





"""simple docstring"""




import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




class  _a						(      unittest.TestCase				):






				def 				snake_case	( self		:    Any							)	->      Any:




										'''simple docstring'''



										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






				@property
				def 				snake_case	( self		:    Optional[int]							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					List[str]     =    1
										_UpperCamelCase			:					Union[str, Any]     =    3
										_UpperCamelCase			:					Tuple     =    (3_2, 3_2)

										_UpperCamelCase			:					Any     =    floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0							)							).to(lowerCAmelCase__							)
										return image






				@property
				def 				snake_case	( self		:    Tuple							)	->      Union[str, Any]:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase			:					Dict     =    UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4), layers_per_block=2, sample_size=3_2, in_channels=4, out_channels=4, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''), up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''), cross_attention_dim=3_2, )
										return model






				@property
				def 				snake_case	( self		:    Dict							)	->      List[Any]:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase			:					List[str]     =    AutoencoderKL(
										    block_out_channels=[3_2, 6_4], in_channels=3, out_channels=3, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''], up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''], latent_channels=4, )
										return model






				@property
				def 				snake_case	( self		:    List[str]							)	->      Tuple:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase			:					Any     =    CLIPTextConfig(
										    bos_token_id=0, eos_token_id=2, hidden_size=3_2, intermediate_size=3_7, layer_norm_eps=1e-0_5, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1_0_0_0, )
										return CLIPTextModel(lowerCAmelCase__							)






				@property
				def 				snake_case	( self		:    Dict							)	->      str:




										'''simple docstring'''



										def extract(*lowerCAmelCase__		:    int, **lowerCAmelCase__		:    int							):




																class  _a						:






																				def __init__( self		:    str							)	->      Optional[Any]:




																										'''simple docstring'''



																										_UpperCamelCase			:					Optional[int]     =    torch.ones([0]							)






																				def 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    List[str]							)	->      Tuple:




																										'''simple docstring'''



																										self.pixel_values.to(lowerCAmelCase__							)
																										return self

																return Out()

										return extract






				def 				snake_case	( self		:    List[str]							)	->      int:




										'''simple docstring'''



										_UpperCamelCase			:					Any     =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										_UpperCamelCase			:					Optional[Any]     =    self.dummy_cond_unet
										_UpperCamelCase			:					int     =    DDIMScheduler(
										    beta_start=0.00_085, beta_end=0.012, beta_schedule='''scaled_linear''', clip_sample=lowerCAmelCase__, set_alpha_to_one=lowerCAmelCase__, )

										_UpperCamelCase			:					Union[str, Any]     =    self.dummy_vae
										_UpperCamelCase			:					Any     =    self.dummy_text_encoder
										_UpperCamelCase			:					Any     =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										# make sure here that pndm scheduler skips prk
										_UpperCamelCase			:					Tuple     =    StableDiffusionPipeline(
										    unet=lowerCAmelCase__, scheduler=lowerCAmelCase__, vae=lowerCAmelCase__, text_encoder=lowerCAmelCase__, tokenizer=lowerCAmelCase__, safety_checker=lowerCAmelCase__, feature_extractor=self.dummy_extractor, )
										_UpperCamelCase			:					str     =    sd_pipe.to(lowerCAmelCase__							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Optional[Any]     =    '''A painting of a squirrel eating a burger'''

										_UpperCamelCase			:					Any     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					List[Any]     =    sd_pipe([prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np'''							)
										_UpperCamelCase			:					Optional[Any]     =    output.images

										_UpperCamelCase			:					str     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					List[Any]     =    sd_pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np''', return_dict=lowerCAmelCase__, )[0]

										_UpperCamelCase			:					List[Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Tuple     =    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase			:					Tuple     =    np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867]							)

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






				def 				snake_case	( self		:    str							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										_UpperCamelCase			:					Optional[int]     =    self.dummy_cond_unet
										_UpperCamelCase			:					List[str]     =    PNDMScheduler(skip_prk_steps=lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    self.dummy_vae
										_UpperCamelCase			:					Optional[Any]     =    self.dummy_text_encoder
										_UpperCamelCase			:					Union[str, Any]     =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										# make sure here that pndm scheduler skips prk
										_UpperCamelCase			:					int     =    StableDiffusionPipeline(
										    unet=lowerCAmelCase__, scheduler=lowerCAmelCase__, vae=lowerCAmelCase__, text_encoder=lowerCAmelCase__, tokenizer=lowerCAmelCase__, safety_checker=lowerCAmelCase__, feature_extractor=self.dummy_extractor, )
										_UpperCamelCase			:					List[str]     =    sd_pipe.to(lowerCAmelCase__							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Tuple     =    '''A painting of a squirrel eating a burger'''
										_UpperCamelCase			:					Union[str, Any]     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					str     =    sd_pipe([prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np'''							)

										_UpperCamelCase			:					Dict     =    output.images

										_UpperCamelCase			:					List[Any]     =    torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
										_UpperCamelCase			:					int     =    sd_pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=6.0, num_inference_steps=2, output_type='''np''', return_dict=lowerCAmelCase__, )[0]

										_UpperCamelCase			:					Union[str, Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Union[str, Any]     =    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_UpperCamelCase			:					Optional[Any]     =    np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994]							)

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






				def 				snake_case	( self		:    List[str]							)	->      int:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    StableDiffusionPipeline.from_pretrained(
										    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe''', safety_checker=lowerCAmelCase__							)
										assert isinstance(lowerCAmelCase__, lowerCAmelCase__							)
										assert isinstance(pipe.scheduler, lowerCAmelCase__							)
										assert pipe.safety_checker is None

										_UpperCamelCase			:					Optional[Any]     =    pipe('''example prompt''', num_inference_steps=2							).images[0]
										assert image is not None

										# check that there's no error when saving a pipeline with one of the models being None
										with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(lowerCAmelCase__							)
																_UpperCamelCase			:					List[str]     =    StableDiffusionPipeline.from_pretrained(lowerCAmelCase__							)

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										_UpperCamelCase			:					int     =    pipe('''example prompt''', num_inference_steps=2							).images[0]
										assert image is not None






				@unittest.skipIf(torch_device != '''cuda''', '''This test requires a GPU'''							)
				def 				snake_case	( self		:    Any							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Any     =    self.dummy_cond_unet
										_UpperCamelCase			:					Union[str, Any]     =    PNDMScheduler(skip_prk_steps=lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    self.dummy_vae
										_UpperCamelCase			:					List[Any]     =    self.dummy_text_encoder
										_UpperCamelCase			:					Union[str, Any]     =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										# put models in fp16
										_UpperCamelCase			:					int     =    unet.half()
										_UpperCamelCase			:					Any     =    vae.half()
										_UpperCamelCase			:					List[Any]     =    bert.half()

										# make sure here that pndm scheduler skips prk
										_UpperCamelCase			:					Optional[int]     =    StableDiffusionPipeline(
										    unet=lowerCAmelCase__, scheduler=lowerCAmelCase__, vae=lowerCAmelCase__, text_encoder=lowerCAmelCase__, tokenizer=lowerCAmelCase__, safety_checker=lowerCAmelCase__, feature_extractor=self.dummy_extractor, )
										_UpperCamelCase			:					Optional[int]     =    sd_pipe.to(lowerCAmelCase__							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Optional[Any]     =    '''A painting of a squirrel eating a burger'''
										_UpperCamelCase			:					List[str]     =    sd_pipe([prompt], num_inference_steps=2, output_type='''np'''							).images

										assert image.shape == (1, 6_4, 6_4, 3)




@nightly
@require_torch_gpu
class  _a						(      unittest.TestCase				):






				def 				snake_case	( self		:    str							)	->      List[Any]:




										'''simple docstring'''



										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






				def 				snake_case	( self		:    int							)	->      Any:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    '''cuda'''
										_UpperCamelCase			:					List[Any]     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
										_UpperCamelCase			:					int     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					str     =    '''a photo of a cat'''
										_UpperCamelCase			:					Tuple     =    {
										    '''editing_prompt''': ['''sunglasses'''],
										    '''reverse_editing_direction''': [False],
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': 0.95,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Optional[int]     =    3
										_UpperCamelCase			:					int     =    7

										# no sega enabled
										_UpperCamelCase			:					Tuple     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					Optional[Any]     =    output.images
										_UpperCamelCase			:					Tuple     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					List[str]     =    [
										    0.34_673_113,
										    0.38_492_733,
										    0.37_597_352,
										    0.34_086_335,
										    0.35_650_748,
										    0.35_579_205,
										    0.3_384_763,
										    0.34_340_236,
										    0.3_573_271,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					Dict     =    output.images
										_UpperCamelCase			:					int     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Optional[int]     =    [
										    0.41_887_826,
										    0.37_728_766,
										    0.30_138_272,
										    0.41_416_335,
										    0.41_664_985,
										    0.36_283_392,
										    0.36_191_246,
										    0.43_364_465,
										    0.43_001_732,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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






				def 				snake_case	( self		:    Tuple							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    '''cuda'''
										_UpperCamelCase			:					str     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
										_UpperCamelCase			:					List[Any]     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					int     =    '''an image of a crowded boulevard, realistic, 4k'''
										_UpperCamelCase			:					Tuple     =    {
										    '''editing_prompt''': '''crowd, crowded, people''',
										    '''reverse_editing_direction''': True,
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 8.3,
										    '''edit_threshold''': 0.9,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Dict     =    9
										_UpperCamelCase			:					Optional[Any]     =    7

										# no sega enabled
										_UpperCamelCase			:					Optional[int]     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					List[str]     =    output.images
										_UpperCamelCase			:					Union[str, Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Dict     =    [
										    0.43_497_998,
										    0.91_814_065,
										    0.7_540_739,
										    0.55_580_205,
										    0.8_467_265,
										    0.5_389_691,
										    0.62_574_506,
										    0.58_897_763,
										    0.50_926_757,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					int     =    output.images
										_UpperCamelCase			:					Optional[int]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					List[Any]     =    [
										    0.3_089_719,
										    0.30_500_144,
										    0.29_016_042,
										    0.30_630_964,
										    0.325_687,
										    0.29_419_225,
										    0.2_908_091,
										    0.28_723_598,
										    0.27_696_294,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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






				def 				snake_case	( self		:    str							)	->      str:




										'''simple docstring'''



										_UpperCamelCase			:					int     =    '''cuda'''
										_UpperCamelCase			:					str     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
										_UpperCamelCase			:					Union[str, Any]     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					Optional[int]     =    '''a castle next to a river'''
										_UpperCamelCase			:					Optional[int]     =    {
										    '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
										    '''reverse_editing_direction''': False,
										    '''edit_warmup_steps''': [1_5, 1_8],
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': [0.9, 0.8],
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Tuple     =    4_8
										_UpperCamelCase			:					List[str]     =    7

										# no sega enabled
										_UpperCamelCase			:					Union[str, Any]     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					Optional[Any]     =    output.images
										_UpperCamelCase			:					Optional[Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					str     =    [
										    0.75_163_555,
										    0.76_037_145,
										    0.61_785,
										    0.9_189_673,
										    0.8_627_701,
										    0.85_189_694,
										    0.8_512_813,
										    0.87_012_076,
										    0.8_312_857,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					int     =    output.images
										_UpperCamelCase			:					List[str]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Dict     =    [
										    0.73_553_365,
										    0.7_537_271,
										    0.74_341_905,
										    0.66_480_356,
										    0.6_472_925,
										    0.63_039_416,
										    0.64_812_905,
										    0.6_749_717,
										    0.6_517_102,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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






				def 				snake_case	( self		:    Union[str, Any]							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    '''cuda'''
										_UpperCamelCase			:					List[str]     =    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''', torch_dtype=torch.floataa							)
										_UpperCamelCase			:					int     =    pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase			:					List[Any]     =    '''a photo of a cat'''
										_UpperCamelCase			:					str     =    {
										    '''editing_prompt''': ['''sunglasses'''],
										    '''reverse_editing_direction''': [False],
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': 0.95,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										_UpperCamelCase			:					Tuple     =    3
										_UpperCamelCase			:					List[str]     =    7

										# no sega enabled
										_UpperCamelCase			:					Any     =    torch.Generator(lowerCAmelCase__							)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					int     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, )

										_UpperCamelCase			:					Union[str, Any]     =    output.images
										_UpperCamelCase			:					Union[str, Any]     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					List[Any]     =    [
										    0.34_887_695,
										    0.3_876_953,
										    0.375,
										    0.34_423_828,
										    0.3_581_543,
										    0.35_717_773,
										    0.3_383_789,
										    0.34_570_312,
										    0.359_375,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[int]     =    pipe(
										    [prompt], generator=lowerCAmelCase__, guidance_scale=lowerCAmelCase__, num_inference_steps=5_0, output_type='''np''', width=5_1_2, height=5_1_2, **lowerCAmelCase__, )

										_UpperCamelCase			:					Tuple     =    output.images
										_UpperCamelCase			:					int     =    image[0, -3:, -3:, -1]
										_UpperCamelCase			:					Optional[Any]     =    [
										    0.42_285_156,
										    0.36_914_062,
										    0.29_077_148,
										    0.42_041_016,
										    0.41_918_945,
										    0.35_498_047,
										    0.3_618_164,
										    0.4_423_828,
										    0.43_115_234,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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










"""simple docstring"""




def        a_				(							_lowercase = 10**6   ):

						if n <= 0:
												raise ValueError('''Please enter an integer greater than 0'''   )

						_UpperCamelCase			:					Any     =    list(range(n + 1   )   )
						for number in range(2       ,						n + 1   ):
												if phi[number] == number:
																		phi[number] -= 1
																		for multiple in range(number * 2       ,						n + 1       ,						_lowercase   ):
																								_UpperCamelCase			:					List[str]     =    (phi[multiple] // number) * (number - 1)

						_UpperCamelCase			:					Union[str, Any]     =    1
						for number in range(1       ,						n + 1   ):
												if (answer / phi[answer]) < (number / phi[number]):
																		_UpperCamelCase			:					List[str]     =    number

						return answer


if __name__ == "__main__":
						print(solution())



