


'''simple docstring'''






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


a_    :			Optional[Any]      				=			logging.get_logger(__name__)

a_    :			Tuple      				=			{
    """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        a   (   __UpperCAmelCase				):
 _lowerCAmelCase           =		'''biogpt'''
 def __init__(       self					, __magic_name__=4_23_84					, __magic_name__=10_24					, __magic_name__=24					, __magic_name__=16					, __magic_name__=40_96					, __magic_name__="gelu"					, __magic_name__=0.1					, __magic_name__=0.1					, __magic_name__=10_24					, __magic_name__=0.0_2					, __magic_name__=1e-12					, __magic_name__=True					, __magic_name__=True					, __magic_name__=0.0					, __magic_name__=0.0					, __magic_name__=1					, __magic_name__=0					, __magic_name__=2					, **__magic_name__					, )    ->							Optional[Any]:
      _a						=							vocab_size
      _a						=							max_position_embeddings
      _a						=							hidden_size
      _a						=							num_hidden_layers
      _a						=							num_attention_heads
      _a						=							intermediate_size
      _a						=							hidden_act
      _a						=							hidden_dropout_prob
      _a						=							attention_probs_dropout_prob
      _a						=							initializer_range
      _a						=							layer_norm_eps
      _a						=							scale_embedding
      _a						=							use_cache
      _a						=							layerdrop
      _a						=							activation_dropout
      super().__init__(pad_token_id=__magic_name__					, bos_token_id=__magic_name__					, eos_token_id=__magic_name__					, **__magic_name__  )








from ..utils import is_flax_available, is_torch_available


if is_torch_available():
    from .autoencoder_kl import AutoencoderKL
    from .controlnet import ControlNetModel
    from .dual_transformer_ad import DualTransformeraDModel
    from .modeling_utils import ModelMixin
    from .prior_transformer import PriorTransformer
    from .ta_film_transformer import TaFilmDecoder
    from .transformer_ad import TransformeraDModel
    from .unet_ad import UNetaDModel
    from .unet_ad import UNetaDModel
    from .unet_ad_condition import UNetaDConditionModel
    from .unet_ad_condition import UNetaDConditionModel
    from .vq_model import VQModel

if is_flax_available():
    from .controlnet_flax import FlaxControlNetModel
    from .unet_ad_condition_flax import FlaxUNetaDConditionModel
    from .vae_flax import FlaxAutoencoderKL


