





"""simple docstring"""





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 		lowercase					(					unittest.TestCase					):





      @property
      def     _snake_case			(       self )		->     str:
            torch.manual_seed(0 )
            lowerCAmelCase  						=       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     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       self.dummy_uncond_unet
            lowerCAmelCase  						=       ScoreSdeVeScheduler()

            lowerCAmelCase  						=       ScoreSdeVePipeline(unet=__a      ,						scheduler=__a )
            sde_ve.to(__a )
            sde_ve.set_progress_bar_config(disable=__a )

            lowerCAmelCase  						=       torch.manual_seed(0 )
            lowerCAmelCase  						=       sde_ve(num_inference_steps=2      ,						output_type="""numpy"""      ,						generator=__a ).images

            lowerCAmelCase  						=       torch.manual_seed(0 )
            lowerCAmelCase  						=       sde_ve(num_inference_steps=2      ,						output_type="""numpy"""      ,						generator=__a      ,						return_dict=__a )[
                0
            ]

            lowerCAmelCase  						=       image[0, -3:, -3:, -1]
            lowerCAmelCase  						=       image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            lowerCAmelCase  						=       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 		lowercase					(					unittest.TestCase					):





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       """google/ncsnpp-church-256"""
            lowerCAmelCase  						=       UNetaDModel.from_pretrained(__a )

            lowerCAmelCase  						=       ScoreSdeVeScheduler.from_pretrained(__a )

            lowerCAmelCase  						=       ScoreSdeVePipeline(unet=__a      ,						scheduler=__a )
            sde_ve.to(__a )
            sde_ve.set_progress_bar_config(disable=__a )

            lowerCAmelCase  						=       torch.manual_seed(0 )
            lowerCAmelCase  						=       sde_ve(num_inference_steps=10      ,						output_type="""numpy"""      ,						generator=__a ).images

            lowerCAmelCase  						=       image[0, -3:, -3:, -1]

            assert image.shape == (1, 256, 256, 3)

            lowerCAmelCase  						=       np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )

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














import math
def 					snake_case_						(   lowerCAmelCase_				:  int					):

							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(lowerCAmelCase_					) + 1					)				,				6					):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True
def 					snake_case_						(   lowerCAmelCase_				:  int = 10001					):

							try:
														__lowercase					:							Optional[int]			=   int(lowerCAmelCase_					)
							except (TypeError, ValueError):
														raise TypeError("""Parameter nth must be int or castable to int."""					) from None
							if nth <= 0:
														raise ValueError("""Parameter nth must be greater than or equal to one."""					)
							__lowercase					:							list[int]			=   []
							__lowercase					:							str			=   2
							while len(lowerCAmelCase_					) < nth:
														if is_prime(lowerCAmelCase_					):
																					primes.append(lowerCAmelCase_					)
																					num += 1
														else:
																					num += 1
							return primes[len(lowerCAmelCase_					) - 1]


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