



"""simple docstring"""
from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

_UpperCAmelCase     =	TypeVar("""T""")





def      __magic_name__				(							lowercase ):
   return (position - 1) // 2





def      __magic_name__				(							lowercase ):
   return (2 * position) + 1





def      __magic_name__				(							lowercase ):
   return (2 * position) + 2
class 					a	(  Generic[T]       ):



  def __init__(						self :					str		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[]
     SCREAMING_SNAKE_CASE_: int     			={}
     SCREAMING_SNAKE_CASE_: List[str]     			=0



  def __len__(						self :					Union[str, Any]		)     ->					Dict:


     '''simple docstring'''




     return self.elements



  def __repr__(						self :					Dict		)     ->					Any:


     '''simple docstring'''




     return str(self.heap		)



  def 	lowerCamelCase__       (						self :					int		)     ->					str:


     '''simple docstring'''




     return self.elements == 0



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					T       ,		lowerCAmelCase :					int		)     ->					Union[str, Any]:


     '''simple docstring'''




     self.heap.append((elem, weight)		)
     SCREAMING_SNAKE_CASE_: str     			=self.elements
     self.elements += 1
     self._bubble_up(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					Dict:


     '''simple docstring'''




     if self.elements > 1:
        self._swap_nodes(0       ,		self.elements - 1		)
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=self.heap.pop()
     del self.position_map[elem]
     self.elements -= 1
     if self.elements > 0:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.heap[0]
        self._bubble_down(lowerCAmelCase		)
     return elem



  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					T       ,		lowerCAmelCase :					int		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=self.position_map[elem]
     SCREAMING_SNAKE_CASE_: str     			=(elem, weight)
     if position > 0:
        SCREAMING_SNAKE_CASE_: Any     			=get_parent_position(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: int     			=self.heap[parent_position]
        if parent_weight > weight:
           self._bubble_up(lowerCAmelCase		)
        else:
           self._bubble_down(lowerCAmelCase		)
     else:
        self._bubble_down(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[int]       ,		lowerCAmelCase :					T		)     ->					Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self.position_map[elem]
     if curr_pos == 0:
        return None
     SCREAMING_SNAKE_CASE_: List[Any]     			=get_parent_position(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=self.heap[curr_pos]
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.heap[parent_position]
     if parent_weight > weight:
        self._swap_nodes(lowerCAmelCase       ,		lowerCAmelCase		)
        return self._bubble_up(lowerCAmelCase		)
     return None



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					T		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=self.position_map[elem]
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=self.heap[curr_pos]
     SCREAMING_SNAKE_CASE_: List[Any]     			=get_child_left_position(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Any     			=get_child_right_position(lowerCAmelCase		)
     if child_left_position < self.elements and child_right_position < self.elements:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=self.heap[child_left_position]
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Any     			=self.heap[child_right_position]
        if child_right_weight < child_left_weight and child_right_weight < weight:
           self._swap_nodes(lowerCAmelCase       ,		lowerCAmelCase		)
           return self._bubble_down(lowerCAmelCase		)
     if child_left_position < self.elements:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=self.heap[child_left_position]
        if child_left_weight < weight:
           self._swap_nodes(lowerCAmelCase       ,		lowerCAmelCase		)
           return self._bubble_down(lowerCAmelCase		)
     else:
        return None
     if child_right_position < self.elements:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Any     			=self.heap[child_right_position]
        if child_right_weight < weight:
           self._swap_nodes(lowerCAmelCase       ,		lowerCAmelCase		)
           return self._bubble_down(lowerCAmelCase		)
     return None





  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=self.heap[nodea_pos][0]
     SCREAMING_SNAKE_CASE_: List[str]     			=self.heap[nodea_pos][0]
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=(
         self.heap[nodea_pos],
         self.heap[nodea_pos],
     )
     SCREAMING_SNAKE_CASE_: str     			=nodea_pos
     SCREAMING_SNAKE_CASE_: List[str]     			=nodea_pos







class 					a	(  Generic[T]       ):



  def __init__(						self :					List[Any]		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			={}
     SCREAMING_SNAKE_CASE_: Dict     			=0



  def __repr__(						self :					Optional[int]		)     ->					Union[str, Any]:


     '''simple docstring'''




     return str(self.connections		)



  def __len__(						self :					List[str]		)     ->					int:


     '''simple docstring'''




     return self.nodes



  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					T		)     ->					Tuple:


     '''simple docstring'''




     if node not in self.connections:
        SCREAMING_SNAKE_CASE_: Dict     			={}
        self.nodes += 1





  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					T       ,		lowerCAmelCase :					T       ,		lowerCAmelCase :					int		)     ->					List[str]:


     '''simple docstring'''




     self.add_node(lowerCAmelCase		)
     self.add_node(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=weight
     SCREAMING_SNAKE_CASE_: Dict     			=weight







def      __magic_name__				(							lowercase							,			):
   SCREAMING_SNAKE_CASE_: Any     			={node: maxsize for node in graph.connections}
   SCREAMING_SNAKE_CASE_: str     			={node: None for node in graph.connections}

   SCREAMING_SNAKE_CASE_: Dict     			=MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(__UpperCAmelCase							,			__UpperCAmelCase )

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=priority_queue.extract_min()
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         SCREAMING_SNAKE_CASE_: List[Any]     			=dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(__UpperCAmelCase							,			dist[neighbour] )
         SCREAMING_SNAKE_CASE_: Dict     			=node

    # running prim's algorithm
   while not priority_queue.is_empty():
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            SCREAMING_SNAKE_CASE_: int     			=dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(__UpperCAmelCase							,			dist[neighbour] )
            SCREAMING_SNAKE_CASE_: List[Any]     			=node
   return dist, parent








"""simple docstring"""
from typing import TYPE_CHECKING

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


_UpperCAmelCase     =			{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XLMForMultipleChoice""",
       """XLMForQuestionAnswering""",
       """XLMForQuestionAnsweringSimple""",
       """XLMForSequenceClassification""",
       """XLMForTokenClassification""",
       """XLMModel""",
       """XLMPreTrainedModel""",
       """XLMWithLMHeadModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXLMForMultipleChoice""",
       """TFXLMForQuestionAnsweringSimple""",
       """TFXLMForSequenceClassification""",
       """TFXLMForTokenClassification""",
       """TFXLMMainLayer""",
       """TFXLMModel""",
       """TFXLMPreTrainedModel""",
       """TFXLMWithLMHeadModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

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



