




from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
	import torch

if is_torch_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm


__lowerCamelCase   :						Tuple				=			logging.get_logger(__name__)


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=[
			    '''no_inference''',
			    '''no_cuda''',
			    '''no_tpu''',
			    '''no_speed''',
			    '''no_memory''',
			    '''no_env_print''',
			    '''no_multi_process''',
			]

			def __init__( self			:   Dict       ,						**lowerCamelCase_			:   List[str]  )						->   Any:
								for deprecated_arg in self.deprecated_args:
													if deprecated_arg in kwargs:
																		__magic_name__							:	Optional[Any]    							=							deprecated_arg[3:]
																		setattr(self       ,						lowerCamelCase_       ,						not kwargs.pop(lowerCamelCase_  )  )
																		logger.warning(
																		    F'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
																		    F''' {positive_arg}={kwargs[positive_arg]}'''  )

								__magic_name__							:	Optional[int]    							=							kwargs.pop('''torchscript'''       ,						self.torchscript  )
								__magic_name__							:	int    							=							kwargs.pop('''torch_xla_tpu_print_metrics'''       ,						self.torch_xla_tpu_print_metrics  )
								__magic_name__							:	str    							=							kwargs.pop('''fp16_opt_level'''       ,						self.fpaa_opt_level  )
								super().__init__(**lowerCamelCase_  )

			UpperCamelCase__								=field(default=_lowerCamelCase				,metadata={'''help''': '''Trace the models using torchscript'''}						)
			UpperCamelCase__								=field(default=_lowerCamelCase				,metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}						)
			UpperCamelCase__								=field(
			    default='''O1'''				,metadata={
			        '''help''': (
			            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
			            '''See details at https://nvidia.github.io/apex/amp.html'''
			        )
			    }				,)

			@cached_property
			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Tuple["torch.device", int]:
								requires_backends(self       ,						['''torch''']  )
								logger.info('''PyTorch: setting up devices'''  )
								if not self.cuda:
													__magic_name__							:	Any    							=							torch.device('''cpu'''  )
													__magic_name__							:	List[str]    							=							0
								elif is_torch_tpu_available():
													__magic_name__							:	str    							=							xm.xla_device()
													__magic_name__							:	Union[str, Any]    							=							0
								else:
													__magic_name__							:	List[str]    							=							torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''  )
													__magic_name__							:	Union[str, Any]    							=							torch.cuda.device_count()
								return device, n_gpu

			@property
			def 							UpperCAmelCase__       ( self			:   int  )						->   Optional[int]:
								return is_torch_tpu_available() and self.tpu

			@property
			def 							UpperCAmelCase__       ( self			:   Any  )						->   int:
								requires_backends(self       ,						['''torch''']  )
								# TODO(PVP): currently only single GPU is supported
								return torch.cuda.current_device()

			@property
			def 							UpperCAmelCase__       ( self			:   str  )						->   "torch.device":
								requires_backends(self       ,						['''torch''']  )
								return self._setup_devices[0]

			@property
			def 							UpperCAmelCase__       ( self			:   Any  )						->   Optional[Any]:
								requires_backends(self       ,						['''torch''']  )
								return self._setup_devices[1]


			@property
			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   List[Any]:
								return self.n_gpu > 0









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__)



