


"""simple docstring"""





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 		snake_case_(       a__			):
     __UpperCamelCase  =					42
     __UpperCamelCase  =					42






     def __init__(	self  :					int					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					int				):
           super().__init__()
           self.register_modules(unet=lowercase__					,				scheduler=lowercase__				)






     @torch.no_grad()
     def __call__(	self  :					List[str]					,				UpperCamelCase_  :					List[Any] = 1					,				UpperCamelCase_  :					Union[str, Any] = 2_0_0_0					,				UpperCamelCase_  :					List[Any] = None					,				UpperCamelCase_  :					List[Any] = "pil"					,				UpperCamelCase_  :					Optional[int] = True					,				**UpperCamelCase_  :					Tuple					,				):
           lowerCAmelCase					:	Optional[int]       =   self.unet.config.sample_size
           lowerCAmelCase					:	int       =   (batch_size, 3, img_size, img_size)

           lowerCAmelCase					:	Dict       =   self.unet

           lowerCAmelCase					:	List[str]       =   randn_tensor(lowercase__					,				generator=lowercase__				) * self.scheduler.init_noise_sigma
           lowerCAmelCase					:	Tuple       =   sample.to(self.device				)

           self.scheduler.set_timesteps(lowercase__				)
           self.scheduler.set_sigmas(lowercase__				)

           for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
                 lowerCAmelCase					:	Dict       =   self.scheduler.sigmas[i] * torch.ones(shape[0]					,				device=self.device				)

                 # correction step
                 for _ in range(self.scheduler.config.correct_steps				):
                       lowerCAmelCase					:	Dict       =   self.unet(lowercase__					,				lowercase__				).sample
                       lowerCAmelCase					:	Dict       =   self.scheduler.step_correct(lowercase__					,				lowercase__					,				generator=lowercase__				).prev_sample

                 # prediction step
                 lowerCAmelCase					:	int       =   model(lowercase__					,				lowercase__				).sample
                 lowerCAmelCase					:	List[Any]       =   self.scheduler.step_pred(lowercase__					,				lowercase__					,				lowercase__					,				generator=lowercase__				)

                 lowerCAmelCase					:	List[Any]       =   output.prev_sample, output.prev_sample_mean

           lowerCAmelCase					:	List[str]       =   sample_mean.clamp(0					,				1				)
           lowerCAmelCase					:	List[Any]       =   sample.cpu().permute(0					,				2					,				3					,				1				).numpy()
           if output_type == "pil":
                 lowerCAmelCase					:	Any       =   self.numpy_to_pil(lowercase__				)

           if not return_dict:
                 return (sample,)

           return ImagePipelineOutput(images=lowercase__				)






"""simple docstring"""





from torch import nn



class 		snake_case_(       nn.Module			):






     def __init__(	self  :					int					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					int				):
           super().__init__()
           lowerCAmelCase					:	str       =   class_size
           lowerCAmelCase					:	Dict       =   embed_size
           # self.mlp1 = nn.Linear(embed_size, embed_size)
           # self.mlp2 = (nn.Linear(embed_size, class_size))
           lowerCAmelCase					:	Any       =   nn.Linear(UpperCamelCase_					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					Tuple				):
           # hidden_state = nn.functional.relu(self.mlp1(hidden_state))
           # hidden_state = self.mlp2(hidden_state)
           lowerCAmelCase					:	int       =   self.mlp(UpperCamelCase_				)
           return logits


