




def 						__A						(      __lowerCamelCase       ) ->			list[int]:

	if num <= 0:
		raise ValueError("""Input must be a positive integer"""       )

	a									=				[True] * (num + 1)

	a									=				2
	while p * p <= num:
		if primes[p]:
			for i in range(p * p				,     num + 1				,     __lowerCamelCase       ):
				a									=				False
		p += 1

	return [prime for prime in range(2				,     num + 1       ) if primes[prime]]


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__UpperCamelCase   :			List[Any]          = int(input("Enter a positive integer: ").strip())
		print(prime_sieve_eratosthenes(user_num))







import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





def 						__A						(      __lowerCamelCase       ) ->			Optional[int]:
	a									=				[tensor.shape for tensor in tensor_list]
	return all(shape == shapes[0] for shape in shapes[1:]       )

class 						__lowerCAmelCase      (      __magic_name__ ,			__magic_name__ ,			__magic_name__ ,			unittest.TestCase					):
							UpperCamelCase__   	=    StableDiffusionLatentUpscalePipeline
							UpperCamelCase__   	=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    '''height''',
							    '''width''',
							    '''cross_attention_kwargs''',
							    '''negative_prompt_embeds''',
							    '''prompt_embeds''',
							}
							UpperCamelCase__   	=    PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
							UpperCamelCase__   	=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							UpperCamelCase__   	=    frozenset(
							    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							UpperCamelCase__   	=    frozenset([]					)

							UpperCamelCase__   	=    True
							@property
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				1
								a									=				4
								a									=				(16, 16)

								a									=				floats_tensor((batch_size, num_channels) + sizes						, rng=random.Random(0							)							).to(__magic_name__							)
								return image
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								torch.manual_seed(0							)
								a									=				UNetaDConditionModel(
								    act_fn="""gelu"""						, attention_head_dim=8						, norm_num_groups=__magic_name__						, block_out_channels=[32, 32, 64, 64]						, time_cond_proj_dim=160						, conv_in_kernel=1						, conv_out_kernel=1						, cross_attention_dim=32						, down_block_types=(
								        """KDownBlock2D""",
								        """KCrossAttnDownBlock2D""",
								        """KCrossAttnDownBlock2D""",
								        """KCrossAttnDownBlock2D""",
								    )						, in_channels=8						, mid_block_type=__magic_name__						, only_cross_attention=__magic_name__						, out_channels=5						, resnet_time_scale_shift="""scale_shift"""						, time_embedding_type="""fourier"""						, timestep_post_act="""gelu"""						, up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")						, )
								a									=				AutoencoderKL(
								    block_out_channels=[32, 32, 64, 64]						, in_channels=3						, out_channels=3						, down_block_types=[
								        """DownEncoderBlock2D""",
								        """DownEncoderBlock2D""",
								        """DownEncoderBlock2D""",
								        """DownEncoderBlock2D""",
								    ]						, up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]						, latent_channels=4						, )
								a									=				EulerDiscreteScheduler(prediction_type="""sample"""							)
								a									=				CLIPTextConfig(
								    bos_token_id=0						, eos_token_id=2						, hidden_size=32						, intermediate_size=37						, layer_norm_eps=1E-0_5						, num_attention_heads=4						, num_hidden_layers=5						, pad_token_id=1						, vocab_size=1000						, hidden_act="""quick_gelu"""						, projection_dim=512						, )
								a									=				CLIPTextModel(__magic_name__							)
								a									=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

								a									=				{
								    """unet""": model.eval(),
								    """vae""": vae.eval(),
								    """scheduler""": scheduler,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								}

								return components
							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:Tuple						, __magic_name__		:Optional[int]=0							):







								'''simple docstring'''







								if str(__magic_name__							).startswith("""mps"""							):
									a									=				torch.manual_seed(__magic_name__							)
								else:
									a									=				torch.Generator(device=__magic_name__							).manual_seed(__magic_name__							)
								a									=				{
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """image""": self.dummy_image.cpu(),
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """output_type""": """numpy""",
								}
								return inputs
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				"""cpu"""

								a									=				self.get_dummy_components()
								a									=				self.pipeline_class(**__magic_name__							)
								pipe.to(__magic_name__							)
								pipe.set_progress_bar_config(disable=__magic_name__							)

								a									=				self.get_dummy_inputs(__magic_name__							)
								a									=				pipe(**__magic_name__							).images
								a									=				image[0, -3:, -3:, -1]

								self.assertEqual(image.shape						, (1, 256, 256, 3)							)
								a									=				np.array(
								    [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]							)
								a									=				np.abs(image_slice.flatten() - expected_slice							).max()
								self.assertLessEqual(__magic_name__						, 1E-3							)
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								super().test_attention_slicing_forward_pass(expected_max_diff=7E-3							)
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								super().test_cpu_offload_forward_pass(expected_max_diff=3E-3							)
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3							)
							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								super().test_inference_batch_single_identical(expected_max_diff=7E-3							)
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3							)
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								super().test_save_load_local(expected_max_difference=3E-3							)
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								super().test_save_load_optional_components(expected_max_difference=3E-3							)





							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				[
								    """DDIMScheduler""",
								    """DDPMScheduler""",
								    """PNDMScheduler""",
								    """HeunDiscreteScheduler""",
								    """EulerAncestralDiscreteScheduler""",
								    """KDPM2DiscreteScheduler""",
								    """KDPM2AncestralDiscreteScheduler""",
								    """DPMSolverSDEScheduler""",
								]
								a									=				self.get_dummy_components()
								a									=				self.pipeline_class(**__magic_name__							)

								# make sure that PNDM does not need warm-up
								pipe.scheduler.register_to_config(skip_prk_steps=__magic_name__							)

								pipe.to(__magic_name__							)
								pipe.set_progress_bar_config(disable=__magic_name__							)
								a									=				self.get_dummy_inputs(__magic_name__							)
								a									=				2

								a									=				[]
								for scheduler_enum in KarrasDiffusionSchedulers:
									if scheduler_enum.name in skip_schedulers:
										# no sigma schedulers are not supported
										# no schedulers
										continue

									a									=				getattr(__magic_name__						, scheduler_enum.name							)
									a									=				scheduler_cls.from_config(pipe.scheduler.config							)
									a									=				pipe(**__magic_name__							)[0]
									outputs.append(__magic_name__							)

								assert check_same_shape(__magic_name__							)







@require_torch_gpu
@slow
class 						__lowerCAmelCase      (      unittest.TestCase					):
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()
							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								a									=				torch.manual_seed(33							)

								a									=				StableDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""						, torch_dtype=torch.floataa							)
								pipe.to("""cuda"""							)

								a									=				StableDiffusionLatentUpscalePipeline.from_pretrained(
								    """stabilityai/sd-x2-latent-upscaler"""						, torch_dtype=torch.floataa							)
								upscaler.to("""cuda"""							)

								a									=				"""a photo of an astronaut high resolution, unreal engine, ultra realistic"""

								a									=				pipe(__magic_name__						, generator=__magic_name__						, output_type="""latent"""							).images

								a									=				upscaler(
								    prompt=__magic_name__						, image=__magic_name__						, num_inference_steps=20						, guidance_scale=0						, generator=__magic_name__						, output_type="""np"""						, ).images[0]

								a									=				load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"""							)
								assert np.abs((expected_image - image).mean()							) < 5E-2





							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a									=				torch.manual_seed(33							)

								a									=				StableDiffusionLatentUpscalePipeline.from_pretrained(
								    """stabilityai/sd-x2-latent-upscaler"""						, torch_dtype=torch.floataa							)
								upscaler.to("""cuda"""							)

								a									=				"""the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""

								a									=				load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"""							)

								a									=				upscaler(
								    prompt=__magic_name__						, image=__magic_name__						, num_inference_steps=20						, guidance_scale=0						, generator=__magic_name__						, output_type="""np"""						, ).images[0]

								a									=				load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"""							)
								assert np.abs((expected_image - image).max()							) < 5E-2

