



class 				__snake_case						:




							def __init__(       self     :       str				,				A_     :       Dict				,				A_     :       Optional[int]):
									lowerCAmelCase_			:     str									= name
									lowerCAmelCase_			:     List[Any]									= val




							def __str__(       self     :       List[str]):
									return F"""{self.__class__.__name__}({self.name}, {self.val})"""







							def __lt__(       self     :       List[str]				,				A_     :       Tuple):
									return self.val < other.val





class 				__snake_case						:




							def __init__(       self     :       int				,				A_     :       Dict):
									lowerCAmelCase_			:     List[str]									= {}
									lowerCAmelCase_			:     Any									= {}
									lowerCAmelCase_			:     Dict									= self.build_heap(A_)




							def __getitem__(       self     :       List[str]				,				A_     :       List[Any]):
									return self.get_value(A_)




							def    UpperCAmelCase__			(       self     :       List[Any]				,				A_     :       str):
									return (idx - 1) // 2




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       List[str]):
									return idx * 2 + 1




							def    UpperCAmelCase__			(       self     :       str				,				A_     :       List[Any]):
									return idx * 2 + 2




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Tuple):
									return self.heap_dict[key]




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Tuple):
									lowerCAmelCase_			:     Dict									= len(A_) - 1
									lowerCAmelCase_			:     List[Any]									= self.get_parent_idx(A_)

									for idx, i in enumerate(A_):
											lowerCAmelCase_			:     List[str]									= idx
											lowerCAmelCase_			:     Any									= i.val

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




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       int				,				A_     :       Tuple):
									while True:
											lowerCAmelCase_			:     Union[str, Any]									= self.get_left_child_idx(A_)  # noqa: E741
											lowerCAmelCase_			:     int									= self.get_right_child_idx(A_)

											lowerCAmelCase_			:     int									= idx
											if l < len(A_) and array[l] < array[idx]:
													lowerCAmelCase_			:     Any									= l
											if r < len(A_) and array[r] < array[smallest]:
													lowerCAmelCase_			:     List[str]									= r

											if smallest != idx:
													lowerCAmelCase_					,  lowerCAmelCase_			:     List[Any]									= array[smallest], array[idx]
													(
													    (
													    lowerCAmelCase_
													)					,  (
													    lowerCAmelCase_
													)					,  
													)			:     List[Any]									= (
													    self.idx_of_element[array[smallest]],
													    self.idx_of_element[array[idx]],
													)
													lowerCAmelCase_			:     Union[str, Any]									= smallest
											else:
													break




							def    UpperCAmelCase__			(       self     :       Tuple				,				A_     :       List[str]):
									lowerCAmelCase_			:     Any									= self.get_parent_idx(A_)
									while p >= 0 and self.heap[p] > self.heap[idx]:
											lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= self.heap[idx], self.heap[p]
											lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= (
											    self.idx_of_element[self.heap[idx]],
											    self.idx_of_element[self.heap[p]],
											)
											lowerCAmelCase_			:     int									= p
											lowerCAmelCase_			:     Tuple									= self.get_parent_idx(A_)




							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									return self.heap[0]




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[int]									= self.heap[-1], self.heap[0]
									lowerCAmelCase_					,  lowerCAmelCase_			:     Any									= (
									    self.idx_of_element[self.heap[-1]],
									    self.idx_of_element[self.heap[0]],
									)

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




							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       Any):
									self.heap.append(A_)
									lowerCAmelCase_			:     Optional[int]									= len(self.heap) - 1
									lowerCAmelCase_			:     Optional[int]									= node.val
									self.sift_up(len(self.heap) - 1)




							def    UpperCAmelCase__			(       self     :       List[Any]):
									return len(self.heap) == 0







							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       Optional[Any]				,				A_     :       int):
									assert (
									    self.heap[self.idx_of_element[node]].val > new_value
									), "newValue must be less that current value"
									lowerCAmelCase_			:     Tuple									= new_value
									lowerCAmelCase_			:     Tuple									= new_value
									self.sift_up(self.idx_of_element[node])





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

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

# Generating Min-Heap from array
A__  :  Optional[Any]								=					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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_			     =  {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["RoFormerTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RoFormerForCausalLM",
				    "RoFormerForMaskedLM",
				    "RoFormerForMultipleChoice",
				    "RoFormerForQuestionAnswering",
				    "RoFormerForSequenceClassification",
				    "RoFormerForTokenClassification",
				    "RoFormerLayer",
				    "RoFormerModel",
				    "RoFormerPreTrainedModel",
				    "load_tf_weights_in_roformer",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRoFormerForCausalLM",
				    "TFRoFormerForMaskedLM",
				    "TFRoFormerForMultipleChoice",
				    "TFRoFormerForQuestionAnswering",
				    "TFRoFormerForSequenceClassification",
				    "TFRoFormerForTokenClassification",
				    "TFRoFormerLayer",
				    "TFRoFormerModel",
				    "TFRoFormerPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FlaxRoFormerForMaskedLM",
				    "FlaxRoFormerForMultipleChoice",
				    "FlaxRoFormerForQuestionAnswering",
				    "FlaxRoFormerForSequenceClassification",
				    "FlaxRoFormerForTokenClassification",
				    "FlaxRoFormerModel",
				    "FlaxRoFormerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






