




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


UpperCamelCase__ :   Any						=							logging.get_logger(__name__)

UpperCamelCase__ :   Any						=							{
    '''Visual-Attention-Network/van-base''': (
        '''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
    ),
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       List[Any]						=     "van"






				def __init__(			self      ,snake_case__=224      ,snake_case__=3      ,snake_case__=[7, 3, 3, 3]      ,snake_case__=[4, 2, 2, 2]      ,snake_case__=[64, 128, 320, 512]      ,snake_case__=[3, 3, 12, 3]      ,snake_case__=[8, 8, 4, 4]      ,snake_case__="gelu"      ,snake_case__=0.02      ,snake_case__=1E-6      ,snake_case__=1E-2      ,snake_case__=0.0      ,snake_case__=0.0      ,**snake_case__      ,):
							super().__init__(**snake_case__ )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      image_size
							SCREAMING_SNAKE_CASE_   : List[str]					  =      num_channels
							SCREAMING_SNAKE_CASE_   : Dict					  =      patch_sizes
							SCREAMING_SNAKE_CASE_   : str					  =      strides
							SCREAMING_SNAKE_CASE_   : str					  =      hidden_sizes
							SCREAMING_SNAKE_CASE_   : str					  =      depths
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      mlp_ratios
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      hidden_act
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      layer_norm_eps
							SCREAMING_SNAKE_CASE_   : int					  =      layer_scale_init_value
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      drop_path_rate
							SCREAMING_SNAKE_CASE_   : List[str]					  =      dropout_rate





"""simple docstring"""




from typing import TYPE_CHECKING

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


A__ :       List[str]				= {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       Dict				= [
      'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BioGptForCausalLM',
      'BioGptForTokenClassification',
      'BioGptForSequenceClassification',
      'BioGptModel',
      'BioGptPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

  A__ :       int				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
