


"""simple docstring"""



class 		__snake_case :





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						str									=					name
     __A   :						Optional[int]									=					val

 def __str__(   self  ):



     '''simple docstring'''




     return F"""{self.__class__.__name__}({self.name}, {self.val})"""







 def __lt__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return self.val < other.val






class 		__snake_case :





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					{}
     __A   :						List[str]									=					{}
     __A   :						Dict									=					self.build_heap(__lowerCamelCase  )

 def __getitem__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return self.get_value(__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return (idx - 1) // 2

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return idx * 2 + 1

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return idx * 2 + 2

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     return self.heap_dict[key]

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						int									=					len(__lowerCamelCase  ) - 1
     __A   :						List[str]									=					self.get_parent_idx(__lowerCamelCase  )

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

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     while True:
         __A   :						Dict									=					self.get_left_child_idx(__lowerCamelCase  )  # noqa: E741
         __A   :						List[str]									=					self.get_right_child_idx(__lowerCamelCase  )

         __A   :						Any									=					idx
         if l < len(__lowerCamelCase  ) and array[l] < array[idx]:
             __A   :						str									=					l
         if r < len(__lowerCamelCase  ) and array[r] < array[smallest]:
             __A   :						Tuple									=					r

         if smallest != idx:
             __A			,	__A   :						Dict									=					array[smallest], array[idx]
             (
                 (
                 __A				
             )			,	(
                 __A				
             )			,					
             )   :						List[Any]									=					(
                 self.idx_of_element[array[smallest]],
                 self.idx_of_element[array[idx]],
             )
             __A   :						Dict									=					smallest
         else:
             break

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Dict									=					self.get_parent_idx(__lowerCamelCase  )
     while p >= 0 and self.heap[p] > self.heap[idx]:
         __A			,	__A   :						List[str]									=					self.heap[idx], self.heap[p]
         __A			,	__A   :						Tuple									=					(
             self.idx_of_element[self.heap[idx]],
             self.idx_of_element[self.heap[p]],
         )
         __A   :						Tuple									=					p
         __A   :						Tuple									=					self.get_parent_idx(__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return self.heap[0]

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A			,	__A   :						Optional[int]									=					self.heap[-1], self.heap[0]
     __A			,	__A   :						List[Any]									=					(
         self.idx_of_element[self.heap[-1]],
         self.idx_of_element[self.heap[0]],
     )

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     self.heap.append(__lowerCamelCase  )
     __A   :						Optional[Any]									=					len(self.heap  ) - 1
     __A   :						str									=					node.val
     self.sift_up(len(self.heap  ) - 1  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return len(self.heap  ) == 0







 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     assert (
         self.heap[self.idx_of_element[node]].val > new_value
     ), "newValue must be less that current value"
     __A   :						int									=					new_value
     __A   :						Optional[int]									=					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()











"""simple docstring"""



from typing import TYPE_CHECKING

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


a_			=      {
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TapasForMaskedLM""",
           """TapasForQuestionAnswering""",
           """TapasForSequenceClassification""",
           """TapasModel""",
           """TapasPreTrainedModel""",
           """load_tf_weights_in_tapas""",
       ]
try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFTapasForMaskedLM""",
           """TFTapasForQuestionAnswering""",
           """TFTapasForSequenceClassification""",
           """TFTapasModel""",
           """TFTapasPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
       from .tokenization_tapas import TapasTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tapas import (
                  TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TapasForMaskedLM,
                  TapasForQuestionAnswering,
                  TapasForSequenceClassification,
                  TapasModel,
                  TapasPreTrainedModel,
                  load_tf_weights_in_tapas,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_tapas import (
                  TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFTapasForMaskedLM,
                  TFTapasForQuestionAnswering,
                  TFTapasForSequenceClassification,
                  TFTapasModel,
                  TFTapasPreTrainedModel,
              )


else:
       import sys

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




