




from __future__ import annotations

from typing import Any


class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						int	)	->       None:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      num_of_nodes
						__UpperCamelCase					:	list[list[int]]             =      []
						__UpperCamelCase					:	dict[int, int]             =      {}






	def 					lowerCamelCase__     (					self		:						Optional[Any]					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						int	)	->       None:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						int	)	->       int:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						int	)	->       None:







						"""simple docstring"""







						if self.m_component[u_node] != u_node:
											for k in self.m_component:
																__UpperCamelCase					:	List[str]             =      self.find_component(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						list[int]					,     lowerCAmelCase		:						int					,     lowerCAmelCase		:						int	)	->       None:







						"""simple docstring"""







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

						elif component_size[u_node] >= component_size[v_node]:
											__UpperCamelCase					:	int             =      self.find_component(lowerCAmelCase	)
											component_size[u_node] += component_size[v_node]
											self.set_component(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       None:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      []
						__UpperCamelCase					:	Union[str, Any]             =      0

						__UpperCamelCase					:	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	)

						__UpperCamelCase					:	List[str]             =      self.m_num_of_nodes

						while num_of_components > 1:
											for edge in self.m_edges:
																__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Optional[Any]             =      edge

																__UpperCamelCase					:	Union[str, Any]             =      self.m_component[u]
																__UpperCamelCase					:	List[str]             =      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
																										):
																															__UpperCamelCase					:	Optional[int]             =      [u, v, w]

											for edge in minimum_weight_edge:
																if isinstance(lowerCAmelCase					,     lowerCAmelCase	):
																					__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Optional[Any]             =      edge

																					__UpperCamelCase					:	Optional[int]             =      self.m_component[u]
																					__UpperCamelCase					:	str             =      self.m_component[v]

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

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






def 				A__ ()     ->						None:
					pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()












# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce





def 				A__ (snake_case   :		List[Any]				)     ->						Optional[Any]:
					return (torch.arange(state.num_processes				) + 1.0 + (state.num_processes * state.process_index)).to(state.device				)





def 				A__ (snake_case   :		int				)     ->						Any:
					__UpperCamelCase					:	List[str]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	List[Any]             =      gather(snake_case				)
					assert gathered_tensor.tolist() == list(range(1		,	state.num_processes**2 + 1				)				)





def 				A__ (snake_case   :		Optional[Any]				)     ->						Dict:
					__UpperCamelCase					:	Any             =      [state.process_index]
					__UpperCamelCase					:	int             =      gather_object(snake_case				)
					assert len(snake_case				) == state.num_processes, F'''{gathered_obj}, {len(snake_case				)} != {state.num_processes}'''
					assert gathered_obj == list(range(state.num_processes				)				), F'''{gathered_obj} != {list(range(state.num_processes				)				)}'''





def 				A__ (snake_case   :		Any				)     ->						Optional[Any]:
					__UpperCamelCase					:	Optional[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	Any             =      broadcast(snake_case				)
					assert broadcasted_tensor.shape == torch.Size([state.num_processes]				)
					assert broadcasted_tensor.tolist() == list(range(1		,	state.num_processes + 1				)				)





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					# We need to pad the tensor with one more element if we are the main process
					# to ensure that we can pad
					if state.is_main_process:
										__UpperCamelCase					:	List[Any]             =      torch.arange(state.num_processes + 1				).to(state.device				)
					else:
										__UpperCamelCase					:	List[str]             =      torch.arange(state.num_processes				).to(state.device				)
					__UpperCamelCase					:	List[str]             =      pad_across_processes(snake_case				)
					assert padded_tensor.shape == torch.Size([state.num_processes + 1]				)
					if not state.is_main_process:
										assert padded_tensor.tolist() == list(range(0		,	state.num_processes				)				) + [0]





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	Optional[int]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""sum"""				)
					__UpperCamelCase					:	Optional[int]             =      torch.tensor([4.0, 6]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Optional[Any]				)     ->						Optional[Any]:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	List[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""mean"""				)
					__UpperCamelCase					:	Union[str, Any]             =      torch.tensor([2.0, 3]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Tuple				)     ->						int:
					# For xla_spawn (TPUs)
					main()





def 				A__ ()     ->						List[Any]:
					__UpperCamelCase					:	List[Any]             =      PartialState()
					state.print(F'''State: {state}'''				)
					state.print("""testing gather"""				)
					test_gather(snake_case				)
					state.print("""testing gather_object"""				)
					test_gather_object(snake_case				)
					state.print("""testing broadcast"""				)
					test_broadcast(snake_case				)
					state.print("""testing pad_across_processes"""				)
					test_pad_across_processes(snake_case				)
					state.print("""testing reduce_sum"""				)
					test_reduce_sum(snake_case				)
					state.print("""testing reduce_mean"""				)
					test_reduce_mean(snake_case				)


if __name__ == "__main__":
	main()






