
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			:     Optional[Any]           =   'src/transformers'
__lowerCAmelCase			:     List[Any]           =   'docs/source/en/tasks'

def a__    (       A_,     A_,     A_					):






  '''simple docstring'''





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

  __magic_name__				     =       start_index
  while not lines[end_index].startswith(_lowerCAmelCase					):
    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			:     int           =   direct_transformers_import(TRANSFORMERS_PATH)

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

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       TASK_GUIDE_TO_MODELS[task_guide]
  __magic_name__				     =       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowerCAmelCase,     set()					)
  __magic_name__				     =       {
      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 a__    (       A_,     A_=False					):






  '''simple docstring'''





  __magic_name__				     =       _find_text_in_file(
      filename=os.path.join(_lowerCAmelCase,     _lowerCAmelCase					),     start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->""",     end_prompt="""<!--End of the generated tip-->""",     )

  __magic_name__				     =       get_model_list_for_task(_lowerCAmelCase					)

  if current_list != new_list:
    if overwrite:
      with open(os.path.join(_lowerCAmelCase,     _lowerCAmelCase					),     """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			:     Tuple           =   argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    __lowerCAmelCase			:     Tuple           =   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 PIL import Image





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Image:

      def brightness(_lowerCAmelCase		) -> float:
            return 128 + level + (c - 128)

      if not -255.0 <= level <= 255.0:
            raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""		)
      return img.point(_lowerCAmelCase		)


if __name__ == "__main__":
       # Load image
       with Image.open("image_data/lena.jpg") as img:
              # Change brightness to 100
              __a          =		change_brightness(img, 100)
              brigt_img.save("image_data/lena_brightness.png", format="png")
