





'''simple docstring'''






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)  # pylint: disable=invalid-name
class 		lowerCamelCase	(   __snake_case							):



	def __init__( self		,   a_		,   a_=768						):
			super().__init__(a__						)
			lowerCAmelCase  :     Dict       						= proj_size

			lowerCAmelCase  :     Any       						= CLIPVisionModel(a__						)
			lowerCAmelCase  :     List[str]       						= PaintByExampleMapper(a__						)
			lowerCAmelCase  :     Optional[int]       						= nn.LayerNorm(config.hidden_size						)
			lowerCAmelCase  :     Any       						= nn.Linear(config.hidden_size		,   self.proj_size						)

			# uncondition for scaling
			lowerCAmelCase  :     Optional[Any]       						= nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_=False						):
			lowerCAmelCase  :     List[str]       						= self.model(pixel_values=a__						)
			lowerCAmelCase  :     List[Any]       						= clip_output.pooler_output
			lowerCAmelCase  :     Dict       						= self.mapper(latent_states[:, None]						)
			lowerCAmelCase  :     Tuple       						= self.final_layer_norm(a__						)
			lowerCAmelCase  :     List[Any]       						= self.proj_out(a__						)
			if return_uncond_vector:
					return latent_states, self.uncond_vector

			return latent_states






class 		lowerCamelCase	(   nn.Module							):



	def __init__( self		,   a_						):
			super().__init__()
			lowerCAmelCase  :     Optional[Any]       						= (config.num_hidden_layers + 1) // 5
			lowerCAmelCase  :     Optional[int]       						= config.hidden_size
			lowerCAmelCase  :     int       						= 1
			lowerCAmelCase  :     str       						= nn.ModuleList(
			    [
			        BasicTransformerBlock(a__		,   a__		,   a__		,   activation_fn="gelu"		,   attention_bias=a__						)
			        for _ in range(a__						)
			    ]						)





	def 	_lowerCamelCase     ( self		,   a_						):
			for block in self.blocks:
					lowerCAmelCase  :     Any       						= block(a__						)

			return hidden_states







from ..utils import DummyObject, requires_backends






class    _UpperCamelCase     (       metaclass=__snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			['note_seq']

				def __init__(    self					, *a__					, **a__ )						->					Optional[int]:
					requires_backends(self					, ["""note_seq"""] )

				@classmethod
				def   _UpperCAmelCase       (    cls					, *a__					, **a__ )						->					Dict:
					requires_backends(cls					, ["""note_seq"""] )

				@classmethod
				def   _UpperCAmelCase       (    cls					, *a__					, **a__ )						->					Optional[int]:
					requires_backends(cls					, ["""note_seq"""] )



