

from collections.abc import Sequence
from queue import Queue





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A					,						_A					,						_A=None					,						_A=None							):
												__lowerCAmelCase					=							start
												__lowerCAmelCase					=							end
												__lowerCAmelCase					=							val
												__lowerCAmelCase					=							(start + end) // 2
												__lowerCAmelCase					=							left
												__lowerCAmelCase					=							right




							def __repr__( self							):
												return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A							):
												__lowerCAmelCase					=							collection
												__lowerCAmelCase					=							function
												if self.collection:
																	__lowerCAmelCase					=							self._build_tree(0					,						len(_A							) - 1							)




							def        A__  ( self					,						_A					,						_A							):
												self._update_tree(self.root					,						_A					,						_A							)




							def        A__  ( self					,						_A					,						_A							):
												return self._query_range(self.root					,						_A					,						_A							)




							def        A__  ( self					,						_A					,						_A							):
												if start == end:
																	return SegmentTreeNode(_A					,						_A					,						self.collection[start]							)
												__lowerCAmelCase					=							(start + end) // 2
												__lowerCAmelCase					=							self._build_tree(_A					,						_A							)
												__lowerCAmelCase					=							self._build_tree(mid + 1					,						_A							)
												return SegmentTreeNode(_A					,						_A					,						self.fn(left.val					,						right.val							)					,						_A					,						_A							)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												if node.start == i and node.end == i:
																	__lowerCAmelCase					=							val
																	return
												if i <= node.mid:
																	self._update_tree(node.left					,						_A					,						_A							)
												else:
																	self._update_tree(node.right					,						_A					,						_A							)
												__lowerCAmelCase					=							self.fn(node.left.val					,						node.right.val							)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												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        A__  ( self							):
												if self.root is not None:
																	__lowerCAmelCase					=							Queue()
																	queue.put(self.root							)
																	while not queue.empty():
																						__lowerCAmelCase					=							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('''*''' * 5_0)
				lowerCamelCase      				=     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()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase      				=     {
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecAudioForAudioFrameClassification''',
		    '''Data2VecAudioForCTC''',
		    '''Data2VecAudioForSequenceClassification''',
		    '''Data2VecAudioForXVector''',
		    '''Data2VecAudioModel''',
		    '''Data2VecAudioPreTrainedModel''',
		]
		lowerCamelCase      				=     [
		    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecTextForCausalLM''',
		    '''Data2VecTextForMaskedLM''',
		    '''Data2VecTextForMultipleChoice''',
		    '''Data2VecTextForQuestionAnswering''',
		    '''Data2VecTextForSequenceClassification''',
		    '''Data2VecTextForTokenClassification''',
		    '''Data2VecTextModel''',
		    '''Data2VecTextPreTrainedModel''',
		]
		lowerCamelCase      				=     [
		    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecVisionForImageClassification''',
		    '''Data2VecVisionForMaskedImageModeling''',
		    '''Data2VecVisionForSemanticSegmentation''',
		    '''Data2VecVisionModel''',
		    '''Data2VecVisionPreTrainedModel''',
		]

if is_tf_available():
		lowerCamelCase      				=     [
		    '''TFData2VecVisionForImageClassification''',
		    '''TFData2VecVisionForSemanticSegmentation''',
		    '''TFData2VecVisionModel''',
		    '''TFData2VecVisionPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
		from .configuration_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecTextConfig,
		    DataaVecTextOnnxConfig,
		)
		from .configuration_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecVisionConfig,
		    DataaVecVisionOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dataavec_audio import (
				    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecAudioForAudioFrameClassification,
				    DataaVecAudioForCTC,
				    DataaVecAudioForSequenceClassification,
				    DataaVecAudioForXVector,
				    DataaVecAudioModel,
				    DataaVecAudioPreTrainedModel,
				)
				from .modeling_dataavec_text import (
				    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecTextForCausalLM,
				    DataaVecTextForMaskedLM,
				    DataaVecTextForMultipleChoice,
				    DataaVecTextForQuestionAnswering,
				    DataaVecTextForSequenceClassification,
				    DataaVecTextForTokenClassification,
				    DataaVecTextModel,
				    DataaVecTextPreTrainedModel,
				)
				from .modeling_dataavec_vision import (
				    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecVisionForImageClassification,
				    DataaVecVisionForMaskedImageModeling,
				    DataaVecVisionForSemanticSegmentation,
				    DataaVecVisionModel,
				    DataaVecVisionPreTrainedModel,
				)
		if is_tf_available():
				from .modeling_tf_dataavec_vision import (
				    TFDataaVecVisionForImageClassification,
				    TFDataaVecVisionForSemanticSegmentation,
				    TFDataaVecVisionModel,
				    TFDataaVecVisionPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase      				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






