






from typing import TYPE_CHECKING

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


snake_case__   :						Dict													=   {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Dict													=   ["""XLNetTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						int													=   ["""XLNetTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Any													=   [
     """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """XLNetForMultipleChoice""",
     """XLNetForQuestionAnswering""",
     """XLNetForQuestionAnsweringSimple""",
     """XLNetForSequenceClassification""",
     """XLNetForTokenClassification""",
     """XLNetLMHeadModel""",
     """XLNetModel""",
     """XLNetPreTrainedModel""",
     """load_tf_weights_in_xlnet""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Optional[Any]													=   [
     """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFXLNetForMultipleChoice""",
     """TFXLNetForQuestionAnsweringSimple""",
     """TFXLNetForSequenceClassification""",
     """TFXLNetForTokenClassification""",
     """TFXLNetLMHeadModel""",
     """TFXLNetMainLayer""",
     """TFXLNetModel""",
     """TFXLNetPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlnet import XLNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlnet_fast import XLNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlnet import (
      XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLNetForMultipleChoice,
      XLNetForQuestionAnswering,
      XLNetForQuestionAnsweringSimple,
      XLNetForSequenceClassification,
      XLNetForTokenClassification,
      XLNetLMHeadModel,
      XLNetModel,
      XLNetPreTrainedModel,
      load_tf_weights_in_xlnet,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlnet import (
      TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLNetForMultipleChoice,
      TFXLNetForQuestionAnsweringSimple,
      TFXLNetForSequenceClassification,
      TFXLNetForTokenClassification,
      TFXLNetLMHeadModel,
      TFXLNetMainLayer,
      TFXLNetModel,
      TFXLNetPreTrainedModel,
  )

else:
 import sys

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













from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

snake_case__   :						List[str]													=   TypeVar("""T""")






def      _snake_case     (__lowercase):
   return (position - 1) // 2






def      _snake_case     (__lowercase):
   return (2 * position) + 1






def      _snake_case     (__lowercase):
   return (2 * position) + 2


class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  []
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




  def __len__(  self			)      ->			int:
     return self.elements




  def __repr__(  self			)      ->			str:
     return str(self.heap			)




  def  _UpperCAmelCase  (  self			)      ->			bool:
     # Check if the priority queue is empty
     return self.elements == 0




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an element with given priority to the queue
     self.heap.append((elem, weight)			)
     UpperCamelCase_						=  self.elements
     self.elements += 1
     self._bubble_up(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			T:
     # Remove and return the element with lowest weight (highest priority)
     if self.elements > 1:
        self._swap_nodes(0		,		self.elements - 1			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap.pop()
     del self.position_map[elem]
     self.elements -= 1
     if self.elements > 0:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[0]
        self._bubble_down(_UpperCAmelCase			)
     return elem




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Update the weight of the given key
     UpperCamelCase_						=  self.position_map[elem]
     UpperCamelCase_						=  (elem, weight)
     if position > 0:
        UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
        if parent_weight > weight:
           self._bubble_up(_UpperCAmelCase			)
        else:
           self._bubble_down(_UpperCAmelCase			)
     else:
        self._bubble_down(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Place a node at the proper position (upward movement) [to be used internally
     # only]
     UpperCamelCase_						=  self.position_map[elem]
     if curr_pos == 0:
        return None
     UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[curr_pos]
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
     if parent_weight > weight:
        self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
        return self._bubble_up(_UpperCAmelCase			)
     return None




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Place a node at the proper position (downward movement) [to be used
     # internally only]
     UpperCamelCase_						=  self.position_map[elem]
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[curr_pos]
     UpperCamelCase_						=  get_child_left_position(_UpperCAmelCase			)
     UpperCamelCase_						=  get_child_right_position(_UpperCAmelCase			)
     if child_left_position < self.elements and child_right_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_left_position]
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_right_position]
        if child_right_weight < child_left_weight and child_right_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     if child_left_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_left_position]
        if child_left_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     else:
        return None
     if child_right_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_right_position]
        if child_right_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     return None




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Swap the nodes at the given positions
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_		,   UpperCamelCase_						=  (
         self.heap[nodea_pos],
         self.heap[nodea_pos],
     )
     UpperCamelCase_						=  nodea_pos
     UpperCamelCase_						=  nodea_pos




class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




  def __repr__(  self			)      ->			str:
     return str(self.connections			)




  def __len__(  self			)      ->			int:
     return self.nodes




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Add a node in the graph if it is not in the graph
     if node not in self.connections:
        UpperCamelCase_						=  {}
        self.nodes += 1




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an edge between 2 nodes in the graph
     self.add_node(_UpperCAmelCase			)
     self.add_node(_UpperCAmelCase			)
     UpperCamelCase_						=  weight
     UpperCamelCase_						=  weight






def      _snake_case     (__lowercase     ,   ):
   UpperCamelCase_						=  {node: maxsize for node in graph.connections}
   UpperCamelCase_						=  {node: None for node in graph.connections}

   UpperCamelCase_						=  MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(__lowercase     ,   __lowercase)

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   UpperCamelCase_						=  priority_queue.extract_min()
   UpperCamelCase_						=  0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(__lowercase     ,   dist[neighbour])
         UpperCamelCase_						=  node

    # running prim's algorithm
   while not priority_queue.is_empty():
      UpperCamelCase_						=  priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(__lowercase     ,   dist[neighbour])
            UpperCamelCase_						=  node
   return dist, parent





