



"""simple docstring"""






import math

class 	__lowerCAmelCase							:


					'''simple docstring'''


					def __init__(      self    ,       a   ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						len(a   )
										snake_case_						:Union[str, Any]								=						[0] * (
										    4 * self.N
										)  # approximate the overall size of segment tree with array N
										self.build(1    ,       0    ,       self.N - 1   )







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										return idx * 2







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										return idx * 2 + 1







					def 		_a (      self    ,       a    ,       a    ,       a   ):  # noqa: E741




										"""simple docstring"""





										if l == r:
															snake_case_						:str								=						A[l]
										else:
															snake_case_						:Tuple								=						(l + r) // 2
															self.build(self.left(a   )    ,       a    ,       a   )
															self.build(self.right(a   )    ,       mid + 1    ,       a   )
															snake_case_						:Union[str, Any]								=						max(self.st[self.left(a   )]    ,       self.st[self.right(a   )]   )







					def 		_a (      self    ,       a    ,       a    ,       a   ):




										"""simple docstring"""





										return self.update_recursive(1    ,       0    ,       self.N - 1    ,       a - 1    ,       b - 1    ,       a   )







					def 		_a (      self    ,       a    ,       a    ,       a    ,       a    ,       a    ,       a   ):  # noqa: E741




										"""simple docstring"""





										if r < a or l > b:
															return True
										if l == r:
															snake_case_						:Tuple								=						val
															return True
										snake_case_						:Any								=						(l + r) // 2
										self.update_recursive(self.left(a   )    ,       a    ,       a    ,       a    ,       a    ,       a   )
										self.update_recursive(self.right(a   )    ,       mid + 1    ,       a    ,       a    ,       a    ,       a   )
										snake_case_						:Tuple								=						max(self.st[self.left(a   )]    ,       self.st[self.right(a   )]   )
										return True







					def 		_a (      self    ,       a    ,       a   ):




										"""simple docstring"""





										return self.query_recursive(1    ,       0    ,       self.N - 1    ,       a - 1    ,       b - 1   )







					def 		_a (      self    ,       a    ,       a    ,       a    ,       a    ,       a   ):  # noqa: E741




										"""simple docstring"""





										if r < a or l > b:
															return -math.inf
										if l >= a and r <= b:
															return self.st[idx]
										snake_case_						:List[Any]								=						(l + r) // 2
										snake_case_						:Tuple								=						self.query_recursive(self.left(a   )    ,       a    ,       a    ,       a    ,       a   )
										snake_case_						:Any								=						self.query_recursive(self.right(a   )    ,       mid + 1    ,       a    ,       a    ,       a   )
										return max(a    ,       a   )







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Dict								=						[]
										for i in range(1    ,       N + 1   ):
															show_list += [self.query(a    ,       a   )]
										print(a   )


if __name__ == "__main__":
				__UpperCAmelCase      :      Dict   					=					[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
				__UpperCAmelCase      :      Optional[int]   					=					15
				__UpperCAmelCase      :      List[Any]   					=					SegmentTree(A)
				print(segt.query(4, 6))
				print(segt.query(7, 11))
				print(segt.query(7, 12))
				segt.update(1, 3, 1_11)
				print(segt.query(1, 15))
				segt.update(7, 8, 2_35)
				segt.show_data()






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__UpperCAmelCase      :      Optional[int]   					=					{
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase      :      int   					=					['SegformerFeatureExtractor']
				__UpperCAmelCase      :      Dict   					=					['SegformerImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase      :      Optional[Any]   					=					[
				    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SegformerDecodeHead',
				    'SegformerForImageClassification',
				    'SegformerForSemanticSegmentation',
				    'SegformerLayer',
				    'SegformerModel',
				    'SegformerPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase      :      Dict   					=					[
				    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFSegformerDecodeHead',
				    'TFSegformerForImageClassification',
				    'TFSegformerForSemanticSegmentation',
				    'TFSegformerModel',
				    'TFSegformerPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_segformer import SegformerFeatureExtractor
								from .image_processing_segformer import SegformerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_segformer import (
								    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SegformerDecodeHead,
								    SegformerForImageClassification,
								    SegformerForSemanticSegmentation,
								    SegformerLayer,
								    SegformerModel,
								    SegformerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_segformer import (
								    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSegformerDecodeHead,
								    TFSegformerForImageClassification,
								    TFSegformerForSemanticSegmentation,
								    TFSegformerModel,
								    TFSegformerPreTrainedModel,
								)

else:
				import sys

				__UpperCAmelCase      :      Optional[Any]   					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

