'''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 					lowerCamelCase_							(	unittest.TestCase							):




					'''simple docstring'''






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






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	1
							_snake_case     	=	3
							_snake_case     	=	(32, 32)

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






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							torch.manual_seed(0			)
							_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,  )
							return model






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							torch.manual_seed(0			)
							_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,  )
							return model






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							torch.manual_seed(0			)
							_snake_case     	=	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=1_000,  )
							return CLIPTextModel(__UpperCamelCase			)






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							def extract(*__UpperCamelCase,  **__UpperCamelCase			):





									class 					lowerCamelCase_							:




														'''simple docstring'''






														def __init__(   self			):
																_snake_case     	=	torch.ones([0]			)






														def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase			):
																self.pixel_values.to(__UpperCamelCase			)
																return self




									return Out()

							return extract






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	"cpu"  # ensure determinism for the device-dependent torch.Generator
							_snake_case     	=	self.dummy_cond_unet
							_snake_case     	=	DDIMScheduler(
							    beta_start=0.0_00_85,  beta_end=0.0_12,  beta_schedule="scaled_linear",  clip_sample=__UpperCamelCase,  set_alpha_to_one=__UpperCamelCase,  )

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

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

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

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

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

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

							assert image.shape == (1, 64, 64, 3)
							_snake_case     	=	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     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	"cpu"  # ensure determinism for the device-dependent torch.Generator
							_snake_case     	=	self.dummy_cond_unet
							_snake_case     	=	PNDMScheduler(skip_prk_steps=__UpperCamelCase			)
							_snake_case     	=	self.dummy_vae
							_snake_case     	=	self.dummy_text_encoder
							_snake_case     	=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

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

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

							_snake_case     	=	output.images

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

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

							assert image.shape == (1, 64, 64, 3)
							_snake_case     	=	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     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	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

							_snake_case     	=	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			)
									_snake_case     	=	StableDiffusionPipeline.from_pretrained(__UpperCamelCase			)

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

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

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

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

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





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




					'''simple docstring'''






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






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	"cuda"
							_snake_case     	=	StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"			)
							_snake_case     	=	pipe.to(__UpperCamelCase			)
							pipe.set_progress_bar_config(disable=__UpperCamelCase			)

							_snake_case     	=	"a photo of a cat"
							_snake_case     	=	{
							    "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,
							}

							_snake_case     	=	3
							_snake_case     	=	7

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

							_snake_case     	=	output.images
							_snake_case     	=	image[0, -3:, -3:, -1]
							_snake_case     	=	[
							    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			)
							_snake_case     	=	pipe(
							    [prompt],  generator=__UpperCamelCase,  guidance_scale=__UpperCamelCase,  num_inference_steps=50,  output_type="np",  width=512,  height=512,  **__UpperCamelCase,  )

							_snake_case     	=	output.images
							_snake_case     	=	image[0, -3:, -3:, -1]
							_snake_case     	=	[
							    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     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	"cuda"
							_snake_case     	=	StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"			)
							_snake_case     	=	pipe.to(__UpperCamelCase			)
							pipe.set_progress_bar_config(disable=__UpperCamelCase			)

							_snake_case     	=	"an image of a crowded boulevard, realistic, 4k"
							_snake_case     	=	{
							    "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,
							}

							_snake_case     	=	9
							_snake_case     	=	7

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

							_snake_case     	=	output.images
							_snake_case     	=	image[0, -3:, -3:, -1]
							_snake_case     	=	[
							    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			)
							_snake_case     	=	pipe(
							    [prompt],  generator=__UpperCamelCase,  guidance_scale=__UpperCamelCase,  num_inference_steps=50,  output_type="np",  width=512,  height=512,  **__UpperCamelCase,  )

							_snake_case     	=	output.images
							_snake_case     	=	image[0, -3:, -3:, -1]
							_snake_case     	=	[
							    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     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	"cuda"
							_snake_case     	=	StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"			)
							_snake_case     	=	pipe.to(__UpperCamelCase			)
							pipe.set_progress_bar_config(disable=__UpperCamelCase			)

							_snake_case     	=	"a castle next to a river"
							_snake_case     	=	{
							    "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,
							}

							_snake_case     	=	48
							_snake_case     	=	7

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

							_snake_case     	=	output.images
							_snake_case     	=	image[0, -3:, -3:, -1]
							_snake_case     	=	[
							    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			)
							_snake_case     	=	pipe(
							    [prompt],  generator=__UpperCamelCase,  guidance_scale=__UpperCamelCase,  num_inference_steps=50,  output_type="np",  width=512,  height=512,  **__UpperCamelCase,  )

							_snake_case     	=	output.images
							_snake_case     	=	image[0, -3:, -3:, -1]
							_snake_case     	=	[
							    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     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	"cuda"
							_snake_case     	=	StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",  torch_dtype=torch.floataa			)
							_snake_case     	=	pipe.to(__UpperCamelCase			)
							pipe.set_progress_bar_config(disable=__UpperCamelCase			)

							_snake_case     	=	"a photo of a cat"
							_snake_case     	=	{
							    "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,
							}

							_snake_case     	=	3
							_snake_case     	=	7

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

							_snake_case     	=	output.images
							_snake_case     	=	image[0, -3:, -3:, -1]
							_snake_case     	=	[
							    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			)
							_snake_case     	=	pipe(
							    [prompt],  generator=__UpperCamelCase,  guidance_scale=__UpperCamelCase,  num_inference_steps=50,  output_type="np",  width=512,  height=512,  **__UpperCamelCase,  )

							_snake_case     	=	output.images
							_snake_case     	=	image[0, -3:, -3:, -1]
							_snake_case     	=	[
							    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'''







import math





class 					lowerCamelCase_							:




					'''simple docstring'''






					def __init__(   self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase			):
							_snake_case     	=	x
							_snake_case     	=	y
							_snake_case     	=	z






					def __repr__(   self			):
							return f'''Point({self.x}, {self.y}, {self.z})'''






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2  )  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





