






"""simple docstring"""



import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						unittest.TestCase   ):
   SCREAMING_SNAKE_CASE_    =StableDiffusionDiffEditPipeline
   SCREAMING_SNAKE_CASE_    =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''height''', '''width''', '''image'''} | {'''image_latents'''}
   SCREAMING_SNAKE_CASE_    =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'''image'''} | {'''image_latents'''}
   SCREAMING_SNAKE_CASE_    =frozenset(
       []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   SCREAMING_SNAKE_CASE_    =frozenset([]   )
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        torch.manual_seed(0							)
        UpperCAmelCase__						:    List[Any]							=							UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)							,			layers_per_block=2							,			sample_size=3_2							,			in_channels=4							,			out_channels=4							,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,			up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,			cross_attention_dim=3_2							,			attention_head_dim=(2, 4)							,			use_linear_projection=snake_case__							,			)
        UpperCAmelCase__						:    int							=							DDIMScheduler(
            beta_start=0.0_0085							,			beta_end=0.012							,			beta_schedule="scaled_linear"							,			clip_sample=snake_case__							,			set_alpha_to_one=snake_case__							,			)
        UpperCAmelCase__						:    Tuple							=							DDIMInverseScheduler(
            beta_start=0.0_0085							,			beta_end=0.012							,			beta_schedule="scaled_linear"							,			clip_sample=snake_case__							,			set_alpha_to_zero=snake_case__							,			)
        torch.manual_seed(0							)
        UpperCAmelCase__						:    Optional[Any]							=							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							,			sample_size=1_2_8							,			)
        torch.manual_seed(0							)
        UpperCAmelCase__						:    Any							=							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							,			hidden_act="gelu"							,			projection_dim=5_1_2							,			)
        UpperCAmelCase__						:    List[str]							=							CLIPTextModel(snake_case__							)
        UpperCAmelCase__						:    int							=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

        UpperCAmelCase__						:    int							=							{
            "unet": unet,
            "scheduler": scheduler,
            "inverse_scheduler": inverse_scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }

        return components
   def       __a							(		self      :	List[str]							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	List[str]=0							):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							floats_tensor((1, 1_6, 1_6)							,			rng=random.Random(snake_case__							)							).to(snake_case__							)
        UpperCAmelCase__						:    Union[str, Any]							=							floats_tensor((1, 2, 4, 1_6, 1_6)							,			rng=random.Random(snake_case__							)							).to(snake_case__							)
        if str(snake_case__							).startswith("mps"							):
             UpperCAmelCase__						:    Optional[Any]							=							torch.manual_seed(snake_case__							)
        else:
             UpperCAmelCase__						:    Tuple							=							torch.Generator(device=snake_case__							).manual_seed(snake_case__							)
        UpperCAmelCase__						:    Any							=							{
            "prompt": "a dog and a newt",
            "mask_image": mask,
            "image_latents": latents,
            "generator": generator,
            "num_inference_steps": 2,
            "inpaint_strength": 1.0,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }

        return inputs
   def       __a							(		self      :	List[str]							,			snake_case__      :	List[Any]							,			snake_case__      :	List[Any]=0							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							floats_tensor((1, 3, 3_2, 3_2)							,			rng=random.Random(snake_case__							)							).to(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							image.cpu().permute(0							,			2							,			3							,			1							)[0]
        UpperCAmelCase__						:    List[Any]							=							Image.fromarray(np.uinta(snake_case__							)							).convert("RGB"							)
        if str(snake_case__							).startswith("mps"							):
             UpperCAmelCase__						:    Tuple							=							torch.manual_seed(snake_case__							)
        else:
             UpperCAmelCase__						:    List[str]							=							torch.Generator(device=snake_case__							).manual_seed(snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							{
            "image": image,
            "source_prompt": "a cat and a frog",
            "target_prompt": "a dog and a newt",
            "generator": generator,
            "num_inference_steps": 2,
            "num_maps_per_mask": 2,
            "mask_encode_strength": 1.0,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }

        return inputs
   def       __a							(		self      :	int							,			snake_case__      :	int							,			snake_case__      :	str=0							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							floats_tensor((1, 3, 3_2, 3_2)							,			rng=random.Random(snake_case__							)							).to(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							image.cpu().permute(0							,			2							,			3							,			1							)[0]
        UpperCAmelCase__						:    Any							=							Image.fromarray(np.uinta(snake_case__							)							).convert("RGB"							)
        if str(snake_case__							).startswith("mps"							):
             UpperCAmelCase__						:    int							=							torch.manual_seed(snake_case__							)
        else:
             UpperCAmelCase__						:    Optional[Any]							=							torch.Generator(device=snake_case__							).manual_seed(snake_case__							)
        UpperCAmelCase__						:    List[Any]							=							{
            "image": image,
            "prompt": "a cat and a frog",
            "generator": generator,
            "num_inference_steps": 2,
            "inpaint_strength": 1.0,
            "guidance_scale": 6.0,
            "decode_latents": True,
            "output_type": "numpy",
        }
        return inputs
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        if not hasattr(self.pipeline_class							,			"_optional_components"							):
             return

        UpperCAmelCase__						:    List[str]							=							self.get_dummy_components()
        UpperCAmelCase__						:    Union[str, Any]							=							self.pipeline_class(**snake_case__							)
        pipe.to(snake_case__							)
        pipe.set_progress_bar_config(disable=snake_case__							)

        # set all optional components to None and update pipeline config accordingly
        for optional_component in pipe._optional_components:
             setattr(snake_case__							,			snake_case__							,			snake_case__							)
        pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}							)

        UpperCAmelCase__						:    List[str]							=							self.get_dummy_inputs(snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							pipe(**snake_case__							)[0]

        with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(snake_case__							)
             UpperCAmelCase__						:    Optional[Any]							=							self.pipeline_class.from_pretrained(snake_case__							)
             pipe_loaded.to(snake_case__							)
             pipe_loaded.set_progress_bar_config(disable=snake_case__							)

        for optional_component in pipe._optional_components:
             self.assertTrue(
                 getattr(snake_case__							,			snake_case__							) is None							,			f'`{optional_component}` did not stay set to None after loading.'							,			)

        UpperCAmelCase__						:    Union[str, Any]							=							self.get_dummy_inputs(snake_case__							)
        UpperCAmelCase__						:    List[Any]							=							pipe_loaded(**snake_case__							)[0]

        UpperCAmelCase__						:    int							=							np.abs(output - output_loaded							).max()
        self.assertLess(snake_case__							,			1e-4							)
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							"cpu"

        UpperCAmelCase__						:    Optional[int]							=							self.get_dummy_components()
        UpperCAmelCase__						:    Optional[Any]							=							self.pipeline_class(**snake_case__							)
        pipe.to(snake_case__							)
        pipe.set_progress_bar_config(disable=snake_case__							)

        UpperCAmelCase__						:    Tuple							=							self.get_dummy_mask_inputs(snake_case__							)
        UpperCAmelCase__						:    Any							=							pipe.generate_mask(**snake_case__							)
        UpperCAmelCase__						:    List[str]							=							mask[0, -3:, -3:]

        self.assertEqual(mask.shape							,			(1, 1_6, 1_6)							)
        UpperCAmelCase__						:    Optional[Any]							=							np.array([0] * 9							)
        UpperCAmelCase__						:    Optional[int]							=							np.abs(mask_slice.flatten() - expected_slice							).max()
        self.assertLessEqual(snake_case__							,			1e-3							)
        self.assertEqual(mask[0, -3, -4]							,			0							)
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							"cpu"

        UpperCAmelCase__						:    int							=							self.get_dummy_components()
        UpperCAmelCase__						:    List[str]							=							self.pipeline_class(**snake_case__							)
        pipe.to(snake_case__							)
        pipe.set_progress_bar_config(disable=snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							self.get_dummy_inversion_inputs(snake_case__							)
        UpperCAmelCase__						:    str							=							pipe.invert(**snake_case__							).images
        UpperCAmelCase__						:    Union[str, Any]							=							image[0, -1, -3:, -3:]

        self.assertEqual(image.shape							,			(2, 3_2, 3_2, 3)							)
        UpperCAmelCase__						:    List[Any]							=							np.array(
            [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]							,			)
        UpperCAmelCase__						:    Optional[int]							=							np.abs(image_slice.flatten() - expected_slice							).max()
        self.assertLessEqual(snake_case__							,			1e-3							)
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        super().test_inference_batch_single_identical(expected_max_diff=5e-3							)




   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							"cpu"

        UpperCAmelCase__						:    List[Any]							=							self.get_dummy_components()

        UpperCAmelCase__						:    List[Any]							=							{"beta_start": 0.0_0085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
        UpperCAmelCase__						:    Optional[Any]							=							DPMSolverMultistepScheduler(**snake_case__							)
        UpperCAmelCase__						:    str							=							DPMSolverMultistepInverseScheduler(**snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							self.pipeline_class(**snake_case__							)
        pipe.to(snake_case__							)
        pipe.set_progress_bar_config(disable=snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							self.get_dummy_inversion_inputs(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							pipe.invert(**snake_case__							).images
        UpperCAmelCase__						:    Dict							=							image[0, -1, -3:, -3:]

        self.assertEqual(image.shape							,			(2, 3_2, 3_2, 3)							)
        UpperCAmelCase__						:    int							=							np.array(
            [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]							,			)
        UpperCAmelCase__						:    Dict							=							np.abs(image_slice.flatten() - expected_slice							).max()
        self.assertLessEqual(snake_case__							,			1e-3							)




@require_torch_gpu
@slow
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   @classmethod
   def       __a							(		cls      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"							)

        UpperCAmelCase__						:    str							=							raw_image.convert("RGB"							).resize((7_6_8, 7_6_8)							)

        UpperCAmelCase__						:    Optional[Any]							=							raw_image
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							torch.manual_seed(0							)

        UpperCAmelCase__						:    Dict							=							StableDiffusionDiffEditPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2-1"							,			safety_checker=snake_case__							,			torch_dtype=torch.floataa							)
        UpperCAmelCase__						:    Union[str, Any]							=							DDIMScheduler.from_config(pipe.scheduler.config							)
        UpperCAmelCase__						:    Optional[Any]							=							DDIMInverseScheduler.from_config(pipe.scheduler.config							)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=snake_case__							)

        UpperCAmelCase__						:    Optional[Any]							=							"a bowl of fruit"
        UpperCAmelCase__						:    Optional[int]							=							"a bowl of pears"

        UpperCAmelCase__						:    Union[str, Any]							=							pipe.generate_mask(
            image=self.raw_image							,			source_prompt=snake_case__							,			target_prompt=snake_case__							,			generator=snake_case__							,			)

        UpperCAmelCase__						:    str							=							pipe.invert(
            prompt=snake_case__							,			image=self.raw_image							,			inpaint_strength=0.7							,			generator=snake_case__							).latents

        UpperCAmelCase__						:    Union[str, Any]							=							pipe(
            prompt=snake_case__							,			mask_image=snake_case__							,			image_latents=snake_case__							,			generator=snake_case__							,			negative_prompt=snake_case__							,			inpaint_strength=0.7							,			output_type="numpy"							,			).images[0]

        UpperCAmelCase__						:    Tuple							=							(
            np.array(
                load_image(
                    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                    "/diffedit/pears.png"							).resize((7_6_8, 7_6_8)							)							)
            / 2_5_5
        )
        assert np.abs((expected_image - image).max()							) < 5e-1




   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							torch.manual_seed(0							)

        UpperCAmelCase__						:    Union[str, Any]							=							StableDiffusionDiffEditPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2-1"							,			safety_checker=snake_case__							,			torch_dtype=torch.floataa							)
        UpperCAmelCase__						:    List[str]							=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
        UpperCAmelCase__						:    List[Any]							=							DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config							)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							"a bowl of fruit"
        UpperCAmelCase__						:    Union[str, Any]							=							"a bowl of pears"

        UpperCAmelCase__						:    List[Any]							=							pipe.generate_mask(
            image=self.raw_image							,			source_prompt=snake_case__							,			target_prompt=snake_case__							,			generator=snake_case__							,			)

        UpperCAmelCase__						:    Optional[Any]							=							pipe.invert(
            prompt=snake_case__							,			image=self.raw_image							,			inpaint_strength=0.7							,			generator=snake_case__							,			num_inference_steps=2_5							,			).latents

        UpperCAmelCase__						:    Optional[int]							=							pipe(
            prompt=snake_case__							,			mask_image=snake_case__							,			image_latents=snake_case__							,			generator=snake_case__							,			negative_prompt=snake_case__							,			inpaint_strength=0.7							,			num_inference_steps=2_5							,			output_type="numpy"							,			).images[0]

        UpperCAmelCase__						:    List[str]							=							(
            np.array(
                load_image(
                    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                    "/diffedit/pears.png"							).resize((7_6_8, 7_6_8)							)							)
            / 2_5_5
        )
        assert np.abs((expected_image - image).max()							) < 5e-1










"""simple docstring"""



from __future__ import annotations

from math import pow, sqrt


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : float			,					snake_case       : float			,					snake_case       : float				)->		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(snake_case			,					2				) - pow(snake_case			,					2				)				)}
     elif reactance == 0:
          return {"reactance": sqrt(pow(snake_case			,					2				) - pow(snake_case			,					2				)				)}
     elif impedance == 0:
          return {"impedance": sqrt(pow(snake_case			,					2				) + pow(snake_case			,					2				)				)}
     else:
          raise ValueError("Exactly one argument must be 0"				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


