






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 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''donut-swin'''

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







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




       """simple docstring"""


       super().__init__(**__UpperCAmelCase  )

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__				   =		{
    "configuration_groupvit": [
        "GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "GroupViTConfig",
        "GroupViTOnnxConfig",
        "GroupViTTextConfig",
        "GroupViTVisionConfig",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GroupViTModel",
         "GroupViTPreTrainedModel",
         "GroupViTTextModel",
         "GroupViTVisionModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFGroupViTModel",
         "TFGroupViTPreTrainedModel",
         "TFGroupViTTextModel",
         "TFGroupViTVisionModel",
     ]

if TYPE_CHECKING:
     from .configuration_groupvit import (
         GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         GroupViTConfig,
         GroupViTOnnxConfig,
         GroupViTTextConfig,
         GroupViTVisionConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_groupvit import (
              GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              GroupViTModel,
              GroupViTPreTrainedModel,
              GroupViTTextModel,
              GroupViTVisionModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_groupvit import (
              TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGroupViTModel,
              TFGroupViTPreTrainedModel,
              TFGroupViTTextModel,
              TFGroupViTVisionModel,
          )

else:
     import sys

     UpperCAmelCase__				   =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


