
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      :				Tuple          = "docs/source/en/tasks"


def    A (      lowercase					, lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

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

		UpperCamelCase						=  start_index
		while not lines[end_index].startswith(__snake_case				):
				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[str]          = direct_transformers_import(TRANSFORMERS_PATH)

_UpperCAmelCase      :				Dict          = {
    "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[Any]          = {
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}


def    A (      lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  TASK_GUIDE_TO_MODELS[task_guide]
		UpperCamelCase						=  SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__snake_case					, set()				)
		UpperCamelCase						=  {
		    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 (      lowercase					, lowercase=False				)     ->						Dict:
		'''simple docstring'''

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

		UpperCamelCase						=  get_model_list_for_task(__snake_case				)

		if current_list != new_list:
				if overwrite:
						with open(os.path.join(__snake_case					, __snake_case				)					, '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      :				Dict          = argparse.ArgumentParser()
					parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
					_UpperCAmelCase      :				Tuple          = 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 (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  int(lowercase				)
		if decimal in (0, 1):  # Exit cases for the recursion
				return str(lowercase				)
		UpperCamelCase			,						UpperCamelCase						=  divmod(lowercase					, 2				)
		return binary_recursive(lowercase				) + str(lowercase				)


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  str(lowercase				).strip()
		if not number:
				raise ValueError('No input value was provided'				)
		UpperCamelCase						=  '-' if number.startswith('-'				) else ''
		UpperCamelCase						=  number.lstrip('-'				)
		if not number.isnumeric():
				raise ValueError('Input value is not an integer'				)
		return f'''{negative}0b{binary_recursive(int(lowercase				)				)}'''


if __name__ == "__main__":
					from doctest import testmod

					testmod()
