

"""simple docstring"""




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


A_				:      Any           =			logging.get_logger(__name__)

A_				:      List[str]           =			{
    "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    (A__      ):
			lowerCamelCase__ :		Dict       		=				'donut-swin'

			lowerCamelCase__ :		Any       		=				{
			    'num_attention_heads': 'num_heads',
			    'num_hidden_layers': 'num_layers',
			}

			def __init__(       self					:    int     ,					__UpperCAmelCase					:    List[str]=2_2_4     ,					__UpperCAmelCase					:    List[Any]=4     ,					__UpperCAmelCase					:    Optional[Any]=3     ,					__UpperCAmelCase					:    List[Any]=9_6     ,					__UpperCAmelCase					:    List[Any]=[2, 2, 6, 2]     ,					__UpperCAmelCase					:    Dict=[3, 6, 1_2, 2_4]     ,					__UpperCAmelCase					:    Tuple=7     ,					__UpperCAmelCase					:    str=4.0     ,					__UpperCAmelCase					:    List[Any]=True     ,					__UpperCAmelCase					:    Union[str, Any]=0.0     ,					__UpperCAmelCase					:    Union[str, Any]=0.0     ,					__UpperCAmelCase					:    int=0.1     ,					__UpperCAmelCase					:    Union[str, Any]="gelu"     ,					__UpperCAmelCase					:    Tuple=False     ,					__UpperCAmelCase					:    Optional[int]=0.02     ,					__UpperCAmelCase					:    Any=1e-5     ,					**__UpperCAmelCase					:    Union[str, Any]     ,					) ->     Optional[int]:
						super().__init__(**__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   image_size
						SCREAMING_SNAKE_CASE__										=   patch_size
						SCREAMING_SNAKE_CASE__										=   num_channels
						SCREAMING_SNAKE_CASE__										=   embed_dim
						SCREAMING_SNAKE_CASE__										=   depths
						SCREAMING_SNAKE_CASE__										=   len(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   num_heads
						SCREAMING_SNAKE_CASE__										=   window_size
						SCREAMING_SNAKE_CASE__										=   mlp_ratio
						SCREAMING_SNAKE_CASE__										=   qkv_bias
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   drop_path_rate
						SCREAMING_SNAKE_CASE__										=   hidden_act
						SCREAMING_SNAKE_CASE__										=   use_absolute_embeddings
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   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
						SCREAMING_SNAKE_CASE__										=   int(embed_dim * 2 ** (len(__UpperCAmelCase							) - 1)							)





"""simple docstring"""




from __future__ import annotations

def 						A							(  snake_case__     ):




			'''simple docstring'''


			for i in range(1		,					len(matrix[0]     )     ):
						matrix[0][i] += matrix[0][i - 1]

			# preprocessing the first column
			for i in range(1		,					len(snake_case__     )     ):
						matrix[i][0] += matrix[i - 1][0]

			# updating the path cost for current position
			for i in range(1		,					len(snake_case__     )     ):
						for j in range(1		,					len(matrix[0]     )     ):
									matrix[i][j] += min(matrix[i - 1][j]		,					matrix[i][j - 1]     )

			return matrix[-1][-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()


