code: import unittest
  
  import torch
  
  from diffusers import DDIMScheduler, TextToVideoZeroPipeline
  from diffusers.utils import load_pt, require_torch_gpu, slow
  
  from ..test_pipelines_common import assert_mean_pixel_difference
  
  
  
  
  @slow
  @require_torch_gpu
  class    __lowerCAmelCase  (       unittest.TestCase						):
  
  
  
   '''simple docstring'''
  
   def 					__snake_case					(			self       :		List[str]  ) ->							int:
          _UpperCamelCase     :       Dict  						=  'runwayml/stable-diffusion-v1-5'
          _UpperCamelCase     :       int  						=  TextToVideoZeroPipeline.from_pretrained(lowerCamelCase       ,			torch_dtype=torch.floataa  ).to('cuda'  )
          _UpperCamelCase     :       Optional[Any]  						=  DDIMScheduler.from_config(pipe.scheduler.config  )
          _UpperCamelCase     :       Dict  						=  torch.Generator(device='cuda'  ).manual_seed(0  )
  
          _UpperCamelCase     :       List[str]  						=  'A bear is playing a guitar on Times Square'
          _UpperCamelCase     :       Tuple  						=  pipe(prompt=lowerCamelCase       ,			generator=lowerCamelCase  ).images
  
          _UpperCamelCase     :       int  						=  load_pt(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'  )
  
          assert_mean_pixel_difference(lowerCamelCase       ,			lowerCamelCase  )
  
  
  
  
style_context: import math
  from collections.abc import Generator
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
         _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)				))
         for i in (n for n in numbers if n > 1):
                for j in range(2		,						lowercase_				):
                       if (i % j) == 0:
                              break
                else:
                       yield i
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
         _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)				))
         for i in (n for n in numbers if n > 1):
                # only need to check for factors up to sqrt(i)
                _UpperCamelCase     :       Any  						=  int(math.sqrt(lowercase_				)				) + 1
                for j in range(2		,						lowercase_				):
                       if (i % j) == 0:
                              break
                else:
                       yield i
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
         _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)		,						2				))
         # It's useless to test even numbers as they will not be prime
         if max_n > 2:
                yield 2  # Because 2 will not be tested, it's necessary to yield it now
         for i in (n for n in numbers if n > 1):
                _UpperCamelCase     :       Tuple  						=  int(math.sqrt(lowercase_				)				) + 1
                for j in range(3		,						lowercase_		,						2				):
                       # As we removed the even numbers, we don't need them now
                       if (i % j) == 0:
                              break
                else:
                       yield i
  
  
  def SCREAMING_SNAKE_CASE				(     ):
         from timeit import timeit
  
         _UpperCamelCase     :       Optional[Any]  						=  'from __main__ import slow_primes, primes, fast_primes'
         print(timeit('slow_primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
         print(timeit('primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
         print(timeit('fast_primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				int(input('''Calculate primes up to:\n>> ''').strip())
   for ret in primes(number):
    print(ret)
   benchmark()
  
  
  
  