




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 			a__       (					SCREAMING_SNAKE_CASE_      ):
     a		:			UNetaDModel
     a		:			PNDMScheduler







     def __init__(	self  ,		A  ,		A )  ->    int:



          '''simple docstring'''



          super().__init__()

          a      =      PNDMScheduler.from_config(scheduler.config )

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







     @torch.no_grad()
     def __call__(	self  ,		A = 1  ,		A = 50  ,		A = None  ,		A = "pil"  ,		A = True  ,		**A  ,		)  ->    Union[ImagePipelineOutput, Tuple]:



          '''simple docstring'''



          a      =      randn_tensor(
              (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)  ,		generator=lowercase_  ,		device=self.device  ,		)

          self.scheduler.set_timesteps(lowercase_ )
          for t in self.progress_bar(self.scheduler.timesteps ):
               a      =      self.unet(lowercase_  ,		lowercase_ ).sample

               a      =      self.scheduler.step(lowercase_  ,		lowercase_  ,		lowercase_ ).prev_sample

          a      =      (image / 2 + 0.5).clamp(0  ,		1 )
          a      =      image.cpu().permute(0  ,		2  ,		3  ,		1 ).numpy()
          if output_type == "pil":
               a      =      self.numpy_to_pil(lowercase_ )

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=lowercase_ )



"""simple docstring"""

import doctest

import projectq
from projectq.ops import H, Measure




def 			lowercase__  (    lowerCAmelCase :    projectq.cengines._main.MainEngine       )       ->					int:






    """simple docstring"""




    UpperCAmelCase  	=    quantum_engine.allocate_qubit()
    H | qubit
    Measure | qubit
    return int(lowerCAmelCase       )


if __name__ == "__main__":
   doctest.testmod()

   # initialises a new quantum backend
   SCREAMING_SNAKE_CASE_			    =  projectq.MainEngine()

   # Generate a list of 10 random numbers
   SCREAMING_SNAKE_CASE_			    =  [get_random_number(quantum_engine) for _ in range(10)]

   # Flushes the quantum engine from memory
   quantum_engine.flush()

   print('''Random numbers''', random_numbers_list)



