
from __future__ import annotations

from typing import Any







class     lowercase		:




							def __init__(    self       ,A__):
												lowercase						     =						num_of_nodes
												lowercase						     =						[]
												lowercase						     =						{}




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




							def 			A__				(    self       ,A__):

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




							def 			A__				(    self       ,A__):

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




							def 			A__				(    self       ,A__       ,A__       ,A__):

												if component_size[u_node] <= component_size[v_node]:
																	lowercase						     =						v_node
																	component_size[v_node] += component_size[u_node]
																	self.set_component(A__)

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





							def 			A__				(    self):
												lowercase						     =						[]
												lowercase						     =						0

												lowercase						     =						[-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)

												lowercase						     =						self.m_num_of_nodes

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

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

																	for edge in minimum_weight_edge:
																						if isinstance(A__       ,A__):
																											lowercase     ,						lowercase     ,						lowercase						     =						edge

																											lowercase						     =						self.m_component[u]
																											lowercase						     =						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

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



def 	UpperCamelCase   (							):
					'''simple docstring'''


if __name__ == "__main__":
							import doctest

							doctest.testmod()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__		:Dict			  =   {
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:List[str]			  =   [
							    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "LukeForEntityClassification",
							    "LukeForEntityPairClassification",
							    "LukeForEntitySpanClassification",
							    "LukeForMultipleChoice",
							    "LukeForQuestionAnswering",
							    "LukeForSequenceClassification",
							    "LukeForTokenClassification",
							    "LukeForMaskedLM",
							    "LukeModel",
							    "LukePreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
							from .tokenization_luke import LukeTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_luke import (
														    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LukeForEntityClassification,
														    LukeForEntityPairClassification,
														    LukeForEntitySpanClassification,
														    LukeForMaskedLM,
														    LukeForMultipleChoice,
														    LukeForQuestionAnswering,
														    LukeForSequenceClassification,
														    LukeForTokenClassification,
														    LukeModel,
														    LukePreTrainedModel,
														)

else:
							import sys

							lowercase__		:Tuple			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




