
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
lowercase						:      str									= 'src/transformers'
lowercase						:      Union[str, Any]									= 'docs/source/en/tasks'




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any]		,						_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    List[Any])      ->					Tuple:






							'''simple docstring'''





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

							__UpperCamelCase			:     List[str]         =      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.
lowercase						:      Dict									= direct_transformers_import(TRANSFORMERS_PATH)

lowercase						:      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`).
lowercase						:      Optional[Any]									= {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any])      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      TASK_GUIDE_TO_MODELS[task_guide]
							__UpperCamelCase			:     str         =      SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowerCamelCase		,						set())
							__UpperCamelCase			:     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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    int=False)      ->					Any:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     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-->"		,						)

							__UpperCamelCase			:     str         =      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__":
		lowercase						:      Tuple									= argparse.ArgumentParser()
		parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
		lowercase						:      Dict									= parser.parse_args()

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

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Sequence[float]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int)      ->					tuple[int | None, int | None, float]:






							'''simple docstring'''





							if not arr:
														return None, None, 0
							if low == high:
														return low, high, arr[low]

							__UpperCamelCase			:     Tuple         =      (low + high) // 2
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      max_subarray(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      max_subarray(_lowerCamelCase		,						mid + 1		,						_lowerCamelCase)
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      max_cross_sum(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
							if left_sum >= right_sum and left_sum >= cross_sum:
														return left_low, left_high, left_sum
							elif right_sum >= left_sum and right_sum >= cross_sum:
														return right_low, right_high, right_sum
							return cross_left, cross_right, cross_sum




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Sequence[float]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int)      ->					tuple[int, int, float]:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      float("-inf"), -1
							__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      float("-inf"), -1

							__UpperCamelCase			:     int | float         =      0
							for i in range(_lowerCamelCase		,						low - 1		,						-1):
														summ += arr[i]
														if summ > left_sum:
																					__UpperCamelCase			:     Tuple         =      summ
																					__UpperCamelCase			:     Optional[int]         =      i

							__UpperCamelCase			:     Union[str, Any]         =      0
							for i in range(mid + 1		,						high + 1):
														summ += arr[i]
														if summ > right_sum:
																					__UpperCamelCase			:     Union[str, Any]         =      summ
																					__UpperCamelCase			:     Optional[int]         =      i

							return max_left, max_right, (left_sum + right_sum)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					float:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      [randint(1		,						_lowerCamelCase) for _ in range(_lowerCamelCase)]
							__UpperCamelCase			:     Optional[Any]         =      time.time()
							max_subarray(_lowerCamelCase		,						0		,						input_size - 1)
							__UpperCamelCase			:     Any         =      time.time()
							return end - start




def 			_SCREAMING_SNAKE_CASE      (	)      ->					None:






							'''simple docstring'''





							__UpperCamelCase			:     Dict         =      [10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
							__UpperCamelCase			:     Union[str, Any]         =      [time_max_subarray(_lowerCamelCase) for input_size in input_sizes]
							print("No of Inputs\t\tTime Taken")
							for input_size, runtime in zip(_lowerCamelCase		,						_lowerCamelCase):
														print(_lowerCamelCase		,						"\t\t"		,						_lowerCamelCase)
							plt.plot(_lowerCamelCase		,						_lowerCamelCase)
							plt.xlabel("Number of Inputs")
							plt.ylabel("Time taken in seconds")
							plt.show()


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

		testmod()