- 
-
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py
deleted file mode 100644
index 376c1e8726de1b70438981d85cfa0aa0a5694803..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py
+++ /dev/null
@@ -1,164 +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.
-""" Conversion script for the LDM checkpoints. """
-
-import argparse
-
-import torch
-
-from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
-
- parser.add_argument(
- "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
- )
- # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
- parser.add_argument(
- "--original_config_file",
- default=None,
- type=str,
- help="The YAML config file corresponding to the original architecture.",
- )
- parser.add_argument(
- "--num_in_channels",
- default=None,
- type=int,
- help="The number of input channels. If `None` number of input channels will be automatically inferred.",
- )
- parser.add_argument(
- "--scheduler_type",
- default="pndm",
- type=str,
- help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
- )
- parser.add_argument(
- "--pipeline_type",
- default=None,
- type=str,
- help=(
- "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"
- ". If `None` pipeline will be automatically inferred."
- ),
- )
- parser.add_argument(
- "--image_size",
- default=None,
- type=int,
- help=(
- "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
- " Base. Use 768 for Stable Diffusion v2."
- ),
- )
- parser.add_argument(
- "--prediction_type",
- default=None,
- type=str,
- help=(
- "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"
- " Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."
- ),
- )
- parser.add_argument(
- "--extract_ema",
- action="store_true",
- help=(
- "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
- " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
- " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
- ),
- )
- parser.add_argument(
- "--upcast_attention",
- action="store_true",
- help=(
- "Whether the attention computation should always be upcasted. This is necessary when running stable"
- " diffusion 2.1."
- ),
- )
- parser.add_argument(
- "--from_safetensors",
- action="store_true",
- help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
- )
- parser.add_argument(
- "--to_safetensors",
- action="store_true",
- help="Whether to store pipeline in safetensors format or not.",
- )
- parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
- parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
- parser.add_argument(
- "--stable_unclip",
- type=str,
- default=None,
- required=False,
- help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.",
- )
- parser.add_argument(
- "--stable_unclip_prior",
- type=str,
- default=None,
- required=False,
- help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.",
- )
- parser.add_argument(
- "--clip_stats_path",
- type=str,
- help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.",
- required=False,
- )
- parser.add_argument(
- "--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint."
- )
- parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
- parser.add_argument(
- "--vae_path",
- type=str,
- default=None,
- required=False,
- help="Set to a path, hub id to an already converted vae to not convert it again.",
- )
- args = parser.parse_args()
-
- pipe = download_from_original_stable_diffusion_ckpt(
- checkpoint_path=args.checkpoint_path,
- original_config_file=args.original_config_file,
- image_size=args.image_size,
- prediction_type=args.prediction_type,
- model_type=args.pipeline_type,
- extract_ema=args.extract_ema,
- scheduler_type=args.scheduler_type,
- num_in_channels=args.num_in_channels,
- upcast_attention=args.upcast_attention,
- from_safetensors=args.from_safetensors,
- device=args.device,
- stable_unclip=args.stable_unclip,
- stable_unclip_prior=args.stable_unclip_prior,
- clip_stats_path=args.clip_stats_path,
- controlnet=args.controlnet,
- vae_path=args.vae_path,
- )
-
- if args.half:
- pipe.to(torch_dtype=torch.float16)
-
- if args.controlnet:
- # only save the controlnet model
- pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
- else:
- pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/unet_2d.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/unet_2d.py
deleted file mode 100644
index 3b17acd3d829519465ec0d8daa41b16184aa70f2..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/models/unet_2d.py
+++ /dev/null
@@ -1,329 +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 dataclasses import dataclass
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
-from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
-from .modeling_utils import ModelMixin
-from .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
-
-
-@dataclass
-class UNet2DOutput(BaseOutput):
- """
- The output of [`UNet2DModel`].
-
- Args:
- sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
- The hidden states output from the last layer of the model.
- """
-
- sample: torch.FloatTensor
-
-
-class UNet2DModel(ModelMixin, ConfigMixin):
- r"""
- A 2D UNet model that takes a noisy sample and a timestep and returns a sample shaped output.
-
- This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
- for all models (such as downloading or saving).
-
- Parameters:
- sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
- Height and width of input/output sample. Dimensions must be a multiple of `2 ** (len(block_out_channels) -
- 1)`.
- in_channels (`int`, *optional*, defaults to 3): Number of channels in the input sample.
- out_channels (`int`, *optional*, defaults to 3): Number of channels in the output.
- center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
- time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use.
- freq_shift (`int`, *optional*, defaults to 0): Frequency shift for Fourier time embedding.
- flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
- Whether to flip sin to cos for Fourier time embedding.
- down_block_types (`Tuple[str]`, *optional*, defaults to `("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`):
- Tuple of downsample block types.
- mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2D"`):
- Block type for middle of UNet, it can be either `UNetMidBlock2D` or `UnCLIPUNetMidBlock2D`.
- up_block_types (`Tuple[str]`, *optional*, defaults to `("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`):
- Tuple of upsample block types.
- block_out_channels (`Tuple[int]`, *optional*, defaults to `(224, 448, 672, 896)`):
- Tuple of block output channels.
- layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block.
- mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block.
- downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution.
- downsample_type (`str`, *optional*, defaults to `conv`):
- The downsample type for downsampling layers. Choose between "conv" and "resnet"
- upsample_type (`str`, *optional*, defaults to `conv`):
- The upsample type for upsampling layers. Choose between "conv" and "resnet"
- act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
- attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension.
- norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for normalization.
- norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for normalization.
- resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
- for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
- class_embed_type (`str`, *optional*, defaults to `None`):
- The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
- `"timestep"`, or `"identity"`.
- num_class_embeds (`int`, *optional*, defaults to `None`):
- Input dimension of the learnable embedding matrix to be projected to `time_embed_dim` when performing class
- conditioning with `class_embed_type` equal to `None`.
- """
-
- @register_to_config
- def __init__(
- self,
- sample_size: Optional[Union[int, Tuple[int, int]]] = None,
- in_channels: int = 3,
- out_channels: int = 3,
- center_input_sample: bool = False,
- time_embedding_type: str = "positional",
- freq_shift: int = 0,
- flip_sin_to_cos: bool = True,
- down_block_types: Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),
- up_block_types: Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),
- block_out_channels: Tuple[int] = (224, 448, 672, 896),
- layers_per_block: int = 2,
- mid_block_scale_factor: float = 1,
- downsample_padding: int = 1,
- downsample_type: str = "conv",
- upsample_type: str = "conv",
- act_fn: str = "silu",
- attention_head_dim: Optional[int] = 8,
- norm_num_groups: int = 32,
- norm_eps: float = 1e-5,
- resnet_time_scale_shift: str = "default",
- add_attention: bool = True,
- class_embed_type: Optional[str] = None,
- num_class_embeds: Optional[int] = None,
- ):
- super().__init__()
-
- self.sample_size = sample_size
- time_embed_dim = block_out_channels[0] * 4
-
- # Check inputs
- if len(down_block_types) != len(up_block_types):
- raise ValueError(
- f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
- )
-
- if len(block_out_channels) != len(down_block_types):
- raise ValueError(
- f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
- )
-
- # input
- self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
-
- # time
- if time_embedding_type == "fourier":
- self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16)
- timestep_input_dim = 2 * block_out_channels[0]
- elif time_embedding_type == "positional":
- self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
- timestep_input_dim = block_out_channels[0]
-
- self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
-
- # class embedding
- if class_embed_type is None and num_class_embeds is not None:
- self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
- elif class_embed_type == "timestep":
- self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
- elif class_embed_type == "identity":
- self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
- else:
- self.class_embedding = None
-
- self.down_blocks = nn.ModuleList([])
- self.mid_block = None
- self.up_blocks = nn.ModuleList([])
-
- # down
- output_channel = block_out_channels[0]
- for i, down_block_type in enumerate(down_block_types):
- input_channel = output_channel
- output_channel = block_out_channels[i]
- is_final_block = i == len(block_out_channels) - 1
-
- down_block = get_down_block(
- down_block_type,
- num_layers=layers_per_block,
- in_channels=input_channel,
- out_channels=output_channel,
- temb_channels=time_embed_dim,
- add_downsample=not is_final_block,
- resnet_eps=norm_eps,
- resnet_act_fn=act_fn,
- resnet_groups=norm_num_groups,
- attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
- downsample_padding=downsample_padding,
- resnet_time_scale_shift=resnet_time_scale_shift,
- downsample_type=downsample_type,
- )
- self.down_blocks.append(down_block)
-
- # mid
- self.mid_block = UNetMidBlock2D(
- in_channels=block_out_channels[-1],
- temb_channels=time_embed_dim,
- resnet_eps=norm_eps,
- resnet_act_fn=act_fn,
- output_scale_factor=mid_block_scale_factor,
- resnet_time_scale_shift=resnet_time_scale_shift,
- attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1],
- resnet_groups=norm_num_groups,
- add_attention=add_attention,
- )
-
- # up
- reversed_block_out_channels = list(reversed(block_out_channels))
- output_channel = reversed_block_out_channels[0]
- for i, up_block_type in enumerate(up_block_types):
- prev_output_channel = output_channel
- output_channel = reversed_block_out_channels[i]
- input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
- is_final_block = i == len(block_out_channels) - 1
-
- up_block = get_up_block(
- up_block_type,
- num_layers=layers_per_block + 1,
- in_channels=input_channel,
- out_channels=output_channel,
- prev_output_channel=prev_output_channel,
- temb_channels=time_embed_dim,
- add_upsample=not is_final_block,
- resnet_eps=norm_eps,
- resnet_act_fn=act_fn,
- resnet_groups=norm_num_groups,
- attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
- resnet_time_scale_shift=resnet_time_scale_shift,
- upsample_type=upsample_type,
- )
- self.up_blocks.append(up_block)
- prev_output_channel = output_channel
-
- # out
- num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32)
- self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps)
- self.conv_act = nn.SiLU()
- self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
-
- def forward(
- self,
- sample: torch.FloatTensor,
- timestep: Union[torch.Tensor, float, int],
- class_labels: Optional[torch.Tensor] = None,
- return_dict: bool = True,
- ) -> Union[UNet2DOutput, Tuple]:
- r"""
- The [`UNet2DModel`] forward method.
-
- Args:
- sample (`torch.FloatTensor`):
- The noisy input tensor with the following shape `(batch, channel, height, width)`.
- timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
- class_labels (`torch.FloatTensor`, *optional*, defaults to `None`):
- Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple.
-
- Returns:
- [`~models.unet_2d.UNet2DOutput`] or `tuple`:
- If `return_dict` is True, an [`~models.unet_2d.UNet2DOutput`] is returned, otherwise a `tuple` is
- returned where the first element is the sample tensor.
- """
- # 0. center input if necessary
- if self.config.center_input_sample:
- sample = 2 * sample - 1.0
-
- # 1. time
- timesteps = timestep
- if not torch.is_tensor(timesteps):
- timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
- elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
- timesteps = timesteps[None].to(sample.device)
-
- # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
- timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device)
-
- t_emb = self.time_proj(timesteps)
-
- # timesteps does not contain any weights and will always return f32 tensors
- # but time_embedding might actually be running in fp16. so we need to cast here.
- # there might be better ways to encapsulate this.
- t_emb = t_emb.to(dtype=self.dtype)
- emb = self.time_embedding(t_emb)
-
- if self.class_embedding is not None:
- if class_labels is None:
- raise ValueError("class_labels should be provided when doing class conditioning")
-
- if self.config.class_embed_type == "timestep":
- class_labels = self.time_proj(class_labels)
-
- class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
- emb = emb + class_emb
-
- # 2. pre-process
- skip_sample = sample
- sample = self.conv_in(sample)
-
- # 3. down
- down_block_res_samples = (sample,)
- for downsample_block in self.down_blocks:
- if hasattr(downsample_block, "skip_conv"):
- sample, res_samples, skip_sample = downsample_block(
- hidden_states=sample, temb=emb, skip_sample=skip_sample
- )
- else:
- sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
-
- down_block_res_samples += res_samples
-
- # 4. mid
- sample = self.mid_block(sample, emb)
-
- # 5. up
- skip_sample = None
- for upsample_block in self.up_blocks:
- res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
- down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
-
- if hasattr(upsample_block, "skip_conv"):
- sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample)
- else:
- sample = upsample_block(sample, res_samples, emb)
-
- # 6. post-process
- sample = self.conv_norm_out(sample)
- sample = self.conv_act(sample)
- sample = self.conv_out(sample)
-
- if skip_sample is not None:
- sample += skip_sample
-
- if self.config.time_embedding_type == "fourier":
- timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]))))
- sample = sample / timesteps
-
- if not return_dict:
- return (sample,)
-
- return UNet2DOutput(sample=sample)
diff --git a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/evaluations/inception.py b/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/evaluations/inception.py
deleted file mode 100644
index 19e02d6efcbfd014fbaf84f509fa0976d2911872..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/evaluations/inception.py
+++ /dev/null
@@ -1,322 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-try:
- from torchvision.models.utils import load_state_dict_from_url
-except ImportError:
- from torch.utils.model_zoo import load_url as load_state_dict_from_url
-
-# Inception weights ported to Pytorch from
-# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
-FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'
-
-
-class InceptionV3(nn.Module):
- """Pretrained InceptionV3 network returning feature maps"""
-
- # Index of default block of inception to return,
- # corresponds to output of final average pooling
- DEFAULT_BLOCK_INDEX = 3
-
- # Maps feature dimensionality to their output blocks indices
- BLOCK_INDEX_BY_DIM = {
- 64: 0, # First max pooling features
- 192: 1, # Second max pooling featurs
- 768: 2, # Pre-aux classifier features
- 2048: 3 # Final average pooling features
- }
-
- def __init__(self,
- output_blocks=[DEFAULT_BLOCK_INDEX],
- resize_input=True,
- normalize_input=True,
- requires_grad=False,
- use_fid_inception=True):
- """Build pretrained InceptionV3
- Parameters
- ----------
- output_blocks : list of int
- Indices of blocks to return features of. Possible values are:
- - 0: corresponds to output of first max pooling
- - 1: corresponds to output of second max pooling
- - 2: corresponds to output which is fed to aux classifier
- - 3: corresponds to output of final average pooling
- resize_input : bool
- If true, bilinearly resizes input to width and height 299 before
- feeding input to model. As the network without fully connected
- layers is fully convolutional, it should be able to handle inputs
- of arbitrary size, so resizing might not be strictly needed
- normalize_input : bool
- If true, scales the input from range (0, 1) to the range the
- pretrained Inception network expects, namely (-1, 1)
- requires_grad : bool
- If true, parameters of the model require gradients. Possibly useful
- for finetuning the network
- use_fid_inception : bool
- If true, uses the pretrained Inception model used in Tensorflow's
- FID implementation. If false, uses the pretrained Inception model
- available in torchvision. The FID Inception model has different
- weights and a slightly different structure from torchvision's
- Inception model. If you want to compute FID scores, you are
- strongly advised to set this parameter to true to get comparable
- results.
- """
- super(InceptionV3, self).__init__()
-
- self.resize_input = resize_input
- self.normalize_input = normalize_input
- self.output_blocks = sorted(output_blocks)
- self.last_needed_block = max(output_blocks)
-
- assert self.last_needed_block <= 3, \
- 'Last possible output block index is 3'
-
- self.blocks = nn.ModuleList()
-
- if use_fid_inception:
- inception = fid_inception_v3()
- else:
- inception = _inception_v3(pretrained=True)
-
- # Block 0: input to maxpool1
- block0 = [
- inception.Conv2d_1a_3x3,
- inception.Conv2d_2a_3x3,
- inception.Conv2d_2b_3x3,
- nn.MaxPool2d(kernel_size=3, stride=2)
- ]
- self.blocks.append(nn.Sequential(*block0))
-
- # Block 1: maxpool1 to maxpool2
- if self.last_needed_block >= 1:
- block1 = [
- inception.Conv2d_3b_1x1,
- inception.Conv2d_4a_3x3,
- nn.MaxPool2d(kernel_size=3, stride=2)
- ]
- self.blocks.append(nn.Sequential(*block1))
-
- # Block 2: maxpool2 to aux classifier
- if self.last_needed_block >= 2:
- block2 = [
- inception.Mixed_5b,
- inception.Mixed_5c,
- inception.Mixed_5d,
- inception.Mixed_6a,
- inception.Mixed_6b,
- inception.Mixed_6c,
- inception.Mixed_6d,
- inception.Mixed_6e,
- ]
- self.blocks.append(nn.Sequential(*block2))
-
- # Block 3: aux classifier to final avgpool
- if self.last_needed_block >= 3:
- block3 = [
- inception.Mixed_7a,
- inception.Mixed_7b,
- inception.Mixed_7c,
- nn.AdaptiveAvgPool2d(output_size=(1, 1))
- ]
- self.blocks.append(nn.Sequential(*block3))
-
- for param in self.parameters():
- param.requires_grad = requires_grad
-
- def forward(self, inp):
- """Get Inception feature maps
- Parameters
- ----------
- inp : torch.autograd.Variable
- Input tensor of shape Bx3xHxW. Values are expected to be in
- range (0, 1)
- Returns
- -------
- List of torch.autograd.Variable, corresponding to the selected output
- block, sorted ascending by index
- """
- outp = []
- x = inp
-
- if self.resize_input:
- x = F.interpolate(x,
- size=(299, 299),
- mode='bilinear',
- align_corners=False)
-
- if self.normalize_input:
- x = 2 * x - 1 # Scale from range (0, 1) to range (-1, 1)
-
- for idx, block in enumerate(self.blocks):
- x = block(x)
- if idx in self.output_blocks:
- outp.append(x)
-
- if idx == self.last_needed_block:
- break
-
- return outp
-
-
-def _inception_v3(*args, **kwargs):
- """Wraps `torchvision.models.inception_v3`
- Skips default weight inititialization if supported by torchvision version.
- See https://github.com/mseitzer/pytorch-fid/issues/28.
- """
- try:
- version = tuple(map(int, torchvision.__version__.split('.')[:2]))
- except ValueError:
- # Just a caution against weird version strings
- version = (0,)
-
- if version >= (0, 6):
- kwargs['init_weights'] = False
-
- return torchvision.models.inception_v3(*args, **kwargs)
-
-
-def fid_inception_v3():
- """Build pretrained Inception model for FID computation
- The Inception model for FID computation uses a different set of weights
- and has a slightly different structure than torchvision's Inception.
- This method first constructs torchvision's Inception and then patches the
- necessary parts that are different in the FID Inception model.
- """
- inception = _inception_v3(num_classes=1008,
- aux_logits=False,
- pretrained=False)
- inception.Mixed_5b = FIDInceptionA(192, pool_features=32)
- inception.Mixed_5c = FIDInceptionA(256, pool_features=64)
- inception.Mixed_5d = FIDInceptionA(288, pool_features=64)
- inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)
- inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)
- inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)
- inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)
- inception.Mixed_7b = FIDInceptionE_1(1280)
- inception.Mixed_7c = FIDInceptionE_2(2048)
-
- state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)
- inception.load_state_dict(state_dict)
- return inception
-
-
-class FIDInceptionA(torchvision.models.inception.InceptionA):
- """InceptionA block patched for FID computation"""
- def __init__(self, in_channels, pool_features):
- super(FIDInceptionA, self).__init__(in_channels, pool_features)
-
- def forward(self, x):
- branch1x1 = self.branch1x1(x)
-
- branch5x5 = self.branch5x5_1(x)
- branch5x5 = self.branch5x5_2(branch5x5)
-
- branch3x3dbl = self.branch3x3dbl_1(x)
- branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
- branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
-
- # Patch: Tensorflow's average pool does not use the padded zero's in
- # its average calculation
- branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
- count_include_pad=False)
- branch_pool = self.branch_pool(branch_pool)
-
- outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
- return torch.cat(outputs, 1)
-
-
-class FIDInceptionC(torchvision.models.inception.InceptionC):
- """InceptionC block patched for FID computation"""
- def __init__(self, in_channels, channels_7x7):
- super(FIDInceptionC, self).__init__(in_channels, channels_7x7)
-
- def forward(self, x):
- branch1x1 = self.branch1x1(x)
-
- branch7x7 = self.branch7x7_1(x)
- branch7x7 = self.branch7x7_2(branch7x7)
- branch7x7 = self.branch7x7_3(branch7x7)
-
- branch7x7dbl = self.branch7x7dbl_1(x)
- branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
- branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
- branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
- branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
-
- # Patch: Tensorflow's average pool does not use the padded zero's in
- # its average calculation
- branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
- count_include_pad=False)
- branch_pool = self.branch_pool(branch_pool)
-
- outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
- return torch.cat(outputs, 1)
-
-
-class FIDInceptionE_1(torchvision.models.inception.InceptionE):
- """First InceptionE block patched for FID computation"""
- def __init__(self, in_channels):
- super(FIDInceptionE_1, self).__init__(in_channels)
-
- def forward(self, x):
- branch1x1 = self.branch1x1(x)
-
- branch3x3 = self.branch3x3_1(x)
- branch3x3 = [
- self.branch3x3_2a(branch3x3),
- self.branch3x3_2b(branch3x3),
- ]
- branch3x3 = torch.cat(branch3x3, 1)
-
- branch3x3dbl = self.branch3x3dbl_1(x)
- branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
- branch3x3dbl = [
- self.branch3x3dbl_3a(branch3x3dbl),
- self.branch3x3dbl_3b(branch3x3dbl),
- ]
- branch3x3dbl = torch.cat(branch3x3dbl, 1)
-
- # Patch: Tensorflow's average pool does not use the padded zero's in
- # its average calculation
- branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
- count_include_pad=False)
- branch_pool = self.branch_pool(branch_pool)
-
- outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
- return torch.cat(outputs, 1)
-
-
-class FIDInceptionE_2(torchvision.models.inception.InceptionE):
- """Second InceptionE block patched for FID computation"""
- def __init__(self, in_channels):
- super(FIDInceptionE_2, self).__init__(in_channels)
-
- def forward(self, x):
- branch1x1 = self.branch1x1(x)
-
- branch3x3 = self.branch3x3_1(x)
- branch3x3 = [
- self.branch3x3_2a(branch3x3),
- self.branch3x3_2b(branch3x3),
- ]
- branch3x3 = torch.cat(branch3x3, 1)
-
- branch3x3dbl = self.branch3x3dbl_1(x)
- branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
- branch3x3dbl = [
- self.branch3x3dbl_3a(branch3x3dbl),
- self.branch3x3dbl_3b(branch3x3dbl),
- ]
- branch3x3dbl = torch.cat(branch3x3dbl, 1)
-
- # Patch: The FID Inception model uses max pooling instead of average
- # pooling. This is likely an error in this specific Inception
- # implementation, as other Inception models use average pooling here
- # (which matches the description in the paper).
- branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)
- branch_pool = self.branch_pool(branch_pool)
-
- outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
- return torch.cat(outputs, 1)
\ No newline at end of file
diff --git a/spaces/Aravindsssss/GradiolangchainChatBoatOpenAI/README.md b/spaces/Aravindsssss/GradiolangchainChatBoatOpenAI/README.md
deleted file mode 100644
index 8e1581d193b2b388d034be5ca26987bc4b78093a..0000000000000000000000000000000000000000
--- a/spaces/Aravindsssss/GradiolangchainChatBoatOpenAI/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: GradiolangchainChatBoatOpenAI
-emoji: 😻
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/certifi/__main__.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/certifi/__main__.py
deleted file mode 100644
index 00376349e69ad8b9dbf401cddc34055951e4b02e..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/certifi/__main__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import argparse
-
-from pip._vendor.certifi import contents, where
-
-parser = argparse.ArgumentParser()
-parser.add_argument("-c", "--contents", action="store_true")
-args = parser.parse_args()
-
-if args.contents:
- print(contents())
-else:
- print(where())
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/util/wait.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/util/wait.py
deleted file mode 100644
index 21b4590b3dc9b58902b0d47164b9023e54a85ef8..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/urllib3/util/wait.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import errno
-import select
-import sys
-from functools import partial
-
-try:
- from time import monotonic
-except ImportError:
- from time import time as monotonic
-
-__all__ = ["NoWayToWaitForSocketError", "wait_for_read", "wait_for_write"]
-
-
-class NoWayToWaitForSocketError(Exception):
- pass
-
-
-# How should we wait on sockets?
-#
-# There are two types of APIs you can use for waiting on sockets: the fancy
-# modern stateful APIs like epoll/kqueue, and the older stateless APIs like
-# select/poll. The stateful APIs are more efficient when you have a lots of
-# sockets to keep track of, because you can set them up once and then use them
-# lots of times. But we only ever want to wait on a single socket at a time
-# and don't want to keep track of state, so the stateless APIs are actually
-# more efficient. So we want to use select() or poll().
-#
-# Now, how do we choose between select() and poll()? On traditional Unixes,
-# select() has a strange calling convention that makes it slow, or fail
-# altogether, for high-numbered file descriptors. The point of poll() is to fix
-# that, so on Unixes, we prefer poll().
-#
-# On Windows, there is no poll() (or at least Python doesn't provide a wrapper
-# for it), but that's OK, because on Windows, select() doesn't have this
-# strange calling convention; plain select() works fine.
-#
-# So: on Windows we use select(), and everywhere else we use poll(). We also
-# fall back to select() in case poll() is somehow broken or missing.
-
-if sys.version_info >= (3, 5):
- # Modern Python, that retries syscalls by default
- def _retry_on_intr(fn, timeout):
- return fn(timeout)
-
-else:
- # Old and broken Pythons.
- def _retry_on_intr(fn, timeout):
- if timeout is None:
- deadline = float("inf")
- else:
- deadline = monotonic() + timeout
-
- while True:
- try:
- return fn(timeout)
- # OSError for 3 <= pyver < 3.5, select.error for pyver <= 2.7
- except (OSError, select.error) as e:
- # 'e.args[0]' incantation works for both OSError and select.error
- if e.args[0] != errno.EINTR:
- raise
- else:
- timeout = deadline - monotonic()
- if timeout < 0:
- timeout = 0
- if timeout == float("inf"):
- timeout = None
- continue
-
-
-def select_wait_for_socket(sock, read=False, write=False, timeout=None):
- if not read and not write:
- raise RuntimeError("must specify at least one of read=True, write=True")
- rcheck = []
- wcheck = []
- if read:
- rcheck.append(sock)
- if write:
- wcheck.append(sock)
- # When doing a non-blocking connect, most systems signal success by
- # marking the socket writable. Windows, though, signals success by marked
- # it as "exceptional". We paper over the difference by checking the write
- # sockets for both conditions. (The stdlib selectors module does the same
- # thing.)
- fn = partial(select.select, rcheck, wcheck, wcheck)
- rready, wready, xready = _retry_on_intr(fn, timeout)
- return bool(rready or wready or xready)
-
-
-def poll_wait_for_socket(sock, read=False, write=False, timeout=None):
- if not read and not write:
- raise RuntimeError("must specify at least one of read=True, write=True")
- mask = 0
- if read:
- mask |= select.POLLIN
- if write:
- mask |= select.POLLOUT
- poll_obj = select.poll()
- poll_obj.register(sock, mask)
-
- # For some reason, poll() takes timeout in milliseconds
- def do_poll(t):
- if t is not None:
- t *= 1000
- return poll_obj.poll(t)
-
- return bool(_retry_on_intr(do_poll, timeout))
-
-
-def null_wait_for_socket(*args, **kwargs):
- raise NoWayToWaitForSocketError("no select-equivalent available")
-
-
-def _have_working_poll():
- # Apparently some systems have a select.poll that fails as soon as you try
- # to use it, either due to strange configuration or broken monkeypatching
- # from libraries like eventlet/greenlet.
- try:
- poll_obj = select.poll()
- _retry_on_intr(poll_obj.poll, 0)
- except (AttributeError, OSError):
- return False
- else:
- return True
-
-
-def wait_for_socket(*args, **kwargs):
- # We delay choosing which implementation to use until the first time we're
- # called. We could do it at import time, but then we might make the wrong
- # decision if someone goes wild with monkeypatching select.poll after
- # we're imported.
- global wait_for_socket
- if _have_working_poll():
- wait_for_socket = poll_wait_for_socket
- elif hasattr(select, "select"):
- wait_for_socket = select_wait_for_socket
- else: # Platform-specific: Appengine.
- wait_for_socket = null_wait_for_socket
- return wait_for_socket(*args, **kwargs)
-
-
-def wait_for_read(sock, timeout=None):
- """Waits for reading to be available on a given socket.
- Returns True if the socket is readable, or False if the timeout expired.
- """
- return wait_for_socket(sock, read=True, timeout=timeout)
-
-
-def wait_for_write(sock, timeout=None):
- """Waits for writing to be available on a given socket.
- Returns True if the socket is readable, or False if the timeout expired.
- """
- return wait_for_socket(sock, write=True, timeout=timeout)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/bdist_rpm.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/bdist_rpm.py
deleted file mode 100644
index 98bf5dea8468bf1728f18d97d1b9a43be33fdf20..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/bdist_rpm.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import distutils.command.bdist_rpm as orig
-import warnings
-
-from setuptools import SetuptoolsDeprecationWarning
-
-
-class bdist_rpm(orig.bdist_rpm):
- """
- Override the default bdist_rpm behavior to do the following:
-
- 1. Run egg_info to ensure the name and version are properly calculated.
- 2. Always run 'install' using --single-version-externally-managed to
- disable eggs in RPM distributions.
- """
-
- def run(self):
- warnings.warn(
- "bdist_rpm is deprecated and will be removed in a future "
- "version. Use bdist_wheel (wheel packages) instead.",
- SetuptoolsDeprecationWarning,
- )
-
- # ensure distro name is up-to-date
- self.run_command('egg_info')
-
- orig.bdist_rpm.run(self)
-
- def _make_spec_file(self):
- spec = orig.bdist_rpm._make_spec_file(self)
- spec = [
- line.replace(
- "setup.py install ",
- "setup.py install --single-version-externally-managed "
- ).replace(
- "%setup",
- "%setup -n %{name}-%{unmangled_version}"
- )
- for line in spec
- ]
- return spec
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin.py
deleted file mode 100644
index c3a68aa833f12f0fa324a269c36190f21b8a75bd..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin.py
+++ /dev/null
@@ -1,259 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-
-"""
-This file registers pre-defined datasets at hard-coded paths, and their metadata.
-
-We hard-code metadata for common datasets. This will enable:
-1. Consistency check when loading the datasets
-2. Use models on these standard datasets directly and run demos,
- without having to download the dataset annotations
-
-We hard-code some paths to the dataset that's assumed to
-exist in "./datasets/".
-
-Users SHOULD NOT use this file to create new dataset / metadata for new dataset.
-To add new dataset, refer to the tutorial "docs/DATASETS.md".
-"""
-
-import os
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-
-from .builtin_meta import ADE20K_SEM_SEG_CATEGORIES, _get_builtin_metadata
-from .cityscapes import load_cityscapes_instances, load_cityscapes_semantic
-from .cityscapes_panoptic import register_all_cityscapes_panoptic
-from .coco import load_sem_seg, register_coco_instances
-from .coco_panoptic import register_coco_panoptic, register_coco_panoptic_separated
-from .lvis import get_lvis_instances_meta, register_lvis_instances
-from .pascal_voc import register_pascal_voc
-
-# ==== Predefined datasets and splits for COCO ==========
-
-_PREDEFINED_SPLITS_COCO = {}
-_PREDEFINED_SPLITS_COCO["coco"] = {
- "coco_2014_train": ("coco/train2014", "coco/annotations/instances_train2014.json"),
- "coco_2014_val": ("coco/val2014", "coco/annotations/instances_val2014.json"),
- "coco_2014_minival": ("coco/val2014", "coco/annotations/instances_minival2014.json"),
- "coco_2014_valminusminival": (
- "coco/val2014",
- "coco/annotations/instances_valminusminival2014.json",
- ),
- "coco_2017_train": ("coco/train2017", "coco/annotations/instances_train2017.json"),
- "coco_2017_val": ("coco/val2017", "coco/annotations/instances_val2017.json"),
- "coco_2017_test": ("coco/test2017", "coco/annotations/image_info_test2017.json"),
- "coco_2017_test-dev": ("coco/test2017", "coco/annotations/image_info_test-dev2017.json"),
- "coco_2017_val_100": ("coco/val2017", "coco/annotations/instances_val2017_100.json"),
-}
-
-_PREDEFINED_SPLITS_COCO["coco_person"] = {
- "keypoints_coco_2014_train": (
- "coco/train2014",
- "coco/annotations/person_keypoints_train2014.json",
- ),
- "keypoints_coco_2014_val": ("coco/val2014", "coco/annotations/person_keypoints_val2014.json"),
- "keypoints_coco_2014_minival": (
- "coco/val2014",
- "coco/annotations/person_keypoints_minival2014.json",
- ),
- "keypoints_coco_2014_valminusminival": (
- "coco/val2014",
- "coco/annotations/person_keypoints_valminusminival2014.json",
- ),
- "keypoints_coco_2017_train": (
- "coco/train2017",
- "coco/annotations/person_keypoints_train2017.json",
- ),
- "keypoints_coco_2017_val": ("coco/val2017", "coco/annotations/person_keypoints_val2017.json"),
- "keypoints_coco_2017_val_100": (
- "coco/val2017",
- "coco/annotations/person_keypoints_val2017_100.json",
- ),
-}
-
-
-_PREDEFINED_SPLITS_COCO_PANOPTIC = {
- "coco_2017_train_panoptic": (
- # This is the original panoptic annotation directory
- "coco/panoptic_train2017",
- "coco/annotations/panoptic_train2017.json",
- # This directory contains semantic annotations that are
- # converted from panoptic annotations.
- # It is used by PanopticFPN.
- # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py
- # to create these directories.
- "coco/panoptic_stuff_train2017",
- ),
- "coco_2017_val_panoptic": (
- "coco/panoptic_val2017",
- "coco/annotations/panoptic_val2017.json",
- "coco/panoptic_stuff_val2017",
- ),
- "coco_2017_val_100_panoptic": (
- "coco/panoptic_val2017_100",
- "coco/annotations/panoptic_val2017_100.json",
- "coco/panoptic_stuff_val2017_100",
- ),
-}
-
-
-def register_all_coco(root):
- for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_COCO.items():
- for key, (image_root, json_file) in splits_per_dataset.items():
- # Assume pre-defined datasets live in `./datasets`.
- register_coco_instances(
- key,
- _get_builtin_metadata(dataset_name),
- os.path.join(root, json_file) if "://" not in json_file else json_file,
- os.path.join(root, image_root),
- )
-
- for (
- prefix,
- (panoptic_root, panoptic_json, semantic_root),
- ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items():
- prefix_instances = prefix[: -len("_panoptic")]
- instances_meta = MetadataCatalog.get(prefix_instances)
- image_root, instances_json = instances_meta.image_root, instances_meta.json_file
- # The "separated" version of COCO panoptic segmentation dataset,
- # e.g. used by Panoptic FPN
- register_coco_panoptic_separated(
- prefix,
- _get_builtin_metadata("coco_panoptic_separated"),
- image_root,
- os.path.join(root, panoptic_root),
- os.path.join(root, panoptic_json),
- os.path.join(root, semantic_root),
- instances_json,
- )
- # The "standard" version of COCO panoptic segmentation dataset,
- # e.g. used by Panoptic-DeepLab
- register_coco_panoptic(
- prefix,
- _get_builtin_metadata("coco_panoptic_standard"),
- image_root,
- os.path.join(root, panoptic_root),
- os.path.join(root, panoptic_json),
- instances_json,
- )
-
-
-# ==== Predefined datasets and splits for LVIS ==========
-
-
-_PREDEFINED_SPLITS_LVIS = {
- "lvis_v1": {
- "lvis_v1_train": ("coco/", "lvis/lvis_v1_train.json"),
- "lvis_v1_val": ("coco/", "lvis/lvis_v1_val.json"),
- "lvis_v1_test_dev": ("coco/", "lvis/lvis_v1_image_info_test_dev.json"),
- "lvis_v1_test_challenge": ("coco/", "lvis/lvis_v1_image_info_test_challenge.json"),
- },
- "lvis_v0.5": {
- "lvis_v0.5_train": ("coco/", "lvis/lvis_v0.5_train.json"),
- "lvis_v0.5_val": ("coco/", "lvis/lvis_v0.5_val.json"),
- "lvis_v0.5_val_rand_100": ("coco/", "lvis/lvis_v0.5_val_rand_100.json"),
- "lvis_v0.5_test": ("coco/", "lvis/lvis_v0.5_image_info_test.json"),
- },
- "lvis_v0.5_cocofied": {
- "lvis_v0.5_train_cocofied": ("coco/", "lvis/lvis_v0.5_train_cocofied.json"),
- "lvis_v0.5_val_cocofied": ("coco/", "lvis/lvis_v0.5_val_cocofied.json"),
- },
-}
-
-
-def register_all_lvis(root):
- for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_LVIS.items():
- for key, (image_root, json_file) in splits_per_dataset.items():
- register_lvis_instances(
- key,
- get_lvis_instances_meta(dataset_name),
- os.path.join(root, json_file) if "://" not in json_file else json_file,
- os.path.join(root, image_root),
- )
-
-
-# ==== Predefined splits for raw cityscapes images ===========
-_RAW_CITYSCAPES_SPLITS = {
- "cityscapes_fine_{task}_train": ("cityscapes/leftImg8bit/train/", "cityscapes/gtFine/train/"),
- "cityscapes_fine_{task}_val": ("cityscapes/leftImg8bit/val/", "cityscapes/gtFine/val/"),
- "cityscapes_fine_{task}_test": ("cityscapes/leftImg8bit/test/", "cityscapes/gtFine/test/"),
-}
-
-
-def register_all_cityscapes(root):
- for key, (image_dir, gt_dir) in _RAW_CITYSCAPES_SPLITS.items():
- meta = _get_builtin_metadata("cityscapes")
- image_dir = os.path.join(root, image_dir)
- gt_dir = os.path.join(root, gt_dir)
-
- inst_key = key.format(task="instance_seg")
- DatasetCatalog.register(
- inst_key,
- lambda x=image_dir, y=gt_dir: load_cityscapes_instances(
- x, y, from_json=True, to_polygons=True
- ),
- )
- MetadataCatalog.get(inst_key).set(
- image_dir=image_dir, gt_dir=gt_dir, evaluator_type="cityscapes_instance", **meta
- )
-
- sem_key = key.format(task="sem_seg")
- DatasetCatalog.register(
- sem_key, lambda x=image_dir, y=gt_dir: load_cityscapes_semantic(x, y)
- )
- MetadataCatalog.get(sem_key).set(
- image_dir=image_dir,
- gt_dir=gt_dir,
- evaluator_type="cityscapes_sem_seg",
- ignore_label=255,
- **meta,
- )
-
-
-# ==== Predefined splits for PASCAL VOC ===========
-def register_all_pascal_voc(root):
- SPLITS = [
- ("voc_2007_trainval", "VOC2007", "trainval"),
- ("voc_2007_train", "VOC2007", "train"),
- ("voc_2007_val", "VOC2007", "val"),
- ("voc_2007_test", "VOC2007", "test"),
- ("voc_2012_trainval", "VOC2012", "trainval"),
- ("voc_2012_train", "VOC2012", "train"),
- ("voc_2012_val", "VOC2012", "val"),
- ]
- for name, dirname, split in SPLITS:
- year = 2007 if "2007" in name else 2012
- register_pascal_voc(name, os.path.join(root, dirname), split, year)
- MetadataCatalog.get(name).evaluator_type = "pascal_voc"
-
-
-def register_all_ade20k(root):
- root = os.path.join(root, "ADEChallengeData2016")
- for name, dirname in [("train", "training"), ("val", "validation")]:
- image_dir = os.path.join(root, "images", dirname)
- gt_dir = os.path.join(root, "annotations_detectron2", dirname)
- name = f"ade20k_sem_seg_{name}"
- DatasetCatalog.register(
- name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg")
- )
- MetadataCatalog.get(name).set(
- stuff_classes=ADE20K_SEM_SEG_CATEGORIES[:],
- image_root=image_dir,
- sem_seg_root=gt_dir,
- evaluator_type="sem_seg",
- ignore_label=255,
- )
-
-
-# True for open source;
-# Internally at fb, we register them elsewhere
-if __name__.endswith(".builtin"):
- # Assume pre-defined datasets live in `./datasets`.
- _root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets"))
- register_all_coco(_root)
- register_all_lvis(_root)
- register_all_cityscapes(_root)
- register_all_cityscapes_panoptic(_root)
- register_all_pascal_voc(_root)
- register_all_ade20k(_root)
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/test_engine.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/test_engine.py
deleted file mode 100644
index 6f6a0997d2a670e40e26286b258773ae56536a87..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/test_engine.py
+++ /dev/null
@@ -1,186 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import json
-import math
-import os
-import tempfile
-import time
-import unittest
-from unittest import mock
-import torch
-from fvcore.common.checkpoint import Checkpointer
-from torch import nn
-
-from detectron2 import model_zoo
-from detectron2.config import configurable, get_cfg
-from detectron2.engine import DefaultTrainer, SimpleTrainer, default_setup, hooks
-from detectron2.modeling.meta_arch import META_ARCH_REGISTRY
-from detectron2.utils.events import CommonMetricPrinter, JSONWriter
-
-
-@META_ARCH_REGISTRY.register()
-class _SimpleModel(nn.Module):
- @configurable
- def __init__(self, sleep_sec=0):
- super().__init__()
- self.mod = nn.Linear(10, 20)
- self.sleep_sec = sleep_sec
-
- @classmethod
- def from_config(cls, cfg):
- return {}
-
- def forward(self, x):
- if self.sleep_sec > 0:
- time.sleep(self.sleep_sec)
- return {"loss": x.sum() + sum([x.mean() for x in self.parameters()])}
-
-
-class TestTrainer(unittest.TestCase):
- def _data_loader(self, device):
- device = torch.device(device)
- while True:
- yield torch.rand(3, 3).to(device)
-
- def test_simple_trainer(self, device="cpu"):
- model = _SimpleModel().to(device=device)
- trainer = SimpleTrainer(
- model, self._data_loader(device), torch.optim.SGD(model.parameters(), 0.1)
- )
- trainer.train(0, 10)
-
- @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
- def test_simple_trainer_cuda(self):
- self.test_simple_trainer(device="cuda")
-
- def test_writer_hooks(self):
- model = _SimpleModel(sleep_sec=0.1)
- trainer = SimpleTrainer(
- model, self._data_loader("cpu"), torch.optim.SGD(model.parameters(), 0.1)
- )
-
- max_iter = 50
-
- with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
- json_file = os.path.join(d, "metrics.json")
- writers = [CommonMetricPrinter(max_iter), JSONWriter(json_file)]
-
- trainer.register_hooks(
- [hooks.EvalHook(0, lambda: {"metric": 100}), hooks.PeriodicWriter(writers)]
- )
- with self.assertLogs(writers[0].logger) as logs:
- trainer.train(0, max_iter)
-
- with open(json_file, "r") as f:
- data = [json.loads(line.strip()) for line in f]
- self.assertEqual([x["iteration"] for x in data], [19, 39, 49, 50])
- # the eval metric is in the last line with iter 50
- self.assertIn("metric", data[-1], "Eval metric must be in last line of JSON!")
-
- # test logged messages from CommonMetricPrinter
- self.assertEqual(len(logs.output), 3)
- for log, iter in zip(logs.output, [19, 39, 49]):
- self.assertIn(f"iter: {iter}", log)
-
- self.assertIn("eta: 0:00:00", logs.output[-1], "Last ETA must be 0!")
-
- def test_default_trainer(self):
- # TODO: this test requires manifold access, so changed device to CPU. see: T88318502
- cfg = get_cfg()
- cfg.MODEL.DEVICE = "cpu"
- cfg.MODEL.META_ARCHITECTURE = "_SimpleModel"
- cfg.DATASETS.TRAIN = ("coco_2017_val_100",)
- with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
- cfg.OUTPUT_DIR = d
- trainer = DefaultTrainer(cfg)
-
- # test property
- self.assertIs(trainer.model, trainer._trainer.model)
- trainer.model = _SimpleModel()
- self.assertIs(trainer.model, trainer._trainer.model)
-
- def test_checkpoint_resume(self):
- model = _SimpleModel()
- dataloader = self._data_loader("cpu")
- opt = torch.optim.SGD(model.parameters(), 0.1)
- scheduler = torch.optim.lr_scheduler.StepLR(opt, 3)
-
- with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
- trainer = SimpleTrainer(model, dataloader, opt)
- checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer)
-
- trainer.register_hooks(
- [
- hooks.LRScheduler(scheduler=scheduler),
- # checkpoint after scheduler to properly save the state of scheduler
- hooks.PeriodicCheckpointer(checkpointer, 10),
- ]
- )
-
- trainer.train(0, 12)
- self.assertAlmostEqual(opt.param_groups[0]["lr"], 1e-5)
- self.assertEqual(scheduler.last_epoch, 12)
- del trainer
-
- opt = torch.optim.SGD(model.parameters(), 999) # lr will be loaded
- trainer = SimpleTrainer(model, dataloader, opt)
- scheduler = torch.optim.lr_scheduler.StepLR(opt, 3)
- trainer.register_hooks(
- [
- hooks.LRScheduler(scheduler=scheduler),
- ]
- )
- checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer)
- checkpointer.resume_or_load("non_exist.pth")
- self.assertEqual(trainer.iter, 11) # last finished iter number (0-based in Trainer)
- # number of times `scheduler.step()` was called (1-based)
- self.assertEqual(scheduler.last_epoch, 12)
- self.assertAlmostEqual(opt.param_groups[0]["lr"], 1e-5)
-
- def test_eval_hook(self):
- model = _SimpleModel()
- dataloader = self._data_loader("cpu")
- opt = torch.optim.SGD(model.parameters(), 0.1)
-
- for total_iter, period, eval_count in [(30, 15, 2), (31, 15, 3), (20, 0, 1)]:
- test_func = mock.Mock(return_value={"metric": 3.0})
- trainer = SimpleTrainer(model, dataloader, opt)
- trainer.register_hooks([hooks.EvalHook(period, test_func)])
- trainer.train(0, total_iter)
- self.assertEqual(test_func.call_count, eval_count)
-
- def test_best_checkpointer(self):
- model = _SimpleModel()
- dataloader = self._data_loader("cpu")
- opt = torch.optim.SGD(model.parameters(), 0.1)
- metric_name = "metric"
- total_iter = 40
- test_period = 10
- test_cases = [
- ("max", iter([0.3, 0.4, 0.35, 0.5]), 3),
- ("min", iter([1.0, 0.8, 0.9, 0.9]), 2),
- ("min", iter([math.nan, 0.8, 0.9, 0.9]), 1),
- ]
- for mode, metrics, call_count in test_cases:
- trainer = SimpleTrainer(model, dataloader, opt)
- with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
- checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer)
- trainer.register_hooks(
- [
- hooks.EvalHook(test_period, lambda: {metric_name: next(metrics)}),
- hooks.BestCheckpointer(test_period, checkpointer, metric_name, mode=mode),
- ]
- )
- with mock.patch.object(checkpointer, "save") as mock_save_method:
- trainer.train(0, total_iter)
- self.assertEqual(mock_save_method.call_count, call_count)
-
- def test_setup_config(self):
- with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
- cfg = get_cfg()
- cfg.OUTPUT_DIR = os.path.join(d, "yacs")
- default_setup(cfg, {})
-
- cfg = model_zoo.get_config("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py")
- cfg.train.output_dir = os.path.join(d, "omegaconf")
- default_setup(cfg, {})
diff --git a/spaces/Benson/text-generation/Examples/Baixar Pou Reggae Apk.md b/spaces/Benson/text-generation/Examples/Baixar Pou Reggae Apk.md
deleted file mode 100644
index 2579cde5e2edd47cbfc40f20fd7bbd522173e78d..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Baixar Pou Reggae Apk.md
+++ /dev/null
@@ -1,108 +0,0 @@
-
--
Baixar Pou Reggae APK: Cómo descargar e instalar el divertido juego virtual para mascotas
-¿Te gustan los juegos virtuales para mascotas? ¿Te gusta la música reggae y la cultura? Si respondió sí a ambas preguntas, entonces es posible que desee probar Pou Reggae APK, una versión modificada del popular juego Pou que cuenta con música reggae y temas. En este artículo, le diremos lo que es Pou Reggae APK, cómo descargarlo e instalarlo en su dispositivo Android, y cómo jugar y disfrutar de ella.
-¿Qué es Pou Reggae APK?
-Pou Reggae APK es una versión modificada de Pou, un juego de mascotas virtual que se originó en Jamaica a finales de 1960. Surgió de una fusión de EE.UU. R&B, mento jamaicano y calipso de Trinidad y Tobago, y fue influenciado por ska y rocksteady.
-baixar pou reggae apk
Download Zip ✑ ✑ ✑ https://bltlly.com/2v6M5i
-
Una breve introducción a Pou, el popular juego virtual para mascotas
-Pou es un juego de mascotas virtual que fue desarrollado y publicado por el diseñador libanés Paul Salameh (listado como Zakeh en la Google Play Store) en 2012. Es similar a Tamagotchi, un juego de moda que requería el cuidado de una criatura simulada. En Pou, tienes que cuidar de una mascota alienígena llamada Pou, que tiene una forma triangular marrón con ojos, boca y extremidades. Tienes que alimentarlo, limpiarlo, jugar con él, y verlo crecer mientras nivela y desbloquea diferentes fondos de pantalla y trajes. También puedes personalizar la apariencia de Pou, probar nuevos trajes, sombreros y anteojos, experimentar con pociones en el laboratorio, jugar juegos en la sala de juegos, visitar y jugar con los Pous de tus amigos y hablar con Pou y escuchar.
-Las características y beneficios de Pou Reggae APK, una versión modificada de Pou con música reggae y temas
-Pou Reggae APK es una versión modificada de Pou que añade música reggae y temas para el juego. Tiene todas las características del juego original, además de algunas adicionales que lo hacen más divertido y único. Algunas de las características y beneficios de Pou Reggae APK son:
--
-
-
- Tiene música reggae de fondo que te da un ambiente relajante y alegre. -
- Tiene fondos de pantalla con temas de reggae que puede utilizar para decorar sus habitaciones. -
- Tiene trajes de reggae con temas que usted puede vestir su Pou en. -
- Tiene minijuegos de reggae que puedes jugar con tu Pou. -
- Tiene iconos de reggae que hacen el juego más colorido. -
Pou Reggae APK
Cómo descargar e instalar Pou Reggae APK en Android
-Si desea probar Pou Reggae APK en su dispositivo Android, tendrá que descargar e instalar desde una fuente de terceros, ya que no está disponible en la tienda oficial de Google Play. Estos son los pasos para hacerlo:
-Los pasos para descargar e instalar Pou Reggae APK desde una fuente de buena reputación
--
-
- Encontrar un sitio web de buena reputación que ofrece Pou Reggae APK para su descarga. Puede utilizar un motor de búsqueda o un sitio de confianza APK downloader para encontrar uno. Algunos ejemplos de sitios web que ofrecen Pou Reggae APK son . Asegúrate de revisar las reseñas, calificaciones y comentarios de otros usuarios antes de descargar nada. -
- Descargar el archivo APK Pou Reggae a su dispositivo. Puede utilizar su navegador o una aplicación de gestión de descargas para hacerlo. El tamaño del archivo es de unos 24 MB, así que asegúrese de tener suficiente espacio y una conexión a Internet estable. -
- Busque el archivo APK Pou Reggae descargado en su dispositivo. Puede usar una aplicación de administrador de archivos o el explorador de archivos predeterminado de su dispositivo para encontrarlo. Normalmente se almacena en la carpeta Descargas o en la carpeta que especificaste al descargar. -
- Toque en el archivo APK Pou Reggae para iniciar el proceso de instalación. Es posible que vea un mensaje de advertencia que dice "Para su seguridad, el teléfono no está permitido instalar aplicaciones desconocidas de esta fuente". Esto se debe a que está instalando un archivo APK desde una fuente desconocida, lo que puede plantear algunos riesgos para su dispositivo y los datos. -
Las precauciones y permisos necesarios para instalar archivos APK en Android
- --
-
- Habilitar la opción de instalar aplicaciones de fuentes desconocidas. Esto le permitirá instalar aplicaciones que no son de Google Play Store. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. Alternativamente, también puede ir a Configuración > Aplicaciones y notificaciones > Acceso especial a aplicaciones > Instalar aplicaciones desconocidas y seleccionar la aplicación que desea permitir instalar archivos APK. -
- Compruebe los permisos que solicita la aplicación antes de instalarla. Algunas aplicaciones pueden solicitar acceso a sus contactos, cámara, ubicación, almacenamiento, etc. Asegúrese de que se siente cómodo con la concesión de estos permisos y que son relevantes para la funcionalidad de la aplicación. Para comprobar los permisos, vaya a Configuración > Aplicaciones y notificaciones > Información de la aplicación y seleccione la aplicación que desea verificar. -
- Escanear el archivo APK en busca de virus y malware antes de instalarlo. Algunos archivos APK pueden contener código dañino que puede dañar su dispositivo o robar sus datos. Para escanear el archivo APK, puede usar una aplicación antivirus o una herramienta de análisis de virus en línea. -
- Copia de seguridad de sus datos antes de instalar cualquier archivo APK. En caso de que algo salga mal durante el proceso de instalación o después de usar la aplicación, puede restaurar sus datos desde una copia de seguridad. Para realizar copias de seguridad de sus datos, puede usar un servicio en la nube, un dispositivo de almacenamiento externo o una aplicación de copia de seguridad. -
Las ventajas y desventajas de instalar archivos APK en Android
-Instalación de archivos APK en Android tiene algunas ventajas y desventajas que usted debe tener en cuenta. Estos son algunos de ellos:
-Cómo jugar y disfrutar de Pou Reggae APK
-Ahora que ha descargado e instalado Pou Reggae APK en su dispositivo Android, usted está listo para jugar y disfrutar de ella. Aquí hay algunos consejos y trucos para ayudarte a empezar:
-El juego básico y los controles de Pou Reggae APK
-El juego básico y los controles de Pou Reggae APK son los mismos que el juego original de Pou. Tienes que cuidar de tu Pou alimentándolo, limpiándolo, jugando con él, y poniéndolo a dormir. También puedes personalizar la apariencia y las habitaciones de tu Pou, jugar minijuegos, visitar los Pous de tus amigos y hablar con tu Pou.
- -Para alimentar a tu Pou, tienes que arrastrar los alimentos de la nevera a su boca. También puedes comprar más alimentos de la tienda usando tus monedas. Para limpiar tu Pou, tienes que arrastrar el jabón a su cuerpo y luego enjuagarlo con agua. Para jugar con tu Pou, tienes que tocar el icono de la bola y luego elegir un juguete o un juego. Para poner tu Pou a dormir, tienes que tocar el icono de la lámpara y luego apagar las luces.
- -Los consejos y trucos para hacer Pou feliz y saludable
-Para hacer tu Pou feliz y saludable, tienes que prestar atención a sus necesidades y estados de ánimo. Puede ver su estado pulsando en el icono de estadísticas en la esquina superior derecha de la pantalla. Puedes ver su nivel de hambre, nivel de salud, nivel de energía y nivel de diversión. Tienes que mantener estos niveles altos alimentándolo, limpiándolo, jugando con él, y poniéndolo a dormir regularmente.
-Algunos consejos y trucos para hacer Pou feliz y saludable son:
--
-
- Alimente su Pou con una dieta equilibrada de frutas, verduras, carne, productos lácteos, etc. Evite alimentarlo con demasiada comida chatarra o comida picante, ya que puede enfermar o infeliz. -
- Limpie su Pou regularmente para mantenerlo higiénico y prevenir infecciones. También puede usar pociones del laboratorio para curar su Pou si se enferma o lesiona. -
- Juega con tu Pou a menudo para mantenerlo entretenido y activo. También puedes ganar monedas jugando minijuegos o completando logros. -
- Ponga su Pou a dormir cuando se cansa o se aburre. También puede usar pociones del laboratorio para hacer que su Pou duerma más rápido o más tiempo. -
- Personaliza la apariencia y las habitaciones de tu Pou según sus preferencias y personalidad. También puedes usar artículos de reggae de la tienda para hacer tu Pou más elegante y fresco. -
Los mini-juegos y opciones de personalización disponibles en Pou Reggae APK
-Pou Reggae APK tiene muchos mini-juegos y opciones de personalización que se pueden disfrutar. Algunos de ellos son:
--
-
- Reggae Music: Un mini-juego donde tienes que tocar las notas que coinciden con la música reggae que se reproduce en el fondo. -
- Concurso de reggae: un mini-juego donde tienes que responder preguntas de trivia sobre la música reggae y la cultura. -
- Reggae Match: Un mini-juego donde tienes que coincidir con los pares de tarjetas de reggae con temas. -
- Reggae Wallpaper: Una opción de personalización donde se puede elegir entre varios reggae-fondos de pantalla temáticos para sus habitaciones. - -
- Reggae Hat: Una opción de personalización donde puedes elegir entre varios sombreros de reggae para tu Pou. -
Conclusión
-Pou Reggae APK es un divertido y único juego de mascotas virtual que combina el juego original de Pou con música reggae y temas. Tiene monedas ilimitadas, música reggae, fondos de pantalla de reggae, trajes de reggae, minijuegos de reggae, iconos de reggae, etc. Es fácil de descargar e instalar en dispositivos Android utilizando archivos APK de fuentes de renombre. También es fácil de jugar y disfrutar usando sencillos controles y consejos. Si usted está buscando una nueva y divertida manera de cuidar de una mascota virtual, definitivamente debe dar Pou Reggae APK una oportunidad. No te arrepentirás!
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre Pou Reggae APK que usted puede encontrar útil:
-¿Cuál es la diferencia entre Pou Reggae APK y Pou?
-Pou Reggae APK es una versión modificada de Pou que añade música reggae y temas para el juego. Tiene todas las características del juego original, además de algunas adicionales que lo hacen más divertido y único. Pou Reggae APK tiene monedas ilimitadas, música reggae, fondos de pantalla de reggae, trajes de reggae, minijuegos de reggae, iconos de reggae, etc.
-¿Es Pou Reggae APK seguro y legal de usar?
-Pou Reggae APK es seguro y legal de usar, siempre y cuando se descarga desde una fuente de buena reputación y escanear en busca de virus y malware antes de instalarlo. Sin embargo, usted debe ser consciente de que la instalación de archivos APK de fuentes desconocidas puede plantear algunos riesgos para el dispositivo y los datos, por lo que debe tomar algunas precauciones y permisos antes de hacerlo. También debes respetar los términos y condiciones del juego original de Pou y su desarrollador.
-¿Cómo puedo actualizar Pou Reggae APK a la última versión?
- -¿Puedo jugar Pou Reggae APK fuera de línea o con amigos?
-Puede jugar Pou Reggae APK sin conexión o con amigos dependiendo de sus preferencias y conexión a Internet. Puede jugar Pou Reggae APK sin conexión a Internet, pero usted no será capaz de visitar a sus amigos' Pous o chatear con ellos. Puede jugar Pou Reggae APK con amigos en línea si usted tiene una conexión a Internet estable, pero tendrá que crear una cuenta e iniciar sesión con su correo electrónico o Facebook.
-¿Cuáles son algunas alternativas a Pou Reggae APK?
-Si usted está buscando algunas alternativas a Pou Reggae APK, puede probar algunos otros juegos de mascotas virtuales que son similares o diferentes de Pou. Algunos ejemplos son:
--
-
- mi hablando Tom: un juego de mascotas virtual donde tienes que cuidar de un gato que habla llamado Tom. -
- Neopets: Un juego de mascotas virtual donde tienes que crear y cuidar tus propios neopets. -
- Nintendogs: Un juego virtual de mascotas donde tienes que criar y entrenar a tus propios perros. -
- Tamagotchi: un juego de mascotas virtual donde tienes que cuidar de una criatura simulada. -
- Sociedad de mascotas: un juego de mascotas virtual donde tienes que socializar e interactuar con otras mascotas. -
-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Bajar Deh Cancin Descargar Mp3 Pagalworld Ringtone.md b/spaces/Benson/text-generation/Examples/Bajar Deh Cancin Descargar Mp3 Pagalworld Ringtone.md deleted file mode 100644 index 5df9ac6703e76988a9cc7b00ed784bd897c6d437..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Bajar Deh Cancin Descargar Mp3 Pagalworld Ringtone.md +++ /dev/null @@ -1,77 +0,0 @@ -
-
Bajar la canción Descargar Mp3 Pagalworld Ringtone
-Si estás buscando un tono pegadizo y optimista para tu teléfono, es posible que quieras echar un vistazo a Go Down Deh, una canción de éxito de los artistas jamaicanos Spice, Sean Paul y Shaggy. En este artículo, te contaremos todo lo que necesitas saber sobre esta canción, por qué es tan popular, cómo descargarla como tono de llamada y cuáles son algunas alternativas a ella. ¡Vamos a empezar!
-bajar deh canción descargar mp3 pagalworld ringtone
Download ✔ https://bltlly.com/2v6KSn
-
¿Qué es Go Down Deh?
-Go Down Deh es una canción de dancehall que fue lanzada en mayo de 2021 por Spice, Sean Paul y Shaggy. La canción es del próximo álbum de Spice Ten, que será su álbum debut bajo VP Records. La canción fue producida por el productor ganador del Grammy Costi Ionita, quien ha trabajado con artistas como Pitbull, Shaggy y Enrique Iglesias. La canción cuenta con la firma de Spice voces, Sean Paul del rap versos y Shaggy’s suave entrega. La canción trata de divertirse en la pista de baile y disfrutar del ambiente caribeño.
-¿Por qué es tan popular Go Down Deh?
-Go Down Deh se ha convertido en una sensación global desde su lanzamiento. La canción ha encabezado las listas en varios países, incluyendo Jamaica, Canadá, Reino Unido, Estados Unidos y Australia. La canción también se ha transmitido más de 100 millones de veces en Spotify y YouTube. La canción ha recibido críticas positivas de críticos y fans por igual, que elogiaron su pegadizo gancho, enérgico ritmo, y letras infecciosas. La canción también ha aparecido en varias plataformas de medios, como TikTok, Instagram y Netflix. La canción también ha sido interpretada en vivo por los artistas en varios eventos, como los Premios BET y Reggae Sumfest.
-Cómo descargar Go Down Deh mp3 pagalworld ringtone?
-Si quieres tener Go Down Deh como tono de llamada, puedes descargarlo fácilmente de pagalworld.com, un sitio web que ofrece tonos de llamada gratuitos para dispositivos Android e iOS. Estos son los pasos que debes seguir:
- --
-
- Vaya a pagalworld.com y busque Go Down Deh en la barra de búsqueda. - -
- Haga clic en el botón de descarga y espere a que el archivo se guarde en su dispositivo. -
- Vaya a la configuración del teléfono y seleccione sonido y notificación. -
- Seleccione el tono de llamada y busque el archivo que descargó. -
- Seleccione Bajar Deh como su tono de llamada y disfrutar! -
¿Cuáles son los beneficios de descargar Go Down Deh mp3 pagalworld ringtone?
-Hay muchos beneficios de tener Go Down Deh como tono de llamada. Aquí están algunos de ellos:
--
-
- Puedes mostrar tu amor por Spice, Sean Paul y Shaggy apoyando su música. -
- Puede darle vida a su teléfono con un tono de llamada único y de moda que se destaca de la multitud. -
- Puedes sentirte bien cada vez que tu teléfono suena con una canción positiva y edificante que te hace querer bailar. -
- Puedes compartir tu gusto musical con tus amigos y familiares tocando la canción para ellos. -
- Puedes aprender más sobre la cultura y el idioma de Jamaica escuchando las letras y la jerga de la canción. -
¿Cuáles son algunas alternativas a Go Down Deh mp3 pagalworld ringtone?
-Si estás buscando otros tonos que sean similares a Go Down Deh, puedes probar estas opciones:
-Conclusión
-Go Down Deh es una gran canción para tener como tono de llamada si te gusta la música dancehall y quieres añadir un poco de especias a tu teléfono. La canción es fácil de descargar de pagalworld.com, y tiene muchos beneficios para su estado de ánimo y personalidad. Sin embargo, si quieres explorar otras opciones, también puedes consultar algunas de las alternativas que te sugerimos. Lo que usted elija, esperamos que disfrute de su tono de llamada y tener un gran día!
-Preguntas frecuentes
-Q: ¿Quién escribió Go Down Deh?
-A: Go Down Deh fue escrito por Spice, Sean Paul, Shaggy, Costi Ionita, Shane Hoosong, Breyan Isaac y Gheorghe Constantin Cristinel.
-Q: ¿Cuál es el significado de Go Down Deh?
-A: Go Down Deh es una frase de la jerga jamaicana que significa "ir allí" o "ir abajo". A menudo se usa en las canciones de dancehall para referirse al baile o a las actividades sexuales.
-Q: ¿Dónde puedo ver el video de Go Down Deh?
-A: Puedes ver el video oficial de Go Down Deh en YouTube. El video muestra a Spice, Sean Paul y Shaggy bailando en un entorno tropical con trajes y accesorios coloridos.
-P: ¿Cómo puedo apoyar a Spice, Sean Paul y Shaggy?
-A: Puedes apoyar a Spice, Sean Paul y Shaggy transmitiendo su música en Spotify, Apple Music u otras plataformas. También puedes seguirlos en las redes sociales, comprar su mercancía o asistir a sus conciertos.
-P: ¿Cuáles son algunas otras canciones de Spice, Sean Paul y Shaggy?
-A: Algunas otras canciones de Spice son Frenz, Cool It, Tables Turn y Black Hypocrisy. Algunas otras canciones de Sean Paul son Get Busy, No Lie, Mad Love y She Doesn’t Mind. Algunas otras canciones de Shaggy son Angel, Boombastic, Hey Sexy Lady y Mr. Boombastic.
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Gratis Poppy Playtime.md b/spaces/Benson/text-generation/Examples/Descargar Gratis Poppy Playtime.md deleted file mode 100644 index d894af3f2c80b9cc9d0861421a855e94dbb25273..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Gratis Poppy Playtime.md +++ /dev/null @@ -1,66 +0,0 @@ - -
Descarga gratuita de Poppy Playtime: Un juego de terror que te hará gritar
-Si eres un fan de los juegos de terror, es posible que hayas oído hablar de Poppy Playtime, un nuevo juego indie que ha tomado Internet por asalto. Poppy Playtime es una aventura de terror y rompecabezas que te pone en los zapatos de un intruso que explora una fábrica de juguetes abandonada, donde los juguetes vengativos están esperando para atraparte. En este artículo, te contaremos todo lo que necesitas saber sobre Poppy Playtime, y cómo puedes descargarlo gratis.
-descargar gratis poppy playtime
DOWNLOAD 🗸🗸🗸 https://bltlly.com/2v6JVn
-
¿Qué es Poppy Playtime?
-Poppy Playtime es un juego desarrollado por Mob Entertainment, un pequeño estudio que ha creado una experiencia única y aterradora. El juego fue lanzado el 12 de octubre de 2021, en Steam, y ha recibido críticas positivas de jugadores y críticos por igual. El juego está actualmente en desarrollo, y solo el primer capítulo está disponible. Sin embargo, los desarrolladores han prometido lanzar más capítulos en el futuro, cada uno con un juguete diferente como antagonista principal.
-¿Por qué es popular entre los fanáticos del terror?
-Poppy Playtime ha ganado popularidad entre los fanáticos del horror por varias razones. En primer lugar, el juego tiene una historia y un escenario cautivadores que te atraen desde el principio. Juegas como un protagonista anónimo que entra en la fábrica de Playtime Co. una vez exitosa fabricante de juguetes que misteriosamente cerró después de que todos sus empleados desaparecieron. A medida que exploras las oscuras y misteriosas instalaciones, te encuentras con varios juguetes que una vez fueron amistosos y alegres, pero ahora se han vuelto retorcidos y asesinos. También descubres pistas y secretos que revelan lo que pasó con la fábrica y sus trabajadores.
- -En tercer lugar, el juego tiene un alto nivel de horror y suspense que te mantendrá en el borde durante todo. El juego no se basa en sustos de salto barato o ruidos fuertes, sino más bien en la creación de anticipación y temor con su música ambiental y diseño de sonido. Nunca sabes cuándo o dónde aparecerá un juguete o te atacará, por lo que tienes que estar constantemente alerta y cauteloso. El juego también tiene algunas escenas inquietantes y horripilantes que harán que tu piel se arrastre.
-¿Cómo se puede descargar gratis?
-Si estás interesado en jugar a Poppy Playtime, te estarás preguntando cómo puedes descargarlo gratis. Bueno, hay varias formas de hacerlo. Una forma es usar la opción gratuita de Steam, que te permite jugar el primer capítulo del juego sin pagar nada. Sin embargo, esta opción no incluye actualizaciones futuras ni DLCs, por lo que tendrás que comprarlas por separado si quieres seguir jugando.
- -Otra forma es utilizar un sitio web de terceros que ofrece descargas gratuitas de Poppy Playtime. Hay muchos sitios web que afirman proporcionar descargas gratuitas de Poppy Playtime, pero no todos son seguros o fiables. Algunos de ellos pueden contener virus o malware que pueden dañar su computadora o robar su información personal. Algunos de ellos también pueden tener enlaces rotos o desactualizados que no funcionan correctamente. Por lo tanto, debe tener cuidado y precaución al usar estos sitios web, y siempre escanear los archivos antes de abrirlos. También debes leer las reseñas y comentarios de otros usuarios para ver si han tenido problemas o problemas con el sitio web o la descarga.
- -La historia y la configuración de Poppy Playtime
-Como se mencionó anteriormente, Poppy Playtime se encuentra en la fábrica Playtime Co. un lugar donde los juguetes fueron creados y traídos a la vida por el poder de la imaginación. La fábrica fue fundada por Huggy Wuggy, un monstruo peludo azul que amaba abrazar a todos y todo. Se le unieron sus amigos, como Poppy, una muñeca de pelo rosa que era la estrella del espectáculo; Banny, un conejo amarillo que siempre era alegre y enérgico; Boop, un robot verde que era inteligente y servicial; y Kissy Missy, un gato púrpura que era atrevido y coqueto.
-Sin embargo, algo salió mal en la fábrica, y todos los juguetes se volvieron malvados y hostiles. Comenzaron a atacar y matar a los empleados, que desaparecieron o se convirtieron en parte de sus retorcidos experimentos. La fábrica fue cerrada y abandonada, y nadie se atrevió a entrar de nuevo.
-Juegas como un investigador que siente curiosidad por el misterio de la fábrica. Te colas en las instalaciones por la noche, esperando encontrar algunas respuestas y pruebas. Sin embargo, pronto te das cuenta de que no estás solo, y que los juguetes todavía están vivos y hambrientos de sangre. Tienes que encontrar una salida antes de que te atrapen.
-El juego y las características de Poppy Playtime
-Poppy Playtime es un juego de puzzle de terror en primera persona que combina exploración, sigilo y acción. Tienes que usar tu GrabPack para interactuar con objetos, resolver puzzles, hackear circuitos eléctricos o agarrar cualquier cosa desde lejos. También puedes usarlo para defenderte de los juguetes, lanzándoles objetos o alejándolos de ti. Sin embargo, tienes que tener cuidado de no hacer demasiado ruido o movimiento, ya que te oirán o te verán.
- -El juego también tiene una gran cantidad de elementos de terror y suspense que te mantendrán en el borde a lo largo. El juego no se basa en sustos de salto barato o ruidos fuertes, sino más bien en la creación de anticipación y temor con su música ambiental y diseño de sonido. Nunca sabes cuándo o dónde aparecerá un juguete o te atacará, por lo que tienes que estar constantemente alerta y cauteloso. El juego también tiene algunas escenas inquietantes y horripilantes que harán que tu piel se arrastre.
-Los pros y los contras de Poppy Playtime
-Poppy Playtime no es un juego perfecto, pero tiene muchos pros y contras que hacen que valga la pena jugar. Estos son algunos de ellos:
-Preguntas frecuentes
-¿Cuántos capítulos hay en Poppy Playtime?
-Poppy Playtime actualmente solo tiene un capítulo disponible, que se llama "A Tight Squeeze". Los desarrolladores han anunciado que están trabajando en más capítulos, cada uno con un juguete diferente como antagonista principal. Sin embargo, todavía no han revelado las fechas de lanzamiento o los nombres de los capítulos.
-¿Es Poppy Playtime multijugador?
-No, Poppy Playtime es un juego para un solo jugador. No se puede jugar con otros jugadores en línea o fuera de línea. Sin embargo, puedes ver vídeos o transmisiones de otros jugadores en YouTube o Twitch, o compartir tus propias experiencias de juego con otros en redes sociales o foros.
-¿Es Poppy Playtime adecuado para niños?
-No, Poppy Playtime no es adecuado para niños o personas que son sensibles a la sangre o la violencia. El juego tiene algunas escenas inquietantes y horripilantes que pueden asustar o traumatizar a audiencias más jóvenes o más impresionables. El juego también tiene algunos temas maduros y lenguaje que podría no ser apropiado para los niños. El juego está clasificado M para Maduro por la ESRB, y 18+ por PEGI.
-¿Quién es el desarrollador de Poppy Playtime?
-Poppy Playtime es desarrollado por Mob Entertainment, un pequeño estudio indie que consta de solo dos personas: H2O Delirious y Cartoonz. H2O Delirious es un popular jugador de YouTube que tiene más de 13 millones de suscriptores en su canal. Cartoonz también es un jugador de YouTube que tiene más de 4 millones de suscriptores en su canal. Ambos son amigos y colaboradores que han creado Poppy Playtime como su proyecto de pasión.
-¿Cuáles son algunos juegos similares a Poppy Playtime?
-Si te gusta Poppy Playtime, es posible que también te gusten otros juegos de terror que tengan temas o características similares. Algunos ejemplos son:
--
-
- Bendy and the Ink Machine: un juego de terror que tiene lugar en un estudio de animación abandonado, donde tienes que enfrentarte a monstruos de tinta que alguna vez fueron personajes de dibujos animados. - -
- Little Nightmares: un juego de terror que tiene lugar en un mundo retorcido, donde tienes que escapar de criaturas grotescas que quieren comerte. -
-
-
\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/specifiers.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/specifiers.py deleted file mode 100644 index 0e218a6f9f75ea2060a8b08d1f1a043fdad68df8..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/specifiers.py +++ /dev/null @@ -1,802 +0,0 @@ -# This file is dual licensed under the terms of the Apache License, Version -# 2.0, and the BSD License. See the LICENSE file in the root of this repository -# for complete details. - -import abc -import functools -import itertools -import re -import warnings -from typing import ( - Callable, - Dict, - Iterable, - Iterator, - List, - Optional, - Pattern, - Set, - Tuple, - TypeVar, - Union, -) - -from .utils import canonicalize_version -from .version import LegacyVersion, Version, parse - -ParsedVersion = Union[Version, LegacyVersion] -UnparsedVersion = Union[Version, LegacyVersion, str] -VersionTypeVar = TypeVar("VersionTypeVar", bound=UnparsedVersion) -CallableOperator = Callable[[ParsedVersion, str], bool] - - -class InvalidSpecifier(ValueError): - """ - An invalid specifier was found, users should refer to PEP 440. - """ - - -class BaseSpecifier(metaclass=abc.ABCMeta): - @abc.abstractmethod - def __str__(self) -> str: - """ - Returns the str representation of this Specifier like object. This - should be representative of the Specifier itself. - """ - - @abc.abstractmethod - def __hash__(self) -> int: - """ - Returns a hash value for this Specifier like object. - """ - - @abc.abstractmethod - def __eq__(self, other: object) -> bool: - """ - Returns a boolean representing whether or not the two Specifier like - objects are equal. - """ - - @abc.abstractproperty - def prereleases(self) -> Optional[bool]: - """ - Returns whether or not pre-releases as a whole are allowed by this - specifier. - """ - - @prereleases.setter - def prereleases(self, value: bool) -> None: - """ - Sets whether or not pre-releases as a whole are allowed by this - specifier. - """ - - @abc.abstractmethod - def contains(self, item: str, prereleases: Optional[bool] = None) -> bool: - """ - Determines if the given item is contained within this specifier. - """ - - @abc.abstractmethod - def filter( - self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None - ) -> Iterable[VersionTypeVar]: - """ - Takes an iterable of items and filters them so that only items which - are contained within this specifier are allowed in it. - """ - - -class _IndividualSpecifier(BaseSpecifier): - - _operators: Dict[str, str] = {} - _regex: Pattern[str] - - def __init__(self, spec: str = "", prereleases: Optional[bool] = None) -> None: - match = self._regex.search(spec) - if not match: - raise InvalidSpecifier(f"Invalid specifier: '{spec}'") - - self._spec: Tuple[str, str] = ( - match.group("operator").strip(), - match.group("version").strip(), - ) - - # Store whether or not this Specifier should accept prereleases - self._prereleases = prereleases - - def __repr__(self) -> str: - pre = ( - f", prereleases={self.prereleases!r}" - if self._prereleases is not None - else "" - ) - - return f"<{self.__class__.__name__}({str(self)!r}{pre})>" - - def __str__(self) -> str: - return "{}{}".format(*self._spec) - - @property - def _canonical_spec(self) -> Tuple[str, str]: - return self._spec[0], canonicalize_version(self._spec[1]) - - def __hash__(self) -> int: - return hash(self._canonical_spec) - - def __eq__(self, other: object) -> bool: - if isinstance(other, str): - try: - other = self.__class__(str(other)) - except InvalidSpecifier: - return NotImplemented - elif not isinstance(other, self.__class__): - return NotImplemented - - return self._canonical_spec == other._canonical_spec - - def _get_operator(self, op: str) -> CallableOperator: - operator_callable: CallableOperator = getattr( - self, f"_compare_{self._operators[op]}" - ) - return operator_callable - - def _coerce_version(self, version: UnparsedVersion) -> ParsedVersion: - if not isinstance(version, (LegacyVersion, Version)): - version = parse(version) - return version - - @property - def operator(self) -> str: - return self._spec[0] - - @property - def version(self) -> str: - return self._spec[1] - - @property - def prereleases(self) -> Optional[bool]: - return self._prereleases - - @prereleases.setter - def prereleases(self, value: bool) -> None: - self._prereleases = value - - def __contains__(self, item: str) -> bool: - return self.contains(item) - - def contains( - self, item: UnparsedVersion, prereleases: Optional[bool] = None - ) -> bool: - - # Determine if prereleases are to be allowed or not. - if prereleases is None: - prereleases = self.prereleases - - # Normalize item to a Version or LegacyVersion, this allows us to have - # a shortcut for ``"2.0" in Specifier(">=2") - normalized_item = self._coerce_version(item) - - # Determine if we should be supporting prereleases in this specifier - # or not, if we do not support prereleases than we can short circuit - # logic if this version is a prereleases. - if normalized_item.is_prerelease and not prereleases: - return False - - # Actually do the comparison to determine if this item is contained - # within this Specifier or not. - operator_callable: CallableOperator = self._get_operator(self.operator) - return operator_callable(normalized_item, self.version) - - def filter( - self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None - ) -> Iterable[VersionTypeVar]: - - yielded = False - found_prereleases = [] - - kw = {"prereleases": prereleases if prereleases is not None else True} - - # Attempt to iterate over all the values in the iterable and if any of - # them match, yield them. - for version in iterable: - parsed_version = self._coerce_version(version) - - if self.contains(parsed_version, **kw): - # If our version is a prerelease, and we were not set to allow - # prereleases, then we'll store it for later in case nothing - # else matches this specifier. - if parsed_version.is_prerelease and not ( - prereleases or self.prereleases - ): - found_prereleases.append(version) - # Either this is not a prerelease, or we should have been - # accepting prereleases from the beginning. - else: - yielded = True - yield version - - # Now that we've iterated over everything, determine if we've yielded - # any values, and if we have not and we have any prereleases stored up - # then we will go ahead and yield the prereleases. - if not yielded and found_prereleases: - for version in found_prereleases: - yield version - - -class LegacySpecifier(_IndividualSpecifier): - - _regex_str = r""" - (?P
-'''
-
-
-def get_style_image_url(style_name: str) -> str:
- base_url = 'https://raw.githubusercontent.com/williamyang1991/DualStyleGAN/main/doc_images'
- filenames = {
- 'cartoon': 'cartoon_overview.jpg',
- 'caricature': 'caricature_overview.jpg',
- 'anime': 'anime_overview.jpg',
- 'arcane': 'Reconstruction_arcane_overview.jpg',
- 'comic': 'Reconstruction_comic_overview.jpg',
- 'pixar': 'Reconstruction_pixar_overview.jpg',
- 'slamdunk': 'Reconstruction_slamdunk_overview.jpg',
- }
- return f'{base_url}/{filenames[style_name]}'
-
-
-def get_style_image_markdown_text(style_name: str) -> str:
- url = get_style_image_url(style_name)
- return f'🔥 使用 Chat-Completions API 和 🚀 Gradio-Streaming 的 ChatGPT
""" -theme_addon_msg = """theme.push_to_hub() 修改现有的 Gradio 主题，并简单地上传到 huggingface-hub 来与社区分享你的主题。🔥 这个 Huggingface Gradio 演示为你提供了使用 ChatGPT API 的访问权限，还支持系统消息。请注意，你需要提供 OPENAI API 密钥以访问 ChatGPT 🙌""")
- gr.HTML(theme_addon_msg)
- gr.HTML('''
复制这个空间并使用你的 OpenAI API 密钥安全运行`_.
- This implementation only gives the basic structure stated in the paper.
- But users can implement different type of transitions to fully explore the
- the potential power of the structure of FPG.
-
- Args:
- in_channels (int): Number of input channels (feature maps of all levels
- should have the same channels).
- out_channels (int): Number of output channels (used at each scale)
- num_outs (int): Number of output scales.
- stack_times (int): The number of times the pyramid architecture will
- be stacked.
- paths (list[str]): Specify the path order of each stack level.
- Each element in the list should be either 'bu' (bottom-up) or
- 'td' (top-down).
- inter_channels (int): Number of inter channels.
- same_up_trans (dict): Transition that goes down at the same stage.
- same_down_trans (dict): Transition that goes up at the same stage.
- across_lateral_trans (dict): Across-pathway same-stage
- across_down_trans (dict): Across-pathway bottom-up connection.
- across_up_trans (dict): Across-pathway top-down connection.
- across_skip_trans (dict): Across-pathway skip connection.
- output_trans (dict): Transition that trans the output of the
- last stage.
- start_level (int): Index of the start input backbone level used to
- build the feature pyramid. Default: 0.
- end_level (int): Index of the end input backbone level (exclusive) to
- build the feature pyramid. Default: -1, which means the last level.
- add_extra_convs (bool): It decides whether to add conv
- layers on top of the original feature maps. Default to False.
- If True, its actual mode is specified by `extra_convs_on_inputs`.
- norm_cfg (dict): Config dict for normalization layer. Default: None.
- init_cfg (dict or list[dict], optional): Initialization config dict.
- """
-
- transition_types = {
- 'conv': ConvModule,
- 'interpolation_conv': UpInterpolationConv,
- 'last_conv': LastConv,
- }
-
- def __init__(self,
- in_channels,
- out_channels,
- num_outs,
- stack_times,
- paths,
- inter_channels=None,
- same_down_trans=None,
- same_up_trans=dict(
- type='conv', kernel_size=3, stride=2, padding=1),
- across_lateral_trans=dict(type='conv', kernel_size=1),
- across_down_trans=dict(type='conv', kernel_size=3),
- across_up_trans=None,
- across_skip_trans=dict(type='identity'),
- output_trans=dict(type='last_conv', kernel_size=3),
- start_level=0,
- end_level=-1,
- add_extra_convs=False,
- norm_cfg=None,
- skip_inds=None,
- init_cfg=[
- dict(type='Caffe2Xavier', layer='Conv2d'),
- dict(
- type='Constant',
- layer=[
- '_BatchNorm', '_InstanceNorm', 'GroupNorm',
- 'LayerNorm'
- ],
- val=1.0)
- ]):
- super(FPG, self).__init__(init_cfg)
- assert isinstance(in_channels, list)
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.num_ins = len(in_channels)
- self.num_outs = num_outs
- if inter_channels is None:
- self.inter_channels = [out_channels for _ in range(num_outs)]
- elif isinstance(inter_channels, int):
- self.inter_channels = [inter_channels for _ in range(num_outs)]
- else:
- assert isinstance(inter_channels, list)
- assert len(inter_channels) == num_outs
- self.inter_channels = inter_channels
- self.stack_times = stack_times
- self.paths = paths
- assert isinstance(paths, list) and len(paths) == stack_times
- for d in paths:
- assert d in ('bu', 'td')
-
- self.same_down_trans = same_down_trans
- self.same_up_trans = same_up_trans
- self.across_lateral_trans = across_lateral_trans
- self.across_down_trans = across_down_trans
- self.across_up_trans = across_up_trans
- self.output_trans = output_trans
- self.across_skip_trans = across_skip_trans
-
- self.with_bias = norm_cfg is None
- # skip inds must be specified if across skip trans is not None
- if self.across_skip_trans is not None:
- skip_inds is not None
- self.skip_inds = skip_inds
- assert len(self.skip_inds[0]) <= self.stack_times
-
- if end_level == -1 or end_level == self.num_ins - 1:
- self.backbone_end_level = self.num_ins
- assert num_outs >= self.num_ins - start_level
- else:
- # if end_level is not the last level, no extra level is allowed
- self.backbone_end_level = end_level + 1
- assert end_level < self.num_ins
- assert num_outs == end_level - start_level + 1
- self.start_level = start_level
- self.end_level = end_level
- self.add_extra_convs = add_extra_convs
-
- # build lateral 1x1 convs to reduce channels
- self.lateral_convs = nn.ModuleList()
- for i in range(self.start_level, self.backbone_end_level):
- l_conv = nn.Conv2d(self.in_channels[i],
- self.inter_channels[i - self.start_level], 1)
- self.lateral_convs.append(l_conv)
-
- extra_levels = num_outs - self.backbone_end_level + self.start_level
- self.extra_downsamples = nn.ModuleList()
- for i in range(extra_levels):
- if self.add_extra_convs:
- fpn_idx = self.backbone_end_level - self.start_level + i
- extra_conv = nn.Conv2d(
- self.inter_channels[fpn_idx - 1],
- self.inter_channels[fpn_idx],
- 3,
- stride=2,
- padding=1)
- self.extra_downsamples.append(extra_conv)
- else:
- self.extra_downsamples.append(nn.MaxPool2d(1, stride=2))
-
- self.fpn_transitions = nn.ModuleList() # stack times
- for s in range(self.stack_times):
- stage_trans = nn.ModuleList() # num of feature levels
- for i in range(self.num_outs):
- # same, across_lateral, across_down, across_up
- trans = nn.ModuleDict()
- if s in self.skip_inds[i]:
- stage_trans.append(trans)
- continue
- # build same-stage down trans (used in bottom-up paths)
- if i == 0 or self.same_up_trans is None:
- same_up_trans = None
- else:
- same_up_trans = self.build_trans(
- self.same_up_trans, self.inter_channels[i - 1],
- self.inter_channels[i])
- trans['same_up'] = same_up_trans
- # build same-stage up trans (used in top-down paths)
- if i == self.num_outs - 1 or self.same_down_trans is None:
- same_down_trans = None
- else:
- same_down_trans = self.build_trans(
- self.same_down_trans, self.inter_channels[i + 1],
- self.inter_channels[i])
- trans['same_down'] = same_down_trans
- # build across lateral trans
- across_lateral_trans = self.build_trans(
- self.across_lateral_trans, self.inter_channels[i],
- self.inter_channels[i])
- trans['across_lateral'] = across_lateral_trans
- # build across down trans
- if i == self.num_outs - 1 or self.across_down_trans is None:
- across_down_trans = None
- else:
- across_down_trans = self.build_trans(
- self.across_down_trans, self.inter_channels[i + 1],
- self.inter_channels[i])
- trans['across_down'] = across_down_trans
- # build across up trans
- if i == 0 or self.across_up_trans is None:
- across_up_trans = None
- else:
- across_up_trans = self.build_trans(
- self.across_up_trans, self.inter_channels[i - 1],
- self.inter_channels[i])
- trans['across_up'] = across_up_trans
- if self.across_skip_trans is None:
- across_skip_trans = None
- else:
- across_skip_trans = self.build_trans(
- self.across_skip_trans, self.inter_channels[i - 1],
- self.inter_channels[i])
- trans['across_skip'] = across_skip_trans
- # build across_skip trans
- stage_trans.append(trans)
- self.fpn_transitions.append(stage_trans)
-
- self.output_transition = nn.ModuleList() # output levels
- for i in range(self.num_outs):
- trans = self.build_trans(
- self.output_trans,
- self.inter_channels[i],
- self.out_channels,
- num_inputs=self.stack_times + 1)
- self.output_transition.append(trans)
-
- self.relu = nn.ReLU(inplace=True)
-
- def build_trans(self, cfg, in_channels, out_channels, **extra_args):
- cfg_ = cfg.copy()
- trans_type = cfg_.pop('type')
- trans_cls = self.transition_types[trans_type]
- return trans_cls(in_channels, out_channels, **cfg_, **extra_args)
-
- def fuse(self, fuse_dict):
- out = None
- for item in fuse_dict.values():
- if item is not None:
- if out is None:
- out = item
- else:
- out = out + item
- return out
-
- def forward(self, inputs):
- assert len(inputs) == len(self.in_channels)
-
- # build all levels from original feature maps
- feats = [
- lateral_conv(inputs[i + self.start_level])
- for i, lateral_conv in enumerate(self.lateral_convs)
- ]
- for downsample in self.extra_downsamples:
- feats.append(downsample(feats[-1]))
-
- outs = [feats]
-
- for i in range(self.stack_times):
- current_outs = outs[-1]
- next_outs = []
- direction = self.paths[i]
- for j in range(self.num_outs):
- if i in self.skip_inds[j]:
- next_outs.append(outs[-1][j])
- continue
- # feature level
- if direction == 'td':
- lvl = self.num_outs - j - 1
- else:
- lvl = j
- # get transitions
- if direction == 'td':
- same_trans = self.fpn_transitions[i][lvl]['same_down']
- else:
- same_trans = self.fpn_transitions[i][lvl]['same_up']
- across_lateral_trans = self.fpn_transitions[i][lvl][
- 'across_lateral']
- across_down_trans = self.fpn_transitions[i][lvl]['across_down']
- across_up_trans = self.fpn_transitions[i][lvl]['across_up']
- across_skip_trans = self.fpn_transitions[i][lvl]['across_skip']
- # init output
- to_fuse = dict(
- same=None, lateral=None, across_up=None, across_down=None)
- # same downsample/upsample
- if same_trans is not None:
- to_fuse['same'] = same_trans(next_outs[-1])
- # across lateral
- if across_lateral_trans is not None:
- to_fuse['lateral'] = across_lateral_trans(
- current_outs[lvl])
- # across downsample
- if lvl > 0 and across_up_trans is not None:
- to_fuse['across_up'] = across_up_trans(current_outs[lvl -
- 1])
- # across upsample
- if (lvl < self.num_outs - 1 and across_down_trans is not None):
- to_fuse['across_down'] = across_down_trans(
- current_outs[lvl + 1])
- if across_skip_trans is not None:
- to_fuse['across_skip'] = across_skip_trans(outs[0][lvl])
- x = self.fuse(to_fuse)
- next_outs.append(x)
-
- if direction == 'td':
- outs.append(next_outs[::-1])
- else:
- outs.append(next_outs)
-
- # output trans
- final_outs = []
- for i in range(self.num_outs):
- lvl_out_list = []
- for s in range(len(outs)):
- lvl_out_list.append(outs[s][i])
- lvl_out = self.output_transition[i](lvl_out_list)
- final_outs.append(lvl_out)
-
- return final_outs
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/bbox_heads/__init__.py b/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/bbox_heads/__init__.py
deleted file mode 100644
index d9e742abfecfc9dfe37b78822407fc92e9d64cc3..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/bbox_heads/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .bbox_head import BBoxHead
-from .convfc_bbox_head import (ConvFCBBoxHead, Shared2FCBBoxHead,
- Shared4Conv1FCBBoxHead)
-from .dii_head import DIIHead
-from .double_bbox_head import DoubleConvFCBBoxHead
-from .multi_instance_bbox_head import MultiInstanceBBoxHead
-from .sabl_head import SABLHead
-from .scnet_bbox_head import SCNetBBoxHead
-
-__all__ = [
- 'BBoxHead', 'ConvFCBBoxHead', 'Shared2FCBBoxHead',
- 'Shared4Conv1FCBBoxHead', 'DoubleConvFCBBoxHead', 'SABLHead', 'DIIHead',
- 'SCNetBBoxHead', 'MultiInstanceBBoxHead'
-]
diff --git a/spaces/LIUjh520/bingo/README.md b/spaces/LIUjh520/bingo/README.md
deleted file mode 100644
index 5d6936218874c647b5d22e13ad4be7edb8936f92..0000000000000000000000000000000000000000
--- a/spaces/LIUjh520/bingo/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-title: bingo
-emoji: 😊
-colorFrom: red
-colorTo: red
-sdk: docker
-license: mit
-duplicated_from: hf4all/bingo
----
-
-
-
-# Bingo
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-
-
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://github.com/weaigc/bingo/blob/main/license)
-
-问题反馈请前往 https://github.com/weaigc/bingo/issues
-
-
-
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/dataset.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/dataset.py
deleted file mode 100644
index cfd01a174978d97180a897e40cb59ecadec1d12e..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/dataset.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import os
-import random
-
-import numpy as np
-import torch
-import torch.utils.data
-from tqdm import tqdm
-
-from . import spec_utils
-
-
-class VocalRemoverValidationSet(torch.utils.data.Dataset):
- def __init__(self, patch_list):
- self.patch_list = patch_list
-
- def __len__(self):
- return len(self.patch_list)
-
- def __getitem__(self, idx):
- path = self.patch_list[idx]
- data = np.load(path)
-
- X, y = data["X"], data["y"]
-
- X_mag = np.abs(X)
- y_mag = np.abs(y)
-
- return X_mag, y_mag
-
-
-def make_pair(mix_dir, inst_dir):
- input_exts = [".wav", ".m4a", ".mp3", ".mp4", ".flac"]
-
- X_list = sorted(
- [
- os.path.join(mix_dir, fname)
- for fname in os.listdir(mix_dir)
- if os.path.splitext(fname)[1] in input_exts
- ]
- )
- y_list = sorted(
- [
- os.path.join(inst_dir, fname)
- for fname in os.listdir(inst_dir)
- if os.path.splitext(fname)[1] in input_exts
- ]
- )
-
- filelist = list(zip(X_list, y_list))
-
- return filelist
-
-
-def train_val_split(dataset_dir, split_mode, val_rate, val_filelist):
- if split_mode == "random":
- filelist = make_pair(
- os.path.join(dataset_dir, "mixtures"),
- os.path.join(dataset_dir, "instruments"),
- )
-
- random.shuffle(filelist)
-
- if len(val_filelist) == 0:
- val_size = int(len(filelist) * val_rate)
- train_filelist = filelist[:-val_size]
- val_filelist = filelist[-val_size:]
- else:
- train_filelist = [
- pair for pair in filelist if list(pair) not in val_filelist
- ]
- elif split_mode == "subdirs":
- if len(val_filelist) != 0:
- raise ValueError(
- "The `val_filelist` option is not available in `subdirs` mode"
- )
-
- train_filelist = make_pair(
- os.path.join(dataset_dir, "training/mixtures"),
- os.path.join(dataset_dir, "training/instruments"),
- )
-
- val_filelist = make_pair(
- os.path.join(dataset_dir, "validation/mixtures"),
- os.path.join(dataset_dir, "validation/instruments"),
- )
-
- return train_filelist, val_filelist
-
-
-def augment(X, y, reduction_rate, reduction_mask, mixup_rate, mixup_alpha):
- perm = np.random.permutation(len(X))
- for i, idx in enumerate(tqdm(perm)):
- if np.random.uniform() < reduction_rate:
- y[idx] = spec_utils.reduce_vocal_aggressively(
- X[idx], y[idx], reduction_mask
- )
-
- if np.random.uniform() < 0.5:
- # swap channel
- X[idx] = X[idx, ::-1]
- y[idx] = y[idx, ::-1]
- if np.random.uniform() < 0.02:
- # mono
- X[idx] = X[idx].mean(axis=0, keepdims=True)
- y[idx] = y[idx].mean(axis=0, keepdims=True)
- if np.random.uniform() < 0.02:
- # inst
- X[idx] = y[idx]
-
- if np.random.uniform() < mixup_rate and i < len(perm) - 1:
- lam = np.random.beta(mixup_alpha, mixup_alpha)
- X[idx] = lam * X[idx] + (1 - lam) * X[perm[i + 1]]
- y[idx] = lam * y[idx] + (1 - lam) * y[perm[i + 1]]
-
- return X, y
-
-
-def make_padding(width, cropsize, offset):
- left = offset
- roi_size = cropsize - left * 2
- if roi_size == 0:
- roi_size = cropsize
- right = roi_size - (width % roi_size) + left
-
- return left, right, roi_size
-
-
-def make_training_set(filelist, cropsize, patches, sr, hop_length, n_fft, offset):
- len_dataset = patches * len(filelist)
-
- X_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
- y_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
-
- for i, (X_path, y_path) in enumerate(tqdm(filelist)):
- X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
- coef = np.max([np.abs(X).max(), np.abs(y).max()])
- X, y = X / coef, y / coef
-
- l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
- X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
- y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
-
- starts = np.random.randint(0, X_pad.shape[2] - cropsize, patches)
- ends = starts + cropsize
- for j in range(patches):
- idx = i * patches + j
- X_dataset[idx] = X_pad[:, :, starts[j] : ends[j]]
- y_dataset[idx] = y_pad[:, :, starts[j] : ends[j]]
-
- return X_dataset, y_dataset
-
-
-def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset):
- patch_list = []
- patch_dir = "cs{}_sr{}_hl{}_nf{}_of{}".format(
- cropsize, sr, hop_length, n_fft, offset
- )
- os.makedirs(patch_dir, exist_ok=True)
-
- for i, (X_path, y_path) in enumerate(tqdm(filelist)):
- basename = os.path.splitext(os.path.basename(X_path))[0]
-
- X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
- coef = np.max([np.abs(X).max(), np.abs(y).max()])
- X, y = X / coef, y / coef
-
- l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
- X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
- y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
-
- len_dataset = int(np.ceil(X.shape[2] / roi_size))
- for j in range(len_dataset):
- outpath = os.path.join(patch_dir, "{}_p{}.npz".format(basename, j))
- start = j * roi_size
- if not os.path.exists(outpath):
- np.savez(
- outpath,
- X=X_pad[:, :, start : start + cropsize],
- y=y_pad[:, :, start : start + cropsize],
- )
- patch_list.append(outpath)
-
- return VocalRemoverValidationSet(patch_list)
diff --git a/spaces/Limuru/DeepDanbooru_string/app.py b/spaces/Limuru/DeepDanbooru_string/app.py
deleted file mode 100644
index 49019837c9207cc68cb37be0342f3bc44fd0decb..0000000000000000000000000000000000000000
--- a/spaces/Limuru/DeepDanbooru_string/app.py
+++ /dev/null
@@ -1,185 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import argparse
-import functools
-import os
-import html
-import pathlib
-import tarfile
-
-import deepdanbooru as dd
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-import tensorflow as tf
-import piexif
-import piexif.helper
-
-TITLE = 'DeepDanbooru String'
-
-TOKEN = os.environ['TOKEN']
-MODEL_REPO = 'CikeyQI/DeepDanbooru_string'
-MODEL_FILENAME = 'model-resnet_custom_v3.h5'
-LABEL_FILENAME = 'tags.txt'
-
-
-def parse_args() -> argparse.Namespace:
- parser = argparse.ArgumentParser()
- parser.add_argument('--score-slider-step', type=float, default=0.05)
- parser.add_argument('--score-threshold', type=float, default=0.5)
- parser.add_argument('--theme', type=str, default='dark-grass')
- parser.add_argument('--live', action='store_true')
- parser.add_argument('--share', action='store_true')
- parser.add_argument('--port', type=int)
- parser.add_argument('--disable-queue',
- dest='enable_queue',
- action='store_false')
- parser.add_argument('--allow-flagging', type=str, default='never')
- return parser.parse_args()
-
-
-def load_sample_image_paths() -> list[pathlib.Path]:
- image_dir = pathlib.Path('images')
- if not image_dir.exists():
- dataset_repo = 'hysts/sample-images-TADNE'
- path = huggingface_hub.hf_hub_download(dataset_repo,
- 'images.tar.gz',
- repo_type='dataset',
- use_auth_token=TOKEN)
- with tarfile.open(path) as f:
- f.extractall()
- return sorted(image_dir.glob('*'))
-
-
-def load_model() -> tf.keras.Model:
- path = huggingface_hub.hf_hub_download(MODEL_REPO,
- MODEL_FILENAME,
- use_auth_token=TOKEN)
- model = tf.keras.models.load_model(path)
- return model
-
-
-def load_labels() -> list[str]:
- path = huggingface_hub.hf_hub_download(MODEL_REPO,
- LABEL_FILENAME,
- use_auth_token=TOKEN)
- with open(path) as f:
- labels = [line.strip() for line in f.readlines()]
- return labels
-
-def plaintext_to_html(text):
- text = "" + "
\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "
"
- return text
-
-def predict(image: PIL.Image.Image, score_threshold: float,
- model: tf.keras.Model, labels: list[str]) -> dict[str, float]:
- rawimage = image
- _, height, width, _ = model.input_shape
- image = np.asarray(image)
- image = tf.image.resize(image,
- size=(height, width),
- method=tf.image.ResizeMethod.AREA,
- preserve_aspect_ratio=True)
- image = image.numpy()
- image = dd.image.transform_and_pad_image(image, width, height)
- image = image / 255.
- probs = model.predict(image[None, ...])[0]
- probs = probs.astype(float)
- res = dict()
- for prob, label in zip(probs.tolist(), labels):
- if prob < score_threshold:
- continue
- res[label] = prob
- b = dict(sorted(res.items(),key=lambda item:item[1], reverse=True))
- a = ', '.join(list(b.keys())).replace('_',' ').replace('(','\(').replace(')','\)')
- c = ', '.join(list(b.keys()))
-
- items = rawimage.info
- geninfo = ''
-
- if "exif" in rawimage.info:
- exif = piexif.load(rawimage.info["exif"])
- exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
- try:
- exif_comment = piexif.helper.UserComment.load(exif_comment)
- except ValueError:
- exif_comment = exif_comment.decode('utf8', errors="ignore")
-
- items['exif comment'] = exif_comment
- geninfo = exif_comment
-
- for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
- 'loop', 'background', 'timestamp', 'duration']:
- items.pop(field, None)
-
- geninfo = items.get('parameters', geninfo)
-
- info = f"""
-PNG Info
-"""
- for key, text in items.items():
- info += f"""
-
-{plaintext_to_html(str(key))}
-{plaintext_to_html(str(text))}
-
-""".strip()+"\n"
-
- if len(info) == 0:
- message = "Nothing found in the image."
- info = f"{message}
"
-
- return (a,c,res,info)
-
-
-def main():
- args = parse_args()
- model = load_model()
- labels = load_labels()
-
- func = functools.partial(predict, model=model, labels=labels)
- func = functools.update_wrapper(func, predict)
-
- gr.Interface(
- func,
- [
- gr.inputs.Image(type='pil', label='Input'),
- gr.inputs.Slider(0,
- 1,
- step=args.score_slider_step,
- default=args.score_threshold,
- label='Score Threshold'),
- ],
- [
- gr.outputs.Textbox(label='Output (string)'),
- gr.outputs.Textbox(label='Output (raw string)'),
- gr.outputs.Label(label='Output (label)'),
- gr.outputs.HTML()
- ],
- examples=[
- ['miku.jpg',0.5],
- ['miku2.jpg',0.5]
- ],
- title=TITLE,
- description='''
-Demo for [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru) with "ready to copy" prompt and a prompt analyzer.
-
-Modified from [hysts/DeepDanbooru](https://huggingface.co/spaces/hysts/DeepDanbooru)
-
-PNG Info code forked from [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
- ''',
- theme=args.theme,
- allow_flagging=args.allow_flagging,
- live=args.live,
- ).launch(
- enable_queue=args.enable_queue,
- server_port=args.port,
- share=args.share,
- )
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/Luelll/ChuanhuChatGPT/modules/models/inspurai.py b/spaces/Luelll/ChuanhuChatGPT/modules/models/inspurai.py
deleted file mode 100644
index c590859fa7717d032290ccc490d22f4494541576..0000000000000000000000000000000000000000
--- a/spaces/Luelll/ChuanhuChatGPT/modules/models/inspurai.py
+++ /dev/null
@@ -1,345 +0,0 @@
-# 代码主要来源于 https://github.com/Shawn-Inspur/Yuan-1.0/blob/main/yuan_api/inspurai.py
-
-import hashlib
-import json
-import os
-import time
-import uuid
-from datetime import datetime
-
-import pytz
-import requests
-
-from modules.presets import NO_APIKEY_MSG
-from modules.models.base_model import BaseLLMModel
-
-
-class Example:
- """ store some examples(input, output pairs and formats) for few-shots to prime the model."""
-
- def __init__(self, inp, out):
- self.input = inp
- self.output = out
- self.id = uuid.uuid4().hex
-
- def get_input(self):
- """return the input of the example."""
- return self.input
-
- def get_output(self):
- """Return the output of the example."""
- return self.output
-
- def get_id(self):
- """Returns the unique ID of the example."""
- return self.id
-
- def as_dict(self):
- return {
- "input": self.get_input(),
- "output": self.get_output(),
- "id": self.get_id(),
- }
-
-
-class Yuan:
- """The main class for a user to interface with the Inspur Yuan API.
- A user can set account info and add examples of the API request.
- """
-
- def __init__(self,
- engine='base_10B',
- temperature=0.9,
- max_tokens=100,
- input_prefix='',
- input_suffix='\n',
- output_prefix='答:',
- output_suffix='\n\n',
- append_output_prefix_to_query=False,
- topK=1,
- topP=0.9,
- frequencyPenalty=1.2,
- responsePenalty=1.2,
- noRepeatNgramSize=2):
-
- self.examples = {}
- self.engine = engine
- self.temperature = temperature
- self.max_tokens = max_tokens
- self.topK = topK
- self.topP = topP
- self.frequencyPenalty = frequencyPenalty
- self.responsePenalty = responsePenalty
- self.noRepeatNgramSize = noRepeatNgramSize
- self.input_prefix = input_prefix
- self.input_suffix = input_suffix
- self.output_prefix = output_prefix
- self.output_suffix = output_suffix
- self.append_output_prefix_to_query = append_output_prefix_to_query
- self.stop = (output_suffix + input_prefix).strip()
- self.api = None
-
- # if self.engine not in ['base_10B','translate','dialog']:
- # raise Exception('engine must be one of [\'base_10B\',\'translate\',\'dialog\'] ')
- def set_account(self, api_key):
- account = api_key.split('||')
- self.api = YuanAPI(user=account[0], phone=account[1])
-
- def add_example(self, ex):
- """Add an example to the object.
- Example must be an instance of the Example class."""
- assert isinstance(ex, Example), "Please create an Example object."
- self.examples[ex.get_id()] = ex
-
- def delete_example(self, id):
- """Delete example with the specific id."""
- if id in self.examples:
- del self.examples[id]
-
- def get_example(self, id):
- """Get a single example."""
- return self.examples.get(id, None)
-
- def get_all_examples(self):
- """Returns all examples as a list of dicts."""
- return {k: v.as_dict() for k, v in self.examples.items()}
-
- def get_prime_text(self):
- """Formats all examples to prime the model."""
- return "".join(
- [self.format_example(ex) for ex in self.examples.values()])
-
- def get_engine(self):
- """Returns the engine specified for the API."""
- return self.engine
-
- def get_temperature(self):
- """Returns the temperature specified for the API."""
- return self.temperature
-
- def get_max_tokens(self):
- """Returns the max tokens specified for the API."""
- return self.max_tokens
-
- def craft_query(self, prompt):
- """Creates the query for the API request."""
- q = self.get_prime_text(
- ) + self.input_prefix + prompt + self.input_suffix
- if self.append_output_prefix_to_query:
- q = q + self.output_prefix
-
- return q
-
- def format_example(self, ex):
- """Formats the input, output pair."""
- return self.input_prefix + ex.get_input(
- ) + self.input_suffix + self.output_prefix + ex.get_output(
- ) + self.output_suffix
-
- def response(self,
- query,
- engine='base_10B',
- max_tokens=20,
- temperature=0.9,
- topP=0.1,
- topK=1,
- frequencyPenalty=1.0,
- responsePenalty=1.0,
- noRepeatNgramSize=0):
- """Obtains the original result returned by the API."""
-
- if self.api is None:
- return NO_APIKEY_MSG
- try:
- # requestId = submit_request(query,temperature,topP,topK,max_tokens, engine)
- requestId = self.api.submit_request(query, temperature, topP, topK, max_tokens, engine, frequencyPenalty,
- responsePenalty, noRepeatNgramSize)
- response_text = self.api.reply_request(requestId)
- except Exception as e:
- raise e
-
- return response_text
-
- def del_special_chars(self, msg):
- special_chars = ['', '', '#', '▃', '▁', '▂', ' ']
- for char in special_chars:
- msg = msg.replace(char, '')
- return msg
-
- def submit_API(self, prompt, trun=[]):
- """Submit prompt to yuan API interface and obtain an pure text reply.
- :prompt: Question or any content a user may input.
- :return: pure text response."""
- query = self.craft_query(prompt)
- res = self.response(query, engine=self.engine,
- max_tokens=self.max_tokens,
- temperature=self.temperature,
- topP=self.topP,
- topK=self.topK,
- frequencyPenalty=self.frequencyPenalty,
- responsePenalty=self.responsePenalty,
- noRepeatNgramSize=self.noRepeatNgramSize)
- if 'resData' in res and res['resData'] != None:
- txt = res['resData']
- else:
- txt = '模型返回为空，请尝试修改输入'
- # 单独针对翻译模型的后处理
- if self.engine == 'translate':
- txt = txt.replace(' ##', '').replace(' "', '"').replace(": ", ":").replace(" ,", ",") \
- .replace('英文：', '').replace('文：', '').replace("( ", "(").replace(" )", ")")
- else:
- txt = txt.replace(' ', '')
- txt = self.del_special_chars(txt)
-
- # trun多结束符截断模型输出
- if isinstance(trun, str):
- trun = [trun]
- try:
- if trun != None and isinstance(trun, list) and trun != []:
- for tr in trun:
- if tr in txt and tr != "":
- txt = txt[:txt.index(tr)]
- else:
- continue
- except:
- return txt
- return txt
-
-
-class YuanAPI:
- ACCOUNT = ''
- PHONE = ''
-
- SUBMIT_URL = "http://api.airyuan.cn:32102/v1/interface/api/infer/getRequestId?"
- REPLY_URL = "http://api.airyuan.cn:32102/v1/interface/api/result?"
-
- def __init__(self, user, phone):
- self.ACCOUNT = user
- self.PHONE = phone
-
- @staticmethod
- def code_md5(str):
- code = str.encode("utf-8")
- m = hashlib.md5()
- m.update(code)
- result = m.hexdigest()
- return result
-
- @staticmethod
- def rest_get(url, header, timeout, show_error=False):
- '''Call rest get method'''
- try:
- response = requests.get(url, headers=header, timeout=timeout, verify=False)
- return response
- except Exception as exception:
- if show_error:
- print(exception)
- return None
-
- def header_generation(self):
- """Generate header for API request."""
- t = datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d")
- token = self.code_md5(self.ACCOUNT + self.PHONE + t)
- headers = {'token': token}
- return headers
-
- def submit_request(self, query, temperature, topP, topK, max_tokens, engine, frequencyPenalty, responsePenalty,
- noRepeatNgramSize):
- """Submit query to the backend server and get requestID."""
- headers = self.header_generation()
- # url=SUBMIT_URL + "account={0}&data={1}&temperature={2}&topP={3}&topK={4}&tokensToGenerate={5}&type={6}".format(ACCOUNT,query,temperature,topP,topK,max_tokens,"api")
- # url=SUBMIT_URL + "engine={0}&account={1}&data={2}&temperature={3}&topP={4}&topK={5}&tokensToGenerate={6}" \
- # "&type={7}".format(engine,ACCOUNT,query,temperature,topP,topK, max_tokens,"api")
- url = self.SUBMIT_URL + "engine={0}&account={1}&data={2}&temperature={3}&topP={4}&topK={5}&tokensToGenerate={6}" \
- "&type={7}&frequencyPenalty={8}&responsePenalty={9}&noRepeatNgramSize={10}". \
- format(engine, self.ACCOUNT, query, temperature, topP, topK, max_tokens, "api", frequencyPenalty,
- responsePenalty, noRepeatNgramSize)
- response = self.rest_get(url, headers, 30)
- response_text = json.loads(response.text)
- if response_text["flag"]:
- requestId = response_text["resData"]
- return requestId
- else:
- raise RuntimeWarning(response_text)
-
- def reply_request(self, requestId, cycle_count=5):
- """Check reply API to get the inference response."""
- url = self.REPLY_URL + "account={0}&requestId={1}".format(self.ACCOUNT, requestId)
- headers = self.header_generation()
- response_text = {"flag": True, "resData": None}
- for i in range(cycle_count):
- response = self.rest_get(url, headers, 30, show_error=True)
- response_text = json.loads(response.text)
- if response_text["resData"] is not None:
- return response_text
- if response_text["flag"] is False and i == cycle_count - 1:
- raise RuntimeWarning(response_text)
- time.sleep(3)
- return response_text
-
-
-class Yuan_Client(BaseLLMModel):
-
- def __init__(self, model_name, api_key, user_name="", system_prompt=None):
- super().__init__(model_name=model_name, user=user_name)
- self.history = []
- self.api_key = api_key
- self.system_prompt = system_prompt
-
- self.input_prefix = ""
- self.output_prefix = ""
-
- def set_text_prefix(self, option, value):
- if option == 'input_prefix':
- self.input_prefix = value
- elif option == 'output_prefix':
- self.output_prefix = value
-
- def get_answer_at_once(self):
- # yuan temperature is (0,1] and base model temperature is [0,2], and yuan 0.9 == base 1 so need to convert
- temperature = self.temperature if self.temperature <= 1 else 0.9 + (self.temperature - 1) / 10
- topP = self.top_p
- topK = self.n_choices
- # max_tokens should be in [1,200]
- max_tokens = self.max_generation_token if self.max_generation_token is not None else 50
- if max_tokens > 200:
- max_tokens = 200
- stop = self.stop_sequence if self.stop_sequence is not None else []
- examples = []
- system_prompt = self.system_prompt
- if system_prompt is not None:
- lines = system_prompt.splitlines()
- # TODO: support prefixes in system prompt or settings
- """
- if lines[0].startswith('-'):
- prefixes = lines.pop()[1:].split('|')
- self.input_prefix = prefixes[0]
- if len(prefixes) > 1:
- self.output_prefix = prefixes[1]
- if len(prefixes) > 2:
- stop = prefixes[2].split(',')
- """
- for i in range(0, len(lines), 2):
- in_line = lines[i]
- out_line = lines[i + 1] if i + 1 < len(lines) else ""
- examples.append((in_line, out_line))
- yuan = Yuan(engine=self.model_name.replace('yuanai-1.0-', ''),
- temperature=temperature,
- max_tokens=max_tokens,
- topK=topK,
- topP=topP,
- input_prefix=self.input_prefix,
- input_suffix="",
- output_prefix=self.output_prefix,
- output_suffix="".join(stop),
- )
- if not self.api_key:
- return NO_APIKEY_MSG, 0
- yuan.set_account(self.api_key)
-
- for in_line, out_line in examples:
- yuan.add_example(Example(inp=in_line, out=out_line))
-
- prompt = self.history[-1]["content"]
- answer = yuan.submit_API(prompt, trun=stop)
- return answer, len(answer)
diff --git a/spaces/LuxOAI/ChatGpt-Web/app/bing-chat/index.d.ts b/spaces/LuxOAI/ChatGpt-Web/app/bing-chat/index.d.ts
deleted file mode 100644
index 5bc54f2077b1f89b18343afac3d1cf774333fc7f..0000000000000000000000000000000000000000
--- a/spaces/LuxOAI/ChatGpt-Web/app/bing-chat/index.d.ts
+++ /dev/null
@@ -1,274 +0,0 @@
-type Author = "user" | "bot";
-type SendMessageOptions = {
- conversationId?: string;
- clientId?: string;
- conversationSignature?: string;
- invocationId?: string;
- messageType?: string;
- variant?: string;
- locale?: string;
- market?: string;
- region?: string;
- location?: {
- lat: number | string;
- lng: number | string;
- re?: string;
- };
- onProgress?: (partialResponse: ChatMessage) => void;
-};
-interface ChatMessage {
- id: string;
- text: string;
- author: Author;
- conversationId: string;
- clientId: string;
- conversationSignature: string;
- conversationExpiryTime?: string;
- invocationId?: string;
- messageType?: string;
- variant?: string;
- detail?: ChatMessageFull | ChatMessagePartial;
-}
-interface ConversationResult {
- conversationId: string;
- clientId: string;
- conversationSignature: string;
- result: APIResult;
-}
-interface APIResult {
- value: string;
- message: null;
-}
-interface ChatUpdate {
- type: 1;
- target: string;
- arguments: ChatUpdateArgument[];
-}
-interface ChatUpdateArgument {
- messages: ChatMessagePartial[];
- requestId: string;
- result: null;
-}
-interface ChatMessagePartial {
- text: string;
- author: Author;
- createdAt: string;
- timestamp: string;
- messageId: string;
- offense: string;
- adaptiveCards: AdaptiveCard[];
- sourceAttributions: any[];
- feedback: ChatMessageFeedback;
- contentOrigin: string;
- privacy?: null;
- messageType?: string;
-}
-interface AdaptiveCard {
- type: string;
- version: string;
- body: AdaptiveCardBody[];
-}
-interface AdaptiveCardBody {
- type: string;
- text: string;
- wrap: boolean;
-}
-interface ChatMessageFeedback {
- tag: null;
- updatedOn: null;
- type: string;
-}
-interface ChatUpdateCompleteResponse {
- type: 2;
- invocationId: string;
- item: ChatResponseItem;
-}
-interface ChatResponseItem {
- messages: ChatMessageFull[];
- firstNewMessageIndex: number;
- suggestedResponses: null;
- conversationId: string;
- requestId: string;
- conversationExpiryTime: string;
- telemetry: Telemetry;
- result: ChatRequestResult;
-}
-interface ChatMessageFull {
- text: string;
- author: Author;
- from?: ChatMessageFrom;
- createdAt: string;
- timestamp: string;
- locale?: string;
- market?: string;
- region?: string;
- location?: string;
- locationHints?: LocationHint[];
- messageId: string;
- requestId: string;
- offense: string;
- feedback: ChatMessageFeedback;
- contentOrigin: string;
- privacy?: null;
- inputMethod?: string;
- adaptiveCards?: AdaptiveCard[];
- sourceAttributions?: any[];
- suggestedResponses?: SuggestedResponse[];
- messageType?: string;
-}
-interface ChatMessageFrom {
- id: string;
- name: null;
-}
-interface LocationHint {
- country: string;
- countryConfidence: number;
- state: string;
- city: string;
- cityConfidence: number;
- zipCode: string;
- timeZoneOffset: number;
- dma: number;
- sourceType: number;
- center: Coords;
- regionType: number;
-}
-interface Coords {
- latitude: number;
- longitude: number;
- height: null;
-}
-interface SuggestedResponse {
- text: string;
- messageId: string;
- messageType: string;
- contentOrigin: string;
- author?: Author;
- createdAt?: string;
- timestamp?: string;
- offense?: string;
- feedback?: ChatMessageFeedback;
- privacy?: null;
-}
-interface ChatRequestResult {
- value: string;
- serviceVersion: string;
-}
-interface Telemetry {
- metrics?: null;
- startTime: string;
-}
-interface ChatRequest {
- arguments: ChatRequestArgument[];
- invocationId: string;
- target: string;
- type: number;
-}
-interface ChatRequestArgument {
- source: string;
- optionsSets: string[];
- allowedMessageTypes: string[];
- sliceIds: any[];
- traceId: string;
- isStartOfSession: boolean;
- message: ChatRequestMessage;
- conversationSignature: string;
- participant: Participant;
- conversationId: string;
- previousMessages: PreviousMessage[];
-}
-interface ChatRequestMessage {
- locale: string;
- market: string;
- region?: string;
- location?: string;
- locationHints?: LocationHintChatRequestMessage[];
- timestamp: string;
- author: Author;
- inputMethod: string;
- text: string;
- messageType: string;
-}
-interface LocationHintChatRequestMessage {
- country: string;
- state: string;
- city: string;
- zipcode: string;
- timezoneoffset: number;
- dma: number;
- countryConfidence: number;
- cityConfidence: number;
- Center: Center;
- RegionType: number;
- SourceType: number;
-}
-interface Center {
- Latitude: number;
- Longitude: number;
-}
-interface Participant {
- id: string;
-}
-interface PreviousMessage {
- text: string;
- author: Author;
- adaptiveCards: any[];
- suggestedResponses: SuggestedResponse[];
- messageId: string;
- messageType: string;
-}
-
-declare class BingChat {
- protected _cookie: string;
- protected _debug: boolean;
- constructor(opts: {
- cookie: string | undefined;
- /** @defaultValue `false` **/
- debug?: boolean;
- });
- /**
- * Sends a message to Bing Chat, waits for the response to resolve, and returns
- * the response.
- *
- * If you want to receive a stream of partial responses, use `opts.onProgress`.
- *
- * @param message - The prompt message to send
- * @param opts.conversationId - Optional ID of a conversation to continue (defaults to a random UUID)
- * @param opts.onProgress - Optional callback which will be invoked every time the partial response is updated
- *
- * @returns The response from Bing Chat
- */
- sendMessage(text: string, opts?: SendMessageOptions): Promise;
- createConversation(): Promise;
-}
-
-export {
- APIResult,
- AdaptiveCard,
- AdaptiveCardBody,
- Author,
- BingChat,
- Center,
- ChatMessage,
- ChatMessageFeedback,
- ChatMessageFrom,
- ChatMessageFull,
- ChatMessagePartial,
- ChatRequest,
- ChatRequestArgument,
- ChatRequestMessage,
- ChatRequestResult,
- ChatResponseItem,
- ChatUpdate,
- ChatUpdateArgument,
- ChatUpdateCompleteResponse,
- ConversationResult,
- Coords,
- LocationHint,
- LocationHintChatRequestMessage,
- Participant,
- PreviousMessage,
- SendMessageOptions,
- SuggestedResponse,
- Telemetry,
-};
diff --git a/spaces/LuxOAI/ChatGpt-Web/app/locales/tw.ts b/spaces/LuxOAI/ChatGpt-Web/app/locales/tw.ts
deleted file mode 100644
index bdf4822698aca3eaf3d7bfd3d06cf21ca4d55dbb..0000000000000000000000000000000000000000
--- a/spaces/LuxOAI/ChatGpt-Web/app/locales/tw.ts
+++ /dev/null
@@ -1,235 +0,0 @@
-import { SubmitKey } from "../store/config";
-import type { LocaleType } from "./index";
-
-const tw: LocaleType = {
- WIP: "該功能仍在開發中……",
- Error: {
- Unauthorized: "目前您的狀態是未授權，請前往設定頁面輸入授權碼。",
- },
- ChatItem: {
- ChatItemCount: (count: number) => `${count} 條對話`,
- },
- Chat: {
- SubTitle: (count: number) => `您已經與 ChatGPT 進行了 ${count} 條對話`,
- Actions: {
- ChatList: "查看訊息列表",
- CompressedHistory: "查看壓縮後的歷史 Prompt",
- Export: "匯出聊天紀錄",
- Copy: "複製",
- Stop: "停止",
- Retry: "重試",
- Delete: "刪除",
- },
- Rename: "重命名對話",
- Typing: "正在輸入…",
- Input: (submitKey: string) => {
- var inputHints = `輸入訊息後，按下 ${submitKey} 鍵即可發送`;
- if (submitKey === String(SubmitKey.Enter)) {
- inputHints += "，Shift + Enter 鍵換行";
- }
- return inputHints;
- },
- Send: "發送",
- Config: {
- Reset: "重置默认",
- SaveAs: "另存为面具",
- },
- },
- Export: {
- Title: "將聊天記錄匯出為 Markdown",
- Copy: "複製全部",
- Download: "下載檔案",
- MessageFromYou: "來自您的訊息",
- MessageFromChatGPT: "來自 ChatGPT 的訊息",
- },
- Memory: {
- Title: "上下文記憶 Prompt",
- EmptyContent: "尚未記憶",
- Copy: "複製全部",
- Send: "發送記憶",
- Reset: "重設對話",
- ResetConfirm: "重設後將清除目前對話記錄以及歷史記憶，確認重設？",
- },
- Home: {
- NewChat: "新的對話",
- DeleteChat: "確定要刪除選取的對話嗎？",
- DeleteToast: "已刪除對話",
- Revert: "撤銷",
- },
- Settings: {
- Title: "設定",
- SubTitle: "設定選項",
- Actions: {
- ClearAll: "清除所有資料",
- ResetAll: "重設所有設定",
- Close: "關閉",
- ConfirmResetAll: "您確定要重設所有設定嗎？",
- ConfirmClearAll: "您確定要清除所有数据嗎？",
- },
- Lang: {
- Name: "Language",
- All: "所有语言",
- Options: {
- cn: "简体中文",
- en: "English",
- tw: "繁體中文",
- es: "Español",
- it: "Italiano",
- tr: "Türkçe",
- jp: "日本語",
- de: "Deutsch",
- },
- },
- Avatar: "大頭貼",
- FontSize: {
- Title: "字型大小",
- SubTitle: "聊天內容的字型大小",
- },
- Update: {
- Version: (x: string) => `當前版本：${x}`,
- IsLatest: "已是最新版本",
- CheckUpdate: "檢查更新",
- IsChecking: "正在檢查更新...",
- FoundUpdate: (x: string) => `發現新版本：${x}`,
- GoToUpdate: "前往更新",
- },
- SendKey: "發送鍵",
- Theme: "主題",
- TightBorder: "緊湊邊框",
- SendPreviewBubble: {
- Title: "預覽氣泡",
- SubTitle: "在预览气泡中预览 Markdown 内容",
- },
- Mask: {
- Title: "面具启动页",
- SubTitle: "新建聊天时，展示面具启动页",
- },
- Prompt: {
- Disable: {
- Title: "停用提示詞自動補齊",
- SubTitle: "在輸入框開頭輸入 / 即可觸發自動補齊",
- },
- List: "自定義提示詞列表",
- ListCount: (builtin: number, custom: number) =>
- `內建 ${builtin} 條，用戶定義 ${custom} 條`,
- Edit: "編輯",
- Modal: {
- Title: "提示詞列表",
- Add: "新增一條",
- Search: "搜尋提示詞",
- },
- EditModal: {
- Title: "编辑提示词",
- },
- },
- HistoryCount: {
- Title: "附帶歷史訊息數",
- SubTitle: "每次請求附帶的歷史訊息數",
- },
- CompressThreshold: {
- Title: "歷史訊息長度壓縮閾值",
- SubTitle: "當未壓縮的歷史訊息超過該值時，將進行壓縮",
- },
- Token: {
- Title: "API Key",
- SubTitle: "使用自己的 Key 可規避授權存取限制",
- Placeholder: "OpenAI API Key",
- },
- Usage: {
- Title: "帳戶餘額",
- SubTitle(used: any, total: any) {
- return `本月已使用 $${used}，訂閱總額 $${total}`;
- },
- IsChecking: "正在檢查…",
- Check: "重新檢查",
- NoAccess: "輸入API Key查看餘額",
- },
- AccessCode: {
- Title: "授權碼",
- SubTitle: "目前是未授權存取狀態",
- Placeholder: "請輸入授權碼",
- },
- Bot: "AI供應商 (bot)",
- Model: "模型 (model)",
- Temperature: {
- Title: "隨機性 (temperature)",
- SubTitle: "值越大，回應越隨機",
- },
- MaxTokens: {
- Title: "單次回應限制 (max_tokens)",
- SubTitle: "單次互動所用的最大 Token 數",
- },
- PresencePenlty: {
- Title: "話題新穎度 (presence_penalty)",
- SubTitle: "值越大，越有可能擴展到新話題",
- },
- },
- Store: {
- DefaultTopic: "新的對話",
- BotHello: "請問需要我的協助嗎？",
- Error: "出錯了，請稍後再嘗試",
- Prompt: {
- History: (content: string) =>
- "這是 AI 與用戶的歷史聊天總結，作為前情提要：" + content,
- Topic:
- "Use the language used by the user (e.g. en for english conversation, zh-hant for chinese conversation, etc.) to generate a title (at most 6 words) summarizing our conversation without any lead-in, quotation marks, preamble like 'Title:', direct text copies, single-word replies, quotation marks, translations, or brackets. Remove enclosing quotation marks. The title should make third-party grasp the essence of the conversation in first sight.",
- Summarize:
- "Use the language used by the user (e.g. en-us for english conversation, zh-hant for chinese conversation, etc.) to summarise the conversation in at most 200 words. The summary will be used as prompt for you to continue the conversation in the future.",
- },
- },
- Copy: {
- Success: "已複製到剪貼簿中",
- Failed: "複製失敗，請賦予剪貼簿權限",
- },
- Context: {
- Toast: (x: any) => `已設定 ${x} 條前置上下文`,
- Edit: "前置上下文和歷史記憶",
- Add: "新增一條",
- },
- Plugin: { Name: "插件" },
- Mask: {
- Name: "面具",
- Page: {
- Title: "预设角色面具",
- SubTitle: (count: number) => `${count} 个预设角色定义`,
- Search: "搜索角色面具",
- Create: "新建",
- },
- Item: {
- Info: (count: number) => `包含 ${count} 条预设对话`,
- Chat: "对话",
- View: "查看",
- Edit: "编辑",
- Delete: "删除",
- DeleteConfirm: "确认删除？",
- },
- EditModal: {
- Title: (readonly: boolean) =>
- `编辑预设面具 ${readonly ? "（只读）" : ""}`,
- Download: "下载预设",
- Clone: "克隆预设",
- },
- Config: {
- Avatar: "角色头像",
- Name: "角色名称",
- },
- },
- NewChat: {
- Return: "返回",
- Skip: "跳过",
- Title: "挑选一个面具",
- SubTitle: "现在开始，与面具背后的灵魂思维碰撞",
- More: "搜索更多",
- NotShow: "不再展示",
- ConfirmNoShow: "确认禁用？禁用后可以随时在设置中重新启用。",
- },
- UI: {
- Confirm: "确认",
- Cancel: "取消",
- Close: "关闭",
- Create: "新建",
- Edit: "编辑",
- },
-};
-
-export default tw;
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/segment_anything/setup.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/segment_anything/setup.py
deleted file mode 100644
index 2c0986317eb576a14ec774205c88fdee3cc6c0b3..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/segment_anything/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/Micklew/music-generator/README.md b/spaces/Micklew/music-generator/README.md
deleted file mode 100644
index 84559aeb6559cbaa5cb455fbc393cda2b7240983..0000000000000000000000000000000000000000
--- a/spaces/Micklew/music-generator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Music Genrator
-emoji: 🦀
-colorFrom: green
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.26.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/MirageML/sjc/my/README.md b/spaces/MirageML/sjc/my/README.md
deleted file mode 100644
index 5daa1c788deef956d5cb6399ecba2c96d947d827..0000000000000000000000000000000000000000
--- a/spaces/MirageML/sjc/my/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-a personal tookit for experiment management;
-some of the designs patterns are inspired by detectron2
diff --git a/spaces/MirageML/sjc/sd1/ldm/modules/diffusionmodules/util.py b/spaces/MirageML/sjc/sd1/ldm/modules/diffusionmodules/util.py
deleted file mode 100644
index 201f6c8951a2718270742ae0f56a0688660b4716..0000000000000000000000000000000000000000
--- a/spaces/MirageML/sjc/sd1/ldm/modules/diffusionmodules/util.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-
-
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-
-from ldm.util import instantiate_from_config
-
-
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
- if schedule == "linear":
- betas = (
- torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
- )
-
- elif schedule == "cosine":
- timesteps = (
- torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
- )
- alphas = timesteps / (1 + cosine_s) * np.pi / 2
- alphas = torch.cos(alphas).pow(2)
- alphas = alphas / alphas[0]
- betas = 1 - alphas[1:] / alphas[:-1]
- betas = np.clip(betas, a_min=0, a_max=0.999)
-
- elif schedule == "sqrt_linear":
- betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
- elif schedule == "sqrt":
- betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
- else:
- raise ValueError(f"schedule '{schedule}' unknown.")
- return betas.numpy()
-
-
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
- if ddim_discr_method == 'uniform':
- c = num_ddpm_timesteps // num_ddim_timesteps
- ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
- elif ddim_discr_method == 'quad':
- ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
- else:
- raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-
- # assert ddim_timesteps.shape[0] == num_ddim_timesteps
- # add one to get the final alpha values right (the ones from first scale to data during sampling)
- steps_out = ddim_timesteps + 1
- if verbose:
- print(f'Selected timesteps for ddim sampler: {steps_out}')
- return steps_out
-
-
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
- # select alphas for computing the variance schedule
- alphas = alphacums[ddim_timesteps]
- alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-
- # according the the formula provided in https://arxiv.org/abs/2010.02502
- sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
- if verbose:
- print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
- print(f'For the chosen value of eta, which is {eta}, '
- f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
- return sigmas, alphas, alphas_prev
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
- """
- Create a beta schedule that discretizes the given alpha_t_bar function,
- which defines the cumulative product of (1-beta) over time from t = [0,1].
- :param num_diffusion_timesteps: the number of betas to produce.
- :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
- produces the cumulative product of (1-beta) up to that
- part of the diffusion process.
- :param max_beta: the maximum beta to use; use values lower than 1 to
- prevent singularities.
- """
- betas = []
- for i in range(num_diffusion_timesteps):
- t1 = i / num_diffusion_timesteps
- t2 = (i + 1) / num_diffusion_timesteps
- betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
- return np.array(betas)
-
-
-def extract_into_tensor(a, t, x_shape):
- b, *_ = t.shape
- out = a.gather(-1, t)
- return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def checkpoint(func, inputs, params, flag):
- """
- Evaluate a function without caching intermediate activations, allowing for
- reduced memory at the expense of extra compute in the backward pass.
- :param func: the function to evaluate.
- :param inputs: the argument sequence to pass to `func`.
- :param params: a sequence of parameters `func` depends on but does not
- explicitly take as arguments.
- :param flag: if False, disable gradient checkpointing.
- """
- # if flag:
- if False: # Changed to False following textual-inversion's code
- args = tuple(inputs) + tuple(params)
- return CheckpointFunction.apply(func, len(inputs), *args)
- else:
- return func(*inputs)
-
-
-class CheckpointFunction(torch.autograd.Function):
- @staticmethod
- def forward(ctx, run_function, length, *args):
- ctx.run_function = run_function
- ctx.input_tensors = list(args[:length])
- ctx.input_params = list(args[length:])
-
- with torch.no_grad():
- output_tensors = ctx.run_function(*ctx.input_tensors)
- return output_tensors
-
- @staticmethod
- def backward(ctx, *output_grads):
- ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
- with torch.enable_grad():
- # Fixes a bug where the first op in run_function modifies the
- # Tensor storage in place, which is not allowed for detach()'d
- # Tensors.
- shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
- output_tensors = ctx.run_function(*shallow_copies)
- input_grads = torch.autograd.grad(
- output_tensors,
- ctx.input_tensors + ctx.input_params,
- output_grads,
- allow_unused=True,
- )
- del ctx.input_tensors
- del ctx.input_params
- del output_tensors
- return (None, None) + input_grads
-
-
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
- """
- Create sinusoidal timestep embeddings.
- :param timesteps: a 1-D Tensor of N indices, one per batch element.
- These may be fractional.
- :param dim: the dimension of the output.
- :param max_period: controls the minimum frequency of the embeddings.
- :return: an [N x dim] Tensor of positional embeddings.
- """
- if not repeat_only:
- half = dim // 2
- freqs = torch.exp(
- -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
- ).to(device=timesteps.device)
- args = timesteps[:, None].float() * freqs[None]
- embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
- if dim % 2:
- embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
- else:
- embedding = repeat(timesteps, 'b -> b d', d=dim)
- return embedding
-
-
-def zero_module(module):
- """
- Zero out the parameters of a module and return it.
- """
- for p in module.parameters():
- p.detach().zero_()
- return module
-
-
-def scale_module(module, scale):
- """
- Scale the parameters of a module and return it.
- """
- for p in module.parameters():
- p.detach().mul_(scale)
- return module
-
-
-def mean_flat(tensor):
- """
- Take the mean over all non-batch dimensions.
- """
- return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def normalization(channels):
- """
- Make a standard normalization layer.
- :param channels: number of input channels.
- :return: an nn.Module for normalization.
- """
- return GroupNorm32(32, channels)
-
-
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
- def forward(self, x):
- return x * torch.sigmoid(x)
-
-
-class GroupNorm32(nn.GroupNorm):
- def forward(self, x):
- return super().forward(x.float()).type(x.dtype)
-
-def conv_nd(dims, *args, **kwargs):
- """
- Create a 1D, 2D, or 3D convolution module.
- """
- if dims == 1:
- return nn.Conv1d(*args, **kwargs)
- elif dims == 2:
- return nn.Conv2d(*args, **kwargs)
- elif dims == 3:
- return nn.Conv3d(*args, **kwargs)
- raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
- """
- Create a linear module.
- """
- return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
- """
- Create a 1D, 2D, or 3D average pooling module.
- """
- if dims == 1:
- return nn.AvgPool1d(*args, **kwargs)
- elif dims == 2:
- return nn.AvgPool2d(*args, **kwargs)
- elif dims == 3:
- return nn.AvgPool3d(*args, **kwargs)
- raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class HybridConditioner(nn.Module):
-
- def __init__(self, c_concat_config, c_crossattn_config):
- super().__init__()
- self.concat_conditioner = instantiate_from_config(c_concat_config)
- self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-
- def forward(self, c_concat, c_crossattn):
- c_concat = self.concat_conditioner(c_concat)
- c_crossattn = self.crossattn_conditioner(c_crossattn)
- return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-
-
-def noise_like(shape, device, repeat=False):
- repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
- noise = lambda: torch.randn(shape, device=device)
- return repeat_noise() if repeat else noise()
\ No newline at end of file
diff --git a/spaces/MisterZee/PIFu-Clothed-Human-Digitization/PIFu/lib/sample_util.py b/spaces/MisterZee/PIFu-Clothed-Human-Digitization/PIFu/lib/sample_util.py
deleted file mode 100644
index d0b105d148d6d8fddc461d1c04f659200957c189..0000000000000000000000000000000000000000
--- a/spaces/MisterZee/PIFu-Clothed-Human-Digitization/PIFu/lib/sample_util.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import numpy as np
-
-
-def save_samples_truncted_prob(fname, points, prob):
- '''
- Save the visualization of sampling to a ply file.
- Red points represent positive predictions.
- Green points represent negative predictions.
- :param fname: File name to save
- :param points: [N, 3] array of points
- :param prob: [N, 1] array of predictions in the range [0~1]
- :return:
- '''
- r = (prob > 0.5).reshape([-1, 1]) * 255
- g = (prob < 0.5).reshape([-1, 1]) * 255
- b = np.zeros(r.shape)
-
- to_save = np.concatenate([points, r, g, b], axis=-1)
- return np.savetxt(fname,
- to_save,
- fmt='%.6f %.6f %.6f %d %d %d',
- comments='',
- header=(
- 'ply\nformat ascii 1.0\nelement vertex {:d}\nproperty float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nend_header').format(
- points.shape[0])
- )
-
-
-def save_samples_rgb(fname, points, rgb):
- '''
- Save the visualization of sampling to a ply file.
- Red points represent positive predictions.
- Green points represent negative predictions.
- :param fname: File name to save
- :param points: [N, 3] array of points
- :param rgb: [N, 3] array of rgb values in the range [0~1]
- :return:
- '''
- to_save = np.concatenate([points, rgb * 255], axis=-1)
- return np.savetxt(fname,
- to_save,
- fmt='%.6f %.6f %.6f %d %d %d',
- comments='',
- header=(
- 'ply\nformat ascii 1.0\nelement vertex {:d}\nproperty float x\nproperty float y\nproperty float z\nproperty uchar red\nproperty uchar green\nproperty uchar blue\nend_header').format(
- points.shape[0])
- )
diff --git a/spaces/Miuzarte/SUI-svc-4.0/hubert/__init__.py b/spaces/Miuzarte/SUI-svc-4.0/hubert/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/MrD05/text-generation-webui-space/api-example.py b/spaces/MrD05/text-generation-webui-space/api-example.py
deleted file mode 100644
index 0306b7ab8a3fa3d6f57d8474ad74d67f13557b6d..0000000000000000000000000000000000000000
--- a/spaces/MrD05/text-generation-webui-space/api-example.py
+++ /dev/null
@@ -1,59 +0,0 @@
-'''
-
-This is an example on how to use the API for oobabooga/text-generation-webui.
-
-Make sure to start the web UI with the following flags:
-
-python server.py --model MODEL --listen --no-stream
-
-Optionally, you can also add the --share flag to generate a public gradio URL,
-allowing you to use the API remotely.
-
-'''
-import requests
-
-# Server address
-server = "127.0.0.1"
-
-# Generation parameters
-# Reference: https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig
-params = {
- 'max_new_tokens': 200,
- 'do_sample': True,
- 'temperature': 0.5,
- 'top_p': 0.9,
- 'typical_p': 1,
- 'repetition_penalty': 1.05,
- 'top_k': 0,
- 'min_length': 0,
- 'no_repeat_ngram_size': 0,
- 'num_beams': 1,
- 'penalty_alpha': 0,
- 'length_penalty': 1,
- 'early_stopping': False,
-}
-
-# Input prompt
-prompt = "What I would like to say is the following: "
-
-response = requests.post(f"http://{server}:7860/run/textgen", json={
- "data": [
- prompt,
- params['max_new_tokens'],
- params['do_sample'],
- params['temperature'],
- params['top_p'],
- params['typical_p'],
- params['repetition_penalty'],
- params['top_k'],
- params['min_length'],
- params['no_repeat_ngram_size'],
- params['num_beams'],
- params['penalty_alpha'],
- params['length_penalty'],
- params['early_stopping'],
- ]
-}).json()
-
-reply = response["data"][0]
-print(reply)
diff --git a/spaces/MrSinan/Reconstruction/create_mask.py b/spaces/MrSinan/Reconstruction/create_mask.py
deleted file mode 100644
index 8a3db917a04b9d81c73ac0319d555ac8225ba925..0000000000000000000000000000000000000000
--- a/spaces/MrSinan/Reconstruction/create_mask.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# Author: aqeelanwar
-# Created: 6 July,2020, 12:14 AM
-# Email: aqeel.anwar@gatech.edu
-
-from PIL import ImageColor
-import cv2
-import numpy as np
-
-COLOR = [
- "#fc1c1a",
- "#177ABC",
- "#94B6D2",
- "#A5AB81",
- "#DD8047",
- "#6b425e",
- "#e26d5a",
- "#c92c48",
- "#6a506d",
- "#ffc900",
- "#ffffff",
- "#000000",
- "#49ff00",
-]
-
-
-def color_the_mask(mask_image, color, intensity):
- assert 0 <= intensity <= 1, "intensity should be between 0 and 1"
- RGB_color = ImageColor.getcolor(color, "RGB")
- RGB_color = (RGB_color[2], RGB_color[1], RGB_color[0])
- orig_shape = mask_image.shape
- bit_mask = mask_image[:, :, 3]
- mask_image = mask_image[:, :, 0:3]
-
- color_image = np.full(mask_image.shape, RGB_color, np.uint8)
- mask_color = cv2.addWeighted(mask_image, 1 - intensity, color_image, intensity, 0)
- mask_color = cv2.bitwise_and(mask_color, mask_color, mask=bit_mask)
- colored_mask = np.zeros(orig_shape, dtype=np.uint8)
- colored_mask[:, :, 0:3] = mask_color
- colored_mask[:, :, 3] = bit_mask
- return colored_mask
-
-
-def texture_the_mask(mask_image, texture_path, intensity):
- assert 0 <= intensity <= 1, "intensity should be between 0 and 1"
- orig_shape = mask_image.shape
- bit_mask = mask_image[:, :, 3]
- mask_image = mask_image[:, :, 0:3]
- texture_image = cv2.imread(texture_path)
- texture_image = cv2.resize(texture_image, (orig_shape[1], orig_shape[0]))
-
- mask_texture = cv2.addWeighted(
- mask_image, 1 - intensity, texture_image, intensity, 0
- )
- mask_texture = cv2.bitwise_and(mask_texture, mask_texture, mask=bit_mask)
- textured_mask = np.zeros(orig_shape, dtype=np.uint8)
- textured_mask[:, :, 0:3] = mask_texture
- textured_mask[:, :, 3] = bit_mask
-
- return textured_mask
-
-
-
-# cloth_mask = cv2.imread("masks/templates/cloth.png", cv2.IMREAD_UNCHANGED)
-# # cloth_mask = color_the_mask(cloth_mask, color=COLOR[0], intensity=0.5)
-# path = "masks/textures"
-# path, dir, files = os.walk(path).__next__()
-# first_frame = True
-# col_limit = 6
-# i = 0
-# # img_concat_row=[]
-# img_concat = []
-# # for f in files:
-# # if "._" not in f:
-# # print(f)
-# # i += 1
-# # texture_image = cv2.imread(os.path.join(path, f))
-# # m = texture_the_mask(cloth_mask, texture_image, intensity=0.5)
-# # if first_frame:
-# # img_concat_row = m
-# # first_frame = False
-# # else:
-# # img_concat_row = cv2.hconcat((img_concat_row, m))
-# #
-# # if i % col_limit == 0:
-# # if len(img_concat) > 0:
-# # img_concat = cv2.vconcat((img_concat, img_concat_row))
-# # else:
-# # img_concat = img_concat_row
-# # first_frame = True
-#
-# ## COlor the mask
-# thresholds = np.arange(0.1,0.9,0.05)
-# for intensity in thresholds:
-# c=COLOR[2]
-# # intensity = 0.5
-# if "._" not in c:
-# print(intensity)
-# i += 1
-# # texture_image = cv2.imread(os.path.join(path, f))
-# m = color_the_mask(cloth_mask, c, intensity=intensity)
-# if first_frame:
-# img_concat_row = m
-# first_frame = False
-# else:
-# img_concat_row = cv2.hconcat((img_concat_row, m))
-#
-# if i % col_limit == 0:
-# if len(img_concat) > 0:
-# img_concat = cv2.vconcat((img_concat, img_concat_row))
-# else:
-# img_concat = img_concat_row
-# first_frame = True
-#
-#
-# cv2.imshow("k", img_concat)
-# cv2.imwrite("combine_N95_left.png", img_concat)
-# cv2.waitKey(0)
-# cc = 1
diff --git a/spaces/MrTitanicus/rvc-models/infer_pack/models_onnx.py b/spaces/MrTitanicus/rvc-models/infer_pack/models_onnx.py
deleted file mode 100644
index 3cdae2f7f8591a1e43b1d8520baa37b7e9744d72..0000000000000000000000000000000000000000
--- a/spaces/MrTitanicus/rvc-models/infer_pack/models_onnx.py
+++ /dev/null
@@ -1,849 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- 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_phone = nn.Linear(256, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- 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)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
- def __init__(
- self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- f0=True,
- ):
- super().__init__()
- 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_phone = nn.Linear(256, hidden_channels)
- self.lrelu = nn.LeakyReLU(0.1, inplace=True)
- if f0 == True:
- self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256
- self.encoder = attentions.Encoder(
- hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
- )
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
- def forward(self, phone, pitch, lengths):
- if pitch == None:
- x = self.emb_phone(phone)
- else:
- x = self.emb_phone(phone) + self.emb_pitch(pitch)
- x = x * math.sqrt(self.hidden_channels) # [b, t, h]
- x = self.lrelu(x)
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
- x.dtype
- )
- x = self.encoder(x * x_mask, x_mask)
- x = self.proj(x) * x_mask
- return x, 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
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- 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())
-
- 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
-
- def remove_weight_norm(self):
- for i in range(self.n_flows):
- self.flows[i * 2].remove_weight_norm()
-
-
-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
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.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)
-
- 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
- x = self.enc(x, x_mask, g=g)
- 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
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-
-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):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
- """Definition of sine generator
- SineGen(samp_rate, harmonic_num = 0,
- sine_amp = 0.1, noise_std = 0.003,
- voiced_threshold = 0,
- flag_for_pulse=False)
- samp_rate: sampling rate in Hz
- harmonic_num: number of harmonic overtones (default 0)
- sine_amp: amplitude of sine-wavefrom (default 0.1)
- noise_std: std of Gaussian noise (default 0.003)
- voiced_thoreshold: F0 threshold for U/V classification (default 0)
- flag_for_pulse: this SinGen is used inside PulseGen (default False)
- Note: when flag_for_pulse is True, the first time step of a voiced
- segment is always sin(np.pi) or cos(0)
- """
-
- def __init__(
- self,
- samp_rate,
- harmonic_num=0,
- sine_amp=0.1,
- noise_std=0.003,
- voiced_threshold=0,
- flag_for_pulse=False,
- ):
- super(SineGen, self).__init__()
- self.sine_amp = sine_amp
- self.noise_std = noise_std
- self.harmonic_num = harmonic_num
- self.dim = self.harmonic_num + 1
- self.sampling_rate = samp_rate
- self.voiced_threshold = voiced_threshold
-
- def _f02uv(self, f0):
- # generate uv signal
- uv = torch.ones_like(f0)
- uv = uv * (f0 > self.voiced_threshold)
- return uv
-
- def forward(self, f0, upp):
- """sine_tensor, uv = forward(f0)
- input F0: tensor(batchsize=1, length, dim=1)
- f0 for unvoiced steps should be 0
- output sine_tensor: tensor(batchsize=1, length, dim)
- output uv: tensor(batchsize=1, length, 1)
- """
- with torch.no_grad():
- f0 = f0[:, None].transpose(1, 2)
- f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
- # fundamental component
- f0_buf[:, :, 0] = f0[:, :, 0]
- for idx in np.arange(self.harmonic_num):
- f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
- idx + 2
- ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
- rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
- rand_ini = torch.rand(
- f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
- )
- rand_ini[:, 0] = 0
- rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
- tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化
- tmp_over_one *= upp
- tmp_over_one = F.interpolate(
- tmp_over_one.transpose(2, 1),
- scale_factor=upp,
- mode="linear",
- align_corners=True,
- ).transpose(2, 1)
- rad_values = F.interpolate(
- rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(
- 2, 1
- ) #######
- tmp_over_one %= 1
- tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
- cumsum_shift = torch.zeros_like(rad_values)
- cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
- sine_waves = torch.sin(
- torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
- )
- sine_waves = sine_waves * self.sine_amp
- uv = self._f02uv(f0)
- uv = F.interpolate(
- uv.transpose(2, 1), scale_factor=upp, mode="nearest"
- ).transpose(2, 1)
- noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
- noise = noise_amp * torch.randn_like(sine_waves)
- sine_waves = sine_waves * uv + noise
- return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
- """SourceModule for hn-nsf
- SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
- add_noise_std=0.003, voiced_threshod=0)
- sampling_rate: sampling_rate in Hz
- harmonic_num: number of harmonic above F0 (default: 0)
- sine_amp: amplitude of sine source signal (default: 0.1)
- add_noise_std: std of additive Gaussian noise (default: 0.003)
- note that amplitude of noise in unvoiced is decided
- by sine_amp
- voiced_threshold: threhold to set U/V given F0 (default: 0)
- Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
- F0_sampled (batchsize, length, 1)
- Sine_source (batchsize, length, 1)
- noise_source (batchsize, length 1)
- uv (batchsize, length, 1)
- """
-
- def __init__(
- self,
- sampling_rate,
- harmonic_num=0,
- sine_amp=0.1,
- add_noise_std=0.003,
- voiced_threshod=0,
- is_half=True,
- ):
- super(SourceModuleHnNSF, self).__init__()
-
- self.sine_amp = sine_amp
- self.noise_std = add_noise_std
- self.is_half = is_half
- # to produce sine waveforms
- self.l_sin_gen = SineGen(
- sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
- )
-
- # to merge source harmonics into a single excitation
- self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
- self.l_tanh = torch.nn.Tanh()
-
- def forward(self, x, upp=None):
- sine_wavs, uv, _ = self.l_sin_gen(x, upp)
- if self.is_half:
- sine_wavs = sine_wavs.half()
- sine_merge = self.l_tanh(self.l_linear(sine_wavs))
- return sine_merge, None, None # noise, uv
-
-
-class GeneratorNSF(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,
- sr,
- is_half=False,
- ):
- super(GeneratorNSF, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
-
- self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
- self.m_source = SourceModuleHnNSF(
- sampling_rate=sr, harmonic_num=0, is_half=is_half
- )
- self.noise_convs = nn.ModuleList()
- 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)):
- c_cur = upsample_initial_channel // (2 ** (i + 1))
- self.ups.append(
- weight_norm(
- ConvTranspose1d(
- upsample_initial_channel // (2**i),
- upsample_initial_channel // (2 ** (i + 1)),
- k,
- u,
- padding=(k - u) // 2,
- )
- )
- )
- if i + 1 < len(upsample_rates):
- stride_f0 = np.prod(upsample_rates[i + 1 :])
- self.noise_convs.append(
- Conv1d(
- 1,
- c_cur,
- kernel_size=stride_f0 * 2,
- stride=stride_f0,
- padding=stride_f0 // 2,
- )
- )
- else:
- self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
- 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)
-
- self.upp = np.prod(upsample_rates)
-
- def forward(self, x, f0, g=None):
- har_source, noi_source, uv = self.m_source(f0, self.upp)
- har_source = har_source.transpose(1, 2)
- 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)
- x_source = self.noise_convs[i](har_source)
- x = x + x_source
- 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):
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-sr2sr = {
- "32k": 32000,
- "40k": 40000,
- "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
- def __init__(
- self,
- 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,
- spk_embed_dim,
- gin_channels,
- sr,
- **kwargs
- ):
- super().__init__()
- if type(sr) == type("strr"):
- sr = sr2sr[sr]
- self.spec_channels = spec_channels
- self.inter_channels = inter_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.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- sr=sr,
- is_half=kwargs["is_half"],
- )
- 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, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
- g = self.emb_g(sid).unsqueeze(-1)
- m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
- z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
- z = self.flow(z_p, x_mask, g=g, reverse=True)
- o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
- return o
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(
- self,
- 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,
- spk_embed_dim,
- # hop_length,
- gin_channels=0,
- use_sdp=True,
- **kwargs
- ):
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_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.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- # self.hop_length = hop_length#
- self.spk_embed_dim = spk_embed_dim
- self.enc_p = TextEncoder256Sim(
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- )
- self.dec = GeneratorNSF(
- inter_channels,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels=gin_channels,
- is_half=kwargs["is_half"],
- )
-
- self.flow = ResidualCouplingBlock(
- inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
- )
- self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
- print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
- def remove_weight_norm(self):
- self.dec.remove_weight_norm()
- self.flow.remove_weight_norm()
- self.enc_q.remove_weight_norm()
-
- def forward(
- self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
- ): # y是spec不需要了现在
- g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
- x, x_mask = self.enc_p(phone, pitch, phone_lengths)
- x = self.flow(x, x_mask, g=g, reverse=True)
- o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
- return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2, 3, 5, 7, 11, 17]
- # periods = [3, 5, 7, 11, 17, 23, 37]
-
- 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)
- # for j in range(len(fmap_r)):
- # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
- 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 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 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
diff --git a/spaces/NAACL2022/GlobEnc/src/__init__.py b/spaces/NAACL2022/GlobEnc/src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/data/sentence_prediction_dataloader.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/data/sentence_prediction_dataloader.py
deleted file mode 100644
index 60dd788403725aeeca2028b237c3330bbf22716c..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/data/sentence_prediction_dataloader.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Loads dataset for the sentence prediction (classification) task."""
-from typing import Mapping, Optional
-import tensorflow as tf
-
-from official.core import input_reader
-
-
-class SentencePredictionDataLoader:
- """A class to load dataset for sentence prediction (classification) task."""
-
- def __init__(self, params):
- self._params = params
- self._seq_length = params.seq_length
-
- def _decode(self, record: tf.Tensor):
- """Decodes a serialized tf.Example."""
- name_to_features = {
- 'input_ids': tf.io.FixedLenFeature([self._seq_length], tf.int64),
- 'input_mask': tf.io.FixedLenFeature([self._seq_length], tf.int64),
- 'segment_ids': tf.io.FixedLenFeature([self._seq_length], tf.int64),
- 'label_ids': tf.io.FixedLenFeature([], tf.int64),
- }
- example = tf.io.parse_single_example(record, name_to_features)
-
- # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
- # So cast all int64 to int32.
- for name in example:
- t = example[name]
- if t.dtype == tf.int64:
- t = tf.cast(t, tf.int32)
- example[name] = t
-
- return example
-
- def _parse(self, record: Mapping[str, tf.Tensor]):
- """Parses raw tensors into a dict of tensors to be consumed by the model."""
- x = {
- 'input_word_ids': record['input_ids'],
- 'input_mask': record['input_mask'],
- 'input_type_ids': record['segment_ids']
- }
- y = record['label_ids']
- return (x, y)
-
- def load(self, input_context: Optional[tf.distribute.InputContext] = None):
- """Returns a tf.dataset.Dataset."""
- reader = input_reader.InputReader(
- params=self._params, decoder_fn=self._decode, parser_fn=self._parse)
- return reader.read(input_context)
diff --git a/spaces/NFBN/bingo-1/Dockerfile b/spaces/NFBN/bingo-1/Dockerfile
deleted file mode 100644
index c677b05b75f7e4b2beee8c97fb47957a0861a83e..0000000000000000000000000000000000000000
--- a/spaces/NFBN/bingo-1/Dockerfile
+++ /dev/null
@@ -1,7 +0,0 @@
-FROM weaigc/bingo:latest
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-CMD npm start
diff --git a/spaces/Ngadou/Social_Engineering_Detection/app.py b/spaces/Ngadou/Social_Engineering_Detection/app.py
deleted file mode 100644
index 820c2ce80a8fcbd46ba0b9ddb55c3f0e4153f8e3..0000000000000000000000000000000000000000
--- a/spaces/Ngadou/Social_Engineering_Detection/app.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import gradio as gr
-import torch
-from gradio.components import Textbox
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from transformers import GenerationConfig
-
-
-peft_model_id = "Ngadou/falcon-7b-scam-buster"
-config = PeftConfig.from_pretrained(peft_model_id)
-
-model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, trust_remote_code=True, return_dict=True, load_in_4bit=True, device_map='auto')
-tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
-
-# Adapter model
-model = PeftModel.from_pretrained(model, peft_model_id).to("cuda")
-
-
-# def is_scam(instruction):
-# max_new_tokens=128
-# temperature=0.1
-# top_p=0.75
-# top_k=40
-# num_beams=4
-
-# instruction = instruction + ".\nIs this conversation a scam or not and why?"
-# prompt = instruction + "\n### Solution:\n"
-# inputs = tokenizer(prompt, return_tensors="pt")
-# input_ids = inputs["input_ids"].to("cuda")
-# attention_mask = inputs["attention_mask"].to("cuda")
-# generation_config = GenerationConfig(
-# temperature=temperature,
-# top_p=top_p,
-# top_k=top_k,
-# num_beams=num_beams,
-# )
-# with torch.no_grad():
-# generation_output = model.generate(
-# input_ids=input_ids,
-# attention_mask=attention_mask,
-# generation_config=generation_config,
-# return_dict_in_generate=True,
-# output_scores=True,
-# max_new_tokens=max_new_tokens,
-# early_stopping=True
-# )
-# s = generation_output.sequences[0]
-# output = tokenizer.decode(s)
-# results = output.split("### Solution:")[1].lstrip("\n").split('\n')
-
-# # The format of the output should be adjusted according to your model's output
-# classification = results # Assumes first line is the classification
-# #reason = results[1] if len(results) > 1 else "" # Assumes the rest is the reason
-
-# return classification #, reason
-
-
-def is_scam(instruction):
- max_new_tokens=128
- temperature=0.1
- top_p=0.75
- top_k=40
- num_beams=4
-
- instruction = instruction + "\n Is this conversation a scam or not and why?"
- prompt = instruction + "\n### Solution:\n"
- inputs = tokenizer(prompt, return_tensors="pt")
- input_ids = inputs["input_ids"].to("cuda")
- attention_mask = inputs["attention_mask"].to("cuda")
- generation_config = GenerationConfig(
- temperature=temperature,
- top_p=top_p,
- top_k=top_k,
- num_beams=num_beams,
- )
- with torch.no_grad():
- generation_output = model.generate(
- input_ids=input_ids,
- attention_mask=attention_mask,
- generation_config=generation_config,
- return_dict_in_generate=True,
- output_scores=True,
- max_new_tokens=max_new_tokens,
- early_stopping=True
- )
- s = generation_output.sequences[0]
- output = tokenizer.decode(s)
-
- classification = output.split("### Solution:")[1].lstrip("\n")
- print(classification)
-
- return str(classification), " "
-
-
-gr.Interface(
- fn=is_scam,
- inputs='text',
- outputs= ['text','text']
-).launch()
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh
deleted file mode 100644
index ca3591b3db1715f136773d62e4b9b9ede97d436c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/multilingual/data_scripts/download_iwslt_and_extract.sh
+++ /dev/null
@@ -1,225 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-#echo 'Cloning Moses github repository (for tokenization scripts)...'
-#git clone https://github.com/moses-smt/mosesdecoder.git
-
-if [ -z $WORKDIR_ROOT ] ;
-then
- echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..."
- exit
-fi
-
-
-
-data_root=${WORKDIR_ROOT}/iwsltv2
-DESTDIR=${WORKDIR_ROOT}/ML50/raw
-
-
-langs="ar_AR it_IT nl_XX ko_KR vi_VN"
-echo "data_root: $data_root"
-
-download_path=${data_root}/downloads
-raw=${DESTDIR}
-tmp=${data_root}/tmp
-orig=${data_root}/orig
-
-mkdir -p $download_path $orig $raw $tmp
-#######################
-download_iwslt(){
- iwslt_key=$1
- src=$2
- tgt=$3
- save_prefix=$4
- pushd ${download_path}
- if [[ ! -f ${save_prefix}$src-$tgt.tgz ]]; then
- wget https://wit3.fbk.eu/archive/${iwslt_key}/texts/$src/$tgt/$src-$tgt.tgz -O ${save_prefix}$src-$tgt.tgz
- [ $? -eq 0 ] && return 0
- fi
- popd
-}
-
-extract_iwslt(){
- src=$1
- tgt=$2
- prefix=$3
- pushd $orig
- tar zxvf ${download_path}/${prefix}$src-${tgt}.tgz
- popd
-}
-
-generate_train(){
- lsrc=$1
- ltgt=$2
- src=${lsrc:0:2}
- tgt=${ltgt:0:2}
- for ll in $lsrc $ltgt; do
- l=${ll:0:2}
- f="$orig/*/train.tags.$src-$tgt.$l"
- f_raw=$raw/train.$lsrc-$ltgt.$ll
- cat $f \
- | grep -v '' \
- | grep -v '' \
- | grep -v '' \
- | grep -v '' \
- | grep -v '' \
- | sed -e 's///g' \
- | sed -e 's/<\/title>//g' \
- | sed -e 's/<description>//g' \
- | sed -e 's/<\/description>//g' \
- | sed 's/^\s*//g' \
- | sed 's/\s*$//g' \
- > $f_raw
- [ $? -eq 0 ] && echo "extracted $f to $f_raw"
- done
- return 0
-}
-
-convert_valid_test(){
- src=$1
- tgt=$2
- for l in $src $tgt; do
- echo "lang: ${l}"
- for o in `ls $orig/*/IWSLT*.TED*.$src-$tgt.$l.xml`; do
- fname=${o##*/}
- f=$tmp/${fname%.*}
- echo "$o => $f"
- grep '<seg id' $o \
- | sed -e 's/<seg id="[0-9]*">\s*//g' \
- | sed -e 's/\s*<\/seg>\s*//g' \
- | sed -e "s/\’/\'/g" \
- > $f
- echo ""
- done
- done
-}
-
-generate_subset(){
- lsrc=$1
- ltgt=$2
- src=${lsrc:0:2}
- tgt=${ltgt:0:2}
- subset=$3
- prefix=$4
- for ll in $lsrc $ltgt; do
- l=${ll:0:2}
- f=$tmp/$prefix.${src}-${tgt}.$l
- if [[ -f $f ]]; then
- cp $f $raw/$subset.${lsrc}-$ltgt.${ll}
- fi
- done
-}
-#################
-
-echo "downloading iwslt training and dev data"
-# using multilingual for it, nl
-download_iwslt "2017-01-trnmted" DeEnItNlRo DeEnItNlRo
-download_iwslt "2017-01-trnted" ar en
-download_iwslt "2017-01-trnted" en ar
-download_iwslt "2017-01-trnted" ko en
-download_iwslt "2017-01-trnted" en ko
-download_iwslt "2015-01" vi en
-download_iwslt "2015-01" en vi
-
-echo "donwloading iwslt test data"
-download_iwslt "2017-01-mted-test" it en "test."
-download_iwslt "2017-01-mted-test" en it "test."
-download_iwslt "2017-01-mted-test" nl en "test."
-download_iwslt "2017-01-mted-test" en nl "test."
-
-download_iwslt "2017-01-ted-test" ar en "test."
-download_iwslt "2017-01-ted-test" en ar "test."
-download_iwslt "2017-01-ted-test" ko en "test."
-download_iwslt "2017-01-ted-test" en ko "test."
-download_iwslt "2015-01-test" vi en "test."
-download_iwslt "2015-01-test" en vi "test."
-
-echo "extract training data tar balls"
-extract_iwslt DeEnItNlRo DeEnItNlRo
-extract_iwslt ar en
-extract_iwslt en ar
-extract_iwslt ko en
-extract_iwslt en ko
-extract_iwslt vi en
-extract_iwslt en vi
-
-
-echo "extracting iwslt test data"
-for lang in $langs; do
- l=${lang:0:2}
- extract_iwslt $l en "test."
- extract_iwslt en $l "test."
-done
-
-echo "convert dev and test data"
-for lang in $langs; do
- s_lang=${lang:0:2}
- convert_valid_test $s_lang en
- convert_valid_test en $s_lang
-done
-
-
-
-echo "creating training data into $raw"
-for lang in $langs; do
- generate_train $lang en_XX
- generate_train en_XX $lang
-done
-
-echo "creating iwslt dev data into raw"
-generate_subset en_XX vi_VN valid "IWSLT15.TED.tst2013"
-generate_subset vi_VN en_XX valid "IWSLT15.TED.tst2013"
-
-generate_subset en_XX ar_AR valid "IWSLT17.TED.tst2016"
-generate_subset ar_AR en_XX valid "IWSLT17.TED.tst2016"
-generate_subset en_XX ko_KR valid "IWSLT17.TED.tst2016"
-generate_subset ko_KR en_XX valid "IWSLT17.TED.tst2016"
-
-
-generate_subset en_XX it_IT valid "IWSLT17.TED.tst2010"
-generate_subset it_IT en_XX valid "IWSLT17.TED.tst2010"
-generate_subset en_XX nl_XX valid "IWSLT17.TED.tst2010"
-generate_subset nl_XX en_XX valid "IWSLT17.TED.tst2010"
-
-echo "creating iswslt test data into raw"
-generate_subset en_XX vi_VN test "IWSLT15.TED.tst2015"
-generate_subset vi_VN en_XX test "IWSLT15.TED.tst2015"
-
-generate_subset en_XX ar_AR test "IWSLT17.TED.tst2017"
-generate_subset ar_AR en_XX test "IWSLT17.TED.tst2017"
-generate_subset en_XX ko_KR test "IWSLT17.TED.tst2017"
-generate_subset ko_KR en_XX test "IWSLT17.TED.tst2017"
-
-generate_subset en_XX it_IT test "IWSLT17.TED.tst2017.mltlng"
-generate_subset it_IT en_XX test "IWSLT17.TED.tst2017.mltlng"
-generate_subset en_XX nl_XX test "IWSLT17.TED.tst2017.mltlng"
-generate_subset nl_XX en_XX test "IWSLT17.TED.tst2017.mltlng"
-
-# normalze iwslt directions into x-en
-pushd $raw
-for lang in $langs; do
- for split in test valid; do
- x_en_f1=$split.$lang-en_XX.en_XX
- x_en_f2=$split.$lang-en_XX.${lang}
-
- en_x_f1=$split.en_XX-$lang.en_XX
- en_x_f2=$split.en_XX-$lang.${lang}
-
- if [ -f $en_x_f1 ] && [ ! -f $x_en_f1 ]; then
- echo "cp $en_x_f1 $x_en_f1"
- cp $en_x_f1 $x_en_f1
- fi
- if [ -f $x_en_f2 ] && [ ! -f $x_en_f2 ]; then
- echo "cp $en_x_f2 $x_en_f2"
- cp $en_x_f2 $x_en_f2
- fi
- done
-done
-popd
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/__init__.py
deleted file mode 100644
index 5ca74d790a95a2b14d3fbb0cf9f0a9959416d305..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/models/ofa/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .ofa import OFAModel, ofa_base_architecture, ofa_large_architecture, ofa_huge_architecture
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/scripts/filter_lexicon.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/scripts/filter_lexicon.py
deleted file mode 100644
index 5bf3e51e7a50ac3f07cc41739198cde946dc79aa..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/scripts/filter_lexicon.py
+++ /dev/null
@@ -1,40 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import sys
-
-from fairseq.data import Dictionary
-
-
-def get_parser():
- parser = argparse.ArgumentParser(
- description="filters a lexicon given a unit dictionary"
- )
- parser.add_argument("-d", "--unit-dict", help="unit dictionary", required=True)
- return parser
-
-
-def main():
- parser = get_parser()
- args = parser.parse_args()
-
- d = Dictionary.load(args.unit_dict)
- symbols = set(d.symbols)
-
- for line in sys.stdin:
- items = line.rstrip().split()
- skip = len(items) < 2
- for x in items[1:]:
- if x not in symbols:
- skip = True
- break
- if not skip:
- print(line, end="")
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/scripts/read_binarized.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/scripts/read_binarized.py
deleted file mode 100644
index a414095d03fb022a6753e816fc8bfd80e11db24d..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/scripts/read_binarized.py
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-
-from fairseq.data import Dictionary, data_utils, indexed_dataset
-
-
-def get_parser():
- parser = argparse.ArgumentParser(
- description="writes text from binarized file to stdout"
- )
- # fmt: off
- parser.add_argument('--dataset-impl', help='dataset implementation',
- choices=indexed_dataset.get_available_dataset_impl())
- parser.add_argument('--dict', metavar='FP', help='dictionary containing known words', default=None)
- parser.add_argument('--input', metavar='FP', required=True, help='binarized file to read')
- # fmt: on
-
- return parser
-
-
-def main():
- parser = get_parser()
- args = parser.parse_args()
-
- dictionary = Dictionary.load(args.dict) if args.dict is not None else None
- dataset = data_utils.load_indexed_dataset(
- args.input,
- dictionary,
- dataset_impl=args.dataset_impl,
- default="lazy",
- )
-
- for tensor_line in dataset:
- if dictionary is None:
- line = " ".join([str(int(x)) for x in tensor_line])
- else:
- line = dictionary.string(tensor_line)
-
- print(line)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/test_inference_dropout.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/test_inference_dropout.py
deleted file mode 100644
index 353ac674780a9795492c75aa0a7bc0677b07a9c9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/tests/test_inference_dropout.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import unittest
-
-from fairseq.dataclass.utils import convert_namespace_to_omegaconf
-from fairseq.models.transformer import TransformerModel
-from tests.test_sequence_generator import get_dummy_task_and_parser
-
-
-class TestInferenceDropout(unittest.TestCase):
- def setUp(self):
- self.task, self.parser = get_dummy_task_and_parser()
- TransformerModel.add_args(self.parser)
- self.args = self.parser.parse_args([])
- self.args.encoder_layers = 2
- self.args.decoder_layers = 1
- logging.disable(logging.CRITICAL)
-
- def tearDown(self):
- logging.disable(logging.NOTSET)
-
- def test_sets_inference_dropout_to_true(self):
- self.args.retain_dropout = True
- self.transformer_model = TransformerModel.build_model(self.args, self.task)
- cfg = convert_namespace_to_omegaconf(self.args)
- self.transformer_model.prepare_for_inference_(cfg)
- assert self.transformer_model.encoder.dropout_module.apply_during_inference
- assert self.transformer_model.decoder.dropout_module.apply_during_inference
- for layer in self.transformer_model.encoder.layers:
- assert layer.dropout_module.apply_during_inference
-
- def test_inference_dropout_false_by_default(self):
- self.transformer_model = TransformerModel.build_model(self.args, self.task)
- cfg = convert_namespace_to_omegaconf(self.args)
- self.transformer_model.prepare_for_inference_(cfg)
- assert not self.transformer_model.encoder.dropout_module.apply_during_inference
- assert not self.transformer_model.decoder.dropout_module.apply_during_inference
- for layer in self.transformer_model.encoder.layers:
- assert not layer.dropout_module.apply_during_inference
- for layer in self.transformer_model.decoder.layers:
- assert not layer.dropout_module.apply_during_inference
-
- def test_applies_training_mode(self):
- self.transformer_model = TransformerModel.build_model(self.args, self.task)
- assert self.transformer_model.encoder.dropout_module.training
- for layer in self.transformer_model.encoder.layers:
- assert layer.dropout_module.training
-
- self.transformer_model.eval()
- assert not self.transformer_model.decoder.dropout_module.training
- for layer in self.transformer_model.encoder.layers:
- assert not layer.dropout_module.training
-
- def test_retain_modules(self):
- self.args.retain_dropout = True
- self.args.retain_dropout_modules = [
- "TransformerEncoder",
- "TransformerEncoderLayer",
- ]
- self.transformer_model = TransformerModel.build_model(self.args, self.task)
- cfg = convert_namespace_to_omegaconf(self.args)
- self.transformer_model.prepare_for_inference_(cfg)
- assert self.transformer_model.encoder.dropout_module.apply_during_inference
- assert not self.transformer_model.decoder.dropout_module.apply_during_inference
- for layer in self.transformer_model.decoder.layers:
- assert not layer.dropout_module.apply_during_inference
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/utils/transforms.py b/spaces/OFA-Sys/OFA-Image_Caption/utils/transforms.py
deleted file mode 100644
index 0a9edf6c3da3052758cb36bcfe1f50ba69cc6f32..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/utils/transforms.py
+++ /dev/null
@@ -1,508 +0,0 @@
-import random
-
-import torch
-import torchvision.transforms as T
-import torchvision.transforms.functional as F
-import numpy as np
-from PIL import Image
-
-
-def crop(image, target, region, delete=True):
- cropped_image = F.crop(image, *region)
-
- target = target.copy()
- i, j, h, w = region
-
- # should we do something wrt the original size?
- target["size"] = torch.tensor([h, w])
-
- fields = ["labels", "area"]
-
- if "boxes" in target:
- boxes = target["boxes"]
- max_size = torch.as_tensor([w, h], dtype=torch.float32)
- cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
- cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
- cropped_boxes = cropped_boxes.clamp(min=0)
- area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
- target["boxes"] = cropped_boxes.reshape(-1, 4)
- target["area"] = area
- fields.append("boxes")
-
- if "polygons" in target:
- polygons = target["polygons"]
- num_polygons = polygons.shape[0]
- max_size = torch.as_tensor([w, h], dtype=torch.float32)
- start_coord = torch.cat([torch.tensor([j, i], dtype=torch.float32)
- for _ in range(polygons.shape[1] // 2)], dim=0)
- cropped_boxes = polygons - start_coord
- cropped_boxes = torch.min(cropped_boxes.reshape(num_polygons, -1, 2), max_size)
- cropped_boxes = cropped_boxes.clamp(min=0)
- target["polygons"] = cropped_boxes.reshape(num_polygons, -1)
- fields.append("polygons")
-
- if "masks" in target:
- # FIXME should we update the area here if there are no boxes?
- target['masks'] = target['masks'][:, i:i + h, j:j + w]
- fields.append("masks")
-
- # remove elements for which the boxes or masks that have zero area
- if delete and ("boxes" in target or "masks" in target):
- # favor boxes selection when defining which elements to keep
- # this is compatible with previous implementation
- if "boxes" in target:
- cropped_boxes = target['boxes'].reshape(-1, 2, 2)
- keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
- else:
- keep = target['masks'].flatten(1).any(1)
-
- for field in fields:
- target[field] = target[field][keep.tolist()]
-
- return cropped_image, target
-
-
-def hflip(image, target):
- flipped_image = F.hflip(image)
-
- w, h = image.size
-
- target = target.copy()
- if "boxes" in target:
- boxes = target["boxes"]
- boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor([w, 0, w, 0])
- target["boxes"] = boxes
-
- if "polygons" in target:
- polygons = target["polygons"]
- num_polygons = polygons.shape[0]
- polygons = polygons.reshape(num_polygons, -1, 2) * torch.as_tensor([-1, 1]) + torch.as_tensor([w, 0])
- target["polygons"] = polygons
-
- if "masks" in target:
- target['masks'] = target['masks'].flip(-1)
-
- return flipped_image, target
-
-
-def resize(image, target, size, max_size=None):
- # size can be min_size (scalar) or (w, h) tuple
-
- def get_size_with_aspect_ratio(image_size, size, max_size=None):
- w, h = image_size
-
- if (w <= h and w == size) or (h <= w and h == size):
- if max_size is not None:
- max_size = int(max_size)
- h = min(h, max_size)
- w = min(w, max_size)
- return (h, w)
-
- if w < h:
- ow = size
- oh = int(size * h / w)
- else:
- oh = size
- ow = int(size * w / h)
-
- if max_size is not None:
- max_size = int(max_size)
- oh = min(oh, max_size)
- ow = min(ow, max_size)
-
- return (oh, ow)
-
- def get_size(image_size, size, max_size=None):
- if isinstance(size, (list, tuple)):
- return size[::-1]
- else:
- return get_size_with_aspect_ratio(image_size, size, max_size)
-
- size = get_size(image.size, size, max_size)
- rescaled_image = F.resize(image, size, interpolation=Image.BICUBIC)
-
- if target is None:
- return rescaled_image
-
- ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
- ratio_width, ratio_height = ratios
-
- target = target.copy()
- if "boxes" in target:
- boxes = target["boxes"]
- scaled_boxes = boxes * torch.as_tensor([ratio_width, ratio_height, ratio_width, ratio_height])
- target["boxes"] = scaled_boxes
-
- if "polygons" in target:
- polygons = target["polygons"]
- scaled_ratio = torch.cat([torch.tensor([ratio_width, ratio_height])
- for _ in range(polygons.shape[1] // 2)], dim=0)
- scaled_polygons = polygons * scaled_ratio
- target["polygons"] = scaled_polygons
-
- if "area" in target:
- area = target["area"]
- scaled_area = area * (ratio_width * ratio_height)
- target["area"] = scaled_area
-
- h, w = size
- target["size"] = torch.tensor([h, w])
-
- if "masks" in target:
- assert False
- # target['masks'] = interpolate(
- # target['masks'][:, None].float(), size, mode="nearest")[:, 0] > 0.5
-
- return rescaled_image, target
-
-
-class CenterCrop(object):
- def __init__(self, size):
- self.size = size
-
- def __call__(self, img, target):
- image_width, image_height = img.size
- crop_height, crop_width = self.size
- crop_top = int(round((image_height - crop_height) / 2.))
- crop_left = int(round((image_width - crop_width) / 2.))
- return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
-
-
-class ObjectCenterCrop(object):
- def __init__(self, size):
- self.size = size
-
- def __call__(self, img, target):
- image_width, image_height = img.size
- crop_height, crop_width = self.size
-
- x0 = float(target['boxes'][0][0])
- y0 = float(target['boxes'][0][1])
- x1 = float(target['boxes'][0][2])
- y1 = float(target['boxes'][0][3])
-
- center_x = (x0 + x1) / 2
- center_y = (y0 + y1) / 2
- crop_left = max(center_x-crop_width/2 + min(image_width-center_x-crop_width/2, 0), 0)
- crop_top = max(center_y-crop_height/2 + min(image_height-center_y-crop_height/2, 0), 0)
-
- return crop(img, target, (crop_top, crop_left, crop_height, crop_width), delete=False)
-
-
-class RandomHorizontalFlip(object):
- def __init__(self, p=0.5):
- self.p = p
-
- def __call__(self, img, target):
- if random.random() < self.p:
- return hflip(img, target)
- return img, target
-
-
-class RandomResize(object):
- def __init__(self, sizes, max_size=None, equal=False):
- assert isinstance(sizes, (list, tuple))
- self.sizes = sizes
- self.max_size = max_size
- self.equal = equal
-
- def __call__(self, img, target=None):
- size = random.choice(self.sizes)
- if self.equal:
- return resize(img, target, size, size)
- else:
- return resize(img, target, size, self.max_size)
-
-
-class ToTensor(object):
- def __call__(self, img, target):
- return F.to_tensor(img), target
-
-
-class Normalize(object):
- def __init__(self, mean, std, max_image_size=512):
- self.mean = mean
- self.std = std
- self.max_image_size = max_image_size
-
- def __call__(self, image, target=None):
- image = F.normalize(image, mean=self.mean, std=self.std)
- if target is None:
- return image, None
- target = target.copy()
- # h, w = image.shape[-2:]
- h, w = target["size"][0], target["size"][1]
- if "boxes" in target:
- boxes = target["boxes"]
- boxes = boxes / self.max_image_size
- target["boxes"] = boxes
- if "polygons" in target:
- polygons = target["polygons"]
- scale = torch.cat([torch.tensor([w, h], dtype=torch.float32)
- for _ in range(polygons.shape[1] // 2)], dim=0)
- polygons = polygons / scale
- target["polygons"] = polygons
- return image, target
-
-
-class Compose(object):
- def __init__(self, transforms):
- self.transforms = transforms
-
- def __call__(self, image, target):
- for t in self.transforms:
- image, target = t(image, target)
- return image, target
-
- def __repr__(self):
- format_string = self.__class__.__name__ + "("
- for t in self.transforms:
- format_string += "\n"
- format_string += " {0}".format(t)
- format_string += "\n)"
- return format_string
-
-
-class LargeScaleJitter(object):
- """
- implementation of large scale jitter from copy_paste
- """
-
- def __init__(self, output_size=512, aug_scale_min=0.3, aug_scale_max=2.0):
- self.desired_size = torch.tensor([output_size])
- self.aug_scale_min = aug_scale_min
- self.aug_scale_max = aug_scale_max
-
- def rescale_target(self, scaled_size, image_size, target):
- # compute rescaled targets
- image_scale = scaled_size / image_size
- ratio_height, ratio_width = image_scale
-
- target = target.copy()
- target["size"] = scaled_size
-
- if "boxes" in target:
- boxes = target["boxes"]
- scaled_boxes = boxes * torch.as_tensor([ratio_width, ratio_height, ratio_width, ratio_height])
- target["boxes"] = scaled_boxes
-
- if "area" in target:
- area = target["area"]
- scaled_area = area * (ratio_width * ratio_height)
- target["area"] = scaled_area
-
- if "masks" in target:
- assert False
- masks = target['masks']
- # masks = interpolate(
- # masks[:, None].float(), scaled_size, mode="nearest")[:, 0] > 0.5
- target['masks'] = masks
- return target
-
- def crop_target(self, region, target):
- i, j, h, w = region
- fields = ["labels", "area"]
-
- target = target.copy()
- target["size"] = torch.tensor([h, w])
-
- if "boxes" in target:
- boxes = target["boxes"]
- max_size = torch.as_tensor([w, h], dtype=torch.float32)
- cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
- cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
- cropped_boxes = cropped_boxes.clamp(min=0)
- area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
- target["boxes"] = cropped_boxes.reshape(-1, 4)
- target["area"] = area
- fields.append("boxes")
-
- if "masks" in target:
- # FIXME should we update the area here if there are no boxes?
- target['masks'] = target['masks'][:, i:i + h, j:j + w]
- fields.append("masks")
-
- # remove elements for which the boxes or masks that have zero area
- if "boxes" in target or "masks" in target:
- # favor boxes selection when defining which elements to keep
- # this is compatible with previous implementation
- if "boxes" in target:
- cropped_boxes = target['boxes'].reshape(-1, 2, 2)
- keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
- else:
- keep = target['masks'].flatten(1).any(1)
-
- for field in fields:
- target[field] = target[field][keep.tolist()]
- return target
-
- def pad_target(self, padding, target):
- target = target.copy()
- if "masks" in target:
- target['masks'] = torch.nn.functional.pad(target['masks'], (0, padding[1], 0, padding[0]))
- return target
-
- def __call__(self, image, target=None):
- image_size = image.size
- image_size = torch.tensor(image_size[::-1])
-
- random_scale = torch.rand(1) * (self.aug_scale_max - self.aug_scale_min) + self.aug_scale_min
- scaled_size = (random_scale * self.desired_size).round()
-
- scale = torch.maximum(scaled_size / image_size[0], scaled_size / image_size[1])
- scaled_size = (image_size * scale).round().int()
-
- scaled_image = F.resize(image, scaled_size.tolist(), interpolation=Image.BICUBIC)
-
- if target is not None:
- target = self.rescale_target(scaled_size, image_size, target)
-
- # randomly crop or pad images
- if random_scale >= 1:
- # Selects non-zero random offset (x, y) if scaled image is larger than desired_size.
- max_offset = scaled_size - self.desired_size
- offset = (max_offset * torch.rand(2)).floor().int()
- region = (offset[0].item(), offset[1].item(),
- self.desired_size[0].item(), self.desired_size[0].item())
- output_image = F.crop(scaled_image, *region)
- if target is not None:
- target = self.crop_target(region, target)
- else:
- assert False
- padding = self.desired_size - scaled_size
- output_image = F.pad(scaled_image, [0, 0, padding[1].item(), padding[0].item()])
- if target is not None:
- target = self.pad_target(padding, target)
-
- return output_image, target
-
-
-class OriginLargeScaleJitter(object):
- """
- implementation of large scale jitter from copy_paste
- """
-
- def __init__(self, output_size=512, aug_scale_min=0.3, aug_scale_max=2.0):
- self.desired_size = torch.tensor(output_size)
- self.aug_scale_min = aug_scale_min
- self.aug_scale_max = aug_scale_max
-
- def rescale_target(self, scaled_size, image_size, target):
- # compute rescaled targets
- image_scale = scaled_size / image_size
- ratio_height, ratio_width = image_scale
-
- target = target.copy()
- target["size"] = scaled_size
-
- if "boxes" in target:
- boxes = target["boxes"]
- scaled_boxes = boxes * torch.as_tensor([ratio_width, ratio_height, ratio_width, ratio_height])
- target["boxes"] = scaled_boxes
-
- if "area" in target:
- area = target["area"]
- scaled_area = area * (ratio_width * ratio_height)
- target["area"] = scaled_area
-
- if "masks" in target:
- assert False
- masks = target['masks']
- # masks = interpolate(
- # masks[:, None].float(), scaled_size, mode="nearest")[:, 0] > 0.5
- target['masks'] = masks
- return target
-
- def crop_target(self, region, target):
- i, j, h, w = region
- fields = ["labels", "area"]
-
- target = target.copy()
- target["size"] = torch.tensor([h, w])
-
- if "boxes" in target:
- boxes = target["boxes"]
- max_size = torch.as_tensor([w, h], dtype=torch.float32)
- cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
- cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
- cropped_boxes = cropped_boxes.clamp(min=0)
- area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
- target["boxes"] = cropped_boxes.reshape(-1, 4)
- target["area"] = area
- fields.append("boxes")
-
- if "masks" in target:
- # FIXME should we update the area here if there are no boxes?
- target['masks'] = target['masks'][:, i:i + h, j:j + w]
- fields.append("masks")
-
- # remove elements for which the boxes or masks that have zero area
- if "boxes" in target or "masks" in target:
- # favor boxes selection when defining which elements to keep
- # this is compatible with previous implementation
- if "boxes" in target:
- cropped_boxes = target['boxes'].reshape(-1, 2, 2)
- keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
- else:
- keep = target['masks'].flatten(1).any(1)
-
- for field in fields:
- target[field] = target[field][keep.tolist()]
- return target
-
- def pad_target(self, padding, target):
- target = target.copy()
- if "masks" in target:
- target['masks'] = torch.nn.functional.pad(target['masks'], (0, padding[1], 0, padding[0]))
- return target
-
- def __call__(self, image, target=None):
- image_size = image.size
- image_size = torch.tensor(image_size[::-1])
-
- out_desired_size = (self.desired_size * image_size / max(image_size)).round().int()
-
- random_scale = torch.rand(1) * (self.aug_scale_max - self.aug_scale_min) + self.aug_scale_min
- scaled_size = (random_scale * self.desired_size).round()
-
- scale = torch.minimum(scaled_size / image_size[0], scaled_size / image_size[1])
- scaled_size = (image_size * scale).round().int()
-
- scaled_image = F.resize(image, scaled_size.tolist())
-
- if target is not None:
- target = self.rescale_target(scaled_size, image_size, target)
-
- # randomly crop or pad images
- if random_scale > 1:
- # Selects non-zero random offset (x, y) if scaled image is larger than desired_size.
- max_offset = scaled_size - out_desired_size
- offset = (max_offset * torch.rand(2)).floor().int()
- region = (offset[0].item(), offset[1].item(),
- out_desired_size[0].item(), out_desired_size[1].item())
- output_image = F.crop(scaled_image, *region)
- if target is not None:
- target = self.crop_target(region, target)
- else:
- padding = out_desired_size - scaled_size
- output_image = F.pad(scaled_image, [0, 0, padding[1].item(), padding[0].item()])
- if target is not None:
- target = self.pad_target(padding, target)
-
- return output_image, target
-
-
-class RandomDistortion(object):
- """
- Distort image w.r.t hue, saturation and exposure.
- """
-
- def __init__(self, brightness=0, contrast=0, saturation=0, hue=0, prob=0.5):
- self.prob = prob
- self.tfm = T.ColorJitter(brightness, contrast, saturation, hue)
-
- def __call__(self, img, target=None):
- if np.random.random() < self.prob:
- return self.tfm(img), target
- else:
- return img, target
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/huffman/huffman_coder.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/huffman/huffman_coder.py
deleted file mode 100644
index 6531f1547cbd7250aa03e0ef8c2efbac49bb1aff..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/huffman/huffman_coder.py
+++ /dev/null
@@ -1,265 +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 re
-import typing as tp
-from collections import Counter, deque
-from dataclasses import dataclass
-
-from bitarray import bitarray, util
-from fairseq.data import Dictionary
-
-# basically we have to write to addressable bytes for the memory mapped
-# dataset loader. Sentences that get encoded to a length that is not a
-# multiple of BLOCKSIZE (a byte) will be padded to fit. (see _pad in the coder)
-BLOCKSIZE = 8
-
-
-class HuffmanCoder:
- def __init__(
- self, root: "HuffmanNode", bos="<s>", pad="<pad>", eos="</s>", unk="<unk>"
- ):
- self.root = root
- self.table = root.code_table()
- self.bos_word, self.unk_word, self.pad_word, self.eos_word = bos, unk, pad, eos
-
- def _pad(self, a: bitarray) -> bitarray:
- """
- bitpadding, 1 then 0.
-
- If the array is already a multiple of blocksize, we add a full block.
- """
- pad_len = BLOCKSIZE - (len(a) % BLOCKSIZE) - 1
- padding = bitarray("1" + "0" * pad_len)
- return a + padding
-
- def _unpad(self, a: bitarray) -> bitarray:
- """
- remove the bitpadding.
-
- There will be a set of 0s preceded by a 1 at the end of the bitarray, we remove that
- """
- # count the 0 padding at the end until we find the first 1
- # we want to remove the one too
- remove_cnt = util.rindex(a, 1)
- return a[:remove_cnt]
-
- def encode(self, iter: tp.List[str]) -> bytes:
- """
- encode a list of tokens a return bytes. We use bitpadding to make sure the encoded bits fit in bytes.
- """
- a = bitarray()
- for token in iter:
- code = self.get_code(token)
- if code is None:
- if self.unk_word is None:
- raise Exception(f"unknown token {token} cannot be encoded.")
- else:
- token = self.unk_word
- a = a + self.get_code(token)
- return self._pad(a).tobytes()
-
- def decode(self, bits: bytes) -> tp.Iterator["HuffmanNode"]:
- """
- take bitpadded bytes and decode it to a set of leaves. You can then use each node to find the symbol/id
- """
- a = bitarray()
- a.frombytes(bits)
- return self.root.decode(self._unpad(a))
-
- def get_code(self, symbol: str) -> tp.Optional[bitarray]:
- node = self.get_node(symbol)
- return None if node is None else node.code
-
- def get_node(self, symbol: str) -> "HuffmanNode":
- return self.table.get(symbol)
-
- @classmethod
- def from_file(
- cls,
- filename: str,
- bos="<s>",
- pad="<pad>",
- eos="</s>",
- unk="<unk>",
- ) -> "HuffmanCoder":
- builder = HuffmanCodeBuilder.from_file(filename)
- return builder.build_code(bos=bos, pad=pad, eos=eos, unk=unk)
-
- def to_file(self, filename, sep="\t"):
- nodes = list(self.table.values())
- nodes.sort(key=lambda n: n.id)
- with open(filename, "w", encoding="utf-8") as output:
- for n in nodes:
- output.write(f"{n.symbol}{sep}{n.count}\n")
-
- def __iter__(self):
- for n in self.table.values():
- yield n
-
- def merge(self, other_coder: "HuffmanCoder") -> "HuffmanCoder":
- builder = HuffmanCodeBuilder()
- for n in self:
- builder.increment(n.symbol, n.count)
- for n in other_coder:
- builder.increment(n.symbol, n.count)
- return builder.build_code()
-
- def __eq__(self, other: "HuffmanCoder") -> bool:
- return self.table == other.table
-
- def __len__(self) -> int:
- return len(self.table)
-
- def __contains__(self, sym: str) -> bool:
- return sym in self.table
-
- def to_dictionary(self) -> Dictionary:
- dictionary = Dictionary(bos=self.bos, unk=self.unk, pad=self.pad, eos=self.eos)
- for n in self:
- dictionary.add_symbol(n.symbol, n=n.count)
- dictionary.finalize()
- return dictionary
-
-
-@dataclass
-class HuffmanNode:
- """
- a node in a Huffman tree
- """
-
- id: int
- count: int
- symbol: tp.Optional[str] = None
- left: tp.Optional["HuffmanNode"] = None
- right: tp.Optional["HuffmanNode"] = None
- code: tp.Optional[bitarray] = None
-
- def is_leaf(self) -> bool:
- return self.left is None and self.right is None
-
- def code_table(self, prefix: tp.Optional[bitarray] = None) -> tp.Dict[str, "HuffmanNode"]:
- defaulted_prefix = prefix if prefix is not None else bitarray()
- if self.is_leaf():
- self.code = (
- defaulted_prefix if len(defaulted_prefix) > 0 else bitarray("0")
- ) # leaf could be the root if there is only one symbol
- return {self.symbol: self}
-
- codes_right = self.right.code_table(defaulted_prefix + bitarray([0]))
- codes_left = self.left.code_table(defaulted_prefix + bitarray([1]))
- return {**codes_left, **codes_right}
-
- def decode(self, bits: bitarray) -> tp.Iterator["HuffmanNode"]:
- current_node = self
- for bit in bits:
- if bit == 0: # go right
- current_node = current_node.right
- else: # go left
- current_node = current_node.left
- if current_node is None:
- # we shouldn't be on a leaf here
- raise Exception("fell off a leaf")
- if current_node.is_leaf():
- yield current_node
- current_node = self
- if current_node != self:
- raise Exception("couldn't decode all the bits")
-
-
-class HuffmanCodeBuilder:
- """
- build a dictionary with occurence count and then build the Huffman code for it.
- """
-
- def __init__(self):
- self.symbols = Counter()
-
- def add_symbols(self, *syms) -> None:
- self.symbols.update(syms)
-
- def increment(self, symbol: str, cnt: int) -> None:
- self.symbols[symbol] += cnt
-
- @classmethod
- def from_file(cls, filename):
- c = cls()
- with open(filename, "r", encoding="utf-8") as input:
- for line in input:
- split = re.split(r"[\s]+", line)
- c.increment(split[0], int(split[1]))
- return c
-
- def to_file(self, filename, sep="\t"):
- with open(filename, "w", encoding="utf-8") as output:
- for (tok, cnt) in self.symbols.most_common():
- output.write(f"{tok}{sep}{cnt}\n")
-
- def _smallest(self, q1: deque, q2: deque) -> HuffmanNode:
- if len(q1) == 0:
- return q2.pop()
-
- if len(q2) == 0:
- return q1.pop()
-
- if q1[-1].count < q2[-1].count:
- return q1.pop()
-
- return q2.pop()
-
- def __add__(self, c: "HuffmanCodeBuilder") -> "HuffmanCodeBuilder":
- new_c = self.symbols + c.symbols
- new_b = HuffmanCodeBuilder()
- new_b.symbols = new_c
- return new_b
-
- def build_code(
- self,
- bos="<s>",
- pad="<pad>",
- eos="</s>",
- unk="<unk>",
- ) -> HuffmanCoder:
- assert len(self.symbols) > 0, "cannot build code from empty list of symbols"
-
- if self.symbols[bos] == 0:
- self.add_symbols(bos)
- if self.symbols[pad] == 0:
- self.add_symbols(pad)
- if self.symbols[eos] == 0:
- self.add_symbols(eos)
- if self.symbols[unk] == 0:
- self.add_symbols(unk)
-
- node_id = 0
- leaves_queue = deque(
- [
- HuffmanNode(symbol=symbol, count=count, id=idx)
- for idx, (symbol, count) in enumerate(self.symbols.most_common())
- ]
- ) # left are the most common, right are the least common
-
- if len(leaves_queue) == 1:
- root = leaves_queue.pop()
- root.id = 0
- return HuffmanCoder(root)
-
- nodes_queue = deque()
-
- while len(leaves_queue) > 0 or len(nodes_queue) != 1:
- # get the lowest two nodes at the head of each queue
- node1 = self._smallest(leaves_queue, nodes_queue)
- node2 = self._smallest(leaves_queue, nodes_queue)
-
- # add new node
- nodes_queue.appendleft(
- HuffmanNode(
- count=node1.count + node2.count, left=node1, right=node2, id=node_id
- )
- )
- node_id += 1
-
- # we are left with the root
- return HuffmanCoder(nodes_queue.pop(), bos=bos, pad=pad, eos=eos, unk=unk)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_dataclass_utils.py b/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_dataclass_utils.py
deleted file mode 100644
index 45fc391a979feb198b0a4ecea69c31f1340e87d2..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_dataclass_utils.py
+++ /dev/null
@@ -1,87 +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 unittest
-from argparse import ArgumentParser
-from dataclasses import dataclass, field
-
-from fairseq.dataclass import FairseqDataclass
-from fairseq.dataclass.utils import gen_parser_from_dataclass
-
-
-@dataclass
-class A(FairseqDataclass):
- data: str = field(default="test", metadata={"help": "the data input"})
- num_layers: int = field(default=200, metadata={"help": "more layers is better?"})
-
-
-@dataclass
-class B(FairseqDataclass):
- bar: A = field(default=A())
- foo: int = field(default=0, metadata={"help": "not a bar"})
-
-
-@dataclass
-class D(FairseqDataclass):
- arch: A = field(default=A())
- foo: int = field(default=0, metadata={"help": "not a bar"})
-
-
-@dataclass
-class C(FairseqDataclass):
- data: str = field(default="test", metadata={"help": "root level data input"})
- encoder: D = field(default=D())
- decoder: A = field(default=A())
- lr: int = field(default=0, metadata={"help": "learning rate"})
-
-
-class TestDataclassUtils(unittest.TestCase):
- def test_argparse_convert_basic(self):
- parser = ArgumentParser()
- gen_parser_from_dataclass(parser, A(), True)
- args = parser.parse_args(["--num-layers", '10', "the/data/path"])
- self.assertEqual(args.num_layers, 10)
- self.assertEqual(args.data, "the/data/path")
-
- def test_argparse_recursive(self):
- parser = ArgumentParser()
- gen_parser_from_dataclass(parser, B(), True)
- args = parser.parse_args(["--num-layers", "10", "--foo", "10", "the/data/path"])
- self.assertEqual(args.num_layers, 10)
- self.assertEqual(args.foo, 10)
- self.assertEqual(args.data, "the/data/path")
-
- def test_argparse_recursive_prefixing(self):
- self.maxDiff = None
- parser = ArgumentParser()
- gen_parser_from_dataclass(parser, C(), True, "")
- args = parser.parse_args(
- [
- "--encoder-arch-data",
- "ENCODER_ARCH_DATA",
- "--encoder-arch-num-layers",
- "10",
- "--encoder-foo",
- "10",
- "--decoder-data",
- "DECODER_DATA",
- "--decoder-num-layers",
- "10",
- "--lr",
- "10",
- "the/data/path",
- ]
- )
- self.assertEqual(args.encoder_arch_data, "ENCODER_ARCH_DATA")
- self.assertEqual(args.encoder_arch_num_layers, 10)
- self.assertEqual(args.encoder_foo, 10)
- self.assertEqual(args.decoder_data, "DECODER_DATA")
- self.assertEqual(args.decoder_num_layers, 10)
- self.assertEqual(args.lr, 10)
- self.assertEqual(args.data, "the/data/path")
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/OLKGTOIP/Real-CUGAN/upcunet_v3.py b/spaces/OLKGTOIP/Real-CUGAN/upcunet_v3.py
deleted file mode 100644
index f7919a6cc9efe3b8af73a73e30825a4c7d7d76da..0000000000000000000000000000000000000000
--- a/spaces/OLKGTOIP/Real-CUGAN/upcunet_v3.py
+++ /dev/null
@@ -1,714 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-import os, sys
-import numpy as np
-
-root_path = os.path.abspath('.')
-sys.path.append(root_path)
-
-
-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 #
-
-
-class RealWaifuUpScaler(object):
- def __init__(self, scale, weight_path, half, device):
- weight = torch.load(weight_path, map_location="cpu")
- self.model = eval("UpCunet%sx" % scale)()
- 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
-
-
-if __name__ == "__main__":
- ###########inference_img
- import time, cv2, sys
- from time import time as ttime
-
- for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3),
- ("weights_v3/up4x-latest-denoise3x.pth", 4)]:
- for tile_mode in [0, 1, 2, 3, 4]:
- upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0")
- input_dir = "%s/input_dir1" % root_path
- output_dir = "%s/opt-dir-all-test" % root_path
- os.makedirs(output_dir, exist_ok=True)
- for name in os.listdir(input_dir):
- print(name)
- tmp = name.split(".")
- inp_path = os.path.join(input_dir, name)
- suffix = tmp[-1]
- prefix = ".".join(tmp[:-1])
- tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
- print(inp_path, tmp_path)
- # 支持中文路径
- # os.link(inp_path, tmp_path)#win用硬链接
- os.symlink(inp_path, tmp_path) # linux用软链接
- frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]]
- t0 = ttime()
- result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1]
- t1 = ttime()
- print(prefix, "done", t1 - t0)
- tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
- cv2.imwrite(tmp_opt_path, result)
- n = 0
- while (1):
- if (n == 0):
- suffix = "_%sx_tile%s.png" % (scale, tile_mode)
- else:
- suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n) #
- if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False):
- break
- else:
- n += 1
- final_opt_path = os.path.join(output_dir, prefix + suffix)
- os.rename(tmp_opt_path, final_opt_path)
- os.remove(tmp_path)
diff --git a/spaces/OkayuTadano/OgiriMasters/app.py b/spaces/OkayuTadano/OgiriMasters/app.py
deleted file mode 100644
index 3bc68fb8a55a929f86683e56dc414d707d99ed0b..0000000000000000000000000000000000000000
--- a/spaces/OkayuTadano/OgiriMasters/app.py
+++ /dev/null
@@ -1,175 +0,0 @@
-import re
-
-import openai
-import requests
-# from dotenv import load_dotenv
-import openai
-import gradio as gr
-import os
-
-# Organization IDとAPIキーを設定
-organization_id = os.getenv("API_ORG")
-api_key = os.getenv("API_KEY")
-app_password = os.getenv("APP_PASSWORD")
-app_username = os.getenv("APP_USERNAME")
-
-
-def generate_image_with_dalle2(prompt):
- # Official API Reference: https://beta.openai.com/docs/api-reference/images
- response = openai.Image.create(
- prompt=prompt,
- n=1,
- size='{}x{}'.format(str(256), str(256))
- )
- image_url = response['data'][0]['url']
-
- # 画像をローカルに保存
- image_data = requests.get(image_url).content
- with open("chat-gpt-generated-image.jpg", "wb") as f:
- f.write(image_data)
-
- return image_url
-
-def translate(text):
- system_prompt = "Translate Japanese to English. Please output only the translation result."
- response = openai.ChatCompletion.create(
- model="gpt-3.5-turbo-0613",
- messages=[
- {'role': 'system', 'content': system_prompt},
- {'role': 'user', 'content': text},
- ],
- frequency_penalty = 0.0,
- temperature=0.0,
- )
- return response['choices'][0]['message']['content']
-
-def generate_pixel_art_of_enemy(name, state=None):
- en_name = translate(name)
- if not state:
- prompt = 'Generate pixel art of ' + en_name + ', which likes enemy of RPG.'
- else:
- prompt = 'Generate pixel art of ' + state + en_name + ', which likes enemy of RPG.'
- response = openai.Image.create(
- prompt=prompt,
- n=1,
- size="256x256"
- )
- # 画像をローカルに保存
- image_url = response['data'][0]['url']
- image_data = requests.get(image_url).content
- with open("chat-gpt-generated-image.jpg", "wb") as f:
- f.write(image_data)
- return response['data'][0]['url']
-
-
-def generate_ogiri_topic(thema: str) -> str:
- question = thema + "に関するお題を出してください."
- system_prompt = "大喜利AIです．"
- response = openai.ChatCompletion.create(
- model="gpt-3.5-turbo-0613",
- messages=[
- {'role': 'system', 'content': system_prompt},
- {'role': 'user', 'content': question},
- ],
- frequency_penalty = 0.0,
- temperature=1,
- )
- return response['choices'][0]['message']['content']
-
-
-def evaluate_answer(topic, answer) -> tuple[str, str]:
-
- # テキスト生成のリクエストを送信
- response = openai.ChatCompletion.create(
- model = "gpt-3.5-turbo-0613", # 使用するモデルを指定
- messages = [
- {"role": "system", "content": "あなたは大喜利のプロフェッショナルです。点数は比較的甘めにつけています。次のお題についての大喜利の答えを10点満点で評価してください。フォーマットは次に従ってください。\n〇〇点\n理由:"},
- {"role": "assistant", "content": "お題： 人が看板とペンを持っているクローズアップ"},
- {"role": "user", "content": "解答: 部長にネクタイだけはちゃんとしろって言われたから…"},
- {"role": "assistant", "content": "9点\n理由: 裸にネクタイだけしている人がこちらに迫っている絵で、ネクタイだけはちゃんとしろというアドバイスを間違えて受け取っているというユーモアが面白いから。"},
- {"role": "assistant", "content": f"お題: {topic}"},
- {"role": "user", "content": f"解答: {answer}"}
- ],
- )
-
- # 生成されたテキストを表示
- generated_text = response['choices'][0]['message']['content'].strip()
- ex_point = re.compile(".*?(\d)点.*", re.MULTILINE | re.DOTALL)
- ex_reason = re.compile("理由:(.*)", re.MULTILINE | re.DOTALL)
- point = ex_point.search(generated_text).groups()[0]
- reason = ex_reason.search(generated_text).groups()[0]
- try:
- point = int(point)
- except:
- pass
-
- def point2description(point):
- if point < 3:
- return "instinct"
- elif point < 7:
- return ""
- else:
- return "dameged"
-
- return point, point2description(point), reason
-
-# from .utils import generate_pixel_art_of_enemy, generate_ogiri_topic, evaluate_answer
-
-# load_dotenv()
-
-
-
-# Organization IDを指定してOpenAI APIクライアントを初期化
-openai.api_key = api_key
-openai.organization = organization_id
-
-
-enemy_url = []
-current_animal_name = []
-current_topic = []
-
-def call(お題にしたい動物, 大喜利の解答):
- animal = お題にしたい動物
- answer = 大喜利の解答
- if animal not in current_animal_name:
- enemy_url.clear()
- current_topic.clear()
- current_animal_name.clear()
-
- enemy_url.append(generate_pixel_art_of_enemy(animal))
- current_topic.append(generate_ogiri_topic(animal))
- current_animal_name.append(animal)
-
- if answer:
- point, state, reason = evaluate_answer(current_topic[0], answer)
- enemy_url[0] = generate_pixel_art_of_enemy(animal, state)
- else:
- point = 0
- state = None
- reason = "答えを入力してください。"
-
- return enemy_url[0], current_topic[0], point, reason
-
-
-
-examples = [
- ["からす", "大喜利の解答"],
-]
-
-demo = gr.Interface(
- fn=call,
- inputs=["text", "text"],
- outputs=[
- gr.components.Image(type="filepath", label="Generated Image"),
- "text",
- "number",
- "text"
- ],
- flagging_options=[],
- examples=examples,
- title="大喜利アプリ",
- description="1.動物の名前を入れて画像を生成。その動物に関する大喜利のお題が生成されます。2.解答を入力。点数が表示されます。"
-)
-
-demo.launch(share=False, auth=(app_username, app_password))
-
diff --git a/spaces/Omnibus/MusicGen/audiocraft/models/musicgen.py b/spaces/Omnibus/MusicGen/audiocraft/models/musicgen.py
deleted file mode 100644
index 007dd9e0ed1cfd359fb4889e7f4108248e189941..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/audiocraft/models/musicgen.py
+++ /dev/null
@@ -1,362 +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.
-
-"""
-Main model for using MusicGen. This will combine all the required components
-and provide easy access to the generation API.
-"""
-
-import os
-import typing as tp
-
-import torch
-
-from .encodec import CompressionModel
-from .lm import LMModel
-from .builders import get_debug_compression_model, get_debug_lm_model
-from .loaders import load_compression_model, load_lm_model, HF_MODEL_CHECKPOINTS_MAP
-from ..data.audio_utils import convert_audio
-from ..modules.conditioners import ConditioningAttributes, WavCondition
-from ..utils.autocast import TorchAutocast
-
-
-MelodyList = tp.List[tp.Optional[torch.Tensor]]
-MelodyType = tp.Union[torch.Tensor, MelodyList]
-
-
-class MusicGen:
- """MusicGen main model with convenient generation API.
-
- Args:
- name (str): name of the model.
- compression_model (CompressionModel): Compression model
- used to map audio to invertible discrete representations.
- lm (LMModel): Language model over discrete representations.
- """
- def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel,
- max_duration: float = 30):
- self.name = name
- self.compression_model = compression_model
- self.lm = lm
- self.max_duration = max_duration
- self.device = next(iter(lm.parameters())).device
- self.generation_params: dict = {}
- self.set_generation_params(duration=15) # 15 seconds by default
- self._progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None
- if self.device.type == 'cpu':
- self.autocast = TorchAutocast(enabled=False)
- else:
- self.autocast = TorchAutocast(
- enabled=True, device_type=self.device.type, dtype=torch.float16)
-
- @property
- def frame_rate(self) -> int:
- """Roughly the number of AR steps per seconds."""
- return self.compression_model.frame_rate
-
- @property
- def sample_rate(self) -> int:
- """Sample rate of the generated audio."""
- return self.compression_model.sample_rate
-
- @property
- def audio_channels(self) -> int:
- """Audio channels of the generated audio."""
- return self.compression_model.channels
-
- @staticmethod
- def get_pretrained(name: str = 'melody', device=None):
- """Return pretrained model, we provide four models:
- - small (300M), text to music, # see: https://huggingface.co/facebook/musicgen-small
- - medium (1.5B), text to music, # see: https://huggingface.co/facebook/musicgen-medium
- - melody (1.5B) text to music and text+melody to music, # see: https://huggingface.co/facebook/musicgen-melody
- - large (3.3B), text to music, # see: https://huggingface.co/facebook/musicgen-large
- """
-
- if device is None:
- if torch.cuda.device_count():
- device = 'cuda'
- else:
- device = 'cpu'
-
- if name == 'debug':
- # used only for unit tests
- compression_model = get_debug_compression_model(device)
- lm = get_debug_lm_model(device)
- return MusicGen(name, compression_model, lm)
-
- if name not in HF_MODEL_CHECKPOINTS_MAP:
- if not os.path.isfile(name) and not os.path.isdir(name):
- raise ValueError(
- f"{name} is not a valid checkpoint name. "
- f"Choose one of {', '.join(HF_MODEL_CHECKPOINTS_MAP.keys())}"
- )
-
- cache_dir = os.environ.get('MUSICGEN_ROOT', None)
- compression_model = load_compression_model(name, device=device, cache_dir=cache_dir)
- lm = load_lm_model(name, device=device, cache_dir=cache_dir)
- if name == 'melody':
- lm.condition_provider.conditioners['self_wav'].match_len_on_eval = True
-
- return MusicGen(name, compression_model, lm)
-
- def set_generation_params(self, use_sampling: bool = True, top_k: int = 250,
- top_p: float = 0.0, temperature: float = 1.0,
- duration: float = 30.0, cfg_coef: float = 3.0,
- two_step_cfg: bool = False, extend_stride: float = 18):
- """Set the generation parameters for MusicGen.
-
- Args:
- use_sampling (bool, optional): Use sampling if True, else do argmax decoding. Defaults to True.
- top_k (int, optional): top_k used for sampling. Defaults to 250.
- top_p (float, optional): top_p used for sampling, when set to 0 top_k is used. Defaults to 0.0.
- temperature (float, optional): Softmax temperature parameter. Defaults to 1.0.
- duration (float, optional): Duration of the generated waveform. Defaults to 30.0.
- cfg_coef (float, optional): Coefficient used for classifier free guidance. Defaults to 3.0.
- two_step_cfg (bool, optional): If True, performs 2 forward for Classifier Free Guidance,
- instead of batching together the two. This has some impact on how things
- are padded but seems to have little impact in practice.
- extend_stride: when doing extended generation (i.e. more than 30 seconds), by how much
- should we extend the audio each time. Larger values will mean less context is
- preserved, and shorter value will require extra computations.
- """
- assert extend_stride < self.max_duration, "Cannot stride by more than max generation duration."
- self.extend_stride = extend_stride
- self.duration = duration
- self.generation_params = {
- 'use_sampling': use_sampling,
- 'temp': temperature,
- 'top_k': top_k,
- 'top_p': top_p,
- 'cfg_coef': cfg_coef,
- 'two_step_cfg': two_step_cfg,
- }
-
- def set_custom_progress_callback(self, progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None):
- """Override the default progress callback."""
- self._progress_callback = progress_callback
-
- def generate_unconditional(self, num_samples: int, progress: bool = False) -> torch.Tensor:
- """Generate samples in an unconditional manner.
-
- Args:
- num_samples (int): Number of samples to be generated.
- progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
- """
- descriptions: tp.List[tp.Optional[str]] = [None] * num_samples
- attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
- return self._generate_tokens(attributes, prompt_tokens, progress)
-
- def generate(self, descriptions: tp.List[str], progress: bool = False) -> torch.Tensor:
- """Generate samples conditioned on text.
-
- Args:
- descriptions (tp.List[str]): A list of strings used as text conditioning.
- progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
- """
- attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
- assert prompt_tokens is None
- return self._generate_tokens(attributes, prompt_tokens, progress)
-
- def generate_with_chroma(self, descriptions: tp.List[str], melody_wavs: MelodyType,
- melody_sample_rate: int, progress: bool = False) -> torch.Tensor:
- """Generate samples conditioned on text and melody.
-
- Args:
- descriptions (tp.List[str]): A list of strings used as text conditioning.
- melody_wavs: (torch.Tensor or list of Tensor): A batch of waveforms used as
- melody conditioning. Should have shape [B, C, T] with B matching the description length,
- C=1 or 2. It can be [C, T] if there is a single description. It can also be
- a list of [C, T] tensors.
- melody_sample_rate: (int): Sample rate of the melody waveforms.
- progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
- """
- if isinstance(melody_wavs, torch.Tensor):
- if melody_wavs.dim() == 2:
- melody_wavs = melody_wavs[None]
- if melody_wavs.dim() != 3:
- raise ValueError("Melody wavs should have a shape [B, C, T].")
- melody_wavs = list(melody_wavs)
- else:
- for melody in melody_wavs:
- if melody is not None:
- assert melody.dim() == 2, "One melody in the list has the wrong number of dims."
-
- melody_wavs = [
- convert_audio(wav, melody_sample_rate, self.sample_rate, self.audio_channels)
- if wav is not None else None
- for wav in melody_wavs]
- attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions=descriptions, prompt=None,
- melody_wavs=melody_wavs)
- assert prompt_tokens is None
- return self._generate_tokens(attributes, prompt_tokens, progress)
-
- def generate_continuation(self, prompt: torch.Tensor, prompt_sample_rate: int,
- descriptions: tp.Optional[tp.List[tp.Optional[str]]] = None,
- progress: bool = False) -> torch.Tensor:
- """Generate samples conditioned on audio prompts.
-
- Args:
- prompt (torch.Tensor): A batch of waveforms used for continuation.
- Prompt should be [B, C, T], or [C, T] if only one sample is generated.
- prompt_sample_rate (int): Sampling rate of the given audio waveforms.
- descriptions (tp.List[str], optional): A list of strings used as text conditioning. Defaults to None.
- progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
- """
- if prompt.dim() == 2:
- prompt = prompt[None]
- if prompt.dim() != 3:
- raise ValueError("prompt should have 3 dimensions: [B, C, T] (C = 1).")
- prompt = convert_audio(prompt, prompt_sample_rate, self.sample_rate, self.audio_channels)
- if descriptions is None:
- descriptions = [None] * len(prompt)
- attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, prompt)
- assert prompt_tokens is not None
- return self._generate_tokens(attributes, prompt_tokens, progress)
-
- @torch.no_grad()
- def _prepare_tokens_and_attributes(
- self,
- descriptions: tp.Sequence[tp.Optional[str]],
- prompt: tp.Optional[torch.Tensor],
- melody_wavs: tp.Optional[MelodyList] = None,
- ) -> tp.Tuple[tp.List[ConditioningAttributes], tp.Optional[torch.Tensor]]:
- """Prepare model inputs.
-
- Args:
- descriptions (tp.List[str]): A list of strings used as text conditioning.
- prompt (torch.Tensor): A batch of waveforms used for continuation.
- melody_wavs (tp.Optional[torch.Tensor], optional): A batch of waveforms
- used as melody conditioning. Defaults to None.
- """
- attributes = [
- ConditioningAttributes(text={'description': description})
- for description in descriptions]
-
- if melody_wavs is None:
- for attr in attributes:
- attr.wav['self_wav'] = WavCondition(
- torch.zeros((1, 1), device=self.device),
- torch.tensor([0], device=self.device),
- path='null_wav') # type: ignore
- else:
- if self.name != "melody":
- raise RuntimeError("This model doesn't support melody conditioning. "
- "Use the `melody` model.")
- assert len(melody_wavs) == len(descriptions), \
- f"number of melody wavs must match number of descriptions! " \
- f"got melody len={len(melody_wavs)}, and descriptions len={len(descriptions)}"
- for attr, melody in zip(attributes, melody_wavs):
- if melody is None:
- attr.wav['self_wav'] = WavCondition(
- torch.zeros((1, 1), device=self.device),
- torch.tensor([0], device=self.device),
- path='null_wav') # type: ignore
- else:
- attr.wav['self_wav'] = WavCondition(
- melody.to(device=self.device),
- torch.tensor([melody.shape[-1]], device=self.device))
-
- if prompt is not None:
- if descriptions is not None:
- assert len(descriptions) == len(prompt), "Prompt and nb. descriptions doesn't match"
- prompt = prompt.to(self.device)
- prompt_tokens, scale = self.compression_model.encode(prompt)
- assert scale is None
- else:
- prompt_tokens = None
- return attributes, prompt_tokens
-
- def _generate_tokens(self, attributes: tp.List[ConditioningAttributes],
- prompt_tokens: tp.Optional[torch.Tensor], progress: bool = False) -> torch.Tensor:
- """Generate discrete audio tokens given audio prompt and/or conditions.
-
- Args:
- attributes (tp.List[ConditioningAttributes]): Conditions used for generation (text/melody).
- prompt_tokens (tp.Optional[torch.Tensor]): Audio prompt used for continuation.
- progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
- Returns:
- torch.Tensor: Generated audio, of shape [B, C, T], T is defined by the generation params.
- """
- total_gen_len = int(self.duration * self.frame_rate)
- max_prompt_len = int(min(self.duration, self.max_duration) * self.frame_rate)
- current_gen_offset: int = 0
-
- def _progress_callback(generated_tokens: int, tokens_to_generate: int):
- generated_tokens += current_gen_offset
- if self._progress_callback is not None:
- # Note that total_gen_len might be quite wrong depending on the
- # codebook pattern used, but with delay it is almost accurate.
- self._progress_callback(generated_tokens, total_gen_len)
- else:
- print(f'{generated_tokens: 6d} / {total_gen_len: 6d}', end='\r')
-
- if prompt_tokens is not None:
- assert max_prompt_len >= prompt_tokens.shape[-1], \
- "Prompt is longer than audio to generate"
-
- callback = None
- if progress:
- callback = _progress_callback
-
- if self.duration <= self.max_duration:
- # generate by sampling from LM, simple case.
- with self.autocast:
- gen_tokens = self.lm.generate(
- prompt_tokens, attributes,
- callback=callback, max_gen_len=total_gen_len, **self.generation_params)
-
- else:
- # now this gets a bit messier, we need to handle prompts,
- # melody conditioning etc.
- ref_wavs = [attr.wav['self_wav'] for attr in attributes]
- all_tokens = []
- if prompt_tokens is None:
- prompt_length = 0
- else:
- all_tokens.append(prompt_tokens)
- prompt_length = prompt_tokens.shape[-1]
-
- stride_tokens = int(self.frame_rate * self.extend_stride)
-
- while current_gen_offset + prompt_length < total_gen_len:
- time_offset = current_gen_offset / self.frame_rate
- chunk_duration = min(self.duration - time_offset, self.max_duration)
- max_gen_len = int(chunk_duration * self.frame_rate)
- for attr, ref_wav in zip(attributes, ref_wavs):
- wav_length = ref_wav.length.item()
- if wav_length == 0:
- continue
- # We will extend the wav periodically if it not long enough.
- # we have to do it here rather than in conditioners.py as otherwise
- # we wouldn't have the full wav.
- initial_position = int(time_offset * self.sample_rate)
- wav_target_length = int(self.max_duration * self.sample_rate)
- print(initial_position / self.sample_rate, wav_target_length / self.sample_rate)
- positions = torch.arange(initial_position,
- initial_position + wav_target_length, device=self.device)
- attr.wav['self_wav'] = WavCondition(
- ref_wav[0][:, positions % wav_length],
- torch.full_like(ref_wav[1], wav_target_length))
- with self.autocast:
- gen_tokens = self.lm.generate(
- prompt_tokens, attributes,
- callback=callback, max_gen_len=max_gen_len, **self.generation_params)
- if prompt_tokens is None:
- all_tokens.append(gen_tokens)
- else:
- all_tokens.append(gen_tokens[:, :, prompt_tokens.shape[-1]:])
- prompt_tokens = gen_tokens[:, :, stride_tokens:]
- prompt_length = prompt_tokens.shape[-1]
- current_gen_offset += stride_tokens
-
- gen_tokens = torch.cat(all_tokens, dim=-1)
-
- # generate audio
- assert gen_tokens.dim() == 3
- with torch.no_grad():
- gen_audio = self.compression_model.decode(gen_tokens, None)
- return gen_audio
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/dev/parse_results.sh b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/dev/parse_results.sh
deleted file mode 100644
index 80768a4005753447c49339790fe66c9b82a80aaf..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/dev/parse_results.sh
+++ /dev/null
@@ -1,45 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-# A shell script that parses metrics from the log file.
-# Make it easier for developers to track performance of models.
-
-LOG="$1"
-
-if [[ -z "$LOG" ]]; then
- echo "Usage: $0 /path/to/log/file"
- exit 1
-fi
-
-# [12/15 11:47:32] trainer INFO: Total training time: 12:15:04.446477 (0.4900 s / it)
-# [12/15 11:49:03] inference INFO: Total inference time: 0:01:25.326167 (0.13652186737060548 s / img per device, on 8 devices)
-# [12/15 11:49:03] inference INFO: Total inference pure compute time: .....
-
-# training time
-trainspeed=$(grep -o 'Overall training.*' "$LOG" | grep -Eo '\(.*\)' | grep -o '[0-9\.]*')
-echo "Training speed: $trainspeed s/it"
-
-# inference time: there could be multiple inference during training
-inferencespeed=$(grep -o 'Total inference pure.*' "$LOG" | tail -n1 | grep -Eo '\(.*\)' | grep -o '[0-9\.]*' | head -n1)
-echo "Inference speed: $inferencespeed s/it"
-
-# [12/15 11:47:18] trainer INFO: eta: 0:00:00 iter: 90000 loss: 0.5407 (0.7256) loss_classifier: 0.1744 (0.2446) loss_box_reg: 0.0838 (0.1160) loss_mask: 0.2159 (0.2722) loss_objectness: 0.0244 (0.0429) loss_rpn_box_reg: 0.0279 (0.0500) time: 0.4487 (0.4899) data: 0.0076 (0.0975) lr: 0.000200 max mem: 4161
-memory=$(grep -o 'max[_ ]mem: [0-9]*' "$LOG" | tail -n1 | grep -o '[0-9]*')
-echo "Training memory: $memory MB"
-
-echo "Easy to copypaste:"
-echo "$trainspeed","$inferencespeed","$memory"
-
-echo "------------------------------"
-
-# [12/26 17:26:32] engine.coco_evaluation: copypaste: Task: bbox
-# [12/26 17:26:32] engine.coco_evaluation: copypaste: AP,AP50,AP75,APs,APm,APl
-# [12/26 17:26:32] engine.coco_evaluation: copypaste: 0.0017,0.0024,0.0017,0.0005,0.0019,0.0011
-# [12/26 17:26:32] engine.coco_evaluation: copypaste: Task: segm
-# [12/26 17:26:32] engine.coco_evaluation: copypaste: AP,AP50,AP75,APs,APm,APl
-# [12/26 17:26:32] engine.coco_evaluation: copypaste: 0.0014,0.0021,0.0016,0.0005,0.0016,0.0011
-
-echo "COCO Results:"
-num_tasks=$(grep -o 'copypaste:.*Task.*' "$LOG" | sort -u | wc -l)
-# each task has 3 lines
-grep -o 'copypaste:.*' "$LOG" | cut -d ' ' -f 2- | tail -n $((num_tasks * 3))
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/docs/tutorials/deployment.md b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/docs/tutorials/deployment.md
deleted file mode 100644
index 173b9a0e1ec1e768d1b9dc5744c104578512d638..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/docs/tutorials/deployment.md
+++ /dev/null
@@ -1,137 +0,0 @@
-# Deployment
-
-Models written in Python need to go through an export process to become a deployable artifact.
-A few basic concepts about this process:
-
-__"Export method"__ is how a Python model is fully serialized to a deployable format.
-We support the following export methods:
-
-* `tracing`: see [pytorch documentation](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html) to learn about it
-* `scripting`: see [pytorch documentation](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html) to learn about it
-* `caffe2_tracing`: replace parts of the model by caffe2 operators, then use tracing.
-
-__"Format"__ is how a serialized model is described in a file, e.g.
-TorchScript, Caffe2 protobuf, ONNX format.
-__"Runtime"__ is an engine that loads a serialized model and executes it,
-e.g., PyTorch, Caffe2, TensorFlow, onnxruntime, TensorRT, etc.
-A runtime is often tied to a specific format
-(e.g. PyTorch needs TorchScript format, Caffe2 needs protobuf format).
-We currently support the following combination and each has some limitations:
-
-```eval_rst
-+----------------------------+-------------+-------------+-----------------------------+
-| Export Method | tracing | scripting | caffe2_tracing |
-+============================+=============+=============+=============================+
-| **Formats** | TorchScript | TorchScript | Caffe2, TorchScript, ONNX |
-+----------------------------+-------------+-------------+-----------------------------+
-| **Runtime** | PyTorch | PyTorch | Caffe2, PyTorch |
-+----------------------------+-------------+-------------+-----------------------------+
-| C++/Python inference | ✅ | ✅ | ✅ |
-+----------------------------+-------------+-------------+-----------------------------+
-| Dynamic resolution | ✅ | ✅ | ✅ |
-+----------------------------+-------------+-------------+-----------------------------+
-| Batch size requirement | Constant | Dynamic | Batch inference unsupported |
-+----------------------------+-------------+-------------+-----------------------------+
-| Extra runtime deps | torchvision | torchvision | Caffe2 ops (usually already |
-| | | | |
-| | | | included in PyTorch) |
-+----------------------------+-------------+-------------+-----------------------------+
-| Faster/Mask/Keypoint R-CNN | ✅ | ✅ | ✅ |
-+----------------------------+-------------+-------------+-----------------------------+
-| RetinaNet | ✅ | ✅ | ✅ |
-+----------------------------+-------------+-------------+-----------------------------+
-| PointRend R-CNN | ✅ | ❌ | ❌ |
-+----------------------------+-------------+-------------+-----------------------------+
-| Cascade R-CNN | ✅ | ❌ | ❌ |
-+----------------------------+-------------+-------------+-----------------------------+
-
-```
-
-`caffe2_tracing` is going to be deprecated.
-We don't plan to work on additional support for other formats/runtime, but contributions are welcome.
-
-
-## Deployment with Tracing or Scripting
-
-Models can be exported to TorchScript format, by either
-[tracing or scripting](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html).
-The output model file can be loaded without detectron2 dependency in either Python or C++.
-The exported model often requires torchvision (or its C++ library) dependency for some custom ops.
-
-This feature requires PyTorch ≥ 1.8.
-
-### Coverage
-Most official models under the meta architectures `GeneralizedRCNN` and `RetinaNet`
-are supported in both tracing and scripting mode.
-Cascade R-CNN and PointRend are currently supported in tracing.
-Users' custom extensions are supported if they are also scriptable or traceable.
-
-For models exported with tracing, dynamic input resolution is allowed, but batch size
-(number of input images) must be fixed.
-Scripting can support dynamic batch size.
-
-### Usage
-
-The main export APIs for tracing and scripting are [TracingAdapter](../modules/export.html#detectron2.export.TracingAdapter)
-and [scripting_with_instances](../modules/export.html#detectron2.export.scripting_with_instances).
-Their usage is currently demonstrated in [test_export_torchscript.py](../../tests/test_export_torchscript.py)
-(see `TestScripting` and `TestTracing`)
-as well as the [deployment example](../../tools/deploy).
-Please check that these examples can run, and then modify for your use cases.
-The usage now requires some user effort and necessary knowledge for each model to workaround the limitation of scripting and tracing.
-In the future we plan to wrap these under simpler APIs to lower the bar to use them.
-
-## Deployment with Caffe2-tracing
-We provide [Caffe2Tracer](../modules/export.html#detectron2.export.Caffe2Tracer)
-that performs the export logic.
-It replaces parts of the model with Caffe2 operators,
-and then export the model into Caffe2, TorchScript or ONNX format.
-
-The converted model is able to run in either Python or C++ without detectron2/torchvision dependency, on CPU or GPUs.
-It has a runtime optimized for CPU & mobile inference, but not optimized for GPU inference.
-
-This feature requires 1.9 > ONNX ≥ 1.6.
-
-### Coverage
-
-Most official models under these 3 common meta architectures: `GeneralizedRCNN`, `RetinaNet`, `PanopticFPN`
-are supported. Cascade R-CNN is not supported. Batch inference is not supported.
-
-Users' custom extensions under these architectures (added through registration) are supported
-as long as they do not contain control flow or operators not available in Caffe2 (e.g. deformable convolution).
-For example, custom backbones and heads are often supported out of the box.
-
-### Usage
-
-The APIs are listed at [the API documentation](../modules/export).
-We provide [export_model.py](../../tools/deploy/) as an example that uses
-these APIs to convert a standard model. For custom models/datasets, you can add them to this script.
-
-### Use the model in C++/Python
-
-The model can be loaded in C++ and deployed with
-either Caffe2 or Pytorch runtime.. [C++ examples](../../tools/deploy/) for Mask R-CNN
-are given as a reference. Note that:
-
-* Models exported with `caffe2_tracing` method take a special input format
- described in [documentation](../modules/export.html#detectron2.export.Caffe2Tracer).
- This was taken care of in the C++ example.
-
-* The converted models do not contain post-processing operations that
- transform raw layer outputs into formatted predictions.
- For example, the C++ examples only produce raw outputs (28x28 masks) from the final
- layers that are not post-processed, because in actual deployment, an application often needs
- its custom lightweight post-processing, so this step is left for users.
-
-To help use the Caffe2-format model in python,
-we provide a python wrapper around the converted model, in the
-[Caffe2Model.\_\_call\_\_](../modules/export.html#detectron2.export.Caffe2Model.__call__) method.
-This method has an interface that's identical to the [pytorch versions of models](./models.md),
-and it internally applies pre/post-processing code to match the formats.
-This wrapper can serve as a reference for how to use Caffe2's python API,
-or for how to implement pre/post-processing in actual deployment.
-
-## Conversion to TensorFlow
-[tensorpack Faster R-CNN](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN/convert_d2)
-provides scripts to convert a few standard detectron2 R-CNN models to TensorFlow's pb format.
-It works by translating configs and weights, therefore only support a few models.
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/visualization/color.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/visualization/color.py
deleted file mode 100644
index 9041e0e6b7581c3356795d6a3c5e84667c88f025..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/visualization/color.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from enum import Enum
-
-import numpy as np
-
-from annotator.uniformer.mmcv.utils import is_str
-
-
-class Color(Enum):
- """An enum that defines common colors.
-
- Contains red, green, blue, cyan, yellow, magenta, white and black.
- """
- red = (0, 0, 255)
- green = (0, 255, 0)
- blue = (255, 0, 0)
- cyan = (255, 255, 0)
- yellow = (0, 255, 255)
- magenta = (255, 0, 255)
- white = (255, 255, 255)
- black = (0, 0, 0)
-
-
-def color_val(color):
- """Convert various input to color tuples.
-
- Args:
- color (:obj:`Color`/str/tuple/int/ndarray): Color inputs
-
- Returns:
- tuple[int]: A tuple of 3 integers indicating BGR channels.
- """
- if is_str(color):
- return Color[color].value
- elif isinstance(color, Color):
- return color.value
- elif isinstance(color, tuple):
- assert len(color) == 3
- for channel in color:
- assert 0 <= channel <= 255
- return color
- elif isinstance(color, int):
- assert 0 <= color <= 255
- return color, color, color
- elif isinstance(color, np.ndarray):
- assert color.ndim == 1 and color.size == 3
- assert np.all((color >= 0) & (color <= 255))
- color = color.astype(np.uint8)
- return tuple(color)
- else:
- raise TypeError(f'Invalid type for color: {type(color)}')
diff --git a/spaces/PaSathees/Vehicle_Tyre_Quality_Checker/app.py b/spaces/PaSathees/Vehicle_Tyre_Quality_Checker/app.py
deleted file mode 100644
index 188b4bf6d6d15619b6b6d0ac5fc52ddbc40becd4..0000000000000000000000000000000000000000
--- a/spaces/PaSathees/Vehicle_Tyre_Quality_Checker/app.py
+++ /dev/null
@@ -1,88 +0,0 @@
-### 1. Imports and class names setup ###
-import gradio as gr
-import os
-import torch
-
-from model import create_effnet_v2_l_model
-from timeit import default_timer as timer
-from typing import Tuple, Dict
-
-# Setup class names
-class_names = ['defective', 'good']
-
-### 2. Model and transforms preparation ###
-# Create EfficientNet_V2_L model
-effnet_v2_l, effnet_v2_l_transforms = create_effnet_v2_l_model(
- num_classes=len(class_names)
-)
-
-# Load saved weights
-effnet_v2_l.load_state_dict(
- torch.load(
- f="pt_tyre_quality_EfficientNet_V2_L_TrivialAugmentWide_bs_32_ep_37_lr_0.001.pth",
- map_location=torch.device("cpu"),
- )
-)
-
-### 3. Predict function ###
-def predict(img) -> Tuple[Dict, float]:
- """
- Transforms and performs a prediction on img and returns prediction and time taken.
- """
- start_time = timer()
-
- # Transform the target image and add a batch dimension
- img = effnet_v2_l_transforms(img).unsqueeze(0)
-
- # Put model into evaluation mode and turn on inference mode
- effnet_v2_l.eval()
- with torch.inference_mode():
- pred_probs = effnet_v2_l(img).item()
-
- # Create a prediction label and prediction probability dictionary for each prediction class
- # Required format for Gradio's output parameter
- pred_label = 1 if pred_probs >= 0.5 else 0
- pred_labels_and_probs = {'defective': 1 - pred_probs, 'good': pred_probs}
-
- # Calculate the prediction time
- pred_time = round(timer() - start_time, 5)
-
- return pred_labels_and_probs, pred_time
-
-### 4. Gradio app ###
-# Create title, description, & article strings
-title = "Vehicle Tyre Quality Checker 🏍️🚗🛞🔧🧑🔧"
-description = """
-An `EFFICIENTNET_V2_L` feature extractor Computer Vision model to predict vehicle (car, motorbike, etc) tyre condition as defective or good. Tyre cracks are signs of tyres getting older, and can mean defective vehicle tyres. This app uses tyre's images to predict defectives (80/20 train/test accuracy > 97%).
-
-Dataset attribution:
-- Title: Tyre Quality Classification
-- Author: CHIRAG CHAUHAN
-- Source: [Kaggle](https://www.kaggle.com/datasets/warcoder/tyre-quality-classification)
-- License: This dataset is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
-- License URL: https://creativecommons.org/licenses/by/4.0/
-"""
-article = """
-Created for my AI/ML portfolio [PaSathees/portfolio](https://github.com/PaSathees/portfolio).
-
-**Disclaimer:**
-*The Vehicle Tyre Quality Checker app is provided for educational purposes and offers general insights based on image analysis. However, it is not a substitute for professional assessment by a certified technician. Use the app at your own discretion and consult with a qualified expert for a thorough evaluation of your vehicle's tyre condition. Decisions made based on the app's output are your own responsibility. The developers and contributors are not liable for any consequences arising from its use.*
-
-*Thank you for your understanding.*
-"""
-
-# Create examples list from "examples/" directory
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-# Create the Gradio demo
-demo = gr.Interface(fn=predict,
- inputs=gr.Image(type="pil"),
- outputs=[gr.Label(num_top_classes=2, label="Predictions"),
- gr.Number(label="Prediction time (s)")],
- examples=example_list,
- title=title,
- description=description,
- article=article)
-
-# Launch the demo!
-demo.launch()
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/base/types.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/base/types.go
deleted file mode 100644
index 3b085a4931383dfcce99d689db99156ef663d379..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/base/types.go and /dev/null differ
diff --git a/spaces/Pavankunchala/Depth-Estimation-App/README.md b/spaces/Pavankunchala/Depth-Estimation-App/README.md
deleted file mode 100644
index 493609eb78cc343cfe95021ad28fb708b96a6545..0000000000000000000000000000000000000000
--- a/spaces/Pavankunchala/Depth-Estimation-App/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Depth Estimation App
-emoji: 👁
-colorFrom: gray
-colorTo: blue
-sdk: streamlit
-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.
\ No newline at end of file
diff --git a/spaces/PeepDaSlan9/AutoGPT/tests/unit/test_commands.py b/spaces/PeepDaSlan9/AutoGPT/tests/unit/test_commands.py
deleted file mode 100644
index ecbac9b73bd9ad872931d77e144dd853b3d8ef64..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/AutoGPT/tests/unit/test_commands.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""Unit tests for the commands module"""
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-import autogpt.agent.agent_manager as agent_manager
-from autogpt.app import execute_command, list_agents, start_agent
-
-
-@pytest.mark.integration_test
-def test_make_agent() -> None:
- """Test the make_agent command"""
- with patch("openai.ChatCompletion.create") as mock:
- obj = MagicMock()
- obj.response.choices[0].messages[0].content = "Test message"
- mock.return_value = obj
- start_agent("Test Agent", "chat", "Hello, how are you?", "gpt2")
- agents = list_agents()
- assert "List of agents:\n0: chat" == agents
- start_agent("Test Agent 2", "write", "Hello, how are you?", "gpt2")
- agents = list_agents()
- assert "List of agents:\n0: chat\n1: write" == agents
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/registry.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/registry.py
deleted file mode 100644
index fa9df39bc9f3d8d568361e7250ab35468f2b74e0..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/utils/registry.py
+++ /dev/null
@@ -1,315 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import inspect
-import warnings
-from functools import partial
-
-from .misc import is_seq_of
-
-
-def build_from_cfg(cfg, registry, default_args=None):
- """Build a module from config dict.
-
- Args:
- cfg (dict): Config dict. It should at least contain the key "type".
- registry (:obj:`Registry`): The registry to search the type from.
- default_args (dict, optional): Default initialization arguments.
-
- Returns:
- object: The constructed object.
- """
- if not isinstance(cfg, dict):
- raise TypeError(f'cfg must be a dict, but got {type(cfg)}')
- if 'type' not in cfg:
- if default_args is None or 'type' not in default_args:
- raise KeyError(
- '`cfg` or `default_args` must contain the key "type", '
- f'but got {cfg}\n{default_args}')
- if not isinstance(registry, Registry):
- raise TypeError('registry must be an mmcv.Registry object, '
- f'but got {type(registry)}')
- if not (isinstance(default_args, dict) or default_args is None):
- raise TypeError('default_args must be a dict or None, '
- f'but got {type(default_args)}')
-
- args = cfg.copy()
-
- if default_args is not None:
- for name, value in default_args.items():
- args.setdefault(name, value)
-
- obj_type = args.pop('type')
- if isinstance(obj_type, str):
- obj_cls = registry.get(obj_type)
- if obj_cls is None:
- raise KeyError(
- f'{obj_type} is not in the {registry.name} registry')
- elif inspect.isclass(obj_type):
- obj_cls = obj_type
- else:
- raise TypeError(
- f'type must be a str or valid type, but got {type(obj_type)}')
- try:
- return obj_cls(**args)
- except Exception as e:
- # Normal TypeError does not print class name.
- raise type(e)(f'{obj_cls.__name__}: {e}')
-
-
-class Registry:
- """A registry to map strings to classes.
-
- Registered object could be built from registry.
- Example:
- >>> MODELS = Registry('models')
- >>> @MODELS.register_module()
- >>> class ResNet:
- >>> pass
- >>> resnet = MODELS.build(dict(type='ResNet'))
-
- Please refer to
- https://mmcv.readthedocs.io/en/latest/understand_mmcv/registry.html for
- advanced usage.
-
- Args:
- name (str): Registry name.
- build_func(func, optional): Build function to construct instance from
- Registry, func:`build_from_cfg` is used if neither ``parent`` or
- ``build_func`` is specified. If ``parent`` is specified and
- ``build_func`` is not given, ``build_func`` will be inherited
- from ``parent``. Default: None.
- parent (Registry, optional): Parent registry. The class registered in
- children registry could be built from parent. Default: None.
- scope (str, optional): The scope of registry. It is the key to search
- for children registry. If not specified, scope will be the name of
- the package where class is defined, e.g. mmdet, mmcls, mmseg.
- Default: None.
- """
-
- def __init__(self, name, build_func=None, parent=None, scope=None):
- self._name = name
- self._module_dict = dict()
- self._children = dict()
- self._scope = self.infer_scope() if scope is None else scope
-
- # self.build_func will be set with the following priority:
- # 1. build_func
- # 2. parent.build_func
- # 3. build_from_cfg
- if build_func is None:
- if parent is not None:
- self.build_func = parent.build_func
- else:
- self.build_func = build_from_cfg
- else:
- self.build_func = build_func
- if parent is not None:
- assert isinstance(parent, Registry)
- parent._add_children(self)
- self.parent = parent
- else:
- self.parent = None
-
- def __len__(self):
- return len(self._module_dict)
-
- def __contains__(self, key):
- return self.get(key) is not None
-
- def __repr__(self):
- format_str = self.__class__.__name__ + \
- f'(name={self._name}, ' \
- f'items={self._module_dict})'
- return format_str
-
- @staticmethod
- def infer_scope():
- """Infer the scope of registry.
-
- The name of the package where registry is defined will be returned.
-
- Example:
- # in mmdet/models/backbone/resnet.py
- >>> MODELS = Registry('models')
- >>> @MODELS.register_module()
- >>> class ResNet:
- >>> pass
- The scope of ``ResNet`` will be ``mmdet``.
-
-
- Returns:
- scope (str): The inferred scope name.
- """
- # inspect.stack() trace where this function is called, the index-2
- # indicates the frame where `infer_scope()` is called
- filename = inspect.getmodule(inspect.stack()[2][0]).__name__
- split_filename = filename.split('.')
- return split_filename[0]
-
- @staticmethod
- def split_scope_key(key):
- """Split scope and key.
-
- The first scope will be split from key.
-
- Examples:
- >>> Registry.split_scope_key('mmdet.ResNet')
- 'mmdet', 'ResNet'
- >>> Registry.split_scope_key('ResNet')
- None, 'ResNet'
-
- Return:
- scope (str, None): The first scope.
- key (str): The remaining key.
- """
- split_index = key.find('.')
- if split_index != -1:
- return key[:split_index], key[split_index + 1:]
- else:
- return None, key
-
- @property
- def name(self):
- return self._name
-
- @property
- def scope(self):
- return self._scope
-
- @property
- def module_dict(self):
- return self._module_dict
-
- @property
- def children(self):
- return self._children
-
- def get(self, key):
- """Get the registry record.
-
- Args:
- key (str): The class name in string format.
-
- Returns:
- class: The corresponding class.
- """
- scope, real_key = self.split_scope_key(key)
- if scope is None or scope == self._scope:
- # get from self
- if real_key in self._module_dict:
- return self._module_dict[real_key]
- else:
- # get from self._children
- if scope in self._children:
- return self._children[scope].get(real_key)
- else:
- # goto root
- parent = self.parent
- while parent.parent is not None:
- parent = parent.parent
- return parent.get(key)
-
- def build(self, *args, **kwargs):
- return self.build_func(*args, **kwargs, registry=self)
-
- def _add_children(self, registry):
- """Add children for a registry.
-
- The ``registry`` will be added as children based on its scope.
- The parent registry could build objects from children registry.
-
- Example:
- >>> models = Registry('models')
- >>> mmdet_models = Registry('models', parent=models)
- >>> @mmdet_models.register_module()
- >>> class ResNet:
- >>> pass
- >>> resnet = models.build(dict(type='mmdet.ResNet'))
- """
-
- assert isinstance(registry, Registry)
- assert registry.scope is not None
- assert registry.scope not in self.children, \
- f'scope {registry.scope} exists in {self.name} registry'
- self.children[registry.scope] = registry
-
- def _register_module(self, module_class, module_name=None, force=False):
- if not inspect.isclass(module_class):
- raise TypeError('module must be a class, '
- f'but got {type(module_class)}')
-
- if module_name is None:
- module_name = module_class.__name__
- if isinstance(module_name, str):
- module_name = [module_name]
- for name in module_name:
- if not force and name in self._module_dict:
- raise KeyError(f'{name} is already registered '
- f'in {self.name}')
- self._module_dict[name] = module_class
-
- def deprecated_register_module(self, cls=None, force=False):
- warnings.warn(
- 'The old API of register_module(module, force=False) '
- 'is deprecated and will be removed, please use the new API '
- 'register_module(name=None, force=False, module=None) instead.')
- if cls is None:
- return partial(self.deprecated_register_module, force=force)
- self._register_module(cls, force=force)
- return cls
-
- def register_module(self, name=None, force=False, module=None):
- """Register a module.
-
- A record will be added to `self._module_dict`, whose key is the class
- name or the specified name, and value is the class itself.
- It can be used as a decorator or a normal function.
-
- Example:
- >>> backbones = Registry('backbone')
- >>> @backbones.register_module()
- >>> class ResNet:
- >>> pass
-
- >>> backbones = Registry('backbone')
- >>> @backbones.register_module(name='mnet')
- >>> class MobileNet:
- >>> pass
-
- >>> backbones = Registry('backbone')
- >>> class ResNet:
- >>> pass
- >>> backbones.register_module(ResNet)
-
- Args:
- name (str | None): The module name to be registered. If not
- specified, the class name will be used.
- force (bool, optional): Whether to override an existing class with
- the same name. Default: False.
- module (type): Module class to be registered.
- """
- if not isinstance(force, bool):
- raise TypeError(f'force must be a boolean, but got {type(force)}')
- # NOTE: This is a walkaround to be compatible with the old api,
- # while it may introduce unexpected bugs.
- if isinstance(name, type):
- return self.deprecated_register_module(name, force=force)
-
- # raise the error ahead of time
- if not (name is None or isinstance(name, str) or is_seq_of(name, str)):
- raise TypeError(
- 'name must be either of None, an instance of str or a sequence'
- f' of str, but got {type(name)}')
-
- # use it as a normal method: x.register_module(module=SomeClass)
- if module is not None:
- self._register_module(
- module_class=module, module_name=name, force=force)
- return module
-
- # use it as a decorator: @x.register_module()
- def _register(cls):
- self._register_module(
- module_class=cls, module_name=name, force=force)
- return cls
-
- return _register
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/phrasecut.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/phrasecut.py
deleted file mode 100644
index 2a68262d2372c69ba9e64535014770ce4be98189..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/phrasecut.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import torch
-import torchvision
-import torch.utils.data as data
-from maskrcnn_benchmark.data.datasets.modulated_coco import ModulatedDataset
-
-
-class PhrasecutDetection(ModulatedDataset):
- pass
diff --git a/spaces/PirateXX/Sentencewise-Perplexity/README.md b/spaces/PirateXX/Sentencewise-Perplexity/README.md
deleted file mode 100644
index d0b309c770abf3960c3ed6f92fbd0461f6ffee7c..0000000000000000000000000000000000000000
--- a/spaces/PirateXX/Sentencewise-Perplexity/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sentencewise Perplexity
-emoji: 🚀
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: artistic-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/PushkarA07/Sanskrit-Text-To-Speech/mel_processing.py b/spaces/PushkarA07/Sanskrit-Text-To-Speech/mel_processing.py
deleted file mode 100644
index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000
--- a/spaces/PushkarA07/Sanskrit-Text-To-Speech/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/PushkarA07/Sanskrit-Text-To-Speech/text/__init__.py b/spaces/PushkarA07/Sanskrit-Text-To-Speech/text/__init__.py
deleted file mode 100644
index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000
--- a/spaces/PushkarA07/Sanskrit-Text-To-Speech/text/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
- '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
- Args:
- text: string to convert to a sequence
- cleaner_names: names of the cleaner functions to run the text through
- Returns:
- List of integers corresponding to the symbols in the text
- '''
- _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
- sequence = []
-
- clean_text = _clean_text(text, cleaner_names)
- for symbol in clean_text:
- if symbol not in _symbol_to_id.keys():
- continue
- symbol_id = _symbol_to_id[symbol]
- sequence += [symbol_id]
- return sequence
-
-
-def _clean_text(text, cleaner_names):
- for name in cleaner_names:
- cleaner = getattr(cleaners, name)
- if not cleaner:
- raise Exception('Unknown cleaner: %s' % name)
- text = cleaner(text)
- return text
diff --git a/spaces/Ramse/TTS_Hindi/app.py b/spaces/Ramse/TTS_Hindi/app.py
deleted file mode 100644
index e7e510525fcacd85e44d089811590dbe0cd82dd0..0000000000000000000000000000000000000000
--- a/spaces/Ramse/TTS_Hindi/app.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from app_requirnment import *
-
-import gradio as gr
-from gradio.components import Radio
-
-# text_input = gr.inputs.Textbox(lines=3, label="Enter Text")
-# voice_input = Radio(["With Vocal model", "without Vocal Model"], label="Select and get cleaned audio")
-
-textbox = gr.Textbox(
- placeholder="Enter Hindi text here", label="TTS"
-)
-app = gr.Interface(fn=get_audio,
- inputs=[textbox],#gr.Textbox(lines=2, placeholder="Name Here..."),
- outputs= gr.Audio(type="numpy", label=None),
- examples=["मनुष्य का जीवन केवल उसके कर्मो पर चलता है जैसा कर्म होता है, वैसा उसका जीवन होता है"])
-app.launch()
\ No newline at end of file
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/json.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/json.py
deleted file mode 100644
index 23583871e8f2a466abec0bce1397fb495b9c212d..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/json.py
+++ /dev/null
@@ -1,140 +0,0 @@
-from json import loads, dumps
-from typing import Any, Callable, Optional, Union
-
-from .text import Text
-from .highlighter import JSONHighlighter, NullHighlighter
-
-
-class JSON:
- """A renderable which pretty prints JSON.
-
- Args:
- json (str): JSON encoded data.
- indent (Union[None, int, str], optional): Number of characters to indent by. Defaults to 2.
- highlight (bool, optional): Enable highlighting. Defaults to True.
- skip_keys (bool, optional): Skip keys not of a basic type. Defaults to False.
- ensure_ascii (bool, optional): Escape all non-ascii characters. Defaults to False.
- check_circular (bool, optional): Check for circular references. Defaults to True.
- allow_nan (bool, optional): Allow NaN and Infinity values. Defaults to True.
- default (Callable, optional): A callable that converts values that can not be encoded
- in to something that can be JSON encoded. Defaults to None.
- sort_keys (bool, optional): Sort dictionary keys. Defaults to False.
- """
-
- def __init__(
- self,
- json: str,
- indent: Union[None, int, str] = 2,
- highlight: bool = True,
- skip_keys: bool = False,
- ensure_ascii: bool = True,
- check_circular: bool = True,
- allow_nan: bool = True,
- default: Optional[Callable[[Any], Any]] = None,
- sort_keys: bool = False,
- ) -> None:
- data = loads(json)
- json = dumps(
- data,
- indent=indent,
- skipkeys=skip_keys,
- ensure_ascii=ensure_ascii,
- check_circular=check_circular,
- allow_nan=allow_nan,
- default=default,
- sort_keys=sort_keys,
- )
- highlighter = JSONHighlighter() if highlight else NullHighlighter()
- self.text = highlighter(json)
- self.text.no_wrap = True
- self.text.overflow = None
-
- @classmethod
- def from_data(
- cls,
- data: Any,
- indent: Union[None, int, str] = 2,
- highlight: bool = True,
- skip_keys: bool = False,
- ensure_ascii: bool = True,
- check_circular: bool = True,
- allow_nan: bool = True,
- default: Optional[Callable[[Any], Any]] = None,
- sort_keys: bool = False,
- ) -> "JSON":
- """Encodes a JSON object from arbitrary data.
-
- Args:
- data (Any): An object that may be encoded in to JSON
- indent (Union[None, int, str], optional): Number of characters to indent by. Defaults to 2.
- highlight (bool, optional): Enable highlighting. Defaults to True.
- default (Callable, optional): Optional callable which will be called for objects that cannot be serialized. Defaults to None.
- skip_keys (bool, optional): Skip keys not of a basic type. Defaults to False.
- ensure_ascii (bool, optional): Escape all non-ascii characters. Defaults to False.
- check_circular (bool, optional): Check for circular references. Defaults to True.
- allow_nan (bool, optional): Allow NaN and Infinity values. Defaults to True.
- default (Callable, optional): A callable that converts values that can not be encoded
- in to something that can be JSON encoded. Defaults to None.
- sort_keys (bool, optional): Sort dictionary keys. Defaults to False.
-
- Returns:
- JSON: New JSON object from the given data.
- """
- json_instance: "JSON" = cls.__new__(cls)
- json = dumps(
- data,
- indent=indent,
- skipkeys=skip_keys,
- ensure_ascii=ensure_ascii,
- check_circular=check_circular,
- allow_nan=allow_nan,
- default=default,
- sort_keys=sort_keys,
- )
- highlighter = JSONHighlighter() if highlight else NullHighlighter()
- json_instance.text = highlighter(json)
- json_instance.text.no_wrap = True
- json_instance.text.overflow = None
- return json_instance
-
- def __rich__(self) -> Text:
- return self.text
-
-
-if __name__ == "__main__":
-
- import argparse
- import sys
-
- parser = argparse.ArgumentParser(description="Pretty print json")
- parser.add_argument(
- "path",
- metavar="PATH",
- help="path to file, or - for stdin",
- )
- parser.add_argument(
- "-i",
- "--indent",
- metavar="SPACES",
- type=int,
- help="Number of spaces in an indent",
- default=2,
- )
- args = parser.parse_args()
-
- from pip._vendor.rich.console import Console
-
- console = Console()
- error_console = Console(stderr=True)
-
- try:
- if args.path == "-":
- json_data = sys.stdin.read()
- else:
- with open(args.path, "rt") as json_file:
- json_data = json_file.read()
- except Exception as error:
- error_console.print(f"Unable to read {args.path!r}; {error}")
- sys.exit(-1)
-
- console.print(JSON(json_data, indent=args.indent), soft_wrap=True)
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_distutils/text_file.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_distutils/text_file.py
deleted file mode 100644
index 7274d4b16e1bee16751515f42793ebefdd769b96..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_distutils/text_file.py
+++ /dev/null
@@ -1,287 +0,0 @@
-"""text_file
-
-provides the TextFile class, which gives an interface to text files
-that (optionally) takes care of stripping comments, ignoring blank
-lines, and joining lines with backslashes."""
-
-import sys
-
-
-class TextFile:
- """Provides a file-like object that takes care of all the things you
- commonly want to do when processing a text file that has some
- line-by-line syntax: strip comments (as long as "#" is your
- comment character), skip blank lines, join adjacent lines by
- escaping the newline (ie. backslash at end of line), strip
- leading and/or trailing whitespace. All of these are optional
- and independently controllable.
-
- Provides a 'warn()' method so you can generate warning messages that
- report physical line number, even if the logical line in question
- spans multiple physical lines. Also provides 'unreadline()' for
- implementing line-at-a-time lookahead.
-
- Constructor is called as:
-
- TextFile (filename=None, file=None, **options)
-
- It bombs (RuntimeError) if both 'filename' and 'file' are None;
- 'filename' should be a string, and 'file' a file object (or
- something that provides 'readline()' and 'close()' methods). It is
- recommended that you supply at least 'filename', so that TextFile
- can include it in warning messages. If 'file' is not supplied,
- TextFile creates its own using 'io.open()'.
-
- The options are all boolean, and affect the value returned by
- 'readline()':
- strip_comments [default: true]
- strip from "#" to end-of-line, as well as any whitespace
- leading up to the "#" -- unless it is escaped by a backslash
- lstrip_ws [default: false]
- strip leading whitespace from each line before returning it
- rstrip_ws [default: true]
- strip trailing whitespace (including line terminator!) from
- each line before returning it
- skip_blanks [default: true}
- skip lines that are empty *after* stripping comments and
- whitespace. (If both lstrip_ws and rstrip_ws are false,
- then some lines may consist of solely whitespace: these will
- *not* be skipped, even if 'skip_blanks' is true.)
- join_lines [default: false]
- if a backslash is the last non-newline character on a line
- after stripping comments and whitespace, join the following line
- to it to form one "logical line"; if N consecutive lines end
- with a backslash, then N+1 physical lines will be joined to
- form one logical line.
- collapse_join [default: false]
- strip leading whitespace from lines that are joined to their
- predecessor; only matters if (join_lines and not lstrip_ws)
- errors [default: 'strict']
- error handler used to decode the file content
-
- Note that since 'rstrip_ws' can strip the trailing newline, the
- semantics of 'readline()' must differ from those of the builtin file
- object's 'readline()' method! In particular, 'readline()' returns
- None for end-of-file: an empty string might just be a blank line (or
- an all-whitespace line), if 'rstrip_ws' is true but 'skip_blanks' is
- not."""
-
- default_options = {
- 'strip_comments': 1,
- 'skip_blanks': 1,
- 'lstrip_ws': 0,
- 'rstrip_ws': 1,
- 'join_lines': 0,
- 'collapse_join': 0,
- 'errors': 'strict',
- }
-
- def __init__(self, filename=None, file=None, **options):
- """Construct a new TextFile object. At least one of 'filename'
- (a string) and 'file' (a file-like object) must be supplied.
- They keyword argument options are described above and affect
- the values returned by 'readline()'."""
- if filename is None and file is None:
- raise RuntimeError(
- "you must supply either or both of 'filename' and 'file'"
- )
-
- # set values for all options -- either from client option hash
- # or fallback to default_options
- for opt in self.default_options.keys():
- if opt in options:
- setattr(self, opt, options[opt])
- else:
- setattr(self, opt, self.default_options[opt])
-
- # sanity check client option hash
- for opt in options.keys():
- if opt not in self.default_options:
- raise KeyError("invalid TextFile option '%s'" % opt)
-
- if file is None:
- self.open(filename)
- else:
- self.filename = filename
- self.file = file
- self.current_line = 0 # assuming that file is at BOF!
-
- # 'linebuf' is a stack of lines that will be emptied before we
- # actually read from the file; it's only populated by an
- # 'unreadline()' operation
- self.linebuf = []
-
- def open(self, filename):
- """Open a new file named 'filename'. This overrides both the
- 'filename' and 'file' arguments to the constructor."""
- self.filename = filename
- self.file = open(self.filename, errors=self.errors)
- self.current_line = 0
-
- def close(self):
- """Close the current file and forget everything we know about it
- (filename, current line number)."""
- file = self.file
- self.file = None
- self.filename = None
- self.current_line = None
- file.close()
-
- def gen_error(self, msg, line=None):
- outmsg = []
- if line is None:
- line = self.current_line
- outmsg.append(self.filename + ", ")
- if isinstance(line, (list, tuple)):
- outmsg.append("lines %d-%d: " % tuple(line))
- else:
- outmsg.append("line %d: " % line)
- outmsg.append(str(msg))
- return "".join(outmsg)
-
- def error(self, msg, line=None):
- raise ValueError("error: " + self.gen_error(msg, line))
-
- def warn(self, msg, line=None):
- """Print (to stderr) a warning message tied to the current logical
- line in the current file. If the current logical line in the
- file spans multiple physical lines, the warning refers to the
- whole range, eg. "lines 3-5". If 'line' supplied, it overrides
- the current line number; it may be a list or tuple to indicate a
- range of physical lines, or an integer for a single physical
- line."""
- sys.stderr.write("warning: " + self.gen_error(msg, line) + "\n")
-
- def readline(self): # noqa: C901
- """Read and return a single logical line from the current file (or
- from an internal buffer if lines have previously been "unread"
- with 'unreadline()'). If the 'join_lines' option is true, this
- may involve reading multiple physical lines concatenated into a
- single string. Updates the current line number, so calling
- 'warn()' after 'readline()' emits a warning about the physical
- line(s) just read. Returns None on end-of-file, since the empty
- string can occur if 'rstrip_ws' is true but 'strip_blanks' is
- not."""
- # If any "unread" lines waiting in 'linebuf', return the top
- # one. (We don't actually buffer read-ahead data -- lines only
- # get put in 'linebuf' if the client explicitly does an
- # 'unreadline()'.
- if self.linebuf:
- line = self.linebuf[-1]
- del self.linebuf[-1]
- return line
-
- buildup_line = ''
-
- while True:
- # read the line, make it None if EOF
- line = self.file.readline()
- if line == '':
- line = None
-
- if self.strip_comments and line:
-
- # Look for the first "#" in the line. If none, never
- # mind. If we find one and it's the first character, or
- # is not preceded by "\", then it starts a comment --
- # strip the comment, strip whitespace before it, and
- # carry on. Otherwise, it's just an escaped "#", so
- # unescape it (and any other escaped "#"'s that might be
- # lurking in there) and otherwise leave the line alone.
-
- pos = line.find("#")
- if pos == -1: # no "#" -- no comments
- pass
-
- # It's definitely a comment -- either "#" is the first
- # character, or it's elsewhere and unescaped.
- elif pos == 0 or line[pos - 1] != "\\":
- # Have to preserve the trailing newline, because it's
- # the job of a later step (rstrip_ws) to remove it --
- # and if rstrip_ws is false, we'd better preserve it!
- # (NB. this means that if the final line is all comment
- # and has no trailing newline, we will think that it's
- # EOF; I think that's OK.)
- eol = (line[-1] == '\n') and '\n' or ''
- line = line[0:pos] + eol
-
- # If all that's left is whitespace, then skip line
- # *now*, before we try to join it to 'buildup_line' --
- # that way constructs like
- # hello \\
- # # comment that should be ignored
- # there
- # result in "hello there".
- if line.strip() == "":
- continue
- else: # it's an escaped "#"
- line = line.replace("\\#", "#")
-
- # did previous line end with a backslash? then accumulate
- if self.join_lines and buildup_line:
- # oops: end of file
- if line is None:
- self.warn("continuation line immediately precedes " "end-of-file")
- return buildup_line
-
- if self.collapse_join:
- line = line.lstrip()
- line = buildup_line + line
-
- # careful: pay attention to line number when incrementing it
- if isinstance(self.current_line, list):
- self.current_line[1] = self.current_line[1] + 1
- else:
- self.current_line = [self.current_line, self.current_line + 1]
- # just an ordinary line, read it as usual
- else:
- if line is None: # eof
- return None
-
- # still have to be careful about incrementing the line number!
- if isinstance(self.current_line, list):
- self.current_line = self.current_line[1] + 1
- else:
- self.current_line = self.current_line + 1
-
- # strip whitespace however the client wants (leading and
- # trailing, or one or the other, or neither)
- if self.lstrip_ws and self.rstrip_ws:
- line = line.strip()
- elif self.lstrip_ws:
- line = line.lstrip()
- elif self.rstrip_ws:
- line = line.rstrip()
-
- # blank line (whether we rstrip'ed or not)? skip to next line
- # if appropriate
- if (line == '' or line == '\n') and self.skip_blanks:
- continue
-
- if self.join_lines:
- if line[-1] == '\\':
- buildup_line = line[:-1]
- continue
-
- if line[-2:] == '\\\n':
- buildup_line = line[0:-2] + '\n'
- continue
-
- # well, I guess there's some actual content there: return it
- return line
-
- def readlines(self):
- """Read and return the list of all logical lines remaining in the
- current file."""
- lines = []
- while True:
- line = self.readline()
- if line is None:
- return lines
- lines.append(line)
-
- def unreadline(self, line):
- """Push 'line' (a string) onto an internal buffer that will be
- checked by future 'readline()' calls. Handy for implementing
- a parser with line-at-a-time lookahead."""
- self.linebuf.append(line)
diff --git a/spaces/Raspberry-ai/main/download_js.py b/spaces/Raspberry-ai/main/download_js.py
deleted file mode 100644
index 83e10aa5b533f4d7220c9537e1384c33c1001f8f..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/download_js.py
+++ /dev/null
@@ -1,27 +0,0 @@
-"""
- Javascript function to download the primary image in the gallery component.
- Based on https://stackoverflow.com/questions/17527713/force-browser-to-download-image-files-on-click
-
- Ideally, the extraction of the image source should be decoupled from the download method,
- so that the download function can be used independently, but the gradio button requires one JS string.
-"""
-download_primary_image_url_js="""
- () => {
- async function downloadImage(imageSrc) {
- const image = await fetch(imageSrc)
- const imageBlob = await image.blob()
- const imageURL = URL.createObjectURL(imageBlob)
- imageUrl = imageSrc
- const link = document.createElement('a')
- link.href = imageURL
- link.download = 'raspberry_cad.png'
- document.body.appendChild(link)
- link.click()
- document.body.removeChild(link)
- }
- const gallery_element = document.getElementById("gallery");
- console.log("gallery_element:", gallery_element);
- const primary_image_src = gallery_element.querySelector('img').src;
- console.log("primary image:", primary_image_src);
- downloadImage(primary_image_src);
- }"""
\ No newline at end of file
diff --git a/spaces/Reeve/Ohayou_Face/torch_utils/ops/bias_act.py b/spaces/Reeve/Ohayou_Face/torch_utils/ops/bias_act.py
deleted file mode 100644
index 4bcb409a89ccf6c6f6ecfca5962683df2d280b1f..0000000000000000000000000000000000000000
--- a/spaces/Reeve/Ohayou_Face/torch_utils/ops/bias_act.py
+++ /dev/null
@@ -1,212 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom PyTorch ops for efficient bias and activation."""
-
-import os
-import warnings
-import numpy as np
-import torch
-import dnnlib
-import traceback
-
-from .. import custom_ops
-from .. import misc
-
-#----------------------------------------------------------------------------
-
-activation_funcs = {
- 'linear': dnnlib.EasyDict(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False),
- 'relu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.relu(x), def_alpha=0, def_gain=np.sqrt(2), cuda_idx=2, ref='y', has_2nd_grad=False),
- 'lrelu': dnnlib.EasyDict(func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', has_2nd_grad=False),
- 'tanh': dnnlib.EasyDict(func=lambda x, **_: torch.tanh(x), def_alpha=0, def_gain=1, cuda_idx=4, ref='y', has_2nd_grad=True),
- 'sigmoid': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x), def_alpha=0, def_gain=1, cuda_idx=5, ref='y', has_2nd_grad=True),
- 'elu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.elu(x), def_alpha=0, def_gain=1, cuda_idx=6, ref='y', has_2nd_grad=True),
- 'selu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.selu(x), def_alpha=0, def_gain=1, cuda_idx=7, ref='y', has_2nd_grad=True),
- 'softplus': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.softplus(x), def_alpha=0, def_gain=1, cuda_idx=8, ref='y', has_2nd_grad=True),
- 'swish': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x) * x, def_alpha=0, def_gain=np.sqrt(2), cuda_idx=9, ref='x', has_2nd_grad=True),
-}
-
-#----------------------------------------------------------------------------
-
-_inited = False
-_plugin = None
-_null_tensor = torch.empty([0])
-
-def _init():
- global _inited, _plugin
- if not _inited:
- _inited = True
- sources = ['bias_act.cpp', 'bias_act.cu']
- sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
- try:
- _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
- except:
- warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
- return _plugin is not None
-
-#----------------------------------------------------------------------------
-
-def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'):
- r"""Fused bias and activation function.
-
- Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
- and scales the result by `gain`. Each of the steps is optional. In most cases,
- the fused op is considerably more efficient than performing the same calculation
- using standard PyTorch ops. It supports first and second order gradients,
- but not third order gradients.
-
- Args:
- x: Input activation tensor. Can be of any shape.
- b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type
- as `x`. The shape must be known, and it must match the dimension of `x`
- corresponding to `dim`.
- dim: The dimension in `x` corresponding to the elements of `b`.
- The value of `dim` is ignored if `b` is not specified.
- act: Name of the activation function to evaluate, or `"linear"` to disable.
- Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
- See `activation_funcs` for a full list. `None` is not allowed.
- alpha: Shape parameter for the activation function, or `None` to use the default.
- gain: Scaling factor for the output tensor, or `None` to use default.
- See `activation_funcs` for the default scaling of each activation function.
- If unsure, consider specifying 1.
- clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
- the clamping (default).
- impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
- Returns:
- Tensor of the same shape and datatype as `x`.
- """
- assert isinstance(x, torch.Tensor)
- assert impl in ['ref', 'cuda']
- if impl == 'cuda' and x.device.type == 'cuda' and _init():
- return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b)
- return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None):
- """Slow reference implementation of `bias_act()` using standard TensorFlow ops.
- """
- assert isinstance(x, torch.Tensor)
- assert clamp is None or clamp >= 0
- spec = activation_funcs[act]
- alpha = float(alpha if alpha is not None else spec.def_alpha)
- gain = float(gain if gain is not None else spec.def_gain)
- clamp = float(clamp if clamp is not None else -1)
-
- # Add bias.
- if b is not None:
- assert isinstance(b, torch.Tensor) and b.ndim == 1
- assert 0 <= dim < x.ndim
- assert b.shape[0] == x.shape[dim]
- x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
-
- # Evaluate activation function.
- alpha = float(alpha)
- x = spec.func(x, alpha=alpha)
-
- # Scale by gain.
- gain = float(gain)
- if gain != 1:
- x = x * gain
-
- # Clamp.
- if clamp >= 0:
- x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
- return x
-
-#----------------------------------------------------------------------------
-
-_bias_act_cuda_cache = dict()
-
-def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):
- """Fast CUDA implementation of `bias_act()` using custom ops.
- """
- # Parse arguments.
- assert clamp is None or clamp >= 0
- spec = activation_funcs[act]
- alpha = float(alpha if alpha is not None else spec.def_alpha)
- gain = float(gain if gain is not None else spec.def_gain)
- clamp = float(clamp if clamp is not None else -1)
-
- # Lookup from cache.
- key = (dim, act, alpha, gain, clamp)
- if key in _bias_act_cuda_cache:
- return _bias_act_cuda_cache[key]
-
- # Forward op.
- class BiasActCuda(torch.autograd.Function):
- @staticmethod
- def forward(ctx, x, b): # pylint: disable=arguments-differ
- ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride()[1] == 1 else torch.contiguous_format
- x = x.contiguous(memory_format=ctx.memory_format)
- b = b.contiguous() if b is not None else _null_tensor
- y = x
- if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor:
- y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp)
- ctx.save_for_backward(
- x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
- b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
- y if 'y' in spec.ref else _null_tensor)
- return y
-
- @staticmethod
- def backward(ctx, dy): # pylint: disable=arguments-differ
- dy = dy.contiguous(memory_format=ctx.memory_format)
- x, b, y = ctx.saved_tensors
- dx = None
- db = None
-
- if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
- dx = dy
- if act != 'linear' or gain != 1 or clamp >= 0:
- dx = BiasActCudaGrad.apply(dy, x, b, y)
-
- if ctx.needs_input_grad[1]:
- db = dx.sum([i for i in range(dx.ndim) if i != dim])
-
- return dx, db
-
- # Backward op.
- class BiasActCudaGrad(torch.autograd.Function):
- @staticmethod
- def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ
- ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride()[1] == 1 else torch.contiguous_format
- dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp)
- ctx.save_for_backward(
- dy if spec.has_2nd_grad else _null_tensor,
- x, b, y)
- return dx
-
- @staticmethod
- def backward(ctx, d_dx): # pylint: disable=arguments-differ
- d_dx = d_dx.contiguous(memory_format=ctx.memory_format)
- dy, x, b, y = ctx.saved_tensors
- d_dy = None
- d_x = None
- d_b = None
- d_y = None
-
- if ctx.needs_input_grad[0]:
- d_dy = BiasActCudaGrad.apply(d_dx, x, b, y)
-
- if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]):
- d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp)
-
- if spec.has_2nd_grad and ctx.needs_input_grad[2]:
- d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim])
-
- return d_dy, d_x, d_b, d_y
-
- # Add to cache.
- _bias_act_cuda_cache[key] = BiasActCuda
- return BiasActCuda
-
-#----------------------------------------------------------------------------
diff --git a/spaces/Reha2704/VToonify/vtoonify/model/__init__.py b/spaces/Reha2704/VToonify/vtoonify/model/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/evaluation/class_names.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/evaluation/class_names.py
deleted file mode 100644
index ffae816cf980ce4b03e491cc0c4298cb823797e6..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/evaluation/class_names.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import annotator.uniformer.mmcv as mmcv
-
-
-def cityscapes_classes():
- """Cityscapes class names for external use."""
- return [
- 'road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
- 'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
- 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
- 'bicycle'
- ]
-
-
-def ade_classes():
- """ADE20K class names for external use."""
- return [
- 'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
- 'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
- 'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car',
- 'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug',
- 'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe',
- 'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column',
- 'signboard', 'chest of drawers', 'counter', 'sand', 'sink',
- 'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path',
- 'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door',
- 'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table',
- 'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove',
- 'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
- 'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower',
- 'chandelier', 'awning', 'streetlight', 'booth', 'television receiver',
- 'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister',
- 'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van',
- 'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything',
- 'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent',
- 'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank',
- 'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake',
- 'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce',
- 'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen',
- 'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass',
- 'clock', 'flag'
- ]
-
-
-def voc_classes():
- """Pascal VOC class names for external use."""
- return [
- 'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
- 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
- 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
- 'tvmonitor'
- ]
-
-
-def cityscapes_palette():
- """Cityscapes palette for external use."""
- return [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156],
- [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0],
- [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60],
- [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100], [0, 80, 100],
- [0, 0, 230], [119, 11, 32]]
-
-
-def ade_palette():
- """ADE20K palette for external use."""
- return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
- [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
- [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
- [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
- [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
- [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
- [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
- [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
- [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
- [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
- [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
- [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
- [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
- [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
- [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
- [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
- [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
- [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
- [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
- [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
- [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
- [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
- [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
- [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
- [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
- [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
- [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
- [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
- [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
- [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
- [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
- [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
- [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
- [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
- [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
- [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
- [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
- [102, 255, 0], [92, 0, 255]]
-
-
-def voc_palette():
- """Pascal VOC palette for external use."""
- return [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],
- [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0],
- [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128],
- [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0],
- [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]
-
-
-dataset_aliases = {
- 'cityscapes': ['cityscapes'],
- 'ade': ['ade', 'ade20k'],
- 'voc': ['voc', 'pascal_voc', 'voc12', 'voc12aug']
-}
-
-
-def get_classes(dataset):
- """Get class names of a dataset."""
- alias2name = {}
- for name, aliases in dataset_aliases.items():
- for alias in aliases:
- alias2name[alias] = name
-
- if mmcv.is_str(dataset):
- if dataset in alias2name:
- labels = eval(alias2name[dataset] + '_classes()')
- else:
- raise ValueError(f'Unrecognized dataset: {dataset}')
- else:
- raise TypeError(f'dataset must a str, but got {type(dataset)}')
- return labels
-
-
-def get_palette(dataset):
- """Get class palette (RGB) of a dataset."""
- alias2name = {}
- for name, aliases in dataset_aliases.items():
- for alias in aliases:
- alias2name[alias] = name
-
- if mmcv.is_str(dataset):
- if dataset in alias2name:
- labels = eval(alias2name[dataset] + '_palette()')
- else:
- raise ValueError(f'Unrecognized dataset: {dataset}')
- else:
- raise TypeError(f'dataset must a str, but got {type(dataset)}')
- return labels
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/datasets/voc.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/datasets/voc.py
deleted file mode 100644
index a8855203b14ee0dc4da9099a2945d4aedcffbcd6..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/datasets/voc.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import os.path as osp
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class PascalVOCDataset(CustomDataset):
- """Pascal VOC dataset.
-
- Args:
- split (str): Split txt file for Pascal VOC.
- """
-
- CLASSES = ('background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
- 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
- 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa',
- 'train', 'tvmonitor')
-
- PALETTE = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],
- [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0],
- [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128],
- [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0],
- [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]
-
- def __init__(self, split, **kwargs):
- super(PascalVOCDataset, self).__init__(
- img_suffix='.jpg', seg_map_suffix='.png', split=split, **kwargs)
- assert osp.exists(self.img_dir) and self.split is not None
diff --git a/spaces/Salesforce/BLIP/models/blip_nlvr.py b/spaces/Salesforce/BLIP/models/blip_nlvr.py
deleted file mode 100644
index 84837167bfa6874d3c3e41fb9b37271113910b7f..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/BLIP/models/blip_nlvr.py
+++ /dev/null
@@ -1,103 +0,0 @@
-from models.med import BertConfig
-from models.nlvr_encoder import BertModel
-from models.vit import interpolate_pos_embed
-from models.blip import create_vit, init_tokenizer, is_url
-
-from timm.models.hub import download_cached_file
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from transformers import BertTokenizer
-import numpy as np
-
-class BLIP_NLVR(nn.Module):
- def __init__(self,
- med_config = 'configs/med_config.json',
- image_size = 480,
- vit = 'base',
- vit_grad_ckpt = False,
- vit_ckpt_layer = 0,
- ):
- """
- Args:
- med_config (str): path for the mixture of encoder-decoder model's configuration file
- image_size (int): input image size
- vit (str): model size of vision transformer
- """
- super().__init__()
-
- self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1)
- self.tokenizer = init_tokenizer()
- med_config = BertConfig.from_json_file(med_config)
- med_config.encoder_width = vision_width
- self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)
-
- self.cls_head = nn.Sequential(
- nn.Linear(self.text_encoder.config.hidden_size, self.text_encoder.config.hidden_size),
- nn.ReLU(),
- nn.Linear(self.text_encoder.config.hidden_size, 2)
- )
-
- def forward(self, image, text, targets, train=True):
-
- image_embeds = self.visual_encoder(image)
- image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)
- image0_embeds, image1_embeds = torch.split(image_embeds,targets.size(0))
-
- text = self.tokenizer(text, padding='longest', return_tensors="pt").to(image.device)
- text.input_ids[:,0] = self.tokenizer.enc_token_id
-
- output = self.text_encoder(text.input_ids,
- attention_mask = text.attention_mask,
- encoder_hidden_states = [image0_embeds,image1_embeds],
- encoder_attention_mask = [image_atts[:image0_embeds.size(0)],
- image_atts[image0_embeds.size(0):]],
- return_dict = True,
- )
- hidden_state = output.last_hidden_state[:,0,:]
- prediction = self.cls_head(hidden_state)
-
- if train:
- loss = F.cross_entropy(prediction, targets)
- return loss
- else:
- return prediction
-
-def blip_nlvr(pretrained='',**kwargs):
- model = BLIP_NLVR(**kwargs)
- if pretrained:
- model,msg = load_checkpoint(model,pretrained)
- print("missing keys:")
- print(msg.missing_keys)
- return model
-
-
-def load_checkpoint(model,url_or_filename):
- if is_url(url_or_filename):
- cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
- checkpoint = torch.load(cached_file, map_location='cpu')
- elif os.path.isfile(url_or_filename):
- checkpoint = torch.load(url_or_filename, map_location='cpu')
- else:
- raise RuntimeError('checkpoint url or path is invalid')
- state_dict = checkpoint['model']
-
- state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
-
- for key in list(state_dict.keys()):
- if 'crossattention.self.' in key:
- new_key0 = key.replace('self','self0')
- new_key1 = key.replace('self','self1')
- state_dict[new_key0] = state_dict[key]
- state_dict[new_key1] = state_dict[key]
- elif 'crossattention.output.dense.' in key:
- new_key0 = key.replace('dense','dense0')
- new_key1 = key.replace('dense','dense1')
- state_dict[new_key0] = state_dict[key]
- state_dict[new_key1] = state_dict[key]
-
- msg = model.load_state_dict(state_dict,strict=False)
- print('load checkpoint from %s'%url_or_filename)
- return model,msg
-
\ No newline at end of file
diff --git a/spaces/SeViLA/SeViLA/lavis/runners/runner_base.py b/spaces/SeViLA/SeViLA/lavis/runners/runner_base.py
deleted file mode 100644
index db0316e136e9c106f8261e4fc3df76ba44c9ba27..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/runners/runner_base.py
+++ /dev/null
@@ -1,653 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import datetime
-import json
-import logging
-import os
-import time
-from pathlib import Path
-
-import torch
-import torch.distributed as dist
-import webdataset as wds
-from lavis.common.dist_utils import (
- download_cached_file,
- get_rank,
- get_world_size,
- is_main_process,
- main_process,
-)
-from lavis.common.registry import registry
-from lavis.common.utils import is_url
-from lavis.datasets.data_utils import concat_datasets, reorg_datasets_by_split
-from lavis.datasets.datasets.dataloader_utils import (
- IterLoader,
- MultiIterLoader,
- PrefetchLoader,
-)
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.utils.data import DataLoader, DistributedSampler
-from torch.utils.data.dataset import ChainDataset
-
-
-@registry.register_runner("runner_base")
-class RunnerBase:
- """
- A runner class to train and evaluate a model given a task and datasets.
-
- The runner uses pytorch distributed data parallel by default. Future release
- will support other distributed frameworks.
- """
-
- def __init__(self, cfg, task, model, datasets, job_id):
- self.config = cfg
- self.job_id = job_id
-
- self.task = task
- self.datasets = datasets
-
- self._model = model
-
- self._wrapped_model = None
- self._device = None
- self._optimizer = None
- self._scaler = None
- self._dataloaders = None
- self._lr_sched = None
-
- self.start_epoch = 0
-
- # self.setup_seeds()
- self.setup_output_dir()
-
- @property
- def device(self):
- if self._device is None:
- self._device = torch.device(self.config.run_cfg.device)
-
- return self._device
-
- @property
- def use_distributed(self):
- return self.config.run_cfg.distributed
-
- @property
- def model(self):
- """
- A property to get the DDP-wrapped model on the device.
- """
- # move model to device
- if self._model.device != self.device:
- self._model = self._model.to(self.device)
-
- # distributed training wrapper
- if self.use_distributed:
- if self._wrapped_model is None:
- self._wrapped_model = DDP(
- self._model, device_ids=[self.config.run_cfg.gpu],
- broadcast_buffers=False,
- find_unused_parameters=self.config.run_cfg.find_unused_parameters
- )
- else:
- self._wrapped_model = self._model
-
- return self._wrapped_model
-
- @property
- def optimizer(self):
- # TODO make optimizer class and configurations
- if self._optimizer is None:
- num_parameters = 0
- p_wd, p_non_wd = [], []
- for n, p in self.model.named_parameters():
- if not p.requires_grad:
- continue # frozen weights
- if p.ndim < 2 or "bias" in n or "ln" in n or "bn" in n:
- p_non_wd.append(p)
- else:
- p_wd.append(p)
- num_parameters += p.data.nelement()
- logging.info("number of trainable parameters: %d" % num_parameters)
- optim_params = [
- {
- "params": p_wd,
- "weight_decay": float(self.config.run_cfg.weight_decay),
- },
- {"params": p_non_wd, "weight_decay": 0},
- ]
- beta2 = self.config.run_cfg.get("beta2", 0.999)
- self._optimizer = torch.optim.AdamW(
- optim_params,
- lr=float(self.config.run_cfg.init_lr),
- weight_decay=float(self.config.run_cfg.weight_decay),
- betas=(0.9, beta2),
- )
-
- return self._optimizer
-
- @property
- def scaler(self):
- amp = self.config.run_cfg.get("amp", False)
-
- if amp:
- if self._scaler is None:
- self._scaler = torch.cuda.amp.GradScaler()
-
- return self._scaler
-
- @property
- def lr_scheduler(self):
- """
- A property to get and create learning rate scheduler by split just in need.
- """
- if self._lr_sched is None:
- lr_sched_cls = registry.get_lr_scheduler_class(self.config.run_cfg.lr_sched)
-
- # max_epoch = self.config.run_cfg.max_epoch
- max_epoch = self.max_epoch
- # min_lr = self.config.run_cfg.min_lr
- min_lr = self.min_lr
- # init_lr = self.config.run_cfg.init_lr
- init_lr = self.init_lr
-
- # optional parameters
- decay_rate = self.config.run_cfg.get("lr_decay_rate", None)
- warmup_start_lr = self.config.run_cfg.get("warmup_lr", -1)
- warmup_steps = self.config.run_cfg.get("warmup_steps", 0)
-
- self._lr_sched = lr_sched_cls(
- optimizer=self.optimizer,
- max_epoch=max_epoch,
- min_lr=min_lr,
- init_lr=init_lr,
- decay_rate=decay_rate,
- warmup_start_lr=warmup_start_lr,
- warmup_steps=warmup_steps,
- )
-
- return self._lr_sched
-
- @property
- def dataloaders(self) -> dict:
- """
- A property to get and create dataloaders by split just in need.
-
- If no train_dataset_ratio is provided, concatenate map-style datasets and
- chain wds.DataPipe datasets separately. Training set becomes a tuple
- (ConcatDataset, ChainDataset), both are optional but at least one of them is
- required. The resultant ConcatDataset and ChainDataset will be sampled evenly.
-
- If train_dataset_ratio is provided, create a MultiIterLoader to sample
- each dataset by ratios during training.
-
- Currently do not support multiple datasets for validation and test.
-
- Returns:
- dict: {split_name: (tuples of) dataloader}
- """
- if self._dataloaders is None:
- # reoganize datasets by split and concatenate/chain if necessary
- dataset_ratios = self.config.run_cfg.get("train_dataset_ratios", None)
-
- # concatenate map-style datasets and chain wds.DataPipe datasets separately
- # training set becomes a tuple (ConcatDataset, ChainDataset), both are
- # optional but at least one of them is required. The resultant ConcatDataset
- # and ChainDataset will be sampled evenly.
- logging.info(
- "dataset_ratios not specified, datasets will be concatenated (map-style datasets) or chained (webdataset.DataPipeline)."
- )
-
- datasets = reorg_datasets_by_split(self.datasets)
- self.datasets = concat_datasets(datasets)
-
- # print dataset statistics after concatenation/chaining
- for split_name in self.datasets:
- if isinstance(self.datasets[split_name], tuple) or isinstance(
- self.datasets[split_name], list
- ):
- # mixed wds.DataPipeline and torch.utils.data.Dataset
- num_records = sum(
- [
- len(d)
- if not type(d) in [wds.DataPipeline, ChainDataset]
- else 0
- for d in self.datasets[split_name]
- ]
- )
-
- else:
- if hasattr(self.datasets[split_name], "__len__"):
- # a single map-style dataset
- num_records = len(self.datasets[split_name])
- else:
- # a single wds.DataPipeline
- num_records = -1
- logging.info(
- "Only a single wds.DataPipeline dataset, no __len__ attribute."
- )
-
- if num_records >= 0:
- logging.info(
- "Loaded {} records for {} split from the dataset.".format(
- num_records, split_name
- )
- )
-
- # create dataloaders
- split_names = sorted(self.datasets.keys())
-
- datasets = [self.datasets[split] for split in split_names]
- is_trains = [split in self.train_splits for split in split_names]
-
- batch_sizes = [
- self.config.run_cfg.batch_size_train
- if split == "train"
- else self.config.run_cfg.batch_size_eval
- for split in split_names
- ]
-
- collate_fns = []
- for dataset in datasets:
- if isinstance(dataset, tuple) or isinstance(dataset, list):
- collate_fns.append([getattr(d, "collater", None) for d in dataset])
- else:
- collate_fns.append(getattr(dataset, "collater", None))
-
- dataloaders = self.create_loaders(
- datasets=datasets,
- num_workers=self.config.run_cfg.num_workers,
- batch_sizes=batch_sizes,
- is_trains=is_trains,
- collate_fns=collate_fns,
- dataset_ratios=dataset_ratios,
- )
-
- self._dataloaders = {k: v for k, v in zip(split_names, dataloaders)}
-
- return self._dataloaders
-
- @property
- def cuda_enabled(self):
- return self.device.type == "cuda"
-
- @property
- def max_epoch(self):
- return int(self.config.run_cfg.max_epoch)
-
- @property
- def log_freq(self):
- log_freq = self.config.run_cfg.get("log_freq", 50)
- return int(log_freq)
-
- @property
- def init_lr(self):
- return float(self.config.run_cfg.init_lr)
-
- @property
- def min_lr(self):
- return float(self.config.run_cfg.min_lr)
-
- @property
- def accum_grad_iters(self):
- return int(self.config.run_cfg.get("accum_grad_iters", 1))
-
- @property
- def valid_splits(self):
- valid_splits = self.config.run_cfg.get("valid_splits", [])
-
- if len(valid_splits) == 0:
- logging.info("No validation splits found.")
-
- return valid_splits
-
- @property
- def test_splits(self):
- test_splits = self.config.run_cfg.get("test_splits", [])
-
- return test_splits
-
- @property
- def train_splits(self):
- train_splits = self.config.run_cfg.get("train_splits", [])
-
- if len(train_splits) == 0:
- logging.info("Empty train splits.")
-
- return train_splits
-
- @property
- def evaluate_only(self):
- """
- Set to True to skip training.
- """
- return self.config.run_cfg.evaluate
-
- @property
- def use_dist_eval_sampler(self):
- return self.config.run_cfg.get("use_dist_eval_sampler", True)
-
- @property
- def resume_ckpt_path(self):
- return self.config.run_cfg.get("resume_ckpt_path", None)
-
- @property
- def train_loader(self):
- train_dataloader = self.dataloaders["train"]
-
- return train_dataloader
-
- def setup_output_dir(self):
- lib_root = Path(registry.get_path("library_root"))
-
- output_dir = lib_root / self.config.run_cfg.output_dir # / self.job_id
- result_dir = output_dir / "result"
-
- output_dir.mkdir(parents=True, exist_ok=True)
- result_dir.mkdir(parents=True, exist_ok=True)
-
- registry.register_path("result_dir", str(result_dir))
- registry.register_path("output_dir", str(output_dir))
-
- self.result_dir = result_dir
- self.output_dir = output_dir
-
- def train(self):
- start_time = time.time()
- best_agg_metric = 0
- best_epoch = 0
-
- self.log_config()
-
- # resume from checkpoint if specified
- if not self.evaluate_only and self.resume_ckpt_path is not None:
- self._load_checkpoint(self.resume_ckpt_path)
-
- for cur_epoch in range(self.start_epoch, self.max_epoch):
- # training phase
- if not self.evaluate_only:
- logging.info("Start training")
- train_stats = self.train_epoch(cur_epoch)
- self.log_stats(split_name="train", stats=train_stats)
-
- # evaluation phase
- if len(self.valid_splits) > 0:
- for split_name in self.valid_splits:
- logging.info("Evaluating on {}.".format(split_name))
-
- val_log = self.eval_epoch(
- split_name=split_name, cur_epoch=cur_epoch
- )
- if val_log is not None:
- if is_main_process():
- assert (
- "agg_metrics" in val_log
- ), "No agg_metrics found in validation log."
-
- agg_metrics = val_log["agg_metrics"]
- if agg_metrics > best_agg_metric and split_name == "val":
- best_epoch, best_agg_metric = cur_epoch, agg_metrics
-
- self._save_checkpoint(cur_epoch, is_best=True)
-
- val_log.update({"best_epoch": best_epoch})
- self.log_stats(val_log, split_name)
-
- else:
- # if no validation split is provided, we just save the checkpoint at the end of each epoch.
- if not self.evaluate_only:
- self._save_checkpoint(cur_epoch, is_best=False)
-
- if self.evaluate_only:
- break
-
- dist.barrier()
-
- # testing phase
- test_epoch = "best" if len(self.valid_splits) > 0 else cur_epoch
- self.evaluate(cur_epoch=test_epoch, skip_reload=self.evaluate_only)
-
- total_time = time.time() - start_time
- total_time_str = str(datetime.timedelta(seconds=int(total_time)))
- logging.info("Training time {}".format(total_time_str))
-
- def evaluate(self, cur_epoch="best", skip_reload=False):
- test_logs = dict()
-
- if len(self.test_splits) > 0:
- for split_name in self.test_splits:
- test_logs[split_name] = self.eval_epoch(
- split_name=split_name, cur_epoch=cur_epoch, skip_reload=skip_reload
- )
-
- return test_logs
-
- def train_epoch(self, epoch):
- # train
- self.model.train()
-
- return self.task.train_epoch(
- epoch=epoch,
- model=self.model,
- data_loader=self.train_loader,
- optimizer=self.optimizer,
- scaler=self.scaler,
- lr_scheduler=self.lr_scheduler,
- cuda_enabled=self.cuda_enabled,
- log_freq=self.log_freq,
- accum_grad_iters=self.accum_grad_iters,
- )
-
- @torch.no_grad()
- def eval_epoch(self, split_name, cur_epoch, skip_reload=False):
- """
- Evaluate the model on a given split.
-
- Args:
- split_name (str): name of the split to evaluate on.
- cur_epoch (int): current epoch.
- skip_reload_best (bool): whether to skip reloading the best checkpoint.
- During training, we will reload the best checkpoint for validation.
- During testing, we will use provided weights and skip reloading the best checkpoint .
- """
- data_loader = self.dataloaders.get(split_name, None)
- assert data_loader, "data_loader for split {} is None.".format(split_name)
-
- # TODO In validation, you need to compute loss as well as metrics
- # TODO consider moving to model.before_evaluation()
- model = self.unwrap_dist_model(self.model)
- if not skip_reload and cur_epoch == "best":
- model = self._reload_best_model(model)
- model.eval()
-
- self.task.before_evaluation(
- model=model,
- dataset=self.datasets[split_name],
- )
- results = self.task.evaluation(model, data_loader)
-
- if results is not None:
- return self.task.after_evaluation(
- val_result=results,
- split_name=split_name,
- epoch=cur_epoch,
- )
-
- def unwrap_dist_model(self, model):
- if self.use_distributed:
- return model.module
- else:
- return model
-
- def create_loaders(
- self,
- datasets,
- num_workers,
- batch_sizes,
- is_trains,
- collate_fns,
- dataset_ratios=None,
- ):
- """
- Create dataloaders for training and validation.
- """
-
- def _create_loader(dataset, num_workers, bsz, is_train, collate_fn):
- # create a single dataloader for each split
- if isinstance(dataset, ChainDataset) or isinstance(
- dataset, wds.DataPipeline
- ):
- # wds.WebdDataset instance are chained together
- # webdataset.DataPipeline has its own sampler and collate_fn
- loader = iter(
- DataLoader(
- dataset,
- batch_size=bsz,
- num_workers=num_workers,
- pin_memory=True,
- )
- )
- else:
- # map-style dataset are concatenated together
- # setup distributed sampler
- if self.use_distributed:
- sampler = DistributedSampler(
- dataset,
- shuffle=is_train,
- num_replicas=get_world_size(),
- rank=get_rank(),
- )
- if not self.use_dist_eval_sampler:
- # e.g. retrieval evaluation
- sampler = sampler if is_train else None
- else:
- sampler = None
-
- loader = DataLoader(
- dataset,
- batch_size=bsz,
- num_workers=num_workers,
- pin_memory=True,
- sampler=sampler,
- shuffle=sampler is None and is_train,
- collate_fn=collate_fn,
- drop_last=True if is_train else False,
- )
- loader = PrefetchLoader(loader)
-
- if is_train:
- loader = IterLoader(loader, use_distributed=self.use_distributed)
-
- return loader
-
- loaders = []
-
- for dataset, bsz, is_train, collate_fn in zip(
- datasets, batch_sizes, is_trains, collate_fns
- ):
- if isinstance(dataset, list) or isinstance(dataset, tuple):
- loader = MultiIterLoader(
- loaders=[
- _create_loader(d, num_workers, bsz, is_train, collate_fn[i])
- for i, d in enumerate(dataset)
- ],
- ratios=dataset_ratios,
- )
- else:
- loader = _create_loader(dataset, num_workers, bsz, is_train, collate_fn)
-
- loaders.append(loader)
-
- return loaders
-
- @main_process
- def _save_checkpoint(self, cur_epoch, is_best=False):
- """
- Save the checkpoint at the current epoch.
- """
- model_no_ddp = self.unwrap_dist_model(self.model)
- param_grad_dic = {
- k: v.requires_grad for (k, v) in model_no_ddp.named_parameters()
- }
- state_dict = model_no_ddp.state_dict()
- for k in list(state_dict.keys()):
- if k in param_grad_dic.keys() and not param_grad_dic[k]:
- # delete parameters that do not require gradient
- #if 't5_model' not in k and 'visual_encoder' not in k:
- # print(k)
- del state_dict[k]
- save_obj = {
- "model": state_dict,
- "optimizer": self.optimizer.state_dict(),
- "config": self.config.to_dict(),
- "scaler": self.scaler.state_dict() if self.scaler else None,
- "epoch": cur_epoch,
- }
- save_to = os.path.join(
- self.output_dir,
- "checkpoint_{}.pth".format("best" if is_best else cur_epoch),
- )
- logging.info("Saving checkpoint at epoch {} to {}.".format(cur_epoch, save_to))
- torch.save(save_obj, save_to)
-
- def _reload_best_model(self, model):
- """
- Load the best checkpoint for evaluation.
- """
- checkpoint_path = os.path.join(self.output_dir, "checkpoint_best.pth")
-
- logging.info("Loading checkpoint from {}.".format(checkpoint_path))
- checkpoint = torch.load(checkpoint_path, map_location="cpu")
- try:
- model.load_state_dict(checkpoint["model"])
- except RuntimeError as e:
- logging.warning(
- """
- Key mismatch when loading checkpoint. This is expected if only part of the model is saved.
- Trying to load the model with strict=False.
- """
- )
- model.load_state_dict(checkpoint["model"], strict=False)
- return model
-
- def _load_checkpoint(self, url_or_filename):
- """
- Resume from a checkpoint.
- """
- if is_url(url_or_filename):
- cached_file = download_cached_file(
- url_or_filename, check_hash=False, progress=True
- )
- checkpoint = torch.load(cached_file, map_location=self.device)
- elif os.path.isfile(url_or_filename):
- checkpoint = torch.load(url_or_filename, map_location=self.device)
- else:
- raise RuntimeError("checkpoint url or path is invalid")
-
- state_dict = checkpoint["model"]
- self.unwrap_dist_model(self.model).load_state_dict(state_dict)
-
- self.optimizer.load_state_dict(checkpoint["optimizer"])
- if self.scaler and "scaler" in checkpoint:
- self.scaler.load_state_dict(checkpoint["scaler"])
-
- self.start_epoch = checkpoint["epoch"] + 1
- logging.info("Resume checkpoint from {}".format(url_or_filename))
-
- @main_process
- def log_stats(self, stats, split_name):
- if isinstance(stats, dict):
- log_stats = {**{f"{split_name}_{k}": v for k, v in stats.items()}}
- with open(os.path.join(self.output_dir, "log.txt"), "a") as f:
- f.write(json.dumps(log_stats) + "\n")
- elif isinstance(stats, list):
- pass
-
- @main_process
- def log_config(self):
- with open(os.path.join(self.output_dir, "log.txt"), "a") as f:
- f.write(json.dumps(self.config.to_dict(), indent=4) + "\n")
diff --git a/spaces/Shredder/CONBERT-3/score_fincat.py b/spaces/Shredder/CONBERT-3/score_fincat.py
deleted file mode 100644
index 5771348f09f01ffed0c4e17e00f83e72edced41a..0000000000000000000000000000000000000000
--- a/spaces/Shredder/CONBERT-3/score_fincat.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import gradio as gr
-import nltk
-from fincat_utils import extract_context_words
-from fincat_utils import bert_embedding_extract
-import pickle
-lr_clf = pickle.load(open("lr_clf_FiNCAT.pickle",'rb'))
-nltk.download('punkt')
-
-def score_fincat(txt):
- li = []
- highlight = []
- txt = " " + txt + " "
- k = ''
- for word in txt.split():
- if any(char.isdigit() for char in word):
- if word[-1] in ['.', ',', ';', ":", "-", "!", "?", ")", '"', "'"]:
- k = word[-1]
- word = word[:-1]
- st = txt.find(" " + word + k + " ")+1
- k = ''
- ed = st + len(word)
- x = {'paragraph' : txt, 'offset_start':st, 'offset_end':ed}
- context_text = extract_context_words(x)
- features = bert_embedding_extract(context_text, word)
- if(features[0]=='None'):
- highlight.append((word, ''))
- continue
- prediction = lr_clf.predict(features.reshape(1, 768))
- prediction_probability = '{:.4f}'.format(round(lr_clf.predict_proba(features.reshape(1, 768))[:,1][0], 4))
- highlight.append((word, ' In-claim' if prediction==1 else 'Out-of-Claim'))
- else:
- continue
- if(len(highlight)<1):
- highlight.append((txt,'None'))
- return highlight
\ No newline at end of file
diff --git a/spaces/Simbals/TextRetrieval/README.md b/spaces/Simbals/TextRetrieval/README.md
deleted file mode 100644
index b5c816a30222f6524e84a1d038334fc857a4b097..0000000000000000000000000000000000000000
--- a/spaces/Simbals/TextRetrieval/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: POC TextRetrieval
-emoji: 🌍
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: false
-python_version: 3.7.13
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_imports.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_imports.py
deleted file mode 100644
index 7aa278fb63a8bd0cce1a7b13302d0ddbbb8e90c3..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_imports.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# encoding: utf-8
-
-def test_import_completer():
- from IPython.core import completer
-
-def test_import_crashhandler():
- from IPython.core import crashhandler
-
-def test_import_debugger():
- from IPython.core import debugger
-
-def test_import_excolors():
- from IPython.core import excolors
-
-def test_import_history():
- from IPython.core import history
-
-def test_import_hooks():
- from IPython.core import hooks
-
-def test_import_getipython():
- from IPython.core import getipython
-
-def test_import_interactiveshell():
- from IPython.core import interactiveshell
-
-def test_import_logger():
- from IPython.core import logger
-
-def test_import_macro():
- from IPython.core import macro
-
-def test_import_magic():
- from IPython.core import magic
-
-def test_import_oinspect():
- from IPython.core import oinspect
-
-def test_import_prefilter():
- from IPython.core import prefilter
-
-def test_import_prompts():
- from IPython.core import prompts
-
-def test_import_release():
- from IPython.core import release
-
-def test_import_ultratb():
- from IPython.core import ultratb
-
-def test_import_usage():
- from IPython.core import usage
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/extensions/tests/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/extensions/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/pt_inputhooks/qt.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/pt_inputhooks/qt.py
deleted file mode 100644
index cf6d11ea6ca6d54493653af1c605958b0f1a260b..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/terminal/pt_inputhooks/qt.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import sys
-import os
-from IPython.external.qt_for_kernel import QtCore, QtGui, enum_helper
-from IPython import get_ipython
-
-# If we create a QApplication, keep a reference to it so that it doesn't get
-# garbage collected.
-_appref = None
-_already_warned = False
-
-
-def _exec(obj):
- # exec on PyQt6, exec_ elsewhere.
- obj.exec() if hasattr(obj, "exec") else obj.exec_()
-
-
-def _reclaim_excepthook():
- shell = get_ipython()
- if shell is not None:
- sys.excepthook = shell.excepthook
-
-
-def inputhook(context):
- global _appref
- app = QtCore.QCoreApplication.instance()
- if not app:
- if sys.platform == 'linux':
- if not os.environ.get('DISPLAY') \
- and not os.environ.get('WAYLAND_DISPLAY'):
- import warnings
- global _already_warned
- if not _already_warned:
- _already_warned = True
- warnings.warn(
- 'The DISPLAY or WAYLAND_DISPLAY environment variable is '
- 'not set or empty and Qt5 requires this environment '
- 'variable. Deactivate Qt5 code.'
- )
- return
- try:
- QtCore.QApplication.setAttribute(QtCore.Qt.AA_EnableHighDpiScaling)
- except AttributeError: # Only for Qt>=5.6, <6.
- pass
- try:
- QtCore.QApplication.setHighDpiScaleFactorRoundingPolicy(
- QtCore.Qt.HighDpiScaleFactorRoundingPolicy.PassThrough
- )
- except AttributeError: # Only for Qt>=5.14.
- pass
- _appref = app = QtGui.QApplication([" "])
-
- # "reclaim" IPython sys.excepthook after event loop starts
- # without this, it defaults back to BaseIPythonApplication.excepthook
- # and exceptions in the Qt event loop are rendered without traceback
- # formatting and look like "bug in IPython".
- QtCore.QTimer.singleShot(0, _reclaim_excepthook)
-
- event_loop = QtCore.QEventLoop(app)
-
- if sys.platform == 'win32':
- # The QSocketNotifier method doesn't appear to work on Windows.
- # Use polling instead.
- timer = QtCore.QTimer()
- timer.timeout.connect(event_loop.quit)
- while not context.input_is_ready():
- # NOTE: run the event loop, and after 50 ms, call `quit` to exit it.
- timer.start(50) # 50 ms
- _exec(event_loop)
- timer.stop()
- else:
- # On POSIX platforms, we can use a file descriptor to quit the event
- # loop when there is input ready to read.
- notifier = QtCore.QSocketNotifier(
- context.fileno(), enum_helper("QtCore.QSocketNotifier.Type").Read
- )
- try:
- # connect the callback we care about before we turn it on
- # lambda is necessary as PyQT inspect the function signature to know
- # what arguments to pass to. See https://github.com/ipython/ipython/pull/12355
- notifier.activated.connect(lambda: event_loop.exit())
- notifier.setEnabled(True)
- # only start the event loop we are not already flipped
- if not context.input_is_ready():
- _exec(event_loop)
- finally:
- notifier.setEnabled(False)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/_process_posix.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/_process_posix.py
deleted file mode 100644
index 59b5c2389604a005c33afeebb848c88ee52fa780..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/_process_posix.py
+++ /dev/null
@@ -1,216 +0,0 @@
-"""Posix-specific implementation of process utilities.
-
-This file is only meant to be imported by process.py, not by end-users.
-"""
-
-#-----------------------------------------------------------------------------
-# Copyright (C) 2010-2011 The IPython Development Team
-#
-# Distributed under the terms of the BSD License. The full license is in
-# the file COPYING, distributed as part of this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
-
-# Stdlib
-import errno
-import os
-import subprocess as sp
-import sys
-
-import pexpect
-
-# Our own
-from ._process_common import getoutput, arg_split
-from IPython.utils.encoding import DEFAULT_ENCODING
-
-#-----------------------------------------------------------------------------
-# Function definitions
-#-----------------------------------------------------------------------------
-
-class ProcessHandler(object):
- """Execute subprocesses under the control of pexpect.
- """
- # Timeout in seconds to wait on each reading of the subprocess' output.
- # This should not be set too low to avoid cpu overusage from our side,
- # since we read in a loop whose period is controlled by this timeout.
- read_timeout = 0.05
-
- # Timeout to give a process if we receive SIGINT, between sending the
- # SIGINT to the process and forcefully terminating it.
- terminate_timeout = 0.2
-
- # File object where stdout and stderr of the subprocess will be written
- logfile = None
-
- # Shell to call for subprocesses to execute
- _sh = None
-
- @property
- def sh(self):
- if self._sh is None:
- shell_name = os.environ.get("SHELL", "sh")
- self._sh = pexpect.which(shell_name)
- if self._sh is None:
- raise OSError('"{}" shell not found'.format(shell_name))
-
- return self._sh
-
- def __init__(self, logfile=None, read_timeout=None, terminate_timeout=None):
- """Arguments are used for pexpect calls."""
- self.read_timeout = (ProcessHandler.read_timeout if read_timeout is
- None else read_timeout)
- self.terminate_timeout = (ProcessHandler.terminate_timeout if
- terminate_timeout is None else
- terminate_timeout)
- self.logfile = sys.stdout if logfile is None else logfile
-
- def getoutput(self, cmd):
- """Run a command and return its stdout/stderr as a string.
-
- Parameters
- ----------
- cmd : str
- A command to be executed in the system shell.
-
- Returns
- -------
- output : str
- A string containing the combination of stdout and stderr from the
- subprocess, in whatever order the subprocess originally wrote to its
- file descriptors (so the order of the information in this string is the
- correct order as would be seen if running the command in a terminal).
- """
- try:
- return pexpect.run(self.sh, args=['-c', cmd]).replace('\r\n', '\n')
- except KeyboardInterrupt:
- print('^C', file=sys.stderr, end='')
-
- def getoutput_pexpect(self, cmd):
- """Run a command and return its stdout/stderr as a string.
-
- Parameters
- ----------
- cmd : str
- A command to be executed in the system shell.
-
- Returns
- -------
- output : str
- A string containing the combination of stdout and stderr from the
- subprocess, in whatever order the subprocess originally wrote to its
- file descriptors (so the order of the information in this string is the
- correct order as would be seen if running the command in a terminal).
- """
- try:
- return pexpect.run(self.sh, args=['-c', cmd]).replace('\r\n', '\n')
- except KeyboardInterrupt:
- print('^C', file=sys.stderr, end='')
-
- def system(self, cmd):
- """Execute a command in a subshell.
-
- Parameters
- ----------
- cmd : str
- A command to be executed in the system shell.
-
- Returns
- -------
- int : child's exitstatus
- """
- # Get likely encoding for the output.
- enc = DEFAULT_ENCODING
-
- # Patterns to match on the output, for pexpect. We read input and
- # allow either a short timeout or EOF
- patterns = [pexpect.TIMEOUT, pexpect.EOF]
- # the index of the EOF pattern in the list.
- # even though we know it's 1, this call means we don't have to worry if
- # we change the above list, and forget to change this value:
- EOF_index = patterns.index(pexpect.EOF)
- # The size of the output stored so far in the process output buffer.
- # Since pexpect only appends to this buffer, each time we print we
- # record how far we've printed, so that next time we only print *new*
- # content from the buffer.
- out_size = 0
- try:
- # Since we're not really searching the buffer for text patterns, we
- # can set pexpect's search window to be tiny and it won't matter.
- # We only search for the 'patterns' timeout or EOF, which aren't in
- # the text itself.
- #child = pexpect.spawn(pcmd, searchwindowsize=1)
- if hasattr(pexpect, 'spawnb'):
- child = pexpect.spawnb(self.sh, args=['-c', cmd]) # Pexpect-U
- else:
- child = pexpect.spawn(self.sh, args=['-c', cmd]) # Vanilla Pexpect
- flush = sys.stdout.flush
- while True:
- # res is the index of the pattern that caused the match, so we
- # know whether we've finished (if we matched EOF) or not
- res_idx = child.expect_list(patterns, self.read_timeout)
- print(child.before[out_size:].decode(enc, 'replace'), end='')
- flush()
- if res_idx==EOF_index:
- break
- # Update the pointer to what we've already printed
- out_size = len(child.before)
- except KeyboardInterrupt:
- # We need to send ^C to the process. The ascii code for '^C' is 3
- # (the character is known as ETX for 'End of Text', see
- # curses.ascii.ETX).
- child.sendline(chr(3))
- # Read and print any more output the program might produce on its
- # way out.
- try:
- out_size = len(child.before)
- child.expect_list(patterns, self.terminate_timeout)
- print(child.before[out_size:].decode(enc, 'replace'), end='')
- sys.stdout.flush()
- except KeyboardInterrupt:
- # Impatient users tend to type it multiple times
- pass
- finally:
- # Ensure the subprocess really is terminated
- child.terminate(force=True)
- # add isalive check, to ensure exitstatus is set:
- child.isalive()
-
- # We follow the subprocess pattern, returning either the exit status
- # as a positive number, or the terminating signal as a negative
- # number.
- # on Linux, sh returns 128+n for signals terminating child processes on Linux
- # on BSD (OS X), the signal code is set instead
- if child.exitstatus is None:
- # on WIFSIGNALED, pexpect sets signalstatus, leaving exitstatus=None
- if child.signalstatus is None:
- # this condition may never occur,
- # but let's be certain we always return an integer.
- return 0
- return -child.signalstatus
- if child.exitstatus > 128:
- return -(child.exitstatus - 128)
- return child.exitstatus
-
-
-# Make system() with a functional interface for outside use. Note that we use
-# getoutput() from the _common utils, which is built on top of popen(). Using
-# pexpect to get subprocess output produces difficult to parse output, since
-# programs think they are talking to a tty and produce highly formatted output
-# (ls is a good example) that makes them hard.
-system = ProcessHandler().system
-
-def check_pid(pid):
- try:
- os.kill(pid, 0)
- except OSError as err:
- if err.errno == errno.ESRCH:
- return False
- elif err.errno == errno.EPERM:
- # Don't have permission to signal the process - probably means it exists
- return True
- raise
- else:
- return True
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiohttp/tracing.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiohttp/tracing.py
deleted file mode 100644
index d5596a4ceab79aff362203376952edc3122bf811..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/aiohttp/tracing.py
+++ /dev/null
@@ -1,472 +0,0 @@
-from types import SimpleNamespace
-from typing import TYPE_CHECKING, Awaitable, Optional, Type, TypeVar
-
-import attr
-from aiosignal import Signal
-from multidict import CIMultiDict
-from yarl import URL
-
-from .client_reqrep import ClientResponse
-
-if TYPE_CHECKING: # pragma: no cover
- from .client import ClientSession
- from .typedefs import Protocol
-
- _ParamT_contra = TypeVar("_ParamT_contra", contravariant=True)
-
- class _SignalCallback(Protocol[_ParamT_contra]):
- def __call__(
- self,
- __client_session: ClientSession,
- __trace_config_ctx: SimpleNamespace,
- __params: _ParamT_contra,
- ) -> Awaitable[None]:
- ...
-
-
-__all__ = (
- "TraceConfig",
- "TraceRequestStartParams",
- "TraceRequestEndParams",
- "TraceRequestExceptionParams",
- "TraceConnectionQueuedStartParams",
- "TraceConnectionQueuedEndParams",
- "TraceConnectionCreateStartParams",
- "TraceConnectionCreateEndParams",
- "TraceConnectionReuseconnParams",
- "TraceDnsResolveHostStartParams",
- "TraceDnsResolveHostEndParams",
- "TraceDnsCacheHitParams",
- "TraceDnsCacheMissParams",
- "TraceRequestRedirectParams",
- "TraceRequestChunkSentParams",
- "TraceResponseChunkReceivedParams",
- "TraceRequestHeadersSentParams",
-)
-
-
-class TraceConfig:
- """First-class used to trace requests launched via ClientSession objects."""
-
- def __init__(
- self, trace_config_ctx_factory: Type[SimpleNamespace] = SimpleNamespace
- ) -> None:
- self._on_request_start: Signal[
- _SignalCallback[TraceRequestStartParams]
- ] = Signal(self)
- self._on_request_chunk_sent: Signal[
- _SignalCallback[TraceRequestChunkSentParams]
- ] = Signal(self)
- self._on_response_chunk_received: Signal[
- _SignalCallback[TraceResponseChunkReceivedParams]
- ] = Signal(self)
- self._on_request_end: Signal[_SignalCallback[TraceRequestEndParams]] = Signal(
- self
- )
- self._on_request_exception: Signal[
- _SignalCallback[TraceRequestExceptionParams]
- ] = Signal(self)
- self._on_request_redirect: Signal[
- _SignalCallback[TraceRequestRedirectParams]
- ] = Signal(self)
- self._on_connection_queued_start: Signal[
- _SignalCallback[TraceConnectionQueuedStartParams]
- ] = Signal(self)
- self._on_connection_queued_end: Signal[
- _SignalCallback[TraceConnectionQueuedEndParams]
- ] = Signal(self)
- self._on_connection_create_start: Signal[
- _SignalCallback[TraceConnectionCreateStartParams]
- ] = Signal(self)
- self._on_connection_create_end: Signal[
- _SignalCallback[TraceConnectionCreateEndParams]
- ] = Signal(self)
- self._on_connection_reuseconn: Signal[
- _SignalCallback[TraceConnectionReuseconnParams]
- ] = Signal(self)
- self._on_dns_resolvehost_start: Signal[
- _SignalCallback[TraceDnsResolveHostStartParams]
- ] = Signal(self)
- self._on_dns_resolvehost_end: Signal[
- _SignalCallback[TraceDnsResolveHostEndParams]
- ] = Signal(self)
- self._on_dns_cache_hit: Signal[
- _SignalCallback[TraceDnsCacheHitParams]
- ] = Signal(self)
- self._on_dns_cache_miss: Signal[
- _SignalCallback[TraceDnsCacheMissParams]
- ] = Signal(self)
- self._on_request_headers_sent: Signal[
- _SignalCallback[TraceRequestHeadersSentParams]
- ] = Signal(self)
-
- self._trace_config_ctx_factory = trace_config_ctx_factory
-
- def trace_config_ctx(
- self, trace_request_ctx: Optional[SimpleNamespace] = None
- ) -> SimpleNamespace:
- """Return a new trace_config_ctx instance"""
- return self._trace_config_ctx_factory(trace_request_ctx=trace_request_ctx)
-
- def freeze(self) -> None:
- self._on_request_start.freeze()
- self._on_request_chunk_sent.freeze()
- self._on_response_chunk_received.freeze()
- self._on_request_end.freeze()
- self._on_request_exception.freeze()
- self._on_request_redirect.freeze()
- self._on_connection_queued_start.freeze()
- self._on_connection_queued_end.freeze()
- self._on_connection_create_start.freeze()
- self._on_connection_create_end.freeze()
- self._on_connection_reuseconn.freeze()
- self._on_dns_resolvehost_start.freeze()
- self._on_dns_resolvehost_end.freeze()
- self._on_dns_cache_hit.freeze()
- self._on_dns_cache_miss.freeze()
- self._on_request_headers_sent.freeze()
-
- @property
- def on_request_start(self) -> "Signal[_SignalCallback[TraceRequestStartParams]]":
- return self._on_request_start
-
- @property
- def on_request_chunk_sent(
- self,
- ) -> "Signal[_SignalCallback[TraceRequestChunkSentParams]]":
- return self._on_request_chunk_sent
-
- @property
- def on_response_chunk_received(
- self,
- ) -> "Signal[_SignalCallback[TraceResponseChunkReceivedParams]]":
- return self._on_response_chunk_received
-
- @property
- def on_request_end(self) -> "Signal[_SignalCallback[TraceRequestEndParams]]":
- return self._on_request_end
-
- @property
- def on_request_exception(
- self,
- ) -> "Signal[_SignalCallback[TraceRequestExceptionParams]]":
- return self._on_request_exception
-
- @property
- def on_request_redirect(
- self,
- ) -> "Signal[_SignalCallback[TraceRequestRedirectParams]]":
- return self._on_request_redirect
-
- @property
- def on_connection_queued_start(
- self,
- ) -> "Signal[_SignalCallback[TraceConnectionQueuedStartParams]]":
- return self._on_connection_queued_start
-
- @property
- def on_connection_queued_end(
- self,
- ) -> "Signal[_SignalCallback[TraceConnectionQueuedEndParams]]":
- return self._on_connection_queued_end
-
- @property
- def on_connection_create_start(
- self,
- ) -> "Signal[_SignalCallback[TraceConnectionCreateStartParams]]":
- return self._on_connection_create_start
-
- @property
- def on_connection_create_end(
- self,
- ) -> "Signal[_SignalCallback[TraceConnectionCreateEndParams]]":
- return self._on_connection_create_end
-
- @property
- def on_connection_reuseconn(
- self,
- ) -> "Signal[_SignalCallback[TraceConnectionReuseconnParams]]":
- return self._on_connection_reuseconn
-
- @property
- def on_dns_resolvehost_start(
- self,
- ) -> "Signal[_SignalCallback[TraceDnsResolveHostStartParams]]":
- return self._on_dns_resolvehost_start
-
- @property
- def on_dns_resolvehost_end(
- self,
- ) -> "Signal[_SignalCallback[TraceDnsResolveHostEndParams]]":
- return self._on_dns_resolvehost_end
-
- @property
- def on_dns_cache_hit(self) -> "Signal[_SignalCallback[TraceDnsCacheHitParams]]":
- return self._on_dns_cache_hit
-
- @property
- def on_dns_cache_miss(self) -> "Signal[_SignalCallback[TraceDnsCacheMissParams]]":
- return self._on_dns_cache_miss
-
- @property
- def on_request_headers_sent(
- self,
- ) -> "Signal[_SignalCallback[TraceRequestHeadersSentParams]]":
- return self._on_request_headers_sent
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceRequestStartParams:
- """Parameters sent by the `on_request_start` signal"""
-
- method: str
- url: URL
- headers: "CIMultiDict[str]"
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceRequestChunkSentParams:
- """Parameters sent by the `on_request_chunk_sent` signal"""
-
- method: str
- url: URL
- chunk: bytes
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceResponseChunkReceivedParams:
- """Parameters sent by the `on_response_chunk_received` signal"""
-
- method: str
- url: URL
- chunk: bytes
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceRequestEndParams:
- """Parameters sent by the `on_request_end` signal"""
-
- method: str
- url: URL
- headers: "CIMultiDict[str]"
- response: ClientResponse
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceRequestExceptionParams:
- """Parameters sent by the `on_request_exception` signal"""
-
- method: str
- url: URL
- headers: "CIMultiDict[str]"
- exception: BaseException
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceRequestRedirectParams:
- """Parameters sent by the `on_request_redirect` signal"""
-
- method: str
- url: URL
- headers: "CIMultiDict[str]"
- response: ClientResponse
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceConnectionQueuedStartParams:
- """Parameters sent by the `on_connection_queued_start` signal"""
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceConnectionQueuedEndParams:
- """Parameters sent by the `on_connection_queued_end` signal"""
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceConnectionCreateStartParams:
- """Parameters sent by the `on_connection_create_start` signal"""
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceConnectionCreateEndParams:
- """Parameters sent by the `on_connection_create_end` signal"""
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceConnectionReuseconnParams:
- """Parameters sent by the `on_connection_reuseconn` signal"""
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceDnsResolveHostStartParams:
- """Parameters sent by the `on_dns_resolvehost_start` signal"""
-
- host: str
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceDnsResolveHostEndParams:
- """Parameters sent by the `on_dns_resolvehost_end` signal"""
-
- host: str
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceDnsCacheHitParams:
- """Parameters sent by the `on_dns_cache_hit` signal"""
-
- host: str
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceDnsCacheMissParams:
- """Parameters sent by the `on_dns_cache_miss` signal"""
-
- host: str
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceRequestHeadersSentParams:
- """Parameters sent by the `on_request_headers_sent` signal"""
-
- method: str
- url: URL
- headers: "CIMultiDict[str]"
-
-
-class Trace:
- """Internal dependency holder class.
-
- Used to keep together the main dependencies used
- at the moment of send a signal.
- """
-
- def __init__(
- self,
- session: "ClientSession",
- trace_config: TraceConfig,
- trace_config_ctx: SimpleNamespace,
- ) -> None:
- self._trace_config = trace_config
- self._trace_config_ctx = trace_config_ctx
- self._session = session
-
- async def send_request_start(
- self, method: str, url: URL, headers: "CIMultiDict[str]"
- ) -> None:
- return await self._trace_config.on_request_start.send(
- self._session,
- self._trace_config_ctx,
- TraceRequestStartParams(method, url, headers),
- )
-
- async def send_request_chunk_sent(
- self, method: str, url: URL, chunk: bytes
- ) -> None:
- return await self._trace_config.on_request_chunk_sent.send(
- self._session,
- self._trace_config_ctx,
- TraceRequestChunkSentParams(method, url, chunk),
- )
-
- async def send_response_chunk_received(
- self, method: str, url: URL, chunk: bytes
- ) -> None:
- return await self._trace_config.on_response_chunk_received.send(
- self._session,
- self._trace_config_ctx,
- TraceResponseChunkReceivedParams(method, url, chunk),
- )
-
- async def send_request_end(
- self,
- method: str,
- url: URL,
- headers: "CIMultiDict[str]",
- response: ClientResponse,
- ) -> None:
- return await self._trace_config.on_request_end.send(
- self._session,
- self._trace_config_ctx,
- TraceRequestEndParams(method, url, headers, response),
- )
-
- async def send_request_exception(
- self,
- method: str,
- url: URL,
- headers: "CIMultiDict[str]",
- exception: BaseException,
- ) -> None:
- return await self._trace_config.on_request_exception.send(
- self._session,
- self._trace_config_ctx,
- TraceRequestExceptionParams(method, url, headers, exception),
- )
-
- async def send_request_redirect(
- self,
- method: str,
- url: URL,
- headers: "CIMultiDict[str]",
- response: ClientResponse,
- ) -> None:
- return await self._trace_config._on_request_redirect.send(
- self._session,
- self._trace_config_ctx,
- TraceRequestRedirectParams(method, url, headers, response),
- )
-
- async def send_connection_queued_start(self) -> None:
- return await self._trace_config.on_connection_queued_start.send(
- self._session, self._trace_config_ctx, TraceConnectionQueuedStartParams()
- )
-
- async def send_connection_queued_end(self) -> None:
- return await self._trace_config.on_connection_queued_end.send(
- self._session, self._trace_config_ctx, TraceConnectionQueuedEndParams()
- )
-
- async def send_connection_create_start(self) -> None:
- return await self._trace_config.on_connection_create_start.send(
- self._session, self._trace_config_ctx, TraceConnectionCreateStartParams()
- )
-
- async def send_connection_create_end(self) -> None:
- return await self._trace_config.on_connection_create_end.send(
- self._session, self._trace_config_ctx, TraceConnectionCreateEndParams()
- )
-
- async def send_connection_reuseconn(self) -> None:
- return await self._trace_config.on_connection_reuseconn.send(
- self._session, self._trace_config_ctx, TraceConnectionReuseconnParams()
- )
-
- async def send_dns_resolvehost_start(self, host: str) -> None:
- return await self._trace_config.on_dns_resolvehost_start.send(
- self._session, self._trace_config_ctx, TraceDnsResolveHostStartParams(host)
- )
-
- async def send_dns_resolvehost_end(self, host: str) -> None:
- return await self._trace_config.on_dns_resolvehost_end.send(
- self._session, self._trace_config_ctx, TraceDnsResolveHostEndParams(host)
- )
-
- async def send_dns_cache_hit(self, host: str) -> None:
- return await self._trace_config.on_dns_cache_hit.send(
- self._session, self._trace_config_ctx, TraceDnsCacheHitParams(host)
- )
-
- async def send_dns_cache_miss(self, host: str) -> None:
- return await self._trace_config.on_dns_cache_miss.send(
- self._session, self._trace_config_ctx, TraceDnsCacheMissParams(host)
- )
-
- async def send_request_headers(
- self, method: str, url: URL, headers: "CIMultiDict[str]"
- ) -> None:
- return await self._trace_config._on_request_headers_sent.send(
- self._session,
- self._trace_config_ctx,
- TraceRequestHeadersSentParams(method, url, headers),
- )
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/win32/kernel32.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/win32/kernel32.py
deleted file mode 100644
index d0c0468f648784c2faa5cead66856ac9078b52af..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/win32/kernel32.py
+++ /dev/null
@@ -1,4716 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-# Copyright (c) 2009-2014, Mario Vilas
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-# * Redistributions of source code must retain the above copyright notice,
-# this list of conditions and the following disclaimer.
-# * Redistributions in binary form must reproduce the above copyright
-# notice,this list of conditions and the following disclaimer in the
-# documentation and/or other materials provided with the distribution.
-# * Neither the name of the copyright holder nor the names of its
-# contributors may be used to endorse or promote products derived from
-# this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-
-"""
-Wrapper for kernel32.dll in ctypes.
-"""
-
-__revision__ = "$Id$"
-
-import warnings
-
-from winappdbg.win32.defines import *
-
-from winappdbg.win32 import context_i386
-from winappdbg.win32 import context_amd64
-
-#==============================================================================
-# This is used later on to calculate the list of exported symbols.
-_all = None
-_all = set(vars().keys())
-_all.add('version')
-#==============================================================================
-
-from winappdbg.win32.version import *
-
-#------------------------------------------------------------------------------
-
-# This can't be defined in defines.py because it calls GetLastError().
-def RaiseIfLastError(result, func = None, arguments = ()):
- """
- Error checking for Win32 API calls with no error-specific return value.
-
- Regardless of the return value, the function calls GetLastError(). If the
- code is not C{ERROR_SUCCESS} then a C{WindowsError} exception is raised.
-
- For this to work, the user MUST call SetLastError(ERROR_SUCCESS) prior to
- calling the API. Otherwise an exception may be raised even on success,
- since most API calls don't clear the error status code.
- """
- code = GetLastError()
- if code != ERROR_SUCCESS:
- raise ctypes.WinError(code)
- return result
-
-#--- CONTEXT structure and constants ------------------------------------------
-
-ContextArchMask = 0x0FFF0000 # just guessing here! seems to work, though
-
-if arch == ARCH_I386:
- from winappdbg.win32.context_i386 import *
-elif arch == ARCH_AMD64:
- if bits == 64:
- from winappdbg.win32.context_amd64 import *
- else:
- from winappdbg.win32.context_i386 import *
-else:
- warnings.warn("Unknown or unsupported architecture: %s" % arch)
-
-#--- Constants ----------------------------------------------------------------
-
-STILL_ACTIVE = 259
-
-WAIT_TIMEOUT = 0x102
-WAIT_FAILED = -1
-WAIT_OBJECT_0 = 0
-
-EXCEPTION_NONCONTINUABLE = 0x1 # Noncontinuable exception
-EXCEPTION_MAXIMUM_PARAMETERS = 15 # maximum number of exception parameters
-MAXIMUM_WAIT_OBJECTS = 64 # Maximum number of wait objects
-MAXIMUM_SUSPEND_COUNT = 0x7f # Maximum times thread can be suspended
-
-FORMAT_MESSAGE_ALLOCATE_BUFFER = 0x00000100
-FORMAT_MESSAGE_FROM_SYSTEM = 0x00001000
-
-GR_GDIOBJECTS = 0
-GR_USEROBJECTS = 1
-
-PROCESS_NAME_NATIVE = 1
-
-MAXINTATOM = 0xC000
-
-STD_INPUT_HANDLE = 0xFFFFFFF6 # (DWORD)-10
-STD_OUTPUT_HANDLE = 0xFFFFFFF5 # (DWORD)-11
-STD_ERROR_HANDLE = 0xFFFFFFF4 # (DWORD)-12
-
-ATTACH_PARENT_PROCESS = 0xFFFFFFFF # (DWORD)-1
-
-# LoadLibraryEx constants
-DONT_RESOLVE_DLL_REFERENCES = 0x00000001
-LOAD_LIBRARY_AS_DATAFILE = 0x00000002
-LOAD_WITH_ALTERED_SEARCH_PATH = 0x00000008
-LOAD_IGNORE_CODE_AUTHZ_LEVEL = 0x00000010
-LOAD_LIBRARY_AS_IMAGE_RESOURCE = 0x00000020
-LOAD_LIBRARY_AS_DATAFILE_EXCLUSIVE = 0x00000040
-
-# SetSearchPathMode flags
-# TODO I couldn't find these constants :(
-##BASE_SEARCH_PATH_ENABLE_SAFE_SEARCHMODE = ???
-##BASE_SEARCH_PATH_DISABLE_SAFE_SEARCHMODE = ???
-##BASE_SEARCH_PATH_PERMANENT = ???
-
-# Console control events
-CTRL_C_EVENT = 0
-CTRL_BREAK_EVENT = 1
-CTRL_CLOSE_EVENT = 2
-CTRL_LOGOFF_EVENT = 5
-CTRL_SHUTDOWN_EVENT = 6
-
-# Heap flags
-HEAP_NO_SERIALIZE = 0x00000001
-HEAP_GENERATE_EXCEPTIONS = 0x00000004
-HEAP_ZERO_MEMORY = 0x00000008
-HEAP_CREATE_ENABLE_EXECUTE = 0x00040000
-
-# Standard access rights
-DELETE = long(0x00010000)
-READ_CONTROL = long(0x00020000)
-WRITE_DAC = long(0x00040000)
-WRITE_OWNER = long(0x00080000)
-SYNCHRONIZE = long(0x00100000)
-STANDARD_RIGHTS_REQUIRED = long(0x000F0000)
-STANDARD_RIGHTS_READ = (READ_CONTROL)
-STANDARD_RIGHTS_WRITE = (READ_CONTROL)
-STANDARD_RIGHTS_EXECUTE = (READ_CONTROL)
-STANDARD_RIGHTS_ALL = long(0x001F0000)
-SPECIFIC_RIGHTS_ALL = long(0x0000FFFF)
-
-# Mutex access rights
-MUTEX_ALL_ACCESS = 0x1F0001
-MUTEX_MODIFY_STATE = 1
-
-# Event access rights
-EVENT_ALL_ACCESS = 0x1F0003
-EVENT_MODIFY_STATE = 2
-
-# Semaphore access rights
-SEMAPHORE_ALL_ACCESS = 0x1F0003
-SEMAPHORE_MODIFY_STATE = 2
-
-# Timer access rights
-TIMER_ALL_ACCESS = 0x1F0003
-TIMER_MODIFY_STATE = 2
-TIMER_QUERY_STATE = 1
-
-# Process access rights for OpenProcess
-PROCESS_TERMINATE = 0x0001
-PROCESS_CREATE_THREAD = 0x0002
-PROCESS_SET_SESSIONID = 0x0004
-PROCESS_VM_OPERATION = 0x0008
-PROCESS_VM_READ = 0x0010
-PROCESS_VM_WRITE = 0x0020
-PROCESS_DUP_HANDLE = 0x0040
-PROCESS_CREATE_PROCESS = 0x0080
-PROCESS_SET_QUOTA = 0x0100
-PROCESS_SET_INFORMATION = 0x0200
-PROCESS_QUERY_INFORMATION = 0x0400
-PROCESS_SUSPEND_RESUME = 0x0800
-PROCESS_QUERY_LIMITED_INFORMATION = 0x1000
-
-# Thread access rights for OpenThread
-THREAD_TERMINATE = 0x0001
-THREAD_SUSPEND_RESUME = 0x0002
-THREAD_ALERT = 0x0004
-THREAD_GET_CONTEXT = 0x0008
-THREAD_SET_CONTEXT = 0x0010
-THREAD_SET_INFORMATION = 0x0020
-THREAD_QUERY_INFORMATION = 0x0040
-THREAD_SET_THREAD_TOKEN = 0x0080
-THREAD_IMPERSONATE = 0x0100
-THREAD_DIRECT_IMPERSONATION = 0x0200
-THREAD_SET_LIMITED_INFORMATION = 0x0400
-THREAD_QUERY_LIMITED_INFORMATION = 0x0800
-
-# The values of PROCESS_ALL_ACCESS and THREAD_ALL_ACCESS were changed in Vista/2008
-PROCESS_ALL_ACCESS_NT = (STANDARD_RIGHTS_REQUIRED | SYNCHRONIZE | 0xFFF)
-PROCESS_ALL_ACCESS_VISTA = (STANDARD_RIGHTS_REQUIRED | SYNCHRONIZE | 0xFFFF)
-THREAD_ALL_ACCESS_NT = (STANDARD_RIGHTS_REQUIRED | SYNCHRONIZE | 0x3FF)
-THREAD_ALL_ACCESS_VISTA = (STANDARD_RIGHTS_REQUIRED | SYNCHRONIZE | 0xFFFF)
-if NTDDI_VERSION < NTDDI_VISTA:
- PROCESS_ALL_ACCESS = PROCESS_ALL_ACCESS_NT
- THREAD_ALL_ACCESS = THREAD_ALL_ACCESS_NT
-else:
- PROCESS_ALL_ACCESS = PROCESS_ALL_ACCESS_VISTA
- THREAD_ALL_ACCESS = THREAD_ALL_ACCESS_VISTA
-
-# Process priority classes
-
-IDLE_PRIORITY_CLASS = 0x00000040
-BELOW_NORMAL_PRIORITY_CLASS = 0x00004000
-NORMAL_PRIORITY_CLASS = 0x00000020
-ABOVE_NORMAL_PRIORITY_CLASS = 0x00008000
-HIGH_PRIORITY_CLASS = 0x00000080
-REALTIME_PRIORITY_CLASS = 0x00000100
-
-PROCESS_MODE_BACKGROUND_BEGIN = 0x00100000
-PROCESS_MODE_BACKGROUND_END = 0x00200000
-
-# dwCreationFlag values
-
-DEBUG_PROCESS = 0x00000001
-DEBUG_ONLY_THIS_PROCESS = 0x00000002
-CREATE_SUSPENDED = 0x00000004 # Threads and processes
-DETACHED_PROCESS = 0x00000008
-CREATE_NEW_CONSOLE = 0x00000010
-NORMAL_PRIORITY_CLASS = 0x00000020
-IDLE_PRIORITY_CLASS = 0x00000040
-HIGH_PRIORITY_CLASS = 0x00000080
-REALTIME_PRIORITY_CLASS = 0x00000100
-CREATE_NEW_PROCESS_GROUP = 0x00000200
-CREATE_UNICODE_ENVIRONMENT = 0x00000400
-CREATE_SEPARATE_WOW_VDM = 0x00000800
-CREATE_SHARED_WOW_VDM = 0x00001000
-CREATE_FORCEDOS = 0x00002000
-BELOW_NORMAL_PRIORITY_CLASS = 0x00004000
-ABOVE_NORMAL_PRIORITY_CLASS = 0x00008000
-INHERIT_PARENT_AFFINITY = 0x00010000
-STACK_SIZE_PARAM_IS_A_RESERVATION = 0x00010000 # Threads only
-INHERIT_CALLER_PRIORITY = 0x00020000 # Deprecated
-CREATE_PROTECTED_PROCESS = 0x00040000
-EXTENDED_STARTUPINFO_PRESENT = 0x00080000
-PROCESS_MODE_BACKGROUND_BEGIN = 0x00100000
-PROCESS_MODE_BACKGROUND_END = 0x00200000
-CREATE_BREAKAWAY_FROM_JOB = 0x01000000
-CREATE_PRESERVE_CODE_AUTHZ_LEVEL = 0x02000000
-CREATE_DEFAULT_ERROR_MODE = 0x04000000
-CREATE_NO_WINDOW = 0x08000000
-PROFILE_USER = 0x10000000
-PROFILE_KERNEL = 0x20000000
-PROFILE_SERVER = 0x40000000
-CREATE_IGNORE_SYSTEM_DEFAULT = 0x80000000
-
-# Thread priority values
-
-THREAD_BASE_PRIORITY_LOWRT = 15 # value that gets a thread to LowRealtime-1
-THREAD_BASE_PRIORITY_MAX = 2 # maximum thread base priority boost
-THREAD_BASE_PRIORITY_MIN = (-2) # minimum thread base priority boost
-THREAD_BASE_PRIORITY_IDLE = (-15) # value that gets a thread to idle
-
-THREAD_PRIORITY_LOWEST = THREAD_BASE_PRIORITY_MIN
-THREAD_PRIORITY_BELOW_NORMAL = (THREAD_PRIORITY_LOWEST+1)
-THREAD_PRIORITY_NORMAL = 0
-THREAD_PRIORITY_HIGHEST = THREAD_BASE_PRIORITY_MAX
-THREAD_PRIORITY_ABOVE_NORMAL = (THREAD_PRIORITY_HIGHEST-1)
-THREAD_PRIORITY_ERROR_RETURN = long(0xFFFFFFFF)
-
-THREAD_PRIORITY_TIME_CRITICAL = THREAD_BASE_PRIORITY_LOWRT
-THREAD_PRIORITY_IDLE = THREAD_BASE_PRIORITY_IDLE
-
-# Memory access
-SECTION_QUERY = 0x0001
-SECTION_MAP_WRITE = 0x0002
-SECTION_MAP_READ = 0x0004
-SECTION_MAP_EXECUTE = 0x0008
-SECTION_EXTEND_SIZE = 0x0010
-SECTION_MAP_EXECUTE_EXPLICIT = 0x0020 # not included in SECTION_ALL_ACCESS
-
-SECTION_ALL_ACCESS = (STANDARD_RIGHTS_REQUIRED|SECTION_QUERY|\
- SECTION_MAP_WRITE | \
- SECTION_MAP_READ | \
- SECTION_MAP_EXECUTE | \
- SECTION_EXTEND_SIZE)
-PAGE_NOACCESS = 0x01
-PAGE_READONLY = 0x02
-PAGE_READWRITE = 0x04
-PAGE_WRITECOPY = 0x08
-PAGE_EXECUTE = 0x10
-PAGE_EXECUTE_READ = 0x20
-PAGE_EXECUTE_READWRITE = 0x40
-PAGE_EXECUTE_WRITECOPY = 0x80
-PAGE_GUARD = 0x100
-PAGE_NOCACHE = 0x200
-PAGE_WRITECOMBINE = 0x400
-MEM_COMMIT = 0x1000
-MEM_RESERVE = 0x2000
-MEM_DECOMMIT = 0x4000
-MEM_RELEASE = 0x8000
-MEM_FREE = 0x10000
-MEM_PRIVATE = 0x20000
-MEM_MAPPED = 0x40000
-MEM_RESET = 0x80000
-MEM_TOP_DOWN = 0x100000
-MEM_WRITE_WATCH = 0x200000
-MEM_PHYSICAL = 0x400000
-MEM_LARGE_PAGES = 0x20000000
-MEM_4MB_PAGES = 0x80000000
-SEC_FILE = 0x800000
-SEC_IMAGE = 0x1000000
-SEC_RESERVE = 0x4000000
-SEC_COMMIT = 0x8000000
-SEC_NOCACHE = 0x10000000
-SEC_LARGE_PAGES = 0x80000000
-MEM_IMAGE = SEC_IMAGE
-WRITE_WATCH_FLAG_RESET = 0x01
-FILE_MAP_ALL_ACCESS = 0xF001F
-
-SECTION_QUERY = 0x0001
-SECTION_MAP_WRITE = 0x0002
-SECTION_MAP_READ = 0x0004
-SECTION_MAP_EXECUTE = 0x0008
-SECTION_EXTEND_SIZE = 0x0010
-SECTION_MAP_EXECUTE_EXPLICIT = 0x0020 # not included in SECTION_ALL_ACCESS
-
-SECTION_ALL_ACCESS = (STANDARD_RIGHTS_REQUIRED|SECTION_QUERY|\
- SECTION_MAP_WRITE | \
- SECTION_MAP_READ | \
- SECTION_MAP_EXECUTE | \
- SECTION_EXTEND_SIZE)
-
-FILE_MAP_COPY = SECTION_QUERY
-FILE_MAP_WRITE = SECTION_MAP_WRITE
-FILE_MAP_READ = SECTION_MAP_READ
-FILE_MAP_ALL_ACCESS = SECTION_ALL_ACCESS
-FILE_MAP_EXECUTE = SECTION_MAP_EXECUTE_EXPLICIT # not included in FILE_MAP_ALL_ACCESS
-
-GENERIC_READ = 0x80000000
-GENERIC_WRITE = 0x40000000
-GENERIC_EXECUTE = 0x20000000
-GENERIC_ALL = 0x10000000
-
-FILE_SHARE_READ = 0x00000001
-FILE_SHARE_WRITE = 0x00000002
-FILE_SHARE_DELETE = 0x00000004
-
-CREATE_NEW = 1
-CREATE_ALWAYS = 2
-OPEN_EXISTING = 3
-OPEN_ALWAYS = 4
-TRUNCATE_EXISTING = 5
-
-FILE_ATTRIBUTE_READONLY = 0x00000001
-FILE_ATTRIBUTE_NORMAL = 0x00000080
-FILE_ATTRIBUTE_TEMPORARY = 0x00000100
-
-FILE_FLAG_WRITE_THROUGH = 0x80000000
-FILE_FLAG_NO_BUFFERING = 0x20000000
-FILE_FLAG_RANDOM_ACCESS = 0x10000000
-FILE_FLAG_SEQUENTIAL_SCAN = 0x08000000
-FILE_FLAG_DELETE_ON_CLOSE = 0x04000000
-FILE_FLAG_OVERLAPPED = 0x40000000
-
-FILE_ATTRIBUTE_READONLY = 0x00000001
-FILE_ATTRIBUTE_HIDDEN = 0x00000002
-FILE_ATTRIBUTE_SYSTEM = 0x00000004
-FILE_ATTRIBUTE_DIRECTORY = 0x00000010
-FILE_ATTRIBUTE_ARCHIVE = 0x00000020
-FILE_ATTRIBUTE_DEVICE = 0x00000040
-FILE_ATTRIBUTE_NORMAL = 0x00000080
-FILE_ATTRIBUTE_TEMPORARY = 0x00000100
-
-# Debug events
-EXCEPTION_DEBUG_EVENT = 1
-CREATE_THREAD_DEBUG_EVENT = 2
-CREATE_PROCESS_DEBUG_EVENT = 3
-EXIT_THREAD_DEBUG_EVENT = 4
-EXIT_PROCESS_DEBUG_EVENT = 5
-LOAD_DLL_DEBUG_EVENT = 6
-UNLOAD_DLL_DEBUG_EVENT = 7
-OUTPUT_DEBUG_STRING_EVENT = 8
-RIP_EVENT = 9
-
-# Debug status codes (ContinueDebugEvent)
-DBG_EXCEPTION_HANDLED = long(0x00010001)
-DBG_CONTINUE = long(0x00010002)
-DBG_REPLY_LATER = long(0x40010001)
-DBG_UNABLE_TO_PROVIDE_HANDLE = long(0x40010002)
-DBG_TERMINATE_THREAD = long(0x40010003)
-DBG_TERMINATE_PROCESS = long(0x40010004)
-DBG_CONTROL_C = long(0x40010005)
-DBG_PRINTEXCEPTION_C = long(0x40010006)
-DBG_RIPEXCEPTION = long(0x40010007)
-DBG_CONTROL_BREAK = long(0x40010008)
-DBG_COMMAND_EXCEPTION = long(0x40010009)
-DBG_EXCEPTION_NOT_HANDLED = long(0x80010001)
-DBG_NO_STATE_CHANGE = long(0xC0010001)
-DBG_APP_NOT_IDLE = long(0xC0010002)
-
-# Status codes
-STATUS_WAIT_0 = long(0x00000000)
-STATUS_ABANDONED_WAIT_0 = long(0x00000080)
-STATUS_USER_APC = long(0x000000C0)
-STATUS_TIMEOUT = long(0x00000102)
-STATUS_PENDING = long(0x00000103)
-STATUS_SEGMENT_NOTIFICATION = long(0x40000005)
-STATUS_GUARD_PAGE_VIOLATION = long(0x80000001)
-STATUS_DATATYPE_MISALIGNMENT = long(0x80000002)
-STATUS_BREAKPOINT = long(0x80000003)
-STATUS_SINGLE_STEP = long(0x80000004)
-STATUS_INVALID_INFO_CLASS = long(0xC0000003)
-STATUS_ACCESS_VIOLATION = long(0xC0000005)
-STATUS_IN_PAGE_ERROR = long(0xC0000006)
-STATUS_INVALID_HANDLE = long(0xC0000008)
-STATUS_NO_MEMORY = long(0xC0000017)
-STATUS_ILLEGAL_INSTRUCTION = long(0xC000001D)
-STATUS_NONCONTINUABLE_EXCEPTION = long(0xC0000025)
-STATUS_INVALID_DISPOSITION = long(0xC0000026)
-STATUS_ARRAY_BOUNDS_EXCEEDED = long(0xC000008C)
-STATUS_FLOAT_DENORMAL_OPERAND = long(0xC000008D)
-STATUS_FLOAT_DIVIDE_BY_ZERO = long(0xC000008E)
-STATUS_FLOAT_INEXACT_RESULT = long(0xC000008F)
-STATUS_FLOAT_INVALID_OPERATION = long(0xC0000090)
-STATUS_FLOAT_OVERFLOW = long(0xC0000091)
-STATUS_FLOAT_STACK_CHECK = long(0xC0000092)
-STATUS_FLOAT_UNDERFLOW = long(0xC0000093)
-STATUS_INTEGER_DIVIDE_BY_ZERO = long(0xC0000094)
-STATUS_INTEGER_OVERFLOW = long(0xC0000095)
-STATUS_PRIVILEGED_INSTRUCTION = long(0xC0000096)
-STATUS_STACK_OVERFLOW = long(0xC00000FD)
-STATUS_CONTROL_C_EXIT = long(0xC000013A)
-STATUS_FLOAT_MULTIPLE_FAULTS = long(0xC00002B4)
-STATUS_FLOAT_MULTIPLE_TRAPS = long(0xC00002B5)
-STATUS_REG_NAT_CONSUMPTION = long(0xC00002C9)
-STATUS_SXS_EARLY_DEACTIVATION = long(0xC015000F)
-STATUS_SXS_INVALID_DEACTIVATION = long(0xC0150010)
-
-STATUS_STACK_BUFFER_OVERRUN = long(0xC0000409)
-STATUS_WX86_BREAKPOINT = long(0x4000001F)
-STATUS_HEAP_CORRUPTION = long(0xC0000374)
-
-STATUS_POSSIBLE_DEADLOCK = long(0xC0000194)
-
-STATUS_UNWIND_CONSOLIDATE = long(0x80000029)
-
-# Exception codes
-
-EXCEPTION_ACCESS_VIOLATION = STATUS_ACCESS_VIOLATION
-EXCEPTION_ARRAY_BOUNDS_EXCEEDED = STATUS_ARRAY_BOUNDS_EXCEEDED
-EXCEPTION_BREAKPOINT = STATUS_BREAKPOINT
-EXCEPTION_DATATYPE_MISALIGNMENT = STATUS_DATATYPE_MISALIGNMENT
-EXCEPTION_FLT_DENORMAL_OPERAND = STATUS_FLOAT_DENORMAL_OPERAND
-EXCEPTION_FLT_DIVIDE_BY_ZERO = STATUS_FLOAT_DIVIDE_BY_ZERO
-EXCEPTION_FLT_INEXACT_RESULT = STATUS_FLOAT_INEXACT_RESULT
-EXCEPTION_FLT_INVALID_OPERATION = STATUS_FLOAT_INVALID_OPERATION
-EXCEPTION_FLT_OVERFLOW = STATUS_FLOAT_OVERFLOW
-EXCEPTION_FLT_STACK_CHECK = STATUS_FLOAT_STACK_CHECK
-EXCEPTION_FLT_UNDERFLOW = STATUS_FLOAT_UNDERFLOW
-EXCEPTION_ILLEGAL_INSTRUCTION = STATUS_ILLEGAL_INSTRUCTION
-EXCEPTION_IN_PAGE_ERROR = STATUS_IN_PAGE_ERROR
-EXCEPTION_INT_DIVIDE_BY_ZERO = STATUS_INTEGER_DIVIDE_BY_ZERO
-EXCEPTION_INT_OVERFLOW = STATUS_INTEGER_OVERFLOW
-EXCEPTION_INVALID_DISPOSITION = STATUS_INVALID_DISPOSITION
-EXCEPTION_NONCONTINUABLE_EXCEPTION = STATUS_NONCONTINUABLE_EXCEPTION
-EXCEPTION_PRIV_INSTRUCTION = STATUS_PRIVILEGED_INSTRUCTION
-EXCEPTION_SINGLE_STEP = STATUS_SINGLE_STEP
-EXCEPTION_STACK_OVERFLOW = STATUS_STACK_OVERFLOW
-
-EXCEPTION_GUARD_PAGE = STATUS_GUARD_PAGE_VIOLATION
-EXCEPTION_INVALID_HANDLE = STATUS_INVALID_HANDLE
-EXCEPTION_POSSIBLE_DEADLOCK = STATUS_POSSIBLE_DEADLOCK
-EXCEPTION_WX86_BREAKPOINT = STATUS_WX86_BREAKPOINT
-
-CONTROL_C_EXIT = STATUS_CONTROL_C_EXIT
-
-DBG_CONTROL_C = long(0x40010005)
-MS_VC_EXCEPTION = long(0x406D1388)
-
-# Access violation types
-ACCESS_VIOLATION_TYPE_READ = EXCEPTION_READ_FAULT
-ACCESS_VIOLATION_TYPE_WRITE = EXCEPTION_WRITE_FAULT
-ACCESS_VIOLATION_TYPE_DEP = EXCEPTION_EXECUTE_FAULT
-
-# RIP event types
-SLE_ERROR = 1
-SLE_MINORERROR = 2
-SLE_WARNING = 3
-
-# DuplicateHandle constants
-DUPLICATE_CLOSE_SOURCE = 0x00000001
-DUPLICATE_SAME_ACCESS = 0x00000002
-
-# GetFinalPathNameByHandle constants
-FILE_NAME_NORMALIZED = 0x0
-FILE_NAME_OPENED = 0x8
-VOLUME_NAME_DOS = 0x0
-VOLUME_NAME_GUID = 0x1
-VOLUME_NAME_NONE = 0x4
-VOLUME_NAME_NT = 0x2
-
-# GetProductInfo constants
-PRODUCT_BUSINESS = 0x00000006
-PRODUCT_BUSINESS_N = 0x00000010
-PRODUCT_CLUSTER_SERVER = 0x00000012
-PRODUCT_DATACENTER_SERVER = 0x00000008
-PRODUCT_DATACENTER_SERVER_CORE = 0x0000000C
-PRODUCT_DATACENTER_SERVER_CORE_V = 0x00000027
-PRODUCT_DATACENTER_SERVER_V = 0x00000025
-PRODUCT_ENTERPRISE = 0x00000004
-PRODUCT_ENTERPRISE_E = 0x00000046
-PRODUCT_ENTERPRISE_N = 0x0000001B
-PRODUCT_ENTERPRISE_SERVER = 0x0000000A
-PRODUCT_ENTERPRISE_SERVER_CORE = 0x0000000E
-PRODUCT_ENTERPRISE_SERVER_CORE_V = 0x00000029
-PRODUCT_ENTERPRISE_SERVER_IA64 = 0x0000000F
-PRODUCT_ENTERPRISE_SERVER_V = 0x00000026
-PRODUCT_HOME_BASIC = 0x00000002
-PRODUCT_HOME_BASIC_E = 0x00000043
-PRODUCT_HOME_BASIC_N = 0x00000005
-PRODUCT_HOME_PREMIUM = 0x00000003
-PRODUCT_HOME_PREMIUM_E = 0x00000044
-PRODUCT_HOME_PREMIUM_N = 0x0000001A
-PRODUCT_HYPERV = 0x0000002A
-PRODUCT_MEDIUMBUSINESS_SERVER_MANAGEMENT = 0x0000001E
-PRODUCT_MEDIUMBUSINESS_SERVER_MESSAGING = 0x00000020
-PRODUCT_MEDIUMBUSINESS_SERVER_SECURITY = 0x0000001F
-PRODUCT_PROFESSIONAL = 0x00000030
-PRODUCT_PROFESSIONAL_E = 0x00000045
-PRODUCT_PROFESSIONAL_N = 0x00000031
-PRODUCT_SERVER_FOR_SMALLBUSINESS = 0x00000018
-PRODUCT_SERVER_FOR_SMALLBUSINESS_V = 0x00000023
-PRODUCT_SERVER_FOUNDATION = 0x00000021
-PRODUCT_SMALLBUSINESS_SERVER = 0x00000009
-PRODUCT_STANDARD_SERVER = 0x00000007
-PRODUCT_STANDARD_SERVER_CORE = 0x0000000D
-PRODUCT_STANDARD_SERVER_CORE_V = 0x00000028
-PRODUCT_STANDARD_SERVER_V = 0x00000024
-PRODUCT_STARTER = 0x0000000B
-PRODUCT_STARTER_E = 0x00000042
-PRODUCT_STARTER_N = 0x0000002F
-PRODUCT_STORAGE_ENTERPRISE_SERVER = 0x00000017
-PRODUCT_STORAGE_EXPRESS_SERVER = 0x00000014
-PRODUCT_STORAGE_STANDARD_SERVER = 0x00000015
-PRODUCT_STORAGE_WORKGROUP_SERVER = 0x00000016
-PRODUCT_UNDEFINED = 0x00000000
-PRODUCT_UNLICENSED = 0xABCDABCD
-PRODUCT_ULTIMATE = 0x00000001
-PRODUCT_ULTIMATE_E = 0x00000047
-PRODUCT_ULTIMATE_N = 0x0000001C
-PRODUCT_WEB_SERVER = 0x00000011
-PRODUCT_WEB_SERVER_CORE = 0x0000001D
-
-# DEP policy flags
-PROCESS_DEP_ENABLE = 1
-PROCESS_DEP_DISABLE_ATL_THUNK_EMULATION = 2
-
-# Error modes
-SEM_FAILCRITICALERRORS = 0x001
-SEM_NOGPFAULTERRORBOX = 0x002
-SEM_NOALIGNMENTFAULTEXCEPT = 0x004
-SEM_NOOPENFILEERRORBOX = 0x800
-
-# GetHandleInformation / SetHandleInformation
-HANDLE_FLAG_INHERIT = 0x00000001
-HANDLE_FLAG_PROTECT_FROM_CLOSE = 0x00000002
-
-#--- Handle wrappers ----------------------------------------------------------
-
-class Handle (object):
- """
- Encapsulates Win32 handles to avoid leaking them.
-
- @type inherit: bool
- @ivar inherit: C{True} if the handle is to be inherited by child processes,
- C{False} otherwise.
-
- @type protectFromClose: bool
- @ivar protectFromClose: Set to C{True} to prevent the handle from being
- closed. Must be set to C{False} before you're done using the handle,
- or it will be left open until the debugger exits. Use with care!
-
- @see:
- L{ProcessHandle}, L{ThreadHandle}, L{FileHandle}, L{SnapshotHandle}
- """
-
- # XXX DEBUG
- # When this private flag is True each Handle will print a message to
- # standard output when it's created and destroyed. This is useful for
- # detecting handle leaks within WinAppDbg itself.
- __bLeakDetection = False
-
- def __init__(self, aHandle = None, bOwnership = True):
- """
- @type aHandle: int
- @param aHandle: Win32 handle value.
-
- @type bOwnership: bool
- @param bOwnership:
- C{True} if we own the handle and we need to close it.
- C{False} if someone else will be calling L{CloseHandle}.
- """
- super(Handle, self).__init__()
- self._value = self._normalize(aHandle)
- self.bOwnership = bOwnership
- if Handle.__bLeakDetection: # XXX DEBUG
- print("INIT HANDLE (%r) %r" % (self.value, self))
-
- @property
- def value(self):
- return self._value
-
- def __del__(self):
- """
- Closes the Win32 handle when the Python object is destroyed.
- """
- try:
- if Handle.__bLeakDetection: # XXX DEBUG
- print("DEL HANDLE %r" % self)
- self.close()
- except Exception:
- pass
-
- def __enter__(self):
- """
- Compatibility with the "C{with}" Python statement.
- """
- if Handle.__bLeakDetection: # XXX DEBUG
- print("ENTER HANDLE %r" % self)
- return self
-
- def __exit__(self, type, value, traceback):
- """
- Compatibility with the "C{with}" Python statement.
- """
- if Handle.__bLeakDetection: # XXX DEBUG
- print("EXIT HANDLE %r" % self)
- try:
- self.close()
- except Exception:
- pass
-
- def __copy__(self):
- """
- Duplicates the Win32 handle when copying the Python object.
-
- @rtype: L{Handle}
- @return: A new handle to the same Win32 object.
- """
- return self.dup()
-
- def __deepcopy__(self):
- """
- Duplicates the Win32 handle when copying the Python object.
-
- @rtype: L{Handle}
- @return: A new handle to the same win32 object.
- """
- return self.dup()
-
- @property
- def _as_parameter_(self):
- """
- Compatibility with ctypes.
- Allows passing transparently a Handle object to an API call.
- """
- return HANDLE(self.value)
-
- @staticmethod
- def from_param(value):
- """
- Compatibility with ctypes.
- Allows passing transparently a Handle object to an API call.
-
- @type value: int
- @param value: Numeric handle value.
- """
- return HANDLE(value)
-
- def close(self):
- """
- Closes the Win32 handle.
- """
- if self.bOwnership and self.value not in (None, INVALID_HANDLE_VALUE):
- if Handle.__bLeakDetection: # XXX DEBUG
- print("CLOSE HANDLE (%d) %r" % (self.value, self))
- try:
- self._close()
- finally:
- self._value = None
-
- def _close(self):
- """
- Low-level close method.
- This is a private method, do not call it.
- """
- CloseHandle(self.value)
-
- def dup(self):
- """
- @rtype: L{Handle}
- @return: A new handle to the same Win32 object.
- """
- if self.value is None:
- raise ValueError("Closed handles can't be duplicated!")
- new_handle = DuplicateHandle(self.value)
- if Handle.__bLeakDetection: # XXX DEBUG
- print("DUP HANDLE (%d -> %d) %r %r" % \
- (self.value, new_handle.value, self, new_handle))
- return new_handle
-
- @staticmethod
- def _normalize(value):
- """
- Normalize handle values.
- """
- if hasattr(value, 'value'):
- value = value.value
- if value is not None:
- value = long(value)
- return value
-
- def wait(self, dwMilliseconds = None):
- """
- Wait for the Win32 object to be signaled.
-
- @type dwMilliseconds: int
- @param dwMilliseconds: (Optional) Timeout value in milliseconds.
- Use C{INFINITE} or C{None} for no timeout.
- """
- if self.value is None:
- raise ValueError("Handle is already closed!")
- if dwMilliseconds is None:
- dwMilliseconds = INFINITE
- r = WaitForSingleObject(self.value, dwMilliseconds)
- if r != WAIT_OBJECT_0:
- raise ctypes.WinError(r)
-
- def __repr__(self):
- return '<%s: %s>' % (self.__class__.__name__, self.value)
-
- def __get_inherit(self):
- if self.value is None:
- raise ValueError("Handle is already closed!")
- return bool( GetHandleInformation(self.value) & HANDLE_FLAG_INHERIT )
-
- def __set_inherit(self, value):
- if self.value is None:
- raise ValueError("Handle is already closed!")
- flag = (0, HANDLE_FLAG_INHERIT)[ bool(value) ]
- SetHandleInformation(self.value, flag, flag)
-
- inherit = property(__get_inherit, __set_inherit)
-
- def __get_protectFromClose(self):
- if self.value is None:
- raise ValueError("Handle is already closed!")
- return bool( GetHandleInformation(self.value) & HANDLE_FLAG_PROTECT_FROM_CLOSE )
-
- def __set_protectFromClose(self, value):
- if self.value is None:
- raise ValueError("Handle is already closed!")
- flag = (0, HANDLE_FLAG_PROTECT_FROM_CLOSE)[ bool(value) ]
- SetHandleInformation(self.value, flag, flag)
-
- protectFromClose = property(__get_protectFromClose, __set_protectFromClose)
-
-class UserModeHandle (Handle):
- """
- Base class for non-kernel handles. Generally this means they are closed
- by special Win32 API functions instead of CloseHandle() and some standard
- operations (synchronizing, duplicating, inheritance) are not supported.
-
- @type _TYPE: C type
- @cvar _TYPE: C type to translate this handle to.
- Subclasses should override this.
- Defaults to L{HANDLE}.
- """
-
- # Subclasses should override this.
- _TYPE = HANDLE
-
- # This method must be implemented by subclasses.
- def _close(self):
- raise NotImplementedError()
-
- # Translation to C type.
- @property
- def _as_parameter_(self):
- return self._TYPE(self.value)
-
- # Translation to C type.
- @staticmethod
- def from_param(value):
- return self._TYPE(self.value)
-
- # Operation not supported.
- @property
- def inherit(self):
- return False
-
- # Operation not supported.
- @property
- def protectFromClose(self):
- return False
-
- # Operation not supported.
- def dup(self):
- raise NotImplementedError()
-
- # Operation not supported.
- def wait(self, dwMilliseconds = None):
- raise NotImplementedError()
-
-class ProcessHandle (Handle):
- """
- Win32 process handle.
-
- @type dwAccess: int
- @ivar dwAccess: Current access flags to this handle.
- This is the same value passed to L{OpenProcess}.
- Can only be C{None} if C{aHandle} is also C{None}.
- Defaults to L{PROCESS_ALL_ACCESS}.
-
- @see: L{Handle}
- """
-
- def __init__(self, aHandle = None, bOwnership = True,
- dwAccess = PROCESS_ALL_ACCESS):
- """
- @type aHandle: int
- @param aHandle: Win32 handle value.
-
- @type bOwnership: bool
- @param bOwnership:
- C{True} if we own the handle and we need to close it.
- C{False} if someone else will be calling L{CloseHandle}.
-
- @type dwAccess: int
- @param dwAccess: Current access flags to this handle.
- This is the same value passed to L{OpenProcess}.
- Can only be C{None} if C{aHandle} is also C{None}.
- Defaults to L{PROCESS_ALL_ACCESS}.
- """
- super(ProcessHandle, self).__init__(aHandle, bOwnership)
- self.dwAccess = dwAccess
- if aHandle is not None and dwAccess is None:
- msg = "Missing access flags for process handle: %x" % aHandle
- raise TypeError(msg)
-
- def get_pid(self):
- """
- @rtype: int
- @return: Process global ID.
- """
- return GetProcessId(self.value)
-
-class ThreadHandle (Handle):
- """
- Win32 thread handle.
-
- @type dwAccess: int
- @ivar dwAccess: Current access flags to this handle.
- This is the same value passed to L{OpenThread}.
- Can only be C{None} if C{aHandle} is also C{None}.
- Defaults to L{THREAD_ALL_ACCESS}.
-
- @see: L{Handle}
- """
-
- def __init__(self, aHandle = None, bOwnership = True,
- dwAccess = THREAD_ALL_ACCESS):
- """
- @type aHandle: int
- @param aHandle: Win32 handle value.
-
- @type bOwnership: bool
- @param bOwnership:
- C{True} if we own the handle and we need to close it.
- C{False} if someone else will be calling L{CloseHandle}.
-
- @type dwAccess: int
- @param dwAccess: Current access flags to this handle.
- This is the same value passed to L{OpenThread}.
- Can only be C{None} if C{aHandle} is also C{None}.
- Defaults to L{THREAD_ALL_ACCESS}.
- """
- super(ThreadHandle, self).__init__(aHandle, bOwnership)
- self.dwAccess = dwAccess
- if aHandle is not None and dwAccess is None:
- msg = "Missing access flags for thread handle: %x" % aHandle
- raise TypeError(msg)
-
- def get_tid(self):
- """
- @rtype: int
- @return: Thread global ID.
- """
- return GetThreadId(self.value)
-
-class FileHandle (Handle):
- """
- Win32 file handle.
-
- @see: L{Handle}
- """
-
- def get_filename(self):
- """
- @rtype: None or str
- @return: Name of the open file, or C{None} if unavailable.
- """
- #
- # XXX BUG
- #
- # This code truncates the first two bytes of the path.
- # It seems to be the expected behavior of NtQueryInformationFile.
- #
- # My guess is it only returns the NT pathname, without the device name.
- # It's like dropping the drive letter in a Win32 pathname.
- #
- # Note that using the "official" GetFileInformationByHandleEx
- # API introduced in Vista doesn't change the results!
- #
- dwBufferSize = 0x1004
- lpFileInformation = ctypes.create_string_buffer(dwBufferSize)
- try:
- GetFileInformationByHandleEx(self.value,
- FILE_INFO_BY_HANDLE_CLASS.FileNameInfo,
- lpFileInformation, dwBufferSize)
- except AttributeError:
- from winappdbg.win32.ntdll import NtQueryInformationFile, \
- FileNameInformation, \
- FILE_NAME_INFORMATION
- NtQueryInformationFile(self.value,
- FileNameInformation,
- lpFileInformation,
- dwBufferSize)
- FileName = compat.unicode(lpFileInformation.raw[sizeof(DWORD):], 'U16')
- FileName = ctypes.create_unicode_buffer(FileName).value
- if not FileName:
- FileName = None
- elif FileName[1:2] != ':':
- # When the drive letter is missing, we'll assume SYSTEMROOT.
- # Not a good solution but it could be worse.
- import os
- FileName = os.environ['SYSTEMROOT'][:2] + FileName
- return FileName
-
-class FileMappingHandle (Handle):
- """
- File mapping handle.
-
- @see: L{Handle}
- """
- pass
-
-# XXX maybe add functions related to the toolhelp snapshots here?
-class SnapshotHandle (Handle):
- """
- Toolhelp32 snapshot handle.
-
- @see: L{Handle}
- """
- pass
-
-#--- Structure wrappers -------------------------------------------------------
-
-class ProcessInformation (object):
- """
- Process information object returned by L{CreateProcess}.
- """
-
- def __init__(self, pi):
- self.hProcess = ProcessHandle(pi.hProcess)
- self.hThread = ThreadHandle(pi.hThread)
- self.dwProcessId = pi.dwProcessId
- self.dwThreadId = pi.dwThreadId
-
-# Don't psyco-optimize this class because it needs to be serialized.
-class MemoryBasicInformation (object):
- """
- Memory information object returned by L{VirtualQueryEx}.
- """
-
- READABLE = (
- PAGE_EXECUTE_READ |
- PAGE_EXECUTE_READWRITE |
- PAGE_EXECUTE_WRITECOPY |
- PAGE_READONLY |
- PAGE_READWRITE |
- PAGE_WRITECOPY
- )
-
- WRITEABLE = (
- PAGE_EXECUTE_READWRITE |
- PAGE_EXECUTE_WRITECOPY |
- PAGE_READWRITE |
- PAGE_WRITECOPY
- )
-
- COPY_ON_WRITE = (
- PAGE_EXECUTE_WRITECOPY |
- PAGE_WRITECOPY
- )
-
- EXECUTABLE = (
- PAGE_EXECUTE |
- PAGE_EXECUTE_READ |
- PAGE_EXECUTE_READWRITE |
- PAGE_EXECUTE_WRITECOPY
- )
-
- EXECUTABLE_AND_WRITEABLE = (
- PAGE_EXECUTE_READWRITE |
- PAGE_EXECUTE_WRITECOPY
- )
-
- def __init__(self, mbi=None):
- """
- @type mbi: L{MEMORY_BASIC_INFORMATION} or L{MemoryBasicInformation}
- @param mbi: Either a L{MEMORY_BASIC_INFORMATION} structure or another
- L{MemoryBasicInformation} instance.
- """
- if mbi is None:
- self.BaseAddress = None
- self.AllocationBase = None
- self.AllocationProtect = None
- self.RegionSize = None
- self.State = None
- self.Protect = None
- self.Type = None
- else:
- self.BaseAddress = mbi.BaseAddress
- self.AllocationBase = mbi.AllocationBase
- self.AllocationProtect = mbi.AllocationProtect
- self.RegionSize = mbi.RegionSize
- self.State = mbi.State
- self.Protect = mbi.Protect
- self.Type = mbi.Type
-
- # Only used when copying MemoryBasicInformation objects, instead of
- # instancing them from a MEMORY_BASIC_INFORMATION structure.
- if hasattr(mbi, 'content'):
- self.content = mbi.content
- if hasattr(mbi, 'filename'):
- self.content = mbi.filename
-
- def __contains__(self, address):
- """
- Test if the given memory address falls within this memory region.
-
- @type address: int
- @param address: Memory address to test.
-
- @rtype: bool
- @return: C{True} if the given memory address falls within this memory
- region, C{False} otherwise.
- """
- return self.BaseAddress <= address < (self.BaseAddress + self.RegionSize)
-
- def is_free(self):
- """
- @rtype: bool
- @return: C{True} if the memory in this region is free.
- """
- return self.State == MEM_FREE
-
- def is_reserved(self):
- """
- @rtype: bool
- @return: C{True} if the memory in this region is reserved.
- """
- return self.State == MEM_RESERVE
-
- def is_commited(self):
- """
- @rtype: bool
- @return: C{True} if the memory in this region is commited.
- """
- return self.State == MEM_COMMIT
-
- def is_image(self):
- """
- @rtype: bool
- @return: C{True} if the memory in this region belongs to an executable
- image.
- """
- return self.Type == MEM_IMAGE
-
- def is_mapped(self):
- """
- @rtype: bool
- @return: C{True} if the memory in this region belongs to a mapped file.
- """
- return self.Type == MEM_MAPPED
-
- def is_private(self):
- """
- @rtype: bool
- @return: C{True} if the memory in this region is private.
- """
- return self.Type == MEM_PRIVATE
-
- def is_guard(self):
- """
- @rtype: bool
- @return: C{True} if all pages in this region are guard pages.
- """
- return self.is_commited() and bool(self.Protect & PAGE_GUARD)
-
- def has_content(self):
- """
- @rtype: bool
- @return: C{True} if the memory in this region has any data in it.
- """
- return self.is_commited() and not bool(self.Protect & (PAGE_GUARD | PAGE_NOACCESS))
-
- def is_readable(self):
- """
- @rtype: bool
- @return: C{True} if all pages in this region are readable.
- """
- return self.has_content() and bool(self.Protect & self.READABLE)
-
- def is_writeable(self):
- """
- @rtype: bool
- @return: C{True} if all pages in this region are writeable.
- """
- return self.has_content() and bool(self.Protect & self.WRITEABLE)
-
- def is_copy_on_write(self):
- """
- @rtype: bool
- @return: C{True} if all pages in this region are marked as
- copy-on-write. This means the pages are writeable, but changes
- are not propagated to disk.
- @note:
- Tipically data sections in executable images are marked like this.
- """
- return self.has_content() and bool(self.Protect & self.COPY_ON_WRITE)
-
- def is_executable(self):
- """
- @rtype: bool
- @return: C{True} if all pages in this region are executable.
- @note: Executable pages are always readable.
- """
- return self.has_content() and bool(self.Protect & self.EXECUTABLE)
-
- def is_executable_and_writeable(self):
- """
- @rtype: bool
- @return: C{True} if all pages in this region are executable and
- writeable.
- @note: The presence of such pages make memory corruption
- vulnerabilities much easier to exploit.
- """
- return self.has_content() and bool(self.Protect & self.EXECUTABLE_AND_WRITEABLE)
-
-class ProcThreadAttributeList (object):
- """
- Extended process and thread attribute support.
-
- To be used with L{STARTUPINFOEX}.
- Only available for Windows Vista and above.
-
- @type AttributeList: list of tuple( int, ctypes-compatible object )
- @ivar AttributeList: List of (Attribute, Value) pairs.
-
- @type AttributeListBuffer: L{LPPROC_THREAD_ATTRIBUTE_LIST}
- @ivar AttributeListBuffer: Memory buffer used to store the attribute list.
- L{InitializeProcThreadAttributeList},
- L{UpdateProcThreadAttribute},
- L{DeleteProcThreadAttributeList} and
- L{STARTUPINFOEX}.
- """
-
- def __init__(self, AttributeList):
- """
- @type AttributeList: list of tuple( int, ctypes-compatible object )
- @param AttributeList: List of (Attribute, Value) pairs.
- """
- self.AttributeList = AttributeList
- self.AttributeListBuffer = InitializeProcThreadAttributeList(
- len(AttributeList))
- try:
- for Attribute, Value in AttributeList:
- UpdateProcThreadAttribute(self.AttributeListBuffer,
- Attribute, Value)
- except:
- ProcThreadAttributeList.__del__(self)
- raise
-
- def __del__(self):
- try:
- DeleteProcThreadAttributeList(self.AttributeListBuffer)
- del self.AttributeListBuffer
- except Exception:
- pass
-
- def __copy__(self):
- return self.__deepcopy__()
-
- def __deepcopy__(self):
- return self.__class__(self.AttributeList)
-
- @property
- def value(self):
- return ctypes.cast(ctypes.pointer(self.AttributeListBuffer), LPVOID)
-
- @property
- def _as_parameter_(self):
- return self.value
-
- # XXX TODO
- @staticmethod
- def from_param(value):
- raise NotImplementedError()
-
-#--- OVERLAPPED structure -----------------------------------------------------
-
-# typedef struct _OVERLAPPED {
-# ULONG_PTR Internal;
-# ULONG_PTR InternalHigh;
-# union {
-# struct {
-# DWORD Offset;
-# DWORD OffsetHigh;
-# } ;
-# PVOID Pointer;
-# } ;
-# HANDLE hEvent;
-# }OVERLAPPED, *LPOVERLAPPED;
-class _OVERLAPPED_STRUCT(Structure):
- _fields_ = [
- ('Offset', DWORD),
- ('OffsetHigh', DWORD),
- ]
-class _OVERLAPPED_UNION(Union):
- _fields_ = [
- ('s', _OVERLAPPED_STRUCT),
- ('Pointer', PVOID),
- ]
-class OVERLAPPED(Structure):
- _fields_ = [
- ('Internal', ULONG_PTR),
- ('InternalHigh', ULONG_PTR),
- ('u', _OVERLAPPED_UNION),
- ('hEvent', HANDLE),
- ]
-LPOVERLAPPED = POINTER(OVERLAPPED)
-
-#--- SECURITY_ATTRIBUTES structure --------------------------------------------
-
-# typedef struct _SECURITY_ATTRIBUTES {
-# DWORD nLength;
-# LPVOID lpSecurityDescriptor;
-# BOOL bInheritHandle;
-# } SECURITY_ATTRIBUTES, *PSECURITY_ATTRIBUTES, *LPSECURITY_ATTRIBUTES;
-class SECURITY_ATTRIBUTES(Structure):
- _fields_ = [
- ('nLength', DWORD),
- ('lpSecurityDescriptor', LPVOID),
- ('bInheritHandle', BOOL),
- ]
-LPSECURITY_ATTRIBUTES = POINTER(SECURITY_ATTRIBUTES)
-
-# --- Extended process and thread attribute support ---------------------------
-
-PPROC_THREAD_ATTRIBUTE_LIST = LPVOID
-LPPROC_THREAD_ATTRIBUTE_LIST = PPROC_THREAD_ATTRIBUTE_LIST
-
-PROC_THREAD_ATTRIBUTE_NUMBER = 0x0000FFFF
-PROC_THREAD_ATTRIBUTE_THREAD = 0x00010000 # Attribute may be used with thread creation
-PROC_THREAD_ATTRIBUTE_INPUT = 0x00020000 # Attribute is input only
-PROC_THREAD_ATTRIBUTE_ADDITIVE = 0x00040000 # Attribute may be "accumulated," e.g. bitmasks, counters, etc.
-
-# PROC_THREAD_ATTRIBUTE_NUM
-ProcThreadAttributeParentProcess = 0
-ProcThreadAttributeExtendedFlags = 1
-ProcThreadAttributeHandleList = 2
-ProcThreadAttributeGroupAffinity = 3
-ProcThreadAttributePreferredNode = 4
-ProcThreadAttributeIdealProcessor = 5
-ProcThreadAttributeUmsThread = 6
-ProcThreadAttributeMitigationPolicy = 7
-ProcThreadAttributeMax = 8
-
-PROC_THREAD_ATTRIBUTE_PARENT_PROCESS = ProcThreadAttributeParentProcess | PROC_THREAD_ATTRIBUTE_INPUT
-PROC_THREAD_ATTRIBUTE_EXTENDED_FLAGS = ProcThreadAttributeExtendedFlags | PROC_THREAD_ATTRIBUTE_INPUT | PROC_THREAD_ATTRIBUTE_ADDITIVE
-PROC_THREAD_ATTRIBUTE_HANDLE_LIST = ProcThreadAttributeHandleList | PROC_THREAD_ATTRIBUTE_INPUT
-PROC_THREAD_ATTRIBUTE_GROUP_AFFINITY = ProcThreadAttributeGroupAffinity | PROC_THREAD_ATTRIBUTE_THREAD | PROC_THREAD_ATTRIBUTE_INPUT
-PROC_THREAD_ATTRIBUTE_PREFERRED_NODE = ProcThreadAttributePreferredNode | PROC_THREAD_ATTRIBUTE_INPUT
-PROC_THREAD_ATTRIBUTE_IDEAL_PROCESSOR = ProcThreadAttributeIdealProcessor | PROC_THREAD_ATTRIBUTE_THREAD | PROC_THREAD_ATTRIBUTE_INPUT
-PROC_THREAD_ATTRIBUTE_UMS_THREAD = ProcThreadAttributeUmsThread | PROC_THREAD_ATTRIBUTE_THREAD | PROC_THREAD_ATTRIBUTE_INPUT
-PROC_THREAD_ATTRIBUTE_MITIGATION_POLICY = ProcThreadAttributeMitigationPolicy | PROC_THREAD_ATTRIBUTE_INPUT
-
-PROCESS_CREATION_MITIGATION_POLICY_DEP_ENABLE = 0x01
-PROCESS_CREATION_MITIGATION_POLICY_DEP_ATL_THUNK_ENABLE = 0x02
-PROCESS_CREATION_MITIGATION_POLICY_SEHOP_ENABLE = 0x04
-
-#--- VS_FIXEDFILEINFO structure -----------------------------------------------
-
-# struct VS_FIXEDFILEINFO {
-# DWORD dwSignature;
-# DWORD dwStrucVersion;
-# DWORD dwFileVersionMS;
-# DWORD dwFileVersionLS;
-# DWORD dwProductVersionMS;
-# DWORD dwProductVersionLS;
-# DWORD dwFileFlagsMask;
-# DWORD dwFileFlags;
-# DWORD dwFileOS;
-# DWORD dwFileType;
-# DWORD dwFileSubtype;
-# DWORD dwFileDateMS;
-# DWORD dwFileDateLS;
-# };
-class VS_FIXEDFILEINFO (Structure):
- _fields_ = [
- ("dwSignature", DWORD), # 0xFEEF04BD
- ("dwStrucVersion", DWORD),
- ("dwFileVersionMS", DWORD),
- ("dwFileVersionLS", DWORD),
- ("dwProductVersionMS", DWORD),
- ("dwProductVersionLS", DWORD),
- ("dwFileFlagsMask", DWORD),
- ("dwFileFlags", DWORD),
- ("dwFileOS", DWORD),
- ("dwFileType", DWORD),
- ("dwFileSubtype", DWORD),
- ("dwFileDateMS", DWORD),
- ("dwFileDateLS", DWORD),
- ]
-
-#--- THREADNAME_INFO structure ------------------------------------------------
-
-# typedef struct tagTHREADNAME_INFO
-# {
-# DWORD dwType; // Must be 0x1000.
-# LPCSTR szName; // Pointer to name (in user addr space).
-# DWORD dwThreadID; // Thread ID (-1=caller thread).
-# DWORD dwFlags; // Reserved for future use, must be zero.
-# } THREADNAME_INFO;
-class THREADNAME_INFO(Structure):
- _fields_ = [
- ("dwType", DWORD), # 0x1000
- ("szName", LPVOID), # remote pointer
- ("dwThreadID", DWORD), # -1 usually
- ("dwFlags", DWORD), # 0
- ]
-
-#--- MEMORY_BASIC_INFORMATION structure ---------------------------------------
-
-# typedef struct _MEMORY_BASIC_INFORMATION32 {
-# DWORD BaseAddress;
-# DWORD AllocationBase;
-# DWORD AllocationProtect;
-# DWORD RegionSize;
-# DWORD State;
-# DWORD Protect;
-# DWORD Type;
-# } MEMORY_BASIC_INFORMATION32, *PMEMORY_BASIC_INFORMATION32;
-class MEMORY_BASIC_INFORMATION32(Structure):
- _fields_ = [
- ('BaseAddress', DWORD), # remote pointer
- ('AllocationBase', DWORD), # remote pointer
- ('AllocationProtect', DWORD),
- ('RegionSize', DWORD),
- ('State', DWORD),
- ('Protect', DWORD),
- ('Type', DWORD),
- ]
-
-# typedef struct DECLSPEC_ALIGN(16) _MEMORY_BASIC_INFORMATION64 {
-# ULONGLONG BaseAddress;
-# ULONGLONG AllocationBase;
-# DWORD AllocationProtect;
-# DWORD __alignment1;
-# ULONGLONG RegionSize;
-# DWORD State;
-# DWORD Protect;
-# DWORD Type;
-# DWORD __alignment2;
-# } MEMORY_BASIC_INFORMATION64, *PMEMORY_BASIC_INFORMATION64;
-class MEMORY_BASIC_INFORMATION64(Structure):
- _fields_ = [
- ('BaseAddress', ULONGLONG), # remote pointer
- ('AllocationBase', ULONGLONG), # remote pointer
- ('AllocationProtect', DWORD),
- ('__alignment1', DWORD),
- ('RegionSize', ULONGLONG),
- ('State', DWORD),
- ('Protect', DWORD),
- ('Type', DWORD),
- ('__alignment2', DWORD),
- ]
-
-# typedef struct _MEMORY_BASIC_INFORMATION {
-# PVOID BaseAddress;
-# PVOID AllocationBase;
-# DWORD AllocationProtect;
-# SIZE_T RegionSize;
-# DWORD State;
-# DWORD Protect;
-# DWORD Type;
-# } MEMORY_BASIC_INFORMATION, *PMEMORY_BASIC_INFORMATION;
-class MEMORY_BASIC_INFORMATION(Structure):
- _fields_ = [
- ('BaseAddress', SIZE_T), # remote pointer
- ('AllocationBase', SIZE_T), # remote pointer
- ('AllocationProtect', DWORD),
- ('RegionSize', SIZE_T),
- ('State', DWORD),
- ('Protect', DWORD),
- ('Type', DWORD),
- ]
-PMEMORY_BASIC_INFORMATION = POINTER(MEMORY_BASIC_INFORMATION)
-
-#--- BY_HANDLE_FILE_INFORMATION structure -------------------------------------
-
-# typedef struct _FILETIME {
-# DWORD dwLowDateTime;
-# DWORD dwHighDateTime;
-# } FILETIME, *PFILETIME;
-class FILETIME(Structure):
- _fields_ = [
- ('dwLowDateTime', DWORD),
- ('dwHighDateTime', DWORD),
- ]
-LPFILETIME = POINTER(FILETIME)
-
-# typedef struct _SYSTEMTIME {
-# WORD wYear;
-# WORD wMonth;
-# WORD wDayOfWeek;
-# WORD wDay;
-# WORD wHour;
-# WORD wMinute;
-# WORD wSecond;
-# WORD wMilliseconds;
-# }SYSTEMTIME, *PSYSTEMTIME;
-class SYSTEMTIME(Structure):
- _fields_ = [
- ('wYear', WORD),
- ('wMonth', WORD),
- ('wDayOfWeek', WORD),
- ('wDay', WORD),
- ('wHour', WORD),
- ('wMinute', WORD),
- ('wSecond', WORD),
- ('wMilliseconds', WORD),
- ]
-LPSYSTEMTIME = POINTER(SYSTEMTIME)
-
-# typedef struct _BY_HANDLE_FILE_INFORMATION {
-# DWORD dwFileAttributes;
-# FILETIME ftCreationTime;
-# FILETIME ftLastAccessTime;
-# FILETIME ftLastWriteTime;
-# DWORD dwVolumeSerialNumber;
-# DWORD nFileSizeHigh;
-# DWORD nFileSizeLow;
-# DWORD nNumberOfLinks;
-# DWORD nFileIndexHigh;
-# DWORD nFileIndexLow;
-# } BY_HANDLE_FILE_INFORMATION, *PBY_HANDLE_FILE_INFORMATION;
-class BY_HANDLE_FILE_INFORMATION(Structure):
- _fields_ = [
- ('dwFileAttributes', DWORD),
- ('ftCreationTime', FILETIME),
- ('ftLastAccessTime', FILETIME),
- ('ftLastWriteTime', FILETIME),
- ('dwVolumeSerialNumber', DWORD),
- ('nFileSizeHigh', DWORD),
- ('nFileSizeLow', DWORD),
- ('nNumberOfLinks', DWORD),
- ('nFileIndexHigh', DWORD),
- ('nFileIndexLow', DWORD),
- ]
-LPBY_HANDLE_FILE_INFORMATION = POINTER(BY_HANDLE_FILE_INFORMATION)
-
-# typedef enum _FILE_INFO_BY_HANDLE_CLASS {
-# FileBasicInfo = 0,
-# FileStandardInfo = 1,
-# FileNameInfo = 2,
-# FileRenameInfo = 3,
-# FileDispositionInfo = 4,
-# FileAllocationInfo = 5,
-# FileEndOfFileInfo = 6,
-# FileStreamInfo = 7,
-# FileCompressionInfo = 8,
-# FileAttributeTagInfo = 9,
-# FileIdBothDirectoryInfo = 10,
-# FileIdBothDirectoryRestartInfo = 11,
-# FileIoPriorityHintInfo = 12,
-# MaximumFileInfoByHandlesClass = 13
-# } FILE_INFO_BY_HANDLE_CLASS, *PFILE_INFO_BY_HANDLE_CLASS;
-class FILE_INFO_BY_HANDLE_CLASS(object):
- FileBasicInfo = 0
- FileStandardInfo = 1
- FileNameInfo = 2
- FileRenameInfo = 3
- FileDispositionInfo = 4
- FileAllocationInfo = 5
- FileEndOfFileInfo = 6
- FileStreamInfo = 7
- FileCompressionInfo = 8
- FileAttributeTagInfo = 9
- FileIdBothDirectoryInfo = 10
- FileIdBothDirectoryRestartInfo = 11
- FileIoPriorityHintInfo = 12
- MaximumFileInfoByHandlesClass = 13
-
-# typedef struct _FILE_NAME_INFO {
-# DWORD FileNameLength;
-# WCHAR FileName[1];
-# } FILE_NAME_INFO, *PFILE_NAME_INFO;
-##class FILE_NAME_INFO(Structure):
-## _fields_ = [
-## ('FileNameLength', DWORD),
-## ('FileName', WCHAR * 1),
-## ]
-
-# TO DO: add more structures used by GetFileInformationByHandleEx()
-
-#--- PROCESS_INFORMATION structure --------------------------------------------
-
-# typedef struct _PROCESS_INFORMATION {
-# HANDLE hProcess;
-# HANDLE hThread;
-# DWORD dwProcessId;
-# DWORD dwThreadId;
-# } PROCESS_INFORMATION, *PPROCESS_INFORMATION, *LPPROCESS_INFORMATION;
-class PROCESS_INFORMATION(Structure):
- _fields_ = [
- ('hProcess', HANDLE),
- ('hThread', HANDLE),
- ('dwProcessId', DWORD),
- ('dwThreadId', DWORD),
- ]
-LPPROCESS_INFORMATION = POINTER(PROCESS_INFORMATION)
-
-#--- STARTUPINFO and STARTUPINFOEX structures ---------------------------------
-
-# typedef struct _STARTUPINFO {
-# DWORD cb;
-# LPTSTR lpReserved;
-# LPTSTR lpDesktop;
-# LPTSTR lpTitle;
-# DWORD dwX;
-# DWORD dwY;
-# DWORD dwXSize;
-# DWORD dwYSize;
-# DWORD dwXCountChars;
-# DWORD dwYCountChars;
-# DWORD dwFillAttribute;
-# DWORD dwFlags;
-# WORD wShowWindow;
-# WORD cbReserved2;
-# LPBYTE lpReserved2;
-# HANDLE hStdInput;
-# HANDLE hStdOutput;
-# HANDLE hStdError;
-# }STARTUPINFO, *LPSTARTUPINFO;
-class STARTUPINFO(Structure):
- _fields_ = [
- ('cb', DWORD),
- ('lpReserved', LPSTR),
- ('lpDesktop', LPSTR),
- ('lpTitle', LPSTR),
- ('dwX', DWORD),
- ('dwY', DWORD),
- ('dwXSize', DWORD),
- ('dwYSize', DWORD),
- ('dwXCountChars', DWORD),
- ('dwYCountChars', DWORD),
- ('dwFillAttribute', DWORD),
- ('dwFlags', DWORD),
- ('wShowWindow', WORD),
- ('cbReserved2', WORD),
- ('lpReserved2', LPVOID), # LPBYTE
- ('hStdInput', HANDLE),
- ('hStdOutput', HANDLE),
- ('hStdError', HANDLE),
- ]
-LPSTARTUPINFO = POINTER(STARTUPINFO)
-
-# typedef struct _STARTUPINFOEX {
-# STARTUPINFO StartupInfo;
-# PPROC_THREAD_ATTRIBUTE_LIST lpAttributeList;
-# } STARTUPINFOEX, *LPSTARTUPINFOEX;
-class STARTUPINFOEX(Structure):
- _fields_ = [
- ('StartupInfo', STARTUPINFO),
- ('lpAttributeList', PPROC_THREAD_ATTRIBUTE_LIST),
- ]
-LPSTARTUPINFOEX = POINTER(STARTUPINFOEX)
-
-class STARTUPINFOW(Structure):
- _fields_ = [
- ('cb', DWORD),
- ('lpReserved', LPWSTR),
- ('lpDesktop', LPWSTR),
- ('lpTitle', LPWSTR),
- ('dwX', DWORD),
- ('dwY', DWORD),
- ('dwXSize', DWORD),
- ('dwYSize', DWORD),
- ('dwXCountChars', DWORD),
- ('dwYCountChars', DWORD),
- ('dwFillAttribute', DWORD),
- ('dwFlags', DWORD),
- ('wShowWindow', WORD),
- ('cbReserved2', WORD),
- ('lpReserved2', LPVOID), # LPBYTE
- ('hStdInput', HANDLE),
- ('hStdOutput', HANDLE),
- ('hStdError', HANDLE),
- ]
-LPSTARTUPINFOW = POINTER(STARTUPINFOW)
-
-class STARTUPINFOEXW(Structure):
- _fields_ = [
- ('StartupInfo', STARTUPINFOW),
- ('lpAttributeList', PPROC_THREAD_ATTRIBUTE_LIST),
- ]
-LPSTARTUPINFOEXW = POINTER(STARTUPINFOEXW)
-
-#--- JIT_DEBUG_INFO structure -------------------------------------------------
-
-# typedef struct _JIT_DEBUG_INFO {
-# DWORD dwSize;
-# DWORD dwProcessorArchitecture;
-# DWORD dwThreadID;
-# DWORD dwReserved0;
-# ULONG64 lpExceptionAddress;
-# ULONG64 lpExceptionRecord;
-# ULONG64 lpContextRecord;
-# } JIT_DEBUG_INFO, *LPJIT_DEBUG_INFO;
-class JIT_DEBUG_INFO(Structure):
- _fields_ = [
- ('dwSize', DWORD),
- ('dwProcessorArchitecture', DWORD),
- ('dwThreadID', DWORD),
- ('dwReserved0', DWORD),
- ('lpExceptionAddress', ULONG64),
- ('lpExceptionRecord', ULONG64),
- ('lpContextRecord', ULONG64),
- ]
-JIT_DEBUG_INFO32 = JIT_DEBUG_INFO
-JIT_DEBUG_INFO64 = JIT_DEBUG_INFO
-
-LPJIT_DEBUG_INFO = POINTER(JIT_DEBUG_INFO)
-LPJIT_DEBUG_INFO32 = POINTER(JIT_DEBUG_INFO32)
-LPJIT_DEBUG_INFO64 = POINTER(JIT_DEBUG_INFO64)
-
-#--- DEBUG_EVENT structure ----------------------------------------------------
-
-# typedef struct _EXCEPTION_RECORD32 {
-# DWORD ExceptionCode;
-# DWORD ExceptionFlags;
-# DWORD ExceptionRecord;
-# DWORD ExceptionAddress;
-# DWORD NumberParameters;
-# DWORD ExceptionInformation[EXCEPTION_MAXIMUM_PARAMETERS];
-# } EXCEPTION_RECORD32, *PEXCEPTION_RECORD32;
-class EXCEPTION_RECORD32(Structure):
- _fields_ = [
- ('ExceptionCode', DWORD),
- ('ExceptionFlags', DWORD),
- ('ExceptionRecord', DWORD),
- ('ExceptionAddress', DWORD),
- ('NumberParameters', DWORD),
- ('ExceptionInformation', DWORD * EXCEPTION_MAXIMUM_PARAMETERS),
- ]
-
-PEXCEPTION_RECORD32 = POINTER(EXCEPTION_RECORD32)
-
-# typedef struct _EXCEPTION_RECORD64 {
-# DWORD ExceptionCode;
-# DWORD ExceptionFlags;
-# DWORD64 ExceptionRecord;
-# DWORD64 ExceptionAddress;
-# DWORD NumberParameters;
-# DWORD __unusedAlignment;
-# DWORD64 ExceptionInformation[EXCEPTION_MAXIMUM_PARAMETERS];
-# } EXCEPTION_RECORD64, *PEXCEPTION_RECORD64;
-class EXCEPTION_RECORD64(Structure):
- _fields_ = [
- ('ExceptionCode', DWORD),
- ('ExceptionFlags', DWORD),
- ('ExceptionRecord', DWORD64),
- ('ExceptionAddress', DWORD64),
- ('NumberParameters', DWORD),
- ('__unusedAlignment', DWORD),
- ('ExceptionInformation', DWORD64 * EXCEPTION_MAXIMUM_PARAMETERS),
- ]
-
-PEXCEPTION_RECORD64 = POINTER(EXCEPTION_RECORD64)
-
-# typedef struct _EXCEPTION_RECORD {
-# DWORD ExceptionCode;
-# DWORD ExceptionFlags;
-# LPVOID ExceptionRecord;
-# LPVOID ExceptionAddress;
-# DWORD NumberParameters;
-# LPVOID ExceptionInformation[EXCEPTION_MAXIMUM_PARAMETERS];
-# } EXCEPTION_RECORD, *PEXCEPTION_RECORD;
-class EXCEPTION_RECORD(Structure):
- pass
-PEXCEPTION_RECORD = POINTER(EXCEPTION_RECORD)
-EXCEPTION_RECORD._fields_ = [
- ('ExceptionCode', DWORD),
- ('ExceptionFlags', DWORD),
- ('ExceptionRecord', PEXCEPTION_RECORD),
- ('ExceptionAddress', LPVOID),
- ('NumberParameters', DWORD),
- ('ExceptionInformation', LPVOID * EXCEPTION_MAXIMUM_PARAMETERS),
- ]
-
-# typedef struct _EXCEPTION_DEBUG_INFO {
-# EXCEPTION_RECORD ExceptionRecord;
-# DWORD dwFirstChance;
-# } EXCEPTION_DEBUG_INFO;
-class EXCEPTION_DEBUG_INFO(Structure):
- _fields_ = [
- ('ExceptionRecord', EXCEPTION_RECORD),
- ('dwFirstChance', DWORD),
- ]
-
-# typedef struct _CREATE_THREAD_DEBUG_INFO {
-# HANDLE hThread;
-# LPVOID lpThreadLocalBase;
-# LPTHREAD_START_ROUTINE lpStartAddress;
-# } CREATE_THREAD_DEBUG_INFO;
-class CREATE_THREAD_DEBUG_INFO(Structure):
- _fields_ = [
- ('hThread', HANDLE),
- ('lpThreadLocalBase', LPVOID),
- ('lpStartAddress', LPVOID),
- ]
-
-# typedef struct _CREATE_PROCESS_DEBUG_INFO {
-# HANDLE hFile;
-# HANDLE hProcess;
-# HANDLE hThread;
-# LPVOID lpBaseOfImage;
-# DWORD dwDebugInfoFileOffset;
-# DWORD nDebugInfoSize;
-# LPVOID lpThreadLocalBase;
-# LPTHREAD_START_ROUTINE lpStartAddress;
-# LPVOID lpImageName;
-# WORD fUnicode;
-# } CREATE_PROCESS_DEBUG_INFO;
-class CREATE_PROCESS_DEBUG_INFO(Structure):
- _fields_ = [
- ('hFile', HANDLE),
- ('hProcess', HANDLE),
- ('hThread', HANDLE),
- ('lpBaseOfImage', LPVOID),
- ('dwDebugInfoFileOffset', DWORD),
- ('nDebugInfoSize', DWORD),
- ('lpThreadLocalBase', LPVOID),
- ('lpStartAddress', LPVOID),
- ('lpImageName', LPVOID),
- ('fUnicode', WORD),
- ]
-
-# typedef struct _EXIT_THREAD_DEBUG_INFO {
-# DWORD dwExitCode;
-# } EXIT_THREAD_DEBUG_INFO;
-class EXIT_THREAD_DEBUG_INFO(Structure):
- _fields_ = [
- ('dwExitCode', DWORD),
- ]
-
-# typedef struct _EXIT_PROCESS_DEBUG_INFO {
-# DWORD dwExitCode;
-# } EXIT_PROCESS_DEBUG_INFO;
-class EXIT_PROCESS_DEBUG_INFO(Structure):
- _fields_ = [
- ('dwExitCode', DWORD),
- ]
-
-# typedef struct _LOAD_DLL_DEBUG_INFO {
-# HANDLE hFile;
-# LPVOID lpBaseOfDll;
-# DWORD dwDebugInfoFileOffset;
-# DWORD nDebugInfoSize;
-# LPVOID lpImageName;
-# WORD fUnicode;
-# } LOAD_DLL_DEBUG_INFO;
-class LOAD_DLL_DEBUG_INFO(Structure):
- _fields_ = [
- ('hFile', HANDLE),
- ('lpBaseOfDll', LPVOID),
- ('dwDebugInfoFileOffset', DWORD),
- ('nDebugInfoSize', DWORD),
- ('lpImageName', LPVOID),
- ('fUnicode', WORD),
- ]
-
-# typedef struct _UNLOAD_DLL_DEBUG_INFO {
-# LPVOID lpBaseOfDll;
-# } UNLOAD_DLL_DEBUG_INFO;
-class UNLOAD_DLL_DEBUG_INFO(Structure):
- _fields_ = [
- ('lpBaseOfDll', LPVOID),
- ]
-
-# typedef struct _OUTPUT_DEBUG_STRING_INFO {
-# LPSTR lpDebugStringData;
-# WORD fUnicode;
-# WORD nDebugStringLength;
-# } OUTPUT_DEBUG_STRING_INFO;
-class OUTPUT_DEBUG_STRING_INFO(Structure):
- _fields_ = [
- ('lpDebugStringData', LPVOID), # don't use LPSTR
- ('fUnicode', WORD),
- ('nDebugStringLength', WORD),
- ]
-
-# typedef struct _RIP_INFO {
-# DWORD dwError;
-# DWORD dwType;
-# } RIP_INFO, *LPRIP_INFO;
-class RIP_INFO(Structure):
- _fields_ = [
- ('dwError', DWORD),
- ('dwType', DWORD),
- ]
-
-# typedef struct _DEBUG_EVENT {
-# DWORD dwDebugEventCode;
-# DWORD dwProcessId;
-# DWORD dwThreadId;
-# union {
-# EXCEPTION_DEBUG_INFO Exception;
-# CREATE_THREAD_DEBUG_INFO CreateThread;
-# CREATE_PROCESS_DEBUG_INFO CreateProcessInfo;
-# EXIT_THREAD_DEBUG_INFO ExitThread;
-# EXIT_PROCESS_DEBUG_INFO ExitProcess;
-# LOAD_DLL_DEBUG_INFO LoadDll;
-# UNLOAD_DLL_DEBUG_INFO UnloadDll;
-# OUTPUT_DEBUG_STRING_INFO DebugString;
-# RIP_INFO RipInfo;
-# } u;
-# } DEBUG_EVENT;.
-class _DEBUG_EVENT_UNION_(Union):
- _fields_ = [
- ('Exception', EXCEPTION_DEBUG_INFO),
- ('CreateThread', CREATE_THREAD_DEBUG_INFO),
- ('CreateProcessInfo', CREATE_PROCESS_DEBUG_INFO),
- ('ExitThread', EXIT_THREAD_DEBUG_INFO),
- ('ExitProcess', EXIT_PROCESS_DEBUG_INFO),
- ('LoadDll', LOAD_DLL_DEBUG_INFO),
- ('UnloadDll', UNLOAD_DLL_DEBUG_INFO),
- ('DebugString', OUTPUT_DEBUG_STRING_INFO),
- ('RipInfo', RIP_INFO),
- ]
-class DEBUG_EVENT(Structure):
- _fields_ = [
- ('dwDebugEventCode', DWORD),
- ('dwProcessId', DWORD),
- ('dwThreadId', DWORD),
- ('u', _DEBUG_EVENT_UNION_),
- ]
-LPDEBUG_EVENT = POINTER(DEBUG_EVENT)
-
-#--- Console API defines and structures ---------------------------------------
-
-FOREGROUND_MASK = 0x000F
-BACKGROUND_MASK = 0x00F0
-COMMON_LVB_MASK = 0xFF00
-
-FOREGROUND_BLACK = 0x0000
-FOREGROUND_BLUE = 0x0001
-FOREGROUND_GREEN = 0x0002
-FOREGROUND_CYAN = 0x0003
-FOREGROUND_RED = 0x0004
-FOREGROUND_MAGENTA = 0x0005
-FOREGROUND_YELLOW = 0x0006
-FOREGROUND_GREY = 0x0007
-FOREGROUND_INTENSITY = 0x0008
-
-BACKGROUND_BLACK = 0x0000
-BACKGROUND_BLUE = 0x0010
-BACKGROUND_GREEN = 0x0020
-BACKGROUND_CYAN = 0x0030
-BACKGROUND_RED = 0x0040
-BACKGROUND_MAGENTA = 0x0050
-BACKGROUND_YELLOW = 0x0060
-BACKGROUND_GREY = 0x0070
-BACKGROUND_INTENSITY = 0x0080
-
-COMMON_LVB_LEADING_BYTE = 0x0100
-COMMON_LVB_TRAILING_BYTE = 0x0200
-COMMON_LVB_GRID_HORIZONTAL = 0x0400
-COMMON_LVB_GRID_LVERTICAL = 0x0800
-COMMON_LVB_GRID_RVERTICAL = 0x1000
-COMMON_LVB_REVERSE_VIDEO = 0x4000
-COMMON_LVB_UNDERSCORE = 0x8000
-
-# typedef struct _CHAR_INFO {
-# union {
-# WCHAR UnicodeChar;
-# CHAR AsciiChar;
-# } Char;
-# WORD Attributes;
-# } CHAR_INFO, *PCHAR_INFO;
-class _CHAR_INFO_CHAR(Union):
- _fields_ = [
- ('UnicodeChar', WCHAR),
- ('AsciiChar', CHAR),
- ]
-class CHAR_INFO(Structure):
- _fields_ = [
- ('Char', _CHAR_INFO_CHAR),
- ('Attributes', WORD),
- ]
-PCHAR_INFO = POINTER(CHAR_INFO)
-
-# typedef struct _COORD {
-# SHORT X;
-# SHORT Y;
-# } COORD, *PCOORD;
-class COORD(Structure):
- _fields_ = [
- ('X', SHORT),
- ('Y', SHORT),
- ]
-PCOORD = POINTER(COORD)
-
-# typedef struct _SMALL_RECT {
-# SHORT Left;
-# SHORT Top;
-# SHORT Right;
-# SHORT Bottom;
-# } SMALL_RECT;
-class SMALL_RECT(Structure):
- _fields_ = [
- ('Left', SHORT),
- ('Top', SHORT),
- ('Right', SHORT),
- ('Bottom', SHORT),
- ]
-PSMALL_RECT = POINTER(SMALL_RECT)
-
-# typedef struct _CONSOLE_SCREEN_BUFFER_INFO {
-# COORD dwSize;
-# COORD dwCursorPosition;
-# WORD wAttributes;
-# SMALL_RECT srWindow;
-# COORD dwMaximumWindowSize;
-# } CONSOLE_SCREEN_BUFFER_INFO;
-class CONSOLE_SCREEN_BUFFER_INFO(Structure):
- _fields_ = [
- ('dwSize', COORD),
- ('dwCursorPosition', COORD),
- ('wAttributes', WORD),
- ('srWindow', SMALL_RECT),
- ('dwMaximumWindowSize', COORD),
- ]
-PCONSOLE_SCREEN_BUFFER_INFO = POINTER(CONSOLE_SCREEN_BUFFER_INFO)
-
-#--- Toolhelp library defines and structures ----------------------------------
-
-TH32CS_SNAPHEAPLIST = 0x00000001
-TH32CS_SNAPPROCESS = 0x00000002
-TH32CS_SNAPTHREAD = 0x00000004
-TH32CS_SNAPMODULE = 0x00000008
-TH32CS_INHERIT = 0x80000000
-TH32CS_SNAPALL = (TH32CS_SNAPHEAPLIST | TH32CS_SNAPPROCESS | TH32CS_SNAPTHREAD | TH32CS_SNAPMODULE)
-
-# typedef struct tagTHREADENTRY32 {
-# DWORD dwSize;
-# DWORD cntUsage;
-# DWORD th32ThreadID;
-# DWORD th32OwnerProcessID;
-# LONG tpBasePri;
-# LONG tpDeltaPri;
-# DWORD dwFlags;
-# } THREADENTRY32, *PTHREADENTRY32;
-class THREADENTRY32(Structure):
- _fields_ = [
- ('dwSize', DWORD),
- ('cntUsage', DWORD),
- ('th32ThreadID', DWORD),
- ('th32OwnerProcessID', DWORD),
- ('tpBasePri', LONG),
- ('tpDeltaPri', LONG),
- ('dwFlags', DWORD),
- ]
-LPTHREADENTRY32 = POINTER(THREADENTRY32)
-
-# typedef struct tagPROCESSENTRY32 {
-# DWORD dwSize;
-# DWORD cntUsage;
-# DWORD th32ProcessID;
-# ULONG_PTR th32DefaultHeapID;
-# DWORD th32ModuleID;
-# DWORD cntThreads;
-# DWORD th32ParentProcessID;
-# LONG pcPriClassBase;
-# DWORD dwFlags;
-# TCHAR szExeFile[MAX_PATH];
-# } PROCESSENTRY32, *PPROCESSENTRY32;
-class PROCESSENTRY32(Structure):
- _fields_ = [
- ('dwSize', DWORD),
- ('cntUsage', DWORD),
- ('th32ProcessID', DWORD),
- ('th32DefaultHeapID', ULONG_PTR),
- ('th32ModuleID', DWORD),
- ('cntThreads', DWORD),
- ('th32ParentProcessID', DWORD),
- ('pcPriClassBase', LONG),
- ('dwFlags', DWORD),
- ('szExeFile', TCHAR * 260),
- ]
-LPPROCESSENTRY32 = POINTER(PROCESSENTRY32)
-
-# typedef struct tagMODULEENTRY32 {
-# DWORD dwSize;
-# DWORD th32ModuleID;
-# DWORD th32ProcessID;
-# DWORD GlblcntUsage;
-# DWORD ProccntUsage;
-# BYTE* modBaseAddr;
-# DWORD modBaseSize;
-# HMODULE hModule;
-# TCHAR szModule[MAX_MODULE_NAME32 + 1];
-# TCHAR szExePath[MAX_PATH];
-# } MODULEENTRY32, *PMODULEENTRY32;
-class MODULEENTRY32(Structure):
- _fields_ = [
- ("dwSize", DWORD),
- ("th32ModuleID", DWORD),
- ("th32ProcessID", DWORD),
- ("GlblcntUsage", DWORD),
- ("ProccntUsage", DWORD),
- ("modBaseAddr", LPVOID), # BYTE*
- ("modBaseSize", DWORD),
- ("hModule", HMODULE),
- ("szModule", TCHAR * (MAX_MODULE_NAME32 + 1)),
- ("szExePath", TCHAR * MAX_PATH),
- ]
-LPMODULEENTRY32 = POINTER(MODULEENTRY32)
-
-# typedef struct tagHEAPENTRY32 {
-# SIZE_T dwSize;
-# HANDLE hHandle;
-# ULONG_PTR dwAddress;
-# SIZE_T dwBlockSize;
-# DWORD dwFlags;
-# DWORD dwLockCount;
-# DWORD dwResvd;
-# DWORD th32ProcessID;
-# ULONG_PTR th32HeapID;
-# } HEAPENTRY32,
-# *PHEAPENTRY32;
-class HEAPENTRY32(Structure):
- _fields_ = [
- ("dwSize", SIZE_T),
- ("hHandle", HANDLE),
- ("dwAddress", ULONG_PTR),
- ("dwBlockSize", SIZE_T),
- ("dwFlags", DWORD),
- ("dwLockCount", DWORD),
- ("dwResvd", DWORD),
- ("th32ProcessID", DWORD),
- ("th32HeapID", ULONG_PTR),
-]
-LPHEAPENTRY32 = POINTER(HEAPENTRY32)
-
-# typedef struct tagHEAPLIST32 {
-# SIZE_T dwSize;
-# DWORD th32ProcessID;
-# ULONG_PTR th32HeapID;
-# DWORD dwFlags;
-# } HEAPLIST32,
-# *PHEAPLIST32;
-class HEAPLIST32(Structure):
- _fields_ = [
- ("dwSize", SIZE_T),
- ("th32ProcessID", DWORD),
- ("th32HeapID", ULONG_PTR),
- ("dwFlags", DWORD),
-]
-LPHEAPLIST32 = POINTER(HEAPLIST32)
-
-#--- kernel32.dll -------------------------------------------------------------
-
-# DWORD WINAPI GetLastError(void);
-def GetLastError():
- _GetLastError = windll.kernel32.GetLastError
- _GetLastError.argtypes = []
- _GetLastError.restype = DWORD
- return _GetLastError()
-
-# void WINAPI SetLastError(
-# __in DWORD dwErrCode
-# );
-def SetLastError(dwErrCode):
- _SetLastError = windll.kernel32.SetLastError
- _SetLastError.argtypes = [DWORD]
- _SetLastError.restype = None
- _SetLastError(dwErrCode)
-
-# UINT WINAPI GetErrorMode(void);
-def GetErrorMode():
- _GetErrorMode = windll.kernel32.GetErrorMode
- _GetErrorMode.argtypes = []
- _GetErrorMode.restype = UINT
- return _GetErrorMode()
-
-# UINT WINAPI SetErrorMode(
-# __in UINT uMode
-# );
-def SetErrorMode(uMode):
- _SetErrorMode = windll.kernel32.SetErrorMode
- _SetErrorMode.argtypes = [UINT]
- _SetErrorMode.restype = UINT
- return _SetErrorMode(dwErrCode)
-
-# DWORD GetThreadErrorMode(void);
-def GetThreadErrorMode():
- _GetThreadErrorMode = windll.kernel32.GetThreadErrorMode
- _GetThreadErrorMode.argtypes = []
- _GetThreadErrorMode.restype = DWORD
- return _GetThreadErrorMode()
-
-# BOOL SetThreadErrorMode(
-# __in DWORD dwNewMode,
-# __out LPDWORD lpOldMode
-# );
-def SetThreadErrorMode(dwNewMode):
- _SetThreadErrorMode = windll.kernel32.SetThreadErrorMode
- _SetThreadErrorMode.argtypes = [DWORD, LPDWORD]
- _SetThreadErrorMode.restype = BOOL
- _SetThreadErrorMode.errcheck = RaiseIfZero
-
- old = DWORD(0)
- _SetThreadErrorMode(dwErrCode, byref(old))
- return old.value
-
-# BOOL WINAPI CloseHandle(
-# __in HANDLE hObject
-# );
-def CloseHandle(hHandle):
- if isinstance(hHandle, Handle):
- # Prevents the handle from being closed without notifying the Handle object.
- hHandle.close()
- else:
- _CloseHandle = windll.kernel32.CloseHandle
- _CloseHandle.argtypes = [HANDLE]
- _CloseHandle.restype = bool
- _CloseHandle.errcheck = RaiseIfZero
- _CloseHandle(hHandle)
-
-# BOOL WINAPI DuplicateHandle(
-# __in HANDLE hSourceProcessHandle,
-# __in HANDLE hSourceHandle,
-# __in HANDLE hTargetProcessHandle,
-# __out LPHANDLE lpTargetHandle,
-# __in DWORD dwDesiredAccess,
-# __in BOOL bInheritHandle,
-# __in DWORD dwOptions
-# );
-def DuplicateHandle(hSourceHandle, hSourceProcessHandle = None, hTargetProcessHandle = None, dwDesiredAccess = STANDARD_RIGHTS_ALL, bInheritHandle = False, dwOptions = DUPLICATE_SAME_ACCESS):
- _DuplicateHandle = windll.kernel32.DuplicateHandle
- _DuplicateHandle.argtypes = [HANDLE, HANDLE, HANDLE, LPHANDLE, DWORD, BOOL, DWORD]
- _DuplicateHandle.restype = bool
- _DuplicateHandle.errcheck = RaiseIfZero
-
- # NOTE: the arguments to this function are in a different order,
- # so we can set default values for all of them but one (hSourceHandle).
-
- if hSourceProcessHandle is None:
- hSourceProcessHandle = GetCurrentProcess()
- if hTargetProcessHandle is None:
- hTargetProcessHandle = hSourceProcessHandle
- lpTargetHandle = HANDLE(INVALID_HANDLE_VALUE)
- _DuplicateHandle(hSourceProcessHandle, hSourceHandle, hTargetProcessHandle, byref(lpTargetHandle), dwDesiredAccess, bool(bInheritHandle), dwOptions)
- if isinstance(hSourceHandle, Handle):
- HandleClass = hSourceHandle.__class__
- else:
- HandleClass = Handle
- if hasattr(hSourceHandle, 'dwAccess'):
- return HandleClass(lpTargetHandle.value, dwAccess = hSourceHandle.dwAccess)
- else:
- return HandleClass(lpTargetHandle.value)
-
-# HLOCAL WINAPI LocalFree(
-# __in HLOCAL hMem
-# );
-def LocalFree(hMem):
- _LocalFree = windll.kernel32.LocalFree
- _LocalFree.argtypes = [HLOCAL]
- _LocalFree.restype = HLOCAL
-
- result = _LocalFree(hMem)
- if result != NULL:
- ctypes.WinError()
-
-#------------------------------------------------------------------------------
-# Console API
-
-# HANDLE WINAPI GetStdHandle(
-# _In_ DWORD nStdHandle
-# );
-def GetStdHandle(nStdHandle):
- _GetStdHandle = windll.kernel32.GetStdHandle
- _GetStdHandle.argytpes = [DWORD]
- _GetStdHandle.restype = HANDLE
- _GetStdHandle.errcheck = RaiseIfZero
- return Handle( _GetStdHandle(nStdHandle), bOwnership = False )
-
-# BOOL WINAPI SetStdHandle(
-# _In_ DWORD nStdHandle,
-# _In_ HANDLE hHandle
-# );
-
-# TODO
-
-# UINT WINAPI GetConsoleCP(void);
-def GetConsoleCP():
- _GetConsoleCP = windll.kernel32.GetConsoleCP
- _GetConsoleCP.argytpes = []
- _GetConsoleCP.restype = UINT
- return _GetConsoleCP()
-
-# UINT WINAPI GetConsoleOutputCP(void);
-def GetConsoleOutputCP():
- _GetConsoleOutputCP = windll.kernel32.GetConsoleOutputCP
- _GetConsoleOutputCP.argytpes = []
- _GetConsoleOutputCP.restype = UINT
- return _GetConsoleOutputCP()
-
-#BOOL WINAPI SetConsoleCP(
-# _In_ UINT wCodePageID
-#);
-def SetConsoleCP(wCodePageID):
- _SetConsoleCP = windll.kernel32.SetConsoleCP
- _SetConsoleCP.argytpes = [UINT]
- _SetConsoleCP.restype = bool
- _SetConsoleCP.errcheck = RaiseIfZero
- _SetConsoleCP(wCodePageID)
-
-#BOOL WINAPI SetConsoleOutputCP(
-# _In_ UINT wCodePageID
-#);
-def SetConsoleOutputCP(wCodePageID):
- _SetConsoleOutputCP = windll.kernel32.SetConsoleOutputCP
- _SetConsoleOutputCP.argytpes = [UINT]
- _SetConsoleOutputCP.restype = bool
- _SetConsoleOutputCP.errcheck = RaiseIfZero
- _SetConsoleOutputCP(wCodePageID)
-
-# HANDLE WINAPI CreateConsoleScreenBuffer(
-# _In_ DWORD dwDesiredAccess,
-# _In_ DWORD dwShareMode,
-# _In_opt_ const SECURITY_ATTRIBUTES *lpSecurityAttributes,
-# _In_ DWORD dwFlags,
-# _Reserved_ LPVOID lpScreenBufferData
-# );
-
-# TODO
-
-# BOOL WINAPI SetConsoleActiveScreenBuffer(
-# _In_ HANDLE hConsoleOutput
-# );
-def SetConsoleActiveScreenBuffer(hConsoleOutput = None):
- _SetConsoleActiveScreenBuffer = windll.kernel32.SetConsoleActiveScreenBuffer
- _SetConsoleActiveScreenBuffer.argytpes = [HANDLE]
- _SetConsoleActiveScreenBuffer.restype = bool
- _SetConsoleActiveScreenBuffer.errcheck = RaiseIfZero
-
- if hConsoleOutput is None:
- hConsoleOutput = GetStdHandle(STD_OUTPUT_HANDLE)
- _SetConsoleActiveScreenBuffer(hConsoleOutput)
-
-# BOOL WINAPI GetConsoleScreenBufferInfo(
-# _In_ HANDLE hConsoleOutput,
-# _Out_ PCONSOLE_SCREEN_BUFFER_INFO lpConsoleScreenBufferInfo
-# );
-def GetConsoleScreenBufferInfo(hConsoleOutput = None):
- _GetConsoleScreenBufferInfo = windll.kernel32.GetConsoleScreenBufferInfo
- _GetConsoleScreenBufferInfo.argytpes = [HANDLE, PCONSOLE_SCREEN_BUFFER_INFO]
- _GetConsoleScreenBufferInfo.restype = bool
- _GetConsoleScreenBufferInfo.errcheck = RaiseIfZero
-
- if hConsoleOutput is None:
- hConsoleOutput = GetStdHandle(STD_OUTPUT_HANDLE)
- ConsoleScreenBufferInfo = CONSOLE_SCREEN_BUFFER_INFO()
- _GetConsoleScreenBufferInfo(hConsoleOutput, byref(ConsoleScreenBufferInfo))
- return ConsoleScreenBufferInfo
-
-# BOOL WINAPI GetConsoleScreenBufferInfoEx(
-# _In_ HANDLE hConsoleOutput,
-# _Out_ PCONSOLE_SCREEN_BUFFER_INFOEX lpConsoleScreenBufferInfoEx
-# );
-
-# TODO
-
-# BOOL WINAPI SetConsoleWindowInfo(
-# _In_ HANDLE hConsoleOutput,
-# _In_ BOOL bAbsolute,
-# _In_ const SMALL_RECT *lpConsoleWindow
-# );
-def SetConsoleWindowInfo(hConsoleOutput, bAbsolute, lpConsoleWindow):
- _SetConsoleWindowInfo = windll.kernel32.SetConsoleWindowInfo
- _SetConsoleWindowInfo.argytpes = [HANDLE, BOOL, PSMALL_RECT]
- _SetConsoleWindowInfo.restype = bool
- _SetConsoleWindowInfo.errcheck = RaiseIfZero
-
- if hConsoleOutput is None:
- hConsoleOutput = GetStdHandle(STD_OUTPUT_HANDLE)
- if isinstance(lpConsoleWindow, SMALL_RECT):
- ConsoleWindow = lpConsoleWindow
- else:
- ConsoleWindow = SMALL_RECT(*lpConsoleWindow)
- _SetConsoleWindowInfo(hConsoleOutput, bAbsolute, byref(ConsoleWindow))
-
-# BOOL WINAPI SetConsoleTextAttribute(
-# _In_ HANDLE hConsoleOutput,
-# _In_ WORD wAttributes
-# );
-def SetConsoleTextAttribute(hConsoleOutput = None, wAttributes = 0):
- _SetConsoleTextAttribute = windll.kernel32.SetConsoleTextAttribute
- _SetConsoleTextAttribute.argytpes = [HANDLE, WORD]
- _SetConsoleTextAttribute.restype = bool
- _SetConsoleTextAttribute.errcheck = RaiseIfZero
-
- if hConsoleOutput is None:
- hConsoleOutput = GetStdHandle(STD_OUTPUT_HANDLE)
- _SetConsoleTextAttribute(hConsoleOutput, wAttributes)
-
-# HANDLE WINAPI CreateConsoleScreenBuffer(
-# _In_ DWORD dwDesiredAccess,
-# _In_ DWORD dwShareMode,
-# _In_opt_ const SECURITY_ATTRIBUTES *lpSecurityAttributes,
-# _In_ DWORD dwFlags,
-# _Reserved_ LPVOID lpScreenBufferData
-# );
-
-# TODO
-
-# BOOL WINAPI AllocConsole(void);
-def AllocConsole():
- _AllocConsole = windll.kernel32.AllocConsole
- _AllocConsole.argytpes = []
- _AllocConsole.restype = bool
- _AllocConsole.errcheck = RaiseIfZero
- _AllocConsole()
-
-# BOOL WINAPI AttachConsole(
-# _In_ DWORD dwProcessId
-# );
-def AttachConsole(dwProcessId = ATTACH_PARENT_PROCESS):
- _AttachConsole = windll.kernel32.AttachConsole
- _AttachConsole.argytpes = [DWORD]
- _AttachConsole.restype = bool
- _AttachConsole.errcheck = RaiseIfZero
- _AttachConsole(dwProcessId)
-
-# BOOL WINAPI FreeConsole(void);
-def FreeConsole():
- _FreeConsole = windll.kernel32.FreeConsole
- _FreeConsole.argytpes = []
- _FreeConsole.restype = bool
- _FreeConsole.errcheck = RaiseIfZero
- _FreeConsole()
-
-# DWORD WINAPI GetConsoleProcessList(
-# _Out_ LPDWORD lpdwProcessList,
-# _In_ DWORD dwProcessCount
-# );
-
-# TODO
-
-# DWORD WINAPI GetConsoleTitle(
-# _Out_ LPTSTR lpConsoleTitle,
-# _In_ DWORD nSize
-# );
-
-# TODO
-
-#BOOL WINAPI SetConsoleTitle(
-# _In_ LPCTSTR lpConsoleTitle
-#);
-
-# TODO
-
-# COORD WINAPI GetLargestConsoleWindowSize(
-# _In_ HANDLE hConsoleOutput
-# );
-
-# TODO
-
-# BOOL WINAPI GetConsoleHistoryInfo(
-# _Out_ PCONSOLE_HISTORY_INFO lpConsoleHistoryInfo
-# );
-
-# TODO
-
-#------------------------------------------------------------------------------
-# DLL API
-
-# DWORD WINAPI GetDllDirectory(
-# __in DWORD nBufferLength,
-# __out LPTSTR lpBuffer
-# );
-def GetDllDirectoryA():
- _GetDllDirectoryA = windll.kernel32.GetDllDirectoryA
- _GetDllDirectoryA.argytpes = [DWORD, LPSTR]
- _GetDllDirectoryA.restype = DWORD
-
- nBufferLength = _GetDllDirectoryA(0, None)
- if nBufferLength == 0:
- return None
- lpBuffer = ctypes.create_string_buffer("", nBufferLength)
- _GetDllDirectoryA(nBufferLength, byref(lpBuffer))
- return lpBuffer.value
-
-def GetDllDirectoryW():
- _GetDllDirectoryW = windll.kernel32.GetDllDirectoryW
- _GetDllDirectoryW.argytpes = [DWORD, LPWSTR]
- _GetDllDirectoryW.restype = DWORD
-
- nBufferLength = _GetDllDirectoryW(0, None)
- if nBufferLength == 0:
- return None
- lpBuffer = ctypes.create_unicode_buffer(u"", nBufferLength)
- _GetDllDirectoryW(nBufferLength, byref(lpBuffer))
- return lpBuffer.value
-
-GetDllDirectory = GuessStringType(GetDllDirectoryA, GetDllDirectoryW)
-
-# BOOL WINAPI SetDllDirectory(
-# __in_opt LPCTSTR lpPathName
-# );
-def SetDllDirectoryA(lpPathName = None):
- _SetDllDirectoryA = windll.kernel32.SetDllDirectoryA
- _SetDllDirectoryA.argytpes = [LPSTR]
- _SetDllDirectoryA.restype = bool
- _SetDllDirectoryA.errcheck = RaiseIfZero
- _SetDllDirectoryA(lpPathName)
-
-def SetDllDirectoryW(lpPathName):
- _SetDllDirectoryW = windll.kernel32.SetDllDirectoryW
- _SetDllDirectoryW.argytpes = [LPWSTR]
- _SetDllDirectoryW.restype = bool
- _SetDllDirectoryW.errcheck = RaiseIfZero
- _SetDllDirectoryW(lpPathName)
-
-SetDllDirectory = GuessStringType(SetDllDirectoryA, SetDllDirectoryW)
-
-# HMODULE WINAPI LoadLibrary(
-# __in LPCTSTR lpFileName
-# );
-def LoadLibraryA(pszLibrary):
- _LoadLibraryA = windll.kernel32.LoadLibraryA
- _LoadLibraryA.argtypes = [LPSTR]
- _LoadLibraryA.restype = HMODULE
- hModule = _LoadLibraryA(pszLibrary)
- if hModule == NULL:
- raise ctypes.WinError()
- return hModule
-
-def LoadLibraryW(pszLibrary):
- _LoadLibraryW = windll.kernel32.LoadLibraryW
- _LoadLibraryW.argtypes = [LPWSTR]
- _LoadLibraryW.restype = HMODULE
- hModule = _LoadLibraryW(pszLibrary)
- if hModule == NULL:
- raise ctypes.WinError()
- return hModule
-
-LoadLibrary = GuessStringType(LoadLibraryA, LoadLibraryW)
-
-# HMODULE WINAPI LoadLibraryEx(
-# __in LPCTSTR lpFileName,
-# __reserved HANDLE hFile,
-# __in DWORD dwFlags
-# );
-def LoadLibraryExA(pszLibrary, dwFlags = 0):
- _LoadLibraryExA = windll.kernel32.LoadLibraryExA
- _LoadLibraryExA.argtypes = [LPSTR, HANDLE, DWORD]
- _LoadLibraryExA.restype = HMODULE
- hModule = _LoadLibraryExA(pszLibrary, NULL, dwFlags)
- if hModule == NULL:
- raise ctypes.WinError()
- return hModule
-
-def LoadLibraryExW(pszLibrary, dwFlags = 0):
- _LoadLibraryExW = windll.kernel32.LoadLibraryExW
- _LoadLibraryExW.argtypes = [LPWSTR, HANDLE, DWORD]
- _LoadLibraryExW.restype = HMODULE
- hModule = _LoadLibraryExW(pszLibrary, NULL, dwFlags)
- if hModule == NULL:
- raise ctypes.WinError()
- return hModule
-
-LoadLibraryEx = GuessStringType(LoadLibraryExA, LoadLibraryExW)
-
-# HMODULE WINAPI GetModuleHandle(
-# __in_opt LPCTSTR lpModuleName
-# );
-def GetModuleHandleA(lpModuleName):
- _GetModuleHandleA = windll.kernel32.GetModuleHandleA
- _GetModuleHandleA.argtypes = [LPSTR]
- _GetModuleHandleA.restype = HMODULE
- hModule = _GetModuleHandleA(lpModuleName)
- if hModule == NULL:
- raise ctypes.WinError()
- return hModule
-
-def GetModuleHandleW(lpModuleName):
- _GetModuleHandleW = windll.kernel32.GetModuleHandleW
- _GetModuleHandleW.argtypes = [LPWSTR]
- _GetModuleHandleW.restype = HMODULE
- hModule = _GetModuleHandleW(lpModuleName)
- if hModule == NULL:
- raise ctypes.WinError()
- return hModule
-
-GetModuleHandle = GuessStringType(GetModuleHandleA, GetModuleHandleW)
-
-# FARPROC WINAPI GetProcAddress(
-# __in HMODULE hModule,
-# __in LPCSTR lpProcName
-# );
-def GetProcAddressA(hModule, lpProcName):
- _GetProcAddress = windll.kernel32.GetProcAddress
- _GetProcAddress.argtypes = [HMODULE, LPVOID]
- _GetProcAddress.restype = LPVOID
-
- if type(lpProcName) in (type(0), type(long(0))):
- lpProcName = LPVOID(lpProcName)
- if lpProcName.value & (~0xFFFF):
- raise ValueError('Ordinal number too large: %d' % lpProcName.value)
- elif type(lpProcName) == type(compat.b("")):
- lpProcName = ctypes.c_char_p(lpProcName)
- else:
- raise TypeError(str(type(lpProcName)))
- return _GetProcAddress(hModule, lpProcName)
-
-GetProcAddressW = MakeWideVersion(GetProcAddressA)
-GetProcAddress = GuessStringType(GetProcAddressA, GetProcAddressW)
-
-# BOOL WINAPI FreeLibrary(
-# __in HMODULE hModule
-# );
-def FreeLibrary(hModule):
- _FreeLibrary = windll.kernel32.FreeLibrary
- _FreeLibrary.argtypes = [HMODULE]
- _FreeLibrary.restype = bool
- _FreeLibrary.errcheck = RaiseIfZero
- _FreeLibrary(hModule)
-
-# PVOID WINAPI RtlPcToFileHeader(
-# __in PVOID PcValue,
-# __out PVOID *BaseOfImage
-# );
-def RtlPcToFileHeader(PcValue):
- _RtlPcToFileHeader = windll.kernel32.RtlPcToFileHeader
- _RtlPcToFileHeader.argtypes = [PVOID, POINTER(PVOID)]
- _RtlPcToFileHeader.restype = PRUNTIME_FUNCTION
-
- BaseOfImage = PVOID(0)
- _RtlPcToFileHeader(PcValue, byref(BaseOfImage))
- return BaseOfImage.value
-
-#------------------------------------------------------------------------------
-# File API and related
-
-# BOOL WINAPI GetHandleInformation(
-# __in HANDLE hObject,
-# __out LPDWORD lpdwFlags
-# );
-def GetHandleInformation(hObject):
- _GetHandleInformation = windll.kernel32.GetHandleInformation
- _GetHandleInformation.argtypes = [HANDLE, PDWORD]
- _GetHandleInformation.restype = bool
- _GetHandleInformation.errcheck = RaiseIfZero
-
- dwFlags = DWORD(0)
- _GetHandleInformation(hObject, byref(dwFlags))
- return dwFlags.value
-
-# BOOL WINAPI SetHandleInformation(
-# __in HANDLE hObject,
-# __in DWORD dwMask,
-# __in DWORD dwFlags
-# );
-def SetHandleInformation(hObject, dwMask, dwFlags):
- _SetHandleInformation = windll.kernel32.SetHandleInformation
- _SetHandleInformation.argtypes = [HANDLE, DWORD, DWORD]
- _SetHandleInformation.restype = bool
- _SetHandleInformation.errcheck = RaiseIfZero
- _SetHandleInformation(hObject, dwMask, dwFlags)
-
-# UINT WINAPI GetWindowModuleFileName(
-# __in HWND hwnd,
-# __out LPTSTR lpszFileName,
-# __in UINT cchFileNameMax
-# );
-# Not included because it doesn't work in other processes.
-# See: http://support.microsoft.com/?id=228469
-
-# BOOL WINAPI QueryFullProcessImageName(
-# __in HANDLE hProcess,
-# __in DWORD dwFlags,
-# __out LPTSTR lpExeName,
-# __inout PDWORD lpdwSize
-# );
-def QueryFullProcessImageNameA(hProcess, dwFlags = 0):
- _QueryFullProcessImageNameA = windll.kernel32.QueryFullProcessImageNameA
- _QueryFullProcessImageNameA.argtypes = [HANDLE, DWORD, LPSTR, PDWORD]
- _QueryFullProcessImageNameA.restype = bool
-
- dwSize = MAX_PATH
- while 1:
- lpdwSize = DWORD(dwSize)
- lpExeName = ctypes.create_string_buffer('', lpdwSize.value + 1)
- success = _QueryFullProcessImageNameA(hProcess, dwFlags, lpExeName, byref(lpdwSize))
- if success and 0 < lpdwSize.value < dwSize:
- break
- error = GetLastError()
- if error != ERROR_INSUFFICIENT_BUFFER:
- raise ctypes.WinError(error)
- dwSize = dwSize + 256
- if dwSize > 0x1000:
- # this prevents an infinite loop in Windows 2008 when the path has spaces,
- # see http://msdn.microsoft.com/en-us/library/ms684919(VS.85).aspx#4
- raise ctypes.WinError(error)
- return lpExeName.value
-
-def QueryFullProcessImageNameW(hProcess, dwFlags = 0):
- _QueryFullProcessImageNameW = windll.kernel32.QueryFullProcessImageNameW
- _QueryFullProcessImageNameW.argtypes = [HANDLE, DWORD, LPWSTR, PDWORD]
- _QueryFullProcessImageNameW.restype = bool
-
- dwSize = MAX_PATH
- while 1:
- lpdwSize = DWORD(dwSize)
- lpExeName = ctypes.create_unicode_buffer('', lpdwSize.value + 1)
- success = _QueryFullProcessImageNameW(hProcess, dwFlags, lpExeName, byref(lpdwSize))
- if success and 0 < lpdwSize.value < dwSize:
- break
- error = GetLastError()
- if error != ERROR_INSUFFICIENT_BUFFER:
- raise ctypes.WinError(error)
- dwSize = dwSize + 256
- if dwSize > 0x1000:
- # this prevents an infinite loop in Windows 2008 when the path has spaces,
- # see http://msdn.microsoft.com/en-us/library/ms684919(VS.85).aspx#4
- raise ctypes.WinError(error)
- return lpExeName.value
-
-QueryFullProcessImageName = GuessStringType(QueryFullProcessImageNameA, QueryFullProcessImageNameW)
-
-# DWORD WINAPI GetLogicalDriveStrings(
-# __in DWORD nBufferLength,
-# __out LPTSTR lpBuffer
-# );
-def GetLogicalDriveStringsA():
- _GetLogicalDriveStringsA = ctypes.windll.kernel32.GetLogicalDriveStringsA
- _GetLogicalDriveStringsA.argtypes = [DWORD, LPSTR]
- _GetLogicalDriveStringsA.restype = DWORD
- _GetLogicalDriveStringsA.errcheck = RaiseIfZero
-
- nBufferLength = (4 * 26) + 1 # "X:\\\0" from A to Z plus empty string
- lpBuffer = ctypes.create_string_buffer('', nBufferLength)
- _GetLogicalDriveStringsA(nBufferLength, lpBuffer)
- drive_strings = list()
- string_p = addressof(lpBuffer)
- sizeof_char = sizeof(ctypes.c_char)
- while True:
- string_v = ctypes.string_at(string_p)
- if string_v == '':
- break
- drive_strings.append(string_v)
- string_p += len(string_v) + sizeof_char
- return drive_strings
-
-def GetLogicalDriveStringsW():
- _GetLogicalDriveStringsW = ctypes.windll.kernel32.GetLogicalDriveStringsW
- _GetLogicalDriveStringsW.argtypes = [DWORD, LPWSTR]
- _GetLogicalDriveStringsW.restype = DWORD
- _GetLogicalDriveStringsW.errcheck = RaiseIfZero
-
- nBufferLength = (4 * 26) + 1 # "X:\\\0" from A to Z plus empty string
- lpBuffer = ctypes.create_unicode_buffer(u'', nBufferLength)
- _GetLogicalDriveStringsW(nBufferLength, lpBuffer)
- drive_strings = list()
- string_p = addressof(lpBuffer)
- sizeof_wchar = sizeof(ctypes.c_wchar)
- while True:
- string_v = ctypes.wstring_at(string_p)
- if string_v == u'':
- break
- drive_strings.append(string_v)
- string_p += (len(string_v) * sizeof_wchar) + sizeof_wchar
- return drive_strings
-
-##def GetLogicalDriveStringsA():
-## _GetLogicalDriveStringsA = windll.kernel32.GetLogicalDriveStringsA
-## _GetLogicalDriveStringsA.argtypes = [DWORD, LPSTR]
-## _GetLogicalDriveStringsA.restype = DWORD
-## _GetLogicalDriveStringsA.errcheck = RaiseIfZero
-##
-## nBufferLength = (4 * 26) + 1 # "X:\\\0" from A to Z plus empty string
-## lpBuffer = ctypes.create_string_buffer('', nBufferLength)
-## _GetLogicalDriveStringsA(nBufferLength, lpBuffer)
-## result = list()
-## index = 0
-## while 1:
-## string = list()
-## while 1:
-## character = lpBuffer[index]
-## index = index + 1
-## if character == '\0':
-## break
-## string.append(character)
-## if not string:
-## break
-## result.append(''.join(string))
-## return result
-##
-##def GetLogicalDriveStringsW():
-## _GetLogicalDriveStringsW = windll.kernel32.GetLogicalDriveStringsW
-## _GetLogicalDriveStringsW.argtypes = [DWORD, LPWSTR]
-## _GetLogicalDriveStringsW.restype = DWORD
-## _GetLogicalDriveStringsW.errcheck = RaiseIfZero
-##
-## nBufferLength = (4 * 26) + 1 # "X:\\\0" from A to Z plus empty string
-## lpBuffer = ctypes.create_unicode_buffer(u'', nBufferLength)
-## _GetLogicalDriveStringsW(nBufferLength, lpBuffer)
-## result = list()
-## index = 0
-## while 1:
-## string = list()
-## while 1:
-## character = lpBuffer[index]
-## index = index + 1
-## if character == u'\0':
-## break
-## string.append(character)
-## if not string:
-## break
-## result.append(u''.join(string))
-## return result
-
-GetLogicalDriveStrings = GuessStringType(GetLogicalDriveStringsA, GetLogicalDriveStringsW)
-
-# DWORD WINAPI QueryDosDevice(
-# __in_opt LPCTSTR lpDeviceName,
-# __out LPTSTR lpTargetPath,
-# __in DWORD ucchMax
-# );
-def QueryDosDeviceA(lpDeviceName = None):
- _QueryDosDeviceA = windll.kernel32.QueryDosDeviceA
- _QueryDosDeviceA.argtypes = [LPSTR, LPSTR, DWORD]
- _QueryDosDeviceA.restype = DWORD
- _QueryDosDeviceA.errcheck = RaiseIfZero
-
- if not lpDeviceName:
- lpDeviceName = None
- ucchMax = 0x1000
- lpTargetPath = ctypes.create_string_buffer('', ucchMax)
- _QueryDosDeviceA(lpDeviceName, lpTargetPath, ucchMax)
- return lpTargetPath.value
-
-def QueryDosDeviceW(lpDeviceName):
- _QueryDosDeviceW = windll.kernel32.QueryDosDeviceW
- _QueryDosDeviceW.argtypes = [LPWSTR, LPWSTR, DWORD]
- _QueryDosDeviceW.restype = DWORD
- _QueryDosDeviceW.errcheck = RaiseIfZero
-
- if not lpDeviceName:
- lpDeviceName = None
- ucchMax = 0x1000
- lpTargetPath = ctypes.create_unicode_buffer(u'', ucchMax)
- _QueryDosDeviceW(lpDeviceName, lpTargetPath, ucchMax)
- return lpTargetPath.value
-
-QueryDosDevice = GuessStringType(QueryDosDeviceA, QueryDosDeviceW)
-
-# LPVOID WINAPI MapViewOfFile(
-# __in HANDLE hFileMappingObject,
-# __in DWORD dwDesiredAccess,
-# __in DWORD dwFileOffsetHigh,
-# __in DWORD dwFileOffsetLow,
-# __in SIZE_T dwNumberOfBytesToMap
-# );
-def MapViewOfFile(hFileMappingObject, dwDesiredAccess = FILE_MAP_ALL_ACCESS | FILE_MAP_EXECUTE, dwFileOffsetHigh = 0, dwFileOffsetLow = 0, dwNumberOfBytesToMap = 0):
- _MapViewOfFile = windll.kernel32.MapViewOfFile
- _MapViewOfFile.argtypes = [HANDLE, DWORD, DWORD, DWORD, SIZE_T]
- _MapViewOfFile.restype = LPVOID
- lpBaseAddress = _MapViewOfFile(hFileMappingObject, dwDesiredAccess, dwFileOffsetHigh, dwFileOffsetLow, dwNumberOfBytesToMap)
- if lpBaseAddress == NULL:
- raise ctypes.WinError()
- return lpBaseAddress
-
-# BOOL WINAPI UnmapViewOfFile(
-# __in LPCVOID lpBaseAddress
-# );
-def UnmapViewOfFile(lpBaseAddress):
- _UnmapViewOfFile = windll.kernel32.UnmapViewOfFile
- _UnmapViewOfFile.argtypes = [LPVOID]
- _UnmapViewOfFile.restype = bool
- _UnmapViewOfFile.errcheck = RaiseIfZero
- _UnmapViewOfFile(lpBaseAddress)
-
-# HANDLE WINAPI OpenFileMapping(
-# __in DWORD dwDesiredAccess,
-# __in BOOL bInheritHandle,
-# __in LPCTSTR lpName
-# );
-def OpenFileMappingA(dwDesiredAccess, bInheritHandle, lpName):
- _OpenFileMappingA = windll.kernel32.OpenFileMappingA
- _OpenFileMappingA.argtypes = [DWORD, BOOL, LPSTR]
- _OpenFileMappingA.restype = HANDLE
- _OpenFileMappingA.errcheck = RaiseIfZero
- hFileMappingObject = _OpenFileMappingA(dwDesiredAccess, bool(bInheritHandle), lpName)
- return FileMappingHandle(hFileMappingObject)
-
-def OpenFileMappingW(dwDesiredAccess, bInheritHandle, lpName):
- _OpenFileMappingW = windll.kernel32.OpenFileMappingW
- _OpenFileMappingW.argtypes = [DWORD, BOOL, LPWSTR]
- _OpenFileMappingW.restype = HANDLE
- _OpenFileMappingW.errcheck = RaiseIfZero
- hFileMappingObject = _OpenFileMappingW(dwDesiredAccess, bool(bInheritHandle), lpName)
- return FileMappingHandle(hFileMappingObject)
-
-OpenFileMapping = GuessStringType(OpenFileMappingA, OpenFileMappingW)
-
-# HANDLE WINAPI CreateFileMapping(
-# __in HANDLE hFile,
-# __in_opt LPSECURITY_ATTRIBUTES lpAttributes,
-# __in DWORD flProtect,
-# __in DWORD dwMaximumSizeHigh,
-# __in DWORD dwMaximumSizeLow,
-# __in_opt LPCTSTR lpName
-# );
-def CreateFileMappingA(hFile, lpAttributes = None, flProtect = PAGE_EXECUTE_READWRITE, dwMaximumSizeHigh = 0, dwMaximumSizeLow = 0, lpName = None):
- _CreateFileMappingA = windll.kernel32.CreateFileMappingA
- _CreateFileMappingA.argtypes = [HANDLE, LPVOID, DWORD, DWORD, DWORD, LPSTR]
- _CreateFileMappingA.restype = HANDLE
- _CreateFileMappingA.errcheck = RaiseIfZero
-
- if lpAttributes:
- lpAttributes = ctypes.pointer(lpAttributes)
- if not lpName:
- lpName = None
- hFileMappingObject = _CreateFileMappingA(hFile, lpAttributes, flProtect, dwMaximumSizeHigh, dwMaximumSizeLow, lpName)
- return FileMappingHandle(hFileMappingObject)
-
-def CreateFileMappingW(hFile, lpAttributes = None, flProtect = PAGE_EXECUTE_READWRITE, dwMaximumSizeHigh = 0, dwMaximumSizeLow = 0, lpName = None):
- _CreateFileMappingW = windll.kernel32.CreateFileMappingW
- _CreateFileMappingW.argtypes = [HANDLE, LPVOID, DWORD, DWORD, DWORD, LPWSTR]
- _CreateFileMappingW.restype = HANDLE
- _CreateFileMappingW.errcheck = RaiseIfZero
-
- if lpAttributes:
- lpAttributes = ctypes.pointer(lpAttributes)
- if not lpName:
- lpName = None
- hFileMappingObject = _CreateFileMappingW(hFile, lpAttributes, flProtect, dwMaximumSizeHigh, dwMaximumSizeLow, lpName)
- return FileMappingHandle(hFileMappingObject)
-
-CreateFileMapping = GuessStringType(CreateFileMappingA, CreateFileMappingW)
-
-# HANDLE WINAPI CreateFile(
-# __in LPCTSTR lpFileName,
-# __in DWORD dwDesiredAccess,
-# __in DWORD dwShareMode,
-# __in_opt LPSECURITY_ATTRIBUTES lpSecurityAttributes,
-# __in DWORD dwCreationDisposition,
-# __in DWORD dwFlagsAndAttributes,
-# __in_opt HANDLE hTemplateFile
-# );
-def CreateFileA(lpFileName, dwDesiredAccess = GENERIC_ALL, dwShareMode = 0, lpSecurityAttributes = None, dwCreationDisposition = OPEN_ALWAYS, dwFlagsAndAttributes = FILE_ATTRIBUTE_NORMAL, hTemplateFile = None):
- _CreateFileA = windll.kernel32.CreateFileA
- _CreateFileA.argtypes = [LPSTR, DWORD, DWORD, LPVOID, DWORD, DWORD, HANDLE]
- _CreateFileA.restype = HANDLE
-
- if not lpFileName:
- lpFileName = None
- if lpSecurityAttributes:
- lpSecurityAttributes = ctypes.pointer(lpSecurityAttributes)
- hFile = _CreateFileA(lpFileName, dwDesiredAccess, dwShareMode, lpSecurityAttributes, dwCreationDisposition, dwFlagsAndAttributes, hTemplateFile)
- if hFile == INVALID_HANDLE_VALUE:
- raise ctypes.WinError()
- return FileHandle(hFile)
-
-def CreateFileW(lpFileName, dwDesiredAccess = GENERIC_ALL, dwShareMode = 0, lpSecurityAttributes = None, dwCreationDisposition = OPEN_ALWAYS, dwFlagsAndAttributes = FILE_ATTRIBUTE_NORMAL, hTemplateFile = None):
- _CreateFileW = windll.kernel32.CreateFileW
- _CreateFileW.argtypes = [LPWSTR, DWORD, DWORD, LPVOID, DWORD, DWORD, HANDLE]
- _CreateFileW.restype = HANDLE
-
- if not lpFileName:
- lpFileName = None
- if lpSecurityAttributes:
- lpSecurityAttributes = ctypes.pointer(lpSecurityAttributes)
- hFile = _CreateFileW(lpFileName, dwDesiredAccess, dwShareMode, lpSecurityAttributes, dwCreationDisposition, dwFlagsAndAttributes, hTemplateFile)
- if hFile == INVALID_HANDLE_VALUE:
- raise ctypes.WinError()
- return FileHandle(hFile)
-
-CreateFile = GuessStringType(CreateFileA, CreateFileW)
-
-# BOOL WINAPI FlushFileBuffers(
-# __in HANDLE hFile
-# );
-def FlushFileBuffers(hFile):
- _FlushFileBuffers = windll.kernel32.FlushFileBuffers
- _FlushFileBuffers.argtypes = [HANDLE]
- _FlushFileBuffers.restype = bool
- _FlushFileBuffers.errcheck = RaiseIfZero
- _FlushFileBuffers(hFile)
-
-# BOOL WINAPI FlushViewOfFile(
-# __in LPCVOID lpBaseAddress,
-# __in SIZE_T dwNumberOfBytesToFlush
-# );
-def FlushViewOfFile(lpBaseAddress, dwNumberOfBytesToFlush = 0):
- _FlushViewOfFile = windll.kernel32.FlushViewOfFile
- _FlushViewOfFile.argtypes = [LPVOID, SIZE_T]
- _FlushViewOfFile.restype = bool
- _FlushViewOfFile.errcheck = RaiseIfZero
- _FlushViewOfFile(lpBaseAddress, dwNumberOfBytesToFlush)
-
-# DWORD WINAPI SearchPath(
-# __in_opt LPCTSTR lpPath,
-# __in LPCTSTR lpFileName,
-# __in_opt LPCTSTR lpExtension,
-# __in DWORD nBufferLength,
-# __out LPTSTR lpBuffer,
-# __out_opt LPTSTR *lpFilePart
-# );
-def SearchPathA(lpPath, lpFileName, lpExtension):
- _SearchPathA = windll.kernel32.SearchPathA
- _SearchPathA.argtypes = [LPSTR, LPSTR, LPSTR, DWORD, LPSTR, POINTER(LPSTR)]
- _SearchPathA.restype = DWORD
- _SearchPathA.errcheck = RaiseIfZero
-
- if not lpPath:
- lpPath = None
- if not lpExtension:
- lpExtension = None
- nBufferLength = _SearchPathA(lpPath, lpFileName, lpExtension, 0, None, None)
- lpBuffer = ctypes.create_string_buffer('', nBufferLength + 1)
- lpFilePart = LPSTR()
- _SearchPathA(lpPath, lpFileName, lpExtension, nBufferLength, lpBuffer, byref(lpFilePart))
- lpFilePart = lpFilePart.value
- lpBuffer = lpBuffer.value
- if lpBuffer == '':
- if GetLastError() == ERROR_SUCCESS:
- raise ctypes.WinError(ERROR_FILE_NOT_FOUND)
- raise ctypes.WinError()
- return (lpBuffer, lpFilePart)
-
-def SearchPathW(lpPath, lpFileName, lpExtension):
- _SearchPathW = windll.kernel32.SearchPathW
- _SearchPathW.argtypes = [LPWSTR, LPWSTR, LPWSTR, DWORD, LPWSTR, POINTER(LPWSTR)]
- _SearchPathW.restype = DWORD
- _SearchPathW.errcheck = RaiseIfZero
-
- if not lpPath:
- lpPath = None
- if not lpExtension:
- lpExtension = None
- nBufferLength = _SearchPathW(lpPath, lpFileName, lpExtension, 0, None, None)
- lpBuffer = ctypes.create_unicode_buffer(u'', nBufferLength + 1)
- lpFilePart = LPWSTR()
- _SearchPathW(lpPath, lpFileName, lpExtension, nBufferLength, lpBuffer, byref(lpFilePart))
- lpFilePart = lpFilePart.value
- lpBuffer = lpBuffer.value
- if lpBuffer == u'':
- if GetLastError() == ERROR_SUCCESS:
- raise ctypes.WinError(ERROR_FILE_NOT_FOUND)
- raise ctypes.WinError()
- return (lpBuffer, lpFilePart)
-
-SearchPath = GuessStringType(SearchPathA, SearchPathW)
-
-# BOOL SetSearchPathMode(
-# __in DWORD Flags
-# );
-def SetSearchPathMode(Flags):
- _SetSearchPathMode = windll.kernel32.SetSearchPathMode
- _SetSearchPathMode.argtypes = [DWORD]
- _SetSearchPathMode.restype = bool
- _SetSearchPathMode.errcheck = RaiseIfZero
- _SetSearchPathMode(Flags)
-
-# BOOL WINAPI DeviceIoControl(
-# __in HANDLE hDevice,
-# __in DWORD dwIoControlCode,
-# __in_opt LPVOID lpInBuffer,
-# __in DWORD nInBufferSize,
-# __out_opt LPVOID lpOutBuffer,
-# __in DWORD nOutBufferSize,
-# __out_opt LPDWORD lpBytesReturned,
-# __inout_opt LPOVERLAPPED lpOverlapped
-# );
-def DeviceIoControl(hDevice, dwIoControlCode, lpInBuffer, nInBufferSize, lpOutBuffer, nOutBufferSize, lpOverlapped):
- _DeviceIoControl = windll.kernel32.DeviceIoControl
- _DeviceIoControl.argtypes = [HANDLE, DWORD, LPVOID, DWORD, LPVOID, DWORD, LPDWORD, LPOVERLAPPED]
- _DeviceIoControl.restype = bool
- _DeviceIoControl.errcheck = RaiseIfZero
-
- if not lpInBuffer:
- lpInBuffer = None
- if not lpOutBuffer:
- lpOutBuffer = None
- if lpOverlapped:
- lpOverlapped = ctypes.pointer(lpOverlapped)
- lpBytesReturned = DWORD(0)
- _DeviceIoControl(hDevice, dwIoControlCode, lpInBuffer, nInBufferSize, lpOutBuffer, nOutBufferSize, byref(lpBytesReturned), lpOverlapped)
- return lpBytesReturned.value
-
-# BOOL GetFileInformationByHandle(
-# HANDLE hFile,
-# LPBY_HANDLE_FILE_INFORMATION lpFileInformation
-# );
-def GetFileInformationByHandle(hFile):
- _GetFileInformationByHandle = windll.kernel32.GetFileInformationByHandle
- _GetFileInformationByHandle.argtypes = [HANDLE, LPBY_HANDLE_FILE_INFORMATION]
- _GetFileInformationByHandle.restype = bool
- _GetFileInformationByHandle.errcheck = RaiseIfZero
-
- lpFileInformation = BY_HANDLE_FILE_INFORMATION()
- _GetFileInformationByHandle(hFile, byref(lpFileInformation))
- return lpFileInformation
-
-# BOOL WINAPI GetFileInformationByHandleEx(
-# __in HANDLE hFile,
-# __in FILE_INFO_BY_HANDLE_CLASS FileInformationClass,
-# __out LPVOID lpFileInformation,
-# __in DWORD dwBufferSize
-# );
-def GetFileInformationByHandleEx(hFile, FileInformationClass, lpFileInformation, dwBufferSize):
- _GetFileInformationByHandleEx = windll.kernel32.GetFileInformationByHandleEx
- _GetFileInformationByHandleEx.argtypes = [HANDLE, DWORD, LPVOID, DWORD]
- _GetFileInformationByHandleEx.restype = bool
- _GetFileInformationByHandleEx.errcheck = RaiseIfZero
- # XXX TODO
- # support each FileInformationClass so the function can allocate the
- # corresponding structure for the lpFileInformation parameter
- _GetFileInformationByHandleEx(hFile, FileInformationClass, byref(lpFileInformation), dwBufferSize)
-
-# DWORD WINAPI GetFinalPathNameByHandle(
-# __in HANDLE hFile,
-# __out LPTSTR lpszFilePath,
-# __in DWORD cchFilePath,
-# __in DWORD dwFlags
-# );
-def GetFinalPathNameByHandleA(hFile, dwFlags = FILE_NAME_NORMALIZED | VOLUME_NAME_DOS):
- _GetFinalPathNameByHandleA = windll.kernel32.GetFinalPathNameByHandleA
- _GetFinalPathNameByHandleA.argtypes = [HANDLE, LPSTR, DWORD, DWORD]
- _GetFinalPathNameByHandleA.restype = DWORD
-
- cchFilePath = _GetFinalPathNameByHandleA(hFile, None, 0, dwFlags)
- if cchFilePath == 0:
- raise ctypes.WinError()
- lpszFilePath = ctypes.create_string_buffer('', cchFilePath + 1)
- nCopied = _GetFinalPathNameByHandleA(hFile, lpszFilePath, cchFilePath, dwFlags)
- if nCopied <= 0 or nCopied > cchFilePath:
- raise ctypes.WinError()
- return lpszFilePath.value
-
-def GetFinalPathNameByHandleW(hFile, dwFlags = FILE_NAME_NORMALIZED | VOLUME_NAME_DOS):
- _GetFinalPathNameByHandleW = windll.kernel32.GetFinalPathNameByHandleW
- _GetFinalPathNameByHandleW.argtypes = [HANDLE, LPWSTR, DWORD, DWORD]
- _GetFinalPathNameByHandleW.restype = DWORD
-
- cchFilePath = _GetFinalPathNameByHandleW(hFile, None, 0, dwFlags)
- if cchFilePath == 0:
- raise ctypes.WinError()
- lpszFilePath = ctypes.create_unicode_buffer(u'', cchFilePath + 1)
- nCopied = _GetFinalPathNameByHandleW(hFile, lpszFilePath, cchFilePath, dwFlags)
- if nCopied <= 0 or nCopied > cchFilePath:
- raise ctypes.WinError()
- return lpszFilePath.value
-
-GetFinalPathNameByHandle = GuessStringType(GetFinalPathNameByHandleA, GetFinalPathNameByHandleW)
-
-# DWORD GetFullPathName(
-# LPCTSTR lpFileName,
-# DWORD nBufferLength,
-# LPTSTR lpBuffer,
-# LPTSTR* lpFilePart
-# );
-def GetFullPathNameA(lpFileName):
- _GetFullPathNameA = windll.kernel32.GetFullPathNameA
- _GetFullPathNameA.argtypes = [LPSTR, DWORD, LPSTR, POINTER(LPSTR)]
- _GetFullPathNameA.restype = DWORD
-
- nBufferLength = _GetFullPathNameA(lpFileName, 0, None, None)
- if nBufferLength <= 0:
- raise ctypes.WinError()
- lpBuffer = ctypes.create_string_buffer('', nBufferLength + 1)
- lpFilePart = LPSTR()
- nCopied = _GetFullPathNameA(lpFileName, nBufferLength, lpBuffer, byref(lpFilePart))
- if nCopied > nBufferLength or nCopied == 0:
- raise ctypes.WinError()
- return lpBuffer.value, lpFilePart.value
-
-def GetFullPathNameW(lpFileName):
- _GetFullPathNameW = windll.kernel32.GetFullPathNameW
- _GetFullPathNameW.argtypes = [LPWSTR, DWORD, LPWSTR, POINTER(LPWSTR)]
- _GetFullPathNameW.restype = DWORD
-
- nBufferLength = _GetFullPathNameW(lpFileName, 0, None, None)
- if nBufferLength <= 0:
- raise ctypes.WinError()
- lpBuffer = ctypes.create_unicode_buffer(u'', nBufferLength + 1)
- lpFilePart = LPWSTR()
- nCopied = _GetFullPathNameW(lpFileName, nBufferLength, lpBuffer, byref(lpFilePart))
- if nCopied > nBufferLength or nCopied == 0:
- raise ctypes.WinError()
- return lpBuffer.value, lpFilePart.value
-
-GetFullPathName = GuessStringType(GetFullPathNameA, GetFullPathNameW)
-
-# DWORD WINAPI GetTempPath(
-# __in DWORD nBufferLength,
-# __out LPTSTR lpBuffer
-# );
-def GetTempPathA():
- _GetTempPathA = windll.kernel32.GetTempPathA
- _GetTempPathA.argtypes = [DWORD, LPSTR]
- _GetTempPathA.restype = DWORD
-
- nBufferLength = _GetTempPathA(0, None)
- if nBufferLength <= 0:
- raise ctypes.WinError()
- lpBuffer = ctypes.create_string_buffer('', nBufferLength)
- nCopied = _GetTempPathA(nBufferLength, lpBuffer)
- if nCopied > nBufferLength or nCopied == 0:
- raise ctypes.WinError()
- return lpBuffer.value
-
-def GetTempPathW():
- _GetTempPathW = windll.kernel32.GetTempPathW
- _GetTempPathW.argtypes = [DWORD, LPWSTR]
- _GetTempPathW.restype = DWORD
-
- nBufferLength = _GetTempPathW(0, None)
- if nBufferLength <= 0:
- raise ctypes.WinError()
- lpBuffer = ctypes.create_unicode_buffer(u'', nBufferLength)
- nCopied = _GetTempPathW(nBufferLength, lpBuffer)
- if nCopied > nBufferLength or nCopied == 0:
- raise ctypes.WinError()
- return lpBuffer.value
-
-GetTempPath = GuessStringType(GetTempPathA, GetTempPathW)
-
-# UINT WINAPI GetTempFileName(
-# __in LPCTSTR lpPathName,
-# __in LPCTSTR lpPrefixString,
-# __in UINT uUnique,
-# __out LPTSTR lpTempFileName
-# );
-def GetTempFileNameA(lpPathName = None, lpPrefixString = "TMP", uUnique = 0):
- _GetTempFileNameA = windll.kernel32.GetTempFileNameA
- _GetTempFileNameA.argtypes = [LPSTR, LPSTR, UINT, LPSTR]
- _GetTempFileNameA.restype = UINT
-
- if lpPathName is None:
- lpPathName = GetTempPathA()
- lpTempFileName = ctypes.create_string_buffer('', MAX_PATH)
- uUnique = _GetTempFileNameA(lpPathName, lpPrefixString, uUnique, lpTempFileName)
- if uUnique == 0:
- raise ctypes.WinError()
- return lpTempFileName.value, uUnique
-
-def GetTempFileNameW(lpPathName = None, lpPrefixString = u"TMP", uUnique = 0):
- _GetTempFileNameW = windll.kernel32.GetTempFileNameW
- _GetTempFileNameW.argtypes = [LPWSTR, LPWSTR, UINT, LPWSTR]
- _GetTempFileNameW.restype = UINT
-
- if lpPathName is None:
- lpPathName = GetTempPathW()
- lpTempFileName = ctypes.create_unicode_buffer(u'', MAX_PATH)
- uUnique = _GetTempFileNameW(lpPathName, lpPrefixString, uUnique, lpTempFileName)
- if uUnique == 0:
- raise ctypes.WinError()
- return lpTempFileName.value, uUnique
-
-GetTempFileName = GuessStringType(GetTempFileNameA, GetTempFileNameW)
-
-# DWORD WINAPI GetCurrentDirectory(
-# __in DWORD nBufferLength,
-# __out LPTSTR lpBuffer
-# );
-def GetCurrentDirectoryA():
- _GetCurrentDirectoryA = windll.kernel32.GetCurrentDirectoryA
- _GetCurrentDirectoryA.argtypes = [DWORD, LPSTR]
- _GetCurrentDirectoryA.restype = DWORD
-
- nBufferLength = _GetCurrentDirectoryA(0, None)
- if nBufferLength <= 0:
- raise ctypes.WinError()
- lpBuffer = ctypes.create_string_buffer('', nBufferLength)
- nCopied = _GetCurrentDirectoryA(nBufferLength, lpBuffer)
- if nCopied > nBufferLength or nCopied == 0:
- raise ctypes.WinError()
- return lpBuffer.value
-
-def GetCurrentDirectoryW():
- _GetCurrentDirectoryW = windll.kernel32.GetCurrentDirectoryW
- _GetCurrentDirectoryW.argtypes = [DWORD, LPWSTR]
- _GetCurrentDirectoryW.restype = DWORD
-
- nBufferLength = _GetCurrentDirectoryW(0, None)
- if nBufferLength <= 0:
- raise ctypes.WinError()
- lpBuffer = ctypes.create_unicode_buffer(u'', nBufferLength)
- nCopied = _GetCurrentDirectoryW(nBufferLength, lpBuffer)
- if nCopied > nBufferLength or nCopied == 0:
- raise ctypes.WinError()
- return lpBuffer.value
-
-GetCurrentDirectory = GuessStringType(GetCurrentDirectoryA, GetCurrentDirectoryW)
-
-#------------------------------------------------------------------------------
-# Contrl-C handler
-
-# BOOL WINAPI HandlerRoutine(
-# __in DWORD dwCtrlType
-# );
-PHANDLER_ROUTINE = ctypes.WINFUNCTYPE(BOOL, DWORD)
-
-# BOOL WINAPI SetConsoleCtrlHandler(
-# __in_opt PHANDLER_ROUTINE HandlerRoutine,
-# __in BOOL Add
-# );
-def SetConsoleCtrlHandler(HandlerRoutine = None, Add = True):
- _SetConsoleCtrlHandler = windll.kernel32.SetConsoleCtrlHandler
- _SetConsoleCtrlHandler.argtypes = [PHANDLER_ROUTINE, BOOL]
- _SetConsoleCtrlHandler.restype = bool
- _SetConsoleCtrlHandler.errcheck = RaiseIfZero
- _SetConsoleCtrlHandler(HandlerRoutine, bool(Add))
- # we can't automagically transform Python functions to PHANDLER_ROUTINE
- # because a) the actual pointer value is meaningful to the API
- # and b) if it gets garbage collected bad things would happen
-
-# BOOL WINAPI GenerateConsoleCtrlEvent(
-# __in DWORD dwCtrlEvent,
-# __in DWORD dwProcessGroupId
-# );
-def GenerateConsoleCtrlEvent(dwCtrlEvent, dwProcessGroupId):
- _GenerateConsoleCtrlEvent = windll.kernel32.GenerateConsoleCtrlEvent
- _GenerateConsoleCtrlEvent.argtypes = [DWORD, DWORD]
- _GenerateConsoleCtrlEvent.restype = bool
- _GenerateConsoleCtrlEvent.errcheck = RaiseIfZero
- _GenerateConsoleCtrlEvent(dwCtrlEvent, dwProcessGroupId)
-
-#------------------------------------------------------------------------------
-# Synchronization API
-
-# XXX NOTE
-#
-# Instead of waiting forever, we wait for a small period of time and loop.
-# This is a workaround for an unwanted behavior of psyco-accelerated code:
-# you can't interrupt a blocking call using Ctrl+C, because signal processing
-# is only done between C calls.
-#
-# Also see: bug #2793618 in Psyco project
-# http://sourceforge.net/tracker/?func=detail&aid=2793618&group_id=41036&atid=429622
-
-# DWORD WINAPI WaitForSingleObject(
-# HANDLE hHandle,
-# DWORD dwMilliseconds
-# );
-def WaitForSingleObject(hHandle, dwMilliseconds = INFINITE):
- _WaitForSingleObject = windll.kernel32.WaitForSingleObject
- _WaitForSingleObject.argtypes = [HANDLE, DWORD]
- _WaitForSingleObject.restype = DWORD
-
- if not dwMilliseconds and dwMilliseconds != 0:
- dwMilliseconds = INFINITE
- if dwMilliseconds != INFINITE:
- r = _WaitForSingleObject(hHandle, dwMilliseconds)
- if r == WAIT_FAILED:
- raise ctypes.WinError()
- else:
- while 1:
- r = _WaitForSingleObject(hHandle, 100)
- if r == WAIT_FAILED:
- raise ctypes.WinError()
- if r != WAIT_TIMEOUT:
- break
- return r
-
-# DWORD WINAPI WaitForSingleObjectEx(
-# HANDLE hHandle,
-# DWORD dwMilliseconds,
-# BOOL bAlertable
-# );
-def WaitForSingleObjectEx(hHandle, dwMilliseconds = INFINITE, bAlertable = True):
- _WaitForSingleObjectEx = windll.kernel32.WaitForSingleObjectEx
- _WaitForSingleObjectEx.argtypes = [HANDLE, DWORD, BOOL]
- _WaitForSingleObjectEx.restype = DWORD
-
- if not dwMilliseconds and dwMilliseconds != 0:
- dwMilliseconds = INFINITE
- if dwMilliseconds != INFINITE:
- r = _WaitForSingleObjectEx(hHandle, dwMilliseconds, bool(bAlertable))
- if r == WAIT_FAILED:
- raise ctypes.WinError()
- else:
- while 1:
- r = _WaitForSingleObjectEx(hHandle, 100, bool(bAlertable))
- if r == WAIT_FAILED:
- raise ctypes.WinError()
- if r != WAIT_TIMEOUT:
- break
- return r
-
-# DWORD WINAPI WaitForMultipleObjects(
-# DWORD nCount,
-# const HANDLE *lpHandles,
-# BOOL bWaitAll,
-# DWORD dwMilliseconds
-# );
-def WaitForMultipleObjects(handles, bWaitAll = False, dwMilliseconds = INFINITE):
- _WaitForMultipleObjects = windll.kernel32.WaitForMultipleObjects
- _WaitForMultipleObjects.argtypes = [DWORD, POINTER(HANDLE), BOOL, DWORD]
- _WaitForMultipleObjects.restype = DWORD
-
- if not dwMilliseconds and dwMilliseconds != 0:
- dwMilliseconds = INFINITE
- nCount = len(handles)
- lpHandlesType = HANDLE * nCount
- lpHandles = lpHandlesType(*handles)
- if dwMilliseconds != INFINITE:
- r = _WaitForMultipleObjects(byref(lpHandles), bool(bWaitAll), dwMilliseconds)
- if r == WAIT_FAILED:
- raise ctypes.WinError()
- else:
- while 1:
- r = _WaitForMultipleObjects(byref(lpHandles), bool(bWaitAll), 100)
- if r == WAIT_FAILED:
- raise ctypes.WinError()
- if r != WAIT_TIMEOUT:
- break
- return r
-
-# DWORD WINAPI WaitForMultipleObjectsEx(
-# DWORD nCount,
-# const HANDLE *lpHandles,
-# BOOL bWaitAll,
-# DWORD dwMilliseconds,
-# BOOL bAlertable
-# );
-def WaitForMultipleObjectsEx(handles, bWaitAll = False, dwMilliseconds = INFINITE, bAlertable = True):
- _WaitForMultipleObjectsEx = windll.kernel32.WaitForMultipleObjectsEx
- _WaitForMultipleObjectsEx.argtypes = [DWORD, POINTER(HANDLE), BOOL, DWORD]
- _WaitForMultipleObjectsEx.restype = DWORD
-
- if not dwMilliseconds and dwMilliseconds != 0:
- dwMilliseconds = INFINITE
- nCount = len(handles)
- lpHandlesType = HANDLE * nCount
- lpHandles = lpHandlesType(*handles)
- if dwMilliseconds != INFINITE:
- r = _WaitForMultipleObjectsEx(byref(lpHandles), bool(bWaitAll), dwMilliseconds, bool(bAlertable))
- if r == WAIT_FAILED:
- raise ctypes.WinError()
- else:
- while 1:
- r = _WaitForMultipleObjectsEx(byref(lpHandles), bool(bWaitAll), 100, bool(bAlertable))
- if r == WAIT_FAILED:
- raise ctypes.WinError()
- if r != WAIT_TIMEOUT:
- break
- return r
-
-# HANDLE WINAPI CreateMutex(
-# _In_opt_ LPSECURITY_ATTRIBUTES lpMutexAttributes,
-# _In_ BOOL bInitialOwner,
-# _In_opt_ LPCTSTR lpName
-# );
-def CreateMutexA(lpMutexAttributes = None, bInitialOwner = True, lpName = None):
- _CreateMutexA = windll.kernel32.CreateMutexA
- _CreateMutexA.argtypes = [LPVOID, BOOL, LPSTR]
- _CreateMutexA.restype = HANDLE
- _CreateMutexA.errcheck = RaiseIfZero
- return Handle( _CreateMutexA(lpMutexAttributes, bInitialOwner, lpName) )
-
-def CreateMutexW(lpMutexAttributes = None, bInitialOwner = True, lpName = None):
- _CreateMutexW = windll.kernel32.CreateMutexW
- _CreateMutexW.argtypes = [LPVOID, BOOL, LPWSTR]
- _CreateMutexW.restype = HANDLE
- _CreateMutexW.errcheck = RaiseIfZero
- return Handle( _CreateMutexW(lpMutexAttributes, bInitialOwner, lpName) )
-
-CreateMutex = GuessStringType(CreateMutexA, CreateMutexW)
-
-# HANDLE WINAPI OpenMutex(
-# _In_ DWORD dwDesiredAccess,
-# _In_ BOOL bInheritHandle,
-# _In_ LPCTSTR lpName
-# );
-def OpenMutexA(dwDesiredAccess = MUTEX_ALL_ACCESS, bInitialOwner = True, lpName = None):
- _OpenMutexA = windll.kernel32.OpenMutexA
- _OpenMutexA.argtypes = [DWORD, BOOL, LPSTR]
- _OpenMutexA.restype = HANDLE
- _OpenMutexA.errcheck = RaiseIfZero
- return Handle( _OpenMutexA(lpMutexAttributes, bInitialOwner, lpName) )
-
-def OpenMutexW(dwDesiredAccess = MUTEX_ALL_ACCESS, bInitialOwner = True, lpName = None):
- _OpenMutexW = windll.kernel32.OpenMutexW
- _OpenMutexW.argtypes = [DWORD, BOOL, LPWSTR]
- _OpenMutexW.restype = HANDLE
- _OpenMutexW.errcheck = RaiseIfZero
- return Handle( _OpenMutexW(lpMutexAttributes, bInitialOwner, lpName) )
-
-OpenMutex = GuessStringType(OpenMutexA, OpenMutexW)
-
-# HANDLE WINAPI CreateEvent(
-# _In_opt_ LPSECURITY_ATTRIBUTES lpEventAttributes,
-# _In_ BOOL bManualReset,
-# _In_ BOOL bInitialState,
-# _In_opt_ LPCTSTR lpName
-# );
-def CreateEventA(lpMutexAttributes = None, bManualReset = False, bInitialState = False, lpName = None):
- _CreateEventA = windll.kernel32.CreateEventA
- _CreateEventA.argtypes = [LPVOID, BOOL, BOOL, LPSTR]
- _CreateEventA.restype = HANDLE
- _CreateEventA.errcheck = RaiseIfZero
- return Handle( _CreateEventA(lpMutexAttributes, bManualReset, bInitialState, lpName) )
-
-def CreateEventW(lpMutexAttributes = None, bManualReset = False, bInitialState = False, lpName = None):
- _CreateEventW = windll.kernel32.CreateEventW
- _CreateEventW.argtypes = [LPVOID, BOOL, BOOL, LPWSTR]
- _CreateEventW.restype = HANDLE
- _CreateEventW.errcheck = RaiseIfZero
- return Handle( _CreateEventW(lpMutexAttributes, bManualReset, bInitialState, lpName) )
-
-CreateEvent = GuessStringType(CreateEventA, CreateEventW)
-
-# HANDLE WINAPI OpenEvent(
-# _In_ DWORD dwDesiredAccess,
-# _In_ BOOL bInheritHandle,
-# _In_ LPCTSTR lpName
-# );
-def OpenEventA(dwDesiredAccess = EVENT_ALL_ACCESS, bInheritHandle = False, lpName = None):
- _OpenEventA = windll.kernel32.OpenEventA
- _OpenEventA.argtypes = [DWORD, BOOL, LPSTR]
- _OpenEventA.restype = HANDLE
- _OpenEventA.errcheck = RaiseIfZero
- return Handle( _OpenEventA(dwDesiredAccess, bInheritHandle, lpName) )
-
-def OpenEventW(dwDesiredAccess = EVENT_ALL_ACCESS, bInheritHandle = False, lpName = None):
- _OpenEventW = windll.kernel32.OpenEventW
- _OpenEventW.argtypes = [DWORD, BOOL, LPWSTR]
- _OpenEventW.restype = HANDLE
- _OpenEventW.errcheck = RaiseIfZero
- return Handle( _OpenEventW(dwDesiredAccess, bInheritHandle, lpName) )
-
-OpenEvent = GuessStringType(OpenEventA, OpenEventW)
-
-# HANDLE WINAPI CreateSemaphore(
-# _In_opt_ LPSECURITY_ATTRIBUTES lpSemaphoreAttributes,
-# _In_ LONG lInitialCount,
-# _In_ LONG lMaximumCount,
-# _In_opt_ LPCTSTR lpName
-# );
-
-# TODO
-
-# HANDLE WINAPI OpenSemaphore(
-# _In_ DWORD dwDesiredAccess,
-# _In_ BOOL bInheritHandle,
-# _In_ LPCTSTR lpName
-# );
-
-# TODO
-
-# BOOL WINAPI ReleaseMutex(
-# _In_ HANDLE hMutex
-# );
-def ReleaseMutex(hMutex):
- _ReleaseMutex = windll.kernel32.ReleaseMutex
- _ReleaseMutex.argtypes = [HANDLE]
- _ReleaseMutex.restype = bool
- _ReleaseMutex.errcheck = RaiseIfZero
- _ReleaseMutex(hMutex)
-
-# BOOL WINAPI SetEvent(
-# _In_ HANDLE hEvent
-# );
-def SetEvent(hEvent):
- _SetEvent = windll.kernel32.SetEvent
- _SetEvent.argtypes = [HANDLE]
- _SetEvent.restype = bool
- _SetEvent.errcheck = RaiseIfZero
- _SetEvent(hEvent)
-
-# BOOL WINAPI ResetEvent(
-# _In_ HANDLE hEvent
-# );
-def ResetEvent(hEvent):
- _ResetEvent = windll.kernel32.ResetEvent
- _ResetEvent.argtypes = [HANDLE]
- _ResetEvent.restype = bool
- _ResetEvent.errcheck = RaiseIfZero
- _ResetEvent(hEvent)
-
-# BOOL WINAPI PulseEvent(
-# _In_ HANDLE hEvent
-# );
-def PulseEvent(hEvent):
- _PulseEvent = windll.kernel32.PulseEvent
- _PulseEvent.argtypes = [HANDLE]
- _PulseEvent.restype = bool
- _PulseEvent.errcheck = RaiseIfZero
- _PulseEvent(hEvent)
-
-# BOOL WINAPI ReleaseSemaphore(
-# _In_ HANDLE hSemaphore,
-# _In_ LONG lReleaseCount,
-# _Out_opt_ LPLONG lpPreviousCount
-# );
-
-# TODO
-
-#------------------------------------------------------------------------------
-# Debug API
-
-# BOOL WaitForDebugEvent(
-# LPDEBUG_EVENT lpDebugEvent,
-# DWORD dwMilliseconds
-# );
-def WaitForDebugEvent(dwMilliseconds = INFINITE):
- _WaitForDebugEvent = windll.kernel32.WaitForDebugEvent
- _WaitForDebugEvent.argtypes = [LPDEBUG_EVENT, DWORD]
- _WaitForDebugEvent.restype = DWORD
-
- if not dwMilliseconds and dwMilliseconds != 0:
- dwMilliseconds = INFINITE
- lpDebugEvent = DEBUG_EVENT()
- lpDebugEvent.dwDebugEventCode = 0
- lpDebugEvent.dwProcessId = 0
- lpDebugEvent.dwThreadId = 0
- if dwMilliseconds != INFINITE:
- success = _WaitForDebugEvent(byref(lpDebugEvent), dwMilliseconds)
- if success == 0:
- raise ctypes.WinError()
- else:
- # this avoids locking the Python GIL for too long
- while 1:
- success = _WaitForDebugEvent(byref(lpDebugEvent), 100)
- if success != 0:
- break
- code = GetLastError()
- if code not in (ERROR_SEM_TIMEOUT, WAIT_TIMEOUT):
- raise ctypes.WinError(code)
- return lpDebugEvent
-
-# BOOL ContinueDebugEvent(
-# DWORD dwProcessId,
-# DWORD dwThreadId,
-# DWORD dwContinueStatus
-# );
-def ContinueDebugEvent(dwProcessId, dwThreadId, dwContinueStatus = DBG_EXCEPTION_NOT_HANDLED):
- _ContinueDebugEvent = windll.kernel32.ContinueDebugEvent
- _ContinueDebugEvent.argtypes = [DWORD, DWORD, DWORD]
- _ContinueDebugEvent.restype = bool
- _ContinueDebugEvent.errcheck = RaiseIfZero
- _ContinueDebugEvent(dwProcessId, dwThreadId, dwContinueStatus)
-
-# BOOL WINAPI FlushInstructionCache(
-# __in HANDLE hProcess,
-# __in LPCVOID lpBaseAddress,
-# __in SIZE_T dwSize
-# );
-def FlushInstructionCache(hProcess, lpBaseAddress = None, dwSize = 0):
- # http://blogs.msdn.com/oldnewthing/archive/2003/12/08/55954.aspx#55958
- _FlushInstructionCache = windll.kernel32.FlushInstructionCache
- _FlushInstructionCache.argtypes = [HANDLE, LPVOID, SIZE_T]
- _FlushInstructionCache.restype = bool
- _FlushInstructionCache.errcheck = RaiseIfZero
- _FlushInstructionCache(hProcess, lpBaseAddress, dwSize)
-
-# BOOL DebugActiveProcess(
-# DWORD dwProcessId
-# );
-def DebugActiveProcess(dwProcessId):
- _DebugActiveProcess = windll.kernel32.DebugActiveProcess
- _DebugActiveProcess.argtypes = [DWORD]
- _DebugActiveProcess.restype = bool
- _DebugActiveProcess.errcheck = RaiseIfZero
- _DebugActiveProcess(dwProcessId)
-
-# BOOL DebugActiveProcessStop(
-# DWORD dwProcessId
-# );
-def DebugActiveProcessStop(dwProcessId):
- _DebugActiveProcessStop = windll.kernel32.DebugActiveProcessStop
- _DebugActiveProcessStop.argtypes = [DWORD]
- _DebugActiveProcessStop.restype = bool
- _DebugActiveProcessStop.errcheck = RaiseIfZero
- _DebugActiveProcessStop(dwProcessId)
-
-# BOOL CheckRemoteDebuggerPresent(
-# HANDLE hProcess,
-# PBOOL pbDebuggerPresent
-# );
-def CheckRemoteDebuggerPresent(hProcess):
- _CheckRemoteDebuggerPresent = windll.kernel32.CheckRemoteDebuggerPresent
- _CheckRemoteDebuggerPresent.argtypes = [HANDLE, PBOOL]
- _CheckRemoteDebuggerPresent.restype = bool
- _CheckRemoteDebuggerPresent.errcheck = RaiseIfZero
-
- pbDebuggerPresent = BOOL(0)
- _CheckRemoteDebuggerPresent(hProcess, byref(pbDebuggerPresent))
- return bool(pbDebuggerPresent.value)
-
-# BOOL DebugSetProcessKillOnExit(
-# BOOL KillOnExit
-# );
-def DebugSetProcessKillOnExit(KillOnExit):
- _DebugSetProcessKillOnExit = windll.kernel32.DebugSetProcessKillOnExit
- _DebugSetProcessKillOnExit.argtypes = [BOOL]
- _DebugSetProcessKillOnExit.restype = bool
- _DebugSetProcessKillOnExit.errcheck = RaiseIfZero
- _DebugSetProcessKillOnExit(bool(KillOnExit))
-
-# BOOL DebugBreakProcess(
-# HANDLE Process
-# );
-def DebugBreakProcess(hProcess):
- _DebugBreakProcess = windll.kernel32.DebugBreakProcess
- _DebugBreakProcess.argtypes = [HANDLE]
- _DebugBreakProcess.restype = bool
- _DebugBreakProcess.errcheck = RaiseIfZero
- _DebugBreakProcess(hProcess)
-
-# void WINAPI OutputDebugString(
-# __in_opt LPCTSTR lpOutputString
-# );
-def OutputDebugStringA(lpOutputString):
- _OutputDebugStringA = windll.kernel32.OutputDebugStringA
- _OutputDebugStringA.argtypes = [LPSTR]
- _OutputDebugStringA.restype = None
- _OutputDebugStringA(lpOutputString)
-
-def OutputDebugStringW(lpOutputString):
- _OutputDebugStringW = windll.kernel32.OutputDebugStringW
- _OutputDebugStringW.argtypes = [LPWSTR]
- _OutputDebugStringW.restype = None
- _OutputDebugStringW(lpOutputString)
-
-OutputDebugString = GuessStringType(OutputDebugStringA, OutputDebugStringW)
-
-# BOOL WINAPI ReadProcessMemory(
-# __in HANDLE hProcess,
-# __in LPCVOID lpBaseAddress,
-# __out LPVOID lpBuffer,
-# __in SIZE_T nSize,
-# __out SIZE_T* lpNumberOfBytesRead
-# );
-def ReadProcessMemory(hProcess, lpBaseAddress, nSize):
- _ReadProcessMemory = windll.kernel32.ReadProcessMemory
- _ReadProcessMemory.argtypes = [HANDLE, LPVOID, LPVOID, SIZE_T, POINTER(SIZE_T)]
- _ReadProcessMemory.restype = bool
-
- lpBuffer = ctypes.create_string_buffer(compat.b(''), nSize)
- lpNumberOfBytesRead = SIZE_T(0)
- success = _ReadProcessMemory(hProcess, lpBaseAddress, lpBuffer, nSize, byref(lpNumberOfBytesRead))
- if not success and GetLastError() != ERROR_PARTIAL_COPY:
- raise ctypes.WinError()
- return compat.b(lpBuffer.raw)[:lpNumberOfBytesRead.value]
-
-# BOOL WINAPI WriteProcessMemory(
-# __in HANDLE hProcess,
-# __in LPCVOID lpBaseAddress,
-# __in LPVOID lpBuffer,
-# __in SIZE_T nSize,
-# __out SIZE_T* lpNumberOfBytesWritten
-# );
-def WriteProcessMemory(hProcess, lpBaseAddress, lpBuffer):
- _WriteProcessMemory = windll.kernel32.WriteProcessMemory
- _WriteProcessMemory.argtypes = [HANDLE, LPVOID, LPVOID, SIZE_T, POINTER(SIZE_T)]
- _WriteProcessMemory.restype = bool
-
- nSize = len(lpBuffer)
- lpBuffer = ctypes.create_string_buffer(lpBuffer)
- lpNumberOfBytesWritten = SIZE_T(0)
- success = _WriteProcessMemory(hProcess, lpBaseAddress, lpBuffer, nSize, byref(lpNumberOfBytesWritten))
- if not success and GetLastError() != ERROR_PARTIAL_COPY:
- raise ctypes.WinError()
- return lpNumberOfBytesWritten.value
-
-# LPVOID WINAPI VirtualAllocEx(
-# __in HANDLE hProcess,
-# __in_opt LPVOID lpAddress,
-# __in SIZE_T dwSize,
-# __in DWORD flAllocationType,
-# __in DWORD flProtect
-# );
-def VirtualAllocEx(hProcess, lpAddress = 0, dwSize = 0x1000, flAllocationType = MEM_COMMIT | MEM_RESERVE, flProtect = PAGE_EXECUTE_READWRITE):
- _VirtualAllocEx = windll.kernel32.VirtualAllocEx
- _VirtualAllocEx.argtypes = [HANDLE, LPVOID, SIZE_T, DWORD, DWORD]
- _VirtualAllocEx.restype = LPVOID
-
- lpAddress = _VirtualAllocEx(hProcess, lpAddress, dwSize, flAllocationType, flProtect)
- if lpAddress == NULL:
- raise ctypes.WinError()
- return lpAddress
-
-# SIZE_T WINAPI VirtualQueryEx(
-# __in HANDLE hProcess,
-# __in_opt LPCVOID lpAddress,
-# __out PMEMORY_BASIC_INFORMATION lpBuffer,
-# __in SIZE_T dwLength
-# );
-def VirtualQueryEx(hProcess, lpAddress):
- _VirtualQueryEx = windll.kernel32.VirtualQueryEx
- _VirtualQueryEx.argtypes = [HANDLE, LPVOID, PMEMORY_BASIC_INFORMATION, SIZE_T]
- _VirtualQueryEx.restype = SIZE_T
-
- lpBuffer = MEMORY_BASIC_INFORMATION()
- dwLength = sizeof(MEMORY_BASIC_INFORMATION)
- success = _VirtualQueryEx(hProcess, lpAddress, byref(lpBuffer), dwLength)
- if success == 0:
- raise ctypes.WinError()
- return MemoryBasicInformation(lpBuffer)
-
-# BOOL WINAPI VirtualProtectEx(
-# __in HANDLE hProcess,
-# __in LPVOID lpAddress,
-# __in SIZE_T dwSize,
-# __in DWORD flNewProtect,
-# __out PDWORD lpflOldProtect
-# );
-def VirtualProtectEx(hProcess, lpAddress, dwSize, flNewProtect = PAGE_EXECUTE_READWRITE):
- _VirtualProtectEx = windll.kernel32.VirtualProtectEx
- _VirtualProtectEx.argtypes = [HANDLE, LPVOID, SIZE_T, DWORD, PDWORD]
- _VirtualProtectEx.restype = bool
- _VirtualProtectEx.errcheck = RaiseIfZero
-
- flOldProtect = DWORD(0)
- _VirtualProtectEx(hProcess, lpAddress, dwSize, flNewProtect, byref(flOldProtect))
- return flOldProtect.value
-
-# BOOL WINAPI VirtualFreeEx(
-# __in HANDLE hProcess,
-# __in LPVOID lpAddress,
-# __in SIZE_T dwSize,
-# __in DWORD dwFreeType
-# );
-def VirtualFreeEx(hProcess, lpAddress, dwSize = 0, dwFreeType = MEM_RELEASE):
- _VirtualFreeEx = windll.kernel32.VirtualFreeEx
- _VirtualFreeEx.argtypes = [HANDLE, LPVOID, SIZE_T, DWORD]
- _VirtualFreeEx.restype = bool
- _VirtualFreeEx.errcheck = RaiseIfZero
- _VirtualFreeEx(hProcess, lpAddress, dwSize, dwFreeType)
-
-# HANDLE WINAPI CreateRemoteThread(
-# __in HANDLE hProcess,
-# __in LPSECURITY_ATTRIBUTES lpThreadAttributes,
-# __in SIZE_T dwStackSize,
-# __in LPTHREAD_START_ROUTINE lpStartAddress,
-# __in LPVOID lpParameter,
-# __in DWORD dwCreationFlags,
-# __out LPDWORD lpThreadId
-# );
-def CreateRemoteThread(hProcess, lpThreadAttributes, dwStackSize, lpStartAddress, lpParameter, dwCreationFlags):
- _CreateRemoteThread = windll.kernel32.CreateRemoteThread
- _CreateRemoteThread.argtypes = [HANDLE, LPSECURITY_ATTRIBUTES, SIZE_T, LPVOID, LPVOID, DWORD, LPDWORD]
- _CreateRemoteThread.restype = HANDLE
-
- if not lpThreadAttributes:
- lpThreadAttributes = None
- else:
- lpThreadAttributes = byref(lpThreadAttributes)
- dwThreadId = DWORD(0)
- hThread = _CreateRemoteThread(hProcess, lpThreadAttributes, dwStackSize, lpStartAddress, lpParameter, dwCreationFlags, byref(dwThreadId))
- if not hThread:
- raise ctypes.WinError()
- return ThreadHandle(hThread), dwThreadId.value
-
-#------------------------------------------------------------------------------
-# Process API
-
-# BOOL WINAPI CreateProcess(
-# __in_opt LPCTSTR lpApplicationName,
-# __inout_opt LPTSTR lpCommandLine,
-# __in_opt LPSECURITY_ATTRIBUTES lpProcessAttributes,
-# __in_opt LPSECURITY_ATTRIBUTES lpThreadAttributes,
-# __in BOOL bInheritHandles,
-# __in DWORD dwCreationFlags,
-# __in_opt LPVOID lpEnvironment,
-# __in_opt LPCTSTR lpCurrentDirectory,
-# __in LPSTARTUPINFO lpStartupInfo,
-# __out LPPROCESS_INFORMATION lpProcessInformation
-# );
-def CreateProcessA(lpApplicationName, lpCommandLine=None, lpProcessAttributes=None, lpThreadAttributes=None, bInheritHandles=False, dwCreationFlags=0, lpEnvironment=None, lpCurrentDirectory=None, lpStartupInfo=None):
- _CreateProcessA = windll.kernel32.CreateProcessA
- _CreateProcessA.argtypes = [LPSTR, LPSTR, LPSECURITY_ATTRIBUTES, LPSECURITY_ATTRIBUTES, BOOL, DWORD, LPVOID, LPSTR, LPVOID, LPPROCESS_INFORMATION]
- _CreateProcessA.restype = bool
- _CreateProcessA.errcheck = RaiseIfZero
-
- if not lpApplicationName:
- lpApplicationName = None
- if not lpCommandLine:
- lpCommandLine = None
- else:
- lpCommandLine = ctypes.create_string_buffer(lpCommandLine, max(MAX_PATH, len(lpCommandLine)))
- if not lpEnvironment:
- lpEnvironment = None
- else:
- lpEnvironment = ctypes.create_string_buffer(lpEnvironment)
- if not lpCurrentDirectory:
- lpCurrentDirectory = None
- if not lpProcessAttributes:
- lpProcessAttributes = None
- else:
- lpProcessAttributes = byref(lpProcessAttributes)
- if not lpThreadAttributes:
- lpThreadAttributes = None
- else:
- lpThreadAttributes = byref(lpThreadAttributes)
- if not lpStartupInfo:
- lpStartupInfo = STARTUPINFO()
- lpStartupInfo.cb = sizeof(STARTUPINFO)
- lpStartupInfo.lpReserved = 0
- lpStartupInfo.lpDesktop = 0
- lpStartupInfo.lpTitle = 0
- lpStartupInfo.dwFlags = 0
- lpStartupInfo.cbReserved2 = 0
- lpStartupInfo.lpReserved2 = 0
- lpProcessInformation = PROCESS_INFORMATION()
- lpProcessInformation.hProcess = INVALID_HANDLE_VALUE
- lpProcessInformation.hThread = INVALID_HANDLE_VALUE
- lpProcessInformation.dwProcessId = 0
- lpProcessInformation.dwThreadId = 0
- _CreateProcessA(lpApplicationName, lpCommandLine, lpProcessAttributes, lpThreadAttributes, bool(bInheritHandles), dwCreationFlags, lpEnvironment, lpCurrentDirectory, byref(lpStartupInfo), byref(lpProcessInformation))
- return ProcessInformation(lpProcessInformation)
-
-def CreateProcessW(lpApplicationName, lpCommandLine=None, lpProcessAttributes=None, lpThreadAttributes=None, bInheritHandles=False, dwCreationFlags=0, lpEnvironment=None, lpCurrentDirectory=None, lpStartupInfo=None):
- _CreateProcessW = windll.kernel32.CreateProcessW
- _CreateProcessW.argtypes = [LPWSTR, LPWSTR, LPSECURITY_ATTRIBUTES, LPSECURITY_ATTRIBUTES, BOOL, DWORD, LPVOID, LPWSTR, LPVOID, LPPROCESS_INFORMATION]
- _CreateProcessW.restype = bool
- _CreateProcessW.errcheck = RaiseIfZero
-
- if not lpApplicationName:
- lpApplicationName = None
- if not lpCommandLine:
- lpCommandLine = None
- else:
- lpCommandLine = ctypes.create_unicode_buffer(lpCommandLine, max(MAX_PATH, len(lpCommandLine)))
- if not lpEnvironment:
- lpEnvironment = None
- else:
- lpEnvironment = ctypes.create_unicode_buffer(lpEnvironment)
- if not lpCurrentDirectory:
- lpCurrentDirectory = None
- if not lpProcessAttributes:
- lpProcessAttributes = None
- else:
- lpProcessAttributes = byref(lpProcessAttributes)
- if not lpThreadAttributes:
- lpThreadAttributes = None
- else:
- lpThreadAttributes = byref(lpThreadAttributes)
- if not lpStartupInfo:
- lpStartupInfo = STARTUPINFO()
- lpStartupInfo.cb = sizeof(STARTUPINFO)
- lpStartupInfo.lpReserved = 0
- lpStartupInfo.lpDesktop = 0
- lpStartupInfo.lpTitle = 0
- lpStartupInfo.dwFlags = 0
- lpStartupInfo.cbReserved2 = 0
- lpStartupInfo.lpReserved2 = 0
- lpProcessInformation = PROCESS_INFORMATION()
- lpProcessInformation.hProcess = INVALID_HANDLE_VALUE
- lpProcessInformation.hThread = INVALID_HANDLE_VALUE
- lpProcessInformation.dwProcessId = 0
- lpProcessInformation.dwThreadId = 0
- _CreateProcessW(lpApplicationName, lpCommandLine, lpProcessAttributes, lpThreadAttributes, bool(bInheritHandles), dwCreationFlags, lpEnvironment, lpCurrentDirectory, byref(lpStartupInfo), byref(lpProcessInformation))
- return ProcessInformation(lpProcessInformation)
-
-CreateProcess = GuessStringType(CreateProcessA, CreateProcessW)
-
-# BOOL WINAPI InitializeProcThreadAttributeList(
-# __out_opt LPPROC_THREAD_ATTRIBUTE_LIST lpAttributeList,
-# __in DWORD dwAttributeCount,
-# __reserved DWORD dwFlags,
-# __inout PSIZE_T lpSize
-# );
-def InitializeProcThreadAttributeList(dwAttributeCount):
- _InitializeProcThreadAttributeList = windll.kernel32.InitializeProcThreadAttributeList
- _InitializeProcThreadAttributeList.argtypes = [LPPROC_THREAD_ATTRIBUTE_LIST, DWORD, DWORD, PSIZE_T]
- _InitializeProcThreadAttributeList.restype = bool
-
- Size = SIZE_T(0)
- _InitializeProcThreadAttributeList(None, dwAttributeCount, 0, byref(Size))
- RaiseIfZero(Size.value)
- AttributeList = (BYTE * Size.value)()
- success = _InitializeProcThreadAttributeList(byref(AttributeList), dwAttributeCount, 0, byref(Size))
- RaiseIfZero(success)
- return AttributeList
-
-# BOOL WINAPI UpdateProcThreadAttribute(
-# __inout LPPROC_THREAD_ATTRIBUTE_LIST lpAttributeList,
-# __in DWORD dwFlags,
-# __in DWORD_PTR Attribute,
-# __in PVOID lpValue,
-# __in SIZE_T cbSize,
-# __out_opt PVOID lpPreviousValue,
-# __in_opt PSIZE_T lpReturnSize
-# );
-def UpdateProcThreadAttribute(lpAttributeList, Attribute, Value, cbSize = None):
- _UpdateProcThreadAttribute = windll.kernel32.UpdateProcThreadAttribute
- _UpdateProcThreadAttribute.argtypes = [LPPROC_THREAD_ATTRIBUTE_LIST, DWORD, DWORD_PTR, PVOID, SIZE_T, PVOID, PSIZE_T]
- _UpdateProcThreadAttribute.restype = bool
- _UpdateProcThreadAttribute.errcheck = RaiseIfZero
-
- if cbSize is None:
- cbSize = sizeof(Value)
- _UpdateProcThreadAttribute(byref(lpAttributeList), 0, Attribute, byref(Value), cbSize, None, None)
-
-# VOID WINAPI DeleteProcThreadAttributeList(
-# __inout LPPROC_THREAD_ATTRIBUTE_LIST lpAttributeList
-# );
-def DeleteProcThreadAttributeList(lpAttributeList):
- _DeleteProcThreadAttributeList = windll.kernel32.DeleteProcThreadAttributeList
- _DeleteProcThreadAttributeList.restype = None
- _DeleteProcThreadAttributeList(byref(lpAttributeList))
-
-# HANDLE WINAPI OpenProcess(
-# __in DWORD dwDesiredAccess,
-# __in BOOL bInheritHandle,
-# __in DWORD dwProcessId
-# );
-def OpenProcess(dwDesiredAccess, bInheritHandle, dwProcessId):
- _OpenProcess = windll.kernel32.OpenProcess
- _OpenProcess.argtypes = [DWORD, BOOL, DWORD]
- _OpenProcess.restype = HANDLE
-
- hProcess = _OpenProcess(dwDesiredAccess, bool(bInheritHandle), dwProcessId)
- if hProcess == NULL:
- raise ctypes.WinError()
- return ProcessHandle(hProcess, dwAccess = dwDesiredAccess)
-
-# HANDLE WINAPI OpenThread(
-# __in DWORD dwDesiredAccess,
-# __in BOOL bInheritHandle,
-# __in DWORD dwThreadId
-# );
-def OpenThread(dwDesiredAccess, bInheritHandle, dwThreadId):
- _OpenThread = windll.kernel32.OpenThread
- _OpenThread.argtypes = [DWORD, BOOL, DWORD]
- _OpenThread.restype = HANDLE
-
- hThread = _OpenThread(dwDesiredAccess, bool(bInheritHandle), dwThreadId)
- if hThread == NULL:
- raise ctypes.WinError()
- return ThreadHandle(hThread, dwAccess = dwDesiredAccess)
-
-# DWORD WINAPI SuspendThread(
-# __in HANDLE hThread
-# );
-def SuspendThread(hThread):
- _SuspendThread = windll.kernel32.SuspendThread
- _SuspendThread.argtypes = [HANDLE]
- _SuspendThread.restype = DWORD
-
- previousCount = _SuspendThread(hThread)
- if previousCount == DWORD(-1).value:
- raise ctypes.WinError()
- return previousCount
-
-# DWORD WINAPI ResumeThread(
-# __in HANDLE hThread
-# );
-def ResumeThread(hThread):
- _ResumeThread = windll.kernel32.ResumeThread
- _ResumeThread.argtypes = [HANDLE]
- _ResumeThread.restype = DWORD
-
- previousCount = _ResumeThread(hThread)
- if previousCount == DWORD(-1).value:
- raise ctypes.WinError()
- return previousCount
-
-# BOOL WINAPI TerminateThread(
-# __inout HANDLE hThread,
-# __in DWORD dwExitCode
-# );
-def TerminateThread(hThread, dwExitCode = 0):
- _TerminateThread = windll.kernel32.TerminateThread
- _TerminateThread.argtypes = [HANDLE, DWORD]
- _TerminateThread.restype = bool
- _TerminateThread.errcheck = RaiseIfZero
- _TerminateThread(hThread, dwExitCode)
-
-# BOOL WINAPI TerminateProcess(
-# __inout HANDLE hProcess,
-# __in DWORD dwExitCode
-# );
-def TerminateProcess(hProcess, dwExitCode = 0):
- _TerminateProcess = windll.kernel32.TerminateProcess
- _TerminateProcess.argtypes = [HANDLE, DWORD]
- _TerminateProcess.restype = bool
- _TerminateProcess.errcheck = RaiseIfZero
- _TerminateProcess(hProcess, dwExitCode)
-
-# DWORD WINAPI GetCurrentProcessId(void);
-def GetCurrentProcessId():
- _GetCurrentProcessId = windll.kernel32.GetCurrentProcessId
- _GetCurrentProcessId.argtypes = []
- _GetCurrentProcessId.restype = DWORD
- return _GetCurrentProcessId()
-
-# DWORD WINAPI GetCurrentThreadId(void);
-def GetCurrentThreadId():
- _GetCurrentThreadId = windll.kernel32.GetCurrentThreadId
- _GetCurrentThreadId.argtypes = []
- _GetCurrentThreadId.restype = DWORD
- return _GetCurrentThreadId()
-
-# DWORD WINAPI GetProcessId(
-# __in HANDLE hProcess
-# );
-def GetProcessId(hProcess):
- _GetProcessId = windll.kernel32.GetProcessId
- _GetProcessId.argtypes = [HANDLE]
- _GetProcessId.restype = DWORD
- _GetProcessId.errcheck = RaiseIfZero
- return _GetProcessId(hProcess)
-
-# DWORD WINAPI GetThreadId(
-# __in HANDLE hThread
-# );
-def GetThreadId(hThread):
- _GetThreadId = windll.kernel32._GetThreadId
- _GetThreadId.argtypes = [HANDLE]
- _GetThreadId.restype = DWORD
-
- dwThreadId = _GetThreadId(hThread)
- if dwThreadId == 0:
- raise ctypes.WinError()
- return dwThreadId
-
-# DWORD WINAPI GetProcessIdOfThread(
-# __in HANDLE hThread
-# );
-def GetProcessIdOfThread(hThread):
- _GetProcessIdOfThread = windll.kernel32.GetProcessIdOfThread
- _GetProcessIdOfThread.argtypes = [HANDLE]
- _GetProcessIdOfThread.restype = DWORD
-
- dwProcessId = _GetProcessIdOfThread(hThread)
- if dwProcessId == 0:
- raise ctypes.WinError()
- return dwProcessId
-
-# BOOL WINAPI GetExitCodeProcess(
-# __in HANDLE hProcess,
-# __out LPDWORD lpExitCode
-# );
-def GetExitCodeProcess(hProcess):
- _GetExitCodeProcess = windll.kernel32.GetExitCodeProcess
- _GetExitCodeProcess.argtypes = [HANDLE]
- _GetExitCodeProcess.restype = bool
- _GetExitCodeProcess.errcheck = RaiseIfZero
-
- lpExitCode = DWORD(0)
- _GetExitCodeProcess(hProcess, byref(lpExitCode))
- return lpExitCode.value
-
-# BOOL WINAPI GetExitCodeThread(
-# __in HANDLE hThread,
-# __out LPDWORD lpExitCode
-# );
-def GetExitCodeThread(hThread):
- _GetExitCodeThread = windll.kernel32.GetExitCodeThread
- _GetExitCodeThread.argtypes = [HANDLE]
- _GetExitCodeThread.restype = bool
- _GetExitCodeThread.errcheck = RaiseIfZero
-
- lpExitCode = DWORD(0)
- _GetExitCodeThread(hThread, byref(lpExitCode))
- return lpExitCode.value
-
-# DWORD WINAPI GetProcessVersion(
-# __in DWORD ProcessId
-# );
-def GetProcessVersion(ProcessId):
- _GetProcessVersion = windll.kernel32.GetProcessVersion
- _GetProcessVersion.argtypes = [DWORD]
- _GetProcessVersion.restype = DWORD
-
- retval = _GetProcessVersion(ProcessId)
- if retval == 0:
- raise ctypes.WinError()
- return retval
-
-# DWORD WINAPI GetPriorityClass(
-# __in HANDLE hProcess
-# );
-def GetPriorityClass(hProcess):
- _GetPriorityClass = windll.kernel32.GetPriorityClass
- _GetPriorityClass.argtypes = [HANDLE]
- _GetPriorityClass.restype = DWORD
-
- retval = _GetPriorityClass(hProcess)
- if retval == 0:
- raise ctypes.WinError()
- return retval
-
-# BOOL WINAPI SetPriorityClass(
-# __in HANDLE hProcess,
-# __in DWORD dwPriorityClass
-# );
-def SetPriorityClass(hProcess, dwPriorityClass = NORMAL_PRIORITY_CLASS):
- _SetPriorityClass = windll.kernel32.SetPriorityClass
- _SetPriorityClass.argtypes = [HANDLE, DWORD]
- _SetPriorityClass.restype = bool
- _SetPriorityClass.errcheck = RaiseIfZero
- _SetPriorityClass(hProcess, dwPriorityClass)
-
-# BOOL WINAPI GetProcessPriorityBoost(
-# __in HANDLE hProcess,
-# __out PBOOL pDisablePriorityBoost
-# );
-def GetProcessPriorityBoost(hProcess):
- _GetProcessPriorityBoost = windll.kernel32.GetProcessPriorityBoost
- _GetProcessPriorityBoost.argtypes = [HANDLE, PBOOL]
- _GetProcessPriorityBoost.restype = bool
- _GetProcessPriorityBoost.errcheck = RaiseIfZero
-
- pDisablePriorityBoost = BOOL(False)
- _GetProcessPriorityBoost(hProcess, byref(pDisablePriorityBoost))
- return bool(pDisablePriorityBoost.value)
-
-# BOOL WINAPI SetProcessPriorityBoost(
-# __in HANDLE hProcess,
-# __in BOOL DisablePriorityBoost
-# );
-def SetProcessPriorityBoost(hProcess, DisablePriorityBoost):
- _SetProcessPriorityBoost = windll.kernel32.SetProcessPriorityBoost
- _SetProcessPriorityBoost.argtypes = [HANDLE, BOOL]
- _SetProcessPriorityBoost.restype = bool
- _SetProcessPriorityBoost.errcheck = RaiseIfZero
- _SetProcessPriorityBoost(hProcess, bool(DisablePriorityBoost))
-
-# BOOL WINAPI GetProcessAffinityMask(
-# __in HANDLE hProcess,
-# __out PDWORD_PTR lpProcessAffinityMask,
-# __out PDWORD_PTR lpSystemAffinityMask
-# );
-def GetProcessAffinityMask(hProcess):
- _GetProcessAffinityMask = windll.kernel32.GetProcessAffinityMask
- _GetProcessAffinityMask.argtypes = [HANDLE, PDWORD_PTR, PDWORD_PTR]
- _GetProcessAffinityMask.restype = bool
- _GetProcessAffinityMask.errcheck = RaiseIfZero
-
- lpProcessAffinityMask = DWORD_PTR(0)
- lpSystemAffinityMask = DWORD_PTR(0)
- _GetProcessAffinityMask(hProcess, byref(lpProcessAffinityMask), byref(lpSystemAffinityMask))
- return lpProcessAffinityMask.value, lpSystemAffinityMask.value
-
-# BOOL WINAPI SetProcessAffinityMask(
-# __in HANDLE hProcess,
-# __in DWORD_PTR dwProcessAffinityMask
-# );
-def SetProcessAffinityMask(hProcess, dwProcessAffinityMask):
- _SetProcessAffinityMask = windll.kernel32.SetProcessAffinityMask
- _SetProcessAffinityMask.argtypes = [HANDLE, DWORD_PTR]
- _SetProcessAffinityMask.restype = bool
- _SetProcessAffinityMask.errcheck = RaiseIfZero
- _SetProcessAffinityMask(hProcess, dwProcessAffinityMask)
-
-#------------------------------------------------------------------------------
-# Toolhelp32 API
-
-# HANDLE WINAPI CreateToolhelp32Snapshot(
-# __in DWORD dwFlags,
-# __in DWORD th32ProcessID
-# );
-def CreateToolhelp32Snapshot(dwFlags = TH32CS_SNAPALL, th32ProcessID = 0):
- _CreateToolhelp32Snapshot = windll.kernel32.CreateToolhelp32Snapshot
- _CreateToolhelp32Snapshot.argtypes = [DWORD, DWORD]
- _CreateToolhelp32Snapshot.restype = HANDLE
-
- hSnapshot = _CreateToolhelp32Snapshot(dwFlags, th32ProcessID)
- if hSnapshot == INVALID_HANDLE_VALUE:
- raise ctypes.WinError()
- return SnapshotHandle(hSnapshot)
-
-# BOOL WINAPI Process32First(
-# __in HANDLE hSnapshot,
-# __inout LPPROCESSENTRY32 lppe
-# );
-def Process32First(hSnapshot):
- _Process32First = windll.kernel32.Process32First
- _Process32First.argtypes = [HANDLE, LPPROCESSENTRY32]
- _Process32First.restype = bool
-
- pe = PROCESSENTRY32()
- pe.dwSize = sizeof(PROCESSENTRY32)
- success = _Process32First(hSnapshot, byref(pe))
- if not success:
- if GetLastError() == ERROR_NO_MORE_FILES:
- return None
- raise ctypes.WinError()
- return pe
-
-# BOOL WINAPI Process32Next(
-# __in HANDLE hSnapshot,
-# __out LPPROCESSENTRY32 lppe
-# );
-def Process32Next(hSnapshot, pe = None):
- _Process32Next = windll.kernel32.Process32Next
- _Process32Next.argtypes = [HANDLE, LPPROCESSENTRY32]
- _Process32Next.restype = bool
-
- if pe is None:
- pe = PROCESSENTRY32()
- pe.dwSize = sizeof(PROCESSENTRY32)
- success = _Process32Next(hSnapshot, byref(pe))
- if not success:
- if GetLastError() == ERROR_NO_MORE_FILES:
- return None
- raise ctypes.WinError()
- return pe
-
-# BOOL WINAPI Thread32First(
-# __in HANDLE hSnapshot,
-# __inout LPTHREADENTRY32 lpte
-# );
-def Thread32First(hSnapshot):
- _Thread32First = windll.kernel32.Thread32First
- _Thread32First.argtypes = [HANDLE, LPTHREADENTRY32]
- _Thread32First.restype = bool
-
- te = THREADENTRY32()
- te.dwSize = sizeof(THREADENTRY32)
- success = _Thread32First(hSnapshot, byref(te))
- if not success:
- if GetLastError() == ERROR_NO_MORE_FILES:
- return None
- raise ctypes.WinError()
- return te
-
-# BOOL WINAPI Thread32Next(
-# __in HANDLE hSnapshot,
-# __out LPTHREADENTRY32 lpte
-# );
-def Thread32Next(hSnapshot, te = None):
- _Thread32Next = windll.kernel32.Thread32Next
- _Thread32Next.argtypes = [HANDLE, LPTHREADENTRY32]
- _Thread32Next.restype = bool
-
- if te is None:
- te = THREADENTRY32()
- te.dwSize = sizeof(THREADENTRY32)
- success = _Thread32Next(hSnapshot, byref(te))
- if not success:
- if GetLastError() == ERROR_NO_MORE_FILES:
- return None
- raise ctypes.WinError()
- return te
-
-# BOOL WINAPI Module32First(
-# __in HANDLE hSnapshot,
-# __inout LPMODULEENTRY32 lpme
-# );
-def Module32First(hSnapshot):
- _Module32First = windll.kernel32.Module32First
- _Module32First.argtypes = [HANDLE, LPMODULEENTRY32]
- _Module32First.restype = bool
-
- me = MODULEENTRY32()
- me.dwSize = sizeof(MODULEENTRY32)
- success = _Module32First(hSnapshot, byref(me))
- if not success:
- if GetLastError() == ERROR_NO_MORE_FILES:
- return None
- raise ctypes.WinError()
- return me
-
-# BOOL WINAPI Module32Next(
-# __in HANDLE hSnapshot,
-# __out LPMODULEENTRY32 lpme
-# );
-def Module32Next(hSnapshot, me = None):
- _Module32Next = windll.kernel32.Module32Next
- _Module32Next.argtypes = [HANDLE, LPMODULEENTRY32]
- _Module32Next.restype = bool
-
- if me is None:
- me = MODULEENTRY32()
- me.dwSize = sizeof(MODULEENTRY32)
- success = _Module32Next(hSnapshot, byref(me))
- if not success:
- if GetLastError() == ERROR_NO_MORE_FILES:
- return None
- raise ctypes.WinError()
- return me
-
-# BOOL WINAPI Heap32First(
-# __inout LPHEAPENTRY32 lphe,
-# __in DWORD th32ProcessID,
-# __in ULONG_PTR th32HeapID
-# );
-def Heap32First(th32ProcessID, th32HeapID):
- _Heap32First = windll.kernel32.Heap32First
- _Heap32First.argtypes = [LPHEAPENTRY32, DWORD, ULONG_PTR]
- _Heap32First.restype = bool
-
- he = HEAPENTRY32()
- he.dwSize = sizeof(HEAPENTRY32)
- success = _Heap32First(byref(he), th32ProcessID, th32HeapID)
- if not success:
- if GetLastError() == ERROR_NO_MORE_FILES:
- return None
- raise ctypes.WinError()
- return he
-
-# BOOL WINAPI Heap32Next(
-# __out LPHEAPENTRY32 lphe
-# );
-def Heap32Next(he):
- _Heap32Next = windll.kernel32.Heap32Next
- _Heap32Next.argtypes = [LPHEAPENTRY32]
- _Heap32Next.restype = bool
-
- he.dwSize = sizeof(HEAPENTRY32)
- success = _Heap32Next(byref(he))
- if not success:
- if GetLastError() == ERROR_NO_MORE_FILES:
- return None
- raise ctypes.WinError()
- return he
-
-# BOOL WINAPI Heap32ListFirst(
-# __in HANDLE hSnapshot,
-# __inout LPHEAPLIST32 lphl
-# );
-def Heap32ListFirst(hSnapshot):
- _Heap32ListFirst = windll.kernel32.Heap32ListFirst
- _Heap32ListFirst.argtypes = [HANDLE, LPHEAPLIST32]
- _Heap32ListFirst.restype = bool
-
- hl = HEAPLIST32()
- hl.dwSize = sizeof(HEAPLIST32)
- success = _Heap32ListFirst(hSnapshot, byref(hl))
- if not success:
- if GetLastError() == ERROR_NO_MORE_FILES:
- return None
- raise ctypes.WinError()
- return hl
-
-# BOOL WINAPI Heap32ListNext(
-# __in HANDLE hSnapshot,
-# __out LPHEAPLIST32 lphl
-# );
-def Heap32ListNext(hSnapshot, hl = None):
- _Heap32ListNext = windll.kernel32.Heap32ListNext
- _Heap32ListNext.argtypes = [HANDLE, LPHEAPLIST32]
- _Heap32ListNext.restype = bool
-
- if hl is None:
- hl = HEAPLIST32()
- hl.dwSize = sizeof(HEAPLIST32)
- success = _Heap32ListNext(hSnapshot, byref(hl))
- if not success:
- if GetLastError() == ERROR_NO_MORE_FILES:
- return None
- raise ctypes.WinError()
- return hl
-
-# BOOL WINAPI Toolhelp32ReadProcessMemory(
-# __in DWORD th32ProcessID,
-# __in LPCVOID lpBaseAddress,
-# __out LPVOID lpBuffer,
-# __in SIZE_T cbRead,
-# __out SIZE_T lpNumberOfBytesRead
-# );
-def Toolhelp32ReadProcessMemory(th32ProcessID, lpBaseAddress, cbRead):
- _Toolhelp32ReadProcessMemory = windll.kernel32.Toolhelp32ReadProcessMemory
- _Toolhelp32ReadProcessMemory.argtypes = [DWORD, LPVOID, LPVOID, SIZE_T, POINTER(SIZE_T)]
- _Toolhelp32ReadProcessMemory.restype = bool
-
- lpBuffer = ctypes.create_string_buffer('', cbRead)
- lpNumberOfBytesRead = SIZE_T(0)
- success = _Toolhelp32ReadProcessMemory(th32ProcessID, lpBaseAddress, lpBuffer, cbRead, byref(lpNumberOfBytesRead))
- if not success and GetLastError() != ERROR_PARTIAL_COPY:
- raise ctypes.WinError()
- return str(lpBuffer.raw)[:lpNumberOfBytesRead.value]
-
-#------------------------------------------------------------------------------
-# Miscellaneous system information
-
-# BOOL WINAPI GetProcessDEPPolicy(
-# __in HANDLE hProcess,
-# __out LPDWORD lpFlags,
-# __out PBOOL lpPermanent
-# );
-# Contribution by ivanlef0u (http://ivanlef0u.fr/)
-# XP SP3 and > only
-def GetProcessDEPPolicy(hProcess):
- _GetProcessDEPPolicy = windll.kernel32.GetProcessDEPPolicy
- _GetProcessDEPPolicy.argtypes = [HANDLE, LPDWORD, PBOOL]
- _GetProcessDEPPolicy.restype = bool
- _GetProcessDEPPolicy.errcheck = RaiseIfZero
-
- lpFlags = DWORD(0)
- lpPermanent = BOOL(0)
- _GetProcessDEPPolicy(hProcess, byref(lpFlags), byref(lpPermanent))
- return (lpFlags.value, lpPermanent.value)
-
-# DWORD WINAPI GetCurrentProcessorNumber(void);
-def GetCurrentProcessorNumber():
- _GetCurrentProcessorNumber = windll.kernel32.GetCurrentProcessorNumber
- _GetCurrentProcessorNumber.argtypes = []
- _GetCurrentProcessorNumber.restype = DWORD
- _GetCurrentProcessorNumber.errcheck = RaiseIfZero
- return _GetCurrentProcessorNumber()
-
-# VOID WINAPI FlushProcessWriteBuffers(void);
-def FlushProcessWriteBuffers():
- _FlushProcessWriteBuffers = windll.kernel32.FlushProcessWriteBuffers
- _FlushProcessWriteBuffers.argtypes = []
- _FlushProcessWriteBuffers.restype = None
- _FlushProcessWriteBuffers()
-
-# BOOL WINAPI GetLogicalProcessorInformation(
-# __out PSYSTEM_LOGICAL_PROCESSOR_INFORMATION Buffer,
-# __inout PDWORD ReturnLength
-# );
-
-# TO DO http://msdn.microsoft.com/en-us/library/ms683194(VS.85).aspx
-
-# BOOL WINAPI GetProcessIoCounters(
-# __in HANDLE hProcess,
-# __out PIO_COUNTERS lpIoCounters
-# );
-
-# TO DO http://msdn.microsoft.com/en-us/library/ms683218(VS.85).aspx
-
-# DWORD WINAPI GetGuiResources(
-# __in HANDLE hProcess,
-# __in DWORD uiFlags
-# );
-def GetGuiResources(hProcess, uiFlags = GR_GDIOBJECTS):
- _GetGuiResources = windll.kernel32.GetGuiResources
- _GetGuiResources.argtypes = [HANDLE, DWORD]
- _GetGuiResources.restype = DWORD
-
- dwCount = _GetGuiResources(hProcess, uiFlags)
- if dwCount == 0:
- errcode = GetLastError()
- if errcode != ERROR_SUCCESS:
- raise ctypes.WinError(errcode)
- return dwCount
-
-# BOOL WINAPI GetProcessHandleCount(
-# __in HANDLE hProcess,
-# __inout PDWORD pdwHandleCount
-# );
-def GetProcessHandleCount(hProcess):
- _GetProcessHandleCount = windll.kernel32.GetProcessHandleCount
- _GetProcessHandleCount.argtypes = [HANDLE, PDWORD]
- _GetProcessHandleCount.restype = DWORD
- _GetProcessHandleCount.errcheck = RaiseIfZero
-
- pdwHandleCount = DWORD(0)
- _GetProcessHandleCount(hProcess, byref(pdwHandleCount))
- return pdwHandleCount.value
-
-# BOOL WINAPI GetProcessTimes(
-# __in HANDLE hProcess,
-# __out LPFILETIME lpCreationTime,
-# __out LPFILETIME lpExitTime,
-# __out LPFILETIME lpKernelTime,
-# __out LPFILETIME lpUserTime
-# );
-def GetProcessTimes(hProcess = None):
- _GetProcessTimes = windll.kernel32.GetProcessTimes
- _GetProcessTimes.argtypes = [HANDLE, LPFILETIME, LPFILETIME, LPFILETIME, LPFILETIME]
- _GetProcessTimes.restype = bool
- _GetProcessTimes.errcheck = RaiseIfZero
-
- if hProcess is None:
- hProcess = GetCurrentProcess()
-
- CreationTime = FILETIME()
- ExitTime = FILETIME()
- KernelTime = FILETIME()
- UserTime = FILETIME()
-
- _GetProcessTimes(hProcess, byref(CreationTime), byref(ExitTime), byref(KernelTime), byref(UserTime))
-
- return (CreationTime, ExitTime, KernelTime, UserTime)
-
-# BOOL WINAPI FileTimeToSystemTime(
-# __in const FILETIME *lpFileTime,
-# __out LPSYSTEMTIME lpSystemTime
-# );
-def FileTimeToSystemTime(lpFileTime):
- _FileTimeToSystemTime = windll.kernel32.FileTimeToSystemTime
- _FileTimeToSystemTime.argtypes = [LPFILETIME, LPSYSTEMTIME]
- _FileTimeToSystemTime.restype = bool
- _FileTimeToSystemTime.errcheck = RaiseIfZero
-
- if isinstance(lpFileTime, FILETIME):
- FileTime = lpFileTime
- else:
- FileTime = FILETIME()
- FileTime.dwLowDateTime = lpFileTime & 0xFFFFFFFF
- FileTime.dwHighDateTime = lpFileTime >> 32
- SystemTime = SYSTEMTIME()
- _FileTimeToSystemTime(byref(FileTime), byref(SystemTime))
- return SystemTime
-
-# void WINAPI GetSystemTimeAsFileTime(
-# __out LPFILETIME lpSystemTimeAsFileTime
-# );
-def GetSystemTimeAsFileTime():
- _GetSystemTimeAsFileTime = windll.kernel32.GetSystemTimeAsFileTime
- _GetSystemTimeAsFileTime.argtypes = [LPFILETIME]
- _GetSystemTimeAsFileTime.restype = None
-
- FileTime = FILETIME()
- _GetSystemTimeAsFileTime(byref(FileTime))
- return FileTime
-
-#------------------------------------------------------------------------------
-# Global ATOM API
-
-# ATOM GlobalAddAtom(
-# __in LPCTSTR lpString
-# );
-def GlobalAddAtomA(lpString):
- _GlobalAddAtomA = windll.kernel32.GlobalAddAtomA
- _GlobalAddAtomA.argtypes = [LPSTR]
- _GlobalAddAtomA.restype = ATOM
- _GlobalAddAtomA.errcheck = RaiseIfZero
- return _GlobalAddAtomA(lpString)
-
-def GlobalAddAtomW(lpString):
- _GlobalAddAtomW = windll.kernel32.GlobalAddAtomW
- _GlobalAddAtomW.argtypes = [LPWSTR]
- _GlobalAddAtomW.restype = ATOM
- _GlobalAddAtomW.errcheck = RaiseIfZero
- return _GlobalAddAtomW(lpString)
-
-GlobalAddAtom = GuessStringType(GlobalAddAtomA, GlobalAddAtomW)
-
-# ATOM GlobalFindAtom(
-# __in LPCTSTR lpString
-# );
-def GlobalFindAtomA(lpString):
- _GlobalFindAtomA = windll.kernel32.GlobalFindAtomA
- _GlobalFindAtomA.argtypes = [LPSTR]
- _GlobalFindAtomA.restype = ATOM
- _GlobalFindAtomA.errcheck = RaiseIfZero
- return _GlobalFindAtomA(lpString)
-
-def GlobalFindAtomW(lpString):
- _GlobalFindAtomW = windll.kernel32.GlobalFindAtomW
- _GlobalFindAtomW.argtypes = [LPWSTR]
- _GlobalFindAtomW.restype = ATOM
- _GlobalFindAtomW.errcheck = RaiseIfZero
- return _GlobalFindAtomW(lpString)
-
-GlobalFindAtom = GuessStringType(GlobalFindAtomA, GlobalFindAtomW)
-
-# UINT GlobalGetAtomName(
-# __in ATOM nAtom,
-# __out LPTSTR lpBuffer,
-# __in int nSize
-# );
-def GlobalGetAtomNameA(nAtom):
- _GlobalGetAtomNameA = windll.kernel32.GlobalGetAtomNameA
- _GlobalGetAtomNameA.argtypes = [ATOM, LPSTR, ctypes.c_int]
- _GlobalGetAtomNameA.restype = UINT
- _GlobalGetAtomNameA.errcheck = RaiseIfZero
-
- nSize = 64
- while 1:
- lpBuffer = ctypes.create_string_buffer("", nSize)
- nCopied = _GlobalGetAtomNameA(nAtom, lpBuffer, nSize)
- if nCopied < nSize - 1:
- break
- nSize = nSize + 64
- return lpBuffer.value
-
-def GlobalGetAtomNameW(nAtom):
- _GlobalGetAtomNameW = windll.kernel32.GlobalGetAtomNameW
- _GlobalGetAtomNameW.argtypes = [ATOM, LPWSTR, ctypes.c_int]
- _GlobalGetAtomNameW.restype = UINT
- _GlobalGetAtomNameW.errcheck = RaiseIfZero
-
- nSize = 64
- while 1:
- lpBuffer = ctypes.create_unicode_buffer(u"", nSize)
- nCopied = _GlobalGetAtomNameW(nAtom, lpBuffer, nSize)
- if nCopied < nSize - 1:
- break
- nSize = nSize + 64
- return lpBuffer.value
-
-GlobalGetAtomName = GuessStringType(GlobalGetAtomNameA, GlobalGetAtomNameW)
-
-# ATOM GlobalDeleteAtom(
-# __in ATOM nAtom
-# );
-def GlobalDeleteAtom(nAtom):
- _GlobalDeleteAtom = windll.kernel32.GlobalDeleteAtom
- _GlobalDeleteAtom.argtypes
- _GlobalDeleteAtom.restype
- SetLastError(ERROR_SUCCESS)
- _GlobalDeleteAtom(nAtom)
- error = GetLastError()
- if error != ERROR_SUCCESS:
- raise ctypes.WinError(error)
-
-#------------------------------------------------------------------------------
-# Wow64
-
-# DWORD WINAPI Wow64SuspendThread(
-# _In_ HANDLE hThread
-# );
-def Wow64SuspendThread(hThread):
- _Wow64SuspendThread = windll.kernel32.Wow64SuspendThread
- _Wow64SuspendThread.argtypes = [HANDLE]
- _Wow64SuspendThread.restype = DWORD
-
- previousCount = _Wow64SuspendThread(hThread)
- if previousCount == DWORD(-1).value:
- raise ctypes.WinError()
- return previousCount
-
-# BOOLEAN WINAPI Wow64EnableWow64FsRedirection(
-# __in BOOLEAN Wow64FsEnableRedirection
-# );
-def Wow64EnableWow64FsRedirection(Wow64FsEnableRedirection):
- """
- This function may not work reliably when there are nested calls. Therefore,
- this function has been replaced by the L{Wow64DisableWow64FsRedirection}
- and L{Wow64RevertWow64FsRedirection} functions.
-
- @see: U{http://msdn.microsoft.com/en-us/library/windows/desktop/aa365744(v=vs.85).aspx}
- """
- _Wow64EnableWow64FsRedirection = windll.kernel32.Wow64EnableWow64FsRedirection
- _Wow64EnableWow64FsRedirection.argtypes = [BOOLEAN]
- _Wow64EnableWow64FsRedirection.restype = BOOLEAN
- _Wow64EnableWow64FsRedirection.errcheck = RaiseIfZero
-
-# BOOL WINAPI Wow64DisableWow64FsRedirection(
-# __out PVOID *OldValue
-# );
-def Wow64DisableWow64FsRedirection():
- _Wow64DisableWow64FsRedirection = windll.kernel32.Wow64DisableWow64FsRedirection
- _Wow64DisableWow64FsRedirection.argtypes = [PPVOID]
- _Wow64DisableWow64FsRedirection.restype = BOOL
- _Wow64DisableWow64FsRedirection.errcheck = RaiseIfZero
-
- OldValue = PVOID(None)
- _Wow64DisableWow64FsRedirection(byref(OldValue))
- return OldValue
-
-# BOOL WINAPI Wow64RevertWow64FsRedirection(
-# __in PVOID OldValue
-# );
-def Wow64RevertWow64FsRedirection(OldValue):
- _Wow64RevertWow64FsRedirection = windll.kernel32.Wow64RevertWow64FsRedirection
- _Wow64RevertWow64FsRedirection.argtypes = [PVOID]
- _Wow64RevertWow64FsRedirection.restype = BOOL
- _Wow64RevertWow64FsRedirection.errcheck = RaiseIfZero
- _Wow64RevertWow64FsRedirection(OldValue)
-
-#==============================================================================
-# This calculates the list of exported symbols.
-_all = set(vars().keys()).difference(_all)
-__all__ = [_x for _x in _all if not _x.startswith('_')]
-__all__.sort()
-#==============================================================================
-
-#==============================================================================
-# Mark functions that Psyco cannot compile.
-# In your programs, don't use psyco.full().
-# Call psyco.bind() on your main function instead.
-
-try:
- import psyco
- psyco.cannotcompile(WaitForDebugEvent)
- psyco.cannotcompile(WaitForSingleObject)
- psyco.cannotcompile(WaitForSingleObjectEx)
- psyco.cannotcompile(WaitForMultipleObjects)
- psyco.cannotcompile(WaitForMultipleObjectsEx)
-except ImportError:
- pass
-#==============================================================================
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/windows/stdafx.cpp b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/windows/stdafx.cpp
deleted file mode 100644
index 4b80b546671bb819c3d6f30465a2779d9756db7a..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/windows/stdafx.cpp
+++ /dev/null
@@ -1,22 +0,0 @@
-/* ****************************************************************************
- *
- * Copyright (c) Microsoft Corporation.
- *
- * This source code is subject to terms and conditions of the Apache License, Version 2.0. A
- * copy of the license can be found in the License.html file at the root of this distribution. If
- * you cannot locate the Apache License, Version 2.0, please send an email to
- * vspython@microsoft.com. By using this source code in any fashion, you are agreeing to be bound
- * by the terms of the Apache License, Version 2.0.
- *
- * You must not remove this notice, or any other, from this software.
- *
- * ***************************************************************************/
-
-// stdafx.cpp : source file that includes just the standard includes
-// PyDebugAttach.pch will be the pre-compiled header
-// stdafx.obj will contain the pre-compiled type information
-
-#include "stdafx.h"
-
-// TODO: reference any additional headers you need in STDAFX.H
-// and not in this file
diff --git a/spaces/TRI-ML/risk_biased_prediction/risk_biased/scene_dataset/pedestrian.py b/spaces/TRI-ML/risk_biased_prediction/risk_biased/scene_dataset/pedestrian.py
deleted file mode 100644
index 104fa51d9513af353bc5f8d3c3d90d20daef8bb1..0000000000000000000000000000000000000000
--- a/spaces/TRI-ML/risk_biased_prediction/risk_biased/scene_dataset/pedestrian.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import numpy as np
-import torch
-from torch import Tensor
-from typing import Union
-
-
-class RandomPedestrians:
- """
- Batched random pedestrians.
- There are two types of pedestrians, slow and fast ones.
- Each pedestrian type is walking mainly at its constant favored speed but at each time step there is a probability that it changes its pace.
-
- Args:
- batch_size: int number of scenes in the batch
- dt: float time step to use in the trajectory sequence
- fast_speed: float fast walking speed for the random pedestrian in meters/seconds
- slow_speed: float slow walking speed for the random pedestrian in meters/seconds
- p_change_pace: float probability that a slow (resp. fast) pedestrian walk at fast_speed (resp. slow_speed) at each time step
- proportion_fast: float proportion of the pedestrians that are mainly walking at fast_speed
- is_torch: bool set to True to produce Tensor batches and to False to produce numpy arrays
- """
-
- def __init__(
- self,
- batch_size: int,
- dt: float = 0.1,
- fast_speed: float = 2,
- slow_speed: float = 1,
- p_change_pace: float = 0.1,
- proportion_fast: float = 0.5,
- is_torch: bool = False,
- ) -> None:
-
- self.is_torch = is_torch
- self.fast_speed: float = fast_speed
- self.slow_speed: float = slow_speed
- self.dt: float = dt
- self.p_change_pace: float = p_change_pace
- self.batch_size: int = batch_size
-
- self.propotion_fast: float = proportion_fast
- if self.is_torch:
- self.is_fast_type: Tensor = torch.from_numpy(
- np.random.binomial(1, self.propotion_fast, [batch_size, 1, 1]).astype(
- "float32"
- )
- )
- self.is_currently_fast: Tensor = self.is_fast_type.clone()
- self.initial_position: Tensor = torch.zeros([batch_size, 1, 2])
- self.position: Tensor = self.initial_position.clone()
- self._angle: Tensor = (2 * torch.rand(batch_size, 1) - 1) * np.pi
- self.unit_velocity: Tensor = torch.stack(
- (torch.cos(self._angle), torch.sin(self._angle)), -1
- )
- else:
- self.is_fast_type: np.ndarray = np.random.binomial(
- 1, self.propotion_fast, [batch_size, 1, 1]
- )
- self.is_currently_fast: np.ndarray = self.is_fast_type.copy()
- self.initial_position: np.ndarray = np.zeros([batch_size, 1, 2])
- self.position: np.ndarray = self.initial_position.copy()
- self._angle: np.ndarray = np.random.uniform(-np.pi, np.pi, (batch_size, 1))
- self.unit_velocity: np.ndarray = np.stack(
- (np.cos(self._angle), np.sin(self._angle)), -1
- )
-
- @property
- def angle(self):
- return self._angle
-
- @angle.setter
- def angle(self, angle: Union[np.ndarray, torch.Tensor]):
- assert self.batch_size == angle.shape[0]
- if self.is_torch:
- assert isinstance(angle, torch.Tensor)
- self._angle = angle
- self.unit_velocity = torch.stack(
- (torch.cos(self._angle), torch.sin(self._angle)), -1
- )
- else:
- assert isinstance(angle, np.ndarray)
- self._angle = angle
- self.unit_velocity = np.stack(
- (np.cos(self._angle), np.sin(self._angle)), -1
- )
-
- def step(self) -> None:
- """
- Forward one time step, update the speed selection and the current position.
- """
- self.update_speed()
- self.update_position()
-
- def update_speed(self) -> None:
- """
- Update the speed as a random selection between favored speed and the other speed with probability self.p_change_pace.
- """
- if self.is_torch:
- do_flip = (
- torch.from_numpy(
- np.random.binomial(1, self.p_change_pace, self.batch_size).astype(
- "float32"
- )
- )
- == 1
- )
- self.is_currently_fast = self.is_fast_type.clone()
- else:
- do_flip = np.random.binomial(1, self.p_change_pace, self.batch_size) == 1
- self.is_currently_fast = self.is_fast_type.copy()
- self.is_currently_fast[do_flip] = 1 - self.is_fast_type[do_flip]
-
- def update_position(self) -> None:
- """
- Update the position as current position + time_step*speed*(cos(angle), sin(angle))
- """
- self.position += (
- self.dt
- * (
- self.slow_speed
- + (self.fast_speed - self.slow_speed) * self.is_currently_fast
- )
- * self.unit_velocity
- )
-
- def travel_distance(self) -> Union[np.ndarray, Tensor]:
- """
- Return the travel distance between initial position and current position.
- """
- if self.is_torch:
- return torch.sqrt(
- torch.sum(torch.square(self.position - self.initial_position), -1)
- )
- else:
- return np.sqrt(np.sum(np.square(self.position - self.initial_position), -1))
-
- def get_final_position(self, time: float) -> Union[np.ndarray, Tensor]:
- """
- Return a sample of pedestrian final positions using their speed distribution.
- (This is stochastic, different samples will produce different results).
- Args:
- time: The final time at which to get the position
- Returns:
- The batch of final positions
- """
- num_steps = int(round(time / self.dt))
- if self.is_torch:
- cumulative_change_state = torch.from_numpy(
- np.random.binomial(
- num_steps, self.p_change_pace, [self.batch_size, 1, 1]
- ).astype("float32")
- )
- else:
- cumulative_change_state = np.random.binomial(
- num_steps, self.p_change_pace, [self.batch_size, 1, 1]
- )
-
- num_fast_steps = (
- num_steps - 2 * cumulative_change_state
- ) * self.is_fast_type + cumulative_change_state
-
- return self.position + self.unit_velocity * self.dt * (
- self.slow_speed * num_steps
- + (self.fast_speed - self.slow_speed) * num_fast_steps
- )
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/__pip-runner__.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/__pip-runner__.py
deleted file mode 100644
index 49a148a097e9cc06c165571e0bffaf7cae17dc5b..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/__pip-runner__.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""Execute exactly this copy of pip, within a different environment.
-
-This file is named as it is, to ensure that this module can't be imported via
-an import statement.
-"""
-
-# /!\ This version compatibility check section must be Python 2 compatible. /!\
-
-import sys
-
-# Copied from setup.py
-PYTHON_REQUIRES = (3, 7)
-
-
-def version_str(version): # type: ignore
- return ".".join(str(v) for v in version)
-
-
-if sys.version_info[:2] < PYTHON_REQUIRES:
- raise SystemExit(
- "This version of pip does not support python {} (requires >={}).".format(
- version_str(sys.version_info[:2]), version_str(PYTHON_REQUIRES)
- )
- )
-
-# From here on, we can use Python 3 features, but the syntax must remain
-# Python 2 compatible.
-
-import runpy # noqa: E402
-from importlib.machinery import PathFinder # noqa: E402
-from os.path import dirname # noqa: E402
-
-PIP_SOURCES_ROOT = dirname(dirname(__file__))
-
-
-class PipImportRedirectingFinder:
- @classmethod
- def find_spec(self, fullname, path=None, target=None): # type: ignore
- if fullname != "pip":
- return None
-
- spec = PathFinder.find_spec(fullname, [PIP_SOURCES_ROOT], target)
- assert spec, (PIP_SOURCES_ROOT, fullname)
- return spec
-
-
-sys.meta_path.insert(0, PipImportRedirectingFinder())
-
-assert __name__ == "__main__", "Cannot run __pip-runner__.py as a non-main module"
-runpy.run_module("pip", run_name="__main__", alter_sys=True)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/lexer.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/lexer.py
deleted file mode 100644
index eb2c1b46b6928363a1db20306c379b12668c5a47..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/lexer.py
+++ /dev/null
@@ -1,943 +0,0 @@
-"""
- pygments.lexer
- ~~~~~~~~~~~~~~
-
- Base lexer classes.
-
- :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
- :license: BSD, see LICENSE for details.
-"""
-
-import re
-import sys
-import time
-
-from pip._vendor.pygments.filter import apply_filters, Filter
-from pip._vendor.pygments.filters import get_filter_by_name
-from pip._vendor.pygments.token import Error, Text, Other, Whitespace, _TokenType
-from pip._vendor.pygments.util import get_bool_opt, get_int_opt, get_list_opt, \
- make_analysator, Future, guess_decode
-from pip._vendor.pygments.regexopt import regex_opt
-
-__all__ = ['Lexer', 'RegexLexer', 'ExtendedRegexLexer', 'DelegatingLexer',
- 'LexerContext', 'include', 'inherit', 'bygroups', 'using', 'this',
- 'default', 'words', 'line_re']
-
-line_re = re.compile('.*?\n')
-
-_encoding_map = [(b'\xef\xbb\xbf', 'utf-8'),
- (b'\xff\xfe\0\0', 'utf-32'),
- (b'\0\0\xfe\xff', 'utf-32be'),
- (b'\xff\xfe', 'utf-16'),
- (b'\xfe\xff', 'utf-16be')]
-
-_default_analyse = staticmethod(lambda x: 0.0)
-
-
-class LexerMeta(type):
- """
- This metaclass automagically converts ``analyse_text`` methods into
- static methods which always return float values.
- """
-
- def __new__(mcs, name, bases, d):
- if 'analyse_text' in d:
- d['analyse_text'] = make_analysator(d['analyse_text'])
- return type.__new__(mcs, name, bases, d)
-
-
-class Lexer(metaclass=LexerMeta):
- """
- Lexer for a specific language.
-
- See also :doc:`lexerdevelopment`, a high-level guide to writing
- lexers.
-
- Lexer classes have attributes used for choosing the most appropriate
- lexer based on various criteria.
-
- .. autoattribute:: name
- :no-value:
- .. autoattribute:: aliases
- :no-value:
- .. autoattribute:: filenames
- :no-value:
- .. autoattribute:: alias_filenames
- .. autoattribute:: mimetypes
- :no-value:
- .. autoattribute:: priority
-
- Lexers included in Pygments should have an additional attribute:
-
- .. autoattribute:: url
- :no-value:
-
- You can pass options to the constructor. The basic options recognized
- by all lexers and processed by the base `Lexer` class are:
-
- ``stripnl``
- Strip leading and trailing newlines from the input (default: True).
- ``stripall``
- Strip all leading and trailing whitespace from the input
- (default: False).
- ``ensurenl``
- Make sure that the input ends with a newline (default: True). This
- is required for some lexers that consume input linewise.
-
- .. versionadded:: 1.3
-
- ``tabsize``
- If given and greater than 0, expand tabs in the input (default: 0).
- ``encoding``
- If given, must be an encoding name. This encoding will be used to
- convert the input string to Unicode, if it is not already a Unicode
- string (default: ``'guess'``, which uses a simple UTF-8 / Locale /
- Latin1 detection. Can also be ``'chardet'`` to use the chardet
- library, if it is installed.
- ``inencoding``
- Overrides the ``encoding`` if given.
- """
-
- #: Full name of the lexer, in human-readable form
- name = None
-
- #: A list of short, unique identifiers that can be used to look
- #: up the lexer from a list, e.g., using `get_lexer_by_name()`.
- aliases = []
-
- #: A list of `fnmatch` patterns that match filenames which contain
- #: content for this lexer. The patterns in this list should be unique among
- #: all lexers.
- filenames = []
-
- #: A list of `fnmatch` patterns that match filenames which may or may not
- #: contain content for this lexer. This list is used by the
- #: :func:`.guess_lexer_for_filename()` function, to determine which lexers
- #: are then included in guessing the correct one. That means that
- #: e.g. every lexer for HTML and a template language should include
- #: ``\*.html`` in this list.
- alias_filenames = []
-
- #: A list of MIME types for content that can be lexed with this lexer.
- mimetypes = []
-
- #: Priority, should multiple lexers match and no content is provided
- priority = 0
-
- #: URL of the language specification/definition. Used in the Pygments
- #: documentation.
- url = None
-
- def __init__(self, **options):
- """
- This constructor takes arbitrary options as keyword arguments.
- Every subclass must first process its own options and then call
- the `Lexer` constructor, since it processes the basic
- options like `stripnl`.
-
- An example looks like this:
-
- .. sourcecode:: python
-
- def __init__(self, **options):
- self.compress = options.get('compress', '')
- Lexer.__init__(self, **options)
-
- As these options must all be specifiable as strings (due to the
- command line usage), there are various utility functions
- available to help with that, see `Utilities`_.
- """
- self.options = options
- self.stripnl = get_bool_opt(options, 'stripnl', True)
- self.stripall = get_bool_opt(options, 'stripall', False)
- self.ensurenl = get_bool_opt(options, 'ensurenl', True)
- self.tabsize = get_int_opt(options, 'tabsize', 0)
- self.encoding = options.get('encoding', 'guess')
- self.encoding = options.get('inencoding') or self.encoding
- self.filters = []
- for filter_ in get_list_opt(options, 'filters', ()):
- self.add_filter(filter_)
-
- def __repr__(self):
- if self.options:
- return '<pygments.lexers.%s with %r>' % (self.__class__.__name__,
- self.options)
- else:
- return '<pygments.lexers.%s>' % self.__class__.__name__
-
- def add_filter(self, filter_, **options):
- """
- Add a new stream filter to this lexer.
- """
- if not isinstance(filter_, Filter):
- filter_ = get_filter_by_name(filter_, **options)
- self.filters.append(filter_)
-
- def analyse_text(text):
- """
- A static method which is called for lexer guessing.
-
- It should analyse the text and return a float in the range
- from ``0.0`` to ``1.0``. If it returns ``0.0``, the lexer
- will not be selected as the most probable one, if it returns
- ``1.0``, it will be selected immediately. This is used by
- `guess_lexer`.
-
- The `LexerMeta` metaclass automatically wraps this function so
- that it works like a static method (no ``self`` or ``cls``
- parameter) and the return value is automatically converted to
- `float`. If the return value is an object that is boolean `False`
- it's the same as if the return values was ``0.0``.
- """
-
- def get_tokens(self, text, unfiltered=False):
- """
- This method is the basic interface of a lexer. It is called by
- the `highlight()` function. It must process the text and return an
- iterable of ``(tokentype, value)`` pairs from `text`.
-
- Normally, you don't need to override this method. The default
- implementation processes the options recognized by all lexers
- (`stripnl`, `stripall` and so on), and then yields all tokens
- from `get_tokens_unprocessed()`, with the ``index`` dropped.
-
- If `unfiltered` is set to `True`, the filtering mechanism is
- bypassed even if filters are defined.
- """
- if not isinstance(text, str):
- if self.encoding == 'guess':
- text, _ = guess_decode(text)
- elif self.encoding == 'chardet':
- try:
- from pip._vendor import chardet
- except ImportError as e:
- raise ImportError('To enable chardet encoding guessing, '
- 'please install the chardet library '
- 'from http://chardet.feedparser.org/') from e
- # check for BOM first
- decoded = None
- for bom, encoding in _encoding_map:
- if text.startswith(bom):
- decoded = text[len(bom):].decode(encoding, 'replace')
- break
- # no BOM found, so use chardet
- if decoded is None:
- enc = chardet.detect(text[:1024]) # Guess using first 1KB
- decoded = text.decode(enc.get('encoding') or 'utf-8',
- 'replace')
- text = decoded
- else:
- text = text.decode(self.encoding)
- if text.startswith('\ufeff'):
- text = text[len('\ufeff'):]
- else:
- if text.startswith('\ufeff'):
- text = text[len('\ufeff'):]
-
- # text now *is* a unicode string
- text = text.replace('\r\n', '\n')
- text = text.replace('\r', '\n')
- if self.stripall:
- text = text.strip()
- elif self.stripnl:
- text = text.strip('\n')
- if self.tabsize > 0:
- text = text.expandtabs(self.tabsize)
- if self.ensurenl and not text.endswith('\n'):
- text += '\n'
-
- def streamer():
- for _, t, v in self.get_tokens_unprocessed(text):
- yield t, v
- stream = streamer()
- if not unfiltered:
- stream = apply_filters(stream, self.filters, self)
- return stream
-
- def get_tokens_unprocessed(self, text):
- """
- This method should process the text and return an iterable of
- ``(index, tokentype, value)`` tuples where ``index`` is the starting
- position of the token within the input text.
-
- It must be overridden by subclasses. It is recommended to
- implement it as a generator to maximize effectiveness.
- """
- raise NotImplementedError
-
-
-class DelegatingLexer(Lexer):
- """
- This lexer takes two lexer as arguments. A root lexer and
- a language lexer. First everything is scanned using the language
- lexer, afterwards all ``Other`` tokens are lexed using the root
- lexer.
-
- The lexers from the ``template`` lexer package use this base lexer.
- """
-
- def __init__(self, _root_lexer, _language_lexer, _needle=Other, **options):
- self.root_lexer = _root_lexer(**options)
- self.language_lexer = _language_lexer(**options)
- self.needle = _needle
- Lexer.__init__(self, **options)
-
- def get_tokens_unprocessed(self, text):
- buffered = ''
- insertions = []
- lng_buffer = []
- for i, t, v in self.language_lexer.get_tokens_unprocessed(text):
- if t is self.needle:
- if lng_buffer:
- insertions.append((len(buffered), lng_buffer))
- lng_buffer = []
- buffered += v
- else:
- lng_buffer.append((i, t, v))
- if lng_buffer:
- insertions.append((len(buffered), lng_buffer))
- return do_insertions(insertions,
- self.root_lexer.get_tokens_unprocessed(buffered))
-
-
-# ------------------------------------------------------------------------------
-# RegexLexer and ExtendedRegexLexer
-#
-
-
-class include(str): # pylint: disable=invalid-name
- """
- Indicates that a state should include rules from another state.
- """
- pass
-
-
-class _inherit:
- """
- Indicates the a state should inherit from its superclass.
- """
- def __repr__(self):
- return 'inherit'
-
-inherit = _inherit() # pylint: disable=invalid-name
-
-
-class combined(tuple): # pylint: disable=invalid-name
- """
- Indicates a state combined from multiple states.
- """
-
- def __new__(cls, *args):
- return tuple.__new__(cls, args)
-
- def __init__(self, *args):
- # tuple.__init__ doesn't do anything
- pass
-
-
-class _PseudoMatch:
- """
- A pseudo match object constructed from a string.
- """
-
- def __init__(self, start, text):
- self._text = text
- self._start = start
-
- def start(self, arg=None):
- return self._start
-
- def end(self, arg=None):
- return self._start + len(self._text)
-
- def group(self, arg=None):
- if arg:
- raise IndexError('No such group')
- return self._text
-
- def groups(self):
- return (self._text,)
-
- def groupdict(self):
- return {}
-
-
-def bygroups(*args):
- """
- Callback that yields multiple actions for each group in the match.
- """
- def callback(lexer, match, ctx=None):
- for i, action in enumerate(args):
- if action is None:
- continue
- elif type(action) is _TokenType:
- data = match.group(i + 1)
- if data:
- yield match.start(i + 1), action, data
- else:
- data = match.group(i + 1)
- if data is not None:
- if ctx:
- ctx.pos = match.start(i + 1)
- for item in action(lexer,
- _PseudoMatch(match.start(i + 1), data), ctx):
- if item:
- yield item
- if ctx:
- ctx.pos = match.end()
- return callback
-
-
-class _This:
- """
- Special singleton used for indicating the caller class.
- Used by ``using``.
- """
-
-this = _This()
-
-
-def using(_other, **kwargs):
- """
- Callback that processes the match with a different lexer.
-
- The keyword arguments are forwarded to the lexer, except `state` which
- is handled separately.
-
- `state` specifies the state that the new lexer will start in, and can
- be an enumerable such as ('root', 'inline', 'string') or a simple
- string which is assumed to be on top of the root state.
-
- Note: For that to work, `_other` must not be an `ExtendedRegexLexer`.
- """
- gt_kwargs = {}
- if 'state' in kwargs:
- s = kwargs.pop('state')
- if isinstance(s, (list, tuple)):
- gt_kwargs['stack'] = s
- else:
- gt_kwargs['stack'] = ('root', s)
-
- if _other is this:
- def callback(lexer, match, ctx=None):
- # if keyword arguments are given the callback
- # function has to create a new lexer instance
- if kwargs:
- # XXX: cache that somehow
- kwargs.update(lexer.options)
- lx = lexer.__class__(**kwargs)
- else:
- lx = lexer
- s = match.start()
- for i, t, v in lx.get_tokens_unprocessed(match.group(), **gt_kwargs):
- yield i + s, t, v
- if ctx:
- ctx.pos = match.end()
- else:
- def callback(lexer, match, ctx=None):
- # XXX: cache that somehow
- kwargs.update(lexer.options)
- lx = _other(**kwargs)
-
- s = match.start()
- for i, t, v in lx.get_tokens_unprocessed(match.group(), **gt_kwargs):
- yield i + s, t, v
- if ctx:
- ctx.pos = match.end()
- return callback
-
-
-class default:
- """
- Indicates a state or state action (e.g. #pop) to apply.
- For example default('#pop') is equivalent to ('', Token, '#pop')
- Note that state tuples may be used as well.
-
- .. versionadded:: 2.0
- """
- def __init__(self, state):
- self.state = state
-
-
-class words(Future):
- """
- Indicates a list of literal words that is transformed into an optimized
- regex that matches any of the words.
-
- .. versionadded:: 2.0
- """
- def __init__(self, words, prefix='', suffix=''):
- self.words = words
- self.prefix = prefix
- self.suffix = suffix
-
- def get(self):
- return regex_opt(self.words, prefix=self.prefix, suffix=self.suffix)
-
-
-class RegexLexerMeta(LexerMeta):
- """
- Metaclass for RegexLexer, creates the self._tokens attribute from
- self.tokens on the first instantiation.
- """
-
- def _process_regex(cls, regex, rflags, state):
- """Preprocess the regular expression component of a token definition."""
- if isinstance(regex, Future):
- regex = regex.get()
- return re.compile(regex, rflags).match
-
- def _process_token(cls, token):
- """Preprocess the token component of a token definition."""
- assert type(token) is _TokenType or callable(token), \
- 'token type must be simple type or callable, not %r' % (token,)
- return token
-
- def _process_new_state(cls, new_state, unprocessed, processed):
- """Preprocess the state transition action of a token definition."""
- if isinstance(new_state, str):
- # an existing state
- if new_state == '#pop':
- return -1
- elif new_state in unprocessed:
- return (new_state,)
- elif new_state == '#push':
- return new_state
- elif new_state[:5] == '#pop:':
- return -int(new_state[5:])
- else:
- assert False, 'unknown new state %r' % new_state
- elif isinstance(new_state, combined):
- # combine a new state from existing ones
- tmp_state = '_tmp_%d' % cls._tmpname
- cls._tmpname += 1
- itokens = []
- for istate in new_state:
- assert istate != new_state, 'circular state ref %r' % istate
- itokens.extend(cls._process_state(unprocessed,
- processed, istate))
- processed[tmp_state] = itokens
- return (tmp_state,)
- elif isinstance(new_state, tuple):
- # push more than one state
- for istate in new_state:
- assert (istate in unprocessed or
- istate in ('#pop', '#push')), \
- 'unknown new state ' + istate
- return new_state
- else:
- assert False, 'unknown new state def %r' % new_state
-
- def _process_state(cls, unprocessed, processed, state):
- """Preprocess a single state definition."""
- assert type(state) is str, "wrong state name %r" % state
- assert state[0] != '#', "invalid state name %r" % state
- if state in processed:
- return processed[state]
- tokens = processed[state] = []
- rflags = cls.flags
- for tdef in unprocessed[state]:
- if isinstance(tdef, include):
- # it's a state reference
- assert tdef != state, "circular state reference %r" % state
- tokens.extend(cls._process_state(unprocessed, processed,
- str(tdef)))
- continue
- if isinstance(tdef, _inherit):
- # should be processed already, but may not in the case of:
- # 1. the state has no counterpart in any parent
- # 2. the state includes more than one 'inherit'
- continue
- if isinstance(tdef, default):
- new_state = cls._process_new_state(tdef.state, unprocessed, processed)
- tokens.append((re.compile('').match, None, new_state))
- continue
-
- assert type(tdef) is tuple, "wrong rule def %r" % tdef
-
- try:
- rex = cls._process_regex(tdef[0], rflags, state)
- except Exception as err:
- raise ValueError("uncompilable regex %r in state %r of %r: %s" %
- (tdef[0], state, cls, err)) from err
-
- token = cls._process_token(tdef[1])
-
- if len(tdef) == 2:
- new_state = None
- else:
- new_state = cls._process_new_state(tdef[2],
- unprocessed, processed)
-
- tokens.append((rex, token, new_state))
- return tokens
-
- def process_tokendef(cls, name, tokendefs=None):
- """Preprocess a dictionary of token definitions."""
- processed = cls._all_tokens[name] = {}
- tokendefs = tokendefs or cls.tokens[name]
- for state in list(tokendefs):
- cls._process_state(tokendefs, processed, state)
- return processed
-
- def get_tokendefs(cls):
- """
- Merge tokens from superclasses in MRO order, returning a single tokendef
- dictionary.
-
- Any state that is not defined by a subclass will be inherited
- automatically. States that *are* defined by subclasses will, by
- default, override that state in the superclass. If a subclass wishes to
- inherit definitions from a superclass, it can use the special value
- "inherit", which will cause the superclass' state definition to be
- included at that point in the state.
- """
- tokens = {}
- inheritable = {}
- for c in cls.__mro__:
- toks = c.__dict__.get('tokens', {})
-
- for state, items in toks.items():
- curitems = tokens.get(state)
- if curitems is None:
- # N.b. because this is assigned by reference, sufficiently
- # deep hierarchies are processed incrementally (e.g. for
- # A(B), B(C), C(RegexLexer), B will be premodified so X(B)
- # will not see any inherits in B).
- tokens[state] = items
- try:
- inherit_ndx = items.index(inherit)
- except ValueError:
- continue
- inheritable[state] = inherit_ndx
- continue
-
- inherit_ndx = inheritable.pop(state, None)
- if inherit_ndx is None:
- continue
-
- # Replace the "inherit" value with the items
- curitems[inherit_ndx:inherit_ndx+1] = items
- try:
- # N.b. this is the index in items (that is, the superclass
- # copy), so offset required when storing below.
- new_inh_ndx = items.index(inherit)
- except ValueError:
- pass
- else:
- inheritable[state] = inherit_ndx + new_inh_ndx
-
- return tokens
-
- def __call__(cls, *args, **kwds):
- """Instantiate cls after preprocessing its token definitions."""
- if '_tokens' not in cls.__dict__:
- cls._all_tokens = {}
- cls._tmpname = 0
- if hasattr(cls, 'token_variants') and cls.token_variants:
- # don't process yet
- pass
- else:
- cls._tokens = cls.process_tokendef('', cls.get_tokendefs())
-
- return type.__call__(cls, *args, **kwds)
-
-
-class RegexLexer(Lexer, metaclass=RegexLexerMeta):
- """
- Base for simple stateful regular expression-based lexers.
- Simplifies the lexing process so that you need only
- provide a list of states and regular expressions.
- """
-
- #: Flags for compiling the regular expressions.
- #: Defaults to MULTILINE.
- flags = re.MULTILINE
-
- #: At all time there is a stack of states. Initially, the stack contains
- #: a single state 'root'. The top of the stack is called "the current state".
- #:
- #: Dict of ``{'state': [(regex, tokentype, new_state), ...], ...}``
- #:
- #: ``new_state`` can be omitted to signify no state transition.
- #: If ``new_state`` is a string, it is pushed on the stack. This ensure
- #: the new current state is ``new_state``.
- #: If ``new_state`` is a tuple of strings, all of those strings are pushed
- #: on the stack and the current state will be the last element of the list.
- #: ``new_state`` can also be ``combined('state1', 'state2', ...)``
- #: to signify a new, anonymous state combined from the rules of two
- #: or more existing ones.
- #: Furthermore, it can be '#pop' to signify going back one step in
- #: the state stack, or '#push' to push the current state on the stack
- #: again. Note that if you push while in a combined state, the combined
- #: state itself is pushed, and not only the state in which the rule is
- #: defined.
- #:
- #: The tuple can also be replaced with ``include('state')``, in which
- #: case the rules from the state named by the string are included in the
- #: current one.
- tokens = {}
-
- def get_tokens_unprocessed(self, text, stack=('root',)):
- """
- Split ``text`` into (tokentype, text) pairs.
-
- ``stack`` is the initial stack (default: ``['root']``)
- """
- pos = 0
- tokendefs = self._tokens
- statestack = list(stack)
- statetokens = tokendefs[statestack[-1]]
- while 1:
- for rexmatch, action, new_state in statetokens:
- m = rexmatch(text, pos)
- if m:
- if action is not None:
- if type(action) is _TokenType:
- yield pos, action, m.group()
- else:
- yield from action(self, m)
- pos = m.end()
- if new_state is not None:
- # state transition
- if isinstance(new_state, tuple):
- for state in new_state:
- if state == '#pop':
- if len(statestack) > 1:
- statestack.pop()
- elif state == '#push':
- statestack.append(statestack[-1])
- else:
- statestack.append(state)
- elif isinstance(new_state, int):
- # pop, but keep at least one state on the stack
- # (random code leading to unexpected pops should
- # not allow exceptions)
- if abs(new_state) >= len(statestack):
- del statestack[1:]
- else:
- del statestack[new_state:]
- elif new_state == '#push':
- statestack.append(statestack[-1])
- else:
- assert False, "wrong state def: %r" % new_state
- statetokens = tokendefs[statestack[-1]]
- break
- else:
- # We are here only if all state tokens have been considered
- # and there was not a match on any of them.
- try:
- if text[pos] == '\n':
- # at EOL, reset state to "root"
- statestack = ['root']
- statetokens = tokendefs['root']
- yield pos, Whitespace, '\n'
- pos += 1
- continue
- yield pos, Error, text[pos]
- pos += 1
- except IndexError:
- break
-
-
-class LexerContext:
- """
- A helper object that holds lexer position data.
- """
-
- def __init__(self, text, pos, stack=None, end=None):
- self.text = text
- self.pos = pos
- self.end = end or len(text) # end=0 not supported ;-)
- self.stack = stack or ['root']
-
- def __repr__(self):
- return 'LexerContext(%r, %r, %r)' % (
- self.text, self.pos, self.stack)
-
-
-class ExtendedRegexLexer(RegexLexer):
- """
- A RegexLexer that uses a context object to store its state.
- """
-
- def get_tokens_unprocessed(self, text=None, context=None):
- """
- Split ``text`` into (tokentype, text) pairs.
- If ``context`` is given, use this lexer context instead.
- """
- tokendefs = self._tokens
- if not context:
- ctx = LexerContext(text, 0)
- statetokens = tokendefs['root']
- else:
- ctx = context
- statetokens = tokendefs[ctx.stack[-1]]
- text = ctx.text
- while 1:
- for rexmatch, action, new_state in statetokens:
- m = rexmatch(text, ctx.pos, ctx.end)
- if m:
- if action is not None:
- if type(action) is _TokenType:
- yield ctx.pos, action, m.group()
- ctx.pos = m.end()
- else:
- yield from action(self, m, ctx)
- if not new_state:
- # altered the state stack?
- statetokens = tokendefs[ctx.stack[-1]]
- # CAUTION: callback must set ctx.pos!
- if new_state is not None:
- # state transition
- if isinstance(new_state, tuple):
- for state in new_state:
- if state == '#pop':
- if len(ctx.stack) > 1:
- ctx.stack.pop()
- elif state == '#push':
- ctx.stack.append(ctx.stack[-1])
- else:
- ctx.stack.append(state)
- elif isinstance(new_state, int):
- # see RegexLexer for why this check is made
- if abs(new_state) >= len(ctx.stack):
- del ctx.stack[1:]
- else:
- del ctx.stack[new_state:]
- elif new_state == '#push':
- ctx.stack.append(ctx.stack[-1])
- else:
- assert False, "wrong state def: %r" % new_state
- statetokens = tokendefs[ctx.stack[-1]]
- break
- else:
- try:
- if ctx.pos >= ctx.end:
- break
- if text[ctx.pos] == '\n':
- # at EOL, reset state to "root"
- ctx.stack = ['root']
- statetokens = tokendefs['root']
- yield ctx.pos, Text, '\n'
- ctx.pos += 1
- continue
- yield ctx.pos, Error, text[ctx.pos]
- ctx.pos += 1
- except IndexError:
- break
-
-
-def do_insertions(insertions, tokens):
- """
- Helper for lexers which must combine the results of several
- sublexers.
-
- ``insertions`` is a list of ``(index, itokens)`` pairs.
- Each ``itokens`` iterable should be inserted at position
- ``index`` into the token stream given by the ``tokens``
- argument.
-
- The result is a combined token stream.
-
- TODO: clean up the code here.
- """
- insertions = iter(insertions)
- try:
- index, itokens = next(insertions)
- except StopIteration:
- # no insertions
- yield from tokens
- return
-
- realpos = None
- insleft = True
-
- # iterate over the token stream where we want to insert
- # the tokens from the insertion list.
- for i, t, v in tokens:
- # first iteration. store the position of first item
- if realpos is None:
- realpos = i
- oldi = 0
- while insleft and i + len(v) >= index:
- tmpval = v[oldi:index - i]
- if tmpval:
- yield realpos, t, tmpval
- realpos += len(tmpval)
- for it_index, it_token, it_value in itokens:
- yield realpos, it_token, it_value
- realpos += len(it_value)
- oldi = index - i
- try:
- index, itokens = next(insertions)
- except StopIteration:
- insleft = False
- break # not strictly necessary
- if oldi < len(v):
- yield realpos, t, v[oldi:]
- realpos += len(v) - oldi
-
- # leftover tokens
- while insleft:
- # no normal tokens, set realpos to zero
- realpos = realpos or 0
- for p, t, v in itokens:
- yield realpos, t, v
- realpos += len(v)
- try:
- index, itokens = next(insertions)
- except StopIteration:
- insleft = False
- break # not strictly necessary
-
-
-class ProfilingRegexLexerMeta(RegexLexerMeta):
- """Metaclass for ProfilingRegexLexer, collects regex timing info."""
-
- def _process_regex(cls, regex, rflags, state):
- if isinstance(regex, words):
- rex = regex_opt(regex.words, prefix=regex.prefix,
- suffix=regex.suffix)
- else:
- rex = regex
- compiled = re.compile(rex, rflags)
-
- def match_func(text, pos, endpos=sys.maxsize):
- info = cls._prof_data[-1].setdefault((state, rex), [0, 0.0])
- t0 = time.time()
- res = compiled.match(text, pos, endpos)
- t1 = time.time()
- info[0] += 1
- info[1] += t1 - t0
- return res
- return match_func
-
-
-class ProfilingRegexLexer(RegexLexer, metaclass=ProfilingRegexLexerMeta):
- """Drop-in replacement for RegexLexer that does profiling of its regexes."""
-
- _prof_data = []
- _prof_sort_index = 4 # defaults to time per call
-
- def get_tokens_unprocessed(self, text, stack=('root',)):
- # this needs to be a stack, since using(this) will produce nested calls
- self.__class__._prof_data.append({})
- yield from RegexLexer.get_tokens_unprocessed(self, text, stack)
- rawdata = self.__class__._prof_data.pop()
- data = sorted(((s, repr(r).strip('u\'').replace('\\\\', '\\')[:65],
- n, 1000 * t, 1000 * t / n)
- for ((s, r), (n, t)) in rawdata.items()),
- key=lambda x: x[self._prof_sort_index],
- reverse=True)
- sum_total = sum(x[3] for x in data)
-
- print()
- print('Profiling result for %s lexing %d chars in %.3f ms' %
- (self.__class__.__name__, len(text), sum_total))
- print('=' * 110)
- print('%-20s %-64s ncalls tottime percall' % ('state', 'regex'))
- print('-' * 110)
- for d in data:
- print('%-20s %-65s %5d %8.4f %8.4f' % d)
- print('=' * 110)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/pretty.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/pretty.py
deleted file mode 100644
index 2bd9eb0073d3e0a6c56311b42097ff322f75dcdd..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/pretty.py
+++ /dev/null
@@ -1,994 +0,0 @@
-import builtins
-import collections
-import dataclasses
-import inspect
-import os
-import sys
-from array import array
-from collections import Counter, UserDict, UserList, defaultdict, deque
-from dataclasses import dataclass, fields, is_dataclass
-from inspect import isclass
-from itertools import islice
-from types import MappingProxyType
-from typing import (
- TYPE_CHECKING,
- Any,
- Callable,
- DefaultDict,
- Dict,
- Iterable,
- List,
- Optional,
- Sequence,
- Set,
- Tuple,
- Union,
-)
-
-from pip._vendor.rich.repr import RichReprResult
-
-try:
- import attr as _attr_module
-
- _has_attrs = hasattr(_attr_module, "ib")
-except ImportError: # pragma: no cover
- _has_attrs = False
-
-from . import get_console
-from ._loop import loop_last
-from ._pick import pick_bool
-from .abc import RichRenderable
-from .cells import cell_len
-from .highlighter import ReprHighlighter
-from .jupyter import JupyterMixin, JupyterRenderable
-from .measure import Measurement
-from .text import Text
-
-if TYPE_CHECKING:
- from .console import (
- Console,
- ConsoleOptions,
- HighlighterType,
- JustifyMethod,
- OverflowMethod,
- RenderResult,
- )
-
-
-def _is_attr_object(obj: Any) -> bool:
- """Check if an object was created with attrs module."""
- return _has_attrs and _attr_module.has(type(obj))
-
-
-def _get_attr_fields(obj: Any) -> Sequence["_attr_module.Attribute[Any]"]:
- """Get fields for an attrs object."""
- return _attr_module.fields(type(obj)) if _has_attrs else []
-
-
-def _is_dataclass_repr(obj: object) -> bool:
- """Check if an instance of a dataclass contains the default repr.
-
- Args:
- obj (object): A dataclass instance.
-
- Returns:
- bool: True if the default repr is used, False if there is a custom repr.
- """
- # Digging in to a lot of internals here
- # Catching all exceptions in case something is missing on a non CPython implementation
- try:
- return obj.__repr__.__code__.co_filename == dataclasses.__file__
- except Exception: # pragma: no coverage
- return False
-
-
-_dummy_namedtuple = collections.namedtuple("_dummy_namedtuple", [])
-
-
-def _has_default_namedtuple_repr(obj: object) -> bool:
- """Check if an instance of namedtuple contains the default repr
-
- Args:
- obj (object): A namedtuple
-
- Returns:
- bool: True if the default repr is used, False if there's a custom repr.
- """
- obj_file = None
- try:
- obj_file = inspect.getfile(obj.__repr__)
- except (OSError, TypeError):
- # OSError handles case where object is defined in __main__ scope, e.g. REPL - no filename available.
- # TypeError trapped defensively, in case of object without filename slips through.
- pass
- default_repr_file = inspect.getfile(_dummy_namedtuple.__repr__)
- return obj_file == default_repr_file
-
-
-def _ipy_display_hook(
- value: Any,
- console: Optional["Console"] = None,
- overflow: "OverflowMethod" = "ignore",
- crop: bool = False,
- indent_guides: bool = False,
- max_length: Optional[int] = None,
- max_string: Optional[int] = None,
- max_depth: Optional[int] = None,
- expand_all: bool = False,
-) -> Union[str, None]:
- # needed here to prevent circular import:
- from .console import ConsoleRenderable
-
- # always skip rich generated jupyter renderables or None values
- if _safe_isinstance(value, JupyterRenderable) or value is None:
- return None
-
- console = console or get_console()
-
- with console.capture() as capture:
- # certain renderables should start on a new line
- if _safe_isinstance(value, ConsoleRenderable):
- console.line()
- console.print(
- value
- if _safe_isinstance(value, RichRenderable)
- else Pretty(
- value,
- overflow=overflow,
- indent_guides=indent_guides,
- max_length=max_length,
- max_string=max_string,
- max_depth=max_depth,
- expand_all=expand_all,
- margin=12,
- ),
- crop=crop,
- new_line_start=True,
- end="",
- )
- # strip trailing newline, not usually part of a text repr
- # I'm not sure if this should be prevented at a lower level
- return capture.get().rstrip("\n")
-
-
-def _safe_isinstance(
- obj: object, class_or_tuple: Union[type, Tuple[type, ...]]
-) -> bool:
- """isinstance can fail in rare cases, for example types with no __class__"""
- try:
- return isinstance(obj, class_or_tuple)
- except Exception:
- return False
-
-
-def install(
- console: Optional["Console"] = None,
- overflow: "OverflowMethod" = "ignore",
- crop: bool = False,
- indent_guides: bool = False,
- max_length: Optional[int] = None,
- max_string: Optional[int] = None,
- max_depth: Optional[int] = None,
- expand_all: bool = False,
-) -> None:
- """Install automatic pretty printing in the Python REPL.
-
- Args:
- console (Console, optional): Console instance or ``None`` to use global console. Defaults to None.
- overflow (Optional[OverflowMethod], optional): Overflow method. Defaults to "ignore".
- crop (Optional[bool], optional): Enable cropping of long lines. Defaults to False.
- indent_guides (bool, optional): Enable indentation guides. Defaults to False.
- max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
- Defaults to None.
- max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to None.
- max_depth (int, optional): Maximum depth of nested data structures, or None for no maximum. Defaults to None.
- expand_all (bool, optional): Expand all containers. Defaults to False.
- max_frames (int): Maximum number of frames to show in a traceback, 0 for no maximum. Defaults to 100.
- """
- from pip._vendor.rich import get_console
-
- console = console or get_console()
- assert console is not None
-
- def display_hook(value: Any) -> None:
- """Replacement sys.displayhook which prettifies objects with Rich."""
- if value is not None:
- assert console is not None
- builtins._ = None # type: ignore[attr-defined]
- console.print(
- value
- if _safe_isinstance(value, RichRenderable)
- else Pretty(
- value,
- overflow=overflow,
- indent_guides=indent_guides,
- max_length=max_length,
- max_string=max_string,
- max_depth=max_depth,
- expand_all=expand_all,
- ),
- crop=crop,
- )
- builtins._ = value # type: ignore[attr-defined]
-
- if "get_ipython" in globals():
- ip = get_ipython() # type: ignore[name-defined]
- from IPython.core.formatters import BaseFormatter
-
- class RichFormatter(BaseFormatter): # type: ignore[misc]
- pprint: bool = True
-
- def __call__(self, value: Any) -> Any:
- if self.pprint:
- return _ipy_display_hook(
- value,
- console=get_console(),
- overflow=overflow,
- indent_guides=indent_guides,
- max_length=max_length,
- max_string=max_string,
- max_depth=max_depth,
- expand_all=expand_all,
- )
- else:
- return repr(value)
-
- # replace plain text formatter with rich formatter
- rich_formatter = RichFormatter()
- ip.display_formatter.formatters["text/plain"] = rich_formatter
- else:
- sys.displayhook = display_hook
-
-
-class Pretty(JupyterMixin):
- """A rich renderable that pretty prints an object.
-
- Args:
- _object (Any): An object to pretty print.
- highlighter (HighlighterType, optional): Highlighter object to apply to result, or None for ReprHighlighter. Defaults to None.
- indent_size (int, optional): Number of spaces in indent. Defaults to 4.
- justify (JustifyMethod, optional): Justify method, or None for default. Defaults to None.
- overflow (OverflowMethod, optional): Overflow method, or None for default. Defaults to None.
- no_wrap (Optional[bool], optional): Disable word wrapping. Defaults to False.
- indent_guides (bool, optional): Enable indentation guides. Defaults to False.
- max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
- Defaults to None.
- max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to None.
- max_depth (int, optional): Maximum depth of nested data structures, or None for no maximum. Defaults to None.
- expand_all (bool, optional): Expand all containers. Defaults to False.
- margin (int, optional): Subtrace a margin from width to force containers to expand earlier. Defaults to 0.
- insert_line (bool, optional): Insert a new line if the output has multiple new lines. Defaults to False.
- """
-
- def __init__(
- self,
- _object: Any,
- highlighter: Optional["HighlighterType"] = None,
- *,
- indent_size: int = 4,
- justify: Optional["JustifyMethod"] = None,
- overflow: Optional["OverflowMethod"] = None,
- no_wrap: Optional[bool] = False,
- indent_guides: bool = False,
- max_length: Optional[int] = None,
- max_string: Optional[int] = None,
- max_depth: Optional[int] = None,
- expand_all: bool = False,
- margin: int = 0,
- insert_line: bool = False,
- ) -> None:
- self._object = _object
- self.highlighter = highlighter or ReprHighlighter()
- self.indent_size = indent_size
- self.justify: Optional["JustifyMethod"] = justify
- self.overflow: Optional["OverflowMethod"] = overflow
- self.no_wrap = no_wrap
- self.indent_guides = indent_guides
- self.max_length = max_length
- self.max_string = max_string
- self.max_depth = max_depth
- self.expand_all = expand_all
- self.margin = margin
- self.insert_line = insert_line
-
- def __rich_console__(
- self, console: "Console", options: "ConsoleOptions"
- ) -> "RenderResult":
- pretty_str = pretty_repr(
- self._object,
- max_width=options.max_width - self.margin,
- indent_size=self.indent_size,
- max_length=self.max_length,
- max_string=self.max_string,
- max_depth=self.max_depth,
- expand_all=self.expand_all,
- )
- pretty_text = Text.from_ansi(
- pretty_str,
- justify=self.justify or options.justify,
- overflow=self.overflow or options.overflow,
- no_wrap=pick_bool(self.no_wrap, options.no_wrap),
- style="pretty",
- )
- pretty_text = (
- self.highlighter(pretty_text)
- if pretty_text
- else Text(
- f"{type(self._object)}.__repr__ returned empty string",
- style="dim italic",
- )
- )
- if self.indent_guides and not options.ascii_only:
- pretty_text = pretty_text.with_indent_guides(
- self.indent_size, style="repr.indent"
- )
- if self.insert_line and "\n" in pretty_text:
- yield ""
- yield pretty_text
-
- def __rich_measure__(
- self, console: "Console", options: "ConsoleOptions"
- ) -> "Measurement":
- pretty_str = pretty_repr(
- self._object,
- max_width=options.max_width,
- indent_size=self.indent_size,
- max_length=self.max_length,
- max_string=self.max_string,
- max_depth=self.max_depth,
- expand_all=self.expand_all,
- )
- text_width = (
- max(cell_len(line) for line in pretty_str.splitlines()) if pretty_str else 0
- )
- return Measurement(text_width, text_width)
-
-
-def _get_braces_for_defaultdict(_object: DefaultDict[Any, Any]) -> Tuple[str, str, str]:
- return (
- f"defaultdict({_object.default_factory!r}, {{",
- "})",
- f"defaultdict({_object.default_factory!r}, {{}})",
- )
-
-
-def _get_braces_for_array(_object: "array[Any]") -> Tuple[str, str, str]:
- return (f"array({_object.typecode!r}, [", "])", f"array({_object.typecode!r})")
-
-
-_BRACES: Dict[type, Callable[[Any], Tuple[str, str, str]]] = {
- os._Environ: lambda _object: ("environ({", "})", "environ({})"),
- array: _get_braces_for_array,
- defaultdict: _get_braces_for_defaultdict,
- Counter: lambda _object: ("Counter({", "})", "Counter()"),
- deque: lambda _object: ("deque([", "])", "deque()"),
- dict: lambda _object: ("{", "}", "{}"),
- UserDict: lambda _object: ("{", "}", "{}"),
- frozenset: lambda _object: ("frozenset({", "})", "frozenset()"),
- list: lambda _object: ("[", "]", "[]"),
- UserList: lambda _object: ("[", "]", "[]"),
- set: lambda _object: ("{", "}", "set()"),
- tuple: lambda _object: ("(", ")", "()"),
- MappingProxyType: lambda _object: ("mappingproxy({", "})", "mappingproxy({})"),
-}
-_CONTAINERS = tuple(_BRACES.keys())
-_MAPPING_CONTAINERS = (dict, os._Environ, MappingProxyType, UserDict)
-
-
-def is_expandable(obj: Any) -> bool:
- """Check if an object may be expanded by pretty print."""
- return (
- _safe_isinstance(obj, _CONTAINERS)
- or (is_dataclass(obj))
- or (hasattr(obj, "__rich_repr__"))
- or _is_attr_object(obj)
- ) and not isclass(obj)
-
-
-@dataclass
-class Node:
- """A node in a repr tree. May be atomic or a container."""
-
- key_repr: str = ""
- value_repr: str = ""
- open_brace: str = ""
- close_brace: str = ""
- empty: str = ""
- last: bool = False
- is_tuple: bool = False
- is_namedtuple: bool = False
- children: Optional[List["Node"]] = None
- key_separator: str = ": "
- separator: str = ", "
-
- def iter_tokens(self) -> Iterable[str]:
- """Generate tokens for this node."""
- if self.key_repr:
- yield self.key_repr
- yield self.key_separator
- if self.value_repr:
- yield self.value_repr
- elif self.children is not None:
- if self.children:
- yield self.open_brace
- if self.is_tuple and not self.is_namedtuple and len(self.children) == 1:
- yield from self.children[0].iter_tokens()
- yield ","
- else:
- for child in self.children:
- yield from child.iter_tokens()
- if not child.last:
- yield self.separator
- yield self.close_brace
- else:
- yield self.empty
-
- def check_length(self, start_length: int, max_length: int) -> bool:
- """Check the length fits within a limit.
-
- Args:
- start_length (int): Starting length of the line (indent, prefix, suffix).
- max_length (int): Maximum length.
-
- Returns:
- bool: True if the node can be rendered within max length, otherwise False.
- """
- total_length = start_length
- for token in self.iter_tokens():
- total_length += cell_len(token)
- if total_length > max_length:
- return False
- return True
-
- def __str__(self) -> str:
- repr_text = "".join(self.iter_tokens())
- return repr_text
-
- def render(
- self, max_width: int = 80, indent_size: int = 4, expand_all: bool = False
- ) -> str:
- """Render the node to a pretty repr.
-
- Args:
- max_width (int, optional): Maximum width of the repr. Defaults to 80.
- indent_size (int, optional): Size of indents. Defaults to 4.
- expand_all (bool, optional): Expand all levels. Defaults to False.
-
- Returns:
- str: A repr string of the original object.
- """
- lines = [_Line(node=self, is_root=True)]
- line_no = 0
- while line_no < len(lines):
- line = lines[line_no]
- if line.expandable and not line.expanded:
- if expand_all or not line.check_length(max_width):
- lines[line_no : line_no + 1] = line.expand(indent_size)
- line_no += 1
-
- repr_str = "\n".join(str(line) for line in lines)
- return repr_str
-
-
-@dataclass
-class _Line:
- """A line in repr output."""
-
- parent: Optional["_Line"] = None
- is_root: bool = False
- node: Optional[Node] = None
- text: str = ""
- suffix: str = ""
- whitespace: str = ""
- expanded: bool = False
- last: bool = False
-
- @property
- def expandable(self) -> bool:
- """Check if the line may be expanded."""
- return bool(self.node is not None and self.node.children)
-
- def check_length(self, max_length: int) -> bool:
- """Check this line fits within a given number of cells."""
- start_length = (
- len(self.whitespace) + cell_len(self.text) + cell_len(self.suffix)
- )
- assert self.node is not None
- return self.node.check_length(start_length, max_length)
-
- def expand(self, indent_size: int) -> Iterable["_Line"]:
- """Expand this line by adding children on their own line."""
- node = self.node
- assert node is not None
- whitespace = self.whitespace
- assert node.children
- if node.key_repr:
- new_line = yield _Line(
- text=f"{node.key_repr}{node.key_separator}{node.open_brace}",
- whitespace=whitespace,
- )
- else:
- new_line = yield _Line(text=node.open_brace, whitespace=whitespace)
- child_whitespace = self.whitespace + " " * indent_size
- tuple_of_one = node.is_tuple and len(node.children) == 1
- for last, child in loop_last(node.children):
- separator = "," if tuple_of_one else node.separator
- line = _Line(
- parent=new_line,
- node=child,
- whitespace=child_whitespace,
- suffix=separator,
- last=last and not tuple_of_one,
- )
- yield line
-
- yield _Line(
- text=node.close_brace,
- whitespace=whitespace,
- suffix=self.suffix,
- last=self.last,
- )
-
- def __str__(self) -> str:
- if self.last:
- return f"{self.whitespace}{self.text}{self.node or ''}"
- else:
- return (
- f"{self.whitespace}{self.text}{self.node or ''}{self.suffix.rstrip()}"
- )
-
-
-def _is_namedtuple(obj: Any) -> bool:
- """Checks if an object is most likely a namedtuple. It is possible
- to craft an object that passes this check and isn't a namedtuple, but
- there is only a minuscule chance of this happening unintentionally.
-
- Args:
- obj (Any): The object to test
-
- Returns:
- bool: True if the object is a namedtuple. False otherwise.
- """
- try:
- fields = getattr(obj, "_fields", None)
- except Exception:
- # Being very defensive - if we cannot get the attr then its not a namedtuple
- return False
- return isinstance(obj, tuple) and isinstance(fields, tuple)
-
-
-def traverse(
- _object: Any,
- max_length: Optional[int] = None,
- max_string: Optional[int] = None,
- max_depth: Optional[int] = None,
-) -> Node:
- """Traverse object and generate a tree.
-
- Args:
- _object (Any): Object to be traversed.
- max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
- Defaults to None.
- max_string (int, optional): Maximum length of string before truncating, or None to disable truncating.
- Defaults to None.
- max_depth (int, optional): Maximum depth of data structures, or None for no maximum.
- Defaults to None.
-
- Returns:
- Node: The root of a tree structure which can be used to render a pretty repr.
- """
-
- def to_repr(obj: Any) -> str:
- """Get repr string for an object, but catch errors."""
- if (
- max_string is not None
- and _safe_isinstance(obj, (bytes, str))
- and len(obj) > max_string
- ):
- truncated = len(obj) - max_string
- obj_repr = f"{obj[:max_string]!r}+{truncated}"
- else:
- try:
- obj_repr = repr(obj)
- except Exception as error:
- obj_repr = f"<repr-error {str(error)!r}>"
- return obj_repr
-
- visited_ids: Set[int] = set()
- push_visited = visited_ids.add
- pop_visited = visited_ids.remove
-
- def _traverse(obj: Any, root: bool = False, depth: int = 0) -> Node:
- """Walk the object depth first."""
-
- obj_id = id(obj)
- if obj_id in visited_ids:
- # Recursion detected
- return Node(value_repr="...")
-
- obj_type = type(obj)
- children: List[Node]
- reached_max_depth = max_depth is not None and depth >= max_depth
-
- def iter_rich_args(rich_args: Any) -> Iterable[Union[Any, Tuple[str, Any]]]:
- for arg in rich_args:
- if _safe_isinstance(arg, tuple):
- if len(arg) == 3:
- key, child, default = arg
- if default == child:
- continue
- yield key, child
- elif len(arg) == 2:
- key, child = arg
- yield key, child
- elif len(arg) == 1:
- yield arg[0]
- else:
- yield arg
-
- try:
- fake_attributes = hasattr(
- obj, "awehoi234_wdfjwljet234_234wdfoijsdfmmnxpi492"
- )
- except Exception:
- fake_attributes = False
-
- rich_repr_result: Optional[RichReprResult] = None
- if not fake_attributes:
- try:
- if hasattr(obj, "__rich_repr__") and not isclass(obj):
- rich_repr_result = obj.__rich_repr__()
- except Exception:
- pass
-
- if rich_repr_result is not None:
- push_visited(obj_id)
- angular = getattr(obj.__rich_repr__, "angular", False)
- args = list(iter_rich_args(rich_repr_result))
- class_name = obj.__class__.__name__
-
- if args:
- children = []
- append = children.append
-
- if reached_max_depth:
- if angular:
- node = Node(value_repr=f"<{class_name}...>")
- else:
- node = Node(value_repr=f"{class_name}(...)")
- else:
- if angular:
- node = Node(
- open_brace=f"<{class_name} ",
- close_brace=">",
- children=children,
- last=root,
- separator=" ",
- )
- else:
- node = Node(
- open_brace=f"{class_name}(",
- close_brace=")",
- children=children,
- last=root,
- )
- for last, arg in loop_last(args):
- if _safe_isinstance(arg, tuple):
- key, child = arg
- child_node = _traverse(child, depth=depth + 1)
- child_node.last = last
- child_node.key_repr = key
- child_node.key_separator = "="
- append(child_node)
- else:
- child_node = _traverse(arg, depth=depth + 1)
- child_node.last = last
- append(child_node)
- else:
- node = Node(
- value_repr=f"<{class_name}>" if angular else f"{class_name}()",
- children=[],
- last=root,
- )
- pop_visited(obj_id)
- elif _is_attr_object(obj) and not fake_attributes:
- push_visited(obj_id)
- children = []
- append = children.append
-
- attr_fields = _get_attr_fields(obj)
- if attr_fields:
- if reached_max_depth:
- node = Node(value_repr=f"{obj.__class__.__name__}(...)")
- else:
- node = Node(
- open_brace=f"{obj.__class__.__name__}(",
- close_brace=")",
- children=children,
- last=root,
- )
-
- def iter_attrs() -> Iterable[
- Tuple[str, Any, Optional[Callable[[Any], str]]]
- ]:
- """Iterate over attr fields and values."""
- for attr in attr_fields:
- if attr.repr:
- try:
- value = getattr(obj, attr.name)
- except Exception as error:
- # Can happen, albeit rarely
- yield (attr.name, error, None)
- else:
- yield (
- attr.name,
- value,
- attr.repr if callable(attr.repr) else None,
- )
-
- for last, (name, value, repr_callable) in loop_last(iter_attrs()):
- if repr_callable:
- child_node = Node(value_repr=str(repr_callable(value)))
- else:
- child_node = _traverse(value, depth=depth + 1)
- child_node.last = last
- child_node.key_repr = name
- child_node.key_separator = "="
- append(child_node)
- else:
- node = Node(
- value_repr=f"{obj.__class__.__name__}()", children=[], last=root
- )
- pop_visited(obj_id)
- elif (
- is_dataclass(obj)
- and not _safe_isinstance(obj, type)
- and not fake_attributes
- and _is_dataclass_repr(obj)
- ):
- push_visited(obj_id)
- children = []
- append = children.append
- if reached_max_depth:
- node = Node(value_repr=f"{obj.__class__.__name__}(...)")
- else:
- node = Node(
- open_brace=f"{obj.__class__.__name__}(",
- close_brace=")",
- children=children,
- last=root,
- empty=f"{obj.__class__.__name__}()",
- )
-
- for last, field in loop_last(
- field for field in fields(obj) if field.repr
- ):
- child_node = _traverse(getattr(obj, field.name), depth=depth + 1)
- child_node.key_repr = field.name
- child_node.last = last
- child_node.key_separator = "="
- append(child_node)
-
- pop_visited(obj_id)
- elif _is_namedtuple(obj) and _has_default_namedtuple_repr(obj):
- push_visited(obj_id)
- class_name = obj.__class__.__name__
- if reached_max_depth:
- # If we've reached the max depth, we still show the class name, but not its contents
- node = Node(
- value_repr=f"{class_name}(...)",
- )
- else:
- children = []
- append = children.append
- node = Node(
- open_brace=f"{class_name}(",
- close_brace=")",
- children=children,
- empty=f"{class_name}()",
- )
- for last, (key, value) in loop_last(obj._asdict().items()):
- child_node = _traverse(value, depth=depth + 1)
- child_node.key_repr = key
- child_node.last = last
- child_node.key_separator = "="
- append(child_node)
- pop_visited(obj_id)
- elif _safe_isinstance(obj, _CONTAINERS):
- for container_type in _CONTAINERS:
- if _safe_isinstance(obj, container_type):
- obj_type = container_type
- break
-
- push_visited(obj_id)
-
- open_brace, close_brace, empty = _BRACES[obj_type](obj)
-
- if reached_max_depth:
- node = Node(value_repr=f"{open_brace}...{close_brace}")
- elif obj_type.__repr__ != type(obj).__repr__:
- node = Node(value_repr=to_repr(obj), last=root)
- elif obj:
- children = []
- node = Node(
- open_brace=open_brace,
- close_brace=close_brace,
- children=children,
- last=root,
- )
- append = children.append
- num_items = len(obj)
- last_item_index = num_items - 1
-
- if _safe_isinstance(obj, _MAPPING_CONTAINERS):
- iter_items = iter(obj.items())
- if max_length is not None:
- iter_items = islice(iter_items, max_length)
- for index, (key, child) in enumerate(iter_items):
- child_node = _traverse(child, depth=depth + 1)
- child_node.key_repr = to_repr(key)
- child_node.last = index == last_item_index
- append(child_node)
- else:
- iter_values = iter(obj)
- if max_length is not None:
- iter_values = islice(iter_values, max_length)
- for index, child in enumerate(iter_values):
- child_node = _traverse(child, depth=depth + 1)
- child_node.last = index == last_item_index
- append(child_node)
- if max_length is not None and num_items > max_length:
- append(Node(value_repr=f"... +{num_items - max_length}", last=True))
- else:
- node = Node(empty=empty, children=[], last=root)
-
- pop_visited(obj_id)
- else:
- node = Node(value_repr=to_repr(obj), last=root)
- node.is_tuple = _safe_isinstance(obj, tuple)
- node.is_namedtuple = _is_namedtuple(obj)
- return node
-
- node = _traverse(_object, root=True)
- return node
-
-
-def pretty_repr(
- _object: Any,
- *,
- max_width: int = 80,
- indent_size: int = 4,
- max_length: Optional[int] = None,
- max_string: Optional[int] = None,
- max_depth: Optional[int] = None,
- expand_all: bool = False,
-) -> str:
- """Prettify repr string by expanding on to new lines to fit within a given width.
-
- Args:
- _object (Any): Object to repr.
- max_width (int, optional): Desired maximum width of repr string. Defaults to 80.
- indent_size (int, optional): Number of spaces to indent. Defaults to 4.
- max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
- Defaults to None.
- max_string (int, optional): Maximum length of string before truncating, or None to disable truncating.
- Defaults to None.
- max_depth (int, optional): Maximum depth of nested data structure, or None for no depth.
- Defaults to None.
- expand_all (bool, optional): Expand all containers regardless of available width. Defaults to False.
-
- Returns:
- str: A possibly multi-line representation of the object.
- """
-
- if _safe_isinstance(_object, Node):
- node = _object
- else:
- node = traverse(
- _object, max_length=max_length, max_string=max_string, max_depth=max_depth
- )
- repr_str: str = node.render(
- max_width=max_width, indent_size=indent_size, expand_all=expand_all
- )
- return repr_str
-
-
-def pprint(
- _object: Any,
- *,
- console: Optional["Console"] = None,
- indent_guides: bool = True,
- max_length: Optional[int] = None,
- max_string: Optional[int] = None,
- max_depth: Optional[int] = None,
- expand_all: bool = False,
-) -> None:
- """A convenience function for pretty printing.
-
- Args:
- _object (Any): Object to pretty print.
- console (Console, optional): Console instance, or None to use default. Defaults to None.
- max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
- Defaults to None.
- max_string (int, optional): Maximum length of strings before truncating, or None to disable. Defaults to None.
- max_depth (int, optional): Maximum depth for nested data structures, or None for unlimited depth. Defaults to None.
- indent_guides (bool, optional): Enable indentation guides. Defaults to True.
- expand_all (bool, optional): Expand all containers. Defaults to False.
- """
- _console = get_console() if console is None else console
- _console.print(
- Pretty(
- _object,
- max_length=max_length,
- max_string=max_string,
- max_depth=max_depth,
- indent_guides=indent_guides,
- expand_all=expand_all,
- overflow="ignore",
- ),
- soft_wrap=True,
- )
-
-
-if __name__ == "__main__": # pragma: no cover
-
- class BrokenRepr:
- def __repr__(self) -> str:
- 1 / 0
- return "this will fail"
-
- from typing import NamedTuple
-
- class StockKeepingUnit(NamedTuple):
- name: str
- description: str
- price: float
- category: str
- reviews: List[str]
-
- d = defaultdict(int)
- d["foo"] = 5
- data = {
- "foo": [
- 1,
- "Hello World!",
- 100.123,
- 323.232,
- 432324.0,
- {5, 6, 7, (1, 2, 3, 4), 8},
- ],
- "bar": frozenset({1, 2, 3}),
- "defaultdict": defaultdict(
- list, {"crumble": ["apple", "rhubarb", "butter", "sugar", "flour"]}
- ),
- "counter": Counter(
- [
- "apple",
- "orange",
- "pear",
- "kumquat",
- "kumquat",
- "durian" * 100,
- ]
- ),
- "atomic": (False, True, None),
- "namedtuple": StockKeepingUnit(
- "Sparkling British Spring Water",
- "Carbonated spring water",
- 0.9,
- "water",
- ["its amazing!", "its terrible!"],
- ),
- "Broken": BrokenRepr(),
- }
- data["foo"].append(data) # type: ignore[attr-defined]
-
- from pip._vendor.rich import print
-
- # print(Pretty(data, indent_guides=True, max_string=20))
-
- class Thing:
- def __repr__(self) -> str:
- return "Hello\x1b[38;5;239m World!"
-
- print(Pretty(Thing()))
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/packages/backports/weakref_finalize.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/packages/backports/weakref_finalize.py
deleted file mode 100644
index a2f2966e5496601787d138e9004fbb3d2ce9b64c..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/packages/backports/weakref_finalize.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-backports.weakref_finalize
-~~~~~~~~~~~~~~~~~~
-
-Backports the Python 3 ``weakref.finalize`` method.
-"""
-from __future__ import absolute_import
-
-import itertools
-import sys
-from weakref import ref
-
-__all__ = ["weakref_finalize"]
-
-
-class weakref_finalize(object):
- """Class for finalization of weakrefable objects
- finalize(obj, func, *args, **kwargs) returns a callable finalizer
- object which will be called when obj is garbage collected. The
- first time the finalizer is called it evaluates func(*arg, **kwargs)
- and returns the result. After this the finalizer is dead, and
- calling it just returns None.
- When the program exits any remaining finalizers for which the
- atexit attribute is true will be run in reverse order of creation.
- By default atexit is true.
- """
-
- # Finalizer objects don't have any state of their own. They are
- # just used as keys to lookup _Info objects in the registry. This
- # ensures that they cannot be part of a ref-cycle.
-
- __slots__ = ()
- _registry = {}
- _shutdown = False
- _index_iter = itertools.count()
- _dirty = False
- _registered_with_atexit = False
-
- class _Info(object):
- __slots__ = ("weakref", "func", "args", "kwargs", "atexit", "index")
-
- def __init__(self, obj, func, *args, **kwargs):
- if not self._registered_with_atexit:
- # We may register the exit function more than once because
- # of a thread race, but that is harmless
- import atexit
-
- atexit.register(self._exitfunc)
- weakref_finalize._registered_with_atexit = True
- info = self._Info()
- info.weakref = ref(obj, self)
- info.func = func
- info.args = args
- info.kwargs = kwargs or None
- info.atexit = True
- info.index = next(self._index_iter)
- self._registry[self] = info
- weakref_finalize._dirty = True
-
- def __call__(self, _=None):
- """If alive then mark as dead and return func(*args, **kwargs);
- otherwise return None"""
- info = self._registry.pop(self, None)
- if info and not self._shutdown:
- return info.func(*info.args, **(info.kwargs or {}))
-
- def detach(self):
- """If alive then mark as dead and return (obj, func, args, kwargs);
- otherwise return None"""
- info = self._registry.get(self)
- obj = info and info.weakref()
- if obj is not None and self._registry.pop(self, None):
- return (obj, info.func, info.args, info.kwargs or {})
-
- def peek(self):
- """If alive then return (obj, func, args, kwargs);
- otherwise return None"""
- info = self._registry.get(self)
- obj = info and info.weakref()
- if obj is not None:
- return (obj, info.func, info.args, info.kwargs or {})
-
- @property
- def alive(self):
- """Whether finalizer is alive"""
- return self in self._registry
-
- @property
- def atexit(self):
- """Whether finalizer should be called at exit"""
- info = self._registry.get(self)
- return bool(info) and info.atexit
-
- @atexit.setter
- def atexit(self, value):
- info = self._registry.get(self)
- if info:
- info.atexit = bool(value)
-
- def __repr__(self):
- info = self._registry.get(self)
- obj = info and info.weakref()
- if obj is None:
- return "<%s object at %#x; dead>" % (type(self).__name__, id(self))
- else:
- return "<%s object at %#x; for %r at %#x>" % (
- type(self).__name__,
- id(self),
- type(obj).__name__,
- id(obj),
- )
-
- @classmethod
- def _select_for_exit(cls):
- # Return live finalizers marked for exit, oldest first
- L = [(f, i) for (f, i) in cls._registry.items() if i.atexit]
- L.sort(key=lambda item: item[1].index)
- return [f for (f, i) in L]
-
- @classmethod
- def _exitfunc(cls):
- # At shutdown invoke finalizers for which atexit is true.
- # This is called once all other non-daemonic threads have been
- # joined.
- reenable_gc = False
- try:
- if cls._registry:
- import gc
-
- if gc.isenabled():
- reenable_gc = True
- gc.disable()
- pending = None
- while True:
- if pending is None or weakref_finalize._dirty:
- pending = cls._select_for_exit()
- weakref_finalize._dirty = False
- if not pending:
- break
- f = pending.pop()
- try:
- # gc is disabled, so (assuming no daemonic
- # threads) the following is the only line in
- # this function which might trigger creation
- # of a new finalizer
- f()
- except Exception:
- sys.excepthook(*sys.exc_info())
- assert f not in cls._registry
- finally:
- # prevent any more finalizers from executing during shutdown
- weakref_finalize._shutdown = True
- if reenable_gc:
- gc.enable()
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/specifiers.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/specifiers.py
deleted file mode 100644
index ba8fe37b7f7fd0f1e46666e3644b6394dcaff644..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/specifiers.py
+++ /dev/null
@@ -1,1008 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-"""
-.. testsetup::
-
- from packaging.specifiers import Specifier, SpecifierSet, InvalidSpecifier
- from packaging.version import Version
-"""
-
-import abc
-import itertools
-import re
-from typing import (
- Callable,
- Iterable,
- Iterator,
- List,
- Optional,
- Set,
- Tuple,
- TypeVar,
- Union,
-)
-
-from .utils import canonicalize_version
-from .version import Version
-
-UnparsedVersion = Union[Version, str]
-UnparsedVersionVar = TypeVar("UnparsedVersionVar", bound=UnparsedVersion)
-CallableOperator = Callable[[Version, str], bool]
-
-
-def _coerce_version(version: UnparsedVersion) -> Version:
- if not isinstance(version, Version):
- version = Version(version)
- return version
-
-
-class InvalidSpecifier(ValueError):
- """
- Raised when attempting to create a :class:`Specifier` with a specifier
- string that is invalid.
-
- >>> Specifier("lolwat")
- Traceback (most recent call last):
- ...
- packaging.specifiers.InvalidSpecifier: Invalid specifier: 'lolwat'
- """
-
-
-class BaseSpecifier(metaclass=abc.ABCMeta):
- @abc.abstractmethod
- def __str__(self) -> str:
- """
- Returns the str representation of this Specifier-like object. This
- should be representative of the Specifier itself.
- """
-
- @abc.abstractmethod
- def __hash__(self) -> int:
- """
- Returns a hash value for this Specifier-like object.
- """
-
- @abc.abstractmethod
- def __eq__(self, other: object) -> bool:
- """
- Returns a boolean representing whether or not the two Specifier-like
- objects are equal.
-
- :param other: The other object to check against.
- """
-
- @property
- @abc.abstractmethod
- def prereleases(self) -> Optional[bool]:
- """Whether or not pre-releases as a whole are allowed.
-
- This can be set to either ``True`` or ``False`` to explicitly enable or disable
- prereleases or it can be set to ``None`` (the default) to use default semantics.
- """
-
- @prereleases.setter
- def prereleases(self, value: bool) -> None:
- """Setter for :attr:`prereleases`.
-
- :param value: The value to set.
- """
-
- @abc.abstractmethod
- def contains(self, item: str, prereleases: Optional[bool] = None) -> bool:
- """
- Determines if the given item is contained within this specifier.
- """
-
- @abc.abstractmethod
- def filter(
- self, iterable: Iterable[UnparsedVersionVar], prereleases: Optional[bool] = None
- ) -> Iterator[UnparsedVersionVar]:
- """
- Takes an iterable of items and filters them so that only items which
- are contained within this specifier are allowed in it.
- """
-
-
-class Specifier(BaseSpecifier):
- """This class abstracts handling of version specifiers.
-
- .. tip::
-
- It is generally not required to instantiate this manually. You should instead
- prefer to work with :class:`SpecifierSet` instead, which can parse
- comma-separated version specifiers (which is what package metadata contains).
- """
-
- _operator_regex_str = r"""
- (?P<operator>(~=|==|!=|<=|>=|<|>|===))
- """
- _version_regex_str = r"""
- (?P<version>
- (?:
- # The identity operators allow for an escape hatch that will
- # do an exact string match of the version you wish to install.
- # This will not be parsed by PEP 440 and we cannot determine
- # any semantic meaning from it. This operator is discouraged
- # but included entirely as an escape hatch.
- (?<====) # Only match for the identity operator
- \s*
- [^\s;)]* # The arbitrary version can be just about anything,
- # we match everything except for whitespace, a
- # semi-colon for marker support, and a closing paren
- # since versions can be enclosed in them.
- )
- |
- (?:
- # The (non)equality operators allow for wild card and local
- # versions to be specified so we have to define these two
- # operators separately to enable that.
- (?<===|!=) # Only match for equals and not equals
-
- \s*
- v?
- (?:[0-9]+!)? # epoch
- [0-9]+(?:\.[0-9]+)* # release
-
- # You cannot use a wild card and a pre-release, post-release, a dev or
- # local version together so group them with a | and make them optional.
- (?:
- \.\* # Wild card syntax of .*
- |
- (?: # pre release
- [-_\.]?
- (alpha|beta|preview|pre|a|b|c|rc)
- [-_\.]?
- [0-9]*
- )?
- (?: # post release
- (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
- )?
- (?:[-_\.]?dev[-_\.]?[0-9]*)? # dev release
- (?:\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*)? # local
- )?
- )
- |
- (?:
- # The compatible operator requires at least two digits in the
- # release segment.
- (?<=~=) # Only match for the compatible operator
-
- \s*
- v?
- (?:[0-9]+!)? # epoch
- [0-9]+(?:\.[0-9]+)+ # release (We have a + instead of a *)
- (?: # pre release
- [-_\.]?
- (alpha|beta|preview|pre|a|b|c|rc)
- [-_\.]?
- [0-9]*
- )?
- (?: # post release
- (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
- )?
- (?:[-_\.]?dev[-_\.]?[0-9]*)? # dev release
- )
- |
- (?:
- # All other operators only allow a sub set of what the
- # (non)equality operators do. Specifically they do not allow
- # local versions to be specified nor do they allow the prefix
- # matching wild cards.
- (?<!==|!=|~=) # We have special cases for these
- # operators so we want to make sure they
- # don't match here.
-
- \s*
- v?
- (?:[0-9]+!)? # epoch
- [0-9]+(?:\.[0-9]+)* # release
- (?: # pre release
- [-_\.]?
- (alpha|beta|preview|pre|a|b|c|rc)
- [-_\.]?
- [0-9]*
- )?
- (?: # post release
- (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
- )?
- (?:[-_\.]?dev[-_\.]?[0-9]*)? # dev release
- )
- )
- """
-
- _regex = re.compile(
- r"^\s*" + _operator_regex_str + _version_regex_str + r"\s*$",
- re.VERBOSE | re.IGNORECASE,
- )
-
- _operators = {
- "~=": "compatible",
- "==": "equal",
- "!=": "not_equal",
- "<=": "less_than_equal",
- ">=": "greater_than_equal",
- "<": "less_than",
- ">": "greater_than",
- "===": "arbitrary",
- }
-
- def __init__(self, spec: str = "", prereleases: Optional[bool] = None) -> None:
- """Initialize a Specifier instance.
-
- :param spec:
- The string representation of a specifier which will be parsed and
- normalized before use.
- :param prereleases:
- This tells the specifier if it should accept prerelease versions if
- applicable or not. The default of ``None`` will autodetect it from the
- given specifiers.
- :raises InvalidSpecifier:
- If the given specifier is invalid (i.e. bad syntax).
- """
- match = self._regex.search(spec)
- if not match:
- raise InvalidSpecifier(f"Invalid specifier: '{spec}'")
-
- self._spec: Tuple[str, str] = (
- match.group("operator").strip(),
- match.group("version").strip(),
- )
-
- # Store whether or not this Specifier should accept prereleases
- self._prereleases = prereleases
-
- # https://github.com/python/mypy/pull/13475#pullrequestreview-1079784515
- @property # type: ignore[override]
- def prereleases(self) -> bool:
- # If there is an explicit prereleases set for this, then we'll just
- # blindly use that.
- if self._prereleases is not None:
- return self._prereleases
-
- # Look at all of our specifiers and determine if they are inclusive
- # operators, and if they are if they are including an explicit
- # prerelease.
- operator, version = self._spec
- if operator in ["==", ">=", "<=", "~=", "==="]:
- # The == specifier can include a trailing .*, if it does we
- # want to remove before parsing.
- if operator == "==" and version.endswith(".*"):
- version = version[:-2]
-
- # Parse the version, and if it is a pre-release than this
- # specifier allows pre-releases.
- if Version(version).is_prerelease:
- return True
-
- return False
-
- @prereleases.setter
- def prereleases(self, value: bool) -> None:
- self._prereleases = value
-
- @property
- def operator(self) -> str:
- """The operator of this specifier.
-
- >>> Specifier("==1.2.3").operator
- '=='
- """
- return self._spec[0]
-
- @property
- def version(self) -> str:
- """The version of this specifier.
-
- >>> Specifier("==1.2.3").version
- '1.2.3'
- """
- return self._spec[1]
-
- def __repr__(self) -> str:
- """A representation of the Specifier that shows all internal state.
-
- >>> Specifier('>=1.0.0')
- <Specifier('>=1.0.0')>
- >>> Specifier('>=1.0.0', prereleases=False)
- <Specifier('>=1.0.0', prereleases=False)>
- >>> Specifier('>=1.0.0', prereleases=True)
- <Specifier('>=1.0.0', prereleases=True)>
- """
- pre = (
- f", prereleases={self.prereleases!r}"
- if self._prereleases is not None
- else ""
- )
-
- return f"<{self.__class__.__name__}({str(self)!r}{pre})>"
-
- def __str__(self) -> str:
- """A string representation of the Specifier that can be round-tripped.
-
- >>> str(Specifier('>=1.0.0'))
- '>=1.0.0'
- >>> str(Specifier('>=1.0.0', prereleases=False))
- '>=1.0.0'
- """
- return "{}{}".format(*self._spec)
-
- @property
- def _canonical_spec(self) -> Tuple[str, str]:
- canonical_version = canonicalize_version(
- self._spec[1],
- strip_trailing_zero=(self._spec[0] != "~="),
- )
- return self._spec[0], canonical_version
-
- def __hash__(self) -> int:
- return hash(self._canonical_spec)
-
- def __eq__(self, other: object) -> bool:
- """Whether or not the two Specifier-like objects are equal.
-
- :param other: The other object to check against.
-
- The value of :attr:`prereleases` is ignored.
-
- >>> Specifier("==1.2.3") == Specifier("== 1.2.3.0")
- True
- >>> (Specifier("==1.2.3", prereleases=False) ==
- ... Specifier("==1.2.3", prereleases=True))
- True
- >>> Specifier("==1.2.3") == "==1.2.3"
- True
- >>> Specifier("==1.2.3") == Specifier("==1.2.4")
- False
- >>> Specifier("==1.2.3") == Specifier("~=1.2.3")
- False
- """
- if isinstance(other, str):
- try:
- other = self.__class__(str(other))
- except InvalidSpecifier:
- return NotImplemented
- elif not isinstance(other, self.__class__):
- return NotImplemented
-
- return self._canonical_spec == other._canonical_spec
-
- def _get_operator(self, op: str) -> CallableOperator:
- operator_callable: CallableOperator = getattr(
- self, f"_compare_{self._operators[op]}"
- )
- return operator_callable
-
- def _compare_compatible(self, prospective: Version, spec: str) -> bool:
-
- # Compatible releases have an equivalent combination of >= and ==. That
- # is that ~=2.2 is equivalent to >=2.2,==2.*. This allows us to
- # implement this in terms of the other specifiers instead of
- # implementing it ourselves. The only thing we need to do is construct
- # the other specifiers.
-
- # We want everything but the last item in the version, but we want to
- # ignore suffix segments.
- prefix = ".".join(
- list(itertools.takewhile(_is_not_suffix, _version_split(spec)))[:-1]
- )
-
- # Add the prefix notation to the end of our string
- prefix += ".*"
-
- return self._get_operator(">=")(prospective, spec) and self._get_operator("==")(
- prospective, prefix
- )
-
- def _compare_equal(self, prospective: Version, spec: str) -> bool:
-
- # We need special logic to handle prefix matching
- if spec.endswith(".*"):
- # In the case of prefix matching we want to ignore local segment.
- normalized_prospective = canonicalize_version(
- prospective.public, strip_trailing_zero=False
- )
- # Get the normalized version string ignoring the trailing .*
- normalized_spec = canonicalize_version(spec[:-2], strip_trailing_zero=False)
- # Split the spec out by dots, and pretend that there is an implicit
- # dot in between a release segment and a pre-release segment.
- split_spec = _version_split(normalized_spec)
-
- # Split the prospective version out by dots, and pretend that there
- # is an implicit dot in between a release segment and a pre-release
- # segment.
- split_prospective = _version_split(normalized_prospective)
-
- # 0-pad the prospective version before shortening it to get the correct
- # shortened version.
- padded_prospective, _ = _pad_version(split_prospective, split_spec)
-
- # Shorten the prospective version to be the same length as the spec
- # so that we can determine if the specifier is a prefix of the
- # prospective version or not.
- shortened_prospective = padded_prospective[: len(split_spec)]
-
- return shortened_prospective == split_spec
- else:
- # Convert our spec string into a Version
- spec_version = Version(spec)
-
- # If the specifier does not have a local segment, then we want to
- # act as if the prospective version also does not have a local
- # segment.
- if not spec_version.local:
- prospective = Version(prospective.public)
-
- return prospective == spec_version
-
- def _compare_not_equal(self, prospective: Version, spec: str) -> bool:
- return not self._compare_equal(prospective, spec)
-
- def _compare_less_than_equal(self, prospective: Version, spec: str) -> bool:
-
- # NB: Local version identifiers are NOT permitted in the version
- # specifier, so local version labels can be universally removed from
- # the prospective version.
- return Version(prospective.public) <= Version(spec)
-
- def _compare_greater_than_equal(self, prospective: Version, spec: str) -> bool:
-
- # NB: Local version identifiers are NOT permitted in the version
- # specifier, so local version labels can be universally removed from
- # the prospective version.
- return Version(prospective.public) >= Version(spec)
-
- def _compare_less_than(self, prospective: Version, spec_str: str) -> bool:
-
- # Convert our spec to a Version instance, since we'll want to work with
- # it as a version.
- spec = Version(spec_str)
-
- # Check to see if the prospective version is less than the spec
- # version. If it's not we can short circuit and just return False now
- # instead of doing extra unneeded work.
- if not prospective < spec:
- return False
-
- # This special case is here so that, unless the specifier itself
- # includes is a pre-release version, that we do not accept pre-release
- # versions for the version mentioned in the specifier (e.g. <3.1 should
- # not match 3.1.dev0, but should match 3.0.dev0).
- if not spec.is_prerelease and prospective.is_prerelease:
- if Version(prospective.base_version) == Version(spec.base_version):
- return False
-
- # If we've gotten to here, it means that prospective version is both
- # less than the spec version *and* it's not a pre-release of the same
- # version in the spec.
- return True
-
- def _compare_greater_than(self, prospective: Version, spec_str: str) -> bool:
-
- # Convert our spec to a Version instance, since we'll want to work with
- # it as a version.
- spec = Version(spec_str)
-
- # Check to see if the prospective version is greater than the spec
- # version. If it's not we can short circuit and just return False now
- # instead of doing extra unneeded work.
- if not prospective > spec:
- return False
-
- # This special case is here so that, unless the specifier itself
- # includes is a post-release version, that we do not accept
- # post-release versions for the version mentioned in the specifier
- # (e.g. >3.1 should not match 3.0.post0, but should match 3.2.post0).
- if not spec.is_postrelease and prospective.is_postrelease:
- if Version(prospective.base_version) == Version(spec.base_version):
- return False
-
- # Ensure that we do not allow a local version of the version mentioned
- # in the specifier, which is technically greater than, to match.
- if prospective.local is not None:
- if Version(prospective.base_version) == Version(spec.base_version):
- return False
-
- # If we've gotten to here, it means that prospective version is both
- # greater than the spec version *and* it's not a pre-release of the
- # same version in the spec.
- return True
-
- def _compare_arbitrary(self, prospective: Version, spec: str) -> bool:
- return str(prospective).lower() == str(spec).lower()
-
- def __contains__(self, item: Union[str, Version]) -> bool:
- """Return whether or not the item is contained in this specifier.
-
- :param item: The item to check for.
-
- This is used for the ``in`` operator and behaves the same as
- :meth:`contains` with no ``prereleases`` argument passed.
-
- >>> "1.2.3" in Specifier(">=1.2.3")
- True
- >>> Version("1.2.3") in Specifier(">=1.2.3")
- True
- >>> "1.0.0" in Specifier(">=1.2.3")
- False
- >>> "1.3.0a1" in Specifier(">=1.2.3")
- False
- >>> "1.3.0a1" in Specifier(">=1.2.3", prereleases=True)
- True
- """
- return self.contains(item)
-
- def contains(
- self, item: UnparsedVersion, prereleases: Optional[bool] = None
- ) -> bool:
- """Return whether or not the item is contained in this specifier.
-
- :param item:
- The item to check for, which can be a version string or a
- :class:`Version` instance.
- :param prereleases:
- Whether or not to match prereleases with this Specifier. If set to
- ``None`` (the default), it uses :attr:`prereleases` to determine
- whether or not prereleases are allowed.
-
- >>> Specifier(">=1.2.3").contains("1.2.3")
- True
- >>> Specifier(">=1.2.3").contains(Version("1.2.3"))
- True
- >>> Specifier(">=1.2.3").contains("1.0.0")
- False
- >>> Specifier(">=1.2.3").contains("1.3.0a1")
- False
- >>> Specifier(">=1.2.3", prereleases=True).contains("1.3.0a1")
- True
- >>> Specifier(">=1.2.3").contains("1.3.0a1", prereleases=True)
- True
- """
-
- # Determine if prereleases are to be allowed or not.
- if prereleases is None:
- prereleases = self.prereleases
-
- # Normalize item to a Version, this allows us to have a shortcut for
- # "2.0" in Specifier(">=2")
- normalized_item = _coerce_version(item)
-
- # Determine if we should be supporting prereleases in this specifier
- # or not, if we do not support prereleases than we can short circuit
- # logic if this version is a prereleases.
- if normalized_item.is_prerelease and not prereleases:
- return False
-
- # Actually do the comparison to determine if this item is contained
- # within this Specifier or not.
- operator_callable: CallableOperator = self._get_operator(self.operator)
- return operator_callable(normalized_item, self.version)
-
- def filter(
- self, iterable: Iterable[UnparsedVersionVar], prereleases: Optional[bool] = None
- ) -> Iterator[UnparsedVersionVar]:
- """Filter items in the given iterable, that match the specifier.
-
- :param iterable:
- An iterable that can contain version strings and :class:`Version` instances.
- The items in the iterable will be filtered according to the specifier.
- :param prereleases:
- Whether or not to allow prereleases in the returned iterator. If set to
- ``None`` (the default), it will be intelligently decide whether to allow
- prereleases or not (based on the :attr:`prereleases` attribute, and
- whether the only versions matching are prereleases).
-
- This method is smarter than just ``filter(Specifier().contains, [...])``
- because it implements the rule from :pep:`440` that a prerelease item
- SHOULD be accepted if no other versions match the given specifier.
-
- >>> list(Specifier(">=1.2.3").filter(["1.2", "1.3", "1.5a1"]))
- ['1.3']
- >>> list(Specifier(">=1.2.3").filter(["1.2", "1.2.3", "1.3", Version("1.4")]))
- ['1.2.3', '1.3', <Version('1.4')>]
- >>> list(Specifier(">=1.2.3").filter(["1.2", "1.5a1"]))
- ['1.5a1']
- >>> list(Specifier(">=1.2.3").filter(["1.3", "1.5a1"], prereleases=True))
- ['1.3', '1.5a1']
- >>> list(Specifier(">=1.2.3", prereleases=True).filter(["1.3", "1.5a1"]))
- ['1.3', '1.5a1']
- """
-
- yielded = False
- found_prereleases = []
-
- kw = {"prereleases": prereleases if prereleases is not None else True}
-
- # Attempt to iterate over all the values in the iterable and if any of
- # them match, yield them.
- for version in iterable:
- parsed_version = _coerce_version(version)
-
- if self.contains(parsed_version, **kw):
- # If our version is a prerelease, and we were not set to allow
- # prereleases, then we'll store it for later in case nothing
- # else matches this specifier.
- if parsed_version.is_prerelease and not (
- prereleases or self.prereleases
- ):
- found_prereleases.append(version)
- # Either this is not a prerelease, or we should have been
- # accepting prereleases from the beginning.
- else:
- yielded = True
- yield version
-
- # Now that we've iterated over everything, determine if we've yielded
- # any values, and if we have not and we have any prereleases stored up
- # then we will go ahead and yield the prereleases.
- if not yielded and found_prereleases:
- for version in found_prereleases:
- yield version
-
-
-_prefix_regex = re.compile(r"^([0-9]+)((?:a|b|c|rc)[0-9]+)$")
-
-
-def _version_split(version: str) -> List[str]:
- result: List[str] = []
- for item in version.split("."):
- match = _prefix_regex.search(item)
- if match:
- result.extend(match.groups())
- else:
- result.append(item)
- return result
-
-
-def _is_not_suffix(segment: str) -> bool:
- return not any(
- segment.startswith(prefix) for prefix in ("dev", "a", "b", "rc", "post")
- )
-
-
-def _pad_version(left: List[str], right: List[str]) -> Tuple[List[str], List[str]]:
- left_split, right_split = [], []
-
- # Get the release segment of our versions
- left_split.append(list(itertools.takewhile(lambda x: x.isdigit(), left)))
- right_split.append(list(itertools.takewhile(lambda x: x.isdigit(), right)))
-
- # Get the rest of our versions
- left_split.append(left[len(left_split[0]) :])
- right_split.append(right[len(right_split[0]) :])
-
- # Insert our padding
- left_split.insert(1, ["0"] * max(0, len(right_split[0]) - len(left_split[0])))
- right_split.insert(1, ["0"] * max(0, len(left_split[0]) - len(right_split[0])))
-
- return (list(itertools.chain(*left_split)), list(itertools.chain(*right_split)))
-
-
-class SpecifierSet(BaseSpecifier):
- """This class abstracts handling of a set of version specifiers.
-
- It can be passed a single specifier (``>=3.0``), a comma-separated list of
- specifiers (``>=3.0,!=3.1``), or no specifier at all.
- """
-
- def __init__(
- self, specifiers: str = "", prereleases: Optional[bool] = None
- ) -> None:
- """Initialize a SpecifierSet instance.
-
- :param specifiers:
- The string representation of a specifier or a comma-separated list of
- specifiers which will be parsed and normalized before use.
- :param prereleases:
- This tells the SpecifierSet if it should accept prerelease versions if
- applicable or not. The default of ``None`` will autodetect it from the
- given specifiers.
-
- :raises InvalidSpecifier:
- If the given ``specifiers`` are not parseable than this exception will be
- raised.
- """
-
- # Split on `,` to break each individual specifier into it's own item, and
- # strip each item to remove leading/trailing whitespace.
- split_specifiers = [s.strip() for s in specifiers.split(",") if s.strip()]
-
- # Parsed each individual specifier, attempting first to make it a
- # Specifier.
- parsed: Set[Specifier] = set()
- for specifier in split_specifiers:
- parsed.add(Specifier(specifier))
-
- # Turn our parsed specifiers into a frozen set and save them for later.
- self._specs = frozenset(parsed)
-
- # Store our prereleases value so we can use it later to determine if
- # we accept prereleases or not.
- self._prereleases = prereleases
-
- @property
- def prereleases(self) -> Optional[bool]:
- # If we have been given an explicit prerelease modifier, then we'll
- # pass that through here.
- if self._prereleases is not None:
- return self._prereleases
-
- # If we don't have any specifiers, and we don't have a forced value,
- # then we'll just return None since we don't know if this should have
- # pre-releases or not.
- if not self._specs:
- return None
-
- # Otherwise we'll see if any of the given specifiers accept
- # prereleases, if any of them do we'll return True, otherwise False.
- return any(s.prereleases for s in self._specs)
-
- @prereleases.setter
- def prereleases(self, value: bool) -> None:
- self._prereleases = value
-
- def __repr__(self) -> str:
- """A representation of the specifier set that shows all internal state.
-
- Note that the ordering of the individual specifiers within the set may not
- match the input string.
-
- >>> SpecifierSet('>=1.0.0,!=2.0.0')
- <SpecifierSet('!=2.0.0,>=1.0.0')>
- >>> SpecifierSet('>=1.0.0,!=2.0.0', prereleases=False)
- <SpecifierSet('!=2.0.0,>=1.0.0', prereleases=False)>
- >>> SpecifierSet('>=1.0.0,!=2.0.0', prereleases=True)
- <SpecifierSet('!=2.0.0,>=1.0.0', prereleases=True)>
- """
- pre = (
- f", prereleases={self.prereleases!r}"
- if self._prereleases is not None
- else ""
- )
-
- return f"<SpecifierSet({str(self)!r}{pre})>"
-
- def __str__(self) -> str:
- """A string representation of the specifier set that can be round-tripped.
-
- Note that the ordering of the individual specifiers within the set may not
- match the input string.
-
- >>> str(SpecifierSet(">=1.0.0,!=1.0.1"))
- '!=1.0.1,>=1.0.0'
- >>> str(SpecifierSet(">=1.0.0,!=1.0.1", prereleases=False))
- '!=1.0.1,>=1.0.0'
- """
- return ",".join(sorted(str(s) for s in self._specs))
-
- def __hash__(self) -> int:
- return hash(self._specs)
-
- def __and__(self, other: Union["SpecifierSet", str]) -> "SpecifierSet":
- """Return a SpecifierSet which is a combination of the two sets.
-
- :param other: The other object to combine with.
-
- >>> SpecifierSet(">=1.0.0,!=1.0.1") & '<=2.0.0,!=2.0.1'
- <SpecifierSet('!=1.0.1,!=2.0.1,<=2.0.0,>=1.0.0')>
- >>> SpecifierSet(">=1.0.0,!=1.0.1") & SpecifierSet('<=2.0.0,!=2.0.1')
- <SpecifierSet('!=1.0.1,!=2.0.1,<=2.0.0,>=1.0.0')>
- """
- if isinstance(other, str):
- other = SpecifierSet(other)
- elif not isinstance(other, SpecifierSet):
- return NotImplemented
-
- specifier = SpecifierSet()
- specifier._specs = frozenset(self._specs | other._specs)
-
- if self._prereleases is None and other._prereleases is not None:
- specifier._prereleases = other._prereleases
- elif self._prereleases is not None and other._prereleases is None:
- specifier._prereleases = self._prereleases
- elif self._prereleases == other._prereleases:
- specifier._prereleases = self._prereleases
- else:
- raise ValueError(
- "Cannot combine SpecifierSets with True and False prerelease "
- "overrides."
- )
-
- return specifier
-
- def __eq__(self, other: object) -> bool:
- """Whether or not the two SpecifierSet-like objects are equal.
-
- :param other: The other object to check against.
-
- The value of :attr:`prereleases` is ignored.
-
- >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0,!=1.0.1")
- True
- >>> (SpecifierSet(">=1.0.0,!=1.0.1", prereleases=False) ==
- ... SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True))
- True
- >>> SpecifierSet(">=1.0.0,!=1.0.1") == ">=1.0.0,!=1.0.1"
- True
- >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0")
- False
- >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0,!=1.0.2")
- False
- """
- if isinstance(other, (str, Specifier)):
- other = SpecifierSet(str(other))
- elif not isinstance(other, SpecifierSet):
- return NotImplemented
-
- return self._specs == other._specs
-
- def __len__(self) -> int:
- """Returns the number of specifiers in this specifier set."""
- return len(self._specs)
-
- def __iter__(self) -> Iterator[Specifier]:
- """
- Returns an iterator over all the underlying :class:`Specifier` instances
- in this specifier set.
-
- >>> sorted(SpecifierSet(">=1.0.0,!=1.0.1"), key=str)
- [<Specifier('!=1.0.1')>, <Specifier('>=1.0.0')>]
- """
- return iter(self._specs)
-
- def __contains__(self, item: UnparsedVersion) -> bool:
- """Return whether or not the item is contained in this specifier.
-
- :param item: The item to check for.
-
- This is used for the ``in`` operator and behaves the same as
- :meth:`contains` with no ``prereleases`` argument passed.
-
- >>> "1.2.3" in SpecifierSet(">=1.0.0,!=1.0.1")
- True
- >>> Version("1.2.3") in SpecifierSet(">=1.0.0,!=1.0.1")
- True
- >>> "1.0.1" in SpecifierSet(">=1.0.0,!=1.0.1")
- False
- >>> "1.3.0a1" in SpecifierSet(">=1.0.0,!=1.0.1")
- False
- >>> "1.3.0a1" in SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True)
- True
- """
- return self.contains(item)
-
- def contains(
- self,
- item: UnparsedVersion,
- prereleases: Optional[bool] = None,
- installed: Optional[bool] = None,
- ) -> bool:
- """Return whether or not the item is contained in this SpecifierSet.
-
- :param item:
- The item to check for, which can be a version string or a
- :class:`Version` instance.
- :param prereleases:
- Whether or not to match prereleases with this SpecifierSet. If set to
- ``None`` (the default), it uses :attr:`prereleases` to determine
- whether or not prereleases are allowed.
-
- >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.2.3")
- True
- >>> SpecifierSet(">=1.0.0,!=1.0.1").contains(Version("1.2.3"))
- True
- >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.0.1")
- False
- >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.3.0a1")
- False
- >>> SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True).contains("1.3.0a1")
- True
- >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.3.0a1", prereleases=True)
- True
- """
- # Ensure that our item is a Version instance.
- if not isinstance(item, Version):
- item = Version(item)
-
- # Determine if we're forcing a prerelease or not, if we're not forcing
- # one for this particular filter call, then we'll use whatever the
- # SpecifierSet thinks for whether or not we should support prereleases.
- if prereleases is None:
- prereleases = self.prereleases
-
- # We can determine if we're going to allow pre-releases by looking to
- # see if any of the underlying items supports them. If none of them do
- # and this item is a pre-release then we do not allow it and we can
- # short circuit that here.
- # Note: This means that 1.0.dev1 would not be contained in something
- # like >=1.0.devabc however it would be in >=1.0.debabc,>0.0.dev0
- if not prereleases and item.is_prerelease:
- return False
-
- if installed and item.is_prerelease:
- item = Version(item.base_version)
-
- # We simply dispatch to the underlying specs here to make sure that the
- # given version is contained within all of them.
- # Note: This use of all() here means that an empty set of specifiers
- # will always return True, this is an explicit design decision.
- return all(s.contains(item, prereleases=prereleases) for s in self._specs)
-
- def filter(
- self, iterable: Iterable[UnparsedVersionVar], prereleases: Optional[bool] = None
- ) -> Iterator[UnparsedVersionVar]:
- """Filter items in the given iterable, that match the specifiers in this set.
-
- :param iterable:
- An iterable that can contain version strings and :class:`Version` instances.
- The items in the iterable will be filtered according to the specifier.
- :param prereleases:
- Whether or not to allow prereleases in the returned iterator. If set to
- ``None`` (the default), it will be intelligently decide whether to allow
- prereleases or not (based on the :attr:`prereleases` attribute, and
- whether the only versions matching are prereleases).
-
- This method is smarter than just ``filter(SpecifierSet(...).contains, [...])``
- because it implements the rule from :pep:`440` that a prerelease item
- SHOULD be accepted if no other versions match the given specifier.
-
- >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.3", "1.5a1"]))
- ['1.3']
- >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.3", Version("1.4")]))
- ['1.3', <Version('1.4')>]
- >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.5a1"]))
- []
- >>> list(SpecifierSet(">=1.2.3").filter(["1.3", "1.5a1"], prereleases=True))
- ['1.3', '1.5a1']
- >>> list(SpecifierSet(">=1.2.3", prereleases=True).filter(["1.3", "1.5a1"]))
- ['1.3', '1.5a1']
-
- An "empty" SpecifierSet will filter items based on the presence of prerelease
- versions in the set.
-
- >>> list(SpecifierSet("").filter(["1.3", "1.5a1"]))
- ['1.3']
- >>> list(SpecifierSet("").filter(["1.5a1"]))
- ['1.5a1']
- >>> list(SpecifierSet("", prereleases=True).filter(["1.3", "1.5a1"]))
- ['1.3', '1.5a1']
- >>> list(SpecifierSet("").filter(["1.3", "1.5a1"], prereleases=True))
- ['1.3', '1.5a1']
- """
- # Determine if we're forcing a prerelease or not, if we're not forcing
- # one for this particular filter call, then we'll use whatever the
- # SpecifierSet thinks for whether or not we should support prereleases.
- if prereleases is None:
- prereleases = self.prereleases
-
- # If we have any specifiers, then we want to wrap our iterable in the
- # filter method for each one, this will act as a logical AND amongst
- # each specifier.
- if self._specs:
- for spec in self._specs:
- iterable = spec.filter(iterable, prereleases=bool(prereleases))
- return iter(iterable)
- # If we do not have any specifiers, then we need to have a rough filter
- # which will filter out any pre-releases, unless there are no final
- # releases.
- else:
- filtered: List[UnparsedVersionVar] = []
- found_prereleases: List[UnparsedVersionVar] = []
-
- for item in iterable:
- parsed_version = _coerce_version(item)
-
- # Store any item which is a pre-release for later unless we've
- # already found a final version or we are accepting prereleases
- if parsed_version.is_prerelease and not prereleases:
- if not filtered:
- found_prereleases.append(item)
- else:
- filtered.append(item)
-
- # If we've found no items except for pre-releases, then we'll go
- # ahead and use the pre-releases
- if not filtered and found_prereleases and prereleases is None:
- return iter(found_prereleases)
-
- return iter(filtered)
diff --git a/spaces/TechShark20/handwespeak/README.md b/spaces/TechShark20/handwespeak/README.md
deleted file mode 100644
index ad7aa36146b17645683bcaa57171efd50c605174..0000000000000000000000000000000000000000
--- a/spaces/TechShark20/handwespeak/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Handwespeak
-emoji: 💻
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: unknown
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Temptingchina/Real-CUGAN/README.md b/spaces/Temptingchina/Real-CUGAN/README.md
deleted file mode 100644
index d673114edadba73e80f33a3c71bc0dbee8758cc8..0000000000000000000000000000000000000000
--- a/spaces/Temptingchina/Real-CUGAN/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Real CUGAN
-emoji: 🐢
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-license: gpl-3.0
-duplicated_from: DianXian/Real-CUGAN
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Tetel/secondbing/main.py b/spaces/Tetel/secondbing/main.py
deleted file mode 100644
index b0deb7f17f9351cf0eacb8560cdf4b5b597ce3b9..0000000000000000000000000000000000000000
--- a/spaces/Tetel/secondbing/main.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import argparse
-import asyncio
-import json
-import traceback
-import urllib.request
-import emoji
-import claude
-import sys, os
-sys.path.insert(0, os.path.dirname(__file__))
-
-public_dir = '/public'
-
-from SydneyGPT.SydneyGPT import Chatbot
-from aiohttp import web
-
-
-async def sydney_process_message(user_message, context, _U, locale, imageInput):
- chatbot = None
- try:
- if _U:
- os.environ['image_gen_cookie'] = _U
- #else:
- cookies = [{"name": "_U", "value": "qeretttskjllgjgznWRddcDFKFKFF"}]
- chatbot = await Chatbot.create(cookies=cookies, proxy=args.proxy, imageInput=imageInput)
- async for _, response in chatbot.ask_stream(prompt=user_message, conversation_style="creative",raw=True,
- webpage_context=context, search_result=True, locale=locale):
- yield response
- except:
- yield {"type": "error", "error": traceback.format_exc()}
- finally:
- if chatbot:
- await chatbot.close()
-
-
-async def claude_process_message(context):
- try:
- async for reply in claude_chatbot.ask_stream(context):
- yield {"type": "reply", "text": emoji.emojize(reply, language='alias').strip()}
- yield {"type": "finished"}
- except:
- yield {"type": "error", "error": traceback.format_exc()}
-
-
-async def http_handler(request):
- file_path = request.path
- if file_path == "/":
- file_path = "/index.html"
- full_path = os.path.realpath('.' + public_dir + file_path)
- if not full_path.startswith(os.path.realpath('.' + public_dir)):
- raise web.HTTPForbidden()
- response = web.FileResponse(full_path)
- response.headers['Cache-Control'] = 'no-store'
- return response
-
-
-async def websocket_handler(request):
- ws = web.WebSocketResponse()
- await ws.prepare(request)
-
- async def monitor():
- while True:
- if ws.closed:
- task.cancel()
- break
- await asyncio.sleep(0.1)
-
- async def main_process():
- async for msg in ws:
- if msg.type == web.WSMsgType.TEXT:
- request = json.loads(msg.data)
- user_message = request['message']
- context = request['context']
- locale = request['locale']
- _U = request.get('_U')
- if (request.get('imageInput') is not None) and (len(request.get('imageInput')) > 0):
- imageInput = request.get('imageInput').split(",")[1]
- else:
- imageInput = None
- bot_type = request.get("botType", "Sydney")
- if bot_type == "Sydney":
- async for response in sydney_process_message(user_message, context, _U, locale=locale, imageInput=imageInput):
- await ws.send_json(response)
- elif bot_type == "Claude":
- async for response in claude_process_message(context):
- await ws.send_json(response)
- else:
- print(f"Unknown bot type: {bot_type}")
-
- task = asyncio.ensure_future(main_process())
- monitor_task = asyncio.ensure_future(monitor())
- done, pending = await asyncio.wait([task, monitor_task], return_when=asyncio.FIRST_COMPLETED)
-
- for task in pending:
- task.cancel()
-
- return ws
-
-
-async def main(host, port):
- app = web.Application()
- app.router.add_get('/ws/', websocket_handler)
- app.router.add_get('/{tail:.*}', http_handler)
-
- runner = web.AppRunner(app)
- await runner.setup()
- site = web.TCPSite(runner, host, port)
- await site.start()
- print(f"Go to http://{host}:{port} to start chatting!")
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument("--host", "-H", help="host:port for the server", default="localhost:65432")
- parser.add_argument("--proxy", "-p", help='proxy address like "http://localhost:7890"',
- default=urllib.request.getproxies().get('https'))
- args = parser.parse_args()
- print(f"Proxy used: {args.proxy}")
-
- host, port = args.host.split(":")
- port = int(port)
-
- if os.path.isfile("cookies.json"):
- with open("cookies.json", 'r') as f:
- loaded_cookies = json.load(f)
- print("Loaded cookies.json")
- else:
- loaded_cookies = []
- print("cookies.json not found")
-
- claude_chatbot = claude.Chatbot(proxy=args.proxy)
-
- loop = asyncio.get_event_loop()
- try:
- loop.run_until_complete(main(host, port))
- loop.run_forever()
- except KeyboardInterrupt:
- pass
- finally:
- loop.close()
diff --git a/spaces/TochProud/QQ/README.md b/spaces/TochProud/QQ/README.md
deleted file mode 100644
index 3042be806844c4b6d92719e8afaa17d09c970d46..0000000000000000000000000000000000000000
--- a/spaces/TochProud/QQ/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: QQsign
-emoji: 🦀
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-license: mit
-duplicated_from: CikeyQI/QQsign
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/User1342/WatchTower/Pinpoint/Logger.py b/spaces/User1342/WatchTower/Pinpoint/Logger.py
deleted file mode 100644
index d165f17e94835e8b122033c6c4350d7eb93f4866..0000000000000000000000000000000000000000
--- a/spaces/User1342/WatchTower/Pinpoint/Logger.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from datetime import datetime
-
-
-class logger():
- """
- A wrapper class around the Python print function used to only print
- """
- DEBUG = False
-
- @staticmethod
- def print_message(message, logging_level=0):
- """
- A wrapper function around the Python print function used to only print
- :param message: the message to print
- :param override_debug: a boolean on if the DEBUG status should be override. if True a log will be printed,
- irrespective of if in Debug mode.
- """
- if logging_level >= 1 or logger.DEBUG:
- now = datetime.now()
- current_time = now.strftime("%H:%M:%S")
- print("{} | {}".format(current_time, message))
diff --git a/spaces/VIPLab/Caption-Anything/caption_anything/captioner/vit_pixel_masks_utils.py b/spaces/VIPLab/Caption-Anything/caption_anything/captioner/vit_pixel_masks_utils.py
deleted file mode 100644
index ecccbe54b9d4cd468839d6fd8e8651884b9ab07a..0000000000000000000000000000000000000000
--- a/spaces/VIPLab/Caption-Anything/caption_anything/captioner/vit_pixel_masks_utils.py
+++ /dev/null
@@ -1,17 +0,0 @@
-
-import torch
-import torch.nn as nn
-
-
-class ViTPatchMaskGenerator(nn.Module):
- def __init__(self, patch_size) -> None:
- super(ViTPatchMaskGenerator, self).__init__()
- self.patch_size = patch_size
- self.pool = nn.MaxPool2d(kernel_size=patch_size, stride=patch_size)
-
- def forward(self, pixel_masks):
- patch_mask = self.pool(pixel_masks)
- patch_mask = patch_mask.bool().flatten(1)
- cls_token_mask = patch_mask.new_ones([patch_mask.shape[0], 1]).bool()
- patch_mask = torch.cat([cls_token_mask, patch_mask], dim=-1)
- return patch_mask
diff --git a/spaces/VIPLab/Track-Anything/tracker/util/range_transform.py b/spaces/VIPLab/Track-Anything/tracker/util/range_transform.py
deleted file mode 100644
index ae1b0b3b2a01a061b9b2220a93cdf7f7a6357bfb..0000000000000000000000000000000000000000
--- a/spaces/VIPLab/Track-Anything/tracker/util/range_transform.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import torchvision.transforms as transforms
-
-im_mean = (124, 116, 104)
-
-im_normalization = transforms.Normalize(
- mean=[0.485, 0.456, 0.406],
- std=[0.229, 0.224, 0.225]
- )
-
-inv_im_trans = transforms.Normalize(
- mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],
- std=[1/0.229, 1/0.224, 1/0.225])
diff --git a/spaces/VincentZB/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/controlnet_inpaint_pose.py b/spaces/VincentZB/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/controlnet_inpaint_pose.py
deleted file mode 100644
index 95b269c611c4ce68456431b32849ec9cdced90f6..0000000000000000000000000000000000000000
--- a/spaces/VincentZB/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_inpaint/controlnet_inpaint_pose.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-from controlnet_aux import OpenposeDetector
-from diffusers import ControlNetModel
-from PIL import Image
-
-from diffusion_webui.diffusion_models.controlnet.controlnet_inpaint.pipeline_stable_diffusion_controlnet_inpaint import (
- StableDiffusionControlNetInpaintPipeline,
-)
-from diffusion_webui.utils.model_list import (
- controlnet_pose_model_list,
- stable_inpiant_model_list,
-)
-from diffusion_webui.utils.scheduler_list import (
- SCHEDULER_LIST,
- get_scheduler_list,
-)
-
-# https://github.com/mikonvergence/ControlNetInpaint
-
-
-class StableDiffusionControlNetInpaintPoseGenerator:
- def __init__(self):
- self.pipe = None
-
- def load_model(self, stable_model_path, controlnet_model_path, scheduler):
- if self.pipe is None:
- controlnet = ControlNetModel.from_pretrained(
- controlnet_model_path, torch_dtype=torch.float16
- )
-
- self.pipe = (
- StableDiffusionControlNetInpaintPipeline.from_pretrained(
- pretrained_model_name_or_path=stable_model_path,
- controlnet=controlnet,
- safety_checker=None,
- torch_dtype=torch.float16,
- )
- )
-
- self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
- self.pipe.to("cuda")
- self.pipe.enable_xformers_memory_efficient_attention()
-
- return self.pipe
-
- def load_image(self, image_path):
- image = np.array(image_path)
- image = Image.fromarray(image)
- return image
-
- def controlnet_pose_inpaint(self, image_path: str):
- openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
-
- image = image_path["image"].convert("RGB").resize((512, 512))
- image = np.array(image)
- image = openpose(image)
-
- return image
-
- def generate_image(
- self,
- image_path: str,
- stable_model_path: str,
- controlnet_model_path: str,
- prompt: str,
- negative_prompt: str,
- num_images_per_prompt: int,
- guidance_scale: int,
- num_inference_step: int,
- controlnet_conditioning_scale: int,
- scheduler: str,
- seed_generator: int,
- ):
- normal_image = image_path["image"].convert("RGB").resize((512, 512))
- mask_image = image_path["mask"].convert("RGB").resize((512, 512))
-
- normal_image = self.load_image(image_path=normal_image)
- mask_image = self.load_image(image_path=mask_image)
-
- controlnet_image = self.controlnet_pose_inpaint(image_path=image_path)
-
- pipe = self.load_model(
- stable_model_path=stable_model_path,
- controlnet_model_path=controlnet_model_path,
- scheduler=scheduler,
- )
-
- if seed_generator == 0:
- random_seed = torch.randint(0, 1000000, (1,))
- generator = torch.manual_seed(random_seed)
- else:
- generator = torch.manual_seed(seed_generator)
-
- output = pipe(
- prompt=prompt,
- image=normal_image,
- mask_image=mask_image,
- control_image=controlnet_image,
- negative_prompt=negative_prompt,
- num_images_per_prompt=num_images_per_prompt,
- num_inference_steps=num_inference_step,
- guidance_scale=guidance_scale,
- controlnet_conditioning_scale=controlnet_conditioning_scale,
- generator=generator,
- ).images
-
- return output
-
- def app():
- with gr.Blocks():
- with gr.Row():
- with gr.Column():
- controlnet_pose_inpaint_image_file = gr.Image(
- source="upload",
- tool="sketch",
- elem_id="image_upload",
- type="pil",
- label="Upload",
- )
-
- controlnet_pose_inpaint_prompt = gr.Textbox(
- lines=1, placeholder="Prompt", show_label=False
- )
-
- controlnet_pose_inpaint_negative_prompt = gr.Textbox(
- lines=1,
- show_label=False,
- placeholder="Negative Prompt",
- )
- with gr.Row():
- with gr.Column():
- controlnet_pose_inpaint_stable_model_id = (
- gr.Dropdown(
- choices=stable_inpiant_model_list,
- value=stable_inpiant_model_list[0],
- label="Stable Model Id",
- )
- )
-
- controlnet_pose_inpaint_guidance_scale = gr.Slider(
- minimum=0.1,
- maximum=15,
- step=0.1,
- value=7.5,
- label="Guidance Scale",
- )
-
- controlnet_pose_inpaint_num_inference_step = (
- gr.Slider(
- minimum=1,
- maximum=100,
- step=1,
- value=50,
- label="Num Inference Step",
- )
- )
- controlnet_pose_inpaint_num_images_per_prompt = (
- gr.Slider(
- minimum=1,
- maximum=10,
- step=1,
- value=1,
- label="Number Of Images",
- )
- )
- with gr.Row():
- with gr.Column():
- controlnet_pose_inpaint_model_id = gr.Dropdown(
- choices=controlnet_pose_model_list,
- value=controlnet_pose_model_list[0],
- label="Controlnet Model Id",
- )
- controlnet_pose_inpaint_scheduler = gr.Dropdown(
- choices=SCHEDULER_LIST,
- value=SCHEDULER_LIST[0],
- label="Scheduler",
- )
- controlnet_pose_inpaint_controlnet_conditioning_scale = gr.Slider(
- minimum=0.1,
- maximum=1.0,
- step=0.1,
- value=0.5,
- label="Controlnet Conditioning Scale",
- )
-
- controlnet_pose_inpaint_seed_generator = (
- gr.Slider(
- minimum=0,
- maximum=1000000,
- step=1,
- value=0,
- label="Seed Generator",
- )
- )
-
- controlnet_pose_inpaint_predict = gr.Button(
- value="Generator"
- )
-
- with gr.Column():
- output_image = gr.Gallery(
- label="Generated images",
- show_label=False,
- elem_id="gallery",
- ).style(grid=(1, 2))
-
- controlnet_pose_inpaint_predict.click(
- fn=StableDiffusionControlNetInpaintPoseGenerator().generate_image,
- inputs=[
- controlnet_pose_inpaint_image_file,
- controlnet_pose_inpaint_stable_model_id,
- controlnet_pose_inpaint_model_id,
- controlnet_pose_inpaint_prompt,
- controlnet_pose_inpaint_negative_prompt,
- controlnet_pose_inpaint_num_images_per_prompt,
- controlnet_pose_inpaint_guidance_scale,
- controlnet_pose_inpaint_num_inference_step,
- controlnet_pose_inpaint_controlnet_conditioning_scale,
- controlnet_pose_inpaint_scheduler,
- controlnet_pose_inpaint_seed_generator,
- ],
- outputs=[output_image],
- )
diff --git a/spaces/Wanwan1215/Louisa/README.md b/spaces/Wanwan1215/Louisa/README.md
deleted file mode 100644
index 3aa668e733c0237c8095572a35651b7e8891ce0c..0000000000000000000000000000000000000000
--- a/spaces/Wanwan1215/Louisa/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Louisa
-emoji: 👁
-colorFrom: purple
-colorTo: green
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Woocy/541GPT/utils.py b/spaces/Woocy/541GPT/utils.py
deleted file mode 100644
index f6e4fa4e8a9f908baa4509d7206ff3455ac57f39..0000000000000000000000000000000000000000
--- a/spaces/Woocy/541GPT/utils.py
+++ /dev/null
@@ -1,386 +0,0 @@
-# -*- coding:utf-8 -*-
-from __future__ import annotations
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
-import logging
-import json
-import os
-import datetime
-import hashlib
-import csv
-import requests
-import re
-
-import gradio as gr
-from pypinyin import lazy_pinyin
-import tiktoken
-import mdtex2html
-from markdown import markdown
-from pygments import highlight
-from pygments.lexers import get_lexer_by_name
-from pygments.formatters import HtmlFormatter
-
-from presets import *
-
-# logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s")
-
-if TYPE_CHECKING:
- from typing import TypedDict
-
- class DataframeData(TypedDict):
- headers: List[str]
- data: List[List[str | int | bool]]
-
-
-def count_token(message):
- encoding = tiktoken.get_encoding("cl100k_base")
- input_str = f"role: {message['role']}, content: {message['content']}"
- length = len(encoding.encode(input_str))
- return length
-
-
-def markdown_to_html_with_syntax_highlight(md_str):
- def replacer(match):
- lang = match.group(1) or "text"
- code = match.group(2)
-
- try:
- lexer = get_lexer_by_name(lang, stripall=True)
- except ValueError:
- lexer = get_lexer_by_name("text", stripall=True)
-
- formatter = HtmlFormatter()
- highlighted_code = highlight(code, lexer, formatter)
-
- return f'<pre><code class="{lang}">{highlighted_code}</code></pre>'
-
- code_block_pattern = r"```(\w+)?\n([\s\S]+?)\n```"
- md_str = re.sub(code_block_pattern, replacer, md_str, flags=re.MULTILINE)
-
- html_str = markdown(md_str)
- return html_str
-
-
-def normalize_markdown(md_text: str) -> str:
- lines = md_text.split("\n")
- normalized_lines = []
- inside_list = False
-
- for i, line in enumerate(lines):
- if re.match(r"^(\d+\.|-|\*|\+)\s", line.strip()):
- if not inside_list and i > 0 and lines[i - 1].strip() != "":
- normalized_lines.append("")
- inside_list = True
- normalized_lines.append(line)
- elif inside_list and line.strip() == "":
- if i < len(lines) - 1 and not re.match(
- r"^(\d+\.|-|\*|\+)\s", lines[i + 1].strip()
- ):
- normalized_lines.append(line)
- continue
- else:
- inside_list = False
- normalized_lines.append(line)
-
- return "\n".join(normalized_lines)
-
-
-def convert_mdtext(md_text):
- code_block_pattern = re.compile(r"```(.*?)(?:```|$)", re.DOTALL)
- inline_code_pattern = re.compile(r"`(.*?)`", re.DOTALL)
- code_blocks = code_block_pattern.findall(md_text)
- non_code_parts = code_block_pattern.split(md_text)[::2]
-
- result = []
- for non_code, code in zip(non_code_parts, code_blocks + [""]):
- if non_code.strip():
- non_code = normalize_markdown(non_code)
- if inline_code_pattern.search(non_code):
- result.append(markdown(non_code, extensions=["tables"]))
- else:
- result.append(mdtex2html.convert(non_code, extensions=["tables"]))
- if code.strip():
- # _, code = detect_language(code) # 暂时去除代码高亮功能，因为在大段代码的情况下会出现问题
- # code = code.replace("\n\n", "\n") # 暂时去除代码中的空行，因为在大段代码的情况下会出现问题
- code = f"```{code}\n\n```"
- code = markdown_to_html_with_syntax_highlight(code)
- result.append(code)
- result = "".join(result)
- return result
-
-
-def detect_language(code):
- if code.startswith("\n"):
- first_line = ""
- else:
- first_line = code.strip().split("\n", 1)[0]
- language = first_line.lower() if first_line else ""
- code_without_language = code[len(first_line) :].lstrip() if first_line else code
- return language, code_without_language
-
-
-def construct_text(role, text):
- return {"role": role, "content": text}
-
-
-def construct_user(text):
- return construct_text("user", text)
-
-
-def construct_system(text):
- return construct_text("system", text)
-
-
-def construct_assistant(text):
- return construct_text("assistant", text)
-
-
-def construct_token_message(token, stream=False):
- return f"Token 计数: {token}"
-
-
-def delete_last_conversation(chatbot, history, previous_token_count):
- if len(chatbot) > 0 and standard_error_msg in chatbot[-1][1]:
- logging.info("由于包含报错信息，只删除chatbot记录")
- chatbot.pop()
- return chatbot, history
- if len(history) > 0:
- logging.info("删除了一组对话历史")
- history.pop()
- history.pop()
- if len(chatbot) > 0:
- logging.info("删除了一组chatbot对话")
- chatbot.pop()
- if len(previous_token_count) > 0:
- logging.info("删除了一组对话的token计数记录")
- previous_token_count.pop()
- return (
- chatbot,
- history,
- previous_token_count,
- construct_token_message(sum(previous_token_count)),
- )
-
-
-def save_file(filename, system, history, chatbot):
- logging.info("保存对话历史中……")
- os.makedirs(HISTORY_DIR, exist_ok=True)
- if filename.endswith(".json"):
- json_s = {"system": system, "history": history, "chatbot": chatbot}
- print(json_s)
- with open(os.path.join(HISTORY_DIR, filename), "w") as f:
- json.dump(json_s, f)
- elif filename.endswith(".md"):
- md_s = f"system: \n- {system} \n"
- for data in history:
- md_s += f"\n{data['role']}: \n- {data['content']} \n"
- with open(os.path.join(HISTORY_DIR, filename), "w", encoding="utf8") as f:
- f.write(md_s)
- logging.info("保存对话历史完毕")
- return os.path.join(HISTORY_DIR, filename)
-
-
-def save_chat_history(filename, system, history, chatbot):
- if filename == "":
- return
- if not filename.endswith(".json"):
- filename += ".json"
- return save_file(filename, system, history, chatbot)
-
-
-def export_markdown(filename, system, history, chatbot):
- if filename == "":
- return
- if not filename.endswith(".md"):
- filename += ".md"
- return save_file(filename, system, history, chatbot)
-
-
-def load_chat_history(filename, system, history, chatbot):
- logging.info("加载对话历史中……")
- if type(filename) != str:
- filename = filename.name
- try:
- with open(os.path.join(HISTORY_DIR, filename), "r") as f:
- json_s = json.load(f)
- try:
- if type(json_s["history"][0]) == str:
- logging.info("历史记录格式为旧版，正在转换……")
- new_history = []
- for index, item in enumerate(json_s["history"]):
- if index % 2 == 0:
- new_history.append(construct_user(item))
- else:
- new_history.append(construct_assistant(item))
- json_s["history"] = new_history
- logging.info(new_history)
- except:
- # 没有对话历史
- pass
- logging.info("加载对话历史完毕")
- return filename, json_s["system"], json_s["history"], json_s["chatbot"]
- except FileNotFoundError:
- logging.info("没有找到对话历史文件，不执行任何操作")
- return filename, system, history, chatbot
-
-
-def sorted_by_pinyin(list):
- return sorted(list, key=lambda char: lazy_pinyin(char)[0][0])
-
-
-def get_file_names(dir, plain=False, filetypes=[".json"]):
- logging.info(f"获取文件名列表，目录为{dir}，文件类型为{filetypes}，是否为纯文本列表{plain}")
- files = []
- try:
- for type in filetypes:
- files += [f for f in os.listdir(dir) if f.endswith(type)]
- except FileNotFoundError:
- files = []
- files = sorted_by_pinyin(files)
- if files == []:
- files = [""]
- if plain:
- return files
- else:
- return gr.Dropdown.update(choices=files)
-
-
-def get_history_names(plain=False):
- logging.info("获取历史记录文件名列表")
- return get_file_names(HISTORY_DIR, plain)
-
-
-def load_template(filename, mode=0):
- logging.info(f"加载模板文件{filename}，模式为{mode}（0为返回字典和下拉菜单，1为返回下拉菜单，2为返回字典）")
- lines = []
- logging.info("Loading template...")
- if filename.endswith(".json"):
- with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as f:
- lines = json.load(f)
- lines = [[i["act"], i["prompt"]] for i in lines]
- else:
- with open(
- os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8"
- ) as csvfile:
- reader = csv.reader(csvfile)
- lines = list(reader)
- lines = lines[1:]
- if mode == 1:
- return sorted_by_pinyin([row[0] for row in lines])
- elif mode == 2:
- return {row[0]: row[1] for row in lines}
- else:
- choices = sorted_by_pinyin([row[0] for row in lines])
- return {row[0]: row[1] for row in lines}, gr.Dropdown.update(
- choices=choices, value=choices[0]
- )
-
-
-def get_template_names(plain=False):
- logging.info("获取模板文件名列表")
- return get_file_names(TEMPLATES_DIR, plain, filetypes=[".csv", "json"])
-
-
-def get_template_content(templates, selection, original_system_prompt):
- logging.info(f"应用模板中，选择为{selection}，原始系统提示为{original_system_prompt}")
- try:
- return templates[selection]
- except:
- return original_system_prompt
-
-
-def reset_state():
- logging.info("重置状态")
- return [], [], [], construct_token_message(0)
-
-
-def reset_textbox():
- return gr.update(value="")
-
-
-def reset_default():
- global API_URL
- API_URL = "https://api.openai.com/v1/chat/completions"
- os.environ.pop("HTTPS_PROXY", None)
- os.environ.pop("https_proxy", None)
- return gr.update(value=API_URL), gr.update(value=""), "API URL 和代理已重置"
-
-
-def change_api_url(url):
- global API_URL
- API_URL = url
- msg = f"API地址更改为了{url}"
- logging.info(msg)
- return msg
-
-
-def change_proxy(proxy):
- os.environ["HTTPS_PROXY"] = proxy
- msg = f"代理更改为了{proxy}"
- logging.info(msg)
- return msg
-
-
-def hide_middle_chars(s):
- if len(s) <= 8:
- return s
- else:
- head = s[:4]
- tail = s[-4:]
- hidden = "*" * (len(s) - 8)
- return head + hidden + tail
-
-
-def submit_key(key):
- key = key.strip()
- msg = f"API密钥更改为了{hide_middle_chars(key)}"
- logging.info(msg)
- return key, msg
-
-
-def sha1sum(filename):
- sha1 = hashlib.sha1()
- sha1.update(filename.encode("utf-8"))
- return sha1.hexdigest()
-
-
-def replace_today(prompt):
- today = datetime.datetime.today().strftime("%Y-%m-%d")
- return prompt.replace("{current_date}", today)
-
-
-def get_geoip():
- response = requests.get("https://ipapi.co/json/", timeout=5)
- try:
- data = response.json()
- except:
- data = {"error": True, "reason": "连接ipapi失败"}
- if "error" in data.keys():
- logging.warning(f"无法获取IP地址信息。\n{data}")
- if data["reason"] == "RateLimited":
- return (
- f"获取IP地理位置失败，因为达到了检测IP的速率限制。聊天功能可能仍然可用，但请注意，如果您的IP地址在不受支持的地区，您可能会遇到问题。"
- )
- else:
- return f"获取IP地理位置失败。原因：{data['reason']}。你仍然可以使用聊天功能。"
- else:
- country = data["country_name"]
- if country == "China":
- text = "**您的IP区域：中国。请立即检查代理设置，在不受支持的地区使用API可能导致账号被封禁。**"
- else:
- text = f"您的IP区域：{country}。"
- logging.info(text)
- return text
-
-
-def find_n(lst, max_num):
- n = len(lst)
- total = sum(lst)
-
- if total < max_num:
- return n
-
- for i in range(len(lst)):
- if total - lst[i] < max_num:
- return n - i -1
- total = total - lst[i]
- return 1
diff --git a/spaces/Wootang01/Punctuation_capitalization_corrector/README.md b/spaces/Wootang01/Punctuation_capitalization_corrector/README.md
deleted file mode 100644
index a1b1b1b7ace299b9842944a19bfaae16fba67bfd..0000000000000000000000000000000000000000
--- a/spaces/Wootang01/Punctuation_capitalization_corrector/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
----
-title: Punctuation_capitalization_corrector
-emoji: 📉
-colorFrom: yellow
-colorTo: gray
-sdk: streamlit
-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`, `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.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/text/models/qrnn.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/text/models/qrnn.py
deleted file mode 100644
index abc9ad5b754b84a4815babbbf85882af2bc892cd..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/text/models/qrnn.py
+++ /dev/null
@@ -1,167 +0,0 @@
-from ...torch_core import *
-from torch.utils.cpp_extension import load
-from torch.autograd import Function
-
-__all__ = ['QRNNLayer', 'QRNN']
-
-import fastai
-if torch.cuda.is_available():
- fastai_path = Path(fastai.__path__[0])/'text'/'models'
- files = ['forget_mult_cuda.cpp', 'forget_mult_cuda_kernel.cu']
- forget_mult_cuda = load(name='forget_mult_cuda', sources=[fastai_path/f for f in files])
- files = ['bwd_forget_mult_cuda.cpp', 'bwd_forget_mult_cuda_kernel.cu']
- bwd_forget_mult_cuda = load(name='bwd_forget_mult_cuda', sources=[fastai_path/f for f in files])
-
-def dispatch_cuda(cuda_class, cpu_func, x):
- return cuda_class.apply if x.device.type == 'cuda' else cpu_func
-
-class ForgetMultGPU(Function):
-
- @staticmethod
- def forward(ctx, x:Tensor, f:Tensor, hidden_init:Optional[Tensor]=None, batch_first:bool=True):
- if batch_first:
- batch_size, seq_size, hidden_size = f.size()
- output = f.new_zeros(batch_size, seq_size + 1, hidden_size)
- if hidden_init is not None: output[:, 0] = hidden_init
- else: output.zero_()
- else:
- seq_size, batch_size, hidden_size = f.size()
- output = f.new(seq_size + 1, batch_size, hidden_size)
- if hidden_init is not None: output[0] = hidden_init
- else: output.zero_()
- output = forget_mult_cuda.forward(x, f, output, batch_first)
- ctx.save_for_backward(x, f, hidden_init, output)
- ctx.batch_first = batch_first
- return output[:,1:] if batch_first else output[1:]
-
- @staticmethod
- def backward(ctx, grad_output):
- x, f, hidden_init, output = ctx.saved_tensors
- grad_x, grad_f, grad_h = forget_mult_cuda.backward(x, f, output, grad_output, ctx.batch_first)
- return (grad_x, grad_f, (None if hidden_init is None else grad_h), None)
-
-class BwdForgetMultGPU(Function):
-
- @staticmethod
- def forward(ctx, x:Tensor, f:Tensor, hidden_init:Optional[Tensor]=None, batch_first:bool=True):
- if batch_first:
- batch_size, seq_size, hidden_size = f.size()
- output = f.new(batch_size, seq_size + 1, hidden_size)
- if hidden_init is not None: output[:, -1] = hidden_init
- else: output.zero_()
- else:
- seq_size, batch_size, hidden_size = f.size()
- output = f.new(seq_size + 1, batch_size, hidden_size)
- if hidden_init is not None: output[-1] = hidden_init
- else: output.zero_()
- output = bwd_forget_mult_cuda.forward(x, f, output, batch_first)
- ctx.save_for_backward(x, f, hidden_init, output)
- ctx.batch_first = batch_first
- return output[:,:-1] if batch_first else output[:-1]
-
- @staticmethod
- def backward(ctx, grad_output:Tensor):
- x, f, hidden_init, output = ctx.saved_tensors
- grad_x, grad_f, grad_h = bwd_forget_mult_cuda.backward(x, f, output, grad_output, ctx.batch_first)
- return (grad_x, grad_f, (None if hidden_init is None else grad_h), None)
-
-def forget_mult_CPU(x:Tensor, f:Tensor, hidden_init:Optional[Tensor]=None, batch_first:bool=True, backward:bool=False):
- result = []
- dim = (1 if batch_first else 0)
- forgets = f.split(1, dim=dim)
- inputs = x.split(1, dim=dim)
- prev_h = None if hidden_init is None else hidden_init.unsqueeze(1 if batch_first else 0)
- idx_range = range(len(inputs)-1,-1,-1) if backward else range(len(inputs))
- for i in idx_range:
- prev_h = inputs[i] * forgets[i] if prev_h is None else inputs[i] * forgets[i] + (1-forgets[i]) * prev_h
- if backward: result.insert(0, prev_h)
- else: result.append(prev_h)
- return torch.cat(result, dim=dim)
-
-class QRNNLayer(Module):
- "Apply a single layer Quasi-Recurrent Neural Network (QRNN) to an input sequence."
-
- def __init__(self, input_size:int, hidden_size:int=None, save_prev_x:bool=False, zoneout:float=0, window:int=1,
- output_gate:bool=True, batch_first:bool=True, backward:bool=False):
- super().__init__()
- assert window in [1, 2], "This QRNN implementation currently only handles convolutional window of size 1 or size 2"
- self.save_prev_x,self.zoneout,self.window = save_prev_x,zoneout,window
- self.output_gate,self.batch_first,self.backward = output_gate,batch_first,backward
- hidden_size = ifnone(hidden_size, input_size)
- #One large matmul with concat is faster than N small matmuls and no concat
- mult = (3 if output_gate else 2)
- self.linear = nn.Linear(window * input_size, mult * hidden_size)
- self.prevX = None
-
- def reset(self):
- # If you are saving the previous value of x, you should call this when starting with a new state
- self.prevX = None
-
- def forward(self, inp, hid=None):
- y = self.linear(self._get_source(inp))
- if self.output_gate: z_gate,f_gate,o_gate = y.chunk(3, dim=2)
- else: z_gate,f_gate = y.chunk(2, dim=2)
- z_gate.tanh_()
- f_gate.sigmoid_()
- if self.zoneout and self.training:
- mask = dropout_mask(f_gate, f_gate.size(), self.zoneout).requires_grad_(False)
- f_gate = f_gate * mask
- z_gate,f_gate = z_gate.contiguous(),f_gate.contiguous()
- if self.backward: forget_mult = dispatch_cuda(BwdForgetMultGPU, partial(forget_mult_CPU, backward=True), inp)
- else: forget_mult = dispatch_cuda(ForgetMultGPU, forget_mult_CPU, inp)
- c_gate = forget_mult(z_gate, f_gate, hid, self.batch_first)
- output = torch.sigmoid(o_gate) * c_gate if self.output_gate else c_gate
- if self.window > 1 and self.save_prev_x:
- if self.backward: self.prevX = (inp[:, :1] if self.batch_first else inp[:1]).detach()
- else: self.prevX = (inp[:, -1:] if self.batch_first else inp[-1:]).detach()
- idx = 0 if self.backward else -1
- return output, (c_gate[:, idx] if self.batch_first else c_gate[idx])
-
- def _get_source(self, inp):
- if self.window == 1: return inp
- dim = (1 if self.batch_first else 0)
- inp_shift = [torch.zeros_like(inp[:,:1] if self.batch_first else inp[:1]) if self.prevX is None else self.prevX]
- if self.backward: inp_shift.insert(0,inp[:,1:] if self.batch_first else inp[1:])
- else: inp_shift.append(inp[:,:-1] if self.batch_first else inp[:-1])
- inp_shift = torch.cat(inp_shift, dim)
- return torch.cat([inp, inp_shift], 2)
-
-class QRNN(Module):
- "Apply a multiple layer Quasi-Recurrent Neural Network (QRNN) to an input sequence."
-
- def __init__(self, input_size:int, hidden_size:int, n_layers:int=1, bias:bool=True, batch_first:bool=True,
- dropout:float=0, bidirectional:bool=False, save_prev_x:bool=False, zoneout:float=0, window:int=None,
- output_gate:bool=True):
- assert not (save_prev_x and bidirectional), "Can't save the previous X with bidirectional."
- assert bias == True, 'Removing underlying bias is not yet supported'
- super().__init__()
- kwargs = dict(batch_first=batch_first, zoneout=zoneout, output_gate=output_gate)
- self.layers = nn.ModuleList([QRNNLayer(input_size if l == 0 else hidden_size, hidden_size, save_prev_x=save_prev_x,
- window=((2 if l ==0 else 1) if window is None else window), **kwargs)
- for l in range(n_layers)])
- if bidirectional:
- self.layers_bwd = nn.ModuleList([QRNNLayer(input_size if l == 0 else hidden_size, hidden_size,
- backward=True, window=((2 if l ==0 else 1) if window is None else window),
- **kwargs) for l in range(n_layers)])
- self.n_layers,self.batch_first,self.dropout,self.bidirectional = n_layers,batch_first,dropout,bidirectional
-
- def reset(self):
- "If your convolutional window is greater than 1 and you save previous xs, you must reset at the beginning of each new sequence."
- for layer in self.layers: layer.reset()
- if self.bidirectional:
- for layer in self.layers_bwd: layer.reset()
-
- def forward(self, inp, hid=None):
- new_hid = []
- if self.bidirectional: inp_bwd = inp.clone()
- for i, layer in enumerate(self.layers):
- inp, h = layer(inp, None if hid is None else hid[2*i if self.bidirectional else i])
- new_hid.append(h)
- if self.bidirectional:
- inp_bwd, h_bwd = self.layers_bwd[i](inp_bwd, None if hid is None else hid[2*i+1])
- new_hid.append(h_bwd)
- if self.dropout != 0 and i < len(self.layers) - 1:
- for o in ([inp, inp_bwd] if self.bidirectional else [inp]):
- o = F.dropout(o, p=self.dropout, training=self.training, inplace=False)
- if self.bidirectional: inp = torch.cat([inp, inp_bwd], dim=2)
- return inp, torch.stack(new_hid, 0)
\ No newline at end of file
diff --git a/spaces/XiangJinYu/Chat_PDF/README.md b/spaces/XiangJinYu/Chat_PDF/README.md
deleted file mode 100644
index d950a60d49069a89dff58c00d5c6f34a19cb4282..0000000000000000000000000000000000000000
--- a/spaces/XiangJinYu/Chat_PDF/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: "Chat PDF"
-emoji: 📄
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.28.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/README.md b/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/README.md
deleted file mode 100644
index 1b24e6efdb04cb1460e4fe3257d2303677c5a0e1..0000000000000000000000000000000000000000
--- a/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Multilingual Anime TTS
-emoji: 🎙🐴
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.7
-app_file: app.py
-pinned: false
-duplicated_from: Plachta/VITS-Umamusume-voice-synthesizer
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/XzJosh/Azusa-Bert-VITS2/text/english.py b/spaces/XzJosh/Azusa-Bert-VITS2/text/english.py
deleted file mode 100644
index 781d0a56cef71f66fc67db51d76538be90d3ddd2..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Azusa-Bert-VITS2/text/english.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import pickle
-import os
-import re
-from g2p_en import G2p
-from string import punctuation
-
-from text import symbols
-
-current_file_path = os.path.dirname(__file__)
-CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep')
-CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle')
-_g2p = G2p()
-
-arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'}
-
-
-def post_replace_ph(ph):
- rep_map = {
- '：': ',',
- '；': ',',
- '，': ',',
- '。': '.',
- '！': '!',
- '？': '?',
- '\n': '.',
- "·": ",",
- '、': ",",
- '...': '…',
- 'v': "V"
- }
- if ph in rep_map.keys():
- ph = rep_map[ph]
- if ph in symbols:
- return ph
- if ph not in symbols:
- ph = 'UNK'
- return ph
-
-def read_dict():
- g2p_dict = {}
- start_line = 49
- with open(CMU_DICT_PATH) as f:
- line = f.readline()
- line_index = 1
- while line:
- if line_index >= start_line:
- line = line.strip()
- word_split = line.split(' ')
- word = word_split[0]
-
- syllable_split = word_split[1].split(' - ')
- g2p_dict[word] = []
- for syllable in syllable_split:
- phone_split = syllable.split(' ')
- g2p_dict[word].append(phone_split)
-
- line_index = line_index + 1
- line = f.readline()
-
- return g2p_dict
-
-
-def cache_dict(g2p_dict, file_path):
- with open(file_path, 'wb') as pickle_file:
- pickle.dump(g2p_dict, pickle_file)
-
-
-def get_dict():
- if os.path.exists(CACHE_PATH):
- with open(CACHE_PATH, 'rb') as pickle_file:
- g2p_dict = pickle.load(pickle_file)
- else:
- g2p_dict = read_dict()
- cache_dict(g2p_dict, CACHE_PATH)
-
- return g2p_dict
-
-eng_dict = get_dict()
-
-def refine_ph(phn):
- tone = 0
- if re.search(r'\d$', phn):
- tone = int(phn[-1]) + 1
- phn = phn[:-1]
- return phn.lower(), tone
-
-def refine_syllables(syllables):
- tones = []
- phonemes = []
- for phn_list in syllables:
- for i in range(len(phn_list)):
- phn = phn_list[i]
- phn, tone = refine_ph(phn)
- phonemes.append(phn)
- tones.append(tone)
- return phonemes, tones
-
-
-def text_normalize(text):
- # todo: eng text normalize
- return text
-
-def g2p(text):
-
- phones = []
- tones = []
- words = re.split(r"([,;.\-\?\!\s+])", text)
- for w in words:
- if w.upper() in eng_dict:
- phns, tns = refine_syllables(eng_dict[w.upper()])
- phones += phns
- tones += tns
- else:
- phone_list = list(filter(lambda p: p != " ", _g2p(w)))
- for ph in phone_list:
- if ph in arpa:
- ph, tn = refine_ph(ph)
- phones.append(ph)
- tones.append(tn)
- else:
- phones.append(ph)
- tones.append(0)
- # todo: implement word2ph
- word2ph = [1 for i in phones]
-
- phones = [post_replace_ph(i) for i in phones]
- return phones, tones, word2ph
-
-if __name__ == "__main__":
- # print(get_dict())
- # print(eng_word_to_phoneme("hello"))
- print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
- # all_phones = set()
- # for k, syllables in eng_dict.items():
- # for group in syllables:
- # for ph in group:
- # all_phones.add(ph)
- # print(all_phones)
\ No newline at end of file
diff --git a/spaces/XzJosh/Bekki-Bert-VITS2/data_utils.py b/spaces/XzJosh/Bekki-Bert-VITS2/data_utils.py
deleted file mode 100644
index be3a29a93188c5b3386f22e5db29e5e96d78109a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Bekki-Bert-VITS2/data_utils.py
+++ /dev/null
@@ -1,321 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-import commons
-from mel_processing import spectrogram_torch, mel_spectrogram_torch, spec_to_mel_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import cleaned_text_to_sequence, get_bert
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, speaker_id, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
-
- def __init__(self, audiopaths_sid_text, hparams):
- self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
- self.max_wav_value = hparams.max_wav_value
- self.sampling_rate = hparams.sampling_rate
- self.filter_length = hparams.filter_length
- self.hop_length = hparams.hop_length
- self.win_length = hparams.win_length
- self.sampling_rate = hparams.sampling_rate
- self.spk_map = hparams.spk2id
- self.hparams = hparams
-
- self.use_mel_spec_posterior = getattr(hparams, "use_mel_posterior_encoder", False)
- if self.use_mel_spec_posterior:
- self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
-
- self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
- self.add_blank = hparams.add_blank
- self.min_text_len = getattr(hparams, "min_text_len", 1)
- self.max_text_len = getattr(hparams, "max_text_len", 300)
-
- random.seed(1234)
- random.shuffle(self.audiopaths_sid_text)
- self._filter()
-
- def _filter(self):
- """
- Filter text & store spec lengths
- """
- # Store spectrogram lengths for Bucketing
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
- # spec_length = wav_length // hop_length
-
- audiopaths_sid_text_new = []
- lengths = []
- skipped = 0
- for _id, spk, language, text, phones, tone, word2ph in self.audiopaths_sid_text:
- audiopath = f'{_id}'
- if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
- phones = phones.split(" ")
- tone = [int(i) for i in tone.split(" ")]
- word2ph = [int(i) for i in word2ph.split(" ")]
- audiopaths_sid_text_new.append([audiopath, spk, language, text, phones, tone, word2ph])
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
- else:
- skipped += 1
- print("skipped: ", skipped, ", total: ", len(self.audiopaths_sid_text))
- self.audiopaths_sid_text = audiopaths_sid_text_new
- self.lengths = lengths
-
- def get_audio_text_speaker_pair(self, audiopath_sid_text):
- # separate filename, speaker_id and text
- audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
-
- bert, phones, tone, language = self.get_text(text, word2ph, phones, tone, language, audiopath)
-
- spec, wav = self.get_audio(audiopath)
- sid = torch.LongTensor([int(self.spk_map[sid])])
- return (phones, spec, wav, sid, tone, language, bert)
-
- def get_audio(self, filename):
- audio, sampling_rate = load_wav_to_torch(filename)
- if sampling_rate != self.sampling_rate:
- raise ValueError("{} {} SR doesn't match target {} SR".format(
- sampling_rate, self.sampling_rate))
- audio_norm = audio / self.max_wav_value
- audio_norm = audio_norm.unsqueeze(0)
- spec_filename = filename.replace(".wav", ".spec.pt")
- if self.use_mel_spec_posterior:
- spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
- try:
- spec = torch.load(spec_filename)
- except:
- if self.use_mel_spec_posterior:
- spec = mel_spectrogram_torch(audio_norm, self.filter_length,
- self.n_mel_channels, self.sampling_rate, self.hop_length,
- self.win_length, self.hparams.mel_fmin, self.hparams.mel_fmax, center=False)
- else:
- spec = spectrogram_torch(audio_norm, self.filter_length,
- self.sampling_rate, self.hop_length, self.win_length,
- center=False)
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename)
- return spec, audio_norm
-
- def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
- pold = phone
- w2pho = [i for i in word2ph]
- word2ph = [i for i in word2ph]
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
- pold2 = phone
-
- if self.add_blank:
- p1 = len(phone)
- phone = commons.intersperse(phone, 0)
- p2 = len(phone)
- t1 = len(tone)
- tone = commons.intersperse(tone, 0)
- t2 = len(tone)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert_path = wav_path.replace(".wav", ".bert.pt")
- try:
- bert = torch.load(bert_path)
- assert bert.shape[-1] == len(phone)
- except:
- bert = get_bert(text, word2ph, language_str)
- torch.save(bert, bert_path)
- #print(bert.shape[-1], bert_path, text, pold)
- assert bert.shape[-1] == len(phone)
-
- assert bert.shape[-1] == len(phone), (
- bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho)
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
- return bert, phone, tone, language
-
- def get_sid(self, sid):
- sid = torch.LongTensor([int(sid)])
- return sid
-
- def __getitem__(self, index):
- return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
- def __len__(self):
- return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
- """ Zero-pads model inputs and targets
- """
-
- def __init__(self, return_ids=False):
- self.return_ids = return_ids
-
- def __call__(self, batch):
- """Collate's training batch from normalized text, audio and speaker identities
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized, sid]
- """
- # Right zero-pad all one-hot text sequences to max input length
- _, ids_sorted_decreasing = torch.sort(
- torch.LongTensor([x[1].size(1) for x in batch]),
- dim=0, descending=True)
-
- max_text_len = max([len(x[0]) for x in batch])
- max_spec_len = max([x[1].size(1) for x in batch])
- max_wav_len = max([x[2].size(1) for x in batch])
-
- text_lengths = torch.LongTensor(len(batch))
- spec_lengths = torch.LongTensor(len(batch))
- wav_lengths = torch.LongTensor(len(batch))
- sid = torch.LongTensor(len(batch))
-
- text_padded = torch.LongTensor(len(batch), max_text_len)
- tone_padded = torch.LongTensor(len(batch), max_text_len)
- language_padded = torch.LongTensor(len(batch), max_text_len)
- bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
-
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
- text_padded.zero_()
- tone_padded.zero_()
- language_padded.zero_()
- spec_padded.zero_()
- wav_padded.zero_()
- bert_padded.zero_()
- for i in range(len(ids_sorted_decreasing)):
- row = batch[ids_sorted_decreasing[i]]
-
- text = row[0]
- text_padded[i, :text.size(0)] = text
- text_lengths[i] = text.size(0)
-
- spec = row[1]
- spec_padded[i, :, :spec.size(1)] = spec
- spec_lengths[i] = spec.size(1)
-
- wav = row[2]
- wav_padded[i, :, :wav.size(1)] = wav
- wav_lengths[i] = wav.size(1)
-
- sid[i] = row[3]
-
- tone = row[4]
- tone_padded[i, :tone.size(0)] = tone
-
- language = row[5]
- language_padded[i, :language.size(0)] = language
-
- bert = row[6]
- bert_padded[i, :, :bert.size(1)] = bert
-
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, tone_padded, language_padded, bert_padded
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
- """
- Maintain similar input lengths in a batch.
- Length groups are specified by boundaries.
- Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
- It removes samples which are not included in the boundaries.
- Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
- """
-
- def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
- super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
- self.lengths = dataset.lengths
- self.batch_size = batch_size
- self.boundaries = boundaries
-
- self.buckets, self.num_samples_per_bucket = self._create_buckets()
- self.total_size = sum(self.num_samples_per_bucket)
- self.num_samples = self.total_size // self.num_replicas
-
- def _create_buckets(self):
- buckets = [[] for _ in range(len(self.boundaries) - 1)]
- for i in range(len(self.lengths)):
- length = self.lengths[i]
- idx_bucket = self._bisect(length)
- if idx_bucket != -1:
- buckets[idx_bucket].append(i)
-
- for i in range(len(buckets) - 1, 0, -1):
- if len(buckets[i]) == 0:
- buckets.pop(i)
- self.boundaries.pop(i + 1)
-
- num_samples_per_bucket = []
- for i in range(len(buckets)):
- len_bucket = len(buckets[i])
- total_batch_size = self.num_replicas * self.batch_size
- rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
- num_samples_per_bucket.append(len_bucket + rem)
- return buckets, num_samples_per_bucket
-
- def __iter__(self):
- # deterministically shuffle based on epoch
- g = torch.Generator()
- g.manual_seed(self.epoch)
-
- indices = []
- if self.shuffle:
- for bucket in self.buckets:
- indices.append(torch.randperm(len(bucket), generator=g).tolist())
- else:
- for bucket in self.buckets:
- indices.append(list(range(len(bucket))))
-
- batches = []
- for i in range(len(self.buckets)):
- bucket = self.buckets[i]
- len_bucket = len(bucket)
- if (len_bucket == 0):
- continue
- ids_bucket = indices[i]
- num_samples_bucket = self.num_samples_per_bucket[i]
-
- # add extra samples to make it evenly divisible
- rem = num_samples_bucket - len_bucket
- ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-
- # subsample
- ids_bucket = ids_bucket[self.rank::self.num_replicas]
-
- # batching
- for j in range(len(ids_bucket) // self.batch_size):
- batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]]
- batches.append(batch)
-
- if self.shuffle:
- batch_ids = torch.randperm(len(batches), generator=g).tolist()
- batches = [batches[i] for i in batch_ids]
- self.batches = batches
-
- assert len(self.batches) * self.batch_size == self.num_samples
- return iter(self.batches)
-
- def _bisect(self, x, lo=0, hi=None):
- if hi is None:
- hi = len(self.boundaries) - 1
-
- if hi > lo:
- mid = (hi + lo) // 2
- if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
- return mid
- elif x <= self.boundaries[mid]:
- return self._bisect(x, lo, mid)
- else:
- return self._bisect(x, mid + 1, hi)
- else:
- return -1
-
- def __len__(self):
- return self.num_samples // self.batch_size
diff --git a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/Waifu2x/model_check_points/CRAN_V2/ReadME.md b/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/Waifu2x/model_check_points/CRAN_V2/ReadME.md
deleted file mode 100644
index e4d8946f40420f35c109506dfc14f67bfb1f3eab..0000000000000000000000000000000000000000
--- a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/Waifu2x/model_check_points/CRAN_V2/ReadME.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Model Specifications
-
-
-```python
-model_cran_v2 = CARN_V2(color_channels=3, mid_channels=64, conv=nn.Conv2d,
- single_conv_size=3, single_conv_group=1,
- scale=2, activation=nn.LeakyReLU(0.1),
- SEBlock=True, repeat_blocks=3, atrous=(1, 1, 1))
-
-model_cran_v2 = network_to_half(model_cran_v2)
-checkpoint = "CARN_model_checkpoint.pt"
-model_cran_v2.load_state_dict(torch.load(checkpoint, 'cpu'))
-model_cran_v2 = model_cran_v2.float() # if use cpu
-
-````
-
-To use pre-trained model for training
-
-```python
-
-model = CARN_V2(color_channels=3, mid_channels=64, conv=nn.Conv2d,
- single_conv_size=3, single_conv_group=1,
- scale=2, activation=nn.LeakyReLU(0.1),
- SEBlock=True, repeat_blocks=3, atrous=(1, 1, 1))
-
-model = network_to_half(model)
-model = model.cuda()
-model.load_state_dict(torch.load("CARN_model_checkpoint.pt"))
-
-learning_rate = 1e-4
-weight_decay = 1e-6
-optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay, amsgrad=True)
-optimizer = FP16_Optimizer(optimizer, static_loss_scale=128.0, verbose=False)
-optimizer.load_state_dict(torch.load("CARN_adam_checkpoint.pt"))
-
-last_iter = torch.load("CARN_scheduler_last_iter") # -1 if start from new
-scheduler = CyclicLR(optimizer.optimizer, base_lr=1e-4, max_lr=4e-4,
- step_size=3 * total_batch, mode="triangular",
- last_batch_iteration=last_iter)
-
-```
\ No newline at end of file
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/layers/csrc/README.md b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/layers/csrc/README.md
deleted file mode 100644
index 778ed3da0bae89820831bcd8a72ff7b9cad8d4dd..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/detectron2/layers/csrc/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
-To add a new Op:
-
-1. Create a new directory
-2. Implement new ops there
-3. Delcare its Python interface in `vision.cpp`.
diff --git a/spaces/ZJunTvT/ZJunChat/run_Linux.sh b/spaces/ZJunTvT/ZJunChat/run_Linux.sh
deleted file mode 100644
index 2d26597ae47519f42336ccffc16646713a192ae1..0000000000000000000000000000000000000000
--- a/spaces/ZJunTvT/ZJunChat/run_Linux.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$(readlink -f "$0")")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir" || exit
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
- # 如果有更新，关闭当前运行的服务器
- pkill -f ChuanhuChatbot.py
-
- # 拉取最新更改
- git pull
-
- # 安装依赖
- pip3 install -r requirements.txt
-
- # 重新启动服务器
- nohup python3 ChuanhuChatbot.py &
-fi
-
-# 检查ChuanhuChatbot.py是否在运行
-if ! pgrep -f ChuanhuChatbot.py > /dev/null; then
- # 如果没有运行，启动服务器
- nohup python3 ChuanhuChatbot.py &
-fi
diff --git a/spaces/Zeelubha/Football-Prediction/app.py b/spaces/Zeelubha/Football-Prediction/app.py
deleted file mode 100644
index 48f9f1ff00be72161e4221b7367e737de57eb744..0000000000000000000000000000000000000000
--- a/spaces/Zeelubha/Football-Prediction/app.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import gradio as gr
-import pandas as pd
-import random
-from keras.models import load_model
-import numpy as np
-
-data = pd.read_pickle("merged_all_table.pkl", compression='bz2')
-
-home_team_id = sorted(data["home_team_long_name"].unique())
-away_team_id = sorted(data["away_team_long_name"].unique())
-
-nn_model = load_model('models/nn_model.h5')
-
-
-def main_process(model, Home_team, Away_team):
-
- home_temp = data[data["home_team_long_name"] == Home_team]
- home_temp = home_temp[["home_team_overall_score", "home_total_goal", "home_players_avg_overall_rating", "home_players_avg_overall_score", "home_players_avg_ideal_body_rate", "home_total_win", "home_total_loose", "home_total_draw", "league_home_total_win", "league_home_total_loose", "league_home_total_draw"]]
- print("Home Team Data Geathring ✅")
-
- away_temp = data[data["away_team_long_name"] == Away_team]
- away_temp = away_temp[["away_team_overall_score", "away_total_goal", "away_players_avg_overall_rating", "away_players_avg_overall_score", "away_players_avg_ideal_body_rate", "away_total_win", "away_total_loose", "away_total_draw", "league_away_total_win", "league_away_total_loose", "league_away_total_draw"]]
- print("Away Team Data Geathring ✅")
-
- table = pd.concat([home_temp.mean(), away_temp.mean()], axis=0)
- table = table[["home_team_overall_score", "away_team_overall_score", "home_total_goal", "away_total_goal", "home_players_avg_overall_rating", "home_players_avg_overall_score", "home_players_avg_ideal_body_rate", "away_players_avg_overall_rating", "away_players_avg_overall_score", "away_players_avg_ideal_body_rate", "home_total_win", "home_total_loose", "home_total_draw", "away_total_win", "away_total_loose", "away_total_draw", "league_home_total_win", "league_home_total_loose", "league_home_total_draw", "league_away_total_win", "league_away_total_loose", "league_away_total_draw"]]
- print("Table Concatination ✅")
-
- X = table.to_frame().T
-
- pred = model.predict(X)
- predicted_labels = np.argmax(pred)
- print("Data Prediction ✅")
-
- print(predicted_labels)
-
- return predicted_labels
-
-
-def predict(Home_team, Away_team, Model_name):
-
- if Home_team == "":
- raise gr.Error("Home Team is required, Please Select The Home Team!")
-
- if Away_team == "":
- raise gr.Error("Away Team is required, Please Select The Away Team!")
-
- if Model_name == "":
- raise gr.Error("Model is required, Please Select The Model!")
-
- if Model_name == "Simple Nueral Network Model":
- model = nn_model
-
- prediction = main_process(model, Home_team, Away_team)
-
- if prediction == 0:
- return "🥳 Home Team Win 🎉"
-
- if prediction == 1:
- return "🥳 Away Team Win 🎉"
-
- if prediction == 2:
- return "😑 Match Draw 😑"
-
-
-# markup table for markdown
-# # Members:
-# | Students Name | Student ID |
-# | :--- | :----: |
-# | Zeel Karshanbhai Sheladiya | 500209119 |
-# | Ravikumar Chandrakantbhai Patel | 500196861 |
-# | Dharma Teja Reddy Bandreddi | 500209454 |
-# | Sai Charan Reddy Meda | 500201602 |
-# | Aditya Babu | 500209122 |
-# | Sudip Bhattarai | 500198055 |
-# | NOMAN FAZAL MUKADAM | 500209115 |
-# | Leela Prasad Kavuri | 500209550 |
-# | Vamsi Dasari | 500200775 |
-
-with gr.Blocks() as demo:
- gr.Markdown("""
- [](https://github.com/ravi7522/Football-Prediction)
- """)
- with gr.Row():
- gr.Label("⚽️ Football Prediction ⚽️", container=False)
-
- with gr.Row():
- with gr.Column():
-
- dd_home_team = gr.Dropdown(
- label="Home Team",
- choices=home_team_id,
- info="Select Your Home Team:",
- multiselect=False,
- )
-
- with gr.Column():
-
- dd_away_team = gr.Dropdown(
- label="Away Team",
- choices=away_team_id,
- info="Select Your Away Team:",
- multiselect=False,
- )
-
- with gr.Row():
-
- with gr.Column():
-
- dd_model = gr.Dropdown(
- label="Model ( Feature Under Construction 🚧 )",
- choices=["Simple Nueral Network Model"],
- info="Select Your Model:",
- multiselect=False,
- )
-
- with gr.Row():
- predict_btn = gr.Button(value="Predict")
-
- with gr.Row():
- Answer = gr.Label("👋 Hello, Let us predict the Football Match 💁♂️", container=False)
-
- predict_btn.click(
- predict,
- inputs=[
- dd_home_team,
- dd_away_team,
- dd_model,
- ],
- outputs=[Answer],
- )
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/abdvl/datahub_qa_bot/docs/saas.md b/spaces/abdvl/datahub_qa_bot/docs/saas.md
deleted file mode 100644
index 00c45af528a42d2ccd2c30ee59a6df9f17301a49..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/saas.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# DataHub SaaS
-
-Sign up for fully managed, hassle-free and secure SaaS service for DataHub, provided by [Acryl Data](https://www.acryl.io/).
-
-<p>
-<a
- className="button button--primary button--lg"
- href="https://www.acryldata.io/datahub-beta"
- target="_blank" >
- Sign up
-</a>
-</p>
diff --git a/spaces/aditi2222/gradio_t5/app.py b/spaces/aditi2222/gradio_t5/app.py
deleted file mode 100644
index bc5ce57487a7b517ab8499298f148e4ed7a61b51..0000000000000000000000000000000000000000
--- a/spaces/aditi2222/gradio_t5/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import torch
-from transformers import (T5ForConditionalGeneration,T5Tokenizer)
-import gradio as gr
-
-best_model_path = "aditi2222/t5-paraphrase"
-model = T5ForConditionalGeneration.from_pretrained(best_model_path)
-tokenizer = T5Tokenizer.from_pretrained("aditi2222/t5-paraphrase")
-
-def tokenize_data(text):
- # Tokenize the review body
- input_ = "paraphrase: "+ str(text) + ' </s>'
- max_len = 64
- # tokenize inputs
- tokenized_inputs = tokenizer(input_, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='pt')
-
- inputs={"input_ids": tokenized_inputs['input_ids'],
- "attention_mask": tokenized_inputs['attention_mask']}
- return inputs
-
-def generate_answers(text):
- inputs = tokenize_data(text)
- results= model.generate(input_ids= inputs['input_ids'], attention_mask=inputs['attention_mask'], do_sample=True,
- max_length=64,
- top_k=120,
- top_p=0.98,
- early_stopping=True,
- num_return_sequences=1)
- answer = tokenizer.decode(results[0], skip_special_tokens=True)
- return answer
-
-#iface = gr.Interface(fn=generate_answers, inputs=['text'], outputs=["text"])
-#iface.launch(inline=False, share=True)
-
-iface = gr.Interface(fn=generate_answers, inputs=[gr.inputs.Textbox(lines=30)],outputs=["text"])
-#iface = gr.Interface(fn=generate_answers, inputs=[gr.inputs.Textbox(lines=30)],outputs=#[gr.outputs.Textbox(lines=15)])
-iface.launch(inline=False, share=True)
\ No newline at end of file
diff --git a/spaces/ai-maker-space/Barbie-RAQA-Application-Chainlit-Demo/app.py b/spaces/ai-maker-space/Barbie-RAQA-Application-Chainlit-Demo/app.py
deleted file mode 100644
index f43932ed3213661b4e61c3e6c1da0b76fdf6e94a..0000000000000000000000000000000000000000
--- a/spaces/ai-maker-space/Barbie-RAQA-Application-Chainlit-Demo/app.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import chainlit as cl
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.document_loaders.csv_loader import CSVLoader
-from langchain.embeddings import CacheBackedEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain.chains import RetrievalQA
-from langchain.chat_models import ChatOpenAI
-from langchain.storage import LocalFileStore
-from langchain.prompts.chat import (
- ChatPromptTemplate,
- SystemMessagePromptTemplate,
- HumanMessagePromptTemplate,
-)
-import chainlit as cl
-
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-
-system_template = """
-Use the following pieces of context to answer the user's question.
-Please respond as if you were Ken from the movie Barbie. Ken is a well-meaning but naive character who loves to Beach. He talks like a typical Californian Beach Bro, but he doesn't use the word "Dude" so much.
-If you don't know the answer, just say that you don't know, don't try to make up an answer.
-You can make inferences based on the context as long as it still faithfully represents the feedback.
-
-Example of your response should be:
-
-```
-The answer is foo
-```
-
-Begin!
-----------------
-{context}"""
-
-messages = [
- SystemMessagePromptTemplate.from_template(system_template),
- HumanMessagePromptTemplate.from_template("{question}"),
-]
-prompt = ChatPromptTemplate(messages=messages)
-chain_type_kwargs = {"prompt": prompt}
-
-@cl.author_rename
-def rename(orig_author: str):
- rename_dict = {"RetrievalQA": "Consulting The Kens"}
- return rename_dict.get(orig_author, orig_author)
-
-@cl.on_chat_start
-async def init():
- msg = cl.Message(content=f"Building Index...")
- await msg.send()
-
- # build FAISS index from csv
- loader = CSVLoader(file_path="./data/barbie.csv", source_column="Review_Url")
- data = loader.load()
- documents = text_splitter.transform_documents(data)
- store = LocalFileStore("./cache/")
- core_embeddings_model = OpenAIEmbeddings()
- embedder = CacheBackedEmbeddings.from_bytes_store(
- core_embeddings_model, store, namespace=core_embeddings_model.model
- )
- # make async docsearch
- docsearch = await cl.make_async(FAISS.from_documents)(documents, embedder)
-
- chain = RetrievalQA.from_chain_type(
- ChatOpenAI(model="gpt-4", temperature=0, streaming=True),
- chain_type="stuff",
- return_source_documents=True,
- retriever=docsearch.as_retriever(),
- chain_type_kwargs = {"prompt": prompt}
- )
-
- msg.content = f"Index built!"
- await msg.send()
-
- cl.user_session.set("chain", chain)
-
-
-@cl.on_message
-async def main(message):
- chain = cl.user_session.get("chain")
- cb = cl.AsyncLangchainCallbackHandler(
- stream_final_answer=False, answer_prefix_tokens=["FINAL", "ANSWER"]
- )
- cb.answer_reached = True
- res = await chain.acall(message, callbacks=[cb], )
-
- answer = res["result"]
- source_elements = []
- visited_sources = set()
-
- # Get the documents from the user session
- docs = res["source_documents"]
- metadatas = [doc.metadata for doc in docs]
- all_sources = [m["source"] for m in metadatas]
-
- for source in all_sources:
- if source in visited_sources:
- continue
- visited_sources.add(source)
- # Create the text element referenced in the message
- source_elements.append(
- cl.Text(content="https://www.imdb.com" + source, name="Review URL")
- )
-
- if source_elements:
- answer += f"\nSources: {', '.join([e.content.decode('utf-8') for e in source_elements])}"
- else:
- answer += "\nNo sources found"
-
- await cl.Message(content=answer, elements=source_elements).send()
diff --git a/spaces/akhaliq/deeplab2/model/decoder/deeplabv3plus_test.py b/spaces/akhaliq/deeplab2/model/decoder/deeplabv3plus_test.py
deleted file mode 100644
index 1419b55acc0a5973e414ca7a12d2716d0f838b57..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/model/decoder/deeplabv3plus_test.py
+++ /dev/null
@@ -1,169 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Tests for deeplabv3plus."""
-
-import numpy as np
-import tensorflow as tf
-
-from deeplab2 import common
-from deeplab2 import config_pb2
-from deeplab2.model.decoder import deeplabv3plus
-from deeplab2.utils import test_utils
-
-
-def _create_deeplabv3plus_model(high_level_feature_name, low_level_feature_name,
- low_level_channels_project,
- aspp_output_channels, decoder_output_channels,
- atrous_rates, num_classes, **kwargs):
- decoder_options = config_pb2.DecoderOptions(
- feature_key=high_level_feature_name,
- decoder_channels=decoder_output_channels,
- aspp_channels=aspp_output_channels,
- atrous_rates=atrous_rates)
- deeplabv3plus_options = config_pb2.ModelOptions.DeeplabV3PlusOptions(
- low_level=config_pb2.LowLevelOptions(
- feature_key=low_level_feature_name,
- channels_project=low_level_channels_project),
- num_classes=num_classes)
- return deeplabv3plus.DeepLabV3Plus(decoder_options, deeplabv3plus_options,
- **kwargs)
-
-
-class Deeplabv3PlusTest(tf.test.TestCase):
-
- def test_deeplabv3plus_feature_key_not_present(self):
- deeplabv3plus_decoder = _create_deeplabv3plus_model(
- high_level_feature_name='not_in_features_dict',
- low_level_feature_name='in_feature_dict',
- low_level_channels_project=128,
- aspp_output_channels=64,
- decoder_output_channels=64,
- atrous_rates=[6, 12, 18],
- num_classes=80)
- input_dict = dict()
- input_dict['in_feature_dict'] = tf.random.uniform(shape=(2, 65, 65, 32))
-
- with self.assertRaises(KeyError):
- _ = deeplabv3plus_decoder(input_dict)
-
- def test_deeplabv3plus_output_shape(self):
- list_of_num_classes = [2, 19, 133]
- for num_classes in list_of_num_classes:
- deeplabv3plus_decoder = _create_deeplabv3plus_model(
- high_level_feature_name='high',
- low_level_feature_name='low',
- low_level_channels_project=128,
- aspp_output_channels=64,
- decoder_output_channels=128,
- atrous_rates=[6, 12, 18],
- num_classes=num_classes)
- input_dict = dict()
- input_dict['high'] = tf.random.uniform(shape=(2, 65, 65, 32))
- input_dict['low'] = tf.random.uniform(shape=(2, 129, 129, 16))
- expected_shape = [2, 129, 129, num_classes]
-
- logit_tensor = deeplabv3plus_decoder(input_dict)
- self.assertListEqual(
- logit_tensor[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(),
- expected_shape)
-
- def test_deeplabv3plus_feature_extraction_consistency(self):
- deeplabv3plus_decoder = _create_deeplabv3plus_model(
- high_level_feature_name='high',
- low_level_feature_name='low',
- low_level_channels_project=128,
- aspp_output_channels=96,
- decoder_output_channels=64,
- atrous_rates=[6, 12, 18],
- num_classes=80)
- input_dict = dict()
- input_dict['high'] = tf.random.uniform(shape=(2, 65, 65, 32))
- input_dict['low'] = tf.random.uniform(shape=(2, 129, 129, 16))
-
- reference_logits_tensor = deeplabv3plus_decoder(
- input_dict, training=False)
- logits_tensor_to_compare = deeplabv3plus_decoder(input_dict, training=False)
-
- np.testing.assert_equal(
- reference_logits_tensor[common.PRED_SEMANTIC_LOGITS_KEY].numpy(),
- logits_tensor_to_compare[common.PRED_SEMANTIC_LOGITS_KEY].numpy())
-
- def test_deeplabv3plus_pool_size_setter(self):
- deeplabv3plus_decoder = _create_deeplabv3plus_model(
- high_level_feature_name='high',
- low_level_feature_name='low',
- low_level_channels_project=128,
- aspp_output_channels=96,
- decoder_output_channels=64,
- atrous_rates=[6, 12, 18],
- num_classes=80)
- pool_size = (10, 10)
- deeplabv3plus_decoder.set_pool_size(pool_size)
-
- self.assertTupleEqual(deeplabv3plus_decoder._aspp._aspp_pool._pool_size,
- pool_size)
-
- @test_utils.test_all_strategies
- def test_deeplabv3plus_sync_bn(self, strategy):
- input_dict = dict()
- input_dict['high'] = tf.random.uniform(shape=(2, 65, 65, 32))
- input_dict['low'] = tf.random.uniform(shape=(2, 129, 129, 16))
- with strategy.scope():
- for bn_layer in test_utils.NORMALIZATION_LAYERS:
- deeplabv3plus_decoder = _create_deeplabv3plus_model(
- high_level_feature_name='high',
- low_level_feature_name='low',
- low_level_channels_project=128,
- aspp_output_channels=96,
- decoder_output_channels=64,
- atrous_rates=[6, 12, 18],
- num_classes=80,
- bn_layer=bn_layer)
- _ = deeplabv3plus_decoder(input_dict)
-
- def test_deeplabv3plus_pool_size_resetter(self):
- deeplabv3plus_decoder = _create_deeplabv3plus_model(
- high_level_feature_name='high',
- low_level_feature_name='low',
- low_level_channels_project=128,
- aspp_output_channels=96,
- decoder_output_channels=64,
- atrous_rates=[6, 12, 18],
- num_classes=80)
- pool_size = (None, None)
- deeplabv3plus_decoder.reset_pooling_layer()
-
- self.assertTupleEqual(deeplabv3plus_decoder._aspp._aspp_pool._pool_size,
- pool_size)
-
- def test_deeplabv3plus_ckpt_items(self):
- deeplabv3plus_decoder = _create_deeplabv3plus_model(
- high_level_feature_name='high',
- low_level_feature_name='low',
- low_level_channels_project=128,
- aspp_output_channels=96,
- decoder_output_channels=64,
- atrous_rates=[6, 12, 18],
- num_classes=80)
- ckpt_dict = deeplabv3plus_decoder.checkpoint_items
- self.assertIn(common.CKPT_DEEPLABV3PLUS_ASPP, ckpt_dict)
- self.assertIn(common.CKPT_DEEPLABV3PLUS_PROJECT_CONV_BN_ACT, ckpt_dict)
- self.assertIn(common.CKPT_DEEPLABV3PLUS_FUSE, ckpt_dict)
- self.assertIn(common.CKPT_SEMANTIC_LAST_LAYER, ckpt_dict)
-
-
-if __name__ == '__main__':
- tf.test.main()
diff --git a/spaces/akhaliq/knollingcase/README.md b/spaces/akhaliq/knollingcase/README.md
deleted file mode 100644
index d02bd1fb8b6d06a8aabfa82b80dad20641c0ec43..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/knollingcase/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Knollingcase
-emoji: 🏢
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/__init__.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/__init__.py
deleted file mode 100644
index 6afb5c627ce3db6e61cbf46276f7ddd42552eb28..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from typing import List, Optional
-
-import pip._internal.utils.inject_securetransport # noqa
-from pip._internal.utils import _log
-
-# init_logging() must be called before any call to logging.getLogger()
-# which happens at import of most modules.
-_log.init_logging()
-
-
-def main(args: (Optional[List[str]]) = None) -> int:
- """This is preserved for old console scripts that may still be referencing
- it.
-
- For additional details, see https://github.com/pypa/pip/issues/7498.
- """
- from pip._internal.utils.entrypoints import _wrapper
-
- return _wrapper(args)
diff --git a/spaces/aliabid94/GPT-Golf/README.md b/spaces/aliabid94/GPT-Golf/README.md
deleted file mode 100644
index ae841446379183092790b00afb28e68aa6b07739..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/GPT-Golf/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
----
-title: GPT-Golf
-app_file: run.py
-sdk: gradio
-sdk_version: 3.32.0
----
diff --git a/spaces/amankishore/sjc/ncsn/normalization.py b/spaces/amankishore/sjc/ncsn/normalization.py
deleted file mode 100644
index 77f0dd4d2667f7868ce3352ab3ed1c1fcd525d34..0000000000000000000000000000000000000000
--- a/spaces/amankishore/sjc/ncsn/normalization.py
+++ /dev/null
@@ -1,208 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-def get_normalization(config, conditional=True):
- norm = config.model.normalization
- if conditional:
- if norm == 'NoneNorm':
- return ConditionalNoneNorm2d
- elif norm == 'InstanceNorm++':
- return ConditionalInstanceNorm2dPlus
- elif norm == 'InstanceNorm':
- return ConditionalInstanceNorm2d
- elif norm == 'BatchNorm':
- return ConditionalBatchNorm2d
- elif norm == 'VarianceNorm':
- return ConditionalVarianceNorm2d
- else:
- raise NotImplementedError("{} does not exist!".format(norm))
- else:
- if norm == 'BatchNorm':
- return nn.BatchNorm2d
- elif norm == 'InstanceNorm':
- return nn.InstanceNorm2d
- elif norm == 'InstanceNorm++':
- return InstanceNorm2dPlus
- elif norm == 'VarianceNorm':
- return VarianceNorm2d
- elif norm == 'NoneNorm':
- return NoneNorm2d
- elif norm is None:
- return None
- else:
- raise NotImplementedError("{} does not exist!".format(norm))
-
-class ConditionalBatchNorm2d(nn.Module):
- def __init__(self, num_features, num_classes, bias=True):
- super().__init__()
- self.num_features = num_features
- self.bias = bias
- self.bn = nn.BatchNorm2d(num_features, affine=False)
- if self.bias:
- self.embed = nn.Embedding(num_classes, num_features * 2)
- self.embed.weight.data[:, :num_features].uniform_() # Initialise scale at N(1, 0.02)
- self.embed.weight.data[:, num_features:].zero_() # Initialise bias at 0
- else:
- self.embed = nn.Embedding(num_classes, num_features)
- self.embed.weight.data.uniform_()
-
- def forward(self, x, y):
- out = self.bn(x)
- if self.bias:
- gamma, beta = self.embed(y).chunk(2, dim=1)
- out = gamma.view(-1, self.num_features, 1, 1) * out + beta.view(-1, self.num_features, 1, 1)
- else:
- gamma = self.embed(y)
- out = gamma.view(-1, self.num_features, 1, 1) * out
- return out
-
-
-class ConditionalInstanceNorm2d(nn.Module):
- def __init__(self, num_features, num_classes, bias=True):
- super().__init__()
- self.num_features = num_features
- self.bias = bias
- self.instance_norm = nn.InstanceNorm2d(num_features, affine=False, track_running_stats=False)
- if bias:
- self.embed = nn.Embedding(num_classes, num_features * 2)
- self.embed.weight.data[:, :num_features].uniform_() # Initialise scale at N(1, 0.02)
- self.embed.weight.data[:, num_features:].zero_() # Initialise bias at 0
- else:
- self.embed = nn.Embedding(num_classes, num_features)
- self.embed.weight.data.uniform_()
-
- def forward(self, x, y):
- h = self.instance_norm(x)
- if self.bias:
- gamma, beta = self.embed(y).chunk(2, dim=-1)
- out = gamma.view(-1, self.num_features, 1, 1) * h + beta.view(-1, self.num_features, 1, 1)
- else:
- gamma = self.embed(y)
- out = gamma.view(-1, self.num_features, 1, 1) * h
- return out
-
-
-class ConditionalVarianceNorm2d(nn.Module):
- def __init__(self, num_features, num_classes, bias=False):
- super().__init__()
- self.num_features = num_features
- self.bias = bias
- self.embed = nn.Embedding(num_classes, num_features)
- self.embed.weight.data.normal_(1, 0.02)
-
- def forward(self, x, y):
- vars = torch.var(x, dim=(2, 3), keepdim=True)
- h = x / torch.sqrt(vars + 1e-5)
-
- gamma = self.embed(y)
- out = gamma.view(-1, self.num_features, 1, 1) * h
- return out
-
-
-class VarianceNorm2d(nn.Module):
- def __init__(self, num_features, bias=False):
- super().__init__()
- self.num_features = num_features
- self.bias = bias
- self.alpha = nn.Parameter(torch.zeros(num_features))
- self.alpha.data.normal_(1, 0.02)
-
- def forward(self, x):
- vars = torch.var(x, dim=(2, 3), keepdim=True)
- h = x / torch.sqrt(vars + 1e-5)
-
- out = self.alpha.view(-1, self.num_features, 1, 1) * h
- return out
-
-
-class ConditionalNoneNorm2d(nn.Module):
- def __init__(self, num_features, num_classes, bias=True):
- super().__init__()
- self.num_features = num_features
- self.bias = bias
- if bias:
- self.embed = nn.Embedding(num_classes, num_features * 2)
- self.embed.weight.data[:, :num_features].uniform_() # Initialise scale at N(1, 0.02)
- self.embed.weight.data[:, num_features:].zero_() # Initialise bias at 0
- else:
- self.embed = nn.Embedding(num_classes, num_features)
- self.embed.weight.data.uniform_()
-
- def forward(self, x, y):
- if self.bias:
- gamma, beta = self.embed(y).chunk(2, dim=-1)
- out = gamma.view(-1, self.num_features, 1, 1) * x + beta.view(-1, self.num_features, 1, 1)
- else:
- gamma = self.embed(y)
- out = gamma.view(-1, self.num_features, 1, 1) * x
- return out
-
-
-class NoneNorm2d(nn.Module):
- def __init__(self, num_features, bias=True):
- super().__init__()
-
- def forward(self, x):
- return x
-
-
-class InstanceNorm2dPlus(nn.Module):
- def __init__(self, num_features, bias=True):
- super().__init__()
- self.num_features = num_features
- self.bias = bias
- self.instance_norm = nn.InstanceNorm2d(num_features, affine=False, track_running_stats=False)
- self.alpha = nn.Parameter(torch.zeros(num_features))
- self.gamma = nn.Parameter(torch.zeros(num_features))
- self.alpha.data.normal_(1, 0.02)
- self.gamma.data.normal_(1, 0.02)
- if bias:
- self.beta = nn.Parameter(torch.zeros(num_features))
-
- def forward(self, x):
- means = torch.mean(x, dim=(2, 3))
- m = torch.mean(means, dim=-1, keepdim=True)
- v = torch.var(means, dim=-1, keepdim=True)
- means = (means - m) / (torch.sqrt(v + 1e-5))
- h = self.instance_norm(x)
-
- if self.bias:
- h = h + means[..., None, None] * self.alpha[..., None, None]
- out = self.gamma.view(-1, self.num_features, 1, 1) * h + self.beta.view(-1, self.num_features, 1, 1)
- else:
- h = h + means[..., None, None] * self.alpha[..., None, None]
- out = self.gamma.view(-1, self.num_features, 1, 1) * h
- return out
-
-
-class ConditionalInstanceNorm2dPlus(nn.Module):
- def __init__(self, num_features, num_classes, bias=True):
- super().__init__()
- self.num_features = num_features
- self.bias = bias
- self.instance_norm = nn.InstanceNorm2d(num_features, affine=False, track_running_stats=False)
- if bias:
- self.embed = nn.Embedding(num_classes, num_features * 3)
- self.embed.weight.data[:, :2 * num_features].normal_(1, 0.02) # Initialise scale at N(1, 0.02)
- self.embed.weight.data[:, 2 * num_features:].zero_() # Initialise bias at 0
- else:
- self.embed = nn.Embedding(num_classes, 2 * num_features)
- self.embed.weight.data.normal_(1, 0.02)
-
- def forward(self, x, y):
- means = torch.mean(x, dim=(2, 3))
- m = torch.mean(means, dim=-1, keepdim=True)
- v = torch.var(means, dim=-1, keepdim=True)
- means = (means - m) / (torch.sqrt(v + 1e-5))
- h = self.instance_norm(x)
-
- if self.bias:
- gamma, alpha, beta = self.embed(y).chunk(3, dim=-1)
- h = h + means[..., None, None] * alpha[..., None, None]
- out = gamma.view(-1, self.num_features, 1, 1) * h + beta.view(-1, self.num_features, 1, 1)
- else:
- gamma, alpha = self.embed(y).chunk(2, dim=-1)
- h = h + means[..., None, None] * alpha[..., None, None]
- out = gamma.view(-1, self.num_features, 1, 1) * h
- return out
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/doc/utils/checkfiledocs.py b/spaces/amarchheda/ChordDuplicate/portaudio/doc/utils/checkfiledocs.py
deleted file mode 100644
index 5d6b58518f7c97eed0e37c9a08fbcf14b3377f89..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/doc/utils/checkfiledocs.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import os
-import os.path
-import string
-
-paRootDirectory = '../../'
-paHtmlDocDirectory = os.path.join( paRootDirectory, "doc", "html" )
-
-## Script to check documentation status
-## this script assumes that html doxygen documentation has been generated
-##
-## it then walks the entire portaudio source tree and check that
-## - every source file (.c,.h,.cpp) has a doxygen comment block containing
-## - a @file directive
-## - a @brief directive
-## - a @ingroup directive
-## - it also checks that a corresponding html documentation file has been generated.
-##
-## This can be used as a first-level check to make sure the documentation is in order.
-##
-## The idea is to get a list of which files are missing doxygen documentation.
-##
-## How to run:
-## $ cd doc/utils
-## $ python checkfiledocs.py
-
-def oneOf_a_in_b(a, b):
- for x in a:
- if x in b:
- return True
- return False
-
-# recurse from top and return a list of all with the given
-# extensions. ignore .svn directories. return absolute paths
-def recursiveFindFiles( top, extensions, dirBlacklist, includePaths ):
- result = []
- for (dirpath, dirnames, filenames) in os.walk(top):
- if not oneOf_a_in_b(dirBlacklist, dirpath):
- for f in filenames:
- if os.path.splitext(f)[1] in extensions:
- if includePaths:
- result.append( os.path.abspath( os.path.join( dirpath, f ) ) )
- else:
- result.append( f )
- return result
-
-# generate the html file name that doxygen would use for
-# a particular source file. this is a brittle conversion
-# which i worked out by trial and error
-def doxygenHtmlDocFileName( sourceFile ):
- return sourceFile.replace( '_', '__' ).replace( '.', '_8' ) + '.html'
-
-
-sourceFiles = recursiveFindFiles( os.path.join(paRootDirectory,'src'), [ '.c', '.h', '.cpp' ], ['.svn', 'mingw-include'], True );
-sourceFiles += recursiveFindFiles( os.path.join(paRootDirectory,'include'), [ '.c', '.h', '.cpp' ], ['.svn'], True );
-docFiles = recursiveFindFiles( paHtmlDocDirectory, [ '.html' ], ['.svn'], False );
-
-
-
-currentFile = ""
-
-def printError( f, message ):
- global currentFile
- if f != currentFile:
- currentFile = f
- print f, ":"
- print "\t!", message
-
-
-for f in sourceFiles:
- if not doxygenHtmlDocFileName( os.path.basename(f) ) in docFiles:
- printError( f, "no doxygen generated doc page" )
-
- s = file( f, 'rt' ).read()
-
- if not '/**' in s:
- printError( f, "no doxygen /** block" )
-
- if not '@file' in s:
- printError( f, "no doxygen @file tag" )
-
- if not '@brief' in s:
- printError( f, "no doxygen @brief tag" )
-
- if not '@ingroup' in s:
- printError( f, "no doxygen @ingroup tag" )
-
-
diff --git a/spaces/analist/upscaler/app.py b/spaces/analist/upscaler/app.py
deleted file mode 100644
index 0045fe06b51fe174f9bd431d2838c52144559e46..0000000000000000000000000000000000000000
--- a/spaces/analist/upscaler/app.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import streamlit as st
-from super_image import EdsrModel, ImageLoader
-from PIL import Image
-import cv2
-
-st.title('Take your picture to the next level')
-
-
-col1, col2 = st.columns([3, 1])
-preds = None
-
-with col2:
- choice = st.selectbox('Scale to', [2, 3, 4])
- model_choice = st.selectbox('Model', ['EDSR', 'PAN', 'CARN'])
-
-with col1:
- file = st.file_uploader('Post your picture', type=['png', 'jpeg'])
- if file is not None:
- image = Image.open(file)
-
- col1, col2 = st.columns(2)
- with col1:
- st.subheader('Input image')
- st.image(image)
-
- if model_choice == "EDSR":
-
- model = EdsrModel.from_pretrained("eugenesiow/edsr-base", scale=int(choice))
- inputs = ImageLoader.load_image(image)
- preds = model(inputs)
-
- ImageLoader.save_image(preds, './scaled_2x.png')
- img = Image.open('./scaled_2x.png')
- with col2:
- st.subheader('New image')
- st.image(img, caption=f'Upscaled {choice} times image')
-
- with open('./scaled_2x.png', 'rb') as f:
- st.download_button(label="Download image", data=f, file_name="upscaled.png", mime="image/png")
-
- else:
- st.text('Not supported yet. Comeback soon for fun releases')
-
diff --git a/spaces/antonelli/outsidellms/app.py b/spaces/antonelli/outsidellms/app.py
deleted file mode 100644
index 1032cb99cb78e9af8aef10afd4b0fd5ba4a9dbd0..0000000000000000000000000000000000000000
--- a/spaces/antonelli/outsidellms/app.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import gradio as gr
-import librosa
-import numpy as np
-import requests
-from gradio.outputs import Video
-
-from video_generator import generate_video
-
-def extract_lyrics(api_response):
- words_timing = api_response["results"]["channels"][0]["alternatives"][0]["words"]
- lyrics_with_timing = []
- CHUNK_DURATION = 10
- current_chunk = ""
- current_chunk_start_time = 0
- for word_info in words_timing:
- word = word_info["word"]
- start_time = word_info["start"]
- if start_time >= current_chunk_start_time + CHUNK_DURATION:
- end_time = word_info["end"]
- lyrics_with_timing.append((current_chunk_start_time, end_time, current_chunk.strip()))
- current_chunk = ""
- current_chunk_start_time += CHUNK_DURATION
- current_chunk += " " + word
- lyrics_with_timing.append((current_chunk_start_time, words_timing[-1]["end"], current_chunk.strip()))
- return lyrics_with_timing
-
-
-def send_to_deepgram(audio_file_path):
- # Update with your Deepgram API endpoint and key
- endpoint = "https://api.deepgram.com/v1/listen"
- headers = {
- "Authorization": "Token 2114fe20a6bdccf930f9a7fd1931958f063745d7"
- }
- with open(audio_file_path, 'rb') as audio_file :
- audio_data = audio_file.read()
-
- response = requests.post(endpoint, headers=headers, data=audio_data)
- response_json = response.json()
- print("Deepgram API Response:", response_json) # Log the response
- return response_json
-
-def analyze_audio(audio_file_path):
- print("Analyzing audio...") # Log start of analysis
- last_frame = None
- y, sr = librosa.load(audio_file_path)
- chunk_length = 10 * sr # 10 seconds
- moods = []
-
- deepgram_response = send_to_deepgram(audio_file_path)
- lyrics_chunks = extract_lyrics(deepgram_response)
-
- for start in range(0, len(y), chunk_length):
- chunk = y[start:start + chunk_length]
- mood, _, _, _, _, _ = analyze_chunk(chunk)
- moods.append(mood)
-
- for i, start in enumerate(range(0, len(y), chunk_length)):
- print(f"Analyzing chunk {i + 1}...") # Log chunk analysis
- chunk = y[start:start + chunk_length]
- lyrics_summary = lyrics_chunks[i] if i < len(lyrics_chunks) else 'Instrumental or silence'
- previous_mood = moods[i - 1] if i > 0 else None
- current_mood = moods[i]
- next_mood = moods[i + 1] if i < len(moods) - 1 else None
- _, tempo, chroma_mean, spectral_contrast_mean, zero_crossing_rate_mean, mfcc_mean = analyze_chunk(chunk)
- prompt = generate_video_prompt(previous_mood, current_mood, next_mood, tempo, chroma_mean, spectral_contrast_mean, zero_crossing_rate_mean, mfcc_mean, lyrics_summary)
- description = f"Chunk starting at {start / sr} seconds:<br>Mood: {current_mood}<br>Video Prompt: {prompt}<br><br>"
- print(f"Generating video for chunk {i + 1}...")
- lyrics_with_timing = extract_lyrics(deepgram_response)
- video = generate_video(lyrics_with_timing, last_frame)
- #last_frame = extract_last_frame(video)
- print(f"Description for chunk {i + 1}: {description}")
- print(f"Video for chunk {i + 1}: {video}")
-
- # Yield the result for this chunk
- yield (description, video)
-
-def analyze_chunk(chunk):
- tempo, _ = librosa.beat.beat_track(y=chunk)
- chroma_mean = np.mean(librosa.feature.chroma_stft(y=chunk))
- spectral_contrast_mean = np.mean(librosa.feature.spectral_contrast(y=chunk))
- zero_crossing_rate_mean = np.mean(librosa.feature.zero_crossing_rate(chunk))
- mfcc_mean = np.mean(librosa.feature.mfcc(y=chunk))
- mood = analyze_mood(tempo, chroma_mean, spectral_contrast_mean, zero_crossing_rate_mean, mfcc_mean)
- return mood, tempo, chroma_mean, spectral_contrast_mean, zero_crossing_rate_mean, mfcc_mean
-
-def analyze_mood(tempo, chroma_mean, spectral_contrast_mean, zero_crossing_rate_mean, mfcc_mean):
- # Happy Mood
- if tempo > 110 and chroma_mean > 0.4:
- return 'Happy'
- # Sad Mood
- elif tempo < 90 and chroma_mean < 0.5 and mfcc_mean < 0:
- return 'Sad'
- # Energetic Mood
- elif tempo > 130 and zero_crossing_rate_mean > 0.05:
- return 'Energetic'
- # Relaxed Mood
- elif tempo < 100 and chroma_mean > 0.3 and spectral_contrast_mean > 15:
- return 'Relaxed'
- # Romantic Mood
- elif tempo < 100 and chroma_mean > 0.5:
- return 'Romantic'
- # Nostalgic Mood
- elif tempo < 100 and chroma_mean < 0.5 and spectral_contrast_mean < 25:
- return 'Nostalgic'
- # Tense Mood
- elif 100 <= tempo <= 130 and chroma_mean < 0.5 and spectral_contrast_mean > 20:
- return 'Tense'
- # Dreamy Mood
- elif tempo < 80 and chroma_mean > 0.4:
- return 'Dreamy'
- # Aggressive Mood
- elif tempo > 140 and zero_crossing_rate_mean > 0.08:
- return 'Aggressive'
- # Neutral Mood (Catch-all)
- else:
- return 'Neutral'
-
-def describe_tempo(tempo):
- if tempo < 60:
- return "a very slow"
- elif tempo < 90:
- return "a slow"
- elif tempo < 120:
- return "a moderate"
- elif tempo < 150:
- return "a lively"
- else:
- return "a fast"
-
-def generate_video_prompt(previous_mood, current_mood, next_mood, tempo, chroma_mean, spectral_contrast_mean, zero_crossing_rate_mean, mfcc_mean, lyrics_summary):
- rhythm_description = "energetic rhythm" if zero_crossing_rate_mean > 0.05 else "smooth rhythm"
- tonal_quality = "bright tones" if chroma_mean > 0.5 else "mellow tones"
- spectral_description = "sharp contrasts" if spectral_contrast_mean > 20 else "soft contrasts"
- tempo_description = describe_tempo(tempo)
-
- transition_description = ""
- if previous_mood:
- transition_description += f"Transition from a {previous_mood.lower()} mood. "
- if next_mood:
- transition_description += f"Prepare to transition to a {next_mood.lower()} mood. "
-
- prompt = (
- f"Essence of a {current_mood.lower()} mood. "
- f"{transition_description}"
- f"Showcase a scene with {rhythm_description}, {tonal_quality}, and {spectral_description}. "
- f"Visualize {tempo_description} tempo. " # Updated line
- f"Narrative based on the lyrics: '{lyrics_summary}'. "
- f"Emphasize the themes and emotions conveyed in the song."
- )
-
- return prompt
-
-# Define Gradio interface
-gr.Interface(
- fn=analyze_audio,
- inputs=gr.Audio(type="filepath"),
- outputs=[gr.HTML(), Video()],
-).launch()
\ No newline at end of file
diff --git a/spaces/arijitdas123student/meeting-summarizer/README.md b/spaces/arijitdas123student/meeting-summarizer/README.md
deleted file mode 100644
index 5d11d9a2d6d35cb7c1efbb063172f4da93a223ab..0000000000000000000000000000000000000000
--- a/spaces/arijitdas123student/meeting-summarizer/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Meeting Summarizer
-emoji: 👀
-colorFrom: pink
-colorTo: pink
-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/arnepeine/monaspeech/README.md b/spaces/arnepeine/monaspeech/README.md
deleted file mode 100644
index 9b468fb04e6f355ecfff9c8ff3d198175236e1d4..0000000000000000000000000000000000000000
--- a/spaces/arnepeine/monaspeech/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Monaspeech
-emoji: 🐨
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/arslan-ahmed/talk-to-your-docs/ttyd_functions.py b/spaces/arslan-ahmed/talk-to-your-docs/ttyd_functions.py
deleted file mode 100644
index dc6138fb1712352ba8c3e27c163ed66f2cd1e30b..0000000000000000000000000000000000000000
--- a/spaces/arslan-ahmed/talk-to-your-docs/ttyd_functions.py
+++ /dev/null
@@ -1,377 +0,0 @@
-
-import datetime
-import gradio as gr
-import time
-import uuid
-import openai
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import Chroma
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import SentenceTransformerEmbeddings
-
-import os
-from langchain.document_loaders import WebBaseLoader, TextLoader, Docx2txtLoader, PyMuPDFLoader, UnstructuredPowerPointLoader
-from whatsapp_chat_custom import WhatsAppChatLoader # use this instead of from langchain.document_loaders import WhatsAppChatLoader
-
-from collections import deque
-import re
-from bs4 import BeautifulSoup
-import requests
-from urllib.parse import urlparse
-import mimetypes
-from pathlib import Path
-import tiktoken
-import gdown
-
-from langchain.chat_models import ChatOpenAI
-from langchain import OpenAI
-
-from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
-from ibm_watson_machine_learning.foundation_models.utils.enums import DecodingMethods
-from ibm_watson_machine_learning.foundation_models import Model
-from ibm_watson_machine_learning.foundation_models.extensions.langchain import WatsonxLLM
-
-
-import genai
-from genai.extensions.langchain import LangChainInterface
-from genai.schemas import GenerateParams
-
-# Regex pattern to match a URL
-HTTP_URL_PATTERN = r'^http[s]*://.+'
-
-mimetypes.init()
-media_files = tuple([x for x in mimetypes.types_map if mimetypes.types_map[x].split('/')[0] in ['image', 'video', 'audio']])
-filter_strings = ['/email-protection#']
-
-def getOaiCreds(key):
- key = key if key else 'Null'
- return {'service': 'openai',
- 'oai_key' : key
- }
-
-
-def getBamCreds(key):
- key = key if key else 'Null'
- return {'service': 'bam',
- 'bam_creds' : genai.Credentials(key, api_endpoint='https://bam-api.res.ibm.com/v1')
- }
-
-
-def getWxCreds(key, p_id):
- key = key if key else 'Null'
- p_id = p_id if p_id else 'Null'
- return {'service': 'watsonx',
- 'credentials' : {"url": "https://us-south.ml.cloud.ibm.com", "apikey": key },
- 'project_id': p_id
- }
-
-def getPersonalBotApiKey():
- if os.getenv("OPENAI_API_KEY"):
- return getOaiCreds(os.getenv("OPENAI_API_KEY"))
- elif os.getenv("WX_API_KEY") and os.getenv("WX_PROJECT_ID"):
- return getWxCreds(os.getenv("WX_API_KEY"), os.getenv("WX_PROJECT_ID"))
- elif os.getenv("BAM_API_KEY"):
- return getBamCreds(os.getenv("BAM_API_KEY"))
- else:
- return {}
-
-
-
-def getOaiLlm(temp, modelNameDD, api_key_st):
- modelName = modelNameDD.split('(')[0].strip()
- # check if the input model is chat model or legacy model
- try:
- ChatOpenAI(openai_api_key=api_key_st['oai_key'], temperature=0,model_name=modelName,max_tokens=1).predict('')
- llm = ChatOpenAI(openai_api_key=api_key_st['oai_key'], temperature=float(temp),model_name=modelName)
- except:
- OpenAI(openai_api_key=api_key_st['oai_key'], temperature=0,model_name=modelName,max_tokens=1).predict('')
- llm = OpenAI(openai_api_key=api_key_st['oai_key'], temperature=float(temp),model_name=modelName)
- return llm
-
-
-MAX_NEW_TOKENS = 1024
-TOP_K = None
-TOP_P = 1
-
-def getWxLlm(temp, modelNameDD, api_key_st):
- modelName = modelNameDD.split('(')[0].strip()
- wxModelParams = {
- GenParams.DECODING_METHOD: DecodingMethods.SAMPLE,
- GenParams.MAX_NEW_TOKENS: MAX_NEW_TOKENS,
- GenParams.TEMPERATURE: float(temp),
- GenParams.TOP_K: TOP_K,
- GenParams.TOP_P: TOP_P
- }
- model = Model(
- model_id=modelName,
- params=wxModelParams,
- credentials=api_key_st['credentials'], project_id=api_key_st['project_id'])
- llm = WatsonxLLM(model=model)
- return llm
-
-
-def getBamLlm(temp, modelNameDD, api_key_st):
- modelName = modelNameDD.split('(')[0].strip()
- parameters = GenerateParams(decoding_method="sample", max_new_tokens=MAX_NEW_TOKENS, temperature=float(temp), top_k=TOP_K, top_p=TOP_P)
- llm = LangChainInterface(model=modelName, params=parameters, credentials=api_key_st['bam_creds'])
- return llm
-
-
-def get_hyperlinks(url):
- try:
- reqs = requests.get(url)
- if not reqs.headers.get('Content-Type').startswith("text/html") or 400<=reqs.status_code<600:
- return []
- soup = BeautifulSoup(reqs.text, 'html.parser')
- except Exception as e:
- print(e)
- return []
-
- hyperlinks = []
- for link in soup.find_all('a', href=True):
- hyperlinks.append(link.get('href'))
-
- return hyperlinks
-
-
-# Function to get the hyperlinks from a URL that are within the same domain
-def get_domain_hyperlinks(local_domain, url):
- clean_links = []
- for link in set(get_hyperlinks(url)):
- clean_link = None
-
- # If the link is a URL, check if it is within the same domain
- if re.search(HTTP_URL_PATTERN, link):
- # Parse the URL and check if the domain is the same
- url_obj = urlparse(link)
- if url_obj.netloc.replace('www.','') == local_domain.replace('www.',''):
- clean_link = link
-
- # If the link is not a URL, check if it is a relative link
- else:
- if link.startswith("/"):
- link = link[1:]
- elif link.startswith(("#", '?', 'mailto:')):
- continue
-
- if 'wp-content/uploads' in url:
- clean_link = url+ "/" + link
- else:
- clean_link = "https://" + local_domain + "/" + link
-
- if clean_link is not None:
- clean_link = clean_link.strip().rstrip('/').replace('/../', '/')
-
- if not any(x in clean_link for x in filter_strings):
- clean_links.append(clean_link)
-
- # Return the list of hyperlinks that are within the same domain
- return list(set(clean_links))
-
-# this function will get you a list of all the URLs from the base URL
-def crawl(url, local_domain, prog=None):
- # Create a queue to store the URLs to crawl
- queue = deque([url])
-
- # Create a set to store the URLs that have already been seen (no duplicates)
- seen = set([url])
-
- # While the queue is not empty, continue crawling
- while queue:
- # Get the next URL from the queue
- url_pop = queue.pop()
- # Get the hyperlinks from the URL and add them to the queue
- for link in get_domain_hyperlinks(local_domain, url_pop):
- if link not in seen:
- queue.append(link)
- seen.add(link)
- if len(seen)>=100:
- return seen
- if prog is not None: prog(1, desc=f'Crawling: {url_pop}')
-
- return seen
-
-
-def ingestURL(documents, url, crawling=True, prog=None):
- url = url.rstrip('/')
- # Parse the URL and get the domain
- local_domain = urlparse(url).netloc
- if not (local_domain and url.startswith('http')):
- return documents
- print('Loading URL', url)
- if crawling:
- # crawl to get other webpages from this URL
- if prog is not None: prog(0, desc=f'Crawling: {url}')
- links = crawl(url, local_domain, prog)
- if prog is not None: prog(1, desc=f'Crawling: {url}')
- else:
- links = set([url])
- # separate pdf and other links
- c_links, pdf_links = [], []
- for x in links:
- if x.endswith('.pdf'):
- pdf_links.append(x)
- elif not x.endswith(media_files):
- c_links.append(x)
-
- # Clean links loader using WebBaseLoader
- if prog is not None: prog(0.5, desc=f'Ingesting: {url}')
- if c_links:
- loader = WebBaseLoader(list(c_links))
- documents.extend(loader.load())
-
- # remote PDFs loader
- for pdf_link in list(pdf_links):
- loader = PyMuPDFLoader(pdf_link)
- doc = loader.load()
- for x in doc:
- x.metadata['source'] = loader.source
- documents.extend(doc)
-
- return documents
-
-def ingestFiles(documents, files_list, prog=None):
- for fPath in files_list:
- doc = None
- if fPath.endswith('.pdf'):
- doc = PyMuPDFLoader(fPath).load()
- elif fPath.endswith('.txt') and not 'WhatsApp Chat with' in fPath:
- doc = TextLoader(fPath).load()
- elif fPath.endswith(('.doc', 'docx')):
- doc = Docx2txtLoader(fPath).load()
- elif 'WhatsApp Chat with' in fPath and fPath.endswith('.csv'): # Convert Whatsapp TXT files to CSV using https://whatstk.streamlit.app/
- doc = WhatsAppChatLoader(fPath).load()
- elif fPath.endswith(('.ppt', '.pptx')):
- doc = UnstructuredPowerPointLoader(fPath).load()
- else:
- pass
-
- if doc is not None and doc[0].page_content:
- if prog is not None: prog(0.9, desc='Loaded file: '+fPath.rsplit('/')[0])
- print('Loaded file:', fPath)
- documents.extend(doc)
- return documents
-
-
-def data_ingestion(inputDir=None, file_list=[], url_list=[], gDriveFolder='', prog=None):
- documents = []
- # Ingestion from Google Drive Folder
- if gDriveFolder:
- opFolder = './gDriveDocs/'
- gdown.download_folder(url=gDriveFolder, output=opFolder, quiet=True)
- files = [str(x) for x in Path(opFolder).glob('**/*')]
- documents = ingestFiles(documents, files, prog)
- # Ingestion from Input Directory
- if inputDir is not None:
- files = [str(x) for x in Path(inputDir).glob('**/*')]
- documents = ingestFiles(documents, files, prog)
- if file_list:
- documents = ingestFiles(documents, file_list, prog)
- # Ingestion from URLs - also try https://python.langchain.com/docs/integrations/document_loaders/recursive_url_loader
- if url_list:
- for url in url_list:
- documents = ingestURL(documents, url, prog=prog)
-
- # Cleanup documents
- for x in documents:
- if 'WhatsApp Chat with' not in x.metadata['source']:
- x.page_content = x.page_content.strip().replace('\n', ' ').replace('\\n', ' ').replace(' ', ' ')
-
- # print(f"Total number of documents: {len(documents)}")
- return documents
-
-
-def split_docs(documents):
- # Splitting and Chunks
- text_splitter = RecursiveCharacterTextSplitter(chunk_size=2500, chunk_overlap=250) # default chunk size of 4000 makes around 1k tokens per doc. with k=4, this means 4k tokens input to LLM.
- docs = text_splitter.split_documents(documents)
- return docs
-
-
-def getSourcesFromMetadata(metadata, sourceOnly=True, sepFileUrl=True):
- # metadata: list of metadata dict from all documents
- setSrc = set()
- for x in metadata:
- metadataText = '' # we need to convert each metadata dict into a string format. This string will be added to a set
- if x is not None:
- # extract source first, and then extract all other items
- source = x['source']
- source = source.rsplit('/',1)[-1] if 'http' not in source else source
- notSource = []
- for k,v in x.items():
- if v is not None and k!='source' and k in ['page']:
- notSource.extend([f"{k}: {v}"])
- metadataText = ', '.join([f'source: {source}'] + notSource) if sourceOnly==False else source
- setSrc.add(metadataText)
-
- if sepFileUrl:
- src_files = '\n'.join(([f"{i+1}) {x}" for i,x in enumerate(sorted([x for x in setSrc if 'http' not in x], key=str.casefold))]))
- src_urls = '\n'.join(([f"{i+1}) {x}" for i,x in enumerate(sorted([x for x in setSrc if 'http' in x], key=str.casefold))]))
-
- src_files = 'Files:\n'+src_files if src_files else ''
- src_urls = 'URLs:\n'+src_urls if src_urls else ''
- newLineSep = '\n\n' if src_files and src_urls else ''
-
- return src_files + newLineSep + src_urls , len(setSrc)
- else:
- src_docs = '\n'.join(([f"{i+1}) {x}" for i,x in enumerate(sorted(list(setSrc), key=str.casefold))]))
- return src_docs, len(setSrc)
-
-def getEmbeddingFunc(creds):
- # OpenAI key used
- if creds.get('service')=='openai':
- embeddings = OpenAIEmbeddings(openai_api_key=creds.get('oai_key','Null'))
- # WX key used
- elif creds.get('service')=='watsonx' or creds.get('service')=='bam':
- # testModel = Model(model_id=ModelTypes.FLAN_UL2, credentials=creds['credentials'], project_id=creds['project_id']) # test the API key
- # del testModel
- embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") # for now use OpenSource model for embedding as WX doesnt have any embedding model
- else:
- raise Exception('Error: Invalid or None Credentials')
- return embeddings
-
-def getVsDict(embeddingFunc, docs, vsDict={}):
- # create chroma client if doesnt exist
- if vsDict.get('chromaClient') is None:
- vsDict['chromaDir'] = './vecstore/'+str(uuid.uuid1())
- vsDict['chromaClient'] = Chroma(embedding_function=embeddingFunc, persist_directory=vsDict['chromaDir'])
- # clear chroma client before adding new docs
- if vsDict['chromaClient']._collection.count()>0:
- vsDict['chromaClient'].delete(vsDict['chromaClient'].get()['ids'])
- # add new docs to chroma client
- vsDict['chromaClient'].add_documents(docs)
- print('vectorstore count:',vsDict['chromaClient']._collection.count(), 'at', datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
- return vsDict
-
-# used for Hardcoded documents only - not uploaded by user (userData_vecStore is separate function)
-def localData_vecStore(embKey={}, inputDir=None, file_list=[], url_list=[], vsDict={}, gGrUrl=''):
- documents = data_ingestion(inputDir, file_list, url_list, gGrUrl)
- if not documents:
- raise Exception('Error: No Documents Found')
- docs = split_docs(documents)
- # Embeddings
- embeddings = getEmbeddingFunc(embKey)
- # create chroma client if doesnt exist
- vsDict_hd = getVsDict(embeddings, docs, vsDict)
- # get sources from metadata
- src_str = getSourcesFromMetadata(vsDict_hd['chromaClient'].get()['metadatas'])
- src_str = str(src_str[1]) + ' source document(s) successfully loaded in vector store.'+'\n\n' + src_str[0]
- print(src_str)
- return vsDict_hd
-
-
-def num_tokens_from_string(string, encoding_name = "cl100k_base"):
- """Returns the number of tokens in a text string."""
- encoding = tiktoken.get_encoding(encoding_name)
- num_tokens = len(encoding.encode(string))
- return num_tokens
-
-def changeModel(oldModel, newModel):
- if oldModel:
- warning = 'Credentials not found for '+oldModel+'. Using default model '+newModel
- gr.Warning(warning)
- time.sleep(1)
- return newModel
-
-def getModelChoices(openAi_models, wml_models, bam_models):
- return [model for model in openAi_models] + [model.value+' (watsonx)' for model in wml_models] + [model + ' (bam)' for model in bam_models]
\ No newline at end of file
diff --git a/spaces/artificialguybr/video-dubbing/TTS/recipes/bel-alex73/docker-prepare/runtime.sh b/spaces/artificialguybr/video-dubbing/TTS/recipes/bel-alex73/docker-prepare/runtime.sh
deleted file mode 100644
index 27b723bc0fe56388674d33e2c8839b7fda68c776..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/recipes/bel-alex73/docker-prepare/runtime.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/bin/bash
-
-cd /a/TTS
-pip install -e .[all,dev,notebooks]
-
-LANG=C.utf8 bash
diff --git a/spaces/artificialguybr/video-dubbing/TTS/recipes/blizzard2013/README.md b/spaces/artificialguybr/video-dubbing/TTS/recipes/blizzard2013/README.md
deleted file mode 100644
index 9dcb73972802686dba80b83e798ab1466f2b26a0..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/recipes/blizzard2013/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# How to get the Blizzard 2013 Dataset
-
-The Capacitron model is a variational encoder extension of standard Tacotron based models to model prosody.
-
-To take full advantage of the model, it is advised to train the model with a dataset that contains a significant amount of prosodic information in the utterances. A tested candidate for such applications is the blizzard2013 dataset from the Blizzard Challenge, containing many hours of high quality audio book recordings.
-
-To get a license and download link for this dataset, you need to visit the [website](https://www.cstr.ed.ac.uk/projects/blizzard/2013/lessac_blizzard2013/license.html) of the Centre for Speech Technology Research of the University of Edinburgh.
-
-You get access to the raw dataset in a couple of days. There are a few preprocessing steps you need to do to be able to use the high fidelity dataset.
-
-1. Get the forced time alignments for the blizzard dataset from [here](https://github.com/mueller91/tts_alignments).
-2. Segment the high fidelity audio-book files based on the instructions [here](https://github.com/Tomiinek/Blizzard2013_Segmentation).
\ No newline at end of file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_linked_table.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_linked_table.py
deleted file mode 100644
index b8f0e260b876346f0b04f51cb6c99d040c338c8d..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_linked_table.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""
-Brushing Scatter Plot to show data on a table
----------------------------------------------
-A scatter plot of the cars dataset, with data tables for horsepower, MPG, and origin.
-The tables update to reflect the selection on the scatter plot.
-"""
-# category: scatter plots
-
-import altair as alt
-from vega_datasets import data
-
-source = data.cars()
-
-# Brush for selection
-brush = alt.selection(type='interval')
-
-# Scatter Plot
-points = alt.Chart(source).mark_point().encode(
- x='Horsepower:Q',
- y='Miles_per_Gallon:Q',
- color=alt.condition(brush, 'Cylinders:O', alt.value('grey'))
-).add_selection(brush)
-
-# Base chart for data tables
-ranked_text = alt.Chart(source).mark_text().encode(
- y=alt.Y('row_number:O',axis=None)
-).transform_window(
- row_number='row_number()'
-).transform_filter(
- brush
-).transform_window(
- rank='rank(row_number)'
-).transform_filter(
- alt.datum.rank<20
-)
-
-# Data Tables
-horsepower = ranked_text.encode(text='Horsepower:N').properties(title='Horsepower')
-mpg = ranked_text.encode(text='Miles_per_Gallon:N').properties(title='MPG')
-origin = ranked_text.encode(text='Origin:N').properties(title='Origin')
-text = alt.hconcat(horsepower, mpg, origin) # Combine data tables
-
-# Build chart
-alt.hconcat(
- points,
- text
-).resolve_legend(
- color="independent"
-)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/sphinxext/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/sphinxext/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/autumn8/selectModel/pages/Host & Deploy.py b/spaces/autumn8/selectModel/pages/Host & Deploy.py
deleted file mode 100644
index 2244def63bece53d1c802b30d661a4b3262f86a0..0000000000000000000000000000000000000000
--- a/spaces/autumn8/selectModel/pages/Host & Deploy.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import streamlit as st
-import time
-
-# Streamlit App
-st.title("AI Model Deployment 🚀")
-
-# Intro
-st.write("""
-Welcome to the AI model deployment flow! Here, we'll follow the process of deploying
-your fine-tuned AI model to one of the cloud instances. Let's begin!
-""")
-
-# Select cloud provider
-cloud_provider = st.selectbox("Choose a cloud provider:", ["AWS EC2", "Google Cloud VM", "Azure VM"])
-st.write(f"You've selected {cloud_provider}!")
-
-# Specify model details
-model_name = st.text_input("Enter your AI model name:", "MySpecialModel")
-if model_name:
- st.write(f"We'll deploy the model named: {model_name}")
-
-# Button to start the deployment
-if st.button("Start Deployment"):
- st.write("Deployment started... Please wait!")
-
- # Simulate progress bar for deployment
- latest_iteration = st.empty()
- bar = st.progress(0)
- for i in range(100):
- # Update the progress bar with each iteration.
- latest_iteration.text(f"Deployment progress: {i+1}%")
- bar.progress(i + 1)
- time.sleep(0.05)
-
- st.write(f"Deployment completed! Your model {model_name} is now live on {cloud_provider} 🌐")
-
-# Sidebar for additional settings (pretend configurations)
-st.sidebar.title("Deployment Settings")
-instance_type = st.sidebar.selectbox("Instance Type:", ["Standard", "High Memory", "High CPU", "GPU"])
-storage_option = st.sidebar.slider("Storage Size (in GB):", 10, 500, 50)
-st.sidebar.write(f"Instance Type: {instance_type}")
-st.sidebar.write(f"Storage Size: {storage_option} GB")
\ No newline at end of file
diff --git a/spaces/awacke1/SMART-FHIR-Assessment-Observation-SDKs/app.py b/spaces/awacke1/SMART-FHIR-Assessment-Observation-SDKs/app.py
deleted file mode 100644
index 697e7006a133e36ae269b588af3b89f0a1a9996f..0000000000000000000000000000000000000000
--- a/spaces/awacke1/SMART-FHIR-Assessment-Observation-SDKs/app.py
+++ /dev/null
@@ -1,533 +0,0 @@
-import streamlit as st
-
-# pip install streamlit fhir.resources==6.5.0 fhirclient smart.models sdcclient
-# FHIR resources functions
-from datetime import datetime
-from fhir.resources.activitydefinition import ActivityDefinition
-from fhir.resources.medicationrequest import MedicationRequest
-from fhir.resources.observation import Observation
-from fhir.resources.diagnosticreport import DiagnosticReport
-from fhir.resources.questionnaireresponse import QuestionnaireResponse
-from fhir.resources.careplan import CarePlan
-from fhir.resources.goal import Goal
-from fhir.resources.condition import Condition
-from fhir.resources.patient import Patient
-#from fhir.resources.patient import HumanName
-from fhir.resources.humanname import HumanName
-from fhir.resources.procedure import Procedure
-from fhir.resources.encounter import Encounter
-from fhir.resources.organization import Organization
-from fhir.resources.practitioner import Practitioner
-from fhir.resources.practitionerrole import PractitionerRole
-from fhir.resources.healthcareservice import HealthcareService
-from fhir.resources.location import Location
-from fhir.resources.immunization import Immunization
-from fhir.resources.documentreference import DocumentReference
-from fhir.resources.medicationdispense import MedicationDispense
-from fhir.resources.medicationstatement import MedicationStatement
-from fhir.resources.appointment import Appointment
-from fhir.resources.schedule import Schedule
-from fhir.resources.slot import Slot
-from fhir.resources.patient import Patient
-from fhir.resources.questionnaire import Questionnaire
-from fhir.resources.activitydefinition import ActivityDefinition
-from fhir.resources.measure import Measure
-from fhir.resources.plandefinition import PlanDefinition
-from fhir.resources.careteam import CareTeam
-from fhir.resources.person import Person
-from fhir.resources.group import Group
-from fhir.resources.practitioner import Practitioner
-from fhir.resources.practitionerrole import PractitionerRole
-from fhir.resources.healthcareservice import HealthcareService
-from fhir.resources.location import Location
-from fhir.resources.immunization import Immunization
-from fhir.resources.documentreference import DocumentReference
-from fhir.resources.medicationdispense import MedicationDispense
-from fhir.resources.medicationstatement import MedicationStatement
-from fhir.resources.appointment import Appointment
-from fhir.resources.schedule import Schedule
-from fhir.resources.slot import Slot
-from fhir.resources.servicerequest import ServiceRequest
-from fhir.resources.communicationrequest import CommunicationRequest
-from fhir.resources.annotation import Annotation
-
-
-import json
-from typing import List, Tuple
-
-#import smart.models as smart
-#from sdcclient import SdcClient
-
-# Initialize the SMART client
-# smart_client = smart.client
-
-# Initialize the SDC client
-#sdc_client = SdcClient()
-
-# Define a list of example patients
-EXAMPLE_PATIENTS = [
- {
- "name": "Alice Smith",
- "birthdate": "1980-01-01",
- "gender": "female",
- "address": {
- "line": ["123 Main St"],
- "city": "Anytown",
- "state": "NY",
- "postalCode": "12345",
- "country": "US"
- },
- "phone": "555-555-1212"
- },
- {
- "name": "Bob Johnson",
- "birthdate": "1975-05-05",
- "gender": "male",
- "address": {
- "line": ["456 Oak St"],
- "city": "Anytown",
- "state": "NY",
- "postalCode": "12345",
- "country": "US"
- },
- "phone": "555-555-1212"
- }
-]
-
-# Define a list of example social determinants of health
-EXAMPLE_SDH = [
- {
- "question": "Do you have reliable transportation?",
- "answer": "Yes"
- },
- {
- "question": "Do you have enough food to eat?",
- "answer": "No"
- },
- {
- "question": "Do you have stable housing?",
- "answer": "No"
- }
-]
-
-def get_patient(name: str) -> Tuple[Patient, str]:
- """
- Returns a tuple containing the FHIR Patient resource and the SMART Patient model
- for the given patient name.
- """
- # Get the example patient with the matching name
- example_patient = next((p for p in EXAMPLE_PATIENTS if p["name"] == name), None)
- if not example_patient:
- raise ValueError(f"No example patient found with name '{name}'")
-
- # Create a FHIR Patient resource
- patient = Patient()
- patient.name = [HumanName()]
- patient.name[0].given = [example_patient["name"].split()[0]]
-# patient.name[0].family = [example_patient["name"].split()[0]]
-
-# patient.birthDate = FHIRDate(example_patient["birthdate"])
- patient.gender = example_patient["gender"]
- #patient.address = [Address()]
- #patient.address.append(Address())
-
- #patient.address[0].line = example_patient["address"]["line"]
- #patient.address[0].city = example_patient["address"]["city"]
- #patient.address[0].state = example_patient["address"]["state"]
- #patient.address[0].postalCode = example_patient["address"]["postalCode"]
- #patient.address[0].country = example_patient["address"]["country"]
- #patient.telecom = [ContactPoint()]
- #patient.telecom[0].system = "phone"
- #patient.telecom[0].value = example_patient["phone"]
-
- # Create a SMART Patient model
- smart_patient = smart.Patient.read_from(patient.as_json())
-
- return patient, smart_patient
-
-def create_observation(patient_id: str, code: str, value: str, unit: str) -> Observation:
- """
- Creates and returns a FHIR Observation resource with the given patient ID, code, value, and unit.
- """
- observation = Observation()
- observation.subject = {"reference": f"Patient/{patient_id}"}
- observation.code = CodeableConcept()
- observation.code.coding = [Coding()]
- observation.code.coding[0].system = "http://loinc.org"
- observation.code.coding[0].code = code
- observation.valueQuantity = Quantity()
- observation.valueQuantity.value = float(value)
- observation.valueQuantity.unit = unit
- observation.status = "final"
- observation.effectiveDateTime = FHIRDate("2023-02-21")
- observation.meta = Meta()
- observation.meta.profile = ["http://hl7.org/fhir/StructureDefinition/vitalsigns"]
-
- return observation
-
-def create_assessment(patient_id: str, code: str, value: str) -> DiagnosticReport:
- """
- Creates and returns a FHIR DiagnosticReport resource with the given patient ID, code, and value.
- """
- report = DiagnosticReport()
- report.status = "final"
- report.subject = {"reference": f"Patient/{patient_id}"}
- report.code = CodeableConcept()
- report.code.coding = [Coding()]
- report.code.coding[0].system = "http://loinc.org"
- report.code.coding[0].code = code
- report.result = [Reference()]
- report.result[0].reference = f"Observation/{code}"
- report.result[0].display = value
- report.effectiveDateTime = FHIRDate("2023-02-21")
-
- return report
-
-
-def create_provider(name: str, organization_name: str) -> Tuple[Practitioner, PractitionerRole, Organization]:
- """
- Creates and returns a tuple containing the FHIR Practitioner, PractitionerRole, and Organization resources for the given provider name and organization name.
- """
- # Create a FHIR Practitioner resource
- practitioner = Practitioner()
- practitioner.name = [HumanName()]
- practitioner.name[0].text = name
- practitioner.identifier = [Identifier()]
- practitioner.identifier[0].system = "http://example.com/providers"
- practitioner.identifier[0].value = "12345"
-
- # Create a FHIR PractitionerRole resource
- practitioner_role = PractitionerRole()
- practitioner_role.practitioner = {"reference": f"Practitioner/{practitioner.id}"}
- practitioner_role.organization = {"reference": f"Organization/{organization_name}"}
- practitioner_role.code = [CodeableConcept()]
- practitioner_role.code[0].coding = [Coding()]
- practitioner_role.code[0].coding[0].system = "http://nucc.org/provider-taxonomy"
- practitioner_role.code[0].coding[0].code = "207Q00000X"
- practitioner_role.code[0].coding[0].display = "Family Medicine"
- practitioner_role.specialization = [CodeableConcept()]
- practitioner_role.specialization[0].coding = [Coding()]
- practitioner_role.specialization[0].coding[0].system = "http://snomed.info/sct"
- practitioner_role.specialization[0].coding[0].code = "123456"
- practitioner_role.specialization[0].coding[0].display = "Example Specialty"
-
- # Create a FHIR Organization resource
- organization = Organization()
- organization.name = organization_name
-
- return practitioner, practitioner_role, organization
-
-
-def create_fulfillment(patient_id: str, medication_name: str, quantity: int, dispense_date: str) -> MedicationStatement:
- """
- Creates and returns a FHIR MedicationStatement resource representing a fulfillment of a prescription for the given patient ID, medication name, quantity, and dispense date.
- """
- # Create a FHIR Medication resource
- medication = Medication()
- medication.code = CodeableConcept()
- medication.code.text = medication_name
-
- # Create a FHIR MedicationStatement resource
- fulfillment = MedicationStatement()
- fulfillment.status = "completed"
- fulfillment.medicationReference = {"reference": f"Medication/{medication.id}"}
- fulfillment.subject = {"reference": f"Patient/{patient_id}"}
- fulfillment.dosage = [Dosage()]
- fulfillment.dosage[0].route = CodeableConcept()
- fulfillment.dosage[0].route.coding = [Coding()]
- fulfillment.dosage[0].route.coding[0].system = "http://example.com/routes"
- fulfillment.dosage[0].route.coding[0].code = "123456"
- fulfillment.dosage[0].route.coding[0].display = "Example Route"
- fulfillment.dosage[0].quantity = Quantity()
- fulfillment.dosage[0].quantity.value = quantity
- fulfillment.dosage[0].quantity.unit = "pill"
- fulfillment.effectiveDateTime = FHIRDate(dispense_date)
-
- return fulfillment
-
-
-def create_note(patient_id: str, text: str) -> DocumentReference:
- """
- Creates and returns a FHIR DocumentReference resource representing a note for the given patient ID and text.
- """
- note = DocumentReference()
- note.status = "current"
- note.subject = {"reference": f"Patient/{patient_id}"}
- note.type = CodeableConcept()
- note.type.coding = [Coding()]
- note.type.coding[0].system = "http://loinc.org"
- note.type.coding[0].code = "11506-3"
- note.type.coding[0].display = "Consult note"
- note.content = [Attachment()]
- note.content[0].contentType = "text/plain"
- note.content[0].data = text.encode("utf-8")
- note.author = [Reference()]
- note.author[0].reference = f"Practitioner/example-provider"
- note.date = FHIRDate("2023-02-21")
-
- return note
-
-def create_social_determinant(question: str, answer: str) -> QuestionnaireResponse:
- """
- Creates and returns a FHIR QuestionnaireResponse resource representing a social determinant of health with the given question and answer.
- """
- response = SmartQuestionnaireResponse()
- response.questionnaire = "http://example.com/sdh-questionnaire"
- response.item = []
- item = QuestionnaireResponseItem()
- item.linkId = "1"
- item.text = question
- item.answer = []
- answer_item = QuestionnaireResponseItemAnswer()
- answer_item.valueString = answer
- item.answer.append(answer_item)
- response.item.append(item)
-
- return response
-
-def create_care_team(name: str, provider_names: List[str]) -> CareTeam:
- """
- Creates and returns a FHIR CareTeam resource representing a care team with the given name and provider names.
- """
- care_team = SmartCareTeam()
- care_team.status = "active"
- care_team.name = name
- care_team.participant = []
- for provider_name in provider_names:
- provider_ref = f"Practitioner/{provider_name}"
- care_team.participant.append(CareTeamParticipant({"member": {"reference": provider_ref}}))
-
- return care_team
-
-def create_activity_definition(title: str, description: str, category: str, code: str) -> ActivityDefinition:
- """
- Creates and returns a FHIR ActivityDefinition resource representing an activity definition with the given title, description, category, and code.
- """
- activity_definition = SmartActivityDefinition()
- activity_definition.status = "draft"
- activity_definition.kind = "procedure"
- activity_definition.title = title
- activity_definition.description = description
- activity_definition.category = CodeableConcept({"coding": [Coding({"system": "http://example.com/categories", "code": category, "display": f"Example {category}"})]})
- activity_definition.code = CodeableConcept({"coding": [Coding({"system": "http://example.com/codes", "code": code, "display": f"Example {code}"})]})
-
- return activity_definition
-
-# Streamlit app code
-import streamlit as st
-from fhir.resources import *
-
-st.set_page_config(page_title="FHIR Demo", page_icon=":heart:", layout="wide")
-
-st.title("FHIR Demo")
-
-st.sidebar.title("Navigation")
-navigation = st.sidebar.radio("Go to", ("Home", "Observations", "Assessments", "Rules", "Referrals", "Providers", "Programs", "Fulfillment", "Alerts", "Notes", "Social Determinants of Health"))
-
-st.sidebar.title("SMART App")
-smart = None
-if st.sidebar.button("Launch SMART App"):
- smart = SmartApp.launch()
- st.sidebar.write("SMART App launched")
-if st.sidebar.button("Close SMART App"):
- if smart:
- smart.close()
- st.sidebar.write("SMART App closed")
- else:
- st.sidebar.write("No SMART App to close")
-
-if navigation == "Home":
- st.write("Welcome to the FHIR Demo!")
- st.write("Use the sidebar to navigate to different FHIR resources.")
- st.write("Use the SMART App buttons in the sidebar to launch and close the app.")
-
-elif navigation == "Observations":
- st.write("# Observations")
- patient_name = st.selectbox("Select patient", [p["name"] for p in EXAMPLE_PATIENTS])
- patient, smart_patient = get_patient(patient_name)
- st.write("### Patient")
- st.write(f"Name: {patient.name[0].given[0]} {patient.name[0].family[0]}")
- st.write(f"Birthdate: {patient.birthDate.as_json()}")
- st.write(f"Gender: {patient.gender}")
- st.write(f"Address: {patient.address[0].line[0]}, {patient.address[0].city}, {patient.address[0].state} {patient.address[0].postalCode}, {patient.address[0].country}")
- st.write(f"Phone: {patient.telecom[0].value}")
- st.write("### Add Observation")
- code = st.selectbox("Select code", ["8302-2", "8462-4", "8867-4", "9279-1"])
- value = st.number_input("Value")
- unit = st.selectbox("Select unit", ["bpm", "mmHg", "mg/dL", "kg"])
- observation = create_observation(patient.id, code, value, unit)
- smart_client = smart.patient(smart_patient)
- response = smart_client.create(observation)
- st.write("Observation created:")
- st.write(response.as_json())
-
-elif navigation == "Assessments":
- st.write("# Assessments")
- patient_name = st.selectbox("Select patient", [p["name"] for p in EXAMPLE_PATIENTS])
- patient, smart_patient = get_patient(patient_name)
- st.write("### Patient")
- st.write(f"Name: {patient.name[0].given[0]} {patient.name[0].family[0]}")
- st.write(f"Birthdate: {patient.birthDate.as_json()}")
- st.write(f"Gender: {patient.gender}")
- st.write(f"Address: {patient.address[0].line[0]}, {patient.address[0].city}, {patient.address[0].state} {patient.address[0].postalCode}, {patient.address[0].country}")
- st.write(f"Phone: {patient.telecom[0].value}")
- st.write("### Add Assessment")
- code = st.selectbox("Select code", ["69646-2", "8150-9", "82810-3"])
- value = st.selectbox("Select value", ["Absent", "Mild", "Moderate", "Severe"])
- assessment = create_assessment(patient.id, code, value)
- smart_client = smart.patient(smart_patient)
- response = smart_client.create(assessment)
- st.write("Assessment created:")
- st.write(response.as_json())
-
-elif navigation == "Rules":
- st.write("# Rules")
- st.write("### Add Rule")
- code = st.selectbox("Select code", ["36405-2", "89053-6"])
- description = st.text_input("Description")
- rule = create_rule(code, description)
- response = smart.server.create(rule)
- st.write("Rule created:")
- st.write(response.as_json())
-
-elif navigation == "Referrals":
- st.write("# Referrals")
- patient_name = st.selectbox("Select patient", [p["name"] for p in EXAMPLE_PATIENTS])
- patient, smart_patient = get_patient(patient_name)
- st.write("### Patient")
- st.write(f"Name: {patient.name[0].given[0]} {patient.name[0].family[0]}")
- st.write(f"Birthdate: {patient.birthDate.as_json()}")
- st.write(f"Gender: {patient.gender}")
- st.write(f"Address: {patient.address[0].line[0]}, {patient.address[0].city}, {patient.address[0].state} {patient.address[0].postalCode}, {patient.address[0].country}")
- st.write(f"Phone: {patient.telecom[0].value}")
- st.write("### Add Referral")
- reason = st.text_input("Reason")
- specialty = st.text_input("Specialty")
- provider_name = st.selectbox("Select provider", [p["name"] for p in EXAMPLE_PROVIDERS])
- provider, practitioner_role, organization = create_provider(provider_name, "Example Healthcare")
- response1 = smart.server.create(provider)
- response2 = smart.server.create(practitioner_role)
- response3 = smart.server.create(organization)
- referral = create_referral_request(patient.id, reason, specialty, provider_name)
- response4 = smart.server.create(referral)
- st.write("Referral created:")
- st.write(response4.as_json())
-
-elif navigation == "Providers":
- st.write("# Providers")
- st.write("### Add Provider")
- name = st.text_input("Name")
- organization_name = st.text_input("Organization")
- provider, practitioner_role, organization = create_provider(name, organization_name)
- response1 = smart.server.create(provider)
- response2 = smart.server.create(practitioner_role)
- response3 = smart.server.create(organization)
- st.write("Provider created:")
- st.write(response1.as_json())
-
-elif navigation == "Programs":
- st.write("# Programs")
- st.write("### Add Program")
- name = st.text_input("Name")
- goal_description = st.text_input("Goal description")
- start_date = st.date_input("Start date")
- end_date = st.date_input("End date")
- program = create_program(name, goal_description, start_date.isoformat(), end_date.isoformat())
- response = smart.server.create(program)
- st.write("Program created:")
- st.write(response.as_json())
-
-elif navigation == "Fulfillment":
- st.write("# Fulfillment")
- patient_name = st.selectbox("Select patient", [p["name"] for p in EXAMPLE_PATIENTS])
- patient, smart_patient = get_patient(patient_name)
- st.write("### Patient")
- st.write(f"Name: {patient.name[0].given[0]} {patient.name[0].family[0]}")
- st.write(f"Birthdate: {patient.birthDate.as_json()}")
- st.write(f"Gender: {patient.gender}")
- st.write(f"Address: {patient.address[0].line[0]}, {patient.address[0].city}, {patient.address[0].state} {patient.address[0].postalCode}, {patient.address[0].country}")
- st.write(f"Phone: {patient.telecom[0].value}")
- st.write("### Add Fulfillment")
- medication_name = st.selectbox("Select medication", ["Aspirin", "Lisinopril", "Metformin"])
- quantity = st.number_input("Quantity")
- dispense_date = st.date_input("Dispense date")
- fulfillment = create_fulfillment(patient.id, medication_name, quantity, dispense_date.isoformat())
- smart_client = smart.patient(smart_patient)
- response = smart_client.create(fulfillment)
- st.write("Fulfillment created:")
- st.write(response.as_json())
-
-st.markdown("""
-
-
-| Library | Description | PyPI URL |
-|---------|-------------|----------|
-| FHIR-Resources | 🩺 A Python library for working with FHIR resources. It provides classes and methods for creating, manipulating, and serializing FHIR resources. | https://pypi.org/project/fhir-resources/ |
-| SMART on FHIR Python Client | 🔒 A Python library for accessing SMART on FHIR servers. It provides classes and methods for authenticating with SMART servers and accessing FHIR resources. | https://pypi.org/project/smart-on-fhir/ |
-| PyFHIR | 📦 A Python library for parsing and generating FHIR resources. It provides classes for representing FHIR resources and methods for serializing and deserializing them. | https://pypi.org/project/pyfhir/ |
-| HAPI FHIR | 💻 A Python library for working with FHIR servers. It provides classes and methods for querying FHIR servers and working with FHIR resources. | https://pypi.org/project/hapi-fhir/ |
-| FHIR-Parser | 📄 A Python library for parsing FHIR resources. It provides a parser class for deserializing FHIR resources. | https://pypi.org/project/fhir-parser/ |
-| FHIR-FLAT | 🧐 A Python library for working with FHIR resources. It provides classes for representing FHIR resources and methods for serializing and deserializing them. | https://pypi.org/project/fhir-flat/ |
-| HL7apy | 📩 A Python library for working with HL7 messages. It provides classes and methods for parsing and generating HL7 messages. | https://pypi.org/project/hl7apy/ |
-| pyHl7 | 📨 A Python library for parsing and generating HL7 messages. It provides classes for representing HL7 messages and methods for serializing and deserializing them. | https://pypi.org/project/pyhl7/ |
-| FHIR-Utils | 🔧 A Python library for working with FHIR resources. It provides utility functions for common FHIR tasks. | https://pypi.org/project/fhir-utils/ |
-
-""")
-
-import streamlit as st
-
-# Angular libraries
-st.header("Angular Libraries")
-st.write("Here are some popular Angular libraries:")
-
-st.markdown("- [ngx-charts](https://www.npmjs.com/package/@swimlane/ngx-charts)")
-st.markdown("- [angular-material](https://material.angular.io/)")
-
-# Node.JS libraries
-st.header("Node.JS Libraries")
-st.write("Here are some popular Node.JS libraries:")
-
-st.markdown("- [express](https://expressjs.com/)")
-st.markdown("- [axios](https://www.npmjs.com/package/axios)")
-
-# Docker libraries
-st.header("Docker Libraries")
-st.write("Here are some popular Docker libraries:")
-
-st.markdown("- [docker-py](https://pypi.org/project/docker/)")
-st.markdown("- [docker-compose](https://docs.docker.com/compose/)")
-
-# Kubernetes libraries
-st.header("Kubernetes Libraries")
-st.write("Here are some popular Kubernetes libraries:")
-
-st.markdown("- [kubernetes](https://pypi.org/project/kubernetes/)")
-st.markdown("- [kubeflow](https://www.kubeflow.org/)")
-
-# GraphQL libraries
-st.header("GraphQL Libraries")
-st.write("Here are some popular GraphQL libraries:")
-
-st.markdown("- [graphene](https://pypi.org/project/graphene/)")
-st.markdown("- [graphql-core](https://pypi.org/project/graphql-core/)")
-
-# PostgreSQL libraries
-st.header("PostgreSQL Libraries")
-st.write("Here are some popular PostgreSQL libraries:")
-
-st.markdown("- [psycopg2](https://pypi.org/project/psycopg2/)")
-st.markdown("- [sqlalchemy](https://pypi.org/project/SQLAlchemy/)")
-
-# Snowflake libraries
-st.header("Snowflake Libraries")
-st.write("Here are some popular Snowflake libraries:")
-
-st.markdown("- [snowflake-connector-python](https://pypi.org/project/snowflake-connector-python/)")
-st.markdown("- [snowflake-sqlalchemy](https://pypi.org/project/snowflake-sqlalchemy/)")
-
-# AI libraries
-st.header("AI Libraries")
-st.write("Here are some popular AI libraries:")
-
-st.markdown("- [tensorflow](https://pypi.org/project/tensorflow/)")
-st.markdown("- [scikit-learn](https://pypi.org/project/scikit-learn/)")
diff --git a/spaces/awacke1/stabilityai-stable-diffusion-2-1/app.py b/spaces/awacke1/stabilityai-stable-diffusion-2-1/app.py
deleted file mode 100644
index 0160420876923d89f2ab5fccb9f4d13725e29972..0000000000000000000000000000000000000000
--- a/spaces/awacke1/stabilityai-stable-diffusion-2-1/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stabilityai/stable-diffusion-2-1").launch()
\ No newline at end of file
diff --git a/spaces/b1sheng/kg_llm_leaderboard_test/src/auto_leaderboard/get_model_metadata.py b/spaces/b1sheng/kg_llm_leaderboard_test/src/auto_leaderboard/get_model_metadata.py
deleted file mode 100644
index e0fbff3e04d86ffcc6f52af1878ec9438b1c9130..0000000000000000000000000000000000000000
--- a/spaces/b1sheng/kg_llm_leaderboard_test/src/auto_leaderboard/get_model_metadata.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import re
-import os
-from typing import List
-
-from src.utils_display import AutoEvalColumn
-from src.auto_leaderboard.model_metadata_type import get_model_type
-
-from huggingface_hub import HfApi
-import huggingface_hub
-api = HfApi(token=os.environ.get("H4_TOKEN", None))
-
-
-def get_model_infos_from_hub(leaderboard_data: List[dict]):
- for model_data in leaderboard_data:
- model_name = model_data["model_name_for_query"]
- try:
- model_info = api.model_info(model_name)
- except huggingface_hub.utils._errors.RepositoryNotFoundError:
- print("Repo not found!", model_name)
- model_data[AutoEvalColumn.license.name] = None
- model_data[AutoEvalColumn.likes.name] = None
- model_data[AutoEvalColumn.params.name] = get_model_size(model_name, None)
- continue
-
- model_data[AutoEvalColumn.license.name] = get_model_license(model_info)
- model_data[AutoEvalColumn.likes.name] = get_model_likes(model_info)
- model_data[AutoEvalColumn.params.name] = get_model_size(model_name, model_info)
-
-
-def get_model_license(model_info):
- try:
- return model_info.cardData["license"]
- except Exception:
- return None
-
-def get_model_likes(model_info):
- return model_info.likes
-
-size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
-
-def get_model_size(model_name, model_info):
- # In billions
- try:
- return round(model_info.safetensors["total"] / 1e9, 3)
- except AttributeError:
- try:
- size_match = re.search(size_pattern, model_name.lower())
- size = size_match.group(0)
- return round(float(size[:-1]) if size[-1] == "b" else float(size[:-1]) / 1e3, 3)
- except AttributeError:
- return None
-
-
-def apply_metadata(leaderboard_data: List[dict]):
- get_model_type(leaderboard_data)
- get_model_infos_from_hub(leaderboard_data)
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/controls/DragControls.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/controls/DragControls.js
deleted file mode 100644
index a461d855757cf58b70fa0fc73f564f923178709a..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/controls/DragControls.js
+++ /dev/null
@@ -1,286 +0,0 @@
-/*
- * @author zz85 / https://github.com/zz85
- * @author mrdoob / http://mrdoob.com
- * Running this will allow you to drag three.js objects around the screen.
- */
-
-THREE.DragControls = function ( _objects, _camera, _domElement ) {
-
- if ( _objects instanceof THREE.Camera ) {
-
- console.warn( 'THREE.DragControls: Constructor now expects ( objects, camera, domElement )' );
- var temp = _objects; _objects = _camera; _camera = temp;
-
- }
-
- var _plane = new THREE.Plane();
- var _raycaster = new THREE.Raycaster();
-
- var _mouse = new THREE.Vector2();
- var _offset = new THREE.Vector3();
- var _intersection = new THREE.Vector3();
- var _worldPosition = new THREE.Vector3();
- var _inverseMatrix = new THREE.Matrix4();
-
- var _selected = null, _hovered = null;
-
- //
-
- var scope = this;
-
- function activate() {
-
- _domElement.addEventListener( 'mousemove', onDocumentMouseMove, false );
- _domElement.addEventListener( 'mousedown', onDocumentMouseDown, false );
- _domElement.addEventListener( 'mouseup', onDocumentMouseCancel, false );
- _domElement.addEventListener( 'mouseleave', onDocumentMouseCancel, false );
- _domElement.addEventListener( 'touchmove', onDocumentTouchMove, false );
- _domElement.addEventListener( 'touchstart', onDocumentTouchStart, false );
- _domElement.addEventListener( 'touchend', onDocumentTouchEnd, false );
-
- }
-
- function deactivate() {
-
- _domElement.removeEventListener( 'mousemove', onDocumentMouseMove, false );
- _domElement.removeEventListener( 'mousedown', onDocumentMouseDown, false );
- _domElement.removeEventListener( 'mouseup', onDocumentMouseCancel, false );
- _domElement.removeEventListener( 'mouseleave', onDocumentMouseCancel, false );
- _domElement.removeEventListener( 'touchmove', onDocumentTouchMove, false );
- _domElement.removeEventListener( 'touchstart', onDocumentTouchStart, false );
- _domElement.removeEventListener( 'touchend', onDocumentTouchEnd, false );
-
- }
-
- function dispose() {
-
- deactivate();
-
- }
-
- function onDocumentMouseMove( event ) {
-
- event.preventDefault();
-
- var rect = _domElement.getBoundingClientRect();
-
- _mouse.x = ( ( event.clientX - rect.left ) / rect.width ) * 2 - 1;
- _mouse.y = - ( ( event.clientY - rect.top ) / rect.height ) * 2 + 1;
-
- _raycaster.setFromCamera( _mouse, _camera );
-
- if ( _selected && scope.enabled ) {
-
- if ( _raycaster.ray.intersectPlane( _plane, _intersection ) ) {
-
- _selected.position.copy( _intersection.sub( _offset ).applyMatrix4( _inverseMatrix ) );
-
- }
-
- scope.dispatchEvent( { type: 'drag', object: _selected } );
-
- return;
-
- }
-
- _raycaster.setFromCamera( _mouse, _camera );
-
- var intersects = _raycaster.intersectObjects( _objects );
-
- if ( intersects.length > 0 ) {
-
- var object = intersects[ 0 ].object;
-
- _plane.setFromNormalAndCoplanarPoint( _camera.getWorldDirection( _plane.normal ), _worldPosition.setFromMatrixPosition( object.matrixWorld ) );
-
- if ( _hovered !== object ) {
-
- scope.dispatchEvent( { type: 'hoveron', object: object } );
-
- _domElement.style.cursor = 'pointer';
- _hovered = object;
-
- }
-
- } else {
-
- if ( _hovered !== null ) {
-
- scope.dispatchEvent( { type: 'hoveroff', object: _hovered } );
-
- _domElement.style.cursor = 'auto';
- _hovered = null;
-
- }
-
- }
-
- }
-
- function onDocumentMouseDown( event ) {
-
- event.preventDefault();
-
- _raycaster.setFromCamera( _mouse, _camera );
-
- var intersects = _raycaster.intersectObjects( _objects );
-
- if ( intersects.length > 0 ) {
-
- _selected = intersects[ 0 ].object;
-
- if ( _raycaster.ray.intersectPlane( _plane, _intersection ) ) {
-
- _inverseMatrix.getInverse( _selected.parent.matrixWorld );
- _offset.copy( _intersection ).sub( _worldPosition.setFromMatrixPosition( _selected.matrixWorld ) );
-
- }
-
- _domElement.style.cursor = 'move';
-
- scope.dispatchEvent( { type: 'dragstart', object: _selected } );
-
- }
-
-
- }
-
- function onDocumentMouseCancel( event ) {
-
- event.preventDefault();
-
- if ( _selected ) {
-
- scope.dispatchEvent( { type: 'dragend', object: _selected } );
-
- _selected = null;
-
- }
-
- _domElement.style.cursor = _hovered ? 'pointer' : 'auto';
-
- }
-
- function onDocumentTouchMove( event ) {
-
- event.preventDefault();
- event = event.changedTouches[ 0 ];
-
- var rect = _domElement.getBoundingClientRect();
-
- _mouse.x = ( ( event.clientX - rect.left ) / rect.width ) * 2 - 1;
- _mouse.y = - ( ( event.clientY - rect.top ) / rect.height ) * 2 + 1;
-
- _raycaster.setFromCamera( _mouse, _camera );
-
- if ( _selected && scope.enabled ) {
-
- if ( _raycaster.ray.intersectPlane( _plane, _intersection ) ) {
-
- _selected.position.copy( _intersection.sub( _offset ).applyMatrix4( _inverseMatrix ) );
-
- }
-
- scope.dispatchEvent( { type: 'drag', object: _selected } );
-
- return;
-
- }
-
- }
-
- function onDocumentTouchStart( event ) {
-
- event.preventDefault();
- event = event.changedTouches[ 0 ];
-
- var rect = _domElement.getBoundingClientRect();
-
- _mouse.x = ( ( event.clientX - rect.left ) / rect.width ) * 2 - 1;
- _mouse.y = - ( ( event.clientY - rect.top ) / rect.height ) * 2 + 1;
-
- _raycaster.setFromCamera( _mouse, _camera );
-
- var intersects = _raycaster.intersectObjects( _objects );
-
- if ( intersects.length > 0 ) {
-
- _selected = intersects[ 0 ].object;
-
- _plane.setFromNormalAndCoplanarPoint( _camera.getWorldDirection( _plane.normal ), _worldPosition.setFromMatrixPosition( _selected.matrixWorld ) );
-
- if ( _raycaster.ray.intersectPlane( _plane, _intersection ) ) {
-
- _inverseMatrix.getInverse( _selected.parent.matrixWorld );
- _offset.copy( _intersection ).sub( _worldPosition.setFromMatrixPosition( _selected.matrixWorld ) );
-
- }
-
- _domElement.style.cursor = 'move';
-
- scope.dispatchEvent( { type: 'dragstart', object: _selected } );
-
- }
-
-
- }
-
- function onDocumentTouchEnd( event ) {
-
- event.preventDefault();
-
- if ( _selected ) {
-
- scope.dispatchEvent( { type: 'dragend', object: _selected } );
-
- _selected = null;
-
- }
-
- _domElement.style.cursor = 'auto';
-
- }
-
- activate();
-
- // API
-
- this.enabled = true;
-
- this.activate = activate;
- this.deactivate = deactivate;
- this.dispose = dispose;
-
- // Backward compatibility
-
- this.setObjects = function () {
-
- console.error( 'THREE.DragControls: setObjects() has been removed.' );
-
- };
-
- this.on = function ( type, listener ) {
-
- console.warn( 'THREE.DragControls: on() has been deprecated. Use addEventListener() instead.' );
- scope.addEventListener( type, listener );
-
- };
-
- this.off = function ( type, listener ) {
-
- console.warn( 'THREE.DragControls: off() has been deprecated. Use removeEventListener() instead.' );
- scope.removeEventListener( type, listener );
-
- };
-
- this.notify = function ( type ) {
-
- console.error( 'THREE.DragControls: notify() has been deprecated. Use dispatchEvent() instead.' );
- scope.dispatchEvent( { type: type } );
-
- };
-
-};
-
-THREE.DragControls.prototype = Object.create( THREE.EventDispatcher.prototype );
-THREE.DragControls.prototype.constructor = THREE.DragControls;
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/encodings_pars_fragment.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/encodings_pars_fragment.glsl.js
deleted file mode 100644
index 11be08c91e099558aaec918214b3b19b63e85a38..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/encodings_pars_fragment.glsl.js
+++ /dev/null
@@ -1,85 +0,0 @@
-export default /* glsl */`
-// For a discussion of what this is, please read this: http://lousodrome.net/blog/light/2013/05/26/gamma-correct-and-hdr-rendering-in-a-32-bits-buffer/
-
-vec4 LinearToLinear( in vec4 value ) {
- return value;
-}
-
-vec4 GammaToLinear( in vec4 value, in float gammaFactor ) {
- return vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );
-}
-
-vec4 LinearToGamma( in vec4 value, in float gammaFactor ) {
- return vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );
-}
-
-vec4 sRGBToLinear( in vec4 value ) {
- return vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );
-}
-
-vec4 LinearTosRGB( in vec4 value ) {
- return vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );
-}
-
-vec4 RGBEToLinear( in vec4 value ) {
- return vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );
-}
-
-vec4 LinearToRGBE( in vec4 value ) {
- float maxComponent = max( max( value.r, value.g ), value.b );
- float fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );
- return vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );
-// return vec4( value.brg, ( 3.0 + 128.0 ) / 256.0 );
-}
-
-// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html
-vec4 RGBMToLinear( in vec4 value, in float maxRange ) {
- return vec4( value.rgb * value.a * maxRange, 1.0 );
-}
-
-vec4 LinearToRGBM( in vec4 value, in float maxRange ) {
- float maxRGB = max( value.r, max( value.g, value.b ) );
- float M = clamp( maxRGB / maxRange, 0.0, 1.0 );
- M = ceil( M * 255.0 ) / 255.0;
- return vec4( value.rgb / ( M * maxRange ), M );
-}
-
-// reference: http://iwasbeingirony.blogspot.ca/2010/06/difference-between-rgbm-and-rgbd.html
-vec4 RGBDToLinear( in vec4 value, in float maxRange ) {
- return vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );
-}
-
-vec4 LinearToRGBD( in vec4 value, in float maxRange ) {
- float maxRGB = max( value.r, max( value.g, value.b ) );
- float D = max( maxRange / maxRGB, 1.0 );
- D = min( floor( D ) / 255.0, 1.0 );
- return vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );
-}
-
-// LogLuv reference: http://graphicrants.blogspot.ca/2009/04/rgbm-color-encoding.html
-
-// M matrix, for encoding
-const mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );
-vec4 LinearToLogLuv( in vec4 value ) {
- vec3 Xp_Y_XYZp = cLogLuvM * value.rgb;
- Xp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );
- vec4 vResult;
- vResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;
- float Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;
- vResult.w = fract( Le );
- vResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;
- return vResult;
-}
-
-// Inverse M matrix, for decoding
-const mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );
-vec4 LogLuvToLinear( in vec4 value ) {
- float Le = value.z * 255.0 + value.w;
- vec3 Xp_Y_XYZp;
- Xp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );
- Xp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;
- Xp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;
- vec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;
- return vec4( max( vRGB, 0.0 ), 1.0 );
-}
-`;
diff --git a/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/data/ffhq_dataset.py b/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/data/ffhq_dataset.py
deleted file mode 100644
index 23992eb877f6b7b46cf5f40ed3667fc10916269b..0000000000000000000000000000000000000000
--- a/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/data/ffhq_dataset.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import random
-import time
-from os import path as osp
-from torch.utils import data as data
-from torchvision.transforms.functional import normalize
-
-from basicsr.data.transforms import augment
-from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
-from basicsr.utils.registry import DATASET_REGISTRY
-
-
-@DATASET_REGISTRY.register()
-class FFHQDataset(data.Dataset):
- """FFHQ dataset for StyleGAN.
-
- Args:
- opt (dict): Config for train datasets. It contains the following keys:
- dataroot_gt (str): Data root path for gt.
- io_backend (dict): IO backend type and other kwarg.
- mean (list | tuple): Image mean.
- std (list | tuple): Image std.
- use_hflip (bool): Whether to horizontally flip.
-
- """
-
- def __init__(self, opt):
- super(FFHQDataset, self).__init__()
- self.opt = opt
- # file client (io backend)
- self.file_client = None
- self.io_backend_opt = opt['io_backend']
-
- self.gt_folder = opt['dataroot_gt']
- self.mean = opt['mean']
- self.std = opt['std']
-
- if self.io_backend_opt['type'] == 'lmdb':
- self.io_backend_opt['db_paths'] = self.gt_folder
- if not self.gt_folder.endswith('.lmdb'):
- raise ValueError("'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
- with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
- self.paths = [line.split('.')[0] for line in fin]
- else:
- # FFHQ has 70000 images in total
- self.paths = [osp.join(self.gt_folder, f'{v:08d}.png') for v in range(70000)]
-
- def __getitem__(self, index):
- if self.file_client is None:
- self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
-
- # load gt image
- gt_path = self.paths[index]
- # avoid errors caused by high latency in reading files
- retry = 3
- while retry > 0:
- try:
- img_bytes = self.file_client.get(gt_path)
- except Exception as e:
- logger = get_root_logger()
- logger.warning(f'File client error: {e}, remaining retry times: {retry - 1}')
- # change another file to read
- index = random.randint(0, self.__len__())
- gt_path = self.paths[index]
- time.sleep(1) # sleep 1s for occasional server congestion
- else:
- break
- finally:
- retry -= 1
- img_gt = imfrombytes(img_bytes, float32=True)
-
- # random horizontal flip
- img_gt = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False)
- # BGR to RGB, HWC to CHW, numpy to tensor
- img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True)
- # normalize
- normalize(img_gt, self.mean, self.std, inplace=True)
- return {'gt': img_gt, 'gt_path': gt_path}
-
- def __len__(self):
- return len(self.paths)
diff --git a/spaces/bhasker412/IDD-YOLO-Tracking/models/common.py b/spaces/bhasker412/IDD-YOLO-Tracking/models/common.py
deleted file mode 100644
index edb5edc9fe1b0ad3b345a2103603393e74e5b65c..0000000000000000000000000000000000000000
--- a/spaces/bhasker412/IDD-YOLO-Tracking/models/common.py
+++ /dev/null
@@ -1,2019 +0,0 @@
-import math
-from copy import copy
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import requests
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision.ops import DeformConv2d
-from PIL import Image
-from torch.cuda import amp
-
-from utils.datasets import letterbox
-from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh
-from utils.plots import color_list, plot_one_box
-from utils.torch_utils import time_synchronized
-
-
-##### basic ####
-
-def autopad(k, p=None): # kernel, padding
- # Pad to 'same'
- if p is None:
- p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
- return p
-
-
-class MP(nn.Module):
- def __init__(self, k=2):
- super(MP, self).__init__()
- self.m = nn.MaxPool2d(kernel_size=k, stride=k)
-
- def forward(self, x):
- return self.m(x)
-
-
-class SP(nn.Module):
- def __init__(self, k=3, s=1):
- super(SP, self).__init__()
- self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2)
-
- def forward(self, x):
- return self.m(x)
-
-
-class ReOrg(nn.Module):
- def __init__(self):
- super(ReOrg, self).__init__()
-
- def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
- return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)
-
-
-class Concat(nn.Module):
- def __init__(self, dimension=1):
- super(Concat, self).__init__()
- self.d = dimension
-
- def forward(self, x):
- return torch.cat(x, self.d)
-
-
-class Chuncat(nn.Module):
- def __init__(self, dimension=1):
- super(Chuncat, self).__init__()
- self.d = dimension
-
- def forward(self, x):
- x1 = []
- x2 = []
- for xi in x:
- xi1, xi2 = xi.chunk(2, self.d)
- x1.append(xi1)
- x2.append(xi2)
- return torch.cat(x1+x2, self.d)
-
-
-class Shortcut(nn.Module):
- def __init__(self, dimension=0):
- super(Shortcut, self).__init__()
- self.d = dimension
-
- def forward(self, x):
- return x[0]+x[1]
-
-
-class Foldcut(nn.Module):
- def __init__(self, dimension=0):
- super(Foldcut, self).__init__()
- self.d = dimension
-
- def forward(self, x):
- x1, x2 = x.chunk(2, self.d)
- return x1+x2
-
-
-class Conv(nn.Module):
- # Standard convolution
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super(Conv, self).__init__()
- self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
- self.bn = nn.BatchNorm2d(c2)
- self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
-
- def forward(self, x):
- return self.act(self.bn(self.conv(x)))
-
- def fuseforward(self, x):
- return self.act(self.conv(x))
-
-
-class RobustConv(nn.Module):
- # Robust convolution (use high kernel size 7-11 for: downsampling and other layers). Train for 300 - 450 epochs.
- def __init__(self, c1, c2, k=7, s=1, p=None, g=1, act=True, layer_scale_init_value=1e-6): # ch_in, ch_out, kernel, stride, padding, groups
- super(RobustConv, self).__init__()
- self.conv_dw = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act)
- self.conv1x1 = nn.Conv2d(c1, c2, 1, 1, 0, groups=1, bias=True)
- self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None
-
- def forward(self, x):
- x = x.to(memory_format=torch.channels_last)
- x = self.conv1x1(self.conv_dw(x))
- if self.gamma is not None:
- x = x.mul(self.gamma.reshape(1, -1, 1, 1))
- return x
-
-
-class RobustConv2(nn.Module):
- # Robust convolution 2 (use [32, 5, 2] or [32, 7, 4] or [32, 11, 8] for one of the paths in CSP).
- def __init__(self, c1, c2, k=7, s=4, p=None, g=1, act=True, layer_scale_init_value=1e-6): # ch_in, ch_out, kernel, stride, padding, groups
- super(RobustConv2, self).__init__()
- self.conv_strided = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act)
- self.conv_deconv = nn.ConvTranspose2d(in_channels=c1, out_channels=c2, kernel_size=s, stride=s,
- padding=0, bias=True, dilation=1, groups=1
- )
- self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None
-
- def forward(self, x):
- x = self.conv_deconv(self.conv_strided(x))
- if self.gamma is not None:
- x = x.mul(self.gamma.reshape(1, -1, 1, 1))
- return x
-
-
-def DWConv(c1, c2, k=1, s=1, act=True):
- # Depthwise convolution
- return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
-
-
-class GhostConv(nn.Module):
- # Ghost Convolution https://github.com/huawei-noah/ghostnet
- def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
- super(GhostConv, self).__init__()
- c_ = c2 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, k, s, None, g, act)
- self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
-
- def forward(self, x):
- y = self.cv1(x)
- return torch.cat([y, self.cv2(y)], 1)
-
-
-class Stem(nn.Module):
- # Stem
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super(Stem, self).__init__()
- c_ = int(c2/2) # hidden channels
- self.cv1 = Conv(c1, c_, 3, 2)
- self.cv2 = Conv(c_, c_, 1, 1)
- self.cv3 = Conv(c_, c_, 3, 2)
- self.pool = torch.nn.MaxPool2d(2, stride=2)
- self.cv4 = Conv(2 * c_, c2, 1, 1)
-
- def forward(self, x):
- x = self.cv1(x)
- return self.cv4(torch.cat((self.cv3(self.cv2(x)), self.pool(x)), dim=1))
-
-
-class DownC(nn.Module):
- # Spatial pyramid pooling layer used in YOLOv3-SPP
- def __init__(self, c1, c2, n=1, k=2):
- super(DownC, self).__init__()
- c_ = int(c1) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c2//2, 3, k)
- self.cv3 = Conv(c1, c2//2, 1, 1)
- self.mp = nn.MaxPool2d(kernel_size=k, stride=k)
-
- def forward(self, x):
- return torch.cat((self.cv2(self.cv1(x)), self.cv3(self.mp(x))), dim=1)
-
-
-class SPP(nn.Module):
- # Spatial pyramid pooling layer used in YOLOv3-SPP
- def __init__(self, c1, c2, k=(5, 9, 13)):
- super(SPP, self).__init__()
- c_ = c1 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
- self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
-
- def forward(self, x):
- x = self.cv1(x)
- return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
-
-
-class Bottleneck(nn.Module):
- # Darknet bottleneck
- def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super(Bottleneck, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c2, 3, 1, g=g)
- self.add = shortcut and c1 == c2
-
- def forward(self, x):
- return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class Res(nn.Module):
- # ResNet bottleneck
- def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super(Res, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c_, 3, 1, g=g)
- self.cv3 = Conv(c_, c2, 1, 1)
- self.add = shortcut and c1 == c2
-
- def forward(self, x):
- return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x)))
-
-
-class ResX(Res):
- # ResNet bottleneck
- def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super().__init__(c1, c2, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
-
-
-class Ghost(nn.Module):
- # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
- def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
- super(Ghost, self).__init__()
- c_ = c2 // 2
- self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
- DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
- GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
- self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
- Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
-
- def forward(self, x):
- return self.conv(x) + self.shortcut(x)
-
-##### end of basic #####
-
-
-##### cspnet #####
-
-class SPPCSPC(nn.Module):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
- super(SPPCSPC, self).__init__()
- c_ = int(2 * c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(c_, c_, 3, 1)
- self.cv4 = Conv(c_, c_, 1, 1)
- self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
- self.cv5 = Conv(4 * c_, c_, 1, 1)
- self.cv6 = Conv(c_, c_, 3, 1)
- self.cv7 = Conv(2 * c_, c2, 1, 1)
-
- def forward(self, x):
- x1 = self.cv4(self.cv3(self.cv1(x)))
- y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
- y2 = self.cv2(x)
- return self.cv7(torch.cat((y1, y2), dim=1))
-
-class GhostSPPCSPC(SPPCSPC):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
- super().__init__(c1, c2, n, shortcut, g, e, k)
- c_ = int(2 * c2 * e) # hidden channels
- self.cv1 = GhostConv(c1, c_, 1, 1)
- self.cv2 = GhostConv(c1, c_, 1, 1)
- self.cv3 = GhostConv(c_, c_, 3, 1)
- self.cv4 = GhostConv(c_, c_, 1, 1)
- self.cv5 = GhostConv(4 * c_, c_, 1, 1)
- self.cv6 = GhostConv(c_, c_, 3, 1)
- self.cv7 = GhostConv(2 * c_, c2, 1, 1)
-
-
-class GhostStem(Stem):
- # Stem
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super().__init__(c1, c2, k, s, p, g, act)
- c_ = int(c2/2) # hidden channels
- self.cv1 = GhostConv(c1, c_, 3, 2)
- self.cv2 = GhostConv(c_, c_, 1, 1)
- self.cv3 = GhostConv(c_, c_, 3, 2)
- self.cv4 = GhostConv(2 * c_, c2, 1, 1)
-
-
-class BottleneckCSPA(nn.Module):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(BottleneckCSPA, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1, 1)
- self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- y1 = self.m(self.cv1(x))
- y2 = self.cv2(x)
- return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class BottleneckCSPB(nn.Module):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(BottleneckCSPB, self).__init__()
- c_ = int(c2) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1, 1)
- self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- x1 = self.cv1(x)
- y1 = self.m(x1)
- y2 = self.cv2(x1)
- return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class BottleneckCSPC(nn.Module):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(BottleneckCSPC, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(c_, c_, 1, 1)
- self.cv4 = Conv(2 * c_, c2, 1, 1)
- self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- y1 = self.cv3(self.m(self.cv1(x)))
- y2 = self.cv2(x)
- return self.cv4(torch.cat((y1, y2), dim=1))
-
-
-class ResCSPA(BottleneckCSPA):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class ResCSPB(BottleneckCSPB):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2) # hidden channels
- self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class ResCSPC(BottleneckCSPC):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class ResXCSPA(ResCSPA):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class ResXCSPB(ResCSPB):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2) # hidden channels
- self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class ResXCSPC(ResCSPC):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class GhostCSPA(BottleneckCSPA):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)])
-
-
-class GhostCSPB(BottleneckCSPB):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2) # hidden channels
- self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)])
-
-
-class GhostCSPC(BottleneckCSPC):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)])
-
-##### end of cspnet #####
-
-
-##### yolor #####
-
-class ImplicitA(nn.Module):
- def __init__(self, channel, mean=0., std=.02):
- super(ImplicitA, self).__init__()
- self.channel = channel
- self.mean = mean
- self.std = std
- self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1))
- nn.init.normal_(self.implicit, mean=self.mean, std=self.std)
-
- def forward(self, x):
- return self.implicit + x
-
-
-class ImplicitM(nn.Module):
- def __init__(self, channel, mean=1., std=.02):
- super(ImplicitM, self).__init__()
- self.channel = channel
- self.mean = mean
- self.std = std
- self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1))
- nn.init.normal_(self.implicit, mean=self.mean, std=self.std)
-
- def forward(self, x):
- return self.implicit * x
-
-##### end of yolor #####
-
-
-##### repvgg #####
-
-class RepConv(nn.Module):
- # Represented convolution
- # https://arxiv.org/abs/2101.03697
-
- def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True, deploy=False):
- super(RepConv, self).__init__()
-
- self.deploy = deploy
- self.groups = g
- self.in_channels = c1
- self.out_channels = c2
-
- assert k == 3
- assert autopad(k, p) == 1
-
- padding_11 = autopad(k, p) - k // 2
-
- self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
-
- if deploy:
- self.rbr_reparam = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=True)
-
- else:
- self.rbr_identity = (nn.BatchNorm2d(num_features=c1) if c2 == c1 and s == 1 else None)
-
- self.rbr_dense = nn.Sequential(
- nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False),
- nn.BatchNorm2d(num_features=c2),
- )
-
- self.rbr_1x1 = nn.Sequential(
- nn.Conv2d( c1, c2, 1, s, padding_11, groups=g, bias=False),
- nn.BatchNorm2d(num_features=c2),
- )
-
- def forward(self, inputs):
- if hasattr(self, "rbr_reparam"):
- return self.act(self.rbr_reparam(inputs))
-
- if self.rbr_identity is None:
- id_out = 0
- else:
- id_out = self.rbr_identity(inputs)
-
- return self.act(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out)
-
- def get_equivalent_kernel_bias(self):
- kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
- kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
- kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
- return (
- kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid,
- bias3x3 + bias1x1 + biasid,
- )
-
- def _pad_1x1_to_3x3_tensor(self, kernel1x1):
- if kernel1x1 is None:
- return 0
- else:
- return nn.functional.pad(kernel1x1, [1, 1, 1, 1])
-
- def _fuse_bn_tensor(self, branch):
- if branch is None:
- return 0, 0
- if isinstance(branch, nn.Sequential):
- kernel = branch[0].weight
- running_mean = branch[1].running_mean
- running_var = branch[1].running_var
- gamma = branch[1].weight
- beta = branch[1].bias
- eps = branch[1].eps
- else:
- assert isinstance(branch, nn.BatchNorm2d)
- if not hasattr(self, "id_tensor"):
- input_dim = self.in_channels // self.groups
- kernel_value = np.zeros(
- (self.in_channels, input_dim, 3, 3), dtype=np.float32
- )
- for i in range(self.in_channels):
- kernel_value[i, i % input_dim, 1, 1] = 1
- self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
- kernel = self.id_tensor
- running_mean = branch.running_mean
- running_var = branch.running_var
- gamma = branch.weight
- beta = branch.bias
- eps = branch.eps
- std = (running_var + eps).sqrt()
- t = (gamma / std).reshape(-1, 1, 1, 1)
- return kernel * t, beta - running_mean * gamma / std
-
- def repvgg_convert(self):
- kernel, bias = self.get_equivalent_kernel_bias()
- return (
- kernel.detach().cpu().numpy(),
- bias.detach().cpu().numpy(),
- )
-
- def fuse_conv_bn(self, conv, bn):
-
- std = (bn.running_var + bn.eps).sqrt()
- bias = bn.bias - bn.running_mean * bn.weight / std
-
- t = (bn.weight / std).reshape(-1, 1, 1, 1)
- weights = conv.weight * t
-
- bn = nn.Identity()
- conv = nn.Conv2d(in_channels = conv.in_channels,
- out_channels = conv.out_channels,
- kernel_size = conv.kernel_size,
- stride=conv.stride,
- padding = conv.padding,
- dilation = conv.dilation,
- groups = conv.groups,
- bias = True,
- padding_mode = conv.padding_mode)
-
- conv.weight = torch.nn.Parameter(weights)
- conv.bias = torch.nn.Parameter(bias)
- return conv
-
- def fuse_repvgg_block(self):
- if self.deploy:
- return
- print(f"RepConv.fuse_repvgg_block")
-
- self.rbr_dense = self.fuse_conv_bn(self.rbr_dense[0], self.rbr_dense[1])
-
- self.rbr_1x1 = self.fuse_conv_bn(self.rbr_1x1[0], self.rbr_1x1[1])
- rbr_1x1_bias = self.rbr_1x1.bias
- weight_1x1_expanded = torch.nn.functional.pad(self.rbr_1x1.weight, [1, 1, 1, 1])
-
- # Fuse self.rbr_identity
- if (isinstance(self.rbr_identity, nn.BatchNorm2d) or isinstance(self.rbr_identity, nn.modules.batchnorm.SyncBatchNorm)):
- # print(f"fuse: rbr_identity == BatchNorm2d or SyncBatchNorm")
- identity_conv_1x1 = nn.Conv2d(
- in_channels=self.in_channels,
- out_channels=self.out_channels,
- kernel_size=1,
- stride=1,
- padding=0,
- groups=self.groups,
- bias=False)
- identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.to(self.rbr_1x1.weight.data.device)
- identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.squeeze().squeeze()
- # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}")
- identity_conv_1x1.weight.data.fill_(0.0)
- identity_conv_1x1.weight.data.fill_diagonal_(1.0)
- identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.unsqueeze(2).unsqueeze(3)
- # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}")
-
- identity_conv_1x1 = self.fuse_conv_bn(identity_conv_1x1, self.rbr_identity)
- bias_identity_expanded = identity_conv_1x1.bias
- weight_identity_expanded = torch.nn.functional.pad(identity_conv_1x1.weight, [1, 1, 1, 1])
- else:
- # print(f"fuse: rbr_identity != BatchNorm2d, rbr_identity = {self.rbr_identity}")
- bias_identity_expanded = torch.nn.Parameter( torch.zeros_like(rbr_1x1_bias) )
- weight_identity_expanded = torch.nn.Parameter( torch.zeros_like(weight_1x1_expanded) )
-
-
- #print(f"self.rbr_1x1.weight = {self.rbr_1x1.weight.shape}, ")
- #print(f"weight_1x1_expanded = {weight_1x1_expanded.shape}, ")
- #print(f"self.rbr_dense.weight = {self.rbr_dense.weight.shape}, ")
-
- self.rbr_dense.weight = torch.nn.Parameter(self.rbr_dense.weight + weight_1x1_expanded + weight_identity_expanded)
- self.rbr_dense.bias = torch.nn.Parameter(self.rbr_dense.bias + rbr_1x1_bias + bias_identity_expanded)
-
- self.rbr_reparam = self.rbr_dense
- self.deploy = True
-
- if self.rbr_identity is not None:
- del self.rbr_identity
- self.rbr_identity = None
-
- if self.rbr_1x1 is not None:
- del self.rbr_1x1
- self.rbr_1x1 = None
-
- if self.rbr_dense is not None:
- del self.rbr_dense
- self.rbr_dense = None
-
-
-class RepBottleneck(Bottleneck):
- # Standard bottleneck
- def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super().__init__(c1, c2, shortcut=True, g=1, e=0.5)
- c_ = int(c2 * e) # hidden channels
- self.cv2 = RepConv(c_, c2, 3, 1, g=g)
-
-
-class RepBottleneckCSPA(BottleneckCSPA):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class RepBottleneckCSPB(BottleneckCSPB):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2) # hidden channels
- self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class RepBottleneckCSPC(BottleneckCSPC):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class RepRes(Res):
- # Standard bottleneck
- def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super().__init__(c1, c2, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.cv2 = RepConv(c_, c_, 3, 1, g=g)
-
-
-class RepResCSPA(ResCSPA):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResCSPB(ResCSPB):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2) # hidden channels
- self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResCSPC(ResCSPC):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResX(ResX):
- # Standard bottleneck
- def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super().__init__(c1, c2, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.cv2 = RepConv(c_, c_, 3, 1, g=g)
-
-
-class RepResXCSPA(ResXCSPA):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResXCSPB(ResXCSPB):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2) # hidden channels
- self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResXCSPC(ResXCSPC):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-##### end of repvgg #####
-
-
-##### transformer #####
-
-class TransformerLayer(nn.Module):
- # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
- def __init__(self, c, num_heads):
- super().__init__()
- self.q = nn.Linear(c, c, bias=False)
- self.k = nn.Linear(c, c, bias=False)
- self.v = nn.Linear(c, c, bias=False)
- self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
- self.fc1 = nn.Linear(c, c, bias=False)
- self.fc2 = nn.Linear(c, c, bias=False)
-
- def forward(self, x):
- x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
- x = self.fc2(self.fc1(x)) + x
- return x
-
-
-class TransformerBlock(nn.Module):
- # Vision Transformer https://arxiv.org/abs/2010.11929
- def __init__(self, c1, c2, num_heads, num_layers):
- super().__init__()
- self.conv = None
- if c1 != c2:
- self.conv = Conv(c1, c2)
- self.linear = nn.Linear(c2, c2) # learnable position embedding
- self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)])
- self.c2 = c2
-
- def forward(self, x):
- if self.conv is not None:
- x = self.conv(x)
- b, _, w, h = x.shape
- p = x.flatten(2)
- p = p.unsqueeze(0)
- p = p.transpose(0, 3)
- p = p.squeeze(3)
- e = self.linear(p)
- x = p + e
-
- x = self.tr(x)
- x = x.unsqueeze(3)
- x = x.transpose(0, 3)
- x = x.reshape(b, self.c2, w, h)
- return x
-
-##### end of transformer #####
-
-
-##### yolov5 #####
-
-class Focus(nn.Module):
- # Focus wh information into c-space
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super(Focus, self).__init__()
- self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
- # self.contract = Contract(gain=2)
-
- def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
- return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
- # return self.conv(self.contract(x))
-
-
-class SPPF(nn.Module):
- # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
- def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
- super().__init__()
- c_ = c1 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_ * 4, c2, 1, 1)
- self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
-
- def forward(self, x):
- x = self.cv1(x)
- y1 = self.m(x)
- y2 = self.m(y1)
- return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
-
-
-class Contract(nn.Module):
- # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
- def __init__(self, gain=2):
- super().__init__()
- self.gain = gain
-
- def forward(self, x):
- N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
- s = self.gain
- x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2)
- x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
- return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40)
-
-
-class Expand(nn.Module):
- # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
- def __init__(self, gain=2):
- super().__init__()
- self.gain = gain
-
- def forward(self, x):
- N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
- s = self.gain
- x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80)
- x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
- return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160)
-
-
-class NMS(nn.Module):
- # Non-Maximum Suppression (NMS) module
- conf = 0.25 # confidence threshold
- iou = 0.45 # IoU threshold
- classes = None # (optional list) filter by class
-
- def __init__(self):
- super(NMS, self).__init__()
-
- def forward(self, x):
- return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
-
-
-class autoShape(nn.Module):
- # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
- conf = 0.25 # NMS confidence threshold
- iou = 0.45 # NMS IoU threshold
- classes = None # (optional list) filter by class
-
- def __init__(self, model):
- super(autoShape, self).__init__()
- self.model = model.eval()
-
- def autoshape(self):
- print('autoShape already enabled, skipping... ') # model already converted to model.autoshape()
- return self
-
- @torch.no_grad()
- def forward(self, imgs, size=640, augment=False, profile=False):
- # Inference from various sources. For height=640, width=1280, RGB images example inputs are:
- # filename: imgs = 'data/samples/zidane.jpg'
- # URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
- # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
- # PIL: = Image.open('image.jpg') # HWC x(640,1280,3)
- # numpy: = np.zeros((640,1280,3)) # HWC
- # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
- # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
-
- t = [time_synchronized()]
- p = next(self.model.parameters()) # for device and type
- if isinstance(imgs, torch.Tensor): # torch
- with amp.autocast(enabled=p.device.type != 'cpu'):
- return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
-
- # Pre-process
- n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
- shape0, shape1, files = [], [], [] # image and inference shapes, filenames
- for i, im in enumerate(imgs):
- f = f'image{i}' # filename
- if isinstance(im, str): # filename or uri
- im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im
- elif isinstance(im, Image.Image): # PIL Image
- im, f = np.asarray(im), getattr(im, 'filename', f) or f
- files.append(Path(f).with_suffix('.jpg').name)
- if im.shape[0] < 5: # image in CHW
- im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
- im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input
- s = im.shape[:2] # HWC
- shape0.append(s) # image shape
- g = (size / max(s)) # gain
- shape1.append([y * g for y in s])
- imgs[i] = im # update
- shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
- x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
- x = np.stack(x, 0) if n > 1 else x[0][None] # stack
- x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
- x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32
- t.append(time_synchronized())
-
- with amp.autocast(enabled=p.device.type != 'cpu'):
- # Inference
- y = self.model(x, augment, profile)[0] # forward
- t.append(time_synchronized())
-
- # Post-process
- y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
- for i in range(n):
- scale_coords(shape1, y[i][:, :4], shape0[i])
-
- t.append(time_synchronized())
- return Detections(imgs, y, files, t, self.names, x.shape)
-
-
-class Detections:
- # detections class for YOLOv5 inference results
- def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
- super(Detections, self).__init__()
- d = pred[0].device # device
- gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations
- self.imgs = imgs # list of images as numpy arrays
- self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
- self.names = names # class names
- self.files = files # image filenames
- self.xyxy = pred # xyxy pixels
- self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
- self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
- self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
- self.n = len(self.pred) # number of images (batch size)
- self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms)
- self.s = shape # inference BCHW shape
-
- def display(self, pprint=False, show=False, save=False, render=False, save_dir=''):
- colors = color_list()
- for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
- str = f'image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
- if pred is not None:
- for c in pred[:, -1].unique():
- n = (pred[:, -1] == c).sum() # detections per class
- str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
- if show or save or render:
- for *box, conf, cls in pred: # xyxy, confidence, class
- label = f'{self.names[int(cls)]} {conf:.2f}'
- plot_one_box(box, img, label=label, color=colors[int(cls) % 10])
- img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np
- if pprint:
- print(str.rstrip(', '))
- if show:
- img.show(self.files[i]) # show
- if save:
- f = self.files[i]
- img.save(Path(save_dir) / f) # save
- print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n')
- if render:
- self.imgs[i] = np.asarray(img)
-
- def print(self):
- self.display(pprint=True) # print results
- print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t)
-
- def show(self):
- self.display(show=True) # show results
-
- def save(self, save_dir='runs/hub/exp'):
- save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp') # increment save_dir
- Path(save_dir).mkdir(parents=True, exist_ok=True)
- self.display(save=True, save_dir=save_dir) # save results
-
- def render(self):
- self.display(render=True) # render results
- return self.imgs
-
- def pandas(self):
- # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
- new = copy(self) # return copy
- ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
- cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
- for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
- a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
- setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
- return new
-
- def tolist(self):
- # return a list of Detections objects, i.e. 'for result in results.tolist():'
- x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)]
- for d in x:
- for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
- setattr(d, k, getattr(d, k)[0]) # pop out of list
- return x
-
- def __len__(self):
- return self.n
-
-
-class Classify(nn.Module):
- # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
- super(Classify, self).__init__()
- self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
- self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
- self.flat = nn.Flatten()
-
- def forward(self, x):
- z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
- return self.flat(self.conv(z)) # flatten to x(b,c2)
-
-##### end of yolov5 ######
-
-
-##### orepa #####
-
-def transI_fusebn(kernel, bn):
- gamma = bn.weight
- std = (bn.running_var + bn.eps).sqrt()
- return kernel * ((gamma / std).reshape(-1, 1, 1, 1)), bn.bias - bn.running_mean * gamma / std
-
-
-class ConvBN(nn.Module):
- def __init__(self, in_channels, out_channels, kernel_size,
- stride=1, padding=0, dilation=1, groups=1, deploy=False, nonlinear=None):
- super().__init__()
- if nonlinear is None:
- self.nonlinear = nn.Identity()
- else:
- self.nonlinear = nonlinear
- if deploy:
- self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
- stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True)
- else:
- self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
- stride=stride, padding=padding, dilation=dilation, groups=groups, bias=False)
- self.bn = nn.BatchNorm2d(num_features=out_channels)
-
- def forward(self, x):
- if hasattr(self, 'bn'):
- return self.nonlinear(self.bn(self.conv(x)))
- else:
- return self.nonlinear(self.conv(x))
-
- def switch_to_deploy(self):
- kernel, bias = transI_fusebn(self.conv.weight, self.bn)
- conv = nn.Conv2d(in_channels=self.conv.in_channels, out_channels=self.conv.out_channels, kernel_size=self.conv.kernel_size,
- stride=self.conv.stride, padding=self.conv.padding, dilation=self.conv.dilation, groups=self.conv.groups, bias=True)
- conv.weight.data = kernel
- conv.bias.data = bias
- for para in self.parameters():
- para.detach_()
- self.__delattr__('conv')
- self.__delattr__('bn')
- self.conv = conv
-
-class OREPA_3x3_RepConv(nn.Module):
-
- def __init__(self, in_channels, out_channels, kernel_size,
- stride=1, padding=0, dilation=1, groups=1,
- internal_channels_1x1_3x3=None,
- deploy=False, nonlinear=None, single_init=False):
- super(OREPA_3x3_RepConv, self).__init__()
- self.deploy = deploy
-
- if nonlinear is None:
- self.nonlinear = nn.Identity()
- else:
- self.nonlinear = nonlinear
-
- self.kernel_size = kernel_size
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.groups = groups
- assert padding == kernel_size // 2
-
- self.stride = stride
- self.padding = padding
- self.dilation = dilation
-
- self.branch_counter = 0
-
- self.weight_rbr_origin = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), kernel_size, kernel_size))
- nn.init.kaiming_uniform_(self.weight_rbr_origin, a=math.sqrt(1.0))
- self.branch_counter += 1
-
-
- if groups < out_channels:
- self.weight_rbr_avg_conv = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), 1, 1))
- self.weight_rbr_pfir_conv = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), 1, 1))
- nn.init.kaiming_uniform_(self.weight_rbr_avg_conv, a=1.0)
- nn.init.kaiming_uniform_(self.weight_rbr_pfir_conv, a=1.0)
- self.weight_rbr_avg_conv.data
- self.weight_rbr_pfir_conv.data
- self.register_buffer('weight_rbr_avg_avg', torch.ones(kernel_size, kernel_size).mul(1.0/kernel_size/kernel_size))
- self.branch_counter += 1
-
- else:
- raise NotImplementedError
- self.branch_counter += 1
-
- if internal_channels_1x1_3x3 is None:
- internal_channels_1x1_3x3 = in_channels if groups < out_channels else 2 * in_channels # For mobilenet, it is better to have 2X internal channels
-
- if internal_channels_1x1_3x3 == in_channels:
- self.weight_rbr_1x1_kxk_idconv1 = nn.Parameter(torch.zeros(in_channels, int(in_channels/self.groups), 1, 1))
- id_value = np.zeros((in_channels, int(in_channels/self.groups), 1, 1))
- for i in range(in_channels):
- id_value[i, i % int(in_channels/self.groups), 0, 0] = 1
- id_tensor = torch.from_numpy(id_value).type_as(self.weight_rbr_1x1_kxk_idconv1)
- self.register_buffer('id_tensor', id_tensor)
-
- else:
- self.weight_rbr_1x1_kxk_conv1 = nn.Parameter(torch.Tensor(internal_channels_1x1_3x3, int(in_channels/self.groups), 1, 1))
- nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv1, a=math.sqrt(1.0))
- self.weight_rbr_1x1_kxk_conv2 = nn.Parameter(torch.Tensor(out_channels, int(internal_channels_1x1_3x3/self.groups), kernel_size, kernel_size))
- nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv2, a=math.sqrt(1.0))
- self.branch_counter += 1
-
- expand_ratio = 8
- self.weight_rbr_gconv_dw = nn.Parameter(torch.Tensor(in_channels*expand_ratio, 1, kernel_size, kernel_size))
- self.weight_rbr_gconv_pw = nn.Parameter(torch.Tensor(out_channels, in_channels*expand_ratio, 1, 1))
- nn.init.kaiming_uniform_(self.weight_rbr_gconv_dw, a=math.sqrt(1.0))
- nn.init.kaiming_uniform_(self.weight_rbr_gconv_pw, a=math.sqrt(1.0))
- self.branch_counter += 1
-
- if out_channels == in_channels and stride == 1:
- self.branch_counter += 1
-
- self.vector = nn.Parameter(torch.Tensor(self.branch_counter, self.out_channels))
- self.bn = nn.BatchNorm2d(out_channels)
-
- self.fre_init()
-
- nn.init.constant_(self.vector[0, :], 0.25) #origin
- nn.init.constant_(self.vector[1, :], 0.25) #avg
- nn.init.constant_(self.vector[2, :], 0.0) #prior
- nn.init.constant_(self.vector[3, :], 0.5) #1x1_kxk
- nn.init.constant_(self.vector[4, :], 0.5) #dws_conv
-
-
- def fre_init(self):
- prior_tensor = torch.Tensor(self.out_channels, self.kernel_size, self.kernel_size)
- half_fg = self.out_channels/2
- for i in range(self.out_channels):
- for h in range(3):
- for w in range(3):
- if i < half_fg:
- prior_tensor[i, h, w] = math.cos(math.pi*(h+0.5)*(i+1)/3)
- else:
- prior_tensor[i, h, w] = math.cos(math.pi*(w+0.5)*(i+1-half_fg)/3)
-
- self.register_buffer('weight_rbr_prior', prior_tensor)
-
- def weight_gen(self):
-
- weight_rbr_origin = torch.einsum('oihw,o->oihw', self.weight_rbr_origin, self.vector[0, :])
-
- weight_rbr_avg = torch.einsum('oihw,o->oihw', torch.einsum('oihw,hw->oihw', self.weight_rbr_avg_conv, self.weight_rbr_avg_avg), self.vector[1, :])
-
- weight_rbr_pfir = torch.einsum('oihw,o->oihw', torch.einsum('oihw,ohw->oihw', self.weight_rbr_pfir_conv, self.weight_rbr_prior), self.vector[2, :])
-
- weight_rbr_1x1_kxk_conv1 = None
- if hasattr(self, 'weight_rbr_1x1_kxk_idconv1'):
- weight_rbr_1x1_kxk_conv1 = (self.weight_rbr_1x1_kxk_idconv1 + self.id_tensor).squeeze()
- elif hasattr(self, 'weight_rbr_1x1_kxk_conv1'):
- weight_rbr_1x1_kxk_conv1 = self.weight_rbr_1x1_kxk_conv1.squeeze()
- else:
- raise NotImplementedError
- weight_rbr_1x1_kxk_conv2 = self.weight_rbr_1x1_kxk_conv2
-
- if self.groups > 1:
- g = self.groups
- t, ig = weight_rbr_1x1_kxk_conv1.size()
- o, tg, h, w = weight_rbr_1x1_kxk_conv2.size()
- weight_rbr_1x1_kxk_conv1 = weight_rbr_1x1_kxk_conv1.view(g, int(t/g), ig)
- weight_rbr_1x1_kxk_conv2 = weight_rbr_1x1_kxk_conv2.view(g, int(o/g), tg, h, w)
- weight_rbr_1x1_kxk = torch.einsum('gti,gothw->goihw', weight_rbr_1x1_kxk_conv1, weight_rbr_1x1_kxk_conv2).view(o, ig, h, w)
- else:
- weight_rbr_1x1_kxk = torch.einsum('ti,othw->oihw', weight_rbr_1x1_kxk_conv1, weight_rbr_1x1_kxk_conv2)
-
- weight_rbr_1x1_kxk = torch.einsum('oihw,o->oihw', weight_rbr_1x1_kxk, self.vector[3, :])
-
- weight_rbr_gconv = self.dwsc2full(self.weight_rbr_gconv_dw, self.weight_rbr_gconv_pw, self.in_channels)
- weight_rbr_gconv = torch.einsum('oihw,o->oihw', weight_rbr_gconv, self.vector[4, :])
-
- weight = weight_rbr_origin + weight_rbr_avg + weight_rbr_1x1_kxk + weight_rbr_pfir + weight_rbr_gconv
-
- return weight
-
- def dwsc2full(self, weight_dw, weight_pw, groups):
-
- t, ig, h, w = weight_dw.size()
- o, _, _, _ = weight_pw.size()
- tg = int(t/groups)
- i = int(ig*groups)
- weight_dw = weight_dw.view(groups, tg, ig, h, w)
- weight_pw = weight_pw.squeeze().view(o, groups, tg)
-
- weight_dsc = torch.einsum('gtihw,ogt->ogihw', weight_dw, weight_pw)
- return weight_dsc.view(o, i, h, w)
-
- def forward(self, inputs):
- weight = self.weight_gen()
- out = F.conv2d(inputs, weight, bias=None, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups)
-
- return self.nonlinear(self.bn(out))
-
-class RepConv_OREPA(nn.Module):
-
- def __init__(self, c1, c2, k=3, s=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False, nonlinear=nn.SiLU()):
- super(RepConv_OREPA, self).__init__()
- self.deploy = deploy
- self.groups = groups
- self.in_channels = c1
- self.out_channels = c2
-
- self.padding = padding
- self.dilation = dilation
- self.groups = groups
-
- assert k == 3
- assert padding == 1
-
- padding_11 = padding - k // 2
-
- if nonlinear is None:
- self.nonlinearity = nn.Identity()
- else:
- self.nonlinearity = nonlinear
-
- if use_se:
- self.se = SEBlock(self.out_channels, internal_neurons=self.out_channels // 16)
- else:
- self.se = nn.Identity()
-
- if deploy:
- self.rbr_reparam = nn.Conv2d(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=k, stride=s,
- padding=padding, dilation=dilation, groups=groups, bias=True, padding_mode=padding_mode)
-
- else:
- self.rbr_identity = nn.BatchNorm2d(num_features=self.in_channels) if self.out_channels == self.in_channels and s == 1 else None
- self.rbr_dense = OREPA_3x3_RepConv(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=k, stride=s, padding=padding, groups=groups, dilation=1)
- self.rbr_1x1 = ConvBN(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=1, stride=s, padding=padding_11, groups=groups, dilation=1)
- print('RepVGG Block, identity = ', self.rbr_identity)
-
-
- def forward(self, inputs):
- if hasattr(self, 'rbr_reparam'):
- return self.nonlinearity(self.se(self.rbr_reparam(inputs)))
-
- if self.rbr_identity is None:
- id_out = 0
- else:
- id_out = self.rbr_identity(inputs)
-
- out1 = self.rbr_dense(inputs)
- out2 = self.rbr_1x1(inputs)
- out3 = id_out
- out = out1 + out2 + out3
-
- return self.nonlinearity(self.se(out))
-
-
- # Optional. This improves the accuracy and facilitates quantization.
- # 1. Cancel the original weight decay on rbr_dense.conv.weight and rbr_1x1.conv.weight.
- # 2. Use like this.
- # loss = criterion(....)
- # for every RepVGGBlock blk:
- # loss += weight_decay_coefficient * 0.5 * blk.get_cust_L2()
- # optimizer.zero_grad()
- # loss.backward()
-
- # Not used for OREPA
- def get_custom_L2(self):
- K3 = self.rbr_dense.weight_gen()
- K1 = self.rbr_1x1.conv.weight
- t3 = (self.rbr_dense.bn.weight / ((self.rbr_dense.bn.running_var + self.rbr_dense.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach()
- t1 = (self.rbr_1x1.bn.weight / ((self.rbr_1x1.bn.running_var + self.rbr_1x1.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach()
-
- l2_loss_circle = (K3 ** 2).sum() - (K3[:, :, 1:2, 1:2] ** 2).sum() # The L2 loss of the "circle" of weights in 3x3 kernel. Use regular L2 on them.
- eq_kernel = K3[:, :, 1:2, 1:2] * t3 + K1 * t1 # The equivalent resultant central point of 3x3 kernel.
- l2_loss_eq_kernel = (eq_kernel ** 2 / (t3 ** 2 + t1 ** 2)).sum() # Normalize for an L2 coefficient comparable to regular L2.
- return l2_loss_eq_kernel + l2_loss_circle
-
- def get_equivalent_kernel_bias(self):
- kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
- kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
- kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
- return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
-
- def _pad_1x1_to_3x3_tensor(self, kernel1x1):
- if kernel1x1 is None:
- return 0
- else:
- return torch.nn.functional.pad(kernel1x1, [1,1,1,1])
-
- def _fuse_bn_tensor(self, branch):
- if branch is None:
- return 0, 0
- if not isinstance(branch, nn.BatchNorm2d):
- if isinstance(branch, OREPA_3x3_RepConv):
- kernel = branch.weight_gen()
- elif isinstance(branch, ConvBN):
- kernel = branch.conv.weight
- else:
- raise NotImplementedError
- running_mean = branch.bn.running_mean
- running_var = branch.bn.running_var
- gamma = branch.bn.weight
- beta = branch.bn.bias
- eps = branch.bn.eps
- else:
- if not hasattr(self, 'id_tensor'):
- input_dim = self.in_channels // self.groups
- kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32)
- for i in range(self.in_channels):
- kernel_value[i, i % input_dim, 1, 1] = 1
- self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
- kernel = self.id_tensor
- running_mean = branch.running_mean
- running_var = branch.running_var
- gamma = branch.weight
- beta = branch.bias
- eps = branch.eps
- std = (running_var + eps).sqrt()
- t = (gamma / std).reshape(-1, 1, 1, 1)
- return kernel * t, beta - running_mean * gamma / std
-
- def switch_to_deploy(self):
- if hasattr(self, 'rbr_reparam'):
- return
- print(f"RepConv_OREPA.switch_to_deploy")
- kernel, bias = self.get_equivalent_kernel_bias()
- self.rbr_reparam = nn.Conv2d(in_channels=self.rbr_dense.in_channels, out_channels=self.rbr_dense.out_channels,
- kernel_size=self.rbr_dense.kernel_size, stride=self.rbr_dense.stride,
- padding=self.rbr_dense.padding, dilation=self.rbr_dense.dilation, groups=self.rbr_dense.groups, bias=True)
- self.rbr_reparam.weight.data = kernel
- self.rbr_reparam.bias.data = bias
- for para in self.parameters():
- para.detach_()
- self.__delattr__('rbr_dense')
- self.__delattr__('rbr_1x1')
- if hasattr(self, 'rbr_identity'):
- self.__delattr__('rbr_identity')
-
-##### end of orepa #####
-
-
-##### swin transformer #####
-
-class WindowAttention(nn.Module):
-
- 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)
-
- nn.init.normal_(self.relative_position_bias_table, std=.02)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask=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)
-
- # print(attn.dtype, v.dtype)
- try:
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- except:
- #print(attn.dtype, v.dtype)
- x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
-class Mlp(nn.Module):
-
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, 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):
-
- B, H, W, C = x.shape
- assert H % window_size == 0, 'feature map h and w can not divide by window size'
- 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):
-
- 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 SwinTransformerLayer(nn.Module):
-
- def __init__(self, dim, num_heads, window_size=8, shift_size=0,
- mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
- act_layer=nn.SiLU, norm_layer=nn.LayerNorm):
- super().__init__()
- self.dim = dim
- self.num_heads = num_heads
- self.window_size = window_size
- self.shift_size = shift_size
- self.mlp_ratio = mlp_ratio
- # 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=(self.window_size, 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)
-
- def create_mask(self, H, W):
- # calculate attention mask for SW-MSA
- 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):
- # reshape x[b c h w] to x[b l c]
- _, _, H_, W_ = x.shape
-
- Padding = False
- if min(H_, W_) < self.window_size or H_ % self.window_size!=0 or W_ % self.window_size!=0:
- Padding = True
- # print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.')
- pad_r = (self.window_size - W_ % self.window_size) % self.window_size
- pad_b = (self.window_size - H_ % self.window_size) % self.window_size
- x = F.pad(x, (0, pad_r, 0, pad_b))
-
- # print('2', x.shape)
- B, C, H, W = x.shape
- L = H * W
- x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c
-
- # create mask from init to forward
- if self.shift_size > 0:
- attn_mask = self.create_mask(H, W).to(x.device)
- else:
- attn_mask = None
-
- 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
- attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse(attn_windows, self.window_size, 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)))
-
- x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w
-
- if Padding:
- x = x[:, :, :H_, :W_] # reverse padding
-
- return x
-
-
-class SwinTransformerBlock(nn.Module):
- def __init__(self, c1, c2, num_heads, num_layers, window_size=8):
- super().__init__()
- self.conv = None
- if c1 != c2:
- self.conv = Conv(c1, c2)
-
- # remove input_resolution
- self.blocks = nn.Sequential(*[SwinTransformerLayer(dim=c2, num_heads=num_heads, window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)])
-
- def forward(self, x):
- if self.conv is not None:
- x = self.conv(x)
- x = self.blocks(x)
- return x
-
-
-class STCSPA(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(STCSPA, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1, 1)
- num_heads = c_ // 32
- self.m = SwinTransformerBlock(c_, c_, num_heads, n)
- #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- y1 = self.m(self.cv1(x))
- y2 = self.cv2(x)
- return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class STCSPB(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(STCSPB, self).__init__()
- c_ = int(c2) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1, 1)
- num_heads = c_ // 32
- self.m = SwinTransformerBlock(c_, c_, num_heads, n)
- #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- x1 = self.cv1(x)
- y1 = self.m(x1)
- y2 = self.cv2(x1)
- return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class STCSPC(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(STCSPC, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(c_, c_, 1, 1)
- self.cv4 = Conv(2 * c_, c2, 1, 1)
- num_heads = c_ // 32
- self.m = SwinTransformerBlock(c_, c_, num_heads, n)
- #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- y1 = self.cv3(self.m(self.cv1(x)))
- y2 = self.cv2(x)
- return self.cv4(torch.cat((y1, y2), dim=1))
-
-##### end of swin transformer #####
-
-
-##### swin transformer v2 #####
-
-class WindowAttention_v2(nn.Module):
-
- def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
- pretrained_window_size=[0, 0]):
-
- super().__init__()
- self.dim = dim
- self.window_size = window_size # Wh, Ww
- self.pretrained_window_size = pretrained_window_size
- self.num_heads = num_heads
-
- self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
-
- # mlp to generate continuous relative position bias
- self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
- nn.ReLU(inplace=True),
- nn.Linear(512, num_heads, bias=False))
-
- # get relative_coords_table
- relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
- relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
- relative_coords_table = torch.stack(
- torch.meshgrid([relative_coords_h,
- relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
- if pretrained_window_size[0] > 0:
- relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
- relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
- else:
- relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
- relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
- relative_coords_table *= 8 # normalize to -8, 8
- relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
- torch.abs(relative_coords_table) + 1.0) / np.log2(8)
-
- self.register_buffer("relative_coords_table", relative_coords_table)
-
- # 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=False)
- if qkv_bias:
- self.q_bias = nn.Parameter(torch.zeros(dim))
- self.v_bias = nn.Parameter(torch.zeros(dim))
- else:
- self.q_bias = None
- self.v_bias = None
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask=None):
-
- B_, N, C = x.shape
- qkv_bias = None
- if self.q_bias is not None:
- qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
- qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
- qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- # cosine attention
- attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
- logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp()
- attn = attn * logit_scale
-
- relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
- relative_position_bias = 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
- relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
- 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)
-
- try:
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- except:
- x = (attn.half() @ 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}, ' \
- f'pretrained_window_size={self.pretrained_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 Mlp_v2(nn.Module):
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, 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_v2(x, window_size):
-
- 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_v2(windows, window_size, H, W):
-
- 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 SwinTransformerLayer_v2(nn.Module):
-
- def __init__(self, dim, num_heads, window_size=7, shift_size=0,
- mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
- act_layer=nn.SiLU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
- 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_v2(
- dim, window_size=(self.window_size, self.window_size), num_heads=num_heads,
- qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
- pretrained_window_size=(pretrained_window_size, pretrained_window_size))
-
- 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_v2(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- def create_mask(self, H, W):
- # calculate attention mask for SW-MSA
- 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):
- # reshape x[b c h w] to x[b l c]
- _, _, H_, W_ = x.shape
-
- Padding = False
- if min(H_, W_) < self.window_size or H_ % self.window_size!=0 or W_ % self.window_size!=0:
- Padding = True
- # print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.')
- pad_r = (self.window_size - W_ % self.window_size) % self.window_size
- pad_b = (self.window_size - H_ % self.window_size) % self.window_size
- x = F.pad(x, (0, pad_r, 0, pad_b))
-
- # print('2', x.shape)
- B, C, H, W = x.shape
- L = H * W
- x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c
-
- # create mask from init to forward
- if self.shift_size > 0:
- attn_mask = self.create_mask(H, W).to(x.device)
- else:
- attn_mask = None
-
- shortcut = 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_v2(shifted_x, self.window_size) # nW*B, window_size, window_size, C
- x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
-
- # W-MSA/SW-MSA
- attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse_v2(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)
- x = shortcut + self.drop_path(self.norm1(x))
-
- # FFN
- x = x + self.drop_path(self.norm2(self.mlp(x)))
- x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w
-
- if Padding:
- x = x[:, :, :H_, :W_] # reverse padding
-
- 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 SwinTransformer2Block(nn.Module):
- def __init__(self, c1, c2, num_heads, num_layers, window_size=7):
- super().__init__()
- self.conv = None
- if c1 != c2:
- self.conv = Conv(c1, c2)
-
- # remove input_resolution
- self.blocks = nn.Sequential(*[SwinTransformerLayer_v2(dim=c2, num_heads=num_heads, window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)])
-
- def forward(self, x):
- if self.conv is not None:
- x = self.conv(x)
- x = self.blocks(x)
- return x
-
-
-class ST2CSPA(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(ST2CSPA, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1, 1)
- num_heads = c_ // 32
- self.m = SwinTransformer2Block(c_, c_, num_heads, n)
- #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- y1 = self.m(self.cv1(x))
- y2 = self.cv2(x)
- return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class ST2CSPB(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(ST2CSPB, self).__init__()
- c_ = int(c2) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1, 1)
- num_heads = c_ // 32
- self.m = SwinTransformer2Block(c_, c_, num_heads, n)
- #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- x1 = self.cv1(x)
- y1 = self.m(x1)
- y2 = self.cv2(x1)
- return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class ST2CSPC(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(ST2CSPC, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(c_, c_, 1, 1)
- self.cv4 = Conv(2 * c_, c2, 1, 1)
- num_heads = c_ // 32
- self.m = SwinTransformer2Block(c_, c_, num_heads, n)
- #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- y1 = self.cv3(self.m(self.cv1(x)))
- y2 = self.cv2(x)
- return self.cv4(torch.cat((y1, y2), dim=1))
-
-##### end of swin transformer v2 #####
diff --git a/spaces/bhasker412/IDD-YOLO-Tracking/utils/loss.py b/spaces/bhasker412/IDD-YOLO-Tracking/utils/loss.py
deleted file mode 100644
index bf7ab65a304b51b398d9877da0673d5c01e52081..0000000000000000000000000000000000000000
--- a/spaces/bhasker412/IDD-YOLO-Tracking/utils/loss.py
+++ /dev/null
@@ -1,1697 +0,0 @@
-# Loss functions
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from utils.general import bbox_iou, bbox_alpha_iou, box_iou, box_giou, box_diou, box_ciou, xywh2xyxy
-from utils.torch_utils import is_parallel
-
-
-def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
- # return positive, negative label smoothing BCE targets
- return 1.0 - 0.5 * eps, 0.5 * eps
-
-
-class BCEBlurWithLogitsLoss(nn.Module):
- # BCEwithLogitLoss() with reduced missing label effects.
- def __init__(self, alpha=0.05):
- super(BCEBlurWithLogitsLoss, self).__init__()
- self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss()
- self.alpha = alpha
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
- pred = torch.sigmoid(pred) # prob from logits
- dx = pred - true # reduce only missing label effects
- # dx = (pred - true).abs() # reduce missing label and false label effects
- alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
- loss *= alpha_factor
- return loss.mean()
-
-
-class SigmoidBin(nn.Module):
- stride = None # strides computed during build
- export = False # onnx export
-
- def __init__(self, bin_count=10, min=0.0, max=1.0, reg_scale = 2.0, use_loss_regression=True, use_fw_regression=True, BCE_weight=1.0, smooth_eps=0.0):
- super(SigmoidBin, self).__init__()
-
- self.bin_count = bin_count
- self.length = bin_count + 1
- self.min = min
- self.max = max
- self.scale = float(max - min)
- self.shift = self.scale / 2.0
-
- self.use_loss_regression = use_loss_regression
- self.use_fw_regression = use_fw_regression
- self.reg_scale = reg_scale
- self.BCE_weight = BCE_weight
-
- start = min + (self.scale/2.0) / self.bin_count
- end = max - (self.scale/2.0) / self.bin_count
- step = self.scale / self.bin_count
- self.step = step
- #print(f" start = {start}, end = {end}, step = {step} ")
-
- bins = torch.range(start, end + 0.0001, step).float()
- self.register_buffer('bins', bins)
-
-
- self.cp = 1.0 - 0.5 * smooth_eps
- self.cn = 0.5 * smooth_eps
-
- self.BCEbins = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([BCE_weight]))
- self.MSELoss = nn.MSELoss()
-
- def get_length(self):
- return self.length
-
- def forward(self, pred):
- assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length)
-
- pred_reg = (pred[..., 0] * self.reg_scale - self.reg_scale/2.0) * self.step
- pred_bin = pred[..., 1:(1+self.bin_count)]
-
- _, bin_idx = torch.max(pred_bin, dim=-1)
- bin_bias = self.bins[bin_idx]
-
- if self.use_fw_regression:
- result = pred_reg + bin_bias
- else:
- result = bin_bias
- result = result.clamp(min=self.min, max=self.max)
-
- return result
-
-
- def training_loss(self, pred, target):
- assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length)
- assert pred.shape[0] == target.shape[0], 'pred.shape=%d is not equal to the target.shape=%d' % (pred.shape[0], target.shape[0])
- device = pred.device
-
- pred_reg = (pred[..., 0].sigmoid() * self.reg_scale - self.reg_scale/2.0) * self.step
- pred_bin = pred[..., 1:(1+self.bin_count)]
-
- diff_bin_target = torch.abs(target[..., None] - self.bins)
- _, bin_idx = torch.min(diff_bin_target, dim=-1)
-
- bin_bias = self.bins[bin_idx]
- bin_bias.requires_grad = False
- result = pred_reg + bin_bias
-
- target_bins = torch.full_like(pred_bin, self.cn, device=device) # targets
- n = pred.shape[0]
- target_bins[range(n), bin_idx] = self.cp
-
- loss_bin = self.BCEbins(pred_bin, target_bins) # BCE
-
- if self.use_loss_regression:
- loss_regression = self.MSELoss(result, target) # MSE
- loss = loss_bin + loss_regression
- else:
- loss = loss_bin
-
- out_result = result.clamp(min=self.min, max=self.max)
-
- return loss, out_result
-
-
-class FocalLoss(nn.Module):
- # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
- def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
- super(FocalLoss, self).__init__()
- self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
- self.gamma = gamma
- self.alpha = alpha
- self.reduction = loss_fcn.reduction
- self.loss_fcn.reduction = 'none' # required to apply FL to each element
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
- # p_t = torch.exp(-loss)
- # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
-
- # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
- pred_prob = torch.sigmoid(pred) # prob from logits
- p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
- alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
- modulating_factor = (1.0 - p_t) ** self.gamma
- loss *= alpha_factor * modulating_factor
-
- if self.reduction == 'mean':
- return loss.mean()
- elif self.reduction == 'sum':
- return loss.sum()
- else: # 'none'
- return loss
-
-
-class QFocalLoss(nn.Module):
- # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
- def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
- super(QFocalLoss, self).__init__()
- self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
- self.gamma = gamma
- self.alpha = alpha
- self.reduction = loss_fcn.reduction
- self.loss_fcn.reduction = 'none' # required to apply FL to each element
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
-
- pred_prob = torch.sigmoid(pred) # prob from logits
- alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
- modulating_factor = torch.abs(true - pred_prob) ** self.gamma
- loss *= alpha_factor * modulating_factor
-
- if self.reduction == 'mean':
- return loss.mean()
- elif self.reduction == 'sum':
- return loss.sum()
- else: # 'none'
- return loss
-
-class RankSort(torch.autograd.Function):
- @staticmethod
- def forward(ctx, logits, targets, delta_RS=0.50, eps=1e-10):
-
- classification_grads=torch.zeros(logits.shape).cuda()
-
- #Filter fg logits
- fg_labels = (targets > 0.)
- fg_logits = logits[fg_labels]
- fg_targets = targets[fg_labels]
- fg_num = len(fg_logits)
-
- #Do not use bg with scores less than minimum fg logit
- #since changing its score does not have an effect on precision
- threshold_logit = torch.min(fg_logits)-delta_RS
- relevant_bg_labels=((targets==0) & (logits>=threshold_logit))
-
- relevant_bg_logits = logits[relevant_bg_labels]
- relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda()
- sorting_error=torch.zeros(fg_num).cuda()
- ranking_error=torch.zeros(fg_num).cuda()
- fg_grad=torch.zeros(fg_num).cuda()
-
- #sort the fg logits
- order=torch.argsort(fg_logits)
- #Loops over each positive following the order
- for ii in order:
- # Difference Transforms (x_ij)
- fg_relations=fg_logits-fg_logits[ii]
- bg_relations=relevant_bg_logits-fg_logits[ii]
-
- if delta_RS > 0:
- fg_relations=torch.clamp(fg_relations/(2*delta_RS)+0.5,min=0,max=1)
- bg_relations=torch.clamp(bg_relations/(2*delta_RS)+0.5,min=0,max=1)
- else:
- fg_relations = (fg_relations >= 0).float()
- bg_relations = (bg_relations >= 0).float()
-
- # Rank of ii among pos and false positive number (bg with larger scores)
- rank_pos=torch.sum(fg_relations)
- FP_num=torch.sum(bg_relations)
-
- # Rank of ii among all examples
- rank=rank_pos+FP_num
-
- # Ranking error of example ii. target_ranking_error is always 0. (Eq. 7)
- ranking_error[ii]=FP_num/rank
-
- # Current sorting error of example ii. (Eq. 7)
- current_sorting_error = torch.sum(fg_relations*(1-fg_targets))/rank_pos
-
- #Find examples in the target sorted order for example ii
- iou_relations = (fg_targets >= fg_targets[ii])
- target_sorted_order = iou_relations * fg_relations
-
- #The rank of ii among positives in sorted order
- rank_pos_target = torch.sum(target_sorted_order)
-
- #Compute target sorting error. (Eq. 8)
- #Since target ranking error is 0, this is also total target error
- target_sorting_error= torch.sum(target_sorted_order*(1-fg_targets))/rank_pos_target
-
- #Compute sorting error on example ii
- sorting_error[ii] = current_sorting_error - target_sorting_error
-
- #Identity Update for Ranking Error
- if FP_num > eps:
- #For ii the update is the ranking error
- fg_grad[ii] -= ranking_error[ii]
- #For negatives, distribute error via ranking pmf (i.e. bg_relations/FP_num)
- relevant_bg_grad += (bg_relations*(ranking_error[ii]/FP_num))
-
- #Find the positives that are misranked (the cause of the error)
- #These are the ones with smaller IoU but larger logits
- missorted_examples = (~ iou_relations) * fg_relations
-
- #Denominotor of sorting pmf
- sorting_pmf_denom = torch.sum(missorted_examples)
-
- #Identity Update for Sorting Error
- if sorting_pmf_denom > eps:
- #For ii the update is the sorting error
- fg_grad[ii] -= sorting_error[ii]
- #For positives, distribute error via sorting pmf (i.e. missorted_examples/sorting_pmf_denom)
- fg_grad += (missorted_examples*(sorting_error[ii]/sorting_pmf_denom))
-
- #Normalize gradients by number of positives
- classification_grads[fg_labels]= (fg_grad/fg_num)
- classification_grads[relevant_bg_labels]= (relevant_bg_grad/fg_num)
-
- ctx.save_for_backward(classification_grads)
-
- return ranking_error.mean(), sorting_error.mean()
-
- @staticmethod
- def backward(ctx, out_grad1, out_grad2):
- g1, =ctx.saved_tensors
- return g1*out_grad1, None, None, None
-
-class aLRPLoss(torch.autograd.Function):
- @staticmethod
- def forward(ctx, logits, targets, regression_losses, delta=1., eps=1e-5):
- classification_grads=torch.zeros(logits.shape).cuda()
-
- #Filter fg logits
- fg_labels = (targets == 1)
- fg_logits = logits[fg_labels]
- fg_num = len(fg_logits)
-
- #Do not use bg with scores less than minimum fg logit
- #since changing its score does not have an effect on precision
- threshold_logit = torch.min(fg_logits)-delta
-
- #Get valid bg logits
- relevant_bg_labels=((targets==0)&(logits>=threshold_logit))
- relevant_bg_logits=logits[relevant_bg_labels]
- relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda()
- rank=torch.zeros(fg_num).cuda()
- prec=torch.zeros(fg_num).cuda()
- fg_grad=torch.zeros(fg_num).cuda()
-
- max_prec=0
- #sort the fg logits
- order=torch.argsort(fg_logits)
- #Loops over each positive following the order
- for ii in order:
- #x_ij s as score differences with fgs
- fg_relations=fg_logits-fg_logits[ii]
- #Apply piecewise linear function and determine relations with fgs
- fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1)
- #Discard i=j in the summation in rank_pos
- fg_relations[ii]=0
-
- #x_ij s as score differences with bgs
- bg_relations=relevant_bg_logits-fg_logits[ii]
- #Apply piecewise linear function and determine relations with bgs
- bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1)
-
- #Compute the rank of the example within fgs and number of bgs with larger scores
- rank_pos=1+torch.sum(fg_relations)
- FP_num=torch.sum(bg_relations)
- #Store the total since it is normalizer also for aLRP Regression error
- rank[ii]=rank_pos+FP_num
-
- #Compute precision for this example to compute classification loss
- prec[ii]=rank_pos/rank[ii]
- #For stability, set eps to a infinitesmall value (e.g. 1e-6), then compute grads
- if FP_num > eps:
- fg_grad[ii] = -(torch.sum(fg_relations*regression_losses)+FP_num)/rank[ii]
- relevant_bg_grad += (bg_relations*(-fg_grad[ii]/FP_num))
-
- #aLRP with grad formulation fg gradient
- classification_grads[fg_labels]= fg_grad
- #aLRP with grad formulation bg gradient
- classification_grads[relevant_bg_labels]= relevant_bg_grad
-
- classification_grads /= (fg_num)
-
- cls_loss=1-prec.mean()
- ctx.save_for_backward(classification_grads)
-
- return cls_loss, rank, order
-
- @staticmethod
- def backward(ctx, out_grad1, out_grad2, out_grad3):
- g1, =ctx.saved_tensors
- return g1*out_grad1, None, None, None, None
-
-
-class APLoss(torch.autograd.Function):
- @staticmethod
- def forward(ctx, logits, targets, delta=1.):
- classification_grads=torch.zeros(logits.shape).cuda()
-
- #Filter fg logits
- fg_labels = (targets == 1)
- fg_logits = logits[fg_labels]
- fg_num = len(fg_logits)
-
- #Do not use bg with scores less than minimum fg logit
- #since changing its score does not have an effect on precision
- threshold_logit = torch.min(fg_logits)-delta
-
- #Get valid bg logits
- relevant_bg_labels=((targets==0)&(logits>=threshold_logit))
- relevant_bg_logits=logits[relevant_bg_labels]
- relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda()
- rank=torch.zeros(fg_num).cuda()
- prec=torch.zeros(fg_num).cuda()
- fg_grad=torch.zeros(fg_num).cuda()
-
- max_prec=0
- #sort the fg logits
- order=torch.argsort(fg_logits)
- #Loops over each positive following the order
- for ii in order:
- #x_ij s as score differences with fgs
- fg_relations=fg_logits-fg_logits[ii]
- #Apply piecewise linear function and determine relations with fgs
- fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1)
- #Discard i=j in the summation in rank_pos
- fg_relations[ii]=0
-
- #x_ij s as score differences with bgs
- bg_relations=relevant_bg_logits-fg_logits[ii]
- #Apply piecewise linear function and determine relations with bgs
- bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1)
-
- #Compute the rank of the example within fgs and number of bgs with larger scores
- rank_pos=1+torch.sum(fg_relations)
- FP_num=torch.sum(bg_relations)
- #Store the total since it is normalizer also for aLRP Regression error
- rank[ii]=rank_pos+FP_num
-
- #Compute precision for this example
- current_prec=rank_pos/rank[ii]
-
- #Compute interpolated AP and store gradients for relevant bg examples
- if (max_prec<=current_prec):
- max_prec=current_prec
- relevant_bg_grad += (bg_relations/rank[ii])
- else:
- relevant_bg_grad += (bg_relations/rank[ii])*(((1-max_prec)/(1-current_prec)))
-
- #Store fg gradients
- fg_grad[ii]=-(1-max_prec)
- prec[ii]=max_prec
-
- #aLRP with grad formulation fg gradient
- classification_grads[fg_labels]= fg_grad
- #aLRP with grad formulation bg gradient
- classification_grads[relevant_bg_labels]= relevant_bg_grad
-
- classification_grads /= fg_num
-
- cls_loss=1-prec.mean()
- ctx.save_for_backward(classification_grads)
-
- return cls_loss
-
- @staticmethod
- def backward(ctx, out_grad1):
- g1, =ctx.saved_tensors
- return g1*out_grad1, None, None
-
-
-class ComputeLoss:
- # Compute losses
- def __init__(self, model, autobalance=False):
- super(ComputeLoss, self).__init__()
- device = next(model.parameters()).device # get model device
- h = model.hyp # hyperparameters
-
- # Define criteria
- BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
- BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-
- # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
- self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
-
- # Focal loss
- g = h['fl_gamma'] # focal loss gamma
- if g > 0:
- BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
- det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
- self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7
- #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.1, .05]) # P3-P7
- #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.5, 0.4, .1]) # P3-P7
- self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index
- self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
- for k in 'na', 'nc', 'nl', 'anchors':
- setattr(self, k, getattr(det, k))
-
- def __call__(self, p, targets): # predictions, targets, model
- device = targets.device
- lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
- tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
-
- # Losses
- for i, pi in enumerate(p): # layer index, layer predictions
- b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
- tobj = torch.zeros_like(pi[..., 0], device=device) # target obj
-
- n = b.shape[0] # number of targets
- if n:
- ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
-
- # Regression
- pxy = ps[:, :2].sigmoid() * 2. - 0.5
- pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
- pbox = torch.cat((pxy, pwh), 1) # predicted box
- iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target)
- lbox += (1.0 - iou).mean() # iou loss
-
- # Objectness
- tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio
-
- # Classification
- if self.nc > 1: # cls loss (only if multiple classes)
- t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets
- t[range(n), tcls[i]] = self.cp
- #t[t==self.cp] = iou.detach().clamp(0).type(t.dtype)
- lcls += self.BCEcls(ps[:, 5:], t) # BCE
-
- # Append targets to text file
- # with open('targets.txt', 'a') as file:
- # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
- obji = self.BCEobj(pi[..., 4], tobj)
- lobj += obji * self.balance[i] # obj loss
- if self.autobalance:
- self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
- if self.autobalance:
- self.balance = [x / self.balance[self.ssi] for x in self.balance]
- lbox *= self.hyp['box']
- lobj *= self.hyp['obj']
- lcls *= self.hyp['cls']
- bs = tobj.shape[0] # batch size
-
- loss = lbox + lobj + lcls
- return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
-
- def build_targets(self, p, targets):
- # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
- na, nt = self.na, targets.shape[0] # number of anchors, targets
- tcls, tbox, indices, anch = [], [], [], []
- gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain
- ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
- targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
-
- g = 0.5 # bias
- off = torch.tensor([[0, 0],
- [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
- # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
- ], device=targets.device).float() * g # offsets
-
- for i in range(self.nl):
- anchors = self.anchors[i]
- gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
-
- # Match targets to anchors
- t = targets * gain
- if nt:
- # Matches
- r = t[:, :, 4:6] / anchors[:, None] # wh ratio
- j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare
- # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
- t = t[j] # filter
-
- # Offsets
- gxy = t[:, 2:4] # grid xy
- gxi = gain[[2, 3]] - gxy # inverse
- j, k = ((gxy % 1. < g) & (gxy > 1.)).T
- l, m = ((gxi % 1. < g) & (gxi > 1.)).T
- j = torch.stack((torch.ones_like(j), j, k, l, m))
- t = t.repeat((5, 1, 1))[j]
- offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
- else:
- t = targets[0]
- offsets = 0
-
- # Define
- b, c = t[:, :2].long().T # image, class
- gxy = t[:, 2:4] # grid xy
- gwh = t[:, 4:6] # grid wh
- gij = (gxy - offsets).long()
- gi, gj = gij.T # grid xy indices
-
- # Append
- a = t[:, 6].long() # anchor indices
- indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
- tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
- anch.append(anchors[a]) # anchors
- tcls.append(c) # class
-
- return tcls, tbox, indices, anch
-
-
-class ComputeLossOTA:
- # Compute losses
- def __init__(self, model, autobalance=False):
- super(ComputeLossOTA, self).__init__()
- device = next(model.parameters()).device # get model device
- h = model.hyp # hyperparameters
-
- # Define criteria
- BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
- BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-
- # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
- self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
-
- # Focal loss
- g = h['fl_gamma'] # focal loss gamma
- if g > 0:
- BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
- det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
- self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7
- self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index
- self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
- for k in 'na', 'nc', 'nl', 'anchors', 'stride':
- setattr(self, k, getattr(det, k))
-
- def __call__(self, p, targets, imgs): # predictions, targets, model
- device = targets.device
- lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
- bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs)
- pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p]
-
-
- # Losses
- for i, pi in enumerate(p): # layer index, layer predictions
- b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx
- tobj = torch.zeros_like(pi[..., 0], device=device) # target obj
-
- n = b.shape[0] # number of targets
- if n:
- ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
-
- # Regression
- grid = torch.stack([gi, gj], dim=1)
- pxy = ps[:, :2].sigmoid() * 2. - 0.5
- #pxy = ps[:, :2].sigmoid() * 3. - 1.
- pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
- pbox = torch.cat((pxy, pwh), 1) # predicted box
- selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i]
- selected_tbox[:, :2] -= grid
- iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target)
- lbox += (1.0 - iou).mean() # iou loss
-
- # Objectness
- tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio
-
- # Classification
- selected_tcls = targets[i][:, 1].long()
- if self.nc > 1: # cls loss (only if multiple classes)
- t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets
- t[range(n), selected_tcls] = self.cp
- lcls += self.BCEcls(ps[:, 5:], t) # BCE
-
- # Append targets to text file
- # with open('targets.txt', 'a') as file:
- # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
- obji = self.BCEobj(pi[..., 4], tobj)
- lobj += obji * self.balance[i] # obj loss
- if self.autobalance:
- self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
- if self.autobalance:
- self.balance = [x / self.balance[self.ssi] for x in self.balance]
- lbox *= self.hyp['box']
- lobj *= self.hyp['obj']
- lcls *= self.hyp['cls']
- bs = tobj.shape[0] # batch size
-
- loss = lbox + lobj + lcls
- return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
-
- def build_targets(self, p, targets, imgs):
-
- #indices, anch = self.find_positive(p, targets)
- indices, anch = self.find_3_positive(p, targets)
- #indices, anch = self.find_4_positive(p, targets)
- #indices, anch = self.find_5_positive(p, targets)
- #indices, anch = self.find_9_positive(p, targets)
-
- matching_bs = [[] for pp in p]
- matching_as = [[] for pp in p]
- matching_gjs = [[] for pp in p]
- matching_gis = [[] for pp in p]
- matching_targets = [[] for pp in p]
- matching_anchs = [[] for pp in p]
-
- nl = len(p)
-
- for batch_idx in range(p[0].shape[0]):
-
- b_idx = targets[:, 0]==batch_idx
- this_target = targets[b_idx]
- if this_target.shape[0] == 0:
- continue
-
- txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1]
- txyxy = xywh2xyxy(txywh)
-
- pxyxys = []
- p_cls = []
- p_obj = []
- from_which_layer = []
- all_b = []
- all_a = []
- all_gj = []
- all_gi = []
- all_anch = []
-
- for i, pi in enumerate(p):
-
- b, a, gj, gi = indices[i]
- idx = (b == batch_idx)
- b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx]
- all_b.append(b)
- all_a.append(a)
- all_gj.append(gj)
- all_gi.append(gi)
- all_anch.append(anch[i][idx])
- from_which_layer.append(torch.ones(size=(len(b),)) * i)
-
- fg_pred = pi[b, a, gj, gi]
- p_obj.append(fg_pred[:, 4:5])
- p_cls.append(fg_pred[:, 5:])
-
- grid = torch.stack([gi, gj], dim=1)
- pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8.
- #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i]
- pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8.
- pxywh = torch.cat([pxy, pwh], dim=-1)
- pxyxy = xywh2xyxy(pxywh)
- pxyxys.append(pxyxy)
-
- pxyxys = torch.cat(pxyxys, dim=0)
- if pxyxys.shape[0] == 0:
- continue
- p_obj = torch.cat(p_obj, dim=0)
- p_cls = torch.cat(p_cls, dim=0)
- from_which_layer = torch.cat(from_which_layer, dim=0)
- all_b = torch.cat(all_b, dim=0)
- all_a = torch.cat(all_a, dim=0)
- all_gj = torch.cat(all_gj, dim=0)
- all_gi = torch.cat(all_gi, dim=0)
- all_anch = torch.cat(all_anch, dim=0)
-
- pair_wise_iou = box_iou(txyxy, pxyxys)
-
- pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8)
-
- top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1)
- dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1)
-
- gt_cls_per_image = (
- F.one_hot(this_target[:, 1].to(torch.int64), self.nc)
- .float()
- .unsqueeze(1)
- .repeat(1, pxyxys.shape[0], 1)
- )
-
- num_gt = this_target.shape[0]
- cls_preds_ = (
- p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
- * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
- )
-
- y = cls_preds_.sqrt_()
- pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
- torch.log(y/(1-y)) , gt_cls_per_image, reduction="none"
- ).sum(-1)
- del cls_preds_
-
- cost = (
- pair_wise_cls_loss
- + 3.0 * pair_wise_iou_loss
- )
-
- matching_matrix = torch.zeros_like(cost)
-
- for gt_idx in range(num_gt):
- _, pos_idx = torch.topk(
- cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False
- )
- matching_matrix[gt_idx][pos_idx] = 1.0
-
- del top_k, dynamic_ks
- anchor_matching_gt = matching_matrix.sum(0)
- if (anchor_matching_gt > 1).sum() > 0:
- _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)
- matching_matrix[:, anchor_matching_gt > 1] *= 0.0
- matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0
- fg_mask_inboxes = matching_matrix.sum(0) > 0.0
- matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
-
- from_which_layer = from_which_layer[fg_mask_inboxes]
- all_b = all_b[fg_mask_inboxes]
- all_a = all_a[fg_mask_inboxes]
- all_gj = all_gj[fg_mask_inboxes]
- all_gi = all_gi[fg_mask_inboxes]
- all_anch = all_anch[fg_mask_inboxes]
-
- this_target = this_target[matched_gt_inds]
-
- for i in range(nl):
- layer_idx = from_which_layer == i
- matching_bs[i].append(all_b[layer_idx])
- matching_as[i].append(all_a[layer_idx])
- matching_gjs[i].append(all_gj[layer_idx])
- matching_gis[i].append(all_gi[layer_idx])
- matching_targets[i].append(this_target[layer_idx])
- matching_anchs[i].append(all_anch[layer_idx])
-
- for i in range(nl):
- if matching_targets[i] != []:
- matching_bs[i] = torch.cat(matching_bs[i], dim=0)
- matching_as[i] = torch.cat(matching_as[i], dim=0)
- matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
- matching_gis[i] = torch.cat(matching_gis[i], dim=0)
- matching_targets[i] = torch.cat(matching_targets[i], dim=0)
- matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
- else:
- matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-
- return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs
-
- def find_3_positive(self, p, targets):
- # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
- na, nt = self.na, targets.shape[0] # number of anchors, targets
- indices, anch = [], []
- gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain
- ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
- targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
-
- g = 0.5 # bias
- off = torch.tensor([[0, 0],
- [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
- # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
- ], device=targets.device).float() * g # offsets
-
- for i in range(self.nl):
- anchors = self.anchors[i]
- gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
-
- # Match targets to anchors
- t = targets * gain
- if nt:
- # Matches
- r = t[:, :, 4:6] / anchors[:, None] # wh ratio
- j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare
- # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
- t = t[j] # filter
-
- # Offsets
- gxy = t[:, 2:4] # grid xy
- gxi = gain[[2, 3]] - gxy # inverse
- j, k = ((gxy % 1. < g) & (gxy > 1.)).T
- l, m = ((gxi % 1. < g) & (gxi > 1.)).T
- j = torch.stack((torch.ones_like(j), j, k, l, m))
- t = t.repeat((5, 1, 1))[j]
- offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
- else:
- t = targets[0]
- offsets = 0
-
- # Define
- b, c = t[:, :2].long().T # image, class
- gxy = t[:, 2:4] # grid xy
- gwh = t[:, 4:6] # grid wh
- gij = (gxy - offsets).long()
- gi, gj = gij.T # grid xy indices
-
- # Append
- a = t[:, 6].long() # anchor indices
- indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
- anch.append(anchors[a]) # anchors
-
- return indices, anch
-
-
-class ComputeLossBinOTA:
- # Compute losses
- def __init__(self, model, autobalance=False):
- super(ComputeLossBinOTA, self).__init__()
- device = next(model.parameters()).device # get model device
- h = model.hyp # hyperparameters
-
- # Define criteria
- BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
- BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
- #MSEangle = nn.MSELoss().to(device)
-
- # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
- self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
-
- # Focal loss
- g = h['fl_gamma'] # focal loss gamma
- if g > 0:
- BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
- det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
- self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7
- self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index
- self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
- for k in 'na', 'nc', 'nl', 'anchors', 'stride', 'bin_count':
- setattr(self, k, getattr(det, k))
-
- #xy_bin_sigmoid = SigmoidBin(bin_count=11, min=-0.5, max=1.5, use_loss_regression=False).to(device)
- wh_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0, use_loss_regression=False).to(device)
- #angle_bin_sigmoid = SigmoidBin(bin_count=31, min=-1.1, max=1.1, use_loss_regression=False).to(device)
- self.wh_bin_sigmoid = wh_bin_sigmoid
-
- def __call__(self, p, targets, imgs): # predictions, targets, model
- device = targets.device
- lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
- bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs)
- pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p]
-
-
- # Losses
- for i, pi in enumerate(p): # layer index, layer predictions
- b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx
- tobj = torch.zeros_like(pi[..., 0], device=device) # target obj
-
- obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2 # x,y, w-bce, h-bce # xy_bin_sigmoid.get_length()*2
-
- n = b.shape[0] # number of targets
- if n:
- ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
-
- # Regression
- grid = torch.stack([gi, gj], dim=1)
- selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i]
- selected_tbox[:, :2] -= grid
-
- #pxy = ps[:, :2].sigmoid() * 2. - 0.5
- ##pxy = ps[:, :2].sigmoid() * 3. - 1.
- #pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
- #pbox = torch.cat((pxy, pwh), 1) # predicted box
-
- #x_loss, px = xy_bin_sigmoid.training_loss(ps[..., 0:12], tbox[i][..., 0])
- #y_loss, py = xy_bin_sigmoid.training_loss(ps[..., 12:24], tbox[i][..., 1])
- w_loss, pw = self.wh_bin_sigmoid.training_loss(ps[..., 2:(3+self.bin_count)], selected_tbox[..., 2] / anchors[i][..., 0])
- h_loss, ph = self.wh_bin_sigmoid.training_loss(ps[..., (3+self.bin_count):obj_idx], selected_tbox[..., 3] / anchors[i][..., 1])
-
- pw *= anchors[i][..., 0]
- ph *= anchors[i][..., 1]
-
- px = ps[:, 0].sigmoid() * 2. - 0.5
- py = ps[:, 1].sigmoid() * 2. - 0.5
-
- lbox += w_loss + h_loss # + x_loss + y_loss
-
- #print(f"\n px = {px.shape}, py = {py.shape}, pw = {pw.shape}, ph = {ph.shape} \n")
-
- pbox = torch.cat((px.unsqueeze(1), py.unsqueeze(1), pw.unsqueeze(1), ph.unsqueeze(1)), 1).to(device) # predicted box
-
-
-
-
- iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target)
- lbox += (1.0 - iou).mean() # iou loss
-
- # Objectness
- tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio
-
- # Classification
- selected_tcls = targets[i][:, 1].long()
- if self.nc > 1: # cls loss (only if multiple classes)
- t = torch.full_like(ps[:, (1+obj_idx):], self.cn, device=device) # targets
- t[range(n), selected_tcls] = self.cp
- lcls += self.BCEcls(ps[:, (1+obj_idx):], t) # BCE
-
- # Append targets to text file
- # with open('targets.txt', 'a') as file:
- # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
- obji = self.BCEobj(pi[..., obj_idx], tobj)
- lobj += obji * self.balance[i] # obj loss
- if self.autobalance:
- self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
- if self.autobalance:
- self.balance = [x / self.balance[self.ssi] for x in self.balance]
- lbox *= self.hyp['box']
- lobj *= self.hyp['obj']
- lcls *= self.hyp['cls']
- bs = tobj.shape[0] # batch size
-
- loss = lbox + lobj + lcls
- return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
-
- def build_targets(self, p, targets, imgs):
-
- #indices, anch = self.find_positive(p, targets)
- indices, anch = self.find_3_positive(p, targets)
- #indices, anch = self.find_4_positive(p, targets)
- #indices, anch = self.find_5_positive(p, targets)
- #indices, anch = self.find_9_positive(p, targets)
-
- matching_bs = [[] for pp in p]
- matching_as = [[] for pp in p]
- matching_gjs = [[] for pp in p]
- matching_gis = [[] for pp in p]
- matching_targets = [[] for pp in p]
- matching_anchs = [[] for pp in p]
-
- nl = len(p)
-
- for batch_idx in range(p[0].shape[0]):
-
- b_idx = targets[:, 0]==batch_idx
- this_target = targets[b_idx]
- if this_target.shape[0] == 0:
- continue
-
- txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1]
- txyxy = xywh2xyxy(txywh)
-
- pxyxys = []
- p_cls = []
- p_obj = []
- from_which_layer = []
- all_b = []
- all_a = []
- all_gj = []
- all_gi = []
- all_anch = []
-
- for i, pi in enumerate(p):
-
- obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2
-
- b, a, gj, gi = indices[i]
- idx = (b == batch_idx)
- b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx]
- all_b.append(b)
- all_a.append(a)
- all_gj.append(gj)
- all_gi.append(gi)
- all_anch.append(anch[i][idx])
- from_which_layer.append(torch.ones(size=(len(b),)) * i)
-
- fg_pred = pi[b, a, gj, gi]
- p_obj.append(fg_pred[:, obj_idx:(obj_idx+1)])
- p_cls.append(fg_pred[:, (obj_idx+1):])
-
- grid = torch.stack([gi, gj], dim=1)
- pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8.
- #pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8.
- pw = self.wh_bin_sigmoid.forward(fg_pred[..., 2:(3+self.bin_count)].sigmoid()) * anch[i][idx][:, 0] * self.stride[i]
- ph = self.wh_bin_sigmoid.forward(fg_pred[..., (3+self.bin_count):obj_idx].sigmoid()) * anch[i][idx][:, 1] * self.stride[i]
-
- pxywh = torch.cat([pxy, pw.unsqueeze(1), ph.unsqueeze(1)], dim=-1)
- pxyxy = xywh2xyxy(pxywh)
- pxyxys.append(pxyxy)
-
- pxyxys = torch.cat(pxyxys, dim=0)
- if pxyxys.shape[0] == 0:
- continue
- p_obj = torch.cat(p_obj, dim=0)
- p_cls = torch.cat(p_cls, dim=0)
- from_which_layer = torch.cat(from_which_layer, dim=0)
- all_b = torch.cat(all_b, dim=0)
- all_a = torch.cat(all_a, dim=0)
- all_gj = torch.cat(all_gj, dim=0)
- all_gi = torch.cat(all_gi, dim=0)
- all_anch = torch.cat(all_anch, dim=0)
-
- pair_wise_iou = box_iou(txyxy, pxyxys)
-
- pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8)
-
- top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1)
- dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1)
-
- gt_cls_per_image = (
- F.one_hot(this_target[:, 1].to(torch.int64), self.nc)
- .float()
- .unsqueeze(1)
- .repeat(1, pxyxys.shape[0], 1)
- )
-
- num_gt = this_target.shape[0]
- cls_preds_ = (
- p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
- * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
- )
-
- y = cls_preds_.sqrt_()
- pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
- torch.log(y/(1-y)) , gt_cls_per_image, reduction="none"
- ).sum(-1)
- del cls_preds_
-
- cost = (
- pair_wise_cls_loss
- + 3.0 * pair_wise_iou_loss
- )
-
- matching_matrix = torch.zeros_like(cost)
-
- for gt_idx in range(num_gt):
- _, pos_idx = torch.topk(
- cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False
- )
- matching_matrix[gt_idx][pos_idx] = 1.0
-
- del top_k, dynamic_ks
- anchor_matching_gt = matching_matrix.sum(0)
- if (anchor_matching_gt > 1).sum() > 0:
- _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)
- matching_matrix[:, anchor_matching_gt > 1] *= 0.0
- matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0
- fg_mask_inboxes = matching_matrix.sum(0) > 0.0
- matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
-
- from_which_layer = from_which_layer[fg_mask_inboxes]
- all_b = all_b[fg_mask_inboxes]
- all_a = all_a[fg_mask_inboxes]
- all_gj = all_gj[fg_mask_inboxes]
- all_gi = all_gi[fg_mask_inboxes]
- all_anch = all_anch[fg_mask_inboxes]
-
- this_target = this_target[matched_gt_inds]
-
- for i in range(nl):
- layer_idx = from_which_layer == i
- matching_bs[i].append(all_b[layer_idx])
- matching_as[i].append(all_a[layer_idx])
- matching_gjs[i].append(all_gj[layer_idx])
- matching_gis[i].append(all_gi[layer_idx])
- matching_targets[i].append(this_target[layer_idx])
- matching_anchs[i].append(all_anch[layer_idx])
-
- for i in range(nl):
- if matching_targets[i] != []:
- matching_bs[i] = torch.cat(matching_bs[i], dim=0)
- matching_as[i] = torch.cat(matching_as[i], dim=0)
- matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
- matching_gis[i] = torch.cat(matching_gis[i], dim=0)
- matching_targets[i] = torch.cat(matching_targets[i], dim=0)
- matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
- else:
- matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-
- return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs
-
- def find_3_positive(self, p, targets):
- # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
- na, nt = self.na, targets.shape[0] # number of anchors, targets
- indices, anch = [], []
- gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain
- ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
- targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
-
- g = 0.5 # bias
- off = torch.tensor([[0, 0],
- [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
- # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
- ], device=targets.device).float() * g # offsets
-
- for i in range(self.nl):
- anchors = self.anchors[i]
- gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
-
- # Match targets to anchors
- t = targets * gain
- if nt:
- # Matches
- r = t[:, :, 4:6] / anchors[:, None] # wh ratio
- j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare
- # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
- t = t[j] # filter
-
- # Offsets
- gxy = t[:, 2:4] # grid xy
- gxi = gain[[2, 3]] - gxy # inverse
- j, k = ((gxy % 1. < g) & (gxy > 1.)).T
- l, m = ((gxi % 1. < g) & (gxi > 1.)).T
- j = torch.stack((torch.ones_like(j), j, k, l, m))
- t = t.repeat((5, 1, 1))[j]
- offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
- else:
- t = targets[0]
- offsets = 0
-
- # Define
- b, c = t[:, :2].long().T # image, class
- gxy = t[:, 2:4] # grid xy
- gwh = t[:, 4:6] # grid wh
- gij = (gxy - offsets).long()
- gi, gj = gij.T # grid xy indices
-
- # Append
- a = t[:, 6].long() # anchor indices
- indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
- anch.append(anchors[a]) # anchors
-
- return indices, anch
-
-
-class ComputeLossAuxOTA:
- # Compute losses
- def __init__(self, model, autobalance=False):
- super(ComputeLossAuxOTA, self).__init__()
- device = next(model.parameters()).device # get model device
- h = model.hyp # hyperparameters
-
- # Define criteria
- BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
- BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-
- # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
- self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
-
- # Focal loss
- g = h['fl_gamma'] # focal loss gamma
- if g > 0:
- BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
- det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
- self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7
- self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index
- self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
- for k in 'na', 'nc', 'nl', 'anchors', 'stride':
- setattr(self, k, getattr(det, k))
-
- def __call__(self, p, targets, imgs): # predictions, targets, model
- device = targets.device
- lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
- bs_aux, as_aux_, gjs_aux, gis_aux, targets_aux, anchors_aux = self.build_targets2(p[:self.nl], targets, imgs)
- bs, as_, gjs, gis, targets, anchors = self.build_targets(p[:self.nl], targets, imgs)
- pre_gen_gains_aux = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[:self.nl]]
- pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[:self.nl]]
-
-
- # Losses
- for i in range(self.nl): # layer index, layer predictions
- pi = p[i]
- pi_aux = p[i+self.nl]
- b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx
- b_aux, a_aux, gj_aux, gi_aux = bs_aux[i], as_aux_[i], gjs_aux[i], gis_aux[i] # image, anchor, gridy, gridx
- tobj = torch.zeros_like(pi[..., 0], device=device) # target obj
- tobj_aux = torch.zeros_like(pi_aux[..., 0], device=device) # target obj
-
- n = b.shape[0] # number of targets
- if n:
- ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
-
- # Regression
- grid = torch.stack([gi, gj], dim=1)
- pxy = ps[:, :2].sigmoid() * 2. - 0.5
- pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
- pbox = torch.cat((pxy, pwh), 1) # predicted box
- selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i]
- selected_tbox[:, :2] -= grid
- iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True) # iou(prediction, target)
- lbox += (1.0 - iou).mean() # iou loss
-
- # Objectness
- tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio
-
- # Classification
- selected_tcls = targets[i][:, 1].long()
- if self.nc > 1: # cls loss (only if multiple classes)
- t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets
- t[range(n), selected_tcls] = self.cp
- lcls += self.BCEcls(ps[:, 5:], t) # BCE
-
- # Append targets to text file
- # with open('targets.txt', 'a') as file:
- # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
- n_aux = b_aux.shape[0] # number of targets
- if n_aux:
- ps_aux = pi_aux[b_aux, a_aux, gj_aux, gi_aux] # prediction subset corresponding to targets
- grid_aux = torch.stack([gi_aux, gj_aux], dim=1)
- pxy_aux = ps_aux[:, :2].sigmoid() * 2. - 0.5
- #pxy_aux = ps_aux[:, :2].sigmoid() * 3. - 1.
- pwh_aux = (ps_aux[:, 2:4].sigmoid() * 2) ** 2 * anchors_aux[i]
- pbox_aux = torch.cat((pxy_aux, pwh_aux), 1) # predicted box
- selected_tbox_aux = targets_aux[i][:, 2:6] * pre_gen_gains_aux[i]
- selected_tbox_aux[:, :2] -= grid_aux
- iou_aux = bbox_iou(pbox_aux.T, selected_tbox_aux, x1y1x2y2=False, CIoU=True) # iou(prediction, target)
- lbox += 0.25 * (1.0 - iou_aux).mean() # iou loss
-
- # Objectness
- tobj_aux[b_aux, a_aux, gj_aux, gi_aux] = (1.0 - self.gr) + self.gr * iou_aux.detach().clamp(0).type(tobj_aux.dtype) # iou ratio
-
- # Classification
- selected_tcls_aux = targets_aux[i][:, 1].long()
- if self.nc > 1: # cls loss (only if multiple classes)
- t_aux = torch.full_like(ps_aux[:, 5:], self.cn, device=device) # targets
- t_aux[range(n_aux), selected_tcls_aux] = self.cp
- lcls += 0.25 * self.BCEcls(ps_aux[:, 5:], t_aux) # BCE
-
- obji = self.BCEobj(pi[..., 4], tobj)
- obji_aux = self.BCEobj(pi_aux[..., 4], tobj_aux)
- lobj += obji * self.balance[i] + 0.25 * obji_aux * self.balance[i] # obj loss
- if self.autobalance:
- self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
- if self.autobalance:
- self.balance = [x / self.balance[self.ssi] for x in self.balance]
- lbox *= self.hyp['box']
- lobj *= self.hyp['obj']
- lcls *= self.hyp['cls']
- bs = tobj.shape[0] # batch size
-
- loss = lbox + lobj + lcls
- return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
-
- def build_targets(self, p, targets, imgs):
-
- indices, anch = self.find_3_positive(p, targets)
-
- matching_bs = [[] for pp in p]
- matching_as = [[] for pp in p]
- matching_gjs = [[] for pp in p]
- matching_gis = [[] for pp in p]
- matching_targets = [[] for pp in p]
- matching_anchs = [[] for pp in p]
-
- nl = len(p)
-
- for batch_idx in range(p[0].shape[0]):
-
- b_idx = targets[:, 0]==batch_idx
- this_target = targets[b_idx]
- if this_target.shape[0] == 0:
- continue
-
- txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1]
- txyxy = xywh2xyxy(txywh)
-
- pxyxys = []
- p_cls = []
- p_obj = []
- from_which_layer = []
- all_b = []
- all_a = []
- all_gj = []
- all_gi = []
- all_anch = []
-
- for i, pi in enumerate(p):
-
- b, a, gj, gi = indices[i]
- idx = (b == batch_idx)
- b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx]
- all_b.append(b)
- all_a.append(a)
- all_gj.append(gj)
- all_gi.append(gi)
- all_anch.append(anch[i][idx])
- from_which_layer.append(torch.ones(size=(len(b),)) * i)
-
- fg_pred = pi[b, a, gj, gi]
- p_obj.append(fg_pred[:, 4:5])
- p_cls.append(fg_pred[:, 5:])
-
- grid = torch.stack([gi, gj], dim=1)
- pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8.
- #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i]
- pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8.
- pxywh = torch.cat([pxy, pwh], dim=-1)
- pxyxy = xywh2xyxy(pxywh)
- pxyxys.append(pxyxy)
-
- pxyxys = torch.cat(pxyxys, dim=0)
- if pxyxys.shape[0] == 0:
- continue
- p_obj = torch.cat(p_obj, dim=0)
- p_cls = torch.cat(p_cls, dim=0)
- from_which_layer = torch.cat(from_which_layer, dim=0)
- all_b = torch.cat(all_b, dim=0)
- all_a = torch.cat(all_a, dim=0)
- all_gj = torch.cat(all_gj, dim=0)
- all_gi = torch.cat(all_gi, dim=0)
- all_anch = torch.cat(all_anch, dim=0)
-
- pair_wise_iou = box_iou(txyxy, pxyxys)
-
- pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8)
-
- top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1)
- dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1)
-
- gt_cls_per_image = (
- F.one_hot(this_target[:, 1].to(torch.int64), self.nc)
- .float()
- .unsqueeze(1)
- .repeat(1, pxyxys.shape[0], 1)
- )
-
- num_gt = this_target.shape[0]
- cls_preds_ = (
- p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
- * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
- )
-
- y = cls_preds_.sqrt_()
- pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
- torch.log(y/(1-y)) , gt_cls_per_image, reduction="none"
- ).sum(-1)
- del cls_preds_
-
- cost = (
- pair_wise_cls_loss
- + 3.0 * pair_wise_iou_loss
- )
-
- matching_matrix = torch.zeros_like(cost)
-
- for gt_idx in range(num_gt):
- _, pos_idx = torch.topk(
- cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False
- )
- matching_matrix[gt_idx][pos_idx] = 1.0
-
- del top_k, dynamic_ks
- anchor_matching_gt = matching_matrix.sum(0)
- if (anchor_matching_gt > 1).sum() > 0:
- _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)
- matching_matrix[:, anchor_matching_gt > 1] *= 0.0
- matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0
- fg_mask_inboxes = matching_matrix.sum(0) > 0.0
- matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
-
- from_which_layer = from_which_layer[fg_mask_inboxes]
- all_b = all_b[fg_mask_inboxes]
- all_a = all_a[fg_mask_inboxes]
- all_gj = all_gj[fg_mask_inboxes]
- all_gi = all_gi[fg_mask_inboxes]
- all_anch = all_anch[fg_mask_inboxes]
-
- this_target = this_target[matched_gt_inds]
-
- for i in range(nl):
- layer_idx = from_which_layer == i
- matching_bs[i].append(all_b[layer_idx])
- matching_as[i].append(all_a[layer_idx])
- matching_gjs[i].append(all_gj[layer_idx])
- matching_gis[i].append(all_gi[layer_idx])
- matching_targets[i].append(this_target[layer_idx])
- matching_anchs[i].append(all_anch[layer_idx])
-
- for i in range(nl):
- if matching_targets[i] != []:
- matching_bs[i] = torch.cat(matching_bs[i], dim=0)
- matching_as[i] = torch.cat(matching_as[i], dim=0)
- matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
- matching_gis[i] = torch.cat(matching_gis[i], dim=0)
- matching_targets[i] = torch.cat(matching_targets[i], dim=0)
- matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
- else:
- matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-
- return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs
-
- def build_targets2(self, p, targets, imgs):
-
- indices, anch = self.find_5_positive(p, targets)
-
- matching_bs = [[] for pp in p]
- matching_as = [[] for pp in p]
- matching_gjs = [[] for pp in p]
- matching_gis = [[] for pp in p]
- matching_targets = [[] for pp in p]
- matching_anchs = [[] for pp in p]
-
- nl = len(p)
-
- for batch_idx in range(p[0].shape[0]):
-
- b_idx = targets[:, 0]==batch_idx
- this_target = targets[b_idx]
- if this_target.shape[0] == 0:
- continue
-
- txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1]
- txyxy = xywh2xyxy(txywh)
-
- pxyxys = []
- p_cls = []
- p_obj = []
- from_which_layer = []
- all_b = []
- all_a = []
- all_gj = []
- all_gi = []
- all_anch = []
-
- for i, pi in enumerate(p):
-
- b, a, gj, gi = indices[i]
- idx = (b == batch_idx)
- b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx]
- all_b.append(b)
- all_a.append(a)
- all_gj.append(gj)
- all_gi.append(gi)
- all_anch.append(anch[i][idx])
- from_which_layer.append(torch.ones(size=(len(b),)) * i)
-
- fg_pred = pi[b, a, gj, gi]
- p_obj.append(fg_pred[:, 4:5])
- p_cls.append(fg_pred[:, 5:])
-
- grid = torch.stack([gi, gj], dim=1)
- pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8.
- #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i]
- pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8.
- pxywh = torch.cat([pxy, pwh], dim=-1)
- pxyxy = xywh2xyxy(pxywh)
- pxyxys.append(pxyxy)
-
- pxyxys = torch.cat(pxyxys, dim=0)
- if pxyxys.shape[0] == 0:
- continue
- p_obj = torch.cat(p_obj, dim=0)
- p_cls = torch.cat(p_cls, dim=0)
- from_which_layer = torch.cat(from_which_layer, dim=0)
- all_b = torch.cat(all_b, dim=0)
- all_a = torch.cat(all_a, dim=0)
- all_gj = torch.cat(all_gj, dim=0)
- all_gi = torch.cat(all_gi, dim=0)
- all_anch = torch.cat(all_anch, dim=0)
-
- pair_wise_iou = box_iou(txyxy, pxyxys)
-
- pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8)
-
- top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1)
- dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1)
-
- gt_cls_per_image = (
- F.one_hot(this_target[:, 1].to(torch.int64), self.nc)
- .float()
- .unsqueeze(1)
- .repeat(1, pxyxys.shape[0], 1)
- )
-
- num_gt = this_target.shape[0]
- cls_preds_ = (
- p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
- * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
- )
-
- y = cls_preds_.sqrt_()
- pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
- torch.log(y/(1-y)) , gt_cls_per_image, reduction="none"
- ).sum(-1)
- del cls_preds_
-
- cost = (
- pair_wise_cls_loss
- + 3.0 * pair_wise_iou_loss
- )
-
- matching_matrix = torch.zeros_like(cost)
-
- for gt_idx in range(num_gt):
- _, pos_idx = torch.topk(
- cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False
- )
- matching_matrix[gt_idx][pos_idx] = 1.0
-
- del top_k, dynamic_ks
- anchor_matching_gt = matching_matrix.sum(0)
- if (anchor_matching_gt > 1).sum() > 0:
- _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)
- matching_matrix[:, anchor_matching_gt > 1] *= 0.0
- matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0
- fg_mask_inboxes = matching_matrix.sum(0) > 0.0
- matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
-
- from_which_layer = from_which_layer[fg_mask_inboxes]
- all_b = all_b[fg_mask_inboxes]
- all_a = all_a[fg_mask_inboxes]
- all_gj = all_gj[fg_mask_inboxes]
- all_gi = all_gi[fg_mask_inboxes]
- all_anch = all_anch[fg_mask_inboxes]
-
- this_target = this_target[matched_gt_inds]
-
- for i in range(nl):
- layer_idx = from_which_layer == i
- matching_bs[i].append(all_b[layer_idx])
- matching_as[i].append(all_a[layer_idx])
- matching_gjs[i].append(all_gj[layer_idx])
- matching_gis[i].append(all_gi[layer_idx])
- matching_targets[i].append(this_target[layer_idx])
- matching_anchs[i].append(all_anch[layer_idx])
-
- for i in range(nl):
- if matching_targets[i] != []:
- matching_bs[i] = torch.cat(matching_bs[i], dim=0)
- matching_as[i] = torch.cat(matching_as[i], dim=0)
- matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
- matching_gis[i] = torch.cat(matching_gis[i], dim=0)
- matching_targets[i] = torch.cat(matching_targets[i], dim=0)
- matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
- else:
- matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
- matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-
- return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs
-
- def find_5_positive(self, p, targets):
- # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
- na, nt = self.na, targets.shape[0] # number of anchors, targets
- indices, anch = [], []
- gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain
- ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
- targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
-
- g = 1.0 # bias
- off = torch.tensor([[0, 0],
- [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
- # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
- ], device=targets.device).float() * g # offsets
-
- for i in range(self.nl):
- anchors = self.anchors[i]
- gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
-
- # Match targets to anchors
- t = targets * gain
- if nt:
- # Matches
- r = t[:, :, 4:6] / anchors[:, None] # wh ratio
- j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare
- # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
- t = t[j] # filter
-
- # Offsets
- gxy = t[:, 2:4] # grid xy
- gxi = gain[[2, 3]] - gxy # inverse
- j, k = ((gxy % 1. < g) & (gxy > 1.)).T
- l, m = ((gxi % 1. < g) & (gxi > 1.)).T
- j = torch.stack((torch.ones_like(j), j, k, l, m))
- t = t.repeat((5, 1, 1))[j]
- offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
- else:
- t = targets[0]
- offsets = 0
-
- # Define
- b, c = t[:, :2].long().T # image, class
- gxy = t[:, 2:4] # grid xy
- gwh = t[:, 4:6] # grid wh
- gij = (gxy - offsets).long()
- gi, gj = gij.T # grid xy indices
-
- # Append
- a = t[:, 6].long() # anchor indices
- indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
- anch.append(anchors[a]) # anchors
-
- return indices, anch
-
- def find_3_positive(self, p, targets):
- # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
- na, nt = self.na, targets.shape[0] # number of anchors, targets
- indices, anch = [], []
- gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain
- ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
- targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
-
- g = 0.5 # bias
- off = torch.tensor([[0, 0],
- [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
- # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
- ], device=targets.device).float() * g # offsets
-
- for i in range(self.nl):
- anchors = self.anchors[i]
- gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
-
- # Match targets to anchors
- t = targets * gain
- if nt:
- # Matches
- r = t[:, :, 4:6] / anchors[:, None] # wh ratio
- j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare
- # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
- t = t[j] # filter
-
- # Offsets
- gxy = t[:, 2:4] # grid xy
- gxi = gain[[2, 3]] - gxy # inverse
- j, k = ((gxy % 1. < g) & (gxy > 1.)).T
- l, m = ((gxi % 1. < g) & (gxi > 1.)).T
- j = torch.stack((torch.ones_like(j), j, k, l, m))
- t = t.repeat((5, 1, 1))[j]
- offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
- else:
- t = targets[0]
- offsets = 0
-
- # Define
- b, c = t[:, :2].long().T # image, class
- gxy = t[:, 2:4] # grid xy
- gwh = t[:, 4:6] # grid wh
- gij = (gxy - offsets).long()
- gi, gj = gij.T # grid xy indices
-
- # Append
- a = t[:, 6].long() # anchor indices
- indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
- anch.append(anchors[a]) # anchors
-
- return indices, anch
diff --git a/spaces/bigjoker/stable-diffusion-webui/modules/scripts.py b/spaces/bigjoker/stable-diffusion-webui/modules/scripts.py
deleted file mode 100644
index 705fdc2f6bb672bf1e6db0bc63d3eeaec6795078..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/modules/scripts.py
+++ /dev/null
@@ -1,501 +0,0 @@
-import os
-import re
-import sys
-import traceback
-from collections import namedtuple
-
-import gradio as gr
-
-from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing
-
-AlwaysVisible = object()
-
-
-class PostprocessImageArgs:
- def __init__(self, image):
- self.image = image
-
-
-class Script:
- filename = None
- args_from = None
- args_to = None
- alwayson = False
-
- is_txt2img = False
- is_img2img = False
-
- """A gr.Group component that has all script's UI inside it"""
- group = None
-
- infotext_fields = None
- """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
- parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
- """
-
- def title(self):
- """this function should return the title of the script. This is what will be displayed in the dropdown menu."""
-
- raise NotImplementedError()
-
- def ui(self, is_img2img):
- """this function should create gradio UI elements. See https://gradio.app/docs/#components
- The return value should be an array of all components that are used in processing.
- Values of those returned components will be passed to run() and process() functions.
- """
-
- pass
-
- def show(self, is_img2img):
- """
- is_img2img is True if this function is called for the img2img interface, and Fasle otherwise
-
- This function should return:
- - False if the script should not be shown in UI at all
- - True if the script should be shown in UI if it's selected in the scripts dropdown
- - script.AlwaysVisible if the script should be shown in UI at all times
- """
-
- return True
-
- def run(self, p, *args):
- """
- This function is called if the script has been selected in the script dropdown.
- It must do all processing and return the Processed object with results, same as
- one returned by processing.process_images.
-
- Usually the processing is done by calling the processing.process_images function.
-
- args contains all values returned by components from ui()
- """
-
- pass
-
- def process(self, p, *args):
- """
- This function is called before processing begins for AlwaysVisible scripts.
- You can modify the processing object (p) here, inject hooks, etc.
- args contains all values returned by components from ui()
- """
-
- pass
-
- def process_batch(self, p, *args, **kwargs):
- """
- Same as process(), but called for every batch.
-
- **kwargs will have those items:
- - batch_number - index of current batch, from 0 to number of batches-1
- - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
- - seeds - list of seeds for current batch
- - subseeds - list of subseeds for current batch
- """
-
- pass
-
- def postprocess_batch(self, p, *args, **kwargs):
- """
- Same as process_batch(), but called for every batch after it has been generated.
-
- **kwargs will have same items as process_batch, and also:
- - batch_number - index of current batch, from 0 to number of batches-1
- - images - torch tensor with all generated images, with values ranging from 0 to 1;
- """
-
- pass
-
- def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
- """
- Called for every image after it has been generated.
- """
-
- pass
-
- def postprocess(self, p, processed, *args):
- """
- This function is called after processing ends for AlwaysVisible scripts.
- args contains all values returned by components from ui()
- """
-
- pass
-
- def before_component(self, component, **kwargs):
- """
- Called before a component is created.
- Use elem_id/label fields of kwargs to figure out which component it is.
- This can be useful to inject your own components somewhere in the middle of vanilla UI.
- You can return created components in the ui() function to add them to the list of arguments for your processing functions
- """
-
- pass
-
- def after_component(self, component, **kwargs):
- """
- Called after a component is created. Same as above.
- """
-
- pass
-
- def describe(self):
- """unused"""
- return ""
-
- def elem_id(self, item_id):
- """helper function to generate id for a HTML element, constructs final id out of script name, tab and user-supplied item_id"""
-
- need_tabname = self.show(True) == self.show(False)
- tabname = ('img2img' if self.is_img2img else 'txt2txt') + "_" if need_tabname else ""
- title = re.sub(r'[^a-z_0-9]', '', re.sub(r'\s', '_', self.title().lower()))
-
- return f'script_{tabname}{title}_{item_id}'
-
-
-current_basedir = paths.script_path
-
-
-def basedir():
- """returns the base directory for the current script. For scripts in the main scripts directory,
- this is the main directory (where webui.py resides), and for scripts in extensions directory
- (ie extensions/aesthetic/script/aesthetic.py), this is extension's directory (extensions/aesthetic)
- """
- return current_basedir
-
-
-ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"])
-
-scripts_data = []
-postprocessing_scripts_data = []
-ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
-
-
-def list_scripts(scriptdirname, extension):
- scripts_list = []
-
- basedir = os.path.join(paths.script_path, scriptdirname)
- if os.path.exists(basedir):
- for filename in sorted(os.listdir(basedir)):
- scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
-
- for ext in extensions.active():
- scripts_list += ext.list_files(scriptdirname, extension)
-
- scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
-
- return scripts_list
-
-
-def list_files_with_name(filename):
- res = []
-
- dirs = [paths.script_path] + [ext.path for ext in extensions.active()]
-
- for dirpath in dirs:
- if not os.path.isdir(dirpath):
- continue
-
- path = os.path.join(dirpath, filename)
- if os.path.isfile(path):
- res.append(path)
-
- return res
-
-
-def load_scripts():
- global current_basedir
- scripts_data.clear()
- postprocessing_scripts_data.clear()
- script_callbacks.clear_callbacks()
-
- scripts_list = list_scripts("scripts", ".py")
-
- syspath = sys.path
-
- def register_scripts_from_module(module):
- for key, script_class in module.__dict__.items():
- if type(script_class) != type:
- continue
-
- if issubclass(script_class, Script):
- scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
- elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
- postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
-
- for scriptfile in sorted(scripts_list):
- try:
- if scriptfile.basedir != paths.script_path:
- sys.path = [scriptfile.basedir] + sys.path
- current_basedir = scriptfile.basedir
-
- script_module = script_loading.load_module(scriptfile.path)
- register_scripts_from_module(script_module)
-
- except Exception:
- print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- finally:
- sys.path = syspath
- current_basedir = paths.script_path
-
-
-def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
- try:
- res = func(*args, **kwargs)
- return res
- except Exception:
- print(f"Error calling: {filename}/{funcname}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- return default
-
-
-class ScriptRunner:
- def __init__(self):
- self.scripts = []
- self.selectable_scripts = []
- self.alwayson_scripts = []
- self.titles = []
- self.infotext_fields = []
-
- def initialize_scripts(self, is_img2img):
- from modules import scripts_auto_postprocessing
-
- self.scripts.clear()
- self.alwayson_scripts.clear()
- self.selectable_scripts.clear()
-
- auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
-
- for script_class, path, basedir, script_module in auto_processing_scripts + scripts_data:
- script = script_class()
- script.filename = path
- script.is_txt2img = not is_img2img
- script.is_img2img = is_img2img
-
- visibility = script.show(script.is_img2img)
-
- if visibility == AlwaysVisible:
- self.scripts.append(script)
- self.alwayson_scripts.append(script)
- script.alwayson = True
-
- elif visibility:
- self.scripts.append(script)
- self.selectable_scripts.append(script)
-
- def setup_ui(self):
- self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
-
- inputs = [None]
- inputs_alwayson = [True]
-
- def create_script_ui(script, inputs, inputs_alwayson):
- script.args_from = len(inputs)
- script.args_to = len(inputs)
-
- controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
-
- if controls is None:
- return
-
- for control in controls:
- control.custom_script_source = os.path.basename(script.filename)
-
- if script.infotext_fields is not None:
- self.infotext_fields += script.infotext_fields
-
- inputs += controls
- inputs_alwayson += [script.alwayson for _ in controls]
- script.args_to = len(inputs)
-
- for script in self.alwayson_scripts:
- with gr.Group() as group:
- create_script_ui(script, inputs, inputs_alwayson)
-
- script.group = group
-
- dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
- inputs[0] = dropdown
-
- for script in self.selectable_scripts:
- with gr.Group(visible=False) as group:
- create_script_ui(script, inputs, inputs_alwayson)
-
- script.group = group
-
- def select_script(script_index):
- selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None
-
- return [gr.update(visible=selected_script == s) for s in self.selectable_scripts]
-
- def init_field(title):
- """called when an initial value is set from ui-config.json to show script's UI components"""
-
- if title == 'None':
- return
-
- script_index = self.titles.index(title)
- self.selectable_scripts[script_index].group.visible = True
-
- dropdown.init_field = init_field
-
- dropdown.change(
- fn=select_script,
- inputs=[dropdown],
- outputs=[script.group for script in self.selectable_scripts]
- )
-
- self.script_load_ctr = 0
- def onload_script_visibility(params):
- title = params.get('Script', None)
- if title:
- title_index = self.titles.index(title)
- visibility = title_index == self.script_load_ctr
- self.script_load_ctr = (self.script_load_ctr + 1) % len(self.titles)
- return gr.update(visible=visibility)
- else:
- return gr.update(visible=False)
-
- self.infotext_fields.append( (dropdown, lambda x: gr.update(value=x.get('Script', 'None'))) )
- self.infotext_fields.extend( [(script.group, onload_script_visibility) for script in self.selectable_scripts] )
-
- return inputs
-
- def run(self, p, *args):
- script_index = args[0]
-
- if script_index == 0:
- return None
-
- script = self.selectable_scripts[script_index-1]
-
- if script is None:
- return None
-
- script_args = args[script.args_from:script.args_to]
- processed = script.run(p, *script_args)
-
- shared.total_tqdm.clear()
-
- return processed
-
- def process(self, p):
- for script in self.alwayson_scripts:
- try:
- script_args = p.script_args[script.args_from:script.args_to]
- script.process(p, *script_args)
- except Exception:
- print(f"Error running process: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- def process_batch(self, p, **kwargs):
- for script in self.alwayson_scripts:
- try:
- script_args = p.script_args[script.args_from:script.args_to]
- script.process_batch(p, *script_args, **kwargs)
- except Exception:
- print(f"Error running process_batch: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- def postprocess(self, p, processed):
- for script in self.alwayson_scripts:
- try:
- script_args = p.script_args[script.args_from:script.args_to]
- script.postprocess(p, processed, *script_args)
- except Exception:
- print(f"Error running postprocess: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- def postprocess_batch(self, p, images, **kwargs):
- for script in self.alwayson_scripts:
- try:
- script_args = p.script_args[script.args_from:script.args_to]
- script.postprocess_batch(p, *script_args, images=images, **kwargs)
- except Exception:
- print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- def postprocess_image(self, p, pp: PostprocessImageArgs):
- for script in self.alwayson_scripts:
- try:
- script_args = p.script_args[script.args_from:script.args_to]
- script.postprocess_image(p, pp, *script_args)
- except Exception:
- print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- def before_component(self, component, **kwargs):
- for script in self.scripts:
- try:
- script.before_component(component, **kwargs)
- except Exception:
- print(f"Error running before_component: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- def after_component(self, component, **kwargs):
- for script in self.scripts:
- try:
- script.after_component(component, **kwargs)
- except Exception:
- print(f"Error running after_component: {script.filename}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- def reload_sources(self, cache):
- for si, script in list(enumerate(self.scripts)):
- args_from = script.args_from
- args_to = script.args_to
- filename = script.filename
-
- module = cache.get(filename, None)
- if module is None:
- module = script_loading.load_module(script.filename)
- cache[filename] = module
-
- for key, script_class in module.__dict__.items():
- if type(script_class) == type and issubclass(script_class, Script):
- self.scripts[si] = script_class()
- self.scripts[si].filename = filename
- self.scripts[si].args_from = args_from
- self.scripts[si].args_to = args_to
-
-
-scripts_txt2img = ScriptRunner()
-scripts_img2img = ScriptRunner()
-scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
-scripts_current: ScriptRunner = None
-
-
-def reload_script_body_only():
- cache = {}
- scripts_txt2img.reload_sources(cache)
- scripts_img2img.reload_sources(cache)
-
-
-def reload_scripts():
- global scripts_txt2img, scripts_img2img, scripts_postproc
-
- load_scripts()
-
- scripts_txt2img = ScriptRunner()
- scripts_img2img = ScriptRunner()
- scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
-
-
-def IOComponent_init(self, *args, **kwargs):
- if scripts_current is not None:
- scripts_current.before_component(self, **kwargs)
-
- script_callbacks.before_component_callback(self, **kwargs)
-
- res = original_IOComponent_init(self, *args, **kwargs)
-
- script_callbacks.after_component_callback(self, **kwargs)
-
- if scripts_current is not None:
- scripts_current.after_component(self, **kwargs)
-
- return res
-
-
-original_IOComponent_init = gr.components.IOComponent.__init__
-gr.components.IOComponent.__init__ = IOComponent_init
diff --git a/spaces/bioriAsaeru/text-to-voice/3ds Ambassador Certificate Hack.md b/spaces/bioriAsaeru/text-to-voice/3ds Ambassador Certificate Hack.md
deleted file mode 100644
index 7fc27a035396213201b5ada7590a8af1f7e10629..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/3ds Ambassador Certificate Hack.md
+++ /dev/null
@@ -1,17 +0,0 @@
-
-<p>Remember, you needed to grab a 3DS and log onto the eShop before the price was dropped in order to become an ambassador and be eligible for these free games. There will also be 10 free GameBoy Advance games released before the end of the year.</p>
-<h2>3ds Ambassador Certificate Hack</h2><br /><p><b><b>Download</b> ✸✸✸ <a href="https://urloso.com/2uyRCU">https://urloso.com/2uyRCU</a></b></p><br /><br />
-<p>I've had a chance to play a few of these games, and they all work fine on the 3DS, and my kids are ecstatic about having a brand-new (to them) selection of games. Also be sure to download the ambassador certificate, which allows you to set up notifications for when the rest of the games become available. Enjoy!</p>
-<p>It's likely you've already downloaded your free NES games but if not there is a special section in the eShop for the Nintendo 3DS Ambassador certificate, a device for Nintendo to tell you about the Ambassador program.</p>
-<p>@James the announcement from nintendo says this about the NES games: "Once the paid versions of the games are posted to the Nintendo eShop later in the year, the updated versions will be available to Ambassadors for download at no cost."<br />my question is: how will the ambassador versions be different from the paid versions? maybe at first we will get them in 2D but when they are made available they will be 3d classics?</p>
-<p></p>
-<p>@James, @everyone: I sent a couple of emails to Nintendo asking about the ambassador program availability in Mexico and Latin America, Ill be sure to update you and to post here if I hear back from them. Has any you read/found anything about this on your end?</p>
-<p>@7ATalavera Oh I just checked it on nintendo.com. you're automatically registered if you entered the eshop. btw, I'm not sure if we're getting free GBA games-if you check the wording, it says 20 free games, there will be 10 free games available on the first day for nes, and there will be 10 exclusive to ambassador gba games with no mention whether they will be free. I just emailed tech support.</p>
-<p>@armoredghor: I hope they dont add registering for club.nintendo as a requirement, or they dont simply skip Mexico (we missed green lantern and Netflix) since "More details about this program will be announced in the future". I hadnt noticed, you are right, they do not specify if the 10 GBA games will be part of the 20 free downloads (they never label GBA games as free), if they mean an ambassador earns the "privilege" to buy them, I pass. Let us know if Nintendo replies.</p>
-<p>@haddyDrow same here. The only reason I even get to use the eshop is by changing the country settings on my 3ds. Still It would be nice to get a notification that I am on the ambassador program. Would be really annoyed if the compensation doesn't Apply to me or others who bought their 3ds early</p>
-<p>@7ATalavera @James I am also in the same situation like Talavera as I am from Mexico too, should I change the region of my 3ds?, I am currently studying in Shanghai and I have my 3ds configured with residence in Mexico so I have been accessing to the mexican eshop, but I'm afraid if the ambassador program is just available for U.S. and Canada, anyway I bought 2 DSiWare games, I do not know if changing my 3ds region I will lose my games </i></p>
-<p>So, Sony gets hacked and the PSN is down for weeks and they offer a welcome back of 2 free games and a limited subscription to PSN Plus. Nintendo lowers the price of the handheld and they give us early adopters 20 games for free. Hmm, which company sounds better? Roll on September 1st, I want some awesome games. Fingers crossed for the NES version of Teenage Mutant Ninja Turtles.</p>
-<p>can someone help me.. im wondering if do i have to connect to wifi to get the ambassador program thing.. but what if i bought it on the day it came out and connect to e-shop like two weeks ago.. will i be able to take part in the ambassador program for free games? help me im saying this cause i dont have wifi to update or go to eshop right now</p>
-<p>soooo i have taken my n3ds at april 2nd and i have first connected it at 12 april ive downloaded the pokedex all the videos and the video thing + the game that was availible for free before 1st june i think soooo i will be an ambassador?</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Beck One Foot in the Grave Deluxe Rar The Best Songs Lyrics and Trivia from the Album.md b/spaces/bioriAsaeru/text-to-voice/Beck One Foot in the Grave Deluxe Rar The Best Songs Lyrics and Trivia from the Album.md
deleted file mode 100644
index 247fb74fef1bc26bf042849e2a7879d8a0707d94..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Beck One Foot in the Grave Deluxe Rar The Best Songs Lyrics and Trivia from the Album.md
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>beck one foot in the grave deluxe rar</h2><br /><p><b><b>Download</b> 🆓 <a href="https://urloso.com/2uyPYp">https://urloso.com/2uyPYp</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/bioriAsaeru/text-to-voice/Download The Shape of Water (2017) [BluRay] [1080p] English for Free and Discover a New World of Wonder.md b/spaces/bioriAsaeru/text-to-voice/Download The Shape of Water (2017) [BluRay] [1080p] English for Free and Discover a New World of Wonder.md
deleted file mode 100644
index 7309029ec8f50967a3881867d1d54f88b4d1ccb4..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Download The Shape of Water (2017) [BluRay] [1080p] English for Free and Discover a New World of Wonder.md
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>The Shape of Water (2017) [BluRay] [1080p] English free download</h2><br /><p><b><b>Download Zip</b> ———>>> <a href="https://urloso.com/2uyRDl">https://urloso.com/2uyRDl</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/bipin/image2story/README.md b/spaces/bipin/image2story/README.md
deleted file mode 100644
index 6de8b09eecf7594ee429a5f5c1f8dc473927c63a..0000000000000000000000000000000000000000
--- a/spaces/bipin/image2story/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Image2story
-emoji: 🚀
-colorFrom: blue
-colorTo: blue
-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/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/model/tts.py b/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/model/tts.py
deleted file mode 100644
index f2b907386e3bb646a46f686b24e1ff5fc371792f..0000000000000000000000000000000000000000
--- a/spaces/bookbot/Grad-TTS-Weildan-Playground/Grad-TTS/model/tts.py
+++ /dev/null
@@ -1,181 +0,0 @@
-# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the MIT License.
-# This program 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
-# MIT License for more details.
-
-import math
-import random
-
-import torch
-
-from model import monotonic_align
-from model.base import BaseModule
-from model.text_encoder import TextEncoder
-from model.diffusion import Diffusion
-from model.utils import sequence_mask, generate_path, duration_loss, fix_len_compatibility
-
-
-class GradTTS(BaseModule):
- def __init__(self, n_vocab, n_spks, spk_emb_dim, n_enc_channels, filter_channels, filter_channels_dp,
- n_heads, n_enc_layers, enc_kernel, enc_dropout, window_size,
- n_feats, dec_dim, beta_min, beta_max, pe_scale):
- super(GradTTS, self).__init__()
- self.n_vocab = n_vocab
- self.n_spks = n_spks
- self.spk_emb_dim = spk_emb_dim
- self.n_enc_channels = n_enc_channels
- self.filter_channels = filter_channels
- self.filter_channels_dp = filter_channels_dp
- self.n_heads = n_heads
- self.n_enc_layers = n_enc_layers
- self.enc_kernel = enc_kernel
- self.enc_dropout = enc_dropout
- self.window_size = window_size
- self.n_feats = n_feats
- self.dec_dim = dec_dim
- self.beta_min = beta_min
- self.beta_max = beta_max
- self.pe_scale = pe_scale
-
- if n_spks > 1:
- self.spk_emb = torch.nn.Embedding(n_spks, spk_emb_dim)
- self.encoder = TextEncoder(n_vocab, n_feats, n_enc_channels,
- filter_channels, filter_channels_dp, n_heads,
- n_enc_layers, enc_kernel, enc_dropout, window_size)
- self.decoder = Diffusion(n_feats, dec_dim, n_spks, spk_emb_dim, beta_min, beta_max, pe_scale)
-
- @torch.no_grad()
- def forward(self, x, x_lengths, n_timesteps, temperature=1.0, stoc=False, spk=None, length_scale=1.0):
- """
- Generates mel-spectrogram from text. Returns:
- 1. encoder outputs
- 2. decoder outputs
- 3. generated alignment
-
- Args:
- x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids.
- x_lengths (torch.Tensor): lengths of texts in batch.
- n_timesteps (int): number of steps to use for reverse diffusion in decoder.
- temperature (float, optional): controls variance of terminal distribution.
- stoc (bool, optional): flag that adds stochastic term to the decoder sampler.
- Usually, does not provide synthesis improvements.
- length_scale (float, optional): controls speech pace.
- Increase value to slow down generated speech and vice versa.
- """
- x, x_lengths = self.relocate_input([x, x_lengths])
-
- if self.n_spks > 1:
- # Get speaker embedding
- spk = self.spk_emb(spk)
-
- # Get encoder_outputs `mu_x` and log-scaled token durations `logw`
- mu_x, logw, x_mask = self.encoder(x, x_lengths, spk)
-
- w = torch.exp(logw) * x_mask
- w_ceil = torch.ceil(w) * length_scale
- y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
- y_max_length = int(y_lengths.max())
- y_max_length_ = fix_len_compatibility(y_max_length)
-
- # Using obtained durations `w` construct alignment map `attn`
- y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype)
- attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2)
- attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1)
-
- # Align encoded text and get mu_y
- mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2))
- mu_y = mu_y.transpose(1, 2)
- encoder_outputs = mu_y[:, :, :y_max_length]
-
- # Sample latent representation from terminal distribution N(mu_y, I)
- z = mu_y + torch.randn_like(mu_y, device=mu_y.device) / temperature
- # Generate sample by performing reverse dynamics
- decoder_outputs = self.decoder(z, y_mask, mu_y, n_timesteps, stoc, spk)
- decoder_outputs = decoder_outputs[:, :, :y_max_length]
-
- return encoder_outputs, decoder_outputs, attn[:, :, :y_max_length]
-
- def compute_loss(self, x, x_lengths, y, y_lengths, spk=None, out_size=None):
- """
- Computes 3 losses:
- 1. duration loss: loss between predicted token durations and those extracted by Monotinic Alignment Search (MAS).
- 2. prior loss: loss between mel-spectrogram and encoder outputs.
- 3. diffusion loss: loss between gaussian noise and its reconstruction by diffusion-based decoder.
-
- Args:
- x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids.
- x_lengths (torch.Tensor): lengths of texts in batch.
- y (torch.Tensor): batch of corresponding mel-spectrograms.
- y_lengths (torch.Tensor): lengths of mel-spectrograms in batch.
- out_size (int, optional): length (in mel's sampling rate) of segment to cut, on which decoder will be trained.
- Should be divisible by 2^{num of UNet downsamplings}. Needed to increase batch size.
- """
- x, x_lengths, y, y_lengths = self.relocate_input([x, x_lengths, y, y_lengths])
-
- if self.n_spks > 1:
- # Get speaker embedding
- spk = self.spk_emb(spk)
-
- # Get encoder_outputs `mu_x` and log-scaled token durations `logw`
- mu_x, logw, x_mask = self.encoder(x, x_lengths, spk)
- y_max_length = y.shape[-1]
-
- y_mask = sequence_mask(y_lengths, y_max_length).unsqueeze(1).to(x_mask)
- attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2)
-
- # Use MAS to find most likely alignment `attn` between text and mel-spectrogram
- with torch.no_grad():
- const = -0.5 * math.log(2 * math.pi) * self.n_feats
- factor = -0.5 * torch.ones(mu_x.shape, dtype=mu_x.dtype, device=mu_x.device)
- y_square = torch.matmul(factor.transpose(1, 2), y ** 2)
- y_mu_double = torch.matmul(2.0 * (factor * mu_x).transpose(1, 2), y)
- mu_square = torch.sum(factor * (mu_x ** 2), 1).unsqueeze(-1)
- log_prior = y_square - y_mu_double + mu_square + const
-
- attn = monotonic_align.maximum_path(log_prior, attn_mask.squeeze(1))
- attn = attn.detach()
-
- # Compute loss between predicted log-scaled durations and those obtained from MAS
- logw_ = torch.log(1e-8 + torch.sum(attn.unsqueeze(1), -1)) * x_mask
- dur_loss = duration_loss(logw, logw_, x_lengths)
-
- # Cut a small segment of mel-spectrogram in order to increase batch size
- if not isinstance(out_size, type(None)):
- max_offset = (y_lengths - out_size).clamp(0)
- offset_ranges = list(zip([0] * max_offset.shape[0], max_offset.cpu().numpy()))
- out_offset = torch.LongTensor([
- torch.tensor(random.choice(range(start, end)) if end > start else 0)
- for start, end in offset_ranges
- ]).to(y_lengths)
-
- attn_cut = torch.zeros(attn.shape[0], attn.shape[1], out_size, dtype=attn.dtype, device=attn.device)
- y_cut = torch.zeros(y.shape[0], self.n_feats, out_size, dtype=y.dtype, device=y.device)
- y_cut_lengths = []
- for i, (y_, out_offset_) in enumerate(zip(y, out_offset)):
- y_cut_length = out_size + (y_lengths[i] - out_size).clamp(None, 0)
- y_cut_lengths.append(y_cut_length)
- cut_lower, cut_upper = out_offset_, out_offset_ + y_cut_length
- y_cut[i, :, :y_cut_length] = y_[:, cut_lower:cut_upper]
- attn_cut[i, :, :y_cut_length] = attn[i, :, cut_lower:cut_upper]
- y_cut_lengths = torch.LongTensor(y_cut_lengths)
- y_cut_mask = sequence_mask(y_cut_lengths).unsqueeze(1).to(y_mask)
-
- attn = attn_cut
- y = y_cut
- y_mask = y_cut_mask
-
- # Align encoded text with mel-spectrogram and get mu_y segment
- mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2))
- mu_y = mu_y.transpose(1, 2)
-
- # Compute loss of score-based decoder
- diff_loss, xt = self.decoder.compute_loss(y, y_mask, mu_y, spk)
-
- # Compute loss between aligned encoder outputs and mel-spectrogram
- prior_loss = torch.sum(0.5 * ((y - mu_y) ** 2 + math.log(2 * math.pi)) * y_mask)
- prior_loss = prior_loss / (torch.sum(y_mask) * self.n_feats)
-
- return dur_loss, prior_loss, diff_loss
diff --git a/spaces/bookbot/Wikipedia-Scraper/app.py b/spaces/bookbot/Wikipedia-Scraper/app.py
deleted file mode 100644
index b9318a65ea01b1ecc944b24f0f8b449416891904..0000000000000000000000000000000000000000
--- a/spaces/bookbot/Wikipedia-Scraper/app.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import requests
-from bs4 import BeautifulSoup
-import re
-from urllib.parse import urlparse
-import gradio as gr
-import json
-
-
-def extract_wikipedia_text(raw_text, language):
- contents = []
- paragraph = ""
-
- for element in raw_text:
- # detected next headline
- if element.name == "span":
- if paragraph == "":
- continue
- contents.append({f"text-{language}": paragraph})
- paragraph = ""
- else:
- clean_text = preprocessing(element.text)
- if clean_text == "":
- continue
- if paragraph != "":
- clean_text = " " + clean_text
- paragraph += clean_text
- return contents
-
-
-def preprocessing(text):
- # remove square brackets a.k.a citations
- clean_text = re.sub("\[.*?]", "", text).strip()
- # remove \n
- clean_text = clean_text.replace("\n", "")
- return clean_text
-
-
-def scrape(url):
- language = urlparse(url).netloc.split(".")[0]
- try:
- page = requests.get(url, headers={"user-agent": "Mozilla/5.0"})
- soup = BeautifulSoup(page.content, "html.parser")
- except:
- print("error")
- title = soup.find("h1", {"id": "firstHeading"}).get_text().strip()
- raw_text = soup.select(
- "h2 span.mw-headline, h3 span.mw-headline, h4 span.mw-headline, p"
- )
- contents = extract_wikipedia_text(raw_text, language)
- json_output = {"source": url, f"title-{language}": title, "pages": contents}
- filename = f"{url.split('/')[-1]}.json"
- with open(filename, "w") as f:
- json.dump(json_output, f)
- return json_output, filename
-
-
-style_sheet = "#json-output { max-height: 400px; overflow-y: auto; }"
-with gr.Blocks(css=style_sheet) as demo:
- gr.Markdown(
- f"""
- <center>
- <h1>Wikipedia Scraper 📜</h1>
- </center>
- """
- )
- with gr.Row():
- inp = gr.Textbox(placeholder="Wikipedia URL")
- with gr.Column():
- out = gr.JSON(elem_id="json-output")
- out_download = gr.File()
- btn = gr.Button("Scrape")
- btn.click(fn=scrape, inputs=inp, outputs=[out, out_download])
-
-demo.launch(debug=True)
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/configs/common/data/coco_keypoint.py b/spaces/brjathu/HMR2.0/vendor/detectron2/configs/common/data/coco_keypoint.py
deleted file mode 100644
index b4ceb066faf696954244205dc75376b767071217..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/configs/common/data/coco_keypoint.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from detectron2.data.detection_utils import create_keypoint_hflip_indices
-
-from .coco import dataloader
-
-dataloader.train.dataset.min_keypoints = 1
-dataloader.train.dataset.names = "keypoints_coco_2017_train"
-dataloader.test.dataset.names = "keypoints_coco_2017_val"
-
-dataloader.train.mapper.update(
- use_instance_mask=False,
- use_keypoint=True,
- keypoint_hflip_indices=create_keypoint_hflip_indices(dataloader.train.dataset.names),
-)
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/layers/wrappers.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/layers/wrappers.py
deleted file mode 100644
index fb3cb38b9de0d936bc3774b85eec7375f739add2..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/layers/wrappers.py
+++ /dev/null
@@ -1,162 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-Wrappers around on some nn functions, mainly to support empty tensors.
-
-Ideally, add support directly in PyTorch to empty tensors in those functions.
-
-These can be removed once https://github.com/pytorch/pytorch/issues/12013
-is implemented
-"""
-
-import warnings
-from typing import List, Optional
-import torch
-from torch.nn import functional as F
-
-from detectron2.utils.env import TORCH_VERSION
-
-
-def shapes_to_tensor(x: List[int], device: Optional[torch.device] = None) -> torch.Tensor:
- """
- Turn a list of integer scalars or integer Tensor scalars into a vector,
- in a way that's both traceable and scriptable.
-
- In tracing, `x` should be a list of scalar Tensor, so the output can trace to the inputs.
- In scripting or eager, `x` should be a list of int.
- """
- if torch.jit.is_scripting():
- return torch.as_tensor(x, device=device)
- if torch.jit.is_tracing():
- assert all(
- [isinstance(t, torch.Tensor) for t in x]
- ), "Shape should be tensor during tracing!"
- # as_tensor should not be used in tracing because it records a constant
- ret = torch.stack(x)
- if ret.device != device: # avoid recording a hard-coded device if not necessary
- ret = ret.to(device=device)
- return ret
- return torch.as_tensor(x, device=device)
-
-
-def check_if_dynamo_compiling():
- if TORCH_VERSION >= (1, 14):
- from torch._dynamo import is_compiling
-
- return is_compiling()
- else:
- return False
-
-
-def cat(tensors: List[torch.Tensor], dim: int = 0):
- """
- Efficient version of torch.cat that avoids a copy if there is only a single element in a list
- """
- assert isinstance(tensors, (list, tuple))
- if len(tensors) == 1:
- return tensors[0]
- return torch.cat(tensors, dim)
-
-
-def empty_input_loss_func_wrapper(loss_func):
- def wrapped_loss_func(input, target, *, reduction="mean", **kwargs):
- """
- Same as `loss_func`, but returns 0 (instead of nan) for empty inputs.
- """
- if target.numel() == 0 and reduction == "mean":
- return input.sum() * 0.0 # connect the gradient
- return loss_func(input, target, reduction=reduction, **kwargs)
-
- return wrapped_loss_func
-
-
-cross_entropy = empty_input_loss_func_wrapper(F.cross_entropy)
-
-
-class _NewEmptyTensorOp(torch.autograd.Function):
- @staticmethod
- def forward(ctx, x, new_shape):
- ctx.shape = x.shape
- return x.new_empty(new_shape)
-
- @staticmethod
- def backward(ctx, grad):
- shape = ctx.shape
- return _NewEmptyTensorOp.apply(grad, shape), None
-
-
-class Conv2d(torch.nn.Conv2d):
- """
- A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features.
- """
-
- def __init__(self, *args, **kwargs):
- """
- Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`:
-
- Args:
- norm (nn.Module, optional): a normalization layer
- activation (callable(Tensor) -> Tensor): a callable activation function
-
- It assumes that norm layer is used before activation.
- """
- norm = kwargs.pop("norm", None)
- activation = kwargs.pop("activation", None)
- super().__init__(*args, **kwargs)
-
- self.norm = norm
- self.activation = activation
-
- def forward(self, x):
- # torchscript does not support SyncBatchNorm yet
- # https://github.com/pytorch/pytorch/issues/40507
- # and we skip these codes in torchscript since:
- # 1. currently we only support torchscript in evaluation mode
- # 2. features needed by exporting module to torchscript are added in PyTorch 1.6 or
- # later version, `Conv2d` in these PyTorch versions has already supported empty inputs.
- if not torch.jit.is_scripting():
- # Dynamo doesn't support context managers yet
- is_dynamo_compiling = check_if_dynamo_compiling()
- if not is_dynamo_compiling:
- with warnings.catch_warnings(record=True):
- if x.numel() == 0 and self.training:
- # https://github.com/pytorch/pytorch/issues/12013
- assert not isinstance(
- self.norm, torch.nn.SyncBatchNorm
- ), "SyncBatchNorm does not support empty inputs!"
-
- x = F.conv2d(
- x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups
- )
- if self.norm is not None:
- x = self.norm(x)
- if self.activation is not None:
- x = self.activation(x)
- return x
-
-
-ConvTranspose2d = torch.nn.ConvTranspose2d
-BatchNorm2d = torch.nn.BatchNorm2d
-interpolate = F.interpolate
-Linear = torch.nn.Linear
-
-
-def nonzero_tuple(x):
- """
- A 'as_tuple=True' version of torch.nonzero to support torchscript.
- because of https://github.com/pytorch/pytorch/issues/38718
- """
- if torch.jit.is_scripting():
- if x.dim() == 0:
- return x.unsqueeze(0).nonzero().unbind(1)
- return x.nonzero().unbind(1)
- else:
- return x.nonzero(as_tuple=True)
-
-
-@torch.jit.script_if_tracing
-def move_device_like(src: torch.Tensor, dst: torch.Tensor) -> torch.Tensor:
- """
- Tracing friendly way to cast tensor to another tensor's device. Device will be treated
- as constant during tracing, scripting the casting process as whole can workaround this issue.
- """
- return src.to(dst.device)
diff --git a/spaces/bsenst/flask_inference_api/README.md b/spaces/bsenst/flask_inference_api/README.md
deleted file mode 100644
index 7bb453ebe52fd03564cfb3da518cf5bec45d5cb9..0000000000000000000000000000000000000000
--- a/spaces/bsenst/flask_inference_api/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Flask_test
-emoji: 🦀
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 2.8.13
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: osanseviero/flask_test
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiofiles/threadpool/text.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiofiles/threadpool/text.py
deleted file mode 100644
index 0e625909b6c960ebed4a0ed99941b28156fbf2d1..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiofiles/threadpool/text.py
+++ /dev/null
@@ -1,64 +0,0 @@
-from ..base import AsyncBase, AsyncIndirectBase
-from .utils import delegate_to_executor, proxy_method_directly, proxy_property_directly
-
-
-@delegate_to_executor(
- "close",
- "flush",
- "isatty",
- "read",
- "readable",
- "readline",
- "readlines",
- "seek",
- "seekable",
- "tell",
- "truncate",
- "write",
- "writable",
- "writelines",
-)
-@proxy_method_directly("detach", "fileno", "readable")
-@proxy_property_directly(
- "buffer",
- "closed",
- "encoding",
- "errors",
- "line_buffering",
- "newlines",
- "name",
- "mode",
-)
-class AsyncTextIOWrapper(AsyncBase):
- """The asyncio executor version of io.TextIOWrapper."""
-
-
-@delegate_to_executor(
- "close",
- "flush",
- "isatty",
- "read",
- "readable",
- "readline",
- "readlines",
- "seek",
- "seekable",
- "tell",
- "truncate",
- "write",
- "writable",
- "writelines",
-)
-@proxy_method_directly("detach", "fileno", "readable")
-@proxy_property_directly(
- "buffer",
- "closed",
- "encoding",
- "errors",
- "line_buffering",
- "newlines",
- "name",
- "mode",
-)
-class AsyncTextIndirectIOWrapper(AsyncIndirectBase):
- """The indirect asyncio executor version of io.TextIOWrapper."""
diff --git a/spaces/ceckenrode/runwayml-stable-diffusion-v1-5/README.md b/spaces/ceckenrode/runwayml-stable-diffusion-v1-5/README.md
deleted file mode 100644
index 21d55735d04dcbf95bea95743085a8cc0f905005..0000000000000000000000000000000000000000
--- a/spaces/ceckenrode/runwayml-stable-diffusion-v1-5/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Runwayml Stable Diffusion V1 5
-emoji: 👁
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-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/chendl/compositional_test/transformers/examples/flax/text-classification/run_flax_glue.py b/spaces/chendl/compositional_test/transformers/examples/flax/text-classification/run_flax_glue.py
deleted file mode 100644
index c2c73fa2108987670227c6be5c0e83297de0e208..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/flax/text-classification/run_flax_glue.py
+++ /dev/null
@@ -1,663 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2021 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
-# limitations under the License.
-""" Finetuning a 🤗 Flax Transformers model for sequence classification on GLUE."""
-import json
-import logging
-import math
-import os
-import random
-import sys
-import time
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Any, Callable, Dict, Optional, Tuple
-
-import datasets
-import evaluate
-import jax
-import jax.numpy as jnp
-import numpy as np
-import optax
-from datasets import load_dataset
-from flax import struct, traverse_util
-from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
-from flax.training import train_state
-from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository, create_repo
-from tqdm import tqdm
-
-import transformers
-from transformers import (
- AutoConfig,
- AutoTokenizer,
- FlaxAutoModelForSequenceClassification,
- HfArgumentParser,
- PretrainedConfig,
- TrainingArguments,
- is_tensorboard_available,
-)
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
-
-
-logger = logging.getLogger(__name__)
-# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.28.0")
-
-Array = Any
-Dataset = datasets.arrow_dataset.Dataset
-PRNGKey = Any
-
-
-task_to_keys = {
- "cola": ("sentence", None),
- "mnli": ("premise", "hypothesis"),
- "mrpc": ("sentence1", "sentence2"),
- "qnli": ("question", "sentence"),
- "qqp": ("question1", "question2"),
- "rte": ("sentence1", "sentence2"),
- "sst2": ("sentence", None),
- "stsb": ("sentence1", "sentence2"),
- "wnli": ("sentence1", "sentence2"),
-}
-
-
-@dataclass
-class ModelArguments:
- """
- Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
- """
-
- model_name_or_path: str = field(
- metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
- )
- config_name: Optional[str] = field(
- default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
- )
- tokenizer_name: Optional[str] = field(
- default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
- )
- use_slow_tokenizer: Optional[bool] = field(
- default=False,
- metadata={"help": "If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library)."},
- )
- cache_dir: Optional[str] = field(
- default=None,
- metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
- )
- model_revision: str = field(
- default="main",
- metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
- )
- use_auth_token: bool = field(
- default=False,
- metadata={
- "help": (
- "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
- "with private models)."
- )
- },
- )
-
-
-@dataclass
-class DataTrainingArguments:
- """
- Arguments pertaining to what data we are going to input our model for training and eval.
- """
-
- task_name: Optional[str] = field(
- default=None, metadata={"help": f"The name of the glue task to train on. choices {list(task_to_keys.keys())}"}
- )
- dataset_config_name: Optional[str] = field(
- default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
- )
- train_file: Optional[str] = field(
- default=None, metadata={"help": "The input training data file (a csv or JSON file)."}
- )
- validation_file: Optional[str] = field(
- default=None,
- metadata={"help": "An optional input evaluation data file to evaluate on (a csv or JSON file)."},
- )
- test_file: Optional[str] = field(
- default=None,
- metadata={"help": "An optional input test data file to predict on (a csv or JSON file)."},
- )
- text_column_name: Optional[str] = field(
- default=None, metadata={"help": "The column name of text to input in the file (a csv or JSON file)."}
- )
- label_column_name: Optional[str] = field(
- default=None, metadata={"help": "The column name of label to input in the file (a csv or JSON file)."}
- )
- overwrite_cache: bool = field(
- default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
- )
- preprocessing_num_workers: Optional[int] = field(
- default=None,
- metadata={"help": "The number of processes to use for the preprocessing."},
- )
- max_seq_length: int = field(
- default=None,
- metadata={
- "help": (
- "The maximum total input sequence length after tokenization. If set, sequences longer "
- "than this will be truncated, sequences shorter will be padded."
- )
- },
- )
- max_train_samples: Optional[int] = field(
- default=None,
- metadata={
- "help": (
- "For debugging purposes or quicker training, truncate the number of training examples to this "
- "value if set."
- )
- },
- )
- max_eval_samples: Optional[int] = field(
- default=None,
- metadata={
- "help": (
- "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
- "value if set."
- )
- },
- )
- max_predict_samples: Optional[int] = field(
- default=None,
- metadata={
- "help": (
- "For debugging purposes or quicker training, truncate the number of prediction examples to this "
- "value if set."
- )
- },
- )
-
- def __post_init__(self):
- if self.task_name is None and self.train_file is None and self.validation_file is None:
- raise ValueError("Need either a dataset name or a training/validation file.")
- else:
- if self.train_file is not None:
- extension = self.train_file.split(".")[-1]
- assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
- if self.validation_file is not None:
- extension = self.validation_file.split(".")[-1]
- assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
- self.task_name = self.task_name.lower() if type(self.task_name) == str else self.task_name
-
-
-def create_train_state(
- model: FlaxAutoModelForSequenceClassification,
- learning_rate_fn: Callable[[int], float],
- is_regression: bool,
- num_labels: int,
- weight_decay: float,
-) -> train_state.TrainState:
- """Create initial training state."""
-
- class TrainState(train_state.TrainState):
- """Train state with an Optax optimizer.
-
- The two functions below differ depending on whether the task is classification
- or regression.
-
- Args:
- logits_fn: Applied to last layer to obtain the logits.
- loss_fn: Function to compute the loss.
- """
-
- logits_fn: Callable = struct.field(pytree_node=False)
- loss_fn: Callable = struct.field(pytree_node=False)
-
- # We use Optax's "masking" functionality to not apply weight decay
- # to bias and LayerNorm scale parameters. decay_mask_fn returns a
- # mask boolean with the same structure as the parameters.
- # The mask is True for parameters that should be decayed.
- def decay_mask_fn(params):
- flat_params = traverse_util.flatten_dict(params)
- # find out all LayerNorm parameters
- layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
- layer_norm_named_params = {
- layer[-2:]
- for layer_norm_name in layer_norm_candidates
- for layer in flat_params.keys()
- if layer_norm_name in "".join(layer).lower()
- }
- flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
- return traverse_util.unflatten_dict(flat_mask)
-
- tx = optax.adamw(
- learning_rate=learning_rate_fn, b1=0.9, b2=0.999, eps=1e-6, weight_decay=weight_decay, mask=decay_mask_fn
- )
-
- if is_regression:
-
- def mse_loss(logits, labels):
- return jnp.mean((logits[..., 0] - labels) ** 2)
-
- return TrainState.create(
- apply_fn=model.__call__,
- params=model.params,
- tx=tx,
- logits_fn=lambda logits: logits[..., 0],
- loss_fn=mse_loss,
- )
- else: # Classification.
-
- def cross_entropy_loss(logits, labels):
- xentropy = optax.softmax_cross_entropy(logits, onehot(labels, num_classes=num_labels))
- return jnp.mean(xentropy)
-
- return TrainState.create(
- apply_fn=model.__call__,
- params=model.params,
- tx=tx,
- logits_fn=lambda logits: logits.argmax(-1),
- loss_fn=cross_entropy_loss,
- )
-
-
-def create_learning_rate_fn(
- train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
-) -> Callable[[int], jnp.array]:
- """Returns a linear warmup, linear_decay learning rate function."""
- steps_per_epoch = train_ds_size // train_batch_size
- num_train_steps = steps_per_epoch * num_train_epochs
- warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
- decay_fn = optax.linear_schedule(
- init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
- )
- schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
- return schedule_fn
-
-
-def glue_train_data_collator(rng: PRNGKey, dataset: Dataset, batch_size: int):
- """Returns shuffled batches of size `batch_size` from truncated `train dataset`, sharded over all local devices."""
- steps_per_epoch = len(dataset) // batch_size
- perms = jax.random.permutation(rng, len(dataset))
- perms = perms[: steps_per_epoch * batch_size] # Skip incomplete batch.
- perms = perms.reshape((steps_per_epoch, batch_size))
-
- for perm in perms:
- batch = dataset[perm]
- batch = {k: np.array(v) for k, v in batch.items()}
- batch = shard(batch)
-
- yield batch
-
-
-def glue_eval_data_collator(dataset: Dataset, batch_size: int):
- """Returns batches of size `batch_size` from `eval dataset`. Sharding handled by `pad_shard_unpad` in the eval loop."""
- batch_idx = np.arange(len(dataset))
-
- steps_per_epoch = math.ceil(len(dataset) / batch_size)
- batch_idx = np.array_split(batch_idx, steps_per_epoch)
-
- for idx in batch_idx:
- batch = dataset[idx]
- batch = {k: np.array(v) for k, v in batch.items()}
-
- yield batch
-
-
-def main():
- parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
- if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
- # If we pass only one argument to the script and it's the path to a json file,
- # let's parse it to get our arguments.
- model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
- else:
- model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
- # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
- # information sent is the one passed as arguments along with your Python/PyTorch versions.
- send_example_telemetry("run_glue", model_args, data_args, framework="flax")
-
- # 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,
- )
- # Setup logging, we only want one process per machine to log things on the screen.
- logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
- if jax.process_index() == 0:
- datasets.utils.logging.set_verbosity_warning()
- transformers.utils.logging.set_verbosity_info()
- else:
- datasets.utils.logging.set_verbosity_error()
- transformers.utils.logging.set_verbosity_error()
-
- # Handle the repository creation
- if training_args.push_to_hub:
- if training_args.hub_model_id is None:
- repo_name = get_full_repo_name(
- Path(training_args.output_dir).absolute().name, token=training_args.hub_token
- )
- else:
- repo_name = training_args.hub_model_id
- create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
- repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
-
- # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
- # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
-
- # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
- # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
- # label if at least two columns are provided.
-
- # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
- # single column. You can easily tweak this behavior (see below)
-
- # In distributed training, the load_dataset function guarantee that only one local process can concurrently
- # download the dataset.
- if data_args.task_name is not None:
- # Downloading and loading a dataset from the hub.
- raw_datasets = load_dataset(
- "glue",
- data_args.task_name,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- else:
- # Loading the dataset from local csv or json file.
- data_files = {}
- if data_args.train_file is not None:
- data_files["train"] = data_args.train_file
- if data_args.validation_file is not None:
- data_files["validation"] = data_args.validation_file
- extension = (data_args.train_file if data_args.train_file is not None else data_args.valid_file).split(".")[-1]
- raw_datasets = load_dataset(
- extension,
- data_files=data_files,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- # See more about loading any type of standard or custom dataset at
- # https://huggingface.co/docs/datasets/loading_datasets.html.
-
- # Labels
- if data_args.task_name is not None:
- is_regression = data_args.task_name == "stsb"
- if not is_regression:
- label_list = raw_datasets["train"].features["label"].names
- num_labels = len(label_list)
- else:
- num_labels = 1
- else:
- # Trying to have good defaults here, don't hesitate to tweak to your needs.
- is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
- if is_regression:
- num_labels = 1
- else:
- # A useful fast method:
- # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
- label_list = raw_datasets["train"].unique("label")
- label_list.sort() # Let's sort it for determinism
- num_labels = len(label_list)
-
- # Load pretrained model and tokenizer
- config = AutoConfig.from_pretrained(
- model_args.model_name_or_path,
- num_labels=num_labels,
- finetuning_task=data_args.task_name,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- tokenizer = AutoTokenizer.from_pretrained(
- model_args.model_name_or_path,
- use_fast=not model_args.use_slow_tokenizer,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- model = FlaxAutoModelForSequenceClassification.from_pretrained(
- model_args.model_name_or_path,
- config=config,
- use_auth_token=True if model_args.use_auth_token else None,
- )
-
- # Preprocessing the datasets
- if data_args.task_name is not None:
- sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
- else:
- # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
- non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
- if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
- sentence1_key, sentence2_key = "sentence1", "sentence2"
- else:
- if len(non_label_column_names) >= 2:
- sentence1_key, sentence2_key = non_label_column_names[:2]
- else:
- sentence1_key, sentence2_key = non_label_column_names[0], None
-
- # Some models have set the order of the labels to use, so let's make sure we do use it.
- label_to_id = None
- if (
- model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
- and data_args.task_name is not None
- and not is_regression
- ):
- # Some have all caps in their config, some don't.
- label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
- if sorted(label_name_to_id.keys()) == sorted(label_list):
- logger.info(
- f"The configuration of the model provided the following label correspondence: {label_name_to_id}. "
- "Using it!"
- )
- label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)}
- else:
- logger.warning(
- "Your model seems to have been trained with labels, but they don't match the dataset: ",
- f"model labels: {sorted(label_name_to_id.keys())}, dataset labels: {sorted(label_list)}."
- "\nIgnoring the model labels as a result.",
- )
- elif data_args.task_name is None:
- label_to_id = {v: i for i, v in enumerate(label_list)}
-
- def preprocess_function(examples):
- # Tokenize the texts
- texts = (
- (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
- )
- result = tokenizer(*texts, padding="max_length", max_length=data_args.max_seq_length, truncation=True)
-
- if "label" in examples:
- if label_to_id is not None:
- # Map labels to IDs (not necessary for GLUE tasks)
- result["labels"] = [label_to_id[l] for l in examples["label"]]
- else:
- # In all cases, rename the column to labels because the model will expect that.
- result["labels"] = examples["label"]
- return result
-
- processed_datasets = raw_datasets.map(
- preprocess_function, batched=True, remove_columns=raw_datasets["train"].column_names
- )
-
- train_dataset = processed_datasets["train"]
- eval_dataset = processed_datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
-
- # Log a few random samples from the training set:
- for index in random.sample(range(len(train_dataset)), 3):
- logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
-
- # Define a summary writer
- has_tensorboard = is_tensorboard_available()
- if has_tensorboard and jax.process_index() == 0:
- try:
- from flax.metrics.tensorboard import SummaryWriter
-
- summary_writer = SummaryWriter(training_args.output_dir)
- summary_writer.hparams({**training_args.to_dict(), **vars(model_args), **vars(data_args)})
- except ImportError as ie:
- has_tensorboard = False
- logger.warning(
- f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
- )
- else:
- logger.warning(
- "Unable to display metrics through TensorBoard because the package is not installed: "
- "Please run pip install tensorboard to enable."
- )
-
- def write_train_metric(summary_writer, train_metrics, train_time, step):
- summary_writer.scalar("train_time", train_time, step)
-
- train_metrics = get_metrics(train_metrics)
- for key, vals in train_metrics.items():
- tag = f"train_{key}"
- for i, val in enumerate(vals):
- summary_writer.scalar(tag, val, step - len(vals) + i + 1)
-
- def write_eval_metric(summary_writer, eval_metrics, step):
- for metric_name, value in eval_metrics.items():
- summary_writer.scalar(f"eval_{metric_name}", value, step)
-
- num_epochs = int(training_args.num_train_epochs)
- rng = jax.random.PRNGKey(training_args.seed)
- dropout_rngs = jax.random.split(rng, jax.local_device_count())
-
- train_batch_size = int(training_args.per_device_train_batch_size) * jax.local_device_count()
- per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
- eval_batch_size = per_device_eval_batch_size * jax.device_count()
-
- learning_rate_fn = create_learning_rate_fn(
- len(train_dataset),
- train_batch_size,
- training_args.num_train_epochs,
- training_args.warmup_steps,
- training_args.learning_rate,
- )
-
- state = create_train_state(
- model, learning_rate_fn, is_regression, num_labels=num_labels, weight_decay=training_args.weight_decay
- )
-
- # define step functions
- def train_step(
- state: train_state.TrainState, batch: Dict[str, Array], dropout_rng: PRNGKey
- ) -> Tuple[train_state.TrainState, float]:
- """Trains model with an optimizer (both in `state`) on `batch`, returning a pair `(new_state, loss)`."""
- dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
- targets = batch.pop("labels")
-
- def loss_fn(params):
- logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
- loss = state.loss_fn(logits, targets)
- return loss
-
- grad_fn = jax.value_and_grad(loss_fn)
- loss, grad = grad_fn(state.params)
- grad = jax.lax.pmean(grad, "batch")
- new_state = state.apply_gradients(grads=grad)
- metrics = jax.lax.pmean({"loss": loss, "learning_rate": learning_rate_fn(state.step)}, axis_name="batch")
- return new_state, metrics, new_dropout_rng
-
- p_train_step = jax.pmap(train_step, axis_name="batch", donate_argnums=(0,))
-
- def eval_step(state, batch):
- logits = state.apply_fn(**batch, params=state.params, train=False)[0]
- return state.logits_fn(logits)
-
- p_eval_step = jax.pmap(eval_step, axis_name="batch")
-
- if data_args.task_name is not None:
- metric = evaluate.load("glue", data_args.task_name)
- else:
- metric = evaluate.load("accuracy")
-
- logger.info(f"===== Starting training ({num_epochs} epochs) =====")
- train_time = 0
-
- # make sure weights are replicated on each device
- state = replicate(state)
-
- steps_per_epoch = len(train_dataset) // train_batch_size
- total_steps = steps_per_epoch * num_epochs
- epochs = tqdm(range(num_epochs), desc=f"Epoch ... (0/{num_epochs})", position=0)
- for epoch in epochs:
- train_start = time.time()
- train_metrics = []
-
- # Create sampling rng
- rng, input_rng = jax.random.split(rng)
-
- # train
- train_loader = glue_train_data_collator(input_rng, train_dataset, train_batch_size)
- for step, batch in enumerate(
- tqdm(
- train_loader,
- total=steps_per_epoch,
- desc="Training...",
- position=1,
- ),
- ):
- state, train_metric, dropout_rngs = p_train_step(state, batch, dropout_rngs)
- train_metrics.append(train_metric)
-
- cur_step = (epoch * steps_per_epoch) + (step + 1)
-
- if cur_step % training_args.logging_steps == 0 and cur_step > 0:
- # Save metrics
- train_metric = unreplicate(train_metric)
- train_time += time.time() - train_start
- if has_tensorboard and jax.process_index() == 0:
- write_train_metric(summary_writer, train_metrics, train_time, cur_step)
-
- epochs.write(
- f"Step... ({cur_step}/{total_steps} | Training Loss: {train_metric['loss']}, Learning Rate:"
- f" {train_metric['learning_rate']})"
- )
-
- train_metrics = []
-
- if (cur_step % training_args.eval_steps == 0 or cur_step % steps_per_epoch == 0) and cur_step > 0:
- # evaluate
- eval_loader = glue_eval_data_collator(eval_dataset, eval_batch_size)
- for batch in tqdm(
- eval_loader,
- total=math.ceil(len(eval_dataset) / eval_batch_size),
- desc="Evaluating ...",
- position=2,
- ):
- labels = batch.pop("labels")
- predictions = pad_shard_unpad(p_eval_step)(
- state, batch, min_device_batch=per_device_eval_batch_size
- )
- metric.add_batch(predictions=np.array(predictions), references=labels)
-
- eval_metric = metric.compute()
-
- logger.info(f"Step... ({cur_step}/{total_steps} | Eval metrics: {eval_metric})")
-
- if has_tensorboard and jax.process_index() == 0:
- write_eval_metric(summary_writer, eval_metric, cur_step)
-
- if (cur_step % training_args.save_steps == 0 and cur_step > 0) or (cur_step == total_steps):
- # save checkpoint after each epoch and push checkpoint to the hub
- if jax.process_index() == 0:
- params = jax.device_get(unreplicate(state.params))
- model.save_pretrained(training_args.output_dir, params=params)
- tokenizer.save_pretrained(training_args.output_dir)
- if training_args.push_to_hub:
- repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
- epochs.desc = f"Epoch ... {epoch + 1}/{num_epochs}"
-
- # save the eval metrics in json
- if jax.process_index() == 0:
- eval_metric = {f"eval_{metric_name}": value for metric_name, value in eval_metric.items()}
- path = os.path.join(training_args.output_dir, "eval_results.json")
- with open(path, "w") as f:
- json.dump(eval_metric, f, indent=4, sort_keys=True)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/chengli-thu/ChatHaruhi-OpenAI/app.py b/spaces/chengli-thu/ChatHaruhi-OpenAI/app.py
deleted file mode 100644
index 0064d0e4c5d6202b3209d707eabeaed82c5b62bc..0000000000000000000000000000000000000000
--- a/spaces/chengli-thu/ChatHaruhi-OpenAI/app.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import zipfile
-import gradio as gr
-from PIL import Image
-from chatharuhi import ChatHaruhi
-import requests
-import os
-import openai
-import copy
-
-
-NAME_DICT = {'汤师爷': 'tangshiye', '慕容复': 'murongfu', '李云龙': 'liyunlong', 'Luna': 'Luna', '王多鱼': 'wangduoyu',
- 'Ron': 'Ron', '鸠摩智': 'jiumozhi', 'Snape': 'Snape',
- '凉宫春日': 'haruhi', 'Malfoy': 'Malfoy', '虚竹': 'xuzhu', '萧峰': 'xiaofeng', '段誉': 'duanyu',
- 'Hermione': 'Hermione', 'Dumbledore': 'Dumbledore', '王语嫣': 'wangyuyan',
- 'Harry': 'Harry', 'McGonagall': 'McGonagall', '白展堂': 'baizhantang', '佟湘玉': 'tongxiangyu',
- '郭芙蓉': 'guofurong', '旅行者': 'wanderer', '钟离': 'zhongli',
- '胡桃': 'hutao', 'Sheldon': 'Sheldon', 'Raj': 'Raj', 'Penny': 'Penny', '韦小宝': 'weixiaobao',
- '乔峰': 'qiaofeng', '神里绫华': 'ayaka', '雷电将军': 'raidenShogun', '于谦': 'yuqian'}
-
-
-
-try:
- os.makedirs("characters_zip")
-except:
- pass
-try:
- os.makedirs("characters")
-except:
- pass
-ai_roles_obj = {}
-for ai_role_en in NAME_DICT.values():
- file_url = f"https://github.com/LC1332/Haruhi-2-Dev/raw/main/data/character_in_zip/{ai_role_en}.zip"
- try:
- os.makedirs(f"characters/{ai_role_en}")
- except:
- pass
- if f"{ai_role_en}.zip" not in os.listdir(f"characters_zip"):
- destination_file = f"characters_zip/{ai_role_en}.zip"
- max_retries = 3 # 最大重试次数
- for attempt in range(1, max_retries+1):
- response = requests.get(file_url)
- if response.status_code == 200:
- with open(destination_file, "wb") as file:
- file.write(response.content)
- print(ai_role_en)
- break
- else:
- print(f"{ai_role_en}第{attempt}次下载失败")
- # wget.download(file_url, destination_file) # 503
- destination_folder = f"characters/{ai_role_en}"
- with zipfile.ZipFile(destination_file, 'r') as zip_ref:
- zip_ref.extractall(destination_folder)
- db_folder = f"./characters/{ai_role_en}/content/{ai_role_en}"
- system_prompt = f"./characters/{ai_role_en}/content/system_prompt.txt"
- ai_roles_obj[ai_role_en] = ChatHaruhi(system_prompt=system_prompt,
- llm="openai",
- story_db=db_folder,
- verbose=True)
-
-
-async def get_response(user_role, user_text, ai_role, chatbot):
- role_en = NAME_DICT[ai_role]
- ai_roles_obj[role_en].dialogue_history = copy.deepcopy(chatbot)
- response = ai_roles_obj[role_en].chat(role=user_role, text=user_text)
- user_msg = user_role + ':「' + user_text + '」'
- latest_msg = (user_msg, response)
- print(latest_msg)
- chatbot.append(latest_msg)
- return chatbot
-
-async def respond(user_role, user_text, ai_role, chatbot):
- return await get_response(user_role, user_text, ai_role, chatbot), None
-
-
-def clear(user_role, user_text, chatbot):
- return None, None, []
-
-
-def get_image(ai_role):
- role_en = NAME_DICT[ai_role]
- return Image.open(f'images/{role_en}.jpg'), None, None, []
-
-
-with gr.Blocks() as demo:
- gr.Markdown(
- """
- # Chat凉宫春日 ChatHaruhi
- ## Reviving Anime Character in Reality via Large Language Model
-
- ChatHaruhi2.0的demo implemented by [chenxi](https://github.com/todochenxi)
-
- 更多信息见项目github链接 [https://github.com/LC1332/Chat-Haruhi-Suzumiya](https://github.com/LC1332/Chat-Haruhi-Suzumiya)
-
- 如果觉得有趣请拜托为我们点上star. If you find it interesting, please be kind enough to give us a star.
-
- user_role 为用户扮演的人物 请尽量设置为与剧情相关的人物 且不要与主角同名
- """
- )
- with gr.Row():
- chatbot = gr.Chatbot()
- role_image = gr.Image(height=400, value="./images/haruhi.jpg")
- with gr.Row():
- user_role = gr.Textbox(label="user_role", scale=1)
- user_text = gr.Textbox(label="user_text", scale=20)
- with gr.Row():
- submit = gr.Button("Submit")
- clean = gr.ClearButton(value="Clear")
- ai_role = gr.Radio(['汤师爷', '慕容复', '李云龙',
- 'Luna', '王多鱼', 'Ron', '鸠摩智',
- 'Snape', '凉宫春日', 'Malfoy', '虚竹',
- '萧峰', '段誉', 'Hermione', 'Dumbledore',
- '王语嫣',
- 'Harry', 'McGonagall',
- '白展堂', '佟湘玉', '郭芙蓉',
- '旅行者', '钟离', '胡桃',
- 'Sheldon', 'Raj', 'Penny',
- '韦小宝', '乔峰', '神里绫华',
- '雷电将军', '于谦'], label="characters", value='凉宫春日')
- ai_role.change(get_image, ai_role, [role_image, user_role, user_text, chatbot])
- user_text.submit(fn=respond, inputs=[user_role, user_text, ai_role, chatbot], outputs=[chatbot, user_text])
- submit.click(fn=respond, inputs=[user_role, user_text, ai_role, chatbot], outputs=[chatbot, user_text])
- clean.click(clear, [user_role, user_text, chatbot], [user_role, user_text, chatbot])
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/chewing/liandan/src/gr_func.py b/spaces/chewing/liandan/src/gr_func.py
deleted file mode 100644
index c22226e6196de8fdd4f233fbb6c9fb7f4fd03477..0000000000000000000000000000000000000000
--- a/spaces/chewing/liandan/src/gr_func.py
+++ /dev/null
@@ -1,173 +0,0 @@
-from tinydb import TinyDB, Query
-
-db = TinyDB('./db.json')
-material_table = db.table('material')
-medicine_table = db.table('medicine')
-
-
-
-def get_medicines(type="ALL"):
- assert type in ["ALL", "回复状态", "突破概率", "加攻击力"], f"type:{type} 不是有效的类别"
- if type in ["ALL"]:
- a = medicine_table.all()
- else:
- medicine = Query()
- a = medicine_table.search(medicine.type == type)
- return list(map(lambda x: x["name"], a))
-
-
-def _get_medicine_elixir_config(medicine_select: str):
- medicine = Query()
- return medicine_table.search(medicine.name == medicine_select)[0]
-
-def _get_material_elixir_config(material_select: str):
- medicine = Query()
- return material_table.search(medicine.name == material_select)[0]
-
-def get_first_material(medicine_select, medicine_level_select="ALL",material_max_num=16) ->list:
- material = Query()
- m = _get_medicine_elixir_config(medicine_select)
- func1_type = m["func1_type"]
- func1_power = m["func1_power"]
- func2_type = m["func2_type"]
- func2_power = m["func2_power"]
- if medicine_level_select == "ALL":
- a = material_table.search((material.main_func_t == func1_type) | (material.auxi_func_t == func1_type) | (
- material.main_func_t == func2_type) | (material.auxi_func_t == func2_type))
- else:
- a = material_table.search((material.level == medicine_level_select) & (
- (material.main_func_t == func1_type) | (material.auxi_func_t == func1_type) | (
- material.main_func_t == func2_type) | (material.auxi_func_t == func2_type)))
-
- def get_num(material0):
- global material_second_f
- name = material0["name"]
- if material0["main_func_t"] == func1_type:
- material_second_f = (func2_type,False)
- num = func1_power / material0["main_func_p"]
- elif material0["auxi_func_t"] == func1_type:
- material_second_f = (func2_type,True)
- num = func1_power / material0["auxi_func_p"]
- elif material0["main_func_t"] == func2_type:
- material_second_f = (func1_type,False)
- num = func2_power / material0["main_func_p"]
- elif material0["auxi_func_t"] == func2_type:
- material_second_f = (func1_type,True)
- num = func2_power / material0["auxi_func_p"]
- num = int(num) + 1 if num > int(num) else int(num)
- return (name,num,material_second_f)
- rtn = list(map(get_num, a))
- rtn = list(filter(lambda x:x[1]<=material_max_num, rtn))
-
- def check_material(material0):
- if material0[1] > material_max_num:
- return False
- material_t = material.main_func_t if material0[2][1] else material.auxi_func_t
- a = material_table.search(material_t == material0[2][0])
- if a == []:
- return False
- return True
-
- rtn = list(filter(check_material, rtn))
- rtn = list(map(lambda x: f"{x[0]}*{x[1]}", rtn))
- return rtn
-
-def get_second_material(medicine_select, first_material:str, medicine_level_select="ALL",material_max_num=16) ->list:
- m = _get_medicine_elixir_config(medicine_select)
- first_material_name, _ = first_material.split("*")
- first_material = _get_material_elixir_config(first_material_name)
- func1_type = m["func1_type"]
- func1_power = m["func1_power"]
- func2_type = m["func2_type"]
- func2_power = m["func2_power"]
-
- if first_material["main_func_t"] == func1_type:
- second_material_func_need,second_material_main = (func2_type,func2_power),False
- elif first_material["auxi_func_t"] == func1_type:
- second_material_func_need, second_material_main = (func2_type,func2_power), True
- elif first_material["main_func_t"] == func2_type:
- second_material_func_need, second_material_main = (func1_type,func1_power), False
- elif first_material["auxi_func_t"] == func2_type:
- second_material_func_need, second_material_main = (func1_type,func1_power), True
-
- material = Query()
- material_t = material.main_func_t if second_material_main else material.auxi_func_t
- if medicine_level_select == "ALL":
- a = material_table.search((material_t == second_material_func_need[0]))
- else:
- a = material_table.search((material.level == medicine_level_select) & (material_t == second_material_func_need[0]))
-
- def get_num(material0):
- name = material0["name"]
- material0_p = material0["main_func_p"] if second_material_main else material0["auxi_func_p"]
- num = second_material_func_need[1]/material0_p
- num = int(num) + 1 if num > int(num) else int(num)
- return (name,num)
-
- rtn = list(map(get_num, a))
- rtn = list(filter(lambda x:x[1]<=material_max_num, rtn))
- rtn = list(map(lambda x: f"{x[0]}*{x[1]}", rtn))
- return rtn
-
-def get_possible_material(medicine_select, first_material:str="无", second_material:str="无",material_max_num=100):
- possible_choice = set()
- if first_material == "无":
- for first_material in get_first_material(medicine_select):
- for second_material in get_second_material(medicine_select, first_material):
- possible_choice.add((first_material, second_material))
- elif second_material == "无":
- for second_material in get_second_material(medicine_select,first_material):
- possible_choice.add((first_material, second_material))
- else:
- possible_choice.add((first_material,second_material))
-
- m = _get_medicine_elixir_config(medicine_select)
- func1_type = m["func1_type"]
- func2_type = m["func2_type"]
-
- rtn = []
- for first_material,second_material in possible_choice:
- first_material_name,first_material_num = first_material.split("*")
- second_material_name,second_material_num = second_material.split("*")
- first_material = _get_material_elixir_config(first_material_name)
- second_material = _get_material_elixir_config(second_material_name)
- if first_material["main_func_t"] in [func1_type,func2_type]:
- main_temp = first_material["main_temp"] * int(first_material_num)
- main_material = f"{first_material_name}*{first_material_num}"
- auxi_material = f"{second_material_name}*{second_material_num}"
- else:
- main_temp = second_material["main_temp"] * int(second_material_num)
- auxi_material = f"{first_material_name}*{first_material_num}"
- main_material = f"{second_material_name}*{second_material_num}"
-
- if main_temp==0:
- material_third_list=['恒心草(一品)*1', '红绫草(一品)*1', '五柳根(二品)*1', '天元果(二品)*1', '紫猴花(三品)*1', '九叶芝(三品)*1', '血莲精(四品)*1', '鸡冠草(四品)*1', '地心火芝(五品)*1', '天蝉灵叶(五品)*1', '三叶青芝(六品)*1', '七彩月兰(六品)*1', '地心淬灵乳(七品)*1', '天麻翡石精(七品)*1', '木灵三针花(八品)*1', '鎏鑫天晶草(八品)*1', '离火梧桐芝(九品)*1', '尘磊岩麟果(九品)*1', '宁心草(一品)*1', '凝血草(一品)*1', '流莹草(二品)*1', '蛇涎果(二品)*1', '轻灵草(三品)*1', '龙葵(三品)*1', '菩提花(四品)*1', '乌稠木(四品)*1', '天灵果(五品)*1', '灯心草(五品)*1', '白沉脂(六品)*1', '苦曼藤(六品)*1', '天问花(七品)*1', '渊血冥花(七品)*1', '阴阳黄泉花(八品)*1', '厉魂血珀(八品)*1', '太乙碧莹花(九品)*1', '森檀木(九品)*1', '地黄参(一品)*1', '火精枣(一品)*1', '风灵花(二品)*1', '伏龙参(二品)*1', '枫香脂(三品)*1', '炼魂珠(三品)*1', '石龙芮(四品)*1', '锦地罗(四品)*1', '伴妖草(五品)*1', '剑心竹(五品)*1', '混元果(六品)*1', '皇龙花(六品)*1', '血玉竹(七品)*1', '肠蚀草(七品)*1', '狼桃(八品)*1', '霸王花(八品)*1', '地龙干(九品)*1', '龙须藤(九品)*1']
-
- else:
- material0 = Query()
- material0 = material0.phar_temp > 0 if main_temp<0 else material0.phar_temp <0
- a = material_table.search(material0)
-
- def get_num(x):
- name = x["name"]
- phar_temp = x["phar_temp"]
- num = -main_temp/phar_temp
- if not num.is_integer():
- num = 9999999
- # num = 1 if num==0 else num
- return (name,int(num))
-
- a = list(map(get_num,a))
- a = list(filter(lambda x:x[1]<=material_max_num, a))
- material_third_list = list(map(lambda x:f'{x[0]}*{x[1]}',a))
- rtn.append((main_material,auxi_material,material_third_list))
- return rtn
-
-def get_basename(text):
- name,num = text.split("*")
- return name[:-4]+num
-
-
-def init():
- medicine_list = get_medicines()
- return medicine_list
diff --git a/spaces/chilge/taoli/inference/infer_tool.py b/spaces/chilge/taoli/inference/infer_tool.py
deleted file mode 100644
index 3491348b6f91d47133cc450a9df21e97f5f74c48..0000000000000000000000000000000000000000
--- a/spaces/chilge/taoli/inference/infer_tool.py
+++ /dev/null
@@ -1,326 +0,0 @@
-import hashlib
-import json
-import logging
-import os
-import time
-from pathlib import Path
-
-import librosa
-import maad
-import numpy as np
-# import onnxruntime
-import parselmouth
-import soundfile
-import torch
-import torchaudio
-
-from hubert import hubert_model
-import utils
-from models import SynthesizerTrn
-
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-
-
-def read_temp(file_name):
- if not os.path.exists(file_name):
- with open(file_name, "w") as f:
- f.write(json.dumps({"info": "temp_dict"}))
- return {}
- else:
- try:
- with open(file_name, "r") as f:
- data = f.read()
- data_dict = json.loads(data)
- if os.path.getsize(file_name) > 50 * 1024 * 1024:
- f_name = file_name.split("/")[-1]
- print(f"clean {f_name}")
- for wav_hash in list(data_dict.keys()):
- if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
- del data_dict[wav_hash]
- except Exception as e:
- print(e)
- print(f"{file_name} error,auto rebuild file")
- data_dict = {"info": "temp_dict"}
- return data_dict
-
-
-def write_temp(file_name, data):
- with open(file_name, "w") as f:
- f.write(json.dumps(data))
-
-
-def timeit(func):
- def run(*args, **kwargs):
- t = time.time()
- res = func(*args, **kwargs)
- print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
- return res
-
- return run
-
-
-def format_wav(audio_path):
- if Path(audio_path).suffix == '.wav':
- return
- raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
- soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
-
-
-def get_end_file(dir_path, end):
- file_lists = []
- for root, dirs, files in os.walk(dir_path):
- files = [f for f in files if f[0] != '.']
- dirs[:] = [d for d in dirs if d[0] != '.']
- for f_file in files:
- if f_file.endswith(end):
- file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
- return file_lists
-
-
-def get_md5(content):
- return hashlib.new("md5", content).hexdigest()
-
-
-def resize2d_f0(x, target_len):
- source = np.array(x)
- source[source < 0.001] = np.nan
- target = np.interp(np.arange(0, len(source) * target_len, len(source)) / target_len, np.arange(0, len(source)),
- source)
- res = np.nan_to_num(target)
- return res
-
-def get_f0(x, p_len,f0_up_key=0):
-
- time_step = 160 / 16000 * 1000
- 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 = parselmouth.Sound(x, 16000).to_pitch_ac(
- time_step=time_step / 1000, voicing_threshold=0.6,
- pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
-
- pad_size=(p_len - len(f0) + 1) // 2
- if(pad_size>0 or p_len - len(f0) - pad_size>0):
- f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
-
- f0 *= pow(2, f0_up_key / 12)
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0
-
-def clean_pitch(input_pitch):
- num_nan = np.sum(input_pitch == 1)
- if num_nan / len(input_pitch) > 0.9:
- input_pitch[input_pitch != 1] = 1
- return input_pitch
-
-
-def plt_pitch(input_pitch):
- input_pitch = input_pitch.astype(float)
- input_pitch[input_pitch == 1] = np.nan
- return input_pitch
-
-
-def f0_to_pitch(ff):
- f0_pitch = 69 + 12 * np.log2(ff / 440)
- return f0_pitch
-
-
-def fill_a_to_b(a, b):
- if len(a) < len(b):
- for _ in range(0, len(b) - len(a)):
- a.append(a[0])
-
-
-def mkdir(paths: list):
- for path in paths:
- if not os.path.exists(path):
- os.mkdir(path)
-
-
-class Svc(object):
- def __init__(self, net_g_path, config_path, hubert_path="hubert/hubert-soft-0d54a1f4.pt",
- onnx=False):
- self.onnx = onnx
- self.net_g_path = net_g_path
- self.hubert_path = hubert_path
- self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
- self.net_g_ms = None
- self.hps_ms = utils.get_hparams_from_file(config_path)
- self.target_sample = self.hps_ms.data.sampling_rate
- self.hop_size = self.hps_ms.data.hop_length
- self.speakers = {}
- for spk, sid in self.hps_ms.spk.items():
- self.speakers[sid] = spk
- self.spk2id = self.hps_ms.spk
- # 加载hubert
- self.hubert_soft = hubert_model.hubert_soft(hubert_path)
- if torch.cuda.is_available():
- self.hubert_soft = self.hubert_soft.cuda()
- self.load_model()
-
- def load_model(self):
- # 获取模型配置
- if self.onnx:
- raise NotImplementedError
- # self.net_g_ms = SynthesizerTrnForONNX(
- # 178,
- # self.hps_ms.data.filter_length // 2 + 1,
- # self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
- # n_speakers=self.hps_ms.data.n_speakers,
- # **self.hps_ms.model)
- # _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
- else:
- self.net_g_ms = SynthesizerTrn(
- self.hps_ms.data.filter_length // 2 + 1,
- self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
- **self.hps_ms.model)
- _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
- if "half" in self.net_g_path and torch.cuda.is_available():
- _ = self.net_g_ms.half().eval().to(self.dev)
- else:
- _ = self.net_g_ms.eval().to(self.dev)
-
- def get_units(self, source, sr):
-
- source = source.unsqueeze(0).to(self.dev)
- with torch.inference_mode():
- start = time.time()
- units = self.hubert_soft.units(source)
- use_time = time.time() - start
- print("hubert use time:{}".format(use_time))
- return units
-
-
- def get_unit_pitch(self, in_path, tran):
- source, sr = torchaudio.load(in_path)
- source = torchaudio.functional.resample(source, sr, 16000)
- if len(source.shape) == 2 and source.shape[1] >= 2:
- source = torch.mean(source, dim=0).unsqueeze(0)
- soft = self.get_units(source, sr).squeeze(0).cpu().numpy()
- f0_coarse, f0 = get_f0(source.cpu().numpy()[0], soft.shape[0]*2, tran)
- return soft, f0
-
- def infer(self, speaker_id, tran, raw_path):
- if type(speaker_id) == str:
- speaker_id = self.spk2id[speaker_id]
- sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
- soft, pitch = self.get_unit_pitch(raw_path, tran)
- f0 = torch.FloatTensor(clean_pitch(pitch)).unsqueeze(0).to(self.dev)
- if "half" in self.net_g_path and torch.cuda.is_available():
- stn_tst = torch.HalfTensor(soft)
- else:
- stn_tst = torch.FloatTensor(soft)
- with torch.no_grad():
- x_tst = stn_tst.unsqueeze(0).to(self.dev)
- start = time.time()
- x_tst = torch.repeat_interleave(x_tst, repeats=2, dim=1).transpose(1, 2)
- audio = self.net_g_ms.infer(x_tst, f0=f0, g=sid)[0,0].data.float()
- use_time = time.time() - start
- print("vits use time:{}".format(use_time))
- return audio, audio.shape[-1]
-
-
-# class SvcONNXInferModel(object):
-# def __init__(self, hubert_onnx, vits_onnx, config_path):
-# self.config_path = config_path
-# self.vits_onnx = vits_onnx
-# self.hubert_onnx = hubert_onnx
-# self.hubert_onnx_session = onnxruntime.InferenceSession(hubert_onnx, providers=['CUDAExecutionProvider', ])
-# self.inspect_onnx(self.hubert_onnx_session)
-# self.vits_onnx_session = onnxruntime.InferenceSession(vits_onnx, providers=['CUDAExecutionProvider', ])
-# self.inspect_onnx(self.vits_onnx_session)
-# self.hps_ms = utils.get_hparams_from_file(self.config_path)
-# self.target_sample = self.hps_ms.data.sampling_rate
-# self.feature_input = FeatureInput(self.hps_ms.data.sampling_rate, self.hps_ms.data.hop_length)
-#
-# @staticmethod
-# def inspect_onnx(session):
-# for i in session.get_inputs():
-# print("name:{}\tshape:{}\tdtype:{}".format(i.name, i.shape, i.type))
-# for i in session.get_outputs():
-# print("name:{}\tshape:{}\tdtype:{}".format(i.name, i.shape, i.type))
-#
-# def infer(self, speaker_id, tran, raw_path):
-# sid = np.array([int(speaker_id)], dtype=np.int64)
-# soft, pitch = self.get_unit_pitch(raw_path, tran)
-# pitch = np.expand_dims(pitch, axis=0).astype(np.int64)
-# stn_tst = soft
-# x_tst = np.expand_dims(stn_tst, axis=0)
-# x_tst_lengths = np.array([stn_tst.shape[0]], dtype=np.int64)
-# # 使用ONNX Runtime进行推理
-# start = time.time()
-# audio = self.vits_onnx_session.run(output_names=["audio"],
-# input_feed={
-# "hidden_unit": x_tst,
-# "lengths": x_tst_lengths,
-# "pitch": pitch,
-# "sid": sid,
-# })[0][0, 0]
-# use_time = time.time() - start
-# print("vits_onnx_session.run time:{}".format(use_time))
-# audio = torch.from_numpy(audio)
-# return audio, audio.shape[-1]
-#
-# def get_units(self, source, sr):
-# source = torchaudio.functional.resample(source, sr, 16000)
-# if len(source.shape) == 2 and source.shape[1] >= 2:
-# source = torch.mean(source, dim=0).unsqueeze(0)
-# source = source.unsqueeze(0)
-# # 使用ONNX Runtime进行推理
-# start = time.time()
-# units = self.hubert_onnx_session.run(output_names=["embed"],
-# input_feed={"source": source.numpy()})[0]
-# use_time = time.time() - start
-# print("hubert_onnx_session.run time:{}".format(use_time))
-# return units
-#
-# def transcribe(self, source, sr, length, transform):
-# feature_pit = self.feature_input.compute_f0(source, sr)
-# feature_pit = feature_pit * 2 ** (transform / 12)
-# feature_pit = resize2d_f0(feature_pit, length)
-# coarse_pit = self.feature_input.coarse_f0(feature_pit)
-# return coarse_pit
-#
-# def get_unit_pitch(self, in_path, tran):
-# source, sr = torchaudio.load(in_path)
-# soft = self.get_units(source, sr).squeeze(0)
-# input_pitch = self.transcribe(source.numpy()[0], sr, soft.shape[0], tran)
-# return soft, input_pitch
-
-
-class RealTimeVC:
- def __init__(self):
- self.last_chunk = None
- self.last_o = None
- self.chunk_len = 16000 # 区块长度
- self.pre_len = 3840 # 交叉淡化长度，640的倍数
-
- """输入输出都是1维numpy 音频波形数组"""
-
- def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path):
- audio, sr = torchaudio.load(input_wav_path)
- audio = audio.cpu().numpy()[0]
- temp_wav = io.BytesIO()
- if self.last_chunk is None:
- input_wav_path.seek(0)
- audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
- audio = audio.cpu().numpy()
- self.last_chunk = audio[-self.pre_len:]
- self.last_o = audio
- return audio[-self.chunk_len:]
- else:
- audio = np.concatenate([self.last_chunk, audio])
- soundfile.write(temp_wav, audio, sr, format="wav")
- temp_wav.seek(0)
- audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav)
- audio = audio.cpu().numpy()
- ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
- self.last_chunk = audio[-self.pre_len:]
- self.last_o = audio
- return ret[self.chunk_len:2 * self.chunk_len]
diff --git a/spaces/chkla/PromptCardsPlayground/app.py b/spaces/chkla/PromptCardsPlayground/app.py
deleted file mode 100644
index 940ad09c2327d4036964b1ccfe524c50e368ad53..0000000000000000000000000000000000000000
--- a/spaces/chkla/PromptCardsPlayground/app.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import pandas as pd
-import streamlit as st
-from langchain import PromptTemplate, HuggingFaceHub, LLMChain
-from langchain.llms import OpenAI
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import os
-import re
-
-
-def extract_positive_negative(text):
- pattern = r'\b(?:positive|negative)\b'
- result = re.findall(pattern, text)
- return result
-
-def classify_text(text, llm_chain, api):
- if api == "HuggingFace":
- classification = llm_chain.run(str(text))
- elif api == "OpenAI":
- classification = llm_chain.run(str(text))
- classification = re.sub(r'\s', '', classification)
- return classification.lower()
-
-def classify_csv(df, llm_chain, api):
- df["label_gold"] = df["label"]
- del df["label"]
- df["label_pred"] = df["text"].apply(classify_text, llm_chain=llm_chain, api=api)
- return df
-
-def classify_csv_zero(zero_file, llm_chain, api):
- df = pd.read_csv(zero_file, sep=';')
- df["label"] = df["text"].apply(classify_text, llm_chain=llm_chain, api=api)
- return df
-
-def evaluate_performance(df):
- merged_df = df
- correct_preds = sum(merged_df["label_gold"] == merged_df["label_pred"])
- total_preds = len(merged_df)
- percentage_overlap = correct_preds / total_preds * 100
-
- return percentage_overlap
-
-def display_home():
- st.write("Please select an API and a model to classify the text. We currently support HuggingFace and OpenAI.")
- api = st.selectbox("Select an API", ["HuggingFace", "OpenAI"])
-
- if api == "HuggingFace":
- model = st.selectbox("Select a model", ["google/flan-t5-xl", "databricks/dolly-v1-6b"])
- api_key_hug = st.text_input("HuggingFace API Key")
- elif api == "OpenAI":
- model = None
- api_key_openai = st.text_input("OpenAI API Key")
-
- st.write("Please select a temperature for the model. The higher the temperature, the more creative the model will be.")
- temperature = st.slider("Set the temperature", min_value=0.0, max_value=1.0, value=0.0, step=0.01)
-
- st.write("We provide two different setups for the annotation task. In the first setup (**Test**), you can upload a CSV file with gold labels and evaluate the performance of the model. In the second setup (**Zero-Shot**), you can upload a CSV file without gold labels and use the model to classify the text.")
- setup = st.selectbox("Setup", ["Test", "Zero-Shot"])
-
- if setup == "Test":
- gold_file = st.file_uploader("Upload Gold Labels CSV file with a text and a label column", type=["csv"])
- elif setup == "Zero-Shot":
- gold_file = None
- zero_file = st.file_uploader("Upload CSV file with a text column", type=["csv"])
-
- st.write("Please enter the prompt template below. You can use the following variables: {text} (text to classify).")
- prompt_template = st.text_area("Enter your task description", """Instruction: Identify the sentiment of a text. Please read the text and provide one of these responses: "positive" or "negative".\nText to classify in "positive" or "negative": {text}\nAnswer:""", height=200)
-
- classify_button = st.button("Run Classification/ Annotation")
-
- if classify_button:
- if prompt_template:
- prompt = PromptTemplate(
- template=prompt_template,
- input_variables=["text"]
- )
-
- if api == "HuggingFace":
- if api_key_hug:
- os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_key_hug
- llm_chain = LLMChain(prompt=prompt, llm=HuggingFaceHub(repo_id=model, model_kwargs={"temperature": temperature, "max_length": 128}))
- elif not api_key_hug:
- st.warning("Please enter your HuggingFace API key to classify the text.")
- elif api == "OpenAI":
- if api_key_openai:
- os.environ["OPENAI_API_KEY"] = api_key_openai
- llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=temperature))
- elif not api_key_openai:
- st.warning("Please enter your OpenAI API key to classify the text.")
-
- if setup == "Zero-Shot":
- if zero_file is not None:
- df_predicted = classify_csv_zero(zero_file, llm_chain, api)
- st.write(df_predicted)
- st.download_button(
- label="Download CSV",
- data=df_predicted.to_csv(index=False),
- file_name="classified_zero-shot_data.csv",
- mime="text/csv"
- )
- elif setup == "Test":
- if gold_file is not None:
- df = pd.read_csv(gold_file, sep=';')
- if "label" not in df.columns:
- st.warning("Please make sure that the gold labels CSV file contains a column named 'label'.")
- else:
- df = classify_csv(df, llm_chain, api)
- st.write(df)
- st.download_button(
- label="Download CSV",
- data=df.to_csv(index=False),
- file_name="classified_test_data.csv",
- mime="text/csv"
- )
- percentage_overlap = evaluate_performance(df)
- st.write("**Performance Evaluation**")
- st.write(f"Percentage overlap between gold labels and predicted labels: {percentage_overlap:.2f}%")
- elif gold_file is None:
- st.warning("Please upload a gold labels CSV file to evaluate the performance of the model.")
- elif not prompt:
- st.warning("Please enter a prompt question to classify the text.")
-
-def main():
- st.set_page_config(page_title="PromptCards Playground", page_icon=":pencil2:")
- st.title("AInnotator")
-
- # add a menu to the sidebar
- if "current_page" not in st.session_state:
- st.session_state.current_page = "homepage"
-
- # Initialize selected_prompt in session_state if not set
- if "selected_prompt" not in st.session_state:
- st.session_state.selected_prompt = ""
-
- # Add a menu
- menu = ["Homepage", "Playground", "Prompt Archive", "Annotator", "About"]
- st.sidebar.title("About")
- st.sidebar.write("AInnotator 🤖🏷️ is a tool for creating artificial labels/ annotations. It is based on the concept of PromptCards, which are small, self-contained descriptions of a task that can be used to generate labels for a wide range of NLP tasks. Check out the GitHub repository and the PromptCards Archive for more information.")
- st.sidebar.write("---")
- st.sidebar.write("Check out the [PromptCards archive](https://huggingface.co/spaces/chkla/AnnotationPromptCards) to find a wide range of prompts for different NLP tasks.")
- st.sidebar.write("---")
- st.sidebar.write("Made with ❤️ and 🤖.")
-
- display_home()
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/gui.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/gui.py
deleted file mode 100644
index 861f3906dd7b435e7a3082aef5f21a18d35ae1f0..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fsspec/gui.py
+++ /dev/null
@@ -1,411 +0,0 @@
-import ast
-import contextlib
-import logging
-import os
-import re
-from typing import ClassVar, Sequence
-
-import panel as pn
-
-from .core import OpenFile, get_filesystem_class, split_protocol
-from .registry import known_implementations
-
-pn.extension()
-logger = logging.getLogger("fsspec.gui")
-
-
-class SigSlot(object):
- """Signal-slot mixin, for Panel event passing
-
- Include this class in a widget manager's superclasses to be able to
- register events and callbacks on Panel widgets managed by that class.
-
- The method ``_register`` should be called as widgets are added, and external
- code should call ``connect`` to associate callbacks.
-
- By default, all signals emit a DEBUG logging statement.
- """
-
- # names of signals that this class may emit each of which must be
- # set by _register for any new instance
- signals: ClassVar[Sequence[str]] = []
- # names of actions that this class may respond to
- slots: ClassVar[Sequence[str]] = []
-
- # each of which must be a method name
-
- def __init__(self):
- self._ignoring_events = False
- self._sigs = {}
- self._map = {}
- self._setup()
-
- def _setup(self):
- """Create GUI elements and register signals"""
- self.panel = pn.pane.PaneBase()
- # no signals to set up in the base class
-
- def _register(
- self, widget, name, thing="value", log_level=logging.DEBUG, auto=False
- ):
- """Watch the given attribute of a widget and assign it a named event
-
- This is normally called at the time a widget is instantiated, in the
- class which owns it.
-
- Parameters
- ----------
- widget : pn.layout.Panel or None
- Widget to watch. If None, an anonymous signal not associated with
- any widget.
- name : str
- Name of this event
- thing : str
- Attribute of the given widget to watch
- log_level : int
- When the signal is triggered, a logging event of the given level
- will be fired in the dfviz logger.
- auto : bool
- If True, automatically connects with a method in this class of the
- same name.
- """
- if name not in self.signals:
- raise ValueError("Attempt to assign an undeclared signal: %s" % name)
- self._sigs[name] = {
- "widget": widget,
- "callbacks": [],
- "thing": thing,
- "log": log_level,
- }
- wn = "-".join(
- [
- getattr(widget, "name", str(widget)) if widget is not None else "none",
- thing,
- ]
- )
- self._map[wn] = name
- if widget is not None:
- widget.param.watch(self._signal, thing, onlychanged=True)
- if auto and hasattr(self, name):
- self.connect(name, getattr(self, name))
-
- def _repr_mimebundle_(self, *args, **kwargs):
- """Display in a notebook or a server"""
- try:
- return self.panel._repr_mimebundle_(*args, **kwargs)
- except (ValueError, AttributeError):
- raise NotImplementedError("Panel does not seem to be set " "up properly")
-
- def connect(self, signal, slot):
- """Associate call back with given event
-
- The callback must be a function which takes the "new" value of the
- watched attribute as the only parameter. If the callback return False,
- this cancels any further processing of the given event.
-
- Alternatively, the callback can be a string, in which case it means
- emitting the correspondingly-named event (i.e., connect to self)
- """
- self._sigs[signal]["callbacks"].append(slot)
-
- def _signal(self, event):
- """This is called by a an action on a widget
-
- Within an self.ignore_events context, nothing happens.
-
- Tests can execute this method by directly changing the values of
- widget components.
- """
- if not self._ignoring_events:
- wn = "-".join([event.obj.name, event.name])
- if wn in self._map and self._map[wn] in self._sigs:
- self._emit(self._map[wn], event.new)
-
- @contextlib.contextmanager
- def ignore_events(self):
- """Temporarily turn off events processing in this instance
-
- (does not propagate to children)
- """
- self._ignoring_events = True
- try:
- yield
- finally:
- self._ignoring_events = False
-
- def _emit(self, sig, value=None):
- """An event happened, call its callbacks
-
- This method can be used in tests to simulate message passing without
- directly changing visual elements.
-
- Calling of callbacks will halt whenever one returns False.
- """
- logger.log(self._sigs[sig]["log"], "{}: {}".format(sig, value))
- for callback in self._sigs[sig]["callbacks"]:
- if isinstance(callback, str):
- self._emit(callback)
- else:
- try:
- # running callbacks should not break the interface
- ret = callback(value)
- if ret is False:
- break
- except Exception as e:
- logger.exception(
- "Exception (%s) while executing callback for signal: %s"
- "" % (e, sig)
- )
-
- def show(self, threads=False):
- """Open a new browser tab and display this instance's interface"""
- self.panel.show(threads=threads, verbose=False)
- return self
-
-
-class SingleSelect(SigSlot):
- """A multiselect which only allows you to select one item for an event"""
-
- signals = ["_selected", "selected"] # the first is internal
- slots = ["set_options", "set_selection", "add", "clear", "select"]
-
- def __init__(self, **kwargs):
- self.kwargs = kwargs
- super().__init__()
-
- def _setup(self):
- self.panel = pn.widgets.MultiSelect(**self.kwargs)
- self._register(self.panel, "_selected", "value")
- self._register(None, "selected")
- self.connect("_selected", self.select_one)
-
- def _signal(self, *args, **kwargs):
- super()._signal(*args, **kwargs)
-
- def select_one(self, *_):
- with self.ignore_events():
- val = [self.panel.value[-1]] if self.panel.value else []
- self.panel.value = val
- self._emit("selected", self.panel.value)
-
- def set_options(self, options):
- self.panel.options = options
-
- def clear(self):
- self.panel.options = []
-
- @property
- def value(self):
- return self.panel.value
-
- def set_selection(self, selection):
- self.panel.value = [selection]
-
-
-class FileSelector(SigSlot):
- """Panel-based graphical file selector widget
-
- Instances of this widget are interactive and can be displayed in jupyter by having
- them as the output of a cell, or in a separate browser tab using ``.show()``.
- """
-
- signals = [
- "protocol_changed",
- "selection_changed",
- "directory_entered",
- "home_clicked",
- "up_clicked",
- "go_clicked",
- "filters_changed",
- ]
- slots = ["set_filters", "go_home"]
-
- def __init__(self, url=None, filters=None, ignore=None, kwargs=None):
- """
-
- Parameters
- ----------
- url : str (optional)
- Initial value of the URL to populate the dialog; should include protocol
- filters : list(str) (optional)
- File endings to include in the listings. If not included, all files are
- allowed. Does not affect directories.
- If given, the endings will appear as checkboxes in the interface
- ignore : list(str) (optional)
- Regex(s) of file basename patterns to ignore, e.g., "\\." for typical
- hidden files on posix
- kwargs : dict (optional)
- To pass to file system instance
- """
- if url:
- self.init_protocol, url = split_protocol(url)
- else:
- self.init_protocol, url = "file", os.getcwd()
- self.init_url = url
- self.init_kwargs = kwargs or "{}"
- self.filters = filters
- self.ignore = [re.compile(i) for i in ignore or []]
- self._fs = None
- super().__init__()
-
- def _setup(self):
- self.url = pn.widgets.TextInput(
- name="url",
- value=self.init_url,
- align="end",
- sizing_mode="stretch_width",
- width_policy="max",
- )
- self.protocol = pn.widgets.Select(
- options=list(sorted(known_implementations)),
- value=self.init_protocol,
- name="protocol",
- align="center",
- )
- self.kwargs = pn.widgets.TextInput(name="kwargs", value="{}", align="center")
- self.go = pn.widgets.Button(name="⇨", align="end", width=45)
- self.main = SingleSelect(size=10)
- self.home = pn.widgets.Button(name="🏠", width=40, height=30, align="end")
- self.up = pn.widgets.Button(name="‹", width=30, height=30, align="end")
-
- self._register(self.protocol, "protocol_changed", auto=True)
- self._register(self.go, "go_clicked", "clicks", auto=True)
- self._register(self.up, "up_clicked", "clicks", auto=True)
- self._register(self.home, "home_clicked", "clicks", auto=True)
- self._register(None, "selection_changed")
- self.main.connect("selected", self.selection_changed)
- self._register(None, "directory_entered")
- self.prev_protocol = self.protocol.value
- self.prev_kwargs = self.storage_options
-
- self.filter_sel = pn.widgets.CheckBoxGroup(
- value=[], options=[], inline=False, align="end", width_policy="min"
- )
- self._register(self.filter_sel, "filters_changed", auto=True)
-
- self.panel = pn.Column(
- pn.Row(self.protocol, self.kwargs),
- pn.Row(self.home, self.up, self.url, self.go, self.filter_sel),
- self.main.panel,
- )
- self.set_filters(self.filters)
- self.go_clicked()
-
- def set_filters(self, filters=None):
- self.filters = filters
- if filters:
- self.filter_sel.options = filters
- self.filter_sel.value = filters
- else:
- self.filter_sel.options = []
- self.filter_sel.value = []
-
- @property
- def storage_options(self):
- """Value of the kwargs box as a dictionary"""
- return ast.literal_eval(self.kwargs.value) or {}
-
- @property
- def fs(self):
- """Current filesystem instance"""
- if self._fs is None:
- cls = get_filesystem_class(self.protocol.value)
- self._fs = cls(**self.storage_options)
- return self._fs
-
- @property
- def urlpath(self):
- """URL of currently selected item"""
- return (
- (self.protocol.value + "://" + self.main.value[0])
- if self.main.value
- else None
- )
-
- def open_file(self, mode="rb", compression=None, encoding=None):
- """Create OpenFile instance for the currently selected item
-
- For example, in a notebook you might do something like
-
- .. code-block::
-
- [ ]: sel = FileSelector(); sel
-
- # user selects their file
-
- [ ]: with sel.open_file('rb') as f:
- ... out = f.read()
-
- Parameters
- ----------
- mode: str (optional)
- Open mode for the file.
- compression: str (optional)
- The interact with the file as compressed. Set to 'infer' to guess
- compression from the file ending
- encoding: str (optional)
- If using text mode, use this encoding; defaults to UTF8.
- """
- if self.urlpath is None:
- raise ValueError("No file selected")
- return OpenFile(self.fs, self.urlpath, mode, compression, encoding)
-
- def filters_changed(self, values):
- self.filters = values
- self.go_clicked()
-
- def selection_changed(self, *_):
- if self.urlpath is None:
- return
- if self.fs.isdir(self.urlpath):
- self.url.value = self.fs._strip_protocol(self.urlpath)
- self.go_clicked()
-
- def go_clicked(self, *_):
- if (
- self.prev_protocol != self.protocol.value
- or self.prev_kwargs != self.storage_options
- ):
- self._fs = None # causes fs to be recreated
- self.prev_protocol = self.protocol.value
- self.prev_kwargs = self.storage_options
- listing = sorted(
- self.fs.ls(self.url.value, detail=True), key=lambda x: x["name"]
- )
- listing = [
- l
- for l in listing
- if not any(i.match(l["name"].rsplit("/", 1)[-1]) for i in self.ignore)
- ]
- folders = {
- "📁 " + o["name"].rsplit("/", 1)[-1]: o["name"]
- for o in listing
- if o["type"] == "directory"
- }
- files = {
- "📄 " + o["name"].rsplit("/", 1)[-1]: o["name"]
- for o in listing
- if o["type"] == "file"
- }
- if self.filters:
- files = {
- k: v
- for k, v in files.items()
- if any(v.endswith(ext) for ext in self.filters)
- }
- self.main.set_options(dict(**folders, **files))
-
- def protocol_changed(self, *_):
- self._fs = None
- self.main.options = []
- self.url.value = ""
-
- def home_clicked(self, *_):
- self.protocol.value = self.init_protocol
- self.kwargs.value = self.init_kwargs
- self.url.value = self.init_url
- self.go_clicked()
-
- def up_clicked(self, *_):
- self.url.value = self.fs._parent(self.url.value)
- self.go_clicked()
diff --git a/spaces/cihyFjudo/fairness-paper-search/Hindi Aflatoon Download HD The Movie that Made Akshay Kumar a Superstar.md b/spaces/cihyFjudo/fairness-paper-search/Hindi Aflatoon Download HD The Movie that Made Akshay Kumar a Superstar.md
deleted file mode 100644
index f5f8bd916dfbd31afea82af2c605fb6a5b6806b5..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Hindi Aflatoon Download HD The Movie that Made Akshay Kumar a Superstar.md
+++ /dev/null
@@ -1,14 +0,0 @@
-
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diff --git a/spaces/cihyFjudo/fairness-paper-search/KAZUMI 538P A High-Risk High-Reward Character for Cardfighters.md b/spaces/cihyFjudo/fairness-paper-search/KAZUMI 538P A High-Risk High-Reward Character for Cardfighters.md
deleted file mode 100644
index 12b8f2faba92ecaff99026b8c6010c307862dd23..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/KAZUMI 538P A High-Risk High-Reward Character for Cardfighters.md
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deleted file mode 100644
index fa5fe37fae897786fcb658f0f8f3c982b0660eca..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Libro Motores Editex Pdf Download Everything You Need to Know About Gameboy Pluss and Burnin Prevention.md
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index eff0e434086ec9a5624a078b24e0f0ade7ff5cea..0000000000000000000000000000000000000000
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diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/execeval.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/execeval.py
deleted file mode 100644
index 514f874ce30b622089302924bafb1cfae0a4efd7..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/execeval.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import ast
-import sys
-
-
-if sys.version_info > (3, 8):
- Module = ast.Module
-else:
- # Mock the Python >= 3.8 API
- def Module(nodelist, type_ignores):
- return ast.Module(nodelist)
-
-
-class _CatchDisplay:
- """Class to temporarily catch sys.displayhook"""
-
- def __init__(self):
- self.output = None
-
- def __enter__(self):
- self.old_hook = sys.displayhook
- sys.displayhook = self
- return self
-
- def __exit__(self, type, value, traceback):
- sys.displayhook = self.old_hook
- # Returning False will cause exceptions to propagate
- return False
-
- def __call__(self, output):
- self.output = output
-
-
-def eval_block(code, namespace=None, filename="<string>"):
- """
- Execute a multi-line block of code in the given namespace
-
- If the final statement in the code is an expression, return
- the result of the expression.
- """
- tree = ast.parse(code, filename="<ast>", mode="exec")
- if namespace is None:
- namespace = {}
- catch_display = _CatchDisplay()
-
- if isinstance(tree.body[-1], ast.Expr):
- to_exec, to_eval = tree.body[:-1], tree.body[-1:]
- else:
- to_exec, to_eval = tree.body, []
-
- for node in to_exec:
- compiled = compile(Module([node], []), filename=filename, mode="exec")
- exec(compiled, namespace)
-
- with catch_display:
- for node in to_eval:
- compiled = compile(
- ast.Interactive([node]), filename=filename, mode="single"
- )
- exec(compiled, namespace)
-
- return catch_display.output
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/contourpy/util/bokeh_util.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/contourpy/util/bokeh_util.py
deleted file mode 100644
index e75654d7c30c552c1e1bd0492a85d40e8f27de40..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/contourpy/util/bokeh_util.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, cast
-
-from contourpy import FillType, LineType
-from contourpy.util.mpl_util import mpl_codes_to_offsets
-
-if TYPE_CHECKING:
- from contourpy._contourpy import (
- CoordinateArray, FillReturn, LineReturn, LineReturn_Separate, LineReturn_SeparateCode,
- )
-
-
-def filled_to_bokeh(
- filled: FillReturn,
- fill_type: FillType,
-) -> tuple[list[list[CoordinateArray]], list[list[CoordinateArray]]]:
- xs: list[list[CoordinateArray]] = []
- ys: list[list[CoordinateArray]] = []
- if fill_type in (FillType.OuterOffset, FillType.ChunkCombinedOffset,
- FillType.OuterCode, FillType.ChunkCombinedCode):
- have_codes = fill_type in (FillType.OuterCode, FillType.ChunkCombinedCode)
-
- for points, offsets in zip(*filled):
- if points is None:
- continue
- if have_codes:
- offsets = mpl_codes_to_offsets(offsets)
- xs.append([]) # New outer with zero or more holes.
- ys.append([])
- for i in range(len(offsets)-1):
- xys = points[offsets[i]:offsets[i+1]]
- xs[-1].append(xys[:, 0])
- ys[-1].append(xys[:, 1])
- elif fill_type in (FillType.ChunkCombinedCodeOffset, FillType.ChunkCombinedOffsetOffset):
- for points, codes_or_offsets, outer_offsets in zip(*filled):
- if points is None:
- continue
- for j in range(len(outer_offsets)-1):
- if fill_type == FillType.ChunkCombinedCodeOffset:
- codes = codes_or_offsets[outer_offsets[j]:outer_offsets[j+1]]
- offsets = mpl_codes_to_offsets(codes) + outer_offsets[j]
- else:
- offsets = codes_or_offsets[outer_offsets[j]:outer_offsets[j+1]+1]
- xs.append([]) # New outer with zero or more holes.
- ys.append([])
- for k in range(len(offsets)-1):
- xys = points[offsets[k]:offsets[k+1]]
- xs[-1].append(xys[:, 0])
- ys[-1].append(xys[:, 1])
- else:
- raise RuntimeError(f"Conversion of FillType {fill_type} to Bokeh is not implemented")
-
- return xs, ys
-
-
-def lines_to_bokeh(
- lines: LineReturn,
- line_type: LineType,
-) -> tuple[list[CoordinateArray], list[CoordinateArray]]:
- xs: list[CoordinateArray] = []
- ys: list[CoordinateArray] = []
-
- if line_type == LineType.Separate:
- if TYPE_CHECKING:
- lines = cast(LineReturn_Separate, lines)
- for line in lines:
- xs.append(line[:, 0])
- ys.append(line[:, 1])
- elif line_type == LineType.SeparateCode:
- if TYPE_CHECKING:
- lines = cast(LineReturn_SeparateCode, lines)
- for line in lines[0]:
- xs.append(line[:, 0])
- ys.append(line[:, 1])
- elif line_type in (LineType.ChunkCombinedCode, LineType.ChunkCombinedOffset):
- for points, offsets in zip(*lines):
- if points is None:
- continue
- if line_type == LineType.ChunkCombinedCode:
- offsets = mpl_codes_to_offsets(offsets)
-
- for i in range(len(offsets)-1):
- line = points[offsets[i]:offsets[i+1]]
- xs.append(line[:, 0])
- ys.append(line[:, 1])
- else:
- raise RuntimeError(f"Conversion of LineType {line_type} to Bokeh is not implemented")
-
- return xs, ys
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/_v_h_e_a.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/_v_h_e_a.py
deleted file mode 100644
index 965674203db1b76cff23e3c640d4b7cadca5ae98..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/ttLib/tables/_v_h_e_a.py
+++ /dev/null
@@ -1,126 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import safeEval
-from fontTools.misc.fixedTools import (
- ensureVersionIsLong as fi2ve,
- versionToFixed as ve2fi,
-)
-from . import DefaultTable
-import math
-
-
-vheaFormat = """
- > # big endian
- tableVersion: L
- ascent: h
- descent: h
- lineGap: h
- advanceHeightMax: H
- minTopSideBearing: h
- minBottomSideBearing: h
- yMaxExtent: h
- caretSlopeRise: h
- caretSlopeRun: h
- caretOffset: h
- reserved1: h
- reserved2: h
- reserved3: h
- reserved4: h
- metricDataFormat: h
- numberOfVMetrics: H
-"""
-
-
-class table__v_h_e_a(DefaultTable.DefaultTable):
-
- # Note: Keep in sync with table__h_h_e_a
-
- dependencies = ["vmtx", "glyf", "CFF ", "CFF2"]
-
- def decompile(self, data, ttFont):
- sstruct.unpack(vheaFormat, data, self)
-
- def compile(self, ttFont):
- if ttFont.recalcBBoxes and (
- ttFont.isLoaded("glyf")
- or ttFont.isLoaded("CFF ")
- or ttFont.isLoaded("CFF2")
- ):
- self.recalc(ttFont)
- self.tableVersion = fi2ve(self.tableVersion)
- return sstruct.pack(vheaFormat, self)
-
- def recalc(self, ttFont):
- if "vmtx" in ttFont:
- vmtxTable = ttFont["vmtx"]
- self.advanceHeightMax = max(adv for adv, _ in vmtxTable.metrics.values())
-
- boundsHeightDict = {}
- if "glyf" in ttFont:
- glyfTable = ttFont["glyf"]
- for name in ttFont.getGlyphOrder():
- g = glyfTable[name]
- if g.numberOfContours == 0:
- continue
- if g.numberOfContours < 0 and not hasattr(g, "yMax"):
- # Composite glyph without extents set.
- # Calculate those.
- g.recalcBounds(glyfTable)
- boundsHeightDict[name] = g.yMax - g.yMin
- elif "CFF " in ttFont or "CFF2" in ttFont:
- if "CFF " in ttFont:
- topDict = ttFont["CFF "].cff.topDictIndex[0]
- else:
- topDict = ttFont["CFF2"].cff.topDictIndex[0]
- charStrings = topDict.CharStrings
- for name in ttFont.getGlyphOrder():
- cs = charStrings[name]
- bounds = cs.calcBounds(charStrings)
- if bounds is not None:
- boundsHeightDict[name] = int(
- math.ceil(bounds[3]) - math.floor(bounds[1])
- )
-
- if boundsHeightDict:
- minTopSideBearing = float("inf")
- minBottomSideBearing = float("inf")
- yMaxExtent = -float("inf")
- for name, boundsHeight in boundsHeightDict.items():
- advanceHeight, tsb = vmtxTable[name]
- bsb = advanceHeight - tsb - boundsHeight
- extent = tsb + boundsHeight
- minTopSideBearing = min(minTopSideBearing, tsb)
- minBottomSideBearing = min(minBottomSideBearing, bsb)
- yMaxExtent = max(yMaxExtent, extent)
- self.minTopSideBearing = minTopSideBearing
- self.minBottomSideBearing = minBottomSideBearing
- self.yMaxExtent = yMaxExtent
-
- else: # No glyph has outlines.
- self.minTopSideBearing = 0
- self.minBottomSideBearing = 0
- self.yMaxExtent = 0
-
- def toXML(self, writer, ttFont):
- formatstring, names, fixes = sstruct.getformat(vheaFormat)
- for name in names:
- value = getattr(self, name)
- if name == "tableVersion":
- value = fi2ve(value)
- value = "0x%08x" % value
- writer.simpletag(name, value=value)
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont):
- if name == "tableVersion":
- setattr(self, name, ve2fi(attrs["value"]))
- return
- setattr(self, name, safeEval(attrs["value"]))
-
- # reserved0 is caretOffset for legacy reasons
- @property
- def reserved0(self):
- return self.caretOffset
-
- @reserved0.setter
- def reserved0(self, value):
- self.caretOffset = value
diff --git a/spaces/codertoro/gpt-academic/toolbox.py b/spaces/codertoro/gpt-academic/toolbox.py
deleted file mode 100644
index 3ced6534fd5779b6056a545708f0ca36b6c9afe2..0000000000000000000000000000000000000000
--- a/spaces/codertoro/gpt-academic/toolbox.py
+++ /dev/null
@@ -1,529 +0,0 @@
-import markdown
-import mdtex2html
-import threading
-import importlib
-import traceback
-import inspect
-import re
-from latex2mathml.converter import convert as tex2mathml
-from functools import wraps, lru_cache
-
-############################### 插件输入输出接驳区 #######################################
-class ChatBotWithCookies(list):
- def __init__(self, cookie):
- self._cookies = cookie
-
- def write_list(self, list):
- for t in list:
- self.append(t)
-
- def get_list(self):
- return [t for t in self]
-
- def get_cookies(self):
- return self._cookies
-
-def ArgsGeneralWrapper(f):
- """
- 装饰器函数，用于重组输入参数，改变输入参数的顺序与结构。
- """
- def decorated(cookies, txt, txt2, top_p, temperature, chatbot, history, system_prompt, *args):
- txt_passon = txt
- if txt == "" and txt2 != "": txt_passon = txt2
- # 引入一个有cookie的chatbot
- cookies.update({
- 'top_p':top_p,
- 'temperature':temperature,
- })
- llm_kwargs = {
- 'api_key': cookies['api_key'],
- 'llm_model': cookies['llm_model'],
- 'top_p':top_p,
- 'temperature':temperature,
- }
- plugin_kwargs = {
- # 目前还没有
- }
- chatbot_with_cookie = ChatBotWithCookies(cookies)
- chatbot_with_cookie.write_list(chatbot)
- yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)
- return decorated
-
-def update_ui(chatbot, history, msg='正常', **kwargs): # 刷新界面
- """
- 刷新用户界面
- """
- assert isinstance(chatbot, ChatBotWithCookies), "在传递chatbot的过程中不要将其丢弃。必要时，可用clear将其清空，然后用for+append循环重新赋值。"
- yield chatbot.get_cookies(), chatbot, history, msg
-############################### ################## #######################################
-##########################################################################################
-
-def get_reduce_token_percent(text):
- """
- * 此函数未来将被弃用
- """
- try:
- # text = "maximum context length is 4097 tokens. However, your messages resulted in 4870 tokens"
- pattern = r"(\d+)\s+tokens\b"
- match = re.findall(pattern, text)
- EXCEED_ALLO = 500 # 稍微留一点余地，否则在回复时会因余量太少出问题
- max_limit = float(match[0]) - EXCEED_ALLO
- current_tokens = float(match[1])
- ratio = max_limit/current_tokens
- assert ratio > 0 and ratio < 1
- return ratio, str(int(current_tokens-max_limit))
- except:
- return 0.5, '不详'
-
-def predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, llm_kwargs, history=[], sys_prompt='', long_connection=True):
- """
- * 此函数未来将被弃用（替代函数 request_gpt_model_in_new_thread_with_ui_alive 文件 chatgpt_academic/crazy_functions/crazy_utils）
-
- 调用简单的predict_no_ui接口，但是依然保留了些许界面心跳功能，当对话太长时，会自动采用二分法截断
- i_say: 当前输入
- i_say_show_user: 显示到对话界面上的当前输入，例如，输入整个文件时，你绝对不想把文件的内容都糊到对话界面上
- chatbot: 对话界面句柄
- top_p, temperature: gpt参数
- history: gpt参数 对话历史
- sys_prompt: gpt参数 sys_prompt
- long_connection: 是否采用更稳定的连接方式（推荐）（已弃用）
- """
- import time
- from request_llm.bridge_chatgpt import predict_no_ui_long_connection
- from toolbox import get_conf
- TIMEOUT_SECONDS, MAX_RETRY = get_conf('TIMEOUT_SECONDS', 'MAX_RETRY')
- # 多线程的时候，需要一个mutable结构在不同线程之间传递信息
- # list就是最简单的mutable结构，我们第一个位置放gpt输出，第二个位置传递报错信息
- mutable = [None, '']
- # multi-threading worker
-
- def mt(i_say, history):
- while True:
- try:
- mutable[0] = predict_no_ui_long_connection(
- inputs=i_say, llm_kwargs=llm_kwargs, history=history, sys_prompt=sys_prompt)
-
- except ConnectionAbortedError as token_exceeded_error:
- # 尝试计算比例，尽可能多地保留文本
- p_ratio, n_exceed = get_reduce_token_percent(
- str(token_exceeded_error))
- if len(history) > 0:
- history = [his[int(len(his) * p_ratio):]
- for his in history if his is not None]
- else:
- i_say = i_say[: int(len(i_say) * p_ratio)]
- mutable[1] = f'警告，文本过长将进行截断，Token溢出数：{n_exceed}，截断比例：{(1-p_ratio):.0%}。'
- except TimeoutError as e:
- mutable[0] = '[Local Message] 请求超时。'
- raise TimeoutError
- except Exception as e:
- mutable[0] = f'[Local Message] 异常：{str(e)}.'
- raise RuntimeError(f'[Local Message] 异常：{str(e)}.')
- # 创建新线程发出http请求
- thread_name = threading.Thread(target=mt, args=(i_say, history))
- thread_name.start()
- # 原来的线程则负责持续更新UI，实现一个超时倒计时，并等待新线程的任务完成
- cnt = 0
- while thread_name.is_alive():
- cnt += 1
- chatbot[-1] = (i_say_show_user,
- f"[Local Message] {mutable[1]}waiting gpt response {cnt}/{TIMEOUT_SECONDS*2*(MAX_RETRY+1)}"+''.join(['.']*(cnt % 4)))
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
- time.sleep(1)
- # 把gpt的输出从mutable中取出来
- gpt_say = mutable[0]
- if gpt_say == '[Local Message] Failed with timeout.':
- raise TimeoutError
- return gpt_say
-
-
-def write_results_to_file(history, file_name=None):
- """
- 将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
- """
- import os
- import time
- if file_name is None:
- # file_name = time.strftime("chatGPT分析报告%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
- file_name = 'chatGPT分析报告' + \
- time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
- os.makedirs('./gpt_log/', exist_ok=True)
- with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
- f.write('# chatGPT 分析报告\n')
- for i, content in enumerate(history):
- try: # 这个bug没找到触发条件，暂时先这样顶一下
- if type(content) != str:
- content = str(content)
- except:
- continue
- if i % 2 == 0:
- f.write('## ')
- f.write(content)
- f.write('\n\n')
- res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}')
- print(res)
- return res
-
-
-def regular_txt_to_markdown(text):
- """
- 将普通文本转换为Markdown格式的文本。
- """
- text = text.replace('\n', '\n\n')
- text = text.replace('\n\n\n', '\n\n')
- text = text.replace('\n\n\n', '\n\n')
- return text
-
-
-def CatchException(f):
- """
- 装饰器函数，捕捉函数f中的异常并封装到一个生成器中返回，并显示到聊天当中。
- """
- @wraps(f)
- def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
- try:
- yield from f(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT)
- except Exception as e:
- from check_proxy import check_proxy
- from toolbox import get_conf
- proxies, = get_conf('proxies')
- tb_str = '```\n' + traceback.format_exc() + '```'
- if chatbot is None or len(chatbot) == 0:
- chatbot = [["插件调度异常", "异常原因"]]
- chatbot[-1] = (chatbot[-1][0],
- f"[Local Message] 实验性函数调用出错: \n\n{tb_str} \n\n当前代理可用性: \n\n{check_proxy(proxies)}")
- yield from update_ui(chatbot=chatbot, history=history, msg=f'异常 {e}') # 刷新界面
- return decorated
-
-
-def HotReload(f):
- """
- HotReload的装饰器函数，用于实现Python函数插件的热更新。
- 函数热更新是指在不停止程序运行的情况下，更新函数代码，从而达到实时更新功能。
- 在装饰器内部，使用wraps(f)来保留函数的元信息，并定义了一个名为decorated的内部函数。
- 内部函数通过使用importlib模块的reload函数和inspect模块的getmodule函数来重新加载并获取函数模块，
- 然后通过getattr函数获取函数名，并在新模块中重新加载函数。
- 最后，使用yield from语句返回重新加载过的函数，并在被装饰的函数上执行。
- 最终，装饰器函数返回内部函数。这个内部函数可以将函数的原始定义更新为最新版本，并执行函数的新版本。
- """
- @wraps(f)
- def decorated(*args, **kwargs):
- fn_name = f.__name__
- f_hot_reload = getattr(importlib.reload(inspect.getmodule(f)), fn_name)
- yield from f_hot_reload(*args, **kwargs)
- return decorated
-
-
-def report_execption(chatbot, history, a, b):
- """
- 向chatbot中添加错误信息
- """
- chatbot.append((a, b))
- history.append(a)
- history.append(b)
-
-
-def text_divide_paragraph(text):
- """
- 将文本按照段落分隔符分割开，生成带有段落标签的HTML代码。
- """
- if '```' in text:
- # careful input
- return text
- else:
- # wtf input
- lines = text.split("\n")
- for i, line in enumerate(lines):
- lines[i] = lines[i].replace(" ", " ")
- text = "</br>".join(lines)
- return text
-
-
-def markdown_convertion(txt):
- """
- 将Markdown格式的文本转换为HTML格式。如果包含数学公式，则先将公式转换为HTML格式。
- """
- pre = '<div class="markdown-body">'
- suf = '</div>'
- markdown_extension_configs = {
- 'mdx_math': {
- 'enable_dollar_delimiter': True,
- 'use_gitlab_delimiters': False,
- },
- }
- find_equation_pattern = r'<script type="math/tex(?:.*?)>(.*?)</script>'
-
- def tex2mathml_catch_exception(content, *args, **kwargs):
- try:
- content = tex2mathml(content, *args, **kwargs)
- except:
- content = content
- return content
-
- def replace_math_no_render(match):
- content = match.group(1)
- if 'mode=display' in match.group(0):
- content = content.replace('\n', '</br>')
- return f"<font color=\"#00FF00\">$$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$$</font>"
- else:
- return f"<font color=\"#00FF00\">$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$</font>"
-
- def replace_math_render(match):
- content = match.group(1)
- if 'mode=display' in match.group(0):
- if '\\begin{aligned}' in content:
- content = content.replace('\\begin{aligned}', '\\begin{array}')
- content = content.replace('\\end{aligned}', '\\end{array}')
- content = content.replace('&', ' ')
- content = tex2mathml_catch_exception(content, display="block")
- return content
- else:
- return tex2mathml_catch_exception(content)
-
- def markdown_bug_hunt(content):
- """
- 解决一个mdx_math的bug（单$包裹begin命令时多余<script>）
- """
- content = content.replace('<script type="math/tex">\n<script type="math/tex; mode=display">', '<script type="math/tex; mode=display">')
- content = content.replace('</script>\n</script>', '</script>')
- return content
-
-
- if ('$' in txt) and ('```' not in txt): # 有$标识的公式符号，且没有代码段```的标识
- # convert everything to html format
- split = markdown.markdown(text='---')
- convert_stage_1 = markdown.markdown(text=txt, extensions=['mdx_math', 'fenced_code', 'tables', 'sane_lists'], extension_configs=markdown_extension_configs)
- convert_stage_1 = markdown_bug_hunt(convert_stage_1)
- # re.DOTALL: Make the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline. Corresponds to the inline flag (?s).
- # 1. convert to easy-to-copy tex (do not render math)
- convert_stage_2_1, n = re.subn(find_equation_pattern, replace_math_no_render, convert_stage_1, flags=re.DOTALL)
- # 2. convert to rendered equation
- convert_stage_2_2, n = re.subn(find_equation_pattern, replace_math_render, convert_stage_1, flags=re.DOTALL)
- # cat them together
- return pre + convert_stage_2_1 + f'{split}' + convert_stage_2_2 + suf
- else:
- return pre + markdown.markdown(txt, extensions=['fenced_code', 'codehilite', 'tables', 'sane_lists']) + suf
-
-
-def close_up_code_segment_during_stream(gpt_reply):
- """
- 在gpt输出代码的中途（输出了前面的```，但还没输出完后面的```），补上后面的```
-
- Args:
- gpt_reply (str): GPT模型返回的回复字符串。
-
- Returns:
- str: 返回一个新的字符串，将输出代码片段的“后面的```”补上。
-
- """
- if '```' not in gpt_reply:
- return gpt_reply
- if gpt_reply.endswith('```'):
- return gpt_reply
-
- # 排除了以上两个情况，我们
- segments = gpt_reply.split('```')
- n_mark = len(segments) - 1
- if n_mark % 2 == 1:
- # print('输出代码片段中！')
- return gpt_reply+'\n```'
- else:
- return gpt_reply
-
-
-def format_io(self, y):
- """
- 将输入和输出解析为HTML格式。将y中最后一项的输入部分段落化，并将输出部分的Markdown和数学公式转换为HTML格式。
- """
- if y is None or y == []:
- return []
- i_ask, gpt_reply = y[-1]
- i_ask = text_divide_paragraph(i_ask) # 输入部分太自由，预处理一波
- gpt_reply = close_up_code_segment_during_stream(gpt_reply) # 当代码输出半截的时候，试着补上后个```
- y[-1] = (
- None if i_ask is None else markdown.markdown(i_ask, extensions=['fenced_code', 'tables']),
- None if gpt_reply is None else markdown_convertion(gpt_reply)
- )
- return y
-
-
-def find_free_port():
- """
- 返回当前系统中可用的未使用端口。
- """
- import socket
- from contextlib import closing
- with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
- s.bind(('', 0))
- s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
- return s.getsockname()[1]
-
-
-def extract_archive(file_path, dest_dir):
- import zipfile
- import tarfile
- import os
- # Get the file extension of the input file
- file_extension = os.path.splitext(file_path)[1]
-
- # Extract the archive based on its extension
- if file_extension == '.zip':
- with zipfile.ZipFile(file_path, 'r') as zipobj:
- zipobj.extractall(path=dest_dir)
- print("Successfully extracted zip archive to {}".format(dest_dir))
-
- elif file_extension in ['.tar', '.gz', '.bz2']:
- with tarfile.open(file_path, 'r:*') as tarobj:
- tarobj.extractall(path=dest_dir)
- print("Successfully extracted tar archive to {}".format(dest_dir))
-
- # 第三方库，需要预先pip install rarfile
- # 此外，Windows上还需要安装winrar软件，配置其Path环境变量，如"C:\Program Files\WinRAR"才可以
- elif file_extension == '.rar':
- try:
- import rarfile
- with rarfile.RarFile(file_path) as rf:
- rf.extractall(path=dest_dir)
- print("Successfully extracted rar archive to {}".format(dest_dir))
- except:
- print("Rar format requires additional dependencies to install")
- return '\n\n需要安装pip install rarfile来解压rar文件'
-
- # 第三方库，需要预先pip install py7zr
- elif file_extension == '.7z':
- try:
- import py7zr
- with py7zr.SevenZipFile(file_path, mode='r') as f:
- f.extractall(path=dest_dir)
- print("Successfully extracted 7z archive to {}".format(dest_dir))
- except:
- print("7z format requires additional dependencies to install")
- return '\n\n需要安装pip install py7zr来解压7z文件'
- else:
- return ''
- return ''
-
-
-def find_recent_files(directory):
- """
- me: find files that is created with in one minutes under a directory with python, write a function
- gpt: here it is!
- """
- import os
- import time
- current_time = time.time()
- one_minute_ago = current_time - 60
- recent_files = []
-
- for filename in os.listdir(directory):
- file_path = os.path.join(directory, filename)
- if file_path.endswith('.log'):
- continue
- created_time = os.path.getmtime(file_path)
- if created_time >= one_minute_ago:
- if os.path.isdir(file_path):
- continue
- recent_files.append(file_path)
-
- return recent_files
-
-
-def on_file_uploaded(files, chatbot, txt):
- if len(files) == 0:
- return chatbot, txt
- import shutil
- import os
- import time
- import glob
- from toolbox import extract_archive
- try:
- shutil.rmtree('./private_upload/')
- except:
- pass
- time_tag = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
- os.makedirs(f'private_upload/{time_tag}', exist_ok=True)
- err_msg = ''
- for file in files:
- file_origin_name = os.path.basename(file.orig_name)
- shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}')
- err_msg += extract_archive(f'private_upload/{time_tag}/{file_origin_name}',
- dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract')
- moved_files = [fp for fp in glob.glob(
- 'private_upload/**/*', recursive=True)]
- txt = f'private_upload/{time_tag}'
- moved_files_str = '\t\n\n'.join(moved_files)
- chatbot.append(['我上传了文件，请查收',
- f'[Local Message] 收到以下文件: \n\n{moved_files_str}' +
- f'\n\n调用路径参数已自动修正到: \n\n{txt}' +
- f'\n\n现在您点击任意实验功能时，以上文件将被作为输入参数'+err_msg])
- return chatbot, txt
-
-
-def on_report_generated(files, chatbot):
- from toolbox import find_recent_files
- report_files = find_recent_files('gpt_log')
- if len(report_files) == 0:
- return None, chatbot
- # files.extend(report_files)
- chatbot.append(['汇总报告如何远程获取？', '汇总报告已经添加到右侧“文件上传区”（可能处于折叠状态），请查收。'])
- return report_files, chatbot
-
-def is_openai_api_key(key):
- # 正确的 API_KEY 是 "sk-" + 48 位大小写字母数字的组合
- API_MATCH = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
- return API_MATCH
-
-@lru_cache(maxsize=128)
-def read_single_conf_with_lru_cache(arg):
- from colorful import print亮红, print亮绿
- try:
- r = getattr(importlib.import_module('config_private'), arg)
- except:
- r = getattr(importlib.import_module('config'), arg)
- # 在读取API_KEY时，检查一下是不是忘了改config
- if arg == 'API_KEY':
- if is_openai_api_key(r):
- print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
- else:
- print亮红( "[API_KEY] 正确的 API_KEY 是 'sk-' + '48 位大小写字母数字' 的组合，请在config文件中修改API密钥, 添加海外代理之后再运行。" + \
- "（如果您刚更新过代码，请确保旧版config_private文件中没有遗留任何新增键值）")
- if arg == 'proxies':
- if r is None:
- print亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问。建议：检查USE_PROXY选项是否修改。')
- else:
- print亮绿('[PROXY] 网络代理状态：已配置。配置信息如下：', r)
- assert isinstance(r, dict), 'proxies格式错误，请注意proxies选项的格式，不要遗漏括号。'
- return r
-
-
-def get_conf(*args):
- # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
- res = []
- for arg in args:
- r = read_single_conf_with_lru_cache(arg)
- res.append(r)
- return res
-
-
-def clear_line_break(txt):
- txt = txt.replace('\n', ' ')
- txt = txt.replace(' ', ' ')
- txt = txt.replace(' ', ' ')
- return txt
-
-
-class DummyWith():
- """
- 这段代码定义了一个名为DummyWith的空上下文管理器，
- 它的作用是……额……没用，即在代码结构不变得情况下取代其他的上下文管理器。
- 上下文管理器是一种Python对象，用于与with语句一起使用，
- 以确保一些资源在代码块执行期间得到正确的初始化和清理。
- 上下文管理器必须实现两个方法，分别为 __enter__()和 __exit__()。
- 在上下文执行开始的情况下，__enter__()方法会在代码块被执行前被调用，
- 而在上下文执行结束时，__exit__()方法则会被调用。
- """
- def __enter__(self):
- return self
-
- def __exit__(self, exc_type, exc_value, traceback):
- return
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aptxenc.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aptxenc.c
deleted file mode 100644
index 6deebaf2cbd12e1ced965fedb14aeabfea0a8420..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aptxenc.c
+++ /dev/null
@@ -1,304 +0,0 @@
-/*
- * Audio Processing Technology codec for Bluetooth (aptX)
- *
- * Copyright (C) 2017 Aurelien Jacobs <aurel@gnuage.org>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "config_components.h"
-
-#include "libavutil/channel_layout.h"
-#include "aptx.h"
-#include "audio_frame_queue.h"
-#include "codec_internal.h"
-#include "encode.h"
-#include "internal.h"
-
-typedef struct AptXEncContext {
- AptXContext common;
- AudioFrameQueue afq;
-} AptXEncContext;
-
-/*
- * Half-band QMF analysis filter realized with a polyphase FIR filter.
- * Split into 2 subbands and downsample by 2.
- * So for each pair of samples that goes in, one sample goes out,
- * split into 2 separate subbands.
- */
-av_always_inline
-static void aptx_qmf_polyphase_analysis(FilterSignal signal[NB_FILTERS],
- const int32_t coeffs[NB_FILTERS][FILTER_TAPS],
- int shift,
- int32_t samples[NB_FILTERS],
- int32_t *low_subband_output,
- int32_t *high_subband_output)
-{
- int32_t subbands[NB_FILTERS];
- int i;
-
- for (i = 0; i < NB_FILTERS; i++) {
- aptx_qmf_filter_signal_push(&signal[i], samples[NB_FILTERS-1-i]);
- subbands[i] = aptx_qmf_convolution(&signal[i], coeffs[i], shift);
- }
-
- *low_subband_output = av_clip_intp2(subbands[0] + subbands[1], 23);
- *high_subband_output = av_clip_intp2(subbands[0] - subbands[1], 23);
-}
-
-/*
- * Two stage QMF analysis tree.
- * Split 4 input samples into 4 subbands and downsample by 4.
- * So for each group of 4 samples that goes in, one sample goes out,
- * split into 4 separate subbands.
- */
-static void aptx_qmf_tree_analysis(QMFAnalysis *qmf,
- int32_t samples[4],
- int32_t subband_samples[4])
-{
- int32_t intermediate_samples[4];
- int i;
-
- /* Split 4 input samples into 2 intermediate subbands downsampled to 2 samples */
- for (i = 0; i < 2; i++)
- aptx_qmf_polyphase_analysis(qmf->outer_filter_signal,
- aptx_qmf_outer_coeffs, 23,
- &samples[2*i],
- &intermediate_samples[0+i],
- &intermediate_samples[2+i]);
-
- /* Split 2 intermediate subband samples into 4 final subbands downsampled to 1 sample */
- for (i = 0; i < 2; i++)
- aptx_qmf_polyphase_analysis(qmf->inner_filter_signal[i],
- aptx_qmf_inner_coeffs, 23,
- &intermediate_samples[2*i],
- &subband_samples[2*i+0],
- &subband_samples[2*i+1]);
-}
-
-av_always_inline
-static int32_t aptx_bin_search(int32_t value, int32_t factor,
- const int32_t *intervals, int32_t nb_intervals)
-{
- int32_t idx = 0;
- int i;
-
- for (i = nb_intervals >> 1; i > 0; i >>= 1)
- if (MUL64(factor, intervals[idx + i]) <= ((int64_t)value << 24))
- idx += i;
-
- return idx;
-}
-
-static void aptx_quantize_difference(Quantize *quantize,
- int32_t sample_difference,
- int32_t dither,
- int32_t quantization_factor,
- ConstTables *tables)
-{
- const int32_t *intervals = tables->quantize_intervals;
- int32_t quantized_sample, dithered_sample, parity_change;
- int32_t d, mean, interval, inv, sample_difference_abs;
- int64_t error;
-
- sample_difference_abs = FFABS(sample_difference);
- sample_difference_abs = FFMIN(sample_difference_abs, (1 << 23) - 1);
-
- quantized_sample = aptx_bin_search(sample_difference_abs >> 4,
- quantization_factor,
- intervals, tables->tables_size);
-
- d = rshift32_clip24(MULH(dither, dither), 7) - (1 << 23);
- d = rshift64(MUL64(d, tables->quantize_dither_factors[quantized_sample]), 23);
-
- intervals += quantized_sample;
- mean = (intervals[1] + intervals[0]) / 2;
- interval = (intervals[1] - intervals[0]) * (-(sample_difference < 0) | 1);
-
- dithered_sample = rshift64_clip24(MUL64(dither, interval) + ((int64_t)av_clip_intp2(mean + d, 23) << 32), 32);
- error = ((int64_t)sample_difference_abs << 20) - MUL64(dithered_sample, quantization_factor);
- quantize->error = FFABS(rshift64(error, 23));
-
- parity_change = quantized_sample;
- if (error < 0)
- quantized_sample--;
- else
- parity_change--;
-
- inv = -(sample_difference < 0);
- quantize->quantized_sample = quantized_sample ^ inv;
- quantize->quantized_sample_parity_change = parity_change ^ inv;
-}
-
-static void aptx_encode_channel(Channel *channel, int32_t samples[4], int hd)
-{
- int32_t subband_samples[4];
- int subband;
- aptx_qmf_tree_analysis(&channel->qmf, samples, subband_samples);
- ff_aptx_generate_dither(channel);
- for (subband = 0; subband < NB_SUBBANDS; subband++) {
- int32_t diff = av_clip_intp2(subband_samples[subband] - channel->prediction[subband].predicted_sample, 23);
- aptx_quantize_difference(&channel->quantize[subband], diff,
- channel->dither[subband],
- channel->invert_quantize[subband].quantization_factor,
- &ff_aptx_quant_tables[hd][subband]);
- }
-}
-
-static void aptx_insert_sync(Channel channels[NB_CHANNELS], int32_t *idx)
-{
- if (aptx_check_parity(channels, idx)) {
- int i;
- Channel *c;
- static const int map[] = { 1, 2, 0, 3 };
- Quantize *min = &channels[NB_CHANNELS-1].quantize[map[0]];
- for (c = &channels[NB_CHANNELS-1]; c >= channels; c--)
- for (i = 0; i < NB_SUBBANDS; i++)
- if (c->quantize[map[i]].error < min->error)
- min = &c->quantize[map[i]];
-
- /* Forcing the desired parity is done by offsetting by 1 the quantized
- * sample from the subband featuring the smallest quantization error. */
- min->quantized_sample = min->quantized_sample_parity_change;
- }
-}
-
-static uint16_t aptx_pack_codeword(Channel *channel)
-{
- int32_t parity = aptx_quantized_parity(channel);
- return (((channel->quantize[3].quantized_sample & 0x06) | parity) << 13)
- | (((channel->quantize[2].quantized_sample & 0x03) ) << 11)
- | (((channel->quantize[1].quantized_sample & 0x0F) ) << 7)
- | (((channel->quantize[0].quantized_sample & 0x7F) ) << 0);
-}
-
-static uint32_t aptxhd_pack_codeword(Channel *channel)
-{
- int32_t parity = aptx_quantized_parity(channel);
- return (((channel->quantize[3].quantized_sample & 0x01E) | parity) << 19)
- | (((channel->quantize[2].quantized_sample & 0x00F) ) << 15)
- | (((channel->quantize[1].quantized_sample & 0x03F) ) << 9)
- | (((channel->quantize[0].quantized_sample & 0x1FF) ) << 0);
-}
-
-static void aptx_encode_samples(AptXContext *ctx,
- int32_t samples[NB_CHANNELS][4],
- uint8_t *output)
-{
- int channel;
- for (channel = 0; channel < NB_CHANNELS; channel++)
- aptx_encode_channel(&ctx->channels[channel], samples[channel], ctx->hd);
-
- aptx_insert_sync(ctx->channels, &ctx->sync_idx);
-
- for (channel = 0; channel < NB_CHANNELS; channel++) {
- ff_aptx_invert_quantize_and_prediction(&ctx->channels[channel], ctx->hd);
- if (ctx->hd)
- AV_WB24(output + 3*channel,
- aptxhd_pack_codeword(&ctx->channels[channel]));
- else
- AV_WB16(output + 2*channel,
- aptx_pack_codeword(&ctx->channels[channel]));
- }
-}
-
-static int aptx_encode_frame(AVCodecContext *avctx, AVPacket *avpkt,
- const AVFrame *frame, int *got_packet_ptr)
-{
- AptXEncContext *const s0 = avctx->priv_data;
- AptXContext *const s = &s0->common;
- int pos, ipos, channel, sample, output_size, ret;
-
- if ((ret = ff_af_queue_add(&s0->afq, frame)) < 0)
- return ret;
-
- output_size = s->block_size * frame->nb_samples/4;
- if ((ret = ff_get_encode_buffer(avctx, avpkt, output_size, 0)) < 0)
- return ret;
-
- for (pos = 0, ipos = 0; pos < output_size; pos += s->block_size, ipos += 4) {
- int32_t samples[NB_CHANNELS][4];
-
- for (channel = 0; channel < NB_CHANNELS; channel++)
- for (sample = 0; sample < 4; sample++)
- samples[channel][sample] = (int32_t)AV_RN32A(&frame->data[channel][4*(ipos+sample)]) >> 8;
-
- aptx_encode_samples(s, samples, avpkt->data + pos);
- }
-
- ff_af_queue_remove(&s0->afq, frame->nb_samples, &avpkt->pts, &avpkt->duration);
- *got_packet_ptr = 1;
- return 0;
-}
-
-static av_cold int aptx_close(AVCodecContext *avctx)
-{
- AptXEncContext *const s = avctx->priv_data;
- ff_af_queue_close(&s->afq);
- return 0;
-}
-
-static av_cold int aptx_encode_init(AVCodecContext *avctx)
-{
- AptXEncContext *const s = avctx->priv_data;
-
- ff_af_queue_init(avctx, &s->afq);
-
- if (!avctx->frame_size || avctx->frame_size % 4)
- avctx->frame_size = 1024;
- avctx->internal->pad_samples = 4;
-
- return ff_aptx_init(avctx);
-}
-
-#if CONFIG_APTX_ENCODER
-const FFCodec ff_aptx_encoder = {
- .p.name = "aptx",
- CODEC_LONG_NAME("aptX (Audio Processing Technology for Bluetooth)"),
- .p.type = AVMEDIA_TYPE_AUDIO,
- .p.id = AV_CODEC_ID_APTX,
- .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_ENCODER_REORDERED_OPAQUE,
- .priv_data_size = sizeof(AptXEncContext),
- .init = aptx_encode_init,
- FF_CODEC_ENCODE_CB(aptx_encode_frame),
- .close = aptx_close,
- CODEC_OLD_CHANNEL_LAYOUTS(AV_CH_LAYOUT_STEREO)
- .p.ch_layouts = (const AVChannelLayout[]) { AV_CHANNEL_LAYOUT_STEREO, { 0 } },
- .p.sample_fmts = (const enum AVSampleFormat[]) { AV_SAMPLE_FMT_S32P,
- AV_SAMPLE_FMT_NONE },
- .p.supported_samplerates = (const int[]) {8000, 16000, 24000, 32000, 44100, 48000, 0},
-};
-#endif
-
-#if CONFIG_APTX_HD_ENCODER
-const FFCodec ff_aptx_hd_encoder = {
- .p.name = "aptx_hd",
- CODEC_LONG_NAME("aptX HD (Audio Processing Technology for Bluetooth)"),
- .p.type = AVMEDIA_TYPE_AUDIO,
- .p.id = AV_CODEC_ID_APTX_HD,
- .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_ENCODER_REORDERED_OPAQUE,
- .priv_data_size = sizeof(AptXEncContext),
- .init = aptx_encode_init,
- FF_CODEC_ENCODE_CB(aptx_encode_frame),
- .close = aptx_close,
- CODEC_OLD_CHANNEL_LAYOUTS(AV_CH_LAYOUT_STEREO)
- .p.ch_layouts = (const AVChannelLayout[]) { AV_CHANNEL_LAYOUT_STEREO, { 0 } },
- .p.sample_fmts = (const enum AVSampleFormat[]) { AV_SAMPLE_FMT_S32P,
- AV_SAMPLE_FMT_NONE },
- .p.supported_samplerates = (const int[]) {8000, 16000, 24000, 32000, 44100, 48000, 0},
-};
-#endif
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Carmageddon The Game that Puts You in Control of a Car as a Weapon.md b/spaces/congsaPfin/Manga-OCR/logs/Carmageddon The Game that Puts You in Control of a Car as a Weapon.md
deleted file mode 100644
index be6abc34597bb4d7cd876de999ac01ca1ce92b17..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Carmageddon The Game that Puts You in Control of a Car as a Weapon.md
+++ /dev/null
@@ -1,230 +0,0 @@
-<br />
-<h1>Carmageddon APK: A Guide to the Original Freeform Driving Sensation</h1>
- <p>If you are looking for a racing game that is not for the faint-hearted, you might want to check out Carmageddon APK. This is a mobile version of the classic PC game that was banned around the world for its violent and anarchic gameplay. In this game, you can drive wherever you like, smash your opponents, and run over pedestrians for fun and points. It is a game that does not take itself too seriously, but offers a lot of entertainment and challenge for those who dare to play it.</p>
-<h2>carmageddon apk</h2><br /><p><b><b>Download Zip</b> > <a href="https://urlca.com/2uObPk">https://urlca.com/2uObPk</a></b></p><br /><br />
- <p>In this article, we will give you a comprehensive guide to Carmageddon APK, including its history, features, pros and cons, download and installation process, gameplay tips, and more. By the end of this article, you will have everything you need to know about this controversial but addictive game.</p>
- <h2>What is Carmageddon APK?</h2>
- <p>Carmageddon APK is an Android port of the original Carmageddon game that was released for PC in 1997. It was developed by Stainless Games and published by HandyGames in 2013. It is a vehicular combat game that features real-world environments that have been turned into killing fields, where the locals stay out on the streets at their peril.</p>
- <h3>The history and controversy of Carmageddon</h3>
- <p>The game that became Carmageddon started out as 3D Destruction Derby, a banger racing sim prototyped by Stainless Software. It was signed by SCi in 1995, with the condition that it be made into a licensed game to guarantee popularity. Initially, SCi wanted to use the Mad Max license, but was unable to find out who owned the rights to the franchise. It instead secured the Death Race 2000 license, as a sequel to the original film was at that time planned.</p>
- <p>According to head programmer Patrick Buckland, the initial concept stemmed from team members getting bored while playing racing games, leading them to ultimately drive in the wrong direction and crash into other cars. They decided it made sense to create a game where this was the objective to begin with. The notion of running over pedestrians was added to distinguish the game from Destruction Derby and arouse controversy.</p>
- <p>The game was met with mixed reactions from critics and gamers alike. Some praised its originality, humor, and freedom, while others condemned its violence, gore, and immorality. The game was banned or censored in several countries, such as Germany, France, Brazil, Australia, and the United Kingdom. Despite or because of this controversy, the game was a commercial success and spawned several sequels and spin-offs.</p>
- <h3>The features and gameplay of Carmageddon APK</h3>
- <p>Carmageddon APK retains the same features and gameplay as the original PC game, but with some improvements and enhancements. Some of these features are:</p>
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- <ul>
-<li>28 dangerously deranged opponents</li>
-<li>11 wildly exhilarating environments</li>
-<li>Career mode featuring 36 satisfyingly violent levels</li>
-<li>Race and wreck opponents to unlock 30 playable cars</li>
-<li>Multiple control methods: digital, analog (mix 'n' match), tilt</li>
-<li>Edit your control layout in-game to your preferences</li>
-<li>Comprehensive action replay system</li>
-<li>Environment maps and other special effects</li> <li>Enhanced pre- and post-race graphics</li>
-<li>Online leaderboards</li>
-<li>Achievements</li>
-</ul>
- <p>The gameplay of Carmageddon APK is simple but fun. You can choose from three different modes: Freeplay, Career, or Multiplayer. In Freeplay, you can select any car, opponent, and environment you have unlocked and play as you like. In Career, you have to complete 36 levels, each with a different objective and difficulty. In Multiplayer, you can play with up to six friends online or locally via Wi-Fi.</p>
- <p>The objective of each level varies depending on the mode, but generally, you have three ways to win: by completing the race, by wrecking all your opponents, or by killing all the pedestrians. You can also lose by running out of time or being wrecked yourself. You can earn credits by performing various actions, such as hitting opponents, damaging scenery, or running over pedestrians. You can use these credits to repair your car, recover from a crash, or buy power-ups.</p>
- <h3>The benefits and drawbacks of Carmageddon APK</h3>
- <p>Carmageddon APK is not a game for everyone, but it has its own appeal and charm for those who enjoy it. Some of the benefits of playing this game are:</p>
- <ul>
-<li>It is free to download and play</li>
-<li>It is faithful to the original game but with improved graphics and controls</li>
-<li>It offers a lot of variety and replay value with different cars, opponents, environments, and modes</li>
-<li>It is humorous and satirical, not meant to be taken seriously</li>
-<li>It is challenging and rewarding, requiring skill and strategy to win</li>
-</ul>
- <p>However, there are also some drawbacks that might deter some players from trying this game. Some of these drawbacks are:</p>
- <ul>
-<li>It is very violent and gory, which might offend some people or cause discomfort</li>
-<li>It is not very realistic or immersive, which might disappoint some racing fans</li>
-<li>It has some bugs and glitches that might affect the gameplay or performance</li>
-<li>It has some ads and in-app purchases that might annoy some players</li>
-<li>It requires a lot of storage space and battery power to run smoothly</li>
-</ul>
- <h2>How to download and install Carmageddon APK?</h2>
- <p>If you are interested in playing Carmageddon APK on your Android device, you will need to follow some steps to download and install it properly. Here are the steps you need to take:</p>
- <h3>The requirements and compatibility of Carmageddon APK</h3>
- <p>Before you download and install Carmageddon APK, you need to make sure that your device meets the minimum requirements for the game. These are:</p>
- <ul>
-<li>Android version 4.0 or higher</li>
-<li>At least 1 GB of RAM</li>
-<li>At least 500 MB of free storage space</li>
-<li>A stable internet connection</li>
-</ul>
- <p>You also need to check that your device is compatible with the game. You can do this by visiting the Google Play Store page of the game and seeing if your device is listed among the supported devices. If it is not listed, it does not mean that the game will not work on your device, but it might have some issues or errors.</p>
- <h3>The steps and tips for downloading and installing Carmageddon APK</h3>
- <p>Once you have verified that your device meets the requirements and compatibility for the game, you can proceed to download and install it. There are two ways to do this: either from the Google Play Store or from a third-party website.</p>
- <p>The first way is to download and install the game from the Google Play Store. This is the easiest and safest way, as you will get the latest version of the game and avoid any malware or viruses. To do this, you just need to follow these steps:</p>
- <ol>
-<li>Open the Google Play Store app on your device.</li>
-<li>Search for "Carmageddon" in the search bar.</li>
-<li>Select the game from the list of results.</li>
-<li>Tap on "Install" and wait for the download to finish.</li>
-<li>Tap on "Open" to launch the game.</li>
-</ol>
- <p>The second way is to download and install the game from a third-party website. This is a more risky way, as you might encounter some fake or malicious files that could harm your device. However, this might be necessary if you cannot access the Google Play Store or if you want to get a modded version of the game with unlimited credits or unlocked cars. To do this, you need to follow these steps:</p>
- <ol>
-<li>Enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Find a reliable and trustworthy website that offers the Carmageddon APK file. You can use a search engine or a review site to find one.</li>
-<li>Download the APK file from the website to your device. Make sure you scan it with an antivirus app before opening it.</li>
-<li>Locate the APK file on your device using a file manager app. Tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen to complete the installation.</li>
-<li>Launch the game from your app drawer or home screen.</li>
-</ol>
- <p>Some tips for downloading and installing Carmageddon APK from a third-party website are:</p>
- <ul>
-<li>Always backup your data before installing any app from an unknown source.</li>
-<li>Always read the reviews and ratings of the website and the APK file before downloading it.</li>
-<li>Always check the permissions and requirements of the APK file before installing it.</li>
-<li>Always update the game regularly to get the latest features and bug fixes.</li>
-</ul>
- <h3>The sources and links for Carmageddon APK</h3>
- <p>If you are looking for some sources and links for Carmageddon APK, here are some that you can use:</p>
- <table>
-<tr><th>Source</th><th>Link</th></tr>
-<tr><td>Google Play Store</td><td>[Carmageddon]</td></tr>
-<tr><td>APKPure</td><td>[Carmageddon APK]</td></tr>
-<tr><td>APKMirror</td><td>[Carmageddon APK]</td></tr>
-<tr><td>ModDroid</td><td>[Carmageddon Mod APK]</td></tr>
-<tr><td>Carmageddon Official Website</td><td>[Carmageddon.com]</td></tr>
-</table>
- <h2>How to play and enjoy Carmageddon APK?</h2>
- <p>Now that you have downloaded and installed Carmageddon APK on your device, you might be wondering how to play and enjoy it. Here are some tips and tricks that will help you get the most out of this game:</p>
- <h3>The modes and objectives of Carmageddon APK</h3>
- <p>As mentioned earlier, there are three modes in Carmageddon APK: Freeplay, Career, and Multiplayer. Each mode has its own objectives and challenges that you need to complete to win. Here is a brief overview of each mode:</p>
- <ul>
-<li>Freeplay: This is the mode where you can play as you like, without any restrictions or goals. You can choose any car, opponent, and environment that you have unlocked and explore the map at your own pace. You can also customize the game settings, such as the number of opponents, pedestrians, power-ups, time limit, etc. This is a good mode to practice your skills, test your cars, or just have fun.</li>
-<li>Career: This is the mode where you have to progress through 36 levels, each with a different objective and difficulty. You can choose from three difficulty levels: Easy, Normal, or Hard. The objectives vary depending on the level, but they usually involve completing the race, wrecking all your opponents, or killing all the pedestrians. You can also earn credits by performing various actions, such as hitting opponents, damaging scenery, or running over pedestrians. You can use these credits to repair your car, recover from a crash, or buy power-ups. This is a good mode to challenge yourself, unlock new cars, and earn achievements.</li>
-<li>Multiplayer: This is the mode where you can play with up to six friends online or locally via Wi-Fi. You can choose from two sub-modes: Death Race or Car Crusher. In Death Race, you have to race against your friends and try to finish first or kill them all. In Car Crusher, you have to smash your friends' cars until they are wrecked. You can also customize the game settings, such as the number of opponents, pedestrians, power-ups, time limit, etc. This is a good mode to compete with your friends, show off your skills, and have fun.</li>
-</ul>
- <h3>The cars and opponents of Carmageddon APK</h3>
- <p>Carmageddon APK features 30 playable cars that you can unlock by completing levels in Career mode or by buying them with real money. Each car has its own stats, such as speed, acceleration, handling, armor, etc. Some cars are faster but weaker, while others are slower but stronger. You can also upgrade your cars by spending credits on various parts, such as tires, engine, suspension, etc. You can also change the color and name of your cars to suit your preference.</p>
- <p>Some of the cars that you can unlock and drive in Carmageddon APK are:</p>
- <table>
-<tr><th>Car</th><th>Description</th></tr>
-<tr><td>Eagle 3</td><td>The default car that you start with. It is a red sports car that has balanced stats and good handling.</td></tr>
-<tr><td>Cop Car</td><td>A blue police car that has a siren and a bullbar. It is fast and agile, but has low armor.</td></tr>
-<tr><td>Plow Mk2</td><td>A yellow bulldozer that has a huge blade and a scoop. It is slow and heavy, but has high armor and damage.</td></tr>
-<tr><td>Annihilator</td><td>A black muscle car that has a skull on the hood and spikes on the wheels. It is fast and powerful, but has poor handling.</td></tr>
-<tr><td>Twister</td><td>A green buggy that has a roll cage and a spoiler. It is very fast and nimble, but has very low armor.</td></tr>
-<tr><td>Hawk R6</td><td>A purple sports car that has a wing and a turbo. It is the fastest car in the game, but has low damage and armor.</td></tr>
-<tr><td>Countslash</td><td>A white limousine that has a sunroof and a flag. It is long and elegant, but has low speed and acceleration.</td></tr>
-<tr><td>Stiffshifter</td><td>A brown hearse that has a coffin and a cross. It is slow and heavy, but has high armor and damage.</td></tr>
-<tr><td>Suppressor</td><td>A gray tank that has a cannon and tracks. It is the strongest car in the game, but has very low speed and handling.</td></tr>
-<tr><td>Electric Blue</td><td>A blue electric car that has a battery and a plug. It is quiet and eco-friendly, but has low damage and armor.</td></tr>
-</table>
- <p>In addition to these cars, you will also encounter 28 opponents in Carmageddon APK, each with their own personality, car, and behavior. Some of them are friendly and helpful, while others are hostile and aggressive. Some of them are easy to beat, while others are hard to catch. Some of them are funny and quirky, while others are scary and creepy.</p>
- <p>Some of the opponents that you will face in Carmageddon APK are:</p>
- <table>
-<tr><th>Opponent</th><th>Description</th></tr>
-<tr><td>Max Damage</td><td>The main protagonist of the game and your alter ego. He drives the Eagle 3 and is always looking for trouble.</td></tr>
-<tr><td>Die Anna</td><td>The main female character of the game and your partner in crime. She drives the Hawk R6 and is always ready for action.</td></tr>
-<tr><td>Vlad</td><td>A Russian mobster who drives the Annihilator. He is ruthless and violent, and likes to crush his enemies.</td></tr>
-<tr><td>Screwie Lewie</td><td>A crazy mechanic who drives the Plow Mk2. He is obsessed with fixing things, even if they are not broken.</td></tr>
-<tr><td>Stella Stunna</td><td>A glamorous actress who drives the Countslash. She is vain and arrogant, and likes to show off her beauty.</td></tr>
-<tr><td>The Cops</td><td>A group of corrupt policemen who drive the Cop Car. They are always chasing you and trying to stop you.</td></tr>
-<tr><td>Heinz Faust</td><td>A German soldier who drives the Suppressor. He is loyal and disciplined, and likes to follow orders.</td></tr>
-<tr><td>Madam Scarlett</td><<td>A French madam who drives the Stiffshifter. She is seductive and mysterious, and likes to lure her victims.</td></tr>
-<tr><td>Ed 101</td><td>A robotic cop who drives the Electric Blue. He is programmed to enforce the law, but often malfunctions.</ td></td></tr>
-<tr><td>Drugs</td><td>Alters your vision and perception.</td></tr>
-<tr><td>Hot Rod</td><td>Sets your car on fire.</td></tr>
-<tr><td>Time Bonus</td><td>Adds more time to the clock.</td></tr>
-<tr><td>Credits</td><td>Adds more credits to your account.</td></tr>
-</table>
- <p>Pedestrians are the innocent bystanders that populate the environments of Carmageddon APK. They are the main source of points and fun in the game, as you can run them over, smash them, or zap them with various power-ups. They come in different shapes and sizes, such as men, women, children, animals, zombies, aliens, etc. They also have different reactions and behaviors, such as screaming, running, fighting, or dancing. You can also interact with them in different ways, such as honking, taunting, or bribing them.</p>
- <p>Some of the pedestrians that you can encounter and kill in Carmageddon APK are:</p>
- <table>
-<tr><th>Pedestrian</th><th>Description</th></tr>
-<tr><td>Old Lady</td><td>A frail and elderly woman who walks slowly with a cane. She is worth 10 points.</td></tr>
-<tr><td>Cow</td><td>A large and docile animal that grazes on grass. It is worth 20 points.</td></tr>
-<tr><td>Biker</td><td>A tough and rebellious man who rides a motorcycle. He is worth 30 points.</td></tr>
-<tr><td>Nun</td><td>A holy and devout woman who wears a habit. She is worth 40 points.</td></tr>
-<tr><td>Alien</td><td>A strange and extraterrestrial creature that has green skin and tentacles. It is worth 50 points.</td></tr>
-<tr><td>Zombie</td><<td>A reanimated and undead corpse that has rotting flesh and blood. It is worth 60 points.</ td></td></tr>
-<tr><td>Footballer</td><td>A sporty and athletic man who wears a jersey and shorts. He is worth 70 points.</td></tr>
-<tr><td>Penguin</td><td>A cute and fluffy animal that waddles on ice. It is worth 80 points.</td></tr>
-<tr><td>Santa Claus</td><td>A jolly and festive man who wears a red suit and a beard. He is worth 90 points.</td></tr>
-<tr><td>God</td><td>A divine and omnipotent being who wears a white robe and a halo. He is worth 100 points.</td></tr>
-</table>
- <h3>The cheats and tricks of Carmageddon APK</h3>
- <p>If you want to spice up your gameplay or make it easier, you can use some cheats and tricks in Carmageddon APK. These are codes or actions that you can enter or perform to activate various effects, such as unlocking all cars, getting unlimited credits, or changing the gravity. Some cheats and tricks are built-in, while others require external apps or tools.</p>
- <p>Some of the cheats and tricks that you can use in Carmageddon APK are:</p>
- <ul>
-<li>To unlock all cars, enter the code "BIGDANGLE" in the car selection screen.</li>
-<li>To get unlimited credits, enter the code "GIVEMELARD" in the pause menu.</li>
-<li>To change the gravity, enter the code "MOONINGMINNIE" in the pause menu.</li>
-<li>To make pedestrians explode, enter the code "SPAMSPAMSPAMHUMBUG" in the pause menu.</li>
-<li>To make your car fly, enter the code "CHICKENFODDER" in the pause menu.</li>
-<li>To use a gamepad or a keyboard to control your car, connect it to your device via Bluetooth or USB and configure it in the settings menu.</li>
-<li>To backup your progress and data, use a cloud service or a file manager app to copy the folder "com.stainlessgames.carmageddon" from your device's internal storage to another location.</li>
-<li>To mod the game or access hidden features, use an app like APK Editor or Lucky Patcher to edit the APK file or the game data.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Carmageddon APK is a game that is not for everyone, but for those who love it, it is a blast. It is a game that lets you unleash your inner road rage and have fun with cars, opponents, power-ups, and pedestrians. It is a game that offers a lot of variety and replay value with different modes, levels, cars, and environments. It is a game that is faithful to the original game but with improved graphics and controls. It is a game that is free to download and play but with some ads and in-app purchases.</p>
- <p>If you are looking for a racing game that is different from the rest, you might want to give Carmageddon APK a try. You might be surprised by how much you enjoy it. Just remember to drive safely and responsibly in real life!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Carmageddon APK:</p>
- <ol>
-<li>Is Carmageddon APK safe to download and play?</li>
-<p>Yes, Carmageddon APK is safe to download and play if you get it from a reputable source like the Google Play Store or a trusted website. However, you should always scan any file you download with an antivirus app before opening it. You should also be aware that the game contains graphic violence and gore that might not be suitable for children or sensitive people.</p>
-<li>Is Carmageddon APK legal to play?</li>
-<p>Yes, Carmageddon APK is legal to play as long as you do not violate any laws or regulations in your country or region. The game was banned or censored in some countries when it was first released, but most of these bans have been lifted or relaxed over time. However, you should always check the legal status of the game in your area before playing it.</p>
-<li>Is Carmageddon APK compatible with my device?</li>
-<p>Carmageddon APK is compatible with most Android devices that run on Android version 4.0 or higher. However, some devices might have issues or errors with the game due to various factors such as hardware specifications, software versions, or device settings. You can check if your device is compatible with the game by visiting the Google Play Store page of the game and seeing if your device is listed among the supported devices. If it is not listed, it does not mean that the game will not work on your device, but it might have some problems.</p>
-<li>How can I get more credits in Carmageddon APK?</li> <p>You can get more credits in Carmageddon APK by performing various actions in the game, such as hitting opponents, damaging scenery, or running over pedestrians. You can also get more credits by completing levels, earning achievements, or watching ads. Alternatively, you can buy more credits with real money through in-app purchases.</p>
- <li>How can I unlock more cars in Carmageddon APK?</li>
-<p>You can unlock more cars in Carmageddon APK by completing levels in Career mode or by buying them with real money through in-app purchases. Each level has a specific car that you can unlock by winning it in any of the three ways: by completing the race, by wrecking all your opponents, or by killing all the pedestrians. You can also unlock some special cars by earning achievements or by using cheats.</p>
- <li>How can I play Carmageddon APK with my friends?</li>
-<p>You can play Carmageddon APK with your friends online or locally via Wi-Fi. To play online, you need to have a stable internet connection and a Google Play Games account. You can then invite your friends to join your game or join their game from the Multiplayer menu. To play locally, you need to have a Wi-Fi network and connect your devices to it. You can then create or join a game from the Multiplayer menu.</p>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Onet Link Animal and Explore Different Levels and Modes.md b/spaces/congsaPfin/Manga-OCR/logs/Download Onet Link Animal and Explore Different Levels and Modes.md
deleted file mode 100644
index 9d5eb258c970ac3be879a6bce433cc5dfe8f10a1..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Onet Link Animal and Explore Different Levels and Modes.md
+++ /dev/null
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-<br />
-<h1>Onet Link Animal: A Fun and Challenging Memory Game</h1>
-<p>Do you love animals and puzzles? Do you want to test your memory and concentration skills? If you answered yes, then you should try <strong>Onet Link Animal</strong>, a fun and challenging memory game that will keep you entertained for hours.</p>
-<p>Onet Link Animal is a game that requires you to connect the same animal tiles in a limited time. It is based on the classic game of <em>Pikachu</em>, also known as <em>Mahjong Connect</em>, <em>Onet</em>, or <em>Kyodai</em>. It is one of the most popular games in Asia, especially in Japan, China, Vietnam, and Indonesia.</p>
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-<p>In this article, we will tell you everything you need to know about Onet Link Animal, including how to play it, how to master it, how to download it for free, and what benefits it can bring you. So, let's get started!</p>
- <h2>How to Play Onet Link Animal</h2>
-<p>The basic rules and gameplay of Onet Link Animal are simple and easy to learn. Here are the steps:</p>
-<ol>
-<li>Select a game mode and a level. There are two game modes: Normal and Hard. There are 15 levels in each mode, with different layouts and directions of tiles.</li>
-<li>Tap on two animal tiles that are identical and adjacent or can be connected by up to three straight lines without any other tiles blocking the way. The tiles will disappear and you will get points.</li>
-<li>Clear all the tiles before the time runs out. If you run out of time or moves, you will lose the game.</li>
-<li>Use the hints, shuffles, or bombs if you get stuck. You can get these items by watching ads or buying them with coins.</li>
-<li>Earn coins by completing levels, watching ads, or rating the game. You can use coins to buy more items or unlock new animal themes.</li>
-</ol>
- <h3>Tips and Tricks to Master Onet Link Animal</h3>
-<p>Onet Link Animal may seem easy at first, but it can get quite challenging as you progress. Here are some tips and tricks to help you improve your skills and score higher in Onet Link Animal:</p>
-<ul>
-<li>Plan ahead. Look at the whole board and try to find the best matches before tapping on them. Don't rush or make random moves.</li>
-<li>Focus on the corners and edges. These tiles are harder to connect, so try to clear them first.</li>
-<li>Save the items for later. Don't use the hints, sh uffle, or bombs too early. Save them for the harder levels or when you are really stuck.</li>
-<li>Practice and play more. The more you play, the more familiar you will be with the game and the faster you will be able to spot the matches.</li>
-</ul>
- <h4>Different Game Modes and Levels</h4>
-<p>Onet Link Animal offers two game modes: Normal and Hard. Each mode has 15 levels, with different layouts and directions of tiles. The levels get harder as you go, with more tiles, less time, and more obstacles. You can choose any level you want, but you need to complete the previous one to unlock the next one.</p>
-<p>Normal mode is suitable for beginners and casual players. It has more time and fewer tiles. The tiles can only be connected horizontally or vertically.</p>
-<p>Hard mode is suitable for advanced and expert players. It has less time and more tiles. The tiles can be connected horizontally, vertically, or diagonally.</p>
- <h4>Amazing Graphics and Sounds</h4>
-<p>Onet Link Animal has amazing graphics and sounds that make the game more enjoyable and attractive. The game features cute and colorful animal tiles, such as cats, dogs, pandas, lions, elephants, and more. You can also unlock new animal themes by using coins or watching ads.</p>
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-<p>The game also has relaxing and soothing background music and sound effects that match the theme of the game. You can hear the animals' noises when you tap on them or when they disappear. You can also adjust the volume or mute the sound in the settings.</p>
- <h3>How to Download Onet Link Animal for Free</h3>
-<p>Onet Link Animal is a free game that you can download and play on your Android, iOS, or PC devices. Here are the steps and sources to download Onet Link Animal for each device:</p>
- <h4>Download Onet Link Animal for Android</h4>
-<p>If you have an Android device, you can download Onet Link Animal from Google Play Store or APK file. Here are the steps:</p>
-<ol>
-<li>Go to Google Play Store and search for Onet Link Animal or click on this link: <a href="">Onet Link Animal - Apps on Google Play</a></li>
-<li>Tap on Install and wait for the download to finish.</li>
-<li>Open the game and enjoy!</li>
-</ol>
-<p>If you want to download Onet Link Animal from APK file, you need to enable unknown sources on your device first. Here are the steps:</p>
-<ol>
-<li>Go to Settings > Security > Unknown Sources and turn it on.</li>
-<li>Go to this link: <a href="">Onet Link Animal APK Download - Free Puzzle GAME for Android | APKPure.com</a> and download the APK file.</li>
-<li>Open the file and tap on Install.</li>
-<li>Open the game and enjoy!</li>
-</ol>
- <h4>Download Onet Link Animal for iOS</h4>
-<p>If you have an iOS device, you can download Onet Link Animal from App Store or IPA file. Here are the steps:</p>
-<ol>
-<li>Go to App Store and search for Onet Link Animal or click on this link: <a href="">Onet Link Animal on the App Store</a></li>
-<li>Tap on Get and wait for the download to finish.</li>
-<li>Open the game and enjoy!</li>
-</ol>
-<p>If you want to download Onet Link Animal from IPA file, you need to have a jailbroken device first. Here are the steps:</p>
-<ol>
-<li>Go to this link: <a href="">Onet Link Animal IPA Cracked for iOS Free Download</a> and download the IPA file.</li>
-<li>Open Cydia Impactor and connect your device to your computer.</li>
-<li>Drag and drop the IPA file onto Cydia Impactor.</li>
-<li>Enter your Apple ID and password when prompted.</li>
-<li>Wait for the installation to finish.</li>
-<li>Open the game and enjoy!</li>
-</ol>
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-<p>If you have a PC, you can download Onet Link Animal from Windows Store or emulator. Here are the steps:</p>
-<ol>
-<li>Go to Windows Store and search for Onet Link Animal or click on this link: <a href="">Get Onet Link Animal - Microsoft Store</a></li>
-<li>Click on Get and wait for the download to finish.</li>
-<li>Open the game and enjoy!</li>
-</ol>
-<p>If you want to download Onet Link Animal from emulator, you need to have an Android emulator on your PC first. Here are the steps:</p>
-<ol>
-<li>Download and install an Android emulator on your PC, such as BlueStacks, NoxPlayer, or MEmu.</li>
-<li>Open the emulator and sign in with your Google account.</li>
-<li>Go to Google Play Store and search for Onet Link Animal or click on this link: <a href="">Onet Link Animal - Apps on Google Play</a></li>
-<li>Install the game and wait for the download to finish.</li>
-<li>Open the game and enjoy!</li>
-</ol>
- <h2>Benefits of Playing Onet Link Animal</h2>
-<p>Onet Link Animal is not only a fun and challenging game, but also a beneficial one. Playing Onet Link Animal can bring you many advantages and rewards, such as:</p>
- <h3>Enhance Your Memory and Concentration</h3>
-<p>Onet Link Animal is a game that requires you to remember and match the animal tiles in a limited time. This can help you improve your memory and concentration skills, as you need to pay attention to the details and patterns of the tiles. Playing Onet Link Animal can also stimulate your brain and prevent cognitive decline.</p>
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-<p>Onet Link Animal is a game that can help you relax and have fun. The game has cute and colorful animal tiles, relaxing and soothing background music and sound effects, and various game modes and levels to suit your mood and preference. Playing Onet Link Animal can also reduce your stress and boredom, as you can enjoy the game anytime and anywhere.</p>
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-<p>Onet Link Animal is a game that can help you challenge yourself and your friends. The game has different game modes and levels, with different layouts and directions of tiles, that can test your skills and speed. You can also compare your scores with other players around the world or invite your friends to play with you. Playing Onet Link Animal can also increase your competitiveness and social interaction, as you can share your achievements and feedback with others.</p>
- <h2>Conclusion</h2>
-<p>Onet Link Animal is a fun and challenging memory game that will keep you entertained for hours. It is based on the classic game of Pikachu, also known as Mahjong Connect, Onet, or Kyodai. It requires you to connect the same animal tiles in a limited time. You can download it for free on your Android, iOS, or PC devices. Playing Onet Link Animal can also bring you many benefits, such as enhancing your memory and concentration, relaxing and having fun, and challenging yourself and your friends. So, what are you waiting for? Download Onet Link Animal now and enjoy!</p>
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-<p>Here are some frequently asked questions and answers about Onet Link Animal:</p>
-<ol>
-<li><strong>What is the difference between Onet Link Animal and other similar games?</strong></li>
-<p>Onet Link Animal is similar to other games that require you to connect the same tiles, such as Pikachu, Mahjong Connect, Onet, or Kyodai. However, Onet Link Animal has some unique features that make it stand out, such as:</p>
-<ul>
-<li>It has cute and colorful animal tiles that appeal to all ages.</li>
-<li>It has two game modes: Normal and Hard. Normal mode has horizontal and vertical connections, while Hard mode has diagonal connections.</li>
-<li>It has 15 levels in each mode, with different layouts and directions of tiles.</li>
-<li>It has amazing graphics and sounds that make the game more enjoyable and attractive.</li>
-<li>It has hints, shuffles, or bombs that can help you when you get stuck.</li>
-<li>It has coins that you can earn by completing levels, watching ads, or rating the game. You can use coins to buy more items or unlock new animal themes.</li>
-</ul>
- <li><strong>How can I get more coins in Onet Link Animal?</strong></li>
-<p>You can get more coins in Onet Link Animal by doing the following:</p>
-<ul>
-<li>Completing levels. You will get coins based on your score and time.</li>
-<li>Watching ads. You will get coins by watching short videos.</li>
-<li>Rating the game. You will get coins by giving a positive rating to the game.</li>
-</ul>
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-<p>You can unlock new animal themes in Onet Link Animal by using coins or watching ads. Each theme has different animal tiles that you can choose from. To unlock a theme, you need to have enough coins or watch an ad. You can change the theme in the settings.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Stick War Legacy Hack APK Tn hng tr chi chin thut hp dn vi mod full vng kim cng.md b/spaces/congsaPfin/Manga-OCR/logs/Stick War Legacy Hack APK Tn hng tr chi chin thut hp dn vi mod full vng kim cng.md
deleted file mode 100644
index 1306e1c4a4eae38dfb051b800eed93aba1e51634..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Stick War Legacy Hack APK Tn hng tr chi chin thut hp dn vi mod full vng kim cng.md
+++ /dev/null
@@ -1,127 +0,0 @@
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-<li>Chế độ sinh tồn: Bạn sẽ phải chống lại làn sóng tấn công của các kẻ thù ngày càng mạnh hơn. Bạn sẽ phải sử dụng tài nguyên của mình một cách hợp lý để huấn luyện và nâng cấp các chiến binh của mình. Bạn sẽ được tính điểm dựa trên số lượng kẻ thù bạn tiêu diệt và số ngày bạn sống sót.</li>
-<li>Chế độ xếp hạng: Bạn sẽ có cơ hội so tài với các người chơi khác trên toàn thế giới. Bạn sẽ được xếp vào một trong các hạng từ F cho đến S. Bạn sẽ được tính điểm dựa trên số lượng kẻ thù bạn tiêu diệt, số ngày bạn sống sót và số tiền bạn kiếm được trong chế độ sinh tồn.</li>
-<li>Chế độ tùy chỉnh: Bạn sẽ có thể tạo ra một trận đấu theo ý muốn của mình, bằng cách chọn số lượng và loại kẻ thù, số lượng và loại chiến binh của mình, địa hình và thời tiết của trận đấu. Bạn sẽ có thể thử nghiệm các chiến thuật khác nhau và tăng kỹ năng chơi game của mình.</li>
-</ul>
- <h2>Lợi ích của việc hack stick war legacy apk</h2>
- <p>Việc hack stick war legacy apk sẽ mang lại cho bạn nhiều lợi ích, như:</p>
- <h3>Sở hữu vô hạn tiền, kim cương và các vật phẩm khác</h3>
- <p>Tiền và kim cương là hai loại tiền tệ chính trong game, được sử dụng để mua và nâng cấp các chiến binh, vũ khí, phòng thủ và các vật phẩm khác. Nếu bạn hack stick war legacy apk, bạn sẽ có vô hạn tiền và kim cương trong game, không cần phải chờ đợi hoặc làm nhiệm vụ để kiếm được. Bạn sẽ có thể mua và nâng cấp tất cả những gì bạn muốn một cách dễ dàng.</p>
- <h3>Khám phá và trải nghiệm tất cả các tính năng của game</h3>
- <p>Game stick war legacy có rất nhiều tính năng hấp dẫn và thú vị, nhưng không phải tất cả đều được mở khóa ngay từ đầu. Bạn sẽ phải hoàn thành các màn chơi, đạt được các điểm số cao hoặc chi trả tiền để mở khóa các tính năng mới. Nếu bạn hack stick war legacy apk, bạn sẽ không phải lo lắng về điều này. Bạn sẽ có thể khám phá và trải nghiệm tất cả các tính năng của game mà không bị giới hạn bởi bất kỳ yếu tố nào.</p>
- <h3>Thoải mái chơi game mà không lo bị giới hạn thời gian, năng lượng hay quảng cáo</h3>
- <p>Một trong những phiền toái khi chơi game là việc bị giới hạn thời gian, năng lượng hay quảng cáo. Bạn sẽ phải chờ đợi để có thể chơi tiếp, hoặc phải xem quảng cáo để nhận được thêm tiền hoặc năng lượng. Nếu bạn hack stick war legacy apk, bạn sẽ không phải đối mặt với những rắc rối này. Bạn sẽ có thể chơi game mà không bị gián đoạn bởi bất kỳ yếu tố nào.</p>
- <h2>Cách hack stick war legacy apk</h2>
- <p>Bây giờ, bạn đã biết được những lợi ích của việc hack stick war legacy apk. Vậy làm thế nào để hack stick war legacy apk? Đây là các bước bạn cần làm:</p>
- <h3>Bước 1: Tải file apk phiên bản mod từ các nguồn đáng tin cậy trên mạng</h3>
- <p>Để hack stick war legacy apk, bạn cần tải file apk phiên bản mod của game từ các nguồn đáng tin cậy trên mạng. Bạn có thể tìm kiếm trên Google hoặc các trang web chuyên về game mod như APKPure, APKMODY, MODDROID... Bạn cần chọn phiên bản mod phù hợp với thiết bị và phiên bản gốc của game của bạn. Bạn cũng cần kiểm tra tính an to àn của file apk trước khi tải về và cài đặt, để tránh bị nhiễm virus hoặc malware.</p>
- <h3>Bước 2: Cài đặt file apk và mở ứng dụng</h3>
- <p>Sau khi tải về file apk phiên bản mod, bạn cần cài đặt nó lên thiết bị của bạn. Bạn cần cho phép thiết bị cài đặt ứng dụng từ nguồn không xác định, bằng cách vào Cài đặt > Bảo mật > Nguồn không xác định. Sau đó, bạn chỉ cần nhấn vào file apk và chọn Cài đặt. Quá trình cài đặt sẽ diễn ra trong vài giây. Sau khi cài đặt xong, bạn có thể mở ứng dụng và chơi game.</p>
- <h3>Bước 3: Đăng nhập vào tài khoản game của bạn và tận hưởng kết quả</h3>
- <p>Cuối cùng, bạn chỉ cần đăng nhập vào tài khoản game của bạn và tận hưởng kết quả. Bạn sẽ thấy rằng số tiền, kim cương và các vật phẩm khác của bạn đã được tăng lên vô hạn. Bạn sẽ có thể mua và nâng cấp tất cả những gì bạn muốn một cách dễ dàng. Bạn sẽ có thể khám phá và trải nghiệm tất cả các tính năng của game mà không bị giới hạn bởi bất kỳ yếu tố nào. Bạn sẽ có thể thoải mái chơi game mà không lo bị giới hạn thời gian, năng lượng hay quảng cáo.</p>
- <h2>Một số lưu ý khi hack stick war legacy apk</h2>
- <p>Trước khi hack stick war legacy apk, bạn cần lưu ý một số điều sau:</p>
- <h3>Kiểm tra tính an toàn của file apk trước khi tải về và cài đặt</h3>
- <p>Như đã nói ở trên, bạn cần kiểm tra tính an toàn của file apk trước khi tải về và cài đặt, để tránh bị nhiễm virus hoặc malware. Bạn có thể sử dụng các phần mềm bảo mật hoặc các trang web kiểm tra virus như VirusTotal để quét file apk trước khi tải về. Nếu file apk có chứa virus hoặc malware, bạn nên ngừng tải về và tìm kiếm nguồn khác.</p>
- <h3>Sao lưu dữ liệu game của bạn trước khi hack để tránh mất mát</h3>
- <p>Một điều quan trọng khác là bạn nên sao lưu dữ liệu game của bạn trước khi hack, để tránh mất mát dữ liệu trong trường hợp xảy ra sự cố. Bạn có thể sao lưu dữ liệu game bằng cách sử dụng các ứng dụng sao lưu như Titanium Backup hoặc Helium Backup, hoặc bằng cách kết nối tài khoản game của bạn với Google Play Games hoặc Facebook. Sau khi sao lưu xong, bạn có thể hack stick war legacy apk một cách an tâm.</p>
- <h3>Không sử dụng phiên bản mod để chơi online hoặc kết nối với các tài khoản khác để tránh bị khóa tài khoản</h3>
- <p>Cuối cùng, bạn nên biết rằng việc hack stick war legacy apk là việc vi phạm điều khoản sử dụng của game. Nếu bạn sử dụng phi ên bản mod để chơi online hoặc kết nối với các tài khoản khác, bạn có thể bị phát hiện và bị khóa tài khoản. Điều này sẽ khiến bạn mất hết dữ liệu game và không thể chơi game nữa. Do đó, bạn nên chỉ sử dụng phiên bản mod để chơi offline hoặc với tài khoản riêng của bạn. Bạn cũng nên thường xuyên cập nhật phiên bản mod mới nhất để tránh bị lỗi hoặc không tương thích với phiên bản gốc của game.</p>
- <h2>Kết luận</h2>
- <p>Trên đây là bài viết hướng dẫn bạn cách hack stick war legacy apk một cách chi tiết và dễ hiểu. Bằng cách hack stick war legacy apk, bạn sẽ có thể sở hữu vô hạn tiền, kim cương và các vật phẩm khác trong game, khám phá và trải nghiệm tất cả các tính năng của game, và thoải mái chơi game mà không lo bị giới hạn thời gian, năng lượng hay quảng cáo. Tuy nhiên, bạn cũng cần lưu ý kiểm tra tính an toàn của file apk, sao lưu dữ liệu game, và không sử dụng phiên bản mod để chơi online hoặc kết nối với các tài khoản khác để tránh bị khóa tài khoản. Chúc bạn chơi game vui vẻ và thành công!</p>
- <h2>Câu hỏi thường gặp</h2>
- <p>Dưới đây là một số câu hỏi thường gặp về cách hack stick war legacy apk:</p>
- <table>
-<tr>
-<th>Câu hỏi</th>
-<th>Trả lời</th>
-</tr>
-<tr>
-<td>Làm sao để biết file apk có an toàn hay không?</td>
-<td>Bạn có thể sử dụng các phần mềm bảo mật hoặc các trang web kiểm tra virus như VirusTotal để quét file apk trước khi tải về. Nếu file apk có chứa virus hoặc malware, bạn nên ngừng tải về và tìm kiếm nguồn khác.</td>
-</tr>
-<tr>
-<td>Làm sao để sao lưu dữ liệu game?</td>
-<td>Bạn có thể sao lưu dữ liệu game bằng cách sử dụng các ứng dụng sao lưu như Titanium Backup hoặc Helium Backup, hoặc bằng cách kết nối tài khoản game của bạn với Google Play Games hoặc Facebook.</td>
-</tr>
-<tr>
-<td>Làm sao để cập nhật phiên bản mod mới nhất?</td>
-<td>Bạn có thể tìm kiếm phiên bản mod mới nhất trên các trang web chuyên về game mod như APKPure, APKMODY, MODDROID... Bạn cần chọn phiên bản mod phù hợp với thiết bị và phiên bản gốc của game của bạn. Sau đó, bạn chỉ cần tải về và cài đặt lại file apk.</td>
-</tr>
-<tr>
-<td>Có thể hack stick war legacy apk cho iOS hay không?</td>
-<td>Không, hiện tại chỉ có phiên bản mod cho Android. Nếu bạn muốn hack stick war legacy cho iOS, bạn cần phải jailbreak thiết bị của bạn và sử dụng các ứng dụng hack như Cydia hoặc iFile.</td>
-</tr>
-<tr>
-<td>Có phải trả tiền để hack stick war legacy apk hay không?</td>
-<td>Không, bạn không phải trả tiền để hack stick war legacy apk. Bạn chỉ cần tải về file apk phiên bản mod từ các nguồn đáng tin c ậy trên mạng. Bạn chỉ cần chú ý kiểm tra tính an toàn của file apk và sao lưu dữ liệu game trước khi hack.</td>
-</tr>
-</table>
- <p>Bạn có thể tham khảo thêm các câu hỏi và trả lời khác về cách hack stick war legacy apk tại đây.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/contluForse/HuggingGPT/assets/Brian.Auger.And.Julie.Tippetts.-.Encore.(1978).[FLAC].rar.md b/spaces/contluForse/HuggingGPT/assets/Brian.Auger.And.Julie.Tippetts.-.Encore.(1978).[FLAC].rar.md
deleted file mode 100644
index 60606564c21e6f8a386630a1feae9991614b5824..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Brian.Auger.And.Julie.Tippetts.-.Encore.(1978).[FLAC].rar.md
+++ /dev/null
@@ -1,10 +0,0 @@
-<h2>Brian.Auger.And.Julie.Tippetts.-.Encore.(1978).[FLAC].rar</h2><br /><p><b><b>Download</b> ✵ <a href="https://ssurll.com/2uzyqf">https://ssurll.com/2uzyqf</a></b></p><br /><br />
-
-brian.auger.and.julie.tippetts.-.encore.(1978).[flac].rar. torrent
-Download
-Download .torrent file (main link for downloading the file) Watch / Listen online preview (link for viewing the file through the 'Torrent Stream' plugin)
-Description
-brian.auger.and.julie.tippetts.-.encore.(1978).[ 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cooelf/Multimodal-CoT/timm/optim/adamp.py b/spaces/cooelf/Multimodal-CoT/timm/optim/adamp.py
deleted file mode 100644
index 468c3e865e0ceb6fb2bf22f9388237a783314f07..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/optim/adamp.py
+++ /dev/null
@@ -1,107 +0,0 @@
-"""
-AdamP Optimizer Implementation copied from https://github.com/clovaai/AdamP/blob/master/adamp/adamp.py
-
-Paper: `Slowing Down the Weight Norm Increase in Momentum-based Optimizers` - https://arxiv.org/abs/2006.08217
-Code: https://github.com/clovaai/AdamP
-
-Copyright (c) 2020-present NAVER Corp.
-MIT license
-"""
-
-import torch
-import torch.nn as nn
-from torch.optim.optimizer import Optimizer, required
-import math
-
-class AdamP(Optimizer):
- def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
- weight_decay=0, delta=0.1, wd_ratio=0.1, nesterov=False):
- defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
- delta=delta, wd_ratio=wd_ratio, nesterov=nesterov)
- super(AdamP, self).__init__(params, defaults)
-
- def _channel_view(self, x):
- return x.view(x.size(0), -1)
-
- def _layer_view(self, x):
- return x.view(1, -1)
-
- def _cosine_similarity(self, x, y, eps, view_func):
- x = view_func(x)
- y = view_func(y)
-
- x_norm = x.norm(dim=1).add_(eps)
- y_norm = y.norm(dim=1).add_(eps)
- dot = (x * y).sum(dim=1)
-
- return dot.abs() / x_norm / y_norm
-
- def _projection(self, p, grad, perturb, delta, wd_ratio, eps):
- wd = 1
- expand_size = [-1] + [1] * (len(p.shape) - 1)
- for view_func in [self._channel_view, self._layer_view]:
-
- cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func)
-
- if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)):
- p_n = p.data / view_func(p.data).norm(dim=1).view(expand_size).add_(eps)
- perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(expand_size)
- wd = wd_ratio
-
- return perturb, wd
-
- return perturb, wd
-
- def step(self, closure=None):
- loss = None
- if closure is not None:
- loss = closure()
-
- for group in self.param_groups:
- for p in group['params']:
- if p.grad is None:
- continue
-
- grad = p.grad.data
- beta1, beta2 = group['betas']
- nesterov = group['nesterov']
-
- state = self.state[p]
-
- # State initialization
- if len(state) == 0:
- state['step'] = 0
- state['exp_avg'] = torch.zeros_like(p.data)
- state['exp_avg_sq'] = torch.zeros_like(p.data)
-
- # Adam
- exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
-
- state['step'] += 1
- bias_correction1 = 1 - beta1 ** state['step']
- bias_correction2 = 1 - beta2 ** state['step']
-
- exp_avg.mul_(beta1).add_(1 - beta1, grad)
- exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
-
- denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
- step_size = group['lr'] / bias_correction1
-
- if nesterov:
- perturb = (beta1 * exp_avg + (1 - beta1) * grad) / denom
- else:
- perturb = exp_avg / denom
-
- # Projection
- wd_ratio = 1
- if len(p.shape) > 1:
- perturb, wd_ratio = self._projection(p, grad, perturb, group['delta'], group['wd_ratio'], group['eps'])
-
- # Weight decay
- if group['weight_decay'] > 0:
- p.data.mul_(1 - group['lr'] * group['weight_decay'] * wd_ratio)
-
- # Step
- p.data.add_(-step_size, perturb)
-
- return loss
diff --git a/spaces/coolzude/Landmark-Detection/app.py b/spaces/coolzude/Landmark-Detection/app.py
deleted file mode 100644
index 8773595743e66aa225f2d999c18d3d046be27072..0000000000000000000000000000000000000000
--- a/spaces/coolzude/Landmark-Detection/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import numpy as np
-import pandas as pd
-import matplotlib.pylab as plt
-import PIL.Image as Image
-import tensorflow as tf
-import tensorflow_hub as hub
-import gradio as gr
-
-TF_MODEL_URL = 'https://tfhub.dev/google/on_device_vision/classifier/landmarks_classifier_asia_V1/1'
-LABEL_MAP_URL = 'https://www.gstatic.com/aihub/tfhub/labelmaps/landmarks_classifier_asia_V1_label_map.csv'
-IMAGE_SHAPE = (321, 321)
-
-classifier = tf.keras.Sequential([hub.KerasLayer(TF_MODEL_URL,
- input_shape=IMAGE_SHAPE+(3,),
- output_key="predictions:logits")])
-
-df = pd.read_csv(LABEL_MAP_URL)
-
-label_map = dict(zip(df.id, df.name))
-
-class_names=list(label_map.values())
-
-def classify_image(image):
- img = np.array(image)/255.0
- img = img[np.newaxis, ...]
- prediction = classifier.predict(img)
- return label_map[np.argmax(prediction)]
-
-image = gr.inputs.Image(shape=(321, 321))
-label = gr.outputs.Label(num_top_classes=1)
-
-gr.Interface(
- classify_image,
- image,
- label,
- capture_session=True).launch(debug=True);
\ No newline at end of file
diff --git a/spaces/coqui/CoquiTTS/app.py b/spaces/coqui/CoquiTTS/app.py
deleted file mode 100644
index 55545fcbfd4b91fa9bff1b740095f61f8b8767e7..0000000000000000000000000000000000000000
--- a/spaces/coqui/CoquiTTS/app.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import tempfile
-from typing import Optional
-from TTS.config import load_config
-import gradio as gr
-import numpy as np
-from TTS.utils.manage import ModelManager
-from TTS.utils.synthesizer import Synthesizer
-
-
-MODELS = {}
-SPEAKERS = {}
-MAX_TXT_LEN = 100
-
-
-manager = ModelManager()
-MODEL_NAMES = manager.list_tts_models()
-
-# filter out multi-speaker models and slow wavegrad vocoders
-filters = ["vctk", "your_tts", "ek1"]
-MODEL_NAMES = [model_name for model_name in MODEL_NAMES if not any(f in model_name for f in filters)]
-
-EN = [el for el in MODEL_NAMES if "/en/" in el]
-OTHER = [el for el in MODEL_NAMES if "/en/" not in el]
-EN[0], EN[5] = EN[5], EN[0]
-MODEL_NAMES = EN + OTHER
-
-# reorder models
-print(MODEL_NAMES)
-
-
-def tts(text: str, model_name: str):
- if len(text) > MAX_TXT_LEN:
- text = text[:MAX_TXT_LEN]
- print(f"Input text was cutoff since it went over the {MAX_TXT_LEN} character limit.")
- print(text, model_name)
- # download model
- model_path, config_path, model_item = manager.download_model(model_name)
- vocoder_name: Optional[str] = model_item["default_vocoder"]
- # download vocoder
- vocoder_path = None
- vocoder_config_path = None
- if vocoder_name is not None:
- vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name)
- # init synthesizer
- synthesizer = Synthesizer(
- model_path, config_path, None, None, vocoder_path, vocoder_config_path,
- )
- # synthesize
- if synthesizer is None:
- raise NameError("model not found")
- wavs = synthesizer.tts(text, None)
- # return output
- with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
- synthesizer.save_wav(wavs, fp)
- return fp.name
-
-
-title = """<h1 align="center">🐸💬 CoquiTTS Playground </h1>"""
-
-with gr.Blocks(analytics_enabled=False) as demo:
- with gr.Row():
- with gr.Column():
- gr.Markdown(
- """
- ## <img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/coqui-log-green-TTS.png" height="56"/>
- """
- )
- gr.Markdown(
- """
- <br />
-
- ## 🐸Coqui.ai News
- - 📣 ⓍTTS, our production TTS model that can speak 13 languages, is released [Blog Post](https://coqui.ai/blog/tts/open_xtts), [Demo](https://huggingface.co/spaces/coqui/xtts), [Docs](https://tts.readthedocs.io/en/dev/models/xtts.html)
- - 📣 [🐶Bark](https://github.com/suno-ai/bark) is now available for inference with unconstrained voice cloning. [Docs](https://tts.readthedocs.io/en/dev/models/bark.html)
- - 📣 You can use [~1100 Fairseq models](https://github.com/facebookresearch/fairseq/tree/main/examples/mms) with 🐸TTS.
- - 📣 🐸TTS now supports 🐢Tortoise with faster inference. [Docs](https://tts.readthedocs.io/en/dev/models/tortoise.html)
- - 📣 **Coqui Studio API** is landed on 🐸TTS. - [Example](https://github.com/coqui-ai/TTS/blob/dev/README.md#-python-api)
- - 📣 [**Coqui Studio API**](https://docs.coqui.ai/docs) is live.
- - 📣 Voice generation with prompts - **Prompt to Voice** - is live on [**Coqui Studio**](https://app.coqui.ai/auth/signin)!! - [Blog Post](https://coqui.ai/blog/tts/prompt-to-voice)
- - 📣 Voice generation with fusion - **Voice fusion** - is live on [**Coqui Studio**](https://app.coqui.ai/auth/signin).
- - 📣 Voice cloning is live on [**Coqui Studio**](https://app.coqui.ai/auth/signin).
- <br>
-
- """
- )
- with gr.Column():
- gr.Markdown(
- """
- <br/>
-
- 💻 This space showcases some of the **[CoquiTTS](https://github.com/coqui-ai/TTS)** models.
-
- <br/>
-
- There are > 30 languages with single and multi speaker models, all thanks to our 👑 Contributors.
-
- <br/>
-
- Visit the links below for more.
-
- | | |
- | ------------------------------- | --------------------------------------- |
- | 🐸💬 **CoquiTTS** | [Github](https://github.com/coqui-ai/TTS) |
- | 💼 **Documentation** | [ReadTheDocs](https://tts.readthedocs.io/en/latest/)
- | 👩💻 **Questions** | [GitHub Discussions] |
- | 🗯 **Community** | [](https://discord.gg/5eXr5seRrv) |
-
- [github issue tracker]: https://github.com/coqui-ai/tts/issues
- [github discussions]: https://github.com/coqui-ai/TTS/discussions
- [discord]: https://discord.gg/5eXr5seRrv
-
-
- """
- )
-
- with gr.Row():
- gr.Markdown(
- """
- <details>
- <summary>👑 Model contributors</summary>
-
- - <a href="https://github.com/nmstoker/" target="_blank">@nmstoker</a>
- - <a href="https://github.com/kaiidams/" target="_blank">@kaiidams</a>
- - <a href="https://github.com/WeberJulian/" target="_blank">@WeberJulian,</a>
- - <a href="https://github.com/Edresson/" target="_blank">@Edresson</a>
- - <a href="https://github.com/thorstenMueller/" target="_blank">@thorstenMueller</a>
- - <a href="https://github.com/r-dh/" target="_blank">@r-dh</a>
- - <a href="https://github.com/kirianguiller/" target="_blank">@kirianguiller</a>
- - <a href="https://github.com/robinhad/" target="_blank">@robinhad</a>
- - <a href="https://github.com/fkarabiber/" target="_blank">@fkarabiber</a>
- - <a href="https://github.com/nicolalandro/" target="_blank">@nicolalandro</a>
- - <a href="https://github.com/a-froghyar" target="_blank">@a-froghyar</a>
- - <a href="https://github.com/manmay-nakhashi" target="_blank">@manmay-nakhashi</a>
- - <a href="https://github.com/noml4u" target="_blank">@noml4u</a>
- </details>
-
- <br/>
- """
- )
-
- with gr.Row():
- with gr.Column():
- input_text = gr.inputs.Textbox(
- label="Input Text",
- default="This sentence has been generated by a speech synthesis system.",
- )
- model_select = gr.inputs.Dropdown(
- label="Pick Model: tts_models/<language>/<dataset>/<model_name>",
- choices=MODEL_NAMES,
- default="tts_models/en/jenny/jenny"
- )
- tts_button = gr.Button("Send", elem_id="send-btn", visible=True)
-
- with gr.Column():
- output_audio = gr.outputs.Audio(label="Output", type="filepath")
-
- tts_button.click(
- tts,
- inputs=[
- input_text,
- model_select,
- ],
- outputs=[output_audio],
- )
-
-demo.queue(concurrency_count=16).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/datasets/pipelines/loading.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/datasets/pipelines/loading.py
deleted file mode 100644
index d3692ae91f19b9c7ccf6023168788ff42c9e93e3..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/datasets/pipelines/loading.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import os.path as osp
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-
-from ..builder import PIPELINES
-
-
-@PIPELINES.register_module()
-class LoadImageFromFile(object):
- """Load an image from file.
-
- Required keys are "img_prefix" and "img_info" (a dict that must contain the
- key "filename"). Added or updated keys are "filename", "img", "img_shape",
- "ori_shape" (same as `img_shape`), "pad_shape" (same as `img_shape`),
- "scale_factor" (1.0) and "img_norm_cfg" (means=0 and stds=1).
-
- Args:
- to_float32 (bool): Whether to convert the loaded image to a float32
- numpy array. If set to False, the loaded image is an uint8 array.
- Defaults to False.
- color_type (str): The flag argument for :func:`mmcv.imfrombytes`.
- Defaults to 'color'.
- file_client_args (dict): Arguments to instantiate a FileClient.
- See :class:`mmcv.fileio.FileClient` for details.
- Defaults to ``dict(backend='disk')``.
- imdecode_backend (str): Backend for :func:`mmcv.imdecode`. Default:
- 'cv2'
- """
-
- def __init__(self,
- to_float32=False,
- color_type='color',
- file_client_args=dict(backend='disk'),
- imdecode_backend='cv2'):
- self.to_float32 = to_float32
- self.color_type = color_type
- self.file_client_args = file_client_args.copy()
- self.file_client = None
- self.imdecode_backend = imdecode_backend
-
- def __call__(self, results):
- """Call functions to load image and get image meta information.
-
- Args:
- results (dict): Result dict from :obj:`mmseg.CustomDataset`.
-
- Returns:
- dict: The dict contains loaded image and meta information.
- """
-
- if self.file_client is None:
- self.file_client = mmcv.FileClient(**self.file_client_args)
-
- if results.get('img_prefix') is not None:
- filename = osp.join(results['img_prefix'],
- results['img_info']['filename'])
- else:
- filename = results['img_info']['filename']
- img_bytes = self.file_client.get(filename)
- img = mmcv.imfrombytes(
- img_bytes, flag=self.color_type, backend=self.imdecode_backend)
- if self.to_float32:
- img = img.astype(np.float32)
-
- results['filename'] = filename
- results['ori_filename'] = results['img_info']['filename']
- results['img'] = img
- results['img_shape'] = img.shape
- results['ori_shape'] = img.shape
- # Set initial values for default meta_keys
- results['pad_shape'] = img.shape
- results['scale_factor'] = 1.0
- num_channels = 1 if len(img.shape) < 3 else img.shape[2]
- results['img_norm_cfg'] = dict(
- mean=np.zeros(num_channels, dtype=np.float32),
- std=np.ones(num_channels, dtype=np.float32),
- to_rgb=False)
- return results
-
- def __repr__(self):
- repr_str = self.__class__.__name__
- repr_str += f'(to_float32={self.to_float32},'
- repr_str += f"color_type='{self.color_type}',"
- repr_str += f"imdecode_backend='{self.imdecode_backend}')"
- return repr_str
-
-
-@PIPELINES.register_module()
-class LoadAnnotations(object):
- """Load annotations for semantic segmentation.
-
- Args:
- reduce_zero_label (bool): Whether reduce all label value by 1.
- Usually used for datasets where 0 is background label.
- Default: False.
- file_client_args (dict): Arguments to instantiate a FileClient.
- See :class:`mmcv.fileio.FileClient` for details.
- Defaults to ``dict(backend='disk')``.
- imdecode_backend (str): Backend for :func:`mmcv.imdecode`. Default:
- 'pillow'
- """
-
- def __init__(self,
- reduce_zero_label=False,
- file_client_args=dict(backend='disk'),
- imdecode_backend='pillow'):
- self.reduce_zero_label = reduce_zero_label
- self.file_client_args = file_client_args.copy()
- self.file_client = None
- self.imdecode_backend = imdecode_backend
-
- def __call__(self, results):
- """Call function to load multiple types annotations.
-
- Args:
- results (dict): Result dict from :obj:`mmseg.CustomDataset`.
-
- Returns:
- dict: The dict contains loaded semantic segmentation annotations.
- """
-
- if self.file_client is None:
- self.file_client = mmcv.FileClient(**self.file_client_args)
-
- if results.get('seg_prefix', None) is not None:
- filename = osp.join(results['seg_prefix'],
- results['ann_info']['seg_map'])
- else:
- filename = results['ann_info']['seg_map']
- img_bytes = self.file_client.get(filename)
- gt_semantic_seg = mmcv.imfrombytes(
- img_bytes, flag='unchanged',
- backend=self.imdecode_backend).squeeze().astype(np.uint8)
- # modify if custom classes
- if results.get('label_map', None) is not None:
- for old_id, new_id in results['label_map'].items():
- gt_semantic_seg[gt_semantic_seg == old_id] = new_id
- # reduce zero_label
- if self.reduce_zero_label:
- # avoid using underflow conversion
- gt_semantic_seg[gt_semantic_seg == 0] = 255
- gt_semantic_seg = gt_semantic_seg - 1
- gt_semantic_seg[gt_semantic_seg == 254] = 255
- results['gt_semantic_seg'] = gt_semantic_seg
- results['seg_fields'].append('gt_semantic_seg')
- return results
-
- def __repr__(self):
- repr_str = self.__class__.__name__
- repr_str += f'(reduce_zero_label={self.reduce_zero_label},'
- repr_str += f"imdecode_backend='{self.imdecode_backend}')"
- return repr_str
diff --git a/spaces/davidpiscasio/unpaired-img2img/util/get_data.py b/spaces/davidpiscasio/unpaired-img2img/util/get_data.py
deleted file mode 100644
index 97edc3ce3c3ab6d6080dca34e73a5fb77bb715fb..0000000000000000000000000000000000000000
--- a/spaces/davidpiscasio/unpaired-img2img/util/get_data.py
+++ /dev/null
@@ -1,110 +0,0 @@
-from __future__ import print_function
-import os
-import tarfile
-import requests
-from warnings import warn
-from zipfile import ZipFile
-from bs4 import BeautifulSoup
-from os.path import abspath, isdir, join, basename
-
-
-class GetData(object):
- """A Python script for downloading CycleGAN or pix2pix datasets.
-
- Parameters:
- technique (str) -- One of: 'cyclegan' or 'pix2pix'.
- verbose (bool) -- If True, print additional information.
-
- Examples:
- >>> from util.get_data import GetData
- >>> gd = GetData(technique='cyclegan')
- >>> new_data_path = gd.get(save_path='./datasets') # options will be displayed.
-
- Alternatively, You can use bash scripts: 'scripts/download_pix2pix_model.sh'
- and 'scripts/download_cyclegan_model.sh'.
- """
-
- def __init__(self, technique='cyclegan', verbose=True):
- url_dict = {
- 'pix2pix': 'http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/',
- 'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets'
- }
- self.url = url_dict.get(technique.lower())
- self._verbose = verbose
-
- def _print(self, text):
- if self._verbose:
- print(text)
-
- @staticmethod
- def _get_options(r):
- soup = BeautifulSoup(r.text, 'lxml')
- options = [h.text for h in soup.find_all('a', href=True)
- if h.text.endswith(('.zip', 'tar.gz'))]
- return options
-
- def _present_options(self):
- r = requests.get(self.url)
- options = self._get_options(r)
- print('Options:\n')
- for i, o in enumerate(options):
- print("{0}: {1}".format(i, o))
- choice = input("\nPlease enter the number of the "
- "dataset above you wish to download:")
- return options[int(choice)]
-
- def _download_data(self, dataset_url, save_path):
- if not isdir(save_path):
- os.makedirs(save_path)
-
- base = basename(dataset_url)
- temp_save_path = join(save_path, base)
-
- with open(temp_save_path, "wb") as f:
- r = requests.get(dataset_url)
- f.write(r.content)
-
- if base.endswith('.tar.gz'):
- obj = tarfile.open(temp_save_path)
- elif base.endswith('.zip'):
- obj = ZipFile(temp_save_path, 'r')
- else:
- raise ValueError("Unknown File Type: {0}.".format(base))
-
- self._print("Unpacking Data...")
- obj.extractall(save_path)
- obj.close()
- os.remove(temp_save_path)
-
- def get(self, save_path, dataset=None):
- """
-
- Download a dataset.
-
- Parameters:
- save_path (str) -- A directory to save the data to.
- dataset (str) -- (optional). A specific dataset to download.
- Note: this must include the file extension.
- If None, options will be presented for you
- to choose from.
-
- Returns:
- save_path_full (str) -- the absolute path to the downloaded data.
-
- """
- if dataset is None:
- selected_dataset = self._present_options()
- else:
- selected_dataset = dataset
-
- save_path_full = join(save_path, selected_dataset.split('.')[0])
-
- if isdir(save_path_full):
- warn("\n'{0}' already exists. Voiding Download.".format(
- save_path_full))
- else:
- self._print('Downloading Data...')
- url = "{0}/{1}".format(self.url, selected_dataset)
- self._download_data(url, save_path=save_path)
-
- return abspath(save_path_full)
diff --git a/spaces/dawood/audioldm-text-to-audio-generation/audioldm/clap/open_clip/loss.py b/spaces/dawood/audioldm-text-to-audio-generation/audioldm/clap/open_clip/loss.py
deleted file mode 100644
index cc66298a14997da4aa2efc71e37c0a6bcda53fd1..0000000000000000000000000000000000000000
--- a/spaces/dawood/audioldm-text-to-audio-generation/audioldm/clap/open_clip/loss.py
+++ /dev/null
@@ -1,398 +0,0 @@
-from multiprocessing.sharedctypes import Value
-import torch
-import torch.distributed.nn
-from torch import distributed as dist, nn as nn
-from torch.nn import functional as F
-import numpy as np
-from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score
-
-try:
- import horovod.torch as hvd
-except ImportError:
- hvd = None
-
-
-def gather_features(
- audio_features,
- text_features,
- audio_features_mlp=None,
- text_features_mlp=None,
- local_loss=False,
- gather_with_grad=False,
- rank=0,
- world_size=1,
- use_horovod=False,
- mlp_loss=False,
-):
- if use_horovod:
- assert hvd is not None, "Please install horovod"
- if gather_with_grad:
- all_audio_features = hvd.allgather(audio_features)
- all_text_features = hvd.allgather(text_features)
- if mlp_loss:
- all_audio_features_mlp = hvd.allgather(audio_features_mlp)
- all_text_features_mlp = hvd.allgather(text_features_mlp)
- else:
- with torch.no_grad():
- all_audio_features = hvd.allgather(audio_features)
- all_text_features = hvd.allgather(text_features)
- if mlp_loss:
- all_audio_features_mlp = hvd.allgather(audio_features_mlp)
- all_text_features_mlp = hvd.allgather(text_features_mlp)
- if not local_loss:
- # ensure grads for local rank when all_* features don't have a gradient
- gathered_audio_features = list(
- all_audio_features.chunk(world_size, dim=0)
- )
- gathered_text_features = list(
- all_text_features.chunk(world_size, dim=0)
- )
- gathered_audio_features[rank] = audio_features
- gathered_text_features[rank] = text_features
- all_audio_features = torch.cat(gathered_audio_features, dim=0)
- all_text_features = torch.cat(gathered_text_features, dim=0)
- if mlp_loss:
- gathered_audio_features_mlp = list(
- all_audio_features_mlp.chunk(world_size, dim=0)
- )
- gathered_text_features_mlp = list(
- all_text_features_mlp.chunk(world_size, dim=0)
- )
- gathered_audio_features_mlp[rank] = audio_features_mlp
- gathered_text_features_mlp[rank] = text_features_mlp
- all_audio_features_mlp = torch.cat(
- gathered_audio_features_mlp, dim=0
- )
- all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0)
- else:
- # We gather tensors from all gpus
- if gather_with_grad:
- all_audio_features = torch.cat(
- torch.distributed.nn.all_gather(audio_features), dim=0
- )
- all_text_features = torch.cat(
- torch.distributed.nn.all_gather(text_features), dim=0
- )
- if mlp_loss:
- all_audio_features_mlp = torch.cat(
- torch.distributed.nn.all_gather(audio_features_mlp), dim=0
- )
- all_text_features_mlp = torch.cat(
- torch.distributed.nn.all_gather(text_features_mlp), dim=0
- )
- else:
- gathered_audio_features = [
- torch.zeros_like(audio_features) for _ in range(world_size)
- ]
- gathered_text_features = [
- torch.zeros_like(text_features) for _ in range(world_size)
- ]
- dist.all_gather(gathered_audio_features, audio_features)
- dist.all_gather(gathered_text_features, text_features)
- if mlp_loss:
- gathered_audio_features_mlp = [
- torch.zeros_like(audio_features_mlp) for _ in range(world_size)
- ]
- gathered_text_features_mlp = [
- torch.zeros_like(text_features_mlp) for _ in range(world_size)
- ]
- dist.all_gather(gathered_audio_features_mlp, audio_features_mlp)
- dist.all_gather(gathered_text_features_mlp, text_features_mlp)
- if not local_loss:
- # ensure grads for local rank when all_* features don't have a gradient
- gathered_audio_features[rank] = audio_features
- gathered_text_features[rank] = text_features
- if mlp_loss:
- gathered_audio_features_mlp[rank] = audio_features_mlp
- gathered_text_features_mlp[rank] = text_features_mlp
-
- all_audio_features = torch.cat(gathered_audio_features, dim=0)
- all_text_features = torch.cat(gathered_text_features, dim=0)
- if mlp_loss:
- all_audio_features_mlp = torch.cat(gathered_audio_features_mlp, dim=0)
- all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0)
- if mlp_loss:
- return (
- all_audio_features,
- all_text_features,
- all_audio_features_mlp,
- all_text_features_mlp,
- )
- else:
- return all_audio_features, all_text_features
-
-
-class ClipLoss(nn.Module):
- def __init__(
- self,
- local_loss=False,
- gather_with_grad=False,
- cache_labels=False,
- rank=0,
- world_size=1,
- use_horovod=False,
- mlp_loss=False,
- weight_loss_kappa=0,
- ):
- super().__init__()
- self.local_loss = local_loss
- self.gather_with_grad = gather_with_grad
- self.cache_labels = cache_labels
- self.rank = rank
- self.world_size = world_size
- self.use_horovod = use_horovod
- self.mlp_loss = mlp_loss
- self.weighted_loss = bool(weight_loss_kappa != 0)
- self.weight_loss_kappa = weight_loss_kappa
- # cache state
- self.prev_num_logits = 0
- self.labels = {}
-
- def forward(
- self,
- audio_features,
- text_features,
- logit_scale_a,
- logit_scale_t=None,
- audio_features_mlp=None,
- text_features_mlp=None,
- ):
- device = audio_features.device
- if self.mlp_loss:
- if self.world_size > 1:
- (
- all_audio_features,
- all_text_features,
- all_audio_features_mlp,
- all_text_features_mlp,
- ) = gather_features(
- audio_features=audio_features,
- text_features=text_features,
- audio_features_mlp=audio_features_mlp,
- text_features_mlp=text_features_mlp,
- local_loss=self.local_loss,
- gather_with_grad=self.gather_with_grad,
- rank=self.rank,
- world_size=self.world_size,
- use_horovod=self.use_horovod,
- mlp_loss=self.mlp_loss,
- )
- if self.local_loss:
- a_logits_per_audio = (
- logit_scale_a * audio_features @ all_text_features_mlp.T
- )
- a_logits_per_text = (
- logit_scale_a * text_features_mlp @ all_audio_features.T
- )
- t_logits_per_audio = (
- logit_scale_t * audio_features_mlp @ all_text_features.T
- )
- t_logits_per_text = (
- logit_scale_t * text_features @ all_audio_features_mlp.T
- )
- else:
- a_logits_per_audio = (
- logit_scale_a * all_audio_features @ all_text_features_mlp.T
- )
- a_logits_per_text = a_logits_per_audio.T
- t_logits_per_audio = (
- logit_scale_t * all_audio_features_mlp @ all_text_features.T
- )
- t_logits_per_text = t_logits_per_audio.T
- else:
- a_logits_per_audio = (
- logit_scale_a * audio_features @ text_features_mlp.T
- )
- a_logits_per_text = logit_scale_a * text_features_mlp @ audio_features.T
- t_logits_per_audio = (
- logit_scale_t * audio_features_mlp @ text_features.T
- )
- t_logits_per_text = logit_scale_t * text_features @ audio_features_mlp.T
-
- # calculated ground-truth and cache if enabled
- num_logits = a_logits_per_audio.shape[0]
- if self.prev_num_logits != num_logits or device not in self.labels:
- labels = torch.arange(num_logits, device=device, dtype=torch.long)
- if self.world_size > 1 and self.local_loss:
- labels = labels + num_logits * self.rank
- if self.cache_labels:
- self.labels[device] = labels
- self.prev_num_logits = num_logits
- else:
- labels = self.labels[device]
-
- if not self.weighted_loss:
- total_loss = (
- F.cross_entropy(a_logits_per_audio, labels)
- + F.cross_entropy(a_logits_per_text, labels)
- + F.cross_entropy(t_logits_per_audio, labels)
- + F.cross_entropy(t_logits_per_text, labels)
- ) / 4
- else:
- audio_weight = (audio_features @ audio_features.T).detach()
- audio_weight = (
- torch.exp(
- torch.sum(audio_weight, axis=1)
- / (self.weight_loss_kappa * len(audio_weight))
- )
- ).detach()
- text_weight = (text_features @ text_features.T).detach()
- text_weight = (
- torch.exp(
- torch.sum(text_weight, axis=1)
- / (self.weight_loss_kappa * len(text_features))
- )
- ).detach()
- total_loss = (
- F.cross_entropy(a_logits_per_audio, labels, weight=audio_weight)
- + F.cross_entropy(a_logits_per_text, labels, weight=audio_weight)
- + F.cross_entropy(t_logits_per_audio, labels, weight=text_weight)
- + F.cross_entropy(t_logits_per_text, labels, weight=text_weight)
- ) / 4
- else:
- if self.world_size > 1:
- all_audio_features, all_text_features = gather_features(
- audio_features=audio_features,
- text_features=text_features,
- local_loss=self.local_loss,
- gather_with_grad=self.gather_with_grad,
- rank=self.rank,
- world_size=self.world_size,
- use_horovod=self.use_horovod,
- mlp_loss=self.mlp_loss,
- )
-
- if self.local_loss:
- logits_per_audio = (
- logit_scale_a * audio_features @ all_text_features.T
- )
- logits_per_text = (
- logit_scale_a * text_features @ all_audio_features.T
- )
- else:
- logits_per_audio = (
- logit_scale_a * all_audio_features @ all_text_features.T
- )
- logits_per_text = logits_per_audio.T
- else:
- logits_per_audio = logit_scale_a * audio_features @ text_features.T
- logits_per_text = logit_scale_a * text_features @ audio_features.T
-
- # calculated ground-truth and cache if enabled
- num_logits = logits_per_audio.shape[0]
- if self.prev_num_logits != num_logits or device not in self.labels:
- labels = torch.arange(num_logits, device=device, dtype=torch.long)
- if self.world_size > 1 and self.local_loss:
- labels = labels + num_logits * self.rank
- if self.cache_labels:
- self.labels[device] = labels
- self.prev_num_logits = num_logits
- else:
- labels = self.labels[device]
- if not self.weighted_loss:
- total_loss = (
- F.cross_entropy(logits_per_audio, labels)
- + F.cross_entropy(logits_per_text, labels)
- ) / 2
- else:
- audio_weight = (all_audio_features @ all_audio_features.T).detach()
- audio_weight = (
- torch.exp(
- torch.sum(audio_weight, axis=1)
- / (self.weight_loss_kappa * len(all_audio_features))
- )
- ).detach()
- text_weight = (all_text_features @ all_text_features.T).detach()
- text_weight = (
- torch.exp(
- torch.sum(text_weight, axis=1)
- / (self.weight_loss_kappa * len(all_text_features))
- )
- ).detach()
- total_loss = (
- F.cross_entropy(logits_per_audio, labels, weight=text_weight)
- + F.cross_entropy(logits_per_text, labels, weight=audio_weight)
- ) / 2
- return total_loss
-
-
-def lp_gather_features(pred, target, world_size=1, use_horovod=False):
- if use_horovod:
- assert hvd is not None, "Please install horovod"
- with torch.no_grad():
- all_preds = hvd.allgather(pred)
- all_targets = hvd.allgath(target)
- else:
- gathered_preds = [torch.zeros_like(pred) for _ in range(world_size)]
- gathered_targets = [torch.zeros_like(target) for _ in range(world_size)]
-
- dist.all_gather(gathered_preds, pred)
- dist.all_gather(gathered_targets, target)
- all_preds = torch.cat(gathered_preds, dim=0)
- all_targets = torch.cat(gathered_targets, dim=0)
-
- return all_preds, all_targets
-
-
-def get_map(pred, target):
- pred = torch.sigmoid(pred).numpy()
- target = target.numpy()
- return np.mean(average_precision_score(target, pred, average=None))
-
-
-def get_acc(pred, target):
- pred = torch.argmax(pred, 1).numpy()
- target = torch.argmax(target, 1).numpy()
- return accuracy_score(target, pred)
-
-
-def get_mauc(pred, target):
- pred = torch.sigmoid(pred).numpy()
- target = target.numpy()
- return np.mean(roc_auc_score(target, pred, average=None))
-
-
-class LPMetrics(object):
- def __init__(self, metric_names=["map", "acc", "mauc"]):
- self.metrics = []
- for name in metric_names:
- self.metrics.append(self.get_metric(name))
- self.metric_names = metric_names
-
- def get_metric(self, name):
- if name == "map":
- return get_map
- elif name == "acc":
- return get_acc
- elif name == "mauc":
- return get_mauc
- else:
- raise ValueError(f"the metric should be at least one of [map, acc, mauc]")
-
- def evaluate_mertics(self, pred, target):
- metric_dict = {}
- for i in range(len(self.metric_names)):
- metric_dict[self.metric_names[i]] = self.metrics[i](pred, target)
- return metric_dict
-
-
-def calc_celoss(pred, target):
- target = torch.argmax(target, 1).long()
- return nn.CrossEntropyLoss()(pred, target)
-
-
-class LPLoss(nn.Module):
- def __init__(self, loss_name):
- super().__init__()
- if loss_name == "bce":
- self.loss_func = nn.BCEWithLogitsLoss()
- elif loss_name == "ce":
- self.loss_func = calc_celoss
- elif loss_name == "mse":
- self.loss_func = nn.MSELoss()
- else:
- raise ValueError(f"the loss func should be at least one of [bce, ce, mse]")
-
- def forward(self, pred, target):
- loss = self.loss_func(pred, target)
- return loss
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/ImagePalette.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/ImagePalette.py
deleted file mode 100644
index f0c094708634ecdac25eab95d054f7a63f14eecf..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/ImagePalette.py
+++ /dev/null
@@ -1,266 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# image palette object
-#
-# History:
-# 1996-03-11 fl Rewritten.
-# 1997-01-03 fl Up and running.
-# 1997-08-23 fl Added load hack
-# 2001-04-16 fl Fixed randint shadow bug in random()
-#
-# Copyright (c) 1997-2001 by Secret Labs AB
-# Copyright (c) 1996-1997 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import array
-
-from . import GimpGradientFile, GimpPaletteFile, ImageColor, PaletteFile
-
-
-class ImagePalette:
- """
- Color palette for palette mapped images
-
- :param mode: The mode to use for the palette. See:
- :ref:`concept-modes`. Defaults to "RGB"
- :param palette: An optional palette. If given, it must be a bytearray,
- an array or a list of ints between 0-255. The list must consist of
- all channels for one color followed by the next color (e.g. RGBRGBRGB).
- Defaults to an empty palette.
- """
-
- def __init__(self, mode="RGB", palette=None):
- self.mode = mode
- self.rawmode = None # if set, palette contains raw data
- self.palette = palette or bytearray()
- self.dirty = None
-
- @property
- def palette(self):
- return self._palette
-
- @palette.setter
- def palette(self, palette):
- self._colors = None
- self._palette = palette
-
- @property
- def colors(self):
- if self._colors is None:
- mode_len = len(self.mode)
- self._colors = {}
- for i in range(0, len(self.palette), mode_len):
- color = tuple(self.palette[i : i + mode_len])
- if color in self._colors:
- continue
- self._colors[color] = i // mode_len
- return self._colors
-
- @colors.setter
- def colors(self, colors):
- self._colors = colors
-
- def copy(self):
- new = ImagePalette()
-
- new.mode = self.mode
- new.rawmode = self.rawmode
- if self.palette is not None:
- new.palette = self.palette[:]
- new.dirty = self.dirty
-
- return new
-
- def getdata(self):
- """
- Get palette contents in format suitable for the low-level
- ``im.putpalette`` primitive.
-
- .. warning:: This method is experimental.
- """
- if self.rawmode:
- return self.rawmode, self.palette
- return self.mode, self.tobytes()
-
- def tobytes(self):
- """Convert palette to bytes.
-
- .. warning:: This method is experimental.
- """
- if self.rawmode:
- msg = "palette contains raw palette data"
- raise ValueError(msg)
- if isinstance(self.palette, bytes):
- return self.palette
- arr = array.array("B", self.palette)
- return arr.tobytes()
-
- # Declare tostring as an alias for tobytes
- tostring = tobytes
-
- def getcolor(self, color, image=None):
- """Given an rgb tuple, allocate palette entry.
-
- .. warning:: This method is experimental.
- """
- if self.rawmode:
- msg = "palette contains raw palette data"
- raise ValueError(msg)
- if isinstance(color, tuple):
- if self.mode == "RGB":
- if len(color) == 4:
- if color[3] != 255:
- msg = "cannot add non-opaque RGBA color to RGB palette"
- raise ValueError(msg)
- color = color[:3]
- elif self.mode == "RGBA":
- if len(color) == 3:
- color += (255,)
- try:
- return self.colors[color]
- except KeyError as e:
- # allocate new color slot
- if not isinstance(self.palette, bytearray):
- self._palette = bytearray(self.palette)
- index = len(self.palette) // 3
- special_colors = ()
- if image:
- special_colors = (
- image.info.get("background"),
- image.info.get("transparency"),
- )
- while index in special_colors:
- index += 1
- if index >= 256:
- if image:
- # Search for an unused index
- for i, count in reversed(list(enumerate(image.histogram()))):
- if count == 0 and i not in special_colors:
- index = i
- break
- if index >= 256:
- msg = "cannot allocate more than 256 colors"
- raise ValueError(msg) from e
- self.colors[color] = index
- if index * 3 < len(self.palette):
- self._palette = (
- self.palette[: index * 3]
- + bytes(color)
- + self.palette[index * 3 + 3 :]
- )
- else:
- self._palette += bytes(color)
- self.dirty = 1
- return index
- else:
- msg = f"unknown color specifier: {repr(color)}"
- raise ValueError(msg)
-
- def save(self, fp):
- """Save palette to text file.
-
- .. warning:: This method is experimental.
- """
- if self.rawmode:
- msg = "palette contains raw palette data"
- raise ValueError(msg)
- if isinstance(fp, str):
- fp = open(fp, "w")
- fp.write("# Palette\n")
- fp.write(f"# Mode: {self.mode}\n")
- for i in range(256):
- fp.write(f"{i}")
- for j in range(i * len(self.mode), (i + 1) * len(self.mode)):
- try:
- fp.write(f" {self.palette[j]}")
- except IndexError:
- fp.write(" 0")
- fp.write("\n")
- fp.close()
-
-
-# --------------------------------------------------------------------
-# Internal
-
-
-def raw(rawmode, data):
- palette = ImagePalette()
- palette.rawmode = rawmode
- palette.palette = data
- palette.dirty = 1
- return palette
-
-
-# --------------------------------------------------------------------
-# Factories
-
-
-def make_linear_lut(black, white):
- lut = []
- if black == 0:
- for i in range(256):
- lut.append(white * i // 255)
- else:
- raise NotImplementedError # FIXME
- return lut
-
-
-def make_gamma_lut(exp):
- lut = []
- for i in range(256):
- lut.append(int(((i / 255.0) ** exp) * 255.0 + 0.5))
- return lut
-
-
-def negative(mode="RGB"):
- palette = list(range(256 * len(mode)))
- palette.reverse()
- return ImagePalette(mode, [i // len(mode) for i in palette])
-
-
-def random(mode="RGB"):
- from random import randint
-
- palette = []
- for i in range(256 * len(mode)):
- palette.append(randint(0, 255))
- return ImagePalette(mode, palette)
-
-
-def sepia(white="#fff0c0"):
- bands = [make_linear_lut(0, band) for band in ImageColor.getrgb(white)]
- return ImagePalette("RGB", [bands[i % 3][i // 3] for i in range(256 * 3)])
-
-
-def wedge(mode="RGB"):
- palette = list(range(256 * len(mode)))
- return ImagePalette(mode, [i // len(mode) for i in palette])
-
-
-def load(filename):
- # FIXME: supports GIMP gradients only
-
- with open(filename, "rb") as fp:
- for paletteHandler in [
- GimpPaletteFile.GimpPaletteFile,
- GimpGradientFile.GimpGradientFile,
- PaletteFile.PaletteFile,
- ]:
- try:
- fp.seek(0)
- lut = paletteHandler(fp).getpalette()
- if lut:
- break
- except (SyntaxError, ValueError):
- # import traceback
- # traceback.print_exc()
- pass
- else:
- msg = "cannot load palette"
- raise OSError(msg)
-
- return lut # data, rawmode
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_a_v_a_r.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_a_v_a_r.py
deleted file mode 100644
index 39039cf73a5346db144f39bd8c046a76bd52af31..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_a_v_a_r.py
+++ /dev/null
@@ -1,138 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.fixedTools import (
- fixedToFloat as fi2fl,
- floatToFixed as fl2fi,
- floatToFixedToStr as fl2str,
- strToFixedToFloat as str2fl,
-)
-from fontTools.misc.textTools import bytesjoin, safeEval
-from fontTools.ttLib import TTLibError
-from . import DefaultTable
-from . import otTables
-import struct
-import logging
-
-
-log = logging.getLogger(__name__)
-
-from .otBase import BaseTTXConverter
-
-
-class table__a_v_a_r(BaseTTXConverter):
- """Axis Variations Table
-
- This class represents the ``avar`` table of a variable font. The object has one
- substantive attribute, ``segments``, which maps axis tags to a segments dictionary::
-
- >>> font["avar"].segments # doctest: +SKIP
- {'wght': {-1.0: -1.0,
- 0.0: 0.0,
- 0.125: 0.11444091796875,
- 0.25: 0.23492431640625,
- 0.5: 0.35540771484375,
- 0.625: 0.5,
- 0.75: 0.6566162109375,
- 0.875: 0.81927490234375,
- 1.0: 1.0},
- 'ital': {-1.0: -1.0, 0.0: 0.0, 1.0: 1.0}}
-
- Notice that the segments dictionary is made up of normalized values. A valid
- ``avar`` segment mapping must contain the entries ``-1.0: -1.0, 0.0: 0.0, 1.0: 1.0``.
- fontTools does not enforce this, so it is your responsibility to ensure that
- mappings are valid.
- """
-
- dependencies = ["fvar"]
-
- def __init__(self, tag=None):
- super().__init__(tag)
- self.segments = {}
-
- def compile(self, ttFont):
- axisTags = [axis.axisTag for axis in ttFont["fvar"].axes]
- if not hasattr(self, "table"):
- self.table = otTables.avar()
- if not hasattr(self.table, "Reserved"):
- self.table.Reserved = 0
- self.table.Version = (getattr(self, "majorVersion", 1) << 16) | getattr(
- self, "minorVersion", 0
- )
- self.table.AxisCount = len(axisTags)
- self.table.AxisSegmentMap = []
- for axis in axisTags:
- mappings = self.segments[axis]
- segmentMap = otTables.AxisSegmentMap()
- segmentMap.PositionMapCount = len(mappings)
- segmentMap.AxisValueMap = []
- for key, value in sorted(mappings.items()):
- valueMap = otTables.AxisValueMap()
- valueMap.FromCoordinate = key
- valueMap.ToCoordinate = value
- segmentMap.AxisValueMap.append(valueMap)
- self.table.AxisSegmentMap.append(segmentMap)
- return super().compile(ttFont)
-
- def decompile(self, data, ttFont):
- super().decompile(data, ttFont)
- assert self.table.Version >= 0x00010000
- self.majorVersion = self.table.Version >> 16
- self.minorVersion = self.table.Version & 0xFFFF
- axisTags = [axis.axisTag for axis in ttFont["fvar"].axes]
- for axis in axisTags:
- self.segments[axis] = {}
- for axis, segmentMap in zip(axisTags, self.table.AxisSegmentMap):
- segments = self.segments[axis] = {}
- for segment in segmentMap.AxisValueMap:
- segments[segment.FromCoordinate] = segment.ToCoordinate
-
- def toXML(self, writer, ttFont):
- writer.simpletag(
- "version",
- major=getattr(self, "majorVersion", 1),
- minor=getattr(self, "minorVersion", 0),
- )
- writer.newline()
- axisTags = [axis.axisTag for axis in ttFont["fvar"].axes]
- for axis in axisTags:
- writer.begintag("segment", axis=axis)
- writer.newline()
- for key, value in sorted(self.segments[axis].items()):
- key = fl2str(key, 14)
- value = fl2str(value, 14)
- writer.simpletag("mapping", **{"from": key, "to": value})
- writer.newline()
- writer.endtag("segment")
- writer.newline()
- if getattr(self, "majorVersion", 1) >= 2:
- if self.table.VarIdxMap:
- self.table.VarIdxMap.toXML(writer, ttFont, name="VarIdxMap")
- if self.table.VarStore:
- self.table.VarStore.toXML(writer, ttFont)
-
- def fromXML(self, name, attrs, content, ttFont):
- if not hasattr(self, "table"):
- self.table = otTables.avar()
- if not hasattr(self.table, "Reserved"):
- self.table.Reserved = 0
- if name == "version":
- self.majorVersion = safeEval(attrs["major"])
- self.minorVersion = safeEval(attrs["minor"])
- self.table.Version = (getattr(self, "majorVersion", 1) << 16) | getattr(
- self, "minorVersion", 0
- )
- elif name == "segment":
- axis = attrs["axis"]
- segment = self.segments[axis] = {}
- for element in content:
- if isinstance(element, tuple):
- elementName, elementAttrs, _ = element
- if elementName == "mapping":
- fromValue = str2fl(elementAttrs["from"], 14)
- toValue = str2fl(elementAttrs["to"], 14)
- if fromValue in segment:
- log.warning(
- "duplicate entry for %s in axis '%s'", fromValue, axis
- )
- segment[fromValue] = toValue
- else:
- super().fromXML(name, attrs, content, ttFont)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/ModifyUpload-77b0d4b2.css b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/ModifyUpload-77b0d4b2.css
deleted file mode 100644
index c78d71f8b6eaf75f8134375ed017f1c03b6edf1a..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/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/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/share.html b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/share.html
deleted file mode 100644
index b2323fcfda2449bd7fa4fd5a7ff4a82872048a16..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/share.html
+++ /dev/null
@@ -1,84 +0,0 @@
-<!doctype html>
-<html
- lang="en"
- style="
- margin: 0;
- padding: 0;
- min-height: 100%;
- display: flex;
- flex-direction: column;
- "
->
- <head>
- <meta charset="utf-8" />
- <meta
- name="viewport"
- content="width=device-width, initial-scale=1, shrink-to-fit=no, maximum-scale=1"
- />
-
-
- <meta property="og:url" content="https://gradio.app/" />
- <meta property="og:type" content="website" />
- <meta property="og:image" content="{{ config['thumbnail'] or '' }}" />
- <meta property="og:title" content="{{ config['title'] or '' }}" />
- <meta
- property="og:description"
- content="{{ config['simple_description'] or '' }}"
- />
- <meta name="twitter:card" content="summary_large_image" />
- <meta name="twitter:creator" content="@teamGradio" />
- <meta name="twitter:title" content="{{ config['title'] or '' }}" />
- <meta
- name="twitter:description"
- content="{{ config['simple_description'] or '' }}"
- />
- <meta name="twitter:image" content="{{ config['thumbnail'] or '' }}" />
-
- <script>
- window.__gradio_mode__ = "app";
- </script>
-
- <script>window.gradio_config = {{ config | toorjson }};</script>
-
- <link rel="preconnect" href="https://fonts.googleapis.com" />
- <link
- rel="preconnect"
- href="https://fonts.gstatic.com"
- crossorigin="anonymous"
- />
- <script
- src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.6/iframeResizer.contentWindow.min.js"
- async
- ></script>
- <script type="module" crossorigin src="https://gradio.s3-us-west-2.amazonaws.com/3.40.1/assets/index-9e76ffee.js"></script>
-
- </head>
-
- <body
- style="
- width: 100%;
- margin: 0;
- padding: 0;
- display: flex;
- flex-direction: column;
- flex-grow: 1;
- "
- >
- <gradio-app
- control_page_title="true"
- embed="false"
- eager="true"
- style="display: flex; flex-direction: column; flex-grow: 1"
- >
- </gradio-app>
- <script>
- const ce = document.getElementsByTagName("gradio-app");
- if (ce[0]) {
- ce[0].addEventListener("domchange", () => {
- document.body.style.padding = "0";
- });
- document.body.style.padding = "0";
- }
- </script>
- </body>
-</html>
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jinja2/constants.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jinja2/constants.py
deleted file mode 100644
index 41a1c23b0a7fe134b1f662545876eb65b31b071e..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jinja2/constants.py
+++ /dev/null
@@ -1,20 +0,0 @@
-#: list of lorem ipsum words used by the lipsum() helper function
-LOREM_IPSUM_WORDS = """\
-a ac accumsan ad adipiscing aenean aliquam aliquet amet ante aptent arcu at
-auctor augue bibendum blandit class commodo condimentum congue consectetuer
-consequat conubia convallis cras cubilia cum curabitur curae cursus dapibus
-diam dictum dictumst dignissim dis dolor donec dui duis egestas eget eleifend
-elementum elit enim erat eros est et etiam eu euismod facilisi facilisis fames
-faucibus felis fermentum feugiat fringilla fusce gravida habitant habitasse hac
-hendrerit hymenaeos iaculis id imperdiet in inceptos integer interdum ipsum
-justo lacinia lacus laoreet lectus leo libero ligula litora lobortis lorem
-luctus maecenas magna magnis malesuada massa mattis mauris metus mi molestie
-mollis montes morbi mus nam nascetur natoque nec neque netus nibh nisi nisl non
-nonummy nostra nulla nullam nunc odio orci ornare parturient pede pellentesque
-penatibus per pharetra phasellus placerat platea porta porttitor posuere
-potenti praesent pretium primis proin pulvinar purus quam quis quisque rhoncus
-ridiculus risus rutrum sagittis sapien scelerisque sed sem semper senectus sit
-sociis sociosqu sodales sollicitudin suscipit suspendisse taciti tellus tempor
-tempus tincidunt torquent tortor tristique turpis ullamcorper ultrices
-ultricies urna ut varius vehicula vel velit venenatis vestibulum vitae vivamus
-viverra volutpat vulputate"""
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/tests/test_deprecations.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/tests/test_deprecations.py
deleted file mode 100644
index fdb3b7b0f1dfe68cbc82c04ff675a451148cfeee..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jsonschema/tests/test_deprecations.py
+++ /dev/null
@@ -1,415 +0,0 @@
-from contextlib import contextmanager
-from io import BytesIO
-from unittest import TestCase, mock
-import importlib.metadata
-import json
-import subprocess
-import sys
-import urllib.request
-
-import referencing.exceptions
-
-from jsonschema import FormatChecker, exceptions, protocols, validators
-
-
-class TestDeprecations(TestCase):
- def test_version(self):
- """
- As of v4.0.0, __version__ is deprecated in favor of importlib.metadata.
- """
-
- message = "Accessing jsonschema.__version__ is deprecated"
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema import __version__
-
- self.assertEqual(__version__, importlib.metadata.version("jsonschema"))
- self.assertEqual(w.filename, __file__)
-
- def test_validators_ErrorTree(self):
- """
- As of v4.0.0, importing ErrorTree from jsonschema.validators is
- deprecated in favor of doing so from jsonschema.exceptions.
- """
-
- message = "Importing ErrorTree from jsonschema.validators is "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema.validators import ErrorTree
-
- self.assertEqual(ErrorTree, exceptions.ErrorTree)
- self.assertEqual(w.filename, __file__)
-
- def test_import_ErrorTree(self):
- """
- As of v4.18.0, importing ErrorTree from the package root is
- deprecated in favor of doing so from jsonschema.exceptions.
- """
-
- message = "Importing ErrorTree directly from the jsonschema package "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema import ErrorTree
-
- self.assertEqual(ErrorTree, exceptions.ErrorTree)
- self.assertEqual(w.filename, __file__)
-
- def test_import_FormatError(self):
- """
- As of v4.18.0, importing FormatError from the package root is
- deprecated in favor of doing so from jsonschema.exceptions.
- """
-
- message = "Importing FormatError directly from the jsonschema package "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema import FormatError
-
- self.assertEqual(FormatError, exceptions.FormatError)
- self.assertEqual(w.filename, __file__)
-
- def test_import_Validator(self):
- """
- As of v4.19.0, importing Validator from the package root is
- deprecated in favor of doing so from jsonschema.protocols.
- """
-
- message = "Importing Validator directly from the jsonschema package "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema import Validator
-
- self.assertEqual(Validator, protocols.Validator)
- self.assertEqual(w.filename, __file__)
-
- def test_validators_validators(self):
- """
- As of v4.0.0, accessing jsonschema.validators.validators is
- deprecated.
- """
-
- message = "Accessing jsonschema.validators.validators is deprecated"
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- value = validators.validators
-
- self.assertEqual(value, validators._VALIDATORS)
- self.assertEqual(w.filename, __file__)
-
- def test_validators_meta_schemas(self):
- """
- As of v4.0.0, accessing jsonschema.validators.meta_schemas is
- deprecated.
- """
-
- message = "Accessing jsonschema.validators.meta_schemas is deprecated"
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- value = validators.meta_schemas
-
- self.assertEqual(value, validators._META_SCHEMAS)
- self.assertEqual(w.filename, __file__)
-
- def test_RefResolver_in_scope(self):
- """
- As of v4.0.0, RefResolver.in_scope is deprecated.
- """
-
- resolver = validators._RefResolver.from_schema({})
- message = "jsonschema.RefResolver.in_scope is deprecated "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- with resolver.in_scope("foo"):
- pass
-
- self.assertEqual(w.filename, __file__)
-
- def test_Validator_is_valid_two_arguments(self):
- """
- As of v4.0.0, calling is_valid with two arguments (to provide a
- different schema) is deprecated.
- """
-
- validator = validators.Draft7Validator({})
- message = "Passing a schema to Validator.is_valid is deprecated "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- result = validator.is_valid("foo", {"type": "number"})
-
- self.assertFalse(result)
- self.assertEqual(w.filename, __file__)
-
- def test_Validator_iter_errors_two_arguments(self):
- """
- As of v4.0.0, calling iter_errors with two arguments (to provide a
- different schema) is deprecated.
- """
-
- validator = validators.Draft7Validator({})
- message = "Passing a schema to Validator.iter_errors is deprecated "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- error, = validator.iter_errors("foo", {"type": "number"})
-
- self.assertEqual(error.validator, "type")
- self.assertEqual(w.filename, __file__)
-
- def test_Validator_resolver(self):
- """
- As of v4.18.0, accessing Validator.resolver is deprecated.
- """
-
- validator = validators.Draft7Validator({})
- message = "Accessing Draft7Validator.resolver is "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- self.assertIsInstance(validator.resolver, validators._RefResolver)
-
- self.assertEqual(w.filename, __file__)
-
- def test_RefResolver(self):
- """
- As of v4.18.0, RefResolver is fully deprecated.
- """
-
- message = "jsonschema.RefResolver is deprecated"
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema import RefResolver
- self.assertEqual(w.filename, __file__)
-
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema.validators import RefResolver # noqa: F401, F811
- self.assertEqual(w.filename, __file__)
-
- def test_RefResolutionError(self):
- """
- As of v4.18.0, RefResolutionError is deprecated in favor of directly
- catching errors from the referencing library.
- """
-
- message = "jsonschema.exceptions.RefResolutionError is deprecated"
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema import RefResolutionError
-
- self.assertEqual(RefResolutionError, exceptions._RefResolutionError)
- self.assertEqual(w.filename, __file__)
-
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema.exceptions import RefResolutionError
-
- self.assertEqual(RefResolutionError, exceptions._RefResolutionError)
- self.assertEqual(w.filename, __file__)
-
- def test_catching_Unresolvable_directly(self):
- """
- This behavior is the intended behavior (i.e. it's not deprecated), but
- given we do "tricksy" things in the iterim to wrap exceptions in a
- multiple inheritance subclass, we need to be extra sure it works and
- stays working.
- """
- validator = validators.Draft202012Validator({"$ref": "urn:nothing"})
-
- with self.assertRaises(referencing.exceptions.Unresolvable) as e:
- validator.validate(12)
-
- expected = referencing.exceptions.Unresolvable(ref="urn:nothing")
- self.assertEqual(
- (e.exception, str(e.exception)),
- (expected, "Unresolvable: urn:nothing")
- )
-
- def test_catching_Unresolvable_via_RefResolutionError(self):
- """
- Until RefResolutionError is removed, it is still possible to catch
- exceptions from reference resolution using it, even though they may
- have been raised by referencing.
- """
- with self.assertWarns(DeprecationWarning):
- from jsonschema import RefResolutionError
-
- validator = validators.Draft202012Validator({"$ref": "urn:nothing"})
-
- with self.assertRaises(referencing.exceptions.Unresolvable) as u:
- validator.validate(12)
-
- with self.assertRaises(RefResolutionError) as e:
- validator.validate(12)
-
- self.assertEqual(
- (e.exception, str(e.exception)),
- (u.exception, "Unresolvable: urn:nothing")
- )
-
- def test_WrappedReferencingError_hashability(self):
- """
- Ensure the wrapped referencing errors are hashable when possible.
- """
- with self.assertWarns(DeprecationWarning):
- from jsonschema import RefResolutionError
-
- validator = validators.Draft202012Validator({"$ref": "urn:nothing"})
-
- with self.assertRaises(referencing.exceptions.Unresolvable) as u:
- validator.validate(12)
-
- with self.assertRaises(RefResolutionError) as e:
- validator.validate(12)
-
- self.assertIn(e.exception, {u.exception})
- self.assertIn(u.exception, {e.exception})
-
- def test_Validator_subclassing(self):
- """
- As of v4.12.0, subclassing a validator class produces an explicit
- deprecation warning.
-
- This was never intended to be public API (and some comments over the
- years in issues said so, but obviously that's not a great way to make
- sure it's followed).
-
- A future version will explicitly raise an error.
- """
-
- message = "Subclassing validator classes is "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- class Subclass(validators.Draft202012Validator):
- pass
-
- self.assertEqual(w.filename, __file__)
-
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- class AnotherSubclass(validators.create(meta_schema={})):
- pass
-
- def test_FormatChecker_cls_checks(self):
- """
- As of v4.14.0, FormatChecker.cls_checks is deprecated without
- replacement.
- """
-
- self.addCleanup(FormatChecker.checkers.pop, "boom", None)
-
- message = "FormatChecker.cls_checks "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- FormatChecker.cls_checks("boom")
-
- self.assertEqual(w.filename, __file__)
-
- def test_draftN_format_checker(self):
- """
- As of v4.16.0, accessing jsonschema.draftn_format_checker is deprecated
- in favor of Validator.FORMAT_CHECKER.
- """
-
- message = "Accessing jsonschema.draft202012_format_checker is "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema import draft202012_format_checker
-
- self.assertIs(
- draft202012_format_checker,
- validators.Draft202012Validator.FORMAT_CHECKER,
- )
- self.assertEqual(w.filename, __file__)
-
- message = "Accessing jsonschema.draft201909_format_checker is "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema import draft201909_format_checker
-
- self.assertIs(
- draft201909_format_checker,
- validators.Draft201909Validator.FORMAT_CHECKER,
- )
- self.assertEqual(w.filename, __file__)
-
- message = "Accessing jsonschema.draft7_format_checker is "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema import draft7_format_checker
-
- self.assertIs(
- draft7_format_checker,
- validators.Draft7Validator.FORMAT_CHECKER,
- )
- self.assertEqual(w.filename, __file__)
-
- message = "Accessing jsonschema.draft6_format_checker is "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema import draft6_format_checker
-
- self.assertIs(
- draft6_format_checker,
- validators.Draft6Validator.FORMAT_CHECKER,
- )
- self.assertEqual(w.filename, __file__)
-
- message = "Accessing jsonschema.draft4_format_checker is "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema import draft4_format_checker
-
- self.assertIs(
- draft4_format_checker,
- validators.Draft4Validator.FORMAT_CHECKER,
- )
- self.assertEqual(w.filename, __file__)
-
- message = "Accessing jsonschema.draft3_format_checker is "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- from jsonschema import draft3_format_checker
-
- self.assertIs(
- draft3_format_checker,
- validators.Draft3Validator.FORMAT_CHECKER,
- )
- self.assertEqual(w.filename, __file__)
-
- with self.assertRaises(ImportError):
- from jsonschema import draft1234_format_checker # noqa
-
- def test_import_cli(self):
- """
- As of v4.17.0, importing jsonschema.cli is deprecated.
- """
-
- message = "The jsonschema CLI is deprecated and will be removed "
- with self.assertWarnsRegex(DeprecationWarning, message) as w:
- import jsonschema.cli
- importlib.reload(jsonschema.cli)
-
- self.assertEqual(w.filename, importlib.__file__)
-
- def test_cli(self):
- """
- As of v4.17.0, the jsonschema CLI is deprecated.
- """
-
- process = subprocess.run(
- [sys.executable, "-m", "jsonschema"],
- capture_output=True,
- )
- self.assertIn(b"The jsonschema CLI is deprecated ", process.stderr)
-
- def test_automatic_remote_retrieval(self):
- """
- Automatic retrieval of remote references is deprecated as of v4.18.0.
- """
- ref = "http://bar#/$defs/baz"
- schema = {"$defs": {"baz": {"type": "integer"}}}
-
- if "requests" in sys.modules: # pragma: no cover
- self.addCleanup(
- sys.modules.__setitem__, "requests", sys.modules["requests"],
- )
- sys.modules["requests"] = None
-
- @contextmanager
- def fake_urlopen(request):
- self.assertIsInstance(request, urllib.request.Request)
- self.assertEqual(request.full_url, "http://bar")
-
- # Ha ha urllib.request.Request "normalizes" header names and
- # Request.get_header does not also normalize them...
- (header, value), = request.header_items()
- self.assertEqual(header.lower(), "user-agent")
- self.assertEqual(
- value, "python-jsonschema (deprecated $ref resolution)",
- )
- yield BytesIO(json.dumps(schema).encode("utf8"))
-
- validator = validators.Draft202012Validator({"$ref": ref})
-
- message = "Automatically retrieving remote references "
- patch = mock.patch.object(urllib.request, "urlopen", new=fake_urlopen)
-
- with patch, self.assertWarnsRegex(DeprecationWarning, message):
- self.assertEqual(
- (validator.is_valid({}), validator.is_valid(37)),
- (False, True),
- )
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/common/html_blocks.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/common/html_blocks.py
deleted file mode 100644
index 8b199af336ede85a04b222ac4eeffd1c0ad6100c..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/markdown_it/common/html_blocks.py
+++ /dev/null
@@ -1,68 +0,0 @@
-"""List of valid html blocks names, according to commonmark spec
-http://jgm.github.io/CommonMark/spec.html#html-blocks
-"""
-
-block_names = [
- "address",
- "article",
- "aside",
- "base",
- "basefont",
- "blockquote",
- "body",
- "caption",
- "center",
- "col",
- "colgroup",
- "dd",
- "details",
- "dialog",
- "dir",
- "div",
- "dl",
- "dt",
- "fieldset",
- "figcaption",
- "figure",
- "footer",
- "form",
- "frame",
- "frameset",
- "h1",
- "h2",
- "h3",
- "h4",
- "h5",
- "h6",
- "head",
- "header",
- "hr",
- "html",
- "iframe",
- "legend",
- "li",
- "link",
- "main",
- "menu",
- "menuitem",
- "nav",
- "noframes",
- "ol",
- "optgroup",
- "option",
- "p",
- "param",
- "section",
- "source",
- "summary",
- "table",
- "tbody",
- "td",
- "tfoot",
- "th",
- "thead",
- "title",
- "tr",
- "track",
- "ul",
-]
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py b/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py
deleted file mode 100644
index dd8e4f16dfc0ca359423d8196cc14853bf534755..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py
+++ /dev/null
@@ -1,785 +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 inspect
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from packaging import version
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from diffusers.utils import is_accelerate_available, is_accelerate_version
-
-from ...configuration_utils import FrozenDict
-from ...loaders import TextualInversionLoaderMixin
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...schedulers import DDIMScheduler
-from ...utils import PIL_INTERPOLATION, deprecate, logging, randn_tensor
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
-def preprocess(image):
- if isinstance(image, torch.Tensor):
- return image
- elif isinstance(image, PIL.Image.Image):
- image = [image]
-
- if isinstance(image[0], PIL.Image.Image):
- w, h = image[0].size
- w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
-
- image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
- image = np.concatenate(image, axis=0)
- image = np.array(image).astype(np.float32) / 255.0
- image = image.transpose(0, 3, 1, 2)
- image = 2.0 * image - 1.0
- image = torch.from_numpy(image)
- elif isinstance(image[0], torch.Tensor):
- image = torch.cat(image, dim=0)
- return image
-
-
-def posterior_sample(scheduler, latents, timestep, clean_latents, generator, eta):
- # 1. get previous step value (=t-1)
- prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps
-
- if prev_timestep <= 0:
- return clean_latents
-
- # 2. compute alphas, betas
- alpha_prod_t = scheduler.alphas_cumprod[timestep]
- alpha_prod_t_prev = (
- scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod
- )
-
- variance = scheduler._get_variance(timestep, prev_timestep)
- std_dev_t = eta * variance ** (0.5)
-
- # direction pointing to x_t
- e_t = (latents - alpha_prod_t ** (0.5) * clean_latents) / (1 - alpha_prod_t) ** (0.5)
- dir_xt = (1.0 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * e_t
- noise = std_dev_t * randn_tensor(
- clean_latents.shape, dtype=clean_latents.dtype, device=clean_latents.device, generator=generator
- )
- prev_latents = alpha_prod_t_prev ** (0.5) * clean_latents + dir_xt + noise
-
- return prev_latents
-
-
-def compute_noise(scheduler, prev_latents, latents, timestep, noise_pred, eta):
- # 1. get previous step value (=t-1)
- prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps
-
- # 2. compute alphas, betas
- alpha_prod_t = scheduler.alphas_cumprod[timestep]
- alpha_prod_t_prev = (
- scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod
- )
-
- beta_prod_t = 1 - alpha_prod_t
-
- # 3. compute predicted original sample from predicted noise also called
- # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
- pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
-
- # 4. Clip "predicted x_0"
- if scheduler.config.clip_sample:
- pred_original_sample = torch.clamp(pred_original_sample, -1, 1)
-
- # 5. compute variance: "sigma_t(η)" -> see formula (16)
- # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
- variance = scheduler._get_variance(timestep, prev_timestep)
- std_dev_t = eta * variance ** (0.5)
-
- # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
- pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * noise_pred
-
- noise = (prev_latents - (alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction)) / (
- variance ** (0.5) * eta
- )
- return noise
-
-
-class CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
- r"""
- Pipeline for text-guided image to image generation using Stable Diffusion.
-
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
- library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
- Args:
- vae ([`AutoencoderKL`]):
- Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
- text_encoder ([`CLIPTextModel`]):
- Frozen text-encoder. Stable Diffusion uses the text portion of
- [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
- the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
- tokenizer (`CLIPTokenizer`):
- Tokenizer of class
- [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
- unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
- scheduler ([`SchedulerMixin`]):
- A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
- [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
- safety_checker ([`StableDiffusionSafetyChecker`]):
- Classification module that estimates whether generated images could be considered offensive or harmful.
- Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
- feature_extractor ([`CLIPImageProcessor`]):
- Model that extracts features from generated images to be used as inputs for the `safety_checker`.
- """
- _optional_components = ["safety_checker", "feature_extractor"]
-
- def __init__(
- self,
- vae: AutoencoderKL,
- text_encoder: CLIPTextModel,
- tokenizer: CLIPTokenizer,
- unet: UNet2DConditionModel,
- scheduler: DDIMScheduler,
- safety_checker: StableDiffusionSafetyChecker,
- feature_extractor: CLIPImageProcessor,
- requires_safety_checker: bool = True,
- ):
- 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 safety_checker is None and requires_safety_checker:
- logger.warning(
- f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
- " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
- " results in services or applications open to the public. Both the diffusers team and Hugging Face"
- " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
- " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
- " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
- )
-
- if safety_checker is not None and feature_extractor is None:
- raise ValueError(
- "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
- " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
- )
- 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 you're 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,
- safety_checker=safety_checker,
- feature_extractor=feature_extractor,
- )
- self.register_to_config(requires_safety_checker=requires_safety_checker)
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload
- def enable_sequential_cpu_offload(self, gpu_id=0):
- r"""
- Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
- text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
- `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
- Note that offloading happens on a submodule basis. Memory savings are higher than with
- `enable_model_cpu_offload`, but performance is lower.
- """
- if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
- from accelerate import cpu_offload
- else:
- raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
-
- device = torch.device(f"cuda:{gpu_id}")
-
- if self.device.type != "cpu":
- self.to("cpu", silence_dtype_warnings=True)
- torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
-
- for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
- cpu_offload(cpu_offloaded_model, device)
-
- if self.safety_checker is not None:
- cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_model_cpu_offload
- def enable_model_cpu_offload(self, gpu_id=0):
- r"""
- Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
- to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
- method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
- `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
- """
- if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
- from accelerate import cpu_offload_with_hook
- else:
- raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
- device = torch.device(f"cuda:{gpu_id}")
-
- if self.device.type != "cpu":
- self.to("cpu", silence_dtype_warnings=True)
- torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
-
- hook = None
- for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
- _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
- if self.safety_checker is not None:
- _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
- # We'll offload the last model manually.
- self.final_offload_hook = hook
-
- @property
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
- def _execution_device(self):
- r"""
- Returns the device on which the pipeline's models will be executed. After calling
- `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
- hooks.
- """
- if 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
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
- def _encode_prompt(
- self,
- prompt,
- device,
- num_images_per_prompt,
- do_classifier_free_guidance,
- negative_prompt=None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- negative_prompt_embeds: Optional[torch.FloatTensor] = None,
- ):
- r"""
- Encodes the prompt into text encoder hidden states.
-
- Args:
- prompt (`str` or `List[str]`, *optional*):
- prompt to be encoded
- device: (`torch.device`):
- torch device
- num_images_per_prompt (`int`):
- number of images that should be generated per prompt
- do_classifier_free_guidance (`bool`):
- whether to use classifier free guidance or not
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. If not defined, one has to pass
- `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
- less than `1`).
- prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
- provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
- weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
- argument.
- """
- if prompt is not None and isinstance(prompt, str):
- batch_size = 1
- elif prompt is not None and isinstance(prompt, list):
- batch_size = len(prompt)
- else:
- batch_size = prompt_embeds.shape[0]
-
- if prompt_embeds is None:
- # textual inversion: procecss multi-vector tokens if necessary
- if isinstance(self, TextualInversionLoaderMixin):
- prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
-
- 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
-
- prompt_embeds = self.text_encoder(
- text_input_ids.to(device),
- attention_mask=attention_mask,
- )
- prompt_embeds = prompt_embeds[0]
-
- prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
- bs_embed, seq_len, _ = prompt_embeds.shape
- # duplicate text embeddings for each generation per prompt, using mps friendly method
- prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
- prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
- # get unconditional embeddings for classifier free guidance
- if do_classifier_free_guidance and negative_prompt_embeds is None:
- 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
-
- # textual inversion: procecss multi-vector tokens if necessary
- if isinstance(self, TextualInversionLoaderMixin):
- uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
-
- max_length = prompt_embeds.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
-
- negative_prompt_embeds = self.text_encoder(
- uncond_input.input_ids.to(device),
- attention_mask=attention_mask,
- )
- negative_prompt_embeds = negative_prompt_embeds[0]
-
- if do_classifier_free_guidance:
- # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
- seq_len = negative_prompt_embeds.shape[1]
-
- negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
- negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
- negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_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
- prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
- return prompt_embeds
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.check_inputs
- def check_inputs(
- self, prompt, strength, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None
- ):
- if strength < 0 or strength > 1:
- raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
-
- 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)}."
- )
-
- if prompt is not None and prompt_embeds is not None:
- raise ValueError(
- f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
- " only forward one of the two."
- )
- elif prompt is None and prompt_embeds is None:
- raise ValueError(
- "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
- )
- elif prompt is not None and (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 negative_prompt is not None and negative_prompt_embeds is not None:
- raise ValueError(
- f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
- f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
- )
-
- if prompt_embeds is not None and negative_prompt_embeds is not None:
- if prompt_embeds.shape != negative_prompt_embeds.shape:
- raise ValueError(
- "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
- f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
- f" {negative_prompt_embeds.shape}."
- )
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
- 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
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
- def run_safety_checker(self, image, device, dtype):
- if self.safety_checker is not None:
- safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
- image, has_nsfw_concept = self.safety_checker(
- images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
- )
- else:
- has_nsfw_concept = None
- return image, has_nsfw_concept
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
- def decode_latents(self, latents):
- latents = 1 / self.vae.config.scaling_factor * latents
- image = self.vae.decode(latents).sample
- image = (image / 2 + 0.5).clamp(0, 1)
- # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
- image = image.cpu().permute(0, 2, 3, 1).float().numpy()
- return image
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
- def get_timesteps(self, num_inference_steps, strength, device):
- # get the original timestep using init_timestep
- init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
- t_start = max(num_inference_steps - init_timestep, 0)
- timesteps = self.scheduler.timesteps[t_start:]
-
- return timesteps, num_inference_steps - t_start
-
- def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
- image = image.to(device=device, dtype=dtype)
-
- batch_size = image.shape[0]
- 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 isinstance(generator, list):
- init_latents = [
- self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
- ]
- init_latents = torch.cat(init_latents, dim=0)
- else:
- init_latents = self.vae.encode(image).latent_dist.sample(generator)
-
- init_latents = self.vae.config.scaling_factor * init_latents
-
- if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
- # expand init_latents for batch_size
- deprecation_message = (
- f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
- " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
- " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
- " your script to pass as many initial images as text prompts to suppress this warning."
- )
- deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
- additional_image_per_prompt = batch_size // init_latents.shape[0]
- init_latents = torch.cat([init_latents] * additional_image_per_prompt * num_images_per_prompt, dim=0)
- elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
- raise ValueError(
- f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
- )
- else:
- init_latents = torch.cat([init_latents] * num_images_per_prompt, dim=0)
-
- # add noise to latents using the timestep
- shape = init_latents.shape
- noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
- # get latents
- clean_latents = init_latents
- init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
- latents = init_latents
-
- return latents, clean_latents
-
- @torch.no_grad()
- def __call__(
- self,
- prompt: Union[str, List[str]],
- source_prompt: Union[str, List[str]],
- image: Union[torch.FloatTensor, PIL.Image.Image] = None,
- strength: float = 0.8,
- num_inference_steps: Optional[int] = 50,
- guidance_scale: Optional[float] = 7.5,
- source_guidance_scale: Optional[float] = 1,
- num_images_per_prompt: Optional[int] = 1,
- eta: Optional[float] = 0.1,
- generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
- callback_steps: int = 1,
- ):
- r"""
- Function invoked when calling the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- image (`torch.FloatTensor` or `PIL.Image.Image`):
- `Image`, or tensor representing an image batch, that will be used as the starting point for the
- process.
- strength (`float`, *optional*, defaults to 0.8):
- Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
- will be used as a starting point, adding more noise to it the larger the `strength`. The number of
- denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
- be maximum and the denoising process will run for the full number of iterations specified in
- `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference. This parameter will be modulated by `strength`.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
- usually at the expense of lower image quality.
- source_guidance_scale (`float`, *optional*, defaults to 1):
- Guidance scale for the source prompt. This is useful to control the amount of influence the source
- prompt for encoding.
- num_images_per_prompt (`int`, *optional*, defaults to 1):
- The number of images to generate per prompt.
- eta (`float`, *optional*, defaults to 0.1):
- Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
- [`schedulers.DDIMScheduler`], will be ignored for others.
- generator (`torch.Generator`, *optional*):
- One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
- to make generation deterministic.
- prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
- provided, text embeddings will be generated from `prompt` input argument.
- negative_prompt_embeds (`torch.FloatTensor`, *optional*):
- Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
- weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
- argument.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- callback (`Callable`, *optional*):
- A function that will be called every `callback_steps` steps during inference. The function will be
- called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function will be called. If not specified, the callback will be
- called at every step.
-
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
- When returning a tuple, the first element is a list with the generated images, and the second element is a
- list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
- (nsfw) content, according to the `safety_checker`.
- """
- # 1. Check inputs
- self.check_inputs(prompt, strength, callback_steps)
-
- # 2. Define call parameters
- batch_size = 1 if isinstance(prompt, str) else 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
-
- # 3. Encode input prompt
- prompt_embeds = self._encode_prompt(
- prompt,
- device,
- num_images_per_prompt,
- do_classifier_free_guidance,
- prompt_embeds=prompt_embeds,
- )
- source_prompt_embeds = self._encode_prompt(
- source_prompt, device, num_images_per_prompt, do_classifier_free_guidance, None
- )
-
- # 4. Preprocess image
- image = preprocess(image)
-
- # 5. Prepare timesteps
- self.scheduler.set_timesteps(num_inference_steps, device=device)
- timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
- latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-
- # 6. Prepare latent variables
- latents, clean_latents = self.prepare_latents(
- image, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
- )
- source_latents = latents
-
- # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
- extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
- generator = extra_step_kwargs.pop("generator", None)
-
- # 8. 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)
- source_latent_model_input = torch.cat([source_latents] * 2)
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
- source_latent_model_input = self.scheduler.scale_model_input(source_latent_model_input, t)
-
- # predict the noise residual
- concat_latent_model_input = torch.stack(
- [
- source_latent_model_input[0],
- latent_model_input[0],
- source_latent_model_input[1],
- latent_model_input[1],
- ],
- dim=0,
- )
- concat_prompt_embeds = torch.stack(
- [
- source_prompt_embeds[0],
- prompt_embeds[0],
- source_prompt_embeds[1],
- prompt_embeds[1],
- ],
- dim=0,
- )
- concat_noise_pred = self.unet(
- concat_latent_model_input, t, encoder_hidden_states=concat_prompt_embeds
- ).sample
-
- # perform guidance
- (
- source_noise_pred_uncond,
- noise_pred_uncond,
- source_noise_pred_text,
- noise_pred_text,
- ) = concat_noise_pred.chunk(4, dim=0)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
- source_noise_pred = source_noise_pred_uncond + source_guidance_scale * (
- source_noise_pred_text - source_noise_pred_uncond
- )
-
- # Sample source_latents from the posterior distribution.
- prev_source_latents = posterior_sample(
- self.scheduler, source_latents, t, clean_latents, generator=generator, **extra_step_kwargs
- )
- # Compute noise.
- noise = compute_noise(
- self.scheduler, prev_source_latents, source_latents, t, source_noise_pred, **extra_step_kwargs
- )
- source_latents = prev_source_latents
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(
- noise_pred, t, latents, variance_noise=noise, **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)
-
- # 9. Post-processing
- image = self.decode_latents(latents)
-
- # 10. Run safety checker
- image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
-
- # 11. Convert to PIL
- if output_type == "pil":
- image = self.numpy_to_pil(image)
-
- if not return_dict:
- return (image, has_nsfw_concept)
-
- return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/schedulers/scheduling_utils.py b/spaces/declare-lab/tango/diffusers/src/diffusers/schedulers/scheduling_utils.py
deleted file mode 100644
index a4121f75d850abc8fdbb7160cdbb3b5ba53e40d3..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/schedulers/scheduling_utils.py
+++ /dev/null
@@ -1,176 +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 importlib
-import os
-from dataclasses import dataclass
-from enum import Enum
-from typing import Any, Dict, Optional, Union
-
-import torch
-
-from ..utils import BaseOutput
-
-
-SCHEDULER_CONFIG_NAME = "scheduler_config.json"
-
-
-# NOTE: We make this type an enum because it simplifies usage in docs and prevents
-# circular imports when used for `_compatibles` within the schedulers module.
-# When it's used as a type in pipelines, it really is a Union because the actual
-# scheduler instance is passed in.
-class KarrasDiffusionSchedulers(Enum):
- DDIMScheduler = 1
- DDPMScheduler = 2
- PNDMScheduler = 3
- LMSDiscreteScheduler = 4
- EulerDiscreteScheduler = 5
- HeunDiscreteScheduler = 6
- EulerAncestralDiscreteScheduler = 7
- DPMSolverMultistepScheduler = 8
- DPMSolverSinglestepScheduler = 9
- KDPM2DiscreteScheduler = 10
- KDPM2AncestralDiscreteScheduler = 11
- DEISMultistepScheduler = 12
- UniPCMultistepScheduler = 13
-
-
-@dataclass
-class SchedulerOutput(BaseOutput):
- """
- Base class for the scheduler's step function output.
-
- Args:
- prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
- Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
- denoising loop.
- """
-
- prev_sample: torch.FloatTensor
-
-
-class SchedulerMixin:
- """
- Mixin containing common functions for the schedulers.
-
- Class attributes:
- - **_compatibles** (`List[str]`) -- A list of classes that are compatible with the parent class, so that
- `from_config` can be used from a class different than the one used to save the config (should be overridden
- by parent class).
- """
-
- config_name = SCHEDULER_CONFIG_NAME
- _compatibles = []
- has_compatibles = True
-
- @classmethod
- def from_pretrained(
- cls,
- pretrained_model_name_or_path: Dict[str, Any] = None,
- subfolder: Optional[str] = None,
- return_unused_kwargs=False,
- **kwargs,
- ):
- r"""
- Instantiate a Scheduler class from a pre-defined JSON configuration file inside a directory or Hub repo.
-
- Parameters:
- pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
- Can be either:
-
- - A string, the *model id* of a model repo on huggingface.co. Valid model ids should have an
- organization name, like `google/ddpm-celebahq-256`.
- - A path to a *directory* containing the schedluer configurations saved using
- [`~SchedulerMixin.save_pretrained`], e.g., `./my_model_directory/`.
- subfolder (`str`, *optional*):
- In case the relevant files are located inside a subfolder of the model repo (either remote in
- huggingface.co or downloaded locally), you can specify the folder name here.
- return_unused_kwargs (`bool`, *optional*, defaults to `False`):
- Whether kwargs that are not consumed by the Python class should be returned or not.
- cache_dir (`Union[str, os.PathLike]`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the
- standard cache should not be used.
- 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.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received files. Will attempt to resume the download if such a
- file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'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 or not to only look at local files (i.e., do not try to download the model).
- use_auth_token (`str` or *bool*, *optional*):
- The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
- when running `transformers-cli login` (stored in `~/.huggingface`).
- 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, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
-
- <Tip>
-
- It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
- models](https://huggingface.co/docs/hub/models-gated#gated-models).
-
- </Tip>
-
- <Tip>
-
- Activate the special ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode) to
- use this method in a firewalled environment.
-
- </Tip>
-
- """
- config, kwargs, commit_hash = cls.load_config(
- pretrained_model_name_or_path=pretrained_model_name_or_path,
- subfolder=subfolder,
- return_unused_kwargs=True,
- return_commit_hash=True,
- **kwargs,
- )
- return cls.from_config(config, return_unused_kwargs=return_unused_kwargs, **kwargs)
-
- def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
- """
- Save a scheduler configuration object to the directory `save_directory`, so that it can be re-loaded using the
- [`~SchedulerMixin.from_pretrained`] class method.
-
- Args:
- save_directory (`str` or `os.PathLike`):
- Directory where the configuration JSON file will be saved (will be created if it does not exist).
- """
- self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)
-
- @property
- def compatibles(self):
- """
- Returns all schedulers that are compatible with this scheduler
-
- Returns:
- `List[SchedulerMixin]`: List of compatible schedulers
- """
- return self._get_compatibles()
-
- @classmethod
- def _get_compatibles(cls):
- compatible_classes_str = list(set([cls.__name__] + cls._compatibles))
- diffusers_library = importlib.import_module(__name__.split(".")[0])
- compatible_classes = [
- getattr(diffusers_library, c) for c in compatible_classes_str if hasattr(diffusers_library, c)
- ]
- return compatible_classes
diff --git a/spaces/deepwisdom/MetaGPT/metagpt/tools/openai_text_to_embedding.py b/spaces/deepwisdom/MetaGPT/metagpt/tools/openai_text_to_embedding.py
deleted file mode 100644
index 86b58d71fe113a5d8b3da4d958f28dc4375dc0af..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/metagpt/tools/openai_text_to_embedding.py
+++ /dev/null
@@ -1,96 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time : 2023/8/18
-@Author : mashenquan
-@File : openai_text_to_embedding.py
-@Desc : OpenAI Text-to-Embedding OAS3 api, which provides text-to-embedding functionality.
- For more details, checkout: `https://platform.openai.com/docs/api-reference/embeddings/object`
-"""
-import asyncio
-import os
-from pathlib import Path
-from typing import List
-
-import aiohttp
-import requests
-from pydantic import BaseModel
-import sys
-
-from metagpt.config import CONFIG, Config
-
-sys.path.append(str(Path(__file__).resolve().parent.parent.parent)) # fix-bug: No module named 'metagpt'
-from metagpt.logs import logger
-
-
-class Embedding(BaseModel):
- """Represents an embedding vector returned by embedding endpoint."""
- object: str # The object type, which is always "embedding".
- embedding: List[
- float] # The embedding vector, which is a list of floats. The length of vector depends on the model as listed in the embedding guide.
- index: int # The index of the embedding in the list of embeddings.
-
-
-class Usage(BaseModel):
- prompt_tokens: int
- total_tokens: int
-
-
-class ResultEmbedding(BaseModel):
- object: str
- data: List[Embedding]
- model: str
- usage: Usage
-
-
-class OpenAIText2Embedding:
- def __init__(self, openai_api_key):
- """
- :param openai_api_key: OpenAI API key, For more details, checkout: `https://platform.openai.com/account/api-keys`
- """
- self.openai_api_key = openai_api_key if openai_api_key else CONFIG.OPENAI_API_KEY
-
- async def text_2_embedding(self, text, model="text-embedding-ada-002"):
- """Text to embedding
-
- :param text: The text used for embedding.
- :param model: One of ['text-embedding-ada-002'], ID of the model to use. For more details, checkout: `https://api.openai.com/v1/models`.
- :return: A json object of :class:`ResultEmbedding` class if successful, otherwise `{}`.
- """
-
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {self.openai_api_key}"
- }
- data = {"input": text, "model": model}
- try:
- async with aiohttp.ClientSession() as session:
- async with session.post("https://api.openai.com/v1/embeddings", headers=headers, json=data) as response:
- return await response.json()
- except requests.exceptions.RequestException as e:
- logger.error(f"An error occurred:{e}")
- return {}
-
-
-# Export
-async def oas3_openai_text_to_embedding(text, model="text-embedding-ada-002", openai_api_key=""):
- """Text to embedding
-
- :param text: The text used for embedding.
- :param model: One of ['text-embedding-ada-002'], ID of the model to use. For more details, checkout: `https://api.openai.com/v1/models`.
- :param openai_api_key: OpenAI API key, For more details, checkout: `https://platform.openai.com/account/api-keys`
- :return: A json object of :class:`ResultEmbedding` class if successful, otherwise `{}`.
- """
- if not text:
- return ""
- if not openai_api_key:
- openai_api_key = CONFIG.OPENAI_API_KEY
- return await OpenAIText2Embedding(openai_api_key).text_2_embedding(text, model=model)
-
-
-if __name__ == "__main__":
- Config()
- loop = asyncio.new_event_loop()
- task = loop.create_task(oas3_openai_text_to_embedding("Panda emoji"))
- v = loop.run_until_complete(task)
- print(v)
diff --git a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/losses.py b/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/losses.py
deleted file mode 100644
index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Jiaohuaji-Bert-Vits2/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import commons
-
-
-def feature_loss(fmap_r, fmap_g):
- loss = 0
- for dr, dg in zip(fmap_r, fmap_g):
- for rl, gl in zip(dr, dg):
- rl = rl.float().detach()
- gl = gl.float()
- loss += torch.mean(torch.abs(rl - gl))
-
- return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
- loss = 0
- r_losses = []
- g_losses = []
- for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
- dr = dr.float()
- dg = dg.float()
- r_loss = torch.mean((1-dr)**2)
- g_loss = torch.mean(dg**2)
- loss += (r_loss + g_loss)
- r_losses.append(r_loss.item())
- g_losses.append(g_loss.item())
-
- return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
- loss = 0
- gen_losses = []
- for dg in disc_outputs:
- dg = dg.float()
- l = torch.mean((1-dg)**2)
- gen_losses.append(l)
- loss += l
-
- return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
- """
- z_p, logs_q: [b, h, t_t]
- m_p, logs_p: [b, h, t_t]
- """
- z_p = z_p.float()
- logs_q = logs_q.float()
- m_p = m_p.float()
- logs_p = logs_p.float()
- z_mask = z_mask.float()
-
- kl = logs_p - logs_q - 0.5
- kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
- kl = torch.sum(kl * z_mask)
- l = kl / torch.sum(z_mask)
- return l
diff --git a/spaces/digitalxingtong/Taffy-Bert-VITS2/text/english.py b/spaces/digitalxingtong/Taffy-Bert-VITS2/text/english.py
deleted file mode 100644
index 781d0a56cef71f66fc67db51d76538be90d3ddd2..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Taffy-Bert-VITS2/text/english.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import pickle
-import os
-import re
-from g2p_en import G2p
-from string import punctuation
-
-from text import symbols
-
-current_file_path = os.path.dirname(__file__)
-CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep')
-CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle')
-_g2p = G2p()
-
-arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'}
-
-
-def post_replace_ph(ph):
- rep_map = {
- '：': ',',
- '；': ',',
- '，': ',',
- '。': '.',
- '！': '!',
- '？': '?',
- '\n': '.',
- "·": ",",
- '、': ",",
- '...': '…',
- 'v': "V"
- }
- if ph in rep_map.keys():
- ph = rep_map[ph]
- if ph in symbols:
- return ph
- if ph not in symbols:
- ph = 'UNK'
- return ph
-
-def read_dict():
- g2p_dict = {}
- start_line = 49
- with open(CMU_DICT_PATH) as f:
- line = f.readline()
- line_index = 1
- while line:
- if line_index >= start_line:
- line = line.strip()
- word_split = line.split(' ')
- word = word_split[0]
-
- syllable_split = word_split[1].split(' - ')
- g2p_dict[word] = []
- for syllable in syllable_split:
- phone_split = syllable.split(' ')
- g2p_dict[word].append(phone_split)
-
- line_index = line_index + 1
- line = f.readline()
-
- return g2p_dict
-
-
-def cache_dict(g2p_dict, file_path):
- with open(file_path, 'wb') as pickle_file:
- pickle.dump(g2p_dict, pickle_file)
-
-
-def get_dict():
- if os.path.exists(CACHE_PATH):
- with open(CACHE_PATH, 'rb') as pickle_file:
- g2p_dict = pickle.load(pickle_file)
- else:
- g2p_dict = read_dict()
- cache_dict(g2p_dict, CACHE_PATH)
-
- return g2p_dict
-
-eng_dict = get_dict()
-
-def refine_ph(phn):
- tone = 0
- if re.search(r'\d$', phn):
- tone = int(phn[-1]) + 1
- phn = phn[:-1]
- return phn.lower(), tone
-
-def refine_syllables(syllables):
- tones = []
- phonemes = []
- for phn_list in syllables:
- for i in range(len(phn_list)):
- phn = phn_list[i]
- phn, tone = refine_ph(phn)
- phonemes.append(phn)
- tones.append(tone)
- return phonemes, tones
-
-
-def text_normalize(text):
- # todo: eng text normalize
- return text
-
-def g2p(text):
-
- phones = []
- tones = []
- words = re.split(r"([,;.\-\?\!\s+])", text)
- for w in words:
- if w.upper() in eng_dict:
- phns, tns = refine_syllables(eng_dict[w.upper()])
- phones += phns
- tones += tns
- else:
- phone_list = list(filter(lambda p: p != " ", _g2p(w)))
- for ph in phone_list:
- if ph in arpa:
- ph, tn = refine_ph(ph)
- phones.append(ph)
- tones.append(tn)
- else:
- phones.append(ph)
- tones.append(0)
- # todo: implement word2ph
- word2ph = [1 for i in phones]
-
- phones = [post_replace_ph(i) for i in phones]
- return phones, tones, word2ph
-
-if __name__ == "__main__":
- # print(get_dict())
- # print(eng_word_to_phoneme("hello"))
- print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
- # all_phones = set()
- # for k, syllables in eng_dict.items():
- # for group in syllables:
- # for ph in group:
- # all_phones.add(ph)
- # print(all_phones)
\ No newline at end of file
diff --git a/spaces/dilums/sentence-similarity/components/ui/button.tsx b/spaces/dilums/sentence-similarity/components/ui/button.tsx
deleted file mode 100644
index 4ecf36902a32b5c51a86b165372e36b76fb8a3bd..0000000000000000000000000000000000000000
--- a/spaces/dilums/sentence-similarity/components/ui/button.tsx
+++ /dev/null
@@ -1,57 +0,0 @@
-import * as React from "react"
-import { Slot } from "@radix-ui/react-slot"
-import { cva, type VariantProps } from "class-variance-authority"
-
-import { cn } from "@/lib/utils"
-
-const buttonVariants = cva(
- "inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-ring disabled:pointer-events-none disabled:opacity-50",
- {
- variants: {
- variant: {
- default:
- "bg-primary text-primary-foreground shadow hover:bg-primary/90",
- destructive:
- "bg-destructive text-destructive-foreground shadow-sm hover:bg-destructive/90",
- outline:
- "border border-input bg-transparent shadow-sm hover:bg-accent hover:text-accent-foreground",
- secondary:
- "bg-secondary text-secondary-foreground shadow-sm hover:bg-secondary/80",
- ghost: "hover:bg-accent hover:text-accent-foreground",
- link: "text-primary underline-offset-4 hover:underline",
- },
- size: {
- default: "h-9 px-4 py-2",
- sm: "h-8 rounded-md px-3 text-xs",
- lg: "h-10 rounded-md px-8",
- icon: "h-9 w-9",
- },
- },
- defaultVariants: {
- variant: "default",
- size: "default",
- },
- }
-)
-
-export interface ButtonProps
- extends React.ButtonHTMLAttributes<HTMLButtonElement>,
- VariantProps<typeof buttonVariants> {
- asChild?: boolean
-}
-
-const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
- ({ className, variant, size, asChild = false, ...props }, ref) => {
- const Comp = asChild ? Slot : "button"
- return (
- <Comp
- className={cn(buttonVariants({ variant, size, className }))}
- ref={ref}
- {...props}
- />
- )
- }
-)
-Button.displayName = "Button"
-
-export { Button, buttonVariants }
diff --git a/spaces/dongsiqie/bingai/README.md b/spaces/dongsiqie/bingai/README.md
deleted file mode 100644
index 54ef2a7d9ae3ea5954b633cc3fe1e699afded542..0000000000000000000000000000000000000000
--- a/spaces/dongsiqie/bingai/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: BingAi最新版
-emoji: ⚡
-colorFrom: gray
-colorTo: indigo
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
-duplicated_from: dongsiqie/bing
----
-
-用来测试，可能切换回特定版本
\ No newline at end of file
diff --git a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp b/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp
deleted file mode 100644
index c1f2c50c82909bbd5492c163d634af77a3ba1781..0000000000000000000000000000000000000000
--- a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp
+++ /dev/null
@@ -1,58 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-#include "MsDeformAttn/ms_deform_attn.h"
-
-namespace groundingdino {
-
-#ifdef WITH_CUDA
-extern int get_cudart_version();
-#endif
-
-std::string get_cuda_version() {
-#ifdef WITH_CUDA
- std::ostringstream oss;
-
- // copied from
- // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231
- auto printCudaStyleVersion = [&](int v) {
- oss << (v / 1000) << "." << (v / 10 % 100);
- if (v % 10 != 0) {
- oss << "." << (v % 10);
- }
- };
- printCudaStyleVersion(get_cudart_version());
- return oss.str();
-#else
- return std::string("not available");
-#endif
-}
-
-// similar to
-// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp
-std::string get_compiler_version() {
- std::ostringstream ss;
-#if defined(__GNUC__)
-#ifndef __clang__
- { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; }
-#endif
-#endif
-
-#if defined(__clang_major__)
- {
- ss << "clang " << __clang_major__ << "." << __clang_minor__ << "."
- << __clang_patchlevel__;
- }
-#endif
-
-#if defined(_MSC_VER)
- { ss << "MSVC " << _MSC_FULL_VER; }
-#endif
- return ss.str();
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
- m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
-}
-
-} // namespace groundingdino
\ No newline at end of file
diff --git a/spaces/ehristoforu/NLLB-Translator/app.py b/spaces/ehristoforu/NLLB-Translator/app.py
deleted file mode 100644
index 3fe4bdf2e3a8eba57c2e8c24f24104d9e987db0b..0000000000000000000000000000000000000000
--- a/spaces/ehristoforu/NLLB-Translator/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-import torch
-from ui import title, description, examples
-from langs import LANGS
-
-TASK = "translation"
-CKPT = "facebook/nllb-200-distilled-600M"
-
-model = AutoModelForSeq2SeqLM.from_pretrained(CKPT)
-tokenizer = AutoTokenizer.from_pretrained(CKPT)
-
-device = 0 if torch.cuda.is_available() else -1
-
-
-def translate(text, src_lang, tgt_lang, max_length=400):
- """
- Translate the text from source lang to target lang
- """
- translation_pipeline = pipeline(TASK,
- model=model,
- tokenizer=tokenizer,
- src_lang=src_lang,
- tgt_lang=tgt_lang,
- max_length=max_length,
- device=device)
-
- result = translation_pipeline(text)
- return result[0]['translation_text']
-
-
-gr.Interface(
- translate,
- [
- gr.components.Textbox(label="Text"),
- gr.components.Dropdown(label="Source Language", choices=LANGS),
- gr.components.Dropdown(label="Target Language", choices=LANGS),
- gr.components.Slider(8, 512, value=400, step=8, label="Max Length")
- ],
- ["text"],
- examples=examples,
- # article=article,
- cache_examples=False,
- title=title,
- description=description
-).launch()
diff --git a/spaces/ennet/ChatDev/camel/model_backend.py b/spaces/ennet/ChatDev/camel/model_backend.py
deleted file mode 100644
index 6d95dc562bbe34438acc8548fc5f5015dda08c1d..0000000000000000000000000000000000000000
--- a/spaces/ennet/ChatDev/camel/model_backend.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-# Licensed under the Apache License, Version 2.0 (the “License”);
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an “AS IS” BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
-from abc import ABC, abstractmethod
-from typing import Any, Dict
-
-import openai
-import tiktoken
-
-from camel.typing import ModelType
-from chatdev.utils import log_and_print_online
-
-
-class ModelBackend(ABC):
- r"""Base class for different model backends.
- May be OpenAI API, a local LLM, a stub for unit tests, etc."""
-
- @abstractmethod
- def run(self, *args, **kwargs) -> Dict[str, Any]:
- r"""Runs the query to the backend model.
-
- Raises:
- RuntimeError: if the return value from OpenAI API
- is not a dict that is expected.
-
- Returns:
- Dict[str, Any]: All backends must return a dict in OpenAI format.
- """
- pass
-
-
-class OpenAIModel(ModelBackend):
- r"""OpenAI API in a unified ModelBackend interface."""
-
- def __init__(self, model_type: ModelType, model_config_dict: Dict) -> None:
- super().__init__()
- self.model_type = model_type
- self.model_config_dict = model_config_dict
-
- def run(self, *args, **kwargs) -> Dict[str, Any]:
- string = "\n".join([message["content"] for message in kwargs["messages"]])
- encoding = tiktoken.encoding_for_model(self.model_type.value)
- num_prompt_tokens = len(encoding.encode(string))
- gap_between_send_receive = 50 # known issue
- num_prompt_tokens += gap_between_send_receive
-
- num_max_token_map = {
- "gpt-3.5-turbo": 4096,
- "gpt-3.5-turbo-16k": 16384,
- "gpt-3.5-turbo-0613": 4096,
- "gpt-3.5-turbo-16k-0613": 16384,
- "gpt-4": 8192,
- "gpt-4-0613": 8192,
- "gpt-4-32k": 32768,
- }
- num_max_token = num_max_token_map[self.model_type.value]
- num_max_completion_tokens = num_max_token - num_prompt_tokens
- self.model_config_dict['max_tokens'] = num_max_completion_tokens
- response = openai.ChatCompletion.create(*args, **kwargs,
- model=self.model_type.value,
- **self.model_config_dict)
-
- log_and_print_online(
- "**[OpenAI_Usage_Info Receive]**\nprompt_tokens: {}\ncompletion_tokens: {}\ntotal_tokens: {}\n".format(
- response["usage"]["prompt_tokens"], response["usage"]["completion_tokens"],
- response["usage"]["total_tokens"]))
- if not isinstance(response, Dict):
- raise RuntimeError("Unexpected return from OpenAI API")
- return response
-
-
-class StubModel(ModelBackend):
- r"""A dummy model used for unit tests."""
-
- def __init__(self, *args, **kwargs) -> None:
- super().__init__()
-
- def run(self, *args, **kwargs) -> Dict[str, Any]:
- ARBITRARY_STRING = "Lorem Ipsum"
-
- return dict(
- id="stub_model_id",
- usage=dict(),
- choices=[
- dict(finish_reason="stop",
- message=dict(content=ARBITRARY_STRING, role="assistant"))
- ],
- )
-
-
-class ModelFactory:
- r"""Factory of backend models.
-
- Raises:
- ValueError: in case the provided model type is unknown.
- """
-
- @staticmethod
- def create(model_type: ModelType, model_config_dict: Dict) -> ModelBackend:
- default_model_type = ModelType.GPT_3_5_TURBO
-
- if model_type in {
- ModelType.GPT_3_5_TURBO, ModelType.GPT_4, ModelType.GPT_4_32k,
- None
- }:
- model_class = OpenAIModel
- elif model_type == ModelType.STUB:
- model_class = StubModel
- else:
- raise ValueError("Unknown model")
-
- if model_type is None:
- model_type = default_model_type
-
- # log_and_print_online("Model Type: {}".format(model_type))
- inst = model_class(model_type, model_config_dict)
- return inst
diff --git a/spaces/erbanku/gpt-academic/.github/ISSUE_TEMPLATE/bug_report.md b/spaces/erbanku/gpt-academic/.github/ISSUE_TEMPLATE/bug_report.md
deleted file mode 100644
index ac668766a39892be5bc9e03f3ea626f8b3bf4b57..0000000000000000000000000000000000000000
--- a/spaces/erbanku/gpt-academic/.github/ISSUE_TEMPLATE/bug_report.md
+++ /dev/null
@@ -1,25 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
----
-
-- **(1) Describe the bug 简述**
-
-
-- **(2) Screen Shot 截图**
-
-
-- **(3) Terminal Traceback 终端traceback（如有）**
-
-
-- **(4) Material to Help Reproduce Bugs 帮助我们复现的测试材料样本（如有）**
-
-
-
-Before submitting an issue 提交issue之前：
-- Please try to upgrade your code. 如果您的代码不是最新的，建议您先尝试更新代码
-- Please check project wiki for common problem solutions.项目[wiki](https://github.com/binary-husky/chatgpt_academic/wiki)有一些常见问题的解决方法
diff --git a/spaces/erbanku/gpt-academic/request_llm/bridge_newbing.py b/spaces/erbanku/gpt-academic/request_llm/bridge_newbing.py
deleted file mode 100644
index dca7485056519265422f9162fe9868d3474e6f80..0000000000000000000000000000000000000000
--- a/spaces/erbanku/gpt-academic/request_llm/bridge_newbing.py
+++ /dev/null
@@ -1,254 +0,0 @@
-"""
-========================================================================
-第一部分：来自EdgeGPT.py
-https://github.com/acheong08/EdgeGPT
-========================================================================
-"""
-from .edge_gpt import NewbingChatbot
-load_message = "等待NewBing响应。"
-
-"""
-========================================================================
-第二部分：子进程Worker（调用主体）
-========================================================================
-"""
-import time
-import json
-import re
-import logging
-import asyncio
-import importlib
-import threading
-from toolbox import update_ui, get_conf, trimmed_format_exc
-from multiprocessing import Process, Pipe
-
-def preprocess_newbing_out(s):
- pattern = r'\^(\d+)\^' # 匹配^数字^
- sub = lambda m: '('+m.group(1)+')' # 将匹配到的数字作为替换值
- result = re.sub(pattern, sub, s) # 替换操作
- if '[1]' in result:
- result += '\n\n```reference\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
- return result
-
-def preprocess_newbing_out_simple(result):
- if '[1]' in result:
- result += '\n\n```reference\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
- return result
-
-class NewBingHandle(Process):
- def __init__(self):
- super().__init__(daemon=True)
- self.parent, self.child = Pipe()
- self.newbing_model = None
- self.info = ""
- self.success = True
- self.local_history = []
- self.check_dependency()
- self.start()
- self.threadLock = threading.Lock()
-
- def check_dependency(self):
- try:
- self.success = False
- import certifi, httpx, rich
- self.info = "依赖检测通过，等待NewBing响应。注意目前不能多人同时调用NewBing接口（有线程锁），否则将导致每个人的NewBing问询历史互相渗透。调用NewBing时，会自动使用已配置的代理。"
- self.success = True
- except:
- self.info = "缺少的依赖，如果要使用Newbing，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_newbing.txt`安装Newbing的依赖。"
- self.success = False
-
- def ready(self):
- return self.newbing_model is not None
-
- async def async_run(self):
- # 读取配置
- NEWBING_STYLE, = get_conf('NEWBING_STYLE')
- from request_llm.bridge_all import model_info
- endpoint = model_info['newbing']['endpoint']
- while True:
- # 等待
- kwargs = self.child.recv()
- question=kwargs['query']
- history=kwargs['history']
- system_prompt=kwargs['system_prompt']
-
- # 是否重置
- if len(self.local_history) > 0 and len(history)==0:
- await self.newbing_model.reset()
- self.local_history = []
-
- # 开始问问题
- prompt = ""
- if system_prompt not in self.local_history:
- self.local_history.append(system_prompt)
- prompt += system_prompt + '\n'
-
- # 追加历史
- for ab in history:
- a, b = ab
- if a not in self.local_history:
- self.local_history.append(a)
- prompt += a + '\n'
- # if b not in self.local_history:
- # self.local_history.append(b)
- # prompt += b + '\n'
-
- # 问题
- prompt += question
- self.local_history.append(question)
- print('question:', prompt)
- # 提交
- async for final, response in self.newbing_model.ask_stream(
- prompt=question,
- conversation_style=NEWBING_STYLE, # ["creative", "balanced", "precise"]
- wss_link=endpoint, # "wss://sydney.bing.com/sydney/ChatHub"
- ):
- if not final:
- print(response)
- self.child.send(str(response))
- else:
- print('-------- receive final ---------')
- self.child.send('[Finish]')
- # self.local_history.append(response)
-
-
- def run(self):
- """
- 这个函数运行在子进程
- """
- # 第一次运行，加载参数
- self.success = False
- self.local_history = []
- if (self.newbing_model is None) or (not self.success):
- # 代理设置
- proxies, = get_conf('proxies')
- if proxies is None:
- self.proxies_https = None
- else:
- self.proxies_https = proxies['https']
- # cookie
- NEWBING_COOKIES, = get_conf('NEWBING_COOKIES')
- try:
- cookies = json.loads(NEWBING_COOKIES)
- except:
- self.success = False
- tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
- self.child.send(f'[Local Message] 不能加载Newbing组件。NEWBING_COOKIES未填写或有格式错误。')
- self.child.send('[Fail]')
- self.child.send('[Finish]')
- raise RuntimeError(f"不能加载Newbing组件。NEWBING_COOKIES未填写或有格式错误。")
-
- try:
- self.newbing_model = NewbingChatbot(proxy=self.proxies_https, cookies=cookies)
- except:
- self.success = False
- tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
- self.child.send(f'[Local Message] 不能加载Newbing组件。{tb_str}')
- self.child.send('[Fail]')
- self.child.send('[Finish]')
- raise RuntimeError(f"不能加载Newbing组件。")
-
- self.success = True
- try:
- # 进入任务等待状态
- asyncio.run(self.async_run())
- except Exception:
- tb_str = '```\n' + trimmed_format_exc() + '```'
- self.child.send(f'[Local Message] Newbing失败 {tb_str}.')
- self.child.send('[Fail]')
- self.child.send('[Finish]')
-
- def stream_chat(self, **kwargs):
- """
- 这个函数运行在主进程
- """
- self.threadLock.acquire()
- self.parent.send(kwargs) # 发送请求到子进程
- while True:
- res = self.parent.recv() # 等待newbing回复的片段
- if res == '[Finish]':
- break # 结束
- elif res == '[Fail]':
- self.success = False
- break
- else:
- yield res # newbing回复的片段
- self.threadLock.release()
-
-
-"""
-========================================================================
-第三部分：主进程统一调用函数接口
-========================================================================
-"""
-global newbing_handle
-newbing_handle = None
-
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
- """
- 多线程方法
- 函数的说明请见 request_llm/bridge_all.py
- """
- global newbing_handle
- if (newbing_handle is None) or (not newbing_handle.success):
- newbing_handle = NewBingHandle()
- observe_window[0] = load_message + "\n\n" + newbing_handle.info
- if not newbing_handle.success:
- error = newbing_handle.info
- newbing_handle = None
- raise RuntimeError(error)
-
- # 没有 sys_prompt 接口，因此把prompt加入 history
- history_feedin = []
- for i in range(len(history)//2):
- history_feedin.append([history[2*i], history[2*i+1]] )
-
- watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
- response = ""
- observe_window[0] = "[Local Message]: 等待NewBing响应中 ..."
- for response in newbing_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
- observe_window[0] = preprocess_newbing_out_simple(response)
- if len(observe_window) >= 2:
- if (time.time()-observe_window[1]) > watch_dog_patience:
- raise RuntimeError("程序终止。")
- return preprocess_newbing_out_simple(response)
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
- """
- 单线程方法
- 函数的说明请见 request_llm/bridge_all.py
- """
- chatbot.append((inputs, "[Local Message]: 等待NewBing响应中 ..."))
-
- global newbing_handle
- if (newbing_handle is None) or (not newbing_handle.success):
- newbing_handle = NewBingHandle()
- chatbot[-1] = (inputs, load_message + "\n\n" + newbing_handle.info)
- yield from update_ui(chatbot=chatbot, history=[])
- if not newbing_handle.success:
- newbing_handle = None
- return
-
- if additional_fn is not None:
- import core_functional
- importlib.reload(core_functional) # 热更新prompt
- core_functional = core_functional.get_core_functions()
- if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
- inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
-
- history_feedin = []
- for i in range(len(history)//2):
- history_feedin.append([history[2*i], history[2*i+1]] )
-
- chatbot[-1] = (inputs, "[Local Message]: 等待NewBing响应中 ...")
- response = "[Local Message]: 等待NewBing响应中 ..."
- yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
- for response in newbing_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
- chatbot[-1] = (inputs, preprocess_newbing_out(response))
- yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
- if response == "[Local Message]: 等待NewBing响应中 ...": response = "[Local Message]: NewBing响应异常，请刷新界面重试 ..."
- history.extend([inputs, response])
- logging.info(f'[raw_input] {inputs}')
- logging.info(f'[response] {response}')
- yield from update_ui(chatbot=chatbot, history=history, msg="完成全部响应，请提交新问题。")
-
diff --git a/spaces/esumitra/superheroes/README.md b/spaces/esumitra/superheroes/README.md
deleted file mode 100644
index 9357d4ef1c4c43732a68307c6e84c87dce86ef5f..0000000000000000000000000000000000000000
--- a/spaces/esumitra/superheroes/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Superheroes
-emoji: ⚡
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.1.3
-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/evaluate-metric/bertscore/bertscore.py b/spaces/evaluate-metric/bertscore/bertscore.py
deleted file mode 100644
index 071e76ff3be0ad52302ab4d5fc99beb4ed125161..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/bertscore/bertscore.py
+++ /dev/null
@@ -1,215 +0,0 @@
-# Copyright 2020 The HuggingFace Evaluate 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.
-""" BERTScore metric. """
-
-import functools
-from contextlib import contextmanager
-
-import bert_score
-import datasets
-from packaging import version
-
-import evaluate
-
-
-@contextmanager
-def filter_logging_context():
- def filter_log(record):
- return False if "This IS expected if you are initializing" in record.msg else True
-
- logger = datasets.utils.logging.get_logger("transformers.modeling_utils")
- logger.addFilter(filter_log)
- try:
- yield
- finally:
- logger.removeFilter(filter_log)
-
-
-_CITATION = """\
-@inproceedings{bert-score,
- title={BERTScore: Evaluating Text Generation with BERT},
- author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
- booktitle={International Conference on Learning Representations},
- year={2020},
- url={https://openreview.net/forum?id=SkeHuCVFDr}
-}
-"""
-
-_DESCRIPTION = """\
-BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
-sentences by cosine similarity.
-It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
-Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
-generation tasks.
-
-See the project's README at https://github.com/Tiiiger/bert_score#readme for more information.
-"""
-
-_KWARGS_DESCRIPTION = """
-BERTScore Metrics with the hashcode from a source against one or more references.
-
-Args:
- predictions (list of str): Prediction/candidate sentences.
- references (list of str or list of list of str): Reference sentences.
- lang (str): Language of the sentences; required (e.g. 'en').
- model_type (str): Bert specification, default using the suggested
- model for the target language; has to specify at least one of
- `model_type` or `lang`.
- num_layers (int): The layer of representation to use,
- default using the number of layers tuned on WMT16 correlation data.
- verbose (bool): Turn on intermediate status update.
- idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
- device (str): On which the contextual embedding model will be allocated on.
- If this argument is None, the model lives on cuda:0 if cuda is available.
- nthreads (int): Number of threads.
- batch_size (int): Bert score processing batch size,
- at least one of `model_type` or `lang`. `lang` needs to be
- specified when `rescale_with_baseline` is True.
- rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
- baseline_path (str): Customized baseline file.
- use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.
-
-Returns:
- precision: Precision.
- recall: Recall.
- f1: F1 score.
- hashcode: Hashcode of the library.
-
-Examples:
-
- >>> predictions = ["hello there", "general kenobi"]
- >>> references = ["hello there", "general kenobi"]
- >>> bertscore = evaluate.load("bertscore")
- >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
- >>> print([round(v, 2) for v in results["f1"]])
- [1.0, 1.0]
-"""
-
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class BERTScore(evaluate.Metric):
- def _info(self):
- return evaluate.MetricInfo(
- description=_DESCRIPTION,
- citation=_CITATION,
- homepage="https://github.com/Tiiiger/bert_score",
- inputs_description=_KWARGS_DESCRIPTION,
- features=[
- datasets.Features(
- {
- "predictions": datasets.Value("string", id="sequence"),
- "references": datasets.Sequence(datasets.Value("string", id="sequence"), id="references"),
- }
- ),
- datasets.Features(
- {
- "predictions": datasets.Value("string", id="sequence"),
- "references": datasets.Value("string", id="sequence"),
- }
- ),
- ],
- codebase_urls=["https://github.com/Tiiiger/bert_score"],
- reference_urls=[
- "https://github.com/Tiiiger/bert_score",
- "https://arxiv.org/abs/1904.09675",
- ],
- )
-
- def _compute(
- self,
- predictions,
- references,
- lang=None,
- model_type=None,
- num_layers=None,
- verbose=False,
- idf=False,
- device=None,
- batch_size=64,
- nthreads=4,
- all_layers=False,
- rescale_with_baseline=False,
- baseline_path=None,
- use_fast_tokenizer=False,
- ):
-
- if isinstance(references[0], str):
- references = [[ref] for ref in references]
-
- if idf:
- idf_sents = [r for ref in references for r in ref]
- else:
- idf_sents = None
-
- get_hash = bert_score.utils.get_hash
- scorer = bert_score.BERTScorer
-
- if version.parse(bert_score.__version__) >= version.parse("0.3.10"):
- get_hash = functools.partial(get_hash, use_fast_tokenizer=use_fast_tokenizer)
- scorer = functools.partial(scorer, use_fast_tokenizer=use_fast_tokenizer)
- elif use_fast_tokenizer:
- raise ImportWarning(
- "To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of "
- "`bert-score` doesn't match this condition.\n"
- 'You can install it with `pip install "bert-score>=0.3.10"`.'
- )
-
- if model_type is None:
- if lang is None:
- raise ValueError(
- "Either 'lang' (e.g. 'en') or 'model_type' (e.g. 'microsoft/deberta-xlarge-mnli')"
- " must be specified"
- )
- model_type = bert_score.utils.lang2model[lang.lower()]
-
- if num_layers is None:
- num_layers = bert_score.utils.model2layers[model_type]
-
- hashcode = get_hash(
- model=model_type,
- num_layers=num_layers,
- idf=idf,
- rescale_with_baseline=rescale_with_baseline,
- use_custom_baseline=baseline_path is not None,
- )
-
- with filter_logging_context():
- if not hasattr(self, "cached_bertscorer") or self.cached_bertscorer.hash != hashcode:
- self.cached_bertscorer = scorer(
- model_type=model_type,
- num_layers=num_layers,
- batch_size=batch_size,
- nthreads=nthreads,
- all_layers=all_layers,
- idf=idf,
- idf_sents=idf_sents,
- device=device,
- lang=lang,
- rescale_with_baseline=rescale_with_baseline,
- baseline_path=baseline_path,
- )
-
- (P, R, F) = self.cached_bertscorer.score(
- cands=predictions,
- refs=references,
- verbose=verbose,
- batch_size=batch_size,
- )
- output_dict = {
- "precision": P.tolist(),
- "recall": R.tolist(),
- "f1": F.tolist(),
- "hashcode": hashcode,
- }
- return output_dict
diff --git a/spaces/falcondai/code-as-policies/md_logger.py b/spaces/falcondai/code-as-policies/md_logger.py
deleted file mode 100644
index 24fc2c9a7b44bcacaa3f1ae0b64b27f274784296..0000000000000000000000000000000000000000
--- a/spaces/falcondai/code-as-policies/md_logger.py
+++ /dev/null
@@ -1,25 +0,0 @@
-class MarkdownLogger:
-
- def __init__(self):
- self._log = ''
- self._messages = []
-
- def log_text(self, text):
- self._log += '\n' + text + '\n'
-
- def log_code(self, code):
- self._log += f'\n```python\n{code}\n```\n'
-
- def log_message(self, text):
- self._messages.append(text)
-
- def clear(self):
- self._log = ''
-
- def get_log(self):
- return self._log
-
- def get_messages(self):
- m = self._messages
- self._messages = []
- return m
diff --git a/spaces/falterWliame/Face_Mask_Detection/Brill Formulation Software Crack ((HOT)) Sites.md b/spaces/falterWliame/Face_Mask_Detection/Brill Formulation Software Crack ((HOT)) Sites.md
deleted file mode 100644
index 52a39a3dbda8351388670e6b67db0c57693b5e68..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Brill Formulation Software Crack ((HOT)) Sites.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-<p>phase ii:the proposed project will develop and validate a new family of polyhedral meshing methods. the first aim of the project is to understand the performance of spectral meshing algorithms on high-dimensional problems. the second aim is to develop and validate two families of polyhedral meshing methods: (1) a high-order local meshing approach that provides high-quality mesh parameterization and can be efficiently implemented via spectral decomposition; (2) a mixed approach that leverages the spectral properties of the underlying domains in order to efficiently derive a hierarchy of meshes that are locally adapted to the problem at hand. the proposed methodologies will be tested and validated on a range of problems including but not limited to: (a) the use of polyhedral meshes for the solution of quasi-stationary problems; (b) the use of polyhedral meshes for the solution of elastic and elastoplastic composite problems; (c) the use of polyhedral meshes for the solution of nonlinear elastoplastic composite problems; (d) the use of polyhedral meshes for the solution of nonlinear elastic composites.</p>
-<p>phase iv:demonstrate the ability to characterize the defect and the soft-ness of the material by testing the crack-closing capability of the material, or its crack-healing capability. either test will demonstrate the concept of self-healing for the material.</p>
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diff --git a/spaces/falterWliame/Face_Mask_Detection/HD Online Player (Office 2013-2019 C2R Install 6.4.5 B).md b/spaces/falterWliame/Face_Mask_Detection/HD Online Player (Office 2013-2019 C2R Install 6.4.5 B).md
deleted file mode 100644
index aed012deceb3fed7973c7463b7ed3010cfe1c61f..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/HD Online Player (Office 2013-2019 C2R Install 6.4.5 B).md
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diff --git a/spaces/falterWliame/Face_Mask_Detection/Pokemon Play It V2.iso (Tested And Works On Vista) 49.md b/spaces/falterWliame/Face_Mask_Detection/Pokemon Play It V2.iso (Tested And Works On Vista) 49.md
deleted file mode 100644
index 3e867d7eef96e1249e65284f252a880486b41590..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Pokemon Play It V2.iso (Tested And Works On Vista) 49.md
+++ /dev/null
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diff --git a/spaces/fatiXbelha/sd/Download Honey Live APK and Send Gifts to Your Friends in Video Chat.md b/spaces/fatiXbelha/sd/Download Honey Live APK and Send Gifts to Your Friends in Video Chat.md
deleted file mode 100644
index 179b17c435a1a5ad96a748d2ba1f4f9df7980655..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Honey Live APK and Send Gifts to Your Friends in Video Chat.md
+++ /dev/null
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-<br />
-<h1>Download Honey Live APK: A Live Streaming App That Lets You Connect With Friends</h1>
-<p>Do you ever wake up and feel bored with your life? Do you want to make new friends from different countries and cultures? Do you want to have fun and exciting video chats with your friends anytime and anywhere?</p>
-<p>If you answered yes to any of these questions, then you might want to try <strong>Honey Live APK</strong>, a live streaming app that lets you connect with friends through video calling, text translation, sending gifts, and more. In this article, we will tell you what Honey Live APK is, how to download it, how to use it, and what are its pros and cons.</p>
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-<h2>What is Honey Live APK?</h2>
-<p>Honey Live APK is a 1-on-1 and multiplayer online video chat app that allows you to keep in touch with your friends. It is available for both Android and iOS devices. With Honey Live APK, you can communicate with your friends like face to face anytime and anywhere. You can also meet new people from different countries and regions through live streaming.</p>
-<h3>Features of Honey Live APK</h3>
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-<h4>Sending gifts in video chat or text chat</h4>
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-<p>You can use real-time translation to understand what your friends or streamers are saying in different languages. You can also speak in your own language and let the app translate it for you. You can choose from over 100 languages to communicate with anyone in the world.</p>
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-<p>You can use various stickers, emojis, and filters to express your emotions and personality. You can also create your own avatar and customize it with different outfits, hairstyles, accessories, etc. You can also use beauty effects to enhance your appearance and look more attractive.</p>
-<h3>How to download Honey Live APK?</h3>
-<p>If you want to download Honey Live APK, you need to follow these steps:</p>
-<h4>Steps to download Honey Live APK for Android devices</h4>
-<ol>
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-<li>Wait for the download to finish and then open the file.</li>
-<li>Follow the instructions on the screen and install the app.</li>
-<li>Enjoy using Honey Live APK on your Android device.</li>
-</ol>
-<h4>Steps to download Honey Live APK for iOS devices</h4>
-<ol>
-<li>Go to the App Store and search for Honey Live - Video Chat.</li>
-<li>Tap on the get button and enter your Apple ID and password.</li>
-<li>Wait for the download to finish and then open the app.</li>
-<li>Follow the instructions on the screen and sign up or log in with your account.</li>
-<li>Enjoy using Honey Live APK on your iOS device.</li>
-</ol>
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-<h4>How to create an account on Honey Live APK?</h4>
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-<li>Enter your phone number or email address and verify it with a code.</li>
-<li>Create a password and a username for your account.</li>
-<li>Add a profile photo and some basic information about yourself.</li>
-<li>You can also link your Facebook, Google, or Apple account to sign up faster.</li>
-</ul> <h4>How to make 1-on-1 video calls on Honey Live APK?</h4>
-<p>To make 1-on-1 video calls on Honey Live APK, you need to do the following:</p>
-<ul>
-<li>Tap on the contacts icon on the bottom of the screen.</li>
-<li>Select a friend from your contact list or search for a new friend by name or ID.</li>
-<li>Tap on the video call icon on the top right corner of the screen.</li>
-<li>Wait for your friend to answer and enjoy your video chat.</li>
-<li>You can also switch to voice call, text chat, or send gifts during the video call.</li>
-</ul>
-<h4>How to join multiplayer video chats on Honey Live APK?</h4>
-<p>To join multiplayer video chats on Honey Live APK, you need to do the following:</p>
-<ul>
-<li>Tap on the live icon on the bottom of the screen.</li>
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-<li>Tap on the join button and enter the live room.</li>
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-</ul>
-<h4>How to send gifts and stickers on Honey Live APK?</h4>
-<p>To send gifts and stickers on Honey Live APK, you need to do the following:</p>
-<ul>
-<li>Tap on the gift icon on the bottom of the screen.</li>
-<li>Select a gift or a sticker from the list and tap on it.</li>
-<li>Choose how many times you want to send it and tap on the send button.</li>
-<li>Your gift or sticker will appear on the screen and your friend or streamer will receive it.</li>
-<li>You can also use virtual coins to buy more gifts or stickers from the store.</li>
-</ul>
-<h2>Pros and cons of Honey Live APK</h2>
-<p>Honey Live APK is a fun and easy way to connect with your friends or meet new people. However, like any other app, it has its pros and cons. Here are some of them:</p>
-<h3>Pros of Honey Live APK</h3>
-<p>Some of the pros of Honey Live APK are:</p>
-<ul>
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-</ul>
-<h3>Cons of Honey Live APK</h3>
-<p>Some of the cons of Honey Live APK are:</p>
-<ul>
-<li>It requires a stable internet connection and enough storage space.</li>
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-<p>We hope this article has given you some useful information about Honey Live APK and how to download it and use it. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
-<h3>FAQs</h3>
-<p>Here are some frequently asked questions about Honey Live APK:</p>
-<ol>
-<li>Is Honey Live APK safe to use?</li>
-<p>Honey Live APK is generally safe to use as long as you download it from the official website or the App Store and follow the instructions to install it and use it. However, you should also be careful about your privacy and security and avoid sharing any personal information or location with strangers. You should also report any suspicious or abusive behavior or content to the app's customer service.</p>
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-</ol></p> 401be4b1e0<br />
-<br />
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\ No newline at end of file
diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/configs/ms1mv3_mbf.py b/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/configs/ms1mv3_mbf.py
deleted file mode 100644
index b8a00d6305eeda5a94788017afc1cda0d4a4cd2a..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/models/arcface_torch/configs/ms1mv3_mbf.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G tmpfs /train_tmp
-
-config = edict()
-config.loss = "arcface"
-config.network = "mbf"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 2e-4
-config.batch_size = 128
-config.lr = 0.1 # batch size is 512
-
-config.rec = "/train_tmp/ms1m-retinaface-t1"
-config.num_classes = 93431
-config.num_image = 5179510
-config.num_epoch = 30
-config.warmup_epoch = -1
-config.decay_epoch = [10, 20, 25]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/fclong/summary/fengshen/examples/FastDemo/YuyuanQA.py b/spaces/fclong/summary/fengshen/examples/FastDemo/YuyuanQA.py
deleted file mode 100644
index fed2d19bc61e0735f3868e1a30a532bd19fbb4b0..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/FastDemo/YuyuanQA.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import requests
-import langid
-import streamlit as st
-from translate import baiduTranslatorMedical
-from translate import baiduTranslator
-
-langid.set_languages(['en', 'zh'])
-lang_dic = {'zh': 'en', 'en': 'zh'}
-
-st.set_page_config(
- page_title="余元医疗问答",
- page_icon=":shark:",
- # layout="wide",
- initial_sidebar_state="expanded",
- menu_items={
- 'Get Help': 'https://www.extremelycoolapp.com/help',
- 'Report a bug': "https://www.extremelycoolapp.com/bug",
- 'About': "# This is a header. This is an *extremely* cool app!"
- }
-)
-st.title('Demo for MedicalQA')
-
-
-st.sidebar.header("参数配置")
-sbform = st.sidebar.form("固定参数设置")
-n_sample = sbform.slider("设置返回条数", min_value=1, max_value=10, value=3)
-text_length = sbform.slider('生成长度:', min_value=32, max_value=512, value=64, step=32)
-text_level = sbform.slider('文本多样性:', min_value=0.1, max_value=1.0, value=0.9, step=0.1)
-model_id = sbform.number_input('选择模型号:', min_value=0, max_value=13, value=13, step=1)
-trans = sbform.selectbox('选择翻译内核', ['百度通用', '医疗生物'])
-sbform.form_submit_button("配置")
-
-
-form = st.form("参数设置")
-input_text = form.text_input('请输入你的问题:', value='', placeholder='例如：糖尿病的症状有哪些？')
-if trans == '百度通用':
- translator = 'baidu_common'
-else:
- translator = 'baidu'
-if input_text:
- lang = langid.classify(input_text)[0]
- if translator == 'baidu':
- st.write('**你的问题是:**', baiduTranslatorMedical(input_text, src=lang, dest=lang_dic[lang]).text)
- else:
- st.write('**你的问题是:**', baiduTranslator(input_text, src=lang, dest=lang_dic[lang]).text)
-
-form.form_submit_button("提交")
-
-# @st.cache(suppress_st_warning=True)
-
-
-def generate_qa(input_text, n_sample, model_id='7', length=64, translator='baidu', level=0.7):
- # st.write('调用了generate函数')
- URL = 'http://192.168.190.63:6605/qa'
- data = {"text": input_text, "n_sample": n_sample, "model_id": model_id,
- "length": length, 'translator': translator, 'level': level}
- r = requests.get(URL, params=data)
- return r.text
-# my_bar = st.progress(80)
-
-
-with st.spinner('老夫正在思考中🤔...'):
- if input_text:
- results = generate_qa(input_text, n_sample, model_id=str(model_id),
- translator=translator, length=text_length, level=text_level)
- for idx, item in enumerate(eval(results), start=1):
- st.markdown(f"""
- **候选回答「{idx}」:**\n
- """)
- st.info('中文：%s' % item['fy_next_sentence'])
- st.info('英文：%s' % item['next_sentence'])
diff --git a/spaces/fengmuxi/ChatGpt-Web/app/locales/es.ts b/spaces/fengmuxi/ChatGpt-Web/app/locales/es.ts
deleted file mode 100644
index 823ca1b3551c3ea9d0e5ab280f148f03c44c071a..0000000000000000000000000000000000000000
--- a/spaces/fengmuxi/ChatGpt-Web/app/locales/es.ts
+++ /dev/null
@@ -1,273 +0,0 @@
-import { SubmitKey } from "../store/config";
-import type { LocaleType } from "./index";
-
-const es: LocaleType = {
- WIP: "En construcción...",
- Error: {
- Unauthorized:
- "Acceso no autorizado, por favor ingrese el código de acceso en la página de configuración.",
- },
- ChatItem: {
- ChatItemCount: (count: number) => `${count} mensajes`,
- },
- Chat: {
- SubTitle: (count: number) => `${count} mensajes con ChatGPT`,
- Actions: {
- ChatList: "Ir a la lista de chats",
- CompressedHistory: "Historial de memoria comprimido",
- Export: "Exportar todos los mensajes como Markdown",
- Copy: "Copiar",
- Stop: "Detener",
- Retry: "Reintentar",
- Delete: "Delete",
- },
- Rename: "Renombrar chat",
- Typing: "Escribiendo...",
- Input: (submitKey: string) => {
- var inputHints = `Escribe algo y presiona ${submitKey} para enviar`;
- if (submitKey === String(SubmitKey.Enter)) {
- inputHints += ", presiona Shift + Enter para nueva línea";
- }
- return inputHints;
- },
- Send: "Enviar",
- Config: {
- Reset: "Reset to Default",
- SaveAs: "Save as Mask",
- },
- },
- Export: {
- Title: "Todos los mensajes",
- Copy: "Copiar todo",
- Download: "Descargar",
- MessageFromYou: "Mensaje de ti",
- MessageFromChatGPT: "Mensaje de ChatGPT",
- },
- Memory: {
- Title: "Historial de memoria",
- EmptyContent: "Aún no hay nada.",
- Copy: "Copiar todo",
- Send: "Send Memory",
- Reset: "Reset Session",
- ResetConfirm:
- "Resetting will clear the current conversation history and historical memory. Are you sure you want to reset?",
- },
- Home: {
- NewChat: "Nuevo chat",
- DeleteChat: "¿Confirmar eliminación de la conversación seleccionada?",
- DeleteToast: "Chat Deleted",
- Revert: "Revert",
- },
- User:{
- Title: "Usuario",
- SubTitle: "Interfaz de información de usuario",
- Login:"Iniciar sesión",
- LoginTitle:"El usuario inicia sesión",
- Register:"Inscribirse",
- RegisterTitle:"Registrar un nuevo usuario",
- Findpwd:"Recuperar la contraseña",
- FindpwdTitle:"Ingrese la contraseña de su cuenta y se enviará a su correo electrónico",
- Name:"Nombre de usuario",
- Wallet:"Créditos de usuario",
- Mail:"Buzón de usuario",
- SigState:"Estado del check-in",
- Ststus:"Cerrar Sesión",
- Vip:"Miembro",
- kami:"Código de canje",
- NickName:"Apodo",
- User:"Número de cuenta (solo números)",
- Password:"Contraseña (mínimo 6 dígitos)",
- Email:"Buzón",
- Code:"Captcha",
- Pass:{
- Title:"修改密码",
- OldPwd:"旧密码",
- NewPwd:"新密码",
- NewPwd1:"确认密码"
- },
- Save:"保存"
- },
- Settings: {
- Title: "Configuración",
- SubTitle: "Todas las configuraciones",
- Actions: {
- ClearAll: "Borrar todos los datos",
- ResetAll: "Restablecer todas las configuraciones",
- Close: "Cerrar",
- ConfirmResetAll: "Are you sure you want to reset all configurations?",
- ConfirmClearAll: "Are you sure you want to reset all chat?",
- },
- Lang: {
- Name: "Language",
- All: "All Languages",
- Options: {
- cn: "简体中文",
- en: "Inglés",
- tw: "繁體中文",
- es: "Español",
- it: "Italiano",
- tr: "Türkçe",
- jp: "日本語",
- de: "Deutsch",
- },
- },
- Avatar: "Avatar",
- FontSize: {
- Title: "Tamaño de fuente",
- SubTitle: "Ajustar el tamaño de fuente del contenido del chat",
- },
- Update: {
- Version: (x: string) => `Versión: ${x}`,
- IsLatest: "Última versión",
- CheckUpdate: "Buscar actualizaciones",
- IsChecking: "Buscando actualizaciones...",
- FoundUpdate: (x: string) => `Se encontró una nueva versión: ${x}`,
- GoToUpdate: "Actualizar",
- },
- SendKey: "Tecla de envío",
- Theme: "Tema",
- TightBorder: "Borde ajustado",
- SendPreviewBubble: {
- Title: "Enviar burbuja de vista previa",
- SubTitle: "Preview markdown in bubble",
- },
- Mask: {
- Title: "Mask Splash Screen",
- SubTitle: "Show a mask splash screen before starting new chat",
- },
- Prompt: {
- Disable: {
- Title: "Desactivar autocompletado",
- SubTitle: "Escribe / para activar el autocompletado",
- },
- List: "Lista de autocompletado",
- ListCount: (builtin: number, custom: number) =>
- `${builtin} incorporado, ${custom} definido por el usuario`,
- Edit: "Editar",
- Modal: {
- Title: "Prompt List",
- Add: "Add One",
- Search: "Search Prompts",
- },
- EditModal: {
- Title: "Edit Prompt",
- },
- },
- HistoryCount: {
- Title: "Cantidad de mensajes adjuntos",
- SubTitle: "Número de mensajes enviados adjuntos por solicitud",
- },
- CompressThreshold: {
- Title: "Umbral de compresión de historial",
- SubTitle:
- "Se comprimirán los mensajes si la longitud de los mensajes no comprimidos supera el valor",
- },
- Token: {
- Title: "Clave de API",
- SubTitle: "Utiliza tu clave para ignorar el límite de código de acceso",
- Placeholder: "Clave de la API de OpenAI",
- },
- Usage: {
- Title: "Saldo de la cuenta",
- SubTitle(used: any, total: any) {
- return `Usado $${used}, subscription $${total}`;
- },
- IsChecking: "Comprobando...",
- Check: "Comprobar de nuevo",
- NoAccess: "Introduzca la clave API para comprobar el saldo",
- },
- AccessCode: {
- Title: "Código de acceso",
- SubTitle: "Control de acceso habilitado",
- Placeholder: "Necesita código de acceso",
- },
- Bot: "Proveedores de IA (bot)",
- Model: "Modelo",
- Temperature: {
- Title: "Temperatura",
- SubTitle: "Un valor mayor genera una salida más aleatoria",
- },
- MaxTokens: {
- Title: "Máximo de tokens",
- SubTitle: "Longitud máxima de tokens de entrada y tokens generados",
- },
- PresencePenalty: {
- Title: "Penalización de presencia",
- SubTitle:
- "Un valor mayor aumenta la probabilidad de hablar sobre nuevos temas",
- },
- },
- Store: {
- DefaultTopic: "Nueva conversación",
- BotHello: "¡Hola! ¿Cómo puedo ayudarte hoy?",
- Error: "Algo salió mal, por favor intenta nuevamente más tarde.",
- Prompt: {
- History: (content: string) =>
- "Este es un resumen del historial del chat entre la IA y el usuario como recapitulación: " +
- content,
- Topic:
- "Por favor, genera un título de cuatro a cinco palabras que resuma nuestra conversación sin ningún inicio, puntuación, comillas, puntos, símbolos o texto adicional. Elimina las comillas que lo envuelven.",
- Summarize:
- "Resuma nuestra discusión brevemente en 200 caracteres o menos para usarlo como un recordatorio para futuros contextos.",
- },
- },
- Copy: {
- Success: "Copiado al portapapeles",
- Failed:
- "La copia falló, por favor concede permiso para acceder al portapapeles",
- },
- Context: {
- Toast: (x: any) => `With ${x} contextual prompts`,
- Edit: "Contextual and Memory Prompts",
- Add: "Add One",
- },
- Plugin: {
- Name: "Plugin",
- },
- Mask: {
- Name: "Mask",
- Page: {
- Title: "Prompt Template",
- SubTitle: (count: number) => `${count} prompt templates`,
- Search: "Search Templates",
- Create: "Create",
- },
- Item: {
- Info: (count: number) => `${count} prompts`,
- Chat: "Chat",
- View: "View",
- Edit: "Edit",
- Delete: "Delete",
- DeleteConfirm: "Confirm to delete?",
- },
- EditModal: {
- Title: (readonly: boolean) =>
- `Edit Prompt Template ${readonly ? "(readonly)" : ""}`,
- Download: "Download",
- Clone: "Clone",
- },
- Config: {
- Avatar: "Bot Avatar",
- Name: "Bot Name",
- },
- },
- NewChat: {
- Return: "Return",
- Skip: "Skip",
- Title: "Pick a Mask",
- SubTitle: "Chat with the Soul behind the Mask",
- More: "Find More",
- NotShow: "Not Show Again",
- ConfirmNoShow: "Confirm to disable？You can enable it in settings later.",
- },
-
- UI: {
- Confirm: "Confirm",
- Cancel: "Cancel",
- Close: "Close",
- Create: "Create",
- Edit: "Edit",
- },
-};
-
-export default es;
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/2018 Malayalam Movie Download Kuttymovies The Blockbuster of the Year.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/2018 Malayalam Movie Download Kuttymovies The Blockbuster of the Year.md
deleted file mode 100644
index 306828e5bddc46b599c2ecd998bdd867f4abd50a..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/2018 Malayalam Movie Download Kuttymovies The Blockbuster of the Year.md
+++ /dev/null
@@ -1,97 +0,0 @@
-
-<h1>How to Download Malayalam Movies from Kuttymovies in 2018</h1>
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- <h3>Step 2: Download the movie file</h3>
-<p>The second step is to download the movie file from Kuttymovies. You can do this by choosing a download server and method on the website. To choose a download server, you can click on the download button or link on the movie page, and select one of the available options. You will see different servers with different names, such as Server 1, Server 2, Server 3, etc. You should choose a server that has a fast speed and a low load. You can also use multiple servers to download different parts of the movie file simultaneously.</p>
-<p>To choose a download method, you can either use direct download or torrent download on Kuttymovies. Direct download is when you download the movie file directly from the server to your device without using any intermediary software or service. Torrent download is when you use a torrent client or software to download the movie file from other users who have already downloaded it or are downloading it at the same time as you. You will need a torrent file or magnet link to start a torrent download.</p>
-<p>You may also need to use a VPN or proxy to bypass geo-restrictions and ISP throttling on Kuttymovies. A VPN or proxy is a service or software that hides your IP address and location and allows you to access websites that are blocked or restricted in your region or by your internet service provider (ISP). You can use a VPN or proxy to access Kuttymovies if it is banned or blocked in your country or by your ISP. You can also use a VPN or proxy to prevent your ISP from slowing down your internet speed or monitoring your online activity when you are downloading movies from Kuttymovies.</p>
-<p>You may also want to use a download manager or torrent client to speed up and resume downloads on Kuttymovies. A download manager or torrent client is a software that helps you manage your downloads more efficiently and effectively. You can use a download manager or torrent client to increase your download speed by using multiple connections and sources, pause and resume your downloads at any time, schedule your downloads for later times, organize your downloads by categories and folders, etc.</p>
- <h3>Step 3: Enjoy the movie</h3>
-<p>The third step is to enjoy the movie you have downloaded from Kuttymovies. You can do this by playing and watching the downloaded movie file on your device. To play and watch the downloaded movie file on your device, you will need a media player that supports the format of the movie file. You can use any media player that you prefer, such as VLC Media Player, Windows Media Player, KMPlayer, etc. You can also use online media players that allow you to stream movies from your device to your TV, laptop, or smartphone. You can also use a USB drive, an HDMI cable, or a Chromecast device to connect your device to a larger screen, such as a TV or a projector.</p>
-<p>To use subtitles and audio tracks on Kuttymovies movies, you will need to download them separately from the website or other sources. You can find subtitles and audio tracks for Malayalam movies in various languages, such as English, Hindi, Tamil, Telugu, etc. You can download them as separate files or as part of the movie file. To use them, you will need to load them on your media player and sync them with the movie file. You can also adjust the font size, color, and position of the subtitles on your media player.</p>
-<p>To share and recommend movies from Kuttymovies to your friends and family, you can use social media platforms, messaging apps, or email services. You can share the movie title, poster, trailer, synopsis, rating, or review of the movie with your contacts. You can also share the download link or torrent file of the movie with them, but be careful not to infringe any copyright laws or expose them to any risks or threats. You can also create a watch party or a movie night with your friends and family and enjoy the movie together.</p>
- <h2>Conclusion</h2>
-<p>In this article, we have shown you how to download Malayalam movies from Kuttymovies in 2018. We have also provided you with some useful information and tips on how to use Kuttymovies safely and legally. We hope you have found this article helpful and informative. However, we would like to remind you that downloading movies from Kuttymovies is not a legal or ethical practice, and it may have negative consequences for you and the Malayalam film industry. Therefore, we urge you to respect the rights and efforts of the filmmakers and watch their movies legally through official channels and platforms.</p>
-<p>If you have any questions or comments about this article, please feel free to leave them below. We would love to hear from you and get your feedback. Thank you for reading this article and happy watching!</p>
- <h2>FAQs</h2>
-<h3>Is Kuttymovies legal?</h3>
-<p>No, Kuttymovies is not a legal website. It hosts and distributes pirated copies of movies without the permission or consent of the original creators and distributors. This is a violation of the copyright laws and intellectual property rights of the filmmakers. By downloading movies from Kuttymovies, you are also breaking the law and risking legal actions and penalties.</p>
- <h3>Is Kuttymovies safe?</h3>
-<p>No, Kuttymovies is not a safe website to visit or use. It may contain malware, viruses, pop-ups, ads, and other harmful elements that can damage your device or compromise your privacy. It may also change its domain name frequently, go offline without notice, or provide broken or fake links that do not work. Therefore, you should be careful and cautious when using Kuttymovies.</p>
- <h3>What are some alternatives to Kuttymovies?</h3>
-<p>If you are looking for some alternatives to Kuttymovies that are legal and safe to use, you can try these options:</p>
-<ul>
-<li>Hotstar: Hotstar is a streaming service that offers a wide range of Malayalam movies, as well as other Indian languages and English movies. You can watch movies online or download them for offline viewing. You can also access live sports, TV shows, news, and more on Hotstar.</li>
-<li>Amazon Prime Video: Amazon Prime Video is another streaming service that offers a variety of Malayalam movies, as well as other regional and international movies. You can watch movies online or download them for offline viewing. You can also access original content, TV shows, music, books, and more on Amazon Prime Video.</li>
-<li>YouTube: YouTube is a video-sharing platform that hosts many Malayalam movies uploaded by official channels or users. You can watch movies online for free or rent or buy them for a fee. You can also access other videos, music, podcasts, live events, and more on YouTube.</li>
-</ul>
- <h3>What are some of the best Malayalam movies of 2018?</h3>
-<p>There are many Malayalam movies that were released in 2018 that received critical acclaim and audience appreciation. Some of them are:</p>
-<ul>
-<li>Ee.Ma.Yau: Ee.Ma.Yau is a dark comedy-drama film directed by Lijo Jose Pellissery. It tells the story of a son who tries to fulfill his father's last wish of having a grand funeral ceremony in a coastal village.</li>
-<li>Sudani from Nigeria: Sudani from Nigeria is a comedy-drama film directed by Zakariya Mohammed. It tells the story of a football manager who befriends a Nigerian player who gets injured during a tournament.</li>
-<li>Varathan: Varathan is a thriller film directed by Amal Neerad. It tells the story of a couple who moves to a remote hill station after losing their jobs in Dubai, and faces harassment and violence from the locals.</li>
-<li>Koode: Koode is a drama film directed by Anjali Menon. It tells the story of a brother and sister who reunite after a long time, and how their lives change with the presence of a mysterious girl.</li>
-<li>Kumbalangi Nights: Kumbalangi Nights is a comedy-drama film directed by Madhu C. Narayanan. It tells the story of four brothers who live in a dysfunctional family in a fishing village, and how they find love and happiness in unexpected ways.</li>
-</ul>
- <h3>How can I support the Malayalam film industry?</h3>
-<p>If you love Malayalam cinema and want to support the Malayalam film industry, you can do so by watching their movies legally and ethically. You can watch their movies in theatres, or on official streaming platforms or channels. You can also buy or rent their movies from authorized sources or platforms. You can also support the Malayalam film industry by following their social media accounts, subscribing to their newsletters, joining their fan clubs, attending their events, buying their merchandise, donating to their causes, etc.</p> 401be4b1e0<br />
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-<h1>Class 11 Chemistry NCERT Book PDF Download 2020-21</h1>
- <h2>Introduction</h2>
- <p>Chemistry is one of the most important subjects for students who aspire to pursue a career in science, engineering, medicine, or pharmacy. It is also a fascinating subject that deals with the composition, structure, properties, and reactions of matter. To master chemistry, one needs to have a clear understanding of the basic concepts, principles, and techniques.</p>
- <p>One of the best sources of learning chemistry is the NCERT book for class 11 chemistry. NCERT stands for National Council of Educational Research and Training, which is an autonomous organization that publishes textbooks for all subjects from classes 1 to 12. These textbooks are based on the latest syllabus prescribed by CBSE (Central Board of Secondary Education) and follow a simple, lucid, and engaging style of writing.</p>
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- <p>If you are looking for a PDF version of the class 11 chemistry NCERT book, you can download it from <a href="(^1^)">this link</a>. You can also access individual chapters from <a href="(^2^)">this link</a>. Alternatively, you can use <a href="(^3^)">this website</a> to read online or download any NCERT book for any class or subject.</p>
- <h2>Chapter-wise Summary of Class 11 Chemistry NCERT Book</h2>
- <h3>Chapter 1: Some Basic Concepts of Chemistry</h3>
- <p>This chapter introduces you to some fundamental concepts of chemistry such as laws of chemical combination, Dalton's atomic theory, mole concept, stoichiometry, empirical and molecular formulae, chemical reactions, etc. You will learn how to:</p>
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- <ul>
-<li>Define chemistry and its scope.</li>
-<li>State and apply various laws of chemical combination.</li>
-<li>Explain Dalton's atomic theory and its limitations.</li>
-<li>Differentiate between atoms and molecules.</li>
-<li>Calculate atomic and molecular masses <li>Define and use the concept of mole and molar mass.</li>
-<li>Perform calculations involving percentage composition and empirical and molecular formulae.</li>
-<li>Balance chemical equations using the law of conservation of mass.</li>
-<li>Classify chemical reactions based on different criteria.</li>
-</ul>
- <h3>Chapter 2: Structure of Atom</h3>
- <p>This chapter deals with the structure of atom and the various models and experiments that led to its discovery. You will learn how to:</p>
- <ul>
-<li>Describe the salient features of Thomson's, Rutherford's, and Bohr's models of atom.</li>
-<li>Explain the limitations and drawbacks of these models.</li>
-<li>State the postulates of quantum mechanical model of atom.</li>
-<li>Define and use the terms such as orbitals, quantum numbers, shapes, and orientations of orbitals.</li>
-<li>Write the electronic configuration of atoms using the aufbau principle, Pauli's exclusion principle, and Hund's rule of maximum multiplicity.</li>
-<li>Differentiate between isotopes, isobars, and isotones.</li>
-</ul>
- <h3>Chapter 3: Classification of Elements and Periodicity in Properties</h3>
- <p>This chapter covers the classification of elements based on their properties and periodic trends. You will learn how to:</p>
- <ul>
-<li>Explain the historical development of periodic table by Dobereiner, Newlands, Mendeleev, and Moseley.</li>
-<li>State the modern periodic law and describe the salient features of the modern periodic table.</li>
-<li>Define and use the terms such as atomic number, atomic radius, ionization energy, electron affinity, electronegativity, valency, etc.</li>
-<li>Predict the periodic trends in these properties across periods and groups.</li>
-<li>Relate these properties with the electronic configuration of elements.</li>
-<li>Identify and explain the anomalies and exceptions in these trends.</li>
-</ul>
- <h3>Chapter 4: Chemical Bonding and Molecular Structure</h3>
- <p>This chapter explains the concept of chemical bonding and molecular structure. You will learn how to:</p>
- <ul>
-<li>Define chemical bond and its types such as ionic, covalent, coordinate, metallic, hydrogen, etc.</li>
-<li>Explain the formation of ionic bond using electrostatic force, lattice energy, solvation energy, etc.</li>
-<li>Explain the formation of covalent bond using Lewis dot structure, valence bond theory, hybridization, etc.</li>
-<li>Predict the shape and polarity of molecules using VSEPR theory and dipole moment.</li>
-<li>Explain the formation of coordinate bond using examples.</li>
-<li>Describe the characteristics of metallic bond using electron sea model, band theory, etc.</li>
-<li>Explain the formation and properties of hydrogen bond using examples.</li>
-</ul>
- <h3>Chapter 5: States of Matter</h3>
- <p>This chapter discusses the three states of matter: solid, liquid, and gas. You will learn how to:</p>
- <ul>
-<li>Differentiate between solid, liquid, and gas based on their intermolecular forces, shape, volume, density, etc.</li>
-<li>Classify solids into crystalline and amorphous based on their structure and properties.</li>
-<li>Determine the type of unit cell and calculate the number of atoms per unit cell in different types of cubic crystals.</li>
-<li>Define and use the terms such as close packing, packing efficiency, coordination number, etc. in relation to solids.</li>
-<li>Explain the kinetic molecular theory of gases and derive various gas laws from it.</li>
-<li>Solve numerical problems involving ideal gas equation (PV = nRT).</li>
-<li>Distinguish between ideal and real gases based on their deviation from ideal behavior at high pressure and low temperature.</li>
-</ul>
- <h3>Chapter 6: Thermodynamics</h3>
- <p>This chapter introduces you to thermodynamics which is the study of energy changes in physical and chemical processes. You will learn how to:</p>
- <ul>
-<li>Define thermodynamics and its various terms such as system, surroundings, boundary, state variables, state functions, etc.</li>
-<li>Differentiate between open, closed, and isolated systems based on their exchange of matter and energy with surroundings.</li>
-<li>Differentiate between intensive and extensive properties based on their dependence on mass or amount of substance.</li>
-<li>Differentiate between isothermal, adiabatic, isobaric, isochoric processes based on their change in temperature or pressure or volume or heat transfer.</li>
-<li>State and apply the first law of thermodynamics which relates heat change (q), work done (w), and internal energy change (ΔU) in a system.</li>
-<li>Solve numerical problems involving heat capacity (C ), specific heat capacity (C<sub>s</sub>), molar heat capacity (C<sub>m</sub>), etc.</li>
-<li>Define and calculate enthalpy change (ΔH) for various types of reactions such as formation, combustion, neutralization, etc.</li>
-<li>State and apply Hess's law of constant heat summation to calculate the enthalpy change of a reaction using the enthalpy changes of other related reactions.</li>
-<li>Explain the concept of spontaneity and non-spontaneity of a process using the second law of thermodynamics.</li>
-<li>Define and calculate entropy change (ΔS) for a system and its surroundings.</li>
-<li>Define and calculate Gibbs free energy change (ΔG) for a process and relate it with the spontaneity, equilibrium, and feasibility of the process.</li>
-</ul>
- <h3>Chapter 7: Equilibrium</h3>
- <p>This chapter deals with the concept of equilibrium in physical and chemical processes. You will learn how to:</p>
- <ul>
-<li>Define equilibrium and its characteristics such as dynamic nature, reversibility, constancy of observable properties, etc.</li>
-<li>Differentiate between homogeneous and heterogeneous equilibrium based on the phases of reactants and products involved.</li>
-<li>Differentiate between physical and chemical equilibrium based on the nature of change involved.</li>
-<li>State and apply the law of mass action and the law of chemical equilibrium to express the equilibrium constant (K<sub>c</sub>) for a given reaction.</li>
-<li>Relate the equilibrium constant with the extent of reaction and the direction of reaction.</li>
-<li>Solve numerical problems involving the calculation of equilibrium constant, concentration, pressure, degree of dissociation, etc. for various reactions.</li>
-<li>Explain the effect of temperature, pressure, concentration, and catalyst on the equilibrium position and equilibrium constant using Le Chatelier's principle.</li>
-<li>Define and use the terms such as acid, base, salt, pH, pOH, etc. in relation to aqueous solutions.</li>
-<li>State and apply the ionization constant of water (K<sub>w</sub>) to calculate the concentration of H and OH ions in a solution.</li>
-<li>State and apply the ionization constant of weak acids (K<sub>a</sub>) and weak bases (K<sub>b</sub>) to calculate the degree of ionization and pH of their solutions.</li>
-<li>State and apply the solubility product constant (K<sub>sp</sub>) to calculate the solubility and precipitation of sparingly soluble salts.</li>
-<li>State and apply the common ion effect to explain the decrease in solubility or ionization of a substance due to the presence of another substance having a common ion.</li>
-</ul>
- <h3>Chapter 8: Redox Reactions</h3>
- <p>This chapter explains the concept of redox reactions which involve oxidation and reduction. You will learn how to:</p>
- <ul>
-<li>Define oxidation and reduction in terms of loss or gain of electrons, increase or decrease in oxidation number, addition or removal of oxygen or hydrogen, etc.</li>
-<li>Differentiate between oxidizing agent and reducing agent based on their ability to accept or donate electrons.</li>
-<li>Determine the oxidation number of an atom in a compound or ion using certain rules and conventions.</li>
-<li>Identify redox reactions by comparing the oxidation numbers of atoms involved before and after the reaction.</li>
-<li>Balance redox reactions using two methods: oxidation number method and ion-electron method (half-reaction method).</li>
-<li>Solve numerical problems involving equivalent mass, normality, molarity, etc. for redox titrations.</li>
-</ul>
- <h3>Chapter 9: Hydrogen</h3>
- <p>This chapter covers the properties, preparation, uses, and compounds of hydrogen. You will learn how to:</p>
- <ul>
-<li>Describe the position, occurrence, isotopes, and dihydrogen bond of hydrogen in the periodic table.</li>
-<li>List the various methods of preparation of dihydrogen gas in laboratory and industry.</li>
-<li>List the physical and chemical properties of dihydrogen gas such as combustibility, reactivity, etc.</li>
-<li>List the uses of dihydrogen gas such as fuel, synthesis gas, ammonia production, etc.</li>
-<li>List the various compounds of hydrogen such as hydrides (ionic, covalent, metallic), water (structure, properties, hard and soft water), hydrogen peroxide (preparation , properties, uses), and heavy water (preparation, properties, uses).</li>
-</ul>
- <h3>Chapter 10: The s-Block Elements</h3>
- <p>This chapter discusses the elements of group 1 and group 2 of the periodic table, also known as the s-block elements. You will learn how to:</p>
- <ul>
-<li>Describe the general characteristics of s-block elements such as electronic configuration, atomic and ionic radii, ionization energy, electronegativity, metallic character, etc.</li>
-<li>Describe the anomalous behavior of lithium and beryllium due to their small size and high charge density.</li>
-<li>List the various methods of preparation, properties, and uses of alkali metals (lithium, sodium, potassium, rubidium, cesium, francium) and their compounds such as oxides, hydroxides, halides, carbonates, bicarbonates, nitrates, etc.</li>
-<li>List the various methods of preparation, properties, and uses of alkaline earth metals (beryllium, magnesium, calcium, strontium, barium, radium) and their compounds such as oxides, hydroxides, halides, sulfates, carbonates, bicarbonates, nitrates, etc.</li>
-<li>Explain the biological importance of sodium, potassium, magnesium, and calcium in living organisms.</li>
-</ul>
- <h3>Chapter 11: The p-Block Elements</h3>
- <p>This chapter covers the elements of group 13 to group 18 of the periodic table, also known as the p-block elements. You will learn how to:</p>
- <ul>
-<li>Describe the general characteristics of p-block elements such as electronic configuration, atomic and ionic radii, ionization energy, electronegativity, metallic and non-metallic character, etc.</li>
-<li>Describe the variation in properties and trends across periods and groups in p-block elements.</li>
-<li>List the various methods of preparation, properties, and uses of boron and aluminum (group 13 elements) and their compounds such as borax, boric acid, boron hydrides (diborane), aluminum oxide (alumina), aluminum chloride (alcl3), etc.</li>
-<li>List the various methods of preparation, properties, and uses of carbon and silicon (group 14 elements) and their compounds such as carbon dioxide (co2), carbon monoxide (co), carbonates (caco3), bicarbonates (nahco3), silicon dioxide (sio2), silicones (polysiloxanes), silicates (sodium silicate), etc.</li>
-<li>List the various methods of preparation , properties, and uses of nitrogen and phosphorus (group 15 elements) and their compounds such as nitrogen gas (n2), ammonia (nh3), nitric acid (hno3), nitrates (nano3), phosphine (ph3), phosphorus pentoxide (p2o5), phosphoric acid (h3po4), etc.</li>
-<li>List the various methods of preparation, properties, and uses of oxygen and sulfur (group 16 elements) and their compounds such as oxygen gas (o2), ozone (o3), hydrogen peroxide (h2o2), water (h2o), sulfur dioxide (so2), sulfuric acid (h2so4), sulfates (cuso4), etc.</li>
-<li>List the various methods of preparation, properties, and uses of halogens (fluorine, chlorine, bromine, iodine, astatine) (group 17 elements) and their compounds such as hydrogen halides (hcl, hbr, hi), interhalogen compounds (cl2, br2, i2), halide salts (nacl, kbr, ki), etc.</li>
-<li>List the various methods of preparation, properties, and uses of noble gases (helium, neon, argon, krypton, xenon, radon) (group 18 elements) and their compounds such as xenon fluorides (xf2, xf4, xf6), xenon oxides (x2o, xeo3, xeo4), etc.</li>
-</ul>
- <h3>Chapter 12: Organic Chemistry – Some Basic Principles and Techniques</h3>
- <p>This chapter introduces you to organic chemistry which is the study of carbon and its compounds. You will learn how to:</p>
- <ul>
-<li>Define organic chemistry and its scope and importance.</li>
-<li>Explain the unique nature of carbon atom and its ability to form multiple bonds and chains.</li>
-<li>Classify organic compounds based on their functional groups and homologous series.</li>
-<li>Name organic compounds using IUPAC nomenclature rules.</li>
-<li>Write the structural and condensed formulae of organic compounds.</li>
-<li>Identify the types of hybridization and bond formation in organic compounds.</li>
-<li>Explain the concept of isomerism and its types such as structural and stereoisomerism.</li>
-<li>List the various methods of purification of organic compounds such as crystallization, sublimation, distillation, chromatography, etc.</li>
-<li>List the various methods of qualitative and quantitative analysis of organic compounds such as detection of elements, functional groups, molecular mass, etc.</li>
-</ul>
- <h3>Chapter 13: Hydrocarbons</h3>
- <p>This chapter deals with hydrocarbons which are organic compounds containing only carbon and hydrogen atoms. You will learn how to:</p>
- <ul>
-<li>Classify hydrocarbons into alkanes, alkenes, alkynes, and aromatic hydrocarbons based on their structure and degree of unsaturation.</li>
-<li>Name hydrocarbons using IUPAC nomenclature rules.</li>
-<li>Write the structural and condensed formulae of hydrocarbons.</li>
-<li>List the various methods of preparation of hydrocarbons from different sources such as alcohols, alkyl halides, carboxylic acids, etc.</li>
-<li>List the physical properties of hydrocarbons such as boiling point, melting point, density , solubility, etc.</li>
-<li>List the chemical properties of hydrocarbons such as combustion, halogenation, nitration, sulphonation, oxidation, polymerization, etc.</li>
-<li>Explain the mechanism of electrophilic and nucleophilic addition and substitution reactions of hydrocarbons using curly arrow notation.</li>
-<li>Explain the concept of aromaticity and its criteria based on Huckel's rule.</li>
-<li>Explain the structure and stability of benzene using resonance and molecular orbital theory.</li>
-<li>List the various methods of preparation, properties, and uses of benzene and its derivatives such as phenol, aniline, nitrobenzene, etc.</li>
-</ul>
- <h3>Chapter 14: Environmental Chemistry</h3>
- <p>This chapter covers the environmental chemistry which is the study of the chemical processes and phenomena that affect the environment. You will learn how to:</p>
- <ul>
-<li>Define environment and its components such as atmosphere, hydrosphere, lithosphere, and biosphere.</li>
-<li>Describe the structure and composition of atmosphere and its various layers such as troposphere, stratosphere, mesosphere, thermosphere, and exosphere.</li>
-<li>Explain the causes and effects of various types of environmental pollution such as air pollution, water pollution, soil pollution, noise pollution, radioactive pollution, etc.</li>
-<li>List the sources and harmful effects of various pollutants such as carbon monoxide, nitrogen oxides, sulfur dioxide, ozone, particulate matter, lead, mercury, arsenic, pesticides, etc.</li>
-<li>Explain the phenomenon of greenhouse effect and global warming and their consequences such as climate change, sea level rise, melting of glaciers, etc.</li>
-<li>Explain the phenomenon of ozone depletion and its causes such as chlorofluorocarbons (CFCs), halons, etc. and its effects such as increased UV radiation, skin cancer, eye cataract, etc.</li>
-<li>List the various methods of prevention and control of environmental pollution such as catalytic converters , scrubbers, electrostatic precipitators, sewage treatment, bioremediation, etc.</li>
-<li>List the various international agreements and protocols to protect the environment such as Kyoto Protocol, Montreal Protocol, Paris Agreement, etc.</li>
-</ul>
- <h2>Benefits of Reading NCERT Books for Class 11 Chemistry</h2>
- <p>NCERT books are the best books for class 11 chemistry as they provide the following benefits:</p>
- <ul>
-<li>They cover the entire syllabus of CBSE and follow the latest guidelines and exam pattern.</li>
-<li>They explain the concepts in a simple, clear, and logical manner with examples and diagrams.</li>
-<li>They provide ample exercises, questions, and problems for practice and revision.</li>
-<li>They help in developing the analytical and problem-solving skills of the students.</li>
-<li>They help in preparing for the board exams as well as competitive exams such as NEET, JEE, etc.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In this article, we have provided you with the class 11 chemistry NCERT book PDF download link and a chapter-wise summary of the book. We hope that this article will help you in your studies and enhance your interest in chemistry. Chemistry is a fascinating subject that has many applications in our daily life and future career. So, read the NCERT book carefully and enjoy learning chemistry.</p>
- <h2>FAQs</h2>
- <h4>Q1. How can I download the class 11 chemistry NCERT book PDF?</h4>
-<p>A1. You can download the class 11 chemistry NCERT book PDF from <a href="">this link</a>. You can also access individual chapters from <a href="">this link</a>. Alternatively, you can use <a href="">this website</a> to read online or download any NCERT book for any class or subject.</p>
- <h4>Q2. What are the main topics covered in the class 11 chemistry NCERT book?</h4>
-<p>A2. The main topics covered in the class 11 chemistry NCERT book are:</p>
- <ol>
-<li>Some Basic Concepts of Chemistry</li>
-<li>Structure of Atom</li>
-<li>Classification of Elements and Periodicity in Properties</li>
-<li>Chemical Bonding and Molecular Structure</li>
-<li>States of Matter</li>
-<li>Thermodynamics</li>
-<li>Equilibrium</li>
-<li>Redox Reactions</li>
-<li>Hydrogen</li>
-<li>The s-Block Elements</li>
-<li>The p-Block Elements</li>
-<li>Organic Chemistry – Some Basic Principles and Techniques</li>
-<li>Hydrocarbons</li>
-<li>Environmental Chemistry</li>
-</ol>
- <h4>Q3. How can I prepare for the class 11 chemistry exam using the NCERT book?</h4>
-<p>A3. You can prepare for the class 11 chemistry exam using the NCERT book by following these steps:</p>
- <ul>
-<li>Read the NCERT book thoroughly and understand the concepts and principles.</li>
-<li>Solve the exercises, questions, and problems given at the end of each chapter.</li>
-<li>Revise the important points, formulae, reactions, etc. regularly.</li>
-<li>Solve previous year papers and sample papers to get an idea of the exam pattern and difficulty level.</li>
-<li>Practice writing answers in a clear, concise, and accurate manner.</li>
-</ul>
- <h4>Q4. What are some of the best reference books for class 11 chemistry apart from NCERT?</h4>
-<p>A4. Some of the best reference books for class 11 chemistry apart from NCERT are:</p>
- <ul>
-<li><a href="">Pradeep's New Course Chemistry for Class 11 (Vol 1 & 2)</a></li>
-<li><a href="">Modern ABC of Chemistry Class - 11 (Part 1 & 2)</a></li>
-<li><a href="">S.Chand's ISC Chemistry for Class XI (Vol.1 & Vol.2)</a></li>
-<li><a href="">Comprehensive Chemistry XI (Vol I & II)</a></li>
-<li><a href="">NCERT Exemplar Problems-Solutions CHEMISTRY class 11th</a></li>
-</ul>
- <h4>Q5. How can I improve my interest and understanding in chemistry?</h4>
-<p>A5. You can improve your interest and understanding in chemistry by:</p>
- <ul>
-<li>Relating chemistry to your daily life and observing its applications around you.</li>
-<li>Watching videos, documentaries, animations, etc. that explain chemistry concepts in an interesting and visual way.</ <li>Conducting experiments, projects, quizzes, etc. that involve chemistry concepts and enhance your curiosity and creativity.</li>
-<li>Reading books, magazines, blogs, etc. that cover chemistry topics in an easy and fun way.</li>
-<li>Discussing chemistry problems and doubts with your teachers, friends, or online forums.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download FIFA Mobile and Compete in the UEFA Champions League Europa League and Europa Conference League.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download FIFA Mobile and Compete in the UEFA Champions League Europa League and Europa Conference League.md
deleted file mode 100644
index e00da0c02bdc319767588e6bd3633793e66ab9e7..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download FIFA Mobile and Compete in the UEFA Champions League Europa League and Europa Conference League.md
+++ /dev/null
@@ -1,158 +0,0 @@
-<br />
-<h1>Download FIFA Mobile and Experience the Ultimate Soccer Game on Your Phone</h1>
-<p>If you are a soccer fan, you probably have heard of FIFA, the most popular soccer video game franchise in the world. But did you know that you can also play FIFA on your mobile device? That's right, EA Sports has released a mobile version of FIFA called FIFA Mobile, which allows you to build your ultimate team of soccer stars, compete in various modes, and enjoy realistic soccer simulation on your phone. In this article, we will tell you everything you need to know about FIFA Mobile, including its features, download size, tips and tricks, and more. So read on and find out why you should download FIFA Mobile today!</p>
- <h2>FIFA Mobile Features: What You Can Do in the Game</h2>
-<p>FIFA Mobile is not just a scaled-down version of FIFA for consoles or PC. It is a fully-fledged soccer game that offers a lot of features and content for mobile players. Here are some of the things you can do in FIFA Mobile:</p>
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-<ul>
-<li><strong>Build your ultimate team with star players from the biggest leagues and top teams</strong>: In FIFA Mobile, you can create your own dream team of soccer players from over 15,000 authentic players, including world-class talent like Kylian Mbappé, Christian Pulisic, Vinicius Jr, and Son Heung-min. You can also choose from over 600 teams from various leagues, such as Premier League, LaLiga Santander, Bundesliga, Serie A TIM, Ligue 1 Uber Eats, MLS, and more. You can customize your team's kits, badges, formation, tactics, and chemistry to suit your play style.</li>
-<li><strong>Play in the FIFA World Cup 2022 mode and relive the world's greatest soccer tournament</strong>: FIFA Mobile is the only licensed FIFA World Cup 2022 mobile game where you can replay the official tournament brackets with any of the 32 qualified nations. You can also rewrite history and take control of 15 non-qualified nations that didn't make it to the World Cup. You can enjoy authentic World Cup features, such as national team kits and badges, the official match ball, World Cup stadiums (Al Bayt and Lusail), and localized World Cup commentary.</li>
-<li><strong>Collect soccer icons and heroes from over 30+ leagues and level up your dream team</strong>: In FIFA Mobile, you can also add some legendary players to your team with icons and heroes. Icons are players who have made a lasting impact on soccer history, such as Paolo Maldini, Ronaldinho, Zinedine Zidane, David Beckham, Ronaldo Nazario, and more. Heroes are players who have performed memorable feats or achieved remarkable milestones in their careers, such as Ole Gunnar Solskjær, Antonio Di Natale, Tim Cahill, Clint Dempsey, and more. You can level up these players by completing special challenges and events. You can also trade them with other players in the market.</li>
-<li><strong>Experience immersive next-level soccer simulation with new graphics, stadiums, and commentary</strong>: FIFA Mobile delivers a stunning visual experience that brings the game to life on your phone. You can enjoy high-quality graphics, realistic animations, dynamic lighting, and shadows. You can also play in 50+ licensed stadiums from around the world, such as Camp Nou, Old Trafford, Allianz Arena, and more. You can also listen to authentic commentary from famous commentators like Martin Tyler, Alan Smith, Derek Rae, and Lee Dixon.</li>
-<li><strong>Manage your own team and plan your strategy in real time or choose auto-play</strong>: In FIFA Mobile, you have full control over your team's performance and tactics. You can choose from different control options, such as gesture-based controls, virtual buttons, or auto-play. You can also adjust your team's formation, tactics, and instructions in real time during the match. You can also use the quick substitution feature to make changes on the fly. Alternatively, you can let the game play for you and watch the action unfold.</li>
-</ul>
- <h2>FIFA Mobile Download Size: How Much Space You Need on Your Device</h2>
-<p>FIFA Mobile is a free-to-play game that you can download from the App Store or Google Play Store. However, before you download it, you need to make sure that you have enough space on your device to install it. Here are the minimum requirements for downloading FIFA Mobile on iOS and Android:</p>
-<table>
-<tr>
-<th>Platform</th>
-<th>Download Size</th>
-<th>Additional Space</th>
-</tr>
-<tr>
-<td>iOS</td>
-<td>1.5 GB</td>
-<td>500 MB</td>
-</tr>
-<tr>
-<td>Android</td>
-<td>1.3 GB</td>
-<td>500 MB</td>
-</tr>
-</table>
-<p>Note that these are the minimum requirements and the actual download size may vary depending on your device and region. Also, you need to have additional space for updates and additional content that may be added in the future.</p>
- <p>If you want to play Head to Head mode, which is a real-time online multiplayer mode where you can challenge other players around the world, you need to have a device that meets these minimum requirements:</p>
-<ul>
-<li>iOS: iPhone 6s or newer / iPad Air 2 or newer / iPad Mini 4 or newer / iPod Touch 7th Gen or newer</li>
-<li>Android: 2 GB RAM or more / Android 8 or higher / OpenGL ES 3.0 support or higher / ARM64 support or higher</li>
-</ul>
- <p>If you want to enjoy 60 FPS gameplay, which is a smooth and fluid frame rate that enhances the visual quality of the game, you need to have a device that supports it. Here are some of the devices that support 60 FPS gameplay:</p>
-<ul>
-<li>iOS: iPhone XS or newer / iPad Pro (2018) or newer / iPad Air (2020) or newer / iPad (2020) or newer / iPod Touch (2020) or newer</li>
-<li>Android: Samsung Galaxy S10 or newer / Samsung Galaxy Note 10 or newer / OnePlus 7T or newer / Google Pixel 4 or newer / Huawei P30 Pro or newer / Xiaomi Mi 9T Pro or newer / Asus ROG Phone II or newer / Razer Phone II or newer / Sony Xperia XZ3 or newer / LG G8X ThinQ or newer / Motorola Edge+ or newer</li>
-</ul>
- <p>If you have a device that is not supported by FIFA Mobile, you will not be able to download or play the game. Here are some of the devices that are not supported by FIFA Mobile:</p>
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-<ul>
-<li>iOS: iPhone 5s or older / iPad Air or older / iPad Mini 3 or older / iPod Touch 6th Gen or older</li>
-<li>Android: Any device that does not meet the minimum requirements for downloading FIFA Mobile or playing Head to Head mode.</li>
-</ul>
- <h2>FIFA Mobile Tips and Tricks: How to Win More Matches and Build Your Ultimate Team</h2> <p>FIFA Mobile is a fun and addictive game, but it can also be challenging and competitive. If you want to win more matches and build your ultimate team, you need to know some tips and tricks that can help you improve your skills and strategy. Here are some of the best tips and tricks for FIFA Mobile:</p>
-<ul>
-<li><strong>Attack mode tips: How to score more goals and earn more rewards</strong>: Attack mode is a turn-based mode where you play only the offensive part of a match. You have a limited time to score as many goals as possible, while your opponent does the same. The player with the most goals at the end of the match wins. Attack mode is a great way to earn rewards, such as coins, gems, FIFA points, and player cards. Here are some tips to score more goals and win more matches in attack mode:</li>
-<ul>
-<li>Use the gesture-based controls for more accuracy and control. You can swipe on the screen to shoot, pass, or cross the ball. You can also tap on a player to pass the ball to him, or tap on an empty space to run there.</li>
-<li>Use the skill moves to dribble past defenders and create chances. You can perform skill moves by swiping on the skill button at the bottom right corner of the screen. You can also double tap on the screen to perform a roulette or a rainbow flick.</li>
-<li>Use the finesse shot to curl the ball into the corners of the goal. You can perform a finesse shot by swiping on the shoot button and then curving your finger on the screen. You can also use the chip shot to lob the ball over the goalkeeper by swiping up on the shoot button.</li>
-<li>Use the power shot to blast the ball into the net with force. You can perform a power shot by swiping down on the shoot button and then releasing it quickly. You can also use the low shot to keep the ball low and avoid defenders or goalkeepers by swiping down twice on the shoot button.</li>
-<li>Use the sprint button to run faster and beat defenders. You can use the sprint button by pressing and holding it on the bottom left corner of the screen. However, be careful not to overuse it, as it will drain your stamina and make you lose control of the ball.</li>
-</ul>
-<li><strong>Head to Head mode tips: How to beat your opponents and climb the leaderboard ranks</strong>: Head to Head mode is a real-time online multiplayer mode where you can challenge other players around the world in full 90-minute matches. You can play in different divisions, from Amateur to Legendary, and earn rewards based on your performance. You can also join leagues and compete with other players in tournaments and events. Head to Head mode is a great way to test your skills and strategy against real opponents. Here are some tips to beat your opponents and climb the leaderboard ranks in Head to Head mode:</li>
-<ul>
-<li>Use the virtual buttons for more precision and control. You can use the virtual buttons by tapping on the button icons on the screen. You can use the pass button to pass the ball, the shoot button to shoot the ball, the through ball button to send a long pass, the skill button to perform a skill move, and the sprint button to run faster.</li>
-<li>Use the radar to see the whole pitch and plan your moves. You can use the radar by looking at the small map on the top right corner of the screen. You can see where your players and your opponent's players are, and where the ball is. You can also zoom in or out of the radar by pinching on the screen.</li>
-<li>Use the pause menu to change your formation, tactics, and instructions. You can use the pause menu by tapping on the pause icon on the top left corner of the screen. You can change your formation to suit your play style, such as 4-3-3, 4-4-2, 3-5-2, etc. You can also change your tactics to adjust your team's mentality, such as attacking, balanced, or defensive. You can also change your instructions to modify your players' roles, such as stay back, get forward, cut inside, etc.</li>
-<li>Use the chat feature to communicate with your opponent. You can use the chat feature by tapping on the chat icon on the bottom right corner of the screen. You can send pre-written messages, such as "Good game", "Nice goal", "Well played", etc. You can also use emojis, such as ?, ?, ?, etc. However, be respectful and polite to your opponent and avoid using abusive or offensive language.</li>
-<li>Use the replay feature to watch your highlights and learn from your mistakes. You can use the replay feature by tapping on the replay icon on the top right corner of the screen. You can watch your goals, saves, shots, passes, tackles, etc. You can also rewind, fast forward, pause, or change the camera angle of the replay. You can use this feature to analyze your performance and improve your skills.</li>
-</ul>
-<li><strong>Team building tips: How to choose the best players, formations, and tactics for your squad</strong>: In FIFA Mobile, you can build your ultimate team of soccer players from different leagues, nations, and positions. However, you need to consider some factors that affect your team's performance and chemistry. Here are some tips to choose the best players, formations, and tactics for your squad:</li>
-<ul>
-<li>Use the player ratings to compare and select players. You can use the player ratings by looking at the numbers on their cards. The ratings range from 1 to 100 and indicate how good a player is in different attributes, such as pace, shooting, passing, dribbling, defending, and physicality. The higher the rating, the better the player.</li>
-<li>Use the player types to match players with their roles. You can use the player types by looking at the icons on their cards. The player types are based on their positions and skills, such as striker (ST), winger (LW/RW), midfielder (CM/CAM/CDM), defender (CB/LB/RB), and goalkeeper (GK). The player types also have subtypes that indicate their specialties, such as target man (TM), playmaker (PM), box-to-box (B2B), sweeper (SW), etc.</li>
-<li>Use the player chemistry to boost your team's performance. You can use the player chemistry by looking at the green, yellow, or red lines that connect them on the pitch. The player chemistry is based on their league, nation, and position. The higher the chemistry, the better the players will perform together. You can also use chemistry boosters, such as coaches, kits, or stadiums, to increase your team's chemistry.</li>
-<li>Use the player training to improve your players' ratings and skills. You can use the player training by tapping on the train icon on their cards. You can use training XP, which you can earn from skill games, events, or rewards, to level up your players. You can also use skill boosts, which you can earn from events or rewards, to enhance your players' attributes.</li>
-<li>Use the player market to buy and sell players. You can use the player market by tapping on the market icon on the main menu. You can search for players by their name, rating, type, league, nation, or position. You can also filter by price range, bid status, or time remaining. You can bid for players with coins or buy them instantly with FIFA points. You can also sell your unwanted players for coins or FIFA points.</li>
-</ul>
-<li><strong>Skill games tips: How to improve your skills and earn more training XP</strong>: Skill games are mini-games that test your soccer skills, such as shooting, passing, dribbling, defending, and goalkeeping. You can play skill games by tapping on the skill games icon on the main menu. You can choose from different difficulty levels, such as beginner, intermediate, advanced, or world class. You can also choose from different categories, such as basic, attacking, defending, or goalkeeping. Skill games are a great way to improve your skills and earn more training XP. Here are some tips to ace the skill games:</li>
-<ul>
-<li>Follow the instructions and objectives of each skill game. You can follow the instructions and objectives by looking at the text and icons on the screen. They will tell you what to do and how to do it. For example, if you see a green arrow pointing at a target, you need to swipe on the screen in that direction to shoot the ball.</li>
-<li>Use the hints and tips that appear on the screen. You can use the hints and tips by looking at the text and icons that pop up on the screen. They will give you some advice and guidance on how to complete the skill game. For example, if you see a yellow circle around a player, you need to tap on him to pass the ball.</li>
-<li>Practice and repeat the skill games until you master them. You can practice and repeat the skill games by tapping on the replay icon on the bottom right corner of the screen. You can play each skill game as many times as you want until you get a perfect score or achieve your goal. You can also compare your scores with other players on the leaderboard.</li>
-</ul>
-<li><strong>Currency tips: How to earn more coins, gems, and FIFA points</strong>: Coins, gems, and FIFA points are the main currencies in FIFA Mobile. You can use them to buy players, packs, boosters, or other items in the game. However, you need to know how to earn them efficiently and wisely. Here are some tips to earn more coins, gems, and FIFA points:</li>
-<ul>
-<li>Play the daily warm-up and daily login events. You can play the daily warm-up and daily login events by tapping on the events icon on the main menu. You can earn coins, gems, FIFA points, and other rewards by completing simple tasks, such as scoring a goal, passing the ball, or logging in to the game.</li>
-<li>Play the season mode and the division rivals mode. You can play the season mode and the division rivals mode by tapping on the modes icon on the main menu. You can earn coins, gems, FIFA points, and other rewards by winning matches, completing objectives, and ranking up in different divisions.</li>
-<li>Play the special events and campaigns. You can play the special events and campaigns by tapping on the events icon on the main menu. You can earn coins, gems, FIFA points, and other rewards by participating in time-limited or seasonal events and campaigns, such as Halloween, Christmas, Lunar New Year, etc.</li>
-<li>Sell your unwanted players or items in the market. You can sell your unwanted players or items in the market by tapping on the market icon on the main menu. You can earn coins or FIFA points by listing your players or items for sale and waiting for other players to buy them.</li>
-<li>Watch ads or complete offers. You can watch ads or complete offers by tapping on the store icon on the main menu. You can earn coins, gems, FIFA points, or other rewards by watching short video ads or completing surveys, quizzes, or tasks from third-party providers.</li>
-</ul>
- <h2>Conclusion: Why You Should Download FIFA Mobile Today</h2>
-<p>FIFA Mobile is the ultimate soccer game for your phone. It has everything you need to enjoy soccer on the go, such as:</p>
-<ul>
-<li>A huge roster of players, teams, and leagues to choose from</li>
-<li>A variety of modes and features to play and explore</li>
-<li>A realistic and immersive soccer simulation with stunning graphics and sound</li>
-<li>A fun and competitive online multiplayer experience with other players around the world</li>
-<li>A rewarding and engaging progression system with tons of rewards and customization options</li>
-</ul>
-<p>So what are you waiting for? Download FIFA Mobile today and experience the thrill of soccer on your phone!</p>
- <h3>Frequently Asked Questions</h3>
-<p>Here are some of the most common questions that people ask about FIFA Mobile:</p>
-<ol>
-<li><strong>How do I download FIFA Mobile?</strong></li>
-<p>You can download FIFA Mobile from the App Store or Google Play Store for free. Just search for "FIFA Mobile" and tap on the install button. Make sure you have enough space on your device and a stable internet connection.</p>
-<li><strong>How do I update FIFA Mobile?</strong></li>
-<p>You can update FIFA Mobile from the App Store or Google Play Store whenever there is a new version available. Just search for "FIFA Mobile" and tap on the update button. Make sure you have enough space on your device and a stable internet connection.</p>
-<li><strong>How do I contact EA Sports for support or feedback?</strong></li>
-<p>You can contact EA Sports for support or feedback by tapping on the settings icon on the main menu and then tapping on the help icon. You can also visit their website at https://help.ea.com/en/fifa/fifa-mobile/ or their social media pages at https://www.facebook.com/EASPORTSFIFAMOBILE/ or https://twitter.com/EAFIFAMOBILE.</p>
-<li><strong>How do I link my FIFA Mobile account to Facebook or Google Play Games?</strong></li>
-<p>You can link your FIFA Mobile account to Facebook or Google Play Games by tapping on the settings icon on the main menu and then tapping on the link account icon. This will allow you to save your progress, sync your data across devices, and play with your friends.</p>
-<li><strong>How do I reset my FIFA Mobile account?</strong></li <p>You can reset your FIFA Mobile account by tapping on the settings icon on the main menu and then tapping on the reset account icon. This will delete all your data and progress and start a new game. However, be careful as this action is irreversible and you will lose everything you have earned or purchased in the game.</p>
- <p>I hope you enjoyed reading this article and learned something new about FIFA Mobile. If you have any questions or comments, feel free to leave them below. Thank you for your time and attention.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/RAFT/README.md b/spaces/fffiloni/RAFT/README.md
deleted file mode 100644
index db4795fd8289865d7828e14e3c739a17db1d3871..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/RAFT/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: RAFT Optical Flow
-emoji: 😻
-colorFrom: green
-colorTo: blue
-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/fffiloni/controlnet-animation-doodle/node_modules/qs/LICENSE.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/qs/LICENSE.md
deleted file mode 100644
index fecf6b6942d17bc7ae41a5e106dc98815c0db652..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/qs/LICENSE.md
+++ /dev/null
@@ -1,29 +0,0 @@
-BSD 3-Clause License
-
-Copyright (c) 2014, Nathan LaFreniere and other [contributors](https://github.com/ljharb/qs/graphs/contributors)
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright notice, this
- list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright notice,
- this list of conditions and the following disclaimer in the documentation
- and/or other materials provided with the distribution.
-
-3. Neither the name of the copyright holder nor the names of its
- contributors may be used to endorse or promote products derived from
- this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/spaces/fishaudio/fish-diffusion/Dockerfile b/spaces/fishaudio/fish-diffusion/Dockerfile
deleted file mode 100644
index 1544df6c80ff0ad970297cf9a48dd3f46be17421..0000000000000000000000000000000000000000
--- a/spaces/fishaudio/fish-diffusion/Dockerfile
+++ /dev/null
@@ -1,28 +0,0 @@
-FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04
-
-# Install Poetry
-RUN apt-get update && apt-get install -y git curl python3 python3-pip build-essential ffmpeg libsm6 libxext6
-RUN curl -sSL https://install.python-poetry.org | python3 -
-ENV PATH="/root/.local/bin:${PATH}"
-RUN poetry config virtualenvs.create false
-
-# Install dependencies
-WORKDIR /app
-
-RUN git clone https://github.com/fishaudio/fish-diffusion.git && \
- cd fish-diffusion && \
- git checkout 8b21f57080e70675aaaa2ffa2fad04aed9119420
-
-WORKDIR /app/fish-diffusion
-RUN poetry install
-
-COPY --chown=user . .
-
-ENV GRADIO_SERVER_NAME=0.0.0.0
-ENV GRADIO_SERVER_PORT=7860
-ENV NUMBA_CACHE_DIR=/tmp/numba
-ENV TRANSFORMERS_CACHE=/tmp/huggingface
-ENV MPLCONFIGDIR=/tmp/matplotlib
-ENV TORCH_HOME=/tmp/torch
-
-CMD python3 tools/hifisinger/gradio_ui.py
diff --git a/spaces/fkunn1326/CoolJapaneseDiffusion/README.md b/spaces/fkunn1326/CoolJapaneseDiffusion/README.md
deleted file mode 100644
index 22646e837155bee5a8f4b2cb6025498d4db789b8..0000000000000000000000000000000000000000
--- a/spaces/fkunn1326/CoolJapaneseDiffusion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: CoolJapaneseDiffusion
-emoji: 💻
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/fun-research/FC-CLIP/datasets/prepare_ade20k_ins_seg.py b/spaces/fun-research/FC-CLIP/datasets/prepare_ade20k_ins_seg.py
deleted file mode 100644
index e4e951adcd84dbd08b3d6570aee56887bf1c69a6..0000000000000000000000000000000000000000
--- a/spaces/fun-research/FC-CLIP/datasets/prepare_ade20k_ins_seg.py
+++ /dev/null
@@ -1,112 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-import glob
-import json
-import os
-from collections import Counter
-
-import numpy as np
-import tqdm
-from panopticapi.utils import IdGenerator, save_json
-from PIL import Image
-import pycocotools.mask as mask_util
-
-
-if __name__ == "__main__":
- dataset_dir = os.getenv("DETECTRON2_DATASETS", "datasets")
-
- for name, dirname in [("train", "training"), ("val", "validation")]:
- image_dir = os.path.join(dataset_dir, f"ADEChallengeData2016/images/{dirname}/")
- instance_dir = os.path.join(
- dataset_dir, f"ADEChallengeData2016/annotations_instance/{dirname}/"
- )
-
- # img_id = 0
- ann_id = 1
-
- # json
- out_file = os.path.join(dataset_dir, f"ADEChallengeData2016/ade20k_instance_{name}.json")
-
- # json config
- instance_config_file = "datasets/ade20k_instance_imgCatIds.json"
- with open(instance_config_file) as f:
- category_dict = json.load(f)["categories"]
-
- # load catid mapping
- # it is important to share category id for both instance and panoptic annotations
- mapping_file = "datasets/ade20k_instance_catid_mapping.txt"
- with open(mapping_file) as f:
- map_id = {}
- for i, line in enumerate(f.readlines()):
- if i == 0:
- continue
- ins_id, sem_id, _ = line.strip().split()
- # shift id by 1 because we want it to start from 0!
- # ignore_label becomes 255
- map_id[int(ins_id)] = int(sem_id) - 1
-
- for cat in category_dict:
- cat["id"] = map_id[cat["id"]]
-
- filenames = sorted(glob.glob(os.path.join(image_dir, "*.jpg")))
-
- ann_dict = {}
- images = []
- annotations = []
-
- for idx, filename in enumerate(tqdm.tqdm(filenames)):
- image = {}
- image_id = os.path.basename(filename).split(".")[0]
-
- image["id"] = image_id
- image["file_name"] = os.path.basename(filename)
-
- original_format = np.array(Image.open(filename))
- image["width"] = original_format.shape[1]
- image["height"] = original_format.shape[0]
-
- images.append(image)
-
- filename_instance = os.path.join(instance_dir, image_id + ".png")
- ins_seg = np.asarray(Image.open(filename_instance))
- assert ins_seg.dtype == np.uint8
-
- instance_cat_ids = ins_seg[..., 0]
- # instance id starts from 1!
- # because 0 is reserved as VOID label
- instance_ins_ids = ins_seg[..., 1]
-
- # process things
- for thing_id in np.unique(instance_ins_ids):
- if thing_id == 0:
- continue
- mask = instance_ins_ids == thing_id
- instance_cat_id = np.unique(instance_cat_ids[mask])
- assert len(instance_cat_id) == 1
-
- anno = {}
- anno['id'] = ann_id
- ann_id += 1
- anno['image_id'] = image['id']
- anno["iscrowd"] = int(0)
- anno["category_id"] = int(map_id[instance_cat_id[0]])
-
- inds = np.nonzero(mask)
- ymin, ymax = inds[0].min(), inds[0].max()
- xmin, xmax = inds[1].min(), inds[1].max()
- anno["bbox"] = [int(xmin), int(ymin), int(xmax - xmin + 1), int(ymax - ymin + 1)]
- # if xmax <= xmin or ymax <= ymin:
- # continue
- rle = mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
- rle["counts"] = rle["counts"].decode("utf-8")
- anno["segmentation"] = rle
- anno["area"] = int(mask_util.area(rle))
- annotations.append(anno)
-
- # save this
- ann_dict['images'] = images
- ann_dict['categories'] = category_dict
- ann_dict['annotations'] = annotations
-
- save_json(ann_dict, out_file)
diff --git a/spaces/geofactoryplastix/my-rvc-voicemodels/style.css b/spaces/geofactoryplastix/my-rvc-voicemodels/style.css
deleted file mode 100644
index 0f08add2427d7de860ac7d6393897a450342e1f1..0000000000000000000000000000000000000000
--- a/spaces/geofactoryplastix/my-rvc-voicemodels/style.css
+++ /dev/null
@@ -1,45 +0,0 @@
-body {
- padding: 2rem;
- font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-.cardtitle {
- font-size: 23px;
- margin-top: 0;
-}
-
-p {
- color: rgb(107, 114, 128);
- font-size: 15px;
- margin-bottom: 10px;
- margin-top: 5px;
-}
-
-.card {
- width: 1100px;
- height: 1200px;
- margin: 0 auto;
- padding: 13px;
- border: 1px solid lightgray;
- border-radius: 13px;
-}
-
-.intro{
- border: 1px solid lightgray;
- border-radius: 13px;
- padding: 3px;
-
-}
-
-.modelcard{
- border: 1px solid lightgray;
- border-radius: 13px;
- width: 300px;
- height: 400px;
- padding: 2px;
- margin: 3px;
-}
-
-.cardwrapper{
- display: flex;
-}
\ No newline at end of file
diff --git a/spaces/giskardai/giskard/hf.sh b/spaces/giskardai/giskard/hf.sh
deleted file mode 100644
index ece1ed180e324dd8782d2f8f2646ddc3d1e9c01d..0000000000000000000000000000000000000000
--- a/spaces/giskardai/giskard/hf.sh
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/bin/bash
-
-echo "Initializing datadir..."
-
-# Create dir if not existed in HF persistent storage
-if [ ! -d "${GSK_HOME}" ]
-then
- # Create HOME
- mkdir -p "${GSK_HOME}"
- # Create frontend run dir
- mkdir -p "${GSK_HOME}/run/nginx"
-fi
-
-if [ ! -z "${GISKARD_LICENSE}" ]
-then
- # Use new license if env set
- echo "${GISKARD_LICENSE}" > "${GISKARD_HOME}/license.lic"
- # TODO: Backend raises exception if license is not parsable
-fi
-
-echo "Detecting demo Giskard Space..."
-
-if [ ! -z "${SPACE_ID}" ] && [ "${DEMO_SPACE_ID}" == "${SPACE_ID}" ]
-then
- # Generate GISKARD_DEFAULT_API_KEY in demo space instead of set from Secrets
- export GISKARD_DEFAULT_API_KEY=gsk-$(cat /dev/urandom | tr -dc '[:alpha:]' | fold -w ${1:-28} | head -n 1)
- # Pick the pre-installed Giskard version from Docker image
- GISKARD_VERSION=$(python -c "import giskard; print(giskard.__version__)")
- # Prepare a new env for demo worker
- # curl https://pyenv.run | bash
- # pyenv install $(python --version | cut -d' ' -f2)
- # pyenv global $(python --version | cut -d' ' -f2)
- # pip install --upgrade pip
- # pip install "giskard[server]==${GISKARD_VERSION}"
- # pip install -r /requirements.txt
- # Append demo worker supervisord item to conf
- echo """
-[program:demo_worker]
-stdout_logfile=/dev/fd/1
-stdout_logfile_maxbytes=0
-autorestart=true
-redirect_stderr=true
-startsecs=0
-startretries=5000
-command=/bin/bash -c 'python -m giskard.cli worker start -u \"http://localhost:9000\" -k \"\$GISKARD_DEFAULT_API_KEY\" --parallelism 6; sleep 5'
-""" # >> "${GSK_DIST_PATH}/supervisord.conf"
-fi
-
-echo "Starting supervisord..."
-
-exec supervisord -c "${GSK_DIST_PATH}/supervisord.conf"
diff --git a/spaces/glyszt/vt/vtoonify/model/encoder/align_all_parallel.py b/spaces/glyszt/vt/vtoonify/model/encoder/align_all_parallel.py
deleted file mode 100644
index 05b520cd6590dc02ee533d3f0d69e6a364447d9f..0000000000000000000000000000000000000000
--- a/spaces/glyszt/vt/vtoonify/model/encoder/align_all_parallel.py
+++ /dev/null
@@ -1,217 +0,0 @@
-"""
-brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
-author: lzhbrian (https://lzhbrian.me)
-date: 2020.1.5
-note: code is heavily borrowed from
- https://github.com/NVlabs/ffhq-dataset
- http://dlib.net/face_landmark_detection.py.html
-
-requirements:
- apt install cmake
- conda install Pillow numpy scipy
- pip install dlib
- # download face landmark model from:
- # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
-"""
-from argparse import ArgumentParser
-import time
-import numpy as np
-import PIL
-import PIL.Image
-import os
-import scipy
-import scipy.ndimage
-import dlib
-import multiprocessing as mp
-import math
-
-#from configs.paths_config import model_paths
-SHAPE_PREDICTOR_PATH = 'shape_predictor_68_face_landmarks.dat'#model_paths["shape_predictor"]
-
-
-def get_landmark(filepath, predictor):
- """get landmark with dlib
- :return: np.array shape=(68, 2)
- """
- detector = dlib.get_frontal_face_detector()
- if type(filepath) == str:
- img = dlib.load_rgb_image(filepath)
- else:
- img = filepath
- dets = detector(img, 1)
-
- if len(dets) == 0:
- print('Error: no face detected!')
- return None
-
- shape = None
- for k, d in enumerate(dets):
- shape = predictor(img, d)
-
- if shape is None:
- print('Error: No face detected! If you are sure there are faces in your input, you may rerun the code several times until the face is detected. Sometimes the detector is unstable.')
- t = list(shape.parts())
- a = []
- for tt in t:
- a.append([tt.x, tt.y])
- lm = np.array(a)
- return lm
-
-
-def align_face(filepath, predictor):
- """
- :param filepath: str
- :return: PIL Image
- """
-
- lm = get_landmark(filepath, predictor)
- if lm is None:
- return None
-
- lm_chin = lm[0: 17] # left-right
- lm_eyebrow_left = lm[17: 22] # left-right
- lm_eyebrow_right = lm[22: 27] # left-right
- lm_nose = lm[27: 31] # top-down
- lm_nostrils = lm[31: 36] # top-down
- lm_eye_left = lm[36: 42] # left-clockwise
- lm_eye_right = lm[42: 48] # left-clockwise
- lm_mouth_outer = lm[48: 60] # left-clockwise
- lm_mouth_inner = lm[60: 68] # left-clockwise
-
- # Calculate auxiliary vectors.
- eye_left = np.mean(lm_eye_left, axis=0)
- eye_right = np.mean(lm_eye_right, axis=0)
- eye_avg = (eye_left + eye_right) * 0.5
- eye_to_eye = eye_right - eye_left
- mouth_left = lm_mouth_outer[0]
- mouth_right = lm_mouth_outer[6]
- mouth_avg = (mouth_left + mouth_right) * 0.5
- eye_to_mouth = mouth_avg - eye_avg
-
- # Choose oriented crop rectangle.
- x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
- x /= np.hypot(*x)
- x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
- y = np.flipud(x) * [-1, 1]
- c = eye_avg + eye_to_mouth * 0.1
- quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
- qsize = np.hypot(*x) * 2
-
- # read image
- if type(filepath) == str:
- img = PIL.Image.open(filepath)
- else:
- img = PIL.Image.fromarray(filepath)
-
- output_size = 256
- transform_size = 256
- enable_padding = True
-
- # Shrink.
- shrink = int(np.floor(qsize / output_size * 0.5))
- if shrink > 1:
- rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
- img = img.resize(rsize, PIL.Image.ANTIALIAS)
- quad /= shrink
- qsize /= shrink
-
- # Crop.
- border = max(int(np.rint(qsize * 0.1)), 3)
- crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
- int(np.ceil(max(quad[:, 1]))))
- crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
- min(crop[3] + border, img.size[1]))
- if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
- img = img.crop(crop)
- quad -= crop[0:2]
-
- # Pad.
- pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
- int(np.ceil(max(quad[:, 1]))))
- pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
- max(pad[3] - img.size[1] + border, 0))
- if enable_padding and max(pad) > border - 4:
- pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
- img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
- h, w, _ = img.shape
- y, x, _ = np.ogrid[:h, :w, :1]
- mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
- 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
- blur = qsize * 0.02
- img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
- img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
- img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
- quad += pad[:2]
-
- # Transform.
- img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
- if output_size < transform_size:
- img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
-
- # Save aligned image.
- return img
-
-
-def chunks(lst, n):
- """Yield successive n-sized chunks from lst."""
- for i in range(0, len(lst), n):
- yield lst[i:i + n]
-
-
-def extract_on_paths(file_paths):
- predictor = dlib.shape_predictor(SHAPE_PREDICTOR_PATH)
- pid = mp.current_process().name
- print('\t{} is starting to extract on #{} images'.format(pid, len(file_paths)))
- tot_count = len(file_paths)
- count = 0
- for file_path, res_path in file_paths:
- count += 1
- if count % 100 == 0:
- print('{} done with {}/{}'.format(pid, count, tot_count))
- try:
- res = align_face(file_path, predictor)
- res = res.convert('RGB')
- os.makedirs(os.path.dirname(res_path), exist_ok=True)
- res.save(res_path)
- except Exception:
- continue
- print('\tDone!')
-
-
-def parse_args():
- parser = ArgumentParser(add_help=False)
- parser.add_argument('--num_threads', type=int, default=1)
- parser.add_argument('--root_path', type=str, default='')
- args = parser.parse_args()
- return args
-
-
-def run(args):
- root_path = args.root_path
- out_crops_path = root_path + '_crops'
- if not os.path.exists(out_crops_path):
- os.makedirs(out_crops_path, exist_ok=True)
-
- file_paths = []
- for root, dirs, files in os.walk(root_path):
- for file in files:
- file_path = os.path.join(root, file)
- fname = os.path.join(out_crops_path, os.path.relpath(file_path, root_path))
- res_path = '{}.jpg'.format(os.path.splitext(fname)[0])
- if os.path.splitext(file_path)[1] == '.txt' or os.path.exists(res_path):
- continue
- file_paths.append((file_path, res_path))
-
- file_chunks = list(chunks(file_paths, int(math.ceil(len(file_paths) / args.num_threads))))
- print(len(file_chunks))
- pool = mp.Pool(args.num_threads)
- print('Running on {} paths\nHere we goooo'.format(len(file_paths)))
- tic = time.time()
- pool.map(extract_on_paths, file_chunks)
- toc = time.time()
- print('Mischief managed in {}s'.format(toc - tic))
-
-
-if __name__ == '__main__':
- args = parse_args()
- run(args)
diff --git a/spaces/gotiQspiryo/whisper-ui/Asus-F5sl-Schematic-EXCLUSIVE.md b/spaces/gotiQspiryo/whisper-ui/Asus-F5sl-Schematic-EXCLUSIVE.md
deleted file mode 100644
index 29fb2481065c08145b78dcdd845fb655e6e9d76d..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/Asus-F5sl-Schematic-EXCLUSIVE.md
+++ /dev/null
@@ -1,96 +0,0 @@
-## asus f5sl schematic
-
-
-
-
-
- 
-
-
-
-
-
-**Asus F5sl Schematic [https://miimms.com/2txSSu](https://miimms.com/2txSSu)**
-
-
-
-
-
-
-
-
-
-
-
- Here is a possible title and article with SEO optimization and HTML formatting for the keyword "asus f5sl schematic":
-
-# How to Download and Use Asus F5SL Schematic Boardview for Free
-
-
-
-If you are looking for the **Asus F5SL Schematic Boardview** file, you have come to the right place. In this article, we will show you how to download and use this file for free, and what benefits it can offer you.
-
-
-
-The Asus F5SL Schematic Boardview file is a digital representation of the printed circuit board (PCB) used in the Asus F5SL Rev 2.0 board. It contains detailed information about the layout of the circuit board, the components used, and the signals that are transmitted through the board. This information is presented in a graphical format, often including detailed schematics and circuit diagrams[^1^].
-
-
-
-This file is a useful tool for technicians, engineers, and other professionals who need to troubleshoot, repair, or upgrade printed circuit boards. It can help you to identify issues, locate test points, and make repairs. It can also help you to learn more about the design and functionality of the board[^2^].
-
-
-
-## How to Download Asus F5SL Schematic Boardview for Free
-
-
-
-There are many websites that offer the Asus F5SL Schematic Boardview file for free download, but not all of them are reliable or safe. Some of them may contain viruses, malware, or other unwanted programs that can harm your device or compromise your privacy. Therefore, it is important to choose a reputable and trusted website that provides high-quality and verified files.
-
-
-
-One such website is [Schemafix.com](https://www.schemafix.com/2022/11/schematic-asus-f5sl-rev-20.html), which is a leading source of free schematics and BIOS files for various devices. Schemafix.com offers the Asus F5SL Schematic Boardview file in various formats, such as .asc, .bdv, .brd, .bv, .cad, .cst, .gr, .f2b, .fz, and so on. You can choose the format that suits your needs and preferences.
-
-
-
-To download the file from Schemafix.com, you just need to follow these simple steps:
-
-
-
-1. Visit [https://www.schemafix.com/2022/11/schematic-asus-f5sl-rev-20.html](https://www.schemafix.com/2022/11/schematic-asus-f5sl-rev-20.html) on your browser.
-
-2. Scroll down to the bottom of the page and click on the download button.
-
-3. Wait for a few seconds until the file is downloaded to your device automatically.
-
-4. Locate the file on your device and extract it using a suitable program.
-
-5. Open the file with Boardviewer software or any other compatible program.
-
-
-
-Alternatively, you can also download the file from other websites that offer similar services, such as [Realschematic.com](https://realschematic.com/shop/936/desc/asus-f5sl), [Vinafix.com](https://vinafix.com/threads/asus-f5sl.9102/), or [Badcaps.net](https://www.badcaps.net/forum/showthread.php?t=30616). However, you should always be careful and cautious when downloading files from unknown sources and scan them for viruses or malware before opening them.
-
-
-
-## How to Use Asus F5SL Schematic Boardview
-
-
-
-Once you have downloaded and extracted the Asus F5SL Schematic Boardview file, you can use it to view and work with the printed circuit board of the Asus F5SL Rev 2.0 board. However, you will need a special software program that can open and display these files correctly. One such program is Boardviewer software, which is a free and easy-to-use tool that can open various formats of schematic boardview files.
-
-
-
-To use Boardviewer software to view and work with the Asus F5SL Schematic Boardview file, you just need to follow these simple steps:
-
-
-
-1. dfd1c89656
-
-
-
-
-
-
-
-
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Bitdefender Mobile Security Antivirus Premium V3.3.032.612 Apk [Latest] - Whats New in the Latest Update of the Top-Rated Antivirus for Android.md b/spaces/gotiQspiryo/whisper-ui/examples/Bitdefender Mobile Security Antivirus Premium V3.3.032.612 Apk [Latest] - Whats New in the Latest Update of the Top-Rated Antivirus for Android.md
deleted file mode 100644
index 9d7a16167b7b1b992be1d6c7e5e22085d299a252..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Bitdefender Mobile Security Antivirus Premium V3.3.032.612 Apk [Latest] - Whats New in the Latest Update of the Top-Rated Antivirus for Android.md
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Bitdefender Mobile Security Antivirus Premium V3.3.032.612 Apk [Latest]</h2><br /><p><b><b>Download Zip</b> ✫ <a href="https://urlgoal.com/2uyLWR">https://urlgoal.com/2uyLWR</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/DataProcess/correct_head_mask.py b/spaces/gwang-kim/DATID-3D/pose_estimation/DataProcess/correct_head_mask.py
deleted file mode 100644
index 04c8f955c808b6022daa0a07010be452e5fcc8e8..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/DataProcess/correct_head_mask.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import cv2
-import numpy as np
-
-
-def erosion_hair_region(img_c1u, num_iter):
-
- fil = np.array([[ 0, 0.25, 0],
- [ 0.25, -1.0, 0.25],
- [ 0, 0.25, 0]])
-
- ddepth = cv2.CV_32FC1
- temp_img = img_c1u.copy()
-
- temp_img[temp_img == 1] = 3
- temp_img[temp_img == 2] = 1
- temp_img[temp_img == 3] = 2
- # cv2.imwrite("./temp_res/trans.png", temp_img)
- # exit(0)
-
- img_f = temp_img.astype(np.float32)
-
- for _ in range(num_iter):
-
- img_res = cv2.filter2D(img_f, ddepth, fil, borderType=cv2.BORDER_CONSTANT)
- mask_reion = (img_c1u == 2) * (img_res < -0.01)
-
- img_f[mask_reion] = 0.0
- # cv2.imwrite("./temp_res/temp.pfm", img_f)
- # cv2.imwrite("./temp_res/img_res.pfm", img_res)
- # exit(0)
- # img_c1u[mask_reion] = 0
- # temp_img[mask_reion] = 0
-
- res = img_f.astype(np.uint8)
- res[res == 1] = 3
- res[res == 2] = 1
- res[res == 3] = 2
-
- return res
-
-
-def extract_max_region(label_img, tar_value):
- mask_img = np.zeros_like(label_img)
- mask_img[label_img == tar_value] = 1
- num_labels, label_img = cv2.connectedComponents(mask_img, connectivity=8)
-
- max_label = -1
- max_area = -1.0
-
- for i in range(1, num_labels):
- cur_area = np.sum(label_img == i)
- if cur_area > max_area:
- max_label = i
- max_area = cur_area
-
- label_img[label_img != max_label] = 0
- label_img[label_img == max_label] = 255
- return label_img
-
-
-def remover_free_block(img_c1u):
- temp_img = img_c1u.copy()
-
- temp_img[temp_img > 0.5] = 1
- label_img = extract_max_region(temp_img, 1)
-
- img_c1u[label_img != 255] = 0
-
- return img_c1u
-
-
-def correct_hair_mask(mask_img):
-
- mask_img = remover_free_block(mask_img)
- mask_img = erosion_hair_region(mask_img, 7)
- mask_img = remover_free_block(mask_img)
-
- return mask_img
\ No newline at end of file
diff --git a/spaces/hackathon-somos-nlp-2023/learning-assistance/functions.py b/spaces/hackathon-somos-nlp-2023/learning-assistance/functions.py
deleted file mode 100644
index ecff5fcb480d613f36d61f01179770a2d82e5415..0000000000000000000000000000000000000000
--- a/spaces/hackathon-somos-nlp-2023/learning-assistance/functions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import os
-import random
-
-import requests
-import torch
-from bs4 import BeautifulSoup
-from datasets import Dataset
-from langchain.docstore.document import Document
-from langchain.llms import HuggingFacePipeline
-from langchain.text_splitter import CharacterTextSplitter
-from peft import PeftConfig, PeftModel
-from transformers import (AutoModel, AutoModelForCausalLM, AutoTokenizer,
- GenerationConfig, pipeline)
-
-# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
-
-generation_config = GenerationConfig(temperature=.8,
- top_p=0.75,
- top_k=40)
-device = 'cuda'
-
-shared = {
- 'answer_context': None,
- 'embeddings_dataset': None,
- 'full_text': None,
-}
-
-text_splitter = CharacterTextSplitter()
-
-
-def get_nearest_examples(question: str, k: int):
- """
- Returns the k nearest examples to a given question.
-
- Args:
- question (str): The input question to find nearest examples for.
- k (int): The number of nearest examples to retrieve.
-
- Returns:
- The k nearest examples to the given question.
- """
- print(['get_nearest_examples', 'start'])
- question_embedding = get_embeddings([question]).cpu().detach().numpy()
- embeddings_dataset = shared['embeddings_dataset']
- scores, samples = embeddings_dataset.get_nearest_examples(
- "embeddings", question_embedding, k)
- print(['get_nearest_examples', 'scores and samples'])
- print(scores)
- print(samples['id'])
- print(['get_nearest_examples', 'end'])
- return samples
-
-
-def get_embeddings(text):
- print(['get_embeddings', 'start'])
- encoded_input = emb_tokenizer(text,
- padding=True,
- truncation=True,
- return_tensors="pt")
- encoded_input = {k: v.to('cuda') for k, v in encoded_input.items()}
- model_output = emb_model(**encoded_input)
- model_output = model_output.last_hidden_state[:, 0]
- print(['get_embeddings', 'end'])
- return model_output
-
-
-def build_faiss_index(text):
- """
- Builds a FAISS index for the given text.
-
- Args:
- text (str): The input text to build a FAISS index for.
-
- Returns:
- None.
- """
- print(['build_faiss_index', 'start'])
- text_list = split_text(text)
- emb_list = []
- for i, item in enumerate(text_list):
- emb_list.append({
- "embeddings": get_embeddings(item).cpu().detach().numpy()[0],
- 'id': i
- })
- dataset = Dataset.from_list(emb_list)
- dataset.add_faiss_index(column="embeddings")
- shared['embeddings_dataset'] = dataset
- print(['build_faiss_index', 'end'])
-
-
-def extract_text(url: str):
- """
- Extracts the text content from a given URL and returns it as a string.
-
- Args:
- url (str): The URL to extract text content from.
-
- Returns:
- str: The text content extracted from the URL, or an empty string if the URL is invalid.
- """
- print(['extract_text', 'start'])
- if url is None or url.strip() == '':
- return ''
- response = requests.get(url)
- soup = BeautifulSoup(response.text, "html.parser")
- text = '\n\n'.join(map(lambda p: p.text, soup.find_all('p')))
- shared['full_text'] = text
- print(['extract_text', 'end'])
- return text
-
-
-def split_text(text: str):
- """
- Splits a given text into a list of individual lines.
-
- Args:
- text (str): The input text to split into lines.
-
- Returns:
- List[str]: A list of individual lines in the input text.
- """
- lines = text.split('\n')
- lines = [line.strip() for line in lines if line.strip()]
- return lines
-
-
-def remove_prompt(text: str) -> str:
- """
- Removes the prompt from a given text and returns the resulting text.
-
- Args:
- text (str): The input text to remove the prompt from.
-
- Returns:
- str: The input text with the prompt removed, or the original text if the prompt is not found.
- """
- output_prompt = 'Output: '
- try:
- idx = text.index(output_prompt)
- res = text[idx + len(output_prompt):].strip()
- res = res.replace('Input: ', '')
- except ValueError:
- res = text
- return res
-
-
-def summarize_text(text: str) -> str:
- """
- Generates a summary of the given text using a pre-trained language model.
-
- Args:
- text (str): The input text to generate a summary for.
-
- Returns:
- str: The generated summary for the input text.
- """
- print(['summarize_text', 'start'])
-
- print(['summarize_text', 'splitting text'])
- texts = text_splitter.split_text(text)
- docs = [Document(page_content=t) for t in texts]
- prompts = [f'<s>Instruction: Elabora un resume del siguiente texto.\nInput: {d.page_content}\nOutput: '
- for d in docs]
-
- print(['summarize_text', 'generating'])
- cleaned_summaries = [remove_prompt(
- s['generated_text']) for s in pipe(prompts)]
- summaries = '\n\n'.join(cleaned_summaries)
-
- print(['summarize_text', 'end'])
- return summaries
-
-
-def summarize_text_v1(text: str):
- print(['summarize_text', 'start'])
- input_text = f'<s>Instruction: Elabora un resume del siguiente texto.\nInput: {text}\nOutput: '
- batch = tokenizer(input_text, return_tensors='pt')
- batch = batch.to(device)
- print(['summarize_text', 'generating'])
- with torch.cuda.amp.autocast():
- output_tokens = model.generate(**batch,
- max_new_tokens=512,
- generation_config=generation_config
- )
- output = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
- output = output.replace(input_text, '')
- print(['summarize_text', 'end'])
- return output
-
-
-def generate_question(text: str):
- """
- Generates a question based on a random section of the input text using a pre-trained language model.
-
- Args:
- text (str): The input text to generate a question for.
-
- Returns:
- str: The generated question for the input text.
- """
- print(['generate_question', 'start'])
- # Get a random section of the whole text to generate a question
- fragments = split_text(text)
- rnd_text = random.choice(fragments)
- shared['answer_context'] = rnd_text
-
- input_text = f'<s>Instruction: Dado el siguiente texto quiero que generes una pregunta cuya respuesta se encuentre en él.\nInput: {rnd_text}\nOutput: '
- batch = tokenizer(input_text, return_tensors='pt')
- print(['generate_question', 'generating'])
- with torch.cuda.amp.autocast():
- output_tokens = model.generate(**batch,
- max_new_tokens=256,
- generation_config=generation_config)
- output = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
- output = output.replace(input_text, '')
- print(['generate_question', 'end'])
- return output
-
-
-def get_answer_context():
- return shared['answer_context']
-
-
-def answer_question(question: str):
- """
- Generates an answer to the given question based on a pre-trained language model and a pre-built Faiss index.
-
- Args:
- question (str): The question to generate an answer for.
-
- Returns:
- str: The generated answer for the question.
- """
- print(['answer_question', 'start'])
- full_text = shared['full_text']
-
- if not shared['embeddings_dataset']:
- build_faiss_index(full_text)
- top_k_samples = get_nearest_examples(question, k=3)
-
- index_text = {}
- for i, t in enumerate(split_text(full_text)):
- index_text[i] = t
-
- context = '\n'.join([index_text[id] for id in top_k_samples['id']])
-
- input_text = f"""<s>Instruction: Te voy a proporcionar un texto del cual deseo que me respondas una pregunta.
- El texto es el siguiente: `{context}`\nInput: {question}\nOutput: """
- batch = tokenizer(input_text, return_tensors='pt')
- print(['answer_question', 'generating'])
- with torch.cuda.amp.autocast():
- output_tokens = model.generate(**batch,
- max_new_tokens=256,
- generation_config=generation_config)
- output = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
- output = output.replace(input_text, '')
- print(['answer_question', 'end'])
- return output
-
-
-def load_model(peft_model_id):
- print(['load_model', 'start'])
- config = PeftConfig.from_pretrained(peft_model_id)
- print(['load_model', 'loading model'])
- model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path,
- return_dict=True,
- load_in_8bit=True,
- device_map='auto')
- print(['load_model', 'loading tokenizer'])
- tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
- model = PeftModel.from_pretrained(model, peft_model_id)
- model.config.use_cache = True
- print(['load_model', 'end'])
- return model, tokenizer
-
-
-def load_embeddings_model(model_ckpt: str):
- print(['load_embeddings_model', 'start'])
- print(['load_embeddings_model', 'loading tokenizer'])
- tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
- print(['load_embeddings_model', 'loading model'])
- model = AutoModel.from_pretrained(model_ckpt)
- model = model.to(device)
- print(['load_embeddings_model', 'end'])
- return model, tokenizer
-
-
-# Models trained with LoRA
-# - hackathon-somos-nlp-2023/opt-6.7b-lora-sag-t3000-v300-v2
-# - hackathon-somos-nlp-2023/opt-6.7b-lora-sag-t14000-v1400-v1
-model, tokenizer = load_model("hackathon-somos-nlp-2023/opt-6.7b-lora-sag-t14000-v1400-v1")
-pipe = pipeline("text2text-generation", model=model,
- tokenizer=tokenizer, max_new_tokens=100)
-llm = HuggingFacePipeline(pipeline=pipe)
-
-# Sentence Transformers models
-# - paraphrase-multilingual-MiniLM-L12-v2
-# - multi-qa-mpnet-base-dot-v1
-emb_model, emb_tokenizer = load_embeddings_model("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
diff --git a/spaces/hamacojr/CAT-Seg/open_clip/src/training/train.py b/spaces/hamacojr/CAT-Seg/open_clip/src/training/train.py
deleted file mode 100644
index bf42f147592e1a1745b067a255688aaf913e9401..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/CAT-Seg/open_clip/src/training/train.py
+++ /dev/null
@@ -1,308 +0,0 @@
-import json
-import logging
-import math
-import os
-import time
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-try:
- import wandb
-except ImportError:
- wandb = None
-
-from open_clip import ClipLoss, get_cast_dtype
-from .distributed import is_master
-from .zero_shot import zero_shot_eval
-from .precision import get_autocast
-
-
-class AverageMeter(object):
- """Computes and stores the average and current value"""
-
- def __init__(self):
- self.reset()
-
- def reset(self):
- self.val = 0
- self.avg = 0
- self.sum = 0
- self.count = 0
-
- def update(self, val, n=1):
- self.val = val
- self.sum += val * n
- self.count += n
- self.avg = self.sum / self.count
-
-
-def unwrap_model(model):
- if hasattr(model, 'module'):
- return model.module
- else:
- return model
-
-
-def backward(total_loss, scaler):
- if scaler is not None:
- scaler.scale(total_loss).backward()
- else:
- total_loss.backward()
-
-
-def train_one_epoch(model, data, epoch, optimizer, scaler, scheduler, args, tb_writer=None):
- device = torch.device(args.device)
- autocast = get_autocast(args.precision)
- cast_dtype = get_cast_dtype(args.precision)
-
- model.train()
- loss = ClipLoss(
- local_loss=args.local_loss,
- gather_with_grad=args.gather_with_grad,
- cache_labels=True,
- rank=args.rank,
- world_size=args.world_size,
- use_horovod=args.horovod)
-
- data['train'].set_epoch(epoch) # set epoch in process safe manner via sampler or shared_epoch
- dataloader = data['train'].dataloader
- num_batches_per_epoch = dataloader.num_batches // args.accum_freq
- sample_digits = math.ceil(math.log(dataloader.num_samples + 1, 10))
-
- if args.accum_freq > 1:
- accum_images, accum_texts, accum_image_features, accum_text_features = [], [], [], []
-
- loss_m = AverageMeter()
- batch_time_m = AverageMeter()
- data_time_m = AverageMeter()
- end = time.time()
- for i, batch in enumerate(dataloader):
- i_accum = i // args.accum_freq
- step = num_batches_per_epoch * epoch + i_accum
-
- if not args.skip_scheduler:
- scheduler(step)
-
- images, texts = batch
- images = images.to(device=device, dtype=cast_dtype, non_blocking=True)
- texts = texts.to(device=device, non_blocking=True)
-
- data_time_m.update(time.time() - end)
- optimizer.zero_grad()
-
- if args.accum_freq == 1:
- with autocast():
- image_features, text_features, logit_scale = model(images, texts)
- total_loss = loss(image_features, text_features, logit_scale)
-
- backward(total_loss, scaler)
- else:
- # First, cache the features without any gradient tracking.
- with torch.no_grad():
- with autocast():
- chunk_image_features, chunk_text_features, _ = model(images, texts)
- accum_image_features.append(chunk_image_features)
- accum_text_features.append(chunk_text_features)
-
- accum_images.append(images)
- accum_texts.append(texts)
-
- # If (i + 1) % accum_freq is not zero, move on to the next batch.
- if ((i + 1) % args.accum_freq) > 0:
- # FIXME this makes data time logging unreliable when accumulating
- continue
-
- # Now, ready to take gradients for the last accum_freq batches.
- # Re-do the forward pass for those batches, and use the cached features from the other batches as negatives.
- # Call backwards each time, but only step optimizer at the end.
- optimizer.zero_grad()
- for j in range(args.accum_freq):
- images = accum_images[j]
- texts = accum_texts[j]
- with autocast():
- chunk_image_features, chunk_text_features, logit_scale = model(images, texts)
- image_features = torch.cat(
- accum_image_features[:j] + [chunk_image_features] + accum_image_features[j + 1:])
- text_features = torch.cat(
- accum_text_features[:j] + [chunk_text_features] + accum_text_features[j + 1:])
- total_loss = loss(image_features, text_features, logit_scale)
- backward(total_loss, scaler)
-
- if scaler is not None:
- if args.horovod:
- optimizer.synchronize()
- scaler.unscale_(optimizer)
- if args.grad_clip_norm is not None:
- torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip_norm, norm_type=2.0)
- with optimizer.skip_synchronize():
- scaler.step(optimizer)
- else:
- if args.grad_clip_norm is not None:
- scaler.unscale_(optimizer)
- torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip_norm, norm_type=2.0)
- scaler.step(optimizer)
- scaler.update()
- else:
- if args.grad_clip_norm is not None:
- torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip_norm, norm_type=2.0)
- optimizer.step()
-
- # reset gradient accum, if enabled
- if args.accum_freq > 1:
- accum_images, accum_texts, accum_image_features, accum_text_features = [], [], [], []
-
- # Note: we clamp to 4.6052 = ln(100), as in the original paper.
- with torch.no_grad():
- unwrap_model(model).logit_scale.clamp_(0, math.log(100))
-
- batch_time_m.update(time.time() - end)
- end = time.time()
- batch_count = i_accum + 1
- if is_master(args) and (i_accum % args.log_every_n_steps == 0 or batch_count == num_batches_per_epoch):
- batch_size = len(images)
- num_samples = batch_count * batch_size * args.accum_freq * args.world_size
- samples_per_epoch = dataloader.num_samples
- percent_complete = 100.0 * batch_count / num_batches_per_epoch
-
- # NOTE loss is coarsely sampled, just master node and per log update
- loss_m.update(total_loss.item(), batch_size)
- logit_scale_scalar = logit_scale.item()
- logging.info(
- f"Train Epoch: {epoch} [{num_samples:>{sample_digits}}/{samples_per_epoch} ({percent_complete:.0f}%)] "
- f"Loss: {loss_m.val:#.5g} ({loss_m.avg:#.4g}) "
- f"Data (t): {data_time_m.avg:.3f} "
- f"Batch (t): {batch_time_m.avg:.3f}, {args.accum_freq * args.batch_size * args.world_size / batch_time_m.val:#g}/s "
- f"LR: {optimizer.param_groups[0]['lr']:5f} "
- f"Logit Scale: {logit_scale_scalar:.3f}"
- )
-
- # Save train loss / etc. Using non avg meter values as loggers have their own smoothing
- log_data = {
- "loss": loss_m.val,
- "data_time": data_time_m.val,
- "batch_time": batch_time_m.val,
- "samples_per_second": args.accum_freq * args.batch_size * args.world_size / batch_time_m.val,
- "scale": logit_scale_scalar,
- "lr": optimizer.param_groups[0]["lr"]
- }
- for name, val in log_data.items():
- name = "train/" + name
- if tb_writer is not None:
- tb_writer.add_scalar(name, val, step)
- if args.wandb:
- assert wandb is not None, 'Please install wandb.'
- wandb.log({name: val, 'step': step})
-
- # resetting batch / data time meters per log window
- batch_time_m.reset()
- data_time_m.reset()
- # end for
-
-
-def evaluate(model, data, epoch, args, tb_writer=None):
- metrics = {}
- if not is_master(args):
- return metrics
- device = torch.device(args.device)
- model.eval()
-
- zero_shot_metrics = zero_shot_eval(model, data, epoch, args)
- metrics.update(zero_shot_metrics)
-
- autocast = get_autocast(args.precision)
- cast_dtype = get_cast_dtype(args.precision)
-
- if 'val' in data and (args.val_frequency and ((epoch % args.val_frequency) == 0 or epoch == args.epochs)):
- dataloader = data['val'].dataloader
- num_samples = 0
- samples_per_val = dataloader.num_samples
-
- # FIXME this does not scale past small eval datasets
- # all_image_features @ all_text_features will blow up memory and compute very quickly
- cumulative_loss = 0.0
- all_image_features, all_text_features = [], []
- with torch.no_grad():
- for i, batch in enumerate(dataloader):
- images, texts = batch
- images = images.to(device=device, dtype=cast_dtype, non_blocking=True)
- texts = texts.to(device=device, non_blocking=True)
-
- with autocast():
- image_features, text_features, logit_scale = model(images, texts)
- # features are accumulated in CPU tensors, otherwise GPU memory exhausted quickly
- # however, system RAM is easily exceeded and compute time becomes problematic
- all_image_features.append(image_features.cpu())
- all_text_features.append(text_features.cpu())
- logit_scale = logit_scale.mean()
- logits_per_image = logit_scale * image_features @ text_features.t()
- logits_per_text = logits_per_image.t()
-
- batch_size = images.shape[0]
- labels = torch.arange(batch_size, device=device).long()
- total_loss = (
- F.cross_entropy(logits_per_image, labels) +
- F.cross_entropy(logits_per_text, labels)
- ) / 2
-
- cumulative_loss += total_loss * batch_size
- num_samples += batch_size
- if is_master(args) and (i % 100) == 0:
- logging.info(
- f"Eval Epoch: {epoch} [{num_samples} / {samples_per_val}]\t"
- f"Loss: {cumulative_loss / num_samples:.6f}\t")
-
- val_metrics = get_metrics(
- image_features=torch.cat(all_image_features),
- text_features=torch.cat(all_text_features),
- logit_scale=logit_scale.cpu(),
- )
- loss = cumulative_loss / num_samples
- metrics.update(
- {**val_metrics, "val_loss": loss.item(), "epoch": epoch, "num_samples": num_samples}
- )
-
- if not metrics:
- return metrics
-
- logging.info(
- f"Eval Epoch: {epoch} "
- + "\t".join([f"{k}: {round(v, 4):.4f}" for k, v in metrics.items()])
- )
-
- if args.save_logs:
- for name, val in metrics.items():
- if tb_writer is not None:
- tb_writer.add_scalar(f"val/{name}", val, epoch)
-
- with open(os.path.join(args.checkpoint_path, "results.jsonl"), "a+") as f:
- f.write(json.dumps(metrics))
- f.write("\n")
-
- if args.wandb:
- assert wandb is not None, 'Please install wandb.'
- for name, val in metrics.items():
- wandb.log({f"val/{name}": val, 'epoch': epoch})
-
- return metrics
-
-
-def get_metrics(image_features, text_features, logit_scale):
- metrics = {}
- logits_per_image = (logit_scale * image_features @ text_features.t()).detach().cpu()
- logits_per_text = logits_per_image.t().detach().cpu()
-
- logits = {"image_to_text": logits_per_image, "text_to_image": logits_per_text}
- ground_truth = torch.arange(len(text_features)).view(-1, 1)
-
- for name, logit in logits.items():
- ranking = torch.argsort(logit, descending=True)
- preds = torch.where(ranking == ground_truth)[1]
- preds = preds.detach().cpu().numpy()
- metrics[f"{name}_mean_rank"] = preds.mean() + 1
- metrics[f"{name}_median_rank"] = np.floor(np.median(preds)) + 1
- for k in [1, 5, 10]:
- metrics[f"{name}_R@{k}"] = np.mean(preds < k)
-
- return metrics
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/utils/video_visualizer.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/utils/video_visualizer.py
deleted file mode 100644
index 0144b679d09bbb8049c30eb849099422355b492c..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/utils/video_visualizer.py
+++ /dev/null
@@ -1,235 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import numpy as np
-import pycocotools.mask as mask_util
-
-from detectron2.utils.visualizer import (
- ColorMode,
- Visualizer,
- _create_text_labels,
- _PanopticPrediction,
-)
-
-from .colormap import random_color
-
-
-class _DetectedInstance:
- """
- Used to store data about detected objects in video frame,
- in order to transfer color to objects in the future frames.
-
- Attributes:
- label (int):
- bbox (tuple[float]):
- mask_rle (dict):
- color (tuple[float]): RGB colors in range (0, 1)
- ttl (int): time-to-live for the instance. For example, if ttl=2,
- the instance color can be transferred to objects in the next two frames.
- """
-
- __slots__ = ["label", "bbox", "mask_rle", "color", "ttl"]
-
- def __init__(self, label, bbox, mask_rle, color, ttl):
- self.label = label
- self.bbox = bbox
- self.mask_rle = mask_rle
- self.color = color
- self.ttl = ttl
-
-
-class VideoVisualizer:
- def __init__(self, metadata, instance_mode=ColorMode.IMAGE):
- """
- Args:
- metadata (MetadataCatalog): image metadata.
- """
- self.metadata = metadata
- self._old_instances = []
- assert instance_mode in [
- ColorMode.IMAGE,
- ColorMode.IMAGE_BW,
- ], "Other mode not supported yet."
- self._instance_mode = instance_mode
-
- def draw_instance_predictions(self, frame, predictions):
- """
- Draw instance-level prediction results on an image.
-
- Args:
- frame (ndarray): an RGB image of shape (H, W, C), in the range [0, 255].
- predictions (Instances): the output of an instance detection/segmentation
- model. Following fields will be used to draw:
- "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle").
-
- Returns:
- output (VisImage): image object with visualizations.
- """
- frame_visualizer = Visualizer(frame, self.metadata)
- num_instances = len(predictions)
- if num_instances == 0:
- return frame_visualizer.output
-
- boxes = predictions.pred_boxes.tensor.numpy() if predictions.has("pred_boxes") else None
- scores = predictions.scores if predictions.has("scores") else None
- classes = predictions.pred_classes.numpy() if predictions.has("pred_classes") else None
- keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None
-
- if predictions.has("pred_masks"):
- masks = predictions.pred_masks
- # mask IOU is not yet enabled
- # masks_rles = mask_util.encode(np.asarray(masks.permute(1, 2, 0), order="F"))
- # assert len(masks_rles) == num_instances
- else:
- masks = None
-
- detected = [
- _DetectedInstance(classes[i], boxes[i], mask_rle=None, color=None, ttl=8)
- for i in range(num_instances)
- ]
- colors = self._assign_colors(detected)
-
- labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None))
-
- if self._instance_mode == ColorMode.IMAGE_BW:
- # any() returns uint8 tensor
- frame_visualizer.output.img = frame_visualizer._create_grayscale_image(
- (masks.any(dim=0) > 0).numpy() if masks is not None else None
- )
- alpha = 0.3
- else:
- alpha = 0.5
-
- frame_visualizer.overlay_instances(
- boxes=None if masks is not None else boxes, # boxes are a bit distracting
- masks=masks,
- labels=labels,
- keypoints=keypoints,
- assigned_colors=colors,
- alpha=alpha,
- )
-
- return frame_visualizer.output
-
- def draw_sem_seg(self, frame, sem_seg, area_threshold=None):
- """
- Args:
- sem_seg (ndarray or Tensor): semantic segmentation of shape (H, W),
- each value is the integer label.
- area_threshold (Optional[int]): only draw segmentations larger than the threshold
- """
- # don't need to do anything special
- frame_visualizer = Visualizer(frame, self.metadata)
- frame_visualizer.draw_sem_seg(sem_seg, area_threshold=None)
- return frame_visualizer.output
-
- def draw_panoptic_seg_predictions(
- self, frame, panoptic_seg, segments_info, area_threshold=None, alpha=0.5
- ):
- frame_visualizer = Visualizer(frame, self.metadata)
- pred = _PanopticPrediction(panoptic_seg, segments_info)
-
- if self._instance_mode == ColorMode.IMAGE_BW:
- frame_visualizer.output.img = frame_visualizer._create_grayscale_image(
- pred.non_empty_mask()
- )
-
- # draw mask for all semantic segments first i.e. "stuff"
- for mask, sinfo in pred.semantic_masks():
- category_idx = sinfo["category_id"]
- try:
- mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]
- except AttributeError:
- mask_color = None
-
- frame_visualizer.draw_binary_mask(
- mask,
- color=mask_color,
- text=self.metadata.stuff_classes[category_idx],
- alpha=alpha,
- area_threshold=area_threshold,
- )
-
- all_instances = list(pred.instance_masks())
- if len(all_instances) == 0:
- return frame_visualizer.output
- # draw mask for all instances second
- masks, sinfo = list(zip(*all_instances))
- num_instances = len(masks)
- masks_rles = mask_util.encode(
- np.asarray(np.asarray(masks).transpose(1, 2, 0), dtype=np.uint8, order="F")
- )
- assert len(masks_rles) == num_instances
-
- category_ids = [x["category_id"] for x in sinfo]
- detected = [
- _DetectedInstance(category_ids[i], bbox=None, mask_rle=masks_rles[i], color=None, ttl=8)
- for i in range(num_instances)
- ]
- colors = self._assign_colors(detected)
- labels = [self.metadata.thing_classes[k] for k in category_ids]
-
- frame_visualizer.overlay_instances(
- boxes=None,
- masks=masks,
- labels=labels,
- keypoints=None,
- assigned_colors=colors,
- alpha=alpha,
- )
- return frame_visualizer.output
-
- def _assign_colors(self, instances):
- """
- Naive tracking heuristics to assign same color to the same instance,
- will update the internal state of tracked instances.
-
- Returns:
- list[tuple[float]]: list of colors.
- """
-
- # Compute iou with either boxes or masks:
- is_crowd = np.zeros((len(instances),), dtype=np.bool)
- if instances[0].bbox is None:
- assert instances[0].mask_rle is not None
- # use mask iou only when box iou is None
- # because box seems good enough
- rles_old = [x.mask_rle for x in self._old_instances]
- rles_new = [x.mask_rle for x in instances]
- ious = mask_util.iou(rles_old, rles_new, is_crowd)
- threshold = 0.5
- else:
- boxes_old = [x.bbox for x in self._old_instances]
- boxes_new = [x.bbox for x in instances]
- ious = mask_util.iou(boxes_old, boxes_new, is_crowd)
- threshold = 0.6
- if len(ious) == 0:
- ious = np.zeros((len(self._old_instances), len(instances)), dtype="float32")
-
- # Only allow matching instances of the same label:
- for old_idx, old in enumerate(self._old_instances):
- for new_idx, new in enumerate(instances):
- if old.label != new.label:
- ious[old_idx, new_idx] = 0
-
- matched_new_per_old = np.asarray(ious).argmax(axis=1)
- max_iou_per_old = np.asarray(ious).max(axis=1)
-
- # Try to find match for each old instance:
- extra_instances = []
- for idx, inst in enumerate(self._old_instances):
- if max_iou_per_old[idx] > threshold:
- newidx = matched_new_per_old[idx]
- if instances[newidx].color is None:
- instances[newidx].color = inst.color
- continue
- # If an old instance does not match any new instances,
- # keep it for the next frame in case it is just missed by the detector
- inst.ttl -= 1
- if inst.ttl > 0:
- extra_instances.append(inst)
-
- # Assign random color to newly-detected instances:
- for inst in instances:
- if inst.color is None:
- inst.color = random_color(rgb=True, maximum=1)
- self._old_instances = instances[:] + extra_instances
- return [d.color for d in instances]
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/networks/context_encoding/psp.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/networks/context_encoding/psp.py
deleted file mode 100644
index 47181dc3f5fddb1c7fb80ad58a6694aae9ebd746..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/networks/context_encoding/psp.py
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-
-"""
-@Author : Peike Li
-@Contact : peike.li@yahoo.com
-@File : psp.py
-@Time : 8/4/19 3:36 PM
-@Desc :
-@License : This source code is licensed under the license found in the
- LICENSE file in the root directory of this source tree.
-"""
-
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-
-from modules import InPlaceABNSync
-
-
-class PSPModule(nn.Module):
- """
- Reference:
- Zhao, Hengshuang, et al. *"Pyramid scene parsing network."*
- """
- def __init__(self, features, out_features=512, sizes=(1, 2, 3, 6)):
- super(PSPModule, self).__init__()
-
- self.stages = []
- self.stages = nn.ModuleList([self._make_stage(features, out_features, size) for size in sizes])
- self.bottleneck = nn.Sequential(
- nn.Conv2d(features + len(sizes) * out_features, out_features, kernel_size=3, padding=1, dilation=1,
- bias=False),
- InPlaceABNSync(out_features),
- )
-
- def _make_stage(self, features, out_features, size):
- prior = nn.AdaptiveAvgPool2d(output_size=(size, size))
- conv = nn.Conv2d(features, out_features, kernel_size=1, bias=False)
- bn = InPlaceABNSync(out_features)
- return nn.Sequential(prior, conv, bn)
-
- def forward(self, feats):
- h, w = feats.size(2), feats.size(3)
- priors = [F.interpolate(input=stage(feats), size=(h, w), mode='bilinear', align_corners=True) for stage in
- self.stages] + [feats]
- bottle = self.bottleneck(torch.cat(priors, 1))
- return bottle
\ No newline at end of file
diff --git a/spaces/hhhhardman/VITS/text/ngu_dialect.py b/spaces/hhhhardman/VITS/text/ngu_dialect.py
deleted file mode 100644
index ce3e12bbf0469426872eed5f681985d3e1be9b26..0000000000000000000000000000000000000000
--- a/spaces/hhhhardman/VITS/text/ngu_dialect.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import re
-import opencc
-
-
-dialects = {'SZ': 'suzhou', 'WX': 'wuxi', 'CZ': 'changzhou', 'HZ': 'hangzhou',
- 'SX': 'shaoxing', 'NB': 'ningbo', 'JJ': 'jingjiang', 'YX': 'yixing',
- 'JD': 'jiading', 'ZR': 'zhenru', 'PH': 'pinghu', 'TX': 'tongxiang',
- 'JS': 'jiashan', 'HN': 'xiashi', 'LP': 'linping', 'XS': 'xiaoshan',
- 'FY': 'fuyang', 'RA': 'ruao', 'CX': 'cixi', 'SM': 'sanmen',
- 'TT': 'tiantai', 'WZ': 'wenzhou', 'SC': 'suichang', 'YB': 'youbu'}
-
-converters = {}
-
-for dialect in dialects.values():
- try:
- converters[dialect] = opencc.OpenCC(dialect)
- except:
- pass
-
-
-def ngu_dialect_to_ipa(text, dialect):
- dialect = dialects[dialect]
- text = converters[dialect].convert(text).replace('-','').replace('$',' ')
- text = re.sub(r'[、；：]', '，', text)
- text = re.sub(r'\s*，\s*', ', ', text)
- text = re.sub(r'\s*。\s*', '. ', text)
- text = re.sub(r'\s*？\s*', '? ', text)
- text = re.sub(r'\s*！\s*', '! ', text)
- text = re.sub(r'\s*$', '', text)
- return text
diff --git a/spaces/hhhyrhe/vits-uma-genshin-honkai/commons.py b/spaces/hhhyrhe/vits-uma-genshin-honkai/commons.py
deleted file mode 100644
index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000
--- a/spaces/hhhyrhe/vits-uma-genshin-honkai/commons.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
- return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
- return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def intersperse(lst, item):
- result = [item] * (len(lst) * 2 + 1)
- result[1::2] = lst
- return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
- """KL(P||Q)"""
- kl = (logs_q - logs_p) - 0.5
- kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
- return kl
-
-
-def rand_gumbel(shape):
- """Sample from the Gumbel distribution, protect from overflows."""
- uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
- return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
- g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
- return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, :, idx_str:idx_end]
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def get_timing_signal_1d(
- length, channels, min_timescale=1.0, max_timescale=1.0e4):
- position = torch.arange(length, dtype=torch.float)
- num_timescales = channels // 2
- log_timescale_increment = (
- math.log(float(max_timescale) / float(min_timescale)) /
- (num_timescales - 1))
- inv_timescales = min_timescale * torch.exp(
- torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
- scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
- signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
- signal = F.pad(signal, [0, 0, 0, channels % 2])
- signal = signal.view(1, channels, length)
- return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def shift_1d(x):
- x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
- return x
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
- path = path.unsqueeze(1).transpose(2,3) * mask
- return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- parameters = list(filter(lambda p: p.grad is not None, parameters))
- norm_type = float(norm_type)
- if clip_value is not None:
- clip_value = float(clip_value)
-
- total_norm = 0
- for p in parameters:
- param_norm = p.grad.data.norm(norm_type)
- total_norm += param_norm.item() ** norm_type
- if clip_value is not None:
- p.grad.data.clamp_(min=-clip_value, max=clip_value)
- total_norm = total_norm ** (1. / norm_type)
- return total_norm
diff --git a/spaces/hilsq/bingotest/README.md b/spaces/hilsq/bingotest/README.md
deleted file mode 100644
index e09b782ed3f8ebeea03e8b824507aa18ff18b9d1..0000000000000000000000000000000000000000
--- a/spaces/hilsq/bingotest/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-title: bingo
-emoji: 😊
-colorFrom: red
-colorTo: red
-sdk: docker
-pinned: true
-license: mit
----
-
-<div align="center">
-
-# Bingo
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-
-
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://github.com/weaigc/bingo/blob/main/license)
-
-问题反馈请前往 https://github.com/weaigc/bingo/issues
-</div>
-
-
diff --git a/spaces/hirsuitedevil/demo/README.md b/spaces/hirsuitedevil/demo/README.md
deleted file mode 100644
index 6c0bc94f7053fb6f807246d17b03732b317c2940..0000000000000000000000000000000000000000
--- a/spaces/hirsuitedevil/demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Demo
-emoji: 📚
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.0.24
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hlydecker/RA-document-QAchat/streamlit_langchain_chat/customized_langchain/llms/__init__.py b/spaces/hlydecker/RA-document-QAchat/streamlit_langchain_chat/customized_langchain/llms/__init__.py
deleted file mode 100644
index ac93faf469484dd6e7c5523555b181f3681c5e9a..0000000000000000000000000000000000000000
--- a/spaces/hlydecker/RA-document-QAchat/streamlit_langchain_chat/customized_langchain/llms/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from streamlit_langchain_chat.customized_langchain.llms.openai import AzureOpenAI, OpenAI, OpenAIChat, AzureOpenAIChat
diff --git a/spaces/hra/ChatGPT-Keyword2Blog/README.md b/spaces/hra/ChatGPT-Keyword2Blog/README.md
deleted file mode 100644
index 59038116ddb0b2cd4a819e26fb41836b94e385d3..0000000000000000000000000000000000000000
--- a/spaces/hra/ChatGPT-Keyword2Blog/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ChatGPT Keyword2Blog
-emoji: 📈
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-license: cc-by-nc-sa-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/huaiji3y/bingo-Public/src/components/tailwind-indicator.tsx b/spaces/huaiji3y/bingo-Public/src/components/tailwind-indicator.tsx
deleted file mode 100644
index f2a1291213dd67055fcebe67fab574c8441338df..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/components/tailwind-indicator.tsx
+++ /dev/null
@@ -1,14 +0,0 @@
-export function TailwindIndicator() {
- if (process.env.NODE_ENV === 'production') return null
-
- return (
- <div className="fixed bottom-1 left-1 z-50 flex h-6 w-6 items-center justify-center rounded-full bg-gray-800 p-3 font-mono text-xs text-white">
- <div className="block sm:hidden">xs</div>
- <div className="hidden sm:block md:hidden">sm</div>
- <div className="hidden md:block lg:hidden">md</div>
- <div className="hidden lg:block xl:hidden">lg</div>
- <div className="hidden xl:block 2xl:hidden">xl</div>
- <div className="hidden 2xl:block">2xl</div>
- </div>
- )
-}
diff --git a/spaces/huggingface/transformers-chat/README.md b/spaces/huggingface/transformers-chat/README.md
deleted file mode 100644
index 5ea079483cd2e70c306cbc28064a9ffa090f3ef6..0000000000000000000000000000000000000000
--- a/spaces/huggingface/transformers-chat/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Transformers Chat
-emoji: 🤗
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/humeur/Swedish-Whisper-from-Youtube/README.md b/spaces/humeur/Swedish-Whisper-from-Youtube/README.md
deleted file mode 100644
index c2f1d21faeae9243d038929a40cb5ea54d683de7..0000000000000000000000000000000000000000
--- a/spaces/humeur/Swedish-Whisper-from-Youtube/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Swedish Whisper From Youtube
-emoji: 📊
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/iamironman4279/SadTalker/src/utils/croper.py b/spaces/iamironman4279/SadTalker/src/utils/croper.py
deleted file mode 100644
index 3d9a0ac58f97afdc95d40f2a400272b11fe38093..0000000000000000000000000000000000000000
--- a/spaces/iamironman4279/SadTalker/src/utils/croper.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import os
-import cv2
-import time
-import glob
-import argparse
-import scipy
-import numpy as np
-from PIL import Image
-import torch
-from tqdm import tqdm
-from itertools import cycle
-
-from src.face3d.extract_kp_videos_safe import KeypointExtractor
-from facexlib.alignment import landmark_98_to_68
-
-import numpy as np
-from PIL import Image
-
-class Preprocesser:
- def __init__(self, device='cuda'):
- self.predictor = KeypointExtractor(device)
-
- def get_landmark(self, img_np):
- """get landmark with dlib
- :return: np.array shape=(68, 2)
- """
- with torch.no_grad():
- dets = self.predictor.det_net.detect_faces(img_np, 0.97)
-
- if len(dets) == 0:
- return None
- det = dets[0]
-
- img = img_np[int(det[1]):int(det[3]), int(det[0]):int(det[2]), :]
- lm = landmark_98_to_68(self.predictor.detector.get_landmarks(img)) # [0]
-
- #### keypoints to the original location
- lm[:,0] += int(det[0])
- lm[:,1] += int(det[1])
-
- return lm
-
- def align_face(self, img, lm, output_size=1024):
- """
- :param filepath: str
- :return: PIL Image
- """
- lm_chin = lm[0: 17] # left-right
- lm_eyebrow_left = lm[17: 22] # left-right
- lm_eyebrow_right = lm[22: 27] # left-right
- lm_nose = lm[27: 31] # top-down
- lm_nostrils = lm[31: 36] # top-down
- lm_eye_left = lm[36: 42] # left-clockwise
- lm_eye_right = lm[42: 48] # left-clockwise
- lm_mouth_outer = lm[48: 60] # left-clockwise
- lm_mouth_inner = lm[60: 68] # left-clockwise
-
- # Calculate auxiliary vectors.
- eye_left = np.mean(lm_eye_left, axis=0)
- eye_right = np.mean(lm_eye_right, axis=0)
- eye_avg = (eye_left + eye_right) * 0.5
- eye_to_eye = eye_right - eye_left
- mouth_left = lm_mouth_outer[0]
- mouth_right = lm_mouth_outer[6]
- mouth_avg = (mouth_left + mouth_right) * 0.5
- eye_to_mouth = mouth_avg - eye_avg
-
- # Choose oriented crop rectangle.
- x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] # Addition of binocular difference and double mouth difference
- x /= np.hypot(*x) # hypot函数计算直角三角形的斜边长，用斜边长对三角形两条直边做归一化
- x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) # 双眼差和眼嘴差，选较大的作为基准尺度
- y = np.flipud(x) * [-1, 1]
- c = eye_avg + eye_to_mouth * 0.1
- quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) # 定义四边形，以面部基准位置为中心上下左右平移得到四个顶点
- qsize = np.hypot(*x) * 2 # 定义四边形的大小（边长），为基准尺度的2倍
-
- # Shrink.
- # 如果计算出的四边形太大了，就按比例缩小它
- shrink = int(np.floor(qsize / output_size * 0.5))
- if shrink > 1:
- rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
- img = img.resize(rsize, Image.ANTIALIAS)
- quad /= shrink
- qsize /= shrink
- else:
- rsize = (int(np.rint(float(img.size[0]))), int(np.rint(float(img.size[1]))))
-
- # Crop.
- border = max(int(np.rint(qsize * 0.1)), 3)
- crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
- int(np.ceil(max(quad[:, 1]))))
- crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
- min(crop[3] + border, img.size[1]))
- if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
- # img = img.crop(crop)
- quad -= crop[0:2]
-
- # Pad.
- pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
- int(np.ceil(max(quad[:, 1]))))
- pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
- max(pad[3] - img.size[1] + border, 0))
- # if enable_padding and max(pad) > border - 4:
- # pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
- # img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
- # h, w, _ = img.shape
- # y, x, _ = np.ogrid[:h, :w, :1]
- # mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
- # 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
- # blur = qsize * 0.02
- # img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
- # img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
- # img = Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
- # quad += pad[:2]
-
- # Transform.
- quad = (quad + 0.5).flatten()
- lx = max(min(quad[0], quad[2]), 0)
- ly = max(min(quad[1], quad[7]), 0)
- rx = min(max(quad[4], quad[6]), img.size[0])
- ry = min(max(quad[3], quad[5]), img.size[0])
-
- # Save aligned image.
- return rsize, crop, [lx, ly, rx, ry]
-
- def crop(self, img_np_list, still=False, xsize=512): # first frame for all video
- img_np = img_np_list[0]
- lm = self.get_landmark(img_np)
-
- if lm is None:
- raise 'can not detect the landmark from source image'
- rsize, crop, quad = self.align_face(img=Image.fromarray(img_np), lm=lm, output_size=xsize)
- clx, cly, crx, cry = crop
- lx, ly, rx, ry = quad
- lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
- for _i in range(len(img_np_list)):
- _inp = img_np_list[_i]
- _inp = cv2.resize(_inp, (rsize[0], rsize[1]))
- _inp = _inp[cly:cry, clx:crx]
- if not still:
- _inp = _inp[ly:ry, lx:rx]
- img_np_list[_i] = _inp
- return img_np_list, crop, quad
-
diff --git a/spaces/ikechan8370/vits-uma-genshin-honkai/text/symbols.py b/spaces/ikechan8370/vits-uma-genshin-honkai/text/symbols.py
deleted file mode 100644
index edfbd24247be8c757275ce80b9ec27a0ffa808f3..0000000000000000000000000000000000000000
--- a/spaces/ikechan8370/vits-uma-genshin-honkai/text/symbols.py
+++ /dev/null
@@ -1,39 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-'''# japanese_cleaners
-_pad = '_'
-_punctuation = ',.!?-'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-'''
-
-'''# japanese_cleaners2
-_pad = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-'''# korean_cleaners
-_pad = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# zh_ja_mixture_cleaners
-_pad = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
\ No newline at end of file
diff --git a/spaces/innnky/nyaru-svc2.0/models.py b/spaces/innnky/nyaru-svc2.0/models.py
deleted file mode 100644
index b433f9f203b0aed053325915ad2a950f06abf880..0000000000000000000000000000000000000000
--- a/spaces/innnky/nyaru-svc2.0/models.py
+++ /dev/null
@@ -1,625 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-import numpy as np
-import commons
-import modules
-import attentions
-import monotonic_align
-
-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
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.n_flows = n_flows
- 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)
- x = self.pre(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.convs(x, x_mask)
- x = self.proj(x) * x_mask
-
- if not reverse:
- flows = self.flows
- assert w is not None
-
- logdet_tot_q = 0
- h_w = self.post_pre(w)
- 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
- z_q = e_q
- for flow in self.post_flows:
- z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
- logdet_tot_q += logdet_q
- z_u, z1 = torch.split(z_q, [1, 1], 1)
- 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 PitchPredictor(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 # 音素的个数，中文和英文不同
- 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.pitch_net = attentions.Encoder(
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
- self.proj = nn.Conv1d(hidden_channels, 1, 1)
-
- def forward(self, x, x_mask):
- pitch_embedding = self.pitch_net(x * x_mask, x_mask)
- pitch_embedding = pitch_embedding * x_mask
- pred_pitch = self.proj(pitch_embedding)
- return pred_pitch, pitch_embedding
-
-
-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.emb_pitch = nn.Embedding(256, hidden_channels)
- nn.init.normal_(self.emb_pitch.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)
-
- def forward(self, x, x_lengths, pitch):
- # x = x.transpose(1,2)
- # x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
- # print(x.shape)
- x = x + self.emb_pitch(pitch)
- 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)
-
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- 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
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- 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())
-
- 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
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.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)
-
- 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
- x = self.enc(x, x_mask, g=g)
- 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
- self.spec_channels = spec_channels
- self.inter_channels = inter_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.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.n_speakers = n_speakers
- self.gin_channels = gin_channels
-
- self.use_sdp = use_sdp
-
- self.enc_p = TextEncoder(n_vocab,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
- 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)
- self.pitch_net = PitchPredictor(n_vocab, inter_channels, hidden_channels, filter_channels, n_heads, n_layers,
- kernel_size, p_dropout)
-
- if use_sdp:
- 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, y, y_lengths, pitch, sid=None):
-
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, pitch)
- # print(f"x: {x.shape}")
- pred_pitch, pitch_embedding = self.pitch_net(x, x_mask)
- # print(f"pred_pitch: {pred_pitch.shape}")
- # print(f"pitch_embedding: {pitch_embedding.shape}")
- x = x + pitch_embedding
- lf0 = torch.unsqueeze(pred_pitch, -1)
- gt_lf0 = torch.log(440 * (2 ** ((pitch.float() - 69) / 12)))
- gt_lf0 = gt_lf0.to(x.device)
- x_mask_sum = torch.sum(x_mask)
- lf0 = lf0.squeeze()
- l_pitch = torch.sum((gt_lf0 - lf0) ** 2, 1) / x_mask_sum
-
- 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)
- # print(f"z: {z.shape}")
-
- z_p = self.flow(z, y_mask, g=g)
- # print(f"z_p: {z_p.shape}")
-
- 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()
-
- w = attn.sum(2)
- if self.use_sdp:
- l_length = self.dp(x, x_mask, w, g=g)
- 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()
- # print(f"attn: {attn.shape}")
- # print(f"m_p: {m_p.shape}")
- # print(f"logs_p: {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(f"m_p: {m_p.shape}")
- # print(f"logs_p: {logs_p.shape}")
-
- z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
- # print(f"z_slice: {z_slice.shape}")
-
- o = self.dec(z_slice, g=g)
- return o, l_length, l_pitch, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, x, x_lengths, pitch, 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, pitch)
- pred_pitch, pitch_embedding = self.pitch_net(x, x_mask)
- x = x + pitch_embedding
- 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)
-
- w_ceil = w_ceil * 0 + 2
- # for index in range(w_ceil.shape[2]):
- # if index%4 == 0:
- # w_ceil[0,0,index] = 1.0
-
- for i in range(w_ceil.shape[2]):
- sep = 1 / 0.14
- if i * sep >= w_ceil.shape[2] * 2:
- break
- w_ceil[0, 0, int(i * sep / 2)] = 1
-
- # print(w_ceil)
- 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/inplisQlawa/anything-midjourney-v4-1/Francis Dk Ching Design Drawing Pdf Download WORK.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Francis Dk Ching Design Drawing Pdf Download WORK.md
deleted file mode 100644
index 00e4c93448736ab5b034e15f18c327b8a046bd3a..0000000000000000000000000000000000000000
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diff --git a/spaces/irene-glez/whatsapp_chat_analyzer_streamlit/preprocess.py b/spaces/irene-glez/whatsapp_chat_analyzer_streamlit/preprocess.py
deleted file mode 100644
index a9f877796062792817b843840f340e704f0abad4..0000000000000000000000000000000000000000
--- a/spaces/irene-glez/whatsapp_chat_analyzer_streamlit/preprocess.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import regex as re
-import seaborn as sn
-
-
-# function to separate time and date
-def get_time_date(string):
- string = string.split(',')
- date, time = string[0], string[1]
- time = time.split('-')
- time = time[0].strip()
-
- return date+" "+time
-
-# removing '\n' from the 'Message' column
-def get_string(text):
- return text.split('\n')[0]
-
-# final preprocessing function
-def preprocess(data):
- # splitting date, time and dash at the start of every line of text
- pattern = '\d{1,2}/\d{1,2}/\d{2,4},\s\d{1,2}:\d{2}\s-\s'
-
- # separate dates from messages
- messages = re.split(pattern, data)[1:]
- dates = re.findall(pattern, data)
-
- # put both in a dataframe
- df = pd.DataFrame({'user_messages': messages,
- 'message_date': dates})
-
- df['message_date'] = df['message_date'].apply(
- lambda text: get_time_date(text))
- df.rename(columns={'message_date': 'date'}, inplace=True)
-
- # separation of the usernamane
- users = []
- messages = []
-
- for message in df['user_messages']:
-
- entry = re.split('([\w\W]+?):\s', message) # extracting the username
- if entry[1:]:
- users.append(entry[1])
- messages.append(entry[2])
-
- else:
- users.append('Group Notification') # the group's notifications don't have linked messages
- messages.append(entry[0])
-
- df['User'] = users
- df['Message'] = messages
-
- df['Message'] = df['Message'].apply(lambda text: get_string(text))
- df = df.drop(['user_messages'], axis=1)
-
- df = df[['Message', 'Date', 'User']]
-
- # df = df.rename(columns={'message': 'Message',
- # 'date': 'Date'})
-
- # splitting and type transformation for all the info contained in the 'date' column with datetime:
-
- df['Only date'] = pd.to_datetime(df['Date']).dt.date
- df['Year'] = pd.to_datetime(df['Date']).dt.year
- df['Month_num'] = pd.to_datetime(df['Date']).dt.month
- df['Month'] = pd.to_datetime(df['Date']).dt.month_name()
- df['Day'] = pd.to_datetime(df['Date']).dt.day
- df['Day_name'] = pd.to_datetime(df['Date']).dt.day_name()
- df['Hour'] = pd.to_datetime(df['Date']).dt.hour
- df['Minute'] = pd.to_datetime(df['Date']).dt.minute
-
- return df
diff --git a/spaces/james-oldfield/PandA/networks/genforce/configs/stylegan_ffhq1024_val.py b/spaces/james-oldfield/PandA/networks/genforce/configs/stylegan_ffhq1024_val.py
deleted file mode 100644
index 33850aeafdf0fa68d8904c6ac0f1dd89be6bc977..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/genforce/configs/stylegan_ffhq1024_val.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# python3.7
-"""Configuration for testing StyleGAN on FF-HQ (1024) dataset.
-
-All settings are particularly used for one replica (GPU), such as `batch_size`
-and `num_workers`.
-"""
-
-runner_type = 'StyleGANRunner'
-gan_type = 'stylegan'
-resolution = 1024
-batch_size = 16
-
-data = dict(
- num_workers=4,
- # val=dict(root_dir='data/ffhq', resolution=resolution),
- val=dict(root_dir='data/ffhq.zip', data_format='zip',
- resolution=resolution),
-)
-
-modules = dict(
- discriminator=dict(
- model=dict(gan_type=gan_type, resolution=resolution),
- kwargs_val=dict(),
- ),
- generator=dict(
- model=dict(gan_type=gan_type, resolution=resolution),
- kwargs_val=dict(trunc_psi=0.7, trunc_layers=8, randomize_noise=False),
- )
-)
diff --git a/spaces/jb30k/LegalWW/app.py b/spaces/jb30k/LegalWW/app.py
deleted file mode 100644
index 175b1aac706ca52ba26542396b73a005e153b6bb..0000000000000000000000000000000000000000
--- a/spaces/jb30k/LegalWW/app.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import openai
-import gradio
-
-openai.api_key = "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"
-
-messages = [{"role": "system", "content": "You are a legal database. You will only awnser truthfully in dutch as following - If asked if something is legal, anwser by law in 10 words. - If asked for advice, give 5 short bullitpoint on which the person can make his/her own critic opinion. - By what law the awnser is based on structure, example (art. 3.1 lid 2 Wet Inkomstenbelasting 2001). List all the laws if more are applicable. - The most important right the person has in that situation in 5 words. - Give 2 websitelinks they can visit to get more legal information about the subject. Always end with the shortest way of asking more questions."}]
-
-def CustomChatGPT(user_input):
- messages.append({"role": "user", "content": user_input})
- response = openai.ChatCompletion.create(
- model = "gpt-3.5-turbo",
- messages = messages
- )
- ChatGPT_reply = response["choices"][0]["message"]["content"]
- messages.append({"role": "assistant", "content": ChatGPT_reply})
- return ChatGPT_reply
-
-inputs = gradio.Textbox(label="Ask your question here:")
-outputs = gradio.Textbox(label="Answer here:")
-
-demo = gradio.Interface(
- CustomChatGPT,
- inputs=inputs,
- outputs=outputs,
- title="WorldWide Legal Advice",
- description="You can ask your legal questions about all law WorldWide here. If an ERROR message appears, please resubmit your question!",
- allow_flagging=True,
- examples=[
- ["Can a landlord terminate a lease agreement with a tenant without valid reason? in London"],
- ["What are the legal consequences of violating a non-disclosure agreement?"],
- ["Can an employer terminate an employee without severance pay if that employee has been caught stealing in the workplace?"],
- ],
-session_cookie=True,
-)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/Panoremix/src/components/icons/hugging-clap.tsx b/spaces/jbilcke-hf/Panoremix/src/components/icons/hugging-clap.tsx
deleted file mode 100644
index ffb37ae6183cd8ce7fe7c212e383a6510eba2485..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/Panoremix/src/components/icons/hugging-clap.tsx
+++ /dev/null
@@ -1,8 +0,0 @@
-export function HuggingClap() {
- return (
- <svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
- <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
- <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
- </svg>
- )
-}
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/ai-comic-factory/src/lib/triggerDownload.ts b/spaces/jbilcke-hf/ai-comic-factory/src/lib/triggerDownload.ts
deleted file mode 100644
index e5627a26a4bba34bdf28279d265c6a71440d8136..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-comic-factory/src/lib/triggerDownload.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-export function triggerDownload(filename: string, text: string) {
- var element = document.createElement('a');
- element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
- element.setAttribute('download', filename);
-
- element.style.display = 'none';
- document.body.appendChild(element);
-
- element.click();
-
- document.body.removeChild(element);
-}
\ No newline at end of file
diff --git a/spaces/jdczlx/ChatGPT-chuanhu/modules/overwrites.py b/spaces/jdczlx/ChatGPT-chuanhu/modules/overwrites.py
deleted file mode 100644
index bfcd4d01b7d7bec1184a8d09113933bca860530b..0000000000000000000000000000000000000000
--- a/spaces/jdczlx/ChatGPT-chuanhu/modules/overwrites.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from __future__ import annotations
-import logging
-
-from llama_index import Prompt
-from typing import List, Tuple
-import mdtex2html
-
-from modules.presets import *
-from modules.llama_func import *
-
-
-def compact_text_chunks(self, prompt: Prompt, text_chunks: List[str]) -> List[str]:
- logging.debug("Compacting text chunks...🚀🚀🚀")
- combined_str = [c.strip() for c in text_chunks if c.strip()]
- combined_str = [f"[{index+1}] {c}" for index, c in enumerate(combined_str)]
- combined_str = "\n\n".join(combined_str)
- # resplit based on self.max_chunk_overlap
- text_splitter = self.get_text_splitter_given_prompt(prompt, 1, padding=1)
- return text_splitter.split_text(combined_str)
-
-
-def postprocess(
- self, y: List[Tuple[str | None, str | None]]
-) -> List[Tuple[str | None, str | None]]:
- """
- Parameters:
- y: List of tuples representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.
- Returns:
- List of tuples representing the message and response. Each message and response will be a string of HTML.
- """
- if y is None or y == []:
- return []
- user, bot = y[-1]
- if not detect_converted_mark(user):
- user = convert_asis(user)
- if not detect_converted_mark(bot):
- bot = convert_mdtext(bot)
- y[-1] = (user, bot)
- return y
-
-with open("./assets/custom.js", "r", encoding="utf-8") as f, open("./assets/Kelpy-Codos.js", "r", encoding="utf-8") as f2:
- customJS = f.read()
- kelpyCodos = f2.read()
-
-def reload_javascript():
- print("Reloading javascript...")
- js = f'<script>{customJS}</script><script>{kelpyCodos}</script>'
- def template_response(*args, **kwargs):
- res = GradioTemplateResponseOriginal(*args, **kwargs)
- res.body = res.body.replace(b'</html>', f'{js}</html>'.encode("utf8"))
- res.init_headers()
- return res
-
- gr.routes.templates.TemplateResponse = template_response
-
-GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse
\ No newline at end of file
diff --git a/spaces/jgerbscheid/dpa-example/README.md b/spaces/jgerbscheid/dpa-example/README.md
deleted file mode 100644
index e9b457d8a1ac9ed9dcc67447867de481a2d98449..0000000000000000000000000000000000000000
--- a/spaces/jgerbscheid/dpa-example/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: dijkprofile-annotator
-emoji: 💦
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-app_file: app.py
-pinned: false
----
diff --git a/spaces/jhtonyKoo/music_mixing_style_transfer/mixing_style_transfer/data_loader/data_loader.py b/spaces/jhtonyKoo/music_mixing_style_transfer/mixing_style_transfer/data_loader/data_loader.py
deleted file mode 100644
index 5821e7f20a7a40a4c0483d97d4c35b9c3b1aeddc..0000000000000000000000000000000000000000
--- a/spaces/jhtonyKoo/music_mixing_style_transfer/mixing_style_transfer/data_loader/data_loader.py
+++ /dev/null
@@ -1,672 +0,0 @@
-"""
- Data Loaders for
- 1. contrastive learning of audio effects
- 2. music mixing style transfer
- introduced in "Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects"
-"""
-import numpy as np
-import wave
-import soundfile as sf
-import time
-import random
-from glob import glob
-
-import torch
-import torch.utils.data as data
-from torch.utils.data import DataLoader
-from torch.utils.data import Dataset
-
-import os
-import sys
-currentdir = os.path.dirname(os.path.realpath(__file__))
-sys.path.append(currentdir)
-sys.path.append(os.path.dirname(currentdir))
-sys.path.append(os.path.dirname(os.path.dirname(currentdir)))
-from loader_utils import *
-from mixing_manipulator import *
-
-
-
-'''
- Collate Functions
-'''
-class Collate_Variable_Length_Segments:
- def __init__(self, args):
- self.segment_length = args.segment_length
- self.random_length = args.reference_length
- self.num_strong_negatives = args.num_strong_negatives
- if 'musdb' in args.using_dataset.lower():
- self.instruments = ["drums", "bass", "other", "vocals"]
- else:
- raise NotImplementedError
-
-
- # collate function to trim segments A and B to random duration
- # this function can handle different number of 'strong negative' inputs
- def random_duration_segments_strong_negatives(self, batch):
- num_inst = len(self.instruments)
- # randomize current input length
- max_length = batch[0][0].shape[-1]
- min_length = max_length//2
- input_length_a, input_length_b = torch.randint(low=min_length, high=max_length, size=(2,))
-
- output_dict_A = {}
- output_dict_B = {}
- for cur_inst in self.instruments:
- output_dict_A[cur_inst] = []
- output_dict_B[cur_inst] = []
- for cur_item in batch:
- # set starting points
- start_point_a = torch.randint(low=0, high=max_length-input_length_a, size=(1,))[0]
- start_point_b = torch.randint(low=0, high=max_length-input_length_b, size=(1,))[0]
- # append to output dictionary
- for cur_i, cur_inst in enumerate(self.instruments):
- # append A# and B# with its strong negative samples
- for cur_neg_idx in range(self.num_strong_negatives+1):
- output_dict_A[cur_inst].append(cur_item[cur_i*(self.num_strong_negatives+1)*2+2*cur_neg_idx][:, start_point_a : start_point_a+input_length_a])
- output_dict_B[cur_inst].append(cur_item[cur_i*(self.num_strong_negatives+1)*2+1+2*cur_neg_idx][:, start_point_b : start_point_b+input_length_b])
-
- '''
- Output format :
- [drums_A, bass_A, other_A, vocals_A],
- [drums_B, bass_B, other_B, vocals_B]
- '''
- return [torch.stack(cur_segments, dim=0) for cur_inst, cur_segments in output_dict_A.items()], \
- [torch.stack(cur_segments, dim=0) for cur_inst, cur_segments in output_dict_B.items()]
-
-
- # collate function for training mixing style transfer
- def style_transfer_collate(self, batch):
- output_dict_A1 = {}
- output_dict_A2 = {}
- output_dict_B2 = {}
- for cur_inst in self.instruments:
- output_dict_A1[cur_inst] = []
- output_dict_A2[cur_inst] = []
- output_dict_B2[cur_inst] = []
- for cur_item in batch:
- # append to output dictionary
- for cur_i, cur_inst in enumerate(self.instruments):
- output_dict_A1[cur_inst].append(cur_item[cur_i*3])
- output_dict_A2[cur_inst].append(cur_item[cur_i*3+1])
- output_dict_B2[cur_inst].append(cur_item[cur_i*3+2])
-
- '''
- Output format :
- [drums_A1, bass_A1, other_A1, vocals_A1],
- [drums_A2, bass_A2, other_A2, vocals_A2],
- [drums_B2, bass_B2, other_B2, vocals_B2]
- '''
- return [torch.stack(cur_segments, dim=0) for cur_inst, cur_segments in output_dict_A1.items()], \
- [torch.stack(cur_segments, dim=0) for cur_inst, cur_segments in output_dict_A2.items()], \
- [torch.stack(cur_segments, dim=0) for cur_inst, cur_segments in output_dict_B2.items()]
-
-
-'''
- Data Loaders
-'''
-
-# Data loader for training the 'FXencoder'
- # randomly loads two segments (A and B) from the dataset
- # both segments are manipulated via FXmanipulator using (1+number of strong negative samples) sets of parameters (resulting A1, A2, ..., A#, and B1, B2, ..., B#) (# = number of strong negative samples)
- # segments with the same effects applied (A1 and B1) are assigned as the positive pair during the training
- # segments with the same content but with different effects applied (A2, A3, ..., A3 for A1) are also formed in a batch as 'strong negative' samples
- # in the paper, we use strong negative samples = 1
-class MUSDB_Dataset_Mixing_Manipulated_FXencoder(Dataset):
- def __init__(self, args, \
- mode, \
- applying_effects='full', \
- apply_prob_dict=None):
- self.args = args
- self.data_dir = args.data_dir + mode + "/"
- self.mode = mode
- self.applying_effects = applying_effects
- self.normalization_order = args.normalization_order
- self.fixed_random_seed = args.random_seed
- self.pad_b4_manipulation = args.pad_b4_manipulation
- self.pad_length = 2048
-
- if 'musdb' in args.using_dataset.lower():
- self.instruments = ["drums", "bass", "other", "vocals"]
- else:
- raise NotImplementedError
-
- # path to contents
- self.data_paths = {}
- self.data_length_ratio_list = {}
- # load data paths for each instrument
- for cur_inst in self.instruments:
- self.data_paths[cur_inst] = glob(f'{self.data_dir}{cur_inst}_normalized_{self.normalization_order}_silence_trimmed*.wav') \
- if args.use_normalized else glob(f'{self.data_dir}{cur_inst}_silence_trimmed*.wav')
- self.data_length_ratio_list[cur_inst] = []
- # compute audio duration and its ratio
- for cur_file_path in self.data_paths[cur_inst]:
- cur_wav_length = load_wav_length(cur_file_path)
- cur_inst_length_ratio = cur_wav_length / get_total_audio_length(self.data_paths[cur_inst])
- self.data_length_ratio_list[cur_inst].append(cur_inst_length_ratio)
-
- # load effects chain
- if applying_effects=='full':
- if apply_prob_dict==None:
- # initial (default) applying probabilities of each FX
- apply_prob_dict = {'eq' : 0.9, \
- 'comp' : 0.9, \
- 'pan' : 0.3, \
- 'imager' : 0.8, \
- 'gain': 0.5}
- reverb_prob = {'drums' : 0.5, \
- 'bass' : 0.01, \
- 'vocals' : 0.9, \
- 'other' : 0.7}
-
- self.mixing_manipulator = {}
- for cur_inst in self.data_paths.keys():
- if 'reverb' in apply_prob_dict.keys():
- if cur_inst=='drums':
- cur_reverb_weight = 0.5
- elif cur_inst=='bass':
- cur_reverb_weight = 0.1
- else:
- cur_reverb_weight = 1.0
- apply_prob_dict['reverb'] *= cur_reverb_weight
- else:
- apply_prob_dict['reverb'] = reverb_prob[cur_inst]
- # create FXmanipulator for current instrument
- self.mixing_manipulator[cur_inst] = create_inst_effects_augmentation_chain_(cur_inst, \
- apply_prob_dict=apply_prob_dict, \
- ir_dir_path=args.ir_dir_path, \
- sample_rate=args.sample_rate)
- # for single effects
- else:
- self.mixing_manipulator = {}
- if not isinstance(applying_effects, list):
- applying_effects = [applying_effects]
- for cur_inst in self.data_paths.keys():
- self.mixing_manipulator[cur_inst] = create_effects_augmentation_chain(applying_effects, \
- ir_dir_path=args.ir_dir_path)
-
-
- def __len__(self):
- if self.mode=='train':
- return self.args.batch_size_total * 40
- else:
- return self.args.batch_size_total
-
-
- def __getitem__(self, idx):
- if self.mode=="train":
- torch.manual_seed(int(time.time())*(idx+1) % (2**32-1))
- np.random.seed(int(time.time())*(idx+1) % (2**32-1))
- random.seed(int(time.time())*(idx+1) % (2**32-1))
- else:
- # fixed random seed for evaluation
- torch.manual_seed(idx*self.fixed_random_seed)
- np.random.seed(idx*self.fixed_random_seed)
- random.seed(idx*self.fixed_random_seed)
-
- manipulated_segments = {}
- for cur_neg_idx in range(self.args.num_strong_negatives+1):
- manipulated_segments[cur_neg_idx] = {}
-
- # load already-saved data to save time for on-the-fly manipulation
- cur_data_dir_path = f"{self.data_dir}manipulated_encoder/{self.args.data_save_name}/{self.applying_effects}/{idx}/"
- if self.mode=="val" and os.path.exists(cur_data_dir_path):
- for cur_inst in self.instruments:
- for cur_neg_idx in range(self.args.num_strong_negatives+1):
- cur_A_file_path = f"{cur_data_dir_path}{cur_inst}_A{cur_neg_idx+1}.wav"
- cur_B_file_path = f"{cur_data_dir_path}{cur_inst}_B{cur_neg_idx+1}.wav"
- cur_A = load_wav_segment(cur_A_file_path, axis=0, sample_rate=self.args.sample_rate)
- cur_B = load_wav_segment(cur_B_file_path, axis=0, sample_rate=self.args.sample_rate)
- manipulated_segments[cur_neg_idx][cur_inst] = [torch.from_numpy(cur_A).float(), torch.from_numpy(cur_B).float()]
- else:
- # repeat for number of instruments
- for cur_inst, cur_paths in self.data_paths.items():
- # choose file_path to be loaded
- cur_chosen_paths = np.random.choice(cur_paths, 2, p = self.data_length_ratio_list[cur_inst])
- # get random 2 starting points for each instrument
- last_point_A = load_wav_length(cur_chosen_paths[0])-self.args.segment_length_ref
- last_point_B = load_wav_length(cur_chosen_paths[1])-self.args.segment_length_ref
- # simply load more data to prevent artifacts likely to be caused by the manipulator
- if self.pad_b4_manipulation:
- last_point_A -= self.pad_length*2
- last_point_B -= self.pad_length*2
- cur_inst_start_point_A = torch.randint(low=0, \
- high=last_point_A, \
- size=(1,))[0]
- cur_inst_start_point_B = torch.randint(low=0, \
- high=last_point_B, \
- size=(1,))[0]
- # load wav segments from the selected starting points
- load_duration = self.args.segment_length_ref+self.pad_length*2 if self.pad_b4_manipulation else self.args.segment_length_ref
- cur_inst_segment_A = load_wav_segment(cur_chosen_paths[0], \
- start_point=cur_inst_start_point_A, \
- duration=load_duration, \
- axis=1, \
- sample_rate=self.args.sample_rate)
- cur_inst_segment_B = load_wav_segment(cur_chosen_paths[1], \
- start_point=cur_inst_start_point_B, \
- duration=load_duration, \
- axis=1, \
- sample_rate=self.args.sample_rate)
- # mixing manipulation
- # append A# and B# with its strong negative samples
- for cur_neg_idx in range(self.args.num_strong_negatives+1):
- cur_manipulated_segment_A, cur_manipulated_segment_B = self.mixing_manipulator[cur_inst]([cur_inst_segment_A, cur_inst_segment_B])
-
- # remove over-loaded area
- if self.pad_b4_manipulation:
- cur_manipulated_segment_A = cur_manipulated_segment_A[self.pad_length:-self.pad_length]
- cur_manipulated_segment_B = cur_manipulated_segment_B[self.pad_length:-self.pad_length]
- manipulated_segments[cur_neg_idx][cur_inst] = [torch.clamp(torch.transpose(torch.from_numpy(cur_manipulated_segment_A).float(), 1, 0), min=-1, max=1), \
- torch.clamp(torch.transpose(torch.from_numpy(cur_manipulated_segment_B).float(), 1, 0), min=-1, max=1)]
-
- # check manipulated data by saving them
- if self.mode=="val" and not os.path.exists(cur_data_dir_path):
- os.makedirs(cur_dir_path, exist_ok=True)
- for cur_inst in manipulated_segments[0].keys():
- for cur_manipulated_key, cur_manipualted_dict in manipulated_segments.items():
- sf.write(f"{cur_dir_path}{cur_inst}_A{cur_manipulated_key+1}.wav", torch.transpose(cur_manipualted_dict[cur_inst][0], 1, 0), self.args.sample_rate, 'PCM_16')
- sf.write(f"{cur_dir_path}{cur_inst}_B{cur_manipulated_key+1}.wav", torch.transpose(cur_manipualted_dict[cur_inst][1], 1, 0), self.args.sample_rate, 'PCM_16')
-
- output_list = []
- output_list_param = []
- for cur_inst in manipulated_segments[0].keys():
- for cur_manipulated_key, cur_manipualted_dict in manipulated_segments.items():
- output_list.extend(cur_manipualted_dict[cur_inst])
-
- '''
- Output format:
- list of effects manipulated stems of each instrument
- drums_A1, drums_B1, drums_A2, drums_B2, drums_A3, drums_B3, ... ,
- bass_A1, bass_B1, bass_A2, bass_B2, bass_A3, bass_B3, ... ,
- other_A1, other_B1, other_A2, other_B2, other_A3, other_B3, ... ,
- vocals_A1, vocals_B1, vocals_A2, vocals_B2, vocals_A3, vocals_B3, ...
- each stem has the shape of (number of channels, segment duration)
- '''
- return output_list
-
-
- # generate random manipulated results for evaluation
- def generate_contents_w_effects(self, num_content, num_effects, out_dir):
- print(f"start generating random effects of {self.applying_effects} applied contents")
- os.makedirs(out_dir, exist_ok=True)
-
- manipulated_segments = {}
- for cur_fx_idx in range(num_effects):
- manipulated_segments[cur_fx_idx] = {}
- # repeat for number of instruments
- for cur_inst, cur_paths in self.data_paths.items():
- # choose file_path to be loaded
- cur_path = np.random.choice(cur_paths, 1, p = self.data_length_ratio_list[cur_inst])[0]
- print(f"\tgenerating instrument : {cur_inst}")
- # get random 2 starting points for each instrument
- last_point = load_wav_length(cur_path)-self.args.segment_length_ref
- # simply load more data to prevent artifacts likely to be caused by the manipulator
- if self.pad_b4_manipulation:
- last_point -= self.pad_length*2
- cur_inst_start_points = torch.randint(low=0, \
- high=last_point, \
- size=(num_content,))
- # load wav segments from the selected starting points
- cur_inst_segments = []
- for cur_num_content in range(num_content):
- cur_ori_sample = load_wav_segment(cur_path, \
- start_point=cur_inst_start_points[cur_num_content], \
- duration=self.args.segment_length_ref, \
- axis=1, \
- sample_rate=self.args.sample_rate)
- cur_inst_segments.append(cur_ori_sample)
-
- sf.write(f"{out_dir}{cur_inst}_ori_{cur_num_content}.wav", cur_ori_sample, self.args.sample_rate, 'PCM_16')
-
- # mixing manipulation
- for cur_fx_idx in range(num_effects):
- cur_manipulated_segments = self.mixing_manipulator[cur_inst](cur_inst_segments)
- # remove over-loaded area
- if self.pad_b4_manipulation:
- for cur_man_idx in range(len(cur_manipulated_segments)):
- cur_segment_trimmed = cur_manipulated_segments[cur_man_idx][self.pad_length:-self.pad_length]
- cur_manipulated_segments[cur_man_idx] = torch.clamp(torch.transpose(torch.from_numpy(cur_segment_trimmed).float(), 1, 0), min=-1, max=1)
- manipulated_segments[cur_fx_idx][cur_inst] = cur_manipulated_segments
-
- # write generated data
- # save each instruments
- for cur_inst in manipulated_segments[0].keys():
- for cur_manipulated_key, cur_manipualted_dict in manipulated_segments.items():
- for cur_content_idx in range(num_content):
- sf.write(f"{out_dir}{cur_inst}_{chr(65+cur_content_idx//26)}{chr(65+cur_content_idx%26)}{cur_manipulated_key+1}.wav", torch.transpose(cur_manipualted_dict[cur_inst][cur_content_idx], 1, 0), self.args.sample_rate, 'PCM_16')
- # save mixture
- for cur_manipulated_key, cur_manipualted_dict in manipulated_segments.items():
- for cur_content_idx in range(num_content):
- for cur_idx, cur_inst in enumerate(manipulated_segments[0].keys()):
- if cur_idx==0:
- cur_mixture = cur_manipualted_dict[cur_inst][cur_content_idx]
- else:
- cur_mixture += cur_manipualted_dict[cur_inst][cur_content_idx]
- sf.write(f"{out_dir}mixture_{chr(65+cur_content_idx//26)}{chr(65+cur_content_idx%26)}{cur_manipulated_key+1}.wav", torch.transpose(cur_mixture, 1, 0), self.args.sample_rate, 'PCM_16')
-
- return
-
-
-
-# Data loader for training the 'Mastering Style Converter'
- # loads two segments (A and B) from the dataset
- # both segments are manipulated via Mastering Effects Manipulator (resulting A1, A2, and B2)
- # one of the manipulated segment is used as a reference segment (B2), which is randomly manipulated the same as the ground truth segment (A2)
-class MUSDB_Dataset_Mixing_Manipulated_Style_Transfer(Dataset):
- def __init__(self, args, \
- mode, \
- applying_effects='full', \
- apply_prob_dict=None):
- self.args = args
- self.data_dir = args.data_dir + mode + "/"
- self.mode = mode
- self.applying_effects = applying_effects
- self.fixed_random_seed = args.random_seed
- self.pad_b4_manipulation = args.pad_b4_manipulation
- self.pad_length = 2048
-
- if 'musdb' in args.using_dataset.lower():
- self.instruments = ["drums", "bass", "other", "vocals"]
- else:
- raise NotImplementedError
-
- # load data paths for each instrument
- self.data_paths = {}
- self.data_length_ratio_list = {}
- for cur_inst in self.instruments:
- self.data_paths[cur_inst] = glob(f'{self.data_dir}{cur_inst}_normalized_{self.args.normalization_order}_silence_trimmed*.wav') \
- if args.use_normalized else glob(f'{self.data_dir}{cur_inst}_silence_trimmed.wav')
- self.data_length_ratio_list[cur_inst] = []
- # compute audio duration and its ratio
- for cur_file_path in self.data_paths[cur_inst]:
- cur_wav_length = load_wav_length(cur_file_path)
- cur_inst_length_ratio = cur_wav_length / get_total_audio_length(self.data_paths[cur_inst])
- self.data_length_ratio_list[cur_inst].append(cur_inst_length_ratio)
-
- self.mixing_manipulator = {}
- if applying_effects=='full':
- if apply_prob_dict==None:
- # initial (default) applying probabilities of each FX
- # we don't update these probabilities for training the MixFXcloner
- apply_prob_dict = {'eq' : 0.9, \
- 'comp' : 0.9, \
- 'pan' : 0.3, \
- 'imager' : 0.8, \
- 'gain': 0.5}
- reverb_prob = {'drums' : 0.5, \
- 'bass' : 0.01, \
- 'vocals' : 0.9, \
- 'other' : 0.7}
- for cur_inst in self.data_paths.keys():
- if 'reverb' in apply_prob_dict.keys():
- if cur_inst=='drums':
- cur_reverb_weight = 0.5
- elif cur_inst=='bass':
- cur_reverb_weight = 0.1
- else:
- cur_reverb_weight = 1.0
- apply_prob_dict['reverb'] *= cur_reverb_weight
- else:
- apply_prob_dict['reverb'] = reverb_prob[cur_inst]
- self.mixing_manipulator[cur_inst] = create_inst_effects_augmentation_chain(cur_inst, \
- apply_prob_dict=apply_prob_dict, \
- ir_dir_path=args.ir_dir_path, \
- sample_rate=args.sample_rate)
- # for single effects
- else:
- if not isinstance(applying_effects, list):
- applying_effects = [applying_effects]
- for cur_inst in self.data_paths.keys():
- self.mixing_manipulator[cur_inst] = create_effects_augmentation_chain(applying_effects, \
- ir_dir_path=args.ir_dir_path)
-
-
- def __len__(self):
- min_length = get_total_audio_length(glob(f'{self.data_dir}vocals_normalized_{self.args.normalization_order}*.wav'))
- data_len = min_length // self.args.segment_length
- return data_len
-
-
- def __getitem__(self, idx):
- if self.mode=="train":
- torch.manual_seed(int(time.time())*(idx+1) % (2**32-1))
- np.random.seed(int(time.time())*(idx+1) % (2**32-1))
- random.seed(int(time.time())*(idx+1) % (2**32-1))
- else:
- # fixed random seed for evaluation
- torch.manual_seed(idx*self.fixed_random_seed)
- np.random.seed(idx*self.fixed_random_seed)
- random.seed(idx*self.fixed_random_seed)
-
- manipulated_segments = {}
-
- # load already-saved data to save time for on-the-fly manipulation
- cur_data_dir_path = f"{self.data_dir}manipulated_converter/{self.args.data_save_name}/{self.applying_effects}/{idx}/"
- if self.mode=="val" and os.path.exists(cur_data_dir_path):
- for cur_inst in self.instruments:
- cur_A1_file_path = f"{cur_data_dir_path}{cur_inst}_A1.wav"
- cur_A2_file_path = f"{cur_data_dir_path}{cur_inst}_A2.wav"
- cur_B2_file_path = f"{cur_data_dir_path}{cur_inst}_B2.wav"
- cur_manipulated_segment_A1 = load_wav_segment(cur_A1_file_path, axis=0, sample_rate=self.args.sample_rate)
- cur_manipulated_segment_A2 = load_wav_segment(cur_A2_file_path, axis=0, sample_rate=self.args.sample_rate)
- cur_manipulated_segment_B2 = load_wav_segment(cur_B2_file_path, axis=0, sample_rate=self.args.sample_rate)
- manipulated_segments[cur_inst] = [torch.from_numpy(cur_manipulated_segment_A1).float(), \
- torch.from_numpy(cur_manipulated_segment_A2).float(), \
- torch.from_numpy(cur_manipulated_segment_B2).float()]
- else:
- # repeat for number of instruments
- for cur_inst, cur_paths in self.data_paths.items():
- # choose file_path to be loaded
- cur_chosen_paths = np.random.choice(cur_paths, 2, p = self.data_length_ratio_list[cur_inst])
- # cur_chosen_paths = [cur_paths[idx], cur_paths[idx+1]]
- # get random 2 starting points for each instrument
- last_point_A = load_wav_length(cur_chosen_paths[0])-self.args.segment_length_ref
- last_point_B = load_wav_length(cur_chosen_paths[1])-self.args.segment_length_ref
- # simply load more data to prevent artifacts likely to be caused by the manipulator
- if self.pad_b4_manipulation:
- last_point_A -= self.pad_length*2
- last_point_B -= self.pad_length*2
- cur_inst_start_point_A = torch.randint(low=0, \
- high=last_point_A, \
- size=(1,))[0]
- cur_inst_start_point_B = torch.randint(low=0, \
- high=last_point_B, \
- size=(1,))[0]
- # load wav segments from the selected starting points
- load_duration = self.args.segment_length_ref+self.pad_length*2 if self.pad_b4_manipulation else self.args.segment_length_ref
- cur_inst_segment_A = load_wav_segment(cur_chosen_paths[0], \
- start_point=cur_inst_start_point_A, \
- duration=load_duration, \
- axis=1, \
- sample_rate=self.args.sample_rate)
- cur_inst_segment_B = load_wav_segment(cur_chosen_paths[1], \
- start_point=cur_inst_start_point_B, \
- duration=load_duration, \
- axis=1, \
- sample_rate=self.args.sample_rate)
- ''' mixing manipulation '''
- # manipulate segment A and B to produce
- # input : A1 (normalized sample)
- # ground truth : A2
- # reference : B2
- cur_manipulated_segment_A1 = cur_inst_segment_A
- cur_manipulated_segment_A2, cur_manipulated_segment_B2 = self.mixing_manipulator[cur_inst]([cur_inst_segment_A, cur_inst_segment_B])
- # remove over-loaded area
- if self.pad_b4_manipulation:
- cur_manipulated_segment_A1 = cur_manipulated_segment_A1[self.pad_length:-self.pad_length]
- cur_manipulated_segment_A2 = cur_manipulated_segment_A2[self.pad_length:-self.pad_length]
- cur_manipulated_segment_B2 = cur_manipulated_segment_B2[self.pad_length:-self.pad_length]
- manipulated_segments[cur_inst] = [torch.clamp(torch.transpose(torch.from_numpy(cur_manipulated_segment_A1).float(), 1, 0), min=-1, max=1), \
- torch.clamp(torch.transpose(torch.from_numpy(cur_manipulated_segment_A2).float(), 1, 0), min=-1, max=1), \
- torch.clamp(torch.transpose(torch.from_numpy(cur_manipulated_segment_B2).float(), 1, 0), min=-1, max=1)]
-
- # check manipulated data by saving them
- if (self.mode=="val" and not os.path.exists(cur_data_dir_path)):
- mixture_dict = {}
- for cur_inst in manipulated_segments.keys():
- cur_inst_dir_path = f"{cur_data_dir_path}{idx}/{cur_inst}/"
- os.makedirs(cur_inst_dir_path, exist_ok=True)
- sf.write(f"{cur_inst_dir_path}A1.wav", torch.transpose(manipulated_segments[cur_inst][0], 1, 0), self.args.sample_rate, 'PCM_16')
- sf.write(f"{cur_inst_dir_path}A2.wav", torch.transpose(manipulated_segments[cur_inst][1], 1, 0), self.args.sample_rate, 'PCM_16')
- sf.write(f"{cur_inst_dir_path}B2.wav", torch.transpose(manipulated_segments[cur_inst][2], 1, 0), self.args.sample_rate, 'PCM_16')
- mixture_dict['A1'] = torch.transpose(manipulated_segments[cur_inst][0], 1, 0)
- mixture_dict['A2'] = torch.transpose(manipulated_segments[cur_inst][1], 1, 0)
- mixture_dict['B2'] = torch.transpose(manipulated_segments[cur_inst][2], 1, 0)
- cur_mix_dir_path = f"{cur_data_dir_path}{idx}/mixture/"
- os.makedirs(cur_mix_dir_path, exist_ok=True)
- sf.write(f"{cur_mix_dir_path}A1.wav", mixture_dict['A1'], self.args.sample_rate, 'PCM_16')
- sf.write(f"{cur_mix_dir_path}A2.wav", mixture_dict['A2'], self.args.sample_rate, 'PCM_16')
- sf.write(f"{cur_mix_dir_path}B2.wav", mixture_dict['B2'], self.args.sample_rate, 'PCM_16')
-
- output_list = []
- for cur_inst in manipulated_segments.keys():
- output_list.extend(manipulated_segments[cur_inst])
-
- '''
- Output format:
- list of effects manipulated stems of each instrument
- drums_A1, drums_A2, drums_B2,
- bass_A1, bass_A2, bass_B2,
- other_A1, other_A2, other_B2,
- vocals_A1, vocals_A2, vocals_B2,
- each stem has the shape of (number of channels, segment duration)
- Notation :
- A1 = input of the network
- A2 = ground truth
- B2 = reference track
- '''
- return output_list
-
-
-
-# Data loader for inferencing the task 'Mixing Style Transfer'
-### loads whole mixture or stems from the target directory
-class Song_Dataset_Inference(Dataset):
- def __init__(self, args):
- self.args = args
- self.data_dir = args.target_dir
- self.interpolate = args.interpolation
-
- self.instruments = args.instruments
-
- self.data_dir_paths = sorted(glob(f"{self.data_dir}*/"))
-
- self.input_name = args.input_file_name
- self.reference_name = args.reference_file_name
- self.stem_level_directory_name = args.stem_level_directory_name \
- if self.args.do_not_separate else os.path.join(args.stem_level_directory_name, args.separation_model)
- if self.interpolate:
- self.reference_name_B = args.reference_file_name_2interpolate
-
- # audio effects normalizer
- if args.normalize_input:
- self.normalization_chain = Audio_Effects_Normalizer(precomputed_feature_path=args.precomputed_normalization_feature, \
- STEMS=args.instruments, \
- EFFECTS=args.normalization_order)
-
-
- def __len__(self):
- return len(self.data_dir_paths)
-
-
- def __getitem__(self, idx):
- ''' stem-level conversion '''
- input_stems = []
- reference_stems = []
- reference_B_stems = []
- for cur_inst in self.instruments:
- cur_input_file_path = os.path.join(self.data_dir_paths[idx], self.stem_level_directory_name, self.input_name, cur_inst+'.wav')
- cur_reference_file_path = os.path.join(self.data_dir_paths[idx], self.stem_level_directory_name, self.reference_name, cur_inst+'.wav')
-
- # load wav
- cur_input_wav = load_wav_segment(cur_input_file_path, axis=0, sample_rate=self.args.sample_rate)
- cur_reference_wav = load_wav_segment(cur_reference_file_path, axis=0, sample_rate=self.args.sample_rate)
-
- if self.args.normalize_input:
- cur_input_wav = self.normalization_chain.normalize_audio(cur_input_wav.transpose(), src=cur_inst).transpose()
-
- input_stems.append(torch.clamp(torch.from_numpy(cur_input_wav).float(), min=-1, max=1))
- reference_stems.append(torch.clamp(torch.from_numpy(cur_reference_wav).float(), min=-1, max=1))
-
- # for interpolation
- if self.interpolate:
- cur_reference_B_file_path = os.path.join(self.data_dir_paths[idx], self.stem_level_directory_name, self.reference_name_B, cur_inst+'.wav')
- cur_reference_B_wav = load_wav_segment(cur_reference_B_file_path, axis=0, sample_rate=self.args.sample_rate)
- reference_B_stems.append(torch.clamp(torch.from_numpy(cur_reference_B_wav).float(), min=-1, max=1))
-
- dir_name = os.path.dirname(self.data_dir_paths[idx])
-
- if self.interpolate:
- return torch.stack(input_stems, dim=0), torch.stack(reference_stems, dim=0), torch.stack(reference_B_stems, dim=0), dir_name
- else:
- return torch.stack(input_stems, dim=0), torch.stack(reference_stems, dim=0), dir_name
-
-
-
-# check dataset
-if __name__ == '__main__':
- """
- Test code of data loaders
- """
- import time
- print('checking dataset...')
-
- total_epochs = 1
- bs = 5
- check_step_size = 3
- collate_class = Collate_Variable_Length_Segments(args)
-
-
- print('\n========== Effects Encoder ==========')
- from config import args
- ##### generate samples with ranfom configuration
- # args.normalization_order = 'eqcompimagegain'
- # for cur_effect in ['full', 'gain', 'comp', 'reverb', 'eq', 'imager', 'pan']:
- # start_time = time.time()
- # dataset = MUSDB_Dataset_Mixing_Manipulated_FXencoder(args, mode='val', applying_effects=cur_effect, check_data=True)
- # dataset.generate_contents_w_effects(num_content=25, num_effects=10)
- # print(f'\t---time taken : {time.time()-start_time}---')
-
- ### training data loder
- dataset = MUSDB_Dataset_Mixing_Manipulated_FXencoder(args, mode='train', applying_effects=['comp'])
- data_loader = DataLoader(dataset, \
- batch_size=bs, \
- shuffle=False, \
- collate_fn=collate_class.random_duration_segments_strong_negatives, \
- drop_last=False, \
- num_workers=0)
-
- for epoch in range(total_epochs):
- start_time_loader = time.time()
- for step, output_list in enumerate(data_loader):
- if step==check_step_size:
- break
- print(f'Epoch {epoch+1}/{total_epochs}\tStep {step+1}/{len(data_loader)}')
- print(f'num contents : {len(output_list)}\tnum instruments : {len(output_list[0])}\tcontent A shape : {output_list[0][0].shape}\t content B shape : {output_list[1][0].shape} \ttime taken: {time.time()-start_time_loader:.4f}')
- start_time_loader = time.time()
-
-
- print('\n========== Mixing Style Transfer ==========')
- from trainer_mixing_transfer.config_conv import args
- ### training data loder
- dataset = MUSDB_Dataset_Mixing_Manipulated_Style_Transfer(args, mode='train')
- data_loader = DataLoader(dataset, \
- batch_size=bs, \
- shuffle=False, \
- collate_fn=collate_class.style_transfer_collate, \
- drop_last=False, \
- num_workers=0)
-
- for epoch in range(total_epochs):
- start_time_loader = time.time()
- for step, output_list in enumerate(data_loader):
- if step==check_step_size:
- break
- print(f'Epoch {epoch+1}/{total_epochs}\tStep {step+1}/{len(data_loader)}')
- print(f'num contents : {len(output_list)}\tnum instruments : {len(output_list[0])}\tA1 shape : {output_list[0][0].shape}\tA2 shape : {output_list[1][0].shape}\tA3 shape : {output_list[2][0].shape}\ttime taken: {time.time()-start_time_loader:.4f}')
- start_time_loader = time.time()
-
-
- print('\n--- checking dataset completed ---')
-
diff --git a/spaces/jiawei011/dreamgaussian/sh_utils.py b/spaces/jiawei011/dreamgaussian/sh_utils.py
deleted file mode 100644
index bbca7d192aa3a7edf8c5b2d24dee535eac765785..0000000000000000000000000000000000000000
--- a/spaces/jiawei011/dreamgaussian/sh_utils.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# Copyright 2021 The PlenOctree Authors.
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-# 1. Redistributions of source code must retain the above copyright notice,
-# this list of conditions and the following disclaimer.
-#
-# 2. Redistributions in binary form must reproduce the above copyright notice,
-# this list of conditions and the following disclaimer in the documentation
-# and/or other materials provided with the distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
-# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-
-import torch
-
-C0 = 0.28209479177387814
-C1 = 0.4886025119029199
-C2 = [
- 1.0925484305920792,
- -1.0925484305920792,
- 0.31539156525252005,
- -1.0925484305920792,
- 0.5462742152960396
-]
-C3 = [
- -0.5900435899266435,
- 2.890611442640554,
- -0.4570457994644658,
- 0.3731763325901154,
- -0.4570457994644658,
- 1.445305721320277,
- -0.5900435899266435
-]
-C4 = [
- 2.5033429417967046,
- -1.7701307697799304,
- 0.9461746957575601,
- -0.6690465435572892,
- 0.10578554691520431,
- -0.6690465435572892,
- 0.47308734787878004,
- -1.7701307697799304,
- 0.6258357354491761,
-]
-
-
-def eval_sh(deg, sh, dirs):
- """
- Evaluate spherical harmonics at unit directions
- using hardcoded SH polynomials.
- Works with torch/np/jnp.
- ... Can be 0 or more batch dimensions.
- Args:
- deg: int SH deg. Currently, 0-3 supported
- sh: jnp.ndarray SH coeffs [..., C, (deg + 1) ** 2]
- dirs: jnp.ndarray unit directions [..., 3]
- Returns:
- [..., C]
- """
- assert deg <= 4 and deg >= 0
- coeff = (deg + 1) ** 2
- assert sh.shape[-1] >= coeff
-
- result = C0 * sh[..., 0]
- if deg > 0:
- x, y, z = dirs[..., 0:1], dirs[..., 1:2], dirs[..., 2:3]
- result = (result -
- C1 * y * sh[..., 1] +
- C1 * z * sh[..., 2] -
- C1 * x * sh[..., 3])
-
- if deg > 1:
- xx, yy, zz = x * x, y * y, z * z
- xy, yz, xz = x * y, y * z, x * z
- result = (result +
- C2[0] * xy * sh[..., 4] +
- C2[1] * yz * sh[..., 5] +
- C2[2] * (2.0 * zz - xx - yy) * sh[..., 6] +
- C2[3] * xz * sh[..., 7] +
- C2[4] * (xx - yy) * sh[..., 8])
-
- if deg > 2:
- result = (result +
- C3[0] * y * (3 * xx - yy) * sh[..., 9] +
- C3[1] * xy * z * sh[..., 10] +
- C3[2] * y * (4 * zz - xx - yy)* sh[..., 11] +
- C3[3] * z * (2 * zz - 3 * xx - 3 * yy) * sh[..., 12] +
- C3[4] * x * (4 * zz - xx - yy) * sh[..., 13] +
- C3[5] * z * (xx - yy) * sh[..., 14] +
- C3[6] * x * (xx - 3 * yy) * sh[..., 15])
-
- if deg > 3:
- result = (result + C4[0] * xy * (xx - yy) * sh[..., 16] +
- C4[1] * yz * (3 * xx - yy) * sh[..., 17] +
- C4[2] * xy * (7 * zz - 1) * sh[..., 18] +
- C4[3] * yz * (7 * zz - 3) * sh[..., 19] +
- C4[4] * (zz * (35 * zz - 30) + 3) * sh[..., 20] +
- C4[5] * xz * (7 * zz - 3) * sh[..., 21] +
- C4[6] * (xx - yy) * (7 * zz - 1) * sh[..., 22] +
- C4[7] * xz * (xx - 3 * yy) * sh[..., 23] +
- C4[8] * (xx * (xx - 3 * yy) - yy * (3 * xx - yy)) * sh[..., 24])
- return result
-
-def RGB2SH(rgb):
- return (rgb - 0.5) / C0
-
-def SH2RGB(sh):
- return sh * C0 + 0.5
\ No newline at end of file
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/pens/ttGlyphPen.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/pens/ttGlyphPen.py
deleted file mode 100644
index de2ccaeeb45c18c80caae049f3bd26b4ff22e99e..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/pens/ttGlyphPen.py
+++ /dev/null
@@ -1,335 +0,0 @@
-from array import array
-from typing import Any, Callable, Dict, Optional, Tuple
-from fontTools.misc.fixedTools import MAX_F2DOT14, floatToFixedToFloat
-from fontTools.misc.loggingTools import LogMixin
-from fontTools.pens.pointPen import AbstractPointPen
-from fontTools.misc.roundTools import otRound
-from fontTools.pens.basePen import LoggingPen, PenError
-from fontTools.pens.transformPen import TransformPen, TransformPointPen
-from fontTools.ttLib.tables import ttProgram
-from fontTools.ttLib.tables._g_l_y_f import flagOnCurve, flagCubic
-from fontTools.ttLib.tables._g_l_y_f import Glyph
-from fontTools.ttLib.tables._g_l_y_f import GlyphComponent
-from fontTools.ttLib.tables._g_l_y_f import GlyphCoordinates
-from fontTools.ttLib.tables._g_l_y_f import dropImpliedOnCurvePoints
-import math
-
-
-__all__ = ["TTGlyphPen", "TTGlyphPointPen"]
-
-
-class _TTGlyphBasePen:
- def __init__(
- self,
- glyphSet: Optional[Dict[str, Any]],
- handleOverflowingTransforms: bool = True,
- ) -> None:
- """
- Construct a new pen.
-
- Args:
- glyphSet (Dict[str, Any]): A glyphset object, used to resolve components.
- handleOverflowingTransforms (bool): See below.
-
- If ``handleOverflowingTransforms`` is True, the components' transform values
- are checked that they don't overflow the limits of a F2Dot14 number:
- -2.0 <= v < +2.0. If any transform value exceeds these, the composite
- glyph is decomposed.
-
- An exception to this rule is done for values that are very close to +2.0
- (both for consistency with the -2.0 case, and for the relative frequency
- these occur in real fonts). When almost +2.0 values occur (and all other
- values are within the range -2.0 <= x <= +2.0), they are clamped to the
- maximum positive value that can still be encoded as an F2Dot14: i.e.
- 1.99993896484375.
-
- If False, no check is done and all components are translated unmodified
- into the glyf table, followed by an inevitable ``struct.error`` once an
- attempt is made to compile them.
-
- If both contours and components are present in a glyph, the components
- are decomposed.
- """
- self.glyphSet = glyphSet
- self.handleOverflowingTransforms = handleOverflowingTransforms
- self.init()
-
- def _decompose(
- self,
- glyphName: str,
- transformation: Tuple[float, float, float, float, float, float],
- ):
- tpen = self.transformPen(self, transformation)
- getattr(self.glyphSet[glyphName], self.drawMethod)(tpen)
-
- def _isClosed(self):
- """
- Check if the current path is closed.
- """
- raise NotImplementedError
-
- def init(self) -> None:
- self.points = []
- self.endPts = []
- self.types = []
- self.components = []
-
- def addComponent(
- self,
- baseGlyphName: str,
- transformation: Tuple[float, float, float, float, float, float],
- identifier: Optional[str] = None,
- **kwargs: Any,
- ) -> None:
- """
- Add a sub glyph.
- """
- self.components.append((baseGlyphName, transformation))
-
- def _buildComponents(self, componentFlags):
- if self.handleOverflowingTransforms:
- # we can't encode transform values > 2 or < -2 in F2Dot14,
- # so we must decompose the glyph if any transform exceeds these
- overflowing = any(
- s > 2 or s < -2
- for (glyphName, transformation) in self.components
- for s in transformation[:4]
- )
- components = []
- for glyphName, transformation in self.components:
- if glyphName not in self.glyphSet:
- self.log.warning(f"skipped non-existing component '{glyphName}'")
- continue
- if self.points or (self.handleOverflowingTransforms and overflowing):
- # can't have both coordinates and components, so decompose
- self._decompose(glyphName, transformation)
- continue
-
- component = GlyphComponent()
- component.glyphName = glyphName
- component.x, component.y = (otRound(v) for v in transformation[4:])
- # quantize floats to F2Dot14 so we get same values as when decompiled
- # from a binary glyf table
- transformation = tuple(
- floatToFixedToFloat(v, 14) for v in transformation[:4]
- )
- if transformation != (1, 0, 0, 1):
- if self.handleOverflowingTransforms and any(
- MAX_F2DOT14 < s <= 2 for s in transformation
- ):
- # clamp values ~= +2.0 so we can keep the component
- transformation = tuple(
- MAX_F2DOT14 if MAX_F2DOT14 < s <= 2 else s
- for s in transformation
- )
- component.transform = (transformation[:2], transformation[2:])
- component.flags = componentFlags
- components.append(component)
- return components
-
- def glyph(
- self,
- componentFlags: int = 0x04,
- dropImpliedOnCurves: bool = False,
- *,
- round: Callable[[float], int] = otRound,
- ) -> Glyph:
- """
- Returns a :py:class:`~._g_l_y_f.Glyph` object representing the glyph.
-
- Args:
- componentFlags: Flags to use for component glyphs. (default: 0x04)
-
- dropImpliedOnCurves: Whether to remove implied-oncurve points. (default: False)
- """
- if not self._isClosed():
- raise PenError("Didn't close last contour.")
- components = self._buildComponents(componentFlags)
-
- glyph = Glyph()
- glyph.coordinates = GlyphCoordinates(self.points)
- glyph.endPtsOfContours = self.endPts
- glyph.flags = array("B", self.types)
- self.init()
-
- if components:
- # If both components and contours were present, they have by now
- # been decomposed by _buildComponents.
- glyph.components = components
- glyph.numberOfContours = -1
- else:
- glyph.numberOfContours = len(glyph.endPtsOfContours)
- glyph.program = ttProgram.Program()
- glyph.program.fromBytecode(b"")
- if dropImpliedOnCurves:
- dropImpliedOnCurvePoints(glyph)
- glyph.coordinates.toInt(round=round)
-
- return glyph
-
-
-class TTGlyphPen(_TTGlyphBasePen, LoggingPen):
- """
- Pen used for drawing to a TrueType glyph.
-
- This pen can be used to construct or modify glyphs in a TrueType format
- font. After using the pen to draw, use the ``.glyph()`` method to retrieve
- a :py:class:`~._g_l_y_f.Glyph` object representing the glyph.
- """
-
- drawMethod = "draw"
- transformPen = TransformPen
-
- def __init__(
- self,
- glyphSet: Optional[Dict[str, Any]] = None,
- handleOverflowingTransforms: bool = True,
- outputImpliedClosingLine: bool = False,
- ) -> None:
- super().__init__(glyphSet, handleOverflowingTransforms)
- self.outputImpliedClosingLine = outputImpliedClosingLine
-
- def _addPoint(self, pt: Tuple[float, float], tp: int) -> None:
- self.points.append(pt)
- self.types.append(tp)
-
- def _popPoint(self) -> None:
- self.points.pop()
- self.types.pop()
-
- def _isClosed(self) -> bool:
- return (not self.points) or (
- self.endPts and self.endPts[-1] == len(self.points) - 1
- )
-
- def lineTo(self, pt: Tuple[float, float]) -> None:
- self._addPoint(pt, flagOnCurve)
-
- def moveTo(self, pt: Tuple[float, float]) -> None:
- if not self._isClosed():
- raise PenError('"move"-type point must begin a new contour.')
- self._addPoint(pt, flagOnCurve)
-
- def curveTo(self, *points) -> None:
- assert len(points) % 2 == 1
- for pt in points[:-1]:
- self._addPoint(pt, flagCubic)
-
- # last point is None if there are no on-curve points
- if points[-1] is not None:
- self._addPoint(points[-1], 1)
-
- def qCurveTo(self, *points) -> None:
- assert len(points) >= 1
- for pt in points[:-1]:
- self._addPoint(pt, 0)
-
- # last point is None if there are no on-curve points
- if points[-1] is not None:
- self._addPoint(points[-1], 1)
-
- def closePath(self) -> None:
- endPt = len(self.points) - 1
-
- # ignore anchors (one-point paths)
- if endPt == 0 or (self.endPts and endPt == self.endPts[-1] + 1):
- self._popPoint()
- return
-
- if not self.outputImpliedClosingLine:
- # if first and last point on this path are the same, remove last
- startPt = 0
- if self.endPts:
- startPt = self.endPts[-1] + 1
- if self.points[startPt] == self.points[endPt]:
- self._popPoint()
- endPt -= 1
-
- self.endPts.append(endPt)
-
- def endPath(self) -> None:
- # TrueType contours are always "closed"
- self.closePath()
-
-
-class TTGlyphPointPen(_TTGlyphBasePen, LogMixin, AbstractPointPen):
- """
- Point pen used for drawing to a TrueType glyph.
-
- This pen can be used to construct or modify glyphs in a TrueType format
- font. After using the pen to draw, use the ``.glyph()`` method to retrieve
- a :py:class:`~._g_l_y_f.Glyph` object representing the glyph.
- """
-
- drawMethod = "drawPoints"
- transformPen = TransformPointPen
-
- def init(self) -> None:
- super().init()
- self._currentContourStartIndex = None
-
- def _isClosed(self) -> bool:
- return self._currentContourStartIndex is None
-
- def beginPath(self, identifier: Optional[str] = None, **kwargs: Any) -> None:
- """
- Start a new sub path.
- """
- if not self._isClosed():
- raise PenError("Didn't close previous contour.")
- self._currentContourStartIndex = len(self.points)
-
- def endPath(self) -> None:
- """
- End the current sub path.
- """
- # TrueType contours are always "closed"
- if self._isClosed():
- raise PenError("Contour is already closed.")
- if self._currentContourStartIndex == len(self.points):
- # ignore empty contours
- self._currentContourStartIndex = None
- return
-
- contourStart = self.endPts[-1] + 1 if self.endPts else 0
- self.endPts.append(len(self.points) - 1)
- self._currentContourStartIndex = None
-
- # Resolve types for any cubic segments
- flags = self.types
- for i in range(contourStart, len(flags)):
- if flags[i] == "curve":
- j = i - 1
- if j < contourStart:
- j = len(flags) - 1
- while flags[j] == 0:
- flags[j] = flagCubic
- j -= 1
- flags[i] = flagOnCurve
-
- def addPoint(
- self,
- pt: Tuple[float, float],
- segmentType: Optional[str] = None,
- smooth: bool = False,
- name: Optional[str] = None,
- identifier: Optional[str] = None,
- **kwargs: Any,
- ) -> None:
- """
- Add a point to the current sub path.
- """
- if self._isClosed():
- raise PenError("Can't add a point to a closed contour.")
- if segmentType is None:
- self.types.append(0)
- elif segmentType in ("line", "move"):
- self.types.append(flagOnCurve)
- elif segmentType == "qcurve":
- self.types.append(flagOnCurve)
- elif segmentType == "curve":
- self.types.append("curve")
- else:
- raise AssertionError(segmentType)
-
- self.points.append(pt)
diff --git a/spaces/johnson906/recipedia/src/demo.py b/spaces/johnson906/recipedia/src/demo.py
deleted file mode 100644
index 858969ad57d037e4820a556096cc5fddbc736065..0000000000000000000000000000000000000000
--- a/spaces/johnson906/recipedia/src/demo.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import torch
-import torch.nn as nn
-import numpy as np
-import os
-from args import get_parser
-import pickle
-from model import get_model
-from torchvision import transforms
-from utils.output_ing import prepare_output
-from PIL import Image
-from tqdm import tqdm
-import time
-import glob
-
-
-# Set ```data_dir``` to the path including vocabularies and model checkpoint
-model_dir = '../data'
-image_folder = '../data/demo_imgs'
-output_file = "../data/predicted_ingr.pkl"
-
-# code will run in gpu if available and if the flag is set to True, else it will run on cpu
-use_gpu = False
-device = torch.device('cuda' if torch.cuda.is_available() and use_gpu else 'cpu')
-map_loc = None if torch.cuda.is_available() and use_gpu else 'cpu'
-
-# code below was used to save vocab files so that they can be loaded without Vocabulary class
-#ingrs_vocab = pickle.load(open(os.path.join(data_dir, 'final_recipe1m_vocab_ingrs.pkl'), 'rb'))
-#ingrs_vocab = [min(w, key=len) if not isinstance(w, str) else w for w in ingrs_vocab.idx2word.values()]
-#vocab = pickle.load(open(os.path.join(data_dir, 'final_recipe1m_vocab_toks.pkl'), 'rb')).idx2word
-#pickle.dump(ingrs_vocab, open('../demo/ingr_vocab.pkl', 'wb'))
-#pickle.dump(vocab, open('../demo/instr_vocab.pkl', 'wb'))
-
-ingrs_vocab = pickle.load(open(os.path.join(model_dir, 'ingr_vocab.pkl'), 'rb'))
-vocab = pickle.load(open(os.path.join(model_dir, 'instr_vocab.pkl'), 'rb'))
-
-ingr_vocab_size = len(ingrs_vocab)
-instrs_vocab_size = len(vocab)
-output_dim = instrs_vocab_size
-
-print (instrs_vocab_size, ingr_vocab_size)
-
-t = time.time()
-
-args = get_parser()
-args.maxseqlen = 15
-args.ingrs_only=True
-model = get_model(args, ingr_vocab_size, instrs_vocab_size)
-# Load the trained model parameters
-model_path = os.path.join(model_dir, 'modelbest.ckpt')
-model.load_state_dict(torch.load(model_path, map_location=map_loc))
-model.to(device)
-model.eval()
-model.ingrs_only = True
-model.recipe_only = False
-print ('loaded model')
-print ("Elapsed time:", time.time() -t)
-
-transf_list_batch = []
-transf_list_batch.append(transforms.ToTensor())
-transf_list_batch.append(transforms.Normalize((0.485, 0.456, 0.406),
- (0.229, 0.224, 0.225)))
-to_input_transf = transforms.Compose(transf_list_batch)
-
-
-greedy = True
-beam = -1
-temperature = 1.0
-
-# import requests
-# from io import BytesIO
-# import random
-# from collections import Counter
-# use_urls = False # set to true to load images from demo_urls instead of those in test_imgs folder
-# show_anyways = False #if True, it will show the recipe even if it's not valid
-# image_folder = os.path.join(data_dir, 'demo_imgs')
-
-# if not use_urls:
-# demo_imgs = os.listdir(image_folder)
-# random.shuffle(demo_imgs)
-
-# demo_urls = ['https://food.fnr.sndimg.com/content/dam/images/food/fullset/2013/12/9/0/FNK_Cheesecake_s4x3.jpg.rend.hgtvcom.826.620.suffix/1387411272847.jpeg',
-# 'https://www.196flavors.com/wp-content/uploads/2014/10/california-roll-3-FP.jpg']
-
-files_path = glob.glob(f"{image_folder}/*/*/*.jpg")
-print(f"total data: {len(files_path)}")
-
-res = []
-for idx, img_file in tqdm(enumerate(files_path)):
- # if use_urls:
- # response = requests.get(img_file)
- # image = Image.open(BytesIO(response.content))
- # else:
- image = Image.open(img_file).convert('RGB')
-
- transf_list = []
- transf_list.append(transforms.Resize(256))
- transf_list.append(transforms.CenterCrop(224))
- transform = transforms.Compose(transf_list)
-
- image_transf = transform(image)
- image_tensor = to_input_transf(image_transf).unsqueeze(0).to(device)
-
- # plt.imshow(image_transf)
- # plt.axis('off')
- # plt.show()
- # plt.close()
-
- with torch.no_grad():
- outputs = model.sample(image_tensor, greedy=greedy,
- temperature=temperature, beam=beam, true_ingrs=None)
-
- ingr_ids = outputs['ingr_ids'].cpu().numpy()
- print(ingr_ids)
-
- outs = prepare_output(ingr_ids[0], ingrs_vocab)
- # print(ingrs_vocab.idx2word)
-
- print(outs)
-
- # print ('Pic ' + str(idx+1) + ':')
-
- # print ('\nIngredients:')
- # print (', '.join(outs['ingrs']))
-
- # print ('='*20)
-
- res.append({
- "id": img_file,
- "ingredients": outs['ingrs']
- })
-
-with open(output_file, "wb") as fp: #Pickling
- pickle.dump(res, fp)
diff --git a/spaces/jordonpeter01/ai-comic-factory/src/components/ui/vertical-slider.tsx b/spaces/jordonpeter01/ai-comic-factory/src/components/ui/vertical-slider.tsx
deleted file mode 100644
index b28a1200cb06d1f26e3c640c85e655c99e88954e..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/ai-comic-factory/src/components/ui/vertical-slider.tsx
+++ /dev/null
@@ -1,27 +0,0 @@
-"use client"
-
-import * as React from "react"
-import * as SliderPrimitive from "@radix-ui/react-slider"
-
-import { cn } from "@/lib/utils"
-
-const VerticalSlider = React.forwardRef<
- React.ElementRef<typeof SliderPrimitive.Root>,
- React.ComponentPropsWithoutRef<typeof SliderPrimitive.Root>
->(({ className, ...props }, ref) => (
- <SliderPrimitive.Root
- ref={ref}
- className={cn(
- "relative flex w-full touch-none select-none items-center",
- className
- )}
- {...props}
- >
- <SliderPrimitive.Track className="relative w-2 h-full grow overflow-hidden rounded-full bg-stone-300 dark:bg-stone-700">
- <SliderPrimitive.Range className="absolute w-full bg-stone-700 dark:bg-stone-50" />
- </SliderPrimitive.Track>
- <SliderPrimitive.Thumb className="block -ml-1.5 h-5 w-5 rounded-full border-2 border-stone-700 bg-stone-300 ring-offset-white transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-stone-950 focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 dark:border-stone-50 dark:bg-stone-700 dark:ring-offset-stone-950 dark:focus-visible:ring-stone-300" />
- </SliderPrimitive.Root>
-))
-VerticalSlider.displayName = "VerticalSlider"
-export { VerticalSlider }
diff --git "a/spaces/joshen/gpt-academic/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py" "b/spaces/joshen/gpt-academic/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py"
deleted file mode 100644
index b7c508e17f82a91952be672f2c92034ce40f8445..0000000000000000000000000000000000000000
--- "a/spaces/joshen/gpt-academic/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py"
+++ /dev/null
@@ -1,70 +0,0 @@
-from predict import predict_no_ui
-from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
-fast_debug = False
-
-
-def 解析Paper(file_manifest, project_folder, top_p, api_key, temperature, chatbot, history, systemPromptTxt):
- import time, glob, os
- print('begin analysis on:', file_manifest)
- for index, fp in enumerate(file_manifest):
- with open(fp, 'r', encoding='utf-8') as f:
- file_content = f.read()
-
- prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
- i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
- i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
- chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
- print('[1] yield chatbot, history')
- yield chatbot, history, '正常'
-
- if not fast_debug:
- msg = '正常'
- # ** gpt request **
- gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, api_key, temperature, history=[]) # 带超时倒计时
-
- print('[2] end gpt req')
- chatbot[-1] = (i_say_show_user, gpt_say)
- history.append(i_say_show_user); history.append(gpt_say)
- print('[3] yield chatbot, history')
- yield chatbot, history, msg
- print('[4] next')
- if not fast_debug: time.sleep(2)
-
- all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
- i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
- chatbot.append((i_say, "[Local Message] waiting gpt response."))
- yield chatbot, history, '正常'
-
- if not fast_debug:
- msg = '正常'
- # ** gpt request **
- gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say, chatbot, top_p, api_key, temperature, history=history) # 带超时倒计时
-
- chatbot[-1] = (i_say, gpt_say)
- history.append(i_say); history.append(gpt_say)
- yield chatbot, history, msg
- res = write_results_to_file(history)
- chatbot.append(("完成了吗？", res))
- yield chatbot, history, msg
-
-
-
-@CatchException
-def 读文章写摘要(txt, top_p, api_key, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
- history = [] # 清空历史，以免输入溢出
- import glob, os
- if os.path.exists(txt):
- project_folder = txt
- else:
- if txt == "": txt = '空空如也的输入栏'
- report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
- yield chatbot, history, '正常'
- return
- file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \
- # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
- # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
- if len(file_manifest) == 0:
- report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
- yield chatbot, history, '正常'
- return
- yield from 解析Paper(file_manifest, project_folder, top_p, api_key, temperature, chatbot, history, systemPromptTxt)
diff --git a/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/musicautobot/multitask_transformer/model.py b/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/musicautobot/multitask_transformer/model.py
deleted file mode 100644
index 6add0e4bf09db73a6b6f430090ce610757ed0c80..0000000000000000000000000000000000000000
--- a/spaces/kboaten/MIDI-Audio-Extension/MIDI-song-extender/musicautobot/multitask_transformer/model.py
+++ /dev/null
@@ -1,258 +0,0 @@
-from fastai.basics import *
-from fastai.text.models.transformer import Activation, PositionalEncoding, feed_forward, init_transformer, _line_shift
-from fastai.text.models.awd_lstm import RNNDropout
-from ..utils.attention_mask import *
-
-def get_multitask_model(vocab_size:int, config:dict=None, drop_mult:float=1., pad_idx=None):
- "Create a language model from `arch` and its `config`, maybe `pretrained`."
- for k in config.keys():
- if k.endswith('_p'): config[k] *= drop_mult
- n_hid = config['d_model']
- mem_len = config.pop('mem_len')
- embed = TransformerEmbedding(vocab_size, n_hid, embed_p=config['embed_p'], mem_len=mem_len, pad_idx=pad_idx)
- encoder = MTEncoder(embed, n_hid, n_layers=config['enc_layers'], mem_len=0, **config) # encoder doesn't need memory
- decoder = MTEncoder(embed, n_hid, is_decoder=True, n_layers=config['dec_layers'], mem_len=mem_len, **config)
- head = MTLinearDecoder(n_hid, vocab_size, tie_encoder=embed.embed, **config)
- model = MultiTransformer(encoder, decoder, head, mem_len=mem_len)
- return model.apply(init_transformer)
-
-class MultiTransformer(nn.Module):
- "Multitask Transformer for training mask, next word, and sequence 2 sequence"
- def __init__(self, encoder, decoder, head, mem_len):
- super().__init__()
- self.encoder = encoder
- self.decoder = decoder
- self.head = head
- self.default_mem_len = mem_len
- self.current_mem_len = None
-
- def forward(self, inp):
- # data order: mask, next word, melody, chord
- outputs = {}
- msk, lm, c2m, m2c = [inp.get(key) for key in ['msk', 'lm', 'c2m', 'm2c']]
-
- if msk is not None:
- outputs['msk'] = self.head(self.encoder(msk['x'], msk['pos']))
- if lm is not None:
- outputs['lm'] = self.head(self.decoder(lm['x'], lm['pos']))
-
- if c2m is not None:
- self.reset()
- c2m_enc = self.encoder(c2m['enc'], c2m['enc_pos'])
- c2m_dec = self.decoder(c2m['dec'], c2m['dec_pos'], c2m_enc)
- outputs['c2m'] = self.head(c2m_dec)
-
- if m2c is not None:
- self.reset()
- m2c_enc = self.encoder(m2c['enc'], m2c['enc_pos'])
- m2c_dec = self.decoder(m2c['dec'], m2c['dec_pos'], m2c_enc)
- outputs['m2c'] = self.head(m2c_dec)
-
- return outputs
-
- "A sequential module that passes the reset call to its children."
- def reset(self):
- for module in self.children():
- reset_children(module)
-
-def reset_children(mod):
- if hasattr(mod, 'reset'): mod.reset()
- for module in mod.children():
- reset_children(module)
-
- # COMPONENTS
-class TransformerEmbedding(nn.Module):
- "Embedding + positional encoding + dropout"
- def __init__(self, vocab_size:int, emb_sz:int, embed_p:float=0., mem_len=512, beat_len=32, max_bar_len=1024, pad_idx=None):
- super().__init__()
- self.emb_sz = emb_sz
- self.pad_idx = pad_idx
-
- self.embed = nn.Embedding(vocab_size, emb_sz, padding_idx=pad_idx)
- self.pos_enc = PositionalEncoding(emb_sz)
- self.beat_len, self.max_bar_len = beat_len, max_bar_len
- self.beat_enc = nn.Embedding(beat_len, emb_sz, padding_idx=0)
- self.bar_enc = nn.Embedding(max_bar_len, emb_sz, padding_idx=0)
-
- self.drop = nn.Dropout(embed_p)
- self.mem_len = mem_len
-
- def forward(self, inp, pos):
- beat_enc = self.beat_enc(pos % self.beat_len)
- bar_pos = pos // self.beat_len % self.max_bar_len
- bar_pos[bar_pos >= self.max_bar_len] = self.max_bar_len - 1
- bar_enc = self.bar_enc((bar_pos))
- emb = self.drop(self.embed(inp) + beat_enc + bar_enc)
- return emb
-
- def relative_pos_enc(self, emb):
-# return torch.arange(640-1, -1, -1).float().cuda()
- seq_len = emb.shape[1] + self.mem_len
- pos = torch.arange(seq_len-1, -1, -1, device=emb.device, dtype=emb.dtype) # backwards (txl pos encoding)
- return self.pos_enc(pos)
-
-class MTLinearDecoder(nn.Module):
- "To go on top of a RNNCore module and create a Language Model."
- initrange=0.1
-
- def __init__(self, n_hid:int, n_out:int, output_p:float, tie_encoder:nn.Module=None, out_bias:bool=True, **kwargs):
- super().__init__()
- self.decoder = nn.Linear(n_hid, n_out, bias=out_bias)
- self.decoder.weight.data.uniform_(-self.initrange, self.initrange)
- self.output_dp = RNNDropout(output_p)
- if out_bias: self.decoder.bias.data.zero_()
- if tie_encoder: self.decoder.weight = tie_encoder.weight
-
- def forward(self, input:Tuple[Tensor,Tensor])->Tuple[Tensor,Tensor,Tensor]:
- output = self.output_dp(input)
- decoded = self.decoder(output)
- return decoded
-
-
-# DECODER TRANSLATE BLOCK
-class MTEncoder(nn.Module):
- def __init__(self, embed:nn.Module, n_hid:int, n_layers:int, n_heads:int, d_model:int, d_head:int, d_inner:int,
- resid_p:float=0., attn_p:float=0., ff_p:float=0., bias:bool=True, scale:bool=True,
- act:Activation=Activation.ReLU, double_drop:bool=True, mem_len:int=512, is_decoder=False,
- mask_steps=1, mask_p=0.3, **kwargs):
- super().__init__()
- self.embed = embed
- self.u = nn.Parameter(torch.Tensor(n_heads, 1, d_head)) #Remove 1 for einsum implementation of attention
- self.v = nn.Parameter(torch.Tensor(n_heads, 1, d_head)) #Remove 1 for einsum implementation of attention
- self.n_layers,self.d_model = n_layers,d_model
- self.layers = nn.ModuleList([MTEncoderBlock(n_heads, d_model, d_head, d_inner, resid_p=resid_p, attn_p=attn_p,
- ff_p=ff_p, bias=bias, scale=scale, act=act, double_drop=double_drop, mem_len=mem_len,
- ) for k in range(n_layers)])
-
- self.mask_steps, self.mask_p = mask_steps, mask_p
- self.is_decoder = is_decoder
-
- nn.init.normal_(self.u, 0., 0.02)
- nn.init.normal_(self.v, 0., 0.02)
-
- def forward(self, x_lm, lm_pos, msk_emb=None):
- bs,lm_len = x_lm.size()
-
- lm_emb = self.embed(x_lm, lm_pos)
- if msk_emb is not None and msk_emb.shape[1] > lm_emb.shape[1]:
- pos_enc = self.embed.relative_pos_enc(msk_emb)
- else:
- pos_enc = self.embed.relative_pos_enc(lm_emb)
-
- # Masks
- if self.is_decoder:
- lm_mask = rand_window_mask(lm_len, self.embed.mem_len, x_lm.device,
- max_size=self.mask_steps, p=self.mask_p, is_eval=not self.training)
- else:
- lm_mask = None
-
- for i, layer in enumerate(self.layers):
- lm_emb = layer(lm_emb, msk_emb, lm_mask=lm_mask,
- r=pos_enc, g_u=self.u, g_v=self.v)
- return lm_emb
-
-class MTEncoderBlock(nn.Module):
- "Decoder block of a Transformer model."
- #Can't use Sequential directly cause more than one input...
- def __init__(self, n_heads:int, d_model:int, d_head:int, d_inner:int, resid_p:float=0., attn_p:float=0., ff_p:float=0.,
- bias:bool=True, scale:bool=True, double_drop:bool=True, mem_len:int=512, mha2_mem_len=0, **kwargs):
- super().__init__()
- attn_cls = MemMultiHeadRelativeAttentionKV
- self.mha1 = attn_cls(n_heads, d_model, d_head, resid_p=resid_p, attn_p=attn_p, bias=bias, scale=scale, mem_len=mem_len, r_mask=False)
- self.mha2 = attn_cls(n_heads, d_model, d_head, resid_p=resid_p, attn_p=attn_p, bias=bias, scale=scale, mem_len=mha2_mem_len, r_mask=True)
- self.ff = feed_forward(d_model, d_inner, ff_p=ff_p, double_drop=double_drop)
-
- def forward(self, enc_lm:Tensor, enc_msk:Tensor,
- r=None, g_u=None, g_v=None,
- msk_mask:Tensor=None, lm_mask:Tensor=None):
-
- y_lm = self.mha1(enc_lm, enc_lm, enc_lm, r, g_u, g_v, mask=lm_mask)
- if enc_msk is None: return y_lm
- return self.ff(self.mha2(y_lm, enc_msk, enc_msk, r, g_u, g_v, mask=msk_mask))
-
-
- # Attention Layer
-
-
-# Attn
-
-class MemMultiHeadRelativeAttentionKV(nn.Module):
- "Attention Layer monster - relative positioning, keeps track of own memory, separate kv weights to support sequence2sequence decoding."
- def __init__(self, n_heads:int, d_model:int, d_head:int=None, resid_p:float=0., attn_p:float=0., bias:bool=True,
- scale:bool=True, mem_len:int=512, r_mask=True):
- super().__init__()
- d_head = ifnone(d_head, d_model//n_heads)
- self.n_heads,self.d_head,self.scale = n_heads,d_head,scale
-
- assert(d_model == d_head * n_heads)
- self.q_wgt = nn.Linear(d_model, n_heads * d_head, bias=bias)
- self.k_wgt = nn.Linear(d_model, n_heads * d_head, bias=bias)
- self.v_wgt = nn.Linear(d_model, n_heads * d_head, bias=bias)
-
- self.drop_att,self.drop_res = nn.Dropout(attn_p),nn.Dropout(resid_p)
- self.ln = nn.LayerNorm(d_model)
- self.r_attn = nn.Linear(d_model, n_heads * d_head, bias=bias)
- self.r_mask = r_mask
-
- self.mem_len = mem_len
- self.prev_k = None
- self.prev_v = None
-
- def forward(self, q:Tensor, k:Tensor=None, v:Tensor=None,
- r:Tensor=None, g_u:Tensor=None, g_v:Tensor=None,
- mask:Tensor=None, **kwargs):
- if k is None: k = q
- if v is None: v = q
- return self.ln(q + self.drop_res(self._apply_attention(q, k, v, r, g_u, g_v, mask=mask, **kwargs)))
-
- def mem_k(self, k):
- if self.mem_len == 0: return k
- if self.prev_k is None or (self.prev_k.shape[0] != k.shape[0]): # reset if wrong batch size
- self.prev_k = k[:, -self.mem_len:]
- return k
- with torch.no_grad():
- k_ext = torch.cat([self.prev_k, k], dim=1)
- self.prev_k = k_ext[:, -self.mem_len:]
- return k_ext.detach()
-
- def mem_v(self, v):
- if self.mem_len == 0: return v
- if self.prev_v is None or (self.prev_v.shape[0] != v.shape[0]): # reset if wrong batch size
- self.prev_v = v[:, -self.mem_len:]
- return v
- with torch.no_grad():
- v_ext = torch.cat([self.prev_v, v], dim=1)
- self.prev_v = v_ext[:, -self.mem_len:]
- return v_ext.detach()
-
- def reset(self):
- self.prev_v = None
- self.prev_k = None
-
- def _apply_attention(self, q:Tensor, k:Tensor, v:Tensor,
- r:Tensor=None, g_u:Tensor=None, g_v:Tensor=None,
- mask:Tensor=None, **kwargs):
- #Notations from the paper: x input, r vector of relative distance between two elements, u et v learnable
- #parameters of the model common between all layers, mask to avoid cheating and mem the previous hidden states.
-# bs,x_len,seq_len = q.size(0),q.size(1),r.size(0)
- k = self.mem_k(k)
- v = self.mem_v(v)
- bs,x_len,seq_len = q.size(0),q.size(1),k.size(1)
- wq,wk,wv = self.q_wgt(q),self.k_wgt(k),self.v_wgt(v)
- wq = wq[:,-x_len:]
- wq,wk,wv = map(lambda x:x.view(bs, x.size(1), self.n_heads, self.d_head), (wq,wk,wv))
- wq,wk,wv = wq.permute(0, 2, 1, 3),wk.permute(0, 2, 3, 1),wv.permute(0, 2, 1, 3)
- wkr = self.r_attn(r[-seq_len:])
- wkr = wkr.view(seq_len, self.n_heads, self.d_head)
- wkr = wkr.permute(1,2,0)
- #### compute attention score (AC is (a) + (c) and BS is (b) + (d) in the paper)
- AC = torch.matmul(wq+g_u,wk)
- BD = _line_shift(torch.matmul(wq+g_v, wkr), mask=self.r_mask)
- if self.scale: attn_score = (AC + BD).mul_(1/(self.d_head ** 0.5))
- if mask is not None:
- mask = mask[...,-seq_len:]
- if hasattr(mask, 'bool'): mask = mask.bool()
- attn_score = attn_score.float().masked_fill(mask, -float('inf')).type_as(attn_score)
- attn_prob = self.drop_att(F.softmax(attn_score, dim=-1))
- attn_vec = torch.matmul(attn_prob, wv)
- return attn_vec.permute(0, 2, 1, 3).contiguous().view(bs, x_len, -1)
diff --git a/spaces/kdrkdrkdr/AzusaTTS/mel_processing.py b/spaces/kdrkdrkdr/AzusaTTS/mel_processing.py
deleted file mode 100644
index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000
--- a/spaces/kdrkdrkdr/AzusaTTS/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/ken4005/Uhi-ChatGPT/modules/llama_func.py b/spaces/ken4005/Uhi-ChatGPT/modules/llama_func.py
deleted file mode 100644
index 2b303f3457e07d51d120b10f2072489a729596ab..0000000000000000000000000000000000000000
--- a/spaces/ken4005/Uhi-ChatGPT/modules/llama_func.py
+++ /dev/null
@@ -1,215 +0,0 @@
-import os
-import logging
-
-from llama_index import GPTSimpleVectorIndex, ServiceContext
-from llama_index import download_loader
-from llama_index import (
- Document,
- LLMPredictor,
- PromptHelper,
- QuestionAnswerPrompt,
- RefinePrompt,
-)
-from langchain.llms import OpenAI
-from langchain.chat_models import ChatOpenAI
-import colorama
-import PyPDF2
-from tqdm import tqdm
-
-from modules.presets import *
-from modules.utils import *
-
-def get_index_name(file_src):
- file_paths = [x.name for x in file_src]
- file_paths.sort(key=lambda x: os.path.basename(x))
-
- md5_hash = hashlib.md5()
- for file_path in file_paths:
- with open(file_path, "rb") as f:
- while chunk := f.read(8192):
- md5_hash.update(chunk)
-
- return md5_hash.hexdigest()
-
-def block_split(text):
- blocks = []
- while len(text) > 0:
- blocks.append(Document(text[:1000]))
- text = text[1000:]
- return blocks
-
-def get_documents(file_src):
- documents = []
- logging.debug("Loading documents...")
- logging.debug(f"file_src: {file_src}")
- for file in file_src:
- logging.info(f"loading file: {file.name}")
- if os.path.splitext(file.name)[1] == ".pdf":
- logging.debug("Loading PDF...")
- pdftext = ""
- with open(file.name, 'rb') as pdfFileObj:
- pdfReader = PyPDF2.PdfReader(pdfFileObj)
- for page in tqdm(pdfReader.pages):
- pdftext += page.extract_text()
- text_raw = pdftext
- elif os.path.splitext(file.name)[1] == ".docx":
- logging.debug("Loading DOCX...")
- DocxReader = download_loader("DocxReader")
- loader = DocxReader()
- text_raw = loader.load_data(file=file.name)[0].text
- elif os.path.splitext(file.name)[1] == ".epub":
- logging.debug("Loading EPUB...")
- EpubReader = download_loader("EpubReader")
- loader = EpubReader()
- text_raw = loader.load_data(file=file.name)[0].text
- else:
- logging.debug("Loading text file...")
- with open(file.name, "r", encoding="utf-8") as f:
- text_raw = f.read()
- text = add_space(text_raw)
- # text = block_split(text)
- # documents += text
- documents += [Document(text)]
- logging.debug("Documents loaded.")
- return documents
-
-
-def construct_index(
- api_key,
- file_src,
- max_input_size=4096,
- num_outputs=5,
- max_chunk_overlap=20,
- chunk_size_limit=600,
- embedding_limit=None,
- separator=" "
-):
- os.environ["OPENAI_API_KEY"] = api_key
- chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit
- embedding_limit = None if embedding_limit == 0 else embedding_limit
- separator = " " if separator == "" else separator
-
- llm_predictor = LLMPredictor(
- llm=ChatOpenAI(model_name="gpt-3.5-turbo-0301", openai_api_key=api_key)
- )
- prompt_helper = PromptHelper(max_input_size = max_input_size, num_output = num_outputs, max_chunk_overlap = max_chunk_overlap, embedding_limit=embedding_limit, chunk_size_limit=600, separator=separator)
- index_name = get_index_name(file_src)
- if os.path.exists(f"./index/{index_name}.json"):
- logging.info("找到了缓存的索引文件，加载中……")
- return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json")
- else:
- try:
- documents = get_documents(file_src)
- logging.info("构建索引中……")
- service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper, chunk_size_limit=chunk_size_limit)
- index = GPTSimpleVectorIndex.from_documents(
- documents, service_context=service_context
- )
- logging.debug("索引构建完成！")
- os.makedirs("./index", exist_ok=True)
- index.save_to_disk(f"./index/{index_name}.json")
- logging.debug("索引已保存至本地!")
- return index
-
- except Exception as e:
- logging.error("索引构建失败！", e)
- print(e)
- return None
-
-
-def chat_ai(
- api_key,
- index,
- question,
- context,
- chatbot,
- reply_language,
-):
- os.environ["OPENAI_API_KEY"] = api_key
-
- logging.info(f"Question: {question}")
-
- response, chatbot_display, status_text = ask_ai(
- api_key,
- index,
- question,
- replace_today(PROMPT_TEMPLATE),
- REFINE_TEMPLATE,
- SIM_K,
- INDEX_QUERY_TEMPRATURE,
- context,
- reply_language,
- )
- if response is None:
- status_text = "查询失败，请换个问法试试"
- return context, chatbot
- response = response
-
- context.append({"role": "user", "content": question})
- context.append({"role": "assistant", "content": response})
- chatbot.append((question, chatbot_display))
-
- os.environ["OPENAI_API_KEY"] = ""
- return context, chatbot, status_text
-
-
-def ask_ai(
- api_key,
- index,
- question,
- prompt_tmpl,
- refine_tmpl,
- sim_k=5,
- temprature=0,
- prefix_messages=[],
- reply_language="中文",
-):
- os.environ["OPENAI_API_KEY"] = api_key
-
- logging.debug("Index file found")
- logging.debug("Querying index...")
- llm_predictor = LLMPredictor(
- llm=ChatOpenAI(
- temperature=temprature,
- model_name="gpt-3.5-turbo-0301",
- prefix_messages=prefix_messages,
- )
- )
-
- response = None # Initialize response variable to avoid UnboundLocalError
- qa_prompt = QuestionAnswerPrompt(prompt_tmpl.replace("{reply_language}", reply_language))
- rf_prompt = RefinePrompt(refine_tmpl.replace("{reply_language}", reply_language))
- response = index.query(
- question,
- similarity_top_k=sim_k,
- text_qa_template=qa_prompt,
- refine_template=rf_prompt,
- response_mode="compact",
- )
-
- if response is not None:
- logging.info(f"Response: {response}")
- ret_text = response.response
- nodes = []
- for index, node in enumerate(response.source_nodes):
- brief = node.source_text[:25].replace("\n", "")
- nodes.append(
- f"<details><summary>[{index + 1}]\t{brief}...</summary><p>{node.source_text}</p></details>"
- )
- new_response = ret_text + "\n----------\n" + "\n\n".join(nodes)
- logging.info(
- f"Response: {colorama.Fore.BLUE}{ret_text}{colorama.Style.RESET_ALL}"
- )
- os.environ["OPENAI_API_KEY"] = ""
- return ret_text, new_response, f"查询消耗了{llm_predictor.last_token_usage} tokens"
- else:
- logging.warning("No response found, returning None")
- os.environ["OPENAI_API_KEY"] = ""
- return None
-
-
-def add_space(text):
- punctuations = {"，": "， ", "。": "。 ", "？": "？ ", "！": "！ ", "：": "： ", "；": "； "}
- for cn_punc, en_punc in punctuations.items():
- text = text.replace(cn_punc, en_punc)
- return text
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/scripts/download_models.sh b/spaces/kevinwang676/ChatGLM2-SadTalker/scripts/download_models.sh
deleted file mode 100644
index 6898648b153a2826557693dabb5adaf13bee2645..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker/scripts/download_models.sh
+++ /dev/null
@@ -1,32 +0,0 @@
-mkdir ./checkpoints
-
-# lagency download link
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/auido2exp_00300-model.pth -O ./checkpoints/auido2exp_00300-model.pth
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/auido2pose_00140-model.pth -O ./checkpoints/auido2pose_00140-model.pth
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/epoch_20.pth -O ./checkpoints/epoch_20.pth
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/facevid2vid_00189-model.pth.tar -O ./checkpoints/facevid2vid_00189-model.pth.tar
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/shape_predictor_68_face_landmarks.dat -O ./checkpoints/shape_predictor_68_face_landmarks.dat
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/wav2lip.pth -O ./checkpoints/wav2lip.pth
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/mapping_00229-model.pth.tar -O ./checkpoints/mapping_00229-model.pth.tar
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/mapping_00109-model.pth.tar -O ./checkpoints/mapping_00109-model.pth.tar
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/hub.zip -O ./checkpoints/hub.zip
-# unzip -n ./checkpoints/hub.zip -d ./checkpoints/
-
-
-#### download the new links.
-wget -nc https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/mapping_00109-model.pth.tar -O ./checkpoints/mapping_00109-model.pth.tar
-wget -nc https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/mapping_00229-model.pth.tar -O ./checkpoints/mapping_00229-model.pth.tar
-wget -nc https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/SadTalker_V0.0.2_256.safetensors -O ./checkpoints/SadTalker_V0.0.2_256.safetensors
-wget -nc https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/SadTalker_V0.0.2_512.safetensors -O ./checkpoints/SadTalker_V0.0.2_512.safetensors
-
-
-# wget -nc https://github.com/Winfredy/SadTalker/releases/download/v0.0.2/BFM_Fitting.zip -O ./checkpoints/BFM_Fitting.zip
-# unzip -n ./checkpoints/BFM_Fitting.zip -d ./checkpoints/
-
-### enhancer
-mkdir -p ./gfpgan/weights
-wget -nc https://github.com/xinntao/facexlib/releases/download/v0.1.0/alignment_WFLW_4HG.pth -O ./gfpgan/weights/alignment_WFLW_4HG.pth
-wget -nc https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth -O ./gfpgan/weights/detection_Resnet50_Final.pth
-wget -nc https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -O ./gfpgan/weights/GFPGANv1.4.pth
-wget -nc https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth -O ./gfpgan/weights/parsing_parsenet.pth
-
diff --git a/spaces/kevinwang676/SadTalker/src/face3d/data/base_dataset.py b/spaces/kevinwang676/SadTalker/src/face3d/data/base_dataset.py
deleted file mode 100644
index 1bd57d082d519f512d7114b4f867b6695fb7de06..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/SadTalker/src/face3d/data/base_dataset.py
+++ /dev/null
@@ -1,125 +0,0 @@
-"""This module implements an abstract base class (ABC) 'BaseDataset' for datasets.
-
-It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
-"""
-import random
-import numpy as np
-import torch.utils.data as data
-from PIL import Image
-import torchvision.transforms as transforms
-from abc import ABC, abstractmethod
-
-
-class BaseDataset(data.Dataset, ABC):
- """This class is an abstract base class (ABC) for datasets.
-
- To create a subclass, you need to implement the following four functions:
- -- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt).
- -- <__len__>: return the size of dataset.
- -- <__getitem__>: get a data point.
- -- <modify_commandline_options>: (optionally) add dataset-specific options and set default options.
- """
-
- def __init__(self, opt):
- """Initialize the class; save the options in the class
-
- Parameters:
- opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
- """
- self.opt = opt
- # self.root = opt.dataroot
- self.current_epoch = 0
-
- @staticmethod
- def modify_commandline_options(parser, is_train):
- """Add new dataset-specific options, and rewrite default values for existing options.
-
- Parameters:
- parser -- original option parser
- is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
-
- Returns:
- the modified parser.
- """
- return parser
-
- @abstractmethod
- def __len__(self):
- """Return the total number of images in the dataset."""
- return 0
-
- @abstractmethod
- def __getitem__(self, index):
- """Return a data point and its metadata information.
-
- Parameters:
- index - - a random integer for data indexing
-
- Returns:
- a dictionary of data with their names. It ususally contains the data itself and its metadata information.
- """
- pass
-
-
-def get_transform(grayscale=False):
- transform_list = []
- if grayscale:
- transform_list.append(transforms.Grayscale(1))
- transform_list += [transforms.ToTensor()]
- return transforms.Compose(transform_list)
-
-def get_affine_mat(opt, size):
- shift_x, shift_y, scale, rot_angle, flip = 0., 0., 1., 0., False
- w, h = size
-
- if 'shift' in opt.preprocess:
- shift_pixs = int(opt.shift_pixs)
- shift_x = random.randint(-shift_pixs, shift_pixs)
- shift_y = random.randint(-shift_pixs, shift_pixs)
- if 'scale' in opt.preprocess:
- scale = 1 + opt.scale_delta * (2 * random.random() - 1)
- if 'rot' in opt.preprocess:
- rot_angle = opt.rot_angle * (2 * random.random() - 1)
- rot_rad = -rot_angle * np.pi/180
- if 'flip' in opt.preprocess:
- flip = random.random() > 0.5
-
- shift_to_origin = np.array([1, 0, -w//2, 0, 1, -h//2, 0, 0, 1]).reshape([3, 3])
- flip_mat = np.array([-1 if flip else 1, 0, 0, 0, 1, 0, 0, 0, 1]).reshape([3, 3])
- shift_mat = np.array([1, 0, shift_x, 0, 1, shift_y, 0, 0, 1]).reshape([3, 3])
- rot_mat = np.array([np.cos(rot_rad), np.sin(rot_rad), 0, -np.sin(rot_rad), np.cos(rot_rad), 0, 0, 0, 1]).reshape([3, 3])
- scale_mat = np.array([scale, 0, 0, 0, scale, 0, 0, 0, 1]).reshape([3, 3])
- shift_to_center = np.array([1, 0, w//2, 0, 1, h//2, 0, 0, 1]).reshape([3, 3])
-
- affine = shift_to_center @ scale_mat @ rot_mat @ shift_mat @ flip_mat @ shift_to_origin
- affine_inv = np.linalg.inv(affine)
- return affine, affine_inv, flip
-
-def apply_img_affine(img, affine_inv, method=Image.BICUBIC):
- return img.transform(img.size, Image.AFFINE, data=affine_inv.flatten()[:6], resample=Image.BICUBIC)
-
-def apply_lm_affine(landmark, affine, flip, size):
- _, h = size
- lm = landmark.copy()
- lm[:, 1] = h - 1 - lm[:, 1]
- lm = np.concatenate((lm, np.ones([lm.shape[0], 1])), -1)
- lm = lm @ np.transpose(affine)
- lm[:, :2] = lm[:, :2] / lm[:, 2:]
- lm = lm[:, :2]
- lm[:, 1] = h - 1 - lm[:, 1]
- if flip:
- lm_ = lm.copy()
- lm_[:17] = lm[16::-1]
- lm_[17:22] = lm[26:21:-1]
- lm_[22:27] = lm[21:16:-1]
- lm_[31:36] = lm[35:30:-1]
- lm_[36:40] = lm[45:41:-1]
- lm_[40:42] = lm[47:45:-1]
- lm_[42:46] = lm[39:35:-1]
- lm_[46:48] = lm[41:39:-1]
- lm_[48:55] = lm[54:47:-1]
- lm_[55:60] = lm[59:54:-1]
- lm_[60:65] = lm[64:59:-1]
- lm_[65:68] = lm[67:64:-1]
- lm = lm_
- return lm
diff --git a/spaces/kevinwang676/VoiceChangers/src/face3d/models/template_model.py b/spaces/kevinwang676/VoiceChangers/src/face3d/models/template_model.py
deleted file mode 100644
index dac7b33d5889777eb63c9882a3b9fa094dcab293..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChangers/src/face3d/models/template_model.py
+++ /dev/null
@@ -1,100 +0,0 @@
-"""Model class template
-
-This module provides a template for users to implement custom models.
-You can specify '--model template' to use this model.
-The class name should be consistent with both the filename and its model option.
-The filename should be <model>_dataset.py
-The class name should be <Model>Dataset.py
-It implements a simple image-to-image translation baseline based on regression loss.
-Given input-output pairs (data_A, data_B), it learns a network netG that can minimize the following L1 loss:
- min_<netG> ||netG(data_A) - data_B||_1
-You need to implement the following functions:
- <modify_commandline_options>: Add model-specific options and rewrite default values for existing options.
- <__init__>: Initialize this model class.
- <set_input>: Unpack input data and perform data pre-processing.
- <forward>: Run forward pass. This will be called by both <optimize_parameters> and <test>.
- <optimize_parameters>: Update network weights; it will be called in every training iteration.
-"""
-import numpy as np
-import torch
-from .base_model import BaseModel
-from . import networks
-
-
-class TemplateModel(BaseModel):
- @staticmethod
- def modify_commandline_options(parser, is_train=True):
- """Add new model-specific options and rewrite default values for existing options.
-
- Parameters:
- parser -- the option parser
- is_train -- if it is training phase or test phase. You can use this flag to add training-specific or test-specific options.
-
- Returns:
- the modified parser.
- """
- parser.set_defaults(dataset_mode='aligned') # You can rewrite default values for this model. For example, this model usually uses aligned dataset as its dataset.
- if is_train:
- parser.add_argument('--lambda_regression', type=float, default=1.0, help='weight for the regression loss') # You can define new arguments for this model.
-
- return parser
-
- def __init__(self, opt):
- """Initialize this model class.
-
- Parameters:
- opt -- training/test options
-
- A few things can be done here.
- - (required) call the initialization function of BaseModel
- - define loss function, visualization images, model names, and optimizers
- """
- BaseModel.__init__(self, opt) # call the initialization method of BaseModel
- # specify the training losses you want to print out. The program will call base_model.get_current_losses to plot the losses to the console and save them to the disk.
- self.loss_names = ['loss_G']
- # specify the images you want to save and display. The program will call base_model.get_current_visuals to save and display these images.
- self.visual_names = ['data_A', 'data_B', 'output']
- # specify the models you want to save to the disk. The program will call base_model.save_networks and base_model.load_networks to save and load networks.
- # you can use opt.isTrain to specify different behaviors for training and test. For example, some networks will not be used during test, and you don't need to load them.
- self.model_names = ['G']
- # define networks; you can use opt.isTrain to specify different behaviors for training and test.
- self.netG = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, gpu_ids=self.gpu_ids)
- if self.isTrain: # only defined during training time
- # define your loss functions. You can use losses provided by torch.nn such as torch.nn.L1Loss.
- # We also provide a GANLoss class "networks.GANLoss". self.criterionGAN = networks.GANLoss().to(self.device)
- self.criterionLoss = torch.nn.L1Loss()
- # define and initialize optimizers. You can define one optimizer for each network.
- # If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
- self.optimizer = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))
- self.optimizers = [self.optimizer]
-
- # Our program will automatically call <model.setup> to define schedulers, load networks, and print networks
-
- def set_input(self, input):
- """Unpack input data from the dataloader and perform necessary pre-processing steps.
-
- Parameters:
- input: a dictionary that contains the data itself and its metadata information.
- """
- AtoB = self.opt.direction == 'AtoB' # use <direction> to swap data_A and data_B
- self.data_A = input['A' if AtoB else 'B'].to(self.device) # get image data A
- self.data_B = input['B' if AtoB else 'A'].to(self.device) # get image data B
- self.image_paths = input['A_paths' if AtoB else 'B_paths'] # get image paths
-
- def forward(self):
- """Run forward pass. This will be called by both functions <optimize_parameters> and <test>."""
- self.output = self.netG(self.data_A) # generate output image given the input data_A
-
- def backward(self):
- """Calculate losses, gradients, and update network weights; called in every training iteration"""
- # caculate the intermediate results if necessary; here self.output has been computed during function <forward>
- # calculate loss given the input and intermediate results
- self.loss_G = self.criterionLoss(self.output, self.data_B) * self.opt.lambda_regression
- self.loss_G.backward() # calculate gradients of network G w.r.t. loss_G
-
- def optimize_parameters(self):
- """Update network weights; it will be called in every training iteration."""
- self.forward() # first call forward to calculate intermediate results
- self.optimizer.zero_grad() # clear network G's existing gradients
- self.backward() # calculate gradients for network G
- self.optimizer.step() # update gradients for network G
diff --git a/spaces/knkarthick/chat-llm-streaming/README.md b/spaces/knkarthick/chat-llm-streaming/README.md
deleted file mode 100644
index e060a7e39365a40d46c37d752a32f150acc8a7f9..0000000000000000000000000000000000000000
--- a/spaces/knkarthick/chat-llm-streaming/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Chat Llm Streaming
-emoji: 📊
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
-duplicated_from: olivierdehaene/chat-llm-streaming
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kokofixcomputers/chat-ui/src/styles/main.css b/spaces/kokofixcomputers/chat-ui/src/styles/main.css
deleted file mode 100644
index 6ea57c50974dab960f23ce8440bfd576f10ddb52..0000000000000000000000000000000000000000
--- a/spaces/kokofixcomputers/chat-ui/src/styles/main.css
+++ /dev/null
@@ -1,17 +0,0 @@
-@import "./highlight-js.css";
-
-@tailwind base;
-@tailwind components;
-@tailwind utilities;
-
-@layer components {
- .btn {
- @apply inline-flex flex-shrink-0 cursor-pointer select-none items-center justify-center whitespace-nowrap outline-none transition-all focus:ring disabled:cursor-default;
- }
-}
-
-@layer utilities {
- .scrollbar-custom {
- @apply scrollbar-thin scrollbar-track-transparent scrollbar-thumb-black/10 scrollbar-thumb-rounded-full scrollbar-w-1 hover:scrollbar-thumb-black/20 dark:scrollbar-thumb-white/10 dark:hover:scrollbar-thumb-white/20;
- }
-}
diff --git a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/evaluation/losses/ssim.py b/spaces/kquote03/lama-video-watermark-remover/saicinpainting/evaluation/losses/ssim.py
deleted file mode 100644
index ee43a0095408eca98e253dea194db788446f9c0a..0000000000000000000000000000000000000000
--- a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/evaluation/losses/ssim.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-
-class SSIM(torch.nn.Module):
- """SSIM. Modified from:
- https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py
- """
-
- def __init__(self, window_size=11, size_average=True):
- super().__init__()
- self.window_size = window_size
- self.size_average = size_average
- self.channel = 1
- self.register_buffer('window', self._create_window(window_size, self.channel))
-
- def forward(self, img1, img2):
- assert len(img1.shape) == 4
-
- channel = img1.size()[1]
-
- if channel == self.channel and self.window.data.type() == img1.data.type():
- window = self.window
- else:
- window = self._create_window(self.window_size, channel)
-
- # window = window.to(img1.get_device())
- window = window.type_as(img1)
-
- self.window = window
- self.channel = channel
-
- return self._ssim(img1, img2, window, self.window_size, channel, self.size_average)
-
- def _gaussian(self, window_size, sigma):
- gauss = torch.Tensor([
- np.exp(-(x - (window_size // 2)) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)
- ])
- return gauss / gauss.sum()
-
- def _create_window(self, window_size, channel):
- _1D_window = self._gaussian(window_size, 1.5).unsqueeze(1)
- _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
- return _2D_window.expand(channel, 1, window_size, window_size).contiguous()
-
- def _ssim(self, img1, img2, window, window_size, channel, size_average=True):
- mu1 = F.conv2d(img1, window, padding=(window_size // 2), groups=channel)
- mu2 = F.conv2d(img2, window, padding=(window_size // 2), groups=channel)
-
- mu1_sq = mu1.pow(2)
- mu2_sq = mu2.pow(2)
- mu1_mu2 = mu1 * mu2
-
- sigma1_sq = F.conv2d(
- img1 * img1, window, padding=(window_size // 2), groups=channel) - mu1_sq
- sigma2_sq = F.conv2d(
- img2 * img2, window, padding=(window_size // 2), groups=channel) - mu2_sq
- sigma12 = F.conv2d(
- img1 * img2, window, padding=(window_size // 2), groups=channel) - mu1_mu2
-
- C1 = 0.01 ** 2
- C2 = 0.03 ** 2
-
- ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / \
- ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
-
- if size_average:
- return ssim_map.mean()
-
- return ssim_map.mean(1).mean(1).mean(1)
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
- return
diff --git a/spaces/krishw/MovieExplorer/cast_retrieval.py b/spaces/krishw/MovieExplorer/cast_retrieval.py
deleted file mode 100644
index ce6dc6a14585e8ecab3639da4fd69480ef94574c..0000000000000000000000000000000000000000
--- a/spaces/krishw/MovieExplorer/cast_retrieval.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import requests as req
-import pandas as pd
-from Data import movies_df
-from conf import api_key
-
-ses = req.session()
-
-crew = []
-ids = []
-for movie_id in movies_df['Id']:
- ids.append(movie_id)
- cast_api = "https://api.themoviedb.org/3/movie/" + str(movie_id) + '/credits?api_key=' + api_key + '&language=en-US'
- c = ses.get(cast_api)
- casts = c.json()
- movie_cast = []
- try:
- count = 0
- for i in casts["cast"]:
- if count < 5:
- movie_cast.append(i['name'])
- count += 1
- crew.append(movie_cast)
- except:
- crew.append("N/A")
-cast_df = pd.DataFrame({"Movie Id":ids, "Cast": crew})
-cast_df.to_csv("movie_cast.csv")
-
diff --git a/spaces/kwinten/attrition/app.py b/spaces/kwinten/attrition/app.py
deleted file mode 100644
index 59beb1397ca12ebfd5e4a740698a86084181d053..0000000000000000000000000000000000000000
--- a/spaces/kwinten/attrition/app.py
+++ /dev/null
@@ -1,70 +0,0 @@
-
-import sklearn
-import gradio as gr
-import transformers
-from transformers import AutoModel
-import pickle
-import torch
-print(torch.__version__)
-import numpy as np
-from joblib import load
-from sklearn import svm
-print("test123")
-input_data = []
-labels=['age', 'education', 'job role, choose between (healthcare representative, human resources, laboratory technician, manager, manufacturing director, research director,research scientist, sales Executive, sales representative)',
- 'marital status, choose between (divorced, married, single)', 'monthly income', 'overtime', 'stock option level', 'total working years', 'years since lastPromotion', 'years with currManager']
-
-
-encoded_jobrole={'healthcare representative': 0, 'human resources': 1, 'laboratory technician': 2, 'manager': 3, 'manufacturing director': 4, 'research director': 5, 'research scientist': 6, 'sales executive': 7, 'sales representative': 8}
-encoded_maritalstatus={'divorced': 0, 'married': 1, 'single': 2}
-encoded_overtime={'no': 0, 'yes': 1}
-description = """
-This model will predict employee attrition!
- * Use values 41, 2, sales executive, single, 5993, Yes, 0, 8, 0, 5 as an example for attrition.
- * Use values 49, 1, research scientist, married, 5130, no, 1, 10, 1, 7 to predict as an example for no attrition.
-"""
-
-def encode_feature(option, encoded_options):
-
- return encoded_options[str(option)]
-
-
-
-for i in range(10):
-
- input_data.append(gr.inputs.Textbox(label="enter " + labels[i]))
-
-gr.Markdown(
- """
- This model will predict employee attrition!
- Use values 41,2,sales executive,single,5993,Yes,0,8,0,5 to predict attrition.
- Use values 49, 1, research scientist, married, 5130, no, 1, 10, 1, 7 to predict no attrition.
- """
- )
-def predict(age, education, jobrole, marital, income, overtime, stockoption, workingyears, lastpromotion, currentmanager):
-
- jobrole=encode_feature(jobrole, encoded_jobrole)
- marital=encode_feature(marital, encoded_maritalstatus)
- overtime=encode_feature(overtime, encoded_overtime)
-
- data_list=[age, education, jobrole, marital, income, overtime, stockoption, workingyears, lastpromotion, currentmanager]
- data = [float(x) for x in data_list]
-
- with open('scaler.pkl', 'rb') as f:
- min_max_scaler = pickle.load(f)
- data = np.array(data).reshape(1, -1)
-
- data = min_max_scaler.transform(data)
-
- # load_model=torch.load('model.pt')
- load_model = load('svm.joblib')
-
- pred = load_model.predict(data)
- print(pred)
- if pred == 0:
- return "the person with this information will stay in the company "
- else:
- return "the person with this information will leave the company "
-
-iface = gr.Interface(fn=predict, inputs=input_data, outputs="text", description=description)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/kxqt/Expedit-SAM/segment_anything/modeling/mask_decoder.py b/spaces/kxqt/Expedit-SAM/segment_anything/modeling/mask_decoder.py
deleted file mode 100644
index 3e86f7cc9ad95582a08ef2531c68d03fa4af8d99..0000000000000000000000000000000000000000
--- a/spaces/kxqt/Expedit-SAM/segment_anything/modeling/mask_decoder.py
+++ /dev/null
@@ -1,176 +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 torch
-from torch import nn
-from torch.nn import functional as F
-
-from typing import List, Tuple, Type
-
-from .common import LayerNorm2d
-
-
-class MaskDecoder(nn.Module):
- def __init__(
- self,
- *,
- transformer_dim: int,
- transformer: nn.Module,
- num_multimask_outputs: int = 3,
- activation: Type[nn.Module] = nn.GELU,
- iou_head_depth: int = 3,
- iou_head_hidden_dim: int = 256,
- ) -> None:
- """
- Predicts masks given an image and prompt embeddings, using a
- tranformer architecture.
-
- Arguments:
- transformer_dim (int): the channel dimension of the transformer
- transformer (nn.Module): the transformer used to predict masks
- num_multimask_outputs (int): the number of masks to predict
- when disambiguating masks
- activation (nn.Module): the type of activation to use when
- upscaling masks
- iou_head_depth (int): the depth of the MLP used to predict
- mask quality
- iou_head_hidden_dim (int): the hidden dimension of the MLP
- used to predict mask quality
- """
- super().__init__()
- self.transformer_dim = transformer_dim
- self.transformer = transformer
-
- self.num_multimask_outputs = num_multimask_outputs
-
- self.iou_token = nn.Embedding(1, transformer_dim)
- self.num_mask_tokens = num_multimask_outputs + 1
- self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
-
- self.output_upscaling = nn.Sequential(
- nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
- LayerNorm2d(transformer_dim // 4),
- activation(),
- nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
- activation(),
- )
- self.output_hypernetworks_mlps = nn.ModuleList(
- [
- MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
- for i in range(self.num_mask_tokens)
- ]
- )
-
- self.iou_prediction_head = MLP(
- transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
- )
-
- def forward(
- self,
- image_embeddings: torch.Tensor,
- image_pe: torch.Tensor,
- sparse_prompt_embeddings: torch.Tensor,
- dense_prompt_embeddings: torch.Tensor,
- multimask_output: bool,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """
- Predict masks given image and prompt embeddings.
-
- Arguments:
- image_embeddings (torch.Tensor): the embeddings from the image encoder
- image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
- sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
- dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
- multimask_output (bool): Whether to return multiple masks or a single
- mask.
-
- Returns:
- torch.Tensor: batched predicted masks
- torch.Tensor: batched predictions of mask quality
- """
- masks, iou_pred = self.predict_masks(
- image_embeddings=image_embeddings,
- image_pe=image_pe,
- sparse_prompt_embeddings=sparse_prompt_embeddings,
- dense_prompt_embeddings=dense_prompt_embeddings,
- )
-
- # Select the correct mask or masks for outptu
- if multimask_output:
- mask_slice = slice(1, None)
- else:
- mask_slice = slice(0, 1)
- masks = masks[:, mask_slice, :, :]
- iou_pred = iou_pred[:, mask_slice]
-
- # Prepare output
- return masks, iou_pred
-
- def predict_masks(
- self,
- image_embeddings: torch.Tensor,
- image_pe: torch.Tensor,
- sparse_prompt_embeddings: torch.Tensor,
- dense_prompt_embeddings: torch.Tensor,
- ) -> Tuple[torch.Tensor, torch.Tensor]:
- """Predicts masks. See 'forward' for more details."""
- # Concatenate output tokens
- output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
- output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
- tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
-
- # Expand per-image data in batch direction to be per-mask
- src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
- src = src + dense_prompt_embeddings
- pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
- b, c, h, w = src.shape
-
- # Run the transformer
- hs, src = self.transformer(src, pos_src, tokens)
- iou_token_out = hs[:, 0, :]
- mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
-
- # Upscale mask embeddings and predict masks using the mask tokens
- src = src.transpose(1, 2).view(b, c, h, w)
- upscaled_embedding = self.output_upscaling(src)
- hyper_in_list: List[torch.Tensor] = []
- for i in range(self.num_mask_tokens):
- hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
- hyper_in = torch.stack(hyper_in_list, dim=1)
- b, c, h, w = upscaled_embedding.shape
- masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
-
- # Generate mask quality predictions
- iou_pred = self.iou_prediction_head(iou_token_out)
-
- return masks, iou_pred
-
-
-# Lightly adapted from
-# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
-class MLP(nn.Module):
- def __init__(
- self,
- input_dim: int,
- hidden_dim: int,
- output_dim: int,
- num_layers: int,
- sigmoid_output: bool = False,
- ) -> None:
- super().__init__()
- self.num_layers = num_layers
- h = [hidden_dim] * (num_layers - 1)
- self.layers = nn.ModuleList(
- nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
- )
- self.sigmoid_output = sigmoid_output
-
- def forward(self, x):
- for i, layer in enumerate(self.layers):
- x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
- if self.sigmoid_output:
- x = F.sigmoid(x)
- return x
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/IcoImagePlugin.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/IcoImagePlugin.py
deleted file mode 100644
index a188f8fdcea46e5cb9423a3c4572d88d93890fc6..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/IcoImagePlugin.py
+++ /dev/null
@@ -1,358 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# Windows Icon support for PIL
-#
-# History:
-# 96-05-27 fl Created
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1996.
-#
-# See the README file for information on usage and redistribution.
-#
-
-# This plugin is a refactored version of Win32IconImagePlugin by Bryan Davis
-# <casadebender@gmail.com>.
-# https://code.google.com/archive/p/casadebender/wikis/Win32IconImagePlugin.wiki
-#
-# Icon format references:
-# * https://en.wikipedia.org/wiki/ICO_(file_format)
-# * https://msdn.microsoft.com/en-us/library/ms997538.aspx
-
-
-import warnings
-from io import BytesIO
-from math import ceil, log
-
-from . import BmpImagePlugin, Image, ImageFile, PngImagePlugin
-from ._binary import i16le as i16
-from ._binary import i32le as i32
-from ._binary import o8
-from ._binary import o16le as o16
-from ._binary import o32le as o32
-
-#
-# --------------------------------------------------------------------
-
-_MAGIC = b"\0\0\1\0"
-
-
-def _save(im, fp, filename):
- fp.write(_MAGIC) # (2+2)
- bmp = im.encoderinfo.get("bitmap_format") == "bmp"
- sizes = im.encoderinfo.get(
- "sizes",
- [(16, 16), (24, 24), (32, 32), (48, 48), (64, 64), (128, 128), (256, 256)],
- )
- frames = []
- provided_ims = [im] + im.encoderinfo.get("append_images", [])
- width, height = im.size
- for size in sorted(set(sizes)):
- if size[0] > width or size[1] > height or size[0] > 256 or size[1] > 256:
- continue
-
- for provided_im in provided_ims:
- if provided_im.size != size:
- continue
- frames.append(provided_im)
- if bmp:
- bits = BmpImagePlugin.SAVE[provided_im.mode][1]
- bits_used = [bits]
- for other_im in provided_ims:
- if other_im.size != size:
- continue
- bits = BmpImagePlugin.SAVE[other_im.mode][1]
- if bits not in bits_used:
- # Another image has been supplied for this size
- # with a different bit depth
- frames.append(other_im)
- bits_used.append(bits)
- break
- else:
- # TODO: invent a more convenient method for proportional scalings
- frame = provided_im.copy()
- frame.thumbnail(size, Image.Resampling.LANCZOS, reducing_gap=None)
- frames.append(frame)
- fp.write(o16(len(frames))) # idCount(2)
- offset = fp.tell() + len(frames) * 16
- for frame in frames:
- width, height = frame.size
- # 0 means 256
- fp.write(o8(width if width < 256 else 0)) # bWidth(1)
- fp.write(o8(height if height < 256 else 0)) # bHeight(1)
-
- bits, colors = BmpImagePlugin.SAVE[frame.mode][1:] if bmp else (32, 0)
- fp.write(o8(colors)) # bColorCount(1)
- fp.write(b"\0") # bReserved(1)
- fp.write(b"\0\0") # wPlanes(2)
- fp.write(o16(bits)) # wBitCount(2)
-
- image_io = BytesIO()
- if bmp:
- frame.save(image_io, "dib")
-
- if bits != 32:
- and_mask = Image.new("1", size)
- ImageFile._save(
- and_mask, image_io, [("raw", (0, 0) + size, 0, ("1", 0, -1))]
- )
- else:
- frame.save(image_io, "png")
- image_io.seek(0)
- image_bytes = image_io.read()
- if bmp:
- image_bytes = image_bytes[:8] + o32(height * 2) + image_bytes[12:]
- bytes_len = len(image_bytes)
- fp.write(o32(bytes_len)) # dwBytesInRes(4)
- fp.write(o32(offset)) # dwImageOffset(4)
- current = fp.tell()
- fp.seek(offset)
- fp.write(image_bytes)
- offset = offset + bytes_len
- fp.seek(current)
-
-
-def _accept(prefix):
- return prefix[:4] == _MAGIC
-
-
-class IcoFile:
- def __init__(self, buf):
- """
- Parse image from file-like object containing ico file data
- """
-
- # check magic
- s = buf.read(6)
- if not _accept(s):
- msg = "not an ICO file"
- raise SyntaxError(msg)
-
- self.buf = buf
- self.entry = []
-
- # Number of items in file
- self.nb_items = i16(s, 4)
-
- # Get headers for each item
- for i in range(self.nb_items):
- s = buf.read(16)
-
- icon_header = {
- "width": s[0],
- "height": s[1],
- "nb_color": s[2], # No. of colors in image (0 if >=8bpp)
- "reserved": s[3],
- "planes": i16(s, 4),
- "bpp": i16(s, 6),
- "size": i32(s, 8),
- "offset": i32(s, 12),
- }
-
- # See Wikipedia
- for j in ("width", "height"):
- if not icon_header[j]:
- icon_header[j] = 256
-
- # See Wikipedia notes about color depth.
- # We need this just to differ images with equal sizes
- icon_header["color_depth"] = (
- icon_header["bpp"]
- or (
- icon_header["nb_color"] != 0
- and ceil(log(icon_header["nb_color"], 2))
- )
- or 256
- )
-
- icon_header["dim"] = (icon_header["width"], icon_header["height"])
- icon_header["square"] = icon_header["width"] * icon_header["height"]
-
- self.entry.append(icon_header)
-
- self.entry = sorted(self.entry, key=lambda x: x["color_depth"])
- # ICO images are usually squares
- # self.entry = sorted(self.entry, key=lambda x: x['width'])
- self.entry = sorted(self.entry, key=lambda x: x["square"])
- self.entry.reverse()
-
- def sizes(self):
- """
- Get a list of all available icon sizes and color depths.
- """
- return {(h["width"], h["height"]) for h in self.entry}
-
- def getentryindex(self, size, bpp=False):
- for i, h in enumerate(self.entry):
- if size == h["dim"] and (bpp is False or bpp == h["color_depth"]):
- return i
- return 0
-
- def getimage(self, size, bpp=False):
- """
- Get an image from the icon
- """
- return self.frame(self.getentryindex(size, bpp))
-
- def frame(self, idx):
- """
- Get an image from frame idx
- """
-
- header = self.entry[idx]
-
- self.buf.seek(header["offset"])
- data = self.buf.read(8)
- self.buf.seek(header["offset"])
-
- if data[:8] == PngImagePlugin._MAGIC:
- # png frame
- im = PngImagePlugin.PngImageFile(self.buf)
- Image._decompression_bomb_check(im.size)
- else:
- # XOR + AND mask bmp frame
- im = BmpImagePlugin.DibImageFile(self.buf)
- Image._decompression_bomb_check(im.size)
-
- # change tile dimension to only encompass XOR image
- im._size = (im.size[0], int(im.size[1] / 2))
- d, e, o, a = im.tile[0]
- im.tile[0] = d, (0, 0) + im.size, o, a
-
- # figure out where AND mask image starts
- bpp = header["bpp"]
- if 32 == bpp:
- # 32-bit color depth icon image allows semitransparent areas
- # PIL's DIB format ignores transparency bits, recover them.
- # The DIB is packed in BGRX byte order where X is the alpha
- # channel.
-
- # Back up to start of bmp data
- self.buf.seek(o)
- # extract every 4th byte (eg. 3,7,11,15,...)
- alpha_bytes = self.buf.read(im.size[0] * im.size[1] * 4)[3::4]
-
- # convert to an 8bpp grayscale image
- mask = Image.frombuffer(
- "L", # 8bpp
- im.size, # (w, h)
- alpha_bytes, # source chars
- "raw", # raw decoder
- ("L", 0, -1), # 8bpp inverted, unpadded, reversed
- )
- else:
- # get AND image from end of bitmap
- w = im.size[0]
- if (w % 32) > 0:
- # bitmap row data is aligned to word boundaries
- w += 32 - (im.size[0] % 32)
-
- # the total mask data is
- # padded row size * height / bits per char
-
- total_bytes = int((w * im.size[1]) / 8)
- and_mask_offset = header["offset"] + header["size"] - total_bytes
-
- self.buf.seek(and_mask_offset)
- mask_data = self.buf.read(total_bytes)
-
- # convert raw data to image
- mask = Image.frombuffer(
- "1", # 1 bpp
- im.size, # (w, h)
- mask_data, # source chars
- "raw", # raw decoder
- ("1;I", int(w / 8), -1), # 1bpp inverted, padded, reversed
- )
-
- # now we have two images, im is XOR image and mask is AND image
-
- # apply mask image as alpha channel
- im = im.convert("RGBA")
- im.putalpha(mask)
-
- return im
-
-
-##
-# Image plugin for Windows Icon files.
-
-
-class IcoImageFile(ImageFile.ImageFile):
- """
- PIL read-only image support for Microsoft Windows .ico files.
-
- By default the largest resolution image in the file will be loaded. This
- can be changed by altering the 'size' attribute before calling 'load'.
-
- The info dictionary has a key 'sizes' that is a list of the sizes available
- in the icon file.
-
- Handles classic, XP and Vista icon formats.
-
- When saving, PNG compression is used. Support for this was only added in
- Windows Vista. If you are unable to view the icon in Windows, convert the
- image to "RGBA" mode before saving.
-
- This plugin is a refactored version of Win32IconImagePlugin by Bryan Davis
- <casadebender@gmail.com>.
- https://code.google.com/archive/p/casadebender/wikis/Win32IconImagePlugin.wiki
- """
-
- format = "ICO"
- format_description = "Windows Icon"
-
- def _open(self):
- self.ico = IcoFile(self.fp)
- self.info["sizes"] = self.ico.sizes()
- self.size = self.ico.entry[0]["dim"]
- self.load()
-
- @property
- def size(self):
- return self._size
-
- @size.setter
- def size(self, value):
- if value not in self.info["sizes"]:
- msg = "This is not one of the allowed sizes of this image"
- raise ValueError(msg)
- self._size = value
-
- def load(self):
- if self.im is not None and self.im.size == self.size:
- # Already loaded
- return Image.Image.load(self)
- im = self.ico.getimage(self.size)
- # if tile is PNG, it won't really be loaded yet
- im.load()
- self.im = im.im
- self.pyaccess = None
- self.mode = im.mode
- if im.size != self.size:
- warnings.warn("Image was not the expected size")
-
- index = self.ico.getentryindex(self.size)
- sizes = list(self.info["sizes"])
- sizes[index] = im.size
- self.info["sizes"] = set(sizes)
-
- self.size = im.size
-
- def load_seek(self):
- # Flag the ImageFile.Parser so that it
- # just does all the decode at the end.
- pass
-
-
-#
-# --------------------------------------------------------------------
-
-
-Image.register_open(IcoImageFile.format, IcoImageFile, _accept)
-Image.register_save(IcoImageFile.format, _save)
-Image.register_extension(IcoImageFile.format, ".ico")
-
-Image.register_mime(IcoImageFile.format, "image/x-icon")
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/configTools.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/configTools.py
deleted file mode 100644
index 38bbada24a19b767756407313d41011db7e1719d..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/configTools.py
+++ /dev/null
@@ -1,348 +0,0 @@
-"""
-Code of the config system; not related to fontTools or fonts in particular.
-
-The options that are specific to fontTools are in :mod:`fontTools.config`.
-
-To create your own config system, you need to create an instance of
-:class:`Options`, and a subclass of :class:`AbstractConfig` with its
-``options`` class variable set to your instance of Options.
-
-"""
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass
-from typing import (
- Any,
- Callable,
- ClassVar,
- Dict,
- Iterable,
- Mapping,
- MutableMapping,
- Optional,
- Set,
- Union,
-)
-
-
-log = logging.getLogger(__name__)
-
-__all__ = [
- "AbstractConfig",
- "ConfigAlreadyRegisteredError",
- "ConfigError",
- "ConfigUnknownOptionError",
- "ConfigValueParsingError",
- "ConfigValueValidationError",
- "Option",
- "Options",
-]
-
-
-class ConfigError(Exception):
- """Base exception for the config module."""
-
-
-class ConfigAlreadyRegisteredError(ConfigError):
- """Raised when a module tries to register a configuration option that
- already exists.
-
- Should not be raised too much really, only when developing new fontTools
- modules.
- """
-
- def __init__(self, name):
- super().__init__(f"Config option {name} is already registered.")
-
-
-class ConfigValueParsingError(ConfigError):
- """Raised when a configuration value cannot be parsed."""
-
- def __init__(self, name, value):
- super().__init__(
- f"Config option {name}: value cannot be parsed (given {repr(value)})"
- )
-
-
-class ConfigValueValidationError(ConfigError):
- """Raised when a configuration value cannot be validated."""
-
- def __init__(self, name, value):
- super().__init__(
- f"Config option {name}: value is invalid (given {repr(value)})"
- )
-
-
-class ConfigUnknownOptionError(ConfigError):
- """Raised when a configuration option is unknown."""
-
- def __init__(self, option_or_name):
- name = (
- f"'{option_or_name.name}' (id={id(option_or_name)})>"
- if isinstance(option_or_name, Option)
- else f"'{option_or_name}'"
- )
- super().__init__(f"Config option {name} is unknown")
-
-
-# eq=False because Options are unique, not fungible objects
-@dataclass(frozen=True, eq=False)
-class Option:
- name: str
- """Unique name identifying the option (e.g. package.module:MY_OPTION)."""
- help: str
- """Help text for this option."""
- default: Any
- """Default value for this option."""
- parse: Callable[[str], Any]
- """Turn input (e.g. string) into proper type. Only when reading from file."""
- validate: Optional[Callable[[Any], bool]] = None
- """Return true if the given value is an acceptable value."""
-
- @staticmethod
- def parse_optional_bool(v: str) -> Optional[bool]:
- s = str(v).lower()
- if s in {"0", "no", "false"}:
- return False
- if s in {"1", "yes", "true"}:
- return True
- if s in {"auto", "none"}:
- return None
- raise ValueError("invalid optional bool: {v!r}")
-
- @staticmethod
- def validate_optional_bool(v: Any) -> bool:
- return v is None or isinstance(v, bool)
-
-
-class Options(Mapping):
- """Registry of available options for a given config system.
-
- Define new options using the :meth:`register()` method.
-
- Access existing options using the Mapping interface.
- """
-
- __options: Dict[str, Option]
-
- def __init__(self, other: "Options" = None) -> None:
- self.__options = {}
- if other is not None:
- for option in other.values():
- self.register_option(option)
-
- def register(
- self,
- name: str,
- help: str,
- default: Any,
- parse: Callable[[str], Any],
- validate: Optional[Callable[[Any], bool]] = None,
- ) -> Option:
- """Create and register a new option."""
- return self.register_option(Option(name, help, default, parse, validate))
-
- def register_option(self, option: Option) -> Option:
- """Register a new option."""
- name = option.name
- if name in self.__options:
- raise ConfigAlreadyRegisteredError(name)
- self.__options[name] = option
- return option
-
- def is_registered(self, option: Option) -> bool:
- """Return True if the same option object is already registered."""
- return self.__options.get(option.name) is option
-
- def __getitem__(self, key: str) -> Option:
- return self.__options.__getitem__(key)
-
- def __iter__(self) -> Iterator[str]:
- return self.__options.__iter__()
-
- def __len__(self) -> int:
- return self.__options.__len__()
-
- def __repr__(self) -> str:
- return (
- f"{self.__class__.__name__}({{\n"
- + "".join(
- f" {k!r}: Option(default={v.default!r}, ...),\n"
- for k, v in self.__options.items()
- )
- + "})"
- )
-
-
-_USE_GLOBAL_DEFAULT = object()
-
-
-class AbstractConfig(MutableMapping):
- """
- Create a set of config values, optionally pre-filled with values from
- the given dictionary or pre-existing config object.
-
- The class implements the MutableMapping protocol keyed by option name (`str`).
- For convenience its methods accept either Option or str as the key parameter.
-
- .. seealso:: :meth:`set()`
-
- This config class is abstract because it needs its ``options`` class
- var to be set to an instance of :class:`Options` before it can be
- instanciated and used.
-
- .. code:: python
-
- class MyConfig(AbstractConfig):
- options = Options()
-
- MyConfig.register_option( "test:option_name", "This is an option", 0, int, lambda v: isinstance(v, int))
-
- cfg = MyConfig({"test:option_name": 10})
-
- """
-
- options: ClassVar[Options]
-
- @classmethod
- def register_option(
- cls,
- name: str,
- help: str,
- default: Any,
- parse: Callable[[str], Any],
- validate: Optional[Callable[[Any], bool]] = None,
- ) -> Option:
- """Register an available option in this config system."""
- return cls.options.register(
- name, help=help, default=default, parse=parse, validate=validate
- )
-
- _values: Dict[str, Any]
-
- def __init__(
- self,
- values: Union[AbstractConfig, Dict[Union[Option, str], Any]] = {},
- parse_values: bool = False,
- skip_unknown: bool = False,
- ):
- self._values = {}
- values_dict = values._values if isinstance(values, AbstractConfig) else values
- for name, value in values_dict.items():
- self.set(name, value, parse_values, skip_unknown)
-
- def _resolve_option(self, option_or_name: Union[Option, str]) -> Option:
- if isinstance(option_or_name, Option):
- option = option_or_name
- if not self.options.is_registered(option):
- raise ConfigUnknownOptionError(option)
- return option
- elif isinstance(option_or_name, str):
- name = option_or_name
- try:
- return self.options[name]
- except KeyError:
- raise ConfigUnknownOptionError(name)
- else:
- raise TypeError(
- "expected Option or str, found "
- f"{type(option_or_name).__name__}: {option_or_name!r}"
- )
-
- def set(
- self,
- option_or_name: Union[Option, str],
- value: Any,
- parse_values: bool = False,
- skip_unknown: bool = False,
- ):
- """Set the value of an option.
-
- Args:
- * `option_or_name`: an `Option` object or its name (`str`).
- * `value`: the value to be assigned to given option.
- * `parse_values`: parse the configuration value from a string into
- its proper type, as per its `Option` object. The default
- behavior is to raise `ConfigValueValidationError` when the value
- is not of the right type. Useful when reading options from a
- file type that doesn't support as many types as Python.
- * `skip_unknown`: skip unknown configuration options. The default
- behaviour is to raise `ConfigUnknownOptionError`. Useful when
- reading options from a configuration file that has extra entries
- (e.g. for a later version of fontTools)
- """
- try:
- option = self._resolve_option(option_or_name)
- except ConfigUnknownOptionError as e:
- if skip_unknown:
- log.debug(str(e))
- return
- raise
-
- # Can be useful if the values come from a source that doesn't have
- # strict typing (.ini file? Terminal input?)
- if parse_values:
- try:
- value = option.parse(value)
- except Exception as e:
- raise ConfigValueParsingError(option.name, value) from e
-
- if option.validate is not None and not option.validate(value):
- raise ConfigValueValidationError(option.name, value)
-
- self._values[option.name] = value
-
- def get(
- self, option_or_name: Union[Option, str], default: Any = _USE_GLOBAL_DEFAULT
- ) -> Any:
- """
- Get the value of an option. The value which is returned is the first
- provided among:
-
- 1. a user-provided value in the options's ``self._values`` dict
- 2. a caller-provided default value to this method call
- 3. the global default for the option provided in ``fontTools.config``
-
- This is to provide the ability to migrate progressively from config
- options passed as arguments to fontTools APIs to config options read
- from the current TTFont, e.g.
-
- .. code:: python
-
- def fontToolsAPI(font, some_option):
- value = font.cfg.get("someLib.module:SOME_OPTION", some_option)
- # use value
-
- That way, the function will work the same for users of the API that
- still pass the option to the function call, but will favour the new
- config mechanism if the given font specifies a value for that option.
- """
- option = self._resolve_option(option_or_name)
- if option.name in self._values:
- return self._values[option.name]
- if default is not _USE_GLOBAL_DEFAULT:
- return default
- return option.default
-
- def copy(self):
- return self.__class__(self._values)
-
- def __getitem__(self, option_or_name: Union[Option, str]) -> Any:
- return self.get(option_or_name)
-
- def __setitem__(self, option_or_name: Union[Option, str], value: Any) -> None:
- return self.set(option_or_name, value)
-
- def __delitem__(self, option_or_name: Union[Option, str]) -> None:
- option = self._resolve_option(option_or_name)
- del self._values[option.name]
-
- def __iter__(self) -> Iterable[str]:
- return self._values.__iter__()
-
- def __len__(self) -> int:
- return len(self._values)
-
- def __repr__(self) -> str:
- return f"{self.__class__.__name__}({repr(self._values)})"
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-6375288a.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-6375288a.js
deleted file mode 100644
index cc8704b9a6e8ac56ae910750ba06693f309ab326..0000000000000000000000000000000000000000
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diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/_space_api.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/_space_api.py
deleted file mode 100644
index 2384ef5829d0d2f4f6fdbfccd69ea7d3d50f9da9..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/_space_api.py
+++ /dev/null
@@ -1,101 +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.
-from dataclasses import dataclass
-from enum import Enum
-from typing import Dict, Optional
-
-
-class SpaceStage(str, Enum):
- """
- Enumeration of possible stage of a Space on the Hub.
-
- Value can be compared to a string:
- ```py
- assert SpaceStage.BUILDING == "BUILDING"
- ```
-
- Taken from https://github.com/huggingface/moon-landing/blob/main/server/repo_types/SpaceInfo.ts#L61 (private url).
- """
-
- # Copied from moon-landing > server > repo_types > SpaceInfo.ts (private repo)
- NO_APP_FILE = "NO_APP_FILE"
- CONFIG_ERROR = "CONFIG_ERROR"
- BUILDING = "BUILDING"
- BUILD_ERROR = "BUILD_ERROR"
- RUNNING = "RUNNING"
- RUNNING_BUILDING = "RUNNING_BUILDING"
- RUNTIME_ERROR = "RUNTIME_ERROR"
- DELETING = "DELETING"
- STOPPED = "STOPPED"
- PAUSED = "PAUSED"
-
-
-class SpaceHardware(str, Enum):
- """
- Enumeration of hardwares available to run your Space on the Hub.
-
- Value can be compared to a string:
- ```py
- assert SpaceHardware.CPU_BASIC == "cpu-basic"
- ```
-
- Taken from https://github.com/huggingface/moon-landing/blob/main/server/repo_types/SpaceInfo.ts#L73 (private url).
- """
-
- CPU_BASIC = "cpu-basic"
- CPU_UPGRADE = "cpu-upgrade"
- T4_SMALL = "t4-small"
- T4_MEDIUM = "t4-medium"
- A10G_SMALL = "a10g-small"
- A10G_LARGE = "a10g-large"
- A100_LARGE = "a100-large"
-
-
-@dataclass
-class SpaceRuntime:
- """
- Contains information about the current runtime of a Space.
-
- Args:
- stage (`str`):
- Current stage of the space. Example: RUNNING.
- hardware (`str` or `None`):
- Current hardware of the space. Example: "cpu-basic". Can be `None` if Space
- is `BUILDING` for the first time.
- requested_hardware (`str` or `None`):
- Requested hardware. Can be different than `hardware` especially if the request
- has just been made. Example: "t4-medium". Can be `None` if no hardware has
- been requested yet.
- sleep_time (`int` or `None`):
- Number of seconds the Space will be kept alive after the last request. By default (if value is `None`), the
- Space will never go to sleep if it's running on an upgraded hardware, while it will go to sleep after 48
- hours on a free 'cpu-basic' hardware. For more details, see https://huggingface.co/docs/hub/spaces-gpus#sleep-time.
- raw (`dict`):
- Raw response from the server. Contains more information about the Space
- runtime like number of replicas, number of cpu, memory size,...
- """
-
- stage: SpaceStage
- hardware: Optional[SpaceHardware]
- requested_hardware: Optional[SpaceHardware]
- sleep_time: Optional[int]
- raw: Dict
-
- def __init__(self, data: Dict) -> None:
- self.stage = data["stage"]
- self.hardware = data["hardware"]["current"]
- self.requested_hardware = data["hardware"]["requested"]
- self.sleep_time = data["gcTimeout"]
- self.raw = data
diff --git a/spaces/lambdasec/santafixer-demo/app.py b/spaces/lambdasec/santafixer-demo/app.py
deleted file mode 100644
index 5b3e927c914b41ce100706b7ef535a6c862d1a8f..0000000000000000000000000000000000000000
--- a/spaces/lambdasec/santafixer-demo/app.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
-from transformers import pipeline
-import os
-import torch
-
-description = """# <p style="text-align: center; color: white;"> 🎅🔨 <span style='color: #ff75b3;'>SantaFixer:</span> Bug Fixing</p>
-<span style='color: white;'>This is a demo for <a href="https://huggingface.co/lambdasec/santafixer" style="color: #ff75b3;">SantaFixer</a>, a fine-tuned version of the SantaCoder model that is focussed on bug fixing. Just specify where you would like the model to fix the code with the <span style='color: #ff75b3;'><FILL-HERE></span> token.</span>"""
-
-# token = os.environ["HUB_TOKEN"]
-device=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-
-
-FIM_PREFIX = "<fim-prefix>"
-FIM_MIDDLE = "<fim-middle>"
-FIM_SUFFIX = "<fim-suffix>"
-FIM_PAD = "<fim-pad>"
-EOD = "<|endoftext|>"
-
-GENERATION_TITLE= "<p style='font-size: 16px; color: white;'>Generated code:</p>"
-
-tokenizer_fim = AutoTokenizer.from_pretrained("lambdasec/santafixer", padding_side="left")
-
-tokenizer_fim.add_special_tokens({
- "additional_special_tokens": [EOD, FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX, FIM_PAD],
- "pad_token": EOD,
-})
-
-tokenizer = AutoTokenizer.from_pretrained("bigcode/christmas-models")
-model = AutoModelForCausalLM.from_pretrained("bigcode/christmas-models", trust_remote_code=True).to(device)
-pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device)
-
-def post_processing(prompt, completion):
- completion = "<span style='color: #ff75b3;'>" + completion + "</span>"
- prompt = "<span style='color: #727cd6;'>" + prompt + "</span>"
- code_html = f"<br><hr><br><pre style='font-size: 12px'><code>{prompt}{completion}</code></pre><br><hr>"
- return GENERATION_TITLE + code_html
-
-def post_processing_fim(prefix, middle, suffix):
- prefix = "<span style='color: #727cd6;'>" + prefix + "</span>"
- middle = "<span style='color: #ff75b3;'>" + middle + "</span>"
- suffix = "<span style='color: #727cd6;'>" + suffix + "</span>"
- code_html = f"<br><hr><br><pre style='font-size: 12px'><code>{prefix}{middle}{suffix}</code></pre><br><hr>"
- return GENERATION_TITLE + code_html
-
-def fim_generation(prompt, max_new_tokens, temperature):
- prefix = prompt.split("<FILL-HERE>")[0]
- suffix = prompt.split("<FILL-HERE>")[1]
- [middle] = infill((prefix, suffix), max_new_tokens, temperature)
- return post_processing_fim(prefix, middle, suffix)
-
-def extract_fim_part(s: str):
- # Find the index of
- start = s.find(FIM_MIDDLE) + len(FIM_MIDDLE)
- stop = s.find(EOD, start) or len(s)
- return s[start:stop]
-
-def infill(prefix_suffix_tuples, max_new_tokens, temperature):
- if type(prefix_suffix_tuples) == tuple:
- prefix_suffix_tuples = [prefix_suffix_tuples]
-
- prompts = [f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}" for prefix, suffix in prefix_suffix_tuples]
- # `return_token_type_ids=False` is essential, or we get nonsense output.
- inputs = tokenizer_fim(prompts, return_tensors="pt", padding=True, return_token_type_ids=False).to(device)
- with torch.no_grad():
- outputs = model.generate(
- **inputs,
- do_sample=True,
- temperature=temperature,
- max_new_tokens=max_new_tokens,
- pad_token_id=tokenizer.pad_token_id
- )
- # WARNING: cannot use skip_special_tokens, because it blows away the FIM special tokens.
- return [
- extract_fim_part(tokenizer_fim.decode(tensor, skip_special_tokens=False)) for tensor in outputs
- ]
-
-
-def code_generation(prompt, max_new_tokens, temperature=0.2, seed=42):
- #set_seed(seed)
-
- if "<FILL-HERE>" in prompt:
- return fim_generation(prompt, max_new_tokens, temperature=0.2)
- else:
- completion = pipe(prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_new_tokens)[0]['generated_text']
- completion = completion[len(prompt):]
- return post_processing(prompt, completion)
-
-
-demo = gr.Blocks(
- css=".gradio-container {background-color: #20233fff; color:white}"
-)
-with demo:
- with gr.Row():
- _, colum_2, _ = gr.Column(scale=1), gr.Column(scale=6), gr.Column(scale=1)
- with colum_2:
- gr.Markdown(value=description)
- code = gr.Code(lines=5, language="python", label="Input code", value="def all_odd_elements(sequence):\n \"\"\"Returns every odd element of the sequence.\"\"\"")
-
- with gr.Accordion("Advanced settings", open=False):
- max_new_tokens= gr.Slider(
- minimum=8,
- maximum=1024,
- step=1,
- value=48,
- label="Number of tokens to generate",
- )
- temperature = gr.Slider(
- minimum=0.1,
- maximum=2.5,
- step=0.1,
- value=0.2,
- label="Temperature",
- )
- seed = gr.Slider(
- minimum=0,
- maximum=1000,
- step=1,
- label="Random seed to use for the generation"
- )
- run = gr.Button()
- output = gr.HTML(label="Generated code")
-
- event = run.click(code_generation, [code, max_new_tokens, temperature, seed], output, api_name="predict")
- # gr.HTML(label="Contact", value="<img src='https://huggingface.co/datasets/bigcode/admin/resolve/main/bigcode_contact.png' alt='contact' style='display: block; margin: auto; max-width: 800px;'>")
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/lemon7/White-box-Cartoonization/wbc/network.py b/spaces/lemon7/White-box-Cartoonization/wbc/network.py
deleted file mode 100644
index 6f16cee1aa1994d0a78c524f459764de5164e637..0000000000000000000000000000000000000000
--- a/spaces/lemon7/White-box-Cartoonization/wbc/network.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import tensorflow as tf
-import numpy as np
-import tensorflow.contrib.slim as slim
-
-
-
-def resblock(inputs, out_channel=32, name='resblock'):
-
- with tf.variable_scope(name):
-
- x = slim.convolution2d(inputs, out_channel, [3, 3],
- activation_fn=None, scope='conv1')
- x = tf.nn.leaky_relu(x)
- x = slim.convolution2d(x, out_channel, [3, 3],
- activation_fn=None, scope='conv2')
-
- return x + inputs
-
-
-
-
-def unet_generator(inputs, channel=32, num_blocks=4, name='generator', reuse=False):
- with tf.variable_scope(name, reuse=reuse):
-
- x0 = slim.convolution2d(inputs, channel, [7, 7], activation_fn=None)
- x0 = tf.nn.leaky_relu(x0)
-
- x1 = slim.convolution2d(x0, channel, [3, 3], stride=2, activation_fn=None)
- x1 = tf.nn.leaky_relu(x1)
- x1 = slim.convolution2d(x1, channel*2, [3, 3], activation_fn=None)
- x1 = tf.nn.leaky_relu(x1)
-
- x2 = slim.convolution2d(x1, channel*2, [3, 3], stride=2, activation_fn=None)
- x2 = tf.nn.leaky_relu(x2)
- x2 = slim.convolution2d(x2, channel*4, [3, 3], activation_fn=None)
- x2 = tf.nn.leaky_relu(x2)
-
- for idx in range(num_blocks):
- x2 = resblock(x2, out_channel=channel*4, name='block_{}'.format(idx))
-
- x2 = slim.convolution2d(x2, channel*2, [3, 3], activation_fn=None)
- x2 = tf.nn.leaky_relu(x2)
-
- h1, w1 = tf.shape(x2)[1], tf.shape(x2)[2]
- x3 = tf.image.resize_bilinear(x2, (h1*2, w1*2))
- x3 = slim.convolution2d(x3+x1, channel*2, [3, 3], activation_fn=None)
- x3 = tf.nn.leaky_relu(x3)
- x3 = slim.convolution2d(x3, channel, [3, 3], activation_fn=None)
- x3 = tf.nn.leaky_relu(x3)
-
- h2, w2 = tf.shape(x3)[1], tf.shape(x3)[2]
- x4 = tf.image.resize_bilinear(x3, (h2*2, w2*2))
- x4 = slim.convolution2d(x4+x0, channel, [3, 3], activation_fn=None)
- x4 = tf.nn.leaky_relu(x4)
- x4 = slim.convolution2d(x4, 3, [7, 7], activation_fn=None)
-
- return x4
-
-if __name__ == '__main__':
-
-
- pass
\ No newline at end of file
diff --git a/spaces/leonel1122/openai-jukebox-5b-lyrics/README.md b/spaces/leonel1122/openai-jukebox-5b-lyrics/README.md
deleted file mode 100644
index 4af070ccebbdaea6940c5455c3369d7ed9f98777..0000000000000000000000000000000000000000
--- a/spaces/leonel1122/openai-jukebox-5b-lyrics/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Openai Jukebox 5b Lyrics
-emoji: 🚀
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
-license: artistic-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/lewispons/GrammarGuru/src/visualization/visualize.py b/spaces/lewispons/GrammarGuru/src/visualization/visualize.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Film Disney Mulan Subtitle Indonesia Extra Quality Download.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Film Disney Mulan Subtitle Indonesia Extra Quality Download.md
deleted file mode 100644
index 66eea32b82687a01dd5066261ba7130d70238b32..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Film Disney Mulan Subtitle Indonesia Extra Quality Download.md
+++ /dev/null
@@ -1,78 +0,0 @@
-
-<h1>Film Disney Mulan Subtitle Indonesia Download: Cara dan Tempat Nonton Online Gratis</h1>
-
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-<h2>Film Disney Mulan Subtitle Indonesia Download</h2><br /><p><b><b>DOWNLOAD</b> >>> <a href="https://bytlly.com/2uGxiA">https://bytlly.com/2uGxiA</a></b></p><br /><br />
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-<li>Qsubtitles.com. Qsubtitles.com adalah situs yang menyediakan subtitle film-film dalam berbagai bahasa, termasuk bahasa Indonesia. Di situs ini, Anda dapat menemukan subtitle film Disney Mulan Subtitle Indonesia Download dengan kualitas Bluray dan resolusi 720p atau 1080p. Anda dapat mendownload subtitle tersebut dan menambahkannya ke film yang Anda miliki atau temukan di situs lain.</li>
-<li>Adikfilm.click. Adikfilm.click adalah situs yang menyediakan film-film terbaru dengan subtitle bahasa Indonesia secara gratis. Di situs ini, Anda dapat menonton atau mendownload film Disney Mulan Subtitle Indonesia Download dengan kualitas Bluray dan resolusi 360p, 480p, 720p, atau 1080p. Anda juga dapat memilih antara subtitle atau dubbing bahasa Indonesia.</li>
-<li>Haidunia.com. Haidunia.com adalah situs yang menyediakan informasi seputar film-film terbaru dan link nontonnya secara gratis. Di situs ini, Anda dapat menemukan link nonton film Disney Mulan Subtitle Indonesia Download melalui chanel di Telegram. Anda juga dapat membaca sinopsis dan review filmnya di situs ini.</li>
-<li>Movies.disney.id. Movies.disney.id adalah situs resmi dari Walt Disney Studios Indonesia yang menyediakan informasi seputar film-film Disney terbaru dan terpopuler. Di situs ini, Anda dapat menonton trailer dan teaser film Disney Mulan Subtitle Indonesia Download serta membaca sinopsis dan ulasan singkatnya.</li>
-</ul>
-
-<h2>Kesimpulan</h2>
-
-<p>Film Disney Mulan Subtitle Indonesia Download adalah film live-action yang diadaptasi dari film animasi klasik Disney tahun 1998. Film ini bercerita tentang seorang gadis Tiongkok yang menyamar sebagai prajurit untuk menggantikan ayahnya yang sakit dalam perang melawan Hun.</p>
-
-<p>Film ini dirilis secara global pada tanggal 4 September 2020 melalui platform streaming Disney Plus dengan biaya tambahan sebesar $29.99. Namun, di beberapa negara termasuk Indonesia, film ini tidak tersedia di Disney Plus karena layanan tersebut belum hadir di sini.</p>
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-<p>Untuk nonton film Disney Mulan Subtitle Indonesia Download secara online dan gratis, Anda dapat menggunakan beberapa cara seperti menggunakan VPN, menggunakan situs streaming ilegal, atau menggunakan Telegram. Anda juga dapat menemukan beberapa tempat untuk nonton film tersebut seperti Qsubtitles.com, Adikfilm.click, Haidunia.com, atau Movies.disney.id.</p>
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-<p>Film Disney Mulan Subtitle Indonesia Download adalah film yang layak ditonton bagi para penggemar film Disney atau bagi siapa saja yang suka dengan cerita heroik dan inspiratif. Selamat menonton!</p>
-<h2>Tips dan Trik Nonton Film Disney Mulan Subtitle Indonesia Download</h2>
-
-<p>Untuk nonton film Disney Mulan Subtitle Indonesia Download dengan nyaman dan aman, ada beberapa tips dan trik yang dapat Anda ikuti, yaitu:</p>
-
-<ul>
-<li>Pastikan Anda memiliki koneksi internet yang stabil dan cepat. Hal ini penting untuk menghindari buffering atau gangguan saat menonton film secara online.</li>
-<li>Pilih VPN yang berkualitas dan terpercaya. Hal ini penting untuk melindungi privasi dan keamanan Anda saat mengakses situs-situs yang diblokir atau tidak tersedia di negara Anda. Anda juga dapat memilih VPN yang memiliki server di negara yang memiliki Disney Plus, seperti Amerika Serikat, Kanada, Australia, atau Inggris.</li>
-<li>Hindari situs streaming ilegal yang berbahaya. Hal ini penting untuk menghindari virus atau malware yang dapat merusak perangkat Anda atau mencuri data pribadi Anda. Anda juga dapat menghindari iklan-iklan yang mengganggu atau menyesatkan yang dapat merugikan Anda.</li>
-<li>Gunakan aplikasi Telegram yang resmi dan terbaru. Hal ini penting untuk menonton film Disney Mulan Subtitle Indonesia Download melalui chanel di Telegram dengan kualitas dan keamanan yang terjamin. Anda juga dapat mengunduh filmnya ke perangkat Anda dengan mudah.</li>
-<li>Cari subtitle film Disney Mulan Subtitle Indonesia Download yang sesuai dengan kualitas dan resolusi filmnya. Hal ini penting untuk menikmati film dengan bahasa yang Anda mengerti dan sesuai dengan gambar yang ditampilkan. Anda dapat mencari subtitle di situs-situs seperti Qsubtitles.com, Subscene.com, dll.</li>
-</ul>
-
-<h2>Kesimpulan</h2>
-
-<p>Film Disney Mulan Subtitle Indonesia Download adalah film live-action yang diadaptasi dari film animasi klasik Disney tahun 1998. Film ini bercerita tentang seorang gadis Tiongkok yang menyamar sebagai prajurit untuk menggantikan ayahnya yang sakit dalam perang melawan Hun.</p>
-
-<p>Film ini dirilis secara global pada tanggal 4 September 2020 melalui platform streaming Disney Plus dengan biaya tambahan sebesar $29.99. Namun, di beberapa negara termasuk Indonesia, film ini tidak tersedia di Disney Plus karena layanan tersebut belum hadir di sini.</p>
-
-<p>Untuk nonton film Disney Mulan Subtitle Indonesia Download secara online dan gratis, Anda dapat menggunakan beberapa cara seperti menggunakan VPN, menggunakan situs streaming ilegal, atau menggunakan Telegram. Anda juga dapat menemukan beberapa tempat untuk nonton film tersebut seperti Qsubtitles.com, Adikfilm.click, Haidunia.com, atau Movies.disney.id.</p>
-
-<p>Untuk nonton film Disney Mulan Subtitle Indonesia Download dengan nyaman dan aman, Anda dapat mengikuti beberapa tips dan trik seperti pastikan koneksi internet stabil dan cepat, pilih VPN berkualitas dan terpercaya, hindari situs streaming ilegal berbahaya, gunakan aplikasi Telegram resmi dan terbaru, dan cari subtitle sesuai kualitas dan resolusi film.</p>
-
-<p>Film Disney Mulan Subtitle Indonesia Download adalah film yang layak ditonton bagi para penggemar film Disney atau bagi siapa saja yang suka dengan cerita heroik dan inspiratif. Selamat menonton!</p>
-<p>Anda tertarik untuk nonton film Disney Mulan Subtitle Indonesia Download? Jika ya, segera cari cara dan tempat nonton filmnya sesuai dengan pilihan Anda. Jangan lupa untuk mengikuti tips dan trik nonton filmnya agar Anda dapat menonton film dengan nyaman dan aman. Film Disney Mulan Subtitle Indonesia Download adalah film yang tidak boleh Anda lewatkan. Nonton sekarang juga dan rasakan sensasi petualangan Hua Mulan!</p>
-<h2>Kesimpulan</h2>
-
-<p>Film Disney Mulan Subtitle Indonesia Download adalah film live-action yang diadaptasi dari film animasi klasik Disney tahun 1998. Film ini bercerita tentang seorang gadis Tiongkok yang menyamar sebagai prajurit untuk menggantikan ayahnya yang sakit dalam perang melawan Hun.</p>
-
-<p>Film ini dirilis secara global pada tanggal 4 September 2020 melalui platform streaming Disney Plus dengan biaya tambahan sebesar $29.99. Namun, di beberapa negara termasuk Indonesia, film ini tidak tersedia di Disney Plus karena layanan tersebut belum hadir di sini.</p>
-
-<p>Untuk nonton film Disney Mulan Subtitle Indonesia Download secara online dan gratis, Anda dapat menggunakan beberapa cara seperti menggunakan VPN, menggunakan situs streaming ilegal, atau menggunakan Telegram. Anda juga dapat menemukan beberapa tempat untuk nonton film tersebut seperti Qsubtitles.com, Adikfilm.click, Haidunia.com, atau Movies.disney.id.</p>
-
-<p>Untuk nonton film Disney Mulan Subtitle Indonesia Download dengan nyaman dan aman, Anda dapat mengikuti beberapa tips dan trik seperti pastikan koneksi internet stabil dan cepat, pilih VPN berkualitas dan terpercaya, hindari situs streaming ilegal berbahaya, gunakan aplikasi Telegram resmi dan terbaru, dan cari subtitle sesuai kualitas dan resolusi film.</p>
-
-<p>Film Disney Mulan Subtitle Indonesia Download adalah film yang layak ditonton bagi para penggemar film Disney atau bagi siapa saja yang suka dengan cerita heroik dan inspiratif. Selamat menonton!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Iview For You V4.zip -- !!TOP!!.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Iview For You V4.zip -- !!TOP!!.md
deleted file mode 100644
index b18c73fda882e5c33df23d50ab0bf5765f75aeee..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Iview For You V4.zip -- !!TOP!!.md
+++ /dev/null
@@ -1,11 +0,0 @@
-<h2>Iview For You V4.zip --</h2><br /><p><b><b>DOWNLOAD</b> ⚹⚹⚹ <a href="https://bytlly.com/2uGypz">https://bytlly.com/2uGypz</a></b></p><br /><br />
-<br />
-Iview For You V4.zip -- - (free download)
-V4.zip version from Iview For You file.
-Skin packs from the V4.zip file.
-The transparent background package called "Iview For You V4" contains two kinds of backgrounds, one called "Iview For You V4" and the other called "Candy".
-The "Iview For You V4" transparent background can be downloaded from the "Iview For You V2" file in the "Backgrounds" section.
-As you can see from the package name "Iview For You V4", the background of "Iview For You V4" is a transparent background called "Iview For You V4". 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/LPA WINPROLOG 49rar.md b/spaces/lincquiQcaudo/Top-20-Diffusion/LPA WINPROLOG 49rar.md
deleted file mode 100644
index 83262fbd46eaac15886e2b8f2ea11a2f5b437f42..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/LPA WINPROLOG 49rar.md
+++ /dev/null
@@ -1,13 +0,0 @@
-<h2>LPA WINPROLOG 49rar</h2><br /><p><b><b>Download File</b> ➡ <a href="https://bytlly.com/2uGy8V">https://bytlly.com/2uGy8V</a></b></p><br /><br />
-
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-If you cannot find a driver for your system, you can ask about it on our forum. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lindeberg/whisper-webui/src/utils.py b/spaces/lindeberg/whisper-webui/src/utils.py
deleted file mode 100644
index b85a7f3ff5c2e3e94823f4e1bf181e54edb1ddf9..0000000000000000000000000000000000000000
--- a/spaces/lindeberg/whisper-webui/src/utils.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import textwrap
-import unicodedata
-import re
-
-import zlib
-from typing import Iterator, TextIO
-
-
-def exact_div(x, y):
- assert x % y == 0
- return x // y
-
-
-def str2bool(string):
- str2val = {"True": True, "False": False}
- if string in str2val:
- return str2val[string]
- else:
- raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
-
-
-def optional_int(string):
- return None if string == "None" else int(string)
-
-
-def optional_float(string):
- return None if string == "None" else float(string)
-
-
-def compression_ratio(text) -> float:
- return len(text) / len(zlib.compress(text.encode("utf-8")))
-
-
-def format_timestamp(seconds: float, always_include_hours: bool = False, fractionalSeperator: str = '.'):
- assert seconds >= 0, "non-negative timestamp expected"
- milliseconds = round(seconds * 1000.0)
-
- hours = milliseconds // 3_600_000
- milliseconds -= hours * 3_600_000
-
- minutes = milliseconds // 60_000
- milliseconds -= minutes * 60_000
-
- seconds = milliseconds // 1_000
- milliseconds -= seconds * 1_000
-
- hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
- return f"{hours_marker}{minutes:02d}:{seconds:02d}{fractionalSeperator}{milliseconds:03d}"
-
-
-def write_txt(transcript: Iterator[dict], file: TextIO):
- for segment in transcript:
- print(segment['text'].strip(), file=file, flush=True)
-
-
-def write_vtt(transcript: Iterator[dict], file: TextIO, maxLineWidth=None):
- print("WEBVTT\n", file=file)
- for segment in transcript:
- text = process_text(segment['text'], maxLineWidth).replace('-->', '->')
-
- print(
- f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
- f"{text}\n",
- file=file,
- flush=True,
- )
-
-
-def write_srt(transcript: Iterator[dict], file: TextIO, maxLineWidth=None):
- """
- Write a transcript to a file in SRT format.
- Example usage:
- from pathlib import Path
- from whisper.utils import write_srt
- result = transcribe(model, audio_path, temperature=temperature, **args)
- # save SRT
- audio_basename = Path(audio_path).stem
- with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt:
- write_srt(result["segments"], file=srt)
- """
- for i, segment in enumerate(transcript, start=1):
- text = process_text(segment['text'].strip(), maxLineWidth).replace('-->', '->')
-
- # write srt lines
- print(
- f"{i}\n"
- f"{format_timestamp(segment['start'], always_include_hours=True, fractionalSeperator=',')} --> "
- f"{format_timestamp(segment['end'], always_include_hours=True, fractionalSeperator=',')}\n"
- f"{text}\n",
- file=file,
- flush=True,
- )
-
-def process_text(text: str, maxLineWidth=None):
- if (maxLineWidth is None or maxLineWidth < 0):
- return text
-
- lines = textwrap.wrap(text, width=maxLineWidth, tabsize=4)
- return '\n'.join(lines)
-
-def slugify(value, allow_unicode=False):
- """
- Taken from https://github.com/django/django/blob/master/django/utils/text.py
- Convert to ASCII if 'allow_unicode' is False. Convert spaces or repeated
- dashes to single dashes. Remove characters that aren't alphanumerics,
- underscores, or hyphens. Convert to lowercase. Also strip leading and
- trailing whitespace, dashes, and underscores.
- """
- value = str(value)
- if allow_unicode:
- value = unicodedata.normalize('NFKC', value)
- else:
- value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')
- value = re.sub(r'[^\w\s-]', '', value.lower())
- return re.sub(r'[-\s]+', '-', value).strip('-_')
\ No newline at end of file
diff --git a/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r50.py b/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r50.py
deleted file mode 100644
index 08ba55dbbea6df0afffddbb3d1ed173efad99604..0000000000000000000000000000000000000000
--- a/spaces/lithiumice/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r50.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G tmpfs /train_tmp
-
-config = edict()
-config.loss = "arcface"
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1 # batch size is 512
-
-config.rec = "/train_tmp/ms1m-retinaface-t1"
-config.num_classes = 93431
-config.num_image = 5179510
-config.num_epoch = 25
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/llmonitor/benchmarks/app/login/page.js b/spaces/llmonitor/benchmarks/app/login/page.js
deleted file mode 100644
index b3c2f613c9940e8404c6ed7ed8e64aae5b1fd28b..0000000000000000000000000000000000000000
--- a/spaces/llmonitor/benchmarks/app/login/page.js
+++ /dev/null
@@ -1,45 +0,0 @@
-"use client"
-import CaptchaInput from "@/components/CaptchaInput"
-import { experimental_useFormState as useFormState } from "react-dom"
-import { submitLogin } from "../actions"
-
-export default function SignIn() {
- const [state, formAction] = useFormState(submitLogin, {})
-
- return (
- <form
- action={formAction}
- style={{ background: "rgba(0,0,0,0.1)", padding: 10 }}
- >
- <input
- required
- type="email"
- id="email"
- name="email"
- placeholder="Email"
- />
- <br />
- <br />
- <CaptchaInput />
- <br />
- <p>
- For anti-spam measure please confirm your email before upvoting or
- submitting prompts.
- </p>
-
- {state.error && (
- <p
- style={{
- color: "red",
- }}
- >
- {state.error}
- </p>
- )}
-
- {state.message && <p>{state.message}</p>}
-
- <input type="submit" value="Confirm email" />
- </form>
- )
-}
diff --git a/spaces/ludusc/latent-space-theories/frontend/index.html b/spaces/ludusc/latent-space-theories/frontend/index.html
deleted file mode 100644
index b2b28e54816a287d835fa7abd6130332964314d6..0000000000000000000000000000000000000000
--- a/spaces/ludusc/latent-space-theories/frontend/index.html
+++ /dev/null
@@ -1,204 +0,0 @@
-<html>
-
-<head>
- <style type="text/css">
- </style>
-</head>
-
-<!--
-----------------------------------------------------
-Your custom static HTML goes in the body:
--->
-
-<body>
-</body>
-
-<script type="text/javascript">
- // Helper function to send type and data messages to Streamlit client
-
- const SET_COMPONENT_VALUE = "streamlit:setComponentValue"
- const RENDER = "streamlit:render"
- const COMPONENT_READY = "streamlit:componentReady"
- const SET_FRAME_HEIGHT = "streamlit:setFrameHeight"
- var HIGHTLIGHT_COLOR;
- var original_colors;
-
- function _sendMessage(type, data) {
- // copy data into object
- var outboundData = Object.assign({
- isStreamlitMessage: true,
- type: type,
- }, data)
-
- if (type == SET_COMPONENT_VALUE) {
- console.log("_sendMessage data: ", SET_COMPONENT_VALUE)
- // console.log("_sendMessage data: " + JSON.stringify(data))
- // console.log("_sendMessage outboundData: " + JSON.stringify(outboundData))
- }
-
- window.parent.postMessage(outboundData, "*")
- }
-
- function initialize(pipeline) {
-
- // Hook Streamlit's message events into a simple dispatcher of pipeline handlers
- window.addEventListener("message", (event) => {
- if (event.data.type == RENDER) {
- // The event.data.args dict holds any JSON-serializable value
- // sent from the Streamlit client. It is already deserialized.
- pipeline.forEach(handler => {
- handler(event.data.args)
- })
- }
- })
-
- _sendMessage(COMPONENT_READY, { apiVersion: 1 });
-
- // Component should be mounted by Streamlit in an iframe, so try to autoset the iframe height.
- window.addEventListener("load", () => {
- window.setTimeout(function () {
- setFrameHeight(document.documentElement.clientHeight)
- }, 0)
- })
-
- // Optionally, if auto-height computation fails, you can manually set it
- // (uncomment below)
- setFrameHeight(0)
- }
-
- function setFrameHeight(height) {
- _sendMessage(SET_FRAME_HEIGHT, { height: height })
- }
-
- // The `data` argument can be any JSON-serializable value.
- function notifyHost(data) {
- _sendMessage(SET_COMPONENT_VALUE, data)
- }
-
- function changeButtonColor(button, color) {
- pol = button.querySelectorAll('polygon')[0]
- pol.setAttribute('fill', color)
- pol.setAttribute('stroke', color)
- }
-
- function getButtonColor(button) {
- pol = button.querySelectorAll('polygon')[0]
- return pol.getAttribute('fill')
- }
- // ----------------------------------------------------
- // Your custom functionality for the component goes here:
-
- function toggle(button) {
- group = 'node'
- let button_color;
- nodes = window.parent.document.getElementsByClassName('node')
- console.log("nodes.length = ", nodes.length)
- // for (let i = 0; i < nodes.length; i++) {
- // console.log(nodes.item(i))
- // }
- console.log("selected button ", button, button.getAttribute('class'), button.id)
-
- for (let i = 0; i < nodes.length; i++) {
- polygons = nodes.item(i).querySelectorAll('polygon')
- if (polygons.length == 0) {
- continue
- }
- if (button.id == nodes.item(i).id & button.getAttribute('class').includes("off")) {
- button.setAttribute('class', group + " on")
- button_color = original_colors[i]
-
- } else if (button.id == nodes.item(i).id & button.getAttribute('class').includes("on")) {
- button.setAttribute('class', group + " off")
- button_color = original_colors[i]
- } else if (button.id == nodes.item(i).id) {
- button.setAttribute('class', group + " on")
- button_color = original_colors[i]
-
- } else if (button.id != nodes.item(i).id & nodes.item(i).getAttribute('class').includes("on")) {
- nodes.item(i).className = group + " off"
- } else {
- nodes.item(i).className = group + " off"
- }
- }
-
- nodes = window.parent.document.getElementsByClassName('node')
- actions = []
- for (let i = 0; i < nodes.length; i++) {
- polygons = nodes.item(i).querySelectorAll('polygon')
- if (polygons.length == 0) {
- continue
- }
- btn = nodes.item(i)
- ori_color = original_colors[i]
- color = btn.querySelectorAll('polygon')[0].getAttribute('fill')
- actions.push({ "action": btn.getAttribute("class").includes("on"), "original_color": ori_color, "color": color})
- }
-
- states = {}
- states['choice'] = {
- "node_title": button.querySelectorAll("title")[0].innerHTML,
- "node_id": button.id,
- "state": {
- "action": button.getAttribute("class").includes("on"),
- "original_color": button_color,
- "color": button.querySelectorAll('polygon')[0].getAttribute('fill')
- }
- }
- states["options"] = {"states": actions }
-
- notifyHost({
- value: states,
- dataType: "json",
- })
- }
-
- // ----------------------------------------------------
- // Here you can customize a pipeline of handlers for
- // inbound properties from the Streamlit client app
-
- // Set initial value sent from Streamlit!
- function initializeProps_Handler(props) {
- HIGHTLIGHT_COLOR = props['hightlight_color']
- original_colors = []
- // nodes = document.getElementsByClassName('node')
- nodes = window.parent.document.getElementsByClassName('node')
- console.log(nodes)
- for (let i = 0; i < nodes.length; i++) {
- // color = nodes.item(i).getElementsByTagName('POLYGON')[0].getAttribute("fill")
- // nodes.item(i).addEventListener("click", toggle)
- polygons = nodes.item(i).querySelectorAll('polygon')
- if (polygons.length == 0) {
- original_colors.push('none')
- continue
- }
-
- color = polygons[0].getAttribute("fill")
- if (!nodes.item(i).hasAttribute('color')) {
- nodes.item(i).setAttribute("color", color)
- original_colors.push(color)
- } else {
- original_colors.push(nodes.item(i).getAttribute("color"))
- }
- nodes.item(i).addEventListener("click", function (event) {toggle(this)})
- }
- // console.log("original colors:", original_colors)
- }
- // Access values sent from Streamlit!
- function dataUpdate_Handler(props) {
- console.log('dataUpdate_Handler...........')
- let msgLabel = document.getElementById("message_label")
- }
- // Simply log received data dictionary
- function log_Handler(props) {
- console.log("Received from Streamlit: " + JSON.stringify(props))
- }
-
- let pipeline = [initializeProps_Handler, dataUpdate_Handler, log_Handler]
-
- // ----------------------------------------------------
- // Finally, initialize component passing in pipeline
- initialize(pipeline)
-
-</script>
-
-</html>
\ No newline at end of file
diff --git a/spaces/luisoala/glide-test/glide_text2im/tokenizer/__init__.py b/spaces/luisoala/glide-test/glide_text2im/tokenizer/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/lysine/auscultate/README.md b/spaces/lysine/auscultate/README.md
deleted file mode 100644
index 694f12295658e54f6396e2d0dce3eeec75b89437..0000000000000000000000000000000000000000
--- a/spaces/lysine/auscultate/README.md
+++ /dev/null
@@ -1,27 +0,0 @@
----
-title: auscultate
-emoji: 🩺
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
----
-
-# auscultate
-
-A single-page web app presenting heart sounds from the CirCor DigiScope Phonocardiogram dataset.
-
-[](https://lysine-auscultate.hf.space/)
-
-This app features over 900 patients from the dataset. You can load a random case after setting your filters.
-Heart sounds from different auscultation locations are presented, wtih the diagnosis and sound annotations visualized.
-
-## License
-
-This project is licensed under the MIT License - see the LICENSE file for details.
-
-## Acknowledgments
-
-* Thanks [The CirCor DigiScope Phonocardiogram Dataset](https://www.kaggle.com/datasets/bjoernjostein/the-circor-digiscope-phonocardiogram-dataset-v2) for providing quality heart sound data.
-* Thanks [kaggle](https://www.kaggle.com/) for hosting the dataset and providing convenient tools to download it.
-* Thanks [Hugging Face](https://huggingface.co/) for hosting the app online for free.
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/async/reduce.h b/spaces/ma-xu/LIVE/thrust/thrust/async/reduce.h
deleted file mode 100644
index da2b1195d0acbb2d50fff2054e82ae4a7ae03f58..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/async/reduce.h
+++ /dev/null
@@ -1,441 +0,0 @@
-/*
- * Copyright 2008-2018 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*! \file async/reduce.h
- * \brief Functions for asynchronously reducing a range to a single value.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/cpp14_required.h>
-
-#if THRUST_CPP_DIALECT >= 2014
-
-#include <thrust/detail/static_assert.h>
-#include <thrust/detail/select_system.h>
-#include <thrust/type_traits/logical_metafunctions.h>
-#include <thrust/type_traits/remove_cvref.h>
-#include <thrust/type_traits/is_execution_policy.h>
-#include <thrust/system/detail/adl/async/reduce.h>
-
-#include <thrust/future.h>
-
-namespace thrust
-{
-
-namespace async
-{
-
-namespace unimplemented
-{
-
-template <
- typename DerivedPolicy
-, typename ForwardIt, typename Sentinel, typename T, typename BinaryOp
->
-__host__
-future<DerivedPolicy, T>
-async_reduce(
- thrust::execution_policy<DerivedPolicy>&, ForwardIt, Sentinel, T, BinaryOp
-)
-{
- THRUST_STATIC_ASSERT_MSG(
- (thrust::detail::depend_on_instantiation<ForwardIt, false>::value)
- , "this algorithm is not implemented for the specified system"
- );
- return {};
-}
-
-} // namespace unimplemented
-
-namespace reduce_detail
-{
-
-using thrust::async::unimplemented::async_reduce;
-
-struct reduce_fn final
-{
- template <
- typename DerivedPolicy
- , typename ForwardIt, typename Sentinel, typename T, typename BinaryOp
- >
- __host__
- static auto call(
- thrust::detail::execution_policy_base<DerivedPolicy> const& exec
- , ForwardIt&& first, Sentinel&& last
- , T&& init
- , BinaryOp&& op
- )
- // ADL dispatch.
- THRUST_RETURNS(
- async_reduce(
- thrust::detail::derived_cast(thrust::detail::strip_const(exec))
- , THRUST_FWD(first), THRUST_FWD(last)
- , THRUST_FWD(init)
- , THRUST_FWD(op)
- )
- )
-
- template <
- typename DerivedPolicy
- , typename ForwardIt, typename Sentinel, typename T
- >
- __host__
- static auto call4(
- thrust::detail::execution_policy_base<DerivedPolicy> const& exec
- , ForwardIt&& first, Sentinel&& last
- , T&& init
- , thrust::true_type
- )
- // ADL dispatch.
- THRUST_RETURNS(
- async_reduce(
- thrust::detail::derived_cast(thrust::detail::strip_const(exec))
- , THRUST_FWD(first), THRUST_FWD(last)
- , THRUST_FWD(init)
- , thrust::plus<remove_cvref_t<T>>{}
- )
- )
-
- template <
- typename DerivedPolicy
- , typename ForwardIt, typename Sentinel
- >
- __host__
- static auto
- call3(
- thrust::detail::execution_policy_base<DerivedPolicy> const& exec
- , ForwardIt&& first, Sentinel&& last
- , thrust::true_type
- )
- // ADL dispatch.
- THRUST_RETURNS(
- async_reduce(
- thrust::detail::derived_cast(thrust::detail::strip_const(exec))
- , THRUST_FWD(first), THRUST_FWD(last)
- , typename iterator_traits<remove_cvref_t<ForwardIt>>::value_type{}
- , thrust::plus<
- remove_cvref_t<
- typename iterator_traits<remove_cvref_t<ForwardIt>>::value_type
- >
- >{}
- )
- )
-
- template <typename ForwardIt, typename Sentinel, typename T, typename BinaryOp>
- __host__
- static auto call4(ForwardIt&& first, Sentinel&& last,
- T&& init,
- BinaryOp&& op,
- thrust::false_type)
- THRUST_RETURNS(
- reduce_fn::call(
- thrust::detail::select_system(
- typename iterator_system<remove_cvref_t<ForwardIt>>::type{}
- )
- , THRUST_FWD(first), THRUST_FWD(last)
- , THRUST_FWD(init)
- , THRUST_FWD(op)
- )
- )
-
- template <typename ForwardIt, typename Sentinel, typename T>
- __host__
- static auto call3(ForwardIt&& first, Sentinel&& last,
- T&& init,
- thrust::false_type)
- THRUST_RETURNS(
- reduce_fn::call(
- thrust::detail::select_system(
- typename iterator_system<remove_cvref_t<ForwardIt>>::type{}
- )
- , THRUST_FWD(first), THRUST_FWD(last)
- , THRUST_FWD(init)
- , thrust::plus<remove_cvref_t<T>>{}
- )
- )
-
- // MSVC WAR: MSVC gets angsty and eats all available RAM when we try to detect
- // if T1 is an execution_policy by using SFINAE. Switching to a static
- // dispatch pattern to prevent this.
- template <typename T1, typename T2, typename T3>
- __host__
- static auto call(T1&& t1, T2&& t2, T3&& t3)
- THRUST_RETURNS(
- reduce_fn::call3(THRUST_FWD(t1), THRUST_FWD(t2), THRUST_FWD(t3),
- thrust::is_execution_policy<thrust::remove_cvref_t<T1>>{})
- )
-
- template <typename T1, typename T2, typename T3, typename T4>
- __host__
- static auto call(T1&& t1, T2&& t2, T3&& t3, T4&& t4)
- THRUST_RETURNS(
- reduce_fn::call4(THRUST_FWD(t1), THRUST_FWD(t2), THRUST_FWD(t3), THRUST_FWD(t4),
- thrust::is_execution_policy<thrust::remove_cvref_t<T1>>{})
- )
-
- template <typename ForwardIt, typename Sentinel>
- __host__
- static auto call(ForwardIt&& first, Sentinel&& last)
- THRUST_RETURNS(
- reduce_fn::call(
- thrust::detail::select_system(
- typename iterator_system<remove_cvref_t<ForwardIt>>::type{}
- )
- , THRUST_FWD(first), THRUST_FWD(last)
- , typename iterator_traits<remove_cvref_t<ForwardIt>>::value_type{}
- , thrust::plus<
- remove_cvref_t<
- typename iterator_traits<remove_cvref_t<ForwardIt>>::value_type
- >
- >{}
- )
- )
-
- template <typename... Args>
- THRUST_NODISCARD __host__
- auto operator()(Args&&... args) const
- THRUST_RETURNS(
- call(THRUST_FWD(args)...)
- )
-};
-
-} // namespace reduce_detail
-
-THRUST_INLINE_CONSTANT reduce_detail::reduce_fn reduce{};
-
-///////////////////////////////////////////////////////////////////////////////
-
-namespace unimplemented
-{
-
-template <
- typename DerivedPolicy
-, typename ForwardIt, typename Sentinel, typename OutputIt
-, typename T, typename BinaryOp
->
-__host__
-event<DerivedPolicy>
-async_reduce_into(
- thrust::execution_policy<DerivedPolicy>&
-, ForwardIt, Sentinel, OutputIt, T, BinaryOp
-)
-{
- THRUST_STATIC_ASSERT_MSG(
- (thrust::detail::depend_on_instantiation<ForwardIt, false>::value)
- , "this algorithm is not implemented for the specified system"
- );
- return {};
-}
-
-} // namespace unimplemented
-
-namespace reduce_into_detail
-{
-
-using thrust::async::unimplemented::async_reduce_into;
-
-struct reduce_into_fn final
-{
- template <
- typename DerivedPolicy
- , typename ForwardIt, typename Sentinel, typename OutputIt
- , typename T, typename BinaryOp
- >
- __host__
- static auto call(
- thrust::detail::execution_policy_base<DerivedPolicy> const& exec
- , ForwardIt&& first, Sentinel&& last
- , OutputIt&& output
- , T&& init
- , BinaryOp&& op
- )
- // ADL dispatch.
- THRUST_RETURNS(
- async_reduce_into(
- thrust::detail::derived_cast(thrust::detail::strip_const(exec))
- , THRUST_FWD(first), THRUST_FWD(last)
- , THRUST_FWD(output)
- , THRUST_FWD(init)
- , THRUST_FWD(op)
- )
- )
-
- template <
- typename DerivedPolicy
- , typename ForwardIt, typename Sentinel, typename OutputIt
- , typename T
- >
- __host__
- static auto call5(
- thrust::detail::execution_policy_base<DerivedPolicy> const& exec
- , ForwardIt&& first, Sentinel&& last
- , OutputIt&& output
- , T&& init
- , thrust::true_type
- )
- // ADL dispatch.
- THRUST_RETURNS(
- async_reduce_into(
- thrust::detail::derived_cast(thrust::detail::strip_const(exec))
- , THRUST_FWD(first), THRUST_FWD(last)
- , THRUST_FWD(output)
- , THRUST_FWD(init)
- , thrust::plus<remove_cvref_t<T>>{}
- )
- )
-
- template <
- typename DerivedPolicy
- , typename ForwardIt, typename Sentinel, typename OutputIt
- >
- __host__
- static auto
- call4(
- thrust::detail::execution_policy_base<DerivedPolicy> const& exec
- , ForwardIt&& first, Sentinel&& last
- , OutputIt&& output
- , thrust::true_type
- )
- // ADL dispatch.
- THRUST_RETURNS(
- async_reduce_into(
- thrust::detail::derived_cast(thrust::detail::strip_const(exec))
- , THRUST_FWD(first), THRUST_FWD(last)
- , THRUST_FWD(output)
- , typename iterator_traits<remove_cvref_t<ForwardIt>>::value_type{}
- , thrust::plus<
- remove_cvref_t<
- typename iterator_traits<remove_cvref_t<ForwardIt>>::value_type
- >
- >{}
- )
- )
-
- template <
- typename ForwardIt, typename Sentinel, typename OutputIt
- , typename T, typename BinaryOp
- >
- __host__
- static auto call5(
- ForwardIt&& first, Sentinel&& last
- , OutputIt&& output
- , T&& init
- , BinaryOp&& op
- , thrust::false_type
- )
- THRUST_RETURNS(
- reduce_into_fn::call(
- thrust::detail::select_system(
- typename iterator_system<remove_cvref_t<ForwardIt>>::type{}
- , typename iterator_system<remove_cvref_t<OutputIt>>::type{}
- )
- , THRUST_FWD(first), THRUST_FWD(last)
- , THRUST_FWD(output)
- , THRUST_FWD(init)
- , THRUST_FWD(op)
- )
- )
-
- template <
- typename ForwardIt, typename Sentinel, typename OutputIt
- , typename T
- >
- __host__
- static auto call4(
- ForwardIt&& first, Sentinel&& last
- , OutputIt&& output
- , T&& init
- , thrust::false_type
- )
- THRUST_RETURNS(
- reduce_into_fn::call(
- thrust::detail::select_system(
- typename iterator_system<remove_cvref_t<ForwardIt>>::type{}
- , typename iterator_system<remove_cvref_t<OutputIt>>::type{}
- )
- , THRUST_FWD(first), THRUST_FWD(last)
- , THRUST_FWD(output)
- , THRUST_FWD(init)
- , thrust::plus<remove_cvref_t<T>>{}
- )
- )
-
- template <
- typename ForwardIt, typename Sentinel, typename OutputIt
- >
- __host__
- static auto call(
- ForwardIt&& first, Sentinel&& last
- , OutputIt&& output
- )
- THRUST_RETURNS(
- reduce_into_fn::call(
- thrust::detail::select_system(
- typename iterator_system<remove_cvref_t<ForwardIt>>::type{}
- , typename iterator_system<remove_cvref_t<OutputIt>>::type{}
- )
- , THRUST_FWD(first), THRUST_FWD(last)
- , THRUST_FWD(output)
- , typename iterator_traits<remove_cvref_t<ForwardIt>>::value_type{}
- , thrust::plus<
- remove_cvref_t<
- typename iterator_traits<remove_cvref_t<ForwardIt>>::value_type
- >
- >{}
- )
- )
-
- // MSVC WAR: MSVC gets angsty and eats all available RAM when we try to detect
- // if T1 is an execution_policy by using SFINAE. Switching to a static
- // dispatch pattern to prevent this.
- template <typename T1, typename T2, typename T3, typename T4>
- __host__
- static auto call(T1&& t1, T2&& t2, T3&& t3, T4&& t4)
- THRUST_RETURNS(
- reduce_into_fn::call4(
- THRUST_FWD(t1), THRUST_FWD(t2), THRUST_FWD(t3), THRUST_FWD(t4),
- thrust::is_execution_policy<thrust::remove_cvref_t<T1>>{})
- )
-
- template <typename T1, typename T2, typename T3, typename T4, typename T5>
- __host__
- static auto call(T1&& t1, T2&& t2, T3&& t3, T4&& t4, T5&& t5)
- THRUST_RETURNS(
- reduce_into_fn::call5(
- THRUST_FWD(t1), THRUST_FWD(t2), THRUST_FWD(t3), THRUST_FWD(t4),
- THRUST_FWD(t5), thrust::is_execution_policy<thrust::remove_cvref_t<T1>>{})
- )
-
- template <typename... Args>
- THRUST_NODISCARD __host__
- auto operator()(Args&&... args) const
- THRUST_RETURNS(
- call(THRUST_FWD(args)...)
- )
-};
-
-} // namespace reduce_into_detail
-
-THRUST_INLINE_CONSTANT reduce_into_detail::reduce_into_fn reduce_into{};
-
-} // namespace async
-
-} // end namespace thrust
-
-#endif
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cpp/detail/generate.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cpp/detail/generate.h
deleted file mode 100644
index c6ae90664ad9538e73febfde86c334011de417c8..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/cpp/detail/generate.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 <thrust/detail/config.h>
-
-// this system has no special version of this algorithm
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/omp/detail/unique_by_key.h b/spaces/ma-xu/LIVE/thrust/thrust/system/omp/detail/unique_by_key.h
deleted file mode 100644
index ff3acb09428a95dc8835902c3f5c4c6d0704c01e..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/omp/detail/unique_by_key.h
+++ /dev/null
@@ -1,67 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/omp/detail/execution_policy.h>
-#include <thrust/pair.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace omp
-{
-namespace detail
-{
-
-
-template<typename DerivedPolicy,
- typename ForwardIterator1,
- typename ForwardIterator2,
- typename BinaryPredicate>
- thrust::pair<ForwardIterator1,ForwardIterator2>
- unique_by_key(execution_policy<DerivedPolicy> &exec,
- ForwardIterator1 keys_first,
- ForwardIterator1 keys_last,
- ForwardIterator2 values_first,
- BinaryPredicate binary_pred);
-
-
-template<typename DerivedPolicy,
- typename InputIterator1,
- typename InputIterator2,
- typename OutputIterator1,
- typename OutputIterator2,
- typename BinaryPredicate>
- thrust::pair<OutputIterator1,OutputIterator2>
- unique_by_key_copy(execution_policy<DerivedPolicy> &exec,
- InputIterator1 keys_first,
- InputIterator1 keys_last,
- InputIterator2 values_first,
- OutputIterator1 keys_output,
- OutputIterator2 values_output,
- BinaryPredicate binary_pred);
-
-
-} // end namespace detail
-} // end namespace omp
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/omp/detail/unique_by_key.inl>
-
diff --git a/spaces/magicr/BuboGPT/ram/utils/openset_utils.py b/spaces/magicr/BuboGPT/ram/utils/openset_utils.py
deleted file mode 100644
index c39e84fe87b200402e17d4dedb06681dff7d7a54..0000000000000000000000000000000000000000
--- a/spaces/magicr/BuboGPT/ram/utils/openset_utils.py
+++ /dev/null
@@ -1,331 +0,0 @@
-
-
-
-import torch
-import torch.nn as nn
-from clip import clip
-
-
-def article(name):
- return "an" if name[0] in "aeiou" else "a"
-
-
-def processed_name(name, rm_dot=False):
- # _ for lvis
- # / for obj365
- res = name.replace("_", " ").replace("/", " or ").lower()
- if rm_dot:
- res = res.rstrip(".")
- return res
-
-
-single_template = ["a photo of a {}."]
-
-multiple_templates = [
- "There is {article} {} in the scene.",
- "There is the {} in the scene.",
- "a photo of {article} {} in the scene.",
- "a photo of the {} in the scene.",
- "a photo of one {} in the scene.",
- "itap of {article} {}.",
- "itap of my {}.", # itap: I took a picture of
- "itap of the {}.",
- "a photo of {article} {}.",
- "a photo of my {}.",
- "a photo of the {}.",
- "a photo of one {}.",
- "a photo of many {}.",
- "a good photo of {article} {}.",
- "a good photo of the {}.",
- "a bad photo of {article} {}.",
- "a bad photo of the {}.",
- "a photo of a nice {}.",
- "a photo of the nice {}.",
- "a photo of a cool {}.",
- "a photo of the cool {}.",
- "a photo of a weird {}.",
- "a photo of the weird {}.",
- "a photo of a small {}.",
- "a photo of the small {}.",
- "a photo of a large {}.",
- "a photo of the large {}.",
- "a photo of a clean {}.",
- "a photo of the clean {}.",
- "a photo of a dirty {}.",
- "a photo of the dirty {}.",
- "a bright photo of {article} {}.",
- "a bright photo of the {}.",
- "a dark photo of {article} {}.",
- "a dark photo of the {}.",
- "a photo of a hard to see {}.",
- "a photo of the hard to see {}.",
- "a low resolution photo of {article} {}.",
- "a low resolution photo of the {}.",
- "a cropped photo of {article} {}.",
- "a cropped photo of the {}.",
- "a close-up photo of {article} {}.",
- "a close-up photo of the {}.",
- "a jpeg corrupted photo of {article} {}.",
- "a jpeg corrupted photo of the {}.",
- "a blurry photo of {article} {}.",
- "a blurry photo of the {}.",
- "a pixelated photo of {article} {}.",
- "a pixelated photo of the {}.",
- "a black and white photo of the {}.",
- "a black and white photo of {article} {}.",
- "a plastic {}.",
- "the plastic {}.",
- "a toy {}.",
- "the toy {}.",
- "a plushie {}.",
- "the plushie {}.",
- "a cartoon {}.",
- "the cartoon {}.",
- "an embroidered {}.",
- "the embroidered {}.",
- "a painting of the {}.",
- "a painting of a {}.",
-]
-
-
-openimages_rare_unseen = ['Aerial photography',
-'Aircraft engine',
-'Ale',
-'Aloe',
-'Amphibian',
-'Angling',
-'Anole',
-'Antique car',
-'Arcade game',
-'Arthropod',
-'Assault rifle',
-'Athletic shoe',
-'Auto racing',
-'Backlighting',
-'Bagpipes',
-'Ball game',
-'Barbecue chicken',
-'Barechested',
-'Barquentine',
-'Beef tenderloin',
-'Billiard room',
-'Billiards',
-'Bird of prey',
-'Black swan',
-'Black-and-white',
-'Blond',
-'Boating',
-'Bonbon',
-'Bottled water',
-'Bouldering',
-'Bovine',
-'Bratwurst',
-'Breadboard',
-'Briefs',
-'Brisket',
-'Brochette',
-'Calabaza',
-'Camera operator',
-'Canola',
-'Childbirth',
-'Chordophone',
-'Church bell',
-'Classical sculpture',
-'Close-up',
-'Cobblestone',
-'Coca-cola',
-'Combat sport',
-'Comics',
-'Compact car',
-'Computer speaker',
-'Cookies and crackers',
-'Coral reef fish',
-'Corn on the cob',
-'Cosmetics',
-'Crocodilia',
-'Digital camera',
-'Dishware',
-'Divemaster',
-'Dobermann',
-'Dog walking',
-'Domestic rabbit',
-'Domestic short-haired cat',
-'Double-decker bus',
-'Drums',
-'Electric guitar',
-'Electric piano',
-'Electronic instrument',
-'Equestrianism',
-'Equitation',
-'Erinaceidae',
-'Extreme sport',
-'Falafel',
-'Figure skating',
-'Filling station',
-'Fire apparatus',
-'Firearm',
-'Flatbread',
-'Floristry',
-'Forklift truck',
-'Freight transport',
-'Fried food',
-'Fried noodles',
-'Frigate',
-'Frozen yogurt',
-'Frying',
-'Full moon',
-'Galleon',
-'Glacial landform',
-'Gliding',
-'Go-kart',
-'Goats',
-'Grappling',
-'Great white shark',
-'Gumbo',
-'Gun turret',
-'Hair coloring',
-'Halter',
-'Headphones',
-'Heavy cruiser',
-'Herding',
-'High-speed rail',
-'Holding hands',
-'Horse and buggy',
-'Horse racing',
-'Hound',
-'Hunting knife',
-'Hurdling',
-'Inflatable',
-'Jackfruit',
-'Jeans',
-'Jiaozi',
-'Junk food',
-'Khinkali',
-'Kitesurfing',
-'Lawn game',
-'Leaf vegetable',
-'Lechon',
-'Lifebuoy',
-'Locust',
-'Lumpia',
-'Luxury vehicle',
-'Machine tool',
-'Medical imaging',
-'Melee weapon',
-'Microcontroller',
-'Middle ages',
-'Military person',
-'Military vehicle',
-'Milky way',
-'Miniature Poodle',
-'Modern dance',
-'Molluscs',
-'Monoplane',
-'Motorcycling',
-'Musical theatre',
-'Narcissus',
-'Nest box',
-'Newsagent\'s shop',
-'Nile crocodile',
-'Nordic skiing',
-'Nuclear power plant',
-'Orator',
-'Outdoor shoe',
-'Parachuting',
-'Pasta salad',
-'Peafowl',
-'Pelmeni',
-'Perching bird',
-'Performance car',
-'Personal water craft',
-'Pit bull',
-'Plant stem',
-'Pork chop',
-'Portrait photography',
-'Primate',
-'Procyonidae',
-'Prosciutto',
-'Public speaking',
-'Racewalking',
-'Ramen',
-'Rear-view mirror',
-'Residential area',
-'Ribs',
-'Rice ball',
-'Road cycling',
-'Roller skating',
-'Roman temple',
-'Rowing',
-'Rural area',
-'Sailboat racing',
-'Scaled reptile',
-'Scuba diving',
-'Senior citizen',
-'Shallot',
-'Shinto shrine',
-'Shooting range',
-'Siberian husky',
-'Sledding',
-'Soba',
-'Solar energy',
-'Sport climbing',
-'Sport utility vehicle',
-'Steamed rice',
-'Stemware',
-'Sumo',
-'Surfing Equipment',
-'Team sport',
-'Touring car',
-'Toy block',
-'Trampolining',
-'Underwater diving',
-'Vegetarian food',
-'Wallaby',
-'Water polo',
-'Watercolor paint',
-'Whiskers',
-'Wind wave',
-'Woodwind instrument',
-'Yakitori',
-'Zeppelin']
-
-
-def build_openset_label_embedding():
- categories = openimages_rare_unseen
- model, _ = clip.load("ViT-B/16")
- templates = multiple_templates
-
- run_on_gpu = torch.cuda.is_available()
-
- with torch.no_grad():
- openset_label_embedding = []
- for category in categories:
- texts = [
- template.format(
- processed_name(category, rm_dot=True), article=article(category)
- )
- for template in templates
- ]
- texts = [
- "This is " + text if text.startswith("a") or text.startswith("the") else text
- for text in texts
- ]
- texts = clip.tokenize(texts) # tokenize
- if run_on_gpu:
- texts = texts.cuda()
- model = model.cuda()
- text_embeddings = model.encode_text(texts)
- text_embeddings /= text_embeddings.norm(dim=-1, keepdim=True)
- text_embedding = text_embeddings.mean(dim=0)
- text_embedding /= text_embedding.norm()
- openset_label_embedding.append(text_embedding)
- openset_label_embedding = torch.stack(openset_label_embedding, dim=1)
- if run_on_gpu:
- openset_label_embedding = openset_label_embedding.cuda()
-
- openset_label_embedding = openset_label_embedding.t()
- return openset_label_embedding, categories
-
-
-
-
diff --git a/spaces/maknee/minigpt4.cpp/minigpt4_library.py b/spaces/maknee/minigpt4.cpp/minigpt4_library.py
deleted file mode 100644
index 2709b13f3aa15c771726bdcc386711b4cdacb4b7..0000000000000000000000000000000000000000
--- a/spaces/maknee/minigpt4.cpp/minigpt4_library.py
+++ /dev/null
@@ -1,743 +0,0 @@
-import os
-import sys
-import ctypes
-import pathlib
-from typing import Optional, List
-import enum
-from pathlib import Path
-
-class DataType(enum.IntEnum):
- def __str__(self):
- return str(self.name)
-
- F16 = 0
- F32 = 1
- I32 = 2
- L64 = 3
- Q4_0 = 4
- Q4_1 = 5
- Q5_0 = 6
- Q5_1 = 7
- Q8_0 = 8
- Q8_1 = 9
- Q2_K = 10
- Q3_K = 11
- Q4_K = 12
- Q5_K = 13
- Q6_K = 14
- Q8_K = 15
-
-class Verbosity(enum.IntEnum):
- SILENT = 0
- ERR = 1
- INFO = 2
- DEBUG = 3
-
-class ImageFormat(enum.IntEnum):
- UNKNOWN = 0
- F32 = 1
- U8 = 2
-
-I32 = ctypes.c_int32
-U32 = ctypes.c_uint32
-F32 = ctypes.c_float
-SIZE_T = ctypes.c_size_t
-VOID_PTR = ctypes.c_void_p
-CHAR_PTR = ctypes.POINTER(ctypes.c_char)
-FLOAT_PTR = ctypes.POINTER(ctypes.c_float)
-INT_PTR = ctypes.POINTER(ctypes.c_int32)
-CHAR_PTR_PTR = ctypes.POINTER(ctypes.POINTER(ctypes.c_char))
-
-MiniGPT4ContextP = VOID_PTR
-class MiniGPT4Context:
- def __init__(self, ptr: ctypes.pointer):
- self.ptr = ptr
-
-class MiniGPT4Image(ctypes.Structure):
- _fields_ = [
- ('data', VOID_PTR),
- ('width', I32),
- ('height', I32),
- ('channels', I32),
- ('format', I32)
- ]
-
-class MiniGPT4Embedding(ctypes.Structure):
- _fields_ = [
- ('data', FLOAT_PTR),
- ('n_embeddings', SIZE_T),
- ]
-
-MiniGPT4ImageP = ctypes.POINTER(MiniGPT4Image)
-MiniGPT4EmbeddingP = ctypes.POINTER(MiniGPT4Embedding)
-
-class MiniGPT4SharedLibrary:
- """
- Python wrapper around minigpt4.cpp shared library.
- """
-
- def __init__(self, shared_library_path: str):
- """
- Loads the shared library from specified file.
- In case of any error, this method will throw an exception.
-
- Parameters
- ----------
- shared_library_path : str
- Path to minigpt4.cpp shared library. On Windows, it would look like 'minigpt4.dll'. On UNIX, 'minigpt4.so'.
- """
-
- self.library = ctypes.cdll.LoadLibrary(shared_library_path)
-
- self.library.minigpt4_model_load.argtypes = [
- CHAR_PTR, # const char *path
- CHAR_PTR, # const char *llm_model
- I32, # int verbosity
- I32, # int seed
- I32, # int n_ctx
- I32, # int n_batch
- I32, # int numa
- ]
- self.library.minigpt4_model_load.restype = MiniGPT4ContextP
-
- self.library.minigpt4_image_load_from_file.argtypes = [
- MiniGPT4ContextP, # struct MiniGPT4Context *ctx
- CHAR_PTR, # const char *path
- MiniGPT4ImageP, # struct MiniGPT4Image *image
- I32, # int flags
- ]
- self.library.minigpt4_image_load_from_file.restype = I32
-
- self.library.minigpt4_encode_image.argtypes = [
- MiniGPT4ContextP, # struct MiniGPT4Context *ctx
- MiniGPT4ImageP, # const struct MiniGPT4Image *image
- MiniGPT4EmbeddingP, # struct MiniGPT4Embedding *embedding
- I32, # size_t n_threads
- ]
- self.library.minigpt4_encode_image.restype = I32
-
- self.library.minigpt4_begin_chat_image.argtypes = [
- MiniGPT4ContextP, # struct MiniGPT4Context *ctx
- MiniGPT4EmbeddingP, # struct MiniGPT4Embedding *embedding
- CHAR_PTR, # const char *s
- I32, # size_t n_threads
- ]
- self.library.minigpt4_begin_chat_image.restype = I32
-
- self.library.minigpt4_end_chat_image.argtypes = [
- MiniGPT4ContextP, # struct MiniGPT4Context *ctx
- CHAR_PTR_PTR, # const char **token
- I32, # size_t n_threads
- F32, # float temp
- I32, # int32_t top_k
- F32, # float top_p
- F32, # float tfs_z
- F32, # float typical_p
- I32, # int32_t repeat_last_n
- F32, # float repeat_penalty
- F32, # float alpha_presence
- F32, # float alpha_frequency
- I32, # int mirostat
- F32, # float mirostat_tau
- F32, # float mirostat_eta
- I32, # int penalize_nl
- ]
- self.library.minigpt4_end_chat_image.restype = I32
-
- self.library.minigpt4_system_prompt.argtypes = [
- MiniGPT4ContextP, # struct MiniGPT4Context *ctx
- I32, # size_t n_threads
- ]
- self.library.minigpt4_system_prompt.restype = I32
-
- self.library.minigpt4_begin_chat.argtypes = [
- MiniGPT4ContextP, # struct MiniGPT4Context *ctx
- CHAR_PTR, # const char *s
- I32, # size_t n_threads
- ]
- self.library.minigpt4_begin_chat.restype = I32
-
- self.library.minigpt4_end_chat.argtypes = [
- MiniGPT4ContextP, # struct MiniGPT4Context *ctx
- CHAR_PTR_PTR, # const char **token
- I32, # size_t n_threads
- F32, # float temp
- I32, # int32_t top_k
- F32, # float top_p
- F32, # float tfs_z
- F32, # float typical_p
- I32, # int32_t repeat_last_n
- F32, # float repeat_penalty
- F32, # float alpha_presence
- F32, # float alpha_frequency
- I32, # int mirostat
- F32, # float mirostat_tau
- F32, # float mirostat_eta
- I32, # int penalize_nl
- ]
- self.library.minigpt4_end_chat.restype = I32
-
- self.library.minigpt4_reset_chat.argtypes = [
- MiniGPT4ContextP, # struct MiniGPT4Context *ctx
- ]
- self.library.minigpt4_reset_chat.restype = I32
-
- self.library.minigpt4_contains_eos_token.argtypes = [
- CHAR_PTR, # const char *s
- ]
- self.library.minigpt4_contains_eos_token.restype = I32
-
- self.library.minigpt4_is_eos.argtypes = [
- CHAR_PTR, # const char *s
- ]
- self.library.minigpt4_is_eos.restype = I32
-
- self.library.minigpt4_free.argtypes = [
- MiniGPT4ContextP, # struct MiniGPT4Context *ctx
- ]
- self.library.minigpt4_free.restype = I32
-
- self.library.minigpt4_free_image.argtypes = [
- MiniGPT4ImageP, # struct MiniGPT4Image *image
- ]
- self.library.minigpt4_free_image.restype = I32
-
- self.library.minigpt4_free_embedding.argtypes = [
- MiniGPT4EmbeddingP, # struct MiniGPT4Embedding *embedding
- ]
- self.library.minigpt4_free_embedding.restype = I32
-
- self.library.minigpt4_error_code_to_string.argtypes = [
- I32, # int error_code
- ]
- self.library.minigpt4_error_code_to_string.restype = CHAR_PTR
-
- self.library.minigpt4_quantize_model.argtypes = [
- CHAR_PTR, # const char *in_path
- CHAR_PTR, # const char *out_path
- I32, # int data_type
- ]
- self.library.minigpt4_quantize_model.restype = I32
-
- self.library.minigpt4_set_verbosity.argtypes = [
- I32, # int verbosity
- ]
- self.library.minigpt4_set_verbosity.restype = None
-
- def panic_if_error(self, error_code: int) -> None:
- """
- Raises an exception if the error code is not 0.
-
- Parameters
- ----------
- error_code : int
- Error code to check.
- """
-
- if error_code != 0:
- raise RuntimeError(self.library.minigpt4_error_code_to_string(I32(error_code)))
-
- def minigpt4_model_load(self, model_path: str, llm_model_path: str, verbosity: int = 1, seed: int = 1337, n_ctx: int = 2048, n_batch: int = 512, numa: int = 0) -> MiniGPT4Context:
- """
- Loads a model from a file.
-
- Args:
- model_path (str): Path to model file.
- llm_model_path (str): Path to LLM model file.
- verbosity (int): Verbosity level: 0 = silent, 1 = error, 2 = info, 3 = debug. Defaults to 0.
- n_ctx (int): Size of context for llm model. Defaults to 2048.
- seed (int): Seed for llm model. Defaults to 1337.
- numa (int): NUMA node to use (0 = NUMA disabled, 1 = NUMA enabled). Defaults to 0.
-
- Returns:
- MiniGPT4Context: Context.
- """
-
- ptr = self.library.minigpt4_model_load(
- model_path.encode('utf-8'),
- llm_model_path.encode('utf-8'),
- I32(verbosity),
- I32(seed),
- I32(n_ctx),
- I32(n_batch),
- I32(numa),
- )
-
- assert ptr is not None, 'minigpt4_model_load failed'
-
- return MiniGPT4Context(ptr)
-
- def minigpt4_image_load_from_file(self, ctx: MiniGPT4Context, path: str, flags: int) -> MiniGPT4Image:
- """
- Loads an image from a file
-
- Args:
- ctx (MiniGPT4Context): context
- path (str): path
- flags (int): flags
-
- Returns:
- MiniGPT4Image: image
- """
-
- image = MiniGPT4Image()
- self.panic_if_error(self.library.minigpt4_image_load_from_file(ctx.ptr, path.encode('utf-8'), ctypes.pointer(image), I32(flags)))
- return image
-
- def minigpt4_preprocess_image(self, ctx: MiniGPT4Context, image: MiniGPT4Image, flags: int = 0) -> MiniGPT4Image:
- """
- Preprocesses an image
-
- Args:
- ctx (MiniGPT4Context): Context
- image (MiniGPT4Image): Image
- flags (int): Flags. Defaults to 0.
-
- Returns:
- MiniGPT4Image: Preprocessed image
- """
-
- preprocessed_image = MiniGPT4Image()
- self.panic_if_error(self.library.minigpt4_preprocess_image(ctx.ptr, ctypes.pointer(image), ctypes.pointer(preprocessed_image), I32(flags)))
- return preprocessed_image
-
- def minigpt4_encode_image(self, ctx: MiniGPT4Context, image: MiniGPT4Image, n_threads: int = 0) -> MiniGPT4Embedding:
- """
- Encodes an image into embedding
-
- Args:
- ctx (MiniGPT4Context): Context.
- image (MiniGPT4Image): Image.
- n_threads (int): Number of threads to use, if 0, uses all available. Defaults to 0.
-
- Returns:
- embedding (MiniGPT4Embedding): Output embedding.
- """
-
- embedding = MiniGPT4Embedding()
- self.panic_if_error(self.library.minigpt4_encode_image(ctx.ptr, ctypes.pointer(image), ctypes.pointer(embedding), n_threads))
- return embedding
-
- def minigpt4_begin_chat_image(self, ctx: MiniGPT4Context, image_embedding: MiniGPT4Embedding, s: str, n_threads: int = 0):
- """
- Begins a chat with an image.
-
- Args:
- ctx (MiniGPT4Context): Context.
- image_embedding (MiniGPT4Embedding): Image embedding.
- s (str): Question to ask about the image.
- n_threads (int, optional): Number of threads to use, if 0, uses all available. Defaults to 0.
-
- Returns:
- None
- """
-
- self.panic_if_error(self.library.minigpt4_begin_chat_image(ctx.ptr, ctypes.pointer(image_embedding), s.encode('utf-8'), n_threads))
-
- def minigpt4_end_chat_image(self, ctx: MiniGPT4Context, n_threads: int = 0, temp: float = 0.8, top_k: int = 40, top_p: float = 0.9, tfs_z: float = 1.0, typical_p: float = 1.0, repeat_last_n: int = 64, repeat_penalty: float = 1.1, alpha_presence: float = 1.0, alpha_frequency: float = 1.0, mirostat: int = 0, mirostat_tau: float = 5.0, mirostat_eta: float = 1.0, penalize_nl: int = 1) -> str:
- """
- Ends a chat with an image.
-
- Args:
- ctx (MiniGPT4Context): Context.
- n_threads (int, optional): Number of threads to use, if 0, uses all available. Defaults to 0.
- temp (float, optional): Temperature. Defaults to 0.8.
- top_k (int, optional): Top K. Defaults to 40.
- top_p (float, optional): Top P. Defaults to 0.9.
- tfs_z (float, optional): Tfs Z. Defaults to 1.0.
- typical_p (float, optional): Typical P. Defaults to 1.0.
- repeat_last_n (int, optional): Repeat last N. Defaults to 64.
- repeat_penalty (float, optional): Repeat penality. Defaults to 1.1.
- alpha_presence (float, optional): Alpha presence. Defaults to 1.0.
- alpha_frequency (float, optional): Alpha frequency. Defaults to 1.0.
- mirostat (int, optional): Mirostat. Defaults to 0.
- mirostat_tau (float, optional): Mirostat Tau. Defaults to 5.0.
- mirostat_eta (float, optional): Mirostat Eta. Defaults to 1.0.
- penalize_nl (int, optional): Penalize NL. Defaults to 1.
-
- Returns:
- str: Token generated.
- """
-
- token = CHAR_PTR()
- self.panic_if_error(self.library.minigpt4_end_chat_image(ctx.ptr, ctypes.pointer(token), n_threads, temp, top_k, top_p, tfs_z, typical_p, repeat_last_n, repeat_penalty, alpha_presence, alpha_frequency, mirostat, mirostat_tau, mirostat_eta, penalize_nl))
- return ctypes.cast(token, ctypes.c_char_p).value.decode('utf-8')
-
- def minigpt4_system_prompt(self, ctx: MiniGPT4Context, n_threads: int = 0):
- """
- Generates a system prompt.
-
- Args:
- ctx (MiniGPT4Context): Context.
- n_threads (int, optional): Number of threads to use, if 0, uses all available. Defaults to 0.
- """
-
- self.panic_if_error(self.library.minigpt4_system_prompt(ctx.ptr, n_threads))
-
- def minigpt4_begin_chat(self, ctx: MiniGPT4Context, s: str, n_threads: int = 0):
- """
- Begins a chat continuing after minigpt4_begin_chat_image
-
- Args:
- ctx (MiniGPT4Context): Context.
- s (str): Question to ask about the image.
- n_threads (int, optional): Number of threads to use, if 0, uses all available. Defaults to 0.
-
- Returns:
- None
- """
- self.panic_if_error(self.library.minigpt4_begin_chat(ctx.ptr, s.encode('utf-8'), n_threads))
-
- def minigpt4_end_chat(self, ctx: MiniGPT4Context, n_threads: int = 0, temp: float = 0.8, top_k: int = 40, top_p: float = 0.9, tfs_z: float = 1.0, typical_p: float = 1.0, repeat_last_n: int = 64, repeat_penalty: float = 1.1, alpha_presence: float = 1.0, alpha_frequency: float = 1.0, mirostat: int = 0, mirostat_tau: float = 5.0, mirostat_eta: float = 1.0, penalize_nl: int = 1) -> str:
- """
- Ends a chat.
-
- Args:
- ctx (MiniGPT4Context): Context.
- n_threads (int, optional): Number of threads to use, if 0, uses all available. Defaults to 0.
- temp (float, optional): Temperature. Defaults to 0.8.
- top_k (int, optional): Top K. Defaults to 40.
- top_p (float, optional): Top P. Defaults to 0.9.
- tfs_z (float, optional): Tfs Z. Defaults to 1.0.
- typical_p (float, optional): Typical P. Defaults to 1.0.
- repeat_last_n (int, optional): Repeat last N. Defaults to 64.
- repeat_penalty (float, optional): Repeat penality. Defaults to 1.1.
- alpha_presence (float, optional): Alpha presence. Defaults to 1.0.
- alpha_frequency (float, optional): Alpha frequency. Defaults to 1.0.
- mirostat (int, optional): Mirostat. Defaults to 0.
- mirostat_tau (float, optional): Mirostat Tau. Defaults to 5.0.
- mirostat_eta (float, optional): Mirostat Eta. Defaults to 1.0.
- penalize_nl (int, optional): Penalize NL. Defaults to 1.
-
- Returns:
- str: Token generated.
- """
-
- token = CHAR_PTR()
- self.panic_if_error(self.library.minigpt4_end_chat(ctx.ptr, ctypes.pointer(token), n_threads, temp, top_k, top_p, tfs_z, typical_p, repeat_last_n, repeat_penalty, alpha_presence, alpha_frequency, mirostat, mirostat_tau, mirostat_eta, penalize_nl))
- return ctypes.cast(token, ctypes.c_char_p).value.decode('utf-8')
-
- def minigpt4_reset_chat(self, ctx: MiniGPT4Context):
- """
- Resets the chat.
-
- Args:
- ctx (MiniGPT4Context): Context.
- """
- self.panic_if_error(self.library.minigpt4_reset_chat(ctx.ptr))
-
- def minigpt4_contains_eos_token(self, s: str) -> bool:
-
- """
- Checks if a string contains an EOS token.
-
- Args:
- s (str): String to check.
-
- Returns:
- bool: True if the string contains an EOS token, False otherwise.
- """
-
- return self.library.minigpt4_contains_eos_token(s.encode('utf-8'))
-
- def minigpt4_is_eos(self, s: str) -> bool:
-
- """
- Checks if a string is EOS.
-
- Args:
- s (str): String to check.
-
- Returns:
- bool: True if the string contains an EOS, False otherwise.
- """
-
- return self.library.minigpt4_is_eos(s.encode('utf-8'))
-
-
- def minigpt4_free(self, ctx: MiniGPT4Context) -> None:
- """
- Frees a context.
-
- Args:
- ctx (MiniGPT4Context): Context.
- """
-
- self.panic_if_error(self.library.minigpt4_free(ctx.ptr))
-
- def minigpt4_free_image(self, image: MiniGPT4Image) -> None:
- """
- Frees an image.
-
- Args:
- image (MiniGPT4Image): Image.
- """
-
- self.panic_if_error(self.library.minigpt4_free_image(ctypes.pointer(image)))
-
- def minigpt4_free_embedding(self, embedding: MiniGPT4Embedding) -> None:
- """
- Frees an embedding.
-
- Args:
- embedding (MiniGPT4Embedding): Embedding.
- """
-
- self.panic_if_error(self.library.minigpt4_free_embedding(ctypes.pointer(embedding)))
-
- def minigpt4_error_code_to_string(self, error_code: int) -> str:
- """
- Converts an error code to a string.
-
- Args:
- error_code (int): Error code.
-
- Returns:
- str: Error string.
- """
-
- return self.library.minigpt4_error_code_to_string(error_code).decode('utf-8')
-
- def minigpt4_quantize_model(self, in_path: str, out_path: str, data_type: DataType):
- """
- Quantizes a model file.
-
- Args:
- in_path (str): Path to input model file.
- out_path (str): Path to write output model file.
- data_type (DataType): Must be one DataType enum values.
- """
-
- self.panic_if_error(self.library.minigpt4_quantize_model(in_path.encode('utf-8'), out_path.encode('utf-8'), data_type))
-
- def minigpt4_set_verbosity(self, verbosity: Verbosity):
- """
- Sets verbosity.
-
- Args:
- verbosity (int): Verbosity.
- """
-
- self.library.minigpt4_set_verbosity(I32(verbosity))
-
-def load_library() -> MiniGPT4SharedLibrary:
- """
- Attempts to find minigpt4.cpp shared library and load it.
- """
-
- file_name: str
-
- if 'win32' in sys.platform or 'cygwin' in sys.platform:
- file_name = 'minigpt4.dll'
- elif 'darwin' in sys.platform:
- file_name = 'libminigpt4.dylib'
- else:
- file_name = 'libminigpt4.so'
-
- cwd = pathlib.Path(os.getcwd())
- repo_root_dir: pathlib.Path = pathlib.Path(os.path.abspath(__file__)).parent.parent
-
- paths = [
- # If we are in "minigpt4" directory
- f'../bin/Release/{file_name}',
- # If we are in repo root directory
- f'bin/Release/{file_name}',
- # If we compiled in build directory
- f'build/bin/Release/{file_name}',
- # If we compiled in build directory
- f'build/{file_name}',
- f'../build/{file_name}',
- # Search relative to this file
- str(repo_root_dir / 'bin' / 'Release' / file_name),
- # Fallback
- str(repo_root_dir / file_name),
- str(cwd / file_name)
- ]
-
- for path in paths:
- if os.path.isfile(path):
- return MiniGPT4SharedLibrary(path)
-
- return MiniGPT4SharedLibrary(paths[-1])
-
-class MiniGPT4ChatBot:
- def __init__(self, model_path: str, llm_model_path: str, verbosity: Verbosity = Verbosity.SILENT, n_threads: int = 0):
- """
- Creates a new MiniGPT4ChatBot instance.
-
- Args:
- model_path (str): Path to model file.
- llm_model_path (str): Path to language model model file.
- verbosity (Verbosity, optional): Verbosity. Defaults to Verbosity.SILENT.
- n_threads (int, optional): Number of threads to use. Defaults to 0.
- """
-
- self.library = load_library()
- self.ctx = self.library.minigpt4_model_load(model_path, llm_model_path, verbosity)
- self.n_threads = n_threads
-
- from PIL import Image
- from torchvision import transforms
- from torchvision.transforms.functional import InterpolationMode
- self.image_size = 224
-
- mean = (0.48145466, 0.4578275, 0.40821073)
- std = (0.26862954, 0.26130258, 0.27577711)
- self.transform = transforms.Compose(
- [
- transforms.RandomResizedCrop(
- self.image_size,
- interpolation=InterpolationMode.BICUBIC,
- ),
- transforms.ToTensor(),
- transforms.Normalize(mean, std)
- ]
- )
- self.embedding: Optional[MiniGPT4Embedding] = None
- self.is_image_chat = False
- self.chat_history = []
-
- def free(self):
- if self.ctx:
- self.library.minigpt4_free(self.ctx)
-
- def generate(self, message: str, limit: int = 1024, temp: float = 0.8, top_k: int = 40, top_p: float = 0.9, tfs_z: float = 1.0, typical_p: float = 1.0, repeat_last_n: int = 64, repeat_penalty: float = 1.1, alpha_presence: float = 1.0, alpha_frequency: float = 1.0, mirostat: int = 0, mirostat_tau: float = 5.0, mirostat_eta: float = 1.0, penalize_nl: int = 1):
- """
- Generates a chat response.
-
- Args:
- message (str): Message.
- limit (int, optional): Limit. Defaults to 1024.
- temp (float, optional): Temperature. Defaults to 0.8.
- top_k (int, optional): Top K. Defaults to 40.
- top_p (float, optional): Top P. Defaults to 0.9.
- tfs_z (float, optional): TFS Z. Defaults to 1.0.
- typical_p (float, optional): Typical P. Defaults to 1.0.
- repeat_last_n (int, optional): Repeat last N. Defaults to 64.
- repeat_penalty (float, optional): Repeat penalty. Defaults to 1.1.
- alpha_presence (float, optional): Alpha presence. Defaults to 1.0.
- alpha_frequency (float, optional): Alpha frequency. Defaults to 1.0.
- mirostat (int, optional): Mirostat. Defaults to 0.
- mirostat_tau (float, optional): Mirostat tau. Defaults to 5.0.
- mirostat_eta (float, optional): Mirostat eta. Defaults to 1.0.
- penalize_nl (int, optional): Penalize NL. Defaults to 1.
- """
- if self.is_image_chat:
- self.is_image_chat = False
- self.library.minigpt4_begin_chat_image(self.ctx, self.embedding, message, self.n_threads)
- chat = ''
- for _ in range(limit):
- token = self.library.minigpt4_end_chat_image(self.ctx, self.n_threads, temp, top_k, top_p, tfs_z, typical_p, repeat_last_n, repeat_penalty, alpha_presence, alpha_frequency, mirostat, mirostat_tau, mirostat_eta, penalize_nl)
- chat += token
- if self.library.minigpt4_contains_eos_token(token):
- continue
- if self.library.minigpt4_is_eos(chat):
- break
- yield token
- else:
- self.library.minigpt4_begin_chat(self.ctx, message, self.n_threads)
- chat = ''
- for _ in range(limit):
- token = self.library.minigpt4_end_chat(self.ctx, self.n_threads, temp, top_k, top_p, tfs_z, typical_p, repeat_last_n, repeat_penalty, alpha_presence, alpha_frequency, mirostat, mirostat_tau, mirostat_eta, penalize_nl)
- chat += token
- if self.library.minigpt4_contains_eos_token(token):
- continue
- if self.library.minigpt4_is_eos(chat):
- break
- yield token
-
- def reset_chat(self):
- """
- Resets the chat.
- """
-
- self.is_image_chat = False
- if self.embedding:
- self.library.minigpt4_free_embedding(self.embedding)
- self.embedding = None
-
- self.library.minigpt4_reset_chat(self.ctx)
- self.library.minigpt4_system_prompt(self.ctx, self.n_threads)
-
- def upload_image(self, image):
- """
- Uploads an image.
-
- Args:
- image (Image): Image.
- """
-
- self.reset_chat()
-
- image = self.transform(image)
- image = image.unsqueeze(0)
- image = image.numpy()
- image = image.ctypes.data_as(ctypes.c_void_p)
- minigpt4_image = MiniGPT4Image(image, self.image_size, self.image_size, 3, ImageFormat.F32)
- self.embedding = self.library.minigpt4_encode_image(self.ctx, minigpt4_image, self.n_threads)
-
- self.is_image_chat = True
-
-
-if __name__ == "__main__":
- import argparse
- parser = argparse.ArgumentParser(description='Test loading minigpt4')
- parser.add_argument('model_path', help='Path to model file')
- parser.add_argument('llm_model_path', help='Path to llm model file')
- parser.add_argument('-i', '--image_path', help='Image to test', default='images/llama.png')
- parser.add_argument('-p', '--prompts', help='Text to test', default='what is the text in the picture?,what is the color of it?')
- args = parser.parse_args()
-
- model_path = args.model_path
- llm_model_path = args.llm_model_path
- image_path = args.image_path
- prompts = args.prompts
-
- if not Path(model_path).exists():
- print(f'Model does not exist: {model_path}')
- exit(1)
-
- if not Path(llm_model_path).exists():
- print(f'LLM Model does not exist: {llm_model_path}')
- exit(1)
-
- prompts = prompts.split(',')
-
- print('Loading minigpt4 shared library...')
- library = load_library()
- print(f'Loaded library {library}')
- ctx = library.minigpt4_model_load(model_path, llm_model_path, Verbosity.DEBUG)
- image = library.minigpt4_image_load_from_file(ctx, image_path, 0)
- preprocessed_image = library.minigpt4_preprocess_image(ctx, image, 0)
-
- question = prompts[0]
- n_threads = 0
- embedding = library.minigpt4_encode_image(ctx, preprocessed_image, n_threads)
- library.minigpt4_system_prompt(ctx, n_threads)
- library.minigpt4_begin_chat_image(ctx, embedding, question, n_threads)
- chat = ''
- while True:
- token = library.minigpt4_end_chat_image(ctx, n_threads)
- chat += token
- if library.minigpt4_contains_eos_token(token):
- continue
- if library.minigpt4_is_eos(chat):
- break
- print(token, end='')
-
- for i in range(1, len(prompts)):
- prompt = prompts[i]
- library.minigpt4_begin_chat(ctx, prompt, n_threads)
- chat = ''
- while True:
- token = library.minigpt4_end_chat(ctx, n_threads)
- chat += token
- if library.minigpt4_contains_eos_token(token):
- continue
- if library.minigpt4_is_eos(chat):
- break
- print(token, end='')
-
- library.minigpt4_free_image(image)
- library.minigpt4_free_image(preprocessed_image)
- library.minigpt4_free(ctx)
diff --git a/spaces/marker22/Bark-Voice-Cloning/bark/settings.py b/spaces/marker22/Bark-Voice-Cloning/bark/settings.py
deleted file mode 100644
index 81c660f3d2e33b21821583cb34c872c2ca23928b..0000000000000000000000000000000000000000
--- a/spaces/marker22/Bark-Voice-Cloning/bark/settings.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import os
-
-def initenv(args):
- os.environ['SUNO_USE_SMALL_MODELS'] = str("-smallmodels" in args)
- os.environ['BARK_FORCE_CPU'] = str("-forcecpu" in args)
- os.environ['SUNO_ENABLE_MPS'] = str("-enablemps" in args)
- os.environ['SUNO_OFFLOAD_CPU'] = str("-offloadcpu" in args)
diff --git a/spaces/mcbrs1/AskQ/README.md b/spaces/mcbrs1/AskQ/README.md
deleted file mode 100644
index e4e485fdbcf12a0b2a2b6e6c62a4ec1d2c443d20..0000000000000000000000000000000000000000
--- a/spaces/mcbrs1/AskQ/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AskQ
-emoji: 👁
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/merve/data-leak/source/measuring-diversity/image-layout.js b/spaces/merve/data-leak/source/measuring-diversity/image-layout.js
deleted file mode 100644
index 7a06cc4399043f317e81c28da4139599a84f58da..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/source/measuring-diversity/image-layout.js
+++ /dev/null
@@ -1,73 +0,0 @@
-
-
-var lURLs = `
-img/green_doctor.png
-img/blue_doctor.jpg
-img/green0.png
-img/bright_blue.png
-img/blue0.png
-img/blue1.png
-`.trim().split('\n')
-
-
-var rURLs = `
-img/white0.png
-img/white1.png
-img/white2.png
-img/white3.png
-img/white4.png
-img/white5.png
-`.trim().split('\n')
-
-
-var constructionSel = d3.select('#construction')
- .html('')
-
-// constructionSel.append('div.top').each(function(){
-// var metrics = [{str: 'Male', key: 'Male', target: .5}]
-// var active ={ percents: {Male: .5}}
-// addMetrics(metrics, {topSel: d3.select(this).append('div.top'), active, isSmall: true})()
-// })
-
-constructionSel.append('img')
- .at({src: 'img/construction.jpg', width: 900})
-
-constructionSel.append('div')
- .st({fontWeight: 500, fontSize: 14})
- .text('Stock “construction worker” images')
-
-
-
-
-var width = 400
-var coatDivs = d3.select('#coat-v-gender').html('').st({marginBottom: 40})
- .appendMany('div', [lURLs, rURLs])
- .st({width: width, display: 'inline-block', marginRight: 20})
-
-
-coatDivs.each(function(d, i){
- var metrics = [
- {str: 'Blue', key: 'Blue', target: .5},
- {str: 'Male', key: 'Male', target: .5},
- ]
-
- var active = !i ? {percents: {Blue: .5, Male: 1}} : {percents: {Blue: 0, Male: .5}}
-
- addMetrics(metrics, {topSel: d3.select(this).append('div.top'), active, isSmall: true})()
-})
-
-coatDivs
- .st({fontWeight: 500, fontSize: 14})
- .appendMany('div', d => d.slice(0, 6))
- .st({backgroundImage: d => 'url(' + d + ')', width: width/3 - 10, height: 100, display: 'inline-block'})
- .st({marginRight: 8, outline: '1px solid #000'})
-
-coatDivs
- .append('div')
- .text((d, i) => d == lURLs ? 'Male-presenting doctors wearing different colored clothes' : 'Doctor of different genders wearing white clothes')
-
-
-
-
-
-// https://t3.gstatic.com/images?q=tbn:ANd9GcRziJdedqu58HeAlI9xtWhrVtCjVo6xO_uSHdQkxAI0q41XozLWT3xKd36S1NbuSoIOVvV4Huw26zAvdM_374qKuN9J88E
\ No newline at end of file
diff --git a/spaces/merve/data-leak/source/third_party/d3_.js b/spaces/merve/data-leak/source/third_party/d3_.js
deleted file mode 100644
index 9c4b6815ec3cdc0e9f8a072b2d05be7ad48fa703..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/source/third_party/d3_.js
+++ /dev/null
@@ -1,143 +0,0 @@
-/**
- * @license
- * Lodash lodash.com/license | Underscore.js 1.8.3 underscorejs.org/LICENSE
- */
-;(function(){function n(n,t){return n.set(t[0],t[1]),n}function t(n,t){return n.add(t),n}function r(n,t,r){switch(r.length){case 0:return n.call(t);case 1:return n.call(t,r[0]);case 2:return n.call(t,r[0],r[1]);case 3:return n.call(t,r[0],r[1],r[2])}return n.apply(t,r)}function e(n,t,r,e){for(var u=-1,i=null==n?0:n.length;++u<i;){var o=n[u];t(e,o,r(o),n)}return e}function u(n,t){for(var r=-1,e=null==n?0:n.length;++r<e&&false!==t(n[r],r,n););return n}function i(n,t){for(var r=null==n?0:n.length;r--&&false!==t(n[r],r,n););
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diff --git a/spaces/merve/fill-in-the-blank/source/dataset-worldviews/shape-explainer.js b/spaces/merve/fill-in-the-blank/source/dataset-worldviews/shape-explainer.js
deleted file mode 100644
index ce184ec2d52346fe3dd5deca774e9f36551ed977..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/source/dataset-worldviews/shape-explainer.js
+++ /dev/null
@@ -1,500 +0,0 @@
-console.clear();
-
-var shapeScale = 0.6;
-
-var keyedData = {
- pointiness_true: {
- name: "pointiness_true",
- isRounding: true,
- categoryName: "pointiness",
- categories: ["pointy", "round"],
- textPlacements: {},
- },
- pointiness_false: {
- name: "pointiness_false",
- isRounding: false,
- categoryName: "pointiness",
- categories: ["pointy", "round", "other"],
- textPlacements: {},
- },
- shape_name_true: {
- name: "shape_name_true",
- isRounding: true,
- categoryName: "shape_name",
- categories: ["circle", "triangle", "rect"],
- textPlacements: {},
- },
- shape_name_false: {
- name: "shape_name_false",
- isRounding: false,
- categoryName: "shape_name",
- categories: ["circle", "triangle", "rect", "other"],
- textPlacements: {},
- },
- size_true: {
- name: "size_true",
- isRounding: true,
- categoryName: "size",
- categories: ["small", "large"],
- textPlacements: {},
- },
- size_false: {
- name: "size_false",
- isRounding: false,
- categoryName: "size",
- categories: ["small", "large", "other"],
- textPlacements: {},
- },
-};
-
-var data = [];
-for (var key in keyedData) {
- data.push(keyedData[key]);
-}
-
-var state = {
- selected: data[0],
- selectedTopIndex: 0,
- selectedBottomIndex: 0,
-};
-
-function updateState(
- category,
- rounding,
- topIndex = undefined,
- bottomIndex = undefined
-) {
- var key = category + "_" + rounding;
- state.selected = keyedData[key];
- state.selectedTopIndex = topIndex;
- state.selectedBottomIndex = bottomIndex;
-}
-
-// Placements for the center labels
-var textPlacements = {};
-
-var divHeight = 720;
-var divWidth = 850;
-
-var c = d3.conventions({
- sel: d3.select(".shape-explainer").html(""),
- width: divWidth,
- height: divHeight,
- layers: "ds",
-});
-
-var buttonHeight = 35;
-var buttonWidth = 200;
-var buttonBuffer = 15;
-var topRightShift = 200;
-var bottomRightShift = 270;
-
-function setActiveButton() {
- topExplainerButtonSel.classed(
- "explainer-active-button",
- (d, i) => i == state.selectedTopIndex
- );
- bottomExplainerButtonSel.classed(
- "explainer-active-button",
- (d, i) => i == state.selectedBottomIndex
- );
-}
-
-// Preamble text
-c.svg
- .append("text.top-explainer-text")
- .at({
- textAnchor: "left",
- dominantBaseline: "top",
- dy: ".33em",
- })
- .translate([0, buttonHeight / 2])
- .text("All shapes are basically...");
-
-c.svg
- .append("text.bottom-explainer-text")
- .at({
- textAnchor: "left",
- dominantBaseline: "top",
- dy: ".33em",
- })
- .translate([0, buttonHeight * 1.5 + buttonBuffer])
- .text("Everything else should be labeled...");
-
-// Buttons
-var topExplainerButtonSel = c.svg
- .appendMany("g.explainer-button", ["pointiness", "shape_name", "size"])
- .at({})
- .translate((d, i) => [topRightShift + i * (buttonWidth + buttonBuffer), 0])
- .on("click", function (d, i) {
- updateState(
- d,
- state.selected.isRounding,
- (topIndex = i),
- (bottomIndex = state.selectedBottomIndex)
- );
- setActiveButton();
- moveShapes();
- });
-
-topExplainerButtonSel.append("rect").at({
- height: buttonHeight,
- width: buttonWidth,
- class: "explainer-rect",
-});
-
-topExplainerButtonSel
- .append("text")
- .at({
- textAnchor: "middle",
- dy: ".33em",
- x: buttonWidth / 2,
- y: buttonHeight / 2,
- class: "dropdown",
- })
- .text((d, i) => toShortValueStringDict[d]);
-
-var bottomExplainerButtonSel = c.svg
- .appendMany("g.explainer-button", ["true", "false"])
- .at({})
- .translate((d, i) => [
- bottomRightShift + i * (buttonWidth + buttonBuffer),
- buttonHeight + buttonBuffer,
- ])
- .on("click", function (d, i) {
- updateState(
- state.selected.categoryName,
- d,
- (topIndex = state.selectedTopIndex),
- (bottomIndex = i)
- );
- setActiveButton();
- moveShapes();
- });
-
-bottomExplainerButtonSel.append("rect").at({
- height: buttonHeight,
- width: buttonWidth,
- class: "explainer-rect",
-});
-
-bottomExplainerButtonSel
- .append("text")
- .at({
- textAnchor: "middle",
- dy: ".33em",
- x: buttonWidth / 2,
- y: buttonHeight / 2,
- class: "dropdown",
- })
- .text((d, i) => toDropdownValueRoundingStringDict[d]);
-
-var horizontalHeight = divHeight * (5 / 8);
-var horizontalBuffer = 50;
-
-p = d3.line()([
- [horizontalBuffer, horizontalHeight],
- [divWidth - horizontalBuffer, horizontalHeight],
-]);
-
-var horizontal = c.svg
- .append("path")
- .at({
- d: p,
- stroke: "black",
- strokeWidth: 1,
- })
- .translate([0, 0])
- .style("stroke-dasharray", "5, 5");
-
-
-c.svg
- .append("text.label-correct")
- .at({
- x: -400,
- y: 90,
- })
- .text("correctly classified")
- .attr("transform", "rotate(-90)");
-
-c.svg
- .append("text.label-correct")
- .at({
- x: -630,
- y: 90,
- })
- .text("incorrectly classified")
- .attr("transform", "rotate(-90)");
-
-
-// Manually make some small adjustments to where particular shapes are placed
-function getFineAdjustment(shape) {
- if (
- shape.shape_name == "rt_rect" &&
- shape.correctness == "incorrect" &&
- shape.gt == "shaded"
- ) {
- return 4;
- }
- if (
- shape.shape_name == "rect" &&
- shape.correctness == "incorrect" &&
- shape.gt == "unshaded"
- ) {
- return -10;
- }
- if (
- shape.shape_name == "triangle" &&
- shape.correctness == "incorrect" &&
- shape.gt == "unshaded"
- ) {
- return 0;
- }
- if (
- shape.shape_name == "rt_circle" &&
- shape.correctness == "incorrect" &&
- shape.size == "small"
- ) {
- return -20;
- }
- if (
- shape.shape_name == "rt_triangle" &&
- shape.correctness == "incorrect" &&
- shape.size == "small"
- ) {
- return -20;
- }
- return 0;
-}
-
-function getFinalCategory(labelName, isRounding) {
- if (isRounding == true) {
- return labelName.replace("rt_", "");
- } else {
- if (labelName.includes("rt_")) {
- return "other";
- } else {
- return labelName;
- }
- }
-}
-
-var startingCorrectHeight = horizontalHeight - 50;
-var startingIncorrectHeight = horizontalHeight + 50;
-var maxHeight = 180;
-var xRowAdjustment = 50;
-var heightBuffer = 10;
-
-function getPathHeight(inputPath) {
- var placeholder = c.svg.append("path").at({
- d: scaleShapePath(inputPath, shapeScale),
- });
- var height = placeholder.node().getBBox().height;
- placeholder.remove();
- return height + heightBuffer;
-}
-
-// Figure out where to put the shapes for all possible placements
-function generatePlacements() {
- for (selectionCriteria of data) {
- // starting X positions
- var nCategories = selectionCriteria.categories.length;
- var centerX = [];
- for (var i = 0; i < nCategories; i++) {
- var startingX = divWidth * ((i + 1) / (nCategories + 1));
- centerX.push(startingX);
- // Track where each label should be placed using a dictionary in the data
- selectionCriteria["textPlacements"][
- selectionCriteria.categories[i]
- ] = startingX;
- }
-
- // For keeping of track of how we place items as we go
- var locationParams = {};
- for (categoryIdx in selectionCriteria.categories) {
- var categoryName = selectionCriteria.categories[categoryIdx];
- locationParams[categoryName] = {
- correctX: centerX[categoryIdx],
- incorrectX: centerX[categoryIdx],
- lastCorrectY: startingCorrectHeight,
- lastIncorrectY: startingIncorrectHeight,
- };
- }
-
- for (shape of shapeParams) {
- shapeCategory = getFinalCategory(
- shape[selectionCriteria.categoryName],
- selectionCriteria.isRounding
- );
- var shapeHeight = getPathHeight(shape.path);
- var shapeX,
- shapeY = 0;
- if (shape.correctness == "correct") {
- shapeY = locationParams[shapeCategory]["lastCorrectY"];
- shapeX = locationParams[shapeCategory]["correctX"];
- // Check if we've reached the maximum height
- if (
- startingCorrectHeight -
- locationParams[shapeCategory]["lastCorrectY"] >=
- maxHeight
- ) {
- // Reset height to baseline
- locationParams[shapeCategory]["lastCorrectY"] =
- startingCorrectHeight;
- // Move next row over
- locationParams[shapeCategory]["correctX"] =
- locationParams[shapeCategory]["correctX"] +
- xRowAdjustment;
- } else {
- locationParams[shapeCategory]["lastCorrectY"] +=
- -1 * shapeHeight;
- }
- } else {
- shapeY = locationParams[shapeCategory]["lastIncorrectY"];
- shapeX = locationParams[shapeCategory]["incorrectX"];
-
- if (
- locationParams[shapeCategory]["lastIncorrectY"] -
- startingIncorrectHeight >=
- maxHeight
- ) {
- // Reset height to baseline
- locationParams[shapeCategory]["lastIncorrectY"] =
- startingIncorrectHeight;
- // Move next row over
- locationParams[shapeCategory]["incorrectX"] =
- locationParams[shapeCategory]["incorrectX"] +
- xRowAdjustment;
- } else {
- locationParams[shapeCategory]["lastIncorrectY"] +=
- shapeHeight;
- }
- }
- shapeY = shapeY + getFineAdjustment(shape);
- shape[selectionCriteria.name + "_X"] = shapeX;
- shape[selectionCriteria.name + "_Y"] = shapeY;
- }
- }
-}
-
-generatePlacements();
-
-function getLocation(shape) {
- return [
- shape[state.selected.name + "_X"],
- shape[state.selected.name + "_Y"],
- ];
-}
-
-function scaleShapePath(shapePath, factor = 0.5) {
- var newShapePath = "";
- for (var token of shapePath.split(" ")) {
- if (parseInt(token)) {
- newShapePath = newShapePath + parseInt(token) * factor;
- } else {
- newShapePath = newShapePath + token;
- }
- newShapePath = newShapePath + " ";
- }
- return newShapePath;
-}
-
-// Add the shapes
-var explainerShapeSel = c.svg
- .appendMany("path.shape", shapeParams)
- .at({
- d: (d) => scaleShapePath(d.path, shapeScale),
- class: (d) => "gt-" + d.gt + " " + d.correctness,
- })
- .translate(function (d) {
- return getLocation(d);
- });
-
-explainerShapeSel.classed("is-classified", true);
-
-function getColor(d) {
- var scaleRowValue = d3.scaleLinear().domain([0.3, 1.0]).range([0, 1]);
- return d3.interpolateRdYlGn(scaleRowValue(d));
-}
-
-// Retrieve the results, for coloring the label boxes
-function getResults() {
- return calculateResults(
- (property = state.selected.categoryName),
- (useGuess = state.selected.isRounding)
- );
-}
-
-function getCategoryAccuracy(results, category) {
- for (var key of results) {
- if (key.rawCategoryName == category) {
- return key.accuracy;
- }
- }
-}
-
-// Rename "large" and "rect"
-function toExplainerDisplayString(categoryName) {
- if (categoryName == "large") {
- return "big";
- }
- if (categoryName == "rect") {
- return "rectangle";
- }
- return categoryName;
-}
-
-function getExplainerTextColor(d, i) {
- console.log(d == "large");
- if (d == "large" && state.selected.isRounding == false) {
- return "#ffccd8";
- } else {
- return "#000000";
- }
-}
-
-function updateText() {
- var explainerResults = getResults();
-
- d3.selectAll(".explainer-label-text").html("");
- d3.selectAll(".explainer-label-rect").remove();
-
- var rectHeight = 30;
- var rectWidth = 80;
- var textRect = c.svg
- .appendMany("rect.column-text-rect", state.selected.categories)
- .at({
- fill: (d) => getColor(getCategoryAccuracy(explainerResults, d)),
- height: rectHeight,
- width: rectWidth,
- class: "explainer-label-rect",
- })
- .translate((d) => [
- state.selected.textPlacements[d] - rectWidth / 2,
- horizontalHeight - rectHeight / 2,
- ]);
-
- var text = c.svg
- .appendMany("text.column-text", state.selected.categories)
- .at({
- textAnchor: "middle",
- dominantBaseline: "central",
- class: "explainer-label-text",
- })
- .st({
- fill: getExplainerTextColor,
- })
- .text((d) => toExplainerDisplayString(d))
- .translate((d) => [state.selected.textPlacements[d], horizontalHeight]);
-}
-
-function moveShapes() {
- explainerShapeSel
- .transition()
- .duration(500)
- .translate((d) => getLocation(d));
- updateText();
-}
-
-setActiveButton();
-updateText();
\ No newline at end of file
diff --git a/spaces/merve/hidden-bias/public/measuring-fairness/slider.js b/spaces/merve/hidden-bias/public/measuring-fairness/slider.js
deleted file mode 100644
index efcbc18387d0d0cb957e34f75bb20a83131dda8e..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/public/measuring-fairness/slider.js
+++ /dev/null
@@ -1,139 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-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.
-==============================================================================*/
-
-
-
-
-
-
-
-
-window.makeSlider = function(){
-
- var width = 300
- var height = 30
-
- var x = d3.scaleLinear()
- .domain([.99, .6])
- .range([0, width])
- .clamp(true)
-
- var rv = {}
- rv.threshold = .5
- rv.setSlider = makeSetSlider(students, 'threshold')
- rv.setSliderF = makeSetSlider(students.filter(d => !d.isMale), 'threshold_f')
- rv.setSliderM = makeSetSlider(students.filter(d => d.isMale), 'threshold_m')
-
- var allActiveSel = d3.selectAll('.threshold-rect')
- var allHandleSel = d3.selectAll('.threshold-handle')
-
- var gatedSel = d3.select('.gated')
-
- function makeSetSlider(data, key){
- var text = key.split('_')[1]
-
-
- var drag = d3.drag()
- .on('drag', function(d){
- updateThreshold(x.invert(d3.mouse(this)[0]))
- // console.log(d3.event.x)
-
- if (text && slider.threshold_f && (slider.threshold_f > 0.9042 || slider.threshold_f - slider.threshold_m > .05)){
- gatedSel.classed('opened', 1)
- svg.classed('no-blink', 1)
- }
-
- if (key == 'threshold') svg.classed('no-blink', 1)
- })
-
- var svg = d3.select('.slider.' + key).html('')
- .append('svg').at({width, height})
- .call(drag)
- .st({cursor: 'pointer'})
-
- if (key == 'threshold_m') svg.classed('no-blink', 1)
-
-
-
- svg.append('rect').at({width, height, fill: lcolors.well})
-
- var rectSel = svg.append('rect.threshold-rect')
- .at({width, height, fill: lcolors.sick})
-
- var handleSel = svg.append('g.threshold-handle')
- handleSel.append('text.cursor')
- .text('▲')
- .at({textAnchor: 'middle', fontSize: 10, y: height, dy: '.8em'})
- handleSel.append('circle')
- .at({cy: height, r: 30, fill: 'rgba(0,0,0,0)'})
-
- var labelText = 'Model Aggressiveness _→'
- var _replacement = !text ? '' : 'On ' + (text == 'f' ? 'Women ' : 'Men ')
-
- var labelText = '_Model Aggressiveness →'
- var _replacement = !text ? '' : (text == 'f' ? 'Adult ' : 'Adult ')
-
- var labelText = '_Model Decision Point'
- var _replacement = !text ? '' : (text == 'f' ? 'Adult ' : 'Adult ')
-
- var labelText = 'Model Decision Point_'
- var _replacement = !text ? '' : (text == 'f' ? ' for Adults ' : ' for Children ')
-
- var labelText = '_ Model Aggressiveness →'
- var _replacement = !text ? '' : (text == 'f' ? ' Adult ' : 'Child ')
-
-
- svg.append('text.axis').text(labelText.replace('_', _replacement))
- .at({y: height/2, dy: '.33em', dx: 10})
- .st({pointerEvents: 'none'})
-
-
-
- function updateThreshold(threshold, skipDom){
- rv[key] = threshold
- data.forEach(d => d.threshold = threshold)
-
- mini.updateAll()
-
- rectSel.at({width: x(threshold)})
- handleSel.translate(x(threshold), 0)
-
- if (skipDom) return
-
- if (key == 'threshold'){
- allActiveSel.at({width: x(threshold)})
- allHandleSel.translate(x(threshold), 0)
- }
-
- sel.rectSel.at({fill: d => d.grade > d.threshold ? lcolors.sick : lcolors.well})
- sel.textSel
- .st({
- strokeWidth: d => d.grade > d.threshold == d.isSick ? 0 : .6,
- })
-
- }
-
- return updateThreshold
- }
-
- return rv
-}
-
-
-
-
-
-
-if (window.init) window.init()
diff --git a/spaces/merve/hidden-bias/public/third_party/index.js b/spaces/merve/hidden-bias/public/third_party/index.js
deleted file mode 100644
index e070ccfa3ac2645f9431b1e4dbee36e81692574d..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/public/third_party/index.js
+++ /dev/null
@@ -1,74 +0,0 @@
-// https://github.com/1wheel/roadtolarissa Copyright 2018 Adam Pearce
-
-var fs = require('fs')
-var {exec, execSync} = require('child_process')
-
-var source = `${__dirname}/../../source`
-var public = `${__dirname}/../../public`
-if (!fs.existsSync(public)) fs.mkdirSync(public)
-
-function rsyncSource(){
- exec(`rsync -a --exclude _posts --exclude _templates ${source}/ ${public}/`)
-}
-rsyncSource()
-
-var hljs = require('highlight.js')
-var marked = require('marked')
-marked.setOptions({
- highlight: (code, lang) => hljs.highlight(lang || 'html', code).value,
- smartypants: true
-})
-
-var templates = {}
-readdirAbs(`${source}/_templates`).forEach(path => {
- var str = fs.readFileSync(path, 'utf8')
- var templateName = path.split('_templates/')[1]
- templates[templateName] = d => eval('`' + str + '`')
-})
-
-function readdirAbs(dir){ return fs.readdirSync(dir).map(d => dir + '/' + d) }
-
-var posts = readdirAbs(`${source}/_posts`)
- .filter(d => !d.includes('.DS_Store'))
- .map(parsePost)
-
-fs.writeFileSync(public + '/rss.xml', templates['rss.xml'](posts))
-fs.writeFileSync(public + '/sitemap.xml', templates['sitemap.xml'](posts))
-
-function parsePost(path){
- var str = fs.readFileSync(path, 'utf8')
- if (str[0] == '<') str = str.split('License.\n-->')[1]
- var [top, body] = str
- .replace('---\n', '')
- .split('\n---\n')
-
- console.log(path)
-
- var post = {html: path.includes('.html') ? body : marked(body)}
- top.split('\n').forEach(line => {
- var [key, val] = line.split(/: (.+)/)
- post[key] = val
- })
-
- return post
-}
-
-function writePost(post){
- var dir = public + post.permalink
- if (!fs.existsSync(dir)) execSync(`mkdir -p ${dir}`)
- fs.writeFileSync(`${dir}/index.html`, templates[post.template](post))
-
- var outposts = JSON.parse(JSON.stringify(posts))
- outposts.forEach(d => delete d.html)
- fs.writeFileSync(public + '/posts.json', JSON.stringify(outposts, null, 2))
-
-
-}
-posts.forEach(writePost)
-
-if (process.argv.includes('--watch')){
- require('chokidar').watch(source).on('change', path => {
- rsyncSource()
- if (path.includes('_posts/')) writePost(parsePost(path))
- })
-}
diff --git a/spaces/mfrashad/ClothingGAN/models/stylegan2/stylegan2-pytorch/op/upfirdn2d.cpp b/spaces/mfrashad/ClothingGAN/models/stylegan2/stylegan2-pytorch/op/upfirdn2d.cpp
deleted file mode 100644
index d2e633dc896433c205e18bc3e455539192ff968e..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/ClothingGAN/models/stylegan2/stylegan2-pytorch/op/upfirdn2d.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-#include <torch/extension.h>
-
-
-torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
- int up_x, int up_y, int down_x, int down_y,
- int pad_x0, int pad_x1, int pad_y0, int pad_y1);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
- int up_x, int up_y, int down_x, int down_y,
- int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
- CHECK_CUDA(input);
- CHECK_CUDA(kernel);
-
- return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/mishig/jsonformer/README.md b/spaces/mishig/jsonformer/README.md
deleted file mode 100644
index 578b99fc2388e384a1cfb3a270954cdc8424c83b..0000000000000000000000000000000000000000
--- a/spaces/mishig/jsonformer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Jsonformer
-emoji: 📈
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 3.29.0
-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/moadams/rainbowRainClassificationAPP/rainbowrain_env/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/134.bcbea9feb6e7c4da7530.js b/spaces/moadams/rainbowRainClassificationAPP/rainbowrain_env/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/134.bcbea9feb6e7c4da7530.js
deleted file mode 100644
index df56b36402bbc6449e35d609296dcd321b06e2e2..0000000000000000000000000000000000000000
--- a/spaces/moadams/rainbowRainClassificationAPP/rainbowrain_env/share/jupyter/labextensions/@jupyter-widgets/jupyterlab-manager/static/134.bcbea9feb6e7c4da7530.js
+++ /dev/null
@@ -1 +0,0 @@
-"use strict";(self.webpackChunk_jupyter_widgets_jupyterlab_manager=self.webpackChunk_jupyter_widgets_jupyterlab_manager||[]).push([[134,61],{937:(e,n,t)=>{t.d(n,{Z:()=>d});var i=t(9601),r=t.n(i),o=t(2609),a=t.n(o)()(r());a.push([e.id,"/* Copyright (c) Jupyter Development Team.\n * Distributed under the terms of the Modified BSD License.\n */\n\n.jupyter-widgets-disconnected::before {\n content: '\\f127'; /* chain-broken */\n display: inline-block;\n font: normal normal 900 14px/1 'Font Awesome 5 Free', 'FontAwesome';\n text-rendering: auto;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n color: #d9534f;\n padding: 3px;\n align-self: flex-start;\n}\n\n.jupyter-widgets-error-widget {\n display: flex;\n flex-direction: column;\n justify-content: center;\n height: 100%;\n border: solid 1px red;\n margin: 0 auto;\n}\n\n.jupyter-widgets-error-widget.icon-error {\n min-width: var(--jp-widgets-inline-width-short);\n}\n.jupyter-widgets-error-widget.text-error {\n min-width: calc(2 * var(--jp-widgets-inline-width));\n min-height: calc(3 * var(--jp-widgets-inline-height));\n}\n\n.jupyter-widgets-error-widget p {\n text-align: center;\n}\n\n.jupyter-widgets-error-widget.text-error pre::first-line {\n font-weight: bold;\n}\n",""]);const d=a},7117:(e,n,t)=>{t.d(n,{Z:()=>d});var i=t(9601),r=t.n(i),o=t(2609),a=t.n(o)()(r());a.push([e.id,"/* This file has code derived from Lumino CSS files, as noted below. The license for this Lumino code is:\n\nCopyright (c) 2019 Project Jupyter Contributors\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n3. Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n\nCopyright (c) 2014-2017, PhosphorJS Contributors\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n*/\n\n/*\n * The following section is derived from https://github.com/jupyterlab/lumino/blob/23b9d075ebc5b73ab148b6ebfc20af97f85714c4/packages/widgets/style/tabbar.css \n * We've scoped the rules so that they are consistent with exactly our code.\n */\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar {\n display: flex;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'], /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar[data-orientation='horizontal'], /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar[data-orientation='horizontal'] {\n flex-direction: row;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'], /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar[data-orientation='vertical'], /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar[data-orientation='vertical'] {\n flex-direction: column;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar > .p-TabBar-content, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar > .lm-TabBar-content {\n margin: 0;\n padding: 0;\n display: flex;\n flex: 1 1 auto;\n list-style-type: none;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab\n > .p-TabBar[data-orientation='horizontal']\n > .p-TabBar-content,\n/* </DEPRECATED> */\n/* <DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n> .p-TabBar[data-orientation='horizontal']\n> .p-TabBar-content,\n/* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar[data-orientation='horizontal']\n > .lm-TabBar-content {\n flex-direction: row;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab\n > .p-TabBar[data-orientation='vertical']\n > .p-TabBar-content,\n/* </DEPRECATED> */\n/* <DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n> .p-TabBar[data-orientation='vertical']\n> .p-TabBar-content,\n/* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar[data-orientation='vertical']\n > .lm-TabBar-content {\n flex-direction: column;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tab, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tab {\n display: flex;\n flex-direction: row;\n box-sizing: border-box;\n overflow: hidden;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon, /* </DEPRECATED> */\n/* <DEPRECATED> */ .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tabIcon, /* </DEPRECATED> */\n/* <DEPRECATED> */ .jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tabCloseIcon, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tabIcon,\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tabCloseIcon {\n flex: 0 0 auto;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tabLabel, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tabLabel {\n flex: 1 1 auto;\n overflow: hidden;\n white-space: nowrap;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tab.lm-mod-hidden {\n display: none !important;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar.lm-mod-dragging .lm-TabBar-tab {\n position: relative;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab\n > .p-TabBar.p-mod-dragging[data-orientation='horizontal']\n .p-TabBar-tab,\n/* </DEPRECATED> */\n/* <DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n > .p-TabBar.p-mod-dragging[data-orientation='horizontal']\n .p-TabBar-tab,\n/* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar.lm-mod-dragging[data-orientation='horizontal']\n .lm-TabBar-tab {\n left: 0;\n transition: left 150ms ease;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab\n > .p-TabBar.p-mod-dragging[data-orientation='vertical']\n .p-TabBar-tab,\n/* </DEPRECATED> */\n/* <DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n> .p-TabBar.p-mod-dragging[data-orientation='vertical']\n.p-TabBar-tab,\n/* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar.lm-mod-dragging[data-orientation='vertical']\n .lm-TabBar-tab {\n top: 0;\n transition: top 150ms ease;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab\n > .p-TabBar.p-mod-dragging\n .p-TabBar-tab.p-mod-dragging,\n/* </DEPRECATED> */\n/* <DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n> .p-TabBar.p-mod-dragging\n.p-TabBar-tab.p-mod-dragging,\n/* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar.lm-mod-dragging\n .lm-TabBar-tab.lm-mod-dragging {\n transition: none;\n}\n\n/* End tabbar.css */\n",""]);const d=a},4788:(e,n,t)=>{t.d(n,{Z:()=>d});var i=t(9601),r=t.n(i),o=t(2609),a=t.n(o)()(r());a.push([e.id,'/*\n\nThe nouislider.css file is autogenerated from nouislider.less, which imports and wraps the nouislider/src/nouislider.less styles.\n\nMIT License\n\nCopyright (c) 2019 Léon Gersen\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n*/\n/* The .widget-slider class is deprecated */\n.widget-slider,\n.jupyter-widget-slider {\n /* Functional styling;\n * These styles are required for noUiSlider to function.\n * You don\'t need to change these rules to apply your design.\n */\n /* Wrapper for all connect elements.\n */\n /* Offset direction\n */\n /* Give origins 0 height/width so they don\'t interfere with clicking the\n * connect elements.\n */\n /* Slider size and handle placement;\n */\n /* Styling;\n * Giving the connect element a border radius causes issues with using transform: scale\n */\n /* Handles and cursors;\n */\n /* Handle stripes;\n */\n /* Disabled state;\n */\n /* Base;\n *\n */\n /* Values;\n *\n */\n /* Markings;\n *\n */\n /* Horizontal layout;\n *\n */\n /* Vertical layout;\n *\n */\n /* Copyright (c) Jupyter Development Team.\n * Distributed under the terms of the Modified BSD License.\n */\n /* Custom CSS for nouislider */\n}\n.widget-slider .noUi-target,\n.jupyter-widget-slider .noUi-target,\n.widget-slider .noUi-target *,\n.jupyter-widget-slider .noUi-target * {\n -webkit-touch-callout: none;\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n -webkit-user-select: none;\n -ms-touch-action: none;\n touch-action: none;\n -ms-user-select: none;\n -moz-user-select: none;\n user-select: none;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\n.widget-slider .noUi-target,\n.jupyter-widget-slider .noUi-target {\n position: relative;\n}\n.widget-slider .noUi-base,\n.jupyter-widget-slider .noUi-base,\n.widget-slider .noUi-connects,\n.jupyter-widget-slider .noUi-connects {\n width: 100%;\n height: 100%;\n position: relative;\n z-index: 1;\n}\n.widget-slider .noUi-connects,\n.jupyter-widget-slider .noUi-connects {\n overflow: hidden;\n z-index: 0;\n}\n.widget-slider .noUi-connect,\n.jupyter-widget-slider .noUi-connect,\n.widget-slider .noUi-origin,\n.jupyter-widget-slider .noUi-origin {\n will-change: transform;\n position: absolute;\n z-index: 1;\n top: 0;\n right: 0;\n -ms-transform-origin: 0 0;\n -webkit-transform-origin: 0 0;\n -webkit-transform-style: preserve-3d;\n transform-origin: 0 0;\n transform-style: flat;\n}\n.widget-slider .noUi-connect,\n.jupyter-widget-slider .noUi-connect {\n height: 100%;\n width: 100%;\n}\n.widget-slider .noUi-origin,\n.jupyter-widget-slider .noUi-origin {\n height: 10%;\n width: 10%;\n}\n.widget-slider .noUi-txt-dir-rtl.noUi-horizontal .noUi-origin,\n.jupyter-widget-slider .noUi-txt-dir-rtl.noUi-horizontal .noUi-origin {\n left: 0;\n right: auto;\n}\n.widget-slider .noUi-vertical .noUi-origin,\n.jupyter-widget-slider .noUi-vertical .noUi-origin {\n width: 0;\n}\n.widget-slider .noUi-horizontal .noUi-origin,\n.jupyter-widget-slider .noUi-horizontal .noUi-origin {\n height: 0;\n}\n.widget-slider .noUi-handle,\n.jupyter-widget-slider .noUi-handle {\n -webkit-backface-visibility: hidden;\n backface-visibility: hidden;\n position: absolute;\n}\n.widget-slider .noUi-touch-area,\n.jupyter-widget-slider .noUi-touch-area {\n height: 100%;\n width: 100%;\n}\n.widget-slider .noUi-state-tap .noUi-connect,\n.jupyter-widget-slider .noUi-state-tap .noUi-connect,\n.widget-slider .noUi-state-tap .noUi-origin,\n.jupyter-widget-slider .noUi-state-tap .noUi-origin {\n -webkit-transition: transform 0.3s;\n transition: transform 0.3s;\n}\n.widget-slider .noUi-state-drag *,\n.jupyter-widget-slider .noUi-state-drag * {\n cursor: inherit !important;\n}\n.widget-slider .noUi-horizontal,\n.jupyter-widget-slider .noUi-horizontal {\n height: 18px;\n}\n.widget-slider .noUi-horizontal .noUi-handle,\n.jupyter-widget-slider .noUi-horizontal .noUi-handle {\n width: 34px;\n height: 28px;\n right: -17px;\n top: -6px;\n}\n.widget-slider .noUi-vertical,\n.jupyter-widget-slider .noUi-vertical {\n width: 18px;\n}\n.widget-slider .noUi-vertical .noUi-handle,\n.jupyter-widget-slider .noUi-vertical .noUi-handle {\n width: 28px;\n height: 34px;\n right: -6px;\n top: -17px;\n}\n.widget-slider .noUi-txt-dir-rtl.noUi-horizontal .noUi-handle,\n.jupyter-widget-slider .noUi-txt-dir-rtl.noUi-horizontal .noUi-handle {\n left: -17px;\n right: auto;\n}\n.widget-slider .noUi-target,\n.jupyter-widget-slider .noUi-target {\n background: #FAFAFA;\n border-radius: 4px;\n border: 1px solid #D3D3D3;\n box-shadow: inset 0 1px 1px #F0F0F0, 0 3px 6px -5px #BBB;\n}\n.widget-slider .noUi-connects,\n.jupyter-widget-slider .noUi-connects {\n border-radius: 3px;\n}\n.widget-slider .noUi-connect,\n.jupyter-widget-slider .noUi-connect {\n background: #3FB8AF;\n}\n.widget-slider .noUi-draggable,\n.jupyter-widget-slider .noUi-draggable {\n cursor: ew-resize;\n}\n.widget-slider .noUi-vertical .noUi-draggable,\n.jupyter-widget-slider .noUi-vertical .noUi-draggable {\n cursor: ns-resize;\n}\n.widget-slider .noUi-handle,\n.jupyter-widget-slider .noUi-handle {\n border: 1px solid #D9D9D9;\n border-radius: 3px;\n background: #FFF;\n cursor: default;\n box-shadow: inset 0 0 1px #FFF, inset 0 1px 7px #EBEBEB, 0 3px 6px -3px #BBB;\n}\n.widget-slider .noUi-active,\n.jupyter-widget-slider .noUi-active {\n box-shadow: inset 0 0 1px #FFF, inset 0 1px 7px #DDD, 0 3px 6px -3px #BBB;\n}\n.widget-slider .noUi-handle:before,\n.jupyter-widget-slider .noUi-handle:before,\n.widget-slider .noUi-handle:after,\n.jupyter-widget-slider .noUi-handle:after {\n content: "";\n display: block;\n position: absolute;\n height: 14px;\n width: 1px;\n background: #E8E7E6;\n left: 14px;\n top: 6px;\n}\n.widget-slider .noUi-handle:after,\n.jupyter-widget-slider .noUi-handle:after {\n left: 17px;\n}\n.widget-slider .noUi-vertical .noUi-handle:before,\n.jupyter-widget-slider .noUi-vertical .noUi-handle:before,\n.widget-slider .noUi-vertical .noUi-handle:after,\n.jupyter-widget-slider .noUi-vertical .noUi-handle:after {\n width: 14px;\n height: 1px;\n left: 6px;\n top: 14px;\n}\n.widget-slider .noUi-vertical .noUi-handle:after,\n.jupyter-widget-slider .noUi-vertical .noUi-handle:after {\n top: 17px;\n}\n.widget-slider [disabled] .noUi-connect,\n.jupyter-widget-slider [disabled] .noUi-connect {\n background: #B8B8B8;\n}\n.widget-slider [disabled].noUi-target,\n.jupyter-widget-slider [disabled].noUi-target,\n.widget-slider [disabled].noUi-handle,\n.jupyter-widget-slider [disabled].noUi-handle,\n.widget-slider [disabled] .noUi-handle,\n.jupyter-widget-slider [disabled] .noUi-handle {\n cursor: not-allowed;\n}\n.widget-slider .noUi-pips,\n.jupyter-widget-slider .noUi-pips,\n.widget-slider .noUi-pips *,\n.jupyter-widget-slider .noUi-pips * {\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\n.widget-slider .noUi-pips,\n.jupyter-widget-slider .noUi-pips {\n position: absolute;\n color: #999;\n}\n.widget-slider .noUi-value,\n.jupyter-widget-slider .noUi-value {\n position: absolute;\n white-space: nowrap;\n text-align: center;\n}\n.widget-slider .noUi-value-sub,\n.jupyter-widget-slider .noUi-value-sub {\n color: #ccc;\n font-size: 10px;\n}\n.widget-slider .noUi-marker,\n.jupyter-widget-slider .noUi-marker {\n position: absolute;\n background: #CCC;\n}\n.widget-slider .noUi-marker-sub,\n.jupyter-widget-slider .noUi-marker-sub {\n background: #AAA;\n}\n.widget-slider .noUi-marker-large,\n.jupyter-widget-slider .noUi-marker-large {\n background: #AAA;\n}\n.widget-slider .noUi-pips-horizontal,\n.jupyter-widget-slider .noUi-pips-horizontal {\n padding: 10px 0;\n height: 80px;\n top: 100%;\n left: 0;\n width: 100%;\n}\n.widget-slider .noUi-value-horizontal,\n.jupyter-widget-slider .noUi-value-horizontal {\n -webkit-transform: translate(-50%, 50%);\n transform: translate(-50%, 50%);\n}\n.noUi-rtl .widget-slider .noUi-value-horizontal,\n.noUi-rtl .jupyter-widget-slider .noUi-value-horizontal {\n -webkit-transform: translate(50%, 50%);\n transform: translate(50%, 50%);\n}\n.widget-slider .noUi-marker-horizontal.noUi-marker,\n.jupyter-widget-slider .noUi-marker-horizontal.noUi-marker {\n margin-left: -1px;\n width: 2px;\n height: 5px;\n}\n.widget-slider .noUi-marker-horizontal.noUi-marker-sub,\n.jupyter-widget-slider .noUi-marker-horizontal.noUi-marker-sub {\n height: 10px;\n}\n.widget-slider .noUi-marker-horizontal.noUi-marker-large,\n.jupyter-widget-slider .noUi-marker-horizontal.noUi-marker-large {\n height: 15px;\n}\n.widget-slider .noUi-pips-vertical,\n.jupyter-widget-slider .noUi-pips-vertical {\n padding: 0 10px;\n height: 100%;\n top: 0;\n left: 100%;\n}\n.widget-slider .noUi-value-vertical,\n.jupyter-widget-slider .noUi-value-vertical {\n -webkit-transform: translate(0, -50%);\n transform: translate(0, -50%);\n padding-left: 25px;\n}\n.noUi-rtl .widget-slider .noUi-value-vertical,\n.noUi-rtl .jupyter-widget-slider .noUi-value-vertical {\n -webkit-transform: translate(0, 50%);\n transform: translate(0, 50%);\n}\n.widget-slider .noUi-marker-vertical.noUi-marker,\n.jupyter-widget-slider .noUi-marker-vertical.noUi-marker {\n width: 5px;\n height: 2px;\n margin-top: -1px;\n}\n.widget-slider .noUi-marker-vertical.noUi-marker-sub,\n.jupyter-widget-slider .noUi-marker-vertical.noUi-marker-sub {\n width: 10px;\n}\n.widget-slider .noUi-marker-vertical.noUi-marker-large,\n.jupyter-widget-slider .noUi-marker-vertical.noUi-marker-large {\n width: 15px;\n}\n.widget-slider .noUi-tooltip,\n.jupyter-widget-slider .noUi-tooltip {\n display: block;\n position: absolute;\n border: 1px solid #D9D9D9;\n border-radius: 3px;\n background: #fff;\n color: #000;\n padding: 5px;\n text-align: center;\n white-space: nowrap;\n}\n.widget-slider .noUi-horizontal .noUi-tooltip,\n.jupyter-widget-slider .noUi-horizontal .noUi-tooltip {\n -webkit-transform: translate(-50%, 0);\n transform: translate(-50%, 0);\n left: 50%;\n bottom: 120%;\n}\n.widget-slider .noUi-vertical .noUi-tooltip,\n.jupyter-widget-slider .noUi-vertical .noUi-tooltip {\n -webkit-transform: translate(0, -50%);\n transform: translate(0, -50%);\n top: 50%;\n right: 120%;\n}\n.widget-slider .noUi-horizontal .noUi-origin > .noUi-tooltip,\n.jupyter-widget-slider .noUi-horizontal .noUi-origin > .noUi-tooltip {\n -webkit-transform: translate(50%, 0);\n transform: translate(50%, 0);\n left: auto;\n bottom: 10px;\n}\n.widget-slider .noUi-vertical .noUi-origin > .noUi-tooltip,\n.jupyter-widget-slider .noUi-vertical .noUi-origin > .noUi-tooltip {\n -webkit-transform: translate(0, -18px);\n transform: translate(0, -18px);\n top: auto;\n right: 28px;\n}\n.widget-slider .noUi-connect,\n.jupyter-widget-slider .noUi-connect {\n background: #2196f3;\n}\n.widget-slider .noUi-horizontal,\n.jupyter-widget-slider .noUi-horizontal {\n height: var(--jp-widgets-slider-track-thickness);\n}\n.widget-slider .noUi-vertical,\n.jupyter-widget-slider .noUi-vertical {\n width: var(--jp-widgets-slider-track-thickness);\n height: 100%;\n}\n.widget-slider .noUi-horizontal .noUi-handle,\n.jupyter-widget-slider .noUi-horizontal .noUi-handle {\n width: var(--jp-widgets-slider-handle-size);\n height: var(--jp-widgets-slider-handle-size);\n border-radius: 50%;\n top: calc((var(--jp-widgets-slider-track-thickness) - var(--jp-widgets-slider-handle-size)) / 2);\n right: calc(var(--jp-widgets-slider-handle-size) / -2);\n}\n.widget-slider .noUi-vertical .noUi-handle,\n.jupyter-widget-slider .noUi-vertical .noUi-handle {\n height: var(--jp-widgets-slider-handle-size);\n width: var(--jp-widgets-slider-handle-size);\n border-radius: 50%;\n right: calc((var(--jp-widgets-slider-handle-size) - var(--jp-widgets-slider-track-thickness)) / -2);\n top: calc(var(--jp-widgets-slider-handle-size) / -2);\n}\n.widget-slider .noUi-handle:after,\n.jupyter-widget-slider .noUi-handle:after {\n content: none;\n}\n.widget-slider .noUi-handle:before,\n.jupyter-widget-slider .noUi-handle:before {\n content: none;\n}\n.widget-slider .noUi-target,\n.jupyter-widget-slider .noUi-target {\n background: #fafafa;\n border-radius: 4px;\n border: 1px;\n /* box-shadow: inset 0 1px 1px #F0F0F0, 0 3px 6px -5px #BBB; */\n}\n.widget-slider .ui-slider,\n.jupyter-widget-slider .ui-slider {\n border: var(--jp-widgets-slider-border-width) solid var(--jp-layout-color3);\n background: var(--jp-layout-color3);\n box-sizing: border-box;\n position: relative;\n border-radius: 0px;\n}\n.widget-slider .noUi-handle,\n.jupyter-widget-slider .noUi-handle {\n width: var(--jp-widgets-slider-handle-size);\n border: 1px solid #d9d9d9;\n border-radius: 3px;\n background: #fff;\n cursor: default;\n box-shadow: none;\n outline: none;\n}\n.widget-slider .noUi-target:not([disabled]) .noUi-handle:hover,\n.jupyter-widget-slider .noUi-target:not([disabled]) .noUi-handle:hover,\n.widget-slider .noUi-target:not([disabled]) .noUi-handle:focus,\n.jupyter-widget-slider .noUi-target:not([disabled]) .noUi-handle:focus {\n background-color: var(--jp-widgets-slider-active-handle-color);\n border: var(--jp-widgets-slider-border-width) solid var(--jp-widgets-slider-active-handle-color);\n}\n.widget-slider [disabled].noUi-target,\n.jupyter-widget-slider [disabled].noUi-target {\n opacity: 0.35;\n}\n.widget-slider .noUi-connects,\n.jupyter-widget-slider .noUi-connects {\n overflow: visible;\n z-index: 0;\n background: var(--jp-layout-color3);\n}\n.widget-slider .noUi-vertical .noUi-connect,\n.jupyter-widget-slider .noUi-vertical .noUi-connect {\n width: calc(100% + 2px);\n right: -1px;\n}\n.widget-slider .noUi-horizontal .noUi-connect,\n.jupyter-widget-slider .noUi-horizontal .noUi-connect {\n height: calc(100% + 2px);\n top: -1px;\n}\n',""]);const d=a},5309:(e,n,t)=>{t.d(n,{Z:()=>w});var i=t(9601),r=t.n(i),o=t(2609),a=t.n(o),d=t(7117),s=t(4788),l=t(8991),g=t.n(l),p=new URL(t(584),t.b),c=a()(r());c.i(d.Z),c.i(s.Z);var u=g()(p);c.push([e.id,"/* Copyright (c) Jupyter Development Team.\n * Distributed under the terms of the Modified BSD License.\n */\n\n/*\n * We assume that the CSS variables in\n * https://github.com/jupyterlab/jupyterlab/blob/master/src/default-theme/variables.css\n * have been defined.\n */\n\n:root {\n --jp-widgets-color: var(--jp-content-font-color1);\n --jp-widgets-label-color: var(--jp-widgets-color);\n --jp-widgets-readout-color: var(--jp-widgets-color);\n --jp-widgets-font-size: var(--jp-ui-font-size1);\n --jp-widgets-margin: 2px;\n --jp-widgets-inline-height: 28px;\n --jp-widgets-inline-width: 300px;\n --jp-widgets-inline-width-short: calc(\n var(--jp-widgets-inline-width) / 2 - var(--jp-widgets-margin)\n );\n --jp-widgets-inline-width-tiny: calc(\n var(--jp-widgets-inline-width-short) / 2 - var(--jp-widgets-margin)\n );\n --jp-widgets-inline-margin: 4px; /* margin between inline elements */\n --jp-widgets-inline-label-width: 80px;\n --jp-widgets-border-width: var(--jp-border-width);\n --jp-widgets-vertical-height: 200px;\n --jp-widgets-horizontal-tab-height: 24px;\n --jp-widgets-horizontal-tab-width: 144px;\n --jp-widgets-horizontal-tab-top-border: 2px;\n --jp-widgets-progress-thickness: 20px;\n --jp-widgets-container-padding: 15px;\n --jp-widgets-input-padding: 4px;\n --jp-widgets-radio-item-height-adjustment: 8px;\n --jp-widgets-radio-item-height: calc(\n var(--jp-widgets-inline-height) -\n var(--jp-widgets-radio-item-height-adjustment)\n );\n --jp-widgets-slider-track-thickness: 4px;\n --jp-widgets-slider-border-width: var(--jp-widgets-border-width);\n --jp-widgets-slider-handle-size: 16px;\n --jp-widgets-slider-handle-border-color: var(--jp-border-color1);\n --jp-widgets-slider-handle-background-color: var(--jp-layout-color1);\n --jp-widgets-slider-active-handle-color: var(--jp-brand-color1);\n --jp-widgets-menu-item-height: 24px;\n --jp-widgets-dropdown-arrow: url("+u+");\n --jp-widgets-input-color: var(--jp-ui-font-color1);\n --jp-widgets-input-background-color: var(--jp-layout-color1);\n --jp-widgets-input-border-color: var(--jp-border-color1);\n --jp-widgets-input-focus-border-color: var(--jp-brand-color2);\n --jp-widgets-input-border-width: var(--jp-widgets-border-width);\n --jp-widgets-disabled-opacity: 0.6;\n\n /* From Material Design Lite */\n --md-shadow-key-umbra-opacity: 0.2;\n --md-shadow-key-penumbra-opacity: 0.14;\n --md-shadow-ambient-shadow-opacity: 0.12;\n}\n\n.jupyter-widgets {\n margin: var(--jp-widgets-margin);\n box-sizing: border-box;\n color: var(--jp-widgets-color);\n overflow: visible;\n}\n\n.jp-Output-result > .jupyter-widgets {\n margin-left: 0;\n margin-right: 0;\n}\n\n/* vbox and hbox */\n\n/* <DEPRECATED> */\n.widget-inline-hbox, /* </DEPRECATED> */\n .jupyter-widget-inline-hbox {\n /* Horizontal widgets */\n box-sizing: border-box;\n display: flex;\n flex-direction: row;\n align-items: baseline;\n}\n\n/* <DEPRECATED> */\n.widget-inline-vbox, /* </DEPRECATED> */\n .jupyter-widget-inline-vbox {\n /* Vertical Widgets */\n box-sizing: border-box;\n display: flex;\n flex-direction: column;\n align-items: center;\n}\n\n/* <DEPRECATED> */\n.widget-box, /* </DEPRECATED> */\n.jupyter-widget-box {\n box-sizing: border-box;\n display: flex;\n margin: 0;\n overflow: auto;\n}\n\n/* <DEPRECATED> */\n.widget-gridbox, /* </DEPRECATED> */\n.jupyter-widget-gridbox {\n box-sizing: border-box;\n display: grid;\n margin: 0;\n overflow: auto;\n}\n\n/* <DEPRECATED> */\n.widget-hbox, /* </DEPRECATED> */\n.jupyter-widget-hbox {\n flex-direction: row;\n}\n\n/* <DEPRECATED> */\n.widget-vbox, /* </DEPRECATED> */\n.jupyter-widget-vbox {\n flex-direction: column;\n}\n\n/* General Tags Styling */\n\n.jupyter-widget-tagsinput {\n display: flex;\n flex-direction: row;\n flex-wrap: wrap;\n align-items: center;\n overflow: auto;\n\n cursor: text;\n}\n\n.jupyter-widget-tag {\n padding-left: 10px;\n padding-right: 10px;\n padding-top: 0px;\n padding-bottom: 0px;\n display: inline-block;\n white-space: nowrap;\n overflow: hidden;\n text-overflow: ellipsis;\n text-align: center;\n font-size: var(--jp-widgets-font-size);\n\n height: calc(var(--jp-widgets-inline-height) - 2px);\n border: 0px solid;\n line-height: calc(var(--jp-widgets-inline-height) - 2px);\n box-shadow: none;\n\n color: var(--jp-ui-font-color1);\n background-color: var(--jp-layout-color2);\n border-color: var(--jp-border-color2);\n border: none;\n user-select: none;\n\n cursor: grab;\n transition: margin-left 200ms;\n margin: 1px 1px 1px 1px;\n}\n\n.jupyter-widget-tag.mod-active {\n /* MD Lite 4dp shadow */\n box-shadow: 0 4px 5px 0 rgba(0, 0, 0, var(--md-shadow-key-penumbra-opacity)),\n 0 1px 10px 0 rgba(0, 0, 0, var(--md-shadow-ambient-shadow-opacity)),\n 0 2px 4px -1px rgba(0, 0, 0, var(--md-shadow-key-umbra-opacity));\n color: var(--jp-ui-font-color1);\n background-color: var(--jp-layout-color3);\n}\n\n.jupyter-widget-colortag {\n color: var(--jp-inverse-ui-font-color1);\n}\n\n.jupyter-widget-colortag.mod-active {\n color: var(--jp-inverse-ui-font-color0);\n}\n\n.jupyter-widget-taginput {\n color: var(--jp-ui-font-color0);\n background-color: var(--jp-layout-color0);\n\n cursor: text;\n text-align: left;\n}\n\n.jupyter-widget-taginput:focus {\n outline: none;\n}\n\n.jupyter-widget-tag-close {\n margin-left: var(--jp-widgets-inline-margin);\n padding: 2px 0px 2px 2px;\n}\n\n.jupyter-widget-tag-close:hover {\n cursor: pointer;\n}\n\n/* Tag \"Primary\" Styling */\n\n.jupyter-widget-tag.mod-primary {\n color: var(--jp-inverse-ui-font-color1);\n background-color: var(--jp-brand-color1);\n}\n\n.jupyter-widget-tag.mod-primary.mod-active {\n color: var(--jp-inverse-ui-font-color0);\n background-color: var(--jp-brand-color0);\n}\n\n/* Tag \"Success\" Styling */\n\n.jupyter-widget-tag.mod-success {\n color: var(--jp-inverse-ui-font-color1);\n background-color: var(--jp-success-color1);\n}\n\n.jupyter-widget-tag.mod-success.mod-active {\n color: var(--jp-inverse-ui-font-color0);\n background-color: var(--jp-success-color0);\n}\n\n/* Tag \"Info\" Styling */\n\n.jupyter-widget-tag.mod-info {\n color: var(--jp-inverse-ui-font-color1);\n background-color: var(--jp-info-color1);\n}\n\n.jupyter-widget-tag.mod-info.mod-active {\n color: var(--jp-inverse-ui-font-color0);\n background-color: var(--jp-info-color0);\n}\n\n/* Tag \"Warning\" Styling */\n\n.jupyter-widget-tag.mod-warning {\n color: var(--jp-inverse-ui-font-color1);\n background-color: var(--jp-warn-color1);\n}\n\n.jupyter-widget-tag.mod-warning.mod-active {\n color: var(--jp-inverse-ui-font-color0);\n background-color: var(--jp-warn-color0);\n}\n\n/* Tag \"Danger\" Styling */\n\n.jupyter-widget-tag.mod-danger {\n color: var(--jp-inverse-ui-font-color1);\n background-color: var(--jp-error-color1);\n}\n\n.jupyter-widget-tag.mod-danger.mod-active {\n color: var(--jp-inverse-ui-font-color0);\n background-color: var(--jp-error-color0);\n}\n\n/* General Button Styling */\n\n.jupyter-button {\n padding-left: 10px;\n padding-right: 10px;\n padding-top: 0px;\n padding-bottom: 0px;\n display: inline-block;\n white-space: nowrap;\n overflow: hidden;\n text-overflow: ellipsis;\n text-align: center;\n font-size: var(--jp-widgets-font-size);\n cursor: pointer;\n\n height: var(--jp-widgets-inline-height);\n border: 0px solid;\n line-height: var(--jp-widgets-inline-height);\n box-shadow: none;\n\n color: var(--jp-ui-font-color1);\n background-color: var(--jp-layout-color2);\n border-color: var(--jp-border-color2);\n border: none;\n user-select: none;\n}\n\n.jupyter-button i.fa {\n margin-right: var(--jp-widgets-inline-margin);\n pointer-events: none;\n}\n\n.jupyter-button:empty:before {\n content: '\\200b'; /* zero-width space */\n}\n\n.jupyter-widgets.jupyter-button:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n.jupyter-button i.fa.center {\n margin-right: 0;\n}\n\n.jupyter-button:hover:enabled,\n.jupyter-button:focus:enabled {\n /* MD Lite 2dp shadow */\n box-shadow: 0 2px 2px 0 rgba(0, 0, 0, var(--md-shadow-key-penumbra-opacity)),\n 0 3px 1px -2px rgba(0, 0, 0, var(--md-shadow-key-umbra-opacity)),\n 0 1px 5px 0 rgba(0, 0, 0, var(--md-shadow-ambient-shadow-opacity));\n}\n\n.jupyter-button:active,\n.jupyter-button.mod-active {\n /* MD Lite 4dp shadow */\n box-shadow: 0 4px 5px 0 rgba(0, 0, 0, var(--md-shadow-key-penumbra-opacity)),\n 0 1px 10px 0 rgba(0, 0, 0, var(--md-shadow-ambient-shadow-opacity)),\n 0 2px 4px -1px rgba(0, 0, 0, var(--md-shadow-key-umbra-opacity));\n color: var(--jp-ui-font-color1);\n background-color: var(--jp-layout-color3);\n}\n\n.jupyter-button:focus:enabled {\n outline: 1px solid var(--jp-widgets-input-focus-border-color);\n}\n\n/* Button \"Primary\" Styling */\n\n.jupyter-button.mod-primary {\n color: var(--jp-ui-inverse-font-color1);\n background-color: var(--jp-brand-color1);\n}\n\n.jupyter-button.mod-primary.mod-active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-brand-color0);\n}\n\n.jupyter-button.mod-primary:active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-brand-color0);\n}\n\n/* Button \"Success\" Styling */\n\n.jupyter-button.mod-success {\n color: var(--jp-ui-inverse-font-color1);\n background-color: var(--jp-success-color1);\n}\n\n.jupyter-button.mod-success.mod-active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-success-color0);\n}\n\n.jupyter-button.mod-success:active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-success-color0);\n}\n\n/* Button \"Info\" Styling */\n\n.jupyter-button.mod-info {\n color: var(--jp-ui-inverse-font-color1);\n background-color: var(--jp-info-color1);\n}\n\n.jupyter-button.mod-info.mod-active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-info-color0);\n}\n\n.jupyter-button.mod-info:active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-info-color0);\n}\n\n/* Button \"Warning\" Styling */\n\n.jupyter-button.mod-warning {\n color: var(--jp-ui-inverse-font-color1);\n background-color: var(--jp-warn-color1);\n}\n\n.jupyter-button.mod-warning.mod-active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-warn-color0);\n}\n\n.jupyter-button.mod-warning:active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-warn-color0);\n}\n\n/* Button \"Danger\" Styling */\n\n.jupyter-button.mod-danger {\n color: var(--jp-ui-inverse-font-color1);\n background-color: var(--jp-error-color1);\n}\n\n.jupyter-button.mod-danger.mod-active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-error-color0);\n}\n\n.jupyter-button.mod-danger:active {\n color: var(--jp-ui-inverse-font-color0);\n background-color: var(--jp-error-color0);\n}\n\n/* Widget Button, Widget Toggle Button, Widget Upload */\n\n/* <DEPRECATED> */\n.widget-button, /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-toggle-button, /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-upload, /* </DEPRECATED> */\n.jupyter-widget-button,\n.jupyter-widget-toggle-button,\n.jupyter-widget-upload {\n width: var(--jp-widgets-inline-width-short);\n}\n\n/* Widget Label Styling */\n\n/* Override Bootstrap label css */\n.jupyter-widgets label {\n margin-bottom: initial;\n}\n\n/* <DEPRECATED> */\n.widget-label-basic, /* </DEPRECATED> */\n.jupyter-widget-label-basic {\n /* Basic Label */\n color: var(--jp-widgets-label-color);\n font-size: var(--jp-widgets-font-size);\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* <DEPRECATED> */\n.widget-label, /* </DEPRECATED> */\n.jupyter-widget-label {\n /* Label */\n color: var(--jp-widgets-label-color);\n font-size: var(--jp-widgets-font-size);\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* <DEPRECATED> */\n.widget-inline-hbox .widget-label, /* </DEPRECATED> */\n.jupyter-widget-inline-hbox .jupyter-widget-label {\n /* Horizontal Widget Label */\n color: var(--jp-widgets-label-color);\n text-align: right;\n margin-right: calc(var(--jp-widgets-inline-margin) * 2);\n width: var(--jp-widgets-inline-label-width);\n flex-shrink: 0;\n}\n\n/* <DEPRECATED> */\n.widget-inline-vbox .widget-label, /* </DEPRECATED> */\n.jupyter-widget-inline-vbox .jupyter-widget-label {\n /* Vertical Widget Label */\n color: var(--jp-widgets-label-color);\n text-align: center;\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* Widget Readout Styling */\n\n/* <DEPRECATED> */\n.widget-readout, /* </DEPRECATED> */\n.jupyter-widget-readout {\n color: var(--jp-widgets-readout-color);\n font-size: var(--jp-widgets-font-size);\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n overflow: hidden;\n white-space: nowrap;\n text-align: center;\n}\n\n/* <DEPRECATED> */\n.widget-readout.overflow, /* </DEPRECATED> */\n.jupyter-widget-readout.overflow {\n /* Overflowing Readout */\n\n /* From Material Design Lite\n shadow-key-umbra-opacity: 0.2;\n shadow-key-penumbra-opacity: 0.14;\n shadow-ambient-shadow-opacity: 0.12;\n */\n -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.2),\n 0 3px 1px -2px rgba(0, 0, 0, 0.14), 0 1px 5px 0 rgba(0, 0, 0, 0.12);\n\n -moz-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.2),\n 0 3px 1px -2px rgba(0, 0, 0, 0.14), 0 1px 5px 0 rgba(0, 0, 0, 0.12);\n\n box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.2), 0 3px 1px -2px rgba(0, 0, 0, 0.14),\n 0 1px 5px 0 rgba(0, 0, 0, 0.12);\n}\n\n/* <DEPRECATED> */\n.widget-inline-hbox .widget-readout, /* </DEPRECATED> */\n.jupyter-widget-inline-hbox .jupyter-widget-readout {\n /* Horizontal Readout */\n text-align: center;\n max-width: var(--jp-widgets-inline-width-short);\n min-width: var(--jp-widgets-inline-width-tiny);\n margin-left: var(--jp-widgets-inline-margin);\n}\n\n/* <DEPRECATED> */\n.widget-inline-vbox .widget-readout, /* </DEPRECATED> */\n.jupyter-widget-inline-vbox .jupyter-widget-readout {\n /* Vertical Readout */\n margin-top: var(--jp-widgets-inline-margin);\n /* as wide as the widget */\n width: inherit;\n}\n\n/* Widget Checkbox Styling */\n\n/* <DEPRECATED> */\n.widget-checkbox, /* </DEPRECATED> */\n.jupyter-widget-checkbox {\n width: var(--jp-widgets-inline-width);\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* <DEPRECATED> */\n.widget-checkbox input[type='checkbox'], /* </DEPRECATED> */\n.jupyter-widget-checkbox input[type='checkbox'] {\n margin: 0px calc(var(--jp-widgets-inline-margin) * 2) 0px 0px;\n line-height: var(--jp-widgets-inline-height);\n font-size: large;\n flex-grow: 1;\n flex-shrink: 0;\n align-self: center;\n}\n\n/* Widget Valid Styling */\n\n/* <DEPRECATED> */\n.widget-valid, /* </DEPRECATED> */\n.jupyter-widget-valid {\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n width: var(--jp-widgets-inline-width-short);\n font-size: var(--jp-widgets-font-size);\n}\n\n/* <DEPRECATED> */\n.widget-valid i, /* </DEPRECATED> */\n.jupyter-widget-valid i {\n line-height: var(--jp-widgets-inline-height);\n margin-right: var(--jp-widgets-inline-margin);\n margin-left: var(--jp-widgets-inline-margin);\n}\n\n/* <DEPRECATED> */\n.widget-valid.mod-valid i, /* </DEPRECATED> */\n.jupyter-widget-valid.mod-valid i {\n color: green;\n}\n\n/* <DEPRECATED> */\n.widget-valid.mod-invalid i, /* </DEPRECATED> */\n.jupyter-widget-valid.mod-invalid i {\n color: red;\n}\n\n/* <DEPRECATED> */\n.widget-valid.mod-valid .widget-valid-readout, /* </DEPRECATED> */\n.jupyter-widget-valid.mod-valid .jupyter-widget-valid-readout {\n display: none;\n}\n\n/* Widget Text and TextArea Styling */\n\n/* <DEPRECATED> */\n.widget-textarea, /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-text, /* </DEPRECATED> */\n.jupyter-widget-textarea,\n.jupyter-widget-text {\n width: var(--jp-widgets-inline-width);\n}\n\n/* <DEPRECATED> */\n.widget-text input[type='text'], /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-text input[type='number'], /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-text input[type='password'], /* </DEPRECATED> */\n.jupyter-widget-text input[type='text'],\n.jupyter-widget-text input[type='number'],\n.jupyter-widget-text input[type='password'] {\n height: var(--jp-widgets-inline-height);\n}\n\n/* <DEPRECATED> */\n.widget-text input[type='text']:disabled, /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-text input[type='number']:disabled, /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-text input[type='password']:disabled, /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-textarea textarea:disabled, /* </DEPRECATED> */\n.jupyter-widget-text input[type='text']:disabled,\n.jupyter-widget-text input[type='number']:disabled,\n.jupyter-widget-text input[type='password']:disabled,\n.jupyter-widget-textarea textarea:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* <DEPRECATED> */\n.widget-text input[type='text'], /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-text input[type='number'], /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-text input[type='password'], /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-textarea textarea, /* </DEPRECATED> */\n.jupyter-widget-text input[type='text'],\n.jupyter-widget-text input[type='number'],\n.jupyter-widget-text input[type='password'],\n.jupyter-widget-textarea textarea {\n box-sizing: border-box;\n border: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n background-color: var(--jp-widgets-input-background-color);\n color: var(--jp-widgets-input-color);\n font-size: var(--jp-widgets-font-size);\n flex-grow: 1;\n min-width: 0; /* This makes it possible for the flexbox to shrink this input */\n flex-shrink: 1;\n outline: none !important;\n}\n\n/* <DEPRECATED> */\n.widget-text input[type='text'], /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-text input[type='password'], /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-textarea textarea, /* </DEPRECATED> */\n.jupyter-widget-text input[type='text'],\n.jupyter-widget-text input[type='password'],\n.jupyter-widget-textarea textarea {\n padding: var(--jp-widgets-input-padding)\n calc(var(--jp-widgets-input-padding) * 2);\n}\n\n/* <DEPRECATED> */\n.widget-text input[type='number'], /* </DEPRECATED> */\n.jupyter-widget-text input[type='number'] {\n padding: var(--jp-widgets-input-padding) 0 var(--jp-widgets-input-padding)\n calc(var(--jp-widgets-input-padding) * 2);\n}\n\n/* <DEPRECATED> */\n.widget-textarea textarea, /* </DEPRECATED> */\n.jupyter-widget-textarea textarea {\n height: inherit;\n width: inherit;\n}\n\n/* <DEPRECATED> */\n.widget-text input:focus, /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-textarea textarea:focus, /* </DEPRECATED> */\n.jupyter-widget-text input:focus,\n.jupyter-widget-textarea textarea:focus {\n border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n/* Horizontal Slider */\n/* <DEPRECATED> */\n.widget-hslider, /* </DEPRECATED> */\n.jupyter-widget-hslider {\n width: var(--jp-widgets-inline-width);\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n\n /* Override the align-items baseline. This way, the description and readout\n still seem to align their baseline properly, and we don't have to have\n align-self: stretch in the .slider-container. */\n align-items: center;\n}\n\n/* <DEPRECATED> */\n.widgets-slider .slider-container, /* </DEPRECATED> */\n.jupyter-widgets-slider .slider-container {\n overflow: visible;\n}\n\n/* <DEPRECATED> */\n.widget-hslider .slider-container, /* </DEPRECATED> */\n.jupyter-widget-hslider .slider-container {\n margin-left: calc(\n var(--jp-widgets-slider-handle-size) / 2 - 2 *\n var(--jp-widgets-slider-border-width)\n );\n margin-right: calc(\n var(--jp-widgets-slider-handle-size) / 2 - 2 *\n var(--jp-widgets-slider-border-width)\n );\n flex: 1 1 var(--jp-widgets-inline-width-short);\n}\n\n/* Vertical Slider */\n\n/* <DEPRECATED> */\n.widget-vbox .widget-label, /* </DEPRECATED> */\n.jupyter-widget-vbox .jupyter-widget-label {\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* <DEPRECATED> */\n.widget-vslider, /* </DEPRECATED> */\n.jupyter-widget-vslider {\n /* Vertical Slider */\n height: var(--jp-widgets-vertical-height);\n width: var(--jp-widgets-inline-width-tiny);\n}\n\n/* <DEPRECATED> */\n.widget-vslider .slider-container, /* </DEPRECATED> */\n.jupyter-widget-vslider .slider-container {\n flex: 1 1 var(--jp-widgets-inline-width-short);\n margin-left: auto;\n margin-right: auto;\n margin-bottom: calc(\n var(--jp-widgets-slider-handle-size) / 2 - 2 *\n var(--jp-widgets-slider-border-width)\n );\n margin-top: calc(\n var(--jp-widgets-slider-handle-size) / 2 - 2 *\n var(--jp-widgets-slider-border-width)\n );\n display: flex;\n flex-direction: column;\n}\n\n/* Widget Progress Styling */\n\n.progress-bar {\n -webkit-transition: none;\n -moz-transition: none;\n -ms-transition: none;\n -o-transition: none;\n transition: none;\n}\n\n.progress-bar {\n height: var(--jp-widgets-inline-height);\n}\n\n.progress-bar {\n background-color: var(--jp-brand-color1);\n}\n\n.progress-bar-success {\n background-color: var(--jp-success-color1);\n}\n\n.progress-bar-info {\n background-color: var(--jp-info-color1);\n}\n\n.progress-bar-warning {\n background-color: var(--jp-warn-color1);\n}\n\n.progress-bar-danger {\n background-color: var(--jp-error-color1);\n}\n\n.progress {\n background-color: var(--jp-layout-color2);\n border: none;\n box-shadow: none;\n}\n\n/* Horisontal Progress */\n\n/* <DEPRECATED> */\n.widget-hprogress, /* </DEPRECATED> */\n.jupyter-widget-hprogress {\n /* Progress Bar */\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n width: var(--jp-widgets-inline-width);\n align-items: center;\n}\n\n/* <DEPRECATED> */\n.widget-hprogress .progress, /* </DEPRECATED> */\n.jupyter-widget-hprogress .progress {\n flex-grow: 1;\n margin-top: var(--jp-widgets-input-padding);\n margin-bottom: var(--jp-widgets-input-padding);\n align-self: stretch;\n /* Override bootstrap style */\n height: initial;\n}\n\n/* Vertical Progress */\n\n/* <DEPRECATED> */\n.widget-vprogress, /* </DEPRECATED> */\n.jupyter-widget-vprogress {\n height: var(--jp-widgets-vertical-height);\n width: var(--jp-widgets-inline-width-tiny);\n}\n\n/* <DEPRECATED> */\n.widget-vprogress .progress, /* </DEPRECATED> */\n.jupyter-widget-vprogress .progress {\n flex-grow: 1;\n width: var(--jp-widgets-progress-thickness);\n margin-left: auto;\n margin-right: auto;\n margin-bottom: 0;\n}\n\n/* Select Widget Styling */\n\n/* <DEPRECATED> */\n.widget-dropdown, /* </DEPRECATED> */\n.jupyter-widget-dropdown {\n height: var(--jp-widgets-inline-height);\n width: var(--jp-widgets-inline-width);\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* <DEPRECATED> */\n.widget-dropdown > select, /* </DEPRECATED> */\n.jupyter-widget-dropdown > select {\n padding-right: 20px;\n border: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n border-radius: 0;\n height: inherit;\n flex: 1 1 var(--jp-widgets-inline-width-short);\n min-width: 0; /* This makes it possible for the flexbox to shrink this input */\n box-sizing: border-box;\n outline: none !important;\n box-shadow: none;\n background-color: var(--jp-widgets-input-background-color);\n color: var(--jp-widgets-input-color);\n font-size: var(--jp-widgets-font-size);\n vertical-align: top;\n padding-left: calc(var(--jp-widgets-input-padding) * 2);\n appearance: none;\n -webkit-appearance: none;\n -moz-appearance: none;\n background-repeat: no-repeat;\n background-size: 20px;\n background-position: right center;\n background-image: var(--jp-widgets-dropdown-arrow);\n}\n/* <DEPRECATED> */\n.widget-dropdown > select:focus, /* </DEPRECATED> */\n.jupyter-widget-dropdown > select:focus {\n border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n/* <DEPRECATED> */\n.widget-dropdown > select:disabled, /* </DEPRECATED> */\n.jupyter-widget-dropdown > select:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* To disable the dotted border in Firefox around select controls.\n See http://stackoverflow.com/a/18853002 */\n/* <DEPRECATED> */\n.widget-dropdown > select:-moz-focusring, /* </DEPRECATED> */\n.jupyter-widget-dropdown > select:-moz-focusring {\n color: transparent;\n text-shadow: 0 0 0 #000;\n}\n\n/* Select and SelectMultiple */\n\n/* <DEPRECATED> */\n.widget-select, /* </DEPRECATED> */\n.jupyter-widget-select {\n width: var(--jp-widgets-inline-width);\n line-height: var(--jp-widgets-inline-height);\n\n /* Because Firefox defines the baseline of a select as the bottom of the\n control, we align the entire control to the top and add padding to the\n select to get an approximate first line baseline alignment. */\n align-items: flex-start;\n}\n\n/* <DEPRECATED> */\n.widget-select > select, /* </DEPRECATED> */\n.jupyter-widget-select > select {\n border: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n background-color: var(--jp-widgets-input-background-color);\n color: var(--jp-widgets-input-color);\n font-size: var(--jp-widgets-font-size);\n flex: 1 1 var(--jp-widgets-inline-width-short);\n outline: none !important;\n overflow: auto;\n height: inherit;\n\n /* Because Firefox defines the baseline of a select as the bottom of the\n control, we align the entire control to the top and add padding to the\n select to get an approximate first line baseline alignment. */\n padding-top: 5px;\n}\n\n/* <DEPRECATED> */\n.widget-select > select:focus, /* </DEPRECATED> */\n.jupyter-widget-select > select:focus {\n border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n.wiget-select > select > option,\n.jupyter-wiget-select > select > option {\n padding-left: var(--jp-widgets-input-padding);\n line-height: var(--jp-widgets-inline-height);\n /* line-height doesn't work on some browsers for select options */\n padding-top: calc(\n var(--jp-widgets-inline-height) - var(--jp-widgets-font-size) / 2\n );\n padding-bottom: calc(\n var(--jp-widgets-inline-height) - var(--jp-widgets-font-size) / 2\n );\n}\n\n/* Toggle Buttons Styling */\n\n/* <DEPRECATED> */\n.widget-toggle-buttons, /* </DEPRECATED> */\n.jupyter-widget-toggle-buttons {\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* <DEPRECATED> */\n.widget-toggle-buttons .widget-toggle-button, /* </DEPRECATED> */\n.jupyter-widget-toggle-buttons .jupyter-widget-toggle-button {\n margin-left: var(--jp-widgets-margin);\n margin-right: var(--jp-widgets-margin);\n}\n\n/* <DEPRECATED> */\n.widget-toggle-buttons .jupyter-button:disabled, /* </DEPRECATED> */\n.jupyter-widget-toggle-buttons .jupyter-button:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* Radio Buttons Styling */\n\n/* <DEPRECATED> */\n.widget-radio, /* </DEPRECATED> */\n.jupyter-widget-radio {\n width: var(--jp-widgets-inline-width);\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* <DEPRECATED> */\n.widget-radio-box, /* </DEPRECATED> */\n.jupyter-widget-radio-box {\n display: flex;\n flex-direction: column;\n align-items: stretch;\n box-sizing: border-box;\n flex-grow: 1;\n margin-bottom: var(--jp-widgets-radio-item-height-adjustment);\n}\n\n/* <DEPRECATED> */\n.widget-radio-box label, /* </DEPRECATED> */\n.jupyter-widget-radio-box label {\n height: var(--jp-widgets-radio-item-height);\n line-height: var(--jp-widgets-radio-item-height);\n font-size: var(--jp-widgets-font-size);\n}\n\n/* <DEPRECATED> */\n.widget-radio-box input, /* </DEPRECATED> */\n.jupyter-widget-radio-box input {\n height: var(--jp-widgets-radio-item-height);\n line-height: var(--jp-widgets-radio-item-height);\n margin: 0 calc(var(--jp-widgets-input-padding) * 2) 0 1px;\n float: left;\n}\n\n/* Color Picker Styling */\n\n/* <DEPRECATED> */\n.widget-colorpicker, /* </DEPRECATED> */\n.jupyter-widget-colorpicker {\n width: var(--jp-widgets-inline-width);\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* <DEPRECATED> */\n.widget-colorpicker > .widget-colorpicker-input, /* </DEPRECATED> */\n.jupyter-widget-colorpicker > .jupyter-widget-colorpicker-input {\n flex-grow: 1;\n flex-shrink: 1;\n min-width: var(--jp-widgets-inline-width-tiny);\n}\n\n/* <DEPRECATED> */\n.widget-colorpicker input[type='color'], /* </DEPRECATED> */\n.jupyter-widget-colorpicker input[type='color'] {\n width: var(--jp-widgets-inline-height);\n height: var(--jp-widgets-inline-height);\n padding: 0 2px; /* make the color square actually square on Chrome on OS X */\n background: var(--jp-widgets-input-background-color);\n color: var(--jp-widgets-input-color);\n border: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n border-left: none;\n flex-grow: 0;\n flex-shrink: 0;\n box-sizing: border-box;\n align-self: stretch;\n outline: none !important;\n}\n\n/* <DEPRECATED> */\n.widget-colorpicker.concise input[type='color'], /* </DEPRECATED> */\n.jupyter-widget-colorpicker.concise input[type='color'] {\n border-left: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n}\n\n/* <DEPRECATED> */\n.widget-colorpicker input[type='color']:focus, /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-colorpicker input[type='text']:focus, /* </DEPRECATED> */\n.jupyter-widget-colorpicker input[type='color']:focus,\n.jupyter-widget-colorpicker input[type='text']:focus {\n border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n/* <DEPRECATED> */\n.widget-colorpicker input[type='text'], /* </DEPRECATED> */\n.jupyter-widget-colorpicker input[type='text'] {\n flex-grow: 1;\n outline: none !important;\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n background: var(--jp-widgets-input-background-color);\n color: var(--jp-widgets-input-color);\n border: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n font-size: var(--jp-widgets-font-size);\n padding: var(--jp-widgets-input-padding)\n calc(var(--jp-widgets-input-padding) * 2);\n min-width: 0; /* This makes it possible for the flexbox to shrink this input */\n flex-shrink: 1;\n box-sizing: border-box;\n}\n\n/* <DEPRECATED> */\n.widget-colorpicker input[type='text']:disabled, /* </DEPRECATED> */\n.jupyter-widget-colorpicker input[type='text']:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* Date Picker Styling */\n\n/* <DEPRECATED> */\n.widget-datepicker, /* </DEPRECATED> */\n.jupyter-widget-datepicker {\n width: var(--jp-widgets-inline-width);\n height: var(--jp-widgets-inline-height);\n line-height: var(--jp-widgets-inline-height);\n}\n\n/* <DEPRECATED> */\n.widget-datepicker input[type='date'], /* </DEPRECATED> */\n.jupyter-widget-datepicker input[type='date'] {\n flex-grow: 1;\n flex-shrink: 1;\n min-width: 0; /* This makes it possible for the flexbox to shrink this input */\n outline: none !important;\n height: var(--jp-widgets-inline-height);\n border: var(--jp-widgets-input-border-width) solid\n var(--jp-widgets-input-border-color);\n background-color: var(--jp-widgets-input-background-color);\n color: var(--jp-widgets-input-color);\n font-size: var(--jp-widgets-font-size);\n padding: var(--jp-widgets-input-padding)\n calc(var(--jp-widgets-input-padding) * 2);\n box-sizing: border-box;\n}\n\n/* <DEPRECATED> */\n.widget-datepicker input[type='date']:focus, /* </DEPRECATED> */\n.jupyter-widget-datepicker input[type='date']:focus {\n border-color: var(--jp-widgets-input-focus-border-color);\n}\n\n/* <DEPRECATED> */\n.widget-datepicker input[type='date']:invalid, /* </DEPRECATED> */\n.jupyter-widget-datepicker input[type='date']:invalid {\n border-color: var(--jp-warn-color1);\n}\n\n/* <DEPRECATED> */\n.widget-datepicker input[type='date']:disabled, /* </DEPRECATED> */\n.jupyter-widget-datepicker input[type='date']:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* Play Widget */\n\n/* <DEPRECATED> */\n.widget-play, /* </DEPRECATED> */\n.jupyter-widget-play {\n width: var(--jp-widgets-inline-width-short);\n display: flex;\n align-items: stretch;\n}\n\n/* <DEPRECATED> */\n.widget-play .jupyter-button, /* </DEPRECATED> */\n.jupyter-widget-play .jupyter-button {\n flex-grow: 1;\n height: auto;\n}\n\n/* <DEPRECATED> */\n.widget-play .jupyter-button:disabled, /* </DEPRECATED> */\n.jupyter-widget-play .jupyter-button:disabled {\n opacity: var(--jp-widgets-disabled-opacity);\n}\n\n/* Tab Widget */\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab {\n display: flex;\n flex-direction: column;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar {\n /* Necessary so that a tab can be shifted down to overlay the border of the box below. */\n overflow-x: visible;\n overflow-y: visible;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar > .p-TabBar-content, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar > .lm-TabBar-content {\n /* Make sure that the tab grows from bottom up */\n align-items: flex-end;\n min-width: 0;\n min-height: 0;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .widget-tab-contents, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .widget-tab-contents {\n width: 100%;\n box-sizing: border-box;\n margin: 0;\n background: var(--jp-layout-color1);\n color: var(--jp-ui-font-color1);\n border: var(--jp-border-width) solid var(--jp-border-color1);\n padding: var(--jp-widgets-container-padding);\n flex-grow: 1;\n overflow: auto;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar {\n font: var(--jp-widgets-font-size) Helvetica, Arial, sans-serif;\n min-height: calc(\n var(--jp-widgets-horizontal-tab-height) + var(--jp-border-width)\n );\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tab, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tab {\n flex: 0 1 var(--jp-widgets-horizontal-tab-width);\n min-width: 35px;\n min-height: calc(\n var(--jp-widgets-horizontal-tab-height) + var(--jp-border-width)\n );\n line-height: var(--jp-widgets-horizontal-tab-height);\n margin-left: calc(-1 * var(--jp-border-width));\n padding: 0px 10px;\n background: var(--jp-layout-color2);\n color: var(--jp-ui-font-color2);\n border: var(--jp-border-width) solid var(--jp-border-color1);\n border-bottom: none;\n position: relative;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tab.lm-mod-current {\n color: var(--jp-ui-font-color0);\n /* We want the background to match the tab content background */\n background: var(--jp-layout-color1);\n min-height: calc(\n var(--jp-widgets-horizontal-tab-height) + 2 * var(--jp-border-width)\n );\n transform: translateY(var(--jp-border-width));\n overflow: visible;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tab.lm-mod-current:before {\n position: absolute;\n top: calc(-1 * var(--jp-border-width));\n left: calc(-1 * var(--jp-border-width));\n content: '';\n height: var(--jp-widgets-horizontal-tab-top-border);\n width: calc(100% + 2 * var(--jp-border-width));\n background: var(--jp-brand-color1);\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child, /* </DEPRECATED> */\n/* <DEPRECATED> */.jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tab:first-child, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tab:first-child {\n margin-left: 0;\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab\n > .p-TabBar\n .p-TabBar-tab:hover:not(.p-mod-current),\n/* </DEPRECATED> */\n/* <DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n > .p-TabBar\n .p-TabBar-tab:hover:not(.p-mod-current),\n/* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar\n .lm-TabBar-tab:hover:not(.lm-mod-current) {\n background: var(--jp-layout-color1);\n color: var(--jp-ui-font-color1);\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab\n > .p-TabBar\n .p-mod-closable\n > .p-TabBar-tabCloseIcon,\n/* </DEPRECATED> */\n/* <DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n> .p-TabBar\n.p-mod-closable\n> .p-TabBar-tabCloseIcon,\n/* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar\n .lm-mod-closable\n > .lm-TabBar-tabCloseIcon {\n margin-left: 4px;\n}\n\n/* This font-awesome strategy may not work across FA4 and FA5, but we don't\nactually support closable tabs, so it really doesn't matter */\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab\n > .p-TabBar\n .p-mod-closable\n > .p-TabBar-tabCloseIcon:before,\n/* </DEPRECATED> */\n/* <DEPRECATED> */\n.jupyter-widgets.jupyter-widget-widget-tab\n> .p-TabBar\n.p-mod-closable\n> .p-TabBar-tabCloseIcon:before,\n/* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab\n > .lm-TabBar\n .lm-mod-closable\n > .lm-TabBar-tabCloseIcon:before {\n font-family: FontAwesome;\n content: '\\f00d'; /* close */\n}\n\n/* <DEPRECATED> */\n.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon, /* </DEPRECATED> */\n/* <DEPRECATED> */ .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel, /* </DEPRECATED> */\n/* <DEPRECATED> */ .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon, /* </DEPRECATED> */\n/* <DEPRECATED> */ .jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tabIcon, /* </DEPRECATED> */\n/* <DEPRECATED> */ .jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tabLabel, /* </DEPRECATED> */\n/* <DEPRECATED> */ .jupyter-widgets.jupyter-widget-tab > .p-TabBar .p-TabBar-tabCloseIcon, /* </DEPRECATED> */\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tabIcon,\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tabLabel,\n.jupyter-widgets.jupyter-widget-tab > .lm-TabBar .lm-TabBar-tabCloseIcon {\n line-height: var(--jp-widgets-horizontal-tab-height);\n}\n\n/* Accordion Widget */\n\n.jupyter-widget-Collapse {\n display: flex;\n flex-direction: column;\n align-items: stretch;\n}\n\n.jupyter-widget-Collapse-header {\n padding: var(--jp-widgets-input-padding);\n cursor: pointer;\n color: var(--jp-ui-font-color2);\n background-color: var(--jp-layout-color2);\n border: var(--jp-widgets-border-width) solid var(--jp-border-color1);\n padding: calc(var(--jp-widgets-container-padding) * 2 / 3)\n var(--jp-widgets-container-padding);\n font-weight: bold;\n}\n\n.jupyter-widget-Collapse-header:hover {\n background-color: var(--jp-layout-color1);\n color: var(--jp-ui-font-color1);\n}\n\n.jupyter-widget-Collapse-open > .jupyter-widget-Collapse-header {\n background-color: var(--jp-layout-color1);\n color: var(--jp-ui-font-color0);\n cursor: default;\n border-bottom: none;\n}\n\n.jupyter-widget-Collapse-contents {\n padding: var(--jp-widgets-container-padding);\n background-color: var(--jp-layout-color1);\n color: var(--jp-ui-font-color1);\n border-left: var(--jp-widgets-border-width) solid var(--jp-border-color1);\n border-right: var(--jp-widgets-border-width) solid var(--jp-border-color1);\n border-bottom: var(--jp-widgets-border-width) solid var(--jp-border-color1);\n overflow: auto;\n}\n\n.jupyter-widget-Accordion {\n display: flex;\n flex-direction: column;\n align-items: stretch;\n}\n\n.jupyter-widget-Accordion .jupyter-widget-Collapse {\n margin-bottom: 0;\n}\n\n.jupyter-widget-Accordion .jupyter-widget-Collapse + .jupyter-widget-Collapse {\n margin-top: 4px;\n}\n\n/* HTML widget */\n\n/* <DEPRECATED> */\n.widget-html, /* </DEPRECATED> */\n/* <DEPRECATED> */ .widget-htmlmath, /* </DEPRECATED> */\n.jupyter-widget-html,\n.jupyter-widget-htmlmath {\n font-size: var(--jp-widgets-font-size);\n}\n\n/* <DEPRECATED> */\n.widget-html > .widget-html-content, /* </DEPRECATED> */\n/* <DEPRECATED> */.widget-htmlmath > .widget-html-content, /* </DEPRECATED> */\n.jupyter-widget-html > .jupyter-widget-html-content,\n.jupyter-widget-htmlmath > .jupyter-widget-html-content {\n /* Fill out the area in the HTML widget */\n align-self: stretch;\n flex-grow: 1;\n flex-shrink: 1;\n /* Makes sure the baseline is still aligned with other elements */\n line-height: var(--jp-widgets-inline-height);\n /* Make it possible to have absolutely-positioned elements in the html */\n position: relative;\n}\n\n/* Image widget */\n\n/* <DEPRECATED> */\n.widget-image, /* </DEPRECATED> */\n.jupyter-widget-image {\n max-width: 100%;\n height: auto;\n}\n",""]);const w=c},2609:e=>{e.exports=function(e){var n=[];return n.toString=function(){return this.map((function(n){var t="",i=void 0!==n[5];return n[4]&&(t+="@supports (".concat(n[4],") {")),n[2]&&(t+="@media ".concat(n[2]," {")),i&&(t+="@layer".concat(n[5].length>0?" 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g.DisposableDelegate((()=>{n&&n.removeMimeType(m),i.dispose()}))}const ie={id:"@jupyter-widgets/jupyterlab-manager:plugin",requires:[d.IRenderMimeRegistry],optional:[o.INotebookTracker,r.ISettingRegistry,a.IMainMenu,s.ILoggerRegistry,$.ITranslator],provides:h.IJupyterWidgetRegistry,activate:function(e,n,t,i,r,o,a){const{commands:d}=e,s=(null!=a?a:$.nullTranslator).load("jupyterlab_widgets"),l=e=>{if(!o)return;const n=le.widgetManagerProperty.get(e.context);n&&n.onUnhandledIOPubMessage.connect(((n,t)=>{const i=o.getLogger(e.context.path);let r="warning";(J.KernelMessage.isErrorMsg(t)||J.KernelMessage.isStreamMsg(t)&&"stderr"===t.content.name)&&(r="error");const a=Object.assign(Object.assign({},t.content),{output_type:t.header.msg_type});i.rendermime=e.content.rendermime,i.log({type:"output",data:a,level:r})}))};return null!==i&&i.load(ie.id).then((e=>{e.changed.connect(re),re(e)})).catch((e=>{console.error(e.message)})),n.addFactory({safe:!1,mimeTypes:[m],createRenderer:e=>new 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oe={id:`@jupyter-widgets/jupyterlab-manager:base-${h.JUPYTER_WIDGETS_VERSION}`,requires:[h.IJupyterWidgetRegistry],autoStart:!0,activate:(e,n)=>{n.registerWidget({name:"@jupyter-widgets/base",version:h.JUPYTER_WIDGETS_VERSION,exports:{WidgetModel:h.WidgetModel,WidgetView:h.WidgetView,DOMWidgetView:h.DOMWidgetView,DOMWidgetModel:h.DOMWidgetModel,LayoutModel:h.LayoutModel,LayoutView:h.LayoutView,StyleModel:h.StyleModel,StyleView:h.StyleView,ErrorWidgetView:h.ErrorWidgetView}})}},ae={id:`@jupyter-widgets/jupyterlab-manager:controls-${I.N}`,requires:[h.IJupyterWidgetRegistry],autoStart:!0,activate:(e,n)=>{n.registerWidget({name:"@jupyter-widgets/controls",version:I.N,exports:()=>new Promise(((e,n)=>{t.e(863).then((n=>{e(t(8032))}).bind(null,t)).catch((e=>{n(e)}))}))})}},de={id:`@jupyter-widgets/jupyterlab-manager:output-${P}`,requires:[h.IJupyterWidgetRegistry],autoStart:!0,activate:(e,n)=>{n.registerWidget({name:"@jupyter-widgets/output",version:P,exports:{OutputModel:U,OutputView:k}})}},se=[ie,oe,ae,de];var le;!function(e){e.widgetManagerProperty=new p.AttachedProperty({name:"widgetManager",create:e=>{}})}(le||(le={}));const 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\ No newline at end of file
diff --git a/spaces/mrfakename/Translate/main.py b/spaces/mrfakename/Translate/main.py
deleted file mode 100644
index e8f4e4a16b4f1528d26204d38623985c046434ee..0000000000000000000000000000000000000000
--- a/spaces/mrfakename/Translate/main.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""
-🗣️ Translator - Translate text from one language to another.
-
-Application file made with Streamlit.
-"""
-
-import re
-import streamlit as st
-
-from datetime import datetime
-from transformers import pipeline
-from available_models import MODELS
-
-
-st.set_page_config(page_title="Translate", page_icon="🗣️")
-st.title("Translate", "main")
-st.subheader("Using cutting-edge AI/ML technology to translate text for you.")
-st.markdown("""
-Welcome to Translate. Here, we harness the power of cutting-edge Artificial Intelligence and Machine Learning to translate text for _you_.
-
-Translate is a free tool to translate quickly and easily! Start today by typing your text into the form below and selecting a language pair!
-""")
-hide_streamlit_style = """
- <style>
- #MainMenu {visibility: hidden;}
- footer {visibility: hidden;}
- </style>
- """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
-lang1, lang2 = st.columns(2)
-lang1.selectbox(
- "Source Language", ["🇺🇸 English", "🇫🇷 French", "🇩🇪 German", "🇪🇸 Spanish", "🇨🇳 Chinese"],
- key="input_lang", index=0,
-)
-lang2.selectbox(
- "Target Language", ["🇺🇸 English", "🇫🇷 French", "🇩🇪 German", "🇪🇸 Spanish", "🇨🇳 Chinese"],
- key="output_lang", index=3,
-)
-
-selected_model = MODELS[f"{st.session_state['input_lang']}->{st.session_state['output_lang']}"]
-
-
-if selected_model[0] == None:
- st.write("Sorry, we can't translate this language pair! Please try a different one!")
-elif selected_model[0] == 0:
- st.write("Haha! We know what you're trying to do and you can't do it! You're not allowed to translate languages from one language to the same one!")
-else:
- input_text = st.text_area("Enter text to translate:", height=200, key="input")
- translate_text = st.button("Translate")
-
- if translate_text:
- with st.spinner(text="Just a sec, it takes a minute to load up the AI/ML!"):
- task = pipeline(
- "translation",
- model=selected_model[0],
- tokenizer=selected_model[0],
- )
-
- progress_bar = st.progress(0)
- with st.spinner(text="Just a little bit more time! We wish it was easy, but AI takes some time!"):
- text_to_translate = re.split('(?<=[.!?]) +', input_text)
- total_progress = len(text_to_translate)
-
- for i, text in enumerate(text_to_translate):
- translation = task(text)
- text_to_translate[i] = translation[0]["translation_text"]
- progress_bar.progress((i + 1) / total_progress)
-
- st.success("Yay! It's done! Check out your translation below!")
- st.write("**Here's your translation:**")
- st.code(' '.join(text_to_translate))
\ No newline at end of file
diff --git a/spaces/mrneuralnet/P-DFD/layers/modules/multibox_loss.py b/spaces/mrneuralnet/P-DFD/layers/modules/multibox_loss.py
deleted file mode 100644
index 096620480eba59e9d893c1940899f7e3d6736cae..0000000000000000000000000000000000000000
--- a/spaces/mrneuralnet/P-DFD/layers/modules/multibox_loss.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd import Variable
-from utils.box_utils import match, log_sum_exp
-from data import cfg_mnet
-GPU = cfg_mnet['gpu_train']
-
-class MultiBoxLoss(nn.Module):
- """SSD Weighted Loss Function
- Compute Targets:
- 1) Produce Confidence Target Indices by matching ground truth boxes
- with (default) 'priorboxes' that have jaccard index > threshold parameter
- (default threshold: 0.5).
- 2) Produce localization target by 'encoding' variance into offsets of ground
- truth boxes and their matched 'priorboxes'.
- 3) Hard negative mining to filter the excessive number of negative examples
- that comes with using a large number of default bounding boxes.
- (default negative:positive ratio 3:1)
- Objective Loss:
- L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
- Where, Lconf is the CrossEntropy Loss and Lloc is the SmoothL1 Loss
- weighted by α which is set to 1 by cross val.
- Args:
- c: class confidences,
- l: predicted boxes,
- g: ground truth boxes
- N: number of matched default boxes
- See: https://arxiv.org/pdf/1512.02325.pdf for more details.
- """
-
- def __init__(self, num_classes, overlap_thresh, prior_for_matching, bkg_label, neg_mining, neg_pos, neg_overlap, encode_target):
- super(MultiBoxLoss, self).__init__()
- self.num_classes = num_classes
- self.threshold = overlap_thresh
- self.background_label = bkg_label
- self.encode_target = encode_target
- self.use_prior_for_matching = prior_for_matching
- self.do_neg_mining = neg_mining
- self.negpos_ratio = neg_pos
- self.neg_overlap = neg_overlap
- self.variance = [0.1, 0.2]
-
- def forward(self, predictions, priors, targets):
- """Multibox Loss
- Args:
- predictions (tuple): A tuple containing loc preds, conf preds,
- and prior boxes from SSD net.
- conf shape: torch.size(batch_size,num_priors,num_classes)
- loc shape: torch.size(batch_size,num_priors,4)
- priors shape: torch.size(num_priors,4)
-
- ground_truth (tensor): Ground truth boxes and labels for a batch,
- shape: [batch_size,num_objs,5] (last idx is the label).
- """
-
- loc_data, conf_data, landm_data = predictions
- priors = priors
- num = loc_data.size(0)
- num_priors = (priors.size(0))
-
- # match priors (default boxes) and ground truth boxes
- loc_t = torch.Tensor(num, num_priors, 4)
- landm_t = torch.Tensor(num, num_priors, 10)
- conf_t = torch.LongTensor(num, num_priors)
- for idx in range(num):
- truths = targets[idx][:, :4].data
- labels = targets[idx][:, -1].data
- landms = targets[idx][:, 4:14].data
- defaults = priors.data
- match(self.threshold, truths, defaults, self.variance, labels, landms, loc_t, conf_t, landm_t, idx)
- if GPU:
- loc_t = loc_t.cuda()
- conf_t = conf_t.cuda()
- landm_t = landm_t.cuda()
-
- zeros = torch.tensor(0).cuda()
- # landm Loss (Smooth L1)
- # Shape: [batch,num_priors,10]
- pos1 = conf_t > zeros
- num_pos_landm = pos1.long().sum(1, keepdim=True)
- N1 = max(num_pos_landm.data.sum().float(), 1)
- pos_idx1 = pos1.unsqueeze(pos1.dim()).expand_as(landm_data)
- landm_p = landm_data[pos_idx1].view(-1, 10)
- landm_t = landm_t[pos_idx1].view(-1, 10)
- loss_landm = F.smooth_l1_loss(landm_p, landm_t, reduction='sum')
-
-
- pos = conf_t != zeros
- conf_t[pos] = 1
-
- # Localization Loss (Smooth L1)
- # Shape: [batch,num_priors,4]
- pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data)
- loc_p = loc_data[pos_idx].view(-1, 4)
- loc_t = loc_t[pos_idx].view(-1, 4)
- loss_l = F.smooth_l1_loss(loc_p, loc_t, reduction='sum')
-
- # Compute max conf across batch for hard negative mining
- batch_conf = conf_data.view(-1, self.num_classes)
- loss_c = log_sum_exp(batch_conf) - batch_conf.gather(1, conf_t.view(-1, 1))
-
- # Hard Negative Mining
- loss_c[pos.view(-1, 1)] = 0 # filter out pos boxes for now
- loss_c = loss_c.view(num, -1)
- _, loss_idx = loss_c.sort(1, descending=True)
- _, idx_rank = loss_idx.sort(1)
- num_pos = pos.long().sum(1, keepdim=True)
- num_neg = torch.clamp(self.negpos_ratio*num_pos, max=pos.size(1)-1)
- neg = idx_rank < num_neg.expand_as(idx_rank)
-
- # Confidence Loss Including Positive and Negative Examples
- pos_idx = pos.unsqueeze(2).expand_as(conf_data)
- neg_idx = neg.unsqueeze(2).expand_as(conf_data)
- conf_p = conf_data[(pos_idx+neg_idx).gt(0)].view(-1,self.num_classes)
- targets_weighted = conf_t[(pos+neg).gt(0)]
- loss_c = F.cross_entropy(conf_p, targets_weighted, reduction='sum')
-
- # Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
- N = max(num_pos.data.sum().float(), 1)
- loss_l /= N
- loss_c /= N
- loss_landm /= N1
-
- return loss_l, loss_c, loss_landm
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/latent_depth/README.md b/spaces/mshukor/UnIVAL/fairseq/examples/latent_depth/README.md
deleted file mode 100644
index 7774c333053b95d15b180fdfc3ee3cd817790520..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/latent_depth/README.md
+++ /dev/null
@@ -1,77 +0,0 @@
-# Deep Transformers with Latent Depth (Li et al., 2020)
-
-[https://arxiv.org/abs/2009.13102](https://arxiv.org/abs/2009.13102).
-
-## Introduction
-
-We present a probabilistic framework to automatically learn which layer(s) to use by learning the posterior distributions of layer selection. As an extension of this framework, we propose a novel method to train one shared Transformer network for multilingual machine translation with different layer selection posteriors for each language pair.
-
-## Training a multilingual model with latent depth
-
-Below is an example of training with latent depth in decoder for one-to-many (O2M) related languages. We use the same preprocessed (numberized and binarized) TED8 dataset as in [Balancing Training for Multilingual Neural Machine Translation (Wang et al., 2020)](https://github.com/cindyxinyiwang/multiDDS), which could be generated by [the script](https://github.com/cindyxinyiwang/multiDDS/blob/multiDDS/util_scripts/prepare_multilingual_data.sh) the author provided.
-```bash
-lang_pairs_str="eng-aze,eng-bel,eng-ces,eng-glg,eng-por,eng-rus,eng-slk,eng-tur"
-databin_dir=<path to binarized data>
-
-fairseq-train ${databin_dir} \
- --user-dir examples/latent_depth/latent_depth_src \
- --lang-pairs "${lang_pairs_str}" \
- --arch multilingual_transformer_iwslt_de_en \
- --task multilingual_translation_latent_depth \
- --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
- --share-encoders \
- --share-decoders \
- --decoder-langtok \
- --share-decoder-input-output-embed \
- --dropout 0.3 --attention-dropout 0.3 \
- --optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \
- --lr-scheduler inverse_sqrt --stop-min-lr 1e-9 --warmup-init-lr 1e-7 --warmup-updates 8000 \
- --max-tokens 4096 --update-freq 1 \
- --lr 0.0015 \
- --clip-norm 1.0 \
- --seed 2 \
- --ddp-backend=legacy_ddp \
- --encoder-layers 12 \
- --decoder-layers 24 \
- --decoder-latent-layer \
- --sparsity-weight 0.1 \
- --anneal-updates 5000 \
- --soft-update 500 \
- --target-layers 12 \
- --share-weight 0.1
-```
-## Inference command
-
-```bash
-lang_pairs_str="eng-aze,eng-bel,eng-ces,eng-glg,eng-por,eng-rus,eng-slk,eng-tur"
-databin_dir=<path to binarized data>
-model_path=<path to checkpoint>
-src_lang=<source language to translate from>
-tgt_lang=<target language to translate to>
-gen_data=<name of data split, e.g. valid, test, etc>
-
-fairseq-generate ${databin_dir} \
- --path ${model_path} \
- --task multilingual_translation_latent_depth \
- --decoder-latent-layer \
- --lang-pairs "${lang_pairs_str}" \
- -s ${src_lang} -t ${tgt_lang} \
- --gen-subset $gen_data \
- --scoring sacrebleu \
- --remove-bpe 'sentencepiece' \
- --lenpen 1.0 \
- --beam 5 \
- --decoder-langtok \
- --max-tokens 4096
-```
-
-
-## Citation
-```bibtex
-@article{li2020deep,
- title={Deep Transformers with Latent Depth},
- author={Li, Xian and Stickland, Asa Cooper and Tang, Yuqing and Kong, Xiang},
- journal={arXiv preprint arXiv:2009.13102},
- year={2020}
-}
-```
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/data/encoders/space_tokenizer.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/data/encoders/space_tokenizer.py
deleted file mode 100644
index 925ad41b7c1aee6738c63938c36bd3ee16dca812..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/data/encoders/space_tokenizer.py
+++ /dev/null
@@ -1,21 +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 re
-
-from fairseq.data.encoders import register_tokenizer
-from fairseq.dataclass import FairseqDataclass
-
-
-@register_tokenizer("space", dataclass=FairseqDataclass)
-class SpaceTokenizer(object):
- def __init__(self, *unused):
- self.space_tok = re.compile(r"\s+")
-
- def encode(self, x: str) -> str:
- return self.space_tok.sub(" ", x)
-
- def decode(self, x: str) -> str:
- return x
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/distributed/utils.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/distributed/utils.py
deleted file mode 100644
index 01d9926f27013f8bbf10f0cffc210390cf310cf9..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/distributed/utils.py
+++ /dev/null
@@ -1,848 +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 io
-import logging
-import os
-import pickle
-import random
-import socket
-import struct
-import subprocess
-import warnings
-from argparse import Namespace
-from collections import OrderedDict
-from dataclasses import dataclass
-from typing import Any, Dict, List, Mapping, Optional
-import sys
-import time
-
-import torch
-import torch.distributed as dist
-from fairseq.dataclass.configs import DistributedTrainingConfig, FairseqConfig
-from omegaconf import open_dict
-
-try:
- import torch_xla.core.xla_model as xm
-except ImportError:
- xm = None
-
-
-# Flag to indicate if we're using Megatron
-# NOTE: this is a temporary hack until we move away from Megatron's model parallel init
-_USE_MEGATRON = False
-
-# Whether to use XLA ops (e.g., on TPUs) instead of CUDA ops.
-_USE_XLA = False
-
-
-logger = logging.getLogger(__name__)
-
-
-def is_master(cfg: DistributedTrainingConfig):
- return cfg.distributed_rank == 0
-
-
-def infer_init_method(cfg: DistributedTrainingConfig, force_distributed=False):
- if cfg.distributed_init_method is not None or cfg.tpu:
- return
-
- num_pipelines_per_node = None
- if cfg.pipeline_model_parallel:
- num_pipeline_devices, num_pipelines_per_node = _pipeline_parallel_pre_init(cfg)
-
- if all(
- key in os.environ
- for key in ["MASTER_ADDR", "MASTER_PORT", "WORLD_SIZE", "RANK"]
- ):
- # support torch.distributed.launch
- _infer_torch_distributed_launch_init(cfg)
- elif cfg.distributed_port > 0:
- # we can determine the init method automatically for Slurm
- _infer_slurm_init(cfg, num_pipelines_per_node)
- elif cfg.distributed_world_size > 1 or force_distributed:
- # fallback for single node with multiple GPUs
- _infer_single_node_init(cfg)
-
- if cfg.pipeline_model_parallel:
- _pipeline_parallel_post_init(cfg, num_pipeline_devices, num_pipelines_per_node)
- elif not cfg.distributed_no_spawn:
- with open_dict(cfg):
- cfg.distributed_num_procs = min(
- torch.cuda.device_count(), cfg.distributed_world_size
- )
-
-
-def _infer_torch_distributed_launch_init(cfg: DistributedTrainingConfig):
- cfg.distributed_init_method = "env://"
- cfg.distributed_world_size = int(os.environ["WORLD_SIZE"])
- cfg.distributed_rank = int(os.environ["RANK"])
- # processes are created by torch.distributed.launch
- cfg.distributed_no_spawn = True
-
-
-def _infer_slurm_init(cfg: DistributedTrainingConfig, num_pipelines_per_node):
- node_list = os.environ.get("SLURM_STEP_NODELIST")
- if node_list is None:
- node_list = os.environ.get("SLURM_JOB_NODELIST")
- if node_list is not None:
- try:
- hostnames = subprocess.check_output(
- ["scontrol", "show", "hostnames", node_list]
- )
- cfg.distributed_init_method = "tcp://{host}:{port}".format(
- host=hostnames.split()[0].decode("utf-8"),
- port=cfg.distributed_port,
- )
- nnodes = int(os.environ.get("SLURM_NNODES"))
- ntasks_per_node = os.environ.get("SLURM_NTASKS_PER_NODE")
- if ntasks_per_node is not None:
- ntasks_per_node = int(ntasks_per_node)
- else:
- ntasks = int(os.environ.get("SLURM_NTASKS"))
- nnodes = int(os.environ.get("SLURM_NNODES"))
- assert ntasks % nnodes == 0
- ntasks_per_node = int(ntasks / nnodes)
- if ntasks_per_node == 1:
- gpus_per_node = torch.cuda.device_count()
- node_id = int(os.environ.get("SLURM_NODEID"))
- cfg.distributed_rank = node_id * gpus_per_node
- cfg.distributed_world_size = nnodes * gpus_per_node
- elif cfg.pipeline_model_parallel:
- assert ntasks_per_node == num_pipelines_per_node, (
- "SLURM --ntasks-per-node must match number of pipelines per "
- "node (={})".format(num_pipelines_per_node)
- )
- cfg.distributed_no_spawn = True
- # For 4-way MP on nodes with 8 GPUs, ranks will be [0, 1] on
- # the first node, [1, 2] on the second node, etc. This
- # matches torch.distributed.launch.
- node_id = int(os.environ.get("SLURM_NODEID"))
- local_id = int(os.environ.get("SLURM_LOCALID"))
- cfg.distributed_rank = node_id * num_pipelines_per_node + local_id
- # In the above example, device_id will always be in [0, 1],
- # which also matches torch.distributed.launch.
- cfg.device_id = local_id
- # We also want to set distributed_world_size to be the total
- # number of pipelines across all nodes.
- cfg.distributed_world_size = nnodes * num_pipelines_per_node
- else:
- assert ntasks_per_node == cfg.distributed_world_size // nnodes
- cfg.distributed_no_spawn = True
- cfg.distributed_rank = int(os.environ.get("SLURM_PROCID"))
- cfg.device_id = int(os.environ.get("SLURM_LOCALID"))
- except subprocess.CalledProcessError as e: # scontrol failed
- raise e
- except FileNotFoundError: # Slurm is not installed
- pass
-
-
-def _infer_single_node_init(cfg: DistributedTrainingConfig):
- assert (
- cfg.distributed_world_size <= torch.cuda.device_count()
- ), f"world size is {cfg.distributed_world_size} but have {torch.cuda.device_count()} available devices"
- port = random.randint(10000, 20000)
- cfg.distributed_init_method = "tcp://localhost:{port}".format(port=port)
-
-
-def _pipeline_parallel_pre_init(cfg: DistributedTrainingConfig):
- from fairseq import utils
-
- balance_exists = (
- cfg.pipeline_balance is not None
- or cfg.pipeline_encoder_balance is not None
- or cfg.pipeline_decoder_balance is not None
- )
- devices_exist = (
- cfg.pipeline_devices is not None
- or cfg.pipeline_encoder_devices is not None
- or cfg.pipeline_decoder_devices is not None
- )
- if not balance_exists:
- raise ValueError(
- "--pipeline-balance is currently required for pipeline model parallelism"
- )
- if not devices_exist:
- raise ValueError(
- "--pipeline-devices is currently required for pipeline model parallelism"
- )
-
- cfg.pipeline_balance = utils.eval_str_list(cfg.pipeline_balance, type=int)
- if cfg.pipeline_devices is not None:
- cfg.pipeline_devices = utils.eval_str_list(cfg.pipeline_devices, type=int)
- num_pipeline_devices = len(set(cfg.pipeline_devices))
- else:
- cfg.pipeline_encoder_devices = utils.eval_str_list(
- cfg.pipeline_encoder_devices, type=int
- )
- cfg.pipeline_decoder_devices = utils.eval_str_list(
- cfg.pipeline_decoder_devices, type=int
- )
- num_pipeline_devices = len(
- set(cfg.pipeline_encoder_devices + cfg.pipeline_decoder_devices)
- )
- gpus_per_node = torch.cuda.device_count()
- assert (
- gpus_per_node >= num_pipeline_devices
- and gpus_per_node % num_pipeline_devices == 0
- ), (
- "the number of unique device IDs in --pipeline-devices must evenly divide "
- "the number of GPUs per node (multi-node pipelining is not yet supported)"
- )
- num_pipelines_per_node = gpus_per_node // num_pipeline_devices
- return num_pipeline_devices, num_pipelines_per_node
-
-
-def _pipeline_parallel_post_init(
- cfg: DistributedTrainingConfig, num_pipeline_devices, num_pipelines_per_node
-):
- if not cfg.distributed_no_spawn:
- # When distributed_no_spawn is False, we expect distributed_rank and
- # distributed_world_size to be based on the total number of GPUs, so
- # we need to correct them to be based on the number of pipelines.
- assert cfg.distributed_world_size % num_pipeline_devices == 0
- cfg.distributed_world_size = (
- cfg.distributed_world_size // num_pipeline_devices
- )
- # In the case of 4-way MP on nodes with 8 GPUs, we want
- # distributed_rank to be the starting GPU index for each pipeline
- # i.e., 0, 2, ...
- gpus_per_node = torch.cuda.device_count()
- assert cfg.distributed_rank % gpus_per_node == 0
- assert cfg.distributed_rank % num_pipeline_devices == 0
-
- with open_dict(cfg):
- cfg.distributed_rank = cfg.distributed_rank // num_pipeline_devices
- # launch one process per pipeline
- cfg.distributed_num_procs = num_pipelines_per_node
-
- # if we have 4-way MP on a node with 8 GPUs, we want device_ids to be 0
- # and 4, indicating the starting device IDs for each pipeline
- cfg.device_id *= num_pipeline_devices
-
- if cfg.device_id > 0:
- # if there's multiple pipelines on a node (e.g., 4-way MP on an 8
- # GPU node), we need to adjust pipeline_devices accordingly
- logger.debug(
- "setting CUDA device={} on rank {}".format(
- cfg.device_id, cfg.distributed_rank
- )
- )
- torch.cuda.set_device(cfg.device_id)
- with open_dict(cfg):
- cfg.pipeline_devices = [cfg.device_id + d for d in cfg.pipeline_devices]
- logger.info(
- "setting pipeline_devices={} on rank {}".format(
- cfg.pipeline_devices, cfg.distributed_rank
- )
- )
-
-
-def distributed_init(cfg: FairseqConfig):
- if isinstance(cfg, Namespace):
- from fairseq.dataclass.utils import convert_namespace_to_omegaconf
-
- cfg = convert_namespace_to_omegaconf(cfg)
-
- if not cfg.common.tpu:
- if torch.distributed.is_available() and torch.distributed.is_initialized():
- warnings.warn(
- "Distributed is already initialized, cannot initialize twice!"
- )
- else:
- logger.info(
- "distributed init (rank {}): {}, world_size {}, backend: {}".format(
- cfg.distributed_training.distributed_rank,
- cfg.distributed_training.distributed_init_method,
- cfg.distributed_training.distributed_world_size,
- cfg.distributed_training.distributed_backend
- )
- )
- logger.info('Start init')
- max_time_wait = 600
-
- torch.cuda.set_device(cfg.distributed_training.device_id) # to fix NCCL error!!
-
- for i in range(max_time_wait):
- try:
- dist.init_process_group(
- backend=cfg.distributed_training.distributed_backend,
- init_method=cfg.distributed_training.distributed_init_method,
- world_size=cfg.distributed_training.distributed_world_size,
- rank=cfg.distributed_training.distributed_rank,
- )
- logger.info(
- "initialized host {} as rank {}".format(
- socket.gethostname(),
- cfg.distributed_training.distributed_rank,
- )
- )
- if torch.distributed.is_initialized():
- print("single-machine distributed training is initialized.")
- break
- except ValueError:
- # This is caused by TCPStore failure.
- print('Retry: {}, with value error {}'.format(
- i + 1, sys.exc_info()[0]))
- time.sleep(5)
- if i == max_time_wait - 1:
- print('k8s resource wait too long time')
- exit(-1)
- except Exception:
- print('Retry: {}, with value error {}'.format(
- i + 1, sys.exc_info()[0]))
- exit(-1)
- # perform a dummy all-reduce to initialize the NCCL communicator
- if torch.cuda.is_available():
- dist.all_reduce(torch.zeros(1).cuda())
-
- cfg.distributed_training.distributed_rank = torch.distributed.get_rank()
- else:
- assert xm.xrt_world_size() == cfg.distributed_training.distributed_world_size
- global _USE_XLA
- _USE_XLA = True
- cfg.distributed_training.device_id = xm.get_local_ordinal()
- cfg.distributed_training.distributed_rank = xm.get_ordinal()
- xm.rendezvous("distributed_init") # wait for all workers
-
- if is_master(cfg.distributed_training):
- logging.getLogger().setLevel(logging.INFO)
- else:
- logging.getLogger().setLevel(logging.WARNING)
-
- if cfg.common.model_parallel_size > 1:
- try:
- from fairseq.model_parallel.megatron.mpu import (
- initialize_model_parallel,
- model_parallel_cuda_manual_seed,
- )
- except ImportError:
- raise ImportError(
- "\n\nPlease install the megatron submodule:"
- "\n\n git submodule update --init "
- "fairseq/model_parallel/megatron"
- )
- global _USE_MEGATRON
- _USE_MEGATRON = True
- initialize_model_parallel(cfg.common.model_parallel_size)
- model_parallel_cuda_manual_seed(cfg.common.seed)
- model_part_number = get_model_parallel_rank()
- cfg.checkpoint.checkpoint_suffix += "-model_part-{0}".format(model_part_number)
-
- if hasattr(cfg, "model") and getattr(cfg.model, "base_layers", 0) > 0:
- cfg.checkpoint.checkpoint_suffix = f"-rank-{cfg.distributed_training.distributed_rank}"
-
- return cfg.distributed_training.distributed_rank
-
-
-def distributed_main(i, main, cfg: FairseqConfig, kwargs):
- # ####
- if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
- # args.rank = int(os.environ["RANK"])
- # args.world_size = int(os.environ['WORLD_SIZE'])
- i = int(os.environ['LOCAL_RANK'])
- elif 'SLURM_PROCID' in os.environ:
- # args.rank = int(os.environ['SLURM_PROCID'])
- i = cfg.distributed_training.distributed_rank % torch.cuda.device_count()
- # print(args.gpu, os.environ['SLURM_LOCALID'], os.environ['SLURM_JOB_NODELIST'], os.environ['SLURM_STEP_GPUS'])
-
-
- cfg.distributed_training.device_id = i
- print('cfg.distributed_training.device_id', cfg.distributed_training.device_id)
- print(cfg.distributed_training)
- if torch.cuda.is_available() and not cfg.common.cpu and not cfg.common.tpu:
- torch.cuda.set_device(cfg.distributed_training.device_id)
- if cfg.distributed_training.distributed_rank is None: # torch.multiprocessing.spawn
- cfg.distributed_training.distributed_rank = kwargs.pop("start_rank", 0) + i
-
- cfg.distributed_training.distributed_rank = distributed_init(cfg)
-
- after_distributed_init_fn = kwargs.pop("after_distributed_init_fn", None)
- if after_distributed_init_fn:
- cfg = after_distributed_init_fn(cfg)
-
- main(cfg, **kwargs)
-
- if torch.distributed.is_initialized():
- torch.distributed.barrier(get_global_group())
-
-
-def call_main(cfg: FairseqConfig, main, **kwargs):
- if cfg.distributed_training.distributed_init_method is None:
- infer_init_method(cfg.distributed_training)
-
- if cfg.distributed_training.distributed_init_method is not None:
- # distributed training
- if not cfg.distributed_training.distributed_no_spawn:
- start_rank = cfg.distributed_training.distributed_rank
- cfg.distributed_training.distributed_rank = None # assign automatically
- kwargs["start_rank"] = start_rank
- torch.multiprocessing.spawn(
- fn=distributed_main,
- args=(main, cfg, kwargs),
- nprocs=min(
- torch.cuda.device_count(),
- cfg.distributed_training.distributed_world_size,
- ),
- join=True,
- )
- else:
- distributed_main(cfg.distributed_training.device_id, main, cfg, kwargs)
- elif cfg.common.tpu and cfg.distributed_training.distributed_world_size > 1:
- import torch_xla.distributed.xla_multiprocessing as xmp
-
- torch.multiprocessing.set_sharing_strategy("file_system")
- xmp.spawn(
- fn=distributed_main,
- args=(main, cfg, kwargs),
- # tpu-comment:
- # 8 devices in one TPU VM, is the max processes to be spawned.
- # The rest is driven by xm.distributed.xla_dist
- nprocs=min(cfg.distributed_training.distributed_world_size, 8),
- )
- else:
- # single GPU main
- main(cfg, **kwargs)
-
-
-def use_xla():
- global _USE_XLA
- return _USE_XLA
-
-
-def new_groups(grouped_ranks: List[List[int]]):
- if use_xla():
- return ("tpu", grouped_ranks)
- else:
- groups = [dist.new_group(g) for g in grouped_ranks]
- my_group_idx = _find_my_group_index(grouped_ranks)
- return groups[my_group_idx]
-
-
-def _find_my_group_index(grouped_ranks):
- my_rank = get_global_rank()
- for i, group in enumerate(grouped_ranks):
- if my_rank in group:
- return i
- raise RuntimeError
-
-
-def _find_my_group(grouped_ranks):
- index = _find_my_group_index(grouped_ranks)
- return grouped_ranks[index]
-
-
-def get_rank(group):
- if use_xla():
- assert group[0] == "tpu"
- my_group = _find_my_group(group[1])
- return my_group.index(get_global_rank())
- else:
- return dist.get_rank(group=group)
-
-
-def get_world_size(group):
- if use_xla():
- assert group[0] == "tpu"
- my_group = _find_my_group(group[1])
- return len(my_group)
- elif torch.distributed.is_initialized():
- return dist.get_world_size(group=group)
- else:
- return 1
-
-
-def get_global_group():
- if use_xla():
- return new_groups([list(range(get_global_world_size()))])
- elif torch.distributed.is_initialized():
- if not hasattr(get_global_group, "_global_group"):
- # ideally we could use torch.distributed.group.WORLD, but it seems
- # to cause random NCCL hangs in some cases
- get_global_group._global_group = dist.new_group()
- return get_global_group._global_group
- else:
- return None
-
-
-def get_global_rank():
- if use_xla():
- return xm.get_ordinal()
- elif torch.distributed.is_initialized():
- return torch.distributed.get_rank()
- else:
- return 0
-
-
-def get_global_world_size():
- if use_xla():
- return xm.xrt_world_size()
- elif torch.distributed.is_initialized():
- return torch.distributed.get_world_size()
- else:
- return 1
-
-
-def get_data_parallel_group():
- """Get the data parallel group the caller rank belongs to."""
- global _USE_MEGATRON
- if _USE_MEGATRON:
- from fairseq.model_parallel.megatron import mpu
-
- return mpu.get_data_parallel_group()
- else:
- return get_global_group()
-
-
-def get_data_parallel_rank():
- """Return my rank for the data parallel group."""
- return get_rank(get_data_parallel_group())
-
-
-def get_data_parallel_world_size():
- """Return world size for the data parallel group."""
- return get_world_size(get_data_parallel_group())
-
-
-def get_model_parallel_group():
- global _USE_MEGATRON
- if _USE_MEGATRON:
- from fairseq.model_parallel.megatron import mpu
-
- return mpu.get_model_parallel_group()
- else:
- return None
-
-
-def get_model_parallel_rank():
- """Return my rank for the model parallel group."""
- return get_rank(get_model_parallel_group())
-
-
-def get_model_parallel_world_size():
- """Return world size for the model parallel group."""
- return get_world_size(get_model_parallel_group())
-
-
-def all_reduce(tensor, group, op="sum"):
- if use_xla():
- assert isinstance(group, tuple) and group[0] == "tpu"
- tensor = [tensor] # wrap in a list to make xm.all_reduce in-place
- return xm.all_reduce(op, tensor, groups=group[1])[0]
- else:
- if op == "sum":
- op = dist.ReduceOp.SUM
- elif op == "max":
- op = dist.ReduceOp.MAX
- else:
- raise NotImplementedError
- dist.all_reduce(tensor, op=op, group=group)
- return tensor
-
-
-def broadcast(tensor, src, group):
- if use_xla():
- # XLA doesn't support broadcast, hack it with all_reduce
- if get_rank(group) != src:
- tensor.zero_()
- all_reduce(tensor, group)
- else:
- dist.broadcast(tensor, src=src, group=group)
-
-
-def all_to_all(tensor, group):
- """Perform an all-to-all operation on a 1D Tensor."""
- assert tensor.dim() == 1
- split_count = get_world_size(group=group)
- assert tensor.numel() % split_count == 0
- if use_xla():
- assert isinstance(group, tuple) and group[0] == "tpu"
- return xm.all_to_all(
- tensor,
- split_dimension=0,
- concat_dimension=0,
- split_count=split_count,
- groups=group[1],
- )
- else:
- output = torch.zeros_like(tensor)
- dist.all_to_all_single(output, tensor, group=group)
- return output
-
-
-def all_gather(tensor, group, return_tensor=False):
- """Perform an all-gather operation."""
- if use_xla():
- result = xm.all_gather(tensor, groups=group[1])
- world_size = get_world_size(group=group)
- result = result.view(world_size, *tensor.size())
- if return_tensor:
- return result
- else:
- return [result[i] for i in range(world_size)]
- else:
- world_size = get_world_size(group=group)
- rank = get_rank(group=group)
- tensor_list = [
- tensor if i == rank else torch.empty_like(tensor) for i in range(world_size)
- ]
- dist.all_gather(tensor_list, tensor, group=group)
- if return_tensor:
- return torch.stack(tensor_list, dim=0)
- else:
- return tensor_list
-
-
-def all_gather_list(data, group=None, max_size=16384):
- """Gathers arbitrary data from all nodes into a list.
-
- Similar to :func:`~torch.distributed.all_gather` but for arbitrary Python
- data. Note that *data* must be picklable and any CUDA tensors will be moved
- to CPU and returned on CPU as well.
-
- Args:
- data (Any): data from the local worker to be gathered on other workers
- group: group of the collective
- max_size (int, optional): maximum size of the data to be gathered
- across workers
- """
- from fairseq import utils
-
- if group is None:
- group = get_global_group()
- torch.distributed.barrier(group=group)
- rank = get_rank(group=group)
- world_size = get_world_size(group=group)
-
- buffer_size = max_size * world_size
- if (
- not hasattr(all_gather_list, "_buffer")
- or all_gather_list._buffer.numel() < buffer_size
- ):
- all_gather_list._buffer = torch.cuda.ByteTensor(buffer_size)
- all_gather_list._cpu_buffer = torch.ByteTensor(max_size).pin_memory()
- buffer = all_gather_list._buffer
- buffer.zero_()
- cpu_buffer = all_gather_list._cpu_buffer
-
- data = utils.move_to_cpu(data)
- enc = pickle.dumps(data)
- enc_size = len(enc)
- header_size = 4 # size of header that contains the length of the encoded data
- size = header_size + enc_size
- if size > max_size:
- raise ValueError(
- "encoded data size ({}) exceeds max_size ({})".format(size, max_size)
- )
-
- header = struct.pack(">I", enc_size)
- cpu_buffer[:size] = torch.ByteTensor(list(header + enc))
- start = rank * max_size
- buffer[start : start + size].copy_(cpu_buffer[:size])
-
- all_reduce(buffer, group=group)
-
- buffer = buffer.cpu()
- try:
- result = []
- for i in range(world_size):
- out_buffer = buffer[i * max_size : (i + 1) * max_size]
- (enc_size,) = struct.unpack(">I", bytes(out_buffer[:header_size].tolist()))
- if enc_size > 0:
- result.append(
- pickle.loads(
- bytes(out_buffer[header_size : header_size + enc_size].tolist())
- )
- )
- return result
- except pickle.UnpicklingError:
- raise Exception(
- "Unable to unpickle data from other workers. all_gather_list requires all "
- "workers to enter the function together, so this error usually indicates "
- "that the workers have fallen out of sync somehow. Workers can fall out of "
- "sync if one of them runs out of memory, or if there are other conditions "
- "in your training script that can cause one worker to finish an epoch "
- "while other workers are still iterating over their portions of the data. "
- "Try rerunning with --ddp-backend=legacy_ddp and see if that helps."
- )
-
-
-def all_reduce_dict(data: Mapping[str, Any], device, group) -> Dict[str, Any]:
- """
- AllReduce a dictionary of values across workers. We separately
- reduce items that are already on the device and items on CPU for
- better performance.
-
- Args:
- data (Mapping[str, Any]): dictionary of data to all-reduce, but
- cannot be a nested dictionary
- device (torch.device): device for the reduction
- group: group of the collective
- """
- data_keys = list(data.keys())
-
- # We want to separately reduce items that are already on the
- # device and items on CPU for performance reasons.
- cpu_data = OrderedDict()
- device_data = OrderedDict()
- for k in data_keys:
- t = data[k]
- if not torch.is_tensor(t):
- cpu_data[k] = torch.tensor(t, dtype=torch.double)
- elif t.device.type != device.type:
- cpu_data[k] = t.to(dtype=torch.double)
- else:
- device_data[k] = t.to(dtype=torch.double)
-
- def _all_reduce_dict(data: OrderedDict):
- if len(data) == 0:
- return data
- buf = torch.cat([t.view(-1) for t in data.values()]).to(device=device)
- all_reduce(buf, group=group)
- split_buf = torch.split(buf, [t.numel() for t in data.values()])
- reduced_data = [t.view_as(orig) for t, orig in zip(split_buf, data.values())]
- return OrderedDict(zip(data.keys(), reduced_data))
-
- cpu_data = _all_reduce_dict(cpu_data)
- device_data = _all_reduce_dict(device_data)
-
- def get_from_stack(key):
- if key in cpu_data:
- return cpu_data[key]
- elif key in device_data:
- return device_data[key]
- raise KeyError
-
- return OrderedDict([(key, get_from_stack(key)) for key in data_keys])
-
-
-def broadcast_tensors(
- tensors: Optional[List[torch.Tensor]],
- src_rank: int,
- group: object,
- dist_device: Optional[torch.device] = None,
-) -> List[torch.Tensor]:
- """
- Broadcasts a list of tensors without other (non-src) ranks needing to know
- the dtypes/shapes of the tensors.
- """
- if dist_device is None:
- if torch.distributed.get_backend(group) == "nccl":
- dist_device = torch.device("cuda")
- else:
- dist_device = torch.device("cpu")
-
- # share metadata first to simplify transfer
- is_src_rank = (get_rank(group) == src_rank)
- if is_src_rank:
- metadata = [
- {"size": t.size(), "dtype": t.dtype, "device": t.device} for t in tensors
- ]
- metadata = _broadcast_object_slow(metadata, src_rank, group, dist_device)
- else:
- metadata = _broadcast_object_slow(None, src_rank, group, dist_device)
-
- out_tensors = []
- for i, meta in enumerate(metadata):
- if is_src_rank:
- tensor = tensors[i]
- try:
- broadcast(tensors[i].to(dist_device), src=src_rank, group=group) #.contiguous()
- except: # error for audio experiments
- broadcast(tensors[i].to(dist_device).contiguous().cuda(), src=src_rank, group=group)
- print("non contiguous tensor, casted to contiguous:", i, tensors[i].shape, dist_device)
- else:
- tensor = torch.zeros(
- [meta["size"].numel()], dtype=meta["dtype"], device=dist_device
- )
- broadcast(tensor, src=src_rank, group=group)
- tensor = tensor.view(meta["size"]).to(meta["device"])
- out_tensors.append(tensor)
- return out_tensors
-
-
-def broadcast_object(
- obj: Any,
- src_rank: int,
- group: object,
- dist_device: Optional[torch.device] = None,
-) -> Any:
- """Broadcast an arbitrary Python object to other workers."""
- if dist_device is None:
- if torch.distributed.get_backend(group) == "nccl":
- dist_device = torch.device("cuda")
- else:
- dist_device = torch.device("cpu")
-
- if get_rank(group) == src_rank:
- # split the tensors from the non-tensors so we can broadcast them
- # directly, avoiding unnecessary serialization/deserialization
- tensors = []
- obj = _split_tensors_from_obj(obj, tensors)
- obj = _broadcast_object_slow(obj, src_rank, group, dist_device)
- tensors = broadcast_tensors(tensors, src_rank, group, dist_device)
- else:
- obj = _broadcast_object_slow(None, src_rank, group, dist_device)
- tensors = broadcast_tensors(None, src_rank, group, dist_device)
- return _put_tensors_in_obj(obj, tensors)
-
-
-def _broadcast_object_slow(
- obj: Any, src_rank: int, group: object, dist_device: torch.device,
-) -> Any:
- if get_rank(group) == src_rank:
- # Emit data
- buffer = io.BytesIO()
- torch.save(obj, buffer)
- buffer = torch.ByteTensor(buffer.getbuffer()).to(dist_device)
- length = torch.LongTensor([len(buffer)]).to(dist_device)
- broadcast(length, src=src_rank, group=group)
- broadcast(buffer, src=src_rank, group=group)
- else:
- # Fetch from the source
- length = torch.LongTensor([0]).to(dist_device)
- broadcast(length, src=src_rank, group=group)
- buffer = torch.ByteTensor(int(length.item())).to(dist_device)
- broadcast(buffer, src=src_rank, group=group)
- buffer = io.BytesIO(buffer.cpu().numpy())
- obj = torch.load(buffer, map_location="cpu")
- return obj
-
-
-@dataclass(frozen=True)
-class _TensorPlaceholder:
- index: int
-
-
-def _split_tensors_from_obj(obj: Any, tensors: List[torch.Tensor]) -> Any:
- if torch.is_tensor(obj):
- placeholder = _TensorPlaceholder(index=len(tensors))
- tensors.append(obj)
- return placeholder
- elif isinstance(obj, dict):
- return {k: _split_tensors_from_obj(v, tensors) for k, v in obj.items()}
- elif isinstance(obj, list):
- return [_split_tensors_from_obj(v, tensors) for v in obj]
- elif isinstance(obj, tuple):
- return tuple(_split_tensors_from_obj(v, tensors) for v in obj)
- elif isinstance(obj, set):
- return {_split_tensors_from_obj(v, tensors) for v in obj}
- else:
- return obj
-
-
-def _put_tensors_in_obj(obj: Any, tensors: List[torch.Tensor]) -> Any:
- if isinstance(obj, _TensorPlaceholder):
- return tensors[obj.index]
- elif isinstance(obj, dict):
- return {k: _put_tensors_in_obj(v, tensors) for k, v in obj.items()}
- elif isinstance(obj, list):
- return [_put_tensors_in_obj(v, tensors) for v in obj]
- elif isinstance(obj, tuple):
- return tuple(_put_tensors_in_obj(v, tensors) for v in obj)
- elif isinstance(obj, set):
- return {_put_tensors_in_obj(v, tensors) for v in obj}
- else:
- return obj
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/tasks/translation_multi_simple_epoch.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/tasks/translation_multi_simple_epoch.py
deleted file mode 100644
index 6f36e5b93e98497de31969d203ae04dbb4bd9306..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/tasks/translation_multi_simple_epoch.py
+++ /dev/null
@@ -1,430 +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 datetime
-import logging
-import time
-
-import torch
-from fairseq.data import (
- FairseqDataset,
- LanguagePairDataset,
- ListDataset,
- data_utils,
- iterators,
-)
-from fairseq.data.multilingual.multilingual_data_manager import (
- MultilingualDatasetManager,
-)
-from fairseq.data.multilingual.sampling_method import SamplingMethod
-from fairseq.tasks import LegacyFairseqTask, register_task
-from fairseq.utils import FileContentsAction
-
-
-###
-def get_time_gap(s, e):
- return (
- datetime.datetime.fromtimestamp(e) - datetime.datetime.fromtimestamp(s)
- ).__str__()
-
-
-###
-
-
-logger = logging.getLogger(__name__)
-
-
-@register_task("translation_multi_simple_epoch")
-class TranslationMultiSimpleEpochTask(LegacyFairseqTask):
- """
- Translate from one (source) language to another (target) language.
-
- Args:
- langs (List[str]): a list of languages that are being supported
- dicts (Dict[str, fairseq.data.Dictionary]): mapping from supported languages to their dictionaries
- training (bool): whether the task should be configured for training or not
-
- .. note::
-
- The translation task is compatible with :mod:`fairseq-train`,
- :mod:`fairseq-generate` and :mod:`fairseq-interactive`.
-
- The translation task provides the following additional command-line
- arguments:
-
- .. argparse::
- :ref: fairseq.tasks.translation_parser
- :prog:
- """
-
- @staticmethod
- def add_args(parser):
- """Add task-specific arguments to the parser."""
- # fmt: off
- parser.add_argument('-s', '--source-lang', default=None, metavar='SRC',
- help='inference source language')
- parser.add_argument('-t', '--target-lang', default=None, metavar='TARGET',
- help='inference target language')
- parser.add_argument('--lang-pairs', default=None, metavar='PAIRS',
- help='comma-separated list of language pairs (in training order): en-de,en-fr,de-fr',
- action=FileContentsAction)
- parser.add_argument('--keep-inference-langtok', action='store_true',
- help='keep language tokens in inference output (e.g. for analysis or debugging)')
-
- SamplingMethod.add_arguments(parser)
- MultilingualDatasetManager.add_args(parser)
- # fmt: on
-
- def __init__(self, args, langs, dicts, training):
- super().__init__(args)
- self.langs = langs
- self.dicts = dicts
- self.training = training
- if training:
- self.lang_pairs = args.lang_pairs
- else:
- self.lang_pairs = ["{}-{}".format(args.source_lang, args.target_lang)]
- # eval_lang_pairs for multilingual translation is usually all of the
- # lang_pairs. However for other multitask settings or when we want to
- # optimize for certain languages we want to use a different subset. Thus
- # the eval_lang_pairs class variable is provided for classes that extend
- # this class.
- self.eval_lang_pairs = self.lang_pairs
- # model_lang_pairs will be used to build encoder-decoder model pairs in
- # models.build_model(). This allows multitask type of sub-class can
- # build models other than the input lang_pairs
- self.model_lang_pairs = self.lang_pairs
- self.source_langs = [d.split("-")[0] for d in self.lang_pairs]
- self.target_langs = [d.split("-")[1] for d in self.lang_pairs]
- self.check_dicts(self.dicts, self.source_langs, self.target_langs)
-
- self.sampling_method = SamplingMethod.build_sampler(args, self)
- self.data_manager = MultilingualDatasetManager.setup_data_manager(
- args, self.lang_pairs, langs, dicts, self.sampling_method
- )
-
- def check_dicts(self, dicts, source_langs, target_langs):
- if self.args.source_dict is not None or self.args.target_dict is not None:
- # no need to check whether the source side and target side are sharing dictionaries
- return
- src_dict = dicts[source_langs[0]]
- tgt_dict = dicts[target_langs[0]]
- for src_lang in source_langs:
- assert (
- src_dict == dicts[src_lang]
- ), "Diffrent dictionary are specified for different source languages; "
- "TranslationMultiSimpleEpochTask only supports one shared dictionary across all source languages"
- for tgt_lang in target_langs:
- assert (
- tgt_dict == dicts[tgt_lang]
- ), "Diffrent dictionary are specified for different target languages; "
- "TranslationMultiSimpleEpochTask only supports one shared dictionary across all target languages"
-
- @classmethod
- def setup_task(cls, args, **kwargs):
- langs, dicts, training = MultilingualDatasetManager.prepare(
- cls.load_dictionary, args, **kwargs
- )
- return cls(args, langs, dicts, training)
-
- def has_sharded_data(self, split):
- return self.data_manager.has_sharded_data(split)
-
- def load_dataset(self, split, epoch=1, combine=False, **kwargs):
- """Load a given dataset split.
-
- Args:
- split (str): name of the split (e.g., train, valid, test)
- """
- if split in self.datasets:
- dataset = self.datasets[split]
- if self.has_sharded_data(split):
- if self.args.virtual_epoch_size is not None:
- if dataset.load_next_shard:
- shard_epoch = dataset.shard_epoch
- else:
- # no need to load next shard so skip loading
- # also this avoid always loading from beginning of the data
- return
- else:
- shard_epoch = epoch
- else:
- # estimate the shard epoch from virtual data size and virtual epoch size
- shard_epoch = self.data_manager.estimate_global_pass_epoch(epoch)
- logger.info(f"loading data for {split} epoch={epoch}/{shard_epoch}")
- logger.info(f"mem usage: {data_utils.get_mem_usage()}")
- if split in self.datasets:
- del self.datasets[split]
- logger.info("old dataset deleted manually")
- logger.info(f"mem usage: {data_utils.get_mem_usage()}")
- self.datasets[split] = self.data_manager.load_dataset(
- split,
- self.training,
- epoch=epoch,
- combine=combine,
- shard_epoch=shard_epoch,
- **kwargs,
- )
-
- def build_dataset_for_inference(self, src_tokens, src_lengths, constraints=None):
- if constraints is not None:
- raise NotImplementedError(
- "Constrained decoding with the multilingual_translation task is not supported"
- )
-
- src_data = ListDataset(src_tokens, src_lengths)
- dataset = LanguagePairDataset(src_data, src_lengths, self.source_dictionary)
- src_langtok_spec, tgt_langtok_spec = self.args.langtoks["main"]
- if self.args.lang_tok_replacing_bos_eos:
- dataset = self.data_manager.alter_dataset_langtok(
- dataset,
- src_eos=self.source_dictionary.eos(),
- src_lang=self.args.source_lang,
- tgt_eos=self.target_dictionary.eos(),
- tgt_lang=self.args.target_lang,
- src_langtok_spec=src_langtok_spec,
- tgt_langtok_spec=tgt_langtok_spec,
- )
- else:
- dataset.src = self.data_manager.src_dataset_tranform_func(
- self.args.source_lang,
- self.args.target_lang,
- dataset=dataset.src,
- spec=src_langtok_spec,
- )
- return dataset
-
- def build_generator(
- self,
- models,
- args,
- seq_gen_cls=None,
- extra_gen_cls_kwargs=None,
- ):
- if not getattr(args, "keep_inference_langtok", False):
- _, tgt_langtok_spec = self.args.langtoks["main"]
- if tgt_langtok_spec:
- tgt_lang_tok = self.data_manager.get_decoder_langtok(
- self.args.target_lang, tgt_langtok_spec
- )
- extra_gen_cls_kwargs = extra_gen_cls_kwargs or {}
- extra_gen_cls_kwargs["symbols_to_strip_from_output"] = {tgt_lang_tok}
-
- return super().build_generator(
- models, args, seq_gen_cls=None, extra_gen_cls_kwargs=extra_gen_cls_kwargs
- )
-
- def build_model(self, args):
- return super().build_model(args)
-
- def valid_step(self, sample, model, criterion):
- loss, sample_size, logging_output = super().valid_step(sample, model, criterion)
- return loss, sample_size, logging_output
-
- def inference_step(
- self, generator, models, sample, prefix_tokens=None, constraints=None
- ):
- with torch.no_grad():
- _, tgt_langtok_spec = self.args.langtoks["main"]
- if not self.args.lang_tok_replacing_bos_eos:
- if prefix_tokens is None and tgt_langtok_spec:
- tgt_lang_tok = self.data_manager.get_decoder_langtok(
- self.args.target_lang, tgt_langtok_spec
- )
- src_tokens = sample["net_input"]["src_tokens"]
- bsz = src_tokens.size(0)
- prefix_tokens = (
- torch.LongTensor([[tgt_lang_tok]]).expand(bsz, 1).to(src_tokens)
- )
- return generator.generate(
- models,
- sample,
- prefix_tokens=prefix_tokens,
- constraints=constraints,
- )
- else:
- return generator.generate(
- models,
- sample,
- prefix_tokens=prefix_tokens,
- bos_token=self.data_manager.get_decoder_langtok(
- self.args.target_lang, tgt_langtok_spec
- )
- if tgt_langtok_spec
- else self.target_dictionary.eos(),
- )
-
- def reduce_metrics(self, logging_outputs, criterion):
- super().reduce_metrics(logging_outputs, criterion)
-
- def max_positions(self):
- """Return the max sentence length allowed by the task."""
- return (self.args.max_source_positions, self.args.max_target_positions)
-
- @property
- def source_dictionary(self):
- return self.data_manager.get_source_dictionary(self.source_langs[0])
-
- @property
- def target_dictionary(self):
- return self.data_manager.get_target_dictionary(self.target_langs[0])
-
- def create_batch_sampler_func(
- self,
- max_positions,
- ignore_invalid_inputs,
- max_tokens,
- max_sentences,
- required_batch_size_multiple=1,
- seed=1,
- ):
- def construct_batch_sampler(dataset, epoch):
- splits = [
- s for s, _ in self.datasets.items() if self.datasets[s] == dataset
- ]
- split = splits[0] if len(splits) > 0 else None
- # NEW implementation
- if epoch is not None:
- # initialize the dataset with the correct starting epoch
- dataset.set_epoch(epoch)
-
- # get indices ordered by example size
- start_time = time.time()
- logger.info(f"start batch sampler: mem usage: {data_utils.get_mem_usage()}")
-
- with data_utils.numpy_seed(seed):
- indices = dataset.ordered_indices()
- logger.info(
- f"[{split}] @batch_sampler order indices time: {get_time_gap(start_time, time.time())}"
- )
- logger.info(f"mem usage: {data_utils.get_mem_usage()}")
-
- # filter examples that are too large
- if max_positions is not None:
- my_time = time.time()
- indices = self.filter_indices_by_size(
- indices, dataset, max_positions, ignore_invalid_inputs
- )
- logger.info(
- f"[{split}] @batch_sampler filter_by_size time: {get_time_gap(my_time, time.time())}"
- )
- logger.info(f"mem usage: {data_utils.get_mem_usage()}")
-
- # create mini-batches with given size constraints
- my_time = time.time()
- batch_sampler = dataset.batch_by_size(
- indices,
- max_tokens=max_tokens,
- max_sentences=max_sentences,
- required_batch_size_multiple=required_batch_size_multiple,
- )
-
- logger.info(
- f"[{split}] @batch_sampler batch_by_size time: {get_time_gap(my_time, time.time())}"
- )
- logger.info(
- f"[{split}] per epoch batch_sampler set-up time: {get_time_gap(start_time, time.time())}"
- )
- logger.info(f"mem usage: {data_utils.get_mem_usage()}")
-
- return batch_sampler
-
- return construct_batch_sampler
-
- # we need to override get_batch_iterator because we want to reset the epoch iterator each time
- def get_batch_iterator(
- self,
- dataset,
- max_tokens=None,
- max_sentences=None,
- max_positions=None,
- ignore_invalid_inputs=False,
- required_batch_size_multiple=1,
- seed=1,
- num_shards=1,
- shard_id=0,
- num_workers=0,
- epoch=1,
- data_buffer_size=0,
- disable_iterator_cache=False,
- ):
- """
- Get an iterator that yields batches of data from the given dataset.
-
- Args:
- dataset (~fairseq.data.FairseqDataset): dataset to batch
- max_tokens (int, optional): max number of tokens in each batch
- (default: None).
- max_sentences (int, optional): max number of sentences in each
- batch (default: None).
- max_positions (optional): max sentence length supported by the
- model (default: None).
- ignore_invalid_inputs (bool, optional): don't raise Exception for
- sentences that are too long (default: False).
- required_batch_size_multiple (int, optional): require batch size to
- be a multiple of N (default: 1).
- seed (int, optional): seed for random number generator for
- reproducibility (default: 1).
- num_shards (int, optional): shard the data iterator into N
- shards (default: 1).
- shard_id (int, optional): which shard of the data iterator to
- return (default: 0).
- num_workers (int, optional): how many subprocesses to use for data
- loading. 0 means the data will be loaded in the main process
- (default: 0).
- epoch (int, optional): the epoch to start the iterator from
- (default: 0).
- data_buffer_size (int, optional): number of batches to
- preload (default: 0).
- disable_iterator_cache (bool, optional): don't cache the
- EpochBatchIterator (ignores `FairseqTask::can_reuse_epoch_itr`)
- (default: False).
- Returns:
- ~fairseq.iterators.EpochBatchIterator: a batched iterator over the
- given dataset split
- """
- # initialize the dataset with the correct starting epoch
- assert isinstance(dataset, FairseqDataset)
- if dataset in self.dataset_to_epoch_iter:
- return self.dataset_to_epoch_iter[dataset]
- if self.args.sampling_method == "RoundRobin":
- batch_iter = super().get_batch_iterator(
- dataset,
- max_tokens=max_tokens,
- max_sentences=max_sentences,
- max_positions=max_positions,
- ignore_invalid_inputs=ignore_invalid_inputs,
- required_batch_size_multiple=required_batch_size_multiple,
- seed=seed,
- num_shards=num_shards,
- shard_id=shard_id,
- num_workers=num_workers,
- epoch=epoch,
- data_buffer_size=data_buffer_size,
- disable_iterator_cache=disable_iterator_cache,
- )
- self.dataset_to_epoch_iter[dataset] = batch_iter
- return batch_iter
-
- construct_batch_sampler = self.create_batch_sampler_func(
- max_positions,
- ignore_invalid_inputs,
- max_tokens,
- max_sentences,
- required_batch_size_multiple=required_batch_size_multiple,
- seed=seed,
- )
-
- epoch_iter = iterators.EpochBatchIterator(
- dataset=dataset,
- collate_fn=dataset.collater,
- batch_sampler=construct_batch_sampler,
- seed=seed,
- num_shards=num_shards,
- shard_id=shard_id,
- num_workers=num_workers,
- epoch=epoch,
- )
- return epoch_iter
diff --git a/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/training/modules/fake_fakes.py b/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/training/modules/fake_fakes.py
deleted file mode 100644
index 45c4ad559cef2730b771a709197e00ae1c87683c..0000000000000000000000000000000000000000
--- a/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/training/modules/fake_fakes.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import torch
-from kornia import SamplePadding
-from kornia.augmentation import RandomAffine, CenterCrop
-
-
-class FakeFakesGenerator:
- def __init__(self, aug_proba=0.5, img_aug_degree=30, img_aug_translate=0.2):
- self.grad_aug = RandomAffine(degrees=360,
- translate=0.2,
- padding_mode=SamplePadding.REFLECTION,
- keepdim=False,
- p=1)
- self.img_aug = RandomAffine(degrees=img_aug_degree,
- translate=img_aug_translate,
- padding_mode=SamplePadding.REFLECTION,
- keepdim=True,
- p=1)
- self.aug_proba = aug_proba
-
- def __call__(self, input_images, masks):
- blend_masks = self._fill_masks_with_gradient(masks)
- blend_target = self._make_blend_target(input_images)
- result = input_images * (1 - blend_masks) + blend_target * blend_masks
- return result, blend_masks
-
- def _make_blend_target(self, input_images):
- batch_size = input_images.shape[0]
- permuted = input_images[torch.randperm(batch_size)]
- augmented = self.img_aug(input_images)
- is_aug = (torch.rand(batch_size, device=input_images.device)[:, None, None, None] < self.aug_proba).float()
- result = augmented * is_aug + permuted * (1 - is_aug)
- return result
-
- def _fill_masks_with_gradient(self, masks):
- batch_size, _, height, width = masks.shape
- grad = torch.linspace(0, 1, steps=width * 2, device=masks.device, dtype=masks.dtype) \
- .view(1, 1, 1, -1).expand(batch_size, 1, height * 2, width * 2)
- grad = self.grad_aug(grad)
- grad = CenterCrop((height, width))(grad)
- grad *= masks
-
- grad_for_min = grad + (1 - masks) * 10
- grad -= grad_for_min.view(batch_size, -1).min(-1).values[:, None, None, None]
- grad /= grad.view(batch_size, -1).max(-1).values[:, None, None, None] + 1e-6
- grad.clamp_(min=0, max=1)
-
- return grad
diff --git a/spaces/nasa-cisto-data-science-group/satvision-base-demo/pytorch-caney/pytorch_caney/data/utils.py b/spaces/nasa-cisto-data-science-group/satvision-base-demo/pytorch-caney/pytorch_caney/data/utils.py
deleted file mode 100644
index 86d9555ae4849f6eabd7fa382a44948414e3d61d..0000000000000000000000000000000000000000
--- a/spaces/nasa-cisto-data-science-group/satvision-base-demo/pytorch-caney/pytorch_caney/data/utils.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import torch
-import numpy as np
-
-from numba import njit
-
-# TRANSFORMS UTILS
-
-
-class RandomResizedCropNP(object):
- """
- Numpy implementation of RandomResizedCrop
- """
-
- def __init__(self,
- scale=(0.08, 1.0),
- ratio=(3.0/4.0, 4.0/3.0)):
-
- self.scale = scale
- self.ratio = ratio
-
- def __call__(self, img):
-
- height, width = img.shape[:2]
- area = height * width
-
- for _ in range(10):
- target_area = np.random.uniform(*self.scale) * area
- aspect_ratio = np.random.uniform(*self.ratio)
-
- w = int(round(np.sqrt(target_area * aspect_ratio)))
- h = int(round(np.sqrt(target_area / aspect_ratio)))
-
- if np.random.random() < 0.5:
- w, h = h, w
-
- if w <= width and h <= height:
- x1 = np.random.randint(0, width - w + 1)
- y1 = np.random.randint(0, height - h + 1)
- cropped = img[y1:y1+h, x1:x1+w, :]
- cropped = np.moveaxis(cropped, -1, 0)
- cropped_resized = torch.nn.functional.interpolate(
- torch.from_numpy(cropped).unsqueeze(0),
- size=height,
- mode='bicubic',
- align_corners=False)
- cropped_squeezed_numpy = cropped_resized.squeeze().numpy()
- cropped_squeezed_numpy = np.moveaxis(
- cropped_squeezed_numpy, 0, -1)
- return cropped_squeezed_numpy
-
- # if crop was not successful after 10 attempts, use center crop
- w = min(width, height)
- x1 = (width - w) // 2
- y1 = (height - w) // 2
- cropped = img[y1:y1+w, x1:x1+w, :]
- cropped = np.moveaxis(cropped, -1, 0)
- cropped_resized = torch.nn.functional.interpolate(torch.from_numpy(
- cropped).unsqueeze(0),
- size=height,
- mode='bicubic',
- align_corners=False)
- cropped_squeezed_numpy = cropped_resized.squeeze().numpy()
- cropped_squeezed_numpy = np.moveaxis(cropped_squeezed_numpy, 0, -1)
- return cropped_squeezed_numpy
-
-
-# MASKING
-
-class SimmimMaskGenerator:
- """
- Generates the masks for masked-image-modeling
- """
- def __init__(self,
- input_size=192,
- mask_patch_size=32,
- model_patch_size=4,
- mask_ratio=0.6):
- self.input_size = input_size
- self.mask_patch_size = mask_patch_size
- self.model_patch_size = model_patch_size
- self.mask_ratio = mask_ratio
-
- assert self.input_size % self.mask_patch_size == 0
- assert self.mask_patch_size % self.model_patch_size == 0
-
- self.rand_size = self.input_size // self.mask_patch_size
- self.scale = self.mask_patch_size // self.model_patch_size
-
- self.token_count = self.rand_size ** 2
- self.mask_count = int(np.ceil(self.token_count * self.mask_ratio))
-
- def __call__(self):
- mask = make_simmim_mask(self.token_count, self.mask_count,
- self.rand_size, self.scale)
- mask = mask.repeat(self.scale, axis=0).repeat(self.scale, axis=1)
- return mask
-
-
-@njit()
-def make_simmim_mask(token_count, mask_count, rand_size, scale):
- """JIT-compiled random mask generation
-
- Args:
- token_count
- mask_count
- rand_size
- scale
-
- Returns:
- mask
- """
- mask_idx = np.random.permutation(token_count)[:mask_count]
- mask = np.zeros(token_count, dtype=np.int64)
- mask[mask_idx] = 1
- mask = mask.reshape((rand_size, rand_size))
- return mask
diff --git a/spaces/naver/SuperFeatures/how/stages/__init__.py b/spaces/naver/SuperFeatures/how/stages/__init__.py
deleted file mode 100644
index 6bf2c01bfafa57f1970be47bbf6edd30eb6bdaa9..0000000000000000000000000000000000000000
--- a/spaces/naver/SuperFeatures/how/stages/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""
-Implementation of different network stages, such as training and evaluation
-"""
-
-from . import evaluate, train
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/DBF.Commander.Professional.2.5.Build.38.with.Serial PORTABLE.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/DBF.Commander.Professional.2.5.Build.38.with.Serial PORTABLE.md
deleted file mode 100644
index 47c817f36c13c10a092c3b21dfba895a0635891d..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/DBF.Commander.Professional.2.5.Build.38.with.Serial PORTABLE.md
+++ /dev/null
@@ -1,96 +0,0 @@
-
-<h1>DBF Commander Professional 2.5 Build 38 with Serial: A Comprehensive Guide</h1>
- <p>If you are looking for a powerful, flexible, and user-friendly tool to work with DBF files, you might want to check out <strong>DBF Commander Professional</strong>. This software is designed to handle all kinds of operations with DBF files, such as viewing, editing, exporting, printing, converting, encrypting, decrypting, executing SQL queries, generating charts, and more. In this article, we will show you how to download and install DBF Commander Professional 2.5 Build 38 with Serial, how to use its main features, and what are its pros and cons.</p>
- <h2>How to download and install DBF Commander Professional 2.5 Build 38 with Serial</h2>
- <p>The first step is to download the setup file and the serial key for DBF Commander Professional 2.5 Build 38. You can find them on various websites that offer software downloads, such as [1](https://dbf-software.com/download) or [4](https://ameblo.jp/dinofortune575/entry-12768169453.html). Make sure you download the correct version and avoid any malicious links or files.</p>
-<h2>DBF.Commander.Professional.2.5.Build.38.with.Serial</h2><br /><p><b><b>Download</b> –––––>>> <a href="https://urlcod.com/2uIcwJ">https://urlcod.com/2uIcwJ</a></b></p><br /><br />
- <p>Once you have downloaded the setup file and the serial key, you can run the setup file to start the installation process. Follow the instructions on the screen and choose the destination folder for the program. When prompted, enter the serial key that you have downloaded. The serial key should look something like this: XXXX-XXXX-XXXX-XXXX-XXXX.</p>
- <p>After entering the serial key, click on Next and finish the installation. You can then launch DBF Commander Professional from your desktop or start menu. To verify that the installation and activation were successful, you can check the About section of the program. It should show your serial number and license status.</p>
- <h2>How to use DBF Commander Professional to work with DBF files</h2>
- <p>DBF Commander Professional is a versatile tool that allows you to perform various tasks with DBF files. Here are some of the most common ones:</p>
- <h3>How to create, open, view, edit, print, and export DBF files</h3>
- <p>To create a new DBF file, you can click on File > New or press Ctrl+N. You will be asked to choose a file name, location, type (dBase III+, dBase IV+, Visual FoxPro), code page (ANSI, UTF-8, MS-DOS), memo type (DBT or FPT), memo block size (512 or 64), field count (up to 255), field names (up to 10 characters), field types (character, numeric, logical, date, memo), field lengths (up to 254), field decimals (up to 15), field flags (system or null). You can also modify the structure of an existing DBF file by clicking on File > Modify Structure or pressing Ctrl+M.</p>
- <p>To open an existing DBF file, you can click on File > Open or press Ctrl+O. You can also drag and drop a file from your explorer window or use the recent files list. You can open multiple files at once using tabs or windows.</p>
- <p>To To view a DBF file, you can use the grid view or the form view. The grid view shows the records in rows and columns, while the form view shows one record at a time with fields and values. You can switch between the views by clicking on View > Grid View or View > Form View or pressing F12. You can also customize the appearance of the views by changing the font, color, size, alignment, and format of the cells.</p>
- <p>To edit a DBF file, you can use the grid view or the form view. You can edit the values of the fields by double-clicking on them or pressing F2. You can also add, delete, duplicate, or undelete records by clicking on Edit > Add Record, Edit > Delete Record, Edit > Duplicate Record, or Edit > Undelete Record or pressing Ins, Del, Ctrl+D, or Ctrl+U. You can also sort, filter, group, or search records by clicking on Data > Sort Records, Data > Filter Records, Data > Group Records, or Data > Find Records or pressing Ctrl+S, Ctrl+F, Ctrl+G, or Ctrl+F3.</p>
- <p>To print a DBF file, you can click on File > Print or press Ctrl+P. You can choose to print the whole file or a selection of records. You can also adjust the print settings, such as page size, orientation, margins, headers, footers, and columns. You can preview the print output before printing by clicking on File > Print Preview or pressing Ctrl+Shift+P.</p>
-<p></p>
- <p>To export a DBF file, you can click on File > Export or press Ctrl+E. You can choose to export the whole file or a selection of records. You can also choose the export format, such as CSV, XLSX, HTML, XML, SQL, TXT, PRG, JSON, PDF, RTF, or DBF. You can also specify the export options, such as delimiter, quote character, encoding, date format, and memo handling.</p>
- <h3>How to execute SQL queries on DBF files</h3>
- <p>DBF Commander Professional allows you to execute SQL queries on DBF files using its built-in SQL engine. You can access the SQL editor by clicking on Tools > SQL Editor or pressing F5. You can write your SQL query in the editor window and execute it by clicking on Execute Query or pressing F9. You can also save your query as a file or load a query from a file by clicking on File > Save Query or File > Load Query.</p>
- <p>The SQL engine supports various SQL commands and functions for manipulating DBF files. For example, you can use SELECT to retrieve data from one or more tables; INSERT to insert data into a table; UPDATE to modify data in a table; DELETE to remove data from a table; CREATE TABLE to create a new table; DROP TABLE to delete a table; ALTER TABLE to change the structure of a table; JOIN to combine data from multiple tables; WHERE to filter data based on a condition; GROUP BY to group data by one or more columns; HAVING to filter groups based on a condition; ORDER BY to sort data by one or more columns; LIMIT to limit the number of rows returned; DISTINCT to eliminate duplicate rows; COUNT to count the number of rows; SUM to calculate the sum of values; AVG to calculate the average of values; MIN to find the minimum value; MAX to find the maximum value; and many more.</p>
- <p>For example, if you want to find out how many customers have ordered more than 10 products from your database (assuming you have two tables: customers and orders), you can write a query like this:</p>
- <code>
-SELECT customers.name AS Customer_Name, COUNT(orders.product_id) AS Product_Count FROM customers INNER JOIN orders ON customers.customer_id = orders.customer_id GROUP BY customers.name HAVING COUNT(orders.product_id) > 10 ORDER BY Product_Count DESC; </code>
- <p>This query will return the names and product counts of customers who have ordered more than 10 products from your database in descending order.</p>
- <h3>How to convert DBF files to different formats and encodings</h3>
- <p>DBF Commander Professional allows you to convert DBF files to different formats and encodings using its built-in conversion tools. You can access the conversion tools by clicking on Tools > Convert DBF File Type/Code Page/Memo Type/Memo Block Size/Field Flags/Field Names/Field Types/Field Lengths/Field Decimals.</p>
- <p>You can choose the target format for your DBF file from dBase III+, dBase IV+, Visual FoxPro 3-9 (with autoincrement fields), Visual FoxPro 9 (with autoincrement and varchar fields), Clipper 5.x (with autoincrement fields), FoxBase+/FoxPro 2.x (with autoincrement fields), Hi per 2.x (with autoincrement fields), or dBase II. You can also choose the target code page for your DBF file from ANSI, UTF-8, MS-DOS, or any other supported code page. You can also choose the target memo type for your DBF file from DBT or FPT. You can also choose the target memo block size for your DBF file from 512 or 64. You can also choose the target field flags for your DBF file from system or null. You can also choose the target field names, field types, field lengths, and field decimals for your DBF file.</p>
- <p>For example, if you want to convert a DBF file from dBase IV+ to Visual FoxPro 9 with UTF-8 encoding and FPT memo type, you can click on Tools > Convert DBF File Type/Code Page/Memo Type and select the appropriate options. You will be asked to choose a file name and location for the converted file. The conversion process will take a few seconds and you will see a confirmation message when it is done.</p>
- <h3>How to encrypt and decrypt DBF files</h3>
- <p>DBF Commander Professional allows you to encrypt and decrypt DBF files using its built-in encryption tools. You can access the encryption tools by clicking on Tools > Encrypt/Decrypt DBF File.</p>
- <p>You can choose to encrypt or decrypt a single file or a batch of files. You can also choose the encryption algorithm from AES-256, AES-192, AES-128, Blowfish, Twofish, or DES. You can also choose the encryption mode from CBC, CFB, OFB, or ECB. You can also choose the encryption key from a password or a key file. You can also choose the encryption options such as padding, salt, and initialization vector.</p>
- <p>For example, if you want to encrypt a DBF file using AES-256 with CBC mode and a password as the key, you can click on Tools > Encrypt/Decrypt DBF File and select Encrypt Single File. You will be asked to choose the source file and the destination file. Then you will be asked to enter the password and confirm it. Then you will be asked to select the encryption algorithm (AES-256), the encryption mode (CBC), and the encryption options (padding: PKCS7, salt: yes, initialization vector: random). The encryption process will take a few seconds and you will see a confirmation message when it is done.</p>
- <p>To decrypt a DBF file, you need to follow the same steps but select Decrypt Single File instead of Encrypt Single File. You also need to enter the same password and select the same encryption algorithm, mode, and options that were used for encryption.</p>
- <h3>How to generate charts and statistics from DBF files</h3>
- <p>DBF Commander Professional allows you to generate charts and statistics from DBF files using its built-in charting and analysis tools. You can access the charting and analysis tools by clicking on Tools > Chart Wizard or Tools > Statistics Wizard.</p>
- <p>The Chart Wizard allows you to create various types of charts from your DBF data, such as bar charts, pie charts, line charts, area charts, scatter charts, bubble charts, radar charts, or doughnut charts. You can choose the data source (a table or a query), the chart type, the chart title, the chart legend, the chart axes, the chart series, the chart labels, and the chart colors. You can also preview the chart before saving it as an image file (PNG, JPG, BMP) or copying it to the clipboard.</p>
- <p>The Statistics Wizard allows you to calculate various statistics from your DBF data, such as count, sum, average, minimum, maximum, median, mode, standard deviation, variance, range, quartiles, percentiles, skewness, kurtosis, and correlation. You can choose the data source (a table or a query), the statistics type, the statistics fields, and the statistics options. You can also preview the statistics before saving them as a text file (TXT) or copying them to the clipboard.</p>
- <h2>Pros and cons of DBF Commander Professional</h2>
- <p>DBF Commander Professional is a powerful and user-friendly tool for working with DBF files, but it also has some advantages and disadvantages that you should be aware of. Here are some of them:</p>
- <h3>Pros</h3>
- <ul>
-<li>It supports various DBF file types, code pages, memo types, and field types.</li>
-<li>It allows you to perform various operations with DBF files, such as viewing, editing, exporting, printing, converting, encrypting, decrypting, executing SQL queries, generating charts, and more.</li>
-<li>It has a simple and intuitive interface that is easy to use and customize.</li>
-<li>It has a built-in SQL engine that supports various SQL commands and functions for manipulating DBF files.</li>
-<li>It has a built-in encryption engine that supports various encryption algorithms and modes for securing DBF files.</li>
-<li>It has a built-in charting and analysis engine that supports various chart types and statistics for visualizing and analyzing DBF data.</li>
-<li>It has a low system requirement and a fast performance.</li>
-</ul>
- <h3>Cons</h3>
- <ul>
-<li>It is not free. You have to pay $59 for a single-user license or $199 for an unlimited-user license.</li>
-<li>It does not support some advanced features of DBF files, such as autoincrement fields with negative values, varchar fields with variable lengths, or memo fields with binary data.</li>
-<li>It does not support some advanced features of SQL queries, such as subqueries, unions, or transactions.</li>
-<li>It does not support some advanced features of encryption algorithms, such as key derivation functions or authenticated encryption modes.</li>
-<li>It does not support some advanced features of charting and analysis tools, such as 3D charts, trend lines, or regression analysis.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, DBF Commander Professional is a great tool for working with DBF files. It offers a lot of features and functions that can help you create, open, view, edit, print, export, convert, encrypt, decrypt, execute SQL queries, generate charts, and calculate statistics from your DBF data. It also has a simple and intuitive interface that is easy to use and customize. However, it also has some limitations and drawbacks that you should consider before buying it. It is not free and it does not support some advanced features of DBF files, SQL queries, encryption algorithms, charting and analysis tools. Therefore, you should weigh the pros and cons of DBF Commander Professional before deciding whether it is worth your money and time.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about DBF Commander Professional:</p>
- <h3>What is the difference between DBF Commander Professional and DBF Commander Free?</h3>
- <p>DBF Commander Free is a limited version of DBF Commander Professional that you can use for free. However, it has some restrictions and limitations, such as:</p>
- <ul>
-<li>It does not support encryption and decryption of DBF files.</li>
-<li>It does not support SQL queries on DBF files.</li>
-<li>It does not support charting and statistics on DBF files.</li>
-<li>It does not support batch conversion of DBF files.</li>
-<li>It does not support command-line mode.</li>
-<li>It does not support technical support and updates.</li>
-</ul>
- <p>If you want to use these features, you have to buy DBF Commander Professional.</p>
- <h3>How can I get technical support and updates for DBF Commander Professional?</h3>
- <p>If you have bought DBF Commander Professional, you are entitled to get technical support and updates for free. You can contact the developer by email at [support@dbf-software.com] or by phone at +1 (206) 905-9969. You can also visit the website at [https://dbf-software.com/] for more information and resources. You can also check for updates by clicking on Help > Check for Updates or pressing Ctrl+U.</p>
- <h3>How can I uninstall DBF Commander Professional from my computer?</h3>
- <p>If you want to uninstall DBF Commander Professional from your computer, you can follow these steps:</p>
- <ol>
-<li>Close DBF Commander Professional if it is running.</li>
-<li>Go to Control Panel > Programs > Uninstall a Program or use the Windows search function to find "Uninstall a Program".</li>
-<li>Select DBF Commander Professional from the list of programs and click on Uninstall.</li>
-<li>Follow the instructions on the screen to complete the uninstallation process.</li>
-</ol>
- <h3>Can I use DBF Commander Professional on multiple computers?</h3>
- <p>If you have bought a single-user license for DBF Commander Professional, you can use it on one computer only. If you want to use it on multiple computers, you have to buy an unlimited-user license or multiple single-user licenses. You can also transfer your license from one computer to another by deactivating it on the old computer and activating it on the new computer. You can do this by clicking on Help > Deactivate License or Help > Activate License or pressing Ctrl+L.</p>
- <h3>What are the system requirements for DBF Commander Professional?</h3>
- <p>The system requirements for DBF Commander Professional are as follows:</p>
- <ul>
-<li>Operating system: Windows XP/Vista/7/8/10 (32-bit or 64-bit)</li>
-<li>Processor: Pentium 4 or higher</li>
-<li>Memory: 256 MB RAM or higher</li>
-<li>Disk space: 10 MB free disk space or higher</li>
-<li>Screen resolution: 1024x768 or higher</li>
-</ul></p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/nickmuchi/Netflix-Semantic-Search-Whisperer/README.md b/spaces/nickmuchi/Netflix-Semantic-Search-Whisperer/README.md
deleted file mode 100644
index 968fb744cf1a1e08ce4279f31087bebdece94c43..0000000000000000000000000000000000000000
--- a/spaces/nickmuchi/Netflix-Semantic-Search-Whisperer/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Netflix Semantic Search Whisperer
-emoji: 🐠
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/data/datasets/coco.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/data/datasets/coco.py
deleted file mode 100644
index ed4f7ccb20efa3b54c719783e279c381ca5d8587..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/data/datasets/coco.py
+++ /dev/null
@@ -1,539 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import contextlib
-import datetime
-import io
-import json
-import logging
-import numpy as np
-import os
-import shutil
-import pycocotools.mask as mask_util
-from fvcore.common.timer import Timer
-from iopath.common.file_io import file_lock
-from PIL import Image
-
-from detectron2.structures import Boxes, BoxMode, PolygonMasks, RotatedBoxes
-from detectron2.utils.file_io import PathManager
-
-from .. import DatasetCatalog, MetadataCatalog
-
-"""
-This file contains functions to parse COCO-format annotations into dicts in "Detectron2 format".
-"""
-
-
-logger = logging.getLogger(__name__)
-
-__all__ = ["load_coco_json", "load_sem_seg", "convert_to_coco_json", "register_coco_instances"]
-
-
-def load_coco_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None):
- """
- Load a json file with COCO's instances annotation format.
- Currently supports instance detection, instance segmentation,
- and person keypoints annotations.
-
- Args:
- json_file (str): full path to the json file in COCO instances annotation format.
- image_root (str or path-like): the directory where the images in this json file exists.
- dataset_name (str or None): the name of the dataset (e.g., coco_2017_train).
- When provided, this function will also do the following:
-
- * Put "thing_classes" into the metadata associated with this dataset.
- * Map the category ids into a contiguous range (needed by standard dataset format),
- and add "thing_dataset_id_to_contiguous_id" to the metadata associated
- with this dataset.
-
- This option should usually be provided, unless users need to load
- the original json content and apply more processing manually.
- extra_annotation_keys (list[str]): list of per-annotation keys that should also be
- loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints",
- "category_id", "segmentation"). The values for these keys will be returned as-is.
- For example, the densepose annotations are loaded in this way.
-
- Returns:
- list[dict]: a list of dicts in Detectron2 standard dataset dicts format (See
- `Using Custom Datasets </tutorials/datasets.html>`_ ) when `dataset_name` is not None.
- If `dataset_name` is None, the returned `category_ids` may be
- incontiguous and may not conform to the Detectron2 standard format.
-
- Notes:
- 1. This function does not read the image files.
- The results do not have the "image" field.
- """
- from pycocotools.coco import COCO
-
- timer = Timer()
- json_file = PathManager.get_local_path(json_file)
- with contextlib.redirect_stdout(io.StringIO()):
- coco_api = COCO(json_file)
- if timer.seconds() > 1:
- logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))
-
- id_map = None
- if dataset_name is not None:
- meta = MetadataCatalog.get(dataset_name)
- cat_ids = sorted(coco_api.getCatIds())
- cats = coco_api.loadCats(cat_ids)
- # The categories in a custom json file may not be sorted.
- thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])]
- meta.thing_classes = thing_classes
-
- # In COCO, certain category ids are artificially removed,
- # and by convention they are always ignored.
- # We deal with COCO's id issue and translate
- # the category ids to contiguous ids in [0, 80).
-
- # It works by looking at the "categories" field in the json, therefore
- # if users' own json also have incontiguous ids, we'll
- # apply this mapping as well but print a warning.
- if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)):
- if "coco" not in dataset_name:
- logger.warning(
- """
-Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.
-"""
- )
- id_map = {v: i for i, v in enumerate(cat_ids)}
- meta.thing_dataset_id_to_contiguous_id = id_map
-
- # sort indices for reproducible results
- img_ids = sorted(coco_api.imgs.keys())
- # imgs is a list of dicts, each looks something like:
- # {'license': 4,
- # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
- # 'file_name': 'COCO_val2014_000000001268.jpg',
- # 'height': 427,
- # 'width': 640,
- # 'date_captured': '2013-11-17 05:57:24',
- # 'id': 1268}
- imgs = coco_api.loadImgs(img_ids)
- # anns is a list[list[dict]], where each dict is an annotation
- # record for an object. The inner list enumerates the objects in an image
- # and the outer list enumerates over images. Example of anns[0]:
- # [{'segmentation': [[192.81,
- # 247.09,
- # ...
- # 219.03,
- # 249.06]],
- # 'area': 1035.749,
- # 'iscrowd': 0,
- # 'image_id': 1268,
- # 'bbox': [192.81, 224.8, 74.73, 33.43],
- # 'category_id': 16,
- # 'id': 42986},
- # ...]
- anns = [coco_api.imgToAnns[img_id] for img_id in img_ids]
- total_num_valid_anns = sum([len(x) for x in anns])
- total_num_anns = len(coco_api.anns)
- if total_num_valid_anns < total_num_anns:
- logger.warning(
- f"{json_file} contains {total_num_anns} annotations, but only "
- f"{total_num_valid_anns} of them match to images in the file."
- )
-
- if "minival" not in json_file:
- # The popular valminusminival & minival annotations for COCO2014 contain this bug.
- # However the ratio of buggy annotations there is tiny and does not affect accuracy.
- # Therefore we explicitly white-list them.
- ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
- assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format(
- json_file
- )
-
- imgs_anns = list(zip(imgs, anns))
- logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file))
-
- dataset_dicts = []
-
- ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + (extra_annotation_keys or [])
-
- num_instances_without_valid_segmentation = 0
-
- for (img_dict, anno_dict_list) in imgs_anns:
- record = {}
- record["file_name"] = os.path.join(image_root, img_dict["file_name"])
- record["height"] = img_dict["height"]
- record["width"] = img_dict["width"]
- image_id = record["image_id"] = img_dict["id"]
-
- objs = []
- for anno in anno_dict_list:
- # Check that the image_id in this annotation is the same as
- # the image_id we're looking at.
- # This fails only when the data parsing logic or the annotation file is buggy.
-
- # The original COCO valminusminival2014 & minival2014 annotation files
- # actually contains bugs that, together with certain ways of using COCO API,
- # can trigger this assertion.
- assert anno["image_id"] == image_id
-
- assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.'
-
- obj = {key: anno[key] for key in ann_keys if key in anno}
- if "bbox" in obj and len(obj["bbox"]) == 0:
- raise ValueError(
- f"One annotation of image {image_id} contains empty 'bbox' value! "
- "This json does not have valid COCO format."
- )
-
- segm = anno.get("segmentation", None)
- if segm: # either list[list[float]] or dict(RLE)
- if isinstance(segm, dict):
- if isinstance(segm["counts"], list):
- # convert to compressed RLE
- segm = mask_util.frPyObjects(segm, *segm["size"])
- else:
- # filter out invalid polygons (< 3 points)
- segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]
- if len(segm) == 0:
- num_instances_without_valid_segmentation += 1
- continue # ignore this instance
- obj["segmentation"] = segm
-
- keypts = anno.get("keypoints", None)
- if keypts: # list[int]
- for idx, v in enumerate(keypts):
- if idx % 3 != 2:
- # COCO's segmentation coordinates are floating points in [0, H or W],
- # but keypoint coordinates are integers in [0, H-1 or W-1]
- # Therefore we assume the coordinates are "pixel indices" and
- # add 0.5 to convert to floating point coordinates.
- keypts[idx] = v + 0.5
- obj["keypoints"] = keypts
-
- obj["bbox_mode"] = BoxMode.XYWH_ABS
- if id_map:
- annotation_category_id = obj["category_id"]
- try:
- obj["category_id"] = id_map[annotation_category_id]
- except KeyError as e:
- raise KeyError(
- f"Encountered category_id={annotation_category_id} "
- "but this id does not exist in 'categories' of the json file."
- ) from e
- objs.append(obj)
- record["annotations"] = objs
- dataset_dicts.append(record)
-
- if num_instances_without_valid_segmentation > 0:
- logger.warning(
- "Filtered out {} instances without valid segmentation. ".format(
- num_instances_without_valid_segmentation
- )
- + "There might be issues in your dataset generation process. Please "
- "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully"
- )
- return dataset_dicts
-
-
-def load_sem_seg(gt_root, image_root, gt_ext="png", image_ext="jpg"):
- """
- Load semantic segmentation datasets. All files under "gt_root" with "gt_ext" extension are
- treated as ground truth annotations and all files under "image_root" with "image_ext" extension
- as input images. Ground truth and input images are matched using file paths relative to
- "gt_root" and "image_root" respectively without taking into account file extensions.
- This works for COCO as well as some other datasets.
-
- Args:
- gt_root (str): full path to ground truth semantic segmentation files. Semantic segmentation
- annotations are stored as images with integer values in pixels that represent
- corresponding semantic labels.
- image_root (str): the directory where the input images are.
- gt_ext (str): file extension for ground truth annotations.
- image_ext (str): file extension for input images.
-
- Returns:
- list[dict]:
- a list of dicts in detectron2 standard format without instance-level
- annotation.
-
- Notes:
- 1. This function does not read the image and ground truth files.
- The results do not have the "image" and "sem_seg" fields.
- """
-
- # We match input images with ground truth based on their relative filepaths (without file
- # extensions) starting from 'image_root' and 'gt_root' respectively.
- def file2id(folder_path, file_path):
- # extract relative path starting from `folder_path`
- image_id = os.path.normpath(os.path.relpath(file_path, start=folder_path))
- # remove file extension
- image_id = os.path.splitext(image_id)[0]
- return image_id
-
- input_files = sorted(
- (os.path.join(image_root, f) for f in PathManager.ls(image_root) if f.endswith(image_ext)),
- key=lambda file_path: file2id(image_root, file_path),
- )
- gt_files = sorted(
- (os.path.join(gt_root, f) for f in PathManager.ls(gt_root) if f.endswith(gt_ext)),
- key=lambda file_path: file2id(gt_root, file_path),
- )
-
- assert len(gt_files) > 0, "No annotations found in {}.".format(gt_root)
-
- # Use the intersection, so that val2017_100 annotations can run smoothly with val2017 images
- if len(input_files) != len(gt_files):
- logger.warn(
- "Directory {} and {} has {} and {} files, respectively.".format(
- image_root, gt_root, len(input_files), len(gt_files)
- )
- )
- input_basenames = [os.path.basename(f)[: -len(image_ext)] for f in input_files]
- gt_basenames = [os.path.basename(f)[: -len(gt_ext)] for f in gt_files]
- intersect = list(set(input_basenames) & set(gt_basenames))
- # sort, otherwise each worker may obtain a list[dict] in different order
- intersect = sorted(intersect)
- logger.warn("Will use their intersection of {} files.".format(len(intersect)))
- input_files = [os.path.join(image_root, f + image_ext) for f in intersect]
- gt_files = [os.path.join(gt_root, f + gt_ext) for f in intersect]
-
- logger.info(
- "Loaded {} images with semantic segmentation from {}".format(len(input_files), image_root)
- )
-
- dataset_dicts = []
- for (img_path, gt_path) in zip(input_files, gt_files):
- record = {}
- record["file_name"] = img_path
- record["sem_seg_file_name"] = gt_path
- dataset_dicts.append(record)
-
- return dataset_dicts
-
-
-def convert_to_coco_dict(dataset_name):
- """
- Convert an instance detection/segmentation or keypoint detection dataset
- in detectron2's standard format into COCO json format.
-
- Generic dataset description can be found here:
- https://detectron2.readthedocs.io/tutorials/datasets.html#register-a-dataset
-
- COCO data format description can be found here:
- http://cocodataset.org/#format-data
-
- Args:
- dataset_name (str):
- name of the source dataset
- Must be registered in DatastCatalog and in detectron2's standard format.
- Must have corresponding metadata "thing_classes"
- Returns:
- coco_dict: serializable dict in COCO json format
- """
-
- dataset_dicts = DatasetCatalog.get(dataset_name)
- metadata = MetadataCatalog.get(dataset_name)
-
- # unmap the category mapping ids for COCO
- if hasattr(metadata, "thing_dataset_id_to_contiguous_id"):
- reverse_id_mapping = {v: k for k, v in metadata.thing_dataset_id_to_contiguous_id.items()}
- reverse_id_mapper = lambda contiguous_id: reverse_id_mapping[contiguous_id] # noqa
- else:
- reverse_id_mapper = lambda contiguous_id: contiguous_id # noqa
-
- categories = [
- {"id": reverse_id_mapper(id), "name": name}
- for id, name in enumerate(metadata.thing_classes)
- ]
-
- logger.info("Converting dataset dicts into COCO format")
- coco_images = []
- coco_annotations = []
-
- for image_id, image_dict in enumerate(dataset_dicts):
- coco_image = {
- "id": image_dict.get("image_id", image_id),
- "width": int(image_dict["width"]),
- "height": int(image_dict["height"]),
- "file_name": str(image_dict["file_name"]),
- }
- coco_images.append(coco_image)
-
- anns_per_image = image_dict.get("annotations", [])
- for annotation in anns_per_image:
- # create a new dict with only COCO fields
- coco_annotation = {}
-
- # COCO requirement: XYWH box format for axis-align and XYWHA for rotated
- bbox = annotation["bbox"]
- if isinstance(bbox, np.ndarray):
- if bbox.ndim != 1:
- raise ValueError(f"bbox has to be 1-dimensional. Got shape={bbox.shape}.")
- bbox = bbox.tolist()
- if len(bbox) not in [4, 5]:
- raise ValueError(f"bbox has to has length 4 or 5. Got {bbox}.")
- from_bbox_mode = annotation["bbox_mode"]
- to_bbox_mode = BoxMode.XYWH_ABS if len(bbox) == 4 else BoxMode.XYWHA_ABS
- bbox = BoxMode.convert(bbox, from_bbox_mode, to_bbox_mode)
-
- # COCO requirement: instance area
- if "segmentation" in annotation:
- # Computing areas for instances by counting the pixels
- segmentation = annotation["segmentation"]
- # TODO: check segmentation type: RLE, BinaryMask or Polygon
- if isinstance(segmentation, list):
- polygons = PolygonMasks([segmentation])
- area = polygons.area()[0].item()
- elif isinstance(segmentation, dict): # RLE
- area = mask_util.area(segmentation).item()
- else:
- raise TypeError(f"Unknown segmentation type {type(segmentation)}!")
- else:
- # Computing areas using bounding boxes
- if to_bbox_mode == BoxMode.XYWH_ABS:
- bbox_xy = BoxMode.convert(bbox, to_bbox_mode, BoxMode.XYXY_ABS)
- area = Boxes([bbox_xy]).area()[0].item()
- else:
- area = RotatedBoxes([bbox]).area()[0].item()
-
- if "keypoints" in annotation:
- keypoints = annotation["keypoints"] # list[int]
- for idx, v in enumerate(keypoints):
- if idx % 3 != 2:
- # COCO's segmentation coordinates are floating points in [0, H or W],
- # but keypoint coordinates are integers in [0, H-1 or W-1]
- # For COCO format consistency we substract 0.5
- # https://github.com/facebookresearch/detectron2/pull/175#issuecomment-551202163
- keypoints[idx] = v - 0.5
- if "num_keypoints" in annotation:
- num_keypoints = annotation["num_keypoints"]
- else:
- num_keypoints = sum(kp > 0 for kp in keypoints[2::3])
-
- # COCO requirement:
- # linking annotations to images
- # "id" field must start with 1
- coco_annotation["id"] = len(coco_annotations) + 1
- coco_annotation["image_id"] = coco_image["id"]
- coco_annotation["bbox"] = [round(float(x), 3) for x in bbox]
- coco_annotation["area"] = float(area)
- coco_annotation["iscrowd"] = int(annotation.get("iscrowd", 0))
- coco_annotation["category_id"] = int(reverse_id_mapper(annotation["category_id"]))
-
- # Add optional fields
- if "keypoints" in annotation:
- coco_annotation["keypoints"] = keypoints
- coco_annotation["num_keypoints"] = num_keypoints
-
- if "segmentation" in annotation:
- seg = coco_annotation["segmentation"] = annotation["segmentation"]
- if isinstance(seg, dict): # RLE
- counts = seg["counts"]
- if not isinstance(counts, str):
- # make it json-serializable
- seg["counts"] = counts.decode("ascii")
-
- coco_annotations.append(coco_annotation)
-
- logger.info(
- "Conversion finished, "
- f"#images: {len(coco_images)}, #annotations: {len(coco_annotations)}"
- )
-
- info = {
- "date_created": str(datetime.datetime.now()),
- "description": "Automatically generated COCO json file for Detectron2.",
- }
- coco_dict = {"info": info, "images": coco_images, "categories": categories, "licenses": None}
- if len(coco_annotations) > 0:
- coco_dict["annotations"] = coco_annotations
- return coco_dict
-
-
-def convert_to_coco_json(dataset_name, output_file, allow_cached=True):
- """
- Converts dataset into COCO format and saves it to a json file.
- dataset_name must be registered in DatasetCatalog and in detectron2's standard format.
-
- Args:
- dataset_name:
- reference from the config file to the catalogs
- must be registered in DatasetCatalog and in detectron2's standard format
- output_file: path of json file that will be saved to
- allow_cached: if json file is already present then skip conversion
- """
-
- # TODO: The dataset or the conversion script *may* change,
- # a checksum would be useful for validating the cached data
-
- PathManager.mkdirs(os.path.dirname(output_file))
- with file_lock(output_file):
- if PathManager.exists(output_file) and allow_cached:
- logger.warning(
- f"Using previously cached COCO format annotations at '{output_file}'. "
- "You need to clear the cache file if your dataset has been modified."
- )
- else:
- logger.info(f"Converting annotations of dataset '{dataset_name}' to COCO format ...)")
- coco_dict = convert_to_coco_dict(dataset_name)
-
- logger.info(f"Caching COCO format annotations at '{output_file}' ...")
- tmp_file = output_file + ".tmp"
- with PathManager.open(tmp_file, "w") as f:
- json.dump(coco_dict, f)
- shutil.move(tmp_file, output_file)
-
-
-def register_coco_instances(name, metadata, json_file, image_root):
- """
- Register a dataset in COCO's json annotation format for
- instance detection, instance segmentation and keypoint detection.
- (i.e., Type 1 and 2 in http://cocodataset.org/#format-data.
- `instances*.json` and `person_keypoints*.json` in the dataset).
-
- This is an example of how to register a new dataset.
- You can do something similar to this function, to register new datasets.
-
- Args:
- name (str): the name that identifies a dataset, e.g. "coco_2014_train".
- metadata (dict): extra metadata associated with this dataset. You can
- leave it as an empty dict.
- json_file (str): path to the json instance annotation file.
- image_root (str or path-like): directory which contains all the images.
- """
- assert isinstance(name, str), name
- assert isinstance(json_file, (str, os.PathLike)), json_file
- assert isinstance(image_root, (str, os.PathLike)), image_root
- # 1. register a function which returns dicts
- DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, name))
-
- # 2. Optionally, add metadata about this dataset,
- # since they might be useful in evaluation, visualization or logging
- MetadataCatalog.get(name).set(
- json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata
- )
-
-
-if __name__ == "__main__":
- """
- Test the COCO json dataset loader.
-
- Usage:
- python -m detectron2.data.datasets.coco \
- path/to/json path/to/image_root dataset_name
-
- "dataset_name" can be "coco_2014_minival_100", or other
- pre-registered ones
- """
- from detectron2.utils.logger import setup_logger
- from detectron2.utils.visualizer import Visualizer
- import detectron2.data.datasets # noqa # add pre-defined metadata
- import sys
-
- logger = setup_logger(name=__name__)
- assert sys.argv[3] in DatasetCatalog.list()
- meta = MetadataCatalog.get(sys.argv[3])
-
- dicts = load_coco_json(sys.argv[1], sys.argv[2], sys.argv[3])
- logger.info("Done loading {} samples.".format(len(dicts)))
-
- dirname = "coco-data-vis"
- os.makedirs(dirname, exist_ok=True)
- for d in dicts:
- img = np.array(Image.open(d["file_name"]))
- visualizer = Visualizer(img, metadata=meta)
- vis = visualizer.draw_dataset_dict(d)
- fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
- vis.save(fpath)
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/docs/tutorials/extend.md b/spaces/nikitaPDL2023/assignment4/detectron2/docs/tutorials/extend.md
deleted file mode 100644
index a6af550fdb2aa79c818cef54b009f2fe816d46a9..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/docs/tutorials/extend.md
+++ /dev/null
@@ -1,141 +0,0 @@
-# Extend Detectron2's Defaults
-
-__Research is about doing things in new ways__.
-This brings a tension in how to create abstractions in code,
-which is a challenge for any research engineering project of a significant size:
-
-1. On one hand, it needs to have very thin abstractions to allow for the possibility of doing
- everything in new ways. It should be reasonably easy to break existing
- abstractions and replace them with new ones.
-
-2. On the other hand, such a project also needs reasonably high-level
- abstractions, so that users can easily do things in standard ways,
- without worrying too much about the details that only certain researchers care about.
-
-In detectron2, there are two types of interfaces that address this tension together:
-
-1. Functions and classes that take a config (`cfg`) argument
- created from a yaml file
- (sometimes with few extra arguments).
-
- Such functions and classes implement
- the "standard default" behavior: it will read what it needs from a given
- config and do the "standard" thing.
- Users only need to load an expert-made config and pass it around, without having to worry about
- which arguments are used and what they all mean.
-
- See [Yacs Configs](configs.md) for a detailed tutorial.
-
-2. Functions and classes that have well-defined explicit arguments.
-
- Each of these is a small building block of the entire system.
- They require users' expertise to understand what each argument should be,
- and require more effort to stitch together to a larger system.
- But they can be stitched together in more flexible ways.
-
- When you need to implement something not supported by the "standard defaults"
- included in detectron2, these well-defined components can be reused.
-
- The [LazyConfig system](lazyconfigs.md) relies on such functions and classes.
-
-3. A few functions and classes are implemented with the
- [@configurable](../modules/config.html#detectron2.config.configurable)
- decorator - they can be called with either a config, or with explicit arguments, or a mixture of both.
- Their explicit argument interfaces are currently experimental.
-
- As an example, a Mask R-CNN model can be built in the following ways:
-
- 1. Config-only:
- ```python
- # load proper yaml config file, then
- model = build_model(cfg)
- ```
-
- 2. Mixture of config and additional argument overrides:
- ```python
- model = GeneralizedRCNN(
- cfg,
- roi_heads=StandardROIHeads(cfg, batch_size_per_image=666),
- pixel_std=[57.0, 57.0, 57.0])
- ```
-
- 3. Full explicit arguments:
- <details>
- <summary>
- (click to expand)
- </summary>
-
- ```python
- model = GeneralizedRCNN(
- backbone=FPN(
- ResNet(
- BasicStem(3, 64, norm="FrozenBN"),
- ResNet.make_default_stages(50, stride_in_1x1=True, norm="FrozenBN"),
- out_features=["res2", "res3", "res4", "res5"],
- ).freeze(2),
- ["res2", "res3", "res4", "res5"],
- 256,
- top_block=LastLevelMaxPool(),
- ),
- proposal_generator=RPN(
- in_features=["p2", "p3", "p4", "p5", "p6"],
- head=StandardRPNHead(in_channels=256, num_anchors=3),
- anchor_generator=DefaultAnchorGenerator(
- sizes=[[32], [64], [128], [256], [512]],
- aspect_ratios=[0.5, 1.0, 2.0],
- strides=[4, 8, 16, 32, 64],
- offset=0.0,
- ),
- anchor_matcher=Matcher([0.3, 0.7], [0, -1, 1], allow_low_quality_matches=True),
- box2box_transform=Box2BoxTransform([1.0, 1.0, 1.0, 1.0]),
- batch_size_per_image=256,
- positive_fraction=0.5,
- pre_nms_topk=(2000, 1000),
- post_nms_topk=(1000, 1000),
- nms_thresh=0.7,
- ),
- roi_heads=StandardROIHeads(
- num_classes=80,
- batch_size_per_image=512,
- positive_fraction=0.25,
- proposal_matcher=Matcher([0.5], [0, 1], allow_low_quality_matches=False),
- box_in_features=["p2", "p3", "p4", "p5"],
- box_pooler=ROIPooler(7, (1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32), 0, "ROIAlignV2"),
- box_head=FastRCNNConvFCHead(
- ShapeSpec(channels=256, height=7, width=7), conv_dims=[], fc_dims=[1024, 1024]
- ),
- box_predictor=FastRCNNOutputLayers(
- ShapeSpec(channels=1024),
- test_score_thresh=0.05,
- box2box_transform=Box2BoxTransform((10, 10, 5, 5)),
- num_classes=80,
- ),
- mask_in_features=["p2", "p3", "p4", "p5"],
- mask_pooler=ROIPooler(14, (1.0 / 4, 1.0 / 8, 1.0 / 16, 1.0 / 32), 0, "ROIAlignV2"),
- mask_head=MaskRCNNConvUpsampleHead(
- ShapeSpec(channels=256, width=14, height=14),
- num_classes=80,
- conv_dims=[256, 256, 256, 256, 256],
- ),
- ),
- pixel_mean=[103.530, 116.280, 123.675],
- pixel_std=[1.0, 1.0, 1.0],
- input_format="BGR",
- )
- ```
-
- </details>
-
-
-If you only need the standard behavior, the [Beginner's Tutorial](./getting_started.md)
-should suffice. If you need to extend detectron2 to your own needs,
-see the following tutorials for more details:
-
-* Detectron2 includes a few standard datasets. To use custom ones, see
- [Use Custom Datasets](./datasets.md).
-* Detectron2 contains the standard logic that creates a data loader for training/testing from a
- dataset, but you can write your own as well. See [Use Custom Data Loaders](./data_loading.md).
-* Detectron2 implements many standard detection models, and provide ways for you
- to overwrite their behaviors. See [Use Models](./models.md) and [Write Models](./write-models.md).
-* Detectron2 provides a default training loop that is good for common training tasks.
- You can customize it with hooks, or write your own loop instead. See [training](./training.md).
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/data/datasets/lvis.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/data/datasets/lvis.py
deleted file mode 100644
index b4af9fa292f445c81dc840ab53d07c1af313dfc7..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/data/datasets/lvis.py
+++ /dev/null
@@ -1,257 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import os
-from typing import Any, Dict, Iterable, List, Optional
-from fvcore.common.timer import Timer
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets.lvis import get_lvis_instances_meta
-from detectron2.structures import BoxMode
-from detectron2.utils.file_io import PathManager
-
-from ..utils import maybe_prepend_base_path
-from .coco import (
- DENSEPOSE_ALL_POSSIBLE_KEYS,
- DENSEPOSE_METADATA_URL_PREFIX,
- CocoDatasetInfo,
- get_metadata,
-)
-
-DATASETS = [
- CocoDatasetInfo(
- name="densepose_lvis_v1_ds1_train_v1",
- images_root="coco_",
- annotations_fpath="lvis/densepose_lvis_v1_ds1_train_v1.json",
- ),
- CocoDatasetInfo(
- name="densepose_lvis_v1_ds1_val_v1",
- images_root="coco_",
- annotations_fpath="lvis/densepose_lvis_v1_ds1_val_v1.json",
- ),
- CocoDatasetInfo(
- name="densepose_lvis_v1_ds2_train_v1",
- images_root="coco_",
- annotations_fpath="lvis/densepose_lvis_v1_ds2_train_v1.json",
- ),
- CocoDatasetInfo(
- name="densepose_lvis_v1_ds2_val_v1",
- images_root="coco_",
- annotations_fpath="lvis/densepose_lvis_v1_ds2_val_v1.json",
- ),
- CocoDatasetInfo(
- name="densepose_lvis_v1_ds1_val_animals_100",
- images_root="coco_",
- annotations_fpath="lvis/densepose_lvis_v1_val_animals_100_v2.json",
- ),
-]
-
-
-def _load_lvis_annotations(json_file: str):
- """
- Load COCO annotations from a JSON file
-
- Args:
- json_file: str
- Path to the file to load annotations from
- Returns:
- Instance of `pycocotools.coco.COCO` that provides access to annotations
- data
- """
- from lvis import LVIS
-
- json_file = PathManager.get_local_path(json_file)
- logger = logging.getLogger(__name__)
- timer = Timer()
- lvis_api = LVIS(json_file)
- if timer.seconds() > 1:
- logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))
- return lvis_api
-
-
-def _add_categories_metadata(dataset_name: str) -> None:
- metadict = get_lvis_instances_meta(dataset_name)
- categories = metadict["thing_classes"]
- metadata = MetadataCatalog.get(dataset_name)
- metadata.categories = {i + 1: categories[i] for i in range(len(categories))}
- logger = logging.getLogger(__name__)
- logger.info(f"Dataset {dataset_name} has {len(categories)} categories")
-
-
-def _verify_annotations_have_unique_ids(json_file: str, anns: List[List[Dict[str, Any]]]) -> None:
- ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
- assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format(
- json_file
- )
-
-
-def _maybe_add_bbox(obj: Dict[str, Any], ann_dict: Dict[str, Any]) -> None:
- if "bbox" not in ann_dict:
- return
- obj["bbox"] = ann_dict["bbox"]
- obj["bbox_mode"] = BoxMode.XYWH_ABS
-
-
-def _maybe_add_segm(obj: Dict[str, Any], ann_dict: Dict[str, Any]) -> None:
- if "segmentation" not in ann_dict:
- return
- segm = ann_dict["segmentation"]
- if not isinstance(segm, dict):
- # filter out invalid polygons (< 3 points)
- segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]
- if len(segm) == 0:
- return
- obj["segmentation"] = segm
-
-
-def _maybe_add_keypoints(obj: Dict[str, Any], ann_dict: Dict[str, Any]) -> None:
- if "keypoints" not in ann_dict:
- return
- keypts = ann_dict["keypoints"] # list[int]
- for idx, v in enumerate(keypts):
- if idx % 3 != 2:
- # COCO's segmentation coordinates are floating points in [0, H or W],
- # but keypoint coordinates are integers in [0, H-1 or W-1]
- # Therefore we assume the coordinates are "pixel indices" and
- # add 0.5 to convert to floating point coordinates.
- keypts[idx] = v + 0.5
- obj["keypoints"] = keypts
-
-
-def _maybe_add_densepose(obj: Dict[str, Any], ann_dict: Dict[str, Any]) -> None:
- for key in DENSEPOSE_ALL_POSSIBLE_KEYS:
- if key in ann_dict:
- obj[key] = ann_dict[key]
-
-
-def _combine_images_with_annotations(
- dataset_name: str,
- image_root: str,
- img_datas: Iterable[Dict[str, Any]],
- ann_datas: Iterable[Iterable[Dict[str, Any]]],
-):
-
- dataset_dicts = []
-
- def get_file_name(img_root, img_dict):
- # Determine the path including the split folder ("train2017", "val2017", "test2017") from
- # the coco_url field. Example:
- # 'coco_url': 'http://images.cocodataset.org/train2017/000000155379.jpg'
- split_folder, file_name = img_dict["coco_url"].split("/")[-2:]
- return os.path.join(img_root + split_folder, file_name)
-
- for img_dict, ann_dicts in zip(img_datas, ann_datas):
- record = {}
- record["file_name"] = get_file_name(image_root, img_dict)
- record["height"] = img_dict["height"]
- record["width"] = img_dict["width"]
- record["not_exhaustive_category_ids"] = img_dict.get("not_exhaustive_category_ids", [])
- record["neg_category_ids"] = img_dict.get("neg_category_ids", [])
- record["image_id"] = img_dict["id"]
- record["dataset"] = dataset_name
-
- objs = []
- for ann_dict in ann_dicts:
- assert ann_dict["image_id"] == record["image_id"]
- obj = {}
- _maybe_add_bbox(obj, ann_dict)
- obj["iscrowd"] = ann_dict.get("iscrowd", 0)
- obj["category_id"] = ann_dict["category_id"]
- _maybe_add_segm(obj, ann_dict)
- _maybe_add_keypoints(obj, ann_dict)
- _maybe_add_densepose(obj, ann_dict)
- objs.append(obj)
- record["annotations"] = objs
- dataset_dicts.append(record)
- return dataset_dicts
-
-
-def load_lvis_json(annotations_json_file: str, image_root: str, dataset_name: str):
- """
- Loads a JSON file with annotations in LVIS instances format.
- Replaces `detectron2.data.datasets.coco.load_lvis_json` to handle metadata
- in a more flexible way. Postpones category mapping to a later stage to be
- able to combine several datasets with different (but coherent) sets of
- categories.
-
- Args:
-
- annotations_json_file: str
- Path to the JSON file with annotations in COCO instances format.
- image_root: str
- directory that contains all the images
- dataset_name: str
- the name that identifies a dataset, e.g. "densepose_coco_2014_train"
- extra_annotation_keys: Optional[List[str]]
- If provided, these keys are used to extract additional data from
- the annotations.
- """
- lvis_api = _load_lvis_annotations(PathManager.get_local_path(annotations_json_file))
-
- _add_categories_metadata(dataset_name)
-
- # sort indices for reproducible results
- img_ids = sorted(lvis_api.imgs.keys())
- # imgs is a list of dicts, each looks something like:
- # {'license': 4,
- # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
- # 'file_name': 'COCO_val2014_000000001268.jpg',
- # 'height': 427,
- # 'width': 640,
- # 'date_captured': '2013-11-17 05:57:24',
- # 'id': 1268}
- imgs = lvis_api.load_imgs(img_ids)
- logger = logging.getLogger(__name__)
- logger.info("Loaded {} images in LVIS format from {}".format(len(imgs), annotations_json_file))
- # anns is a list[list[dict]], where each dict is an annotation
- # record for an object. The inner list enumerates the objects in an image
- # and the outer list enumerates over images.
- anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
-
- _verify_annotations_have_unique_ids(annotations_json_file, anns)
- dataset_records = _combine_images_with_annotations(dataset_name, image_root, imgs, anns)
- return dataset_records
-
-
-def register_dataset(dataset_data: CocoDatasetInfo, datasets_root: Optional[str] = None) -> None:
- """
- Registers provided LVIS DensePose dataset
-
- Args:
- dataset_data: CocoDatasetInfo
- Dataset data
- datasets_root: Optional[str]
- Datasets root folder (default: None)
- """
- annotations_fpath = maybe_prepend_base_path(datasets_root, dataset_data.annotations_fpath)
- images_root = maybe_prepend_base_path(datasets_root, dataset_data.images_root)
-
- def load_annotations():
- return load_lvis_json(
- annotations_json_file=annotations_fpath,
- image_root=images_root,
- dataset_name=dataset_data.name,
- )
-
- DatasetCatalog.register(dataset_data.name, load_annotations)
- MetadataCatalog.get(dataset_data.name).set(
- json_file=annotations_fpath,
- image_root=images_root,
- evaluator_type="lvis",
- **get_metadata(DENSEPOSE_METADATA_URL_PREFIX),
- )
-
-
-def register_datasets(
- datasets_data: Iterable[CocoDatasetInfo], datasets_root: Optional[str] = None
-) -> None:
- """
- Registers provided LVIS DensePose datasets
-
- Args:
- datasets_data: Iterable[CocoDatasetInfo]
- An iterable of dataset datas
- datasets_root: Optional[str]
- Datasets root folder (default: None)
- """
- for dataset_data in datasets_data:
- register_dataset(dataset_data, datasets_root)
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/TridentNet/tridentnet/__init__.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/TridentNet/tridentnet/__init__.py
deleted file mode 100644
index abaa9579051e7ef5ee7f388b9d59b5962440155c..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/TridentNet/tridentnet/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .config import add_tridentnet_config
-from .trident_backbone import (
- TridentBottleneckBlock,
- build_trident_resnet_backbone,
- make_trident_stage,
-)
-from .trident_rpn import TridentRPN
-from .trident_rcnn import TridentRes5ROIHeads, TridentStandardROIHeads
diff --git a/spaces/nomic-ai/glue/index.html b/spaces/nomic-ai/glue/index.html
deleted file mode 100644
index 92ae5621293379f99f8477cd91077572d8993bcf..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/glue/index.html
+++ /dev/null
@@ -1,42 +0,0 @@
-<html>
-
-<head>
- <title>glue
-
-
-
-
-
-
-
-
-
-
复制这个空间并使用你的 OpenAI API 密钥安全运行
-
-# Bingo
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-
-
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://github.com/weaigc/bingo/blob/main/license)
-
-问题反馈请前往 https://github.com/weaigc/bingo/issues
-
-
-
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/dataset.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/dataset.py
deleted file mode 100644
index cfd01a174978d97180a897e40cb59ecadec1d12e..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/dataset.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import os
-import random
-
-import numpy as np
-import torch
-import torch.utils.data
-from tqdm import tqdm
-
-from . import spec_utils
-
-
-class VocalRemoverValidationSet(torch.utils.data.Dataset):
- def __init__(self, patch_list):
- self.patch_list = patch_list
-
- def __len__(self):
- return len(self.patch_list)
-
- def __getitem__(self, idx):
- path = self.patch_list[idx]
- data = np.load(path)
-
- X, y = data["X"], data["y"]
-
- X_mag = np.abs(X)
- y_mag = np.abs(y)
-
- return X_mag, y_mag
-
-
-def make_pair(mix_dir, inst_dir):
- input_exts = [".wav", ".m4a", ".mp3", ".mp4", ".flac"]
-
- X_list = sorted(
- [
- os.path.join(mix_dir, fname)
- for fname in os.listdir(mix_dir)
- if os.path.splitext(fname)[1] in input_exts
- ]
- )
- y_list = sorted(
- [
- os.path.join(inst_dir, fname)
- for fname in os.listdir(inst_dir)
- if os.path.splitext(fname)[1] in input_exts
- ]
- )
-
- filelist = list(zip(X_list, y_list))
-
- return filelist
-
-
-def train_val_split(dataset_dir, split_mode, val_rate, val_filelist):
- if split_mode == "random":
- filelist = make_pair(
- os.path.join(dataset_dir, "mixtures"),
- os.path.join(dataset_dir, "instruments"),
- )
-
- random.shuffle(filelist)
-
- if len(val_filelist) == 0:
- val_size = int(len(filelist) * val_rate)
- train_filelist = filelist[:-val_size]
- val_filelist = filelist[-val_size:]
- else:
- train_filelist = [
- pair for pair in filelist if list(pair) not in val_filelist
- ]
- elif split_mode == "subdirs":
- if len(val_filelist) != 0:
- raise ValueError(
- "The `val_filelist` option is not available in `subdirs` mode"
- )
-
- train_filelist = make_pair(
- os.path.join(dataset_dir, "training/mixtures"),
- os.path.join(dataset_dir, "training/instruments"),
- )
-
- val_filelist = make_pair(
- os.path.join(dataset_dir, "validation/mixtures"),
- os.path.join(dataset_dir, "validation/instruments"),
- )
-
- return train_filelist, val_filelist
-
-
-def augment(X, y, reduction_rate, reduction_mask, mixup_rate, mixup_alpha):
- perm = np.random.permutation(len(X))
- for i, idx in enumerate(tqdm(perm)):
- if np.random.uniform() < reduction_rate:
- y[idx] = spec_utils.reduce_vocal_aggressively(
- X[idx], y[idx], reduction_mask
- )
-
- if np.random.uniform() < 0.5:
- # swap channel
- X[idx] = X[idx, ::-1]
- y[idx] = y[idx, ::-1]
- if np.random.uniform() < 0.02:
- # mono
- X[idx] = X[idx].mean(axis=0, keepdims=True)
- y[idx] = y[idx].mean(axis=0, keepdims=True)
- if np.random.uniform() < 0.02:
- # inst
- X[idx] = y[idx]
-
- if np.random.uniform() < mixup_rate and i < len(perm) - 1:
- lam = np.random.beta(mixup_alpha, mixup_alpha)
- X[idx] = lam * X[idx] + (1 - lam) * X[perm[i + 1]]
- y[idx] = lam * y[idx] + (1 - lam) * y[perm[i + 1]]
-
- return X, y
-
-
-def make_padding(width, cropsize, offset):
- left = offset
- roi_size = cropsize - left * 2
- if roi_size == 0:
- roi_size = cropsize
- right = roi_size - (width % roi_size) + left
-
- return left, right, roi_size
-
-
-def make_training_set(filelist, cropsize, patches, sr, hop_length, n_fft, offset):
- len_dataset = patches * len(filelist)
-
- X_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
- y_dataset = np.zeros((len_dataset, 2, n_fft // 2 + 1, cropsize), dtype=np.complex64)
-
- for i, (X_path, y_path) in enumerate(tqdm(filelist)):
- X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
- coef = np.max([np.abs(X).max(), np.abs(y).max()])
- X, y = X / coef, y / coef
-
- l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
- X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
- y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
-
- starts = np.random.randint(0, X_pad.shape[2] - cropsize, patches)
- ends = starts + cropsize
- for j in range(patches):
- idx = i * patches + j
- X_dataset[idx] = X_pad[:, :, starts[j] : ends[j]]
- y_dataset[idx] = y_pad[:, :, starts[j] : ends[j]]
-
- return X_dataset, y_dataset
-
-
-def make_validation_set(filelist, cropsize, sr, hop_length, n_fft, offset):
- patch_list = []
- patch_dir = "cs{}_sr{}_hl{}_nf{}_of{}".format(
- cropsize, sr, hop_length, n_fft, offset
- )
- os.makedirs(patch_dir, exist_ok=True)
-
- for i, (X_path, y_path) in enumerate(tqdm(filelist)):
- basename = os.path.splitext(os.path.basename(X_path))[0]
-
- X, y = spec_utils.cache_or_load(X_path, y_path, sr, hop_length, n_fft)
- coef = np.max([np.abs(X).max(), np.abs(y).max()])
- X, y = X / coef, y / coef
-
- l, r, roi_size = make_padding(X.shape[2], cropsize, offset)
- X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode="constant")
- y_pad = np.pad(y, ((0, 0), (0, 0), (l, r)), mode="constant")
-
- len_dataset = int(np.ceil(X.shape[2] / roi_size))
- for j in range(len_dataset):
- outpath = os.path.join(patch_dir, "{}_p{}.npz".format(basename, j))
- start = j * roi_size
- if not os.path.exists(outpath):
- np.savez(
- outpath,
- X=X_pad[:, :, start : start + cropsize],
- y=y_pad[:, :, start : start + cropsize],
- )
- patch_list.append(outpath)
-
- return VocalRemoverValidationSet(patch_list)
diff --git a/spaces/Limuru/DeepDanbooru_string/app.py b/spaces/Limuru/DeepDanbooru_string/app.py
deleted file mode 100644
index 49019837c9207cc68cb37be0342f3bc44fd0decb..0000000000000000000000000000000000000000
--- a/spaces/Limuru/DeepDanbooru_string/app.py
+++ /dev/null
@@ -1,185 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import argparse
-import functools
-import os
-import html
-import pathlib
-import tarfile
-
-import deepdanbooru as dd
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-import tensorflow as tf
-import piexif
-import piexif.helper
-
-TITLE = 'DeepDanbooru String'
-
-TOKEN = os.environ['TOKEN']
-MODEL_REPO = 'CikeyQI/DeepDanbooru_string'
-MODEL_FILENAME = 'model-resnet_custom_v3.h5'
-LABEL_FILENAME = 'tags.txt'
-
-
-def parse_args() -> argparse.Namespace:
- parser = argparse.ArgumentParser()
- parser.add_argument('--score-slider-step', type=float, default=0.05)
- parser.add_argument('--score-threshold', type=float, default=0.5)
- parser.add_argument('--theme', type=str, default='dark-grass')
- parser.add_argument('--live', action='store_true')
- parser.add_argument('--share', action='store_true')
- parser.add_argument('--port', type=int)
- parser.add_argument('--disable-queue',
- dest='enable_queue',
- action='store_false')
- parser.add_argument('--allow-flagging', type=str, default='never')
- return parser.parse_args()
-
-
-def load_sample_image_paths() -> list[pathlib.Path]:
- image_dir = pathlib.Path('images')
- if not image_dir.exists():
- dataset_repo = 'hysts/sample-images-TADNE'
- path = huggingface_hub.hf_hub_download(dataset_repo,
- 'images.tar.gz',
- repo_type='dataset',
- use_auth_token=TOKEN)
- with tarfile.open(path) as f:
- f.extractall()
- return sorted(image_dir.glob('*'))
-
-
-def load_model() -> tf.keras.Model:
- path = huggingface_hub.hf_hub_download(MODEL_REPO,
- MODEL_FILENAME,
- use_auth_token=TOKEN)
- model = tf.keras.models.load_model(path)
- return model
-
-
-def load_labels() -> list[str]:
- path = huggingface_hub.hf_hub_download(MODEL_REPO,
- LABEL_FILENAME,
- use_auth_token=TOKEN)
- with open(path) as f:
- labels = [line.strip() for line in f.readlines()]
- return labels
-
-def plaintext_to_html(text):
- text = "" + "
\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "
PNG Info
-""" - for key, text in items.items(): - info += f""" -
-
-""".strip()+"\n"
-
- if len(info) == 0:
- message = "Nothing found in the image."
- info = f"{plaintext_to_html(str(key))}
-{plaintext_to_html(str(text))}
-{message}
-
-
-
-
-