
"""simple docstring"""



from __future__ import annotations

from typing import Any

class  lowerCAmelCase__							:







     def __init__(				self     :     Optional[int]       , _lowerCamelCase     :     int     ):
       _snake_case   	=			num_of_nodes
       _snake_case   	=			[]
       _snake_case   	=			{}







     def 	lowercase      (				self     :     str       , _lowerCamelCase     :     int       , _lowerCamelCase     :     int       , _lowerCamelCase     :     int     ):
       self.m_edges.append([u_node, v_node, weight]     )







     def 	lowercase      (				self     :     int       , _lowerCamelCase     :     int     ):


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







     def 	lowercase      (				self     :     Optional[Any]       , _lowerCamelCase     :     int     ):


       if self.m_component[u_node] != u_node:
         for k in self.m_component:
           _snake_case   	=			self.find_component(_SCREAMING_SNAKE_CASE     )







     def 	lowercase      (				self     :     List[str]       , _lowerCamelCase     :     list[int]       , _lowerCamelCase     :     int       , _lowerCamelCase     :     int     ):


       if component_size[u_node] <= component_size[v_node]:
         _snake_case   	=			v_node
         component_size[v_node] += component_size[u_node]
         self.set_component(_SCREAMING_SNAKE_CASE     )

       elif component_size[u_node] >= component_size[v_node]:
         _snake_case   	=			self.find_component(_SCREAMING_SNAKE_CASE     )
         component_size[u_node] += component_size[v_node]
         self.set_component(_SCREAMING_SNAKE_CASE     )







     def 	lowercase      (				self     :     int     ):
       _snake_case   	=			[]
       _snake_case   	=			0

       _snake_case   	=			[-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     )

       _snake_case   	=			self.m_num_of_nodes

       while num_of_components > 1:
         for edge in self.m_edges:
           _snake_case   	=			edge

           _snake_case   	=			self.m_component[u]
           _snake_case   	=			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
               ):
                 _snake_case   	=			[u, v, w]

         for edge in minimum_weight_edge:
           if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE     ):
             _snake_case   	=			edge

             _snake_case   	=			self.m_component[u]
             _snake_case   	=			self.m_component[v]

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

         _snake_case   	=			[-1] * self.m_num_of_nodes
       print(f'''The total weight of the minimal spanning tree is: {mst_weight}'''     )






def    _UpperCAmelCase (					)	->   None:
  pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()








"""simple docstring"""

from typing import TYPE_CHECKING

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


A_						:    Optional[Any]				=      {
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Tuple				=      ['SqueezeBertTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    int				=      [
							    'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SqueezeBertForMaskedLM',
							    'SqueezeBertForMultipleChoice',
							    'SqueezeBertForQuestionAnswering',
							    'SqueezeBertForSequenceClassification',
							    'SqueezeBertForTokenClassification',
							    'SqueezeBertModel',
							    'SqueezeBertModule',
							    'SqueezeBertPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_squeezebert import (
							    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SqueezeBertConfig,
							    SqueezeBertOnnxConfig,
							)
							from .tokenization_squeezebert import SqueezeBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_squeezebert import (
														    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SqueezeBertForMaskedLM,
														    SqueezeBertForMultipleChoice,
														    SqueezeBertForQuestionAnswering,
														    SqueezeBertForSequenceClassification,
														    SqueezeBertForTokenClassification,
														    SqueezeBertModel,
														    SqueezeBertModule,
														    SqueezeBertPreTrainedModel,
														)

else:
							import sys

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





