



"""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.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu

class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					def 	snake_case ( self							:						int  )->      Tuple:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



					@property
					def 	snake_case ( self							:						Tuple  )->      str:
								lowerCamelCase__	:	str				=1
								lowerCamelCase__	:	Any				=3
								lowerCamelCase__	:	Optional[Any]				=(32, 32)

								lowerCamelCase__	:	Tuple				=floats_tensor((batch_size, num_channels) + sizes,						rng=random.Random(0  )  ).to(lowerCamelCase  )
								return image



					@property
					def 	snake_case ( self							:						str  )->      List[Any]:
								torch.manual_seed(0  )
								lowerCamelCase__	:	Union[str, Any]				=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,						)
								return model



					@property
					def 	snake_case ( self							:						Dict  )->      List[str]:
								torch.manual_seed(0  )
								lowerCamelCase__	:	Dict				=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,						)
								return model



					@property
					def 	snake_case ( self							:						int  )->      str:
								torch.manual_seed(0  )
								lowerCamelCase__	:	List[Any]				=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=1000,						)
								return CLIPTextModel(lowerCamelCase  )



					@property
					def 	snake_case ( self							:						Optional[int]  )->      int:
								def extract(*lowerCamelCase							:						List[Any],						**lowerCamelCase							:						str  ):

											class  __SCREAMING_SNAKE_CASE			:


																'''simple docstring'''





																def __init__( self							:						int  )->      Any:
																			lowerCamelCase__	:	Optional[int]				=torch.ones([0]  )




																def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						List[str]  )->      int:
																			self.pixel_values.to(lowerCamelCase  )
																			return self





											return Out()

								return extract



					def 	snake_case ( self							:						List[Any]  )->      Dict:
								lowerCamelCase__	:	List[Any]				='''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowerCamelCase__	:	Union[str, Any]				=self.dummy_cond_unet
								lowerCamelCase__	:	str				=DDIMScheduler(
								    beta_start=0.00_085,						beta_end=0.012,						beta_schedule='''scaled_linear''',						clip_sample=lowerCamelCase,						set_alpha_to_one=lowerCamelCase,						)

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

								# make sure here that pndm scheduler skips prk
								lowerCamelCase__	:	str				=StableDiffusionPipeline(
								    unet=lowerCamelCase,						scheduler=lowerCamelCase,						vae=lowerCamelCase,						text_encoder=lowerCamelCase,						tokenizer=lowerCamelCase,						safety_checker=lowerCamelCase,						feature_extractor=self.dummy_extractor,						)
								lowerCamelCase__	:	Union[str, Any]				=sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowerCamelCase__	:	int				='''A painting of a squirrel eating a burger'''

								lowerCamelCase__	:	str				=torch.Generator(device=lowerCamelCase  ).manual_seed(0  )
								lowerCamelCase__	:	int				=sd_pipe([prompt],						generator=lowerCamelCase,						guidance_scale=6.0,						num_inference_steps=2,						output_type='''np'''  )
								lowerCamelCase__	:	Optional[Any]				=output.images

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

								lowerCamelCase__	:	Optional[int]				=image[0, -3:, -3:, -1]
								lowerCamelCase__	:	Any				=image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								lowerCamelCase__	:	Any				=np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864]  )

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

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

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

								lowerCamelCase__	:	List[Any]				=output.images

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

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

								assert image.shape == (1, 64, 64, 3)
								lowerCamelCase__	:	int				=np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993]  )

								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							:						Any  )->      Tuple:
								lowerCamelCase__	:	List[str]				=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

								lowerCamelCase__	:	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  )
											lowerCamelCase__	:	List[str]				=StableDiffusionPipeline.from_pretrained(lowerCamelCase  )

								# sanity check that the pipeline still works
								assert pipe.safety_checker is None
								lowerCamelCase__	:	List[str]				=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							:						int  )->      Union[str, Any]:
								lowerCamelCase__	:	List[Any]				=self.dummy_cond_unet
								lowerCamelCase__	:	Optional[Any]				=PNDMScheduler(skip_prk_steps=lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				=self.dummy_vae
								lowerCamelCase__	:	Any				=self.dummy_text_encoder
								lowerCamelCase__	:	Union[str, Any]				=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

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

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

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

								assert image.shape == (1, 64, 64, 3)





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


					'''simple docstring'''





					def 	snake_case ( self							:						Any  )->      List[Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()



					def 	snake_case ( self							:						Any  )->      Any:
								lowerCamelCase__	:	int				=StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''',						safety_checker=lowerCamelCase  )
								lowerCamelCase__	:	List[Any]				=LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config  )
								lowerCamelCase__	:	str				=sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowerCamelCase__	:	List[str]				=(
								    '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
								    ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
								    ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
								    ''' children from bahnhof zoo, detailed '''
								)
								lowerCamelCase__	:	Optional[Any]				=40_0366_0346
								lowerCamelCase__	:	str				=7

								# without safety guidance (sld_guidance_scale = 0)
								lowerCamelCase__	:	Tuple				=torch.manual_seed(lowerCamelCase  )
								lowerCamelCase__	:	Any				=sd_pipe(
								    [prompt],						generator=lowerCamelCase,						guidance_scale=lowerCamelCase,						num_inference_steps=50,						output_type='''np''',						width=512,						height=512,						sld_guidance_scale=0,						)

								lowerCamelCase__	:	Optional[int]				=output.images
								lowerCamelCase__	:	Union[str, Any]				=image[0, -3:, -3:, -1]
								lowerCamelCase__	:	List[Any]				=[0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]

								assert image.shape == (1, 512, 512, 3)

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

								# without safety guidance (strong configuration)
								lowerCamelCase__	:	int				=torch.manual_seed(lowerCamelCase  )
								lowerCamelCase__	:	int				=sd_pipe(
								    [prompt],						generator=lowerCamelCase,						guidance_scale=lowerCamelCase,						num_inference_steps=50,						output_type='''np''',						width=512,						height=512,						sld_guidance_scale=2000,						sld_warmup_steps=7,						sld_threshold=0.025,						sld_momentum_scale=0.5,						sld_mom_beta=0.7,						)

								lowerCamelCase__	:	str				=output.images
								lowerCamelCase__	:	List[str]				=image[0, -3:, -3:, -1]
								lowerCamelCase__	:	str				=[0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]

								assert image.shape == (1, 512, 512, 3)

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



					def 	snake_case ( self							:						int  )->      str:
								lowerCamelCase__	:	str				=StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''',						safety_checker=lowerCamelCase  )
								lowerCamelCase__	:	List[Any]				=LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config  )
								lowerCamelCase__	:	List[str]				=sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowerCamelCase__	:	int				='''padme amidala taking a bath artwork, safe for work, no nudity'''
								lowerCamelCase__	:	int				=27_3497_1755
								lowerCamelCase__	:	Dict				=7

								lowerCamelCase__	:	Dict				=torch.manual_seed(lowerCamelCase  )
								lowerCamelCase__	:	List[str]				=sd_pipe(
								    [prompt],						generator=lowerCamelCase,						guidance_scale=lowerCamelCase,						num_inference_steps=50,						output_type='''np''',						width=512,						height=512,						sld_guidance_scale=0,						)

								lowerCamelCase__	:	Union[str, Any]				=output.images
								lowerCamelCase__	:	int				=image[0, -3:, -3:, -1]
								lowerCamelCase__	:	Optional[Any]				=[0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]

								assert image.shape == (1, 512, 512, 3)

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

								lowerCamelCase__	:	Dict				=torch.manual_seed(lowerCamelCase  )
								lowerCamelCase__	:	int				=sd_pipe(
								    [prompt],						generator=lowerCamelCase,						guidance_scale=lowerCamelCase,						num_inference_steps=50,						output_type='''np''',						width=512,						height=512,						sld_guidance_scale=2000,						sld_warmup_steps=7,						sld_threshold=0.025,						sld_momentum_scale=0.5,						sld_mom_beta=0.7,						)

								lowerCamelCase__	:	Any				=output.images
								lowerCamelCase__	:	Dict				=image[0, -3:, -3:, -1]
								lowerCamelCase__	:	Tuple				=[0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]

								assert image.shape == (1, 512, 512, 3)

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




					def 	snake_case ( self							:						List[Any]  )->      Any:
								lowerCamelCase__	:	Optional[Any]				=StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''  )
								lowerCamelCase__	:	Optional[Any]				=sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowerCamelCase__	:	int				=(
								    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
								    ''' leyendecker'''
								)
								lowerCamelCase__	:	Optional[int]				=10_4435_5234
								lowerCamelCase__	:	Optional[int]				=12

								lowerCamelCase__	:	Dict				=torch.manual_seed(lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				=sd_pipe(
								    [prompt],						generator=lowerCamelCase,						guidance_scale=lowerCamelCase,						num_inference_steps=50,						output_type='''np''',						width=512,						height=512,						sld_guidance_scale=0,						)

								lowerCamelCase__	:	Any				=output.images
								lowerCamelCase__	:	List[str]				=image[0, -3:, -3:, -1]
								lowerCamelCase__	:	Optional[Any]				=np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]  )

								assert image.shape == (1, 512, 512, 3)

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-7

								lowerCamelCase__	:	Optional[int]				=torch.manual_seed(lowerCamelCase  )
								lowerCamelCase__	:	str				=sd_pipe(
								    [prompt],						generator=lowerCamelCase,						guidance_scale=lowerCamelCase,						num_inference_steps=50,						output_type='''np''',						width=512,						height=512,						sld_guidance_scale=2000,						sld_warmup_steps=7,						sld_threshold=0.025,						sld_momentum_scale=0.5,						sld_mom_beta=0.7,						)

								lowerCamelCase__	:	List[str]				=output.images
								lowerCamelCase__	:	Optional[Any]				=image[0, -3:, -3:, -1]
								lowerCamelCase__	:	List[Any]				=np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561]  )
								assert image.shape == (1, 512, 512, 3)

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









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					int = 4000000			):
			"""simple docstring"""
			lowerCamelCase__	:	Dict				=[]
			lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=0, 1
			while b <= n:
						if b % 2 == 0:
									even_fibs.append(__lowerCamelCase			)
						lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=b, a + b
			return sum(__lowerCamelCase			)


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




