





import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[int]:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:      Any			  =UNetaDModel(
       block_out_channels=(32, 64)       , layers_per_block=2       , sample_size=32       , in_channels=3       , out_channels=3       , down_block_types=("DownBlock2D", "AttnDownBlock2D")       , up_block_types=("AttnUpBlock2D", "UpBlock2D")       , )
   return model







 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->int:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.dummy_uncond_unet
   lowerCamelCase__:      Any			  =KarrasVeScheduler()

   lowerCamelCase__:      int			  =KarrasVePipeline(unet=UpperCAmelCase_       , scheduler=UpperCAmelCase_)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:      str			  =torch.manual_seed(0)
   lowerCamelCase__:      int			  =pipe(num_inference_steps=2       , generator=UpperCAmelCase_       , output_type="numpy").images

   lowerCamelCase__:      List[str]			  =torch.manual_seed(0)
   lowerCamelCase__:      str			  =pipe(num_inference_steps=2       , generator=UpperCAmelCase_       , output_type="numpy"       , return_dict=UpperCAmelCase_)[0]

   lowerCamelCase__:      Any			  =image[0, -3:, -3:, -1]
   lowerCamelCase__:      Dict			  =image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   lowerCamelCase__:      Optional[int]			  =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2



@slow
@require_torch
class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->int:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  ="google/ncsnpp-celebahq-256"
   lowerCamelCase__:      Tuple			  =UNetaDModel.from_pretrained(UpperCAmelCase_)
   lowerCamelCase__:      Any			  =KarrasVeScheduler()

   lowerCamelCase__:      Optional[int]			  =KarrasVePipeline(unet=UpperCAmelCase_       , scheduler=UpperCAmelCase_)
   pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:      str			  =torch.manual_seed(0)
   lowerCamelCase__:      Any			  =pipe(num_inference_steps=20       , generator=UpperCAmelCase_       , output_type="numpy").images

   lowerCamelCase__:      Tuple			  =image[0, -3:, -3:, -1]
   assert image.shape == (1, 256, 256, 3)
   lowerCamelCase__:      Dict			  =np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2








import itertools
import math





def      lowerCAmelCase_ (       __a		)     ->		bool:







  """simple docstring"""





  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5	,    int(math.sqrt(__a		) + 1		)	,    6		):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True





def      lowerCAmelCase_ (       )     ->		str:







  """simple docstring"""





  lowerCamelCase__:      Optional[int]			  =2
  while True:
    if is_prime(__a		):
      yield num
    num += 1





def      lowerCAmelCase_ (       __a = 10001		)     ->		int:







  """simple docstring"""





  return next(itertools.islice(prime_generator()	,    nth - 1	,    __a		)		)


if __name__ == "__main__":
     print(f'{solution() = }')

