Update parquet files (step 17 of 296)

spaces/101-5/gpt4free/g4f/.v1/gpt4free/theb/README.md

@@ -1,14 +0,0 @@
-### Example: `theb` (use like openai pypi package) <a name="example-theb"></a>
-
-```python
-# import library
-from gpt4free import theb
-
-# simple streaming completion
-
-while True:
-	x = input()
-	for token in theb.Completion.create(x):
-		print(token, end='', flush=True)
-	print("")
-```

spaces/17TheWord/RealESRGAN/tests/test_dataset.py

@@ -1,151 +0,0 @@
-import pytest
-import yaml
-
-from realesrgan.data.realesrgan_dataset import RealESRGANDataset
-from realesrgan.data.realesrgan_paired_dataset import RealESRGANPairedDataset
-
-
-def test_realesrgan_dataset():
-
-    with open('tests/data/test_realesrgan_dataset.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    dataset = RealESRGANDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 2  # whether to read correct meta info
-    assert dataset.kernel_list == [
-        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
-    ]  # correct initialization the degradation configurations
-    assert dataset.betag_range2 == [0.5, 4]
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 400, 400)
-    assert result['kernel1'].shape == (21, 21)
-    assert result['kernel2'].shape == (21, 21)
-    assert result['sinc_kernel'].shape == (21, 21)
-    assert result['gt_path'] == 'tests/data/gt/baboon.png'
-
-    # ------------------ test lmdb backend -------------------- #
-    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
-    opt['io_backend']['type'] = 'lmdb'
-
-    dataset = RealESRGANDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
-    assert len(dataset.paths) == 2  # whether to read correct meta info
-    assert dataset.kernel_list == [
-        'iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'
-    ]  # correct initialization the degradation configurations
-    assert dataset.betag_range2 == [0.5, 4]
-
-    # test __getitem__
-    result = dataset.__getitem__(1)
-    # check returned keys
-    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 400, 400)
-    assert result['kernel1'].shape == (21, 21)
-    assert result['kernel2'].shape == (21, 21)
-    assert result['sinc_kernel'].shape == (21, 21)
-    assert result['gt_path'] == 'comic'
-
-    # ------------------ test with sinc_prob = 0 -------------------- #
-    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
-    opt['io_backend']['type'] = 'lmdb'
-    opt['sinc_prob'] = 0
-    opt['sinc_prob2'] = 0
-    opt['final_sinc_prob'] = 0
-    dataset = RealESRGANDataset(opt)
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'kernel1', 'kernel2', 'sinc_kernel', 'gt_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 400, 400)
-    assert result['kernel1'].shape == (21, 21)
-    assert result['kernel2'].shape == (21, 21)
-    assert result['sinc_kernel'].shape == (21, 21)
-    assert result['gt_path'] == 'baboon'
-
-    # ------------------ lmdb backend should have paths ends with lmdb -------------------- #
-    with pytest.raises(ValueError):
-        opt['dataroot_gt'] = 'tests/data/gt'
-        opt['io_backend']['type'] = 'lmdb'
-        dataset = RealESRGANDataset(opt)
-
-
-def test_realesrgan_paired_dataset():
-
-    with open('tests/data/test_realesrgan_paired_dataset.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    dataset = RealESRGANPairedDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 2  # whether to read correct meta info
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 128, 128)
-    assert result['lq'].shape == (3, 32, 32)
-    assert result['gt_path'] == 'tests/data/gt/baboon.png'
-    assert result['lq_path'] == 'tests/data/lq/baboon.png'
-
-    # ------------------ test lmdb backend -------------------- #
-    opt['dataroot_gt'] = 'tests/data/gt.lmdb'
-    opt['dataroot_lq'] = 'tests/data/lq.lmdb'
-    opt['io_backend']['type'] = 'lmdb'
-
-    dataset = RealESRGANPairedDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'lmdb'  # io backend
-    assert len(dataset) == 2  # whether to read correct meta info
-
-    # test __getitem__
-    result = dataset.__getitem__(1)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 128, 128)
-    assert result['lq'].shape == (3, 32, 32)
-    assert result['gt_path'] == 'comic'
-    assert result['lq_path'] == 'comic'
-
-    # ------------------ test paired_paths_from_folder -------------------- #
-    opt['dataroot_gt'] = 'tests/data/gt'
-    opt['dataroot_lq'] = 'tests/data/lq'
-    opt['io_backend'] = dict(type='disk')
-    opt['meta_info'] = None
-
-    dataset = RealESRGANPairedDataset(opt)
-    assert dataset.io_backend_opt['type'] == 'disk'  # io backend
-    assert len(dataset) == 2  # whether to read correct meta info
-
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 128, 128)
-    assert result['lq'].shape == (3, 32, 32)
-
-    # ------------------ test normalization -------------------- #
-    dataset.mean = [0.5, 0.5, 0.5]
-    dataset.std = [0.5, 0.5, 0.5]
-    # test __getitem__
-    result = dataset.__getitem__(0)
-    # check returned keys
-    expected_keys = ['gt', 'lq', 'gt_path', 'lq_path']
-    assert set(expected_keys).issubset(set(result.keys()))
-    # check shape and contents
-    assert result['gt'].shape == (3, 128, 128)
-    assert result['lq'].shape == (3, 32, 32)

spaces/17TheWord/vits-models/modules.py

@@ -1,388 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x

spaces/1gistliPinn/ChatGPT4/Examples/BMW Coding E-SYS V.3.24.3 Plken And PIN Utorrent LINK.md

@@ -1,7 +0,0 @@
-<br />
-<p>We propose nonadaptive protocols that can convert any classical message into a secure quantum bit string when used with a quantum channel. This task, which we call superdense coding, goes beyond the standard dense coding by demonstrating the possibility of using one qubit per transmitted photon to secure a long bit string (for example, a secret key for quantum key distribution). The task of superdense coding can also be achieved by using entanglement to secure a bit string.</p>
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-<p>We report the first demonstration of quantum Darwinism: the redundant encoding of information about a decohering system in its environment. For a system to be both decoherent and open, it must acquire memories that become increasingly redundant as the system is shrunk, then protect the redundancy for an extended period. We demonstrate the first effective implementation of the decoherence–protection cycle for quantum Darwinism. As a prototype, we have encoded information about the quantum state of a superconducting flux qubit into its resistance, a form of a memory highly resistant to radiation noise--a decoherence mechanism of a type that can be engineered in a variety of systems. This quantum memory, when transferred to a second qubit, protects the initial code from decoherence up to an appreciable length scale, due to quantum Darwinism.</p> 899543212b<br />
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-<br />

spaces/1line/AutoGPT/CODE_OF_CONDUCT.md

@@ -1,40 +0,0 @@
-# Code of Conduct for auto-gpt
-
-## 1. Purpose
-
-The purpose of this Code of Conduct is to provide guidelines for contributors to the auto-gpt project on GitHub. We aim to create a positive and inclusive environment where all participants can contribute and collaborate effectively. By participating in this project, you agree to abide by this Code of Conduct.
-
-## 2. Scope
-
-This Code of Conduct applies to all contributors, maintainers, and users of the auto-gpt project. It extends to all project spaces, including but not limited to issues, pull requests, code reviews, comments, and other forms of communication within the project.
-
-## 3. Our Standards
-
-We encourage the following behavior:
-
-* Being respectful and considerate to others
-* Actively seeking diverse perspectives
-* Providing constructive feedback and assistance
-* Demonstrating empathy and understanding
-
-We discourage the following behavior:
-
-* Harassment or discrimination of any kind
-* Disrespectful, offensive, or inappropriate language or content
-* Personal attacks or insults
-* Unwarranted criticism or negativity
-
-## 4. Reporting and Enforcement
-
-If you witness or experience any violations of this Code of Conduct, please report them to the project maintainers by email or other appropriate means. The maintainers will investigate and take appropriate action, which may include warnings, temporary or permanent bans, or other measures as necessary.
-
-Maintainers are responsible for ensuring compliance with this Code of Conduct and may take action to address any violations.
-
-## 5. Acknowledgements
-
-This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org/version/2/0/code_of_conduct.html).
-
-## 6. Contact
-
-If you have any questions or concerns, please contact the project maintainers.
-

spaces/1line/AutoGPT/main.py

@@ -1 +0,0 @@
-from autogpt import main

spaces/1phancelerku/anime-remove-background/Download APK for GTA Vice City and Experience the 80s on Your Phone.md

@@ -1,211 +0,0 @@
-
-<h1>Download APK for GTA Vice City: How to Play One of the Best Games Ever on Your Android Device</h1>
- <p>If you are a fan of open-world action-adventure games, you probably have heard of GTA Vice City. This game is one of the most iconic and influential titles in the history of gaming, and it is still loved by millions of players around the world. But did you know that you can play GTA Vice City on your Android device? In this article, we will show you how to download APK for GTA Vice City and enjoy this classic game on your smartphone or tablet.</p>
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- <h2>What is GTA Vice City?</h2>
- <h3>A brief introduction to the game and its features</h3>
- <p>GTA Vice City is a game developed by Rockstar Games and released in 2002 for PlayStation 2, Xbox, and PC. It is the sixth installment in the Grand Theft Auto series, and it is set in a fictional city based on Miami in the 1980s. The game follows the story of Tommy Vercetti, a former mobster who is sent to Vice City by his boss to establish a criminal empire. The game allows you to explore a vast and colorful open world, where you can drive various vehicles, use different weapons, interact with various characters, complete missions, and cause mayhem.</p>
- <h3>Why is GTA Vice City so popular and beloved?</h3>
- <p>GTA Vice City is widely regarded as one of the best games ever made, and it has received critical acclaim and commercial success. Some of the reasons why GTA Vice City is so popular and beloved are:</p>
- <ul>
-<li>It has a captivating and immersive storyline, with memorable characters, dialogues, and voice acting.</li>
-<li>It has a rich and vibrant atmosphere, with a stunning graphics, sound, and music that capture the essence of the 1980s culture.</li>
-<li>It has a fun and addictive gameplay, with a lot of variety, freedom, and replay value.</li>
-<li>It has a huge fan base, with a lot of mods, cheats, guides, and fan-made content available online.</li>
-</ul>
- <h2>How to download APK for GTA Vice City?</h2>
- <h3>The official way: buy the game from Google Play Store</h3>
- <p>The easiest and safest way to download APK for GTA Vice City is to buy the game from Google Play Store. The game was officially released for Android devices in 2012, and it costs $4.99. The game is compatible with most Android devices running Android 7.0 or higher, and it requires about 1.5 GB of free space. To buy the game from Google Play Store, you need to:</p>
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- <ol>
-<li>Open Google Play Store on your device and search for "GTA Vice City".</li>
-<li>Select the game from the results and tap on "Buy".</li>
-<li>Enter your payment details and confirm your purchase.</li>
-<li>Wait for the game to download and install on your device.</li>
-<li>Launch the game from your app drawer or home screen.</li>
-</ol>
- <h3>The unofficial way: download the APK file from a third-party source</h3>
- <p>If you don't want to pay for the game or if your device is not compatible with Google Play Store, you can also download APK for GTA Vice City from a third-party source. However, this method is not recommended, as it may expose your device to malware <p>The unofficial way: download the APK file from a third-party source</p>
- <p>If you don't want to pay for the game or if your device is not compatible with Google Play Store, you can also download APK for GTA Vice City from a third-party source. However, this method is not recommended, as it may expose your device to malware, viruses, or legal issues. If you still want to try this method, you need to follow these steps:</p>
- <ol>
-<li>Find a reliable and trustworthy website that offers the APK file for GTA Vice City. You can search online or use some of the links provided below . Make sure to read the reviews and ratings of the website before downloading anything.</li>
-<li>Download the APK file and the OBB data file from the website. The APK file is the application file that installs the game on your device, while the OBB data file contains the game data and assets. The size of these files may vary depending on the website, but they are usually around 1.5 GB in total.</li>
-<li>Enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on. This will allow you to install apps that are not from Google Play Store.</li>
-<li>Locate the downloaded APK file and OBB data file on your device using a file manager app. You can use any file manager app that you prefer, such as ES File Explorer or ZArchiver.</li>
-<li>Install the APK file by tapping on it and following the instructions on the screen. Do not launch the game yet.</li>
-<li>Extract the OBB data file using a file extractor app, such as ZArchiver. You will get a folder named "com.rockstargames.gtavc". Copy this folder and paste it in the Android/OBB directory on your device's internal storage.</li>
-<li>Launch the game from your app drawer or home screen and enjoy GTA Vice City on your Android device.</li>
-</ol>
- <h4>Pros and cons of using the unofficial way</h4>
- <p>Using the unofficial way to download APK for GTA Vice City has some pros and cons that you should be aware of before trying it. Here are some of them:</p>
- <table>
-<tr>
-<th>Pros</th>
-<th>Cons</th>
-</tr>
-<tr>
-<td>You can get the game for free without paying anything.</td>
-<td>You may violate the intellectual property rights of Rockstar Games and face legal consequences.</td>
-</tr>
-<tr>
-<td>You can play the game on devices that are not compatible with Google Play Store.</td>
-<td>You may encounter bugs, glitches, crashes, or performance issues while playing the game.</td>
-</tr>
-<tr>
-<td>You can access some mods, cheats, or hacks that are not available on the official version.</td>
-<td>You may risk infecting your device with malware, viruses, or spyware that can harm your data or privacy.</td>
-</tr>
-<tr>
-<td>You can update the game manually whenever a new version is available.</td>
-<td>You may not receive any official support or updates from Rockstar Games or Google Play Store.</td>
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-</table>
- <h4>How to install the APK file on your device</h4>
- <p>If you have followed the steps above correctly, you should have installed the APK file and the OBB data file on your device successfully. However, if you face any problems or errors while installing or launching the game, you can try some of these solutions:</p>
- <ul>
-<li>Make sure that you have enough free space on your device's internal storage. You need at least 1.5 GB of free space to install and run GTA Vice City smoothly.</li>
-<li>Make sure that you have downloaded the correct APK file and OBB data file for your device's architecture and Android version. You can check your device's specifications using an app like CPU-Z or Droid Hardware Info.</li>
-<li>Make sure that you have copied and pasted the OBB data folder in the right location on your device's internal storage. The folder name should be "com.rockstargames.gtavc" and it should be inside the Android/OBB directory.</li>
-<li>Make sure that you have enabled the installation of apps from unknown sources on your device's settings. You can disable it after installing GTA Vice City if you want to increase your security.</li>
-<li>Make sure that you have granted all the necessary permissions to GTA Vice City when launching it for the first time. The game may require access to your storage, network, location, microphone, or other features to function properly.</li>
-<li>If none of these solutions work, you may need to uninstall GTA Vice City and download it again from a different website or source. You may also need to restart your device or clear its cache before <p>If none of these solutions work, you may need to uninstall GTA Vice City and download it again from a different website or source. You may also need to restart your device or clear its cache before installing the game.</p>
- <h2>How to play GTA Vice City on your Android device?</h2>
- <h3>The basic controls and gameplay</h3>
- <p>Once you have installed GTA Vice City on your Android device, you can start playing it and enjoy the open-world action-adventure experience. The game has a simple and intuitive control scheme that adapts to your device's screen size and orientation. You can customize the controls to your liking by going to the Settings > Controls menu. Here are some of the basic controls and gameplay features of GTA Vice City:</p>
- <ul>
-<li>To move your character, use the virtual joystick on the left side of the screen. To run, tap and hold the joystick. To jump, swipe up on the joystick.</li>
-<li>To look around, swipe on the right side of the screen. To aim, tap and hold on the right side of the screen. To shoot, tap on the fire button on the bottom right corner of the screen.</li>
-<li>To enter or exit a vehicle, tap on the vehicle icon on the bottom left corner of the screen. To drive a vehicle, use the same joystick as for walking. To accelerate, tap and hold the gas pedal on the bottom right corner of the screen. To brake or reverse, tap and hold the brake pedal on the bottom left corner of the screen. To steer, tilt your device left or right.</li>
-<li>To access the map, pause menu, or mission menu, tap on the icons on the top right corner of the screen. To view your stats, inventory, or options, swipe down from the top of the screen.</li>
-<li>To switch between different weapons, tap on the weapon icon on the top left corner of the screen. To reload your weapon, swipe down on the weapon icon.</li>
-<li>To interact with other characters or objects, tap on them when they are highlighted. To answer a phone call or start a mission, tap on the green phone icon or blue marker respectively.</li>
-</ul>
- <h3>The best tips and tricks to enjoy the game</h3>
- <p>GTA Vice City is a game that offers a lot of fun and excitement, but it can also be challenging and frustrating at times. To help you enjoy the game more and avoid some common pitfalls, here are some of the best tips and tricks that you should know:</p>
- <ul>
-<li>Save your game frequently. You can save your game at any safe house that you own or rent by entering it and tapping on the cassette icon. Saving your game will also restore your health and armor.</li>
-<li>Collect hidden packages, rampages, and unique jumps. These are special items or challenges that are scattered around Vice City. They will reward you with money, weapons, vehicles, or bonuses when you find them or complete them.</li>
-<li>Use taxis, buses, or helicopters to travel faster. These are convenient ways to get around Vice City without having to drive yourself. You can hail a taxi by tapping on it when it is nearby. You can board a bus by entering it when it stops at a bus stop. You can fly a helicopter by finding one at a helipad or airport.</li>
-<li>Buy properties and businesses. These are valuable assets that will generate income for you over time. You can buy properties and businesses by going to their locations and tapping on the for sale sign. Some properties and businesses will also unlock new missions or features for you.</li>
-<li>Pay attention to the radio stations and news reports. These are entertaining sources of information that will keep you updated on what is happening in Vice City. They will also give you hints, tips, or warnings about certain events or situations.</li>
-</ul>
- <h4>How to use cheats and mods</h4>
- <p>If you want to spice up your gameplay or make things easier for yourself, you can use cheats and mods in GTA Vice City. Cheats are codes that you can enter during gameplay to activate various effects, such as unlimited health, ammo, money, or weapons. Mods are modifications that you can install to change or enhance certain aspects of the game, such as graphics, sound, gameplay, or content.</p>
- <p>To use cheats in GTA Vice City, you need to have a keyboard app installed on your device. You can use any keyboard app that you prefer, such as Gboard or SwiftKey. To enter a cheat code, simply open the keyboard app during gameplay and type in the code. You will see a confirmation message if the cheat is activated successfully. You can find a list of cheat codes online . Be careful when using cheats, as they may cause glitches or prevent you from completing some missions or achievements.</p>
- <p>To use mods in GTA Vice City, To use mods in GTA Vice City, you need to have a file manager app and a file extractor app installed on your device. You can use any apps that you prefer, such as ES File Explorer and ZArchiver. To install a mod, you need to follow these steps: <ol>
-<li>Find a reliable and trustworthy website that offers mods for GTA Vice City. You can search online or use some of the links provided below . Make sure to read the reviews and ratings of the website and the mod before downloading anything.</li>
-<li>Download the mod file from the website. The mod file may be in ZIP, RAR, or APK format, depending on the type of mod. The size of the mod file may vary depending on the mod, but they are usually around 100 MB or less.</li>
-<li>Locate the downloaded mod file on your device using a file manager app. You can use any file manager app that you prefer, such as ES File Explorer or ZArchiver.</li>
-<li>Extract the mod file using a file extractor app, such as ZArchiver. You will get a folder or a file that contains the mod data and instructions.</li>
-<li>Follow the instructions provided by the mod creator to install the mod on your device. The instructions may vary depending on the type of mod, but they usually involve copying and pasting some files or folders into the GTA Vice City directory on your device's internal storage.</li>
-<li>Launch GTA Vice City from your app drawer or home screen and enjoy the modded game.</li>
-</ol>
- <p>Be careful when using mods, as they may cause conflicts or errors with the original game or other mods. You may also need to backup your game data before installing any mods, in case something goes wrong. You can find a guide on how to backup your game data online .</p>
- <h4>How to save your progress and avoid bugs</h4>
- <p>GTA Vice City is a game that can be very fun and rewarding, but it can also be very frustrating and annoying if you lose your progress or encounter bugs. To prevent this from happening, you should follow some of these tips:</p>
- <ul>
-<li>Save your game frequently. You can save your game at any safe house that you own or rent by entering it and tapping on the cassette icon. Saving your game will also restore your health and armor.</li>
-<li>Avoid using cheats or mods that may interfere with the game's functionality or stability. Some cheats or mods may cause glitches, crashes, or corruption of your game data.</li>
-<li>Clear your game cache regularly. This will help improve your game's performance and reduce lag or loading times. To clear your game cache, go to Settings > Apps > GTA Vice City > Storage > Clear Cache.</li>
-<li>Update your game and device regularly. This will help fix any bugs or issues that may affect your game's quality or compatibility. To update your game, go to Google Play Store and check for any updates available. To update your device, go to Settings > System > System Update and check for any updates available.</li>
-<li>Contact Rockstar Games or Google Play Store for any technical support or feedback. If you have any problems or questions regarding GTA Vice City, you can contact Rockstar Games through their website  or Google Play Store through their help center . They may be able to help you resolve your issues or provide you with useful information.</li>
-</ul>
- <h2>Conclusion</h2>
- <h3>A summary of the main points and a call to action</h3>
- <p>GTA Vice City is one of the best games ever made, and it is still enjoyable and relevant today. You can play GTA Vice City on your Android device by downloading APK for GTA Vice City from Google Play Store or a third-party source. However, you should be aware of the pros and cons of each method, and follow some tips and tricks to install and play the game smoothly and safely. GTA Vice City is a game that offers a lot of fun and excitement, but it also requires some skill and patience. If you are ready to take on the challenge of becoming a criminal mastermind in Vice City, download APK for GTA Vice City today and start playing!</p>
- <p>If you liked this article, please share it with your friends and family who are also fans of GTA Vice City. You can also leave us a comment below and let us know what you think about GTA Vice City on Android devices. We would love to hear from you!</p>
- <h2>Frequently Asked Questions</h2>
- <h3>Q: How much does GTA Vice City cost on Google Play Store?</h3>
- <p>A: GTA Vice City costs $4.99 on Google Play Store.</p>
- <h3>Q: What are the minimum requirements to play GTA Vice City on Android devices?</h3>
- <p>A: The minimum requirements to play GTA Vice City on Android devices <p>A: The minimum requirements to play GTA Vice City on Android devices are:</p>
- <ul>
-<li>Android 7.0 or higher</li>
-<li>1.5 GB of free space</li>
-<li>1 GB of RAM</li>
-<li>Dual-core 1.2 GHz processor</li>
-<li>Adreno 220, Mali 400, PowerVR SGX540, or Tegra 3 GPU</li>
-</ul>
- <h3>Q: How can I restore my purchases or progress if I delete GTA Vice City or change my device?</h3>
- <p>A: If you have bought GTA Vice City from Google Play Store, you can restore your purchases or progress by logging in to the same Google account that you used to buy the game. You can also use Google Play Games to sync your game data across different devices. To do this, you need to:</p>
- <ol>
-<li>Open Google Play Games on your device and sign in with your Google account.</li>
-<li>Tap on the menu icon and select Settings.</li>
-<li>Enable the option "Automatically sign in to supported games".</li>
-<li>Launch GTA Vice City and tap on the Google Play Games icon on the top right corner of the screen.</li>
-<li>Select your Google account and follow the instructions on the screen.</li>
-</ol>
- <p>If you have downloaded GTA Vice City from a third-party source, you may not be able to restore your purchases or progress easily. You may need to backup your game data manually using a file manager app or a backup app before deleting or changing your device. You can find a guide on how to backup your game data online .</p>
- <h3>Q: How can I play GTA Vice City with a controller or a keyboard and mouse?</h3>
- <p>A: GTA Vice City supports various types of controllers and input devices, such as Bluetooth controllers, USB controllers, keyboards, and mice. To play GTA Vice City with a controller or a keyboard and mouse, you need to:</p>
- <ol>
-<li>Connect your controller or input device to your Android device using Bluetooth, USB, or an adapter.</li>
-<li>Launch GTA Vice City and go to Settings > Controls.</li>
-<li>Select the option "Controller" or "Keyboard & Mouse" depending on your device.</li>
-<li>Customize the buttons or keys according to your preference.</li>
-<li>Enjoy playing GTA Vice City with your controller or input device.</li>
-</ol>
- <h3>Q: How can I change the language or subtitles of GTA Vice City?</h3>
- <p>A: GTA Vice City supports various languages and subtitles, such as English, French, German, Italian, Spanish, Russian, Japanese, and more. To change the language or subtitles of GTA Vice City, you need to:</p>
- <ol>
-<li>Launch GTA Vice City and go to Settings > Display.</li>
-<li>Select the option "Language" or "Subtitles" depending on what you want to change.</li>
-<li>Select the language that you prefer from the list.</li>
-<li>Enjoy playing GTA Vice City in your chosen language or with subtitles.</li>
-</ol>
- <h3>Q: How can I contact Rockstar Games or Google Play Store for any technical support or feedback?</h3>
- <p>A: If you have any problems or questions regarding GTA Vice City, you can contact Rockstar Games or Google Play Store for any technical support or feedback. Here are some of the ways that you can contact them:</p>
- <ul>
-<li>To contact Rockstar Games, you can visit their website  and fill out a support request form. You can also email them at [email protected] or call them at +1-866-922-8694.</li>
-<li>To contact Google Play Store, you can visit their help center  and browse through their articles and FAQs. You can also email them at [email protected] or call them at +1-855-836-3987.</li>
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spaces/1phancelerku/anime-remove-background/Download My Talking Tom Mod Apk Versi Lama untuk Android dan iOS.md

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-<h4>Customize Tom's appearance and home</h4>
-<p>Another feature of My Talking Tom is that you can customize Tom's appearance and home according to your preferences. You can change his fur color, eye color, shape of his ears, nose, mouth, and tail. You can also change his home's wallpaper, furniture, decorations, and more. You can create your own unique style for Tom and his home.</p>
-<h4>Interact with Tom and hear him repeat your words</h4>
-<p>A fun feature of My Talking Tom is that you can interact with Tom and hear him repeat your words in a funny voice. You can talk to him using your device's microphone or type your messages on the screen. You can also record videos of your conversations with Tom and share them with your friends on social media.</p>
-<h4>Explore different mini-games and earn coins</h4>
-<p>Another feature of My Talking Tom is that you can explore different mini-games and earn coins. You can play games such as Flappy Tom, Bubble Shooter, Planet Hop, and more. You can also watch videos and complete tasks to earn more coins. You can use the coins to buy food, clothes, toys, and other items for Tom.</p>
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-<h3>A modified version of the game that offers more benefits</h3>
-<p>My Talking Tom Mod Apk Versi Lama is a modified version of the game that offers more benefits and features than the original game. It is also known as My Talking Tom Old Version Mod Apk or My Talking Tom Hack Apk. It is a free and safe download that does not require root access or any other permissions. It is compatible with most Android devices and does not affect the performance of your device.</p>
-<h3>Advantages of My Talking Tom Mod Apk Versi Lama</h3>
-<h4>No ads to interrupt your gameplay</h4>
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spaces/44ov41za8i/FreeVC/speaker_encoder/params_data.py

@@ -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
-

spaces/52Hz/SRMNet_AWGN_denoising/main_test_SRMNet.py

@@ -1,97 +0,0 @@
-import argparse
-import cv2
-import glob
-import numpy as np
-from collections import OrderedDict
-from skimage import img_as_ubyte
-import os
-import torch
-import requests
-from PIL import Image
-import torchvision.transforms.functional as TF
-import torch.nn.functional as F
-from natsort import natsorted
-from model.SRMNet import SRMNet
-
-def clean_folder(folder):
-    for filename in os.listdir(folder):
-        file_path = os.path.join(folder, filename)
-        try:
-            if os.path.isfile(file_path) or os.path.islink(file_path):
-                os.unlink(file_path)
-            elif os.path.isdir(file_path):
-                shutil.rmtree(file_path)
-        except Exception as e:
-            print('Failed to delete %s. Reason: %s' % (file_path, e))
-            
-def main():
-    parser = argparse.ArgumentParser(description='Demo Image Denoising')
-    parser.add_argument('--input_dir', default='test', type=str, help='Input images')
-    parser.add_argument('--result_dir', default='result', type=str, help='Directory for results')
-    parser.add_argument('--weights',
-                        default='experiments/pretrained_models/AWGN_denoising_SRMNet.pth', type=str,
-                        help='Path to weights')
-
-    args = parser.parse_args()
-
-    inp_dir = args.input_dir
-    out_dir = args.result_dir
-
-    os.makedirs(out_dir, exist_ok=True)
-
-    files = natsorted(glob.glob(os.path.join(inp_dir, '*')))
-
-    if len(files) == 0:
-        raise Exception(f"No files found at {inp_dir}")
-
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-    # Load corresponding models architecture and weights
-    model = SRMNet()
-    model = model.to(device)
-    model.eval()
-    load_checkpoint(model, args.weights)
-    
-
-    mul = 16
-    for file_ in files:
-        img = Image.open(file_).convert('RGB')
-        input_ = TF.to_tensor(img).unsqueeze(0).to(device)
-
-        # Pad the input if not_multiple_of 8
-        h, w = input_.shape[2], input_.shape[3]
-        H, W = ((h + mul) // mul) * mul, ((w + mul) // mul) * mul
-        padh = H - h if h % mul != 0 else 0
-        padw = W - w if w % mul != 0 else 0
-        input_ = F.pad(input_, (0, padw, 0, padh), 'reflect')
-        with torch.no_grad():
-            restored = model(input_)
-
-        restored = torch.clamp(restored, 0, 1)
-        restored = restored[:, :, :h, :w]
-        restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
-        restored = img_as_ubyte(restored[0])
-
-        f = os.path.splitext(os.path.split(file_)[-1])[0]
-        save_img((os.path.join(out_dir, f + '.png')), restored)
-        clean_folder(inp_dir)
-
-def save_img(filepath, img):
-    cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
-
-
-def load_checkpoint(model, weights):
-    checkpoint = torch.load(weights, map_location=torch.device('cpu'))
-    try:
-        model.load_state_dict(checkpoint["state_dict"])
-    except:
-        state_dict = checkpoint["state_dict"]
-        new_state_dict = OrderedDict()
-        for k, v in state_dict.items():
-            name = k[7:]  # remove `module.`
-            new_state_dict[name] = v
-        model.load_state_dict(new_state_dict)
-
-
-if __name__ == '__main__':
-    main()

spaces/AI-Dashboards/Graph.Visualization.Plotly.Sunbursts.Treemaps.WebGL/README.md

@@ -1,12 +0,0 @@
----
-title: 🧠Visualization Plotly Treemaps WebGL🩺
-emoji: 🩺Vis🧠
-colorFrom: indigo
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIConsultant/MusicGen/audiocraft/data/zip.py

@@ -1,76 +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.
-"""Utility for reading some info from inside a zip file.
-"""
-
-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:
-    """Hold a path of file within a zip file.
-
-    Args:
-        path (str): 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 (int): 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 (PathInZip): A PathInZip object representing the file to return a file-like object of.
-        mode (str): 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)

spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/open_clap/loss.py

@@ -1,307 +0,0 @@
-from multiprocessing.sharedctypes import Value
-import torch
-import torch.distributed.nn
-from torch import distributed as dist, nn as nn
-from torch.nn import functional as F
-import numpy as np
-from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score 
-
-try:
-    import horovod.torch as hvd
-except ImportError:
-    hvd = None
-
-
-def gather_features(
-        audio_features,
-        text_features,
-        audio_features_mlp=None, 
-        text_features_mlp=None,
-        local_loss=False,
-        gather_with_grad=False,
-        rank=0,
-        world_size=1,
-        use_horovod=False,
-        mlp_loss=False
-):
-    if use_horovod:
-        assert hvd is not None, 'Please install horovod'
-        if gather_with_grad:
-            all_audio_features = hvd.allgather(audio_features)
-            all_text_features = hvd.allgather(text_features)
-            if mlp_loss:
-                all_audio_features_mlp = hvd.allgather(audio_features_mlp)
-                all_text_features_mlp = hvd.allgather(text_features_mlp)
-        else:
-            with torch.no_grad():
-                all_audio_features = hvd.allgather(audio_features)
-                all_text_features = hvd.allgather(text_features)
-                if mlp_loss:
-                    all_audio_features_mlp = hvd.allgather(audio_features_mlp)
-                    all_text_features_mlp = hvd.allgather(text_features_mlp)
-            if not local_loss:
-                # ensure grads for local rank when all_* features don't have a gradient
-                gathered_audio_features = list(all_audio_features.chunk(world_size, dim=0))
-                gathered_text_features = list(all_text_features.chunk(world_size, dim=0))
-                gathered_audio_features[rank] = audio_features
-                gathered_text_features[rank] = text_features
-                all_audio_features = torch.cat(gathered_audio_features, dim=0)
-                all_text_features = torch.cat(gathered_text_features, dim=0)
-                if mlp_loss:
-                    gathered_audio_features_mlp = list(all_audio_features_mlp.chunk(world_size, dim=0))
-                    gathered_text_features_mlp = list(all_text_features_mlp.chunk(world_size, dim=0))
-                    gathered_audio_features_mlp[rank] = audio_features_mlp
-                    gathered_text_features_mlp[rank] = text_features_mlp
-                    all_audio_features_mlp = torch.cat(gathered_audio_features_mlp, dim=0)
-                    all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0)
-    else:
-        # We gather tensors from all gpus
-        if gather_with_grad:
-            all_audio_features = torch.cat(torch.distributed.nn.all_gather(audio_features), dim=0)
-            all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0)
-            if mlp_loss:
-                all_audio_features_mlp = torch.cat(torch.distributed.nn.all_gather(audio_features_mlp), dim=0)
-                all_text_features_mlp = torch.cat(torch.distributed.nn.all_gather(text_features_mlp), dim=0)
-        else:
-            gathered_audio_features = [torch.zeros_like(audio_features) for _ in range(world_size)]
-            gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)]
-            dist.all_gather(gathered_audio_features, audio_features)
-            dist.all_gather(gathered_text_features, text_features)
-            if mlp_loss:
-                gathered_audio_features_mlp = [torch.zeros_like(audio_features_mlp) for _ in range(world_size)]
-                gathered_text_features_mlp = [torch.zeros_like(text_features_mlp) for _ in range(world_size)]
-                dist.all_gather(gathered_audio_features_mlp, audio_features_mlp)
-                dist.all_gather(gathered_text_features_mlp, text_features_mlp)
-            if not local_loss:
-                # ensure grads for local rank when all_* features don't have a gradient
-                gathered_audio_features[rank] = audio_features
-                gathered_text_features[rank] = text_features
-                if mlp_loss:
-                    gathered_audio_features_mlp[rank] = audio_features_mlp
-                    gathered_text_features_mlp[rank] = text_features_mlp
-
-            all_audio_features = torch.cat(gathered_audio_features, dim=0)
-            all_text_features = torch.cat(gathered_text_features, dim=0)
-            if mlp_loss:
-                all_audio_features_mlp = torch.cat(gathered_audio_features_mlp, dim=0)
-                all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0)
-    if mlp_loss:
-        return all_audio_features, all_text_features, all_audio_features_mlp, all_text_features_mlp
-    else:
-        return all_audio_features, all_text_features
-
-class ClipLoss(nn.Module):
-
-    def __init__(
-            self,
-            local_loss=False,
-            gather_with_grad=False,
-            cache_labels=False,
-            rank=0,
-            world_size=1,
-            use_horovod=False,
-            mlp_loss=False,
-            weight_loss_kappa=0,
-    ):
-        super().__init__()
-        self.local_loss = local_loss
-        self.gather_with_grad = gather_with_grad
-        self.cache_labels = cache_labels
-        self.rank = rank
-        self.world_size = world_size
-        self.use_horovod = use_horovod
-        self.mlp_loss = mlp_loss
-        self.weighted_loss = bool(weight_loss_kappa!=0)
-        self.weight_loss_kappa = weight_loss_kappa
-        # cache state
-        self.prev_num_logits = 0
-        self.labels = {}
-
-    def forward(self, audio_features, text_features, logit_scale_a, logit_scale_t=None, audio_features_mlp=None, text_features_mlp=None):
-        device = audio_features.device
-        if self.mlp_loss:
-            if self.world_size > 1:
-                all_audio_features, all_text_features, all_audio_features_mlp, all_text_features_mlp = gather_features(
-                    audio_features=audio_features,text_features=text_features,
-                    audio_features_mlp=audio_features_mlp,text_features_mlp=text_features_mlp,
-                    local_loss=self.local_loss,gather_with_grad=self.gather_with_grad,
-                    rank=self.rank,world_size=self.world_size,use_horovod=self.use_horovod,
-                    mlp_loss=self.mlp_loss
-                )
-                if self.local_loss:
-                    a_logits_per_audio = logit_scale_a * audio_features @ all_text_features_mlp.T
-                    a_logits_per_text = logit_scale_a * text_features_mlp @ all_audio_features.T
-                    t_logits_per_audio = logit_scale_t * audio_features_mlp @ all_text_features.T
-                    t_logits_per_text = logit_scale_t * text_features @ all_audio_features_mlp.T
-                else:
-                    a_logits_per_audio = logit_scale_a * all_audio_features @ all_text_features_mlp.T
-                    a_logits_per_text = a_logits_per_audio.T
-                    t_logits_per_audio = logit_scale_t * all_audio_features_mlp @ all_text_features.T
-                    t_logits_per_text = t_logits_per_audio.T
-            else:
-                a_logits_per_audio = logit_scale_a * audio_features @ text_features_mlp.T
-                a_logits_per_text = logit_scale_a * text_features_mlp @ audio_features.T
-                t_logits_per_audio = logit_scale_t * audio_features_mlp @ text_features.T
-                t_logits_per_text = logit_scale_t * text_features @ audio_features_mlp.T
-
-            # calculated ground-truth and cache if enabled
-            num_logits = a_logits_per_audio.shape[0]
-            if self.prev_num_logits != num_logits or device not in self.labels:
-                labels = torch.arange(num_logits, device=device, dtype=torch.long)
-                if self.world_size > 1 and self.local_loss:
-                    labels = labels + num_logits * self.rank
-                if self.cache_labels:
-                    self.labels[device] = labels
-                    self.prev_num_logits = num_logits
-            else:
-                labels = self.labels[device]
-
-            if not self.weighted_loss:
-                total_loss = (
-                    F.cross_entropy(a_logits_per_audio, labels) +
-                    F.cross_entropy(a_logits_per_text, labels) + 
-                    F.cross_entropy(t_logits_per_audio, labels) +
-                    F.cross_entropy(t_logits_per_text, labels) 
-                    ) / 4
-            else:
-                audio_weight = (audio_features@audio_features.T).detach()
-                audio_weight = (torch.exp(torch.sum(audio_weight, axis=1)/(self.weight_loss_kappa*len(audio_weight)))).detach()
-                text_weight = (text_features@text_features.T).detach()
-                text_weight = (torch.exp(torch.sum(text_weight, axis=1)/(self.weight_loss_kappa*len(text_features)))).detach()
-                total_loss = (
-                    F.cross_entropy(a_logits_per_audio, labels, weight=audio_weight) +
-                    F.cross_entropy(a_logits_per_text, labels, weight=audio_weight) + 
-                    F.cross_entropy(t_logits_per_audio, labels, weight=text_weight) +
-                    F.cross_entropy(t_logits_per_text, labels, weight=text_weight) 
-                    ) / 4
-        else:
-            if self.world_size > 1:
-                all_audio_features, all_text_features = gather_features(
-                    audio_features=audio_features,text_features=text_features,
-                    local_loss=self.local_loss,gather_with_grad=self.gather_with_grad,
-                    rank=self.rank,world_size=self.world_size,use_horovod=self.use_horovod,
-                    mlp_loss=self.mlp_loss
-                )
-
-                if self.local_loss:
-                    logits_per_audio = logit_scale_a * audio_features @ all_text_features.T
-                    logits_per_text = logit_scale_a * text_features @ all_audio_features.T
-                else:
-                    logits_per_audio = logit_scale_a * all_audio_features @ all_text_features.T
-                    logits_per_text = logits_per_audio.T
-            else:
-                logits_per_audio = logit_scale_a * audio_features @ text_features.T
-                logits_per_text = logit_scale_a * text_features @ audio_features.T
-
-            # calculated ground-truth and cache if enabled
-            num_logits = logits_per_audio.shape[0]
-            if self.prev_num_logits != num_logits or device not in self.labels:
-                labels = torch.arange(num_logits, device=device, dtype=torch.long)
-                if self.world_size > 1 and self.local_loss:
-                    labels = labels + num_logits * self.rank
-                if self.cache_labels:
-                    self.labels[device] = labels
-                    self.prev_num_logits = num_logits
-            else:
-                labels = self.labels[device]
-            if not self.weighted_loss:
-                total_loss = (
-                    F.cross_entropy(logits_per_audio, labels) +
-                    F.cross_entropy(logits_per_text, labels)
-                    ) / 2
-            else:
-                audio_weight = (all_audio_features@all_audio_features.T).detach()
-                audio_weight = (torch.exp(torch.sum(audio_weight, axis=1)/(self.weight_loss_kappa*len(all_audio_features)))).detach()
-                text_weight = (all_text_features@all_text_features.T).detach()
-                text_weight = (torch.exp(torch.sum(text_weight, axis=1)/(self.weight_loss_kappa*len(all_text_features)))).detach()
-                total_loss = (
-                    F.cross_entropy(logits_per_audio, labels, weight=text_weight) +
-                    F.cross_entropy(logits_per_text, labels, weight=audio_weight)
-                    ) / 2
-        return total_loss
-
-def lp_gather_features(
-        pred,
-        target,
-        world_size=1,
-        use_horovod=False
-):
-    if use_horovod:
-        assert hvd is not None, 'Please install horovod'
-        with torch.no_grad():
-            all_preds = hvd.allgather(pred)
-            all_targets = hvd.allgath(target)
-    else:
-        gathered_preds = [torch.zeros_like(pred) for _ in range(world_size)]
-        gathered_targets = [torch.zeros_like(target) for _ in range(world_size)]
-
-        dist.all_gather(gathered_preds, pred)
-        dist.all_gather(gathered_targets, target)
-        all_preds = torch.cat(gathered_preds, dim=0)
-        all_targets = torch.cat(gathered_targets, dim=0)
-
-    return all_preds, all_targets
-
-
-def get_map(pred, target):
-    pred = torch.sigmoid(pred).numpy()
-    target = target.numpy()
-    return np.mean(average_precision_score(target, pred, average=None))
-
-def get_acc(pred, target):
-    pred = torch.argmax(pred,1).numpy()
-    target = torch.argmax(target,1).numpy()
-    return accuracy_score(target, pred)
-
-def get_mauc(pred, target):
-    pred = torch.sigmoid(pred).numpy()
-    target = target.numpy()
-    return np.mean(roc_auc_score(target, pred, average=None))
-
-
-class LPMetrics(object):
-    def __init__(self, metric_names = ['map','acc','mauc']):
-        self.metrics = []
-        for name in metric_names:
-            self.metrics.append(self.get_metric(name))
-        self.metric_names = metric_names
-
-    def get_metric(self,name):
-        if name == 'map':
-            return get_map
-        elif name == 'acc':
-            return get_acc
-        elif name == 'mauc':
-            return get_mauc
-        else:
-            raise ValueError(f'the metric should be at least one of [map, acc, mauc]')
-
-    def evaluate_mertics(self, pred, target):
-        metric_dict = {}
-        for i in range(len(self.metric_names)):
-            metric_dict[self.metric_names[i]] = self.metrics[i](pred, target)
-        return metric_dict
-
-
-def calc_celoss(pred, target):
-    target = torch.argmax(target, 1).long()
-    return nn.CrossEntropyLoss()(pred, target)
-
-
-class LPLoss(nn.Module):
-
-    def __init__(self, loss_name):
-        super().__init__()
-        if loss_name == 'bce':
-            self.loss_func = nn.BCEWithLogitsLoss()
-        elif loss_name == 'ce':
-            self.loss_func = calc_celoss
-        elif loss_name == 'mse':
-            self.loss_func = nn.MSELoss()
-        else:
-            raise ValueError(f'the loss func should be at least one of [bce, ce, mse]')
-
-    def forward(self, pred, target):
-        loss = self.loss_func(pred, target)
-        return loss
-        

spaces/AISloth/1.ChatGPT-HuggingFace-Spaces-NLP-Transformers-Pipeline/README.md

@@ -1,14 +0,0 @@
----
-title: ChatGPTwithAPI
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.20.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: awacke1/1.ChatGPT-HuggingFace-Spaces-NLP-Transformers-Pipeline
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Abhiboken12/travelling_ai/app.py

@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Theb.py

@@ -1,28 +0,0 @@
-import os
-import json
-import time
-import subprocess
-
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://theb.ai'
-model = ['gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-
-    path = os.path.dirname(os.path.realpath(__file__))
-    config = json.dumps({
-        'messages': messages,
-        'model': model}, separators=(',', ':'))
-    
-    cmd = ['python3', f'{path}/helpers/theb.py', config]
-
-    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-
-    for line in iter(p.stdout.readline, b''):
-        yield line.decode('utf-8')
-        
-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]])

spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/order/random.py

@@ -1,21 +0,0 @@
-from __future__ import annotations
-
-import random
-from typing import TYPE_CHECKING, List
-
-from . import order_registry as OrderRegistry
-from .base import BaseOrder
-
-if TYPE_CHECKING:
-    from agentverse.environments import BaseEnvironment
-
-
-@OrderRegistry.register("random")
-class RandomOrder(BaseOrder):
-    """
-    Order for random conversation
-    The agents speak in a random order
-    """
-
-    def get_next_agent_idx(self, environment: BaseEnvironment) -> List[int]:
-        return [random.randint(0, len(environment.agents) - 1)]

spaces/AlekseyCalvin/dreambooth-training3/README.md

@@ -1,14 +0,0 @@
----
-title: Dreambooth Training
-emoji: ☁️
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.11
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: multimodalart/dreambooth-training
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AlexWang/lama/saicinpainting/training/modules/spatial_transform.py

@@ -1,49 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from kornia.geometry.transform import rotate
-
-
-class LearnableSpatialTransformWrapper(nn.Module):
-    def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True):
-        super().__init__()
-        self.impl = impl
-        self.angle = torch.rand(1) * angle_init_range
-        if train_angle:
-            self.angle = nn.Parameter(self.angle, requires_grad=True)
-        self.pad_coef = pad_coef
-
-    def forward(self, x):
-        if torch.is_tensor(x):
-            return self.inverse_transform(self.impl(self.transform(x)), x)
-        elif isinstance(x, tuple):
-            x_trans = tuple(self.transform(elem) for elem in x)
-            y_trans = self.impl(x_trans)
-            return tuple(self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x))
-        else:
-            raise ValueError(f'Unexpected input type {type(x)}')
-
-    def transform(self, x):
-        height, width = x.shape[2:]
-        pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)
-        x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode='reflect')
-        x_padded_rotated = rotate(x_padded, angle=self.angle.to(x_padded))
-        return x_padded_rotated
-
-    def inverse_transform(self, y_padded_rotated, orig_x):
-        height, width = orig_x.shape[2:]
-        pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)
-
-        y_padded = rotate(y_padded_rotated, angle=-self.angle.to(y_padded_rotated))
-        y_height, y_width = y_padded.shape[2:]
-        y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w]
-        return y
-
-
-if __name__ == '__main__':
-    layer = LearnableSpatialTransformWrapper(nn.Identity())
-    x = torch.arange(2* 3 * 15 * 15).view(2, 3, 15, 15).float()
-    y = layer(x)
-    assert x.shape == y.shape
-    assert torch.allclose(x[:, :, 1:, 1:][:, :, :-1, :-1], y[:, :, 1:, 1:][:, :, :-1, :-1])
-    print('all ok')

spaces/Ameaou/academic-chatgpt3.1/crazy_functions/解析项目源代码.py

@@ -1,266 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-
-def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import os, copy
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-    from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-    msg = '正常'
-    inputs_array = []
-    inputs_show_user_array = []
-    history_array = []
-    sys_prompt_array = []
-    report_part_1 = []
-    
-    assert len(file_manifest) <= 512, "源文件太多（超过512个）, 请缩减输入文件的数量。或者，您也可以选择删除此行警告，并修改代码拆分file_manifest列表，从而实现分批次处理。"
-    ############################## <第一步，逐个文件分析，多线程> ##################################
-    for index, fp in enumerate(file_manifest):
-        # 读取文件
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-        prefix = "接下来请你逐文件分析下面的工程" if index==0 else ""
-        i_say = prefix + f'请对下面的程序文件做一个概述文件名是{os.path.relpath(fp, project_folder)}，文件代码是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
-        # 装载请求内容
-        inputs_array.append(i_say)
-        inputs_show_user_array.append(i_say_show_user)
-        history_array.append([])
-        sys_prompt_array.append("你是一个程序架构分析师，正在分析一个源代码项目。你的回答必须简单明了。")
-
-    # 文件读取完成，对每一个源代码文件，生成一个请求线程，发送到chatgpt进行分析
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array = inputs_array,
-        inputs_show_user_array = inputs_show_user_array,
-        history_array = history_array,
-        sys_prompt_array = sys_prompt_array,
-        llm_kwargs = llm_kwargs,
-        chatbot = chatbot,
-        show_user_at_complete = True
-    )
-
-    # 全部文件解析完成，结果写入文件，准备对工程源代码进行汇总分析
-    report_part_1 = copy.deepcopy(gpt_response_collection)
-    history_to_return = report_part_1
-    res = write_results_to_file(report_part_1)
-    chatbot.append(("完成？", "逐个文件分析已完成。" + res + "\n\n正在开始汇总。"))
-    yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面
-
-    ############################## <第二步，综合，单线程，分组+迭代处理> ##################################
-    batchsize = 16  # 10个文件为一组
-    report_part_2 = []
-    previous_iteration_files = []
-    last_iteration_result = ""
-    while True:
-        if len(file_manifest) == 0: break
-        this_iteration_file_manifest = file_manifest[:batchsize]
-        this_iteration_gpt_response_collection = gpt_response_collection[:batchsize*2]
-        file_rel_path = [os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)]
-        # 把“请对下面的程序文件做一个概述” 替换成 精简的 "文件名：{all_file[index]}"
-        for index, content in enumerate(this_iteration_gpt_response_collection):
-            if index%2==0: this_iteration_gpt_response_collection[index] = f"{file_rel_path[index//2]}" # 只保留文件名节省token
-        previous_iteration_files.extend([os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)])
-        previous_iteration_files_string = ', '.join(previous_iteration_files)
-        current_iteration_focus = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)])
-        i_say = f'根据以上分析，对程序的整体功能和构架重新做出概括。然后用一张markdown表格整理每个文件的功能（包括{previous_iteration_files_string}）。'
-        inputs_show_user = f'根据以上分析，对程序的整体功能和构架重新做出概括，由于输入长度限制，可能需要分组处理，本组文件为 {current_iteration_focus} + 已经汇总的文件组。'
-        this_iteration_history = copy.deepcopy(this_iteration_gpt_response_collection) 
-        this_iteration_history.append(last_iteration_result)
-        result = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot, 
-            history=this_iteration_history,   # 迭代之前的分析
-            sys_prompt="你是一个程序架构分析师，正在分析一个项目的源代码。")
-        report_part_2.extend([i_say, result])
-        last_iteration_result = result
-
-        file_manifest = file_manifest[batchsize:]
-        gpt_response_collection = gpt_response_collection[batchsize*2:]
-
-    ############################## <END> ##################################
-    history_to_return.extend(report_part_2)
-    res = write_results_to_file(history_to_return)
-    chatbot.append(("完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面
-
-
-@CatchException
-def 解析项目本身(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob
-    file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
-                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]+ \
-                    [f for f in glob.glob('./request_llm/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
-    project_folder = './'
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-@CatchException
-def 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析一个C项目的头文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)]  + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] #+ \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-@CatchException
-def 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)]  + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析一个Java项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.java', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.jar', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.sh', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何java文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析一个Rect项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.ts', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.tsx', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.js', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.jsx', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何Rect文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
-
-@CatchException
-def 解析一个Golang项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []  # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.go', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/go.mod', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/go.sum', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/go.work', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-    
-    
-@CatchException
-def 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.lua', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何lua文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)    
-    
-        
-@CatchException
-def 解析一个CSharp项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以��输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.cs', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.csproj', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何CSharp文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)    

spaces/Amrrs/fashion-aggregator-duplicated/README.md

@@ -1,13 +0,0 @@
----
-title: Fashion Aggregator
-emoji: 👕
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.9
-app_file: app.py
-pinned: false
-duplicated_from: ryparmar/fashion-aggregator
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/dataset/style_dataset.py

@@ -1,18 +0,0 @@
-from torch.utils.data import Dataset
-from torchvision import datasets
-import torchvision.transforms as T
-from torch.utils.data import DataLoader
-
-class StyleDataset(Dataset):
-    def __init__(self, datadir, batch_size, sampler, image_side_length=256, num_workers=2):
-        transform = T.Compose([
-            T.Resize(size=(image_side_length * 2, image_side_length * 2)),
-            T.RandomCrop(image_side_length),
-            T.ToTensor(),
-        ])
-
-        dataset = datasets.ImageFolder(datadir, transform=transform)
-        dataloader = DataLoader(dataset, batch_size=batch_size, sampler=sampler(len(dataset)),
-                                num_workers=num_workers)
-
-        return dataloader

spaces/Andy1621/uniformer_image_segmentation/configs/dmnet/README.md

@@ -1,39 +0,0 @@
-# Dynamic Multi-scale Filters for Semantic Segmentation
-
-## Introduction
-
-<!-- [ALGORITHM] -->
-
-```latex
-@InProceedings{He_2019_ICCV,
-author = {He, Junjun and Deng, Zhongying and Qiao, Yu},
-title = {Dynamic Multi-Scale Filters for Semantic Segmentation},
-booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
-month = {October},
-year = {2019}
-}
-```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                    | download                                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| DMNet  | R-50-D8  | 512x1024  |   40000 | 7.0      | 3.66           | 77.78 |         79.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes-20201214_115717.log.json)     |
-| DMNet  | R-101-D8 | 512x1024  |   40000 | 10.6     | 2.54           | 78.37 |         79.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes-20201214_115716.log.json) |
-| DMNet  | R-50-D8  | 769x769   |   40000 | 7.9      | 1.57           | 78.49 |         80.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes-20201214_115717.log.json)         |
-| DMNet  | R-101-D8 | 769x769   |   40000 | 12.0     | 1.01           | 77.62 |         78.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes-20201214_115718.log.json)     |
-| DMNet  | R-50-D8  | 512x1024  |   80000 | -        | -              | 79.07 |         80.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes-20201214_115716.log.json)     |
-| DMNet  | R-101-D8 | 512x1024  |   80000 | -        | -              | 79.64 |         80.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes-20201214_115705.log.json) |
-| DMNet  | R-50-D8  | 769x769   |   80000 | -        | -              | 79.22 |         80.55 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes-20201214_115718.log.json)         |
-| DMNet  | R-101-D8 | 769x769   |   80000 | -        | -              | 79.19 |         80.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes-20201214_115716.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                | download                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DMNet  | R-50-D8  | 512x512   |   80000 | 9.4      | 20.95          | 42.37 |         43.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k-20201214_115705.log.json)         |
-| DMNet  | R-101-D8 | 512x512   |   80000 | 13.0     | 13.88          | 45.34 |         46.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k-20201214_115704.log.json)     |
-| DMNet  | R-50-D8  | 512x512   |  160000 | -        | -              | 43.15 |         44.17 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k-20201214_115706.log.json)     |
-| DMNet  | R-101-D8 | 512x512   |  160000 | -        | -              | 45.42 |         46.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k-20201214_115705.log.json) |

spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './pspnet_r50-d8_512x1024_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Andy1621/uniformer_image_segmentation/configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = '../fcn/fcn_r101-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='open-mmlab://resnest101',
-    backbone=dict(
-        type='ResNeSt',
-        stem_channels=128,
-        radix=2,
-        reduction_factor=4,
-        avg_down_stride=True))

spaces/Anitha0531/SpeechtoText/README.md

@@ -1,13 +0,0 @@
----
-title: SpeechtoText
-emoji: 🌖
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/cnn/__init__.py

@@ -1,41 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .alexnet import AlexNet
-# yapf: disable
-from .bricks import (ACTIVATION_LAYERS, CONV_LAYERS, NORM_LAYERS,
-                     PADDING_LAYERS, PLUGIN_LAYERS, UPSAMPLE_LAYERS,
-                     ContextBlock, Conv2d, Conv3d, ConvAWS2d, ConvModule,
-                     ConvTranspose2d, ConvTranspose3d, ConvWS2d,
-                     DepthwiseSeparableConvModule, GeneralizedAttention,
-                     HSigmoid, HSwish, Linear, MaxPool2d, MaxPool3d,
-                     NonLocal1d, NonLocal2d, NonLocal3d, Scale, Swish,
-                     build_activation_layer, build_conv_layer,
-                     build_norm_layer, build_padding_layer, build_plugin_layer,
-                     build_upsample_layer, conv_ws_2d, is_norm)
-from .builder import MODELS, build_model_from_cfg
-# yapf: enable
-from .resnet import ResNet, make_res_layer
-from .utils import (INITIALIZERS, Caffe2XavierInit, ConstantInit, KaimingInit,
-                    NormalInit, PretrainedInit, TruncNormalInit, UniformInit,
-                    XavierInit, bias_init_with_prob, caffe2_xavier_init,
-                    constant_init, fuse_conv_bn, get_model_complexity_info,
-                    initialize, kaiming_init, normal_init, trunc_normal_init,
-                    uniform_init, xavier_init)
-from .vgg import VGG, make_vgg_layer
-
-__all__ = [
-    'AlexNet', 'VGG', 'make_vgg_layer', 'ResNet', 'make_res_layer',
-    'constant_init', 'xavier_init', 'normal_init', 'trunc_normal_init',
-    'uniform_init', 'kaiming_init', 'caffe2_xavier_init',
-    'bias_init_with_prob', 'ConvModule', 'build_activation_layer',
-    'build_conv_layer', 'build_norm_layer', 'build_padding_layer',
-    'build_upsample_layer', 'build_plugin_layer', 'is_norm', 'NonLocal1d',
-    'NonLocal2d', 'NonLocal3d', 'ContextBlock', 'HSigmoid', 'Swish', 'HSwish',
-    'GeneralizedAttention', 'ACTIVATION_LAYERS', 'CONV_LAYERS', 'NORM_LAYERS',
-    'PADDING_LAYERS', 'UPSAMPLE_LAYERS', 'PLUGIN_LAYERS', 'Scale',
-    'get_model_complexity_info', 'conv_ws_2d', 'ConvAWS2d', 'ConvWS2d',
-    'fuse_conv_bn', 'DepthwiseSeparableConvModule', 'Linear', 'Conv2d',
-    'ConvTranspose2d', 'MaxPool2d', 'ConvTranspose3d', 'MaxPool3d', 'Conv3d',
-    'initialize', 'INITIALIZERS', 'ConstantInit', 'XavierInit', 'NormalInit',
-    'TruncNormalInit', 'UniformInit', 'KaimingInit', 'PretrainedInit',
-    'Caffe2XavierInit', 'MODELS', 'build_model_from_cfg'
-]

spaces/Ariharasudhan/YoloV5/README.md

@@ -1,13 +0,0 @@
----
-title: Yolov5
-emoji: 🐨
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.44.1
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/distlib/wheel.py

@@ -1,1082 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2013-2020 Vinay Sajip.
-# Licensed to the Python Software Foundation under a contributor agreement.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-from __future__ import unicode_literals
-
-import base64
-import codecs
-import datetime
-from email import message_from_file
-import hashlib
-import json
-import logging
-import os
-import posixpath
-import re
-import shutil
-import sys
-import tempfile
-import zipfile
-
-from . import __version__, DistlibException
-from .compat import sysconfig, ZipFile, fsdecode, text_type, filter
-from .database import InstalledDistribution
-from .metadata import (Metadata, METADATA_FILENAME, WHEEL_METADATA_FILENAME,
-                       LEGACY_METADATA_FILENAME)
-from .util import (FileOperator, convert_path, CSVReader, CSVWriter, Cache,
-                   cached_property, get_cache_base, read_exports, tempdir,
-                   get_platform)
-from .version import NormalizedVersion, UnsupportedVersionError
-
-logger = logging.getLogger(__name__)
-
-cache = None    # created when needed
-
-if hasattr(sys, 'pypy_version_info'):  # pragma: no cover
-    IMP_PREFIX = 'pp'
-elif sys.platform.startswith('java'):  # pragma: no cover
-    IMP_PREFIX = 'jy'
-elif sys.platform == 'cli':  # pragma: no cover
-    IMP_PREFIX = 'ip'
-else:
-    IMP_PREFIX = 'cp'
-
-VER_SUFFIX = sysconfig.get_config_var('py_version_nodot')
-if not VER_SUFFIX:   # pragma: no cover
-    VER_SUFFIX = '%s%s' % sys.version_info[:2]
-PYVER = 'py' + VER_SUFFIX
-IMPVER = IMP_PREFIX + VER_SUFFIX
-
-ARCH = get_platform().replace('-', '_').replace('.', '_')
-
-ABI = sysconfig.get_config_var('SOABI')
-if ABI and ABI.startswith('cpython-'):
-    ABI = ABI.replace('cpython-', 'cp').split('-')[0]
-else:
-    def _derive_abi():
-        parts = ['cp', VER_SUFFIX]
-        if sysconfig.get_config_var('Py_DEBUG'):
-            parts.append('d')
-        if IMP_PREFIX == 'cp':
-            vi = sys.version_info[:2]
-            if vi < (3, 8):
-                wpm = sysconfig.get_config_var('WITH_PYMALLOC')
-                if wpm is None:
-                    wpm = True
-                if wpm:
-                    parts.append('m')
-                if vi < (3, 3):
-                    us = sysconfig.get_config_var('Py_UNICODE_SIZE')
-                    if us == 4 or (us is None and sys.maxunicode == 0x10FFFF):
-                        parts.append('u')
-        return ''.join(parts)
-    ABI = _derive_abi()
-    del _derive_abi
-
-FILENAME_RE = re.compile(r'''
-(?P<nm>[^-]+)
--(?P<vn>\d+[^-]*)
-(-(?P<bn>\d+[^-]*))?
--(?P<py>\w+\d+(\.\w+\d+)*)
--(?P<bi>\w+)
--(?P<ar>\w+(\.\w+)*)
-\.whl$
-''', re.IGNORECASE | re.VERBOSE)
-
-NAME_VERSION_RE = re.compile(r'''
-(?P<nm>[^-]+)
--(?P<vn>\d+[^-]*)
-(-(?P<bn>\d+[^-]*))?$
-''', re.IGNORECASE | re.VERBOSE)
-
-SHEBANG_RE = re.compile(br'\s*#![^\r\n]*')
-SHEBANG_DETAIL_RE = re.compile(br'^(\s*#!("[^"]+"|\S+))\s+(.*)$')
-SHEBANG_PYTHON = b'#!python'
-SHEBANG_PYTHONW = b'#!pythonw'
-
-if os.sep == '/':
-    to_posix = lambda o: o
-else:
-    to_posix = lambda o: o.replace(os.sep, '/')
-
-if sys.version_info[0] < 3:
-    import imp
-else:
-    imp = None
-    import importlib.machinery
-    import importlib.util
-
-def _get_suffixes():
-    if imp:
-        return [s[0] for s in imp.get_suffixes()]
-    else:
-        return importlib.machinery.EXTENSION_SUFFIXES
-
-def _load_dynamic(name, path):
-    # https://docs.python.org/3/library/importlib.html#importing-a-source-file-directly
-    if imp:
-        return imp.load_dynamic(name, path)
-    else:
-        spec = importlib.util.spec_from_file_location(name, path)
-        module = importlib.util.module_from_spec(spec)
-        sys.modules[name] = module
-        spec.loader.exec_module(module)
-        return module
-
-class Mounter(object):
-    def __init__(self):
-        self.impure_wheels = {}
-        self.libs = {}
-
-    def add(self, pathname, extensions):
-        self.impure_wheels[pathname] = extensions
-        self.libs.update(extensions)
-
-    def remove(self, pathname):
-        extensions = self.impure_wheels.pop(pathname)
-        for k, v in extensions:
-            if k in self.libs:
-                del self.libs[k]
-
-    def find_module(self, fullname, path=None):
-        if fullname in self.libs:
-            result = self
-        else:
-            result = None
-        return result
-
-    def load_module(self, fullname):
-        if fullname in sys.modules:
-            result = sys.modules[fullname]
-        else:
-            if fullname not in self.libs:
-                raise ImportError('unable to find extension for %s' % fullname)
-            result = _load_dynamic(fullname, self.libs[fullname])
-            result.__loader__ = self
-            parts = fullname.rsplit('.', 1)
-            if len(parts) > 1:
-                result.__package__ = parts[0]
-        return result
-
-_hook = Mounter()
-
-
-class Wheel(object):
-    """
-    Class to build and install from Wheel files (PEP 427).
-    """
-
-    wheel_version = (1, 1)
-    hash_kind = 'sha256'
-
-    def __init__(self, filename=None, sign=False, verify=False):
-        """
-        Initialise an instance using a (valid) filename.
-        """
-        self.sign = sign
-        self.should_verify = verify
-        self.buildver = ''
-        self.pyver = [PYVER]
-        self.abi = ['none']
-        self.arch = ['any']
-        self.dirname = os.getcwd()
-        if filename is None:
-            self.name = 'dummy'
-            self.version = '0.1'
-            self._filename = self.filename
-        else:
-            m = NAME_VERSION_RE.match(filename)
-            if m:
-                info = m.groupdict('')
-                self.name = info['nm']
-                # Reinstate the local version separator
-                self.version = info['vn'].replace('_', '-')
-                self.buildver = info['bn']
-                self._filename = self.filename
-            else:
-                dirname, filename = os.path.split(filename)
-                m = FILENAME_RE.match(filename)
-                if not m:
-                    raise DistlibException('Invalid name or '
-                                           'filename: %r' % filename)
-                if dirname:
-                    self.dirname = os.path.abspath(dirname)
-                self._filename = filename
-                info = m.groupdict('')
-                self.name = info['nm']
-                self.version = info['vn']
-                self.buildver = info['bn']
-                self.pyver = info['py'].split('.')
-                self.abi = info['bi'].split('.')
-                self.arch = info['ar'].split('.')
-
-    @property
-    def filename(self):
-        """
-        Build and return a filename from the various components.
-        """
-        if self.buildver:
-            buildver = '-' + self.buildver
-        else:
-            buildver = ''
-        pyver = '.'.join(self.pyver)
-        abi = '.'.join(self.abi)
-        arch = '.'.join(self.arch)
-        # replace - with _ as a local version separator
-        version = self.version.replace('-', '_')
-        return '%s-%s%s-%s-%s-%s.whl' % (self.name, version, buildver,
-                                         pyver, abi, arch)
-
-    @property
-    def exists(self):
-        path = os.path.join(self.dirname, self.filename)
-        return os.path.isfile(path)
-
-    @property
-    def tags(self):
-        for pyver in self.pyver:
-            for abi in self.abi:
-                for arch in self.arch:
-                    yield pyver, abi, arch
-
-    @cached_property
-    def metadata(self):
-        pathname = os.path.join(self.dirname, self.filename)
-        name_ver = '%s-%s' % (self.name, self.version)
-        info_dir = '%s.dist-info' % name_ver
-        wrapper = codecs.getreader('utf-8')
-        with ZipFile(pathname, 'r') as zf:
-            wheel_metadata = self.get_wheel_metadata(zf)
-            wv = wheel_metadata['Wheel-Version'].split('.', 1)
-            file_version = tuple([int(i) for i in wv])
-            # if file_version < (1, 1):
-                # fns = [WHEEL_METADATA_FILENAME, METADATA_FILENAME,
-                       # LEGACY_METADATA_FILENAME]
-            # else:
-                # fns = [WHEEL_METADATA_FILENAME, METADATA_FILENAME]
-            fns = [WHEEL_METADATA_FILENAME, LEGACY_METADATA_FILENAME]
-            result = None
-            for fn in fns:
-                try:
-                    metadata_filename = posixpath.join(info_dir, fn)
-                    with zf.open(metadata_filename) as bf:
-                        wf = wrapper(bf)
-                        result = Metadata(fileobj=wf)
-                        if result:
-                            break
-                except KeyError:
-                    pass
-            if not result:
-                raise ValueError('Invalid wheel, because metadata is '
-                                 'missing: looked in %s' % ', '.join(fns))
-        return result
-
-    def get_wheel_metadata(self, zf):
-        name_ver = '%s-%s' % (self.name, self.version)
-        info_dir = '%s.dist-info' % name_ver
-        metadata_filename = posixpath.join(info_dir, 'WHEEL')
-        with zf.open(metadata_filename) as bf:
-            wf = codecs.getreader('utf-8')(bf)
-            message = message_from_file(wf)
-        return dict(message)
-
-    @cached_property
-    def info(self):
-        pathname = os.path.join(self.dirname, self.filename)
-        with ZipFile(pathname, 'r') as zf:
-            result = self.get_wheel_metadata(zf)
-        return result
-
-    def process_shebang(self, data):
-        m = SHEBANG_RE.match(data)
-        if m:
-            end = m.end()
-            shebang, data_after_shebang = data[:end], data[end:]
-            # Preserve any arguments after the interpreter
-            if b'pythonw' in shebang.lower():
-                shebang_python = SHEBANG_PYTHONW
-            else:
-                shebang_python = SHEBANG_PYTHON
-            m = SHEBANG_DETAIL_RE.match(shebang)
-            if m:
-                args = b' ' + m.groups()[-1]
-            else:
-                args = b''
-            shebang = shebang_python + args
-            data = shebang + data_after_shebang
-        else:
-            cr = data.find(b'\r')
-            lf = data.find(b'\n')
-            if cr < 0 or cr > lf:
-                term = b'\n'
-            else:
-                if data[cr:cr + 2] == b'\r\n':
-                    term = b'\r\n'
-                else:
-                    term = b'\r'
-            data = SHEBANG_PYTHON + term + data
-        return data
-
-    def get_hash(self, data, hash_kind=None):
-        if hash_kind is None:
-            hash_kind = self.hash_kind
-        try:
-            hasher = getattr(hashlib, hash_kind)
-        except AttributeError:
-            raise DistlibException('Unsupported hash algorithm: %r' % hash_kind)
-        result = hasher(data).digest()
-        result = base64.urlsafe_b64encode(result).rstrip(b'=').decode('ascii')
-        return hash_kind, result
-
-    def write_record(self, records, record_path, archive_record_path):
-        records = list(records) # make a copy, as mutated
-        records.append((archive_record_path, '', ''))
-        with CSVWriter(record_path) as writer:
-            for row in records:
-                writer.writerow(row)
-
-    def write_records(self, info, libdir, archive_paths):
-        records = []
-        distinfo, info_dir = info
-        hasher = getattr(hashlib, self.hash_kind)
-        for ap, p in archive_paths:
-            with open(p, 'rb') as f:
-                data = f.read()
-            digest = '%s=%s' % self.get_hash(data)
-            size = os.path.getsize(p)
-            records.append((ap, digest, size))
-
-        p = os.path.join(distinfo, 'RECORD')
-        ap = to_posix(os.path.join(info_dir, 'RECORD'))
-        self.write_record(records, p, ap)
-        archive_paths.append((ap, p))
-
-    def build_zip(self, pathname, archive_paths):
-        with ZipFile(pathname, 'w', zipfile.ZIP_DEFLATED) as zf:
-            for ap, p in archive_paths:
-                logger.debug('Wrote %s to %s in wheel', p, ap)
-                zf.write(p, ap)
-
-    def build(self, paths, tags=None, wheel_version=None):
-        """
-        Build a wheel from files in specified paths, and use any specified tags
-        when determining the name of the wheel.
-        """
-        if tags is None:
-            tags = {}
-
-        libkey = list(filter(lambda o: o in paths, ('purelib', 'platlib')))[0]
-        if libkey == 'platlib':
-            is_pure = 'false'
-            default_pyver = [IMPVER]
-            default_abi = [ABI]
-            default_arch = [ARCH]
-        else:
-            is_pure = 'true'
-            default_pyver = [PYVER]
-            default_abi = ['none']
-            default_arch = ['any']
-
-        self.pyver = tags.get('pyver', default_pyver)
-        self.abi = tags.get('abi', default_abi)
-        self.arch = tags.get('arch', default_arch)
-
-        libdir = paths[libkey]
-
-        name_ver = '%s-%s' % (self.name, self.version)
-        data_dir = '%s.data' % name_ver
-        info_dir = '%s.dist-info' % name_ver
-
-        archive_paths = []
-
-        # First, stuff which is not in site-packages
-        for key in ('data', 'headers', 'scripts'):
-            if key not in paths:
-                continue
-            path = paths[key]
-            if os.path.isdir(path):
-                for root, dirs, files in os.walk(path):
-                    for fn in files:
-                        p = fsdecode(os.path.join(root, fn))
-                        rp = os.path.relpath(p, path)
-                        ap = to_posix(os.path.join(data_dir, key, rp))
-                        archive_paths.append((ap, p))
-                        if key == 'scripts' and not p.endswith('.exe'):
-                            with open(p, 'rb') as f:
-                                data = f.read()
-                            data = self.process_shebang(data)
-                            with open(p, 'wb') as f:
-                                f.write(data)
-
-        # Now, stuff which is in site-packages, other than the
-        # distinfo stuff.
-        path = libdir
-        distinfo = None
-        for root, dirs, files in os.walk(path):
-            if root == path:
-                # At the top level only, save distinfo for later
-                # and skip it for now
-                for i, dn in enumerate(dirs):
-                    dn = fsdecode(dn)
-                    if dn.endswith('.dist-info'):
-                        distinfo = os.path.join(root, dn)
-                        del dirs[i]
-                        break
-                assert distinfo, '.dist-info directory expected, not found'
-
-            for fn in files:
-                # comment out next suite to leave .pyc files in
-                if fsdecode(fn).endswith(('.pyc', '.pyo')):
-                    continue
-                p = os.path.join(root, fn)
-                rp = to_posix(os.path.relpath(p, path))
-                archive_paths.append((rp, p))
-
-        # Now distinfo. Assumed to be flat, i.e. os.listdir is enough.
-        files = os.listdir(distinfo)
-        for fn in files:
-            if fn not in ('RECORD', 'INSTALLER', 'SHARED', 'WHEEL'):
-                p = fsdecode(os.path.join(distinfo, fn))
-                ap = to_posix(os.path.join(info_dir, fn))
-                archive_paths.append((ap, p))
-
-        wheel_metadata = [
-            'Wheel-Version: %d.%d' % (wheel_version or self.wheel_version),
-            'Generator: distlib %s' % __version__,
-            'Root-Is-Purelib: %s' % is_pure,
-        ]
-        for pyver, abi, arch in self.tags:
-            wheel_metadata.append('Tag: %s-%s-%s' % (pyver, abi, arch))
-        p = os.path.join(distinfo, 'WHEEL')
-        with open(p, 'w') as f:
-            f.write('\n'.join(wheel_metadata))
-        ap = to_posix(os.path.join(info_dir, 'WHEEL'))
-        archive_paths.append((ap, p))
-
-        # sort the entries by archive path. Not needed by any spec, but it
-        # keeps the archive listing and RECORD tidier than they would otherwise
-        # be. Use the number of path segments to keep directory entries together,
-        # and keep the dist-info stuff at the end.
-        def sorter(t):
-            ap = t[0]
-            n = ap.count('/')
-            if '.dist-info' in ap:
-                n += 10000
-            return (n, ap)
-        archive_paths = sorted(archive_paths, key=sorter)
-
-        # Now, at last, RECORD.
-        # Paths in here are archive paths - nothing else makes sense.
-        self.write_records((distinfo, info_dir), libdir, archive_paths)
-        # Now, ready to build the zip file
-        pathname = os.path.join(self.dirname, self.filename)
-        self.build_zip(pathname, archive_paths)
-        return pathname
-
-    def skip_entry(self, arcname):
-        """
-        Determine whether an archive entry should be skipped when verifying
-        or installing.
-        """
-        # The signature file won't be in RECORD,
-        # and we  don't currently don't do anything with it
-        # We also skip directories, as they won't be in RECORD
-        # either. See:
-        #
-        # https://github.com/pypa/wheel/issues/294
-        # https://github.com/pypa/wheel/issues/287
-        # https://github.com/pypa/wheel/pull/289
-        #
-        return arcname.endswith(('/', '/RECORD.jws'))
-
-    def install(self, paths, maker, **kwargs):
-        """
-        Install a wheel to the specified paths. If kwarg ``warner`` is
-        specified, it should be a callable, which will be called with two
-        tuples indicating the wheel version of this software and the wheel
-        version in the file, if there is a discrepancy in the versions.
-        This can be used to issue any warnings to raise any exceptions.
-        If kwarg ``lib_only`` is True, only the purelib/platlib files are
-        installed, and the headers, scripts, data and dist-info metadata are
-        not written. If kwarg ``bytecode_hashed_invalidation`` is True, written
-        bytecode will try to use file-hash based invalidation (PEP-552) on
-        supported interpreter versions (CPython 2.7+).
-
-        The return value is a :class:`InstalledDistribution` instance unless
-        ``options.lib_only`` is True, in which case the return value is ``None``.
-        """
-
-        dry_run = maker.dry_run
-        warner = kwargs.get('warner')
-        lib_only = kwargs.get('lib_only', False)
-        bc_hashed_invalidation = kwargs.get('bytecode_hashed_invalidation', False)
-
-        pathname = os.path.join(self.dirname, self.filename)
-        name_ver = '%s-%s' % (self.name, self.version)
-        data_dir = '%s.data' % name_ver
-        info_dir = '%s.dist-info' % name_ver
-
-        metadata_name = posixpath.join(info_dir, LEGACY_METADATA_FILENAME)
-        wheel_metadata_name = posixpath.join(info_dir, 'WHEEL')
-        record_name = posixpath.join(info_dir, 'RECORD')
-
-        wrapper = codecs.getreader('utf-8')
-
-        with ZipFile(pathname, 'r') as zf:
-            with zf.open(wheel_metadata_name) as bwf:
-                wf = wrapper(bwf)
-                message = message_from_file(wf)
-            wv = message['Wheel-Version'].split('.', 1)
-            file_version = tuple([int(i) for i in wv])
-            if (file_version != self.wheel_version) and warner:
-                warner(self.wheel_version, file_version)
-
-            if message['Root-Is-Purelib'] == 'true':
-                libdir = paths['purelib']
-            else:
-                libdir = paths['platlib']
-
-            records = {}
-            with zf.open(record_name) as bf:
-                with CSVReader(stream=bf) as reader:
-                    for row in reader:
-                        p = row[0]
-                        records[p] = row
-
-            data_pfx = posixpath.join(data_dir, '')
-            info_pfx = posixpath.join(info_dir, '')
-            script_pfx = posixpath.join(data_dir, 'scripts', '')
-
-            # make a new instance rather than a copy of maker's,
-            # as we mutate it
-            fileop = FileOperator(dry_run=dry_run)
-            fileop.record = True    # so we can rollback if needed
-
-            bc = not sys.dont_write_bytecode    # Double negatives. Lovely!
-
-            outfiles = []   # for RECORD writing
-
-            # for script copying/shebang processing
-            workdir = tempfile.mkdtemp()
-            # set target dir later
-            # we default add_launchers to False, as the
-            # Python Launcher should be used instead
-            maker.source_dir = workdir
-            maker.target_dir = None
-            try:
-                for zinfo in zf.infolist():
-                    arcname = zinfo.filename
-                    if isinstance(arcname, text_type):
-                        u_arcname = arcname
-                    else:
-                        u_arcname = arcname.decode('utf-8')
-                    if self.skip_entry(u_arcname):
-                        continue
-                    row = records[u_arcname]
-                    if row[2] and str(zinfo.file_size) != row[2]:
-                        raise DistlibException('size mismatch for '
-                                               '%s' % u_arcname)
-                    if row[1]:
-                        kind, value = row[1].split('=', 1)
-                        with zf.open(arcname) as bf:
-                            data = bf.read()
-                        _, digest = self.get_hash(data, kind)
-                        if digest != value:
-                            raise DistlibException('digest mismatch for '
-                                                   '%s' % arcname)
-
-                    if lib_only and u_arcname.startswith((info_pfx, data_pfx)):
-                        logger.debug('lib_only: skipping %s', u_arcname)
-                        continue
-                    is_script = (u_arcname.startswith(script_pfx)
-                                 and not u_arcname.endswith('.exe'))
-
-                    if u_arcname.startswith(data_pfx):
-                        _, where, rp = u_arcname.split('/', 2)
-                        outfile = os.path.join(paths[where], convert_path(rp))
-                    else:
-                        # meant for site-packages.
-                        if u_arcname in (wheel_metadata_name, record_name):
-                            continue
-                        outfile = os.path.join(libdir, convert_path(u_arcname))
-                    if not is_script:
-                        with zf.open(arcname) as bf:
-                            fileop.copy_stream(bf, outfile)
-                        # Issue #147: permission bits aren't preserved. Using
-                        # zf.extract(zinfo, libdir) should have worked, but didn't,
-                        # see https://www.thetopsites.net/article/53834422.shtml
-                        # So ... manually preserve permission bits as given in zinfo
-                        if os.name == 'posix':
-                            # just set the normal permission bits
-                            os.chmod(outfile, (zinfo.external_attr >> 16) & 0x1FF)
-                        outfiles.append(outfile)
-                        # Double check the digest of the written file
-                        if not dry_run and row[1]:
-                            with open(outfile, 'rb') as bf:
-                                data = bf.read()
-                                _, newdigest = self.get_hash(data, kind)
-                                if newdigest != digest:
-                                    raise DistlibException('digest mismatch '
-                                                           'on write for '
-                                                           '%s' % outfile)
-                        if bc and outfile.endswith('.py'):
-                            try:
-                                pyc = fileop.byte_compile(outfile,
-                                                          hashed_invalidation=bc_hashed_invalidation)
-                                outfiles.append(pyc)
-                            except Exception:
-                                # Don't give up if byte-compilation fails,
-                                # but log it and perhaps warn the user
-                                logger.warning('Byte-compilation failed',
-                                               exc_info=True)
-                    else:
-                        fn = os.path.basename(convert_path(arcname))
-                        workname = os.path.join(workdir, fn)
-                        with zf.open(arcname) as bf:
-                            fileop.copy_stream(bf, workname)
-
-                        dn, fn = os.path.split(outfile)
-                        maker.target_dir = dn
-                        filenames = maker.make(fn)
-                        fileop.set_executable_mode(filenames)
-                        outfiles.extend(filenames)
-
-                if lib_only:
-                    logger.debug('lib_only: returning None')
-                    dist = None
-                else:
-                    # Generate scripts
-
-                    # Try to get pydist.json so we can see if there are
-                    # any commands to generate. If this fails (e.g. because
-                    # of a legacy wheel), log a warning but don't give up.
-                    commands = None
-                    file_version = self.info['Wheel-Version']
-                    if file_version == '1.0':
-                        # Use legacy info
-                        ep = posixpath.join(info_dir, 'entry_points.txt')
-                        try:
-                            with zf.open(ep) as bwf:
-                                epdata = read_exports(bwf)
-                            commands = {}
-                            for key in ('console', 'gui'):
-                                k = '%s_scripts' % key
-                                if k in epdata:
-                                    commands['wrap_%s' % key] = d = {}
-                                    for v in epdata[k].values():
-                                        s = '%s:%s' % (v.prefix, v.suffix)
-                                        if v.flags:
-                                            s += ' [%s]' % ','.join(v.flags)
-                                        d[v.name] = s
-                        except Exception:
-                            logger.warning('Unable to read legacy script '
-                                           'metadata, so cannot generate '
-                                           'scripts')
-                    else:
-                        try:
-                            with zf.open(metadata_name) as bwf:
-                                wf = wrapper(bwf)
-                                commands = json.load(wf).get('extensions')
-                                if commands:
-                                    commands = commands.get('python.commands')
-                        except Exception:
-                            logger.warning('Unable to read JSON metadata, so '
-                                           'cannot generate scripts')
-                    if commands:
-                        console_scripts = commands.get('wrap_console', {})
-                        gui_scripts = commands.get('wrap_gui', {})
-                        if console_scripts or gui_scripts:
-                            script_dir = paths.get('scripts', '')
-                            if not os.path.isdir(script_dir):
-                                raise ValueError('Valid script path not '
-                                                 'specified')
-                            maker.target_dir = script_dir
-                            for k, v in console_scripts.items():
-                                script = '%s = %s' % (k, v)
-                                filenames = maker.make(script)
-                                fileop.set_executable_mode(filenames)
-
-                            if gui_scripts:
-                                options = {'gui': True }
-                                for k, v in gui_scripts.items():
-                                    script = '%s = %s' % (k, v)
-                                    filenames = maker.make(script, options)
-                                    fileop.set_executable_mode(filenames)
-
-                    p = os.path.join(libdir, info_dir)
-                    dist = InstalledDistribution(p)
-
-                    # Write SHARED
-                    paths = dict(paths)     # don't change passed in dict
-                    del paths['purelib']
-                    del paths['platlib']
-                    paths['lib'] = libdir
-                    p = dist.write_shared_locations(paths, dry_run)
-                    if p:
-                        outfiles.append(p)
-
-                    # Write RECORD
-                    dist.write_installed_files(outfiles, paths['prefix'],
-                                               dry_run)
-                return dist
-            except Exception:  # pragma: no cover
-                logger.exception('installation failed.')
-                fileop.rollback()
-                raise
-            finally:
-                shutil.rmtree(workdir)
-
-    def _get_dylib_cache(self):
-        global cache
-        if cache is None:
-            # Use native string to avoid issues on 2.x: see Python #20140.
-            base = os.path.join(get_cache_base(), str('dylib-cache'),
-                                '%s.%s' % sys.version_info[:2])
-            cache = Cache(base)
-        return cache
-
-    def _get_extensions(self):
-        pathname = os.path.join(self.dirname, self.filename)
-        name_ver = '%s-%s' % (self.name, self.version)
-        info_dir = '%s.dist-info' % name_ver
-        arcname = posixpath.join(info_dir, 'EXTENSIONS')
-        wrapper = codecs.getreader('utf-8')
-        result = []
-        with ZipFile(pathname, 'r') as zf:
-            try:
-                with zf.open(arcname) as bf:
-                    wf = wrapper(bf)
-                    extensions = json.load(wf)
-                    cache = self._get_dylib_cache()
-                    prefix = cache.prefix_to_dir(pathname)
-                    cache_base = os.path.join(cache.base, prefix)
-                    if not os.path.isdir(cache_base):
-                        os.makedirs(cache_base)
-                    for name, relpath in extensions.items():
-                        dest = os.path.join(cache_base, convert_path(relpath))
-                        if not os.path.exists(dest):
-                            extract = True
-                        else:
-                            file_time = os.stat(dest).st_mtime
-                            file_time = datetime.datetime.fromtimestamp(file_time)
-                            info = zf.getinfo(relpath)
-                            wheel_time = datetime.datetime(*info.date_time)
-                            extract = wheel_time > file_time
-                        if extract:
-                            zf.extract(relpath, cache_base)
-                        result.append((name, dest))
-            except KeyError:
-                pass
-        return result
-
-    def is_compatible(self):
-        """
-        Determine if a wheel is compatible with the running system.
-        """
-        return is_compatible(self)
-
-    def is_mountable(self):
-        """
-        Determine if a wheel is asserted as mountable by its metadata.
-        """
-        return True # for now - metadata details TBD
-
-    def mount(self, append=False):
-        pathname = os.path.abspath(os.path.join(self.dirname, self.filename))
-        if not self.is_compatible():
-            msg = 'Wheel %s not compatible with this Python.' % pathname
-            raise DistlibException(msg)
-        if not self.is_mountable():
-            msg = 'Wheel %s is marked as not mountable.' % pathname
-            raise DistlibException(msg)
-        if pathname in sys.path:
-            logger.debug('%s already in path', pathname)
-        else:
-            if append:
-                sys.path.append(pathname)
-            else:
-                sys.path.insert(0, pathname)
-            extensions = self._get_extensions()
-            if extensions:
-                if _hook not in sys.meta_path:
-                    sys.meta_path.append(_hook)
-                _hook.add(pathname, extensions)
-
-    def unmount(self):
-        pathname = os.path.abspath(os.path.join(self.dirname, self.filename))
-        if pathname not in sys.path:
-            logger.debug('%s not in path', pathname)
-        else:
-            sys.path.remove(pathname)
-            if pathname in _hook.impure_wheels:
-                _hook.remove(pathname)
-            if not _hook.impure_wheels:
-                if _hook in sys.meta_path:
-                    sys.meta_path.remove(_hook)
-
-    def verify(self):
-        pathname = os.path.join(self.dirname, self.filename)
-        name_ver = '%s-%s' % (self.name, self.version)
-        data_dir = '%s.data' % name_ver
-        info_dir = '%s.dist-info' % name_ver
-
-        metadata_name = posixpath.join(info_dir, LEGACY_METADATA_FILENAME)
-        wheel_metadata_name = posixpath.join(info_dir, 'WHEEL')
-        record_name = posixpath.join(info_dir, 'RECORD')
-
-        wrapper = codecs.getreader('utf-8')
-
-        with ZipFile(pathname, 'r') as zf:
-            with zf.open(wheel_metadata_name) as bwf:
-                wf = wrapper(bwf)
-                message = message_from_file(wf)
-            wv = message['Wheel-Version'].split('.', 1)
-            file_version = tuple([int(i) for i in wv])
-            # TODO version verification
-
-            records = {}
-            with zf.open(record_name) as bf:
-                with CSVReader(stream=bf) as reader:
-                    for row in reader:
-                        p = row[0]
-                        records[p] = row
-
-            for zinfo in zf.infolist():
-                arcname = zinfo.filename
-                if isinstance(arcname, text_type):
-                    u_arcname = arcname
-                else:
-                    u_arcname = arcname.decode('utf-8')
-                # See issue #115: some wheels have .. in their entries, but
-                # in the filename ... e.g. __main__..py ! So the check is
-                # updated to look for .. in the directory portions
-                p = u_arcname.split('/')
-                if '..' in p:
-                    raise DistlibException('invalid entry in '
-                                           'wheel: %r' % u_arcname)
-
-                if self.skip_entry(u_arcname):
-                    continue
-                row = records[u_arcname]
-                if row[2] and str(zinfo.file_size) != row[2]:
-                    raise DistlibException('size mismatch for '
-                                           '%s' % u_arcname)
-                if row[1]:
-                    kind, value = row[1].split('=', 1)
-                    with zf.open(arcname) as bf:
-                        data = bf.read()
-                    _, digest = self.get_hash(data, kind)
-                    if digest != value:
-                        raise DistlibException('digest mismatch for '
-                                               '%s' % arcname)
-
-    def update(self, modifier, dest_dir=None, **kwargs):
-        """
-        Update the contents of a wheel in a generic way. The modifier should
-        be a callable which expects a dictionary argument: its keys are
-        archive-entry paths, and its values are absolute filesystem paths
-        where the contents the corresponding archive entries can be found. The
-        modifier is free to change the contents of the files pointed to, add
-        new entries and remove entries, before returning. This method will
-        extract the entire contents of the wheel to a temporary location, call
-        the modifier, and then use the passed (and possibly updated)
-        dictionary to write a new wheel. If ``dest_dir`` is specified, the new
-        wheel is written there -- otherwise, the original wheel is overwritten.
-
-        The modifier should return True if it updated the wheel, else False.
-        This method returns the same value the modifier returns.
-        """
-
-        def get_version(path_map, info_dir):
-            version = path = None
-            key = '%s/%s' % (info_dir, LEGACY_METADATA_FILENAME)
-            if key not in path_map:
-                key = '%s/PKG-INFO' % info_dir
-            if key in path_map:
-                path = path_map[key]
-                version = Metadata(path=path).version
-            return version, path
-
-        def update_version(version, path):
-            updated = None
-            try:
-                v = NormalizedVersion(version)
-                i = version.find('-')
-                if i < 0:
-                    updated = '%s+1' % version
-                else:
-                    parts = [int(s) for s in version[i + 1:].split('.')]
-                    parts[-1] += 1
-                    updated = '%s+%s' % (version[:i],
-                                         '.'.join(str(i) for i in parts))
-            except UnsupportedVersionError:
-                logger.debug('Cannot update non-compliant (PEP-440) '
-                             'version %r', version)
-            if updated:
-                md = Metadata(path=path)
-                md.version = updated
-                legacy = path.endswith(LEGACY_METADATA_FILENAME)
-                md.write(path=path, legacy=legacy)
-                logger.debug('Version updated from %r to %r', version,
-                             updated)
-
-        pathname = os.path.join(self.dirname, self.filename)
-        name_ver = '%s-%s' % (self.name, self.version)
-        info_dir = '%s.dist-info' % name_ver
-        record_name = posixpath.join(info_dir, 'RECORD')
-        with tempdir() as workdir:
-            with ZipFile(pathname, 'r') as zf:
-                path_map = {}
-                for zinfo in zf.infolist():
-                    arcname = zinfo.filename
-                    if isinstance(arcname, text_type):
-                        u_arcname = arcname
-                    else:
-                        u_arcname = arcname.decode('utf-8')
-                    if u_arcname == record_name:
-                        continue
-                    if '..' in u_arcname:
-                        raise DistlibException('invalid entry in '
-                                               'wheel: %r' % u_arcname)
-                    zf.extract(zinfo, workdir)
-                    path = os.path.join(workdir, convert_path(u_arcname))
-                    path_map[u_arcname] = path
-
-            # Remember the version.
-            original_version, _ = get_version(path_map, info_dir)
-            # Files extracted. Call the modifier.
-            modified = modifier(path_map, **kwargs)
-            if modified:
-                # Something changed - need to build a new wheel.
-                current_version, path = get_version(path_map, info_dir)
-                if current_version and (current_version == original_version):
-                    # Add or update local version to signify changes.
-                    update_version(current_version, path)
-                # Decide where the new wheel goes.
-                if dest_dir is None:
-                    fd, newpath = tempfile.mkstemp(suffix='.whl',
-                                                   prefix='wheel-update-',
-                                                   dir=workdir)
-                    os.close(fd)
-                else:
-                    if not os.path.isdir(dest_dir):
-                        raise DistlibException('Not a directory: %r' % dest_dir)
-                    newpath = os.path.join(dest_dir, self.filename)
-                archive_paths = list(path_map.items())
-                distinfo = os.path.join(workdir, info_dir)
-                info = distinfo, info_dir
-                self.write_records(info, workdir, archive_paths)
-                self.build_zip(newpath, archive_paths)
-                if dest_dir is None:
-                    shutil.copyfile(newpath, pathname)
-        return modified
-
-def _get_glibc_version():
-    import platform
-    ver = platform.libc_ver()
-    result = []
-    if ver[0] == 'glibc':
-        for s in ver[1].split('.'):
-            result.append(int(s) if s.isdigit() else 0)
-        result = tuple(result)
-    return result
-
-def compatible_tags():
-    """
-    Return (pyver, abi, arch) tuples compatible with this Python.
-    """
-    versions = [VER_SUFFIX]
-    major = VER_SUFFIX[0]
-    for minor in range(sys.version_info[1] - 1, - 1, -1):
-        versions.append(''.join([major, str(minor)]))
-
-    abis = []
-    for suffix in _get_suffixes():
-        if suffix.startswith('.abi'):
-            abis.append(suffix.split('.', 2)[1])
-    abis.sort()
-    if ABI != 'none':
-        abis.insert(0, ABI)
-    abis.append('none')
-    result = []
-
-    arches = [ARCH]
-    if sys.platform == 'darwin':
-        m = re.match(r'(\w+)_(\d+)_(\d+)_(\w+)$', ARCH)
-        if m:
-            name, major, minor, arch = m.groups()
-            minor = int(minor)
-            matches = [arch]
-            if arch in ('i386', 'ppc'):
-                matches.append('fat')
-            if arch in ('i386', 'ppc', 'x86_64'):
-                matches.append('fat3')
-            if arch in ('ppc64', 'x86_64'):
-                matches.append('fat64')
-            if arch in ('i386', 'x86_64'):
-                matches.append('intel')
-            if arch in ('i386', 'x86_64', 'intel', 'ppc', 'ppc64'):
-                matches.append('universal')
-            while minor >= 0:
-                for match in matches:
-                    s = '%s_%s_%s_%s' % (name, major, minor, match)
-                    if s != ARCH:   # already there
-                        arches.append(s)
-                minor -= 1
-
-    # Most specific - our Python version, ABI and arch
-    for abi in abis:
-        for arch in arches:
-            result.append((''.join((IMP_PREFIX, versions[0])), abi, arch))
-            # manylinux
-            if abi != 'none' and sys.platform.startswith('linux'):
-                arch = arch.replace('linux_', '')
-                parts = _get_glibc_version()
-                if len(parts) == 2:
-                    if parts >= (2, 5):
-                        result.append((''.join((IMP_PREFIX, versions[0])), abi,
-                                       'manylinux1_%s' % arch))
-                    if parts >= (2, 12):
-                        result.append((''.join((IMP_PREFIX, versions[0])), abi,
-                                       'manylinux2010_%s' % arch))
-                    if parts >= (2, 17):
-                        result.append((''.join((IMP_PREFIX, versions[0])), abi,
-                                       'manylinux2014_%s' % arch))
-                    result.append((''.join((IMP_PREFIX, versions[0])), abi,
-                                   'manylinux_%s_%s_%s' % (parts[0], parts[1],
-                                                           arch)))
-
-    # where no ABI / arch dependency, but IMP_PREFIX dependency
-    for i, version in enumerate(versions):
-        result.append((''.join((IMP_PREFIX, version)), 'none', 'any'))
-        if i == 0:
-            result.append((''.join((IMP_PREFIX, version[0])), 'none', 'any'))
-
-    # no IMP_PREFIX, ABI or arch dependency
-    for i, version in enumerate(versions):
-        result.append((''.join(('py', version)), 'none', 'any'))
-        if i == 0:
-            result.append((''.join(('py', version[0])), 'none', 'any'))
-
-    return set(result)
-
-
-COMPATIBLE_TAGS = compatible_tags()
-
-del compatible_tags
-
-
-def is_compatible(wheel, tags=None):
-    if not isinstance(wheel, Wheel):
-        wheel = Wheel(wheel)    # assume it's a filename
-    result = False
-    if tags is None:
-        tags = COMPATIBLE_TAGS
-    for ver, abi, arch in tags:
-        if ver in wheel.pyver and abi in wheel.abi and arch in wheel.arch:
-            result = True
-            break
-    return result

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/importlib_resources/abc.py

@@ -1,137 +0,0 @@
-import abc
-from typing import BinaryIO, Iterable, Text
-
-from ._compat import runtime_checkable, Protocol
-
-
-class ResourceReader(metaclass=abc.ABCMeta):
-    """Abstract base class for loaders to provide resource reading support."""
-
-    @abc.abstractmethod
-    def open_resource(self, resource: Text) -> BinaryIO:
-        """Return an opened, file-like object for binary reading.
-
-        The 'resource' argument is expected to represent only a file name.
-        If the resource cannot be found, FileNotFoundError is raised.
-        """
-        # This deliberately raises FileNotFoundError instead of
-        # NotImplementedError so that if this method is accidentally called,
-        # it'll still do the right thing.
-        raise FileNotFoundError
-
-    @abc.abstractmethod
-    def resource_path(self, resource: Text) -> Text:
-        """Return the file system path to the specified resource.
-
-        The 'resource' argument is expected to represent only a file name.
-        If the resource does not exist on the file system, raise
-        FileNotFoundError.
-        """
-        # This deliberately raises FileNotFoundError instead of
-        # NotImplementedError so that if this method is accidentally called,
-        # it'll still do the right thing.
-        raise FileNotFoundError
-
-    @abc.abstractmethod
-    def is_resource(self, path: Text) -> bool:
-        """Return True if the named 'path' is a resource.
-
-        Files are resources, directories are not.
-        """
-        raise FileNotFoundError
-
-    @abc.abstractmethod
-    def contents(self) -> Iterable[str]:
-        """Return an iterable of entries in `package`."""
-        raise FileNotFoundError
-
-
-@runtime_checkable
-class Traversable(Protocol):
-    """
-    An object with a subset of pathlib.Path methods suitable for
-    traversing directories and opening files.
-    """
-
-    @abc.abstractmethod
-    def iterdir(self):
-        """
-        Yield Traversable objects in self
-        """
-
-    def read_bytes(self):
-        """
-        Read contents of self as bytes
-        """
-        with self.open('rb') as strm:
-            return strm.read()
-
-    def read_text(self, encoding=None):
-        """
-        Read contents of self as text
-        """
-        with self.open(encoding=encoding) as strm:
-            return strm.read()
-
-    @abc.abstractmethod
-    def is_dir(self) -> bool:
-        """
-        Return True if self is a directory
-        """
-
-    @abc.abstractmethod
-    def is_file(self) -> bool:
-        """
-        Return True if self is a file
-        """
-
-    @abc.abstractmethod
-    def joinpath(self, child):
-        """
-        Return Traversable child in self
-        """
-
-    def __truediv__(self, child):
-        """
-        Return Traversable child in self
-        """
-        return self.joinpath(child)
-
-    @abc.abstractmethod
-    def open(self, mode='r', *args, **kwargs):
-        """
-        mode may be 'r' or 'rb' to open as text or binary. Return a handle
-        suitable for reading (same as pathlib.Path.open).
-
-        When opening as text, accepts encoding parameters such as those
-        accepted by io.TextIOWrapper.
-        """
-
-    @abc.abstractproperty
-    def name(self) -> str:
-        """
-        The base name of this object without any parent references.
-        """
-
-
-class TraversableResources(ResourceReader):
-    """
-    The required interface for providing traversable
-    resources.
-    """
-
-    @abc.abstractmethod
-    def files(self):
-        """Return a Traversable object for the loaded package."""
-
-    def open_resource(self, resource):
-        return self.files().joinpath(resource).open('rb')
-
-    def resource_path(self, resource):
-        raise FileNotFoundError(resource)
-
-    def is_resource(self, path):
-        return self.files().joinpath(path).is_file()
-
-    def contents(self):
-        return (item.name for item in self.files().iterdir())

spaces/Avinash-12035/MyGenAIChatBot/README.md

@@ -1,12 +0,0 @@
----
-title: MyGenAIChatBot
-emoji: 🏢
-colorFrom: indigo
-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

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/config.py

@@ -1,87 +0,0 @@
-from detectron2.config import CfgNode as CN
-
-def add_centernet_config(cfg):
-    _C = cfg
-
-    _C.MODEL.CENTERNET = CN()
-    _C.MODEL.CENTERNET.NUM_CLASSES = 80
-    _C.MODEL.CENTERNET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"]
-    _C.MODEL.CENTERNET.FPN_STRIDES = [8, 16, 32, 64, 128]
-    _C.MODEL.CENTERNET.PRIOR_PROB = 0.01
-    _C.MODEL.CENTERNET.INFERENCE_TH = 0.05
-    _C.MODEL.CENTERNET.CENTER_NMS = False
-    _C.MODEL.CENTERNET.NMS_TH_TRAIN = 0.6
-    _C.MODEL.CENTERNET.NMS_TH_TEST = 0.6
-    _C.MODEL.CENTERNET.PRE_NMS_TOPK_TRAIN = 1000
-    _C.MODEL.CENTERNET.POST_NMS_TOPK_TRAIN = 100
-    _C.MODEL.CENTERNET.PRE_NMS_TOPK_TEST = 1000
-    _C.MODEL.CENTERNET.POST_NMS_TOPK_TEST = 100
-    _C.MODEL.CENTERNET.NORM = "GN"
-    _C.MODEL.CENTERNET.USE_DEFORMABLE = False
-    _C.MODEL.CENTERNET.NUM_CLS_CONVS = 4
-    _C.MODEL.CENTERNET.NUM_BOX_CONVS = 4
-    _C.MODEL.CENTERNET.NUM_SHARE_CONVS = 0
-    _C.MODEL.CENTERNET.LOC_LOSS_TYPE = 'giou'
-    _C.MODEL.CENTERNET.SIGMOID_CLAMP = 1e-4
-    _C.MODEL.CENTERNET.HM_MIN_OVERLAP = 0.8
-    _C.MODEL.CENTERNET.MIN_RADIUS = 4
-    _C.MODEL.CENTERNET.SOI = [[0, 80], [64, 160], [128, 320], [256, 640], [512, 10000000]]
-    _C.MODEL.CENTERNET.POS_WEIGHT = 1.
-    _C.MODEL.CENTERNET.NEG_WEIGHT = 1.
-    _C.MODEL.CENTERNET.REG_WEIGHT = 2.
-    _C.MODEL.CENTERNET.HM_FOCAL_BETA = 4
-    _C.MODEL.CENTERNET.HM_FOCAL_ALPHA = 0.25
-    _C.MODEL.CENTERNET.LOSS_GAMMA = 2.0
-    _C.MODEL.CENTERNET.WITH_AGN_HM = False
-    _C.MODEL.CENTERNET.ONLY_PROPOSAL = False
-    _C.MODEL.CENTERNET.AS_PROPOSAL = False
-    _C.MODEL.CENTERNET.IGNORE_HIGH_FP = -1.
-    _C.MODEL.CENTERNET.MORE_POS = False
-    _C.MODEL.CENTERNET.MORE_POS_THRESH = 0.2
-    _C.MODEL.CENTERNET.MORE_POS_TOPK = 9
-    _C.MODEL.CENTERNET.NOT_NORM_REG = True
-    _C.MODEL.CENTERNET.NOT_NMS = False
-    _C.MODEL.CENTERNET.NO_REDUCE = False
-
-    _C.MODEL.ROI_BOX_HEAD.USE_SIGMOID_CE = False
-    _C.MODEL.ROI_BOX_HEAD.PRIOR_PROB = 0.01
-    _C.MODEL.ROI_BOX_HEAD.USE_EQL_LOSS = False
-    _C.MODEL.ROI_BOX_HEAD.CAT_FREQ_PATH = \
-        'datasets/lvis/lvis_v1_train_cat_info.json'
-    _C.MODEL.ROI_BOX_HEAD.EQL_FREQ_CAT = 200
-    _C.MODEL.ROI_BOX_HEAD.USE_FED_LOSS = False
-    _C.MODEL.ROI_BOX_HEAD.FED_LOSS_NUM_CAT = 50
-    _C.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT = 0.5
-    _C.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE = False
-
-    _C.MODEL.BIFPN = CN()
-    _C.MODEL.BIFPN.NUM_LEVELS = 5
-    _C.MODEL.BIFPN.NUM_BIFPN = 6
-    _C.MODEL.BIFPN.NORM = 'GN'
-    _C.MODEL.BIFPN.OUT_CHANNELS = 160
-    _C.MODEL.BIFPN.SEPARABLE_CONV = False
-
-    _C.MODEL.DLA = CN()
-    _C.MODEL.DLA.OUT_FEATURES = ['dla2']
-    _C.MODEL.DLA.USE_DLA_UP = True
-    _C.MODEL.DLA.NUM_LAYERS = 34
-    _C.MODEL.DLA.MS_OUTPUT = False
-    _C.MODEL.DLA.NORM = 'BN'
-    _C.MODEL.DLA.DLAUP_IN_FEATURES = ['dla3', 'dla4', 'dla5']
-    _C.MODEL.DLA.DLAUP_NODE = 'conv'
-
-    _C.SOLVER.RESET_ITER = False
-    _C.SOLVER.TRAIN_ITER = -1
-
-    _C.INPUT.CUSTOM_AUG = ''
-    _C.INPUT.TRAIN_SIZE = 640
-    _C.INPUT.TEST_SIZE = 640
-    _C.INPUT.SCALE_RANGE = (0.1, 2.)
-    # 'default' for fixed short/ long edge, 'square' for max size=INPUT.SIZE
-    _C.INPUT.TEST_INPUT_TYPE = 'default' 
-    
-    _C.DEBUG = False
-    _C.SAVE_DEBUG = False
-    _C.SAVE_PTH = False
-    _C.VIS_THRESH = 0.3
-    _C.DEBUG_SHOW_NAME = False

spaces/BAAI/vid2vid-zero/app.py

@@ -1,72 +0,0 @@
-# Most code is from https://huggingface.co/spaces/Tune-A-Video-library/Tune-A-Video-Training-UI
-
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-from subprocess import getoutput
-
-import gradio as gr
-import torch
-
-from gradio_demo.app_running import create_demo
-from gradio_demo.runner import Runner
-
-TITLE = '# [vid2vid-zero](https://github.com/baaivision/vid2vid-zero)'
-
-ORIGINAL_SPACE_ID = 'BAAI/vid2vid-zero'
-SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
-GPU_DATA = getoutput('nvidia-smi')
-SHARED_UI_WARNING = f'''## Attention - Running doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU.
-<center><a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></center>
-'''
-
-if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID:
-    SETTINGS = f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>'
-else:
-    SETTINGS = 'Settings'
-
-CUDA_NOT_AVAILABLE_WARNING = f'''## Attention - Running on CPU.
-<center>
-You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
-You can use "T4 small/medium" to run this demo.
-</center>
-'''
-
-HF_TOKEN_NOT_SPECIFIED_WARNING = f'''The environment variable `HF_TOKEN` is not specified. Feel free to specify your Hugging Face token with write permission if you don't want to manually provide it for every run.
-<center>
-You can check and create your Hugging Face tokens <a href="https://huggingface.co/settings/tokens" target="_blank">here</a>.
-You can specify environment variables in the "Repository secrets" section of the {SETTINGS} tab.
-</center>
-'''
-
-HF_TOKEN = os.getenv('HF_TOKEN')
-
-
-def show_warning(warning_text: str) -> gr.Blocks:
-    with gr.Blocks() as demo:
-        with gr.Box():
-            gr.Markdown(warning_text)
-    return demo
-
-
-pipe = None
-runner = Runner(HF_TOKEN)
-
-with gr.Blocks(css='gradio_demo/style.css') as demo:
-    gr.HTML('''<center><a href="https://huggingface.co/spaces/BAAI/vid2vid-zero?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your machine''')
-    if not torch.cuda.is_available():
-        show_warning(CUDA_NOT_AVAILABLE_WARNING)
-    # elif SPACE_ID == ORIGINAL_SPACE_ID:
-    #     show_warning(SHARED_UI_WARNING)
-
-    gr.Markdown(TITLE)
-    with gr.Tabs():
-        with gr.TabItem('Zero-shot Testing'):
-            create_demo(runner, pipe)
-
-    if not HF_TOKEN:
-        show_warning(HF_TOKEN_NOT_SPECIFIED_WARNING)
-
-demo.queue(max_size=1).launch(share=False)

spaces/BaitMan/abroader-otters/greeting.md

@@ -1,6 +0,0 @@
-(1) This house is Bitchin'!
-(2) I am vengeance.
-(3) Who said "And there it is! Who's the best hacker in the world people?"
-(4) The name of the move that requires to be at "5.3 Miles" away
-
-Now Combine all that. Every Answer starts with Capital letter, followed by lowercases, no Spaces.

spaces/Banbri/zcvzcv/src/components/ui/command.tsx

@@ -1,155 +0,0 @@
-"use client"
-
-import * as React from "react"
-import { DialogProps } from "@radix-ui/react-dialog"
-import { Command as CommandPrimitive } from "cmdk"
-import { Search } from "lucide-react"
-
-import { cn } from "@/lib/utils"
-import { Dialog, DialogContent } from "@/components/ui/dialog"
-
-const Command = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive>
->(({ className, ...props }, ref) => (
-  <CommandPrimitive
-    ref={ref}
-    className={cn(
-      "flex h-full w-full flex-col overflow-hidden rounded-md bg-white text-stone-950 dark:bg-stone-950 dark:text-stone-50",
-      className
-    )}
-    {...props}
-  />
-))
-Command.displayName = CommandPrimitive.displayName
-
-interface CommandDialogProps extends DialogProps {}
-
-const CommandDialog = ({ children, ...props }: CommandDialogProps) => {
-  return (
-    <Dialog {...props}>
-      <DialogContent className="overflow-hidden p-0 shadow-lg">
-        <Command className="[&_[cmdk-group-heading]]:px-2 [&_[cmdk-group-heading]]:font-medium [&_[cmdk-group-heading]]:text-stone-500 [&_[cmdk-group]:not([hidden])_~[cmdk-group]]:pt-0 [&_[cmdk-group]]:px-2 [&_[cmdk-input-wrapper]_svg]:h-5 [&_[cmdk-input-wrapper]_svg]:w-5 [&_[cmdk-input]]:h-12 [&_[cmdk-item]]:px-2 [&_[cmdk-item]]:py-3 [&_[cmdk-item]_svg]:h-5 [&_[cmdk-item]_svg]:w-5 dark:[&_[cmdk-group-heading]]:text-stone-400">
-          {children}
-        </Command>
-      </DialogContent>
-    </Dialog>
-  )
-}
-
-const CommandInput = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive.Input>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Input>
->(({ className, ...props }, ref) => (
-  <div className="flex items-center border-b px-3" cmdk-input-wrapper="">
-    <Search className="mr-2 h-4 w-4 shrink-0 opacity-50" />
-    <CommandPrimitive.Input
-      ref={ref}
-      className={cn(
-        "flex h-11 w-full rounded-md bg-transparent py-3 text-sm outline-none placeholder:text-stone-500 disabled:cursor-not-allowed disabled:opacity-50 dark:placeholder:text-stone-400",
-        className
-      )}
-      {...props}
-    />
-  </div>
-))
-
-CommandInput.displayName = CommandPrimitive.Input.displayName
-
-const CommandList = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive.List>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive.List>
->(({ className, ...props }, ref) => (
-  <CommandPrimitive.List
-    ref={ref}
-    className={cn("max-h-[300px] overflow-y-auto overflow-x-hidden", className)}
-    {...props}
-  />
-))
-
-CommandList.displayName = CommandPrimitive.List.displayName
-
-const CommandEmpty = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive.Empty>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Empty>
->((props, ref) => (
-  <CommandPrimitive.Empty
-    ref={ref}
-    className="py-6 text-center text-sm"
-    {...props}
-  />
-))
-
-CommandEmpty.displayName = CommandPrimitive.Empty.displayName
-
-const CommandGroup = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive.Group>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Group>
->(({ className, ...props }, ref) => (
-  <CommandPrimitive.Group
-    ref={ref}
-    className={cn(
-      "overflow-hidden p-1 text-stone-950 [&_[cmdk-group-heading]]:px-2 [&_[cmdk-group-heading]]:py-1.5 [&_[cmdk-group-heading]]:text-xs [&_[cmdk-group-heading]]:font-medium [&_[cmdk-group-heading]]:text-stone-500 dark:text-stone-50 dark:[&_[cmdk-group-heading]]:text-stone-400",
-      className
-    )}
-    {...props}
-  />
-))
-
-CommandGroup.displayName = CommandPrimitive.Group.displayName
-
-const CommandSeparator = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive.Separator>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Separator>
->(({ className, ...props }, ref) => (
-  <CommandPrimitive.Separator
-    ref={ref}
-    className={cn("-mx-1 h-px bg-stone-200 dark:bg-stone-800", className)}
-    {...props}
-  />
-))
-CommandSeparator.displayName = CommandPrimitive.Separator.displayName
-
-const CommandItem = React.forwardRef<
-  React.ElementRef<typeof CommandPrimitive.Item>,
-  React.ComponentPropsWithoutRef<typeof CommandPrimitive.Item>
->(({ className, ...props }, ref) => (
-  <CommandPrimitive.Item
-    ref={ref}
-    className={cn(
-      "relative flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none aria-selected:bg-stone-100 aria-selected:text-stone-900 data-[disabled]:pointer-events-none data-[disabled]:opacity-50 dark:aria-selected:bg-stone-800 dark:aria-selected:text-stone-50",
-      className
-    )}
-    {...props}
-  />
-))
-
-CommandItem.displayName = CommandPrimitive.Item.displayName
-
-const CommandShortcut = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLSpanElement>) => {
-  return (
-    <span
-      className={cn(
-        "ml-auto text-xs tracking-widest text-stone-500 dark:text-stone-400",
-        className
-      )}
-      {...props}
-    />
-  )
-}
-CommandShortcut.displayName = "CommandShortcut"
-
-export {
-  Command,
-  CommandDialog,
-  CommandInput,
-  CommandList,
-  CommandEmpty,
-  CommandGroup,
-  CommandItem,
-  CommandShortcut,
-  CommandSeparator,
-}

spaces/Bart92/RVC_HF/diffq/diffq.py

@@ -1,286 +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.
-
-"""
-Differentiable quantizer based on scaled noise injection.
-"""
-from dataclasses import dataclass
-import math
-import typing as tp
-
-import torch
-
-from .base import BaseQuantizer
-from .uniform import uniform_quantize, uniform_unquantize
-from .utils import simple_repr
-
-
-class DiffQuantizer(BaseQuantizer):
-    @dataclass
-    class _QuantizedParam(BaseQuantizer._QuantizedParam):
-        logit: torch.nn.Parameter
-
-    def __init__(self, model: torch.nn.Module, min_size: float = 0.01, float16: bool = False,
-                 group_size: int = 1, min_bits: float = 2, max_bits: float = 15,
-                 param="bits", noise="gaussian",
-                 init_bits: float = 8, extra_bits: float = 0, suffix: str = "_diffq",
-                 exclude: tp.List[str] = [], detect_bound: bool = True):
-        """
-        Differentiable quantizer based on scaled noise injection.
-        For every parameter `p` in the model, this introduces a number of bits parameter
-        `b` with the same dimensions (when group_size = 1).
-        Before each forward, `p` is replaced by `p + U`
-        with U uniform iid noise with range [-d/2, d/2], with `d` the uniform quantization
-        step for `b` bits.
-        This noise approximates the quantization noise in a differentiable manner, both
-        with respect to the unquantized parameter `p` and the number of bits `b`.
-
-        At eveluation (as detected with `model.eval()`), the model is replaced
-        by its true quantized version, and restored when going back to training.
-
-        When doing actual quantization (for serialization, or evaluation),
-        the number of bits is rounded to the nearest integer, and needs to be stored along.
-        This will cost a few bits per dimension. To reduce this cost, one can use `group_size`,
-        which will use a single noise level for multiple weight entries.
-
-        You can use the `DiffQuantizer.model_size` method to get a differentiable estimate of the
-        model size in MB. You can then use this estimate as a penalty in your training loss.
-
-        Args:
-            model (torch.nn.Module): model to quantize
-            min_size (float): minimum size in MB of a parameter to be quantized.
-            float16 (bool): if a layer is smaller than min_size, should we still do float16?
-            group_size (int): weight entries are groupped together to reduce the number
-                of noise scales to store. This should divide the size of all parameters
-                bigger than min_size.
-            min_bits (float): minimal number of bits.
-            max_bits (float): maximal number of bits.
-            init_bits (float): initial number of bits.
-            extra_bits (float): extra bits to add for actual quantization (before roundoff).
-            suffix (str): suffix used for the name of the extra noise scale parameters.
-            exclude (list[str]): list of patterns used to match parameters to exclude.
-                For instance `['bias']` to exclude all bias terms.
-            detect_bound (bool): if True, will detect bound parameters and reuse
-                the same quantized tensor for both, as well as the same number of bits.
-
-        ..Warning::
-            You must call `model.training()` and `model.eval()` for `DiffQuantizer` work properly.
-
-        """
-        self.group_size = group_size
-        self.min_bits = min_bits
-        self.max_bits = max_bits
-        self.init_bits = init_bits
-        self.extra_bits = extra_bits
-        self.suffix = suffix
-        self.param = param
-        self.noise = noise
-        assert noise in ["gaussian", "uniform"]
-        self._optimizer_setup = False
-
-        self._min_noise = 1 / (2 ** self.max_bits - 1)
-        self._max_noise = 1 / (2 ** self.min_bits - 1)
-
-        assert group_size >= 0
-        assert min_bits < init_bits < max_bits, \
-               "init_bits must be between min_bits and max_bits excluded3"
-
-        for name, _ in model.named_parameters():
-            if name.endswith(suffix):
-                raise RuntimeError("The model already has some noise scales parameters, "
-                                   "maybe you used twice a DiffQuantizer on the same model?.")
-
-        super().__init__(model, min_size, float16, exclude, detect_bound)
-
-    def _get_bits(self, logit: torch.Tensor):
-        if self.param == "noise":
-            return torch.log2(1 + 1 / self._get_noise_scale(logit))
-        else:
-            t = torch.sigmoid(logit)
-            return self.max_bits * t + (1 - t) * self.min_bits
-
-    def _get_noise_scale(self, logit: torch.Tensor):
-        if self.param == "noise":
-            t = torch.sigmoid(logit)
-            return torch.exp(t * math.log(self._min_noise) + (1 - t) * math.log(self._max_noise))
-        else:
-            return 1 / (2 ** self._get_bits(logit) - 1)
-
-    def _register_param(self, name, param, module, other):
-        if other is not None:
-            return self.__class__._QuantizedParam(
-               name=name, param=param, module=module, logit=other.logit, other=other)
-        assert self.group_size == 0 or param.numel() % self.group_size == 0
-        # we want the initial number of bits to be init_bits.
-        if self.param == "noise":
-            noise_scale = 1 / (2 ** self.init_bits - 1)
-            t = (math.log(noise_scale) - math.log(self._max_noise)) / (
-                math.log(self._min_noise) - math.log(self._max_noise))
-        else:
-            t = (self.init_bits - self.min_bits) / (self.max_bits - self.min_bits)
-        assert 0 < t < 1
-        logit = torch.logit(torch.tensor(float(t)))
-        assert abs(self._get_bits(logit) - self.init_bits) < 1e-5
-        if self.group_size > 0:
-            nparam = param.numel() // self.group_size
-        else:
-            nparam = 1
-        logit = torch.nn.Parameter(
-            torch.full(
-                (nparam,),
-                logit,
-                device=param.device))
-        module.register_parameter(name + self.suffix, logit)
-        return self.__class__._QuantizedParam(
-           name=name, param=param, module=module, logit=logit, other=None)
-
-    def clear_optimizer(self, optimizer: torch.optim.Optimizer):
-        params = [qp.logit for qp in self._qparams]
-
-        for group in optimizer.param_groups:
-            new_params = []
-            for q in list(group["params"]):
-                matched = False
-                for p in params:
-                    if p is q:
-                        matched = True
-                if not matched:
-                    new_params.append(q)
-            group["params"][:] = new_params
-
-    def setup_optimizer(self, optimizer: torch.optim.Optimizer,
-                        lr: float = 1e-3, **kwargs):
-        """
-        Setup the optimizer to tune the number of bits. In particular, this will deactivate
-        weight decay for the bits parameters.
-
-        Args:
-            optimizer (torch.Optimizer): optimizer to use.
-            lr (float): specific learning rate for the bits parameters. 1e-3
-                is perfect for Adam.,w
-            kwargs (dict): overrides for other optimization parameters for the bits.
-        """
-        assert not self._optimizer_setup
-        self._optimizer_setup = True
-
-        params = [qp.logit for qp in self._qparams]
-
-        for group in optimizer.param_groups:
-            for q in list(group["params"]):
-                for p in params:
-                    if p is q:
-                        raise RuntimeError("You should create the optimizer "
-                                           "before the quantizer!")
-
-        group = {"params": params, "lr": lr, "weight_decay": 0}
-        group.update(kwargs)
-        optimizer.add_param_group(group)
-
-    def no_optimizer(self):
-        """
-        Call this if you do not want to use an optimizer.
-        """
-        self._optimizer_setup = True
-
-    def check_unused(self):
-        for qparam in self._qparams:
-            if qparam.other is not None:
-                continue
-            grad = qparam.param.grad
-            if grad is None or (grad == 0).all():
-                if qparam.logit.grad is not None:
-                    qparam.logit.grad.data.zero_()
-
-    def model_size(self, exact=False):
-        """
-        Differentiable estimate of the model size.
-        The size is returned in MB.
-
-        If `exact` is True, then the output is no longer differentiable but
-        reflect exactly an achievable size, even without compression,
-        i.e.same as returned by `naive_model_size()`.
-        """
-        total = super().model_size()
-        subtotal = 0
-        for qparam in self._qparams:
-            # only count the first appearance of a Parameter
-            if qparam.other is not None:
-                continue
-            bits = self.extra_bits + self._get_bits(qparam.logit)
-            if exact:
-                bits = bits.round().clamp(1, 15)
-            if self.group_size == 0:
-                group_size = qparam.param.numel()
-            else:
-                group_size = self.group_size
-            subtotal += group_size * bits.sum()
-            subtotal += 2 * 32  # param scale
-
-            # Number of bits to represent each number of bits
-            bits_bits = math.ceil(math.log2(1 + (bits.max().round().item() - self.min_bits)))
-            subtotal += 8  # 8 bits for bits_bits
-            subtotal += bits_bits * bits.numel()
-
-        subtotal /= 2 ** 20 * 8  # bits -> MegaBytes
-        return total + subtotal
-
-    def true_model_size(self):
-        """
-        Naive model size without zlib compression.
-        """
-        return self.model_size(exact=True).item()
-
-    def _pre_forward_train(self):
-        if not self._optimizer_setup:
-            raise RuntimeError("You must call `setup_optimizer()` on your optimizer "
-                               "before starting training.")
-        for qparam in self._qparams:
-            if qparam.other is not None:
-                noisy = qparam.other.module._parameters[qparam.other.name]
-            else:
-                bits = self._get_bits(qparam.logit)[:, None]
-                if self.group_size == 0:
-                    p_flat = qparam.param.view(-1)
-                else:
-                    p_flat = qparam.param.view(-1, self.group_size)
-                scale = p_flat.max() - p_flat.min()
-                unit = 1 / (2**bits - 1)
-                if self.noise == "uniform":
-                    noise_source = (torch.rand_like(p_flat) - 0.5)
-                elif self.noise == "gaussian":
-                    noise_source = torch.randn_like(p_flat) / 2
-                noise = scale * unit * noise_source
-                noisy = p_flat + noise
-            # We bypass the checks by PyTorch on parameters being leafs
-            qparam.module._parameters[qparam.name] = noisy.view_as(qparam.param)
-        return True
-
-    def _post_forward_train(self):
-        for qparam in self._qparams:
-            qparam.module._parameters[qparam.name] = qparam.param
-        return True
-
-    def _quantize_param(self, qparam: _QuantizedParam) -> tp.Any:
-        bits = self.extra_bits + self._get_bits(qparam.logit)
-        bits = bits.round().clamp(1, 15)[:, None].byte()
-        if self.group_size == 0:
-            p = qparam.param.data.view(-1)
-        else:
-            p = qparam.param.data.view(-1, self.group_size)
-        levels, scales = uniform_quantize(p, bits)
-        return levels, scales, bits
-
-    def _unquantize_param(self, qparam: _QuantizedParam, quantized: tp.Any) -> torch.Tensor:
-        levels, param_scale, bits = quantized
-        return uniform_unquantize(levels, param_scale, bits).view_as(qparam.param.data)
-
-    def detach(self):
-        super().detach()
-        for qparam in self._qparams:
-            delattr(qparam.module, qparam.name + self.suffix)
-
-    def __repr__(self):
-        return simple_repr(self)

spaces/Bart92/RVC_HF/infer/lib/rmvpe.py

@@ -1,717 +0,0 @@
-import pdb, os
-
-import numpy as np
-import torch
-try:
-    #Fix "Torch not compiled with CUDA enabled"
-    import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
-    if torch.xpu.is_available():
-        from infer.modules.ipex import ipex_init
-        ipex_init()
-except Exception:
-    pass
-import torch.nn as nn
-import torch.nn.functional as F
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-
-import logging
-
-logger = logging.getLogger(__name__)
-
-
-###stft codes from https://github.com/pseeth/torch-stft/blob/master/torch_stft/util.py
-def window_sumsquare(
-    window,
-    n_frames,
-    hop_length=200,
-    win_length=800,
-    n_fft=800,
-    dtype=np.float32,
-    norm=None,
-):
-    """
-    # from librosa 0.6
-    Compute the sum-square envelope of a window function at a given hop length.
-    This is used to estimate modulation effects induced by windowing
-    observations in short-time fourier transforms.
-    Parameters
-    ----------
-    window : string, tuple, number, callable, or list-like
-        Window specification, as in `get_window`
-    n_frames : int > 0
-        The number of analysis frames
-    hop_length : int > 0
-        The number of samples to advance between frames
-    win_length : [optional]
-        The length of the window function.  By default, this matches `n_fft`.
-    n_fft : int > 0
-        The length of each analysis frame.
-    dtype : np.dtype
-        The data type of the output
-    Returns
-    -------
-    wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
-        The sum-squared envelope of the window function
-    """
-    if win_length is None:
-        win_length = n_fft
-
-    n = n_fft + hop_length * (n_frames - 1)
-    x = np.zeros(n, dtype=dtype)
-
-    # Compute the squared window at the desired length
-    win_sq = get_window(window, win_length, fftbins=True)
-    win_sq = normalize(win_sq, norm=norm) ** 2
-    win_sq = pad_center(win_sq, n_fft)
-
-    # Fill the envelope
-    for i in range(n_frames):
-        sample = i * hop_length
-        x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))]
-    return x
-
-
-class STFT(torch.nn.Module):
-    def __init__(
-        self, filter_length=1024, hop_length=512, win_length=None, window="hann"
-    ):
-        """
-        This module implements an STFT using 1D convolution and 1D transpose convolutions.
-        This is a bit tricky so there are some cases that probably won't work as working
-        out the same sizes before and after in all overlap add setups is tough. Right now,
-        this code should work with hop lengths that are half the filter length (50% overlap
-        between frames).
-
-        Keyword Arguments:
-            filter_length {int} -- Length of filters used (default: {1024})
-            hop_length {int} -- Hop length of STFT (restrict to 50% overlap between frames) (default: {512})
-            win_length {[type]} -- Length of the window function applied to each frame (if not specified, it
-                equals the filter length). (default: {None})
-            window {str} -- Type of window to use (options are bartlett, hann, hamming, blackman, blackmanharris)
-                (default: {'hann'})
-        """
-        super(STFT, self).__init__()
-        self.filter_length = filter_length
-        self.hop_length = hop_length
-        self.win_length = win_length if win_length else filter_length
-        self.window = window
-        self.forward_transform = None
-        self.pad_amount = int(self.filter_length / 2)
-        scale = self.filter_length / self.hop_length
-        fourier_basis = np.fft.fft(np.eye(self.filter_length))
-
-        cutoff = int((self.filter_length / 2 + 1))
-        fourier_basis = np.vstack(
-            [np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])]
-        )
-        forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
-        inverse_basis = torch.FloatTensor(
-            np.linalg.pinv(scale * fourier_basis).T[:, None, :]
-        )
-
-        assert filter_length >= self.win_length
-        # get window and zero center pad it to filter_length
-        fft_window = get_window(window, self.win_length, fftbins=True)
-        fft_window = pad_center(fft_window, size=filter_length)
-        fft_window = torch.from_numpy(fft_window).float()
-
-        # window the bases
-        forward_basis *= fft_window
-        inverse_basis *= fft_window
-
-        self.register_buffer("forward_basis", forward_basis.float())
-        self.register_buffer("inverse_basis", inverse_basis.float())
-
-    def transform(self, input_data):
-        """Take input data (audio) to STFT domain.
-
-        Arguments:
-            input_data {tensor} -- Tensor of floats, with shape (num_batch, num_samples)
-
-        Returns:
-            magnitude {tensor} -- Magnitude of STFT with shape (num_batch,
-                num_frequencies, num_frames)
-            phase {tensor} -- Phase of STFT with shape (num_batch,
-                num_frequencies, num_frames)
-        """
-        num_batches = input_data.shape[0]
-        num_samples = input_data.shape[-1]
-
-        self.num_samples = num_samples
-
-        # similar to librosa, reflect-pad the input
-        input_data = input_data.view(num_batches, 1, num_samples)
-        # print(1234,input_data.shape)
-        input_data = F.pad(
-            input_data.unsqueeze(1),
-            (self.pad_amount, self.pad_amount, 0, 0, 0, 0),
-            mode="reflect",
-        ).squeeze(1)
-        # print(2333,input_data.shape,self.forward_basis.shape,self.hop_length)
-        # pdb.set_trace()
-        forward_transform = F.conv1d(
-            input_data, self.forward_basis, stride=self.hop_length, padding=0
-        )
-
-        cutoff = int((self.filter_length / 2) + 1)
-        real_part = forward_transform[:, :cutoff, :]
-        imag_part = forward_transform[:, cutoff:, :]
-
-        magnitude = torch.sqrt(real_part**2 + imag_part**2)
-        # phase = torch.atan2(imag_part.data, real_part.data)
-
-        return magnitude  # , phase
-
-    def inverse(self, magnitude, phase):
-        """Call the inverse STFT (iSTFT), given magnitude and phase tensors produced
-        by the ```transform``` function.
-
-        Arguments:
-            magnitude {tensor} -- Magnitude of STFT with shape (num_batch,
-                num_frequencies, num_frames)
-            phase {tensor} -- Phase of STFT with shape (num_batch,
-                num_frequencies, num_frames)
-
-        Returns:
-            inverse_transform {tensor} -- Reconstructed audio given magnitude and phase. Of
-                shape (num_batch, num_samples)
-        """
-        recombine_magnitude_phase = torch.cat(
-            [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1
-        )
-
-        inverse_transform = F.conv_transpose1d(
-            recombine_magnitude_phase,
-            self.inverse_basis,
-            stride=self.hop_length,
-            padding=0,
-        )
-
-        if self.window is not None:
-            window_sum = window_sumsquare(
-                self.window,
-                magnitude.size(-1),
-                hop_length=self.hop_length,
-                win_length=self.win_length,
-                n_fft=self.filter_length,
-                dtype=np.float32,
-            )
-            # remove modulation effects
-            approx_nonzero_indices = torch.from_numpy(
-                np.where(window_sum > tiny(window_sum))[0]
-            )
-            window_sum = torch.from_numpy(window_sum).to(inverse_transform.device)
-            inverse_transform[:, :, approx_nonzero_indices] /= window_sum[
-                approx_nonzero_indices
-            ]
-
-            # scale by hop ratio
-            inverse_transform *= float(self.filter_length) / self.hop_length
-
-        inverse_transform = inverse_transform[..., self.pad_amount :]
-        inverse_transform = inverse_transform[..., : self.num_samples]
-        inverse_transform = inverse_transform.squeeze(1)
-
-        return inverse_transform
-
-    def forward(self, input_data):
-        """Take input data (audio) to STFT domain and then back to audio.
-
-        Arguments:
-            input_data {tensor} -- Tensor of floats, with shape (num_batch, num_samples)
-
-        Returns:
-            reconstruction {tensor} -- Reconstructed audio given magnitude and phase. Of
-                shape (num_batch, num_samples)
-        """
-        self.magnitude, self.phase = self.transform(input_data)
-        reconstruction = self.inverse(self.magnitude, self.phase)
-        return reconstruction
-
-
-from time import time as ttime
-
-
-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 * nn.N_MELS, nn.N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
-            )
-
-    def forward(self, mel):
-        # print(mel.shape)
-        mel = mel.transpose(-1, -2).unsqueeze(1)
-        x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
-        x = self.fc(x)
-        # print(x.shape)
-        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(
-                # "cpu"if(audio.device.type=="privateuseone") else audio.device
-                audio.device
-            )
-        # fft = torch.stft(#doesn't support pytorch_dml
-        #     # audio.cpu() if(audio.device.type=="privateuseone")else audio,
-        #     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))
-        # print(1111111111)
-        # print(222222222222222,audio.device,self.is_half)
-        if hasattr(self, "stft") == False:
-            # print(n_fft_new,hop_length_new,win_length_new,audio.shape)
-            self.stft = STFT(
-                filter_length=n_fft_new,
-                hop_length=hop_length_new,
-                win_length=win_length_new,
-                window="hann",
-            ).to(audio.device)
-        magnitude = self.stft.transform(audio)  # phase
-        # if (audio.device.type == "privateuseone"):
-        #     magnitude=magnitude.to(audio.device)
-        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))
-        # print(log_mel_spec.device.type)
-        return log_mel_spec
-
-
-class RMVPE:
-    def __init__(self, model_path, is_half, device=None):
-        self.resample_kernel = {}
-        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)
-        if "privateuseone" in str(device):
-            import onnxruntime as ort
-
-            ort_session = ort.InferenceSession(
-                "%s/rmvpe.onnx" % os.environ["rmvpe_root"],
-                providers=["DmlExecutionProvider"],
-            )
-            self.model = ort_session
-        else:
-            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.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="constant"
-            )
-            if "privateuseone" in str(self.device):
-                onnx_input_name = self.model.get_inputs()[0].name
-                onnx_outputs_names = self.model.get_outputs()[0].name
-                hidden = self.model.run(
-                    [onnx_outputs_names],
-                    input_feed={onnx_input_name: mel.cpu().numpy()},
-                )[0]
-            else:
-                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):
-        # torch.cuda.synchronize()
-        t0 = ttime()
-        mel = self.mel_extractor(
-            torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True
-        )
-        # print(123123123,mel.device.type)
-        # torch.cuda.synchronize()
-        t1 = ttime()
-        hidden = self.mel2hidden(mel)
-        # torch.cuda.synchronize()
-        t2 = ttime()
-        # print(234234,hidden.device.type)
-        if "privateuseone" not in str(self.device):
-            hidden = hidden.squeeze(0).cpu().numpy()
-        else:
-            hidden = hidden[0]
-        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 infer_from_audio_with_pitch(self, audio, thred=0.03, f0_min=50, f0_max=1100):
-        audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
-        mel = self.mel_extractor(audio, center=True)
-        hidden = self.mel2hidden(mel)
-        hidden = hidden.squeeze(0).cpu().numpy()
-        if self.is_half == True:
-            hidden = hidden.astype("float32")
-        f0 = self.decode(hidden, thred=thred)
-        f0[(f0 < f0_min) | (f0 > f0_max)] = 0  
-        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__":
-    import librosa
-    import soundfile as sf
-
-    audio, sampling_rate = sf.read(r"C:\Users\liujing04\Desktop\Z\冬之花clip1.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 = r"D:\BaiduNetdiskDownload\RVC-beta-v2-0727AMD_realtime\rmvpe.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()
-    logger.info("%s %.2f", f0.shape, t1 - t0)

spaces/Benson/text-generation/Examples/Descargar Fid Q Ielewe Mitaa Mp3.md

@@ -1,49 +0,0 @@
-
-<h1>Descargar gratis Paint 3D Windows 10: Una guía para principiantes</h1>
-<p>Si estás buscando una forma divertida y fácil de dar rienda suelta a tu creatividad y hacer increíbles diseños 2D y 3D, deberías probar Paint 3D. Paint 3D es una aplicación gratuita que viene con Windows 10 y te permite crear proyectos creativos profesionales o divertidos mediante la combinación de diferentes herramientas y características. No necesitas experiencia en diseño para usar Paint 3D - ¡cualquiera puede hacerlo! </p>
-<p>En este artículo, le mostraremos cómo descargar e instalar Paint 3D en Windows 10, cómo usarlo para crear obras de arte impresionantes y cómo aprender más sobre él. ¡Vamos a empezar! </p>
-<h2>descargar fid q ielewe mitaa mp3</h2><br /><p><b><b>DOWNLOAD</b>  <a href="https://bltlly.com/2v6K2D">https://bltlly.com/2v6K2D</a></b></p><br /><br />
- <h2>Cómo descargar e instalar Paint 3D en Windows 10</h2>
-<p>Descargar e instalar Paint 3D en Windows 10 es muy fácil. Solo sigue estos pasos:</p>
-<ol>
-<li>Abra la aplicación Microsoft Store en su PC. Puede encontrarla en el menú Inicio o escribiendo "store" en el cuadro de búsqueda de la barra de tareas. </li>
-<li>Buscar "Paint 3D" en la aplicación de la tienda y haga clic en el resultado. Verá una página con información sobre la aplicación, capturas de pantalla, comentarios y más. </li>
-<li>Haga clic en el botón "Obtener" para descargar e instalar la aplicación. Es posible que tenga que iniciar sesión con su cuenta de Microsoft si aún no lo ha hecho. </li>
-<li>Una vez instalada la aplicación, puede iniciarla desde el menú Inicio o haciendo clic en el icono de la barra de tareas. También puede anclarlo a la barra de tareas o al menú Inicio para facilitar el acceso. </li>
-</ol>
- <h2>Cómo usar Paint 3D en Windows 10</h2>
-<p>Ahora que has descargado e instalado Paint 3D, estás listo para usarlo. Estos son algunos pasos básicos para ayudarte a empezar:</p>
-<ol>
-<li>Elija un lienzo o un fondo. Cuando abra Paint 3D, verá un lienzo blanco donde puede dibujar o pintar. También puede elegir un color diferente o una imagen personalizada como fondo haciendo clic en el botón "Canvas" en el menú superior. </li>
-
-<li>Crear o importar objetos 2D y 3D. Puede crear sus propios objetos 2D y 3D utilizando las herramientas "3D Shapes" o "3D Doodle" en el lado derecho de la pantalla. La herramienta "3D Shapes" le permite elegir entre diferentes formas básicas como cubos, esferas, conos, cilindros, etc. La herramienta "3D Doodle" le permite dibujar formas a mano alzada que se convierten automáticamente en objetos 3D. También puede importar sus propias imágenes o modelos 2D o 3D haciendo clic en el botón "Insertar" en el menú superior. </li>
-<li>Edita, transforma y personaliza tus objetos. Puede editar, transformar y personalizar sus objetos utilizando la herramienta "Seleccionar" en el lado derecho de la pantalla. La herramienta "Seleccionar" le permite mover, rotar, escalar, copiar, eliminar, agrupar o desagrupar sus objetos. También puede usar la herramienta "Selección mágica" para recortar una parte de una imagen u objeto y convertirlo en un objeto separado. También puede utilizar la herramienta "Paint Bucket" para llenar sus objetos con diferentes colores o texturas. </li>
-<li>Añadir pegatinas, texto y efectos. Puede añadir pegatinas, texto y efectos a sus objetos utilizando las mismas herramientas que utilizó para el lienzo. También puede usar el botón "Mezclar realidad" en el menú superior para colocar sus objetos en una escena del mundo real usando la cámara de su dispositivo. </li>
-<li>Guarda, exporta o comparte tu proyecto. Cuando haya terminado con su proyecto, puede guardarlo como un archivo Paint 3D haciendo clic en el botón "Menú" en la esquina superior izquierda y eligiendo "Guardar". También puede exportarlo como una imagen o video 2D o 3D haciendo clic en el botón "Menú" y eligiendo "Exportar". También puede compartir su proyecto con otros haciendo clic en el botón "Compartir" en el menú superior y elegir entre diferentes opciones como correo electrónico, redes sociales o Remix 3D. </li>
-</ol>
- <h2>Cómo aprender más sobre la pintura 3D en Windows 10</h2>
-<p>Si quieres aprender más sobre Paint 3D y cómo usarlo de manera efectiva, aquí hay algunos consejos y recursos que puedes usar:</p>
-<ul>
-
-<li>Únete a comunidades y foros en línea. Hay muchas comunidades y foros en línea donde puedes hacer preguntas, compartir consejos, obtener comentarios y ver lo que otros están creando con Paint 3D. Puedes unirte a ellos en Facebook, Reddit, Twitter u otras plataformas. </li>
-<li>Echa un vistazo al sitio web de Remix 3D para obtener inspiración y recursos. El sitio web Remix 3D es un lugar donde puedes encontrar miles de modelos 3D que puedes descargar y usar en Paint 3D. También puedes subir tus propias creaciones y remezclar el trabajo de otros. También puede navegar por diferentes colecciones y categorías de modelos basados en temas, estilos o tendencias. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>Paint 3D es una aplicación gratuita que viene con Windows 10 y te permite crear increíbles diseños 2D y 3D con facilidad. Puedes descargarlo e instalarlo desde la aplicación de Microsoft Store y empezar a crear tus propios proyectos siguiendo los pasos de este artículo. También puedes aprender más sobre Paint 3D viendo tutoriales, uniéndote a comunidades en línea y visitando el sitio web de Remix 3D. Paint 3D es una gran manera de expresar tu creatividad y divertirte con el arte digital. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Paint 3D:</p>
-<ol>
-<li><b>¿Cuáles son los requisitos del sistema para Paint 3D? </b><br>
-Paint 3D funciona en cualquier dispositivo que ejecute Windows 10 versión 1709 o posterior. Sin embargo, algunas funciones pueden requerir hardware más avanzado, como una pantalla táctil, un dispositivo de lápiz, una cámara web o una tarjeta gráfica. </li>
-<li><b>¿Cómo puedo usar Paint 3D con otras aplicaciones de Microsoft? </b><br>
-Puedes usar Paint 3D con otras aplicaciones de Microsoft como Word, PowerPoint, Excel, OneNote, Outlook, etc. insertando tus proyectos Paint 3D como imágenes o vídeos en estas aplicaciones. También puedes usar Paint 3D con la aplicación Mixed Reality Viewer para ver tus proyectos Paint 3D en 3D o en realidad aumentada. </li>
-<li><b>¿Cuáles son algunas alternativas a Paint 3D? </b><br>
-
-<li><b>¿Cómo puedo obtener ayuda o soporte para Paint 3D? </b><br>
-Si necesita ayuda o soporte para Paint 3D, puede visitar el sitio web de Microsoft Support y buscar "Paint 3D". Encontrará artículos, videos y guías que pueden ayudarlo con problemas o preguntas comunes. También puede ponerse en contacto con Microsoft Support por teléfono, chat o correo electrónico si necesita más asistencia. </li>
-<li><b>¿Cómo puedo dar comentarios o sugerencias para Paint 3D? </b><br>
-Si desea dar comentarios o sugerencias para Paint 3D, puede usar la aplicación Feedback Hub en su dispositivo Windows 10. También puede calificar y revisar la aplicación en la aplicación de Microsoft Store o dejar un comentario en la página de Facebook de Paint 3D. </li>
-</ol>
- <p>Espero que hayas disfrutado este artículo y hayas aprendido algo nuevo sobre Paint 3D. Si tiene alguna pregunta o comentario, no dude en compartirlos a continuación. ¡Gracias por leer! </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Gratis Bitcoin Bot.md

@@ -1,77 +0,0 @@
-
-<h1>Descargar gratis Bitcoin Bot: Lo que necesitas saber</h1>
-<p>Si usted está interesado en el comercio bitcoin u otras criptomonedas, es posible que haya oído hablar de los robots bitcoin. Estos son programas de software que pueden comprar y vender automáticamente criptomonedas en su nombre, basado en reglas y estrategias predefinidas. Pero, ¿qué son exactamente los robots bitcoin, cómo funcionan, y dónde se puede obtener de forma gratuita? En este artículo, vamos a responder a estas preguntas y más. También revisaremos los tres mejores bots de bitcoin que puedes descargar gratis y comenzar a usar hoy. </p>
-<h2>descargar gratis bitcoin bot</h2><br /><p><b><b>DOWNLOAD</b> &#9733;&#9733;&#9733; <a href="https://bltlly.com/2v6Km5">https://bltlly.com/2v6Km5</a></b></p><br /><br />
- <h2>Cómo funcionan los bots de Bitcoin</h2>
-<p>Los bots Bitcoin son esencialmente programas de computadora que pueden conectarse a varios intercambios de criptomonedas a través de API (interfaces de programación de aplicaciones) y ejecutar operaciones en su nombre. También pueden recopilar y analizar datos de mercado, como movimientos de precios, volumen, tendencias, indicadores y señales. Sobre la base de estos datos, pueden aplicar diferentes estrategias de negociación, tales como especulación, arbitraje, comercio de red, el costo del dólar de promedio, y más. Algunos robots bitcoin también pueden utilizar el aprendizaje automático o la inteligencia artificial para adaptarse a las condiciones cambiantes del mercado y optimizar su rendimiento. </p>
- <h2>Beneficios de usar Bitcoin Bots</h2>
-<p>Hay muchas razones por las que es posible que desee utilizar un bot bitcoin para su comercio de criptografía. Estos son algunos de los principales beneficios:</p>
-<ul>
-<li><strong>Ahorre tiempo:</strong> El comercio de criptomonedas puede ser lento y tedioso. Usted tiene que monitorear constantemente el mercado, hacer investigaciones, tomar decisiones y ejecutar operaciones. Con un bot bitcoin, puedes automatizar estas tareas y dejar que el bot haga el trabajo por ti. </li>
-<li><strong>Reducir el estrés:</strong> El comercio criptomonedas también puede ser estresante y emocional. Tienes que lidiar con la volatilidad del mercado, la incertidumbre, el miedo, la codicia y otros factores psicológicos. Con un bot bitcoin, puede reducir su participación emocional y el comercio más racionalmente. </li>
-
-<li><strong>Evite los errores humanos:</strong> Las criptomonedas comerciales pueden ser propensas a errores humanos, como errores tipográficos, errores de cálculo o problemas técnicos. Estos errores pueden costarle dinero o incluso arruinar toda su cuenta de operaciones. Con un bot bitcoin, puede minimizar estos riesgos confiando en la precisión y fiabilidad del software. </li>
-</ul>
- <h2>Riesgos de usar Bitcoin Bots</h2>
-<p>Aunque los bots bitcoin pueden ofrecer muchas ventajas, también vienen con algunos inconvenientes y peligros. Estos son algunos de los principales riesgos:</p>
-<ul>
-<li><strong>Hacking:</strong> Los bots Bitcoin son programas de software que se ejecutan en su computadora o plataformas en línea. Esto significa que pueden ser hackeados por actores maliciosos que pueden robar sus fondos o manipular sus operaciones. <li><strong>Mal funcionamiento:</strong> Los bots de Bitcoin son programas de software que pueden tener errores, errores o problemas de compatibilidad. Esto significa que pueden fallar o dejar de funcionar en cualquier momento, lo que resulta en pérdidas u oportunidades perdidas. </li>
-<li><strong>Perder dinero:</strong> Los bots de Bitcoin son programas de software que pueden realizar operaciones en su nombre, pero no pueden garantizar las ganancias o evitar pérdidas. Esto significa que aún puede perder dinero debido a la volatilidad del mercado, las malas estrategias o eventos inesperados. </li>
-</ul>
-<p>Por lo tanto, siempre debe ser cuidadoso y cauteloso al usar los bots bitcoin. Nunca debe confiar ciegamente en ellos o invertir más de lo que puede permitirse perder. También debe hacer su propia investigación y debida diligencia antes de elegir un bot bitcoin y usarlo para su comercio. </p>
- <h2>Cómo elegir un bot de Bitcoin</h2>
-<p>Hay muchos bots bitcoin disponibles en Internet, pero no todos son confiables, confiables o eficaces. Algunos de ellos son estafas, algunos de ellos son anticuados, y algunos de ellos son simplemente inútiles. ¿Cómo se puede saber cuáles valen su tiempo y dinero? Aquí hay algunos factores a considerar al elegir un bot bitcoin:</p>
-<p></p>
-<ul>
-
-<li><strong>Rendimiento:</strong> ¿Qué tan bien funciona el bot bitcoin? ¿Tiene un historial probado de generación de resultados consistentes y rentables? ¿Tiene testimonios, reseñas o valoraciones de usuarios o expertos reales? ¿Tiene estadísticas, gráficos o informes que muestren su historial de rendimiento y estado actual? </li>
-<li><strong>Seguridad:</strong> ¿Qué tan seguro es el bot bitcoin? ¿Tiene alguna medida de seguridad, como cifrado, autenticación o verificación, para proteger sus datos y fondos? ¿Tiene sistemas de copia de seguridad, opciones de recuperación o soporte al cliente en caso de emergencias? ¿Tiene alguna reputación o credibilidad en la comunidad criptográfica? </li>
-<li><strong>Price:</strong> ¿Cuánto cuesta el bot bitcoin? ¿Es gratuito, gratuito, basado en suscripción o de pago único? ¿Ofrece descuentos, bonificaciones o reembolsos? ¿Tiene tarifas, comisiones o cargos ocultos? ¿Vale la pena el valor del dinero? </li>
-<li><strong>Comentarios:</strong> ¿Qué dicen otras personas sobre el bot bitcoin? ¿Tiene alguna retroalimentación positiva o negativa de foros en línea, blogs, redes sociales o sitios web? ¿Tiene quejas, problemas o problemas reportados por sus usuarios o críticos? ¿Tiene premios, reconocimientos o avales de fuentes acreditadas? </li>
-</ul>
-<p>Al comparar y evaluar estos factores, puede reducir sus opciones y encontrar el mejor bot bitcoin para sus necesidades y preferencias. </p>
- <h2>Top 3 Bitcoin Bots para descargar gratis</h2>
-<p>Para ahorrarle tiempo y esfuerzo, hemos hecho algunas investigaciones y pruebas y seleccionado los tres mejores bots bitcoin que puede descargar de forma gratuita y comenzar a usar hoy. Aquí hay un breve resumen de cada bot, con sus pros y contras, y un enlace para descargar:</p>
- <h3>Bot de Freebitcoin</h3>
-
-<ul>
-<li><strong>Pros:</strong> Fácil de usar, no requiere registro, no hay cargos involucrados</li>
-<li><strong>Contras:</strong> Características limitadas, bajo potencial de ganancias</li>
-<li><strong>Descargar enlace:</strong> [Freebitcoin Bot]</li>
-</ul>
- <h3>HaasOnline</h3>
-<p>Esta es una plataforma potente y profesional que admite varios bots y estrategias de comercio de criptografía. Puede elegir entre más de 15 bots predefinidos o crear su propio bot personalizado utilizando un editor de arrastrar y soltar. También puede acceder a más de 50 indicadores técnicos y más de 20 intercambios. Con esta plataforma, puede automatizar sus operaciones con precisión y eficiencia. </p>
-<ul>
-<li><strong>Pros:</strong> Características avanzadas, potencial de alto rendimiento</li>
-<li><strong>Contras:</strong> Interfaz compleja, planes de suscripción caros</li>
-<li><strong>Descargar enlace:</strong> [HaasOnline]</li>
-</ul>
- <h3>WunderTrading</h3>
-<p>Esta es una plataforma confiable y fácil de usar que ofrece una gran selección de herramientas de comercio de criptografía y plantillas <li><strong>Instalar y configurar el bot bitcoin:</strong> Dependiendo del tipo de bot que tenga, es posible que necesite instalarlo en su computadora o acceder a ella en línea. También es posible que necesite crear una cuenta, verificar su identidad y configurar sus preferencias. También necesitará conectar el bot a su intercambio(s) preferido proporcionando sus claves de API y otras credenciales. </li>
-<li><strong>Seleccione y pruebe su estrategia de trading:</strong> Dependiendo del tipo de bot que tenga, puede elegir entre una variedad de estrategias de trading o crear las suyas propias. Siempre debes probar tu estrategia antes de usarla con dinero real. Puede utilizar una cuenta de demostración, una herramienta de backtesting o un modo de comercio de papel para ver cómo funciona su estrategia en diferentes escenarios. </li>
-
-<li><strong>Iniciar y supervisar su bot bitcoin:</strong> Una vez que tenga todo listo, puede iniciar su bot bitcoin y dejar que el comercio para usted. Sin embargo, no debe dejarlo desatendido ni olvidarse de él. Siempre debe supervisar su rendimiento, revisar sus registros y revisar sus resultados. También debe estar preparado para detenerlo o modificarlo si es necesario. </li>
-</ol>
- <h2>Consejos y trucos para Bitcoin Bot Trading</h2>
-<p>Para aprovechar al máximo su experiencia y resultados de comercio de bots bitcoin, aquí hay algunos consejos y trucos que puede seguir:</p>
-<ul>
-<li><strong>Haga su investigación:</strong> Antes de elegir un bot bitcoin, usted debe hacer su investigación y comparar diferentes opciones. También debe hacer su investigación sobre el mercado y las estrategias de negociación que desea utilizar. Usted nunca debe confiar en bombo, promesas, o garantías. </li>
-<li><strong>Comience pequeño:</strong> Cuando utilice un bot bitcoin por primera vez, debe comenzar con una pequeña cantidad de dinero que puede permitirse perder. También debe usar una estrategia de bajo riesgo y un entorno conservador. Puede aumentar gradualmente su nivel de inversión y riesgo a medida que gana más experiencia y confianza. </li>
-<li><strong>Diversificar su cartera:</strong> Cuando se utiliza un bot bitcoin, no debe poner todos los huevos en una cesta. Usted debe diversificar su cartera mediante el comercio de diferentes criptomonedas, el uso de diferentes estrategias, o el uso de diferentes bots. De esta manera, puede reducir su exposición a las fluctuaciones del mercado y maximizar sus oportunidades. </li>
-<li><strong>Sigue aprendiendo:</strong> Cuando usas un bot bitcoin, no debes dejar de aprender y mejorar. Siempre debe realizar un seguimiento de su rendimiento, analizar sus resultados y aprender de sus errores. También debe mantenerse al día con las últimas noticias, tendencias y desarrollos en el espacio criptográfico. También deberías estar abierto a probar cosas nuevas y experimentar con nuevas ideas. </li>
-</ul>
- <h2>Conclusión</h2>
-
-<p>Si usted está buscando un bot bitcoin descarga gratuita que puede satisfacer sus necesidades y preferencias, le recomendamos que echa un vistazo a las tres mejores opciones que hemos revisado en este artículo: Freebitcoin Bot, HaasOnline, y WunderTrading. Estos son algunos de los mejores bots bitcoin que puedes descargar gratis y empezar a usar hoy. </p>
-<p>Esperamos que este artículo haya sido informativo y útil para usted. Si usted tiene alguna pregunta o comentario acerca de bitcoin bot trading, no dude en dejarlos abajo. Nos encantaría saber de usted! </p>
- <h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas y respuestas frecuentes sobre el comercio bot bitcoin:</p>
-<ol>
-<li><strong>¿Cuál es el mejor bot bitcoin? </strong> No hay una respuesta definitiva a esta pregunta, ya que diferentes bots bitcoin pueden tener diferentes características, rendimiento, seguridad, precio y comentarios. El mejor bot bitcoin para usted puede depender de sus necesidades personales, preferencias y objetivos. Sin embargo, algunos de los factores que se pueden considerar al elegir un bot bitcoin son sus características, rendimiento, seguridad, precio y comentarios. También puedes ver nuestras tres recomendaciones principales en este artículo: Freebitcoin Bot, HaasOnline y WunderTrading.</li>
-<li><strong>¿Es el comercio bot bitcoin legal? </strong> Bitcoin bot trading es generalmente legal en la mayoría de los países, siempre y cuando siga las reglas y regulaciones de su jurisdicción. Sin embargo, algunos países pueden tener leyes o prohibiciones más estrictas sobre el comercio de criptomonedas o el juego. Por lo tanto, siempre debe comprobar el estado legal de bitcoin bot trading en su país antes de usarlo. </li>
-
-<li><strong>¿Cuánto dinero puedo hacer con el comercio bot bitcoin? </strong> No hay una respuesta definitiva a esta pregunta, ya que la cantidad de dinero que puede hacer con el comercio de bots bitcoin puede depender de varios factores, como las condiciones del mercado, la estrategia comercial, los parámetros comerciales y el propio bot bitcoin. Sin embargo, algunos de los factores que pueden aumentar su potencial de ganancias son el uso de un bot bitcoin fiable y eficaz, la elección de una estrategia comercial rentable y coherente, el establecimiento de parámetros de comercio realistas y razonables, y la diversificación de su cartera. </li>
-<li><strong>¿Cómo puedo aprender más sobre el comercio bot bitcoin? </strong> Si desea aprender más sobre el comercio de bots bitcoin, puede hacer una investigación en línea y leer algunos artículos, blogs, libros o guías sobre el tema. También puede ver algunos videos, podcasts, seminarios web o cursos sobre el tema. También puede unirse a algunos foros en línea, comunidades o grupos donde puede interactuar con otros operadores de bots bitcoin y aprender de sus experiencias e ideas. </li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/args.py

@@ -1,648 +0,0 @@
-# Copyright 2016 Amazon.com, Inc. or its affiliates. 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. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file 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.
-"""Internal module to help with normalizing botocore client args.
-
-This module (and all function/classes within this module) should be
-considered internal, and *not* a public API.
-
-"""
-import copy
-import logging
-import socket
-
-import botocore.exceptions
-import botocore.parsers
-import botocore.serialize
-from botocore.config import Config
-from botocore.endpoint import EndpointCreator
-from botocore.regions import EndpointResolverBuiltins as EPRBuiltins
-from botocore.regions import EndpointRulesetResolver
-from botocore.signers import RequestSigner
-from botocore.utils import ensure_boolean, is_s3_accelerate_url
-
-logger = logging.getLogger(__name__)
-
-
-VALID_REGIONAL_ENDPOINTS_CONFIG = [
-    'legacy',
-    'regional',
-]
-LEGACY_GLOBAL_STS_REGIONS = [
-    'ap-northeast-1',
-    'ap-south-1',
-    'ap-southeast-1',
-    'ap-southeast-2',
-    'aws-global',
-    'ca-central-1',
-    'eu-central-1',
-    'eu-north-1',
-    'eu-west-1',
-    'eu-west-2',
-    'eu-west-3',
-    'sa-east-1',
-    'us-east-1',
-    'us-east-2',
-    'us-west-1',
-    'us-west-2',
-]
-
-
-class ClientArgsCreator:
-    def __init__(
-        self,
-        event_emitter,
-        user_agent,
-        response_parser_factory,
-        loader,
-        exceptions_factory,
-        config_store,
-    ):
-        self._event_emitter = event_emitter
-        self._user_agent = user_agent
-        self._response_parser_factory = response_parser_factory
-        self._loader = loader
-        self._exceptions_factory = exceptions_factory
-        self._config_store = config_store
-
-    def get_client_args(
-        self,
-        service_model,
-        region_name,
-        is_secure,
-        endpoint_url,
-        verify,
-        credentials,
-        scoped_config,
-        client_config,
-        endpoint_bridge,
-        auth_token=None,
-        endpoints_ruleset_data=None,
-        partition_data=None,
-    ):
-        final_args = self.compute_client_args(
-            service_model,
-            client_config,
-            endpoint_bridge,
-            region_name,
-            endpoint_url,
-            is_secure,
-            scoped_config,
-        )
-
-        service_name = final_args['service_name']  # noqa
-        parameter_validation = final_args['parameter_validation']
-        endpoint_config = final_args['endpoint_config']
-        protocol = final_args['protocol']
-        config_kwargs = final_args['config_kwargs']
-        s3_config = final_args['s3_config']
-        partition = endpoint_config['metadata'].get('partition', None)
-        socket_options = final_args['socket_options']
-
-        signing_region = endpoint_config['signing_region']
-        endpoint_region_name = endpoint_config['region_name']
-
-        event_emitter = copy.copy(self._event_emitter)
-        signer = RequestSigner(
-            service_model.service_id,
-            signing_region,
-            endpoint_config['signing_name'],
-            endpoint_config['signature_version'],
-            credentials,
-            event_emitter,
-            auth_token,
-        )
-
-        config_kwargs['s3'] = s3_config
-        new_config = Config(**config_kwargs)
-        endpoint_creator = EndpointCreator(event_emitter)
-
-        endpoint = endpoint_creator.create_endpoint(
-            service_model,
-            region_name=endpoint_region_name,
-            endpoint_url=endpoint_config['endpoint_url'],
-            verify=verify,
-            response_parser_factory=self._response_parser_factory,
-            max_pool_connections=new_config.max_pool_connections,
-            proxies=new_config.proxies,
-            timeout=(new_config.connect_timeout, new_config.read_timeout),
-            socket_options=socket_options,
-            client_cert=new_config.client_cert,
-            proxies_config=new_config.proxies_config,
-        )
-
-        serializer = botocore.serialize.create_serializer(
-            protocol, parameter_validation
-        )
-        response_parser = botocore.parsers.create_parser(protocol)
-
-        ruleset_resolver = self._build_endpoint_resolver(
-            endpoints_ruleset_data,
-            partition_data,
-            client_config,
-            service_model,
-            endpoint_region_name,
-            region_name,
-            endpoint_url,
-            endpoint,
-            is_secure,
-            endpoint_bridge,
-            event_emitter,
-        )
-
-        return {
-            'serializer': serializer,
-            'endpoint': endpoint,
-            'response_parser': response_parser,
-            'event_emitter': event_emitter,
-            'request_signer': signer,
-            'service_model': service_model,
-            'loader': self._loader,
-            'client_config': new_config,
-            'partition': partition,
-            'exceptions_factory': self._exceptions_factory,
-            'endpoint_ruleset_resolver': ruleset_resolver,
-        }
-
-    def compute_client_args(
-        self,
-        service_model,
-        client_config,
-        endpoint_bridge,
-        region_name,
-        endpoint_url,
-        is_secure,
-        scoped_config,
-    ):
-        service_name = service_model.endpoint_prefix
-        protocol = service_model.metadata['protocol']
-        parameter_validation = True
-        if client_config and not client_config.parameter_validation:
-            parameter_validation = False
-        elif scoped_config:
-            raw_value = scoped_config.get('parameter_validation')
-            if raw_value is not None:
-                parameter_validation = ensure_boolean(raw_value)
-
-        # Override the user agent if specified in the client config.
-        user_agent = self._user_agent
-        if client_config is not None:
-            if client_config.user_agent is not None:
-                user_agent = client_config.user_agent
-            if client_config.user_agent_extra is not None:
-                user_agent += ' %s' % client_config.user_agent_extra
-
-        s3_config = self.compute_s3_config(client_config)
-        endpoint_config = self._compute_endpoint_config(
-            service_name=service_name,
-            region_name=region_name,
-            endpoint_url=endpoint_url,
-            is_secure=is_secure,
-            endpoint_bridge=endpoint_bridge,
-            s3_config=s3_config,
-        )
-        endpoint_variant_tags = endpoint_config['metadata'].get('tags', [])
-        # Create a new client config to be passed to the client based
-        # on the final values. We do not want the user to be able
-        # to try to modify an existing client with a client config.
-        config_kwargs = dict(
-            region_name=endpoint_config['region_name'],
-            signature_version=endpoint_config['signature_version'],
-            user_agent=user_agent,
-        )
-        if 'dualstack' in endpoint_variant_tags:
-            config_kwargs.update(use_dualstack_endpoint=True)
-        if 'fips' in endpoint_variant_tags:
-            config_kwargs.update(use_fips_endpoint=True)
-        if client_config is not None:
-            config_kwargs.update(
-                connect_timeout=client_config.connect_timeout,
-                read_timeout=client_config.read_timeout,
-                max_pool_connections=client_config.max_pool_connections,
-                proxies=client_config.proxies,
-                proxies_config=client_config.proxies_config,
-                retries=client_config.retries,
-                client_cert=client_config.client_cert,
-                inject_host_prefix=client_config.inject_host_prefix,
-                tcp_keepalive=client_config.tcp_keepalive,
-            )
-        self._compute_retry_config(config_kwargs)
-        self._compute_connect_timeout(config_kwargs)
-        s3_config = self.compute_s3_config(client_config)
-
-        is_s3_service = self._is_s3_service(service_name)
-
-        if is_s3_service and 'dualstack' in endpoint_variant_tags:
-            if s3_config is None:
-                s3_config = {}
-            s3_config['use_dualstack_endpoint'] = True
-
-        return {
-            'service_name': service_name,
-            'parameter_validation': parameter_validation,
-            'user_agent': user_agent,
-            'endpoint_config': endpoint_config,
-            'protocol': protocol,
-            'config_kwargs': config_kwargs,
-            's3_config': s3_config,
-            'socket_options': self._compute_socket_options(
-                scoped_config, client_config
-            ),
-        }
-
-    def compute_s3_config(self, client_config):
-        s3_configuration = self._config_store.get_config_variable('s3')
-
-        # Next specific client config values takes precedence over
-        # specific values in the scoped config.
-        if client_config is not None:
-            if client_config.s3 is not None:
-                if s3_configuration is None:
-                    s3_configuration = client_config.s3
-                else:
-                    # The current s3_configuration dictionary may be
-                    # from a source that only should be read from so
-                    # we want to be safe and just make a copy of it to modify
-                    # before it actually gets updated.
-                    s3_configuration = s3_configuration.copy()
-                    s3_configuration.update(client_config.s3)
-
-        return s3_configuration
-
-    def _is_s3_service(self, service_name):
-        """Whether the service is S3 or S3 Control.
-
-        Note that throughout this class, service_name refers to the endpoint
-        prefix, not the folder name of the service in botocore/data. For
-        S3 Control, the folder name is 's3control' but the endpoint prefix is
-        's3-control'.
-        """
-        return service_name in ['s3', 's3-control']
-
-    def _compute_endpoint_config(
-        self,
-        service_name,
-        region_name,
-        endpoint_url,
-        is_secure,
-        endpoint_bridge,
-        s3_config,
-    ):
-        resolve_endpoint_kwargs = {
-            'service_name': service_name,
-            'region_name': region_name,
-            'endpoint_url': endpoint_url,
-            'is_secure': is_secure,
-            'endpoint_bridge': endpoint_bridge,
-        }
-        if service_name == 's3':
-            return self._compute_s3_endpoint_config(
-                s3_config=s3_config, **resolve_endpoint_kwargs
-            )
-        if service_name == 'sts':
-            return self._compute_sts_endpoint_config(**resolve_endpoint_kwargs)
-        return self._resolve_endpoint(**resolve_endpoint_kwargs)
-
-    def _compute_s3_endpoint_config(
-        self, s3_config, **resolve_endpoint_kwargs
-    ):
-        force_s3_global = self._should_force_s3_global(
-            resolve_endpoint_kwargs['region_name'], s3_config
-        )
-        if force_s3_global:
-            resolve_endpoint_kwargs['region_name'] = None
-        endpoint_config = self._resolve_endpoint(**resolve_endpoint_kwargs)
-        self._set_region_if_custom_s3_endpoint(
-            endpoint_config, resolve_endpoint_kwargs['endpoint_bridge']
-        )
-        # For backwards compatibility reasons, we want to make sure the
-        # client.meta.region_name will remain us-east-1 if we forced the
-        # endpoint to be the global region. Specifically, if this value
-        # changes to aws-global, it breaks logic where a user is checking
-        # for us-east-1 as the global endpoint such as in creating buckets.
-        if force_s3_global and endpoint_config['region_name'] == 'aws-global':
-            endpoint_config['region_name'] = 'us-east-1'
-        return endpoint_config
-
-    def _should_force_s3_global(self, region_name, s3_config):
-        s3_regional_config = 'legacy'
-        if s3_config and 'us_east_1_regional_endpoint' in s3_config:
-            s3_regional_config = s3_config['us_east_1_regional_endpoint']
-            self._validate_s3_regional_config(s3_regional_config)
-
-        is_global_region = region_name in ('us-east-1', None)
-        return s3_regional_config == 'legacy' and is_global_region
-
-    def _validate_s3_regional_config(self, config_val):
-        if config_val not in VALID_REGIONAL_ENDPOINTS_CONFIG:
-            raise botocore.exceptions.InvalidS3UsEast1RegionalEndpointConfigError(
-                s3_us_east_1_regional_endpoint_config=config_val
-            )
-
-    def _set_region_if_custom_s3_endpoint(
-        self, endpoint_config, endpoint_bridge
-    ):
-        # If a user is providing a custom URL, the endpoint resolver will
-        # refuse to infer a signing region. If we want to default to s3v4,
-        # we have to account for this.
-        if (
-            endpoint_config['signing_region'] is None
-            and endpoint_config['region_name'] is None
-        ):
-            endpoint = endpoint_bridge.resolve('s3')
-            endpoint_config['signing_region'] = endpoint['signing_region']
-            endpoint_config['region_name'] = endpoint['region_name']
-
-    def _compute_sts_endpoint_config(self, **resolve_endpoint_kwargs):
-        endpoint_config = self._resolve_endpoint(**resolve_endpoint_kwargs)
-        if self._should_set_global_sts_endpoint(
-            resolve_endpoint_kwargs['region_name'],
-            resolve_endpoint_kwargs['endpoint_url'],
-            endpoint_config,
-        ):
-            self._set_global_sts_endpoint(
-                endpoint_config, resolve_endpoint_kwargs['is_secure']
-            )
-        return endpoint_config
-
-    def _should_set_global_sts_endpoint(
-        self, region_name, endpoint_url, endpoint_config
-    ):
-        has_variant_tags = endpoint_config and endpoint_config.get(
-            'metadata', {}
-        ).get('tags')
-        if endpoint_url or has_variant_tags:
-            return False
-        return (
-            self._get_sts_regional_endpoints_config() == 'legacy'
-            and region_name in LEGACY_GLOBAL_STS_REGIONS
-        )
-
-    def _get_sts_regional_endpoints_config(self):
-        sts_regional_endpoints_config = self._config_store.get_config_variable(
-            'sts_regional_endpoints'
-        )
-        if not sts_regional_endpoints_config:
-            sts_regional_endpoints_config = 'legacy'
-        if (
-            sts_regional_endpoints_config
-            not in VALID_REGIONAL_ENDPOINTS_CONFIG
-        ):
-            raise botocore.exceptions.InvalidSTSRegionalEndpointsConfigError(
-                sts_regional_endpoints_config=sts_regional_endpoints_config
-            )
-        return sts_regional_endpoints_config
-
-    def _set_global_sts_endpoint(self, endpoint_config, is_secure):
-        scheme = 'https' if is_secure else 'http'
-        endpoint_config['endpoint_url'] = '%s://sts.amazonaws.com' % scheme
-        endpoint_config['signing_region'] = 'us-east-1'
-
-    def _resolve_endpoint(
-        self,
-        service_name,
-        region_name,
-        endpoint_url,
-        is_secure,
-        endpoint_bridge,
-    ):
-        return endpoint_bridge.resolve(
-            service_name, region_name, endpoint_url, is_secure
-        )
-
-    def _compute_socket_options(self, scoped_config, client_config=None):
-        # This disables Nagle's algorithm and is the default socket options
-        # in urllib3.
-        socket_options = [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]
-        client_keepalive = client_config and client_config.tcp_keepalive
-        scoped_keepalive = scoped_config and self._ensure_boolean(
-            scoped_config.get("tcp_keepalive", False)
-        )
-        # Enables TCP Keepalive if specified in client config object or shared config file.
-        if client_keepalive or scoped_keepalive:
-            socket_options.append((socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1))
-        return socket_options
-
-    def _compute_retry_config(self, config_kwargs):
-        self._compute_retry_max_attempts(config_kwargs)
-        self._compute_retry_mode(config_kwargs)
-
-    def _compute_retry_max_attempts(self, config_kwargs):
-        # There's a pre-existing max_attempts client config value that actually
-        # means max *retry* attempts.  There's also a `max_attempts` we pull
-        # from the config store that means *total attempts*, which includes the
-        # intitial request.  We can't change what `max_attempts` means in
-        # client config so we try to normalize everything to a new
-        # "total_max_attempts" variable.  We ensure that after this, the only
-        # configuration for "max attempts" is the 'total_max_attempts' key.
-        # An explicitly provided max_attempts in the client config
-        # overrides everything.
-        retries = config_kwargs.get('retries')
-        if retries is not None:
-            if 'total_max_attempts' in retries:
-                retries.pop('max_attempts', None)
-                return
-            if 'max_attempts' in retries:
-                value = retries.pop('max_attempts')
-                # client config max_attempts means total retries so we
-                # have to add one for 'total_max_attempts' to account
-                # for the initial request.
-                retries['total_max_attempts'] = value + 1
-                return
-        # Otherwise we'll check the config store which checks env vars,
-        # config files, etc.  There is no default value for max_attempts
-        # so if this returns None and we don't set a default value here.
-        max_attempts = self._config_store.get_config_variable('max_attempts')
-        if max_attempts is not None:
-            if retries is None:
-                retries = {}
-                config_kwargs['retries'] = retries
-            retries['total_max_attempts'] = max_attempts
-
-    def _compute_retry_mode(self, config_kwargs):
-        retries = config_kwargs.get('retries')
-        if retries is None:
-            retries = {}
-            config_kwargs['retries'] = retries
-        elif 'mode' in retries:
-            # If there's a retry mode explicitly set in the client config
-            # that overrides everything.
-            return
-        retry_mode = self._config_store.get_config_variable('retry_mode')
-        if retry_mode is None:
-            retry_mode = 'legacy'
-        retries['mode'] = retry_mode
-
-    def _compute_connect_timeout(self, config_kwargs):
-        # Checking if connect_timeout is set on the client config.
-        # If it is not, we check the config_store in case a
-        # non legacy default mode has been configured.
-        connect_timeout = config_kwargs.get('connect_timeout')
-        if connect_timeout is not None:
-            return
-        connect_timeout = self._config_store.get_config_variable(
-            'connect_timeout'
-        )
-        if connect_timeout:
-            config_kwargs['connect_timeout'] = connect_timeout
-
-    def _ensure_boolean(self, val):
-        if isinstance(val, bool):
-            return val
-        else:
-            return val.lower() == 'true'
-
-    def _build_endpoint_resolver(
-        self,
-        endpoints_ruleset_data,
-        partition_data,
-        client_config,
-        service_model,
-        endpoint_region_name,
-        region_name,
-        endpoint_url,
-        endpoint,
-        is_secure,
-        endpoint_bridge,
-        event_emitter,
-    ):
-        if endpoints_ruleset_data is None:
-            return None
-
-        # The legacy EndpointResolver is global to the session, but
-        # EndpointRulesetResolver is service-specific. Builtins for
-        # EndpointRulesetResolver must not be derived from the legacy
-        # endpoint resolver's output, including final_args, s3_config,
-        # etc.
-        s3_config_raw = self.compute_s3_config(client_config) or {}
-        service_name_raw = service_model.endpoint_prefix
-        # Maintain complex logic for s3 and sts endpoints for backwards
-        # compatibility.
-        if service_name_raw in ['s3', 'sts'] or region_name is None:
-            eprv2_region_name = endpoint_region_name
-        else:
-            eprv2_region_name = region_name
-        resolver_builtins = self.compute_endpoint_resolver_builtin_defaults(
-            region_name=eprv2_region_name,
-            service_name=service_name_raw,
-            s3_config=s3_config_raw,
-            endpoint_bridge=endpoint_bridge,
-            client_endpoint_url=endpoint_url,
-            legacy_endpoint_url=endpoint.host,
-        )
-        # botocore does not support client context parameters generically
-        # for every service. Instead, the s3 config section entries are
-        # available as client context parameters. In the future, endpoint
-        # rulesets of services other than s3/s3control may require client
-        # context parameters.
-        client_context = (
-            s3_config_raw if self._is_s3_service(service_name_raw) else {}
-        )
-        sig_version = (
-            client_config.signature_version
-            if client_config is not None
-            else None
-        )
-        return EndpointRulesetResolver(
-            endpoint_ruleset_data=endpoints_ruleset_data,
-            partition_data=partition_data,
-            service_model=service_model,
-            builtins=resolver_builtins,
-            client_context=client_context,
-            event_emitter=event_emitter,
-            use_ssl=is_secure,
-            requested_auth_scheme=sig_version,
-        )
-
-    def compute_endpoint_resolver_builtin_defaults(
-        self,
-        region_name,
-        service_name,
-        s3_config,
-        endpoint_bridge,
-        client_endpoint_url,
-        legacy_endpoint_url,
-    ):
-        # EndpointRulesetResolver rulesets may accept an "SDK::Endpoint" as
-        # input. If the endpoint_url argument of create_client() is set, it
-        # always takes priority.
-        if client_endpoint_url:
-            given_endpoint = client_endpoint_url
-        # If an endpoints.json data file other than the one bundled within
-        # the botocore/data directory is used, the output of legacy
-        # endpoint resolution is provided to EndpointRulesetResolver.
-        elif not endpoint_bridge.resolver_uses_builtin_data():
-            given_endpoint = legacy_endpoint_url
-        else:
-            given_endpoint = None
-
-        # The endpoint rulesets differ from legacy botocore behavior in whether
-        # forcing path style addressing in incompatible situations raises an
-        # exception or silently ignores the config setting. The
-        # AWS_S3_FORCE_PATH_STYLE parameter is adjusted both here and for each
-        # operation so that the ruleset behavior is backwards compatible.
-        if s3_config.get('use_accelerate_endpoint', False):
-            force_path_style = False
-        elif client_endpoint_url is not None and not is_s3_accelerate_url(
-            client_endpoint_url
-        ):
-            force_path_style = s3_config.get('addressing_style') != 'virtual'
-        else:
-            force_path_style = s3_config.get('addressing_style') == 'path'
-
-        return {
-            EPRBuiltins.AWS_REGION: region_name,
-            EPRBuiltins.AWS_USE_FIPS: (
-                # SDK_ENDPOINT cannot be combined with AWS_USE_FIPS
-                given_endpoint is None
-                # use legacy resolver's _resolve_endpoint_variant_config_var()
-                # or default to False if it returns None
-                and endpoint_bridge._resolve_endpoint_variant_config_var(
-                    'use_fips_endpoint'
-                )
-                or False
-            ),
-            EPRBuiltins.AWS_USE_DUALSTACK: (
-                # SDK_ENDPOINT cannot be combined with AWS_USE_DUALSTACK
-                given_endpoint is None
-                # use legacy resolver's _resolve_use_dualstack_endpoint() and
-                # or default to False if it returns None
-                and endpoint_bridge._resolve_use_dualstack_endpoint(
-                    service_name
-                )
-                or False
-            ),
-            EPRBuiltins.AWS_STS_USE_GLOBAL_ENDPOINT: (
-                self._should_set_global_sts_endpoint(
-                    region_name=region_name,
-                    endpoint_url=None,
-                    endpoint_config=None,
-                )
-            ),
-            EPRBuiltins.AWS_S3_USE_GLOBAL_ENDPOINT: (
-                self._should_force_s3_global(region_name, s3_config)
-            ),
-            EPRBuiltins.AWS_S3_ACCELERATE: s3_config.get(
-                'use_accelerate_endpoint', False
-            ),
-            EPRBuiltins.AWS_S3_FORCE_PATH_STYLE: force_path_style,
-            EPRBuiltins.AWS_S3_USE_ARN_REGION: s3_config.get(
-                'use_arn_region', True
-            ),
-            EPRBuiltins.AWS_S3CONTROL_USE_ARN_REGION: s3_config.get(
-                'use_arn_region', False
-            ),
-            EPRBuiltins.AWS_S3_DISABLE_MRAP: s3_config.get(
-                's3_disable_multiregion_access_points', False
-            ),
-            EPRBuiltins.SDK_ENDPOINT: given_endpoint,
-        }

spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/tasks.py

@@ -1,387 +0,0 @@
-# Copyright 2016 Amazon.com, Inc. or its affiliates. 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. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file 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 copy
-import logging
-
-from s3transfer.utils import get_callbacks
-
-logger = logging.getLogger(__name__)
-
-
-class Task:
-    """A task associated to a TransferFuture request
-
-    This is a base class for other classes to subclass from. All subclassed
-    classes must implement the main() method.
-    """
-
-    def __init__(
-        self,
-        transfer_coordinator,
-        main_kwargs=None,
-        pending_main_kwargs=None,
-        done_callbacks=None,
-        is_final=False,
-    ):
-        """
-        :type transfer_coordinator: s3transfer.futures.TransferCoordinator
-        :param transfer_coordinator: The context associated to the
-            TransferFuture for which this Task is associated with.
-
-        :type main_kwargs: dict
-        :param main_kwargs: The keyword args that can be immediately supplied
-            to the _main() method of the task
-
-        :type pending_main_kwargs: dict
-        :param pending_main_kwargs: The keyword args that are depended upon
-            by the result from a dependent future(s). The result returned by
-            the future(s) will be used as the value for the keyword argument
-            when _main() is called. The values for each key can be:
-                * a single future - Once completed, its value will be the
-                  result of that single future
-                * a list of futures - Once all of the futures complete, the
-                  value used will be a list of each completed future result
-                  value in order of when they were originally supplied.
-
-        :type done_callbacks: list of callbacks
-        :param done_callbacks: A list of callbacks to call once the task is
-            done completing. Each callback will be called with no arguments
-            and will be called no matter if the task succeeds or an exception
-            is raised.
-
-        :type is_final: boolean
-        :param is_final: True, to indicate that this task is the final task
-            for the TransferFuture request. By setting this value to True, it
-            will set the result of the entire TransferFuture to the result
-            returned by this task's main() method.
-        """
-        self._transfer_coordinator = transfer_coordinator
-
-        self._main_kwargs = main_kwargs
-        if self._main_kwargs is None:
-            self._main_kwargs = {}
-
-        self._pending_main_kwargs = pending_main_kwargs
-        if pending_main_kwargs is None:
-            self._pending_main_kwargs = {}
-
-        self._done_callbacks = done_callbacks
-        if self._done_callbacks is None:
-            self._done_callbacks = []
-
-        self._is_final = is_final
-
-    def __repr__(self):
-        # These are the general main_kwarg parameters that we want to
-        # display in the repr.
-        params_to_display = [
-            'bucket',
-            'key',
-            'part_number',
-            'final_filename',
-            'transfer_future',
-            'offset',
-            'extra_args',
-        ]
-        main_kwargs_to_display = self._get_kwargs_with_params_to_include(
-            self._main_kwargs, params_to_display
-        )
-        return '{}(transfer_id={}, {})'.format(
-            self.__class__.__name__,
-            self._transfer_coordinator.transfer_id,
-            main_kwargs_to_display,
-        )
-
-    @property
-    def transfer_id(self):
-        """The id for the transfer request that the task belongs to"""
-        return self._transfer_coordinator.transfer_id
-
-    def _get_kwargs_with_params_to_include(self, kwargs, include):
-        filtered_kwargs = {}
-        for param in include:
-            if param in kwargs:
-                filtered_kwargs[param] = kwargs[param]
-        return filtered_kwargs
-
-    def _get_kwargs_with_params_to_exclude(self, kwargs, exclude):
-        filtered_kwargs = {}
-        for param, value in kwargs.items():
-            if param in exclude:
-                continue
-            filtered_kwargs[param] = value
-        return filtered_kwargs
-
-    def __call__(self):
-        """The callable to use when submitting a Task to an executor"""
-        try:
-            # Wait for all of futures this task depends on.
-            self._wait_on_dependent_futures()
-            # Gather up all of the main keyword arguments for main().
-            # This includes the immediately provided main_kwargs and
-            # the values for pending_main_kwargs that source from the return
-            # values from the task's dependent futures.
-            kwargs = self._get_all_main_kwargs()
-            # If the task is not done (really only if some other related
-            # task to the TransferFuture had failed) then execute the task's
-            # main() method.
-            if not self._transfer_coordinator.done():
-                return self._execute_main(kwargs)
-        except Exception as e:
-            self._log_and_set_exception(e)
-        finally:
-            # Run any done callbacks associated to the task no matter what.
-            for done_callback in self._done_callbacks:
-                done_callback()
-
-            if self._is_final:
-                # If this is the final task announce that it is done if results
-                # are waiting on its completion.
-                self._transfer_coordinator.announce_done()
-
-    def _execute_main(self, kwargs):
-        # Do not display keyword args that should not be printed, especially
-        # if they are going to make the logs hard to follow.
-        params_to_exclude = ['data']
-        kwargs_to_display = self._get_kwargs_with_params_to_exclude(
-            kwargs, params_to_exclude
-        )
-        # Log what is about to be executed.
-        logger.debug(f"Executing task {self} with kwargs {kwargs_to_display}")
-
-        return_value = self._main(**kwargs)
-        # If the task is the final task, then set the TransferFuture's
-        # value to the return value from main().
-        if self._is_final:
-            self._transfer_coordinator.set_result(return_value)
-        return return_value
-
-    def _log_and_set_exception(self, exception):
-        # If an exception is ever thrown than set the exception for the
-        # entire TransferFuture.
-        logger.debug("Exception raised.", exc_info=True)
-        self._transfer_coordinator.set_exception(exception)
-
-    def _main(self, **kwargs):
-        """The method that will be ran in the executor
-
-        This method must be implemented by subclasses from Task. main() can
-        be implemented with any arguments decided upon by the subclass.
-        """
-        raise NotImplementedError('_main() must be implemented')
-
-    def _wait_on_dependent_futures(self):
-        # Gather all of the futures into that main() depends on.
-        futures_to_wait_on = []
-        for _, future in self._pending_main_kwargs.items():
-            # If the pending main keyword arg is a list then extend the list.
-            if isinstance(future, list):
-                futures_to_wait_on.extend(future)
-            # If the pending main keyword arg is a future append it to the list.
-            else:
-                futures_to_wait_on.append(future)
-        # Now wait for all of the futures to complete.
-        self._wait_until_all_complete(futures_to_wait_on)
-
-    def _wait_until_all_complete(self, futures):
-        # This is a basic implementation of the concurrent.futures.wait()
-        #
-        # concurrent.futures.wait() is not used instead because of this
-        # reported issue: https://bugs.python.org/issue20319.
-        # The issue would occasionally cause multipart uploads to hang
-        # when wait() was called. With this approach, it avoids the
-        # concurrency bug by removing any association with concurrent.futures
-        # implementation of waiters.
-        logger.debug(
-            '%s about to wait for the following futures %s', self, futures
-        )
-        for future in futures:
-            try:
-                logger.debug('%s about to wait for %s', self, future)
-                future.result()
-            except Exception:
-                # result() can also produce exceptions. We want to ignore
-                # these to be deferred to error handling down the road.
-                pass
-        logger.debug('%s done waiting for dependent futures', self)
-
-    def _get_all_main_kwargs(self):
-        # Copy over all of the kwargs that we know is available.
-        kwargs = copy.copy(self._main_kwargs)
-
-        # Iterate through the kwargs whose values are pending on the result
-        # of a future.
-        for key, pending_value in self._pending_main_kwargs.items():
-            # If the value is a list of futures, iterate though the list
-            # appending on the result from each future.
-            if isinstance(pending_value, list):
-                result = []
-                for future in pending_value:
-                    result.append(future.result())
-            # Otherwise if the pending_value is a future, just wait for it.
-            else:
-                result = pending_value.result()
-            # Add the retrieved value to the kwargs to be sent to the
-            # main() call.
-            kwargs[key] = result
-        return kwargs
-
-
-class SubmissionTask(Task):
-    """A base class for any submission task
-
-    Submission tasks are the top-level task used to submit a series of tasks
-    to execute a particular transfer.
-    """
-
-    def _main(self, transfer_future, **kwargs):
-        """
-        :type transfer_future: s3transfer.futures.TransferFuture
-        :param transfer_future: The transfer future associated with the
-            transfer request that tasks are being submitted for
-
-        :param kwargs: Any additional kwargs that you may want to pass
-            to the _submit() method
-        """
-        try:
-            self._transfer_coordinator.set_status_to_queued()
-
-            # Before submitting any tasks, run all of the on_queued callbacks
-            on_queued_callbacks = get_callbacks(transfer_future, 'queued')
-            for on_queued_callback in on_queued_callbacks:
-                on_queued_callback()
-
-            # Once callbacks have been ran set the status to running.
-            self._transfer_coordinator.set_status_to_running()
-
-            # Call the submit method to start submitting tasks to execute the
-            # transfer.
-            self._submit(transfer_future=transfer_future, **kwargs)
-        except BaseException as e:
-            # If there was an exception raised during the submission of task
-            # there is a chance that the final task that signals if a transfer
-            # is done and too run the cleanup may never have been submitted in
-            # the first place so we need to account accordingly.
-            #
-            # Note that BaseException is caught, instead of Exception, because
-            # for some implementations of executors, specifically the serial
-            # implementation, the SubmissionTask is directly exposed to
-            # KeyboardInterupts and so needs to cleanup and signal done
-            # for those as well.
-
-            # Set the exception, that caused the process to fail.
-            self._log_and_set_exception(e)
-
-            # Wait for all possibly associated futures that may have spawned
-            # from this submission task have finished before we announce the
-            # transfer done.
-            self._wait_for_all_submitted_futures_to_complete()
-
-            # Announce the transfer as done, which will run any cleanups
-            # and done callbacks as well.
-            self._transfer_coordinator.announce_done()
-
-    def _submit(self, transfer_future, **kwargs):
-        """The submission method to be implemented
-
-        :type transfer_future: s3transfer.futures.TransferFuture
-        :param transfer_future: The transfer future associated with the
-            transfer request that tasks are being submitted for
-
-        :param kwargs: Any additional keyword arguments you want to be passed
-            in
-        """
-        raise NotImplementedError('_submit() must be implemented')
-
-    def _wait_for_all_submitted_futures_to_complete(self):
-        # We want to wait for all futures that were submitted to
-        # complete as we do not want the cleanup callbacks or done callbacks
-        # to be called to early. The main problem is any task that was
-        # submitted may have submitted even more during its process and so
-        # we need to account accordingly.
-
-        # First get all of the futures that were submitted up to this point.
-        submitted_futures = self._transfer_coordinator.associated_futures
-        while submitted_futures:
-            # Wait for those futures to complete.
-            self._wait_until_all_complete(submitted_futures)
-            # However, more futures may have been submitted as we waited so
-            # we need to check again for any more associated futures.
-            possibly_more_submitted_futures = (
-                self._transfer_coordinator.associated_futures
-            )
-            # If the current list of submitted futures is equal to the
-            # the list of associated futures for when after the wait completes,
-            # we can ensure no more futures were submitted in waiting on
-            # the current list of futures to complete ultimately meaning all
-            # futures that may have spawned from the original submission task
-            # have completed.
-            if submitted_futures == possibly_more_submitted_futures:
-                break
-            submitted_futures = possibly_more_submitted_futures
-
-
-class CreateMultipartUploadTask(Task):
-    """Task to initiate a multipart upload"""
-
-    def _main(self, client, bucket, key, extra_args):
-        """
-        :param client: The client to use when calling CreateMultipartUpload
-        :param bucket: The name of the bucket to upload to
-        :param key: The name of the key to upload to
-        :param extra_args: A dictionary of any extra arguments that may be
-            used in the initialization.
-
-        :returns: The upload id of the multipart upload
-        """
-        # Create the multipart upload.
-        response = client.create_multipart_upload(
-            Bucket=bucket, Key=key, **extra_args
-        )
-        upload_id = response['UploadId']
-
-        # Add a cleanup if the multipart upload fails at any point.
-        self._transfer_coordinator.add_failure_cleanup(
-            client.abort_multipart_upload,
-            Bucket=bucket,
-            Key=key,
-            UploadId=upload_id,
-        )
-        return upload_id
-
-
-class CompleteMultipartUploadTask(Task):
-    """Task to complete a multipart upload"""
-
-    def _main(self, client, bucket, key, upload_id, parts, extra_args):
-        """
-        :param client: The client to use when calling CompleteMultipartUpload
-        :param bucket: The name of the bucket to upload to
-        :param key: The name of the key to upload to
-        :param upload_id: The id of the upload
-        :param parts: A list of parts to use to complete the multipart upload::
-
-            [{'Etag': etag_value, 'PartNumber': part_number}, ...]
-
-            Each element in the list consists of a return value from
-            ``UploadPartTask.main()``.
-        :param extra_args:  A dictionary of any extra arguments that may be
-            used in completing the multipart transfer.
-        """
-        client.complete_multipart_upload(
-            Bucket=bucket,
-            Key=key,
-            UploadId=upload_id,
-            MultipartUpload={'Parts': parts},
-            **extra_args,
-        )

spaces/Billius/runwayml-stable-diffusion-v1-5-04-07-2023/README.md

@@ -1,13 +0,0 @@
----
-title: Runwayml Stable Diffusion V1 5 04 07 2023
-emoji: 👁
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/datasets/cityscapes.py

@@ -1,333 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import functools
-import glob
-import json
-import logging
-import multiprocessing as mp
-import numpy as np
-import os
-from itertools import chain
-import pycocotools.mask as mask_util
-from fvcore.common.file_io import PathManager
-from PIL import Image
-
-from detectron2.structures import BoxMode
-from detectron2.utils.comm import get_world_size
-from detectron2.utils.logger import setup_logger
-
-try:
-    import cv2  # noqa
-except ImportError:
-    # OpenCV is an optional dependency at the moment
-    pass
-
-
-logger = logging.getLogger(__name__)
-
-
-def get_cityscapes_files(image_dir, gt_dir):
-    files = []
-    # scan through the directory
-    cities = PathManager.ls(image_dir)
-    logger.info(f"{len(cities)} cities found in '{image_dir}'.")
-    for city in cities:
-        city_img_dir = os.path.join(image_dir, city)
-        city_gt_dir = os.path.join(gt_dir, city)
-        for basename in PathManager.ls(city_img_dir):
-            image_file = os.path.join(city_img_dir, basename)
-
-            suffix = "leftImg8bit.png"
-            assert basename.endswith(suffix)
-            basename = basename[: -len(suffix)]
-
-            instance_file = os.path.join(city_gt_dir, basename + "gtFine_instanceIds.png")
-            label_file = os.path.join(city_gt_dir, basename + "gtFine_labelIds.png")
-            json_file = os.path.join(city_gt_dir, basename + "gtFine_polygons.json")
-
-            files.append((image_file, instance_file, label_file, json_file))
-    assert len(files), "No images found in {}".format(image_dir)
-    for f in files[0]:
-        assert PathManager.isfile(f), f
-    return files
-
-
-def load_cityscapes_instances(image_dir, gt_dir, from_json=True, to_polygons=True):
-    """
-    Args:
-        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
-        gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train".
-        from_json (bool): whether to read annotations from the raw json file or the png files.
-        to_polygons (bool): whether to represent the segmentation as polygons
-            (COCO's format) instead of masks (cityscapes's format).
-
-    Returns:
-        list[dict]: a list of dicts in Detectron2 standard format. (See
-        `Using Custom Datasets </tutorials/datasets.html>`_ )
-    """
-    if from_json:
-        assert to_polygons, (
-            "Cityscapes's json annotations are in polygon format. "
-            "Converting to mask format is not supported now."
-        )
-    files = get_cityscapes_files(image_dir, gt_dir)
-
-    logger.info("Preprocessing cityscapes annotations ...")
-    # This is still not fast: all workers will execute duplicate works and will
-    # take up to 10m on a 8GPU server.
-    pool = mp.Pool(processes=max(mp.cpu_count() // get_world_size() // 2, 4))
-
-    ret = pool.map(
-        functools.partial(cityscapes_files_to_dict, from_json=from_json, to_polygons=to_polygons),
-        files,
-    )
-    logger.info("Loaded {} images from {}".format(len(ret), image_dir))
-
-    # Map cityscape ids to contiguous ids
-    from cityscapesscripts.helpers.labels import labels
-
-    labels = [l for l in labels if l.hasInstances and not l.ignoreInEval]
-    dataset_id_to_contiguous_id = {l.id: idx for idx, l in enumerate(labels)}
-    for dict_per_image in ret:
-        for anno in dict_per_image["annotations"]:
-            anno["category_id"] = dataset_id_to_contiguous_id[anno["category_id"]]
-    return ret
-
-
-def load_cityscapes_semantic(image_dir, gt_dir):
-    """
-    Args:
-        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
-        gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train".
-
-    Returns:
-        list[dict]: a list of dict, each has "file_name" and
-            "sem_seg_file_name".
-    """
-    ret = []
-    for image_file in glob.glob(os.path.join(image_dir, "**/*.png")):
-        suffix = "leftImg8bit.png"
-        assert image_file.endswith(suffix)
-        prefix = image_dir
-
-        label_file = gt_dir + image_file[len(prefix) : -len(suffix)] + "gtFine_labelTrainIds.png"
-        assert os.path.isfile(
-            label_file
-        ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py"  # noqa
-
-        json_file = gt_dir + image_file[len(prefix) : -len(suffix)] + "gtFine_polygons.json"
-
-        with PathManager.open(json_file, "r") as f:
-            jsonobj = json.load(f)
-        ret.append(
-            {
-                "file_name": image_file,
-                "sem_seg_file_name": label_file,
-                "height": jsonobj["imgHeight"],
-                "width": jsonobj["imgWidth"],
-            }
-        )
-    return ret
-
-
-def cityscapes_files_to_dict(files, from_json, to_polygons):
-    """
-    Parse cityscapes annotation files to a dict.
-
-    Args:
-        files (tuple): consists of (image_file, instance_id_file, label_id_file, json_file)
-        from_json (bool): whether to read annotations from the raw json file or the png files.
-        to_polygons (bool): whether to represent the segmentation as polygons
-            (COCO's format) instead of masks (cityscapes's format).
-
-    Returns:
-        A dict in Detectron2 Dataset format.
-    """
-    from cityscapesscripts.helpers.labels import id2label, name2label
-
-    image_file, instance_id_file, _, json_file = files
-
-    annos = []
-
-    if from_json:
-        from shapely.geometry import MultiPolygon, Polygon
-
-        with PathManager.open(json_file, "r") as f:
-            jsonobj = json.load(f)
-        ret = {
-            "file_name": image_file,
-            "image_id": os.path.basename(image_file),
-            "height": jsonobj["imgHeight"],
-            "width": jsonobj["imgWidth"],
-        }
-
-        # `polygons_union` contains the union of all valid polygons.
-        polygons_union = Polygon()
-
-        # CityscapesScripts draw the polygons in sequential order
-        # and each polygon *overwrites* existing ones. See
-        # (https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/json2instanceImg.py) # noqa
-        # We use reverse order, and each polygon *avoids* early ones.
-        # This will resolve the ploygon overlaps in the same way as CityscapesScripts.
-        for obj in jsonobj["objects"][::-1]:
-            if "deleted" in obj:  # cityscapes data format specific
-                continue
-            label_name = obj["label"]
-
-            try:
-                label = name2label[label_name]
-            except KeyError:
-                if label_name.endswith("group"):  # crowd area
-                    label = name2label[label_name[: -len("group")]]
-                else:
-                    raise
-            if label.id < 0:  # cityscapes data format
-                continue
-
-            # Cityscapes's raw annotations uses integer coordinates
-            # Therefore +0.5 here
-            poly_coord = np.asarray(obj["polygon"], dtype="f4") + 0.5
-            # CityscapesScript uses PIL.ImageDraw.polygon to rasterize
-            # polygons for evaluation. This function operates in integer space
-            # and draws each pixel whose center falls into the polygon.
-            # Therefore it draws a polygon which is 0.5 "fatter" in expectation.
-            # We therefore dilate the input polygon by 0.5 as our input.
-            poly = Polygon(poly_coord).buffer(0.5, resolution=4)
-
-            if not label.hasInstances or label.ignoreInEval:
-                # even if we won't store the polygon it still contributes to overlaps resolution
-                polygons_union = polygons_union.union(poly)
-                continue
-
-            # Take non-overlapping part of the polygon
-            poly_wo_overlaps = poly.difference(polygons_union)
-            if poly_wo_overlaps.is_empty:
-                continue
-            polygons_union = polygons_union.union(poly)
-
-            anno = {}
-            anno["iscrowd"] = label_name.endswith("group")
-            anno["category_id"] = label.id
-
-            if isinstance(poly_wo_overlaps, Polygon):
-                poly_list = [poly_wo_overlaps]
-            elif isinstance(poly_wo_overlaps, MultiPolygon):
-                poly_list = poly_wo_overlaps.geoms
-            else:
-                raise NotImplementedError("Unknown geometric structure {}".format(poly_wo_overlaps))
-
-            poly_coord = []
-            for poly_el in poly_list:
-                # COCO API can work only with exterior boundaries now, hence we store only them.
-                # TODO: store both exterior and interior boundaries once other parts of the
-                # codebase support holes in polygons.
-                poly_coord.append(list(chain(*poly_el.exterior.coords)))
-            anno["segmentation"] = poly_coord
-            (xmin, ymin, xmax, ymax) = poly_wo_overlaps.bounds
-
-            anno["bbox"] = (xmin, ymin, xmax, ymax)
-            anno["bbox_mode"] = BoxMode.XYXY_ABS
-
-            annos.append(anno)
-    else:
-        # See also the official annotation parsing scripts at
-        # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/instances2dict.py  # noqa
-        with PathManager.open(instance_id_file, "rb") as f:
-            inst_image = np.asarray(Image.open(f), order="F")
-        # ids < 24 are stuff labels (filtering them first is about 5% faster)
-        flattened_ids = np.unique(inst_image[inst_image >= 24])
-
-        ret = {
-            "file_name": image_file,
-            "image_id": os.path.basename(image_file),
-            "height": inst_image.shape[0],
-            "width": inst_image.shape[1],
-        }
-
-        for instance_id in flattened_ids:
-            # For non-crowd annotations, instance_id // 1000 is the label_id
-            # Crowd annotations have <1000 instance ids
-            label_id = instance_id // 1000 if instance_id >= 1000 else instance_id
-            label = id2label[label_id]
-            if not label.hasInstances or label.ignoreInEval:
-                continue
-
-            anno = {}
-            anno["iscrowd"] = instance_id < 1000
-            anno["category_id"] = label.id
-
-            mask = np.asarray(inst_image == instance_id, dtype=np.uint8, order="F")
-
-            inds = np.nonzero(mask)
-            ymin, ymax = inds[0].min(), inds[0].max()
-            xmin, xmax = inds[1].min(), inds[1].max()
-            anno["bbox"] = (xmin, ymin, xmax, ymax)
-            if xmax <= xmin or ymax <= ymin:
-                continue
-            anno["bbox_mode"] = BoxMode.XYXY_ABS
-            if to_polygons:
-                # This conversion comes from D4809743 and D5171122,
-                # when Mask-RCNN was first developed.
-                contours = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[
-                    -2
-                ]
-                polygons = [c.reshape(-1).tolist() for c in contours if len(c) >= 3]
-                # opencv's can produce invalid polygons
-                if len(polygons) == 0:
-                    continue
-                anno["segmentation"] = polygons
-            else:
-                anno["segmentation"] = mask_util.encode(mask[:, :, None])[0]
-            annos.append(anno)
-    ret["annotations"] = annos
-    return ret
-
-
-if __name__ == "__main__":
-    """
-    Test the cityscapes dataset loader.
-
-    Usage:
-        python -m detectron2.data.datasets.cityscapes \
-            cityscapes/leftImg8bit/train cityscapes/gtFine/train
-    """
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("image_dir")
-    parser.add_argument("gt_dir")
-    parser.add_argument("--type", choices=["instance", "semantic"], default="instance")
-    args = parser.parse_args()
-    from detectron2.data.catalog import Metadata
-    from detectron2.utils.visualizer import Visualizer
-    from cityscapesscripts.helpers.labels import labels
-
-    logger = setup_logger(name=__name__)
-
-    dirname = "cityscapes-data-vis"
-    os.makedirs(dirname, exist_ok=True)
-
-    if args.type == "instance":
-        dicts = load_cityscapes_instances(
-            args.image_dir, args.gt_dir, from_json=True, to_polygons=True
-        )
-        logger.info("Done loading {} samples.".format(len(dicts)))
-
-        thing_classes = [k.name for k in labels if k.hasInstances and not k.ignoreInEval]
-        meta = Metadata().set(thing_classes=thing_classes)
-
-    else:
-        dicts = load_cityscapes_semantic(args.image_dir, args.gt_dir)
-        logger.info("Done loading {} samples.".format(len(dicts)))
-
-        stuff_names = [k.name for k in labels if k.trainId != 255]
-        stuff_colors = [k.color for k in labels if k.trainId != 255]
-        meta = Metadata().set(stuff_names=stuff_names, stuff_colors=stuff_colors)
-
-    for d in dicts:
-        img = np.array(Image.open(d["file_name"]))
-        visualizer = Visualizer(img, metadata=meta)
-        vis = visualizer.draw_dataset_dict(d)
-        # cv2.imshow("a", vis.get_image()[:, :, ::-1])
-        # cv2.waitKey()
-        fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
-        vis.save(fpath)

spaces/CVPR/LIVE/thrust/thrust/count.h

@@ -1,235 +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.
- */
-
-
-/*! \file count.h
- *  \brief Counting elements in a range
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-#include <thrust/iterator/iterator_traits.h>
-
-namespace thrust
-{
-
-
-/*! \addtogroup algorithms
- */
-
-/*! \addtogroup reductions
- *  \ingroup algorithms
- *  \{
- */
-
-/*! \addtogroup counting
- *  \ingroup reductions
- *  \{
- */
-
-
-/*! \p count finds the number of elements in <tt>[first,last)</tt> that are equal
- *  to \p value. More precisely, \p count returns the number of iterators \c i in
- *  <tt>[first, last)</tt> such that <tt>*i == value</tt>.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence.
- *  \param last The end of the sequence.
- *  \param value The value to be counted.
- *  \return The number of elements equal to \p value.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator must be a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a> and \c InputIterator's \c value_type must be a model of must be a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *  \tparam EqualityComparable must be a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a> and can be compared for equality with \c InputIterator's \c value_type
- *
- *  The following code snippet demonstrates how to use \p count to 
- *  count the number of instances in a range of a value of interest using the \p thrust::device execution policy:
- *
- *  \code
- *  #include <thrust/count.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  // put 3 1s in a device_vector
- *  thrust::device_vector<int> vec(5,0);
- *  vec[1] = 1;
- *  vec[3] = 1;
- *  vec[4] = 1;
- *  
- *  // count the 1s
- *  int result = thrust::count(thrust::device, vec.begin(), vec.end(), 1);
- *  // result == 3
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/count.html
- */
-template<typename DerivedPolicy, typename InputIterator, typename EqualityComparable>
-__host__ __device__
-  typename thrust::iterator_traits<InputIterator>::difference_type
-    count(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, InputIterator first, InputIterator last, const EqualityComparable& value);
-
-
-
-/*! \p count finds the number of elements in <tt>[first,last)</tt> that are equal
- *  to \p value. More precisely, \p count returns the number of iterators \c i in
- *  <tt>[first, last)</tt> such that <tt>*i == value</tt>.
- *
- *  \param first The beginning of the sequence.
- *  \param last The end of the sequence.
- *  \param value The value to be counted.
- *  \return The number of elements equal to \p value.
- *
- *  \tparam InputIterator must be a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a> and \c InputIterator's \c value_type must be a model of must be a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *  \tparam EqualityComparable must be a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a> and can be compared for equality with \c InputIterator's \c value_type
- *
- *  The following code snippet demonstrates how to use \p count to 
- *  count the number of instances in a range of a value of interest.
- *  \code
- *  #include <thrust/count.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  // put 3 1s in a device_vector
- *  thrust::device_vector<int> vec(5,0);
- *  vec[1] = 1;
- *  vec[3] = 1;
- *  vec[4] = 1;
- *  
- *  // count the 1s
- *  int result = thrust::count(vec.begin(), vec.end(), 1);
- *  // result == 3
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/count.html
- */
-template <typename InputIterator, typename EqualityComparable>
-  typename thrust::iterator_traits<InputIterator>::difference_type
-    count(InputIterator first, InputIterator last, const EqualityComparable& value);
-
-
-/*! \p count_if finds the number of elements in <tt>[first,last)</tt> for which 
- *  a predicate is \c true. More precisely, \p count_if returns the number of iterators
- *  \c i in <tt>[first, last)</tt> such that <tt>pred(*i) == true</tt>.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence.
- *  \param last The end of the sequence.
- *  \param pred The predicate.
- *  \return The number of elements where \p pred is \c true.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator must be a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a> and \c InputIterator's \c value_type must be convertible to \c Predicate's \c argument_type.
- *  \tparam Predicate must be a model of <a href="http://www.sgi.com/tech/stl/Predicate.html">Predicate</a>.
- *
- *  The following code snippet demonstrates how to use \p count to
- *  count the number of odd numbers in a range using the \p thrust::device execution policy:
- *
- *  \code
- *  #include <thrust/count.h>
- *  #include <thrust/device_vector.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  struct is_odd
- *  {
- *    __host__ __device__
- *    bool operator()(int &x)
- *    {
- *      return x & 1;
- *    }
- *  };
- *  ...
- *  // fill a device_vector with even & odd numbers
- *  thrust::device_vector<int> vec(5);
- *  vec[0] = 0;
- *  vec[1] = 1;
- *  vec[2] = 2;
- *  vec[3] = 3;
- *  vec[4] = 4;
- *
- *  // count the odd elements in vec
- *  int result = thrust::count_if(thrust::device, vec.begin(), vec.end(), is_odd());
- *  // result == 2
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/count.html
- */
-template<typename DerivedPolicy, typename InputIterator, typename Predicate>
-__host__ __device__
-  typename thrust::iterator_traits<InputIterator>::difference_type
-    count_if(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, InputIterator first, InputIterator last, Predicate pred);
-
-
-/*! \p count_if finds the number of elements in <tt>[first,last)</tt> for which 
- *  a predicate is \c true. More precisely, \p count_if returns the number of iterators
- *  \c i in <tt>[first, last)</tt> such that <tt>pred(*i) == true</tt>.
- *
- *  \param first The beginning of the sequence.
- *  \param last The end of the sequence.
- *  \param pred The predicate.
- *  \return The number of elements where \p pred is \c true.
- *
- *  \tparam InputIterator must be a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a> and \c InputIterator's \c value_type must be convertible to \c Predicate's \c argument_type.
- *  \tparam Predicate must be a model of <a href="http://www.sgi.com/tech/stl/Predicate.html">Predicate</a>.
- *
- *  The following code snippet demonstrates how to use \p count to
- *  count the number of odd numbers in a range.
- *  \code
- *  #include <thrust/count.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  struct is_odd
- *  {
- *    __host__ __device__
- *    bool operator()(int &x)
- *    {
- *      return x & 1;
- *    }
- *  };
- *  ...
- *  // fill a device_vector with even & odd numbers
- *  thrust::device_vector<int> vec(5);
- *  vec[0] = 0;
- *  vec[1] = 1;
- *  vec[2] = 2;
- *  vec[3] = 3;
- *  vec[4] = 4;
- *
- *  // count the odd elements in vec
- *  int result = thrust::count_if(vec.begin(), vec.end(), is_odd());
- *  // result == 2
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/count.html
- */
-template <typename InputIterator, typename Predicate>
-  typename thrust::iterator_traits<InputIterator>::difference_type
-    count_if(InputIterator first, InputIterator last, Predicate pred);
-
-
-/*! \} // end counting
- *  \} // end reductions
- */
-
-
-} // end thrust
-
-#include <thrust/detail/count.inl>
-

spaces/CVPR/LIVE/thrust/thrust/random/uniform_real_distribution.h

@@ -1,274 +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.
- */
-
-
-/*! \file uniform_real_distribution.h
- *  \brief A uniform distribution of real-valued numbers
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/pair.h>
-#include <thrust/random/detail/random_core_access.h>
-#include <iostream>
-
-namespace thrust
-{
-
-namespace random
-{
-
-
-/*! \addtogroup random_number_distributions
- *  \{
- */
-
-/*! \class uniform_real_distribution
- *  \brief A \p uniform_real_distribution random number distribution produces floating point
- *         uniform random numbers from a half-open interval.
- *
- *  \tparam RealType The type of floating point number to produce.
- *
- *  The following code snippet demonstrates examples of using a \p uniform_real_distribution with a 
- *  random number engine to produce random integers drawn from a given range:
- *
- *  \code
- *  #include <thrust/random/linear_congruential_engine.h>
- *  #include <thrust/random/uniform_real_distribution.h>
- *
- *  int main(void)
- *  {
- *    // create a minstd_rand object to act as our source of randomness
- *    thrust::minstd_rand rng;
- *
- *    // create a uniform_real_distribution to produce floats from [-7,13)
- *    thrust::uniform_real_distribution<float> dist(-7,13);
- *
- *    // write a random number from the range [-7,13) to standard output
- *    std::cout << dist(rng) << std::endl;
- *
- *    // write the range of the distribution, just in case we forgot
- *    std::cout << dist.min() << std::endl;
- *
- *    // -7.0 is printed
- *
- *    std::cout << dist.max() << std::endl;
- *
- *    // 13.0 is printed
- *
- *    // write the parameters of the distribution (which happen to be the bounds) to standard output
- *    std::cout << dist.a() << std::endl;
- *
- *    // -7.0 is printed
- *
- *    std::cout << dist.b() << std::endl;
- *
- *    // 13.0 is printed
- *
- *    return 0;
- *  }
- *  \endcode
- */
-template<typename RealType = double>
-  class uniform_real_distribution
-{
-  public:
-    // types
-    
-    /*! \typedef result_type
-     *  \brief The type of the floating point number produced by this \p uniform_real_distribution.
-     */
-    typedef RealType result_type;
-
-    /*! \typedef param_type
-     *  \brief The type of the object encapsulating this \p uniform_real_distribution's parameters.
-     */
-    typedef thrust::pair<RealType,RealType> param_type;
-
-    // constructors and reset functions
-    
-    /*! This constructor creates a new \p uniform_real_distribution from two values defining the
-     *  half-open interval of the distribution.
-     *  
-     *  \param a The smallest floating point number to potentially produce. Defaults to \c 0.0.
-     *  \param b The smallest number larger than the largest floating point number to potentially produce. Defaults to \c 1.0.
-     */
-    __host__ __device__
-    explicit uniform_real_distribution(RealType a = 0.0, RealType b = 1.0);
-
-    /*! This constructor creates a new \p uniform_real_distribution from a \p param_type object
-     *  encapsulating the range of the distribution.
-     *  
-     *  \param parm A \p param_type object encapsulating the parameters (i.e., the range) of the distribution.
-     */
-    __host__ __device__
-    explicit uniform_real_distribution(const param_type &parm);
-
-    /*! This does nothing.  It is included to conform to the requirements of the RandomDistribution concept.
-     */
-    __host__ __device__
-    void reset(void);
-
-    // generating functions
-
-    /*! This method produces a new uniform random integer drawn from this \p uniform_real_distribution's
-     *  range using a \p UniformRandomNumberGenerator as a source of randomness.
-     *
-     *  \param urng The \p UniformRandomNumberGenerator to use as a source of randomness.
-     */
-    template<typename UniformRandomNumberGenerator>
-    __host__ __device__
-    result_type operator()(UniformRandomNumberGenerator &urng);
-
-    /*! This method produces a new uniform random integer as if by creating a new \p uniform_real_distribution 
-     *  from the given \p param_type object, and calling its <tt>operator()</tt> method with the given
-     *  \p UniformRandomNumberGenerator as a source of randomness.
-     *
-     *  \param urng The \p UniformRandomNumberGenerator to use as a source of randomness.
-     *  \param parm A \p param_type object encapsulating the parameters of the \p uniform_real_distribution
-     *              to draw from.
-     */
-    template<typename UniformRandomNumberGenerator>
-    __host__ __device__
-    result_type operator()(UniformRandomNumberGenerator &urng, const param_type &parm);
-
-    // property functions
-
-    /*! This method returns the value of the parameter with which this \p uniform_real_distribution
-     *  was constructed.
-     *
-     *  \return The lower bound of this \p uniform_real_distribution's half-open interval.
-     */
-    __host__ __device__
-    result_type a(void) const;
-
-    /*! This method returns the value of the parameter with which this \p uniform_real_distribution
-     *  was constructed.
-     *
-     *  \return The upper bound of this \p uniform_real_distribution's half-open interval.
-     */
-    __host__ __device__
-    result_type b(void) const;
-
-    /*! This method returns a \p param_type object encapsulating the parameters with which this
-     *  \p uniform_real_distribution was constructed.
-     *
-     *  \return A \p param_type object enapsulating the half-open interval of this \p uniform_real_distribution.
-     */
-    __host__ __device__
-    param_type param(void) const;
-
-    /*! This method changes the parameters of this \p uniform_real_distribution using the values encapsulated
-     *  in a given \p param_type object.
-     *
-     *  \param parm A \p param_type object encapsulating the new half-open interval of this \p uniform_real_distribution.
-     */
-    __host__ __device__
-    void param(const param_type &parm);
-
-    /*! This method returns the smallest floating point number this \p uniform_real_distribution can potentially produce.
-     *
-     *  \return The lower bound of this \p uniform_real_distribution's half-open interval.
-     */
-    __host__ __device__
-    result_type min THRUST_PREVENT_MACRO_SUBSTITUTION (void) const;
-
-    /*! This method returns the smallest number larger than largest floating point number this \p uniform_real_distribution can potentially produce.
-     *
-     *  \return The upper bound of this \p uniform_real_distribution's half-open interval.
-     */
-    __host__ __device__
-    result_type max THRUST_PREVENT_MACRO_SUBSTITUTION (void) const;
-
-    /*! \cond
-     */
-  private:
-    param_type m_param;
-
-    friend struct thrust::random::detail::random_core_access;
-
-    __host__ __device__
-    bool equal(const uniform_real_distribution &rhs) const;
-
-    template<typename CharT, typename Traits>
-    std::basic_ostream<CharT,Traits>& stream_out(std::basic_ostream<CharT,Traits> &os) const;
-
-    template<typename CharT, typename Traits>
-    std::basic_istream<CharT,Traits>& stream_in(std::basic_istream<CharT,Traits> &is);
-    /*! \endcond
-     */
-}; // end uniform_real_distribution
-
-
-/*! This function checks two \p uniform_real_distributions for equality.
- *  \param lhs The first \p uniform_real_distribution to test.
- *  \param rhs The second \p uniform_real_distribution to test.
- *  \return \c true if \p lhs is equal to \p rhs; \c false, otherwise.
- */
-template<typename RealType>
-__host__ __device__
-bool operator==(const uniform_real_distribution<RealType> &lhs,
-                const uniform_real_distribution<RealType> &rhs);
-
-
-/*! This function checks two \p uniform_real_distributions for inequality.
- *  \param lhs The first \p uniform_real_distribution to test.
- *  \param rhs The second \p uniform_real_distribution to test.
- *  \return \c true if \p lhs is not equal to \p rhs; \c false, otherwise.
- */
-template<typename RealType>
-__host__ __device__
-bool operator!=(const uniform_real_distribution<RealType> &lhs,
-                const uniform_real_distribution<RealType> &rhs);
-
-
-/*! This function streams a uniform_real_distribution to a \p std::basic_ostream.
- *  \param os The \p basic_ostream to stream out to.
- *  \param d The \p uniform_real_distribution to stream out.
- *  \return \p os
- */
-template<typename RealType,
-         typename CharT, typename Traits>
-std::basic_ostream<CharT,Traits>&
-operator<<(std::basic_ostream<CharT,Traits> &os,
-           const uniform_real_distribution<RealType> &d);
-
-
-/*! This function streams a uniform_real_distribution in from a std::basic_istream.
- *  \param is The \p basic_istream to stream from.
- *  \param d The \p uniform_real_distribution to stream in.
- *  \return \p is
- */
-template<typename RealType,
-         typename CharT, typename Traits>
-std::basic_istream<CharT,Traits>&
-operator>>(std::basic_istream<CharT,Traits> &is,
-           uniform_real_distribution<RealType> &d);
-
-
-/*! \} // end random_number_distributions
- */
-
-
-} // end random
-
-using random::uniform_real_distribution;
-
-} // end thrust
-
-#include <thrust/random/detail/uniform_real_distribution.inl>
-

spaces/CVPR/drawings-to-human/static/_app/immutable/pages/index.svelte-b5d75a5f.js

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spaces/CVPR/regionclip-demo/detectron2/layers/batch_norm.py

@@ -1,243 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import torch
-import torch.distributed as dist
-from fvcore.nn.distributed import differentiable_all_reduce
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.utils import comm, env
-
-from .wrappers import BatchNorm2d
-
-
-class FrozenBatchNorm2d(nn.Module):
-    """
-    BatchNorm2d where the batch statistics and the affine parameters are fixed.
-
-    It contains non-trainable buffers called
-    "weight" and "bias", "running_mean", "running_var",
-    initialized to perform identity transformation.
-
-    The pre-trained backbone models from Caffe2 only contain "weight" and "bias",
-    which are computed from the original four parameters of BN.
-    The affine transform `x * weight + bias` will perform the equivalent
-    computation of `(x - running_mean) / sqrt(running_var) * weight + bias`.
-    When loading a backbone model from Caffe2, "running_mean" and "running_var"
-    will be left unchanged as identity transformation.
-
-    Other pre-trained backbone models may contain all 4 parameters.
-
-    The forward is implemented by `F.batch_norm(..., training=False)`.
-    """
-
-    _version = 3
-
-    def __init__(self, num_features, eps=1e-5):
-        super().__init__()
-        self.num_features = num_features
-        self.eps = eps
-        self.register_buffer("weight", torch.ones(num_features))
-        self.register_buffer("bias", torch.zeros(num_features))
-        self.register_buffer("running_mean", torch.zeros(num_features))
-        self.register_buffer("running_var", torch.ones(num_features) - eps)
-
-    def forward(self, x):
-        if x.requires_grad:
-            # When gradients are needed, F.batch_norm will use extra memory
-            # because its backward op computes gradients for weight/bias as well.
-            scale = self.weight * (self.running_var + self.eps).rsqrt()
-            bias = self.bias - self.running_mean * scale
-            scale = scale.reshape(1, -1, 1, 1)
-            bias = bias.reshape(1, -1, 1, 1)
-            out_dtype = x.dtype  # may be half
-            return x * scale.to(out_dtype) + bias.to(out_dtype)
-        else:
-            # When gradients are not needed, F.batch_norm is a single fused op
-            # and provide more optimization opportunities.
-            return F.batch_norm(
-                x,
-                self.running_mean,
-                self.running_var,
-                self.weight,
-                self.bias,
-                training=False,
-                eps=self.eps,
-            )
-
-    def _load_from_state_dict(
-        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-    ):
-        version = local_metadata.get("version", None)
-
-        if version is None or version < 2:
-            # when use offline modules, avoid overwriting running mean and var for loaded weights
-            skip_reset = False
-            for k_n in state_dict: # checkpoint weights
-                if 'ignore_others' in k_n: #if 'offline' in k_n:
-                    skip_reset = True
-            if not skip_reset:
-                # No running_mean/var in early versions
-                # This will silent the warnings
-                if prefix + "running_mean" not in state_dict:
-                    state_dict[prefix + "running_mean"] = torch.zeros_like(self.running_mean)
-                if prefix + "running_var" not in state_dict:
-                    state_dict[prefix + "running_var"] = torch.ones_like(self.running_var)
-
-        # NOTE: if a checkpoint is trained with BatchNorm and loaded (together with
-        # version number) to FrozenBatchNorm, running_var will be wrong. One solution
-        # is to remove the version number from the checkpoint.
-        if version is not None and version < 3:
-            logger = logging.getLogger(__name__)
-            logger.info("FrozenBatchNorm {} is upgraded to version 3.".format(prefix.rstrip(".")))
-            # In version < 3, running_var are used without +eps.
-            state_dict[prefix + "running_var"] -= self.eps
-
-        super()._load_from_state_dict(
-            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-        )
-
-    def __repr__(self):
-        return "FrozenBatchNorm2d(num_features={}, eps={})".format(self.num_features, self.eps)
-
-    @classmethod
-    def convert_frozen_batchnorm(cls, module):
-        """
-        Convert all BatchNorm/SyncBatchNorm in module into FrozenBatchNorm.
-
-        Args:
-            module (torch.nn.Module):
-
-        Returns:
-            If module is BatchNorm/SyncBatchNorm, returns a new module.
-            Otherwise, in-place convert module and return it.
-
-        Similar to convert_sync_batchnorm in
-        https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py
-        """
-        bn_module = nn.modules.batchnorm
-        bn_module = (bn_module.BatchNorm2d, bn_module.SyncBatchNorm)
-        res = module
-        if isinstance(module, bn_module):
-            res = cls(module.num_features)
-            if module.affine:
-                res.weight.data = module.weight.data.clone().detach()
-                res.bias.data = module.bias.data.clone().detach()
-            res.running_mean.data = module.running_mean.data
-            res.running_var.data = module.running_var.data
-            res.eps = module.eps
-        else:
-            for name, child in module.named_children():
-                new_child = cls.convert_frozen_batchnorm(child)
-                if new_child is not child:
-                    res.add_module(name, new_child)
-        return res
-
-
-def get_norm(norm, out_channels):
-    """
-    Args:
-        norm (str or callable): either one of BN, SyncBN, FrozenBN, GN;
-            or a callable that takes a channel number and returns
-            the normalization layer as a nn.Module.
-
-    Returns:
-        nn.Module or None: the normalization layer
-    """
-    if norm is None:
-        return None
-    if isinstance(norm, str):
-        if len(norm) == 0:
-            return None
-        norm = {
-            "BN": BatchNorm2d,
-            # Fixed in https://github.com/pytorch/pytorch/pull/36382
-            "SyncBN": NaiveSyncBatchNorm if env.TORCH_VERSION <= (1, 5) else nn.SyncBatchNorm,
-            "FrozenBN": FrozenBatchNorm2d,
-            "GN": lambda channels: nn.GroupNorm(32, channels),
-            # for debugging:
-            "nnSyncBN": nn.SyncBatchNorm,
-            "naiveSyncBN": NaiveSyncBatchNorm,
-        }[norm]
-    return norm(out_channels)
-
-
-class NaiveSyncBatchNorm(BatchNorm2d):
-    """
-    In PyTorch<=1.5, ``nn.SyncBatchNorm`` has incorrect gradient
-    when the batch size on each worker is different.
-    (e.g., when scale augmentation is used, or when it is applied to mask head).
-
-    This is a slower but correct alternative to `nn.SyncBatchNorm`.
-
-    Note:
-        There isn't a single definition of Sync BatchNorm.
-
-        When ``stats_mode==""``, this module computes overall statistics by using
-        statistics of each worker with equal weight.  The result is true statistics
-        of all samples (as if they are all on one worker) only when all workers
-        have the same (N, H, W). This mode does not support inputs with zero batch size.
-
-        When ``stats_mode=="N"``, this module computes overall statistics by weighting
-        the statistics of each worker by their ``N``. The result is true statistics
-        of all samples (as if they are all on one worker) only when all workers
-        have the same (H, W). It is slower than ``stats_mode==""``.
-
-        Even though the result of this module may not be the true statistics of all samples,
-        it may still be reasonable because it might be preferrable to assign equal weights
-        to all workers, regardless of their (H, W) dimension, instead of putting larger weight
-        on larger images. From preliminary experiments, little difference is found between such
-        a simplified implementation and an accurate computation of overall mean & variance.
-    """
-
-    def __init__(self, *args, stats_mode="", **kwargs):
-        super().__init__(*args, **kwargs)
-        assert stats_mode in ["", "N"]
-        self._stats_mode = stats_mode
-
-    def forward(self, input):
-        if comm.get_world_size() == 1 or not self.training:
-            return super().forward(input)
-
-        B, C = input.shape[0], input.shape[1]
-
-        half_input = input.dtype == torch.float16
-        if half_input:
-            # fp16 does not have good enough numerics for the reduction here
-            input = input.float()
-        mean = torch.mean(input, dim=[0, 2, 3])
-        meansqr = torch.mean(input * input, dim=[0, 2, 3])
-
-        if self._stats_mode == "":
-            assert B > 0, 'SyncBatchNorm(stats_mode="") does not support zero batch size.'
-            vec = torch.cat([mean, meansqr], dim=0)
-            vec = differentiable_all_reduce(vec) * (1.0 / dist.get_world_size())
-            mean, meansqr = torch.split(vec, C)
-            momentum = self.momentum
-        else:
-            if B == 0:
-                vec = torch.zeros([2 * C + 1], device=mean.device, dtype=mean.dtype)
-                vec = vec + input.sum()  # make sure there is gradient w.r.t input
-            else:
-                vec = torch.cat(
-                    [mean, meansqr, torch.ones([1], device=mean.device, dtype=mean.dtype)], dim=0
-                )
-            vec = differentiable_all_reduce(vec * B)
-
-            total_batch = vec[-1].detach()
-            momentum = total_batch.clamp(max=1) * self.momentum  # no update if total_batch is 0
-            mean, meansqr, _ = torch.split(vec / total_batch.clamp(min=1), C)  # avoid div-by-zero
-
-        var = meansqr - mean * mean
-        invstd = torch.rsqrt(var + self.eps)
-        scale = self.weight * invstd
-        bias = self.bias - mean * scale
-        scale = scale.reshape(1, -1, 1, 1)
-        bias = bias.reshape(1, -1, 1, 1)
-
-        self.running_mean += momentum * (mean.detach() - self.running_mean)
-        self.running_var += momentum * (var.detach() - self.running_var)
-        ret = input * scale + bias
-        if half_input:
-            ret = ret.half()
-        return ret

spaces/CamCam17/Alexwww-davide-comic-book-characters/Dockerfile

@@ -1,17 +0,0 @@
-FROM ghcr.io/livebook-dev/livebook:latest-cuda11.8
-
-ENV LIVEBOOK_APP_SERVICE_NAME "🐳 Hugging Face - $SPACE_TITLE"
-ENV LIVEBOOK_APP_SERVICE_URL "https://huggingface.co/spaces/$SPACE_AUTHOR_NAME/$SPACE_REPO_NAME"
-ENV LIVEBOOK_UPDATE_INSTRUCTIONS_URL "https://livebook.dev"
-ENV LIVEBOOK_WITHIN_IFRAME "true"
-ENV LIVEBOOK_APPS_PATH "/public-apps"
-ENV LIVEBOOK_APPS_PATH_WARMUP "manual"
-ENV LIVEBOOK_DATA_PATH "/data"
-ENV LIVEBOOK_PORT 7860
-
-EXPOSE 7860
-USER root
-COPY public-apps/ /public-apps
-RUN mkdir -p /data
-RUN chmod 777 /data
-RUN /app/bin/warmup_apps.sh

spaces/CikeyQI/meme-api/meme_generator/memes/loading/__init__.py

@@ -1,36 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL import ImageFilter
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import make_jpg_or_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def loading(images: List[BuildImage], texts, args):
-    img_big = images[0].convert("RGBA").resize_width(500)
-    img_big = img_big.filter(ImageFilter.GaussianBlur(radius=3))
-    h1 = img_big.height
-    mask = BuildImage.new("RGBA", img_big.size, (0, 0, 0, 32))
-    icon = BuildImage.open(img_dir / "icon.png")
-    img_big.paste(mask, alpha=True).paste(icon, (200, int(h1 / 2) - 50), alpha=True)
-
-    def make(img: BuildImage) -> BuildImage:
-        img_small = img.convert("RGBA").resize_width(100)
-        h2 = max(img_small.height, 80)
-        frame = BuildImage.new("RGBA", (500, h1 + h2 + 10), "white")
-        frame.paste(img_big, alpha=True).paste(
-            img_small, (100, h1 + 5 + (h2 - img_small.height) // 2), alpha=True
-        )
-        frame.draw_text(
-            (210, h1 + 5, 480, h1 + h2 + 5), "不出来", halign="left", max_fontsize=60
-        )
-        return frame
-
-    return make_jpg_or_gif(images[0], make)
-
-
-add_meme("loading", loading, min_images=1, max_images=1, keywords=["加载中"])

spaces/CofAI/chat.b4/g4f/Provider/Providers/helpers/theb.py

@@ -1,48 +0,0 @@
-import json
-import sys
-from re import findall
-from curl_cffi import requests
-
-config = json.loads(sys.argv[1])
-prompt = config['messages'][-1]['content']
-
-headers = {
-    'authority': 'chatbot.theb.ai',
-    'accept': 'application/json, text/plain, */*',
-    'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
-    'content-type': 'application/json',
-    'origin': 'https://chatbot.theb.ai',
-    'referer': 'https://chatbot.theb.ai/',
-    'sec-ch-ua': '"Google Chrome";v="113", "Chromium";v="113", "Not-A.Brand";v="24"',
-    'sec-ch-ua-mobile': '?0',
-    'sec-ch-ua-platform': '"macOS"',
-    'sec-fetch-dest': 'empty',
-    'sec-fetch-mode': 'cors',
-    'sec-fetch-site': 'same-origin',
-    'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36',
-}
-
-json_data = {
-    'prompt': prompt,
-    'options': {}
-}
-
-def format(chunk):
-    try:
-        completion_chunk = findall(r'content":"(.*)"},"fin', chunk.decode())[0]
-        print(completion_chunk, flush=True, end='')
-
-    except Exception as e:
-        print(f'[ERROR] an error occured, retrying... | [[{chunk.decode()}]]', flush=True)
-        return
-
-while True:
-    try: 
-        response = requests.post('https://chatbot.theb.ai/api/chat-process', 
-                            headers=headers, json=json_data, content_callback=format, impersonate='chrome110')
-        
-        exit(0)
-    
-    except Exception as e:
-        print('[ERROR] an error occured, retrying... |', e, flush=True)
-        continue