'''simple docstring'''

import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCamelCase__     :	str				=			'src/transformers'
UpperCamelCase__     :	int				=			'docs/source/en/tasks'







def __UpperCamelCase(   _A					:				Union[str, Any]  ,			_A					:				List[str]  ,			_A					:				Union[str, Any]    ):
   '''simple docstring'''
   with open(_A  ,			'''r'''  ,			encoding='''utf-8'''  ,			newline='''\n'''    ) as f:
      UpperCAmelCase__							:				List[Any]   				=  f.readlines()
   # Find the start prompt.
   UpperCAmelCase__							:				Optional[int]   				=  0
   while not lines[start_index].startswith(_A    ):
      start_index += 1
   start_index += 1

   UpperCAmelCase__							:				str   				=  start_index
   while not lines[end_index].startswith(_A    ):
      end_index += 1
   end_index -= 1

   while len(lines[start_index]    ) <= 1:
      start_index += 1
   while len(lines[end_index]    ) <= 1:
      end_index -= 1
   end_index += 1
   return "".join(lines[start_index:end_index]    ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
UpperCamelCase__     :	List[str]				=			direct_transformers_import(TRANSFORMERS_PATH)

UpperCamelCase__     :	int				=			{
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
UpperCamelCase__     :	Any				=			{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  TASK_GUIDE_TO_MODELS[task_guide]
   UpperCAmelCase__							:				Union[str, Any]   				=  SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_A  ,			set()    )
   UpperCAmelCase__							:				Union[str, Any]   				=  {
       code: name
       for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
       if (code in model_maping_names or code in special_model_types)
   }
   return ", ".join([F'''[{name}](../model_doc/{code})''' for code, name in model_names.items()]    ) + "\n"







def __UpperCamelCase(   _A					:				int  ,			_A					:				Tuple=False    ):
   '''simple docstring'''
   UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[int]   				=  _find_text_in_file(
       filename=os.path.join(_A  ,			_A    )  ,			start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''  ,			end_prompt='''<!--End of the generated tip-->'''  ,			)

   UpperCAmelCase__							:				List[str]   				=  get_model_list_for_task(_A    )

   if current_list != new_list:
      if overwrite:
         with open(os.path.join(_A  ,			_A    )  ,			'''w'''  ,			encoding='''utf-8'''  ,			newline='''\n'''    ) as f:
            f.writelines(lines[:start_index] + [new_list] + lines[end_index:]    )
      else:
         raise ValueError(
             F'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
             ''' to fix this.'''    )


if __name__ == "__main__":
  UpperCamelCase__     :	int				=			argparse.ArgumentParser()
  parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
  UpperCamelCase__     :	int				=			parser.parse_args()

  for task_guide in TASK_GUIDE_TO_MODELS.keys():
    check_model_list_for_task(task_guide, args.fix_and_overwrite)





'''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)




