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


__SCREAMING_SNAKE_CASE						:   int         =			logging.get_logger(__name__)

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



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'data2vec-vision'



							def __init__(						self				,    lowercase_=768				,    lowercase_=12				,    lowercase_=12				,    lowercase_=3_072				,    lowercase_="gelu"				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=0.02				,    lowercase_=1e-12				,    lowercase_=224				,    lowercase_=16				,    lowercase_=3				,    lowercase_=False				,    lowercase_=False				,    lowercase_=False				,    lowercase_=False				,    lowercase_=0.1				,    lowercase_=0.1				,    lowercase_=True				,    lowercase_=[3, 5, 7, 11]				,    lowercase_=[1, 2, 3, 6]				,    lowercase_=True				,    lowercase_=0.4				,    lowercase_=256				,    lowercase_=1				,    lowercase_=False				,    lowercase_=255				,    **lowercase_				,    ):
													super().__init__(**lowercase_    )

													_snake_case						:					int    =   hidden_size
													_snake_case						:					Dict    =   num_hidden_layers
													_snake_case						:					Optional[int]    =   num_attention_heads
													_snake_case						:					Union[str, Any]    =   intermediate_size
													_snake_case						:					Any    =   hidden_act
													_snake_case						:					List[Any]    =   hidden_dropout_prob
													_snake_case						:					Union[str, Any]    =   attention_probs_dropout_prob
													_snake_case						:					List[str]    =   initializer_range
													_snake_case						:					Any    =   layer_norm_eps

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




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		version.parse('1.11'		)



							@property
							def 	UpperCamelCase	(						self    ):
													return OrderedDict(
													    [
													        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
													    ]    )



							@property
							def 	UpperCamelCase	(						self    ):
													return 1e-4
def  snake_case						()	->	Dict:

						'''simple docstring'''



						_snake_case						:					List[str]    =   0
						for i in range(1 ,      1_001						):
												total += i**i
						return str(__lowercase						)[-10:]


if __name__ == "__main__":
				print(solution())