



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
lowerCAmelCase__		:					Optional[Any]							='''src/transformers'''
lowerCAmelCase__		:					Optional[int]							='''docs/source/en/tasks'''






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

							__SCREAMING_SNAKE_CASE									= 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.
lowerCAmelCase__		:					str							=direct_transformers_import(TRANSFORMERS_PATH)

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






def      __lowercase       (			a__       )    ->			int:
							__SCREAMING_SNAKE_CASE									= TASK_GUIDE_TO_MODELS[task_guide]
							__SCREAMING_SNAKE_CASE									= SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a__							,       set()       )
							__SCREAMING_SNAKE_CASE									= {
							    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       (			a__							,       a__=False       )    ->			List[Any]:
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= _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-->'							,       )

							__SCREAMING_SNAKE_CASE									= 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__":
			lowerCAmelCase__		:					Union[str, Any]							=argparse.ArgumentParser()
			parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
			lowerCAmelCase__		:					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 collections import deque

from .hash_table import HashTable

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

													super().__init__(*_A				,							**_A		)






						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= deque([]		) if self.values[key] is None else self.values[key]
													self.values[key].appendleft(_A		)
													__SCREAMING_SNAKE_CASE									= self.values[key]






						def 	_A     (   self		):







													'''simple docstring'''

													return (
													    sum(self.charge_factor - len(_A		) for slot in self.values		)
													    / self.size_table
													    * self.charge_factor
													)






						def 	_A     (   self				,							_A				,							_A=None		):







													'''simple docstring'''

													if not (
													    len(self.values[key]		) == self.charge_factor and self.values.count(_A		) == 0
													):
																				return key
													return super()._collision_resolution(_A				,							_A		)






