





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


__UpperCAmelCase       :   List[Any]				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   List[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       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """donut-swin"""

       lowerCAmelCase__      		=     {
           """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							,				):
         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__)






