


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
a			:			int												=					'''src/transformers'''
a			:			Dict												=					'''docs/source/en/tasks'''


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







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

			__lowercase			       = 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.
a			:			List[Any]												=					direct_transformers_import(TRANSFORMERS_PATH)

a			:			Tuple												=					{
    '''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`).
a			:			str												=					{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = TASK_GUIDE_TO_MODELS[task_guide]
			__lowercase			       = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_UpperCamelCase							,      set()	)
			__lowercase			       = {
			    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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=False	):


			'''simple docstring'''







			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase			       = _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-->'''							,      )

			__lowercase			       = 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__":
	a			:			Union[str, Any]												=					argparse.ArgumentParser()
	parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
	a			:			int												=					parser.parse_args()

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






a			:			Any												=					[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = 0
			while number:
						# Increased Speed Slightly by checking every 5 digits together.
						sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
						number //= 10_00_00

			return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
a			:			list[bool | None]												=					[None] * 10000000
a			:			int												=					True
a			:			Any												=					False


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if CHAINS[number - 1] is not None:
						return CHAINS[number - 1]  # type: ignore

			__lowercase			       = chain(next_number(_UpperCamelCase	)	)
			__lowercase			       = number_chain

			while number < 10_00_00_00:
						__lowercase			       = number_chain
						number *= 10

			return number_chain


def 		lowercase_	(  _UpperCamelCase = 10_00_00_00	):


			'''simple docstring'''







			for i in range(1							,      _UpperCamelCase	):
						if CHAINS[i] is None:
									chain(i + 1	)

			return CHAINS[:number].count(_UpperCamelCase	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f'''{solution() = }''')


