






"""simple docstring"""







class  lowerCamelCase__ :






    def __init__(			self     ,A     ,A ):
          UpperCAmelCase          =	name
          UpperCAmelCase          =	val






    def __str__(			self ):
          return F'''{self.__class__.__name__}({self.name}, {self.val})'''






    def __lt__(			self     ,A ):
          return self.val < other.val







class  lowerCamelCase__ :






    def __init__(			self     ,A ):
          UpperCAmelCase          =	{}
          UpperCAmelCase          =	{}
          UpperCAmelCase          =	self.build_heap(A )






    def __getitem__(			self     ,A ):
          return self.get_value(A )






    def     _UpperCamelCase							(			self     ,A ):
          return (idx - 1) // 2






    def     _UpperCamelCase							(			self     ,A ):
          return idx * 2 + 1






    def     _UpperCamelCase							(			self     ,A ):
          return idx * 2 + 2






    def     _UpperCamelCase							(			self     ,A ):
          return self.heap_dict[key]






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	len(A ) - 1
          UpperCAmelCase          =	self.get_parent_idx(A )

          for idx, i in enumerate(A ):
                UpperCAmelCase          =	idx
                UpperCAmelCase          =	i.val

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






    def     _UpperCamelCase							(			self     ,A     ,A ):
          while True:
                UpperCAmelCase          =	self.get_left_child_idx(A )  # noqa: E741
                UpperCAmelCase          =	self.get_right_child_idx(A )

                UpperCAmelCase          =	idx
                if l < len(A ) and array[l] < array[idx]:
                      UpperCAmelCase          =	l
                if r < len(A ) 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     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	self.get_parent_idx(A )
          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(A )






    def     _UpperCamelCase							(			self ):
          return self.heap[0]






    def     _UpperCamelCase							(			self ):
          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     _UpperCamelCase							(			self     ,A ):
          self.heap.append(A )
          UpperCAmelCase          =	len(self.heap ) - 1
          UpperCAmelCase          =	node.val
          self.sift_up(len(self.heap ) - 1 )






    def     _UpperCamelCase							(			self ):
          return len(self.heap ) == 0






    def     _UpperCamelCase							(			self     ,A     ,A ):
          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] )







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

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

# Generating Min-Heap from array
_UpperCamelCase					      =  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,
)


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

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """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:
  _UpperCamelCase					      =  [
      """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:
  _UpperCamelCase					      =  [
      """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

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



