


'''simple docstring'''






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


a_    :			Tuple      				=			logging.get_logger(__name__)

a_    :			Optional[int]      				=			{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}




class        a   (   _a				):
 _lowerCAmelCase           =		'''data2vec-vision'''
 def __init__(       self					, __magic_name__=7_68					, __magic_name__=12					, __magic_name__=12					, __magic_name__=30_72					, __magic_name__="gelu"					, __magic_name__=0.0					, __magic_name__=0.0					, __magic_name__=0.0_2					, __magic_name__=1e-12					, __magic_name__=2_24					, __magic_name__=16					, __magic_name__=3					, __magic_name__=False					, __magic_name__=False					, __magic_name__=False					, __magic_name__=False					, __magic_name__=0.1					, __magic_name__=0.1					, __magic_name__=True					, __magic_name__=[3, 5, 7, 11]					, __magic_name__=[1, 2, 3, 6]					, __magic_name__=True					, __magic_name__=0.4					, __magic_name__=2_56					, __magic_name__=1					, __magic_name__=False					, __magic_name__=2_55					, **__magic_name__					, )    ->							int:
      super().__init__(**__magic_name__  )

      _a						=							hidden_size
      _a						=							num_hidden_layers
      _a						=							num_attention_heads
      _a						=							intermediate_size
      _a						=							hidden_act
      _a						=							hidden_dropout_prob
      _a						=							attention_probs_dropout_prob
      _a						=							initializer_range
      _a						=							layer_norm_eps

      _a						=							image_size
      _a						=							patch_size
      _a						=							num_channels
      _a						=							use_mask_token
      _a						=							use_absolute_position_embeddings
      _a						=							use_relative_position_bias
      _a						=							use_shared_relative_position_bias
      _a						=							layer_scale_init_value
      _a						=							drop_path_rate
      _a						=							use_mean_pooling
      # decode head attributes (semantic segmentation)
      _a						=							out_indices
      _a						=							pool_scales
      # auxiliary head attributes (semantic segmentation)
      _a						=							use_auxiliary_head
      _a						=							auxiliary_loss_weight
      _a						=							auxiliary_channels
      _a						=							auxiliary_num_convs
      _a						=							auxiliary_concat_input
      _a						=							semantic_loss_ignore_index





class        a   (   _a				):
 _lowerCAmelCase           =		version.parse("""1.11"""				)
 @property
 def 							__UpperCAmelCase						(       self  )    ->							Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
          ]  )




 @property
 def 							__UpperCAmelCase						(       self  )    ->							float:
      return 1e-4








from __future__ import annotations






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						tuple[float, list[float]]:






				"""simple docstring"""


				snake_case_						:    Dict				=  list(range(len(_UpperCamelCase ) ) )
				snake_case_						:    Dict				=  [v / w for v, w in zip(_UpperCamelCase ,				_UpperCamelCase )]
				index.sort(key=lambda _UpperCamelCase : ratio[i] ,				reverse=_UpperCamelCase )

				snake_case_						:    float				=  0
				snake_case_						:    list[float]				=  [0] * len(_UpperCamelCase )
				for i in index:
								if weight[i] <= capacity:
												snake_case_						:    Dict				=  1
												max_value += value[i]
												capacity -= weight[i]
								else:
												snake_case_						:    Union[str, Any]				=  capacity / weight[i]
												max_value += value[i] * capacity / weight[i]
												break

				return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()
