


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

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


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




class    UpperCAmelCase					(       snake_case_    ):




			def __init__(						self							:		Any       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[int]=7_6_8						)     ->   int:







						"""simple docstring"""







						super().__init__(__magic_name__						)
						UpperCamelCase         =					proj_size

						UpperCamelCase         =					CLIPVisionModel(__magic_name__						)
						UpperCamelCase         =					PaintByExampleMapper(__magic_name__						)
						UpperCamelCase         =					nn.LayerNorm(config.hidden_size						)
						UpperCamelCase         =					nn.Linear(config.hidden_size       ,       self.proj_size						)

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





			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		int       ,       __magic_name__							:		Any=False						)     ->   Optional[int]:







						"""simple docstring"""







						UpperCamelCase         =					self.model(pixel_values=__magic_name__						)
						UpperCamelCase         =					clip_output.pooler_output
						UpperCamelCase         =					self.mapper(latent_states[:, None]						)
						UpperCamelCase         =					self.final_layer_norm(__magic_name__						)
						UpperCamelCase         =					self.proj_out(__magic_name__						)
						if return_uncond_vector:
									return latent_states, self.uncond_vector

						return latent_states




class    UpperCAmelCase					(       nn.Module    ):




			def __init__(						self							:		List[str]       ,       __magic_name__							:		Union[str, Any]						)     ->   str:







						"""simple docstring"""







						super().__init__()
						UpperCamelCase         =					(config.num_hidden_layers + 1) // 5
						UpperCamelCase         =					config.hidden_size
						UpperCamelCase         =					1
						UpperCamelCase         =					nn.ModuleList(
						    [
						        BasicTransformerBlock(__magic_name__       ,       __magic_name__       ,       __magic_name__       ,       activation_fn="""gelu"""       ,       attention_bias=__magic_name__						)
						        for _ in range(__magic_name__						)
						    ]						)





			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		Dict						)     ->   List[Any]:







						"""simple docstring"""







						for block in self.blocks:
									UpperCamelCase         =					block(__magic_name__						)

						return hidden_states








from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




