"""simple docstring"""






def       __A (     a_  :int				,    a_  :list)						->						List[str]:
				_enforce_args(__a				,    __a)
				if n == 0:
								return 0
				__a	:  Optional[Any]         =					float('''-inf''')
				for i in range(1				,    n + 1):
								__a	:  List[Any]         =					max(
								    __a				,    prices[i - 1] + naive_cut_rod_recursive(n - i				,    __a))

				return max_revue

def       __A (     a_  :int				,    a_  :list)						->						Dict:
				_enforce_args(__a				,    __a)
				__a	:  Optional[Any]         =					[float('''-inf''') for _ in range(n + 1)]
				return _top_down_cut_rod_recursive(__a				,    __a				,    __a)

def       __A (     a_  :int				,    a_  :list				,    a_  :list)						->						Union[str, Any]:

				if max_rev[n] >= 0:
								return max_rev[n]
				elif n == 0:
								return 0
				else:
								__a	:  Optional[int]         =					float('''-inf''')
								for i in range(1				,    n + 1):
												__a	:  Union[str, Any]         =					max(
												    __a				,    prices[i - 1] + _top_down_cut_rod_recursive(n - i				,    __a				,    __a)				,    )

								__a	:  Dict         =					max_revenue

				return max_rev[n]

def       __A (     a_  :int				,    a_  :list)						->						str:
				_enforce_args(__a				,    __a)

				# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
				# length 0.
				__a	:  Any         =					[float('''-inf''') for _ in range(n + 1)]
				__a	:  str         =					0

				for i in range(1				,    n + 1):
								__a	:  List[Any]         =					max_rev[i]
								for j in range(1				,    i + 1):
												__a	:  Tuple         =					max(__a				,    prices[j - 1] + max_rev[i - j])

								__a	:  Optional[int]         =					max_revenue_i

				return max_rev[n]

def       __A (     a_  :int				,    a_  :list)						->						Optional[Any]:

				if n < 0:
								__a	:  Union[str, Any]         =					F"""n must be greater than or equal to 0. Got n = {n}"""
								raise ValueError(__a)

				if n > len(__a):
								__a	:  Optional[Any]         =					(
								    '''Each integral piece of rod must have a corresponding price. '''
								    F"""Got n = {n} but length of prices = {len(__a)}"""
								)
								raise ValueError(__a)

def       __A (     )						->						Dict:
				__a	:  Dict         =					[6, 10, 12, 15, 20, 23]
				__a	:  Tuple         =					len(__a)

				# the best revenue comes from cutting the rod into 6 pieces, each
				# of length 1 resulting in a revenue of 6 * 6 = 36.
				__a	:  str         =					36

				__a	:  List[Any]         =					top_down_cut_rod(__a				,    __a)
				__a	:  Union[str, Any]         =					bottom_up_cut_rod(__a				,    __a)
				__a	:  Dict         =					naive_cut_rod_recursive(__a				,    __a)

				assert expected_max_revenue == max_rev_top_down
				assert max_rev_top_down == max_rev_bottom_up
				assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
		main()






"""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
A	     =					'''src/transformers'''
A	     =					'''docs/source/en/tasks'''

def       __A (     a_  :List[Any]				,    a_  :List[Any]				,    a_  :List[str])						->						List[str]:
				with open(a_				,    '''r'''				,    encoding='''utf-8'''				,    newline='''\n''') as f:
								__a	:  List[str]         =					f.readlines()
				# Find the start prompt.
				__a	:  Optional[Any]         =					0
				while not lines[start_index].startswith(a_):
								start_index += 1
				start_index += 1

				__a	:  int         =					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.
A	     =					direct_transformers_import(TRANSFORMERS_PATH)

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

def       __A (     a_  :Optional[Any])						->						Any:
				__a	:  List[Any]         =					TASK_GUIDE_TO_MODELS[task_guide]
				__a	:  List[Any]         =					SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a_				,    set())
				__a	:  Any         =					{
				    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_  :Optional[int]				,    a_  :Dict=False)						->						Any:
				__a    ,  __a    ,  __a    ,  __a	:  Any         =					_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	:  Optional[Any]         =					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__":
		A	     =					argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		A	     =					parser.parse_args()

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