import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_=""					,lowerCamelCase_="train"							)			->			List[Any]:



         '''simple docstring'''



         assert os.path.isdir(lowerCamelCase_							)

         UpperCAmelCase__							:				List[Any]   				=  []
         UpperCAmelCase__							:				str   				=  os.listdir(lowerCamelCase_							)
         for story_filename in story_filenames_list:
            if "summary" in story_filename:
               continue
            UpperCAmelCase__							:				int   				=  os.path.join(lowerCamelCase_					,lowerCamelCase_							)
            if not os.path.isfile(lowerCamelCase_							):
               continue
            self.documents.append(lowerCamelCase_							)




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



         '''simple docstring'''



         return len(self.documents							)




      def __getitem__(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  self.documents[idx]
         UpperCAmelCase__							:				Optional[Any]   				=  document_path.split('''/'''							)[-1]
         with open(lowerCamelCase_					,encoding='''utf-8'''							) as source:
            UpperCAmelCase__							:				Tuple   				=  source.read()
            UpperCAmelCase__							:				str   				=  process_story(lowerCamelCase_							)
         return document_name, story_lines, summary_lines







def __UpperCamelCase(   _A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  list(filter(lambda _A    : len(_A    ) != 0  ,			[line.strip() for line in raw_story.split('''\n'''    )]    )    )

   # for some unknown reason some lines miss a period, add it
   UpperCAmelCase__							:				Any   				=  [_add_missing_period(_A    ) for line in nonempty_lines]

   # gather article lines
   UpperCAmelCase__							:				List[str]   				=  []
   UpperCAmelCase__							:				Tuple   				=  deque(_A    )
   while True:
      try:
         UpperCAmelCase__							:				Tuple   				=  lines.popleft()
         if element.startswith('''@highlight'''    ):
            break
         story_lines.append(_A    )
      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__							:				Dict   				=  list(filter(lambda _A    : not t.startswith('''@highlight'''    )  ,			_A    )    )

   return story_lines, summary_lines







def __UpperCamelCase(   _A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  ['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
   if line.startswith('''@highlight'''    ):
      return line
   if line[-1] in END_TOKENS:
      return line
   return line + "."







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				List[Any]  ,			_A					:				Optional[Any]    ):
   '''simple docstring'''
   if len(_A    ) > block_size:
      return sequence[:block_size]
   else:
      sequence.extend([pad_token_id] * (block_size - len(_A    ))    )
      return sequence







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Dict    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  torch.ones_like(_A    )
   UpperCAmelCase__							:				Tuple   				=  sequence == pad_token_id
   UpperCAmelCase__							:				Any   				=  0
   return mask







def __UpperCamelCase(   _A					:				int  ,			_A					:				Optional[Any]  ,			_A					:				str    ):
   '''simple docstring'''
   UpperCAmelCase__							:				str   				=  [tokenizer.encode(_A    ) for line in story_lines]
   UpperCAmelCase__							:				Optional[int]   				=  [token for sentence in story_lines_token_ids for token in sentence]
   UpperCAmelCase__							:				Dict   				=  [tokenizer.encode(_A    ) for line in summary_lines]
   UpperCAmelCase__							:				List[Any]   				=  [token for sentence in summary_lines_token_ids for token in sentence]

   return story_token_ids, summary_token_ids







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Dict    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  []
   for sequence in batch:
      UpperCAmelCase__							:				Any   				=  -1
      UpperCAmelCase__							:				Optional[Any]   				=  []
      for s in sequence:
         if s == separator_token_id:
            sentence_num += 1
         embeddings.append(sentence_num % 2    )
      batch_embeddings.append(_A    )
   return torch.tensor(_A    )





'''simple docstring'''

import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  []
   embed.append(
       (
           F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
           F'''stage{idx}.patch_embed.proj.weight''',
       )    )
   embed.append(
       (
           F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
           F'''stage{idx}.patch_embed.proj.bias''',
       )    )
   embed.append(
       (
           F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
           F'''stage{idx}.patch_embed.norm.weight''',
       )    )
   embed.append(
       (
           F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
           F'''stage{idx}.patch_embed.norm.bias''',
       )    )
   return embed







def __UpperCamelCase(   _A					:				Union[str, Any]  ,			_A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  []
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
           F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
           F'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
       )    )
   attention_weights.append(
       (
           F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
           F'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
       )    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', F'''stage{idx}.blocks.{cnt}.norm1.weight''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', F'''stage{idx}.blocks.{cnt}.norm1.bias''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', F'''stage{idx}.blocks.{cnt}.norm2.weight''')    )
   attention_weights.append(
       (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', F'''stage{idx}.blocks.{cnt}.norm2.bias''')    )
   return attention_weights







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  []
   token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')    )
   return token







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  []
   head.append(('''layernorm.weight''', '''norm.weight''')    )
   head.append(('''layernorm.bias''', '''norm.bias''')    )
   head.append(('''classifier.weight''', '''head.weight''')    )
   head.append(('''classifier.bias''', '''head.bias''')    )
   return head







def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				Dict  ,			_A					:				str  ,			_A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  '''imagenet-1k-id2label.json'''
   UpperCAmelCase__							:				str   				=  10_00

   UpperCAmelCase__							:				str   				=  '''huggingface/label-files'''
   UpperCAmelCase__							:				List[Any]   				=  num_labels
   UpperCAmelCase__							:				Tuple   				=  json.load(open(cached_download(hf_hub_url(_A  ,			_A  ,			repo_type='''dataset'''    )    )  ,			'''r'''    )    )
   UpperCAmelCase__							:				Any   				=  {int(_A    ): v for k, v in idalabel.items()}

   UpperCAmelCase__							:				List[str]   				=  idalabel
   UpperCAmelCase__							:				Any   				=  {v: k for k, v in idalabel.items()}

   UpperCAmelCase__							:				int   				=  CvtConfig(num_labels=_A  ,			idalabel=_A  ,			labelaid=_A    )

   # For depth size 13 (13 = 1+2+10)
   if cvt_model.rsplit('''/'''  ,			1    )[-1][4:6] == "13":
      UpperCAmelCase__							:				Tuple   				=  [1, 2, 10]

   # For depth size 21 (21 = 1+4+16)
   elif cvt_model.rsplit('''/'''  ,			1    )[-1][4:6] == "21":
      UpperCAmelCase__							:				List[str]   				=  [1, 4, 16]

   # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
   else:
      UpperCAmelCase__							:				Optional[int]   				=  [2, 2, 20]
      UpperCAmelCase__							:				str   				=  [3, 12, 16]
      UpperCAmelCase__							:				Union[str, Any]   				=  [1_92, 7_68, 10_24]

   UpperCAmelCase__							:				Optional[int]   				=  CvtForImageClassification(_A    )
   UpperCAmelCase__							:				Any   				=  AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''    )
   UpperCAmelCase__							:				Dict   				=  image_size
   UpperCAmelCase__							:				Union[str, Any]   				=  torch.load(_A  ,			map_location=torch.device('''cpu'''    )    )

   UpperCAmelCase__							:				Union[str, Any]   				=  OrderedDict()
   UpperCAmelCase__							:				str   				=  []

   for idx in range(len(config.depth    )    ):
      if config.cls_token[idx]:
         UpperCAmelCase__							:				Optional[int]   				=  list_of_state_dict + cls_token(_A    )
      UpperCAmelCase__							:				Union[str, Any]   				=  list_of_state_dict + embeddings(_A    )
      for cnt in range(config.depth[idx]    ):
         UpperCAmelCase__							:				str   				=  list_of_state_dict + attention(_A  ,			_A    )

   UpperCAmelCase__							:				int   				=  list_of_state_dict + final()
   for gg in list_of_state_dict:
      print(_A    )
   for i in range(len(_A    )    ):
      UpperCAmelCase__							:				List[Any]   				=  original_weights[list_of_state_dict[i][1]]

   model.load_state_dict(_A    )
   model.save_pretrained(_A    )
   image_processor.save_pretrained(_A    )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
  UpperCamelCase__     :	Tuple				=			argparse.ArgumentParser()
  parser.add_argument(
      '--cvt_model',
      default='cvt-w24',
      type=str,
      help='Name of the cvt model you\'d like to convert.',
  )
  parser.add_argument(
      '--image_size',
      default=384,
      type=int,
      help='Input Image Size',
  )
  parser.add_argument(
      '--cvt_file_name',
      default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
      type=str,
      help='Input Image Size',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
  )

  UpperCamelCase__     :	Dict				=			parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




