





'''simple docstring'''


import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class       UpperCamelCase__  (		__lowerCAmelCase	,__lowerCAmelCase	,__lowerCAmelCase	,unittest.TestCase     ):
	lowerCAmelCase__					:			Optional[int]								=						StableDiffusionInstructPixaPixPipeline
	lowerCAmelCase__					:			Union[str, Any]								=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
	lowerCAmelCase__					:			Dict								=						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	lowerCAmelCase__					:			Any								=						IMAGE_TO_IMAGE_IMAGE_PARAMS
	lowerCAmelCase__					:			Optional[int]								=						IMAGE_TO_IMAGE_IMAGE_PARAMS







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				torch.manual_seed(0		)
				a__     = UNetaDConditionModel(
				    block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=8 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=3_2 , )
				a__     = PNDMScheduler(skip_prk_steps=lowerCamelCase		)
				torch.manual_seed(0		)
				a__     = 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 , )
				torch.manual_seed(0		)
				a__     = CLIPTextConfig(
				    bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
				a__     = CLIPTextModel(lowerCamelCase		)
				a__     = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

				a__     = {
				    "unet": unet,
				    "scheduler": scheduler,
				    "vae": vae,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
				return components







	def 						__a   (				self			:   str , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   Optional[int]=0		):




				'''simple docstring'''
				a__     = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(lowerCamelCase		)		).to(lowerCamelCase		)
				a__     = image.cpu().permute(0 , 2 , 3 , 1		)[0]
				a__     = Image.fromarray(np.uinta(lowerCamelCase		)		).convert("RGB"		)
				if str(lowerCamelCase		).startswith("mps"		):
							a__     = torch.manual_seed(lowerCamelCase		)
				else:
							a__     = torch.Generator(device=lowerCamelCase		).manual_seed(lowerCamelCase		)
				a__     = {
				    "prompt": "A painting of a squirrel eating a burger",
				    "image": image,
				    "generator": generator,
				    "num_inference_steps": 2,
				    "guidance_scale": 6.0,
				    "image_guidance_scale": 1,
				    "output_type": "numpy",
				}
				return inputs







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = "cpu"  # ensure determinism for the device-dependent torch.Generator
				a__     = self.get_dummy_components()
				a__     = StableDiffusionInstructPixaPixPipeline(**lowerCamelCase		)
				a__     = sd_pipe.to(lowerCamelCase		)
				sd_pipe.set_progress_bar_config(disable=lowerCamelCase		)

				a__     = self.get_dummy_inputs(lowerCamelCase		)
				a__     = sd_pipe(**lowerCamelCase		).images
				a__     = image[0, -3:, -3:, -1]
				assert image.shape == (1, 3_2, 3_2, 3)
				a__     = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = "cpu"  # ensure determinism for the device-dependent torch.Generator
				a__     = self.get_dummy_components()
				a__     = StableDiffusionInstructPixaPixPipeline(**lowerCamelCase		)
				a__     = sd_pipe.to(lowerCamelCase		)
				sd_pipe.set_progress_bar_config(disable=lowerCamelCase		)

				a__     = self.get_dummy_inputs(lowerCamelCase		)
				a__     = "french fries"
				a__     = sd_pipe(**lowerCamelCase , negative_prompt=lowerCamelCase		)
				a__     = output.images
				a__     = image[0, -3:, -3:, -1]

				assert image.shape == (1, 3_2, 3_2, 3)
				a__     = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = "cpu"  # ensure determinism for the device-dependent torch.Generator
				a__     = self.get_dummy_components()
				a__     = StableDiffusionInstructPixaPixPipeline(**lowerCamelCase		)
				a__     = sd_pipe.to(lowerCamelCase		)
				sd_pipe.set_progress_bar_config(disable=lowerCamelCase		)

				a__     = self.get_dummy_inputs(lowerCamelCase		)
				a__     = [inputs["prompt"]] * 2

				a__     = np.array(inputs["image"]		).astype(np.floataa		) / 255.0
				a__     = torch.from_numpy(lowerCamelCase		).unsqueeze(0		).to(lowerCamelCase		)
				a__     = image / 2 + 0.5
				a__     = image.permute(0 , 3 , 1 , 2		)
				a__     = image.repeat(2 , 1 , 1 , 1		)

				a__     = sd_pipe(**lowerCamelCase		).images
				a__     = image[-1, -3:, -3:, -1]

				assert image.shape == (2, 3_2, 3_2, 3)
				a__     = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = "cpu"  # ensure determinism for the device-dependent torch.Generator
				a__     = self.get_dummy_components()
				a__     = EulerAncestralDiscreteScheduler(
				    beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear"		)
				a__     = StableDiffusionInstructPixaPixPipeline(**lowerCamelCase		)
				a__     = sd_pipe.to(lowerCamelCase		)
				sd_pipe.set_progress_bar_config(disable=lowerCamelCase		)

				a__     = self.get_dummy_inputs(lowerCamelCase		)
				a__     = sd_pipe(**lowerCamelCase		).images
				a__     = image[0, -3:, -3:, -1]

				a__     = [round(lowerCamelCase , 4		) for x in image_slice.flatten().tolist()]
				print(",".join([str(lowerCamelCase		) for x in slice]		)		)

				assert image.shape == (1, 3_2, 3_2, 3)
				a__     = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				super().test_inference_batch_single_identical(expected_max_diff=3e-3		)







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = self.get_dummy_components()
				a__     = StableDiffusionInstructPixaPixPipeline(**lowerCamelCase		)
				a__     = VaeImageProcessor(do_resize=lowerCamelCase , do_normalize=lowerCamelCase		)
				a__     = pipe.to(lowerCamelCase		)
				pipe.set_progress_bar_config(disable=lowerCamelCase		)

				a__     = pipe(**self.get_dummy_inputs_by_type(lowerCamelCase , input_image_type="pt"		)		)[0]

				a__     = components["vae"]
				a__     = self.get_dummy_inputs_by_type(lowerCamelCase , input_image_type="pt"		)

				for image_param in self.image_latents_params:
							if image_param in inputs.keys():
										a__     = vae.encode(inputs[image_param]		).latent_dist.mode()

				a__     = pipe(**lowerCamelCase		)[0]

				a__     = np.abs(out - out_latents_inputs		).max()
				self.assertLess(lowerCamelCase , 1e-4 , "passing latents as image input generate different result from passing image"		)






@slow
@require_torch_gpu
class       UpperCamelCase__  (		unittest.TestCase     ):







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







	def 						__a   (				self			:   Any , lowerCamelCase			:   List[Any]=0		):




				'''simple docstring'''
				a__     = torch.manual_seed(lowerCamelCase		)
				a__     = load_image(
				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"		)
				a__     = {
				    "prompt": "turn him into a cyborg",
				    "image": image,
				    "generator": generator,
				    "num_inference_steps": 3,
				    "guidance_scale": 7.5,
				    "image_guidance_scale": 1.0,
				    "output_type": "numpy",
				}
				return inputs







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix" , safety_checker=lowerCamelCase		)
				pipe.to(lowerCamelCase		)
				pipe.set_progress_bar_config(disable=lowerCamelCase		)
				pipe.enable_attention_slicing()

				a__     = self.get_inputs()
				a__     = pipe(**lowerCamelCase		).images
				a__     = image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				a__     = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]		)

				assert np.abs(expected_slice - image_slice		).max() < 1e-3







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				a__     = StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix" , safety_checker=lowerCamelCase		)
				a__     = LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
				pipe.to(lowerCamelCase		)
				pipe.set_progress_bar_config(disable=lowerCamelCase		)
				pipe.enable_attention_slicing()

				a__     = self.get_inputs()
				a__     = pipe(**lowerCamelCase		).images
				a__     = image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				a__     = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]		)

				assert np.abs(expected_slice - image_slice		).max() < 1e-3







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix" , safety_checker=lowerCamelCase		)
				a__     = DDIMScheduler.from_config(pipe.scheduler.config		)
				pipe.to(lowerCamelCase		)
				pipe.set_progress_bar_config(disable=lowerCamelCase		)
				pipe.enable_attention_slicing()

				a__     = self.get_inputs()
				a__     = pipe(**lowerCamelCase		).images
				a__     = image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				a__     = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]		)

				assert np.abs(expected_slice - image_slice		).max() < 1e-3







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = 0

				def callback_fn(lowerCamelCase			:   int , lowerCamelCase			:   int , lowerCamelCase			:   torch.FloatTensor		) -> None:
							a__     = True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 1:
										a__     = latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 6_4, 6_4)
										a__     = latents[0, -3:, -3:, -1]
										a__     = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]		)

										assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2
							elif step == 2:
										a__     = latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 6_4, 6_4)
										a__     = latents[0, -3:, -3:, -1]
										a__     = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]		)

										assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2

				a__     = False

				a__     = StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix" , safety_checker=lowerCamelCase , torch_dtype=torch.floataa		)
				a__     = pipe.to(lowerCamelCase		)
				pipe.set_progress_bar_config(disable=lowerCamelCase		)
				pipe.enable_attention_slicing()

				a__     = self.get_inputs()
				pipe(**lowerCamelCase , callback=lowerCamelCase , callback_steps=1		)
				assert callback_fn.has_been_called
				assert number_of_steps == 3







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				a__     = StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    "timbrooks/instruct-pix2pix" , safety_checker=lowerCamelCase , torch_dtype=torch.floataa		)
				a__     = pipe.to(lowerCamelCase		)
				pipe.set_progress_bar_config(disable=lowerCamelCase		)
				pipe.enable_attention_slicing(1		)
				pipe.enable_sequential_cpu_offload()

				a__     = self.get_inputs()
				a__     = pipe(**lowerCamelCase		)

				a__     = torch.cuda.max_memory_allocated()
				# make sure that less than 2.2 GB is allocated
				assert mem_bytes < 2.2 * 1_0**9







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = self.get_inputs()
				# resize to resolution that is divisible by 8 but not 16 or 32
				a__     = inputs["image"].resize((5_0_4, 5_0_4)		)

				a__     = "timbrooks/instruct-pix2pix"
				a__     = StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    lowerCamelCase , safety_checker=lowerCamelCase , )
				pipe.to(lowerCamelCase		)
				pipe.set_progress_bar_config(disable=lowerCamelCase		)
				pipe.enable_attention_slicing()

				a__     = pipe(**lowerCamelCase		)
				a__     = output.images[0]

				a__     = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

				assert image.shape == (5_0_4, 5_0_4, 3)
				a__     = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 5e-3










'''simple docstring'''


from __future__ import annotations

from math import pi







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

			if (inductance, frequency, reactance).count(0     ) != 1:
						raise ValueError("One and only one argument must be 0"     )
			if inductance < 0:
						raise ValueError("Inductance cannot be negative"     )
			if frequency < 0:
						raise ValueError("Frequency cannot be negative"     )
			if reactance < 0:
						raise ValueError("Inductive reactance cannot be negative"     )
			if inductance == 0:
						return {"inductance": reactance / (2 * pi * frequency)}
			elif frequency == 0:
						return {"frequency": reactance / (2 * pi * inductance)}
			elif reactance == 0:
						return {"reactance": 2 * pi * frequency * inductance}
			else:
						raise ValueError("Exactly one argument must be 0"     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()



