

'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase  :		Any  =		{
    '''configuration_clap''': [
        '''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ClapAudioConfig''',
        '''ClapConfig''',
        '''ClapTextConfig''',
    ],
    '''processing_clap''': ['''ClapProcessor'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase  :		Optional[int]  =		[
         '''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ClapModel''',
         '''ClapPreTrainedModel''',
         '''ClapTextModel''',
         '''ClapTextModelWithProjection''',
         '''ClapAudioModel''',
         '''ClapAudioModelWithProjection''',
     ]
     _UpperCAmelCase  :		Optional[Any]  =		['''ClapFeatureExtractor''']

if TYPE_CHECKING:
     from .configuration_clap import (
         CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
         ClapAudioConfig,
         ClapConfig,
         ClapTextConfig,
     )
     from .processing_clap import ClapProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_clap import ClapFeatureExtractor
          from .modeling_clap import (
              CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
              ClapAudioModel,
              ClapAudioModelWithProjection,
              ClapModel,
              ClapPreTrainedModel,
              ClapTextModel,
              ClapTextModelWithProjection,
          )


else:
     import sys

     _UpperCAmelCase  :		Any  =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







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


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    '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							):






 '''simple docstring'''





 __UpperCamelCase       =      'donut-swin'

 __UpperCamelCase       =      {
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }

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

    UpperCAmelCase__						=image_size
    UpperCAmelCase__						=patch_size
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=embed_dim
    UpperCAmelCase__						=depths
    UpperCAmelCase__						=len(A_		)
    UpperCAmelCase__						=num_heads
    UpperCAmelCase__						=window_size
    UpperCAmelCase__						=mlp_ratio
    UpperCAmelCase__						=qkv_bias
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=drop_path_rate
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=use_absolute_embeddings
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=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
    UpperCAmelCase__						=int(embed_dim * 2 ** (len(A_		) - 1)		)



