpenPLC Editor: A software tool for programming Omron PLCs and other PLCs using open source software and hardware platforms.</li>
-<li>SILworX: A software tool for programming and verifying Omron PLCs and other PLCs that comply with the safety standards of IEC 61508.</li>
-</ul>
- <h3>You can compare the features, ratings, reviews, pricing, and more of different software tools</h3>
-<p>You can compare the features, ratings, reviews, pricing, and more of different software tools for programming PLCs to find the best option for you. You can use online sources such as websites, blogs, forums, videos, etc. to find and compare information about different software tools. You can also use online tools such as Software Advice, Capterra, G2, etc. to find and compare software tools based on various criteria such as category, industry, function, etc.</p>
- <h2>Conclusion</h2>
-<p>In this article, we have covered the topic of Cx Programmer 9.1 download torrentl. We have explained what Cx Programmer is and why you need it, how to download and install it from torrent sites, how to use it to create and maintain PLC programs, what are some common problems and troubleshooting tips for it, and what are some alternatives and competitors to it. We hope that this article has been helpful and informative for you.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Cx Programmer 9.1 download torrentl:</p>
-<ol>
-<li>Q: Is Cx Programmer 9.1 free or paid?</li>
-<li>A: Cx Programmer 9.1 is a paid software tool that requires a license key or activation code to use. However, you can download a free trial version of Cx Programmer 9.1 from the Omron website that allows you to use it for a limited time.</li>
-<li>Q: Is Cx Programmer 9.1 legal or illegal?</li>
-<li>A: Cx Programmer 9.1 is a legal software tool that is developed and distributed by Omron Corporation. However, downloading and installing Cx Programmer 9.1 from torrent sites may be illegal in some countries, as it may violate the intellectual property rights of Omron Corporation or contain pirated or cracked versions of Cx Programmer 9.1.</li>
-<li>Q: Is Cx Programmer 9.1 safe or risky?</li>
-<li>A: Cx Programmer 9.1 is a safe software tool that does not contain any viruses, malware, spyware, or other harmful components. However, downloading and installing Cx Programmer 9.1 from torrent sites may be risky, as it may expose your computer to viruses, malware, spyware, or other harmful components that may damage your system or compromise your data.</li>
-<li>Q: Is Cx Programmer 9.1 compatible with Windows 10?</li>
-<li>A: Yes, Cx Programmer 9.1 is compatible with Windows 10 as well as other Windows operating systems such as Windows 8.1/8/7/Vista/XP/2000/NT4.0(SP6a)/Me/98SE.</li>
-<li>Q: Is Cx Programmer 9.1 compatible with other PLC brands or models?</li>
-<li>A: No, Cx Programmer 9.1 is only compatible with Omron PLCs such as CJ1/CJ2/CP1/CS1/CV/CQM1H/C200HX/HG/HE(-ZE)/C200HS/NJ/NX/NY/CPM2A/CPM2C/SRM1(-V2)/C200H/C500/C1000H/C2000H.</li>
-</ol></p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sub314xxl/MusicGen/audiocraft/modules/lstm.py b/spaces/sub314xxl/MusicGen/audiocraft/modules/lstm.py
deleted file mode 100644
index c0866175950c1ca4f6cca98649525e6481853bba..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MusicGen/audiocraft/modules/lstm.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from torch import nn
-
-
-class StreamableLSTM(nn.Module):
-    """LSTM without worrying about the hidden state, nor the layout of the data.
-    Expects input as convolutional layout.
-    """
-    def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True):
-        super().__init__()
-        self.skip = skip
-        self.lstm = nn.LSTM(dimension, dimension, num_layers)
-
-    def forward(self, x):
-        x = x.permute(2, 0, 1)
-        y, _ = self.lstm(x)
-        if self.skip:
-            y = y + x
-        y = y.permute(1, 2, 0)
-        return y
diff --git a/spaces/sudo-ai/zero123plus-demo-space/examples/img_to_mv.py b/spaces/sudo-ai/zero123plus-demo-space/examples/img_to_mv.py
deleted file mode 100644
index 9d3550a8d9605ae0edebdf336e1eef3eebe1fd2e..0000000000000000000000000000000000000000
--- a/spaces/sudo-ai/zero123plus-demo-space/examples/img_to_mv.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import torch
-import requests
-from PIL import Image
-from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
-
-# Load the pipeline
-pipeline = DiffusionPipeline.from_pretrained(
-    "sudo-ai/zero123plus-v1.1", custom_pipeline="sudo-ai/zero123plus-pipeline",
-    torch_dtype=torch.float16
-)
-# Feel free to tune the scheduler
-pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
-    pipeline.scheduler.config, timestep_spacing='trailing'
-)
-pipeline.to('cuda:0')
-# Run the pipeline
-cond = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/lysol.png", stream=True).raw)
-result = pipeline(cond, num_inference_steps=75).images[0]
-result.show()
-result.save("output.png")
diff --git a/spaces/supertori/files/stable-diffusion-webui/test/basic_features/img2img_test.py b/spaces/supertori/files/stable-diffusion-webui/test/basic_features/img2img_test.py
deleted file mode 100644
index 08c5c903e8382ef4b969b01da87bc69fb06ff2b4..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/test/basic_features/img2img_test.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import unittest
-import requests
-from gradio.processing_utils import encode_pil_to_base64
-from PIL import Image
-
-
-class TestImg2ImgWorking(unittest.TestCase):
-    def setUp(self):
-        self.url_img2img = "http://localhost:7860/sdapi/v1/img2img"
-        self.simple_img2img = {
-            "init_images": [encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))],
-            "resize_mode": 0,
-            "denoising_strength": 0.75,
-            "mask": None,
-            "mask_blur": 4,
-            "inpainting_fill": 0,
-            "inpaint_full_res": False,
-            "inpaint_full_res_padding": 0,
-            "inpainting_mask_invert": False,
-            "prompt": "example prompt",
-            "styles": [],
-            "seed": -1,
-            "subseed": -1,
-            "subseed_strength": 0,
-            "seed_resize_from_h": -1,
-            "seed_resize_from_w": -1,
-            "batch_size": 1,
-            "n_iter": 1,
-            "steps": 3,
-            "cfg_scale": 7,
-            "width": 64,
-            "height": 64,
-            "restore_faces": False,
-            "tiling": False,
-            "negative_prompt": "",
-            "eta": 0,
-            "s_churn": 0,
-            "s_tmax": 0,
-            "s_tmin": 0,
-            "s_noise": 1,
-            "override_settings": {},
-            "sampler_index": "Euler a",
-            "include_init_images": False
-            }
-
-    def test_img2img_simple_performed(self):
-        self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
-
-    def test_inpainting_masked_performed(self):
-        self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png"))
-        self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
-
-    def test_inpainting_with_inverted_masked_performed(self):
-        self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png"))
-        self.simple_img2img["inpainting_mask_invert"] = True
-        self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
-
-    def test_img2img_sd_upscale_performed(self):
-        self.simple_img2img["script_name"] = "sd upscale"
-        self.simple_img2img["script_args"] = ["", 8, "Lanczos", 2.0]
-
-        self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Dirt 2 Crack BETTER Only Download.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Dirt 2 Crack BETTER Only Download.md
deleted file mode 100644
index 85b61f88017cf7192b8f4ff83ef8b71581129f21..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Dirt 2 Crack BETTER Only Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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deleted file mode 100644
index 015c14b9e4b16b66d19177e19f68c4b57717863c..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Natholi Oru Cheriya Meenalla 2013 Malayalam DVDRip XviDDDR.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<h2>Natholi Oru Cheriya Meenalla 2013 Malayalam DVDRip XviDDDR</h2><br /><p><b><b>DOWNLOAD</b> &#10038;&#10038;&#10038; <a href="https://cinurl.com/2uEYwz">https://cinurl.com/2uEYwz</a></b></p><br /><br />
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diff --git a/spaces/suvradip2000/space1/README.md b/spaces/suvradip2000/space1/README.md
deleted file mode 100644
index b096e8f73ae10f16910fae3b311d7c1a8b33c75d..0000000000000000000000000000000000000000
--- a/spaces/suvradip2000/space1/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Space1
-emoji: 👁
-colorFrom: gray
-colorTo: green
-sdk: docker
-pinned: false
-license: mit
-app_port: 8001
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
diff --git a/spaces/tang155/bingo/src/components/chat-message.tsx b/spaces/tang155/bingo/src/components/chat-message.tsx
deleted file mode 100644
index bf272d8d7005cfd06c53bd213e09ea217e803549..0000000000000000000000000000000000000000
--- a/spaces/tang155/bingo/src/components/chat-message.tsx
+++ /dev/null
@@ -1,93 +0,0 @@
-import remarkGfm from 'remark-gfm'
-import remarkMath from 'remark-math'
-import supersub from 'remark-supersub'
-import remarkBreaks from 'remark-breaks'
-import { cn } from '@/lib/utils'
-import { CodeBlock } from '@/components/ui/codeblock'
-import { MemoizedReactMarkdown } from '@/components/markdown'
-import { LearnMore } from './learn-more'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { useEffect } from 'react'
-import { TurnCounter } from './turn-counter'
-
-export interface ChatMessageProps {
-  message: ChatMessageModel
-}
-
-export function ChatMessage({ message, ...props }: ChatMessageProps) {
-  useEffect(() => {
-    if (document.body.scrollHeight - window.innerHeight - window.scrollY - 200 < 0) {
-      window.scrollBy(0, 200)
-    }
-  }, [message.text])
-
-  return message.text ? (
-    <div
-      className={cn('text-message', message.author)}
-      {...props}
-    >
-      <div className="text-message-content">
-        <MemoizedReactMarkdown
-          linkTarget="_blank"
-          className="prose break-words dark:prose-invert prose-p:leading-relaxed prose-pre:p-0"
-          remarkPlugins={[remarkGfm, remarkMath, supersub, remarkBreaks]}
-          components={{
-            img(obj) {
-              try {
-                const uri = new URL(obj.src!)
-                const w = uri.searchParams.get('w')
-                const h = uri.searchParams.get('h')
-                if (w && h) {
-                  uri.searchParams.delete('w')
-                  uri.searchParams.delete('h')
-                  return <a style={{ float: 'left', maxWidth: '50%' }} href={uri.toString()} target="_blank" rel="noopener noreferrer"><img src={obj.src} alt={obj.alt} width={w!} height={h!}/></a>
-                }
-              } catch (e) {
-              }
-              return <img src={obj.src} alt={obj.alt} title={obj.title} />
-            },
-            p({ children }) {
-              return <p className="mb-2">{children}</p>
-            },
-            code({ node, inline, className, children, ...props }) {
-              if (children.length) {
-                if (children[0] == '▍') {
-                  return (
-                    <span className="mt-1 animate-pulse cursor-default">▍</span>
-                  )
-                }
-
-                children[0] = (children[0] as string).replace('`▍`', '▍')
-              }
-
-              const match = /language-(\w+)/.exec(className || '')
-
-              if (inline) {
-                return (
-                  <code className={className} {...props}>
-                    {children}
-                  </code>
-                )
-              }
-
-              return (
-                <CodeBlock
-                  key={Math.random()}
-                  language={(match && match[1]) || ''}
-                  value={String(children).replace(/\n$/, '')}
-                  {...props}
-                />
-              )
-            }
-          }}
-        >
-          {message.text}
-        </MemoizedReactMarkdown>
-      </div>
-      <div className="text-message-footer">
-        {message.author === 'bot' && <LearnMore sourceAttributions={message.sourceAttributions} />}
-        {message.author === 'bot' && <TurnCounter throttling={message.throttling} />}
-      </div>
-    </div>
-  ) : null
-}
diff --git a/spaces/tappyness1/error-analysis-cv-segmentations/src/error_analysis.py b/spaces/tappyness1/error-analysis-cv-segmentations/src/error_analysis.py
deleted file mode 100644
index b5950e4e2f60b9285f96b4eefeb7a14650c9f4ee..0000000000000000000000000000000000000000
--- a/spaces/tappyness1/error-analysis-cv-segmentations/src/error_analysis.py
+++ /dev/null
@@ -1,182 +0,0 @@
-from peekingduck.pipeline.nodes.model import yolo as pkd_yolo
-from peekingduck.pipeline.nodes.model import yolact_edge as pkd_yolact
-from src.data_ingestion.data_ingestion import AnnotsGTGetter
-from src.inference import Inference
-from src.confusion_matrix import ConfusionMatrix
-import yaml
-from itertools import product
-import pandas as pd
-import numpy as np
-
-def transform_gt_bbox_format(ground_truth, img_size, format = "coco"):
-    """transforms ground truth bbox format to pascal voc for confusion matrix
-
-    Args:
-        ground_truth (_type_): nx5 numpy array, if coco - n x [class, x, y, w, h], if yolo - n x [class, x-mid, y-mid, w, h]
-        img_size (_type_): [Height * Weight * Dimension] values vector
-        format (str, optional): . Defaults to "coco".
-
-    Returns:
-        _type_: ground_truth. Transformed ground truth to pascal voc format
-    """
-    if format == "coco":
-        ground_truth[:, 3] = (ground_truth[:, 1] + ground_truth[:, 3])/img_size[1]
-        ground_truth[:, 1] = (ground_truth[:, 1]) /img_size[1]
-        ground_truth[:, 4] = (ground_truth[:, 2] + ground_truth[:, 4])/img_size[0]
-        ground_truth[:, 2] = (ground_truth[:, 2]) /img_size[0]
-    
-    return ground_truth
-
-def load_model(cfg_obj, iou_threshold, score_threshold):
-
-    pkd = cfg_obj['error_analysis']['peekingduck']
-    task = cfg_obj['error_analysis']['task']
-
-    if pkd:
-
-        pkd_model = cfg_obj['pkd']['model']
-        # assert task == "seg" and pkd_model == "yolact_edge", "For segmentation tasks, make sure task is seg and pkd_model is yolact_edge"
-        # assert task == "det" and pkd_model == "yolo", "For detection tasks, make sure task is det and pkd_model is yolo"
-        # only instantiates the v4tiny model, but you are free to change this to other pkd model 
-        if pkd_model == "yolo":
-            yolo_ver = cfg_obj['pkd']['yolo_ver']
-            model = pkd_yolo.Node(model_type = yolo_ver, 
-                                  detect= list(cfg_obj['error_analysis']['inference_labels_dict'].keys()),
-                                  iou_threshold = iou_threshold,
-                                  score_threshold = score_threshold)
-
-        if pkd_model == "yolact_edge":
-            yolact_ver = cfg_obj['pkd']['yolact_ver']
-            model = pkd_yolact.Node(model_type = yolact_ver, 
-                                    detect= list(cfg_obj['error_analysis']['inference_labels_dict'].values()), 
-                                    iou_threshold = iou_threshold,
-                                    score_threshold = score_threshold)
-
-    else:
-        # call in your own model
-        # self.model = <your model import here>
-        # make sure that your model has iou_threshold and score_threshold attributes 
-        # you can easily set those attributes in this else block
-        pass
-
-    return model
-
-class ErrorAnalysis:
-
-    def __init__(self,  cfg_path = 'cfg/cfg.yml'):
-        
-        cfg_file = open(cfg_path)
-        self.cfg_obj = yaml.load(cfg_file, Loader=yaml.FullLoader)
-        # self.nms_thresh = self.cfg_obj['error_analysis']['nms_thresholds']
-        self.iou_thresh = self.cfg_obj['error_analysis']['iou_thresholds']
-        self.conf_thresh = self.cfg_obj['error_analysis']['conf_thresholds']
-        self.inference_folder = self.cfg_obj['dataset']['img_folder_path']
-        self.task = self.cfg_obj['error_analysis']['task']
-        base_iou_threshold = self.cfg_obj['visual_tool']['iou_threshold']
-        base_score_threshold = self.cfg_obj['visual_tool']['conf_threshold']
-        
-        self.cm_results = []
-        
-        # instantiate a "base" model with configs already 
-        self.model = load_model(self.cfg_obj, base_iou_threshold, base_score_threshold) 
-
-    def generate_inference(self, img_fname = "000000576052.jpg"):
-        """Run inference on img based on the image file name. Path to the folder is determined by cfg 
-
-        Args:
-            img_fname (str, optional): _description_. Defaults to "000000576052.jpg".
-
-        Returns:
-            ndarray, tuple: if task is 'det': ndarray - n x [x1, y1, x2, y2, score, class], (H, W, D)
-            ndarray, tuple: if task is 'seg': list - n x [[array of binary mask], score, class], (H, W, D)
-        """
-        
-        inference_obj = Inference(self.model, self.cfg_obj)
-        img_path = f"{self.inference_folder}{img_fname}"
-        inference_outputs = inference_obj.run_inference_path(img_path)
-
-        return inference_outputs
-
-    def get_annots(self):
-        """get GT annotations from dataset 
-        """
-
-        annots_obj = AnnotsGTGetter(cfg_obj = self.cfg_obj)
-        self.gt_dict = annots_obj.get_gt_annots()
-
-    def generate_conf_matrix(self,iou_threshold = 0.5, conf_threshold = 0.2):
-        """generate the confusion matrix by running inference on each image
-        """
-
-        num_classes = len(list(self.cfg_obj['error_analysis']['labels_dict'].keys()))
-        ground_truth_format = self.cfg_obj["error_analysis"]["ground_truth_format"]
-        idx_base = self.cfg_obj["error_analysis"]["idx_base"]
-
-        # TODO - currently, Conf Matrix is 0 indexed but all my classes are one-based index. 
-        # need to find a better to resolve this 
-        # Infuriating.
-        cm = ConfusionMatrix(num_classes=num_classes, CONF_THRESHOLD = conf_threshold, IOU_THRESHOLD=iou_threshold)
-        
-        for fname in list(self.gt_dict.keys()):
-
-            inference_output, img_size = self.generate_inference(fname)
-            ground_truth = self.gt_dict[fname].copy()
-                        
-            if self.task == "det":
-
-                # deduct index_base from each inference's class index 
-                inference_output[:, -1] -= idx_base
-                # deduct index_base from each groundtruth's class index 
-                ground_truth[:, 0] -= idx_base
-                # inference is in x1, y1, x2, y2, scores, class, so OK
-                # coco gt is in x, y, width, height - need to change to suit conf matrix
-                # img shape is (H, W, D) so plug in accordingly to normalise 
-                ground_truth = transform_gt_bbox_format(ground_truth=ground_truth, img_size=img_size, format = ground_truth_format)
-
-            else: 
-                # deduct index_base from each groundtruth's class index
-                ground_truth = [[gt[0] - idx_base, gt[1]] for gt in ground_truth]
-
-            cm.process_batch(inference_output, ground_truth, task = self.task)
-        
-        cm.get_PR() 
-
-        return cm.matrix, cm.precision, cm.recall
-    
-    def generate_conf_matrices(self, print_matrix = True):
-        """generates the confidence matrices
-        """
-
-        # get all combinations of the threshold values:
-        combinations = list(product(self.iou_thresh, self.conf_thresh))
-        # print (combinations)
-        comb_cms = {}
-        for comb in combinations:
-            # print (f"IOU: {comb[0]}, Conf: {comb[1]}")  
-            self.model = load_model(self.cfg_obj, iou_threshold=comb[0], score_threshold=comb[1])
-            returned_matrix, precision, recall = self.generate_conf_matrix(iou_threshold = comb[0], conf_threshold = comb[1])
-            # print (returned_matrix)
-            # print (f"precision: {precision}")
-            # print (f"recall: {recall}")
-            comb_cms[f"IOU: {comb[0]}, Conf: {comb[1]}"] = returned_matrix
-            self.cm_results.append([comb[0], comb[1], precision, recall])
-        
-        if print_matrix:
-            for k, v in comb_cms.items():
-                print (k)
-                print (v)
-
-    def proc_pr_table(self):
-        
-        self.cm_table = pd.DataFrame(self.cm_results, columns = ['IOU_Threshold', 'Score Threshold', 'Precision', 'Recall'])
-
-        print (self.cm_table)
-
-
-if __name__ == "__main__":
-    ea_games = ErrorAnalysis()
-    # print (ea_games.generate_inference())
-    ea_games.get_annots()
-    ea_games.generate_conf_matrices()
-    # print (ea_games.generate_conf_matrix())
-    # print (ea_games.gt_dict)
\ No newline at end of file
diff --git a/spaces/tappyness1/one_dash/src/scraper_char_list.py b/spaces/tappyness1/one_dash/src/scraper_char_list.py
deleted file mode 100644
index ad8c7261e415224d709955f26532286acb94ebff..0000000000000000000000000000000000000000
--- a/spaces/tappyness1/one_dash/src/scraper_char_list.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import requests
-from bs4 import BeautifulSoup
-import pandas as pd
-
-def scrape_char_links(char_dict, start_chap = 1, end_chap =5000, continue_last = True):
-    # if continue_last:
-    #     curr_chapts = df['Chapter'].tolist()
-    # else: curr_chapts = []
-    for i in range(start_chap, end_chap):
-        # if i in curr_chapts:
-        #     continue
-        # else:
-        if i % 100 == 0:
-            print (i)
-        # char_list = []
-        URL = f'https://onepiece.fandom.com/wiki/Chapter_{i}'
-        page = requests.get(URL)
-
-        soup = BeautifulSoup(page.content, 'html.parser')
-
-        table = soup.find('table', class_='CharTable')
-        
-        for elem in table.find_all('li'):
-            try:
-                # char_list.append(elem.text)
-                if elem.find('a').get('title') in char_dict:
-                    continue
-                else:
-                    char_dict[elem.find('a').get('title')] = elem.find('a').get('href')
-            except :
-                continue
-    return char_dict
\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Aspentech Aspenone V8.0 Dvd1 (2013) [Extra Quality].md b/spaces/terfces0erbo/CollegeProjectV2/Aspentech Aspenone V8.0 Dvd1 (2013) [Extra Quality].md
deleted file mode 100644
index 37503445975549eb112abdbfbac55c987a9fc271..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Aspentech Aspenone V8.0 Dvd1 (2013) [Extra Quality].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Aspentech Aspenone V8.0 Dvd1 (2013)</h2><br /><p><b><b>Download File</b> ->->->-> <a href="https://bytlly.com/2uGm6k">https://bytlly.com/2uGm6k</a></b></p><br /><br />
-
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-<p></p>
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Gaussian 09 Crack.md b/spaces/terfces0erbo/CollegeProjectV2/Gaussian 09 Crack.md
deleted file mode 100644
index cfeb6fcc437cc9e23fcae1ae94ceb0575798c005..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Gaussian 09 Crack.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<br />
-<h1>How to Download and Install Gaussian 09 for Free</h1>
-<p>Gaussian 09 is a popular software package for computational chemistry that offers a variety of methods and capabilities for studying molecular systems. However, Gaussian 09 is not free and requires a license from Gaussian, Inc. to use it legally. If you are looking for a way to download and install Gaussian 09 for free, you may be tempted to search for a cracked version of the software on the internet. However, this is not a good idea for several reasons:</p>
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-<ul>
-<li>Downloading cracked software is illegal and unethical. You may be violating the intellectual property rights of Gaussian, Inc. and the original developers of the software. You may also be breaking the terms of use of your institution or organization if you are using their computers or networks.</li>
-<li>Downloading cracked software is risky and unsafe. You may expose your computer to viruses, malware, spyware, or other harmful programs that may damage your system or compromise your data. You may also download a corrupted or incomplete version of the software that may not work properly or produce erroneous results.</li>
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-</ul>
-<p>Therefore, we strongly advise you not to download or install Gaussian 09 crack from any unauthorized sources. Instead, we recommend you to use one of the following alternatives:</p>
-<ul>
-<li>Request a trial license from Gaussian, Inc. If you are a new user or a student who wants to try out Gaussian 09 for a limited time, you can request a trial license from Gaussian, Inc. by filling out this form: <a href="https://gaussian.com/trial/">https://gaussian.com/trial/</a>. You will need to provide some basic information about yourself and your intended use of the software. You will also need to agree to abide by the terms and conditions of the trial license.</li>
-<li>Purchase a regular license from Gaussian, Inc. If you are a regular user or a researcher who needs to use Gaussian 09 for an extended period of time, you can purchase a regular license from Gaussian, Inc. by contacting them at this email address: <a href="mailto:sales@gaussian.com">sales@gaussian.com</a>. You will need to provide some details about your institution or organization and your desired configuration of the software. You will also need to agree to pay the license fee and abide by the terms and conditions of the regular license.</li>
-<li>Use an alternative software package for computational chemistry. If you are unable or unwilling to obtain a license for Gaussian 09, you can use an alternative software package for computational chemistry that may offer similar or different features and capabilities. Some examples of such software packages are: <a href="https://www.turbomole.org/">Turbomole</a>, <a href="https://www.nwchem-sw.org/">NWChem</a>, <a href="https://www.psi4.org/">Psi4</a>, <a href="https://www.q-chem.com/">Q-Chem</a>, <a href="https://www.molpro.net/">Molpro</a>, <a href="https://www.gamess-us.org/">GAMESS</a>, <a href="https://orcaforum.kofo.mpg.de/app.php/portal">ORCA</a>, <a href="https://www.cp2k.org/">CP2K</a>, <a href="http://www.openmopac.net/">OpenMOPAC</a>, etc. Some of these software packages are free and open source, while others are commercial and require a license. You will need to check their websites for more information on how to download, install, and use them.</li>
-</ul>
-<p>We hope this article has helped you understand why you should not download or install Gaussian 09 crack and what alternatives you have instead. We hope you will enjoy using computational chemistry software in a legal, ethical, safe, reliable, and supported way.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/testingcodehere/oai-proxy/README.md b/spaces/testingcodehere/oai-proxy/README.md
deleted file mode 100644
index f81d1efdc2f266de21ce32408730ffa5dfd7778d..0000000000000000000000000000000000000000
--- a/spaces/testingcodehere/oai-proxy/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Oai Proxyy
-emoji: 💩
-colorFrom: yellow
-colorTo: green
-sdk: docker
-pinned: false
-duplicated_from: whocars123/oai-proxy
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/therealcyberlord/abstract-art-generation/DCGAN.py b/spaces/therealcyberlord/abstract-art-generation/DCGAN.py
deleted file mode 100644
index 9501b9ca79aa8033cd8b8a47400745b6623c95cb..0000000000000000000000000000000000000000
--- a/spaces/therealcyberlord/abstract-art-generation/DCGAN.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from torch import nn
-import torch
-import torch.nn.functional as F
-    
-# Generator Code
-
-ngf = 64
-num_channels = 3
-
-class Generator(nn.Module):
-    def __init__(self, latent_size):
-        super(Generator, self).__init__()
-
-        self.latent_size = latent_size
-        self.conv1 = nn.ConvTranspose2d(
-            self.latent_size, ngf*8, 4, 1, 0, bias=False)
-        self.bn1 = nn.BatchNorm2d(ngf*8)
-        self.conv2 = nn.ConvTranspose2d(ngf*8, ngf*4, 4, 2, 1, bias=False)
-        self.bn2 = nn.BatchNorm2d(ngf*4)
-        self.conv3 = nn.ConvTranspose2d(ngf*4, ngf*2, 4, 2, 1, bias=False)
-        self.bn3 = nn.BatchNorm2d(ngf*2)
-        self.conv4 = nn.ConvTranspose2d(ngf*2, ngf, 4, 2, 1, bias=False)
-        self.bn4 = nn.BatchNorm2d(ngf)
-
-        self.conv5 = nn.ConvTranspose2d(ngf, num_channels, 4, 2, 1, bias=False)
-
-    def forward(self, x):
-        x = F.relu(self.bn1(self.conv1(x)), inplace=True)
-        x = F.relu(self.bn2(self.conv2(x)), inplace=True)
-        x = F.relu(self.bn3(self.conv3(x)), inplace=True)
-        x = F.relu(self.bn4(self.conv4(x)), inplace=True)
-        return torch.tanh(self.conv5(x))
\ No newline at end of file
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/AutoCAD 2015 Free Download with Crack 64-bit Filehippo A Scam or a Bargain?.md b/spaces/tialenAdioni/chat-gpt-api/logs/AutoCAD 2015 Free Download with Crack 64-bit Filehippo A Scam or a Bargain?.md
deleted file mode 100644
index 28ce3d2961069c5f110f47a96a7701cc1a66dd6d..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/AutoCAD 2015 Free Download with Crack 64-bit Filehippo A Scam or a Bargain?.md	
+++ /dev/null
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-<p>AutoCAD is a popular software for designing and drafting in various fields such as architecture, engineering, construction, and manufacturing. It is widely used by professionals and students alike for creating 2D and 3D models, drawings, and animations.</p>
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Focus Accounting Software How to Manage Your Accounting Tasks with Ease and Efficiency.md b/spaces/tialenAdioni/chat-gpt-api/logs/Focus Accounting Software How to Manage Your Accounting Tasks with Ease and Efficiency.md
deleted file mode 100644
index fc1e4c0f69ecab48a37057a4d5a400aa891b89bd..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Focus Accounting Software How to Manage Your Accounting Tasks with Ease and Efficiency.md	
+++ /dev/null
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-<p>Focus Accounting Software is a powerful and user-friendly software that helps you manage your business finances, inventory, taxation, payroll, and more. Whether you are a small business owner, a freelancer, or a professional accountant, Focus Accounting Software can help you streamline your accounting processes and improve your productivity.</p>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Chess Base The Best Way to Study Chess Openings Middlegames and Endgames.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Chess Base The Best Way to Study Chess Openings Middlegames and Endgames.md
deleted file mode 100644
index cc3fac9abac3be4de7e9ac0dce83e6068875635d..0000000000000000000000000000000000000000
--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Chess Base The Best Way to Study Chess Openings Middlegames and Endgames.md	
+++ /dev/null
@@ -1,117 +0,0 @@
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-<h1>Download ChessBase: A Guide for Chess Enthusiasts</h1>
- <p>If you are a chess player who wants to take your game to the next level, you might have heard of ChessBase. ChessBase is a personal, stand-alone chess database that has become the standard throughout the world. It allows you to access millions of chess games, analyze them with powerful engines, create your own opening repertoire, train your tactical skills, and much more. In this article, we will explain what ChessBase is, what are some of its features, what are some of its alternatives, and what are some of its reviews.</p>
- <h2>What is ChessBase and why is it useful for chess players?</h2>
- <p>ChessBase is a software that helps you manage and study chess games. You can use it to store your own games or import games from various sources, such as online databases, books, magazines, or DVDs. You can also search for games by openings, players, tournaments, positions, themes, or motifs. You can then analyze these games with powerful chess engines, such as Stockfish or Komodo, or with cloud engines that run on remote servers. You can also annotate your games with comments, symbols, arrows, or multimedia files.</p>
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-<li><b>Beauty Medals:</b> This feature allows you to find the most beautiful and instructive games in your database. It automatically assigns medals to games based on criteria such as sacrifices, combinations, endgames, attacks, defenses, or novelties. You can then filter and sort the games by these medals and enjoy the best chess ever played.</li>
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-</ul>
- <h2>What are some of the alternatives to ChessBase and how do they compare?</h2>
- <p>ChessBase is not the only chess database software available. There are several alternatives that offer some similar features or different advantages. Here are some of the most popular ones:</p>
- <table>
-<tr>
-<th>Name</th>
-<th>Description</th>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>ChessBase Online</td>
-<td>A web-based version of ChessBase that allows you to access your database and analysis tools from any device with an internet connection.</td>
-<td>- No installation required<br>- Syncs with your ChessBase account<br>- Compatible with any device</td>
-<td>- Less features than the desktop version<br>- Requires a subscription fee<br>- Depends on internet speed and availability</td>
-</tr>
-<tr>
-<td>SCID</td>
-<td>A free and open-source chess database software that can handle large databases and supports various formats and engines.</td>
-<td>- Free and open-source<br>- Supports many formats and engines<br>- Has many features and options</td>
-<td>- Less user-friendly than ChessBase<br>- Less updated and supported than ChessBase<br>- Has some bugs and compatibility issues</td>
-</tr>
-<tr>
-<td>ChessX</td>
-<td>A free and cross-platform chess database software that allows you to view, edit, and analyze chess games.</td>
-<td>- Free and cross-platform<br>- Simple and intuitive interface<br>- Supports PGN and FEN formats</td>
-<td>- Less features than ChessBase or SCID<br>- Less powerful analysis tools than ChessBase or SCID<br>- Does not support cloud engines or live database</td>
-</tr>
-<tr>
-<td>Chess Assistant</td>
-<td>A commercial chess database software that offers similar features to ChessBase, such as game analysis, opening preparation, training, and online play.</td>
-<td>- Similar features to ChessBase<br>- Includes a large database of games and opening books<br>- Supports cloud engines and live database</td>
-<td>- More expensive than ChessBase<br>- Less user-friendly than ChessBase<br>- Less compatible with other formats and engines than ChessBase</td>
-</tr>
- <h2>What are some of the reviews of ChessBase from users and experts?</h2>
- <p>ChessBase has received many positive reviews from users and experts who praise its functionality, quality, and usefulness. Here are some of the reviews from various sources:</p>
- <blockquote>"ChessBase is an amazing tool for anyone who wants to study chess in a serious way. I use it every day to prepare for my games, to analyze my mistakes, and to learn from the best players in history. It is like having a personal chess coach that guides you to improve your skills."</blockquote>
- <p><i>- Magnus Carlsen, World Chess Champion</i></p>
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- <p><i>- Susan Polgar, Grandmaster and former Women's World Champion</i></p>
- <blockquote>"ChessBase is the best chess software on the market. It is easy to use, fast, reliable, and constantly updated. It has a huge database of games that covers all kinds of openings, players, tournaments, and themes. It also has a great analysis feature that allows you to explore any position with multiple engines and cloud computing. It also has a lot of training options that help you improve your skills in a fun and interactive way."</blockquote>
- <p><i>- John Nunn, Grandmaster and Chess Author</i></p>
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Bhaji In Problem Movie Download With Subtitles In Utorrent _HOT_.md b/spaces/tioseFevbu/cartoon-converter/scripts/Bhaji In Problem Movie Download With Subtitles In Utorrent _HOT_.md
deleted file mode 100644
index 87492d49bdca19bf4554dbbbe749c23391eaeed9..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Bhaji In Problem Movie Download With Subtitles In Utorrent _HOT_.md	
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Blackberry Smart Tool V1.0.0.961 ((BETTER)).md b/spaces/tioseFevbu/cartoon-converter/scripts/Blackberry Smart Tool V1.0.0.961 ((BETTER)).md
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index 9042c7ace1a08eebda82bcda85601b78a6553d67..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Blackberry Smart Tool V1.0.0.961 ((BETTER)).md	
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-<h2>How to Use Blackberry Smart Tool V1.0.0.961</h2>
-<ol>
-<li>Connect your Blackberry phone to your computer using a USB cable.</li>
-<li>Launch Blackberry Smart Tool V1.0.0.961 from your FuriousGold software.</li>
-<li>Select your phone model and click on Connect.</li>
-<li>Choose the operation you want to perform from the tabs on the left side of the software.</li>
-<li>Follow the instructions on the screen and wait for the process to complete.</li>
-<li>Disconnect your phone and enjoy your unlocked and repaired Blackberry device.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Blackberry Smart Tool V1.0.0.961 is a powerful unlocking software for Blackberry phones that can help you solve various issues with your device. Whether you want to unlock your network, remove your password, change your IMEI, or repair your MEP0, you can do it easily with this software. All you need is a FuriousGold dongle and a USB cable to connect your phone to your computer.</p>
-<p>If you have any questions or problems with using Blackberry Smart Tool V1.0.0.961, you can visit the FuriousGold forum[^1^] and get help from other users and experts.</p><h2>Benefits of Using Blackberry Smart Tool V1.0.0.961</h2>
-<p>Using Blackberry Smart Tool V1.0.0.961 can bring you many benefits, such as:</p>
-<ul>
-<li>You can unlock your Blackberry phone from any network and use it with any SIM card of your choice.</li>
-<li>You can remove the password or security lock from your Blackberry phone and access your data without any hassle.</li>
-<li>You can change the IMEI number of your Blackberry phone and avoid any blacklist or warranty issues.</li>
-<li>You can repair the MEP0 counter of your Blackberry phone and restore its functionality.</li>
-<li>You can update the firmware of your Blackberry phone and enjoy the latest features and improvements.</li>
-</ul>
-<h2>Supported Models by Blackberry Smart Tool V1.0.0.961</h2>
-<p>Blackberry Smart Tool V1.0.0.961 supports a wide range of Blackberry models, including:</p>
-<ul>
-<li>Blackberry 8520</li>
-<li>Blackberry 8900</li>
-<li>Blackberry 9000</li>
-<li>Blackberry 9100</li>
-<li>Blackberry 9300</li>
-<li>Blackberry 9500</li>
-<li>Blackberry 9700</li>
-<li>Blackberry 9800</li>
-<li>Blackberry 9900</li>
-<li>And many more...</li>
-</ul></p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Fsuipc4 Serial Keygen And Crack REPACK.md b/spaces/tioseFevbu/cartoon-converter/scripts/Fsuipc4 Serial Keygen And Crack REPACK.md
deleted file mode 100644
index 0bc765d458ded15b7d18291c37430787010d616f..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Fsuipc4 Serial Keygen And Crack REPACK.md	
+++ /dev/null
@@ -1,77 +0,0 @@
-
-<h1>Fsuipc4 Serial Keygen And Crack: How to Use It Safely and Legally</h1>
-<p>If you are a flight simulator enthusiast, you may have heard of Fsuipc4, a software that provides an interface for other programs and add-on aircraft to communicate with Microsoft Flight Simulator X. Fsuipc4 can enhance your simulation experience by allowing you to customize your controls, access more data, and use various add-ons. However, Fsuipc4 is not free, and you need to purchase a license to use its full features.</p>
-<p>Some people may be tempted to use cracked software, such as keygens, cracks, or patches, to get Fsuipc4 for free. However, this is not only illegal, but also risky. Cracked software can expose your computer to malware, viruses, and legal issues. Moreover, cracked software may not work properly or cause damage to your system.</p>
-<h2>Fsuipc4 Serial Keygen And Crack</h2><br /><p><b><b>DOWNLOAD</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://urlcod.com/2uHxtu">https://urlcod.com/2uHxtu</a></b></p><br /><br />
-<p>In this article, we will show you how to use Fsuipc4 serial keygen and crack software safely and legally. We will explain what each type of cracked software does, how to use it correctly, and what precautions you should take. We will also provide some alternatives to using cracked software, such as buying a license or using free software.</p>
-<h2>How to Use Fsuipc4 Serial Keygen and Crack Software Safely and Legally</h2>
-<p>Before we proceed, we want to make it clear that we do not condone or encourage the use of cracked software. This article is for educational purposes only, and we are not responsible for any consequences that may arise from following the instructions below. Use cracked software at your own risk.</p>
-<h3>How to Download Fsuipc4 from a Trusted Source and Install It Correctly</h3>
-<p>The first step is to download Fsuipc4 from a trusted source. You can find the official website of Fsuipc4 here. You can also buy a license from this website if you want to support the developer and enjoy the full features of Fsuipc4.</p>
-<p>Alternatively, you can download Fsuipc4 from other sources, such as simMarket or Steam. However, be careful when downloading from third-party websites, as they may contain malware or viruses. Always scan the downloaded file with an antivirus program before opening it.</p>
-<p>To install Fsuipc4 correctly, follow these steps:</p>
-<ol>
-<li>Extract the downloaded file to a folder on your computer.</li>
-<li>Run the installer file (FSUIPC4.exe) as an administrator.</li>
-<li>Follow the instructions on the screen and choose the destination folder for Fsuipc4. It should be the same folder where your Flight Simulator X is installed.</li>
-<li>Click Finish when the installation is complete.</li>
-<li>Launch Flight Simulator X and check if Fsuipc4 is working. You should see a message saying "FSUIPC4 Status: OK" on the bottom left corner of the screen.</li>
-</ol>
-<h3>How to Use a Keygen to Generate a Valid License Key for Fsuipc4</h3>
-<p>A keygen is a program that generates valid license keys for software. A keygen analyzes the algorithm that the software uses to create license keys and produces similar ones that can activate the software.</p>
-<p>To use a keygen for Fsuipc4, follow these steps:</p>
-<p></p>
-<ol>
-<li>Find a keygen for Fsuipc4 from a reliable source. You can search online for "Fsuipc4 keygen" or "Fsuipc4 serial" and look for reputable websites or forums that offer keygens. However, be careful of fake or malicious keygens that may harm your computer or steal your personal information. Always scan the downloaded file with an antivirus program before opening it.</li>
-<li>Run the keygen file as an administrator. You may need to disable your antivirus program temporarily, as some antivirus programs may flag keygens as potential threats.</li>
-<li>Click on the "Generate" button and wait for the keygen to produce a license key for Fsuipc4. You should see a series of numbers and letters that look like this: XXXX-XXXX-XXXX-XXXX.</li>
-<li>Copy the license key and paste it into the registration window of Fsuipc4. You can access the registration window by launching Flight Simulator X, clicking on "Add-ons" on the menu bar, and selecting "FSUIPC4".</li>
-<li>Click on "Register" and check if the license key is accepted. You should see a message saying "FSUIPC4 Registration Successful" on the screen.</li>
-</ol>
-<h3>How to Use a Crack to Bypass the License Verification of Fsuipc4</h3>
-<p>A crack is a program that modifies the original software to bypass its license verification or protection mechanism. A crack can either replace or patch the original executable file of the software, or create a new file that acts as a loader or emulator for the software.</p>
-<p>To use a crack for Fsuipc4, follow these steps:</p>
-<ol>
-<li>Find a crack for Fsuipc4 from a reliable source. You can search online for "Fsuipc4 crack" or "Fsuipc4 full version" and look for reputable websites or forums that offer cracks. However, be careful of fake or malicious cracks that may harm your computer or steal your personal information. Always scan the downloaded file with an antivirus program before opening it.</li>
-<li>Extract the crack file to a folder on your computer. You should see one or more files that have the same name as Fsuipc4, such as FSUIPC4.dll, FSUIPC4.exe, or FSUIPC4.key.</li>
-<li>Copy the crack file(s) and paste them into the folder where Fsuipc4 is installed. It should be the same folder where your Flight Simulator X is installed. You may need to overwrite or delete the original file(s) of Fsuipc4.</li>
-<li>Launch Flight Simulator X and check if Fsuipc4 is working. You should see a message saying "FSUIPC4 Status: OK" on the bottom left corner of the screen, without asking you to register or enter a license key.</li>
-</ol>
-<h3>How to Use a Patch to Fix Any Bugs or Errors in Fsuipc4</h3>
-<p>A patch is a program that updates or fixes the original software to improve its performance, stability, or compatibility. A patch can either replace or modify some parts of the original software, or add new features or functions to it.</p>
-<p>To use a patch for Fsuipc4, follow these steps:</p>
-<ol>
-<li>Find a patch for Fsuipc4 from a reliable source. You can search online for "Fsuipc4 patch" or "Fsuipc4 update" and look for reputable websites or forums that offer patches. However, be careful of fake or malicious patches that may harm your computer or steal your personal information. Always scan the downloaded file with an antivirus program before opening it.</li>
-<li>Extract the patch file to a folder on your computer. You should see one or more files that have the same name as Fsuipc4, such as FSUIPC4.dll, FSUIPC4.exe, or FSUIPC4.key.</li>
-<li>Copy the patch file(s) and paste them into the folder where Fsuipc4 is installed. It should be the same folder where your Flight Simulator X is installed. You may need to overwrite or delete the original file(s) of Fsuipc4.</li>
-<li>Launch Flight Simulator X and check if Fsuipc4 is working. You should see a message saying "FSUIPC4 Status: OK" on the bottom left corner of the screen, and notice any improvements or changes in Fsuipc4's functionality.</li>
-</ol>
-<h3>How to Avoid Malware, Viruses, and Legal Issues When Using Cracked Software</h3>
-<p>While using cracked software may seem tempting, it comes with many risks and disadvantages. Cracked software can expose your computer to malware, viruses, and legal issues. Here are some tips and warnings to avoid these problems when using cracked software:</p>
-<ul>
-<li>Always download cracked software from trusted sources. Do not click on suspicious links or pop-ups that claim to offer free or cracked software. Do not download cracked software from torrent sites or peer-to-peer networks, as they may contain malware or viruses.</li>
-<li>Always scan the downloaded file with an antivirus program before opening it. Some antivirus programs may flag cracked software as potential threats, so you may need to disable them temporarily or add an exception for the file. However, do not ignore any warnings or alerts that indicate the file is infected or harmful.</li>
-<li>Always backup your data and system before installing or running cracked software. Cracked software may cause damage to your system, corrupt your files, or delete your data. You should have a backup plan in case something goes wrong.</li>
-<li>Always read the instructions and comments from the source of the cracked software. They may provide useful information on how to use the cracked software correctly, what problems or issues to expect, and how to fix them.</li>
-<li>Always use a firewall and a VPN when using cracked software. A firewall can block any unwanted connections or communications from the cracked software, while a VPN can hide your IP address and location from prying eyes. This can protect your privacy and security when using cracked software.</li>
-<li>Never use cracked software for commercial or professional purposes. Cracked software is illegal and unethical, and you may face legal consequences if you use it for profit or work. You may also damage your reputation and credibility if you use cracked software for your projects or clients.</li>
-<li>Never update or register cracked software online. Cracked software may stop working or become unusable if you update it online, as it may detect the crack and disable it. Cracked software may also expose your personal information or credentials if you register it online, as it may send them to the original developer or a third party.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to use Fsuipc4 serial keygen and crack software safely and legally. We have explained what each type of cracked software does, how to use it correctly, and what precautions you should take. We have also provided some alternatives to using cracked software, such as buying a license or using free software.</p>
-<p>However, we want to remind you that using cracked software is not recommended, as it comes with many risks and disadvantages. Cracked software can expose your computer to malware, viruses, and legal issues. Moreover, cracked software may not work properly or cause damage to your system.</p>
-<p>Therefore, we suggest that you avoid using cracked software and support the original developer of Fsuipc4 by purchasing a license from their website. You can also use free software that offers similar features as Fsuipc4, such as XPUIPC for X-Plane or FSUIPC7 for Microsoft Flight Simulator 2020.</p>
-<p>We hope you found this article helpful and informative. If you have any questions or comments, please feel free to share them below. Thank you for reading!</p>
-<h2>FAQs</h2>
-<h3>What is Fsuipc4?</h3>
-<p>Fsuipc4 is a software that provides an interface for other programs and add-on aircraft to communicate with Microsoft Flight Simulator X. Fsuipc4 can enhance your simulation experience by allowing you to customize your controls, access more data, and use various add-ons.</p>
-<h3>What is a keygen?</h3>
-<p>A keygen is a program that generates valid license keys for software. A keygen analyzes the algorithm that the software uses to create license keys and produces similar ones that can activate the software.</p>
-<h3>What is a crack?</h3>
-<p>A crack is a program that modifies the original software to bypass its license verification or protection mechanism. A crack can either replace or patch the original executable file of the software, or create a new file that acts as a loader or emulator for the software.</p>
-<h3>What is a patch?</h3>
-<p>A patch is a program that updates or fixes the original software to improve its performance, stability, or compatibility. A patch can either replace or modify some parts of the original software, or add new features or functions to it.</p>
-<h3>Is using cracked software illegal?</h3>
-<p>Yes, using cracked software is illegal and unethical, as it violates the intellectual property rights of the original developer. Using cracked software can result in legal consequences, such as fines or lawsuits.</p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/HD Online Player (apocalypto Hollywood Movie Hindi Dub) TOP.md b/spaces/tioseFevbu/cartoon-converter/scripts/HD Online Player (apocalypto Hollywood Movie Hindi Dub) TOP.md
deleted file mode 100644
index 7e6492665484ff9caee0fb0c3a611194363937dd..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/HD Online Player (apocalypto Hollywood Movie Hindi Dub) TOP.md	
+++ /dev/null
@@ -1,21 +0,0 @@
-<br />
-<h1>How to Watch Apocalypto in Hindi Dubbed Online</h1>
-<p>Apocalypto is a 2006 epic adventure film directed by Mel Gibson that depicts the decline of the Maya civilization in the 16th century. The film follows a young man named Jaguar Paw who escapes from a raiding party that captures his village and plans to sacrifice him and his people. He must run for his life through the jungle and reach his pregnant wife and son before they are killed.</p>
-<h2>HD Online Player (apocalypto hollywood movie hindi dub)</h2><br /><p><b><b>Download File</b> &#10027;&#10027;&#10027; <a href="https://urlcod.com/2uHwoE">https://urlcod.com/2uHwoE</a></b></p><br /><br />
-<p>If you are a fan of this thrilling and action-packed movie, you might be wondering how to watch it in Hindi dubbed online. There are many websites that claim to offer HD online streaming of Apocalypto in Hindi dubbed, but not all of them are reliable or safe. Some of them might contain malware, viruses, or pop-up ads that can harm your device or compromise your privacy. Others might have low-quality video or audio, or require you to sign up or pay for a subscription.</p>
-<p>To help you avoid these problems, we have compiled a list of some of the best and most trusted websites that offer HD online streaming of Apocalypto in Hindi dubbed. These websites are free to use and do not require any registration or payment. They also have high-quality video and audio, and minimal or no ads. You can enjoy watching Apocalypto in Hindi dubbed online anytime and anywhere with these websites.</p>
-<h2>List of Websites to Watch Apocalypto in Hindi Dubbed Online</h2>
-<ul>
-<li><a href="https://showmyflix.com/hollywood-dubbed/">Showmyflix</a>: This website has a large collection of Hollywood dubbed movies in Hindi, including Apocalypto. You can watch it online in HD quality without any interruption or buffering. You can also browse other genres and categories of movies on this website.</li>
-<li><a href="https://yaroslavsamsonov69.wixsite.com/ridgaitertsalt/post/apocalypto-2006-in-hindi-dubbed">Ridgaitertsalt</a>: This website is dedicated to Apocalypto in Hindi dubbed. You can watch it online in HD quality with clear sound and subtitles. You can also download it to your device for offline viewing. The website is simple and easy to use.</li>
-<li><a href="https://sway.office.com/GJyTGxmQ2ZWNew4h">Sway</a>: This website is a Microsoft service that allows you to create and share interactive presentations and stories. You can watch Apocalypto in Hindi dubbed online on this website using the HD online player embedded in the page. You can also adjust the playback speed, volume, and quality of the video.</li>
-</ul>
-<p>We hope you find this article helpful and enjoy watching Apocalypto in Hindi dubbed online. If you have any questions or suggestions, please feel free to leave a comment below.</p>
-
-<h2>Why You Should Watch Apocalypto in Hindi Dubbed Online</h2>
-<p>Apocalypto is not just a thrilling and action-packed movie, but also a visually stunning and emotionally engaging one. The film showcases the beauty and diversity of the natural environment, the culture and traditions of the Maya people, and the human spirit of survival and resilience. The film also explores themes such as religious fanaticism, elitism and classism, and the decline of civilizations.</p>
-<p>Watching Apocalypto in Hindi dubbed online can enhance your enjoyment and understanding of the film. You can appreciate the dialogue and the performances of the actors better in your native language. You can also relate more to the characters and their struggles, as well as their humor and courage. You can immerse yourself in the story and feel like you are part of the adventure.</p>
-<p>Apocalypto is a film that deserves to be seen by a wide audience. It is a film that showcases the talent and vision of Mel Gibson as a director, as well as the skill and dedication of the cast and crew. It is a film that celebrates the richness and diversity of human cultures, as well as the universal values of love, family, and freedom.</p>
-<p>So what are you waiting for? Watch Apocalypto in Hindi dubbed online today and experience this epic adventure for yourself.</p> 81aa517590<br />
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\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/euckrprober.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/euckrprober.py
deleted file mode 100644
index 154a6d2162bb03e565a37b216d4be6c37800a4d6..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/euckrprober.py
+++ /dev/null
@@ -1,47 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from .chardistribution import EUCKRDistributionAnalysis
-from .codingstatemachine import CodingStateMachine
-from .mbcharsetprober import MultiByteCharSetProber
-from .mbcssm import EUCKR_SM_MODEL
-
-
-class EUCKRProber(MultiByteCharSetProber):
-    def __init__(self):
-        super().__init__()
-        self.coding_sm = CodingStateMachine(EUCKR_SM_MODEL)
-        self.distribution_analyzer = EUCKRDistributionAnalysis()
-        self.reset()
-
-    @property
-    def charset_name(self):
-        return "EUC-KR"
-
-    @property
-    def language(self):
-        return "Korean"
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/mbcsgroupprober.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/mbcsgroupprober.py
deleted file mode 100644
index 94488360c4b15db311fdf88ef42e064d7d7b2c43..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/mbcsgroupprober.py
+++ /dev/null
@@ -1,56 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Universal charset detector code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 2001
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#   Shy Shalom - original C code
-#   Proofpoint, Inc.
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from .big5prober import Big5Prober
-from .charsetgroupprober import CharSetGroupProber
-from .cp949prober import CP949Prober
-from .eucjpprober import EUCJPProber
-from .euckrprober import EUCKRProber
-from .euctwprober import EUCTWProber
-from .gb2312prober import GB2312Prober
-from .johabprober import JOHABProber
-from .sjisprober import SJISProber
-from .utf8prober import UTF8Prober
-
-
-class MBCSGroupProber(CharSetGroupProber):
-    def __init__(self, lang_filter=None):
-        super().__init__(lang_filter=lang_filter)
-        self.probers = [
-            UTF8Prober(),
-            SJISProber(),
-            EUCJPProber(),
-            GB2312Prober(),
-            EUCKRProber(),
-            CP949Prober(),
-            Big5Prober(),
-            EUCTWProber(),
-            JOHABProber(),
-        ]
-        self.reset()
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/padding.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/padding.py
deleted file mode 100644
index 1b2204f59f2ce4d9c8f2cca85326e4d81f8805bb..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/padding.py
+++ /dev/null
@@ -1,141 +0,0 @@
-from typing import cast, List, Optional, Tuple, TYPE_CHECKING, Union
-
-if TYPE_CHECKING:
-    from .console import (
-        Console,
-        ConsoleOptions,
-        RenderableType,
-        RenderResult,
-    )
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .style import Style
-from .segment import Segment
-
-
-PaddingDimensions = Union[int, Tuple[int], Tuple[int, int], Tuple[int, int, int, int]]
-
-
-class Padding(JupyterMixin):
-    """Draw space around content.
-
-    Example:
-        >>> print(Padding("Hello", (2, 4), style="on blue"))
-
-    Args:
-        renderable (RenderableType): String or other renderable.
-        pad (Union[int, Tuple[int]]): Padding for top, right, bottom, and left borders.
-            May be specified with 1, 2, or 4 integers (CSS style).
-        style (Union[str, Style], optional): Style for padding characters. Defaults to "none".
-        expand (bool, optional): Expand padding to fit available width. Defaults to True.
-    """
-
-    def __init__(
-        self,
-        renderable: "RenderableType",
-        pad: "PaddingDimensions" = (0, 0, 0, 0),
-        *,
-        style: Union[str, Style] = "none",
-        expand: bool = True,
-    ):
-        self.renderable = renderable
-        self.top, self.right, self.bottom, self.left = self.unpack(pad)
-        self.style = style
-        self.expand = expand
-
-    @classmethod
-    def indent(cls, renderable: "RenderableType", level: int) -> "Padding":
-        """Make padding instance to render an indent.
-
-        Args:
-            renderable (RenderableType): String or other renderable.
-            level (int): Number of characters to indent.
-
-        Returns:
-            Padding: A Padding instance.
-        """
-
-        return Padding(renderable, pad=(0, 0, 0, level), expand=False)
-
-    @staticmethod
-    def unpack(pad: "PaddingDimensions") -> Tuple[int, int, int, int]:
-        """Unpack padding specified in CSS style."""
-        if isinstance(pad, int):
-            return (pad, pad, pad, pad)
-        if len(pad) == 1:
-            _pad = pad[0]
-            return (_pad, _pad, _pad, _pad)
-        if len(pad) == 2:
-            pad_top, pad_right = cast(Tuple[int, int], pad)
-            return (pad_top, pad_right, pad_top, pad_right)
-        if len(pad) == 4:
-            top, right, bottom, left = cast(Tuple[int, int, int, int], pad)
-            return (top, right, bottom, left)
-        raise ValueError(f"1, 2 or 4 integers required for padding; {len(pad)} given")
-
-    def __repr__(self) -> str:
-        return f"Padding({self.renderable!r}, ({self.top},{self.right},{self.bottom},{self.left}))"
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        style = console.get_style(self.style)
-        if self.expand:
-            width = options.max_width
-        else:
-            width = min(
-                Measurement.get(console, options, self.renderable).maximum
-                + self.left
-                + self.right,
-                options.max_width,
-            )
-        render_options = options.update_width(width - self.left - self.right)
-        if render_options.height is not None:
-            render_options = render_options.update_height(
-                height=render_options.height - self.top - self.bottom
-            )
-        lines = console.render_lines(
-            self.renderable, render_options, style=style, pad=True
-        )
-        _Segment = Segment
-
-        left = _Segment(" " * self.left, style) if self.left else None
-        right = (
-            [_Segment(f'{" " * self.right}', style), _Segment.line()]
-            if self.right
-            else [_Segment.line()]
-        )
-        blank_line: Optional[List[Segment]] = None
-        if self.top:
-            blank_line = [_Segment(f'{" " * width}\n', style)]
-            yield from blank_line * self.top
-        if left:
-            for line in lines:
-                yield left
-                yield from line
-                yield from right
-        else:
-            for line in lines:
-                yield from line
-                yield from right
-        if self.bottom:
-            blank_line = blank_line or [_Segment(f'{" " * width}\n', style)]
-            yield from blank_line * self.bottom
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-        max_width = options.max_width
-        extra_width = self.left + self.right
-        if max_width - extra_width < 1:
-            return Measurement(max_width, max_width)
-        measure_min, measure_max = Measurement.get(console, options, self.renderable)
-        measurement = Measurement(measure_min + extra_width, measure_max + extra_width)
-        measurement = measurement.with_maximum(max_width)
-        return measurement
-
-
-if __name__ == "__main__":  #  pragma: no cover
-    from pip._vendor.rich import print
-
-    print(Padding("Hello, World", (2, 4), style="on blue"))
diff --git a/spaces/tobiascz/SDSdemo/pytorch_grad_cam/ablation_cam_multilayer.py b/spaces/tobiascz/SDSdemo/pytorch_grad_cam/ablation_cam_multilayer.py
deleted file mode 100644
index 9b9dc806d845422e594bd0082dc718b947c593f2..0000000000000000000000000000000000000000
--- a/spaces/tobiascz/SDSdemo/pytorch_grad_cam/ablation_cam_multilayer.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import cv2
-import numpy as np
-import torch
-import tqdm
-from pytorch_grad_cam.base_cam import BaseCAM
-
-
-class AblationLayer(torch.nn.Module):
-    def __init__(self, layer, reshape_transform, indices):
-        super(AblationLayer, self).__init__()
-
-        self.layer = layer
-        self.reshape_transform = reshape_transform
-        # The channels to zero out:
-        self.indices = indices
-
-    def forward(self, x):
-        self.__call__(x)
-
-    def __call__(self, x):
-        output = self.layer(x)
-
-        # Hack to work with ViT,
-        # Since the activation channels are last and not first like in CNNs
-        # Probably should remove it?
-        if self.reshape_transform is not None:
-            output = output.transpose(1, 2)
-
-        for i in range(output.size(0)):
-
-            # Commonly the minimum activation will be 0,
-            # And then it makes sense to zero it out.
-            # However depending on the architecture,
-            # If the values can be negative, we use very negative values
-            # to perform the ablation, deviating from the paper.
-            if torch.min(output) == 0:
-                output[i, self.indices[i], :] = 0
-            else:
-                ABLATION_VALUE = 1e5
-                output[i, self.indices[i], :] = torch.min(
-                    output) - ABLATION_VALUE
-
-        if self.reshape_transform is not None:
-            output = output.transpose(2, 1)
-
-        return output
-
-
-def replace_layer_recursive(model, old_layer, new_layer):
-    for name, layer in model._modules.items():
-        if layer == old_layer:
-            model._modules[name] = new_layer
-            return True
-        elif replace_layer_recursive(layer, old_layer, new_layer):
-            return True
-    return False
-
-
-class AblationCAM(BaseCAM):
-    def __init__(self, model, target_layers, use_cuda=False,
-                 reshape_transform=None):
-        super(AblationCAM, self).__init__(model, target_layers, use_cuda,
-                                          reshape_transform)
-
-        if len(target_layers) > 1:
-            print(
-                "Warning. You are usign Ablation CAM with more than 1 layers. "
-                "This is supported only if all layers have the same output shape")
-
-    def set_ablation_layers(self):
-        self.ablation_layers = []
-        for target_layer in self.target_layers:
-            ablation_layer = AblationLayer(target_layer,
-                                           self.reshape_transform, indices=[])
-            self.ablation_layers.append(ablation_layer)
-            replace_layer_recursive(self.model, target_layer, ablation_layer)
-
-    def unset_ablation_layers(self):
-        # replace the model back to the original state
-        for ablation_layer, target_layer in zip(
-                self.ablation_layers, self.target_layers):
-            replace_layer_recursive(self.model, ablation_layer, target_layer)
-
-    def set_ablation_layer_batch_indices(self, indices):
-        for ablation_layer in self.ablation_layers:
-            ablation_layer.indices = indices
-
-    def trim_ablation_layer_batch_indices(self, keep):
-        for ablation_layer in self.ablation_layers:
-            ablation_layer.indices = ablation_layer.indices[:keep]
-
-    def get_cam_weights(self,
-                        input_tensor,
-                        target_category,
-                        activations,
-                        grads):
-        with torch.no_grad():
-            outputs = self.model(input_tensor).cpu().numpy()
-            original_scores = []
-            for i in range(input_tensor.size(0)):
-                original_scores.append(outputs[i, target_category[i]])
-        original_scores = np.float32(original_scores)
-
-        self.set_ablation_layers()
-
-        if hasattr(self, "batch_size"):
-            BATCH_SIZE = self.batch_size
-        else:
-            BATCH_SIZE = 32
-
-        number_of_channels = activations.shape[1]
-        weights = []
-
-        with torch.no_grad():
-            # Iterate over the input batch
-            for tensor, category in zip(input_tensor, target_category):
-                batch_tensor = tensor.repeat(BATCH_SIZE, 1, 1, 1)
-                for i in tqdm.tqdm(range(0, number_of_channels, BATCH_SIZE)):
-                    self.set_ablation_layer_batch_indices(
-                        list(range(i, i + BATCH_SIZE)))
-
-                    if i + BATCH_SIZE > number_of_channels:
-                        keep = number_of_channels - i
-                        batch_tensor = batch_tensor[:keep]
-                        self.trim_ablation_layer_batch_indices(self, keep)
-                    score = self.model(batch_tensor)[:, category].cpu().numpy()
-                    weights.extend(score)
-
-        weights = np.float32(weights)
-        weights = weights.reshape(activations.shape[:2])
-        original_scores = original_scores[:, None]
-        weights = (original_scores - weights) / original_scores
-
-        # replace the model back to the original state
-        self.unset_ablation_layers()
-        return weights
diff --git a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/modeling/segmentation/loss.py b/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/modeling/segmentation/loss.py
deleted file mode 100644
index 08d662cced182ba55859dc29d8e146e27acb43ac..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/modeling/segmentation/loss.py
+++ /dev/null
@@ -1,64 +0,0 @@
-#!/usr/bin/env python3
-"""
-This file contains specific functions for computing losses on the SEG
-file
-"""
-
-import torch
-
-
-class SEGLossComputation(object):
-    """
-    This class computes the SEG loss.
-    """
-
-    def __init__(self, cfg):
-        self.eps = 1e-6
-        self.cfg = cfg
-
-    def __call__(self, preds, targets):
-        """
-        Arguments:
-            preds (Tensor)
-            targets (list[Tensor])
-            masks (list[Tensor])
-        Returns:
-            seg_loss (Tensor)
-        """
-        image_size = (preds.shape[2], preds.shape[3])
-        segm_targets, masks = self.prepare_targets(targets, image_size)
-        device = preds.device
-        segm_targets = segm_targets.float().to(device)
-        masks = masks.float().to(device)
-        seg_loss = self.dice_loss(preds, segm_targets, masks)
-        return seg_loss
-
-    def dice_loss(self, pred, gt, m):
-        intersection = torch.sum(pred * gt * m)
-        union = torch.sum(pred * m) + torch.sum(gt * m) + self.eps
-        loss = 1 - 2.0 * intersection / union
-        return loss
-
-    def project_masks_on_image(self, mask_polygons, labels, shrink_ratio, image_size):
-        seg_map, training_mask = mask_polygons.convert_seg_map(
-            labels, shrink_ratio, image_size, self.cfg.MODEL.SEG.IGNORE_DIFFICULT
-        )
-        return torch.from_numpy(seg_map), torch.from_numpy(training_mask)
-
-    def prepare_targets(self, targets, image_size):
-        segms = []
-        training_masks = []
-        for target_per_image in targets:
-            segmentation_masks = target_per_image.get_field("masks")
-            labels = target_per_image.get_field("labels")
-            seg_maps_per_image, training_masks_per_image = self.project_masks_on_image(
-                segmentation_masks, labels, self.cfg.MODEL.SEG.SHRINK_RATIO, image_size
-            )
-            segms.append(seg_maps_per_image)
-            training_masks.append(training_masks_per_image)
-        return torch.stack(segms), torch.stack(training_masks)
-
-
-def make_seg_loss_evaluator(cfg):
-    loss_evaluator = SEGLossComputation(cfg)
-    return loss_evaluator
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
deleted file mode 100644
index 53964a3e1d2c9fd2e9eb905c6d6f645f62ea9957..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnest101',
-    backbone=dict(stem_channels=128, depth=101))
diff --git a/spaces/triple-t/ttt-space/frontend/src/app.html b/spaces/triple-t/ttt-space/frontend/src/app.html
deleted file mode 100644
index ec5635220a9b4a52c0987404b81e02607389d83d..0000000000000000000000000000000000000000
--- a/spaces/triple-t/ttt-space/frontend/src/app.html
+++ /dev/null
@@ -1,17 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-	<meta charset="utf-8" />
-	<link rel="icon" href="%sveltekit.assets%/favicon.png" />
-	<meta name="viewport" content="width=device-width" />
-	<script
-		src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
-	%sveltekit.head%
-</head>
-
-<body class="dark:bg-[rgb(11,15,25)] bg-white dark:text-white text-black">
-	<div>%sveltekit.body%</div>
-</body>
-
-</html>
\ No newline at end of file
diff --git a/spaces/ucalyptus/PTI/scripts/__init__.py b/spaces/ucalyptus/PTI/scripts/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Curso de direito constitucional gilmar mendes download pdf um clssico do direito brasileiro.md b/spaces/usbethFlerru/sovits-modelsV2/example/Curso de direito constitucional gilmar mendes download pdf um clssico do direito brasileiro.md
deleted file mode 100644
index 976b8e64ee52aa60c373f05018110bf58e61718a..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Curso de direito constitucional gilmar mendes download pdf um clssico do direito brasileiro.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-<br />
-<p>DOI: 10.11117/9788502179509A jurisdição constitucional exercida na Corte Suprema é de importância vital para um país e para o seu povo, e no Brasil não é diferente. O Supremo Tribunal Federal está localizado no ápice da estrutura do Poder Judiciário, e para ele convergem as questões mais sensíveis da sociedade brasileira. A pretensão do Autor é fazer com que os julgados repercutam em todo o País. Este livro contribui tanto para a reunião de julgados da Corte quanto para a exteriorização das teses jurídicas que são analisadas e discutidas pelos eminentes ministros. Gilmar Mendes selecionou as decisões mais relevantes que proferiu nos primeiros nove anos de atuação. Para facilitar a compreensão e consulta, a classificação temática foi a melhor alternativa. O leitor encontra uma diversidade de assuntos, relacionados a direitos fundamentais, direitos políticos, sistemas eleitorais, controle de constitucionalidade, administração pública e funções essenciais à justiça, podendo-se destacar as decisões de maior repercussão no cenário nacional, como os casos Cesare Battisti, demarcação de terras indígenas Raposa Serra do Sol, Lei da Ficha Limpa, pesquisa científica com células-tronco etc.</p>
-<p>Foi um dos autores, junto com os Professores Ives Gandra da Silva Martins Filho e Sálvio de Figueiredo Teixeira, do texto do Projeto de Lei nº 4.070/98 do Poder Executivo, que resultou na Lei no 9.756/98, que introduziu importantes mudanças na legislação processual civil (por exemplo, a Lei que admitiu decisões monocráticas para o provimento de recursos no caso de manifesta contradição com súmula ou com a jurisprudência do STF e do STJ) e deu nova disciplina ao incidente de inconstitucionalidade (CPC, art. 481).</p>
-<h2>curso de direito constitucional gilmar mendes download pdf</h2><br /><p><b><b>Download</b> &ndash;&ndash;&ndash; <a href="https://urlcod.com/2uyUyV">https://urlcod.com/2uyUyV</a></b></p><br /><br />
-<p>Quando da indicação de Mendes para o STF, Dalmo de Abreu Dallari, professor da Faculdade de Direito da Universidade de São Paulo, afirmou, em artigo de opinião publicado na <i>Folha de S. Paulo</i>, que tal indicação representava um sério risco para a proteção dos direitos no Brasil, o combate à corrupção e a própria normalidade constitucional. Segundo ele, Gilmar Mendes estaria longe de preencher os requisitos necessários para ser membro da mais alta corte do país.[68] Já o irmão de Dalmo, Adilson de Abreu Dallari, professor titular de Direito Administrativo da PUC-SP, não compartilhava da mesma opinião. Em 2008, afirmou que o ministro vinha tendo "uma excelente atuação, com independência e imparcialidade, decidindo de maneira totalmente coerente com seus escritos doutrinários, sem surpresa alguma".[69]</p>
-<p>Em 2002, o jurista e professor da USP Dalmo Dallari publicava um artigo de opinião intitulado "Degradação do Judiciário", no qual criticava a indicação de Gilmar Mendes, então advogado-geral da União, para integrar o Supremo Tribunal Federal - indicação feita antes mesmo que se formalizasse a abertura da vaga na Corte. Segundo Dallari, a aprovação dessa indicação colocaria em risco "a proteção dos direitos no Brasil, o combate à corrupção e a própria normalidade constitucional". Segundo Dallari, o indicado não tinha a "reputação ilibada" necessária para integrar o STF.[68]</p>
-<p>BRASIL. Supremo Tribunal Federal (Plenário). Repercussão no Recurso Extraordinário com Agravo 639.228 Rio de Janeiro. [...] Não apresenta repercussão geral recurso extraordinário que, tendo por objeto a obrigatoriedade de observância dos princípios do contraditório e da ampla defesa, nos casos de indeferimento do pedido de produção de provas em processo judicial, versa sobre tema infraconstitucional. Relator: ministro César Peluso, 16 jun. 2011. Diário de Justiça eletrônico, Brasília, DF, 31 ago. 2011. Disponível em: =AC&docID=626891. Acesso em: 7 nov. 2022.</p>
-<p>BRASIL. Superior Tribunal de Justiça (Corte Especial). Recurso Especial nº 1.798.374 -DF. [...] 1.1. O Código de Processo Civil de 2015 (CPC/2015) introduziu em nosso sistema processual o Incidente de Resolução de Demandas Repetitivas - IRDR (arts. 976 ao 987), técnica de julgamento de processos que envolvam casos repetitivos (art. 928) que tratem da mesma questão de direito, essencialmente voltada para os Tribunais locais (Tribunal de Justiça e Tribunal Regional Federal), com o claro objetivo de proporcionar isonomia e segurança jurídica e atacar a repetição de demandas idênticas, problema crônico do sistema judicial brasileiro [...]. Relator: ministro Mauro Capbell Marques, 18 maio 2022. Diário de Justiça eletrônico, Brasília, DF, 21 jun. 2022d. Disponível em: _registro=201900536793&dt_publicacao=21/06/2022. Acesso em: 18 jun. 2022.</p>
-<p>Este material didático é composto por questões do Enade. O Enade é o Exame Nacional de Desempenho dos Estudantes. Foi criado, juntamente com o Sistema Nacional de Avaliação Superior, pela Lei 10.861, de 14 de abril de 2004. Desde a sua criação, é um componente curricular obrigatório. Este material didático se propõe a comentar questões da área de direito constitucional.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/userzyzz/riffusion-riffusion-model-v1/README.md b/spaces/userzyzz/riffusion-riffusion-model-v1/README.md
deleted file mode 100644
index 5d0b7d8d65f007f9d70e5c441e51594b2288b749..0000000000000000000000000000000000000000
--- a/spaces/userzyzz/riffusion-riffusion-model-v1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Riffusion Riffusion Model V1
-emoji: 🐢
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.13.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/errors.py b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/errors.py
deleted file mode 100644
index 7163d4d2576fc78ed25f36bf3a4ec06981ea94e6..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/errors.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-
-from ultralytics.yolo.utils import emojis
-
-
-class HUBModelError(Exception):
-
-    def __init__(self, message='Model not found. Please check model URL and try again.'):
-        """Create an exception for when a model is not found."""
-        super().__init__(emojis(message))
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/v8/segment/val.py b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/v8/segment/val.py
deleted file mode 100644
index 73c2fe834fc16daf5435f5db90046f5d7698e20e..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/v8/segment/val.py
+++ /dev/null
@@ -1,262 +0,0 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-
-from multiprocessing.pool import ThreadPool
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, NUM_THREADS, ops
-from ultralytics.yolo.utils.checks import check_requirements
-from ultralytics.yolo.utils.metrics import SegmentMetrics, box_iou, mask_iou
-from ultralytics.yolo.utils.plotting import output_to_target, plot_images
-from ultralytics.yolo.v8.detect import DetectionValidator
-
-
-class SegmentationValidator(DetectionValidator):
-
-    def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
-        """Initialize SegmentationValidator and set task to 'segment', metrics to SegmentMetrics."""
-        super().__init__(dataloader, save_dir, pbar, args, _callbacks)
-        self.args.task = 'segment'
-        self.metrics = SegmentMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
-
-    def preprocess(self, batch):
-        """Preprocesses batch by converting masks to float and sending to device."""
-        batch = super().preprocess(batch)
-        batch['masks'] = batch['masks'].to(self.device).float()
-        return batch
-
-    def init_metrics(self, model):
-        """Initialize metrics and select mask processing function based on save_json flag."""
-        super().init_metrics(model)
-        self.plot_masks = []
-        if self.args.save_json:
-            check_requirements('pycocotools>=2.0.6')
-            self.process = ops.process_mask_upsample  # more accurate
-        else:
-            self.process = ops.process_mask  # faster
-
-    def get_desc(self):
-        """Return a formatted description of evaluation metrics."""
-        return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Mask(P',
-                                         'R', 'mAP50', 'mAP50-95)')
-
-    def postprocess(self, preds):
-        """Postprocesses YOLO predictions and returns output detections with proto."""
-        p = ops.non_max_suppression(preds[0],
-                                    self.args.conf,
-                                    self.args.iou,
-                                    labels=self.lb,
-                                    multi_label=True,
-                                    agnostic=self.args.single_cls,
-                                    max_det=self.args.max_det,
-                                    nc=self.nc)
-        proto = preds[1][-1] if len(preds[1]) == 3 else preds[1]  # second output is len 3 if pt, but only 1 if exported
-        return p, proto
-
-    def update_metrics(self, preds, batch):
-        """Metrics."""
-        for si, (pred, proto) in enumerate(zip(preds[0], preds[1])):
-            idx = batch['batch_idx'] == si
-            cls = batch['cls'][idx]
-            bbox = batch['bboxes'][idx]
-            nl, npr = cls.shape[0], pred.shape[0]  # number of labels, predictions
-            shape = batch['ori_shape'][si]
-            correct_masks = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device)  # init
-            correct_bboxes = torch.zeros(npr, self.niou, dtype=torch.bool, device=self.device)  # init
-            self.seen += 1
-
-            if npr == 0:
-                if nl:
-                    self.stats.append((correct_bboxes, correct_masks, *torch.zeros(
-                        (2, 0), device=self.device), cls.squeeze(-1)))
-                    if self.args.plots:
-                        self.confusion_matrix.process_batch(detections=None, labels=cls.squeeze(-1))
-                continue
-
-            # Masks
-            midx = [si] if self.args.overlap_mask else idx
-            gt_masks = batch['masks'][midx]
-            pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=batch['img'][si].shape[1:])
-
-            # Predictions
-            if self.args.single_cls:
-                pred[:, 5] = 0
-            predn = pred.clone()
-            ops.scale_boxes(batch['img'][si].shape[1:], predn[:, :4], shape,
-                            ratio_pad=batch['ratio_pad'][si])  # native-space pred
-
-            # Evaluate
-            if nl:
-                height, width = batch['img'].shape[2:]
-                tbox = ops.xywh2xyxy(bbox) * torch.tensor(
-                    (width, height, width, height), device=self.device)  # target boxes
-                ops.scale_boxes(batch['img'][si].shape[1:], tbox, shape,
-                                ratio_pad=batch['ratio_pad'][si])  # native-space labels
-                labelsn = torch.cat((cls, tbox), 1)  # native-space labels
-                correct_bboxes = self._process_batch(predn, labelsn)
-                # TODO: maybe remove these `self.` arguments as they already are member variable
-                correct_masks = self._process_batch(predn,
-                                                    labelsn,
-                                                    pred_masks,
-                                                    gt_masks,
-                                                    overlap=self.args.overlap_mask,
-                                                    masks=True)
-                if self.args.plots:
-                    self.confusion_matrix.process_batch(predn, labelsn)
-
-            # Append correct_masks, correct_boxes, pconf, pcls, tcls
-            self.stats.append((correct_bboxes, correct_masks, pred[:, 4], pred[:, 5], cls.squeeze(-1)))
-
-            pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
-            if self.args.plots and self.batch_i < 3:
-                self.plot_masks.append(pred_masks[:15].cpu())  # filter top 15 to plot
-
-            # Save
-            if self.args.save_json:
-                pred_masks = ops.scale_image(pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(),
-                                             shape,
-                                             ratio_pad=batch['ratio_pad'][si])
-                self.pred_to_json(predn, batch['im_file'][si], pred_masks)
-            # if self.args.save_txt:
-            #    save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
-
-    def finalize_metrics(self, *args, **kwargs):
-        """Sets speed and confusion matrix for evaluation metrics."""
-        self.metrics.speed = self.speed
-        self.metrics.confusion_matrix = self.confusion_matrix
-
-    def _process_batch(self, detections, labels, pred_masks=None, gt_masks=None, overlap=False, masks=False):
-        """
-        Return correct prediction matrix
-        Arguments:
-            detections (array[N, 6]), x1, y1, x2, y2, conf, class
-            labels (array[M, 5]), class, x1, y1, x2, y2
-        Returns:
-            correct (array[N, 10]), for 10 IoU levels
-        """
-        if masks:
-            if overlap:
-                nl = len(labels)
-                index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1
-                gt_masks = gt_masks.repeat(nl, 1, 1)  # shape(1,640,640) -> (n,640,640)
-                gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
-            if gt_masks.shape[1:] != pred_masks.shape[1:]:
-                gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode='bilinear', align_corners=False)[0]
-                gt_masks = gt_masks.gt_(0.5)
-            iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
-        else:  # boxes
-            iou = box_iou(labels[:, 1:], detections[:, :4])
-
-        correct = np.zeros((detections.shape[0], self.iouv.shape[0])).astype(bool)
-        correct_class = labels[:, 0:1] == detections[:, 5]
-        for i in range(len(self.iouv)):
-            x = torch.where((iou >= self.iouv[i]) & correct_class)  # IoU > threshold and classes match
-            if x[0].shape[0]:
-                matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]),
-                                    1).cpu().numpy()  # [label, detect, iou]
-                if x[0].shape[0] > 1:
-                    matches = matches[matches[:, 2].argsort()[::-1]]
-                    matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
-                    # matches = matches[matches[:, 2].argsort()[::-1]]
-                    matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
-                correct[matches[:, 1].astype(int), i] = True
-        return torch.tensor(correct, dtype=torch.bool, device=detections.device)
-
-    def plot_val_samples(self, batch, ni):
-        """Plots validation samples with bounding box labels."""
-        plot_images(batch['img'],
-                    batch['batch_idx'],
-                    batch['cls'].squeeze(-1),
-                    batch['bboxes'],
-                    batch['masks'],
-                    paths=batch['im_file'],
-                    fname=self.save_dir / f'val_batch{ni}_labels.jpg',
-                    names=self.names,
-                    on_plot=self.on_plot)
-
-    def plot_predictions(self, batch, preds, ni):
-        """Plots batch predictions with masks and bounding boxes."""
-        plot_images(
-            batch['img'],
-            *output_to_target(preds[0], max_det=15),  # not set to self.args.max_det due to slow plotting speed
-            torch.cat(self.plot_masks, dim=0) if len(self.plot_masks) else self.plot_masks,
-            paths=batch['im_file'],
-            fname=self.save_dir / f'val_batch{ni}_pred.jpg',
-            names=self.names,
-            on_plot=self.on_plot)  # pred
-        self.plot_masks.clear()
-
-    def pred_to_json(self, predn, filename, pred_masks):
-        """Save one JSON result."""
-        # Example result = {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
-        from pycocotools.mask import encode  # noqa
-
-        def single_encode(x):
-            """Encode predicted masks as RLE and append results to jdict."""
-            rle = encode(np.asarray(x[:, :, None], order='F', dtype='uint8'))[0]
-            rle['counts'] = rle['counts'].decode('utf-8')
-            return rle
-
-        stem = Path(filename).stem
-        image_id = int(stem) if stem.isnumeric() else stem
-        box = ops.xyxy2xywh(predn[:, :4])  # xywh
-        box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
-        pred_masks = np.transpose(pred_masks, (2, 0, 1))
-        with ThreadPool(NUM_THREADS) as pool:
-            rles = pool.map(single_encode, pred_masks)
-        for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())):
-            self.jdict.append({
-                'image_id': image_id,
-                'category_id': self.class_map[int(p[5])],
-                'bbox': [round(x, 3) for x in b],
-                'score': round(p[4], 5),
-                'segmentation': rles[i]})
-
-    def eval_json(self, stats):
-        """Return COCO-style object detection evaluation metrics."""
-        if self.args.save_json and self.is_coco and len(self.jdict):
-            anno_json = self.data['path'] / 'annotations/instances_val2017.json'  # annotations
-            pred_json = self.save_dir / 'predictions.json'  # predictions
-            LOGGER.info(f'\nEvaluating pycocotools mAP using {pred_json} and {anno_json}...')
-            try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
-                check_requirements('pycocotools>=2.0.6')
-                from pycocotools.coco import COCO  # noqa
-                from pycocotools.cocoeval import COCOeval  # noqa
-
-                for x in anno_json, pred_json:
-                    assert x.is_file(), f'{x} file not found'
-                anno = COCO(str(anno_json))  # init annotations api
-                pred = anno.loadRes(str(pred_json))  # init predictions api (must pass string, not Path)
-                for i, eval in enumerate([COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'segm')]):
-                    if self.is_coco:
-                        eval.params.imgIds = [int(Path(x).stem) for x in self.dataloader.dataset.im_files]  # im to eval
-                    eval.evaluate()
-                    eval.accumulate()
-                    eval.summarize()
-                    idx = i * 4 + 2
-                    stats[self.metrics.keys[idx + 1]], stats[
-                        self.metrics.keys[idx]] = eval.stats[:2]  # update mAP50-95 and mAP50
-            except Exception as e:
-                LOGGER.warning(f'pycocotools unable to run: {e}')
-        return stats
-
-
-def val(cfg=DEFAULT_CFG, use_python=False):
-    """Validate trained YOLO model on validation data."""
-    model = cfg.model or 'yolov8n-seg.pt'
-    data = cfg.data or 'coco128-seg.yaml'
-
-    args = dict(model=model, data=data)
-    if use_python:
-        from ultralytics import YOLO
-        YOLO(model).val(**args)
-    else:
-        validator = SegmentationValidator(args=args)
-        validator(model=args['model'])
-
-
-if __name__ == '__main__':
-    val()
diff --git a/spaces/vama09/HashtagAndCaption/README.md b/spaces/vama09/HashtagAndCaption/README.md
deleted file mode 100644
index f8be605bc0b38f5647db0782cc3a9414ce29ed0a..0000000000000000000000000000000000000000
--- a/spaces/vama09/HashtagAndCaption/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: HashtagAndCaption
-emoji: 🏃
-colorFrom: blue
-colorTo: green
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vishal0501/ICD-DEMO/app.py b/spaces/vishal0501/ICD-DEMO/app.py
deleted file mode 100644
index 838a8e4c8e16b2804155ee82bd434c08f0fdc0c6..0000000000000000000000000000000000000000
--- a/spaces/vishal0501/ICD-DEMO/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import streamlit as st
-import model_class
-
-
-# import streamlit as st
-
-col1, col2 = st.columns(2)
-
-with col1:
-#    st.header("A cat")
-#    st.image("https://static.streamlit.io/examples/cat.jpg")
-    # text_box
-    gptkey = st.text_input(label='Enter GPT KEY',placeholder="Enter GPT-API-KEY",label_visibility="collapsed")
-
-
-
-with col2:
-    pineconekey = st.text_input(label='Enter PINECONE KEY',placeholder="Enter Pinecone API-KEY",label_visibility="collapsed")
-
-
-# text_box
-# query = st.text_input(label='Enter Query',placeholder="Enter Query",label_visibility="collapsed")
-
-query = st.text_area(label='Enter Query',placeholder="Enter Query",height=200)
-
-if st.button('Submit'):
-# if query:
-    model_class.model_obj.setAPIKEY(gptkey, pineconekey)
-    model_class.model_obj.initializer()
-
-    resp = model_class.model_obj.get_response(query)
-
-    # for i in resp:
-        # st.write(i)
-
-    st.write(eval(resp))
\ No newline at end of file
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/configs/_base_/models/nonlocal_r50-d8.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/configs/_base_/models/nonlocal_r50-d8.py
deleted file mode 100644
index 5674a39854cafd1f2e363bac99c58ccae62f24da..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/configs/_base_/models/nonlocal_r50-d8.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='NLHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        dropout_ratio=0.1,
-        reduction=2,
-        use_scale=True,
-        mode='embedded_gaussian',
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/vuu10/EnzRank/app.py b/spaces/vuu10/EnzRank/app.py
deleted file mode 100644
index baeae12d8f3e3c765dea33fe020be05f95af6316..0000000000000000000000000000000000000000
--- a/spaces/vuu10/EnzRank/app.py
+++ /dev/null
@@ -1,186 +0,0 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import re
-from PIL import Image
-import webbrowser
-
-from rdkit import Chem
-from rdkit.Chem import AllChem
-from rdkit.Chem import Draw
-from rdkit.Chem import rdChemReactions as Reactions
-
-import tensorflow as tf
-from tensorflow import keras
-from keras.preprocessing import sequence
-from keras.utils import pad_sequences
-import keras
-from keras import backend as K
-from keras.models import load_model
-import argparse
-import h5py
-import pdb
-
-
-seq_rdic = ['A', 'I', 'L', 'V', 'F', 'W', 'Y', 'N', 'C', 'Q', 'M',
-    'S', 'T', 'D', 'E', 'R', 'H', 'K', 'G', 'P', 'O', 'U', 'X', 'B', 'Z']
-seq_dic = {w: i+1 for i, w in enumerate(seq_rdic)}
-
-
-@st.cache_data
-def encodeSeq(seq, seq_dic):
-    if pd.isnull(seq):
-        return [0]
-    else:
-        return [seq_dic[aa] for aa in seq]
-
-
-@st.cache_resource
-def load_modelfile(model_string):
-	loaded_model = tf.keras.models.load_model(model_string)
-	return loaded_model
-
-
-@st.cache_data
-def prot_feature_gen_from_str_input(prot_input_str, prot_len=2500):
-    Prot_ID = prot_input_str.split(':')[0]
-    Prot_seq = prot_input_str.split(':')[1]
-    Prot_seq = Prot_seq.replace(" ", "")
-    prot_dataframe = pd.DataFrame(
-        {'Protein_ID': Prot_ID, 'Sequence': Prot_seq}, index=[0])
-    prot_dataframe.set_index('Protein_ID')
-
-    prot_dataframe["encoded_sequence"] = prot_dataframe.Sequence.map(
-        lambda a: encodeSeq(a, seq_dic))
-    prot_feature = pad_sequences(
-        prot_dataframe["encoded_sequence"].values, prot_len)
-
-    return prot_feature, Prot_ID
-
-
-@st.cache_data
-def mol_feature_gen_from_str_input(mol_str, kegg_id_flag, kegg_df):
-
-	if kegg_id_flag == 1:
-		KEGG_ID = mol_str
-		kegg_id_loc = kegg_df.index[kegg_df.Compound_ID == KEGG_ID][0]
-		KEGG_ID_info = kegg_df.loc[kegg_id_loc]
-		KEGG_ID_info_df = KEGG_ID_info.to_frame().T.set_index('Compound_ID')
-
-		final_return = KEGG_ID_info_df
-		final_id = KEGG_ID
-
-	else:
-		try:
-			mol_ID = mol_str.split(':')[0]
-			mol_smiles = mol_str.split(':')[1]
-			mol = Chem.MolFromSmiles(mol_smiles)
-			fp1 = AllChem.GetMorganFingerprintAsBitVect(
-			    mol, useChirality=True, radius=2, nBits=2048)
-			fp_list = list(np.array(fp1).astype(float))
-			fp_str = list(map(str, fp_list))
-			mol_fp = '\t'.join(fp_str)
-
-			mol_dict = {}
-			mol_dict['Compound_ID'] = mol_ID
-			mol_dict['Smiles'] = mol_smiles
-			mol_dict['morgan_fp_r2'] = mol_fp
-
-			mol_info_df = pd.DataFrame(mol_dict, index=[0])
-			mol_info_df = mol_info_df.set_index('Compound_ID')
-
-			final_return = mol_info_df
-			final_id = mol_ID
-
-		except Exception as error:
-			print('Something wrong with molecule input string...' + repr(error))
-
-	return final_return, final_id
-
-
-@st.cache_data
-def act_df_gen_mol_feature(mol_id, prot_id):
-	act_df = pd.DataFrame(
-	    {'Protein_ID': prot_id, 'Compound_ID': mol_id}, index=[0])
-
-	return act_df
-
-
-@st.cache_data
-def compound_feature_gen_df_input(act_df, comp_df, comp_len=2048, comp_vec='morgan_fp_r2'):
-	act_df = pd.merge(act_df, comp_df, left_on='Compound_ID', right_index=True)
-	comp_feature = np.stack(act_df[comp_vec].map(lambda fp: fp.split("\t")))
-	comp_feature = comp_feature.astype('float')
-	return comp_feature
-
-
-@st.cache_data
-def model_prediction(compound_feature, enz_feature, model):
-    prediction_vals = model.predict([compound_feature, enz_feature])
-
-    return prediction_vals[0][0]
-
-
-def main():
-	graph = tf.compat.v1.get_default_graph()
-	ld_model = tf.keras.models.load_model('./CNN_model_final/Final_model.model')
-
-	KEGG_compound_read = pd.read_csv('./CNN_data_kegg/kegg_compound.csv', index_col = 'Compound_ID')
-	kegg_df = KEGG_compound_read.reset_index()
-
-	st.image('./Streamlit/header.png', use_column_width=True)
-
-	st.subheader('Enzyme-Substrate Activity Predictor ')
-
-	st.subheader('Enzyme sequence')
-	st.caption('Please follow the input format show in the text box--> id:Sequence')
-
-	enz_str = st.text_input('', value="A0A4P8WFA8:MTKRVLVTGGAGFLGSHLCERLLSEGHEVICLDNFGSGRRKNIKEFEDHPSFKVNDRDVRISESLPSVDRIYHLASRASPADFTQFPVNIALANTQGTRRLLDQARACDARMVFASTSEVYGDPKVHPQPETYTGNVNIRGARGCYDESKRFGETLTVAYQRKYDVDARTVRIFNTYGPRMRPDDGRVVPTFVTQALRGDDLTIYGDGEQTRSFCYVDDLIEGLISLMRVDNPEHNVYNIGKENERTIKELAYEVLGLTDTESDIVYEPLPEDDPGQRRPDITRAKTELDWEPKISLREGLEDTITYFDN")
-
-    # url = 'https://www.genome.jp/dbget-bin/www_bget?rn:R00801'
-    # if st.button('KEformat example'):
-    #     webbrowser.open_new_tab(url)
-
-	st.subheader('Substrate ')
-	st.caption('Please follow the input format show in the text box--> KEGG id or click the checkbox')
-
-	comp_str = st.text_input('', value="C00149")
-	if st.checkbox('If you are entering smiles string click here'):
-		add_info = st.text_area('Additional information (id: Smiles):', "C00149:O[C@@H](CC([O-])=O)C([O-])=O")
-	else:
-		add_info = ''
-
-	if st.button("Predict"):
-    # if session_state.button_search:
-# 		st.subheader('Enzyme-Substrate activity score')
-		with st.spinner('Calculating...'):
-			try:
-# 				st.write('I am inside')
-				prot_feature, prot_id = prot_feature_gen_from_str_input(enz_str)
-				if len(add_info) == 0:
-					kegg_id_flag = 1
-					comp_feature, comp_id = mol_feature_gen_from_str_input(comp_str, kegg_id_flag, kegg_df)
-				else:
-					kegg_id_flag = 0
-					comp_feature, comp_id = mol_feature_gen_from_str_input(add_info, kegg_id_flag, kegg_df)
-				
-				act_dataframe = act_df_gen_mol_feature(comp_id, prot_id)
-# 				st.write(act_dataframe)
-				compound_feature = compound_feature_gen_df_input(act_dataframe, comp_feature)
-# 				st.write(compound_feature)
-            
-			except Exception as e:
-				st.write('Error somewhere...' + repr(e))
-				
-# 			st.write(compound_feature)
-# 			st.write(prot_feature)
-# 			keras.backend.clear_session()
-
-			y = ld_model.predict([compound_feature, prot_feature])
-			
-			subheaderstring = 'EnzRank Score for '+ prot_id +  '-' + comp_id + ' pair:'
-			st.subheader(subheaderstring)
-			st.write(str(y[0][0]))
-			
-if __name__ == '__main__':
-    main()
diff --git a/spaces/whitphx/gradio-static-test/dist/assets/dockerfile-d67bbd50.js b/spaces/whitphx/gradio-static-test/dist/assets/dockerfile-d67bbd50.js
deleted file mode 100644
index 5405cd3af19be5d8cb56dbb55aefa442653e888a..0000000000000000000000000000000000000000
--- a/spaces/whitphx/gradio-static-test/dist/assets/dockerfile-d67bbd50.js
+++ /dev/null
@@ -1,2 +0,0 @@
-function c(n){a(n,"start");var t={},e=n.languageData||{},s=!1;for(var l in n)if(l!=e&&n.hasOwnProperty(l))for(var u=t[l]=[],o=n[l],r=0;r<o.length;r++){var i=o[r];u.push(new w(i,n)),(i.indent||i.dedent)&&(s=!0)}return{name:e.name,startState:function(){return{state:"start",pending:null,indent:s?[]:null}},copyState:function(g){var x={state:g.state,pending:g.pending,indent:g.indent&&g.indent.slice(0)};return g.stack&&(x.stack=g.stack.slice(0)),x},token:v(t),indent:R(t,e),languageData:e}}function a(n,t){if(!n.hasOwnProperty(t))throw new Error("Undefined state "+t+" in simple mode")}function h(n,t){if(!n)return/(?:)/;var e="";return n instanceof RegExp?(n.ignoreCase&&(e="i"),n=n.source):n=String(n),new RegExp((t===!1?"":"^")+"(?:"+n+")",e)}function y(n){if(!n)return null;if(n.apply)return n;if(typeof n=="string")return n.replace(/\./g," ");for(var t=[],e=0;e<n.length;e++)t.push(n[e]&&n[e].replace(/\./g," "));return t}function w(n,t){(n.next||n.push)&&a(t,n.next||n.push),this.regex=h(n.regex),this.token=y(n.token),this.data=n}function v(n){return function(t,e){if(e.pending){var s=e.pending.shift();return e.pending.length==0&&(e.pending=null),t.pos+=s.text.length,s.token}for(var l=n[e.state],u=0;u<l.length;u++){var o=l[u],r=(!o.data.sol||t.sol())&&t.match(o.regex);if(r){o.data.next?e.state=o.data.next:o.data.push?((e.stack||(e.stack=[])).push(e.state),e.state=o.data.push):o.data.pop&&e.stack&&e.stack.length&&(e.state=e.stack.pop()),o.data.indent&&e.indent.push(t.indentation()+t.indentUnit),o.data.dedent&&e.indent.pop();var i=o.token;if(i&&i.apply&&(i=i(r)),r.length>2&&o.token&&typeof o.token!="string"){e.pending=[];for(var g=2;g<r.length;g++)r[g]&&e.pending.push({text:r[g],token:o.token[g-1]});return t.backUp(r[0].length-(r[1]?r[1].length:0)),i[0]}else return i&&i.join?i[0]:i}}return t.next(),null}}function R(n,t){return function(e,s){if(e.indent==null||t.dontIndentStates&&t.doneIndentState.indexOf(e.state)>-1)return null;var l=e.indent.length-1,u=n[e.state];n:for(;;){for(var o=0;o<u.length;o++){var r=u[o];if(r.data.dedent&&r.data.dedentIfLineStart!==!1){var i=r.regex.exec(s);if(i&&i[0]){l--,(r.next||r.push)&&(u=n[r.next||r.push]),s=s.slice(i[0].length);continue n}}}break}return l<0?0:e.indent[l]}}var p="from",$=new RegExp("^(\\s*)\\b("+p+")\\b","i"),d=["run","cmd","entrypoint","shell"],b=new RegExp("^(\\s*)("+d.join("|")+")(\\s+\\[)","i"),f="expose",m=new RegExp("^(\\s*)("+f+")(\\s+)","i"),S=["arg","from","maintainer","label","env","add","copy","volume","user","workdir","onbuild","stopsignal","healthcheck","shell"],E=[p,f].concat(d).concat(S),k="("+E.join("|")+")",I=new RegExp("^(\\s*)"+k+"(\\s*)(#.*)?$","i"),O=new RegExp("^(\\s*)"+k+"(\\s+)","i");const D=c({start:[{regex:/^\s*#.*$/,sol:!0,token:"comment"},{regex:$,token:[null,"keyword"],sol:!0,next:"from"},{regex:I,token:[null,"keyword",null,"error"],sol:!0},{regex:b,token:[null,"keyword",null],sol:!0,next:"array"},{regex:m,token:[null,"keyword",null],sol:!0,next:"expose"},{regex:O,token:[null,"keyword",null],sol:!0,next:"arguments"},{regex:/./,token:null}],from:[{regex:/\s*$/,token:null,next:"start"},{regex:/(\s*)(#.*)$/,token:[null,"error"],next:"start"},{regex:/(\s*\S+\s+)(as)/i,token:[null,"keyword"],next:"start"},{token:null,next:"start"}],single:[{regex:/(?:[^\\']|\\.)/,token:"string"},{regex:/'/,token:"string",pop:!0}],double:[{regex:/(?:[^\\"]|\\.)/,token:"string"},{regex:/"/,token:"string",pop:!0}],array:[{regex:/\]/,token:null,next:"start"},{regex:/"(?:[^\\"]|\\.)*"?/,token:"string"}],expose:[{regex:/\d+$/,token:"number",next:"start"},{regex:/[^\d]+$/,token:null,next:"start"},{regex:/\d+/,token:"number"},{regex:/[^\d]+/,token:null},{token:null,next:"start"}],arguments:[{regex:/^\s*#.*$/,sol:!0,token:"comment"},{regex:/"(?:[^\\"]|\\.)*"?$/,token:"string",next:"start"},{regex:/"/,token:"string",push:"double"},{regex:/'(?:[^\\']|\\.)*'?$/,token:"string",next:"start"},{regex:/'/,token:"string",push:"single"},{regex:/[^#"']+[\\`]$/,token:null},{regex:/[^#"']+$/,token:null,next:"start"},{regex:/[^#"']+/,token:null},{token:null,next:"start"}],languageData:{commentTokens:{line:"#"}}});export{D as dockerFile};
-//# sourceMappingURL=dockerfile-d67bbd50.js.map
diff --git a/spaces/wliu88/StructDiffusionDemo/app_v0.py b/spaces/wliu88/StructDiffusionDemo/app_v0.py
deleted file mode 100644
index 81020debb61c6a7764082a2ea5122c38b52a6194..0000000000000000000000000000000000000000
--- a/spaces/wliu88/StructDiffusionDemo/app_v0.py
+++ /dev/null
@@ -1,282 +0,0 @@
-import os
-import argparse
-import torch
-import trimesh
-import numpy as np
-import pytorch_lightning as pl
-import gradio as gr
-from omegaconf import OmegaConf
-
-import sys
-sys.path.append('./src')
-
-from StructDiffusion.data.semantic_arrangement_demo import SemanticArrangementDataset
-from StructDiffusion.language.tokenizer import Tokenizer
-from StructDiffusion.models.pl_models import ConditionalPoseDiffusionModel
-from StructDiffusion.diffusion.sampler import Sampler
-from StructDiffusion.diffusion.pose_conversion import get_struct_objs_poses
-from StructDiffusion.utils.files import get_checkpoint_path_from_dir
-from StructDiffusion.utils.rearrangement import show_pcs_with_trimesh
-import StructDiffusion.utils.transformations as tra
-
-
-def move_pc_and_create_scene_simple(obj_xyzs, struct_pose, pc_poses_in_struct):
-
-    device = obj_xyzs.device
-
-    # obj_xyzs: B, N, P, 3 or 6
-    # struct_pose: B, 1, 4, 4
-    # pc_poses_in_struct: B, N, 4, 4
-
-    B, N, _, _ = pc_poses_in_struct.shape
-    _, _, P, _ = obj_xyzs.shape
-
-    current_pc_poses = torch.eye(4).repeat(B, N, 1, 1).to(device)  # B, N, 4, 4
-    # print(torch.mean(obj_xyzs, dim=2).shape)
-    current_pc_poses[:, :, :3, 3] = torch.mean(obj_xyzs[:, :, :, :3], dim=2)  # B, N, 4, 4
-    current_pc_poses = current_pc_poses.reshape(B * N, 4, 4)  # B x N, 4, 4
-
-    struct_pose = struct_pose.repeat(1, N, 1, 1) # B, N, 4, 4
-    struct_pose = struct_pose.reshape(B * N, 4, 4)  # B x 1, 4, 4
-    pc_poses_in_struct = pc_poses_in_struct.reshape(B * N, 4, 4)  # B x N, 4, 4
-
-    goal_pc_pose = struct_pose @ pc_poses_in_struct  # B x N, 4, 4
-    # print("goal pc poses")
-    # print(goal_pc_pose)
-    goal_pc_transform = goal_pc_pose @ torch.inverse(current_pc_poses)  # B x N, 4, 4
-
-    # # important: pytorch3d uses row-major ordering, need to transpose each transformation matrix
-    # transpose = tra3d.Transform3d(matrix=goal_pc_transform.transpose(1, 2))
-    # new_obj_xyzs = obj_xyzs.reshape(B * N, P, -1)  # B x N, P, 3
-    # new_obj_xyzs[:, :, :3] = transpose.transform_points(new_obj_xyzs[:, :, :3])
-
-    # a verision that does not rely on pytorch3d
-    new_obj_xyzs = obj_xyzs.reshape(B * N, P, -1)[:, :, :3]  # B x N, P, 3
-    new_obj_xyzs = torch.concat([new_obj_xyzs, torch.ones(B * N, P, 1).to(device)], dim=-1) # B x N, P, 4
-    new_obj_xyzs = torch.einsum('bij,bkj->bki', goal_pc_transform, new_obj_xyzs)[:, :, :3]  # # B x N, P, 3
-
-    # put it back to B, N, P, 3
-    obj_xyzs[:, :, :, :3] = new_obj_xyzs.reshape(B, N, P, -1)
-
-    return obj_xyzs
-
-
-class Infer_Wrapper:
-
-    def __init__(self, args, cfg):
-
-        # load
-        pl.seed_everything(args.eval_random_seed)
-        self.device = (torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu"))
-
-        checkpoint_dir = os.path.join(cfg.WANDB.save_dir, cfg.WANDB.project, args.checkpoint_id, "checkpoints")
-        checkpoint_path = get_checkpoint_path_from_dir(checkpoint_dir)
-
-        self.tokenizer = Tokenizer(cfg.DATASET.vocab_dir)
-        # override ignore_rgb for visualization
-        cfg.DATASET.ignore_rgb = False
-        self.dataset = SemanticArrangementDataset(tokenizer=self.tokenizer, **cfg.DATASET)
-
-        self.sampler = Sampler(ConditionalPoseDiffusionModel, checkpoint_path, self.device)
-
-    def visualize_scene(self, di, session_id):
-        raw_datum = self.dataset.get_raw_data(di)
-        language_command = self.tokenizer.convert_structure_params_to_natural_language(raw_datum["sentence"])
-
-        obj_xyz = raw_datum["pcs"]
-        scene = show_pcs_with_trimesh([xyz[:, :3] for xyz in obj_xyz], [xyz[:, 3:] for xyz in obj_xyz], return_scene=True)
-
-        scene.apply_transform(tra.euler_matrix(np.pi, 0, np.pi/2))
-
-        scene_filename = "./tmp_data/input_scene_{}.glb".format(session_id)
-        scene.export(scene_filename)
-
-        return language_command, scene_filename
-
-    def infer(self, di, session_id, progress=gr.Progress()):
-
-        # di = np.random.choice(len(self.dataset))
-
-        raw_datum = self.dataset.get_raw_data(di)
-        print(self.tokenizer.convert_structure_params_to_natural_language(raw_datum["sentence"]))
-        datum = self.dataset.convert_to_tensors(raw_datum, self.tokenizer)
-        batch = self.dataset.single_datum_to_batch(datum, args.num_samples, self.device, inference_mode=True)
-
-        num_poses = datum["goal_poses"].shape[0]
-        xs = self.sampler.sample(batch, num_poses, progress)
-
-        struct_pose, pc_poses_in_struct = get_struct_objs_poses(xs[0])
-        new_obj_xyzs = move_pc_and_create_scene_simple(batch["pcs"], struct_pose, pc_poses_in_struct)
-
-        # vis
-        vis_obj_xyzs = new_obj_xyzs[:3]
-        if torch.is_tensor(vis_obj_xyzs):
-            if vis_obj_xyzs.is_cuda:
-                vis_obj_xyzs = vis_obj_xyzs.detach().cpu()
-            vis_obj_xyzs = vis_obj_xyzs.numpy()
-
-        # for bi, vis_obj_xyz in enumerate(vis_obj_xyzs):
-        #     if verbose:
-        #         print("example {}".format(bi))
-        #         print(vis_obj_xyz.shape)
-        #
-        #     if trimesh:
-        #         show_pcs_with_trimesh([xyz[:, :3] for xyz in vis_obj_xyz], [xyz[:, 3:] for xyz in vis_obj_xyz])
-        vis_obj_xyz = vis_obj_xyzs[0]
-        scene = show_pcs_with_trimesh([xyz[:, :3] for xyz in vis_obj_xyz], [xyz[:, 3:] for xyz in vis_obj_xyz], return_scene=True)
-
-        scene.apply_transform(tra.euler_matrix(np.pi, 0, np.pi/2))
-
-        scene_filename = "./tmp_data/output_scene_{}.glb".format(session_id)
-        scene.export(scene_filename)
-
-        # pc_filename = "/home/weiyu/Research/StructDiffusion/StructDiffusion/interactive_demo/tmp_data/pc.glb"
-        # scene_filename = "/home/weiyu/Research/StructDiffusion/StructDiffusion/interactive_demo/tmp_data/scene.glb"
-        #
-        # vis_obj_xyz = vis_obj_xyz.reshape(-1, 6)
-        # vis_pc = trimesh.PointCloud(vis_obj_xyz[:, :3], colors=np.concatenate([vis_obj_xyz[:, 3:] * 255, np.ones([vis_obj_xyz.shape[0], 1]) * 255], axis=-1))
-        # vis_pc.export(pc_filename)
-        #
-        # scene = trimesh.Scene()
-        # # add the coordinate frame first
-        # # geom = trimesh.creation.axis(0.01)
-        # # scene.add_geometry(geom)
-        # table = trimesh.creation.box(extents=[1.0, 1.0, 0.02])
-        # table.apply_translation([0.5, 0, -0.01])
-        # table.visual.vertex_colors = [150, 111, 87, 125]
-        # scene.add_geometry(table)
-        # # bounds = trimesh.creation.box(extents=[4.0, 4.0, 4.0])
-        # # bounds = trimesh.creation.icosphere(subdivisions=3, radius=3.1)
-        # # bounds.apply_translation([0, 0, 0])
-        # # bounds.visual.vertex_colors = [30, 30, 30, 30]
-        # # scene.add_geometry(bounds)
-        # # RT_4x4 = np.array([[-0.39560353822208355, -0.9183993826406329, 0.006357240869497738, 0.2651463080169481],
-        # #                    [-0.797630370081598, 0.3401340617616391, -0.4980909683511864, 0.2225696480721997],
-        # #                    [0.45528412367406523, -0.2021172778236285, -0.8671014777611122, 0.9449050652025951],
-        # #                    [0.0, 0.0, 0.0, 1.0]])
-        # # RT_4x4 = np.linalg.inv(RT_4x4)
-        # # RT_4x4 = RT_4x4 @ np.diag([1, -1, -1, 1])
-        # # scene.camera_transform = RT_4x4
-        #
-        # mesh_list = trimesh.util.concatenate(scene.dump())
-        # print(mesh_list)
-        # trimesh.io.export.export_mesh(mesh_list, scene_filename, file_type='obj')
-
-        return scene_filename
-
-    def infer_new(self, di, session_id, progress=gr.Progress()):
-
-        # di = np.random.choice(len(self.dataset))
-
-        raw_datum = self.dataset.get_raw_data(di)
-        print(self.tokenizer.convert_structure_params_to_natural_language(raw_datum["sentence"]))
-        datum = self.dataset.convert_to_tensors(raw_datum, self.tokenizer)
-        batch = self.dataset.single_datum_to_batch(datum, args.num_samples, self.device, inference_mode=True)
-
-        num_poses = datum["goal_poses"].shape[0]
-        xs = self.sampler.sample(batch, num_poses, progress)
-
-        struct_pose, pc_poses_in_struct = get_struct_objs_poses(xs[0])
-        new_obj_xyzs = move_pc_and_create_scene_simple(batch["pcs"], struct_pose, pc_poses_in_struct)
-
-        # vis
-        vis_obj_xyzs = new_obj_xyzs[:3]
-        if torch.is_tensor(vis_obj_xyzs):
-            if vis_obj_xyzs.is_cuda:
-                vis_obj_xyzs = vis_obj_xyzs.detach().cpu()
-            vis_obj_xyzs = vis_obj_xyzs.numpy()
-
-        # for bi, vis_obj_xyz in enumerate(vis_obj_xyzs):
-        #     if verbose:
-        #         print("example {}".format(bi))
-        #         print(vis_obj_xyz.shape)
-        #
-        #     if trimesh:
-        #         show_pcs_with_trimesh([xyz[:, :3] for xyz in vis_obj_xyz], [xyz[:, 3:] for xyz in vis_obj_xyz])
-        vis_obj_xyz = vis_obj_xyzs[0]
-        scene = show_pcs_with_trimesh([xyz[:, :3] for xyz in vis_obj_xyz], [xyz[:, 3:] for xyz in vis_obj_xyz], return_scene=True)
-
-        scene.apply_transform(tra.euler_matrix(np.pi, 0, np.pi/2))
-
-        scene_filename = "./tmp_data/output_scene_{}.glb".format(session_id)
-        scene.export(scene_filename)
-
-        # pc_filename = "/home/weiyu/Research/StructDiffusion/StructDiffusion/interactive_demo/tmp_data/pc.glb"
-        # scene_filename = "/home/weiyu/Research/StructDiffusion/StructDiffusion/interactive_demo/tmp_data/scene.glb"
-        #
-        # vis_obj_xyz = vis_obj_xyz.reshape(-1, 6)
-        # vis_pc = trimesh.PointCloud(vis_obj_xyz[:, :3], colors=np.concatenate([vis_obj_xyz[:, 3:] * 255, np.ones([vis_obj_xyz.shape[0], 1]) * 255], axis=-1))
-        # vis_pc.export(pc_filename)
-        #
-        # scene = trimesh.Scene()
-        # # add the coordinate frame first
-        # # geom = trimesh.creation.axis(0.01)
-        # # scene.add_geometry(geom)
-        # table = trimesh.creation.box(extents=[1.0, 1.0, 0.02])
-        # table.apply_translation([0.5, 0, -0.01])
-        # table.visual.vertex_colors = [150, 111, 87, 125]
-        # scene.add_geometry(table)
-        # # bounds = trimesh.creation.box(extents=[4.0, 4.0, 4.0])
-        # # bounds = trimesh.creation.icosphere(subdivisions=3, radius=3.1)
-        # # bounds.apply_translation([0, 0, 0])
-        # # bounds.visual.vertex_colors = [30, 30, 30, 30]
-        # # scene.add_geometry(bounds)
-        # # RT_4x4 = np.array([[-0.39560353822208355, -0.9183993826406329, 0.006357240869497738, 0.2651463080169481],
-        # #                    [-0.797630370081598, 0.3401340617616391, -0.4980909683511864, 0.2225696480721997],
-        # #                    [0.45528412367406523, -0.2021172778236285, -0.8671014777611122, 0.9449050652025951],
-        # #                    [0.0, 0.0, 0.0, 1.0]])
-        # # RT_4x4 = np.linalg.inv(RT_4x4)
-        # # RT_4x4 = RT_4x4 @ np.diag([1, -1, -1, 1])
-        # # scene.camera_transform = RT_4x4
-        #
-        # mesh_list = trimesh.util.concatenate(scene.dump())
-        # print(mesh_list)
-        # trimesh.io.export.export_mesh(mesh_list, scene_filename, file_type='obj')
-
-        return scene_filename
-
-
-args = OmegaConf.create()
-args.base_config_file = "./configs/base.yaml"
-args.config_file = "./configs/conditional_pose_diffusion.yaml"
-args.checkpoint_id = "ConditionalPoseDiffusion"
-args.eval_random_seed = 42
-args.num_samples = 1
-
-base_cfg = OmegaConf.load(args.base_config_file)
-cfg = OmegaConf.load(args.config_file)
-cfg = OmegaConf.merge(base_cfg, cfg)
-
-infer_wrapper = Infer_Wrapper(args, cfg)
-
-# version 0
-# demo = gr.Interface(
-#     fn=infer_wrapper.run,
-#     inputs=gr.Slider(0, len(infer_wrapper.dataset)),
-#     # clear color range [0-1.0]
-#     outputs=gr.Model3D(clear_color=[0, 0, 0, 0],  label="3D Model")
-# )
-#
-# demo.launch()
-
-# version 1
-demo = gr.Blocks(theme=gr.themes.Soft())
-with demo:
-    gr.Markdown("<p style='text-align:center;font-size:18px'><b>StructDiffusion Demo</b></p>")
-    # font-size:18px
-    gr.Markdown("<p style='text-align:center'>StructDiffusion combines a diffusion model and an object-centric transformer to construct structures given partial-view point clouds and high-level language goals.<br><a href='https://structdiffusion.github.io/'>Website</a> | <a href='https://github.com/StructDiffusion/StructDiffusion'>Code</a></p>")
-
-    session_id = gr.State(value=np.random.randint(0, 1000))
-    data_selection = gr.Number(label="Example No.", minimum=0, maximum=len(infer_wrapper.dataset) - 1, precision=0)
-    input_scene = gr.Model3D(clear_color=[0, 0, 0, 0],  label="Input 3D Scene")
-    language_command = gr.Textbox(label="Input Language Command")
-    output_scene = gr.Model3D(clear_color=[0, 0, 0, 0],  label="Generated 3D Structure")
-
-    b1 = gr.Button("Show Input Language and Scene")
-    b2 = gr.Button("Generate 3D Structure")
-
-    b1.click(infer_wrapper.visualize_scene, inputs=[data_selection, session_id], outputs=[language_command, input_scene])
-    b2.click(infer_wrapper.infer, inputs=[data_selection, session_id], outputs=output_scene)
-
-demo.queue(concurrency_count=10)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/wong26/faster-whisper-webui/LICENSE.md b/spaces/wong26/faster-whisper-webui/LICENSE.md
deleted file mode 100644
index f5f4b8b5ecd27c09e4ef16e9662bcb7bb2bfc76f..0000000000000000000000000000000000000000
--- a/spaces/wong26/faster-whisper-webui/LICENSE.md
+++ /dev/null
@@ -1,195 +0,0 @@
-Apache License
-==============
-
-_Version 2.0, January 2004_  
-_&lt;<http://www.apache.org/licenses/>&gt;_
-
-### Terms and Conditions for use, reproduction, and distribution
-
-#### 1. Definitions
-
-“License” shall mean the terms and conditions for use, reproduction, and
-distribution as defined by Sections 1 through 9 of this document.
-
-“Licensor” shall mean the copyright owner or entity authorized by the copyright
-owner that is granting the License.
-
-“Legal Entity” shall mean the union of the acting entity and all other entities
-that control, are controlled by, or are under common control with that entity.
-For the purposes of this definition, “control” means **(i)** the power, direct or
-indirect, to cause the direction or management of such entity, whether by
-contract or otherwise, or **(ii)** ownership of fifty percent (50%) or more of the
-outstanding shares, or **(iii)** beneficial ownership of such entity.
-
-“You” (or “Your”) shall mean an individual or Legal Entity exercising
-permissions granted by this License.
-
-“Source” form shall mean the preferred form for making modifications, including
-but not limited to software source code, documentation source, and configuration
-files.
-
-“Object” form shall mean any form resulting from mechanical transformation or
-translation of a Source form, including but not limited to compiled object code,
-generated documentation, and conversions to other media types.
-
-“Work” shall mean the work of authorship, whether in Source or Object form, made
-available under the License, as indicated by a copyright notice that is included
-in or attached to the work (an example is provided in the Appendix below).
-
-“Derivative Works” shall mean any work, whether in Source or Object form, that
-is based on (or derived from) the Work and for which the editorial revisions,
-annotations, elaborations, or other modifications represent, as a whole, an
-original work of authorship. For the purposes of this License, Derivative Works
-shall not include works that remain separable from, or merely link (or bind by
-name) to the interfaces of, the Work and Derivative Works thereof.
-
-“Contribution” shall mean any work of authorship, including the original version
-of the Work and any modifications or additions to that Work or Derivative Works
-thereof, that is intentionally submitted to Licensor for inclusion in the Work
-by the copyright owner or by an individual or Legal Entity authorized to submit
-on behalf of the copyright owner. For the purposes of this definition,
-“submitted” means any form of electronic, verbal, or written communication sent
-to the Licensor or its representatives, including but not limited to
-communication on electronic mailing lists, source code control systems, and
-issue tracking systems that are managed by, or on behalf of, the Licensor for
-the purpose of discussing and improving the Work, but excluding communication
-that is conspicuously marked or otherwise designated in writing by the copyright
-owner as “Not a Contribution.”
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-#### 5. Submission of Contributions
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-for inclusion in the Work by You to the Licensor shall be under the terms and
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-Notwithstanding the above, nothing herein shall supersede or modify the terms of
-any separate license agreement you may have executed with Licensor regarding
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-#### 6. Trademarks
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-#### 7. Disclaimer of Warranty
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-Unless required by applicable law or agreed to in writing, Licensor provides the
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-solely responsible for determining the appropriateness of using or
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-permissions under this License.
-
-#### 8. Limitation of Liability
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-contract, or otherwise, unless required by applicable law (such as deliberate
-and grossly negligent acts) or agreed to in writing, shall any Contributor be
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-
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-### APPENDIX: How to apply the Apache License to your work
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-To apply the Apache License to your work, attach the following boilerplate
-notice, with the fields enclosed by brackets `[]` replaced with your own
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-recommend that a file or class name and description of purpose be included on
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-
-    Copyright [yyyy] [name of copyright owner]
-    
-    Licensed under the Apache License, Version 2.0 (the "License");
-    you may not use this file except in compliance with the License.
-    You may obtain a copy of the License at
-    
-      http://www.apache.org/licenses/LICENSE-2.0
-    
-    Unless required by applicable law or agreed to in writing, software
-    distributed under the License is distributed on an "AS IS" BASIS,
-    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-    See the License for the specific language governing permissions and
-    limitations under the License.
-
diff --git a/spaces/xangma/chat-pykg/style.css b/spaces/xangma/chat-pykg/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/xangma/chat-pykg/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/xdecoder/Instruct-X-Decoder/tasks/__init__.py b/spaces/xdecoder/Instruct-X-Decoder/tasks/__init__.py
deleted file mode 100644
index fdf223db638eddb8b08f04195a9bfe1079792920..0000000000000000000000000000000000000000
--- a/spaces/xdecoder/Instruct-X-Decoder/tasks/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from .img_cap import image_captioning
-from .open_inst import open_instseg
-from .open_pano import open_panoseg
-from .open_sem import open_semseg
-from .ref_cap import referring_captioning
-from .ref_in import referring_inpainting
-from .ref_seg import referring_segmentation
-from .text_ret import text_retrieval
-from .reg_ret import region_retrieval
-from .ref_in_gpt3 import referring_inpainting_gpt3
-from . import img_cap, open_inst, open_pano, open_sem, ref_cap, ref_in, ref_seg, text_ret
\ No newline at end of file
diff --git a/spaces/xin/PatentSolver/App/bin/InformationExtractor.py b/spaces/xin/PatentSolver/App/bin/InformationExtractor.py
deleted file mode 100644
index 1e04f9a2af6f232660cd21e5ad4a45a4e2390aa0..0000000000000000000000000000000000000000
--- a/spaces/xin/PatentSolver/App/bin/InformationExtractor.py
+++ /dev/null
@@ -1,588 +0,0 @@
-# -*- coding: utf-8 -*-
-
-#java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer --port 8080
-import nltk
-nltk.download('all')
-import os
-import re
-import json
-import hashlib
-import Levenshtein
-import uuid
-from App.bin import constants
-from collections import OrderedDict
-from nltk import word_tokenize
-
-from App.bin.SharpClassifier import SharpClassifier
-from App.bin.ClassifierWithIncr import ClassifyWithIncr_it
-from App.bin.SentenceClassifier import SentenceClassifier
-from App.bin.ParameterExtractor import ParameterExtractor
-
-class InformationExtractor(object):
-
-    patent_abbreviations = open(constants.ASSETS + "abbreviation_sentence_splitter").read().split()
-    sentence_finder = nltk.data.load('tokenizers/punkt/english.pickle')
-    sentence_finder._params.abbrev_types.update(patent_abbreviations)
-
-    def __init__(self, section, input_folder,file_extension, file_name):
-        self.section = section
-        self.input_folder = input_folder
-        self.file_extension = file_extension
-        self.file_name = file_name
-
-        print("Extracting problem graph")
-
-    #@staticmethod
-
-
-    def discardLines(self, line,lexic):
-        with open (constants.ASSETS+ lexic) as m:
-            exclusion_list = m.read().splitlines()
-            if any(word in line for word in exclusion_list):
-                pass
-            else:
-                return line
-
-
-    def selectLines(self, line, lexic):
-        with open(constants.ASSETS + lexic) as n:
-            inclusion_list = n.read().splitlines()
-            if any(word in line for word in inclusion_list):
-                return line
-
-    def last_cleansing(self, concept):
-        concept = str(concept)
-        concept = concept.lower()
-        if concept.endswith("."):
-            concept = concept.strip(".")
-        concept = re.sub(r'^consequently ','', concept)
-        concept = re.sub(r'^such ', '', concept)
-        concept = re.sub(r'^said ', '', concept)
-        concept = re.sub(r'^\s+', '', concept)
-        concept = re.sub(r'^it is worth noting that ', '', concept)
-        concept = re.sub(r'^example of ', '', concept)
-        concept = re.sub(r'^since ', '', concept)
-        concept = re.sub(r'^\( |\)$ ', '', concept)
-        return concept
-
-    # def get_from_claims(self):
-    #
-    #     section = self.section
-    #     content = []
-    #     sentence_finder = InformationExtractor.sentence_finder
-    #     sentences = sentence_finder.tokenize(section.strip())
-    #     with open(constants.ASSETS + "getFromClaims") as concept:
-    #         # next(concept)
-    #         included_words = concept.read().splitlines()
-    #         include_link_pattern = re.compile('|'.join(included_words))
-
-
-    def get_from_description(self):
-        previous_polarity = ''
-        noise_trash =[]
-
-        content = []
-        include_links = []
-        output_content = []
-        ex_output_content = []
-        output_result=[]
-        output_linked_content = []
-        output_inter_content = []
-        uniq_output_linked_content =[]
-        ex_output_content_linked =[]
-        section = self.section
-        input_folder = self.input_folder
-        file_name = self.file_name
-        file_extension = self.file_extension
-        projectFolder = os.path.basename(os.path.normpath(input_folder))
-        output_file_name = input_folder+"/"+file_name+file_extension.strip("*")
-
-        graphItemId = hashlib.md5(file_name.encode())
-        graphItemIdValue = graphItemId.hexdigest()
-        graphItemIdValue = str(uuid.uuid4())
-        t_sline = ""
-        t_sline_ex =[]
-        compt_Id = 30
-        compt_Id_ex = 40
-
-        root_img_url = 'https://worldwide.espacenet.com/espacenetImage.jpg?flavour=firstPageClipping&locale=en_EP&FT=D&'
-        root_pdf_url = 'https://worldwide.espacenet.com/publicationDetails/originalDocument?'
-
-        if file_name is not None:
-            match = re.search('(^[a-zA-Z]+)(([0-9]+)\s?([a-zA-Z0-9_]+$))', file_name)
-            # CC for country code
-            CC = match.group(1)
-            # NR for Number
-            NR = match.group(2)
-            NR = re.sub(r'\s', '', NR)
-            # KC for Kind code
-            KC = match.group(4)
-
-            urlImg = root_img_url + '&CC=' + CC + '&NR=' + NR + '&KC=' + KC
-            urlPDF = root_pdf_url + 'CC=' + CC + '&NR=' + NR + '&KC=' + KC + '&FT=D&ND=3&date=' + '&DB=&locale=en_EP#'
-
-        sentence_finder = InformationExtractor.sentence_finder
-
-        #section = self.dataCleaner(section)
-        #print(section)
-        sentences = sentence_finder.tokenize(section.strip())
-
-
-        with open(constants.ASSETS + "includeLinks") as concept:
-            # next(concept)
-            included_words = concept.read().splitlines()
-            include_link_pattern = re.compile('|'.join(included_words))
-        #open examplification wordfile
-        with open(constants.ASSETS + "examplificationclues") as examplif:
-            # next(concept)
-            exam_words = examplif.read().splitlines()
-            examplif_word_pattern = re.compile('|'.join(exam_words))
-
-        description_sentences_number = len(sentences)
-        number_of_words = 0
-        for sentence in sentences:
-
-            # with open(constants.DATA + 'sentences.txt', 'a', encoding='utf8') as file_handler:
-            #     for item in sentences:
-            #         file_handler.write("{}\n".format(item))
-            number_of_word = len(nltk.word_tokenize(sentence))
-            number_of_words += number_of_word
-
-
-            sentenced = self.discardLines(sentence, "exclusionList")
-
-
-            if sentenced is not None:
-
-
-                content.append(sentenced)
-                #print("origine=> "+sentence)
-        total_sentences_number = len(sentences)
-        # mean_sentence_length = int(round(number_of_words/total_sentences_number))
-        # print(mean_sentence_length)
-
-        for line in content:
-
-            line = self.selectLines(line, "inclusionList")
-
-
-
-            if line is not None:
-
-                if re.match(include_link_pattern, line):
-                    include_links.append(line)
-                    #print(line)
-                if line.count(',') == 0:
-                    output_content.append(line)
-                    # content.remove(line)
-                if line.count(',') > 0:
-                    output_inter_content.append(line)
-                    content.remove(line)
-        for s in content:
-            # print(s, file_name)
-            sentence = self.discardLines(s, "FilterS")
-            if sentence is not None:
-                if s.count(',') <= 2 and re.match(examplif_word_pattern, s.lower()):
-                    s = str(s)
-                    cs = s.lower()
-                    cs = re.sub(examplif_word_pattern, '', cs)
-                    cs = re.sub('which', 'this/these', cs)
-                    cs = re.sub(r'\.$', '', cs)
-                    #print(s)
-                    if cs.count(',') == 1 and cs.count('such as')==0:
-                        ex_output_content_linked.append(cs)
-                    else:
-                        ex_output_content.append(cs)
-                elif s.count(',') == 1:
-                    s = str(s)
-                    s = s.lower()
-                    s = self.selectLines(s, "OneCommaDiscriminator")
-                    if s is not None:
-                    #s = re.sub('which', 'this/these', s)
-                    #print(s)
-                        s = re.sub(r'^thus, ', '', s)
-                        s = re.sub(r'^preferably, ', '', s)
-                        s = re.sub(r'^conventional ', '', s)
-                        s = re.sub(r'^in particular, ', '', s)
-                        s = re.sub(r'^specifically, ', '', s)
-                        s = re.sub(r'^as necessary, ', '', s)
-                        s = re.sub(', which', ',this/these', s)
-                        s = re.sub(r'\.$', '', s)
-
-                        if s.count(',')==1:
-                            ex_output_content_linked.append(s)
-                        else:
-                            ex_output_content.append(s)
-                else:
-                   pass
-
-        print(len(ex_output_content_linked))
-        ex_output_content_linked = list(set(ex_output_content_linked))
-        for line in ex_output_content_linked:
-            line = line.lower()
-            if 'figure' not in line:
-            #if line.count(',') <= 1:
-                t_sline_ex = line.strip().split(',')
-            #print("outpib"+str(t_sline_ex))
-            for concept in t_sline_ex:
-                #print("outpib" + str(concept))
-                words = nltk.word_tokenize(concept)
-                tagged = nltk.pos_tag(words)
-                #print(tagged)
-                parameters_list = []
-                compteur = 0
-                compt_Id_ex += 1
-                tagged = nltk.pos_tag(word_tokenize(concept))
-                tags = [word for word, pos in tagged if pos == 'VBZ' or pos == 'VBP'  or pos ==  'VBG' or pos == 'MD' or pos == 'JJR']
-                if len(tags) < 1:
-                    continue
-                # classifyT = SentenceClassifier(concept)
-                # polarite = classifyT.classifySentence()
-                classifyT = ClassifyWithIncr_it()
-                polarite = classifyT.main(concept)
-                # if polarite == 'neutre':
-                #     classify = SentenceClassifier(concept)
-                #     polarite = classify.classifySentence()
-                    # print(concept)
-
-                get_parameters = ParameterExtractor(concept)
-                parameters = get_parameters.extract_parameters()
-
-                parameters_list.extend( parameters)
-                # parameters_list=", ".join(parameters_list)
-                # parameters_list = parameters_list
-                #print("Index is: ")
-                #print(t_sline_ex.index(concept))
-                #print(concept)
-
-                clean_concept = self.last_cleansing(concept)
-                # if polarite == 'neutre':
-                #     words = word_tokenize(clean_concept)
-                #     hit = ' '.join([word + '/' + pos for word, pos in nltk.pos_tag(words)])
-                #     noise_trash.append(hit)
-
-                validity = self.discardLines(concept, 'referencing_indices')
-                if t_sline_ex.index(concept) == 0 and validity is not None:
-                    previous_polarity = polarite
-                    values = OrderedDict({
-                        "concept": {
-                            "type": polarite,
-                            "enfants": graphItemIdValue + str(compt_Id_ex + 1),
-                            "id": graphItemIdValue + str(compt_Id_ex),
-                            "sentence": clean_concept,
-                            "source": output_file_name,
-                            "parameters":parameters_list,
-                            "image": urlImg,
-                            "pdf": urlPDF
-                        }
-
-                    })
-
-                else:
-                    print("Previous polarity is : " + str(previous_polarity))
-                    if previous_polarity =='partialSolution' or validity is None:
-                        continue
-                    else:
-                        compteur += 1
-                        values = OrderedDict({
-                            "concept": {
-                                "type": polarite,
-                                "parents": graphItemIdValue + str(compt_Id_ex - 1),
-                                "id": graphItemIdValue + str(compt_Id_ex),
-                                "sentence": clean_concept,
-                                "source": output_file_name,
-                                "parameters": parameters_list,
-                                "image": urlImg,
-                                "pdf": urlPDF
-
-                            }
-
-                        })
-
-                json_string_linkes = json.dumps(values, sort_keys=OrderedDict, indent=4, separators=(',', ': '))
-
-                output_result.append(json_string_linkes)
-
-
-
-        #for line in output_content:
-                #print ("include=> "+line)
-        #just examplification sentences
-        #make a function of that
-        ex_output_content = list(set(ex_output_content))
-        for concept in ex_output_content:
-            tagged = nltk.pos_tag(word_tokenize(concept))
-            tags = [word for word, pos in tagged if
-                    pos == 'VBZ' or pos == 'VBP' or pos == 'VBG' or pos == 'MD' or pos == 'JJR']
-            if len(tags) < 1:
-                continue
-            parameters_list = []
-            concept = concept.lower()
-            compt_Id_ex += 1
-            # classify = SentenceClassifier(sline)
-            # polarite = classify.classifySentence()
-            classifyT = ClassifyWithIncr_it()
-            polarite = classifyT.main(concept)
-
-            # if polarite =='neutre':
-            #     classify = SentenceClassifier(concept)
-            #     polarite = classify.classifySentence()
-                # print(sline)
-
-            #if polarite == 'partialSolution':
-                #print(sline)
-                #Insert a classifier here
-            get_parameters = ParameterExtractor(concept)
-            parameters = get_parameters.extract_parameters()
-
-            clean_concept = self.last_cleansing(concept)
-            parameters_list.extend(parameters)
-            # if polarite == 'neutre':
-            #     words = word_tokenize(clean_concept)
-            #     hit = ' '.join([word + '/' + pos for word, pos in nltk.pos_tag(words)])
-            #     noise_trash.append(hit)
-            # parameters_list = ", ".join(parameters_list)
-            validity = self.discardLines(concept, 'referencing_indices')
-            if polarite != 'partialSolution' and validity is not None:
-
-                values = OrderedDict({
-                    "concept": {
-                        "type": polarite,
-                        "id": graphItemIdValue + str(compt_Id_ex),
-                        "sentence": clean_concept,
-                        "source": output_file_name,
-                        "parameters": parameters_list,
-                        "image": urlImg,
-                        "pdf": urlPDF
-
-
-                    }
-
-                })
-                json_string = json.dumps(values, sort_keys=OrderedDict, indent=4, separators=(',', ': '))
-                output_result.append(json_string)
-
-
-
-        for line in include_links:
-            #print(line)
-            #Put in lower case to improve matching
-            line = line.lower()
-
-            if re.match(r'however', line) and line.count(',') <= 1:
-                line = str(line)
-                sline = re.sub(r'however|,', '', line)
-                if sline not in output_linked_content:
-                    output_linked_content.append(sline)
-            if re.match(r'however', line) and line.count(',') > 1:
-                sline = re.sub(r'^however,?(\s\w+)\s*, that ', '', line)
-                # sline = re.sub(r'however,.+, that ', '', sline)
-                sline = re.sub(r'^however,?(\s\w+)+\s(above), ', '', sline)
-                sline = re.sub(r'^however,?\s\w+ed(\s\w+)+,\s*', '', sline)
-                sline = re.sub(r'^however,?\sif\s(desired|said)\s*,\s', '', sline)
-                sline = re.sub(r'^however,?\s(it)\s(will be appreciated)\s*,\s(that)+\s*', '', sline)
-                sline = re.sub(r'^however,?\s(as|if|because|when|since)\s*(?!is)', '', sline)
-                sline = re.sub(r'^however,?\s*', '', sline)
-                if sline not in output_linked_content:
-                    output_linked_content.append(sline)
-            if re.match(r'if', line) and line.count(',') <= 1:
-                line = str(line)
-                sline = re.sub(r'^if\s?(and when|not|desired|necessary)\s?,?\s*', '', line)
-                sline = re.sub(r'^if,?\s*', '', sline)
-                sline = re.sub(r'^if ', '', sline)
-                if sline not in output_linked_content:
-                    output_linked_content.append(sline)
-                # print (sline)
-
-            if re.match(r'when', line):
-                line = str(line)
-                line = line.lower()
-                sline = re.sub(r'^when\s*', '', line)
-                sline = re.sub(r'^when,?\s*', '', sline)
-                sline = re.sub(r'^when ', '', sline)
-                if sline not in output_linked_content:
-                    output_linked_content.append(sline)
-            if re.match(r'(^since)|(^\w+\s?,\s?since\s?)', line):
-                sline = re.sub(r'^since', '', line)
-                sline = re.sub(r'^\w+\s?,\s?since\s?', '', sline)
-                if sline not in output_linked_content:
-                    output_linked_content.append(sline)
-
-        for line in output_content:
-            line = line.lower()
-            if re.match(r'if', line):
-                line = str(line)
-                sline = re.sub(r'^if ', '', line)
-                if sline not in output_linked_content:
-                    output_content.append(sline)
-                #output_content.remove(line)
-
-        uniq_output_linked_content = list(set(output_linked_content))
-        for line in uniq_output_linked_content:
-            #print("long sentences = > " + line)
-            # line = str(i)
-            #print(line)
-            line = line.lower()
-            if 'figure' in line:
-                uniq_output_linked_content.remove(line)
-            sline = re.sub(r'^\s+', '', line)
-            sline = re.sub(r'^\d+\.+$', '', sline)
-
-            if sline.count(',') <= 1:
-                t_sline = tuple(sline.strip().split(', '))
-                #print("outpib"+str(t_sline))
-        for concept in t_sline:
-            tagged = nltk.pos_tag(word_tokenize(concept))
-            tags = [word for word, pos in tagged if
-                    pos == 'VBZ' or pos == 'VBP' or pos == 'VBG' or pos == 'MD' or pos == 'JJR']
-            if len(tags) < 1:
-                continue
-            else:
-                parameters_list = []
-                compteur = 0
-                compt_Id += 1
-                # classifyT = SentenceClassifier(concept)
-                # polarite = classifyT.classifySentence()
-                tagged = nltk.pos_tag(word_tokenize(concept))
-                tags = [word for word, pos in tagged if pos.startswith('V') or pos == 'JJR']
-                if len(tags) < 1:
-                    continue
-                classifyT = ClassifyWithIncr_it()
-                polarite = classifyT.main(concept)
-
-
-                # if polarite == 'neutre':
-                #     classify = SentenceClassifier(concept)
-                #     polarite = classify.classifySentence()
-                    # print(concept)
-
-                get_parameters = ParameterExtractor(concept)
-                parameters = get_parameters.extract_parameters()
-
-                parameters_list.extend( parameters)
-                # parameters_list=", ".join(parameters_list)
-                # parameters_list = parameters_list
-
-                clean_concept = self.last_cleansing(concept)
-                validity = self.discardLines(concept, 'referencing_indices')
-                # if polarite == 'neutre':
-                #     words = word_tokenize(clean_concept)
-                #     hit = ' '.join([word + '/' + pos for word, pos in nltk.pos_tag(words)])
-                #     noise_trash.append(hit)
-
-
-                if t_sline.index(concept) == 0 and validity is not None:
-                    previous_polarity = polarite
-                    values = OrderedDict({
-                        "concept": {
-                            "type": polarite,
-                            "enfants": graphItemIdValue + str(compt_Id + 1),
-                            "id": graphItemIdValue + str(compt_Id),
-                            "sentence": clean_concept,
-                            "source": output_file_name,
-                            "parameters":parameters_list,
-                            "image": urlImg,
-                            "pdf": urlPDF
-                        }
-
-                    })
-
-                else:
-                    print("Previous polarity is : " + str(previous_polarity))
-                    if previous_polarity =='partialSolutiond' or validity is None:
-                        continue
-                    else:
-                        compteur += 1
-                        values = OrderedDict({
-                            "concept": {
-                                "type": polarite,
-                                "parents": graphItemIdValue + str(compt_Id - 1),
-                                "id": graphItemIdValue + str(compt_Id),
-                                "sentence": clean_concept,
-                                "source": output_file_name,
-                                "parameters": parameters_list,
-                                "image": urlImg,
-                                "pdf": urlPDF
-
-                            }
-
-                        })
-
-                json_string_linked = json.dumps(values, sort_keys=OrderedDict, indent=4, separators=(',', ': '))
-
-                output_result.append(json_string_linked)
-
-
-        uniq_output_content = list(set(output_content))
-        for s in uniq_output_content:
-            for y in uniq_output_content:
-                if s != y:
-                    result = Levenshtein.ratio(s, y)
-                    if result > .7:
-                        # print(s + " :IS SIMILAR TO: " + y)
-                        if len(s) > len(y):
-                            uniq_output_content.remove(y)
-                        elif len(y) < len(s):
-                            uniq_output_content.remove(s)
-
-
-        for concept in uniq_output_content:
-            tagged = nltk.pos_tag(word_tokenize(concept))
-            tags = [word for word, pos in tagged if
-                    pos == 'VBZ' or pos == 'VBP' or pos == 'VBG' or pos == 'MD' or pos == 'JJR']
-            if len(tags) < 1:
-                continue
-            parameters_list = []
-            concept = concept.lower()
-            compt_Id += 1
-            sline = re.sub(r'^if ', '', concept)
-            sline = re.sub(r'^(if|preferably) ', '', sline)
-            sline = re.sub(r'^\s+?said ', '', sline)
-            # classify = SentenceClassifier(sline)
-            # polarite = classify.classifySentence()
-            classifyT = ClassifyWithIncr_it()
-            polarite = classifyT.main(concept)
-            # if polarite =='neutre':
-            #     classify = SentenceClassifier(sline)
-            #     polarite = classify.classifySentence()
-                # print(sline)
-
-            #if polarite == 'partialSolution':
-                #print(sline)
-                #Insert a classifier here
-            get_parameters = ParameterExtractor(concept)
-            parameters = get_parameters.extract_parameters()
-
-            parameters_list.extend(parameters)
-            # parameters_list = ", ".join(parameters_list)
-            clean_concept = self.last_cleansing(sline)
-            # if polarite == 'neutre':
-            #     words = word_tokenize(clean_concept)
-            #     hit = ' '.join([word + '/' + pos for word, pos in nltk.pos_tag(words)])
-            #     noise_trash.append(hit)
-
-            validity = self.discardLines(concept, 'referencing_indices')
-            if polarite !='partialSolution' and validity is not None:
-
-                values = OrderedDict({
-                    "concept": {
-                        "type": polarite,
-                        "id": graphItemIdValue + str(compt_Id),
-                        "sentence": clean_concept,
-                        "source": output_file_name,
-                        "parameters": parameters_list,
-                        "image": urlImg,
-                        "pdf": urlPDF
-                    }
-
-                })
-                json_string = json.dumps(values, sort_keys=OrderedDict, indent=4, separators=(',', ': '))
-                output_result.append(json_string)
-                output_result = list(set(output_result))
-
-
-
-
-
-        output_json = ",".join(output_result)
-        return output_json, total_sentences_number
\ No newline at end of file
diff --git a/spaces/xu1998hz/sescore_english_mt/tests.py b/spaces/xu1998hz/sescore_english_mt/tests.py
deleted file mode 100644
index 601ed757507caebec67493462d11eb4c8901c2a1..0000000000000000000000000000000000000000
--- a/spaces/xu1998hz/sescore_english_mt/tests.py
+++ /dev/null
@@ -1,17 +0,0 @@
-test_cases = [
-    {
-        "predictions": [0, 0],
-        "references": [1, 1],
-        "result": {"metric_score": 0}
-    },
-    {
-        "predictions": [1, 1],
-        "references": [1, 1],
-        "result": {"metric_score": 1}
-    },
-    {
-        "predictions": [1, 0],
-        "references": [1, 1],
-        "result": {"metric_score": 0.5}
-    }
-]
\ No newline at end of file
diff --git a/spaces/xwsm/gpt/crazy_functions/test_project/cpp/longcode/jpge.cpp b/spaces/xwsm/gpt/crazy_functions/test_project/cpp/longcode/jpge.cpp
deleted file mode 100644
index 2e26b71ed5aad0d46478fdbcd3a880be1401f946..0000000000000000000000000000000000000000
--- a/spaces/xwsm/gpt/crazy_functions/test_project/cpp/longcode/jpge.cpp
+++ /dev/null
@@ -1,1049 +0,0 @@
-// jpge.cpp - C++ class for JPEG compression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-// v1.01, Dec. 18, 2010 - Initial release
-// v1.02, Apr. 6, 2011 - Removed 2x2 ordered dither in H2V1 chroma subsampling method load_block_16_8_8(). (The rounding factor was 2, when it should have been 1. Either way, it wasn't helping.)
-// v1.03, Apr. 16, 2011 - Added support for optimized Huffman code tables, optimized dynamic memory allocation down to only 1 alloc.
-//                        Also from Alex Evans: Added RGBA support, linear memory allocator (no longer needed in v1.03).
-// v1.04, May. 19, 2012: Forgot to set m_pFile ptr to NULL in cfile_stream::close(). Thanks to Owen Kaluza for reporting this bug.
-//                       Code tweaks to fix VS2008 static code analysis warnings (all looked harmless).
-//                       Code review revealed method load_block_16_8_8() (used for the non-default H2V1 sampling mode to downsample chroma) somehow didn't get the rounding factor fix from v1.02.
-
-#include "jpge.h"
-
-#include <stdlib.h>
-#include <string.h>
-#if PLATFORM_WINDOWS
-#include <malloc.h>
-#endif
-
-#define JPGE_MAX(a,b) (((a)>(b))?(a):(b))
-#define JPGE_MIN(a,b) (((a)<(b))?(a):(b))
-
-namespace jpge {
-
-static inline void *jpge_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-static inline void jpge_free(void *p) { FMemory::Free(p);; }
-
-// Various JPEG enums and tables.
-enum { M_SOF0 = 0xC0, M_DHT = 0xC4, M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_APP0 = 0xE0 };
-enum { DC_LUM_CODES = 12, AC_LUM_CODES = 256, DC_CHROMA_CODES = 12, AC_CHROMA_CODES = 256, MAX_HUFF_SYMBOLS = 257, MAX_HUFF_CODESIZE = 32 };
-
-static uint8 s_zag[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-static int16 s_std_lum_quant[64] = { 16,11,12,14,12,10,16,14,13,14,18,17,16,19,24,40,26,24,22,22,24,49,35,37,29,40,58,51,61,60,57,51,56,55,64,72,92,78,64,68,87,69,55,56,80,109,81,87,95,98,103,104,103,62,77,113,121,112,100,120,92,101,103,99 };
-static int16 s_std_croma_quant[64] = { 17,18,18,24,21,24,47,26,26,47,99,66,56,66,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99 };
-static uint8 s_dc_lum_bits[17] = { 0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0 };
-static uint8 s_dc_lum_val[DC_LUM_CODES] = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_lum_bits[17] = { 0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d };
-static uint8 s_ac_lum_val[AC_LUM_CODES]  =
-{
-  0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08,0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,
-  0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28,0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,
-  0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89,
-  0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,
-  0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-static uint8 s_dc_chroma_bits[17] = { 0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0 };
-static uint8 s_dc_chroma_val[DC_CHROMA_CODES]  = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_chroma_bits[17] = { 0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77 };
-static uint8 s_ac_chroma_val[AC_CHROMA_CODES] =
-{
-  0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91,0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,
-  0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26,0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,
-  0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87,
-  0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,
-  0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-
-// Low-level helper functions.
-template <class T> inline void clear_obj(T &obj) { memset(&obj, 0, sizeof(obj)); }
-
-const int YR = 19595, YG = 38470, YB = 7471, CB_R = -11059, CB_G = -21709, CB_B = 32768, CR_R = 32768, CR_G = -27439, CR_B = -5329;
-static inline uint8 clamp(int i) { if (static_cast<uint>(i) > 255U) { if (i < 0) i = 0; else if (i > 255) i = 255; } return static_cast<uint8>(i); }
-
-static void RGB_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 3, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGB_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 3, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void RGBA_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 4, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGBA_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 4, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void Y_to_YCC(uint8* pDst, const uint8* pSrc, int num_pixels)
-{
-  for( ; num_pixels; pDst += 3, pSrc++, num_pixels--) { pDst[0] = pSrc[0]; pDst[1] = 128; pDst[2] = 128; }
-}
-
-// Forward DCT - DCT derived from jfdctint.
-#define CONST_BITS  13
-#define ROW_BITS    2
-#define DCT_DESCALE(x, n) (((x) + (((int32)1) << ((n) - 1))) >> (n))
-#define DCT_MUL(var, c) (static_cast<int16>(var) * static_cast<int32>(c))
-#define DCT1D(s0, s1, s2, s3, s4, s5, s6, s7) \
-  int32 t0 = s0 + s7, t7 = s0 - s7, t1 = s1 + s6, t6 = s1 - s6, t2 = s2 + s5, t5 = s2 - s5, t3 = s3 + s4, t4 = s3 - s4; \
-  int32 t10 = t0 + t3, t13 = t0 - t3, t11 = t1 + t2, t12 = t1 - t2; \
-  int32 u1 = DCT_MUL(t12 + t13, 4433); \
-  s2 = u1 + DCT_MUL(t13, 6270); \
-  s6 = u1 + DCT_MUL(t12, -15137); \
-  u1 = t4 + t7; \
-  int32 u2 = t5 + t6, u3 = t4 + t6, u4 = t5 + t7; \
-  int32 z5 = DCT_MUL(u3 + u4, 9633); \
-  t4 = DCT_MUL(t4, 2446); t5 = DCT_MUL(t5, 16819); \
-  t6 = DCT_MUL(t6, 25172); t7 = DCT_MUL(t7, 12299); \
-  u1 = DCT_MUL(u1, -7373); u2 = DCT_MUL(u2, -20995); \
-  u3 = DCT_MUL(u3, -16069); u4 = DCT_MUL(u4, -3196); \
-  u3 += z5; u4 += z5; \
-  s0 = t10 + t11; s1 = t7 + u1 + u4; s3 = t6 + u2 + u3; s4 = t10 - t11; s5 = t5 + u2 + u4; s7 = t4 + u1 + u3;
-
-static void DCT2D(int32 *p)
-{
-  int32 c, *q = p;
-  for (c = 7; c >= 0; c--, q += 8)
-  {
-    int32 s0 = q[0], s1 = q[1], s2 = q[2], s3 = q[3], s4 = q[4], s5 = q[5], s6 = q[6], s7 = q[7];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0] = s0 << ROW_BITS; q[1] = DCT_DESCALE(s1, CONST_BITS-ROW_BITS); q[2] = DCT_DESCALE(s2, CONST_BITS-ROW_BITS); q[3] = DCT_DESCALE(s3, CONST_BITS-ROW_BITS);
-    q[4] = s4 << ROW_BITS; q[5] = DCT_DESCALE(s5, CONST_BITS-ROW_BITS); q[6] = DCT_DESCALE(s6, CONST_BITS-ROW_BITS); q[7] = DCT_DESCALE(s7, CONST_BITS-ROW_BITS);
-  }
-  for (q = p, c = 7; c >= 0; c--, q++)
-  {
-    int32 s0 = q[0*8], s1 = q[1*8], s2 = q[2*8], s3 = q[3*8], s4 = q[4*8], s5 = q[5*8], s6 = q[6*8], s7 = q[7*8];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0*8] = DCT_DESCALE(s0, ROW_BITS+3); q[1*8] = DCT_DESCALE(s1, CONST_BITS+ROW_BITS+3); q[2*8] = DCT_DESCALE(s2, CONST_BITS+ROW_BITS+3); q[3*8] = DCT_DESCALE(s3, CONST_BITS+ROW_BITS+3);
-    q[4*8] = DCT_DESCALE(s4, ROW_BITS+3); q[5*8] = DCT_DESCALE(s5, CONST_BITS+ROW_BITS+3); q[6*8] = DCT_DESCALE(s6, CONST_BITS+ROW_BITS+3); q[7*8] = DCT_DESCALE(s7, CONST_BITS+ROW_BITS+3);
-  }
-}
-
-struct sym_freq { uint m_key, m_sym_index; };
-
-// Radix sorts sym_freq[] array by 32-bit key m_key. Returns ptr to sorted values.
-static inline sym_freq* radix_sort_syms(uint num_syms, sym_freq* pSyms0, sym_freq* pSyms1)
-{
-  const uint cMaxPasses = 4;
-  uint32 hist[256 * cMaxPasses]; clear_obj(hist);
-  for (uint i = 0; i < num_syms; i++) { uint freq = pSyms0[i].m_key; hist[freq & 0xFF]++; hist[256 + ((freq >> 8) & 0xFF)]++; hist[256*2 + ((freq >> 16) & 0xFF)]++; hist[256*3 + ((freq >> 24) & 0xFF)]++; }
-  sym_freq* pCur_syms = pSyms0, *pNew_syms = pSyms1;
-  uint total_passes = cMaxPasses; while ((total_passes > 1) && (num_syms == hist[(total_passes - 1) * 256])) total_passes--;
-  for (uint pass_shift = 0, pass = 0; pass < total_passes; pass++, pass_shift += 8)
-  {
-    const uint32* pHist = &hist[pass << 8];
-    uint offsets[256], cur_ofs = 0;
-    for (uint i = 0; i < 256; i++) { offsets[i] = cur_ofs; cur_ofs += pHist[i]; }
-    for (uint i = 0; i < num_syms; i++)
-      pNew_syms[offsets[(pCur_syms[i].m_key >> pass_shift) & 0xFF]++] = pCur_syms[i];
-    sym_freq* t = pCur_syms; pCur_syms = pNew_syms; pNew_syms = t;
-  }
-  return pCur_syms;
-}
-
-// calculate_minimum_redundancy() originally written by: Alistair Moffat, alistair@cs.mu.oz.au, Jyrki Katajainen, jyrki@diku.dk, November 1996.
-static void calculate_minimum_redundancy(sym_freq *A, int n)
-{
-  int root, leaf, next, avbl, used, dpth;
-  if (n==0) return; else if (n==1) { A[0].m_key = 1; return; }
-  A[0].m_key += A[1].m_key; root = 0; leaf = 2;
-  for (next=1; next < n-1; next++)
-  {
-    if (leaf>=n || A[root].m_key<A[leaf].m_key) { A[next].m_key = A[root].m_key; A[root++].m_key = next; } else A[next].m_key = A[leaf++].m_key;
-    if (leaf>=n || (root<next && A[root].m_key<A[leaf].m_key)) { A[next].m_key += A[root].m_key; A[root++].m_key = next; } else A[next].m_key += A[leaf++].m_key;
-  }
-  A[n-2].m_key = 0;
-  for (next=n-3; next>=0; next--) A[next].m_key = A[A[next].m_key].m_key+1;
-  avbl = 1; used = dpth = 0; root = n-2; next = n-1;
-  while (avbl>0)
-  {
-    while (root>=0 && (int)A[root].m_key==dpth) { used++; root--; }
-    while (avbl>used) { A[next--].m_key = dpth; avbl--; }
-    avbl = 2*used; dpth++; used = 0;
-  }
-}
-
-// Limits canonical Huffman code table's max code size to max_code_size.
-static void huffman_enforce_max_code_size(int *pNum_codes, int code_list_len, int max_code_size)
-{
-  if (code_list_len <= 1) return;
-
-  for (int i = max_code_size + 1; i <= MAX_HUFF_CODESIZE; i++) pNum_codes[max_code_size] += pNum_codes[i];
-
-  uint32 total = 0;
-  for (int i = max_code_size; i > 0; i--)
-    total += (((uint32)pNum_codes[i]) << (max_code_size - i));
-
-  while (total != (1UL << max_code_size))
-  {
-    pNum_codes[max_code_size]--;
-    for (int i = max_code_size - 1; i > 0; i--)
-    {
-      if (pNum_codes[i]) { pNum_codes[i]--; pNum_codes[i + 1] += 2; break; }
-    }
-    total--;
-  }
-}
-
-// Generates an optimized offman table.
-void jpeg_encoder::optimize_huffman_table(int table_num, int table_len)
-{
-  sym_freq syms0[MAX_HUFF_SYMBOLS], syms1[MAX_HUFF_SYMBOLS];
-  syms0[0].m_key = 1; syms0[0].m_sym_index = 0;  // dummy symbol, assures that no valid code contains all 1's
-  int num_used_syms = 1;
-  const uint32 *pSym_count = &m_huff_count[table_num][0];
-  for (int i = 0; i < table_len; i++)
-    if (pSym_count[i]) { syms0[num_used_syms].m_key = pSym_count[i]; syms0[num_used_syms++].m_sym_index = i + 1; }
-  sym_freq* pSyms = radix_sort_syms(num_used_syms, syms0, syms1);
-  calculate_minimum_redundancy(pSyms, num_used_syms);
-
-  // Count the # of symbols of each code size.
-  int num_codes[1 + MAX_HUFF_CODESIZE]; clear_obj(num_codes);
-  for (int i = 0; i < num_used_syms; i++)
-    num_codes[pSyms[i].m_key]++;
-
-  const uint JPGE_CODE_SIZE_LIMIT = 16; // the maximum possible size of a JPEG Huffman code (valid range is [9,16] - 9 vs. 8 because of the dummy symbol)
-  huffman_enforce_max_code_size(num_codes, num_used_syms, JPGE_CODE_SIZE_LIMIT);
-
-  // Compute m_huff_bits array, which contains the # of symbols per code size.
-  clear_obj(m_huff_bits[table_num]);
-  for (int i = 1; i <= (int)JPGE_CODE_SIZE_LIMIT; i++)
-    m_huff_bits[table_num][i] = static_cast<uint8>(num_codes[i]);
-
-  // Remove the dummy symbol added above, which must be in largest bucket.
-  for (int i = JPGE_CODE_SIZE_LIMIT; i >= 1; i--)
-  {
-    if (m_huff_bits[table_num][i]) { m_huff_bits[table_num][i]--; break; }
-  }
-
-  // Compute the m_huff_val array, which contains the symbol indices sorted by code size (smallest to largest).
-  for (int i = num_used_syms - 1; i >= 1; i--)
-    m_huff_val[table_num][num_used_syms - 1 - i] = static_cast<uint8>(pSyms[i].m_sym_index - 1);
-}
-
-// JPEG marker generation.
-void jpeg_encoder::emit_byte(uint8 i)
-{
-  m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_obj(i);
-}
-
-void jpeg_encoder::emit_word(uint i)
-{
-  emit_byte(uint8(i >> 8)); emit_byte(uint8(i & 0xFF));
-}
-
-void jpeg_encoder::emit_marker(int marker)
-{
-  emit_byte(uint8(0xFF)); emit_byte(uint8(marker));
-}
-
-// Emit JFIF marker
-void jpeg_encoder::emit_jfif_app0()
-{
-  emit_marker(M_APP0);
-  emit_word(2 + 4 + 1 + 2 + 1 + 2 + 2 + 1 + 1);
-  emit_byte(0x4A); emit_byte(0x46); emit_byte(0x49); emit_byte(0x46); /* Identifier: ASCII "JFIF" */
-  emit_byte(0);
-  emit_byte(1);      /* Major version */
-  emit_byte(1);      /* Minor version */
-  emit_byte(0);      /* Density unit */
-  emit_word(1);
-  emit_word(1);
-  emit_byte(0);      /* No thumbnail image */
-  emit_byte(0);
-}
-
-// Emit quantization tables
-void jpeg_encoder::emit_dqt()
-{
-  for (int i = 0; i < ((m_num_components == 3) ? 2 : 1); i++)
-  {
-    emit_marker(M_DQT);
-    emit_word(64 + 1 + 2);
-    emit_byte(static_cast<uint8>(i));
-    for (int j = 0; j < 64; j++)
-      emit_byte(static_cast<uint8>(m_quantization_tables[i][j]));
-  }
-}
-
-// Emit start of frame marker
-void jpeg_encoder::emit_sof()
-{
-  emit_marker(M_SOF0);                           /* baseline */
-  emit_word(3 * m_num_components + 2 + 5 + 1);
-  emit_byte(8);                                  /* precision */
-  emit_word(m_image_y);
-  emit_word(m_image_x);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));                                   /* component ID     */
-    emit_byte((m_comp_h_samp[i] << 4) + m_comp_v_samp[i]);  /* h and v sampling */
-    emit_byte(i > 0);                                   /* quant. table num */
-  }
-}
-
-// Emit Huffman table.
-void jpeg_encoder::emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag)
-{
-  emit_marker(M_DHT);
-
-  int length = 0;
-  for (int i = 1; i <= 16; i++)
-    length += bits[i];
-
-  emit_word(length + 2 + 1 + 16);
-  emit_byte(static_cast<uint8>(index + (ac_flag << 4)));
-
-  for (int i = 1; i <= 16; i++)
-    emit_byte(bits[i]);
-
-  for (int i = 0; i < length; i++)
-    emit_byte(val[i]);
-}
-
-// Emit all Huffman tables.
-void jpeg_encoder::emit_dhts()
-{
-  emit_dht(m_huff_bits[0+0], m_huff_val[0+0], 0, false);
-  emit_dht(m_huff_bits[2+0], m_huff_val[2+0], 0, true);
-  if (m_num_components == 3)
-  {
-    emit_dht(m_huff_bits[0+1], m_huff_val[0+1], 1, false);
-    emit_dht(m_huff_bits[2+1], m_huff_val[2+1], 1, true);
-  }
-}
-
-// emit start of scan
-void jpeg_encoder::emit_sos()
-{
-  emit_marker(M_SOS);
-  emit_word(2 * m_num_components + 2 + 1 + 3);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));
-    if (i == 0)
-      emit_byte((0 << 4) + 0);
-    else
-      emit_byte((1 << 4) + 1);
-  }
-  emit_byte(0);     /* spectral selection */
-  emit_byte(63);
-  emit_byte(0);
-}
-
-// Emit all markers at beginning of image file.
-void jpeg_encoder::emit_markers()
-{
-  emit_marker(M_SOI);
-  emit_jfif_app0();
-  emit_dqt();
-  emit_sof();
-  emit_dhts();
-  emit_sos();
-}
-
-// Compute the actual canonical Huffman codes/code sizes given the JPEG huff bits and val arrays.
-void jpeg_encoder::compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val)
-{
-  int i, l, last_p, si;
-  uint8 huff_size[257];
-  uint huff_code[257];
-  uint code;
-
-  int p = 0;
-  for (l = 1; l <= 16; l++)
-    for (i = 1; i <= bits[l]; i++)
-      huff_size[p++] = (char)l;
-
-  huff_size[p] = 0; last_p = p; // write sentinel
-
-  code = 0; si = huff_size[0]; p = 0;
-
-  while (huff_size[p])
-  {
-    while (huff_size[p] == si)
-      huff_code[p++] = code++;
-    code <<= 1;
-    si++;
-  }
-
-  memset(codes, 0, sizeof(codes[0])*256);
-  memset(code_sizes, 0, sizeof(code_sizes[0])*256);
-  for (p = 0; p < last_p; p++)
-  {
-    codes[val[p]]      = huff_code[p];
-    code_sizes[val[p]] = huff_size[p];
-  }
-}
-
-// Quantization table generation.
-void jpeg_encoder::compute_quant_table(int32 *pDst, int16 *pSrc)
-{
-  int32 q;
-  if (m_params.m_quality < 50)
-    q = 5000 / m_params.m_quality;
-  else
-    q = 200 - m_params.m_quality * 2;
-  for (int i = 0; i < 64; i++)
-  {
-    int32 j = *pSrc++; j = (j * q + 50L) / 100L;
-    *pDst++ = JPGE_MIN(JPGE_MAX(j, 1), 255);
-  }
-}
-
-// Higher-level methods.
-void jpeg_encoder::first_pass_init()
-{
-  m_bit_buffer = 0; m_bits_in = 0;
-  memset(m_last_dc_val, 0, 3 * sizeof(m_last_dc_val[0]));
-  m_mcu_y_ofs = 0;
-  m_pass_num = 1;
-}
-
-bool jpeg_encoder::second_pass_init()
-{
-  compute_huffman_table(&m_huff_codes[0+0][0], &m_huff_code_sizes[0+0][0], m_huff_bits[0+0], m_huff_val[0+0]);
-  compute_huffman_table(&m_huff_codes[2+0][0], &m_huff_code_sizes[2+0][0], m_huff_bits[2+0], m_huff_val[2+0]);
-  if (m_num_components > 1)
-  {
-    compute_huffman_table(&m_huff_codes[0+1][0], &m_huff_code_sizes[0+1][0], m_huff_bits[0+1], m_huff_val[0+1]);
-    compute_huffman_table(&m_huff_codes[2+1][0], &m_huff_code_sizes[2+1][0], m_huff_bits[2+1], m_huff_val[2+1]);
-  }
-  first_pass_init();
-  emit_markers();
-  m_pass_num = 2;
-  return true;
-}
-
-bool jpeg_encoder::jpg_open(int p_x_res, int p_y_res, int src_channels)
-{
-  m_num_components = 3;
-  switch (m_params.m_subsampling)
-  {
-    case Y_ONLY:
-    {
-      m_num_components = 1;
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H1V1:
-    {
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H2V1:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 8;
-      break;
-    }
-    case H2V2:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 2;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 16;
-    }
-  }
-
-  m_image_x        = p_x_res; m_image_y = p_y_res;
-  m_image_bpp      = src_channels;
-  m_image_bpl      = m_image_x * src_channels;
-  m_image_x_mcu    = (m_image_x + m_mcu_x - 1) & (~(m_mcu_x - 1));
-  m_image_y_mcu    = (m_image_y + m_mcu_y - 1) & (~(m_mcu_y - 1));
-  m_image_bpl_xlt  = m_image_x * m_num_components;
-  m_image_bpl_mcu  = m_image_x_mcu * m_num_components;
-  m_mcus_per_row   = m_image_x_mcu / m_mcu_x;
-
-  if ((m_mcu_lines[0] = static_cast<uint8*>(jpge_malloc(m_image_bpl_mcu * m_mcu_y))) == NULL) return false;
-  for (int i = 1; i < m_mcu_y; i++)
-    m_mcu_lines[i] = m_mcu_lines[i-1] + m_image_bpl_mcu;
-
-  compute_quant_table(m_quantization_tables[0], s_std_lum_quant);
-  compute_quant_table(m_quantization_tables[1], m_params.m_no_chroma_discrim_flag ? s_std_lum_quant : s_std_croma_quant);
-
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-  m_pOut_buf = m_out_buf;
-
-  if (m_params.m_two_pass_flag)
-  {
-    clear_obj(m_huff_count);
-    first_pass_init();
-  }
-  else
-  {
-    memcpy(m_huff_bits[0+0], s_dc_lum_bits, 17);    memcpy(m_huff_val [0+0], s_dc_lum_val, DC_LUM_CODES);
-    memcpy(m_huff_bits[2+0], s_ac_lum_bits, 17);    memcpy(m_huff_val [2+0], s_ac_lum_val, AC_LUM_CODES);
-    memcpy(m_huff_bits[0+1], s_dc_chroma_bits, 17); memcpy(m_huff_val [0+1], s_dc_chroma_val, DC_CHROMA_CODES);
-    memcpy(m_huff_bits[2+1], s_ac_chroma_bits, 17); memcpy(m_huff_val [2+1], s_ac_chroma_val, AC_CHROMA_CODES);
-    if (!second_pass_init()) return false;   // in effect, skip over the first pass
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-void jpeg_encoder::load_block_8_8_grey(int x)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[i] + x;
-    pDst[0] = pSrc[0] - 128; pDst[1] = pSrc[1] - 128; pDst[2] = pSrc[2] - 128; pDst[3] = pSrc[3] - 128;
-    pDst[4] = pSrc[4] - 128; pDst[5] = pSrc[5] - 128; pDst[6] = pSrc[6] - 128; pDst[7] = pSrc[7] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_8_8(int x, int y, int c)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (8 * 3)) + c;
-  y <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[y + i] + x;
-    pDst[0] = pSrc[0 * 3] - 128; pDst[1] = pSrc[1 * 3] - 128; pDst[2] = pSrc[2 * 3] - 128; pDst[3] = pSrc[3 * 3] - 128;
-    pDst[4] = pSrc[4 * 3] - 128; pDst[5] = pSrc[5 * 3] - 128; pDst[6] = pSrc[6 * 3] - 128; pDst[7] = pSrc[7 * 3] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_16_8(int x, int c)
-{
-  uint8 *pSrc1, *pSrc2;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  int a = 0, b = 2;
-  for (int i = 0; i < 16; i += 2, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pSrc2 = m_mcu_lines[i + 1] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3] + pSrc2[ 0 * 3] + pSrc2[ 1 * 3] + a) >> 2) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3] + pSrc2[ 2 * 3] + pSrc2[ 3 * 3] + b) >> 2) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3] + pSrc2[ 4 * 3] + pSrc2[ 5 * 3] + a) >> 2) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3] + pSrc2[ 6 * 3] + pSrc2[ 7 * 3] + b) >> 2) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3] + pSrc2[ 8 * 3] + pSrc2[ 9 * 3] + a) >> 2) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3] + pSrc2[10 * 3] + pSrc2[11 * 3] + b) >> 2) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3] + pSrc2[12 * 3] + pSrc2[13 * 3] + a) >> 2) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3] + pSrc2[14 * 3] + pSrc2[15 * 3] + b) >> 2) - 128;
-    int temp = a; a = b; b = temp;
-  }
-}
-
-void jpeg_encoder::load_block_16_8_8(int x, int c)
-{
-  uint8 *pSrc1;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3]) >> 1) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3]) >> 1) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3]) >> 1) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3]) >> 1) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3]) >> 1) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3]) >> 1) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3]) >> 1) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3]) >> 1) - 128;
-  }
-}
-
-void jpeg_encoder::load_quantized_coefficients(int component_num)
-{
-  int32 *q = m_quantization_tables[component_num > 0];
-  int16 *pDst = m_coefficient_array;
-  for (int i = 0; i < 64; i++)
-  {
-    sample_array_t j = m_sample_array[s_zag[i]];
-    if (j < 0)
-    {
-      if ((j = -j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>(-(j / *q));
-    }
-    else
-    {
-      if ((j = j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>((j / *q));
-    }
-    q++;
-  }
-}
-
-void jpeg_encoder::flush_output_buffer()
-{
-  if (m_out_buf_left != JPGE_OUT_BUF_SIZE)
-    m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_buf(m_out_buf, JPGE_OUT_BUF_SIZE - m_out_buf_left);
-  m_pOut_buf = m_out_buf;
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-}
-
-void jpeg_encoder::put_bits(uint bits, uint len)
-{
-  m_bit_buffer |= ((uint32)bits << (24 - (m_bits_in += len)));
-  while (m_bits_in >= 8)
-  {
-    uint8 c;
-    #define JPGE_PUT_BYTE(c) { *m_pOut_buf++ = (c); if (--m_out_buf_left == 0) flush_output_buffer(); }
-    JPGE_PUT_BYTE(c = (uint8)((m_bit_buffer >> 16) & 0xFF));
-    if (c == 0xFF) JPGE_PUT_BYTE(0);
-    m_bit_buffer <<= 8;
-    m_bits_in -= 8;
-  }
-}
-
-void jpeg_encoder::code_coefficients_pass_one(int component_num)
-{
-  if (component_num >= 3) return; // just to shut up static analysis
-  int i, run_len, nbits, temp1;
-  int16 *src = m_coefficient_array;
-  uint32 *dc_count = component_num ? m_huff_count[0 + 1] : m_huff_count[0 + 0], *ac_count = component_num ? m_huff_count[2 + 1] : m_huff_count[2 + 0];
-
-  temp1 = src[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = src[0];
-  if (temp1 < 0) temp1 = -temp1;
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  dc_count[nbits]++;
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        ac_count[0xF0]++;
-        run_len -= 16;
-      }
-      if (temp1 < 0) temp1 = -temp1;
-      nbits = 1;
-      while (temp1 >>= 1) nbits++;
-      ac_count[(run_len << 4) + nbits]++;
-      run_len = 0;
-    }
-  }
-  if (run_len) ac_count[0]++;
-}
-
-void jpeg_encoder::code_coefficients_pass_two(int component_num)
-{
-  int i, j, run_len, nbits, temp1, temp2;
-  int16 *pSrc = m_coefficient_array;
-  uint *codes[2];
-  uint8 *code_sizes[2];
-
-  if (component_num == 0)
-  {
-    codes[0] = m_huff_codes[0 + 0]; codes[1] = m_huff_codes[2 + 0];
-    code_sizes[0] = m_huff_code_sizes[0 + 0]; code_sizes[1] = m_huff_code_sizes[2 + 0];
-  }
-  else
-  {
-    codes[0] = m_huff_codes[0 + 1]; codes[1] = m_huff_codes[2 + 1];
-    code_sizes[0] = m_huff_code_sizes[0 + 1]; code_sizes[1] = m_huff_code_sizes[2 + 1];
-  }
-
-  temp1 = temp2 = pSrc[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = pSrc[0];
-
-  if (temp1 < 0)
-  {
-    temp1 = -temp1; temp2--;
-  }
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  put_bits(codes[0][nbits], code_sizes[0][nbits]);
-  if (nbits) put_bits(temp2 & ((1 << nbits) - 1), nbits);
-
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        put_bits(codes[1][0xF0], code_sizes[1][0xF0]);
-        run_len -= 16;
-      }
-      if ((temp2 = temp1) < 0)
-      {
-        temp1 = -temp1;
-        temp2--;
-      }
-      nbits = 1;
-      while (temp1 >>= 1)
-        nbits++;
-      j = (run_len << 4) + nbits;
-      put_bits(codes[1][j], code_sizes[1][j]);
-      put_bits(temp2 & ((1 << nbits) - 1), nbits);
-      run_len = 0;
-    }
-  }
-  if (run_len)
-    put_bits(codes[1][0], code_sizes[1][0]);
-}
-
-void jpeg_encoder::code_block(int component_num)
-{
-  DCT2D(m_sample_array);
-  load_quantized_coefficients(component_num);
-  if (m_pass_num == 1)
-    code_coefficients_pass_one(component_num);
-  else
-    code_coefficients_pass_two(component_num);
-}
-
-void jpeg_encoder::process_mcu_row()
-{
-  if (m_num_components == 1)
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8_grey(i); code_block(0);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i, 0, 0); code_block(0); load_block_8_8(i, 0, 1); code_block(1); load_block_8_8(i, 0, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_16_8_8(i, 1); code_block(1); load_block_16_8_8(i, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_8_8(i * 2 + 0, 1, 0); code_block(0); load_block_8_8(i * 2 + 1, 1, 0); code_block(0);
-      load_block_16_8(i, 1); code_block(1); load_block_16_8(i, 2); code_block(2);
-    }
-  }
-}
-
-bool jpeg_encoder::terminate_pass_one()
-{
-  optimize_huffman_table(0+0, DC_LUM_CODES); optimize_huffman_table(2+0, AC_LUM_CODES);
-  if (m_num_components > 1)
-  {
-    optimize_huffman_table(0+1, DC_CHROMA_CODES); optimize_huffman_table(2+1, AC_CHROMA_CODES);
-  }
-  return second_pass_init();
-}
-
-bool jpeg_encoder::terminate_pass_two()
-{
-  put_bits(0x7F, 7);
-  flush_output_buffer();
-  emit_marker(M_EOI);
-  m_pass_num++; // purposely bump up m_pass_num, for debugging
-  return true;
-}
-
-bool jpeg_encoder::process_end_of_image()
-{
-  if (m_mcu_y_ofs)
-  {
-    if (m_mcu_y_ofs < 16) // check here just to shut up static analysis
-    {
-      for (int i = m_mcu_y_ofs; i < m_mcu_y; i++)
-        memcpy(m_mcu_lines[i], m_mcu_lines[m_mcu_y_ofs - 1], m_image_bpl_mcu);
-    }
-
-    process_mcu_row();
-  }
-
-  if (m_pass_num == 1)
-    return terminate_pass_one();
-  else
-    return terminate_pass_two();
-}
-
-void jpeg_encoder::load_mcu(const void *pSrc)
-{
-  const uint8* Psrc = reinterpret_cast<const uint8*>(pSrc);
-
-  uint8* pDst = m_mcu_lines[m_mcu_y_ofs]; // OK to write up to m_image_bpl_xlt bytes to pDst
-
-  if (m_num_components == 1)
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_Y(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_Y(pDst, Psrc, m_image_x);
-    else
-      memcpy(pDst, Psrc, m_image_x);
-  }
-  else
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_YCC(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_YCC(pDst, Psrc, m_image_x);
-    else
-      Y_to_YCC(pDst, Psrc, m_image_x);
-  }
-
-  // Possibly duplicate pixels at end of scanline if not a multiple of 8 or 16
-  if (m_num_components == 1)
-    memset(m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt, pDst[m_image_bpl_xlt - 1], m_image_x_mcu - m_image_x);
-  else
-  {
-    const uint8 y = pDst[m_image_bpl_xlt - 3 + 0], cb = pDst[m_image_bpl_xlt - 3 + 1], cr = pDst[m_image_bpl_xlt - 3 + 2];
-    uint8 *q = m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt;
-    for (int i = m_image_x; i < m_image_x_mcu; i++)
-    {
-      *q++ = y; *q++ = cb; *q++ = cr;
-    }
-  }
-
-  if (++m_mcu_y_ofs == m_mcu_y)
-  {
-    process_mcu_row();
-    m_mcu_y_ofs = 0;
-  }
-}
-
-void jpeg_encoder::clear()
-{
-  m_mcu_lines[0] = NULL;
-  m_pass_num = 0;
-  m_all_stream_writes_succeeded = true;
-}
-
-jpeg_encoder::jpeg_encoder()
-{
-  clear();
-}
-
-jpeg_encoder::~jpeg_encoder()
-{
-  deinit();
-}
-
-bool jpeg_encoder::init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params)
-{
-  deinit();
-  if (((!pStream) || (width < 1) || (height < 1)) || ((src_channels != 1) && (src_channels != 3) && (src_channels != 4)) || (!comp_params.check_valid())) return false;
-  m_pStream = pStream;
-  m_params = comp_params;
-  return jpg_open(width, height, src_channels);
-}
-
-void jpeg_encoder::deinit()
-{
-  jpge_free(m_mcu_lines[0]);
-  clear();
-}
-
-bool jpeg_encoder::process_scanline(const void* pScanline)
-{
-  if ((m_pass_num < 1) || (m_pass_num > 2)) return false;
-  if (m_all_stream_writes_succeeded)
-  {
-    if (!pScanline)
-    {
-      if (!process_end_of_image()) return false;
-    }
-    else
-    {
-      load_mcu(pScanline);
-    }
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-// Higher level wrappers/examples (optional).
-#include <stdio.h>
-
-class cfile_stream : public output_stream
-{
-   cfile_stream(const cfile_stream &);
-   cfile_stream &operator= (const cfile_stream &);
-
-   FILE* m_pFile;
-   bool m_bStatus;
-
-public:
-   cfile_stream() : m_pFile(NULL), m_bStatus(false) { }
-
-   virtual ~cfile_stream()
-   {
-      close();
-   }
-
-   bool open(const char *pFilename)
-   {
-      close();
-#if defined(_MSC_VER)
-      if (fopen_s(&m_pFile, pFilename, "wb") != 0)
-	  {
-		  return false;
-	  }
-#else
-      m_pFile = fopen(pFilename, "wb");
-#endif
-      m_bStatus = (m_pFile != NULL);
-      return m_bStatus;
-   }
-
-   bool close()
-   {
-      if (m_pFile)
-      {
-         if (fclose(m_pFile) == EOF)
-         {
-            m_bStatus = false;
-         }
-         m_pFile = NULL;
-      }
-      return m_bStatus;
-   }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      m_bStatus = m_bStatus && (fwrite(pBuf, len, 1, m_pFile) == 1);
-      return m_bStatus;
-   }
-
-   uint get_size() const
-   {
-      return m_pFile ? ftell(m_pFile) : 0;
-   }
-};
-
-// Writes JPEG image to file.
-bool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-  cfile_stream dst_stream;
-  if (!dst_stream.open(pFilename))
-    return false;
-
-  jpge::jpeg_encoder dst_image;
-  if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-    return false;
-
-  for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-  {
-    for (int64_t i = 0; i < height; i++)
-    {
-		// i, width, and num_channels are all 64bit
-       const uint8* pBuf = pImage_data + i * width * num_channels;
-       if (!dst_image.process_scanline(pBuf))
-          return false;
-    }
-    if (!dst_image.process_scanline(NULL))
-       return false;
-  }
-
-  dst_image.deinit();
-
-  return dst_stream.close();
-}
-
-class memory_stream : public output_stream
-{
-   memory_stream(const memory_stream &);
-   memory_stream &operator= (const memory_stream &);
-
-   uint8 *m_pBuf;
-   uint64_t m_buf_size, m_buf_ofs;
-
-public:
-   memory_stream(void *pBuf, uint64_t buf_size) : m_pBuf(static_cast<uint8*>(pBuf)), m_buf_size(buf_size), m_buf_ofs(0) { }
-
-   virtual ~memory_stream() { }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      uint64_t buf_remaining = m_buf_size - m_buf_ofs;
-      if ((uint64_t)len > buf_remaining)
-         return false;
-      memcpy(m_pBuf + m_buf_ofs, pBuf, len);
-      m_buf_ofs += len;
-      return true;
-   }
-
-   uint64_t get_size() const
-   {
-      return m_buf_ofs;
-   }
-};
-
-bool compress_image_to_jpeg_file_in_memory(void *pDstBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-   if ((!pDstBuf) || (!buf_size))
-      return false;
-
-   memory_stream dst_stream(pDstBuf, buf_size);
-
-   buf_size = 0;
-
-   jpge::jpeg_encoder dst_image;
-   if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-      return false;
-
-   for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-   {
-     for (int64_t i = 0; i < height; i++)
-     {
-        const uint8* pScanline = pImage_data + i * width * num_channels;
-        if (!dst_image.process_scanline(pScanline))
-           return false;
-     }
-     if (!dst_image.process_scanline(NULL))
-        return false;
-   }
-
-   dst_image.deinit();
-
-   buf_size = dst_stream.get_size();
-   return true;
-}
-
-} // namespace jpge
\ No newline at end of file
diff --git a/spaces/xxx1/zh-clip/models/zhclip/__init__.py b/spaces/xxx1/zh-clip/models/zhclip/__init__.py
deleted file mode 100644
index c51e4f336c855695198fe1dd25ad6478cd70338e..0000000000000000000000000000000000000000
--- a/spaces/xxx1/zh-clip/models/zhclip/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .modeling_zhclip import (
-    ZhCLIPModel,
-)
-
-from .configuration_zhclip import ZhCLIPConfig
-from .processing_zhclip import ZhCLIPProcessor
\ No newline at end of file
diff --git a/spaces/yderre-aubay/midi-player-demo/src/components/Toast.tsx b/spaces/yderre-aubay/midi-player-demo/src/components/Toast.tsx
deleted file mode 100644
index 1781598a5a9d15f18764f381ee1af89ba1560ee4..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/components/Toast.tsx
+++ /dev/null
@@ -1,114 +0,0 @@
-import { keyframes } from "@emotion/react"
-import styled from "@emotion/styled"
-import * as Portal from "@radix-ui/react-portal"
-import Error from "mdi-react/AlertCircleIcon"
-import Warning from "mdi-react/AlertIcon"
-import CheckCircle from "mdi-react/CheckCircleIcon"
-import Info from "mdi-react/InformationIcon"
-import { FC, useEffect, useState } from "react"
-import { Theme } from "../common/theme/Theme"
-import { useTheme } from "../main/hooks/useTheme"
-import { ToastSeverity } from "../main/hooks/useToast"
-
-export interface ToastProps {
-  message: string
-  severity: ToastSeverity
-  onExited: () => void
-}
-
-const contentShow = keyframes`
-  from {
-    opacity: 0;
-    transform: translate(0, 0.5rem) scale(0.96);
-  }
-  to {
-    opacity: 1;
-    transform: translate(0, 0) scale(1);
-  }
-`
-
-const contentHide = keyframes`
-  from {
-    opacity: 1;
-    transform: translate(0, 0) scale(1);
-  }
-  to {
-    opacity: 0;
-    transform: translate(0, 0.5rem) scale(0.96);
-  }
-`
-
-const Root = styled(Portal.Root)`
-  position: fixed;
-  bottom: 2rem;
-  left: 0;
-  right: 0;
-  display: flex;
-`
-
-const Content = styled.div`
-  margin: 0 auto;
-  background: ${({ theme }) => theme.secondaryBackgroundColor};
-  padding: 1rem;
-  border-radius: 0.5rem;
-  font-size: 0.8rem;
-  box-shadow: 0 0.5rem 3rem ${({ theme }) => theme.shadowColor};
-  display: flex;
-  align-items: center;
-
-  animation: ${({ show }: { show: boolean }) =>
-      show ? contentShow : contentHide}
-    500ms cubic-bezier(0.16, 1, 0.3, 1) forwards;
-`
-
-const SeverityIcon: FC<{ severity: ToastSeverity }> = ({ severity }) => {
-  const theme = useTheme()
-  const fill = colorForSeverity(severity, theme)
-  switch (severity) {
-    case "error":
-      return <Error style={{ fill }} />
-    case "info":
-      return <Info style={{ fill }} />
-    case "success":
-      return <CheckCircle style={{ fill }} />
-    case "warning":
-      return <Warning style={{ fill }} />
-  }
-}
-
-const exitDuration = 5000
-
-export const Toast: FC<ToastProps> = ({ message, severity, onExited }) => {
-  const [show, setShow] = useState(true)
-
-  useEffect(() => {
-    const timeout = setTimeout(() => setShow(false), exitDuration - 500)
-    const timeout2 = setTimeout(onExited, exitDuration)
-    return () => {
-      clearTimeout(timeout)
-      clearTimeout(timeout2)
-    }
-  })
-  return (
-    <Root>
-      <Content show={show}>
-        <SeverityIcon severity={severity} />
-        <div style={{ width: "0.5rem" }} />
-        {message}
-      </Content>
-    </Root>
-  )
-}
-
-const colorForSeverity = (severity: ToastSeverity, theme: Theme) => {
-  switch (severity) {
-    case "error":
-      return theme.redColor
-    case "info":
-      return theme.textColor
-    case "success":
-      return theme.greenColor
-    case "warning":
-      return theme.yellowColor
-  }
-}
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/hyperparameter_search.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/hyperparameter_search.py
deleted file mode 100644
index 8dfd60cc39cd31f8fd7621bc3be4b2bf67b19cb8..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/hyperparameter_search.py
+++ /dev/null
@@ -1,141 +0,0 @@
-# coding=utf-8
-# Copyright 2023-present the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .integrations import (
-    is_optuna_available,
-    is_ray_available,
-    is_sigopt_available,
-    is_wandb_available,
-    run_hp_search_optuna,
-    run_hp_search_ray,
-    run_hp_search_sigopt,
-    run_hp_search_wandb,
-)
-from .trainer_utils import (
-    HPSearchBackend,
-    default_hp_space_optuna,
-    default_hp_space_ray,
-    default_hp_space_sigopt,
-    default_hp_space_wandb,
-)
-from .utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class HyperParamSearchBackendBase:
-    name: str
-    pip_package: str = None
-
-    @staticmethod
-    def is_available():
-        raise NotImplementedError
-
-    def run(self, trainer, n_trials: int, direction: str, **kwargs):
-        raise NotImplementedError
-
-    def default_hp_space(self, trial):
-        raise NotImplementedError
-
-    def ensure_available(self):
-        if not self.is_available():
-            raise RuntimeError(
-                f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."
-            )
-
-    @classmethod
-    def pip_install(cls):
-        return f"`pip install {cls.pip_package or cls.name}`"
-
-
-class OptunaBackend(HyperParamSearchBackendBase):
-    name = "optuna"
-
-    @staticmethod
-    def is_available():
-        return is_optuna_available()
-
-    def run(self, trainer, n_trials: int, direction: str, **kwargs):
-        return run_hp_search_optuna(trainer, n_trials, direction, **kwargs)
-
-    def default_hp_space(self, trial):
-        return default_hp_space_optuna(trial)
-
-
-class RayTuneBackend(HyperParamSearchBackendBase):
-    name = "ray"
-    pip_package = "'ray[tune]'"
-
-    @staticmethod
-    def is_available():
-        return is_ray_available()
-
-    def run(self, trainer, n_trials: int, direction: str, **kwargs):
-        return run_hp_search_ray(trainer, n_trials, direction, **kwargs)
-
-    def default_hp_space(self, trial):
-        return default_hp_space_ray(trial)
-
-
-class SigOptBackend(HyperParamSearchBackendBase):
-    name = "sigopt"
-
-    @staticmethod
-    def is_available():
-        return is_sigopt_available()
-
-    def run(self, trainer, n_trials: int, direction: str, **kwargs):
-        return run_hp_search_sigopt(trainer, n_trials, direction, **kwargs)
-
-    def default_hp_space(self, trial):
-        return default_hp_space_sigopt(trial)
-
-
-class WandbBackend(HyperParamSearchBackendBase):
-    name = "wandb"
-
-    @staticmethod
-    def is_available():
-        return is_wandb_available()
-
-    def run(self, trainer, n_trials: int, direction: str, **kwargs):
-        return run_hp_search_wandb(trainer, n_trials, direction, **kwargs)
-
-    def default_hp_space(self, trial):
-        return default_hp_space_wandb(trial)
-
-
-ALL_HYPERPARAMETER_SEARCH_BACKENDS = {
-    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
-}
-
-
-def default_hp_search_backend() -> str:
-    available_backends = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
-    if len(available_backends) > 0:
-        name = available_backends[0].name
-        if len(available_backends) > 1:
-            logger.info(
-                f"{len(available_backends)} hyperparameter search backends available. Using {name} as the default."
-            )
-        return name
-    raise RuntimeError(
-        "No hyperparameter search backend available.\n"
-        + "\n".join(
-            f" - To install {backend.name} run {backend.pip_install()}"
-            for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()
-        )
-    )
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/albert/modeling_tf_albert.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/albert/modeling_tf_albert.py
deleted file mode 100644
index ad35b6182a4e21b1c3c4cc8a62dcd92603f4d7fd..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/albert/modeling_tf_albert.py
+++ /dev/null
@@ -1,1396 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" TF 2.0 ALBERT model."""
-
-
-from __future__ import annotations
-
-import math
-from dataclasses import dataclass
-from typing import Dict, Optional, Tuple, Union
-
-import numpy as np
-import tensorflow as tf
-
-from ...activations_tf import get_tf_activation
-from ...modeling_tf_outputs import (
-    TFBaseModelOutput,
-    TFBaseModelOutputWithPooling,
-    TFMaskedLMOutput,
-    TFMultipleChoiceModelOutput,
-    TFQuestionAnsweringModelOutput,
-    TFSequenceClassifierOutput,
-    TFTokenClassifierOutput,
-)
-from ...modeling_tf_utils import (
-    TFMaskedLanguageModelingLoss,
-    TFModelInputType,
-    TFMultipleChoiceLoss,
-    TFPreTrainedModel,
-    TFQuestionAnsweringLoss,
-    TFSequenceClassificationLoss,
-    TFTokenClassificationLoss,
-    get_initializer,
-    keras_serializable,
-    unpack_inputs,
-)
-from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
-from ...utils import (
-    ModelOutput,
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_albert import AlbertConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "albert-base-v2"
-_CONFIG_FOR_DOC = "AlbertConfig"
-
-TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "albert-base-v1",
-    "albert-large-v1",
-    "albert-xlarge-v1",
-    "albert-xxlarge-v1",
-    "albert-base-v2",
-    "albert-large-v2",
-    "albert-xlarge-v2",
-    "albert-xxlarge-v2",
-    # See all ALBERT models at https://huggingface.co/models?filter=albert
-]
-
-
-class TFAlbertPreTrainingLoss:
-    """
-    Loss function suitable for ALBERT pretraining, that is, the task of pretraining a language model by combining SOP +
-    MLM. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss computation.
-    """
-
-    def hf_compute_loss(self, labels: tf.Tensor, logits: tf.Tensor) -> tf.Tensor:
-        loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
-            from_logits=True, reduction=tf.keras.losses.Reduction.NONE
-        )
-        if self.config.tf_legacy_loss:
-            # make sure only labels that are not equal to -100
-            # are taken into account as loss
-            masked_lm_active_loss = tf.not_equal(tf.reshape(tensor=labels["labels"], shape=(-1,)), -100)
-            masked_lm_reduced_logits = tf.boolean_mask(
-                tensor=tf.reshape(tensor=logits[0], shape=(-1, shape_list(logits[0])[2])),
-                mask=masked_lm_active_loss,
-            )
-            masked_lm_labels = tf.boolean_mask(
-                tensor=tf.reshape(tensor=labels["labels"], shape=(-1,)), mask=masked_lm_active_loss
-            )
-            sentence_order_active_loss = tf.not_equal(
-                tf.reshape(tensor=labels["sentence_order_label"], shape=(-1,)), -100
-            )
-            sentence_order_reduced_logits = tf.boolean_mask(
-                tensor=tf.reshape(tensor=logits[1], shape=(-1, 2)), mask=sentence_order_active_loss
-            )
-            sentence_order_label = tf.boolean_mask(
-                tensor=tf.reshape(tensor=labels["sentence_order_label"], shape=(-1,)), mask=sentence_order_active_loss
-            )
-            masked_lm_loss = loss_fn(y_true=masked_lm_labels, y_pred=masked_lm_reduced_logits)
-            sentence_order_loss = loss_fn(y_true=sentence_order_label, y_pred=sentence_order_reduced_logits)
-            masked_lm_loss = tf.reshape(tensor=masked_lm_loss, shape=(-1, shape_list(sentence_order_loss)[0]))
-            masked_lm_loss = tf.reduce_mean(input_tensor=masked_lm_loss, axis=0)
-
-            return masked_lm_loss + sentence_order_loss
-
-        # Clip negative labels to zero here to avoid NaNs and errors - those positions will get masked later anyway
-        unmasked_lm_losses = loss_fn(y_true=tf.nn.relu(labels["labels"]), y_pred=logits[0])
-        # make sure only labels that are not equal to -100
-        # are taken into account for the loss computation
-        lm_loss_mask = tf.cast(labels["labels"] != -100, dtype=unmasked_lm_losses.dtype)
-        masked_lm_losses = unmasked_lm_losses * lm_loss_mask
-        reduced_masked_lm_loss = tf.reduce_sum(masked_lm_losses) / tf.reduce_sum(lm_loss_mask)
-
-        sop_logits = tf.reshape(logits[1], (-1, 2))
-        # Clip negative labels to zero here to avoid NaNs and errors - those positions will get masked later anyway
-        unmasked_sop_loss = loss_fn(y_true=tf.nn.relu(labels["sentence_order_label"]), y_pred=sop_logits)
-        sop_loss_mask = tf.cast(labels["sentence_order_label"] != -100, dtype=unmasked_sop_loss.dtype)
-
-        masked_sop_loss = unmasked_sop_loss * sop_loss_mask
-        reduced_masked_sop_loss = tf.reduce_sum(masked_sop_loss) / tf.reduce_sum(sop_loss_mask)
-
-        return tf.reshape(reduced_masked_lm_loss + reduced_masked_sop_loss, (1,))
-
-
-class TFAlbertEmbeddings(tf.keras.layers.Layer):
-    """Construct the embeddings from word, position and token_type embeddings."""
-
-    def __init__(self, config: AlbertConfig, **kwargs):
-        super().__init__(**kwargs)
-
-        self.config = config
-        self.embedding_size = config.embedding_size
-        self.max_position_embeddings = config.max_position_embeddings
-        self.initializer_range = config.initializer_range
-        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
-        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
-
-    def build(self, input_shape: tf.TensorShape):
-        with tf.name_scope("word_embeddings"):
-            self.weight = self.add_weight(
-                name="weight",
-                shape=[self.config.vocab_size, self.embedding_size],
-                initializer=get_initializer(self.initializer_range),
-            )
-
-        with tf.name_scope("token_type_embeddings"):
-            self.token_type_embeddings = self.add_weight(
-                name="embeddings",
-                shape=[self.config.type_vocab_size, self.embedding_size],
-                initializer=get_initializer(self.initializer_range),
-            )
-
-        with tf.name_scope("position_embeddings"):
-            self.position_embeddings = self.add_weight(
-                name="embeddings",
-                shape=[self.max_position_embeddings, self.embedding_size],
-                initializer=get_initializer(self.initializer_range),
-            )
-
-        super().build(input_shape)
-
-    # Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings.call
-    def call(
-        self,
-        input_ids: tf.Tensor = None,
-        position_ids: tf.Tensor = None,
-        token_type_ids: tf.Tensor = None,
-        inputs_embeds: tf.Tensor = None,
-        past_key_values_length=0,
-        training: bool = False,
-    ) -> tf.Tensor:
-        """
-        Applies embedding based on inputs tensor.
-
-        Returns:
-            final_embeddings (`tf.Tensor`): output embedding tensor.
-        """
-        if input_ids is None and inputs_embeds is None:
-            raise ValueError("Need to provide either `input_ids` or `input_embeds`.")
-
-        if input_ids is not None:
-            check_embeddings_within_bounds(input_ids, self.config.vocab_size)
-            inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
-
-        input_shape = shape_list(inputs_embeds)[:-1]
-
-        if token_type_ids is None:
-            token_type_ids = tf.fill(dims=input_shape, value=0)
-
-        if position_ids is None:
-            position_ids = tf.expand_dims(
-                tf.range(start=past_key_values_length, limit=input_shape[1] + past_key_values_length), axis=0
-            )
-
-        position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
-        token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
-        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
-        final_embeddings = self.LayerNorm(inputs=final_embeddings)
-        final_embeddings = self.dropout(inputs=final_embeddings, training=training)
-
-        return final_embeddings
-
-
-class TFAlbertAttention(tf.keras.layers.Layer):
-    """Contains the complete attention sublayer, including both dropouts and layer norm."""
-
-    def __init__(self, config: AlbertConfig, **kwargs):
-        super().__init__(**kwargs)
-
-        if config.hidden_size % config.num_attention_heads != 0:
-            raise ValueError(
-                f"The hidden size ({config.hidden_size}) is not a multiple of the number "
-                f"of attention heads ({config.num_attention_heads})"
-            )
-
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-        self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
-        self.output_attentions = config.output_attentions
-
-        self.query = tf.keras.layers.Dense(
-            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
-        )
-        self.key = tf.keras.layers.Dense(
-            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
-        )
-        self.value = tf.keras.layers.Dense(
-            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
-        )
-        self.dense = tf.keras.layers.Dense(
-            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
-        )
-        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
-        # Two different dropout probabilities; see https://github.com/google-research/albert/blob/master/modeling.py#L971-L993
-        self.attention_dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
-        self.output_dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
-
-    def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
-        # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
-        tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
-
-        # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
-        return tf.transpose(tensor, perm=[0, 2, 1, 3])
-
-    def call(
-        self,
-        input_tensor: tf.Tensor,
-        attention_mask: tf.Tensor,
-        head_mask: tf.Tensor,
-        output_attentions: bool,
-        training: bool = False,
-    ) -> Tuple[tf.Tensor]:
-        batch_size = shape_list(input_tensor)[0]
-        mixed_query_layer = self.query(inputs=input_tensor)
-        mixed_key_layer = self.key(inputs=input_tensor)
-        mixed_value_layer = self.value(inputs=input_tensor)
-        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
-        key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
-        value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        # (batch size, num_heads, seq_len_q, seq_len_k)
-        attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
-        dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
-        attention_scores = tf.divide(attention_scores, dk)
-
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in TFAlbertModel call() function)
-            attention_scores = tf.add(attention_scores, attention_mask)
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = stable_softmax(logits=attention_scores, axis=-1)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.attention_dropout(inputs=attention_probs, training=training)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs = tf.multiply(attention_probs, head_mask)
-
-        context_layer = tf.matmul(attention_probs, value_layer)
-        context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
-
-        # (batch_size, seq_len_q, all_head_size)
-        context_layer = tf.reshape(tensor=context_layer, shape=(batch_size, -1, self.all_head_size))
-        self_outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
-        hidden_states = self_outputs[0]
-        hidden_states = self.dense(inputs=hidden_states)
-        hidden_states = self.output_dropout(inputs=hidden_states, training=training)
-        attention_output = self.LayerNorm(inputs=hidden_states + input_tensor)
-
-        # add attentions if we output them
-        outputs = (attention_output,) + self_outputs[1:]
-
-        return outputs
-
-
-class TFAlbertLayer(tf.keras.layers.Layer):
-    def __init__(self, config: AlbertConfig, **kwargs):
-        super().__init__(**kwargs)
-
-        self.attention = TFAlbertAttention(config, name="attention")
-        self.ffn = tf.keras.layers.Dense(
-            units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="ffn"
-        )
-
-        if isinstance(config.hidden_act, str):
-            self.activation = get_tf_activation(config.hidden_act)
-        else:
-            self.activation = config.hidden_act
-
-        self.ffn_output = tf.keras.layers.Dense(
-            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="ffn_output"
-        )
-        self.full_layer_layer_norm = tf.keras.layers.LayerNormalization(
-            epsilon=config.layer_norm_eps, name="full_layer_layer_norm"
-        )
-        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
-
-    def call(
-        self,
-        hidden_states: tf.Tensor,
-        attention_mask: tf.Tensor,
-        head_mask: tf.Tensor,
-        output_attentions: bool,
-        training: bool = False,
-    ) -> Tuple[tf.Tensor]:
-        attention_outputs = self.attention(
-            input_tensor=hidden_states,
-            attention_mask=attention_mask,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            training=training,
-        )
-        ffn_output = self.ffn(inputs=attention_outputs[0])
-        ffn_output = self.activation(ffn_output)
-        ffn_output = self.ffn_output(inputs=ffn_output)
-        ffn_output = self.dropout(inputs=ffn_output, training=training)
-        hidden_states = self.full_layer_layer_norm(inputs=ffn_output + attention_outputs[0])
-
-        # add attentions if we output them
-        outputs = (hidden_states,) + attention_outputs[1:]
-
-        return outputs
-
-
-class TFAlbertLayerGroup(tf.keras.layers.Layer):
-    def __init__(self, config: AlbertConfig, **kwargs):
-        super().__init__(**kwargs)
-
-        self.albert_layers = [
-            TFAlbertLayer(config, name=f"albert_layers_._{i}") for i in range(config.inner_group_num)
-        ]
-
-    def call(
-        self,
-        hidden_states: tf.Tensor,
-        attention_mask: tf.Tensor,
-        head_mask: tf.Tensor,
-        output_attentions: bool,
-        output_hidden_states: bool,
-        training: bool = False,
-    ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
-        layer_hidden_states = () if output_hidden_states else None
-        layer_attentions = () if output_attentions else None
-
-        for layer_index, albert_layer in enumerate(self.albert_layers):
-            if output_hidden_states:
-                layer_hidden_states = layer_hidden_states + (hidden_states,)
-
-            layer_output = albert_layer(
-                hidden_states=hidden_states,
-                attention_mask=attention_mask,
-                head_mask=head_mask[layer_index],
-                output_attentions=output_attentions,
-                training=training,
-            )
-            hidden_states = layer_output[0]
-
-            if output_attentions:
-                layer_attentions = layer_attentions + (layer_output[1],)
-
-        # Add last layer
-        if output_hidden_states:
-            layer_hidden_states = layer_hidden_states + (hidden_states,)
-
-        return tuple(v for v in [hidden_states, layer_hidden_states, layer_attentions] if v is not None)
-
-
-class TFAlbertTransformer(tf.keras.layers.Layer):
-    def __init__(self, config: AlbertConfig, **kwargs):
-        super().__init__(**kwargs)
-
-        self.num_hidden_layers = config.num_hidden_layers
-        self.num_hidden_groups = config.num_hidden_groups
-        # Number of layers in a hidden group
-        self.layers_per_group = int(config.num_hidden_layers / config.num_hidden_groups)
-        self.embedding_hidden_mapping_in = tf.keras.layers.Dense(
-            units=config.hidden_size,
-            kernel_initializer=get_initializer(config.initializer_range),
-            name="embedding_hidden_mapping_in",
-        )
-        self.albert_layer_groups = [
-            TFAlbertLayerGroup(config, name=f"albert_layer_groups_._{i}") for i in range(config.num_hidden_groups)
-        ]
-
-    def call(
-        self,
-        hidden_states: tf.Tensor,
-        attention_mask: tf.Tensor,
-        head_mask: tf.Tensor,
-        output_attentions: bool,
-        output_hidden_states: bool,
-        return_dict: bool,
-        training: bool = False,
-    ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
-        hidden_states = self.embedding_hidden_mapping_in(inputs=hidden_states)
-        all_attentions = () if output_attentions else None
-        all_hidden_states = (hidden_states,) if output_hidden_states else None
-
-        for i in range(self.num_hidden_layers):
-            # Index of the hidden group
-            group_idx = int(i / (self.num_hidden_layers / self.num_hidden_groups))
-            layer_group_output = self.albert_layer_groups[group_idx](
-                hidden_states=hidden_states,
-                attention_mask=attention_mask,
-                head_mask=head_mask[group_idx * self.layers_per_group : (group_idx + 1) * self.layers_per_group],
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                training=training,
-            )
-            hidden_states = layer_group_output[0]
-
-            if output_attentions:
-                all_attentions = all_attentions + layer_group_output[-1]
-
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
-
-        return TFBaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
-        )
-
-
-class TFAlbertPreTrainedModel(TFPreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = AlbertConfig
-    base_model_prefix = "albert"
-
-
-class TFAlbertMLMHead(tf.keras.layers.Layer):
-    def __init__(self, config: AlbertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
-        super().__init__(**kwargs)
-
-        self.config = config
-        self.embedding_size = config.embedding_size
-        self.dense = tf.keras.layers.Dense(
-            config.embedding_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
-        )
-        if isinstance(config.hidden_act, str):
-            self.activation = get_tf_activation(config.hidden_act)
-        else:
-            self.activation = config.hidden_act
-
-        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
-
-        # The output weights are the same as the input embeddings, but there is
-        # an output-only bias for each token.
-        self.decoder = input_embeddings
-
-    def build(self, input_shape: tf.TensorShape):
-        self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
-        self.decoder_bias = self.add_weight(
-            shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="decoder/bias"
-        )
-
-        super().build(input_shape)
-
-    def get_output_embeddings(self) -> tf.keras.layers.Layer:
-        return self.decoder
-
-    def set_output_embeddings(self, value: tf.Variable):
-        self.decoder.weight = value
-        self.decoder.vocab_size = shape_list(value)[0]
-
-    def get_bias(self) -> Dict[str, tf.Variable]:
-        return {"bias": self.bias, "decoder_bias": self.decoder_bias}
-
-    def set_bias(self, value: tf.Variable):
-        self.bias = value["bias"]
-        self.decoder_bias = value["decoder_bias"]
-        self.config.vocab_size = shape_list(value["bias"])[0]
-
-    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
-        hidden_states = self.dense(inputs=hidden_states)
-        hidden_states = self.activation(hidden_states)
-        hidden_states = self.LayerNorm(inputs=hidden_states)
-        seq_length = shape_list(tensor=hidden_states)[1]
-        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.embedding_size])
-        hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True)
-        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size])
-        hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.decoder_bias)
-
-        return hidden_states
-
-
-@keras_serializable
-class TFAlbertMainLayer(tf.keras.layers.Layer):
-    config_class = AlbertConfig
-
-    def __init__(self, config: AlbertConfig, add_pooling_layer: bool = True, **kwargs):
-        super().__init__(**kwargs)
-
-        self.config = config
-
-        self.embeddings = TFAlbertEmbeddings(config, name="embeddings")
-        self.encoder = TFAlbertTransformer(config, name="encoder")
-        self.pooler = (
-            tf.keras.layers.Dense(
-                units=config.hidden_size,
-                kernel_initializer=get_initializer(config.initializer_range),
-                activation="tanh",
-                name="pooler",
-            )
-            if add_pooling_layer
-            else None
-        )
-
-    def get_input_embeddings(self) -> tf.keras.layers.Layer:
-        return self.embeddings
-
-    def set_input_embeddings(self, value: tf.Variable):
-        self.embeddings.weight = value
-        self.embeddings.vocab_size = shape_list(value)[0]
-
-    def _prune_heads(self, heads_to_prune):
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        raise NotImplementedError
-
-    @unpack_inputs
-    def call(
-        self,
-        input_ids: TFModelInputType | None = None,
-        attention_mask: np.ndarray | tf.Tensor | None = None,
-        token_type_ids: np.ndarray | tf.Tensor | None = None,
-        position_ids: np.ndarray | tf.Tensor | None = None,
-        head_mask: np.ndarray | tf.Tensor | None = None,
-        inputs_embeds: np.ndarray | tf.Tensor | None = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        training: bool = False,
-    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = shape_list(input_ids)
-        elif inputs_embeds is not None:
-            input_shape = shape_list(inputs_embeds)[:-1]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        if attention_mask is None:
-            attention_mask = tf.fill(dims=input_shape, value=1)
-
-        if token_type_ids is None:
-            token_type_ids = tf.fill(dims=input_shape, value=0)
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            token_type_ids=token_type_ids,
-            inputs_embeds=inputs_embeds,
-            training=training,
-        )
-
-        # We create a 3D attention mask from a 2D tensor mask.
-        # Sizes are [batch_size, 1, 1, to_seq_length]
-        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
-        # this attention mask is more simple than the triangular masking of causal attention
-        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
-        extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1]))
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-        extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
-        one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
-        ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
-        extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        if head_mask is not None:
-            raise NotImplementedError
-        else:
-            head_mask = [None] * self.config.num_hidden_layers
-
-        encoder_outputs = self.encoder(
-            hidden_states=embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=training,
-        )
-
-        sequence_output = encoder_outputs[0]
-        pooled_output = self.pooler(inputs=sequence_output[:, 0]) if self.pooler is not None else None
-
-        if not return_dict:
-            return (
-                sequence_output,
-                pooled_output,
-            ) + encoder_outputs[1:]
-
-        return TFBaseModelOutputWithPooling(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-        )
-
-
-@dataclass
-class TFAlbertForPreTrainingOutput(ModelOutput):
-    """
-    Output type of [`TFAlbertForPreTraining`].
-
-    Args:
-        prediction_logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        sop_logits (`tf.Tensor` of shape `(batch_size, 2)`):
-            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
-            before SoftMax).
-        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
-            `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    loss: tf.Tensor = None
-    prediction_logits: tf.Tensor = None
-    sop_logits: tf.Tensor = None
-    hidden_states: Tuple[tf.Tensor] | None = None
-    attentions: Tuple[tf.Tensor] | None = None
-
-
-ALBERT_START_DOCSTRING = r"""
-
-    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
-    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
-    behavior.
-
-    <Tip>
-
-    TensorFlow models and layers in `transformers` accept two formats as input:
-
-    - having all inputs as keyword arguments (like PyTorch models), or
-    - having all inputs as a list, tuple or dict in the first positional argument.
-
-    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
-    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
-    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
-    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
-    the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
-    positional argument:
-
-    - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
-    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
-    `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
-    - a dictionary with one or several input Tensors associated to the input names given in the docstring:
-    `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
-
-    Note that when creating models and layers with
-    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
-    about any of this, as you can just pass inputs like you would to any other Python function!
-
-    </Tip>
-
-    Args:
-        config ([`AlbertConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-ALBERT_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
-            [`PreTrainedTokenizer.encode`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
-            1]`:
-
-            - 0 corresponds to a *sentence A* token,
-            - 1 corresponds to a *sentence B* token.
-
-            [What are token type IDs?](../glossary#token-type-ids)
-        position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
-            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
-            config will be used instead.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
-            used instead.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
-            eager mode, in graph mode the value will always be set to True.
-        training (`bool`, *optional*, defaults to `False`):
-            Whether or not to use the model in training mode (some modules like dropout modules have different
-            behaviors between training and evaluation).
-"""
-
-
-@add_start_docstrings(
-    "The bare Albert Model transformer outputting raw hidden-states without any specific head on top.",
-    ALBERT_START_DOCSTRING,
-)
-class TFAlbertModel(TFAlbertPreTrainedModel):
-    def __init__(self, config: AlbertConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
-
-        self.albert = TFAlbertMainLayer(config, name="albert")
-
-    @unpack_inputs
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TFBaseModelOutputWithPooling,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def call(
-        self,
-        input_ids: TFModelInputType | None = None,
-        attention_mask: np.ndarray | tf.Tensor | None = None,
-        token_type_ids: np.ndarray | tf.Tensor | None = None,
-        position_ids: np.ndarray | tf.Tensor | None = None,
-        head_mask: np.ndarray | tf.Tensor | None = None,
-        inputs_embeds: np.ndarray | tf.Tensor | None = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        training: Optional[bool] = False,
-    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
-        outputs = self.albert(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=training,
-        )
-
-        return outputs
-
-
-@add_start_docstrings(
-    """
-    Albert Model with two heads on top for pretraining: a `masked language modeling` head and a `sentence order
-    prediction` (classification) head.
-    """,
-    ALBERT_START_DOCSTRING,
-)
-class TFAlbertForPreTraining(TFAlbertPreTrainedModel, TFAlbertPreTrainingLoss):
-    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
-    _keys_to_ignore_on_load_unexpected = [r"predictions.decoder.weight"]
-
-    def __init__(self, config: AlbertConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
-
-        self.num_labels = config.num_labels
-
-        self.albert = TFAlbertMainLayer(config, name="albert")
-        self.predictions = TFAlbertMLMHead(config, input_embeddings=self.albert.embeddings, name="predictions")
-        self.sop_classifier = TFAlbertSOPHead(config, name="sop_classifier")
-
-    def get_lm_head(self) -> tf.keras.layers.Layer:
-        return self.predictions
-
-    @unpack_inputs
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=TFAlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
-    def call(
-        self,
-        input_ids: TFModelInputType | None = None,
-        attention_mask: np.ndarray | tf.Tensor | None = None,
-        token_type_ids: np.ndarray | tf.Tensor | None = None,
-        position_ids: np.ndarray | tf.Tensor | None = None,
-        head_mask: np.ndarray | tf.Tensor | None = None,
-        inputs_embeds: np.ndarray | tf.Tensor | None = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        labels: np.ndarray | tf.Tensor | None = None,
-        sentence_order_label: np.ndarray | tf.Tensor | None = None,
-        training: Optional[bool] = False,
-    ) -> Union[TFAlbertForPreTrainingOutput, Tuple[tf.Tensor]]:
-        r"""
-        Return:
-
-        Example:
-
-        ```python
-        >>> import tensorflow as tf
-        >>> from transformers import AutoTokenizer, TFAlbertForPreTraining
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
-        >>> model = TFAlbertForPreTraining.from_pretrained("albert-base-v2")
-
-        >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :]
-        >>> # Batch size 1
-        >>> outputs = model(input_ids)
-
-        >>> prediction_logits = outputs.prediction_logits
-        >>> sop_logits = outputs.sop_logits
-        ```"""
-
-        outputs = self.albert(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=training,
-        )
-        sequence_output, pooled_output = outputs[:2]
-        prediction_scores = self.predictions(hidden_states=sequence_output)
-        sop_scores = self.sop_classifier(pooled_output=pooled_output, training=training)
-        total_loss = None
-
-        if labels is not None and sentence_order_label is not None:
-            d_labels = {"labels": labels}
-            d_labels["sentence_order_label"] = sentence_order_label
-            total_loss = self.hf_compute_loss(labels=d_labels, logits=(prediction_scores, sop_scores))
-
-        if not return_dict:
-            output = (prediction_scores, sop_scores) + outputs[2:]
-            return ((total_loss,) + output) if total_loss is not None else output
-
-        return TFAlbertForPreTrainingOutput(
-            loss=total_loss,
-            prediction_logits=prediction_scores,
-            sop_logits=sop_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-class TFAlbertSOPHead(tf.keras.layers.Layer):
-    def __init__(self, config: AlbertConfig, **kwargs):
-        super().__init__(**kwargs)
-
-        self.dropout = tf.keras.layers.Dropout(rate=config.classifier_dropout_prob)
-        self.classifier = tf.keras.layers.Dense(
-            units=config.num_labels,
-            kernel_initializer=get_initializer(config.initializer_range),
-            name="classifier",
-        )
-
-    def call(self, pooled_output: tf.Tensor, training: bool) -> tf.Tensor:
-        dropout_pooled_output = self.dropout(inputs=pooled_output, training=training)
-        logits = self.classifier(inputs=dropout_pooled_output)
-
-        return logits
-
-
-@add_start_docstrings("""Albert Model with a `language modeling` head on top.""", ALBERT_START_DOCSTRING)
-class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss):
-    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
-    _keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions.decoder.weight"]
-
-    def __init__(self, config: AlbertConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
-
-        self.albert = TFAlbertMainLayer(config, add_pooling_layer=False, name="albert")
-        self.predictions = TFAlbertMLMHead(config, input_embeddings=self.albert.embeddings, name="predictions")
-
-    def get_lm_head(self) -> tf.keras.layers.Layer:
-        return self.predictions
-
-    @unpack_inputs
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
-    def call(
-        self,
-        input_ids: TFModelInputType | None = None,
-        attention_mask: np.ndarray | tf.Tensor | None = None,
-        token_type_ids: np.ndarray | tf.Tensor | None = None,
-        position_ids: np.ndarray | tf.Tensor | None = None,
-        head_mask: np.ndarray | tf.Tensor | None = None,
-        inputs_embeds: np.ndarray | tf.Tensor | None = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        labels: np.ndarray | tf.Tensor | None = None,
-        training: Optional[bool] = False,
-    ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
-        r"""
-        labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
-            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
-            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> import tensorflow as tf
-        >>> from transformers import AutoTokenizer, TFAlbertForMaskedLM
-
-        >>> tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
-        >>> model = TFAlbertForMaskedLM.from_pretrained("albert-base-v2")
-
-        >>> # add mask_token
-        >>> inputs = tokenizer(f"The capital of [MASK] is Paris.", return_tensors="tf")
-        >>> logits = model(**inputs).logits
-
-        >>> # retrieve index of [MASK]
-        >>> mask_token_index = tf.where(inputs.input_ids == tokenizer.mask_token_id)[0][1]
-        >>> predicted_token_id = tf.math.argmax(logits[0, mask_token_index], axis=-1)
-        >>> tokenizer.decode(predicted_token_id)
-        'france'
-        ```
-
-        ```python
-        >>> labels = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
-        >>> labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
-        >>> outputs = model(**inputs, labels=labels)
-        >>> round(float(outputs.loss), 2)
-        0.81
-        ```
-        """
-        outputs = self.albert(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=training,
-        )
-        sequence_output = outputs[0]
-        prediction_scores = self.predictions(hidden_states=sequence_output, training=training)
-        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=prediction_scores)
-
-        if not return_dict:
-            output = (prediction_scores,) + outputs[2:]
-
-            return ((loss,) + output) if loss is not None else output
-
-        return TFMaskedLMOutput(
-            loss=loss,
-            logits=prediction_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
-    output) e.g. for GLUE tasks.
-    """,
-    ALBERT_START_DOCSTRING,
-)
-class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClassificationLoss):
-    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
-    _keys_to_ignore_on_load_unexpected = [r"predictions"]
-    _keys_to_ignore_on_load_missing = [r"dropout"]
-
-    def __init__(self, config: AlbertConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
-
-        self.num_labels = config.num_labels
-
-        self.albert = TFAlbertMainLayer(config, name="albert")
-        self.dropout = tf.keras.layers.Dropout(rate=config.classifier_dropout_prob)
-        self.classifier = tf.keras.layers.Dense(
-            units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
-        )
-
-    @unpack_inputs
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint="vumichien/albert-base-v2-imdb",
-        output_type=TFSequenceClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-        expected_output="'LABEL_1'",
-        expected_loss=0.12,
-    )
-    def call(
-        self,
-        input_ids: TFModelInputType | None = None,
-        attention_mask: np.ndarray | tf.Tensor | None = None,
-        token_type_ids: np.ndarray | tf.Tensor | None = None,
-        position_ids: np.ndarray | tf.Tensor | None = None,
-        head_mask: np.ndarray | tf.Tensor | None = None,
-        inputs_embeds: np.ndarray | tf.Tensor | None = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        labels: np.ndarray | tf.Tensor | None = None,
-        training: Optional[bool] = False,
-    ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
-        r"""
-        labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        outputs = self.albert(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=training,
-        )
-        pooled_output = outputs[1]
-        pooled_output = self.dropout(inputs=pooled_output, training=training)
-        logits = self.classifier(inputs=pooled_output)
-        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-
-            return ((loss,) + output) if loss is not None else output
-
-        return TFSequenceClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Albert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
-    Named-Entity-Recognition (NER) tasks.
-    """,
-    ALBERT_START_DOCSTRING,
-)
-class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificationLoss):
-    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
-    _keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"]
-    _keys_to_ignore_on_load_missing = [r"dropout"]
-
-    def __init__(self, config: AlbertConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
-
-        self.num_labels = config.num_labels
-
-        self.albert = TFAlbertMainLayer(config, add_pooling_layer=False, name="albert")
-        classifier_dropout_prob = (
-            config.classifier_dropout_prob
-            if config.classifier_dropout_prob is not None
-            else config.hidden_dropout_prob
-        )
-        self.dropout = tf.keras.layers.Dropout(rate=classifier_dropout_prob)
-        self.classifier = tf.keras.layers.Dense(
-            units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
-        )
-
-    @unpack_inputs
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TFTokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def call(
-        self,
-        input_ids: TFModelInputType | None = None,
-        attention_mask: np.ndarray | tf.Tensor | None = None,
-        token_type_ids: np.ndarray | tf.Tensor | None = None,
-        position_ids: np.ndarray | tf.Tensor | None = None,
-        head_mask: np.ndarray | tf.Tensor | None = None,
-        inputs_embeds: np.ndarray | tf.Tensor | None = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        labels: np.ndarray | tf.Tensor | None = None,
-        training: Optional[bool] = False,
-    ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
-        r"""
-        labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
-        """
-        outputs = self.albert(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=training,
-        )
-        sequence_output = outputs[0]
-        sequence_output = self.dropout(inputs=sequence_output, training=training)
-        logits = self.classifier(inputs=sequence_output)
-        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-
-            return ((loss,) + output) if loss is not None else output
-
-        return TFTokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Albert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
-    layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    ALBERT_START_DOCSTRING,
-)
-class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringLoss):
-    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
-    _keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"]
-
-    def __init__(self, config: AlbertConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
-
-        self.num_labels = config.num_labels
-
-        self.albert = TFAlbertMainLayer(config, add_pooling_layer=False, name="albert")
-        self.qa_outputs = tf.keras.layers.Dense(
-            units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
-        )
-
-    @unpack_inputs
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint="vumichien/albert-base-v2-squad2",
-        output_type=TFQuestionAnsweringModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-        qa_target_start_index=12,
-        qa_target_end_index=13,
-        expected_output="'a nice puppet'",
-        expected_loss=7.36,
-    )
-    def call(
-        self,
-        input_ids: TFModelInputType | None = None,
-        attention_mask: np.ndarray | tf.Tensor | None = None,
-        token_type_ids: np.ndarray | tf.Tensor | None = None,
-        position_ids: np.ndarray | tf.Tensor | None = None,
-        head_mask: np.ndarray | tf.Tensor | None = None,
-        inputs_embeds: np.ndarray | tf.Tensor | None = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        start_positions: np.ndarray | tf.Tensor | None = None,
-        end_positions: np.ndarray | tf.Tensor | None = None,
-        training: Optional[bool] = False,
-    ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
-        r"""
-        start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the start of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
-            Labels for position (index) of the end of the labelled span for computing the token classification loss.
-            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
-            are not taken into account for computing the loss.
-        """
-        outputs = self.albert(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=training,
-        )
-        sequence_output = outputs[0]
-        logits = self.qa_outputs(inputs=sequence_output)
-        start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1)
-        start_logits = tf.squeeze(input=start_logits, axis=-1)
-        end_logits = tf.squeeze(input=end_logits, axis=-1)
-        loss = None
-
-        if start_positions is not None and end_positions is not None:
-            labels = {"start_position": start_positions}
-            labels["end_position"] = end_positions
-            loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits))
-
-        if not return_dict:
-            output = (start_logits, end_logits) + outputs[2:]
-
-            return ((loss,) + output) if loss is not None else output
-
-        return TFQuestionAnsweringModelOutput(
-            loss=loss,
-            start_logits=start_logits,
-            end_logits=end_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Albert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
-    softmax) e.g. for RocStories/SWAG tasks.
-    """,
-    ALBERT_START_DOCSTRING,
-)
-class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
-    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
-    _keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"]
-    _keys_to_ignore_on_load_missing = [r"dropout"]
-
-    def __init__(self, config: AlbertConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
-
-        self.albert = TFAlbertMainLayer(config, name="albert")
-        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
-        self.classifier = tf.keras.layers.Dense(
-            units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
-        )
-
-    @unpack_inputs
-    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TFMultipleChoiceModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def call(
-        self,
-        input_ids: TFModelInputType | None = None,
-        attention_mask: np.ndarray | tf.Tensor | None = None,
-        token_type_ids: np.ndarray | tf.Tensor | None = None,
-        position_ids: np.ndarray | tf.Tensor | None = None,
-        head_mask: np.ndarray | tf.Tensor | None = None,
-        inputs_embeds: np.ndarray | tf.Tensor | None = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        labels: np.ndarray | tf.Tensor | None = None,
-        training: Optional[bool] = False,
-    ) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]:
-        r"""
-        labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
-            where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)
-        """
-
-        if input_ids is not None:
-            num_choices = shape_list(input_ids)[1]
-            seq_length = shape_list(input_ids)[2]
-        else:
-            num_choices = shape_list(inputs_embeds)[1]
-            seq_length = shape_list(inputs_embeds)[2]
-
-        flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
-        flat_attention_mask = (
-            tf.reshape(tensor=attention_mask, shape=(-1, seq_length)) if attention_mask is not None else None
-        )
-        flat_token_type_ids = (
-            tf.reshape(tensor=token_type_ids, shape=(-1, seq_length)) if token_type_ids is not None else None
-        )
-        flat_position_ids = (
-            tf.reshape(tensor=position_ids, shape=(-1, seq_length)) if position_ids is not None else None
-        )
-        flat_inputs_embeds = (
-            tf.reshape(tensor=inputs_embeds, shape=(-1, seq_length, shape_list(inputs_embeds)[3]))
-            if inputs_embeds is not None
-            else None
-        )
-        outputs = self.albert(
-            input_ids=flat_input_ids,
-            attention_mask=flat_attention_mask,
-            token_type_ids=flat_token_type_ids,
-            position_ids=flat_position_ids,
-            head_mask=head_mask,
-            inputs_embeds=flat_inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=training,
-        )
-        pooled_output = outputs[1]
-        pooled_output = self.dropout(inputs=pooled_output, training=training)
-        logits = self.classifier(inputs=pooled_output)
-        reshaped_logits = tf.reshape(tensor=logits, shape=(-1, num_choices))
-        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=reshaped_logits)
-
-        if not return_dict:
-            output = (reshaped_logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TFMultipleChoiceModelOutput(
-            loss=loss,
-            logits=reshaped_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/esm/openfold_utils/data_transforms.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/esm/openfold_utils/data_transforms.py
deleted file mode 100644
index 8d4c17589ae66df2a8fd0ccfe8d6e335004eed9a..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/esm/openfold_utils/data_transforms.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright 2021 AlQuraishi Laboratory
-# Copyright 2021 DeepMind Technologies Limited
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Dict
-
-import numpy as np
-import torch
-
-from . import residue_constants as rc
-from .tensor_utils import tensor_tree_map, tree_map
-
-
-def make_atom14_masks(protein: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
-    """Construct denser atom positions (14 dimensions instead of 37)."""
-    restype_atom14_to_atom37_list = []
-    restype_atom37_to_atom14_list = []
-    restype_atom14_mask_list = []
-
-    for rt in rc.restypes:
-        atom_names = rc.restype_name_to_atom14_names[rc.restype_1to3[rt]]
-        restype_atom14_to_atom37_list.append([(rc.atom_order[name] if name else 0) for name in atom_names])
-        atom_name_to_idx14 = {name: i for i, name in enumerate(atom_names)}
-        restype_atom37_to_atom14_list.append(
-            [(atom_name_to_idx14[name] if name in atom_name_to_idx14 else 0) for name in rc.atom_types]
-        )
-
-        restype_atom14_mask_list.append([(1.0 if name else 0.0) for name in atom_names])
-
-    # Add dummy mapping for restype 'UNK'
-    restype_atom14_to_atom37_list.append([0] * 14)
-    restype_atom37_to_atom14_list.append([0] * 37)
-    restype_atom14_mask_list.append([0.0] * 14)
-
-    restype_atom14_to_atom37 = torch.tensor(
-        restype_atom14_to_atom37_list,
-        dtype=torch.int32,
-        device=protein["aatype"].device,
-    )
-    restype_atom37_to_atom14 = torch.tensor(
-        restype_atom37_to_atom14_list,
-        dtype=torch.int32,
-        device=protein["aatype"].device,
-    )
-    restype_atom14_mask = torch.tensor(
-        restype_atom14_mask_list,
-        dtype=torch.float32,
-        device=protein["aatype"].device,
-    )
-    protein_aatype = protein["aatype"].to(torch.long)
-
-    # create the mapping for (residx, atom14) --> atom37, i.e. an array
-    # with shape (num_res, 14) containing the atom37 indices for this protein
-    residx_atom14_to_atom37 = restype_atom14_to_atom37[protein_aatype]
-    residx_atom14_mask = restype_atom14_mask[protein_aatype]
-
-    protein["atom14_atom_exists"] = residx_atom14_mask
-    protein["residx_atom14_to_atom37"] = residx_atom14_to_atom37.long()
-
-    # create the gather indices for mapping back
-    residx_atom37_to_atom14 = restype_atom37_to_atom14[protein_aatype]
-    protein["residx_atom37_to_atom14"] = residx_atom37_to_atom14.long()
-
-    # create the corresponding mask
-    restype_atom37_mask = torch.zeros([21, 37], dtype=torch.float32, device=protein["aatype"].device)
-    for restype, restype_letter in enumerate(rc.restypes):
-        restype_name = rc.restype_1to3[restype_letter]
-        atom_names = rc.residue_atoms[restype_name]
-        for atom_name in atom_names:
-            atom_type = rc.atom_order[atom_name]
-            restype_atom37_mask[restype, atom_type] = 1
-
-    residx_atom37_mask = restype_atom37_mask[protein_aatype]
-    protein["atom37_atom_exists"] = residx_atom37_mask
-
-    return protein
-
-
-def make_atom14_masks_np(batch: Dict[str, torch.Tensor]) -> Dict[str, np.ndarray]:
-    batch = tree_map(lambda n: torch.tensor(n, device=batch["aatype"].device), batch, np.ndarray)
-    out = tensor_tree_map(lambda t: np.array(t), make_atom14_masks(batch))
-    return out
diff --git a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/vendor.js b/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/vendor.js
deleted file mode 100644
index 099ffc10d6824ec56b99a0b2207658ea0dd1aad4..0000000000000000000000000000000000000000
--- a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/vendor.js
+++ /dev/null
@@ -1,14 +0,0 @@
-module.exports = {
-  prefix(prop) {
-    let match = prop.match(/^(-\w+-)/)
-    if (match) {
-      return match[0]
-    }
-
-    return ''
-  },
-
-  unprefixed(prop) {
-    return prop.replace(/^-\w+-/, '')
-  }
-}
diff --git a/spaces/ysharma/LLaVA_v1/llava/model/language_model/mpt/adapt_tokenizer.py b/spaces/ysharma/LLaVA_v1/llava/model/language_model/mpt/adapt_tokenizer.py
deleted file mode 100644
index e640c157e8f5581953c518df0611a423225ef598..0000000000000000000000000000000000000000
--- a/spaces/ysharma/LLaVA_v1/llava/model/language_model/mpt/adapt_tokenizer.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from typing import Union
-from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
-Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
-NUM_SENTINEL_TOKENS: int = 100
-
-def adapt_tokenizer_for_denoising(tokenizer: Tokenizer):
-    """Adds sentinel tokens and padding token (if missing).
-
-    Expands the tokenizer vocabulary to include sentinel tokens
-    used in mixture-of-denoiser tasks as well as a padding token.
-
-    All added tokens are added as special tokens. No tokens are
-    added if sentinel tokens and padding token already exist.
-    """
-    sentinels_to_add = [f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)]
-    tokenizer.add_tokens(sentinels_to_add, special_tokens=True)
-    if tokenizer.pad_token is None:
-        tokenizer.add_tokens('<pad>', special_tokens=True)
-        tokenizer.pad_token = '<pad>'
-        assert tokenizer.pad_token_id is not None
-    sentinels = ''.join([f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)])
-    _sentinel_token_ids = tokenizer(sentinels, add_special_tokens=False).input_ids
-    tokenizer.sentinel_token_ids = _sentinel_token_ids
-
-class AutoTokenizerForMOD(AutoTokenizer):
-    """AutoTokenizer + Adaptation for MOD.
-
-    A simple wrapper around AutoTokenizer to make instantiating
-    an MOD-adapted tokenizer a bit easier.
-
-    MOD-adapted tokenizers have sentinel tokens (e.g., <extra_id_0>),
-    a padding token, and a property to get the token ids of the
-    sentinel tokens.
-    """
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        """See `AutoTokenizer.from_pretrained` docstring."""
-        tokenizer = super().from_pretrained(*args, **kwargs)
-        adapt_tokenizer_for_denoising(tokenizer)
-        return tokenizer
\ No newline at end of file
diff --git a/spaces/ysharma/Low-rank-Adaptation/README.md b/spaces/ysharma/Low-rank-Adaptation/README.md
deleted file mode 100644
index c0c50c49082f00a71e59d7f29adba8faf67f64e6..0000000000000000000000000000000000000000
--- a/spaces/ysharma/Low-rank-Adaptation/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: LORA-Low-rank-Adaptation
-emoji: 📚
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.12
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/yxmnjxzx/PubMedGPT/README.md b/spaces/yxmnjxzx/PubMedGPT/README.md
deleted file mode 100644
index dca2bc138e1b11dd4cae5b5dcfd82e69cba1398b..0000000000000000000000000000000000000000
--- a/spaces/yxmnjxzx/PubMedGPT/README.md
+++ /dev/null
@@ -1,50 +0,0 @@
----
-title: PubMedGPT
-emoji: 🐢
-colorFrom: blue
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: amanatid/PubMedGPT
----
-# PubMebGPT
-A GPT  engine for the PubMed database. It concerns people that are associated withint the medical sector.
-
-
-
-## **Intro**
-We create a PubMedBot App  using Streamlit, llama_index, Langchain and OpenAI. The PudMed Bot is a Medical Scientist  who
-can answer you  questions that are related to Medical Science. For this reason you need an account on OpenAI and its associated
-api key. More specifically, you need to train our Medical Scientist based on the seacrh query,  number of related files and a 
-specific criterion. The criterion is based on three parameters a)Relevance, b)Publication Date and c)Journal Name.
-
-
-
-### **Quick Start**  
-- Input the OpenAI Api key and press Enter.
-- Input Query, number of files and choose Relevance, Publication Date and Journal Name and files are loaded .
-- The maximum number of files you can enter are 50. 
-- When you see the message "PubMed papers are loaded based on the criteria" you are ready to chat as a User.
-- Under the User ask the question and press the Submit button, wait a little and the PubMed Scientist will respond you.
-
-We are using the default chat model from OpenAI ChatGPT-4.
-
-### Issues
-In the case of a free OpenAI API key takes time to index the loaded pdf files since a free API key has a restricted [rate limits](https://platform.openai.com/docs/guides/rate-limits/overview). To make the process fast, you can use a paid API key. It is not so fast during the training along with the response from the Chatbot engine.
-
-### Future Prospectives 
-We  are looking to improve the layout and be more self-explanatory. Also, we will provide
-option to download the requested pdf files along with their abstracts. 
-
-### **References**
-1. https://github.com/emptycrown/llama-hub/blob/main/loader_hub/papers/arxiv/
-2. https://llamahub.ai/l/papers-pubmed
-3. https://github.com/mmz-001/knowledge_gpt/tree/main
-4. https://pubmed.ncbi.nlm.nih.gov/
-
-
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/zhan66/vits-simple-api/bert_vits2/transforms.py b/spaces/zhan66/vits-simple-api/bert_vits2/transforms.py
deleted file mode 100644
index 12dd72776a6b788932329da169155a289eb645bc..0000000000000000000000000000000000000000
--- a/spaces/zhan66/vits-simple-api/bert_vits2/transforms.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None,
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-        inputs=inputs,
-        unnormalized_widths=unnormalized_widths,
-        unnormalized_heights=unnormalized_heights,
-        unnormalized_derivatives=unnormalized_derivatives,
-        inverse=inverse,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-        **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/zhaoys/wfms-kuiwenc/src/app/manifest.ts b/spaces/zhaoys/wfms-kuiwenc/src/app/manifest.ts
deleted file mode 100644
index 118495dafe945819f2a8d5e46bdec5d314311e4d..0000000000000000000000000000000000000000
--- a/spaces/zhaoys/wfms-kuiwenc/src/app/manifest.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-import { MetadataRoute } from 'next'
-
-export default function manifest(): MetadataRoute.Manifest {
-  return {
-    name: 'BingAI',
-    short_name: 'BingAI',
-    start_url: '/',
-    display: 'standalone',
-    background_color: '#ffffff',
-    lang: 'zh-CN',
-    scope: '/',
-    theme_color: '#ffffff',
-    icons: [
-      {
-        src: '/favicon.svg',
-        sizes: 'any',
-        type: 'image/svg+xml',
-        purpose: 'any'
-      },
-    ]
-  }
-}
diff --git a/spaces/zomehwh/vits-models/text/cleaners.py b/spaces/zomehwh/vits-models/text/cleaners.py
deleted file mode 100644
index 68c9ad24d5a303b68a521fba2e8776c8cc867356..0000000000000000000000000000000000000000
--- a/spaces/zomehwh/vits-models/text/cleaners.py
+++ /dev/null
@@ -1,475 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-import pyopenjtalk
-from jamo import h2j, j2hcj
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba, cn2an
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('mrs', 'misess'),
-  ('mr', 'mister'),
-  ('dr', 'doctor'),
-  ('st', 'saint'),
-  ('co', 'company'),
-  ('jr', 'junior'),
-  ('maj', 'major'),
-  ('gen', 'general'),
-  ('drs', 'doctors'),
-  ('rev', 'reverend'),
-  ('lt', 'lieutenant'),
-  ('hon', 'honorable'),
-  ('sgt', 'sergeant'),
-  ('capt', 'captain'),
-  ('esq', 'esquire'),
-  ('ltd', 'limited'),
-  ('col', 'colonel'),
-  ('ft', 'fort'),
-]]
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
-  ('ㄳ', 'ㄱㅅ'),
-  ('ㄵ', 'ㄴㅈ'),
-  ('ㄶ', 'ㄴㅎ'),
-  ('ㄺ', 'ㄹㄱ'),
-  ('ㄻ', 'ㄹㅁ'),
-  ('ㄼ', 'ㄹㅂ'),
-  ('ㄽ', 'ㄹㅅ'),
-  ('ㄾ', 'ㄹㅌ'),
-  ('ㄿ', 'ㄹㅍ'),
-  ('ㅀ', 'ㄹㅎ'),
-  ('ㅄ', 'ㅂㅅ'),
-  ('ㅘ', 'ㅗㅏ'),
-  ('ㅙ', 'ㅗㅐ'),
-  ('ㅚ', 'ㅗㅣ'),
-  ('ㅝ', 'ㅜㅓ'),
-  ('ㅞ', 'ㅜㅔ'),
-  ('ㅟ', 'ㅜㅣ'),
-  ('ㅢ', 'ㅡㅣ'),
-  ('ㅑ', 'ㅣㅏ'),
-  ('ㅒ', 'ㅣㅐ'),
-  ('ㅕ', 'ㅣㅓ'),
-  ('ㅖ', 'ㅣㅔ'),
-  ('ㅛ', 'ㅣㅗ'),
-  ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('a', '에이'),
-  ('b', '비'),
-  ('c', '시'),
-  ('d', '디'),
-  ('e', '이'),
-  ('f', '에프'),
-  ('g', '지'),
-  ('h', '에이치'),
-  ('i', '아이'),
-  ('j', '제이'),
-  ('k', '케이'),
-  ('l', '엘'),
-  ('m', '엠'),
-  ('n', '엔'),
-  ('o', '오'),
-  ('p', '피'),
-  ('q', '큐'),
-  ('r', '아르'),
-  ('s', '에스'),
-  ('t', '티'),
-  ('u', '유'),
-  ('v', '브이'),
-  ('w', '더블유'),
-  ('x', '엑스'),
-  ('y', '와이'),
-  ('z', '제트')
-]]
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('a', 'ㄟˉ'),
-  ('b', 'ㄅㄧˋ'),
-  ('c', 'ㄙㄧˉ'),
-  ('d', 'ㄉㄧˋ'),
-  ('e', 'ㄧˋ'),
-  ('f', 'ㄝˊㄈㄨˋ'),
-  ('g', 'ㄐㄧˋ'),
-  ('h', 'ㄝˇㄑㄩˋ'),
-  ('i', 'ㄞˋ'),
-  ('j', 'ㄐㄟˋ'),
-  ('k', 'ㄎㄟˋ'),
-  ('l', 'ㄝˊㄛˋ'),
-  ('m', 'ㄝˊㄇㄨˋ'),
-  ('n', 'ㄣˉ'),
-  ('o', 'ㄡˉ'),
-  ('p', 'ㄆㄧˉ'),
-  ('q', 'ㄎㄧㄡˉ'),
-  ('r', 'ㄚˋ'),
-  ('s', 'ㄝˊㄙˋ'),
-  ('t', 'ㄊㄧˋ'),
-  ('u', 'ㄧㄡˉ'),
-  ('v', 'ㄨㄧˉ'),
-  ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
-  ('x', 'ㄝˉㄎㄨˋㄙˋ'),
-  ('y', 'ㄨㄞˋ'),
-  ('z', 'ㄗㄟˋ')
-]]
-
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('ㄅㄛ', 'p⁼wo'),
-  ('ㄆㄛ', 'pʰwo'),
-  ('ㄇㄛ', 'mwo'),
-  ('ㄈㄛ', 'fwo'),
-  ('ㄅ', 'p⁼'),
-  ('ㄆ', 'pʰ'),
-  ('ㄇ', 'm'),
-  ('ㄈ', 'f'),
-  ('ㄉ', 't⁼'),
-  ('ㄊ', 'tʰ'),
-  ('ㄋ', 'n'),
-  ('ㄌ', 'l'),
-  ('ㄍ', 'k⁼'),
-  ('ㄎ', 'kʰ'),
-  ('ㄏ', 'h'),
-  ('ㄐ', 'ʧ⁼'),
-  ('ㄑ', 'ʧʰ'),
-  ('ㄒ', 'ʃ'),
-  ('ㄓ', 'ʦ`⁼'),
-  ('ㄔ', 'ʦ`ʰ'),
-  ('ㄕ', 's`'),
-  ('ㄖ', 'ɹ`'),
-  ('ㄗ', 'ʦ⁼'),
-  ('ㄘ', 'ʦʰ'),
-  ('ㄙ', 's'),
-  ('ㄚ', 'a'),
-  ('ㄛ', 'o'),
-  ('ㄜ', 'ə'),
-  ('ㄝ', 'e'),
-  ('ㄞ', 'ai'),
-  ('ㄟ', 'ei'),
-  ('ㄠ', 'au'),
-  ('ㄡ', 'ou'),
-  ('ㄧㄢ', 'yeNN'),
-  ('ㄢ', 'aNN'),
-  ('ㄧㄣ', 'iNN'),
-  ('ㄣ', 'əNN'),
-  ('ㄤ', 'aNg'),
-  ('ㄧㄥ', 'iNg'),
-  ('ㄨㄥ', 'uNg'),
-  ('ㄩㄥ', 'yuNg'),
-  ('ㄥ', 'əNg'),
-  ('ㄦ', 'əɻ'),
-  ('ㄧ', 'i'),
-  ('ㄨ', 'u'),
-  ('ㄩ', 'ɥ'),
-  ('ˉ', '→'),
-  ('ˊ', '↑'),
-  ('ˇ', '↓↑'),
-  ('ˋ', '↓'),
-  ('˙', ''),
-  ('，', ','),
-  ('。', '.'),
-  ('！', '!'),
-  ('？', '?'),
-  ('—', '-')
-]]
-
-
-def expand_abbreviations(text):
-  for regex, replacement in _abbreviations:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def lowercase(text):
-  return text.lower()
-
-
-def collapse_whitespace(text):
-  return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
-  return unidecode(text)
-
-
-def japanese_to_romaji_with_accent(text):
-  '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-  sentences = re.split(_japanese_marks, text)
-  marks = re.findall(_japanese_marks, text)
-  text = ''
-  for i, sentence in enumerate(sentences):
-    if re.match(_japanese_characters, sentence):
-      if text!='':
-        text+=' '
-      labels = pyopenjtalk.extract_fullcontext(sentence)
-      for n, label in enumerate(labels):
-        phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-        if phoneme not in ['sil','pau']:
-          text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
-        else:
-          continue
-        n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-        a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-        a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-        a3 = int(re.search(r"\+(\d+)/", label).group(1))
-        if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
-          a2_next=-1
-        else:
-          a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-        # Accent phrase boundary
-        if a3 == 1 and a2_next == 1:
-          text += ' '
-        # Falling
-        elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
-          text += '↓'
-        # Rising
-        elif a2 == 1 and a2_next == 2:
-          text += '↑'
-    if i<len(marks):
-      text += unidecode(marks[i]).replace(' ','')
-  return text
-
-
-def latin_to_hangul(text):
-  for regex, replacement in _latin_to_hangul:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def divide_hangul(text):
-  for regex, replacement in _hangul_divided:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def hangul_number(num, sino=True):
-  '''Reference https://github.com/Kyubyong/g2pK'''
-  num = re.sub(',', '', num)
-
-  if num == '0':
-      return '영'
-  if not sino and num == '20':
-      return '스무'
-
-  digits = '123456789'
-  names = '일이삼사오육칠팔구'
-  digit2name = {d: n for d, n in zip(digits, names)}
-  
-  modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
-  decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
-  digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
-  digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
-  spelledout = []
-  for i, digit in enumerate(num):
-    i = len(num) - i - 1
-    if sino:
-      if i == 0:
-        name = digit2name.get(digit, '')
-      elif i == 1:
-        name = digit2name.get(digit, '') + '십'
-        name = name.replace('일십', '십')
-    else:
-      if i == 0:
-        name = digit2mod.get(digit, '')
-      elif i == 1:
-        name = digit2dec.get(digit, '')
-    if digit == '0':
-      if i % 4 == 0:
-        last_three = spelledout[-min(3, len(spelledout)):]
-        if ''.join(last_three) == '':
-          spelledout.append('')
-          continue
-      else:
-        spelledout.append('')
-        continue
-    if i == 2:
-      name = digit2name.get(digit, '') + '백'
-      name = name.replace('일백', '백')
-    elif i == 3:
-      name = digit2name.get(digit, '') + '천'
-      name = name.replace('일천', '천')
-    elif i == 4:
-      name = digit2name.get(digit, '') + '만'
-      name = name.replace('일만', '만')
-    elif i == 5:
-      name = digit2name.get(digit, '') + '십'
-      name = name.replace('일십', '십')
-    elif i == 6:
-      name = digit2name.get(digit, '') + '백'
-      name = name.replace('일백', '백')
-    elif i == 7:
-      name = digit2name.get(digit, '') + '천'
-      name = name.replace('일천', '천')
-    elif i == 8:
-      name = digit2name.get(digit, '') + '억'
-    elif i == 9:
-      name = digit2name.get(digit, '') + '십'
-    elif i == 10:
-      name = digit2name.get(digit, '') + '백'
-    elif i == 11:
-      name = digit2name.get(digit, '') + '천'
-    elif i == 12:
-      name = digit2name.get(digit, '') + '조'
-    elif i == 13:
-      name = digit2name.get(digit, '') + '십'
-    elif i == 14:
-      name = digit2name.get(digit, '') + '백'
-    elif i == 15:
-      name = digit2name.get(digit, '') + '천'
-    spelledout.append(name)
-  return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
-  '''Reference https://github.com/Kyubyong/g2pK'''
-  tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
-  for token in tokens:
-    num, classifier = token
-    if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
-      spelledout = hangul_number(num, sino=False)
-    else:
-      spelledout = hangul_number(num, sino=True)
-    text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
-  # digit by digit for remaining digits
-  digits = '0123456789'
-  names = '영일이삼사오육칠팔구'
-  for d, n in zip(digits, names):
-    text = text.replace(d, n)
-  return text
-
-
-def number_to_chinese(text):
-  numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-  for number in numbers:
-    text = text.replace(number, cn2an.an2cn(number),1)
-  return text
-
-
-def chinese_to_bopomofo(text):
-  text=text.replace('、','，').replace('；','，').replace('：','，')
-  words=jieba.lcut(text,cut_all=False)
-  text=''
-  for word in words:
-    bopomofos=lazy_pinyin(word,BOPOMOFO)
-    if not re.search('[\u4e00-\u9fff]',word):
-      text+=word
-      continue
-    for i in range(len(bopomofos)):
-      if re.match('[\u3105-\u3129]',bopomofos[i][-1]):
-        bopomofos[i]+='ˉ'
-    if text!='':
-      text+=' '
-    text+=''.join(bopomofos)
-  return text
-
-
-def latin_to_bopomofo(text):
-  for regex, replacement in _latin_to_bopomofo:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def bopomofo_to_romaji(text):
-  for regex, replacement in _bopomofo_to_romaji:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def basic_cleaners(text):
-  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def transliteration_cleaners(text):
-  '''Pipeline for non-English text that transliterates to ASCII.'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def japanese_cleaners(text):
-  text=japanese_to_romaji_with_accent(text)
-  if re.match('[A-Za-z]',text[-1]):
-    text += '.'
-  return text
-
-
-def japanese_cleaners2(text):
-  return japanese_cleaners(text).replace('ts','ʦ').replace('...','…')
-
-
-def korean_cleaners(text):
-  '''Pipeline for Korean text'''
-  text = latin_to_hangul(text)
-  text = number_to_hangul(text)
-  text = j2hcj(h2j(text))
-  text = divide_hangul(text)
-  if re.match('[\u3131-\u3163]',text[-1]):
-    text += '.'
-  return text
-
-
-def chinese_cleaners(text):
-  '''Pipeline for Chinese text'''
-  text=number_to_chinese(text)
-  text=chinese_to_bopomofo(text)
-  text=latin_to_bopomofo(text)
-  if re.match('[ˉˊˇˋ˙]',text[-1]):
-    text += '。'
-  return text
-
-
-def zh_ja_mixture_cleaners(text):
-  chinese_texts=re.findall(r'\[ZH\].*?\[ZH\]',text)
-  japanese_texts=re.findall(r'\[JA\].*?\[JA\]',text)
-  for chinese_text in chinese_texts:
-    cleaned_text=number_to_chinese(chinese_text[4:-4])
-    cleaned_text=chinese_to_bopomofo(cleaned_text)
-    cleaned_text=latin_to_bopomofo(cleaned_text)
-    cleaned_text=bopomofo_to_romaji(cleaned_text)
-    cleaned_text=re.sub('i[aoe]',lambda x:'y'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('u[aoəe]',lambda x:'w'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ`'+x.group(2),cleaned_text).replace('ɻ','ɹ`')
-    cleaned_text=re.sub('([ʦs][⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ'+x.group(2),cleaned_text)
-    text = text.replace(chinese_text,cleaned_text+' ',1)
-  for japanese_text in japanese_texts:
-    cleaned_text=japanese_to_romaji_with_accent(japanese_text[4:-4]).replace('ts','ʦ').replace('u','ɯ').replace('...','…')
-    text = text.replace(japanese_text,cleaned_text+' ',1)
-  text=text[:-1]
-  if len(text) and re.match('[A-Za-zɯɹəɥ→↓↑]',text[-1]):
-    text += '.'
-  return text
\ No newline at end of file