


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


snake_case				:    List[str]       				=     logging.get_logger(__name__)
def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Optional[Any] 							=       os.getenv('''SM_HP_MP_PARAMETERS''' ,  '''{}'''						)
      try:
            # Parse it and check the field "partitions" is included, it is required for model parallel.
            a						:Optional[Any] 							=       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.
      a						:Optional[int] 							=       os.getenv('''SM_FRAMEWORK_PARAMS''' ,  '''{}'''						)
      try:
            # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
            a						:int 							=       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 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       field(
     default=''						,    metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}						,    )




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




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

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

       return device




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

       return super().world_size




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




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return False










"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase    :int     =					{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ViTMSNModel',
      'ViTMSNForImageClassification',
      'ViTMSNPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


