



'''simple docstring'''
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


snake_case_ :	List[Any]         =  logging.get_logger(__name__)






@dataclass
class 							lowercase__	(							lowercase   ):
	lowercase__  				=			[
	    """no_inference""",
	    """no_cuda""",
	    """no_tpu""",
	    """no_speed""",
	    """no_memory""",
	    """no_env_print""",
	    """no_multi_process""",
	]





	def __init__( self					:				int ,**lowerCamelCase__					:				List[Any] ):







							'''simple docstring'''
							for deprecated_arg in self.deprecated_args:
													if deprecated_arg in kwargs:
																			_UpperCamelCase					:		List[str] 				=   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]}' )

							_UpperCamelCase					:		Optional[Any] 				=   kwargs.pop('torchscript' ,self.torchscript )
							_UpperCamelCase					:		List[str] 				=   kwargs.pop('torch_xla_tpu_print_metrics' ,self.torch_xla_tpu_print_metrics )
							_UpperCamelCase					:		Optional[Any] 				=   kwargs.pop('fp16_opt_level' ,self.fpaa_opt_level )
							super().__init__(**lowerCamelCase__ )

	lowercase__  				=			field(default=lowercase						,					metadata={"""help""": """Trace the models using torchscript"""}   )
	lowercase__  				=			field(default=lowercase						,					metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}   )
	lowercase__  				=			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] ):







							'''simple docstring'''
							requires_backends(self ,['torch'] )
							logger.info('PyTorch: setting up devices' )
							if not self.cuda:
													_UpperCamelCase					:		Any 				=   torch.device('cpu' )
													_UpperCamelCase					:		Union[str, Any] 				=   0
							elif is_torch_tpu_available():
													_UpperCamelCase					:		Optional[Any] 				=   xm.xla_device()
													_UpperCamelCase					:		Dict 				=   0
							else:
													_UpperCamelCase					:		Tuple 				=   torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
													_UpperCamelCase					:		List[Any] 				=   torch.cuda.device_count()
							return device, n_gpu





	@property
	def 	UpperCamelCase_  ( self					:				Union[str, Any] ):







							'''simple docstring'''
							return is_torch_tpu_available() and self.tpu





	@property
	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							requires_backends(self ,['torch'] )
							# TODO(PVP): currently only single GPU is supported
							return torch.cuda.current_device()





	@property
	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							requires_backends(self ,['torch'] )
							return self._setup_devices[0]





	@property
	def 	UpperCamelCase_  ( self					:				Union[str, Any] ):







							'''simple docstring'''
							requires_backends(self ,['torch'] )
							return self._setup_devices[1]





	@property
	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							return self.n_gpu > 0



"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a      :		List[Any]           =   logging.get_logger(__name__)

_a      :		Any           =   {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			int							=		"swinv2"

     _UpperCamelCase							:			List[str]							=		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(		self				,     a__=224				,     a__=4				,     a__=3				,     a__=96				,     a__=[2, 2, 6, 2]				,     a__=[3, 6, 12, 24]				,     a__=7				,     a__=4.0				,     a__=True				,     a__=0.0				,     a__=0.0				,     a__=0.1				,     a__="gelu"				,     a__=False				,     a__=0.0_2				,     a__=1e-5				,     a__=32				,     **a__				,     ):
       super().__init__(**a__  )

       _lowerCAmelCase						:       int  			=     image_size
       _lowerCAmelCase						:       Optional[Any]  			=     patch_size
       _lowerCAmelCase						:       Any  			=     num_channels
       _lowerCAmelCase						:       List[Any]  			=     embed_dim
       _lowerCAmelCase						:       Optional[int]  			=     depths
       _lowerCAmelCase						:       List[Any]  			=     len(a__  )
       _lowerCAmelCase						:       Any  			=     num_heads
       _lowerCAmelCase						:       Tuple  			=     window_size
       _lowerCAmelCase						:       Tuple  			=     mlp_ratio
       _lowerCAmelCase						:       Any  			=     qkv_bias
       _lowerCAmelCase						:       Optional[int]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Tuple  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       str  			=     drop_path_rate
       _lowerCAmelCase						:       List[str]  			=     hidden_act
       _lowerCAmelCase						:       List[str]  			=     use_absolute_embeddings
       _lowerCAmelCase						:       Optional[int]  			=     layer_norm_eps
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       _lowerCAmelCase						:       Any  			=     encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       _lowerCAmelCase						:       Tuple  			=     int(embed_dim * 2 ** (len(a__  ) - 1)  )
       _lowerCAmelCase						:       Tuple  			=     (0, 0, 0, 0)




