




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[Any]				=			logging.get_logger(__name__)







def 		lowercase__   (		):





					'''simple docstring'''






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

					# Get the sagemaker specific framework parameters from mpi_options variable.
					__magic_name__							:	int    							=							os.getenv('''SM_FRAMEWORK_PARAMS'''							,'''{}'''				)
					try:
										# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
										__magic_name__							:	List[Any]    							=							json.loads(__A				)
										if not mpi_options.get('''sagemaker_mpi_enabled'''							,__A				):
															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    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





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

			def 							UpperCAmelCase__       ( self			:   str  )						->   List[str]:
								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 							UpperCAmelCase__       ( self			:   int  )						->   "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:
													__magic_name__							:	Optional[int]    							=							torch.device('''cpu'''  )
													__magic_name__							:	int    							=							0
								elif is_sagemaker_model_parallel_available():
													__magic_name__							:	Any    							=							smp.local_rank()
													__magic_name__							:	int    							=							torch.device('''cuda'''       ,						lowerCamelCase_  )
													__magic_name__							:	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  )
													__magic_name__							:	Optional[int]    							=							int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''  )  )
													__magic_name__							:	Optional[int]    							=							torch.device('''cuda'''       ,						self.local_rank  )
													__magic_name__							:	str    							=							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
													__magic_name__							:	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.
													__magic_name__							:	Dict    							=							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  )
													__magic_name__							:	str    							=							torch.device('''cuda'''       ,						self.local_rank  )
													__magic_name__							:	Dict    							=							1

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

								return device

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

								return super().world_size

			@property
			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   List[str]:
								return not is_sagemaker_model_parallel_available()


			@property
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Tuple:
								return False









from typing import TYPE_CHECKING

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


__lowerCamelCase   :						int				=			{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						int				=			[
	    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SwinForImageClassification''',
	    '''SwinForMaskedImageModeling''',
	    '''SwinModel''',
	    '''SwinPreTrainedModel''',
	    '''SwinBackbone''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[int]				=			[
	    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSwinForImageClassification''',
	    '''TFSwinForMaskedImageModeling''',
	    '''TFSwinModel''',
	    '''TFSwinPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swin import (
		    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwinBackbone,
		    SwinForImageClassification,
		    SwinForMaskedImageModeling,
		    SwinModel,
		    SwinPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		    TFSwinPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Union[str, Any]				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



