



'''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        UpperCAmelCase__						(		unittest.TestCase			):



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



					@property
					def 							UpperCAmelCase_			(     self  )  ->      int:
						__lowerCAmelCase       				=	1
						__lowerCAmelCase       				=	3
						__lowerCAmelCase       				=	(32, 32)

						__lowerCAmelCase       				=	floats_tensor((batch_size, num_channels) + sizes      ,					rng=random.Random(0  )  ).to(UpperCamelCase  )
						return image



					@property
					def 							UpperCAmelCase_			(     self  )  ->      str:
						torch.manual_seed(0  )
						__lowerCAmelCase       				=	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 							UpperCAmelCase_			(     self  )  ->      Dict:
						torch.manual_seed(0  )
						__lowerCAmelCase       				=	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 							UpperCAmelCase_			(     self  )  ->      List[str]:
						torch.manual_seed(0  )
						__lowerCAmelCase       				=	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(UpperCamelCase  )



					@property
					def 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						def extract(*UpperCamelCase      ,					**UpperCamelCase  ):






							class        UpperCAmelCase__						:



												def __init__(     self  )  ->      Dict:
													__lowerCAmelCase       				=	torch.ones([0]  )



												def 							UpperCAmelCase_			(     self      ,					UpperCamelCase  )  ->      str:
													self.pixel_values.to(UpperCamelCase  )
													return self


							return Out()

						return extract



					def 							UpperCAmelCase_			(     self  )  ->      Any:
						__lowerCAmelCase       				=	"cpu"  # ensure determinism for the device-dependent torch.Generator
						__lowerCAmelCase       				=	self.dummy_cond_unet
						__lowerCAmelCase       				=	DDIMScheduler(
						    beta_start=0.0_00_85      ,					beta_end=0.0_12      ,					beta_schedule="scaled_linear"      ,					clip_sample=UpperCamelCase      ,					set_alpha_to_one=UpperCamelCase      ,					)

						__lowerCAmelCase       				=	self.dummy_vae
						__lowerCAmelCase       				=	self.dummy_text_encoder
						__lowerCAmelCase       				=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

						# make sure here that pndm scheduler skips prk
						__lowerCAmelCase       				=	StableDiffusionPipeline(
						    unet=UpperCamelCase      ,					scheduler=UpperCamelCase      ,					vae=UpperCamelCase      ,					text_encoder=UpperCamelCase      ,					tokenizer=UpperCamelCase      ,					safety_checker=UpperCamelCase      ,					feature_extractor=self.dummy_extractor      ,					)
						__lowerCAmelCase       				=	sd_pipe.to(UpperCamelCase  )
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	"A painting of a squirrel eating a burger"

						__lowerCAmelCase       				=	torch.Generator(device=UpperCamelCase  ).manual_seed(0  )
						__lowerCAmelCase       				=	sd_pipe([prompt]      ,					generator=UpperCamelCase      ,					guidance_scale=6.0      ,					num_inference_steps=2      ,					output_type="np"  )
						__lowerCAmelCase       				=	output.images

						__lowerCAmelCase       				=	torch.Generator(device=UpperCamelCase  ).manual_seed(0  )
						__lowerCAmelCase       				=	sd_pipe(
						    [prompt]      ,					generator=UpperCamelCase      ,					guidance_scale=6.0      ,					num_inference_steps=2      ,					output_type="np"      ,					return_dict=UpperCamelCase      ,					)[0]

						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]
						__lowerCAmelCase       				=	image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						__lowerCAmelCase       				=	np.array([0.57_53, 0.61_14, 0.50_01, 0.50_34, 0.54_70, 0.47_29, 0.49_71, 0.48_67, 0.48_67]  )

						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 							UpperCAmelCase_			(     self  )  ->      Union[str, Any]:
						__lowerCAmelCase       				=	"cpu"  # ensure determinism for the device-dependent torch.Generator
						__lowerCAmelCase       				=	self.dummy_cond_unet
						__lowerCAmelCase       				=	PNDMScheduler(skip_prk_steps=UpperCamelCase  )
						__lowerCAmelCase       				=	self.dummy_vae
						__lowerCAmelCase       				=	self.dummy_text_encoder
						__lowerCAmelCase       				=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

						# make sure here that pndm scheduler skips prk
						__lowerCAmelCase       				=	StableDiffusionPipeline(
						    unet=UpperCamelCase      ,					scheduler=UpperCamelCase      ,					vae=UpperCamelCase      ,					text_encoder=UpperCamelCase      ,					tokenizer=UpperCamelCase      ,					safety_checker=UpperCamelCase      ,					feature_extractor=self.dummy_extractor      ,					)
						__lowerCAmelCase       				=	sd_pipe.to(UpperCamelCase  )
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	"A painting of a squirrel eating a burger"
						__lowerCAmelCase       				=	torch.Generator(device=UpperCamelCase  ).manual_seed(0  )
						__lowerCAmelCase       				=	sd_pipe([prompt]      ,					generator=UpperCamelCase      ,					guidance_scale=6.0      ,					num_inference_steps=2      ,					output_type="np"  )

						__lowerCAmelCase       				=	output.images

						__lowerCAmelCase       				=	torch.Generator(device=UpperCamelCase  ).manual_seed(0  )
						__lowerCAmelCase       				=	sd_pipe(
						    [prompt]      ,					generator=UpperCamelCase      ,					guidance_scale=6.0      ,					num_inference_steps=2      ,					output_type="np"      ,					return_dict=UpperCamelCase      ,					)[0]

						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]
						__lowerCAmelCase       				=	image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						__lowerCAmelCase       				=	np.array([0.51_22, 0.57_12, 0.48_25, 0.50_53, 0.56_46, 0.47_69, 0.51_79, 0.48_94, 0.49_94]  )

						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 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						__lowerCAmelCase       				=	StableDiffusionPipeline.from_pretrained(
						    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"      ,					safety_checker=UpperCamelCase  )
						assert isinstance(UpperCamelCase      ,					UpperCamelCase  )
						assert isinstance(pipe.scheduler      ,					UpperCamelCase  )
						assert pipe.safety_checker is None

						__lowerCAmelCase       				=	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(UpperCamelCase  )
							__lowerCAmelCase       				=	StableDiffusionPipeline.from_pretrained(UpperCamelCase  )

						# sanity check that the pipeline still works
						assert pipe.safety_checker is None
						__lowerCAmelCase       				=	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 							UpperCAmelCase_			(     self  )  ->      str:
						__lowerCAmelCase       				=	self.dummy_cond_unet
						__lowerCAmelCase       				=	PNDMScheduler(skip_prk_steps=UpperCamelCase  )
						__lowerCAmelCase       				=	self.dummy_vae
						__lowerCAmelCase       				=	self.dummy_text_encoder
						__lowerCAmelCase       				=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

						# put models in fp16
						__lowerCAmelCase       				=	unet.half()
						__lowerCAmelCase       				=	vae.half()
						__lowerCAmelCase       				=	bert.half()

						# make sure here that pndm scheduler skips prk
						__lowerCAmelCase       				=	StableDiffusionPipeline(
						    unet=UpperCamelCase      ,					scheduler=UpperCamelCase      ,					vae=UpperCamelCase      ,					text_encoder=UpperCamelCase      ,					tokenizer=UpperCamelCase      ,					safety_checker=UpperCamelCase      ,					feature_extractor=self.dummy_extractor      ,					)
						__lowerCAmelCase       				=	sd_pipe.to(UpperCamelCase  )
						sd_pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	"A painting of a squirrel eating a burger"
						__lowerCAmelCase       				=	sd_pipe([prompt]      ,					num_inference_steps=2      ,					output_type="np"  ).images

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






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



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



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	"cuda"
						__lowerCAmelCase       				=	StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"  )
						__lowerCAmelCase       				=	pipe.to(UpperCamelCase  )
						pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	"a photo of a cat"
						__lowerCAmelCase       				=	{
						    "editing_prompt": ["sunglasses"],
						    "reverse_editing_direction": [False],
						    "edit_warmup_steps": 10,
						    "edit_guidance_scale": 6,
						    "edit_threshold": 0.95,
						    "edit_momentum_scale": 0.5,
						    "edit_mom_beta": 0.6,
						}

						__lowerCAmelCase       				=	3
						__lowerCAmelCase       				=	7

						# no sega enabled
						__lowerCAmelCase       				=	torch.Generator(UpperCamelCase  )
						generator.manual_seed(UpperCamelCase  )
						__lowerCAmelCase       				=	pipe(
						    [prompt]      ,					generator=UpperCamelCase      ,					guidance_scale=UpperCamelCase      ,					num_inference_steps=50      ,					output_type="np"      ,					width=512      ,					height=512      ,					)

						__lowerCAmelCase       				=	output.images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]
						__lowerCAmelCase       				=	[
						    0.34_67_31_13,
						    0.38_49_27_33,
						    0.37_59_73_52,
						    0.34_08_63_35,
						    0.35_65_07_48,
						    0.35_57_92_05,
						    0.3_38_47_63,
						    0.34_34_02_36,
						    0.3_57_32_71,
						]

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

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(UpperCamelCase  )
						__lowerCAmelCase       				=	pipe(
						    [prompt]      ,					generator=UpperCamelCase      ,					guidance_scale=UpperCamelCase      ,					num_inference_steps=50      ,					output_type="np"      ,					width=512      ,					height=512      ,					**UpperCamelCase      ,					)

						__lowerCAmelCase       				=	output.images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]
						__lowerCAmelCase       				=	[
						    0.41_88_78_26,
						    0.37_72_87_66,
						    0.30_13_82_72,
						    0.41_41_63_35,
						    0.41_66_49_85,
						    0.36_28_33_92,
						    0.36_19_12_46,
						    0.43_36_44_65,
						    0.43_00_17_32,
						]

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

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



					def 							UpperCAmelCase_			(     self  )  ->      Tuple:
						__lowerCAmelCase       				=	"cuda"
						__lowerCAmelCase       				=	StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"  )
						__lowerCAmelCase       				=	pipe.to(UpperCamelCase  )
						pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	"an image of a crowded boulevard, realistic, 4k"
						__lowerCAmelCase       				=	{
						    "editing_prompt": "crowd, crowded, people",
						    "reverse_editing_direction": True,
						    "edit_warmup_steps": 10,
						    "edit_guidance_scale": 8.3,
						    "edit_threshold": 0.9,
						    "edit_momentum_scale": 0.5,
						    "edit_mom_beta": 0.6,
						}

						__lowerCAmelCase       				=	9
						__lowerCAmelCase       				=	7

						# no sega enabled
						__lowerCAmelCase       				=	torch.Generator(UpperCamelCase  )
						generator.manual_seed(UpperCamelCase  )
						__lowerCAmelCase       				=	pipe(
						    [prompt]      ,					generator=UpperCamelCase      ,					guidance_scale=UpperCamelCase      ,					num_inference_steps=50      ,					output_type="np"      ,					width=512      ,					height=512      ,					)

						__lowerCAmelCase       				=	output.images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]
						__lowerCAmelCase       				=	[
						    0.43_49_79_98,
						    0.91_81_40_65,
						    0.7_54_07_39,
						    0.55_58_02_05,
						    0.8_46_72_65,
						    0.5_38_96_91,
						    0.62_57_45_06,
						    0.58_89_77_63,
						    0.50_92_67_57,
						]

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

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(UpperCamelCase  )
						__lowerCAmelCase       				=	pipe(
						    [prompt]      ,					generator=UpperCamelCase      ,					guidance_scale=UpperCamelCase      ,					num_inference_steps=50      ,					output_type="np"      ,					width=512      ,					height=512      ,					**UpperCamelCase      ,					)

						__lowerCAmelCase       				=	output.images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]
						__lowerCAmelCase       				=	[
						    0.3_08_97_19,
						    0.30_50_01_44,
						    0.29_01_60_42,
						    0.30_63_09_64,
						    0.32_56_87,
						    0.29_41_92_25,
						    0.2_90_80_91,
						    0.28_72_35_98,
						    0.27_69_62_94,
						]

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

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



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	"cuda"
						__lowerCAmelCase       				=	StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"  )
						__lowerCAmelCase       				=	pipe.to(UpperCamelCase  )
						pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	"a castle next to a river"
						__lowerCAmelCase       				=	{
						    "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
						    "reverse_editing_direction": False,
						    "edit_warmup_steps": [15, 18],
						    "edit_guidance_scale": 6,
						    "edit_threshold": [0.9, 0.8],
						    "edit_momentum_scale": 0.5,
						    "edit_mom_beta": 0.6,
						}

						__lowerCAmelCase       				=	48
						__lowerCAmelCase       				=	7

						# no sega enabled
						__lowerCAmelCase       				=	torch.Generator(UpperCamelCase  )
						generator.manual_seed(UpperCamelCase  )
						__lowerCAmelCase       				=	pipe(
						    [prompt]      ,					generator=UpperCamelCase      ,					guidance_scale=UpperCamelCase      ,					num_inference_steps=50      ,					output_type="np"      ,					width=512      ,					height=512      ,					)

						__lowerCAmelCase       				=	output.images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]
						__lowerCAmelCase       				=	[
						    0.75_16_35_55,
						    0.76_03_71_45,
						    0.6_17_85,
						    0.9_18_96_73,
						    0.8_62_77_01,
						    0.85_18_96_94,
						    0.8_51_28_13,
						    0.87_01_20_76,
						    0.8_31_28_57,
						]

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

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(UpperCamelCase  )
						__lowerCAmelCase       				=	pipe(
						    [prompt]      ,					generator=UpperCamelCase      ,					guidance_scale=UpperCamelCase      ,					num_inference_steps=50      ,					output_type="np"      ,					width=512      ,					height=512      ,					**UpperCamelCase      ,					)

						__lowerCAmelCase       				=	output.images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]
						__lowerCAmelCase       				=	[
						    0.73_55_33_65,
						    0.7_53_72_71,
						    0.74_34_19_05,
						    0.66_48_03_56,
						    0.6_47_29_25,
						    0.63_03_94_16,
						    0.64_81_29_05,
						    0.6_74_97_17,
						    0.6_51_71_02,
						]

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

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



					def 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						__lowerCAmelCase       				=	"cuda"
						__lowerCAmelCase       				=	StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"      ,					torch_dtype=torch.floataa  )
						__lowerCAmelCase       				=	pipe.to(UpperCamelCase  )
						pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	"a photo of a cat"
						__lowerCAmelCase       				=	{
						    "editing_prompt": ["sunglasses"],
						    "reverse_editing_direction": [False],
						    "edit_warmup_steps": 10,
						    "edit_guidance_scale": 6,
						    "edit_threshold": 0.95,
						    "edit_momentum_scale": 0.5,
						    "edit_mom_beta": 0.6,
						}

						__lowerCAmelCase       				=	3
						__lowerCAmelCase       				=	7

						# no sega enabled
						__lowerCAmelCase       				=	torch.Generator(UpperCamelCase  )
						generator.manual_seed(UpperCamelCase  )
						__lowerCAmelCase       				=	pipe(
						    [prompt]      ,					generator=UpperCamelCase      ,					guidance_scale=UpperCamelCase      ,					num_inference_steps=50      ,					output_type="np"      ,					width=512      ,					height=512      ,					)

						__lowerCAmelCase       				=	output.images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]
						__lowerCAmelCase       				=	[
						    0.34_88_76_95,
						    0.3_87_69_53,
						    0.3_75,
						    0.34_42_38_28,
						    0.3_58_15_43,
						    0.35_71_77_73,
						    0.3_38_37_89,
						    0.34_57_03_12,
						    0.35_93_75,
						]

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

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(UpperCamelCase  )
						__lowerCAmelCase       				=	pipe(
						    [prompt]      ,					generator=UpperCamelCase      ,					guidance_scale=UpperCamelCase      ,					num_inference_steps=50      ,					output_type="np"      ,					width=512      ,					height=512      ,					**UpperCamelCase      ,					)

						__lowerCAmelCase       				=	output.images
						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]
						__lowerCAmelCase       				=	[
						    0.42_28_51_56,
						    0.36_91_40_62,
						    0.29_07_71_48,
						    0.42_04_10_16,
						    0.41_91_89_45,
						    0.35_49_80_47,
						    0.3_61_81_64,
						    0.4_42_38_28,
						    0.43_11_52_34,
						]

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

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




'''simple docstring'''



def 	__lowerCAmelCase       (				lowerCamelCase :    float					,		lowerCamelCase :    float       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	float()
	try:
		__lowerCAmelCase       				=	mass * acceleration
	except Exception:
		return -0.0
	return force


if __name__ == "__main__":
	import doctest

	# run doctest
	doctest.testmod()

	# demo
	lowerCAmelCase						:		List[Any]						      = 12.5
	lowerCAmelCase						:		Optional[Any]						      = 1_0
	lowerCAmelCase						:		str						      = newtons_second_law_of_motion(mass, acceleration)
	print('''The force is ''', force, '''N''')