





import math


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  int:

    if not isinstance(_UpperCAmelCase		,			_UpperCAmelCase				):
        lowerCamelCase__					:   str      				=	F"""Input value of [number={number}] must be an integer"""
        raise TypeError(_UpperCAmelCase				)

    if number < 1:
        lowerCamelCase__					:   str      				=	F"""Input value of [number={number}] must be > 0"""
        raise ValueError(_UpperCAmelCase				)
    elif number == 1:
        return 3
    elif number == 2:
        return 5
    else:
        lowerCamelCase__					:   str      				=	int(math.log(number // 3		,			2				)				) + 2

        lowerCamelCase__					:   List[Any]      				=	[3, 5]
        lowerCamelCase__					:   Any      				=	2
        lowerCamelCase__					:   Dict      				=	3
        for block in range(1		,			_UpperCAmelCase				):
            for _ in range(_UpperCAmelCase				):
                proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]				)
                proth_index += 1
            increment *= 2

    return proth_list[number - 1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    for number in range(11):
        _UpperCAmelCase       : int			     =       0
        try:
            _UpperCAmelCase       : int			     =       proth(number)
        except ValueError:
            print(F"""ValueError: there is no {number}th Proth number""")
            continue

        print(F"""The {number}th Proth number: {value}""")












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


_UpperCAmelCase       : Dict			     =       logging.get_logger(__name__)

_UpperCAmelCase       : Union[str, Any]			     =       {
    """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							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	"""donut-swin"""

 UpperCAmelCase__									=	{
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }
 def __init__(   self						:   Tuple    ,  UpperCAmelCase						:   List[str]=224    ,  UpperCAmelCase						:   Optional[Any]=4    ,  UpperCAmelCase						:   Union[str, Any]=3    ,  UpperCAmelCase						:   Union[str, Any]=96    ,  UpperCAmelCase						:   int=[2, 2, 6, 2]    ,  UpperCAmelCase						:   Dict=[3, 6, 12, 24]    ,  UpperCAmelCase						:   Any=7    ,  UpperCAmelCase						:   Optional[int]=4.0    ,  UpperCAmelCase						:   List[Any]=True    ,  UpperCAmelCase						:   Any=0.0    ,  UpperCAmelCase						:   Optional[Any]=0.0    ,  UpperCAmelCase						:   int=0.1    ,  UpperCAmelCase						:   List[str]="gelu"    ,  UpperCAmelCase						:   List[str]=False    ,  UpperCAmelCase						:   str=0.0_2    ,  UpperCAmelCase						:   str=1e-5    ,  **UpperCAmelCase						:   Tuple    ,  )    ->			int:
     super().__init__(**UpperCAmelCase			)

     lowerCamelCase__					:   Optional[Any]      				=	image_size
     lowerCamelCase__					:   Optional[int]      				=	patch_size
     lowerCamelCase__					:   int      				=	num_channels
     lowerCamelCase__					:   int      				=	embed_dim
     lowerCamelCase__					:   Optional[Any]      				=	depths
     lowerCamelCase__					:   Optional[int]      				=	len(UpperCAmelCase			)
     lowerCamelCase__					:   Optional[int]      				=	num_heads
     lowerCamelCase__					:   Tuple      				=	window_size
     lowerCamelCase__					:   Dict      				=	mlp_ratio
     lowerCamelCase__					:   str      				=	qkv_bias
     lowerCamelCase__					:   Tuple      				=	hidden_dropout_prob
     lowerCamelCase__					:   str      				=	attention_probs_dropout_prob
     lowerCamelCase__					:   str      				=	drop_path_rate
     lowerCamelCase__					:   int      				=	hidden_act
     lowerCamelCase__					:   str      				=	use_absolute_embeddings
     lowerCamelCase__					:   List[Any]      				=	layer_norm_eps
     lowerCamelCase__					:   Optional[Any]      				=	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__					:   Optional[int]      				=	int(embed_dim * 2 ** (len(UpperCAmelCase			) - 1)			)





