
"""simple docstring"""

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


SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    					=						{
    '''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      _UpperCAmelCase							(		_snake_case							):
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	'donut-swin'

      __SCREAMING_SNAKE_CASE   :							Dict							      =	{
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
      }




      def __init__(							self   ,       lowercase_=2_2_4   ,       lowercase_=4   ,       lowercase_=3   ,       lowercase_=9_6   ,       lowercase_=[2, 2, 6, 2]   ,       lowercase_=[3, 6, 1_2, 2_4]   ,       lowercase_=7   ,       lowercase_=4.0   ,       lowercase_=True   ,       lowercase_=0.0   ,       lowercase_=0.0   ,       lowercase_=0.1   ,       lowercase_="gelu"   ,       lowercase_=False   ,       lowercase_=0.0_2   ,       lowercase_=1E-5   ,       **lowercase_   ,       )						->					Tuple:
          super().__init__(**lowercase_							)

          UpperCAmelCase									=					image_size
          UpperCAmelCase									=					patch_size
          UpperCAmelCase									=					num_channels
          UpperCAmelCase									=					embed_dim
          UpperCAmelCase									=					depths
          UpperCAmelCase									=					len(lowercase_							)
          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(lowercase_							) - 1)							)





"""simple docstring"""

import copy

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


SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"encoder-decoder"
      __SCREAMING_SNAKE_CASE   :							Any							      =	True




      def __init__(							self   ,       **lowercase_							)						->					str:
          super().__init__(**lowercase_							)
          assert (
              "encoder" in kwargs and "decoder" in kwargs
          ), "Config has to be initialized with encoder and decoder config"
          UpperCAmelCase									=					kwargs.pop('encoder'							)
          UpperCAmelCase									=					encoder_config.pop('model_type'							)
          UpperCAmelCase									=					kwargs.pop('decoder'							)
          UpperCAmelCase									=					decoder_config.pop('model_type'							)

          from ..auto.configuration_auto import AutoConfig

          UpperCAmelCase									=					AutoConfig.for_model(lowercase_   ,       **lowercase_							)
          UpperCAmelCase									=					AutoConfig.for_model(lowercase_   ,       **lowercase_							)
          UpperCAmelCase									=					True




      @classmethod
      def     a_      (							cls   ,       lowercase_   ,       lowercase_   ,       **lowercase_							)						->					PretrainedConfig:
          logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'							)
          UpperCAmelCase									=					True
          UpperCAmelCase									=					True

          return cls(encoder=encoder_config.to_dict()   ,       decoder=decoder_config.to_dict()   ,       **lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					copy.deepcopy(self.__dict__							)
          UpperCAmelCase									=					self.encoder.to_dict()
          UpperCAmelCase									=					self.decoder.to_dict()
          UpperCAmelCase									=					self.__class__.model_type
          return output



