





import inspect
from typing import List, Optional, Tuple, Union

import torch

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


class 			lowerCamelCase						(		SCREAMING_SNAKE_CASE  ):







  def __init__(     self						:   Optional[Any]  ,					__snake_case						:   Optional[Any]  ,					__snake_case						:   Union[str, Any]  ,					__snake_case						:   Dict       )    ->		int:
        super().__init__()
        self.register_modules(vqvae=_UpperCamelCase  ,					unet=_UpperCamelCase  ,					scheduler=_UpperCamelCase       )







  @torch.no_grad()
  def __call__(     self						:   Dict  ,					__snake_case						:   int = 1  ,					__snake_case						:   Any = None  ,					__snake_case						:   str = 0.0  ,					__snake_case						:   Optional[Any] = 50  ,					__snake_case						:   Optional[int] = "pil"  ,					__snake_case						:   Union[str, Any] = True  ,					**__snake_case						:   Tuple  ,					)    ->		Union[Tuple, ImagePipelineOutput]:
        _a							:  List[str]				= randn_tensor(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)  ,					generator=_UpperCamelCase  ,					)
        _a							:  Union[str, Any]				= latents.to(self.device       )

        # scale the initial noise by the standard deviation required by the scheduler
        _a							:  Dict				= latents * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(_UpperCamelCase       )

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        _a							:  Union[str, Any]				= '''eta''' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

        _a							:  Tuple				= {}
        if accepts_eta:
              _a							:  Any				= eta

        for t in self.progress_bar(self.scheduler.timesteps       ):
              _a							:  Optional[int]				= self.scheduler.scale_model_input(_UpperCamelCase  ,					_UpperCamelCase       )
              # predict the noise residual
              _a							:  Dict				= self.unet(_UpperCamelCase  ,					_UpperCamelCase       ).sample
              # compute the previous noisy sample x_t -> x_t-1
              _a							:  Any				= self.scheduler.step(_UpperCamelCase  ,					_UpperCamelCase  ,					_UpperCamelCase  ,					**_UpperCamelCase       ).prev_sample

        # decode the image latents with the VAE
        _a							:  List[Any]				= self.vqvae.decode(_UpperCamelCase       ).sample

        _a							:  int				= (image / 2 + 0.5).clamp(0  ,					1       )
        _a							:  Any				= image.cpu().permute(0  ,					2  ,					3  ,					1       ).numpy()
        if output_type == "pil":
              _a							:  Optional[int]				= self.numpy_to_pil(_UpperCamelCase       )

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=_UpperCamelCase       )









'''simple docstring'''
from statistics import mean, stdev

def 					lowerCamelCase_ ( __UpperCamelCase					:       list							,    __UpperCamelCase					:       int = 3		)					->	list:







     """simple docstring"""
     _A				=							min(__UpperCamelCase		)
     _A				=							max(__UpperCamelCase		)
     # normalize data
     return [round((x - x_min) / (x_max - x_min)							,    __UpperCamelCase		) for x in data]

def 					lowerCamelCase_ ( __UpperCamelCase					:       list							,    __UpperCamelCase					:       int = 3		)					->	list:







     """simple docstring"""
     _A				=							mean(__UpperCamelCase		)
     _A				=							stdev(__UpperCamelCase		)
     # standardize data
     return [round((x - mu) / (sigma)							,    __UpperCamelCase		) for x in data]





