





from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class A_      (    _lowerCamelCase     ):




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






   @torch.no_grad()
   def __call__(self			:List[Any]					,     _UpperCamelCase			:int = 1					,     _UpperCamelCase			:Optional[Union[torch.Generator, List[torch.Generator]]] = None					,     _UpperCamelCase			:int = 1000					,     _UpperCamelCase			:Optional[str] = "pil"					,     _UpperCamelCase			:bool = True					,     )->      Union[ImagePipelineOutput, Tuple]:
         # Sample gaussian noise to begin loop
         if isinstance(self.unet.config.sample_size					,     _UpperCamelCase				):
               __A      =       (
                   batch_size,
                   self.unet.config.in_channels,
                   self.unet.config.sample_size,
                   self.unet.config.sample_size,
               )
         else:
               __A      =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

         if self.device.type == "mps":
               # randn does not work reproducibly on mps
               __A      =       randn_tensor(_UpperCamelCase					,     generator=_UpperCamelCase				)
               __A      =       image.to(self.device				)
         else:
               __A      =       randn_tensor(_UpperCamelCase					,     generator=_UpperCamelCase					,     device=self.device				)

         # set step values
         self.scheduler.set_timesteps(_UpperCamelCase				)

         for t in self.progress_bar(self.scheduler.timesteps				):
               # 1. predict noise model_output
               __A      =       self.unet(_UpperCamelCase					,     _UpperCamelCase				).sample

               # 2. compute previous image: x_t -> x_t-1
               __A      =       self.scheduler.step(_UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase					,     generator=_UpperCamelCase				).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(_UpperCamelCase				)

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=_UpperCamelCase				)












import unittest
from timeit import timeit



def _a       (			lowerCamelCase:     int      ,       lowerCamelCase:     int				)			->		int:







      '''simple docstring'''






      __A      =       first_num if first_num >= second_num else second_num
      __A      =       max_num
      while (common_mult % first_num > 0) or (common_mult % second_num > 0):
            common_mult += max_num
      return common_mult



def _a       (			lowerCamelCase:     int      ,       lowerCamelCase:     int				)			->		int:







      '''simple docstring'''






      return b if a == 0 else greatest_common_divisor(b % a      ,       lowerCamelCase				)



def _a       (			lowerCamelCase:     int      ,       lowerCamelCase:     int				)			->		int:







      '''simple docstring'''






      return first_num // greatest_common_divisor(lowerCamelCase      ,       lowerCamelCase				) * second_num



def _a       (			)			->		Tuple:







      '''simple docstring'''






      __A      =       (
          '''from __main__ import least_common_multiple_slow, least_common_multiple_fast'''
      )
      print(
          '''least_common_multiple_slow():'''      ,       timeit('''least_common_multiple_slow(1000, 999)'''      ,       setup=lowerCamelCase				)      ,       )
      print(
          '''least_common_multiple_fast():'''      ,       timeit('''least_common_multiple_fast(1000, 999)'''      ,       setup=lowerCamelCase				)      ,       )





class A_      (    unittest.TestCase     ):
   lowerCAmelCase__       =				(
       (10, 20),
       (13, 15),
       (4, 31),
       (10, 42),
       (43, 34),
       (5, 12),
       (12, 25),
       (10, 25),
       (6, 9),
   )
   lowerCAmelCase__       =				(20, 195, 124, 210, 1462, 60, 300, 50, 18)




   def _lowerCAmelCase   (self			:List[str]				)->      Union[str, Any]:
         for i, (first_num, second_num) in enumerate(self.test_inputs				):
               __A      =       least_common_multiple_slow(_UpperCamelCase					,     _UpperCamelCase				)
               __A      =       least_common_multiple_fast(_UpperCamelCase					,     _UpperCamelCase				)
               with self.subTest(i=_UpperCamelCase				):
                     self.assertEqual(_UpperCamelCase					,     self.expected_results[i]				)
                     self.assertEqual(_UpperCamelCase					,     self.expected_results[i]				)


if __name__ == "__main__":
       benchmark()
       unittest.main()





