



import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig


class 	lowerCamelCase__ (		_a   ):
						_lowerCAmelCase								= CustomConfig


						def __init__( self						:  str      ,       _a						:  int			):
													super().__init__(_a			)
													a__:       List[Any]											=torch.nn.Linear(config.hidden_size      ,       config.hidden_size			)


						def   _lowerCamelCase			( self						:  Any      ,       _a						:  Optional[int]			):
													return self.linear(_a			)







						def   _lowerCamelCase			( self						:  Tuple      ,       _a						:  List[str]			):
													pass


class 	lowerCamelCase__ (		_a   ):
						_lowerCAmelCase								= NoSuperInitConfig


						def __init__( self						:  Any      ,       _a						:  int			):
													super().__init__(_a			)
													a__:       List[Any]											=torch.nn.Linear(config.attribute      ,       config.attribute			)


						def   _lowerCamelCase			( self						:  Dict      ,       _a						:  Optional[int]			):
													return self.linear(_a			)







						def   _lowerCamelCase			( self						:  Tuple      ,       _a						:  Optional[int]			):
													pass











from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
		from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline





