

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    				=     logging.get_logger(__name__)







def 	_snake_case		(    )       ->      Tuple:

     """simple docstring"""







     _lowerCAmelCase   :					Dict        =    os.getenv("SM_HP_MP_PARAMETERS"		,							"{}"				)
     try:
          # Parse it and check the field "partitions" is included, it is required for model parallel.
          _lowerCAmelCase   :					int        =    json.loads(SCREAMING_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.
     _lowerCAmelCase   :					Union[str, Any]        =    os.getenv("SM_FRAMEWORK_PARAMS"		,							"{}"				)
     try:
          # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
          _lowerCAmelCase   :					Any        =    json.loads(SCREAMING_SNAKE_CASE				)
          if not mpi_options.get("sagemaker_mpi_enabled"		,							SCREAMING_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 A__    ( A ):



 """simple docstring"""







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

 def    __magic_name__       (		self :	Any       ):






      '''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    __magic_name__       (		self :	List[Any]       ):






      '''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:
           _lowerCAmelCase   :					Any        =    torch.device("cpu"       )
           _lowerCAmelCase   :					Any        =    0
      elif is_sagemaker_model_parallel_available():
           _lowerCAmelCase   :					Optional[int]        =    smp.local_rank()
           _lowerCAmelCase   :					Union[str, Any]        =    torch.device("cuda"     ,						A_       )
           _lowerCAmelCase   :					Optional[Any]        =    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   :					int        =    torch.device("cuda"     ,						self.local_rank       )
           _lowerCAmelCase   :					Optional[int]        =    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   :					Tuple        =    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   :					Optional[int]        =    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   :					Optional[Any]        =    torch.device("cuda"     ,						self.local_rank       )
           _lowerCAmelCase   :					List[str]        =    1

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

      return device

 @property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      if is_sagemaker_model_parallel_available():
           return smp.dp_size()

      return super().world_size

 @property
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      return not is_sagemaker_model_parallel_available()


 @property
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      return False









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase    				=     {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     ['YolosFeatureExtractor']
  __UpperCAmelCase    				=     ['YolosImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'YolosForObjectDetection',
      'YolosModel',
      'YolosPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_yolos import YolosFeatureExtractor
    from .image_processing_yolos import YolosImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_yolos import (
        YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
        YolosForObjectDetection,
        YolosModel,
        YolosPreTrainedModel,
    )


else:
  import sys

  __UpperCAmelCase    				=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






