

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
lowerCamelCase           =					'''src/transformers'''
lowerCamelCase           =					'''docs/source/en/tasks'''




def 			UpperCAmelCase__				(			_A      : Optional[int]	,      _A      : List[str]	,      _A      : Dict							):





  '''simple docstring'''







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

  a__  =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.
lowerCamelCase           =					direct_transformers_import(TRANSFORMERS_PATH)

lowerCamelCase           =					{
    '''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`).
lowerCamelCase           =					{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}




def 			UpperCAmelCase__				(			_A      : List[Any]							):





  '''simple docstring'''







  a__  =TASK_GUIDE_TO_MODELS[task_guide]
  a__  =SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_A	,      set()							)
  a__  ={
      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      : Union[str, Any]	,      _A      : List[Any]=False							):





  '''simple docstring'''







  a__,      a__,      a__,      a__  =_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__  =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__":
       lowerCamelCase           =					argparse.ArgumentParser()
       parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
       lowerCamelCase           =					parser.parse_args()

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



from typing import TYPE_CHECKING

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


lowerCamelCase           =					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''MvpTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''MvpForCausalLM''',
           '''MvpForConditionalGeneration''',
           '''MvpForQuestionAnswering''',
           '''MvpForSequenceClassification''',
           '''MvpModel''',
           '''MvpPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
