






'''simple docstring'''

import os
from collections import deque

import torch
from torch.utils.data import Dataset






class     a_						(      lowerCamelCase    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE=""      ,		_SCREAMING_SNAKE_CASE="train"				)  ->				List[str]:



        """simple docstring"""






        assert os.path.isdir(_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = []
        UpperCamelCase      = os.listdir(_SCREAMING_SNAKE_CASE				)
        for story_filename in story_filenames_list:
              if "summary" in story_filename:
                    continue
              UpperCamelCase      = os.path.join(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
              if not os.path.isfile(_SCREAMING_SNAKE_CASE				):
                    continue
              self.documents.append(_SCREAMING_SNAKE_CASE				)

  def __len__(				self				)  ->				Optional[Any]:



        """simple docstring"""






        return len(self.documents				)


  def __getitem__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = self.documents[idx]
        UpperCamelCase      = document_path.split("""/"""				)[-1]
        with open(_SCREAMING_SNAKE_CASE      ,		encoding="""utf-8"""				) as source:
              UpperCamelCase      = source.read()
              UpperCamelCase  ,UpperCamelCase      = process_story(_SCREAMING_SNAKE_CASE				)
        return document_name, story_lines, summary_lines







def    lowercase__			(				__UpperCamelCase   )->			int:
      UpperCamelCase      = list(filter(lambda __UpperCamelCase   : len(__UpperCamelCase   ) != 0		,						[line.strip() for line in raw_story.split("""\n"""   )]   )   )

      # for some unknown reason some lines miss a period, add it
      UpperCamelCase      = [_add_missing_period(__UpperCamelCase   ) for line in nonempty_lines]

      # gather article lines
      UpperCamelCase      = []
      UpperCamelCase      = deque(__UpperCamelCase   )
      while True:
            try:
                  UpperCamelCase      = lines.popleft()
                  if element.startswith("""@highlight"""   ):
                        break
                  story_lines.append(__UpperCamelCase   )
            except IndexError:
                  # if "@highlight" is absent from the file we pop
                  # all elements until there is None, raising an exception.
                  return story_lines, []

    # gather summary lines
      UpperCamelCase      = list(filter(lambda __UpperCamelCase   : not t.startswith("""@highlight"""   )		,						__UpperCamelCase   )   )

      return story_lines, summary_lines







def    lowercase__			(				__UpperCamelCase   )->			str:
      UpperCamelCase      = [""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
      if line.startswith("""@highlight"""   ):
            return line
      if line[-1] in END_TOKENS:
            return line
      return line + "."







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Tuple:
      if len(__UpperCamelCase   ) > block_size:
            return sequence[:block_size]
      else:
            sequence.extend([pad_token_id] * (block_size - len(__UpperCamelCase   ))   )
            return sequence







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Union[str, Any]:
      UpperCamelCase      = torch.ones_like(__UpperCamelCase   )
      UpperCamelCase      = sequence == pad_token_id
      UpperCamelCase      = 0
      return mask







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Union[str, Any]:
      UpperCamelCase      = [tokenizer.encode(__UpperCamelCase   ) for line in story_lines]
      UpperCamelCase      = [token for sentence in story_lines_token_ids for token in sentence]
      UpperCamelCase      = [tokenizer.encode(__UpperCamelCase   ) for line in summary_lines]
      UpperCamelCase      = [token for sentence in summary_lines_token_ids for token in sentence]

      return story_token_ids, summary_token_ids







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			str:
      UpperCamelCase      = []
      for sequence in batch:
            UpperCamelCase      = -1
            UpperCamelCase      = []
            for s in sequence:
                  if s == separator_token_id:
                        sentence_num += 1
                  embeddings.append(sentence_num % 2   )
            batch_embeddings.append(__UpperCamelCase   )
      return torch.tensor(__UpperCamelCase   )








'''simple docstring'''

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     =       logging.get_logger()






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				field(default_factory=lowerCamelCase    )

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = len(list(m.modules()				)				) == 1 or isinstance(_SCREAMING_SNAKE_CASE      ,		nn.Convad				) or isinstance(_SCREAMING_SNAKE_CASE      ,		nn.BatchNormad				)
        if has_not_submodules:
              self.traced.append(_SCREAMING_SNAKE_CASE				)

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Any:



        """simple docstring"""






        for m in self.module.modules():
              self.handles.append(m.register_forward_hook(self._forward_hook				)				)
        self.module(_SCREAMING_SNAKE_CASE				)
        [x.remove() for x in self.handles]
        return self


  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return list(filter(lambda _SCREAMING_SNAKE_CASE				: len(list(x.state_dict().keys()				)				) > 0      ,		self.traced				)				)






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				42
  lowercase							=				0
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				field(default_factory=lowerCamelCase    )

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = Tracker(self.dest				)(_SCREAMING_SNAKE_CASE				).parametrized
        UpperCamelCase      = Tracker(self.src				)(_SCREAMING_SNAKE_CASE				).parametrized

        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: type(_SCREAMING_SNAKE_CASE				) not in self.src_skip      ,		_SCREAMING_SNAKE_CASE				)				)
        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: type(_SCREAMING_SNAKE_CASE				) not in self.dest_skip      ,		_SCREAMING_SNAKE_CASE				)				)

        if len(_SCREAMING_SNAKE_CASE				) != len(_SCREAMING_SNAKE_CASE				):
              raise Exception(
                  F"Numbers of operations are different. Source module has {len(_SCREAMING_SNAKE_CASE				)} operations while"
                  F" destination module has {len(_SCREAMING_SNAKE_CASE				)}."				)

        for dest_m, src_m in zip(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              dest_m.load_state_dict(src_m.state_dict()				)
              if self.verbose == 1:
                    print(F"Transfered from={src_m} to={dest_m}"				)







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase = True   )->			Optional[Any]:
      print(F"Converting {name}..."   )
      with torch.no_grad():
            UpperCamelCase      = timm.create_model(__UpperCamelCase		,						pretrained=__UpperCamelCase   ).eval()
            UpperCamelCase      = ResNetForImageClassification(__UpperCamelCase   ).eval()
            UpperCamelCase      = ModuleTransfer(src=__UpperCamelCase		,						dest=__UpperCamelCase   )
            UpperCamelCase      = torch.randn((1, 3, 224, 224)   )
            module_transfer(__UpperCamelCase   )

      assert torch.allclose(from_model(__UpperCamelCase   )		,						our_model(__UpperCamelCase   ).logits   ), "The model logits don't match the original one."

      UpperCamelCase      = F"resnet{'-'.join(name.split('resnet'   )   )}"
      print(__UpperCamelCase   )

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name		,						commit_message="""Add model"""		,						use_temp_dir=__UpperCamelCase		,						)

            # we can use the convnext one
            UpperCamelCase      = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""   )
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name		,						commit_message="""Add image processor"""		,						use_temp_dir=__UpperCamelCase		,						)

            print(F"Pushed {checkpoint_name}"   )







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase = None		,						__UpperCamelCase = True   )->			str:
      UpperCamelCase      = """imagenet-1k-id2label.json"""
      UpperCamelCase      = 1000
      UpperCamelCase      = (1, num_labels)

      UpperCamelCase      = """huggingface/label-files"""
      UpperCamelCase      = num_labels
      UpperCamelCase      = json.load(open(hf_hub_download(__UpperCamelCase		,						__UpperCamelCase		,						repo_type="""dataset"""   )		,						"""r"""   )   )
      UpperCamelCase      = {int(__UpperCamelCase   ): v for k, v in idalabel.items()}

      UpperCamelCase      = idalabel
      UpperCamelCase      = {v: k for k, v in idalabel.items()}

      UpperCamelCase      = partial(__UpperCamelCase		,						num_labels=__UpperCamelCase		,						idalabel=__UpperCamelCase		,						labelaid=__UpperCamelCase   )

      UpperCamelCase      = {
          """resnet18""": ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]		,						hidden_sizes=[64, 128, 256, 512]		,						layer_type="""basic"""   ),
          """resnet26""": ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type="""bottleneck"""   ),
          """resnet34""": ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]		,						hidden_sizes=[64, 128, 256, 512]		,						layer_type="""basic"""   ),
          """resnet50""": ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type="""bottleneck"""   ),
          """resnet101""": ImageNetPreTrainedConfig(
              depths=[3, 4, 23, 3]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type="""bottleneck"""   ),
          """resnet152""": ImageNetPreTrainedConfig(
              depths=[3, 8, 36, 3]		,						hidden_sizes=[256, 512, 1024, 2048]		,						layer_type="""bottleneck"""   ),
      }

      if model_name:
            convert_weight_and_push(__UpperCamelCase		,						names_to_config[model_name]		,						__UpperCamelCase		,						__UpperCamelCase   )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )
      return config, expected_shape


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default=None,
         type=str,
         help=(
             'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
             ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default=None,
         type=Path,
         required=True,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument(
         '--push_to_hub',
         default=True,
         type=bool,
         required=False,
         help='If True, push model and image processor to the hub.',
     )

     SCREAMING_SNAKE_CASE__     =       parser.parse_args()
     SCREAMING_SNAKE_CASE__     =       args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
