






'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

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


class     _UpperCAmelCase  (						_lowerCamelCase     ):
    __lowerCamelCase:				int						    =      42
    __lowerCamelCase:				int						    =      42




    def __init__(     self					:    Union[str, Any]					,       a					:    List[str]					,       a					:    str     ):






       '''simple docstring'''






       super().__init__()

       lowercase_		:      List[str]          =       PNDMScheduler.from_config(scheduler.config     )

       self.register_modules(unet=__UpperCamelCase					,       scheduler=__UpperCamelCase     )





    @torch.no_grad()
    def __call__(     self					:    Tuple					,       a					:    Optional[Any] = 1					,       a					:    Optional[Any] = 5_0					,       a					:    Union[str, Any] = None					,       a					:    Any = "pil"					,       a					:    str = True					,       **a					:    Optional[int]					,       ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,       generator=__UpperCamelCase					,       device=self.device					,       )

       self.scheduler.set_timesteps(__UpperCamelCase     )
       for t in self.progress_bar(self.scheduler.timesteps     ):
          lowercase_		:      Optional[int]          =       self.unet(__UpperCamelCase					,       __UpperCamelCase     ).sample

          lowercase_		:      Optional[Any]          =       self.scheduler.step(__UpperCamelCase					,       __UpperCamelCase					,       __UpperCamelCase     ).prev_sample

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

       if not return_dict:
          return (image,)

       return ImagePipelineOutput(images=__UpperCamelCase     )












'''simple docstring'''
def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   sequence.append(_UpperCamelCase							)

   lowercase_		:      Union[str, Any]          =       0
   while sequence[index] != target:
      index += 1

   sequence.pop()

   if index == len(_UpperCamelCase							):
      return None

   return index


if __name__ == "__main__":
 UpperCamelCase__	=     input('Enter numbers separated by comma:\n').strip()
 UpperCamelCase__	=     [int(item) for item in user_input.split(',')]

 UpperCamelCase__	=     input('Enter a single number to be found in the list:\n')
 UpperCamelCase__	=     int(target_input)
 UpperCamelCase__	=     sentinel_linear_search(sequence, target)
 if result is not None:
  print(f"""{target} found at positions: {result}""")
 else:
  print('Not found')




