


from __future__ import annotations

from typing import Any
class 		__magic_name__						:





     '''simple docstring'''



     def __init__(					self:Any     ,		_a:Any   ):
       snake_case__						= num_of_nodes
       snake_case__						= []
       snake_case__						= {}

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:Optional[Any]     ,		_a:Union[str, Any]     ,		_a:str   ):
       self.m_edges.append([u_node, v_node, weight]   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:Optional[Any]   ):

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:int   ):

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

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:int     ,		_a:Optional[Any]     ,		_a:Optional[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(_lowercase   )

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







     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       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__				,				snake_case__				,				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(_lowercase     ,		_lowercase   ):
             snake_case__				,				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(_lowercase     ,		_lowercase     ,		_lowercase   )
               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 SCREAMING_SNAKE_CASE	(			)    ->				None:
  pass


if __name__ == "__main__":
   import doctest

   doctest.testmod()




'''simple docstring'''

import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			logging.get_logger(__name__)




def 							_UpperCamelCase				(   )      -> List[Any]:
							# Get the sagemaker specific mp parameters from smp_options variable.
							SCREAMING_SNAKE_CASE_				=				os.getenv('SM_HP_MP_PARAMETERS'		,'{}'   )
							try:
														# Parse it and check the field "partitions" is included, it is required for model parallel.
														SCREAMING_SNAKE_CASE_				=				json.loads(lowerCAmelCase__   )
														if "partitions" not in smp_options:
																					return False
							except json.JSONDecodeError:
														return False

							# Get the sagemaker specific framework parameters from mpi_options variable.
							SCREAMING_SNAKE_CASE_				=				os.getenv('SM_FRAMEWORK_PARAMS'		,'{}'   )
							try:
														# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
														SCREAMING_SNAKE_CASE_				=				json.loads(lowerCAmelCase__   )
														if not mpi_options.get('sagemaker_mpi_enabled'		,lowerCAmelCase__   ):
																					return False
							except json.JSONDecodeError:
														return False
							# Lastly, check if the `smdistributed` module is present.
							return importlib.util.find_spec('smdistributed'   ) is not None


if is_sagemaker_model_parallel_available():
							import smdistributed.modelparallel.torch as smp

							smp.init()






@dataclass
class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     field(
				    default="""""",						metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""},						)



				def   a__					(    self       )     ->							Union[str, Any]:
											super().__post_init__()
											warnings.warn(
											    '`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
											    '`TrainingArguments` instead.',     _lowercase,     )



				@cached_property
				def   a__					(    self       )     ->							"torch.device":
											logger.info('PyTorch: setting up devices'       )
											if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
																		logger.warning(
																		    'torch.distributed process group is initialized, but local_rank == -1. '
																		    'In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'       )
											if self.no_cuda:
																		SCREAMING_SNAKE_CASE_				=				torch.device('cpu'       )
																		SCREAMING_SNAKE_CASE_				=				0
											elif is_sagemaker_model_parallel_available():
																		SCREAMING_SNAKE_CASE_				=				smp.local_rank()
																		SCREAMING_SNAKE_CASE_				=				torch.device('cuda',     _lowercase       )
																		SCREAMING_SNAKE_CASE_				=				1
											elif is_sagemaker_dp_enabled():
																		import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

																		torch.distributed.init_process_group(backend='smddp',     timeout=self.ddp_timeout_delta       )
																		SCREAMING_SNAKE_CASE_				=				int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'       )       )
																		SCREAMING_SNAKE_CASE_				=				torch.device('cuda',     self.local_rank       )
																		SCREAMING_SNAKE_CASE_				=				1
											elif self.local_rank == -1:
																		# if n_gpu is > 1 we'll use nn.DataParallel.
																		# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
																		# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
																		# trigger an error that a device index is missing. Index 0 takes into account the
																		# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
																		# will use the first GPU in that env, i.e. GPU#1
																		SCREAMING_SNAKE_CASE_				=				torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'       )
																		# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
																		# the default value.
																		SCREAMING_SNAKE_CASE_				=				torch.cuda.device_count()
											else:
																		# Here, we'll use torch.distributed.
																		# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
																		if not torch.distributed.is_initialized():
																									torch.distributed.init_process_group(backend='nccl',     timeout=self.ddp_timeout_delta       )
																		SCREAMING_SNAKE_CASE_				=				torch.device('cuda',     self.local_rank       )
																		SCREAMING_SNAKE_CASE_				=				1

											if device.type == "cuda":
																		torch.cuda.set_device(_lowercase       )

											return device



				@property
				def   a__					(    self       )     ->							Optional[Any]:
											if is_sagemaker_model_parallel_available():
																		return smp.dp_size()

											return super().world_size



				@property
				def   a__					(    self       )     ->							Optional[Any]:
											return not is_sagemaker_model_parallel_available()





				@property
				def   a__					(    self       )     ->							Tuple:
											return False





