
'''simple docstring'''


from abc import ABC, abstractmethod
from argparse import ArgumentParser

class 	__lowercase				(  _lowercase    ):

			@staticmethod
			@abstractmethod
			def        UpperCAmelCase__ (A      ):
								raise NotImplementedError()






			@abstractmethod
			def        UpperCAmelCase__ (self      ):
								raise NotImplementedError()




'''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							:   Union[str, Any]					=     logging.get_logger(__name__)




def lowercase_	(   )     ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		os.getenv('''SM_HP_MP_PARAMETERS'''			,       '''{}'''				)
					try:
										# Parse it and check the field "partitions" is included, it is required for model parallel.
										lowerCamelCase_					:      Optional[Any]     =		json.loads(_lowercase				)
										if "partitions" not in smp_options:
															return False
					except json.JSONDecodeError:
										return False

					# Get the sagemaker specific framework parameters from mpi_options variable.
					lowerCamelCase_					:      List[str]     =		os.getenv('''SM_FRAMEWORK_PARAMS'''			,       '''{}'''				)
					try:
										# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
										lowerCamelCase_					:      List[str]     =		json.loads(_lowercase				)
										if not mpi_options.get('''sagemaker_mpi_enabled'''			,       _lowercase				):
															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 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		str	      =			field(
			    default=""      ,     metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}      ,     )

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

			@cached_property
			def        UpperCAmelCase__ (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:
													lowerCamelCase_					:      Optional[int]     =		torch.device('''cpu'''      )
													lowerCamelCase_					:      Dict     =		0
								elif is_sagemaker_model_parallel_available():
													lowerCamelCase_					:      int     =		smp.local_rank()
													lowerCamelCase_					:      str     =		torch.device('''cuda'''			,   A      )
													lowerCamelCase_					:      Tuple     =		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      )
													lowerCamelCase_					:      List[Any]     =		int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''      )      )
													lowerCamelCase_					:      Union[str, Any]     =		torch.device('''cuda'''			,   self.local_rank      )
													lowerCamelCase_					:      List[str]     =		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
													lowerCamelCase_					:      List[str]     =		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.
													lowerCamelCase_					:      Any     =		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      )
													lowerCamelCase_					:      Any     =		torch.device('''cuda'''			,   self.local_rank      )
													lowerCamelCase_					:      List[str]     =		1

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

								return device

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

								return super().world_size

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






			@property
			def        UpperCAmelCase__ (self      ):
								return False


