

'''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


lowerCamelCase			:				List[str] 			=					logging.get_logger(__name__)


def 	_lowerCAmelCase (						)				->      Optional[int]:
     """simple docstring"""




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

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





  def 						A  (		self				:     Any			)     ->   int:





       '''simple docstring'''






       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 						A  (		self				:     List[Any]			)     ->   "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:
            _SCREAMING_SNAKE_CASE							   =torch.device('cpu'			)
            _SCREAMING_SNAKE_CASE							   =0
       elif is_sagemaker_model_parallel_available():
            _SCREAMING_SNAKE_CASE							   =smp.local_rank()
            _SCREAMING_SNAKE_CASE							   =torch.device('cuda'							, _a			)
            _SCREAMING_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			)
            _SCREAMING_SNAKE_CASE							   =int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'			)			)
            _SCREAMING_SNAKE_CASE							   =torch.device('cuda'							, self.local_rank			)
            _SCREAMING_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
            _SCREAMING_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.
            _SCREAMING_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			)
            _SCREAMING_SNAKE_CASE							   =torch.device('cuda'							, self.local_rank			)
            _SCREAMING_SNAKE_CASE							   =1

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

       return device





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





       '''simple docstring'''






       if is_sagemaker_model_parallel_available():
            return smp.dp_size()

       return super().world_size





  @property
  def 						A  (		self				:     Optional[int]			)     ->   Any:





       '''simple docstring'''






       return not is_sagemaker_model_parallel_available()





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





       '''simple docstring'''






       return False



'''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


lowerCamelCase			:				Optional[Any] 			=					logging.get_logger(__name__)

lowerCamelCase			:				Optional[Any] 			=					"T5Config"







class 							A__   (	A__  ):
  A__					=    'mt5'
  A__					=    MTaConfig







class 							A__   (	A__  ):
  A__					=    'mt5'
  A__					=    MTaConfig







class 							A__   (	A__  ):
  A__					=    'mt5'
  A__					=    MTaConfig
