

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				)   ->       List[Any]:
				# Get the sagemaker specific mp parameters from smp_options variable.
				_UpperCAmelCase       :		int 		=    os.getenv("SM_HP_MP_PARAMETERS"  ,      "{}" )
				try:
								# Parse it and check the field "partitions" is included, it is required for model parallel.
								_UpperCAmelCase       :		Union[str, Any] 		=    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       :		str 		=    os.getenv("SM_FRAMEWORK_PARAMS"  ,      "{}" )
				try:
								# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
								_UpperCAmelCase       :		Tuple 		=    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 				a  (	UpperCAmelCase				):
						_lowercase    						=			field(
						    default=""							, metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}							, )


						def        _UpperCAmelCase				(       self	):







										'''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        _UpperCAmelCase				(       self	):







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

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

										return device


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										if is_sagemaker_model_parallel_available():
														return smp.dp_size()

										return super().world_size


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return not is_sagemaker_model_parallel_available()





						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return False





import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										warnings.warn(
										    "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use DeformableDetrImageProcessor instead."						,					A_						,					)
										super().__init__(*A_						,					**A_	)


