




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


_lowerCamelCase      :  Dict      						=						logging.get_logger(__name__)

_lowerCamelCase      :  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        __snake_case  (_a		):
				lowerCAmelCase__				=     "donut-swin"

				lowerCAmelCase__				=     {
				    "num_attention_heads": "num_heads",
				    "num_hidden_layers": "num_layers",
				}



				def __init__(      self      :		List[str]		,	_UpperCAmelCase      :		List[Any]=224		,	_UpperCAmelCase      :		Dict=4		,	_UpperCAmelCase      :		List[str]=3		,	_UpperCAmelCase      :		str=96		,	_UpperCAmelCase      :		str=[2, 2, 6, 2]		,	_UpperCAmelCase      :		Optional[int]=[3, 6, 12, 24]		,	_UpperCAmelCase      :		Tuple=7		,	_UpperCAmelCase      :		Any=4.0		,	_UpperCAmelCase      :		Optional[Any]=True		,	_UpperCAmelCase      :		int=0.0		,	_UpperCAmelCase      :		int=0.0		,	_UpperCAmelCase      :		Optional[int]=0.1		,	_UpperCAmelCase      :		Union[str, Any]="gelu"		,	_UpperCAmelCase      :		str=False		,	_UpperCAmelCase      :		Any=0.02		,	_UpperCAmelCase      :		Dict=1E-5		,	**_UpperCAmelCase      :		Optional[int]		,	)  ->    List[str]:





								'''simple docstring'''




								super().__init__(**_UpperCAmelCase      )

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







def 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : bool = False       ):
				'''simple docstring'''

				if not isinstance(UpperCamelCase_      ,    UpperCamelCase_       ):
								_lowerCAmelCase    :    Tuple										=      F"Expected string as input, found {type(UpperCamelCase_       )}"
								raise ValueError(UpperCamelCase_       )
				if not isinstance(UpperCamelCase_      ,    UpperCamelCase_       ):
								_lowerCAmelCase    :    Optional[Any]										=      F"Expected boolean as use_pascal parameter, found {type(UpperCamelCase_       )}"
								raise ValueError(UpperCamelCase_       )

				_lowerCAmelCase    :    Any										=      input_str.split("""_"""       )

				_lowerCAmelCase    :    str										=      0 if use_pascal else 1

				_lowerCAmelCase    :    Any										=      words[start_index:]

				_lowerCAmelCase    :    List[str]										=      [word[0].upper() + word[1:] for word in words_to_capitalize]

				_lowerCAmelCase    :    Any										=      """""" if use_pascal else words[0]

				return "".join([initial_word, *capitalized_words]       )


if __name__ == "__main__":
	from doctest import testmod

	testmod()

