


class       lowercase_					:





				def __init__(				self       ,  lowercase_       ,  lowercase_)  -> str:
											a__      =name
											a__      =val





				def __str__(				self)  -> Tuple:
											return F"""{self.__class__.__name__}({self.name}, {self.val})"""





				def __lt__(				self       ,  lowercase_)  -> Any:
											return self.val < other.val







class       lowercase_					:





				def __init__(				self       ,  lowercase_)  -> Any:
											a__      ={}
											a__      ={}
											a__      =self.build_heap(lowercase_)





				def __getitem__(				self       ,  lowercase_)  -> List[str]:
											return self.get_value(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Optional[int]:
											return (idx - 1) // 2





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> int:
											return idx * 2 + 1





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> List[Any]:
											return idx * 2 + 2





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Any:
											return self.heap_dict[key]





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> str:
											a__      =len(lowercase_) - 1
											a__      =self.get_parent_idx(lowercase_)

											for idx, i in enumerate(lowercase_):
																		a__      =idx
																		a__      =i.val

											for i in range(lowercase_       ,  -1       ,  -1):
																		self.sift_down(lowercase_       ,  lowercase_)
											return array





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> List[str]:
											while True:
																		a__      =self.get_left_child_idx(lowercase_)  # noqa: E741
																		a__      =self.get_right_child_idx(lowercase_)

																		a__      =idx
																		if l < len(lowercase_) and array[l] < array[idx]:
																									a__      =l
																		if r < len(lowercase_) and array[r] < array[smallest]:
																									a__      =r

																		if smallest != idx:
																									a__    ,     a__      =array[smallest], array[idx]
																									(
																									    (
																									    a__
																									)    ,     (
																									    a__
																									)    ,     
																									)      =(
																									    self.idx_of_element[array[smallest]],
																									    self.idx_of_element[array[idx]],
																									)
																									a__      =smallest
																		else:
																									break





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Dict:
											a__      =self.get_parent_idx(lowercase_)
											while p >= 0 and self.heap[p] > self.heap[idx]:
																		a__    ,     a__      =self.heap[idx], self.heap[p]
																		a__    ,     a__      =(
																		    self.idx_of_element[self.heap[idx]],
																		    self.idx_of_element[self.heap[p]],
																		)
																		a__      =p
																		a__      =self.get_parent_idx(lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											return self.heap[0]





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__    ,     a__      =self.heap[-1], self.heap[0]
											a__    ,     a__      =(
											    self.idx_of_element[self.heap[-1]],
											    self.idx_of_element[self.heap[0]],
											)

											a__      =self.heap.pop()
											del self.idx_of_element[x]
											self.sift_down(0       ,  self.heap)
											return x





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Tuple:
											self.heap.append(lowercase_)
											a__      =len(self.heap) - 1
											a__      =node.val
											self.sift_up(len(self.heap) - 1)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											return len(self.heap) == 0





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> int:
											assert (
											    self.heap[self.idx_of_element[node]].val > new_value
											), "newValue must be less that current value"
											a__      =new_value
											a__      =new_value
											self.sift_up(self.idx_of_element[node])







_lowerCAmelCase:						Tuple   				=       Node('R', -1)
_lowerCAmelCase:						Optional[int]   				=       Node('B', 6)
_lowerCAmelCase:						Tuple   				=       Node('A', 3)
_lowerCAmelCase:						int   				=       Node('X', 1)
_lowerCAmelCase:						List[str]   				=       Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
_lowerCAmelCase:						int   				=       MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
	print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
	print(i)

if __name__ == "__main__":
	import doctest

	doctest.testmod()








"""simple docstring"""


from typing import TYPE_CHECKING

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


__magic_name__       :					int						=			{
    """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:
		__magic_name__       :					List[Any]						=			[
		    """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecAudioForAudioFrameClassification""",
		    """Data2VecAudioForCTC""",
		    """Data2VecAudioForSequenceClassification""",
		    """Data2VecAudioForXVector""",
		    """Data2VecAudioModel""",
		    """Data2VecAudioPreTrainedModel""",
		]
		__magic_name__       :					Optional[int]						=			[
		    """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecTextForCausalLM""",
		    """Data2VecTextForMaskedLM""",
		    """Data2VecTextForMultipleChoice""",
		    """Data2VecTextForQuestionAnswering""",
		    """Data2VecTextForSequenceClassification""",
		    """Data2VecTextForTokenClassification""",
		    """Data2VecTextModel""",
		    """Data2VecTextPreTrainedModel""",
		]
		__magic_name__       :					str						=			[
		    """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecVisionForImageClassification""",
		    """Data2VecVisionForMaskedImageModeling""",
		    """Data2VecVisionForSemanticSegmentation""",
		    """Data2VecVisionModel""",
		    """Data2VecVisionPreTrainedModel""",
		]

if is_tf_available():
		__magic_name__       :					Optional[Any]						=			[
		    """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

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



