



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     a__					(      unittest.TestCase   ):






						@property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											torch.manual_seed(0    )
											__lowerCAmelCase            = UNetaDModel(
											    block_out_channels=(3_2, 6_4)	,    layers_per_block=2	,    sample_size=3_2	,    in_channels=3	,    out_channels=3	,    down_block_types=("DownBlock2D", "AttnDownBlock2D")	,    up_block_types=("AttnUpBlock2D", "UpBlock2D")	,    )
											return model






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.dummy_uncond_unet
											__lowerCAmelCase            = KarrasVeScheduler()

											__lowerCAmelCase            = KarrasVePipeline(unet=_a	,    scheduler=_a    )
											pipe.to(_a    )
											pipe.set_progress_bar_config(disable=_a    )

											__lowerCAmelCase            = torch.manual_seed(0    )
											__lowerCAmelCase            = pipe(num_inference_steps=2	,    generator=_a	,    output_type="numpy"    ).images

											__lowerCAmelCase            = torch.manual_seed(0    )
											__lowerCAmelCase            = pipe(num_inference_steps=2	,    generator=_a	,    output_type="numpy"	,    return_dict=_a    )[0]

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

											assert image.shape == (1, 3_2, 3_2, 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     a__					(      unittest.TestCase   ):






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = """google/ncsnpp-celebahq-256"""
											__lowerCAmelCase            = UNetaDModel.from_pretrained(_a    )
											__lowerCAmelCase            = KarrasVeScheduler()

											__lowerCAmelCase            = KarrasVePipeline(unet=_a	,    scheduler=_a    )
											pipe.to(_a    )
											pipe.set_progress_bar_config(disable=_a    )

											__lowerCAmelCase            = torch.manual_seed(0    )
											__lowerCAmelCase            = pipe(num_inference_steps=2_0	,    generator=_a	,    output_type="numpy"    ).images

											__lowerCAmelCase            = image[0, -3:, -3:, -1]
											assert image.shape == (1, 2_5_6, 2_5_6, 3)
											__lowerCAmelCase            = np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86]    )

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










from math import sqrt







def 			_a   (		SCREAMING_SNAKE_CASE_							:       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(sqrt(SCREAMING_SNAKE_CASE_  ) + 1  )   ,   6  ):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int = 1_00_01  ):
					__lowerCAmelCase            = 0
					__lowerCAmelCase            = 1
					while count != nth and number < 3:
										number += 1
										if is_prime(SCREAMING_SNAKE_CASE_  ):
															count += 1
					while count != nth:
										number += 2
										if is_prime(SCREAMING_SNAKE_CASE_  ):
															count += 1
					return number


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




