


'''simple docstring'''
from __future__ import annotations

from typing import Any

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		int	  =      num_of_nodes
  __A	:		list[list[int]]	  =      []
  __A	:		dict[int, int]	  =      {}


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  self.m_edges.append([u_node, v_node, weight]    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  if self.m_component[u_node] == u_node:
   return u_node
  return self.find_component(self.m_component[u_node]    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  if self.m_component[u_node] != u_node:
   for k in self.m_component:
    __A	:		Union[str, Any]	  =      self.find_component(__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  if component_size[u_node] <= component_size[v_node]:
   __A	:		Any	  =      v_node
   component_size[v_node] += component_size[u_node]
   self.set_component(__lowercase    )

  elif component_size[u_node] >= component_size[v_node]:
   __A	:		List[Any]	  =      self.find_component(__lowercase    )
   component_size[u_node] += component_size[v_node]
   self.set_component(__lowercase    )







 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[Any]	  =      []
  __A	:		Any	  =      0

  __A	:		list[Any]	  =      [-1] * self.m_num_of_nodes

  # A list of components (initialized to all of the nodes)
  for node in range(self.m_num_of_nodes    ):
   self.m_component.update({node: node}    )
   component_size.append(1    )

  __A	:		List[Any]	  =      self.m_num_of_nodes

  while num_of_components > 1:
   for edge in self.m_edges:
    __A  ,__A  ,__A	:		Tuple	  =      edge

    __A	:		List[Any]	  =      self.m_component[u]
    __A	:		Optional[int]	  =      self.m_component[v]

    if u_component != v_component:

     for component in (u_component, v_component):
      if (
          minimum_weight_edge[component] == -1
          or minimum_weight_edge[component][2] > w
      ):
       __A	:		Dict	  =      [u, v, w]

   for edge in minimum_weight_edge:
    if isinstance(__lowercase   ,   __lowercase    ):
     __A  ,__A  ,__A	:		Tuple	  =      edge

     __A	:		str	  =      self.m_component[u]
     __A	:		List[str]	  =      self.m_component[v]

     if u_component != v_component:
      mst_weight += w
      self.union(__lowercase   ,   __lowercase   ,   __lowercase    )
      print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""    )
      num_of_components -= 1

   __A	:		int	  =      [-1] * self.m_num_of_nodes
  print(F"""The total weight of the minimal spanning tree is: {mst_weight}"""    )


def       _lowercase							(					):
 pass


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


UpperCAmelCase_			:  Optional[Any]										=   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Any										=   [
     'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'AdaptiveEmbedding',
     'TransfoXLForSequenceClassification',
     'TransfoXLLMHeadModel',
     'TransfoXLModel',
     'TransfoXLPreTrainedModel',
     'load_tf_weights_in_transfo_xl',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFAdaptiveEmbedding',
     'TFTransfoXLForSequenceClassification',
     'TFTransfoXLLMHeadModel',
     'TFTransfoXLMainLayer',
     'TFTransfoXLModel',
     'TFTransfoXLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
 from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_transfo_xl import (
      TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      AdaptiveEmbedding,
      TransfoXLForSequenceClassification,
      TransfoXLLMHeadModel,
      TransfoXLModel,
      TransfoXLPreTrainedModel,
      load_tf_weights_in_transfo_xl,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_transfo_xl import (
      TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAdaptiveEmbedding,
      TFTransfoXLForSequenceClassification,
      TFTransfoXLLMHeadModel,
      TFTransfoXLMainLayer,
      TFTransfoXLModel,
      TFTransfoXLPreTrainedModel,
  )

else:
 import sys

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






