code: 
  
  """simple docstring"""
  
  
  
  
  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     __UpperCamelCase				(     unittest.TestCase		):
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
  							A    =      "runwayml/stable-diffusion-v1-5"
  							A    =      TextToVideoZeroPipeline.from_pretrained(__SCREAMING_SNAKE_CASE  ,  torch_dtype=torch.floataa).to("cuda")
  							A    =      DDIMScheduler.from_config(pipe.scheduler.config)
  							A    =      torch.Generator(device="cuda").manual_seed(0)
  
  							A    =      "A bear is playing a guitar on Times Square"
  							A    =      pipe(prompt=__SCREAMING_SNAKE_CASE  ,  generator=__SCREAMING_SNAKE_CASE).images
  
  							A    =      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(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if not isinstance(lowercase__    ,							lowercase__   ):
  										raise ValueError("double_factorial() only accepts integral values"   )
  					if n < 0:
  										raise ValueError("double_factorial() not defined for negative values"   )
  					return 1 if n <= 1 else n * double_factorial(n - 2   )
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  