

"""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 							__snake_case	(						self       )      ->      Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Union[str, Any]       =   """runwayml/stable-diffusion-v1-5"""
				_UpperCAmelCase      :							Union[str, Any]       =   TextToVideoZeroPipeline.from_pretrained(_A ,						torch_dtype=torch.floataa       ).to("""cuda"""       )
				_UpperCAmelCase      :							List[str]       =   DDIMScheduler.from_config(pipe.scheduler.config       )
				_UpperCAmelCase      :							int       =   torch.Generator(device="""cuda"""       ).manual_seed(0       )

				_UpperCAmelCase      :							Union[str, Any]       =   """A bear is playing a guitar on Times Square"""
				_UpperCAmelCase      :							List[str]       =   pipe(prompt=_A ,						generator=_A       ).images

				_UpperCAmelCase      :							List[Any]       =   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(_A ,						_A       )








"""simple docstring"""






from math import factorial
def  UpperCamelCase   (     _lowerCAmelCase       :  Dict,					_lowerCAmelCase       :  List[str]	)			->     Dict:
	return factorial(_lowerCAmelCase	) / (factorial(_lowerCAmelCase	) * factorial(n - r	))
def  UpperCamelCase   (     )			->     Tuple:
	_UpperCAmelCase      :							Optional[int]       =   0

	for i in range(1,					101	):
		for j in range(1,					i + 1	):
			if combinations(_lowerCAmelCase,					_lowerCAmelCase	) > 1E6:
				total += 1
	return total


if __name__ == "__main__":
						print(solution())





