


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


__UpperCamelCase    		=       logging.get_logger(__name__)
def        UpperCamelCase_(				)->		Any:
			# Get the sagemaker specific mp parameters from smp_options variable.
			UpperCamelCase__					=       os.getenv("SM_HP_MP_PARAMETERS"				,     "{}"  )
			try:
						# Parse it and check the field "partitions" is included, it is required for model parallel.
						UpperCamelCase__					=       json.loads(_A  )
						if "partitions" not in smp_options:
									return False
			except json.JSONDecodeError:
						return False

			# Get the sagemaker specific framework parameters from mpi_options variable.
			UpperCamelCase__					=       os.getenv("SM_FRAMEWORK_PARAMS"				,     "{}"  )
			try:
						# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
						UpperCamelCase__					=       json.loads(_A  )
						if not mpi_options.get("sagemaker_mpi_enabled"				,     _A  ):
									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        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					str    =		field(
			    default=''					,	metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}					,	)




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						super().__post_init__()
						warnings.warn(
						    "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
						    "`TrainingArguments` instead."      ,						snake_case      ,						)




			@cached_property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						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:
									UpperCamelCase__					=       torch.device("cpu"     )
									UpperCamelCase__					=       0
						elif is_sagemaker_model_parallel_available():
									UpperCamelCase__					=       smp.local_rank()
									UpperCamelCase__					=       torch.device("cuda"      ,						snake_case     )
									UpperCamelCase__					=       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     )
									UpperCamelCase__					=       int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"     )     )
									UpperCamelCase__					=       torch.device("cuda"      ,						self.local_rank     )
									UpperCamelCase__					=       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
									UpperCamelCase__					=       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.
									UpperCamelCase__					=       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     )
									UpperCamelCase__					=       torch.device("cuda"      ,						self.local_rank     )
									UpperCamelCase__					=       1

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

						return device




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if is_sagemaker_model_parallel_available():
									return smp.dp_size()

						return super().world_size




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return not is_sagemaker_model_parallel_available()







			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return False






from typing import Any







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       data
						UpperCamelCase__					=       None







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       None




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.head
						while temp is not None:
									print(temp.data      ,						end=" "     )
									UpperCamelCase__					=       temp.next
						print()




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       Node(snake_case     )
						UpperCamelCase__					=       self.head
						UpperCamelCase__					=       new_node







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						if node_data_a == node_data_a:
									return
						else:
									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									if node_a is None or node_a is None:
												return

									UpperCamelCase__,      UpperCamelCase__					=       node_a.data, node_a.data


if __name__ == "__main__":
						__UpperCamelCase    		=       LinkedList()
						for i in range(5, 0, -1):
												ll.push(i)

						ll.print_list()

						ll.swap_nodes(1, 4)
						print('After swapping')
						ll.print_list()


