






import re

def      lowerCAmelCase__	(    _a       :			str    ):
		return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]"  ,      str_    )]

def      lowerCAmelCase__	(    _a       :			str    ):
		snake_case_				:							List[str]						 =       split_input(str_    )
		return "".join(
		    ["".join([char.capitalize() for char in sub_str]    ) for sub_str in string_split]    )

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			bool  ,      _a       :			str    ):
		try:
				snake_case_				:							Dict						 =       split_input(_a    )
				if upper:
						snake_case_				:							Optional[int]						 =       "".join(
						    [
						        separator.join([char.upper() for char in sub_str]    )
						        for sub_str in string_split
						    ]    )
				else:
						snake_case_				:							List[str]						 =       "".join(
						    [
						        separator.join([char.lower() for char in sub_str]    )
						        for sub_str in string_split
						    ]    )
				return res_str
		except IndexError:
				return "not valid string"

def      lowerCAmelCase__	(    _a       :			str    ):
		return to_simple_case(_a    )

def      lowerCAmelCase__	(    _a       :			str    ):
		try:
				snake_case_				:							str						 =       to_simple_case(_a    )
				return res_str[0].lower() + res_str[1:]
		except IndexError:
				return "not valid string"

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			bool    ):
		return to_complex_case(_a  ,      _a  ,      "_"    )

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			bool    ):
		return to_complex_case(_a  ,      _a  ,      "-"    )


if __name__ == "__main__":
							__import__('''doctest''').testmod()








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


lowercase  :							Any			  =    logging.get_logger(__name__)

lowercase  :							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 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					'donut-swin'

						A     :   Union[str, Any]						=					{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}






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

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