

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					:		List[Any]	  =						logging.get_logger(__name__)
def      a_ (      )			->			Optional[Any]:







				"""simple docstring"""
				# Get the sagemaker specific mp parameters from smp_options variable.
				snake_case__									=     os.getenv('SM_HP_MP_PARAMETERS'      ,   '{}'  )
				try:
								# Parse it and check the field "partitions" is included, it is required for model parallel.
								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.
				snake_case__									=     os.getenv('SM_FRAMEWORK_PARAMS'      ,   '{}'  )
				try:
								# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
								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   __SCREAMING_SNAKE_CASE(	__lowerCamelCase	):
							_UpperCAmelCase     =       field(
							    default=""    ,  metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}    ,  )
							def lowerCAmelCase_ (     self:      str						)		->		List[Any]:
											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:      Dict						)		->		str:
											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:
															snake_case__									=     torch.device('cpu'						)
															snake_case__									=     0
											elif is_sagemaker_model_parallel_available():
															snake_case__									=     smp.local_rank()
															snake_case__									=     torch.device('cuda'       ,	UpperCAmelCase_						)
															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						)
															snake_case__									=     int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'						)						)
															snake_case__									=     torch.device('cuda'       ,	self.local_rank						)
															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
															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.
															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						)
															snake_case__									=     torch.device('cuda'       ,	self.local_rank						)
															snake_case__									=     1

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

											return device
							@property
							def lowerCAmelCase_ (     self:      Dict						)		->		Optional[int]:
											if is_sagemaker_model_parallel_available():
															return smp.dp_size()

											return super().world_size
							@property
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		List[Any]:
											return not is_sagemaker_model_parallel_available()


							@property
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		List[Any]:
											return False












from __future__ import annotations
def      a_ (      _A  )			->			bool:







				"""simple docstring"""
				return len(set(_A  )  ) == len(_A  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




