





"""simple docstring"""





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=1  )						->Any:
  if n_shave_prefix_segments >= 0:
    return ".".join(path.split('''.'''  )[n_shave_prefix_segments:]  )
  else:
    return ".".join(path.split('''.'''  )[:n_shave_prefix_segments]  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=0  )						->Any:
  __UpperCAmelCase    :						str       =						[]
  for old_item in old_list:
    __UpperCAmelCase    :						List[str]       =						old_item.replace('''in_layers.0'''  ,			'''norm1'''  )
    __UpperCAmelCase    :						List[str]       =						new_item.replace('''in_layers.2'''  ,			'''conv1'''  )

    __UpperCAmelCase    :						str       =						new_item.replace('''out_layers.0'''  ,			'''norm2'''  )
    __UpperCAmelCase    :						Tuple       =						new_item.replace('''out_layers.3'''  ,			'''conv2'''  )

    __UpperCAmelCase    :						Any       =						new_item.replace('''emb_layers.1'''  ,			'''time_emb_proj'''  )
    __UpperCAmelCase    :						Union[str, Any]       =						new_item.replace('''skip_connection'''  ,			'''conv_shortcut'''  )

    __UpperCAmelCase    :						List[str]       =						shave_segments(lowerCAmelCase__  ,			n_shave_prefix_segments=lowerCAmelCase__  )

    mapping.append({'''old''': old_item, '''new''': new_item}  )

  return mapping


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=0  )						->Dict:
  __UpperCAmelCase    :						Dict       =						[]
  for old_item in old_list:
    __UpperCAmelCase    :						int       =						old_item

    __UpperCAmelCase    :						Optional[int]       =						new_item.replace('''norm.weight'''  ,			'''group_norm.weight'''  )
    __UpperCAmelCase    :						Any       =						new_item.replace('''norm.bias'''  ,			'''group_norm.bias'''  )

    __UpperCAmelCase    :						str       =						new_item.replace('''proj_out.weight'''  ,			'''proj_attn.weight'''  )
    __UpperCAmelCase    :						List[str]       =						new_item.replace('''proj_out.bias'''  ,			'''proj_attn.bias'''  )

    __UpperCAmelCase    :						int       =						shave_segments(lowerCAmelCase__  ,			n_shave_prefix_segments=lowerCAmelCase__  )

    mapping.append({'''old''': old_item, '''new''': new_item}  )

  return mapping


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=None  )						->Optional[int]:
  assert isinstance(lowerCAmelCase__  ,			lowerCAmelCase__  ), "Paths should be a list of dicts containing 'old' and 'new' keys."

  # Splits the attention layers into three variables.
  if attention_paths_to_split is not None:
    for path, path_map in attention_paths_to_split.items():
      __UpperCAmelCase    :						Optional[int]       =						old_checkpoint[path]
      __UpperCAmelCase    :						List[str]       =						old_tensor.shape[0] // 3

      __UpperCAmelCase    :						Any       =						(-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

      __UpperCAmelCase    :						Union[str, Any]       =						old_tensor.shape[0] // config['''num_head_channels'''] // 3

      __UpperCAmelCase    :						List[str]       =						old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]  )
      __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[Any]       =						old_tensor.split(channels // num_heads  ,			dim=1  )

      __UpperCAmelCase    :						Optional[Any]       =						query.reshape(lowerCAmelCase__  )
      __UpperCAmelCase    :						Optional[int]       =						key.reshape(lowerCAmelCase__  )
      __UpperCAmelCase    :						Dict       =						value.reshape(lowerCAmelCase__  )

  for path in paths:
    __UpperCAmelCase    :						Tuple       =						path['''new''']

    # These have already been assigned
    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    # Global renaming happens here
    __UpperCAmelCase    :						Dict       =						new_path.replace('''middle_block.0'''  ,			'''mid_block.resnets.0'''  )
    __UpperCAmelCase    :						str       =						new_path.replace('''middle_block.1'''  ,			'''mid_block.attentions.0'''  )
    __UpperCAmelCase    :						str       =						new_path.replace('''middle_block.2'''  ,			'''mid_block.resnets.1'''  )

    if additional_replacements is not None:
      for replacement in additional_replacements:
        __UpperCAmelCase    :						int       =						new_path.replace(replacement['''old''']  ,			replacement['''new''']  )

        # proj_attn.weight has to be converted from conv 1D to linear
    if "proj_attn.weight" in new_path:
      __UpperCAmelCase    :						List[str]       =						old_checkpoint[path['''old''']][:, :, 0]
    else:
      __UpperCAmelCase    :						List[Any]       =						old_checkpoint[path['''old''']]


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[int]:
  __UpperCAmelCase    :						Tuple       =						{}

  __UpperCAmelCase    :						Dict       =						checkpoint['''time_embed.0.weight''']
  __UpperCAmelCase    :						List[str]       =						checkpoint['''time_embed.0.bias''']
  __UpperCAmelCase    :						Union[str, Any]       =						checkpoint['''time_embed.2.weight''']
  __UpperCAmelCase    :						str       =						checkpoint['''time_embed.2.bias''']

  __UpperCAmelCase    :						Optional[Any]       =						checkpoint['''input_blocks.0.0.weight''']
  __UpperCAmelCase    :						Union[str, Any]       =						checkpoint['''input_blocks.0.0.bias''']

  __UpperCAmelCase    :						List[Any]       =						checkpoint['''out.0.weight''']
  __UpperCAmelCase    :						str       =						checkpoint['''out.0.bias''']
  __UpperCAmelCase    :						Dict       =						checkpoint['''out.2.weight''']
  __UpperCAmelCase    :						Union[str, Any]       =						checkpoint['''out.2.bias''']

  # Retrieves the keys for the input blocks only
  __UpperCAmelCase    :						Union[str, Any]       =						len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in checkpoint if '''input_blocks''' in layer}  )
  __UpperCAmelCase    :						Tuple       =						{
      layer_id: [key for key in checkpoint if f'''input_blocks.{layer_id}''' in key]
      for layer_id in range(lowerCAmelCase__  )
  }

  # Retrieves the keys for the middle blocks only
  __UpperCAmelCase    :						Dict       =						len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in checkpoint if '''middle_block''' in layer}  )
  __UpperCAmelCase    :						Tuple       =						{
      layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
      for layer_id in range(lowerCAmelCase__  )
  }

  # Retrieves the keys for the output blocks only
  __UpperCAmelCase    :						Union[str, Any]       =						len({'''.'''.join(layer.split('''.'''  )[:2]  ) for layer in checkpoint if '''output_blocks''' in layer}  )
  __UpperCAmelCase    :						List[Any]       =						{
      layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
      for layer_id in range(lowerCAmelCase__  )
  }

  for i in range(1  ,			lowerCAmelCase__  ):
    __UpperCAmelCase    :						Optional[int]       =						(i - 1) // (config['''num_res_blocks'''] + 1)
    __UpperCAmelCase    :						List[str]       =						(i - 1) % (config['''num_res_blocks'''] + 1)

    __UpperCAmelCase    :						str       =						[key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
    __UpperCAmelCase    :						int       =						[key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]

    if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
      __UpperCAmelCase    :						Dict       =						checkpoint[
          f'''input_blocks.{i}.0.op.weight'''
      ]
      __UpperCAmelCase    :						Any       =						checkpoint[
          f'''input_blocks.{i}.0.op.bias'''
      ]
      continue

    __UpperCAmelCase    :						str       =						renew_resnet_paths(lowerCAmelCase__  )
    __UpperCAmelCase    :						Union[str, Any]       =						{'''old''': f'''input_blocks.{i}.0''', '''new''': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
    __UpperCAmelCase    :						str       =						{'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
    assign_to_checkpoint(
        lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__  ,			additional_replacements=[meta_path, resnet_op]  ,			config=lowerCAmelCase__  )

    if len(lowerCAmelCase__  ):
      __UpperCAmelCase    :						Dict       =						renew_attention_paths(lowerCAmelCase__  )
      __UpperCAmelCase    :						List[str]       =						{
          '''old''': f'''input_blocks.{i}.1''',
          '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
      }
      __UpperCAmelCase    :						Dict       =						{
          f'''input_blocks.{i}.1.qkv.bias''': {
              '''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
              '''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
              '''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
          },
          f'''input_blocks.{i}.1.qkv.weight''': {
              '''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
              '''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
              '''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
          },
      }
      assign_to_checkpoint(
          lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__  ,			additional_replacements=[meta_path]  ,			attention_paths_to_split=lowerCAmelCase__  ,			config=lowerCAmelCase__  ,			)

  __UpperCAmelCase    :						Union[str, Any]       =						middle_blocks[0]
  __UpperCAmelCase    :						Union[str, Any]       =						middle_blocks[1]
  __UpperCAmelCase    :						Optional[Any]       =						middle_blocks[2]

  __UpperCAmelCase    :						List[str]       =						renew_resnet_paths(lowerCAmelCase__  )
  assign_to_checkpoint(lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__  ,			config=lowerCAmelCase__  )

  __UpperCAmelCase    :						List[Any]       =						renew_resnet_paths(lowerCAmelCase__  )
  assign_to_checkpoint(lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__  ,			config=lowerCAmelCase__  )

  __UpperCAmelCase    :						Any       =						renew_attention_paths(lowerCAmelCase__  )
  __UpperCAmelCase    :						Tuple       =						{
      '''middle_block.1.qkv.bias''': {
          '''key''': '''mid_block.attentions.0.key.bias''',
          '''query''': '''mid_block.attentions.0.query.bias''',
          '''value''': '''mid_block.attentions.0.value.bias''',
      },
      '''middle_block.1.qkv.weight''': {
          '''key''': '''mid_block.attentions.0.key.weight''',
          '''query''': '''mid_block.attentions.0.query.weight''',
          '''value''': '''mid_block.attentions.0.value.weight''',
      },
  }
  assign_to_checkpoint(
      lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__  ,			attention_paths_to_split=lowerCAmelCase__  ,			config=lowerCAmelCase__  )

  for i in range(lowerCAmelCase__  ):
    __UpperCAmelCase    :						str       =						i // (config['''num_res_blocks'''] + 1)
    __UpperCAmelCase    :						List[str]       =						i % (config['''num_res_blocks'''] + 1)
    __UpperCAmelCase    :						Any       =						[shave_segments(lowerCAmelCase__  ,			2  ) for name in output_blocks[i]]
    __UpperCAmelCase    :						Optional[Any]       =						{}

    for layer in output_block_layers:
      __UpperCAmelCase       ,     __UpperCAmelCase    :						Dict       =						layer.split('''.'''  )[0], shave_segments(lowerCAmelCase__  ,			1  )
      if layer_id in output_block_list:
        output_block_list[layer_id].append(lowerCAmelCase__  )
      else:
        __UpperCAmelCase    :						Any       =						[layer_name]

    if len(lowerCAmelCase__  ) > 1:
      __UpperCAmelCase    :						List[str]       =						[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
      __UpperCAmelCase    :						Optional[Any]       =						[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

      __UpperCAmelCase    :						str       =						renew_resnet_paths(lowerCAmelCase__  )
      __UpperCAmelCase    :						Union[str, Any]       =						renew_resnet_paths(lowerCAmelCase__  )

      __UpperCAmelCase    :						Tuple       =						{'''old''': f'''output_blocks.{i}.0''', '''new''': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
      assign_to_checkpoint(lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__  ,			additional_replacements=[meta_path]  ,			config=lowerCAmelCase__  )

      if ["conv.weight", "conv.bias"] in output_block_list.values():
        __UpperCAmelCase    :						Any       =						list(output_block_list.values()  ).index(['''conv.weight''', '''conv.bias''']  )
        __UpperCAmelCase    :						Union[str, Any]       =						checkpoint[
            f'''output_blocks.{i}.{index}.conv.weight'''
        ]
        __UpperCAmelCase    :						Optional[int]       =						checkpoint[
            f'''output_blocks.{i}.{index}.conv.bias'''
        ]

        # Clear attentions as they have been attributed above.
        if len(lowerCAmelCase__  ) == 2:
          __UpperCAmelCase    :						str       =						[]

      if len(lowerCAmelCase__  ):
        __UpperCAmelCase    :						Tuple       =						renew_attention_paths(lowerCAmelCase__  )
        __UpperCAmelCase    :						Optional[Any]       =						{
            '''old''': f'''output_blocks.{i}.1''',
            '''new''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
        }
        __UpperCAmelCase    :						Dict       =						{
            f'''output_blocks.{i}.1.qkv.bias''': {
                '''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
                '''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
                '''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
            },
            f'''output_blocks.{i}.1.qkv.weight''': {
                '''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
                '''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
                '''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
            },
        }
        assign_to_checkpoint(
            lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__  ,			additional_replacements=[meta_path]  ,			attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions  ) else None  ,			config=lowerCAmelCase__  ,			)
    else:
      __UpperCAmelCase    :						Tuple       =						renew_resnet_paths(lowerCAmelCase__  ,			n_shave_prefix_segments=1  )
      for path in resnet_0_paths:
        __UpperCAmelCase    :						Optional[Any]       =						'''.'''.join(['''output_blocks''', str(lowerCAmelCase__  ), path['''old''']]  )
        __UpperCAmelCase    :						Optional[int]       =						'''.'''.join(['''up_blocks''', str(lowerCAmelCase__  ), '''resnets''', str(lowerCAmelCase__  ), path['''new''']]  )

        __UpperCAmelCase    :						List[Any]       =						checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
  lowercase__				:Optional[int]				   =       argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
  )

  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      required=True,
      help='The config json file corresponding to the architecture.',
  )

  parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

  lowercase__				:Tuple				   =       parser.parse_args()

  lowercase__				:List[str]				   =       torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    lowercase__				:List[str]				   =       json.loads(f.read())

  lowercase__				:Optional[Any]				   =       convert_ldm_checkpoint(checkpoint, config)

  if "ldm" in config:
    del config["ldm"]

  lowercase__				:str				   =       UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    lowercase__				:Tuple				   =       DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
    lowercase__				:Optional[Any]				   =       VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

    lowercase__				:Any				   =       LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)






"""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
lowercase__				:int				   =       'src/transformers'
lowercase__				:List[str]				   =       'docs/source/en/tasks'


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->str:





  """simple docstring"""


  with open(UpperCAmelCase_  ,			'''r'''  ,			encoding='''utf-8'''  ,			newline='''\n'''  ) as f:
    __UpperCAmelCase    :						Union[str, Any]       =						f.readlines()
  # Find the start prompt.
  __UpperCAmelCase    :						Any       =						0
  while not lines[start_index].startswith(UpperCAmelCase_  ):
    start_index += 1
  start_index += 1

  __UpperCAmelCase    :						Optional[Any]       =						start_index
  while not lines[end_index].startswith(UpperCAmelCase_  ):
    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.
lowercase__				:Any				   =       direct_transformers_import(TRANSFORMERS_PATH)

lowercase__				:List[Any]				   =       {
    '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`).
lowercase__				:Union[str, Any]				   =       {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						List[str]       =						TASK_GUIDE_TO_MODELS[task_guide]
  __UpperCAmelCase    :						Dict       =						SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase_  ,			set()  )
  __UpperCAmelCase    :						List[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       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=False  )						->Tuple:





  """simple docstring"""


  __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Dict       =						_find_text_in_file(
      filename=os.path.join(UpperCAmelCase_  ,			UpperCAmelCase_  )  ,			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(UpperCAmelCase_  )

  if current_list != new_list:
    if overwrite:
      with open(os.path.join(UpperCAmelCase_  ,			UpperCAmelCase_  )  ,			'''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__":
  lowercase__				:int				   =       argparse.ArgumentParser()
  parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
  lowercase__				:Optional[Any]				   =       parser.parse_args()

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