





from __future__ import annotations

from typing import Any







class 			__A       :




      """simple docstring"""







      def __init__(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		num_of_nodes
             _lowerCamelCase							:     list[list[int]]	=		[]
             _lowerCamelCase							:     dict[int, int]	=		{}


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





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


      def 				__snake_case			(		self      ,							a__):



             """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      ,							a__):



             """simple docstring"""





             if self.m_component[u_node] != u_node:
                    for k in self.m_component:
                           _lowerCamelCase							:     Any	=		self.find_component(a__)


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             if component_size[u_node] <= component_size[v_node]:
                    _lowerCamelCase							:     Dict	=		v_node
                    component_size[v_node] += component_size[u_node]
                    self.set_component(a__)

             elif component_size[u_node] >= component_size[v_node]:
                    _lowerCamelCase							:     Dict	=		self.find_component(a__)
                    component_size[u_node] += component_size[v_node]
                    self.set_component(a__)



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     int	=		[]
             _lowerCamelCase							:     Optional[int]	=		0

             _lowerCamelCase							:     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)

             _lowerCamelCase							:     int	=		self.m_num_of_nodes

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

                           _lowerCamelCase							:     Tuple	=		self.m_component[u]
                           _lowerCamelCase							:     Dict	=		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
                                         ):
                                                _lowerCamelCase							:     Optional[int]	=		[u, v, w]

                    for edge in minimum_weight_edge:
                           if isinstance(a__      ,							a__):
                                  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     str	=		edge

                                  _lowerCamelCase							:     Dict	=		self.m_component[u]
                                  _lowerCamelCase							:     Optional[int]	=		self.m_component[v]

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

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




def    __UpperCAmelCase(      ):
       pass


if __name__ == "__main__":
       import doctest

       doctest.testmod()








from typing import TYPE_CHECKING

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


_lowerCamelCase			       =      {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OPTForCausalLM',
           'OPTModel',
           'OPTPreTrainedModel',
           'OPTForSequenceClassification',
           'OPTForQuestionAnswering',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      ['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxOPTForCausalLM',
           'FlaxOPTModel',
           'FlaxOPTPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_opt import (
                  OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OPTForCausalLM,
                  OPTForQuestionAnswering,
                  OPTForSequenceClassification,
                  OPTModel,
                  OPTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
       import sys

       _lowerCamelCase			       =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

