



'''simple docstring'''




from __future__ import annotations

from typing import Any







class      _lowerCAmelCase							:


	def __init__(self	,				lowercase							):
				A_     :						List[Any]     =					num_of_nodes
				A_     :						list[list[int]]     =					[]
				A_     :						dict[int, int]     =					{}


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


	def    _a     (self	,				lowercase							):

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


	def    _a     (self	,				lowercase							):

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


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase							):

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

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


	def    _a     (self							):
				A_     :						Dict     =					[]
				A_     :						int     =					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_     :						Optional[Any]     =					self.m_num_of_nodes

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

										A_     :						Optional[int]     =					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_     :						List[Any]     =					[u, v, w]

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

													A_     :						Any     =					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_     :						Dict     =					[-1] * self.m_num_of_nodes
				print(F'The total weight of the minimal spanning tree is: {mst_weight}'							)






def      a      (			):
			'''simple docstring'''












if __name__ == "__main__":
						import doctest

						doctest.testmod()




'''simple docstring'''




from typing import Dict

from .base import GenericTensor, Pipeline







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				if tokenize_kwargs is None:
							A_     :						Optional[Any]     =					{}

				if truncation is not None:
							if "truncation" in tokenize_kwargs:
										raise ValueError(
										    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""							)
							A_     :						str     =					truncation

				A_     :						List[str]     =					tokenize_kwargs

				A_     :						Dict     =					{}
				if return_tensors is not None:
							A_     :						List[Any]     =					return_tensors

				return preprocess_params, {}, postprocess_params


	def    _a     (self	,				lowercase	,				**lowercase							):
				A_     :						Optional[int]     =					self.framework
				A_     :						str     =					self.tokenizer(lowercase	,				return_tensors=lowercase	,				**lowercase							)
				return model_inputs


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.model(**lowercase							)
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=False							):
				# [0] is the first available tensor, logits or last_hidden_state.
				if return_tensors:
							return model_outputs[0]
				if self.framework == "pt":
							return model_outputs[0].tolist()
				elif self.framework == "tf":
							return model_outputs[0].numpy().tolist()


	def __call__(self	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)