



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 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	42
 lowerCAmelCase__							       =	42




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase					):



    '''simple docstring'''
    super().__init__()
    self.register_modules(unet=__UpperCAmelCase						,					scheduler=__UpperCAmelCase					)




 @torch.no_grad()
 def __call__(    self						,					__UpperCAmelCase = 1						,					__UpperCAmelCase = 2000						,					__UpperCAmelCase = None						,					__UpperCAmelCase = "pil"						,					__UpperCAmelCase = True						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.unet.config.sample_size
    __lowerCamelCase    				=     (batch_size, 3, img_size, img_size)

    __lowerCamelCase    				=     self.unet

    __lowerCamelCase    				=     randn_tensor(__UpperCAmelCase						,					generator=__UpperCAmelCase					) * self.scheduler.init_noise_sigma
    __lowerCamelCase    				=     sample.to(self.device					)

    self.scheduler.set_timesteps(__UpperCAmelCase					)
    self.scheduler.set_sigmas(__UpperCAmelCase					)

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

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

       # prediction step
       __lowerCamelCase    				=     model(__UpperCAmelCase						,					__UpperCAmelCase					).sample
       __lowerCamelCase    				=     self.scheduler.step_pred(__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					generator=__UpperCAmelCase					)

       __lowerCamelCase				,__lowerCamelCase    				=     output.prev_sample, output.prev_sample_mean

    __lowerCamelCase    				=     sample_mean.clamp(0						,					1					)
    __lowerCamelCase    				=     sample.cpu().permute(0						,					2						,					3						,					1					).numpy()
    if output_type == "pil":
       __lowerCamelCase    				=     self.numpy_to_pil(__UpperCAmelCase					)

    if not return_dict:
       return (sample,)

    return ImagePipelineOutput(images=__UpperCAmelCase					)











def 		a__      (		_UpperCamelCase      :			str				):
   if not all(x.isalpha() for x in string				):
      raise ValueError('''String must only contain alphabetic characters.'''				)

   __lowerCamelCase    				=     sorted(string.lower()				)
   return len(_UpperCamelCase				) == len(set(_UpperCamelCase				)				)


if __name__ == "__main__":
  a_								=       input("""Enter a string """).strip()

  a_								=       is_isogram(input_str)
  print(f"{input_str} is {'an' if isogram else 'not an'} isogram.")






