
'''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


lowercase__											=logging.get_logger(__name__)






def      UpperCamelCase_				(      ):
	# Get the sagemaker specific mp parameters from smp_options variable.
	a_      				=						os.getenv("""SM_HP_MP_PARAMETERS"""  ,      """{}"""						)
	try:
		# Parse it and check the field "partitions" is included, it is required for model parallel.
		a_      				=						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.
	a_      				=						os.getenv("""SM_FRAMEWORK_PARAMS"""  ,      """{}"""						)
	try:
		# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
		a_      				=						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   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			str       =    field(
			    default=''		,  metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}		,  )




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




			@cached_property
			def 					lowerCAmelCase__					(       self	):
				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:
					a_      				=						torch.device("""cpu"""	)
					a_      				=						0
				elif is_sagemaker_model_parallel_available():
					a_      				=						smp.local_rank()
					a_      				=						torch.device("""cuda"""					,   UpperCAmelCase	)
					a_      				=						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	)
					a_      				=						int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""	)	)
					a_      				=						torch.device("""cuda"""					,   self.local_rank	)
					a_      				=						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
					a_      				=						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.
					a_      				=						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	)
					a_      				=						torch.device("""cuda"""					,   self.local_rank	)
					a_      				=						1

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

				return device




			@property
			def 					lowerCAmelCase__					(       self	):
				if is_sagemaker_model_parallel_available():
					return smp.dp_size()

				return super().world_size




			@property
			def 					lowerCAmelCase__					(       self	):
				return not is_sagemaker_model_parallel_available()






			@property
			def 					lowerCAmelCase__					(       self	):
				return False


'''simple docstring'''
from typing import Any







class   a_ :




			def __init__(       self					,   UpperCAmelCase	):
				a_      				=						data
				a_      				=						None







class   a_ :




			def __init__(       self	):
				a_      				=						None




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




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						Node(UpperCAmelCase	)
				a_      				=						self.head
				a_      				=						new_node






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase	):
				if node_data_a == node_data_a:
					return
				else:
					a_      				=						self.head
					while node_a is not None and node_a.data != node_data_a:
						a_      				=						node_a.next

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

					if node_a is None or node_a is None:
						return

					a_				,  a_      				=						node_a.data, node_a.data


if __name__ == "__main__":
		lowercase__											=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()
