






'''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 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: UNetaDModel
    __lowerCamelCase: ScoreSdeVeScheduler




    def __init__(      self	:							int	,  a	:							UNetaDModel	,  a	:							ScoreSdeVeScheduler      ):






       '''simple docstring'''






       super().__init__()
       self.register_modules(unet=a	,  scheduler=a      )





    @torch.no_grad()
    def __call__(      self	:							str	,  a	:							int = 1	,  a	:							int = 2_0_0_0	,  a	:							Optional[Union[torch.Generator, List[torch.Generator]]] = None	,  a	:							Optional[str] = "pil"	,  a	:							bool = True	,  **a	:							str	,  ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  self.unet.config.sample_size
       lowercase_						:     Tuple							     =  (batch_size, 3, img_size, img_size)

       lowercase_						:     Optional[Any]							     =  self.unet

       lowercase_						:     List[str]							     =  randn_tensor(a	,  generator=a      ) * self.scheduler.init_noise_sigma
       lowercase_						:     Dict							     =  sample.to(self.device      )

       self.scheduler.set_timesteps(a      )
       self.scheduler.set_sigmas(a      )

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

          # correction step
          for _ in range(self.scheduler.config.correct_steps      ):
             lowercase_						:     int							     =  self.unet(a	,  a      ).sample
             lowercase_						:     Optional[Any]							     =  self.scheduler.step_correct(a	,  a	,  generator=a      ).prev_sample

          # prediction step
          lowercase_						:     Any							     =  model(a	,  a      ).sample
          lowercase_						:     Dict							     =  self.scheduler.step_pred(a	,  a	,  a	,  generator=a      )

          lowercase_     ,		lowercase_						:     List[str]							     =  output.prev_sample, output.prev_sample_mean

       lowercase_						:     Tuple							     =  sample_mean.clamp(0	,  1      )
       lowercase_						:     Optional[int]							     =  sample.cpu().permute(0	,  2	,  3	,  1      ).numpy()
       if output_type == "pil":
          lowercase_						:     Any							     =  self.numpy_to_pil(a      )

       if not return_dict:
          return (sample,)

       return ImagePipelineOutput(images=a      )












'''simple docstring'''
UpperCamelCase__			=      {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 1000000,
    "gigajoule": 1000000000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 3600000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 4186800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1055.05585,
    "footpound": 1.35_58_18,
}




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
      lowercase_						:     Union[str, Any]							     =  (
          F"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
          F"""Valid values are: {", ".join(_UpperCamelCase				)}"""
      )
      raise ValueError(_UpperCamelCase				)
   return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




