




import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 			UpperCAmelCase_      (   A		,      A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     StableDiffusionDiffEditPipeline
							a__						  =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
							a__						  =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
							a__						  =     frozenset(
							    []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							a__						  =     frozenset([]       )





							def    _UpperCAmelCase     (					self       :	Dict			)			->					Tuple:
										torch.manual_seed(0			)
										SCREAMING_SNAKE_CASE           =					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       ,     attention_head_dim=(2, 4)       ,     use_linear_projection=a       ,     )
										SCREAMING_SNAKE_CASE           =					DDIMScheduler(
										    beta_start=0.0_0085       ,     beta_end=0.012       ,     beta_schedule="""scaled_linear"""       ,     clip_sample=a       ,     set_alpha_to_one=a       ,     )
										SCREAMING_SNAKE_CASE           =					DDIMInverseScheduler(
										    beta_start=0.0_0085       ,     beta_end=0.012       ,     beta_schedule="""scaled_linear"""       ,     clip_sample=a       ,     set_alpha_to_zero=a       ,     )
										torch.manual_seed(0			)
										SCREAMING_SNAKE_CASE           =					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       ,     sample_size=128       ,     )
										torch.manual_seed(0			)
										SCREAMING_SNAKE_CASE           =					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=1_000       ,     hidden_act="""gelu"""       ,     projection_dim=512       ,     )
										SCREAMING_SNAKE_CASE           =					CLIPTextModel(a			)
										SCREAMING_SNAKE_CASE           =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

										SCREAMING_SNAKE_CASE           =					{
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """inverse_scheduler""": inverse_scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}

										return components





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	Optional[int]       ,     a       :	Optional[Any]=0			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					floats_tensor((1, 16, 16)       ,     rng=random.Random(a			)			).to(a			)
										SCREAMING_SNAKE_CASE           =					floats_tensor((1, 2, 4, 16, 16)       ,     rng=random.Random(a			)			).to(a			)
										if str(a			).startswith("""mps"""			):
													SCREAMING_SNAKE_CASE           =					torch.manual_seed(a			)
										else:
													SCREAMING_SNAKE_CASE           =					torch.Generator(device=a			).manual_seed(a			)
										SCREAMING_SNAKE_CASE           =					{
										    """prompt""": """a dog and a newt""",
										    """mask_image""": mask,
										    """image_latents""": latents,
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """inpaint_strength""": 1.0,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}

										return inputs





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	List[Any]       ,     a       :	Tuple=0			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					floats_tensor((1, 3, 32, 32)       ,     rng=random.Random(a			)			).to(a			)
										SCREAMING_SNAKE_CASE           =					image.cpu().permute(0       ,     2       ,     3       ,     1			)[0]
										SCREAMING_SNAKE_CASE           =					Image.fromarray(np.uinta(a			)			).convert("""RGB"""			)
										if str(a			).startswith("""mps"""			):
													SCREAMING_SNAKE_CASE           =					torch.manual_seed(a			)
										else:
													SCREAMING_SNAKE_CASE           =					torch.Generator(device=a			).manual_seed(a			)
										SCREAMING_SNAKE_CASE           =					{
										    """image""": image,
										    """source_prompt""": """a cat and a frog""",
										    """target_prompt""": """a dog and a newt""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """num_maps_per_mask""": 2,
										    """mask_encode_strength""": 1.0,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}

										return inputs





							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	str       ,     a       :	Optional[Any]=0			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					floats_tensor((1, 3, 32, 32)       ,     rng=random.Random(a			)			).to(a			)
										SCREAMING_SNAKE_CASE           =					image.cpu().permute(0       ,     2       ,     3       ,     1			)[0]
										SCREAMING_SNAKE_CASE           =					Image.fromarray(np.uinta(a			)			).convert("""RGB"""			)
										if str(a			).startswith("""mps"""			):
													SCREAMING_SNAKE_CASE           =					torch.manual_seed(a			)
										else:
													SCREAMING_SNAKE_CASE           =					torch.Generator(device=a			).manual_seed(a			)
										SCREAMING_SNAKE_CASE           =					{
										    """image""": image,
										    """prompt""": """a cat and a frog""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """inpaint_strength""": 1.0,
										    """guidance_scale""": 6.0,
										    """decode_latents""": True,
										    """output_type""": """numpy""",
										}
										return inputs





							def    _UpperCAmelCase     (					self       :	int			)			->					Union[str, Any]:
										if not hasattr(self.pipeline_class       ,     """_optional_components"""			):
													return

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

										# set all optional components to None and update pipeline config accordingly
										for optional_component in pipe._optional_components:
													setattr(a       ,     a       ,     a			)
										pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}			)

										SCREAMING_SNAKE_CASE           =					self.get_dummy_inputs(a			)
										SCREAMING_SNAKE_CASE           =					pipe(**a			)[0]

										with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(a			)
													SCREAMING_SNAKE_CASE           =					self.pipeline_class.from_pretrained(a			)
													pipe_loaded.to(a			)
													pipe_loaded.set_progress_bar_config(disable=a			)

										for optional_component in pipe._optional_components:
													self.assertTrue(
													    getattr(a       ,     a			) is None       ,     f"""`{optional_component}` did not stay set to None after loading."""       ,     )

										SCREAMING_SNAKE_CASE           =					self.get_dummy_inputs(a			)
										SCREAMING_SNAKE_CASE           =					pipe_loaded(**a			)[0]

										SCREAMING_SNAKE_CASE           =					np.abs(output - output_loaded			).max()
										self.assertLess(a       ,     1E-4			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					"""cpu"""

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

										SCREAMING_SNAKE_CASE           =					self.get_dummy_mask_inputs(a			)
										SCREAMING_SNAKE_CASE           =					pipe.generate_mask(**a			)
										SCREAMING_SNAKE_CASE           =					mask[0, -3:, -3:]

										self.assertEqual(mask.shape       ,     (1, 16, 16)			)
										SCREAMING_SNAKE_CASE           =					np.array([0] * 9			)
										SCREAMING_SNAKE_CASE           =					np.abs(mask_slice.flatten() - expected_slice			).max()
										self.assertLessEqual(a       ,     1E-3			)
										self.assertEqual(mask[0, -3, -4]       ,     0			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					"""cpu"""

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

										SCREAMING_SNAKE_CASE           =					self.get_dummy_inversion_inputs(a			)
										SCREAMING_SNAKE_CASE           =					pipe.invert(**a			).images
										SCREAMING_SNAKE_CASE           =					image[0, -1, -3:, -3:]

										self.assertEqual(image.shape       ,     (2, 32, 32, 3)			)
										SCREAMING_SNAKE_CASE           =					np.array(
										    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]       ,     )
										SCREAMING_SNAKE_CASE           =					np.abs(image_slice.flatten() - expected_slice			).max()
										self.assertLessEqual(a       ,     1E-3			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Union[str, Any]:
										super().test_inference_batch_single_identical(expected_max_diff=5E-3			)







							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					"""cpu"""

										SCREAMING_SNAKE_CASE           =					self.get_dummy_components()

										SCREAMING_SNAKE_CASE           =					{"""beta_start""": 0.0_0085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
										SCREAMING_SNAKE_CASE           =					DPMSolverMultistepScheduler(**a			)
										SCREAMING_SNAKE_CASE           =					DPMSolverMultistepInverseScheduler(**a			)

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

										SCREAMING_SNAKE_CASE           =					self.get_dummy_inversion_inputs(a			)
										SCREAMING_SNAKE_CASE           =					pipe.invert(**a			).images
										SCREAMING_SNAKE_CASE           =					image[0, -1, -3:, -3:]

										self.assertEqual(image.shape       ,     (2, 32, 32, 3)			)
										SCREAMING_SNAKE_CASE           =					np.array(
										    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]       ,     )
										SCREAMING_SNAKE_CASE           =					np.abs(image_slice.flatten() - expected_slice			).max()
										self.assertLessEqual(a       ,     1E-3			)





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




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	str			)			->					int:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





							@classmethod
							def    _UpperCAmelCase     (					cls       :	int			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""			)

										SCREAMING_SNAKE_CASE           =					raw_image.convert("""RGB"""			).resize((768, 768)			)

										SCREAMING_SNAKE_CASE           =					raw_image





							def    _UpperCAmelCase     (					self       :	Any			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					torch.manual_seed(0			)

										SCREAMING_SNAKE_CASE           =					StableDiffusionDiffEditPipeline.from_pretrained(
										    """stabilityai/stable-diffusion-2-1"""       ,     safety_checker=a       ,     torch_dtype=torch.floataa			)
										SCREAMING_SNAKE_CASE           =					DDIMScheduler.from_config(pipe.scheduler.config			)
										SCREAMING_SNAKE_CASE           =					DDIMInverseScheduler.from_config(pipe.scheduler.config			)
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=a			)

										SCREAMING_SNAKE_CASE           =					"""a bowl of fruit"""
										SCREAMING_SNAKE_CASE           =					"""a bowl of pears"""

										SCREAMING_SNAKE_CASE           =					pipe.generate_mask(
										    image=self.raw_image       ,     source_prompt=a       ,     target_prompt=a       ,     generator=a       ,     )

										SCREAMING_SNAKE_CASE           =					pipe.invert(
										    prompt=a       ,     image=self.raw_image       ,     inpaint_strength=0.7       ,     generator=a			).latents

										SCREAMING_SNAKE_CASE           =					pipe(
										    prompt=a       ,     mask_image=a       ,     image_latents=a       ,     generator=a       ,     negative_prompt=a       ,     inpaint_strength=0.7       ,     output_type="""numpy"""       ,     ).images[0]

										SCREAMING_SNAKE_CASE           =					(
										    np.array(
										        load_image(
										            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										            """/diffedit/pears.png"""			).resize((768, 768)			)			)
										    / 255
										)
										assert np.abs((expected_image - image).max()			) < 5E-1







							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					torch.manual_seed(0			)

										SCREAMING_SNAKE_CASE           =					StableDiffusionDiffEditPipeline.from_pretrained(
										    """stabilityai/stable-diffusion-2-1"""       ,     safety_checker=a       ,     torch_dtype=torch.floataa			)
										SCREAMING_SNAKE_CASE           =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
										SCREAMING_SNAKE_CASE           =					DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config			)
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=a			)

										SCREAMING_SNAKE_CASE           =					"""a bowl of fruit"""
										SCREAMING_SNAKE_CASE           =					"""a bowl of pears"""

										SCREAMING_SNAKE_CASE           =					pipe.generate_mask(
										    image=self.raw_image       ,     source_prompt=a       ,     target_prompt=a       ,     generator=a       ,     )

										SCREAMING_SNAKE_CASE           =					pipe.invert(
										    prompt=a       ,     image=self.raw_image       ,     inpaint_strength=0.7       ,     generator=a       ,     num_inference_steps=25       ,     ).latents

										SCREAMING_SNAKE_CASE           =					pipe(
										    prompt=a       ,     mask_image=a       ,     image_latents=a       ,     generator=a       ,     negative_prompt=a       ,     inpaint_strength=0.7       ,     num_inference_steps=25       ,     output_type="""numpy"""       ,     ).images[0]

										SCREAMING_SNAKE_CASE           =					(
										    np.array(
										        load_image(
										            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										            """/diffedit/pears.png"""			).resize((768, 768)			)			)
										    / 255
										)
										assert np.abs((expected_image - image).max()			) < 5E-1












from __future__ import annotations

import math
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''

			if num <= 0:
						SCREAMING_SNAKE_CASE           =					f"""{num}: Invalid input, please enter a positive integer."""
						raise ValueError(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					[True] * (num + 1)
			SCREAMING_SNAKE_CASE           =					[]
			SCREAMING_SNAKE_CASE           =					2
			SCREAMING_SNAKE_CASE           =					int(math.sqrt(SCREAMING_SNAKE_CASE   )   )

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

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

						start += 1

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

			return prime


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






