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[int]       =  '''src/transformers'''
__lowerCamelCase					:	List[str]       =  '''docs/source/en/tasks'''




def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		with open(lowerCAmelCase   , "r"   , encoding="utf-8"   , newline="\n"	) as f:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    f.readlines()
		# Find the start prompt.
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    0
		while not lines[start_index].startswith(lowerCAmelCase	):
				start_index += 1
		start_index += 1

		SCREAMING_SNAKE_CASE_  :		Any				      =    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.
__lowerCamelCase					:	Dict       =  direct_transformers_import(TRANSFORMERS_PATH)

__lowerCamelCase					:	Union[str, 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					:	List[Any]       =  {
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}




def       _snake_case	(   lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    TASK_GUIDE_TO_MODELS[task_guide]
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowerCAmelCase   , set()	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    {
		    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							:   List[Any]   , lowerCAmelCase							:   Any=False	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    _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-->"   , )

		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    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__":
		__lowerCamelCase					:	List[str]       =  argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		__lowerCamelCase					:	str       =  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.abc import Sequence
from queue import Queue






class 		a__						:

					def __init__(    self      :       int,_A      :       List[Any],_A      :       Optional[Any],_A      :       Optional[int],_A      :       int=None,_A      :       List[str]=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    start
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    end
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    val
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    left
							SCREAMING_SNAKE_CASE_  :		str				      =    right






					def __repr__(    self      :       Tuple    ):







							"""simple docstring"""






							return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'







class 		a__						:

					def __init__(    self      :       Any,_A      :       Sequence,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    collection
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    function
							if self.collection:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    self._build_tree(0,len(_A    ) - 1    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							self._update_tree(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       Any,_A      :       List[Any]    ):







							"""simple docstring"""






							return self._query_range(self.root,_A,_A    )

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Optional[int],_A      :       int    ):







							"""simple docstring"""






							if start == end:
									return SegmentTreeNode(_A,_A,self.collection[start]    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    (start + end) // 2
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._build_tree(_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self._build_tree(mid + 1,_A    )
							return SegmentTreeNode(_A,_A,self.fn(left.val,right.val    ),_A,_A    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       int,_A      :       Tuple,_A      :       Dict    ):







							"""simple docstring"""






							if node.start == i and node.end == i:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    val
									return
							if i <= node.mid:
									self._update_tree(node.left,_A,_A    )
							else:
									self._update_tree(node.right,_A,_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.fn(node.left.val,node.right.val    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str],_A      :       Optional[int],_A      :       Optional[Any]    ):







							"""simple docstring"""






							if node.start == i and node.end == j:
									return node.val

							if i <= node.mid:
									if j <= node.mid:
											# range in left child tree
											return self._query_range(node.left,_A,_A    )
									else:
											# range in left child tree and right child tree
											return self.fn(
											    self._query_range(node.left,_A,node.mid    ),self._query_range(node.right,node.mid + 1,_A    ),)
							else:
									# range in right child tree
									return self._query_range(node.right,_A,_A    )






					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							if self.root is not None:
									SCREAMING_SNAKE_CASE_  :		int				      =    Queue()
									queue.put(self.root    )
									while not queue.empty():
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    queue.get()
											yield node

											if node.left is not None:
													queue.put(node.left    )

											if node.right is not None:
													queue.put(node.right    )


if __name__ == "__main__":
		import operator

		for fn in [operator.add, max, min]:
				print('''*''' * 50)
				__lowerCamelCase					:	int       =  SegmentTree([2, 1, 5, 3, 4], fn)
				for node in arr.traverse():
						print(node)
				print()

				arr.update(1, 5)
				for node in arr.traverse():
						print(node)
				print()

				print(arr.query_range(3, 4))  # 7
				print(arr.query_range(2, 2))  # 5
				print(arr.query_range(1, 3))  # 13
				print()
