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  )




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()



