

"""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 							SCREAMING_SNAKE_CASE   (						a_       ,				a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			StableDiffusionDiffEditPipeline
					lowercase__												=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
					lowercase__												=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
					lowercase__												=			frozenset(
					    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					lowercase__												=			frozenset([]			)







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						torch.manual_seed(0						)
						lowerCAmelCase__      :		str    							=      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=lowercase_  ,)
						lowerCAmelCase__      :		List[Any]    							=      DDIMScheduler(
						    beta_start=0.0_0085  ,beta_end=0.012  ,beta_schedule='''scaled_linear'''  ,clip_sample=lowercase_  ,set_alpha_to_one=lowercase_  ,)
						lowerCAmelCase__      :		Union[str, Any]    							=      DDIMInverseScheduler(
						    beta_start=0.0_0085  ,beta_end=0.012  ,beta_schedule='''scaled_linear'''  ,clip_sample=lowercase_  ,set_alpha_to_zero=lowercase_  ,)
						torch.manual_seed(0						)
						lowerCAmelCase__      :		str    							=      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						)
						lowerCAmelCase__      :		int    							=      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  ,)
						lowerCAmelCase__      :		int    							=      CLIPTextModel(lowercase_						)
						lowerCAmelCase__      :		Any    							=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

						lowerCAmelCase__      :		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 			__lowerCAmelCase					(			self     :							List[str]  ,lowercase_     :							Optional[Any]  ,lowercase_     :							List[str]=0						):
						lowerCAmelCase__      :		Any    							=      floats_tensor((1, 1_6, 1_6)  ,rng=random.Random(lowercase_						)						).to(lowercase_						)
						lowerCAmelCase__      :		Tuple    							=      floats_tensor((1, 2, 4, 1_6, 1_6)  ,rng=random.Random(lowercase_						)						).to(lowercase_						)
						if str(lowercase_						).startswith('''mps'''						):
							lowerCAmelCase__      :		List[Any]    							=      torch.manual_seed(lowercase_						)
						else:
							lowerCAmelCase__      :		int    							=      torch.Generator(device=lowercase_						).manual_seed(lowercase_						)
						lowerCAmelCase__      :		List[str]    							=      {
						    '''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 			__lowerCAmelCase					(			self     :							Optional[Any]  ,lowercase_     :							Optional[int]  ,lowercase_     :							Dict=0						):
						lowerCAmelCase__      :		List[str]    							=      floats_tensor((1, 3, 3_2, 3_2)  ,rng=random.Random(lowercase_						)						).to(lowercase_						)
						lowerCAmelCase__      :		Optional[Any]    							=      image.cpu().permute(0  ,2  ,3  ,1						)[0]
						lowerCAmelCase__      :		Tuple    							=      Image.fromarray(np.uinta(lowercase_						)						).convert('''RGB'''						)
						if str(lowercase_						).startswith('''mps'''						):
							lowerCAmelCase__      :		Dict    							=      torch.manual_seed(lowercase_						)
						else:
							lowerCAmelCase__      :		List[str]    							=      torch.Generator(device=lowercase_						).manual_seed(lowercase_						)
						lowerCAmelCase__      :		List[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 			__lowerCAmelCase					(			self     :							Tuple  ,lowercase_     :							int  ,lowercase_     :							Optional[Any]=0						):
						lowerCAmelCase__      :		Union[str, Any]    							=      floats_tensor((1, 3, 3_2, 3_2)  ,rng=random.Random(lowercase_						)						).to(lowercase_						)
						lowerCAmelCase__      :		Tuple    							=      image.cpu().permute(0  ,2  ,3  ,1						)[0]
						lowerCAmelCase__      :		int    							=      Image.fromarray(np.uinta(lowercase_						)						).convert('''RGB'''						)
						if str(lowercase_						).startswith('''mps'''						):
							lowerCAmelCase__      :		Any    							=      torch.manual_seed(lowercase_						)
						else:
							lowerCAmelCase__      :		Optional[Any]    							=      torch.Generator(device=lowercase_						).manual_seed(lowercase_						)
						lowerCAmelCase__      :		Tuple    							=      {
						    '''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 			__lowerCAmelCase					(			self     :							Dict						):
						if not hasattr(self.pipeline_class  ,'''_optional_components'''						):
							return

						lowerCAmelCase__      :		Union[str, Any]    							=      self.get_dummy_components()
						lowerCAmelCase__      :		Dict    							=      self.pipeline_class(**lowercase_						)
						pipe.to(lowercase_						)
						pipe.set_progress_bar_config(disable=lowercase_						)

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

						lowerCAmelCase__      :		Dict    							=      self.get_dummy_inputs(lowercase_						)
						lowerCAmelCase__      :		Optional[int]    							=      pipe(**lowercase_						)[0]

						with tempfile.TemporaryDirectory() as tmpdir:
							pipe.save_pretrained(lowercase_						)
							lowerCAmelCase__      :		Tuple    							=      self.pipeline_class.from_pretrained(lowercase_						)
							pipe_loaded.to(lowercase_						)
							pipe_loaded.set_progress_bar_config(disable=lowercase_						)

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

						lowerCAmelCase__      :		Union[str, Any]    							=      self.get_dummy_inputs(lowercase_						)
						lowerCAmelCase__      :		Optional[Any]    							=      pipe_loaded(**lowercase_						)[0]

						lowerCAmelCase__      :		Optional[Any]    							=      np.abs(output - output_loaded						).max()
						self.assertLess(lowercase_  ,1E-4						)







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Optional[Any]    							=      '''cpu'''

						lowerCAmelCase__      :		str    							=      self.get_dummy_components()
						lowerCAmelCase__      :		Any    							=      self.pipeline_class(**lowercase_						)
						pipe.to(lowercase_						)
						pipe.set_progress_bar_config(disable=lowercase_						)

						lowerCAmelCase__      :		List[str]    							=      self.get_dummy_mask_inputs(lowercase_						)
						lowerCAmelCase__      :		Dict    							=      pipe.generate_mask(**lowercase_						)
						lowerCAmelCase__      :		Tuple    							=      mask[0, -3:, -3:]

						self.assertEqual(mask.shape  ,(1, 1_6, 1_6)						)
						lowerCAmelCase__      :		List[str]    							=      np.array([0] * 9						)
						lowerCAmelCase__      :		str    							=      np.abs(mask_slice.flatten() - expected_slice						).max()
						self.assertLessEqual(lowercase_  ,1E-3						)
						self.assertEqual(mask[0, -3, -4]  ,0						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		int    							=      '''cpu'''

						lowerCAmelCase__      :		Dict    							=      self.get_dummy_components()
						lowerCAmelCase__      :		List[Any]    							=      self.pipeline_class(**lowercase_						)
						pipe.to(lowercase_						)
						pipe.set_progress_bar_config(disable=lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      self.get_dummy_inversion_inputs(lowercase_						)
						lowerCAmelCase__      :		Any    							=      pipe.invert(**lowercase_						).images
						lowerCAmelCase__      :		List[str]    							=      image[0, -1, -3:, -3:]

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







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						super().test_inference_batch_single_identical(expected_max_diff=5E-3						)







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		Tuple    							=      '''cpu'''

						lowerCAmelCase__      :		Any    							=      self.get_dummy_components()

						lowerCAmelCase__      :		int    							=      {'''beta_start''': 0.0_0085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
						lowerCAmelCase__      :		Optional[Any]    							=      DPMSolverMultistepScheduler(**lowercase_						)
						lowerCAmelCase__      :		str    							=      DPMSolverMultistepInverseScheduler(**lowercase_						)

						lowerCAmelCase__      :		Optional[Any]    							=      self.pipeline_class(**lowercase_						)
						pipe.to(lowercase_						)
						pipe.set_progress_bar_config(disable=lowercase_						)

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

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







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






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							Dict						):
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







					@classmethod
					def 			__lowerCAmelCase					(			cls     :							List[Any]						):
						lowerCAmelCase__      :		Any    							=      load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''						)

						lowerCAmelCase__      :		List[str]    							=      raw_image.convert('''RGB'''						).resize((7_6_8, 7_6_8)						)

						lowerCAmelCase__      :		List[Any]    							=      raw_image







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						lowerCAmelCase__      :		Any    							=      torch.manual_seed(0						)

						lowerCAmelCase__      :		Optional[Any]    							=      StableDiffusionDiffEditPipeline.from_pretrained(
						    '''stabilityai/stable-diffusion-2-1'''  ,safety_checker=lowercase_  ,torch_dtype=torch.floataa						)
						lowerCAmelCase__      :		Optional[int]    							=      DDIMScheduler.from_config(pipe.scheduler.config						)
						lowerCAmelCase__      :		Optional[Any]    							=      DDIMInverseScheduler.from_config(pipe.scheduler.config						)
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      '''a bowl of fruit'''
						lowerCAmelCase__      :		Optional[Any]    							=      '''a bowl of pears'''

						lowerCAmelCase__      :		Optional[Any]    							=      pipe.generate_mask(
						    image=self.raw_image  ,source_prompt=lowercase_  ,target_prompt=lowercase_  ,generator=lowercase_  ,)

						lowerCAmelCase__      :		Union[str, Any]    							=      pipe.invert(
						    prompt=lowercase_  ,image=self.raw_image  ,inpaint_strength=0.7  ,generator=lowercase_						).latents

						lowerCAmelCase__      :		Dict    							=      pipe(
						    prompt=lowercase_  ,mask_image=lowercase_  ,image_latents=lowercase_  ,generator=lowercase_  ,negative_prompt=lowercase_  ,inpaint_strength=0.7  ,output_type='''numpy'''  ,).images[0]

						lowerCAmelCase__      :		Union[str, Any]    							=      (
						    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 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		int    							=      torch.manual_seed(0						)

						lowerCAmelCase__      :		Tuple    							=      StableDiffusionDiffEditPipeline.from_pretrained(
						    '''stabilityai/stable-diffusion-2-1'''  ,safety_checker=lowercase_  ,torch_dtype=torch.floataa						)
						lowerCAmelCase__      :		List[str]    							=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
						lowerCAmelCase__      :		Optional[Any]    							=      DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config						)
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      '''a bowl of fruit'''
						lowerCAmelCase__      :		Any    							=      '''a bowl of pears'''

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

						lowerCAmelCase__      :		List[str]    							=      pipe.invert(
						    prompt=lowercase_  ,image=self.raw_image  ,inpaint_strength=0.7  ,generator=lowercase_  ,num_inference_steps=2_5  ,).latents

						lowerCAmelCase__      :		Any    							=      pipe(
						    prompt=lowercase_  ,mask_image=lowercase_  ,image_latents=lowercase_  ,generator=lowercase_  ,negative_prompt=lowercase_  ,inpaint_strength=0.7  ,num_inference_steps=2_5  ,output_type='''numpy'''  ,).images[0]

						lowerCAmelCase__      :		List[Any]    							=      (
						    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 typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			42
					lowercase__												=			42







					def __init__(			self     :							int  ,lowercase_     :							UNetaDModel  ,lowercase_     :							ScoreSdeVeScheduler						):
						super().__init__()
						self.register_modules(unet=lowercase_  ,scheduler=lowercase_						)







					@torch.no_grad()
					def __call__(			self     :							List[str]  ,lowercase_     :							int = 1  ,lowercase_     :							int = 2_0_0_0  ,lowercase_     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,lowercase_     :							Optional[str] = "pil"  ,lowercase_     :							bool = True  ,**lowercase_     :							Dict  ,):
						lowerCAmelCase__      :		str    							=      self.unet.config.sample_size
						lowerCAmelCase__      :		int    							=      (batch_size, 3, img_size, img_size)

						lowerCAmelCase__      :		List[Any]    							=      self.unet

						lowerCAmelCase__      :		Tuple    							=      randn_tensor(lowercase_  ,generator=lowercase_						) * self.scheduler.init_noise_sigma
						lowerCAmelCase__      :		List[Any]    							=      sample.to(self.device						)

						self.scheduler.set_timesteps(lowercase_						)
						self.scheduler.set_sigmas(lowercase_						)

						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
							lowerCAmelCase__      :		Tuple    							=      self.scheduler.sigmas[i] * torch.ones(shape[0]  ,device=self.device						)

							# correction step
							for _ in range(self.scheduler.config.correct_steps						):
								lowerCAmelCase__      :		str    							=      self.unet(lowercase_  ,lowercase_						).sample
								lowerCAmelCase__      :		List[str]    							=      self.scheduler.step_correct(lowercase_  ,lowercase_  ,generator=lowercase_						).prev_sample

							# prediction step
							lowerCAmelCase__      :		Dict    							=      model(lowercase_  ,lowercase_						).sample
							lowerCAmelCase__      :		Optional[int]    							=      self.scheduler.step_pred(lowercase_  ,lowercase_  ,lowercase_  ,generator=lowercase_						)

							lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      output.prev_sample, output.prev_sample_mean

						lowerCAmelCase__      :		List[str]    							=      sample_mean.clamp(0  ,1						)
						lowerCAmelCase__      :		Union[str, Any]    							=      sample.cpu().permute(0  ,2  ,3  ,1						).numpy()
						if output_type == "pil":
							lowerCAmelCase__      :		Union[str, Any]    							=      self.numpy_to_pil(lowercase_						)

						if not return_dict:
							return (sample,)

						return ImagePipelineOutput(images=lowercase_						)




