




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


__A        =    logging.get_logger(__name__)

__A        =    {
    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       '''donut-swin'''

			snake_case_   		=       {
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}


			def __init__(      self			,						lowerCamelCase__=224			,						lowerCamelCase__=4			,						lowerCamelCase__=3			,						lowerCamelCase__=96			,						lowerCamelCase__=[2, 2, 6, 2]			,						lowerCamelCase__=[3, 6, 12, 24]			,						lowerCamelCase__=7			,						lowerCamelCase__=4.0			,						lowerCamelCase__=True			,						lowerCamelCase__=0.0			,						lowerCamelCase__=0.0			,						lowerCamelCase__=0.1			,						lowerCamelCase__="gelu"			,						lowerCamelCase__=False			,						lowerCamelCase__=0.02			,						lowerCamelCase__=1e-5			,						**lowerCamelCase__			,						)    ->      str:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__							)

							__lowerCamelCase		       =    image_size
							__lowerCamelCase		       =    patch_size
							__lowerCamelCase		       =    num_channels
							__lowerCamelCase		       =    embed_dim
							__lowerCamelCase		       =    depths
							__lowerCamelCase		       =    len(lowerCamelCase__							)
							__lowerCamelCase		       =    num_heads
							__lowerCamelCase		       =    window_size
							__lowerCamelCase		       =    mlp_ratio
							__lowerCamelCase		       =    qkv_bias
							__lowerCamelCase		       =    hidden_dropout_prob
							__lowerCamelCase		       =    attention_probs_dropout_prob
							__lowerCamelCase		       =    drop_path_rate
							__lowerCamelCase		       =    hidden_act
							__lowerCamelCase		       =    use_absolute_embeddings
							__lowerCamelCase		       =    layer_norm_eps
							__lowerCamelCase		       =    initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							__lowerCamelCase		       =    int(embed_dim * 2 ** (len(lowerCamelCase__							) - 1)							)









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()
