






"""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__	:    Dict			 = 'src/transformers'
A__	:    Union[str, Any]			 = 'docs/source/en/tasks'

def 			_snake_case    (					lowerCamelCase__     :   Dict    ,						lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   Optional[int]  )    ->     Optional[Any]:
							with open(lowerCamelCase__    ,						"r"    ,						encoding="utf-8"    ,						newline="\n"  ) as f:
														lowerCamelCase_	:							Any									=f.readlines()
							# Find the start prompt.
							lowerCamelCase_	:							Optional[int]									=0
							while not lines[start_index].startswith(lowerCamelCase__  ):
														start_index += 1
							start_index += 1

							lowerCamelCase_	:							Optional[int]									=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.
A__	:    int			 = direct_transformers_import(TRANSFORMERS_PATH)

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

def 			_snake_case    (					lowerCamelCase__     :   Dict  )    ->     Any:
							lowerCamelCase_	:							int									=TASK_GUIDE_TO_MODELS[task_guide]
							lowerCamelCase_	:							Optional[Any]									=SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowerCamelCase__    ,						set()  )
							lowerCamelCase_	:							Dict									={
							    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 			_snake_case    (					lowerCamelCase__     :   Optional[int]    ,						lowerCamelCase__     :   Union[str, Any]=False  )    ->     Union[str, Any]:
							lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							List[Any]									=_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-->"    ,						)

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

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











"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   Optional[Any]  )    ->     Optional[int]:
							if not head:
														return True
							# split the list to two parts
							lowerCamelCase_							,						lowerCamelCase_	:							Union[str, Any]									=head.next, head
							while fast and fast.next:
														lowerCamelCase_	:							Optional[Any]									=fast.next.next
														lowerCamelCase_	:							str									=slow.next
							lowerCamelCase_	:							Tuple									=slow.next
							lowerCamelCase_	:							Any									=None  # Don't forget here! But forget still works!
							# reverse the second part
							lowerCamelCase_	:							List[str]									=None
							while second:
														lowerCamelCase_	:							Any									=second.next
														lowerCamelCase_	:							Union[str, Any]									=node
														lowerCamelCase_	:							Union[str, Any]									=second
														lowerCamelCase_	:							Optional[Any]									=nxt
							# compare two parts
							# second part has the same or one less node
							while node:
														if node.val != head.val:
																					return False
														lowerCamelCase_	:							List[str]									=node.next
														lowerCamelCase_	:							Optional[Any]									=head.next
							return True

def 			_snake_case    (					lowerCamelCase__     :   str  )    ->     Optional[int]:
							if not head or not head.next:
														return True

							# 1. Get the midpoint (slow)
							lowerCamelCase_	:							List[str]									=head
							while fast and fast.next:
														lowerCamelCase_							,						lowerCamelCase_	:							Union[str, Any]									=fast.next.next, slow.next

							# 2. Push the second half into the stack
							lowerCamelCase_	:							List[Any]									=[slow.val]
							while slow.next:
														lowerCamelCase_	:							List[Any]									=slow.next
														stack.append(slow.val  )

							# 3. Comparison
							while stack:
														if stack.pop() != cur.val:
																					return False
														lowerCamelCase_	:							Union[str, Any]									=cur.next

							return True

def 			_snake_case    (					lowerCamelCase__     :   Dict  )    ->     Optional[Any]:
							if not head or not head.next:
														return True
							lowerCamelCase_	:							Union[str, Any]									={}
							lowerCamelCase_	:							List[Any]									=0
							while head:
														if head.val in d:
																					d[head.val].append(lowerCamelCase__  )
														else:
																					lowerCamelCase_	:							List[str]									=[pos]
														lowerCamelCase_	:							Optional[int]									=head.next
														pos += 1
							lowerCamelCase_	:							Union[str, Any]									=pos - 1
							lowerCamelCase_	:							Optional[int]									=0
							for v in d.values():
														if len(lowerCamelCase__  ) % 2 != 0:
																					middle += 1
														else:
																					lowerCamelCase_	:							Optional[Any]									=0
																					for i in range(0    ,						len(lowerCamelCase__  )  ):
																												if v[i] + v[len(lowerCamelCase__  ) - 1 - step] != checksum:
																																			return False
																												step += 1
														if middle > 1:
																					return False
							return True



