
"""simple docstring"""


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
		import tensorflow as tf


_lowercase				 =					logging.get_logger(__name__)







@dataclass
class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     int   =     [
						    '''no_inference''',
						    '''no_cuda''',
						    '''no_tpu''',
						    '''no_speed''',
						    '''no_memory''',
						    '''no_env_print''',
						    '''no_multi_process''',
						]


						def __init__(			self  :   Optional[int]	,**A_  :   List[Any]   )  -> str:
							for deprecated_arg in self.deprecated_args:
								if deprecated_arg in kwargs:
									A     = deprecated_arg[3:]
									A     = not kwargs.pop(A_   )
									logger.warning(
									    F'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
									    F' {positive_arg}={kwargs[positive_arg]}'   )
							A     = kwargs.pop('tpu_name'	,self.tpu_name   )
							A     = kwargs.pop('device_idx'	,self.device_idx   )
							A     = kwargs.pop('eager_mode'	,self.eager_mode   )
							A     = kwargs.pop('use_xla'	,self.use_xla   )
							super().__init__(**A_   )

						_lowerCamelCase:     str   =     field(
						    default=_lowercase		,       metadata={'''help''': '''Name of TPU'''}		,       )
						_lowerCamelCase:     int   =     field(
						    default=0		,       metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}		,       )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Benchmark models in eager model.'''}     )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={
						        '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
						    }		,       )


						@cached_property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
							requires_backends(self	,['tf']   )
							A     = None
							if self.tpu:
								try:
									if self.tpu_name:
										A     = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name   )
									else:
										A     = tf.distribute.cluster_resolver.TPUClusterResolver()
								except ValueError:
									A     = None
							return tpu


						@cached_property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
							requires_backends(self	,['tf']   )
							if self.is_tpu:
								tf.config.experimental_connect_to_cluster(self._setup_tpu   )
								tf.tpu.experimental.initialize_tpu_system(self._setup_tpu   )

								A     = tf.distribute.TPUStrategy(self._setup_tpu   )
							else:
								# currently no multi gpu is allowed
								if self.is_gpu:
									# TODO: Currently only single GPU is supported
									tf.config.set_visible_devices(self.gpu_list[self.device_idx]	,'GPU'   )
									A     = tf.distribute.OneDeviceStrategy(device=F'/gpu:{self.device_idx}'   )
								else:
									tf.config.set_visible_devices([]	,'GPU'   )  # disable GPU
									A     = tf.distribute.OneDeviceStrategy(device=F'/cpu:{self.device_idx}'   )

							return strategy


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> bool:
							requires_backends(self	,['tf']   )
							return self._setup_tpu is not None


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> "tf.distribute.Strategy":
							requires_backends(self	,['tf']   )
							return self._setup_strategy


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Dict:
							requires_backends(self	,['tf']   )
							return tf.config.list_physical_devices('GPU'   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> int:
							requires_backends(self	,['tf']   )
							if self.cuda:
								return len(self.gpu_list   )
							return 0




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> bool:
							return self.n_gpu > 0






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase							:       Optional[int]					 =	logging.get_logger(__name__)

lowerCAmelCase							:       Tuple					 =	{
    """facebook/data2vec-vision-base-ft""": (
        """https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
    ),
}





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Union[str, Any]		      =     '''data2vec-vision'''






				def __init__(  self			, _lowerCAmelCase=768			, _lowerCAmelCase=12			, _lowerCAmelCase=12			, _lowerCAmelCase=3072			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=0.0			, _lowerCAmelCase=0.0			, _lowerCAmelCase=0.02			, _lowerCAmelCase=1e-12			, _lowerCAmelCase=224			, _lowerCAmelCase=16			, _lowerCAmelCase=3			, _lowerCAmelCase=False			, _lowerCAmelCase=False			, _lowerCAmelCase=False			, _lowerCAmelCase=False			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=True			, _lowerCAmelCase=[3, 5, 7, 11]			, _lowerCAmelCase=[1, 2, 3, 6]			, _lowerCAmelCase=True			, _lowerCAmelCase=0.4			, _lowerCAmelCase=256			, _lowerCAmelCase=1			, _lowerCAmelCase=False			, _lowerCAmelCase=255			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				str           =   hidden_size
										__SCREAMING_SNAKE_CASE:				Tuple           =   num_hidden_layers
										__SCREAMING_SNAKE_CASE:				Tuple           =   num_attention_heads
										__SCREAMING_SNAKE_CASE:				int           =   intermediate_size
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   hidden_act
										__SCREAMING_SNAKE_CASE:				int           =   hidden_dropout_prob
										__SCREAMING_SNAKE_CASE:				str           =   attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE:				Any           =   initializer_range
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   layer_norm_eps

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   image_size
										__SCREAMING_SNAKE_CASE:				List[str]           =   patch_size
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   num_channels
										__SCREAMING_SNAKE_CASE:				List[Any]           =   use_mask_token
										__SCREAMING_SNAKE_CASE:				Tuple           =   use_absolute_position_embeddings
										__SCREAMING_SNAKE_CASE:				Any           =   use_relative_position_bias
										__SCREAMING_SNAKE_CASE:				Dict           =   use_shared_relative_position_bias
										__SCREAMING_SNAKE_CASE:				Any           =   layer_scale_init_value
										__SCREAMING_SNAKE_CASE:				List[Any]           =   drop_path_rate
										__SCREAMING_SNAKE_CASE:				int           =   use_mean_pooling
										# decode head attributes (semantic segmentation)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   out_indices
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   pool_scales
										# auxiliary head attributes (semantic segmentation)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   use_auxiliary_head
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   auxiliary_loss_weight
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   auxiliary_channels
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   auxiliary_num_convs
										__SCREAMING_SNAKE_CASE:				Any           =   auxiliary_concat_input
										__SCREAMING_SNAKE_CASE:				List[str]           =   semantic_loss_ignore_index





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     version.parse('''1.11'''			)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										    ]					)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return 1e-4



