




import math
from timeit import timeit


def 		_lowerCamelCase				(					UpperCamelCase							)			->		int:





  """simple docstring"""





  _lowercase: Tuple						     =    0
  _lowercase: Union[str, Any]						     =    abs(snake_case__							)
  while True:
    _lowercase: Dict						     =    n // 10
    digits += 1
    if n == 0:
      break
  return digits


def 		_lowerCamelCase				(					UpperCamelCase							)			->		int:





  """simple docstring"""





  return 1 if n == 0 else math.floor(math.log(abs(snake_case__							)					,     10							) + 1							)


def 		_lowerCamelCase				(					UpperCamelCase							)			->		int:





  """simple docstring"""





  return len(str(abs(snake_case__							)							)							)


def 		_lowerCamelCase				(					)			->		None:





  """simple docstring"""





  from collections.abc import Callable

  def benchmark_a_function(UpperCamelCase					,     UpperCamelCase							) -> None:
    _lowercase: Any						     =    F'{func.__name__}({value})'
    _lowercase: List[str]						     =    timeit(F'__main__.{call}'					,     setup='import __main__'							)
    print(F'{call}: {func(snake_case__							)} -- {timing} seconds'							)

  for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
    for func in (num_digits, num_digits_fast, num_digits_faster):
      benchmark_a_function(snake_case__					,     snake_case__							)
    print()


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    image.to(self.device     )

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

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

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       _lowercase: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

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

     if not return_dict:
       return (image,), "This is a local test"

     return ImagePipelineOutput(images=UpperCamelCase_     ), "This is a local test"
