
'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 						a   (				__magic_name__				,__magic_name__				,unittest.TestCase    ):
				_snake_case		    = StableDiffusionSAGPipeline
				_snake_case		    = TEXT_TO_IMAGE_PARAMS
				_snake_case		    = TEXT_TO_IMAGE_BATCH_PARAMS
				_snake_case		    = TEXT_TO_IMAGE_IMAGE_PARAMS
				_snake_case		    = TEXT_TO_IMAGE_IMAGE_PARAMS
				_snake_case		    = False




				def 	__snake_case							(							self	:				Optional[Any]      ):
										torch.manual_seed(0      )
										snake_case       :     Dict =    UNetaDConditionModel(
										    block_out_channels=(32, 64),    layers_per_block=2,    sample_size=32,    in_channels=4,    out_channels=4,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),    cross_attention_dim=32,    )
										snake_case       :     List[str] =    DDIMScheduler(
										    beta_start=0.00085,    beta_end=0.012,    beta_schedule='''scaled_linear''',    clip_sample=SCREAMING_SNAKE_CASE_,    set_alpha_to_one=SCREAMING_SNAKE_CASE_,    )
										torch.manual_seed(0      )
										snake_case       :     Optional[Any] =    AutoencoderKL(
										    block_out_channels=[32, 64],    in_channels=3,    out_channels=3,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],    latent_channels=4,    )
										torch.manual_seed(0      )
										snake_case       :     int =    CLIPTextConfig(
										    bos_token_id=0,    eos_token_id=2,    hidden_size=32,    intermediate_size=37,    layer_norm_eps=1e-05,    num_attention_heads=4,    num_hidden_layers=5,    pad_token_id=1,    vocab_size=10_00,    )
										snake_case       :     Dict =    CLIPTextModel(SCREAMING_SNAKE_CASE_      )
										snake_case       :     str =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

										snake_case       :     str =    {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[int]=0      ):
										if str(SCREAMING_SNAKE_CASE_      ).startswith('''mps'''      ):
																snake_case       :     Optional[Any] =    torch.manual_seed(SCREAMING_SNAKE_CASE_      )
										else:
																snake_case       :     int =    torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(SCREAMING_SNAKE_CASE_      )
										snake_case       :     int =    {
										    '''prompt''': '''.''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 1.0,
										    '''sag_scale''': 1.0,
										    '''output_type''': '''numpy''',
										}
										return inputs




				def 	__snake_case							(							self	:				List[Any]      ):
										super().test_inference_batch_single_identical(expected_max_diff=3e-3      )




@slow
@require_torch_gpu
class 						a   (				unittest.TestCase    ):




				def 	__snake_case							(							self	:				Union[str, Any]      ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     List[Any] =    StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''      )
										snake_case       :     Tuple =    sag_pipe.to(SCREAMING_SNAKE_CASE_      )
										sag_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     List[Any] =    '''.'''
										snake_case       :     Tuple =    torch.manual_seed(0      )
										snake_case       :     Optional[int] =    sag_pipe(
										    [prompt],    generator=SCREAMING_SNAKE_CASE_,    guidance_scale=7.5,    sag_scale=1.0,    num_inference_steps=20,    output_type='''np'''      )

										snake_case       :     Union[str, Any] =    output.images

										snake_case       :     int =    image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										snake_case       :     int =    np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-2




				def 	__snake_case							(							self	:				str      ):
										snake_case       :     Optional[Any] =    StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''      )
										snake_case       :     Dict =    sag_pipe.to(SCREAMING_SNAKE_CASE_      )
										sag_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     Tuple =    '''.'''
										snake_case       :     Union[str, Any] =    torch.manual_seed(0      )
										snake_case       :     str =    sag_pipe(
										    [prompt],    generator=SCREAMING_SNAKE_CASE_,    guidance_scale=7.5,    sag_scale=1.0,    num_inference_steps=20,    output_type='''np'''      )

										snake_case       :     Union[str, Any] =    output.images

										snake_case       :     Optional[int] =    image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										snake_case       :     str =    np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-2




				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     Tuple =    StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''      )
										snake_case       :     int =    sag_pipe.to(SCREAMING_SNAKE_CASE_      )
										sag_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     str =    '''.'''
										snake_case       :     List[Any] =    torch.manual_seed(0      )
										snake_case       :     Tuple =    sag_pipe(
										    [prompt],    width=7_68,    height=5_12,    generator=SCREAMING_SNAKE_CASE_,    guidance_scale=7.5,    sag_scale=1.0,    num_inference_steps=20,    output_type='''np''',    )

										snake_case       :     Optional[int] =    output.images

										assert image.shape == (1, 5_12, 7_68, 3)



'''simple docstring'''





from __future__ import annotations

import math

def      A (       A_							:					int       ):

						if num <= 0:
												snake_case       :     List[Any] =    F"""{num}: Invalid input, please enter a positive integer."""
												raise ValueError(A_       )

						snake_case       :     Optional[Any] =    [True] * (num + 1)
						snake_case       :     List[str] =    []
						snake_case       :     List[Any] =    2
						snake_case       :     str =    int(math.sqrt(A_       )       )

						while start <= end:
												# If start is a prime
												if sieve[start] is True:
																		prime.append(A_       )

																		# Set multiples of start be False
																		for i in range(start * start    ,							num + 1    ,							A_       ):
																								if sieve[i] is True:
																														snake_case       :     int =    False

												start += 1

						for j in range(end + 1    ,							num + 1       ):
												if sieve[j] is True:
																		prime.append(A_       )

						return prime


if __name__ == "__main__":
						print(prime_sieve(int(input("Enter a positive integer: ").strip())))

