






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							:   Optional[int]							=  logging.get_logger(__name__)





def 					lowercase_ (    )     -> Optional[Any]:





				'''simple docstring'''


				snake_case__      :int         =   os.getenv("SM_HP_MP_PARAMETERS"	,	"{}"      )
				try:
								# Parse it and check the field "partitions" is included, it is required for model parallel.
								snake_case__      :Tuple         =   json.loads(__snake_case      )
								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__      :Dict         =   os.getenv("SM_FRAMEWORK_PARAMS"	,	"{}"      )
				try:
								# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
								snake_case__      :List[str]         =   json.loads(__snake_case      )
								if not mpi_options.get("sagemaker_mpi_enabled"	,	__snake_case      ):
												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						(			_A ):
	_A          =   field(
	    default=''       ,    metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}       ,    )






	def    lowerCAmelCase_							(		self       )						->    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       )						->    "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:
									snake_case__      :int         =   torch.device("cpu"       )
									snake_case__      :Any         =   0
					elif is_sagemaker_model_parallel_available():
									snake_case__      :str         =   smp.local_rank()
									snake_case__      :Dict         =   torch.device("cuda"					,UpperCamelCase       )
									snake_case__      :str         =   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__      :str         =   int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"       )       )
									snake_case__      :Dict         =   torch.device("cuda"					,self.local_rank       )
									snake_case__      :Tuple         =   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__      :Dict         =   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__      :Tuple         =   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__      :Optional[int]         =   torch.device("cuda"					,self.local_rank       )
									snake_case__      :Optional[Any]         =   1

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

					return device






	@property
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					if is_sagemaker_model_parallel_available():
									return smp.dp_size()

					return super().world_size






	@property
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					return not is_sagemaker_model_parallel_available()






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







def 					lowercase_ (    __snake_case     :							int      )     -> bool:





				'''simple docstring'''


				if p < 2:
								raise ValueError("p should not be less than 2!"      )
				elif p == 2:
								return True

				snake_case__      :List[str]         =   4
				snake_case__      :Optional[int]         =   (1 << p) - 1
				for _ in range(p - 2      ):
								snake_case__      :List[Any]         =   ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
	print(lucas_lehmer_test(7))
	print(lucas_lehmer_test(1_1))