"""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							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							os.getenv('SM_HP_MP_PARAMETERS'				,			'{}' )
							try:
														# Parse it and check the field "partitions" is included, it is required for model parallel.
														A__		   =							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.
							A__		   =							os.getenv('SM_FRAMEWORK_PARAMS'				,			'{}' )
							try:
														# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
														A__		   =							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 			UpperCamelCase__(	lowercase__       ):
						lowerCAmelCase__     :  str  							=						field(
						    default=''						,  metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}						,  )





						def  snake_case__		(				self   )    -> List[str]:
													super().__post_init__()
													warnings.warn(
													    '`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
													    '`TrainingArguments` instead.'						,__lowercase						,)





						@cached_property
						def  snake_case__		(				self   )    -> "torch.device":
													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__		   =							torch.device('cpu'   )
																				A__		   =							0
													elif is_sagemaker_model_parallel_available():
																				A__		   =							smp.local_rank()
																				A__		   =							torch.device('cuda'						,__lowercase   )
																				A__		   =							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__		   =							int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'   )   )
																				A__		   =							torch.device('cuda'						,self.local_rank   )
																				A__		   =							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__		   =							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__		   =							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__		   =							torch.device('cuda'						,self.local_rank   )
																				A__		   =							1

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

													return device





						@property
						def  snake_case__		(				self   )    -> List[str]:
													if is_sagemaker_model_parallel_available():
																				return smp.dp_size()

													return super().world_size





						@property
						def  snake_case__		(				self   )    -> int:
													return not is_sagemaker_model_parallel_available()





						@property
						def  snake_case__		(				self   )    -> List[Any]:
													return False













# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		:       Union[str, Any]			     = {"configuration_timm_backbone": ["TimmBackboneConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:       Tuple			     = ["TimmBackbone"]


if TYPE_CHECKING:
       from .configuration_timm_backbone import TimmBackboneConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_timm_backbone import TimmBackbone

else:
       import sys

       a		:       List[Any]			     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


