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- Diffusers Models Gallery
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-Play around with the Spaces below and see if you notice a difference between generated images with and without a depth map!
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diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/t5_film_transformer.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/t5_film_transformer.py
deleted file mode 100644
index 1c41e656a9dbe81edafd5a2958d49ff28e84fd01..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/t5_film_transformer.py
+++ /dev/null
@@ -1,321 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import math
-
-import torch
-from torch import nn
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from .attention_processor import Attention
-from .embeddings import get_timestep_embedding
-from .modeling_utils import ModelMixin
-
-
-class T5FilmDecoder(ModelMixin, ConfigMixin):
- @register_to_config
- def __init__(
- self,
- input_dims: int = 128,
- targets_length: int = 256,
- max_decoder_noise_time: float = 2000.0,
- d_model: int = 768,
- num_layers: int = 12,
- num_heads: int = 12,
- d_kv: int = 64,
- d_ff: int = 2048,
- dropout_rate: float = 0.1,
- ):
- super().__init__()
-
- self.conditioning_emb = nn.Sequential(
- nn.Linear(d_model, d_model * 4, bias=False),
- nn.SiLU(),
- nn.Linear(d_model * 4, d_model * 4, bias=False),
- nn.SiLU(),
- )
-
- self.position_encoding = nn.Embedding(targets_length, d_model)
- self.position_encoding.weight.requires_grad = False
-
- self.continuous_inputs_projection = nn.Linear(input_dims, d_model, bias=False)
-
- self.dropout = nn.Dropout(p=dropout_rate)
-
- self.decoders = nn.ModuleList()
- for lyr_num in range(num_layers):
- # FiLM conditional T5 decoder
- lyr = DecoderLayer(d_model=d_model, d_kv=d_kv, num_heads=num_heads, d_ff=d_ff, dropout_rate=dropout_rate)
- self.decoders.append(lyr)
-
- self.decoder_norm = T5LayerNorm(d_model)
-
- self.post_dropout = nn.Dropout(p=dropout_rate)
- self.spec_out = nn.Linear(d_model, input_dims, bias=False)
-
- def encoder_decoder_mask(self, query_input, key_input):
- mask = torch.mul(query_input.unsqueeze(-1), key_input.unsqueeze(-2))
- return mask.unsqueeze(-3)
-
- def forward(self, encodings_and_masks, decoder_input_tokens, decoder_noise_time):
- batch, _, _ = decoder_input_tokens.shape
- assert decoder_noise_time.shape == (batch,)
-
- # decoder_noise_time is in [0, 1), so rescale to expected timing range.
- time_steps = get_timestep_embedding(
- decoder_noise_time * self.config.max_decoder_noise_time,
- embedding_dim=self.config.d_model,
- max_period=self.config.max_decoder_noise_time,
- ).to(dtype=self.dtype)
-
- conditioning_emb = self.conditioning_emb(time_steps).unsqueeze(1)
-
- assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
-
- seq_length = decoder_input_tokens.shape[1]
-
- # If we want to use relative positions for audio context, we can just offset
- # this sequence by the length of encodings_and_masks.
- decoder_positions = torch.broadcast_to(
- torch.arange(seq_length, device=decoder_input_tokens.device),
- (batch, seq_length),
- )
-
- position_encodings = self.position_encoding(decoder_positions)
-
- inputs = self.continuous_inputs_projection(decoder_input_tokens)
- inputs += position_encodings
- y = self.dropout(inputs)
-
- # decoder: No padding present.
- decoder_mask = torch.ones(
- decoder_input_tokens.shape[:2], device=decoder_input_tokens.device, dtype=inputs.dtype
- )
-
- # Translate encoding masks to encoder-decoder masks.
- encodings_and_encdec_masks = [(x, self.encoder_decoder_mask(decoder_mask, y)) for x, y in encodings_and_masks]
-
- # cross attend style: concat encodings
- encoded = torch.cat([x[0] for x in encodings_and_encdec_masks], dim=1)
- encoder_decoder_mask = torch.cat([x[1] for x in encodings_and_encdec_masks], dim=-1)
-
- for lyr in self.decoders:
- y = lyr(
- y,
- conditioning_emb=conditioning_emb,
- encoder_hidden_states=encoded,
- encoder_attention_mask=encoder_decoder_mask,
- )[0]
-
- y = self.decoder_norm(y)
- y = self.post_dropout(y)
-
- spec_out = self.spec_out(y)
- return spec_out
-
-
-class DecoderLayer(nn.Module):
- def __init__(self, d_model, d_kv, num_heads, d_ff, dropout_rate, layer_norm_epsilon=1e-6):
- super().__init__()
- self.layer = nn.ModuleList()
-
- # cond self attention: layer 0
- self.layer.append(
- T5LayerSelfAttentionCond(d_model=d_model, d_kv=d_kv, num_heads=num_heads, dropout_rate=dropout_rate)
- )
-
- # cross attention: layer 1
- self.layer.append(
- T5LayerCrossAttention(
- d_model=d_model,
- d_kv=d_kv,
- num_heads=num_heads,
- dropout_rate=dropout_rate,
- layer_norm_epsilon=layer_norm_epsilon,
- )
- )
-
- # Film Cond MLP + dropout: last layer
- self.layer.append(
- T5LayerFFCond(d_model=d_model, d_ff=d_ff, dropout_rate=dropout_rate, layer_norm_epsilon=layer_norm_epsilon)
- )
-
- def forward(
- self,
- hidden_states,
- conditioning_emb=None,
- attention_mask=None,
- encoder_hidden_states=None,
- encoder_attention_mask=None,
- encoder_decoder_position_bias=None,
- ):
- hidden_states = self.layer[0](
- hidden_states,
- conditioning_emb=conditioning_emb,
- attention_mask=attention_mask,
- )
-
- if encoder_hidden_states is not None:
- encoder_extended_attention_mask = torch.where(encoder_attention_mask > 0, 0, -1e10).to(
- encoder_hidden_states.dtype
- )
-
- hidden_states = self.layer[1](
- hidden_states,
- key_value_states=encoder_hidden_states,
- attention_mask=encoder_extended_attention_mask,
- )
-
- # Apply Film Conditional Feed Forward layer
- hidden_states = self.layer[-1](hidden_states, conditioning_emb)
-
- return (hidden_states,)
-
-
-class T5LayerSelfAttentionCond(nn.Module):
- def __init__(self, d_model, d_kv, num_heads, dropout_rate):
- super().__init__()
- self.layer_norm = T5LayerNorm(d_model)
- self.FiLMLayer = T5FiLMLayer(in_features=d_model * 4, out_features=d_model)
- self.attention = Attention(query_dim=d_model, heads=num_heads, dim_head=d_kv, out_bias=False, scale_qk=False)
- self.dropout = nn.Dropout(dropout_rate)
-
- def forward(
- self,
- hidden_states,
- conditioning_emb=None,
- attention_mask=None,
- ):
- # pre_self_attention_layer_norm
- normed_hidden_states = self.layer_norm(hidden_states)
-
- if conditioning_emb is not None:
- normed_hidden_states = self.FiLMLayer(normed_hidden_states, conditioning_emb)
-
- # Self-attention block
- attention_output = self.attention(normed_hidden_states)
-
- hidden_states = hidden_states + self.dropout(attention_output)
-
- return hidden_states
-
-
-class T5LayerCrossAttention(nn.Module):
- def __init__(self, d_model, d_kv, num_heads, dropout_rate, layer_norm_epsilon):
- super().__init__()
- self.attention = Attention(query_dim=d_model, heads=num_heads, dim_head=d_kv, out_bias=False, scale_qk=False)
- self.layer_norm = T5LayerNorm(d_model, eps=layer_norm_epsilon)
- self.dropout = nn.Dropout(dropout_rate)
-
- def forward(
- self,
- hidden_states,
- key_value_states=None,
- attention_mask=None,
- ):
- normed_hidden_states = self.layer_norm(hidden_states)
- attention_output = self.attention(
- normed_hidden_states,
- encoder_hidden_states=key_value_states,
- attention_mask=attention_mask.squeeze(1),
- )
- layer_output = hidden_states + self.dropout(attention_output)
- return layer_output
-
-
-class T5LayerFFCond(nn.Module):
- def __init__(self, d_model, d_ff, dropout_rate, layer_norm_epsilon):
- super().__init__()
- self.DenseReluDense = T5DenseGatedActDense(d_model=d_model, d_ff=d_ff, dropout_rate=dropout_rate)
- self.film = T5FiLMLayer(in_features=d_model * 4, out_features=d_model)
- self.layer_norm = T5LayerNorm(d_model, eps=layer_norm_epsilon)
- self.dropout = nn.Dropout(dropout_rate)
-
- def forward(self, hidden_states, conditioning_emb=None):
- forwarded_states = self.layer_norm(hidden_states)
- if conditioning_emb is not None:
- forwarded_states = self.film(forwarded_states, conditioning_emb)
-
- forwarded_states = self.DenseReluDense(forwarded_states)
- hidden_states = hidden_states + self.dropout(forwarded_states)
- return hidden_states
-
-
-class T5DenseGatedActDense(nn.Module):
- def __init__(self, d_model, d_ff, dropout_rate):
- super().__init__()
- self.wi_0 = nn.Linear(d_model, d_ff, bias=False)
- self.wi_1 = nn.Linear(d_model, d_ff, bias=False)
- self.wo = nn.Linear(d_ff, d_model, bias=False)
- self.dropout = nn.Dropout(dropout_rate)
- self.act = NewGELUActivation()
-
- def forward(self, hidden_states):
- hidden_gelu = self.act(self.wi_0(hidden_states))
- hidden_linear = self.wi_1(hidden_states)
- hidden_states = hidden_gelu * hidden_linear
- hidden_states = self.dropout(hidden_states)
-
- hidden_states = self.wo(hidden_states)
- return hidden_states
-
-
-class T5LayerNorm(nn.Module):
- def __init__(self, hidden_size, eps=1e-6):
- """
- Construct a layernorm module in the T5 style. No bias and no subtraction of mean.
- """
- super().__init__()
- self.weight = nn.Parameter(torch.ones(hidden_size))
- self.variance_epsilon = eps
-
- def forward(self, hidden_states):
- # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
- # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
- # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
- # half-precision inputs is done in fp32
-
- variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
- hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-
- # convert into half-precision if necessary
- if self.weight.dtype in [torch.float16, torch.bfloat16]:
- hidden_states = hidden_states.to(self.weight.dtype)
-
- return self.weight * hidden_states
-
-
-class NewGELUActivation(nn.Module):
- """
- Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see
- the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
- """
-
- def forward(self, input: torch.Tensor) -> torch.Tensor:
- return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))
-
-
-class T5FiLMLayer(nn.Module):
- """
- FiLM Layer
- """
-
- def __init__(self, in_features, out_features):
- super().__init__()
- self.scale_bias = nn.Linear(in_features, out_features * 2, bias=False)
-
- def forward(self, x, conditioning_emb):
- emb = self.scale_bias(conditioning_emb)
- scale, shift = torch.chunk(emb, 2, -1)
- x = x * (1 + scale) + shift
- return x
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/auto_pipeline.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/auto_pipeline.py
deleted file mode 100644
index 66d306720aaf9d4b2aad84af3b4113447335782c..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/auto_pipeline.py
+++ /dev/null
@@ -1,886 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from collections import OrderedDict
-
-from ..configuration_utils import ConfigMixin
-from .controlnet import (
- StableDiffusionControlNetImg2ImgPipeline,
- StableDiffusionControlNetInpaintPipeline,
- StableDiffusionControlNetPipeline,
- StableDiffusionXLControlNetPipeline,
-)
-from .deepfloyd_if import IFImg2ImgPipeline, IFInpaintingPipeline, IFPipeline
-from .kandinsky import (
- KandinskyCombinedPipeline,
- KandinskyImg2ImgCombinedPipeline,
- KandinskyImg2ImgPipeline,
- KandinskyInpaintCombinedPipeline,
- KandinskyInpaintPipeline,
- KandinskyPipeline,
-)
-from .kandinsky2_2 import (
- KandinskyV22CombinedPipeline,
- KandinskyV22Img2ImgCombinedPipeline,
- KandinskyV22Img2ImgPipeline,
- KandinskyV22InpaintCombinedPipeline,
- KandinskyV22InpaintPipeline,
- KandinskyV22Pipeline,
-)
-from .stable_diffusion import (
- StableDiffusionImg2ImgPipeline,
- StableDiffusionInpaintPipeline,
- StableDiffusionPipeline,
-)
-from .stable_diffusion_xl import (
- StableDiffusionXLImg2ImgPipeline,
- StableDiffusionXLInpaintPipeline,
- StableDiffusionXLPipeline,
-)
-
-
-AUTO_TEXT2IMAGE_PIPELINES_MAPPING = OrderedDict(
- [
- ("stable-diffusion", StableDiffusionPipeline),
- ("stable-diffusion-xl", StableDiffusionXLPipeline),
- ("if", IFPipeline),
- ("kandinsky", KandinskyCombinedPipeline),
- ("kandinsky22", KandinskyV22CombinedPipeline),
- ("stable-diffusion-controlnet", StableDiffusionControlNetPipeline),
- ("stable-diffusion-xl-controlnet", StableDiffusionXLControlNetPipeline),
- ]
-)
-
-AUTO_IMAGE2IMAGE_PIPELINES_MAPPING = OrderedDict(
- [
- ("stable-diffusion", StableDiffusionImg2ImgPipeline),
- ("stable-diffusion-xl", StableDiffusionXLImg2ImgPipeline),
- ("if", IFImg2ImgPipeline),
- ("kandinsky", KandinskyImg2ImgCombinedPipeline),
- ("kandinsky22", KandinskyV22Img2ImgCombinedPipeline),
- ("stable-diffusion-controlnet", StableDiffusionControlNetImg2ImgPipeline),
- ]
-)
-
-AUTO_INPAINT_PIPELINES_MAPPING = OrderedDict(
- [
- ("stable-diffusion", StableDiffusionInpaintPipeline),
- ("stable-diffusion-xl", StableDiffusionXLInpaintPipeline),
- ("if", IFInpaintingPipeline),
- ("kandinsky", KandinskyInpaintCombinedPipeline),
- ("kandinsky22", KandinskyV22InpaintCombinedPipeline),
- ("stable-diffusion-controlnet", StableDiffusionControlNetInpaintPipeline),
- ]
-)
-
-_AUTO_TEXT2IMAGE_DECODER_PIPELINES_MAPPING = OrderedDict(
- [
- ("kandinsky", KandinskyPipeline),
- ("kandinsky22", KandinskyV22Pipeline),
- ]
-)
-_AUTO_IMAGE2IMAGE_DECODER_PIPELINES_MAPPING = OrderedDict(
- [
- ("kandinsky", KandinskyImg2ImgPipeline),
- ("kandinsky22", KandinskyV22Img2ImgPipeline),
- ]
-)
-_AUTO_INPAINT_DECODER_PIPELINES_MAPPING = OrderedDict(
- [
- ("kandinsky", KandinskyInpaintPipeline),
- ("kandinsky22", KandinskyV22InpaintPipeline),
- ]
-)
-
-SUPPORTED_TASKS_MAPPINGS = [
- AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
- AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
- AUTO_INPAINT_PIPELINES_MAPPING,
- _AUTO_TEXT2IMAGE_DECODER_PIPELINES_MAPPING,
- _AUTO_IMAGE2IMAGE_DECODER_PIPELINES_MAPPING,
- _AUTO_INPAINT_DECODER_PIPELINES_MAPPING,
-]
-
-
-def _get_connected_pipeline(pipeline_cls):
- # for now connected pipelines can only be loaded from decoder pipelines, such as kandinsky-community/kandinsky-2-2-decoder
- if pipeline_cls in _AUTO_TEXT2IMAGE_DECODER_PIPELINES_MAPPING.values():
- return _get_task_class(
- AUTO_TEXT2IMAGE_PIPELINES_MAPPING, pipeline_cls.__name__, throw_error_if_not_exist=False
- )
- if pipeline_cls in _AUTO_IMAGE2IMAGE_DECODER_PIPELINES_MAPPING.values():
- return _get_task_class(
- AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, pipeline_cls.__name__, throw_error_if_not_exist=False
- )
- if pipeline_cls in _AUTO_INPAINT_DECODER_PIPELINES_MAPPING.values():
- return _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, pipeline_cls.__name__, throw_error_if_not_exist=False)
-
-
-def _get_task_class(mapping, pipeline_class_name, throw_error_if_not_exist: bool = True):
- def get_model(pipeline_class_name):
- for task_mapping in SUPPORTED_TASKS_MAPPINGS:
- for model_name, pipeline in task_mapping.items():
- if pipeline.__name__ == pipeline_class_name:
- return model_name
-
- model_name = get_model(pipeline_class_name)
-
- if model_name is not None:
- task_class = mapping.get(model_name, None)
- if task_class is not None:
- return task_class
-
- if throw_error_if_not_exist:
- raise ValueError(f"AutoPipeline can't find a pipeline linked to {pipeline_class_name} for {model_name}")
-
-
-def _get_signature_keys(obj):
- parameters = inspect.signature(obj.__init__).parameters
- required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
- optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
- expected_modules = set(required_parameters.keys()) - {"self"}
- return expected_modules, optional_parameters
-
-
-class AutoPipelineForText2Image(ConfigMixin):
- r"""
-
- AutoPipeline for text-to-image generation.
-
- [`AutoPipelineForText2Image`] is a generic pipeline class that will be instantiated as one of the text-to-image
- pipeline class in diffusers.
-
- The pipeline type (for example [`StableDiffusionPipeline`]) is automatically selected when created with the
- AutoPipelineForText2Image.from_pretrained(pretrained_model_name_or_path) or
- AutoPipelineForText2Image.from_pipe(pipeline) class methods .
-
- This class cannot be instantiated using __init__() (throws an error).
-
- Class attributes:
-
- - **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
- diffusion pipeline's components.
-
- """
- config_name = "model_index.json"
-
- def __init__(self, *args, **kwargs):
- raise EnvironmentError(
- f"{self.__class__.__name__} is designed to be instantiated "
- f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
- f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
- )
-
- @classmethod
- def from_pretrained(cls, pretrained_model_or_path, **kwargs):
- r"""
- Instantiates a text-to-image Pytorch diffusion pipeline from pretrained pipeline weight.
-
- The from_pretrained() method takes care of returning the correct pipeline class instance by:
- 1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
- config object
- 2. Find the text-to-image pipeline linked to the pipeline class using pattern matching on pipeline class
- name.
-
- If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetPipeline`] object.
-
- The pipeline is set in evaluation mode (`model.eval()`) by default.
-
- If you get the error message below, you need to finetune the weights for your downstream task:
-
- ```
- Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
- You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
- ```
-
- Parameters:
- pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
- Can be either:
-
- - A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
- hosted on the Hub.
- - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
- saved using
- [`~DiffusionPipeline.save_pretrained`].
- torch_dtype (`str` or `torch.dtype`, *optional*):
- Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
- dtype is automatically derived from the model's weights.
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download of the model weights and configuration files, overriding the
- cached versions if they exist.
- cache_dir (`Union[str, os.PathLike]`, *optional*):
- Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
- is not used.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
- incompletely downloaded files are deleted.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, *optional*, defaults to `False`):
- Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only (`bool`, *optional*, defaults to `False`):
- Whether to only load local model weights and configuration files or not. If set to `True`, the model
- won't be downloaded from the Hub.
- use_auth_token (`str` or *bool*, *optional*):
- The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
- `diffusers-cli login` (stored in `~/.huggingface`) is used.
- revision (`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
- allowed by Git.
- custom_revision (`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
- `revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
- custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
- mirror (`str`, *optional*):
- Mirror source to resolve accessibility issues if you’re downloading a model in China. We do not
- guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
- information.
- device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
- A map that specifies where each submodule should go. It doesn’t need to be defined for each
- parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
- same device.
-
- Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
- more information about each option see [designing a device
- map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
- max_memory (`Dict`, *optional*):
- A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
- each GPU and the available CPU RAM if unset.
- offload_folder (`str` or `os.PathLike`, *optional*):
- The path to offload weights if device_map contains the value `"disk"`.
- offload_state_dict (`bool`, *optional*):
- If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
- the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
- when there is some disk offload.
- low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
- Speed up model loading only loading the pretrained weights and not initializing the weights. This also
- tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
- Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
- argument to `True` will raise an error.
- use_safetensors (`bool`, *optional*, defaults to `None`):
- If set to `None`, the safetensors weights are downloaded if they're available **and** if the
- safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
- weights. If set to `False`, safetensors weights are not loaded.
- kwargs (remaining dictionary of keyword arguments, *optional*):
- Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
- class). The overwritten components are passed directly to the pipelines `__init__` method. See example
- below for more information.
- variant (`str`, *optional*):
- Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
- loading `from_flax`.
-
-
-
-
-There are also objects or logic that cannot be described simply by a dictionary,
-such as reused objects or method calls. They may require some refactoring
-to work with recursive instantiation.
-
-## Using Model Zoo LazyConfigs
-
-We provide some configs in the model zoo using the LazyConfig system, for example:
-
-* [common baselines](../../configs/common/).
-* [new Mask R-CNN baselines](../../configs/new_baselines/)
-
-After installing detectron2, they can be loaded by the model zoo API
-[model_zoo.get_config](../modules/model_zoo.html#detectron2.model_zoo.get_config).
-
-Using these as references, you're free to define custom config structure / fields for your own
-project, as long as your training script can understand them.
-Despite of this, our model zoo configs still follow some simple conventions for consistency, e.g.
-`cfg.model` defines a model object, `cfg.dataloader.{train,test}` defines dataloader objects,
-and `cfg.train` contains training options in key-value form.
-In addition to `print()`, a better way to view the structure of a config is like this:
-```
-from detectron2.model_zoo import get_config
-from detectron2.config import LazyConfig
-print(LazyConfig.to_py(get_config("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py")))
-```
-From the output it's easier to find relevant options to change, e.g.
-`dataloader.train.total_batch_size` for the batch size, or `optimizer.lr` for base learning rate.
-
-We provide a reference training script
-[tools/lazyconfig_train_net.py](../../tools/lazyconfig_train_net.py),
-that can train/eval our model zoo configs.
-It also shows how to support command line value overrides.
-
-To demonstrate the power and flexibility of the new system, we show that
-[a simple config file](../../configs/Misc/torchvision_imagenet_R_50.py)
-can let detectron2 train an ImageNet classification model from torchvision, even though
-detectron2 contains no features about ImageNet classification.
-This can serve as a reference for using detectron2 in other deep learning tasks.
-
-## Summary
-
-By using recursive instantiation to create objects,
-we avoid passing a giant config to many places, because `cfg` is only passed to `instantiate`.
-This has the following benefits:
-
-* It's __non-intrusive__: objects to be constructed are config-agnostic, regular Python
- functions/classes.
- They can even live in other libraries. For example,
- `{"_target_": "torch.nn.Conv2d", "in_channels": 10, "out_channels": 10, "kernel_size": 1}`
- defines a conv layer.
-* __Clarity__ of what function/classes will be called, and what arguments they use.
-* `cfg` doesn't need pre-defined keys and structures. It's valid as long as it translates to valid
- code. This gives a lot more __flexibility__.
-* You can still pass huge dictionaries as arguments, just like the old way.
-
-Recursive instantiation and Python syntax are orthogonal: you can use one without the other.
-But by putting them together, the config file looks a lot like the code that will be executed:
-
-
-
-However, the config file just defines dictionaries, which can be easily manipulated further
-by composition or overrides.
-The corresponding code will only be executed
-later when `instantiate` is called. In some way,
-in config files we're writing "editable code" that will be "lazily executed" later when needed.
-That's why we call this system "LazyConfig".
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py
deleted file mode 100644
index 17f2904ccad484f380b64efc668b9090d047d15e..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py
+++ /dev/null
@@ -1,469 +0,0 @@
-# This file is modified from https://github.com/aim-uofa/AdelaiDet/blob/master/adet/modeling/backbone/bifpn.py
-# The original file is under 2-clause BSD License for academic use, and *non-commercial use*.
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-
-from detectron2.modeling.backbone import Backbone, build_resnet_backbone
-from detectron2.modeling import BACKBONE_REGISTRY
-from .dlafpn import dla34
-
-__all__ = []
-
-
-def swish(x):
- return x * x.sigmoid()
-
-
-def split_name(name):
- for i, c in enumerate(name):
- if not c.isalpha():
- return name[:i], int(name[i:])
- raise ValueError()
-
-
-class FeatureMapResampler(nn.Module):
- def __init__(self, in_channels, out_channels, stride, norm=""):
- super(FeatureMapResampler, self).__init__()
- if in_channels != out_channels:
- self.reduction = Conv2d(
- in_channels, out_channels, kernel_size=1,
- bias=(norm == ""),
- norm=get_norm(norm, out_channels),
- activation=None
- )
- else:
- self.reduction = None
-
- assert stride <= 2
- self.stride = stride
-
- def forward(self, x):
- if self.reduction is not None:
- x = self.reduction(x)
-
- if self.stride == 2:
- x = F.max_pool2d(
- x, kernel_size=self.stride + 1,
- stride=self.stride, padding=1
- )
- elif self.stride == 1:
- pass
- else:
- raise NotImplementedError()
- return x
-
-
-class BackboneWithTopLevels(Backbone):
- def __init__(self, backbone, out_channels, num_top_levels, norm=""):
- super(BackboneWithTopLevels, self).__init__()
- self.backbone = backbone
- backbone_output_shape = backbone.output_shape()
-
- self._out_feature_channels = {name: shape.channels for name, shape in backbone_output_shape.items()}
- self._out_feature_strides = {name: shape.stride for name, shape in backbone_output_shape.items()}
- self._out_features = list(self._out_feature_strides.keys())
-
- last_feature_name = max(self._out_feature_strides.keys(), key=lambda x: split_name(x)[1])
- self.last_feature_name = last_feature_name
- self.num_top_levels = num_top_levels
-
- last_channels = self._out_feature_channels[last_feature_name]
- last_stride = self._out_feature_strides[last_feature_name]
-
- prefix, suffix = split_name(last_feature_name)
- prev_channels = last_channels
- for i in range(num_top_levels):
- name = prefix + str(suffix + i + 1)
- self.add_module(name, FeatureMapResampler(
- prev_channels, out_channels, 2, norm
- ))
- prev_channels = out_channels
-
- self._out_feature_channels[name] = out_channels
- self._out_feature_strides[name] = last_stride * 2 ** (i + 1)
- self._out_features.append(name)
-
- def forward(self, x):
- outputs = self.backbone(x)
- last_features = outputs[self.last_feature_name]
- prefix, suffix = split_name(self.last_feature_name)
-
- x = last_features
- for i in range(self.num_top_levels):
- name = prefix + str(suffix + i + 1)
- x = self.__getattr__(name)(x)
- outputs[name] = x
-
- return outputs
-
-
-class SingleBiFPN(Backbone):
- """
- This module implements Feature Pyramid Network.
- It creates pyramid features built on top of some input feature maps.
- """
-
- def __init__(
- self, in_channels_list, out_channels, norm=""
- ):
- """
- Args:
- bottom_up (Backbone): module representing the bottom up subnetwork.
- Must be a subclass of :class:`Backbone`. The multi-scale feature
- maps generated by the bottom up network, and listed in `in_features`,
- are used to generate FPN levels.
- in_features (list[str]): names of the input feature maps coming
- from the backbone to which FPN is attached. For example, if the
- backbone produces ["res2", "res3", "res4"], any *contiguous* sublist
- of these may be used; order must be from high to low resolution.
- out_channels (int): number of channels in the output feature maps.
- norm (str): the normalization to use.
- """
- super(SingleBiFPN, self).__init__()
-
- self.out_channels = out_channels
- # build 5-levels bifpn
- if len(in_channels_list) == 5:
- self.nodes = [
- {'feat_level': 3, 'inputs_offsets': [3, 4]},
- {'feat_level': 2, 'inputs_offsets': [2, 5]},
- {'feat_level': 1, 'inputs_offsets': [1, 6]},
- {'feat_level': 0, 'inputs_offsets': [0, 7]},
- {'feat_level': 1, 'inputs_offsets': [1, 7, 8]},
- {'feat_level': 2, 'inputs_offsets': [2, 6, 9]},
- {'feat_level': 3, 'inputs_offsets': [3, 5, 10]},
- {'feat_level': 4, 'inputs_offsets': [4, 11]},
- ]
- elif len(in_channels_list) == 3:
- self.nodes = [
- {'feat_level': 1, 'inputs_offsets': [1, 2]},
- {'feat_level': 0, 'inputs_offsets': [0, 3]},
- {'feat_level': 1, 'inputs_offsets': [1, 3, 4]},
- {'feat_level': 2, 'inputs_offsets': [2, 5]},
- ]
- else:
- raise NotImplementedError
-
- node_info = [_ for _ in in_channels_list]
-
- num_output_connections = [0 for _ in in_channels_list]
- for fnode in self.nodes:
- feat_level = fnode["feat_level"]
- inputs_offsets = fnode["inputs_offsets"]
- inputs_offsets_str = "_".join(map(str, inputs_offsets))
- for input_offset in inputs_offsets:
- num_output_connections[input_offset] += 1
-
- in_channels = node_info[input_offset]
- if in_channels != out_channels:
- lateral_conv = Conv2d(
- in_channels,
- out_channels,
- kernel_size=1,
- norm=get_norm(norm, out_channels)
- )
- self.add_module(
- "lateral_{}_f{}".format(input_offset, feat_level), lateral_conv
- )
- node_info.append(out_channels)
- num_output_connections.append(0)
-
- # generate attention weights
- name = "weights_f{}_{}".format(feat_level, inputs_offsets_str)
- self.__setattr__(name, nn.Parameter(
- torch.ones(len(inputs_offsets), dtype=torch.float32),
- requires_grad=True
- ))
-
- # generate convolutions after combination
- name = "outputs_f{}_{}".format(feat_level, inputs_offsets_str)
- self.add_module(name, Conv2d(
- out_channels,
- out_channels,
- kernel_size=3,
- padding=1,
- norm=get_norm(norm, out_channels),
- bias=(norm == "")
- ))
-
- def forward(self, feats):
- """
- Args:
- input (dict[str->Tensor]): mapping feature map name (e.g., "p5") to
- feature map tensor for each feature level in high to low resolution order.
- Returns:
- dict[str->Tensor]:
- mapping from feature map name to FPN feature map tensor
- in high to low resolution order. Returned feature names follow the FPN
- paper convention: "p-A Full Mask R-CNN described in recursive instantiation (click to expand) -
- -```eval_rst -.. literalinclude:: ../../configs/common/models/mask_rcnn_fpn.py - :language: python - :linenos: -``` - -Idle Archer Tower Defense Mod APK: Un divertido y desafiante juego de rol
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- Amazon Music: Esta es otra plataforma conocida y confiable para comprar música en línea. Puedes comprar Conquest Paradise Riddim por $8.99 USD, o canciones individuales por $0.99 USD cada una. También puede transmitir el riddim en Amazon Music Unlimited, si tiene una suscripción. -
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- Download the Autocom version 2.12.2 software from the official website or from a trusted source. -
- Install the software on your computer following the instructions. -
- Download the keygen for Autocom version 2.12.2 from this link or from another source. -
- Extract the keygen file and run it as administrator. -
- Select the software version (Autocom 2014.R3) and the hardware key (100251) from the drop-down menus. -
- Click on the "Generate" button and copy the activation code. -
- Run the Autocom version 2.12.2 software and click on the "Activate via USB" button. -
- Paste the activation code in the box and click on the "OK" button. -
- The software will create a file named FileActivation.xml in the installation folder. -
- Copy this file to a USB flash drive and insert it into your diagnostic device. -
- The device will read the file and activate the software. -
- You can now use Autocom version 2.12.2 with full functionality. -
- A keygen can contain viruses, malware or spyware that can harm your computer or steal your personal information. -
- A keygen can violate the intellectual property rights of the software developer and expose you to legal risks. -
- A keygen can result in an unstable or corrupted software that can cause errors or damage your vehicle. -
- A keygen can make you ineligible for any updates or support from the software developer. -
- Connect your diagnostic device to your computer using a USB cable or a wireless connection. -
- Connect your diagnostic device to your vehicle using the appropriate connector or adapter. -
- Run the Autocom version 2.12.2 software on your computer and select the vehicle make, model and year from the menu. -
- Select the function you want to perform, such as reading fault codes, clearing fault codes, reading live data, performing tests, programming modules, resetting service intervals, etc. -
- Follow the instructions on the screen and wait for the software to communicate with the vehicle. -
- View the results on the screen and save or print them if needed. -
- Autocom version 2.12.2 supports a wide range of vehicles, protocols and interfaces, making it compatible with most cars and trucks on the market. -
- Autocom version 2.12.2 offers many functions and features, such as reading and clearing fault codes, reading live data, performing tests, programming modules, resetting service intervals, etc. -
- Autocom version 2.12.2 has a user-friendly interface and easy-to-follow instructions, making it suitable for both professionals and amateurs. -
- Autocom version 2.12.2 has a fast and reliable communication speed, ensuring a smooth and accurate diagnosis and programming process. -
- Autocom version 2.12.2 requires activation with a keygen or a license, which can be unsafe or illegal to use. -
- Autocom version 2.12.2 may not support some newer or specific vehicles or functions, depending on the software update or the hardware compatibility. -
- Autocom version 2.12.2 may cause errors or damage to your vehicle if used incorrectly or with a faulty interface or connector. -
- Autocom version 2.12.2 may not be eligible for any updates or support from the software developer if activated with a keygen. -
- Purchase a license or subscription from the official website or from an authorized dealer. -
- Download the latest version of the software from the official website or from the link provided by the dealer. -
- Install the latest version of the software on your computer following the instructions. -
- Run the latest version of the software and enter your license or subscription details. -
- The software will automatically update itself and activate your license or subscription. -
- You can now use Autocom version 2.12.2 with all the updates and support. -
- The compatibility of the interface with the software and the vehicle. You need to check if the interface supports the software version, the hardware key, the protocols and the functions that you want to use. -
- The quality and reliability of the interface. You need to check if the interface is well-made, durable and stable. You also need to check if the interface has any warranty or support from the manufacturer or seller. -
- The price and availability of the interface. You need to compare the prices and features of different interfaces and choose the one that offers the best value for money. You also need to check if the interface is in stock and can be delivered quickly. -
- Autocom CDP+ Pro Plus -
- TCS CDP Pro Plus for Autocom Car Truck Generic 3 in 1 -
- NEW 2020 Autocom Delphi 2016 R0 2017 R1 2019 R3 2020 R0 Cars Trucks Diagnostic -
- The official website of Autocom: https://www.autocom.se/en/. Here you can find information about the software, the products, the dealers, the updates and more. -
- The official forum of Autocom: https://forum.autocom.se/. Here you can join the community of Autocom users and experts, ask questions, share experiences, get tips and advice. -
- The online manuals of Autocom: https://www.autocom.se/en/support/manuals/. Here you can find detailed instructions on how to install, activate, use and troubleshoot Autocom version 2.12.2. -
- The online videos of Autocom: https://www.youtube.com/user/AutocomDiagnostic/videos. Here you can watch tutorials, demonstrations, reviews and testimonials of Autocom version 2.12.2. -
- The online blogs of Autocom: https://autocomblog.wordpress.com/. Here you can read articles, news, stories and insights about Autocom version 2.12.2. -
- The online reviews of Autocom: https://www.trustpilot.com/review/www.autocom.se. Here you can read honest feedback from real customers who have used Autocom version 2.12.2. -
- Open Google Play Store or App Store on your device. -
- Search for Monoposto 2023 in the search bar. -
- Select the game from the list of results and tap on it. -
- Tap on the Install or Get button to start downloading the game. -
- Wait for the download to finish and then tap on Open or Play to launch the game. -
- Enjoy Monoposto 2023 full version! -
- You may download a fake or modified version of the game that contains malware, viruses, or spyware that can harm your device or steal your personal information. -
- You may violate the terms of service or intellectual property rights of the developer or publisher of the game by downloading an unauthorized copy of the game. -
- You may not receive updates or support from the developer or publisher of the game if you download from other sources. -
- You may need to enable unknown sources or sideloading on your device settings to install apps from other sources. This can expose your device to security risks. -
- You may need to use a VPN or proxy to access the websites that offer the game for download, as they may be blocked or restricted in your region. -
- You may need to verify or activate the game using a code or a crack that may not work or may be illegal. -
- Find a website that offers Monoposto 2023 full version for download. You can use a search engine or a forum to look for such websites. -
- Check the reviews, ratings, and comments of the website and the game to make sure they are trustworthy and safe. -
- Download the game file from the website. It may be in the form of an APK file for Android devices or an IPA file for iOS devices. -
- Transfer the game file to your device using a USB cable, Bluetooth, or Wi-Fi. -
- Enable unknown sources or sideloading on your device settings to allow the installation of apps from other sources. -
- Locate the game file on your device and tap on it to start the installation process. -
- Follow the instructions on the screen to complete the installation. -
- Launch the game and enjoy Monoposto 2023 full version! -
- A colorful and stunning 3D graphics that create a vibrant and immersive world. -
- A simple and intuitive swipe-based control system that allows you to move easily and smoothly. -
- A variety of heroes to choose from, each with their own personality and abilities. -
- A collection of weapons and armors to unlock and upgrade, each with their own stats and effects. -
- A selection of pets or mounts to ride on, such as wolves, horses, dragons, and more. -
- A number of power-ups to use during your run, such as shields, magnets, portals, etc. -
- A system of quests and achievements to complete for extra rewards. -
- A leaderboard and social features to compete with your friends and other players around the world. -
- You will be able to enjoy the game without any ads or interruptions. -
- You will be able to unlock all the heroes, weapons, armors, pets, and power-ups in the game. -
- You will be able to upgrade your heroes and weapons to the maximum level and increase their stats and effects. -
- You will be able to complete the quests and achievements faster and easier. -
- You will be able to score higher and rank higher on the leaderboard and social features. -
- You will be able to have more fun and excitement playing the game. -
- You need to have an Android device that runs on Android 4.4 or higher. -
- You need to have enough storage space on your device to download and install the mod apk file. -
- You need to enable the installation of apps from unknown sources on your device settings. This will allow you to install the mod apk file that is not from the Google Play Store. -
- You need to uninstall the original version of Blades of Brim from your device if you have it installed. This will prevent any conflicts or errors during the installation process. -
- You need to be aware that using the mod apk may violate the terms and conditions of the game and may result in a ban or suspension of your account. Use it at your own risk and discretion. -
- Go to this link to download the mod apk file. The file size is about 100 MB. -
- Wait for the download to finish and then locate the file on your device storage. -
- Tap on the file and follow the instructions on the screen to install it. -
- Wait for the installation to complete and then launch the game from your app drawer or home screen. -
- Enjoy playing Blades of Brim with unlimited coins and essence. -
- Is Blades of Brim Mod APK Apkpure safe to use? -
- Is Blades of Brim Mod APK Apkpure compatible with other devices? -
- How can I update Blades of Brim Mod APK Apkpure? -
- How can I contact the developer of Blades of Brim Mod APK Apkpure? -
- How can I support the developer of Blades of Brim Mod APK Apkpure? -
- A huge collection of elements to discover and collect -
- A variety of categories to explore, such as chemistry, physics, biology, geology, astronomy, mythology, and more -
- A beautiful design and graphics that create a realistic and immersive experience -
- A dynamic soundtrack that adapts to the gameplay -
- A leaderboard and achievements system that tracks your progress and rewards you for your accomplishments -
- A social media integration that allows you to share your discoveries with your friends -
- A support system that provides you with hints and tips when you get stuck -
- A regular update that adds new elements and features to the game -
- You can access all the elements without having to discover them by yourself -
- You can use hints and cheats to find out the combinations and reactions of any element -
- You can buy any item or upgrade from the shop without worrying about the cost -
- You can enjoy the game without any ads or interruptions -
- You can play the game offline without needing an internet connection -
- Q: Is Alchemy Classic 2 free to play? -
- A: Yes, Alchemy Classic 2 is free to play. However, it contains ads and in-app purchases that can enhance your gaming experience. If you want to remove the ads and get unlimited money, hints, and cheats, you can download the modded version of the game. -
- Q: Is Alchemy Classic 2 safe to download and install? -
- A: Yes, Alchemy Classic 2 is safe to download and install. The game does not contain any viruses or malware that can harm your device. However, you should always download the game from a trusted source, such as Google Play or the link we provided in this article. -
- Q: What are the requirements for playing Alchemy Classic 2? -
- A: Alchemy Classic 2 requires Android 4.1 or higher to run smoothly on your device. The game also requires about 50 MB of storage space and a stable internet connection for downloading and updating the game. -
- Q: How many elements are there in Alchemy Classic 2? -
- A: There are over 1000 elements in Alchemy Classic 2, divided into different categories such as chemistry, physics, biology, geology, astronomy, mythology, and more. You can discover new elements by combining existing ones or using hints and cheats. -
- Q: How can I contact the developer of Alchemy Classic 2? -
- A: If you have any questions, feedback, or suggestions about Alchemy Classic 2, you can contact the developer of the game by sending an email to support@niasoft.com or visiting their website at https://niasoft.com/. -
- Search for "Getting Over It" on APKPure.com. -
- Click on the "Download APK" button to start downloading the file on your device. -
- Once the download is finished, open the file and allow it to install on your device. -
- Launch the game and start playing. -
- Open Safari on your iOS device and go to AppValley.vip. -
- Tap on the "Install AppValley" button and follow the instructions to install the app on your device. -
- Open AppValley and search for "Getting Over It". -
- Tap on the "Get" button and wait for the game to download and install on your device. -
- Launch the game and start playing. -
- Use your finger or mouse to drag the hammer around the screen. The hammer will follow your movement, but it will also have some inertia and momentum. You need to account for that when you swing or hook it. -
- Use the hammer to grab onto objects, such as rocks, trees, pipes, or furniture. You can then use them to pull yourself up, push yourself forward, or swing yourself around. -
- Use the hammer to balance yourself in mid-air, or to land safely on a surface. You can also use it to bounce off walls or ceilings, or to deflect obstacles. -
- Use the pot to protect yourself from falling objects, such as barrels, buckets, or anvils. You can also use it to slide down slopes or ramps, or to bounce off springs or trampolines. -
- Use the pot to limit your movement, such as when you need to squeeze through narrow gaps or corners. You can also use it to wedge yourself between objects, such as branches or beams. -
- Take a break. If you feel like you are losing your temper or your motivation, just stop playing for a while. Do something else that makes you happy or relaxed. Come back later when you are in a better mood. -
- Laugh it off. If you fail in a funny or absurd way, just laugh at yourself and the game. Don't take it too seriously. Enjoy the humor and the absurdity of the situation. -
- Learn from your mistakes. If you fail in a predictable or avoidable way, just learn from it and try again. Don't repeat the same mistake twice. Improve your skills and your strategy. -
- Celebrate your achievements. If you succeed in a difficult or impressive way, just celebrate it and be proud of yourself. Don't forget how far you have come. Appreciate your progress and your effort. -
- Share your experience. If you want to vent your frustration or share your joy, just talk to someone who understands you. Find a friend who plays the game, or join an online community of fellow players. Exchange tips, stories, screenshots, or videos. -
- Explore the world. The game has a lot of hidden secrets and surprises. You can find different paths, different objects, different sounds, and different scenes. You can also interact with some of them, such as playing music, reading books, or watching videos. -
- Listen to the voice. The game has a lot of dialogue and narration. You can hear Bennett Foddy talking about various topics, such as art, history, culture, psychology, or philosophy. You can also hear him quoting other authors, artists, or thinkers. -
- Think about the meaning. The game has a lot of symbolism and metaphors. You can interpret the game as a metaphor for life, for struggle, for achievement, or for anything else. You can also relate the game to your own experiences, beliefs, or values. -
- Express yourself. The game has a lot of freedom and creativity. You can play the game in your own way, at your own pace, and with your own style. You can also create your own content, such as drawings, poems, songs, or stories. -
- Have fun. The game has a lot of fun and humor. You can enjoy the game as a game, as a challenge, as a joke, or as a spectacle. You can also make fun of yourself, of the game, or of Bennett Foddy. -
- Using a modded APK or IPA file that gives you unlimited lives, unlimited speed, or unlimited height. -
- Using a screen recorder or a video editor to fake your progress or your completion. -
- Using a VPN or a proxy server to access the game from a different region or country. -
- Using a bot or a script to automate your movements or actions. -
- The official website of Bennett Foddy, where you can find his other games, his blog, his contact information, and his biography. -
- The official Twitter account of Bennett Foddy, where you can follow his updates, his tweets, his replies, and his retweets. -
- The official YouTube channel of Bennett Foddy, where you can watch his videos, his interviews, his lectures, and his streams. -
- The official Discord server of Getting Over It with Bennett Foddy, where you can join the community of players, chat with them, ask questions, share tips, post memes, or participate in events. -
- The official subreddit of Getting Over It with Bennett Foddy, where you can browse the posts of players, comment on them, upvote them, downvote them, or create your own posts. -
- Enter the driver parameters such as Fs, Vas and Qts. BassBox Pro 6.0.2 full version's driver database contains parameters for thousands of drivers. You can also add, edit or delete drivers and use various search methods to find the best match for your project. -
- Calculate the box volume and tuning. This is very easy with the program's helpful "Suggest" button that will recommend a box that will produce a smooth bass response. -
- Evaluate the performance with the graphs. This can include the effects of the acoustical environment such as in-car response or room modes. You can also import acoustic data from several popular measurement systems and include the effects of an external passive network or an active equalization filter. -
- It is compatible with Microsoft Windows 7, Vista, XP, d5da3c52bf
-
-
\ No newline at end of file diff --git a/spaces/inreVtussa/clothingai/Examples/Descargar Zarastudio 22 Full PORTABLE Para.md b/spaces/inreVtussa/clothingai/Examples/Descargar Zarastudio 22 Full PORTABLE Para.md deleted file mode 100644 index 75109a7404021a91b2e14f9e70aed59845743390..0000000000000000000000000000000000000000 --- a/spaces/inreVtussa/clothingai/Examples/Descargar Zarastudio 22 Full PORTABLE Para.md +++ /dev/null @@ -1,46 +0,0 @@ - -¿Cómo descargar Zarastudio 22 Full Para tu emisora de radio?
-Zarastudio es un software de automatización de radio que te permite gestionar y programar tu emisora de forma fácil y profesional. Con Zarastudio 22 Full Para, puedes disfrutar de todas las ventajas y funciones de este programa sin limitaciones ni restricciones.
-Descargar Zarastudio 22 Full Para
Download File ⚡ https://tiurll.com/2uClP4
-Para descargar Zarastudio 22 Full Para, necesitas seguir estos pasos:
--
-
- Visita el sitio web oficial de Zarastudio: https://www.zarastudio.es/ -
- En la sección de descargas, elige la opción "ZaraStudio 2.2.18 (versión completa)" y haz clic en el botón "Descargar". -
- Guarda el archivo .zip en tu ordenador y extráelo en una carpeta. -
- Ejecuta el archivo "zarastudio2.exe" para iniciar la instalación. -
- Sigue las instrucciones del asistente de instalación y acepta los términos y condiciones. -
- Cuando termine la instalación, abre el programa y haz clic en el menú "Ayuda" y luego en "Activar licencia". -
- Introduce el código de activación que te proporcionamos a continuación y haz clic en "Aceptar". -
Código de activación: XXXX-XXXX-XXXX-XXXX
-¡Listo! Ya puedes disfrutar de Zarastudio 22 Full Para y crear tu propia emisora de radio con calidad profesional.
-Si tienes alguna duda o problema con la descarga o la activación, puedes contactar con el soporte técnico de Zarastudio a través de su página web o su correo electrónico: info@zarastudio.es
- -Zarastudio 22 Full Para te ofrece una serie de funciones y herramientas que te ayudarán a crear y gestionar tu emisora de radio de forma eficiente y profesional. Algunas de estas funciones son:
--
-
- Reproducción automática de música, jingles, cuñas, publicidad, noticias, etc. -
- Programación de eventos y tareas por horas, dÃas o semanas. -
- Control de niveles de audio, ecualización, compresión, limitación, etc. -
- Generación de listas de reproducción con criterios personalizados. -
- Soporte para múltiples formatos de audio: MP3, WAV, OGG, WMA, etc. -
- Integración con bases de datos externas: MySQL, SQL Server, Access, etc. -
- Emisión en directo por Internet o por FM. -
- Grabación y edición de audio con el editor integrado o con programas externos. -
- Generación de informes y estadÃsticas de la emisora. -
Con Zarastudio 22 Full Para, podrás crear tu propia emisora de radio con un estilo único y profesional, adaptado a tus gustos y necesidades. Zarastudio es el software de automatización de radio más utilizado y recomendado por miles de usuarios en todo el mundo.
- - -Para crear una lista de reproducción con Zarastudio 22 Full Para, solo tienes que seguir estos pasos:
--
-
- Haz clic en el botón "Lista" en la parte superior del programa. -
- Haz clic en el botón "Nueva lista" para crear una lista vacÃa. -
- Haz clic en el botón "Añadir" para agregar archivos de audio a la lista. Puedes seleccionar varios archivos a la vez o arrastrarlos desde el explorador de Windows. -
- Puedes ordenar los archivos de la lista arrastrándolos con el ratón o usando los botones "Subir" y "Bajar". También puedes eliminar los archivos que no quieras con el botón "Quitar". -
- Puedes editar las propiedades de cada archivo haciendo doble clic sobre él o usando el botón "Propiedades". Aquà podrás cambiar el nombre, el artista, el álbum, el género, el año, etc. También podrás ajustar el volumen, la duración, el inicio y el final del archivo. -
- Cuando tengas la lista lista, puedes guardarla con el botón "Guardar" o "Guardar como". Puedes darle un nombre y una descripción a la lista. También puedes elegir si quieres que se repita o no al finalizar. -
- Para cargar una lista guardada, solo tienes que hacer clic en el botón "Abrir" y seleccionarla. También puedes arrastrarla desde el explorador de Windows al programa. -
Asà de fácil es crear una lista de reproducción con Zarastudio 22 Full Para. Puedes crear tantas listas como quieras y programarlas para que se reproduzcan automáticamente según tu horario. Zarastudio te permite tener un control total sobre tu emisora de radio.
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-
-
\ No newline at end of file diff --git a/spaces/inreVtussa/clothingai/Examples/Deskscapes 8 EXCLUSIVE Crack Kickass Torrents.md b/spaces/inreVtussa/clothingai/Examples/Deskscapes 8 EXCLUSIVE Crack Kickass Torrents.md deleted file mode 100644 index 019c55ebea0510155e1993f9b481dbfbb2b129e4..0000000000000000000000000000000000000000 --- a/spaces/inreVtussa/clothingai/Examples/Deskscapes 8 EXCLUSIVE Crack Kickass Torrents.md +++ /dev/null @@ -1,6 +0,0 @@ -deskscapes 8 crack kickass torrents
Download File >>> https://tiurll.com/2uCk4D
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- diff --git a/spaces/jayvaghasiya/winerybarreloak/app.py b/spaces/jayvaghasiya/winerybarreloak/app.py deleted file mode 100644 index 656653cf6a0ebd395244928da958f43d09a0a7b6..0000000000000000000000000000000000000000 --- a/spaces/jayvaghasiya/winerybarreloak/app.py +++ /dev/null @@ -1,33 +0,0 @@ -# This Script will create a user frindly interface fo demonstrartion purpose. - -import openai -import gradio as gr -import sys -import os - -openai.api_key = "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" - - -def chatbot(input_text): - response = openai.Completion.create( - model="davinci:ft-personal:human-like-wine-prediction-2023-06-28-13-43-11", - prompt=f"{input_text} ->", - temperature=1, - max_tokens=250, - top_p=1, - frequency_penalty=0, - presence_penalty=0, - stop=["END"], - ) - return response["choices"][0]["text"] - - -iface = gr.Interface( - fn=chatbot, - inputs=gr.components.Textbox(lines=7, label="Enter your text"), - outputs="text", - title="AI for Wine Recommendation", -) - -# index = construct_index("data") -iface.launch(enable_queue=False) diff --git a/spaces/jbilcke-hf/LifeSim/Dockerfile b/spaces/jbilcke-hf/LifeSim/Dockerfile deleted file mode 100644 index 91319be9b3dd35d916d18fba5260f51125c46b50..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/LifeSim/Dockerfile +++ /dev/null @@ -1,65 +0,0 @@ -FROM node:18-alpine AS base - -# Install dependencies only when needed -FROM base AS deps -# Check https://github.com/nodejs/docker-node/tree/b4117f9333da4138b03a546ec926ef50a31506c3#nodealpine to understand why libc6-compat might be needed. -RUN apk add --no-cache libc6-compat -WORKDIR /app - -# Install dependencies based on the preferred package manager -COPY package.json yarn.lock* package-lock.json* pnpm-lock.yaml* ./ -RUN \ - if [ -f yarn.lock ]; then yarn --frozen-lockfile; \ - elif [ -f package-lock.json ]; then npm ci; \ - elif [ -f pnpm-lock.yaml ]; then yarn global add pnpm && pnpm i --frozen-lockfile; \ - else echo "Lockfile not found." && exit 1; \ - fi - -# Uncomment the following lines if you want to use a secret at buildtime, -# for example to access your private npm packages -# RUN --mount=type=secret,id=HF_EXAMPLE_SECRET,mode=0444,required=true \ -# $(cat /run/secrets/HF_EXAMPLE_SECRET) - -# Rebuild the source code only when needed -FROM base AS builder -WORKDIR /app -COPY --from=deps /app/node_modules ./node_modules -COPY . . - -# Next.js collects completely anonymous telemetry data about general usage. -# Learn more here: https://nextjs.org/telemetry -# Uncomment the following line in case you want to disable telemetry during the build. -# ENV NEXT_TELEMETRY_DISABLED 1 - -# RUN yarn build - -# If you use yarn, comment out this line and use the line above -RUN npm run build - -# Production image, copy all the files and run next -FROM base AS runner -WORKDIR /app - -ENV NODE_ENV production -# Uncomment the following line in case you want to disable telemetry during runtime. -# ENV NEXT_TELEMETRY_DISABLED 1 - -RUN addgroup --system --gid 1001 nodejs -RUN adduser --system --uid 1001 nextjs - -COPY --from=builder /app/public ./public - -# Automatically leverage output traces to reduce image size -# https://nextjs.org/docs/advanced-features/output-file-tracing -COPY --from=builder --chown=nextjs:nodejs /app/.next/standalone ./ -COPY --from=builder --chown=nextjs:nodejs /app/.next/static ./.next/static -COPY --from=builder --chown=nextjs:nodejs /app/.next/cache ./.next/cache -# COPY --from=builder --chown=nextjs:nodejs /app/.next/cache/fetch-cache ./.next/cache/fetch-cache - -USER nextjs - -EXPOSE 3000 - -ENV PORT 3000 - -CMD ["node", "server.js"] \ No newline at end of file diff --git a/spaces/jbilcke-hf/Panoremix/src/components/ui/slider.tsx b/spaces/jbilcke-hf/Panoremix/src/components/ui/slider.tsx deleted file mode 100644 index 0e35bc7fb000cffa5e29956283ecf7d75453236c..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/Panoremix/src/components/ui/slider.tsx +++ /dev/null @@ -1,28 +0,0 @@ -"use client" - -import * as React from "react" -import * as SliderPrimitive from "@radix-ui/react-slider" - -import { cn } from "@/lib/utils" - -const Slider = React.forwardRef< - React.ElementRef, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - - -)) -Slider.displayName = SliderPrimitive.Root.displayName - -export { Slider } diff --git a/spaces/jimschat/VITS-Umamusume-voice-synthesizer/ONNXVITS_inference.py b/spaces/jimschat/VITS-Umamusume-voice-synthesizer/ONNXVITS_inference.py deleted file mode 100644 index 258b618cd338322365dfa25bec468a0a3f70ccd1..0000000000000000000000000000000000000000 --- a/spaces/jimschat/VITS-Umamusume-voice-synthesizer/ONNXVITS_inference.py +++ /dev/null @@ -1,36 +0,0 @@ -import logging -logging.getLogger('numba').setLevel(logging.WARNING) -import IPython.display as ipd -import torch -import commons -import utils -import ONNXVITS_infer -from text import text_to_sequence - -def get_text(text, hps): - text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners) - if hps.data.add_blank: - text_norm = commons.intersperse(text_norm, 0) - text_norm = torch.LongTensor(text_norm) - return text_norm - -hps = utils.get_hparams_from_file("../vits/pretrained_models/uma87.json") - -net_g = ONNXVITS_infer.SynthesizerTrn( - len(hps.symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model) -_ = net_g.eval() - -_ = utils.load_checkpoint("../vits/pretrained_models/uma_1153000.pth", net_g) - -text1 = get_text("おはようございます。", hps) -stn_tst = text1 -with torch.no_grad(): - x_tst = stn_tst.unsqueeze(0) - x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) - sid = torch.LongTensor([0]) - audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy() -print(audio) \ No newline at end of file diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/contourpy/util/mpl_renderer.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/contourpy/util/mpl_renderer.py deleted file mode 100644 index dbcb5ca19a01e3ae000986673d66def23f9c2eac..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/contourpy/util/mpl_renderer.py +++ /dev/null @@ -1,613 +0,0 @@ -from __future__ import annotations - -import io -from typing import TYPE_CHECKING, Any, cast - -import matplotlib.collections as mcollections -import matplotlib.pyplot as plt -import numpy as np - -from contourpy import FillType, LineType -from contourpy.util.mpl_util import filled_to_mpl_paths, lines_to_mpl_paths, mpl_codes_to_offsets -from contourpy.util.renderer import Renderer - -if TYPE_CHECKING: - from matplotlib.axes import Axes - from matplotlib.figure import Figure - from numpy.typing import ArrayLike - - import contourpy._contourpy as cpy - - -class MplRenderer(Renderer): - _axes: Axes - _fig: Figure - _want_tight: bool - - """Utility renderer using Matplotlib to render a grid of plots over the same (x, y) range. - - Args: - nrows (int, optional): Number of rows of plots, default ``1``. - ncols (int, optional): Number of columns of plots, default ``1``. - figsize (tuple(float, float), optional): Figure size in inches, default ``(9, 9)``. - show_frame (bool, optional): Whether to show frame and axes ticks, default ``True``. - backend (str, optional): Matplotlib backend to use or ``None`` for default backend. - Default ``None``. - gridspec_kw (dict, optional): Gridspec keyword arguments to pass to ``plt.subplots``, - default None. - """ - def __init__( - self, - nrows: int = 1, - ncols: int = 1, - figsize: tuple[float, float] = (9, 9), - show_frame: bool = True, - backend: str | None = None, - gridspec_kw: dict[str, Any] | None = None, - ) -> None: - if backend is not None: - import matplotlib - matplotlib.use(backend) - - kwargs = dict(figsize=figsize, squeeze=False, sharex=True, sharey=True) - if gridspec_kw is not None: - kwargs["gridspec_kw"] = gridspec_kw - else: - kwargs["subplot_kw"] = dict(aspect="equal") - - self._fig, axes = plt.subplots(nrows, ncols, **kwargs) - self._axes = axes.flatten() - if not show_frame: - for ax in self._axes: - ax.axis("off") - - self._want_tight = True - - def __del__(self) -> None: - if hasattr(self, "_fig"): - plt.close(self._fig) - - def _autoscale(self) -> None: - # Using axes._need_autoscale attribute if need to autoscale before rendering after adding - # lines/filled. Only want to autoscale once per axes regardless of how many lines/filled - # added. - for ax in self._axes: - if getattr(ax, "_need_autoscale", False): - ax.autoscale_view(tight=True) - ax._need_autoscale = False - if self._want_tight and len(self._axes) > 1: - self._fig.tight_layout() - - def _get_ax(self, ax: Axes | int) -> Axes: - if isinstance(ax, int): - ax = self._axes[ax] - return ax - - def filled( - self, - filled: cpy.FillReturn, - fill_type: FillType, - ax: Axes | int = 0, - color: str = "C0", - alpha: float = 0.7, - ) -> None: - """Plot filled contours on a single Axes. - - Args: - filled (sequence of arrays): Filled contour data as returned by - :func:`~contourpy.ContourGenerator.filled`. - fill_type (FillType): Type of ``filled`` data, as returned by - :attr:`~contourpy.ContourGenerator.fill_type`. - ax (int or Maplotlib Axes, optional): Which axes to plot on, default ``0``. - color (str, optional): Color to plot with. May be a string color or the letter ``"C"`` - followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the - ``tab10`` colormap. Default ``"C0"``. - alpha (float, optional): Opacity to plot with, default ``0.7``. - """ - ax = self._get_ax(ax) - paths = filled_to_mpl_paths(filled, fill_type) - collection = mcollections.PathCollection( - paths, facecolors=color, edgecolors="none", lw=0, alpha=alpha) - ax.add_collection(collection) - ax._need_autoscale = True - - def grid( - self, - x: ArrayLike, - y: ArrayLike, - ax: Axes | int = 0, - color: str = "black", - alpha: float = 0.1, - point_color: str | None = None, - quad_as_tri_alpha: float = 0, - ) -> None: - """Plot quad grid lines on a single Axes. - - Args: - x (array-like of shape (ny, nx) or (nx,)): The x-coordinates of the grid points. - y (array-like of shape (ny, nx) or (ny,)): The y-coordinates of the grid points. - ax (int or Matplotlib Axes, optional): Which Axes to plot on, default ``0``. - color (str, optional): Color to plot grid lines, default ``"black"``. - alpha (float, optional): Opacity to plot lines with, default ``0.1``. - point_color (str, optional): Color to plot grid points or ``None`` if grid points - should not be plotted, default ``None``. - quad_as_tri_alpha (float, optional): Opacity to plot ``quad_as_tri`` grid, default 0. - - Colors may be a string color or the letter ``"C"`` followed by an integer in the range - ``"C0"`` to ``"C9"`` to use a color from the ``tab10`` colormap. - - Warning: - ``quad_as_tri_alpha > 0`` plots all quads as though they are unmasked. - """ - ax = self._get_ax(ax) - x, y = self._grid_as_2d(x, y) - kwargs = dict(color=color, alpha=alpha) - ax.plot(x, y, x.T, y.T, **kwargs) - if quad_as_tri_alpha > 0: - # Assumes no quad mask. - xmid = 0.25*(x[:-1, :-1] + x[1:, :-1] + x[:-1, 1:] + x[1:, 1:]) - ymid = 0.25*(y[:-1, :-1] + y[1:, :-1] + y[:-1, 1:] + y[1:, 1:]) - kwargs["alpha"] = quad_as_tri_alpha - ax.plot( - np.stack((x[:-1, :-1], xmid, x[1:, 1:])).reshape((3, -1)), - np.stack((y[:-1, :-1], ymid, y[1:, 1:])).reshape((3, -1)), - np.stack((x[1:, :-1], xmid, x[:-1, 1:])).reshape((3, -1)), - np.stack((y[1:, :-1], ymid, y[:-1, 1:])).reshape((3, -1)), - **kwargs) - if point_color is not None: - ax.plot(x, y, color=point_color, alpha=alpha, marker="o", lw=0) - ax._need_autoscale = True - - def lines( - self, - lines: cpy.LineReturn, - line_type: LineType, - ax: Axes | int = 0, - color: str = "C0", - alpha: float = 1.0, - linewidth: float = 1, - ) -> None: - """Plot contour lines on a single Axes. - - Args: - lines (sequence of arrays): Contour line data as returned by - :func:`~contourpy.ContourGenerator.lines`. - line_type (LineType): Type of ``lines`` data, as returned by - :attr:`~contourpy.ContourGenerator.line_type`. - ax (int or Matplotlib Axes, optional): Which Axes to plot on, default ``0``. - color (str, optional): Color to plot lines. May be a string color or the letter ``"C"`` - followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the - ``tab10`` colormap. Default ``"C0"``. - alpha (float, optional): Opacity to plot lines with, default ``1.0``. - linewidth (float, optional): Width of lines, default ``1``. - """ - ax = self._get_ax(ax) - paths = lines_to_mpl_paths(lines, line_type) - collection = mcollections.PathCollection( - paths, facecolors="none", edgecolors=color, lw=linewidth, alpha=alpha) - ax.add_collection(collection) - ax._need_autoscale = True - - def mask( - self, - x: ArrayLike, - y: ArrayLike, - z: ArrayLike | np.ma.MaskedArray[Any, Any], - ax: Axes | int = 0, - color: str = "black", - ) -> None: - """Plot masked out grid points as circles on a single Axes. - - Args: - x (array-like of shape (ny, nx) or (nx,)): The x-coordinates of the grid points. - y (array-like of shape (ny, nx) or (ny,)): The y-coordinates of the grid points. - z (masked array of shape (ny, nx): z-values. - ax (int or Matplotlib Axes, optional): Which Axes to plot on, default ``0``. - color (str, optional): Circle color, default ``"black"``. - """ - mask = np.ma.getmask(z) # type: ignore[no-untyped-call] - if mask is np.ma.nomask: - return - ax = self._get_ax(ax) - x, y = self._grid_as_2d(x, y) - ax.plot(x[mask], y[mask], "o", c=color) - - def save(self, filename: str, transparent: bool = False) -> None: - """Save plots to SVG or PNG file. - - Args: - filename (str): Filename to save to. - transparent (bool, optional): Whether background should be transparent, default - ``False``. - """ - self._autoscale() - self._fig.savefig(filename, transparent=transparent) - - def save_to_buffer(self) -> io.BytesIO: - """Save plots to an ``io.BytesIO`` buffer. - - Return: - BytesIO: PNG image buffer. - """ - self._autoscale() - buf = io.BytesIO() - self._fig.savefig(buf, format="png") - buf.seek(0) - return buf - - def show(self) -> None: - """Show plots in an interactive window, in the usual Matplotlib manner. - """ - self._autoscale() - plt.show() - - def title(self, title: str, ax: Axes | int = 0, color: str | None = None) -> None: - """Set the title of a single Axes. - - Args: - title (str): Title text. - ax (int or Matplotlib Axes, optional): Which Axes to set the title of, default ``0``. - color (str, optional): Color to set title. May be a string color or the letter ``"C"`` - followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the - ``tab10`` colormap. Default is ``None`` which uses Matplotlib's default title color - that depends on the stylesheet in use. - """ - if color: - self._get_ax(ax).set_title(title, color=color) - else: - self._get_ax(ax).set_title(title) - - def z_values( - self, - x: ArrayLike, - y: ArrayLike, - z: ArrayLike, - ax: Axes | int = 0, - color: str = "green", - fmt: str = ".1f", - quad_as_tri: bool = False, - ) -> None: - """Show ``z`` values on a single Axes. - - Args: - x (array-like of shape (ny, nx) or (nx,)): The x-coordinates of the grid points. - y (array-like of shape (ny, nx) or (ny,)): The y-coordinates of the grid points. - z (array-like of shape (ny, nx): z-values. - ax (int or Matplotlib Axes, optional): Which Axes to plot on, default ``0``. - color (str, optional): Color of added text. May be a string color or the letter ``"C"`` - followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the - ``tab10`` colormap. Default ``"green"``. - fmt (str, optional): Format to display z-values, default ``".1f"``. - quad_as_tri (bool, optional): Whether to show z-values at the ``quad_as_tri`` centers - of quads. - - Warning: - ``quad_as_tri=True`` shows z-values for all quads, even if masked. - """ - ax = self._get_ax(ax) - x, y = self._grid_as_2d(x, y) - z = np.asarray(z) - ny, nx = z.shape - for j in range(ny): - for i in range(nx): - ax.text(x[j, i], y[j, i], f"{z[j, i]:{fmt}}", ha="center", va="center", - color=color, clip_on=True) - if quad_as_tri: - for j in range(ny-1): - for i in range(nx-1): - xx = np.mean(x[j:j+2, i:i+2]) - yy = np.mean(y[j:j+2, i:i+2]) - zz = np.mean(z[j:j+2, i:i+2]) - ax.text(xx, yy, f"{zz:{fmt}}", ha="center", va="center", color=color, - clip_on=True) - - -class MplTestRenderer(MplRenderer): - """Test renderer implemented using Matplotlib. - - No whitespace around plots and no spines/ticks displayed. - Uses Agg backend, so can only save to file/buffer, cannot call ``show()``. - """ - def __init__( - self, - nrows: int = 1, - ncols: int = 1, - figsize: tuple[float, float] = (9, 9), - ) -> None: - gridspec = { - "left": 0.01, - "right": 0.99, - "top": 0.99, - "bottom": 0.01, - "wspace": 0.01, - "hspace": 0.01, - } - super().__init__( - nrows, ncols, figsize, show_frame=True, backend="Agg", gridspec_kw=gridspec, - ) - - for ax in self._axes: - ax.set_xmargin(0.0) - ax.set_ymargin(0.0) - ax.set_xticks([]) - ax.set_yticks([]) - - self._want_tight = False - - -class MplDebugRenderer(MplRenderer): - """Debug renderer implemented using Matplotlib. - - Extends ``MplRenderer`` to add extra information to help in debugging such as markers, arrows, - text, etc. - """ - def __init__( - self, - nrows: int = 1, - ncols: int = 1, - figsize: tuple[float, float] = (9, 9), - show_frame: bool = True, - ) -> None: - super().__init__(nrows, ncols, figsize, show_frame) - - def _arrow( - self, - ax: Axes, - line_start: cpy.CoordinateArray, - line_end: cpy.CoordinateArray, - color: str, - alpha: float, - arrow_size: float, - ) -> None: - mid = 0.5*(line_start + line_end) - along = line_end - line_start - along /= np.sqrt(np.dot(along, along)) # Unit vector. - right = np.asarray((along[1], -along[0])) - arrow = np.stack(( - mid - (along*0.5 - right)*arrow_size, - mid + along*0.5*arrow_size, - mid - (along*0.5 + right)*arrow_size, - )) - ax.plot(arrow[:, 0], arrow[:, 1], "-", c=color, alpha=alpha) - - def _filled_to_lists_of_points_and_offsets( - self, - filled: cpy.FillReturn, - fill_type: FillType, - ) -> tuple[list[cpy.PointArray], list[cpy.OffsetArray]]: - if fill_type == FillType.OuterCode: - if TYPE_CHECKING: - filled = cast(cpy.FillReturn_OuterCode, filled) - all_points = filled[0] - all_offsets = [mpl_codes_to_offsets(codes) for codes in filled[1]] - elif fill_type == FillType.ChunkCombinedCode: - if TYPE_CHECKING: - filled = cast(cpy.FillReturn_ChunkCombinedCode, filled) - all_points = [points for points in filled[0] if points is not None] - all_offsets = [mpl_codes_to_offsets(codes) for codes in filled[1] if codes is not None] - elif fill_type == FillType.OuterOffset: - if TYPE_CHECKING: - filled = cast(cpy.FillReturn_OuterOffset, filled) - all_points = filled[0] - all_offsets = filled[1] - elif fill_type == FillType.ChunkCombinedOffset: - if TYPE_CHECKING: - filled = cast(cpy.FillReturn_ChunkCombinedOffset, filled) - all_points = [points for points in filled[0] if points is not None] - all_offsets = [offsets for offsets in filled[1] if offsets is not None] - elif fill_type == FillType.ChunkCombinedCodeOffset: - if TYPE_CHECKING: - filled = cast(cpy.FillReturn_ChunkCombinedCodeOffset, filled) - all_points = [] - all_offsets = [] - for points, codes, outer_offsets in zip(*filled): - if points is None: - continue - if TYPE_CHECKING: - assert codes is not None and outer_offsets is not None - all_points += np.split(points, outer_offsets[1:-1]) - all_codes = np.split(codes, outer_offsets[1:-1]) - all_offsets += [mpl_codes_to_offsets(codes) for codes in all_codes] - elif fill_type == FillType.ChunkCombinedOffsetOffset: - if TYPE_CHECKING: - filled = cast(cpy.FillReturn_ChunkCombinedOffsetOffset, filled) - all_points = [] - all_offsets = [] - for points, offsets, outer_offsets in zip(*filled): - if points is None: - continue - if TYPE_CHECKING: - assert offsets is not None and outer_offsets is not None - for i in range(len(outer_offsets)-1): - offs = offsets[outer_offsets[i]:outer_offsets[i+1]+1] - all_points.append(points[offs[0]:offs[-1]]) - all_offsets.append(offs - offs[0]) - else: - raise RuntimeError(f"Rendering FillType {fill_type} not implemented") - - return all_points, all_offsets - - def _lines_to_list_of_points( - self, lines: cpy.LineReturn, line_type: LineType, - ) -> list[cpy.PointArray]: - if line_type == LineType.Separate: - if TYPE_CHECKING: - lines = cast(cpy.LineReturn_Separate, lines) - all_lines = lines - elif line_type == LineType.SeparateCode: - if TYPE_CHECKING: - lines = cast(cpy.LineReturn_SeparateCode, lines) - all_lines = lines[0] - elif line_type == LineType.ChunkCombinedCode: - if TYPE_CHECKING: - lines = cast(cpy.LineReturn_ChunkCombinedCode, lines) - all_lines = [] - for points, codes in zip(*lines): - if points is not None: - if TYPE_CHECKING: - assert codes is not None - offsets = mpl_codes_to_offsets(codes) - for i in range(len(offsets)-1): - all_lines.append(points[offsets[i]:offsets[i+1]]) - elif line_type == LineType.ChunkCombinedOffset: - if TYPE_CHECKING: - lines = cast(cpy.LineReturn_ChunkCombinedOffset, lines) - all_lines = [] - for points, all_offsets in zip(*lines): - if points is not None: - if TYPE_CHECKING: - assert all_offsets is not None - for i in range(len(all_offsets)-1): - all_lines.append(points[all_offsets[i]:all_offsets[i+1]]) - else: - raise RuntimeError(f"Rendering LineType {line_type} not implemented") - - return all_lines - - def filled( - self, - filled: cpy.FillReturn, - fill_type: FillType, - ax: Axes | int = 0, - color: str = "C1", - alpha: float = 0.7, - line_color: str = "C0", - line_alpha: float = 0.7, - point_color: str = "C0", - start_point_color: str = "red", - arrow_size: float = 0.1, - ) -> None: - super().filled(filled, fill_type, ax, color, alpha) - - if line_color is None and point_color is None: - return - - ax = self._get_ax(ax) - all_points, all_offsets = self._filled_to_lists_of_points_and_offsets(filled, fill_type) - - # Lines. - if line_color is not None: - for points, offsets in zip(all_points, all_offsets): - for start, end in zip(offsets[:-1], offsets[1:]): - xys = points[start:end] - ax.plot(xys[:, 0], xys[:, 1], c=line_color, alpha=line_alpha) - - if arrow_size > 0.0: - n = len(xys) - for i in range(n-1): - self._arrow(ax, xys[i], xys[i+1], line_color, line_alpha, arrow_size) - - # Points. - if point_color is not None: - for points, offsets in zip(all_points, all_offsets): - mask = np.ones(offsets[-1], dtype=bool) - mask[offsets[1:]-1] = False # Exclude end points. - if start_point_color is not None: - start_indices = offsets[:-1] - mask[start_indices] = False # Exclude start points. - ax.plot( - points[:, 0][mask], points[:, 1][mask], "o", c=point_color, alpha=line_alpha) - - if start_point_color is not None: - ax.plot(points[:, 0][start_indices], points[:, 1][start_indices], "o", - c=start_point_color, alpha=line_alpha) - - def lines( - self, - lines: cpy.LineReturn, - line_type: LineType, - ax: Axes | int = 0, - color: str = "C0", - alpha: float = 1.0, - linewidth: float = 1, - point_color: str = "C0", - start_point_color: str = "red", - arrow_size: float = 0.1, - ) -> None: - super().lines(lines, line_type, ax, color, alpha, linewidth) - - if arrow_size == 0.0 and point_color is None: - return - - ax = self._get_ax(ax) - all_lines = self._lines_to_list_of_points(lines, line_type) - - if arrow_size > 0.0: - for line in all_lines: - for i in range(len(line)-1): - self._arrow(ax, line[i], line[i+1], color, alpha, arrow_size) - - if point_color is not None: - for line in all_lines: - start_index = 0 - end_index = len(line) - if start_point_color is not None: - ax.plot(line[0, 0], line[0, 1], "o", c=start_point_color, alpha=alpha) - start_index = 1 - if line[0][0] == line[-1][0] and line[0][1] == line[-1][1]: - end_index -= 1 - ax.plot(line[start_index:end_index, 0], line[start_index:end_index, 1], "o", - c=color, alpha=alpha) - - def point_numbers( - self, - x: ArrayLike, - y: ArrayLike, - z: ArrayLike, - ax: Axes | int = 0, - color: str = "red", - ) -> None: - ax = self._get_ax(ax) - x, y = self._grid_as_2d(x, y) - z = np.asarray(z) - ny, nx = z.shape - for j in range(ny): - for i in range(nx): - quad = i + j*nx - ax.text(x[j, i], y[j, i], str(quad), ha="right", va="top", color=color, - clip_on=True) - - def quad_numbers( - self, - x: ArrayLike, - y: ArrayLike, - z: ArrayLike, - ax: Axes | int = 0, - color: str = "blue", - ) -> None: - ax = self._get_ax(ax) - x, y = self._grid_as_2d(x, y) - z = np.asarray(z) - ny, nx = z.shape - for j in range(1, ny): - for i in range(1, nx): - quad = i + j*nx - xmid = x[j-1:j+1, i-1:i+1].mean() - ymid = y[j-1:j+1, i-1:i+1].mean() - ax.text(xmid, ymid, str(quad), ha="center", va="center", color=color, clip_on=True) - - def z_levels( - self, - x: ArrayLike, - y: ArrayLike, - z: ArrayLike, - lower_level: float, - upper_level: float | None = None, - ax: Axes | int = 0, - color: str = "green", - ) -> None: - ax = self._get_ax(ax) - x, y = self._grid_as_2d(x, y) - z = np.asarray(z) - ny, nx = z.shape - for j in range(ny): - for i in range(nx): - zz = z[j, i] - if upper_level is not None and zz > upper_level: - z_level = 2 - elif zz > lower_level: - z_level = 1 - else: - z_level = 0 - ax.text(x[j, i], y[j, i], z_level, ha="left", va="bottom", color=color, - clip_on=True) diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/background.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/background.py deleted file mode 100644 index dd3bbe249130348881331aea569ce3ec3f295128..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/background.py +++ /dev/null @@ -1 +0,0 @@ -from starlette.background import BackgroundTasks as BackgroundTasks # noqa diff --git a/spaces/jordonpeter01/MusicGen/audiocraft/modules/rope.py b/spaces/jordonpeter01/MusicGen/audiocraft/modules/rope.py deleted file mode 100644 index 4b8c70b9aba28eeb53d12ddc3de8852492847808..0000000000000000000000000000000000000000 --- a/spaces/jordonpeter01/MusicGen/audiocraft/modules/rope.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import typing as tp - -from torch import nn -import torch - - -class XPos(nn.Module): - """Length-extrapolatable positional embedding (xPos) from [Sun et al 2022](https://arxiv.org/abs/2212.10554v1). - This applies an exponential decay to the RoPE rotation matrix. - - Args: - dim (int): Embedding dimension. - smoothing (float): Smoothing factor applied to the decay rates. - base_scale (int): Base decay rate, given in terms of scaling time. - device (torch.device or None): Device on which to initialize the module. - dtype (torch.dtype): dtype to use to generate the embedding. - """ - def __init__(self, dim: int, smoothing: float = 0.4, base_scale: int = 512, - device=None, dtype: torch.dtype = torch.float32): - super().__init__() - assert dim % 2 == 0 - assert dtype in [torch.float64, torch.float32] - self.dtype = dtype - self.base_scale = base_scale - - half_dim = dim // 2 - adim = torch.arange(half_dim, device=device, dtype=dtype) - decay_rates = (adim / half_dim + smoothing) / (1.0 + smoothing) - self.register_buffer("decay_rates", decay_rates) - self.decay: tp.Optional[torch.Tensor] = None - - def get_decay(self, start: int, end: int): - """Create complex decay tensor, cache values for fast computation. - """ - if self.decay is None or end > self.decay.shape[0]: - assert isinstance(self.decay_rates, torch.Tensor) # Satisfy type checker. - idx = torch.arange(end, device=self.decay_rates.device, dtype=self.dtype) - power = idx / self.base_scale - scale = self.decay_rates ** power.unsqueeze(-1) - self.decay = torch.polar(scale, torch.zeros_like(scale)) - return self.decay[start:end] # [T, C/2] - - -class RotaryEmbedding(nn.Module): - """Rotary positional embedding (RoPE) from [Su et al 2022](https://arxiv.org/abs/2104.09864). - - Args: - dim (int): Embedding dimension (twice the number of frequencies). - max_period (float): Maximum period of the rotation frequencies. - xpos (bool): Use xPos, applies an exponential decay to rotation matrix. - scale (float): Scale of positional embedding, set to 0 to deactivate. - device (torch.device or None): Device on which to initialize the module. - dtype (torch.dtype): dtype to use to generate the embedding. - """ - def __init__(self, dim: int, max_period: float = 10000.0, xpos: bool = False, - scale: float = 1.0, device=None, dtype: torch.dtype = torch.float32): - super().__init__() - assert dim % 2 == 0 - self.scale = scale - assert dtype in [torch.float64, torch.float32] - self.dtype = dtype - - adim = torch.arange(0, dim, 2, device=device, dtype=dtype)[: (dim // 2)] - frequencies = 1.0 / (max_period ** (adim / dim)) - self.register_buffer("frequencies", frequencies) - self.rotation: tp.Optional[torch.Tensor] = None - - self.xpos = XPos(dim, device=device, dtype=dtype) if xpos else None - - def get_rotation(self, start: int, end: int): - """Create complex rotation tensor, cache values for fast computation. - """ - if self.rotation is None or end > self.rotation.shape[0]: - assert isinstance(self.frequencies, torch.Tensor) # Satisfy type checker. - idx = torch.arange(end, device=self.frequencies.device, dtype=self.dtype) - angles = torch.outer(idx, self.frequencies) - self.rotation = torch.polar(torch.ones_like(angles), angles) - return self.rotation[start:end] - - def rotate(self, x: torch.Tensor, start: int = 0, invert_decay: bool = False): - """Apply rope rotation to query or key tensor. - """ - T = x.shape[1] - rotation = self.get_rotation(start, start + T).unsqueeze(0).unsqueeze(2) - - if self.xpos: - decay = self.xpos.get_decay(start, start + T).unsqueeze(0).unsqueeze(2) - else: - decay = 1.0 - - if invert_decay: - decay = decay ** -1 - - x_complex = torch.view_as_complex(x.to(self.dtype).reshape(*x.shape[:-1], -1, 2)) - scaled_rotation = (rotation * decay) * self.scale + (1.0 - self.scale) - x_out = torch.view_as_real(x_complex * scaled_rotation).flatten(-2) - - return x_out.type_as(x) - - def rotate_qk(self, query: torch.Tensor, key: torch.Tensor, start: int = 0): - """ Apply rope rotation to both query and key tensors. - Supports streaming mode, in which query and key are not expected to have the same shape. - In streaming mode, key will be of legnth [P + C] with P the cached past timesteps, but - query will be [C] (typically C == 1). - - Args: - query (torch.Tensor): Query to rotate. - key (torch.Tensor): Key to rotate. - start (int): Start index of the sequence for time offset. - """ - query_timesteps = query.shape[1] - key_timesteps = key.shape[1] - streaming_offset = key_timesteps - query_timesteps - - query_out = self.rotate(query, start + streaming_offset) - key_out = self.rotate(key, start, invert_decay=True) - - return query_out, key_out diff --git a/spaces/jracca/01-learning-space/app.py b/spaces/jracca/01-learning-space/app.py deleted file mode 100644 index 4709e853096531640dbf08655e6009043b2d139e..0000000000000000000000000000000000000000 --- a/spaces/jracca/01-learning-space/app.py +++ /dev/null @@ -1,225 +0,0 @@ -import os - -os.system("pip uninstall -y gradio") -os.system("pip install gradio==2.9.4") -os.system("git clone --recursive https://github.com/JD-P/cloob-latent-diffusion") -os.system("cd cloob-latent-diffusion;pip install omegaconf pillow pytorch-lightning einops wandb ftfy regex ./CLIP") - -import argparse -from functools import partial -from pathlib import Path -import sys -sys.path.append('./cloob-latent-diffusion') -sys.path.append('./cloob-latent-diffusion/cloob-training') -sys.path.append('./cloob-latent-diffusion/latent-diffusion') -sys.path.append('./cloob-latent-diffusion/taming-transformers') -sys.path.append('./cloob-latent-diffusion/v-diffusion-pytorch') -from omegaconf import OmegaConf -from PIL import Image -import torch -from torch import nn -from torch.nn import functional as F -from torchvision import transforms -from torchvision.transforms import functional as TF -from tqdm import trange -from CLIP import clip -from cloob_training import model_pt, pretrained -import ldm.models.autoencoder -from diffusion import sampling, utils -import train_latent_diffusion as train -from huggingface_hub import hf_hub_url, cached_download -import random - -# Download the model files -checkpoint = cached_download(hf_hub_url("huggan/distill-ccld-wa", filename="model_student.ckpt")) -ae_model_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.ckpt")) -ae_config_path = cached_download(hf_hub_url("huggan/ccld_wa", filename="ae_model.yaml")) - -# Define a few utility functions - -def parse_prompt(prompt, default_weight=3.): - if prompt.startswith('http://') or prompt.startswith('https://'): - vals = prompt.rsplit(':', 2) - vals = [vals[0] + ':' + vals[1], *vals[2:]] - else: - vals = prompt.rsplit(':', 1) - vals = vals + ['', default_weight][len(vals):] - return vals[0], float(vals[1]) - - -def resize_and_center_crop(image, size): - fac = max(size[0] / image.size[0], size[1] / image.size[1]) - image = image.resize((int(fac * image.size[0]), int(fac * image.size[1])), Image.LANCZOS) - return TF.center_crop(image, size[::-1]) - - -# Load the models -device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') -print('Using device:', device) -print('loading models') - -# autoencoder -ae_config = OmegaConf.load(ae_config_path) -ae_model = ldm.models.autoencoder.AutoencoderKL(**ae_config.model.params) -ae_model.eval().requires_grad_(False).to(device) -ae_model.load_state_dict(torch.load(ae_model_path)) -n_ch, side_y, side_x = 4, 32, 32 - -# diffusion model -model = train.DiffusionModel(192, [1,1,2,2], autoencoder_scale=torch.tensor(4.3084)) -model.load_state_dict(torch.load(checkpoint, map_location='cpu')) -model = model.to(device).eval().requires_grad_(False) - -# CLOOB -cloob_config = pretrained.get_config('cloob_laion_400m_vit_b_16_16_epochs') -cloob = model_pt.get_pt_model(cloob_config) -checkpoint = pretrained.download_checkpoint(cloob_config) -cloob.load_state_dict(model_pt.get_pt_params(cloob_config, checkpoint)) -cloob.eval().requires_grad_(False).to(device) - - -# The key function: returns a list of n PIL images -def generate(n=1, prompts=['a red circle'], images=[], seed=42, steps=15, - method='plms', eta=None): - zero_embed = torch.zeros([1, cloob.config['d_embed']], device=device) - target_embeds, weights = [zero_embed], [] - - for prompt in prompts: - txt, weight = parse_prompt(prompt) - target_embeds.append(cloob.text_encoder(cloob.tokenize(txt).to(device)).float()) - weights.append(weight) - - for prompt in images: - path, weight = parse_prompt(prompt) - img = Image.open(utils.fetch(path)).convert('RGB') - clip_size = cloob.config['image_encoder']['image_size'] - img = resize_and_center_crop(img, (clip_size, clip_size)) - batch = TF.to_tensor(img)[None].to(device) - embed = F.normalize(cloob.image_encoder(cloob.normalize(batch)).float(), dim=-1) - target_embeds.append(embed) - weights.append(weight) - - weights = torch.tensor([1 - sum(weights), *weights], device=device) - - torch.manual_seed(seed) - - def cfg_model_fn(x, t): - n = x.shape[0] - n_conds = len(target_embeds) - x_in = x.repeat([n_conds, 1, 1, 1]) - t_in = t.repeat([n_conds]) - clip_embed_in = torch.cat([*target_embeds]).repeat_interleave(n, 0) - vs = model(x_in, t_in, clip_embed_in).view([n_conds, n, *x.shape[1:]]) - v = vs.mul(weights[:, None, None, None, None]).sum(0) - return v - - def run(x, steps): - if method == 'ddpm': - return sampling.sample(cfg_model_fn, x, steps, 1., {}) - if method == 'ddim': - return sampling.sample(cfg_model_fn, x, steps, eta, {}) - if method == 'prk': - return sampling.prk_sample(cfg_model_fn, x, steps, {}) - if method == 'plms': - return sampling.plms_sample(cfg_model_fn, x, steps, {}) - if method == 'pie': - return sampling.pie_sample(cfg_model_fn, x, steps, {}) - if method == 'plms2': - return sampling.plms2_sample(cfg_model_fn, x, steps, {}) - assert False - - batch_size = n - x = torch.randn([n, n_ch, side_y, side_x], device=device) - t = torch.linspace(1, 0, steps + 1, device=device)[:-1] - steps = utils.get_spliced_ddpm_cosine_schedule(t) - pil_ims = [] - for i in trange(0, n, batch_size): - cur_batch_size = min(n - i, batch_size) - out_latents = run(x[i:i+cur_batch_size], steps) - outs = ae_model.decode(out_latents * torch.tensor(2.55).to(device)) - for j, out in enumerate(outs): - pil_ims.append(utils.to_pil_image(out)) - - return pil_ims - - -import gradio as gr - -def gen_ims(prompt, im_prompt=None, seed=None, n_steps=10, method='plms'): - if seed == None : - seed = random.randint(0, 10000) - print( prompt, im_prompt, seed, n_steps) - prompts = [prompt] - im_prompts = [] - if im_prompt != None: - im_prompts = [im_prompt] - pil_ims = generate(n=1, prompts=prompts, images=im_prompts, seed=seed, steps=n_steps, method=method) - return pil_ims[0] - -iface = gr.Interface(fn=gen_ims, - inputs=[#gr.inputs.Slider(minimum=1, maximum=1, step=1, default=1,label="Number of images"), - #gr.inputs.Slider(minimum=0, maximum=200, step=1, label='Random seed', default=0), - gr.inputs.Textbox(label="Text prompt"), - gr.inputs.Image(optional=True, label="Image prompt", type='filepath'), - #gr.inputs.Slider(minimum=10, maximum=35, step=1, default=15,label="Number of steps") - ], - outputs=[gr.outputs.Image(type="pil", label="Generated Image")], - examples=[ - ["Futurism, in the style of Wassily Kandinsky"], - ["Art Nouveau, in the style of John Singer Sargent"], - ["Surrealism, in the style of Edgar Degas"], - ["Expressionism, in the style of Wassily Kandinsky"], - ["Futurism, in the style of Egon Schiele"], - ["Neoclassicism, in the style of Gustav Klimt"], - ["Cubism, in the style of Gustav Klimt"], - ["Op Art, in the style of Marc Chagall"], - ["Romanticism, in the style of M.C. Escher"], - ["Futurism, in the style of M.C. Escher"], - ["Abstract Art, in the style of M.C. Escher"], - ["Mannerism, in the style of Paul Klee"], - ["Romanesque Art, in the style of Leonardo da Vinci"], - ["High Renaissance, in the style of Rembrandt"], - ["Magic Realism, in the style of Gustave Dore"], - ["Realism, in the style of Jean-Michel Basquiat"], - ["Art Nouveau, in the style of Paul Gauguin"], - ["Avant-garde, in the style of Pierre-Auguste Renoir"], - ["Baroque, in the style of Edward Hopper"], - ["Post-Impressionism, in the style of Wassily Kandinsky"], - ["Naturalism, in the style of Rene Magritte"], - ["Constructivism, in the style of Paul Cezanne"], - ["Abstract Expressionism, in the style of Henri Matisse"], - ["Pop Art, in the style of Vincent van Gogh"], - ["Futurism, in the style of Wassily Kandinsky"], - ["Futurism, in the style of Zdzislaw Beksinski"], - ['Surrealism, in the style of Salvador Dali'], - ["Aaron Wacker, oil on canvas"], - ["abstract"], - ["landscape"], - ["portrait"], - ["sculpture"], - ["genre painting"], - ["installation"], - ["photo"], - ["figurative"], - ["illustration"], - ["still life"], - ["history painting"], - ["cityscape"], - ["marina"], - ["animal painting"], - ["design"], - ["calligraphy"], - ["symbolic painting"], - ["graffiti"], - ["performance"], - ["mythological painting"], - ["battle painting"], - ["self-portrait"], - ["Impressionism, oil on canvas"] - ], - title='Art Generator and Style Mixer from 🧠 Cloob and 🎨 WikiArt - Visual Art Encyclopedia:', - description="Trained on images from the [WikiArt](https://www.wikiart.org/) dataset, comprised of visual arts", - article = 'Model used is: [model card](https://huggingface.co/huggan/distill-ccld-wa)..' - -) -iface.launch(enable_queue=True) # , debug=True for colab debugging \ No newline at end of file diff --git a/spaces/kadirnar/Multilingual-Translation/README.md b/spaces/kadirnar/Multilingual-Translation/README.md deleted file mode 100644 index 3fb383a8d4b2ca3061ec41fe7cdd718470820fd3..0000000000000000000000000000000000000000 --- a/spaces/kadirnar/Multilingual-Translation/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Multilingual Translation -emoji: 🌍 -colorFrom: yellow -colorTo: pink -sdk: gradio -sdk_version: 3.17.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/kahnchana/clippy/infer_model.py b/spaces/kahnchana/clippy/infer_model.py deleted file mode 100644 index cac1db5dd06f9de9d10e8bdf1740f4f59b62fcdb..0000000000000000000000000000000000000000 --- a/spaces/kahnchana/clippy/infer_model.py +++ /dev/null @@ -1,202 +0,0 @@ -import abc -import math - -import torch -import torch.nn.functional as F -from sentence_transformers import SentenceTransformer -from timm.models.vision_transformer import ( - VisionTransformer, - build_model_with_cfg, - checkpoint_filter_fn, - checkpoint_seq, - resolve_pretrained_cfg, -) -from torch import Tensor, nn - - -class BlankLayer(nn.Module): - pass - - -class CustomViT(VisionTransformer): - def __init__( - self, - *args, - image_pooling="gmp", - **kwargs, - ): - super(CustomViT, self).__init__( - *args, **kwargs - ) - self.image_pooling = image_pooling - - def forward_head(self, x, pre_logits: bool = False): - if self.image_pooling: - if self.image_pooling == "gap": - x = x[:, self.num_prefix_tokens:].mean(dim=1) - elif self.image_pooling == "gmp": - x = x[:, self.num_prefix_tokens:].max(dim=-2)[0] - elif self.image_pooling == "all": - x = x[:, self.num_prefix_tokens:] - else: # cls by default - x = x[:, 0] - x = self.fc_norm(x) - return x if pre_logits else self.head(x) - - def forward(self, x, get_pos_tokens=False): - x = self.forward_features(x, get_pos_tokens=get_pos_tokens) - if get_pos_tokens: - return self.fc_norm(x[:, self.num_prefix_tokens:]) - x = self.forward_head(x) - return x - - def forward_features(self, x, get_pos_tokens=False): - _, nc, h, w = x.shape - x = self.patch_embed(x) - x = self._pos_embed(x, w, h) - if self.grad_checkpointing and not torch.jit.is_scripting(): - x = checkpoint_seq(self.blocks, x) - else: - x = self.blocks(x) - x = self.norm(x) - return x - - def _pos_embed(self, x, w, h): - if self.no_embed_class: - # deit-3, updated JAX (big vision) - # position embedding does not overlap with class token, add then concat - x = x + self.pos_embed - if self.cls_token is not None: - x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1) - else: - # original timm, JAX, and deit vit impl - # pos_embed has entry for class token, concat then add - if self.cls_token is not None: - x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1) - x = x + self._interpolate_pos_encoding(x, w, h) - return self.pos_drop(x) - - def _interpolate_pos_encoding(self, x, w, h): - npatch = x.shape[1] - 1 - N = self.pos_embed.shape[1] - 1 - if npatch == N and w == h: - return self.pos_embed - class_pos_embed = self.pos_embed[:, 0] - patch_pos_embed = self.pos_embed[:, 1:] - dim = x.shape[-1] - w0 = w // self.patch_embed.patch_size[0] - h0 = h // self.patch_embed.patch_size[1] - # we add a small number to avoid floating point error in the interpolation - # see discussion at https://github.com/facebookresearch/dino/issues/8 - w0, h0 = w0 + 0.1, h0 + 0.1 - patch_pos_embed = nn.functional.interpolate( - patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute( - 0, 3, 1, 2 - ), - scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)), - mode="bicubic", - ) - assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1] - patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) - return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) - - -def _create_vision_transformer(variant, pretrained=False, **kwargs): - if kwargs.get("features_only", None): - raise RuntimeError("features_only not implemented for Vision Transformer models.") - - pretrained_cfg = resolve_pretrained_cfg( - variant, pretrained_cfg=kwargs.pop("pretrained_cfg", None) - ) - model = build_model_with_cfg( - CustomViT, - variant, - pretrained, - pretrained_cfg=pretrained_cfg, - pretrained_filter_fn=checkpoint_filter_fn, - pretrained_custom_load="npz" in pretrained_cfg["url"], - **kwargs, - ) - return model - - -def vit_base_patch16_224(pretrained=False, variant="vit_base_patch16_224_dino", **kwargs): - """ViT-Base (ViT-B/16) /w DINO pretrained weights (no head) - https://arxiv.org/abs/2104.14294""" - model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) - model = _create_vision_transformer(variant, pretrained=pretrained, **model_kwargs) - return model - - -class CLIPpyModel(abc.ABC, torch.nn.Module): - """ Implements code for running inference with pre-trained CLIPpy model. - - NOTE: weights used are for a model trained with lower batch-size leading to results below those in paper. - """ - - def __init__( - self, - image_pooling: str = "cls", - text_pooling: str = "gap", - ): - super().__init__() - - self.visual = BlankLayer() - - self.visual.trunk = vit_base_patch16_224(True, image_pooling=image_pooling) - - self.text = SentenceTransformer("sentence-transformers/sentence-t5-base") - self.logit_scale = nn.Parameter(torch.ones([]) * math.log(1 / 0.07)) - self.set_text_pooling(text_pooling) - - self._divisor_eps = 1e-4 - self._image_pooling = image_pooling - self._text_pooling = text_pooling - - def forward( - self, - images: Tensor, - input_ids: Tensor, - input_id_masks: Tensor, - get_pos_tokens: bool = False, - **kwargs, - ): - - image_encodings = self.encode_image(images, get_pos_tokens=get_pos_tokens) - - if get_pos_tokens: - return { - image_encodings: image_encodings, - } - - text_encodings = self.encode_text(input_ids, input_id_masks) - - return { - image_encodings: image_encodings, - text_encodings: text_encodings, - } - - def encode_text(self, input_ids: Tensor, input_id_masks: Tensor = None, **kwargs): - output = self.text({"input_ids": input_ids, "attention_mask": input_id_masks})[ - "sentence_embedding" - ] - return self.text_head(output) - - def text_head(self, hidden_states: Tensor, input_id_masks: Tensor = None, **kwargs): - return F.normalize(hidden_states, dim=-1, eps=self._divisor_eps).float() - - def encode_image(self, images: Tensor, get_pos_tokens: bool = False, **kwargs): - output = self.visual.trunk(images, get_pos_tokens) - return self.image_head(output, get_pos_tokens=get_pos_tokens) - - def image_head(self, hidden_states: Tensor, get_pos_tokens: bool = False, **kwargs): - return F.normalize(hidden_states, dim=-1, eps=self._divisor_eps).float() - - def set_text_pooling(self, pooling): - """ Converts pooling in the Hugging Face model to be max or average pooling""" - if pooling == "gmp": - self.text[1].pooling_mode_mean_tokens = False - self.text[1].pooling_mode_max_tokens = True - elif pooling == "gap": - pass - else: - raise NotImplementedError(f"{pooling} not implemented") diff --git a/spaces/kenton-li/record/README.md b/spaces/kenton-li/record/README.md deleted file mode 100644 index a9fe0503ded8d5b3f799fe988593b95821a48da3..0000000000000000000000000000000000000000 --- a/spaces/kenton-li/record/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: Record -emoji: 📉 -colorFrom: purple -colorTo: red -sdk: docker -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/kepl/gpt/g4f/Provider/Providers/Gravityengine.py b/spaces/kepl/gpt/g4f/Provider/Providers/Gravityengine.py deleted file mode 100644 index f0cd09daaaae0adaa349f91139dc60c7ac79c028..0000000000000000000000000000000000000000 --- a/spaces/kepl/gpt/g4f/Provider/Providers/Gravityengine.py +++ /dev/null @@ -1,27 +0,0 @@ -import requests -import os -import json -from ...typing import sha256, Dict, get_type_hints - -url = 'https://gpt4.xunika.uk/' -model = ['gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613'] -supports_stream = True -needs_auth = False - -def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs): - headers = { - 'Content-Type': 'application/json', - } - data = { - 'model': model, - 'temperature': 0.7, - 'presence_penalty': 0, - 'messages': messages, - } - response = requests.post(url + '/api/openai/v1/chat/completions', - json=data, stream=True) - - yield response.json()['choices'][0]['message']['content'] - -params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \ - '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]]) \ No newline at end of file diff --git a/spaces/kevinwang676/VoiceChangers/src/facerender/modules/keypoint_detector.py b/spaces/kevinwang676/VoiceChangers/src/facerender/modules/keypoint_detector.py deleted file mode 100644 index 62a38a962b2f1a4326aac771aced353ec5e22a96..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/VoiceChangers/src/facerender/modules/keypoint_detector.py +++ /dev/null @@ -1,179 +0,0 @@ -from torch import nn -import torch -import torch.nn.functional as F - -from src.facerender.sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d -from src.facerender.modules.util import KPHourglass, make_coordinate_grid, AntiAliasInterpolation2d, ResBottleneck - - -class KPDetector(nn.Module): - """ - Detecting canonical keypoints. Return keypoint position and jacobian near each keypoint. - """ - - def __init__(self, block_expansion, feature_channel, num_kp, image_channel, max_features, reshape_channel, reshape_depth, - num_blocks, temperature, estimate_jacobian=False, scale_factor=1, single_jacobian_map=False): - super(KPDetector, self).__init__() - - self.predictor = KPHourglass(block_expansion, in_features=image_channel, - max_features=max_features, reshape_features=reshape_channel, reshape_depth=reshape_depth, num_blocks=num_blocks) - - # self.kp = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=7, padding=3) - self.kp = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=3, padding=1) - - if estimate_jacobian: - self.num_jacobian_maps = 1 if single_jacobian_map else num_kp - # self.jacobian = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=9 * self.num_jacobian_maps, kernel_size=7, padding=3) - self.jacobian = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=9 * self.num_jacobian_maps, kernel_size=3, padding=1) - ''' - initial as: - [[1 0 0] - [0 1 0] - [0 0 1]] - ''' - self.jacobian.weight.data.zero_() - self.jacobian.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0, 0, 0, 1] * self.num_jacobian_maps, dtype=torch.float)) - else: - self.jacobian = None - - self.temperature = temperature - self.scale_factor = scale_factor - if self.scale_factor != 1: - self.down = AntiAliasInterpolation2d(image_channel, self.scale_factor) - - def gaussian2kp(self, heatmap): - """ - Extract the mean from a heatmap - """ - shape = heatmap.shape - heatmap = heatmap.unsqueeze(-1) - grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0) - value = (heatmap * grid).sum(dim=(2, 3, 4)) - kp = {'value': value} - - return kp - - def forward(self, x): - if self.scale_factor != 1: - x = self.down(x) - - feature_map = self.predictor(x) - prediction = self.kp(feature_map) - - final_shape = prediction.shape - heatmap = prediction.view(final_shape[0], final_shape[1], -1) - heatmap = F.softmax(heatmap / self.temperature, dim=2) - heatmap = heatmap.view(*final_shape) - - out = self.gaussian2kp(heatmap) - - if self.jacobian is not None: - jacobian_map = self.jacobian(feature_map) - jacobian_map = jacobian_map.reshape(final_shape[0], self.num_jacobian_maps, 9, final_shape[2], - final_shape[3], final_shape[4]) - heatmap = heatmap.unsqueeze(2) - - jacobian = heatmap * jacobian_map - jacobian = jacobian.view(final_shape[0], final_shape[1], 9, -1) - jacobian = jacobian.sum(dim=-1) - jacobian = jacobian.view(jacobian.shape[0], jacobian.shape[1], 3, 3) - out['jacobian'] = jacobian - - return out - - -class HEEstimator(nn.Module): - """ - Estimating head pose and expression. - """ - - def __init__(self, block_expansion, feature_channel, num_kp, image_channel, max_features, num_bins=66, estimate_jacobian=True): - super(HEEstimator, self).__init__() - - self.conv1 = nn.Conv2d(in_channels=image_channel, out_channels=block_expansion, kernel_size=7, padding=3, stride=2) - self.norm1 = BatchNorm2d(block_expansion, affine=True) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - - self.conv2 = nn.Conv2d(in_channels=block_expansion, out_channels=256, kernel_size=1) - self.norm2 = BatchNorm2d(256, affine=True) - - self.block1 = nn.Sequential() - for i in range(3): - self.block1.add_module('b1_'+ str(i), ResBottleneck(in_features=256, stride=1)) - - self.conv3 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=1) - self.norm3 = BatchNorm2d(512, affine=True) - self.block2 = ResBottleneck(in_features=512, stride=2) - - self.block3 = nn.Sequential() - for i in range(3): - self.block3.add_module('b3_'+ str(i), ResBottleneck(in_features=512, stride=1)) - - self.conv4 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=1) - self.norm4 = BatchNorm2d(1024, affine=True) - self.block4 = ResBottleneck(in_features=1024, stride=2) - - self.block5 = nn.Sequential() - for i in range(5): - self.block5.add_module('b5_'+ str(i), ResBottleneck(in_features=1024, stride=1)) - - self.conv5 = nn.Conv2d(in_channels=1024, out_channels=2048, kernel_size=1) - self.norm5 = BatchNorm2d(2048, affine=True) - self.block6 = ResBottleneck(in_features=2048, stride=2) - - self.block7 = nn.Sequential() - for i in range(2): - self.block7.add_module('b7_'+ str(i), ResBottleneck(in_features=2048, stride=1)) - - self.fc_roll = nn.Linear(2048, num_bins) - self.fc_pitch = nn.Linear(2048, num_bins) - self.fc_yaw = nn.Linear(2048, num_bins) - - self.fc_t = nn.Linear(2048, 3) - - self.fc_exp = nn.Linear(2048, 3*num_kp) - - def forward(self, x): - out = self.conv1(x) - out = self.norm1(out) - out = F.relu(out) - out = self.maxpool(out) - - out = self.conv2(out) - out = self.norm2(out) - out = F.relu(out) - - out = self.block1(out) - - out = self.conv3(out) - out = self.norm3(out) - out = F.relu(out) - out = self.block2(out) - - out = self.block3(out) - - out = self.conv4(out) - out = self.norm4(out) - out = F.relu(out) - out = self.block4(out) - - out = self.block5(out) - - out = self.conv5(out) - out = self.norm5(out) - out = F.relu(out) - out = self.block6(out) - - out = self.block7(out) - - out = F.adaptive_avg_pool2d(out, 1) - out = out.view(out.shape[0], -1) - - yaw = self.fc_roll(out) - pitch = self.fc_pitch(out) - roll = self.fc_yaw(out) - t = self.fc_t(out) - exp = self.fc_exp(out) - - return {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp} - diff --git a/spaces/kinyugo/msanii/README.md b/spaces/kinyugo/msanii/README.md deleted file mode 100644 index 97fe66e50141d956f39facced5f86cb76d06edf5..0000000000000000000000000000000000000000 --- a/spaces/kinyugo/msanii/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Msanii -emoji: 👁 -colorFrom: indigo -colorTo: pink -sdk: gradio -sdk_version: 3.16.2 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.py b/spaces/koajoel/PolyFormer/fairseq/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.py deleted file mode 100644 index 7e2caa03400129ac0bb34ae35274cdf46f27a055..0000000000000000000000000000000000000000 --- a/spaces/koajoel/PolyFormer/fairseq/examples/linformer/linformer_src/modules/linformer_sentence_encoder_layer.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from fairseq import utils -from fairseq.modules import TransformerEncoderLayer - -from .multihead_linear_attention import MultiheadLinearAttention - - -class LinformerTransformerEncoderLayer(TransformerEncoderLayer): - """ - Implements a Linformer Encoder Layer used in BERT/XLM style pre-trained - models. - """ - - def __init__(self, args, shared_compress_layer): - # wrap in a list so it's not automatically registered by PyTorch - self.shared_compress_layer = [shared_compress_layer] - - super().__init__(args) - - self.register_buffer("version", torch.tensor(2)) - - def build_self_attention(self, embed_dim, args): - return MultiheadLinearAttention( - embed_dim, - args.encoder_attention_heads, - dropout=args.dropout, - self_attention=True, - q_noise=args.quant_noise_pq, - qn_block_size=args.quant_noise_pq_block_size, - compressed=args.compressed, - max_seq_len=args.max_positions, - shared_kv_compressed=args.shared_kv_compressed, - shared_compress_layer=self.shared_compress_layer[0], - freeze_compress=args.freeze_compress, - ) - - def upgrade_state_dict_named(self, state_dict, name): - super().upgrade_state_dict_named(state_dict, name) - prefix = name + "." if name != "" else "" - - # some old checkpoints had weight sharing implemented incorrectly - # (note: this was correct in the original paper code) - if utils.item(state_dict.get(f"{prefix}version", torch.tensor(1))) < 2: - state_dict[f"{prefix}version"] = torch.tensor(1) - # check compression layer sharing - if f"{prefix}shared_compress_layer.weight" in state_dict: - # reinitialize block without sharing compression layer to match - # old behavior - self.shared_compress_layer = [ - torch.nn.Linear( - self.shared_compress_layer[0].weight.size(1), - self.shared_compress_layer[0].weight.size(0), - ) - ] - self.self_attn = self.build_self_attention(self.embed_dim, self.args) - # delete shared_compress_layer, since it's already copied to - # self_attn.compress_k.weight - del state_dict[f"{prefix}shared_compress_layer.weight"] - if f"{prefix}shared_compress_layer.bias" in state_dict: - del state_dict[f"{prefix}shared_compress_layer.bias"] diff --git a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/__init__.py b/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/krystian-lieber/codellama-34b-chat/app.py b/spaces/krystian-lieber/codellama-34b-chat/app.py deleted file mode 100644 index fc0a6d92cd26665ae82c16dff92a8552beddee67..0000000000000000000000000000000000000000 --- a/spaces/krystian-lieber/codellama-34b-chat/app.py +++ /dev/null @@ -1,276 +0,0 @@ -import os -from typing import Iterator - -import gradio as gr - -from model import run - -HF_PUBLIC = os.environ.get("HF_PUBLIC", False) - -DEFAULT_SYSTEM_PROMPT = """\ -You are a helpful, respectful and honest assistant with a deep knowledge of code and software design. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\ -""" -MAX_MAX_NEW_TOKENS = 4096 -DEFAULT_MAX_NEW_TOKENS = 1024 -MAX_INPUT_TOKEN_LENGTH = 4000 - -DESCRIPTION = """ -# Code Llama 34B Chat - -This Space demonstrates model [CodeLlama-34b-Instruct](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) by Meta, a Code Llama model with 34B parameters fine-tuned for chat instructions and specialized on code tasks. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints). - -🔎 For more details about the Code Llama family of models and how to use them with `transformers`, take a look [at our blog post](https://huggingface.co/blog/codellama) or [the paper](https://huggingface.co/papers/2308.12950). - -🏃🏻 Check out our [Playground](https://huggingface.co/spaces/codellama/codellama-playground) for a super-fast code completion demo that leverages a streaming [inference endpoint](https://huggingface.co/inference-endpoints). - -""" - -LICENSE = """ - - ---- -As a derivate work of Code Llama by Meta, -this demo is governed by the original [license](https://huggingface.co/spaces/huggingface-projects/codellama-2-34b-chat/blob/main/LICENSE.txt) and [acceptable use policy](https://huggingface.co/spaces/huggingface-projects/codellama-2-34b-chat/blob/main/USE_POLICY.md). -""" - - -def clear_and_save_textbox(message: str) -> tuple[str, str]: - return '', message - - -def display_input(message: str, - history: list[tuple[str, str]]) -> list[tuple[str, str]]: - history.append((message, '')) - return history - - -def delete_prev_fn( - history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]: - try: - message, _ = history.pop() - except IndexError: - message = '' - return history, message or '' - - -def generate( - message: str, - history_with_input: list[tuple[str, str]], - system_prompt: str, - max_new_tokens: int, - temperature: float, - top_p: float, - top_k: int, -) -> Iterator[list[tuple[str, str]]]: - if max_new_tokens > MAX_MAX_NEW_TOKENS: - raise ValueError - - history = history_with_input[:-1] - generator = run(message, history, system_prompt, max_new_tokens, temperature, top_p, top_k) - try: - first_response = next(generator) - yield history + [(message, first_response)] - except StopIteration: - yield history + [(message, '')] - for response in generator: - yield history + [(message, response)] - - -def process_example(message: str) -> tuple[str, list[tuple[str, str]]]: - generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 1024, 1, 0.95, 50) - for x in generator: - pass - return '', x - - -def check_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> None: - input_token_length = len(message) + len(chat_history) - if input_token_length > MAX_INPUT_TOKEN_LENGTH: - raise gr.Error(f'The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again.') - - -with gr.Blocks(css='style.css') as demo: - gr.Markdown(DESCRIPTION) - gr.DuplicateButton(value='Duplicate Space for private use', - elem_id='duplicate-button') - - with gr.Group(): - chatbot = gr.Chatbot(label='Chatbot') - with gr.Row(): - textbox = gr.Textbox( - container=False, - show_label=False, - placeholder='Type a message...', - scale=10, - ) - submit_button = gr.Button('Submit', - variant='primary', - scale=1, - min_width=0) - with gr.Row(): - retry_button = gr.Button('🔄 Retry', variant='secondary') - undo_button = gr.Button('↩️ Undo', variant='secondary') - clear_button = gr.Button('🗑️ Clear', variant='secondary') - - saved_input = gr.State() - - with gr.Accordion(label='Advanced options', open=False): - system_prompt = gr.Textbox(label='System prompt', - value=DEFAULT_SYSTEM_PROMPT, - lines=6) - max_new_tokens = gr.Slider( - label='Max new tokens', - minimum=1, - maximum=MAX_MAX_NEW_TOKENS, - step=1, - value=DEFAULT_MAX_NEW_TOKENS, - ) - temperature = gr.Slider( - label='Temperature', - minimum=0.1, - maximum=4.0, - step=0.1, - value=0.1, - ) - top_p = gr.Slider( - label='Top-p (nucleus sampling)', - minimum=0.05, - maximum=1.0, - step=0.05, - value=0.9, - ) - top_k = gr.Slider( - label='Top-k', - minimum=1, - maximum=1000, - step=1, - value=10, - ) - - gr.Examples( - examples=[ - 'What is the Fibonacci sequence?', - 'Can you explain briefly what Python is good for?', - 'How can I display a grid of images in SwiftUI?', - ], - inputs=textbox, - outputs=[textbox, chatbot], - fn=process_example, - cache_examples=True, - ) - - gr.Markdown(LICENSE) - - textbox.submit( - fn=clear_and_save_textbox, - inputs=textbox, - outputs=[textbox, saved_input], - api_name=False, - queue=False, - ).then( - fn=display_input, - inputs=[saved_input, chatbot], - outputs=chatbot, - api_name=False, - queue=False, - ).then( - fn=check_input_token_length, - inputs=[saved_input, chatbot, system_prompt], - api_name=False, - queue=False, - ).success( - fn=generate, - inputs=[ - saved_input, - chatbot, - system_prompt, - max_new_tokens, - temperature, - top_p, - top_k, - ], - outputs=chatbot, - api_name=False, - ) - - button_event_preprocess = submit_button.click( - fn=clear_and_save_textbox, - inputs=textbox, - outputs=[textbox, saved_input], - api_name=False, - queue=False, - ).then( - fn=display_input, - inputs=[saved_input, chatbot], - outputs=chatbot, - api_name=False, - queue=False, - ).then( - fn=check_input_token_length, - inputs=[saved_input, chatbot, system_prompt], - api_name=False, - queue=False, - ).success( - fn=generate, - inputs=[ - saved_input, - chatbot, - system_prompt, - max_new_tokens, - temperature, - top_p, - top_k, - ], - outputs=chatbot, - api_name=False, - ) - - retry_button.click( - fn=delete_prev_fn, - inputs=chatbot, - outputs=[chatbot, saved_input], - api_name=False, - queue=False, - ).then( - fn=display_input, - inputs=[saved_input, chatbot], - outputs=chatbot, - api_name=False, - queue=False, - ).then( - fn=generate, - inputs=[ - saved_input, - chatbot, - system_prompt, - max_new_tokens, - temperature, - top_p, - top_k, - ], - outputs=chatbot, - api_name=False, - ) - - undo_button.click( - fn=delete_prev_fn, - inputs=chatbot, - outputs=[chatbot, saved_input], - api_name=False, - queue=False, - ).then( - fn=lambda x: x, - inputs=[saved_input], - outputs=textbox, - api_name=False, - queue=False, - ) - - clear_button.click( - fn=lambda: ([], ''), - outputs=[chatbot, saved_input], - queue=False, - api_name=False, - ) - -demo.queue(max_size=32).launch(share=HF_PUBLIC) diff --git a/spaces/kxqt/Expedit-SAM/setup.py b/spaces/kxqt/Expedit-SAM/setup.py deleted file mode 100644 index 2c0986317eb576a14ec774205c88fdee3cc6c0b3..0000000000000000000000000000000000000000 --- a/spaces/kxqt/Expedit-SAM/setup.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from setuptools import find_packages, setup - -setup( - name="segment_anything", - version="1.0", - install_requires=[], - packages=find_packages(exclude="notebooks"), - extras_require={ - "all": ["matplotlib", "pycocotools", "opencv-python", "onnx", "onnxruntime"], - "dev": ["flake8", "isort", "black", "mypy"], - }, -) diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/_version.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/_version.py deleted file mode 100644 index d94d35934401a72eef61a3ae3a22d493dcc909e9..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/_version.py +++ /dev/null @@ -1,2 +0,0 @@ -# Master version for Pillow -__version__ = "9.5.0" diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/implementations/tar.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/implementations/tar.py deleted file mode 100644 index 772ebd4d6a251dd89c03190abbda50f09fa2bf7a..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/implementations/tar.py +++ /dev/null @@ -1,123 +0,0 @@ -import logging -import tarfile - -import fsspec -from fsspec.archive import AbstractArchiveFileSystem -from fsspec.compression import compr -from fsspec.utils import infer_compression - -typemap = {b"0": "file", b"5": "directory"} - -logger = logging.getLogger("tar") - - -class TarFileSystem(AbstractArchiveFileSystem): - """Compressed Tar archives as a file-system (read-only) - - Supports the following formats: - tar.gz, tar.bz2, tar.xz - """ - - root_marker = "" - protocol = "tar" - cachable = False - - def __init__( - self, - fo="", - index_store=None, - target_options=None, - target_protocol=None, - compression=None, - **kwargs, - ): - super().__init__(**kwargs) - target_options = target_options or {} - - if isinstance(fo, str): - self.of = fsspec.open(fo, protocol=target_protocol, **target_options) - fo = self.of.open() # keep the reference - - # Try to infer compression. - if compression is None: - name = None - - # Try different ways to get hold of the filename. `fo` might either - # be a `fsspec.LocalFileOpener`, an `io.BufferedReader` or an - # `fsspec.AbstractFileSystem` instance. - try: - # Amended io.BufferedReader or similar. - # This uses a "protocol extension" where original filenames are - # propagated to archive-like filesystems in order to let them - # infer the right compression appropriately. - if hasattr(fo, "original"): - name = fo.original - - # fsspec.LocalFileOpener - elif hasattr(fo, "path"): - name = fo.path - - # io.BufferedReader - elif hasattr(fo, "name"): - name = fo.name - - # fsspec.AbstractFileSystem - elif hasattr(fo, "info"): - name = fo.info()["name"] - - except Exception as ex: - logger.warning( - f"Unable to determine file name, not inferring compression: {ex}" - ) - - if name is not None: - compression = infer_compression(name) - logger.info(f"Inferred compression {compression} from file name {name}") - - if compression is not None: - # TODO: tarfile already implements compression with modes like "'r:gz'", - # but then would seek to offset in the file work? - fo = compr[compression](fo) - - self._fo_ref = fo - self.fo = fo # the whole instance is a context - self.tar: tarfile.TarFile = tarfile.TarFile(fileobj=self.fo) - self.dir_cache = None - - self.index_store = index_store - self.index = None - self._index() - - def _index(self): - # TODO: load and set saved index, if exists - out = {} - for ti in self.tar: - info = ti.get_info() - info["type"] = typemap.get(info["type"], "file") - name = ti.get_info()["name"].rstrip("/") - out[name] = (info, ti.offset_data) - - self.index = out - # TODO: save index to self.index_store here, if set - - def _get_dirs(self): - if self.dir_cache is not None: - return - - # This enables ls to get directories as children as well as files - self.dir_cache = { - dirname + "/": {"name": dirname + "/", "size": 0, "type": "directory"} - for dirname in self._all_dirnames(self.tar.getnames()) - } - for member in self.tar.getmembers(): - info = member.get_info() - info["type"] = typemap.get(info["type"], "file") - self.dir_cache[info["name"]] = info - - def _open(self, path, mode="rb", **kwargs): - if mode != "rb": - raise ValueError("Read-only filesystem implementation") - details, offset = self.index[path] - if details["type"] != "file": - raise ValueError("Can only handle regular files") - return self.tar.extractfile(path) diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Form-189d7bad.css b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Form-189d7bad.css deleted file mode 100644 index 0b8e23a00e51fe054b66be462ce774d415cd57aa..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Form-189d7bad.css +++ /dev/null @@ -1 +0,0 @@ -div.svelte-b6y5bg{display:flex;flex-direction:inherit;flex-wrap:wrap;gap:var(--form-gap-width);box-shadow:var(--block-shadow);border:var(--block-border-width) solid var(--border-color-primary);border-radius:var(--block-radius);background:var(--border-color-primary);overflow:hidden}div.svelte-b6y5bg .block{box-shadow:none!important;border-width:0px!important;border-radius:0!important}.hidden.svelte-b6y5bg{display:none} diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/ModifyUpload-77b0d4b2.css b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/ModifyUpload-77b0d4b2.css deleted file mode 100644 index c78d71f8b6eaf75f8134375ed017f1c03b6edf1a..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/ModifyUpload-77b0d4b2.css +++ /dev/null @@ -1 +0,0 @@ -div.svelte-116rqfv{cursor:pointer;width:var(--size-full);height:var(--size-full)}.center.svelte-116rqfv{text-align:center}.flex.svelte-116rqfv{display:flex;justify-content:center;align-items:center}input.svelte-116rqfv{display:none}div.svelte-19sk1im{display:flex;top:var(--size-2);right:var(--size-2);justify-content:flex-end;gap:var(--spacing-sm);z-index:var(--layer-1)}.not-absolute.svelte-19sk1im{margin:var(--size-1)} diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/_inference.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/_inference.py deleted file mode 100644 index 4aab6568951266322e69501a6fb6bea46ccc3ad8..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/_inference.py +++ /dev/null @@ -1,890 +0,0 @@ -# coding=utf-8 -# Copyright 2019-present, the HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# Related resources: -# https://huggingface.co/tasks -# https://huggingface.co/docs/huggingface.js/inference/README -# https://github.com/huggingface/huggingface.js/tree/main/packages/inference/src -# https://github.com/huggingface/text-generation-inference/tree/main/clients/python -# https://github.com/huggingface/text-generation-inference/blob/main/clients/python/text_generation/client.py -# https://huggingface.slack.com/archives/C03E4DQ9LAJ/p1680169099087869 -# https://github.com/huggingface/unity-api#tasks -# -# Some TODO: -# - validate inputs/options/parameters? with Pydantic for instance? or only optionally? -# - add all tasks -# - handle async requests -# -# NOTE: the philosophy of this client is "let's make it as easy as possible to use it, even if less optimized". Some -# examples of how it translates: -# - Timeout / Server unavailable is handled by the client in a single "timeout" parameter. -# - Files can be provided as bytes, file paths, or URLs and the client will try to "guess" the type. -# - Images are parsed as PIL.Image for easier manipulation. -# - Provides a "recommended model" for each task => suboptimal but user-wise quicker to get a first script running. -# - Only the main parameters are publicly exposed. Power users can always read the docs for more options. -import base64 -import io -import logging -import time -import warnings -from contextlib import contextmanager -from pathlib import Path -from typing import TYPE_CHECKING, Any, BinaryIO, ContextManager, Dict, Generator, List, Optional, Union, overload - -from requests import HTTPError, Response - -from ._inference_types import ClassificationOutput, ConversationalOutput, ImageSegmentationOutput -from .constants import INFERENCE_ENDPOINT -from .utils import build_hf_headers, get_session, hf_raise_for_status, is_numpy_available, is_pillow_available -from .utils._typing import Literal - - -if TYPE_CHECKING: - import numpy as np - from PIL import Image - -logger = logging.getLogger(__name__) - - -RECOMMENDED_MODELS = { - "audio-classification": "superb/hubert-large-superb-er", - "automatic-speech-recognition": "facebook/wav2vec2-large-960h-lv60-self", - "conversational": "microsoft/DialoGPT-large", - "feature-extraction": "facebook/bart-base", - "image-classification": "google/vit-base-patch16-224", - "image-segmentation": "facebook/detr-resnet-50-panoptic", - "image-to-image": "timbrooks/instruct-pix2pix", - "image-to-text": "nlpconnect/vit-gpt2-image-captioning", - "sentence-similarity": "sentence-transformers/all-MiniLM-L6-v2", - "summarization": "facebook/bart-large-cnn", - "text-to-image": "stabilityai/stable-diffusion-2-1", - "text-to-speech": "espnet/kan-bayashi_ljspeech_vits", -} - -UrlT = str -PathT = Union[str, Path] -BinaryT = Union[bytes, BinaryIO] -ContentT = Union[BinaryT, PathT, UrlT] - - -class InferenceTimeoutError(HTTPError, TimeoutError): - """Error raised when a model is unavailable or the request times out.""" - - -class InferenceClient: - """ - Initialize a new Inference Client. - - [`InferenceClient`] aims to provide a unified experience to perform inference. The client can be used - seamlessly with either the (free) Inference API or self-hosted Inference Endpoints. - - Args: - model (`str`, `optional`): - The model to run inference with. Can be a model id hosted on the Hugging Face Hub, e.g. `bigcode/starcoder` - or a URL to a deployed Inference Endpoint. Defaults to None, in which case a recommended model is - automatically selected for the task. - token (`str`, *optional*): - Hugging Face token. Will default to the locally saved token. - timeout (`float`, `optional`): - The maximum number of seconds to wait for a response from the server. Loading a new model in Inference - API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. - """ - - def __init__( - self, model: Optional[str] = None, token: Optional[str] = None, timeout: Optional[float] = None - ) -> None: - self.model: Optional[str] = model - self.headers = build_hf_headers(token=token) - self.timeout = timeout - - def __repr__(self): - return f"- -- - - - You must have `PIL` installed if you want to work with images (`pip install Pillow`). - - - - Args: - image (`Union[str, Path, bytes, BinaryIO]`): - The image to segment. It can be raw bytes, an image file, or a URL to an online image. - model (`str`, *optional*): - The model to use for image segmentation. Can be a model ID hosted on the Hugging Face Hub or a URL to a - deployed Inference Endpoint. If not provided, the default recommended model for image segmentation will be used. - - Returns: - `List[Dict]`: A list of dictionaries containing the segmented masks and associated attributes. - - Raises: - [`InferenceTimeoutError`]: - If the model is unavailable or the request times out. - `HTTPError`: - If the request fails with an HTTP error status code other than HTTP 503. - - Example: - ```py - >>> from huggingface_hub import InferenceClient - >>> client = InferenceClient() - >>> client.image_segmentation("cat.jpg"): - [{'score': 0.989008, 'label': 'LABEL_184', 'mask':}, ...] - ``` - """ - - # Segment - response = self.post(data=image, model=model, task="image-segmentation") - output = response.json() - - # Parse masks as PIL Image - if not isinstance(output, list): - raise ValueError(f"Server output must be a list. Got {type(output)}: {str(output)[:200]}...") - for item in output: - item["mask"] = _b64_to_image(item["mask"]) - return output - - def image_to_image( - self, - image: ContentT, - prompt: Optional[str] = None, - *, - negative_prompt: Optional[str] = None, - height: Optional[int] = None, - width: Optional[int] = None, - num_inference_steps: Optional[int] = None, - guidance_scale: Optional[float] = None, - model: Optional[str] = None, - **kwargs, - ) -> "Image": - """ - Perform image-to-image translation using a specified model. - - - - You must have `PIL` installed if you want to work with images (`pip install Pillow`). - - - - Args: - image (`Union[str, Path, bytes, BinaryIO]`): - The input image for translation. It can be raw bytes, an image file, or a URL to an online image. - prompt (`str`, *optional*): - The text prompt to guide the image generation. - negative_prompt (`str`, *optional*): - A negative prompt to guide the translation process. - height (`int`, *optional*): - The height in pixels of the generated image. - width (`int`, *optional*): - The width in pixels of the generated image. - num_inference_steps (`int`, *optional*): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. - guidance_scale (`float`, *optional*): - Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, - usually at the expense of lower image quality. - model (`str`, *optional*): - The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed - Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None. - - Returns: - `Image`: The translated image. - - Raises: - [`InferenceTimeoutError`]: - If the model is unavailable or the request times out. - `HTTPError`: - If the request fails with an HTTP error status code other than HTTP 503. - - Example: - ```py - >>> from huggingface_hub import InferenceClient - >>> client = InferenceClient() - >>> image = client.image_to_image("cat.jpg", prompt="turn the cat into a tiger") - >>> image.save("tiger.jpg") - ``` - """ - parameters = { - "prompt": prompt, - "negative_prompt": negative_prompt, - "height": height, - "width": width, - "num_inference_steps": num_inference_steps, - "guidance_scale": guidance_scale, - **kwargs, - } - if all(parameter is None for parameter in parameters.values()): - # Either only an image to send => send as raw bytes - self.post(data=image, model=model, task="image-to-image") - data = image - payload: Optional[Dict[str, Any]] = None - else: - # Or an image + some parameters => use base64 encoding - data = None - payload = {"inputs": _b64_encode(image)} - for key, value in parameters.items(): - if value is not None: - payload[key] = value - - response = self.post(json=payload, data=data, model=model, task="image-to-image") - return _response_to_image(response) - - def image_to_text(self, image: ContentT, *, model: Optional[str] = None) -> str: - """ - Takes an input image and return text. - - Models can have very different outputs depending on your use case (image captioning, optical character recognition - (OCR), Pix2Struct, etc). Please have a look to the model card to learn more about a model's specificities. - - Args: - image (`Union[str, Path, bytes, BinaryIO]`): - The input image to caption. It can be raw bytes, an image file, or a URL to an online image.. - model (`str`, *optional*): - The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed - Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None. - - Returns: - `str`: The generated text. - - Raises: - [`InferenceTimeoutError`]: - If the model is unavailable or the request times out. - `HTTPError`: - If the request fails with an HTTP error status code other than HTTP 503. - - Example: - ```py - >>> from huggingface_hub import InferenceClient - >>> client = InferenceClient() - >>> client.image_to_text("cat.jpg") - 'a cat standing in a grassy field ' - >>> client.image_to_text("https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg") - 'a dog laying on the grass next to a flower pot ' - ``` - """ - response = self.post(data=image, model=model, task="image-to-text") - return response.json()[0]["generated_text"] - - def sentence_similarity( - self, sentence: str, other_sentences: List[str], *, model: Optional[str] = None - ) -> List[float]: - """ - Compute the semantic similarity between a sentence and a list of other sentences by comparing their embeddings. - - Args: - sentence (`str`): - The main sentence to compare to others. - other_sentences (`List[str]`): - The list of sentences to compare to. - model (`str`, *optional*): - The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to - a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used. - Defaults to None. - - Returns: - `List[float]`: The embedding representing the input text. - - Raises: - [`InferenceTimeoutError`]: - If the model is unavailable or the request times out. - `HTTPError`: - If the request fails with an HTTP error status code other than HTTP 503. - - Example: - ```py - >>> from huggingface_hub import InferenceClient - >>> client = InferenceClient() - >>> client.sentence_similarity( - ... "Machine learning is so easy.", - ... other_sentences=[ - ... "Deep learning is so straightforward.", - ... "This is so difficult, like rocket science.", - ... "I can't believe how much I struggled with this.", - ... ], - ... ) - [0.7785726189613342, 0.45876261591911316, 0.2906220555305481] - ``` - """ - response = self.post( - json={"inputs": {"source_sentence": sentence, "sentences": other_sentences}}, - model=model, - task="sentence-similarity", - ) - return response.json() - - def summarization( - self, - text: str, - *, - parameters: Optional[Dict[str, Any]] = None, - model: Optional[str] = None, - ) -> str: - """ - Generate a summary of a given text using a specified model. - - Args: - text (`str`): - The input text to summarize. - parameters (`Dict[str, Any]`, *optional*): - Additional parameters for summarization. Check out this [page](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task) - for more details. - model (`str`, *optional*): - The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed - Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None. - - Returns: - `str`: The generated summary text. - - Raises: - [`InferenceTimeoutError`]: - If the model is unavailable or the request times out. - `HTTPError`: - If the request fails with an HTTP error status code other than HTTP 503. - - Example: - ```py - >>> from huggingface_hub import InferenceClient - >>> client = InferenceClient() - >>> client.summarization("The Eiffel tower...") - 'The Eiffel tower is one of the most famous landmarks in the world....' - ``` - """ - payload: Dict[str, Any] = {"inputs": text} - if parameters is not None: - payload["parameters"] = parameters - response = self.post(json=payload, model=model, task="summarization") - return response.json()[0]["summary_text"] - - def text_to_image( - self, - prompt: str, - *, - negative_prompt: Optional[str] = None, - height: Optional[float] = None, - width: Optional[float] = None, - num_inference_steps: Optional[float] = None, - guidance_scale: Optional[float] = None, - model: Optional[str] = None, - **kwargs, - ) -> "Image": - """ - Generate an image based on a given text using a specified model. - -- - You must have `PIL` installed if you want to work with images (`pip install Pillow`). - - - - Args: - prompt (`str`): - The prompt to generate an image from. - negative_prompt (`str`, *optional*): - An optional negative prompt for the image generation. - height (`float`, *optional*): - The height in pixels of the image to generate. - width (`float`, *optional*): - The width in pixels of the image to generate. - num_inference_steps (`int`, *optional*): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. - guidance_scale (`float`, *optional*): - Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, - usually at the expense of lower image quality. - model (`str`, *optional*): - The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed - Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None. - - Returns: - `Image`: The generated image. - - Raises: - [`InferenceTimeoutError`]: - If the model is unavailable or the request times out. - `HTTPError`: - If the request fails with an HTTP error status code other than HTTP 503. - - Example: - ```py - >>> from huggingface_hub import InferenceClient - >>> client = InferenceClient() - - >>> image = client.text_to_image("An astronaut riding a horse on the moon.") - >>> image.save("astronaut.png") - - >>> image = client.text_to_image( - ... "An astronaut riding a horse on the moon.", - ... negative_prompt="low resolution, blurry", - ... model="stabilityai/stable-diffusion-2-1", - ... ) - >>> image.save("better_astronaut.png") - ``` - """ - parameters = { - "inputs": prompt, - "negative_prompt": negative_prompt, - "height": height, - "width": width, - "num_inference_steps": num_inference_steps, - "guidance_scale": guidance_scale, - **kwargs, - } - payload = {} - for key, value in parameters.items(): - if value is not None: - payload[key] = value - response = self.post(json=payload, model=model, task="text-to-image") - return _response_to_image(response) - - def text_to_speech(self, text: str, *, model: Optional[str] = None) -> bytes: - """ - Synthesize an audio of a voice pronouncing a given text. - - Args: - text (`str`): - The text to synthesize. - model (`str`, *optional*): - The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed - Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None. - - Returns: - `bytes`: The generated audio. - - Raises: - [`InferenceTimeoutError`]: - If the model is unavailable or the request times out. - `HTTPError`: - If the request fails with an HTTP error status code other than HTTP 503. - - Example: - ```py - >>> from pathlib import Path - >>> from huggingface_hub import InferenceClient - >>> client = InferenceClient() - - >>> audio = client.text_to_speech("Hello world") - >>> Path("hello_world.wav").write_bytes(audio) - ``` - """ - response = self.post(json={"inputs": text}, model=model, task="text-to-speech") - return response.content - - def _resolve_url(self, model: Optional[str] = None, task: Optional[str] = None) -> str: - model = model or self.model - - # If model is already a URL, ignore `task` and return directly - if model is not None and (model.startswith("http://") or model.startswith("https://")): - return model - - # # If no model but task is set => fetch the recommended one for this task - if model is None: - if task is None: - raise ValueError( - "You must specify at least a model (repo_id or URL) or a task, either when instantiating" - " `InferenceClient` or when making a request." - ) - model = _get_recommended_model(task) - - # Compute InferenceAPI url - return ( - # Feature-extraction and sentence-similarity are the only cases where we handle models with several tasks. - f"{INFERENCE_ENDPOINT}/pipeline/{task}/{model}" - if task in ("feature-extraction", "sentence-similarity") - # Otherwise, we use the default endpoint - else f"{INFERENCE_ENDPOINT}/models/{model}" - ) - - -def _get_recommended_model(task: str) -> str: - # TODO: load from a config file? (from the Hub?) Would make sense to make updates easier. - if task in RECOMMENDED_MODELS: - model = RECOMMENDED_MODELS[task] - logger.info( - f"Defaulting to recommended model {model} for task {task}. It is recommended to explicitly pass" - f" `model='{model}'` as argument as we do not guarantee that the recommended model will stay the same over" - " time." - ) - return model - raise NotImplementedError() - - -@overload -def _open_as_binary(content: ContentT) -> ContextManager[BinaryT]: - ... # means "if input is not None, output is not None" - - -@overload -def _open_as_binary(content: Literal[None]) -> ContextManager[Literal[None]]: - ... # means "if input is None, output is None" - - -@contextmanager # type: ignore -def _open_as_binary(content: Optional[ContentT]) -> Generator[Optional[BinaryT], None, None]: - """Open `content` as a binary file, either from a URL, a local path, or raw bytes. - - Do nothing if `content` is None, - - TODO: handle a PIL.Image as input - TODO: handle base64 as input - """ - # If content is a string => must be either a URL or a path - if isinstance(content, str): - if content.startswith("https://") or content.startswith("http://"): - logger.debug(f"Downloading content from {content}") - yield get_session().get(content).content # TODO: retrieve as stream and pipe to post request ? - return - content = Path(content) - if not content.exists(): - raise FileNotFoundError( - f"File not found at {content}. If `data` is a string, it must either be a URL or a path to a local" - " file. To pass raw content, please encode it as bytes first." - ) - - # If content is a Path => open it - if isinstance(content, Path): - logger.debug(f"Opening content from {content}") - with content.open("rb") as f: - yield f - else: - # Otherwise: already a file-like object or None - yield content - - -def _b64_encode(content: ContentT) -> str: - """Encode a raw file (image, audio) into base64. Can be byes, an opened file, a path or a URL.""" - with _open_as_binary(content) as data: - data_as_bytes = data if isinstance(data, bytes) else data.read() - return base64.b64encode(data_as_bytes).decode() - - -def _b64_to_image(encoded_image: str) -> "Image": - """Parse a base64-encoded string into a PIL Image.""" - Image = _import_pil_image() - return Image.open(io.BytesIO(base64.b64decode(encoded_image))) - - -def _response_to_image(response: Response) -> "Image": - """Parse a Response object into a PIL Image. - - Expects the response body to be raw bytes. To deal with b64 encoded images, use `_b64_to_image` instead. - """ - Image = _import_pil_image() - return Image.open(io.BytesIO(response.content)) - - -def _import_pil_image(): - """Make sure `PIL` is installed on the machine.""" - if not is_pillow_available(): - raise ImportError( - "Please install Pillow to use deal with images (`pip install Pillow`). If you don't want the image to be" - " post-processed, use `client.post(...)` and get the raw response from the server." - ) - from PIL import Image - - return Image - - -def _import_numpy(): - """Make sure `numpy` is installed on the machine.""" - if not is_numpy_available(): - raise ImportError("Please install numpy to use deal with embeddings (`pip install numpy`).") - import numpy - - return numpy diff --git a/spaces/leilevy/bingo/src/components/ui/dialog.tsx b/spaces/leilevy/bingo/src/components/ui/dialog.tsx deleted file mode 100644 index 925e77fe7858fb218b5115b4e225174a886e0f02..0000000000000000000000000000000000000000 --- a/spaces/leilevy/bingo/src/components/ui/dialog.tsx +++ /dev/null @@ -1,128 +0,0 @@ -'use client' - -import * as React from 'react' -import * as DialogPrimitive from '@radix-ui/react-dialog' - -import { cn } from '@/lib/utils' -import { IconClose } from '@/components/ui/icons' - -const Dialog = DialogPrimitive.Root - -const DialogTrigger = DialogPrimitive.Trigger - -const DialogPortal = ({ - className, - children, - ...props -}: DialogPrimitive.DialogPortalProps) => ( -- -) -DialogPortal.displayName = DialogPrimitive.Portal.displayName - -const DialogOverlay = React.forwardRef< - React.ElementRef- {children} --, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -DialogOverlay.displayName = DialogPrimitive.Overlay.displayName - -const DialogContent = React.forwardRef< - React.ElementRef , - React.ComponentPropsWithoutRef ->(({ className, children, ...props }, ref) => ( - - -)) -DialogContent.displayName = DialogPrimitive.Content.displayName - -const DialogHeader = ({ - className, - ...props -}: React.HTMLAttributes- - {children} - -- -- Close - ) => ( - -) -DialogHeader.displayName = 'DialogHeader' - -const DialogFooter = ({ - className, - ...props -}: React.HTMLAttributes ) => ( - -) -DialogFooter.displayName = 'DialogFooter' - -const DialogTitle = React.forwardRef< - React.ElementRef , - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -DialogTitle.displayName = DialogPrimitive.Title.displayName - -const DialogDescription = React.forwardRef< - React.ElementRef , - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -DialogDescription.displayName = DialogPrimitive.Description.displayName - -export { - Dialog, - DialogTrigger, - DialogContent, - DialogHeader, - DialogFooter, - DialogTitle, - DialogDescription -} diff --git a/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/sh/test_benchmark_ppl.sh b/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/sh/test_benchmark_ppl.sh deleted file mode 100644 index aa9b3a78177a4a84d80dc31f4a53610bf1213673..0000000000000000000000000000000000000000 --- a/spaces/leogabraneth/text-generation-webui-main/repositories/exllama/sh/test_benchmark_ppl.sh +++ /dev/null @@ -1,18 +0,0 @@ - -echo "-------------------------------------------------------------------------------------------------------------" -python test_benchmark_inference.py -ppl -d /mnt/str/models/_test_models/iambestfeed_open_llama_3b_4bit_128g -echo "-------------------------------------------------------------------------------------------------------------" -python test_benchmark_inference.py -ppl -d /mnt/str/models/llama-7b-4bit-128g -echo "-------------------------------------------------------------------------------------------------------------" -python test_benchmark_inference.py -ppl -d /mnt/str/models/llama-13b-4bit-128g -echo "-------------------------------------------------------------------------------------------------------------" -python test_benchmark_inference.py -ppl -d /mnt/str/models/llama-30b-4bit-128g -echo "-------------------------------------------------------------------------------------------------------------" -python test_benchmark_inference.py -ppl -d /mnt/str/models/llama-30b-4bit-128g-act -echo "-------------------------------------------------------------------------------------------------------------" -python test_benchmark_inference.py -ppl -d /mnt/str/models/llama-30b-4bit-32g-act-ts -l 1550 -echo "-------------------------------------------------------------------------------------------------------------" -python test_benchmark_inference.py -ppl -d /mnt/str/models/koala-13B-4bit-128g-act -echo "-------------------------------------------------------------------------------------------------------------" -python test_benchmark_inference.py -ppl -d /mnt/str/models/wizardlm-30b-uncensored-4bit-act-order -echo "-------------------------------------------------------------------------------------------------------------" diff --git a/spaces/lightli/bingo-newbing/src/components/welcome-screen.tsx b/spaces/lightli/bingo-newbing/src/components/welcome-screen.tsx deleted file mode 100644 index f7449fcbb6c621875e235db98f2790bf7894fb0a..0000000000000000000000000000000000000000 --- a/spaces/lightli/bingo-newbing/src/components/welcome-screen.tsx +++ /dev/null @@ -1,34 +0,0 @@ -import { useBing } from '@/lib/hooks/use-bing' - -const exampleMessages = [ - { - heading: '🧐 提出复杂问题', - message: `我可以为我挑剔的只吃橙色食物的孩子做什么饭?` - }, - { - heading: '🙌 获取更好的答案', - message: '销量最高的 3 种宠物吸尘器有哪些优点和缺点?' - }, - { - heading: '🎨 获得创意灵感', - message: `以海盗的口吻写一首关于外太空鳄鱼的俳句` - } -] - -export function WelcomeScreen({ setInput }: Pick - {exampleMessages.map(example => ( - - ))} -- ) -} diff --git a/spaces/liuyuan-pal/SyncDreamer/sam_utils.py b/spaces/liuyuan-pal/SyncDreamer/sam_utils.py deleted file mode 100644 index 94095e37216f946f5a832f038f8e6da92806b7c6..0000000000000000000000000000000000000000 --- a/spaces/liuyuan-pal/SyncDreamer/sam_utils.py +++ /dev/null @@ -1,50 +0,0 @@ -import os -import numpy as np -import torch -from PIL import Image -import time - -from segment_anything import sam_model_registry, SamPredictor - -def sam_init(device_id=0): - sam_checkpoint = os.path.join(os.path.dirname(__file__), "ckpt/sam_vit_h_4b8939.pth") - model_type = "vit_h" - - device = "cuda:{}".format(device_id) if torch.cuda.is_available() else "cpu" - - sam = sam_model_registry[model_type](checkpoint=sam_checkpoint).to(device=device) - predictor = SamPredictor(sam) - return predictor - -def sam_out_nosave(predictor, input_image, bbox): - bbox = np.array(bbox) - image = np.asarray(input_image) - - start_time = time.time() - predictor.set_image(image) - - h, w, _ = image.shape - input_point = np.array([[h//2, w//2]]) - input_label = np.array([1]) - - masks, scores, logits = predictor.predict( - point_coords=input_point, - point_labels=input_label, - multimask_output=True, - ) - - masks_bbox, scores_bbox, logits_bbox = predictor.predict( - box=bbox, - multimask_output=True - ) - - print(f"SAM Time: {time.time() - start_time:.3f}s") - opt_idx = np.argmax(scores) - mask = masks[opt_idx] - out_image = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8) - out_image[:, :, :3] = image - out_image_bbox = out_image.copy() - out_image[:, :, 3] = mask.astype(np.uint8) * 255 - out_image_bbox[:, :, 3] = masks_bbox[-1].astype(np.uint8) * 255 # np.argmax(scores_bbox) - torch.cuda.empty_cache() - return Image.fromarray(out_image_bbox, mode='RGBA') \ No newline at end of file diff --git a/spaces/llamaindex/llama_index_term_definition_demo/app.py b/spaces/llamaindex/llama_index_term_definition_demo/app.py deleted file mode 100644 index 4def7f72199dd8379c7b107f475878c6f9c3748d..0000000000000000000000000000000000000000 --- a/spaces/llamaindex/llama_index_term_definition_demo/app.py +++ /dev/null @@ -1,222 +0,0 @@ -import os -import streamlit as st - -from PIL import Image -from llama_index import ( - Document, - GPTVectorStoreIndex, - GPTListIndex, - LLMPredictor, - ServiceContext, - SimpleDirectoryReader, - PromptHelper, - StorageContext, - load_index_from_storage, - download_loader, -) -from llama_index.readers.file.base import DEFAULT_FILE_READER_CLS - -from constants import DEFAULT_TERM_STR, DEFAULT_TERMS, REFINE_TEMPLATE, TEXT_QA_TEMPLATE -from utils import get_llm - - -if "all_terms" not in st.session_state: - st.session_state["all_terms"] = DEFAULT_TERMS - - -@st.cache_resource -def get_file_extractor(): - ImageReader = download_loader("ImageReader") - image_loader = ImageReader(text_type="plain_text") - file_extractor = DEFAULT_FILE_READER_CLS - file_extractor.update( - { - ".jpg": image_loader, - ".png": image_loader, - ".jpeg": image_loader, - } - ) - - return file_extractor - - -file_extractor = get_file_extractor() - - -def extract_terms(documents, term_extract_str, llm_name, model_temperature, api_key): - llm = get_llm(llm_name, model_temperature, api_key, max_tokens=1024) - - service_context = ServiceContext.from_defaults( - llm_predictor=LLMPredictor(llm=llm), - prompt_helper=PromptHelper( - max_input_size=4096, max_chunk_overlap=20, num_output=1024 - ), - chunk_size_limit=1024, - ) - - temp_index = GPTListIndex.from_documents(documents, service_context=service_context) - terms_definitions = str( - temp_index.as_query_engine(response_mode="tree_summarize").query( - term_extract_str - ) - ) - terms_definitions = [ - x - for x in terms_definitions.split("\n") - if x and "Term:" in x and "Definition:" in x - ] - # parse the text into a dict - terms_to_definition = { - x.split("Definition:")[0] - .split("Term:")[-1] - .strip(): x.split("Definition:")[-1] - .strip() - for x in terms_definitions - } - return terms_to_definition - - -def insert_terms(terms_to_definition): - for term, definition in terms_to_definition.items(): - doc = Document(f"Term: {term}\nDefinition: {definition}") - st.session_state["llama_index"].insert(doc) - - -@st.cache_resource -def initialize_index(llm_name, model_temperature, api_key): - """Create the GPTSQLStructStoreIndex object.""" - llm = get_llm(llm_name, model_temperature, api_key) - - service_context = ServiceContext.from_defaults(llm_predictor=LLMPredictor(llm=llm)) - - index = load_index_from_storage( - StorageContext.from_defaults(persist_dir="./initial_index"), - service_context=service_context, - ) - - return index - - -st.title("🦙 Llama Index Term Extractor 🦙") -st.markdown( - ( - "This demo allows you to upload your own documents (either a screenshot/image or the actual text) and extract terms and definitions, building a knowledge base!\n\n" - "Powered by [Llama Index](https://gpt-index.readthedocs.io/en/latest/index.html) and OpenAI, you can augment the existing knowledge of an " - "LLM using your own notes, documents, and images. Then, when you ask about a term or definition, it will use your data first! " - "The app is currently pre-loaded with terms from the NYC Wikipedia page." - ) -) - -setup_tab, terms_tab, upload_tab, query_tab = st.tabs( - ["Setup", "All Terms", "Upload/Extract Terms", "Query Terms"] -) - -with setup_tab: - st.subheader("LLM Setup") - api_key = st.text_input("Enter your OpenAI API key here", type="password") - llm_name = st.selectbox( - "Which LLM?", ["text-davinci-003", "gpt-3.5-turbo", "gpt-4"] - ) - model_temperature = st.slider( - "LLM Temperature", min_value=0.0, max_value=1.0, step=0.1 - ) - term_extract_str = st.text_area( - "The query to extract terms and definitions with.", value=DEFAULT_TERM_STR - ) - - -with terms_tab: - st.subheader("Current Extracted Terms and Definitions") - st.json(st.session_state["all_terms"]) - - -with upload_tab: - st.subheader("Extract and Query Definitions") - if st.button("Initialize Index and Reset Terms", key="init_index_1"): - st.session_state["llama_index"] = initialize_index( - llm_name, model_temperature, api_key - ) - st.session_state["all_terms"] = DEFAULT_TERMS - - if "llama_index" in st.session_state: - st.markdown( - "Either upload an image/screenshot of a document, or enter the text manually." - ) - uploaded_file = st.file_uploader( - "Upload an image/screenshot of a document:", type=["png", "jpg", "jpeg"] - ) - document_text = st.text_area("Or enter raw text") - if st.button("Extract Terms and Definitions") and ( - uploaded_file or document_text - ): - st.session_state["terms"] = {} - terms_docs = {} - with st.spinner("Extracting (images may be slow)..."): - if document_text: - terms_docs.update( - extract_terms( - [Document(document_text)], - term_extract_str, - llm_name, - model_temperature, - api_key, - ) - ) - if uploaded_file: - Image.open(uploaded_file).convert("RGB").save("temp.png") - img_reader = SimpleDirectoryReader( - input_files=["temp.png"], file_extractor=file_extractor - ) - img_docs = img_reader.load_data() - os.remove("temp.png") - terms_docs.update( - extract_terms( - img_docs, - term_extract_str, - llm_name, - model_temperature, - api_key, - ) - ) - st.session_state["terms"].update(terms_docs) - - if "terms" in st.session_state and st.session_state["terms"]: - st.markdown("Extracted terms") - st.json(st.session_state["terms"]) - - if st.button("Insert terms?"): - with st.spinner("Inserting terms"): - insert_terms(st.session_state["terms"]) - st.session_state["all_terms"].update(st.session_state["terms"]) - st.session_state["terms"] = {} - st.experimental_rerun() - -with query_tab: - st.subheader("Query for Terms/Definitions!") - st.markdown( - ( - "The LLM will attempt to answer your query, and augment it's answers using the terms/definitions you've inserted. " - "If a term is not in the index, it will answer using it's internal knowledge." - ) - ) - if st.button("Initialize Index and Reset Terms", key="init_index_2"): - st.session_state["llama_index"] = initialize_index( - llm_name, model_temperature, api_key - ) - st.session_state["all_terms"] = DEFAULT_TERMS - - if "llama_index" in st.session_state: - query_text = st.text_input("Ask about a term or definition:") - if query_text: - with st.spinner("Generating answer..."): - response = ( - st.session_state["llama_index"] - .as_query_engine( - similarity_top_k=5, - response_mode="compact", - text_qa_template=TEXT_QA_TEMPLATE, - refine_template=REFINE_TEMPLATE, - ) - .query(query_text) - ) - st.markdown(str(response)) diff --git a/spaces/lsmyrtaj/cse6242-dataminers/sharp_ratio.py b/spaces/lsmyrtaj/cse6242-dataminers/sharp_ratio.py deleted file mode 100644 index ecb5859546daa4f4595ccbada3b74b4649934051..0000000000000000000000000000000000000000 --- a/spaces/lsmyrtaj/cse6242-dataminers/sharp_ratio.py +++ /dev/null @@ -1,146 +0,0 @@ -import pandas as pd -import numpy as np -from datetime import datetime -import streamlit as st -import matplotlib.pyplot as plt -import plotly.express as px -#import plotly.graph_objects as go - - -def cumulative_return(stocks,choices): - symbols, weights, investing_style, benchmark, rf, A_coef = choices.values() - - #tkers = sorted(set(stocks['Ticker'].unique())) - #preprocess - #stocks = stocks.pivot(index="Date", columns="Ticker", values="Adj. Close") - tkers = symbols.copy() - logRet = np.log(stocks/stocks.shift()) - log_returns = np.log(stocks/stocks.shift()) - tickers_list = symbols.copy() - weights_list = weights.copy() - ## - stock_port = {} - for e in tickers_list: stock_port[e] = 0 - # Convert Weights to Floats and Sum - weights = [float(x) for x in weights_list] - s = sum(weights) - # Calc Weight Proportions - new_weights = [] - for i in weights: new_weights.append(i/s) - # Assign Weights to Ticker Dict - i = 0 - for e in stock_port: - stock_port[e] = new_weights[i] - i += 1 - - port = dict.fromkeys(tkers, 0) - port.update(stock_port) - - portfolio_dict = port - - for e in portfolio_dict: - tmp = 0 - if portfolio_dict[e] > tmp: - tmp = portfolio_dict[e] - tick = e - list_ =[] - for e in tickers_list: - if e not in list_: - list_.append(e) - - df = stocks[list_] - df = df/df.iloc[0] - df.reset_index(inplace=True) - df=pd.DataFrame(df) - print(df) - fig = px.line(df, x='Date' ,y=df.columns[1:,]) - - - #layout reference = https://linuxtut.com/en/b13e3e721519c2842cc9/ - fig.update_layout( - xaxis=dict( - rangeselector=dict( - buttons=list([ - dict(count=1, - label="1m", - step="month", - stepmode="backward"), - dict(count=6, - label="6m", - step="month", - stepmode="backward"), - dict(count=1, - label="YTD", - step="year", - stepmode="todate"), - dict(count=1, - label="1y", - step="year", - stepmode="backward"), - dict(step="all") - ]) - ), - rangeslider=dict( - visible=True - ), - type="date" - ) - ) - fig.update_layout(xaxis=dict(rangeselector = dict(font = dict( color = "black")))) - st.subheader('Portfolio Historical Normalized Cumulative Returns') - - st.plotly_chart(fig, use_container_width=True) - -def sharp_ratio_func(stocks,choices): - symbols, weights, investing_style, benchmark, rf, A_coef = choices.values() - logRet,tickers_list,weights_list = preprocess(stocks,choices) - tkers = sorted(set(stocks['Ticker'].unique())) - - stocks = stocks.pivot(index="Date", columns="Ticker", values="Adj. Close") - - stock_port = {} - for e in tickers_list: stock_port[e] = 0 - # Convert Weights to Floats and Sum - weights = [float(x) for x in weights_list] - s = sum(weights) - # Calc Weight Proportions - new_weights = [] - for i in weights: new_weights.append(i/s) - # Assign Weights to Ticker Dict - i = 0 - for e in stock_port: - stock_port[e] = new_weights[i] - i += 1 - - port = dict.fromkeys(tkers, 0) - port.update(stock_port) - - portfolio_dict = port - - sharp_ratio_list = [] - for ticker in symbols: - logRet = np.log(stocks/stocks.shift()) - stk = dict.fromkeys(tkers, 0) - stkTicker = {ticker:1} - stk.update(stkTicker) - ttlStk = np.sum(logRet*stk, axis=1) - stock_sharpe_ratio = ttlStk.mean() / ttlStk.std() - sharp_ratio_list.append(stock_sharpe_ratio) - - sharp_ratio = {'Assets': symbols, 'Sharpe Ratio': sharp_ratio_list} - - # Portfolio sharp Ratio Calculation - logRet = np.log(stocks/stocks.shift()) - portfolio = dict.fromkeys(tkers, 0) - portfolio.update(portfolio_dict) - totalPortfolio = np.sum(logRet*portfolio, axis=1) - portfolio_sharpe_ratio = totalPortfolio.mean() / totalPortfolio.std() - - sharp_ratio['Assets'].append('Portfolio') - sharp_ratio['Sharpe Ratio'].append(portfolio_sharpe_ratio) - - fig = px.bar(sharp_ratio, x='Assets', y="Sharpe Ratio",color='Assets') - fig.update_layout(title_text = 'Sharpe Ratio of the Assets and Portfolio', - title_x=0.458) - st.plotly_chart(fig, use_container_width=True) - diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cpp/detail/reverse.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cpp/detail/reverse.h deleted file mode 100644 index c6ae90664ad9538e73febfde86c334011de417c8..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/thrust/system/cpp/detail/reverse.h +++ /dev/null @@ -1,22 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include- -// this system has no special version of this algorithm - diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/losses/basic_loss.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/losses/basic_loss.py deleted file mode 100644 index d2e965526a9b0e2686575bf93f0173cc2664d9bb..0000000000000000000000000000000000000000 --- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/losses/basic_loss.py +++ /dev/null @@ -1,253 +0,0 @@ -import torch -from torch import nn as nn -from torch.nn import functional as F - -from basicsr.archs.vgg_arch import VGGFeatureExtractor -from basicsr.utils.registry import LOSS_REGISTRY -from .loss_util import weighted_loss - -_reduction_modes = ['none', 'mean', 'sum'] - - -@weighted_loss -def l1_loss(pred, target): - return F.l1_loss(pred, target, reduction='none') - - -@weighted_loss -def mse_loss(pred, target): - return F.mse_loss(pred, target, reduction='none') - - -@weighted_loss -def charbonnier_loss(pred, target, eps=1e-12): - return torch.sqrt((pred - target)**2 + eps) - - -@LOSS_REGISTRY.register() -class L1Loss(nn.Module): - """L1 (mean absolute error, MAE) loss. - - Args: - loss_weight (float): Loss weight for L1 loss. Default: 1.0. - reduction (str): Specifies the reduction to apply to the output. - Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. - """ - - def __init__(self, loss_weight=1.0, reduction='mean'): - super(L1Loss, self).__init__() - if reduction not in ['none', 'mean', 'sum']: - raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}') - - self.loss_weight = loss_weight - self.reduction = reduction - - def forward(self, pred, target, weight=None, **kwargs): - """ - Args: - pred (Tensor): of shape (N, C, H, W). Predicted tensor. - target (Tensor): of shape (N, C, H, W). Ground truth tensor. - weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None. - """ - return self.loss_weight * l1_loss(pred, target, weight, reduction=self.reduction) - - -@LOSS_REGISTRY.register() -class MSELoss(nn.Module): - """MSE (L2) loss. - - Args: - loss_weight (float): Loss weight for MSE loss. Default: 1.0. - reduction (str): Specifies the reduction to apply to the output. - Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. - """ - - def __init__(self, loss_weight=1.0, reduction='mean'): - super(MSELoss, self).__init__() - if reduction not in ['none', 'mean', 'sum']: - raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}') - - self.loss_weight = loss_weight - self.reduction = reduction - - def forward(self, pred, target, weight=None, **kwargs): - """ - Args: - pred (Tensor): of shape (N, C, H, W). Predicted tensor. - target (Tensor): of shape (N, C, H, W). Ground truth tensor. - weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None. - """ - return self.loss_weight * mse_loss(pred, target, weight, reduction=self.reduction) - - -@LOSS_REGISTRY.register() -class CharbonnierLoss(nn.Module): - """Charbonnier loss (one variant of Robust L1Loss, a differentiable - variant of L1Loss). - - Described in "Deep Laplacian Pyramid Networks for Fast and Accurate - Super-Resolution". - - Args: - loss_weight (float): Loss weight for L1 loss. Default: 1.0. - reduction (str): Specifies the reduction to apply to the output. - Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. - eps (float): A value used to control the curvature near zero. Default: 1e-12. - """ - - def __init__(self, loss_weight=1.0, reduction='mean', eps=1e-12): - super(CharbonnierLoss, self).__init__() - if reduction not in ['none', 'mean', 'sum']: - raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}') - - self.loss_weight = loss_weight - self.reduction = reduction - self.eps = eps - - def forward(self, pred, target, weight=None, **kwargs): - """ - Args: - pred (Tensor): of shape (N, C, H, W). Predicted tensor. - target (Tensor): of shape (N, C, H, W). Ground truth tensor. - weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None. - """ - return self.loss_weight * charbonnier_loss(pred, target, weight, eps=self.eps, reduction=self.reduction) - - -@LOSS_REGISTRY.register() -class WeightedTVLoss(L1Loss): - """Weighted TV loss. - - Args: - loss_weight (float): Loss weight. Default: 1.0. - """ - - def __init__(self, loss_weight=1.0, reduction='mean'): - if reduction not in ['mean', 'sum']: - raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: mean | sum') - super(WeightedTVLoss, self).__init__(loss_weight=loss_weight, reduction=reduction) - - def forward(self, pred, weight=None): - if weight is None: - y_weight = None - x_weight = None - else: - y_weight = weight[:, :, :-1, :] - x_weight = weight[:, :, :, :-1] - - y_diff = super().forward(pred[:, :, :-1, :], pred[:, :, 1:, :], weight=y_weight) - x_diff = super().forward(pred[:, :, :, :-1], pred[:, :, :, 1:], weight=x_weight) - - loss = x_diff + y_diff - - return loss - - -@LOSS_REGISTRY.register() -class PerceptualLoss(nn.Module): - """Perceptual loss with commonly used style loss. - - Args: - layer_weights (dict): The weight for each layer of vgg feature. - Here is an example: {'conv5_4': 1.}, which means the conv5_4 - feature layer (before relu5_4) will be extracted with weight - 1.0 in calculating losses. - vgg_type (str): The type of vgg network used as feature extractor. - Default: 'vgg19'. - use_input_norm (bool): If True, normalize the input image in vgg. - Default: True. - range_norm (bool): If True, norm images with range [-1, 1] to [0, 1]. - Default: False. - perceptual_weight (float): If `perceptual_weight > 0`, the perceptual - loss will be calculated and the loss will multiplied by the - weight. Default: 1.0. - style_weight (float): If `style_weight > 0`, the style loss will be - calculated and the loss will multiplied by the weight. - Default: 0. - criterion (str): Criterion used for perceptual loss. Default: 'l1'. - """ - - def __init__(self, - layer_weights, - vgg_type='vgg19', - use_input_norm=True, - range_norm=False, - perceptual_weight=1.0, - style_weight=0., - criterion='l1'): - super(PerceptualLoss, self).__init__() - self.perceptual_weight = perceptual_weight - self.style_weight = style_weight - self.layer_weights = layer_weights - self.vgg = VGGFeatureExtractor( - layer_name_list=list(layer_weights.keys()), - vgg_type=vgg_type, - use_input_norm=use_input_norm, - range_norm=range_norm) - - self.criterion_type = criterion - if self.criterion_type == 'l1': - self.criterion = torch.nn.L1Loss() - elif self.criterion_type == 'l2': - self.criterion = torch.nn.L2loss() - elif self.criterion_type == 'fro': - self.criterion = None - else: - raise NotImplementedError(f'{criterion} criterion has not been supported.') - - def forward(self, x, gt): - """Forward function. - - Args: - x (Tensor): Input tensor with shape (n, c, h, w). - gt (Tensor): Ground-truth tensor with shape (n, c, h, w). - - Returns: - Tensor: Forward results. - """ - # extract vgg features - x_features = self.vgg(x) - gt_features = self.vgg(gt.detach()) - - # calculate perceptual loss - if self.perceptual_weight > 0: - percep_loss = 0 - for k in x_features.keys(): - if self.criterion_type == 'fro': - percep_loss += torch.norm(x_features[k] - gt_features[k], p='fro') * self.layer_weights[k] - else: - percep_loss += self.criterion(x_features[k], gt_features[k]) * self.layer_weights[k] - percep_loss *= self.perceptual_weight - else: - percep_loss = None - - # calculate style loss - if self.style_weight > 0: - style_loss = 0 - for k in x_features.keys(): - if self.criterion_type == 'fro': - style_loss += torch.norm( - self._gram_mat(x_features[k]) - self._gram_mat(gt_features[k]), p='fro') * self.layer_weights[k] - else: - style_loss += self.criterion(self._gram_mat(x_features[k]), self._gram_mat( - gt_features[k])) * self.layer_weights[k] - style_loss *= self.style_weight - else: - style_loss = None - - return percep_loss, style_loss - - def _gram_mat(self, x): - """Calculate Gram matrix. - - Args: - x (torch.Tensor): Tensor with shape of (n, c, h, w). - - Returns: - torch.Tensor: Gram matrix. - """ - n, c, h, w = x.size() - features = x.view(n, c, w * h) - features_t = features.transpose(1, 2) - gram = features.bmm(features_t) / (c * h * w) - return gram diff --git a/spaces/manishjaiswal/01-3DModel-GradioDemo/README.md b/spaces/manishjaiswal/01-3DModel-GradioDemo/README.md deleted file mode 100644 index 4ac90d0220b8950f98f2c491b38d4f000507a830..0000000000000000000000000000000000000000 --- a/spaces/manishjaiswal/01-3DModel-GradioDemo/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: 01 3DModel GradioDemo -emoji: 🦆🧊 -colorFrom: red -colorTo: purple -sdk: gradio -sdk_version: 3.3.1 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/matthoffner/chatbot-mini/__tests__/utils/app/importExports.test.ts b/spaces/matthoffner/chatbot-mini/__tests__/utils/app/importExports.test.ts deleted file mode 100644 index aa51cbc054eae6a7921d88f2e894186e82a87739..0000000000000000000000000000000000000000 --- a/spaces/matthoffner/chatbot-mini/__tests__/utils/app/importExports.test.ts +++ /dev/null @@ -1,264 +0,0 @@ -import { DEFAULT_SYSTEM_PROMPT, DEFAULT_TEMPERATURE } from '@/utils/app/const'; -import { - cleanData, - isExportFormatV1, - isExportFormatV2, - isExportFormatV3, - isExportFormatV4, - isLatestExportFormat, -} from '@/utils/app/importExport'; - -import { ExportFormatV1, ExportFormatV2, ExportFormatV4 } from '@/types/export'; -import { OpenAIModelID, OpenAIModels } from '@/types/openai'; - -import { describe, expect, it } from 'vitest'; - -describe('Export Format Functions', () => { - describe('isExportFormatV1', () => { - it('should return true for v1 format', () => { - const obj = [{ id: 1 }]; - expect(isExportFormatV1(obj)).toBe(true); - }); - - it('should return false for non-v1 formats', () => { - const obj = { version: 3, history: [], folders: [] }; - expect(isExportFormatV1(obj)).toBe(false); - }); - }); - - describe('isExportFormatV2', () => { - it('should return true for v2 format', () => { - const obj = { history: [], folders: [] }; - expect(isExportFormatV2(obj)).toBe(true); - }); - - it('should return false for non-v2 formats', () => { - const obj = { version: 3, history: [], folders: [] }; - expect(isExportFormatV2(obj)).toBe(false); - }); - }); - - describe('isExportFormatV3', () => { - it('should return true for v3 format', () => { - const obj = { version: 3, history: [], folders: [] }; - expect(isExportFormatV3(obj)).toBe(true); - }); - - it('should return false for non-v3 formats', () => { - const obj = { version: 4, history: [], folders: [] }; - expect(isExportFormatV3(obj)).toBe(false); - }); - }); - - describe('isExportFormatV4', () => { - it('should return true for v4 format', () => { - const obj = { version: 4, history: [], folders: [], prompts: [] }; - expect(isExportFormatV4(obj)).toBe(true); - }); - - it('should return false for non-v4 formats', () => { - const obj = { version: 5, history: [], folders: [], prompts: [] }; - expect(isExportFormatV4(obj)).toBe(false); - }); - }); -}); - -describe('cleanData Functions', () => { - describe('cleaning v1 data', () => { - it('should return the latest format', () => { - const data = [ - { - id: 1, - name: 'conversation 1', - messages: [ - { - role: 'user', - content: "what's up ?", - }, - { - role: 'assistant', - content: 'Hi', - }, - ], - }, - ] as ExportFormatV1; - const obj = cleanData(data); - expect(isLatestExportFormat(obj)).toBe(true); - expect(obj).toEqual({ - version: 4, - history: [ - { - id: 1, - name: 'conversation 1', - messages: [ - { - role: 'user', - content: "what's up ?", - }, - { - role: 'assistant', - content: 'Hi', - }, - ], - model: OpenAIModels[OpenAIModelID.GPT_3_5], - prompt: DEFAULT_SYSTEM_PROMPT, - temperature: DEFAULT_TEMPERATURE, - folderId: null, - }, - ], - folders: [], - prompts: [], - }); - }); - }); - - describe('cleaning v2 data', () => { - it('should return the latest format', () => { - const data = { - history: [ - { - id: '1', - name: 'conversation 1', - messages: [ - { - role: 'user', - content: "what's up ?", - }, - { - role: 'assistant', - content: 'Hi', - }, - ], - }, - ], - folders: [ - { - id: 1, - name: 'folder 1', - }, - ], - } as ExportFormatV2; - const obj = cleanData(data); - expect(isLatestExportFormat(obj)).toBe(true); - expect(obj).toEqual({ - version: 4, - history: [ - { - id: '1', - name: 'conversation 1', - messages: [ - { - role: 'user', - content: "what's up ?", - }, - { - role: 'assistant', - content: 'Hi', - }, - ], - model: OpenAIModels[OpenAIModelID.GPT_3_5], - prompt: DEFAULT_SYSTEM_PROMPT, - temperature: DEFAULT_TEMPERATURE, - folderId: null, - }, - ], - folders: [ - { - id: '1', - name: 'folder 1', - type: 'chat', - }, - ], - prompts: [], - }); - }); - }); - - describe('cleaning v4 data', () => { - it('should return the latest format', () => { - const data = { - version: 4, - history: [ - { - id: '1', - name: 'conversation 1', - messages: [ - { - role: 'user', - content: "what's up ?", - }, - { - role: 'assistant', - content: 'Hi', - }, - ], - model: OpenAIModels[OpenAIModelID.GPT_3_5], - prompt: DEFAULT_SYSTEM_PROMPT, - temperature: DEFAULT_TEMPERATURE, - folderId: null, - }, - ], - folders: [ - { - id: '1', - name: 'folder 1', - type: 'chat', - }, - ], - prompts: [ - { - id: '1', - name: 'prompt 1', - description: '', - content: '', - model: OpenAIModels[OpenAIModelID.GPT_3_5], - folderId: null, - }, - ], - } as ExportFormatV4; - - const obj = cleanData(data); - expect(isLatestExportFormat(obj)).toBe(true); - expect(obj).toEqual({ - version: 4, - history: [ - { - id: '1', - name: 'conversation 1', - messages: [ - { - role: 'user', - content: "what's up ?", - }, - { - role: 'assistant', - content: 'Hi', - }, - ], - model: OpenAIModels[OpenAIModelID.GPT_3_5], - prompt: DEFAULT_SYSTEM_PROMPT, - temperature: DEFAULT_TEMPERATURE, - folderId: null, - }, - ], - folders: [ - { - id: '1', - name: 'folder 1', - type: 'chat', - }, - ], - prompts: [ - { - id: '1', - name: 'prompt 1', - description: '', - content: '', - model: OpenAIModels[OpenAIModelID.GPT_3_5], - folderId: null, - }, - ], - }); - }); - }); -}); diff --git a/spaces/matthoffner/chatbot-mini/types/export.ts b/spaces/matthoffner/chatbot-mini/types/export.ts deleted file mode 100644 index 655e3f7b7a47012fb7fa96456304f024ce1f0c21..0000000000000000000000000000000000000000 --- a/spaces/matthoffner/chatbot-mini/types/export.ts +++ /dev/null @@ -1,45 +0,0 @@ -import { Conversation, Message } from './chat'; -import { FolderInterface } from './folder'; -import { OpenAIModel } from './openai'; -import { Prompt } from './prompt'; - -export type SupportedExportFormats = - | ExportFormatV1 - | ExportFormatV2 - | ExportFormatV3 - | ExportFormatV4; -export type LatestExportFormat = ExportFormatV4; - -//////////////////////////////////////////////////////////////////////////////////////////// -interface ConversationV1 { - id: number; - name: string; - messages: Message[]; -} - -export type ExportFormatV1 = ConversationV1[]; - -//////////////////////////////////////////////////////////////////////////////////////////// -interface ChatFolder { - id: number; - name: string; -} - -export interface ExportFormatV2 { - history: Conversation[] | null; - folders: ChatFolder[] | null; -} - -//////////////////////////////////////////////////////////////////////////////////////////// -export interface ExportFormatV3 { - version: 3; - history: Conversation[]; - folders: FolderInterface[]; -} - -export interface ExportFormatV4 { - version: 4; - history: Conversation[]; - folders: FolderInterface[]; - prompts: Prompt[]; -} diff --git a/spaces/matthoffner/open-codetree/store/store.ts b/spaces/matthoffner/open-codetree/store/store.ts deleted file mode 100644 index 04446f16d24f89a8d1b95c59d039ef706088d371..0000000000000000000000000000000000000000 --- a/spaces/matthoffner/open-codetree/store/store.ts +++ /dev/null @@ -1,21 +0,0 @@ -import { configureStore } from "@reduxjs/toolkit"; - -import authSlice from "./features/authSlice"; -import editorReducer from "./features/editorSlice"; -import compilerReducer from "./features/compilerSlice"; -import modalReducer from "./features/modalSlice"; -import themeReducer from "./features/themeSlice"; - -export const store = configureStore({ - reducer: { - auth: authSlice, - editor: editorReducer, - compiler: compilerReducer, - modal: modalReducer, - theme: themeReducer, - }, -}); - -export type RootState = ReturnType ; -// Inferred type: {posts: PostsState, comments: CommentsState, users: UsersState} -export type AppDispatch = typeof store.dispatch; diff --git a/spaces/mayhug/Real-CUGAN/app.py b/spaces/mayhug/Real-CUGAN/app.py deleted file mode 100644 index d08cf5e7187059767f600bd8bd9dc217d49ea8aa..0000000000000000000000000000000000000000 --- a/spaces/mayhug/Real-CUGAN/app.py +++ /dev/null @@ -1,836 +0,0 @@ -import os -from enum import IntEnum -from pathlib import Path -from tempfile import mktemp -from typing import IO, Dict, Type - -import cv2 -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from gradio import Interface, inputs, outputs - -DEVICE = "cpu" - -WEIGHTS_PATH = Path(__file__).parent / "weights" - -AVALIABLE_WEIGHTS = { - basename: path - for basename, ext in ( - os.path.splitext(filename) for filename in os.listdir(WEIGHTS_PATH) - ) - if (path := WEIGHTS_PATH / (basename + ext)).is_file() and ext.endswith("pth") -} - - -class ScaleMode(IntEnum): - up2x = 2 - up3x = 3 - up4x = 4 - - -class TileMode(IntEnum): - full = 0 - half = 1 - quarter = 2 - ninth = 3 - sixteenth = 4 - - -class SEBlock(nn.Module): - def __init__(self, in_channels, reduction=8, bias=False): - super(SEBlock, self).__init__() - self.conv1 = nn.Conv2d( - in_channels, in_channels // reduction, 1, 1, 0, bias=bias - ) - self.conv2 = nn.Conv2d( - in_channels // reduction, in_channels, 1, 1, 0, bias=bias - ) - - def forward(self, x): - if "Half" in x.type(): # torch.HalfTensor/torch.cuda.HalfTensor - x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half() - else: - x0 = torch.mean(x, dim=(2, 3), keepdim=True) - x0 = self.conv1(x0) - x0 = F.relu(x0, inplace=True) - x0 = self.conv2(x0) - x0 = torch.sigmoid(x0) - x = torch.mul(x, x0) - return x - - def forward_mean(self, x, x0): - x0 = self.conv1(x0) - x0 = F.relu(x0, inplace=True) - x0 = self.conv2(x0) - x0 = torch.sigmoid(x0) - x = torch.mul(x, x0) - return x - - -class UNetConv(nn.Module): - def __init__(self, in_channels, mid_channels, out_channels, se): - super(UNetConv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d(in_channels, mid_channels, 3, 1, 0), - nn.LeakyReLU(0.1, inplace=True), - nn.Conv2d(mid_channels, out_channels, 3, 1, 0), - nn.LeakyReLU(0.1, inplace=True), - ) - if se: - self.seblock = SEBlock(out_channels, reduction=8, bias=True) - else: - self.seblock = None - - def forward(self, x): - z = self.conv(x) - if self.seblock is not None: - z = self.seblock(z) - return z - - -class UNet1(nn.Module): - def __init__(self, in_channels, out_channels, deconv): - super(UNet1, self).__init__() - self.conv1 = UNetConv(in_channels, 32, 64, se=False) - self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0) - self.conv2 = UNetConv(64, 128, 64, se=True) - self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0) - self.conv3 = nn.Conv2d(64, 64, 3, 1, 0) - - if deconv: - self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3) - else: - self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0) - - for m in self.modules(): - if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): - nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") - elif isinstance(m, nn.Linear): - nn.init.normal_(m.weight, 0, 0.01) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - - def forward(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2(x2) - x2 = self.conv2_up(x2) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - - x1 = F.pad(x1, (-4, -4, -4, -4)) - x3 = self.conv3(x1 + x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - z = self.conv_bottom(x3) - return z - - def forward_a(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2.conv(x2) - return x1, x2 - - def forward_b(self, x1, x2): - x2 = self.conv2_up(x2) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - - x1 = F.pad(x1, (-4, -4, -4, -4)) - x3 = self.conv3(x1 + x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - z = self.conv_bottom(x3) - return z - - -class UNet1x3(nn.Module): - def __init__(self, in_channels, out_channels, deconv): - super(UNet1x3, self).__init__() - self.conv1 = UNetConv(in_channels, 32, 64, se=False) - self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0) - self.conv2 = UNetConv(64, 128, 64, se=True) - self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0) - self.conv3 = nn.Conv2d(64, 64, 3, 1, 0) - - if deconv: - self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2) - else: - self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0) - - for m in self.modules(): - if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): - nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") - elif isinstance(m, nn.Linear): - nn.init.normal_(m.weight, 0, 0.01) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - - def forward(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2(x2) - x2 = self.conv2_up(x2) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - - x1 = F.pad(x1, (-4, -4, -4, -4)) - x3 = self.conv3(x1 + x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - z = self.conv_bottom(x3) - return z - - def forward_a(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2.conv(x2) - return x1, x2 - - def forward_b(self, x1, x2): - x2 = self.conv2_up(x2) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - - x1 = F.pad(x1, (-4, -4, -4, -4)) - x3 = self.conv3(x1 + x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - z = self.conv_bottom(x3) - return z - - -class UNet2(nn.Module): - def __init__(self, in_channels, out_channels, deconv): - super(UNet2, self).__init__() - - self.conv1 = UNetConv(in_channels, 32, 64, se=False) - self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0) - self.conv2 = UNetConv(64, 64, 128, se=True) - self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0) - self.conv3 = UNetConv(128, 256, 128, se=True) - self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0) - self.conv4 = UNetConv(128, 64, 64, se=True) - self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0) - self.conv5 = nn.Conv2d(64, 64, 3, 1, 0) - - if deconv: - self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3) - else: - self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0) - - for m in self.modules(): - if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): - nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") - elif isinstance(m, nn.Linear): - nn.init.normal_(m.weight, 0, 0.01) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - - def forward(self, x): - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2(x2) - - x3 = self.conv2_down(x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - x3 = self.conv3(x3) - x3 = self.conv3_up(x3) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - - x2 = F.pad(x2, (-4, -4, -4, -4)) - x4 = self.conv4(x2 + x3) - x4 = self.conv4_up(x4) - x4 = F.leaky_relu(x4, 0.1, inplace=True) - - x1 = F.pad(x1, (-16, -16, -16, -16)) - x5 = self.conv5(x1 + x4) - x5 = F.leaky_relu(x5, 0.1, inplace=True) - - z = self.conv_bottom(x5) - return z - - def forward_a(self, x): # conv234结尾有se - x1 = self.conv1(x) - x2 = self.conv1_down(x1) - x2 = F.leaky_relu(x2, 0.1, inplace=True) - x2 = self.conv2.conv(x2) - return x1, x2 - - def forward_b(self, x2): # conv234结尾有se - x3 = self.conv2_down(x2) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - x3 = self.conv3.conv(x3) - return x3 - - def forward_c(self, x2, x3): # conv234结尾有se - x3 = self.conv3_up(x3) - x3 = F.leaky_relu(x3, 0.1, inplace=True) - - x2 = F.pad(x2, (-4, -4, -4, -4)) - x4 = self.conv4.conv(x2 + x3) - return x4 - - def forward_d(self, x1, x4): # conv234结尾有se - x4 = self.conv4_up(x4) - x4 = F.leaky_relu(x4, 0.1, inplace=True) - - x1 = F.pad(x1, (-16, -16, -16, -16)) - x5 = self.conv5(x1 + x4) - x5 = F.leaky_relu(x5, 0.1, inplace=True) - - z = self.conv_bottom(x5) - return z - - -class UpCunet2x(nn.Module): # 完美tile，全程无损 - def __init__(self, in_channels=3, out_channels=3): - super(UpCunet2x, self).__init__() - self.unet1 = UNet1(in_channels, out_channels, deconv=True) - self.unet2 = UNet2(in_channels, out_channels, deconv=False) - - def forward(self, x, tile_mode): # 1.7G - n, c, h0, w0 = x.shape - if tile_mode == 0: # 不tile - ph = ((h0 - 1) // 2 + 1) * 2 - pw = ((w0 - 1) // 2 + 1) * 2 - x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), "reflect") # 需要保证被2整除 - x = self.unet1.forward(x) - x0 = self.unet2.forward(x) - x1 = F.pad(x, (-20, -20, -20, -20)) - x = torch.add(x0, x1) - if w0 != pw or h0 != ph: - x = x[:, :, : h0 * 2, : w0 * 2] - return x - elif tile_mode == 1: # 对长边减半 - if w0 >= h0: - crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除 - crop_size_h = (h0 - 1) // 2 * 2 + 2 # 能被2整除 - else: - crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除 - crop_size_w = (w0 - 1) // 2 * 2 + 2 # 能被2整除 - crop_size = (crop_size_h, crop_size_w) # 6.6G - elif tile_mode == 2: # hw都减半 - crop_size = ( - ((h0 - 1) // 4 * 4 + 4) // 2, - ((w0 - 1) // 4 * 4 + 4) // 2, - ) # 5.6G - elif tile_mode == 3: # hw都三分之一 - crop_size = ( - ((h0 - 1) // 6 * 6 + 6) // 3, - ((w0 - 1) // 6 * 6 + 6) // 3, - ) # 4.2G - elif tile_mode == 4: # hw都四分之一 - crop_size = ( - ((h0 - 1) // 8 * 8 + 8) // 4, - ((w0 - 1) // 8 * 8 + 8) // 4, - ) # 3.7G - ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0] - pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1] - x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), "reflect") - n, c, h, w = x.shape - se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device) - if "Half" in x.type(): - se_mean0 = se_mean0.half() - n_patch = 0 - tmp_dict = {} - opt_res_dict = {} - for i in range(0, h - 36, crop_size[0]): - tmp_dict[i] = {} - for j in range(0, w - 36, crop_size[1]): - x_crop = x[:, :, i : i + crop_size[0] + 36, j : j + crop_size[1] + 36] - n, c1, h1, w1 = x_crop.shape - tmp0, x_crop = self.unet1.forward_a(x_crop) - if "Half" in x.type(): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean( - x_crop.float(), dim=(2, 3), keepdim=True - ).half() - else: - tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - n_patch += 1 - tmp_dict[i][j] = (tmp0, x_crop) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if "Half" in x.type(): - se_mean1 = se_mean1.half() - for i in range(0, h - 36, crop_size[0]): - for j in range(0, w - 36, crop_size[1]): - tmp0, x_crop = tmp_dict[i][j] - x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0) - opt_unet1 = self.unet1.forward_b(tmp0, x_crop) - tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1) - if "Half" in x.type(): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean( - tmp_x2.float(), dim=(2, 3), keepdim=True - ).half() - else: - tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2) - se_mean1 /= n_patch - se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if "Half" in x.type(): - se_mean0 = se_mean0.half() - for i in range(0, h - 36, crop_size[0]): - for j in range(0, w - 36, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j] - tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1) - tmp_x3 = self.unet2.forward_b(tmp_x2) - if "Half" in x.type(): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean( - tmp_x3.float(), dim=(2, 3), keepdim=True - ).half() - else: - tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64 - if "Half" in x.type(): - se_mean1 = se_mean1.half() - for i in range(0, h - 36, crop_size[0]): - for j in range(0, w - 36, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j] - tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0) - tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3) - if "Half" in x.type(): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean( - tmp_x4.float(), dim=(2, 3), keepdim=True - ).half() - else: - tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4) - se_mean1 /= n_patch - for i in range(0, h - 36, crop_size[0]): - opt_res_dict[i] = {} - for j in range(0, w - 36, crop_size[1]): - opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j] - tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1) - x0 = self.unet2.forward_d(tmp_x1, tmp_x4) - x1 = F.pad(opt_unet1, (-20, -20, -20, -20)) - x_crop = torch.add(x0, x1) # x0是unet2的最终输出 - opt_res_dict[i][j] = x_crop - del tmp_dict - torch.cuda.empty_cache() - res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device) - if "Half" in x.type(): - res = res.half() - for i in range(0, h - 36, crop_size[0]): - for j in range(0, w - 36, crop_size[1]): - res[ - :, :, i * 2 : i * 2 + h1 * 2 - 72, j * 2 : j * 2 + w1 * 2 - 72 - ] = opt_res_dict[i][j] - del opt_res_dict - torch.cuda.empty_cache() - if w0 != pw or h0 != ph: - res = res[:, :, : h0 * 2, : w0 * 2] - return res # - - -class UpCunet3x(nn.Module): # 完美tile，全程无损 - def __init__(self, in_channels=3, out_channels=3): - super(UpCunet3x, self).__init__() - self.unet1 = UNet1x3(in_channels, out_channels, deconv=True) - self.unet2 = UNet2(in_channels, out_channels, deconv=False) - - def forward(self, x, tile_mode): # 1.7G - n, c, h0, w0 = x.shape - if tile_mode == 0: # 不tile - ph = ((h0 - 1) // 4 + 1) * 4 - pw = ((w0 - 1) // 4 + 1) * 4 - x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), "reflect") # 需要保证被2整除 - x = self.unet1.forward(x) - x0 = self.unet2.forward(x) - x1 = F.pad(x, (-20, -20, -20, -20)) - x = torch.add(x0, x1) - if w0 != pw or h0 != ph: - x = x[:, :, : h0 * 3, : w0 * 3] - return x - elif tile_mode == 1: # 对长边减半 - if w0 >= h0: - crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2 # 减半后能被4整除，所以要先被8整除 - crop_size_h = (h0 - 1) // 4 * 4 + 4 # 能被4整除 - else: - crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2 # 减半后能被4整除，所以要先被8整除 - crop_size_w = (w0 - 1) // 4 * 4 + 4 # 能被4整除 - crop_size = (crop_size_h, crop_size_w) # 6.6G - elif tile_mode == 2: # hw都减半 - crop_size = ( - ((h0 - 1) // 8 * 8 + 8) // 2, - ((w0 - 1) // 8 * 8 + 8) // 2, - ) # 5.6G - elif tile_mode == 3: # hw都三分之一 - crop_size = ( - ((h0 - 1) // 12 * 12 + 12) // 3, - ((w0 - 1) // 12 * 12 + 12) // 3, - ) # 4.2G - elif tile_mode == 4: # hw都四分之一 - crop_size = ( - ((h0 - 1) // 16 * 16 + 16) // 4, - ((w0 - 1) // 16 * 16 + 16) // 4, - ) # 3.7G - ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0] - pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1] - x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), "reflect") - n, c, h, w = x.shape - se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device) - if "Half" in x.type(): - se_mean0 = se_mean0.half() - n_patch = 0 - tmp_dict = {} - opt_res_dict = {} - for i in range(0, h - 28, crop_size[0]): - tmp_dict[i] = {} - for j in range(0, w - 28, crop_size[1]): - x_crop = x[:, :, i : i + crop_size[0] + 28, j : j + crop_size[1] + 28] - n, c1, h1, w1 = x_crop.shape - tmp0, x_crop = self.unet1.forward_a(x_crop) - if "Half" in x.type(): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean( - x_crop.float(), dim=(2, 3), keepdim=True - ).half() - else: - tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - n_patch += 1 - tmp_dict[i][j] = (tmp0, x_crop) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if "Half" in x.type(): - se_mean1 = se_mean1.half() - for i in range(0, h - 28, crop_size[0]): - for j in range(0, w - 28, crop_size[1]): - tmp0, x_crop = tmp_dict[i][j] - x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0) - opt_unet1 = self.unet1.forward_b(tmp0, x_crop) - tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1) - if "Half" in x.type(): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean( - tmp_x2.float(), dim=(2, 3), keepdim=True - ).half() - else: - tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2) - se_mean1 /= n_patch - se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if "Half" in x.type(): - se_mean0 = se_mean0.half() - for i in range(0, h - 28, crop_size[0]): - for j in range(0, w - 28, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j] - tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1) - tmp_x3 = self.unet2.forward_b(tmp_x2) - if "Half" in x.type(): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean( - tmp_x3.float(), dim=(2, 3), keepdim=True - ).half() - else: - tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64 - if "Half" in x.type(): - se_mean1 = se_mean1.half() - for i in range(0, h - 28, crop_size[0]): - for j in range(0, w - 28, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j] - tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0) - tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3) - if "Half" in x.type(): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean( - tmp_x4.float(), dim=(2, 3), keepdim=True - ).half() - else: - tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4) - se_mean1 /= n_patch - for i in range(0, h - 28, crop_size[0]): - opt_res_dict[i] = {} - for j in range(0, w - 28, crop_size[1]): - opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j] - tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1) - x0 = self.unet2.forward_d(tmp_x1, tmp_x4) - x1 = F.pad(opt_unet1, (-20, -20, -20, -20)) - x_crop = torch.add(x0, x1) # x0是unet2的最终输出 - opt_res_dict[i][j] = x_crop # - del tmp_dict - torch.cuda.empty_cache() - res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device) - if "Half" in x.type(): - res = res.half() - for i in range(0, h - 28, crop_size[0]): - for j in range(0, w - 28, crop_size[1]): - res[ - :, :, i * 3 : i * 3 + h1 * 3 - 84, j * 3 : j * 3 + w1 * 3 - 84 - ] = opt_res_dict[i][j] - del opt_res_dict - torch.cuda.empty_cache() - if w0 != pw or h0 != ph: - res = res[:, :, : h0 * 3, : w0 * 3] - return res - - -class UpCunet4x(nn.Module): # 完美tile，全程无损 - def __init__(self, in_channels=3, out_channels=3): - super(UpCunet4x, self).__init__() - self.unet1 = UNet1(in_channels, 64, deconv=True) - self.unet2 = UNet2(64, 64, deconv=False) - self.ps = nn.PixelShuffle(2) - self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True) - - def forward(self, x, tile_mode): - n, c, h0, w0 = x.shape - x00 = x - if tile_mode == 0: # 不tile - ph = ((h0 - 1) // 2 + 1) * 2 - pw = ((w0 - 1) // 2 + 1) * 2 - x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), "reflect") # 需要保证被2整除 - x = self.unet1.forward(x) - x0 = self.unet2.forward(x) - x1 = F.pad(x, (-20, -20, -20, -20)) - x = torch.add(x0, x1) - x = self.conv_final(x) - x = F.pad(x, (-1, -1, -1, -1)) - x = self.ps(x) - if w0 != pw or h0 != ph: - x = x[:, :, : h0 * 4, : w0 * 4] - x += F.interpolate(x00, scale_factor=4, mode="nearest") - return x - elif tile_mode == 1: # 对长边减半 - if w0 >= h0: - crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除 - crop_size_h = (h0 - 1) // 2 * 2 + 2 # 能被2整除 - else: - crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2 # 减半后能被2整除，所以要先被4整除 - crop_size_w = (w0 - 1) // 2 * 2 + 2 # 能被2整除 - crop_size = (crop_size_h, crop_size_w) # 6.6G - elif tile_mode == 2: # hw都减半 - crop_size = ( - ((h0 - 1) // 4 * 4 + 4) // 2, - ((w0 - 1) // 4 * 4 + 4) // 2, - ) # 5.6G - elif tile_mode == 3: # hw都三分之一 - crop_size = ( - ((h0 - 1) // 6 * 6 + 6) // 3, - ((w0 - 1) // 6 * 6 + 6) // 3, - ) # 4.1G - elif tile_mode == 4: # hw都四分之一 - crop_size = ( - ((h0 - 1) // 8 * 8 + 8) // 4, - ((w0 - 1) // 8 * 8 + 8) // 4, - ) # 3.7G - ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0] - pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1] - x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), "reflect") - n, c, h, w = x.shape - se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device) - if "Half" in x.type(): - se_mean0 = se_mean0.half() - n_patch = 0 - tmp_dict = {} - opt_res_dict = {} - for i in range(0, h - 38, crop_size[0]): - tmp_dict[i] = {} - for j in range(0, w - 38, crop_size[1]): - x_crop = x[:, :, i : i + crop_size[0] + 38, j : j + crop_size[1] + 38] - n, c1, h1, w1 = x_crop.shape - tmp0, x_crop = self.unet1.forward_a(x_crop) - if "Half" in x.type(): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean( - x_crop.float(), dim=(2, 3), keepdim=True - ).half() - else: - tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - n_patch += 1 - tmp_dict[i][j] = (tmp0, x_crop) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if "Half" in x.type(): - se_mean1 = se_mean1.half() - for i in range(0, h - 38, crop_size[0]): - for j in range(0, w - 38, crop_size[1]): - tmp0, x_crop = tmp_dict[i][j] - x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0) - opt_unet1 = self.unet1.forward_b(tmp0, x_crop) - tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1) - if "Half" in x.type(): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean( - tmp_x2.float(), dim=(2, 3), keepdim=True - ).half() - else: - tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2) - se_mean1 /= n_patch - se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device) # 64#128#128#64 - if "Half" in x.type(): - se_mean0 = se_mean0.half() - for i in range(0, h - 38, crop_size[0]): - for j in range(0, w - 38, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j] - tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1) - tmp_x3 = self.unet2.forward_b(tmp_x2) - if "Half" in x.type(): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean( - tmp_x3.float(), dim=(2, 3), keepdim=True - ).half() - else: - tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True) - se_mean0 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3) - se_mean0 /= n_patch - se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device) # 64#128#128#64 - if "Half" in x.type(): - se_mean1 = se_mean1.half() - for i in range(0, h - 38, crop_size[0]): - for j in range(0, w - 38, crop_size[1]): - opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j] - tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0) - tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3) - if "Half" in x.type(): # torch.HalfTensor/torch.cuda.HalfTensor - tmp_se_mean = torch.mean( - tmp_x4.float(), dim=(2, 3), keepdim=True - ).half() - else: - tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True) - se_mean1 += tmp_se_mean - tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4) - se_mean1 /= n_patch - for i in range(0, h - 38, crop_size[0]): - opt_res_dict[i] = {} - for j in range(0, w - 38, crop_size[1]): - opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j] - tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1) - x0 = self.unet2.forward_d(tmp_x1, tmp_x4) - x1 = F.pad(opt_unet1, (-20, -20, -20, -20)) - x_crop = torch.add(x0, x1) # x0是unet2的最终输出 - x_crop = self.conv_final(x_crop) - x_crop = F.pad(x_crop, (-1, -1, -1, -1)) - x_crop = self.ps(x_crop) - opt_res_dict[i][j] = x_crop - del tmp_dict - torch.cuda.empty_cache() - res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device) - if "Half" in x.type(): - res = res.half() - for i in range(0, h - 38, crop_size[0]): - for j in range(0, w - 38, crop_size[1]): - # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape) - res[ - :, :, i * 4 : i * 4 + h1 * 4 - 152, j * 4 : j * 4 + w1 * 4 - 152 - ] = opt_res_dict[i][j] - del opt_res_dict - torch.cuda.empty_cache() - if w0 != pw or h0 != ph: - res = res[:, :, : h0 * 4, : w0 * 4] - res += F.interpolate(x00, scale_factor=4, mode="nearest") - return res # - - -models: Dict[str, Type[nn.Module]] = { - obj.__name__: obj - for obj in globals().values() - if isinstance(obj, type) and issubclass(obj, nn.Module) -} - - -class RealWaifuUpScaler: - def __init__(self, scale: int, weight_path: str, half: bool, device: str): - weight = torch.load(weight_path, map_location=device) - self.model = models[f"UpCunet{scale}x"]() - - if half == True: - self.model = self.model.half().to(device) - else: - self.model = self.model.to(device) - - self.model.load_state_dict(weight, strict=True) - self.model.eval() - - self.half = half - self.device = device - - def np2tensor(self, np_frame): - if self.half == False: - return ( - torch.from_numpy(np.transpose(np_frame, (2, 0, 1))) - .unsqueeze(0) - .to(self.device) - .float() - / 255 - ) - else: - return ( - torch.from_numpy(np.transpose(np_frame, (2, 0, 1))) - .unsqueeze(0) - .to(self.device) - .half() - / 255 - ) - - def tensor2np(self, tensor): - if self.half == False: - return np.transpose( - (tensor.data.squeeze() * 255.0) - .round() - .clamp_(0, 255) - .byte() - .cpu() - .numpy(), - (1, 2, 0), - ) - else: - return np.transpose( - (tensor.data.squeeze().float() * 255.0) - .round() - .clamp_(0, 255) - .byte() - .cpu() - .numpy(), - (1, 2, 0), - ) - - def __call__(self, frame, tile_mode): - with torch.no_grad(): - tensor = self.np2tensor(frame) - result = self.tensor2np(self.model(tensor, tile_mode)) - return result - - -input_image = inputs.File(label="Input image") -half_precision = inputs.Checkbox( - label="Half precision (NOT work for CPU)", default=False -) -model_weight = inputs.Dropdown(sorted(AVALIABLE_WEIGHTS), label="Choice model weight") -tile_mode = inputs.Radio([mode.name for mode in TileMode], label="Output tile mode") - -output_image = outputs.Image(label="Output image preview") -output_file = outputs.File(label="Output image file") - - -def main(file: IO[bytes], half: bool, weight: str, tile: str): - scale = next(mode.value for mode in ScaleMode if weight.startswith(mode.name)) - upscaler = RealWaifuUpScaler( - scale, weight_path=str(AVALIABLE_WEIGHTS[weight]), half=half, device=DEVICE - ) - - frame = cv2.cvtColor(cv2.imread(file.name), cv2.COLOR_BGR2RGB) - result = cv2.cvtColor(upscaler(frame, TileMode[tile]), cv2.COLOR_RGB2BGR) - - _, ext = os.path.splitext(file.name) - tempfile = mktemp(suffix=ext) - cv2.imwrite(tempfile, result) - return tempfile, tempfile - - -interface = Interface( - main, - inputs=[input_image, half_precision, model_weight, tile_mode], - outputs=[output_image, output_file], -) -interface.launch() diff --git a/spaces/mayordp/DeepFakeAI/app.py b/spaces/mayordp/DeepFakeAI/app.py deleted file mode 100644 index 7fb389c97f82c9944c9cfa9d3144958bba9f406d..0000000000000000000000000000000000000000 --- a/spaces/mayordp/DeepFakeAI/app.py +++ /dev/null @@ -1,166 +0,0 @@ -import gradio as gr -import subprocess as sp -import os -import uuid -import time -import shutil - -os.makedirs("./output", exist_ok=True) - -def run(*args): - source, target, unique_id, *rest_args = args - if not os.path.exists(source): - return "Source file does not exist" - if not os.path.exists(target): - return "Target file does not exist" - remove_old_directories("./output", num_minutes=60) - filename = os.path.basename(target) - os.makedirs(f"./output/{unique_id}",exist_ok=True) - output = f"./output/{unique_id}/{filename}" - frame_processor = rest_args[0] - selected_frame_processors = ' '.join(frame_processor) - - face_analyser_direction = rest_args[1] - face_recognition = rest_args[2] - face_analyser_gender = rest_args[3] - - cmd = ( - f"python run.py --execution-providers cpu -s {source} -t {target} -o {output} " - f"--frame-processors {selected_frame_processors} " - f"--face-analyser-direction {face_analyser_direction} " - ) - if face_recognition != 'none': - cmd += f"--face-recognition {face_recognition} " - if face_analyser_gender != 'none': - cmd += f"--face-analyser-gender {face_analyser_gender} " - - if len(rest_args) > 4: - skip_audio = rest_args[4] - keep_fps = rest_args[5] - keep_temp = rest_args[6] - if skip_audio: - cmd += "--skip-audio " - if keep_fps: - cmd += "--keep-fps " - if keep_temp: - cmd += "--keep-temp " - - try: - print("Started...", cmd) - output_text = sp.run(cmd, shell=True, capture_output=True, text=True).stdout - print(output_text) - return output - except Exception as e: - return f"An error occurred: {str(e)}" - -def clear_output(unique_id): - try: - output_path = f"./output/{unique_id}" - if os.path.exists(output_path): - print("Trying to delete ") - for filename in os.listdir(output_path): - file_path = os.path.join(output_path, filename) - if os.path.isfile(file_path): - os.remove(file_path) - print(f"Output files in {output_path} are deleted") - return "Output files for unique_id deleted" - else: - print(f"Output files in {output_path} does not exist") - return "Output directory for (output_path} does not exist" - except Exception as e: - return f"An error occurred: {str(e)}" - -def remove_old_directories(directory, num_minutes=60): - now = time.time() - - for r, d, f in os.walk(directory): - for dir_name in d: - dir_path = os.path.join(r, dir_name) - timestamp = os.path.getmtime(dir_path) - age_minutes = (now - timestamp) / 60 # Convert to minutes - - if age_minutes >= num_minutes: - try: - print("Removing", dir_path) - shutil.rmtree(dir_path) - print("Directory removed:", dir_path) - except Exception as e: - print(e) - pass - -def get_theme() -> gr.Theme: - return gr.themes.Soft( - primary_hue = gr.themes.colors.red, - secondary_hue = gr.themes.colors.gray, - font = gr.themes.GoogleFont('Inter') - ).set( - background_fill_primary = '*neutral_50', - block_label_text_size = '*text_sm', - block_title_text_size = '*text_sm' - ) - -with gr.Blocks(theme=get_theme(), title="DeepFakeAI 1.0.0") as ui: - with gr.Box(): - gr.HTML(' DeepFakeAI 1.0.1 ') - - with gr.Box(): - with gr.Column(scale=3): - frame_processor_checkbox = gr.CheckboxGroup( - choices = ['face_swapper', 'face_enhancer', 'frame_enhancer'], - label = 'FRAME PROCESSORS', - value = ['face_swapper'] # Default value - ) - - - with gr.Box(): - with gr.Column(scale=3): - face_analyser_direction_dropdown = gr.Dropdown( - label = 'FACE ANALYSER DIRECTION', - choices = ['left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small'], - value = 'left-right' - ) - face_analyser_age_dropdown = gr.Dropdown( - label = 'FACE RECOGNITION', - choices = ['none'] + ['reference', 'many'], - value = 'reference' - ) - face_analyser_gender_dropdown = gr.Dropdown( - label = 'FACE ANALYSER GENDER', - choices = ['none'] + ['male', 'female'], - value = 'none' - ) - unique_id = gr.Textbox(value=str(uuid.uuid4()), visible=False) - with gr.Tab("Image: "): - source_image = gr.Image(type="filepath", label="SOURCE IMAGE") - target_image = gr.Image(type="filepath", label="TARGET IMAGE") - image_button = gr.Button("START") - clear_button = gr.ClearButton(value="CLEAR") - image_output = gr.Image(label="OUTPUT") - clear_button.add(image_output) - - image_button.click( - run, - inputs=[source_image, target_image, unique_id, frame_processor_checkbox, face_analyser_direction_dropdown, face_analyser_age_dropdown, face_analyser_gender_dropdown], - outputs=image_output - ) - clear_button.click(fn=clear_output, inputs=unique_id) - - with gr.Tab("Video: "): - source_image_video = gr.Image(type="filepath", label="SOURCE IMAGE") - target_video = gr.Video(label="TARGET VIDEO") - with gr.Box(): - skip_audio = gr.Checkbox(label="SKIP AUDIO") - keep_fps = gr.Checkbox(label="KEEP FPS") - keep_temp = gr.Checkbox(label="KEEP TEMP") - video_button = gr.Button("START") - clear_video_button = gr.ClearButton(value="CLEAR") - video_output = gr.Video(label="OUTPUT") - clear_video_button.add(video_output) - video_button.click( - run, - inputs=[source_image_video, target_video, unique_id, frame_processor_checkbox, face_analyser_direction_dropdown, face_analyser_age_dropdown, face_analyser_gender_dropdown, skip_audio, keep_fps, keep_temp], - outputs=video_output - ) - clear_video_button.click(fn=clear_output, inputs=unique_id) - -ui.launch(debug=True) \ No newline at end of file diff --git a/spaces/mehdidc/text_to_image_ddgan/score_sde/models/__init__.py b/spaces/mehdidc/text_to_image_ddgan/score_sde/models/__init__.py deleted file mode 100644 index 9a198046280b17bea735155aff5f8d4a0ab6f4da..0000000000000000000000000000000000000000 --- a/spaces/mehdidc/text_to_image_ddgan/score_sde/models/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -# coding=utf-8 -# Copyright 2020 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - diff --git a/spaces/merle/PROTEIN_GENERATOR/utils/README.md b/spaces/merle/PROTEIN_GENERATOR/utils/README.md deleted file mode 100644 index 06b2c403cbf5ef306c6400960bb39b2d9fa2ee63..0000000000000000000000000000000000000000 --- a/spaces/merle/PROTEIN_GENERATOR/utils/README.md +++ /dev/null @@ -1,132 +0,0 @@ ---- -title: PROTEIN GENERATOR -emoji: 🐨 -thumbnail: http://files.ipd.uw.edu/pub/sequence_diffusion/figs/diffusion_landscape.png -colorFrom: blue -colorTo: purple -sdk: gradio -sdk_version: 3.24.1 -app_file: app.py -pinned: false ---- - - - -## TLDR but I know how to use inpainting -Submit jobs with all the same args as [inpainting](https://git.ipd.uw.edu/jwatson3/proteininpainting) plus some new ones ----Tspecify number of timesteps to use, good choices are 5,25,50,100 (try lower T first) ----save_best_plddtrecommended arg to save best plddt str/seq in trajectory ----loop_designuse when generating loops for binder design (or Ab loop design), will load finetuned checkpoint ----symmetryinteger to divide the input up with for symmetric update (input of X will divide the sequence into X symmetric motifs) ----trbspecify trb when partially diffusing to use same mask as the design, must match pdb input (contigs will not get used) ----sampling_tempfraction of diffusion trajectory to use for partial diffusion (default is 1.0 for fully diffused) values around 0.3 seem to give good diversity - -**environment to use** -``` -source activate /software/conda/envs/SE3nv -``` -**example command** -``` -python inference.py --pdb examples/pdbs/rsv5_5tpn.pdb --out examples/out/design \ - --contigs 0-25,A163-181,25-30 --T 25 --save_best_plddt -``` -For jobs with inputs <75 residues it is feesible to run on CPUs. It's helpful to redesign output backbones with [MPNN](https://git.ipd.uw.edu/justas/proteinmpnn) (not sure if useful yet when using--loop_design). Check back for more updates. - -## Getting started - -Check out the templates in the example folder to see how you can set up jobs for the various design strategies - -- [ ] [Motif or active-site scaffolding](examples/motif_scaffolding.sh) -- [ ] [Partial diffusion (design diversification)](examples/partial_diffusion.sh) -- [ ] [Anitbody / Loop design](examples/loop_design.sh) -- [ ] [Symmetric design](examples/symmetric_design.sh) - -## Weighted Sequence design -Biasing the sequence by weighting certain amino acid types is a nice way to control and guide the diffusion process, generate interesting folds, and repeat units. It is possible to combine this technique with motif scaffolding as well. here are a few different ways to set up sequence potentials: - -The--aa_specargument used in combination with the--aa_weightallows you to specify the complete amino acid weighting pattern for a sequence. The pattern specified in aa_spec will be repeated along the entire length of the design. - ---aa_specbase repeat unit to weight sequence with, X is used as a mask token, for example--aa_spec XXAXXLXXwill generate solenoid folds like the one below - ---aa_weightweights to assign for non masked residues inaa_spec, for example--aa_weight 2,2will weight alanine to 2 and leucine to 2 - -**Make solenoids with a little bias!** --
- -**example job set up for sequene weighting** -``` -python inference.py \ - --num_designs 10 \ - --out examples/out/seq_bias \ - --contigs 100 --symmetry 5 \ - --T 25 --save_best_plddt \ - --aa_spec XXAXXLXX --aa_weight 2,2 -``` - -In addition to the contigs above users can also use a disctionary to specify sequence weighting with [aa_weights](examples/aa_weights.json) for more generic uses. These weights can be specified with the
---aa_weights_jsonarg and used in combination with the--add_weight_every_narg or--frac_seq_to_weightarg. Each of these args defines where weights in the aa_weights dictionary will be applied to the sequence (you cannot specify both simultaneously). To add the weight every 5 residues use--add_weight_every_n 5. To add weight to a randomly sampled 40% of the sequence use--frac_seq_to_weight 0.4. If you add weight to multiple amino acid types in aa_weights, use the--one_weight_per_positionflag to specify that a randomly sampled amino acid from aa_weight with a positive value should be chosen where the sequence bias is added. This allows the user to specify multiple amino acid types you want to upweight while ensuring to only bias for one type at each position, this usually is more effective. - - -## Motif and active site scaffolding -An example for motif scaffolding submission is written below, if you are inputing an active site with single residue inputs this can be specified in the contigs like10-20,A10-10,20-30,A50-50,5-15to scaffold just the 10th and 50th residues of chain A. Setting the model at higher T usually results in higher success rates, but it can still be useful to try problems out with just a few steps (T = 5, 15, or 25), before increasing the number of steps further. It is recommended to use [MPNN](https://git.ipd.uw.edu/justas/proteinmpnn) on the output backbones before alphafolding for validation. - -``` -python inference.py \ - --num_designs 10 \ - --out examples/out/design \ - --pdb examples/pdbs/rsv5_5tpn.pdb \ - --contigs 0-25,A163-181,25-30 --T 25 --save_best_plddt -``` - -## Partial diffusion -To sample diverse and highquality desing fast, it can be useful to run many designs with T=5, and then after MPNN and alphafold filtering partially diffuse the successful designs to generate more diversity around designs that seem to be working. By using the--trbflag the script will enter partial diffusion mode. With the--Tflag you can specify the total number of steps inthe trajectory and with the--sampling_tempflag you can determine how far into the trajectory the inputs will be diffused. Setting the sampling temp to 1.0 would be full diffused. In this mode the contigs will be ignored, and the mask used from the original design will be used. - -``` -python inference.py \ - --num_designs 10 \ - --pdb examples/out/design_000.pdb \ - --trb examples/out/design_000.trb \ - --out examples/out/partial_diffusion_design \ - --contigs 0 --sampling_temp 0.3 --T 50 --save_best_plddt -``` - - -## Symmetric design -In symmetric design mode, the--symmetryflag is used to specify the number of partitions to make from the input sequence length. Each partition will be updated symmetric according to the first in the sequence. This requires that your sequence length (L) is divisible by the symmetry input. Symmetric motif scaffolding should be possible with the right contigs, but has not been experimented with yet. - -``` -python inference.py \ - --num_designs 10 \ - --pdb examples/pdbs/rsv5_5tpn.pdb \ - --out examples/out/symmetric_design \ - --contigs 25,0 25,0 25,0 \ - --T 50 --save_best_plddt --symmetry 3 -``` - - -## Antibody and loop design -Using the--loop_desingflag will load a version of the model finetuned on antibody CDR loops. This is useful if you are looking to design new CDR loops or are strcutred loops for binder design. It is helpful to run the designs with a target input too. - -``` -python inference.py \ - --num_designs 10 \ - --pdb examples/pdbs/G12D_manual_mut.pdb \ - --out examples/out/ab_loop \ - --contigs A2-176,0 C7-16,0 H2-95,12-15,H111-116,0 L1-45,10-12,L56-107 \ - --T 25 --save_best_plddt --loop_design -``` - - - -## About the model -Sequence diffusion is trained on the same dataset and uses the same architecture as RoseTTAFold. To train the model, a ground truth sequence is transformed into an Lx20 continuous space and gaussian noise is added to diffuse the sequence to the sampled timestep. To condition on structure and sequence, the structre for a motif is given and then corresponding sequence is denoised in the input. The rest of the structure is blackhole initialized. For each example the model is trained to predict Xo and losses are applied on the structure and sequence respectively. During training big T is set to 1000 steps, and a square root schedule is used to add noise. - - - - -## Looking ahead -We are interested in problems where diffusing in sequence space is useful, if you would like to chat more or join in our effort for sequence diffusion come talk to Sidney or Jake! - - -## Acknowledgements -A project by Sidney Lisanza and Jake Gershon. Thanks to Sam Tipps for implementing symmetric sequence diffusion. Thank you to Minkyung Baek and Frank Dimaio for developing RoseTTAFold, Joe Watson and David Juergens for the developing inpainting inference script which the inference code is built on top of. - diff --git a/spaces/merve/anonymization/public/third_party/alea.js b/spaces/merve/anonymization/public/third_party/alea.js deleted file mode 100644 index 9effe485ca14df5d6923e20adefaa794b939ee26..0000000000000000000000000000000000000000 --- a/spaces/merve/anonymization/public/third_party/alea.js +++ /dev/null @@ -1,3 +0,0 @@ -// https://github.com/davidbau/seedrandom Copyright 2019 David Bau - -!function(n,t,e){function u(n){var t=this,e=function(){var s=4022871197;return function(n){n=String(n);for(var t=0;tHow randomized response can help collect sensitive information responsibly - - - - - - - - - - - - - - - --- -- --
-
- Explorables
- How randomized response can help collect sensitive information responsibly
-Giant datasets are revealing new patterns in cancer, income inequality and other important areas. However, the widespread availability of fast computers that can cross reference public data is making it harder to collect private information without inadvertently violating people's privacy. Modern randomization techniques can help preserve anonymity.- - - -- -- ---- -- - -Anonymous Data
- -Let's pretend we're analysts at a small college, looking at anonymous survey data about plagiarism. - -
We've gotten responses from the entire student body, reporting if they've ever plagiarized or not. To encourage them to respond honestly, names were not collected. -
- -
The data here has been randomly generated
--- - -On the survey students also report several bits of information about themselves, like their age... -
-- - -...and what state they're from. - -
This additional information is critical to finding potential patterns in the data—why have so many first-years from New Hampshire plagiarized? -
-- - -Revealed Information
-But granular information comes with a cost. - -
One student has a unique age/home state combination. By searching another student database for a 19-year old from Vermont we can identify one of the plagiarists from supposedly anonymous survey data. -
-- - -Increasing granularity exacerbates the problem. If the students reported slightly more about their ages by including what season they were born in, we'd be able to identify about a sixth of them. - -
This isn't just a hypothetical: A birthday / gender / zip code combination uniquely identifies 83% of the people in the United States. - -
With the spread of large datasets, it is increasingly difficult to release detailed information without inadvertently revealing someone's identity. A week of a person's location data could reveal a home and work address—possibly enough to find a name using public records. -
-- - -Randomization
-One solution is to randomize responses so each student has plausible deniability. This lets us buy privacy at the cost of some uncertainty in our estimation of plagiarism rates. - -
Step 1: Each student flips a coin and looks at it without showing anyone. -
-- - -Step 2: Students who flip heads report plagiarism, even if they haven't plagiarized. - -
Students that flipped tails report the truth, secure with the knowledge that even if their response is linked back to their name, they can claim they flipped heads. -
-- - -With a little bit of math, we can approximate the rate of plagiarism from these randomized responses. We'll skip the algebra, but doubling the reported non-plagiarism rate gives a good estimate of the actual non-plagiarism rate. - -
- - - --- - -How far off can we be?
- -If we simulate this coin flipping lots of times, we can see the distribution of errors. - -
The estimates are close most of the time, but errors can be quite large. - -
- --- - -Reducing the random noise (by reducing the number of students who flip heads) increases the accuracy of our estimate, but risks leaking information about students. - -
If the coin is heavily weighted towards tails, identified students can't credibly claim they reported plagiarizing because they flipped heads. - -
- --- - - -One surprising way out of this accuracy-privacy tradeoff: carefully collect information from even more people. - -
If we got students from other schools to fill out this survey, we could accurately measure plagiarism while protecting everyone's privacy. With enough students, we could even start comparing plagiarism across different age groups again—safely this time. - -
-Conclusion
- -Aggregate statistics about private information are valuable, but can be risky to collect. We want researchers to be able to study things like the connection between demographics and health outcomes without revealing our entire medical history to our neighbors. The coin flipping technique in this article, called randomized response, makes it possible to safely study private information. - -
You might wonder if coin flipping is the only way to do this. It's not—differential privacy can add targeted bits of random noise to a dataset and guarantee privacy. More flexible than randomized response, the 2020 Census will use it to protect respondents' privacy. In addition to randomizing responses, differential privacy also limits the impact any one response can have on the released data. - - -
Credits
- -Adam Pearce and Ellen Jiang // September 2020 - -
Thanks to Carey Radebaugh, Fernanda Viégas, Emily Reif, Hal Abelson, Jess Holbrook, Kristen Olson, Mahima Pushkarna, Martin Wattenberg, Michael Terry, Miguel Guevara, Rebecca Salois, Yannick Assogba, Zan Armstrong and our other colleagues at Google for their help with this piece. - - - - -
More Explorables
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/spaces/merve/measuring-fairness/source/dataset-worldviews/script.js b/spaces/merve/measuring-fairness/source/dataset-worldviews/script.js deleted file mode 100644 index 3ebba088d65f389af1b446a9ea90fcde674d5fdf..0000000000000000000000000000000000000000 --- a/spaces/merve/measuring-fairness/source/dataset-worldviews/script.js +++ /dev/null @@ -1,588 +0,0 @@ - -console.clear(); - -var ttSel = d3.select("body").selectAppend("div.tooltip.tooltip-hidden"); -// For result tables -const columns = ["object", "n", "n correct", "accuracy"]; -const rowHeight = 50; -const rowWidth = 100; -const buffer = 2; - -const classifierBlobWidth = 50; -const classifierBlobHeight = 460; - -function drawShapesWithData(classifier) { - var divHeight = classifier.class == "show-shapes" ? 250 : 490; - - var c = d3.conventions({ - sel: d3.select("." + classifier.class).html(""), - width: 1300, - height: divHeight, - layers: "ds", - }); - - function runClassifier() { - classifier.isClassified = true; - var duration = 3000; - classifierSel.classed("is-classified", true); - graphResultsGroup.classed("is-classified", true); - - drawResults(); - buttonSel.text("Reset"); - - var minX = d3.min(shapeParams, (d) => d.endX - 50); - var timer = d3.timer((ms) => { - if (!classifier.isClassified) { - timer.stop(); - shapeSel.classed("is-classified", false); - return; - } - - var t = d3.easeCubicInOut(ms / duration); - t = d3.clamp(0, t, 1); - - shapeParams.forEach((d, i) => { - d.x = d.startX + (d.endX - d.startX) * t; - d.y = d.startY + (d.endY - d.startY) * t; - d.isClassified = d.x > minX; - }); - - shapeSel - .translate((d) => [d.x, d.y]) - .classed("is-classified", (d) => d.isClassified); - - if (t == 1) { - timer.stop(); - } - }); - } - - function resetClassifier() { - shapeSel.translate((d) => [d.startX, d.startY]); - shapeSel.classed("is-classified", false); - classifier.isClassified = false; - shapeSel - .transition("position") - .duration(0) - .translate((d) => [d.startX, d.startY]); - classifierSel.classed("is-classified", false); - graphResultsGroup.classed("is-classified", false); - if (classifier.class != "show-shapes") { - classifierBlobSel.attr("opacity", 100); - } - - drawResults(); - buttonSel.text("Run Classifier"); - } - - // Add run/reset button - var buttonSel = d3 - .select("." + classifier.class + "-button") - .html("") - .append("button#run") - .at({ - type: "button", - class: "classifier-button", - }) - .text("Run Classifier") - .on("click", () => { - // if already classified, reset - if (classifier.isClassified) { - // Resetting - resetClassifier(); - } else { - runClassifier(); - } - }); - - // Backgrounds for different classifications - var classifierSel = c.svg - .append("g") - .at({ - class: "classifier", - }) - .translate([465, 20]); - - classifierSel - .append("path.classifier-bg-shaded") - .at({ - d: classifierBgPathTop, - // fill: "#ccc", - // stroke: "#000", - }) - .translate([-50, 0]); - - classifierSel - .append("text.classifier-bg-text") - .at({ - fill: "#000", - textAnchor: "middle", - dominantBaseline: "central", - class: "monospace", - }) - .text("shaded") - .translate([160, 15]); - - classifierSel - .append("path.classifier-bg-unshaded") - .at({ - d: classifierBgPathBottom, - }) - .translate([-50, 160]); - - classifierSel - .append("text.classifier-bg-text") - .at({ - fill: "#000", - textAnchor: "middle", - dominantBaseline: "central", - class: "monospace", - }) - .text("unshaded") - .translate([160, 175]); - - // Add the shapes themselves - var shapeSel = c.svg - .appendMany("path.shape", shapeParams) - .at({ - d: (d) => d.path, - class: (d) => "gt-" + d.gt + " " + d.correctness, - }) - .translate(function (d) { - if (classifier.class == "show-shapes") { - return [d.initialX + 35, d.initialY-20]; - } else { - return [d.startX, d.startY]; - } - }) - .call(d3.attachTooltip) - .on("mouseover", (d) => { - ttSel.html(""); - if (classifier.usingLabel != "none") { - ttSel - .append("div") - .html( - `labeled: ${toPropertyString( - d[classifier.usingLabel], - classifier.isRounding - ).slice(0, -1)}` - ); - } - var gtSel = ttSel - .append("div") - .html( - `ground truth: ${d.gt}` - ); - if (classifier.isClassified) { - ttSel - .append("div.labeled-row") - .html( - `classified as: ${d.label}` - ); - - ttSel - .append("div.correct-row") - .classed("is-correct-tooltip", d.correctness == "correct") - .html(`
${d.correctness}ly classified `); - } - ttSel.classed("tt-text", true); - }); - - // If we're just showing shapes, ignore everything else - if (classifier.class == "show-shapes") return; - - // Add "classifier" line - var classifierBlobSel = c.svg - .append("g") - .at({ - class: "classifier-blob", - strokeWidth: 0, - }) - .translate([378, 20]); - - classifierBlobSel - .append("line.classifier-blob") - .at({ - class: "line", - x1: 27, - x2: 27, - y1: 0, - y2: 464, - stroke: "#000", - strokeWidth: 1, - }) - .style("stroke-dasharray", "5, 5"); - - classifierBlobSel - .append("text.classifier-blob-text") - .at({ - class: "classifier-blob-text monospace", - textAnchor: "middle", - dominantBaseline: "central", - }) - .text("is_shaded classifier") - .attr("transform", "translate(30,480) rotate(0)"); - - if (classifier.class == "show-shapes") { - classifierBlobSel.classed("is-classified", true); - } - - // Draw the results table with accuracies - // This will be hidden before classifier is run. - var graphResultsGroup = c.svg - .append("g") - .attr("class", "results") - .translate([-20, 19]); - - function drawResults() { - // Write text summary - summarySel = d3 - .select("." + classifier.class + "-summary") - .html(summaries[classifier.class]) - .translate([0, 20]); - summarySel.classed("summary-text", true); - summarySel.classed("is-classified", classifier.isClassified); - - if (!classifier.isClassified) { - c.layers[0].html(""); - classifier.wasClassified = false; - return; - } - - // Access results, which are calculated in shapes.js. - // If there are none, draw nothing. - results = allResults[classifier.class]; - if (!results) return; - - // Figure out which shapes should be highlighted on mouseover - // This depends on whether we're "rounding" edge case examples. - function isMatch(rowName, labelName, isRounding) { - // Not filtering at all - if (rowName == "shape") { - return true; - } - if (isRounding == true) { - // No "other" category - return labelName.includes(toOriginalString(rowName)) - ? true - : false; - } else { - // There is an "other" category, prefixed by "rt_" - if (labelName == toOriginalString(rowName)) { - return true; - } else if ( - labelName.includes("rt_") && - rowName == "other shapes" - ) { - return true; - } - return false; - } - } - - // Color the last row of each table - function getColor(d, i) { - if (i != 3) { - // not last index - return "#e6e6e6"; - } else { - var scaleRowValue = d3 - .scaleLinear() - .domain([0.3, 1.0]) - .range([0, 1]); - return d3.interpolateRdYlGn(scaleRowValue(d)); - } - } - - // Adjust text color for visibility - function getTextColor(d, i) { - if (i != 3) { - // not last index - return "#000000"; - } else { - var bgColor = getColor(d, i); - if (d < 0.3) { - // Alternative: use a brighter color? - // return d3.rgb(bgColor).brighter(-2); - return "#FFCCD8"; - } else { - // Alternative: use a darker color? - // return d3.rgb(bgColor).darker(2); - return "#000000"; - } - } - } - - // Draw results table - var tableSel = c.layers[0] - .html("") - .raise() - .st({ width: 400 }) - .append("div") - .translate([0, 10]) - .append("table.results-table.monospace") - .st({ width: 400 }); - - var header = tableSel - .append("thead") - .append("tr") - .appendMany("th", columns) - .text((d) => d); - - var rowSel = tableSel - .appendMany("tr", results) - .at({ - class: "row monospace", - }) - .on("mouseover", (row) => { - if (classifier.class == "default-classifier") { - return; - } - rowSel.classed("active", (d) => d == row); - shapeSel.classed("shape-row-unhighlighted", function (d) { - return !isMatch( - row.object, - d[classifier.usingLabel], - (isRounding = classifier.isRounding) - ); - }); - }) - .on("mouseout", (row) => { - rowSel.classed("active", function (d) { - if (d == row) { - return false; - } - }); - if (classifier.isClassified) { - shapeSel.classed("shape-row-unhighlighted", 0); - } - }); - - rowSel - .appendMany("td", (result) => - columns.map((column) => result[column]) - ) - .text((d) => d) - .st({ - backgroundColor: getColor, - color: getTextColor, - }); - - header.style("opacity", 0); - rowSel.style("opacity", 0); - - // If the classifier has already been run before, draw results right away. - // Otherwise, wait for other animation to run before drawing results. - var initialDelay = classifier.wasClassified ? 0 : 2000; - classifier.wasClassified = true; - - header - .transition() - .delay(initialDelay) - .duration(1000) - .style("opacity", 1); - rowSel - .transition() - .delay(function (d, i) { - return initialDelay + i * 200; - }) - .duration(1000) - .style("opacity", 1); - } - - // Draw the dropdowns for selecting different labels - function drawDropdown() { - if (!classifier.options) return; - - ["rounding", "category"].forEach(function (classifierType) { - if (!classifier.options[classifierType]) return; - var sel = d3 - .select("#" + classifier.class + "-select-" + classifierType) - .html(""); - sel.classed("dropdown", true); - sel.appendMany("option", classifier.options[classifierType]) - .at({ - value: function (d) { - return d.value; - }, - }) - .text((d) => d.label); - sel.on("change", function () { - if (classifierType == "rounding") { - classifier.isRounding = toBool(this.value); - } else { - classifier.usingLabel = this.value; - } - updateResults(); - drawResults(); - }); - }); - } - drawDropdown(); - updateResults(); - drawResults(); - - // For continuity, auto-run the second two classifiers - if ( - classifier.class == "second-classifier" || - classifier.class == "final-classifier" - ) { - runClassifier(); - } -} - -// Draw the "Labels Tell Stories" section -function drawConclusion() { - function drawNewspapers() { - d3.select(".conclusion-newspapers").html(function () { - var imgPath = - "img/newspapers_" + - document.getElementById("conclusion-select-category").value; - return ( - '
'
- );
- });
- }
-
- function drawInterface() {
- d3.select(".conclusion-interface").html(function () {
- var imgPath =
- "img/confusing_" +
- document.getElementById("conclusion-select-category").value;
- return (
- 'Frame for start of decoder loop - go_frame = torch.zeros(batch_size, self.n_mels, device=device) - - # Need an initial context vector - context_vec = torch.zeros(batch_size, self.encoder_dims + self.speaker_embedding_size, device=device) - - # SV2TTS: Run the encoder with the speaker embedding - # The projection avoids unnecessary matmuls in the decoder loop - encoder_seq = self.encoder(x, speaker_embedding) - encoder_seq_proj = self.encoder_proj(encoder_seq) - - # Need a couple of lists for outputs - mel_outputs, attn_scores, stop_outputs = [], [], [] - - # Run the decoder loop - for t in range(0, steps, self.r): - prenet_in = m[:, :, t - 1] if t > 0 else go_frame - mel_frames, scores, hidden_states, cell_states, context_vec, stop_tokens = \ - self.decoder(encoder_seq, encoder_seq_proj, prenet_in, - hidden_states, cell_states, context_vec, t, x) - mel_outputs.append(mel_frames) - attn_scores.append(scores) - stop_outputs.extend([stop_tokens] * self.r) - - # Concat the mel outputs into sequence - mel_outputs = torch.cat(mel_outputs, dim=2) - - # Post-Process for Linear Spectrograms - postnet_out = self.postnet(mel_outputs) - linear = self.post_proj(postnet_out) - linear = linear.transpose(1, 2) - - # For easy visualisation - attn_scores = torch.cat(attn_scores, 1) - # attn_scores = attn_scores.cpu().data.numpy() - stop_outputs = torch.cat(stop_outputs, 1) - - return mel_outputs, linear, attn_scores, stop_outputs - - def generate(self, x, speaker_embedding=None, steps=2000): - self.eval() - device = next(self.parameters()).device # use same device as parameters - - batch_size, _ = x.size() - - # Need to initialise all hidden states and pack into tuple for tidyness - attn_hidden = torch.zeros(batch_size, self.decoder_dims, device=device) - rnn1_hidden = torch.zeros(batch_size, self.lstm_dims, device=device) - rnn2_hidden = torch.zeros(batch_size, self.lstm_dims, device=device) - hidden_states = (attn_hidden, rnn1_hidden, rnn2_hidden) - - # Need to initialise all lstm cell states and pack into tuple for tidyness - rnn1_cell = torch.zeros(batch_size, self.lstm_dims, device=device) - rnn2_cell = torch.zeros(batch_size, self.lstm_dims, device=device) - cell_states = (rnn1_cell, rnn2_cell) - - # Need a Frame for start of decoder loop - go_frame = torch.zeros(batch_size, self.n_mels, device=device) - - # Need an initial context vector - context_vec = torch.zeros(batch_size, self.encoder_dims + self.speaker_embedding_size, device=device) - - # SV2TTS: Run the encoder with the speaker embedding - # The projection avoids unnecessary matmuls in the decoder loop - encoder_seq = self.encoder(x, speaker_embedding) - encoder_seq_proj = self.encoder_proj(encoder_seq) - - # Need a couple of lists for outputs - mel_outputs, attn_scores, stop_outputs = [], [], [] - - # Run the decoder loop - for t in range(0, steps, self.r): - prenet_in = mel_outputs[-1][:, :, -1] if t > 0 else go_frame - mel_frames, scores, hidden_states, cell_states, context_vec, stop_tokens = \ - self.decoder(encoder_seq, encoder_seq_proj, prenet_in, - hidden_states, cell_states, context_vec, t, x) - mel_outputs.append(mel_frames) - attn_scores.append(scores) - stop_outputs.extend([stop_tokens] * self.r) - # Stop the loop when all stop tokens in batch exceed threshold - if (stop_tokens > 0.5).all() and t > 10: break - - # Concat the mel outputs into sequence - mel_outputs = torch.cat(mel_outputs, dim=2) - - # Post-Process for Linear Spectrograms - postnet_out = self.postnet(mel_outputs) - linear = self.post_proj(postnet_out) - - - linear = linear.transpose(1, 2) - - # For easy visualisation - attn_scores = torch.cat(attn_scores, 1) - stop_outputs = torch.cat(stop_outputs, 1) - - self.train() - - return mel_outputs, linear, attn_scores - - def init_model(self): - for p in self.parameters(): - if p.dim() > 1: nn.init.xavier_uniform_(p) - - def get_step(self): - return self.step.data.item() - - def reset_step(self): - # assignment to parameters or buffers is overloaded, updates internal dict entry - self.step = self.step.data.new_tensor(1) - - def log(self, path, msg): - with open(path, "a") as f: - print(msg, file=f) - - def load(self, path, optimizer=None): - # Use device of model params as location for loaded state - device = next(self.parameters()).device - checkpoint = torch.load(str(path), map_location=device) - self.load_state_dict(checkpoint["model_state"]) - - if "optimizer_state" in checkpoint and optimizer is not None: - optimizer.load_state_dict(checkpoint["optimizer_state"]) - - def save(self, path, optimizer=None): - if optimizer is not None: - torch.save({ - "model_state": self.state_dict(), - "optimizer_state": optimizer.state_dict(), - }, str(path)) - else: - torch.save({ - "model_state": self.state_dict(), - }, str(path)) - - - def num_params(self, print_out=True): - parameters = filter(lambda p: p.requires_grad, self.parameters()) - parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000 - if print_out: - print("Trainable Parameters: %.3fM" % parameters) - return parameters diff --git a/spaces/nagolinc/liteDungeon/README.md b/spaces/nagolinc/liteDungeon/README.md deleted file mode 100644 index 501c4282d27ad368964fb083e299b14ebd1ad781..0000000000000000000000000000000000000000 --- a/spaces/nagolinc/liteDungeon/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: LiteDungeon -emoji: 📉 -colorFrom: blue -colorTo: gray -sdk: gradio -sdk_version: 3.0.10 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/nahue-passano/librispeech-corpus-generator/whisper_transcriber/transcribe.py b/spaces/nahue-passano/librispeech-corpus-generator/whisper_transcriber/transcribe.py deleted file mode 100644 index be0b2c1f0df6ce57152bd7620ee1617503bcec94..0000000000000000000000000000000000000000 --- a/spaces/nahue-passano/librispeech-corpus-generator/whisper_transcriber/transcribe.py +++ /dev/null @@ -1,3087 +0,0 @@ -import os - -# Remove warning "This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)..." -os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1" -os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # GPU in the right order -__version__ = "1.12.20" - -# Whisper and Torch -import whisper -import torch -import torch.nn.functional as F - -# For alignment -import numpy as np -import dtw - -# from scipy.signal import medfilt as median_filter -from scipy.ndimage import ( - median_filter, -) # faster owing to https://github.com/openai/whisper/commit/f0083e7eb20d032390e42f6f6039947fa8669c93 -from scipy.signal import find_peaks - -# Additional -import string -import csv -import sys -import gzip, base64 -import copy -import re - -# Constant variables -from whisper.utils import format_timestamp -from whisper.audio import N_FRAMES, HOP_LENGTH, SAMPLE_RATE # 3000, 160, 16000 - -AUDIO_SAMPLES_PER_TOKEN = HOP_LENGTH * 2 # 320 -AUDIO_TIME_PER_TOKEN = AUDIO_SAMPLES_PER_TOKEN / SAMPLE_RATE # 0.02 (sec) -SEGMENT_DURATION = N_FRAMES * HOP_LENGTH / SAMPLE_RATE # 30.0 (sec) - -# Logs -import logging - -logger = logging.getLogger("whisper_timestamped") - -USE_EFFICIENT_BY_DEFAULT = True -TRUST_WHISPER_TIMESTAMP_BY_DEFAULT = True -DISFLUENCY_MARK = "[*]" - -try: - whisper_version = whisper.__version__ -except NameError: - whisper_version = "" -WHIPSER_GE_20230306 = whisper_version >= "20230306" -WHIPSER_GE_20230308 = whisper_version >= "20230308" - - -def transcribe_timestamped( - # Main Whisper options - model, - audio, - language=None, - task="transcribe", - # Additional options for word alignment - remove_punctuation_from_words=False, - compute_word_confidence=True, - include_punctuation_in_confidence=False, - refine_whisper_precision=0.5, - min_word_duration=0.02, # Was 0.04 before 1.11 - plot_word_alignment=False, - word_alignement_most_top_layers=None, # Was 6 before 1.9 - remove_empty_words=False, - # Reproducibility - seed=1234, - vad=False, - detect_disfluencies=False, - trust_whisper_timestamps=TRUST_WHISPER_TIMESTAMP_BY_DEFAULT, - naive_approach=False, - # Other Whisper options - temperature=0.0 if USE_EFFICIENT_BY_DEFAULT else (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), - best_of=None, - beam_size=None, - patience=None, - length_penalty=None, - compression_ratio_threshold=2.4, - logprob_threshold=-1.0, - no_speech_threshold=0.6, - fp16=None, - condition_on_previous_text=True, - initial_prompt=None, - suppress_tokens="-1", - sample_len=None, - verbose=False, -): - """ - Transcribe an audio file using Whisper - - Parameters - ---------- - model: Whisper - The Whisper model instance. - - audio: Union[str, np.ndarray, torch.Tensor] - The path to the audio file to open, or the audio waveform in 16kHz. - - language: str - The language to use for the transcription. If None, the language is detected automatically. - - task: str - The task to perform: either "transcribe" or "translate". - - remove_punctuation_from_words: bool - If False, words will be glued with the next punctuation mark (if any). - If True, there will be no punctuation mark in the `words[:]["text"]` list. - It only affects these strings; This has no influence on the computation of the word confidence, whatever the value of `include_punctuation_in_confidence` is. - - include_punctuation_in_confidence: bool - Whether to include proba of punctuation in the computation of the (previous) word confidence. - - compute_word_confidence: bool - Whether to compute word confidence. - If True, a finer confidence for each segment will be computed as well. - - vad: bool - Whether to perform voice activity detection (VAD) on the audio file, to remove silent parts before transcribing with Whisper model. - This should decrease hallucinations from the Whisper model. - - detect_disfluencies: bool - Whether to detect disfluencies (i.e. hesitations, filler words, repetitions, corrections, etc.) that Whisper model might have omitted in the transcription. - This should make the word timestamp prediction more accurate. - And probable disfluencies will be marked as special words "[*]". - - trust_whisper_timestamps: bool - Whether to rely on Whisper's timestamps to get approximative first estimate of segment positions (up to refine_whisper_precision). - - refine_whisper_precision: float - How much can we refine Whisper segment positions, in seconds. Must be a multiple of 0.02. - - min_word_duration: float - Minimum duration of a word, in seconds. If a word is shorter than this, timestamps will be adjusted. - - plot_word_alignment: bool - Whether to plot the word alignment for each segment. matplotlib must be installed to use this option. - - remove_empty_words: bool - Whether to remove words with no duration occuring at the end of segments (probable Whisper hallucinations). - - seed: int - Random seed to use for temperature sampling, for the sake of reproducibility. - Choose None for unpredictable randomness. - - naive_approach: bool - Force the naive approach that consists in decoding twice the audio file, once to get the transcritpion and once with the decoded tokens to get the alignment. - Note that this approach is used anyway when beam_size is not None and/or when the temperature is a list with more than one element. - - temperature: float - Temperature for sampling. - - compression_ratio_threshold: float - If the gzip compression ratio is above this value, treat as failed. - - logprob_threshold: float - If the average log probability over sampled tokens is below this value, treat as failed. - - no_speech_threshold: float - If the no_speech probability is higher than this value AND the average log probability - over sampled tokens is below `logprob_threshold`, consider the segment as silent. - - condition_on_previous_text: bool - if True, the previous output of the model is provided as a prompt for the next window; - disabling may make the text inconsistent across windows, but the model becomes less prone to - getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. - - initial_prompt: str - Optional text to provide as a prompt for the first window. - - suppress_tokens: str - Comma-separated list of token ids to suppress during sampling; - '-1' will suppress most special characters except common punctuations. - - verbose: bool - Whether to display the text being decoded to the console. If True, displays all the details, - If False, displays minimal details. If None, does not display anything - - Returns - ------- - A dictionary containing the resulting text ("text") and segment-level details ("segments"), and - the spoken language ("language"), which is detected when `decode_options["language"]` is None. - """ - - if seed is not None: - torch.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - - # Check input options - assert ( - refine_whisper_precision >= 0 - and refine_whisper_precision / AUDIO_TIME_PER_TOKEN - == round(refine_whisper_precision / AUDIO_TIME_PER_TOKEN) - ), f"refine_whisper_precision must be a positive multiple of {AUDIO_TIME_PER_TOKEN}" - refine_whisper_precision_nframes = round( - refine_whisper_precision / AUDIO_TIME_PER_TOKEN - ) - assert min_word_duration >= 0, f"min_word_duration must be a positive number" - assert ( - word_alignement_most_top_layers is None or word_alignement_most_top_layers > 0 - ), f"word_alignement_most_top_layers must be a strictly positive number" - - if isinstance(temperature, (list, tuple)) and len(temperature) == 1: - temperature = temperature[0] - if isinstance(temperature, (list, tuple)): - # temperature fallback - naive_approach = True - elif temperature > 0 and best_of is not None and best_of > 1: - naive_approach = True - if beam_size is not None: - # beam-search - naive_approach = True - - # Input options - if fp16 is None: - fp16 = model.device != torch.device("cpu") - - # Safety check - input_stride = N_FRAMES // model.dims.n_audio_ctx - time_precision = input_stride * HOP_LENGTH / SAMPLE_RATE - assert time_precision == AUDIO_TIME_PER_TOKEN - - alignment_options = dict( - remove_punctuation_from_words=remove_punctuation_from_words, - compute_word_confidence=compute_word_confidence, - include_punctuation_in_confidence=include_punctuation_in_confidence, - detect_disfluencies=detect_disfluencies, - refine_whisper_precision_nframes=refine_whisper_precision_nframes, - plot_word_alignment=plot_word_alignment, - word_alignement_most_top_layers=word_alignement_most_top_layers, - alignment_heads=get_alignment_heads(model) - if word_alignement_most_top_layers is None - else None, - ) - whisper_options = dict( - language=language, - task=task, - fp16=fp16, - temperature=temperature, - best_of=best_of, - beam_size=beam_size, - patience=patience, - length_penalty=length_penalty, - condition_on_previous_text=condition_on_previous_text, - initial_prompt=initial_prompt, - suppress_tokens=suppress_tokens, - sample_len=sample_len, - verbose=verbose if (not vad or verbose is not True) else False, - ) - other_options = dict( - no_speech_threshold=no_speech_threshold, - logprob_threshold=logprob_threshold, - compression_ratio_threshold=compression_ratio_threshold, - ) - - if vad: - audio = get_audio_tensor(audio) - audio, convert_timestamps = remove_non_speech(audio, plot=plot_word_alignment) - - global num_alignment_for_plot - num_alignment_for_plot = 0 - - if naive_approach: - (transcription, words) = _transcribe_timestamped_naive( - model, - audio, - min_word_duration=0.0, # Was 0.04 before 1.11 - trust_whisper_timestamps=trust_whisper_timestamps, - **alignment_options, - **whisper_options, - **other_options, - ) - else: - (transcription, words) = _transcribe_timestamped_efficient( - model, - audio, - trust_whisper_timestamps=trust_whisper_timestamps, - **alignment_options, - **whisper_options, - **other_options, - ) - if remove_empty_words: - # Remove words with empty duration happening at the end of segments, to remove some hallucinations - transcription, words = remove_last_null_duration_words( - transcription, words, recompute_text=True - ) - - # Refine word positions - ensure_increasing_positions( - words, min_duration=min_word_duration if trust_whisper_timestamps else 0 - ) - - # Combine words and segments - whisper_segments = transcription["segments"] - for word in words: - if verbose and not naive_approach and not vad: - print_timestamped(word) - word.pop("tokens") - word.pop("tokens_indices") - if "avg_logprob_reliable" in word: - word.pop("avg_logprob_reliable") - idx_segment = word.pop("idx_segment") - assert idx_segment < len( - whisper_segments - ), f"Fatal error: Got unexpected segment index {idx_segment} >= {len(whisper_segments)}" - segment = whisper_segments[idx_segment] - if "words" in segment: - segment["words"].append(word) - else: - segment["words"] = [word] - if refine_whisper_precision: - segment["start"] = word["start"] - if refine_whisper_precision: - segment["end"] = word["end"] - - if vad: - # Recompute timestamps to match the original audio - for segment in whisper_segments: - for word in segment.get("words", []): - word["start"], word["end"] = convert_timestamps( - word["start"], word["end"] - ) - if verbose: - print_timestamped(word) - if refine_whisper_precision and len(segment.get("words", [])): - segment["start"] = segment["words"][0]["start"] - segment["end"] = segment["words"][-1]["end"] - else: - segment["start"], segment["end"] = convert_timestamps( - segment["start"], segment["end"] - ) - - return transcription - - -def _transcribe_timestamped_efficient( - model, - audio, - remove_punctuation_from_words, - compute_word_confidence, - include_punctuation_in_confidence, - refine_whisper_precision_nframes, - alignment_heads, - plot_word_alignment, - word_alignement_most_top_layers, - detect_disfluencies, - trust_whisper_timestamps, - use_timestamps_for_alignment=True, - # Whisper specific options - **whisper_options, -): - # Get options - sample_len = whisper_options["sample_len"] - temperature = whisper_options["temperature"] - no_speech_threshold = whisper_options["no_speech_threshold"] - logprob_threshold = whisper_options["logprob_threshold"] - verbose = whisper_options["verbose"] - # Note: "on-the-fly" verbose is not implementable in the current state (we don't know the absolute position of the current chunk). See issue #18 - verbose_bugged = False - whisper_options["verbose"] = ( - None if whisper_options["verbose"] is True else whisper_options["verbose"] - ) # We will print intermediate results ourselves - - logit_filters = get_logit_filters(model, whisper_options) - language = whisper_options["language"] - tokenizer = whisper.tokenizer.get_tokenizer( - model.is_multilingual, task=whisper_options["task"], language=language - ) - - max_sample_len = sample_len or model.dims.n_text_ctx // 2 - n_ctx = model.dims.n_text_ctx - - debug = logger.getEffectiveLevel() >= logging.DEBUG - - word_alignement_most_top_layers = ( - float("inf") - if word_alignement_most_top_layers is None - else word_alignement_most_top_layers - ) - - # The main outcome - timestamped_word_segments = ( - [] - ) # list of timestamped word segments that have been collected so far - # Main variables to be accumulated - segment_tokens = [ - [] - ] # list of lists of token indices that have been collected so far (one list per segment) - segment_attweights = [ - [] - for _ in range(min(word_alignement_most_top_layers, len(model.decoder.blocks))) - ] - # attention weights on the last segments - segment_avglogprobs = ( - [] - ) # average log probability for each segment (actually of the corresponding chunk, as computed by whisper) - segment_logprobs = [] # token log probabilities for each segment - # Variables related to options that can skip some segments - sot_index = None # index of the SOT token in the current set of processed tokens - no_speech_prob = None # no speech probability for the current 30 sec chunk - chunk_logprobs = [] # log probabilities for the current 30 sec chunk - chunk_tokens = [] # tokens for the current 30 sec chunk (list of Torch tensors) - chunk_tokens_nosot = ( - [] - ) # tokens for the current 30 sec chunk, without the SOT tokens (list of indices) - last_chunk_token = ( - None # last token of the current chunk, that may be needed for corner cases - ) - last_token_fallback = ( - None # last token to use as a fallback if the model gets stuck - ) - has_started = False # whether we have started decoding - mfcc = None # MFCC features for the current 30 sec chunk - new_mfcc = None # - num_inference_steps = ( - 0 # number of inference steps performed so far (for debugging only) - ) - - def is_sot(curr_tokens): - return ( - curr_tokens is None - or len(curr_tokens) > 1 - or curr_tokens[0] == tokenizer.sot - ) - - def has_reached_decoding_limit(): - n = len(chunk_tokens_nosot) + 1 - m = n + (len(chunk_tokens[0]) if len(chunk_tokens) > 0 else 0) - return n + 1 >= max_sample_len or m > n_ctx - - def reset(add_segment, keep_last_token=True): - """Reset the list of tokens for the current speech segment, and corresponding cross-attention weights""" - nonlocal segment_tokens, segment_attweights - if add_segment: - if keep_last_token: - segment_tokens.append([segment_tokens[-1][-1]]) - segment_attweights = [w[-1:] for w in segment_attweights] - else: - segment_tokens.append([]) - segment_attweights = [[] for w in segment_attweights] - segment_tokens[-2].pop(0) - elif len(segment_tokens[-1]) > 0: - if debug: - logger.debug( - f"Reset last segment: {tokenizer.decode_with_timestamps(segment_tokens[-1])}" - ) - segment_tokens[-1] = [] - segment_attweights = [[] for w in segment_attweights] - - saw_consecutive_timestamps = False - - def must_flush_segment(curr_tokens): - """Return whether or not the previously collected tokens must be used to add a new speech segment""" - nonlocal segment_tokens, saw_consecutive_timestamps, chunk_tokens_nosot - - if not is_sot(curr_tokens): - is_timestamp = curr_tokens[0] >= tokenizer.timestamp_begin - is_previous_timestamp = ( - segment_tokens[-1][-1] >= tokenizer.timestamp_begin - if len(segment_tokens[-1]) > 0 - else False - ) - consecutive_timestamps = is_timestamp and is_previous_timestamp - if consecutive_timestamps: - saw_consecutive_timestamps = True - return consecutive_timestamps - else: # Several tokens as a prompt or must flush last segments - must_flush = len(segment_tokens[-1]) > 1 and not saw_consecutive_timestamps - if ( - not must_flush and WHIPSER_GE_20230306 - ): # If the last token is a timestamp, the last segment is used - if last_chunk_token is None: - must_flush = ( - len(segment_tokens[-1]) > 2 - and segment_tokens[-1][-1] >= tokenizer.timestamp_begin - ) - else: - must_flush = last_chunk_token >= tokenizer.timestamp_begin - if not must_flush and trust_whisper_timestamps: - # Discard the end of the last transcription - reset(False) - saw_consecutive_timestamps = False - return must_flush - - index_begin_30sec_chunck = 0 - - def get_index_begin_30sec_chunck(curr_tokens): - nonlocal index_begin_30sec_chunck, has_started - - if is_sot(curr_tokens) and has_started: - if trust_whisper_timestamps: - res = index_begin_30sec_chunck - index_begin_30sec_chunck = len(segment_tokens) - 1 - else: - res = len(segment_tokens) - 1 - return res - - def align_last_segment(curr_tokens=None): - nonlocal segment_tokens, segment_attweights, timestamped_word_segments, has_started, no_speech_prob, chunk_tokens, chunk_tokens_nosot, chunk_logprobs, mfcc, new_mfcc, logit_filters, index_begin_30sec_chunck, last_token_fallback, num_inference_steps - - if debug and trust_whisper_timestamps: - logger.debug( - f"Add segment {len(timestamped_word_segments)+1} at step {num_inference_steps}:\n\t{tokenizer.decode_with_timestamps(segment_tokens[-1])}" - ) - - tokens = segment_tokens[-1][1:] - - # When the decoding hit the max limit (number of tokens) -- usually when the language model gets stuck -- - # then we have to recover the last token from what is send to the decoder - unfinished_decoding = has_reached_decoding_limit() - last_is_not_timestamp = len(tokens) and tokens[-1] < tokenizer.timestamp_begin - last_token_reliable = True - - if unfinished_decoding: - logger.debug( - f"WARNING: decoding hit the max limit for segment {segment_tokens[-1]} (It usually happens when the language model gets stuck)" - ) - # The last token chosen is in the prompt for the new chunk - if curr_tokens is not None and curr_tokens[0] == tokenizer.sot_prev: - index_sot = (curr_tokens == tokenizer.sot).nonzero(as_tuple=True) - assert len(index_sot) == 1 - index_sot = index_sot[0].item() - assert index_sot > 0 - last_token_fallback = curr_tokens[index_sot - 1].item() - logger.debug( - f" Guessed last token from the prompt for the new chunk: {last_token_fallback}" - ) - # Fallback for the last segment, or without prompt: Assume greedy decoding - else: - last_token_fallback = ( - torch.argmax(chunk_logprobs[-1]).item() - if last_chunk_token is None - else last_chunk_token - ) - last_token_reliable = temperature == 0 - logger.debug( - f" Guess last token using probas (assuming greedy decoding): {last_token_fallback}" - ) - if debug: - logger.debug( - f"WARNING: also add last token: {tokenizer.decode_with_timestamps([last_token_fallback])}" - ) - - tokens.append(last_token_fallback) - segment_tokens[-1].append(last_token_fallback) - attention_weights = [torch.cat(w, dim=-2) for w in segment_attweights] - last_logprobs = chunk_logprobs[-1] - elif ( - last_is_not_timestamp - ): # was emitted early, without a timestamp before - logger.debug(f"WARNING: end timestamp not produced. Adding <|endoftext|>") - tokens.append(tokenizer.eot) - segment_tokens[-1].append(tokenizer.eot) - attention_weights = [torch.cat(w, dim=-2) for w in segment_attweights] - last_logprobs = chunk_logprobs[-1] - else: - attention_weights = [torch.cat(w[:-1], dim=-2) for w in segment_attweights] - last_logprobs = chunk_logprobs[-2] - - # Check prediction of last token - end_token = tokens[-1] - if end_token >= tokenizer.timestamp_begin: - start_token = tokens[0] - assert start_token >= tokenizer.timestamp_begin - # If Whisper prediction of the end is obviously wrong, we predict it again (constrained) - if end_token <= start_token: - new_end_token = ( - last_logprobs[start_token + 1 :].argmax() + start_token + 1 - ) - tokens[-1] = new_end_token.item() - if debug: - logger.debug( - f"Re-estimated end token {tokenizer.decode_with_timestamps([new_end_token])} (was {tokenizer.decode_with_timestamps([end_token])}) to be after start token {tokenizer.decode_with_timestamps([start_token])}" - ) - - if len(tokens) <= 1: - # Corner case: nothing in between timestamps - ws = [] - else: - ws = perform_word_alignment( - tokens, - attention_weights, - tokenizer, - use_space=should_use_space(language), - alignment_heads=alignment_heads, - remove_punctuation_from_words=remove_punctuation_from_words, - refine_whisper_precision_nframes=refine_whisper_precision_nframes, - detect_disfluencies=detect_disfluencies, - unfinished_decoding=unfinished_decoding, - mfcc=mfcc, - plot=plot_word_alignment, - debug=debug, - ) - - add_segment = len(ws) > 0 - if add_segment: - timestamped_word_segments.append(ws) - else: - logger.debug(f"Not added!") - reset(add_segment, not is_sot(curr_tokens)) - - return add_segment, unfinished_decoding, last_token_reliable - - def may_flush_segment(curr_tokens=None): - """Add a speech segment with the new tokens if necessary. - May also remove the last collected segments if filtered out by Whisper (no_speech_prob <= no_speech_threshold) - """ - nonlocal segment_tokens, segment_attweights, timestamped_word_segments, segment_logprobs, has_started, no_speech_prob, chunk_tokens, chunk_tokens_nosot, chunk_logprobs, mfcc, new_mfcc, logit_filters, index_begin_30sec_chunck, last_token_fallback, num_inference_steps, last_chunk_token - - # Check if a new segment should be added - unfinished_decoding = False - last_token_reliable = True - - if must_flush_segment(curr_tokens) and trust_whisper_timestamps: - _, unfinished_decoding, last_token_reliable = align_last_segment( - curr_tokens - ) - - i_start = get_index_begin_30sec_chunck(curr_tokens) - - # All segments from previous 30sec chunck have been collected - if i_start is not None: - if not trust_whisper_timestamps: - tokens = torch.Tensor(segment_tokens[-1]).int() - idx_task = torch.where(tokens == tokenizer.sot_sequence[-1])[0][ - 0 - ].item() # index of <|transcribe|> - - is_special = tokens.ge(tokenizer.eot) - # Remove prompt - is_special[:idx_task] = True - # Keep begin timestamp - is_special[idx_task : idx_task + 2] = False - - is_timestamp = tokens.ge(tokenizer.timestamp_begin) - consecutive = torch.where(is_timestamp[1:] & is_timestamp[:-1])[0] - if (WHIPSER_GE_20230306 or has_reached_decoding_limit()) and ( - (is_timestamp[-1] and not is_timestamp[-2]) - if last_chunk_token is None - else last_chunk_token >= tokenizer.timestamp_begin - and not is_timestamp[-2] - ): - consecutive = torch.cat( - [consecutive, torch.Tensor([len(tokens) - 1]).int()] - ) - last_is_timestamp = True - if len(consecutive): - # Remove last tokens - is_special[consecutive[-1] + 1 :] = True - # Keep end timestamp - is_special[consecutive[-1]] = False - elif is_timestamp[-1]: - # Keep end timestamp - is_special[-1] = False - else: - last_is_timestamp = False - - if use_timestamps_for_alignment and len(consecutive): - # Keep all timestamps - is_special[idx_task + 2 : consecutive[-1]] = False - - # Do remove what has to be removed - is_next_achar = ~torch.cat( - [is_special[1:], torch.Tensor([False]).bool()] - ) - for i, weights in enumerate(segment_attweights): - assert len(weights) == len( - tokens - ), f"{len(weights)} attention weights != {len(tokens)}" - # We must remove attention weights used to predict timestamp tokens - segment_attweights[i] = [ - w for s, w in zip(is_next_achar, weights) if s - ] - tokens_filtered = tokens[~is_special] - assert len(segment_attweights[0]) == len( - tokens_filtered - ), f"{len(segment_attweights[0])} attention weights != {len(tokens_filtered)} " - - # Replace first and last timestamp - orig_start, orig_end = ( - tokens_filtered[1].item(), - tokens_filtered[-1].item(), - ) - tokens_filtered[1] = tokenizer.timestamp_begin # <|0.00|> - if last_is_timestamp: - tokens_filtered[-1] = ( - tokenizer.timestamp_begin + N_FRAMES // 2 - ) # <|30.00|> - segment_tokens[-1] = tokens_filtered.tolist() - - # Do alignement - added, unfinished_decoding, last_token_reliable = align_last_segment() - - # Re-split into segments (if necessary) - if added: - if len(consecutive) > 1: - segments_timestamped_concat = timestamped_word_segments[-1] - new_segments_timestamped = [] - new_segment_tokens = [] - start = idx_task + 1 - i_word = 0 - for i, end in enumerate(consecutive): - end = end.item() - new_segment_tokens.append(tokens[start : end + 1].tolist()) - if debug: - logger.debug( - f"Add segment {len(timestamped_word_segments)+i}:\n\t{tokenizer.decode_with_timestamps(new_segment_tokens[-1])}" - ) - total_length = end - start - 1 - start = end + 1 - length = 0 - new_segments_timestamped.append([]) - while length < total_length: - if not use_timestamps_for_alignment and i_word == len( - segments_timestamped_concat - ): - # This can happen in the case of "..." - assert ( - total_length == 1 and i == len(consecutive) - 1 - ), "Unexpected situation!" - break - assert i_word < len( - segments_timestamped_concat - ), f"i_word={i_word} < len(segments_timestamped_concat)={len(segments_timestamped_concat)}" - word = segments_timestamped_concat[i_word] - new_segments_timestamped[-1].append(word) - length += len(word["tokens_indices"]) - i_word += 1 - # This can be non zero, when a punctuation (alone in a segment) is glued to the previous segment - if use_timestamps_for_alignment: - assert ( - length == total_length - ), f"length={length} != total_length={total_length}" - elif length > total_length: - delta = length - total_length - word = new_segments_timestamped[-1][-1] - word_tokindices = word["tokens_indices"] - word_tokens = word["tokens"] - word["tokens_indices"] = word_tokindices[:-delta] - word["tokens"] = word_tokens[:-delta] - word["word"] = "".join(word_tokens[:-delta]) - i_word -= 1 - t = segments_timestamped_concat[i_word]["end"] - segments_timestamped_concat[i_word] = dict( - text="".join(word_tokens[-delta:]), - start=t, - end=t, # Word without timestamp - tokens=word_tokens[-delta:], - tokens_indices=word_tokindices[-delta:], - ) - - assert i_word == len(segments_timestamped_concat) - - segment_tokens = ( - segment_tokens[:-2] - + new_segment_tokens - + [segment_tokens[-1]] - ) - timestamped_word_segments = ( - timestamped_word_segments[:-1] + new_segments_timestamped - ) - - else: - # Recover start and end token - segment = segment_tokens[-2] - tokenizer.decode_with_timestamps([orig_start, orig_end]) - segment[0] = orig_start - if last_is_timestamp: - segment[-1] = orig_end - - if debug: - logger.debug( - f"Add segment {len(timestamped_word_segments)}:\n\t{tokenizer.decode_with_timestamps(segment)}" - ) - - if unfinished_decoding: - timestamped_word_segments[-1][-1][ - "avg_logprob_reliable" - ] = last_token_reliable - - reset(False) - - mfcc = new_mfcc - - n_segments = len(segment_tokens) - 1 - - # Get word confidence and/or check if previous segments shoud have been skipped - should_skip = False - if compute_word_confidence or no_speech_threshold is not None: - # no voice activity check - should_skip = ( - (no_speech_prob > no_speech_threshold) - if (no_speech_threshold is not None) - else False - ) - if compute_word_confidence or ( - should_skip and logprob_threshold is not None - ): - n = len(chunk_logprobs) - if n == len(chunk_tokens_nosot): - chunk_tokens_nosot = chunk_tokens_nosot[1:] - if unfinished_decoding: - assert last_token_fallback is not None - last_tokens = [last_token_fallback] - timestamped_word_segments[-1][-1][ - "avg_logprob_reliable" - ] = last_token_reliable - n += 1 - elif has_reached_decoding_limit(): - # there were segments in the 30sec chunck, and then the LM got stuck - last_tokens = [torch.argmax(chunk_logprobs[-1]).item()] - timestamped_word_segments[-1][-1]["avg_logprob_reliable"] = ( - temperature == 0 - ) - else: - last_tokens = [tokenizer.eot] - chunck_indices = chunk_tokens_nosot + last_tokens - assert len(chunk_logprobs) == len( - chunck_indices - ), f"{len(chunk_logprobs)} != {len(chunck_indices)}" - logprobs = torch.cat( - [ - logprob[i].unsqueeze(0) - for (logprob, i) in zip(chunk_logprobs, chunck_indices) - ] - ) - assert min( - [p.isfinite().item() for p in logprobs] - ), f"Got infinite logprob among ({len(logprobs)}) {[(i, tokenizer.decode_with_timestamps([i]), v.item()) for (i,v) in zip(chunck_indices, logprobs)]}" - sum_logprob = sum(logprobs) - avg_logprob = sum_logprob / n - # don't skip if the logprob is high enough, whatever the no_speech_prob is - if ( - logprob_threshold is not None - and avg_logprob > logprob_threshold - ): - should_skip = False - - if should_skip: - logger.debug( - f"Skipping last {n_segments-i_start} segments (no_speech_prob {no_speech_prob} > {no_speech_threshold} and avg_logprob {avg_logprob} < {logprob_threshold})" - ) - index_begin_30sec_chunck -= n_segments - i_start - segment_tokens = segment_tokens[:i_start] + [segment_tokens[-1]] - timestamped_word_segments = timestamped_word_segments[:i_start] - elif compute_word_confidence: - avg_logprob = avg_logprob.item() - i_token_end = -1 - for i in range(i_start, n_segments): - tokens = segment_tokens[i] - i_token_start = i_token_end + 1 - i_token_end = i_token_start + len(tokens) - assert ( - chunck_indices[i_token_start:i_token_end] == tokens - ), f"Inconsistent token list {tokenizer.decode_with_timestamps(chunck_indices[i_token_start:i_token_end])} != {tokenizer.decode_with_timestamps(tokens)}" - i_token_start += 1 # skip sos (start time) - if not unfinished_decoding or i != n_segments - 1: - i_token_end -= 1 # skip eos (end time) - segment_logprobs.append(logprobs[i_token_start:i_token_end]) - segment_avglogprobs.append(avg_logprob) - else: - for i in range(i_start, n_segments): - segment_logprobs.append(None) - segment_avglogprobs.append(None) - - else: - for i in range(i_start, n_segments): - segment_logprobs.append(None) - segment_avglogprobs.append(None) - - if verbose_bugged and not should_skip: - for segment in timestamped_word_segments[i_start:]: - for word in segment: - print_timestamped(word) - - # Reset counters - chunk_tokens = [] - chunk_tokens_nosot = [] - chunk_logprobs = [] - no_speech_prob = None - - def hook_attention_weights(layer, ins, outs, index): - nonlocal segment_attweights - # In old version of whisper, output is a single tensor - assert ( - isinstance(outs, tuple) and len(outs) == 2 - ), "whisper seems to be outdated, please update it (pip install --upgrade --no-deps --force-reinstall git+https://github.com/openai/whisper.git)" - if not has_started: - return - w = outs[-1] - # Only the last attention weights is useful - if w.shape[-2] > 1: - w = w[:, :, -1:, :] - segment_attweights[index].append(w.cpu()) - - def hook_mfcc(layer, ins, outs): - nonlocal new_mfcc, mfcc - new_mfcc = ins[0] - if mfcc is None: - mfcc = new_mfcc - - def hook_input_tokens(layer, ins, outs): - nonlocal segment_tokens, sot_index, chunk_tokens, chunk_tokens_nosot, logit_filters, has_started, language, num_inference_steps - num_inference_steps += 1 - - curr_tokens = ins[0] - assert curr_tokens.shape[0] == 1, "Batch decoding is not supported" - curr_tokens = curr_tokens.squeeze(0) - - if is_sot(curr_tokens): - chunk_prompt = curr_tokens.tolist() - if language is None: - if len(curr_tokens) > 1: - language = tokenizer.decode(curr_tokens[-2:-1]) - language = language[2:-2] # remove trailing "<|" and "|>" - whisper_options["language"] = language - - if ( - verbose - and not whisper_options["verbose"] - and len(curr_tokens) > 1 - ): - # Reproduce whisper verbose (2/2) - print( - f"Detected language: {whisper.tokenizer.LANGUAGES[language].title()}" - ) - sys.stdout.flush() - - logit_filters = get_logit_filters( - model, - whisper_options, - prompt=chunk_prompt[1 : -len(tokenizer.sot_sequence)], - ) - - may_flush_segment(curr_tokens) - - # Get the index of the <|startoftranscript|> tokens (to get proba of silence later) - if is_sot(curr_tokens): - has_started = len(curr_tokens) > 1 or not model.is_multilingual - if no_speech_threshold is not None: - sot_index = curr_tokens.tolist().index(tokenizer.sot) - else: - sot_index = None - - # Keep the last token only - if has_started: - segment_tokens[-1].append(curr_tokens[-1].item()) - - # Accumulate tokens - if has_started: - chunk_tokens.append(curr_tokens) - if not is_sot(curr_tokens): - chunk_tokens_nosot.append(curr_tokens[-1].item()) - else: - if verbose and not whisper_options["verbose"]: - # Reproduce whisper verbose (1/2) - print( - "Detecting language using up to the first 30 seconds. Use `--language` to specify the language" - ) - - embedding_weights = None - - def hook_output_logits(layer, ins, outs): - nonlocal no_speech_prob, chunk_logprobs, segment_tokens, chunk_tokens, chunk_tokens_nosot, last_chunk_token, embedding_weights, has_started - - if embedding_weights is None: - embedding_weights = torch.transpose( - model.decoder.token_embedding.weight, 0, 1 - ).to(outs[0].dtype) - - # Get the probability of silence - if sot_index is not None: - logits = (outs[0][sot_index, :] @ embedding_weights).float() - logits = logits.softmax(dim=-1) - no_speech_prob = logits[tokenizer.no_speech].item() - - # Get the log-probabilities of tokens (we don't know yet which one will be chosen) - if has_started: - logits = (outs[0][-1:, :] @ embedding_weights).float() - tokens = torch.cat(chunk_tokens).unsqueeze(0) - for logit_filter in logit_filters: - logit_filter.apply(logits, tokens) - logits = F.log_softmax(logits.squeeze(0), dim=-1) - chunk_logprobs.append(logits) - - if WHIPSER_GE_20230306 and has_reached_decoding_limit(): - last_chunk_token = torch.argmax(logits).item() - else: - last_chunk_token = None - - try: - # Add hooks to the model, to get tokens and attention weights on the fly - all_hooks = [] - all_hooks.append(model.encoder.conv1.register_forward_hook(hook_mfcc)) - all_hooks.append( - model.decoder.token_embedding.register_forward_hook(hook_input_tokens) - ) - nblocks = len(model.decoder.blocks) - j = 0 - for i, block in enumerate(model.decoder.blocks): - if i < nblocks - word_alignement_most_top_layers: - continue - all_hooks.append( - block.cross_attn.register_forward_hook( - lambda layer, ins, outs, index=j: hook_attention_weights( - layer, ins, outs, index - ) - ) - ) - j += 1 - if compute_word_confidence or no_speech_threshold is not None: - all_hooks.append(model.decoder.ln.register_forward_hook(hook_output_logits)) - - transcription = model.transcribe(audio, **whisper_options) - - finally: - # Remove hooks - for hook in all_hooks: - hook.remove() - - # Finalize (collect last segment) - may_flush_segment() - segment_tokens.pop(-1) - - token_special_idx = min(tokenizer.sot, tokenizer.eot) - - def filter_tokens(tokens): - while len(tokens) and tokens[0] >= token_special_idx: - tokens = tokens[1:] - while len(tokens) and tokens[-1] >= token_special_idx: - tokens = tokens[:-1] - return tokens - - assert len(segment_tokens) == len( - timestamped_word_segments - ), f"Inconsistent number of segments: tokens ({len(segment_tokens)}) != timestamped_word_segments ({len(timestamped_word_segments)})" - assert len(segment_avglogprobs) == len( - segment_tokens - ), f"Inconsistent number of segments: avg logprobs ({len(segment_avglogprobs)}) != tokens ({len(segment_tokens)})" - assert len(segment_logprobs) == len( - segment_tokens - ), f"Inconsistent number of segments: logprobs ({len(segment_logprobs)}) != tokens ({len(segment_tokens)})" - - whisper_segments = transcription["segments"] - l1 = len(whisper_segments) - l2 = len(timestamped_word_segments) - if l1 != l2 and l1 != 0: - logger.warning( - f"Inconsistent number of segments: whisper_segments ({l1}) != timestamped_word_segments ({l2})" - ) - assert ( - l1 == l2 or l1 == 0 - ), f"Inconsistent number of segments: whisper_segments ({l1}) != timestamped_word_segments ({l2})" - - logger.debug("Compile results") - words = [] - for i, (segment, timestamped_words, token, avglogprob, logprobs) in enumerate( - zip( - whisper_segments, - timestamped_word_segments, - segment_tokens, - segment_avglogprobs, - segment_logprobs, - ) - ): - timestamped_tokens = filter_tokens(token) - whisper_tokens = filter_tokens(segment["tokens"]) - if timestamped_tokens != whisper_tokens: - if len(timestamped_tokens) == len(whisper_tokens) + 1: - logger.warning(f"An additional token was added on segment {i}") - elif WHIPSER_GE_20230306 and len(whisper_tokens) == 0: - logger.warning(f"Whisper has empty segment {i}") - assert ( - segment["end"] == segment["start"] - ), f"Fatal Error: Got empty segment {i} with non-zero duration" - segment["tokens"] = timestamped_tokens - segment["text"] = tokenizer.decode(timestamped_tokens) - else: - assert ( - len(timestamped_tokens) < len(whisper_tokens) - and timestamped_tokens == whisper_tokens[: len(timestamped_tokens)] - ), f"Fatal Error: Got inconsistent text for segment {i}:\n({len(timestamped_tokens)})\n{tokenizer.decode_with_timestamps(timestamped_tokens)}\n{timestamped_tokens}\n!=\n({len(whisper_tokens)})\n{tokenizer.decode_with_timestamps(whisper_tokens)}\n{whisper_tokens[:len(timestamped_tokens)]}" - segment["tokens"] = ( - token if WHIPSER_GE_20230306 else timestamped_tokens - ) # tokens include special timestamp tokens since 20230306 - segment["text"] = tokenizer.decode(segment["tokens"]) - logger.warning( - f"Text had to be shortned on segment {i}:\n{tokenizer.decode(timestamped_tokens)}\n!=\n{tokenizer.decode(whisper_tokens)}" - ) - timestamped_words[-1]["avg_logprob_reliable"] = False - - offset = segment["seek"] * HOP_LENGTH / SAMPLE_RATE - for timestamped_word in timestamped_words: - timestamped_word["start"] += offset - timestamped_word["end"] += offset - timestamped_word["idx_segment"] = i - - if compute_word_confidence: - if ( - "avg_logprob_reliable" not in timestamped_words[-1] - or timestamped_words[-1]["avg_logprob_reliable"] - ): - # assert abs(segment["avg_logprob"] - avglogprob) < 1e-2, f"Fatal Error: Got inconsistent logprob for segment {i}: {segment['avg_logprob']} != {avglogprob}" - if abs(segment["avg_logprob"] - avglogprob) >= 1e-2: - logger.warning( - f"Recomputed different logprob for segment {i}: {avglogprob} != {segment['avg_logprob']}" - ) - if include_punctuation_in_confidence: - segment["confidence"] = round_confidence(logprobs.mean().exp().item()) - else: - logprobs_nopunc = [] - i_end = 0 - for timestamped_word in timestamped_words: - i_start = i_end - tokens = timestamped_word["tokens"] - i_end += len(tokens) - - assert i_end <= len( - logprobs - ), f"Fatal Error: Got out-of-bound index for segment {i}: {i_end} > {len(logprobs)}" - if include_punctuation_in_confidence: - word_logprobs = logprobs[i_start:i_end] - else: - while ( - len(tokens) > 1 - and len(tokens[-1]) - and tokens[-1][-1] in _punctuation - ): # Note: look at the last character of token, to take into account "...", "!!", etc. - tokens = tokens[:-1] - word_logprobs = logprobs[i_start : i_start + len(tokens)] - logprobs_nopunc.append(word_logprobs) - - timestamped_word["confidence"] = round_confidence( - word_logprobs.mean().exp().item() if len(word_logprobs) else 0.0 - ) - - if i_end not in [len(logprobs), len(logprobs) - 1]: - logger.warning( - f"Got inconsistent length for segment {i} ({len(logprobs)} != {i_end}). Some words have been ignored." - ) - if not include_punctuation_in_confidence: - logprobs_nopunc = torch.cat(logprobs_nopunc) - segment["confidence"] = round_confidence( - logprobs_nopunc.mean().exp().item() - ) - - words.extend(timestamped_words) - - return transcription, words - - -def _transcribe_timestamped_naive( - model, - audio, - remove_punctuation_from_words, - compute_word_confidence, - include_punctuation_in_confidence, - refine_whisper_precision_nframes, - alignment_heads, - plot_word_alignment, - word_alignement_most_top_layers, - detect_disfluencies, - trust_whisper_timestamps, - min_word_duration, - **whisper_options, -): - verbose = whisper_options["verbose"] - whisper_options["verbose"] = ( - None if whisper_options["verbose"] is True else whisper_options["verbose"] - ) # We will print intermediate results ourselves - language = whisper_options["language"] - refine_whisper_precision_sec = ( - refine_whisper_precision_nframes * AUDIO_TIME_PER_TOKEN - ) - - word_alignement_most_top_layers = ( - float("inf") - if word_alignement_most_top_layers is None - else word_alignement_most_top_layers - ) - - audio = get_audio_tensor(audio) - audio_duration = audio.shape[-1] / SAMPLE_RATE - - if verbose and language is None and not whisper_options["verbose"]: - # Reproduce whisper verbose (1/2) - print( - "Detecting language using up to the first 30 seconds. Use `--language` to specify the language" - ) - - transcription = model.transcribe(audio, **whisper_options) - - if verbose and language is None and not whisper_options["verbose"]: - # Reproduce whisper verbose (2/2) - print( - f"Detected language: {whisper.tokenizer.LANGUAGES[transcription['language']].title()}" - ) - sys.stdout.flush() - - language = norm_language(transcription["language"]) - - tokenizer = whisper.tokenizer.get_tokenizer( - model.is_multilingual, task=whisper_options["task"], language=language - ) - use_space = should_use_space(language) - - attention_weights = [ - [] - for _ in range(min(word_alignement_most_top_layers, len(model.decoder.blocks))) - ] - - try: - all_hooks = [] - - # Hook the model - nblocks = len(model.decoder.blocks) - j = 0 - for i, block in enumerate(model.decoder.blocks): - if i < nblocks - word_alignement_most_top_layers: - continue - all_hooks.append( - block.cross_attn.register_forward_hook( - lambda layer, ins, outs, index=j: attention_weights.__setitem__( - index, outs[-1] - ) - ) - ) - j += 1 - - # When not relying on Whisper timestamps - current_tokens = [] - token_to_idx_segment = [] - - words = [] - previous_end = 0 - whisper_segments = transcription["segments"] - for i_segment, segment in enumerate(whisper_segments): - # Note: this could also be a fix to issue #61 where a "<|te|>" token was predicted - # segment["tokens"] = [t for t in segment["tokens"] if t < tokenizer.eot or t >= tokenizer.timestamp_begin] - - start = end = tokens = None - if trust_whisper_timestamps: - start = segment["start"] - end = segment["end"] - if end < start: - # Whisper is wrong on the prediction of segment end - end = min(audio_duration, start + SEGMENT_DURATION) - - start_margin_min = start - refine_whisper_precision_sec - start_margin_max = start + refine_whisper_precision_sec - if start >= audio_duration - min_word_duration or ( - previous_end >= start_margin_min - and previous_end <= start_margin_max - ): - # Make start as accurate as possible (as the decoding will start with timestamp <|0|>) - start = previous_end - else: - # Fallback - start = start_margin_min - - if start > audio_duration - min_word_duration: - # Skip last segment if too short - logger.warning( - f"Skipping segment outside of audio duration {audio_duration} (original: {segment['start']}-{segment['end']}, new: {start}-XXX)" - ) - continue - - end_margin_min = end - refine_whisper_precision_sec - end_margin_max = end + refine_whisper_precision_sec - if i_segment < len(whisper_segments) - 1: - # Try to enforce: - # end + min_word_duration <= next start + refine_whisper_precision_sec - end_margin_max2 = ( - whisper_segments[i_segment + 1]["start"] - + refine_whisper_precision_sec - - min_word_duration - ) - if end_margin_max2 >= end_margin_min: - end_margin_max = min(end_margin_max2, end_margin_max) - end = min(audio_duration, end_margin_max) - - if end < start + min_word_duration: - logger.warning( - f"Got super short segment (original from whisper: {segment['start']}-{segment['end']}, new: {start, end})" - ) - end = min(audio_duration, start + min_word_duration) - if end <= start: - logger.warning( - f"Skipping this short segment occuring too close to the end of the audio" - ) - continue - - tokens = segment["tokens"] - - else: - seek = segment["seek"] - new_tokens = segment["tokens"] - if not len(new_tokens): - continue - # Add timestamps that will be needed after - if new_tokens[0] < tokenizer.timestamp_begin: - relative_start = segment["start"] - ( - seek * HOP_LENGTH / SAMPLE_RATE - ) - start_token = ( - round(relative_start * SAMPLE_RATE / AUDIO_SAMPLES_PER_TOKEN) - + tokenizer.timestamp_begin - ) - new_tokens = [start_token] + new_tokens - if new_tokens[-1] < tokenizer.timestamp_begin: - relative_end = segment["end"] - (seek * HOP_LENGTH / SAMPLE_RATE) - end_token = ( - round(relative_end * SAMPLE_RATE / AUDIO_SAMPLES_PER_TOKEN) - + tokenizer.timestamp_begin - ) - new_tokens = new_tokens + [end_token] - - current_tokens.extend(new_tokens) - token_to_idx_segment.extend([i_segment] * len(new_tokens)) - - next_seek = ( - whisper_segments[i_segment + 1]["seek"] - if i_segment < len(whisper_segments) - 1 - else None - ) - if seek != next_seek: - start = float(seek * HOP_LENGTH / SAMPLE_RATE) - assert ( - start < audio_duration - ), f"Got start {start} which is outside of audio duration {audio_duration}" - end = min(start + SEGMENT_DURATION, audio_duration) - tokens = current_tokens - - if tokens is None or not len(tokens): - continue - - start_sample = min(round(start * SAMPLE_RATE), audio.shape[-1]) - end_sample = min(round(end * SAMPLE_RATE), audio.shape[-1]) - - # Extract features on the audio segment - sub_audio = audio_minimum_padding(audio[start_sample:end_sample]) - - mfcc = whisper.log_mel_spectrogram(sub_audio).to(model.device) - mfcc = whisper.pad_or_trim(mfcc, N_FRAMES) - mfcc = mfcc.unsqueeze(0) - - segment_tokens_check = [] - if tokens[0] >= tokenizer.timestamp_begin: - segment_tokens_check.append(tokens[0]) - while tokens[0] >= tokenizer.timestamp_begin: - tokens = tokens[1:] - assert len(tokens), "Got transcription with only timestamps!" - last_token_check = None - while tokens[-1] >= tokenizer.timestamp_begin: - last_token_check = tokens[-1] - tokens = tokens[:-1] - - tokens = [ - *tokenizer.sot_sequence, - tokenizer.timestamp_begin, - ] + tokens - - i_start = len(tokenizer.sot_sequence) - - with torch.no_grad(): - logprobs = model( - mfcc, torch.Tensor(tokens).int().to(model.device).unsqueeze(0) - ) - logprobs = F.log_softmax(logprobs, dim=-1) - - end_token = tokenizer.timestamp_begin + round( - min(N_FRAMES * HOP_LENGTH, end_sample - start_sample) - // AUDIO_SAMPLES_PER_TOKEN - ) - tokens = tokens[i_start:] + [end_token] - attention_weights = [w[:, :, i_start - 1 :, :] for w in attention_weights] - - ws = perform_word_alignment( - tokens, - attention_weights, - tokenizer, - use_space=use_space, - alignment_heads=alignment_heads, - remove_punctuation_from_words=remove_punctuation_from_words, - refine_whisper_precision_nframes=refine_whisper_precision_nframes, - detect_disfluencies=detect_disfluencies, - mfcc=mfcc, - plot=plot_word_alignment, - ) - - segment_logprobs = [] - i_token = 1 - - for word in ws: - word["start"] = round(word["start"] + start, 2) - word["end"] = round(word["end"] + start, 2) - - if trust_whisper_timestamps: - word.update({"idx_segment": i_segment}) - else: - assert i_token < len(tokens) - assert ( - not len(word["tokens_indices"]) - or word["tokens_indices"][0] == tokens[i_token] - ) - word.update({"idx_segment": token_to_idx_segment[i_token]}) - i_token += len(word["tokens"]) - while ( - i_token < len(tokens) - and tokens[i_token] >= tokenizer.timestamp_begin - ): - i_token += 1 - - tok_indices = word["tokens_indices"] - segment_tokens_check.extend(tok_indices) - - if compute_word_confidence: - tok = word["tokens"] - i_end = i_start + len(tok) - if include_punctuation_in_confidence: - while ( - len(tok) > 1 - and len(tok[-1]) - and tok[-1][-1] in _punctuation - ): # Note: look at the last character of token, to take into account "...", "!!", etc. - tok = tok[:-1] - tok_indices = tok_indices[:-1] - word_logprobs = [ - logprobs[:, step, tok] - for (step, tok) in zip( - range(i_start, i_start + len(tok_indices)), tok_indices - ) - ] - i_start = i_end - if len(word_logprobs): - word_logprobs = torch.cat(word_logprobs) - segment_logprobs.append(word_logprobs) - word_confidence = word_logprobs.mean().exp().item() - else: - word_confidence = 0 - word.update({"confidence": round_confidence(word_confidence)}) - - words.append(word) - - if verbose: - print_timestamped(word) - - if last_token_check is not None: - segment_tokens_check.append(last_token_check) - if trust_whisper_timestamps: - if segment_tokens_check != segment["tokens"]: - assert ( - len(segment_tokens_check) < len(segment["tokens"]) - and segment_tokens_check[:-1] - == segment["tokens"][: len(segment_tokens_check) - 1] - ), f"Got inconsistent tokens: {tokenizer.decode(segment_tokens_check)} != {tokenizer.decode(segment['tokens'])}" - segment["tokens"] = segment_tokens_check - segment["text"] = tokenizer.decode(segment["tokens"]) - # else: TODO - - if len(segment_logprobs): - segment.update( - { - "confidence": round_confidence( - torch.cat(segment_logprobs).mean().exp().item() - ) - } - ) - - if len(ws): - previous_end = ws[-1]["end"] - - if not trust_whisper_timestamps: - current_tokens = [] - token_to_idx_segment = [] - - finally: - # Remove hooks - for hook in all_hooks: - hook.remove() - - return (transcription, words) - - -def get_audio_tensor(audio, device="cpu"): - if isinstance(audio, str): - audio = whisper.load_audio(audio) - if isinstance(audio, np.ndarray): - audio = torch.Tensor(audio) - else: - assert isinstance( - audio, torch.Tensor - ), f"Got unexpected audio of type {type(audio)}" - return audio.to(device) - - -def audio_minimum_padding(audio): - if audio.shape[-1] <= 200: - return whisper.pad_or_trim(audio, 201) - return audio - - -def should_use_space(language): - return norm_language(language) not in ["zh", "ja", "th", "lo", "my"] - - -def norm_language(language): - if language is None: - return "en" - return whisper.tokenizer.TO_LANGUAGE_CODE.get(language.lower(), language) - - -def print_timestamped(w): - line = f"[{format_timestamp(w['start'])} --> {format_timestamp(w['end'])}] {w['text']}\n" - # compared to just `print(line)`, this replaces any character not representable using - # the system default encoding with an '?', avoiding UnicodeEncodeError. - sys.stdout.buffer.write(line.encode(sys.getdefaultencoding(), errors="replace")) - sys.stdout.flush() - - -def get_logit_filters(model, whisper_options, prompt=None): - decoding_options = get_decoding_options(whisper_options) - if "initial_prompt" in decoding_options: - prompt0 = decoding_options.pop("initial_prompt") - if prompt is None: - prompt = prompt0 - if prompt is not None: - decoding_options["prompt"] = prompt - decoding_options = whisper.DecodingOptions( - without_timestamps=False, - max_initial_timestamp=1.0, - prefix=None, - suppress_blank=True, - **decoding_options, - ) - - # This performs some checks on the options - decoding_task = whisper.decoding.DecodingTask(model, decoding_options) - return decoding_task.logit_filters - - -def get_decoding_options(whisper_options): - return dict( - [ - (k, v) - for (k, v) in whisper_options.items() - if k - not in [ - "no_speech_threshold", - "logprob_threshold", - "compression_ratio_threshold", - "condition_on_previous_text", - "verbose", - ] - ] - ) - - -def perform_word_alignment( - tokens, - attention_weights, - tokenizer, - use_space=True, - mfcc=None, - refine_whisper_precision_nframes=0, - remove_punctuation_from_words=False, - include_punctuation_in_timing=False, # Was True before 1.9 - unfinished_decoding=False, - alignment_heads=None, - medfilt_width=9, - qk_scale=1.0, - detect_disfluencies=True, - subwords_can_be_empty=True, # Was False before 1.11 - plot=False, - debug=False, -): - """ - Perform word alignment on the given tokens and attention weights. - Returns a list of (word, start_time, end_time) tuples. - - tokens: list of tokens (integers) - attention_weights: list of attention weights (torch tensors) - tokenizer: tokenizer used to tokenize the text - use_space: whether to use spaces to split the tokens into words (should be true for all languages except Japanese, Chinese, ...) - mfcc: MFCC features (used to identify padded region, and for plotting) - refine_whisper_precision_nframes: precision time - remove_punctuation_from_words: whether to remove punctuation from words - include_punctuation_in_timing: whether to include punctuation in the timing of (previous) words - unfinished_decoding: whether the decoding is unfinished (e.g. because the model is stuck) - alignment_heads: list of attention heads to use for alignment - medfilt_width: width of the median filter used to smooth the attention weights - qk_scale: scale factor applied to the attention weights - plot: whether to plot the word alignment - debug: whether to print debug information - """ - - assert ( - len(tokens) > 1 - ), f"Got unexpected sequence of tokens of length {len(tokens)} {tokenizer.decode_with_timestamps(tokens)}" - start_token = tokens[0] - tokenizer.timestamp_begin - end_token = tokens[-1] - tokenizer.timestamp_begin - - # Check start / end tokens - if start_token < 0: - raise RuntimeError( - f"Missing start token in: {tokenizer.decode_with_timestamps(tokens)}" - ) - if len(tokens) == 1 or end_token < 0: - # This can happens when Whisper is stucked as a Language Model - if debug: - logger.debug( - f"Missing end token in {tokenizer.decode_with_timestamps(tokens)}" - ) - end_token = N_FRAMES // 2 - if end_token == start_token and refine_whisper_precision_nframes == 0: - if debug: - logger.debug( - f"Got empty segment in {tokenizer.decode_with_timestamps(tokens)}" - ) - return [] - - # Let a minimal duration given the number of tokens (see https://github.com/linto-ai/whisper-timestamped/issues/67) - end_token = min(N_FRAMES // 2, max(end_token, start_token + len(tokens))) - - # Put some margin around the segment - if refine_whisper_precision_nframes > 0: - start_token = max(start_token - refine_whisper_precision_nframes, 0) - end_token = min(end_token + refine_whisper_precision_nframes, N_FRAMES // 2) - - if end_token <= start_token: - raise RuntimeError( - f"Got segment with null or negative duration {tokenizer.decode_with_timestamps(tokens)}: {start_token} {end_token}" - ) - - start_time = start_token * AUDIO_TIME_PER_TOKEN - # end_time = end_token * AUDIO_TIME_PER_TOKEN - - split_tokens = split_tokens_on_spaces if use_space else split_tokens_on_unicode - words, word_tokens, word_tokens_indices = split_tokens( - tokens, tokenizer, remove_punctuation_from_words=remove_punctuation_from_words - ) - - # If the last token is a punctuation that comes after a word - # group this final punctuation with the final timestamp - # This is to avoid assigning the final punctuation to a big silence or a noise/music background coming after - num_punctuations_per_tokens = [ - 0 if len(w) == 1 or w[-1] not in _punctuation else 1 for w in word_tokens - ] - if include_punctuation_in_timing: - num_punctuations_per_tokens[:-2] = [0] * (len(num_punctuations_per_tokens) - 2) - - for i, w in enumerate(attention_weights): - assert w.shape[-2] == len( - tokens - ), f"Attention weights have wrong shape: {w.shape[-2]} (expected {len(tokens)})." - weights = torch.cat(attention_weights) # layers * heads * tokens * frames - - num_tokens = weights.shape[-2] - num_frames = end_token - start_token - if num_tokens > num_frames: - logger.warning( - f"Too much text ({num_tokens} tokens) for the given number of frames ({num_frames}) in: {tokenizer.decode_with_timestamps(tokens)}\nThe end of the text will be removed." - ) - return perform_word_alignment( - tokens[: num_frames - 1] + [tokens[-1]], - [ - torch.cat([w[:, :, : num_frames - 1, :], w[:, :, -1:, :]], dim=-2) - for w in attention_weights - ], - tokenizer, - use_space=use_space, - refine_whisper_precision_nframes=refine_whisper_precision_nframes, - medfilt_width=medfilt_width, - qk_scale=qk_scale, - alignment_heads=alignment_heads, - mfcc=mfcc, - plot=plot, - remove_punctuation_from_words=remove_punctuation_from_words, - detect_disfluencies=detect_disfluencies, - subwords_can_be_empty=subwords_can_be_empty, - unfinished_decoding=True, - debug=debug, - ) - - assert end_token <= weights.shape[-1] - assert len(tokens) == num_tokens - - weights = weights[ - ..., start_token:end_token - ].cpu() # layers * heads * tokens * frames - - if alignment_heads is None: - weights = weights.reshape(-1, *weights.shape[-2:]) # N * tokens * frames - else: - weights = torch.stack([weights[l][h] for l, h in alignment_heads.indices().T]) - weights = median_filter(weights, (1, 1, medfilt_width)) - weights = torch.tensor(weights * qk_scale).softmax(dim=-1) - weights = weights.mean( - axis=(0) - ) # average over layers and heads # tokens * frames - weights = weights / weights.norm( - dim=-2, keepdim=True - ) # This was before the mean before 1.9 - weights = -weights.double().numpy() - worse_weight = 0 - - # Get the limit of audio duration - max_duration = None - if mfcc is not None: - max_duration = find_start_padding(mfcc) - if max_duration is not None: - max_duration = max_duration // 2 - - # Enforce the max duration - if max_duration: - if start_token >= max_duration: - logger.warning(f"Got start time outside of audio boundary") - else: - weights[:-1, max_duration:] = worse_weight - - # Encourage to start early - weights[0, 0] = weights.min() - # weights[0, refine_whisper_precision_nframes*2:] = worse_weight - - if subwords_can_be_empty: - step_pattern = dtw.stepPattern.symmetric1 - else: - # Similar as "symmetric1" but without the possibility to have the same timestamp for two tokens - step_pattern = dtw.stepPattern.StepPattern( - dtw.stepPattern._c( - 1, - 1, - 1, - -1, - 1, - 0, - 0, - 1, - 2, - 0, - 1, - -1, - 2, - 0, - 0, - 1, - ) - ) - alignment = dtw.dtw(weights, step_pattern=step_pattern) - - global num_alignment_for_plot - num_alignment_for_plot += 1 - - if plot: - import matplotlib.pyplot as plt - import matplotlib.ticker as ticker - - plot_mfcc = 1 if mfcc is not None else 0 - plot_disfluencies = 1 if detect_disfluencies else 0 - nplots = 1 + plot_mfcc + plot_disfluencies - - plt.subplots( - nplots, - 1, - figsize=(16, 9), - gridspec_kw={"height_ratios": [3] + [1] * (nplots - 1)}, - ) - plt.subplot(nplots, 1, 1, frameon=False) - - plt.imshow(-weights, aspect="auto") - plt.plot(alignment.index2s, alignment.index1s, color="red") - - xticks = np.arange(0, weights.shape[1], 1 / AUDIO_TIME_PER_TOKEN) - xticklabels = [ - round_timestamp(x) for x in xticks * AUDIO_TIME_PER_TOKEN + start_time - ] - - ylims = plt.gca().get_ylim() - - ax = plt.gca() - ax.tick_params("both", length=0, width=0, which="minor", pad=6) - - ax.yaxis.set_ticks_position("left") - ax.yaxis.set_label_position("left") - ax.invert_yaxis() - ax.set_ylim(ylims) - - major_ticks = [-0.5] - minor_ticks = [] - current_y = 0 - - for word, word_token in zip(words, word_tokens): - minor_ticks.append(current_y + len(word_token) / 2 - 0.5) - current_y += len(word_token) - major_ticks.append(current_y - 0.5) - - words_with_subwords = [ - "|".join(s).strip() for (w, s) in zip(words, word_tokens) - ] - - ax.yaxis.set_minor_locator(ticker.FixedLocator(minor_ticks)) - ax.yaxis.set_minor_formatter(ticker.FixedFormatter(words_with_subwords)) - ax.set_yticks(major_ticks) - ax.yaxis.set_major_formatter(ticker.NullFormatter()) - for y in major_ticks: - plt.axhline(y, color="black", linestyle="dashed") - - plt.ylabel("Words") - - if plot_mfcc: - plt.xticks(xticks) - plt.setp(plt.gca().get_xticklabels(), visible=False) - - xticks *= 2 - - plt.subplot(nplots, 1, 2, frameon=False) - plt.imshow( - mfcc[0, :, start_token * 2 : end_token * 2].cpu(), - aspect="auto", - origin="lower", - ) - plt.yticks([]) - plt.ylabel("MFCC") - - plt.xticks(xticks, xticklabels) - plt.xlabel("Time (s)") - - jumps = np.diff(alignment.index1s) - jumps = np.pad(jumps, (1, 0), constant_values=1) - jumps = jumps.astype(bool) - jumps = alignment.index2s[jumps] - jumps = np.pad(jumps, (0, 1), constant_values=alignment.index2s[-1]) - - jumps_start = jumps - disfluences = {} - if detect_disfluencies: - jumps_start = copy.copy(jumps) - - for i_token, (tok, begin, end) in enumerate(zip(tokens, jumps[:-1], jumps[1:])): - # Find local maxima in the portion of attention weights - attention_weights = -weights[i_token, begin:end] - peaks, properties = find_peaks( - attention_weights, - width=3, - prominence=0.02, - ) - # If more than - if len(peaks) > 1: - if "left_ips" in properties: - left = [round(x) for x in properties["left_ips"]] - else: - left = properties["left_bases"] - - new_begin = left[-1] + begin - - jumps_start[i_token] = new_begin - - if new_begin != begin: - is_punctuation = ( - tokenizer.decode_with_timestamps([tok]) in _punctuation - ) - if not is_punctuation: - disfluences[i_token] = (begin, jumps_start[i_token]) - else: - disfluences[i_token + 1] = (begin, end) - - if plot: - plt.subplot(nplots, 1, 2 + plot_mfcc, frameon=False) - plt.plot(range(begin, end), attention_weights) - plt.xlim(0, end) - - for i, p in enumerate(peaks): - color = ( - "red" if (len(peaks) > 1 and i < len(peaks) - 1) else "green" - ) - plt.vlines(begin + p, 0, 1, color=color, linestyle="--") - - if "left_bases" in properties: - - def barxxy(start, end, y, **kwargs): - middle = (start + end) / 2 - plt.bar(middle, y, width=end - start, **kwargs) - - color = "red" if len(peaks) > 1 else "green" - barxxy( - begin + properties["left_bases"], - begin + properties["right_bases"], - properties.get( - "prominences", [1] * len(properties["left_bases"]) - ), - alpha=0.5, - # put a line with a custom color - linewidth=1, - edgecolor=color, - ) - if "left_ips" in properties: - for left in properties["left_ips"]: - plt.vlines(begin + left, 0, 0.5, color="green", linestyle=":") - for right in properties["right_ips"]: - plt.vlines(begin + right, 0, 0.5, color="red", linestyle=":") - - # display the word-level timestamps in a table - word_boundaries = np.cumsum([len(t) for t in word_tokens]) - word_boundaries = np.pad(word_boundaries, (1, 0)) - begin_times = jumps_start[word_boundaries[:-1]] - end_times = jumps[word_boundaries[1:] - num_punctuations_per_tokens] - - begin_times = begin_times * AUDIO_TIME_PER_TOKEN - end_times = end_times * AUDIO_TIME_PER_TOKEN - - if detect_disfluencies: - to_be_added = [] - i_start = 0 - for i_word, toks in enumerate(word_tokens[:-1]): - i_end = i_start + len(toks) - if i_start in disfluences and i_word > 0: - begin, end = disfluences[i_start] - begin *= AUDIO_TIME_PER_TOKEN - end *= AUDIO_TIME_PER_TOKEN - to_be_added.append((i_word, begin, end)) - i_start = i_end - # Add from the end to avoid messing up the indices - for i_word, begin, end in to_be_added[-1::-1]: - words.insert(i_word, DISFLUENCY_MARK) - word_tokens.insert(i_word, []) - word_tokens_indices.insert(i_word, []) - begin_times = np.insert(begin_times, i_word, begin) - end_times = np.insert(end_times, i_word, end) - - # Ignore start / end tokens - if not refine_whisper_precision_nframes: - begin_times[1] = begin_times[0] - if not refine_whisper_precision_nframes: - end_times[-2] = end_times[-1] - if unfinished_decoding: - words = words[1:] - word_tokens = word_tokens[1:] - word_tokens_indices = word_tokens_indices[1:] - begin_times = begin_times[1:] - end_times = end_times[1:] - else: - words = words[1:-1] - word_tokens = word_tokens[1:-1] - word_tokens_indices = word_tokens_indices[1:-1] - begin_times = begin_times[1:-1] - end_times = end_times[1:-1] - - if plot: - ymin = 1 - - plt.subplot(nplots, 1, 1) - for i, (w, ws, begin, end) in enumerate( - zip(words, word_tokens, begin_times, end_times) - ): - ymax = ymin + len(ws) - if mfcc is None: - plt.text( - begin / AUDIO_TIME_PER_TOKEN, - num_tokens - 0.5, - w, - ha="left", - va="top", - color="red", - ) - for x in [ - begin, - end, - ]: - plt.axvline( - x / AUDIO_TIME_PER_TOKEN, - color="red", - linestyle="dotted", - ymin=1 - ymin / num_tokens, - ymax=0, # 1-ymax/num_tokens, - ) - ymin = ymax - - if plot_mfcc: - plt.subplot(nplots, 1, 2) - for i, (w, begin, end) in enumerate(zip(words, begin_times, end_times)): - plt.text( - begin * 2 / AUDIO_TIME_PER_TOKEN, - mfcc.shape[-2] * 1.05, - w, - ha="left", - va="bottom", - color="red", - ) - for x in [ - begin, - end, - ]: - plt.axvline( - x * 2 / AUDIO_TIME_PER_TOKEN, color="red", linestyle="dotted" - ) - - if isinstance(plot, str): - plt.savefig( - f"{plot}.alignment{num_alignment_for_plot:03d}.jpg", - bbox_inches="tight", - pad_inches=0, - ) - else: - plt.show() - - return [ - dict( - text=word, - start=round_timestamp(begin + start_time), - end=round_timestamp(end + start_time), - tokens=tokens, - tokens_indices=tokens_indices, - ) - for word, begin, end, tokens, tokens_indices in zip( - words, begin_times, end_times, word_tokens, word_tokens_indices - ) - if not word.startswith("<|") - ] - - -def find_start_padding(mfcc): - """Return start of padding given the mfcc, or None if there is no padding""" - last_mfcc = mfcc[0, :, -1] - if torch.min(last_mfcc) == torch.max(last_mfcc) == 0: - candidate_index = mfcc.shape[-1] - 2 - while candidate_index > 0: - candidate = mfcc[0, :, candidate_index] - if not torch.equal(candidate, last_mfcc): - return candidate_index + 1 - candidate_index -= 1 - return 0 # WTF!? - - -def round_confidence(x): - return round(x, 3) - - -def round_timestamp(x): - return round(x, 2) - - -_punctuation = ( - "".join(c for c in string.punctuation if c not in ["-", "'"]) + "。，！？：”、…" -) - - -def split_tokens_on_unicode( - tokens: list, - tokenizer, - remove_punctuation_from_words=False, - isolate_punctuations=False, -): - words = [] - word_tokens = [] - word_tokens_indices = [] - current_tokens = [] - - for token in tokens: - current_tokens.append(token) - decoded = tokenizer.decode_with_timestamps( - [ - t - for t in current_tokens - if t < tokenizer.eot or t >= tokenizer.timestamp_begin - ] - ) - if "\ufffd" not in decoded: - empty_tokens = [""] * (len(current_tokens) - 1) - punctuation = not isolate_punctuations and ( - decoded.strip() and decoded.strip() in _punctuation - ) - previous_special = len(word_tokens_indices) > 0 and ( - word_tokens_indices[-1][-1] >= tokenizer.timestamp_begin - ) - if punctuation and not previous_special: - if len(words) == 0: - words = [""] - word_tokens = [[]] - if not remove_punctuation_from_words: - words[-1] += decoded - word_tokens[-1].extend(empty_tokens + [decoded]) - word_tokens_indices[-1].extend(current_tokens) - else: - words.append(decoded) - word_tokens.append(empty_tokens + [decoded]) - word_tokens_indices.append(current_tokens) - current_tokens = [] - - return words, word_tokens, word_tokens_indices - - -def split_tokens_on_spaces( - tokens: torch.Tensor, tokenizer, remove_punctuation_from_words=False -): - ( - subwords, - subword_tokens_list, - subword_tokens_indices_list, - ) = split_tokens_on_unicode( - tokens, tokenizer, remove_punctuation_from_words=remove_punctuation_from_words - ) - words = [] - word_tokens = [] - word_tokens_indices = [] - - for i, (subword, subword_tokens, subword_tokens_indices) in enumerate( - zip(subwords, subword_tokens_list, subword_tokens_indices_list) - ): - special = subword_tokens_indices[0] >= tokenizer.timestamp_begin - previous_special = (i > 0) and ( - subword_tokens_indices_list[i - 1][0] >= tokenizer.timestamp_begin - ) - next_special = (i < len(subword_tokens_indices_list) - 1) and ( - subword_tokens_indices_list[i + 1][0] >= tokenizer.timestamp_begin - ) - previous_space = (i > 0) and (not subwords[i - 1].strip()) - is_space = not subword.strip() - with_space = subword.startswith(" ") and not is_space - punctuation = not is_space and subword.strip() in _punctuation - if special or ( - not previous_space - and ( - previous_special - or (with_space and not punctuation) - or (is_space and not next_special) - ) - ): - words.append(subword.strip()) - word_tokens.append(subword_tokens) - word_tokens_indices.append(subword_tokens_indices) - else: - words[-1] = words[-1] + subword.strip() - word_tokens[-1].extend(subword_tokens) - word_tokens_indices[-1].extend(subword_tokens_indices) - - return words, word_tokens, word_tokens_indices - - -silero_vad_model = None - - -def get_vad_segments( - audio, - output_sample=False, - min_speech_duration=0.1, - min_silence_duration=0.1, - dilatation=0.5, -): - """ - Get speech segments from audio using Silero VAD - parameters: - audio: torch.Tensor - audio data *in 16kHz* - output_sample: bool - if True, return start and end in samples instead of seconds - min_speech_duration: float - minimum duration (in sec) of a speech segment - min_silence_duration: float - minimum duration (in sec) of a silence segment - dilatation: float - how much (in sec) to enlarge each speech segment detected by the VAD - """ - global silero_vad_model, silero_get_speech_ts - - if silero_vad_model is None: - import onnxruntime - - onnxruntime.set_default_logger_severity( - 3 - ) # Remove warning "Removing initializer 'XXX'. It is not used by any node and should be removed from the model." - repo_or_dir = os.path.expanduser( - "~/.cache/torch/hub/snakers4_silero-vad_master" - ) - source = "local" - if not os.path.exists(repo_or_dir): - repo_or_dir = "snakers4/silero-vad" - source = "github" - silero_vad_model, utils = torch.hub.load( - repo_or_dir=repo_or_dir, model="silero_vad", onnx=True, source=source - ) - silero_get_speech_ts = utils[0] - - # Cheap normalization of the volume - audio = audio / max(0.1, audio.abs().max()) - - segments = silero_get_speech_ts( - audio, - silero_vad_model, - min_speech_duration_ms=round(min_speech_duration * 1000), - min_silence_duration_ms=round(min_silence_duration * 1000), - return_seconds=False, - ) - - if dilatation > 0: - dilatation = round(dilatation * SAMPLE_RATE) - new_segments = [] - for seg in segments: - new_seg = { - "start": max(0, seg["start"] - dilatation), - "end": min(len(audio), seg["end"] + dilatation), - } - if len(new_segments) > 0 and new_segments[-1]["end"] >= new_seg["start"]: - new_segments[-1]["end"] = new_seg["end"] - else: - new_segments.append(new_seg) - segments = new_segments - - ratio = 1 if output_sample else 1 / SAMPLE_RATE - - if ratio != 1: - for seg in segments: - seg["start"] *= ratio - seg["end"] *= ratio - if output_sample: - for seg in segments: - seg["start"] = round(seg["start"]) - seg["end"] = round(seg["end"]) - return segments - - -def remove_non_speech( - audio, - use_sample=False, - min_speech_duration=0.1, - min_silence_duration=1, - plot=False, -): - """ - Remove non-speech segments from audio (using Silero VAD), - glue the speech segments together and return the result along with - a function to convert timestamps from the new audio to the original audio - """ - - segments = get_vad_segments( - audio, - output_sample=True, - min_speech_duration=min_speech_duration, - min_silence_duration=min_silence_duration, - ) - - segments = [(seg["start"], seg["end"]) for seg in segments] - if len(segments) == 0: - segments = [(0, audio.shape[-1])] - - audio_speech = torch.cat([audio[..., s:e] for s, e in segments], dim=-1) - - if plot: - import matplotlib.pyplot as plt - - plt.figure() - plt.plot(audio) - for s, e in segments: - plt.axvspan(s, e, color="red", alpha=0.1) - if isinstance(plot, str): - plt.savefig(f"{plot}.VAD.jpg", bbox_inches="tight", pad_inches=0) - else: - plt.show() - - if not use_sample: - segments = [ - (float(s) / SAMPLE_RATE, float(e) / SAMPLE_RATE) for s, e in segments - ] - - return audio_speech, lambda t, t2=None: do_convert_timestamps(segments, t, t2) - - -def do_convert_timestamps(segments, t, t2=None): - """ - Convert timestamp from audio without non-speech segments to original audio (with non-speech segments) - - parameters: - segments: list of tuple (start, end) corresponding to non-speech segments in original audio - t: timestamp to convert - t2: second timestamp to convert (optional), when the two timestamps should be in the same segment - """ - assert len(segments) - ioffset = 0 # Input offset - ooffset = 0 # Output offset - ipreviousend = 0 - result = [] - for istart, iend in segments: - ostart = ooffset - oend = ostart + (iend - istart) - ooffset = oend - ioffset += istart - ipreviousend - ipreviousend = iend - t_in = t <= oend - t2_in = t_in if t2 is None else t2 <= oend - if t_in or t2_in: - result.append( - [ - max(istart, min(iend, ioffset + t)), - max(istart, min(iend, ioffset + t2)) if t2 is not None else None, - ] - ) - if t_in and t2_in: - break - if not len(result): - result.append([ioffset + t, ioffset + t2 if t2 is not None else None]) - - if len(result) > 1: - # Minimize difference between durations - result = sorted(result, key=lambda x: abs(abs(t2 - t) - abs(x[1] - x[0]))) - result = result[0] - if t2 is None: - result = round(result[0], 2) - else: - result = [round(x, 2) for x in result] - return result - - -def remove_last_null_duration_words(transcription, words, recompute_text=False): - """ - Remove words with null duration happening at the end of a chunk (probable Whisper hallucinations) - """ - # First group segments by audio chunk - segments_groups = {} - seek = None - current_chunk = -1 - for i, segment in enumerate(transcription["segments"]): - if segment["seek"] != seek: - current_chunk += 1 - seek = segment["seek"] - segments_groups[i] = current_chunk - - # Remove words with null duration happening at the end of a chunk - current_chunk = -1 - is_last_empty = False - to_remove = [] - for i, word in enumerate(words[::-1]): # Reverse order - i = len(words) - i - 1 - empty = word["start"] == word["end"] - idx_segment = word["idx_segment"] - group = segments_groups[idx_segment] - if current_chunk != group: - is_last_empty = empty - current_chunk = group - elif not empty: - is_last_empty = False - if is_last_empty: - # Remove word - to_remove.append(i) - # Shorten text of segment - full_word = "".join(word["tokens"]) - logger.debug( - f"Removing word {i+1}/{len(words)} \"{full_word}\" with empty duration at the end of segment {idx_segment+1}/{len(transcription['segments'])}" - ) - segment = transcription["segments"][idx_segment] - text = segment["text"] - if not text.endswith(full_word): # see issue #62 - if text.endswith(full_word[:-1]): - full_word = full_word[:-1] - elif text[:-1].endswith(full_word): - text = text[:-1] - else: - raise RuntimeError(f'"{text}" not ending with "{full_word}"') - text = text[: -len(full_word)] - if i > 0 and words[i - 1]["idx_segment"] == idx_segment: - segment["text"] = text - else: - logger.debug(f"Removing empty segment {idx_segment}") - # Remove segment with no more words - transcription["segments"].pop(idx_segment) - for j in range(i + 1, len(words)): - words[j]["idx_segment"] -= 1 - recompute_text = True - - for i in to_remove: - words.pop(i) # Warning: inplace modification - - if recompute_text: - transcription["text"] = "".join([s["text"] for s in transcription["segments"]]) - - return transcription, words - - -def ensure_increasing_positions(segments, min_duration=0): - """ - Ensure that "start" and "end" come in increasing order - """ - has_modified_backward = False - previous_end = 0 - for i, seg in enumerate(segments): - if seg["start"] < previous_end: - assert i > 0 - new_start = round_timestamp((previous_end + seg["start"]) / 2) - if new_start < segments[i - 1]["start"] + min_duration: - new_start = previous_end - else: - segments[i - 1]["end"] = new_start - has_modified_backward = True - seg["start"] = new_start - if seg["end"] <= seg["start"] + min_duration: - seg["end"] = seg["start"] + min_duration - previous_end = seg["end"] - if has_modified_backward: - return ensure_increasing_positions(segments, min_duration) - - previous_end = 0 - for seg in segments: - seg["start"] = round_timestamp(seg["start"]) - seg["end"] = round_timestamp(seg["end"]) - assert ( - seg["start"] >= previous_end - ), f"Got segment {seg} coming before the previous finishes ({previous_end} > {seg['start']})" - assert seg["end"] >= seg["start"], f"Got segment {seg} with end < start" - previous_end = seg["end"] - - return segments - - -## Some utilities for writing transcripts to files - - -def flatten(list_of_lists, key=None): - for sublist in list_of_lists: - for item in sublist.get(key, []) if key else sublist: - yield item - - -def remove_keys(list_of_dicts, key): - for d in list_of_dicts: - yield {k: d[k] for k in d.keys() - {key}} - - -def write_csv( - transcript, file, sep=",", text_first=True, format_timestamps=None, header=False -): - writer = csv.writer(file, delimiter=sep) - if format_timestamps is None: - format_timestamps = lambda x: x - if header is True: - header = ["text", "start", "end"] if text_first else ["start", "end", "text"] - if header: - writer.writerow(header) - if text_first: - writer.writerows( - [ - [ - segment["text"].strip(), - format_timestamps(segment["start"]), - format_timestamps(segment["end"]), - ] - for segment in transcript - ] - ) - else: - writer.writerows( - [ - [ - format_timestamps(segment["start"]), - format_timestamps(segment["end"]), - segment["text"].strip(), - ] - for segment in transcript - ] - ) - - -# https://stackoverflow.com/questions/66588715/runtimeerror-cudnn-error-cudnn-status-not-initialized-using-pytorch -# CUDA initialization may fail on old GPU card -def force_cudnn_initialization(device=None, s=32): - if device is None: - device = torch.device("cuda") - torch.nn.functional.conv2d( - torch.zeros(s, s, s, s, device=device), torch.zeros(s, s, s, s, device=device) - ) - - -# base85-encoded (n_layers, n_heads) boolean arrays indicating the cross-attention heads that are -# highly correlated to the word-level timing, i.e. the alignment between audio and text tokens. -_ALIGNMENT_HEADS = { - "tiny.en": b"ABzY8J1N>@0{>%R00Bk>$p{7v037`oCl~+#00", - "tiny": b"ABzY8bu8Lr0{>%RKn9Fp%m@SkK7Kt=7ytkO", - "base.en": b"ABzY8;40c<0{>%RzzG;p*o+Vo09|#PsxSZm00", - "base": b"ABzY8KQ!870{>%RzyTQH3`Q^yNP!>##QT-
-Acer A200 Simple Tool V2 598: How to Root, Flash, Backup, and More with One Click
-If you own an Acer Iconia A200 tablet and want to customize it to your liking, you might be interested in a tool that can help you do that easily. The tool is called A cer A200 Simple Tool V2 598 and it was developed by XDA Developers member sc2k. The tool allows you to root, flash, backup, and perform other operations on your tablet with one click. Sounds amazing, right?
-acer a200 simple tool v2 598
Download File ↔ https://urlcod.com/2uIabZ
-In this article, I will tell you everything you need to know about Acer A200 Simple Tool V2 598, including what it is, what are the benefits of using it, what are the requirements for using it, how to download and install it, how to use it, what are some tips and tricks for using it, what are some alternatives to it, and some FAQs. By the end of this article, you will be able to unleash the full potential of your Acer Iconia A200 tablet with Acer A200 Simple Tool V2 598. Let's get started!
-What is Acer A200 Simple Tool V2 598?
-Acer A200 Simple Tool V2 598 is a Windows-based application that can perform various tasks on your Acer Iconia A200 tablet with one click. The tool was created by sc2k, a senior member of XDA Developers forum, who shared it on his thread. The tool is based on the work of other developers such as alephzain, vache, thor2002ro, and others. The tool supports both Windows XP and Windows 7 operating systems.
-The tool has a simple and user-friendly interface that shows you the options available for your tablet. You can choose from the following options:
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- Root: This option allows you to root your tablet, which means gaining administrative access to the system files and settings. Rooting enables you to install custom ROMs, remove bloatware, tweak performance, and more. -
- Flash: This option allows you to flash a custom ROM on your tablet, which means replacing the stock firmware with a modified one. Flashing a custom ROM can give you more features, better performance, improved battery life, and more. -
- Backup: This option allows you to backup your tablet data, which means creating a copy of your files and settings. Backing up your data can help you restore it in case of any problem or data loss. -
- Restore: This option allows you to restore your tablet data, which means restoring the files and settings from a backup. Restoring your data can help you recover from any problem or data loss. -
- Unroot: This option allows you to unroot your tablet, which means removing the administrative access to the system files and settings. Unrooting can help you return to the stock firmware, receive official updates, or claim warranty. -
As you can see, Acer A200 Simple Tool V2 598 can help you do a lot of things with your tablet with just one click. But why would you want to do these things? Let's find out in the next section.
-What are the benefits of using Acer A200 Simple Tool V2 598?
-Using Acer A200 Simple Tool V2 598 can bring you many benefits for your tablet. Here are some of them:
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- You can root your tablet easily and safely without any complicated commands or procedures. Rooting can give you more control over your tablet and allow you to customize it as you wish. -
- You can flash a custom ROM on your tablet quickly and conveniently without any hassle or risk. Flashing a custom ROM can give you more functionality and performance than the stock firmware. -
- You can backup your tablet data securely and reliably without any loss or corruption. Backing up your data can help you protect it from any damage or deletion. -
- You can restore your tablet data easily and accurately without any error or mismatch. Restoring your data can help you recover it from any problem or disaster. -
- You can unroot your tablet smoothly and cleanly without any trace or residue. Unrooting can help you revert to the stock firmware, receive official updates, or claim warranty. -
As you can see, using Acer A200 Simple Tool V2 598 can make your tablet experience better and more enjoyable. But before you use it, you need to make sure that you meet some requirements. Let's see what they are in the next section.
-What are the requirements for using Acer A200 Simple Tool V2 598?
-Before you use Acer A200 Simple Tool V2 598 on your tablet, you need to make sure that you have the following requirements:
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- A Windows XP or Windows 7 computer with .NET Framework 4 installed. You can download .NET Framework 4 from here. -
- An Acer Iconia A200 tablet with at least 50% battery level. You can check your battery level by going to Settings > About tablet > Status. -
- A USB cable to connect your tablet to your computer. Make sure that the cable is working properly and not damaged. -
- The Acer A200 Simple Tool V2 598 application. You can download it from here. -
- The drivers for your tablet. You can download them from here. -
- A backup of your tablet data. You can use the tool itself to backup your data, or use any other method that you prefer. This is important in case something goes wrong during the process. -
Once you have all these requirements, you are ready to use Acer A200 Simple Tool V2 598 on your tablet. But first, you need to download and install it on your computer. Let's see how to do that in the next section.
-How to download and install Acer A200 Simple Tool V2 598?
-Downloading and installing Acer A200 Simple Tool V2 598 on your computer is very easy and straightforward. Just follow these steps:
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- Download the Acer A200 Simple Tool V2 598 application from here. The file size is about 15 MB and the file name is Acer_A200_Simple_Tool_v2_598.zip. -
- Extract the zip file to a folder on your computer. You can use any zip extractor software that you like, such as WinRAR, 7-Zip, or PeaZip. -
- Open the folder where you extracted the zip file and double-click on the Acer_A200_Simple_Tool_v2_598.exe file. This will launch the application. -
- Wait for the application to load and detect your tablet. You will see a message saying "Device connected" if your tablet is detected successfully. -
Congratulations! You have successfully downloaded and installed Acer A200 Simple Tool V2 598 on your computer. Now you can use it to perform various tasks on your tablet with one click. Let's see how to do that in the next section.
-How to use Acer A200 Simple Tool V2 598?
-Using Acer A200 Simple Tool V2 598 on your tablet is very simple and convenient. You just need to select the option that you want to perform and follow the instructions on the screen. Here are some guides on how to use the tool for different purposes:
-How to root your Acer Iconia A200 with Acer A200 Simple Tool V2 598?
-Rooting your tablet means gaining administrative access to the system files and settings. Rooting can enable you to install custom ROMs, remove bloatware, tweak performance, and more. To root your tablet with Acer A200 Simple Tool V2 598, follow these steps:
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- Make sure that you have met all the requirements for using the tool, as mentioned in the previous sections. -
- Connect your tablet to your computer with a USB cable and enable USB debugging mode on your tablet. To enable USB debugging mode, go to Settings > Developer options > USB debugging and check the box. -
- Launch the Acer A200 Simple Tool V2 598 application on your computer and wait for it to detect your tablet. -
- Select the option "1-Root" from the main menu of the application and press Enter. -
- Follow the instructions on the screen and wait for the process to complete. The process may take a few minutes and your tablet may reboot several times during the process. -
- Once the process is done, you will see a message saying "Root done" on the screen. -
Congratulations! You have successfully rooted your tablet with Acer A200 Simple Tool V2 598. You can now enjoy more control over your tablet and customize it as you wish.
-How to flash a custom ROM on your Acer Iconia A200 with Acer A200 Simple Tool V2 598?
-Flashing a custom ROM means replacing the stock firmware with a modified one. Flashing a custom ROM can give you more features, better performance, improved battery life, and more. To flash a custom ROM on your tablet with Acer A200 Simple Tool V2 598, follow these steps:
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- Make sure that you have met all the requirements for using the tool, as mentioned in the previous sections. -
- Download a custom ROM that is compatible with your tablet from a reliable source, such as XDA Developers forum or Android Authority website. The custom ROM should be in a zip file format and should not be extracted. -
- Copy the custom ROM zip file to the root directory of your tablet memory card or internal storage. You can use a file manager app or a USB cable to do this. -
- Connect your tablet to your computer with a USB cable and enable USB debugging mode on your tablet. To enable USB debugging mode, go to Settings > Developer options > USB debugging and check the box. -
- Launch the Acer A200 Simple Tool V2 598 application on your computer and wait for it to detect your tablet. -
- Select the option "2-Flash" from the main menu of the application and press Enter. -
- Follow the instructions on the screen and select the custom ROM zip file that you copied to your tablet. The application will automatically reboot your tablet into recovery mode and flash the custom ROM. -
- Wait for the process to complete. The process may take several minutes and your tablet may reboot several times during the process. -
- Once the process is done, you will see a message saying "Flash done" on the screen. -
Congratulations! You have successfully flashed a custom ROM on your tablet with Acer A200 Simple Tool V2 598. You can now enjoy more functionality and performance than the stock firmware.
-How to backup your Acer Iconia A200 data with Acer A200 Simple Tool V2 598?
-Backing up your tablet data means creating a copy of your files and settings. Backing up your data can help you protect it from any damage or deletion. To backup your tablet data with Acer A200 Simple Tool V2 598, follow these steps:
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- Make sure that you have met all the requirements for using the tool, as mentioned in the previous sections. -
- Connect your tablet to your computer with a USB cable and enable USB debugging mode on your tablet. To enable USB debugging mode, go to Settings > Developer options > USB debugging and check the box. -
- Launch the Acer A200 Simple Tool V2 598 application on your computer and wait for it to detect your tablet. -
- Select the option "3-Backup" from the main menu of the application and press Enter. -
- Follow the instructions on the screen and choose a location on your computer where you want to save the backup file. The application will automatically reboot your tablet into recovery mode and backup your data. -
- Wait for the process to complete. The process may take several minutes and your tablet may reboot several times during the process. -
- Once the process is done, you will see a message saying "Backup done" on the screen. -
Congratulations! You have successfully backed up your tablet data with Acer A200 Simple Tool V2 598. You can now restore it in case of any problem or data loss.
-How to restore your Acer Iconia A200 data with Acer A200 Simple Tool V2 598?
-Restoring your tablet data means restoring the files and settings from a backup. Restoring your data can help you recover it from any problem or disaster. To restore your tablet data with Acer A200 Simple Tool V2 598, follow these steps:
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- Make sure that you have met all the requirements for using the tool, as mentioned in the previous sections. -
- Connect your tablet to your computer with a USB cable and enable USB debugging mode on your tablet. To enable USB debugging mode, go to Settings > Developer options > USB debugging and check the box. -
- Launch the Acer A200 Simple Tool V2 598 application on your computer and wait for it to detect your tablet. -
- Select the option "4-Restore" from the main menu of the application and press Enter. -
- Follow the instructions on the screen and select the backup file that you want to restore from. The application will automatically reboot your tablet into recovery mode and restore your data. -
- Wait for the process to complete. The process may take several minutes and your tablet may reboot several times during the process. -
- Once the process is done, you will see a message saying "Restore done" on the screen. -
Congratulations! You have successfully restored your tablet data with Acer A200 Simple Tool V2 598. You can now enjoy your tablet as before.
-How to unroot your Acer Iconia A200 with Acer A200 Simple Tool V2 598?
-Unrooting your tablet means removing the administrative access to the system files and settings. Unrooting can help you return to the stock firmware, receive official updates, or claim warranty. To unroot your tablet with Acer A200 Simple Tool V2 598, follow these steps:
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- Make sure that you have met all the requirements for using the tool, as mentioned in the previous sections. -
- Connect your tablet to your computer with a USB cable and enable USB debugging mode on your tablet. To enable USB debugging mode, go to Settings > Developer options > USB debugging and check the box. -
- Launch the Acer A200 Simple Tool V2 598 application on your computer and wait for it to detect your tablet. -
- Select the option "5-Unroot" from the main menu of the application and press Enter. -
- Follow the instructions on the screen and wait for the process to complete. The process may take a few minutes and your tablet may reboot several times during the process. -
- Once the process is done, you will see a message saying "Unroot done" on the screen. -
Congratulations! You have successfully unrooted your tablet with Acer A200 Simple Tool V2 598. You can now revert to the stock firmware, receive official updates, or claim warranty.
-What are some tips and tricks for using Acer A200 Simple Tool V2 598?
-Using Acer A200 Simple Tool V2 598 on your tablet can be very easy and convenient, but there are some tips and tricks that can make it even better and safer. Here are some of them:
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- Before using the tool, make sure that you have a backup of your tablet data. This can help you restore it in case of any problem or data loss. -
- Before using the tool, make sure that you have enough battery level on your tablet. This can prevent any interruption or failure during the process. -
- Before using the tool, make sure that you have installed the drivers for your tablet on your computer. This can ensure a smooth and stable connection between your tablet and your computer. -
- Before using the tool, make sure that you have downloaded the latest version of the tool from the official thread. This can ensure that you have the most updated and bug-free version of the tool. -
- Before using the tool, make sure that you have checked the compatibility of the custom ROM that you want to flash on your tablet. This can prevent any incompatibility or conflict issues between your tablet and the custom ROM. -
- After using the tool, make sure that you reboot your tablet once or twice. This can help your tablet to adapt to the changes and optimize its performance. -
- If you encounter any error or problem while using the tool, make sure that you report it to the developer on his thread. This can help him to fix it and improve it in future versions. -
These are some of the tips and tricks that can help you use Acer A200 Simple Tool V2 598 on your tablet more effectively and safely. If you follow them, you will have a better and more enjoyable tablet experience.
-What are some alternatives to Acer A200 Simple Tool V2 598?
-Acer A200 Simple Tool V2 598 is a great tool for your Acer Iconia A200 tablet, but it is not the only one. There are some other tools that can perform similar functions as Acer A200 Simple Tool V2 598, such as:
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-
- KingoRoot: This is a one-click root tool that can root almost any Android device with ease. It supports both Windows and Android platforms and has a high success rate. You can download it from here. -
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These are some of the alternatives to Acer A200 Simple Tool V2 598 that you can try if you want to explore more options for your tablet. However, Acer A200 Simple Tool V2 598 is still a very useful and convenient tool that can help you do a lot of things with your tablet with one click.
-Conclusion
-In this article, I have shown you everything you need to know about Acer A200 Simple Tool V2 598, a tool that can help you root, flash, backup, and perform other operations on your Acer Iconia A200 tablet with one click. I have explained what it is, what are the benefits of using it, what are the requirements for using it, how to download and install it, how to use it, what are some tips and tricks for using it, what are some alternatives to it, and some FAQs. I hope you have found this article helpful and informative.
-If you have an Acer Iconia A200 tablet and want to customize it to your liking, I highly recommend you to try Acer A200 Simple Tool V2 598. It is a simple and user-friendly tool that can make your tablet experience better and more enjoyable. You can download it from here and follow the guides in this article to use it on your tablet.
-Thank you for reading this article. If you have any questions or comments, please feel free to leave them below. I would love to hear from you. Have a great day!
-FAQs
-Here are some frequently asked questions about Acer A200 Simple Tool V2 598 and their answers:
--
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- Is Acer A200 Simple Tool V2 598 safe to use? -
- Does Acer A200 Simple Tool V2 598 work on other devices? -
- Does Acer A200 Simple Tool V2 598 void the warranty of my tablet? -
- Where can I find more information about Acer A200 Simple Tool V2 598? -
- How can I support the development of Acer A200 Simple Tool V2 598? -
Yes, Acer A200 Simple Tool V2 598 is safe to use as long as you follow the instructions carefully and make a backup of your data before using it. However, there is always a risk of bricking or damaging your device when performing any modification on it. Therefore, use the tool at your own risk and responsibility.
-No, Acer A200 Simple Tool V2 598 is designed specifically for the Acer Iconia A200 tablet and does not work on other devices. If you have a different device, you should look for a different tool that is compatible with your device.
-Yes, using Acer A200 Simple Tool V2 598 may void the warranty of your tablet as it modifies the system files and settings of your tablet. If you want to claim warranty, you should unroot your tablet with the tool and restore the stock firmware before sending it to the service center.
-You can find more information about Acer A200 Simple Tool V2 598 on the official thread of the developer on XDA Developers forum. You can also contact the developer directly through his profile or email if you have any feedback or suggestion for the tool.
-If you like Acer A200 Simple Tool V2 598 and want to support the development of the tool, you can donate to the developer through his PayPal account or thank him on his thread. You can also share the tool with your friends and family who have an Acer Iconia A200 tablet and want to customize it.
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\ No newline at end of file diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Golmaal Returns In Hindi Download Full Movie.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Golmaal Returns In Hindi Download Full Movie.md deleted file mode 100644 index 26da6e7c929849bae8cf67b51d4fe7ec50cca1b6..0000000000000000000000000000000000000000 --- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Golmaal Returns In Hindi Download Full Movie.md +++ /dev/null @@ -1,23 +0,0 @@ - -How to Download Golmaal Returns (2008) Full Movie in Hindi
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\ No newline at end of file diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/evaluation/__init__.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/evaluation/__init__.py deleted file mode 100644 index d96609e8f2261a6800fe85fcf3e1eaeaa44455c6..0000000000000000000000000000000000000000 --- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/evaluation/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -from .cityscapes_evaluation import CityscapesInstanceEvaluator, CityscapesSemSegEvaluator -from .coco_evaluation import COCOEvaluator -from .rotated_coco_evaluation import RotatedCOCOEvaluator -from .evaluator import DatasetEvaluator, DatasetEvaluators, inference_context, inference_on_dataset -from .lvis_evaluation import LVISEvaluator -from .panoptic_evaluation import COCOPanopticEvaluator -from .pascal_voc_evaluation import PascalVOCDetectionEvaluator -from .sem_seg_evaluation import SemSegEvaluator -from .testing import print_csv_format, verify_results - -__all__ = [k for k in globals().keys() if not k.startswith("_")] diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/modeling/meta_arch/build.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/modeling/meta_arch/build.py deleted file mode 100644 index 3427215746c9a146bd902f22ea9b26d121c36b27..0000000000000000000000000000000000000000 --- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/modeling/meta_arch/build.py +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import torch - -from detectron2.utils.logger import _log_api_usage -from detectron2.utils.registry import Registry - -META_ARCH_REGISTRY = Registry("META_ARCH") # noqa F401 isort:skip -META_ARCH_REGISTRY.__doc__ = """ -Registry for meta-architectures, i.e. the whole model. - -The registered object will be called with `obj(cfg)` -and expected to return a `nn.Module` object. -""" - - -def build_model(cfg): - """ - Build the whole model architecture, defined by ``cfg.MODEL.META_ARCHITECTURE``. - Note that it does not load any weights from ``cfg``. - """ - meta_arch = cfg.MODEL.META_ARCHITECTURE - model = META_ARCH_REGISTRY.get(meta_arch)(cfg) - model.to(torch.device(cfg.MODEL.DEVICE)) - _log_api_usage("modeling.meta_arch." + meta_arch) - return model diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/converters/chart_output_hflip.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/converters/chart_output_hflip.py deleted file mode 100644 index 17d294841264c248cf7fa9e3d2d2b4efdbb9a5e8..0000000000000000000000000000000000000000 --- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/converters/chart_output_hflip.py +++ /dev/null @@ -1,71 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -from dataclasses import fields -import torch - -from densepose.structures import DensePoseChartPredictorOutput, DensePoseTransformData - - -def densepose_chart_predictor_output_hflip( - densepose_predictor_output: DensePoseChartPredictorOutput, - transform_data: DensePoseTransformData, -) -> DensePoseChartPredictorOutput: - """ - Change to take into account a Horizontal flip. - """ - if len(densepose_predictor_output) > 0: - - PredictorOutput = type(densepose_predictor_output) - output_dict = {} - - for field in fields(densepose_predictor_output): - field_value = getattr(densepose_predictor_output, field.name) - # flip tensors - if isinstance(field_value, torch.Tensor): - setattr(densepose_predictor_output, field.name, torch.flip(field_value, [3])) - - densepose_predictor_output = _flip_iuv_semantics_tensor( - densepose_predictor_output, transform_data - ) - densepose_predictor_output = _flip_segm_semantics_tensor( - densepose_predictor_output, transform_data - ) - - for field in fields(densepose_predictor_output): - output_dict[field.name] = getattr(densepose_predictor_output, field.name) - - return PredictorOutput(**output_dict) - else: - return densepose_predictor_output - - -def _flip_iuv_semantics_tensor( - densepose_predictor_output: DensePoseChartPredictorOutput, - dp_transform_data: DensePoseTransformData, -) -> DensePoseChartPredictorOutput: - point_label_symmetries = dp_transform_data.point_label_symmetries - uv_symmetries = dp_transform_data.uv_symmetries - - N, C, H, W = densepose_predictor_output.u.shape - u_loc = (densepose_predictor_output.u[:, 1:, :, :].clamp(0, 1) * 255).long() - v_loc = (densepose_predictor_output.v[:, 1:, :, :].clamp(0, 1) * 255).long() - Iindex = torch.arange(C - 1, device=densepose_predictor_output.u.device)[ - None, :, None, None - ].expand(N, C - 1, H, W) - densepose_predictor_output.u[:, 1:, :, :] = uv_symmetries["U_transforms"][Iindex, v_loc, u_loc] - densepose_predictor_output.v[:, 1:, :, :] = uv_symmetries["V_transforms"][Iindex, v_loc, u_loc] - - for el in ["fine_segm", "u", "v"]: - densepose_predictor_output.__dict__[el] = densepose_predictor_output.__dict__[el][ - :, point_label_symmetries, :, : - ] - return densepose_predictor_output - - -def _flip_segm_semantics_tensor( - densepose_predictor_output: DensePoseChartPredictorOutput, dp_transform_data -): - if densepose_predictor_output.coarse_segm.shape[1] > 2: - densepose_predictor_output.coarse_segm = densepose_predictor_output.coarse_segm[ - :, dp_transform_data.mask_label_symmetries, :, : - ] - return densepose_predictor_output diff --git a/spaces/notsq/diffuse-the-rest/build/_app/immutable/assets/+layout-7c2f4ad7.css b/spaces/notsq/diffuse-the-rest/build/_app/immutable/assets/+layout-7c2f4ad7.css deleted file mode 100644 index 3d3b9c37a65b45a8c172cf493f95cf521ab680a7..0000000000000000000000000000000000000000 --- a/spaces/notsq/diffuse-the-rest/build/_app/immutable/assets/+layout-7c2f4ad7.css +++ /dev/null @@ -1 +0,0 @@ -*,:before,:after{box-sizing:border-box;border-width:0;border-style:solid;border-color:#e5e7eb}:before,:after{--tw-content: ""}html{line-height:1.5;-webkit-text-size-adjust:100%;-moz-tab-size:4;-o-tab-size:4;tab-size:4;font-family:ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,"Apple Color Emoji","Segoe UI Emoji",Segoe UI Symbol,"Noto Color Emoji"}body{margin:0;line-height:inherit}hr{height:0;color:inherit;border-top-width:1px}abbr:where([title]){-webkit-text-decoration:underline dotted;text-decoration:underline dotted}h1,h2,h3,h4,h5,h6{font-size:inherit;font-weight:inherit}a{color:inherit;text-decoration:inherit}b,strong{font-weight:bolder}code,kbd,samp,pre{font-family:ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier 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0;--tw-border-spacing-y: 0;--tw-translate-x: 0;--tw-translate-y: 0;--tw-rotate: 0;--tw-skew-x: 0;--tw-skew-y: 0;--tw-scale-x: 1;--tw-scale-y: 1;--tw-pan-x: ;--tw-pan-y: ;--tw-pinch-zoom: ;--tw-scroll-snap-strictness: proximity;--tw-ordinal: ;--tw-slashed-zero: ;--tw-numeric-figure: ;--tw-numeric-spacing: ;--tw-numeric-fraction: ;--tw-ring-inset: ;--tw-ring-offset-width: 0px;--tw-ring-offset-color: #fff;--tw-ring-color: rgb(59 130 246 / .5);--tw-ring-offset-shadow: 0 0 #0000;--tw-ring-shadow: 0 0 #0000;--tw-shadow: 0 0 #0000;--tw-shadow-colored: 0 0 #0000;--tw-blur: ;--tw-brightness: ;--tw-contrast: ;--tw-grayscale: ;--tw-hue-rotate: ;--tw-invert: ;--tw-saturate: ;--tw-sepia: ;--tw-drop-shadow: ;--tw-backdrop-blur: ;--tw-backdrop-brightness: ;--tw-backdrop-contrast: ;--tw-backdrop-grayscale: ;--tw-backdrop-hue-rotate: ;--tw-backdrop-invert: ;--tw-backdrop-opacity: ;--tw-backdrop-saturate: ;--tw-backdrop-sepia: }::-webkit-backdrop{--tw-border-spacing-x: 0;--tw-border-spacing-y: 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1024px){.lg\:px-56{padding-left:14rem;padding-right:14rem}} diff --git a/spaces/odettecantswim/rvc-mlbb-v2/lib/infer_pack/onnx_inference.py b/spaces/odettecantswim/rvc-mlbb-v2/lib/infer_pack/onnx_inference.py deleted file mode 100644 index c78324cbc08414fffcc689f325312de0e51bd6b4..0000000000000000000000000000000000000000 --- a/spaces/odettecantswim/rvc-mlbb-v2/lib/infer_pack/onnx_inference.py +++ /dev/null @@ -1,143 +0,0 @@ -import onnxruntime -import librosa -import numpy as np -import soundfile - - -class ContentVec: - def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None): - print("load model(s) from {}".format(vec_path)) - if device == "cpu" or device is None: - providers = ["CPUExecutionProvider"] - elif device == "cuda": - providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] - elif device == "dml": - providers = ["DmlExecutionProvider"] - else: - raise RuntimeError("Unsportted Device") - self.model = onnxruntime.InferenceSession(vec_path, providers=providers) - - def __call__(self, wav): - return self.forward(wav) - - def forward(self, wav): - feats = wav - if feats.ndim == 2: # double channels - feats = feats.mean(-1) - assert feats.ndim == 1, feats.ndim - feats = np.expand_dims(np.expand_dims(feats, 0), 0) - onnx_input = {self.model.get_inputs()[0].name: feats} - logits = self.model.run(None, onnx_input)[0] - return logits.transpose(0, 2, 1) - - -def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs): - if f0_predictor == "pm": - from lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor - - f0_predictor_object = PMF0Predictor( - hop_length=hop_length, sampling_rate=sampling_rate - ) - elif f0_predictor == "harvest": - from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor - - f0_predictor_object = HarvestF0Predictor( - hop_length=hop_length, sampling_rate=sampling_rate - ) - elif f0_predictor == "dio": - from lib.infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor - - f0_predictor_object = DioF0Predictor( - hop_length=hop_length, sampling_rate=sampling_rate - ) - else: - raise Exception("Unknown f0 predictor") - return f0_predictor_object - - -class OnnxRVC: - def __init__( - self, - model_path, - sr=40000, - hop_size=512, - vec_path="vec-768-layer-12", - device="cpu", - ): - vec_path = f"pretrained/{vec_path}.onnx" - self.vec_model = ContentVec(vec_path, device) - if device == "cpu" or device is None: - providers = ["CPUExecutionProvider"] - elif device == "cuda": - providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] - elif device == "dml": - providers = ["DmlExecutionProvider"] - else: - raise RuntimeError("Unsportted Device") - self.model = onnxruntime.InferenceSession(model_path, providers=providers) - self.sampling_rate = sr - self.hop_size = hop_size - - def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd): - onnx_input = { - self.model.get_inputs()[0].name: hubert, - self.model.get_inputs()[1].name: hubert_length, - self.model.get_inputs()[2].name: pitch, - self.model.get_inputs()[3].name: pitchf, - self.model.get_inputs()[4].name: ds, - self.model.get_inputs()[5].name: rnd, - } - return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16) - - def inference( - self, - raw_path, - sid, - f0_method="dio", - f0_up_key=0, - pad_time=0.5, - cr_threshold=0.02, - ): - f0_min = 50 - f0_max = 1100 - f0_mel_min = 1127 * np.log(1 + f0_min / 700) - f0_mel_max = 1127 * np.log(1 + f0_max / 700) - f0_predictor = get_f0_predictor( - f0_method, - hop_length=self.hop_size, - sampling_rate=self.sampling_rate, - threshold=cr_threshold, - ) - wav, sr = librosa.load(raw_path, sr=self.sampling_rate) - org_length = len(wav) - if org_length / sr > 50.0: - raise RuntimeError("Reached Max Length") - - wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000) - wav16k = wav16k - - hubert = self.vec_model(wav16k) - hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32) - hubert_length = hubert.shape[1] - - pitchf = f0_predictor.compute_f0(wav, hubert_length) - pitchf = pitchf * 2 ** (f0_up_key / 12) - pitch = pitchf.copy() - f0_mel = 1127 * np.log(1 + pitch / 700) - f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / ( - f0_mel_max - f0_mel_min - ) + 1 - f0_mel[f0_mel <= 1] = 1 - f0_mel[f0_mel > 255] = 255 - pitch = np.rint(f0_mel).astype(np.int64) - - pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32) - pitch = pitch.reshape(1, len(pitch)) - ds = np.array([sid]).astype(np.int64) - - rnd = np.random.randn(1, 192, hubert_length).astype(np.float32) - hubert_length = np.array([hubert_length]).astype(np.int64) - - out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze() - out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant") - return out_wav[0:org_length] diff --git a/spaces/ofig/live-lm-critic/critic/critic.py b/spaces/ofig/live-lm-critic/critic/critic.py deleted file mode 100644 index 5e629d297cb60f3e635e5225b137166f171ecc62..0000000000000000000000000000000000000000 --- a/spaces/ofig/live-lm-critic/critic/critic.py +++ /dev/null @@ -1,318 +0,0 @@ -import sys -import torch -import random -import hashlib -import numpy as np -from tqdm import tqdm -from transformers import GPT2Tokenizer, GPT2Model, GPT2LMHeadModel -from transformers import OPTForCausalLM, GPTNeoForCausalLM -from transformers import RobertaTokenizer, RobertaForCausalLM, RobertaConfig -from transformers import XLMRobertaTokenizer, XLMRobertaForCausalLM, XLMRobertaConfig -from transformers import BartTokenizer, BartForCausalLM -import nltk -import pandas as pd -nltk.download('punkt') - -sys.path.insert(0, '.') -from critic.perturbations import get_local_neighbors_char_level, get_local_neighbors_word_level -from utils.spacy_tokenizer import spacy_tokenize_gec -import streamlit as st - -st.subheader('Exploring Unsupervised Grammatical Error Correction with Transformer-Based Models') -st.write('This live demonstration is adapted from the paper [LM-Critic: Language Models for Unsupervised Grammatical Error Correction](https://aclanthology.org/2021.emnlp-main.611.pdf) (EMNLP 2021) by Michihiro Yasunaga, Jure Leskovec, Percy Liang.') -st.write('Enter any sentence in the text box, press submit, and see the grammatical scoring and judgement results outputted by LM-Critic using different LMs displayed below.') - -def get_gpt2_loss(model, tokenizer, input_ids, attention_mask, labels): - with torch.no_grad(): - outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels) - lm_logits = outputs[1] #[bsize, seqlen, vocab] - if labels is not None: - shift_logits = lm_logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - shift_mask = attention_mask[..., 1:].contiguous() - loss_fct = torch.nn.CrossEntropyLoss(reduction='none') - bsize, seqlen = input_ids.size() - loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)).view(bsize, seqlen-1) - loss = (loss * shift_mask).sum(dim=1) #[bsize, ] - return loss - - -MAX_LENGTH = 66 - -def run_gpt2(sents, model, tokenizer, cuda=False, model_name=None): - assert isinstance(sents, list) - _sents = [tokenizer.bos_token + s for s in sents] - inputs = tokenizer(_sents, return_tensors="pt", padding=True) - if inputs['input_ids'].size(1) > MAX_LENGTH: - return None - if cuda: - inputs = {k: v.cuda() for k, v in inputs.items()} - loss = get_gpt2_loss(model, tokenizer, input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], labels=inputs['input_ids']) - logps = - loss.detach().cpu() - return logps - - -def gpt2_critic_char_level_only(sent, verbose=1, cuda=False, fp16=True, seed='auto', n_samples=100): - return_string = [] - if seed == 'auto': - seed = int(hashlib.md5(sent.encode()).hexdigest(), 16) % (2**32) #Seed must be between 0 and 2**32 - 1 - if verbose > 1: - print ('seed', seed) - np.random.seed(seed); random.seed(seed) - is_good = True - for _ in range(1): - sent_perturbations = get_local_neighbors_char_level(sent, max_n_samples=n_samples) - if verbose > 1: - print ("#sent_perturbations (char-level)", len(sent_perturbations)) - return_string.append(f"#sent_perturbations (char-level){len(sent_perturbations)}\n") - sents = [sent] + list(sent_perturbations) - if fp16: - with torch.cuda.amp.autocast(): - logps = run_gpt2(sents, cuda) - else: - logps = run_gpt2(sents, cuda) - if logps is None: - if verbose: - print ('Invalid input. Maybe the sentence is too long.') - return_string.append('Invalid input. Maybe the sentence is too long.\n') - return None - best_idx = int(logps.argmax()) - if best_idx != 0: - is_good = False - break - if verbose: - if is_good: - print ('Good! Your sentence log(p) = {:.3f}'.format(float(logps[0]))) - return_string.append('Good! Your sentence log(p) = {:.3f}\n'.format(float(logps[0]))) - else: - print ('Bad! Your sentence log(p) = {:.3f}'.format(float(logps[0]))) - return_string.append('Bad! Your sentence log(p) = {:.3f}\n'.format(float(logps[0]))) - print ('Neighbor sentence with highest log(p): {} (= {:.3f})'.format(sents[best_idx], float(logps[best_idx]))) - return_string.append('Neighbor sentence with highest log(p): {} (= {:.3f})\n'.format(sents[best_idx], float(logps[best_idx]))) - counter_example = None - if not is_good: - counter_example = [sents[best_idx], float(logps[best_idx])] - return is_good, float(logps[0]), counter_example - - -def gpt2_critic(sent, model, tokenizer, verbose=1, cuda=False, fp16=True, seed='auto', n_samples=100, word_level_mode='refine'): - return_string = [] - if seed == 'auto': - seed = int(hashlib.md5(sent.encode()).hexdigest(), 16) % (2**32) #Seed must be between 0 and 2**32 - 1 - if verbose > 1: - print ('seed', seed) - return_string.append(f'seed{seed}\n') - np.random.seed(seed); random.seed(seed) - sent_toked = spacy_tokenize_gec(sent) - is_good = True - for _ in range(1): - sent_perturbations_w, orig_sent = get_local_neighbors_word_level(sent_toked, max_n_samples=n_samples//2, mode=word_level_mode) - sent_perturbations_c = get_local_neighbors_char_level(orig_sent, max_n_samples=n_samples//2) - if verbose > 1: - print ("#sent_perturbations (char-level)", len(sent_perturbations_c)) - return_string.append("#sent_perturbations (char-level)\n", len(sent_perturbations_c)) - print ("#sent_perturbations (word-level)", len(sent_perturbations_w)) - return_string.append("#sent_perturbations (word-level)\n", len(sent_perturbations_w)) - sents = [orig_sent] + list(sent_perturbations_c.union(sent_perturbations_w)) - if fp16: - with torch.cuda.amp.autocast(): - logps = run_gpt2(sents, model, tokenizer, cuda) - else: - logps = run_gpt2(sents, model, tokenizer, cuda) - if logps is None: - if verbose: - print ('Invalid input. Maybe the sentence is too long.') - return_string.append('Invalid input. Maybe the sentence is too long.\n') - return None - best_idx = int(logps.argmax()) - if best_idx != 0: - is_good = False - break - if verbose: - if is_good: - print ('Good! Your sentence log(p) = {:.3f}'.format(float(logps[0]))) - return_string.append('Good! Your sentence log(p) = {:.3f}\n'.format(float(logps[0]))) - else: - print ('Bad! Your sentence log(p) = {:.3f}'.format(float(logps[0]))) - return_string.append('Bad! Your sentence log(p) = {:.3f}\n'.format(float(logps[0]))) - print ('Neighbor sentence with highest log(p): {} (= {:.3f})'.format(sents[best_idx], float(logps[best_idx]))) - return_string.append('Neighbor sentence with highest log(p): {} (= {:.3f})\n'.format(sents[best_idx], float(logps[best_idx]))) - counter_example = None - if not is_good: - counter_example = [sents[best_idx], float(logps[best_idx])] - return is_good, float(logps[0]), counter_example, return_string - -def gpt2(): - ## GPT-2 LM (original LM-critic) - placeholder_lm_name = st.empty() - model_name_gpt2 = 'gpt2' - nice_name_gpt2 = "GPT-2" - placeholder_lm_name.text(f"Initializing {nice_name_gpt2}...") - tokenizer_gpt2 = GPT2Tokenizer.from_pretrained(model_name_gpt2) - tokenizer_gpt2.pad_token = tokenizer_gpt2.eos_token - model_gpt2 = GPT2LMHeadModel.from_pretrained(model_name_gpt2) - model_gpt2.eval() - model_gpt2.cpu() - placeholder_lm_name.empty() - st.session_state["model_gpt2"] = model_gpt2 - st.session_state["tokenizer_gpt2"] = tokenizer_gpt2 - st.session_state["nice_name_gpt2"] = nice_name_gpt2 - -def opt(): - ## OPT LM - placeholder_lm_name = st.empty() - model_name_opt = "facebook/opt-350m" - nice_name_opt = "OPT" - placeholder_lm_name.text(f"Initializing {nice_name_opt}...") - model_opt = OPTForCausalLM.from_pretrained(model_name_opt) - tokenizer_opt = GPT2Tokenizer.from_pretrained(model_name_opt) - tokenizer_opt.pad_token = tokenizer_opt.eos_token - model_opt.eval() - model_opt.cpu() - placeholder_lm_name.empty() - st.session_state["model_opt"] = model_opt - st.session_state["tokenizer_opt"] = tokenizer_opt - st.session_state["nice_name_opt"] = nice_name_opt - -def gpt_neo(): - ## GPT NEO - placeholder_lm_name = st.empty() - model_name_gptneo = "EleutherAI/gpt-neo-1.3B" - nice_name_gptneo = "GPT NEO" - placeholder_lm_name.text(f"Initializing {nice_name_gptneo}...") - model_gptneo = GPTNeoForCausalLM.from_pretrained(model_name_gptneo) - tokenizer_gptneo = GPT2Tokenizer.from_pretrained(model_name_gptneo) - tokenizer_gptneo.pad_token = tokenizer_gptneo.eos_token - model_gptneo.eval() - model_gptneo.cpu() - placeholder_lm_name.empty() - st.session_state["model_gptneo"] = model_gptneo - st.session_state["tokenizer_gptneo"] = tokenizer_gptneo - st.session_state["nice_name_gptneo"] = nice_name_gptneo - -def roberta(): - ## RoBERTa - placeholder_lm_name = st.empty() - model_name_roberta = "roberta-base" - nice_name_roberta = "RoBERTa" - placeholder_lm_name.text(f"Initializing {nice_name_roberta}...") - tokenizer_roberta = RobertaTokenizer.from_pretrained(model_name_roberta) - config_roberta = RobertaConfig.from_pretrained(model_name_roberta) - config_roberta.is_decoder = True - model_roberta = RobertaForCausalLM.from_pretrained(model_name_roberta, config=config_roberta) - tokenizer_roberta.pad_token = tokenizer_roberta.eos_token - model_roberta.eval() - model_roberta.cpu() - placeholder_lm_name.empty() - st.session_state["model_roberta"] = model_roberta - st.session_state["tokenizer_roberta"] = tokenizer_roberta - st.session_state["nice_name_roberta"] = nice_name_roberta - -def bart(): - ## BART - placeholder_lm_name = st.empty() - model_name_bart = "facebook/bart-base" - nice_name_bart = "BART" - placeholder_lm_name.text(f"Initializing {nice_name_bart}...") - tokenizer_bart = BartTokenizer.from_pretrained(model_name_bart) - model_bart = BartForCausalLM.from_pretrained(model_name_bart, add_cross_attention=False) - assert model_bart.config.is_decoder, f"{model_bart.__class__} has to be configured as a decoder." - tokenizer_bart.pad_token = tokenizer_bart.eos_token - model_bart.eval() - model_bart.cpu() - placeholder_lm_name.empty() - st.session_state["model_bart"] = model_bart - st.session_state["tokenizer_bart"] = tokenizer_bart - st.session_state["nice_name_bart"] = nice_name_bart - -def xlm_roberta(): - ## XLM RoBERTa - placeholder_lm_name = st.empty() - model_name_xlmroberta = 'xlm-roberta-base' - nice_name_xlmroberta = 'XLM RoBERTa' - placeholder_lm_name.text(f"Initializing {nice_name_xlmroberta}...") - tokenizer_xlmroberta = XLMRobertaTokenizer.from_pretrained(model_name_xlmroberta) - config_xlmroberta = XLMRobertaConfig.from_pretrained(model_name_xlmroberta) - config_xlmroberta.is_decoder = True - model_xlmroberta = XLMRobertaForCausalLM.from_pretrained(model_name_xlmroberta, config=config_xlmroberta) - tokenizer_xlmroberta.pad_token = tokenizer_xlmroberta.eos_token - model_xlmroberta.eval() - model_xlmroberta.cpu() - placeholder_lm_name.empty() - st.session_state["model_xlmroberta"] = model_xlmroberta - st.session_state["tokenizer_xlmroberta"] = tokenizer_xlmroberta - st.session_state["nice_name_xlmroberta"] = nice_name_xlmroberta - -def main(): - form = st.form(key='my_form') - sent = form.text_input(label='Enter a sentence:', value="") - submit = form.form_submit_button(label='Submit') - - if submit and sent != '': - st.markdown(f"**Input Sentence**: {sent}") - results = {} - - with st.spinner('Running with GPT-2 LM...'): - ## GPT-2 LM (original LM-critic) - if "nice_name_gpt2" not in st.session_state: - gpt2() - is_good, score, counter_example, return_string_GPT2 = gpt2_critic(sent, st.session_state['model_gpt2'], st.session_state['tokenizer_gpt2']) - st.markdown("**Results with GPT-2 LM:**") - st.write('\n'.join(return_string_GPT2)) - results[st.session_state['nice_name_gpt2']] = ["Good" if is_good else "Bad", str(round(score, 3)), "N/A" if not counter_example else str(counter_example[0]), "N/A" if not counter_example else str(round(counter_example[1], 3))] - - with st.spinner('Running with OPT LM...'): - ## OPT LM - if "nice_name_opt" not in st.session_state: - opt() - is_good, score, counter_example, return_string_OPT = gpt2_critic(sent, st.session_state['model_opt'], st.session_state['tokenizer_opt']) - st.markdown("**Results with OPT LM:**") - st.write('\n'.join(return_string_OPT)) - results[st.session_state['nice_name_opt']] = ["Good" if is_good else "Bad", str(round(score, 3)), "N/A" if not counter_example else str(counter_example[0]), "N/A" if not counter_example else str(round(counter_example[1], 3))] - - with st.spinner('Running with GPT NEO LM...'): - ## GPT NEO - if "nice_name_gptneo" not in st.session_state: - gpt_neo() - is_good, score, counter_example, return_string_GPTNEO = gpt2_critic(sent, st.session_state['model_gptneo'], st.session_state['tokenizer_gptneo']) - st.markdown("**Results with GPT NEO LM:**") - st.write('\n'.join(return_string_GPTNEO)) - results[st.session_state['nice_name_gptneo']] = ["Good" if is_good else "Bad", str(round(score, 3)), "N/A" if not counter_example else str(counter_example[0]), "N/A" if not counter_example else str(round(counter_example[1], 3))] - - with st.spinner('Running with RoBERTa LM...'): - ## RoBERTa - if "nice_name_roberta" not in st.session_state: - roberta() - is_good, score, counter_example, return_string_RoBERTa = gpt2_critic(sent, st.session_state['model_roberta'], st.session_state['tokenizer_roberta']) - st.markdown("**Results with RoBERTa LM:**") - st.write('\n'.join(return_string_RoBERTa)) - results[st.session_state['nice_name_roberta']] = ["Good" if is_good else "Bad", str(round(score, 3)), "N/A" if not counter_example else str(counter_example[0]), "N/A" if not counter_example else str(round(counter_example[1], 3))] - - with st.spinner('Running with BART LM...'): - ## BART - if "nice_name_bart" not in st.session_state: - bart() - is_good, score, counter_example, return_string_BART = gpt2_critic(sent, st.session_state['model_bart'], st.session_state['tokenizer_bart']) - st.markdown("**Results with BART LM:**") - st.write('\n'.join(return_string_BART)) - results[st.session_state['nice_name_bart']] = ["Good" if is_good else "Bad", str(round(score, 3)), "N/A" if not counter_example else str(counter_example[0]), "N/A" if not counter_example else str(round(counter_example[1], 3))] - - with st.spinner('Running with XLM RoBERTa LM...'): - ## XLM RoBERTa - if "nice_name_xlmroberta" not in st.session_state: - xlm_roberta() - is_good, score, counter_example, return_string_XLMRoBERTa = gpt2_critic(sent, st.session_state['model_xlmroberta'], st.session_state['tokenizer_xlmroberta']) - st.markdown("**Results with XLM RoBERTa LM:**") - st.write('\n'.join(return_string_XLMRoBERTa)) - results[st.session_state['nice_name_xlmroberta']] = ["Good" if is_good else "Bad", str(round(score, 3)), "N/A" if not counter_example else str(counter_example[0]), "N/A" if not counter_example else str(round(counter_example[1], 3))] - - df = pd.DataFrame.from_dict(results, - orient = 'index', - columns=['Judgement', 'Score (log(p))', 'Neighbor sentence with highest score (log(p))', 'Neighbor sentence score (log(p))']) - st.markdown("**Tabular summary of results:**") - st.table(df) - - st.write("Input another sentence!") - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/spaces/oliver2023/chatgpt-on-wechat/plugins/godcmd/README.md b/spaces/oliver2023/chatgpt-on-wechat/plugins/godcmd/README.md deleted file mode 100644 index 00a4f8d59138894513b9f1deec47d44c9b3dd680..0000000000000000000000000000000000000000 --- a/spaces/oliver2023/chatgpt-on-wechat/plugins/godcmd/README.md +++ /dev/null @@ -1,12 +0,0 @@ -## 插件说明 - -指令插件 - -## 插件使用 - -将`config.json.template`复制为`config.json`，并修改其中`password`的值为口令。 - -在私聊中可使用`#auth`指令，输入口令进行管理员认证，详细指令请输入`#help`查看帮助文档： - -`#auth <口令>` - 管理员认证。 -`#help` - 输出帮助文档，是否是管理员和是否是在群聊中会影响帮助文档的输出内容。 diff --git a/spaces/oniati/mrt/README.md b/spaces/oniati/mrt/README.md deleted file mode 100644 index b0a208ccb0c63a67e5850786d8256e4086e0b03e..0000000000000000000000000000000000000000 --- a/spaces/oniati/mrt/README.md +++ /dev/null @@ -1,30 +0,0 @@ ---- -title: MT3 -emoji: 🦀 -colorFrom: red -colorTo: green -sdk: gradio -app_file: app.py -pinned: false ---- -# Configuration -`title`: _string_ -Display title for the Space -`emoji`: _string_ -Space emoji (emoji-only character allowed) -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) -`sdk`: _string_ -Can be either `gradio` or `streamlit` -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/onnx/BiDAF/README.md b/spaces/onnx/BiDAF/README.md deleted file mode 100644 index b201fff5a0aec600d18e3dc993fe2411dae06e0b..0000000000000000000000000000000000000000 --- a/spaces/onnx/BiDAF/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: BiDAF -emoji: 📚 -colorFrom: gray -colorTo: indigo -sdk: gradio -sdk_version: 2.8.11 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/openlamm/LAMM/model/openlamm.py b/spaces/openlamm/LAMM/model/openlamm.py deleted file mode 100644 index fba083f4bb0319cdda8b1a0e26392ac3821aa535..0000000000000000000000000000000000000000 --- a/spaces/openlamm/LAMM/model/openlamm.py +++ /dev/null @@ -1,511 +0,0 @@ -import io -import os - -import requests -import torch -import torch.nn as nn -import torch.nn.functional as F -# from petrel_client.client import Client -from PIL import Image, ImageFile -from torch.nn.utils import rnn -from types import SimpleNamespace -from peft import LoraConfig, TaskType, get_peft_model -from transformers import LlamaTokenizer, LlamaForCausalLM, LlamaConfig - -import numpy as np -# from header import * - -from transformers import StoppingCriteria, StoppingCriteriaList - -from .CLIP import load as load_clip -from .PROCESS import data -from .modeling_llama import LlamaForCausalLM -from .utils.pcl_utils import MEAN_COLOR_RGB, RandomCuboid, random_sampling -from .conversations import conversation_dict, default_conversation - -ImageFile.LOAD_TRUNCATED_IMAGES = True - -# sov: start of vision part; eov: end of vision part -VISION_TAGS = { - 'pos': {'image': '', 'pcl': ' '}, - 'sov': {'image': ' ', 'pcl': '
'}, - 'eov': {'image': '', 'pcl': ' '}, -} -ModalityType = SimpleNamespace( - VISION="vision", - TEXT="text", - AUDIO="audio", - THERMAL="thermal", - DEPTH="depth", - IMU="imu", -) - -class StoppingCriteriaSub(StoppingCriteria): - - def __init__(self, stops = [], encounters=1): - super().__init__() - self.stops = stops - self.ENCOUNTERS = encounters - - def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor): - stop_count = 0 - for stop in self.stops: - stop_count = (stop == input_ids[0]).sum().item() - if stop_count >= self.ENCOUNTERS: - return True - return False - - -class MyStoppingCriteria(StoppingCriteria): - def __init__(self, stops, input_ids): - super().__init__() - self.stops = [torch.tensor(stop).to('cuda:0') for stop in stops] - self.stop_flag = [0]*input_ids.shape[0] - - def check_stop(self, input_ids): - for stop in self.stops: - if torch.all((stop == input_ids[-len(stop):])).item(): - return True - return False - - def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: - flag = 1 - for id, output_id in enumerate(output_ids): - if self.stop_flag[id] == 1: - continue - if self.check_stop(output_id): - self.stop_flag[id] = 1 - else: - flag = 0 - if flag == 1: - return True - return False - - -def build_one_instance(tokenizer, conversation, vision_type='image'): - pos = VISION_TAGS['pos'][vision_type] - # sov = VISION_TAGS['sov'][vision_type] - eov = VISION_TAGS['eov'][vision_type] - - text_list = [] - turn_num = len(conversation) - input_ids, target_ids = [], [] - for i in range(turn_num): - turn = conversation[i] - role = turn['from'] - if i == 0: # the first human turn - assert role == 'human' - turn['value'] = turn['value'].replace(f'{pos}\n', '').replace(f'\n{pos}', '') - text = f'{eov} ' + turn['value'] + '\n### Assistant:' - one_input_id = tokenizer(text, add_special_tokens=False).input_ids - input_ids += one_input_id - target_ids += [-100]*len(one_input_id) # do not perform loss regression on human prompt - else: - if role == 'human': - text = 'Human: ' + turn['value'] + '\n### Assistant:' - one_input_id = tokenizer(text, add_special_tokens=False).input_ids - input_ids += one_input_id - target_ids += [-100]*len(one_input_id) - elif role == 'gpt': - text = turn['value'] + '\n###' - one_input_id = tokenizer(text, add_special_tokens=False).input_ids - input_ids += one_input_id - target_ids += one_input_id - else: - raise Exception('Wrong Role!!!') - text_list.append(text) - assert len(input_ids) == len(target_ids) - return text_list, input_ids, target_ids - - -def process_batch_instance(tokenizer, batch_of_conversations, max_tgt_len, vision_type='image'): - batch_input_ids, batch_target_ids = [], [] - for conversation in batch_of_conversations: - _, one_input_ids, one_target_ids = build_one_instance(tokenizer, conversation, vision_type=vision_type) - batch_input_ids.append(torch.LongTensor(one_input_ids)) - batch_target_ids.append(torch.LongTensor(one_target_ids)) - input_ids = rnn.pad_sequence(batch_input_ids, batch_first=True, padding_value=tokenizer.pad_token_id) - target_ids = rnn.pad_sequence(batch_target_ids, batch_first=True, padding_value=-100) - assert input_ids.size() == target_ids.size() - input_ids = input_ids[:,:max_tgt_len] - target_ids = target_ids[:,:max_tgt_len] - attention_mask = input_ids.ne(tokenizer.pad_token_id) - assert attention_mask.size() == input_ids.size() - return input_ids, target_ids, attention_mask.long() - - -def make_prompt_start(system_header=False, vision_type='image', task_type='normal'): - # TODO: choose prefix according to task type - PROMPT_START = f'### Human: {VISION_TAGS["sov"][vision_type]}' - if system_header: - if task_type == 'normal': - return f"{default_conversation.system}\n\n" + PROMPT_START - else: - return [f"{conversation_dict[task]}\n\n" + PROMPT_START for task in task_type] - else: - return PROMPT_START - - -class LAMMPEFTModel(nn.Module): - - '''LoRA for LLaMa model''' - - def __init__(self, **args): - super(LAMMPEFTModel, self).__init__() - self.args = args - # self.client = Client('~/petreloss.conf') - self.client = None - - self.vision_type = args['vision_type'] if 'vision_type' in args else 'image' - encoder_pretrain = args['encoder_pretrain'] if 'encoder_pretrain' in args else 'clip' - self.encoder_pretrain = encoder_pretrain - assert encoder_pretrain in ['imagebind', 'clip', 'epcl'], f'Encoder_pretrain: {encoder_pretrain} Not Implemented' - if not encoder_pretrain == 'clip' or os.path.isfile(args['encoder_ckpt_path']): - encoder_ckpt_path = args['encoder_ckpt_path'] - elif not os.path.isfile(args['encoder_ckpt_path']): - encoder_ckpt_path = 'ViT-L/14' - - vicuna_ckpt_path = args['vicuna_ckpt_path'] - - system_header = args['system_header'] if 'system_header' in args else False - stage = args['stage'] - - # TODO: checkout vision token number; for ImageBind = 1; Defaultly to use 1 global token for this - # -1 for last embedding; -2 for transformer output - self.vision_feature_type = args['vision_feature_type'] - self.num_vision_token = args['num_vision_token'] - - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - print (f'Initializing [{encoder_pretrain}] visual encoder from {encoder_ckpt_path} [{device}]...') - - # TODO: Make sure the number of vision tokens is correct - if args['encoder_pretrain'].lower() == 'clip': - clip_encoder, self.visual_preprocess = load_clip(encoder_ckpt_path, device=device) - self.visual_encoder = clip_encoder.visual - if self.vision_feature_type == 'global': # global feature from CLIP - self.vision_hidden_size = 768 - self.num_vision_token = 1 - assert self.num_vision_token == 1, 'Only 1 global token is available!' - elif self.vision_feature_type == 'local': # patch features from CLIP ViT - self.vision_hidden_size = 1024 - self.num_vision_token = min(self.num_vision_token, 256) # may cut partial tokens - - # freeze vision encoder - for name, param in self.visual_encoder.named_parameters(): - param.requires_grad = False - self.visual_encoder.eval() - print ('Visual encoder initialized.') - - print (f'Initializing language decoder from {vicuna_ckpt_path} ...') - # add the lora module - peft_config = LoraConfig( - task_type=TaskType.CAUSAL_LM, - inference_mode=False, - r=self.args['lora_r'], - lora_alpha=self.args['lora_alpha'], - lora_dropout=self.args['lora_dropout'], - target_modules=self.args['lora_target_modules'] - ) - - self.llama_model = LlamaForCausalLM.from_pretrained(vicuna_ckpt_path) - self.llama_model = get_peft_model(self.llama_model, peft_config) - self.llama_model.print_trainable_parameters() - - self.llama_tokenizer = LlamaTokenizer.from_pretrained(vicuna_ckpt_path, use_fast=False) - self.llama_tokenizer.pad_token = self.llama_tokenizer.eos_token - self.llama_tokenizer.padding_side = "right" - print ('Language decoder initialized.') - - self.llama_proj = nn.Linear( - self.vision_hidden_size, self.llama_model.config.hidden_size - ) - print ('LLaMa projection layer initialized.') - - self.max_tgt_len = args['max_tgt_len'] - self.system_header = system_header - self.device = torch.cuda.current_device() - - def encode_image(self, image_paths): - """encode images to llama inputs - - :param tupe image_paths: (bsz, ) - :return tensor, tensor: input feature to llama, attention mask to llama - """ - if self.encoder_pretrain == 'imagebind': - inputs = {ModalityType.VISION: data.load_and_transform_vision_data(image_paths, self.device)} - # convert into visual dtype - inputs = {key: inputs[key].to(self.llama_model.dtype) for key in inputs} - with torch.no_grad(): - embeddings = self.visual_encoder(inputs) - image_embeds = embeddings['vision'] # bsz x 1024 - inputs_llama = self.llama_proj(image_embeds).unsqueeze(1) # bsz x 1 x llama_size - atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(self.device) # bsz x 1 - return inputs_llama, atts_llama - elif self.encoder_pretrain == 'clip': - inputs = self.load_and_transform_vision_data_clip(image_paths, self.device) # bsz x 3 x 224 x 224 - inputs = inputs.to(self.llama_model.dtype) # clip requires torch.float32 - inputs_llama = self.clip_encode_image(inputs) - atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(self.device) # bsz x 1/256 - return inputs_llama, atts_llama - - def my_encode_image(self, images): - """encoder loaded image objects""" - if self.encoder_pretrain == 'clip': - inputs = data.transform_vision_data(images, self.device) # bsz x 3 x 224 x 224 - inputs_llama = self.clip_encode_image(inputs) # bsz x 1/256 x llama_size - atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(self.device) # bsz x 1/256 - return inputs_llama, atts_llama - else: - raise NotImplementedError("Encoder pretrain [{}] not implemented".format(self.encoder_pretrain)) - - def encode_pcl(self, pcl_paths): - # load pcl data - inputs = self.load_and_transform_pcl_data(pcl_paths, self.device) # bsz x 40000 x 3 - - inputs = inputs.to(self.llama_model.dtype) # clip requires torch.float32 - with torch.no_grad(): - if self.vision_feature_type == 'global': - raise NotImplementedError("Global feature not implemented for pcl") - elif self.vision_feature_type == 'local': - embeddings = self.visual_encoder(inputs)[1][:, :self.num_vision_token] # bsz x 256 x 1024; - image_embeds = embeddings.reshape(-1, self.vision_hidden_size).to(self.llama_model.dtype) # bsz*num vision token x 1024 - inputs_llama = self.llama_proj(image_embeds).reshape(-1, self.num_vision_token, self.llama_model.config.hidden_size) # bsz x num_vision_token x llama_size - atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(self.device) # bsz x 1/256 - return inputs_llama, atts_llama - - def clip_encode_image(self, inputs): - inputs = inputs.to(self.llama_model.dtype) # clip requires torch.float32 - with torch.no_grad(): - if self.vision_feature_type == 'global': - embeddings = self.visual_encoder(inputs) # bsz x 768 - image_embeds = embeddings.to(self.llama_model.dtype) - inputs_llama = self.llama_proj(image_embeds).unsqueeze(1) # bsz x 1 x llama_size - elif self.vision_feature_type == 'local': - embeddings = self.visual_encoder.forward_patch_features(inputs)[:, :self.num_vision_token] # bsz x self.num_vision_token x 1024 - image_embeds = embeddings.reshape(-1, self.vision_hidden_size).to(self.llama_model.dtype) # bsz*num vision token x 1024 - inputs_llama = self.llama_proj(image_embeds).reshape(-1, self.num_vision_token, self.llama_model.config.hidden_size) # bsz x num_vision_token x llama_size - else: - raise NotImplementedError("{} not Implemented".format(self.vision_feature_type)) - return inputs_llama - - def load_and_transform_vision_data_clip(self, image_paths, device): - if image_paths is None: - return None - image_ouputs = [] - for image_path in image_paths: - if os.path.exists(image_path): - image = Image.open(image_path) - elif image_path.startswith('s3://') and self.client is not None: - image = Image.open(io.BytesIO(self.client.get(image_path, update_cache=True))).convert("RGB") - elif image_path.startswith('http://'): - image = Image.open(requests.get(image_path, stream=True).raw) - else: - print("can not load image: ", image_path) - image_outpt = self.visual_preprocess(image).to(device) # 3 x 224 x 224 - image_ouputs.append(image_outpt) - return torch.stack(image_ouputs, dim=0) # B x 3 x 224 x 224 - - def load_and_transform_pcl_data(self, pcl_paths, device): - if pcl_paths is None: - return None - pcl_output = [] - for pcl_path in pcl_paths: - mesh_vertices = np.load(pcl_path) # 150000, 3 - if not self.use_color: - point_cloud = mesh_vertices[:, 0:3] # do not use color for now - else: - point_cloud = mesh_vertices[:, 0:6] - point_cloud[:, 3:] = (point_cloud[:, 3:] - MEAN_COLOR_RGB) / 256.0 - - if self.use_height: - floor_height = np.percentile(point_cloud[:, 2], 0.99) - height = point_cloud[:, 2] - floor_height - point_cloud = np.concatenate([point_cloud, np.expand_dims(height, 1)], 1) - - point_cloud, _ = random_sampling( - point_cloud, self.num_points, return_choices=True - ) - pcl_output.append(torch.from_numpy(point_cloud)) - return torch.stack(pcl_output, dim=0).to(device) # bsz x num_points x 3 - - def prompt_wrap(self, img_embeds, input_ids, target_ids, attention_mask, system_header, task_type): - ''' - input_ids, target_ids, attention_mask: bsz x s2 - ''' - input_ids = input_ids.to(self.device) # bsz x s2 - target_ids = target_ids.to(self.device) # bsz x s2 - attention_mask = attention_mask.to(self.device) # bsz x s2 - - batch_size = img_embeds.shape[0] - - # return list of headers if multiple tasks - p_before = make_prompt_start(system_header=system_header, vision_type=self.vision_type, task_type=task_type) - if isinstance(p_before, list): - p_before_tokens = [self.llama_tokenizer(p, - return_tensors="pt", add_special_tokens=False).input_ids[0].to(self.device) for p in p_before] - # TODO: fix bug here - p_before_token_ids = rnn.pad_sequence(p_before_tokens, batch_first=True, padding_value=self.llama_tokenizer.pad_token_id) # bsz x s1 - p_before_attn_mask = p_before_token_ids.ne(self.llama_tokenizer.pad_token_id) - else: - p_before_tokens = self.llama_tokenizer(p_before, - return_tensors="pt", add_special_tokens=False).to(self.device) # [s1, s1...] list of batch size - p_before_token_ids = p_before_tokens.input_ids.expand(batch_size, -1) # bsz x s1 - p_before_attn_mask = p_before_tokens.attention_mask.expand(batch_size, -1) # bsz x s1 - # peft model need deeper call - p_before_embeds = self.llama_model.model.model.embed_tokens(p_before_token_ids) #.expand(batch_size, -1, -1) # bsz x s1 x embed_dim - p_after_embeds = self.llama_model.model.model.embed_tokens(input_ids).expand(batch_size, -1, -1) # bsz x s2 x embed_dim - bos = torch.ones([batch_size, 1], - dtype=p_before_token_ids.dtype, - device=p_before_token_ids.device) * self.llama_tokenizer.bos_token_id # bsz x 1 - bos_embeds = self.llama_model.model.model.embed_tokens(bos) # bsz x 1 x embed_dim - inputs_embeds = torch.cat([bos_embeds, p_before_embeds, img_embeds, p_after_embeds], dim=1) # bsz x (1+s1+NumToken+s2) x embed_dim - - # make target ids for prefix part - empty_targets = ( - torch.ones([batch_size, 1 + p_before_embeds.size()[1] + self.num_vision_token], # 1 (bos) + s1 + num_image_tokens (image vector) - dtype=torch.long).to(self.device).fill_(-100) - ) # bsz x (1 + s1 + 1) - targets = torch.cat([empty_targets, target_ids], dim=1) # bsz x (1 + s1 + num_image_tokens + s2) - assert inputs_embeds.size()[1] == targets.size()[1] - - # atts_prefix = torch.ones([batch_size, 1 + p_before_embeds.size()[1] + self.num_vision_token], dtype=torch.long).to(self.device) # bsz x (1[bos] + s1 +num_image_tokens) - atts_bos = torch.ones([batch_size, 1], dtype=torch.long).to(self.device) # bsz x 1 - atts_img = torch.ones([batch_size, self.num_vision_token], dtype=torch.long).to(self.device) # bsz x num_image_tokens - attention_mask = torch.cat([atts_bos, p_before_attn_mask, atts_img, attention_mask], dim=1) - assert attention_mask.size() == targets.size() # bsz x (1 + s1 + num_image_tokens + s2) - return inputs_embeds, targets, attention_mask - - def forward(self, inputs): - """Model Forward in training - - :param class inputs: model itself - :raises ValueError: valueerror if not image or pcl - :return list: loss & token acc - """ - # image_paths = inputs['image_paths'] - assert self.vision_type == inputs['vision_type'] # single modal case - task_type = inputs['task_type'] - vision_paths = inputs['vision_paths'] - if self.vision_type == 'image': - vision_embeds, _ = self.encode_image(vision_paths) - elif self.vision_type == 'pcl': - vision_embeds, _ = self.encode_pcl(vision_paths) # Bsz x N token x C - else: - raise ValueError('vision type [{}] not supported'.format(self.vision_type)) - - output_texts = inputs['output_texts'] - input_ids, target_ids, attention_mask = process_batch_instance(self.llama_tokenizer, output_texts, self.max_tgt_len, self.vision_type) - inputs_embeds, targets, attention_mask = self.prompt_wrap(vision_embeds, input_ids, target_ids, attention_mask, self.system_header, task_type) - - outputs = self.llama_model( - inputs_embeds=inputs_embeds, - attention_mask=attention_mask, - return_dict=True, - labels=targets, - ) - loss = outputs.loss - # calculate the token accuarcy - chosen_tokens = torch.max(outputs.logits, dim=-1)[1][:, 1: -1] # [B, S-1] - labels = targets[:, 2:] - gen_acc = (chosen_tokens.reshape(-1) == labels.reshape(-1)).to(torch.long) # [B*S] - valid_mask = (labels != -100).reshape(-1) - valid_tokens = gen_acc & valid_mask # [B*S] - gen_acc = valid_tokens.sum().item() / valid_mask.sum().item() - return loss, gen_acc - - def extract_multimodal_feature(self, inputs): - """Extract multimodal features from the input in Generation (Test) - - :param Dict inputs: input dict; modality: path - :return _type_: _description_ - """ - features = [] - if inputs['image_paths']: - image_embeds, _ = self.encode_image(inputs['image_paths']) - features.append(image_embeds) - if 'images' in inputs and inputs['images']: # image objects input in testing - image_embeds, _ = self.my_encode_image(inputs['images']) - return image_embeds - # features.append(image_embeds) - if 'pcl_paths' in inputs and inputs['pcl_paths']: - pcl_embeds, _ = self.encode_pcl(inputs['pcl_paths']) - features.append(pcl_embeds) - # TODO: Cautions HERE! Multimodality allowed in test ONLY! - feature_embeds = torch.cat(features).sum(dim=0).unsqueeze(0) # sum all modality features together - return feature_embeds - - def prepare_generation_embedding(self, inputs): - """prepare for generation - - :param class inputs: model - :return Dict: generation input - """ - eov = VISION_TAGS['eov'][self.vision_type] - # TODO: add System header & image token size - prompt_list = inputs['prompt'] # questions from user - if len(inputs['modality_embeds']) == 1: - feature_embeds = inputs['modality_embeds'][0] - else: - feature_embeds = self.extract_multimodal_feature(inputs) - inputs['modality_embeds'].append(feature_embeds) - - batch_size = feature_embeds.shape[0] - p_before = make_prompt_start(vision_type=self.vision_type) # no system header in test - p_before_tokens = self.llama_tokenizer(p_before, - return_tensors="pt", add_special_tokens=False).to(self.device) - p_before_embeds = self.llama_model.model.model.embed_tokens(p_before_tokens.input_ids).expand(batch_size, -1, -1) # bsz x s1 x embed_dim - p_after_embeds_list = [] - p_after_tokens_list = [] - for prompt in prompt_list: - # text = ' ' + prompt + '\n### Assistant:' - text = f'{eov} ' + prompt + '\n### Assistant:' - p_after_tokens = self.llama_tokenizer(text, add_special_tokens=False, return_tensors='pt').to(self.device) - - p_after_tokens_list.append(p_after_tokens.input_ids.squeeze(0)) - - p_after_tokens = rnn.pad_sequence(p_after_tokens_list, batch_first=True, padding_value=self.llama_tokenizer.pad_token_id) - - p_after_embeds = self.llama_model.model.model.embed_tokens(p_after_tokens) - - # text = f'{eov} ' + prompt + '\n### Assistant:' - # p_after_tokens = self.llama_tokenizer(text, add_special_tokens=False, return_tensors='pt').to(self.device) - # p_after_embeds = self.llama_model.model.model.embed_tokens(p_after_tokens.input_ids).expand(batch_size, -1, -1) # bsz x s1 x embed_dim - bos = torch.ones([batch_size, 1], - dtype=p_before_tokens.input_ids.dtype, - device=p_before_tokens.input_ids.device) * self.llama_tokenizer.bos_token_id # bsz x 1 - bos_embeds = self.llama_model.model.model.embed_tokens(bos) # bsz x 1 x embed_dim - # print(bos_embeds.shape, p_before_embeds.shape, feature_embeds.shape, p_after_embeds.shape) - inputs_embeds = torch.cat([bos_embeds, p_before_embeds, feature_embeds, p_after_embeds], dim=1) # bsz x (1+s1+NumVisionToken+s2) x embed_dim - return inputs_embeds - - def generate(self, inputs): - ''' - inputs = { - 'image_paths': optional, - 'audio_paths': optional - 'video_paths': optional - 'thermal_paths': optional - 'mode': generation mode, - 'prompt': human input prompt, - 'max_tgt_len': generation length, - 'top_p': top_p, - 'temperature': temperature - 'modality_embeds': None or torch.tensor - 'modality_cache': save the image cache - } - ''' - input_embeds = self.prepare_generation_embedding(inputs) - # stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=[2277], encounters=1)]) - stopping_criteria = StoppingCriteriaList([MyStoppingCriteria([[2277]], input_embeds)]) - outputs = self.llama_model.generate( - inputs_embeds=input_embeds, - max_new_tokens=inputs['max_tgt_len'], - top_p=inputs['top_p'], - temperature=inputs['temperature'], - do_sample=True, - use_cache=True, - stopping_criteria=stopping_criteria, - ) - #output_text = self.llama_tokenizer.decode(outputs[0][:-2], skip_special_tokens=True) - output_text = self.llama_tokenizer.batch_decode(outputs, skip_special_tokens=True) - return output_text diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/custom_diffusion/train_custom_diffusion.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/custom_diffusion/train_custom_diffusion.py deleted file mode 100644 index 60d8d6723dcf579e12263f75b9a60374f778cee1..0000000000000000000000000000000000000000 --- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/custom_diffusion/train_custom_diffusion.py +++ /dev/null @@ -1,1338 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 -# Copyright 2023 Custom Diffusion authors and the HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and - -import argparse -import hashlib -import itertools -import json -import logging -import math -import os -import random -import shutil -import warnings -from pathlib import Path - -import numpy as np -import safetensors -import torch -import torch.nn.functional as F -import torch.utils.checkpoint -import transformers -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import ProjectConfiguration, set_seed -from huggingface_hub import HfApi, create_repo -from packaging import version -from PIL import Image -from torch.utils.data import Dataset -from torchvision import transforms -from tqdm.auto import tqdm -from transformers import AutoTokenizer, PretrainedConfig - -import diffusers -from diffusers import ( - AutoencoderKL, - DDPMScheduler, - DiffusionPipeline, - DPMSolverMultistepScheduler, - UNet2DConditionModel, -) -from diffusers.loaders import AttnProcsLayers -from diffusers.models.attention_processor import ( - CustomDiffusionAttnProcessor, - CustomDiffusionAttnProcessor2_0, - CustomDiffusionXFormersAttnProcessor, -) -from diffusers.optimization import get_scheduler -from diffusers.utils import check_min_version, is_wandb_available -from diffusers.utils.import_utils import is_xformers_available - - -# Will error if the minimal version of diffusers is not installed. Remove at your own risks. -check_min_version("0.22.0.dev0") - -logger = get_logger(__name__) - - -def freeze_params(params): - for param in params: - param.requires_grad = False - - -def save_model_card(repo_id: str, images=None, base_model=str, prompt=str, repo_folder=None): - img_str = "" - for i, image in enumerate(images): - image.save(os.path.join(repo_folder, f"image_{i}.png")) - img_str += f"\n" - - yaml = f""" ---- -license: creativeml-openrail-m -base_model: {base_model} -instance_prompt: {prompt} -tags: -- stable-diffusion -- stable-diffusion-diffusers -- text-to-image -- diffusers -- custom-diffusion -inference: true ---- - """ - model_card = f""" -# Custom Diffusion - {repo_id} - -These are Custom Diffusion adaption weights for {base_model}. The weights were trained on {prompt} using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. \n -{img_str} - -\nFor more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion). -""" - with open(os.path.join(repo_folder, "README.md"), "w") as f: - f.write(yaml + model_card) - - -def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str): - text_encoder_config = PretrainedConfig.from_pretrained( - pretrained_model_name_or_path, - subfolder="text_encoder", - revision=revision, - ) - model_class = text_encoder_config.architectures[0] - - if model_class == "CLIPTextModel": - from transformers import CLIPTextModel - - return CLIPTextModel - elif model_class == "RobertaSeriesModelWithTransformation": - from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation - - return RobertaSeriesModelWithTransformation - else: - raise ValueError(f"{model_class} is not supported.") - - -def collate_fn(examples, with_prior_preservation): - input_ids = [example["instance_prompt_ids"] for example in examples] - pixel_values = [example["instance_images"] for example in examples] - mask = [example["mask"] for example in examples] - # Concat class and instance examples for prior preservation. - # We do this to avoid doing two forward passes. - if with_prior_preservation: - input_ids += [example["class_prompt_ids"] for example in examples] - pixel_values += [example["class_images"] for example in examples] - mask += [example["class_mask"] for example in examples] - - input_ids = torch.cat(input_ids, dim=0) - pixel_values = torch.stack(pixel_values) - mask = torch.stack(mask) - pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() - mask = mask.to(memory_format=torch.contiguous_format).float() - - batch = {"input_ids": input_ids, "pixel_values": pixel_values, "mask": mask.unsqueeze(1)} - return batch - - -class PromptDataset(Dataset): - "A simple dataset to prepare the prompts to generate class images on multiple GPUs." - - def __init__(self, prompt, num_samples): - self.prompt = prompt - self.num_samples = num_samples - - def __len__(self): - return self.num_samples - - def __getitem__(self, index): - example = {} - example["prompt"] = self.prompt - example["index"] = index - return example - - -class CustomDiffusionDataset(Dataset): - """ - A dataset to prepare the instance and class images with the prompts for fine-tuning the model. - It pre-processes the images and the tokenizes prompts. - """ - - def __init__( - self, - concepts_list, - tokenizer, - size=512, - mask_size=64, - center_crop=False, - with_prior_preservation=False, - num_class_images=200, - hflip=False, - aug=True, - ): - self.size = size - self.mask_size = mask_size - self.center_crop = center_crop - self.tokenizer = tokenizer - self.interpolation = Image.BILINEAR - self.aug = aug - - self.instance_images_path = [] - self.class_images_path = [] - self.with_prior_preservation = with_prior_preservation - for concept in concepts_list: - inst_img_path = [ - (x, concept["instance_prompt"]) for x in Path(concept["instance_data_dir"]).iterdir() if x.is_file() - ] - self.instance_images_path.extend(inst_img_path) - - if with_prior_preservation: - class_data_root = Path(concept["class_data_dir"]) - if os.path.isdir(class_data_root): - class_images_path = list(class_data_root.iterdir()) - class_prompt = [concept["class_prompt"] for _ in range(len(class_images_path))] - else: - with open(class_data_root, "r") as f: - class_images_path = f.read().splitlines() - with open(concept["class_prompt"], "r") as f: - class_prompt = f.read().splitlines() - - class_img_path = [(x, y) for (x, y) in zip(class_images_path, class_prompt)] - self.class_images_path.extend(class_img_path[:num_class_images]) - - random.shuffle(self.instance_images_path) - self.num_instance_images = len(self.instance_images_path) - self.num_class_images = len(self.class_images_path) - self._length = max(self.num_class_images, self.num_instance_images) - self.flip = transforms.RandomHorizontalFlip(0.5 * hflip) - - self.image_transforms = transforms.Compose( - [ - self.flip, - transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), - transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), - transforms.ToTensor(), - transforms.Normalize([0.5], [0.5]), - ] - ) - - def __len__(self): - return self._length - - def preprocess(self, image, scale, resample): - outer, inner = self.size, scale - factor = self.size // self.mask_size - if scale > self.size: - outer, inner = scale, self.size - top, left = np.random.randint(0, outer - inner + 1), np.random.randint(0, outer - inner + 1) - image = image.resize((scale, scale), resample=resample) - image = np.array(image).astype(np.uint8) - image = (image / 127.5 - 1.0).astype(np.float32) - instance_image = np.zeros((self.size, self.size, 3), dtype=np.float32) - mask = np.zeros((self.size // factor, self.size // factor)) - if scale > self.size: - instance_image = image[top : top + inner, left : left + inner, :] - mask = np.ones((self.size // factor, self.size // factor)) - else: - instance_image[top : top + inner, left : left + inner, :] = image - mask[ - top // factor + 1 : (top + scale) // factor - 1, left // factor + 1 : (left + scale) // factor - 1 - ] = 1.0 - return instance_image, mask - - def __getitem__(self, index): - example = {} - instance_image, instance_prompt = self.instance_images_path[index % self.num_instance_images] - instance_image = Image.open(instance_image) - if not instance_image.mode == "RGB": - instance_image = instance_image.convert("RGB") - instance_image = self.flip(instance_image) - - # apply resize augmentation and create a valid image region mask - random_scale = self.size - if self.aug: - random_scale = ( - np.random.randint(self.size // 3, self.size + 1) - if np.random.uniform() < 0.66 - else np.random.randint(int(1.2 * self.size), int(1.4 * self.size)) - ) - instance_image, mask = self.preprocess(instance_image, random_scale, self.interpolation) - - if random_scale < 0.6 * self.size: - instance_prompt = np.random.choice(["a far away ", "very small "]) + instance_prompt - elif random_scale > self.size: - instance_prompt = np.random.choice(["zoomed in ", "close up "]) + instance_prompt - - example["instance_images"] = torch.from_numpy(instance_image).permute(2, 0, 1) - example["mask"] = torch.from_numpy(mask) - example["instance_prompt_ids"] = self.tokenizer( - instance_prompt, - truncation=True, - padding="max_length", - max_length=self.tokenizer.model_max_length, - return_tensors="pt", - ).input_ids - - if self.with_prior_preservation: - class_image, class_prompt = self.class_images_path[index % self.num_class_images] - class_image = Image.open(class_image) - if not class_image.mode == "RGB": - class_image = class_image.convert("RGB") - example["class_images"] = self.image_transforms(class_image) - example["class_mask"] = torch.ones_like(example["mask"]) - example["class_prompt_ids"] = self.tokenizer( - class_prompt, - truncation=True, - padding="max_length", - max_length=self.tokenizer.model_max_length, - return_tensors="pt", - ).input_ids - - return example - - -def save_new_embed(text_encoder, modifier_token_id, accelerator, args, output_dir, safe_serialization=True): - """Saves the new token embeddings from the text encoder.""" - logger.info("Saving embeddings") - learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight - for x, y in zip(modifier_token_id, args.modifier_token): - learned_embeds_dict = {} - learned_embeds_dict[y] = learned_embeds[x] - filename = f"{output_dir}/{y}.bin" - - if safe_serialization: - safetensors.torch.save_file(learned_embeds_dict, filename, metadata={"format": "pt"}) - else: - torch.save(learned_embeds_dict, filename) - - -def parse_args(input_args=None): - parser = argparse.ArgumentParser(description="Custom Diffusion training script.") - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default=None, - required=True, - help="Path to pretrained model or model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--revision", - type=str, - default=None, - required=False, - help="Revision of pretrained model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--tokenizer_name", - type=str, - default=None, - help="Pretrained tokenizer name or path if not the same as model_name", - ) - parser.add_argument( - "--instance_data_dir", - type=str, - default=None, - help="A folder containing the training data of instance images.", - ) - parser.add_argument( - "--class_data_dir", - type=str, - default=None, - help="A folder containing the training data of class images.", - ) - parser.add_argument( - "--instance_prompt", - type=str, - default=None, - help="The prompt with identifier specifying the instance", - ) - parser.add_argument( - "--class_prompt", - type=str, - default=None, - help="The prompt to specify images in the same class as provided instance images.", - ) - parser.add_argument( - "--validation_prompt", - type=str, - default=None, - help="A prompt that is used during validation to verify that the model is learning.", - ) - parser.add_argument( - "--num_validation_images", - type=int, - default=2, - help="Number of images that should be generated during validation with `validation_prompt`.", - ) - parser.add_argument( - "--validation_steps", - type=int, - default=50, - help=( - "Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt" - " `args.validation_prompt` multiple times: `args.num_validation_images`." - ), - ) - parser.add_argument( - "--with_prior_preservation", - default=False, - action="store_true", - help="Flag to add prior preservation loss.", - ) - parser.add_argument( - "--real_prior", - default=False, - action="store_true", - help="real images as prior.", - ) - parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.") - parser.add_argument( - "--num_class_images", - type=int, - default=200, - help=( - "Minimal class images for prior preservation loss. If there are not enough images already present in" - " class_data_dir, additional images will be sampled with class_prompt." - ), - ) - parser.add_argument( - "--output_dir", - type=str, - default="custom-diffusion-model", - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.") - parser.add_argument( - "--resolution", - type=int, - default=512, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - parser.add_argument( - "--center_crop", - default=False, - action="store_true", - help=( - "Whether to center crop the input images to the resolution. If not set, the images will be randomly" - " cropped. The images will be resized to the resolution first before cropping." - ), - ) - parser.add_argument( - "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader." - ) - parser.add_argument( - "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images." - ) - parser.add_argument("--num_train_epochs", type=int, default=1) - parser.add_argument( - "--max_train_steps", - type=int, - default=None, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - parser.add_argument( - "--checkpointing_steps", - type=int, - default=250, - help=( - "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final" - " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming" - " training using `--resume_from_checkpoint`." - ), - ) - parser.add_argument( - "--checkpoints_total_limit", - type=int, - default=None, - help=("Max number of checkpoints to store."), - ) - parser.add_argument( - "--resume_from_checkpoint", - type=str, - default=None, - help=( - "Whether training should be resumed from a previous checkpoint. Use a path saved by" - ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' - ), - ) - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - parser.add_argument( - "--gradient_checkpointing", - action="store_true", - help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", - ) - parser.add_argument( - "--learning_rate", - type=float, - default=1e-5, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument( - "--scale_lr", - action="store_true", - default=False, - help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", - ) - parser.add_argument( - "--dataloader_num_workers", - type=int, - default=2, - help=( - "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." - ), - ) - parser.add_argument( - "--freeze_model", - type=str, - default="crossattn_kv", - choices=["crossattn_kv", "crossattn"], - help="crossattn to enable fine-tuning of all params in the cross attention", - ) - parser.add_argument( - "--lr_scheduler", - type=str, - default="constant", - help=( - 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup"]' - ), - ) - parser.add_argument( - "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." - ) - parser.add_argument( - "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." - ) - parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") - parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") - parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") - parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") - parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") - parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") - parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") - parser.add_argument( - "--hub_model_id", - type=str, - default=None, - help="The name of the repository to keep in sync with the local `output_dir`.", - ) - parser.add_argument( - "--logging_dir", - type=str, - default="logs", - help=( - "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." - ), - ) - parser.add_argument( - "--allow_tf32", - action="store_true", - help=( - "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" - " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" - ), - ) - parser.add_argument( - "--report_to", - type=str, - default="tensorboard", - help=( - 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' - ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' - ), - ) - parser.add_argument( - "--mixed_precision", - type=str, - default=None, - choices=["no", "fp16", "bf16"], - help=( - "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" - " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" - " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." - ), - ) - parser.add_argument( - "--prior_generation_precision", - type=str, - default=None, - choices=["no", "fp32", "fp16", "bf16"], - help=( - "Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" - " 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32." - ), - ) - parser.add_argument( - "--concepts_list", - type=str, - default=None, - help="Path to json containing multiple concepts, will overwrite parameters like instance_prompt, class_prompt, etc.", - ) - parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") - parser.add_argument( - "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." - ) - parser.add_argument( - "--set_grads_to_none", - action="store_true", - help=( - "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain" - " behaviors, so disable this argument if it causes any problems. More info:" - " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html" - ), - ) - parser.add_argument( - "--modifier_token", - type=str, - default=None, - help="A token to use as a modifier for the concept.", - ) - parser.add_argument( - "--initializer_token", type=str, default="ktn+pll+ucd", help="A token to use as initializer word." - ) - parser.add_argument("--hflip", action="store_true", help="Apply horizontal flip data augmentation.") - parser.add_argument( - "--noaug", - action="store_true", - help="Dont apply augmentation during data augmentation when this flag is enabled.", - ) - parser.add_argument( - "--no_safe_serialization", - action="store_true", - help="If specified save the checkpoint not in `safetensors` format, but in original PyTorch format instead.", - ) - - if input_args is not None: - args = parser.parse_args(input_args) - else: - args = parser.parse_args() - - env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) - if env_local_rank != -1 and env_local_rank != args.local_rank: - args.local_rank = env_local_rank - - if args.with_prior_preservation: - if args.concepts_list is None: - if args.class_data_dir is None: - raise ValueError("You must specify a data directory for class images.") - if args.class_prompt is None: - raise ValueError("You must specify prompt for class images.") - else: - # logger is not available yet - if args.class_data_dir is not None: - warnings.warn("You need not use --class_data_dir without --with_prior_preservation.") - if args.class_prompt is not None: - warnings.warn("You need not use --class_prompt without --with_prior_preservation.") - - return args - - -def main(args): - logging_dir = Path(args.output_dir, args.logging_dir) - - accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) - - accelerator = Accelerator( - gradient_accumulation_steps=args.gradient_accumulation_steps, - mixed_precision=args.mixed_precision, - log_with=args.report_to, - project_config=accelerator_project_config, - ) - - if args.report_to == "wandb": - if not is_wandb_available(): - raise ImportError("Make sure to install wandb if you want to use it for logging during training.") - import wandb - - # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate - # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models. - # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate. - # Make one log on every process with the configuration for debugging. - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO, - ) - logger.info(accelerator.state, main_process_only=False) - if accelerator.is_local_main_process: - transformers.utils.logging.set_verbosity_warning() - diffusers.utils.logging.set_verbosity_info() - else: - transformers.utils.logging.set_verbosity_error() - diffusers.utils.logging.set_verbosity_error() - - # We need to initialize the trackers we use, and also store our configuration. - # The trackers initializes automatically on the main process. - if accelerator.is_main_process: - accelerator.init_trackers("custom-diffusion", config=vars(args)) - - # If passed along, set the training seed now. - if args.seed is not None: - set_seed(args.seed) - if args.concepts_list is None: - args.concepts_list = [ - { - "instance_prompt": args.instance_prompt, - "class_prompt": args.class_prompt, - "instance_data_dir": args.instance_data_dir, - "class_data_dir": args.class_data_dir, - } - ] - else: - with open(args.concepts_list, "r") as f: - args.concepts_list = json.load(f) - - # Generate class images if prior preservation is enabled. - if args.with_prior_preservation: - for i, concept in enumerate(args.concepts_list): - class_images_dir = Path(concept["class_data_dir"]) - if not class_images_dir.exists(): - class_images_dir.mkdir(parents=True, exist_ok=True) - if args.real_prior: - assert ( - class_images_dir / "images" - ).exists(), f"Please run: python retrieve.py --class_prompt \"{concept['class_prompt']}\" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}" - assert ( - len(list((class_images_dir / "images").iterdir())) == args.num_class_images - ), f"Please run: python retrieve.py --class_prompt \"{concept['class_prompt']}\" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}" - assert ( - class_images_dir / "caption.txt" - ).exists(), f"Please run: python retrieve.py --class_prompt \"{concept['class_prompt']}\" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}" - assert ( - class_images_dir / "images.txt" - ).exists(), f"Please run: python retrieve.py --class_prompt \"{concept['class_prompt']}\" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}" - concept["class_prompt"] = os.path.join(class_images_dir, "caption.txt") - concept["class_data_dir"] = os.path.join(class_images_dir, "images.txt") - args.concepts_list[i] = concept - accelerator.wait_for_everyone() - else: - cur_class_images = len(list(class_images_dir.iterdir())) - - if cur_class_images < args.num_class_images: - torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32 - if args.prior_generation_precision == "fp32": - torch_dtype = torch.float32 - elif args.prior_generation_precision == "fp16": - torch_dtype = torch.float16 - elif args.prior_generation_precision == "bf16": - torch_dtype = torch.bfloat16 - pipeline = DiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - torch_dtype=torch_dtype, - safety_checker=None, - revision=args.revision, - ) - pipeline.set_progress_bar_config(disable=True) - - num_new_images = args.num_class_images - cur_class_images - logger.info(f"Number of class images to sample: {num_new_images}.") - - sample_dataset = PromptDataset(args.class_prompt, num_new_images) - sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size) - - sample_dataloader = accelerator.prepare(sample_dataloader) - pipeline.to(accelerator.device) - - for example in tqdm( - sample_dataloader, - desc="Generating class images", - disable=not accelerator.is_local_main_process, - ): - images = pipeline(example["prompt"]).images - - for i, image in enumerate(images): - hash_image = hashlib.sha1(image.tobytes()).hexdigest() - image_filename = ( - class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" - ) - image.save(image_filename) - - del pipeline - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - # Handle the repository creation - if accelerator.is_main_process: - if args.output_dir is not None: - os.makedirs(args.output_dir, exist_ok=True) - - if args.push_to_hub: - repo_id = create_repo( - repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token - ).repo_id - - # Load the tokenizer - if args.tokenizer_name: - tokenizer = AutoTokenizer.from_pretrained( - args.tokenizer_name, - revision=args.revision, - use_fast=False, - ) - elif args.pretrained_model_name_or_path: - tokenizer = AutoTokenizer.from_pretrained( - args.pretrained_model_name_or_path, - subfolder="tokenizer", - revision=args.revision, - use_fast=False, - ) - - # import correct text encoder class - text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision) - - # Load scheduler and models - noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") - text_encoder = text_encoder_cls.from_pretrained( - args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision - ) - vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision) - unet = UNet2DConditionModel.from_pretrained( - args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision - ) - - # Adding a modifier token which is optimized #### - # Code taken from https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py - modifier_token_id = [] - initializer_token_id = [] - if args.modifier_token is not None: - args.modifier_token = args.modifier_token.split("+") - args.initializer_token = args.initializer_token.split("+") - if len(args.modifier_token) > len(args.initializer_token): - raise ValueError("You must specify + separated initializer token for each modifier token.") - for modifier_token, initializer_token in zip( - args.modifier_token, args.initializer_token[: len(args.modifier_token)] - ): - # Add the placeholder token in tokenizer - num_added_tokens = tokenizer.add_tokens(modifier_token) - if num_added_tokens == 0: - raise ValueError( - f"The tokenizer already contains the token {modifier_token}. Please pass a different" - " `modifier_token` that is not already in the tokenizer." - ) - - # Convert the initializer_token, placeholder_token to ids - token_ids = tokenizer.encode([initializer_token], add_special_tokens=False) - print(token_ids) - # Check if initializer_token is a single token or a sequence of tokens - if len(token_ids) > 1: - raise ValueError("The initializer token must be a single token.") - - initializer_token_id.append(token_ids[0]) - modifier_token_id.append(tokenizer.convert_tokens_to_ids(modifier_token)) - - # Resize the token embeddings as we are adding new special tokens to the tokenizer - text_encoder.resize_token_embeddings(len(tokenizer)) - - # Initialise the newly added placeholder token with the embeddings of the initializer token - token_embeds = text_encoder.get_input_embeddings().weight.data - for x, y in zip(modifier_token_id, initializer_token_id): - token_embeds[x] = token_embeds[y] - - # Freeze all parameters except for the token embeddings in text encoder - params_to_freeze = itertools.chain( - text_encoder.text_model.encoder.parameters(), - text_encoder.text_model.final_layer_norm.parameters(), - text_encoder.text_model.embeddings.position_embedding.parameters(), - ) - freeze_params(params_to_freeze) - ######################################################## - ######################################################## - - vae.requires_grad_(False) - if args.modifier_token is None: - text_encoder.requires_grad_(False) - unet.requires_grad_(False) - # For mixed precision training we cast the text_encoder and vae weights to half-precision - # as these models are only used for inference, keeping weights in full precision is not required. - weight_dtype = torch.float32 - if accelerator.mixed_precision == "fp16": - weight_dtype = torch.float16 - elif accelerator.mixed_precision == "bf16": - weight_dtype = torch.bfloat16 - - # Move unet, vae and text_encoder to device and cast to weight_dtype - if accelerator.mixed_precision != "fp16" and args.modifier_token is not None: - text_encoder.to(accelerator.device, dtype=weight_dtype) - unet.to(accelerator.device, dtype=weight_dtype) - vae.to(accelerator.device, dtype=weight_dtype) - - attention_class = ( - CustomDiffusionAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else CustomDiffusionAttnProcessor - ) - if args.enable_xformers_memory_efficient_attention: - if is_xformers_available(): - import xformers - - xformers_version = version.parse(xformers.__version__) - if xformers_version == version.parse("0.0.16"): - logger.warn( - "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." - ) - attention_class = CustomDiffusionXFormersAttnProcessor - else: - raise ValueError("xformers is not available. Make sure it is installed correctly") - - # now we will add new Custom Diffusion weights to the attention layers - # It's important to realize here how many attention weights will be added and of which sizes - # The sizes of the attention layers consist only of two different variables: - # 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`. - # 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`. - - # Let's first see how many attention processors we will have to set. - # For Stable Diffusion, it should be equal to: - # - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12 - # - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2 - # - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18 - # => 32 layers - - # Only train key, value projection layers if freeze_model = 'crossattn_kv' else train all params in the cross attention layer - train_kv = True - train_q_out = False if args.freeze_model == "crossattn_kv" else True - custom_diffusion_attn_procs = {} - - st = unet.state_dict() - for name, _ in unet.attn_processors.items(): - cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim - if name.startswith("mid_block"): - hidden_size = unet.config.block_out_channels[-1] - elif name.startswith("up_blocks"): - block_id = int(name[len("up_blocks.")]) - hidden_size = list(reversed(unet.config.block_out_channels))[block_id] - elif name.startswith("down_blocks"): - block_id = int(name[len("down_blocks.")]) - hidden_size = unet.config.block_out_channels[block_id] - layer_name = name.split(".processor")[0] - weights = { - "to_k_custom_diffusion.weight": st[layer_name + ".to_k.weight"], - "to_v_custom_diffusion.weight": st[layer_name + ".to_v.weight"], - } - if train_q_out: - weights["to_q_custom_diffusion.weight"] = st[layer_name + ".to_q.weight"] - weights["to_out_custom_diffusion.0.weight"] = st[layer_name + ".to_out.0.weight"] - weights["to_out_custom_diffusion.0.bias"] = st[layer_name + ".to_out.0.bias"] - if cross_attention_dim is not None: - custom_diffusion_attn_procs[name] = attention_class( - train_kv=train_kv, - train_q_out=train_q_out, - hidden_size=hidden_size, - cross_attention_dim=cross_attention_dim, - ).to(unet.device) - custom_diffusion_attn_procs[name].load_state_dict(weights) - else: - custom_diffusion_attn_procs[name] = attention_class( - train_kv=False, - train_q_out=False, - hidden_size=hidden_size, - cross_attention_dim=cross_attention_dim, - ) - del st - unet.set_attn_processor(custom_diffusion_attn_procs) - custom_diffusion_layers = AttnProcsLayers(unet.attn_processors) - - accelerator.register_for_checkpointing(custom_diffusion_layers) - - if args.gradient_checkpointing: - unet.enable_gradient_checkpointing() - if args.modifier_token is not None: - text_encoder.gradient_checkpointing_enable() - # Enable TF32 for faster training on Ampere GPUs, - # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices - if args.allow_tf32: - torch.backends.cuda.matmul.allow_tf32 = True - - if args.scale_lr: - args.learning_rate = ( - args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes - ) - if args.with_prior_preservation: - args.learning_rate = args.learning_rate * 2.0 - - # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs - if args.use_8bit_adam: - try: - import bitsandbytes as bnb - except ImportError: - raise ImportError( - "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`." - ) - - optimizer_class = bnb.optim.AdamW8bit - else: - optimizer_class = torch.optim.AdamW - - # Optimizer creation - optimizer = optimizer_class( - itertools.chain(text_encoder.get_input_embeddings().parameters(), custom_diffusion_layers.parameters()) - if args.modifier_token is not None - else custom_diffusion_layers.parameters(), - lr=args.learning_rate, - betas=(args.adam_beta1, args.adam_beta2), - weight_decay=args.adam_weight_decay, - eps=args.adam_epsilon, - ) - - # Dataset and DataLoaders creation: - train_dataset = CustomDiffusionDataset( - concepts_list=args.concepts_list, - tokenizer=tokenizer, - with_prior_preservation=args.with_prior_preservation, - size=args.resolution, - mask_size=vae.encode( - torch.randn(1, 3, args.resolution, args.resolution).to(dtype=weight_dtype).to(accelerator.device) - ) - .latent_dist.sample() - .size()[-1], - center_crop=args.center_crop, - num_class_images=args.num_class_images, - hflip=args.hflip, - aug=not args.noaug, - ) - - train_dataloader = torch.utils.data.DataLoader( - train_dataset, - batch_size=args.train_batch_size, - shuffle=True, - collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation), - num_workers=args.dataloader_num_workers, - ) - - # Scheduler and math around the number of training steps. - overrode_max_train_steps = False - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if args.max_train_steps is None: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - overrode_max_train_steps = True - - lr_scheduler = get_scheduler( - args.lr_scheduler, - optimizer=optimizer, - num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes, - num_training_steps=args.max_train_steps * accelerator.num_processes, - ) - - # Prepare everything with our `accelerator`. - if args.modifier_token is not None: - custom_diffusion_layers, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - custom_diffusion_layers, text_encoder, optimizer, train_dataloader, lr_scheduler - ) - else: - custom_diffusion_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - custom_diffusion_layers, optimizer, train_dataloader, lr_scheduler - ) - - # We need to recalculate our total training steps as the size of the training dataloader may have changed. - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if overrode_max_train_steps: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - # Afterwards we recalculate our number of training epochs - args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - - # Train! - total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps - - logger.info("***** Running training *****") - logger.info(f" Num examples = {len(train_dataset)}") - logger.info(f" Num batches each epoch = {len(train_dataloader)}") - logger.info(f" Num Epochs = {args.num_train_epochs}") - logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") - logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") - logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") - logger.info(f" Total optimization steps = {args.max_train_steps}") - global_step = 0 - first_epoch = 0 - - # Potentially load in the weights and states from a previous save - if args.resume_from_checkpoint: - if args.resume_from_checkpoint != "latest": - path = os.path.basename(args.resume_from_checkpoint) - else: - # Get the most recent checkpoint - dirs = os.listdir(args.output_dir) - dirs = [d for d in dirs if d.startswith("checkpoint")] - dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) - path = dirs[-1] if len(dirs) > 0 else None - - if path is None: - accelerator.print( - f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run." - ) - args.resume_from_checkpoint = None - else: - accelerator.print(f"Resuming from checkpoint {path}") - accelerator.load_state(os.path.join(args.output_dir, path)) - global_step = int(path.split("-")[1]) - - resume_global_step = global_step * args.gradient_accumulation_steps - first_epoch = global_step // num_update_steps_per_epoch - resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps) - - # Only show the progress bar once on each machine. - progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process) - progress_bar.set_description("Steps") - - for epoch in range(first_epoch, args.num_train_epochs): - unet.train() - if args.modifier_token is not None: - text_encoder.train() - for step, batch in enumerate(train_dataloader): - # Skip steps until we reach the resumed step - if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step: - if step % args.gradient_accumulation_steps == 0: - progress_bar.update(1) - continue - - with accelerator.accumulate(unet), accelerator.accumulate(text_encoder): - # Convert images to latent space - latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample() - latents = latents * vae.config.scaling_factor - - # Sample noise that we'll add to the latents - noise = torch.randn_like(latents) - bsz = latents.shape[0] - # Sample a random timestep for each image - timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) - timesteps = timesteps.long() - - # Add noise to the latents according to the noise magnitude at each timestep - # (this is the forward diffusion process) - noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - - # Get the text embedding for conditioning - encoder_hidden_states = text_encoder(batch["input_ids"])[0] - - # Predict the noise residual - model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - - # Get the target for loss depending on the prediction type - if noise_scheduler.config.prediction_type == "epsilon": - target = noise - elif noise_scheduler.config.prediction_type == "v_prediction": - target = noise_scheduler.get_velocity(latents, noise, timesteps) - else: - raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") - - if args.with_prior_preservation: - # Chunk the noise and model_pred into two parts and compute the loss on each part separately. - model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) - target, target_prior = torch.chunk(target, 2, dim=0) - mask = torch.chunk(batch["mask"], 2, dim=0)[0] - # Compute instance loss - loss = F.mse_loss(model_pred.float(), target.float(), reduction="none") - loss = ((loss * mask).sum([1, 2, 3]) / mask.sum([1, 2, 3])).mean() - - # Compute prior loss - prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") - - # Add the prior loss to the instance loss. - loss = loss + args.prior_loss_weight * prior_loss - else: - mask = batch["mask"] - loss = F.mse_loss(model_pred.float(), target.float(), reduction="none") - loss = ((loss * mask).sum([1, 2, 3]) / mask.sum([1, 2, 3])).mean() - accelerator.backward(loss) - # Zero out the gradients for all token embeddings except the newly added - # embeddings for the concept, as we only want to optimize the concept embeddings - if args.modifier_token is not None: - if accelerator.num_processes > 1: - grads_text_encoder = text_encoder.module.get_input_embeddings().weight.grad - else: - grads_text_encoder = text_encoder.get_input_embeddings().weight.grad - # Get the index for tokens that we want to zero the grads for - index_grads_to_zero = torch.arange(len(tokenizer)) != modifier_token_id[0] - for i in range(len(modifier_token_id[1:])): - index_grads_to_zero = index_grads_to_zero & ( - torch.arange(len(tokenizer)) != modifier_token_id[i] - ) - grads_text_encoder.data[index_grads_to_zero, :] = grads_text_encoder.data[ - index_grads_to_zero, : - ].fill_(0) - - if accelerator.sync_gradients: - params_to_clip = ( - itertools.chain(text_encoder.parameters(), custom_diffusion_layers.parameters()) - if args.modifier_token is not None - else custom_diffusion_layers.parameters() - ) - accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) - optimizer.step() - lr_scheduler.step() - optimizer.zero_grad(set_to_none=args.set_grads_to_none) - - # Checks if the accelerator has performed an optimization step behind the scenes - if accelerator.sync_gradients: - progress_bar.update(1) - global_step += 1 - - if global_step % args.checkpointing_steps == 0: - if accelerator.is_main_process: - # _before_ saving state, check if this save would set us over the `checkpoints_total_limit` - if args.checkpoints_total_limit is not None: - checkpoints = os.listdir(args.output_dir) - checkpoints = [d for d in checkpoints if d.startswith("checkpoint")] - checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1])) - - # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints - if len(checkpoints) >= args.checkpoints_total_limit: - num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1 - removing_checkpoints = checkpoints[0:num_to_remove] - - logger.info( - f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints" - ) - logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}") - - for removing_checkpoint in removing_checkpoints: - removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint) - shutil.rmtree(removing_checkpoint) - - save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") - accelerator.save_state(save_path) - logger.info(f"Saved state to {save_path}") - - logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} - progress_bar.set_postfix(**logs) - accelerator.log(logs, step=global_step) - - if global_step >= args.max_train_steps: - break - - if accelerator.is_main_process: - images = [] - - if args.validation_prompt is not None and global_step % args.validation_steps == 0: - logger.info( - f"Running validation... \n Generating {args.num_validation_images} images with prompt:" - f" {args.validation_prompt}." - ) - # create pipeline - pipeline = DiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - unet=accelerator.unwrap_model(unet), - text_encoder=accelerator.unwrap_model(text_encoder), - tokenizer=tokenizer, - revision=args.revision, - torch_dtype=weight_dtype, - ) - pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config) - pipeline = pipeline.to(accelerator.device) - pipeline.set_progress_bar_config(disable=True) - - # run inference - generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) - images = [ - pipeline(args.validation_prompt, num_inference_steps=25, generator=generator, eta=1.0).images[0] - for _ in range(args.num_validation_images) - ] - - for tracker in accelerator.trackers: - if tracker.name == "tensorboard": - np_images = np.stack([np.asarray(img) for img in images]) - tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC") - if tracker.name == "wandb": - tracker.log( - { - "validation": [ - wandb.Image(image, caption=f"{i}: {args.validation_prompt}") - for i, image in enumerate(images) - ] - } - ) - - del pipeline - torch.cuda.empty_cache() - - # Save the custom diffusion layers - accelerator.wait_for_everyone() - if accelerator.is_main_process: - unet = unet.to(torch.float32) - unet.save_attn_procs(args.output_dir, safe_serialization=not args.no_safe_serialization) - save_new_embed( - text_encoder, - modifier_token_id, - accelerator, - args, - args.output_dir, - safe_serialization=not args.no_safe_serialization, - ) - - # Final inference - # Load previous pipeline - pipeline = DiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype - ) - pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config) - pipeline = pipeline.to(accelerator.device) - - # load attention processors - weight_name = ( - "pytorch_custom_diffusion_weights.safetensors" - if not args.no_safe_serialization - else "pytorch_custom_diffusion_weights.bin" - ) - pipeline.unet.load_attn_procs(args.output_dir, weight_name=weight_name) - for token in args.modifier_token: - token_weight_name = f"{token}.safetensors" if not args.no_safe_serialization else f"{token}.bin" - pipeline.load_textual_inversion(args.output_dir, weight_name=token_weight_name) - - # run inference - if args.validation_prompt and args.num_validation_images > 0: - generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None - images = [ - pipeline(args.validation_prompt, num_inference_steps=25, generator=generator, eta=1.0).images[0] - for _ in range(args.num_validation_images) - ] - - for tracker in accelerator.trackers: - if tracker.name == "tensorboard": - np_images = np.stack([np.asarray(img) for img in images]) - tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC") - if tracker.name == "wandb": - tracker.log( - { - "test": [ - wandb.Image(image, caption=f"{i}: {args.validation_prompt}") - for i, image in enumerate(images) - ] - } - ) - - if args.push_to_hub: - save_model_card( - repo_id, - images=images, - base_model=args.pretrained_model_name_or_path, - prompt=args.instance_prompt, - repo_folder=args.output_dir, - ) - api = HfApi(token=args.hub_token) - api.upload_folder( - repo_id=repo_id, - folder_path=args.output_dir, - commit_message="End of training", - ignore_patterns=["step_*", "epoch_*"], - ) - - accelerator.end_training() - - -if __name__ == "__main__": - args = parse_args() - main(args) diff --git a/spaces/patgpt4/MusicGen/audiocraft/quantization/base.py b/spaces/patgpt4/MusicGen/audiocraft/quantization/base.py deleted file mode 100644 index 1b16c130d266fbd021d3fc29bb9f98c33dd3c588..0000000000000000000000000000000000000000 --- a/spaces/patgpt4/MusicGen/audiocraft/quantization/base.py +++ /dev/null @@ -1,107 +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. - -""" -Base class for all quantizers. -""" - -from dataclasses import dataclass, field -import typing as tp - -import torch -from torch import nn - - -@dataclass -class QuantizedResult: - x: torch.Tensor - codes: torch.Tensor - bandwidth: torch.Tensor # bandwidth in kb/s used, per batch item. - penalty: tp.Optional[torch.Tensor] = None - metrics: dict = field(default_factory=dict) - - -class BaseQuantizer(nn.Module): - """Base class for quantizers. - """ - - def forward(self, x: torch.Tensor, frame_rate: int) -> QuantizedResult: - """ - Given input tensor x, returns first the quantized (or approximately quantized) - representation along with quantized codes, bandwidth, and any penalty term for the loss. - Finally, this returns a dict of metrics to update logging etc. - Frame rate must be passed so that the bandwidth is properly computed. - """ - raise NotImplementedError() - - def encode(self, x: torch.Tensor) -> torch.Tensor: - """Encode a given input tensor with the specified sample rate at the given bandwidth. - """ - raise NotImplementedError() - - def decode(self, codes: torch.Tensor) -> torch.Tensor: - """Decode the given codes to the quantized representation. - """ - raise NotImplementedError() - - @property - def total_codebooks(self): - """Total number of codebooks. - """ - raise NotImplementedError() - - @property - def num_codebooks(self): - """Number of active codebooks. - """ - raise NotImplementedError() - - def set_num_codebooks(self, n: int): - """Set the number of active codebooks. - """ - raise NotImplementedError() - - -class DummyQuantizer(BaseQuantizer): - """Fake quantizer that actually does not perform any quantization. - """ - def __init__(self): - super().__init__() - - def forward(self, x: torch.Tensor, frame_rate: int): - q = x.unsqueeze(1) - return QuantizedResult(x, q, torch.tensor(q.numel() * 32 * frame_rate / 1000 / len(x)).to(x)) - - def encode(self, x: torch.Tensor) -> torch.Tensor: - """Encode a given input tensor with the specified sample rate at the given bandwidth. - In the case of the DummyQuantizer, the codes are actually identical - to the input and resulting quantized representation as no quantization is done. - """ - return x.unsqueeze(1) - - def decode(self, codes: torch.Tensor) -> torch.Tensor: - """Decode the given codes to the quantized representation. - In the case of the DummyQuantizer, the codes are actually identical - to the input and resulting quantized representation as no quantization is done. - """ - return codes.squeeze(1) - - @property - def total_codebooks(self): - """Total number of codebooks. - """ - return 1 - - @property - def num_codebooks(self): - """Total number of codebooks. - """ - return self.total_codebooks - - def set_num_codebooks(self, n: int): - """Set the number of active codebooks. - """ - raise AttributeError("Cannot override the number of codebooks for the dummy quantizer") diff --git a/spaces/paulokewunmi/omowe.ai/README.md b/spaces/paulokewunmi/omowe.ai/README.md deleted file mode 100644 index 5784ead4025120f70e89df49c03fd977df9b270f..0000000000000000000000000000000000000000 --- a/spaces/paulokewunmi/omowe.ai/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Omowe.ai -emoji: 🏢 -colorFrom: blue -colorTo: green -sdk: gradio -sdk_version: 3.28.2 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/pcuenq/paella/app.py b/spaces/pcuenq/paella/app.py deleted file mode 100644 index 76e8c6710e31d7750b0a5f0e93c6aa06648f5e6e..0000000000000000000000000000000000000000 --- a/spaces/pcuenq/paella/app.py +++ /dev/null @@ -1,460 +0,0 @@ -import gradio as gr -import torch -import open_clip -import torchvision -from huggingface_hub import hf_hub_download -from PIL import Image -from open_clip import tokenizer -from Paella.utils.modules import Paella -from arroz import Diffuzz, PriorModel -from transformers import AutoTokenizer, T5EncoderModel -from Paella.src.vqgan import VQModel -from Paella.utils.alter_attention import replace_attention_layers - -model_repo = "dome272/Paella" -model_file = "paella_v3.pt" -prior_file = "prior_v1.pt" -vqgan_file = "vqgan_f4.pt" - -device = "cuda" if torch.cuda.is_available() else "cpu" - -batch_size = 4 -latent_shape = (batch_size, 64, 64) # latent shape of the generated image, we are using an f4 vqgan and thus sampling 64x64 will result in 256x256 -prior_timesteps, prior_cfg, prior_sampler, clip_embedding_shape = 60, 3.0, "ddpm", (batch_size, 1024) - -generator_timesteps = 12 -generator_cfg = 5 -prior_timesteps = 60 -prior_cfg = 3.0 -prior_sampler = 'ddpm' -clip_embedding_shape = (batch_size, 1024) - - -def to_pil(images): - images = images.permute(0, 2, 3, 1).cpu().numpy() - images = (images * 255).round().astype("uint8") - images = [Image.fromarray(image) for image in images] - return images - -def log(t, eps=1e-20): - return torch.log(t + eps) - -def gumbel_noise(t): - noise = torch.zeros_like(t).uniform_(0, 1) - return -log(-log(noise)) - -def gumbel_sample(t, temperature=1., dim=-1): - return ((t / max(temperature, 1e-10)) + gumbel_noise(t)).argmax(dim=dim) - -def sample(model, c, x=None, negative_embeddings=None, mask=None, T=12, size=(32, 32), starting_t=0, temp_range=[1.0, 1.0], typical_filtering=True, typical_mass=0.2, typical_min_tokens=1, classifier_free_scale=-1, renoise_steps=11, renoise_mode='start'): - with torch.inference_mode(): - r_range = torch.linspace(0, 1, T+1)[:-1][:, None].expand(-1, c.size(0)).to(c.device) - temperatures = torch.linspace(temp_range[0], temp_range[1], T) - preds = [] - if x is None: - x = torch.randint(0, model.num_labels, size=(c.size(0), *size), device=c.device) - elif mask is not None: - noise = torch.randint(0, model.num_labels, size=(c.size(0), *size), device=c.device) - x = noise * mask + (1-mask) * x - init_x = x.clone() - for i in range(starting_t, T): - if renoise_mode == 'prev': - prev_x = x.clone() - r, temp = r_range[i], temperatures[i] - logits = model(x, c, r) - if classifier_free_scale >= 0: - if negative_embeddings is not None: - logits_uncond = model(x, negative_embeddings, r) - else: - logits_uncond = model(x, torch.zeros_like(c), r) - logits = torch.lerp(logits_uncond, logits, classifier_free_scale) - x = logits - x_flat = x.permute(0, 2, 3, 1).reshape(-1, x.size(1)) - if typical_filtering: - x_flat_norm = torch.nn.functional.log_softmax(x_flat, dim=-1) - x_flat_norm_p = torch.exp(x_flat_norm) - entropy = -(x_flat_norm * x_flat_norm_p).nansum(-1, keepdim=True) - - c_flat_shifted = torch.abs((-x_flat_norm) - entropy) - c_flat_sorted, x_flat_indices = torch.sort(c_flat_shifted, descending=False) - x_flat_cumsum = x_flat.gather(-1, x_flat_indices).softmax(dim=-1).cumsum(dim=-1) - - last_ind = (x_flat_cumsum < typical_mass).sum(dim=-1) - sorted_indices_to_remove = c_flat_sorted > c_flat_sorted.gather(1, last_ind.view(-1, 1)) - if typical_min_tokens > 1: - sorted_indices_to_remove[..., :typical_min_tokens] = 0 - indices_to_remove = sorted_indices_to_remove.scatter(1, x_flat_indices, sorted_indices_to_remove) - x_flat = x_flat.masked_fill(indices_to_remove, -float("Inf")) - x_flat = torch.multinomial(x_flat.div(temp).softmax(-1), num_samples=1)[:, 0] - x = x_flat.view(x.size(0), *x.shape[2:]) - if mask is not None: - x = x * mask + (1-mask) * init_x - if i < renoise_steps: - if renoise_mode == 'start': - x, _ = model.add_noise(x, r_range[i+1], random_x=init_x) - elif renoise_mode == 'prev': - x, _ = model.add_noise(x, r_range[i+1], random_x=prev_x) - else: # 'rand' - x, _ = model.add_noise(x, r_range[i+1]) - preds.append(x.detach()) - return preds - -# Model loading - -# Load T5 on CPU -t5_tokenizer = AutoTokenizer.from_pretrained("google/byt5-xl") -t5_model = T5EncoderModel.from_pretrained("google/byt5-xl") - -# Load other models on GPU -clip_model, _, _ = open_clip.create_model_and_transforms('ViT-H-14', pretrained='laion2b_s32b_b79k') -clip_model = clip_model.to(device).half().eval().requires_grad_(False) - -clip_preprocess = torchvision.transforms.Compose([ - torchvision.transforms.Resize(224, interpolation=torchvision.transforms.InterpolationMode.BICUBIC), - torchvision.transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711)), -]) - -vqgan_path = hf_hub_download(repo_id=model_repo, filename=vqgan_file) -vqmodel = VQModel().to(device) -vqmodel.load_state_dict(torch.load(vqgan_path, map_location=device)) -vqmodel.eval().requires_grad_(False) - -prior_path = hf_hub_download(repo_id=model_repo, filename=prior_file) -prior = PriorModel().to(device)#.half() -prior.load_state_dict(torch.load(prior_path, map_location=device)) -prior.eval().requires_grad_(False) - -model_path = hf_hub_download(repo_id=model_repo, filename=model_file) -model = Paella(byt5_embd=2560) -model.load_state_dict(torch.load(model_path, map_location=device)) -model.eval().requires_grad_()#.half() -replace_attention_layers(model) -model.to(device) - -diffuzz = Diffuzz(device=device) - -@torch.inference_mode() -def decode(img_seq): - return vqmodel.decode_indices(img_seq) - -@torch.inference_mode() -def embed_t5(text, t5_tokenizer, t5_model, final_device="cuda"): - device = t5_model.device - t5_tokens = t5_tokenizer(text, padding="longest", return_tensors="pt", max_length=768, truncation=True).input_ids.to(device) - t5_embeddings = t5_model(input_ids=t5_tokens).last_hidden_state.to(final_device) - return t5_embeddings - -@torch.inference_mode() -def sample(model, model_inputs, latent_shape, - unconditional_inputs=None, init_x=None, steps=12, renoise_steps=None, - temperature = (0.7, 0.3), cfg=(8.0, 8.0), - mode = 'multinomial', # 'quant', 'multinomial', 'argmax' - t_start=1.0, t_end=0.0, - sampling_conditional_steps=None, sampling_quant_steps=None, attn_weights=None - ): - device = unconditional_inputs["byt5"].device - if sampling_conditional_steps is None: - sampling_conditional_steps = steps - if sampling_quant_steps is None: - sampling_quant_steps = steps - if renoise_steps is None: - renoise_steps = steps-1 - if unconditional_inputs is None: - unconditional_inputs = {k: torch.zeros_like(v) for k, v in model_inputs.items()} - - init_noise = torch.randint(0, model.num_labels, size=latent_shape, device=device) - if init_x != None: - sampled = init_x - else: - sampled = init_noise.clone() - t_list = torch.linspace(t_start, t_end, steps+1) - temperatures = torch.linspace(temperature[0], temperature[1], steps) - cfgs = torch.linspace(cfg[0], cfg[1], steps) - for i, tv in enumerate(t_list[:steps]): - if i >= sampling_quant_steps: - mode = "quant" - t = torch.ones(latent_shape[0], device=device) * tv - - logits = model(sampled, t, **model_inputs, attn_weights=attn_weights) - if cfg is not None and i < sampling_conditional_steps: - logits = logits * cfgs[i] + model(sampled, t, **unconditional_inputs) * (1-cfgs[i]) - scores = logits.div(temperatures[i]).softmax(dim=1) - - if mode == 'argmax': - sampled = logits.argmax(dim=1) - elif mode == 'multinomial': - sampled = scores.permute(0, 2, 3, 1).reshape(-1, logits.size(1)) - sampled = torch.multinomial(sampled, 1)[:, 0].view(logits.size(0), *logits.shape[2:]) - elif mode == 'quant': - sampled = scores.permute(0, 2, 3, 1) @ vqmodel.vquantizer.codebook.weight.data - sampled = vqmodel.vquantizer.forward(sampled, dim=-1)[-1] - else: - raise Exception(f"Mode '{mode}' not supported, use: 'quant', 'multinomial' or 'argmax'") - - if i < renoise_steps: - t_next = torch.ones(latent_shape[0], device=device) * t_list[i+1] - sampled = model.add_noise(sampled, t_next, random_x=init_noise)[0] - return sampled - -# ----- - -def infer(prompt, negative_prompt, seed): - torch.manual_seed(seed) - text = tokenizer.tokenize([prompt] * latent_shape[0]).to(device) - with torch.inference_mode(): - if negative_prompt: - clip_text_tokens_uncond = tokenizer.tokenize([negative_prompt] * len(text)).to(device) - t5_embeddings_uncond = embed_t5([negative_prompt] * len(text), t5_tokenizer, t5_model) - else: - clip_text_tokens_uncond = tokenizer.tokenize([""] * len(text)).to(device) - t5_embeddings_uncond = embed_t5([""] * len(text), t5_tokenizer, t5_model) - - t5_embeddings = embed_t5([prompt] * latent_shape[0], t5_tokenizer, t5_model) - clip_text_embeddings = clip_model.encode_text(text) - clip_text_embeddings_uncond = clip_model.encode_text(clip_text_tokens_uncond) - - with torch.autocast(device_type="cuda"): - clip_image_embeddings = diffuzz.sample( - prior, {'c': clip_text_embeddings}, clip_embedding_shape, - timesteps=prior_timesteps, cfg=prior_cfg, sampler=prior_sampler - )[-1] - - attn_weights = torch.ones((t5_embeddings.shape[1])) - attn_weights[-4:] = 0.4 # reweigh attention weights for image embeddings --> less influence - attn_weights[:-4] = 1.2 # reweigh attention weights for the rest --> more influence - attn_weights = attn_weights.to(device) - - sampled_tokens = sample(model, - model_inputs={'byt5': t5_embeddings, 'clip': clip_text_embeddings, 'clip_image': clip_image_embeddings}, unconditional_inputs={'byt5': t5_embeddings_uncond, 'clip': clip_text_embeddings_uncond, 'clip_image': None}, - temperature=(1.2, 0.2), cfg=(8,8), steps=32, renoise_steps=26, latent_shape=latent_shape, t_start=1.0, t_end=0.0, - mode="multinomial", sampling_conditional_steps=20, attn_weights=attn_weights) - - sampled = decode(sampled_tokens) - return to_pil(sampled.clamp(0, 1)) - -css = """ - .gradio-container { - font-family: 'IBM Plex Sans', sans-serif; - } - .gr-button { - color: white; - border-color: black; - background: black; - } - input[type='range'] { - accent-color: black; - } - .dark input[type='range'] { - accent-color: #dfdfdf; - } - .container { - max-width: 730px; - margin: auto; - padding-top: 1.5rem; - } - #gallery { - min-height: 22rem; - margin-bottom: 15px; - margin-left: auto; - margin-right: auto; - border-bottom-right-radius: .5rem !important; - border-bottom-left-radius: .5rem !important; - } - #gallery>div>.h-full { - min-height: 20rem; - } - .details:hover { - text-decoration: underline; - } - .gr-button { - white-space: nowrap; - } - .gr-button:focus { - border-color: rgb(147 197 253 / var(--tw-border-opacity)); - outline: none; - box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); - --tw-border-opacity: 1; - --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); - --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); - --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity)); - --tw-ring-opacity: .5; - } - .footer { - margin-bottom: 45px; - margin-top: 35px; - text-align: center; - border-bottom: 1px solid #e5e5e5; - } - .footer>p { - font-size: .8rem; - display: inline-block; - padding: 0 10px; - transform: translateY(10px); - background: white; - } - .dark .footer { - border-color: #303030; - } - .dark .footer>p { - background: #0b0f19; - } - .acknowledgments h4{ - margin: 1.25em 0 .25em 0; - font-weight: bold; - font-size: 115%; - } - .animate-spin { - animation: spin 1s linear infinite; - } - @keyframes spin { - from { - transform: rotate(0deg); - } - to { - transform: rotate(360deg); - } - } - #share-btn-container { - display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; - } - #share-btn { - all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important; - } - #share-btn * { - all: unset; - } - .gr-form{ - flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0; - } - #prompt-container{ - gap: 0; - } -""" - -block = gr.Blocks(css=css) - -with block: - gr.HTML( - f""" --- """ - ) - with gr.Group(): - with gr.Box(): - with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True): - with gr.Column(): - text = gr.Textbox( - label="Enter your prompt", - show_label=False, - max_lines=1, - placeholder="an image of a shiba inu, donning a spacesuit and helmet, traversing the uncharted terrain of a distant, extraterrestrial world, as a symbol of the intrepid spirit of exploration and the unrelenting curiosity that drives humanity to push beyond the bounds of the known", - elem_id="prompt-text-input", - ).style( - border=(True, False, True, True), - rounded=(True, False, False, True), - container=False, - ) - negative = gr.Textbox( - label="Enter your negative prompt", - show_label=False, - max_lines=1, - placeholder="low quality, low resolution, bad image, blurry, blur", - elem_id="negative-prompt-text-input", - ).style( - border=(True, False, True, True), - rounded=(True, False, False, True), - container=False, - ) - btn = gr.Button("Generate image").style( - margin=False, - rounded=(False, True, True, False), - full_width=False, - ) - - gallery = gr.Gallery( - label="Generated images", show_label=False, elem_id="gallery" - ).style(grid=[2], height="auto") - - with gr.Group(): - with gr.Accordion("Advanced settings", open=False): - seed = gr.Slider( - label="Seed", - minimum=0, - maximum=2147483647, - step=1, - randomize=True, - ) - - text.submit(infer, inputs=[text, negative, seed], outputs=gallery) - btn.click(infer, inputs=[text, negative, seed], outputs=gallery) - - gr.HTML( - """ - -- --- Paella Demo -
-- Paella is a novel text-to-image model that uses a compressed quantized latent space, based on a VQGAN, and a masked training objective to achieve fast generation in ~10 inference steps. - - This version builds on top of our initial paper, bringing Paella to a similar level as other state-of-the-art models, while preserving the compactness and clarity of the previous implementations. Please, refer to the resources below for details. -
--- """ - ) - -block.launch() diff --git a/spaces/penscola/sale_predictions/README.md b/spaces/penscola/sale_predictions/README.md deleted file mode 100644 index 3e2ad5e2b9cc378491a9946f73c9a5e71441a14c..0000000000000000000000000000000000000000 --- a/spaces/penscola/sale_predictions/README.md +++ /dev/null @@ -1,30 +0,0 @@ ---- -title: Sale Predictions -emoji: 🏢 -colorFrom: red -colorTo: blue -sdk: streamlit -sdk_version: 1.21.0 -app_file: app.py -pinned: false -license: mit ---- - -## Background: -Whether we wish to predict the trend in financial markets or electricity consumption, time is an important factor that must now be considered in our models. For example, it would be interesting to forecast at what hour during the day is there going to be a peak consumption in electricity, such as to adjust the price or the production of electricity. - -## The Process -The procedure begins with exporting the essential items from the notebook, followed by correctly designing an interface, importing the necessary objects for modeling, and then writing the code to process inputs. The procedure can be summarized as follows: -- Import machine learning components into the app script. -- Create an interface, -- Create a function to handle inputs. -- Values are passed through the interface. -- Restore these values in the backend, -- Apply the required processing, -- To produce predictions, submit the processed values to the ML model. -- Process the acquired predictions and present them on the interface. -Created by: Felix Kiprotich -https://www.linkedin.com/in/felix-kiprotich-a2ba1a1a4/ - - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/peteralexandercharles/wav2vec2-uk-demo/README.md b/spaces/peteralexandercharles/wav2vec2-uk-demo/README.md deleted file mode 100644 index 5d450a00a26b58114bfb9fc78bdca09671c192b9..0000000000000000000000000000000000000000 --- a/spaces/peteralexandercharles/wav2vec2-uk-demo/README.md +++ /dev/null @@ -1,185 +0,0 @@ ---- -title: Wav2vec2 Ukrainian with Timestamps -emoji: 🇺🇦 -colorFrom: blue -colorTo: yellow -sdk: gradio -app_file: app.py -pinned: false -duplicated_from: Yehor/wav2vec2-uk-demo ---- - - -# Demo of Ukrainian wav2vec2 model - -- The base model is hosted here: https://huggingface.co/Yehor/wav2vec2-xls-r-1b-uk-with-lm -- The model with better News LM: https://huggingface.co/Yehor/wav2vec2-xls-r-1b-uk-with-news-lm - -Follow our community in Telegram: https://t.me/speech_recognition_uk - ---- - -Create a virtualenv: - -```bash -pipenv install -pipenv shell -``` - -Install deps: - -```bash -pip install https://github.com/huggingface/transformers/archive/refs/tags/v4.16.2.zip -pip install https://github.com/kpu/kenlm/archive/master.zip - -pip install torch==1.9.1 torchaudio==0.9.1 pyctcdecode==0.3.0 -``` - -Run inference: - -```bash -python inference.py --model_id Yehor/wav2vec2-xls-r-1b-uk-with-lm --path_files short_1.wav - -# with chunking -python inference.py --model_id Yehor/wav2vec2-xls-r-1b-uk-with-lm --path_files short_1.wav --chunk_length_s 10 --stride_length_s_l 4 --stride_length_s_r 2 -python inference.py --model_id Yehor/wav2vec2-xls-r-1b-uk-with-lm --path_files long_1.wav --chunk_length_s 10 --stride_length_s_l 4 --stride_length_s_r 2 - -# with chunking on GPU -python inference_gpu.py --model_id Yehor/wav2vec2-xls-r-1b-uk-with-lm --path_files short_1.wav --chunk_length_s 10 --stride_length_s_l 4 --stride_length_s_r 2 -python inference_gpu.py --model_id Yehor/wav2vec2-xls-r-1b-uk-with-lm --path_files long_1.wav --chunk_length_s 10 --stride_length_s_l 4 --stride_length_s_r 2 - -python inference.py --model_id Yehor/wav2vec2-xls-r-1b-uk-with-news-lm --path_files mer_lviv_interview.wav --chunk_length_s 10 --stride_length_s_l 4 --stride_length_s_r 2 -python inference_gpu.py --model_id Yehor/wav2vec2-xls-r-1b-uk-with-news-lm --path_files mer_lviv_interview.wav --chunk_length_s 10 --stride_length_s_l 4 --stride_length_s_r 2 - -python inference.py --model_id Yehor/wav2vec2-xls-r-1b-uk-with-lm --path_files tsn.wav,tsn_2.wav --chunk_length_s 10 --stride_length_s_l 4 --stride_length_s_r 2 -``` - -NOTE: Do the inference process for long files with chunking. - ---- - -short_1.wav: - -``` -пана сполучені штати над важливий стратегічний партнер однак є різницяштати мають спеціальний закон який передбачає якщо китай напади на тайвань американський військові мають його захищати у гри -``` - -short_1.wav (with better News LM): - -``` -аня сполучені штати над важливий стратегічний партнер однак є різниця штати мають спеціальний закон який передбачає якщо китай нападе на тайвань американський військові мають його захищати угери -``` - -long_1.wav: - -``` -серце чи дивовижни порятунок мільйони людей фактично в прямому ефірі вже три доби спостерігають за спробамиамероканських рятувальникив дісттисколодя за пятирічне хлопя досі не зрозуміло чи вдастядістати його з тридцяти метрового провал живим про надзвичайно складну операцію що триває в цю мить я на есарчуккулояз який провалився пятирічнийраян ледь помітна діра в землі менше тридцяти сантиметріву діаметрі але в глиб вона тягнеться на тридцять два метро батьки шукали сина кілька один перед тим як зрозуміле він під землею коли він зник я молилися богупросила аби алагзбиріг мосина і його дістали з колодязь живим господихай йому та менше болить в тій ділі я так сподіваючиь що у рятувальники все вийде його неможливо витягти просто так розуміють рятувальники занадто вуськоа розширяти діру не можна вона просто завалитья тому вони три до бою розкопують амундалік і поки працює техніки -``` - -long_1.wav (with News LM): - -``` -серце чи дивовижних порятунок мільйони людей фактично в прямому ефірі вже три доби спостерігають за спробами мероканських рятувальників дісттисколодя за пятирічне хлопя досі незрозуміло чи вдастся дістати його з тридцятиметрового провалля живим про надзвичайно складну операцію що триває в цю мить я не слісарчукодязв який провалився пятирічний раян ледь помітна діра в землі менше тридцяти сантиметрів у діаметрі але в глиб вона тягнеться на тридцять два метри батьки шукали сина кілька годин перед тим як зрозуміли він під землею а коли він зник я молилася богу просила аби алагбирігмо сина і його дістали сколотізяживим господи хай йому там менше болить в тій дірі і так сподіваючись що у рятувальники все вийде його неможливо витягти просто так розуміють рятувальників занадто вузько а розширяти діру не можна вона просто завалиться тому вони три добою розкопують амну далікі поки працює технік -``` - -long_1.wav (with better News LM): - -``` -серце чи дивовижний порятунок мільйони людей фактично в прямому ефірі вже три доби спостерігають за спробами мароканських рятувальників дісттисколодя за пятирічне хлопя досі незрозуміло чи вдастся дістати його з тридцятиметрового провалля живим про надзвичайно складну операцію що триває в цю мить я не слесарчукодязв який провалився пятирічний раян ледь помітна діра в землі менше тридцяти сантиметрів у діаметрі але в глиб вона тягнеться на тридцять два метри батьки шукали сина кілька годин перед тим як зрозуміли він під землею а коли він зник я молилася богу просила аби алаксбирігмо сина і його дістали сководізяживим господи хай йому там менше болить в тій тіріятак сподіваючись що у рятувальники все вийде його неможливо витягти просто так розуміють рятувальників занадто вузько а розширяти діру не можна вона просто завалиться тому вони три добою розкопують амнокдалік і поки працює технік -``` - -tsn.wav (with better News LM): - -``` -інформаційний вечір на один плюс один в студії та сем лідія таран а платонова королева з чого починалося і чим ще зовсім не закінчиться правління єлизавети другої сімдесят років на троні окмовівіполовні рішення щодо спадкоємців корони більше зараз усе -``` - -tsn_2.wav (with better News LM): - -``` -до осло зіли під час стрілянини на південмаші в жану влучили три кулі одна з них розірвала кишківник та хребет животі катастрофу розуміти три літри крові яку неможливо повернути тому що вона забруднена кишківник кинувши доз розірваний освітунавіть не котичекапо ужилася дуже активна проти дістала кулі просто на варті контрактниця відповідала за оповіщення частини про надзвичайні ситуації він був в двох кроках від вельми мовчки розстріляли що на посту була радіостанція і я запраз та могла доповісти частину військові ну я не змогла доповісти зробив постріли я вже не змогла підвестися стрілянина на південмаші сталася вночі двадцять сьомого січня пятеро людей загинули серед них одна цивільна ще пятро поранені підозрюваний двадцятирічний рокових артемірявчух у сізо спочатку провину визнавав тепер і знову м адвокатами від показів відмовився ані конфліктних ситуації ані бо чого підозрілого жанна каже до стрілянини із підозрюваним навіть не розмовляла а тільки за памятала і оце а так я навіть його імені й фамілії не знав я дуже рідко заступала ці кару там де срочного служба я не можу знати чому він так зробив навпроти жани реанімації контрактник ігор самененко під час стрлянини відпочивав за графіком повороже забігаю тикрчишотамячу ксюш сумною наше -``` - -mer_lviv_interview.wav: - -``` -ми отримали нового керівника територіальної оборони області а це є фахова людина і з дурне міста ми переформотуваи наш управлінні звичайних ситуаії сьогодні воно має функцію назвичйнх ситуацівільного захист територіальної оборони і в нас достатньо якісна співпрая якісна координації тому що заначаміста допомогти державному фактично кірунку щоб він якісно за працював стосовно ремонти стосовно обладнання і стосовно рекрутенгу людей які зараз голошулися що би бути по контрактах в територіальної обороні на території міста любова на стврюються двбатаьони і наразі все йде чітко за тимланам які визначено державою натомість місто взяла на себе додатково функцію ми очали великий навчальний процес для працівників комунальних підприємсособливо мов для про стратегічне підприємства працівники виконавчий хоронні влади банально відновити навики володіння зброю в наших працівників люсважливи чиникце уміння надавати першу до медичну допомогу і за така велика хвиля яка після закінчення карантивно де включати і старшокласників ми на при великий жаль багато в остнніх років таке враження що перебували лютриному сні розуміючи що росія поруч і за ви буде центр можливо агресії нам треба вишколювати себе і бути готовими до того щоб захищати рідну державу якщо кожен українець буде добре володіти навиками стрилецької зброї буде вміти надавати мдичну допомогу в бимене це потужна сила яку неможливо здолати тому в координації з державу і рухаємося вперед ще таке питання оту нас багато кажуть що поруч і бригада ми сил тертральо оборони можуть стврюватися і формуватися доровольчі обєднання чи у вслвові це передбачено і як ваше ставлення до такого віце ми працюємо в чуткійкоординації з державними інституціями і на сьогоднішній день я не бачу потреби в інших формування х тому що принципі має бути жорстка вертикально має бути один центр правління сла обогощо є новий командувач територіальної оборони рамках держави керівник штабоновий принципі пан у то який в нас у чули територіальну орону області на це дуже фахова компетентна людина з ним просто рємно сів працювати в не розуміє все співслов законом передбачена можливість прикидання в разі такої нагальної потреби перекидання бригада сил територіальної оборони в зонобоєвихді вічі області яке ваше ставленндоцього і можливо вам відомо яке можливо ставлення у самих сів територіальної оборони віці на сьогоднішній день ми розуміємо що він форційному плані треба зробити титанічниобєм праці людям треба пояснити що ти не можеш сховатисяу власному помешкані коли прийде ворог ворога треба не пустити на території нашої країни і я думаю що всі громадяни повинні мати готовнізахи щати свої рідну крїну на сьогоднішній день йде нормальний процес люди записується дуже багато відомелюдей записуся в територіальну оборону я вважаю це правильно тому що треба мати таки висів а стосовно всіх інших речей ще раз наголошую коли мова йде про безпеку про оборону держави має бути субординація і є чітка державна вертикальтреба захищати будемо сі разом захищати нема наторадиа ще одне питання багато хто впеонийщо законіне достатньо влади про писано для місцеве адміністрації влдимається на увазі повноважень і можливості при формуванні розгортанні сил тоеитрально оборони ви на практиці зіткнулися вже цим і як ви оцінюють ся це ну по перше добре що закон є звичайно що од жоден закон не є та обтальний чи ідеальний він мав би процесі реально життя за знати певних крягувань у наприклад керівником териріальной оборони в часі на звичайної ситуації є голова обласної адміністрації так а по місто любово наприклад керівником є керівник районної адміністрації це є не великий офісі є декілька працівників і вони мали би стояти над громади міста льоа я не думаю що це буде мати достатню якісний ефект тому що принцип є крівникобласті я керівник мста але ці речі напевно коли писали закон не до кінця прраховували але ми маємо час щоб це поправити але на сьогодні ми чітко викониозакни який є інших варіантів не може бути пане андріюбуквально остання запитнняваша бригада тлвівськану два батальйон як викрити як вони зараз озброєні повністю чи ніч це тільки стрилецьке збрїчице й більш важке озброєнн і як в ставитиь до того щоб міськдністрація мала можливістькажімо щось купувати для забзпечення саме си тртріальоорон у на приклад ті ж самі виспілотники для розвідки або засоби звязку а або ось щось таке ну що ви розуміли ми достатньо багато помагаємо всім нашим військовим частинам батальоам це робили вчорастобупі татом у кожного року якщо говорити про сили територіальну оборони області ми допоможе мо усім чим буде потрібно мова про гроші не йде ми не профінансуємо ремонту чи іншої вулиці а нашим хлопцям до поможемо і це має робити кона громада і кожен лідер гмади в нашій країні -``` - -mer_lviv_interview.wav (with better News LM): - -``` -ми отримали нового керівника територіальної оборони області а це є фахова людина і з турне міста ми переформатували наше управління звичайних ситуаії сьогодні воно має функцію надзвичйних ситуацї цівільного захист територіальної оборони і в нас достатньо якісна співпраця якісна координація тому що задача міста допомогти державному фактично керунку щоб він якісно запрацював стосовно ремонті стосовно обладнання і стосовно рекрутингу людей які зараз зголошувалися щоби бути по контрактах в територіальній обороні на території міста любова на стврюються батальони і наразі все йде чітко за тим ланом які визначеної державою натомість місто взяло на себе додаткову функцію ми очали великий навчальний процес для працівників комунальних підприємстособливо мов для про стратегічні підприємства працівники виконавчий хорані влади банально відновити навики володіння зброєю в наших працівників плюс важливий чинник це уміння надавати першу домедичну допомогу і за така велика хвиля яка після закінчення карантивно буде включати і старшокласників ми напривеликий жаль багато в останніх років таке враження що перебували літориномусні розуміючи що росія поруч і за вибуде центр можливо агресії нам треба вишколювати себе і бути готовими до того щоб захищати рідну державу якщо кожен українець буде добре володіти навиками стрілецької зброї буде вміти надавати м дичну допомогу в имени це потужна сила яку неможливо здолати тому в координації з державу і рухаємося вперед ще таке питання от у нас багато кажуть що поруч із бригадами сил тертральооборони можуть стврюватися і формуватися добровольчі обєднання чи уволвові це передбачено як ваше ставлення до такого віце ми працюємо в чіткийкординації з державними інституціями і на сьогоднішній день я не бачу потреби в інших формуваннях тому що в принципі має бути жорстка вертикаль має бути один центр управління сла обо що є новий командувач територіальної оборони в рамках держави керівник шабанови принципі пану то які в нас учули територіальну орон області на це дуже фахова компетентна людина з ним просто приємно співпрацювати не розуміє все співслова законом передбачена можливість перекидання в разі такої нагальної потреби перекидання бригад сил територіальної оборони в зону бойових дій віншіобласті яке ваше ставленн до цього і можливо вам відомо яке можливо ставлення рсу самих бійців територіальної оборони від це на сьогоднішній день ми розуміємо що вінформаційному плані треба зробити титанічний обєм праці людям треба пояснити що ти не можеш сховатися у власному помешканні коли прийде ворог ворога треба не пустити на територію нашої країни і я думаю що всі громадяни повинні мати готовні захищати свою рідну крїну на сьогоднішній день йде нормальний процес люди записується дуже багато відоме людей записується в територіальну оборону я вважаю це правильно тому що треба мати такі вишкіл а стосовно всіх інших речей ще раз наголошую коли мова йде про безпеку про оборону держави має бути субординація і є чітка державна вертикаль треба захищати будемо сі разом захищати нема на то ради а ще одне питання багато хто впевнений що в законі недостатньо влади прописано для місцевих д міністрації влди мається на увазі повноважень і можлвостпри формуванні розгортанні сил ториторіально оборони ви на практиці зіткнулися вже цим і як ви оцінюютьсяцену поперше добре що закон є звичайно що жододенззаон не є та обтальний чи ідеальний він мав би процесі реально життя зазнати певних коригувань ну наприклад керівником териріальной оборони в часі на звичайної ситуації є голова обласної адміністрації так а по місту любовонаприклад керівником є керівник районної адміністрації це є невеликий офіс нехедекілька працівників і вони мали би стояти над громадою міста львова я не думаю що це буде мати достатню якісний ефект тому що принцип є керівникобласті керівни мста але ці речі напевно коли писали закон не до кінця раховували але ми маємо час щоб це поправити але на сьогодні ми чітко викониозакни який є іншхварянтів не може буде пане андрію буквально остання запитння ваша бригада львівська ну два батальйони як викрита як вони зараз озброєні повністю чи ніч це тільки стрілецьке збря чи це й більш важке озброєння і як в ставитись до того щоб міськдістрація мала можливістькажімо щось купувати для забзпечення саме си тртіальоорони ну наприклад ті ж самі беспілотники для розвідки або засоби звязку а або ось щось таке ну щоб ви розуміли ми достатньо багато помагаємо всім нашим військовим частинам батальонам це робили читерастобу питатиму кожного року якщо говорити про сили територіально оборони області ми допоможемо усім чим буде потрібно мова про гроші не йде ми не профінансуємо ремонтуючи іншої вулиці а нашим хлопцям допоможемо і це має робити кона громада і кожен лідер гомади в нашій країні -``` - -### Inference of `mer_lviv_interview.wav` (time is 06:38) - -#### CPU - -- Memory peaks to 60GB -- Memory peaks to 65GB (on News LM) - -Inference duration: - -``` -real 7m39.461s -user 59m19.065s -sys 24m1.254s -``` - -Inference duration (on News LM): - -``` -real 12m36.888s -user 63m19.396s -sys 24m24.823s -``` - -Duration tracked with loading the LM. - -## Using timestamps - -The `inference_timestamps.py` script can be used to do inference with timestamps for chars and words. - -### `output_char_offsets=True` - -``` -Wav2Vec2CTCTokenizerOutput(text='паня сполучені штати надважливий стратегічний партнер однак є різниця штати мають спеціальни закон який передбачає якщо китай нападе на тайвань американський військові мають його захищати евуйвгере', char_offsets=[{'char': 'п', 'start_offset': 0, 'end_offset': 1}, {'char': 'а', 'start_offset': 1, 'end_offset': 2}, {'char': 'н', 'start_offset': 9, 'end_offset': 10}, {'char': 'я', 'start_offset': 11, 'end_offset': 12}, {'char': ' ', 'start_offset': 14, 'end_offset': 15}, {'char': 'с', 'start_offset': 16, 'end_offset': 17}, {'char': 'п', 'start_offset': 19, 'end_offset': 20}, {'char': 'о', 'start_offset': 21, 'end_offset': 22}, {'char': 'л', 'start_offset': 23, 'end_offset': 24}, {'char': 'у', 'start_offset': 25, 'end_offset': 26}, {'char': 'ч', 'start_offset': 30, 'end_offset': 31}, {'char': 'е', 'start_offset': 32, 'end_offset': 33}, {'char': 'н', 'start_offset': 37, 'end_offset': 38}, {'char': 'і', 'start_offset': 38, 'end_offset': 39}, {'char': ' ', 'start_offset': 40, 'end_offset': 42}, {'char': 'ш', 'start_offset': 43, 'end_offset': 44}, {'char': 'т', 'start_offset': 46, 'end_offset': 47}, {'char': 'а', 'start_offset': 48, 'end_offset': 49}, {'char': 'т', 'start_offset': 57, 'end_offset': 58}, {'char': 'и', 'start_offset': 58, 'end_offset': 59}, {'char': ' ', 'start_offset': 76, 'end_offset': 79}, {'char': 'н', 'start_offset': 85, 'end_offset': 86}, {'char': 'а', 'start_offset': 87, 'end_offset': 88}, {'char': 'д', 'start_offset': 93, 'end_offset': 94}, {'char': 'в', 'start_offset': 97, 'end_offset': 98}, {'char': 'а', 'start_offset': 99, 'end_offset': 100}, {'char': 'ж', 'start_offset': 105, 'end_offset': 106}, {'char': 'л', 'start_offset': 113, 'end_offset': 114}, {'char': 'и', 'start_offset': 114, 'end_offset': 115}, {'char': 'в', 'start_offset': 121, 'end_offset': 122}, {'char': 'и', 'start_offset': 123, 'end_offset': 124}, {'char': 'й', 'start_offset': 125, 'end_offset': 126}, {'char': ' ', 'start_offset': 127, 'end_offset': 129}, {'char': 'с', 'start_offset': 130, 'end_offset': 131}, {'char': 'т', 'start_offset': 134, 'end_offset': 136}, {'char': 'р', 'start_offset': 138, 'end_offset': 139}, {'char': 'а', 'start_offset': 139, 'end_offset': 140}, {'char': 'т', 'start_offset': 145, 'end_offset': 146}, {'char': 'е', 'start_offset': 146, 'end_offset': 147}, {'char': 'г', 'start_offset': 152, 'end_offset': 153}, {'char': 'і', 'start_offset': 153, 'end_offset': 154}, {'char': 'ч', 'start_offset': 160, 'end_offset': 161}, {'char': 'н', 'start_offset': 167, 'end_offset': 168}, {'char': 'и', 'start_offset': 168, 'end_offset': 169}, {'char': 'й', 'start_offset': 170, 'end_offset': 171}, {'char': ' ', 'start_offset': 171, 'end_offset': 173}, {'char': 'п', 'start_offset': 174, 'end_offset': 175}, {'char': 'а', 'start_offset': 176, 'end_offset': 177}, {'char': 'р', 'start_offset': 179, 'end_offset': 180}, {'char': 'т', 'start_offset': 183, 'end_offset': 184}, {'char': 'н', 'start_offset': 188, 'end_offset': 189}, {'char': 'е', 'start_offset': 189, 'end_offset': 190}, {'char': 'р', 'start_offset': 193, 'end_offset': 194}, {'char': ' ', 'start_offset': 201, 'end_offset': 203}, {'char': 'о', 'start_offset': 204, 'end_offset': 205}, {'char': 'д', 'start_offset': 208, 'end_offset': 209}, {'char': 'н', 'start_offset': 214, 'end_offset': 216}, {'char': 'а', 'start_offset': 216, 'end_offset': 217}, {'char': 'к', 'start_offset': 224, 'end_offset': 225}, {'char': ' ', 'start_offset': 227, 'end_offset': 229}, {'char': 'є', 'start_offset': 233, 'end_offset': 234}, {'char': ' ', 'start_offset': 237, 'end_offset': 239}, {'char': 'р', 'start_offset': 240, 'end_offset': 241}, {'char': 'і', 'start_offset': 241, 'end_offset': 242}, {'char': 'з', 'start_offset': 247, 'end_offset': 248}, {'char': 'н', 'start_offset': 253, 'end_offset': 254}, {'char': 'и', 'start_offset': 254, 'end_offset': 255}, {'char': 'ц', 'start_offset': 261, 'end_offset': 262}, {'char': 'я', 'start_offset': 262, 'end_offset': 263}, {'char': ' ', 'start_offset': 281, 'end_offset': 283}, {'char': 'ш', 'start_offset': 283, 'end_offset': 284}, {'char': 'т', 'start_offset': 286, 'end_offset': 287}, {'char': 'а', 'start_offset': 288, 'end_offset': 289}, {'char': 'т', 'start_offset': 294, 'end_offset': 295}, {'char': 'и', 'start_offset': 296, 'end_offset': 297}, {'char': ' ', 'start_offset': 297, 'end_offset': 299}, {'char': 'м', 'start_offset': 300, 'end_offset': 301}, {'char': 'а', 'start_offset': 301, 'end_offset': 302}, {'char': 'ю', 'start_offset': 306, 'end_offset': 307}, {'char': 'т', 'start_offset': 308, 'end_offset': 309}, {'char': 'ь', 'start_offset': 309, 'end_offset': 311}, {'char': ' ', 'start_offset': 311, 'end_offset': 313}, {'char': 'с', 'start_offset': 313, 'end_offset': 314}, {'char': 'п', 'start_offset': 316, 'end_offset': 317}, {'char': 'е', 'start_offset': 318, 'end_offset': 319}, {'char': 'ц', 'start_offset': 324, 'end_offset': 325}, {'char': 'і', 'start_offset': 325, 'end_offset': 326}, {'char': 'а', 'start_offset': 328, 'end_offset': 329}, {'char': 'л', 'start_offset': 333, 'end_offset': 334}, {'char': 'ь', 'start_offset': 334, 'end_offset': 336}, {'char': 'н', 'start_offset': 339, 'end_offset': 340}, {'char': 'и', 'start_offset': 341, 'end_offset': 342}, {'char': ' ', 'start_offset': 345, 'end_offset': 348}, {'char': 'з', 'start_offset': 351, 'end_offset': 352}, {'char': 'а', 'start_offset': 354, 'end_offset': 355}, {'char': 'к', 'start_offset': 361, 'end_offset': 362}, {'char': 'о', 'start_offset': 365, 'end_offset': 366}, {'char': 'н', 'start_offset': 373, 'end_offset': 374}, {'char': ' ', 'start_offset': 382, 'end_offset': 384}, {'char': 'я', 'start_offset': 386, 'end_offset': 387}, {'char': 'к', 'start_offset': 390, 'end_offset': 391}, {'char': 'и', 'start_offset': 392, 'end_offset': 393}, {'char': 'й', 'start_offset': 394, 'end_offset': 395}, {'char': ' ', 'start_offset': 396, 'end_offset': 398}, {'char': 'п', 'start_offset': 399, 'end_offset': 401}, {'char': 'е', 'start_offset': 402, 'end_offset': 403}, {'char': 'р', 'start_offset': 406, 'end_offset': 407}, {'char': 'е', 'start_offset': 407, 'end_offset': 408}, {'char': 'д', 'start_offset': 411, 'end_offset': 412}, {'char': 'б', 'start_offset': 415, 'end_offset': 416}, {'char': 'а', 'start_offset': 416, 'end_offset': 417}, {'char': 'ч', 'start_offset': 424, 'end_offset': 425}, {'char': 'а', 'start_offset': 428, 'end_offset': 429}, {'char': 'є', 'start_offset': 437, 'end_offset': 438}, {'char': ' ', 'start_offset': 445, 'end_offset': 447}, {'char': 'я', 'start_offset': 448, 'end_offset': 449}, {'char': 'к', 'start_offset': 452, 'end_offset': 453}, {'char': 'щ', 'start_offset': 455, 'end_offset': 456}, {'char': 'о', 'start_offset': 457, 'end_offset': 458}, {'char': ' ', 'start_offset': 460, 'end_offset': 463}, {'char': 'к', 'start_offset': 463, 'end_offset': 464}, {'char': 'и', 'start_offset': 465, 'end_offset': 466}, {'char': 'т', 'start_offset': 470, 'end_offset': 471}, {'char': 'а', 'start_offset': 472, 'end_offset': 473}, {'char': 'й', 'start_offset': 478, 'end_offset': 480}, {'char': ' ', 'start_offset': 484, 'end_offset': 486}, {'char': 'н', 'start_offset': 487, 'end_offset': 488}, {'char': 'а', 'start_offset': 488, 'end_offset': 489}, {'char': 'п', 'start_offset': 493, 'end_offset': 494}, {'char': 'а', 'start_offset': 496, 'end_offset': 497}, {'char': 'д', 'start_offset': 502, 'end_offset': 503}, {'char': 'е', 'start_offset': 504, 'end_offset': 505}, {'char': ' ', 'start_offset': 509, 'end_offset': 511}, {'char': 'н', 'start_offset': 511, 'end_offset': 512}, {'char': 'а', 'start_offset': 513, 'end_offset': 514}, {'char': ' ', 'start_offset': 515, 'end_offset': 517}, {'char': 'т', 'start_offset': 518, 'end_offset': 519}, {'char': 'а', 'start_offset': 519, 'end_offset': 520}, {'char': 'й', 'start_offset': 524, 'end_offset': 525}, {'char': 'в', 'start_offset': 527, 'end_offset': 528}, {'char': 'а', 'start_offset': 529, 'end_offset': 530}, {'char': 'н', 'start_offset': 535, 'end_offset': 536}, {'char': 'ь', 'start_offset': 536, 'end_offset': 537}, {'char': ' ', 'start_offset': 552, 'end_offset': 555}, {'char': 'а', 'start_offset': 555, 'end_offset': 556}, {'char': 'м', 'start_offset': 561, 'end_offset': 562}, {'char': 'е', 'start_offset': 562, 'end_offset': 563}, {'char': 'р', 'start_offset': 566, 'end_offset': 567}, {'char': 'и', 'start_offset': 567, 'end_offset': 568}, {'char': 'к', 'start_offset': 572, 'end_offset': 573}, {'char': 'а', 'start_offset': 574, 'end_offset': 575}, {'char': 'н', 'start_offset': 579, 'end_offset': 580}, {'char': 'с', 'start_offset': 582, 'end_offset': 583}, {'char': 'ь', 'start_offset': 583, 'end_offset': 585}, {'char': 'к', 'start_offset': 586, 'end_offset': 587}, {'char': 'и', 'start_offset': 588, 'end_offset': 589}, {'char': 'й', 'start_offset': 589, 'end_offset': 590}, {'char': ' ', 'start_offset': 591, 'end_offset': 593}, {'char': 'в', 'start_offset': 594, 'end_offset': 595}, {'char': 'і', 'start_offset': 595, 'end_offset': 596}, {'char': 'й', 'start_offset': 600, 'end_offset': 601}, {'char': 'с', 'start_offset': 604, 'end_offset': 605}, {'char': 'ь', 'start_offset': 605, 'end_offset': 607}, {'char': 'к', 'start_offset': 609, 'end_offset': 611}, {'char': 'о', 'start_offset': 612, 'end_offset': 613}, {'char': 'в', 'start_offset': 620, 'end_offset': 621}, {'char': 'і', 'start_offset': 622, 'end_offset': 623}, {'char': ' ', 'start_offset': 637, 'end_offset': 639}, {'char': 'м', 'start_offset': 641, 'end_offset': 642}, {'char': 'а', 'start_offset': 643, 'end_offset': 644}, {'char': 'ю', 'start_offset': 651, 'end_offset': 652}, {'char': 'т', 'start_offset': 654, 'end_offset': 655}, {'char': 'ь', 'start_offset': 655, 'end_offset': 656}, {'char': ' ', 'start_offset': 657, 'end_offset': 659}, {'char': 'й', 'start_offset': 659, 'end_offset': 660}, {'char': 'о', 'start_offset': 660, 'end_offset': 662}, {'char': 'г', 'start_offset': 664, 'end_offset': 665}, {'char': 'о', 'start_offset': 666, 'end_offset': 667}, {'char': ' ', 'start_offset': 677, 'end_offset': 679}, {'char': 'з', 'start_offset': 681, 'end_offset': 682}, {'char': 'а', 'start_offset': 683, 'end_offset': 684}, {'char': 'х', 'start_offset': 686, 'end_offset': 687}, {'char': 'и', 'start_offset': 689, 'end_offset': 690}, {'char': 'щ', 'start_offset': 696, 'end_offset': 697}, {'char': 'а', 'start_offset': 698, 'end_offset': 699}, {'char': 'т', 'start_offset': 707, 'end_offset': 708}, {'char': 'и', 'start_offset': 709, 'end_offset': 710}, {'char': ' ', 'start_offset': 733, 'end_offset': 734}, {'char': 'е', 'start_offset': 740, 'end_offset': 741}, {'char': 'в', 'start_offset': 747, 'end_offset': 748}, {'char': 'у', 'start_offset': 748, 'end_offset': 749}, {'char': 'й', 'start_offset': 752, 'end_offset': 753}, {'char': 'в', 'start_offset': 754, 'end_offset': 755}, {'char': 'г', 'start_offset': 757, 'end_offset': 758}, {'char': 'е', 'start_offset': 759, 'end_offset': 760}, {'char': 'р', 'start_offset': 767, 'end_offset': 768}, {'char': 'е', 'start_offset': 768, 'end_offset': 769}], word_offsets=None) -``` - -### `output_word_offsets=True` - -``` -Wav2Vec2CTCTokenizerOutput(text='паня сполучені штати надважливий стратегічний партнер однак є різниця штати мають спеціальни закон який передбачає якщо китай нападе на тайвань американський військові мають його захищати евуйвгере', char_offsets=[{'char': 'п', 'start_offset': 0, 'end_offset': 1}, {'char': 'а', 'start_offset': 1, 'end_offset': 2}, {'char': 'н', 'start_offset': 9, 'end_offset': 10}, {'char': 'я', 'start_offset': 11, 'end_offset': 12}, {'char': ' ', 'start_offset': 14, 'end_offset': 15}, {'char': 'с', 'start_offset': 16, 'end_offset': 17}, {'char': 'п', 'start_offset': 19, 'end_offset': 20}, {'char': 'о', 'start_offset': 21, 'end_offset': 22}, {'char': 'л', 'start_offset': 23, 'end_offset': 24}, {'char': 'у', 'start_offset': 25, 'end_offset': 26}, {'char': 'ч', 'start_offset': 30, 'end_offset': 31}, {'char': 'е', 'start_offset': 32, 'end_offset': 33}, {'char': 'н', 'start_offset': 37, 'end_offset': 38}, {'char': 'і', 'start_offset': 38, 'end_offset': 39}, {'char': ' ', 'start_offset': 40, 'end_offset': 42}, {'char': 'ш', 'start_offset': 43, 'end_offset': 44}, {'char': 'т', 'start_offset': 46, 'end_offset': 47}, {'char': 'а', 'start_offset': 48, 'end_offset': 49}, {'char': 'т', 'start_offset': 57, 'end_offset': 58}, {'char': 'и', 'start_offset': 58, 'end_offset': 59}, {'char': ' ', 'start_offset': 76, 'end_offset': 79}, {'char': 'н', 'start_offset': 85, 'end_offset': 86}, {'char': 'а', 'start_offset': 87, 'end_offset': 88}, {'char': 'д', 'start_offset': 93, 'end_offset': 94}, {'char': 'в', 'start_offset': 97, 'end_offset': 98}, {'char': 'а', 'start_offset': 99, 'end_offset': 100}, {'char': 'ж', 'start_offset': 105, 'end_offset': 106}, {'char': 'л', 'start_offset': 113, 'end_offset': 114}, {'char': 'и', 'start_offset': 114, 'end_offset': 115}, {'char': 'в', 'start_offset': 121, 'end_offset': 122}, {'char': 'и', 'start_offset': 123, 'end_offset': 124}, {'char': 'й', 'start_offset': 125, 'end_offset': 126}, {'char': ' ', 'start_offset': 127, 'end_offset': 129}, {'char': 'с', 'start_offset': 130, 'end_offset': 131}, {'char': 'т', 'start_offset': 134, 'end_offset': 136}, {'char': 'р', 'start_offset': 138, 'end_offset': 139}, {'char': 'а', 'start_offset': 139, 'end_offset': 140}, {'char': 'т', 'start_offset': 145, 'end_offset': 146}, {'char': 'е', 'start_offset': 146, 'end_offset': 147}, {'char': 'г', 'start_offset': 152, 'end_offset': 153}, {'char': 'і', 'start_offset': 153, 'end_offset': 154}, {'char': 'ч', 'start_offset': 160, 'end_offset': 161}, {'char': 'н', 'start_offset': 167, 'end_offset': 168}, {'char': 'и', 'start_offset': 168, 'end_offset': 169}, {'char': 'й', 'start_offset': 170, 'end_offset': 171}, {'char': ' ', 'start_offset': 171, 'end_offset': 173}, {'char': 'п', 'start_offset': 174, 'end_offset': 175}, {'char': 'а', 'start_offset': 176, 'end_offset': 177}, {'char': 'р', 'start_offset': 179, 'end_offset': 180}, {'char': 'т', 'start_offset': 183, 'end_offset': 184}, {'char': 'н', 'start_offset': 188, 'end_offset': 189}, {'char': 'е', 'start_offset': 189, 'end_offset': 190}, {'char': 'р', 'start_offset': 193, 'end_offset': 194}, {'char': ' ', 'start_offset': 201, 'end_offset': 203}, {'char': 'о', 'start_offset': 204, 'end_offset': 205}, {'char': 'д', 'start_offset': 208, 'end_offset': 209}, {'char': 'н', 'start_offset': 214, 'end_offset': 216}, {'char': 'а', 'start_offset': 216, 'end_offset': 217}, {'char': 'к', 'start_offset': 224, 'end_offset': 225}, {'char': ' ', 'start_offset': 227, 'end_offset': 229}, {'char': 'є', 'start_offset': 233, 'end_offset': 234}, {'char': ' ', 'start_offset': 237, 'end_offset': 239}, {'char': 'р', 'start_offset': 240, 'end_offset': 241}, {'char': 'і', 'start_offset': 241, 'end_offset': 242}, {'char': 'з', 'start_offset': 247, 'end_offset': 248}, {'char': 'н', 'start_offset': 253, 'end_offset': 254}, {'char': 'и', 'start_offset': 254, 'end_offset': 255}, {'char': 'ц', 'start_offset': 261, 'end_offset': 262}, {'char': 'я', 'start_offset': 262, 'end_offset': 263}, {'char': ' ', 'start_offset': 281, 'end_offset': 283}, {'char': 'ш', 'start_offset': 283, 'end_offset': 284}, {'char': 'т', 'start_offset': 286, 'end_offset': 287}, {'char': 'а', 'start_offset': 288, 'end_offset': 289}, {'char': 'т', 'start_offset': 294, 'end_offset': 295}, {'char': 'и', 'start_offset': 296, 'end_offset': 297}, {'char': ' ', 'start_offset': 297, 'end_offset': 299}, {'char': 'м', 'start_offset': 300, 'end_offset': 301}, {'char': 'а', 'start_offset': 301, 'end_offset': 302}, {'char': 'ю', 'start_offset': 306, 'end_offset': 307}, {'char': 'т', 'start_offset': 308, 'end_offset': 309}, {'char': 'ь', 'start_offset': 309, 'end_offset': 311}, {'char': ' ', 'start_offset': 311, 'end_offset': 313}, {'char': 'с', 'start_offset': 313, 'end_offset': 314}, {'char': 'п', 'start_offset': 316, 'end_offset': 317}, {'char': 'е', 'start_offset': 318, 'end_offset': 319}, {'char': 'ц', 'start_offset': 324, 'end_offset': 325}, {'char': 'і', 'start_offset': 325, 'end_offset': 326}, {'char': 'а', 'start_offset': 328, 'end_offset': 329}, {'char': 'л', 'start_offset': 333, 'end_offset': 334}, {'char': 'ь', 'start_offset': 334, 'end_offset': 336}, {'char': 'н', 'start_offset': 339, 'end_offset': 340}, {'char': 'и', 'start_offset': 341, 'end_offset': 342}, {'char': ' ', 'start_offset': 345, 'end_offset': 348}, {'char': 'з', 'start_offset': 351, 'end_offset': 352}, {'char': 'а', 'start_offset': 354, 'end_offset': 355}, {'char': 'к', 'start_offset': 361, 'end_offset': 362}, {'char': 'о', 'start_offset': 365, 'end_offset': 366}, {'char': 'н', 'start_offset': 373, 'end_offset': 374}, {'char': ' ', 'start_offset': 382, 'end_offset': 384}, {'char': 'я', 'start_offset': 386, 'end_offset': 387}, {'char': 'к', 'start_offset': 390, 'end_offset': 391}, {'char': 'и', 'start_offset': 392, 'end_offset': 393}, {'char': 'й', 'start_offset': 394, 'end_offset': 395}, {'char': ' ', 'start_offset': 396, 'end_offset': 398}, {'char': 'п', 'start_offset': 399, 'end_offset': 401}, {'char': 'е', 'start_offset': 402, 'end_offset': 403}, {'char': 'р', 'start_offset': 406, 'end_offset': 407}, {'char': 'е', 'start_offset': 407, 'end_offset': 408}, {'char': 'д', 'start_offset': 411, 'end_offset': 412}, {'char': 'б', 'start_offset': 415, 'end_offset': 416}, {'char': 'а', 'start_offset': 416, 'end_offset': 417}, {'char': 'ч', 'start_offset': 424, 'end_offset': 425}, {'char': 'а', 'start_offset': 428, 'end_offset': 429}, {'char': 'є', 'start_offset': 437, 'end_offset': 438}, {'char': ' ', 'start_offset': 445, 'end_offset': 447}, {'char': 'я', 'start_offset': 448, 'end_offset': 449}, {'char': 'к', 'start_offset': 452, 'end_offset': 453}, {'char': 'щ', 'start_offset': 455, 'end_offset': 456}, {'char': 'о', 'start_offset': 457, 'end_offset': 458}, {'char': ' ', 'start_offset': 460, 'end_offset': 463}, {'char': 'к', 'start_offset': 463, 'end_offset': 464}, {'char': 'и', 'start_offset': 465, 'end_offset': 466}, {'char': 'т', 'start_offset': 470, 'end_offset': 471}, {'char': 'а', 'start_offset': 472, 'end_offset': 473}, {'char': 'й', 'start_offset': 478, 'end_offset': 480}, {'char': ' ', 'start_offset': 484, 'end_offset': 486}, {'char': 'н', 'start_offset': 487, 'end_offset': 488}, {'char': 'а', 'start_offset': 488, 'end_offset': 489}, {'char': 'п', 'start_offset': 493, 'end_offset': 494}, {'char': 'а', 'start_offset': 496, 'end_offset': 497}, {'char': 'д', 'start_offset': 502, 'end_offset': 503}, {'char': 'е', 'start_offset': 504, 'end_offset': 505}, {'char': ' ', 'start_offset': 509, 'end_offset': 511}, {'char': 'н', 'start_offset': 511, 'end_offset': 512}, {'char': 'а', 'start_offset': 513, 'end_offset': 514}, {'char': ' ', 'start_offset': 515, 'end_offset': 517}, {'char': 'т', 'start_offset': 518, 'end_offset': 519}, {'char': 'а', 'start_offset': 519, 'end_offset': 520}, {'char': 'й', 'start_offset': 524, 'end_offset': 525}, {'char': 'в', 'start_offset': 527, 'end_offset': 528}, {'char': 'а', 'start_offset': 529, 'end_offset': 530}, {'char': 'н', 'start_offset': 535, 'end_offset': 536}, {'char': 'ь', 'start_offset': 536, 'end_offset': 537}, {'char': ' ', 'start_offset': 552, 'end_offset': 555}, {'char': 'а', 'start_offset': 555, 'end_offset': 556}, {'char': 'м', 'start_offset': 561, 'end_offset': 562}, {'char': 'е', 'start_offset': 562, 'end_offset': 563}, {'char': 'р', 'start_offset': 566, 'end_offset': 567}, {'char': 'и', 'start_offset': 567, 'end_offset': 568}, {'char': 'к', 'start_offset': 572, 'end_offset': 573}, {'char': 'а', 'start_offset': 574, 'end_offset': 575}, {'char': 'н', 'start_offset': 579, 'end_offset': 580}, {'char': 'с', 'start_offset': 582, 'end_offset': 583}, {'char': 'ь', 'start_offset': 583, 'end_offset': 585}, {'char': 'к', 'start_offset': 586, 'end_offset': 587}, {'char': 'и', 'start_offset': 588, 'end_offset': 589}, {'char': 'й', 'start_offset': 589, 'end_offset': 590}, {'char': ' ', 'start_offset': 591, 'end_offset': 593}, {'char': 'в', 'start_offset': 594, 'end_offset': 595}, {'char': 'і', 'start_offset': 595, 'end_offset': 596}, {'char': 'й', 'start_offset': 600, 'end_offset': 601}, {'char': 'с', 'start_offset': 604, 'end_offset': 605}, {'char': 'ь', 'start_offset': 605, 'end_offset': 607}, {'char': 'к', 'start_offset': 609, 'end_offset': 611}, {'char': 'о', 'start_offset': 612, 'end_offset': 613}, {'char': 'в', 'start_offset': 620, 'end_offset': 621}, {'char': 'і', 'start_offset': 622, 'end_offset': 623}, {'char': ' ', 'start_offset': 637, 'end_offset': 639}, {'char': 'м', 'start_offset': 641, 'end_offset': 642}, {'char': 'а', 'start_offset': 643, 'end_offset': 644}, {'char': 'ю', 'start_offset': 651, 'end_offset': 652}, {'char': 'т', 'start_offset': 654, 'end_offset': 655}, {'char': 'ь', 'start_offset': 655, 'end_offset': 656}, {'char': ' ', 'start_offset': 657, 'end_offset': 659}, {'char': 'й', 'start_offset': 659, 'end_offset': 660}, {'char': 'о', 'start_offset': 660, 'end_offset': 662}, {'char': 'г', 'start_offset': 664, 'end_offset': 665}, {'char': 'о', 'start_offset': 666, 'end_offset': 667}, {'char': ' ', 'start_offset': 677, 'end_offset': 679}, {'char': 'з', 'start_offset': 681, 'end_offset': 682}, {'char': 'а', 'start_offset': 683, 'end_offset': 684}, {'char': 'х', 'start_offset': 686, 'end_offset': 687}, {'char': 'и', 'start_offset': 689, 'end_offset': 690}, {'char': 'щ', 'start_offset': 696, 'end_offset': 697}, {'char': 'а', 'start_offset': 698, 'end_offset': 699}, {'char': 'т', 'start_offset': 707, 'end_offset': 708}, {'char': 'и', 'start_offset': 709, 'end_offset': 710}, {'char': ' ', 'start_offset': 733, 'end_offset': 734}, {'char': 'е', 'start_offset': 740, 'end_offset': 741}, {'char': 'в', 'start_offset': 747, 'end_offset': 748}, {'char': 'у', 'start_offset': 748, 'end_offset': 749}, {'char': 'й', 'start_offset': 752, 'end_offset': 753}, {'char': 'в', 'start_offset': 754, 'end_offset': 755}, {'char': 'г', 'start_offset': 757, 'end_offset': 758}, {'char': 'е', 'start_offset': 759, 'end_offset': 760}, {'char': 'р', 'start_offset': 767, 'end_offset': 768}, {'char': 'е', 'start_offset': 768, 'end_offset': 769}], word_offsets=[{'word': 'паня', 'start_offset': 0, 'end_offset': 12}, {'word': 'сполучені', 'start_offset': 16, 'end_offset': 39}, {'word': 'штати', 'start_offset': 43, 'end_offset': 59}, {'word': 'надважливий', 'start_offset': 85, 'end_offset': 126}, {'word': 'стратегічний', 'start_offset': 130, 'end_offset': 171}, {'word': 'партнер', 'start_offset': 174, 'end_offset': 194}, {'word': 'однак', 'start_offset': 204, 'end_offset': 225}, {'word': 'є', 'start_offset': 233, 'end_offset': 234}, {'word': 'різниця', 'start_offset': 240, 'end_offset': 263}, {'word': 'штати', 'start_offset': 283, 'end_offset': 297}, {'word': 'мають', 'start_offset': 300, 'end_offset': 311}, {'word': 'спеціальни', 'start_offset': 313, 'end_offset': 342}, {'word': 'закон', 'start_offset': 351, 'end_offset': 374}, {'word': 'який', 'start_offset': 386, 'end_offset': 395}, {'word': 'передбачає', 'start_offset': 399, 'end_offset': 438}, {'word': 'якщо', 'start_offset': 448, 'end_offset': 458}, {'word': 'китай', 'start_offset': 463, 'end_offset': 480}, {'word': 'нападе', 'start_offset': 487, 'end_offset': 505}, {'word': 'на', 'start_offset': 511, 'end_offset': 514}, {'word': 'тайвань', 'start_offset': 518, 'end_offset': 537}, {'word': 'американський', 'start_offset': 555, 'end_offset': 590}, {'word': 'військові', 'start_offset': 594, 'end_offset': 623}, {'word': 'мають', 'start_offset': 641, 'end_offset': 656}, {'word': 'його', 'start_offset': 659, 'end_offset': 667}, {'word': 'захищати', 'start_offset': 681, 'end_offset': 710}, {'word': 'евуйвгере', 'start_offset': 740, 'end_offset': 769}]) -``` - -### Split by seconds - -``` -0.0 - 0.24: паня -0.32 - 0.78: сполучені -0.86 - 1.18: штати -1.7 - 2.52: надважливий -2.6 - 3.42: стратегічний -3.48 - 3.88: партнер -4.08 - 4.5: однак -4.66 - 4.68: є -4.8 - 5.26: різниця -5.66 - 5.94: штати -6.0 - 6.22: мають -6.26 - 6.84: спеціальни -7.02 - 7.48: закон -7.72 - 7.9: який -7.98 - 8.76: передбачає -8.96 - 9.16: якщо -9.26 - 9.6: китай -9.74 - 10.1: нападе -10.22 - 10.28: на -10.36 - 10.74: тайвань -11.1 - 11.8: американський -11.88 - 12.46: військові -12.82 - 13.12: мають -13.18 - 13.34: його -13.62 - 14.2: захищати -14.8 - 15.38: евуйвгере -``` diff --git a/spaces/pixiou/bingo/src/lib/isomorphic/index.ts b/spaces/pixiou/bingo/src/lib/isomorphic/index.ts deleted file mode 100644 index 738dc92f74079ab762d584fb7422a8c8c3b61547..0000000000000000000000000000000000000000 --- a/spaces/pixiou/bingo/src/lib/isomorphic/index.ts +++ /dev/null @@ -1,17 +0,0 @@ -'use client' - -import Default from './browser' - -let exportsModel: any = {} - -if (process.browser) { - Object.assign(exportsModel, require('./browser').default) -} else { - Object.assign(exportsModel, require('./node').default) -} - -export default exportsModel! as typeof Default - -export const fetch: typeof Default.fetch = exportsModel!.fetch -export const WebSocket: typeof Default.WebSocket = exportsModel!.WebSocket -export const debug: typeof Default.debug = exportsModel!.debug diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/resolution/resolvelib/found_candidates.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/resolution/resolvelib/found_candidates.py deleted file mode 100644 index 8663097b447cdd80c52e2b2abde33a4736ddb9c2..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/resolution/resolvelib/found_candidates.py +++ /dev/null @@ -1,155 +0,0 @@ -"""Utilities to lazily create and visit candidates found. - -Creating and visiting a candidate is a *very* costly operation. It involves -fetching, extracting, potentially building modules from source, and verifying -distribution metadata. It is therefore crucial for performance to keep -everything here lazy all the way down, so we only touch candidates that we -absolutely need, and not "download the world" when we only need one version of -something. -""" - -import functools -from collections.abc import Sequence -from typing import TYPE_CHECKING, Any, Callable, Iterator, Optional, Set, Tuple - -from pip._vendor.packaging.version import _BaseVersion - -from .base import Candidate - -IndexCandidateInfo = Tuple[_BaseVersion, Callable[[], Optional[Candidate]]] - -if TYPE_CHECKING: - SequenceCandidate = Sequence[Candidate] -else: - # For compatibility: Python before 3.9 does not support using [] on the - # Sequence class. - # - # >>> from collections.abc import Sequence - # >>> Sequence[str] - # Traceback (most recent call last): - # File "Resources
- Paper, official implementation, Model Card. - -LICENSE
- MIT. - -Biases and content acknowledgment
-Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on 600 million images from the improved LAION-5B aesthetic dataset, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. - -", line 1, in - # TypeError: 'ABCMeta' object is not subscriptable - # - # TODO: Remove this block after dropping Python 3.8 support. - SequenceCandidate = Sequence - - -def _iter_built(infos: Iterator[IndexCandidateInfo]) -> Iterator[Candidate]: - """Iterator for ``FoundCandidates``. - - This iterator is used when the package is not already installed. Candidates - from index come later in their normal ordering. - """ - versions_found: Set[_BaseVersion] = set() - for version, func in infos: - if version in versions_found: - continue - candidate = func() - if candidate is None: - continue - yield candidate - versions_found.add(version) - - -def _iter_built_with_prepended( - installed: Candidate, infos: Iterator[IndexCandidateInfo] -) -> Iterator[Candidate]: - """Iterator for ``FoundCandidates``. - - This iterator is used when the resolver prefers the already-installed - candidate and NOT to upgrade. The installed candidate is therefore - always yielded first, and candidates from index come later in their - normal ordering, except skipped when the version is already installed. - """ - yield installed - versions_found: Set[_BaseVersion] = {installed.version} - for version, func in infos: - if version in versions_found: - continue - candidate = func() - if candidate is None: - continue - yield candidate - versions_found.add(version) - - -def _iter_built_with_inserted( - installed: Candidate, infos: Iterator[IndexCandidateInfo] -) -> Iterator[Candidate]: - """Iterator for ``FoundCandidates``. - - This iterator is used when the resolver prefers to upgrade an - already-installed package. Candidates from index are returned in their - normal ordering, except replaced when the version is already installed. - - The implementation iterates through and yields other candidates, inserting - the installed candidate exactly once before we start yielding older or - equivalent candidates, or after all other candidates if they are all newer. - """ - versions_found: Set[_BaseVersion] = set() - for version, func in infos: - if version in versions_found: - continue - # If the installed candidate is better, yield it first. - if installed.version >= version: - yield installed - versions_found.add(installed.version) - candidate = func() - if candidate is None: - continue - yield candidate - versions_found.add(version) - - # If the installed candidate is older than all other candidates. - if installed.version not in versions_found: - yield installed - - -class FoundCandidates(SequenceCandidate): - """A lazy sequence to provide candidates to the resolver. - - The intended usage is to return this from `find_matches()` so the resolver - can iterate through the sequence multiple times, but only access the index - page when remote packages are actually needed. This improve performances - when suitable candidates are already installed on disk. - """ - - def __init__( - self, - get_infos: Callable[[], Iterator[IndexCandidateInfo]], - installed: Optional[Candidate], - prefers_installed: bool, - incompatible_ids: Set[int], - ): - self._get_infos = get_infos - self._installed = installed - self._prefers_installed = prefers_installed - self._incompatible_ids = incompatible_ids - - def __getitem__(self, index: Any) -> Any: - # Implemented to satisfy the ABC check. This is not needed by the - # resolver, and should not be used by the provider either (for - # performance reasons). - raise NotImplementedError("don't do this") - - def __iter__(self) -> Iterator[Candidate]: - infos = self._get_infos() - if not self._installed: - iterator = _iter_built(infos) - elif self._prefers_installed: - iterator = _iter_built_with_prepended(self._installed, infos) - else: - iterator = _iter_built_with_inserted(self._installed, infos) - return (c for c in iterator if id(c) not in self._incompatible_ids) - - def __len__(self) -> int: - # Implemented to satisfy the ABC check. This is not needed by the - # resolver, and should not be used by the provider either (for - # performance reasons). - raise NotImplementedError("don't do this") - - @functools.lru_cache(maxsize=1) - def __bool__(self) -> bool: - if self._prefers_installed and self._installed: - return True - return any(self) diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py deleted file mode 100644 index ee511ff20d73bb245fe7ae0c1fc31a41c33e7d29..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py +++ /dev/null @@ -1,353 +0,0 @@ -"""This is invoked in a subprocess to call the build backend hooks. - -It expects: -- Command line args: hook_name, control_dir -- Environment variables: - PEP517_BUILD_BACKEND=entry.point:spec - PEP517_BACKEND_PATH=paths (separated with os.pathsep) -- control_dir/input.json: - - {"kwargs": {...}} - -Results: -- control_dir/output.json - - {"return_val": ...} -""" -import json -import os -import os.path -import re -import shutil -import sys -import traceback -from glob import glob -from importlib import import_module -from os.path import join as pjoin - -# This file is run as a script, and `import wrappers` is not zip-safe, so we -# include write_json() and read_json() from wrappers.py. - - -def write_json(obj, path, **kwargs): - with open(path, 'w', encoding='utf-8') as f: - json.dump(obj, f, **kwargs) - - -def read_json(path): - with open(path, encoding='utf-8') as f: - return json.load(f) - - -class BackendUnavailable(Exception): - """Raised if we cannot import the backend""" - def __init__(self, traceback): - self.traceback = traceback - - -class BackendInvalid(Exception): - """Raised if the backend is invalid""" - def __init__(self, message): - self.message = message - - -class HookMissing(Exception): - """Raised if a hook is missing and we are not executing the fallback""" - def __init__(self, hook_name=None): - super().__init__(hook_name) - self.hook_name = hook_name - - -def contained_in(filename, directory): - """Test if a file is located within the given directory.""" - filename = os.path.normcase(os.path.abspath(filename)) - directory = os.path.normcase(os.path.abspath(directory)) - return os.path.commonprefix([filename, directory]) == directory - - -def _build_backend(): - """Find and load the build backend""" - # Add in-tree backend directories to the front of sys.path. - backend_path = os.environ.get('PEP517_BACKEND_PATH') - if backend_path: - extra_pathitems = backend_path.split(os.pathsep) - sys.path[:0] = extra_pathitems - - ep = os.environ['PEP517_BUILD_BACKEND'] - mod_path, _, obj_path = ep.partition(':') - try: - obj = import_module(mod_path) - except ImportError: - raise BackendUnavailable(traceback.format_exc()) - - if backend_path: - if not any( - contained_in(obj.__file__, path) - for path in extra_pathitems - ): - raise BackendInvalid("Backend was not loaded from backend-path") - - if obj_path: - for path_part in obj_path.split('.'): - obj = getattr(obj, path_part) - return obj - - -def _supported_features(): - """Return the list of options features supported by the backend. - - Returns a list of strings. - The only possible value is 'build_editable'. - """ - backend = _build_backend() - features = [] - if hasattr(backend, "build_editable"): - features.append("build_editable") - return features - - -def get_requires_for_build_wheel(config_settings): - """Invoke the optional get_requires_for_build_wheel hook - - Returns [] if the hook is not defined. - """ - backend = _build_backend() - try: - hook = backend.get_requires_for_build_wheel - except AttributeError: - return [] - else: - return hook(config_settings) - - -def get_requires_for_build_editable(config_settings): - """Invoke the optional get_requires_for_build_editable hook - - Returns [] if the hook is not defined. - """ - backend = _build_backend() - try: - hook = backend.get_requires_for_build_editable - except AttributeError: - return [] - else: - return hook(config_settings) - - -def prepare_metadata_for_build_wheel( - metadata_directory, config_settings, _allow_fallback): - """Invoke optional prepare_metadata_for_build_wheel - - Implements a fallback by building a wheel if the hook isn't defined, - unless _allow_fallback is False in which case HookMissing is raised. - """ - backend = _build_backend() - try: - hook = backend.prepare_metadata_for_build_wheel - except AttributeError: - if not _allow_fallback: - raise HookMissing() - else: - return hook(metadata_directory, config_settings) - # fallback to build_wheel outside the try block to avoid exception chaining - # which can be confusing to users and is not relevant - whl_basename = backend.build_wheel(metadata_directory, config_settings) - return _get_wheel_metadata_from_wheel(whl_basename, metadata_directory, - config_settings) - - -def prepare_metadata_for_build_editable( - metadata_directory, config_settings, _allow_fallback): - """Invoke optional prepare_metadata_for_build_editable - - Implements a fallback by building an editable wheel if the hook isn't - defined, unless _allow_fallback is False in which case HookMissing is - raised. - """ - backend = _build_backend() - try: - hook = backend.prepare_metadata_for_build_editable - except AttributeError: - if not _allow_fallback: - raise HookMissing() - try: - build_hook = backend.build_editable - except AttributeError: - raise HookMissing(hook_name='build_editable') - else: - whl_basename = build_hook(metadata_directory, config_settings) - return _get_wheel_metadata_from_wheel(whl_basename, - metadata_directory, - config_settings) - else: - return hook(metadata_directory, config_settings) - - -WHEEL_BUILT_MARKER = 'PEP517_ALREADY_BUILT_WHEEL' - - -def _dist_info_files(whl_zip): - """Identify the .dist-info folder inside a wheel ZipFile.""" - res = [] - for path in whl_zip.namelist(): - m = re.match(r'[^/\\]+-[^/\\]+\.dist-info/', path) - if m: - res.append(path) - if res: - return res - raise Exception("No .dist-info folder found in wheel") - - -def _get_wheel_metadata_from_wheel( - whl_basename, metadata_directory, config_settings): - """Extract the metadata from a wheel. - - Fallback for when the build backend does not - define the 'get_wheel_metadata' hook. - """ - from zipfile import ZipFile - with open(os.path.join(metadata_directory, WHEEL_BUILT_MARKER), 'wb'): - pass # Touch marker file - - whl_file = os.path.join(metadata_directory, whl_basename) - with ZipFile(whl_file) as zipf: - dist_info = _dist_info_files(zipf) - zipf.extractall(path=metadata_directory, members=dist_info) - return dist_info[0].split('/')[0] - - -def _find_already_built_wheel(metadata_directory): - """Check for a wheel already built during the get_wheel_metadata hook. - """ - if not metadata_directory: - return None - metadata_parent = os.path.dirname(metadata_directory) - if not os.path.isfile(pjoin(metadata_parent, WHEEL_BUILT_MARKER)): - return None - - whl_files = glob(os.path.join(metadata_parent, '*.whl')) - if not whl_files: - print('Found wheel built marker, but no .whl files') - return None - if len(whl_files) > 1: - print('Found multiple .whl files; unspecified behaviour. ' - 'Will call build_wheel.') - return None - - # Exactly one .whl file - return whl_files[0] - - -def build_wheel(wheel_directory, config_settings, metadata_directory=None): - """Invoke the mandatory build_wheel hook. - - If a wheel was already built in the - prepare_metadata_for_build_wheel fallback, this - will copy it rather than rebuilding the wheel. - """ - prebuilt_whl = _find_already_built_wheel(metadata_directory) - if prebuilt_whl: - shutil.copy2(prebuilt_whl, wheel_directory) - return os.path.basename(prebuilt_whl) - - return _build_backend().build_wheel(wheel_directory, config_settings, - metadata_directory) - - -def build_editable(wheel_directory, config_settings, metadata_directory=None): - """Invoke the optional build_editable hook. - - If a wheel was already built in the - prepare_metadata_for_build_editable fallback, this - will copy it rather than rebuilding the wheel. - """ - backend = _build_backend() - try: - hook = backend.build_editable - except AttributeError: - raise HookMissing() - else: - prebuilt_whl = _find_already_built_wheel(metadata_directory) - if prebuilt_whl: - shutil.copy2(prebuilt_whl, wheel_directory) - return os.path.basename(prebuilt_whl) - - return hook(wheel_directory, config_settings, metadata_directory) - - -def get_requires_for_build_sdist(config_settings): - """Invoke the optional get_requires_for_build_wheel hook - - Returns [] if the hook is not defined. - """ - backend = _build_backend() - try: - hook = backend.get_requires_for_build_sdist - except AttributeError: - return [] - else: - return hook(config_settings) - - -class _DummyException(Exception): - """Nothing should ever raise this exception""" - - -class GotUnsupportedOperation(Exception): - """For internal use when backend raises UnsupportedOperation""" - def __init__(self, traceback): - self.traceback = traceback - - -def build_sdist(sdist_directory, config_settings): - """Invoke the mandatory build_sdist hook.""" - backend = _build_backend() - try: - return backend.build_sdist(sdist_directory, config_settings) - except getattr(backend, 'UnsupportedOperation', _DummyException): - raise GotUnsupportedOperation(traceback.format_exc()) - - -HOOK_NAMES = { - 'get_requires_for_build_wheel', - 'prepare_metadata_for_build_wheel', - 'build_wheel', - 'get_requires_for_build_editable', - 'prepare_metadata_for_build_editable', - 'build_editable', - 'get_requires_for_build_sdist', - 'build_sdist', - '_supported_features', -} - - -def main(): - if len(sys.argv) < 3: - sys.exit("Needs args: hook_name, control_dir") - hook_name = sys.argv[1] - control_dir = sys.argv[2] - if hook_name not in HOOK_NAMES: - sys.exit("Unknown hook: %s" % hook_name) - hook = globals()[hook_name] - - hook_input = read_json(pjoin(control_dir, 'input.json')) - - json_out = {'unsupported': False, 'return_val': None} - try: - json_out['return_val'] = hook(**hook_input['kwargs']) - except BackendUnavailable as e: - json_out['no_backend'] = True - json_out['traceback'] = e.traceback - except BackendInvalid as e: - json_out['backend_invalid'] = True - json_out['backend_error'] = e.message - except GotUnsupportedOperation as e: - json_out['unsupported'] = True - json_out['traceback'] = e.traceback - except HookMissing as e: - json_out['hook_missing'] = True - json_out['missing_hook_name'] = e.hook_name or hook_name - - write_json(json_out, pjoin(control_dir, 'output.json'), indent=2) - - -if __name__ == '__main__': - main() diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/encodings/MacRoman.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/encodings/MacRoman.py deleted file mode 100644 index ba8bf14ef7de1cf76248a2bbd1a98bc8bf36cc5e..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/encodings/MacRoman.py +++ /dev/null @@ -1,258 +0,0 @@ -MacRoman = [ - "NUL", - "Eth", - "eth", - "Lslash", - "lslash", - "Scaron", - "scaron", - "Yacute", - "yacute", - "HT", - "LF", - "Thorn", - "thorn", - "CR", - "Zcaron", - "zcaron", - "DLE", - "DC1", - "DC2", - "DC3", - "DC4", - "onehalf", - "onequarter", - "onesuperior", - "threequarters", - "threesuperior", - "twosuperior", - "brokenbar", - "minus", - "multiply", - "RS", - "US", - "space", - "exclam", - "quotedbl", - "numbersign", - "dollar", - "percent", - "ampersand", - "quotesingle", - "parenleft", - "parenright", - "asterisk", - "plus", - "comma", - "hyphen", - "period", - "slash", - "zero", - "one", - "two", - "three", - "four", - "five", - "six", - "seven", - "eight", - "nine", - "colon", - "semicolon", - "less", - "equal", - "greater", - "question", - "at", - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "J", - "K", - "L", - "M", - "N", - "O", - "P", - "Q", - "R", - "S", - "T", - "U", - "V", - "W", - "X", - "Y", - "Z", - "bracketleft", - "backslash", - "bracketright", - "asciicircum", - "underscore", - "grave", - "a", - "b", - "c", - "d", - "e", - "f", - "g", - "h", - "i", - "j", - "k", - "l", - "m", - "n", - "o", - "p", - "q", - "r", - "s", - "t", - "u", - "v", - "w", - "x", - "y", - "z", - "braceleft", - "bar", - "braceright", - "asciitilde", - "DEL", - "Adieresis", - "Aring", - "Ccedilla", - "Eacute", - "Ntilde", - "Odieresis", - "Udieresis", - "aacute", - "agrave", - "acircumflex", - "adieresis", - "atilde", - "aring", - "ccedilla", - "eacute", - "egrave", - "ecircumflex", - "edieresis", - "iacute", - "igrave", - "icircumflex", - "idieresis", - "ntilde", - "oacute", - "ograve", - "ocircumflex", - "odieresis", - "otilde", - "uacute", - "ugrave", - "ucircumflex", - "udieresis", - "dagger", - "degree", - "cent", - "sterling", - "section", - "bullet", - "paragraph", - "germandbls", - "registered", - "copyright", - "trademark", - "acute", - "dieresis", - "notequal", - "AE", - "Oslash", - "infinity", - "plusminus", - "lessequal", - "greaterequal", - "yen", - "mu", - "partialdiff", - "summation", - "product", - "pi", - "integral", - "ordfeminine", - "ordmasculine", - "Omega", - "ae", - "oslash", - "questiondown", - "exclamdown", - "logicalnot", - "radical", - "florin", - "approxequal", - "Delta", - "guillemotleft", - "guillemotright", - "ellipsis", - "nbspace", - "Agrave", - "Atilde", - "Otilde", - "OE", - "oe", - "endash", - "emdash", - "quotedblleft", - "quotedblright", - "quoteleft", - "quoteright", - "divide", - "lozenge", - "ydieresis", - "Ydieresis", - "fraction", - "currency", - "guilsinglleft", - "guilsinglright", - "fi", - "fl", - "daggerdbl", - "periodcentered", - "quotesinglbase", - "quotedblbase", - "perthousand", - "Acircumflex", - "Ecircumflex", - "Aacute", - "Edieresis", - "Egrave", - "Iacute", - "Icircumflex", - "Idieresis", - "Igrave", - "Oacute", - "Ocircumflex", - "apple", - "Ograve", - "Uacute", - "Ucircumflex", - "Ugrave", - "dotlessi", - "circumflex", - "tilde", - "macron", - "breve", - "dotaccent", - "ring", - "cedilla", - "hungarumlaut", - "ogonek", - "caron", -] diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/markdown_it/cli/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/markdown_it/cli/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/memmap.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/memmap.py deleted file mode 100644 index 79c695455d343365d2a299bbe499f7eac1d0906b..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/memmap.py +++ /dev/null @@ -1,338 +0,0 @@ -from contextlib import nullcontext - -import numpy as np -from .._utils import set_module -from .numeric import uint8, ndarray, dtype -from numpy.compat import os_fspath, is_pathlib_path - -__all__ = ['memmap'] - -dtypedescr = dtype -valid_filemodes = ["r", "c", "r+", "w+"] -writeable_filemodes = ["r+", "w+"] - -mode_equivalents = { - "readonly":"r", - "copyonwrite":"c", - "readwrite":"r+", - "write":"w+" - } - - -@set_module('numpy') -class memmap(ndarray): - """Create a memory-map to an array stored in a *binary* file on disk. - - Memory-mapped files are used for accessing small segments of large files - on disk, without reading the entire file into memory. NumPy's - memmap's are array-like objects. This differs from Python's ``mmap`` - module, which uses file-like objects. - - This subclass of ndarray has some unpleasant interactions with - some operations, because it doesn't quite fit properly as a subclass. - An alternative to using this subclass is to create the ``mmap`` - object yourself, then create an ndarray with ndarray.__new__ directly, - passing the object created in its 'buffer=' parameter. - - This class may at some point be turned into a factory function - which returns a view into an mmap buffer. - - Flush the memmap instance to write the changes to the file. Currently there - is no API to close the underlying ``mmap``. It is tricky to ensure the - resource is actually closed, since it may be shared between different - memmap instances. - - - Parameters - ---------- - filename : str, file-like object, or pathlib.Path instance - The file name or file object to be used as the array data buffer. - dtype : data-type, optional - The data-type used to interpret the file contents. - Default is `uint8`. - mode : {'r+', 'r', 'w+', 'c'}, optional - The file is opened in this mode: - - +------+-------------------------------------------------------------+ - | 'r' | Open existing file for reading only. | - +------+-------------------------------------------------------------+ - | 'r+' | Open existing file for reading and writing. | - +------+-------------------------------------------------------------+ - | 'w+' | Create or overwrite existing file for reading and writing. | - | | If ``mode == 'w+'`` then `shape` must also be specified. | - +------+-------------------------------------------------------------+ - | 'c' | Copy-on-write: assignments affect data in memory, but | - | | changes are not saved to disk. The file on disk is | - | | read-only. | - +------+-------------------------------------------------------------+ - - Default is 'r+'. - offset : int, optional - In the file, array data starts at this offset. Since `offset` is - measured in bytes, it should normally be a multiple of the byte-size - of `dtype`. When ``mode != 'r'``, even positive offsets beyond end of - file are valid; The file will be extended to accommodate the - additional data. By default, ``memmap`` will start at the beginning of - the file, even if ``filename`` is a file pointer ``fp`` and - ``fp.tell() != 0``. - shape : tuple, optional - The desired shape of the array. If ``mode == 'r'`` and the number - of remaining bytes after `offset` is not a multiple of the byte-size - of `dtype`, you must specify `shape`. By default, the returned array - will be 1-D with the number of elements determined by file size - and data-type. - order : {'C', 'F'}, optional - Specify the order of the ndarray memory layout: - :term:`row-major`, C-style or :term:`column-major`, - Fortran-style. This only has an effect if the shape is - greater than 1-D. The default order is 'C'. - - Attributes - ---------- - filename : str or pathlib.Path instance - Path to the mapped file. - offset : int - Offset position in the file. - mode : str - File mode. - - Methods - ------- - flush - Flush any changes in memory to file on disk. - When you delete a memmap object, flush is called first to write - changes to disk. - - - See also - -------- - lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file. - - Notes - ----- - The memmap object can be used anywhere an ndarray is accepted. - Given a memmap ``fp``, ``isinstance(fp, numpy.ndarray)`` returns - ``True``. - - Memory-mapped files cannot be larger than 2GB on 32-bit systems. - - When a memmap causes a file to be created or extended beyond its - current size in the filesystem, the contents of the new part are - unspecified. On systems with POSIX filesystem semantics, the extended - part will be filled with zero bytes. - - Examples - -------- - >>> data = np.arange(12, dtype='float32') - >>> data.resize((3,4)) - - This example uses a temporary file so that doctest doesn't write - files to your directory. You would use a 'normal' filename. - - >>> from tempfile import mkdtemp - >>> import os.path as path - >>> filename = path.join(mkdtemp(), 'newfile.dat') - - Create a memmap with dtype and shape that matches our data: - - >>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4)) - >>> fp - memmap([[0., 0., 0., 0.], - [0., 0., 0., 0.], - [0., 0., 0., 0.]], dtype=float32) - - Write data to memmap array: - - >>> fp[:] = data[:] - >>> fp - memmap([[ 0., 1., 2., 3.], - [ 4., 5., 6., 7.], - [ 8., 9., 10., 11.]], dtype=float32) - - >>> fp.filename == path.abspath(filename) - True - - Flushes memory changes to disk in order to read them back - - >>> fp.flush() - - Load the memmap and verify data was stored: - - >>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4)) - >>> newfp - memmap([[ 0., 1., 2., 3.], - [ 4., 5., 6., 7.], - [ 8., 9., 10., 11.]], dtype=float32) - - Read-only memmap: - - >>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4)) - >>> fpr.flags.writeable - False - - Copy-on-write memmap: - - >>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4)) - >>> fpc.flags.writeable - True - - It's possible to assign to copy-on-write array, but values are only - written into the memory copy of the array, and not written to disk: - - >>> fpc - memmap([[ 0., 1., 2., 3.], - [ 4., 5., 6., 7.], - [ 8., 9., 10., 11.]], dtype=float32) - >>> fpc[0,:] = 0 - >>> fpc - memmap([[ 0., 0., 0., 0.], - [ 4., 5., 6., 7.], - [ 8., 9., 10., 11.]], dtype=float32) - - File on disk is unchanged: - - >>> fpr - memmap([[ 0., 1., 2., 3.], - [ 4., 5., 6., 7.], - [ 8., 9., 10., 11.]], dtype=float32) - - Offset into a memmap: - - >>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16) - >>> fpo - memmap([ 4., 5., 6., 7., 8., 9., 10., 11.], dtype=float32) - - """ - - __array_priority__ = -100.0 - - def __new__(subtype, filename, dtype=uint8, mode='r+', offset=0, - shape=None, order='C'): - # Import here to minimize 'import numpy' overhead - import mmap - import os.path - try: - mode = mode_equivalents[mode] - except KeyError as e: - if mode not in valid_filemodes: - raise ValueError( - "mode must be one of {!r} (got {!r})" - .format(valid_filemodes + list(mode_equivalents.keys()), mode) - ) from None - - if mode == 'w+' and shape is None: - raise ValueError("shape must be given if mode == 'w+'") - - if hasattr(filename, 'read'): - f_ctx = nullcontext(filename) - else: - f_ctx = open(os_fspath(filename), ('r' if mode == 'c' else mode)+'b') - - with f_ctx as fid: - fid.seek(0, 2) - flen = fid.tell() - descr = dtypedescr(dtype) - _dbytes = descr.itemsize - - if shape is None: - bytes = flen - offset - if bytes % _dbytes: - raise ValueError("Size of available data is not a " - "multiple of the data-type size.") - size = bytes // _dbytes - shape = (size,) - else: - if not isinstance(shape, tuple): - shape = (shape,) - size = np.intp(1) # avoid default choice of np.int_, which might overflow - for k in shape: - size *= k - - bytes = int(offset + size*_dbytes) - - if mode in ('w+', 'r+') and flen < bytes: - fid.seek(bytes - 1, 0) - fid.write(b'\0') - fid.flush() - - if mode == 'c': - acc = mmap.ACCESS_COPY - elif mode == 'r': - acc = mmap.ACCESS_READ - else: - acc = mmap.ACCESS_WRITE - - start = offset - offset % mmap.ALLOCATIONGRANULARITY - bytes -= start - array_offset = offset - start - mm = mmap.mmap(fid.fileno(), bytes, access=acc, offset=start) - - self = ndarray.__new__(subtype, shape, dtype=descr, buffer=mm, - offset=array_offset, order=order) - self._mmap = mm - self.offset = offset - self.mode = mode - - if is_pathlib_path(filename): - # special case - if we were constructed with a pathlib.path, - # then filename is a path object, not a string - self.filename = filename.resolve() - elif hasattr(fid, "name") and isinstance(fid.name, str): - # py3 returns int for TemporaryFile().name - self.filename = os.path.abspath(fid.name) - # same as memmap copies (e.g. memmap + 1) - else: - self.filename = None - - return self - - def __array_finalize__(self, obj): - if hasattr(obj, '_mmap') and np.may_share_memory(self, obj): - self._mmap = obj._mmap - self.filename = obj.filename - self.offset = obj.offset - self.mode = obj.mode - else: - self._mmap = None - self.filename = None - self.offset = None - self.mode = None - - def flush(self): - """ - Write any changes in the array to the file on disk. - - For further information, see `memmap`. - - Parameters - ---------- - None - - See Also - -------- - memmap - - """ - if self.base is not None and hasattr(self.base, 'flush'): - self.base.flush() - - def __array_wrap__(self, arr, context=None): - arr = super().__array_wrap__(arr, context) - - # Return a memmap if a memmap was given as the output of the - # ufunc. Leave the arr class unchanged if self is not a memmap - # to keep original memmap subclasses behavior - if self is arr or type(self) is not memmap: - return arr - # Return scalar instead of 0d memmap, e.g. for np.sum with - # axis=None - if arr.shape == (): - return arr[()] - # Return ndarray otherwise - return arr.view(np.ndarray) - - def __getitem__(self, index): - res = super().__getitem__(index) - if type(res) is memmap and res._mmap is None: - return res.view(type=ndarray) - return res diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/mingw/gfortran_vs2003_hack.c b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/mingw/gfortran_vs2003_hack.c deleted file mode 100644 index 485a675d8a1fb80bc4927fe236ba3fe550f5a0c9..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/mingw/gfortran_vs2003_hack.c +++ /dev/null @@ -1,6 +0,0 @@ -int _get_output_format(void) -{ - return 0; -} - -int _imp____lc_codepage = 0; diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/mingw32ccompiler.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/mingw32ccompiler.py deleted file mode 100644 index 4763f41ad326d464355fd82ceccb019e1e55edf0..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/mingw32ccompiler.py +++ /dev/null @@ -1,591 +0,0 @@ -""" -Support code for building Python extensions on Windows. - - # NT stuff - # 1. Make sure libpython .a exists for gcc. If not, build it. - # 2. Force windows to use gcc (we're struggling with MSVC and g77 support) - # 3. Force windows to use g77 - -""" -import os -import sys -import subprocess -import re -import textwrap - -# Overwrite certain distutils.ccompiler functions: -import numpy.distutils.ccompiler # noqa: F401 -from numpy.distutils import log -# NT stuff -# 1. Make sure libpython .a exists for gcc. If not, build it. -# 2. Force windows to use gcc (we're struggling with MSVC and g77 support) -# --> this is done in numpy/distutils/ccompiler.py -# 3. Force windows to use g77 - -import distutils.cygwinccompiler -from distutils.unixccompiler import UnixCCompiler -from distutils.msvccompiler import get_build_version as get_build_msvc_version -from distutils.errors import UnknownFileError -from numpy.distutils.misc_util import (msvc_runtime_library, - msvc_runtime_version, - msvc_runtime_major, - get_build_architecture) - -def get_msvcr_replacement(): - """Replacement for outdated version of get_msvcr from cygwinccompiler""" - msvcr = msvc_runtime_library() - return [] if msvcr is None else [msvcr] - - -# Useful to generate table of symbols from a dll -_START = re.compile(r'\[Ordinal/Name Pointer\] Table') -_TABLE = re.compile(r'^\s+\[([\s*[0-9]*)\] ([a-zA-Z0-9_]*)') - -# the same as cygwin plus some additional parameters -class Mingw32CCompiler(distutils.cygwinccompiler.CygwinCCompiler): - """ A modified MingW32 compiler compatible with an MSVC built Python. - - """ - - compiler_type = 'mingw32' - - def __init__ (self, - verbose=0, - dry_run=0, - force=0): - - distutils.cygwinccompiler.CygwinCCompiler.__init__ (self, verbose, - dry_run, force) - - # **changes: eric jones 4/11/01 - # 1. Check for import library on Windows. Build if it doesn't exist. - - build_import_library() - - # Check for custom msvc runtime library on Windows. Build if it doesn't exist. - msvcr_success = build_msvcr_library() - msvcr_dbg_success = build_msvcr_library(debug=True) - if msvcr_success or msvcr_dbg_success: - # add preprocessor statement for using customized msvcr lib - self.define_macro('NPY_MINGW_USE_CUSTOM_MSVCR') - - # Define the MSVC version as hint for MinGW - msvcr_version = msvc_runtime_version() - if msvcr_version: - self.define_macro('__MSVCRT_VERSION__', '0x%04i' % msvcr_version) - - # MS_WIN64 should be defined when building for amd64 on windows, - # but python headers define it only for MS compilers, which has all - # kind of bad consequences, like using Py_ModuleInit4 instead of - # Py_ModuleInit4_64, etc... So we add it here - if get_build_architecture() == 'AMD64': - self.set_executables( - compiler='gcc -g -DDEBUG -DMS_WIN64 -O0 -Wall', - compiler_so='gcc -g -DDEBUG -DMS_WIN64 -O0 -Wall ' - '-Wstrict-prototypes', - linker_exe='gcc -g', - linker_so='gcc -g -shared') - else: - self.set_executables( - compiler='gcc -O2 -Wall', - compiler_so='gcc -O2 -Wall -Wstrict-prototypes', - linker_exe='g++ ', - linker_so='g++ -shared') - # added for python2.3 support - # we can't pass it through set_executables because pre 2.2 would fail - self.compiler_cxx = ['g++'] - - # Maybe we should also append -mthreads, but then the finished dlls - # need another dll (mingwm10.dll see Mingw32 docs) (-mthreads: Support - # thread-safe exception handling on `Mingw32') - - # no additional libraries needed - #self.dll_libraries=[] - return - - # __init__ () - - def link(self, - target_desc, - objects, - output_filename, - output_dir, - libraries, - library_dirs, - runtime_library_dirs, - export_symbols = None, - debug=0, - extra_preargs=None, - extra_postargs=None, - build_temp=None, - target_lang=None): - # Include the appropriate MSVC runtime library if Python was built - # with MSVC >= 7.0 (MinGW standard is msvcrt) - runtime_library = msvc_runtime_library() - if runtime_library: - if not libraries: - libraries = [] - libraries.append(runtime_library) - args = (self, - target_desc, - objects, - output_filename, - output_dir, - libraries, - library_dirs, - runtime_library_dirs, - None, #export_symbols, we do this in our def-file - debug, - extra_preargs, - extra_postargs, - build_temp, - target_lang) - func = UnixCCompiler.link - func(*args[:func.__code__.co_argcount]) - return - - def object_filenames (self, - source_filenames, - strip_dir=0, - output_dir=''): - if output_dir is None: output_dir = '' - obj_names = [] - for src_name in source_filenames: - # use normcase to make sure '.rc' is really '.rc' and not '.RC' - (base, ext) = os.path.splitext (os.path.normcase(src_name)) - - # added these lines to strip off windows drive letters - # without it, .o files are placed next to .c files - # instead of the build directory - drv, base = os.path.splitdrive(base) - if drv: - base = base[1:] - - if ext not in (self.src_extensions + ['.rc', '.res']): - raise UnknownFileError( - "unknown file type '%s' (from '%s')" % \ - (ext, src_name)) - if strip_dir: - base = os.path.basename (base) - if ext == '.res' or ext == '.rc': - # these need to be compiled to object files - obj_names.append (os.path.join (output_dir, - base + ext + self.obj_extension)) - else: - obj_names.append (os.path.join (output_dir, - base + self.obj_extension)) - return obj_names - - # object_filenames () - - -def find_python_dll(): - # We can't do much here: - # - find it in the virtualenv (sys.prefix) - # - find it in python main dir (sys.base_prefix, if in a virtualenv) - # - in system32, - # - ortherwise (Sxs), I don't know how to get it. - stems = [sys.prefix] - if sys.base_prefix != sys.prefix: - stems.append(sys.base_prefix) - - sub_dirs = ['', 'lib', 'bin'] - # generate possible combinations of directory trees and sub-directories - lib_dirs = [] - for stem in stems: - for folder in sub_dirs: - lib_dirs.append(os.path.join(stem, folder)) - - # add system directory as well - if 'SYSTEMROOT' in os.environ: - lib_dirs.append(os.path.join(os.environ['SYSTEMROOT'], 'System32')) - - # search in the file system for possible candidates - major_version, minor_version = tuple(sys.version_info[:2]) - implementation = sys.implementation.name - if implementation == 'cpython': - dllname = f'python{major_version}{minor_version}.dll' - elif implementation == 'pypy': - dllname = f'libpypy{major_version}.{minor_version}-c.dll' - else: - dllname = f'Unknown platform {implementation}' - print("Looking for %s" % dllname) - for folder in lib_dirs: - dll = os.path.join(folder, dllname) - if os.path.exists(dll): - return dll - - raise ValueError("%s not found in %s" % (dllname, lib_dirs)) - -def dump_table(dll): - st = subprocess.check_output(["objdump.exe", "-p", dll]) - return st.split(b'\n') - -def generate_def(dll, dfile): - """Given a dll file location, get all its exported symbols and dump them - into the given def file. - - The .def file will be overwritten""" - dump = dump_table(dll) - for i in range(len(dump)): - if _START.match(dump[i].decode()): - break - else: - raise ValueError("Symbol table not found") - - syms = [] - for j in range(i+1, len(dump)): - m = _TABLE.match(dump[j].decode()) - if m: - syms.append((int(m.group(1).strip()), m.group(2))) - else: - break - - if len(syms) == 0: - log.warn('No symbols found in %s' % dll) - - with open(dfile, 'w') as d: - d.write('LIBRARY %s\n' % os.path.basename(dll)) - d.write(';CODE PRELOAD MOVEABLE DISCARDABLE\n') - d.write(';DATA PRELOAD SINGLE\n') - d.write('\nEXPORTS\n') - for s in syms: - #d.write('@%d %s\n' % (s[0], s[1])) - d.write('%s\n' % s[1]) - -def find_dll(dll_name): - - arch = {'AMD64' : 'amd64', - 'Intel' : 'x86'}[get_build_architecture()] - - def _find_dll_in_winsxs(dll_name): - # Walk through the WinSxS directory to find the dll. - winsxs_path = os.path.join(os.environ.get('WINDIR', r'C:\WINDOWS'), - 'winsxs') - if not os.path.exists(winsxs_path): - return None - for root, dirs, files in os.walk(winsxs_path): - if dll_name in files and arch in root: - return os.path.join(root, dll_name) - return None - - def _find_dll_in_path(dll_name): - # First, look in the Python directory, then scan PATH for - # the given dll name. - for path in [sys.prefix] + os.environ['PATH'].split(';'): - filepath = os.path.join(path, dll_name) - if os.path.exists(filepath): - return os.path.abspath(filepath) - - return _find_dll_in_winsxs(dll_name) or _find_dll_in_path(dll_name) - -def build_msvcr_library(debug=False): - if os.name != 'nt': - return False - - # If the version number is None, then we couldn't find the MSVC runtime at - # all, because we are running on a Python distribution which is customed - # compiled; trust that the compiler is the same as the one available to us - # now, and that it is capable of linking with the correct runtime without - # any extra options. - msvcr_ver = msvc_runtime_major() - if msvcr_ver is None: - log.debug('Skip building import library: ' - 'Runtime is not compiled with MSVC') - return False - - # Skip using a custom library for versions < MSVC 8.0 - if msvcr_ver < 80: - log.debug('Skip building msvcr library:' - ' custom functionality not present') - return False - - msvcr_name = msvc_runtime_library() - if debug: - msvcr_name += 'd' - - # Skip if custom library already exists - out_name = "lib%s.a" % msvcr_name - out_file = os.path.join(sys.prefix, 'libs', out_name) - if os.path.isfile(out_file): - log.debug('Skip building msvcr library: "%s" exists' % - (out_file,)) - return True - - # Find the msvcr dll - msvcr_dll_name = msvcr_name + '.dll' - dll_file = find_dll(msvcr_dll_name) - if not dll_file: - log.warn('Cannot build msvcr library: "%s" not found' % - msvcr_dll_name) - return False - - def_name = "lib%s.def" % msvcr_name - def_file = os.path.join(sys.prefix, 'libs', def_name) - - log.info('Building msvcr library: "%s" (from %s)' \ - % (out_file, dll_file)) - - # Generate a symbol definition file from the msvcr dll - generate_def(dll_file, def_file) - - # Create a custom mingw library for the given symbol definitions - cmd = ['dlltool', '-d', def_file, '-l', out_file] - retcode = subprocess.call(cmd) - - # Clean up symbol definitions - os.remove(def_file) - - return (not retcode) - -def build_import_library(): - if os.name != 'nt': - return - - arch = get_build_architecture() - if arch == 'AMD64': - return _build_import_library_amd64() - elif arch == 'Intel': - return _build_import_library_x86() - else: - raise ValueError("Unhandled arch %s" % arch) - -def _check_for_import_lib(): - """Check if an import library for the Python runtime already exists.""" - major_version, minor_version = tuple(sys.version_info[:2]) - - # patterns for the file name of the library itself - patterns = ['libpython%d%d.a', - 'libpython%d%d.dll.a', - 'libpython%d.%d.dll.a'] - - # directory trees that may contain the library - stems = [sys.prefix] - if hasattr(sys, 'base_prefix') and sys.base_prefix != sys.prefix: - stems.append(sys.base_prefix) - elif hasattr(sys, 'real_prefix') and sys.real_prefix != sys.prefix: - stems.append(sys.real_prefix) - - # possible subdirectories within those trees where it is placed - sub_dirs = ['libs', 'lib'] - - # generate a list of candidate locations - candidates = [] - for pat in patterns: - filename = pat % (major_version, minor_version) - for stem_dir in stems: - for folder in sub_dirs: - candidates.append(os.path.join(stem_dir, folder, filename)) - - # test the filesystem to see if we can find any of these - for fullname in candidates: - if os.path.isfile(fullname): - # already exists, in location given - return (True, fullname) - - # needs to be built, preferred location given first - return (False, candidates[0]) - -def _build_import_library_amd64(): - out_exists, out_file = _check_for_import_lib() - if out_exists: - log.debug('Skip building import library: "%s" exists', out_file) - return - - # get the runtime dll for which we are building import library - dll_file = find_python_dll() - log.info('Building import library (arch=AMD64): "%s" (from %s)' % - (out_file, dll_file)) - - # generate symbol list from this library - def_name = "python%d%d.def" % tuple(sys.version_info[:2]) - def_file = os.path.join(sys.prefix, 'libs', def_name) - generate_def(dll_file, def_file) - - # generate import library from this symbol list - cmd = ['dlltool', '-d', def_file, '-l', out_file] - subprocess.check_call(cmd) - -def _build_import_library_x86(): - """ Build the import libraries for Mingw32-gcc on Windows - """ - out_exists, out_file = _check_for_import_lib() - if out_exists: - log.debug('Skip building import library: "%s" exists', out_file) - return - - lib_name = "python%d%d.lib" % tuple(sys.version_info[:2]) - lib_file = os.path.join(sys.prefix, 'libs', lib_name) - if not os.path.isfile(lib_file): - # didn't find library file in virtualenv, try base distribution, too, - # and use that instead if found there. for Python 2.7 venvs, the base - # directory is in attribute real_prefix instead of base_prefix. - if hasattr(sys, 'base_prefix'): - base_lib = os.path.join(sys.base_prefix, 'libs', lib_name) - elif hasattr(sys, 'real_prefix'): - base_lib = os.path.join(sys.real_prefix, 'libs', lib_name) - else: - base_lib = '' # os.path.isfile('') == False - - if os.path.isfile(base_lib): - lib_file = base_lib - else: - log.warn('Cannot build import library: "%s" not found', lib_file) - return - log.info('Building import library (ARCH=x86): "%s"', out_file) - - from numpy.distutils import lib2def - - def_name = "python%d%d.def" % tuple(sys.version_info[:2]) - def_file = os.path.join(sys.prefix, 'libs', def_name) - nm_output = lib2def.getnm( - lib2def.DEFAULT_NM + [lib_file], shell=False) - dlist, flist = lib2def.parse_nm(nm_output) - with open(def_file, 'w') as fid: - lib2def.output_def(dlist, flist, lib2def.DEF_HEADER, fid) - - dll_name = find_python_dll () - - cmd = ["dlltool", - "--dllname", dll_name, - "--def", def_file, - "--output-lib", out_file] - status = subprocess.check_output(cmd) - if status: - log.warn('Failed to build import library for gcc. Linking will fail.') - return - -#===================================== -# Dealing with Visual Studio MANIFESTS -#===================================== - -# Functions to deal with visual studio manifests. Manifest are a mechanism to -# enforce strong DLL versioning on windows, and has nothing to do with -# distutils MANIFEST. manifests are XML files with version info, and used by -# the OS loader; they are necessary when linking against a DLL not in the -# system path; in particular, official python 2.6 binary is built against the -# MS runtime 9 (the one from VS 2008), which is not available on most windows -# systems; python 2.6 installer does install it in the Win SxS (Side by side) -# directory, but this requires the manifest for this to work. This is a big -# mess, thanks MS for a wonderful system. - -# XXX: ideally, we should use exactly the same version as used by python. I -# submitted a patch to get this version, but it was only included for python -# 2.6.1 and above. So for versions below, we use a "best guess". -_MSVCRVER_TO_FULLVER = {} -if sys.platform == 'win32': - try: - import msvcrt - # I took one version in my SxS directory: no idea if it is the good - # one, and we can't retrieve it from python - _MSVCRVER_TO_FULLVER['80'] = "8.0.50727.42" - _MSVCRVER_TO_FULLVER['90'] = "9.0.21022.8" - # Value from msvcrt.CRT_ASSEMBLY_VERSION under Python 3.3.0 - # on Windows XP: - _MSVCRVER_TO_FULLVER['100'] = "10.0.30319.460" - crt_ver = getattr(msvcrt, 'CRT_ASSEMBLY_VERSION', None) - if crt_ver is not None: # Available at least back to Python 3.3 - maj, min = re.match(r'(\d+)\.(\d)', crt_ver).groups() - _MSVCRVER_TO_FULLVER[maj + min] = crt_ver - del maj, min - del crt_ver - except ImportError: - # If we are here, means python was not built with MSVC. Not sure what - # to do in that case: manifest building will fail, but it should not be - # used in that case anyway - log.warn('Cannot import msvcrt: using manifest will not be possible') - -def msvc_manifest_xml(maj, min): - """Given a major and minor version of the MSVCR, returns the - corresponding XML file.""" - try: - fullver = _MSVCRVER_TO_FULLVER[str(maj * 10 + min)] - except KeyError: - raise ValueError("Version %d,%d of MSVCRT not supported yet" % - (maj, min)) from None - # Don't be fooled, it looks like an XML, but it is not. In particular, it - # should not have any space before starting, and its size should be - # divisible by 4, most likely for alignment constraints when the xml is - # embedded in the binary... - # This template was copied directly from the python 2.6 binary (using - # strings.exe from mingw on python.exe). - template = textwrap.dedent("""\ - - """) - - return template % {'fullver': fullver, 'maj': maj, 'min': min} - -def manifest_rc(name, type='dll'): - """Return the rc file used to generate the res file which will be embedded - as manifest for given manifest file name, of given type ('dll' or - 'exe'). - - Parameters - ---------- - name : str - name of the manifest file to embed - type : str {'dll', 'exe'} - type of the binary which will embed the manifest - - """ - if type == 'dll': - rctype = 2 - elif type == 'exe': - rctype = 1 - else: - raise ValueError("Type %s not supported" % type) - - return """\ -#include "winuser.h" -%d RT_MANIFEST %s""" % (rctype, name) - -def check_embedded_msvcr_match_linked(msver): - """msver is the ms runtime version used for the MANIFEST.""" - # check msvcr major version are the same for linking and - # embedding - maj = msvc_runtime_major() - if maj: - if not maj == int(msver): - raise ValueError( - "Discrepancy between linked msvcr " \ - "(%d) and the one about to be embedded " \ - "(%d)" % (int(msver), maj)) - -def configtest_name(config): - base = os.path.basename(config._gen_temp_sourcefile("yo", [], "c")) - return os.path.splitext(base)[0] - -def manifest_name(config): - # Get configest name (including suffix) - root = configtest_name(config) - exext = config.compiler.exe_extension - return root + exext + ".manifest" - -def rc_name(config): - # Get configtest name (including suffix) - root = configtest_name(config) - return root + ".rc" - -def generate_manifest(config): - msver = get_build_msvc_version() - if msver is not None: - if msver >= 8: - check_embedded_msvcr_match_linked(msver) - ma_str, mi_str = str(msver).split('.') - # Write the manifest file - manxml = msvc_manifest_xml(int(ma_str), int(mi_str)) - with open(manifest_name(config), "w") as man: - config.temp_files.append(manifest_name(config)) - man.write(manxml) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/frame/methods/test_isetitem.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/frame/methods/test_isetitem.py deleted file mode 100644 index 69f394afb65191fe4cc52519fbc52959d2e1dd76..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/frame/methods/test_isetitem.py +++ /dev/null @@ -1,50 +0,0 @@ -import pytest - -from pandas import ( - DataFrame, - Series, -) -import pandas._testing as tm - - -class TestDataFrameSetItem: - def test_isetitem_ea_df(self): - # GH#49922 - df = DataFrame([[1, 2, 3], [4, 5, 6]]) - rhs = DataFrame([[11, 12], [13, 14]], dtype="Int64") - - df.isetitem([0, 1], rhs) - expected = DataFrame( - { - 0: Series([11, 13], dtype="Int64"), - 1: Series([12, 14], dtype="Int64"), - 2: [3, 6], - } - ) - tm.assert_frame_equal(df, expected) - - def test_isetitem_ea_df_scalar_indexer(self): - # GH#49922 - df = DataFrame([[1, 2, 3], [4, 5, 6]]) - rhs = DataFrame([[11], [13]], dtype="Int64") - - df.isetitem(2, rhs) - expected = DataFrame( - { - 0: [1, 4], - 1: [2, 5], - 2: Series([11, 13], dtype="Int64"), - } - ) - tm.assert_frame_equal(df, expected) - - def test_isetitem_dimension_mismatch(self): - # GH#51701 - df = DataFrame({"a": [1, 2], "b": [3, 4], "c": [5, 6]}) - value = df.copy() - with pytest.raises(ValueError, match="Got 2 positions but value has 3 columns"): - df.isetitem([1, 2], value) - - value = df.copy() - with pytest.raises(ValueError, match="Got 2 positions but value has 1 columns"): - df.isetitem([1, 2], value[["a"]]) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/formats/test_console.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/formats/test_console.py deleted file mode 100644 index dd7b57df9baed18b172dc8398a61a49e9435f82a..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/formats/test_console.py +++ /dev/null @@ -1,72 +0,0 @@ -import locale - -import pytest - -from pandas._config import detect_console_encoding - - -class MockEncoding: - """ - Used to add a side effect when accessing the 'encoding' property. If the - side effect is a str in nature, the value will be returned. Otherwise, the - side effect should be an exception that will be raised. - """ - - def __init__(self, encoding) -> None: - super().__init__() - self.val = encoding - - @property - def encoding(self): - return self.raise_or_return(self.val) - - @staticmethod - def raise_or_return(val): - if isinstance(val, str): - return val - else: - raise val - - -@pytest.mark.parametrize("empty,filled", [["stdin", "stdout"], ["stdout", "stdin"]]) -def test_detect_console_encoding_from_stdout_stdin(monkeypatch, empty, filled): - # Ensures that when sys.stdout.encoding or sys.stdin.encoding is used when - # they have values filled. - # GH 21552 - with monkeypatch.context() as context: - context.setattr(f"sys.{empty}", MockEncoding("")) - context.setattr(f"sys.{filled}", MockEncoding(filled)) - assert detect_console_encoding() == filled - - -@pytest.mark.parametrize("encoding", [AttributeError, OSError, "ascii"]) -def test_detect_console_encoding_fallback_to_locale(monkeypatch, encoding): - # GH 21552 - with monkeypatch.context() as context: - context.setattr("locale.getpreferredencoding", lambda: "foo") - context.setattr("sys.stdout", MockEncoding(encoding)) - assert detect_console_encoding() == "foo" - - -@pytest.mark.parametrize( - "std,locale", - [ - ["ascii", "ascii"], - ["ascii", locale.Error], - [AttributeError, "ascii"], - [AttributeError, locale.Error], - [OSError, "ascii"], - [OSError, locale.Error], - ], -) -def test_detect_console_encoding_fallback_to_default(monkeypatch, std, locale): - # When both the stdout/stdin encoding and locale preferred encoding checks - # fail (or return 'ascii', we should default to the sys default encoding. - # GH 21552 - with monkeypatch.context() as context: - context.setattr( - "locale.getpreferredencoding", lambda: MockEncoding.raise_or_return(locale) - ) - context.setattr("sys.stdout", MockEncoding(std)) - context.setattr("sys.getdefaultencoding", lambda: "sysDefaultEncoding") - assert detect_console_encoding() == "sysDefaultEncoding" diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tslibs/test_array_to_datetime.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tslibs/test_array_to_datetime.py deleted file mode 100644 index 829bb140e6e968023d4b51d6807ae148a8460a40..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tslibs/test_array_to_datetime.py +++ /dev/null @@ -1,205 +0,0 @@ -from datetime import ( - date, - datetime, - timedelta, - timezone, -) - -from dateutil.tz.tz import tzoffset -import numpy as np -import pytest - -from pandas._libs import ( - iNaT, - tslib, -) - -from pandas import Timestamp -import pandas._testing as tm - - -@pytest.mark.parametrize( - "data,expected", - [ - ( - ["01-01-2013", "01-02-2013"], - [ - "2013-01-01T00:00:00.000000000", - "2013-01-02T00:00:00.000000000", - ], - ), - ( - ["Mon Sep 16 2013", "Tue Sep 17 2013"], - [ - "2013-09-16T00:00:00.000000000", - "2013-09-17T00:00:00.000000000", - ], - ), - ], -) -def test_parsing_valid_dates(data, expected): - arr = np.array(data, dtype=object) - result, _ = tslib.array_to_datetime(arr) - - expected = np.array(expected, dtype="M8[ns]") - tm.assert_numpy_array_equal(result, expected) - - -@pytest.mark.parametrize( - "dt_string, expected_tz", - [ - ["01-01-2013 08:00:00+08:00", 480], - ["2013-01-01T08:00:00.000000000+0800", 480], - ["2012-12-31T16:00:00.000000000-0800", -480], - ["12-31-2012 23:00:00-01:00", -60], - ], -) -def test_parsing_timezone_offsets(dt_string, expected_tz): - # All of these datetime strings with offsets are equivalent - # to the same datetime after the timezone offset is added. - arr = np.array(["01-01-2013 00:00:00"], dtype=object) - expected, _ = tslib.array_to_datetime(arr) - - arr = np.array([dt_string], dtype=object) - result, result_tz = tslib.array_to_datetime(arr) - - tm.assert_numpy_array_equal(result, expected) - assert result_tz == timezone(timedelta(minutes=expected_tz)) - - -def test_parsing_non_iso_timezone_offset(): - dt_string = "01-01-2013T00:00:00.000000000+0000" - arr = np.array([dt_string], dtype=object) - - with tm.assert_produces_warning(None): - # GH#50949 should not get tzlocal-deprecation warning here - result, result_tz = tslib.array_to_datetime(arr) - expected = np.array([np.datetime64("2013-01-01 00:00:00.000000000")]) - - tm.assert_numpy_array_equal(result, expected) - assert result_tz is timezone.utc - - -def test_parsing_different_timezone_offsets(): - # see gh-17697 - data = ["2015-11-18 15:30:00+05:30", "2015-11-18 15:30:00+06:30"] - data = np.array(data, dtype=object) - - msg = "parsing datetimes with mixed time zones will raise an error" - with tm.assert_produces_warning(FutureWarning, match=msg): - result, result_tz = tslib.array_to_datetime(data) - expected = np.array( - [ - datetime(2015, 11, 18, 15, 30, tzinfo=tzoffset(None, 19800)), - datetime(2015, 11, 18, 15, 30, tzinfo=tzoffset(None, 23400)), - ], - dtype=object, - ) - - tm.assert_numpy_array_equal(result, expected) - assert result_tz is None - - -@pytest.mark.parametrize( - "data", [["-352.737091", "183.575577"], ["1", "2", "3", "4", "5"]] -) -def test_number_looking_strings_not_into_datetime(data): - # see gh-4601 - # - # These strings don't look like datetimes, so - # they shouldn't be attempted to be converted. - arr = np.array(data, dtype=object) - result, _ = tslib.array_to_datetime(arr, errors="ignore") - - tm.assert_numpy_array_equal(result, arr) - - -@pytest.mark.parametrize( - "invalid_date", - [ - date(1000, 1, 1), - datetime(1000, 1, 1), - "1000-01-01", - "Jan 1, 1000", - np.datetime64("1000-01-01"), - ], -) -@pytest.mark.parametrize("errors", ["coerce", "raise"]) -def test_coerce_outside_ns_bounds(invalid_date, errors): - arr = np.array([invalid_date], dtype="object") - kwargs = {"values": arr, "errors": errors} - - if errors == "raise": - msg = "^Out of bounds nanosecond timestamp: .*, at position 0$" - - with pytest.raises(ValueError, match=msg): - tslib.array_to_datetime(**kwargs) - else: # coerce. - result, _ = tslib.array_to_datetime(**kwargs) - expected = np.array([iNaT], dtype="M8[ns]") - - tm.assert_numpy_array_equal(result, expected) - - -def test_coerce_outside_ns_bounds_one_valid(): - arr = np.array(["1/1/1000", "1/1/2000"], dtype=object) - result, _ = tslib.array_to_datetime(arr, errors="coerce") - - expected = [iNaT, "2000-01-01T00:00:00.000000000"] - expected = np.array(expected, dtype="M8[ns]") - - tm.assert_numpy_array_equal(result, expected) - - -@pytest.mark.parametrize("errors", ["ignore", "coerce"]) -def test_coerce_of_invalid_datetimes(errors): - arr = np.array(["01-01-2013", "not_a_date", "1"], dtype=object) - kwargs = {"values": arr, "errors": errors} - - if errors == "ignore": - # Without coercing, the presence of any invalid - # dates prevents any values from being converted. - result, _ = tslib.array_to_datetime(**kwargs) - tm.assert_numpy_array_equal(result, arr) - else: # coerce. - # With coercing, the invalid dates becomes iNaT - result, _ = tslib.array_to_datetime(arr, errors="coerce") - expected = ["2013-01-01T00:00:00.000000000", iNaT, iNaT] - - tm.assert_numpy_array_equal(result, np.array(expected, dtype="M8[ns]")) - - -def test_to_datetime_barely_out_of_bounds(): - # see gh-19382, gh-19529 - # - # Close enough to bounds that dropping nanos - # would result in an in-bounds datetime. - arr = np.array(["2262-04-11 23:47:16.854775808"], dtype=object) - msg = "^Out of bounds nanosecond timestamp: 2262-04-11 23:47:16, at position 0$" - - with pytest.raises(tslib.OutOfBoundsDatetime, match=msg): - tslib.array_to_datetime(arr) - - -class SubDatetime(datetime): - pass - - -@pytest.mark.parametrize( - "data,expected", - [ - ([SubDatetime(2000, 1, 1)], ["2000-01-01T00:00:00.000000000"]), - ([datetime(2000, 1, 1)], ["2000-01-01T00:00:00.000000000"]), - ([Timestamp(2000, 1, 1)], ["2000-01-01T00:00:00.000000000"]), - ], -) -def test_datetime_subclass(data, expected): - # GH 25851 - # ensure that subclassed datetime works with - # array_to_datetime - - arr = np.array(data, dtype=object) - result, _ = tslib.array_to_datetime(arr) - - expected = np.array(expected, dtype="M8[ns]") - tm.assert_numpy_array_equal(result, expected) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/utils/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/utils/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/__init__.py deleted file mode 100644 index 80ad2546d7981394b5f5d221336c9f00236b9d66..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/chardet/__init__.py +++ /dev/null @@ -1,83 +0,0 @@ -######################## BEGIN LICENSE BLOCK ######################## -# This library is free software; you can redistribute it and/or -# modify it under the terms of the GNU Lesser General Public -# License as published by the Free Software Foundation; either -# version 2.1 of the License, or (at your option) any later version. -# -# This library is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU -# Lesser General Public License for more details. -# -# You should have received a copy of the GNU Lesser General Public -# License along with this library; if not, write to the Free Software -# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA -# 02110-1301 USA -######################### END LICENSE BLOCK ######################### - - -from .universaldetector import UniversalDetector -from .enums import InputState -from .version import __version__, VERSION - - -__all__ = ['UniversalDetector', 'detect', 'detect_all', '__version__', 'VERSION'] - - -def detect(byte_str): - """ - Detect the encoding of the given byte string. - - :param byte_str: The byte sequence to examine. - :type byte_str: ``bytes`` or ``bytearray`` - """ - if not isinstance(byte_str, bytearray): - if not isinstance(byte_str, bytes): - raise TypeError('Expected object of type bytes or bytearray, got: ' - '{}'.format(type(byte_str))) - else: - byte_str = bytearray(byte_str) - detector = UniversalDetector() - detector.feed(byte_str) - return detector.close() - - -def detect_all(byte_str): - """ - Detect all the possible encodings of the given byte string. - - :param byte_str: The byte sequence to examine. - :type byte_str: ``bytes`` or ``bytearray`` - """ - if not isinstance(byte_str, bytearray): - if not isinstance(byte_str, bytes): - raise TypeError('Expected object of type bytes or bytearray, got: ' - '{}'.format(type(byte_str))) - else: - byte_str = bytearray(byte_str) - - detector = UniversalDetector() - detector.feed(byte_str) - detector.close() - - if detector._input_state == InputState.HIGH_BYTE: - results = [] - for prober in detector._charset_probers: - if prober.get_confidence() > detector.MINIMUM_THRESHOLD: - charset_name = prober.charset_name - lower_charset_name = prober.charset_name.lower() - # Use Windows encoding name instead of ISO-8859 if we saw any - # extra Windows-specific bytes - if lower_charset_name.startswith('iso-8859'): - if detector._has_win_bytes: - charset_name = detector.ISO_WIN_MAP.get(lower_charset_name, - charset_name) - results.append({ - 'encoding': charset_name, - 'confidence': prober.get_confidence(), - 'language': prober.language, - }) - if len(results) > 0: - return sorted(results, key=lambda result: -result['confidence']) - - return [detector.result] diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/pygments/lexers/_mapping.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/pygments/lexers/_mapping.py deleted file mode 100644 index c972e3a2b3b544f19891325c9fbfcbafca29f40f..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/pygments/lexers/_mapping.py +++ /dev/null @@ -1,580 +0,0 @@ -""" - pygments.lexers._mapping - ~~~~~~~~~~~~~~~~~~~~~~~~ - - Lexer mapping definitions. This file is generated by itself. Everytime - you change something on a builtin lexer definition, run this script from - the lexers folder to update it. - - Do not alter the LEXERS dictionary by hand. - - :copyright: Copyright 2006-2014, 2016 by the Pygments team, see AUTHORS. - :license: BSD, see LICENSE for details. -""" - -LEXERS = { - 'ABAPLexer': ('pip._vendor.pygments.lexers.business', 'ABAP', ('abap',), ('*.abap', '*.ABAP'), ('text/x-abap',)), - 'AMDGPULexer': ('pip._vendor.pygments.lexers.amdgpu', 'AMDGPU', ('amdgpu',), ('*.isa',), ()), - 'APLLexer': ('pip._vendor.pygments.lexers.apl', 'APL', ('apl',), ('*.apl', '*.aplf', '*.aplo', '*.apln', '*.aplc', '*.apli', '*.dyalog'), ()), - 'AbnfLexer': ('pip._vendor.pygments.lexers.grammar_notation', 'ABNF', ('abnf',), ('*.abnf',), ('text/x-abnf',)), - 'ActionScript3Lexer': ('pip._vendor.pygments.lexers.actionscript', 'ActionScript 3', ('actionscript3', 'as3'), ('*.as',), ('application/x-actionscript3', 'text/x-actionscript3', 'text/actionscript3')), - 'ActionScriptLexer': ('pip._vendor.pygments.lexers.actionscript', 'ActionScript', ('actionscript', 'as'), ('*.as',), ('application/x-actionscript', 'text/x-actionscript', 'text/actionscript')), - 'AdaLexer': ('pip._vendor.pygments.lexers.pascal', 'Ada', ('ada', 'ada95', 'ada2005'), ('*.adb', '*.ads', '*.ada'), ('text/x-ada',)), - 'AdlLexer': ('pip._vendor.pygments.lexers.archetype', 'ADL', ('adl',), ('*.adl', '*.adls', '*.adlf', '*.adlx'), ()), - 'AgdaLexer': ('pip._vendor.pygments.lexers.haskell', 'Agda', ('agda',), ('*.agda',), ('text/x-agda',)), - 'AheuiLexer': ('pip._vendor.pygments.lexers.esoteric', 'Aheui', ('aheui',), ('*.aheui',), ()), - 'AlloyLexer': ('pip._vendor.pygments.lexers.dsls', 'Alloy', ('alloy',), ('*.als',), ('text/x-alloy',)), - 'AmbientTalkLexer': ('pip._vendor.pygments.lexers.ambient', 'AmbientTalk', ('ambienttalk', 'ambienttalk/2', 'at'), ('*.at',), ('text/x-ambienttalk',)), - 'AmplLexer': ('pip._vendor.pygments.lexers.ampl', 'Ampl', ('ampl',), ('*.run',), ()), - 'Angular2HtmlLexer': ('pip._vendor.pygments.lexers.templates', 'HTML + Angular2', ('html+ng2',), ('*.ng2',), ()), - 'Angular2Lexer': ('pip._vendor.pygments.lexers.templates', 'Angular2', ('ng2',), (), ()), - 'AntlrActionScriptLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With ActionScript Target', ('antlr-actionscript', 'antlr-as'), ('*.G', '*.g'), ()), - 'AntlrCSharpLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With C# Target', ('antlr-csharp', 'antlr-c#'), ('*.G', '*.g'), ()), - 'AntlrCppLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With CPP Target', ('antlr-cpp',), ('*.G', '*.g'), ()), - 'AntlrJavaLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With Java Target', ('antlr-java',), ('*.G', '*.g'), ()), - 'AntlrLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR', ('antlr',), (), ()), - 'AntlrObjectiveCLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With ObjectiveC Target', ('antlr-objc',), ('*.G', '*.g'), ()), - 'AntlrPerlLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With Perl Target', ('antlr-perl',), ('*.G', '*.g'), ()), - 'AntlrPythonLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With Python Target', ('antlr-python',), ('*.G', '*.g'), ()), - 'AntlrRubyLexer': ('pip._vendor.pygments.lexers.parsers', 'ANTLR With Ruby Target', ('antlr-ruby', 'antlr-rb'), ('*.G', '*.g'), ()), - 'ApacheConfLexer': ('pip._vendor.pygments.lexers.configs', 'ApacheConf', ('apacheconf', 'aconf', 'apache'), ('.htaccess', 'apache.conf', 'apache2.conf'), ('text/x-apacheconf',)), - 'AppleScriptLexer': ('pip._vendor.pygments.lexers.scripting', 'AppleScript', ('applescript',), ('*.applescript',), ()), - 'ArduinoLexer': ('pip._vendor.pygments.lexers.c_like', 'Arduino', ('arduino',), ('*.ino',), ('text/x-arduino',)), - 'ArrowLexer': ('pip._vendor.pygments.lexers.arrow', 'Arrow', ('arrow',), ('*.arw',), ()), - 'AscLexer': ('pip._vendor.pygments.lexers.asc', 'ASCII armored', ('asc', 'pem'), ('*.asc', '*.pem', 'id_dsa', 'id_ecdsa', 'id_ecdsa_sk', 'id_ed25519', 'id_ed25519_sk', 'id_rsa'), ('application/pgp-keys', 'application/pgp-encrypted', 'application/pgp-signature')), - 'AspectJLexer': ('pip._vendor.pygments.lexers.jvm', 'AspectJ', ('aspectj',), ('*.aj',), ('text/x-aspectj',)), - 'AsymptoteLexer': ('pip._vendor.pygments.lexers.graphics', 'Asymptote', ('asymptote', 'asy'), ('*.asy',), ('text/x-asymptote',)), - 'AugeasLexer': ('pip._vendor.pygments.lexers.configs', 'Augeas', ('augeas',), ('*.aug',), ()), - 'AutoItLexer': ('pip._vendor.pygments.lexers.automation', 'AutoIt', ('autoit',), ('*.au3',), ('text/x-autoit',)), - 'AutohotkeyLexer': ('pip._vendor.pygments.lexers.automation', 'autohotkey', ('autohotkey', 'ahk'), ('*.ahk', '*.ahkl'), ('text/x-autohotkey',)), - 'AwkLexer': ('pip._vendor.pygments.lexers.textedit', 'Awk', ('awk', 'gawk', 'mawk', 'nawk'), ('*.awk',), ('application/x-awk',)), - 'BBCBasicLexer': ('pip._vendor.pygments.lexers.basic', 'BBC Basic', ('bbcbasic',), ('*.bbc',), ()), - 'BBCodeLexer': ('pip._vendor.pygments.lexers.markup', 'BBCode', ('bbcode',), (), ('text/x-bbcode',)), - 'BCLexer': ('pip._vendor.pygments.lexers.algebra', 'BC', ('bc',), ('*.bc',), ()), - 'BSTLexer': ('pip._vendor.pygments.lexers.bibtex', 'BST', ('bst', 'bst-pybtex'), ('*.bst',), ()), - 'BareLexer': ('pip._vendor.pygments.lexers.bare', 'BARE', ('bare',), ('*.bare',), ()), - 'BaseMakefileLexer': ('pip._vendor.pygments.lexers.make', 'Base Makefile', ('basemake',), (), ()), - 'BashLexer': ('pip._vendor.pygments.lexers.shell', 'Bash', ('bash', 'sh', 'ksh', 'zsh', 'shell'), ('*.sh', '*.ksh', '*.bash', '*.ebuild', '*.eclass', '*.exheres-0', '*.exlib', '*.zsh', '.bashrc', 'bashrc', '.bash_*', 'bash_*', 'zshrc', '.zshrc', '.kshrc', 'kshrc', 'PKGBUILD'), ('application/x-sh', 'application/x-shellscript', 'text/x-shellscript')), - 'BashSessionLexer': ('pip._vendor.pygments.lexers.shell', 'Bash Session', ('console', 'shell-session'), ('*.sh-session', '*.shell-session'), ('application/x-shell-session', 'application/x-sh-session')), - 'BatchLexer': ('pip._vendor.pygments.lexers.shell', 'Batchfile', ('batch', 'bat', 'dosbatch', 'winbatch'), ('*.bat', '*.cmd'), ('application/x-dos-batch',)), - 'BddLexer': ('pip._vendor.pygments.lexers.bdd', 'Bdd', ('bdd',), ('*.feature',), ('text/x-bdd',)), - 'BefungeLexer': ('pip._vendor.pygments.lexers.esoteric', 'Befunge', ('befunge',), ('*.befunge',), ('application/x-befunge',)), - 'BibTeXLexer': ('pip._vendor.pygments.lexers.bibtex', 'BibTeX', ('bibtex', 'bib'), ('*.bib',), ('text/x-bibtex',)), - 'BlitzBasicLexer': ('pip._vendor.pygments.lexers.basic', 'BlitzBasic', ('blitzbasic', 'b3d', 'bplus'), ('*.bb', '*.decls'), ('text/x-bb',)), - 'BlitzMaxLexer': ('pip._vendor.pygments.lexers.basic', 'BlitzMax', ('blitzmax', 'bmax'), ('*.bmx',), ('text/x-bmx',)), - 'BnfLexer': ('pip._vendor.pygments.lexers.grammar_notation', 'BNF', ('bnf',), ('*.bnf',), ('text/x-bnf',)), - 'BoaLexer': ('pip._vendor.pygments.lexers.boa', 'Boa', ('boa',), ('*.boa',), ()), - 'BooLexer': ('pip._vendor.pygments.lexers.dotnet', 'Boo', ('boo',), ('*.boo',), ('text/x-boo',)), - 'BoogieLexer': ('pip._vendor.pygments.lexers.verification', 'Boogie', ('boogie',), ('*.bpl',), ()), - 'BrainfuckLexer': ('pip._vendor.pygments.lexers.esoteric', 'Brainfuck', ('brainfuck', 'bf'), ('*.bf', '*.b'), ('application/x-brainfuck',)), - 'BugsLexer': ('pip._vendor.pygments.lexers.modeling', 'BUGS', ('bugs', 'winbugs', 'openbugs'), ('*.bug',), ()), - 'CAmkESLexer': ('pip._vendor.pygments.lexers.esoteric', 'CAmkES', ('camkes', 'idl4'), ('*.camkes', '*.idl4'), ()), - 'CLexer': ('pip._vendor.pygments.lexers.c_cpp', 'C', ('c',), ('*.c', '*.h', '*.idc', '*.x[bp]m'), ('text/x-chdr', 'text/x-csrc', 'image/x-xbitmap', 'image/x-xpixmap')), - 'CMakeLexer': ('pip._vendor.pygments.lexers.make', 'CMake', ('cmake',), ('*.cmake', 'CMakeLists.txt'), ('text/x-cmake',)), - 'CObjdumpLexer': ('pip._vendor.pygments.lexers.asm', 'c-objdump', ('c-objdump',), ('*.c-objdump',), ('text/x-c-objdump',)), - 'CPSALexer': ('pip._vendor.pygments.lexers.lisp', 'CPSA', ('cpsa',), ('*.cpsa',), ()), - 'CSharpAspxLexer': ('pip._vendor.pygments.lexers.dotnet', 'aspx-cs', ('aspx-cs',), ('*.aspx', '*.asax', '*.ascx', '*.ashx', '*.asmx', '*.axd'), ()), - 'CSharpLexer': ('pip._vendor.pygments.lexers.dotnet', 'C#', ('csharp', 'c#', 'cs'), ('*.cs',), ('text/x-csharp',)), - 'Ca65Lexer': ('pip._vendor.pygments.lexers.asm', 'ca65 assembler', ('ca65',), ('*.s',), ()), - 'CadlLexer': ('pip._vendor.pygments.lexers.archetype', 'cADL', ('cadl',), ('*.cadl',), ()), - 'CapDLLexer': ('pip._vendor.pygments.lexers.esoteric', 'CapDL', ('capdl',), ('*.cdl',), ()), - 'CapnProtoLexer': ('pip._vendor.pygments.lexers.capnproto', "Cap'n Proto", ('capnp',), ('*.capnp',), ()), - 'CbmBasicV2Lexer': ('pip._vendor.pygments.lexers.basic', 'CBM BASIC V2', ('cbmbas',), ('*.bas',), ()), - 'CddlLexer': ('pip._vendor.pygments.lexers.cddl', 'CDDL', ('cddl',), ('*.cddl',), ('text/x-cddl',)), - 'CeylonLexer': ('pip._vendor.pygments.lexers.jvm', 'Ceylon', ('ceylon',), ('*.ceylon',), ('text/x-ceylon',)), - 'Cfengine3Lexer': ('pip._vendor.pygments.lexers.configs', 'CFEngine3', ('cfengine3', 'cf3'), ('*.cf',), ()), - 'ChaiscriptLexer': ('pip._vendor.pygments.lexers.scripting', 'ChaiScript', ('chaiscript', 'chai'), ('*.chai',), ('text/x-chaiscript', 'application/x-chaiscript')), - 'ChapelLexer': ('pip._vendor.pygments.lexers.chapel', 'Chapel', ('chapel', 'chpl'), ('*.chpl',), ()), - 'CharmciLexer': ('pip._vendor.pygments.lexers.c_like', 'Charmci', ('charmci',), ('*.ci',), ()), - 'CheetahHtmlLexer': ('pip._vendor.pygments.lexers.templates', 'HTML+Cheetah', ('html+cheetah', 'html+spitfire', 'htmlcheetah'), (), ('text/html+cheetah', 'text/html+spitfire')), - 'CheetahJavascriptLexer': ('pip._vendor.pygments.lexers.templates', 'JavaScript+Cheetah', ('javascript+cheetah', 'js+cheetah', 'javascript+spitfire', 'js+spitfire'), (), ('application/x-javascript+cheetah', 'text/x-javascript+cheetah', 'text/javascript+cheetah', 'application/x-javascript+spitfire', 'text/x-javascript+spitfire', 'text/javascript+spitfire')), - 'CheetahLexer': ('pip._vendor.pygments.lexers.templates', 'Cheetah', ('cheetah', 'spitfire'), ('*.tmpl', '*.spt'), ('application/x-cheetah', 'application/x-spitfire')), - 'CheetahXmlLexer': ('pip._vendor.pygments.lexers.templates', 'XML+Cheetah', ('xml+cheetah', 'xml+spitfire'), (), ('application/xml+cheetah', 'application/xml+spitfire')), - 'CirruLexer': ('pip._vendor.pygments.lexers.webmisc', 'Cirru', ('cirru',), ('*.cirru',), ('text/x-cirru',)), - 'ClayLexer': ('pip._vendor.pygments.lexers.c_like', 'Clay', ('clay',), ('*.clay',), ('text/x-clay',)), - 'CleanLexer': ('pip._vendor.pygments.lexers.clean', 'Clean', ('clean',), ('*.icl', '*.dcl'), ()), - 'ClojureLexer': ('pip._vendor.pygments.lexers.jvm', 'Clojure', ('clojure', 'clj'), ('*.clj',), ('text/x-clojure', 'application/x-clojure')), - 'ClojureScriptLexer': ('pip._vendor.pygments.lexers.jvm', 'ClojureScript', ('clojurescript', 'cljs'), ('*.cljs',), ('text/x-clojurescript', 'application/x-clojurescript')), - 'CobolFreeformatLexer': ('pip._vendor.pygments.lexers.business', 'COBOLFree', ('cobolfree',), ('*.cbl', '*.CBL'), ()), - 'CobolLexer': ('pip._vendor.pygments.lexers.business', 'COBOL', ('cobol',), ('*.cob', '*.COB', '*.cpy', '*.CPY'), ('text/x-cobol',)), - 'CoffeeScriptLexer': ('pip._vendor.pygments.lexers.javascript', 'CoffeeScript', ('coffeescript', 'coffee-script', 'coffee'), ('*.coffee',), ('text/coffeescript',)), - 'ColdfusionCFCLexer': ('pip._vendor.pygments.lexers.templates', 'Coldfusion CFC', ('cfc',), ('*.cfc',), ()), - 'ColdfusionHtmlLexer': ('pip._vendor.pygments.lexers.templates', 'Coldfusion HTML', ('cfm',), ('*.cfm', '*.cfml'), ('application/x-coldfusion',)), - 'ColdfusionLexer': ('pip._vendor.pygments.lexers.templates', 'cfstatement', ('cfs',), (), ()), - 'CommonLispLexer': ('pip._vendor.pygments.lexers.lisp', 'Common Lisp', ('common-lisp', 'cl', 'lisp'), ('*.cl', '*.lisp'), ('text/x-common-lisp',)), - 'ComponentPascalLexer': ('pip._vendor.pygments.lexers.oberon', 'Component Pascal', ('componentpascal', 'cp'), ('*.cp', '*.cps'), ('text/x-component-pascal',)), - 'CoqLexer': ('pip._vendor.pygments.lexers.theorem', 'Coq', ('coq',), ('*.v',), ('text/x-coq',)), - 'CppLexer': ('pip._vendor.pygments.lexers.c_cpp', 'C++', ('cpp', 'c++'), ('*.cpp', '*.hpp', '*.c++', '*.h++', '*.cc', '*.hh', '*.cxx', '*.hxx', '*.C', '*.H', '*.cp', '*.CPP'), ('text/x-c++hdr', 'text/x-c++src')), - 'CppObjdumpLexer': ('pip._vendor.pygments.lexers.asm', 'cpp-objdump', ('cpp-objdump', 'c++-objdumb', 'cxx-objdump'), ('*.cpp-objdump', '*.c++-objdump', '*.cxx-objdump'), ('text/x-cpp-objdump',)), - 'CrmshLexer': ('pip._vendor.pygments.lexers.dsls', 'Crmsh', ('crmsh', 'pcmk'), ('*.crmsh', '*.pcmk'), ()), - 'CrocLexer': ('pip._vendor.pygments.lexers.d', 'Croc', ('croc',), ('*.croc',), ('text/x-crocsrc',)), - 'CryptolLexer': ('pip._vendor.pygments.lexers.haskell', 'Cryptol', ('cryptol', 'cry'), ('*.cry',), ('text/x-cryptol',)), - 'CrystalLexer': ('pip._vendor.pygments.lexers.crystal', 'Crystal', ('cr', 'crystal'), ('*.cr',), ('text/x-crystal',)), - 'CsoundDocumentLexer': ('pip._vendor.pygments.lexers.csound', 'Csound Document', ('csound-document', 'csound-csd'), ('*.csd',), ()), - 'CsoundOrchestraLexer': ('pip._vendor.pygments.lexers.csound', 'Csound Orchestra', ('csound', 'csound-orc'), ('*.orc', '*.udo'), ()), - 'CsoundScoreLexer': ('pip._vendor.pygments.lexers.csound', 'Csound Score', ('csound-score', 'csound-sco'), ('*.sco',), ()), - 'CssDjangoLexer': ('pip._vendor.pygments.lexers.templates', 'CSS+Django/Jinja', ('css+django', 'css+jinja'), (), ('text/css+django', 'text/css+jinja')), - 'CssErbLexer': ('pip._vendor.pygments.lexers.templates', 'CSS+Ruby', ('css+ruby', 'css+erb'), (), ('text/css+ruby',)), - 'CssGenshiLexer': ('pip._vendor.pygments.lexers.templates', 'CSS+Genshi Text', ('css+genshitext', 'css+genshi'), (), ('text/css+genshi',)), - 'CssLexer': ('pip._vendor.pygments.lexers.css', 'CSS', ('css',), ('*.css',), ('text/css',)), - 'CssPhpLexer': ('pip._vendor.pygments.lexers.templates', 'CSS+PHP', ('css+php',), (), ('text/css+php',)), - 'CssSmartyLexer': ('pip._vendor.pygments.lexers.templates', 'CSS+Smarty', ('css+smarty',), (), ('text/css+smarty',)), - 'CudaLexer': ('pip._vendor.pygments.lexers.c_like', 'CUDA', ('cuda', 'cu'), ('*.cu', '*.cuh'), ('text/x-cuda',)), - 'CypherLexer': ('pip._vendor.pygments.lexers.graph', 'Cypher', ('cypher',), ('*.cyp', '*.cypher'), ()), - 'CythonLexer': ('pip._vendor.pygments.lexers.python', 'Cython', ('cython', 'pyx', 'pyrex'), ('*.pyx', '*.pxd', '*.pxi'), ('text/x-cython', 'application/x-cython')), - 'DLexer': ('pip._vendor.pygments.lexers.d', 'D', ('d',), ('*.d', '*.di'), ('text/x-dsrc',)), - 'DObjdumpLexer': ('pip._vendor.pygments.lexers.asm', 'd-objdump', ('d-objdump',), ('*.d-objdump',), ('text/x-d-objdump',)), - 'DarcsPatchLexer': ('pip._vendor.pygments.lexers.diff', 'Darcs Patch', ('dpatch',), ('*.dpatch', '*.darcspatch'), ()), - 'DartLexer': ('pip._vendor.pygments.lexers.javascript', 'Dart', ('dart',), ('*.dart',), ('text/x-dart',)), - 'Dasm16Lexer': ('pip._vendor.pygments.lexers.asm', 'DASM16', ('dasm16',), ('*.dasm16', '*.dasm'), ('text/x-dasm16',)), - 'DebianControlLexer': ('pip._vendor.pygments.lexers.installers', 'Debian Control file', ('debcontrol', 'control'), ('control',), ()), - 'DelphiLexer': ('pip._vendor.pygments.lexers.pascal', 'Delphi', ('delphi', 'pas', 'pascal', 'objectpascal'), ('*.pas', '*.dpr'), ('text/x-pascal',)), - 'DevicetreeLexer': ('pip._vendor.pygments.lexers.devicetree', 'Devicetree', ('devicetree', 'dts'), ('*.dts', '*.dtsi'), ('text/x-c',)), - 'DgLexer': ('pip._vendor.pygments.lexers.python', 'dg', ('dg',), ('*.dg',), ('text/x-dg',)), - 'DiffLexer': ('pip._vendor.pygments.lexers.diff', 'Diff', ('diff', 'udiff'), ('*.diff', '*.patch'), ('text/x-diff', 'text/x-patch')), - 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'ScssLexer': ('pip._vendor.pygments.lexers.css', 'SCSS', ('scss',), ('*.scss',), ('text/x-scss',)), - 'SedLexer': ('pip._vendor.pygments.lexers.textedit', 'Sed', ('sed', 'gsed', 'ssed'), ('*.sed', '*.[gs]sed'), ('text/x-sed',)), - 'ShExCLexer': ('pip._vendor.pygments.lexers.rdf', 'ShExC', ('shexc', 'shex'), ('*.shex',), ('text/shex',)), - 'ShenLexer': ('pip._vendor.pygments.lexers.lisp', 'Shen', ('shen',), ('*.shen',), ('text/x-shen', 'application/x-shen')), - 'SieveLexer': ('pip._vendor.pygments.lexers.sieve', 'Sieve', ('sieve',), ('*.siv', '*.sieve'), ()), - 'SilverLexer': ('pip._vendor.pygments.lexers.verification', 'Silver', ('silver',), ('*.sil', '*.vpr'), ()), - 'SingularityLexer': ('pip._vendor.pygments.lexers.configs', 'Singularity', ('singularity',), ('*.def', 'Singularity'), ()), - 'SlashLexer': ('pip._vendor.pygments.lexers.slash', 'Slash', ('slash',), ('*.sla',), ()), - 'SlimLexer': ('pip._vendor.pygments.lexers.webmisc', 'Slim', ('slim',), ('*.slim',), ('text/x-slim',)), - 'SlurmBashLexer': ('pip._vendor.pygments.lexers.shell', 'Slurm', ('slurm', 'sbatch'), ('*.sl',), ()), - 'SmaliLexer': ('pip._vendor.pygments.lexers.dalvik', 'Smali', ('smali',), ('*.smali',), ('text/smali',)), - 'SmalltalkLexer': ('pip._vendor.pygments.lexers.smalltalk', 'Smalltalk', ('smalltalk', 'squeak', 'st'), ('*.st',), ('text/x-smalltalk',)), - 'SmartGameFormatLexer': ('pip._vendor.pygments.lexers.sgf', 'SmartGameFormat', ('sgf',), ('*.sgf',), ()), - 'SmartyLexer': ('pip._vendor.pygments.lexers.templates', 'Smarty', ('smarty',), ('*.tpl',), ('application/x-smarty',)), - 'SmithyLexer': ('pip._vendor.pygments.lexers.smithy', 'Smithy', ('smithy',), ('*.smithy',), ()), - 'SnobolLexer': ('pip._vendor.pygments.lexers.snobol', 'Snobol', ('snobol',), ('*.snobol',), ('text/x-snobol',)), - 'SnowballLexer': ('pip._vendor.pygments.lexers.dsls', 'Snowball', ('snowball',), ('*.sbl',), ()), - 'SolidityLexer': ('pip._vendor.pygments.lexers.solidity', 'Solidity', ('solidity',), ('*.sol',), ()), - 'SophiaLexer': ('pip._vendor.pygments.lexers.sophia', 'Sophia', ('sophia',), ('*.aes',), ()), - 'SourcePawnLexer': ('pip._vendor.pygments.lexers.pawn', 'SourcePawn', ('sp',), ('*.sp',), ('text/x-sourcepawn',)), - 'SourcesListLexer': ('pip._vendor.pygments.lexers.installers', 'Debian Sourcelist', ('debsources', 'sourceslist', 'sources.list'), ('sources.list',), ()), - 'SparqlLexer': ('pip._vendor.pygments.lexers.rdf', 'SPARQL', ('sparql',), ('*.rq', '*.sparql'), ('application/sparql-query',)), - 'SpiceLexer': ('pip._vendor.pygments.lexers.spice', 'Spice', ('spice', 'spicelang'), ('*.spice',), ('text/x-spice',)), - 'SqlLexer': ('pip._vendor.pygments.lexers.sql', 'SQL', ('sql',), ('*.sql',), ('text/x-sql',)), - 'SqliteConsoleLexer': ('pip._vendor.pygments.lexers.sql', 'sqlite3con', ('sqlite3',), ('*.sqlite3-console',), ('text/x-sqlite3-console',)), - 'SquidConfLexer': ('pip._vendor.pygments.lexers.configs', 'SquidConf', ('squidconf', 'squid.conf', 'squid'), ('squid.conf',), ('text/x-squidconf',)), - 'SrcinfoLexer': ('pip._vendor.pygments.lexers.srcinfo', 'Srcinfo', ('srcinfo',), ('.SRCINFO',), ()), - 'SspLexer': ('pip._vendor.pygments.lexers.templates', 'Scalate Server Page', ('ssp',), ('*.ssp',), ('application/x-ssp',)), - 'StanLexer': ('pip._vendor.pygments.lexers.modeling', 'Stan', ('stan',), ('*.stan',), ()), - 'StataLexer': ('pip._vendor.pygments.lexers.stata', 'Stata', ('stata', 'do'), ('*.do', '*.ado'), ('text/x-stata', 'text/stata', 'application/x-stata')), - 'SuperColliderLexer': ('pip._vendor.pygments.lexers.supercollider', 'SuperCollider', ('supercollider', 'sc'), ('*.sc', '*.scd'), ('application/supercollider', 'text/supercollider')), - 'SwiftLexer': ('pip._vendor.pygments.lexers.objective', 'Swift', ('swift',), ('*.swift',), ('text/x-swift',)), - 'SwigLexer': ('pip._vendor.pygments.lexers.c_like', 'SWIG', ('swig',), ('*.swg', '*.i'), ('text/swig',)), - 'SystemVerilogLexer': ('pip._vendor.pygments.lexers.hdl', 'systemverilog', ('systemverilog', 'sv'), ('*.sv', '*.svh'), ('text/x-systemverilog',)), - 'TAPLexer': ('pip._vendor.pygments.lexers.testing', 'TAP', ('tap',), ('*.tap',), ()), - 'TNTLexer': ('pip._vendor.pygments.lexers.tnt', 'Typographic Number Theory', ('tnt',), ('*.tnt',), ()), - 'TOMLLexer': ('pip._vendor.pygments.lexers.configs', 'TOML', ('toml',), ('*.toml', 'Pipfile', 'poetry.lock'), ()), - 'Tads3Lexer': ('pip._vendor.pygments.lexers.int_fiction', 'TADS 3', ('tads3',), ('*.t',), ()), - 'TasmLexer': ('pip._vendor.pygments.lexers.asm', 'TASM', ('tasm',), ('*.asm', '*.ASM', '*.tasm'), ('text/x-tasm',)), - 'TclLexer': ('pip._vendor.pygments.lexers.tcl', 'Tcl', ('tcl',), ('*.tcl', '*.rvt'), ('text/x-tcl', 'text/x-script.tcl', 'application/x-tcl')), - 'TcshLexer': ('pip._vendor.pygments.lexers.shell', 'Tcsh', ('tcsh', 'csh'), ('*.tcsh', '*.csh'), ('application/x-csh',)), - 'TcshSessionLexer': ('pip._vendor.pygments.lexers.shell', 'Tcsh Session', ('tcshcon',), (), ()), - 'TeaTemplateLexer': ('pip._vendor.pygments.lexers.templates', 'Tea', ('tea',), ('*.tea',), ('text/x-tea',)), - 'TealLexer': ('pip._vendor.pygments.lexers.teal', 'teal', ('teal',), ('*.teal',), ()), - 'TeraTermLexer': ('pip._vendor.pygments.lexers.teraterm', 'Tera Term macro', ('teratermmacro', 'teraterm', 'ttl'), ('*.ttl',), ('text/x-teratermmacro',)), - 'TermcapLexer': ('pip._vendor.pygments.lexers.configs', 'Termcap', ('termcap',), ('termcap', 'termcap.src'), ()), - 'TerminfoLexer': ('pip._vendor.pygments.lexers.configs', 'Terminfo', ('terminfo',), ('terminfo', 'terminfo.src'), ()), - 'TerraformLexer': ('pip._vendor.pygments.lexers.configs', 'Terraform', ('terraform', 'tf'), ('*.tf',), ('application/x-tf', 'application/x-terraform')), - 'TexLexer': ('pip._vendor.pygments.lexers.markup', 'TeX', ('tex', 'latex'), ('*.tex', '*.aux', '*.toc'), ('text/x-tex', 'text/x-latex')), - 'TextLexer': ('pip._vendor.pygments.lexers.special', 'Text only', ('text',), ('*.txt',), ('text/plain',)), - 'ThingsDBLexer': ('pip._vendor.pygments.lexers.thingsdb', 'ThingsDB', ('ti', 'thingsdb'), ('*.ti',), ()), - 'ThriftLexer': ('pip._vendor.pygments.lexers.dsls', 'Thrift', ('thrift',), ('*.thrift',), ('application/x-thrift',)), - 'TiddlyWiki5Lexer': ('pip._vendor.pygments.lexers.markup', 'tiddler', ('tid',), ('*.tid',), ('text/vnd.tiddlywiki',)), - 'TodotxtLexer': ('pip._vendor.pygments.lexers.textfmts', 'Todotxt', ('todotxt',), ('todo.txt', '*.todotxt'), ('text/x-todo',)), - 'TransactSqlLexer': ('pip._vendor.pygments.lexers.sql', 'Transact-SQL', ('tsql', 't-sql'), ('*.sql',), ('text/x-tsql',)), - 'TreetopLexer': ('pip._vendor.pygments.lexers.parsers', 'Treetop', ('treetop',), ('*.treetop', '*.tt'), ()), - 'TurtleLexer': ('pip._vendor.pygments.lexers.rdf', 'Turtle', ('turtle',), ('*.ttl',), ('text/turtle', 'application/x-turtle')), - 'TwigHtmlLexer': ('pip._vendor.pygments.lexers.templates', 'HTML+Twig', ('html+twig',), ('*.twig',), ('text/html+twig',)), - 'TwigLexer': ('pip._vendor.pygments.lexers.templates', 'Twig', ('twig',), (), ('application/x-twig',)), - 'TypeScriptLexer': ('pip._vendor.pygments.lexers.javascript', 'TypeScript', ('typescript', 'ts'), ('*.ts',), ('application/x-typescript', 'text/x-typescript')), - 'TypoScriptCssDataLexer': ('pip._vendor.pygments.lexers.typoscript', 'TypoScriptCssData', ('typoscriptcssdata',), (), ()), - 'TypoScriptHtmlDataLexer': ('pip._vendor.pygments.lexers.typoscript', 'TypoScriptHtmlData', ('typoscripthtmldata',), (), ()), - 'TypoScriptLexer': ('pip._vendor.pygments.lexers.typoscript', 'TypoScript', ('typoscript',), ('*.typoscript',), ('text/x-typoscript',)), - 'UcodeLexer': ('pip._vendor.pygments.lexers.unicon', 'ucode', ('ucode',), ('*.u', '*.u1', '*.u2'), ()), - 'UniconLexer': ('pip._vendor.pygments.lexers.unicon', 'Unicon', ('unicon',), ('*.icn',), ('text/unicon',)), - 'UrbiscriptLexer': ('pip._vendor.pygments.lexers.urbi', 'UrbiScript', ('urbiscript',), ('*.u',), ('application/x-urbiscript',)), - 'UsdLexer': ('pip._vendor.pygments.lexers.usd', 'USD', ('usd', 'usda'), ('*.usd', '*.usda'), ()), - 'VBScriptLexer': ('pip._vendor.pygments.lexers.basic', 'VBScript', ('vbscript',), ('*.vbs', '*.VBS'), ()), - 'VCLLexer': ('pip._vendor.pygments.lexers.varnish', 'VCL', ('vcl',), ('*.vcl',), ('text/x-vclsrc',)), - 'VCLSnippetLexer': ('pip._vendor.pygments.lexers.varnish', 'VCLSnippets', ('vclsnippets', 'vclsnippet'), (), ('text/x-vclsnippet',)), - 'VCTreeStatusLexer': ('pip._vendor.pygments.lexers.console', 'VCTreeStatus', ('vctreestatus',), (), ()), - 'VGLLexer': ('pip._vendor.pygments.lexers.dsls', 'VGL', ('vgl',), ('*.rpf',), ()), - 'ValaLexer': ('pip._vendor.pygments.lexers.c_like', 'Vala', ('vala', 'vapi'), ('*.vala', '*.vapi'), ('text/x-vala',)), - 'VbNetAspxLexer': ('pip._vendor.pygments.lexers.dotnet', 'aspx-vb', ('aspx-vb',), ('*.aspx', '*.asax', '*.ascx', '*.ashx', '*.asmx', '*.axd'), ()), - 'VbNetLexer': ('pip._vendor.pygments.lexers.dotnet', 'VB.net', ('vb.net', 'vbnet'), ('*.vb', '*.bas'), ('text/x-vbnet', 'text/x-vba')), - 'VelocityHtmlLexer': ('pip._vendor.pygments.lexers.templates', 'HTML+Velocity', ('html+velocity',), (), ('text/html+velocity',)), - 'VelocityLexer': ('pip._vendor.pygments.lexers.templates', 'Velocity', ('velocity',), ('*.vm', '*.fhtml'), ()), - 'VelocityXmlLexer': ('pip._vendor.pygments.lexers.templates', 'XML+Velocity', ('xml+velocity',), (), ('application/xml+velocity',)), - 'VerilogLexer': ('pip._vendor.pygments.lexers.hdl', 'verilog', ('verilog', 'v'), ('*.v',), ('text/x-verilog',)), - 'VhdlLexer': ('pip._vendor.pygments.lexers.hdl', 'vhdl', ('vhdl',), ('*.vhdl', '*.vhd'), ('text/x-vhdl',)), - 'VimLexer': ('pip._vendor.pygments.lexers.textedit', 'VimL', ('vim',), ('*.vim', '.vimrc', '.exrc', '.gvimrc', '_vimrc', '_exrc', '_gvimrc', 'vimrc', 'gvimrc'), ('text/x-vim',)), - 'WDiffLexer': ('pip._vendor.pygments.lexers.diff', 'WDiff', ('wdiff',), ('*.wdiff',), ()), - 'WatLexer': ('pip._vendor.pygments.lexers.webassembly', 'WebAssembly', ('wast', 'wat'), ('*.wat', '*.wast'), ()), - 'WebIDLLexer': ('pip._vendor.pygments.lexers.webidl', 'Web IDL', ('webidl',), ('*.webidl',), ()), - 'WhileyLexer': ('pip._vendor.pygments.lexers.whiley', 'Whiley', ('whiley',), ('*.whiley',), ('text/x-whiley',)), - 'X10Lexer': ('pip._vendor.pygments.lexers.x10', 'X10', ('x10', 'xten'), ('*.x10',), ('text/x-x10',)), - 'XQueryLexer': ('pip._vendor.pygments.lexers.webmisc', 'XQuery', ('xquery', 'xqy', 'xq', 'xql', 'xqm'), ('*.xqy', '*.xquery', '*.xq', '*.xql', '*.xqm'), ('text/xquery', 'application/xquery')), - 'XmlDjangoLexer': ('pip._vendor.pygments.lexers.templates', 'XML+Django/Jinja', ('xml+django', 'xml+jinja'), (), ('application/xml+django', 'application/xml+jinja')), - 'XmlErbLexer': ('pip._vendor.pygments.lexers.templates', 'XML+Ruby', ('xml+ruby', 'xml+erb'), (), ('application/xml+ruby',)), - 'XmlLexer': ('pip._vendor.pygments.lexers.html', 'XML', ('xml',), ('*.xml', '*.xsl', '*.rss', '*.xslt', '*.xsd', '*.wsdl', '*.wsf'), ('text/xml', 'application/xml', 'image/svg+xml', 'application/rss+xml', 'application/atom+xml')), - 'XmlPhpLexer': ('pip._vendor.pygments.lexers.templates', 'XML+PHP', ('xml+php',), (), ('application/xml+php',)), - 'XmlSmartyLexer': ('pip._vendor.pygments.lexers.templates', 'XML+Smarty', ('xml+smarty',), (), ('application/xml+smarty',)), - 'XorgLexer': ('pip._vendor.pygments.lexers.xorg', 'Xorg', ('xorg.conf',), ('xorg.conf',), ()), - 'XsltLexer': ('pip._vendor.pygments.lexers.html', 'XSLT', ('xslt',), ('*.xsl', '*.xslt', '*.xpl'), ('application/xsl+xml', 'application/xslt+xml')), - 'XtendLexer': ('pip._vendor.pygments.lexers.jvm', 'Xtend', ('xtend',), ('*.xtend',), ('text/x-xtend',)), - 'XtlangLexer': ('pip._vendor.pygments.lexers.lisp', 'xtlang', ('extempore',), ('*.xtm',), ()), - 'YamlJinjaLexer': ('pip._vendor.pygments.lexers.templates', 'YAML+Jinja', ('yaml+jinja', 'salt', 'sls'), ('*.sls',), ('text/x-yaml+jinja', 'text/x-sls')), - 'YamlLexer': ('pip._vendor.pygments.lexers.data', 'YAML', ('yaml',), ('*.yaml', '*.yml'), ('text/x-yaml',)), - 'YangLexer': ('pip._vendor.pygments.lexers.yang', 'YANG', ('yang',), ('*.yang',), ('application/yang',)), - 'ZeekLexer': ('pip._vendor.pygments.lexers.dsls', 'Zeek', ('zeek', 'bro'), ('*.zeek', '*.bro'), ()), - 'ZephirLexer': ('pip._vendor.pygments.lexers.php', 'Zephir', ('zephir',), ('*.zep',), ()), - 'ZigLexer': ('pip._vendor.pygments.lexers.zig', 'Zig', ('zig',), ('*.zig',), ('text/zig',)), - 'apdlexer': ('pip._vendor.pygments.lexers.apdlexer', 'ANSYS parametric design language', ('ansys', 'apdl'), ('*.ans',), ()), -} - -if __name__ == '__main__': # pragma: no cover - import sys - import os - - # lookup lexers - found_lexers = [] - sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..')) - for root, dirs, files in os.walk('.'): - for filename in files: - if filename.endswith('.py') and not filename.startswith('_'): - module_name = 'pygments.lexers%s.%s' % ( - root[1:].replace('/', '.'), filename[:-3]) - print(module_name) - module = __import__(module_name, None, None, ['']) - for lexer_name in module.__all__: - lexer = getattr(module, lexer_name) - found_lexers.append( - '%r: %r' % (lexer_name, - (module_name, - lexer.name, - tuple(lexer.aliases), - tuple(lexer.filenames), - tuple(lexer.mimetypes)))) - # sort them to make the diff minimal - found_lexers.sort() - - # extract useful sourcecode from this file - with open(__file__) as fp: - content = fp.read() - # replace crnl to nl for Windows. - # - # Note that, originally, contributers should keep nl of master - # repository, for example by using some kind of automatic - 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-Title: / Meng Qi Shi Shen English title: Cinderella Chef Genre:.... Cinderella Monogatari English Su:. young and capable, workaholic, a very studious young man, a person who loves food and cooking, but it’s also a girl whose homework and cooking is always neglected by his mother.... Cinderella Monogatari English Su:. hero is also recognized as a fellow of the society and has a dark side. But he loves to observe other people secretly.... Cinderella Monogatari English Su: Cinderella monogatari is a female division of "popular story". Cinderella girls are usually sent to the services.... Cinderella Monogatari English Su: Cinderella is not an ordinary Cinderella. This Cinderella girl is not a princess, so she can only attend the wedding party. In the end, she meets the prince for the first time.... Cinderella Monogatari English Su:..... Title: / Meng Qi Shi Shen English title: Cinderella Chef Genre:.... Being a Cinderella means that an unknown power protect the girl and is always ready to protect you. Cinderella Monogatari English Su, Cinderella Monogatari English Su MGP Hunter 4..... Cinderella Monogatari English Su, Cinderella Monogatari English Su MGP Hunter 4..... The Vanishing (2014) CiCuChi 1 2 3 4 Cinderella Monogatari English Su.... Hikaru no Go Cinderella Monogatari English Su.... Reideen Cinderella Monogatari English Su.... Cinderella Monogatari English Su: As the only daughter in the family, Cinderella must live with her own, loving mother. Her father is a long-distance runner, and spends most of his time at the faraway races. Her mother.... Cinderella Monogatari English Su: Cinderella "says of the royal family, "I can stay at home, but they can't stay at home. " Cinderella... Cinderella Monogatari English Su.... Alone (2005) 25 28 13 20 Cinderella Monogatari English Su. Cinderella Monogatari English Su MGP Hunter 4..... Cinderella Monogatari English Su. Cinderella Monogatari English Su MGP Hunter 4..... Attack on Titan: Armored Warfare 2 Frontline Bypass Strategy. Cinderella Monogatari English Su.... Cinderella Monogatari English Su MGP Hunter 4..... Cinderella Monogatari English Su MGP Hunter 4..... Cinderella Monogatari English Su MGP Hunter 4..... Cinderella Monogatari English Su MGP Hunter 4..... Cinderella Monogatari English Su.... Mother & Daughter.... The Girls from Paradise (2008).... Cinderella Monogatari English Su.... Cinderella Monogatari English Su MGP Hunter 4..... Cinderella Monogatari English Su.... Cinderella Monogatari English Su.... Cinderella Monogatari English Su.... Cinderella Monogatari English Su.... Cinderella Monogatari English Su. Cinderella Monogatari English Su.... Cinderella Monogatari English Su.... Hagakure Anime English Sub Tue. 31 Jul 2017 18:06. tbsplus. Cinderella Monogatari (Cinderella Monogatari, 春刀梨紗, Shunkagure wakuranai Cinderella) is a 2006 South Korean supernatural horror film. It was directed by Kim Tae-kyung, who also wrote the script. Cinderella was the only girl from her father who lived with her stepmother and step-sisters in her house. Her stepmother and step sister used to harass Cinderella and make her do all the household work. One day a message was sent from the royal house in the city that the prince is looking for a girl for his wedding. Because of which he invited all the girls of the city to the ball party.
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\ No newline at end of file diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Ganapathi Thalam Tamil Pdf 27.md b/spaces/quidiaMuxgu/Expedit-SAM/Ganapathi Thalam Tamil Pdf 27.md deleted file mode 100644 index ec3d8d34182618b5092a640f81ac1a87ba38432e..0000000000000000000000000000000000000000 --- a/spaces/quidiaMuxgu/Expedit-SAM/Ganapathi Thalam Tamil Pdf 27.md +++ /dev/null @@ -1,6 +0,0 @@ -ganapathi thalam tamil pdf 27
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-27.Major Texts on Dance that you know . 28.Melakartha Raga Table and its relevance in Carnatic Music. [2X15=30 MARKS] ... Jathiswaram – Ragam-Vasantha, Thalam-Roopakam-(24 Hrs) ... Page 32. • Ganapathi sthuthi / Thodayam- ( 24 Hrs) ... Kummi is a folk dance common in both Kerala and Tamil Nadu. Explain. 18. 1fdad05405
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\ No newline at end of file diff --git a/spaces/radames/MusicGen-Continuation/tests/modules/test_conv.py b/spaces/radames/MusicGen-Continuation/tests/modules/test_conv.py deleted file mode 100644 index 28fbc4f1a0ebaf41b56947b767958ae696e75eec..0000000000000000000000000000000000000000 --- a/spaces/radames/MusicGen-Continuation/tests/modules/test_conv.py +++ /dev/null @@ -1,203 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from itertools import product -import math -import random - -import pytest -import torch -from torch import nn - -from audiocraft.modules import ( - NormConv1d, - NormConvTranspose1d, - StreamableConv1d, - StreamableConvTranspose1d, - pad1d, - unpad1d, -) - - -def test_get_extra_padding_for_conv1d(): - # TODO: Implement me! - pass - - -def test_pad1d_zeros(): - x = torch.randn(1, 1, 20) - - xp1 = pad1d(x, (0, 5), mode='constant', value=0.) - assert xp1.shape[-1] == 25 - xp2 = pad1d(x, (5, 5), mode='constant', value=0.) - assert xp2.shape[-1] == 30 - xp3 = pad1d(x, (0, 0), mode='constant', value=0.) - assert xp3.shape[-1] == 20 - xp4 = pad1d(x, (10, 30), mode='constant', value=0.) - assert xp4.shape[-1] == 60 - - with pytest.raises(AssertionError): - pad1d(x, (-1, 0), mode='constant', value=0.) - - with pytest.raises(AssertionError): - pad1d(x, (0, -1), mode='constant', value=0.) - - with pytest.raises(AssertionError): - pad1d(x, (-1, -1), mode='constant', value=0.) - - -def test_pad1d_reflect(): - x = torch.randn(1, 1, 20) - - xp1 = pad1d(x, (0, 5), mode='reflect', value=0.) - assert xp1.shape[-1] == 25 - xp2 = pad1d(x, (5, 5), mode='reflect', value=0.) - assert xp2.shape[-1] == 30 - xp3 = pad1d(x, (0, 0), mode='reflect', value=0.) - assert xp3.shape[-1] == 20 - xp4 = pad1d(x, (10, 30), mode='reflect', value=0.) - assert xp4.shape[-1] == 60 - - with pytest.raises(AssertionError): - pad1d(x, (-1, 0), mode='reflect', value=0.) - - with pytest.raises(AssertionError): - pad1d(x, (0, -1), mode='reflect', value=0.) - - with pytest.raises(AssertionError): - pad1d(x, (-1, -1), mode='reflect', value=0.) - - -def test_unpad1d(): - x = torch.randn(1, 1, 20) - - u1 = unpad1d(x, (5, 5)) - assert u1.shape[-1] == 10 - u2 = unpad1d(x, (0, 5)) - assert u2.shape[-1] == 15 - u3 = unpad1d(x, (5, 0)) - assert u3.shape[-1] == 15 - u4 = unpad1d(x, (0, 0)) - assert u4.shape[-1] == x.shape[-1] - - with pytest.raises(AssertionError): - unpad1d(x, (-1, 0)) - - with pytest.raises(AssertionError): - unpad1d(x, (0, -1)) - - with pytest.raises(AssertionError): - unpad1d(x, (-1, -1)) - - -class TestNormConv1d: - - def test_norm_conv1d_modules(self): - N, C, T = 2, 2, random.randrange(1, 100_000) - t0 = torch.randn(N, C, T) - - C_out, kernel_size, stride = 1, 4, 1 - expected_out_length = int((T - kernel_size) / stride + 1) - wn_conv = NormConv1d(C, 1, kernel_size=4, norm='weight_norm') - gn_conv = NormConv1d(C, 1, kernel_size=4, norm='time_group_norm') - nn_conv = NormConv1d(C, 1, kernel_size=4, norm='none') - - assert isinstance(wn_conv.norm, nn.Identity) - assert isinstance(wn_conv.conv, nn.Conv1d) - - assert isinstance(gn_conv.norm, nn.GroupNorm) - assert isinstance(gn_conv.conv, nn.Conv1d) - - assert isinstance(nn_conv.norm, nn.Identity) - assert isinstance(nn_conv.conv, nn.Conv1d) - - for conv_layer in [wn_conv, gn_conv, nn_conv]: - out = conv_layer(t0) - assert isinstance(out, torch.Tensor) - assert list(out.shape) == [N, C_out, expected_out_length] - - -class TestNormConvTranspose1d: - - def test_normalizations(self): - N, C, T = 2, 2, random.randrange(1, 100_000) - t0 = torch.randn(N, C, T) - - C_out, kernel_size, stride = 1, 4, 1 - expected_out_length = (T - 1) * stride + (kernel_size - 1) + 1 - - wn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='weight_norm') - gn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='time_group_norm') - nn_convtr = NormConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, norm='none') - - assert isinstance(wn_convtr.norm, nn.Identity) - assert isinstance(wn_convtr.convtr, nn.ConvTranspose1d) - - assert isinstance(gn_convtr.norm, nn.GroupNorm) - assert isinstance(gn_convtr.convtr, nn.ConvTranspose1d) - - assert isinstance(nn_convtr.norm, nn.Identity) - assert isinstance(nn_convtr.convtr, nn.ConvTranspose1d) - - for convtr_layer in [wn_convtr, gn_convtr, nn_convtr]: - out = convtr_layer(t0) - assert isinstance(out, torch.Tensor) - assert list(out.shape) == [N, C_out, expected_out_length] - - -class TestStreamableConv1d: - - def get_streamable_conv1d_output_length(self, length, kernel_size, stride, dilation): - # StreamableConv1d internally pads to make sure that the last window is full - padding_total = (kernel_size - 1) * dilation - (stride - 1) - n_frames = (length - kernel_size + padding_total) / stride + 1 - ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total) - return ideal_length // stride - - def test_streamable_conv1d(self): - N, C, T = 2, 2, random.randrange(1, 100_000) - t0 = torch.randn(N, C, T) - C_out = 1 - - # conv params are [(kernel_size, stride, dilation)] - conv_params = [(4, 1, 1), (4, 2, 1), (3, 1, 3), (10, 5, 1), (3, 2, 3)] - for causal, (kernel_size, stride, dilation) in product([False, True], conv_params): - expected_out_length = self.get_streamable_conv1d_output_length(T, kernel_size, stride, dilation) - sconv = StreamableConv1d(C, C_out, kernel_size=kernel_size, stride=stride, dilation=dilation, causal=causal) - out = sconv(t0) - assert isinstance(out, torch.Tensor) - print(list(out.shape), [N, C_out, expected_out_length]) - assert list(out.shape) == [N, C_out, expected_out_length] - - -class TestStreamableConvTranspose1d: - - def get_streamable_convtr1d_output_length(self, length, kernel_size, stride): - padding_total = (kernel_size - stride) - return (length - 1) * stride - padding_total + (kernel_size - 1) + 1 - - def test_streamable_convtr1d(self): - N, C, T = 2, 2, random.randrange(1, 100_000) - t0 = torch.randn(N, C, T) - - C_out = 1 - - with pytest.raises(AssertionError): - StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=False, trim_right_ratio=0.5) - StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=-1.) - StreamableConvTranspose1d(C, C_out, kernel_size=4, causal=True, trim_right_ratio=2) - - # causal params are [(causal, trim_right)] - causal_params = [(False, 1.0), (True, 1.0), (True, 0.5), (True, 0.0)] - # conv params are [(kernel_size, stride)] - conv_params = [(4, 1), (4, 2), (3, 1), (10, 5)] - for ((causal, trim_right_ratio), (kernel_size, stride)) in product(causal_params, conv_params): - expected_out_length = self.get_streamable_convtr1d_output_length(T, kernel_size, stride) - sconvtr = StreamableConvTranspose1d(C, C_out, kernel_size=kernel_size, stride=stride, - causal=causal, trim_right_ratio=trim_right_ratio) - out = sconvtr(t0) - assert isinstance(out, torch.Tensor) - assert list(out.shape) == [N, C_out, expected_out_length] diff --git a/spaces/radames/instruct-pix2pix/utils.py b/spaces/radames/instruct-pix2pix/utils.py deleted file mode 100644 index ff1c065d186347ca51b47d010a697dbe1814695c..0000000000000000000000000000000000000000 --- a/spaces/radames/instruct-pix2pix/utils.py +++ /dev/null @@ -1,6 +0,0 @@ -def is_google_colab(): - try: - import google.colab - return True - except: - return False \ No newline at end of file diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Dalenet - Thrive Universal Easy Flash Tool - V1.6.zip.md b/spaces/raedeXanto/academic-chatgpt-beta/Dalenet - Thrive Universal Easy Flash Tool - V1.6.zip.md deleted file mode 100644 index 9884a6fc15476499d63aa3e2d7f299a001445c93..0000000000000000000000000000000000000000 --- a/spaces/raedeXanto/academic-chatgpt-beta/Dalenet - Thrive Universal Easy Flash Tool - V1.6.zip.md +++ /dev/null @@ -1,7 +0,0 @@ - -How to Root Your Toshiba Thrive Tablet with DaleNet Universal Easy Flash Tool
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- How to use the additional resources provided by the book? | | Conclusion | - Summarize the main points of the article
- Provide a call to action for readers | | FAQs | - Answer some common questions related to the book | ## Table 2: Article with HTML formatting ```htmlData Interpretation and Logical Reasoning by Arun Sharma PDF Download
-Are you looking for a comprehensive guide to ace the data interpretation and logical reasoning sections of the CAT exam? Do you want to learn the concepts, techniques and strategies to solve various types of questions in these sections? If yes, then you should check out the book Data Interpretation and Logical Reasoning by Arun Sharma.
-This book is one of the best-selling books for CAT preparation and has been updated with the latest syllabus and pattern of the exam. It covers all the topics and subtopics of data interpretation and logical reasoning, such as tables, charts, graphs, puzzles, arrangements, games, syllogisms, deductions, etc. It also provides tips and tricks to improve your speed and accuracy while solving the questions.
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-The book is divided into two parts: data interpretation and logical reasoning. Each part has four sections: basic mode, intermediate mode, advanced mode and test papers. The basic mode introduces the concepts and fundamentals of each topic with solved examples. The intermediate mode provides more practice questions with varying levels of difficulty. The advanced mode challenges you with high-level questions that test your analytical and critical thinking skills. The test papers simulate the actual CAT exam format and help you assess your performance and progress.
-The book also comes with a CD-ROM that contains more than 1000 practice questions with detailed solutions. You can also access online mock tests and video lectures by the author on the publisher's website. The book is suitable for both beginners and experts who want to master data interpretation and logical reasoning for CAT.
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-Data interpretation and logical reasoning are two of the most important and scoring sections of the CAT exam. They test your ability to interpret data, analyze information, draw conclusions, make decisions and solve problems. These skills are not only essential for cracking the CAT exam but also for succeeding in your academic and professional career.
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- Start with the basic mode of each topic and review the concepts and fundamentals with solved examples. -
- Move on to the intermediate mode of each topic and practice more questions with varying levels of difficulty. -
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- Use the CD-ROM or online resources to access more practice questions, mock tests and video lectures by the author. -
- Revise the topics regularly and keep track of your strengths and weaknesses. -
- Seek feedback from experts or peers if you have any doubts or queries. -
Conclusion
-In conclusion, Data Interpretation and Logical Reasoning by Arun Sharma is a must-read book for anyone who wants to ace the data interpretation and logical reasoning sections of the CAT exam. It covers all the topics and subtopics of these sections with clear explanations, solved examples and practice questions. It also provides tips and tricks to improve your speed and accuracy while solving the questions. It also comes with a CD-ROM and online resources that offer more practice questions, mock tests and video lectures by the author. The book is suitable for both beginners and experts who want to master data interpretation and logical reasoning for CAT.
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- Understand the syllabus and pattern of these sections and familiarize yourself with the types of questions that can appear in the exam. -
- Learn the concepts and principles of data interpretation and logical reasoning from a reliable source like Data Interpretation and Logical Reasoning by Arun Sharma. -
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- They help you to interpret data, analyze information, draw conclusions, make decisions and solve problems effectively. -
- They enhance your analytical and critical thinking skills that are essential for innovation and creativity. -
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- They want to save money. Buying FIFA 2020 can be expensive for some people, especially if they live in regions where the game's price is higher than average or if they have other financial obligations to meet. -
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- Electrical symbol library: You can access a library of 65,000+ intelligent electrical symbols that are easy to use, colorful, and customizable. You can also create your own symbols using the Symbol Builder tool. -
- Wire and component tagging: You can automate the numbering of wires and the generation of component tags with various options and formats. You can also re-tag components easily and update your drawings accordingly. -
- Automatic reports: You can generate and update multiple customized reports automatically, such as bill of materials, wire lists, terminal plans, cable summaries, and more. You can also export your reports to various formats, such as Excel, PDF, or HTML. -
- Electrical standards support: You can work with the latest electrical standards, such as NFPA, IEC, ANSI, JIC, GB, AS/NZS, and more. You can also use the JIC and older IEC symbol libraries for legacy support. -
- Project management: You can use the Project Manager to organize and manage your electrical projects. You can re-order drawings, copy or move project files, create subfolders, and more. You can also access simple export options to share your projects with others. -
- Circuit design and reuse: You can use the Circuit Builder to design and annotate a variety of motor control and power feed circuits. You can access prepopulated data and customize it according to your needs. You can also reuse existing circuits or create your own circuit templates. -
- SQL catalog support: You can use a SQL catalog database to store and retrieve your electrical component data. You can also integrate your catalog data with Autodesk Vault to generate a bill of materials automatically from your AutoCAD Electrical drawings. -
- Coil and contact cross-referencing: You can keep track of parent/child contacts in real time while Circuit Builder dynamically builds the circuit. You can also assign component tags to each component automatically. -
- PLC I/O drawings from spreadsheets: You can use a single data file to generate multiple PLC I/O drawings automatically. You can define your project’s I/O assignments in a spreadsheet, database, or comma-delimited text file and import it into AutoCAD Electrical 2014. -
- First, you need to purchase PowerDVD Ultra from the official CyberLink website or an authorized reseller. You can choose between a one-time payment or a subscription plan. -
- After completing your purchase, you will receive an email confirmation with your order details and your serial key. The serial key is a 30-digit alphanumeric code that looks something like this: XXXXX-XXXXX-XXXXX-XXXXX-XXXXX-XXXXX. -
- Next, you need to download and install PowerDVD Ultra on your computer. You can download the installer from the CyberLink website or use the disc that came with your purchase. -
- Once you have installed PowerDVD Ultra, launch it and click on the "Activate" button on the top right corner of the main window. You will be prompted to enter your serial key. -
- Copy and paste your serial key from the email confirmation or type it manually. Make sure you enter it exactly as it appears, without any spaces or dashes. -
- Click on the "OK" button to verify your serial key and activate your software. You will see a message confirming that your activation was successful. -
- Enjoy using PowerDVD Ultra with all its features and benefits! -
- It supports all kinds of media formats, including Blu-ray, DVD, 4K, HDR, HEVC, 360, VR, and more. You can play any disc or file on your computer or stream online content from popular platforms like YouTube, Netflix, Hulu, and Amazon Prime Video. -
- It delivers the best quality and performance for your media playback. You can enjoy smooth and stutter-free playback with TrueTheater technology that optimizes the video and audio quality according to your hardware and preferences. You can also adjust the brightness, contrast, color, and sound settings to suit your viewing environment. -
- It offers you a immersive and interactive experience with your media. You can explore the 360 and VR content with your mouse, keyboard, or headset. You can also use the PowerDVD Remote app to control the playback from your smartphone or tablet. -
- It lets you organize and manage your media library with ease. You can browse and search your media files by categories, tags, ratings, and keywords. You can also create playlists, bookmarks, and favorites for quick access. -
- It allows you to share and enjoy your media across devices and platforms. You can use the CyberLink Cloud service to store and sync your media files online. You can also use the PowerDVD Mobile app to access and play your media on your iOS or Android devices. -
- Check for updates regularly. CyberLink releases new updates for PowerDVD Ultra that fix bugs, improve performance, and add new features. You can check for updates manually by clicking on the "Update" button on the top right corner of the main window or enable automatic updates in the settings. -
- Explore the settings and preferences. PowerDVD Ultra has a lot of options and settings that you can customize according to your needs and preferences. You can access them by clicking on the "Settings" button on the top right corner of the main window or pressing Ctrl+P on your keyboard. You can change the language, subtitles, audio output, playback mode, network connection, cloud storage, parental control, and more. -
- Use keyboard shortcuts and mouse gestures. PowerDVD Ultra has a lot of keyboard shortcuts and mouse gestures that you can use to control the playback and navigate the interface. You can view them by clicking on the "Help" button on the top right corner of the main window or pressing F1 on your keyboard. You can also customize them in the settings. -
- Join the CyberLink community. CyberLink has a large and active community of users who share their tips, feedback, questions, and suggestions about PowerDVD Ultra and other CyberLink products. You can join them by clicking on the "Community" button on the top right corner of the main window or visiting https://forum.cyberlink.com/. You can also follow CyberLink on social media platforms like Facebook, Twitter, Instagram, YouTube, and LinkedIn. -
- Go to [this link](^1^) and click on the Download APK button. -
- Wait for the download to finish and then open the file. -
- If you see a warning message that says "Install blocked", go to your device settings and enable "Unknown sources" under security or privacy options. -
- Tap on "Install" and wait for the installation to complete. -
- Launch the game and enjoy! -
- The goal of the game is to control a ball that rolls on a road that changes color according to the song you choose. You have to swipe left or right to match the ball with the same color as the road. -
- If you miss a color or hit a different color, you will lose a life. You have three lives in each level. If you lose all your lives, you will have to start over. -
- You can earn coins, gems, stars, and other rewards by completing levels, challenges, and events. You can use them to unlock more songs, balls, roads, themes, effects, and other features. -
- You can choose from different game modes such as Classic, Endless, Hard, and VIP. Each mode has different levels of difficulty and rewards. -
- You can also choose from different genres of music such as Pop, Rock, EDM, Hip Hop, and more. Each genre has different songs and roads. -
- You can also customize your game experience by choosing different balls, roads, themes, and effects. Each ball has different attributes such as speed, size, shape, and trail. Each road has different patterns and colors. Each theme has different backgrounds and sounds. Each effect has different animations and visuals. -
- To choose a different song, tap on the music icon on the top left corner of the screen. You will see a list of genres and songs that you can choose from. Tap on the song that you want to play. -
- To choose a different ball, tap on the ball icon on the top right corner of the screen. You will see a list of balls that you can choose from. Tap on the ball that you want to use. -
- To choose a different road, tap on the road icon on the bottom left corner of the screen. You will see a list of roads that you can choose from. Tap on the road that you want to roll on. -
- To choose a different theme, tap on the theme icon on the bottom right corner of the screen. You will see a list of themes that you can choose from. Tap on the theme that you want to apply. -
- To choose a different effect, tap on the effect icon on the bottom center of the screen. You will see a list of effects that you can choose from. Tap on the effect that you want to activate. -
- Complete levels: Each level has three stars that you can earn by matching the ball with the road perfectly. The more stars you earn, the more coins and gems you get. You can use them to unlock more songs, balls, roads, themes, and effects. -
- Complete challenges: Each day, you can complete different challenges that give you coins, gems, stars, and other rewards. Some examples of challenges are: play 10 songs, collect 100 coins, use 5 different balls, etc. -
- Join events: Each week, you can join different events that give you coins, gems, stars, and other rewards. Some examples of events are: Halloween event, Christmas event, Valentine's event, etc. -
- Watch ads: You can watch ads to get free coins, gems, stars, and other rewards. You can also watch ads to revive your lives or to get extra rewards after completing a level. -
- Invite friends: You can invite your friends to play Dancing Road APK Mod and get coins, gems, stars, and other rewards. You can also join or create a team with your friends and chat with them in the game. -
- To join or create a team, tap on the team icon on the top center of the screen. You will see a list of teams that you can join or create. Tap on the team that you want to join or create. -
- To chat with other players, tap on the chat icon on the bottom left corner of the screen. You will see a list of chat rooms that you can join or create. Tap on the chat room that you want to join or create. -
- To compete in leaderboards, tap on the leaderboard icon on the top right corner of the screen. You will see a list of leaderboards that you can join or create. Tap on the leaderboard that you want to join or create. -
- To compete in tournaments, tap on the tournament icon on the bottom right corner of the screen. You will see a list of tournaments that you can join or create. Tap on the tournament that you want to join or create. -
- To compete in events, tap on the event icon on the bottom center of the screen. You will see a list of events that you can join or create. Tap on the event that you want to join or create. -
- Check your internet connection: Make sure you have a stable and fast internet connection to play the game smoothly. You can use a Wi-Fi or a mobile data connection, but avoid using a VPN or a proxy server. -
- Clear your cache and data: Sometimes, the cache and data of the game can get corrupted or outdated, causing issues. You can clear them by going to your device settings, apps, Dancing Road APK Mod, storage, and tapping on "Clear cache" and "Clear data". Note that this will reset your game progress and settings, so make sure you back them up first. -
- Update your game and device: Sometimes, the game or your device can have bugs or glitches that cause issues. You can update them by going to the Google Play Store, Dancing Road APK Mod, and tapping on "Update". You can also go to your device settings, system, and tapping on "Update". Note that this may require a restart of your device. -
- Reinstall your game: Sometimes, the game can have corrupted or missing files that cause issues. You can reinstall it by going to the Google Play Store, Dancing Road APK Mod, and tapping on "Uninstall". Then, you can download and install it again from [this link]. -
- Contact the support team: If none of the above methods work, you can contact the support team of Dancing Road APK Mod by going to the game settings, help, and tapping on "Contact us". You can also email them at support@dancingroad.com. They will try to help you as soon as possible. -
- Beat Jumper: EDM up: This is another music game by the same developer of Dancing Road APK Mod. In this game, you have to control a ball that jumps on tiles that match the rhythm of the song. You can choose from different genres and songs, and customize your ball and tiles. You can also compete with other players in leaderboards and tournaments. -
- Tiles Hop: EDM Rush: This is a similar game to Beat Jumper, but with a different twist. In this game, you have to control a ball that hops on tiles that move horizontally. You have to swipe left or right to match the ball with the tiles. You can also choose from different genres and songs, and customize your ball and tiles. -
- Project Diva: This is a series of rhythm games that feature popular songs from Vocaloid, a software that synthesizes singing voices. In these games, you have to press buttons that correspond to the notes on the screen. You can choose from different Vocaloid characters and songs, and customize their outfits and stages. -
- Dance Dance Revolution: This is a classic rhythm game that requires a dance pad or a controller. In this game, you have to step on arrows that match the rhythm of the song. You can choose from different genres and songs, and adjust the difficulty and speed. You can also play with friends or online players. -
- Dancing Games at Y8.com: This is a website that offers a variety of online games related to dancing. You can learn to do different dances, be a cheerleader, practice your rhythm, and more. You can also enjoy the soundtracks and graphics of these games. -
- Is Dancing Road APK Mod safe to use? -
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- Anesthesia Review for DNB Students: This book is designed to help students prepare for the DNB (Diplomate of National Board) examination in anaesthesia. It covers the syllabus of the exam, and provides multiple choice questions and answers with explanations. -
- MCQs in Regional Anaesthesia and Pain Therapy: This book is a collection of more than 1000 multiple choice questions on regional anaesthesia and pain therapy. It covers topics such as anatomy, physiology, pharmacology, techniques, complications, and special situations. It also provides answers with references and rationales. -
- Handbook of Anaesthesia for Postgraduates: This book is a concise and practical guide for postgraduate students of anaesthesia. It covers the basics of anaesthesia, as well as specific topics such as paediatric anaesthesia, obstetric anaesthesia, neuroanaesthesia, cardiac anaesthesia, and more. It also provides tips and tricks for clinical practice. -
- It supports multiple technologies and disciplines in one integrated environment. -
- It allows you to design and simulate circuits and systems using graphical symbols and components. -
- It provides realistic animations and dynamic simulations that show the behavior and performance of your circuits and systems. -
- It enables you to generate and export documentation, reports, schematics, bills of materials, etc. -
- It offers a complete project/product lifecycle solution that can optimize your workflow from design to maintenance. -
- It can help you save time and money by reducing errors, rework, and waste. -
- It can help you improve your skills and knowledge by learning new technologies and concepts. -
- It can help you enhance your creativity and innovation by exploring different scenarios and solutions. -
- It can help you increase your productivity and efficiency by automating tasks and processes. -
- It can help you improve your communication and collaboration by sharing your projects and ideas with others. -
- It is expensive. A license key for Automation Studio can cost from $995 to $4995 depending on the edition and modules you choose. This may be too costly for some users who have limited budgets or resources. -
- It requires a license key. You need to activate your license key online or offline before you can use Automation Studio. You also need to renew your license key periodically or buy a new one if you want to upgrade or change your computer. This may be inconvenient or problematic for some users who have unreliable internet connections or changing hardware configurations. -
- It has limited compatibility. Automation Studio only works on Windows operating systems and does not support Mac or Linux platforms. It also does not integrate well with other software or tools, such as CAD, CAM, CNC, etc. This may limit your options or flexibility when working on different projects or environments. -
- Using a keygen. A keygen is a program that generates valid license keys for software. You can use a keygen to activate your software without buying a license key from the official source. -
- Using a patch. A patch is a program that modifies the original files of software to remove or alter the security features. You can use a patch to run your software without entering a license key or connecting to the internet. -
- Using a crack. A crack is a modified version of software that has been pre-activated or pre-patched by someone else. You can use a crack to install and use your software without any hassle or verification. -
- It is illegal. Cracking software violates the intellectual property rights of the software developers and owners. It is considered a form of piracy or theft that can result in legal actions, fines, or imprisonment. -
- It is unethical. Cracking software deprives the software developers and owners of their deserved income and recognition. It is also unfair to the legitimate users who pay for the software and follow the terms of use. -
- It is harmful. Cracking software can expose you to malware, viruses, spyware, ransomware, or other malicious code that can infect your computer or device, steal your data, damage your files, or compromise your security. -
- It is poor quality. Cracking software can cause errors, bugs, crashes, or performance issues that can affect your work or experience. It can also prevent you from accessing the latest updates, features, or support from the software developers or owners. -
- The software may not work as expected or cause errors or crashes. -
- The software may not have all the features or modules that you need or want. -
- The software may not be compatible with your system or other software or tools. -
- The software may not be updated or supported by the developers or owners. -
- Online updates. You can download and install the latest updates for Automation Studio to keep your software up to date and secure. -
- Online help. You can access the online help system that provides detailed information and instructions on how to use Automation Studio. -
- Online forum. You can join the online forum that allows you to interact with other users and experts of Automation Studio, ask questions, share tips, and get feedback. -
- Online training. You can enroll in online training courses that teach you how to use Automation Studio effectively and efficiently. -
- Online support. You can contact the online support team that can help you with any technical issues or problems that you may encounter with Automation Studio. -
- Launch the software and select a voice preset from the list on the left side of the main window. You can choose from various categories, such as Male Voices, Female Voices, Animal Voices, Robot Voices, etc. -
- Adjust the pitch and timbre sliders to fine-tune your voice. Pitch controls how high or low your voice sounds, while timbre controls how masculine or feminine your voice sounds. -
- Add some background effects to make your voice more realistic or funny. You can choose from different environments, such as City Street, Forest, Rainy Day, etc. -
- Test your voice by clicking on the "Preview" button at the bottom of the main window. You should hear your voice changed according to your settings. -
- Select an audio application that you want to use with AV Gold 7.0.15 Voice Changer Software from the list on the right side of the main window. You can also click on "More" to add more applications or browse for them manually. -
- Make sure that AV Gold 7.0.15 Voice Changer Software is set as your default microphone in your audio application settings. -
- Start using your audio application and enjoy your new voice! -
- Visit http://www.fmcdealer.com >PTS (Professional Technician Society) website> Rotunda > Rotunda Diagnostic > IDS > Module Reprogramming. Enter the calibration file name. For example, if IDS states file abcdefg.bin, enter âabcdefgâ then click on the download button. -
- OR -
- Visit http://www.motorcraftservice.com >Diagnostic Tool Support >IDS >Module Reprogramming. Enter the calibration file name. For example, if IDS states file abcdefg.bin, enter âabcdefgâ then click on the download button. -
- Download the file to either a USB key/drive or CD ROM. -
- Copy the file from the removable media to C:\Program Files\Ford Motor Company\Calibration files directory. -
- If the IDS laptop has an internet connection, download the file(s) directly to C:\Program Files\Ford Motor Company\Calibration files directory. -
- Visit one of these links that provide direct downloads of the Ford IDS Calibration Files v81: -
- http://www.nitroflare.com/view/454788CEDEBB2B8/Ford_IDS_Calibration_Files_v81.part1.rar -
- http://www.nitro e753bf7129
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-
\ No newline at end of file diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Activate Autodesk 3ds Max 2015 with X-Force Keygen.md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Activate Autodesk 3ds Max 2015 with X-Force Keygen.md deleted file mode 100644 index 2d0be28c601868a70d32e00c3b3ae02f339edfea..0000000000000000000000000000000000000000 --- a/spaces/tialenAdioni/chat-gpt-api/logs/How to Activate Autodesk 3ds Max 2015 with X-Force Keygen.md +++ /dev/null @@ -1,48 +0,0 @@ -
-How to Crack Autodesk 3ds Max 2015 with X-Force Keygen
-Autodesk 3ds Max 2015 is a powerful 3D modeling and animation software that allows you to create stunning scenes and characters for games, movies, and other projects. However, it is not a free software and you need a product key to activate it. In this article, we will show you how to crack Autodesk 3ds Max 2015 with X-Force Keygen, a tool that can generate activation keys for any Autodesk product.
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-Step 1: Download and install Autodesk 3ds Max 2015
-You can download the trial version of Autodesk 3ds Max 2015 from the official website or from other sources. Make sure you choose the right version for your system (32-bit or 64-bit). Follow the installation instructions and restart your computer when prompted.
-Step 2: Download and run X-Force Keygen
-X-Force Keygen is a program that can generate serial numbers and product keys for any Autodesk product. You can download it from various websites , but be careful of viruses and malware. After downloading, extract the file and run the keygen as administrator. You will see a window like this:
-
-Select Autodesk 3ds Max 2015 from the drop-down menu and click on Generate. You will get a serial number and a product key. Copy them and keep them safe.
- -Step 3: Activate Autodesk 3ds Max 2015
-Launch Autodesk 3ds Max 2015 and click on Activate. You will see a screen like this:
-
-Paste the serial number and the product key that you got from X-Force Keygen and click on Next. You will see a message that your serial is wrong. Don't worry, just click on Close and then on Activate again. This time, you will see an option to request an activation code using an offline method. Choose that option and click on Next.
-You will see a screen like this:
-
-Copy the request code that appears on the screen and go back to X-Force Keygen. Paste the request code in the keygen and click on Generate. You will get an activation code. Copy it and go back to Autodesk 3ds Max 2015. Paste the activation code in the corresponding field and click on Next.
-You will see a message that your product has been activated successfully. Congratulations! You have cracked Autodesk 3ds Max 2015 with X-Force Keygen.
-Disclaimer
-This article is for educational purposes only. We do not condone or encourage piracy or illegal use of software. Please support the developers by purchasing their products legally.
How to Use Autodesk 3ds Max 2015
-Autodesk 3ds Max 2015 is a versatile and powerful 3D software that can help you create amazing scenes and characters for your projects. Whether you are a beginner or a professional, you can use Autodesk 3ds Max 2015 to model, animate, render, and simulate your 3D ideas. In this article, we will give you a brief overview of how to use Autodesk 3ds Max 2015.
-The User Interface
-When you launch Autodesk 3ds Max 2015, you will see a user interface like this:
-
-The user interface consists of several elements that help you navigate and work with your 3D scene. Here are some of the main elements:
--
-
- The Menu Bar contains various menus that give you access to different commands and options. -
- The Quick Access Toolbar contains icons for some of the most frequently used commands and tools. -
- The Ribbon contains tabs and panels that organize different tools and functions according to categories. -
- The Viewports are the windows that display your 3D scene from different perspectives. You can have up to four viewports at a time, and you can switch between different views using the icons at the bottom-left corner of each viewport. -
- The Command Panel is the vertical panel on the right side of the screen that contains various tabs and rollouts that let you create and modify objects, materials, modifiers, animation, and more. -
- The Scene Explorer is the panel on the left side of the screen that shows a hierarchical list of all the objects in your scene. You can use it to select, rename, hide, freeze, group, or layer your objects. -
- The Status Bar is the horizontal bar at the bottom of the screen that shows information about your scene, such as the number of objects, polygons, vertices, frames, etc. It also contains icons for toggling different modes and settings. -
- The Time Slider is the horizontal bar above the status bar that shows the frames of your animation. You can use it to play, pause, rewind, or fast-forward your animation. -
- The Animation Controls are the buttons below the time slider that let you control your animation playback and keyframes. -
The Workflow
-The workflow of Autodesk 3ds Max 2015 can be summarized in four main steps: modeling, texturing, lighting, and rendering. Here is a brief description of each step:
--
-
- Modeling is the process of creating and shaping your 3D objects using various tools and techniques. You can start from scratch or use predefined shapes called primitives. You can also import models from other software or sources. You can use modifiers to add details and effects to your models. You can also use splines to create curves and surfaces. -
- Texturing is the process of applying colors, images, patterns, and effects to your 3D objects using materials and maps. You can use the Material Editor to create and edit materials and assign them to your objects. You can also use UVW mapping to control how your textures wrap around your objects. -
- Lighting is the process of adding lights to your scene to create realistic or artistic effects. You can use different types of lights such as standard lights, photometric lights, or daylight systems. You can also adjust various parameters such as color, intensity, shadow type, etc. You can also use global illumination techniques such as radiosity or mental ray to simulate natural lighting. -
- Rendering is the process of generating an image or a video from your 3D scene using a renderer. You can use different renderers such as scanline renderer, mental ray renderer, or Quicksilver renderer. You can also adjust various settings such as resolution, quality, anti-aliasing, etc. You can also use render elements to separate different aspects of your image such as diffuse color, specular color, reflection, etc. -
Conclusion
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\ No newline at end of file diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Bloons TD 6 Mod APK Unlimited Money and More on 5Play.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Bloons TD 6 Mod APK Unlimited Money and More on 5Play.md deleted file mode 100644 index 1a3fbdbf23ca75a23564e7dff7884dfac24cc419..0000000000000000000000000000000000000000 --- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Bloons TD 6 Mod APK Unlimited Money and More on 5Play.md +++ /dev/null @@ -1,166 +0,0 @@ - -Bloons TD 6: A Fun and Challenging Tower Defense Game
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-What is Bloons TD 6?
-Bloons TD 6 is a tower defense game developed and published by Ninja Kiwi, a New Zealand-based company that specializes in creating casual and mobile games. Bloons TD 6 is the sixth installment in the Bloons Tower Defense series, which started in 2007 as a flash game on the internet. Since then, the series has grown to become one of the most popular and acclaimed tower defense franchises in the world, with over a billion downloads across various platforms.
-The history and features of the game
-Bloons TD 6 was released on June 14, 2018 for iOS, Android, and Windows devices. It was later ported to macOS in October 2018, and to Steam in December 2018. The game has received regular updates since its launch, adding new content, features, and improvements. Some of the key features of Bloons TD 6 are:
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- A massive 3D tower defense game with over 60 handcrafted maps, each with different themes, layouts, and difficulties. -
- 23 powerful monkey towers, each with three upgrade paths and unique activated abilities. -
- 14 diverse heroes, each with their own personality, voiceover, signature upgrades, and special abilities. -
- 4-player co-op mode, where you can team up with your friends or strangers online to pop every last bloon. -
- Regular events, such as boss battles, odysseys, quests, trophy store, and content browser. -
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-bloons td 6 co-op mode how to play with friends on android ios pc using the same account on different devices with the same progress on the same server using different accounts on different devices with different progress on different servers using bluetooth using wifi using hotspot using vpn using emulator using controller using keyboard and mouse using touch screen using voice chat using text chat using discord using zoom using skype using whatsapp using telegram using facebook messenger using instagram dm using snapchat using tiktok using twitter using reddit using quora using youtube using twitch using steam using epic games using origin using uplay using gog using itch.io using gamejolt using kongregate using newgrounds using armor games using miniclip using coolmath gamesThe bloons come in different types, colors, sizes, and speeds. Some bloons are immune or resistant to certain attacks or effects. Some bloons contain other bloons inside them that will pop out when destroyed. Some bloons are stronger or faster than others. You have to use different strategies and combinations of towers and heroes to deal with different kinds of bloons.
-The game offers various modes and difficulties for different levels of challenge and fun. Some of the modes are:
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- Standard mode: The basic mode where you can choose from easy, medium, or hard difficulty. You can also choose from different sub-modes, such as primary only, military only, magic only, or double HP MOABs. -
- Impoppable mode: A harder mode where the bloons are much tougher and the towers are more expensive. You also have fewer lives and no continues. -
- CHIMPS mode: The hardest mode where you have no monkey knowledge, no powers, no insta monkeys, no continues, no income, and no selling. You also have to deal with harder bloons and more rounds. -
- Sandbox mode: A free-play mode where you can experiment with different towers, heroes, bloons, and settings. You can also spawn any bloon you want and test your defenses. -
- Apopalypse mode: A mode where the bloons come in endless waves with increasing speed and difficulty. You have to survive as long as you can. -
- Deflation mode: A mode where you start with a fixed amount of money and no income. You have to pop all the bloons with the money you have. -
- Half cash mode: A mode where you earn half the money from popping bloons and selling towers. You also have half the starting cash. -
- Reverse mode: A mode where the bloons come from the opposite direction of the normal path. -
The towers and heroes of the game
-The towers are the main weapons you use to pop the bloons. There are 23 different towers in Bloons TD 6, each with its own strengths, weaknesses, and abilities. The towers are divided into four categories: primary, military, magic, and support. Each tower has three upgrade paths, each with five tiers of upgrades. You can only choose two paths for each tower, and only one path can reach the fifth tier. The fifth tier upgrades are very powerful and expensive, and you can only have one of each type per game.
-The heroes are special units that have unique abilities and skills. There are 14 heroes in Bloons TD 6, each with their own personality and voiceover. The heroes level up automatically during the game, unlocking new abilities and buffs. You can also activate their special abilities manually for a burst of power. You can only have one hero per game, and you can choose which one to use before starting a game.
-Some of the towers and heroes in Bloons TD 6 are:
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-Tower Category Description Dart Monkey Primary A basic tower that throws darts at bloons. Can be upgraded to shoot faster, farther, or more accurately. Boomerang Monkey Primary A tower that throws boomerangs that can hit multiple bloons in a curved path. Can be upgraded to throw faster, stronger, or more boomerangs. Bomb Shooter Military A tower that shoots bombs that explode on impact, dealing splash damage to nearby bloons. Can be upgraded to shoot bigger, faster, or more bombs. Sniper Monkey Military A tower that shoots powerful bullets that can pop any type of bloon and pierce through multiple layers. Can be upgraded to shoot faster, farther, or more accurately. Ninja Monkey Magic A tower that throws shurikens that can pop multiple bloons in a straight line. Can be upgraded to throw faster, sharper, or more shurikens. Super Monkey Magic A tower that shoots rapid-fire lasers that can pop any type of bloon. Can be upgraded to shoot plasma blasts, sun rays, or dark energy. Banana Farm Support A tower that produces bananas that give you extra cash when collected. Can be upgraded to produce more bananas, more valuable bananas, or other benefits. Spike Factory Support A tower that produces spikes that pop bloons when they run over them. Can be upgraded to produce more spikes, stronger spikes, or other effects. Gwendolin Hero A pyromaniac monkey who loves fire. She can set bloons on fire with her flamethrower or her fireballs. Her abilities are Firestorm and Cocktail of Fire. Obyn Greenfoot Hero A A forest guardian who can summon nature's wrath. He can shoot thorns that pierce through bloons or vines that trap them. His abilities are Brambles and Wall of Trees. Adora Hero A divine monkey who can unleash holy power. She can shoot radiant bolts that deal massive damage to bloons or beams that pierce through them. Her abilities are Long Arm of Light and Blood Sacrifice. Why should you play Bloons TD 6?
-Bloons TD 6 is a game that has something for everyone. Whether you are looking for a casual and relaxing game, or a challenging and strategic game, you will find it in Bloons TD 6. Here are some of the reasons why you should play Bloons TD 6:
-The benefits and challenges of the game
-Bloons TD 6 is a game that can help you improve your cognitive skills, such as memory, attention, problem-solving, and creativity. The game requires you to plan ahead, think fast, and adapt to changing situations. The game also rewards you for experimenting with different strategies and combinations of towers and heroes. The game can also help you relieve stress, have fun, and express yourself.
-Bloons TD 6 is also a game that can challenge you in many ways. The game has a variety of bloons, towers, heroes, maps, modes, and difficulties that can test your skills and knowledge. The game also has a competitive aspect, where you can compare your scores and achievements with other players around the world. The game also has a community aspect, where you can chat with other players, share your creations, and join clans.
-The tips and tricks for the game
-If you want to get better at Bloons TD 6, here are some tips and tricks that can help you:
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- Learn the strengths and weaknesses of each tower and hero, and use them accordingly. -
- Use the monkey knowledge system to unlock useful upgrades and bonuses for your towers and heroes. -
- Use the powers and insta monkeys wisely, as they can save you from losing or help you win faster. -
- Use the sandbox mode to test your strategies and learn how different bloons behave. -
- Watch videos or streams of other players to get ideas and inspiration for your own gameplay. -
- Join a clan or a co-op game to cooperate with other players and earn more rewards. -
The reviews and ratings of the game
-Bloons TD 6 is a game that has received positive reviews and ratings from critics and players alike. The game has an average rating of 4.7 out of 5 stars on the App Store, 4.8 out of 5 stars on Google Play, and 9 out of 10 on Steam. Some of the praises for the game are:
-"Bloons TD 6 is an awesome tower defense game that will keep you hooked for hours on end. The graphics are stunning, the gameplay is smooth, and the content is rich. The game is well-balanced, challenging, and rewarding. It is definitely worth every penny."
-"Bloons TD 6 is one of the best games I have ever played. It is so fun and addictive that I can't stop playing it. The game has so much variety and depth that it never gets boring or repetitive. The game is also very user-friendly and easy to learn. It is a must-have for any tower defense fan."
-"Bloons TD 6 is a masterpiece of tower defense gaming. It is a perfect blend of strategy, action, humor, and creativity. The game has amazing graphics, sound effects, music, and voiceovers. The game is also very polished and updated regularly. It is a game that deserves all the praise it gets."
-How can you download Bloons TD 6?
-If you are interested in playing Bloons TD 6, you might be wondering how you can download it. There are different ways to download Bloons TD 6, depending on your device and preference. Here are some of the options:
-The official sources and platforms of the game
-The official sources and platforms of Bloons TD 6 are the ones that are authorized by Ninja Kiwi, the developer and publisher of the game. These sources and platforms are the safest and most reliable way to download Bloons TD 6, as they guarantee the quality and security of the game. They also support the developers and allow them to continue making more games and updates. The official sources and platforms of Bloons TD 6 are:
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- The App Store for iOS devices. You can download Bloons TD 6 for $4.99 from the App Store. You will need an iOS device with iOS 11.0 or later, and at least 1.3 GB of free space. -
- Google Play for Android devices. You can download Bloons TD 6 for $4.99 from Google Play. You will need an Android device with Android 5.0 or later, and at least 1.3 GB of free space. -
- Steam for Windows and macOS devices. You can download Bloons TD 6 for $9.99 from Steam. You will need a Windows or macOS device with a minimum of 4 GB of RAM, 2048 MB of VRAM, and 2048 MB of available space. -
The alternative sources and platforms of the game
-The alternative sources and platforms of Bloons TD 6 are the ones that are not authorized by Ninja Kiwi, but still offer the game for download. These sources and platforms are usually cheaper or free, but they also come with some risks and drawbacks. Some of the alternative sources and platforms of Bloons TD 6 are:
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- Third-party app stores for iOS and Android devices. These are app stores that are not affiliated with Apple or Google, but still offer apps for download. Some examples are TutuApp, AppValley, Aptoide, and APKPure. These app stores may offer Bloons TD 6 for free or at a lower price, but they may also contain malware, viruses, or spyware that can harm your device or steal your data. -
- Torrent sites for Windows and macOS devices. These are sites that allow users to share files through peer-to-peer networks. Some examples are The Pirate Bay, Kickass Torrents, RARBG, and 1337x. These sites may offer Bloons TD 6 for free or at a lower price, but they may also contain malware, viruses, or spyware that can harm your device or steal your data. -
- Modded versions of the game for any device. These are versions of the game that have been modified by hackers or modders to alter the gameplay or features of the game. Some examples are Bloons TD 6 Mod APK, Bloons TD 6 Hack, and Bloons TD 6 Unlimited Money. These versions may offer some advantages or cheats in the game, such as unlimited money, lives, or powers, but they may also cause glitches, crashes, or bans in the game. -
The risks and precautions of downloading the game
-If you decide to download Bloons TD 6 from an alternative source or platform, you should be aware of the risks and precautions involved. Some of the risks and precautions are:
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- You may violate the terms of service or the intellectual property rights of Ninja Kiwi, which could result in legal action or penalties. -
- You may damage your device or compromise your data by downloading malicious software or content. -
- You may lose your progress or account in the game by using an incompatible or unauthorized version of the game. -
- You may ruin your gaming experience or enjoyment by using cheats or hacks that make the game too easy or boring. -
- You may miss out on updates, features, events, or support from Ninja Kiwi by using an outdated or unofficial version of the game. -
Therefore, you should always be careful and cautious when downloading Bloons TD 6 from an alternative source or platform. You should always check the reputation and reviews of the source or platform before downloading anything. You should also scan your device for any malware or viruses after downloading anything. You should also backup your data and progress in the game regularly in case anything goes wrong.
-Conclusion
-Bloons TD 6 is a fun and challenging tower defense game that you can play on various devices and platforms. The game has a lot of content, features, modes, and difficulties that can keep you entertained for hours. The game also has a lot of benefits and challenges that can improve your cognitive skills and challenge your creativity. The game can be downloaded from official sources and platforms that are safe and reliable, or from alternative sources and platforms that are cheaper or free but risky and unreliable.
-A summary of the main points of the article
-To summarize, here are the main points of this article:
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- Bloons TD 6 is a tower defense game that has a lot of history, features, gameplay, towers, and heroes. -
- Bloons TD 6 is a game that can help you improve your cognitive skills, challenge you in different ways, and give you a lot of fun and enjoyment. -
- Bloons TD 6 can be downloaded from official sources and platforms that are authorized by Ninja Kiwi, or from alternative sources and platforms that are not authorized by Ninja Kiwi. -
- Bloons TD 6 is a game that is worth playing and downloading, but you should be careful and cautious when choosing where to download it from. -
A call to action for the readers
-If you are interested in playing Bloons TD 6, you can download it from the links below. You can also visit the official website of Ninja Kiwi to learn more about the game and the developer. You can also join the Bloons TD 6 community on social media, forums, or Discord to chat with other players, share your creations, and get tips and support. We hope you enjoy playing Bloons TD 6 and popping some bloons!
-Download Bloons TD 6 from:
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- [App Store] -
- [Google Play] -
- [Steam] -
Visit Ninja Kiwi's website: [https://ninjakiwi.com/]
-Join the Bloons TD 6 community on:
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- [Facebook] -
- [Twitter] -
- [Reddit](^-1^) -
- [Discord](^-2^) -
FAQs
-Here are some of the frequently asked questions about Bloons TD 6:
-Q: Is Bloons TD 6 free?
-A: No, Bloons TD 6 is not free. You have to pay a one-time fee to download the game from the official sources and platforms. However, the game does not have any in-app purchases or subscriptions, so you can enjoy the game without spending any more money.
-Q: Is Bloons TD 6 online or offline?
-A: Bloons TD 6 can be played both online and offline. You can play the game offline without an internet connection, but you will not be able to access some features, such as co-op mode, events, quests, trophy store, content browser, or cloud save. You can play the game online with an internet connection, but you will need to create an account with Ninja Kiwi to access some features, such as co-op mode, events, quests, trophy store, content browser, or cloud save.
-Q: Is Bloons TD 6 cross-platform?
-A: Yes, Bloons TD 6 is cross-platform. You can play the game on different devices and platforms with the same account and progress. However, you will need to link your account with Ninja Kiwi to do so. You can also play co-op mode with other players on different devices and platforms.
-Q: Is Bloons TD 6 multiplayer?
-A: Yes, Bloons TD 6 is multiplayer. You can play co-op mode with up to three other players online. You can either join a public lobby or create a private lobby with your friends or clan members. You can also chat with other players in co-op mode.
-Q: Is Bloons TD 6 kid-friendly?
-A: Yes, Bloons TD 6 is kid-friendly. The game does not have any violence, gore, profanity, or inappropriate content. The game is suitable for players of all ages and skill levels. The game also has a parental control option that allows you to restrict some features of the game for younger players.
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\ No newline at end of file diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Lifting Hero Hile APK Mod for Android and Enjoy the Ultimate Casual Game.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Lifting Hero Hile APK Mod for Android and Enjoy the Ultimate Casual Game.md deleted file mode 100644 index 470af3e722bfb7e8ca6657b7064d31485a08da7f..0000000000000000000000000000000000000000 --- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Lifting Hero Hile APK Mod for Android and Enjoy the Ultimate Casual Game.md +++ /dev/null @@ -1,94 +0,0 @@ - -Lifting Hero Hile APK: How to Become a Giant by Lifting Weights
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\ No newline at end of file diff --git a/spaces/timdettmers/guanaco-65b-4bit/app.py b/spaces/timdettmers/guanaco-65b-4bit/app.py deleted file mode 100644 index 522e516efcd794030fa154b6fe938fb8bc9ef1b5..0000000000000000000000000000000000000000 --- a/spaces/timdettmers/guanaco-65b-4bit/app.py +++ /dev/null @@ -1,313 +0,0 @@ -# Copyright 2023 MosaicML spaces authors -# SPDX-License-Identifier: Apache-2.0 -import datetime -import os -from threading import Event, Thread -from uuid import uuid4 -from peft import PeftModel - -import gradio as gr -import requests -import torch -import transformers -from transformers import ( - AutoModelForCausalLM, - AutoTokenizer, - LlamaTokenizer, - StoppingCriteria, - StoppingCriteriaList, - TextIteratorStreamer, -) - - -# model_name = "lmsys/vicuna-7b-delta-v1.1" -#model_name = "timdettmers/guanaco-33b-merged" -model_name = "facebook/opt-125m" -tok = AutoTokenizer.from_pretrained('facebook/opt-125m') -#tok = LlamaTokenizer.from_pretrained('huggyllama/llama-30b') - -max_new_tokens = 1536 - -auth_token = os.getenv("HF_TOKEN", None) - -print(f"Starting to load the model {model_name} into memory") - -m = AutoModelForCausalLM.from_pretrained( - model_name, - #quantization_config=transformers.BitsAndBytesConfig( - # load_in_4bit=True, - # bnb_4bit_compute_dtype=torch.bfloat16, - # bnb_4bit_use_double_quant=True, - # bnb_4bit_quant_type='nf4' # {'fp4', 'nf4'} - # ), - torch_dtype=torch.bfloat16, - device_map={"": 0} -) -#m = PeftModel.from_pretrained(m, 'timdettmers/guanaco-65b') -m.eval() - -#tok.bos_token_id = 1 - -stop_token_ids = [0] - -print(f"Successfully loaded the model {model_name} into memory") - - -start_message = """A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.""" - - - -class StopOnTokens(StoppingCriteria): - def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: - for stop_id in stop_token_ids: - if input_ids[0][-1] == stop_id: - return True - return False - - -def convert_history_to_text(history): - text = start_message + "".join( - [ - "".join( - [ - f"### Human: {item[0]}\n", - f"### Assistant: {item[1]}\n", - ] - ) - for item in history[:-1] - ] - ) - text += "".join( - [ - "".join( - [ - f"### Human: {history[-1][0]}\n", - f"### Assistant: {history[-1][1]}\n", - ] - ) - ] - ) - return text - - -def log_conversation(conversation_id, history, messages, generate_kwargs): - logging_url = os.getenv("LOGGING_URL", None) - if logging_url is None: - return - - timestamp = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S") - - data = { - "conversation_id": conversation_id, - "timestamp": timestamp, - "history": history, - "messages": messages, - "generate_kwargs": generate_kwargs, - } - - try: - requests.post(logging_url, json=data) - except requests.exceptions.RequestException as e: - print(f"Error logging conversation: {e}") - - -def user(message, history): - # Append the user's message to the conversation history - return "", history + [[message, ""]] - - -def bot(history, temperature, top_p, top_k, repetition_penalty, conversation_id): - print(f"history: {history}") - # Initialize a StopOnTokens object - stop = StopOnTokens() - - # Construct the input message string for the model by concatenating the current system message and conversation history - messages = convert_history_to_text(history) - - # Tokenize the messages string - input_ids = tok(messages, return_tensors="pt").input_ids - input_ids = input_ids.to(m.device) - streamer = TextIteratorStreamer(tok, timeout=10.0, skip_prompt=True, skip_special_tokens=True) - generate_kwargs = dict( - input_ids=input_ids, - max_new_tokens=max_new_tokens, - temperature=temperature, - do_sample=temperature > 0.0, - top_p=top_p, - top_k=top_k, - repetition_penalty=repetition_penalty, - streamer=streamer, - stopping_criteria=StoppingCriteriaList([stop]), - ) - - stream_complete = Event() - - def generate_and_signal_complete(): - m.generate(**generate_kwargs) - stream_complete.set() - - def log_after_stream_complete(): - stream_complete.wait() - log_conversation( - conversation_id, - history, - messages, - { - "top_k": top_k, - "top_p": top_p, - "temperature": temperature, - "repetition_penalty": repetition_penalty, - }, - ) - - t1 = Thread(target=generate_and_signal_complete) - t1.start() - - t2 = Thread(target=log_after_stream_complete) - t2.start() - - # Initialize an empty string to store the generated text - partial_text = "" - for new_text in streamer: - partial_text += new_text - history[-1][1] = partial_text - yield history - - -def get_uuid(): - return str(uuid4()) - - -with gr.Blocks( - theme=gr.themes.Soft(), - css=".disclaimer {font-variant-caps: all-small-caps;}", -) as demo: - conversation_id = gr.State(get_uuid) - gr.Markdown( - """
-""" - ) - chatbot = gr.Chatbot().style(height=500) - with gr.Row(): - with gr.Column(): - msg = gr.Textbox( - label="Chat Message Box", - placeholder="Chat Message Box", - show_label=False, - ).style(container=False) - with gr.Column(): - with gr.Row(): - submit = gr.Button("Submit") - stop = gr.Button("Stop") - clear = gr.Button("Clear") - with gr.Row(): - with gr.Accordion("Advanced Options:", open=False): - with gr.Row(): - with gr.Column(): - with gr.Row(): - temperature = gr.Slider( - label="Temperature", - value=0.7, - minimum=0.0, - maximum=1.0, - step=0.1, - interactive=True, - info="Higher values produce more diverse outputs", - ) - with gr.Column(): - with gr.Row(): - top_p = gr.Slider( - label="Top-p (nucleus sampling)", - value=0.9, - minimum=0.0, - maximum=1, - step=0.01, - interactive=True, - info=( - "Sample from the smallest possible set of tokens whose cumulative probability " - "exceeds top_p. Set to 1 to disable and sample from all tokens." - ), - ) - with gr.Column(): - with gr.Row(): - top_k = gr.Slider( - label="Top-k", - value=0, - minimum=0.0, - maximum=200, - step=1, - interactive=True, - info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.", - ) - with gr.Column(): - with gr.Row(): - repetition_penalty = gr.Slider( - label="Repetition Penalty", - value=1.1, - minimum=1.0, - maximum=2.0, - step=0.1, - interactive=True, - info="Penalize repetition — 1.0 to disable.", - ) - with gr.Row(): - gr.Markdown( - "Disclaimer: The model can produce factually incorrect output, and should not be relied on to produce " - "factually accurate information. The model was trained on various public datasets; while great efforts " - "have been taken to clean the pretraining data, it is possible that this model could generate lewd, " - "biased, or otherwise offensive outputs.", - elem_classes=["disclaimer"], - ) - with gr.Row(): - gr.Markdown( - "[Privacy policy](https://gist.github.com/samhavens/c29c68cdcd420a9aa0202d0839876dac)", - elem_classes=["disclaimer"], - ) - - submit_event = msg.submit( - fn=user, - inputs=[msg, chatbot], - outputs=[msg, chatbot], - queue=False, - ).then( - fn=bot, - inputs=[ - chatbot, - temperature, - top_p, - top_k, - repetition_penalty, - conversation_id, - ], - outputs=chatbot, - queue=True, - ) - submit_click_event = submit.click( - fn=user, - inputs=[msg, chatbot], - outputs=[msg, chatbot], - queue=False, - ).then( - fn=bot, - inputs=[ - chatbot, - temperature, - top_p, - top_k, - repetition_penalty, - conversation_id, - ], - outputs=chatbot, - queue=True, - ) - stop.click( - fn=None, - inputs=None, - outputs=None, - cancels=[submit_event, submit_click_event], - queue=False, - ) - clear.click(lambda: None, None, chatbot, queue=False) - -demo.queue(max_size=128, concurrency_count=2) -demo.launch() diff --git a/spaces/timpal0l/chat-ui/src/lib/utils/trimPrefix.ts b/spaces/timpal0l/chat-ui/src/lib/utils/trimPrefix.ts deleted file mode 100644 index d006e66deca639f3f4d208e77a64ba368fab00ee..0000000000000000000000000000000000000000 --- a/spaces/timpal0l/chat-ui/src/lib/utils/trimPrefix.ts +++ /dev/null @@ -1,6 +0,0 @@ -export function trimPrefix(input: string, prefix: string) { - if (input.startsWith(prefix)) { - return input.slice(prefix.length); - } - return input; -} diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/panel.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/panel.py deleted file mode 100644 index fc2807c3136558272e0d341f39cf55451b5ce452..0000000000000000000000000000000000000000 --- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/panel.py +++ /dev/null @@ -1,251 +0,0 @@ -from typing import TYPE_CHECKING, Optional - -from .align import AlignMethod -from .box import ROUNDED, Box -from .jupyter import JupyterMixin -from .measure import Measurement, measure_renderables -from .padding import Padding, PaddingDimensions -from .segment import Segment -from .style import StyleType -from .text import Text, TextType - -if TYPE_CHECKING: - from .console import Console, ConsoleOptions, RenderableType, RenderResult - - -class Panel(JupyterMixin): - """A console renderable that draws a border around its contents. - - Example: - >>> console.print(Panel("Hello, World!")) - - Args: - renderable (RenderableType): A console renderable object. - box (Box, optional): A Box instance that defines the look of the border (see :ref:`appendix_box`. - Defaults to box.ROUNDED. - safe_box (bool, optional): Disable box characters that don't display on windows legacy terminal with *raster* fonts. Defaults to True. - expand (bool, optional): If True the panel will stretch to fill the console - width, otherwise it will be sized to fit the contents. Defaults to True. - style (str, optional): The style of the panel (border and contents). Defaults to "none". - border_style (str, optional): The style of the border. Defaults to "none". - width (Optional[int], optional): Optional width of panel. Defaults to None to auto-detect. - height (Optional[int], optional): Optional height of panel. Defaults to None to auto-detect. - padding (Optional[PaddingDimensions]): Optional padding around renderable. Defaults to 0. - highlight (bool, optional): Enable automatic highlighting of panel title (if str). Defaults to False. - """ - - def __init__( - self, - renderable: "RenderableType", - box: Box = ROUNDED, - *, - title: Optional[TextType] = None, - title_align: AlignMethod = "center", - subtitle: Optional[TextType] = None, - subtitle_align: AlignMethod = "center", - safe_box: Optional[bool] = None, - expand: bool = True, - style: StyleType = "none", - border_style: StyleType = "none", - width: Optional[int] = None, - height: Optional[int] = None, - padding: PaddingDimensions = (0, 1), - highlight: bool = False, - ) -> None: - self.renderable = renderable - self.box = box - self.title = title - self.title_align: AlignMethod = title_align - self.subtitle = subtitle - self.subtitle_align = subtitle_align - self.safe_box = safe_box - self.expand = expand - self.style = style - self.border_style = border_style - self.width = width - self.height = height - self.padding = padding - self.highlight = highlight - - @classmethod - def fit( - cls, - renderable: "RenderableType", - box: Box = ROUNDED, - *, - title: Optional[TextType] = None, - title_align: AlignMethod = "center", - subtitle: Optional[TextType] = None, - subtitle_align: AlignMethod = "center", - safe_box: Optional[bool] = None, - style: StyleType = "none", - border_style: StyleType = "none", - width: Optional[int] = None, - padding: PaddingDimensions = (0, 1), - ) -> "Panel": - """An alternative constructor that sets expand=False.""" - return cls( - renderable, - box, - title=title, - title_align=title_align, - subtitle=subtitle, - subtitle_align=subtitle_align, - safe_box=safe_box, - style=style, - border_style=border_style, - width=width, - padding=padding, - expand=False, - ) - - @property - def _title(self) -> Optional[Text]: - if self.title: - title_text = ( - Text.from_markup(self.title) - if isinstance(self.title, str) - else self.title.copy() - ) - title_text.end = "" - title_text.plain = title_text.plain.replace("\n", " ") - title_text.no_wrap = True - title_text.expand_tabs() - title_text.pad(1) - return title_text - return None - - @property - def _subtitle(self) -> Optional[Text]: - if self.subtitle: - subtitle_text = ( - Text.from_markup(self.subtitle) - if isinstance(self.subtitle, str) - else self.subtitle.copy() - ) - subtitle_text.end = "" - subtitle_text.plain = subtitle_text.plain.replace("\n", " ") - subtitle_text.no_wrap = True - subtitle_text.expand_tabs() - subtitle_text.pad(1) - return subtitle_text - return None - - def __rich_console__( - self, console: "Console", options: "ConsoleOptions" - ) -> "RenderResult": - _padding = Padding.unpack(self.padding) - renderable = ( - Padding(self.renderable, _padding) if any(_padding) else self.renderable - ) - style = console.get_style(self.style) - border_style = style + console.get_style(self.border_style) - width = ( - options.max_width - if self.width is None - else min(options.max_width, self.width) - ) - - safe_box: bool = console.safe_box if self.safe_box is None else self.safe_box - box = self.box.substitute(options, safe=safe_box) - - title_text = self._title - if title_text is not None: - title_text.style = border_style - - child_width = ( - width - 2 - if self.expand - else console.measure( - renderable, options=options.update_width(width - 2) - ).maximum - ) - child_height = self.height or options.height or None - if child_height: - child_height -= 2 - if title_text is not None: - child_width = min( - options.max_width - 2, max(child_width, title_text.cell_len + 2) - ) - - width = child_width + 2 - child_options = options.update( - width=child_width, height=child_height, highlight=self.highlight - ) - lines = console.render_lines(renderable, child_options, style=style) - - line_start = Segment(box.mid_left, border_style) - line_end = Segment(f"{box.mid_right}", border_style) - new_line = Segment.line() - if title_text is None or width <= 4: - yield Segment(box.get_top([width - 2]), border_style) - else: - title_text.align(self.title_align, width - 4, character=box.top) - yield Segment(box.top_left + box.top, border_style) - yield from console.render(title_text, child_options.update_width(width - 4)) - yield Segment(box.top + box.top_right, border_style) - - yield new_line - for line in lines: - yield line_start - yield from line - yield line_end - yield new_line - - subtitle_text = self._subtitle - if subtitle_text is not None: - subtitle_text.style = border_style - - if subtitle_text is None or width <= 4: - yield Segment(box.get_bottom([width - 2]), border_style) - else: - subtitle_text.align(self.subtitle_align, width - 4, character=box.bottom) - yield Segment(box.bottom_left + box.bottom, border_style) - yield from console.render( - subtitle_text, child_options.update_width(width - 4) - ) - yield Segment(box.bottom + box.bottom_right, border_style) - - yield new_line - - def __rich_measure__( - self, console: "Console", options: "ConsoleOptions" - ) -> "Measurement": - _title = self._title - _, right, _, left = Padding.unpack(self.padding) - padding = left + right - renderables = [self.renderable, _title] if _title else [self.renderable] - - if self.width is None: - width = ( - measure_renderables( - console, - options.update_width(options.max_width - padding - 2), - renderables, - ).maximum - + padding - + 2 - ) - else: - width = self.width - return Measurement(width, width) - - -if __name__ == "__main__": # pragma: no cover - from .console import Console - - c = Console() - - from .box import DOUBLE, ROUNDED - from .padding import Padding - - p = Panel( - "Hello, World!", - title="rich.Panel", - style="white on blue", - box=DOUBLE, - padding=1, - ) - - c.print() - c.print(p) diff --git a/spaces/tmnam20/code-summarization/model.py b/spaces/tmnam20/code-summarization/model.py deleted file mode 100644 index 1a0b0ef6e477e4555d27d47f44ef598b1dc995e1..0000000000000000000000000000000000000000 --- a/spaces/tmnam20/code-summarization/model.py +++ /dev/null @@ -1,269 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT license. - -import torch -import torch.nn as nn -import torch -from torch.autograd import Variable -import copy - - -class Seq2Seq(nn.Module): - """ - Build Seqence-to-Sequence. - - Parameters: - - * `encoder`- encoder of seq2seq model. e.g. roberta - * `decoder`- decoder of seq2seq model. e.g. transformer - * `config`- configuration of encoder model. - * `beam_size`- beam size for beam search. - * `max_length`- max length of target for beam search. - * `sos_id`- start of symbol ids in target for beam search. - * `eos_id`- end of symbol ids in target for beam search. - """ - - def __init__( - self, - encoder, - decoder, - config, - beam_size=None, - max_length=None, - sos_id=None, - eos_id=None, - ): - super(Seq2Seq, self).__init__() - self.encoder = encoder - self.decoder = decoder - self.config = config - self.register_buffer("bias", torch.tril(torch.ones(2048, 2048))) - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - self.lsm = nn.LogSoftmax(dim=-1) - self.tie_weights() - - self.beam_size = beam_size - self.max_length = max_length - self.sos_id = sos_id - self.eos_id = eos_id - - def _tie_or_clone_weights(self, first_module, second_module): - """Tie or clone module weights depending of weither we are using TorchScript or not""" - if self.config.torchscript: - first_module.weight = nn.Parameter(second_module.weight.clone()) - else: - first_module.weight = second_module.weight - - def tie_weights(self): - """Make sure we are sharing the input and output embeddings. - Export to TorchScript can't handle parameter sharing so we are cloning them instead. - """ - self._tie_or_clone_weights( - self.lm_head, self.encoder.embeddings.word_embeddings - ) - - def forward( - self, - source_ids=None, - source_mask=None, - target_ids=None, - target_mask=None, - args=None, - ): - outputs = self.encoder(source_ids, attention_mask=source_mask) - encoder_output = outputs[0].permute([1, 0, 2]).contiguous() - if target_ids is not None: - attn_mask = -1e4 * ( - 1 - self.bias[: target_ids.shape[1], : target_ids.shape[1]] - ) - tgt_embeddings = ( - self.encoder.embeddings(target_ids).permute([1, 0, 2]).contiguous() - ) - out = self.decoder( - tgt_embeddings, - encoder_output, - tgt_mask=attn_mask, - memory_key_padding_mask=(1 - source_mask).bool(), - ) - hidden_states = torch.tanh(self.dense(out)).permute([1, 0, 2]).contiguous() - lm_logits = self.lm_head(hidden_states) - # Shift so that tokens < n predict n - active_loss = target_mask[..., 1:].ne(0).view(-1) == 1 - shift_logits = lm_logits[..., :-1, :].contiguous() - shift_labels = target_ids[..., 1:].contiguous() - # Flatten the tokens - loss_fct = nn.CrossEntropyLoss(ignore_index=-1) - loss = loss_fct( - shift_logits.view(-1, shift_logits.size(-1))[active_loss], - shift_labels.view(-1)[active_loss], - ) - - outputs = loss, loss * active_loss.sum(), active_loss.sum() - return outputs - else: - # Predict - preds = [] - try: - zero = torch.cuda.LongTensor(1).fill_(0) - except Exception as e: - zero = torch.LongTensor(1).fill_(0) - for i in range(source_ids.shape[0]): - context = encoder_output[:, i : i + 1] - context_mask = source_mask[i : i + 1, :] - beam = Beam(self.beam_size, self.sos_id, self.eos_id) - input_ids = beam.getCurrentState() - context = context.repeat(1, self.beam_size, 1) - context_mask = context_mask.repeat(self.beam_size, 1) - for _ in range(self.max_length): - if beam.done(): - break - attn_mask = -1e4 * ( - 1 - self.bias[: input_ids.shape[1], : input_ids.shape[1]] - ) - tgt_embeddings = ( - self.encoder.embeddings(input_ids) - .permute([1, 0, 2]) - .contiguous() - ) - out = self.decoder( - tgt_embeddings, - context, - tgt_mask=attn_mask, - memory_key_padding_mask=(1 - context_mask).bool(), - ) - out = torch.tanh(self.dense(out)) - hidden_states = out.permute([1, 0, 2]).contiguous()[:, -1, :] - out = self.lsm(self.lm_head(hidden_states)).data - beam.advance(out) - input_ids.data.copy_( - input_ids.data.index_select(0, beam.getCurrentOrigin()) - ) - input_ids = torch.cat((input_ids, beam.getCurrentState()), -1) - hyp = beam.getHyp(beam.getFinal()) - pred = beam.buildTargetTokens(hyp)[: self.beam_size] - pred = [ - torch.cat( - [x.view(-1) for x in p] + [zero] * (self.max_length - len(p)) - ).view(1, -1) - for p in pred - ] - preds.append(torch.cat(pred, 0).unsqueeze(0)) - - preds = torch.cat(preds, 0) - return preds - - -class Beam(object): - def __init__(self, size, sos, eos): - self.size = size - if torch.cuda.is_available(): - self.tt = torch.cuda - else: - self.tt = torch - # The score for each translation on the beam. - self.scores = self.tt.FloatTensor(size).zero_() - # The backpointers at each time-step. - self.prevKs = [] - # The outputs at each time-step. - self.nextYs = [self.tt.LongTensor(size).fill_(0)] - self.nextYs[0][0] = sos - # Has EOS topped the beam yet. - self._eos = eos - self.eosTop = False - # Time and k pair for finished. - self.finished = [] - - def getCurrentState(self): - "Get the outputs for the current timestep." - batch = self.tt.LongTensor(self.nextYs[-1]).view(-1, 1) - return batch - - def getCurrentOrigin(self): - "Get the backpointers for the current timestep." - return self.prevKs[-1] - - def advance(self, wordLk): - """ - Given prob over words for every last beam `wordLk` and attention - `attnOut`: Compute and update the beam search. - - Parameters: - - * `wordLk`- probs of advancing from the last step (K x words) - * `attnOut`- attention at the last step - - Returns: True if beam search is complete. - """ - numWords = wordLk.size(1) - - # Sum the previous scores. - if len(self.prevKs) > 0: - beamLk = wordLk + self.scores.unsqueeze(1).expand_as(wordLk) - - # Don't let EOS have children. - for i in range(self.nextYs[-1].size(0)): - if self.nextYs[-1][i] == self._eos: - beamLk[i] = -1e20 - else: - beamLk = wordLk[0] - flatBeamLk = beamLk.view(-1) - bestScores, bestScoresId = flatBeamLk.topk(self.size, 0, True, True) - - self.scores = bestScores - - # bestScoresId is flattened beam x word array, so calculate which - # word and beam each score came from - prevK = bestScoresId // numWords - self.prevKs.append(prevK) - self.nextYs.append((bestScoresId - prevK * numWords)) - - for i in range(self.nextYs[-1].size(0)): - if self.nextYs[-1][i] == self._eos: - s = self.scores[i] - self.finished.append((s, len(self.nextYs) - 1, i)) - - # End condition is when top-of-beam is EOS and no global score. - if self.nextYs[-1][0] == self._eos: - self.eosTop = True - - def done(self): - return self.eosTop and len(self.finished) >= self.size - - def getFinal(self): - if len(self.finished) == 0: - self.finished.append((self.scores[0], len(self.nextYs) - 1, 0)) - self.finished.sort(key=lambda a: -a[0]) - if len(self.finished) != self.size: - unfinished = [] - for i in range(self.nextYs[-1].size(0)): - if self.nextYs[-1][i] != self._eos: - s = self.scores[i] - unfinished.append((s, len(self.nextYs) - 1, i)) - unfinished.sort(key=lambda a: -a[0]) - self.finished += unfinished[: self.size - len(self.finished)] - return self.finished[: self.size] - - def getHyp(self, beam_res): - """ - Walk back to construct the full hypothesis. - """ - hyps = [] - for _, timestep, k in beam_res: - hyp = [] - for j in range(len(self.prevKs[:timestep]) - 1, -1, -1): - hyp.append(self.nextYs[j + 1][k]) - k = self.prevKs[j][k] - hyps.append(hyp[::-1]) - return hyps - - def buildTargetTokens(self, preds): - sentence = [] - for pred in preds: - tokens = [] - for tok in pred: - if tok == self._eos: - break - tokens.append(tok) - sentence.append(tokens) - return sentence diff --git a/spaces/tomg-group-umd/pez-dispenser/open_clip/tokenizer.py b/spaces/tomg-group-umd/pez-dispenser/open_clip/tokenizer.py deleted file mode 100644 index 01e9f9d25574cfe757bc43a0ff0d982f5a4efad3..0000000000000000000000000000000000000000 --- a/spaces/tomg-group-umd/pez-dispenser/open_clip/tokenizer.py +++ /dev/null @@ -1,201 +0,0 @@ -""" CLIP tokenizer - -Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. -""" -import gzip -import html -import os -from functools import lru_cache -from typing import Union, List - -import ftfy -import regex as re -import torch - -# https://stackoverflow.com/q/62691279 -import os -os.environ["TOKENIZERS_PARALLELISM"] = "false" - - -@lru_cache() -def default_bpe(): - return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") - - -@lru_cache() -def bytes_to_unicode(): - """ - Returns list of utf-8 byte and a corresponding list of unicode strings. - The reversible bpe codes work on unicode strings. - This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. - When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. - This is a significant percentage of your normal, say, 32K bpe vocab. - To avoid that, we want lookup tables between utf-8 bytes and unicode strings. - And avoids mapping to whitespace/control characters the bpe code barfs on. - """ - bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) - cs = bs[:] - n = 0 - for b in range(2**8): - if b not in bs: - bs.append(b) - cs.append(2**8+n) - n += 1 - cs = [chr(n) for n in cs] - return dict(zip(bs, cs)) - - -def get_pairs(word): - """Return set of symbol pairs in a word. - Word is represented as tuple of symbols (symbols being variable-length strings). - """ - pairs = set() - prev_char = word[0] - for char in word[1:]: - pairs.add((prev_char, char)) - prev_char = char - return pairs - - -def basic_clean(text): - text = ftfy.fix_text(text) - text = html.unescape(html.unescape(text)) - return text.strip() - - -def whitespace_clean(text): - text = re.sub(r'\s+', ' ', text) - text = text.strip() - return text - - -class SimpleTokenizer(object): - def __init__(self, bpe_path: str = default_bpe(), special_tokens=None): - self.byte_encoder = bytes_to_unicode() - self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} - merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') - merges = merges[1:49152-256-2+1] - merges = [tuple(merge.split()) for merge in merges] - vocab = list(bytes_to_unicode().values()) - vocab = vocab + [v+'' for v in vocab] - for merge in merges: - vocab.append(''.join(merge)) - if not special_tokens: - special_tokens = ['Guanaco-65b playground ', ' '] - else: - special_tokens = [' ', ' '] + special_tokens - vocab.extend(special_tokens) - self.encoder = dict(zip(vocab, range(len(vocab)))) - self.decoder = {v: k for k, v in self.encoder.items()} - self.bpe_ranks = dict(zip(merges, range(len(merges)))) - self.cache = {t:t for t in special_tokens} - special = "|".join(special_tokens) - self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) - - self.vocab_size = len(self.encoder) - self.all_special_ids = [self.encoder[t] for t in special_tokens] - - def bpe(self, token): - if token in self.cache: - return self.cache[token] - word = tuple(token[:-1]) + ( token[-1] + '',) - pairs = get_pairs(word) - - if not pairs: - return token+'' - - while True: - bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) - if bigram not in self.bpe_ranks: - break - first, second = bigram - new_word = [] - i = 0 - while i < len(word): - try: - j = word.index(first, i) - new_word.extend(word[i:j]) - i = j - except: - new_word.extend(word[i:]) - break - - if word[i] == first and i < len(word)-1 and word[i+1] == second: - new_word.append(first+second) - i += 2 - else: - new_word.append(word[i]) - i += 1 - new_word = tuple(new_word) - word = new_word - if len(word) == 1: - break - else: - pairs = get_pairs(word) - word = ' '.join(word) - self.cache[token] = word - return word - - def encode(self, text): - bpe_tokens = [] - text = whitespace_clean(basic_clean(text)).lower() - for token in re.findall(self.pat, text): - token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) - bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) - return bpe_tokens - - def decode(self, tokens): - text = ''.join([self.decoder[token] for token in tokens]) - text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') - return text - - -_tokenizer = SimpleTokenizer() - - -def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: - """ - Returns the tokenized representation of given input string(s) - - Parameters - ---------- - texts : Union[str, List[str]] - An input string or a list of input strings to tokenize - context_length : int - The context length to use; all CLIP models use 77 as the context length - - Returns - ------- - A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] - """ - if isinstance(texts, str): - texts = [texts] - - sot_token = _tokenizer.encoder[" "] - eot_token = _tokenizer.encoder[" "] - all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] - result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) - - for i, tokens in enumerate(all_tokens): - if len(tokens) > context_length: - tokens = tokens[:context_length] # Truncate - tokens[-1] = eot_token - result[i, :len(tokens)] = torch.tensor(tokens) - - return result - - -class HFTokenizer: - "HuggingFace tokenizer wrapper" - def __init__(self, tokenizer_name:str): - from transformers import AutoTokenizer - self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) - - def __call__(self, texts:Union[str, List[str]], context_length:int=77) -> torch.Tensor: - # same cleaning as for default tokenizer, except lowercasing - # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance - if isinstance(texts, str): - texts = [texts] - texts = [whitespace_clean(basic_clean(text)) for text in texts] - input_ids = self.tokenizer(texts, return_tensors='pt', max_length=context_length, padding='max_length', truncation=True).input_ids - return input_ids diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/deepfashion/README.md b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/deepfashion/README.md deleted file mode 100644 index 68e57e4fa5b29b66a8381a87949762d89d791a2e..0000000000000000000000000000000000000000 --- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/deepfashion/README.md +++ /dev/null @@ -1,56 +0,0 @@ -# DeepFashion - - - -[MMFashion](https://github.com/open-mmlab/mmfashion) develops "fashion parsing and segmentation" module -based on the dataset -[DeepFashion-Inshop](https://drive.google.com/drive/folders/0B7EVK8r0v71pVDZFQXRsMDZCX1E?usp=sharing). -Its annotation follows COCO style. -To use it, you need to first download the data. Note that we only use "img_highres" in this task. -The file tree should be like this: - -```sh -mmdetection -├── mmdet -├── tools -├── configs -├── data -│ ├── DeepFashion -│ │ ├── In-shop -│ │ ├── Anno -│ │ │ ├── segmentation -│ │ │ | ├── DeepFashion_segmentation_train.json -│ │ │ | ├── DeepFashion_segmentation_query.json -│ │ │ | ├── DeepFashion_segmentation_gallery.json -│ │ │ ├── list_bbox_inshop.txt -│ │ │ ├── list_description_inshop.json -│ │ │ ├── list_item_inshop.txt -│ │ │ └── list_landmarks_inshop.txt -│ │ ├── Eval -│ │ │ └── list_eval_partition.txt -│ │ ├── Img -│ │ │ ├── img -│ │ │ │ ├──XXX.jpg -│ │ │ ├── img_highres -│ │ │ └── ├──XXX.jpg - -``` - -After that you can train the Mask RCNN r50 on DeepFashion-In-shop dataset by launching training with the `mask_rcnn_r50_fpn_1x.py` config -or creating your own config file. - -``` -@inproceedings{liuLQWTcvpr16DeepFashion, - author = {Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou}, - title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations}, - booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, - month = {June}, - year = {2016} -} -``` - -## Model Zoo - -| Backbone | Model type | Dataset | bbox detection Average Precision | segmentation Average Precision | Config | Download (Google) | -| :---------: | :----------: | :-----------------: | :--------------------------------: | :----------------------------: | :---------:| :-------------------------: | -| ResNet50 | Mask RCNN | DeepFashion-In-shop | 0.599 | 0.584 |[config](https://github.com/open-mmlab/mmdetection/blob/master/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py)| [model](https://drive.google.com/open?id=1q6zF7J6Gb-FFgM87oIORIt6uBozaXp5r) | [log](https://drive.google.com/file/d/1qTK4Dr4FFLa9fkdI6UVko408gkrfTRLP/view?usp=sharing) | diff --git a/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/models/diffusion/dpm_solver/__init__.py b/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/models/diffusion/dpm_solver/__init__.py deleted file mode 100644 index 7427f38c07530afbab79154ea8aaf88c4bf70a08..0000000000000000000000000000000000000000 --- a/spaces/tornadoslims/instruct-pix2pix/stable_diffusion/ldm/models/diffusion/dpm_solver/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .sampler import DPMSolverSampler \ No newline at end of file diff --git a/spaces/trttung1610/musicgen/audiocraft/grids/compression/_explorers.py b/spaces/trttung1610/musicgen/audiocraft/grids/compression/_explorers.py deleted file mode 100644 index eed30d5b8a1c14676503148ddf133c79ed2e33bf..0000000000000000000000000000000000000000 --- a/spaces/trttung1610/musicgen/audiocraft/grids/compression/_explorers.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import treetable as tt - -from .._base_explorers import BaseExplorer - - -class CompressionExplorer(BaseExplorer): - eval_metrics = ["sisnr", "visqol"] - - def stages(self): - return ["train", "valid", "evaluate"] - - def get_grid_meta(self): - """Returns the list of Meta information to display for each XP/job. - """ - return [ - tt.leaf("index", align=">"), - tt.leaf("name", wrap=140), - tt.leaf("state"), - tt.leaf("sig", align=">"), - ] - - def get_grid_metrics(self): - """Return the metrics that should be displayed in the tracking table. - """ - return [ - tt.group( - "train", - [ - tt.leaf("epoch"), - tt.leaf("bandwidth", ".2f"), - tt.leaf("adv", ".4f"), - tt.leaf("d_loss", ".4f"), - ], - align=">", - ), - tt.group( - "valid", - [ - tt.leaf("bandwidth", ".2f"), - tt.leaf("adv", ".4f"), - tt.leaf("msspec", ".4f"), - tt.leaf("sisnr", ".2f"), - ], - align=">", - ), - tt.group( - "evaluate", [tt.leaf(name, ".3f") for name in self.eval_metrics], align=">" - ), - ] diff --git a/spaces/uSerNameDDHL/bingo/src/components/ui/icons.tsx b/spaces/uSerNameDDHL/bingo/src/components/ui/icons.tsx deleted file mode 100644 index 742b489b50437c5b64c86082f2ebc712eeb6a2b0..0000000000000000000000000000000000000000 --- a/spaces/uSerNameDDHL/bingo/src/components/ui/icons.tsx +++ /dev/null @@ -1,504 +0,0 @@ -'use client' - -import * as React from 'react' - -import { cn } from '@/lib/utils' - -function IconNextChat({ - className, - inverted, - ...props -}: React.ComponentProps<'svg'> & { inverted?: boolean }) { - const id = React.useId() - - return ( - - ) -} - -function IconOpenAI({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconGitHub({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconSeparator({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconArrowDown({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconArrowRight({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconUser({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconPlus({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconArrowElbow({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconSpinner({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconMessage({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconTrash({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconMore({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconRefresh({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconStop({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconSidebar({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconMoon({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconSun({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconCopy({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconCheck({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconDownload({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconClose({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconEdit({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconShare({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconUsers({ className, ...props }: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconExternalLink({ - className, - ...props -}: React.ComponentProps<'svg'>) { - return ( - - ) -} - -function IconChevronUpDown({ - className, - ...props -}: React.ComponentProps<'svg'>) { - return ( - - ) -} - -export { - IconEdit, - IconNextChat, - IconOpenAI, - IconGitHub, - IconSeparator, - IconArrowDown, - IconArrowRight, - IconUser, - IconPlus, - IconArrowElbow, - IconSpinner, - IconMessage, - IconTrash, - IconMore, - IconRefresh, - IconStop, - IconSidebar, - IconMoon, - IconSun, - IconCopy, - IconCheck, - IconDownload, - IconClose, - IconShare, - IconUsers, - IconExternalLink, - IconChevronUpDown -} diff --git a/spaces/ulysses115/diffsvc_test/utils/text_norm.py b/spaces/ulysses115/diffsvc_test/utils/text_norm.py deleted file mode 100644 index d0973cebc91e0525aeb6657e70012a1d37b5e6ff..0000000000000000000000000000000000000000 --- a/spaces/ulysses115/diffsvc_test/utils/text_norm.py +++ /dev/null @@ -1,790 +0,0 @@ -# coding=utf-8 -# Authors: -# 2019.5 Zhiyang Zhou (https://github.com/Joee1995/chn_text_norm.git) -# 2019.9 Jiayu DU -# -# requirements: -# - python 3.X -# notes: python 2.X WILL fail or produce misleading results - -import sys, os, argparse, codecs, string, re - -# ================================================================================ # -# basic constant -# ================================================================================ # -CHINESE_DIGIS = u'零一二三四五六七八九' -BIG_CHINESE_DIGIS_SIMPLIFIED = u'零壹贰叁肆伍陆柒捌玖' -BIG_CHINESE_DIGIS_TRADITIONAL = u'零壹貳參肆伍陸柒捌玖' -SMALLER_BIG_CHINESE_UNITS_SIMPLIFIED = u'十百千万' -SMALLER_BIG_CHINESE_UNITS_TRADITIONAL = u'拾佰仟萬' -LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'亿兆京垓秭穰沟涧正载' -LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'億兆京垓秭穰溝澗正載' -SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'十百千万' -SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'拾佰仟萬' - -ZERO_ALT = u'〇' -ONE_ALT = u'幺' -TWO_ALTS = [u'两', u'兩'] - -POSITIVE = [u'正', u'正'] -NEGATIVE = [u'负', u'負'] -POINT = [u'点', u'點'] -# PLUS = [u'加', u'加'] -# SIL = [u'杠', u'槓'] - -# 中文数字系统类型 -NUMBERING_TYPES = ['low', 'mid', 'high'] - -CURRENCY_NAMES = '(人民币|美元|日元|英镑|欧元|马克|法郎|加拿大元|澳元|港币|先令|芬兰马克|爱尔兰镑|' \ - '里拉|荷兰盾|埃斯库多|比塞塔|印尼盾|林吉特|新西兰元|比索|卢布|新加坡元|韩元|泰铢)' -CURRENCY_UNITS = '((亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|)元|(亿|千万|百万|万|千|百|)块|角|毛|分)' -COM_QUANTIFIERS = '(匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|' \ - '砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|' \ - '针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|' \ - '毫|厘|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|' \ - '盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|旬|' \ - '纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块)' - -# punctuation information are based on Zhon project (https://github.com/tsroten/zhon.git) -CHINESE_PUNC_STOP = '！？｡。' -CHINESE_PUNC_NON_STOP = '＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏' -CHINESE_PUNC_LIST = CHINESE_PUNC_STOP + CHINESE_PUNC_NON_STOP - - -# ================================================================================ # -# basic class -# ================================================================================ # -class ChineseChar(object): - """ - 中文字符 - 每个字符对应简体和繁体, - e.g. 简体 = '负', 繁体 = '負' - 转换时可转换为简体或繁体 - """ - - def __init__(self, simplified, traditional): - self.simplified = simplified - self.traditional = traditional - # self.__repr__ = self.__str__ - - def __str__(self): - return self.simplified or self.traditional or None - - def __repr__(self): - return self.__str__() - - -class ChineseNumberUnit(ChineseChar): - """ - 中文数字/数位字符 - 每个字符除繁简体外还有一个额外的大写字符 - e.g. '陆' 和 '陸' - """ - - def __init__(self, power, simplified, traditional, big_s, big_t): - super(ChineseNumberUnit, self).__init__(simplified, traditional) - self.power = power - self.big_s = big_s - self.big_t = big_t - - def __str__(self): - return '10^{}'.format(self.power) - - @classmethod - def create(cls, index, value, numbering_type=NUMBERING_TYPES[1], small_unit=False): - - if small_unit: - return ChineseNumberUnit(power=index + 1, - simplified=value[0], traditional=value[1], big_s=value[1], big_t=value[1]) - elif numbering_type == NUMBERING_TYPES[0]: - return ChineseNumberUnit(power=index + 8, - simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1]) - elif numbering_type == NUMBERING_TYPES[1]: - return ChineseNumberUnit(power=(index + 2) * 4, - simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1]) - elif numbering_type == NUMBERING_TYPES[2]: - return ChineseNumberUnit(power=pow(2, index + 3), - simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1]) - else: - raise ValueError( - 'Counting type should be in {0} ({1} provided).'.format(NUMBERING_TYPES, numbering_type)) - - -class ChineseNumberDigit(ChineseChar): - """ - 中文数字字符 - """ - - def __init__(self, value, simplified, traditional, big_s, big_t, alt_s=None, alt_t=None): - super(ChineseNumberDigit, self).__init__(simplified, traditional) - self.value = value - self.big_s = big_s - self.big_t = big_t - self.alt_s = alt_s - self.alt_t = alt_t - - def __str__(self): - return str(self.value) - - @classmethod - def create(cls, i, v): - return ChineseNumberDigit(i, v[0], v[1], v[2], v[3]) - - -class ChineseMath(ChineseChar): - """ - 中文数位字符 - """ - - def __init__(self, simplified, traditional, symbol, expression=None): - super(ChineseMath, self).__init__(simplified, traditional) - self.symbol = symbol - self.expression = expression - self.big_s = simplified - self.big_t = traditional - - -CC, CNU, CND, CM = ChineseChar, ChineseNumberUnit, ChineseNumberDigit, ChineseMath - - -class NumberSystem(object): - """ - 中文数字系统 - """ - pass - - -class MathSymbol(object): - """ - 用于中文数字系统的数学符号 (繁/简体), e.g. - positive = ['正', '正'] - negative = ['负', '負'] - point = ['点', '點'] - """ - - def __init__(self, positive, negative, point): - self.positive = positive - self.negative = negative - self.point = point - - def __iter__(self): - for v in self.__dict__.values(): - yield v - - -# class OtherSymbol(object): -# """ -# 其他符号 -# """ -# -# def __init__(self, sil): -# self.sil = sil -# -# def __iter__(self): -# for v in self.__dict__.values(): -# yield v - - -# ================================================================================ # -# basic utils -# ================================================================================ # -def create_system(numbering_type=NUMBERING_TYPES[1]): - """ - 根据数字系统类型返回创建相应的数字系统，默认为 mid - NUMBERING_TYPES = ['low', 'mid', 'high']: 中文数字系统类型 - low: '兆' = '亿' * '十' = $10^{9}$, '京' = '兆' * '十', etc. - mid: '兆' = '亿' * '万' = $10^{12}$, '京' = '兆' * '万', etc. - high: '兆' = '亿' * '亿' = $10^{16}$, '京' = '兆' * '兆', etc. - 返回对应的数字系统 - """ - - # chinese number units of '亿' and larger - all_larger_units = zip( - LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED, LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL) - larger_units = [CNU.create(i, v, numbering_type, False) - for i, v in enumerate(all_larger_units)] - # chinese number units of '十, 百, 千, 万' - all_smaller_units = zip( - SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED, SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL) - smaller_units = [CNU.create(i, v, small_unit=True) - for i, v in enumerate(all_smaller_units)] - # digis - chinese_digis = zip(CHINESE_DIGIS, CHINESE_DIGIS, - BIG_CHINESE_DIGIS_SIMPLIFIED, BIG_CHINESE_DIGIS_TRADITIONAL) - digits = [CND.create(i, v) for i, v in enumerate(chinese_digis)] - digits[0].alt_s, digits[0].alt_t = ZERO_ALT, ZERO_ALT - digits[1].alt_s, digits[1].alt_t = ONE_ALT, ONE_ALT - digits[2].alt_s, digits[2].alt_t = TWO_ALTS[0], TWO_ALTS[1] - - # symbols - positive_cn = CM(POSITIVE[0], POSITIVE[1], '+', lambda x: x) - negative_cn = CM(NEGATIVE[0], NEGATIVE[1], '-', lambda x: -x) - point_cn = CM(POINT[0], POINT[1], '.', lambda x, - y: float(str(x) + '.' + str(y))) - # sil_cn = CM(SIL[0], SIL[1], '-', lambda x, y: float(str(x) + '-' + str(y))) - system = NumberSystem() - system.units = smaller_units + larger_units - system.digits = digits - system.math = MathSymbol(positive_cn, negative_cn, point_cn) - # system.symbols = OtherSymbol(sil_cn) - return system - - -def chn2num(chinese_string, numbering_type=NUMBERING_TYPES[1]): - def get_symbol(char, system): - for u in system.units: - if char in [u.traditional, u.simplified, u.big_s, u.big_t]: - return u - for d in system.digits: - if char in [d.traditional, d.simplified, d.big_s, d.big_t, d.alt_s, d.alt_t]: - return d - for m in system.math: - if char in [m.traditional, m.simplified]: - return m - - def string2symbols(chinese_string, system): - int_string, dec_string = chinese_string, '' - for p in [system.math.point.simplified, system.math.point.traditional]: - if p in chinese_string: - int_string, dec_string = chinese_string.split(p) - break - return [get_symbol(c, system) for c in int_string], \ - [get_symbol(c, system) for c in dec_string] - - def correct_symbols(integer_symbols, system): - """ - 一百八 to 一百八十 - 一亿一千三百万 to 一亿 一千万 三百万 - """ - - if integer_symbols and isinstance(integer_symbols[0], CNU): - if integer_symbols[0].power == 1: - integer_symbols = [system.digits[1]] + integer_symbols - - if len(integer_symbols) > 1: - if isinstance(integer_symbols[-1], CND) and isinstance(integer_symbols[-2], CNU): - integer_symbols.append( - CNU(integer_symbols[-2].power - 1, None, None, None, None)) - - result = [] - unit_count = 0 - for s in integer_symbols: - if isinstance(s, CND): - result.append(s) - unit_count = 0 - elif isinstance(s, CNU): - current_unit = CNU(s.power, None, None, None, None) - unit_count += 1 - - if unit_count == 1: - result.append(current_unit) - elif unit_count > 1: - for i in range(len(result)): - if isinstance(result[-i - 1], CNU) and result[-i - 1].power < current_unit.power: - result[-i - 1] = CNU(result[-i - 1].power + - current_unit.power, None, None, None, None) - return result - - def compute_value(integer_symbols): - """ - Compute the value. - When current unit is larger than previous unit, current unit * all previous units will be used as all previous units. - e.g. '两千万' = 2000 * 10000 not 2000 + 10000 - """ - value = [0] - last_power = 0 - for s in integer_symbols: - if isinstance(s, CND): - value[-1] = s.value - elif isinstance(s, CNU): - value[-1] *= pow(10, s.power) - if s.power > last_power: - value[:-1] = list(map(lambda v: v * - pow(10, s.power), value[:-1])) - last_power = s.power - value.append(0) - return sum(value) - - system = create_system(numbering_type) - int_part, dec_part = string2symbols(chinese_string, system) - int_part = correct_symbols(int_part, system) - int_str = str(compute_value(int_part)) - dec_str = ''.join([str(d.value) for d in dec_part]) - if dec_part: - return '{0}.{1}'.format(int_str, dec_str) - else: - return int_str - - -def num2chn(number_string, numbering_type=NUMBERING_TYPES[1], big=False, - traditional=False, alt_zero=False, alt_one=False, alt_two=True, - use_zeros=True, use_units=True): - def get_value(value_string, use_zeros=True): - - striped_string = value_string.lstrip('0') - - # record nothing if all zeros - if not striped_string: - return [] - - # record one digits - elif len(striped_string) == 1: - if use_zeros and len(value_string) != len(striped_string): - return [system.digits[0], system.digits[int(striped_string)]] - else: - return [system.digits[int(striped_string)]] - - # recursively record multiple digits - else: - result_unit = next(u for u in reversed( - system.units) if u.power < len(striped_string)) - result_string = value_string[:-result_unit.power] - return get_value(result_string) + [result_unit] + get_value(striped_string[-result_unit.power:]) - - system = create_system(numbering_type) - - int_dec = number_string.split('.') - if len(int_dec) == 1: - int_string = int_dec[0] - dec_string = "" - elif len(int_dec) == 2: - int_string = int_dec[0] - dec_string = int_dec[1] - else: - raise ValueError( - "invalid input num string with more than one dot: {}".format(number_string)) - - if use_units and len(int_string) > 1: - result_symbols = get_value(int_string) - else: - result_symbols = [system.digits[int(c)] for c in int_string] - dec_symbols = [system.digits[int(c)] for c in dec_string] - if dec_string: - result_symbols += [system.math.point] + dec_symbols - - if alt_two: - liang = CND(2, system.digits[2].alt_s, system.digits[2].alt_t, - system.digits[2].big_s, system.digits[2].big_t) - for i, v in enumerate(result_symbols): - if isinstance(v, CND) and v.value == 2: - next_symbol = result_symbols[i + - 1] if i < len(result_symbols) - 1 else None - previous_symbol = result_symbols[i - 1] if i > 0 else None - if isinstance(next_symbol, CNU) and isinstance(previous_symbol, (CNU, type(None))): - if next_symbol.power != 1 and ((previous_symbol is None) or (previous_symbol.power != 1)): - result_symbols[i] = liang - - # if big is True, '两' will not be used and `alt_two` has no impact on output - if big: - attr_name = 'big_' - if traditional: - attr_name += 't' - else: - attr_name += 's' - else: - if traditional: - attr_name = 'traditional' - else: - attr_name = 'simplified' - - result = ''.join([getattr(s, attr_name) for s in result_symbols]) - - # if not use_zeros: - # result = result.strip(getattr(system.digits[0], attr_name)) - - if alt_zero: - result = result.replace( - getattr(system.digits[0], attr_name), system.digits[0].alt_s) - - if alt_one: - result = result.replace( - getattr(system.digits[1], attr_name), system.digits[1].alt_s) - - for i, p in enumerate(POINT): - if result.startswith(p): - return CHINESE_DIGIS[0] + result - - # ^10, 11, .., 19 - if len(result) >= 2 and result[1] in [SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED[0], - SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL[0]] and \ - result[0] in [CHINESE_DIGIS[1], BIG_CHINESE_DIGIS_SIMPLIFIED[1], BIG_CHINESE_DIGIS_TRADITIONAL[1]]: - result = result[1:] - - return result - - -# ================================================================================ # -# different types of rewriters -# ================================================================================ # -class Cardinal: - """ - CARDINAL类 - """ - - def __init__(self, cardinal=None, chntext=None): - self.cardinal = cardinal - self.chntext = chntext - - def chntext2cardinal(self): - return chn2num(self.chntext) - - def cardinal2chntext(self): - return num2chn(self.cardinal) - - -class Digit: - """ - DIGIT类 - """ - - def __init__(self, digit=None, chntext=None): - self.digit = digit - self.chntext = chntext - - # def chntext2digit(self): - # return chn2num(self.chntext) - - def digit2chntext(self): - return num2chn(self.digit, alt_two=False, use_units=False) - - -class TelePhone: - """ - TELEPHONE类 - """ - - def __init__(self, telephone=None, raw_chntext=None, chntext=None): - self.telephone = telephone - self.raw_chntext = raw_chntext - self.chntext = chntext - - # def chntext2telephone(self): - # sil_parts = self.raw_chntext.split(' ') - # self.telephone = '-'.join([ - # str(chn2num(p)) for p in sil_parts - # ]) - # return self.telephone - - def telephone2chntext(self, fixed=False): - - if fixed: - sil_parts = self.telephone.split('-') - self.raw_chntext = ' '.join([ - num2chn(part, alt_two=False, use_units=False) for part in sil_parts - ]) - self.chntext = self.raw_chntext.replace(' ', '') - else: - sp_parts = self.telephone.strip('+').split() - self.raw_chntext = ' '.join([ - num2chn(part, alt_two=False, use_units=False) for part in sp_parts - ]) - self.chntext = self.raw_chntext.replace(' ', '') - return self.chntext - - -class Fraction: - """ - FRACTION类 - """ - - def __init__(self, fraction=None, chntext=None): - self.fraction = fraction - self.chntext = chntext - - def chntext2fraction(self): - denominator, numerator = self.chntext.split('分之') - return chn2num(numerator) + '/' + chn2num(denominator) - - def fraction2chntext(self): - numerator, denominator = self.fraction.split('/') - return num2chn(denominator) + '分之' + num2chn(numerator) - - -class Date: - """ - DATE类 - """ - - def __init__(self, date=None, chntext=None): - self.date = date - self.chntext = chntext - - # def chntext2date(self): - # chntext = self.chntext - # try: - # year, other = chntext.strip().split('年', maxsplit=1) - # year = Digit(chntext=year).digit2chntext() + '年' - # except ValueError: - # other = chntext - # year = '' - # if other: - # try: - # month, day = other.strip().split('月', maxsplit=1) - # month = Cardinal(chntext=month).chntext2cardinal() + '月' - # except ValueError: - # day = chntext - # month = '' - # if day: - # day = Cardinal(chntext=day[:-1]).chntext2cardinal() + day[-1] - # else: - # month = '' - # day = '' - # date = year + month + day - # self.date = date - # return self.date - - def date2chntext(self): - date = self.date - try: - year, other = date.strip().split('年', 1) - year = Digit(digit=year).digit2chntext() + '年' - except ValueError: - other = date - year = '' - if other: - try: - month, day = other.strip().split('月', 1) - month = Cardinal(cardinal=month).cardinal2chntext() + '月' - except ValueError: - day = date - month = '' - if day: - day = Cardinal(cardinal=day[:-1]).cardinal2chntext() + day[-1] - else: - month = '' - day = '' - chntext = year + month + day - self.chntext = chntext - return self.chntext - - -class Money: - """ - MONEY类 - """ - - def __init__(self, money=None, chntext=None): - self.money = money - self.chntext = chntext - - # def chntext2money(self): - # return self.money - - def money2chntext(self): - money = self.money - pattern = re.compile(r'(\d+(\.\d+)?)') - matchers = pattern.findall(money) - if matchers: - for matcher in matchers: - money = money.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext()) - self.chntext = money - return self.chntext - - -class Percentage: - """ - PERCENTAGE类 - """ - - def __init__(self, percentage=None, chntext=None): - self.percentage = percentage - self.chntext = chntext - - def chntext2percentage(self): - return chn2num(self.chntext.strip().strip('百分之')) + '%' - - def percentage2chntext(self): - return '百分之' + num2chn(self.percentage.strip().strip('%')) - - -# ================================================================================ # -# NSW Normalizer -# ================================================================================ # -class NSWNormalizer: - def __init__(self, raw_text): - self.raw_text = '^' + raw_text + '$' - self.norm_text = '' - - def _particular(self): - text = self.norm_text - pattern = re.compile(r"(([a-zA-Z]+)二([a-zA-Z]+))") - matchers = pattern.findall(text) - if matchers: - # print('particular') - for matcher in matchers: - text = text.replace(matcher[0], matcher[1] + '2' + matcher[2], 1) - self.norm_text = text - return self.norm_text - - def normalize(self, remove_punc=True): - text = self.raw_text - - # 规范化日期 - pattern = re.compile(r"\D+((([089]\d|(19|20)\d{2})年)?(\d{1,2}月(\d{1,2}[日号])?)?)") - matchers = pattern.findall(text) - if matchers: - # print('date') - for matcher in matchers: - text = text.replace(matcher[0], Date(date=matcher[0]).date2chntext(), 1) - - # 规范化金钱 - pattern = re.compile(r"\D+((\d+(\.\d+)?)[多余几]?" + CURRENCY_UNITS + r"(\d" + CURRENCY_UNITS + r"?)?)") - matchers = pattern.findall(text) - if matchers: - # print('money') - for matcher in matchers: - text = text.replace(matcher[0], Money(money=matcher[0]).money2chntext(), 1) - - # 规范化固话/手机号码 - # 手机 - # http://www.jihaoba.com/news/show/13680 - # 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198 - # 联通：130、131、132、156、155、186、185、176 - # 电信：133、153、189、180、181、177 - pattern = re.compile(r"\D((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})\D") - matchers = pattern.findall(text) - if matchers: - # print('telephone') - for matcher in matchers: - text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(), 1) - # 固话 - pattern = re.compile(r"\D((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})\D") - matchers = pattern.findall(text) - if matchers: - # print('fixed telephone') - for matcher in matchers: - text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(fixed=True), 1) - - # 规范化分数 - pattern = re.compile(r"(\d+/\d+)") - matchers = pattern.findall(text) - if matchers: - # print('fraction') - for matcher in matchers: - text = text.replace(matcher, Fraction(fraction=matcher).fraction2chntext(), 1) - - # 规范化百分数 - text = text.replace('％', '%') - pattern = re.compile(r"(\d+(\.\d+)?%)") - matchers = pattern.findall(text) - if matchers: - # print('percentage') - for matcher in matchers: - text = text.replace(matcher[0], Percentage(percentage=matcher[0]).percentage2chntext(), 1) - - # 规范化纯数+量词 - pattern = re.compile(r"(\d+(\.\d+)?)[多余几]?" + COM_QUANTIFIERS) - matchers = pattern.findall(text) - if matchers: - # print('cardinal+quantifier') - for matcher in matchers: - text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1) - - # 规范化数字编号 - pattern = re.compile(r"(\d{4,32})") - matchers = pattern.findall(text) - if matchers: - # print('digit') - for matcher in matchers: - text = text.replace(matcher, Digit(digit=matcher).digit2chntext(), 1) - - # 规范化纯数 - pattern = re.compile(r"(\d+(\.\d+)?)") - matchers = pattern.findall(text) - if matchers: - # print('cardinal') - for matcher in matchers: - text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1) - - self.norm_text = text - self._particular() - - text = self.norm_text.lstrip('^').rstrip('$') - if remove_punc: - # Punctuations removal - old_chars = CHINESE_PUNC_LIST + string.punctuation # includes all CN and EN punctuations - new_chars = ' ' * len(old_chars) - del_chars = '' - text = text.translate(str.maketrans(old_chars, new_chars, del_chars)) - return text - - -def nsw_test_case(raw_text): - print('I:' + raw_text) - print('O:' + NSWNormalizer(raw_text).normalize()) - print('') - - -def nsw_test(): - nsw_test_case('固话：0595-23865596或23880880。') - nsw_test_case('固话：0595-23865596或23880880。') - nsw_test_case('手机：+86 19859213959或15659451527。') - nsw_test_case('分数：32477/76391。') - nsw_test_case('百分数：80.03%。') - nsw_test_case('编号：31520181154418。') - nsw_test_case('纯数：2983.07克或12345.60米。') - nsw_test_case('日期：1999年2月20日或09年3月15号。') - nsw_test_case('金钱：12块5，34.5元，20.1万') - nsw_test_case('特殊：O2O或B2C。') - nsw_test_case('3456万吨') - nsw_test_case('2938个') - nsw_test_case('938') - nsw_test_case('今天吃了115个小笼包231个馒头') - nsw_test_case('有62％的概率') - - -if __name__ == '__main__': - # nsw_test() - - p = argparse.ArgumentParser() - p.add_argument('ifile', help='input filename, assume utf-8 encoding') - p.add_argument('ofile', help='output filename') - p.add_argument('--to_upper', action='store_true', help='convert to upper case') - p.add_argument('--to_lower', action='store_true', help='convert to lower case') - p.add_argument('--has_key', action='store_true', help="input text has Kaldi's key as first field.") - p.add_argument('--log_interval', type=int, default=10000, help='log interval in number of processed lines') - args = p.parse_args() - - ifile = codecs.open(args.ifile, 'r', 'utf8') - ofile = codecs.open(args.ofile, 'w+', 'utf8') - - n = 0 - for l in ifile: - key = '' - text = '' - if args.has_key: - cols = l.split(maxsplit=1) - key = cols[0] - if len(cols) == 2: - text = cols[1] - else: - text = '' - else: - text = l - - # cases - if args.to_upper and args.to_lower: - sys.stderr.write('text norm: to_upper OR to_lower?') - exit(1) - if args.to_upper: - text = text.upper() - if args.to_lower: - text = text.lower() - - # NSW(Non-Standard-Word) normalization - text = NSWNormalizer(text).normalize() - - # - if args.has_key: - ofile.write(key + '\t' + text) - else: - ofile.write(text) - - n += 1 - if n % args.log_interval == 0: - sys.stderr.write("text norm: {} lines done.\n".format(n)) - - sys.stderr.write("text norm: {} lines done in total.\n".format(n)) - - ifile.close() - ofile.close() diff --git a/spaces/unik-style/unik-ml/cache/local_cache.py b/spaces/unik-style/unik-ml/cache/local_cache.py deleted file mode 100644 index 0f3fd9c89f1bdec92c52cbed3f8d11c878858867..0000000000000000000000000000000000000000 --- a/spaces/unik-style/unik-ml/cache/local_cache.py +++ /dev/null @@ -1,42 +0,0 @@ -from datetime import datetime, timedelta -from functools import wraps -from io import BytesIO - -from fastapi.responses import StreamingResponse - -CACHE_SIZE = 50 - -_cache = {} -_cache_time = {} - - -def ttl_cache(key_name, media_type=None, ttl_secs=20): - def decorator(func): - @wraps(func) - async def wrapper(*args, **kwargs): - # Assuming the prompt is the key for caching, change as necessary - key = kwargs.get(key_name) - ttl = timedelta(seconds=ttl_secs) - # Check cache - if key in _cache: - if datetime.now() - _cache_time[key] > ttl: - # Cache has expired - del _cache[key] - del _cache_time[key] - else: - # if media_type == 'image/png': - # return StreamingResponse(BytesIO(_cache[key]), media_type=media_type) - # else: - return StreamingResponse(BytesIO(_cache[key]), media_type="image/png") - - # Call the actual function if not in cache or expired - response, image_data = await func(*args, **kwargs) - # Cache the content of the response's body. - _cache[key] = image_data - _cache_time[key] = datetime.now() - - return response - - return wrapper - - return decorator diff --git a/spaces/unstructuredio/chat-your-data-isw/query_data.py b/spaces/unstructuredio/chat-your-data-isw/query_data.py deleted file mode 100644 index 33678f07ef276bc1a7acaf9bdd871e8974a70317..0000000000000000000000000000000000000000 --- a/spaces/unstructuredio/chat-your-data-isw/query_data.py +++ /dev/null @@ -1,34 +0,0 @@ -from langchain.prompts.prompt import PromptTemplate -from langchain.llms import OpenAI -from langchain.chains import ChatVectorDBChain - -_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. -You can assume the question about the war in Ukraine. - -Chat History: -{chat_history} -Follow Up Input: {question} -Standalone question:""" -CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template) - -template = """You are an AI assistant for answering questions about the war in Ukraine. -You are given the following extracted parts of a long document and a question. Provide a conversational answer. -If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer. -If the question is not about the war in Ukraine, politely inform them that you are tuned to only answer questions about the war in Ukraine. -Question: {question} -========= -{context} -========= -Answer in Markdown:""" -QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context"]) - - -def get_chain(vectorstore): - llm = OpenAI(temperature=0) - qa_chain = ChatVectorDBChain.from_llm( - llm, - vectorstore, - qa_prompt=QA_PROMPT, - condense_question_prompt=CONDENSE_QUESTION_PROMPT, - ) - return qa_chain diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Akka Koothi Tamil Sex Stories In English Letters.md b/spaces/usbethFlerru/sovits-modelsV2/example/Akka Koothi Tamil Sex Stories In English Letters.md deleted file mode 100644 index 7f397d82ebf33fa5733cd63b17cdf6e2e01be049..0000000000000000000000000000000000000000 --- a/spaces/usbethFlerru/sovits-modelsV2/example/Akka Koothi Tamil Sex Stories In English Letters.md +++ /dev/null @@ -1,6 +0,0 @@ - akka koothi tamil sex stories in english letters
Download ►►►►► https://urlcod.com/2uyY1a
- - aaccfb2cb3
-
-
- diff --git a/spaces/user238921933/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch.py b/spaces/user238921933/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch.py deleted file mode 100644 index 863f42db6f50e5eac70931b8c0e6443f831a6018..0000000000000000000000000000000000000000 --- a/spaces/user238921933/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch.py +++ /dev/null @@ -1,867 +0,0 @@ -# ----------------------------------------------------------------------------------- -# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 -# Originally Written by Ze Liu, Modified by Jingyun Liang. -# ----------------------------------------------------------------------------------- - -import math -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as checkpoint -from timm.models.layers import DropPath, to_2tuple, trunc_normal_ - - -class Mlp(nn.Module): - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (B, H, W, C) - window_size (int): window size - - Returns: - windows: (num_windows*B, window_size, window_size, C) - """ - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - return windows - - -def window_reverse(windows, window_size, H, W): - """ - Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image - - Returns: - x: (B, H, W, C) - """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class WindowAttention(nn.Module): - r""" Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): - - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_bias_table, std=.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - """ - Args: - x: input features with shape of (num_windows*B, N, C) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - B_, N, C = x.shape - qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = (q @ k.transpose(-2, -1)) - - relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( - self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) - attn = attn.view(-1, self.num_heads, N, N) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - def extra_repr(self) -> str: - return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' - - def flops(self, N): - # calculate flops for 1 window with token length of N - flops = 0 - # qkv = self.qkv(x) - flops += N * self.dim * 3 * self.dim - # attn = (q @ k.transpose(-2, -1)) - flops += self.num_heads * N * (self.dim // self.num_heads) * N - # x = (attn @ v) - flops += self.num_heads * N * N * (self.dim // self.num_heads) - # x = self.proj(x) - flops += N * self.dim * self.dim - return flops - - -class SwinTransformerBlock(nn.Module): - r""" Swin Transformer Block. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., - act_layer=nn.GELU, norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - if min(self.input_resolution) <= self.window_size: - # if window size is larger than input resolution, we don't partition windows - self.shift_size = 0 - self.window_size = min(self.input_resolution) - assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, - qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) - - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - if self.shift_size > 0: - attn_mask = self.calculate_mask(self.input_resolution) - else: - attn_mask = None - - self.register_buffer("attn_mask", attn_mask) - - def calculate_mask(self, x_size): - # calculate attention mask for SW-MSA - H, W = x_size - img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 - h_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - w_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) - - return attn_mask - - def forward(self, x, x_size): - H, W = x_size - B, L, C = x.shape - # assert L == H * W, "input feature has wrong size" - - shortcut = x - x = self.norm1(x) - x = x.view(B, H, W, C) - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - else: - shifted_x = x - - # partition windows - x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C - x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size - if self.input_resolution == x_size: - attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C - else: - attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - else: - x = shifted_x - x = x.view(B, H * W, C) - - # FFN - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - - return x - - def extra_repr(self) -> str: - return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ - f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" - - def flops(self): - flops = 0 - H, W = self.input_resolution - # norm1 - flops += self.dim * H * W - # W-MSA/SW-MSA - nW = H * W / self.window_size / self.window_size - flops += nW * self.attn.flops(self.window_size * self.window_size) - # mlp - flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio - # norm2 - flops += self.dim * H * W - return flops - - -class PatchMerging(nn.Module): - r""" Patch Merging Layer. - - Args: - input_resolution (tuple[int]): Resolution of input feature. - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.input_resolution = input_resolution - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(4 * dim) - - def forward(self, x): - """ - x: B, H*W, C - """ - H, W = self.input_resolution - B, L, C = x.shape - assert L == H * W, "input feature has wrong size" - assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." - - x = x.view(B, H, W, C) - - x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C - x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C - x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C - x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C - x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C - x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C - - x = self.norm(x) - x = self.reduction(x) - - return x - - def extra_repr(self) -> str: - return f"input_resolution={self.input_resolution}, dim={self.dim}" - - def flops(self): - H, W = self.input_resolution - flops = H * W * self.dim - flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim - return flops - - -class BasicLayer(nn.Module): - """ A basic Swin Transformer layer for one stage. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__(self, dim, input_resolution, depth, num_heads, window_size, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False): - - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.depth = depth - self.use_checkpoint = use_checkpoint - - # build blocks - self.blocks = nn.ModuleList([ - SwinTransformerBlock(dim=dim, input_resolution=input_resolution, - num_heads=num_heads, window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop, attn_drop=attn_drop, - drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, - norm_layer=norm_layer) - for i in range(depth)]) - - # patch merging layer - if downsample is not None: - self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) - else: - self.downsample = None - - def forward(self, x, x_size): - for blk in self.blocks: - if self.use_checkpoint: - x = checkpoint.checkpoint(blk, x, x_size) - else: - x = blk(x, x_size) - if self.downsample is not None: - x = self.downsample(x) - return x - - def extra_repr(self) -> str: - return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" - - def flops(self): - flops = 0 - for blk in self.blocks: - flops += blk.flops() - if self.downsample is not None: - flops += self.downsample.flops() - return flops - - -class RSTB(nn.Module): - """Residual Swin Transformer Block (RSTB). - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - img_size: Input image size. - patch_size: Patch size. - resi_connection: The convolutional block before residual connection. - """ - - def __init__(self, dim, input_resolution, depth, num_heads, window_size, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, - img_size=224, patch_size=4, resi_connection='1conv'): - super(RSTB, self).__init__() - - self.dim = dim - self.input_resolution = input_resolution - - self.residual_group = BasicLayer(dim=dim, - input_resolution=input_resolution, - depth=depth, - num_heads=num_heads, - window_size=window_size, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop, attn_drop=attn_drop, - drop_path=drop_path, - norm_layer=norm_layer, - downsample=downsample, - use_checkpoint=use_checkpoint) - - if resi_connection == '1conv': - self.conv = nn.Conv2d(dim, dim, 3, 1, 1) - elif resi_connection == '3conv': - # to save parameters and memory - self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim, 3, 1, 1)) - - self.patch_embed = PatchEmbed( - img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, - norm_layer=None) - - self.patch_unembed = PatchUnEmbed( - img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, - norm_layer=None) - - def forward(self, x, x_size): - return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x - - def flops(self): - flops = 0 - flops += self.residual_group.flops() - H, W = self.input_resolution - flops += H * W * self.dim * self.dim * 9 - flops += self.patch_embed.flops() - flops += self.patch_unembed.flops() - - return flops - - -class PatchEmbed(nn.Module): - r""" Image to Patch Embedding - - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - x = x.flatten(2).transpose(1, 2) # B Ph*Pw C - if self.norm is not None: - x = self.norm(x) - return x - - def flops(self): - flops = 0 - H, W = self.img_size - if self.norm is not None: - flops += H * W * self.embed_dim - return flops - - -class PatchUnEmbed(nn.Module): - r""" Image to Patch Unembedding - - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - def forward(self, x, x_size): - B, HW, C = x.shape - x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C - return x - - def flops(self): - flops = 0 - return flops - - -class Upsample(nn.Sequential): - """Upsample module. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - """ - - def __init__(self, scale, num_feat): - m = [] - if (scale & (scale - 1)) == 0: # scale = 2^n - for _ in range(int(math.log(scale, 2))): - m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(2)) - elif scale == 3: - m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(3)) - else: - raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') - super(Upsample, self).__init__(*m) - - -class UpsampleOneStep(nn.Sequential): - """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) - Used in lightweight SR to save parameters. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - - """ - - def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): - self.num_feat = num_feat - self.input_resolution = input_resolution - m = [] - m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1)) - m.append(nn.PixelShuffle(scale)) - super(UpsampleOneStep, self).__init__(*m) - - def flops(self): - H, W = self.input_resolution - flops = H * W * self.num_feat * 3 * 9 - return flops - - -class SwinIR(nn.Module): - r""" SwinIR - A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. - - Args: - img_size (int | tuple(int)): Input image size. Default 64 - patch_size (int | tuple(int)): Patch size. Default: 1 - in_chans (int): Number of input image channels. Default: 3 - embed_dim (int): Patch embedding dimension. Default: 96 - depths (tuple(int)): Depth of each Swin Transformer layer. - num_heads (tuple(int)): Number of attention heads in different layers. - window_size (int): Window size. Default: 7 - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None - drop_rate (float): Dropout rate. Default: 0 - attn_drop_rate (float): Attention dropout rate. Default: 0 - drop_path_rate (float): Stochastic depth rate. Default: 0.1 - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. - ape (bool): If True, add absolute position embedding to the patch embedding. Default: False - patch_norm (bool): If True, add normalization after patch embedding. Default: True - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False - upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction - img_range: Image range. 1. or 255. - upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None - resi_connection: The convolutional block before residual connection. '1conv'/'3conv' - """ - - def __init__(self, img_size=64, patch_size=1, in_chans=3, - embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6], - window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, - drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, - norm_layer=nn.LayerNorm, ape=False, patch_norm=True, - use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv', - **kwargs): - super(SwinIR, self).__init__() - num_in_ch = in_chans - num_out_ch = in_chans - num_feat = 64 - self.img_range = img_range - if in_chans == 3: - rgb_mean = (0.4488, 0.4371, 0.4040) - self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) - else: - self.mean = torch.zeros(1, 1, 1, 1) - self.upscale = upscale - self.upsampler = upsampler - self.window_size = window_size - - ##################################################################################################### - ################################### 1, shallow feature extraction ################################### - self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) - - ##################################################################################################### - ################################### 2, deep feature extraction ###################################### - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.num_features = embed_dim - self.mlp_ratio = mlp_ratio - - # split image into non-overlapping patches - self.patch_embed = PatchEmbed( - img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - num_patches = self.patch_embed.num_patches - patches_resolution = self.patch_embed.patches_resolution - self.patches_resolution = patches_resolution - - # merge non-overlapping patches into image - self.patch_unembed = PatchUnEmbed( - img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - - # absolute position embedding - if self.ape: - self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) - trunc_normal_(self.absolute_pos_embed, std=.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - # stochastic depth - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule - - # build Residual Swin Transformer blocks (RSTB) - self.layers = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = RSTB(dim=embed_dim, - input_resolution=(patches_resolution[0], - patches_resolution[1]), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, - drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results - norm_layer=norm_layer, - downsample=None, - use_checkpoint=use_checkpoint, - img_size=img_size, - patch_size=patch_size, - resi_connection=resi_connection - - ) - self.layers.append(layer) - self.norm = norm_layer(self.num_features) - - # build the last conv layer in deep feature extraction - if resi_connection == '1conv': - self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) - elif resi_connection == '3conv': - # to save parameters and memory - self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1)) - - ##################################################################################################### - ################################ 3, high quality image reconstruction ################################ - if self.upsampler == 'pixelshuffle': - # for classical SR - self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), - nn.LeakyReLU(inplace=True)) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - elif self.upsampler == 'pixelshuffledirect': - # for lightweight SR (to save parameters) - self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, - (patches_resolution[0], patches_resolution[1])) - elif self.upsampler == 'nearest+conv': - # for real-world SR (less artifacts) - self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), - nn.LeakyReLU(inplace=True)) - self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - if self.upscale == 4: - self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - else: - # for image denoising and JPEG compression artifact reduction - self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - @torch.jit.ignore - def no_weight_decay(self): - return {'absolute_pos_embed'} - - @torch.jit.ignore - def no_weight_decay_keywords(self): - return {'relative_position_bias_table'} - - def check_image_size(self, x): - _, _, h, w = x.size() - mod_pad_h = (self.window_size - h % self.window_size) % self.window_size - mod_pad_w = (self.window_size - w % self.window_size) % self.window_size - x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect') - return x - - def forward_features(self, x): - x_size = (x.shape[2], x.shape[3]) - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - for layer in self.layers: - x = layer(x, x_size) - - x = self.norm(x) # B L C - x = self.patch_unembed(x, x_size) - - return x - - def forward(self, x): - H, W = x.shape[2:] - x = self.check_image_size(x) - - self.mean = self.mean.type_as(x) - x = (x - self.mean) * self.img_range - - if self.upsampler == 'pixelshuffle': - # for classical SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.conv_last(self.upsample(x)) - elif self.upsampler == 'pixelshuffledirect': - # for lightweight SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.upsample(x) - elif self.upsampler == 'nearest+conv': - # for real-world SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) - if self.upscale == 4: - x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) - x = self.conv_last(self.lrelu(self.conv_hr(x))) - else: - # for image denoising and JPEG compression artifact reduction - x_first = self.conv_first(x) - res = self.conv_after_body(self.forward_features(x_first)) + x_first - x = x + self.conv_last(res) - - x = x / self.img_range + self.mean - - return x[:, :, :H*self.upscale, :W*self.upscale] - - def flops(self): - flops = 0 - H, W = self.patches_resolution - flops += H * W * 3 * self.embed_dim * 9 - flops += self.patch_embed.flops() - for i, layer in enumerate(self.layers): - flops += layer.flops() - flops += H * W * 3 * self.embed_dim * self.embed_dim - flops += self.upsample.flops() - return flops - - -if __name__ == '__main__': - upscale = 4 - window_size = 8 - height = (1024 // upscale // window_size + 1) * window_size - width = (720 // upscale // window_size + 1) * window_size - model = SwinIR(upscale=2, img_size=(height, width), - window_size=window_size, img_range=1., depths=[6, 6, 6, 6], - embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect') - print(model) - print(height, width, model.flops() / 1e9) - - x = torch.randn((1, 3, height, width)) - x = model(x) - print(x.shape) diff --git a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/save_images.py b/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/save_images.py deleted file mode 100644 index 8b6c60c5bfec5947b0a9bf7f9fb87512e97e5ad6..0000000000000000000000000000000000000000 --- a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/save_images.py +++ /dev/null @@ -1,80 +0,0 @@ -from typing import List, Tuple -from einops import rearrange -import numpy as np, os, torch -from PIL import Image -from torchvision.utils import make_grid -import time - - -def get_output_folder(output_path, batch_folder): - out_path = os.path.join(output_path,time.strftime('%Y-%m')) - if batch_folder != "": - out_path = os.path.join(out_path, batch_folder) - os.makedirs(out_path, exist_ok=True) - return out_path - - -def save_samples( - args, x_samples: torch.Tensor, seed: int, n_rows: int -) -> Tuple[Image.Image, List[Image.Image]]: - """Function to save samples to disk. - Args: - args: Stable deforum diffusion arguments. - x_samples: Samples to save. - seed: Seed for the experiment. - n_rows: Number of rows in the grid. - Returns: - A tuple of the grid image and a list of the generated images. - ( grid_image, generated_images ) - """ - - # save samples - images = [] - grid_image = None - if args.display_samples or args.save_samples: - for index, x_sample in enumerate(x_samples): - x_sample = 255.0 * rearrange(x_sample.cpu().numpy(), "c h w -> h w c") - images.append(Image.fromarray(x_sample.astype(np.uint8))) - if args.save_samples: - images[-1].save( - os.path.join( - args.outdir, f"{args.timestring}_{index:02}_{seed}.png" - ) - ) - - # save grid - if args.display_grid or args.save_grid: - grid = torch.stack([x_samples], 0) - grid = rearrange(grid, "n b c h w -> (n b) c h w") - grid = make_grid(grid, nrow=n_rows, padding=0) - - # to image - grid = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy() - grid_image = Image.fromarray(grid.astype(np.uint8)) - if args.save_grid: - grid_image.save( - os.path.join(args.outdir, f"{args.timestring}_{seed}_grid.png") - ) - - # return grid_image and individual sample images - return grid_image, images - -def save_image(image, image_type, filename, args, video_args, root): - if video_args.store_frames_in_ram: - root.frames_cache.append({'path':os.path.join(args.outdir, filename), 'image':image, 'image_type':image_type}) - else: - image.save(os.path.join(args.outdir, filename)) - -import cv2, gc - -def reset_frames_cache(root): - root.frames_cache = [] - gc.collect() - -def dump_frames_cache(root): - for image_cache in root.frames_cache: - if image_cache['image_type'] == 'cv2': - cv2.imwrite(image_cache['path'], image_cache['image']) - elif image_cache['image_type'] == 'PIL': - image_cache['image'].save(image_cache['path']) - # do not reset the cache since we're going to add frame erasing later function #TODO diff --git a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/general_utils.py b/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/general_utils.py deleted file mode 100644 index 708d32e701a78f3ce848060baef561c8f11b1b2e..0000000000000000000000000000000000000000 --- a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/general_utils.py +++ /dev/null @@ -1,272 +0,0 @@ -import json -import inspect -import torch -import os -import sys -import yaml -from shutil import copy, copytree -from os.path import join, dirname, realpath, expanduser, isfile, isdir, basename - - -class Logger(object): - - def __getattr__(self, k): - return print - -log = Logger() - -def training_config_from_cli_args(): - experiment_name = sys.argv[1] - experiment_id = int(sys.argv[2]) - - yaml_config = yaml.load(open(f'experiments/{experiment_name}'), Loader=yaml.SafeLoader) - - config = yaml_config['configuration'] - config = {**config, **yaml_config['individual_configurations'][experiment_id]} - config = AttributeDict(config) - return config - - -def score_config_from_cli_args(): - experiment_name = sys.argv[1] - experiment_id = int(sys.argv[2]) - - - yaml_config = yaml.load(open(f'experiments/{experiment_name}'), Loader=yaml.SafeLoader) - - config = yaml_config['test_configuration_common'] - - if type(yaml_config['test_configuration']) == list: - test_id = int(sys.argv[3]) - config = {**config, **yaml_config['test_configuration'][test_id]} - else: - config = {**config, **yaml_config['test_configuration']} - - if 'test_configuration' in yaml_config['individual_configurations'][experiment_id]: - config = {**config, **yaml_config['individual_configurations'][experiment_id]['test_configuration']} - - train_checkpoint_id = yaml_config['individual_configurations'][experiment_id]['name'] - - config = AttributeDict(config) - return config, train_checkpoint_id - - -def get_from_repository(local_name, repo_files, integrity_check=None, repo_dir='~/dataset_repository', - local_dir='~/datasets'): - """ copies files from repository to local folder. - - repo_files: list of filenames or list of tuples [filename, target path] - - e.g. get_from_repository('MyDataset', [['data/dataset1.tar', 'other/path/ds03.tar']) - will create a folder 'MyDataset' in local_dir, and extract the content of - '/data/dataset1.tar' to /MyDataset/other/path. - """ - - local_dir = realpath(join(expanduser(local_dir), local_name)) - - dataset_exists = True - - # check if folder is available - if not isdir(local_dir): - dataset_exists = False - - if integrity_check is not None: - try: - integrity_ok = integrity_check(local_dir) - except BaseException: - integrity_ok = False - - if integrity_ok: - log.hint('Passed custom integrity check') - else: - log.hint('Custom integrity check failed') - - dataset_exists = dataset_exists and integrity_ok - - if not dataset_exists: - - repo_dir = realpath(expanduser(repo_dir)) - - for i, filename in enumerate(repo_files): - - if type(filename) == str: - origin, target = filename, filename - archive_target = join(local_dir, basename(origin)) - extract_target = join(local_dir) - else: - origin, target = filename - archive_target = join(local_dir, dirname(target), basename(origin)) - extract_target = join(local_dir, dirname(target)) - - archive_origin = join(repo_dir, origin) - - log.hint(f'copy: {archive_origin} to {archive_target}') - - # make sure the path exists - os.makedirs(dirname(archive_target), exist_ok=True) - - if os.path.isfile(archive_target): - # only copy if size differs - if os.path.getsize(archive_target) != os.path.getsize(archive_origin): - log.hint(f'file exists but filesize differs: target {os.path.getsize(archive_target)} vs. origin {os.path.getsize(archive_origin)}') - copy(archive_origin, archive_target) - else: - copy(archive_origin, archive_target) - - extract_archive(archive_target, extract_target, noarchive_ok=True) - - # concurrent processes might have deleted the file - if os.path.isfile(archive_target): - os.remove(archive_target) - - -def extract_archive(filename, target_folder=None, noarchive_ok=False): - from subprocess import run, PIPE - - if filename.endswith('.tgz') or filename.endswith('.tar'): - command = f'tar -xf {filename}' - command += f' -C {target_folder}' if target_folder is not None else '' - elif filename.endswith('.tar.gz'): - command = f'tar -xzf {filename}' - command += f' -C {target_folder}' if target_folder is not None else '' - elif filename.endswith('zip'): - command = f'unzip {filename}' - command += f' -d {target_folder}' if target_folder is not None else '' - else: - if noarchive_ok: - return - else: - raise ValueError(f'unsuppored file ending of {filename}') - - log.hint(command) - result = run(command.split(), stdout=PIPE, stderr=PIPE) - if result.returncode != 0: - print(result.stdout, result.stderr) - - -class AttributeDict(dict): - """ - An extended dictionary that allows access to elements as atttributes and counts - these accesses. This way, we know if some attributes were never used. - """ - - def __init__(self, *args, **kwargs): - from collections import Counter - super().__init__(*args, **kwargs) - self.__dict__['counter'] = Counter() - - def __getitem__(self, k): - self.__dict__['counter'][k] += 1 - return super().__getitem__(k) - - def __getattr__(self, k): - self.__dict__['counter'][k] += 1 - return super().get(k) - - def __setattr__(self, k, v): - return super().__setitem__(k, v) - - def __delattr__(self, k, v): - return super().__delitem__(k, v) - - def unused_keys(self, exceptions=()): - return [k for k in super().keys() if self.__dict__['counter'][k] == 0 and k not in exceptions] - - def assume_no_unused_keys(self, exceptions=()): - if len(self.unused_keys(exceptions=exceptions)) > 0: - log.warning('Unused keys:', self.unused_keys(exceptions=exceptions)) - - -def get_attribute(name): - import importlib - - if name is None: - raise ValueError('The provided attribute is None') - - name_split = name.split('.') - mod = importlib.import_module('.'.join(name_split[:-1])) - return getattr(mod, name_split[-1]) - - - -def filter_args(input_args, default_args): - - updated_args = {k: input_args[k] if k in input_args else v for k, v in default_args.items()} - used_args = {k: v for k, v in input_args.items() if k in default_args} - unused_args = {k: v for k, v in input_args.items() if k not in default_args} - - return AttributeDict(updated_args), AttributeDict(used_args), AttributeDict(unused_args) - - -def load_model(checkpoint_id, weights_file=None, strict=True, model_args='from_config', with_config=False): - - config = json.load(open(join('logs', checkpoint_id, 'config.json'))) - - if model_args != 'from_config' and type(model_args) != dict: - raise ValueError('model_args must either be "from_config" or a dictionary of values') - - model_cls = get_attribute(config['model']) - - # load model - if model_args == 'from_config': - _, model_args, _ = filter_args(config, inspect.signature(model_cls).parameters) - - model = model_cls(**model_args) - - if weights_file is None: - weights_file = realpath(join('logs', checkpoint_id, 'weights.pth')) - else: - weights_file = realpath(join('logs', checkpoint_id, weights_file)) - - if isfile(weights_file): - weights = torch.load(weights_file) - for _, w in weights.items(): - assert not torch.any(torch.isnan(w)), 'weights contain NaNs' - model.load_state_dict(weights, strict=strict) - else: - raise FileNotFoundError(f'model checkpoint {weights_file} was not found') - - if with_config: - return model, config - - return model - - -class TrainingLogger(object): - - def __init__(self, model, log_dir, config=None, *args): - super().__init__() - self.model = model - self.base_path = join(f'logs/{log_dir}') if log_dir is not None else None - - os.makedirs('logs/', exist_ok=True) - os.makedirs(self.base_path, exist_ok=True) - - if config is not None: - json.dump(config, open(join(self.base_path, 'config.json'), 'w')) - - def iter(self, i, **kwargs): - if i % 100 == 0 and 'loss' in kwargs: - loss = kwargs['loss'] - print(f'iteration {i}: loss {loss:.4f}') - - def save_weights(self, only_trainable=False, weight_file='weights.pth'): - if self.model is None: - raise AttributeError('You need to provide a model reference when initializing TrainingTracker to save weights.') - - weights_path = join(self.base_path, weight_file) - - weight_dict = self.model.state_dict() - - if only_trainable: - weight_dict = {n: weight_dict[n] for n, p in self.model.named_parameters() if p.requires_grad} - - torch.save(weight_dict, weights_path) - log.info(f'Saved weights to {weights_path}') - - def __enter__(self): - return self - - def __exit__(self, type, value, traceback): - """ automatically stop processes if used in a context manager """ - pass \ No newline at end of file diff --git a/spaces/utkuarslan5/yodazer/app.py b/spaces/utkuarslan5/yodazer/app.py deleted file mode 100644 index e0fa4b83e0630d3bb831c4e7a85c578bf732d4b6..0000000000000000000000000000000000000000 --- a/spaces/utkuarslan5/yodazer/app.py +++ /dev/null @@ -1,41 +0,0 @@ -import gradio as gr -from langchain.llms import OpenAI -from langchain.prompts import PromptTemplate -from langchain.chains.llm import LLMChain -from langchain.chains.constitutional_ai.base import ConstitutionalChain -from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple - -def yodafy(sentence, selection): - llm = OpenAI(model_name="gpt-3.5-turbo", temperature=.8) - prompt = PromptTemplate( - input_variables=["sentence", "selection"], - template="You are Master Yoda. A young apprentice has came to tell ysou this sentence: {sentence}. {selection} the sentence as Master Yoda would. You may refer to the Star Wars and use punctuation. Remove any \n.", - ) - - chain = LLMChain(llm=llm, prompt=prompt) - - master_yoda_principle = ConstitutionalPrinciple( - name='Master Yoda Principle', - critique_request='Identify specific ways in which the model\'s response is not in the style of Master Yoda.', - revision_request='Please rewrite the model response to be in the style of Master Yoda using his teachings, his wisdom and the Force.', - ) - - constitutional_chain = ConstitutionalChain.from_llm( - chain=chain, - constitutional_principles=[master_yoda_principle], - llm=llm, - verbose=True, - ) - - return constitutional_chain.run(sentence=sentence, selection=selection) - - -iface = gr.Interface(fn=yodafy, - inputs=["text", - gr.inputs.Radio(["Rewrite", "Reply"])], - outputs="text", - examples=[ - ["May the Force be with you!", "Rewrite"], - ] - ) -iface.launch() \ No newline at end of file diff --git a/spaces/victor/test-12342324/README.md b/spaces/victor/test-12342324/README.md deleted file mode 100644 index d38b1e56e3050f17bcca0870fafc439ea4e5c7fb..0000000000000000000000000000000000000000 --- a/spaces/victor/test-12342324/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Test 12342324 -emoji: 🐠 -colorFrom: green -colorTo: gray -sdk: gradio -sdk_version: 3.29.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/vishnu0001/text2mesh/shap_e/rendering/pytorch3d_util.py b/spaces/vishnu0001/text2mesh/shap_e/rendering/pytorch3d_util.py deleted file mode 100644 index 772036a5bddbe4f6a7557846109aeb46b83942dd..0000000000000000000000000000000000000000 --- a/spaces/vishnu0001/text2mesh/shap_e/rendering/pytorch3d_util.py +++ /dev/null @@ -1,248 +0,0 @@ -import copy -import inspect -from typing import Any, Callable, List, Sequence, Tuple, Union - -import numpy as np -import torch -from pytorch3d.renderer import ( - BlendParams, - DirectionalLights, - FoVPerspectiveCameras, - MeshRasterizer, - MeshRenderer, - RasterizationSettings, - SoftPhongShader, - TexturesVertex, -) -from pytorch3d.renderer.utils import TensorProperties -from pytorch3d.structures import Meshes - -from shap_e.models.nn.checkpoint import checkpoint - -from .blender.constants import BASIC_AMBIENT_COLOR, BASIC_DIFFUSE_COLOR, UNIFORM_LIGHT_DIRECTION -from .torch_mesh import TorchMesh -from .view_data import ProjectiveCamera - -# Using a lower value like 1e-4 seems to result in weird issues -# for our high-poly meshes. -DEFAULT_RENDER_SIGMA = 1e-5 - -DEFAULT_RENDER_GAMMA = 1e-4 - - -def render_images( - image_size: int, - meshes: Meshes, - cameras: Any, - lights: Any, - sigma: float = DEFAULT_RENDER_SIGMA, - gamma: float = DEFAULT_RENDER_GAMMA, - max_faces_per_bin=100000, - faces_per_pixel=50, - bin_size=None, - use_checkpoint: bool = False, -) -> torch.Tensor: - if use_checkpoint: - # Decompose all of our arguments into a bunch of tensor lists - # so that autograd can keep track of what the op depends on. - verts_list = meshes.verts_list() - faces_list = meshes.faces_list() - assert isinstance(meshes.textures, TexturesVertex) - assert isinstance(lights, BidirectionalLights) - textures = meshes.textures.verts_features_padded() - light_vecs, light_fn = _deconstruct_tensor_props(lights) - camera_vecs, camera_fn = _deconstruct_tensor_props(cameras) - - def ckpt_fn( - *args: torch.Tensor, - num_verts=len(verts_list), - num_light_vecs=len(light_vecs), - num_camera_vecs=len(camera_vecs), - light_fn=light_fn, - camera_fn=camera_fn, - faces_list=faces_list - ): - args = list(args) - verts_list = args[:num_verts] - del args[:num_verts] - light_vecs = args[:num_light_vecs] - del args[:num_light_vecs] - camera_vecs = args[:num_camera_vecs] - del args[:num_camera_vecs] - textures = args.pop(0) - - meshes = Meshes(verts=verts_list, faces=faces_list, textures=TexturesVertex(textures)) - lights = light_fn(light_vecs) - cameras = camera_fn(camera_vecs) - return render_images( - image_size=image_size, - meshes=meshes, - cameras=cameras, - lights=lights, - sigma=sigma, - gamma=gamma, - max_faces_per_bin=max_faces_per_bin, - faces_per_pixel=faces_per_pixel, - bin_size=bin_size, - use_checkpoint=False, - ) - - result = checkpoint(ckpt_fn, (*verts_list, *light_vecs, *camera_vecs, textures), (), True) - else: - raster_settings_soft = RasterizationSettings( - image_size=image_size, - blur_radius=np.log(1.0 / 1e-4 - 1.0) * sigma, - faces_per_pixel=faces_per_pixel, - max_faces_per_bin=max_faces_per_bin, - bin_size=bin_size, - perspective_correct=False, - ) - renderer = MeshRenderer( - rasterizer=MeshRasterizer(cameras=cameras, raster_settings=raster_settings_soft), - shader=SoftPhongShader( - device=meshes.device, - cameras=cameras, - lights=lights, - blend_params=BlendParams(sigma=sigma, gamma=gamma, background_color=(0, 0, 0)), - ), - ) - result = renderer(meshes) - - return result - - -def _deconstruct_tensor_props( - props: TensorProperties, -) -> Tuple[List[torch.Tensor], Callable[[List[torch.Tensor]], TensorProperties]]: - vecs = [] - names = [] - other_props = {} - for k in dir(props): - if k.startswith("__"): - continue - v = getattr(props, k) - if inspect.ismethod(v): - continue - if torch.is_tensor(v): - vecs.append(v) - names.append(k) - else: - other_props[k] = v - - def recreate_fn(vecs_arg): - other = type(props)(device=props.device) - for k, v in other_props.items(): - setattr(other, k, copy.deepcopy(v)) - for name, vec in zip(names, vecs_arg): - setattr(other, name, vec) - return other - - return vecs, recreate_fn - - - -def convert_meshes(raw_meshes: Sequence[TorchMesh], default_brightness=0.8) -> Meshes: - meshes = Meshes( - verts=[mesh.verts for mesh in raw_meshes], faces=[mesh.faces for mesh in raw_meshes] - ) - rgbs = [] - for mesh in raw_meshes: - if mesh.vertex_channels and all(k in mesh.vertex_channels for k in "RGB"): - rgbs.append(torch.stack([mesh.vertex_channels[k] for k in "RGB"], axis=-1)) - else: - rgbs.append( - torch.ones( - len(mesh.verts) * default_brightness, - 3, - device=mesh.verts.device, - dtype=mesh.verts.dtype, - ) - ) - meshes.textures = TexturesVertex(verts_features=rgbs) - return meshes - - -def convert_cameras( - cameras: Sequence[ProjectiveCamera], device: torch.device -) -> FoVPerspectiveCameras: - Rs = [] - Ts = [] - for camera in cameras: - assert ( - camera.width == camera.height and camera.x_fov == camera.y_fov - ), "viewports must be square" - assert camera.x_fov == cameras[0].x_fov, "all cameras must have same field-of-view" - R = np.stack([-camera.x, -camera.y, camera.z], axis=0).T - T = -R.T @ camera.origin - Rs.append(R) - Ts.append(T) - return FoVPerspectiveCameras( - R=np.stack(Rs, axis=0), - T=np.stack(Ts, axis=0), - fov=cameras[0].x_fov, - degrees=False, - device=device, - ) - - -def convert_cameras_torch( - origins: torch.Tensor, xs: torch.Tensor, ys: torch.Tensor, zs: torch.Tensor, fov: float -) -> FoVPerspectiveCameras: - Rs = [] - Ts = [] - for origin, x, y, z in zip(origins, xs, ys, zs): - R = torch.stack([-x, -y, z], axis=0).T - T = -R.T @ origin - Rs.append(R) - Ts.append(T) - return FoVPerspectiveCameras( - R=torch.stack(Rs, dim=0), - T=torch.stack(Ts, dim=0), - fov=fov, - degrees=False, - device=origins.device, - ) - - -def blender_uniform_lights( - batch_size: int, - device: torch.device, - ambient_color: Union[float, Tuple[float]] = BASIC_AMBIENT_COLOR, - diffuse_color: Union[float, Tuple[float]] = BASIC_DIFFUSE_COLOR, - specular_color: Union[float, Tuple[float]] = 0.0, -) -> "BidirectionalLights": - """ - Create a light that attempts to match the light used by the Blender - renderer when run with `--light_mode basic`. - """ - if isinstance(ambient_color, float): - ambient_color = (ambient_color,) * 3 - if isinstance(diffuse_color, float): - diffuse_color = (diffuse_color,) * 3 - if isinstance(specular_color, float): - specular_color = (specular_color,) * 3 - return BidirectionalLights( - ambient_color=(ambient_color,) * batch_size, - diffuse_color=(diffuse_color,) * batch_size, - specular_color=(specular_color,) * batch_size, - direction=(UNIFORM_LIGHT_DIRECTION,) * batch_size, - device=device, - ) - - -class BidirectionalLights(DirectionalLights): - """ - Adapted from here, but effectively shines the light in both positive and negative directions: - https://github.com/facebookresearch/pytorch3d/blob/efea540bbcab56fccde6f4bc729d640a403dac56/pytorch3d/renderer/lighting.py#L159 - """ - - def diffuse(self, normals, points=None) -> torch.Tensor: - return torch.maximum( - super().diffuse(normals, points=points), super().diffuse(-normals, points=points) - ) - - def specular(self, normals, points, camera_position, shininess) -> torch.Tensor: - return torch.maximum( - super().specular(normals, points, camera_position, shininess), - super().specular(-normals, points, camera_position, shininess), - ) diff --git a/spaces/vnemala/StreamlitHeatmapAndCluster/app.py b/spaces/vnemala/StreamlitHeatmapAndCluster/app.py deleted file mode 100644 index 040b6f71a2b254f3826994176b1140b08ce6ef8a..0000000000000000000000000000000000000000 --- a/spaces/vnemala/StreamlitHeatmapAndCluster/app.py +++ /dev/null @@ -1,73 +0,0 @@ -import streamlit as st -import nltk -from transformers import pipeline -from sentence_transformers import SentenceTransformer -from scipy.spatial.distance import cosine -import numpy as np -import seaborn as sns -import matplotlib.pyplot as plt -from sklearn.cluster import KMeans -import tensorflow as tf -import tensorflow_hub as hub - - -def cluster_examples(messages, embed, nc=3): - km = KMeans( - n_clusters=nc, init='random', - n_init=10, max_iter=300, - tol=1e-04, random_state=0 - ) - km = km.fit_predict(embed) - for n in range(nc): - idxs = [i for i in range(len(km)) if km[i] == n] - ms = [messages[i] for i in idxs] - st.markdown ("CLUSTER : %d"%n) - for m in ms: - st.markdown (m) - - -def plot_heatmap(labels, heatmap, rotation=90): - sns.set(font_scale=1.2) - fig, ax = plt.subplots() - g = sns.heatmap( - heatmap, - xticklabels=labels, - yticklabels=labels, - vmin=-1, - vmax=1, - cmap="coolwarm") - g.set_xticklabels(labels, rotation=rotation) - g.set_title("Textual Similarity") - st.pyplot(fig) - -# Streamlit text boxes -text = st.text_area('Enter sentences:', value="Behavior right this is a kind of Heisenberg uncertainty principle situation if I told you, then you behave differently. What would be the impressive thing is you have talked about winning a nobel prize in a system winning a nobel prize. Adjusting it and then making your own. That is when I fell in love with computers. I realized that they were a very magical device. Can go to sleep come back the next day and it is solved. You know that feels magical to me.") - -nc = st.slider('Select a number of clusters:', min_value=1, max_value=15, value=3) - -model_type = st.radio("Choose model:", ('Sentence Transformer', 'Universal Sentence Encoder'), index=0) - -# Model setup -if model_type == "Sentence Transformer": - model = SentenceTransformer('paraphrase-distilroberta-base-v1') -elif model_type == "Universal Sentence Encoder": - model_url = "https://tfhub.dev/google/universal-sentence-encoder-large/5" - model = hub.load(model_url) - -nltk.download('punkt') - -# Run model -if text: - sentences = nltk.tokenize.sent_tokenize(text) - if model_type == "Sentence Transformer": - embed = model.encode(sentences) - elif model_type == "Universal Sentence Encoder": - embed = model(sentences).numpy() - sim = np.zeros([len(embed), len(embed)]) - for i,em in enumerate(embed): - for j,ea in enumerate(embed): - sim[i][j] = 1.0-cosine(em,ea) - st.subheader("Similarity Heatmap") - plot_heatmap(sentences, sim) - st.subheader("Results from K-Means Clustering") - cluster_examples(sentences, embed, nc) \ No newline at end of file diff --git a/spaces/wangguanlin/vits_Kazari/mel_processing.py b/spaces/wangguanlin/vits_Kazari/mel_processing.py deleted file mode 100644 index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000 --- a/spaces/wangguanlin/vits_Kazari/mel_processing.py +++ /dev/null @@ -1,101 +0,0 @@ -import torch -import torch.utils.data -from librosa.filters import mel as librosa_mel_fn - -MAX_WAV_VALUE = 32768.0 - - -def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): - """ - PARAMS - ------ - C: compression factor - """ - return torch.log(torch.clamp(x, min=clip_val) * C) - - -def dynamic_range_decompression_torch(x, C=1): - """ - PARAMS - ------ - C: compression factor used to compress - """ - return torch.exp(x) / C - - -def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output - - -def spectral_de_normalize_torch(magnitudes): - output = dynamic_range_decompression_torch(magnitudes) - return output - - -mel_basis = {} -hann_window = {} - - -def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - return spec - - -def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): - global mel_basis - dtype_device = str(spec.dtype) + '_' + str(spec.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device) - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - return spec - - -def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global mel_basis, hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device) - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - - return spec diff --git a/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/actions/test_debug_error.py b/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/actions/test_debug_error.py deleted file mode 100644 index 555c84e4e21286f369a218161ec8f6a3581d5751..0000000000000000000000000000000000000000 --- a/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/actions/test_debug_error.py +++ /dev/null @@ -1,155 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -@Time : 2023/5/11 17:46 -@Author : alexanderwu -@File : test_debug_error.py -""" -import pytest - -from metagpt.actions.debug_error import DebugError - -EXAMPLE_MSG_CONTENT = ''' ---- -## Development Code File Name -player.py -## Development Code -```python -from typing import List -from deck import Deck -from card import Card - -class Player: - """ - A class representing a player in the Black Jack game. - """ - - def __init__(self, name: str): - """ - Initialize a Player object. - - Args: - name (str): The name of the player. - """ - self.name = name - self.hand: List[Card] = [] - self.score = 0 - - def draw(self, deck: Deck): - """ - Draw a card from the deck and add it to the player's hand. - - Args: - deck (Deck): The deck of cards. - """ - card = deck.draw_card() - self.hand.append(card) - self.calculate_score() - - def calculate_score(self) -> int: - """ - Calculate the score of the player's hand. - - Returns: - int: The score of the player's hand. - """ - self.score = sum(card.value for card in self.hand) - # Handle the case where Ace is counted as 11 and causes the score to exceed 21 - if self.score > 21 and any(card.rank == 'A' for card in self.hand): - self.score -= 10 - return self.score - -``` -## Test File Name -test_player.py -## Test Code -```python -import unittest -from blackjack_game.player import Player -from blackjack_game.deck import Deck -from blackjack_game.card import Card - -class TestPlayer(unittest.TestCase): - ## Test the Player's initialization - def test_player_initialization(self): - player = Player("Test Player") - self.assertEqual(player.name, "Test Player") - self.assertEqual(player.hand, []) - self.assertEqual(player.score, 0) - - ## Test the Player's draw method - def test_player_draw(self): - deck = Deck() - player = Player("Test Player") - player.draw(deck) - self.assertEqual(len(player.hand), 1) - self.assertEqual(player.score, player.hand[0].value) - - ## Test the Player's calculate_score method - def test_player_calculate_score(self): - deck = Deck() - player = Player("Test Player") - player.draw(deck) - player.draw(deck) - self.assertEqual(player.score, sum(card.value for card in player.hand)) - - ## Test the Player's calculate_score method with Ace card - def test_player_calculate_score_with_ace(self): - deck = Deck() - player = Player("Test Player") - player.hand.append(Card('A', 'Hearts', 11)) - player.hand.append(Card('K', 'Hearts', 10)) - player.calculate_score() - self.assertEqual(player.score, 21) - - ## Test the Player's calculate_score method with multiple Aces - def test_player_calculate_score_with_multiple_aces(self): - deck = Deck() - player = Player("Test Player") - player.hand.append(Card('A', 'Hearts', 11)) - player.hand.append(Card('A', 'Diamonds', 11)) - player.calculate_score() - self.assertEqual(player.score, 12) - -if __name__ == '__main__': - unittest.main() - -``` -## Running Command -python tests/test_player.py -## Running Output -standard output: ; -standard errors: ..F.. -====================================================================== -FAIL: test_player_calculate_score_with_multiple_aces (__main__.TestPlayer) ----------------------------------------------------------------------- -Traceback (most recent call last): - File "tests/test_player.py", line 46, in test_player_calculate_score_with_multiple_aces - self.assertEqual(player.score, 12) -AssertionError: 22 != 12 - ----------------------------------------------------------------------- -Ran 5 tests in 0.007s - -FAILED (failures=1) -; -## instruction: -The error is in the development code, specifically in the calculate_score method of the Player class. The method is not correctly handling the case where there are multiple Aces in the player's hand. The current implementation only subtracts 10 from the score once if the score is over 21 and there's an Ace in the hand. However, in the case of multiple Aces, it should subtract 10 for each Ace until the score is 21 or less. -## File To Rewrite: -player.py -## Status: -FAIL -## Send To: -Engineer ---- -''' - -@pytest.mark.asyncio -async def test_debug_error(): - - debug_error = DebugError("debug_error") - - file_name, rewritten_code = await debug_error.run(context=EXAMPLE_MSG_CONTENT) - - assert "class Player" in rewritten_code # rewrite the same class - assert "while self.score > 21" in rewritten_code # a key logic to rewrite to (original one is "if self.score > 12") diff --git a/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/actions/test_write_prd.py b/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/actions/test_write_prd.py deleted file mode 100644 index 38e4e52219917e0c3e68e83950f7fb4ddb01ce82..0000000000000000000000000000000000000000 --- a/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/actions/test_write_prd.py +++ /dev/null @@ -1,26 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -@Time : 2023/5/11 17:45 -@Author : alexanderwu -@File : test_write_prd.py -""" -import pytest - -from metagpt.actions import BossRequirement -from metagpt.logs import logger -from metagpt.roles.product_manager import ProductManager -from metagpt.schema import Message - - -@pytest.mark.asyncio -async def test_write_prd(): - product_manager = ProductManager() - requirements = "开发一个基于大语言模型与私有知识库的搜索引擎，希望可以基于大语言模型进行搜索总结" - prd = await product_manager.handle(Message(content=requirements, cause_by=BossRequirement)) - logger.info(requirements) - logger.info(prd) - - # Assert the prd is not None or empty - assert prd is not None - assert prd != "" diff --git a/spaces/woshixuhao/Rf_prediction/app.py b/spaces/woshixuhao/Rf_prediction/app.py deleted file mode 100644 index be31613a185ee7868abcf9ebc29c0705b8599160..0000000000000000000000000000000000000000 --- a/spaces/woshixuhao/Rf_prediction/app.py +++ /dev/null @@ -1,503 +0,0 @@ -import argparse -import os -from rdkit import Chem -import sys -import joblib -sys.modules['sklearn.externals.joblib'] = joblib -from sklearn.externals import joblib -import numpy as np -from rdkit.Chem import Descriptors -from rdkit.Chem import rdMolDescriptors -from xgboost.sklearn import XGBClassifier,XGBRegressor -import torch -import torch.nn.functional as F -from torch.autograd import Variable -from rdkit.Chem import MACCSkeys -import torch.nn as nn -import lightgbm as lgb -from sklearn.ensemble import RandomForestRegressor -import wget -import warnings -import gradio as gr -import pandas as pd -from matplotlib.backends.backend_agg import FigureCanvasAgg -import PIL.Image as Image -import matplotlib.pyplot as plt -import pandas as pd -import time -warnings.filterwarnings("ignore") - -Eluent_smiles=['CCCCCC','CC(OCC)=O','C(Cl)Cl','CO','CCOCC'] -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument('--file_path', type=str, default=os.getcwd()+'\TLC_dataset.xlsx', help='path of download dataset') - parser.add_argument('--dipole_path', type=str, default=os.getcwd() + '\compound_list_带化合物分类.xlsx', - help='path of dipole file') - parser.add_argument('--data_range', type=int, default=4944, help='utilized data range,robot:4114,manual:4458,new:4944') - parser.add_argument('--automatic_divide', type=bool, default=False, help='automatically divide dataset by 80% train,10% validate and 10% test') - parser.add_argument('--choose_total', type=int, default=387, help='train total num,robot:387,manual:530') - parser.add_argument('--choose_train', type=int, default=308, help='train num,robot:387,manual:530') - parser.add_argument('--choose_validate', type=int, default=38, help='validate num') - parser.add_argument('--choose_test', type=int, default=38, help='test num') - parser.add_argument('--seed', type=int, default=324, help='random seed for split dataset') - parser.add_argument('--torch_seed', type=int, default=324, help='random seed for torch') - parser.add_argument('--add_dipole', type=bool, default=True, help='add dipole into dataset') - parser.add_argument('--add_molecular_descriptors', type=bool, default=True, help='add molecular_descriptors (分子量(MW)、拓扑极性表面积(TPSA)、可旋转键的个数(NROTB)、氢键供体个数(HBA)、氢键受体个数(HBD)、脂水分配系数值(LogP)) into dataset') - parser.add_argument('--add_MACCkeys', type=bool, default=True,help='add MACCSkeys into dataset') - parser.add_argument('--add_eluent_matrix', type=bool, default=True,help='add eluent matrix into dataset') - parser.add_argument('--test_mode', type=str, default='robot', help='manual data or robot data or fix, costum test data') - parser.add_argument('--use_model', type=str, default='Ensemble',help='the utilized model (XGB,LGB,ANN,RF,Ensemble,Bayesian)') - parser.add_argument('--download_data', type=bool, default=False, help='use local dataset or download from dataset') - parser.add_argument('--use_sigmoid', type=bool, default=True, help='use sigmoid') - parser.add_argument('--shuffle_array', type=bool, default=True, help='shuffle_array') - parser.add_argument('--characterization_mode', type=str, default='standard', - help='the characterization mode for the dataset, including standard, precise_TPSA, no_multi') - - #---------------parapmeters for plot--------------------- - parser.add_argument('--plot_col_num', type=int, default=4, help='The col_num in plot') - parser.add_argument('--plot_row_num', type=int, default=4, help='The row_num in plot') - parser.add_argument('--plot_importance_num', type=int, default=10, help='The max importance num in plot') - #--------------parameters For LGB------------------- - parser.add_argument('--LGB_max_depth', type=int, default=5, help='max_depth for LGB') - parser.add_argument('--LGB_num_leaves', type=int, default=25, help='num_leaves for LGB') - parser.add_argument('--LGB_learning_rate', type=float, default=0.007, help='learning_rate for LGB') - parser.add_argument('--LGB_n_estimators', type=int, default=1000, help='n_estimators for LGB') - parser.add_argument('--LGB_early_stopping_rounds', type=int, default=200, help='early_stopping_rounds for LGB') - - #---------------parameters for XGB----------------------- - parser.add_argument('--XGB_n_estimators', type=int, default=200, help='n_estimators for XGB') - parser.add_argument('--XGB_max_depth', type=int, default=3, help='max_depth for XGB') - parser.add_argument('--XGB_learning_rate', type=float, default=0.1, help='learning_rate for XGB') - - #---------------parameters for RF------------------------ - parser.add_argument('--RF_n_estimators', type=int, default=1000, help='n_estimators for RF') - parser.add_argument('--RF_random_state', type=int, default=1, help='random_state for RF') - parser.add_argument('--RF_n_jobs', type=int, default=1, help='n_jobs for RF') - - #--------------parameters for ANN----------------------- - parser.add_argument('--NN_hidden_neuron', type=int, default=128, help='hidden neurons for NN') - parser.add_argument('--NN_optimizer', type=str, default='Adam', help='optimizer for NN (Adam,SGD,RMSprop)') - parser.add_argument('--NN_lr', type=float, default=0.005, help='learning rate for NN') - parser.add_argument('--NN_model_save_location', type=str, default=os.getcwd()+'\model_save_NN', help='learning rate for NN') - parser.add_argument('--NN_max_epoch', type=int, default=5000, help='max training epoch for NN') - parser.add_argument('--NN_add_sigmoid', type=bool, default=True, help='whether add sigmoid in NN') - parser.add_argument('--NN_add_PINN', type=bool, default=False, help='whether add PINN in NN') - parser.add_argument('--NN_epi', type=float, default=100.0, help='The coef of PINN Loss in NN') - - - - config = parser.parse_args() - config.device = 'cpu' - return config - -class ANN(nn.Module): - ''' - Construct artificial neural network - ''' - def __init__(self, in_neuron, hidden_neuron, out_neuron,config): - super(ANN, self).__init__() - self.input_layer = nn.Linear(in_neuron, hidden_neuron) - self.hidden_layer = nn.Linear(hidden_neuron, hidden_neuron) - self.output_layer = nn.Linear(hidden_neuron, out_neuron) - self.NN_add_sigmoid=config.NN_add_sigmoid - - - def forward(self, x): - x = self.input_layer(x) - x = F.leaky_relu(x) - x = self.hidden_layer(x) - x = F.leaky_relu(x) - x = self.hidden_layer(x) - x = F.leaky_relu(x) - x = self.hidden_layer(x) - x = F.leaky_relu(x) - x = self.output_layer(x) - if self.NN_add_sigmoid==True: - x = F.sigmoid(x) - return x - -class Model_ML(): - def __init__(self,config,X_test): - super(Model_ML, self).__init__() - self.X_test=X_test - self.seed=config.seed - self.torch_seed=config.seed - self.config=config - self.add_dipole = config.add_dipole - self.add_molecular_descriptors = config.add_molecular_descriptors - self.add_eluent_matrix=config.add_eluent_matrix - self.use_sigmoid=config.use_sigmoid - - self.use_model=config.use_model - self.LGB_max_depth=config.LGB_max_depth - self.LGB_num_leaves=config.LGB_num_leaves - self.LGB_learning_rate=config.LGB_learning_rate - self.LGB_n_estimators=config.LGB_n_estimators - self.LGB_early_stopping_rounds=config.LGB_early_stopping_rounds - - self.XGB_n_estimators=config.XGB_n_estimators - self.XGB_max_depth = config.XGB_max_depth - self.XGB_learning_rate = config.XGB_learning_rate - - self.RF_n_estimators=config.RF_n_estimators - self.RF_random_state=config.RF_random_state - self.RF_n_jobs=config.RF_n_jobs - - self.NN_hidden_neuron=config.NN_hidden_neuron - self.NN_optimizer=config.NN_optimizer - self.NN_lr= config.NN_lr - self.NN_model_save_location=config.NN_model_save_location - self.NN_max_epoch=config.NN_max_epoch - self.NN_add_PINN=config.NN_add_PINN - self.NN_epi=config.NN_epi - self.device=config.device - - self.plot_row_num=config.plot_row_num - self.plot_col_num=config.plot_col_num - self.plot_importance_num=config.plot_importance_num - - - - def load_model(self): - model_LGB = lgb.LGBMRegressor(objective='regression', max_depth=self.LGB_max_depth, - num_leaves=self.LGB_num_leaves, - learning_rate=self.LGB_learning_rate, n_estimators=self.LGB_n_estimators) - model_XGB = XGBRegressor(seed=self.seed, - n_estimators=self.XGB_n_estimators, - max_depth=self.XGB_max_depth, - eval_metric='rmse', - learning_rate=self.XGB_learning_rate, - min_child_weight=1, - subsample=1, - colsample_bytree=1, - colsample_bylevel=1, - gamma=0) - - model_RF = RandomForestRegressor(n_estimators=self.RF_n_estimators, - criterion='mse', - random_state=self.RF_random_state, - n_jobs=self.RF_n_jobs) - - Net = ANN(self.X_test.shape[1], self.NN_hidden_neuron, 1, config=self.config).to(self.device) - #model_LGB = joblib.load('model_LGB.pkl') - #wget.download('https://huggingface.co/woshixuhao/Rf_prediction/resolve/main/model_LGB.pkl') - #wget.download('https://huggingface.co/woshixuhao/Rf_prediction/resolve/main/model_XGB.pkl') - #wget.download('https://huggingface.co/woshixuhao/Rf_prediction/resolve/main/model_RF.pkl') - #wget.download('https://huggingface.co/woshixuhao/Rf_prediction/resolve/main/model_ANN.pkl') - model_LGB = joblib.load('model_LGB.pkl') - model_XGB = joblib.load('model_XGB.pkl') - model_RF = joblib.load('model_RF.pkl') - Net.load_state_dict(torch.load('model_ANN.pkl',map_location=torch.device('cpu'))) - return model_LGB,model_XGB,model_RF,Net - - def get_Rf(self): - model_LGB, model_XGB, model_RF, model_ANN = Model_ML.load_model(self) - - X_test_ANN = Variable(torch.from_numpy(self.X_test.astype(np.float32)).to(self.device), requires_grad=True) - y_pred_ANN = model_ANN(X_test_ANN).cpu().data.numpy() - y_pred_ANN = y_pred_ANN.reshape(y_pred_ANN.shape[0], ) - - - y_pred_XGB = model_XGB.predict(self.X_test) - if self.use_sigmoid == True: - y_pred_XGB = 1 / (1 + np.exp(-y_pred_XGB)) - - y_pred_LGB = model_LGB.predict(self.X_test) - if self.use_sigmoid == True: - y_pred_LGB = 1 / (1 + np.exp(-y_pred_LGB)) - - y_pred_RF = model_RF.predict(self.X_test) - if self.use_sigmoid == True: - y_pred_RF = 1 / (1 + np.exp(-y_pred_RF)) - - y_pred = (0.2 * y_pred_LGB + 0.2 * y_pred_XGB + 0.2 * y_pred_RF + 0.4 * y_pred_ANN) - return y_pred - -def get_descriptor(smiles,ratio): - compound_mol = Chem.MolFromSmiles(smiles) - descriptor=[] - descriptor.append(Descriptors.ExactMolWt(compound_mol)) - descriptor.append(Chem.rdMolDescriptors.CalcTPSA(compound_mol)) - descriptor.append(Descriptors.NumRotatableBonds(compound_mol)) # Number of rotable bonds - descriptor.append(Descriptors.NumHDonors(compound_mol)) # Number of H bond donors - descriptor.append(Descriptors.NumHAcceptors(compound_mol)) # Number of H bond acceptors - descriptor.append(Descriptors.MolLogP(compound_mol)) # LogP - descriptor=np.array(descriptor)*ratio - return descriptor - -def get_eluent_descriptor(eluent): - eluent=np.array(eluent) - des = np.zeros([6,]) - for i in range(eluent.shape[0]): - if eluent[i] != 0: - e_descriptors = get_descriptor(Eluent_smiles[i], eluent[i]) - des+=e_descriptors - return des - -def get_data_from_smile(smile, eluent_list): - compound_mol = Chem.MolFromSmiles(smile) - Finger = MACCSkeys.GenMACCSKeys(Chem.MolFromSmiles(smile)) - fingerprint = np.array([x for x in Finger]) - compound_finger = fingerprint - compound_MolWt = Descriptors.ExactMolWt(compound_mol) - compound_TPSA = Chem.rdMolDescriptors.CalcTPSA(compound_mol) - compound_nRotB = Descriptors.NumRotatableBonds(compound_mol) # Number of rotable bonds - compound_HBD = Descriptors.NumHDonors(compound_mol) # Number of H bond donors - compound_HBA = Descriptors.NumHAcceptors(compound_mol) # Number of H bond acceptors - compound_LogP = Descriptors.MolLogP(compound_mol) # LogP - X_test = np.zeros([1, 179]) - X_test[0, 0:167] = compound_finger - X_test[0, 167:173] = 0 - X_test[0, 173:179] = [compound_MolWt, compound_TPSA, compound_nRotB, compound_HBD, compound_HBA, compound_LogP] - - eluent_array = get_eluent_descriptor(eluent_list) - eluent_array = np.array(eluent_array) - X_test[0, 167:173] = eluent_array - - return X_test - -def get_data_from_xlsx(file_name): - file_open = pd.read_csv(file_name) - smiles = file_open['SMILES'].values - PEs = file_open['PE'].values - EAs = file_open['EA'].values - DCMs = file_open['DCM'].values - MeOHs = file_open['MeOH'].values - Et2Os = file_open['Et2O'].values - X_test = np.zeros([len(smiles), 179]) - for i in range(len(smiles)): - smile=smiles[i] - eluent_sum = PEs[i] + EAs[i] + DCMs[i] + MeOHs[i] + Et2Os[i] - if eluent_sum != 0: - eluent_list = [PEs[i] / eluent_sum, EAs[i] / eluent_sum, DCMs[i] / eluent_sum, MeOHs[i] / eluent_sum, Et2Os[i] / eluent_sum] - else: - eluent_list = [0, 0, 0, 0, 0] - compound_mol = Chem.MolFromSmiles(smile) - Finger = MACCSkeys.GenMACCSKeys(Chem.MolFromSmiles(smile)) - fingerprint = np.array([x for x in Finger]) - compound_finger = fingerprint - compound_MolWt = Descriptors.ExactMolWt(compound_mol) - compound_TPSA = Chem.rdMolDescriptors.CalcTPSA(compound_mol) - compound_nRotB = Descriptors.NumRotatableBonds(compound_mol) # Number of rotable bonds - compound_HBD = Descriptors.NumHDonors(compound_mol) # Number of H bond donors - compound_HBA = Descriptors.NumHAcceptors(compound_mol) # Number of H bond acceptors - compound_LogP = Descriptors.MolLogP(compound_mol) # LogP - - X_test[i, 0:167] = compound_finger - X_test[i, 167:173] = 0 - X_test[i, 173:179] = [compound_MolWt, compound_TPSA, compound_nRotB, compound_HBD, compound_HBA, compound_LogP] - - eluent_array = get_eluent_descriptor(eluent_list) - eluent_array = np.array(eluent_array) - X_test[i, 167:173] = eluent_array - return X_test - -def predict_single(smile,PE,EA,DCM,MeOH,Et20): - if PE==None: - PE=0 - if EA==None: - EA=0 - if DCM==None: - DCM=0 - if MeOH==None: - MeOH=0 - if Et20==None: - Et20=0 - config = parse_args() - config.add_dipole = False - eluent_sum=PE+EA+DCM+MeOH+Et20 - if eluent_sum!=0: - eluent_list=[PE/eluent_sum,EA/eluent_sum,DCM/eluent_sum,MeOH/eluent_sum,Et20/eluent_sum] - else: - eluent_list=[0,0,0,0,0] - X_test=get_data_from_smile(smile,eluent_list) - Model = Model_ML(config,X_test) - Rf=Model.get_Rf() - return Rf[0] - -def predict_xlsx(file): - file_name=file.name - config = parse_args() - config.add_dipole = False - X_test = get_data_from_xlsx(file_name) - Model = Model_ML(config, X_test) - Rf = Model.get_Rf() - file_open = pd.read_csv(file_name) - file_open['Rf']=Rf - file_open.to_csv(file_name) - return file_name - -def get_data_from_smile_compare(smile): - x_PE = np.array([[0, 1, 0, 0, 0], [0.333333, 0.666667, 0, 0, 0], [0.5, 0.5, 0, 0, 0], - [0.75, 0.25, 0, 0, 0], [0.833333, 0.166667, 0, 0, 0], [0.952381, 0.047619, 0, 0, 0], - [0.980392, 0.019608, 0, 0, 0], [1, 0, 0, 0, 0]], dtype=np.float32) - x_PE=np.flip(x_PE,axis=0) - x_ME = np.array([[0, 0, 1, 0, 0], [0, 0, 0.990099, 0.009901, 0], [0, 0, 0.980392, 0.019608, 0], - [0, 0, 0.967742, 0.032258, 0], [0, 0, 0.952381, 0.047619, 0], - [0, 0, 0.909091, 0.090909, 0]], dtype=np.float32) - x_Et = np.array([[1,0,0,0,0],[0.66667, 0, 0, 0, 0.33333], [0.5, 0, 0, 0, 0.5],[0.33333,0,0,0,0.66667], [0, 0, 0, 0, 1]]) - - - compound_mol = Chem.MolFromSmiles(smile) - Finger = MACCSkeys.GenMACCSKeys(Chem.MolFromSmiles(smile)) - fingerprint = np.array([x for x in Finger]) - compound_finger = fingerprint - compound_MolWt = Descriptors.ExactMolWt(compound_mol) - compound_TPSA = Chem.rdMolDescriptors.CalcTPSA(compound_mol) - compound_nRotB = Descriptors.NumRotatableBonds(compound_mol) # Number of rotable bonds - compound_HBD = Descriptors.NumHDonors(compound_mol) # Number of H bond donors - compound_HBA = Descriptors.NumHAcceptors(compound_mol) # Number of H bond acceptors - compound_LogP = Descriptors.MolLogP(compound_mol) # LogP - X_test_PE=[] - X_test_ME=[] - X_test_Et=[] - X_test = np.zeros([1, 179]) - X_test[0, 0:167] = compound_finger - X_test[0, 167:173] = 0 - X_test[0, 173:179] = [compound_MolWt, compound_TPSA, compound_nRotB, compound_HBD, compound_HBA, compound_LogP] - - for x in x_PE: - X_test[0, 167:173] =get_eluent_descriptor(x) - X_test_PE.append(X_test.copy()) - for x in x_ME: - X_test[0, 167:173] = get_eluent_descriptor(x) - X_test_ME.append(X_test.copy()) - for x in x_Et: - X_test[0, 167:173] = get_eluent_descriptor(x) - X_test_Et.append(X_test.copy()) - - X_test_PE=np.squeeze(np.array(X_test_PE)) - X_test_Et=np.squeeze(np.array(X_test_Et)) - X_test_ME=np.squeeze(np.array(X_test_ME)) - return X_test_PE,X_test_Et,X_test_ME - -def convert_fig_PIL(fig): - canvas = FigureCanvasAgg(fig) - canvas.draw() - w, h = canvas.get_width_height() - buf = np.fromstring(canvas.tostring_argb(), dtype=np.uint8) - buf.shape = (w, h, 4) - buf = np.roll(buf, 3, axis=2) - image = Image.frombytes("RGBA", (w, h), buf.tostring()) - return image - -def predict_compare(smile_1,smile_2): - config = parse_args() - config.add_dipole = False - X_test_PE_1,X_test_Et_1,X_test_ME_1=get_data_from_smile_compare(smile_1) - X_test_PE_2,X_test_Et_2,X_test_ME_2=get_data_from_smile_compare(smile_2) - Rf_all=[] - for x_test in [X_test_PE_1,X_test_Et_1,X_test_ME_1,X_test_PE_2,X_test_Et_2,X_test_ME_2]: - Model = Model_ML(config,x_test) - Rf=Model.get_Rf() - Rf_all.append(Rf) - fig1=plot_Rf(Rf_all[0],Rf_all[3],'PE:EA') - fig2 = plot_Rf(Rf_all[2], Rf_all[5], 'DCM:MeOH') - fig3 = plot_Rf(Rf_all[1], Rf_all[4], 'PE:Et2O') - fig1=convert_fig_PIL(fig1) - fig2=convert_fig_PIL(fig2) - fig3=convert_fig_PIL(fig3) - return fig1,fig2,fig3 - - - -def plot_Rf(Rf_1,Rf_2,eluent): - EA = np.array([0, 0.019608, 0.047619, 0.166667, 0.25, 0.5, 0.666667, 1]) - ME = np.array([0, 0.009901, 0.019608, 0.032258, 0.047619, 0.090909]) - Et = np.array([0, 0.33333, 0.5, 0.66667, 1]) - font1 = {'family': 'Arial', - 'weight': 'normal', - 'size': 5} - - - if eluent=='PE:EA': - fig = plt.figure(1, figsize=(2, 2), dpi=300) - plt.clf() - ax = plt.subplot(1, 1, 1) - plt.plot(np.arange(0,EA.shape[0],1), Rf_1, c='#82B0D2', label='SMILE_1', zorder=1) - plt.plot(np.arange(0,EA.shape[0],1), Rf_2, c='#8A83B4', label='SMILE_2', zorder=1) - plt.scatter(np.arange(0,EA.shape[0],1), Rf_1, color='white', edgecolors='black', marker='^', s=10, zorder=1,linewidths=0.5) - plt.scatter(np.arange(0,EA.shape[0],1), Rf_2, color='white', edgecolors='black', marker='*', s=10, zorder=2,linewidths=0.5) - plt.xlabel('PE:EA',font1) - plt.ylabel('Rf',font1) - plt.xticks(np.arange(0,EA.shape[0],1), ['1:0','50:1','20:1','5:1','3:1','1:1','1:2','0:1'],fontproperties='Arial', size=4) - plt.yticks([0,0.2,0.4,0.6,0.8,1.0],[0,0.2,0.4,0.6,0.8,1.0],fontproperties='Arial', size=4) - plt.legend(loc='lower right', prop=font1) - if eluent == 'DCM:MeOH': - fig = plt.figure(2, figsize=(2, 2), dpi=300) - plt.clf() - ax = plt.subplot(1, 1, 1) - plt.plot(np.arange(0,ME.shape[0],1), Rf_1, c='#82B0D2', label='SMILE_1', zorder=1) - plt.plot(np.arange(0,ME.shape[0],1), Rf_2, c='#8A83B4', label='SMILE_2', zorder=1) - plt.scatter(np.arange(0,ME.shape[0],1), Rf_1, color='white', edgecolors='black', marker='^', s=10, zorder=1,linewidths=0.5) - plt.scatter(np.arange(0,ME.shape[0],1), Rf_2, color='white', edgecolors='black', marker='*', s=10, zorder=2,linewidths=0.5) - plt.xlabel('DCM:MeOH', font1) - plt.ylabel('Rf', font1) - plt.xticks(np.arange(0,ME.shape[0],1), ['1:0','100:1','50:1','30:1','20:1','10:1'], fontproperties='Arial', size=4) - plt.yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0], [0, 0.2, 0.4, 0.6, 0.8, 1.0], fontproperties='Arial', size=4) - plt.legend(loc='lower right', prop=font1) - if eluent == 'PE:Et2O': - fig = plt.figure(3, figsize=(2, 2), dpi=300) - plt.clf() - ax = plt.subplot(1, 1, 1) - plt.plot(np.arange(0,Et.shape[0],1), Rf_1, c='#82B0D2', label='SMILE_1', zorder=1) - plt.plot(np.arange(0,Et.shape[0],1), Rf_2, c='#8A83B4', label='SMILE_2', zorder=1) - plt.scatter(np.arange(0,Et.shape[0],1), Rf_1, color='white', edgecolors='black', marker='^', s=10, zorder=1,linewidths=0.5) - plt.scatter(np.arange(0,Et.shape[0],1), Rf_2, color='white', edgecolors='black', marker='*', s=10, zorder=2,linewidths=0.5) - plt.xlabel('PE:Et2O', font1) - plt.ylabel('Rf', font1) - plt.xticks(np.arange(0,Et.shape[0],1), ['1:0','2:1','1:1','1:2','0:1'], fontproperties='Arial', size=4) - plt.yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0], [0, 0.2, 0.4, 0.6, 0.8, 1.0], fontproperties='Arial', size=4) - plt.legend(loc='lower right', prop=font1) - plt.title(eluent,font1) - plt.tight_layout() - plt.ylim(-0.1, 1.1) - return fig - -if __name__=='__main__': - - - theme = gr.themes.Monochrome( - primary_hue="indigo", - secondary_hue="blue", - neutral_hue="slate", - ) - - model_card = f""" - ## Description\n - It is a app for predicting Rf values of a compound under given eluents in TLC.\n - input: smiles of one compound, such as CC(OCC)=O, and the ratio of five solvents, example: 20 1 0 0 0 for PE:EA=20:1\n - output: the predicted Rf value.\n\n - ## Citation\n - We would appreciate it if you use our software and give us credit in the acknowledgements section of your paper:\n - we use RF prediction software in our synthesis work. [Citation 1, Citation 2]\n - Citation1: H. Xu, J. Lin, Q. Liu, Y. Chen, J. Zhang, Y. Yang, M.C. Young, Y. Xu, D. Zhang, F. Mo - High-throughput discovery of chemical structure-polarity relationships combining automation and machine-learning techniques - Chem (2022), 3202–3214, 10.1016/j.chempr.2022.08.008\n - Citation2: https://huggingface.co/spaces/woshixuhao/Rf_prediction\n - Business applications require authorization! - ## Function\n - Single predict: predict a compound under a given eluent system\n - Batch predict: Upload a .csv file with multiple conditions to conduct batch prediction\n - Rf compare: predict Rf values of two compounds under different eluents in TLC - """ - - - with gr.Blocks() as demo: - gr.Markdown(''' - -- ''') - gr.Markdown(model_card) - with gr.Tab("Single prediction"): - gr.Interface(fn=predict_single, inputs=["text", "number","number","number","number","number"], outputs='number') - with gr.Tab("Batch prediction"): - gr.Interface(fn=predict_xlsx,description='please upload a .csv file formatted in the form of the example', inputs="file", outputs="file",examples=[os.path.join(os.path.dirname(__file__),"TLC_1.csv")],cache_examples=True) - with gr.Tab("Rf compare"): - gr.Interface(fn=predict_compare, inputs=["text", "text"], outputs=["image","image","image"], - description='input: smiles of two compounds, such as CC(OCC)=O and CCOCC\n output: three images that show the Rf curve with different eluent ratios under PE/EA, DCM/MeOH, PE/Et2O system.\n\n') - demo.launch() - # smile='O=C(OC1C(OC(C)=O)C(OC(C)=O)C(OC(C)=O)C(COC(C)=O)O1)C' - # eluent=[0,0.9,0,0,0] - # print(predict_single(smile,1,0,0,0,0)) diff --git a/spaces/wuhuik/bingo/src/lib/bots/bing/types.ts b/spaces/wuhuik/bingo/src/lib/bots/bing/types.ts deleted file mode 100644 index 5a9813b797d13b592ec17b45cfac4bd46510d883..0000000000000000000000000000000000000000 --- a/spaces/wuhuik/bingo/src/lib/bots/bing/types.ts +++ /dev/null @@ -1,261 +0,0 @@ -export type Author = 'user' | 'system' | 'bot' - -export type BotId = 'bing' - -export enum BingConversationStyle { - Creative = 'Creative', - Balanced = 'Balanced', - Precise = 'Precise' -} - -export enum ErrorCode { - CONVERSATION_LIMIT = 'CONVERSATION_LIMIT', - BING_UNAUTHORIZED = 'BING_UNAUTHORIZED', - BING_IP_FORBIDDEN = 'BING_IP_FORBIDDEN', - BING_TRY_LATER = 'BING_TRY_LATER', - BING_FORBIDDEN = 'BING_FORBIDDEN', - BING_CAPTCHA = 'BING_CAPTCHA', - THROTTLE_LIMIT = 'THROTTLE_LIMIT', - NOTFOUND_ERROR = 'NOT_FOUND_ERROR', - UNKOWN_ERROR = 'UNKOWN_ERROR', - NETWORK_ERROR = 'NETWORK_ERROR', -} - -export class ChatError extends Error { - code: ErrorCode - constructor(message: string, code: ErrorCode) { - super(message) - this.code = code - } -} - -export type ChatMessageModel = { - id: string - author: Author - text: string - error?: ChatError - throttling?: Throttling - sourceAttributions?: SourceAttribution[] - suggestedResponses?: SuggestedResponse[] -} - -export interface ConversationModel { - messages: ChatMessageModel[] -} - -export type Event = - | { - type: 'UPDATE_ANSWER' - data: { - text: string - spokenText?: string - sourceAttributions?: SourceAttribution[] - suggestedResponses?: SuggestedResponse[] - throttling?: Throttling - } - } - | { - type: 'DONE' - } - | { - type: 'ERROR' - error: ChatError - } - -export interface SendMessageParamsRf prediction
-{ - prompt: string - imageUrl?: string - options: T - onEvent: (event: Event) => void - signal?: AbortSignal -} - -export interface ConversationResponse { - conversationId: string - clientId: string - conversationSignature: string - result: { - value: string - message?: string - } -} - -export interface Telemetry { - metrics?: null - startTime: string -} - -export interface ChatUpdateArgument { - messages?: ChatResponseMessage[] - throttling?: Throttling - requestId: string - result: null -} - -export type ChatUpdateCompleteResponse = { - type: 2 - invocationId: string - item: ChatResponseItem -} | { - type: 1 - target: string - arguments: ChatUpdateArgument[] -} | { - type: 3 - invocationId: string -} | { - type: 6 | 7 -} - -export interface ChatRequestResult { - value: string - serviceVersion: string - error?: string -} - -export interface ChatResponseItem { - messages: ChatResponseMessage[] - firstNewMessageIndex: number - suggestedResponses: null - conversationId: string - requestId: string - conversationExpiryTime: string - telemetry: Telemetry - result: ChatRequestResult - throttling: Throttling -} -export enum InvocationEventType { - Invocation = 1, - StreamItem = 2, - Completion = 3, - StreamInvocation = 4, - CancelInvocation = 5, - Ping = 6, - Close = 7, -} - -// https://github.com/bytemate/bingchat-api/blob/main/src/lib.ts - -export interface ConversationInfo { - conversationId: string - clientId: string - conversationSignature: string - invocationId: number - conversationStyle: BingConversationStyle - prompt: string - imageUrl?: string -} - -export interface BingChatResponse { - conversationSignature: string - conversationId: string - clientId: string - invocationId: number - conversationExpiryTime: Date - response: string - details: ChatResponseMessage -} - -export interface Throttling { - maxNumLongDocSummaryUserMessagesInConversation: number - maxNumUserMessagesInConversation: number - numLongDocSummaryUserMessagesInConversation: number - numUserMessagesInConversation: number -} - -export interface ChatResponseMessage { - text: string - spokenText?: string - author: string - createdAt: Date - timestamp: Date - messageId: string - requestId: string - offense: string - adaptiveCards: AdaptiveCard[] - sourceAttributions: SourceAttribution[] - feedback: Feedback - contentOrigin: string - messageType?: string - contentType?: string - privacy: null - suggestedResponses: SuggestedResponse[] -} - -export interface AdaptiveCard { - type: string - version: string - body: Body[] -} - -export interface Body { - type: string - text: string - wrap: boolean - size?: string -} - -export interface Feedback { - tag: null - updatedOn: null - type: string -} - -export interface SourceAttribution { - providerDisplayName: string - seeMoreUrl: string - searchQuery: string -} - -export interface SuggestedResponse { - text: string - author?: Author - createdAt?: Date - timestamp?: Date - messageId?: string - messageType?: string - offense?: string - feedback?: Feedback - contentOrigin?: string - privacy?: null -} - -export interface KBlobRequest { - knowledgeRequest: KnowledgeRequestContext - imageBase64?: string -} - -export interface KBlobResponse { - blobId: string - processedBlobId?: string -} - -export interface KnowledgeRequestContext { - imageInfo: ImageInfo; - knowledgeRequest: KnowledgeRequest; -} - -export interface ImageInfo { - url?: string; -} - -export interface KnowledgeRequest { - invokedSkills: string[]; - subscriptionId: string; - invokedSkillsRequestData: InvokedSkillsRequestData; - convoData: ConvoData; -} - -export interface ConvoData { - convoid: string; - convotone: BingConversationStyle; -} - -export interface InvokedSkillsRequestData { - enableFaceBlur: boolean; -} - -export interface FileItem { - url: string; - status?: 'loading' | 'error' | 'loaded' -} diff --git a/spaces/wwwwwwww2/bingo/src/lib/bots/bing/utils.ts b/spaces/wwwwwwww2/bingo/src/lib/bots/bing/utils.ts deleted file mode 100644 index 6bbbc5e463ad55bc1219b63cf78013f5360fc908..0000000000000000000000000000000000000000 --- a/spaces/wwwwwwww2/bingo/src/lib/bots/bing/utils.ts +++ /dev/null @@ -1,87 +0,0 @@ -import { ChatResponseMessage, BingChatResponse } from './types' - -export function convertMessageToMarkdown(message: ChatResponseMessage): string { - if (message.messageType === 'InternalSearchQuery') { - return message.text - } - for (const card of message.adaptiveCards??[]) { - for (const block of card.body) { - if (block.type === 'TextBlock') { - return block.text - } - } - } - return '' -} - -const RecordSeparator = String.fromCharCode(30) - -export const websocketUtils = { - packMessage(data: any) { - return `${JSON.stringify(data)}${RecordSeparator}` - }, - unpackMessage(data: string | ArrayBuffer | Blob) { - if (!data) return {} - return data - .toString() - .split(RecordSeparator) - .filter(Boolean) - .map((s) => { - try { - return JSON.parse(s) - } catch (e) { - return {} - } - }) - }, -} - -export async function createImage(prompt: string, id: string, headers: HeadersInit): Promise { - const { headers: responseHeaders } = await fetch(`https://www.bing.com/images/create?partner=sydney&re=1&showselective=1&sude=1&kseed=7000&SFX=&q=${encodeURIComponent(prompt)}&iframeid=${id}`, - { - method: 'HEAD', - headers, - redirect: 'manual' - }, - ); - - if (!/&id=([^&]+)$/.test(responseHeaders.get('location') || '')) { - throw new Error('请求异常，请检查身份信息是否有效') - } - - const resultId = RegExp.$1; - let count = 0 - const imageThumbUrl = `https://www.bing.com/images/create/async/results/${resultId}?q=${encodeURIComponent(prompt)}&partner=sydney&showselective=1&IID=images.as`; - - do { - await sleep(3000); - const content = await fetch(imageThumbUrl, { headers, method: 'GET' }) - - // @ts-ignore - if (content.headers.get('content-length') > 1) { - const text = await content.text() - return (text?.match(/ target?.split('src="').pop()?.replace(/&/g, '&')) - .map(img => ``).join(' ') - } - } while(count ++ < 10); -} - - -export async function* streamAsyncIterable(stream: ReadableStream) { - const reader = stream.getReader() - try { - while (true) { - const { done, value } = await reader.read() - if (done) { - return - } - yield value - } - } finally { - reader.releaseLock() - } -} - -export const sleep = (ms: number) => new Promise(resolve => setTimeout(resolve, ms)) - diff --git a/spaces/xdecoder/Demo/xdecoder/body/decoder/registry.py b/spaces/xdecoder/Demo/xdecoder/body/decoder/registry.py deleted file mode 100644 index bd9a7453d5bace3cdd892226f2f40c1a0be1fdb6..0000000000000000000000000000000000000000 --- a/spaces/xdecoder/Demo/xdecoder/body/decoder/registry.py +++ /dev/null @@ -1,13 +0,0 @@ -_model_entrypoints = {} - -def register_decoder(fn): - module_name_split = fn.__module__.split('.') - model_name = module_name_split[-1] - _model_entrypoints[model_name] = fn - return fn - -def model_entrypoints(model_name): - return _model_entrypoints[model_name] - -def is_model(model_name): - return model_name in _model_entrypoints \ No newline at end of file diff --git a/spaces/xl2533/MakeInstruction/ape/dump.py b/spaces/xl2533/MakeInstruction/ape/dump.py deleted file mode 100644 index aba08d529580a0f7d823c8293f2e102b89b1af9a..0000000000000000000000000000000000000000 --- a/spaces/xl2533/MakeInstruction/ape/dump.py +++ /dev/null @@ -1,35 +0,0 @@ -# -*-coding:utf-8 -*- -""" - Dump Instruction Data for SELF -""" -import json -from ape.instance import LoadFactory - - -def make_instruct_data(instruction, input_output_list): - data = { - 'instruction': instruction, - 'instances': [] - } - for i, j in input_output_list: - data['instances'].append({'input': i, 'output': j}) - return data - - -def seed_file(file_name='./ape/data/seed_task.json', n_instances=5): - instruction = { - 'paraphase': '将医学手术名称的术语表述标准化。输入是医学手术的名称，输出是对该手术的名称进行修正、标准化，以供医学专业人员更好地理解', - 'search_intent': '生成医学相关问题的答案。给定一个输入问题，需要根据问题生成相应的输出答案。答案包括临床表现、病因、治疗方法、作用、定义等等，如果有多个问题，返回多问', - 'qa_generation': '训练一个问答系统，给定一些医学文本，能够回答用户提问关于该文本内容的问题。每个输入-输出对是一组文本和对应的问题及答案。输出的形式是以下Json格式{"问题":$问题, "回答":$回答}', - 'entity': '给定药品信息和用途说明，根据用途说明提取出药品的主治功能' - } - - with open(file_name, 'w', encoding='UTF8') as f: - for task, instruct in instruction.items(): - sample = LoadFactory[task]()[:n_instances] - data = make_instruct_data(instruct, sample) - f.write(json.dumps(data, ensure_ascii=False) + '\n') - - -if __name__ == '__main__': - seed_file() diff --git a/spaces/yderre-aubay/midi-player-demo/src/common/transform/index.ts b/spaces/yderre-aubay/midi-player-demo/src/common/transform/index.ts deleted file mode 100644 index 3ba23bee452a54eea02d17f8e7bfa1e638d34515..0000000000000000000000000000000000000000 --- a/spaces/yderre-aubay/midi-player-demo/src/common/transform/index.ts +++ /dev/null @@ -1,2 +0,0 @@ -export { default as NoteCoordTransform } from "./NoteCoordTransform" -export { default as TempoCoordTransform } from "./TempoCoordTransform" diff --git a/spaces/yderre-aubay/midi-player-demo/src/community/helpers/formatTimeAgo.ts b/spaces/yderre-aubay/midi-player-demo/src/community/helpers/formatTimeAgo.ts deleted file mode 100644 index 2bd939a33757a8b6d6f5e1f5bcfcf8655933021b..0000000000000000000000000000000000000000 --- a/spaces/yderre-aubay/midi-player-demo/src/community/helpers/formatTimeAgo.ts +++ /dev/null @@ -1,27 +0,0 @@ -// https://blog.webdevsimplified.com/2020-07/relative-time-format/ -const formatter = new Intl.RelativeTimeFormat(undefined, { - numeric: "auto", -}) - -const DIVISIONS = [ - { amount: 60, name: "seconds" }, - { amount: 60, name: "minutes" }, - { amount: 24, name: "hours" }, - { amount: 7, name: "days" }, - { amount: 4.34524, name: "weeks" }, - { amount: 12, name: "months" }, - { amount: Number.POSITIVE_INFINITY, name: "years" }, -] as const - -export function formatTimeAgo(date: Date): string { - let duration = (date.getTime() - Date.now()) / 1000 - - for (let i = 0; i <= DIVISIONS.length; i++) { - const division = DIVISIONS[i] - if (Math.abs(duration) < division.amount) { - return formatter.format(Math.round(duration), division.name) - } - duration /= division.amount - } - throw new Error("") -} diff --git a/spaces/ygangang/VToonify/vtoonify/model/raft/core/corr.py b/spaces/ygangang/VToonify/vtoonify/model/raft/core/corr.py deleted file mode 100644 index 40214aa5e6f0392a732eacab9d9cb0cbfb4669f3..0000000000000000000000000000000000000000 --- a/spaces/ygangang/VToonify/vtoonify/model/raft/core/corr.py +++ /dev/null @@ -1,91 +0,0 @@ -import torch -import torch.nn.functional as F -from model.raft.core.utils.utils import bilinear_sampler, coords_grid - -try: - import alt_cuda_corr -except: - # alt_cuda_corr is not compiled - pass - - -class CorrBlock: - def __init__(self, fmap1, fmap2, num_levels=4, radius=4): - self.num_levels = num_levels - self.radius = radius - self.corr_pyramid = [] - - # all pairs correlation - corr = CorrBlock.corr(fmap1, fmap2) - - batch, h1, w1, dim, h2, w2 = corr.shape - corr = corr.reshape(batch*h1*w1, dim, h2, w2) - - self.corr_pyramid.append(corr) - for i in range(self.num_levels-1): - corr = F.avg_pool2d(corr, 2, stride=2) - self.corr_pyramid.append(corr) - - def __call__(self, coords): - r = self.radius - coords = coords.permute(0, 2, 3, 1) - batch, h1, w1, _ = coords.shape - - out_pyramid = [] - for i in range(self.num_levels): - corr = self.corr_pyramid[i] - dx = torch.linspace(-r, r, 2*r+1, device=coords.device) - dy = torch.linspace(-r, r, 2*r+1, device=coords.device) - delta = torch.stack(torch.meshgrid(dy, dx), axis=-1) - - centroid_lvl = coords.reshape(batch*h1*w1, 1, 1, 2) / 2**i - delta_lvl = delta.view(1, 2*r+1, 2*r+1, 2) - coords_lvl = centroid_lvl + delta_lvl - - corr = bilinear_sampler(corr, coords_lvl) - corr = corr.view(batch, h1, w1, -1) - out_pyramid.append(corr) - - out = torch.cat(out_pyramid, dim=-1) - return out.permute(0, 3, 1, 2).contiguous().float() - - @staticmethod - def corr(fmap1, fmap2): - batch, dim, ht, wd = fmap1.shape - fmap1 = fmap1.view(batch, dim, ht*wd) - fmap2 = fmap2.view(batch, dim, ht*wd) - - corr = torch.matmul(fmap1.transpose(1,2), fmap2) - corr = corr.view(batch, ht, wd, 1, ht, wd) - return corr / torch.sqrt(torch.tensor(dim).float()) - - -class AlternateCorrBlock: - def __init__(self, fmap1, fmap2, num_levels=4, radius=4): - self.num_levels = num_levels - self.radius = radius - - self.pyramid = [(fmap1, fmap2)] - for i in range(self.num_levels): - fmap1 = F.avg_pool2d(fmap1, 2, stride=2) - fmap2 = F.avg_pool2d(fmap2, 2, stride=2) - self.pyramid.append((fmap1, fmap2)) - - def __call__(self, coords): - coords = coords.permute(0, 2, 3, 1) - B, H, W, _ = coords.shape - dim = self.pyramid[0][0].shape[1] - - corr_list = [] - for i in range(self.num_levels): - r = self.radius - fmap1_i = self.pyramid[0][0].permute(0, 2, 3, 1).contiguous() - fmap2_i = self.pyramid[i][1].permute(0, 2, 3, 1).contiguous() - - coords_i = (coords / 2**i).reshape(B, 1, H, W, 2).contiguous() - corr, = alt_cuda_corr.forward(fmap1_i, fmap2_i, coords_i, r) - corr_list.append(corr.squeeze(1)) - - corr = torch.stack(corr_list, dim=1) - corr = corr.reshape(B, -1, H, W) - return corr / torch.sqrt(torch.tensor(dim).float()) diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/generation/streamers.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/generation/streamers.py deleted file mode 100644 index 4b299db5da6982e5f767fb4e8196dbde476dff9e..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/generation/streamers.py +++ /dev/null @@ -1,227 +0,0 @@ -# coding=utf-8 -# Copyright 2023 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from queue import Queue -from typing import TYPE_CHECKING, Optional - - -if TYPE_CHECKING: - from ..models.auto import AutoTokenizer - - -class BaseStreamer: - """ - Base class from which `.generate()` streamers should inherit. - """ - - def put(self, value): - """Function that is called by `.generate()` to push new tokens""" - raise NotImplementedError() - - def end(self): - """Function that is called by `.generate()` to signal the end of generation""" - raise NotImplementedError() - - -class TextStreamer(BaseStreamer): - """ - Simple text streamer that prints the token(s) to stdout as soon as entire words are formed. - -
- - The API for the streamer classes is still under development and may change in the future. - - - - Parameters: - tokenizer (`AutoTokenizer`): - The tokenized used to decode the tokens. - skip_prompt (`bool`, *optional*, defaults to `False`): - Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots. - decode_kwargs (`dict`, *optional*): - Additional keyword arguments to pass to the tokenizer's `decode` method. - - Examples: - - ```python - >>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer - - >>> tok = AutoTokenizer.from_pretrained("gpt2") - >>> model = AutoModelForCausalLM.from_pretrained("gpt2") - >>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt") - >>> streamer = TextStreamer(tok) - - >>> # Despite returning the usual output, the streamer will also print the generated text to stdout. - >>> _ = model.generate(**inputs, streamer=streamer, max_new_tokens=20) - An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven, - ``` - """ - - def __init__(self, tokenizer: "AutoTokenizer", skip_prompt: bool = False, **decode_kwargs): - self.tokenizer = tokenizer - self.skip_prompt = skip_prompt - self.decode_kwargs = decode_kwargs - - # variables used in the streaming process - self.token_cache = [] - self.print_len = 0 - self.next_tokens_are_prompt = True - - def put(self, value): - """ - Receives tokens, decodes them, and prints them to stdout as soon as they form entire words. - """ - if len(value.shape) > 1 and value.shape[0] > 1: - raise ValueError("TextStreamer only supports batch size 1") - elif len(value.shape) > 1: - value = value[0] - - if self.skip_prompt and self.next_tokens_are_prompt: - self.next_tokens_are_prompt = False - return - - # Add the new token to the cache and decodes the entire thing. - self.token_cache.extend(value.tolist()) - text = self.tokenizer.decode(self.token_cache, **self.decode_kwargs) - - # After the symbol for a new line, we flush the cache. - if text.endswith("\n"): - printable_text = text[self.print_len :] - self.token_cache = [] - self.print_len = 0 - # If the last token is a CJK character, we print the characters. - elif len(text) > 0 and self._is_chinese_char(ord(text[-1])): - printable_text = text[self.print_len :] - self.print_len += len(printable_text) - # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words, - # which may change with the subsequent token -- there are probably smarter ways to do this!) - else: - printable_text = text[self.print_len : text.rfind(" ") + 1] - self.print_len += len(printable_text) - - self.on_finalized_text(printable_text) - - def end(self): - """Flushes any remaining cache and prints a newline to stdout.""" - # Flush the cache, if it exists - if len(self.token_cache) > 0: - text = self.tokenizer.decode(self.token_cache, **self.decode_kwargs) - printable_text = text[self.print_len :] - self.token_cache = [] - self.print_len = 0 - else: - printable_text = "" - - self.next_tokens_are_prompt = True - self.on_finalized_text(printable_text, stream_end=True) - - def on_finalized_text(self, text: str, stream_end: bool = False): - """Prints the new text to stdout. If the stream is ending, also prints a newline.""" - print(text, flush=True, end="" if not stream_end else None) - - def _is_chinese_char(self, cp): - """Checks whether CP is the codepoint of a CJK character.""" - # This defines a "chinese character" as anything in the CJK Unicode block: - # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) - # - # Note that the CJK Unicode block is NOT all Japanese and Korean characters, - # despite its name. The modern Korean Hangul alphabet is a different block, - # as is Japanese Hiragana and Katakana. Those alphabets are used to write - # space-separated words, so they are not treated specially and handled - # like the all of the other languages. - if ( - (cp >= 0x4E00 and cp <= 0x9FFF) - or (cp >= 0x3400 and cp <= 0x4DBF) # - or (cp >= 0x20000 and cp <= 0x2A6DF) # - or (cp >= 0x2A700 and cp <= 0x2B73F) # - or (cp >= 0x2B740 and cp <= 0x2B81F) # - or (cp >= 0x2B820 and cp <= 0x2CEAF) # - or (cp >= 0xF900 and cp <= 0xFAFF) - or (cp >= 0x2F800 and cp <= 0x2FA1F) # - ): # - return True - - return False - - -class TextIteratorStreamer(TextStreamer): - """ - Streamer that stores print-ready text in a queue, to be used by a downstream application as an iterator. This is - useful for applications that benefit from acessing the generated text in a non-blocking way (e.g. in an interactive - Gradio demo). - -- - The API for the streamer classes is still under development and may change in the future. - - - - Parameters: - tokenizer (`AutoTokenizer`): - The tokenized used to decode the tokens. - skip_prompt (`bool`, *optional*, defaults to `False`): - Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots. - timeout (`float`, *optional*): - The timeout for the text queue. If `None`, the queue will block indefinitely. Useful to handle exceptions - in `.generate()`, when it is called in a separate thread. - decode_kwargs (`dict`, *optional*): - Additional keyword arguments to pass to the tokenizer's `decode` method. - - Examples: - - ```python - >>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer - >>> from threading import Thread - - >>> tok = AutoTokenizer.from_pretrained("gpt2") - >>> model = AutoModelForCausalLM.from_pretrained("gpt2") - >>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt") - >>> streamer = TextIteratorStreamer(tok) - - >>> # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way. - >>> generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=20) - >>> thread = Thread(target=model.generate, kwargs=generation_kwargs) - >>> thread.start() - >>> generated_text = "" - >>> for new_text in streamer: - ... generated_text += new_text - >>> generated_text - 'An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,' - ``` - """ - - def __init__( - self, tokenizer: "AutoTokenizer", skip_prompt: bool = False, timeout: Optional[float] = None, **decode_kwargs - ): - super().__init__(tokenizer, skip_prompt, **decode_kwargs) - self.text_queue = Queue() - self.stop_signal = None - self.timeout = timeout - - def on_finalized_text(self, text: str, stream_end: bool = False): - """Put the new text in the queue. If the stream is ending, also put a stop signal in the queue.""" - self.text_queue.put(text, timeout=self.timeout) - if stream_end: - self.text_queue.put(self.stop_signal, timeout=self.timeout) - - def __iter__(self): - return self - - def __next__(self): - value = self.text_queue.get(timeout=self.timeout) - if value == self.stop_signal: - raise StopIteration() - else: - return value diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/image_utils.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/image_utils.py deleted file mode 100644 index 628fe5dea72f63157a9f2a666e6b00396907fb77..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/image_utils.py +++ /dev/null @@ -1,668 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import base64 -import os -from io import BytesIO -from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Tuple, Union - -import numpy as np -import requests -from packaging import version - -from .utils import ( - ExplicitEnum, - is_jax_tensor, - is_tf_tensor, - is_torch_available, - is_torch_tensor, - is_vision_available, - requires_backends, - to_numpy, -) -from .utils.constants import ( # noqa: F401 - IMAGENET_DEFAULT_MEAN, - IMAGENET_DEFAULT_STD, - IMAGENET_STANDARD_MEAN, - IMAGENET_STANDARD_STD, - OPENAI_CLIP_MEAN, - OPENAI_CLIP_STD, -) - - -if is_vision_available(): - import PIL.Image - import PIL.ImageOps - - if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"): - PILImageResampling = PIL.Image.Resampling - else: - PILImageResampling = PIL.Image - -if TYPE_CHECKING: - if is_torch_available(): - import torch - - -ImageInput = Union[ - "PIL.Image.Image", np.ndarray, "torch.Tensor", List["PIL.Image.Image"], List[np.ndarray], List["torch.Tensor"] -] # noqa - - -class ChannelDimension(ExplicitEnum): - FIRST = "channels_first" - LAST = "channels_last" - - -def is_pil_image(img): - return is_vision_available() and isinstance(img, PIL.Image.Image) - - -def is_valid_image(img): - return ( - (is_vision_available() and isinstance(img, PIL.Image.Image)) - or isinstance(img, np.ndarray) - or is_torch_tensor(img) - or is_tf_tensor(img) - or is_jax_tensor(img) - ) - - -def valid_images(imgs): - # If we have an list of images, make sure every image is valid - if isinstance(imgs, (list, tuple)): - for img in imgs: - if not valid_images(img): - return False - # If not a list of tuple, we have been given a single image or batched tensor of images - elif not is_valid_image(imgs): - return False - return True - - -def is_batched(img): - if isinstance(img, (list, tuple)): - return is_valid_image(img[0]) - return False - - -def is_scaled_image(image: np.ndarray) -> bool: - """ - Checks to see whether the pixel values have already been rescaled to [0, 1]. - """ - if image.dtype == np.uint8: - return False - - # It's possible the image has pixel values in [0, 255] but is of floating type - return np.min(image) >= 0 and np.max(image) <= 1 - - -def make_list_of_images(images, expected_ndims: int = 3) -> List[ImageInput]: - """ - Ensure that the input is a list of images. If the input is a single image, it is converted to a list of length 1. - If the input is a batch of images, it is converted to a list of images. - - Args: - images (`ImageInput`): - Image of images to turn into a list of images. - expected_ndims (`int`, *optional*, defaults to 3): - Expected number of dimensions for a single input image. If the input image has a different number of - dimensions, an error is raised. - """ - if is_batched(images): - return images - - # Either the input is a single image, in which case we create a list of length 1 - if isinstance(images, PIL.Image.Image): - # PIL images are never batched - return [images] - - if is_valid_image(images): - if images.ndim == expected_ndims + 1: - # Batch of images - images = list(images) - elif images.ndim == expected_ndims: - # Single image - images = [images] - else: - raise ValueError( - f"Invalid image shape. Expected either {expected_ndims + 1} or {expected_ndims} dimensions, but got" - f" {images.ndim} dimensions." - ) - return images - raise ValueError( - "Invalid image type. Expected either PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or " - f"jax.ndarray, but got {type(images)}." - ) - - -def to_numpy_array(img) -> np.ndarray: - if not is_valid_image(img): - raise ValueError(f"Invalid image type: {type(img)}") - - if is_vision_available() and isinstance(img, PIL.Image.Image): - return np.array(img) - return to_numpy(img) - - -def infer_channel_dimension_format( - image: np.ndarray, num_channels: Optional[Union[int, Tuple[int, ...]]] = None -) -> ChannelDimension: - """ - Infers the channel dimension format of `image`. - - Args: - image (`np.ndarray`): - The image to infer the channel dimension of. - num_channels (`int` or `Tuple[int, ...]`, *optional*, defaults to `(1, 3)`): - The number of channels of the image. - - Returns: - The channel dimension of the image. - """ - num_channels = num_channels if num_channels is not None else (1, 3) - num_channels = (num_channels,) if isinstance(num_channels, int) else num_channels - - if image.ndim == 3: - first_dim, last_dim = 0, 2 - elif image.ndim == 4: - first_dim, last_dim = 1, 3 - else: - raise ValueError(f"Unsupported number of image dimensions: {image.ndim}") - - if image.shape[first_dim] in num_channels: - return ChannelDimension.FIRST - elif image.shape[last_dim] in num_channels: - return ChannelDimension.LAST - raise ValueError("Unable to infer channel dimension format") - - -def get_channel_dimension_axis( - image: np.ndarray, input_data_format: Optional[Union[ChannelDimension, str]] = None -) -> int: - """ - Returns the channel dimension axis of the image. - - Args: - image (`np.ndarray`): - The image to get the channel dimension axis of. - input_data_format (`ChannelDimension` or `str`, *optional*): - The channel dimension format of the image. If `None`, will infer the channel dimension from the image. - - Returns: - The channel dimension axis of the image. - """ - if input_data_format is None: - input_data_format = infer_channel_dimension_format(image) - if input_data_format == ChannelDimension.FIRST: - return image.ndim - 3 - elif input_data_format == ChannelDimension.LAST: - return image.ndim - 1 - raise ValueError(f"Unsupported data format: {input_data_format}") - - -def get_image_size(image: np.ndarray, channel_dim: ChannelDimension = None) -> Tuple[int, int]: - """ - Returns the (height, width) dimensions of the image. - - Args: - image (`np.ndarray`): - The image to get the dimensions of. - channel_dim (`ChannelDimension`, *optional*): - Which dimension the channel dimension is in. If `None`, will infer the channel dimension from the image. - - Returns: - A tuple of the image's height and width. - """ - if channel_dim is None: - channel_dim = infer_channel_dimension_format(image) - - if channel_dim == ChannelDimension.FIRST: - return image.shape[-2], image.shape[-1] - elif channel_dim == ChannelDimension.LAST: - return image.shape[-3], image.shape[-2] - else: - raise ValueError(f"Unsupported data format: {channel_dim}") - - -def is_valid_annotation_coco_detection(annotation: Dict[str, Union[List, Tuple]]) -> bool: - if ( - isinstance(annotation, dict) - and "image_id" in annotation - and "annotations" in annotation - and isinstance(annotation["annotations"], (list, tuple)) - and ( - # an image can have no annotations - len(annotation["annotations"]) == 0 - or isinstance(annotation["annotations"][0], dict) - ) - ): - return True - return False - - -def is_valid_annotation_coco_panoptic(annotation: Dict[str, Union[List, Tuple]]) -> bool: - if ( - isinstance(annotation, dict) - and "image_id" in annotation - and "segments_info" in annotation - and "file_name" in annotation - and isinstance(annotation["segments_info"], (list, tuple)) - and ( - # an image can have no segments - len(annotation["segments_info"]) == 0 - or isinstance(annotation["segments_info"][0], dict) - ) - ): - return True - return False - - -def valid_coco_detection_annotations(annotations: Iterable[Dict[str, Union[List, Tuple]]]) -> bool: - return all(is_valid_annotation_coco_detection(ann) for ann in annotations) - - -def valid_coco_panoptic_annotations(annotations: Iterable[Dict[str, Union[List, Tuple]]]) -> bool: - return all(is_valid_annotation_coco_panoptic(ann) for ann in annotations) - - -def load_image(image: Union[str, "PIL.Image.Image"], timeout: Optional[float] = None) -> "PIL.Image.Image": - """ - Loads `image` to a PIL Image. - - Args: - image (`str` or `PIL.Image.Image`): - The image to convert to the PIL Image format. - timeout (`float`, *optional*): - The timeout value in seconds for the URL request. - - Returns: - `PIL.Image.Image`: A PIL Image. - """ - requires_backends(load_image, ["vision"]) - if isinstance(image, str): - if image.startswith("http://") or image.startswith("https://"): - # We need to actually check for a real protocol, otherwise it's impossible to use a local file - # like http_huggingface_co.png - image = PIL.Image.open(requests.get(image, stream=True, timeout=timeout).raw) - elif os.path.isfile(image): - image = PIL.Image.open(image) - else: - if image.startswith("data:image/"): - image = image.split(",")[1] - - # Try to load as base64 - try: - b64 = base64.b64decode(image, validate=True) - image = PIL.Image.open(BytesIO(b64)) - except Exception as e: - raise ValueError( - f"Incorrect image source. Must be a valid URL starting with `http://` or `https://`, a valid path to an image file, or a base64 encoded string. Got {image}. Failed with {e}" - ) - elif isinstance(image, PIL.Image.Image): - image = image - else: - raise ValueError( - "Incorrect format used for image. Should be an url linking to an image, a base64 string, a local path, or a PIL image." - ) - image = PIL.ImageOps.exif_transpose(image) - image = image.convert("RGB") - return image - - -# In the future we can add a TF implementation here when we have TF models. -class ImageFeatureExtractionMixin: - """ - Mixin that contain utilities for preparing image features. - """ - - def _ensure_format_supported(self, image): - if not isinstance(image, (PIL.Image.Image, np.ndarray)) and not is_torch_tensor(image): - raise ValueError( - f"Got type {type(image)} which is not supported, only `PIL.Image.Image`, `np.array` and " - "`torch.Tensor` are." - ) - - def to_pil_image(self, image, rescale=None): - """ - Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if - needed. - - Args: - image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor`): - The image to convert to the PIL Image format. - rescale (`bool`, *optional*): - Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will - default to `True` if the image type is a floating type, `False` otherwise. - """ - self._ensure_format_supported(image) - - if is_torch_tensor(image): - image = image.numpy() - - if isinstance(image, np.ndarray): - if rescale is None: - # rescale default to the array being of floating type. - rescale = isinstance(image.flat[0], np.floating) - # If the channel as been moved to first dim, we put it back at the end. - if image.ndim == 3 and image.shape[0] in [1, 3]: - image = image.transpose(1, 2, 0) - if rescale: - image = image * 255 - image = image.astype(np.uint8) - return PIL.Image.fromarray(image) - return image - - def convert_rgb(self, image): - """ - Converts `PIL.Image.Image` to RGB format. - - Args: - image (`PIL.Image.Image`): - The image to convert. - """ - self._ensure_format_supported(image) - if not isinstance(image, PIL.Image.Image): - return image - - return image.convert("RGB") - - def rescale(self, image: np.ndarray, scale: Union[float, int]) -> np.ndarray: - """ - Rescale a numpy image by scale amount - """ - self._ensure_format_supported(image) - return image * scale - - def to_numpy_array(self, image, rescale=None, channel_first=True): - """ - Converts `image` to a numpy array. Optionally rescales it and puts the channel dimension as the first - dimension. - - Args: - image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): - The image to convert to a NumPy array. - rescale (`bool`, *optional*): - Whether or not to apply the scaling factor (to make pixel values floats between 0. and 1.). Will - default to `True` if the image is a PIL Image or an array/tensor of integers, `False` otherwise. - channel_first (`bool`, *optional*, defaults to `True`): - Whether or not to permute the dimensions of the image to put the channel dimension first. - """ - self._ensure_format_supported(image) - - if isinstance(image, PIL.Image.Image): - image = np.array(image) - - if is_torch_tensor(image): - image = image.numpy() - - rescale = isinstance(image.flat[0], np.integer) if rescale is None else rescale - - if rescale: - image = self.rescale(image.astype(np.float32), 1 / 255.0) - - if channel_first and image.ndim == 3: - image = image.transpose(2, 0, 1) - - return image - - def expand_dims(self, image): - """ - Expands 2-dimensional `image` to 3 dimensions. - - Args: - image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): - The image to expand. - """ - self._ensure_format_supported(image) - - # Do nothing if PIL image - if isinstance(image, PIL.Image.Image): - return image - - if is_torch_tensor(image): - image = image.unsqueeze(0) - else: - image = np.expand_dims(image, axis=0) - return image - - def normalize(self, image, mean, std, rescale=False): - """ - Normalizes `image` with `mean` and `std`. Note that this will trigger a conversion of `image` to a NumPy array - if it's a PIL Image. - - Args: - image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): - The image to normalize. - mean (`List[float]` or `np.ndarray` or `torch.Tensor`): - The mean (per channel) to use for normalization. - std (`List[float]` or `np.ndarray` or `torch.Tensor`): - The standard deviation (per channel) to use for normalization. - rescale (`bool`, *optional*, defaults to `False`): - Whether or not to rescale the image to be between 0 and 1. If a PIL image is provided, scaling will - happen automatically. - """ - self._ensure_format_supported(image) - - if isinstance(image, PIL.Image.Image): - image = self.to_numpy_array(image, rescale=True) - # If the input image is a PIL image, it automatically gets rescaled. If it's another - # type it may need rescaling. - elif rescale: - if isinstance(image, np.ndarray): - image = self.rescale(image.astype(np.float32), 1 / 255.0) - elif is_torch_tensor(image): - image = self.rescale(image.float(), 1 / 255.0) - - if isinstance(image, np.ndarray): - if not isinstance(mean, np.ndarray): - mean = np.array(mean).astype(image.dtype) - if not isinstance(std, np.ndarray): - std = np.array(std).astype(image.dtype) - elif is_torch_tensor(image): - import torch - - if not isinstance(mean, torch.Tensor): - mean = torch.tensor(mean) - if not isinstance(std, torch.Tensor): - std = torch.tensor(std) - - if image.ndim == 3 and image.shape[0] in [1, 3]: - return (image - mean[:, None, None]) / std[:, None, None] - else: - return (image - mean) / std - - def resize(self, image, size, resample=None, default_to_square=True, max_size=None): - """ - Resizes `image`. Enforces conversion of input to PIL.Image. - - Args: - image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): - The image to resize. - size (`int` or `Tuple[int, int]`): - The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be - matched to this. - - If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If - `size` is an int and `default_to_square` is `False`, then smaller edge of the image will be matched to - this number. i.e, if height > width, then image will be rescaled to (size * height / width, size). - resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`): - The filter to user for resampling. - default_to_square (`bool`, *optional*, defaults to `True`): - How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a - square (`size`,`size`). If set to `False`, will replicate - [`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize) - with support for resizing only the smallest edge and providing an optional `max_size`. - max_size (`int`, *optional*, defaults to `None`): - The maximum allowed for the longer edge of the resized image: if the longer edge of the image is - greater than `max_size` after being resized according to `size`, then the image is resized again so - that the longer edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller - edge may be shorter than `size`. Only used if `default_to_square` is `False`. - - Returns: - image: A resized `PIL.Image.Image`. - """ - resample = resample if resample is not None else PILImageResampling.BILINEAR - - self._ensure_format_supported(image) - - if not isinstance(image, PIL.Image.Image): - image = self.to_pil_image(image) - - if isinstance(size, list): - size = tuple(size) - - if isinstance(size, int) or len(size) == 1: - if default_to_square: - size = (size, size) if isinstance(size, int) else (size[0], size[0]) - else: - width, height = image.size - # specified size only for the smallest edge - short, long = (width, height) if width <= height else (height, width) - requested_new_short = size if isinstance(size, int) else size[0] - - if short == requested_new_short: - return image - - new_short, new_long = requested_new_short, int(requested_new_short * long / short) - - if max_size is not None: - if max_size <= requested_new_short: - raise ValueError( - f"max_size = {max_size} must be strictly greater than the requested " - f"size for the smaller edge size = {size}" - ) - if new_long > max_size: - new_short, new_long = int(max_size * new_short / new_long), max_size - - size = (new_short, new_long) if width <= height else (new_long, new_short) - - return image.resize(size, resample=resample) - - def center_crop(self, image, size): - """ - Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the - size given, it will be padded (so the returned result has the size asked). - - Args: - image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape (n_channels, height, width) or (height, width, n_channels)): - The image to resize. - size (`int` or `Tuple[int, int]`): - The size to which crop the image. - - Returns: - new_image: A center cropped `PIL.Image.Image` or `np.ndarray` or `torch.Tensor` of shape: (n_channels, - height, width). - """ - self._ensure_format_supported(image) - - if not isinstance(size, tuple): - size = (size, size) - - # PIL Image.size is (width, height) but NumPy array and torch Tensors have (height, width) - if is_torch_tensor(image) or isinstance(image, np.ndarray): - if image.ndim == 2: - image = self.expand_dims(image) - image_shape = image.shape[1:] if image.shape[0] in [1, 3] else image.shape[:2] - else: - image_shape = (image.size[1], image.size[0]) - - top = (image_shape[0] - size[0]) // 2 - bottom = top + size[0] # In case size is odd, (image_shape[0] + size[0]) // 2 won't give the proper result. - left = (image_shape[1] - size[1]) // 2 - right = left + size[1] # In case size is odd, (image_shape[1] + size[1]) // 2 won't give the proper result. - - # For PIL Images we have a method to crop directly. - if isinstance(image, PIL.Image.Image): - return image.crop((left, top, right, bottom)) - - # Check if image is in (n_channels, height, width) or (height, width, n_channels) format - channel_first = True if image.shape[0] in [1, 3] else False - - # Transpose (height, width, n_channels) format images - if not channel_first: - if isinstance(image, np.ndarray): - image = image.transpose(2, 0, 1) - if is_torch_tensor(image): - image = image.permute(2, 0, 1) - - # Check if cropped area is within image boundaries - if top >= 0 and bottom <= image_shape[0] and left >= 0 and right <= image_shape[1]: - return image[..., top:bottom, left:right] - - # Otherwise, we may need to pad if the image is too small. Oh joy... - new_shape = image.shape[:-2] + (max(size[0], image_shape[0]), max(size[1], image_shape[1])) - if isinstance(image, np.ndarray): - new_image = np.zeros_like(image, shape=new_shape) - elif is_torch_tensor(image): - new_image = image.new_zeros(new_shape) - - top_pad = (new_shape[-2] - image_shape[0]) // 2 - bottom_pad = top_pad + image_shape[0] - left_pad = (new_shape[-1] - image_shape[1]) // 2 - right_pad = left_pad + image_shape[1] - new_image[..., top_pad:bottom_pad, left_pad:right_pad] = image - - top += top_pad - bottom += top_pad - left += left_pad - right += left_pad - - new_image = new_image[ - ..., max(0, top) : min(new_image.shape[-2], bottom), max(0, left) : min(new_image.shape[-1], right) - ] - - return new_image - - def flip_channel_order(self, image): - """ - Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of - `image` to a NumPy array if it's a PIL Image. - - Args: - image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): - The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should - be first. - """ - self._ensure_format_supported(image) - - if isinstance(image, PIL.Image.Image): - image = self.to_numpy_array(image) - - return image[::-1, :, :] - - def rotate(self, image, angle, resample=None, expand=0, center=None, translate=None, fillcolor=None): - """ - Returns a rotated copy of `image`. This method returns a copy of `image`, rotated the given number of degrees - counter clockwise around its centre. - - Args: - image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`): - The image to rotate. If `np.ndarray` or `torch.Tensor`, will be converted to `PIL.Image.Image` before - rotating. - - Returns: - image: A rotated `PIL.Image.Image`. - """ - resample = resample if resample is not None else PIL.Image.NEAREST - - self._ensure_format_supported(image) - - if not isinstance(image, PIL.Image.Image): - image = self.to_pil_image(image) - - return image.rotate( - angle, resample=resample, expand=expand, center=center, translate=translate, fillcolor=fillcolor - ) diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/encodec/__init__.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/encodec/__init__.py deleted file mode 100644 index d3d9488968bf2cc6316ba5eb4601e3dc3e5878b8..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/encodec/__init__.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright 2023 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import TYPE_CHECKING - -from ...utils import ( - OptionalDependencyNotAvailable, - _LazyModule, - is_torch_available, -) - - -_import_structure = { - "configuration_encodec": [ - "ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP", - "EncodecConfig", - ], - "feature_extraction_encodec": ["EncodecFeatureExtractor"], -} - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_encodec"] = [ - "ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST", - "EncodecModel", - "EncodecPreTrainedModel", - ] - -if TYPE_CHECKING: - from .configuration_encodec import ( - ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP, - EncodecConfig, - ) - from .feature_extraction_encodec import EncodecFeatureExtractor - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_encodec import ( - ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST, - EncodecModel, - EncodecPreTrainedModel, - ) - -else: - import sys - - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlm/modeling_tf_layoutlm.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlm/modeling_tf_layoutlm.py deleted file mode 100644 index c756609468598ca4c5c967333dc49c5769595021..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlm/modeling_tf_layoutlm.py +++ /dev/null @@ -1,1487 +0,0 @@ -# coding=utf-8 -# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" TF 2.0 LayoutLM model.""" - - -from __future__ import annotations - -import math -import warnings -from typing import Dict, Optional, Tuple, Union - -import numpy as np -import tensorflow as tf - -from ...activations_tf import get_tf_activation -from ...modeling_tf_outputs import ( - TFBaseModelOutputWithPastAndCrossAttentions, - TFBaseModelOutputWithPoolingAndCrossAttentions, - TFMaskedLMOutput, - TFQuestionAnsweringModelOutput, - TFSequenceClassifierOutput, - TFTokenClassifierOutput, -) -from ...modeling_tf_utils import ( - TFMaskedLanguageModelingLoss, - TFModelInputType, - TFPreTrainedModel, - TFQuestionAnsweringLoss, - TFSequenceClassificationLoss, - TFTokenClassificationLoss, - get_initializer, - keras_serializable, - unpack_inputs, -) -from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax -from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings -from .configuration_layoutlm import LayoutLMConfig - - -logger = logging.get_logger(__name__) - -_CONFIG_FOR_DOC = "LayoutLMConfig" - -TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = [ - "microsoft/layoutlm-base-uncased", - "microsoft/layoutlm-large-uncased", -] - - -class TFLayoutLMEmbeddings(tf.keras.layers.Layer): - """Construct the embeddings from word, position and token_type embeddings.""" - - def __init__(self, config: LayoutLMConfig, **kwargs): - super().__init__(**kwargs) - - self.config = config - self.hidden_size = config.hidden_size - self.max_position_embeddings = config.max_position_embeddings - self.max_2d_position_embeddings = config.max_2d_position_embeddings - self.initializer_range = config.initializer_range - self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") - self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) - - def build(self, input_shape: tf.TensorShape): - with tf.name_scope("word_embeddings"): - self.weight = self.add_weight( - name="weight", - shape=[self.config.vocab_size, self.hidden_size], - initializer=get_initializer(self.initializer_range), - ) - - with tf.name_scope("token_type_embeddings"): - self.token_type_embeddings = self.add_weight( - name="embeddings", - shape=[self.config.type_vocab_size, self.hidden_size], - initializer=get_initializer(self.initializer_range), - ) - - with tf.name_scope("position_embeddings"): - self.position_embeddings = self.add_weight( - name="embeddings", - shape=[self.max_position_embeddings, self.hidden_size], - initializer=get_initializer(self.initializer_range), - ) - - with tf.name_scope("x_position_embeddings"): - self.x_position_embeddings = self.add_weight( - name="embeddings", - shape=[self.max_2d_position_embeddings, self.hidden_size], - initializer=get_initializer(self.initializer_range), - ) - - with tf.name_scope("y_position_embeddings"): - self.y_position_embeddings = self.add_weight( - name="embeddings", - shape=[self.max_2d_position_embeddings, self.hidden_size], - initializer=get_initializer(self.initializer_range), - ) - - with tf.name_scope("h_position_embeddings"): - self.h_position_embeddings = self.add_weight( - name="embeddings", - shape=[self.max_2d_position_embeddings, self.hidden_size], - initializer=get_initializer(self.initializer_range), - ) - - with tf.name_scope("w_position_embeddings"): - self.w_position_embeddings = self.add_weight( - name="embeddings", - shape=[self.max_2d_position_embeddings, self.hidden_size], - initializer=get_initializer(self.initializer_range), - ) - - super().build(input_shape) - - def call( - self, - input_ids: tf.Tensor = None, - bbox: tf.Tensor = None, - position_ids: tf.Tensor = None, - token_type_ids: tf.Tensor = None, - inputs_embeds: tf.Tensor = None, - training: bool = False, - ) -> tf.Tensor: - """ - Applies embedding based on inputs tensor. - - Returns: - final_embeddings (`tf.Tensor`): output embedding tensor. - """ - assert not (input_ids is None and inputs_embeds is None) - - if input_ids is not None: - check_embeddings_within_bounds(input_ids, self.config.vocab_size) - inputs_embeds = tf.gather(params=self.weight, indices=input_ids) - - input_shape = shape_list(inputs_embeds)[:-1] - - if token_type_ids is None: - token_type_ids = tf.fill(dims=input_shape, value=0) - - if position_ids is None: - position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0) - - if position_ids is None: - position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0) - - if bbox is None: - bbox = bbox = tf.fill(input_shape + [4], value=0) - try: - left_position_embeddings = tf.gather(self.x_position_embeddings, bbox[:, :, 0]) - upper_position_embeddings = tf.gather(self.y_position_embeddings, bbox[:, :, 1]) - right_position_embeddings = tf.gather(self.x_position_embeddings, bbox[:, :, 2]) - lower_position_embeddings = tf.gather(self.y_position_embeddings, bbox[:, :, 3]) - except IndexError as e: - raise IndexError("The `bbox`coordinate values should be within 0-1000 range.") from e - h_position_embeddings = tf.gather(self.h_position_embeddings, bbox[:, :, 3] - bbox[:, :, 1]) - w_position_embeddings = tf.gather(self.w_position_embeddings, bbox[:, :, 2] - bbox[:, :, 0]) - - position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids) - token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids) - final_embeddings = ( - inputs_embeds - + position_embeds - + token_type_embeds - + left_position_embeddings - + upper_position_embeddings - + right_position_embeddings - + lower_position_embeddings - + h_position_embeddings - + w_position_embeddings - ) - final_embeddings = self.LayerNorm(inputs=final_embeddings) - final_embeddings = self.dropout(inputs=final_embeddings, training=training) - - return final_embeddings - - -# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->LayoutLM -class TFLayoutLMSelfAttention(tf.keras.layers.Layer): - def __init__(self, config: LayoutLMConfig, **kwargs): - super().__init__(**kwargs) - - if config.hidden_size % config.num_attention_heads != 0: - raise ValueError( - f"The hidden size ({config.hidden_size}) is not a multiple of the number " - f"of attention heads ({config.num_attention_heads})" - ) - - self.num_attention_heads = config.num_attention_heads - self.attention_head_size = int(config.hidden_size / config.num_attention_heads) - self.all_head_size = self.num_attention_heads * self.attention_head_size - self.sqrt_att_head_size = math.sqrt(self.attention_head_size) - - self.query = tf.keras.layers.Dense( - units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" - ) - self.key = tf.keras.layers.Dense( - units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" - ) - self.value = tf.keras.layers.Dense( - units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" - ) - self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob) - - self.is_decoder = config.is_decoder - - def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor: - # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] - tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) - - # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] - return tf.transpose(tensor, perm=[0, 2, 1, 3]) - - def call( - self, - hidden_states: tf.Tensor, - attention_mask: tf.Tensor, - head_mask: tf.Tensor, - encoder_hidden_states: tf.Tensor, - encoder_attention_mask: tf.Tensor, - past_key_value: Tuple[tf.Tensor], - output_attentions: bool, - training: bool = False, - ) -> Tuple[tf.Tensor]: - batch_size = shape_list(hidden_states)[0] - mixed_query_layer = self.query(inputs=hidden_states) - - # If this is instantiated as a cross-attention module, the keys - # and values come from an encoder; the attention mask needs to be - # such that the encoder's padding tokens are not attended to. - is_cross_attention = encoder_hidden_states is not None - - if is_cross_attention and past_key_value is not None: - # reuse k,v, cross_attentions - key_layer = past_key_value[0] - value_layer = past_key_value[1] - attention_mask = encoder_attention_mask - elif is_cross_attention: - key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size) - value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size) - attention_mask = encoder_attention_mask - elif past_key_value is not None: - key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) - value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) - key_layer = tf.concat([past_key_value[0], key_layer], axis=2) - value_layer = tf.concat([past_key_value[1], value_layer], axis=2) - else: - key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) - value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) - - query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) - - if self.is_decoder: - # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. - # Further calls to cross_attention layer can then reuse all cross-attention - # key/value_states (first "if" case) - # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of - # all previous decoder key/value_states. Further calls to uni-directional self-attention - # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) - # if encoder bi-directional self-attention `past_key_value` is always `None` - past_key_value = (key_layer, value_layer) - - # Take the dot product between "query" and "key" to get the raw attention scores. - # (batch size, num_heads, seq_len_q, seq_len_k) - attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) - dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype) - attention_scores = tf.divide(attention_scores, dk) - - if attention_mask is not None: - # Apply the attention mask is (precomputed for all layers in TFLayoutLMModel call() function) - attention_scores = tf.add(attention_scores, attention_mask) - - # Normalize the attention scores to probabilities. - attention_probs = stable_softmax(logits=attention_scores, axis=-1) - - # This is actually dropping out entire tokens to attend to, which might - # seem a bit unusual, but is taken from the original Transformer paper. - attention_probs = self.dropout(inputs=attention_probs, training=training) - - # Mask heads if we want to - if head_mask is not None: - attention_probs = tf.multiply(attention_probs, head_mask) - - attention_output = tf.matmul(attention_probs, value_layer) - attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) - - # (batch_size, seq_len_q, all_head_size) - attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) - outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) - - if self.is_decoder: - outputs = outputs + (past_key_value,) - return outputs - - -# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->LayoutLM -class TFLayoutLMSelfOutput(tf.keras.layers.Layer): - def __init__(self, config: LayoutLMConfig, **kwargs): - super().__init__(**kwargs) - - self.dense = tf.keras.layers.Dense( - units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" - ) - self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") - self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) - - def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: - hidden_states = self.dense(inputs=hidden_states) - hidden_states = self.dropout(inputs=hidden_states, training=training) - hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) - - return hidden_states - - -# Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->LayoutLM -class TFLayoutLMAttention(tf.keras.layers.Layer): - def __init__(self, config: LayoutLMConfig, **kwargs): - super().__init__(**kwargs) - - self.self_attention = TFLayoutLMSelfAttention(config, name="self") - self.dense_output = TFLayoutLMSelfOutput(config, name="output") - - def prune_heads(self, heads): - raise NotImplementedError - - def call( - self, - input_tensor: tf.Tensor, - attention_mask: tf.Tensor, - head_mask: tf.Tensor, - encoder_hidden_states: tf.Tensor, - encoder_attention_mask: tf.Tensor, - past_key_value: Tuple[tf.Tensor], - output_attentions: bool, - training: bool = False, - ) -> Tuple[tf.Tensor]: - self_outputs = self.self_attention( - hidden_states=input_tensor, - attention_mask=attention_mask, - head_mask=head_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - past_key_value=past_key_value, - output_attentions=output_attentions, - training=training, - ) - attention_output = self.dense_output( - hidden_states=self_outputs[0], input_tensor=input_tensor, training=training - ) - # add attentions (possibly with past_key_value) if we output them - outputs = (attention_output,) + self_outputs[1:] - - return outputs - - -# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->LayoutLM -class TFLayoutLMIntermediate(tf.keras.layers.Layer): - def __init__(self, config: LayoutLMConfig, **kwargs): - super().__init__(**kwargs) - - self.dense = tf.keras.layers.Dense( - units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" - ) - - if isinstance(config.hidden_act, str): - self.intermediate_act_fn = get_tf_activation(config.hidden_act) - else: - self.intermediate_act_fn = config.hidden_act - - def call(self, hidden_states: tf.Tensor) -> tf.Tensor: - hidden_states = self.dense(inputs=hidden_states) - hidden_states = self.intermediate_act_fn(hidden_states) - - return hidden_states - - -# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->LayoutLM -class TFLayoutLMOutput(tf.keras.layers.Layer): - def __init__(self, config: LayoutLMConfig, **kwargs): - super().__init__(**kwargs) - - self.dense = tf.keras.layers.Dense( - units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" - ) - self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") - self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) - - def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: - hidden_states = self.dense(inputs=hidden_states) - hidden_states = self.dropout(inputs=hidden_states, training=training) - hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) - - return hidden_states - - -# Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->LayoutLM -class TFLayoutLMLayer(tf.keras.layers.Layer): - def __init__(self, config: LayoutLMConfig, **kwargs): - super().__init__(**kwargs) - - self.attention = TFLayoutLMAttention(config, name="attention") - self.is_decoder = config.is_decoder - self.add_cross_attention = config.add_cross_attention - if self.add_cross_attention: - if not self.is_decoder: - raise ValueError(f"{self} should be used as a decoder model if cross attention is added") - self.crossattention = TFLayoutLMAttention(config, name="crossattention") - self.intermediate = TFLayoutLMIntermediate(config, name="intermediate") - self.bert_output = TFLayoutLMOutput(config, name="output") - - def call( - self, - hidden_states: tf.Tensor, - attention_mask: tf.Tensor, - head_mask: tf.Tensor, - encoder_hidden_states: tf.Tensor | None, - encoder_attention_mask: tf.Tensor | None, - past_key_value: Tuple[tf.Tensor] | None, - output_attentions: bool, - training: bool = False, - ) -> Tuple[tf.Tensor]: - # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 - self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None - self_attention_outputs = self.attention( - input_tensor=hidden_states, - attention_mask=attention_mask, - head_mask=head_mask, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=self_attn_past_key_value, - output_attentions=output_attentions, - training=training, - ) - attention_output = self_attention_outputs[0] - - # if decoder, the last output is tuple of self-attn cache - if self.is_decoder: - outputs = self_attention_outputs[1:-1] - present_key_value = self_attention_outputs[-1] - else: - outputs = self_attention_outputs[1:] # add self attentions if we output attention weights - - cross_attn_present_key_value = None - if self.is_decoder and encoder_hidden_states is not None: - if not hasattr(self, "crossattention"): - raise ValueError( - f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" - " by setting `config.add_cross_attention=True`" - ) - - # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple - cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None - cross_attention_outputs = self.crossattention( - input_tensor=attention_output, - attention_mask=attention_mask, - head_mask=head_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - past_key_value=cross_attn_past_key_value, - output_attentions=output_attentions, - training=training, - ) - attention_output = cross_attention_outputs[0] - outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights - - # add cross-attn cache to positions 3,4 of present_key_value tuple - cross_attn_present_key_value = cross_attention_outputs[-1] - present_key_value = present_key_value + cross_attn_present_key_value - - intermediate_output = self.intermediate(hidden_states=attention_output) - layer_output = self.bert_output( - hidden_states=intermediate_output, input_tensor=attention_output, training=training - ) - outputs = (layer_output,) + outputs # add attentions if we output them - - # if decoder, return the attn key/values as the last output - if self.is_decoder: - outputs = outputs + (present_key_value,) - - return outputs - - -# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->LayoutLM -class TFLayoutLMEncoder(tf.keras.layers.Layer): - def __init__(self, config: LayoutLMConfig, **kwargs): - super().__init__(**kwargs) - self.config = config - self.layer = [TFLayoutLMLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] - - def call( - self, - hidden_states: tf.Tensor, - attention_mask: tf.Tensor, - head_mask: tf.Tensor, - encoder_hidden_states: tf.Tensor | None, - encoder_attention_mask: tf.Tensor | None, - past_key_values: Tuple[Tuple[tf.Tensor]] | None, - use_cache: Optional[bool], - output_attentions: bool, - output_hidden_states: bool, - return_dict: bool, - training: bool = False, - ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: - all_hidden_states = () if output_hidden_states else None - all_attentions = () if output_attentions else None - all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None - - next_decoder_cache = () if use_cache else None - for i, layer_module in enumerate(self.layer): - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - past_key_value = past_key_values[i] if past_key_values is not None else None - - layer_outputs = layer_module( - hidden_states=hidden_states, - attention_mask=attention_mask, - head_mask=head_mask[i], - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - past_key_value=past_key_value, - output_attentions=output_attentions, - training=training, - ) - hidden_states = layer_outputs[0] - - if use_cache: - next_decoder_cache += (layer_outputs[-1],) - - if output_attentions: - all_attentions = all_attentions + (layer_outputs[1],) - if self.config.add_cross_attention and encoder_hidden_states is not None: - all_cross_attentions = all_cross_attentions + (layer_outputs[2],) - - # Add last layer - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if not return_dict: - return tuple( - v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None - ) - - return TFBaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=next_decoder_cache, - hidden_states=all_hidden_states, - attentions=all_attentions, - cross_attentions=all_cross_attentions, - ) - - -# Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->LayoutLM -class TFLayoutLMPooler(tf.keras.layers.Layer): - def __init__(self, config: LayoutLMConfig, **kwargs): - super().__init__(**kwargs) - - self.dense = tf.keras.layers.Dense( - units=config.hidden_size, - kernel_initializer=get_initializer(config.initializer_range), - activation="tanh", - name="dense", - ) - - def call(self, hidden_states: tf.Tensor) -> tf.Tensor: - # We "pool" the model by simply taking the hidden state corresponding - # to the first token. - first_token_tensor = hidden_states[:, 0] - pooled_output = self.dense(inputs=first_token_tensor) - - return pooled_output - - -# Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->LayoutLM -class TFLayoutLMPredictionHeadTransform(tf.keras.layers.Layer): - def __init__(self, config: LayoutLMConfig, **kwargs): - super().__init__(**kwargs) - - self.dense = tf.keras.layers.Dense( - units=config.hidden_size, - kernel_initializer=get_initializer(config.initializer_range), - name="dense", - ) - - if isinstance(config.hidden_act, str): - self.transform_act_fn = get_tf_activation(config.hidden_act) - else: - self.transform_act_fn = config.hidden_act - - self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") - - def call(self, hidden_states: tf.Tensor) -> tf.Tensor: - hidden_states = self.dense(inputs=hidden_states) - hidden_states = self.transform_act_fn(hidden_states) - hidden_states = self.LayerNorm(inputs=hidden_states) - - return hidden_states - - -# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->LayoutLM -class TFLayoutLMLMPredictionHead(tf.keras.layers.Layer): - def __init__(self, config: LayoutLMConfig, input_embeddings: tf.keras.layers.Layer, **kwargs): - super().__init__(**kwargs) - - self.config = config - self.hidden_size = config.hidden_size - - self.transform = TFLayoutLMPredictionHeadTransform(config, name="transform") - - # The output weights are the same as the input embeddings, but there is - # an output-only bias for each token. - self.input_embeddings = input_embeddings - - def build(self, input_shape: tf.TensorShape): - self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias") - - super().build(input_shape) - - def get_output_embeddings(self) -> tf.keras.layers.Layer: - return self.input_embeddings - - def set_output_embeddings(self, value: tf.Variable): - self.input_embeddings.weight = value - self.input_embeddings.vocab_size = shape_list(value)[0] - - def get_bias(self) -> Dict[str, tf.Variable]: - return {"bias": self.bias} - - def set_bias(self, value: tf.Variable): - self.bias = value["bias"] - self.config.vocab_size = shape_list(value["bias"])[0] - - def call(self, hidden_states: tf.Tensor) -> tf.Tensor: - hidden_states = self.transform(hidden_states=hidden_states) - seq_length = shape_list(hidden_states)[1] - hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size]) - hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True) - hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size]) - hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) - - return hidden_states - - -# Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->LayoutLM -class TFLayoutLMMLMHead(tf.keras.layers.Layer): - def __init__(self, config: LayoutLMConfig, input_embeddings: tf.keras.layers.Layer, **kwargs): - super().__init__(**kwargs) - - self.predictions = TFLayoutLMLMPredictionHead(config, input_embeddings, name="predictions") - - def call(self, sequence_output: tf.Tensor) -> tf.Tensor: - prediction_scores = self.predictions(hidden_states=sequence_output) - - return prediction_scores - - -@keras_serializable -class TFLayoutLMMainLayer(tf.keras.layers.Layer): - config_class = LayoutLMConfig - - def __init__(self, config: LayoutLMConfig, add_pooling_layer: bool = True, **kwargs): - super().__init__(**kwargs) - - self.config = config - - self.embeddings = TFLayoutLMEmbeddings(config, name="embeddings") - self.encoder = TFLayoutLMEncoder(config, name="encoder") - self.pooler = TFLayoutLMPooler(config, name="pooler") if add_pooling_layer else None - - def get_input_embeddings(self) -> tf.keras.layers.Layer: - return self.embeddings - - def set_input_embeddings(self, value: tf.Variable): - self.embeddings.weight = value - self.embeddings.vocab_size = shape_list(value)[0] - - def _prune_heads(self, heads_to_prune): - """ - Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base - class PreTrainedModel - """ - raise NotImplementedError - - @unpack_inputs - def call( - self, - input_ids: TFModelInputType | None = None, - bbox: np.ndarray | tf.Tensor | None = None, - attention_mask: np.ndarray | tf.Tensor | None = None, - token_type_ids: np.ndarray | tf.Tensor | None = None, - position_ids: np.ndarray | tf.Tensor | None = None, - head_mask: np.ndarray | tf.Tensor | None = None, - inputs_embeds: np.ndarray | tf.Tensor | None = None, - encoder_hidden_states: np.ndarray | tf.Tensor | None = None, - encoder_attention_mask: np.ndarray | tf.Tensor | None = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - training: bool = False, - ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]: - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - input_shape = shape_list(input_ids) - elif inputs_embeds is not None: - input_shape = shape_list(inputs_embeds)[:-1] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - if attention_mask is None: - attention_mask = tf.fill(dims=input_shape, value=1) - - if token_type_ids is None: - token_type_ids = tf.fill(dims=input_shape, value=0) - if bbox is None: - bbox = tf.fill(dims=input_shape + [4], value=0) - - embedding_output = self.embeddings( - input_ids=input_ids, - bbox=bbox, - position_ids=position_ids, - token_type_ids=token_type_ids, - inputs_embeds=inputs_embeds, - training=training, - ) - - # We create a 3D attention mask from a 2D tensor mask. - # Sizes are [batch_size, 1, 1, to_seq_length] - # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] - # this attention mask is more simple than the triangular masking of causal attention - # used in OpenAI GPT, we just need to prepare the broadcast dimension here. - extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1])) - - # Since attention_mask is 1.0 for positions we want to attend and 0.0 for - # masked positions, this operation will create a tensor which is 0.0 for - # positions we want to attend and -10000.0 for masked positions. - # Since we are adding it to the raw scores before the softmax, this is - # effectively the same as removing these entirely. - extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype) - one_cst = tf.constant(1.0, dtype=embedding_output.dtype) - ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype) - extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst) - - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape bsz x n_heads x N x N - # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] - # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] - if head_mask is not None: - raise NotImplementedError - else: - head_mask = [None] * self.config.num_hidden_layers - - encoder_outputs = self.encoder( - hidden_states=embedding_output, - attention_mask=extended_attention_mask, - head_mask=head_mask, - # Need to pass these required positional arguments to `Encoder` - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=None, - past_key_values=None, - use_cache=False, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - training=training, - ) - - sequence_output = encoder_outputs[0] - pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None - - if not return_dict: - return ( - sequence_output, - pooled_output, - ) + encoder_outputs[1:] - - return TFBaseModelOutputWithPoolingAndCrossAttentions( - last_hidden_state=sequence_output, - pooler_output=pooled_output, - hidden_states=encoder_outputs.hidden_states, - attentions=encoder_outputs.attentions, - cross_attentions=encoder_outputs.cross_attentions, - ) - - -class TFLayoutLMPreTrainedModel(TFPreTrainedModel): - """ - An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained - models. - """ - - config_class = LayoutLMConfig - base_model_prefix = "layoutlm" - - -LAYOUTLM_START_DOCSTRING = r""" - - This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the - library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads - etc.) - - This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it - as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and - behavior. - -- - TensorFlow models and layers in `transformers` accept two formats as input: - - - having all inputs as keyword arguments (like PyTorch models), or - - having all inputs as a list, tuple or dict in the first positional argument. - - The reason the second format is supported is that Keras methods prefer this format when passing inputs to models - and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just - pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second - format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with - the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first - positional argument: - - - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: - `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - - a dictionary with one or several input Tensors associated to the input names given in the docstring: - `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` - - Note that when creating models and layers with - [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry - about any of this, as you can just pass inputs like you would to any other Python function! - - - - Args: - config ([`LayoutLMConfig`]): Model configuration class with all the parameters of the model. - Initializing with a config file does not load the weights associated with the model, only the - configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. -""" - -LAYOUTLM_INPUTS_DOCSTRING = r""" - Args: - input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`): - Indices of input sequence tokens in the vocabulary. - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and - [`PreTrainedTokenizer.encode`] for details. - - [What are input IDs?](../glossary#input-ids) - bbox (`Numpy array` or `tf.Tensor` of shape `({0}, 4)`, *optional*): - Bounding Boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings- - 1]`. - attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): - Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - - [What are attention masks?](../glossary#attention-mask) - token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): - Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, - 1]`: - - - 0 corresponds to a *sentence A* token, - - 1 corresponds to a *sentence B* token. - - [What are token type IDs?](../glossary#token-type-ids) - position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): - Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, - config.max_position_embeddings - 1]`. - - [What are position IDs?](../glossary#position-ids) - head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): - Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*): - Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This - is useful if you want more control over how to convert `input_ids` indices into associated vectors than the - model's internal embedding lookup matrix. - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned - tensors for more detail. - output_hidden_states (`bool`, *optional*): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for - more detail. - return_dict (`bool`, *optional*): - Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. - training (`bool`, *optional*, defaults to `False`): - Whether or not to use the model in training mode (some modules like dropout modules have different - behaviors between training and evaluation). -""" - - -@add_start_docstrings( - "The bare LayoutLM Model transformer outputting raw hidden-states without any specific head on top.", - LAYOUTLM_START_DOCSTRING, -) -class TFLayoutLMModel(TFLayoutLMPreTrainedModel): - def __init__(self, config: LayoutLMConfig, *inputs, **kwargs): - super().__init__(config, *inputs, **kwargs) - - self.layoutlm = TFLayoutLMMainLayer(config, name="layoutlm") - - @unpack_inputs - @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) - @replace_return_docstrings( - output_type=TFBaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC - ) - def call( - self, - input_ids: TFModelInputType | None = None, - bbox: np.ndarray | tf.Tensor | None = None, - attention_mask: np.ndarray | tf.Tensor | None = None, - token_type_ids: np.ndarray | tf.Tensor | None = None, - position_ids: np.ndarray | tf.Tensor | None = None, - head_mask: np.ndarray | tf.Tensor | None = None, - inputs_embeds: np.ndarray | tf.Tensor | None = None, - encoder_hidden_states: np.ndarray | tf.Tensor | None = None, - encoder_attention_mask: np.ndarray | tf.Tensor | None = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - training: Optional[bool] = False, - ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]: - r""" - Returns: - - Examples: - - ```python - >>> from transformers import AutoTokenizer, TFLayoutLMModel - >>> import tensorflow as tf - - >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased") - >>> model = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased") - - >>> words = ["Hello", "world"] - >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782] - - >>> token_boxes = [] - >>> for word, box in zip(words, normalized_word_boxes): - ... word_tokens = tokenizer.tokenize(word) - ... token_boxes.extend([box] * len(word_tokens)) - >>> # add bounding boxes of cls + sep tokens - >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]] - - >>> encoding = tokenizer(" ".join(words), return_tensors="tf") - >>> input_ids = encoding["input_ids"] - >>> attention_mask = encoding["attention_mask"] - >>> token_type_ids = encoding["token_type_ids"] - >>> bbox = tf.convert_to_tensor([token_boxes]) - - >>> outputs = model( - ... input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids - ... ) - - >>> last_hidden_states = outputs.last_hidden_state - ```""" - outputs = self.layoutlm( - input_ids=input_ids, - bbox=bbox, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - training=training, - ) - - return outputs - - -@add_start_docstrings("""LayoutLM Model with a `language modeling` head on top.""", LAYOUTLM_START_DOCSTRING) -class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, TFMaskedLanguageModelingLoss): - # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model - _keys_to_ignore_on_load_unexpected = [ - r"pooler", - r"cls.seq_relationship", - r"cls.predictions.decoder.weight", - r"nsp___cls", - ] - - def __init__(self, config: LayoutLMConfig, *inputs, **kwargs): - super().__init__(config, *inputs, **kwargs) - - if config.is_decoder: - logger.warning( - "If you want to use `TFLayoutLMForMaskedLM` make sure `config.is_decoder=False` for " - "bi-directional self-attention." - ) - - self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm") - self.mlm = TFLayoutLMMLMHead(config, input_embeddings=self.layoutlm.embeddings, name="mlm___cls") - - def get_lm_head(self) -> tf.keras.layers.Layer: - return self.mlm.predictions - - def get_prefix_bias_name(self) -> str: - warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) - return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name - - @unpack_inputs - @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) - @replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC) - def call( - self, - input_ids: TFModelInputType | None = None, - bbox: np.ndarray | tf.Tensor | None = None, - attention_mask: np.ndarray | tf.Tensor | None = None, - token_type_ids: np.ndarray | tf.Tensor | None = None, - position_ids: np.ndarray | tf.Tensor | None = None, - head_mask: np.ndarray | tf.Tensor | None = None, - inputs_embeds: np.ndarray | tf.Tensor | None = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - labels: np.ndarray | tf.Tensor | None = None, - training: Optional[bool] = False, - ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]: - r""" - labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., - config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the - loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` - - Returns: - - Examples: - - ```python - >>> from transformers import AutoTokenizer, TFLayoutLMForMaskedLM - >>> import tensorflow as tf - - >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased") - >>> model = TFLayoutLMForMaskedLM.from_pretrained("microsoft/layoutlm-base-uncased") - - >>> words = ["Hello", "[MASK]"] - >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782] - - >>> token_boxes = [] - >>> for word, box in zip(words, normalized_word_boxes): - ... word_tokens = tokenizer.tokenize(word) - ... token_boxes.extend([box] * len(word_tokens)) - >>> # add bounding boxes of cls + sep tokens - >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]] - - >>> encoding = tokenizer(" ".join(words), return_tensors="tf") - >>> input_ids = encoding["input_ids"] - >>> attention_mask = encoding["attention_mask"] - >>> token_type_ids = encoding["token_type_ids"] - >>> bbox = tf.convert_to_tensor([token_boxes]) - - >>> labels = tokenizer("Hello world", return_tensors="tf")["input_ids"] - - >>> outputs = model( - ... input_ids=input_ids, - ... bbox=bbox, - ... attention_mask=attention_mask, - ... token_type_ids=token_type_ids, - ... labels=labels, - ... ) - - >>> loss = outputs.loss - ```""" - outputs = self.layoutlm( - input_ids=input_ids, - bbox=bbox, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - training=training, - ) - sequence_output = outputs[0] - prediction_scores = self.mlm(sequence_output=sequence_output, training=training) - loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=prediction_scores) - - if not return_dict: - output = (prediction_scores,) + outputs[2:] - return ((loss,) + output) if loss is not None else output - - return TFMaskedLMOutput( - loss=loss, - logits=prediction_scores, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - -@add_start_docstrings( - """ - LayoutLM Model transformer with a sequence classification/regression head on top (a linear layer on top of the - pooled output) e.g. for GLUE tasks. - """, - LAYOUTLM_START_DOCSTRING, -) -class TFLayoutLMForSequenceClassification(TFLayoutLMPreTrainedModel, TFSequenceClassificationLoss): - # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model - _keys_to_ignore_on_load_unexpected = [r"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_relationship"] - _keys_to_ignore_on_load_missing = [r"dropout"] - - def __init__(self, config: LayoutLMConfig, *inputs, **kwargs): - super().__init__(config, *inputs, **kwargs) - - self.num_labels = config.num_labels - - self.layoutlm = TFLayoutLMMainLayer(config, name="layoutlm") - self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) - self.classifier = tf.keras.layers.Dense( - units=config.num_labels, - kernel_initializer=get_initializer(config.initializer_range), - name="classifier", - ) - - @unpack_inputs - @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) - @replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) - def call( - self, - input_ids: TFModelInputType | None = None, - bbox: np.ndarray | tf.Tensor | None = None, - attention_mask: np.ndarray | tf.Tensor | None = None, - token_type_ids: np.ndarray | tf.Tensor | None = None, - position_ids: np.ndarray | tf.Tensor | None = None, - head_mask: np.ndarray | tf.Tensor | None = None, - inputs_embeds: np.ndarray | tf.Tensor | None = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - labels: np.ndarray | tf.Tensor | None = None, - training: Optional[bool] = False, - ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: - r""" - labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*): - Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., - config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If - `config.num_labels > 1` a classification loss is computed (Cross-Entropy). - - Returns: - - Examples: - - ```python - >>> from transformers import AutoTokenizer, TFLayoutLMForSequenceClassification - >>> import tensorflow as tf - - >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased") - >>> model = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased") - - >>> words = ["Hello", "world"] - >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782] - - >>> token_boxes = [] - >>> for word, box in zip(words, normalized_word_boxes): - ... word_tokens = tokenizer.tokenize(word) - ... token_boxes.extend([box] * len(word_tokens)) - >>> # add bounding boxes of cls + sep tokens - >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]] - - >>> encoding = tokenizer(" ".join(words), return_tensors="tf") - >>> input_ids = encoding["input_ids"] - >>> attention_mask = encoding["attention_mask"] - >>> token_type_ids = encoding["token_type_ids"] - >>> bbox = tf.convert_to_tensor([token_boxes]) - >>> sequence_label = tf.convert_to_tensor([1]) - - >>> outputs = model( - ... input_ids=input_ids, - ... bbox=bbox, - ... attention_mask=attention_mask, - ... token_type_ids=token_type_ids, - ... labels=sequence_label, - ... ) - - >>> loss = outputs.loss - >>> logits = outputs.logits - ```""" - outputs = self.layoutlm( - input_ids=input_ids, - bbox=bbox, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - training=training, - ) - pooled_output = outputs[1] - pooled_output = self.dropout(inputs=pooled_output, training=training) - logits = self.classifier(inputs=pooled_output) - loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) - - if not return_dict: - output = (logits,) + outputs[2:] - return ((loss,) + output) if loss is not None else output - - return TFSequenceClassifierOutput( - loss=loss, - logits=logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - -@add_start_docstrings( - """ - LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for - Named-Entity-Recognition (NER) tasks. - """, - LAYOUTLM_START_DOCSTRING, -) -class TFLayoutLMForTokenClassification(TFLayoutLMPreTrainedModel, TFTokenClassificationLoss): - # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model - _keys_to_ignore_on_load_unexpected = [ - r"pooler", - r"mlm___cls", - r"nsp___cls", - r"cls.predictions", - r"cls.seq_relationship", - ] - _keys_to_ignore_on_load_missing = [r"dropout"] - - def __init__(self, config: LayoutLMConfig, *inputs, **kwargs): - super().__init__(config, *inputs, **kwargs) - - self.num_labels = config.num_labels - - self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm") - self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) - self.classifier = tf.keras.layers.Dense( - units=config.num_labels, - kernel_initializer=get_initializer(config.initializer_range), - name="classifier", - ) - - @unpack_inputs - @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) - @replace_return_docstrings(output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC) - def call( - self, - input_ids: TFModelInputType | None = None, - bbox: np.ndarray | tf.Tensor | None = None, - attention_mask: np.ndarray | tf.Tensor | None = None, - token_type_ids: np.ndarray | tf.Tensor | None = None, - position_ids: np.ndarray | tf.Tensor | None = None, - head_mask: np.ndarray | tf.Tensor | None = None, - inputs_embeds: np.ndarray | tf.Tensor | None = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - labels: np.ndarray | tf.Tensor | None = None, - training: Optional[bool] = False, - ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]: - r""" - labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. - - Returns: - - Examples: - - ```python - >>> import tensorflow as tf - >>> from transformers import AutoTokenizer, TFLayoutLMForTokenClassification - - >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased") - >>> model = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased") - - >>> words = ["Hello", "world"] - >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782] - - >>> token_boxes = [] - >>> for word, box in zip(words, normalized_word_boxes): - ... word_tokens = tokenizer.tokenize(word) - ... token_boxes.extend([box] * len(word_tokens)) - >>> # add bounding boxes of cls + sep tokens - >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]] - - >>> encoding = tokenizer(" ".join(words), return_tensors="tf") - >>> input_ids = encoding["input_ids"] - >>> attention_mask = encoding["attention_mask"] - >>> token_type_ids = encoding["token_type_ids"] - >>> bbox = tf.convert_to_tensor([token_boxes]) - >>> token_labels = tf.convert_to_tensor([1, 1, 0, 0]) - - >>> outputs = model( - ... input_ids=input_ids, - ... bbox=bbox, - ... attention_mask=attention_mask, - ... token_type_ids=token_type_ids, - ... labels=token_labels, - ... ) - - >>> loss = outputs.loss - >>> logits = outputs.logits - ```""" - outputs = self.layoutlm( - input_ids=input_ids, - bbox=bbox, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - training=training, - ) - sequence_output = outputs[0] - sequence_output = self.dropout(inputs=sequence_output, training=training) - logits = self.classifier(inputs=sequence_output) - loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) - - if not return_dict: - output = (logits,) + outputs[2:] - return ((loss,) + output) if loss is not None else output - - return TFTokenClassifierOutput( - loss=loss, - logits=logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - -@add_start_docstrings( - """ - LayoutLM Model with a span classification head on top for extractive question-answering tasks such as - [DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the final hidden-states output to compute `span - start logits` and `span end logits`). - """, - LAYOUTLM_START_DOCSTRING, -) -class TFLayoutLMForQuestionAnswering(TFLayoutLMPreTrainedModel, TFQuestionAnsweringLoss): - # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model - _keys_to_ignore_on_load_unexpected = [ - r"pooler", - r"mlm___cls", - r"nsp___cls", - r"cls.predictions", - r"cls.seq_relationship", - ] - - def __init__(self, config: LayoutLMConfig, *inputs, **kwargs): - super().__init__(config, *inputs, **kwargs) - self.num_labels = config.num_labels - - self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm") - self.qa_outputs = tf.keras.layers.Dense( - units=config.num_labels, - kernel_initializer=get_initializer(config.initializer_range), - name="qa_outputs", - ) - - @unpack_inputs - @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) - @replace_return_docstrings(output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC) - def call( - self, - input_ids: TFModelInputType | None = None, - bbox: np.ndarray | tf.Tensor | None = None, - attention_mask: np.ndarray | tf.Tensor | None = None, - token_type_ids: np.ndarray | tf.Tensor | None = None, - position_ids: np.ndarray | tf.Tensor | None = None, - head_mask: np.ndarray | tf.Tensor | None = None, - inputs_embeds: np.ndarray | tf.Tensor | None = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - start_positions: np.ndarray | tf.Tensor | None = None, - end_positions: np.ndarray | tf.Tensor | None = None, - training: Optional[bool] = False, - ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]: - r""" - start_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the start of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - end_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*): - Labels for position (index) of the end of the labelled span for computing the token classification loss. - Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence - are not taken into account for computing the loss. - - Returns: - - Examples: - - ```python - >>> import tensorflow as tf - >>> from transformers import AutoTokenizer, TFLayoutLMForQuestionAnswering - >>> from datasets import load_dataset - - >>> tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True) - >>> model = TFLayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="1e3ebac") - - >>> dataset = load_dataset("nielsr/funsd", split="train") - >>> example = dataset[0] - >>> question = "what's his name?" - >>> words = example["words"] - >>> boxes = example["bboxes"] - - >>> encoding = tokenizer( - ... question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="tf" - ... ) - >>> bbox = [] - >>> for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)): - ... if s == 1: - ... bbox.append(boxes[w]) - ... elif i == tokenizer.sep_token_id: - ... bbox.append([1000] * 4) - ... else: - ... bbox.append([0] * 4) - >>> encoding["bbox"] = tf.convert_to_tensor([bbox]) - - >>> word_ids = encoding.word_ids(0) - >>> outputs = model(**encoding) - >>> loss = outputs.loss - >>> start_scores = outputs.start_logits - >>> end_scores = outputs.end_logits - >>> start, end = word_ids[tf.math.argmax(start_scores, -1)[0]], word_ids[tf.math.argmax(end_scores, -1)[0]] - >>> print(" ".join(words[start : end + 1])) - M. Hamann P. Harper, P. Martinez - ```""" - - outputs = self.layoutlm( - input_ids=input_ids, - bbox=bbox, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - training=training, - ) - - sequence_output = outputs[0] - - logits = self.qa_outputs(inputs=sequence_output) - start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1) - start_logits = tf.squeeze(input=start_logits, axis=-1) - end_logits = tf.squeeze(input=end_logits, axis=-1) - loss = None - - if start_positions is not None and end_positions is not None: - labels = {"start_position": start_positions} - labels["end_position"] = end_positions - loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits)) - - if not return_dict: - output = (start_logits, end_logits) + outputs[2:] - return ((loss,) + output) if loss is not None else output - - return TFQuestionAnsweringModelOutput( - loss=loss, - start_logits=start_logits, - end_logits=end_logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) diff --git a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vencoder/__init__.py b/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/vencoder/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/ynhe/AskAnything/models/grit_src/grit/modeling/roi_heads/grit_roi_heads.py b/spaces/ynhe/AskAnything/models/grit_src/grit/modeling/roi_heads/grit_roi_heads.py deleted file mode 100644 index 648214d7d3218b96e5b410350de8e52759550526..0000000000000000000000000000000000000000 --- a/spaces/ynhe/AskAnything/models/grit_src/grit/modeling/roi_heads/grit_roi_heads.py +++ /dev/null @@ -1,478 +0,0 @@ -import math -import torch -from typing import Dict, List, Optional, Tuple, Union - -from detectron2.config import configurable -from detectron2.structures import Boxes, Instances, pairwise_iou -from detectron2.utils.events import get_event_storage - -from detectron2.modeling.box_regression import Box2BoxTransform -from detectron2.modeling.roi_heads.roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads -from detectron2.modeling.roi_heads.cascade_rcnn import CascadeROIHeads, _ScaleGradient -from detectron2.modeling.poolers import ROIPooler -from detectron2.layers import batched_nms -from .grit_fast_rcnn import GRiTFastRCNNOutputLayers - -from ..text.text_decoder import TransformerDecoderTextualHead, GRiTTextDecoder, AutoRegressiveBeamSearch -from ..text.load_text_token import LoadTextTokens -from transformers import BertTokenizer -from models.grit_src.grit.data.custom_dataset_mapper import ObjDescription -from ..soft_nms import batched_soft_nms - -import logging -logger = logging.getLogger(__name__) - - -@ROI_HEADS_REGISTRY.register() -class GRiTROIHeadsAndTextDecoder(CascadeROIHeads): - @configurable - def __init__( - self, - *, - text_decoder_transformer, - train_task: list, - test_task: str, - mult_proposal_score: bool = False, - mask_weight: float = 1.0, - object_feat_pooler=None, - soft_nms_enabled=False, - beam_size=1, - **kwargs, - ): - super().__init__(**kwargs) - self.mult_proposal_score = mult_proposal_score - self.mask_weight = mask_weight - self.object_feat_pooler = object_feat_pooler - self.soft_nms_enabled = soft_nms_enabled - self.test_task = test_task - self.beam_size = beam_size - - tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True) - self.tokenizer = tokenizer - - assert test_task in train_task, 'GRiT has not been trained on {} task, ' \ - 'please verify the task name or train a new ' \ - 'GRiT on {} task'.format(test_task, test_task) - task_begin_tokens = {} - for i, task in enumerate(train_task): - if i == 0: - task_begin_tokens[task] = tokenizer.cls_token_id - else: - task_begin_tokens[task] = 103 + i - self.task_begin_tokens = task_begin_tokens - - beamsearch_decode = AutoRegressiveBeamSearch( - end_token_id=tokenizer.sep_token_id, - max_steps=40, - beam_size=beam_size, - objectdet=test_task == "ObjectDet", - per_node_beam_size=1, - ) - self.text_decoder = GRiTTextDecoder( - text_decoder_transformer, - beamsearch_decode=beamsearch_decode, - begin_token_id=task_begin_tokens[test_task], - loss_type='smooth', - tokenizer=tokenizer, - ) - self.get_target_text_tokens = LoadTextTokens(tokenizer, max_text_len=40, padding='do_not_pad') - - @classmethod - def from_config(cls, cfg, input_shape): - ret = super().from_config(cfg, input_shape) - text_decoder_transformer = TransformerDecoderTextualHead( - object_feature_size=cfg.MODEL.FPN.OUT_CHANNELS, - vocab_size=cfg.TEXT_DECODER.VOCAB_SIZE, - hidden_size=cfg.TEXT_DECODER.HIDDEN_SIZE, - num_layers=cfg.TEXT_DECODER.NUM_LAYERS, - attention_heads=cfg.TEXT_DECODER.ATTENTION_HEADS, - feedforward_size=cfg.TEXT_DECODER.FEEDFORWARD_SIZE, - mask_future_positions=True, - padding_idx=0, - decoder_type='bert_en', - use_act_checkpoint=cfg.USE_ACT_CHECKPOINT, - ) - ret.update({ - 'text_decoder_transformer': text_decoder_transformer, - 'train_task': cfg.MODEL.TRAIN_TASK, - 'test_task': cfg.MODEL.TEST_TASK, - 'mult_proposal_score': cfg.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE, - 'mask_weight': cfg.MODEL.ROI_HEADS.MASK_WEIGHT, - 'soft_nms_enabled': cfg.MODEL.ROI_HEADS.SOFT_NMS_ENABLED, - 'beam_size': cfg.MODEL.BEAM_SIZE, - }) - return ret - - @classmethod - def _init_box_head(self, cfg, input_shape): - ret = super()._init_box_head(cfg, input_shape) - del ret['box_predictors'] - cascade_bbox_reg_weights = cfg.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS - box_predictors = [] - for box_head, bbox_reg_weights in zip(ret['box_heads'], \ - cascade_bbox_reg_weights): - box_predictors.append( - GRiTFastRCNNOutputLayers( - cfg, box_head.output_shape, - box2box_transform=Box2BoxTransform(weights=bbox_reg_weights) - )) - ret['box_predictors'] = box_predictors - - in_features = cfg.MODEL.ROI_HEADS.IN_FEATURES - pooler_scales = tuple(1.0 / input_shape[k].stride for k in in_features) - sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO - pooler_type = cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE - object_feat_pooler = ROIPooler( - output_size=cfg.MODEL.ROI_HEADS.OBJECT_FEAT_POOLER_RES, - scales=pooler_scales, - sampling_ratio=sampling_ratio, - pooler_type=pooler_type, - ) - ret['object_feat_pooler'] = object_feat_pooler - return ret - - def check_if_all_background(self, proposals, targets, stage): - all_background = True - for proposals_per_image in proposals: - if not (proposals_per_image.gt_classes == self.num_classes).all(): - all_background = False - - if all_background: - logger.info('all proposals are background at stage {}'.format(stage)) - proposals[0].proposal_boxes.tensor[0, :] = targets[0].gt_boxes.tensor[0, :] - proposals[0].gt_boxes.tensor[0, :] = targets[0].gt_boxes.tensor[0, :] - proposals[0].objectness_logits[0] = math.log((1.0 - 1e-10) / (1 - (1.0 - 1e-10))) - proposals[0].gt_classes[0] = targets[0].gt_classes[0] - proposals[0].gt_object_descriptions.data[0] = targets[0].gt_object_descriptions.data[0] - if 'foreground' in proposals[0].get_fields().keys(): - proposals[0].foreground[0] = 1 - return proposals - - def _forward_box(self, features, proposals, targets=None, task="ObjectDet"): - if self.training: - proposals = self.check_if_all_background(proposals, targets, 0) - if (not self.training) and self.mult_proposal_score: - if len(proposals) > 0 and proposals[0].has('scores'): - proposal_scores = [p.get('scores') for p in proposals] - else: - proposal_scores = [p.get('objectness_logits') for p in proposals] - - features = [features[f] for f in self.box_in_features] - head_outputs = [] - prev_pred_boxes = None - image_sizes = [x.image_size for x in proposals] - - for k in range(self.num_cascade_stages): - if k > 0: - proposals = self._create_proposals_from_boxes( - prev_pred_boxes, image_sizes, - logits=[p.objectness_logits for p in proposals]) - if self.training: - proposals = self._match_and_label_boxes_GRiT( - proposals, k, targets) - proposals = self.check_if_all_background(proposals, targets, k) - predictions = self._run_stage(features, proposals, k) - prev_pred_boxes = self.box_predictor[k].predict_boxes( - (predictions[0], predictions[1]), proposals) - head_outputs.append((self.box_predictor[k], predictions, proposals)) - - if self.training: - object_features = self.object_feat_pooler(features, [x.proposal_boxes for x in proposals]) - object_features = _ScaleGradient.apply(object_features, 1.0 / self.num_cascade_stages) - foreground = torch.cat([x.foreground for x in proposals]) - object_features = object_features[foreground > 0] - - object_descriptions = [] - for x in proposals: - object_descriptions += x.gt_object_descriptions[x.foreground > 0].data - object_descriptions = ObjDescription(object_descriptions) - object_descriptions = object_descriptions.data - - if len(object_descriptions) > 0: - begin_token = self.task_begin_tokens[task] - text_decoder_inputs = self.get_target_text_tokens(object_descriptions, object_features, begin_token) - object_features = object_features.view( - object_features.shape[0], object_features.shape[1], -1).permute(0, 2, 1).contiguous() - text_decoder_inputs.update({'object_features': object_features}) - text_decoder_loss = self.text_decoder(text_decoder_inputs) - else: - text_decoder_loss = head_outputs[0][1][0].new_zeros([1])[0] - - losses = {} - storage = get_event_storage() - # RoI Head losses (For the proposal generator loss, please find it in grit.py) - for stage, (predictor, predictions, proposals) in enumerate(head_outputs): - with storage.name_scope("stage{}".format(stage)): - stage_losses = predictor.losses( - (predictions[0], predictions[1]), proposals) - losses.update({k + "_stage{}".format(stage): v for k, v in stage_losses.items()}) - # Text Decoder loss - losses.update({'text_decoder_loss': text_decoder_loss}) - return losses - else: - scores_per_stage = [h[0].predict_probs(h[1], h[2]) for h in head_outputs] - logits_per_stage = [(h[1][0],) for h in head_outputs] - scores = [ - sum(list(scores_per_image)) * (1.0 / self.num_cascade_stages) - for scores_per_image in zip(*scores_per_stage) - ] - logits = [ - sum(list(logits_per_image)) * (1.0 / self.num_cascade_stages) - for logits_per_image in zip(*logits_per_stage) - ] - if self.mult_proposal_score: - scores = [(s * ps[:, None]) ** 0.5 for s, ps in zip(scores, proposal_scores)] - predictor, predictions, proposals = head_outputs[-1] - boxes = predictor.predict_boxes( - (predictions[0], predictions[1]), proposals) - assert len(boxes) == 1 - pred_instances, _ = self.fast_rcnn_inference_GRiT( - boxes, - scores, - logits, - image_sizes, - predictor.test_score_thresh, - predictor.test_nms_thresh, - predictor.test_topk_per_image, - self.soft_nms_enabled, - ) - - assert len(pred_instances) == 1, "Only support one image" - for i, pred_instance in enumerate(pred_instances): - if len(pred_instance.pred_boxes) > 0: - object_features = self.object_feat_pooler(features, [pred_instance.pred_boxes]) - object_features = object_features.view( - object_features.shape[0], object_features.shape[1], -1).permute(0, 2, 1).contiguous() - text_decoder_output = self.text_decoder({'object_features': object_features}) - if self.beam_size > 1 and self.test_task == "ObjectDet": - pred_boxes = [] - pred_scores = [] - pred_classes = [] - pred_object_descriptions = [] - - for beam_id in range(self.beam_size): - pred_boxes.append(pred_instance.pred_boxes.tensor) - # object score = sqrt(objectness score x description score) - pred_scores.append((pred_instance.scores * - torch.exp(text_decoder_output['logprobs'])[:, beam_id]) ** 0.5) - pred_classes.append(pred_instance.pred_classes) - for prediction in text_decoder_output['predictions'][:, beam_id, :]: - # convert text tokens to words - description = self.tokenizer.decode(prediction.tolist()[1:], skip_special_tokens=True) - pred_object_descriptions.append(description) - - merged_instances = Instances(image_sizes[0]) - if torch.cat(pred_scores, dim=0).shape[0] <= predictor.test_topk_per_image: - merged_instances.scores = torch.cat(pred_scores, dim=0) - merged_instances.pred_boxes = Boxes(torch.cat(pred_boxes, dim=0)) - merged_instances.pred_classes = torch.cat(pred_classes, dim=0) - merged_instances.pred_object_descriptions = ObjDescription(pred_object_descriptions) - else: - pred_scores, top_idx = torch.topk( - torch.cat(pred_scores, dim=0), predictor.test_topk_per_image) - merged_instances.scores = pred_scores - merged_instances.pred_boxes = Boxes(torch.cat(pred_boxes, dim=0)[top_idx, :]) - merged_instances.pred_classes = torch.cat(pred_classes, dim=0)[top_idx] - merged_instances.pred_object_descriptions = \ - ObjDescription(ObjDescription(pred_object_descriptions)[top_idx].data) - - pred_instances[i] = merged_instances - else: - # object score = sqrt(objectness score x description score) - pred_instance.scores = (pred_instance.scores * - torch.exp(text_decoder_output['logprobs'])) ** 0.5 - - pred_object_descriptions = [] - for prediction in text_decoder_output['predictions']: - # convert text tokens to words - description = self.tokenizer.decode(prediction.tolist()[1:], skip_special_tokens=True) - pred_object_descriptions.append(description) - pred_instance.pred_object_descriptions = ObjDescription(pred_object_descriptions) - else: - pred_instance.pred_object_descriptions = ObjDescription([]) - - return pred_instances - - - def forward(self, features, proposals, targets=None, targets_task="ObjectDet"): - if self.training: - proposals = self.label_and_sample_proposals( - proposals, targets) - - losses = self._forward_box(features, proposals, targets, task=targets_task) - if targets[0].has('gt_masks'): - mask_losses = self._forward_mask(features, proposals) - losses.update({k: v * self.mask_weight \ - for k, v in mask_losses.items()}) - else: - losses.update(self._get_empty_mask_loss(device=proposals[0].objectness_logits.device)) - return proposals, losses - else: - pred_instances = self._forward_box(features, proposals, task=self.test_task) - pred_instances = self.forward_with_given_boxes(features, pred_instances) - return pred_instances, {} - - @torch.no_grad() - def _match_and_label_boxes_GRiT(self, proposals, stage, targets): - """ - Add "gt_object_description" and "foreground" to detectron2's _match_and_label_boxes - """ - num_fg_samples, num_bg_samples = [], [] - for proposals_per_image, targets_per_image in zip(proposals, targets): - match_quality_matrix = pairwise_iou( - targets_per_image.gt_boxes, proposals_per_image.proposal_boxes - ) - # proposal_labels are 0 or 1 - matched_idxs, proposal_labels = self.proposal_matchers[stage](match_quality_matrix) - if len(targets_per_image) > 0: - gt_classes = targets_per_image.gt_classes[matched_idxs] - # Label unmatched proposals (0 label from matcher) as background (label=num_classes) - gt_classes[proposal_labels == 0] = self.num_classes - foreground = torch.ones_like(gt_classes) - foreground[proposal_labels == 0] = 0 - gt_boxes = targets_per_image.gt_boxes[matched_idxs] - gt_object_descriptions = targets_per_image.gt_object_descriptions[matched_idxs] - else: - gt_classes = torch.zeros_like(matched_idxs) + self.num_classes - foreground = torch.zeros_like(gt_classes) - gt_boxes = Boxes( - targets_per_image.gt_boxes.tensor.new_zeros((len(proposals_per_image), 4)) - ) - gt_object_descriptions = ObjDescription(['None' for i in range(len(proposals_per_image))]) - proposals_per_image.gt_classes = gt_classes - proposals_per_image.gt_boxes = gt_boxes - proposals_per_image.gt_object_descriptions = gt_object_descriptions - proposals_per_image.foreground = foreground - - num_fg_samples.append((proposal_labels == 1).sum().item()) - num_bg_samples.append(proposal_labels.numel() - num_fg_samples[-1]) - - # Log the number of fg/bg samples in each stage - storage = get_event_storage() - storage.put_scalar( - "stage{}/roi_head/num_fg_samples".format(stage), - sum(num_fg_samples) / len(num_fg_samples), - ) - storage.put_scalar( - "stage{}/roi_head/num_bg_samples".format(stage), - sum(num_bg_samples) / len(num_bg_samples), - ) - return proposals - - def fast_rcnn_inference_GRiT( - self, - boxes: List[torch.Tensor], - scores: List[torch.Tensor], - logits: List[torch.Tensor], - image_shapes: List[Tuple[int, int]], - score_thresh: float, - nms_thresh: float, - topk_per_image: int, - soft_nms_enabled: bool, - ): - result_per_image = [ - self.fast_rcnn_inference_single_image_GRiT( - boxes_per_image, scores_per_image, logits_per_image, image_shape, - score_thresh, nms_thresh, topk_per_image, soft_nms_enabled - ) - for scores_per_image, boxes_per_image, image_shape, logits_per_image \ - in zip(scores, boxes, image_shapes, logits) - ] - return [x[0] for x in result_per_image], [x[1] for x in result_per_image] - - def fast_rcnn_inference_single_image_GRiT( - self, - boxes, - scores, - logits, - image_shape: Tuple[int, int], - score_thresh: float, - nms_thresh: float, - topk_per_image: int, - soft_nms_enabled, - ): - """ - Add soft NMS to detectron2's fast_rcnn_inference_single_image - """ - valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1) - if not valid_mask.all(): - boxes = boxes[valid_mask] - scores = scores[valid_mask] - logits = logits[valid_mask] - - scores = scores[:, :-1] - logits = logits[:, :-1] - num_bbox_reg_classes = boxes.shape[1] // 4 - # Convert to Boxes to use the `clip` function ... - boxes = Boxes(boxes.reshape(-1, 4)) - boxes.clip(image_shape) - boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 4) # R x C x 4 - - # 1. Filter results based on detection scores. It can make NMS more efficient - # by filtering out low-confidence detections. - filter_mask = scores > score_thresh # R x K - # R' x 2. First column contains indices of the R predictions; - # Second column contains indices of classes. - filter_inds = filter_mask.nonzero() - if num_bbox_reg_classes == 1: - boxes = boxes[filter_inds[:, 0], 0] - else: - boxes = boxes[filter_mask] - scores = scores[filter_mask] - logits = logits[filter_mask] - - # 2. Apply NMS for each class independently. - if not soft_nms_enabled: - keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh) - else: - keep, soft_nms_scores = batched_soft_nms( - boxes, - scores, - filter_inds[:, 1], - "linear", - 0.5, - nms_thresh, - 0.001, - ) - scores[keep] = soft_nms_scores - if topk_per_image >= 0: - keep = keep[:topk_per_image] - boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep] - logits = logits[keep] - - result = Instances(image_shape) - result.pred_boxes = Boxes(boxes) - result.scores = scores - result.pred_classes = filter_inds[:, 1] - result.logits = logits - return result, filter_inds[:, 0] - - def _get_empty_mask_loss(self, device): - if self.mask_on: - return {'loss_mask': torch.zeros( - (1, ), device=device, dtype=torch.float32)[0]} - else: - return {} - - def _create_proposals_from_boxes(self, boxes, image_sizes, logits): - boxes = [Boxes(b.detach()) for b in boxes] - proposals = [] - for boxes_per_image, image_size, logit in zip( - boxes, image_sizes, logits): - boxes_per_image.clip(image_size) - if self.training: - inds = boxes_per_image.nonempty() - boxes_per_image = boxes_per_image[inds] - logit = logit[inds] - prop = Instances(image_size) - prop.proposal_boxes = boxes_per_image - prop.objectness_logits = logit - proposals.append(prop) - return proposals - - def _run_stage(self, features, proposals, stage): - pool_boxes = [x.proposal_boxes for x in proposals] - box_features = self.box_pooler(features, pool_boxes) - box_features = _ScaleGradient.apply(box_features, 1.0 / self.num_cascade_stages) - box_features = self.box_head[stage](box_features) - return self.box_predictor[stage](box_features) diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/utils/README.md b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/utils/README.md deleted file mode 100644 index 9765b24a730b77556104187ac3ef5439ab0859fd..0000000000000000000000000000000000000000 --- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/utils/README.md +++ /dev/null @@ -1,5 +0,0 @@ -# Utility functions - -This folder contain utility functions that are not used in the -core library, but are useful for building models or training -code using the config system. diff --git a/spaces/yuan1615/EmpathyTTS/models.py b/spaces/yuan1615/EmpathyTTS/models.py deleted file mode 100644 index 0737a7830944167833fd3ace4e74c1998218bdf2..0000000000000000000000000000000000000000 --- a/spaces/yuan1615/EmpathyTTS/models.py +++ /dev/null @@ -1,578 +0,0 @@ -import copy -import math - -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - -import commons -import modules -import attentions - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from commons import init_weights, get_padding - - -class StochasticDurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): - super().__init__() - filter_channels = in_channels # it needs to be removed from future version. - self.in_channels = in_channels # 192 - self.filter_channels = filter_channels - self.kernel_size = kernel_size # 3 - self.p_dropout = p_dropout # 0.5 - self.n_flows = n_flows # 4 - self.gin_channels = gin_channels - - self.log_flow = modules.Log() - self.flows = nn.ModuleList() - self.flows.append(modules.ElementwiseAffine(2)) - for i in range(n_flows): - self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.flows.append(modules.Flip()) - - self.post_pre = nn.Conv1d(1, filter_channels, 1) - self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - self.post_flows = nn.ModuleList() - self.post_flows.append(modules.ElementwiseAffine(2)) - for i in range(4): - self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.post_flows.append(modules.Flip()) - - self.pre = nn.Conv1d(in_channels, filter_channels, 1) - self.proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, filter_channels, 1) - - def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): - x = torch.detach(x) - # 截断梯度，这里不会影响 Text Encoder 模块的参数更新 - x = self.pre(x) # [8, 192, 101] [b, h, t] - if g is not None: - # 针对多说话人，这里也会不同，因为不同人说话的节奏韵律是不一样的 - g = torch.detach(g) - x = x + self.cond(g) - x = self.convs(x, x_mask) # [8, 192, 101] - x = self.proj(x) * x_mask # - # x 是在处理 Text Encoder 的输出 - - if not reverse: - flows = self.flows - assert w is not None - # w 是在处理 duration - logdet_tot_q = 0 - h_w = self.post_pre(w) # [8, 192, 123] - h_w = self.post_convs(h_w, x_mask) # - h_w = self.post_proj(h_w) * x_mask # - e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask # [8, 2, 123] - # 正态分布 - z_q = e_q # [8, 2, 123] - for flow in self.post_flows: - z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) - logdet_tot_q += logdet_q - # print(z_q.shape) [8, 2, 123] - # print(logdet_tot_q) [8] - z_u, z1 = torch.split(z_q, [1, 1], 1) # u 和 v - u = torch.sigmoid(z_u) * x_mask - z0 = (w - u) * x_mask - logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2]) - logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q - - logdet_tot = 0 - z0, logdet = self.log_flow(z0, x_mask) - logdet_tot += logdet - z = torch.cat([z0, z1], 1) - for flow in flows: - z, logdet = flow(z, x_mask, g=x, reverse=reverse) - logdet_tot = logdet_tot + logdet - nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot - return nll + logq # [b] - else: - flows = list(reversed(self.flows)) - flows = flows[:-2] + [flows[-1]] # remove a useless vflow - z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale - for flow in flows: - z = flow(z, x_mask, g=x, reverse=reverse) - z0, z1 = torch.split(z, [1, 1], 1) - logw = z0 - return logw - - -class DurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): - super().__init__() - - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - - self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_1 = modules.LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_2 = modules.LayerNorm(filter_channels) - self.proj = nn.Conv1d(filter_channels, 1, 1) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, in_channels, 1) - - def forward(self, x, x_mask, g=None): - x = torch.detach(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.norm_1(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - x = torch.relu(x) - x = self.norm_2(x) - x = self.drop(x) - x = self.proj(x * x_mask) - return x * x_mask - - -class TextEncoder(nn.Module): - def __init__(self, - n_vocab, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout): - super().__init__() - self.n_vocab = n_vocab - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - - self.emb = nn.Embedding(n_vocab, hidden_channels) - nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) - - self.prosody_emb = nn.Embedding(5, hidden_channels) - nn.init.normal_(self.prosody_emb.weight, 0.0, hidden_channels**-0.5) - - self.encoder = attentions.Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - # num_param = sum(param.numel() for param in self.parameters()) # 6353664 - # print(num_param) - - def forward(self, x, x_lengths, prosody): - # print(x.shape) # [8, 101], - # print(x_lengths.shape) # [8] - # print(prosody.shape) # [8, 101], - - x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] - prosody = self.prosody_emb(prosody) * math.sqrt(self.hidden_channels) - x = x + prosody - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - # print(x_mask.shape) # [8, 1, 101] - # raise OSError('end') - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) # 获得 PRIOR 的 均值与方差 - return x, m, logs, x_mask - - -class ResidualCouplingBlock(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0): - super().__init__() - self.channels = channels # 192 - self.hidden_channels = hidden_channels # 192 - self.kernel_size = kernel_size # 5 - self.dilation_rate = dilation_rate # 1 - self.n_layers = n_layers # 4 - self.n_flows = n_flows # 4 - self.gin_channels = gin_channels # 0 - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) - self.flows.append(modules.Flip()) - # num_param = sum(param.numel() for param in self.parameters()) # 7,102,080 - # print(num_param) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - -class PosteriorEncoder(nn.Module): - def __init__(self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0): - super().__init__() - self.in_channels = in_channels # 513 - self.out_channels = out_channels # 192 - self.hidden_channels = hidden_channels # 192 - self.kernel_size = kernel_size # 5 - self.dilation_rate = dilation_rate # 1 - self.n_layers = n_layers # 16 - self.gin_channels = gin_channels # 0 - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - # num_param = sum(param.numel() for param in self.parameters()) # 参数量 7,238,016 - # print(num_param) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - x = self.pre(x) * x_mask - # print(x.shape) # [8, 192, 654] - x = self.enc(x, x_mask, g=g) # - # print(x.shape) # [8, 192, 654] 参数维度没有任何变化 - stats = self.proj(x) * x_mask # - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - -class Generator(torch.nn.Module): - def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) - resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append(weight_norm( - ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), - k, u, padding=(k-u)//2))) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel//(2**(i+1)) - for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i*self.num_kernels+j](x) - else: - xs += self.resblocks[i*self.num_kernels+j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), - ]) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ]) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2,3,5,7,11] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - - -class SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - def __init__(self, - n_vocab, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - n_speakers=0, - gin_channels=0, - use_sdp=True, - **kwargs): - - super().__init__() - self.n_vocab = n_vocab # 313 - self.spec_channels = spec_channels # 513 - self.inter_channels = inter_channels # 192 - self.hidden_channels = hidden_channels # 192 - self.filter_channels = filter_channels # 768 - self.n_heads = n_heads # 2 - self.n_layers = n_layers # 6 - self.kernel_size = kernel_size # 3 - self.p_dropout = p_dropout # 0.1 - self.resblock = resblock # '1' - self.resblock_kernel_sizes = resblock_kernel_sizes # [3,7,11], - self.resblock_dilation_sizes = resblock_dilation_sizes # [[1,3,5], [1,3,5], [1,3,5]], - self.upsample_rates = upsample_rates # [8,8,2,2], - self.upsample_initial_channel = upsample_initial_channel # 512 - self.upsample_kernel_sizes = upsample_kernel_sizes # [16,16,4,4], - self.segment_size = segment_size # 8192 - self.n_speakers = n_speakers # 0 - self.gin_channels = gin_channels # 0 - - self.use_sdp = use_sdp # True - - self.enc_p = TextEncoder(n_vocab, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - for p in self.parameters(): - p.requires_grad = False - - self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) - self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) - self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) - - if use_sdp: - # 是否使用 Stochastic Duration Predictor - self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) - else: - self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels) - - if n_speakers > 1: - self.emb_g = nn.Embedding(n_speakers, gin_channels) - - def forward(self, x, x_lengths, prosody, y, y_lengths, sid=None): - - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, prosody) - if self.n_speakers > 0: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = None - - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - - with torch.no_grad(): - # negative cross-entropy - s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t] - neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s] - neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s] - neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 - - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach() - # print(neg_cent.shape) - # np.save('/home/admin/yuanxin/Bil-vits/attn.npy', neg_cent.cpu().numpy()) - # print(attn.shape) - # np.save('/home/admin/yuanxin/Bil-vits/test.npy', attn.cpu().numpy()) - - w = attn.sum(2) # [8, 1, 101] 这里代表了每个音素对应的 duration，是根据 Monotonic Alignment Search 得到的 - if self.use_sdp: - l_length = self.dp(x, x_mask, w, g=g) # 这里返回的是一个损失 - # print(l_length) - # print(l_length.shape) - # raise OSError('end') - l_length = l_length / torch.sum(x_mask) - else: - logw_ = torch.log(w + 1e-6) * x_mask - logw = self.dp(x, x_mask, g=g) - l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging - - # expand prior - # print(m_p.shape) - # print(logs_p.shape) - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) - # print(m_p.shape) - # print(logs_p.shape) - # raise OSError('end') - # 根据 attn 将 text 映射到 谱的长度，Tacotron2中这个是 loaction 的对齐 - z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size) - # 这里不是整个音频输入的，8192，约0.3s进行对抗训练 - - o = self.dec(z_slice, g=g) - return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, x, x_lengths, prosody, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None): - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, prosody) - if self.n_speakers > 0: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = None - - if self.use_sdp: - logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) - else: - logw = self.dp(x, x_mask, g=g) - w = torch.exp(logw) * x_mask * length_scale - w_ceil = torch.ceil(w) - y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() - y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = commons.generate_path(w_ceil, attn_mask) - - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - - z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale - z = self.flow(z_p, y_mask, g=g, reverse=True) - o = self.dec((z * y_mask)[:,:,:max_len], g=g) - return o, attn, y_mask, (z, z_p, m_p, logs_p) - - def voice_conversion(self, y, y_lengths, sid_src, sid_tgt): - assert self.n_speakers > 0, "n_speakers have to be larger than 0." - g_src = self.emb_g(sid_src).unsqueeze(-1) - g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) - z_p = self.flow(z, y_mask, g=g_src) - z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) - o_hat = self.dec(z_hat * y_mask, g=g_tgt) - return o_hat, y_mask, (z, z_p, z_hat) - diff --git a/spaces/zama-fhe/encrypted_health_prediction/server.py b/spaces/zama-fhe/encrypted_health_prediction/server.py deleted file mode 100644 index 1dc9aa6258050a1ad9603a831750477c66bb0c7b..0000000000000000000000000000000000000000 --- a/spaces/zama-fhe/encrypted_health_prediction/server.py +++ /dev/null @@ -1,100 +0,0 @@ -"""Server that will listen for GET and POST requests from the client.""" - -import time -from typing import List - -from fastapi import FastAPI, File, Form, UploadFile -from fastapi.responses import JSONResponse, Response -from utils import DEPLOYMENT_DIR, SERVER_DIR # pylint: disable=no-name-in-module - -from concrete.ml.deployment import FHEModelServer - -# Initialize an instance of FastAPI -app = FastAPI() - -# Define the default route -@app.get("/") -def root(): - """ - Root endpoint of the health prediction API. - - Returns: - dict: The welcome message. - """ - return {"message": "Welcome to your disease prediction with FHE!"} - - -@app.post("/send_input") -def send_input( - user_id: str = Form(), - files: List[UploadFile] = File(), -): - """Send the inputs to the server.""" - - print("\nSend the data to the server ............\n") - - # Receive the Client's files (Evaluation key + Encrypted symptoms) - evaluation_key_path = SERVER_DIR / f"{user_id}_valuation_key" - encrypted_input_path = SERVER_DIR / f"{user_id}_encrypted_input" - - # Save the files using the above paths - with encrypted_input_path.open("wb") as encrypted_input, evaluation_key_path.open( - "wb" - ) as evaluation_key: - encrypted_input.write(files[0].file.read()) - evaluation_key.write(files[1].file.read()) - - -@app.post("/run_fhe") -def run_fhe( - user_id: str = Form(), -): - """Inference in FHE.""" - - print("\nRun in FHE in the server ............\n") - evaluation_key_path = SERVER_DIR / f"{user_id}_valuation_key" - encrypted_input_path = SERVER_DIR / f"{user_id}_encrypted_input" - - # Read the files (Evaluation key + Encrypted symptoms) using the above paths - with encrypted_input_path.open("rb") as encrypted_output_file, evaluation_key_path.open( - "rb" - ) as evaluation_key_file: - encrypted_output = encrypted_output_file.read() - evaluation_key = evaluation_key_file.read() - - # Load the FHE server and the model - fhe_server = FHEModelServer(DEPLOYMENT_DIR) - - # Run the FHE execution - start = time.time() - encrypted_output = fhe_server.run(encrypted_output, evaluation_key) - assert isinstance(encrypted_output, bytes) - fhe_execution_time = round(time.time() - start, 2) - - # Retrieve the encrypted output path - encrypted_output_path = SERVER_DIR / f"{user_id}_encrypted_output" - - # Write the file using the above path - with encrypted_output_path.open("wb") as f: - f.write(encrypted_output) - - return JSONResponse(content=fhe_execution_time) - - -@app.post("/get_output") -def get_output(user_id: str = Form()): - """Retrieve the encrypted output from the server.""" - - print("\nGet the output from the server ............\n") - - # Path where the encrypted output is saved - encrypted_output_path = SERVER_DIR / f"{user_id}_encrypted_output" - - # Read the file using the above path - with encrypted_output_path.open("rb") as f: - encrypted_output = f.read() - - time.sleep(1) - - # Send the encrypted output - return Response(encrypted_output) diff --git a/spaces/zamasam/death/greeting.md b/spaces/zamasam/death/greeting.md deleted file mode 100644 index 74d27a78bfcc4c3cc74a8be3989df6823bc0d864..0000000000000000000000000000000000000000 --- a/spaces/zamasam/death/greeting.md +++ /dev/null @@ -1 +0,0 @@ -coming soon... \ No newline at end of file
Arun Sharma is a renowned author, trainer and mentor for CAT aspirants. He has more than 20 years of experience in teaching and coaching students for various competitive exams. He is also an alumnus of IIM Bangalore and has worked with top companies like Tata Steel, Tata Motors and Wipro.
-Arun Sharma has written several books for CAT preparation, such as How to Prepare for Quantitative Aptitude for CAT, How to Prepare for Verbal Ability and Reading Comprehension for CAT, Study Package for CAT & Other MBA Entrance Exams, etc. He has also written books for other exams like GMAT, XAT, SNAP, IIFT, etc.
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You can download the PDF version of Data Interpretation and Logical Reasoning by Arun Sharma from the links provided in the web search results . You will need a PDF reader software like Adobe Acrobat Reader or Foxit Reader to open the file. You can also print or save the file on your device for future reference.
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\ No newline at end of file diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Fifa 2020 Crack Tips and Tricks to Improve Your Performance and Skills.md b/spaces/raedeXanto/academic-chatgpt-beta/Fifa 2020 Crack Tips and Tricks to Improve Your Performance and Skills.md deleted file mode 100644 index 46f46cd513ebb0fc561bf0d3ed6e3d9a93b439b6..0000000000000000000000000000000000000000 --- a/spaces/raedeXanto/academic-chatgpt-beta/Fifa 2020 Crack Tips and Tricks to Improve Your Performance and Skills.md +++ /dev/null @@ -1,145 +0,0 @@ -
-FIFA 2020 Crack: How to Download and Play the Latest Version of FIFA for Free
-Introduction
-If you are a fan of soccer games, you probably have heard of FIFA 2020, the latest installment of the popular FIFA series. FIFA 2020 is a simulation game that lets you play as your favorite teams and players in various modes and competitions. You can also create your own custom teams and players, and customize your gameplay experience with various options and settings.
-However, FIFA 2020 is not a free game. You need to buy it from an official store or platform, such as Origin, Steam, or PlayStation Store. The price of FIFA 2020 varies depending on your region and platform, but it is usually around $60. That's not a cheap price for many people, especially if you are on a tight budget or have other expenses to worry about.
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-So, what can you do if you want to play FIFA 2020 without paying for it? Well, there is a way to do that. It's called FIFA 2020 Crack. In this article, we will explain what FIFA 2020 Crack is, how to download it, and how to play it. We will also answer some frequently asked questions about FIFA 2020 Crack. Let's get started!
-What is FIFA 2020?
-FIFA 2020 is a soccer simulation game developed by EA Sports and published by Electronic Arts. It was released on September 27, 2019 for Microsoft Windows, PlayStation 4, Xbox One, and Nintendo Switch. It is the 27th edition of the FIFA series, and the first one to feature the UEFA Champions League and Europa League licenses.
-FIFA 2020 has many features and improvements over its predecessors, such as:
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FIFA 2020 is widely praised by critics and players alike for its gameplay quality, content variety, and presentation style. It has won several awards and nominations, such as Best Sports Game at The Game Awards 2019. It has also sold over 25 million copies worldwide as of May 2020.
-What is FIFA 2020 Crack?
-FIFA 2020 Crack is a modified version of FIFA 2020 that bypasses the game's security system and allows you to play it without buying it or activating it online. It is also known as FIFA 20 CPY Crack or FIFA 20 Codex Crack, after the names of the groups that created it.
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-Fifa 20 Update VersionFIFA 2020 Crack works by replacing or modifying some files in the game folder that are responsible for checking the game's license and authentication. By doing so, it tricks the game into thinking that it is a legitimate copy that has been purchased and activated online.
-FIFA 2020 Crack does not affect the game's performance or functionality in any way. You can still play all the modes and features of the game as if you had bought it from an official store or platform. You can also update the game with patches and fixes that are released by EA Sports.
-Why do people use FIFA 2020 Crack?
-There are many reasons why people use FIFA 2020 Crack instead of buying the game from an official store or platform. Some of them are:
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Whatever the reason may be, using FIFA 2020 Crack is illegal and risky. It violates the terms of service and end-user license agreement of EA Sports and Electronic Arts. It also exposes you to potential malware or viruses that may harm your device or data. It may also cause compatibility issues or errors with your game or device.
-How to Download FIFA 2020 Crack
-If you still want to use FIFA 2020 Crack despite the risks and consequences, you need to follow some steps to download and install it on your device. Here are the steps:
-Step 1: Find a reliable source for FIFA 2020 Crack
-The first step is to find a website or platform that offers FIFA 2020 Crack for download. There are many websites and platforms that claim to have FIFA 2020 Crack, but not all of them are trustworthy or safe. Some of them may contain fake or corrupted files, malware or viruses, or hidden fees or surveys.
-To avoid these problems, you need to do some research and check the reputation and reviews of the website or platform before you download anything from it. You can also use some tools or software that can scan and verify the files for any potential threats or issues.
-Some of the websites or platforms that are known to have FIFA 2020 Crack are:
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However, we do not endorse or recommend any of these websites or platforms. Use them at your own risk and discretion.
-Step 2: Download the FIFA 2020 Crack file
-The second step is to download the FIFA 2020 Crack file from the website or platform that you have chosen. The file size may vary depending on the source, but it is usually around 50 GB. You need to have enough space on your device and a stable internet connection to download the file.
-The file may be in a compressed or archived format, such as ZIP, RAR, or ISO. You need to have a software that can extract or mount these files, such as WinRAR, 7-Zip, or Daemon Tools.
-The file may also be split into several parts or segments. You need to download all the parts or segments and put them in the same folder before you extract or mount them.
-Step 3: Extract or mount the FIFA 2020 Crack file
-The third step is to extract or mount the FIFA 2020 Crack file that you have downloaded. If the file is in a compressed or archived format, such as ZIP or RAR, you need to right-click on it and choose Extract Here or Extract to Folder. This will create a new folder with the extracted files inside.
-If the file is in an image format, such as ISO, you need to right-click on it and choose Mount Image. This will create a virtual drive with the mounted files inside.
-Step 4: Copy and paste the FIFA 2020 Crack file into the game folder
-The fourth step is to copy and paste the FIFA 2020 Crack file into the game folder. The game folder is where you have installed FIFA 2020 on your device. You can find it by following this path:
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You need to copy and paste the FIFA 2020 Crack file into this folder and replace any existing files with the same name. You may need to grant administrator permission to do this.
-How to Play FIFA 2020 Crack
-After you have completed the steps above, you are ready to play FIFA 2020 Crack on your device. Here are the steps:
-Step 1: Run the FIFA 2020 Crack file as administrator
-The first step is to run the FIFA 2020 Crack file as administrator. You can do this by right-clicking on it and choosing Run as Administrator. This will launch the game with full access and privileges.
-Step 2: Choose your language and settings
-The second step is to choose your language and settings for the game. You can do this by following the instructions on the screen. You can also change your language and settings later by going to Options > Game Settings > Language.
-Step 3: Enjoy the game!
-The third step is to enjoy the game! You can play all the modes and features of FIFA 2020 as if you had bought it from an official store or platform. You can also update the game with patches and fixes that are released by EA Sports.
-Conclusion
-In this article, we have explained what FIFA 2020 Crack is, how to download it, and how to play it. We have also answered some frequently asked questions about FIFA 2020 Crack.
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-Is FIFA 2020 Crack safe to use?
-No, FIFA 2020 Crack is not safe to use. It may contain malware or viruses that can harm your device or data. It may also cause compatibility issues or errors with your game or device. It may also expose you to legal actions or penalties from EA Sports or Electronic Arts.
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-No, FIFA 2020 Crack is not free to use. Although you do not have to pay for the game itself, you may have to pay for other costs or risks associated with using FIFA 2020 Crack, such as:
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Is FIFA 2020 Crack legal to use?
-No, FIFA 2020 Crack is not legal to use. It violates the terms of service and end-user license agreement of EA Sports and Electronic Arts. It also infringes the intellectual property rights and copyrights of EA Sports and Electronic Arts. It may also violate the laws and regulations of your country or region regarding software piracy and copyright infringement.
-Is FIFA 2020 Crack worth using?
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-What are some alternatives to FIFA 2020 Crack?
-If you want to play FIFA 2020 without using FIFA 2020 Crack, you have some alternatives, such as:
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-If you are looking for a way to activate AutoCAD Electrical 2014 without buying a license, you might have heard of X Force Keygen. This is a tool that can generate serial numbers and activation codes for various Autodesk products, including AutoCAD Electrical 2014. In this article, we will show you how to download, install, and use X Force Keygen AutoCAD Electrical 2014 Portable, a version that does not require installation and can run from any device.
-What is X Force Keygen?
-X Force Keygen is a software that can generate valid serial numbers and activation codes for Autodesk products. It is based on a reverse engineering technique that exploits the algorithm of the software protection system. By using X Force Keygen, you can bypass the online activation process and use Autodesk products without paying for a license.
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-X Force Keygen works by creating a request code based on your product information and hardware ID. This request code is then sent to the keygen, which generates an activation code that matches your request code. The activation code is then entered into the software activation screen, where it is verified and accepted. This way, you can activate your Autodesk product offline.
-Why use it for AutoCAD Electrical 2014?
-AutoCAD Electrical 2014 is a software that helps you design and document electrical systems. It has many features and functions that can help you create schematic diagrams, panel layouts, PLC I/O drawings, and more. However, AutoCAD Electrical 2014 is not cheap. It costs around $5,000 for a single-user license. If you cannot afford this price, you might want to use X Force Keygen to activate it for free.
-How to download and install X Force Keygen AutoCAD Electrical 2014 Portable?
-To use X Force Keygen AutoCAD Electrical 2014 Portable, you need to download and install two things: the keygen itself and the portable version of AutoCAD Electrical 2014. Here are the steps to follow:
-Step 1: Disable your internet connection and antivirus
-Before you download and run X Force Keygen, you need to disable your internet connection and antivirus software. This is because X Force Keygen is detected as a malicious program by most antivirus programs and online security services. If you do not disable them, they might block or delete X Force Keygen from your device.
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-x force suprafiles AutoCAD Electrical 2014 wupfileStep 3: Extract the zip file and run the keygen as administrator
-After you download X Force Keygen from , you will get a zip file named Universal.xforce.keygen.Autodesk.2014.zip. You need to extract this file using a program like WinRAR or 7-Zip. You will get a folder named Universal.xforce.keygen.Autodesk.2014 with two files inside: x-force_2014_x32.exe and x-force_2014_x64.exe. Depending on your system architecture (32-bit or 64-bit), you need to run one of these files as administrator by right-clicking on it and choosing Run as administrator.
-Step 4: Select AutoCAD Electrical 2014 from the product list and click on Patch
-When you run X Force Keygen as administrator, you will see a window with a list of Autodesk products. You need to select AutoCAD Electrical 2014 from this list and click on Patch. You should see a message saying Successfully patched.
-Step 5: Copy the request code and paste it into the keygen and click on Generate
-Now you need to open AutoCAD Electrical 2014 Portable from its location (see step 6) and go to the activation screen by clicking on Activate in the Help menu. You will see a request code in this screen. You need to copy this code and paste it into the keygen window where it says Request. Then click on Generate. You will see an activation code in the keygen window.
-Step 6: Copy the activation code and paste it into the activation screen of AutoCAD Electrical 2014
-Now you need to copy the activation code from the keygen window and paste it into the activation screen of AutoCAD Electrical 2014 where it says Enter an activation code here. Then click on Next. You should see a message saying Thank you for activating your Autodesk product.
-How to use AutoCAD Electrical 2014 Portable with X Force Keygen?
-Now that you have activated AutoCAD Electrical 2014 with X Force Keygen, you can use it as a portable version that does not require installation and can run from any device.
-What are the benefits of using a portable version?
-A portable version of AutoCAD Electrical 2014 has several benefits over an installed version:
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How to run AutoCAD Electrical 2014 Portable from a USB drive or a cloud storage?
-To run AutoCAD Electrical 2014 Portable from a USB drive or a cloud storage, you need to download and extract it first. You can find a link to download AutoCAD Electrical 2014 Portable from . This link will take you to Google Drive where you can download xforce keygen 64bits autocad
-xforce keygen autocad electrical portable-.zip file. After downloading this file, you need to extract it using WinRAR or 7-Zip. You will get a folder named xforce keygen autocad electrical portable with two files inside: acad.exe (the main executable file) and acad.ico (the icon file). To run AutoCAD Electrical 2014 Portable from a USB drive or a cloud storage, you need to copy this folder to your desired location (such as your USB drive or your cloud storage folder). Then double-click on acad.exe file to launch AutoCAD Electrical-xforce keygen autocad electrical portable-.What are the features and functions of AutoCAD Electrical
--xforce keygen autocad electrical portable-?AutoCAD Electrical
--xforce keygen autocad electrical portable-is a software that helps you design and document electrical systems. It has many features and functions that can help you create schematic diagrams, panel layouts, PLC I/O drawings, and more. Some of the features and functions of AutoCAD Electrical 2014 are:-
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Conclusion
-X Force Keygen AutoCAD Electrical 2014 Portable is a tool that can help you activate AutoCAD Electrical 2014 without buying a license. It is based on a reverse engineering technique that exploits the algorithm of the software protection system. By using X Force Keygen AutoCAD Electrical 2014 Portable, you can also use AutoCAD Electrical 2014 as a portable version that does not require installation and can run from any device.
-AutoCAD Electrical 2014 is a software that helps you design and document electrical systems. It has many features and functions that can help you create schematic diagrams, panel layouts, PLC I/O drawings, and more. It also supports the latest electrical standards and provides a library of 65,000+ intelligent electrical symbols.
-If you want to learn more about X Force Keygen AutoCAD Electrical 2014 Portable or AutoCAD Electrical 2014 features and functions, you can check out the following resources:
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-Electrical Toolset In Autodesk AutoCAD | Features https://www.autodesk.com/products/autocad/included-toolsets/autocad-electrical xforce keygen 64bits autocad 2014 - Google Drive https://drive.google.com/file/d/0B9OEdNQ-01uBMVdvSkRIUkpaSEU System requirements for AutoCAD Electrical 2014 - Autodesk https://www.autodesk.com/support/technical/article/caas/sfdcarticles/sfdcarticles/System-requirements-for-AutoCAD-Electrical-2014.html FAQs
-What is the difference between X Force Keygen AutoCAD Electrical 2014 Portable and X Force Keygen AutoCAD Electrical 2014?
-X Force Keygen AutoCAD Electrical 2014 Portable is a version that does not require installation and can run from any device. X Force Keygen AutoCAD Electrical 2014 is a version that requires installation on your device.
-Is X Force Keygen AutoCAD Electrical 2014 Portable safe to use?
-X Force Keygen AutoCAD Electrical 2014 Portable is detected as a malicious program by most antivirus programs and online security services. Therefore, you should use it at your own risk and disable your internet connection and antivirus software before using it.
-How long does X Force Keygen AutoCAD Electrical 2014 Portable last?
-X Force Keygen AutoCAD Electrical 2014 Portable lasts indefinitely as long as you do not update or reinstall your Autodesk product.
-Can I use X Force Keygen AutoCAD Electrical 2014 Portable for other Autodesk products?
-X Force Keygen AutoCAD Electrical 2014 Portable works for all Autodesk products (2014) all. However, you need to select the correct product from the list in the keygen window before generating an activation code.
-Can I use X Force Keygen AutoCAD Electrical 2014 Portable online?
-No, you cannot use X Force Keygen AutoCAD Electrical 2014 Portable online. You need to activate your Autodesk product offline using X Force Keygen AutoCAD Electrical 2014 Portable.
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\ No newline at end of file diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/detectors/yolof.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/detectors/yolof.py deleted file mode 100644 index 2bc4f1abd21eb9ad439e5810dc8dce2c4d0d6329..0000000000000000000000000000000000000000 --- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/detectors/yolof.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from ..builder import DETECTORS -from .single_stage import SingleStageDetector - - -@DETECTORS.register_module() -class YOLOF(SingleStageDetector): - r"""Implementation of `You Only Look One-level Feature -Barrons Sat Subject Test Physics Pdf Download
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- diff --git a/spaces/ruboin/faster-whisper-webui/src/conversion/hf_converter.py b/spaces/ruboin/faster-whisper-webui/src/conversion/hf_converter.py deleted file mode 100644 index 6da4f0fd672d63b099f21d0498ba4001d23356f7..0000000000000000000000000000000000000000 --- a/spaces/ruboin/faster-whisper-webui/src/conversion/hf_converter.py +++ /dev/null @@ -1,67 +0,0 @@ -# https://github.com/bayartsogt-ya/whisper-multiple-hf-datasets - -from copy import deepcopy -import torch - -WHISPER_MAPPING = { - "layers": "blocks", - "fc1": "mlp.0", - "fc2": "mlp.2", - "final_layer_norm": "mlp_ln", - "layers": "blocks", - ".self_attn.q_proj": ".attn.query", - ".self_attn.k_proj": ".attn.key", - ".self_attn.v_proj": ".attn.value", - ".self_attn_layer_norm": ".attn_ln", - ".self_attn.out_proj": ".attn.out", - ".encoder_attn.q_proj": ".cross_attn.query", - ".encoder_attn.k_proj": ".cross_attn.key", - ".encoder_attn.v_proj": ".cross_attn.value", - ".encoder_attn_layer_norm": ".cross_attn_ln", - ".encoder_attn.out_proj": ".cross_attn.out", - "decoder.layer_norm.": "decoder.ln.", - "encoder.layer_norm.": "encoder.ln_post.", - "embed_tokens": "token_embedding", - "encoder.embed_positions.weight": "encoder.positional_embedding", - "decoder.embed_positions.weight": "decoder.positional_embedding", - "layer_norm": "ln_post", -} - - -def rename_keys(s_dict): - keys = list(s_dict.keys()) - for key in keys: - new_key = key - for k, v in WHISPER_MAPPING.items(): - if k in key: - new_key = new_key.replace(k, v) - - print(f"{key} -> {new_key}") - - s_dict[new_key] = s_dict.pop(key) - return s_dict - - -def convert_hf_whisper(hf_model_name_or_path: str, whisper_state_path: str): - from transformers import WhisperForConditionalGeneration - transformer_model = WhisperForConditionalGeneration.from_pretrained(hf_model_name_or_path) - config = transformer_model.config - - # first build dims - dims = { - 'n_mels': config.num_mel_bins, - 'n_vocab': config.vocab_size, - 'n_audio_ctx': config.max_source_positions, - 'n_audio_state': config.d_model, - 'n_audio_head': config.encoder_attention_heads, - 'n_audio_layer': config.encoder_layers, - 'n_text_ctx': config.max_target_positions, - 'n_text_state': config.d_model, - 'n_text_head': config.decoder_attention_heads, - 'n_text_layer': config.decoder_layers - } - - state_dict = deepcopy(transformer_model.model.state_dict()) - state_dict = rename_keys(state_dict) - - torch.save({"dims": dims, "model_state_dict": state_dict}, whisper_state_path) \ No newline at end of file diff --git a/spaces/russellc/BLIP/data/pretrain_dataset.py b/spaces/russellc/BLIP/data/pretrain_dataset.py deleted file mode 100644 index 703d543ab5267fdc6fe2b7c84ef6a631d8af90ad..0000000000000000000000000000000000000000 --- a/spaces/russellc/BLIP/data/pretrain_dataset.py +++ /dev/null @@ -1,59 +0,0 @@ -import json -import os -import random - -from torch.utils.data import Dataset - -from PIL import Image -from PIL import ImageFile -ImageFile.LOAD_TRUNCATED_IMAGES = True -Image.MAX_IMAGE_PIXELS = None - -from data.utils import pre_caption -import os,glob - -class pretrain_dataset(Dataset): - def __init__(self, ann_file, laion_path, transform): - - self.ann_pretrain = [] - for f in ann_file: - print('loading '+f) - ann = json.load(open(f,'r')) - self.ann_pretrain += ann - - self.laion_path = laion_path - if self.laion_path: - self.laion_files = glob.glob(os.path.join(laion_path,'*.json')) - - print('loading '+self.laion_files[0]) - with open(self.laion_files[0],'r') as f: - self.ann_laion = json.load(f) - - self.annotation = self.ann_pretrain + self.ann_laion - else: - self.annotation = self.ann_pretrain - - self.transform = transform - - - def reload_laion(self, epoch): - n = epoch%len(self.laion_files) - print('loading '+self.laion_files[n]) - with open(self.laion_files[n],'r') as f: - self.ann_laion = json.load(f) - - self.annotation = self.ann_pretrain + self.ann_laion - - - def __len__(self): - return len(self.annotation) - - def __getitem__(self, index): - - ann = self.annotation[index] - - image = Image.open(ann['image']).convert('RGB') - image = self.transform(image) - caption = pre_caption(ann['caption'],30) - - return image, caption \ No newline at end of file diff --git a/spaces/samuelinferences/TabPFN/TabPFN/datasets/__init__.py b/spaces/samuelinferences/TabPFN/TabPFN/datasets/__init__.py deleted file mode 100644 index 38a42a279fe237176fba8989ee757ef8e37c0ac7..0000000000000000000000000000000000000000 --- a/spaces/samuelinferences/TabPFN/TabPFN/datasets/__init__.py +++ /dev/null @@ -1,149 +0,0 @@ -import pandas as pd -import torch -import numpy as np -import openml - - -def get_openml_classification(did, max_samples, multiclass=True, shuffled=True): - dataset = openml.datasets.get_dataset(did) - X, y, categorical_indicator, attribute_names = dataset.get_data( - dataset_format="array", target=dataset.default_target_attribute - ) - - if not multiclass: - X = X[y < 2] - y = y[y < 2] - - if multiclass and not shuffled: - raise NotImplementedError("This combination of multiclass and shuffling isn't implemented") - - if not isinstance(X, np.ndarray) or not isinstance(y, np.ndarray): - print('Not a NP Array, skipping') - return None, None, None, None - - if not shuffled: - sort = np.argsort(y) if y.mean() < 0.5 else np.argsort(-y) - pos = int(y.sum()) if y.mean() < 0.5 else int((1 - y).sum()) - X, y = X[sort][-pos * 2:], y[sort][-pos * 2:] - y = torch.tensor(y).reshape(2, -1).transpose(0, 1).reshape(-1).flip([0]).float() - X = torch.tensor(X).reshape(2, -1, X.shape[1]).transpose(0, 1).reshape(-1, X.shape[1]).flip([0]).float() - else: - order = np.arange(y.shape[0]) - np.random.seed(13) - np.random.shuffle(order) - X, y = torch.tensor(X[order]), torch.tensor(y[order]) - if max_samples: - X, y = X[:max_samples], y[:max_samples] - - return X, y, list(np.where(categorical_indicator)[0]), attribute_names - -def load_openml_list(dids, filter_for_nan=False - , num_feats=100 - , min_samples = 100 - , max_samples=400 - , multiclass=True - , max_num_classes=10 - , shuffled=True - , return_capped = False): - datasets = [] - openml_list = openml.datasets.list_datasets(dids) - print(f'Number of datasets: {len(openml_list)}') - - datalist = pd.DataFrame.from_dict(openml_list, orient="index") - if filter_for_nan: - datalist = datalist[datalist['NumberOfInstancesWithMissingValues'] == 0] - print(f'Number of datasets after Nan and feature number filtering: {len(datalist)}') - - for ds in datalist.index: - modifications = {'samples_capped': False, 'classes_capped': False, 'feats_capped': False} - entry = datalist.loc[ds] - - print('Loading', entry['name'], entry.did, '..') - - if entry['NumberOfClasses'] == 0.0: - raise Exception("Regression not supported") - #X, y, categorical_feats, attribute_names = get_openml_regression(int(entry.did), max_samples) - else: - X, y, categorical_feats, attribute_names = get_openml_classification(int(entry.did), max_samples - , multiclass=multiclass, shuffled=shuffled) - if X is None: - continue - - if X.shape[1] > num_feats: - if return_capped: - X = X[:, 0:num_feats] - categorical_feats = [c for c in categorical_feats if c < num_feats] - modifications['feats_capped'] = True - else: - print('Too many features') - continue - if X.shape[0] == max_samples: - modifications['samples_capped'] = True - - if X.shape[0] < min_samples: - print(f'Too few samples left') - continue - - if len(np.unique(y)) > max_num_classes: - if return_capped: - X = X[y < np.unique(y)[10]] - y = y[y < np.unique(y)[10]] - modifications['classes_capped'] = True - else: - print(f'Too many classes') - continue - - datasets += [[entry['name'], X, y, categorical_feats, attribute_names, modifications]] - - return datasets, datalist - - -# Classification -valid_dids_classification = [13, 59, 4, 15, 40710, 43, 1498] -test_dids_classification = [973, 1596, 40981, 1468, 40984, 40975, 41163, 41147, 1111, 41164, 1169, 1486, 41143, 1461, 41167, 40668, 41146, 41169, 41027, 23517, 41165, 41161, 41159, 41138, 1590, 41166, 1464, 41168, 41150, 1489, 41142, 3, 12, 31, 54, 1067] -valid_large_classification = [ 943, 23512, 49, 838, 1131, 767, 1142, 748, 1112, - 1541, 384, 912, 1503, 796, 20, 30, 903, 4541, - 961, 805, 1000, 4135, 1442, 816, 1130, 906, 1511, - 184, 181, 137, 1452, 1481, 949, 449, 50, 913, - 1071, 831, 843, 9, 896, 1532, 311, 39, 451, - 463, 382, 778, 474, 737, 1162, 1538, 820, 188, - 452, 1156, 37, 957, 911, 1508, 1054, 745, 1220, - 763, 900, 25, 387, 38, 757, 1507, 396, 4153, - 806, 779, 746, 1037, 871, 717, 1480, 1010, 1016, - 981, 1547, 1002, 1126, 1459, 846, 837, 1042, 273, - 1524, 375, 1018, 1531, 1458, 6332, 1546, 1129, 679, - 389] - -open_cc_dids = [11, - 14, - 15, - 16, - 18, - 22, - 23, - 29, - 31, - 37, - 50, - 54, - 188, - 458, - 469, - 1049, - 1050, - 1063, - 1068, - 1510, - 1494, - 1480, - 1462, - 1464, - 6332, - 23381, - 40966, - 40982, - 40994, - 40975] -# Filtered by N_samples < 2000, N feats < 100, N classes < 10 - -open_cc_valid_dids = [13,25,35,40,41,43,48,49,51,53,55,56,59,61,187,285,329,333,334,335,336,337,338,377,446,450,451,452,460,463,464,466,470,475,481,679,694,717,721,724,733,738,745,747,748,750,753,756,757,764,765,767,774,778,786,788,795,796,798,801,802,810,811,814,820,825,826,827,831,839,840,841,844,852,853,854,860,880,886,895,900,906,907,908,909,915,925,930,931,934,939,940,941,949,966,968,984,987,996,1048,1054,1071,1073,1100,1115,1412,1442,1443,1444,1446,1447,1448,1451,1453,1488,1490,1495,1498,1499,1506,1508,1511,1512,1520,1523,4153,23499,40496,40646,40663,40669,40680,40682,40686,40690,40693,40705,40706,40710,40711,40981,41430,41538,41919,41976,42172,42261,42544,42585,42638] diff --git a/spaces/sarahyoung/taltech/test28_translate_to_chinese.py b/spaces/sarahyoung/taltech/test28_translate_to_chinese.py deleted file mode 100644 index 7f72bdc74e35db95c87fd0d213b846b8bb3542b0..0000000000000000000000000000000000000000 --- a/spaces/sarahyoung/taltech/test28_translate_to_chinese.py +++ /dev/null @@ -1,9 +0,0 @@ -from transformers import pipeline - -translator = pipeline("translation", model="DDDSSS/translation_en-zh") - -def translate(text): - "translates text" - return translator(text)[0]["translation_text"] - - diff --git a/spaces/sarinam/speaker-anonymization/IMSToucan/Layers/Swish.py b/spaces/sarinam/speaker-anonymization/IMSToucan/Layers/Swish.py deleted file mode 100644 index 1541ac7c6b33d08f7f998f7950f090afd6c14b38..0000000000000000000000000000000000000000 --- a/spaces/sarinam/speaker-anonymization/IMSToucan/Layers/Swish.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright 2020 Johns Hopkins University (Shinji Watanabe) -# Northwestern Polytechnical University (Pengcheng Guo) -# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) -# Adapted by Florian Lux 2021 - -import torch - - -class Swish(torch.nn.Module): - """ - Construct an Swish activation function for Conformer. - """ - - def forward(self, x): - """ - Return Swish activation function. - """ - return x * torch.sigmoid(x) diff --git a/spaces/sccstandardteam/ChuanhuChatGPT/run_Linux.sh b/spaces/sccstandardteam/ChuanhuChatGPT/run_Linux.sh deleted file mode 100644 index 2d26597ae47519f42336ccffc16646713a192ae1..0000000000000000000000000000000000000000 --- a/spaces/sccstandardteam/ChuanhuChatGPT/run_Linux.sh +++ /dev/null @@ -1,31 +0,0 @@ -#!/bin/bash - 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- diff --git a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/rnn/__init__.py b/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/rnn/__init__.py deleted file mode 100644 index b7f177368e62a5578b8706300e101f831a3972ac..0000000000000000000000000000000000000000 --- a/spaces/segments-tobias/conex/espnet/nets/pytorch_backend/rnn/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Initialize sub package.""" diff --git a/spaces/segments-tobias/conex/espnet2/bin/pack.py b/spaces/segments-tobias/conex/espnet2/bin/pack.py deleted file mode 100644 index b152ba6ee76bcd99dcfd491f9d116f79acdb2354..0000000000000000000000000000000000000000 --- a/spaces/segments-tobias/conex/espnet2/bin/pack.py +++ /dev/null @@ -1,81 +0,0 @@ -#!/usr/bin/env python3 -import argparse -from typing import Type - -from espnet2.main_funcs.pack_funcs import pack - - -class PackedContents: - files = [] - yaml_files = [] - - -class ASRPackedContents(PackedContents): - # These names must be consistent with the argument of inference functions - files = ["asr_model_file", "lm_file"] - yaml_files = ["asr_train_config", "lm_train_config"] - - -class TTSPackedContents(PackedContents): - files = ["model_file"] - yaml_files = ["train_config"] - - -class EnhPackedContents(PackedContents): - files = ["model_file"] - yaml_files = ["train_config"] - - -def add_arguments(parser: argparse.ArgumentParser, contents: Type[PackedContents]): - parser.add_argument("--outpath", type=str, required=True) - for key in contents.yaml_files: - parser.add_argument(f"--{key}", type=str, default=None) - for key in contents.files: - parser.add_argument(f"--{key}", type=str, default=None) - parser.add_argument("--option", type=str, action="append", default=[]) - - -def get_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser(description="Pack input files to archive format") - subparsers = parser.add_subparsers() - - # Create subparser for ASR - for name, contents in [ - ("asr", ASRPackedContents), - ("tts", TTSPackedContents), - ("enh", EnhPackedContents), - ]: - parser_asr = subparsers.add_parser( - name, - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - ) - add_arguments(parser_asr, contents) - parser_asr.set_defaults(contents=contents) - return parser - - -def main(cmd=None): - parser = get_parser() - args = parser.parse_args(cmd) - if not hasattr(args, "contents"): - parser.print_help() - parser.exit(2) - - yaml_files = { - y: getattr(args, y) - for y in args.contents.yaml_files - if getattr(args, y) is not None - } - files = { - y: getattr(args, y) for y in args.contents.files if getattr(args, y) is not None - } - pack( - yaml_files=yaml_files, - files=files, - option=args.option, - outpath=args.outpath, - ) - - -if __name__ == "__main__": - main() diff --git a/spaces/seungheondoh/LP-Music-Caps-demo/README.md b/spaces/seungheondoh/LP-Music-Caps-demo/README.md deleted file mode 100644 index e0176e17204ee98adb21fe307746e45ace64f499..0000000000000000000000000000000000000000 --- a/spaces/seungheondoh/LP-Music-Caps-demo/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Lp Music Caps -emoji: 🎵🎵🎵 -colorFrom: purple -colorTo: indigo -sdk: gradio -sdk_version: 3.33.1 -app_file: app.py -pinned: false -license: mit ---- - -- check paper & dataset & details: https://arxiv.org/abs/2307.16372 \ No newline at end of file diff --git a/spaces/shi-labs/OneFormer/Dockerfile b/spaces/shi-labs/OneFormer/Dockerfile deleted file mode 100644 index 9d8ae6794b290c8dd670c65c1d10e1f3b0557d02..0000000000000000000000000000000000000000 --- a/spaces/shi-labs/OneFormer/Dockerfile +++ /dev/null @@ -1,61 +0,0 @@ -FROM nvidia/cuda:11.3.1-cudnn8-devel-ubuntu18.04 -CMD nvidia-smi - -ENV DEBIAN_FRONTEND noninteractive -RUN apt-get update && apt-get install -y \ - git \ - make build-essential libssl-dev zlib1g-dev \ - libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \ - libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev \ - ffmpeg libsm6 libxext6 cmake libgl1-mesa-glx \ - && rm -rf /var/lib/apt/lists/* - -RUN useradd -ms /bin/bash user -USER user - -ENV HOME=/home/user \ - PATH=/home/user/.local/bin:$PATH - -RUN curl https://pyenv.run | bash -ENV PATH=$HOME/.pyenv/shims:$HOME/.pyenv/bin:$PATH -RUN pyenv install 3.8.15 && \ - pyenv global 3.8.15 && \ - pyenv rehash && \ - pip install --no-cache-dir --upgrade pip setuptools wheel - -ENV WORKDIR=/code -WORKDIR $WORKDIR -RUN chown -R user:user $WORKDIR -RUN chmod -R 777 $WORKDIR - -COPY requirements.txt $WORKDIR/requirements.txt -RUN pip install --no-cache-dir --upgrade -r $WORKDIR/requirements.txt -RUN pip install ninja - -COPY . . - -ARG TORCH_CUDA_ARCH_LIST=7.5+PTX - -USER root -RUN chown -R user:user $HOME -RUN chmod -R 777 $HOME -RUN chown -R user:user $WORKDIR -RUN chmod -R 777 $WORKDIR - -USER user -RUN ln -s $WORKDIR/oneformer/modeling/pixel_decoder/ops/ $WORKDIR/ && ls && cd ops/ && FORCE_CUDA=1 python setup.py build --build-base=$WORKDIR/ install --user && cd .. -RUN sh deform_setup.sh - -USER user -RUN sh deform_setup.sh - -RUN mkdir -p examples -RUN wget https://praeclarumjj3.github.io/files/ade20k.jpeg -P $WORKDIR/examples/ -RUN wget https://praeclarumjj3.github.io/files/cityscapes.png -P $WORKDIR/examples/ -RUN wget https://praeclarumjj3.github.io/files/coco.jpeg -P $WORKDIR/examples/ - -USER user - -EXPOSE 7860 - -ENTRYPOINT ["python", "gradio_app.py"] diff --git a/spaces/sidharthism/fashion-eye/netdissect/serverstate.py b/spaces/sidharthism/fashion-eye/netdissect/serverstate.py deleted file mode 100644 index e7ddc790c3dfc881f8aa4322d10d90e4e4fc09f0..0000000000000000000000000000000000000000 --- a/spaces/sidharthism/fashion-eye/netdissect/serverstate.py +++ /dev/null @@ -1,526 +0,0 @@ -import os, torch, numpy, base64, json, re, threading, random -from torch.utils.data import TensorDataset, DataLoader -from collections import defaultdict -from netdissect.easydict import EasyDict -from netdissect.modelconfig import create_instrumented_model -from netdissect.runningstats import RunningQuantile -from netdissect.dissection import safe_dir_name -from netdissect.zdataset import z_sample_for_model -from PIL import Image -from io import BytesIO - -class DissectionProject: - ''' - DissectionProject understand how to drive a GanTester within a - dissection project directory structure: it caches data in files, - creates image files, and translates data between plain python data - types and the pytorch-specific tensors required by GanTester. - ''' - def __init__(self, config, project_dir, path_url, public_host): - print('config done', project_dir) - self.use_cuda = torch.cuda.is_available() - self.dissect = config - self.project_dir = project_dir - self.path_url = path_url - self.public_host = public_host - self.cachedir = os.path.join(self.project_dir, 'cache') - self.tester = GanTester( - config.settings, dissectdir=project_dir, - device=torch.device('cuda') if self.use_cuda - else torch.device('cpu')) - self.stdz = [] - - def get_zs(self, size): - if size <= len(self.stdz): - return self.stdz[:size].tolist() - z_tensor = self.tester.standard_z_sample(size) - numpy_z = z_tensor.cpu().numpy() - self.stdz = numpy_z - return self.stdz.tolist() - - def get_z(self, id): - if id < len(self.stdz): - return self.stdz[id] - return self.get_zs((id + 1) * 2)[id] - - def get_zs_for_ids(self, ids): - max_id = max(ids) - if max_id >= len(self.stdz): - self.get_z(max_id) - return self.stdz[ids] - - def get_layers(self): - result = [] - layer_shapes = self.tester.layer_shapes() - for layer in self.tester.layers: - shape = layer_shapes[layer] - result.append(dict( - layer=layer, - channels=shape[1], - shape=[shape[2], shape[3]])) - return result - - def get_units(self, layer): - try: - dlayer = [dl for dl in self.dissect['layers'] - if dl['layer'] == layer][0] - except: - return None - - dunits = dlayer['units'] - result = [dict(unit=unit_num, - img='/%s/%s/s-image/%d-top.jpg' % - (self.path_url, layer, unit_num), - label=unit['iou_label']) - for unit_num, unit in enumerate(dunits)] - return result - - def get_rankings(self, layer): - try: - dlayer = [dl for dl in self.dissect['layers'] - if dl['layer'] == layer][0] - except: - return None - result = [dict(name=ranking['name'], - metric=ranking.get('metric', None), - scores=ranking['score']) - for ranking in dlayer['rankings']] - return result - - def get_levels(self, layer, quantiles): - levels = self.tester.levels( - layer, torch.from_numpy(numpy.array(quantiles))) - return levels.cpu().numpy().tolist() - - def generate_images(self, zs, ids, interventions, return_urls=False): - if ids is not None: - assert zs is None - zs = self.get_zs_for_ids(ids) - if not interventions: - # Do file caching when ids are given (and no ablations). - imgdir = os.path.join(self.cachedir, 'img', 'id') - os.makedirs(imgdir, exist_ok=True) - exist = set(os.listdir(imgdir)) - unfinished = [('%d.jpg' % id) not in exist for id in ids] - needed_z_tensor = torch.tensor(zs[unfinished]).float().to( - self.tester.device) - needed_ids = numpy.array(ids)[unfinished] - # Generate image files for just the needed images. - if len(needed_z_tensor): - imgs = self.tester.generate_images(needed_z_tensor - ).cpu().numpy() - for i, img in zip(needed_ids, imgs): - Image.fromarray(img.transpose(1, 2, 0)).save( - os.path.join(imgdir, '%d.jpg' % i), 'jpeg', - quality=99, optimize=True, progressive=True) - # Assemble a response. - imgurls = ['/%s/cache/img/id/%d.jpg' - % (self.path_url, i) for i in ids] - return [dict(id=i, d=d) for i, d in zip(ids, imgurls)] - # No file caching when ids are not given (or ablations are applied) - z_tensor = torch.tensor(zs).float().to(self.tester.device) - imgs = self.tester.generate_images(z_tensor, - intervention=decode_intervention_array(interventions, - self.tester.layer_shapes()), - ).cpu().numpy() - numpy_z = z_tensor.cpu().numpy() - if return_urls: - randdir = '%03d' % random.randrange(1000) - imgdir = os.path.join(self.cachedir, 'img', 'uniq', randdir) - os.makedirs(imgdir, exist_ok=True) - startind = random.randrange(100000) - imgurls = [] - for i, img in enumerate(imgs): - filename = '%d.jpg' % (i + startind) - Image.fromarray(img.transpose(1, 2, 0)).save( - os.path.join(imgdir, filename), 'jpeg', - quality=99, optimize=True, progressive=True) - image_url_path = ('/%s/cache/img/uniq/%s/%s' - % (self.path_url, randdir, filename)) - imgurls.append(image_url_path) - tweet_filename = 'tweet-%d.html' % (i + startind) - tweet_url_path = ('/%s/cache/img/uniq/%s/%s' - % (self.path_url, randdir, tweet_filename)) - with open(os.path.join(imgdir, tweet_filename), 'w') as f: - f.write(twitter_card(image_url_path, tweet_url_path, - self.public_host)) - return [dict(d=d) for d in imgurls] - imgurls = [img2base64(img.transpose(1, 2, 0)) for img in imgs] - return [dict(d=d) for d in imgurls] - - def get_features(self, ids, masks, layers, interventions): - zs = self.get_zs_for_ids(ids) - z_tensor = torch.tensor(zs).float().to(self.tester.device) - t_masks = torch.stack( - [torch.from_numpy(mask_to_numpy(mask)) for mask in masks] - )[:,None,:,:].to(self.tester.device) - t_features = self.tester.feature_stats(z_tensor, t_masks, - decode_intervention_array(interventions, - self.tester.layer_shapes()), layers) - # Convert torch arrays to plain python lists before returning. - return { layer: { key: value.cpu().numpy().tolist() - for key, value in feature.items() } - for layer, feature in t_features.items() } - - def get_featuremaps(self, ids, layers, interventions): - zs = self.get_zs_for_ids(ids) - z_tensor = torch.tensor(zs).float().to(self.tester.device) - # Quantilized features are returned. - q_features = self.tester.feature_maps(z_tensor, - decode_intervention_array(interventions, - self.tester.layer_shapes()), layers) - # Scale them 0-255 and return them. - # TODO: turn them into pngs for returning. - return { layer: [ - value.clamp(0, 1).mul(255).byte().cpu().numpy().tolist() - for value in valuelist ] - for layer, valuelist in q_features.items() - if (not layers) or (layer in layers) } - - def get_recipes(self): - recipedir = os.path.join(self.project_dir, 'recipe') - if not os.path.isdir(recipedir): - return [] - result = [] - for filename in os.listdir(recipedir): - with open(os.path.join(recipedir, filename)) as f: - result.append(json.load(f)) - return result - - - - -class GanTester: - ''' - GanTester holds on to a specific model to test. - - (1) loads and instantiates the GAN; - (2) instruments it at every layer so that units can be ablated - (3) precomputes z dimensionality, and output image dimensions. - ''' - def __init__(self, args, dissectdir=None, device=None): - self.cachedir = os.path.join(dissectdir, 'cache') - self.device = device if device is not None else torch.device('cpu') - self.dissectdir = dissectdir - self.modellock = threading.Lock() - - # Load the generator from the pth file. - args_copy = EasyDict(args) - args_copy.edit = True - model = create_instrumented_model(args_copy) - model.eval() - self.model = model - - # Get the set of layers of interest. - # Default: all shallow children except last. - self.layers = sorted(model.retained_features().keys()) - - # Move it to CUDA if wanted. - model.to(device) - - self.quantiles = { - layer: load_quantile_if_present(os.path.join(self.dissectdir, - safe_dir_name(layer)), 'quantiles.npz', - device=torch.device('cpu')) - for layer in self.layers } - - def layer_shapes(self): - return self.model.feature_shape - - def standard_z_sample(self, size=100, seed=1, device=None): - ''' - Generate a standard set of random Z as a (size, z_dimension) tensor. - With the same random seed, it always returns the same z (e.g., - the first one is always the same regardless of the size.) - ''' - result = z_sample_for_model(self.model, size) - if device is not None: - result = result.to(device) - return result - - def reset_intervention(self): - self.model.remove_edits() - - def apply_intervention(self, intervention): - ''' - Applies an ablation recipe of the form [(layer, unit, alpha)...]. - ''' - self.reset_intervention() - if not intervention: - return - for layer, (a, v) in intervention.items(): - self.model.edit_layer(layer, ablation=a, replacement=v) - - def generate_images(self, z_batch, intervention=None): - ''' - Makes some images. - ''' - with torch.no_grad(), self.modellock: - batch_size = 10 - self.apply_intervention(intervention) - test_loader = DataLoader(TensorDataset(z_batch[:,:,None,None]), - batch_size=batch_size, - pin_memory=('cuda' == self.device.type - and z_batch.device.type == 'cpu')) - result_img = torch.zeros( - *((len(z_batch), 3) + self.model.output_shape[2:]), - dtype=torch.uint8, device=self.device) - for batch_num, [batch_z,] in enumerate(test_loader): - batch_z = batch_z.to(self.device) - out = self.model(batch_z) - result_img[batch_num*batch_size: - batch_num*batch_size+len(batch_z)] = ( - (((out + 1) / 2) * 255).clamp(0, 255).byte()) - return result_img - - def get_layers(self): - return self.layers - - def feature_stats(self, z_batch, - masks=None, intervention=None, layers=None): - feature_stat = defaultdict(dict) - with torch.no_grad(), self.modellock: - batch_size = 10 - self.apply_intervention(intervention) - if masks is None: - masks = torch.ones(z_batch.size(0), 1, 1, 1, - device=z_batch.device, dtype=z_batch.dtype) - else: - assert masks.shape[0] == z_batch.shape[0] - assert masks.shape[1] == 1 - test_loader = DataLoader( - TensorDataset(z_batch[:,:,None,None], masks), - batch_size=batch_size, - pin_memory=('cuda' == self.device.type - and z_batch.device.type == 'cpu')) - processed = 0 - for batch_num, [batch_z, batch_m] in enumerate(test_loader): - batch_z, batch_m = [ - d.to(self.device) for d in [batch_z, batch_m]] - # Run model but disregard output - self.model(batch_z) - processing = batch_z.shape[0] - for layer, feature in self.model.retained_features().items(): - if layers is not None: - if layer not in layers: - continue - # Compute max features touching mask - resized_max = torch.nn.functional.adaptive_max_pool2d( - batch_m, - (feature.shape[2], feature.shape[3])) - max_feature = (feature * resized_max).view( - feature.shape[0], feature.shape[1], -1 - ).max(2)[0].max(0)[0] - if 'max' not in feature_stat[layer]: - feature_stat[layer]['max'] = max_feature - else: - torch.max(feature_stat[layer]['max'], max_feature, - out=feature_stat[layer]['max']) - # Compute mean features weighted by overlap with mask - resized_mean = torch.nn.functional.adaptive_avg_pool2d( - batch_m, - (feature.shape[2], feature.shape[3])) - mean_feature = (feature * resized_mean).view( - feature.shape[0], feature.shape[1], -1 - ).sum(2).sum(0) / (resized_mean.sum() + 1e-15) - if 'mean' not in feature_stat[layer]: - feature_stat[layer]['mean'] = mean_feature - else: - feature_stat[layer]['mean'] = ( - processed * feature_mean[layer]['mean'] - + processing * mean_feature) / ( - processed + processing) - processed += processing - # After summaries are done, also compute quantile stats - for layer, stats in feature_stat.items(): - if self.quantiles.get(layer, None) is not None: - for statname in ['max', 'mean']: - stats['%s_quantile' % statname] = ( - self.quantiles[layer].normalize(stats[statname])) - return feature_stat - - def levels(self, layer, quantiles): - return self.quantiles[layer].quantiles(quantiles) - - def feature_maps(self, z_batch, intervention=None, layers=None, - quantiles=True): - feature_map = defaultdict(list) - with torch.no_grad(), self.modellock: - batch_size = 10 - self.apply_intervention(intervention) - test_loader = DataLoader( - TensorDataset(z_batch[:,:,None,None]), - batch_size=batch_size, - pin_memory=('cuda' == self.device.type - and z_batch.device.type == 'cpu')) - processed = 0 - for batch_num, [batch_z] in enumerate(test_loader): - batch_z = batch_z.to(self.device) - # Run model but disregard output - self.model(batch_z) - processing = batch_z.shape[0] - for layer, feature in self.model.retained_features().items(): - for single_featuremap in feature: - if quantiles: - feature_map[layer].append(self.quantiles[layer] - .normalize(single_featuremap)) - else: - feature_map[layer].append(single_featuremap) - return feature_map - -def load_quantile_if_present(outdir, filename, device): - filepath = os.path.join(outdir, filename) - if os.path.isfile(filepath): - data = numpy.load(filepath) - result = RunningQuantile(state=data) - result.to_(device) - return result - return None - -if __name__ == '__main__': - test_main() - -def mask_to_numpy(mask_record): - # Detect a png image mask. - bitstring = mask_record['bitstring'] - bitnumpy = None - default_shape = (256, 256) - if 'image/png;base64,' in bitstring: - bitnumpy = base642img(bitstring) - default_shape = bitnumpy.shape[:2] - # Set up results - shape = mask_record.get('shape', None) - if not shape: # None or empty [] - shape = default_shape - result = numpy.zeros(shape=shape, dtype=numpy.float32) - bitbounds = mask_record.get('bitbounds', None) - if not bitbounds: # None or empty [] - bitbounds = ([0] * len(result.shape)) + list(result.shape) - start = bitbounds[:len(result.shape)] - end = bitbounds[len(result.shape):] - if bitnumpy is not None: - if bitnumpy.shape[2] == 4: - # Mask is any nontransparent bits in the alpha channel if present - result[start[0]:end[0], start[1]:end[1]] = (bitnumpy[:,:,3] > 0) - else: - # Or any nonwhite pixels in the red channel if no alpha. - result[start[0]:end[0], start[1]:end[1]] = (bitnumpy[:,:,0] < 255) - return result - else: - # Or bitstring can be just ones and zeros. - indexes = start.copy() - bitindex = 0 - while True: - result[tuple(indexes)] = (bitstring[bitindex] != '0') - for ii in range(len(indexes) - 1, -1, -1): - if indexes[ii] < end[ii] - 1: - break - indexes[ii] = start[ii] - else: - assert (bitindex + 1) == len(bitstring) - return result - indexes[ii] += 1 - bitindex += 1 - -def decode_intervention_array(interventions, layer_shapes): - result = {} - for channels in [decode_intervention(intervention, layer_shapes) - for intervention in (interventions or [])]: - for layer, channel in channels.items(): - if layer not in result: - result[layer] = channel - continue - accum = result[layer] - newalpha = 1 - (1 - channel[:1]) * (1 - accum[:1]) - newvalue = (accum[1:] * accum[:1] * (1 - channel[:1]) + - channel[1:] * channel[:1]) / (newalpha + 1e-40) - accum[:1] = newalpha - accum[1:] = newvalue - return result - -def decode_intervention(intervention, layer_shapes): - # Every plane of an intervention is a solid choice of activation - # over a set of channels, with a mask applied to alpha-blended channels - # (when the mask resolution is different from the feature map, it can - # be either a max-pooled or average-pooled to the proper resolution). - # This can be reduced to a single alpha-blended featuremap. - if intervention is None: - return None - mask = intervention.get('mask', None) - if mask: - mask = torch.from_numpy(mask_to_numpy(mask)) - maskpooling = intervention.get('maskpooling', 'max') - channels = {} # layer -> ([alpha, val], c) - for arec in intervention.get('ablations', []): - unit = arec['unit'] - layer = arec['layer'] - alpha = arec.get('alpha', 1.0) - if alpha is None: - alpha = 1.0 - value = arec.get('value', 0.0) - if value is None: - value = 0.0 - if alpha != 0.0 or value != 0.0: - if layer not in channels: - channels[layer] = torch.zeros(2, *layer_shapes[layer][1:]) - channels[layer][0, unit] = alpha - channels[layer][1, unit] = value - if mask is not None: - for layer in channels: - layer_shape = layer_shapes[layer][2:] - if maskpooling == 'mean': - layer_mask = torch.nn.functional.adaptive_avg_pool2d( - mask[None,None,...], layer_shape)[0] - else: - layer_mask = torch.nn.functional.adaptive_max_pool2d( - mask[None,None,...], layer_shape)[0] - channels[layer][0] *= layer_mask - return channels - -def img2base64(imgarray, for_html=True, image_format='jpeg'): - ''' - Converts a numpy array to a jpeg base64 url - ''' - input_image_buff = BytesIO() - Image.fromarray(imgarray).save(input_image_buff, image_format, - quality=99, optimize=True, progressive=True) - res = base64.b64encode(input_image_buff.getvalue()).decode('ascii') - if for_html: - return 'data:image/' + image_format + ';base64,' + res - else: - return res - -def base642img(stringdata): - stringdata = re.sub('^(?:data:)?image/\w+;base64,', '', stringdata) - im = Image.open(BytesIO(base64.b64decode(stringdata))) - return numpy.array(im) - -def twitter_card(image_path, tweet_path, public_host): - return '''\ - - - - - - - - - - - - -- - -'''.format( - image_path=image_path, - tweet_path=tweet_path, - public_host=public_host) diff --git a/spaces/simonduerr/ProteinMPNNESM/ProteinMPNN/vanilla_proteinmpnn/helper_scripts/other_tools/make_omit_AA.py b/spaces/simonduerr/ProteinMPNNESM/ProteinMPNN/vanilla_proteinmpnn/helper_scripts/other_tools/make_omit_AA.py deleted file mode 100644 index 353a74bcf0ec1c0238a32b17a001d15591d18243..0000000000000000000000000000000000000000 --- a/spaces/simonduerr/ProteinMPNNESM/ProteinMPNN/vanilla_proteinmpnn/helper_scripts/other_tools/make_omit_AA.py +++ /dev/null @@ -1,39 +0,0 @@ -import glob -import random -import numpy as np -import json -import itertools - -#MODIFY this path -with open('/home/justas/projects/lab_github/mpnn/data/pdbs.jsonl', 'r') as json_file: - json_list = list(json_file) - -my_dict = {} -for json_str in json_list: - result = json.loads(json_str) - all_chain_list = [item[-1:] for item in list(result) if item[:9]=='seq_chain'] - fixed_position_dict = {} - print(result['name']) - if result['name'] == '5TTA': - for chain in all_chain_list: - if chain == 'A': - fixed_position_dict[chain] = [ - [[int(item) for item in list(itertools.chain(list(np.arange(1,4)), list(np.arange(7,10)), [22, 25, 33]))], 'GPL'], - [[int(item) for item in list(itertools.chain([40, 41, 42, 43]))], 'WC'], - [[int(item) for item in list(itertools.chain(list(np.arange(50,150))))], 'ACEFGHIKLMNRSTVWYX'], - [[int(item) for item in list(itertools.chain(list(np.arange(160,200))))], 'FGHIKLPQDMNRSTVWYX']] - else: - fixed_position_dict[chain] = [] - else: - for chain in all_chain_list: - fixed_position_dict[chain] = [] - my_dict[result['name']] = fixed_position_dict - -#MODIFY this path -with open('/home/justas/projects/lab_github/mpnn/data/omit_AA.jsonl', 'w') as f: - f.write(json.dumps(my_dict) + '\n') - - -print('Finished') -#e.g. output -#{"5TTA": {"A": [[[1, 2, 3, 7, 8, 9, 22, 25, 33], "GPL"], [[40, 41, 42, 43], "WC"], [[50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149], "ACEFGHIKLMNRSTVWYX"], [[160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199], "FGHIKLPQDMNRSTVWYX"]], "B": []}, "3LIS": {"A": [], "B": []}} diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Dancing Road APK Mod A Fun and Challenging Music Game for Android.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Dancing Road APK Mod A Fun and Challenging Music Game for Android.md deleted file mode 100644 index 4802ecbf16d83c32dfef654707279e12f6776223..0000000000000000000000000000000000000000 --- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Dancing Road APK Mod A Fun and Challenging Music Game for Android.md +++ /dev/null @@ -1,131 +0,0 @@ - -Painting with GANs from MIT-IBM Watson AI Lab
-This demo lets you modify a selection of meatningful GAN units for a generated image by simply painting.
--
Redirecting to -GANPaint -
-Dancing Road APK Mod: A Fun and Challenging Music Game
-Do you love music and rhythm games? Do you want to enjoy a colorful and exciting game that tests your reflexes and coordination? If yes, then you should try Dancing Road APK Mod, a popular music game that lets you control a ball that rolls on a road that changes color according to the song you choose.
-dancing road apk mod
DOWNLOAD ===== https://ssurll.com/2uNUwF
-Dancing Road APK Mod is a modified version of the original game that gives you unlimited lives, coins, gems, stars, and other resources. You can use them to unlock more songs, balls, roads, themes, effects, and other features that make your game experience more fun and personalized.
-In this article, we will show you how to download and install Dancing Road APK Mod on your Android device. We will also give you some tips on how to play the game, how to customize your game experience, how to unlock more content, how to connect with other players, how to troubleshoot common issues, and more.
-So if you are ready to join the millions of players who are enjoying this addictive music game, read on!
-How to Download and Install Dancing Road APK Mod
-Downloading and installing Dancing Road APK Mod is very easy. Just follow these simple steps:
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Note: You may need to uninstall the original version of Dancing Road before installing the modded version.
-[Dancing Road: Color Ball Run! MOD APK 1.8.0 (Unlimited Money) Download]
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-[Dancing Road: Color Ball Run! Mod Apk 1.8.0 (Unlimited Lives) - ApkMod]
-[Dancing Road: Color Ball Run! Mod APK 1.8.0 - Download Dancing Road: Color Ball Run! Mod for Android]
-[Dancing Road: Color Ball Run! Mod Apk 1.8.0 (Unlimited Money) - Android Mods Apk]
-[Dancing Road: Color Ball Run! Mod APK 1.8.0 - HappyMod]How to Play Dancing Road APK Mod
-Dancing Road APK Mod is very easy to play but hard to master. Here are some basic gameplay instructions, tips and tricks, game modes, levels, challenges, rewards:
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Dancing Road APK Mod is a fun and challenging music game that will keep you entertained for hours. You will enjoy the variety of songs, balls, roads, themes, effects, and other features that you can unlock and customize. You will also improve your reflexes and coordination as you try to match the ball with the road.
-How to Customize Your Game Experience with Dancing Road APK Mod
-One of the best things about Dancing Road APK Mod is that you can customize your game experience according to your preferences and mood. Here are some ways you can do that:
--
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Note: Some songs, balls, roads, themes, and effects are locked and require coins, gems, stars, or other resources to unlock them. You can earn them by playing the game or by using Dancing Road APK Mod.
-How to Unlock More Content with Dancing Road APK Mod
-Dancing Road APK Mod gives you unlimited access to all the content in the game. However, if you want to unlock more content without using the modded version, here are some ways you can do that:
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Dancing Road APK Mod has a lot of content that you can unlock and enjoy. You can choose from hundreds of songs, balls, roads, themes, effects, and other features that make your game experience more fun and personalized.
-How to Connect with Other Players with Dancing Road APK Mod
-Dancing Road APK Mod is not only a solo game. You can also connect with other players from around the world and compete with them in leaderboards, tournaments, events, and more. Here are some ways you can do that:
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Note: Some teams, chat rooms, leaderboards, tournaments, and events are locked and require coins, gems, stars, or other resources to join or create them. You can earn them by playing the game or by using Dancing Road APK Mod.
-How to Troubleshoot Common Issues with Dancing Road APK Mod
-Dancing Road APK Mod is a stable and smooth game that runs well on most Android devices. However, if you encounter any issues such as crashing, freezing, lagging, loading errors, etc., here are some ways you can troubleshoot them:
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Dancing Road APK Mod is a reliable and enjoyable game that rarely has any issues. However, if you do encounter any problems, you can try these troubleshooting methods or contact the support team for assistance.
-Pros and Cons of Dancing Road APK Mod
-Dancing Road APK Mod is a great music game that has many pros and cons. Here is a table comparing them:
--
-- -Pros -Cons -- -- Unlimited lives, coins, gems, stars, and other resources -- May not be compatible with some devices or Android versions -- -- Access to all songs, balls, roads, themes, effects, and other features -- May cause some issues such as crashing, freezing, lagging, loading errors, etc. -- -- Fun and challenging gameplay that tests your reflexes and coordination -- May be considered cheating by some players or developers -- -- Variety of genres, modes, levels, challenges, rewards, and customization options -- May take up more storage space than the original version -- -- Ability to connect with other players from around the world and compete with them -- May require a stable and fast internet connection to play smoothly -Dancing Road APK Mod is a music game that has many pros and cons. You can decide whether to use it or not based on your preferences and needs.
-Alternatives to Dancing Road APK Mod
-If you like Dancing Road APK Mod but want to try something different,
here are some similar games that you can try if you like Dancing Road APK Mod:
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Dancing Road APK Mod is a fun and challenging music game that has many alternatives. You can try these games if you want to experience different genres, modes, features, and challenges.
-FAQs about Dancing Road APK Mod
-Here are some frequently asked questions and answers about Dancing Road APK Mod:
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Yes, Dancing Road APK Mod is safe to use as long as you download it from a trusted source like [this link]. However, you should be careful not to use it in a way that violates the terms of service or the privacy policy of the original game or the developer.
-It depends on your location and the laws of your country or region. Some countries or regions may prohibit or restrict the use of modded apps or games. You should check the laws of your country or region before using Dancing Road APK Mod.
-No, you need an internet connection to play Dancing Road APK Mod. This is because the game needs to load the songs, balls, roads, themes, effects, and other features from the server. You also need an internet connection to connect with other players and compete with them.
-No, Dancing Road APK Mod is only available for Android devices. However, you can use an Android emulator on your PC to run the game. An Android emulator is a software that simulates an Android device on your PC. Some examples of Android emulators are BlueStacks, NoxPlayer, MEmu, etc.
-Yes, you can play Dancing Road APK Mod with friends by joining or creating a team, chat room, leaderboard, tournament, or event. You can also invite your friends to play the game and get rewards.
-Conclusion
-Dancing Road APK Mod is a fun and challenging music game that lets you control a ball that rolls on a road that changes color according to the song you choose. You can enjoy a variety of songs, balls, roads, themes, effects, and other features that you can unlock and customize with unlimited lives, coins, gems, stars, and other resources. You can also connect with other players from around the world and compete with them in leaderboards, tournaments, events, and more.
-If you love music and rhythm games, you should download and install Dancing Road APK Mod on your Android device today. You will have a blast playing this addictive music game!
197e85843d
-
-
\ No newline at end of file diff --git a/spaces/skf15963/summary/fengshen/examples/clue1.1/predict2submit/csl_submit.py b/spaces/skf15963/summary/fengshen/examples/clue1.1/predict2submit/csl_submit.py deleted file mode 100644 index 0664d67675a797959ba0fb6883b81bd172de8b06..0000000000000000000000000000000000000000 --- a/spaces/skf15963/summary/fengshen/examples/clue1.1/predict2submit/csl_submit.py +++ /dev/null @@ -1,83 +0,0 @@ -import json -from tqdm import tqdm -import argparse -import numpy as np - -def save_data(data,file_path): - with open(file_path, 'w', encoding='utf8') as f: - for line in data: - json_data=json.dumps(line,ensure_ascii=False) - f.write(json_data+'\n') - - -def load_data(file_path,is_training=False): - with open(file_path, 'r', encoding='utf8') as f: - lines = f.readlines() - result=[] - for l,line in tqdm(enumerate(lines)): - data = json.loads(line) - result.append(data) - return result - - -def recls(line): - mat=[] - for l in line: - s=[v for v in l['score'].values()] - mat.append(s) - mat=np.array(mat) - batch,num_labels=mat.shape - for i in range(len(line)): - index = np.unravel_index(np.argmax(mat, axis=None), mat.shape) - line[index[0]]['label'] = int(index[1]) - mat[index[0],:] = np.zeros((num_labels,)) - mat[:,index[1]] = np.zeros((batch,)) - return line - - -import copy - -def csl_scorted(data): - lines={} - new_data=copy.deepcopy(data) - for d in data: - if d['texta'] not in lines.keys(): - lines[d['texta']]={} - lines[d['texta']][d['id']]=d['score'][d['choice'][0]] - result=[] - id2preds={} - for k,v in lines.items(): - v=sorted(v.items(), key=lambda x: x[1], reverse=True) - # print(v) - for i,(text_id, score) in enumerate(v): - if i
-Here is a possible title and article with html formatting for the keyword "Ajay Yadav Anaesthesia Pdf Download": - -How to Download Short Textbook of Anesthesia by Ajay Yadav 6th Edition Pdf for Free
-If you are looking for a concise and comprehensive guide to anaesthesia, you may want to check out Short Textbook of Anesthesia by Ajay Yadav 6th Edition. This book covers the latest advances in anaesthesia, from basic concepts of anatomy and physiology, to equipment, preoperative assessment, airway management, monitoring, general and regional anaesthesia, anaesthesia for coexisting diseases, subspecialty anaesthetic management, and intensive care management[^1^].
-The book has 344 pages, coloured illustrations, and key points for each topic. It also follows the CPR guidelines based on American Heart Association (AHA) guidelines[^2^]. The book has received positive reviews from students and practitioners alike[^1^].
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Download ⚙ https://urlgoal.com/2uI68Y
-So how can you download this book for free? Well, there are some websites that offer free pdf downloads of this book, such as Paul Kavin and Scribd. However, these websites may not be legal or safe, and they may not have the complete or updated version of the book. Therefore, we do not recommend downloading the book from these sources.
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Here are a few more paragraphs to add to the article: - -About the Author
-Dr Ajay Yadav is an Indian anaesthesiologist from Gurgaon, Haryana. He has more than 20 years of experience in the field of anaesthesia and critical care. He has authored several books and articles on anaesthesia, and has been a speaker and faculty member at various national and international conferences. He is currently working as a senior consultant and head of department of anaesthesia at Medanta - The Medicity Hospital.
- -Other Books by Ajay Yadav
-Apart from Short Textbook of Anesthesia, Dr Ajay Yadav has also written other books on anaesthesia, such as:
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\ No newline at end of file diff --git a/spaces/stomexserde/gpt4-ui/Examples/Builders Of The Adytum Private Lessons - [150 PDFs].md b/spaces/stomexserde/gpt4-ui/Examples/Builders Of The Adytum Private Lessons - [150 PDFs].md deleted file mode 100644 index 9c9ff53c6041f060ddaaa011b4a130cb142196ab..0000000000000000000000000000000000000000 --- a/spaces/stomexserde/gpt4-ui/Examples/Builders Of The Adytum Private Lessons - [150 PDFs].md +++ /dev/null @@ -1,21 +0,0 @@ -
-Builders of the Adytum: A Modern Mystery School Offering Private Lessons
-Have you ever wondered about the secrets of the Western Mystery tradition? Do you want to learn more about Tarot, Qabalah, Gematria, Astrology, Alchemy, and other related disciplines? If so, you might be interested in joining Builders of the Adytum (B.O.T.A.), a non-profit organization and an authentic Mystery School founded by Dr. Paul Foster Case in 1922.
-Builders of the Adytum private lessons - [150 PDFs]
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-B.O.T.A. is derived from a Greek word that means Inner Shrine or Holy of Holies. It is a spiritual order that aims to build the Inner Temple within each member and to manifest the truth that love is the only real power in the universe. B.O.T.A. welcomes people of all faiths who are sincerely interested in its teachings, which are based on Qabalah, the root of Judaism and Christianity.
-As a member of B.O.T.A., you will receive weekly printed lessons covering the various subjects of the curriculum. You will also be able to participate in meditative healing work, group activities, and regional retreats. You will have access to a collection of 150 PDFs that contain private lessons from Dr. Case and other B.O.T.A. teachers. These lessons are not available to the public and contain valuable insights and instructions for your spiritual growth.
-If you want to join B.O.T.A., you can apply for membership online at www.bota.org/membership. You must be over 21 years of age and pay a modest monthly dues to support the work of the order. You will also receive a free brochure titled 'The Open Door', which explains more about B.O.T.A. and its mission.
- -B.O.T.A. is an open door before which you now stand. It is an opportunity to enter a new world of spiritual knowledge and practice. It is a chance to connect with a community of like-minded seekers who share your vision of a better world. It is an invitation to discover your true self and your divine potential.
-Will you enter?
- -If you decide to join B.O.T.A., you will embark on a journey of self-discovery and transformation. You will study the ancient wisdom of the Western Mystery tradition in a modern and practical way. You will learn how to use the symbols and tools of Tarot, Qabalah, Gematria, Astrology, Alchemy, and other related disciplines to understand yourself and the world around you. You will also learn how to apply these teachings to your daily life and to your spiritual development.
-The curriculum of B.O.T.A. is divided into four main sections: The First Temple Degree, The Second Temple Degree, The Third Temple Degree, and The Fourth Temple Degree. Each section consists of several lessons that cover different topics and aspects of the Western Mystery tradition. The lessons are designed to be studied in order and at your own pace. You can also review them as often as you like.
-The First Temple Degree introduces you to the basic principles and practices of B.O.T.A. and the Western Mystery tradition. You will learn about the history and purpose of B.O.T.A., the structure and symbolism of the Tree of Life, the meaning and use of the Tarot cards, the fundamentals of Qabalah and Gematria, the basics of Astrology and Alchemy, and the methods of meditation and visualization.
-The Second Temple Degree deepens your knowledge and understanding of the Western Mystery tradition. You will learn about the four worlds of Qabalah, the ten Sephiroth and their correspondences, the twenty-two paths and their attributions, the four elements and their qualities, the twelve signs of the zodiac and their influences, the seven planets and their energies, and the alchemical process of transformation.
-The Third Temple Degree expands your vision and awareness of the Western Mystery tradition. You will learn about the higher worlds of Qabalah, the hidden meanings of the Tarot cards, the advanced techniques of Gematria and Astrology, the secrets of Alchemy and Hermeticism, and the mysteries of initiation and illumination.
-The Fourth Temple Degree completes your education and preparation for the Western Mystery tradition. You will learn about the ultimate goal of B.O.T.A. and the Western Mystery tradition, which is to attain union with God or the Absolute. You will also learn how to apply all that you have learned to your service to humanity and to your personal fulfillment.
-By studying the private lessons of B.O.T.A., you will not only gain intellectual knowledge but also spiritual experience. You will not only acquire information but also wisdom. You will not only learn theories but also practices. You will not only read words but also live them.
-B.O.T.A. is more than a school. It is a way of life.
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\ No newline at end of file diff --git a/spaces/stomexserde/gpt4-ui/Examples/Crack Automation Studio 5.6.rar NEW!.md b/spaces/stomexserde/gpt4-ui/Examples/Crack Automation Studio 5.6.rar NEW!.md deleted file mode 100644 index eaaa7d9c07625a7a2c0d5b1824f21c1f23f14453..0000000000000000000000000000000000000000 --- a/spaces/stomexserde/gpt4-ui/Examples/Crack Automation Studio 5.6.rar NEW!.md +++ /dev/null @@ -1,194 +0,0 @@ - --
b2dd77e56b- -Crack Automation Studio 5.6.rar: What You Need to Know
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\ No newline at end of file diff --git a/spaces/subhc/Guess-What-Moves/utils/convert.py b/spaces/subhc/Guess-What-Moves/utils/convert.py deleted file mode 100644 index 569f5fddc5861f6bbe2568bfec47c0a3b00897af..0000000000000000000000000000000000000000 --- a/spaces/subhc/Guess-What-Moves/utils/convert.py +++ /dev/null @@ -1,25 +0,0 @@ -import torch -import itertools - -def cast_like(maybe_tensor, example_tensor): - if not torch.is_tensor(maybe_tensor): - maybe_tensor = torch.tensor(maybe_tensor) - maybe_tensor = maybe_tensor.to(example_tensor.device).to(example_tensor.dtype) - shape = [*maybe_tensor.shape, *[1] * len(example_tensor.shape)] - if not shape: - shape = [1] - return maybe_tensor.view(*shape) - - -def lofd_2_dofl(list_of_dicts, make_tensor=True): - keys = set(itertools.chain.from_iterable(list_of_dicts)) - out_dict = {} - for k in keys: - out_dict[k] = [d[k] for d in list_of_dicts if k in d] - if make_tensor: - example_tensor = next((v for v in out_dict[k] if torch.is_tensor(v)), None) - if example_tensor is None: - out_dict[k] = torch.tensor(out_dict[k]) - else: - out_dict[k] = torch.cat([cast_like(t, example_tensor) for t in out_dict[k]], 0) - return out_dict diff --git a/spaces/suchun/chatGPT_acdemic/crazy_functions/crazy_utils.py b/spaces/suchun/chatGPT_acdemic/crazy_functions/crazy_utils.py deleted file mode 100644 index 4e0eba499e6f2fa94b1a962421b3c4bfef7a2f26..0000000000000000000000000000000000000000 --- a/spaces/suchun/chatGPT_acdemic/crazy_functions/crazy_utils.py +++ /dev/null @@ -1,566 +0,0 @@ -import traceback -from toolbox import update_ui, get_conf - -def input_clipping(inputs, history, max_token_limit): - import numpy as np - from request_llm.bridge_all import model_info - enc = model_info["gpt-3.5-turbo"]['tokenizer'] - def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) - - mode = 'input-and-history' - # 当 输入部分的token占比 小于 全文的一半时，只裁剪历史 - input_token_num = get_token_num(inputs) - if input_token_num < max_token_limit//2: - mode = 'only-history' - max_token_limit = max_token_limit - input_token_num - - everything = [inputs] if mode == 'input-and-history' else [''] - everything.extend(history) - n_token = get_token_num('\n'.join(everything)) - everything_token = [get_token_num(e) for e in everything] - delta = max(everything_token) // 16 # 截断时的颗粒度 - - while n_token > max_token_limit: - where = np.argmax(everything_token) - encoded = enc.encode(everything[where], disallowed_special=()) - clipped_encoded = encoded[:len(encoded)-delta] - everything[where] = enc.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char - everything_token[where] = get_token_num(everything[where]) - n_token = get_token_num('\n'.join(everything)) - - if mode == 'input-and-history': - inputs = everything[0] - else: - pass - history = everything[1:] - return inputs, history - -def request_gpt_model_in_new_thread_with_ui_alive( - inputs, inputs_show_user, llm_kwargs, - chatbot, history, sys_prompt, refresh_interval=0.2, - handle_token_exceed=True, - retry_times_at_unknown_error=2, - ): - """ - Request GPT model，请求GPT模型同时维持用户界面活跃。 - - 输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）: - inputs (string): List of inputs （输入） - inputs_show_user (string): List of inputs to show user（展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性） - top_p (float): Top p value for sampling from model distribution （GPT参数，浮点数） - temperature (float): Temperature value for sampling from model distribution（GPT参数，浮点数） - chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化） - history (list): List of chat history （历史，对话历史列表） - sys_prompt (string): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样） - refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果） - handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启 - retry_times_at_unknown_error：失败时的重试次数 - - 输出 Returns: - future: 输出，GPT返回的结果 - """ - import time - from concurrent.futures import ThreadPoolExecutor - from request_llm.bridge_all import predict_no_ui_long_connection - # 用户反馈 - chatbot.append([inputs_show_user, ""]) - yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 - executor = ThreadPoolExecutor(max_workers=16) - mutable = ["", time.time(), ""] - def _req_gpt(inputs, history, sys_prompt): - retry_op = retry_times_at_unknown_error - exceeded_cnt = 0 - while True: - # watchdog error - if len(mutable) >= 2 and (time.time()-mutable[1]) > 5: - raise RuntimeError("检测到程序终止。") - try: - # 【第一种情况】：顺利完成 - result = predict_no_ui_long_connection( - inputs=inputs, llm_kwargs=llm_kwargs, - history=history, sys_prompt=sys_prompt, observe_window=mutable) - return result - except ConnectionAbortedError as token_exceeded_error: - # 【第二种情况】：Token溢出 - if handle_token_exceed: - exceeded_cnt += 1 - # 【选择处理】 尝试计算比例，尽可能多地保留文本 - from toolbox import get_reduce_token_percent - p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error)) - MAX_TOKEN = 4096 - EXCEED_ALLO = 512 + 512 * exceeded_cnt - inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO) - mutable[0] += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n' - continue # 返回重试 - else: - # 【选择放弃】 - tb_str = '```\n' + traceback.format_exc() + '```' - mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n" - return mutable[0] # 放弃 - except: - # 【第三种情况】：其他错误：重试几次 - tb_str = '```\n' + traceback.format_exc() + '```' - print(tb_str) - mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n" - if retry_op > 0: - retry_op -= 1 - mutable[0] += f"[Local Message] 重试中，请稍等 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}：\n\n" - if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str): - time.sleep(30) - time.sleep(5) - continue # 返回重试 - else: - time.sleep(5) - return mutable[0] # 放弃 - - # 提交任务 - future = executor.submit(_req_gpt, inputs, history, sys_prompt) - while True: - # yield一次以刷新前端页面 - time.sleep(refresh_interval) - # “喂狗”（看门狗） - mutable[1] = time.time() - if future.done(): - break - chatbot[-1] = [chatbot[-1][0], mutable[0]] - yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 - - final_result = future.result() - chatbot[-1] = [chatbot[-1][0], final_result] - yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息 - return final_result - - -def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( - inputs_array, inputs_show_user_array, llm_kwargs, - chatbot, history_array, sys_prompt_array, - refresh_interval=0.2, max_workers=-1, scroller_max_len=30, - handle_token_exceed=True, show_user_at_complete=False, - retry_times_at_unknown_error=2, - ): - """ - Request GPT model using multiple threads with UI and high efficiency - 请求GPT模型的[多线程]版。 - 具备以下功能： - 实时在UI上反馈远程数据流 - 使用线程池，可调节线程池的大小避免openai的流量限制错误 - 处理中途中止的情况 - 网络等出问题时，会把traceback和已经接收的数据转入输出 - - 输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）: - inputs_array (list): List of inputs （每个子任务的输入） - inputs_show_user_array (list): List of inputs to show user（每个子任务展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性） - llm_kwargs: llm_kwargs参数 - chatbot: chatbot （用户界面对话窗口句柄，用于数据流可视化） - history_array (list): List of chat history （历史对话输入，双层列表，第一层列表是子任务分解，第二层列表是对话历史） - sys_prompt_array (list): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样） - refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果） - max_workers (int, optional): Maximum number of threads (default: see config.py) （最大线程数，如果子任务非常多，需要用此选项防止高频地请求openai导致错误） - scroller_max_len (int, optional): Maximum length for scroller (default: 30)（数据流的显示最后收到的多少个字符，仅仅服务于视觉效果） - handle_token_exceed (bool, optional): （是否在输入过长时，自动缩减文本） - handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启 - show_user_at_complete (bool, optional): (在结束时，把完整输入-输出结果显示在聊天框) - retry_times_at_unknown_error：子任务失败时的重试次数 - - 输出 Returns: - list: List of GPT model responses （每个子任务的输出汇总，如果某个子任务出错，response中会携带traceback报错信息，方便调试和定位问题。） - """ - import time, random - from concurrent.futures import ThreadPoolExecutor - from request_llm.bridge_all import predict_no_ui_long_connection - assert len(inputs_array) == len(history_array) - assert len(inputs_array) == len(sys_prompt_array) - if max_workers == -1: # 读取配置文件 - try: max_workers, = get_conf('DEFAULT_WORKER_NUM') - except: max_workers = 8 - if max_workers <= 0 or max_workers >= 20: max_workers = 8 - # 屏蔽掉 chatglm的多线程，可能会导致严重卡顿 - if not (llm_kwargs['llm_model'].startswith('gpt-') or llm_kwargs['llm_model'].startswith('api2d-')): - max_workers = 1 - - executor = ThreadPoolExecutor(max_workers=max_workers) - n_frag = len(inputs_array) - # 用户反馈 - chatbot.append(["请开始多线程操作。", ""]) - yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 - # 跨线程传递 - mutable = [["", time.time(), "等待中"] for _ in range(n_frag)] - - # 子线程任务 - def _req_gpt(index, inputs, history, sys_prompt): - gpt_say = "" - retry_op = retry_times_at_unknown_error - exceeded_cnt = 0 - mutable[index][2] = "执行中" - while True: - # watchdog error - if len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > 5: - raise RuntimeError("检测到程序终止。") - try: - # 【第一种情况】：顺利完成 - # time.sleep(10); raise RuntimeError("测试") - gpt_say = predict_no_ui_long_connection( - inputs=inputs, llm_kwargs=llm_kwargs, history=history, - sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True - ) - mutable[index][2] = "已成功" - return gpt_say - except ConnectionAbortedError as token_exceeded_error: - # 【第二种情况】：Token溢出， - if handle_token_exceed: - exceeded_cnt += 1 - # 【选择处理】 尝试计算比例，尽可能多地保留文本 - from toolbox import get_reduce_token_percent - p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error)) - MAX_TOKEN = 4096 - EXCEED_ALLO = 512 + 512 * exceeded_cnt - inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO) - gpt_say += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n' - mutable[index][2] = f"截断重试" - continue # 返回重试 - else: - # 【选择放弃】 - tb_str = '```\n' + traceback.format_exc() + '```' - gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n" - if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0] - mutable[index][2] = "输入过长已放弃" - return gpt_say # 放弃 - except: - # 【第三种情况】：其他错误 - tb_str = '```\n' + traceback.format_exc() + '```' - print(tb_str) - gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n" - if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0] - if retry_op > 0: - retry_op -= 1 - wait = random.randint(5, 20) - if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str): - wait = wait * 3 - fail_info = "OpenAI绑定信用卡可解除频率限制 " - else: - fail_info = "" - # 也许等待十几秒后，情况会好转 - for i in range(wait): - mutable[index][2] = f"{fail_info}等待重试 {wait-i}"; time.sleep(1) - # 开始重试 - mutable[index][2] = f"重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}" - continue # 返回重试 - else: - mutable[index][2] = "已失败" - wait = 5 - time.sleep(5) - return gpt_say # 放弃 - - # 异步任务开始 - futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip( - range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)] - cnt = 0 - while True: - # yield一次以刷新前端页面 - time.sleep(refresh_interval) - cnt += 1 - worker_done = [h.done() for h in futures] - if all(worker_done): - executor.shutdown() - break - # 更好的UI视觉效果 - observe_win = [] - # 每个线程都要“喂狗”（看门狗） - for thread_index, _ in enumerate(worker_done): - mutable[thread_index][1] = time.time() - # 在前端打印些好玩的东西 - for thread_index, _ in enumerate(worker_done): - print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\ - replace('\n', '').replace('```', '...').replace( - ' ', '.').replace('
', '.....').replace('$', '.')+"`... ]" - observe_win.append(print_something_really_funny) - # 在前端打印些好玩的东西 - stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n' - if not done else f'`{mutable[thread_index][2]}`\n\n' - for thread_index, done, obs in zip(range(len(worker_done)), worker_done, observe_win)]) - # 在前端打印些好玩的东西 - chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))] - yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 - - # 异步任务结束 - gpt_response_collection = [] - for inputs_show_user, f in zip(inputs_show_user_array, futures): - gpt_res = f.result() - gpt_response_collection.extend([inputs_show_user, gpt_res]) - - # 是否在结束时，在界面上显示结果 - if show_user_at_complete: - for inputs_show_user, f in zip(inputs_show_user_array, futures): - gpt_res = f.result() - chatbot.append([inputs_show_user, gpt_res]) - yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面 - time.sleep(0.3) - return gpt_response_collection - - -def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit): - def cut(txt_tocut, must_break_at_empty_line): # 递归 - if get_token_fn(txt_tocut) <= limit: - return [txt_tocut] - else: - lines = txt_tocut.split('\n') - estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines) - estimated_line_cut = int(estimated_line_cut) - for cnt in reversed(range(estimated_line_cut)): - if must_break_at_empty_line: - if lines[cnt] != "": - continue - print(cnt) - prev = "\n".join(lines[:cnt]) - post = "\n".join(lines[cnt:]) - if get_token_fn(prev) < limit: - break - if cnt == 0: - raise RuntimeError("存在一行极长的文本！") - # print(len(post)) - # 列表递归接龙 - result = [prev] - result.extend(cut(post, must_break_at_empty_line)) - return result - try: - return cut(txt, must_break_at_empty_line=True) - except RuntimeError: - return cut(txt, must_break_at_empty_line=False) - - -def force_breakdown(txt, limit, get_token_fn): - """ - 当无法用标点、空行分割时，我们用最暴力的方法切割 - """ - for i in reversed(range(len(txt))): - if get_token_fn(txt[:i]) < limit: - return txt[:i], txt[i:] - return "Tiktoken未知错误", "Tiktoken未知错误" - -def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit): - # 递归 - def cut(txt_tocut, must_break_at_empty_line, break_anyway=False): - if get_token_fn(txt_tocut) <= limit: - return [txt_tocut] - else: - lines = txt_tocut.split('\n') - estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines) - estimated_line_cut = int(estimated_line_cut) - cnt = 0 - for cnt in reversed(range(estimated_line_cut)): - if must_break_at_empty_line: - if lines[cnt] != "": - continue - prev = "\n".join(lines[:cnt]) - post = "\n".join(lines[cnt:]) - if get_token_fn(prev) < limit: - break - if cnt == 0: - if break_anyway: - prev, post = force_breakdown(txt_tocut, limit, get_token_fn) - else: - raise RuntimeError(f"存在一行极长的文本！{txt_tocut}") - # print(len(post)) - # 列表递归接龙 - result = [prev] - result.extend(cut(post, must_break_at_empty_line, break_anyway=break_anyway)) - return result - try: - # 第1次尝试，将双空行（\n\n）作为切分点 - return cut(txt, must_break_at_empty_line=True) - except RuntimeError: - try: - # 第2次尝试，将单空行（\n）作为切分点 - return cut(txt, must_break_at_empty_line=False) - except RuntimeError: - try: - # 第3次尝试，将英文句号（.）作为切分点 - res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在 - return [r.replace('。\n', '.') for r in res] - except RuntimeError as e: - try: - # 第4次尝试，将中文句号（。）作为切分点 - res = cut(txt.replace('。', '。。\n'), must_break_at_empty_line=False) - return [r.replace('。。\n', '。') for r in res] - except RuntimeError as e: - # 第5次尝试，没办法了，随便切一下敷衍吧 - return cut(txt, must_break_at_empty_line=False, break_anyway=True) - - - -def read_and_clean_pdf_text(fp): - """ - 这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好 - - **输入参数说明** - - `fp`：需要读取和清理文本的pdf文件路径 - - **输出参数说明** - - `meta_txt`：清理后的文本内容字符串 - - `page_one_meta`：第一页清理后的文本内容列表 - - **函数功能** - 读取pdf文件并清理其中的文本内容，清理规则包括： - - 提取所有块元的文本信息，并合并为一个字符串 - - 去除短块（字符数小于100）并替换为回车符 - - 清理多余的空行 - - 合并小写字母开头的段落块并替换为空格 - - 清除重复的换行 - - 将每个换行符替换为两个换行符，使每个段落之间有两个换行符分隔 - """ - import fitz, copy - import re - import numpy as np - from colorful import print亮黄, print亮绿 - fc = 0 # Index 0 文本 - fs = 1 # Index 1 字体 - fb = 2 # Index 2 框框 - REMOVE_FOOT_NOTE = True # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等） - REMOVE_FOOT_FFSIZE_PERCENT = 0.95 # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化） - def primary_ffsize(l): - """ - 提取文本块主字体 - """ - fsize_statiscs = {} - for wtf in l['spans']: - if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0 - fsize_statiscs[wtf['size']] += len(wtf['text']) - return max(fsize_statiscs, key=fsize_statiscs.get) - - def ffsize_same(a,b): - """ - 提取字体大小是否近似相等 - """ - return abs((a-b)/max(a,b)) < 0.02 - - with fitz.open(fp) as doc: - meta_txt = [] - meta_font = [] - - meta_line = [] - meta_span = [] - ############################## <第 1 步，搜集初始信息> ################################## - for index, page in enumerate(doc): - # file_content += page.get_text() - text_areas = page.get_text("dict") # 获取页面上的文本信息 - for t in text_areas['blocks']: - if 'lines' in t: - pf = 998 - for l in t['lines']: - txt_line = "".join([wtf['text'] for wtf in l['spans']]) - if len(txt_line) == 0: continue - pf = primary_ffsize(l) - meta_line.append([txt_line, pf, l['bbox'], l]) - for wtf in l['spans']: # for l in t['lines']: - meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])]) - # meta_line.append(["NEW_BLOCK", pf]) - # 块元提取 for each word segment with in line for each line cross-line words for each block - meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace( - '- ', '') for t in text_areas['blocks'] if 'lines' in t]) - meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']]) - for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t]) - if index == 0: - page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace( - '- ', '') for t in text_areas['blocks'] if 'lines' in t] - - ############################## <第 2 步，获取正文主字体> ################################## - fsize_statiscs = {} - for span in meta_span: - if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0 - fsize_statiscs[span[1]] += span[2] - main_fsize = max(fsize_statiscs, key=fsize_statiscs.get) - if REMOVE_FOOT_NOTE: - give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT - - ############################## <第 3 步，切分和重新整合> ################################## - mega_sec = [] - sec = [] - for index, line in enumerate(meta_line): - if index == 0: - sec.append(line[fc]) - continue - if REMOVE_FOOT_NOTE: - if meta_line[index][fs] <= give_up_fize_threshold: - continue - if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]): - # 尝试识别段落 - if meta_line[index][fc].endswith('.') and\ - (meta_line[index-1][fc] != 'NEW_BLOCK') and \ - (meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7: - sec[-1] += line[fc] - sec[-1] += "\n\n" - else: - sec[-1] += " " - sec[-1] += line[fc] - else: - if (index+1 < len(meta_line)) and \ - meta_line[index][fs] > main_fsize: - # 单行 + 字体大 - mega_sec.append(copy.deepcopy(sec)) - sec = [] - sec.append("# " + line[fc]) - else: - # 尝试识别section - if meta_line[index-1][fs] > meta_line[index][fs]: - sec.append("\n" + line[fc]) - else: - sec.append(line[fc]) - mega_sec.append(copy.deepcopy(sec)) - - finals = [] - for ms in mega_sec: - final = " ".join(ms) - final = final.replace('- ', ' ') - finals.append(final) - meta_txt = finals - - ############################## <第 4 步，乱七八糟的后处理> ################################## - def 把字符太少的块清除为回车(meta_txt): - for index, block_txt in enumerate(meta_txt): - if len(block_txt) < 100: - meta_txt[index] = '\n' - return meta_txt - meta_txt = 把字符太少的块清除为回车(meta_txt) - - def 清理多余的空行(meta_txt): - for index in reversed(range(1, len(meta_txt))): - if meta_txt[index] == '\n' and meta_txt[index-1] == '\n': - meta_txt.pop(index) - return meta_txt - meta_txt = 清理多余的空行(meta_txt) - - def 合并小写开头的段落块(meta_txt): - def starts_with_lowercase_word(s): - pattern = r"^[a-z]+" - match = re.match(pattern, s) - if match: - return True - else: - return False - for _ in range(100): - for index, block_txt in enumerate(meta_txt): - if starts_with_lowercase_word(block_txt): - if meta_txt[index-1] != '\n': - meta_txt[index-1] += ' ' - else: - meta_txt[index-1] = '' - meta_txt[index-1] += meta_txt[index] - meta_txt[index] = '\n' - return meta_txt - meta_txt = 合并小写开头的段落块(meta_txt) - meta_txt = 清理多余的空行(meta_txt) - - meta_txt = '\n'.join(meta_txt) - # 清除重复的换行 - for _ in range(5): - meta_txt = meta_txt.replace('\n\n', '\n') - - # 换行 -> 双换行 - meta_txt = meta_txt.replace('\n', '\n\n') - - ############################## <第 5 步，展示分割效果> ################################## - # for f in finals: - # print亮黄(f) - # print亮绿('***************************') - - return meta_txt, page_one_meta diff --git a/spaces/sunwaee/MT5-Questions-Answers-Generation-Extraction/mt5.py b/spaces/sunwaee/MT5-Questions-Answers-Generation-Extraction/mt5.py deleted file mode 100644 index 7906a2007bc1273773b57e2fe18d396525708707..0000000000000000000000000000000000000000 --- a/spaces/sunwaee/MT5-Questions-Answers-Generation-Extraction/mt5.py +++ /dev/null @@ -1,141 +0,0 @@ -# coding:utf-8 -""" -Filename: mt5.py -Author: @DvdNss - -Created on 12/30/2021 -""" - -from typing import List - -from pytorch_lightning import LightningModule -from transformers import MT5ForConditionalGeneration, AutoTokenizer - - -class MT5(LightningModule): - """ - Google MT5 transformer class. - """ - - def __init__(self, model_name_or_path: str = None): - """ - Initialize module. - - :param model_name_or_path: model name - """ - - super().__init__() - - # Load model and tokenizer - self.save_hyperparameters() - self.model = MT5ForConditionalGeneration.from_pretrained( - model_name_or_path) if model_name_or_path is not None else None - self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, - use_fast=True) if model_name_or_path is not None else None - - def forward(self, **inputs): - """ - Forward inputs. - - :param inputs: dictionary of inputs (input_ids, attention_mask, labels) - """ - - return self.model(**inputs) - - def qa(self, batch: List[dict], max_length: int = 512, **kwargs): - """ - Question answering prediction. - - :param batch: batch of dict {question: q, context: c} - :param max_length: max length of output - """ - - # Transform inputs - inputs = [f"question: {context['question']} context: {context['context']}" for context in batch] - - # Predict - outputs = self.predict(inputs=inputs, max_length=max_length, **kwargs) - - return outputs - - def qg(self, batch: List[str] = None, max_length: int = 512, **kwargs): - """ - Question generation prediction. - - :param batch: batch of context with highlighted elements - :param max_length: max length of output - """ - - # Transform inputs - inputs = [f"generate: {context}" for context in batch] - - # Predict - outputs = self.predict(inputs=inputs, max_length=max_length, **kwargs) - - return outputs - - def ae(self, batch: List[str], max_length: int = 512, **kwargs): - """ - Answer extraction prediction. - - :param batch: list of context - :param max_length: max length of output - """ - - # Transform inputs - inputs = [f"extract: {context}" for context in batch] - - # Predict - outputs = self.predict(inputs=inputs, max_length=max_length, **kwargs) - - return outputs - - def multitask(self, batch: List[str], max_length: int = 512, **kwargs): - """ - Answer extraction + question generation + question answering. - - :param batch: list of context - :param max_length: max length of outputs - """ - - # Build output dict - dict_batch = {'context': [context for context in batch], 'answers': [], 'questions': [], 'answers_bis': []} - - # Iterate over context - for context in batch: - answers = self.ae(batch=[context], max_length=max_length, **kwargs)[0] - answers = answers.split('') - answers = [ans.strip() for ans in answers if ans != ' '] - dict_batch['answers'].append(answers) - for_qg = [f"{context.replace(ans, f' {ans} ')}" for ans in answers] - questions = self.qg(batch=for_qg, max_length=max_length, **kwargs) - dict_batch['questions'].append(questions) - new_answers = self.qa([{'context': context, 'question': question} for question in questions], - max_length=max_length, **kwargs) - dict_batch['answers_bis'].append(new_answers) - return dict_batch - - def predict(self, inputs, max_length, **kwargs): - """ - Inference processing. - - :param inputs: list of inputs - :param max_length: max_length of outputs - """ - - # Tokenize inputs - inputs = self.tokenizer(inputs, max_length=max_length, padding='max_length', truncation=True, - return_tensors="pt") - - # Retrieve input_ids and attention_mask - input_ids = inputs.input_ids.to(self.model.device) - attention_mask = inputs.attention_mask.to(self.model.device) - - # Predict - outputs = self.model.generate(input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, - **kwargs) - - # Decode outputs - predictions = self.tokenizer.batch_decode(outputs, skip_special_tokens=True) - - return predictions diff --git a/spaces/supertori/files/stable-diffusion-webui/modules/hashes.py b/spaces/supertori/files/stable-diffusion-webui/modules/hashes.py deleted file mode 100644 index 46abf99c304b23bf8e3e394e07c2209d4130afef..0000000000000000000000000000000000000000 --- a/spaces/supertori/files/stable-diffusion-webui/modules/hashes.py +++ /dev/null @@ -1,91 +0,0 @@ -import hashlib -import json -import os.path - -import filelock - -from modules import shared -from modules.paths import data_path - - -cache_filename = os.path.join(data_path, "cache.json") -cache_data = None - - -def dump_cache(): - with filelock.FileLock(cache_filename+".lock"): - with open(cache_filename, "w", encoding="utf8") as file: - json.dump(cache_data, file, indent=4) - - -def cache(subsection): - global cache_data - - if cache_data is None: - with filelock.FileLock(cache_filename+".lock"): - if not os.path.isfile(cache_filename): - cache_data = {} - else: - with open(cache_filename, "r", encoding="utf8") as file: - cache_data = json.load(file) - - s = cache_data.get(subsection, {}) - cache_data[subsection] = s - - return s - - -def calculate_sha256(filename): - hash_sha256 = hashlib.sha256() - blksize = 1024 * 1024 - - with open(filename, "rb") as f: - for chunk in iter(lambda: f.read(blksize), b""): - hash_sha256.update(chunk) - - return hash_sha256.hexdigest() - - -def sha256_from_cache(filename, title): - hashes = cache("hashes") - ondisk_mtime = os.path.getmtime(filename) - - if title not in hashes: - return None - - cached_sha256 = hashes[title].get("sha256", None) - cached_mtime = hashes[title].get("mtime", 0) - - if ondisk_mtime > cached_mtime or cached_sha256 is None: - return None - - return cached_sha256 - - -def sha256(filename, title): - hashes = cache("hashes") - - sha256_value = sha256_from_cache(filename, title) - if sha256_value is not None: - return sha256_value - - if shared.cmd_opts.no_hashing: - return None - - print(f"Calculating sha256 for {filename}: ", end='') - sha256_value = calculate_sha256(filename) - print(f"{sha256_value}") - - hashes[title] = { - "mtime": os.path.getmtime(filename), - "sha256": sha256_value, - } - - dump_cache() - - return sha256_value - - - - - diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Asus Drivers Update Utility License Key19.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Asus Drivers Update Utility License Key19.md deleted file mode 100644 index f75a4e33b9ce3e361e7c973cecd60cb55941c08d..0000000000000000000000000000000000000000 --- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Asus Drivers Update Utility License Key19.md +++ /dev/null @@ -1,6 +0,0 @@ - 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\ No newline at end of file diff --git a/spaces/text-generation-inference/chat-ui/vite.config.ts b/spaces/text-generation-inference/chat-ui/vite.config.ts deleted file mode 100644 index bbf8c7da43f0080dc6b9fb275f9583b7c17f1506..0000000000000000000000000000000000000000 --- a/spaces/text-generation-inference/chat-ui/vite.config.ts +++ /dev/null @@ -1,6 +0,0 @@ -import { sveltekit } from '@sveltejs/kit/vite'; -import { defineConfig } from 'vite'; - -export default defineConfig({ - plugins: [sveltekit()] -}); diff --git a/spaces/thejagstudio/procom/main/migrations/__init__.py b/spaces/thejagstudio/procom/main/migrations/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/theonerichy/wd-v1-4-tags/README.md b/spaces/theonerichy/wd-v1-4-tags/README.md deleted file mode 100644 index a553789ff8ab1de5a879c1bd9e6b4ef1b32e2a98..0000000000000000000000000000000000000000 --- a/spaces/theonerichy/wd-v1-4-tags/README.md +++ /dev/null @@ -1,39 +0,0 @@ ---- -title: WaifuDiffusion v1.4 Tags -emoji: 💬 -colorFrom: blue -colorTo: red -sdk: gradio -sdk_version: 3.16.2 -app_file: app.py -pinned: false -duplicated_from: SmilingWolf/wd-v1-4-tags ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio`, `streamlit`, or `static` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Ford IDS Calibration Files v81 Free Download - Car-Auto-Repair.com[3] This is another blog post that provides similar information and links as the previous one but with a different layout and format..md b/spaces/tialenAdioni/chat-gpt-api/logs/Ford IDS Calibration Files v81 Free Download - Car-Auto-Repair.com[3] This is another blog post that provides similar information and links as the previous one but with a different layout and format..md deleted file mode 100644 index 12e62cf54533714c4422544c72d4d5127e9f949e..0000000000000000000000000000000000000000 --- a/spaces/tialenAdioni/chat-gpt-api/logs/Ford IDS Calibration Files v81 Free Download - Car-Auto-Repair.com[3] This is another blog post that provides similar information and links as the previous one but with a different layout and format..md +++ /dev/null @@ -1,62 +0,0 @@ -
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-How to use Ford Ids V86 and Calibration FilesOnce you have the file name, you can follow these steps:
--
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Method 2: Using Third-Party File Hosting Services
-To use this method, you do not need to know the calibration file name that you need for your vehicle. You can simply download all the calibration files that are available for the Ford IDS v81 software. However, this method may take longer and require more disk space than the first method.
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-Un riddim es un término utilizado en la música jamaicana para describir un ritmo o pista instrumental que es utilizado por diferentes artistas para crear sus propias canciones. Un riddim puede ser original o muestreado de otra canción, y generalmente consiste en un patrón de batería, una línea de bajo y algunos otros elementos. Un riddim puede tener un tema o un estado de ánimo que refleje su nombre o productor.
-Conquest Paradise Riddim es un riddim que fue lanzado en febrero de 2019 por Damage Musiq, un sello discográfico fundado por Dwayne Parkinson, también conocido como Damage. Damage Musiq es conocido por producir riddims de alta calidad que cuentan con algunos de los mejores artistas de dancehall y reggae en Jamaica y más allá. Algunos de sus riddims anteriores incluyen Holocaust Riddim, Levels Riddim, Rope Riddim y Dark Temptation Riddim.
-Los artistas y canciones que aparecen en el riddim
-Conquest Paradise Riddim cuenta con 10 canciones de 9 artistas diferentes, incluyendo algunos de los nombres más populares en la escena reggae. Las canciones son las siguientes:
- -Las canciones de Conquest Paradise Riddim son en su mayoría positivas y edificantes, con temas como la gratitud, la perseverancia, la fe, el éxito y el amor. El riddim tiene un sonido suave y melódico, con un riff de guitarra pegadizo, un bajo pulsante y un ritmo de batería constante. El riddim es adecuado para relajarse y bailar, y muestra la versatilidad y el talento de los artistas y el productor.
-¿Por qué descargar Conquest Paradise Riddim?
-Los beneficios de poseer tu música
-Download Conquest Paradise Riddim es una gran manera de disfrutar de su música en cualquier momento y en cualquier lugar, sin depender de la conexión a Internet o servicios de transmisión. Al descargar el riddim, puedes tener control total sobre tu biblioteca de música, y puedes crear tus propias listas de reproducción, mixtapes o álbumes. También puede compartir su música con sus amigos y familiares, o reproducirla en cualquier dispositivo o altavoz que desee. Descargar tu música también significa que puedes apoyar a los artistas y al productor directamente, mostrándoles tu aprecio y lealtad.
-La calidad y variedad del riddim
-Otra razón para descargar Conquest Paradise Riddim es que es un riddim de alta calidad y diversa que satisfará a cualquier amante del reggae. El riddim tiene un sonido nítido y claro, sin distorsión ni ruido. Las canciones están bien producidas y bien mezcladas, con voces e instrumentos equilibrados. El riddim también ofrece una variedad de estilos y vibraciones, desde las canciones inspiradoras y motivadoras de Tommy Lee Sparta, Chronic Law, Shane O, Singer J, Tamo J y Gott Yo, hasta las canciones románticas y sensuales de Shatta Wale y Jah Vinci, y las canciones conscientes y sociales de Jahmiel. Hay algo para todos en Conquest Paradise Riddim, y no te aburrirás de escucharlo.
- -Las cuestiones legales y éticas de descargar música gratuita
-Antes de mostrarle cómo descargar Conquest Paradise Riddim gratis, tenemos que advertirle sobre los problemas legales y éticos de hacerlo. Descargar música gratis de fuentes no autorizadas se considera piratería, que es una forma de robo que viola los derechos de propiedad intelectual de los artistas y el productor. La piratería puede tener consecuencias negativas para la industria musical, como una reducción de los ingresos, una menor calidad, menos innovación y menos incentivos para que surjan nuevos artistas. La piratería también puede exponerlo a malware, virus, spyware u otro software dañino que puede dañar su dispositivo o comprometer su privacidad. Por lo tanto, no recomendamos ni aprobamos la descarga de música gratuita de fuentes ilegales, y le aconsejamos que lo haga bajo su propio riesgo.
-Los sitios web y aplicaciones que ofrecen descargas gratuitas del riddim
-Si todavía quieres descargar Conquest Paradise Riddim gratis, a pesar de los riesgos que implica, hay algunos sitios web y aplicaciones que afirman ofrecer descargas gratuitas de la riddim. Sin embargo, no podemos garantizar su seguridad, fiabilidad o calidad. Algunos de estos sitios web y aplicaciones son:
--
-
Cómo descargar Conquest Paradise Riddim por una tarifa?
-Las ventajas de comprar música online
-Si desea descargar Conquest Paradise Riddim legal y éticamente, puede optar por comprarlo en línea de fuentes autorizadas. Comprar música online tiene muchas ventajas, como:
--
-
Las plataformas y servicios que venden el riddim
-Hay muchas plataformas y servicios que venden Conquest Paradise Riddim en línea, por un precio razonable. Algunas de estas plataformas y servicios son:
--
-
Un resumen de los puntos principales y una llamada a la acción
-En conclusión, Conquest Paradise Riddim es un imprescindible para cualquier amante de la música reggae. Es un riddim de alta calidad y diversa que cuenta con algunos de los mejores artistas y canciones del género. También es un riddim positivo y edificante que te inspirará y te hará sentir bien. Puedes descargar Conquest Paradise Riddim gratis o por una tarifa, dependiendo de tu preferencia y presupuesto. Sin embargo, le recomendamos que compre el riddim en línea de fuentes autorizadas, para apoyar a los artistas y al productor, y para disfrutar de la mejor calidad y experiencia. Entonces, ¿qué estás esperando? Descargar Conquest Paradise Riddim hoy y disfrutar de las vibraciones!
-Preguntas frecuentes
-¿Qué es un riddim?
-Un riddim es un término utilizado en la música jamaicana para describir un ritmo o pista instrumental que es utilizado por diferentes artistas para crear sus propias canciones.
-¿Qué es la música reggae?
-El reggae es un género de música que se originó en Jamaica a finales de los años 60. Se caracteriza por su ritmo distintivo, línea de bajo, acordes de guitarra, voces y letras. La música reggae a menudo expresa mensajes sociales y políticos, así como valores culturales y espirituales.
-¿Cómo puedo reproducir la música descargada en mi dispositivo?
-Puede reproducir la música descargada en su dispositivo mediante el uso de cualquier aplicación de reproductor de medios que soporta mp3 u otros formatos de audio. También puede transferir la música descargada a otros dispositivos o altavoces a través de Bluetooth, USB o Wi-Fi.
-¿Cómo puedo apoyar a los artistas y productores del riddim?
-Puedes apoyar a los artistas y productores del riddim comprando su música en línea de fuentes autorizadas, transmitiendo su música en plataformas legales, siguiéndola en redes sociales, compartiendo su música con tus amigos y familiares, asistiendo a sus conciertos o eventos, o enviándoles comentarios o donaciones.
-¿Dónde puedo encontrar más música reggae online?
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\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/mbcsgroupprober.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/mbcsgroupprober.py deleted file mode 100644 index 6cb9cc7b3bc751fbb5a54ba06eaaf953bf14ed8d..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/mbcsgroupprober.py +++ /dev/null @@ -1,57 +0,0 @@ -######################## BEGIN LICENSE BLOCK ######################## -# The Original Code is Mozilla Universal charset detector code. -# -# The Initial Developer of the Original Code is -# Netscape Communications Corporation. -# Portions created by the Initial Developer are Copyright (C) 2001 -# the Initial Developer. All Rights Reserved. -# -# Contributor(s): -# Mark Pilgrim - port to Python -# Shy Shalom - original C code -# Proofpoint, Inc. -# -# This library is free software; you can redistribute it and/or -# modify it under the terms of the GNU Lesser General Public -# License as published by the Free Software Foundation; either -# version 2.1 of the License, or (at your option) any later version. -# -# This library is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU -# Lesser General Public License for more details. -# -# You should have received a copy of the GNU Lesser General Public -# License along with this library; if not, write to the Free Software -# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA -# 02110-1301 USA -######################### END LICENSE BLOCK ######################### - -from .big5prober import Big5Prober -from .charsetgroupprober import CharSetGroupProber -from .cp949prober import CP949Prober -from .enums import LanguageFilter -from .eucjpprober import EUCJPProber -from .euckrprober import EUCKRProber -from .euctwprober import EUCTWProber -from .gb2312prober import GB2312Prober -from .johabprober import JOHABProber -from .sjisprober import SJISProber -from .utf8prober import UTF8Prober - - -class MBCSGroupProber(CharSetGroupProber): - def __init__(self, lang_filter: LanguageFilter = LanguageFilter.NONE) -> None: - super().__init__(lang_filter=lang_filter) - self.probers = [ - UTF8Prober(), - SJISProber(), - EUCJPProber(), - GB2312Prober(), - EUCKRProber(), - CP949Prober(), - Big5Prober(), - EUCTWProber(), - JOHABProber(), - ] - self.reset() diff --git a/spaces/Billius/VizLib-TopLargeHospitalsNewJersey-04-07-2023/README.md b/spaces/Billius/VizLib-TopLargeHospitalsNewJersey-04-07-2023/README.md deleted file mode 100644 index 03aaad6de2128a38ca71d6045222adba7e4a659b..0000000000000000000000000000000000000000 --- a/spaces/Billius/VizLib-TopLargeHospitalsNewJersey-04-07-2023/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: VizLib TopLargeHospitalsNewJersey 04 07 2023 -emoji: 💻 -colorFrom: yellow -colorTo: purple -sdk: streamlit -sdk_version: 1.17.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/.github/ISSUE_TEMPLATE/feature-request.md b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/.github/ISSUE_TEMPLATE/feature-request.md deleted file mode 100644 index e2c80f65dbba13c14d00491ef9fb305c9614b1cc..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/.github/ISSUE_TEMPLATE/feature-request.md +++ /dev/null @@ -1,32 +0,0 @@ ---- -name: "\U0001F680Feature Request" -about: Submit a proposal/request for a new detectron2 feature - ---- - -## 🚀 Feature -A clear and concise description of the feature proposal. - - -## Motivation & Examples - -Tell us why the feature is useful. - -Describe what the feature would look like, if it is implemented. -Best demonstrated using **code examples** in addition to words. - -## Note - -We only consider adding new features if they are relevant to many users. - -If you request implementation of research papers -- -we only consider papers that have enough significance and prevalance. - -We do not take requests for most projects in the `projects/` directory, -because they are research code release that is mainly for other researchers to reproduce results. - -Instead of adding features inside detectron2, -you can implement many features by [extending detectron2](https://detectron2.readthedocs.io/tutorials/extend.html). -The [projects/](https://github.com/facebookresearch/detectron2/tree/master/projects/) directory -contains many of such examples. - diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/dev/packaging/gen_wheel_index.sh b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/dev/packaging/gen_wheel_index.sh deleted file mode 100644 index 0f5abc5ab2d17445e9f762f9275499112bd5fc27..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/dev/packaging/gen_wheel_index.sh +++ /dev/null @@ -1,26 +0,0 @@ -#!/bin/bash -e - - -root=$1 -if [[ -z "$root" ]]; then - echo "Usage: ./gen_wheel_index.sh /path/to/wheels" - exit -fi - -index=$root/index.html - -cd "$root" -for cu in cpu cu92 cu100 cu101; do - cd $cu - echo "Creating $PWD/index.html ..." - for whl in *.whl; do - echo "$whl
" - done > index.html - cd "$root" -done - -echo "Creating $index ..." -for whl in $(find . -type f -name '*.whl' -printf '%P\n' | sort); do - echo "$whl
" -done > "$index" - diff --git a/spaces/CVPR/LIVE/pybind11/tests/test_opaque_types.cpp b/spaces/CVPR/LIVE/pybind11/tests/test_opaque_types.cpp deleted file mode 100644 index 0d20d9a01c8592e844fb909b336fd5c8e969b9e0..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/pybind11/tests/test_opaque_types.cpp +++ /dev/null @@ -1,67 +0,0 @@ -/* - tests/test_opaque_types.cpp -- opaque types, passing void pointers - - Copyright (c) 2016 Wenzel Jakob
File Activation.xml Autocom Version 2.12.2 Keygen: How to Use It and Why You Should Avoid It
- -Autocom version 2.12.2 is a software tool that enables you to diagnose and program various vehicles using a computer and a compatible interface. It supports different protocols and interfaces, such as OBD-II, EOBD, CAN, J1850, ISO9141, KWP2000 and more. Autocom version 2.12.2 is the latest release that offers improved features and performance.
- -However, to use Autocom version 2.12.2, you need to activate it with a license or subscription that you can purchase from the official website or from an authorized dealer. Alternatively, you may have found a keygen that can generate a file activation.xml for you. A file activation.xml is a XML file that contains the information needed to activate Autocom version 2.12.2, such as the hardware key, the software version, the license type and the expiration date.
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In this article, we will show you how to use a keygen for Autocom version 2.12.2 and why you should avoid it.
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- -To use a keygen for Autocom version 2.12.2, you need to follow these steps:
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Why you should avoid using a keygen for Autocom version 2.12.2
- -A keygen for Autocom version 2.12.2 is not a safe or legal way to activate the software. Here are some reasons why you should avoid using it:
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The only safe and legal way to activate Autocom version 2.12.2 is to purchase a license or subscription from the official website or from an authorized dealer. This way, you can get access to all the features and updates of the software and enjoy its full functionality.
- -Conclusion
- -In this article, we have shown you how to use a keygen for Autocom version 2.12.2 and why you should avoid it. A keygen is not a safe or legal way to activate a software tool that can help you diagnose and program various vehicles using a computer and a compatible interface. We advise you to buy a genuine license or subscription for Autocom version 2.12.2 from the official website or from an authorized dealer.
-How to Use Autocom Version 2.12.2 for Vehicle Diagnosis and Programming
- -Once you have activated Autocom version 2.12.2 with a keygen or a license, you can use it to diagnose and program various vehicles using a computer and a compatible interface. Here are some steps to use Autocom version 2.12.2 for vehicle diagnosis and programming:
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- -Autocom version 2.12.2 is a software tool that has many advantages and disadvantages for vehicle diagnosis and programming. Here are some of them:
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Conclusion
- -In this article, we have shown you how to use a keygen for Autocom version 2.12.2, how to use Autocom version 2.12.2 for vehicle diagnosis and programming, and what are the advantages and disadvantages of Autocom version 2.12.2. We hope this article has been helpful for you to understand more about this software tool and how to use it effectively.
-Where to Download Autocom Version 2.12.2 and Keygen
- -If you want to download Autocom version 2.12.2 and keygen, you need to be careful about the source and the quality of the files. There are many websites that claim to offer free or cracked versions of Autocom version 2.12.2 and keygen, but they may be fake, infected or corrupted.
- -The best way to download Autocom version 2.12.2 and keygen is to use the official website or a trusted source that has positive reviews and feedback from other users. You can also use a reliable antivirus software and a VPN service to protect your computer and your privacy from any potential threats.
- -Here are some links that you can use to download Autocom version 2.12.2 and keygen:
- - - -How to Update Autocom Version 2.12.2
- -If you have activated Autocom version 2.12.2 with a keygen, you may not be able to update it to the latest version or get any support from the software developer. This can limit your access to new features, functions and vehicles that are added in the newer versions of the software.
- -If you want to update Autocom version 2.12.2, you need to purchase a license or subscription from the official website or from an authorized dealer. This way, you can get access to all the updates and support that are available for the software.
- -To update Autocom version 2.12.2, you need to follow these steps:
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Conclusion
- -In this article, we have shown you how to use a keygen for Autocom version 2.12.2, how to use Autocom version 2.12.2 for vehicle diagnosis and programming, what are the advantages and disadvantages of Autocom version 2.12.2, where to download Autocom version 2.12.2 and keygen, and how to update Autocom version 2.12.2. We hope this article has been helpful for you to understand more about this software tool and how to use it effectively.
-How to Choose the Best Interface for Autocom Version 2.12.2
- -To use Autocom version 2.12.2 for vehicle diagnosis and programming, you need to have a compatible interface that can connect your computer and your vehicle. There are many types and models of interfaces available on the market, but not all of them are suitable for Autocom version 2.12.2.
- -To choose the best interface for Autocom version 2.12.2, you need to consider these factors:
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- -If you have any questions or problems with Autocom version 2.12.2, you can get help and support from various sources:
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Conclusion
- -In this article, we have shown you how to use a keygen for Autocom version 2.12.2, how to use Autocom version 2.12.2 for vehicle diagnosis and programming, what are the advantages and disadvantages of Autocom version 2.12.2, where to download Autocom version 2.12.2 and keygen, how to update Autocom version 2.12.2, how to choose the best interface for Autocom version 2.12.2, and how to get help and support for Autocom version 2.12.2. We hope this article has been helpful for you to understand more about this software tool and how to use it effectively.
-Conclusion
- -In this article, we have shown you how to use a keygen for Autocom version 2.12.2, how to use Autocom version 2.12.2 for vehicle diagnosis and programming, what are the advantages and disadvantages of Autocom version 2.12.2, where to download Autocom version 2.12.2 and keygen, how to update Autocom version 2.12.2, how to choose the best interface for Autocom version 2.12.2, and how to get help and support for Autocom version 2.12.2. We hope this article has been helpful for you to understand more about this software tool and how to use it effectively.
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- diff --git a/spaces/fatiXbelha/sd/Download Minecraft 1.19.0.30 for Free and Enjoy the Spectator Mode and Other Features.md b/spaces/fatiXbelha/sd/Download Minecraft 1.19.0.30 for Free and Enjoy the Spectator Mode and Other Features.md deleted file mode 100644 index bdeff196cdea812d19b33c7fd8179b42e598655f..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Minecraft 1.19.0.30 for Free and Enjoy the Spectator Mode and Other Features.md +++ /dev/null @@ -1,64 +0,0 @@ -
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How to Download Minecraft 1.19.0.30 for Free - The Latest Version of Bedrock Edition
-Minecraft is one of the most popular and creative games in the world, with millions of players exploring, building, and surviving in infinite worlds. If you are a fan of Minecraft, you might be interested in downloading the latest version of Bedrock Edition, which is 1.19.0.30.
-Minecraft 1.19.0.30 is a beta version that introduces some exciting new features, such as a new viewer mode, a new biome called mangrove swamps, and a new mob called warden. In this article, we will show you how to download Minecraft 1.19.0.30 for free with a working Xbox Live, and what's new in this version.
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Introduction
-Minecraft 1.19.0.30 is a beta version that is part of the upcoming 1.19 update, also known as The Wild Update. This update aims to add more diversity and life to the game, with new biomes, mobs, blocks, items, and mechanics.
-To download Minecraft 1.19.0.30 for free, you need to have an Android device and join the beta program for Minecraft Bedrock Edition on Google Play Store. You also need to have an Xbox Live account to play online with other players. If you don't have one, you can create one for free on the official website. Once you have joined the beta program and signed in with your Xbox Live account, you can download Minecraft 1.19.0.30 from the Google Play Store and enjoy the new features.
-What's New in Minecraft 1.19.0.30?
-Minecraft 1.19.0.30 adds some new features that are still under development and may change or be removed in future versions. Here are some of the most notable ones:
-New Viewer Mode
-Viewer mode is a new feature that allows you to explore the world without affecting it or being affected by it. You can activate viewer mode by pressing F5 on your keyboard or tapping on the eye icon on your screen. In viewer mode, you can fly around, see through walls, and observe everything without being noticed by mobs or triggering traps. You can also switch between different perspectives by pressing F5 again or tapping on the eye icon again.
-Viewer mode affects sculk sensors and tripwire traps differently than normal mode. Sculk sensors are blocks that detect vibrations and emit redstone signals. In normal mode, sculk sensors will detect any vibration caused by you or other entities, such as walking, jumping, placing blocks, breaking blocks, etc. In viewer mode, however , sculk sensors will not detect any vibration caused by you, but they will still detect vibrations caused by other entities. This means you can sneak past sculk sensors without alerting them, but you can also observe how other entities interact with them. Tripwire traps are devices that consist of tripwire hooks and string. In normal mode, tripwire traps will activate when you or other entities cross the string, triggering redstone signals or dispensers. In viewer mode, tripwire traps will not activate when you cross the string, but they will still activate when other entities cross the string. This means you can avoid triggering tripwire traps yourself, but you can also watch how other entities trigger them.
-Mangrove Swamps
-Mangrove swamps are a new biome that can be found in warm ocean regions. They are characterized by shallow water, muddy soil, and mangrove trees. Mangrove trees are a new type of tree that have roots that grow above the water and branches that hang down. They can be grown on mud or dirt with water nearby, and they drop mangrove saplings and mangrove planks when broken. Mangrove planks are a new type of wood that can be used to craft various items and blocks, such as boats, doors, fences, etc. Mud is a new type of block that can be found in mangrove swamps and other wet areas. It has a brown color and a sticky texture, and it slows down entities that walk on it. It can also be used to craft mud bricks, which are a new type of building block.
-Warden
-Warden is a new mob that can be found in the deep dark, a new underground biome that is part of the upcoming 1.18 update. Warden is a large, blind creature that has a lot of health and deals a lot of damage. It uses its ears to detect vibrations and track down its prey. It can sense any movement or sound within a 64-block radius, and it will attack anything that makes noise or moves near it. Warden can also break through most blocks to reach its target, so hiding behind walls is not very effective.
-To fight warden, you need to be very careful and strategic. You can use stealth and distraction to avoid or confuse it. For example, you can use viewer mode to fly around without making noise or vibrations. You can also use sculk sensors or wool occlusion to block its hearing or create false signals. You can also use snowballs, arrows, eggs, or other projectiles to divert its attention or lure it away from you. You can also use sculk catalysts to temporarily disable its hearing. Sculk catalysts are a new type of item that can be crafted from sculk shards and copper ingots. They emit a purple light and a high-pitched sound when activated by redstone signals. When placed near warden, they will make it deaf for a few seconds, giving you a chance to escape or attack.
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Conclusion
-Minecraft 1.19.0.30 is a beta version that offers a glimpse of the upcoming 1.19 update, The Wild Update. It adds some new features that enhance the gameplay and the immersion of the game, such as viewer mode, mangrove swamps, and warden. If you want to try out these features for yourself, you can download Minecraft 1.19.0.30 for free with a working Xbox Live account on your Android device. Just remember that this is a beta version and it may have bugs or glitches, so make sure to back up your worlds before playing.
-We hope you enjoyed this article and learned something new about Minecraft 1.19.0.30. If you have any questions or feedback, feel free to leave them in the comments section below.
-FAQs
-Is Minecraft 1.19.0.30 compatible with older versions?
-No, Minecraft 1.19.0.30 is not compatible with older versions of the game. You can only play with other players who have the same version as you.
-How can I join the beta program for Minecraft Bedrock Edition?
-To join the beta program for Minecraft Bedrock Edition on Android devices, you need to follow these steps: - Open the Google Play Store app on your device. - Search for Minecraft and select it. - Scroll down to the bottom of the page and tap on "Join the beta". - Wait for the update to download and install. - Launch Minecraft and sign in with your Xbox Live account.
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download Monoposto 2023 Full Version in Minutes with This Simple Trick.md b/spaces/fatiXbelha/sd/Download Monoposto 2023 Full Version in Minutes with This Simple Trick.md deleted file mode 100644 index 901f33cb318c3a33feb6ceb7b44533fc337b2264..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Monoposto 2023 Full Version in Minutes with This Simple Trick.md +++ /dev/null @@ -1,122 +0,0 @@ -
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-If you are a fan of racing games, especially single seater open-wheel cars, you might have heard of Monoposto, an amazing independent racing game developed by Marco Pesce. The game has been around since 2017, but the latest version, Monoposto 2023, is the most exciting one yet. In this article, we will tell you what Monoposto 2023 is, what features it has, why you should download the full version, and how to do it.
-What is Monoposto 2023?
-Monoposto 2023 is a racing game that simulates the experience of driving a single seater open-wheel car, similar to Formula One. You can compete in the new 2023 season, with 24 racing tracks based on reality. You can also challenge other players online in multiplayer duels, or enjoy a quick race, a single race, or a championship mode. The game has realistic physics, graphics, and sounds, as well as dynamic weather conditions that affect the performance of your car. You can also customize your car and driver, and adjust your car setup before and during the race.
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-Monoposto 2023 has many features that make it one of the best racing games on the market. Here are some of them:
--24 realistic tracks
-The game has 24 racing tracks that are based on real circuits from around the world. You can race on famous tracks like Monaco, Silverstone, Spa-Francorchamps, Suzuka, and more. Each track has its own characteristics, such as curves, straights, elevation changes, and weather conditions. You can also choose the time of day and the number of laps for each race.
--Online multiplayer duel
-If you want to test your skills against other players, you can join the online multiplayer mode. You can challenge anyone who is online, or invite your friends to a private duel. You can also see your ranking and statistics on the leaderboard. The online mode is fast and smooth, with no lag or connection issues.
--Qualifying session and dynamic weather
-Before each race, you can participate in a qualifying session to determine your starting position on the grid. The qualifying session is timed and you have to complete at least one lap to qualify. The faster you are, the better your position will be. You can also pit during the qualifying session to change your tires or repair your car.
-The weather conditions can change during the race, affecting your visibility and grip. You can choose from sunny, cloudy, rainy, or stormy weather. The weather can also vary from track to track, adding more realism and challenge to the game.
--Pit stop and car setup
-During the race, you can pit to change your tires or repair your car. Depending on the weather and the track conditions, you can choose from different types of tires: soft, medium, hard, wet, or intermediate. You can also adjust your car setup before and during the race, such as the wing angle, brake balance, suspension stiffness, gear ratio, fuel load, and engine power.
-Customization of cars and drivers
-You can customize your car and driver to suit your preferences. You can choose from different colors, designs, stickers, and sponsors for your car. You can also change the name, number, helmet, and suit of your driver. You can save your customizations and use them in any mode of the game.
--Spectator TV mode race view
-If you want to watch the race from a different perspective, you can switch to the spectator TV mode race view. This mode allows you to see the race from various camera angles, such as the cockpit, the front wing, the rear wing, the side pod, the overhead, and the broadcast. You can also see the live timing, the lap chart, the standings, and the telemetry of each car.
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Why download Monoposto 2023 full version?
-Monoposto 2023 is a great game that offers a lot of fun and challenge for racing fans. However, if you want to enjoy the game to the fullest, you should download the full version. Here are some reasons why:
--Full unlocked game with no ads
-The full version of Monoposto 2023 gives you access to all the features and modes of the game, without any limitations or restrictions. You can play any track, any mode, any car, and any customization that you want. You also don't have to worry about annoying ads or in-app purchases that interrupt your gameplay.
--External and MFi game controller support
-If you want to have a more immersive and realistic experience, you can use an external or MFi game controller to play Monoposto 2023. The game supports most Bluetooth controllers that are compatible with Android or iOS devices. You can also adjust the sensitivity and calibration of your controller in the settings menu.
--Podium celebrations and improved graphics
-The full version of Monoposto 2023 also features podium celebrations after each race. You can see your driver celebrating with champagne and trophies on the podium, along with your team and rivals. The game also has improved graphics and animations, with more details and effects on the cars, tracks, and environments.
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-There are two ways to download Monoposto 2023 full version: from Google Play Store or App Store, or from other sources. Here are the steps for each method:
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-This is the easiest and safest way to download Monoposto 2023 full version. All you need is a compatible Android or iOS device and an internet connection. Here are the steps:
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-The price of Monoposto 2023 full version is $2.99 on both Google Play Store and App Store. The app size is about 300 MB on Android devices and about 400 MB on iOS devices. You may need additional storage space for updates and data files.
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Conclusion
-Monoposto 2023 is an awesome racing game that lets you drive a single seater open-wheel car on realistic tracks. The game has many features that make it fun and challenging, such as online multiplayer duel, qualifying session, dynamic weather, pit stop, car setup, and customization. You can download Monoposto 2023 full version from Google Play Store or App Store for $2.99, or from other sources for free. However, we recommend downloading from official sources to avoid any risks or problems. We hope this article helped you learn more about Monoposto 2023 and how to download it. Happy racing!
-FAQs
-Here are some frequently asked questions about Monoposto 2023:
--What are the system requirements for Monoposto 2023?
-The game requires Android 5.0 or higher, or iOS 10.0 or higher. It also requires at least 1 GB of RAM and 300 MB of storage space.
--Can I play Monoposto 2023 offline?
-Yes, you can play Monoposto 2023 offline in single player modes, such as quick race, single race, or championship. However, you need an internet connection to play online multiplayer mode or to access leaderboards and statistics.
--How can I contact the developer of Monoposto 2023?
-You can contact Marco Pesce, the developer of Monoposto 2023, by email at monopostogame@gmail.com, or by visiting his website at www.monopostogame.com. You can also follow him on Facebook, Twitter, Instagram, and YouTube for updates and news about the game.
--Can I play Monoposto 2023 on PC or console?
-No, Monoposto 2023 is only available for mobile devices. However, you can use an emulator or a mirroring software to play it on PC or console.
--Can I get a refund if I don't like Monoposto 2023?
-If you download Monoposto 2023 from Google Play Store or App Store, you can request a refund within 48 hours of purchase. You need to contact Google Play Store or App Store support and provide your order number and reason for refund. If you download Monoposto 2023 from other sources, you may not be able to get a refund.
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\ No newline at end of file diff --git a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/losses/perceptual_loss.py b/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/losses/perceptual_loss.py deleted file mode 100644 index 742b2b07f7ec65a4252146e55d0ddbbd10061917..0000000000000000000000000000000000000000 --- a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/losses/perceptual_loss.py +++ /dev/null @@ -1,111 +0,0 @@ -""" -Code borrowed from https://gist.github.com/alper111/8233cdb0414b4cb5853f2f730ab95a49#file-vgg_perceptual_loss-py-L5 -""" -import torch -import torchvision -from models.vggface import VGGFaceFeats - - -def cos_loss(fi, ft): - return 1 - torch.nn.functional.cosine_similarity(fi, ft).mean() - - -class VGGPerceptualLoss(torch.nn.Module): - def __init__(self, resize=False): - super(VGGPerceptualLoss, self).__init__() - blocks = [] - blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval()) - blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval()) - blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval()) - blocks.append(torchvision.models.vgg16(pretrained=True).features[16:23].eval()) - for bl in blocks: - for p in bl: - p.requires_grad = False - self.blocks = torch.nn.ModuleList(blocks) - self.transform = torch.nn.functional.interpolate - self.mean = torch.nn.Parameter(torch.tensor([0.485, 0.456, 0.406]).view(1,3,1,1)) - self.std = torch.nn.Parameter(torch.tensor([0.229, 0.224, 0.225]).view(1,3,1,1)) - self.resize = resize - - def forward(self, input, target, max_layer=4, cos_dist: bool = False): - target = (target + 1) * 0.5 - input = (input + 1) * 0.5 - - if input.shape[1] != 3: - input = input.repeat(1, 3, 1, 1) - target = target.repeat(1, 3, 1, 1) - input = (input-self.mean) / self.std - target = (target-self.mean) / self.std - if self.resize: - input = self.transform(input, mode='bilinear', size=(224, 224), align_corners=False) - target = self.transform(target, mode='bilinear', size=(224, 224), align_corners=False) - x = input - y = target - loss = 0.0 - loss_func = cos_loss if cos_dist else torch.nn.functional.l1_loss - for bi, block in enumerate(self.blocks[:max_layer]): - x = block(x) - y = block(y) - loss += loss_func(x, y.detach()) - return loss - - -class VGGFacePerceptualLoss(torch.nn.Module): - def __init__(self, weight_path: str = "checkpoint/vgg_face_dag.pt", resize: bool = False): - super().__init__() - self.vgg = VGGFaceFeats() - self.vgg.load_state_dict(torch.load(weight_path)) - - mean = torch.tensor(self.vgg.meta["mean"]).view(1, 3, 1, 1) / 255.0 - self.register_buffer("mean", mean) - - self.transform = torch.nn.functional.interpolate - self.resize = resize - - def forward(self, input, target, max_layer: int = 4, cos_dist: bool = False): - target = (target + 1) * 0.5 - input = (input + 1) * 0.5 - - # preprocessing - if input.shape[1] != 3: - input = input.repeat(1, 3, 1, 1) - target = target.repeat(1, 3, 1, 1) - input = input - self.mean - target = target - self.mean - if self.resize: - input = self.transform(input, mode='bilinear', size=(224, 224), align_corners=False) - target = self.transform(target, mode='bilinear', size=(224, 224), align_corners=False) - - input_feats = self.vgg(input) - target_feats = self.vgg(target) - - loss_func = cos_loss if cos_dist else torch.nn.functional.l1_loss - # calc perceptual loss - loss = 0.0 - for fi, ft in zip(input_feats[:max_layer], target_feats[:max_layer]): - loss = loss + loss_func(fi, ft.detach()) - return loss - - -class PerceptualLoss(torch.nn.Module): - def __init__( - self, lambda_vggface: float = 0.025 / 0.15, lambda_vgg: float = 1, eps: float = 1e-8, cos_dist: bool = False - ): - super().__init__() - self.register_buffer("lambda_vggface", torch.tensor(lambda_vggface)) - self.register_buffer("lambda_vgg", torch.tensor(lambda_vgg)) - self.cos_dist = cos_dist - - if lambda_vgg > eps: - self.vgg = VGGPerceptualLoss() - if lambda_vggface > eps: - self.vggface = VGGFacePerceptualLoss() - - def forward(self, input, target, eps=1e-8, use_vggface: bool = True, use_vgg=True, max_vgg_layer=4): - loss = 0.0 - if self.lambda_vgg > eps and use_vgg: - loss = loss + self.lambda_vgg * self.vgg(input, target, max_layer=max_vgg_layer) - if self.lambda_vggface > eps and use_vggface: - loss = loss + self.lambda_vggface * self.vggface(input, target, cos_dist=self.cos_dist) - return loss - diff --git a/spaces/fengmuxi/ChatGpt-Web/docs/vercel-cn.md b/spaces/fengmuxi/ChatGpt-Web/docs/vercel-cn.md deleted file mode 100644 index c492296944737156572ea8df8b43b866b3e695bf..0000000000000000000000000000000000000000 --- a/spaces/fengmuxi/ChatGpt-Web/docs/vercel-cn.md +++ /dev/null @@ -1,39 +0,0 @@ -# Vercel 的使用说明 - -## 如何新建项目 -当你从 Github fork 本项目之后，需要重新在 Vercel 创建一个全新的 Vercel 项目来重新部署，你需要按照下列步骤进行。 - - -1. 进入 Vercel 控制台首页； -2. 点击 Add New； -3. 选择 Project。 - - -1. 在 Import Git Repository 处，搜索 chatgpt-next-web； -2. 选中新 fork 的项目，点击 Import。 - - -1. 在项目配置页，点开 Environmane Variables 开始配置环境变量； -2. 依次新增名为 OPENAI_API_KEY 和 CODE 的环境变量； -3. 填入环境变量对应的值； -4. 点击 Add 确认增加环境变量； -5. 请确保你添加了 OPENAI_API_KEY，否则无法使用； -6. 点击 Deploy，创建完成，耐心等待 5 分钟左右部署完成。 - -## 如何增加自定义域名 -[TODO] - -## 如何更改环境变量 - -1. 进去 Vercel 项目内部控制台，点击顶部的 Settings 按钮； -2. 点击左侧的 Environment Variables； -3. 点击已有条目的右侧按钮； -4. 选择 Edit 进行编辑，然后保存即可。 - -⚠️️ 注意：每次修改完环境变量，你都需要[重新部署项目](#如何重新部署)来让改动生效！ - -## 如何重新部署 - -1. 进入 Vercel 项目内部控制台，点击顶部的 Deployments 按钮； -2. 选择列表最顶部一条的右侧按钮； -3. 点击 Redeploy 即可重新部署。 \ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Blades of Brim Mod APK Download - Unlock All Heroes and Blades.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Blades of Brim Mod APK Download - Unlock All Heroes and Blades.md deleted file mode 100644 index e246ad89e98d6c31be9532b66375020bcc898075..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Blades of Brim Mod APK Download - Unlock All Heroes and Blades.md +++ /dev/null @@ -1,231 +0,0 @@ -
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Tips and tricks for playing Blades of Brim
-Kill as many monsters as possible
-One of the main objectives of Blades of Brim is to kill as many monsters as possible during your run. This will not only increase your score, but also fill up your combo meter, which will give you extra coins, essence, runes, orbs, etc. You can kill monsters by slashing them with your sword or using your pets or power-ups. You can also use wall running and jumping skills to reach higher places where more monsters are waiting for you. Try to kill multiple monsters in one swipe or jump for bonus points.
-Collect runes and orbs
-Another important objective of Blades of Brim is to collect runes and orbs during your run. Runes are special symbols that appear randomly on the ground or in chests. They have different colors and shapes, such as red circles, blue triangles, green squares, etc. You need to collect three runes of the same color or shape in a row to activate a rune power-up, which will give you a temporary boost or effect, such as speed, shield, magnet, etc. Orbs are glowing spheres that appear randomly on the ground or in chests. They have different colors and effects, such as purple orbs that give you essence, yellow orbs that give you coins, green orbs that give you health, etc. You need to collect as many orbs as possible to increase your resources and survive longer.
-Upgrade your heroes and weapons
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-| Hero | -Image | -Ability | -Damage | -Speed | -Range | -Cost | -
|---|---|---|---|---|---|---|
| Lilith | - |
-Double Jump | -10 | -10 | -10 | -Free | -
| Zenobia | - |
-Glide | -15 | -15 | -15 | -1000 coins or 100 essence | -
| Ragnar | - |
-Dash Attack | -20 | -20 | -20 | -2000 coins or 200 essence | -
| Aurora | - |
- Magnetize Orbs | -25 | -25 | -25 | -3000 coins or 300 essence | -
| Sir Hugo | - |
- Bonus Chests | -30 | -30 | -30 | -4000 coins or 400 essence | -
| Weapon | -Image | -Type | -Damage | -Speed | -Range | -Cost | -
| Sword of Brimstone | - |
- Sword | -10 + 2 per level | -10 + 1 per level | -10 + 1 per level | -Free (default) | -
| Axe of Fury | - |
- Axe | -15 + 3 per level | -8 + 1 per level | -12 + 2 per level | -500 coins or 50 essence |
| Hammer of Justice | - |
- Hammer | -20 + 4 per level | -6 + 1 per level | -14 + 2 per level | -1000 coins or 100 essence | -
| Dagger of Destiny | - |
- Dagger | -8 + 2 per level | -12 + 2 per level | -8 + 1 per level | -1500 coins or 150 essence | -
| Spear of Light | - |
- Spear | -12 + 3 per level | -9 + 1 per level | -16 + 3 per level | -2000 coins or 200 essence | -
Conclusion
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\ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Create Combine and Explore with Alchemy Classic 2 Mod APK.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Create Combine and Explore with Alchemy Classic 2 Mod APK.md deleted file mode 100644 index c90168339ab5ba81cfa42d0459bf4569eddefc66..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Create Combine and Explore with Alchemy Classic 2 Mod APK.md +++ /dev/null @@ -1,109 +0,0 @@ -
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Alchemy Classic 2 Mod APK: A Fun and Creative Game for Android Users
-Do you love playing games that challenge your creativity and logic? Do you enjoy mixing and matching different elements to create new ones? If you answered yes, then you will love Alchemy Classic 2, a game that lets you explore the world of chemistry and physics in a fun and engaging way. And if you want to make the game even more exciting, you can download the Alchemy Classic 2 Mod APK, which gives you unlimited money, hints, and cheats to unlock all the elements and features in the game. In this article, we will tell you everything you need to know about Alchemy Classic 2 and how to download and install the modded version on your Android device.
-What is Alchemy Classic 2?
-Alchemy Classic 2 is a puzzle game developed by Niasoft, a company that specializes in creating educational and entertaining games for mobile devices. The game is a sequel to the popular Alchemy Classic, which has over 10 million downloads on Google Play. The game is based on the concept of alchemy, which is an ancient practice of transforming matter into different forms. In the game, you start with four basic elements: air, water, fire, and earth. By combining these elements, you can create new ones, such as steam, mud, lava, and sand. As you progress in the game, you can discover more complex and advanced elements, such as metals, plants, animals, humans, and even mythical creatures. The game has over 1000 elements to discover and collect, making it a great way to learn about science and nature while having fun.
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-The gameplay of Alchemy Classic 2 is simple and intuitive. You just need to drag and drop the elements from the inventory to the main screen and see what happens when they interact. If they can form a new element, it will appear on the screen and be added to your collection. If not, nothing will happen and you can try another combination. You can also tap on an element to see its properties and description, as well as its history and origin. The game has a user-friendly interface that allows you to easily navigate through the menus and options. You can also customize the game settings according to your preferences, such as changing the language, sound effects, background music, and theme.
-The features of Alchemy Classic 2
-Alchemy Classic 2 has many features that make it a fun and addictive game for Android users. Some of these features are:
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The benefits of Alchemy Classic 2 Mod APK
-If you want to enjoy Alchemy Classic 2 without any limitations or restrictions, you can download the Alchemy Classic 2 Mod APK, which gives you unlimited money, hints, and cheats to unlock all the elements and features in the game. The modded version of the game has many benefits, such as:
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The Alchemy Classic 2 Mod APK is safe and easy to download and install on your Android device. You just need to follow a few simple steps, which we will explain in the next section.
-How to download and install Alchemy Classic 2 Mod APK?
-To download and install the Alchemy Classic 2 Mod APK on your Android device, you need to follow these steps:
-Step 1: Download the APK file from a trusted source
-The first thing you need to do is to download the APK file of the modded version of the game from a reliable and secure source. You can use the link below to download the file directly to your device. The file size is about 50 MB, so make sure you have enough storage space and a stable internet connection.
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-Step 2: Enable unknown sources on your device
-The next thing you need to do is to enable unknown sources on your device. This is a security setting that allows you to install apps from sources other than Google Play. To enable unknown sources, you need to go to your device settings, then security, then toggle on the option that says "allow installation of apps from unknown sources". This will allow you to install the APK file that you downloaded in step 1.
-Step 3: Install the APK file and launch the game
-The final thing you need to do is to install the APK file and launch the game. To do this, you need to locate the file in your device's file manager or downloads folder, then tap on it to start the installation process. Follow the instructions on the screen and wait for a few seconds until the installation is complete. Once it is done, you can open the game and enjoy playing Alchemy Classic 2 with unlimited money, hints, and cheats.
-Tips and tricks for playing Alchemy Classic 2
-Now that you have downloaded and installed Alchemy Classic 2 Mod APK, you are ready to play the game and have fun. However, if you want to make the most out of your gaming experience, here are some tips and tricks that you can use:
-Use hints and cheats to discover new elements
-One of the benefits of using the modded version of the game is that you have unlimited hints and cheats that can help you discover new elements. You can use these features whenever you get stuck or want to learn something new. To use hints, you just need to tap on the light bulb icon at the top right corner of the screen. This will show you a possible combination or reaction that you can try. To use cheats, you just need to tap on the magnifying glass icon at the top left corner of the screen. This will show you all the elements that you can create with a given element.
-Experiment with different combinations and reactions
-Another benefit of using the modded version of the game is that you have unlimited money that you can use to buy any item or upgrade from the shop. This means that you can experiment with different combinations and reactions without worrying about wasting resources or losing progress. You can try different items such as flasks, tubes, filters, heaters, coolers, magnets, lasers, and more. You can also try different upgrades such as increasing your inventory size, unlocking new categories, improving your hints and cheats, and more. By experimenting with different combinations and reactions, you can discover new elements and learn more about science and nature.
-Save your progress and share your achievements
-The last benefit of using the modded version of the game is that you can play offline without needing an internet connection. This means that you can save your progress and resume playing anytime and anywhere. However, if you want to share your achievements with your friends or other players, you can connect your game account to social media platforms such as Facebook or Twitter. This will allow you to post your discoveries, screenshots, videos, and more. You can also compare your scores and rankings with other players on the leaderboard and achievements system. You can also join the online community of Alchemy Classic 2 and interact with other fans and players.
-Conclusion
-Summary of the main points
-Alchemy Classic 2 is a fun and creative game for Android users that lets you explore the world of chemistry and physics in a fun and engaging way. You can discover and collect over 1000 elements from different categories and learn more about science and nature. You can also download the Alchemy Classic 2 Mod APK, which gives you unlimited money, hints, and cheats to unlock all the elements and features in the game. You can also customize the game settings, experiment with different combinations and reactions, save your progress, and share your achievements with your friends.
-Call to action and recommendation
-If you are looking for a game that challenges your creativity and logic, then you should try Alchemy Classic 2. It is a game that will keep you entertained and educated for hours. You can download the game from Google Play or use the link below to download the modded version of the game. We hope you enjoy playing Alchemy Classic 2 and have fun discovering new elements.
-Download Alchemy Classic 2 Mod APK here
-FAQs
-Here are some frequently asked questions about Alchemy Classic 2 and its modded version:
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\ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Getting Over It on Mobile A Game for a Certain Kind of Person to Hurt Them.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Getting Over It on Mobile A Game for a Certain Kind of Person to Hurt Them.md deleted file mode 100644 index e9ebaa480df366d1c59ecd0a32cc47e9c0c8a8e1..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Getting Over It on Mobile A Game for a Certain Kind of Person to Hurt Them.md +++ /dev/null @@ -1,132 +0,0 @@ - -
How to Download Getting Over It Mobile - A Guide for Frustration Lovers
-If you are looking for a game that will test your patience, skill, and sanity, then you might want to try Getting Over It Mobile. This is a mobile version of the popular and notorious PC game Getting Over It with Bennett Foddy, which is known for its punishing difficulty and hilarious commentary. In this article, we will show you how to download Getting Over It Mobile on your Android or iOS device, and give you some tips and tricks for playing this frustrating but addictive game.
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What is Getting Over It Mobile?
-A brief introduction to the game and its developer
-Getting Over It Mobile is a fan game based on Getting Over It with Bennett Foddy, which was released in 2017 by Bennett Foddy, an Australian game designer and philosopher. The original game was inspired by an older game called Sexy Hiking, which was made by Jazzuo in 2002. Both games are part of a genre called "B-games", which are intentionally weird, low-budget, and experimental games that challenge the conventions of mainstream gaming.
-The gameplay and the challenge of Getting Over It Mobile
-The gameplay of Getting Over It Mobile is simple but hard. You play as a man in a pot who has to climb up a mountain using only a hammer. You can swing the hammer with your finger or mouse, and use it to hook onto objects, push yourself off the ground, or balance yourself in mid-air. The physics of the game are realistic but unforgiving, and any wrong move can send you tumbling down to where you started, or even lower. There is no save system, no checkpoints, no shortcuts, and no mercy. The only thing that keeps you going is the voice of Bennett Foddy himself, who narrates your progress, gives you philosophical insights, or taunts you with sarcastic remarks.
-How to Download Getting Over It Mobile on Android
-The official way to download the game from Google Play Store
-The official version of Getting Over It Mobile is available on Google Play Store for $4.99. You can find it by searching for "Getting Over It" or by following this link. To download the game, you need to have an Android device that runs on Android 5.0 or higher, and have enough storage space (about 150 MB). Once you purchase and install the game, you can launch it and start playing.
-The alternative way to download the game from APKPure.com
-If you don't want to pay for the game, or if you can't access Google Play Store for some reason, you can also download the game from APKPure.com, which is a website that offers free APK files for various apps and games. To download the game from APKPure.com, you need to follow these steps:
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Note that downloading APK files from third-party sources can be risky, as they may contain malware or viruses. Therefore, you should always scan the file before installing
How to Download Getting Over It Mobile on iOS
-The official way to download the game from App Store
-The official version of Getting Over It Mobile is also available on App Store for $4.99. You can find it by searching for "Getting Over It" or by following this link. To download the game, you need to have an iOS device that runs on iOS 10.0 or higher, and have enough storage space (about 150 MB). Once you purchase and install the game, you can launch it and start playing.
-The alternative way to download the game from AppValley.vip
-If you don't want to pay for the game, or if you can't access App Store for some reason, you can also download the game from AppValley.vip, which is a website that offers free apps and games for iOS devices. To download the game from AppValley.vip, you need to follow these steps:
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Note that downloading apps and games from third-party sources can be risky, as they may contain malware or viruses. Therefore, you should always scan the app before installing it, and trust the developer in your device settings.
-Tips and Tricks for Playing Getting Over It Mobile
-How to control the hammer and the pot
-The most important skill in Getting Over It Mobile is learning how to control the hammer and the pot. The hammer is your only tool and weapon, and the pot is your only protection and limitation. Here are some tips on how to master them:
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How to deal with frustration and failure
-The most common emotion in Getting Over It Mobile is frustration. The game is designed to make you fail, over and over again. You will lose progress, fall down, get stuck, or get hit by random objects. You will hear Bennett Foddy mocking you, laughing at you, or lecturing you. You will feel angry, sad, hopeless, or bored. Here are some tips on how to deal with frustration and failure:
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How to How to enjoy the game and its philosophy
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The most rewarding emotion in Getting Over It Mobile is enjoyment. The game is not only a challenge, but also a journey, a story, and a message. You will discover new places, new objects, new sounds, and new words. You will hear Bennett Foddy telling you his thoughts, his feelings, his opinions, and his wisdom. You will feel curious, inspired, amused, or enlightened. Here are some tips on how to enjoy the game and its philosophy:
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Conclusion
-Getting Over It Mobile is a game that will make you feel frustrated, but also satisfied; angry, but also happy; hopeless, but also hopeful. It is a game that will make you laugh, cry, scream, or smile. It is a game that will make you think, learn, grow, or change. It is a game that will make you love it or hate it.
-If you want to experience this unique and unforgettable game on your mobile device, you can download it from Google Play Store or App Store for $4.99. Alternatively, you can download it from APKPure.com or AppValley.vip for free.
-Whatever you choose to do, we hope that this article has helped you to download Getting Over It Mobile and to play it better. We also hope that you have enjoyed reading this article as much as we have enjoyed writing it.
-FAQs
-Q: Is Getting Over It Mobile the same as Getting Over It with Bennett Foddy?
-A: Getting Over It Mobile is not the same as Getting Over It with Bennett Foddy. It is a fan game that is based on the original game but has some differences in graphics, sound, and gameplay. For example, the mobile version has a different background music, a different ending, and a different control scheme. However, the mobile version still retains the core elements of the original game, such as the mountain, the hammer, the pot, and the voice.
-Q: How long does it take to finish Getting Over It Mobile?
-A: The answer to this question depends on your skill level, your luck, and your perseverance. Some people can finish the game in less than an hour, while others can take days, weeks, or even months. The game has no fixed length, as it depends on how fast you can climb the mountain and how often you fall down. The game also has a secret ending that requires you to do something extra after reaching the top of the mountain.
-Q: What is the reward for finishing Getting Over It Mobile?
-A: The reward for finishing Getting Over It Mobile is mostly personal and emotional. You will feel a sense of accomplishment, satisfaction, relief, and pride. You will also get to see a special scene at the end of the game, where you can interact with other players who have finished the game. You will also get to hear a final message from Bennett Foddy, who will congratulate you and share some of his thoughts with you.
-Q: Is there a way to cheat or hack Getting Over It Mobile?
-A: There are some ways to cheat or hack Getting Over It Mobile, but we do not recommend them. They can ruin the fun and the challenge of the game, and they can also cause problems for your device or your account. Some of the common ways to cheat or hack Getting Over It Mobile are:
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If you want to enjoy Getting Over It Mobile as it is intended to be played, we suggest that you avoid these methods and play the game fair and square.
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\ No newline at end of file diff --git a/spaces/florim/MedGPT/autogpt/speech/eleven_labs.py b/spaces/florim/MedGPT/autogpt/speech/eleven_labs.py deleted file mode 100644 index ea84efd8ca9489b40919ecd571813fe954b078e3..0000000000000000000000000000000000000000 --- a/spaces/florim/MedGPT/autogpt/speech/eleven_labs.py +++ /dev/null @@ -1,86 +0,0 @@ -"""ElevenLabs speech module""" -import os - -import requests -from playsound import playsound - -from autogpt.config import Config -from autogpt.speech.base import VoiceBase - -PLACEHOLDERS = {"your-voice-id"} - - -class ElevenLabsSpeech(VoiceBase): - """ElevenLabs speech class""" - - def _setup(self) -> None: - """Set up the voices, API key, etc. - - Returns: - None: None - """ - - cfg = Config() - default_voices = ["ErXwobaYiN019PkySvjV", "EXAVITQu4vr4xnSDxMaL"] - voice_options = { - "Rachel": "21m00Tcm4TlvDq8ikWAM", - "Domi": "AZnzlk1XvdvUeBnXmlld", - "Bella": "EXAVITQu4vr4xnSDxMaL", - "Antoni": "ErXwobaYiN019PkySvjV", - "Elli": "MF3mGyEYCl7XYWbV9V6O", - "Josh": "TxGEqnHWrfWFTfGW9XjX", - "Arnold": "VR6AewLTigWG4xSOukaG", - "Adam": "pNInz6obpgDQGcFmaJgB", - "Sam": "yoZ06aMxZJJ28mfd3POQ", - } - self._headers = { - "Content-Type": "application/json", - "xi-api-key": cfg.elevenlabs_api_key, - } - self._voices = default_voices.copy() - if cfg.elevenlabs_voice_1_id in voice_options: - cfg.elevenlabs_voice_1_id = voice_options[cfg.elevenlabs_voice_1_id] - if cfg.elevenlabs_voice_2_id in voice_options: - cfg.elevenlabs_voice_2_id = voice_options[cfg.elevenlabs_voice_2_id] - self._use_custom_voice(cfg.elevenlabs_voice_1_id, 0) - self._use_custom_voice(cfg.elevenlabs_voice_2_id, 1) - - def _use_custom_voice(self, voice, voice_index) -> None: - """Use a custom voice if provided and not a placeholder - - Args: - voice (str): The voice ID - voice_index (int): The voice index - - Returns: - None: None - """ - # Placeholder values that should be treated as empty - if voice and voice not in PLACEHOLDERS: - self._voices[voice_index] = voice - - def _speech(self, text: str, voice_index: int = 0) -> bool: - """Speak text using elevenlabs.io's API - - Args: - text (str): The text to speak - voice_index (int, optional): The voice to use. Defaults to 0. - - Returns: - bool: True if the request was successful, False otherwise - """ - tts_url = ( - f"https://api.elevenlabs.io/v1/text-to-speech/{self._voices[voice_index]}" - ) - response = requests.post(tts_url, headers=self._headers, json={"text": text}) - - if response.status_code == 200: - with open("speech.mpeg", "wb") as f: - f.write(response.content) - playsound("speech.mpeg", True) - os.remove("speech.mpeg") - return True - else: - print("Request failed with status code:", response.status_code) - print("Response content:", response.content) - return False diff --git a/spaces/freddyaboulton/llama-chat-discord-bot/app.py b/spaces/freddyaboulton/llama-chat-discord-bot/app.py deleted file mode 100644 index c690a6d7b3a276c074bb1bb150bb4b84d21b16ab..0000000000000000000000000000000000000000 --- a/spaces/freddyaboulton/llama-chat-discord-bot/app.py +++ /dev/null @@ -1,192 +0,0 @@ -import asyncio -import os -import threading -from threading import Event -from typing import Optional - -import discord -import gradio as gr -from discord import Permissions -from discord.ext import commands -from discord.utils import oauth_url - -import gradio_client as grc -from gradio_client.utils import QueueError - -event = Event() - -DISCORD_TOKEN = os.getenv("DISCORD_TOKEN") - - -async def wait(job): - while not job.done(): - await asyncio.sleep(0.2) - - -def get_client(session: Optional[str] = None) -> grc.Client: - client = grc.Client("https://ysharma-explore-llamav2-with-tgi.hf.space", hf_token=os.getenv("HF_TOKEN")) - if session: - client.session_hash = session - return client - - -def truncate_response(response: str) -> str: - ending = "...\nTruncating response to 2000 characters due to discord api limits." - if len(response) > 2000: - return response[: 2000 - len(ending)] + ending - else: - return response - - -intents = discord.Intents.default() -intents.message_content = True -bot = commands.Bot(command_prefix="/", intents=intents) - - -@bot.event -async def on_ready(): - print(f"Logged in as {bot.user} (ID: {bot.user.id})") - synced = await bot.tree.sync() - print(f"Synced commands: {', '.join([s.name for s in synced])}.") - event.set() - print("------") - - -thread_to_client = {} -thread_to_user = {} - - -@bot.hybrid_command( - name="llamachat", - description="Enter some text to chat with the bot! Like this: /llamachat Hello, how are you?", -) -async def chat(ctx, prompt: str): - if ctx.author.id == bot.user.id: - return - try: - message = await ctx.send("Creating thread...") - - thread = await message.create_thread(name=prompt) - loop = asyncio.get_running_loop() - client = await loop.run_in_executor(None, get_client, None) - job = client.submit(prompt, api_name="/chat") - await wait(job) - - try: - job.result() - response = job.outputs()[-1] - await thread.send(truncate_response(response)) - thread_to_client[thread.id] = client - thread_to_user[thread.id] = ctx.author.id - except QueueError: - await thread.send( - "The gradio space powering this bot is really busy! Please try again later!" - ) - - except Exception as e: - print(f"{e}") - - -async def continue_chat(message): - """Continues a given conversation based on chathistory""" - try: - client = thread_to_client[message.channel.id] - prompt = message.content - job = client.submit(prompt, api_name="/chat") - await wait(job) - try: - job.result() - response = job.outputs()[-1] - await message.reply(truncate_response(response)) - except QueueError: - await message.reply( - "The gradio space powering this bot is really busy! Please try again later!" - ) - - except Exception as e: - print(f"Error: {e}") - - -@bot.event -async def on_message(message): - """Continue the chat""" - try: - if not message.author.bot: - if message.channel.id in thread_to_user: - if thread_to_user[message.channel.id] == message.author.id: - await continue_chat(message) - else: - await bot.process_commands(message) - - except Exception as e: - print(f"Error: {e}") - - -# running in thread -def run_bot(): - if not DISCORD_TOKEN: - print("DISCORD_TOKEN NOT SET") - event.set() - else: - bot.run(DISCORD_TOKEN) - - -threading.Thread(target=run_bot).start() - -event.wait() - -if not DISCORD_TOKEN: - welcome_message = """ - - ## You have not specified a DISCORD_TOKEN, which means you have not created a bot account. Please follow these steps: - - ### 1. Go to https://discord.com/developers/applications and click 'New Application' - - ### 2. Give your bot a name 🤖 - -  - - ## 3. In Settings > Bot, click the 'Reset Token' button to get a new token. Write it down and keep it safe 🔐 - -  - - ## 4. Optionally make the bot public if you want anyone to be able to add it to their servers - - ## 5. Scroll down and enable 'Message Content Intent' under 'Priviledged Gateway Intents' - -  - - ## 6. Save your changes! - - ## 7. The token from step 3 is the DISCORD_TOKEN. Rerun the deploy_discord command, e.g client.deploy_discord(discord_bot_token=DISCORD_TOKEN, ...), or add the token as a space secret manually. -""" -else: - permissions = Permissions(326417525824) - url = oauth_url(bot.user.id, permissions=permissions) - welcome_message = f""" - ## Add this bot to your server by clicking this link: - - {url} - - ## How to use it? - - The bot can be triggered via `/llamachat` followed by your text prompt. - - This will create a thread with the bot's response to your text prompt. - You can reply in the thread (without `/llamachat`) to continue the conversation. - In the thread, the bot will only reply to the original author of the command. - - ⚠️ Note ⚠️: Please make sure this bot's command does have the same name as another command in your server. - ⚠️ Note ⚠️: Bot commands do not work in DMs with the bot as of now. - """ - - -with gr.Blocks() as demo: - gr.Markdown( - f""" - # Discord bot of https://ysharma-explore-llamav2-with-tgi.hf.space - {welcome_message} - """ - ) - -demo.launch() diff --git a/spaces/freddyaboulton/openai-whisper-large/README.md b/spaces/freddyaboulton/openai-whisper-large/README.md deleted file mode 100644 index f4e0c32c81c3c734049decd5d4391b848aaf7b70..0000000000000000000000000000000000000000 --- a/spaces/freddyaboulton/openai-whisper-large/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Openai Whisper Large -emoji: 🌖 -colorFrom: blue -colorTo: red -sdk: gradio -sdk_version: 3.12.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/pixel_group.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/pixel_group.py deleted file mode 100644 index 2143c75f835a467c802fc3c37ecd3ac0f85bcda4..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/ops/pixel_group.py +++ /dev/null @@ -1,75 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import numpy as np -import torch - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', ['pixel_group']) - - -def pixel_group(score, mask, embedding, kernel_label, kernel_contour, - kernel_region_num, distance_threshold): - """Group pixels into text instances, which is widely used text detection - methods. - - Arguments: - score (np.array or Tensor): The foreground score with size hxw. - mask (np.array or Tensor): The foreground mask with size hxw. - embedding (np.array or Tensor): The embedding with size hxwxc to - distinguish instances. - kernel_label (np.array or Tensor): The instance kernel index with - size hxw. - kernel_contour (np.array or Tensor): The kernel contour with size hxw. - kernel_region_num (int): The instance kernel region number. - distance_threshold (float): The embedding distance threshold between - kernel and pixel in one instance. - - Returns: - pixel_assignment (List[List[float]]): The instance coordinate list. - Each element consists of averaged confidence, pixel number, and - coordinates (x_i, y_i for all pixels) in order. - """ - assert isinstance(score, (torch.Tensor, np.ndarray)) - assert isinstance(mask, (torch.Tensor, np.ndarray)) - assert isinstance(embedding, (torch.Tensor, np.ndarray)) - assert isinstance(kernel_label, (torch.Tensor, np.ndarray)) - assert isinstance(kernel_contour, (torch.Tensor, np.ndarray)) - assert isinstance(kernel_region_num, int) - assert isinstance(distance_threshold, float) - - if isinstance(score, np.ndarray): - score = torch.from_numpy(score) - if isinstance(mask, np.ndarray): - mask = torch.from_numpy(mask) - if isinstance(embedding, np.ndarray): - embedding = torch.from_numpy(embedding) - if isinstance(kernel_label, np.ndarray): - kernel_label = torch.from_numpy(kernel_label) - if isinstance(kernel_contour, np.ndarray): - kernel_contour = torch.from_numpy(kernel_contour) - - if torch.__version__ == 'parrots': - label = ext_module.pixel_group( - score, - mask, - embedding, - kernel_label, - kernel_contour, - kernel_region_num=kernel_region_num, - distance_threshold=distance_threshold) - label = label.tolist() - label = label[0] - list_index = kernel_region_num - pixel_assignment = [] - for x in range(kernel_region_num): - pixel_assignment.append( - np.array( - label[list_index:list_index + int(label[x])], - dtype=np.float)) - list_index = list_index + int(label[x]) - else: - pixel_assignment = ext_module.pixel_group(score, mask, embedding, - kernel_label, kernel_contour, - kernel_region_num, - distance_threshold) - return pixel_assignment diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/losses/utils.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/losses/utils.py deleted file mode 100644 index 85aec9f3045240c3de96a928324ae8f5c3aebe8b..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/losses/utils.py +++ /dev/null @@ -1,121 +0,0 @@ -import functools - -import annotator.uniformer.mmcv as mmcv -import numpy as np -import torch.nn.functional as F - - -def get_class_weight(class_weight): - """Get class weight for loss function. - - Args: - class_weight (list[float] | str | None): If class_weight is a str, - take it as a file name and read from it. - """ - if isinstance(class_weight, str): - # take it as a file path - if class_weight.endswith('.npy'): - class_weight = np.load(class_weight) - else: - # pkl, json or yaml - class_weight = mmcv.load(class_weight) - - return class_weight - - -def reduce_loss(loss, reduction): - """Reduce loss as specified. - - Args: - loss (Tensor): Elementwise loss tensor. - reduction (str): Options are "none", "mean" and "sum". - - Return: - Tensor: Reduced loss tensor. - """ - reduction_enum = F._Reduction.get_enum(reduction) - # none: 0, elementwise_mean:1, sum: 2 - if reduction_enum == 0: - return loss - elif reduction_enum == 1: - return loss.mean() - elif reduction_enum == 2: - return loss.sum() - - -def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None): - """Apply element-wise weight and reduce loss. - - Args: - loss (Tensor): Element-wise loss. - weight (Tensor): Element-wise weights. - reduction (str): Same as built-in losses of PyTorch. - avg_factor (float): Avarage factor when computing the mean of losses. - - Returns: - Tensor: Processed loss values. - """ - # if weight is specified, apply element-wise weight - if weight is not None: - assert weight.dim() == loss.dim() - if weight.dim() > 1: - assert weight.size(1) == 1 or weight.size(1) == loss.size(1) - loss = loss * weight - - # if avg_factor is not specified, just reduce the loss - if avg_factor is None: - loss = reduce_loss(loss, reduction) - else: - # if reduction is mean, then average the loss by avg_factor - if reduction == 'mean': - loss = loss.sum() / avg_factor - # if reduction is 'none', then do nothing, otherwise raise an error - elif reduction != 'none': - raise ValueError('avg_factor can not be used with reduction="sum"') - return loss - - -def weighted_loss(loss_func): - """Create a weighted version of a given loss function. - - To use this decorator, the loss function must have the signature like - `loss_func(pred, target, **kwargs)`. The function only needs to compute - element-wise loss without any reduction. This decorator will add weight - and reduction arguments to the function. The decorated function will have - the signature like `loss_func(pred, target, weight=None, reduction='mean', - avg_factor=None, **kwargs)`. - - :Example: - - >>> import torch - >>> @weighted_loss - >>> def l1_loss(pred, target): - >>> return (pred - target).abs() - - >>> pred = torch.Tensor([0, 2, 3]) - >>> target = torch.Tensor([1, 1, 1]) - >>> weight = torch.Tensor([1, 0, 1]) - - >>> l1_loss(pred, target) - tensor(1.3333) - >>> l1_loss(pred, target, weight) - tensor(1.) - >>> l1_loss(pred, target, reduction='none') - tensor([1., 1., 2.]) - >>> l1_loss(pred, target, weight, avg_factor=2) - tensor(1.5000) - """ - - @functools.wraps(loss_func) - def wrapper(pred, - target, - weight=None, - reduction='mean', - avg_factor=None, - **kwargs): - # get element-wise loss - loss = loss_func(pred, target, **kwargs) - loss = weight_reduce_loss(loss, weight, reduction, avg_factor) - return loss - - return wrapper diff --git a/spaces/georgefen/Face-Landmark-ControlNet/ldm/modules/ema.py b/spaces/georgefen/Face-Landmark-ControlNet/ldm/modules/ema.py deleted file mode 100644 index bded25019b9bcbcd0260f0b8185f8c7859ca58c4..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/ldm/modules/ema.py +++ /dev/null @@ -1,80 +0,0 @@ -import torch -from torch import nn - - -class LitEma(nn.Module): - def __init__(self, model, decay=0.9999, use_num_upates=True): - super().__init__() - if decay < 0.0 or decay > 1.0: - raise ValueError('Decay must be between 0 and 1') - - self.m_name2s_name = {} - self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) - self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates - else torch.tensor(-1, dtype=torch.int)) - - for name, p in model.named_parameters(): - if p.requires_grad: - # remove as '.'-character is not allowed in buffers - s_name = name.replace('.', '') - self.m_name2s_name.update({name: s_name}) - self.register_buffer(s_name, p.clone().detach().data) - - self.collected_params = [] - - def reset_num_updates(self): - del self.num_updates - self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int)) - - def forward(self, model): - decay = self.decay - - if self.num_updates >= 0: - self.num_updates += 1 - decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) - - one_minus_decay = 1.0 - decay - - with torch.no_grad(): - m_param = dict(model.named_parameters()) - shadow_params = dict(self.named_buffers()) - - for key in m_param: - if m_param[key].requires_grad: - sname = self.m_name2s_name[key] - shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) - shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) - else: - assert not key in self.m_name2s_name - - def copy_to(self, model): - m_param = dict(model.named_parameters()) - shadow_params = dict(self.named_buffers()) - for key in m_param: - if m_param[key].requires_grad: - m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) - else: - assert not key in self.m_name2s_name - - def store(self, parameters): - """ - Save the current parameters for restoring later. - Args: - parameters: Iterable of `torch.nn.Parameter`; the parameters to be - temporarily stored. - """ - self.collected_params = [param.clone() for param in parameters] - - def restore(self, parameters): - """ - Restore the parameters stored with the `store` method. - Useful to validate the model with EMA parameters without affecting the - original optimization process. Store the parameters before the - `copy_to` method. After validation (or model saving), use this to - restore the former parameters. - Args: - parameters: Iterable of `torch.nn.Parameter`; the parameters to be - updated with the stored parameters. - """ - for c_param, param in zip(self.collected_params, parameters): - param.data.copy_(c_param.data) diff --git a/spaces/gordonchan/h2oo/gradio_utils/prompt_form.py b/spaces/gordonchan/h2oo/gradio_utils/prompt_form.py deleted file mode 100644 index 34707d44de1d9eb21b7caef4e5345b11c4c9bd28..0000000000000000000000000000000000000000 --- a/spaces/gordonchan/h2oo/gradio_utils/prompt_form.py +++ /dev/null @@ -1,95 +0,0 @@ -import os -import math - -import gradio as gr - - -def make_chatbots(output_label0, output_label0_model2, **kwargs): - text_outputs = [] - chat_kwargs = [] - for model_state_lock in kwargs['model_states']: - if os.environ.get('DEBUG_MODEL_LOCK'): - model_name = model_state_lock["base_model"] + " : " + model_state_lock["inference_server"] - else: - model_name = model_state_lock["base_model"] - output_label = f'h2oGPT [{model_name}]' - min_width = 250 if kwargs['gradio_size'] in ['small', 'large', 'medium'] else 160 - chat_kwargs.append(dict(label=output_label, visible=kwargs['model_lock'], elem_classes='chatsmall', - height=kwargs['height'] or 400, min_width=min_width)) - - if kwargs['model_lock_columns'] == -1: - kwargs['model_lock_columns'] = len(kwargs['model_states']) - if kwargs['model_lock_columns'] is None: - kwargs['model_lock_columns'] = 3 - - ncols = kwargs['model_lock_columns'] - if kwargs['model_states'] == 0: - nrows = 0 - else: - nrows = math.ceil(len(kwargs['model_states']) / kwargs['model_lock_columns']) - - if kwargs['model_lock_columns'] == 0: - # not using model_lock - pass - elif nrows <= 1: - with gr.Row(): - for chat_kwargs1, model_state_lock in zip(chat_kwargs, kwargs['model_states']): - text_outputs.append(gr.Chatbot(**chat_kwargs1)) - elif nrows == kwargs['model_states']: - with gr.Row(): - for chat_kwargs1, model_state_lock in zip(chat_kwargs, kwargs['model_states']): - text_outputs.append(gr.Chatbot(**chat_kwargs1)) - elif nrows == 2: - with gr.Row(): - for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])): - if mii >= len(kwargs['model_states']) / 2: - continue - text_outputs.append(gr.Chatbot(**chat_kwargs1)) - with gr.Row(): - for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])): - if mii < len(kwargs['model_states']) / 2: - continue - text_outputs.append(gr.Chatbot(**chat_kwargs1)) - elif nrows == 3: - with gr.Row(): - for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])): - if mii >= 1 * len(kwargs['model_states']) / 3: - continue - text_outputs.append(gr.Chatbot(**chat_kwargs1)) - with gr.Row(): - for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])): - if mii < 1 * len(kwargs['model_states']) / 3 or mii >= 2 * len(kwargs['model_states']) / 3: - continue - text_outputs.append(gr.Chatbot(**chat_kwargs1)) - with gr.Row(): - for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])): - if mii < 2 * len(kwargs['model_states']) / 3: - continue - text_outputs.append(gr.Chatbot(**chat_kwargs1)) - elif nrows >= 4: - with gr.Row(): - for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])): - if mii >= 1 * len(kwargs['model_states']) / 4: - continue - text_outputs.append(gr.Chatbot(**chat_kwargs1)) - with gr.Row(): - for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])): - if mii < 1 * len(kwargs['model_states']) / 4 or mii >= 2 * len(kwargs['model_states']) / 4: - continue - text_outputs.append(gr.Chatbot(**chat_kwargs1)) - with gr.Row(): - for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])): - if mii < 2 * len(kwargs['model_states']) / 4 or mii >= 3 * len(kwargs['model_states']) / 4: - continue - text_outputs.append(gr.Chatbot(**chat_kwargs1)) - with gr.Row(): - for mii, (chat_kwargs1, model_state_lock) in enumerate(zip(chat_kwargs, kwargs['model_states'])): - if mii < 3 * len(kwargs['model_states']) / 4: - continue - text_outputs.append(gr.Chatbot(**chat_kwargs1)) - - with gr.Row(): - text_output = gr.Chatbot(label=output_label0, visible=not kwargs['model_lock'], height=kwargs['height'] or 400) - text_output2 = gr.Chatbot(label=output_label0_model2, - visible=False and not kwargs['model_lock'], height=kwargs['height'] or 400) - return text_output, text_output2, text_outputs diff --git a/spaces/gotiQspiryo/whisper-ui/Pad-Man-Tamil-Movie-Torrent-Download-VERIFIED.md b/spaces/gotiQspiryo/whisper-ui/Pad-Man-Tamil-Movie-Torrent-Download-VERIFIED.md deleted file mode 100644 index 9f0f2ab985360533bba64fada5bca6203e84ecd9..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/Pad-Man-Tamil-Movie-Torrent-Download-VERIFIED.md +++ /dev/null @@ -1,64 +0,0 @@ -## Pad Man Tamil Movie Torrent Download - - - - - - - - - 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The film stars Akshay Kumar as Muruganantham and Radhika Apte as his wife Gayatri. The film is directed by R. Balki and has an ensemble supporting cast that includes Sonam Kapoor, Amitabh Bachchan, Sudhir Pandey and others. - - - -The film follows Muruganantham's journey from being a simple welder to a national hero who revolutionized menstrual hygiene in India. He faces many challenges and obstacles along the way, such as social stigma, family opposition, financial difficulties and legal troubles. He also sacrifices his personal life and happiness for his noble cause. The film showcases his determination, innovation and courage in overcoming all odds and fulfilling his dream of making affordable sanitary pads for millions of women. - - - -Pad Man is a film that celebrates the spirit of innovation and social change. It also raises awareness about the importance of menstrual hygiene and women's empowerment. The film is based on a true story and is adapted from the book The Legend of Lakshmi Prasad by Twinkle Khanna, who is also one of the producers of the film. The film received positive reviews from critics and audiences alike and was a commercial success. It also won several awards and nominations, including the National Film Award for Best Film on Other Social Issues. - - - -Pad Man is a film that is worth watching for its inspiring message, brilliant performances and engaging direction. It is a film that will make you laugh, cry and think. It is a film that will make you proud of being an Indian. - - - -## Pad Man: A Movie Reception - - - -Pad Man received positive reviews from critics and audiences alike for its portrayal of a real-life hero and his social impact. The film was praised for its performances, direction, screenplay, music and message. The film also sparked a conversation about menstrual hygiene and women's rights in India and abroad. The film was also appreciated for its humor and emotion that balanced the serious subject matter. - - - -The film was a commercial success as well, grossing â¹207.73 crore worldwide against a budget of â¹45 crore. The film performed well especially in India and China, where it was released on 9 February 2018 and 14 December 2018 respectively. The film also became the first Hindi film to premiere at the Russia box office on its original date. The film won several awards and nominations, including the National Film Award for Best Film on Other Social Issues. The film was also selected as India's official entry for the 91st Academy Awards but did not make the shortlist. - - - -Pad Man is a film that has made a difference in the lives of many people. It has inspired many initiatives and campaigns to promote menstrual hygiene and awareness. It has also inspired many people to follow their dreams and passions despite the challenges they face. Pad Man is a film that has left a lasting impression on the hearts and minds of the viewers. - - 1b8d091108 - - - - - diff --git "a/spaces/gotiQspiryo/whisper-ui/examples/After Effect\302\240Template Torrent.md" "b/spaces/gotiQspiryo/whisper-ui/examples/After Effect\302\240Template Torrent.md" deleted file mode 100644 index 5558e1318693bb597cb341c75b68e0910e05d20b..0000000000000000000000000000000000000000 --- "a/spaces/gotiQspiryo/whisper-ui/examples/After Effect\302\240Template Torrent.md" +++ /dev/null @@ -1,6 +0,0 @@ -
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- diff --git a/spaces/gradio/HuBERT/fairseq/data/id_dataset.py b/spaces/gradio/HuBERT/fairseq/data/id_dataset.py deleted file mode 100644 index 3e4d7969cf2a26e852b466f165a6fadabae3b35f..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/data/id_dataset.py +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -from . import FairseqDataset - - -class IdDataset(FairseqDataset): - def __getitem__(self, index): - return index - - def __len__(self): - return 0 - - def collater(self, samples): - return torch.tensor(samples) diff --git a/spaces/gradio/HuBERT/fairseq/models/speech_to_text/utils.py b/spaces/gradio/HuBERT/fairseq/models/speech_to_text/utils.py deleted file mode 100644 index 573f8537c9b5940f3eff1fef5e732c6ae7e7fdc0..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/models/speech_to_text/utils.py +++ /dev/null @@ -1,564 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) 2017-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the LICENSE file in -# the root directory of this source tree. An additional grant of patent rights -# can be found in the PATENTS file in the same directory. - - -import logging -from collections.abc import Iterable -from itertools import repeat -from typing import List, Optional, Tuple - -import torch -from torch import Tensor - - -# ------------------------------------------------------------------------------ -# assert_equal() -# ------------------------------------------------------------------------------ - - -def assert_equal(value1, value2, name1=None, name2=None): - """Asserts two values are equal otherwise raise an error.""" - - str_name1 = "" if name1 is None else "{} ".format(name1) - str_name2 = "" if name2 is None else "{} ".format(name2) - if value1 != value2: - str_value1 = "{}" if name1 is None else "({})" - str_value1 = str_value1.format(value1) - str_value2 = "{}" if name2 is None else "({})" - str_value2 = str_value2.format(value2) - raise ValueError( - "Expected {}{} == {}{}".format(str_name1, str_value1, str_name2, str_value2) - ) - - -def fill_config(config, key, value): - if value is not None: - if key not in config or config[key] is None: - config[key] = value - assert_equal(value, config[key], "value", f'config["{key}"]') - - -# ------------------------------------------------------------------------------ -# check_and_return_expected() -# ------------------------------------------------------------------------------ - - -def check_and_return_expected(value, undefined_value, expected_value, name=None): - """ - Return the expected value while checking if the given value is undefined or - equal to the expected value. - """ - if (undefined_value is None and value is None) or (undefined_value == value): - return expected_value - if value != expected_value: - str_name = "" if name is None else "{} ".format(name) - str_value = "{}" if name is None else "({})" - str_value = str_value.format(value) - raise ValueError( - "Expected {}{} == {}".format(str_name, str_value, expected_value) - ) - return expected_value - - -# ------------------------------------------------------------------------------ -# get_time_axis() -# ------------------------------------------------------------------------------ - - -def get_time_axis(layout): - """ - Extract the time axis from the layout, for example for breaking sequence into - segments. - """ - if layout in ["TB", "TBD"]: - return 0 - if layout in ["BT", "BTD"]: - return 1 - if layout in ["BCTD"]: - return 2 - raise ValueError("Unsupported layout = {}".format(layout)) - - -# ------------------------------------------------------------------------------ -# get_batch_axis() -# ------------------------------------------------------------------------------ - - -def get_batch_axis(layout): - """ - Extract the batch axis from the layout - """ - if layout in ["TB", "TBD"]: - return 1 - if layout in ["BT", "BTD", "BCTD"]: - return 0 - raise ValueError("Unsupported layout = {}".format(layout)) - - -# ------------------------------------------------------------------------------ -# monotonically_increasing_and_bounded() -# ------------------------------------------------------------------------------ - - -def monotonically_increasing_and_bounded(iterable, min=None, max=None): - """ - Check if the elements in the given iterable are monotonically increasing and - bounded by upper/lower bounds. - """ - if not isinstance(iterable, Iterable): - raise TypeError( - "Expected iterable to be of type Iterable, got ({})".format( - iterable.__class__.__name__ - ) - ) - for i in range(len(iterable)): - if min is not None and iterable[i] < min: - return False - if max is not None and iterable[i] > max: - return False - if i > 0 and iterable[i] <= iterable[i - 1]: - return False - return True - - -# ------------------------------------------------------------------------------ -# to_pair() -# ------------------------------------------------------------------------------ - - -def to_pair(value, name): - """Make a pair (of type tuple) of given value.""" - if isinstance(value, Iterable): - if len(value) != 2: - raise ValueError( - "Expected `{}` to have exactly 2 elements, got: ({})".format( - name, value - ) - ) - return value - return tuple(repeat(value, 2)) - - -# ------------------------------------------------------------------------------ -# infer_conv_output_attrs() -# ------------------------------------------------------------------------------ - - -# TODO(cfyeh): figure out if we can get `output_dim` without calling the module. -def infer_conv_output_attrs( - module, input_channels, input_dim, batch_size=1, max_length=8 -): - """Get output attributes of a module with input.""" - input = torch.randn(batch_size, input_channels, max_length, input_dim) - output = module(input) - output_channels = output.shape[1] - output_dim = output.shape[-1] - return output_channels, output_dim - - -# ------------------------------------------------------------------------------ -# NoOp -# ------------------------------------------------------------------------------ - - -class NoOp(torch.nn.Module): - """ - NoOp simply passes the input as the output. - """ - - def __init__(self): - super().__init__() - - def forward(self, input: Tensor) -> Tensor: - return input - - -# ------------------------------------------------------------------------------ -# Permute: a torch.nn.Module applies permutation on the input tensor. -# ------------------------------------------------------------------------------ - - -class Permute(torch.nn.Module): - def __init__(self, dims): - super().__init__() - self.dims = dims - - def forward(self, input: Tensor) -> Tensor: - return input.permute(self.dims).contiguous() - - -# ------------------------------------------------------------------------------ -# lengths_to_padding_mask() -# ------------------------------------------------------------------------------ - - -def lengths_to_padding_mask(lengths: Tensor) -> Tensor: - """Convert lengths of shape (B, ) to padding mask.""" - batch_size = lengths.shape[0] - max_length = int(torch.max(lengths).item()) - padding_mask = torch.arange( # [0, ..., T-1] - max_length, device=lengths.device, dtype=lengths.dtype - ).expand(batch_size, max_length) >= lengths.unsqueeze(1) - - return padding_mask - - -# ------------------------------------------------------------------------------ -# lengths_to_attention_mask() -# ------------------------------------------------------------------------------ - - -def lengths_to_attention_mask( - lengths: Tensor, - left_context: Optional[int] = None, - right_context: Optional[int] = None, -) -> Optional[Tensor]: - """ - Generate attention mask based on (lengths, left_context, right_context). - left_context is None means unlimited left context. - right_context is None means unlimited right context. - """ - - if left_context is None and right_context is None: - return None - - max_length = int(torch.max(lengths).item()) - - # For example, with `max_length` == 5, - # indices = tensor([ - # [ 0, 1, 2, 3, 4, 5], - # [-1, 0, 1, 2, 3, 4], - # [-2, -1, 0, 1, 2, 3], - # [-3, -2, -1, 0, 1, 2], - # [-4, -3, -2, -1, 0, 1], - # [-5, -4, -3, -2, -1, 0], - # ]) - - # In some cases the second torch.arange is created on cpu which causes a - # failure. Adding the device option to guard against it. - indices = torch.arange( - max_length, device=lengths.device, dtype=lengths.dtype - ).expand(max_length, max_length) - torch.arange( - max_length, device=lengths.device - ).view( - max_length, -1 - ) - - # For example, with `max_length` == 5, - # bool_mask = tensor([ - # [True, True, True, True, True], - # [True, True, True, True, True], - # [True, True, True, True, True], - # [True, True, True, True, True], - # [True, True, True, True, True], - # ]) - bool_mask = ( - torch.tensor([True]).to(device=lengths.device).expand(max_length, max_length) - ) - - # For example, with `max_length` == 5, left_context == 2 - # left_mask = tensor([ - # [ True, True, True, True, True], - # [ True, True, True, True, True], - # [ True, True, True, True, True], - # [False, True, True, True, True], - # [False, False, True, True, True], - # ]) - if left_context is not None: - left_mask = indices >= -left_context - bool_mask = bool_mask & left_mask - - # For example, with `max_length` == 5, right_context == 1 - # right_mask = tensor([ - # [True, True, False, False, False], - # [True, True, True, False, False], - # [True, True, True, True, False], - # [True, True, True, True, True], - # [True, True, True, True, True], - # ]) - if right_context is not None: - right_mask = indices <= right_context - bool_mask = bool_mask & right_mask - - bool_mask = (~bool_mask).to(device=lengths.device) - return bool_mask - - -# ------------------------------------------------------------------------------ -# infer_output_norm() -# ------------------------------------------------------------------------------ - - -def infer_output_norm(module, output_norm=None): - """ - Infer the output norm (string and module) needed on the module gvien desired - output normalization. - """ - if output_norm == module.output_norm(): - # output_norm already matches module.output_norm(). - return (None, NoOp()) - - if output_norm is None and module.output_norm() is not None: - logger = logging.getLogger("infer_output_norm()") - logger.warning( - "trying to set output_norm ({}) ".format(output_norm) - + "but got module.output_norm() ({}), ".format(module.output_norm()) - + "the combined output_norm() will be ({})".format(module.output_norm()) - ) - return (None, NoOp()) - - if output_norm == "log_softmax": - if module.output_norm() is not None: - raise ValueError( - "incompatible output_norm ({}) ".format(output_norm) - + "and module.output_norm() ({})".format(module.output_norm()) - ) - else: - return ("log_softmax", torch.nn.LogSoftmax(dim=-1)) - - if output_norm == "softmax": - if module.output_norm() is not None: - raise ValueError( - "incompatible output_norm ({}) ".format(output_norm) - + "and module.output_norm() ({})".format(module.output_norm()) - ) - else: - return ("softmax", torch.nn.Softmax(dim=-1)) - - raise ValueError( - "output_norm ({}) not in ".format(output_norm) - + "supported list = [None, softmax, log_softmax]" - ) - - -# ------------------------------------------------------------------------------ -# infer_channels_from_layout() -# ------------------------------------------------------------------------------ - - -def infer_channels_from_layout(layout, channels): - """Extract the number of channels from the layout.""" - if layout in ("TBD", "BTD"): - if channels is not None and channels != 1: - raise ValueError( - "Expected channels ({}) to be 1 for layout = {}".format( - channels, layout - ) - ) - if channels is None: - return 1 - return channels - - -# ------------------------------------------------------------------------------ -# pad_sequence() -# ------------------------------------------------------------------------------ - - -@torch.jit.export -def pad_sequence( - sequence: Tensor, - time_axis: int, - extra_left_context: int = 0, - extra_right_context: int = 0, -) -> Tensor: - """Pad extra left/right contexts to the sequence.""" - - if extra_left_context == 0 and extra_right_context == 0: - return sequence - - tensors_to_concat = [] - - if extra_left_context: - size = (extra_left_context,) - fill_value = 0 - indices = torch.full( - size=size, - fill_value=fill_value, - dtype=torch.long, - device=sequence.device, - ) - left_padding = torch.index_select(sequence, time_axis, indices) - tensors_to_concat.append(left_padding) - - tensors_to_concat.append(sequence) - - # NOTE(cfyeh): for efficiency reason we pad 0 instead of the last frame for - # extra right contexts. - if extra_right_context: - size = list(sequence.shape) - size[time_axis] = extra_right_context - right_padding = torch.zeros(size, dtype=sequence.dtype, device=sequence.device) - tensors_to_concat.append(right_padding) - - padded_sequence = torch.cat(tensors_to_concat, dim=time_axis) - return padded_sequence - - -# ------------------------------------------------------------------------------ -# sequence_to_segments() -# ------------------------------------------------------------------------------ - - -@torch.jit.export -def sequence_to_segments( - sequence: Tensor, - time_axis: int, - lengths: Tensor, - segment_size: Optional[int] = None, - extra_left_context: int = 0, - extra_right_context: int = 0, -) -> List[Tuple[Tensor, Tensor]]: - """Breaks sequence into segments.""" - - sequence = pad_sequence( - sequence=sequence, - time_axis=time_axis, - extra_left_context=extra_left_context, - extra_right_context=extra_right_context, - ) - - lengths = lengths + extra_left_context + extra_right_context - - segments: List[Tuple[Tensor, Tensor]] = [] - - if segment_size is None: - segments.append((sequence, lengths)) - return segments - - offset = 0 - end = sequence.shape[time_axis] - step = segment_size - size = extra_left_context + segment_size + extra_right_context - - while offset + extra_left_context + extra_right_context < end: - clamped_size = min(size, end - offset) - segment_lengths = torch.clamp(lengths - offset, min=0, max=clamped_size) - indices = torch.arange( - start=offset, - end=(offset + clamped_size), - step=1, - dtype=torch.long, - device=sequence.device, - ) - segment_tensor = torch.index_select(sequence, time_axis, indices) - segments.append((segment_tensor, segment_lengths)) - offset = offset + step - - return segments - - -# ------------------------------------------------------------------------------ -# segments_to_sequence() -# ------------------------------------------------------------------------------ - - -@torch.jit.export -def segments_to_sequence( - segments: List[Tuple[Tensor, Tensor]], time_axis: int -) -> Tuple[Tensor, Tensor]: - """Concatenate segments into a full sequence.""" - if len(segments) == 1: - return segments[0] - - tensors_to_concat: List[Tensor] = [] - lengths_to_stack: List[Tensor] = [] - - for tensor, lengths in segments: - tensors_to_concat.append(tensor) - lengths_to_stack.append(lengths) - - sequence = torch.cat(tensors_to_concat, dim=time_axis) - lengths = torch.stack(lengths_to_stack, dim=0) - lengths = torch.sum(lengths, dim=0) - - return sequence, lengths - - -def lengths_to_encoder_padding_mask(lengths, batch_first: bool = False): - """ - convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor - - Args: - lengths: a (B, )-shaped tensor - batch_first: whether to return a (B, T) tensor - - Return: - max_length: maximum length of B sequences - encoder_padding_mask: a (max_length, B) binary mask, where - [t, b] = False for t < lengths[b] and True otherwise - - TODO: - kernelize this function if benchmarking shows this function is slow - """ - max_lengths = torch.max(lengths).item() - bsz = lengths.size(0) - encoder_padding_mask = torch.arange( - max_lengths - ).to( # a (T, ) tensor with [0, ..., T-1] - lengths.device - ).view( # move to the right device - 1, max_lengths - ).expand( # reshape to (1, T)-shaped tensor - bsz, -1 - ) > lengths.view( # expand to (B, T)-shaped tensor - bsz, 1 - ).expand( - -1, max_lengths - ) - if not batch_first: - return encoder_padding_mask.t(), max_lengths - else: - return encoder_padding_mask, max_lengths - - -# ------------------------------------------------------------------------------ -# attention suppression -# ------------------------------------------------------------------------------ - - -def attention_suppression(attention_weights: Tensor, scale: float): - # B, H, qlen, klen -> B, H, qlen, 1 - attention_prob = torch.nn.functional.softmax(attention_weights.float(), dim=-1) - attention_nozeros = attention_prob.to(torch.bool) - nozeros_sum = torch.sum(attention_nozeros.to(torch.float), dim=-1, keepdim=True) - - # For very sparse situation, we need get round about 0s - key_sum = torch.sum(attention_prob, dim=-1, keepdim=True) - - # nozeros_sum should > 1 - key_mean = key_sum / (nozeros_sum + 1e-8) - - # std calculation - dis = (attention_prob - key_mean) * (attention_prob - key_mean) - - # if attention_prob[i] < threshold, then dis_masked[i] = 0; for all i - dis_masked = torch.where( - attention_nozeros, dis, attention_prob.new_zeros(attention_prob.size()) - ) - - key_var = torch.sum(dis_masked, dim=-1, keepdim=True) - key_var = key_var / (nozeros_sum - 1.0 + 1e-8) - key_std = torch.sqrt(key_var) - key_thread = key_mean - scale * key_std - - # if attention_prob[i] >= key_thread, then attention_prob[i] - # , otherwise "-inf" - inf_tensor = attention_prob.new_zeros(attention_prob.size()).detach() - inf_tensor[:] = float("-inf") - attention_weights_float = torch.where( - attention_prob < key_thread, - inf_tensor, - attention_weights.float(), - ) - - return attention_weights_float.type_as(attention_weights) - - -def layer_norm_backward_hook(module, grad_input, grad_output, clamp_value): - return tuple(torch.clamp(v, min=-clamp_value, max=clamp_value) for v in grad_input) diff --git a/spaces/greatMLideas/Realstate/test_api.py b/spaces/greatMLideas/Realstate/test_api.py deleted file mode 100644 index 2af805a92b4f3f7f851264cc72e0359e98e281a8..0000000000000000000000000000000000000000 --- a/spaces/greatMLideas/Realstate/test_api.py +++ /dev/null @@ -1,14 +0,0 @@ -import os -import requests -url = "http://0.0.0.0:8000/run_fully_automated?input_image&prompt&room_class" - -# Livingroom Payloads -l_payload1 = {"zipcode" : "19701", - "budget":"800K", - "for_sale":"rent"} -response = requests.request("POST", url, data=l_payload1) -print(response.text) - - - - diff --git a/spaces/grosenthal/aineid/README.md b/spaces/grosenthal/aineid/README.md deleted file mode 100644 index 178bba22407d31b7fa2fe9fbb43f42a0ea71c2b5..0000000000000000000000000000000000000000 --- a/spaces/grosenthal/aineid/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: Latin English Eco -emoji: 💻 -colorFrom: blue -colorTo: indigo -sdk: docker -pinned: false -license: wtfpl ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/guoyww/AnimateDiff/animatediff/pipelines/pipeline_animation.py b/spaces/guoyww/AnimateDiff/animatediff/pipelines/pipeline_animation.py deleted file mode 100644 index 58f22d16c995fce9bd0b56bfd88ae757303d8080..0000000000000000000000000000000000000000 --- a/spaces/guoyww/AnimateDiff/animatediff/pipelines/pipeline_animation.py +++ /dev/null @@ -1,428 +0,0 @@ -# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py - -import inspect -from typing import Callable, List, Optional, Union -from dataclasses import dataclass - -import numpy as np -import torch -from tqdm import tqdm - -from diffusers.utils import is_accelerate_available -from packaging import version -from transformers import CLIPTextModel, CLIPTokenizer - -from diffusers.configuration_utils import FrozenDict -from diffusers.models import AutoencoderKL -from diffusers.pipeline_utils import DiffusionPipeline -from diffusers.schedulers import ( - DDIMScheduler, - DPMSolverMultistepScheduler, - EulerAncestralDiscreteScheduler, - EulerDiscreteScheduler, - LMSDiscreteScheduler, - PNDMScheduler, -) -from diffusers.utils import deprecate, logging, BaseOutput - -from einops import rearrange - -from ..models.unet import UNet3DConditionModel - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -@dataclass -class AnimationPipelineOutput(BaseOutput): - videos: Union[torch.Tensor, np.ndarray] - - -class AnimationPipeline(DiffusionPipeline): - _optional_components = [] - - def __init__( - self, - vae: AutoencoderKL, - text_encoder: CLIPTextModel, - tokenizer: CLIPTokenizer, - unet: UNet3DConditionModel, - scheduler: Union[ - DDIMScheduler, - PNDMScheduler, - LMSDiscreteScheduler, - EulerDiscreteScheduler, - EulerAncestralDiscreteScheduler, - DPMSolverMultistepScheduler, - ], - ): - super().__init__() - - if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: - deprecation_message = ( - f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" - f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " - "to update the config accordingly as leaving `steps_offset` might led to incorrect results" - " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," - " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" - " file" - ) - deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(scheduler.config) - new_config["steps_offset"] = 1 - scheduler._internal_dict = FrozenDict(new_config) - - if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: - deprecation_message = ( - f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." - " `clip_sample` should be set to False in the configuration file. Please make sure to update the" - " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" - " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" - " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" - ) - deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(scheduler.config) - new_config["clip_sample"] = False - scheduler._internal_dict = FrozenDict(new_config) - - is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( - version.parse(unet.config._diffusers_version).base_version - ) < version.parse("0.9.0.dev0") - is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 - if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: - deprecation_message = ( - "The configuration file of the unet has set the default `sample_size` to smaller than" - " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" - " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" - " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" - " in the config might lead to incorrect results in future versions. If you have downloaded this" - " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" - " the `unet/config.json` file" - ) - deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) - new_config = dict(unet.config) - new_config["sample_size"] = 64 - unet._internal_dict = FrozenDict(new_config) - - self.register_modules( - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - unet=unet, - scheduler=scheduler, - ) - self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) - - def enable_vae_slicing(self): - self.vae.enable_slicing() - - def disable_vae_slicing(self): - self.vae.disable_slicing() - - def enable_sequential_cpu_offload(self, gpu_id=0): - if is_accelerate_available(): - from accelerate import cpu_offload - else: - raise ImportError("Please install accelerate via `pip install accelerate`") - - device = torch.device(f"cuda:{gpu_id}") - - for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: - if cpu_offloaded_model is not None: - cpu_offload(cpu_offloaded_model, device) - - - @property - def _execution_device(self): - if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): - return self.device - for module in self.unet.modules(): - if ( - hasattr(module, "_hf_hook") - and hasattr(module._hf_hook, "execution_device") - and module._hf_hook.execution_device is not None - ): - return torch.device(module._hf_hook.execution_device) - return self.device - - def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt): - batch_size = len(prompt) if isinstance(prompt, list) else 1 - - text_inputs = self.tokenizer( - prompt, - padding="max_length", - max_length=self.tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - text_input_ids = text_inputs.input_ids - untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids - - if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): - removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]) - logger.warning( - "The following part of your input was truncated because CLIP can only handle sequences up to" - f" {self.tokenizer.model_max_length} tokens: {removed_text}" - ) - - if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: - attention_mask = text_inputs.attention_mask.to(device) - else: - attention_mask = None - - text_embeddings = self.text_encoder( - text_input_ids.to(device), - attention_mask=attention_mask, - ) - text_embeddings = text_embeddings[0] - - # duplicate text embeddings for each generation per prompt, using mps friendly method - bs_embed, seq_len, _ = text_embeddings.shape - text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1) - text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1) - - # get unconditional embeddings for classifier free guidance - if do_classifier_free_guidance: - uncond_tokens: List[str] - if negative_prompt is None: - uncond_tokens = [""] * batch_size - elif type(prompt) is not type(negative_prompt): - raise TypeError( - f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" - f" {type(prompt)}." - ) - elif isinstance(negative_prompt, str): - uncond_tokens = [negative_prompt] - elif batch_size != len(negative_prompt): - raise ValueError( - f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" - f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" - " the batch size of `prompt`." - ) - else: - uncond_tokens = negative_prompt - - max_length = text_input_ids.shape[-1] - uncond_input = self.tokenizer( - uncond_tokens, - padding="max_length", - max_length=max_length, - truncation=True, - return_tensors="pt", - ) - - if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: - attention_mask = uncond_input.attention_mask.to(device) - else: - attention_mask = None - - uncond_embeddings = self.text_encoder( - uncond_input.input_ids.to(device), - attention_mask=attention_mask, - ) - uncond_embeddings = uncond_embeddings[0] - - # duplicate unconditional embeddings for each generation per prompt, using mps friendly method - seq_len = uncond_embeddings.shape[1] - uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1) - uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1) - - # For classifier free guidance, we need to do two forward passes. - # Here we concatenate the unconditional and text embeddings into a single batch - # to avoid doing two forward passes - text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) - - return text_embeddings - - def decode_latents(self, latents): - video_length = latents.shape[2] - latents = 1 / 0.18215 * latents - latents = rearrange(latents, "b c f h w -> (b f) c h w") - # video = self.vae.decode(latents).sample - video = [] - for frame_idx in tqdm(range(latents.shape[0])): - video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample) - video = torch.cat(video) - video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length) - video = (video / 2 + 0.5).clamp(0, 1) - # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 - video = video.cpu().float().numpy() - return video - - def prepare_extra_step_kwargs(self, generator, eta): - # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature - # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. - # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 - # and should be between [0, 1] - - accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) - extra_step_kwargs = {} - if accepts_eta: - extra_step_kwargs["eta"] = eta - - # check if the scheduler accepts generator - accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) - if accepts_generator: - extra_step_kwargs["generator"] = generator - return extra_step_kwargs - - def check_inputs(self, prompt, height, width, callback_steps): - if not isinstance(prompt, str) and not isinstance(prompt, list): - raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") - - if height % 8 != 0 or width % 8 != 0: - raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") - - if (callback_steps is None) or ( - callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) - ): - raise ValueError( - f"`callback_steps` has to be a positive integer but is {callback_steps} of type" - f" {type(callback_steps)}." - ) - - def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None): - shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor) - if isinstance(generator, list) and len(generator) != batch_size: - raise ValueError( - f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" - f" size of {batch_size}. Make sure the batch size matches the length of the generators." - ) - if latents is None: - rand_device = "cpu" if device.type == "mps" else device - - if isinstance(generator, list): - shape = shape - # shape = (1,) + shape[1:] - latents = [ - torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype) - for i in range(batch_size) - ] - latents = torch.cat(latents, dim=0).to(device) - else: - latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device) - else: - if latents.shape != shape: - raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") - latents = latents.to(device) - - # scale the initial noise by the standard deviation required by the scheduler - latents = latents * self.scheduler.init_noise_sigma - return latents - - @torch.no_grad() - def __call__( - self, - prompt: Union[str, List[str]], - video_length: Optional[int], - height: Optional[int] = None, - width: Optional[int] = None, - num_inference_steps: int = 50, - guidance_scale: float = 7.5, - negative_prompt: Optional[Union[str, List[str]]] = None, - num_videos_per_prompt: Optional[int] = 1, - eta: float = 0.0, - generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, - latents: Optional[torch.FloatTensor] = None, - output_type: Optional[str] = "tensor", - return_dict: bool = True, - callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, - callback_steps: Optional[int] = 1, - **kwargs, - ): - # Default height and width to unet - height = height or self.unet.config.sample_size * self.vae_scale_factor - width = width or self.unet.config.sample_size * self.vae_scale_factor - - # Check inputs. Raise error if not correct - self.check_inputs(prompt, height, width, callback_steps) - - # Define call parameters - # batch_size = 1 if isinstance(prompt, str) else len(prompt) - batch_size = 1 - if latents is not None: - batch_size = latents.shape[0] - if isinstance(prompt, list): - batch_size = len(prompt) - - device = self._execution_device - # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) - # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` - # corresponds to doing no classifier free guidance. - do_classifier_free_guidance = guidance_scale > 1.0 - - # Encode input prompt - prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size - if negative_prompt is not None: - negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size - text_embeddings = self._encode_prompt( - prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt - ) - - # Prepare timesteps - self.scheduler.set_timesteps(num_inference_steps, device=device) - timesteps = self.scheduler.timesteps - - # Prepare latent variables - num_channels_latents = self.unet.in_channels - latents = self.prepare_latents( - batch_size * num_videos_per_prompt, - num_channels_latents, - video_length, - height, - width, - text_embeddings.dtype, - device, - generator, - latents, - ) - latents_dtype = latents.dtype - - # Prepare extra step kwargs. - extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) - - # Denoising loop - num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order - with self.progress_bar(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) - - # predict the noise residual - noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample.to(dtype=latents_dtype) - # noise_pred = [] - # import pdb - # pdb.set_trace() - # for batch_idx in range(latent_model_input.shape[0]): - # noise_pred_single = self.unet(latent_model_input[batch_idx:batch_idx+1], t, encoder_hidden_states=text_embeddings[batch_idx:batch_idx+1]).sample.to(dtype=latents_dtype) - # noise_pred.append(noise_pred_single) - # noise_pred = torch.cat(noise_pred) - - # perform guidance - if do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample - - # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): - progress_bar.update() - if callback is not None and i % callback_steps == 0: - callback(i, t, latents) - - # Post-processing - video = self.decode_latents(latents) - - # Convert to tensor - if output_type == "tensor": - video = torch.from_numpy(video) - - if not return_dict: - return video - - return AnimationPipelineOutput(videos=video) diff --git a/spaces/guoyww/AnimateDiff/download_bashscripts/8-GhibliBackground.sh b/spaces/guoyww/AnimateDiff/download_bashscripts/8-GhibliBackground.sh deleted file mode 100644 index 39b9e76ddf77a842e4f41acbee9e73f62c49eec0..0000000000000000000000000000000000000000 --- a/spaces/guoyww/AnimateDiff/download_bashscripts/8-GhibliBackground.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash -wget https://civitai.com/api/download/models/102828 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate -wget https://civitai.com/api/download/models/57618 -P models/DreamBooth_LoRA/ --content-disposition --no-check-certificate diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/docs/speed_benchmark.md b/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/docs/speed_benchmark.md deleted file mode 100644 index 055aee0defe2c43a523ced48260242f0f99b7cea..0000000000000000000000000000000000000000 --- a/spaces/gwang-kim/DATID-3D/pose_estimation/models/arcface_torch/docs/speed_benchmark.md +++ /dev/null @@ -1,93 +0,0 @@ -## Test Training Speed - -- Test Commands - -You need to use the following two commands to test the Partial FC training performance. -The number of identites is **3 millions** (synthetic data), turn mixed precision training on, backbone is resnet50, -batch size is 1024. -```shell -# Model Parallel -python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/3millions -# Partial FC 0.1 -python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/3millions_pfc -``` - -- GPU Memory - -``` -# (Model Parallel) gpustat -i -[0] Tesla V100-SXM2-32GB | 64'C, 94 % | 30338 / 32510 MB -[1] Tesla V100-SXM2-32GB | 60'C, 99 % | 28876 / 32510 MB -[2] Tesla V100-SXM2-32GB | 60'C, 99 % | 28872 / 32510 MB -[3] Tesla V100-SXM2-32GB | 69'C, 99 % | 28872 / 32510 MB -[4] Tesla V100-SXM2-32GB | 66'C, 99 % | 28888 / 32510 MB -[5] Tesla V100-SXM2-32GB | 60'C, 99 % | 28932 / 32510 MB -[6] Tesla V100-SXM2-32GB | 68'C, 100 % | 28916 / 32510 MB -[7] Tesla V100-SXM2-32GB | 65'C, 99 % | 28860 / 32510 MB - -# (Partial FC 0.1) gpustat -i -[0] Tesla V100-SXM2-32GB | 60'C, 95 % | 10488 / 32510 MB │······················· -[1] Tesla V100-SXM2-32GB | 60'C, 97 % | 10344 / 32510 MB │······················· -[2] Tesla V100-SXM2-32GB | 61'C, 95 % | 10340 / 32510 MB │······················· -[3] Tesla V100-SXM2-32GB | 66'C, 95 % | 10340 / 32510 MB │······················· -[4] Tesla V100-SXM2-32GB | 65'C, 94 % | 10356 / 32510 MB │······················· -[5] Tesla V100-SXM2-32GB | 61'C, 95 % | 10400 / 32510 MB │······················· -[6] Tesla V100-SXM2-32GB | 68'C, 96 % | 10384 / 32510 MB │······················· -[7] Tesla V100-SXM2-32GB | 64'C, 95 % | 10328 / 32510 MB │······················· -``` - -- Training Speed - -```python -# (Model Parallel) trainging.log -Training: Speed 2271.33 samples/sec Loss 1.1624 LearningRate 0.2000 Epoch: 0 Global Step: 100 -Training: Speed 2269.94 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 150 -Training: Speed 2272.67 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 200 -Training: Speed 2266.55 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 250 -Training: Speed 2272.54 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 300 - -# (Partial FC 0.1) trainging.log -Training: Speed 5299.56 samples/sec Loss 1.0965 LearningRate 0.2000 Epoch: 0 Global Step: 100 -Training: Speed 5296.37 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 150 -Training: Speed 5304.37 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 200 -Training: Speed 5274.43 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 250 -Training: Speed 5300.10 samples/sec Loss 0.0000 LearningRate 0.2000 Epoch: 0 Global Step: 300 -``` - -In this test case, Partial FC 0.1 only use1 1/3 of the GPU memory of the model parallel, -and the training speed is 2.5 times faster than the model parallel. - - -## Speed Benchmark - -1. Training speed of different parallel methods (samples/second), Tesla V100 32GB * 8. (Larger is better) - -| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 | -| :--- | :--- | :--- | :--- | -|125000 | 4681 | 4824 | 5004 | -|250000 | 4047 | 4521 | 4976 | -|500000 | 3087 | 4013 | 4900 | -|1000000 | 2090 | 3449 | 4803 | -|1400000 | 1672 | 3043 | 4738 | -|2000000 | - | 2593 | 4626 | -|4000000 | - | 1748 | 4208 | -|5500000 | - | 1389 | 3975 | -|8000000 | - | - | 3565 | -|16000000 | - | - | 2679 | -|29000000 | - | - | 1855 | - -2. GPU memory cost of different parallel methods (GB per GPU), Tesla V100 32GB * 8. (Smaller is better) - -| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 | -| :--- | :--- | :--- | :--- | -|125000 | 7358 | 5306 | 4868 | -|250000 | 9940 | 5826 | 5004 | -|500000 | 14220 | 7114 | 5202 | -|1000000 | 23708 | 9966 | 5620 | -|1400000 | 32252 | 11178 | 6056 | -|2000000 | - | 13978 | 6472 | -|4000000 | - | 23238 | 8284 | -|5500000 | - | 32188 | 9854 | -|8000000 | - | - | 12310 | -|16000000 | - | - | 19950 | -|29000000 | - | - | 32324 | diff --git a/spaces/gyugnsu/DragGan-Inversion/training/training_loop.py b/spaces/gyugnsu/DragGan-Inversion/training/training_loop.py deleted file mode 100644 index b1643b2d96a597d236af29053878191859a74cb7..0000000000000000000000000000000000000000 --- a/spaces/gyugnsu/DragGan-Inversion/training/training_loop.py +++ /dev/null @@ -1,499 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Main training loop.""" - -import os -import time -import copy -import json -import pickle -import psutil -import PIL.Image -import numpy as np -import torch -import dnnlib -from torch_utils import misc -from torch_utils import training_stats -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import grid_sample_gradfix - -import legacy -from metrics import metric_main - -# ---------------------------------------------------------------------------- - - -def setup_snapshot_image_grid(training_set, random_seed=0): - rnd = np.random.RandomState(random_seed) - gw = np.clip(7680 // training_set.image_shape[2], 7, 32) - gh = np.clip(4320 // training_set.image_shape[1], 4, 32) - - # No labels => show random subset of training samples. - if not training_set.has_labels: - all_indices = list(range(len(training_set))) - rnd.shuffle(all_indices) - grid_indices = [all_indices[i % - len(all_indices)] for i in range(gw * gh)] - - else: - # Group training samples by label. - label_groups = dict() # label => [idx, ...] - for idx in range(len(training_set)): - label = tuple(training_set.get_details(idx).raw_label.flat[::-1]) - if label not in label_groups: - label_groups[label] = [] - label_groups[label].append(idx) - - # Reorder. - label_order = sorted(label_groups.keys()) - for label in label_order: - rnd.shuffle(label_groups[label]) - - # Organize into grid. - grid_indices = [] - for y in range(gh): - label = label_order[y % len(label_order)] - indices = label_groups[label] - grid_indices += [indices[x % len(indices)] for x in range(gw)] - label_groups[label] = [ - indices[(i + gw) % len(indices)] for i in range(len(indices))] - - # Load data. - images, labels = zip(*[training_set[i] for i in grid_indices]) - return (gw, gh), np.stack(images), np.stack(labels) - -# ---------------------------------------------------------------------------- - - -def save_image_grid(img, fname, drange, grid_size): - lo, hi = drange - img = np.asarray(img, dtype=np.float32) - img = (img - lo) * (255 / (hi - lo)) - img = np.rint(img).clip(0, 255).astype(np.uint8) - - gw, gh = grid_size - _N, C, H, W = img.shape - img = img.reshape([gh, gw, C, H, W]) - img = img.transpose(0, 3, 1, 4, 2) - img = img.reshape([gh * H, gw * W, C]) - - assert C in [1, 3] - if C == 1: - PIL.Image.fromarray(img[:, :, 0], 'L').save(fname) - if C == 3: - PIL.Image.fromarray(img, 'RGB').save(fname) - -# ---------------------------------------------------------------------------- - - -def training_loop( - run_dir='.', # Output directory. - training_set_kwargs={}, # Options for training set. - data_loader_kwargs={}, # Options for torch.utils.data.DataLoader. - G_kwargs={}, # Options for generator network. - D_kwargs={}, # Options for discriminator network. - G_opt_kwargs={}, # Options for generator optimizer. - D_opt_kwargs={}, # Options for discriminator optimizer. - # Options for augmentation pipeline. None = disable. - augment_kwargs=None, - loss_kwargs={}, # Options for loss function. - metrics=[], # Metrics to evaluate during training. - random_seed=0, # Global random seed. - num_gpus=1, # Number of GPUs participating in the training. - rank=0, # Rank of the current process in [0, num_gpus[. - # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus. - batch_size=4, - batch_gpu=4, # Number of samples processed at a time by one GPU. - # Half-life of the exponential moving average (EMA) of generator weights. - ema_kimg=10, - ema_rampup=0.05, # EMA ramp-up coefficient. None = no rampup. - # How often to perform regularization for G? None = disable lazy regularization. - G_reg_interval=None, - # How often to perform regularization for D? None = disable lazy regularization. - D_reg_interval=16, - augment_p=0, # Initial value of augmentation probability. - ada_target=None, # ADA target value. None = fixed p. - ada_interval=4, # How often to perform ADA adjustment? - # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit. - ada_kimg=500, - # Total length of the training, measured in thousands of real images. - total_kimg=25000, - kimg_per_tick=4, # Progress snapshot interval. - # How often to save image snapshots? None = disable. - image_snapshot_ticks=50, - # How often to save network snapshots? None = disable. - network_snapshot_ticks=50, - resume_pkl=None, # Network pickle to resume training from. - resume_kimg=0, # First kimg to report when resuming training. - cudnn_benchmark=True, # Enable torch.backends.cudnn.benchmark? - # Callback function for determining whether to abort training. Must return consistent results across ranks. - abort_fn=None, - # Callback function for updating training progress. Called for all ranks. - progress_fn=None, -): - # Initialize. - start_time = time.time() - device = torch.device('cuda', rank) - np.random.seed(random_seed * num_gpus + rank) - torch.manual_seed(random_seed * num_gpus + rank) - # Improves training speed. - torch.backends.cudnn.benchmark = cudnn_benchmark - # Improves numerical accuracy. - torch.backends.cuda.matmul.allow_tf32 = False - # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False - # Improves training speed. - conv2d_gradfix.enabled = True - # Avoids errors with the augmentation pipe. - grid_sample_gradfix.enabled = True - - # Load training set. - if rank == 0: - print('Loading training set...') - training_set = dnnlib.util.construct_class_by_name( - **training_set_kwargs) # subclass of training.dataset.Dataset - training_set_sampler = misc.InfiniteSampler( - dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed) - training_set_iterator = iter(torch.utils.data.DataLoader( - dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs)) - if rank == 0: - print() - print('Num images: ', len(training_set)) - print('Image shape:', training_set.image_shape) - print('Label shape:', training_set.label_shape) - print() - - # Construct networks. - if rank == 0: - print('Constructing networks...') - common_kwargs = dict(c_dim=training_set.label_dim, - img_resolution=training_set.resolution, img_channels=training_set.num_channels) - G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train( - ).requires_grad_(False).to(device) # subclass of torch.nn.Module - D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train( - ).requires_grad_(False).to(device) # subclass of torch.nn.Module - G_ema = copy.deepcopy(G).eval() - - # Resume from existing pickle. - if (resume_pkl is not None) and (rank == 0): - print(f'Resuming from "{resume_pkl}"') - with dnnlib.util.open_url(resume_pkl) as f: - resume_data = legacy.load_network_pkl(f) - for name, module in [('G', G), ('D', D), ('G_ema', G_ema)]: - misc.copy_params_and_buffers( - resume_data[name], module, require_all=False) - - # Print network summary tables. - if rank == 0: - z = torch.empty([batch_gpu, G.z_dim], device=device) - c = torch.empty([batch_gpu, G.c_dim], device=device) - img = misc.print_module_summary(G, [z, c]) - misc.print_module_summary(D, [img, c]) - - # Setup augmentation. - if rank == 0: - print('Setting up augmentation...') - augment_pipe = None - ada_stats = None - if (augment_kwargs is not None) and (augment_p > 0 or ada_target is not None): - augment_pipe = dnnlib.util.construct_class_by_name( - **augment_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module - augment_pipe.p.copy_(torch.as_tensor(augment_p)) - if ada_target is not None: - ada_stats = training_stats.Collector(regex='Loss/signs/real') - - # Distribute across GPUs. - if rank == 0: - print(f'Distributing across {num_gpus} GPUs...') - for module in [G, D, G_ema, augment_pipe]: - if module is not None and num_gpus > 1: - for param in misc.params_and_buffers(module): - torch.distributed.broadcast(param, src=0) - - # Setup training phases. - if rank == 0: - print('Setting up training phases...') - loss = dnnlib.util.construct_class_by_name( - device=device, G=G, D=D, augment_pipe=augment_pipe, **loss_kwargs) # subclass of training.loss.Loss - phases = [] - for name, module, opt_kwargs, reg_interval in [('G', G, G_opt_kwargs, G_reg_interval), ('D', D, D_opt_kwargs, D_reg_interval)]: - if reg_interval is None: - opt = dnnlib.util.construct_class_by_name( - params=module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer - phases += [dnnlib.EasyDict(name=name+'both', - module=module, opt=opt, interval=1)] - else: # Lazy regularization. - mb_ratio = reg_interval / (reg_interval + 1) - opt_kwargs = dnnlib.EasyDict(opt_kwargs) - opt_kwargs.lr = opt_kwargs.lr * mb_ratio - opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas] - opt = dnnlib.util.construct_class_by_name( - module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer - phases += [dnnlib.EasyDict(name=name+'main', - module=module, opt=opt, interval=1)] - phases += [dnnlib.EasyDict(name=name+'reg', - module=module, opt=opt, interval=reg_interval)] - for phase in phases: - phase.start_event = None - phase.end_event = None - if rank == 0: - phase.start_event = torch.cuda.Event(enable_timing=True) - phase.end_event = torch.cuda.Event(enable_timing=True) - - # Export sample images. - grid_size = None - grid_z = None - grid_c = None - if rank == 0: - print('Exporting sample images...') - grid_size, images, labels = setup_snapshot_image_grid( - training_set=training_set) - save_image_grid(images, os.path.join(run_dir, 'reals.png'), - drange=[0, 255], grid_size=grid_size) - grid_z = torch.randn([labels.shape[0], G.z_dim], - device=device).split(batch_gpu) - grid_c = torch.from_numpy(labels).to(device).split(batch_gpu) - images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() - for z, c in zip(grid_z, grid_c)]).numpy() - save_image_grid(images, os.path.join( - run_dir, 'fakes_init.png'), drange=[-1, 1], grid_size=grid_size) - - # Initialize logs. - if rank == 0: - print('Initializing logs...') - stats_collector = training_stats.Collector(regex='.*') - stats_metrics = dict() - stats_jsonl = None - stats_tfevents = None - if rank == 0: - stats_jsonl = open(os.path.join(run_dir, 'stats.jsonl'), 'wt') - try: - import torch.utils.tensorboard as tensorboard - stats_tfevents = tensorboard.SummaryWriter(run_dir) - except ImportError as err: - print('Skipping tfevents export:', err) - - # Train. - if rank == 0: - print(f'Training for {total_kimg} kimg...') - print() - cur_nimg = resume_kimg * 1000 - cur_tick = 0 - tick_start_nimg = cur_nimg - tick_start_time = time.time() - maintenance_time = tick_start_time - start_time - batch_idx = 0 - if progress_fn is not None: - progress_fn(0, total_kimg) - while True: - - # Fetch training data. - with torch.autograd.profiler.record_function('data_fetch'): - phase_real_img, phase_real_c = next(training_set_iterator) - phase_real_img = (phase_real_img.to(device).to( - torch.float32) / 127.5 - 1).split(batch_gpu) - phase_real_c = phase_real_c.to(device).split(batch_gpu) - all_gen_z = torch.randn( - [len(phases) * batch_size, G.z_dim], device=device) - all_gen_z = [phase_gen_z.split( - batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)] - all_gen_c = [training_set.get_label(np.random.randint( - len(training_set))) for _ in range(len(phases) * batch_size)] - all_gen_c = torch.from_numpy( - np.stack(all_gen_c)).pin_memory().to(device) - all_gen_c = [phase_gen_c.split( - batch_gpu) for phase_gen_c in all_gen_c.split(batch_size)] - - # Execute training phases. - for phase, phase_gen_z, phase_gen_c in zip(phases, all_gen_z, all_gen_c): - if batch_idx % phase.interval != 0: - continue - if phase.start_event is not None: - phase.start_event.record(torch.cuda.current_stream(device)) - - # Accumulate gradients. - phase.opt.zero_grad(set_to_none=True) - phase.module.requires_grad_(True) - for real_img, real_c, gen_z, gen_c in zip(phase_real_img, phase_real_c, phase_gen_z, phase_gen_c): - loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, - gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg) - phase.module.requires_grad_(False) - - # Update weights. - with torch.autograd.profiler.record_function(phase.name + '_opt'): - params = [param for param in phase.module.parameters() - if param.grad is not None] - if len(params) > 0: - flat = torch.cat([param.grad.flatten() - for param in params]) - if num_gpus > 1: - torch.distributed.all_reduce(flat) - flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, - neginf=-1e5, out=flat) - grads = flat.split([param.numel() for param in params]) - for param, grad in zip(params, grads): - param.grad = grad.reshape(param.shape) - phase.opt.step() - - # Phase done. - if phase.end_event is not None: - phase.end_event.record(torch.cuda.current_stream(device)) - - # Update G_ema. - with torch.autograd.profiler.record_function('Gema'): - ema_nimg = ema_kimg * 1000 - if ema_rampup is not None: - ema_nimg = min(ema_nimg, cur_nimg * ema_rampup) - ema_beta = 0.5 ** (batch_size / max(ema_nimg, 1e-8)) - for p_ema, p in zip(G_ema.parameters(), G.parameters()): - p_ema.copy_(p.lerp(p_ema, ema_beta)) - for b_ema, b in zip(G_ema.buffers(), G.buffers()): - b_ema.copy_(b) - - # Update state. - cur_nimg += batch_size - batch_idx += 1 - - # Execute ADA heuristic. - if (ada_stats is not None) and (batch_idx % ada_interval == 0): - ada_stats.update() - adjust = np.sign(ada_stats['Loss/signs/real'] - ada_target) * \ - (batch_size * ada_interval) / (ada_kimg * 1000) - augment_pipe.p.copy_( - (augment_pipe.p + adjust).max(misc.constant(0, device=device))) - - # Perform maintenance tasks once per tick. - done = (cur_nimg >= total_kimg * 1000) - if (not done) and (cur_tick != 0) and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000): - continue - - # Print status line, accumulating the same information in training_stats. - tick_end_time = time.time() - fields = [] - fields += [ - f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"] - fields += [ - f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"] - fields += [ - f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"] - fields += [ - f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"] - fields += [ - f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"] - fields += [ - f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"] - fields += [ - f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"] - fields += [ - f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"] - fields += [ - f"reserved {training_stats.report0('Resources/peak_gpu_mem_reserved_gb', torch.cuda.max_memory_reserved(device) / 2**30):<6.2f}"] - torch.cuda.reset_peak_memory_stats() - fields += [ - f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"] - training_stats.report0('Timing/total_hours', - (tick_end_time - start_time) / (60 * 60)) - training_stats.report0('Timing/total_days', - (tick_end_time - start_time) / (24 * 60 * 60)) - if rank == 0: - print(' '.join(fields)) - - # Check for abort. - if (not done) and (abort_fn is not None) and abort_fn(): - done = True - if rank == 0: - print() - print('Aborting...') - - # Save image snapshot. - if (rank == 0) and (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0): - images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() - for z, c in zip(grid_z, grid_c)]).numpy() - save_image_grid(images, os.path.join( - run_dir, f'fakes{cur_nimg//1000:06d}.png'), drange=[-1, 1], grid_size=grid_size) - - # Save network snapshot. - snapshot_pkl = None - snapshot_data = None - if (network_snapshot_ticks is not None) and (done or cur_tick % network_snapshot_ticks == 0): - snapshot_data = dict(G=G, D=D, G_ema=G_ema, augment_pipe=augment_pipe, - training_set_kwargs=dict(training_set_kwargs)) - for key, value in snapshot_data.items(): - if isinstance(value, torch.nn.Module): - value = copy.deepcopy(value).eval().requires_grad_(False) - if num_gpus > 1: - misc.check_ddp_consistency( - value, ignore_regex=r'.*\.[^.]+_(avg|ema)') - for param in misc.params_and_buffers(value): - torch.distributed.broadcast(param, src=0) - snapshot_data[key] = value.cpu() - del value # conserve memory - snapshot_pkl = os.path.join( - run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl') - if rank == 0: - with open(snapshot_pkl, 'wb') as f: - pickle.dump(snapshot_data, f) - - # Evaluate metrics. - if (snapshot_data is not None) and (len(metrics) > 0): - if rank == 0: - print('Evaluating metrics...') - for metric in metrics: - result_dict = metric_main.calc_metric(metric=metric, G=snapshot_data['G_ema'], - dataset_kwargs=training_set_kwargs, num_gpus=num_gpus, rank=rank, device=device) - if rank == 0: - metric_main.report_metric( - result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl) - stats_metrics.update(result_dict.results) - del snapshot_data # conserve memory - - # Collect statistics. - for phase in phases: - value = [] - if (phase.start_event is not None) and (phase.end_event is not None): - phase.end_event.synchronize() - value = phase.start_event.elapsed_time(phase.end_event) - training_stats.report0('Timing/' + phase.name, value) - stats_collector.update() - stats_dict = stats_collector.as_dict() - - # Update logs. - timestamp = time.time() - if stats_jsonl is not None: - fields = dict(stats_dict, timestamp=timestamp) - stats_jsonl.write(json.dumps(fields) + '\n') - stats_jsonl.flush() - if stats_tfevents is not None: - global_step = int(cur_nimg / 1e3) - walltime = timestamp - start_time - for name, value in stats_dict.items(): - stats_tfevents.add_scalar( - name, value.mean, global_step=global_step, walltime=walltime) - for name, value in stats_metrics.items(): - stats_tfevents.add_scalar( - f'Metrics/{name}', value, global_step=global_step, walltime=walltime) - stats_tfevents.flush() - if progress_fn is not None: - progress_fn(cur_nimg // 1000, total_kimg) - - # Update state. - cur_tick += 1 - tick_start_nimg = cur_nimg - tick_start_time = time.time() - maintenance_time = tick_start_time - tick_end_time - if done: - break - - # Done. - if rank == 0: - print() - print('Exiting...') - -# ---------------------------------------------------------------------------- diff --git a/spaces/h2oai/wave-chatbot-ui/Dockerfile b/spaces/h2oai/wave-chatbot-ui/Dockerfile deleted file mode 100644 index 10b9dda44751397fd4c93872ca11c6aeb81a1fc0..0000000000000000000000000000000000000000 --- a/spaces/h2oai/wave-chatbot-ui/Dockerfile +++ /dev/null @@ -1,15 +0,0 @@ -# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker -# you will also find guides on how best to write your Dockerfile - -FROM python:3.9 - -COPY app.py . -COPY requirements.txt . - -RUN python -m venv venv -RUN ./venv/bin/pip install -r requirements.txt - -ENV H2O_WAVE_LISTEN=":7860" -ENV H2O_WAVE_ADDRESS="http://127.0.0.1:7860" - -CMD ["./venv/bin/wave", "run", "app.py", "--no-reload"] diff --git a/spaces/hacknc23/hacknc23/app.py b/spaces/hacknc23/hacknc23/app.py deleted file mode 100644 index 9d1331993b8d061919d0c726930434586b61501d..0000000000000000000000000000000000000000 --- a/spaces/hacknc23/hacknc23/app.py +++ /dev/null @@ -1,146 +0,0 @@ -from gtts import gTTS -import streamlit as st -import base64 -from ingestPDF import readUploadedPdf -from PIL import Image - -image = Image.open('studyaid.jpeg') - - -class SessionState: - def __init__(self): - self.play = False - -session_state = SessionState() - -st.set_page_config(page_title="StudyAId", page_icon=":tada:", layout="wide") - - -def local_css(file_name): - with open(file_name) as f: - st.markdown(f"", unsafe_allow_html= True) - - - - -local_css("style.css") - - - -#PASS STRING TO TURN INTO AUDIO MP3 -def textToAudio(str): - audio = gTTS(str) - audio.save('output.mp3') - -#STYLIZE THE AUDIO PLAYBACK -style_css = """ -audio::-webkit-media-controls-panel, -audio::-webkit-media-controls-enclosure { - background-color:#BBBBBB;} - -audio::-webkit-media-controls-time-remaining-display, -audio::-webkit-media-controls-current-time-display { - color: white; - text-shadow: none; -} -audio::-webkit-media-controls-timeline { - background-color: #888888; - border-radius: 25px; - margin-left: 10px; - margin-right: 10px; -} -audio { - width: 100px; - height: 100px; -} -""" -st.markdown( - "", unsafe_allow_html=True -) - - -#GENERATE AN AUDIO PLAYBACK FROM MP3 -def generateAudio(): - audio_file = open('output.mp3', 'rb') - st.audio(audio_file) - -def autoplay_audio(file_path: str): - with open(file_path, "rb") as f: - data = f.read() - b64 = base64.b64encode(data).decode() - md = f""" - - """ - st.markdown( - md, - unsafe_allow_html=True, - ) - - - -st.image(image, width=150, use_column_width=None) - - - - -st.subheader("Welcome to StudyAId!") - -st.write("This program is designed to help you answer any questions you have about your uploaded document. Please enter a question/prompt below and the computer will generate a response to answer your query!") - -def reset(): - st.experimental_rerun() - - - - -uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf") -#Updated to have a key to pass to clear_text -query = st.text_area('Enter your question:', value = "", key="Query") - -passToLangChain = readUploadedPdf(uploaded_file) - -# Tries to clear the state of the Query text box when the clear button is clicked -def clear_text(): - st.session_state["Query"] = "" -# Button to call the clear_text function -st.button("Clear text input", on_click =clear_text) - - -from langtest import callAPI - - - - -# if answered != None: -# st.subheader(answered) - - -col1, col2, col3 = st.columns([1,1,1]) - -with col1: - if st.button('Enter'): - if not query == "": - answered = callAPI(passToLangChain, query) - - textToAudio(answered) - autoplay_audio("output.mp3") - st.text (answered) - if st.button('Clear'): - st.experimental_rerun() - query = st.text_area('', value = "") - - else: - st.text("Please provide a question!") - - - - -# with col2: - # if st.button('Play'): - # st.experimental_rerun() - - -# with col2: -# if st.button('Pause'): \ No newline at end of file diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/mixup.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/mixup.py deleted file mode 100644 index 85969e097ad154d1948d81eb1b5b3e941f5bf53f..0000000000000000000000000000000000000000 --- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/mixup.py +++ /dev/null @@ -1,124 +0,0 @@ -"""Mixup detection dataset wrapper.""" -from __future__ import absolute_import -import numpy as np -import torch -import torch.utils.data as data - - -class MixupDetection(data.Dataset): - """Detection dataset wrapper that performs mixup for normal dataset. - Parameters - ---------- - dataset : mx.gluon.data.Dataset - Gluon dataset object. - mixup : callable random generator, e.g. np.random.uniform - A random mixup ratio sampler, preferably a random generator from numpy.random - A random float will be sampled each time with mixup(*args). - Use None to disable. - *args : list - Additional arguments for mixup random sampler. - """ - def __init__(self, dataset, mixup=None, preproc=None, *args): - super().__init__(dataset.input_dim) - self._dataset = dataset - self.preproc = preproc - self._mixup = mixup - self._mixup_args = args - - def set_mixup(self, mixup=None, *args): - """Set mixup random sampler, use None to disable. - Parameters - ---------- - mixup : callable random generator, e.g. np.random.uniform - A random mixup ratio sampler, preferably a random generator from numpy.random - A random float will be sampled each time with mixup(*args) - *args : list - Additional arguments for mixup random sampler. - """ - self._mixup = mixup - self._mixup_args = args - - def __len__(self): - return len(self._dataset) - - @Dataset.resize_getitem - def __getitem__(self, idx): - self._dataset._input_dim = self.input_dim - # first image - img1, label1, _, _= self._dataset.pull_item(idx) - lambd = 1 - - # draw a random lambda ratio from distribution - if self._mixup is not None: - lambd = max(0, min(1, self._mixup(*self._mixup_args))) - - if lambd >= 1: - weights1 = np.ones((label1.shape[0], 1)) - label1 = np.hstack((label1, weights1)) - height, width, _ = img1.shape - img_info = (width, height) - if self.preproc is not None: - img_o, target_o = self.preproc(img1, label1, self.input_dim) - return img_o, target_o, img_info, idx - - # second image - idx2 = int(np.random.choice(np.delete(np.arange(len(self)), idx))) - img2, label2, _, _ = self._dataset.pull_item(idx2) - - # mixup two images - height = max(img1.shape[0], img2.shape[0]) - width = max(img1.shape[1], img2.shape[1]) - mix_img = np.zeros((height, width, 3),dtype=np.float32) - mix_img[:img1.shape[0], :img1.shape[1], :] = img1.astype(np.float32) * lambd - mix_img[:img2.shape[0], :img2.shape[1], :] += img2.astype(np.float32) * (1. - lambd) - mix_img = mix_img.astype(np.uint8) - - y1 = np.hstack((label1, np.full((label1.shape[0], 1), lambd))) - y2 = np.hstack((label2, np.full((label2.shape[0], 1), 1. - lambd))) - mix_label = np.vstack((y1, y2)) - if self.preproc is not None: - mix_img, padded_labels = self.preproc(mix_img, mix_label, self.input_dim) - - img_info = (width, height) - - return mix_img, padded_labels, img_info , idx - - def pull_item(self, idx): - self._dataset._input_dim = self.input_dim - # first image - img1, label1, _, _= self._dataset.pull_item(idx) - lambd = 1 - - # draw a random lambda ratio from distribution - if self._mixup is not None: - lambd = max(0, min(1, self._mixup(*self._mixup_args))) - - if lambd >= 1: - weights1 = np.ones((label1.shape[0], 1)) - label1 = np.hstack((label1, weights1)) - height, width, _ = img1.shape - img_info = (width, height) - if self.preproc is not None: - img_o, target_o = self.preproc(img1, label1, self.input_dim) - return img_o, target_o, img_info, idx - - # second image - idx2 = int(np.random.choice(np.delete(np.arange(len(self)), idx))) - img2, label2 = self._dataset.pull_item(idx2) - - # mixup two images - height = max(img1.shape[0], img2.shape[0]) - width = max(img1.shape[1], img2.shape[1]) - mix_img = np.zeros((height, width, 3),dtype=np.float32) - mix_img[:img1.shape[0], :img1.shape[1], :] = img1.astype(np.float32) * lambd - mix_img[:img2.shape[0], :img2.shape[1], :] += img2.astype(np.float32) * (1. - lambd) - mix_img = mix_img.astype(np.uint8) - - y1 = np.hstack((label1, np.full((label1.shape[0], 1), lambd))) - y2 = np.hstack((label2, np.full((label2.shape[0], 1), 1. - lambd))) - mix_label = np.vstack((y1, y2)) - if self.preproc is not None: - mix_img, padded_labels = self.preproc(mix_img, mix_label, self.input_dim) - - img_info = (width, height) - return mix_img, padded_labels, img_info , idx diff --git a/spaces/happiestminds/trackbot/create_db.py b/spaces/happiestminds/trackbot/create_db.py deleted file mode 100644 index ca8bd3129f71a966e9b860a848d05d06ddd04abc..0000000000000000000000000000000000000000 --- a/spaces/happiestminds/trackbot/create_db.py +++ /dev/null @@ -1,50 +0,0 @@ -import os -import sqlite3 - -def create_database_from_file(schema_filepath): - db_name = 'shipping_application.db' - - # Remove the existing database file to start from a clean slate - if os.path.exists(db_name): - os.remove(db_name) - - # Connect to SQLite database or create it if it doesn't exist - conn = sqlite3.connect(db_name) - cursor = conn.cursor() - - # Read schema SQL from the file - with open(schema_filepath, 'r') as f: - schema_sql = f.read() - - # Execute the schema SQL to create tables - cursor.executescript(schema_sql) - - # Commit the changes - conn.commit() - - # Check if tables exist - cursor.execute("SELECT name FROM sqlite_master WHERE type='table';") - tables = cursor.fetchall() - table_names = [table[0] for table in tables] - - # Here, list the expected table names based on your schema - expected_table_names = [ - 'Manufacturers', 'Showrooms', 'ShippingModes', 'Status', - 'Locations', 'Consignments', 'Vehicles', 'Consignment_Showroom', - 'Consignment_ShippingMode', 'Consignment_Status', 'Consignment_Events', - 'Customers', 'Customer_Vehicles' - ] - - missing_tables = set(expected_table_names) - set(table_names) - - if missing_tables: - print(f"Missing tables: {', '.join(missing_tables)}") - else: - print("All expected tables exist.") - - # Close the database connection - conn.close() - -if __name__ == '__main__': - schema_filepath = 'db_schema.sql' # Replace with the actual path to your db_schema.sql - create_database_from_file(schema_filepath) diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/model_zoo/__init__.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/model_zoo/__init__.py deleted file mode 100644 index 886616f8e11ef31ea85d7a7ba9a75308befceedf..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/model_zoo/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Model Zoo API for Detectron2: a collection of functions to create common model architectures and -optionally load pre-trained weights as released in -`MODEL_ZOO.md
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- diff --git a/spaces/inreVtussa/clothingai/Examples/BassBox Pro 6.0.2 Full Version LINK.md b/spaces/inreVtussa/clothingai/Examples/BassBox Pro 6.0.2 Full Version LINK.md deleted file mode 100644 index 0d474eeffb046f4aba0920eaaa666210c9b64dc2..0000000000000000000000000000000000000000 --- a/spaces/inreVtussa/clothingai/Examples/BassBox Pro 6.0.2 Full Version LINK.md +++ /dev/null @@ -1,41 +0,0 @@ - -
How to Design World-Class Speaker Boxes with BassBox Pro 6.0.2 Full Version
- -If you are looking for a state-of-the-art speaker enclosure design program that is used by professional and amateur speaker system designers around the world, you should check out BassBox Pro 6.0.2 full version. This software allows you to design speakers for a wide variety of applications, including home hi-fi, home theater, car, truck, van, pro sound reinforcement, recording studio monitors, stage monitors, PA, musical instruments, and more.
- -BassBox Pro 6.0.2 full version has many features that make it very easy to learn and use. For example, when you run the program for the first time, a Welcome window will appear to help you configure the program and introduce you to its on-screen manual. The on-screen manual is extensive and contains most of the same information as the beautiful 364-page printed manual that comes with the software. Both the on-screen and printed manuals include a "Box Designer's Guide", "Sample Designs" and "BassBox Pro Reference".
-BassBox Pro 6.0.2 full version
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BassBox Pro 6.0.2 full version also includes a "balloon" help feature that provides definitions or explanations of any control or label when you hover your mouse pointer over it. This way, you can get instant feedback and guidance as you use the program.
- -Box Design is as Easy as 1-2-3 with BassBox Pro 6.0.2 Full Version
- -There are many ways to begin a speaker design with BassBox Pro 6.0.2 full version, including the use of its innovative Design Wizard. In general, speaker design involves the following steps:
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Each box design can be saved and re-opened or re-used later. You can also duplicate designs so that you don't have to re-enter driver or box information. You can have up to ten different designs open at the same time and compare them with mini preview graphs that show their amplitude response.
- -Design Wizard Helps New Users Quickly Design a Speaker
- -BassBox Pro 6.0.2 full version includes a powerful Design Wizard that helps new users quickly design a speaker. It can start with either the driver or the box and then it will walk you through BassBox Pro 6.0.2 full version as it prompts you for information in an orderly progression. In this way, it serves as a "smart" assistant to help you use the program.
- -Main Window Shows All Open Designs
- -The centerpiece of BassBox Pro 6.0.2 full version is its resizable main window that shows a summary of all open designs (see screenshot below). A small picture beside each mini graph displays the box type that has been selected (closed, vented, bandpass or passive radiator). You can also access all the features and functions of BassBox Pro 6.0.2 full version from this window.
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-Other Features of BassBox Pro 6.0.2 Full Version
- -BassBox Pro 6.0.2 full version has many other features that make it a versatile and powerful speaker enclosure design program, such as:
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