






"""simple docstring"""

from __future__ import annotations

from math import pow, sqrt

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase       )						->    dict[str, float]:

		"""simple docstring"""







		if (resistance, reactance, impedance).count(0       ) != 1:
				raise ValueError("One and only one argument must be 0"       )
		if resistance == 0:
				return {"resistance": sqrt(pow(__UpperCamelCase      ,       2       ) - pow(__UpperCamelCase      ,       2       )       )}
		elif reactance == 0:
				return {"reactance": sqrt(pow(__UpperCamelCase      ,       2       ) - pow(__UpperCamelCase      ,       2       )       )}
		elif impedance == 0:
				return {"impedance": sqrt(pow(__UpperCamelCase      ,       2       ) + pow(__UpperCamelCase      ,       2       )       )}
		else:
				raise ValueError("Exactly one argument must be 0"       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()








"""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 		__lowerCamelCase				( unittest.TestCase			):





		'''simple docstring'''






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





		@property
		def    lowerCamelCase       (   self							:	Optional[Any]	):
				lowerCAmelCase_   :							Optional[int]									=			1
				lowerCAmelCase_   :							int									=			3
				lowerCAmelCase_   :							Dict									=			(32, 32)

				lowerCAmelCase_   :							List[Any]									=			floats_tensor((batch_size, num_channels) + sizes						,			rng=random.Random(0	)	).to(a_	)
				return image





		@property
		def    lowerCamelCase       (   self							:	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    lowerCamelCase       (   self							:	Tuple	):
				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    lowerCamelCase       (   self							:	List[str]	):
				torch.manual_seed(0	)
				lowerCAmelCase_   :							Tuple									=			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=10_00						,			)
				return CLIPTextModel(a_	)





		@property
		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				def extract(*a_							:	Tuple						,			**a_							:	Tuple	):


						class 		__lowerCamelCase				:





								'''simple docstring'''






								def __init__(   self							:	Union[str, Any]	):
										lowerCAmelCase_   :							List[str]									=			torch.ones([0]	)





								def    lowerCamelCase       (   self							:	str						,			a_							:	Optional[int]	):
										self.pixel_values.to(a_	)
										return self







						return Out()

				return extract





		def    lowerCamelCase       (   self							:	List[str]	):
				lowerCAmelCase_   :							Dict									=			"cpu"  # ensure determinism for the device-dependent torch.Generator
				lowerCAmelCase_   :							List[Any]									=			self.dummy_cond_unet
				lowerCAmelCase_   :							List[Any]									=			DDIMScheduler(
				    beta_start=0.00085						,			beta_end=0.012						,			beta_schedule="scaled_linear"						,			clip_sample=a_						,			set_alpha_to_one=a_						,			)

				lowerCAmelCase_   :							List[Any]									=			self.dummy_vae
				lowerCAmelCase_   :							List[str]									=			self.dummy_text_encoder
				lowerCAmelCase_   :							List[Any]									=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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

				lowerCAmelCase_   :							str									=			"A painting of a squirrel eating a burger"

				lowerCAmelCase_   :							Any									=			torch.Generator(device=a_	).manual_seed(0	)
				lowerCAmelCase_   :							Union[str, Any]									=			sd_pipe([prompt]						,			generator=a_						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type="np"	)
				lowerCAmelCase_   :							str									=			output.images

				lowerCAmelCase_   :							Dict									=			torch.Generator(device=a_	).manual_seed(0	)
				lowerCAmelCase_   :							str									=			sd_pipe(
				    [prompt]						,			generator=a_						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type="np"						,			return_dict=a_						,			)[0]

				lowerCAmelCase_   :							str									=			image[0, -3:, -3:, -1]
				lowerCAmelCase_   :							List[Any]									=			image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				lowerCAmelCase_   :							Any									=			np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]	)

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

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

				lowerCAmelCase_   :							Optional[Any]									=			"A painting of a squirrel eating a burger"
				lowerCAmelCase_   :							List[Any]									=			torch.Generator(device=a_	).manual_seed(0	)
				lowerCAmelCase_   :							Any									=			sd_pipe([prompt]						,			generator=a_						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type="np"	)

				lowerCAmelCase_   :							Union[str, Any]									=			output.images

				lowerCAmelCase_   :							List[str]									=			torch.Generator(device=a_	).manual_seed(0	)
				lowerCAmelCase_   :							Optional[int]									=			sd_pipe(
				    [prompt]						,			generator=a_						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type="np"						,			return_dict=a_						,			)[0]

				lowerCAmelCase_   :							Dict									=			image[0, -3:, -3:, -1]
				lowerCAmelCase_   :							Any									=			image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				lowerCAmelCase_   :							str									=			np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]	)

				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    lowerCamelCase       (   self							:	Tuple	):
				lowerCAmelCase_   :							Tuple									=			StableDiffusionPipeline.from_pretrained(
				    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"						,			safety_checker=a_	)
				assert isinstance(a_						,			a_	)
				assert isinstance(pipe.scheduler						,			a_	)
				assert pipe.safety_checker is None

				lowerCAmelCase_   :							str									=			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(a_	)
						lowerCAmelCase_   :							List[str]									=			StableDiffusionPipeline.from_pretrained(a_	)

				# sanity check that the pipeline still works
				assert pipe.safety_checker is None
				lowerCAmelCase_   :							Any									=			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    lowerCamelCase       (   self							:	Tuple	):
				lowerCAmelCase_   :							str									=			self.dummy_cond_unet
				lowerCAmelCase_   :							str									=			PNDMScheduler(skip_prk_steps=a_	)
				lowerCAmelCase_   :							Tuple									=			self.dummy_vae
				lowerCAmelCase_   :							Dict									=			self.dummy_text_encoder
				lowerCAmelCase_   :							Tuple									=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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

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

				lowerCAmelCase_   :							List[str]									=			"A painting of a squirrel eating a burger"
				lowerCAmelCase_   :							Optional[Any]									=			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    lowerCamelCase       (   self							:	Optional[int]	):
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





		def    lowerCamelCase       (   self							:	List[str]	):
				lowerCAmelCase_   :							Union[str, Any]									=			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"						,			safety_checker=a_	)
				lowerCAmelCase_   :							Any									=			LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
				lowerCAmelCase_   :							str									=			sd_pipe.to(a_	)
				sd_pipe.set_progress_bar_config(disable=a_	)

				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[int]									=			40_03_66_03_46
				lowerCAmelCase_   :							List[Any]									=			7

				# without safety guidance (sld_guidance_scale = 0)
				lowerCAmelCase_   :							Union[str, Any]									=			torch.manual_seed(a_	)
				lowerCAmelCase_   :							Union[str, Any]									=			sd_pipe(
				    [prompt]						,			generator=a_						,			guidance_scale=a_						,			num_inference_steps=50						,			output_type="np"						,			width=5_12						,			height=5_12						,			sld_guidance_scale=0						,			)

				lowerCAmelCase_   :							Union[str, Any]									=			output.images
				lowerCAmelCase_   :							Optional[Any]									=			image[0, -3:, -3:, -1]
				lowerCAmelCase_   :							Union[str, Any]									=			[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

				assert image.shape == (1, 5_12, 5_12, 3)

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

				# without safety guidance (strong configuration)
				lowerCAmelCase_   :							List[str]									=			torch.manual_seed(a_	)
				lowerCAmelCase_   :							Any									=			sd_pipe(
				    [prompt]						,			generator=a_						,			guidance_scale=a_						,			num_inference_steps=50						,			output_type="np"						,			width=5_12						,			height=5_12						,			sld_guidance_scale=20_00						,			sld_warmup_steps=7						,			sld_threshold=0.025						,			sld_momentum_scale=0.5						,			sld_mom_beta=0.7						,			)

				lowerCAmelCase_   :							Optional[Any]									=			output.images
				lowerCAmelCase_   :							Optional[int]									=			image[0, -3:, -3:, -1]
				lowerCAmelCase_   :							Optional[Any]									=			[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

				assert image.shape == (1, 5_12, 5_12, 3)

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





		def    lowerCamelCase       (   self							:	str	):
				lowerCAmelCase_   :							Any									=			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"						,			safety_checker=a_	)
				lowerCAmelCase_   :							Union[str, Any]									=			LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
				lowerCAmelCase_   :							Optional[Any]									=			sd_pipe.to(a_	)
				sd_pipe.set_progress_bar_config(disable=a_	)

				lowerCAmelCase_   :							Union[str, Any]									=			"padme amidala taking a bath artwork, safe for work, no nudity"
				lowerCAmelCase_   :							Union[str, Any]									=			27_34_97_17_55
				lowerCAmelCase_   :							Union[str, Any]									=			7

				lowerCAmelCase_   :							str									=			torch.manual_seed(a_	)
				lowerCAmelCase_   :							Dict									=			sd_pipe(
				    [prompt]						,			generator=a_						,			guidance_scale=a_						,			num_inference_steps=50						,			output_type="np"						,			width=5_12						,			height=5_12						,			sld_guidance_scale=0						,			)

				lowerCAmelCase_   :							Any									=			output.images
				lowerCAmelCase_   :							int									=			image[0, -3:, -3:, -1]
				lowerCAmelCase_   :							int									=			[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

				assert image.shape == (1, 5_12, 5_12, 3)

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

				lowerCAmelCase_   :							Optional[int]									=			torch.manual_seed(a_	)
				lowerCAmelCase_   :							Union[str, Any]									=			sd_pipe(
				    [prompt]						,			generator=a_						,			guidance_scale=a_						,			num_inference_steps=50						,			output_type="np"						,			width=5_12						,			height=5_12						,			sld_guidance_scale=20_00						,			sld_warmup_steps=7						,			sld_threshold=0.025						,			sld_momentum_scale=0.5						,			sld_mom_beta=0.7						,			)

				lowerCAmelCase_   :							Any									=			output.images
				lowerCAmelCase_   :							Optional[int]									=			image[0, -3:, -3:, -1]
				lowerCAmelCase_   :							Tuple									=			[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

				assert image.shape == (1, 5_12, 5_12, 3)

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





		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				lowerCAmelCase_   :							Dict									=			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"	)
				lowerCAmelCase_   :							Any									=			sd_pipe.to(a_	)
				sd_pipe.set_progress_bar_config(disable=a_	)

				lowerCAmelCase_   :							Tuple									=			(
				    "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
				    " leyendecker"
				)
				lowerCAmelCase_   :							List[Any]									=			10_44_35_52_34
				lowerCAmelCase_   :							Dict									=			12

				lowerCAmelCase_   :							int									=			torch.manual_seed(a_	)
				lowerCAmelCase_   :							List[str]									=			sd_pipe(
				    [prompt]						,			generator=a_						,			guidance_scale=a_						,			num_inference_steps=50						,			output_type="np"						,			width=5_12						,			height=5_12						,			sld_guidance_scale=0						,			)

				lowerCAmelCase_   :							int									=			output.images
				lowerCAmelCase_   :							int									=			image[0, -3:, -3:, -1]
				lowerCAmelCase_   :							int									=			np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	)

				assert image.shape == (1, 5_12, 5_12, 3)

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

				lowerCAmelCase_   :							int									=			torch.manual_seed(a_	)
				lowerCAmelCase_   :							Any									=			sd_pipe(
				    [prompt]						,			generator=a_						,			guidance_scale=a_						,			num_inference_steps=50						,			output_type="np"						,			width=5_12						,			height=5_12						,			sld_guidance_scale=20_00						,			sld_warmup_steps=7						,			sld_threshold=0.025						,			sld_momentum_scale=0.5						,			sld_mom_beta=0.7						,			)

				lowerCAmelCase_   :							Optional[Any]									=			output.images
				lowerCAmelCase_   :							Optional[Any]									=			image[0, -3:, -3:, -1]
				lowerCAmelCase_   :							str									=			np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]	)
				assert image.shape == (1, 5_12, 5_12, 3)

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