"""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   :						List[str]      =    "src/transformers"
_UpperCamelCase   :						str      =    "docs/source/en/tasks"





def 				a_     (      _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : int     ):





       '''simple docstring'''

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

       lowercase__			:		int       =	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.
_UpperCamelCase   :						List[Any]      =    direct_transformers_import(TRANSFORMERS_PATH)

_UpperCamelCase   :						Union[str, 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   :						List[str]      =    {
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def 				a_     (      _lowerCAmelCase  : Any     ):





       '''simple docstring'''

       lowercase__			:		Optional[Any]       =	TASK_GUIDE_TO_MODELS[task_guide]
       lowercase__			:		int       =	SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowerCAmelCase   , set()     )
       lowercase__			:		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 				a_     (      _lowerCAmelCase  : Dict   , _lowerCAmelCase  : List[str]=False     ):





       '''simple docstring'''

       lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__			:		List[str]       =	_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-->'   , )

       lowercase__			:		Dict       =	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__":
       _UpperCamelCase   :						List[str]      =    argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       _UpperCamelCase   :						List[str]      =    parser.parse_args()

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







def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       str:
	if not isinstance(__lowerCamelCase							,			__lowerCamelCase    ):
		raise ValueError("""iterations must be defined as integers"""    )
	if not isinstance(__lowerCamelCase							,			__lowerCamelCase    ) or not number >= 1:
		raise ValueError(
		    """starting number must be
                         and integer and be more than 0"""    )
	if not iterations >= 1:
		raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz"""    )

	a										=							""""""
	while number <= iterations:
		if number % 3 == 0:
			out += "Fizz"
		if number % 5 == 0:
			out += "Buzz"
		if 0 not in (number % 3, number % 5):
			out += str(__lowerCamelCase    )

		# print(out)
		number += 1
		out += " "
	return out


if __name__ == "__main__":
		import doctest

		doctest.testmod()

