




"""simple docstring"""


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 					lowercase(     __lowerCamelCase ):







 '''simple docstring'''





 lowercase__ 					=					"donut-swin"

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


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




   '''simple docstring'''



   super().__init__(**a_		)

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__UpperCAmelCase       :   Union[str, Any]				       =							{}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase       :   str				       =							["BartphoTokenizer"]

if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bartpho import BartphoTokenizer

else:
      import sys

      __UpperCAmelCase       :   Optional[Any]				       =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






