
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__							:							str									=			logging.get_logger(__name__)




def 	A_ (							)      ->    Dict:



						'''simple docstring'''

						__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(_lowerCamelCase )
												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(_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__    (							__lowercase    ):

							"""simple docstring"""






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



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






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



							@cached_property
							def     A__		(  self			)->   "torch.device":





													'''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'''     ,	SCREAMING_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(SCREAMING_SNAKE_CASE_			)

													return device



							@property
							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													if is_sagemaker_model_parallel_available():
																			return smp.dp_size()

													return super().world_size



							@property
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													return not is_sagemaker_model_parallel_available()



							@property
							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													return False






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




