




class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: List[str] ,       snake_case					: List[str] ,       snake_case					: Optional[Any]				):
          __UpperCamelCase  		=						name
          __UpperCamelCase  		=						val







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







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







class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: str ,       snake_case					: Optional[int]				):
          __UpperCamelCase  		=						{}
          __UpperCamelCase  		=						{}
          __UpperCamelCase  		=						self.build_heap(snake_case				)







      def __getitem__(     self					: List[str] ,       snake_case					: Tuple				):
          return self.get_value(snake_case				)







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







      def       snake_case	(     self					: List[str] ,       snake_case					: Any				):
          return idx * 2 + 1







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: Optional[int]				):
          return idx * 2 + 2







      def       snake_case	(     self					: str ,       snake_case					: Any				):
          return self.heap_dict[key]







      def       snake_case	(     self					: List[Any] ,       snake_case					: Union[str, Any]				):
          __UpperCamelCase  		=						len(snake_case				) - 1
          __UpperCamelCase  		=						self.get_parent_idx(snake_case				)

          for idx, i in enumerate(snake_case				):
              __UpperCamelCase  		=						idx
              __UpperCamelCase  		=						i.val

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







      def       snake_case	(     self					: Dict ,       snake_case					: Tuple ,       snake_case					: Optional[int]				):
          while True:
              __UpperCamelCase  		=						self.get_left_child_idx(snake_case				)  # noqa: E741
              __UpperCamelCase  		=						self.get_right_child_idx(snake_case				)

              __UpperCamelCase  		=						idx
              if l < len(snake_case				) and array[l] < array[idx]:
                  __UpperCamelCase  		=						l
              if r < len(snake_case				) and array[r] < array[smallest]:
                  __UpperCamelCase  		=						r

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







      def       snake_case	(     self					: List[Any] ,       snake_case					: Optional[Any]				):
          __UpperCamelCase  		=						self.get_parent_idx(snake_case				)
          while p >= 0 and self.heap[p] > self.heap[idx]:
              __UpperCamelCase		,      __UpperCamelCase  		=						self.heap[idx], self.heap[p]
              __UpperCamelCase		,      __UpperCamelCase  		=						(
                  self.idx_of_element[self.heap[idx]],
                  self.idx_of_element[self.heap[p]],
              )
              __UpperCamelCase  		=						p
              __UpperCamelCase  		=						self.get_parent_idx(snake_case				)







      def       snake_case	(     self					: Optional[int]				):
          return self.heap[0]







      def       snake_case	(     self					: Any				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.heap[-1], self.heap[0]
          __UpperCamelCase		,      __UpperCamelCase  		=						(
              self.idx_of_element[self.heap[-1]],
              self.idx_of_element[self.heap[0]],
          )

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







      def       snake_case	(     self					: List[Any] ,       snake_case					: List[str]				):
          self.heap.append(snake_case				)
          __UpperCamelCase  		=						len(self.heap				) - 1
          __UpperCamelCase  		=						node.val
          self.sift_up(len(self.heap				) - 1				)







      def       snake_case	(     self					: Dict				):
          return len(self.heap				) == 0







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: str ,       snake_case					: List[Any]				):
          assert (
              self.heap[self.idx_of_element[node]].val > new_value
          ), "newValue must be less that current value"
          __UpperCamelCase  		=						new_value
          __UpperCamelCase  		=						new_value
          self.sift_up(self.idx_of_element[node]				)







a_   		=     Node("R", -1)
a_   		=     Node("B", 6)
a_   		=     Node("A", 3)
a_   		=     Node("X", 1)
a_   		=     Node("E", 4)

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

# Generating Min-Heap from array
a_   		=     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()












from typing import TYPE_CHECKING

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


a_   		=     {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongformerForMaskedLM",
     "LongformerForMultipleChoice",
     "LongformerForQuestionAnswering",
     "LongformerForSequenceClassification",
     "LongformerForTokenClassification",
     "LongformerModel",
     "LongformerPreTrainedModel",
     "LongformerSelfAttention",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLongformerForMaskedLM",
     "TFLongformerForMultipleChoice",
     "TFLongformerForQuestionAnswering",
     "TFLongformerForSequenceClassification",
     "TFLongformerForTokenClassification",
     "TFLongformerModel",
     "TFLongformerPreTrainedModel",
     "TFLongformerSelfAttention",
 ]


if TYPE_CHECKING:
 from .configuration_longformer import (
     LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LongformerConfig,
     LongformerOnnxConfig,
 )
 from .tokenization_longformer import LongformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_longformer_fast import LongformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longformer import (
      LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongformerForMaskedLM,
      LongformerForMultipleChoice,
      LongformerForQuestionAnswering,
      LongformerForSequenceClassification,
      LongformerForTokenClassification,
      LongformerModel,
      LongformerPreTrainedModel,
      LongformerSelfAttention,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_longformer import (
      TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLongformerForMaskedLM,
      TFLongformerForMultipleChoice,
      TFLongformerForQuestionAnswering,
      TFLongformerForSequenceClassification,
      TFLongformerForTokenClassification,
      TFLongformerModel,
      TFLongformerPreTrainedModel,
      TFLongformerSelfAttention,
  )

else:
 import sys

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






