






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    :   Optional[int]   			=						"src/transformers"
_UpperCamelCase    :   Dict   			=						"docs/source/en/tasks"




def       snake_case  (A_				:Dict     ,      A_				:List[str]     ,      A_				:str				):







  '''simple docstring'''

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

  a					:      Optional[int]    =       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    :   str   			=						direct_transformers_import(TRANSFORMERS_PATH)

_UpperCamelCase    :   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`).
_UpperCamelCase    :   str   			=						{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}




def       snake_case  (A_				:str				):







  '''simple docstring'''

  a					:      List[str]    =       TASK_GUIDE_TO_MODELS[task_guide]
  a					:      Tuple    =       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(A_     ,      set()				)
  a					:      Optional[int]    =       {
      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       snake_case  (A_				:int     ,      A_				:Tuple=False				):







  '''simple docstring'''

  a					:      Dict    =       _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-->'     ,      )

  a					:      Tuple    =       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    :   Any   			=						argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   _UpperCamelCase    :   str   			=						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"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_UpperCamelCase    :   Optional[int]   			=						{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Dict   			=						[
       'RagModel',
       'RagPreTrainedModel',
       'RagSequenceForGeneration',
       'RagTokenForGeneration',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Optional[int]   			=						[
       'TFRagModel',
       'TFRagPreTrainedModel',
       'TFRagSequenceForGeneration',
       'TFRagTokenForGeneration',
   ]


if TYPE_CHECKING:
   from .configuration_rag import RagConfig
   from .retrieval_rag import RagRetriever
   from .tokenization_rag import RagTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_rag import (
          TFRagModel,
          TFRagPreTrainedModel,
          TFRagSequenceForGeneration,
          TFRagTokenForGeneration,
      )

else:
   import sys

   _UpperCamelCase    :   int   			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




