


"""simple docstring"""







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

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


a__    :							Any            =					logging.get_logger(__name__)  # pylint: disable=invalid-name




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	def __init__(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    int=7_6_8       )	->     str:
						super().__init__(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    proj_size

						__SCREAMING_SNAKE_CASE						       =    CLIPVisionModel(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    PaintByExampleMapper(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    nn.LayerNorm(config.hidden_size       )
						__SCREAMING_SNAKE_CASE						       =    nn.Linear(config.hidden_size      ,      self.proj_size       )

						# uncondition for scaling
						__SCREAMING_SNAKE_CASE						       =    nn.Parameter(torch.randn((1, 1, self.proj_size)       )       )




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Any=False       )	->     List[str]:
						__SCREAMING_SNAKE_CASE						       =    self.model(pixel_values=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    clip_output.pooler_output
						__SCREAMING_SNAKE_CASE						       =    self.mapper(latent_states[:, None]       )
						__SCREAMING_SNAKE_CASE						       =    self.final_layer_norm(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    self.proj_out(UpperCAmelCase__       )
						if return_uncond_vector:
											return latent_states, self.uncond_vector

						return latent_states




class 			UpperCamelCase_     (			nn.Module):

	"""simple docstring"""





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




	def   UpperCAmelCase_				(			self      :    List[Any]      ,      UpperCAmelCase__      :    List[str]       )	->     Union[str, Any]:
						for block in self.blocks:
											__SCREAMING_SNAKE_CASE						       =    block(UpperCAmelCase__       )

						return hidden_states




"""simple docstring"""







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
		from .pipeline_kandinsky import KandinskyPipeline
		from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
		from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
		from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
		from .text_encoder import MultilingualCLIP
