
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


__a  			=					logging.get_logger(__name__)
@dataclass
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[Any]         =	[
	    'no_inference',
	    'no_cuda',
	    'no_tpu',
	    'no_speed',
	    'no_memory',
	    'no_env_print',
	    'no_multi_process',
	]
	def __init__(       self  :			int       ,							**SCREAMING_SNAKE_CASE_  :			Tuple       )       ->     Optional[int]:
					for deprecated_arg in self.deprecated_args:
									if deprecated_arg in kwargs:
													lowercase_       =					deprecated_arg[3:]
													setattr(self       ,							SCREAMING_SNAKE_CASE_       ,							not kwargs.pop(SCREAMING_SNAKE_CASE_       )       )
													logger.warning(
													    f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
													    f''' {positive_arg}={kwargs[positive_arg]}'''       )

					lowercase_       =					kwargs.pop('''torchscript'''       ,							self.torchscript       )
					lowercase_       =					kwargs.pop('''torch_xla_tpu_print_metrics'''       ,							self.torch_xla_tpu_print_metrics       )
					lowercase_       =					kwargs.pop('''fp16_opt_level'''       ,							self.fpaa_opt_level       )
					super().__init__(**SCREAMING_SNAKE_CASE_       )

	a			:bool         =	field(default=UpperCAmelCase ,						metadata={'help': 'Trace the models using torchscript'}							)
	a			:bool         =	field(default=UpperCAmelCase ,						metadata={'help': 'Print Xla/PyTorch tpu metrics'}							)
	a			:str         =	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 						_lowercase  (       self  :			Any       )       ->     Tuple["torch.device", int]:
					requires_backends(self       ,							['''torch''']       )
					logger.info('''PyTorch: setting up devices'''       )
					if not self.cuda:
									lowercase_       =					torch.device('''cpu'''       )
									lowercase_       =					0
					elif is_torch_tpu_available():
									lowercase_       =					xm.xla_device()
									lowercase_       =					0
					else:
									lowercase_       =					torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''       )
									lowercase_       =					torch.cuda.device_count()
					return device, n_gpu
	@property
	def 						_lowercase  (       self  :			List[Any]       )       ->     Union[str, Any]:
					return is_torch_tpu_available() and self.tpu
	@property
	def 						_lowercase  (       self  :			List[Any]       )       ->     int:
					requires_backends(self       ,							['''torch''']       )
					# TODO(PVP): currently only single GPU is supported
					return torch.cuda.current_device()
	@property
	def 						_lowercase  (       self  :			List[Any]       )       ->     "torch.device":
					requires_backends(self       ,							['''torch''']       )
					return self._setup_devices[0]
	@property
	def 						_lowercase  (       self  :			Any       )       ->     int:
					requires_backends(self       ,							['''torch''']       )
					return self._setup_devices[1]




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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__a  			=					{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['DeiTFeatureExtractor']
						__a  			=					['DeiTImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DeiTForImageClassification',
						    'DeiTForImageClassificationWithTeacher',
						    'DeiTForMaskedImageModeling',
						    'DeiTModel',
						    'DeiTPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFDeiTForImageClassification',
						    'TFDeiTForImageClassificationWithTeacher',
						    'TFDeiTForMaskedImageModeling',
						    'TFDeiTModel',
						    'TFDeiTPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deit import DeiTFeatureExtractor
												from .image_processing_deit import DeiTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deit import (
												    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeiTForImageClassification,
												    DeiTForImageClassificationWithTeacher,
												    DeiTForMaskedImageModeling,
												    DeiTModel,
												    DeiTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deit import (
												    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDeiTForImageClassification,
												    TFDeiTForImageClassificationWithTeacher,
												    TFDeiTForMaskedImageModeling,
												    TFDeiTModel,
												    TFDeiTPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




