
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


lowercase						:      str									= logging.get_logger(__name__)




def 			_SCREAMING_SNAKE_CASE      (	)      ->					Dict:






							'''simple docstring'''





							__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			:     List[str]         =      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			:     int         =      os.getenv("SM_FRAMEWORK_PARAMS"		,						"{}")
							try:
														# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
														__UpperCamelCase			:     Any         =      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 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




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







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				Tuple:
								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      _lowerCamelCase       (      self	:Any		)	->				"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:
															__UpperCamelCase			:     Optional[int]         =      torch.device("cpu"		)
															__UpperCamelCase			:     List[str]         =      0
								elif is_sagemaker_model_parallel_available():
															__UpperCamelCase			:     Any         =      smp.local_rank()
															__UpperCamelCase			:     int         =      torch.device("cuda"  ,     a		)
															__UpperCamelCase			:     List[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			:     Union[str, Any]         =      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			:     Optional[Any]         =      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			:     List[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			:     Optional[Any]         =      torch.device("cuda"  ,     self.local_rank		)
															__UpperCamelCase			:     List[str]         =      1

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

								return device







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

								return super().world_size







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







	@property
	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				List[str]:
								return False

from typing import TYPE_CHECKING

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


lowercase						:      List[Any]									= {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}


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

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      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

		lowercase						:      Optional[Any]									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)