"""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        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

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







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									torch.manual_seed(0    )
									UpperCAmelCase__    =							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					,attention_head_dim=(2, 4)					,use_linear_projection=lowerCamelCase__					,)
									UpperCAmelCase__    =							DDIMScheduler(
									    beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule='scaled_linear'					,clip_sample=lowerCamelCase__					,set_alpha_to_one=lowerCamelCase__					,)
									UpperCAmelCase__    =							DDIMInverseScheduler(
									    beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule='scaled_linear'					,clip_sample=lowerCamelCase__					,set_alpha_to_zero=lowerCamelCase__					,)
									torch.manual_seed(0    )
									UpperCAmelCase__    =							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					,sample_size=128					,)
									torch.manual_seed(0    )
									UpperCAmelCase__    =							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					,hidden_act='gelu'					,projection_dim=512					,)
									UpperCAmelCase__    =							CLIPTextModel(lowerCamelCase__    )
									UpperCAmelCase__    =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

									UpperCAmelCase__    =							{
									    '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[Any]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			int=0    ):
									UpperCAmelCase__    =							floats_tensor((1, 16, 16)					,rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
									UpperCAmelCase__    =							floats_tensor((1, 2, 4, 16, 16)					,rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
									if str(lowerCamelCase__    ).startswith('mps'    ):
													UpperCAmelCase__    =							torch.manual_seed(lowerCamelCase__    )
									else:
													UpperCAmelCase__    =							torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    '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     :			str					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			int=0    ):
									UpperCAmelCase__    =							floats_tensor((1, 3, 32, 32)					,rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
									UpperCAmelCase__    =							image.cpu().permute(0					,2					,3					,1    )[0]
									UpperCAmelCase__    =							Image.fromarray(np.uinta(lowerCamelCase__    )    ).convert('RGB'    )
									if str(lowerCamelCase__    ).startswith('mps'    ):
													UpperCAmelCase__    =							torch.manual_seed(lowerCamelCase__    )
									else:
													UpperCAmelCase__    =							torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    '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     :			Any					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			str=0    ):
									UpperCAmelCase__    =							floats_tensor((1, 3, 32, 32)					,rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
									UpperCAmelCase__    =							image.cpu().permute(0					,2					,3					,1    )[0]
									UpperCAmelCase__    =							Image.fromarray(np.uinta(lowerCamelCase__    )    ).convert('RGB'    )
									if str(lowerCamelCase__    ).startswith('mps'    ):
													UpperCAmelCase__    =							torch.manual_seed(lowerCamelCase__    )
									else:
													UpperCAmelCase__    =							torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    '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     :			Any    ):
									if not hasattr(self.pipeline_class					,'_optional_components'    ):
													return

									UpperCAmelCase__    =							self.get_dummy_components()
									UpperCAmelCase__    =							self.pipeline_class(**lowerCamelCase__    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

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

									UpperCAmelCase__    =							self.get_dummy_inputs(lowerCamelCase__    )
									UpperCAmelCase__    =							pipe(**lowerCamelCase__    )[0]

									with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(lowerCamelCase__    )
													UpperCAmelCase__    =							self.pipeline_class.from_pretrained(lowerCamelCase__    )
													pipe_loaded.to(lowerCamelCase__    )
													pipe_loaded.set_progress_bar_config(disable=lowerCamelCase__    )

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

									UpperCAmelCase__    =							self.get_dummy_inputs(lowerCamelCase__    )
									UpperCAmelCase__    =							pipe_loaded(**lowerCamelCase__    )[0]

									UpperCAmelCase__    =							np.abs(output - output_loaded    ).max()
									self.assertLess(lowerCamelCase__					,1e-4    )







					def 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							'cpu'

									UpperCAmelCase__    =							self.get_dummy_components()
									UpperCAmelCase__    =							self.pipeline_class(**lowerCamelCase__    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							self.get_dummy_mask_inputs(lowerCamelCase__    )
									UpperCAmelCase__    =							pipe.generate_mask(**lowerCamelCase__    )
									UpperCAmelCase__    =							mask[0, -3:, -3:]

									self.assertEqual(mask.shape					,(1, 16, 16)    )
									UpperCAmelCase__    =							np.array([0] * 9    )
									UpperCAmelCase__    =							np.abs(mask_slice.flatten() - expected_slice    ).max()
									self.assertLessEqual(lowerCamelCase__					,1e-3    )
									self.assertEqual(mask[0, -3, -4]					,0    )







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							'cpu'

									UpperCAmelCase__    =							self.get_dummy_components()
									UpperCAmelCase__    =							self.pipeline_class(**lowerCamelCase__    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

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

									self.assertEqual(image.shape					,(2, 32, 32, 3)    )
									UpperCAmelCase__    =							np.array(
									    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]					,)
									UpperCAmelCase__    =							np.abs(image_slice.flatten() - expected_slice    ).max()
									self.assertLessEqual(lowerCamelCase__					,1e-3    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									super().test_inference_batch_single_identical(expected_max_diff=5e-3    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							'cpu'

									UpperCAmelCase__    =							self.get_dummy_components()

									UpperCAmelCase__    =							{'beta_start': 0.0_0_0_8_5, 'beta_end': 0.0_1_2, 'beta_schedule': 'scaled_linear'}
									UpperCAmelCase__    =							DPMSolverMultistepScheduler(**lowerCamelCase__    )
									UpperCAmelCase__    =							DPMSolverMultistepInverseScheduler(**lowerCamelCase__    )

									UpperCAmelCase__    =							self.pipeline_class(**lowerCamelCase__    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

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

									self.assertEqual(image.shape					,(2, 32, 32, 3)    )
									UpperCAmelCase__    =							np.array(
									    [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]					,)
									UpperCAmelCase__    =							np.abs(image_slice.flatten() - expected_slice    ).max()
									self.assertLessEqual(lowerCamelCase__					,1e-3    )






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




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







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

									UpperCAmelCase__    =							raw_image.convert('RGB'    ).resize((768, 768)    )

									UpperCAmelCase__    =							raw_image







					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__    =							torch.manual_seed(0    )

									UpperCAmelCase__    =							StableDiffusionDiffEditPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-1'					,safety_checker=lowerCamelCase__					,torch_dtype=torch.floataa    )
									UpperCAmelCase__    =							DDIMScheduler.from_config(pipe.scheduler.config    )
									UpperCAmelCase__    =							DDIMInverseScheduler.from_config(pipe.scheduler.config    )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							'a bowl of fruit'
									UpperCAmelCase__    =							'a bowl of pears'

									UpperCAmelCase__    =							pipe.generate_mask(
									    image=self.raw_image					,source_prompt=lowerCamelCase__					,target_prompt=lowerCamelCase__					,generator=lowerCamelCase__					,)

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

									UpperCAmelCase__    =							pipe(
									    prompt=lowerCamelCase__					,mask_image=lowerCamelCase__					,image_latents=lowerCamelCase__					,generator=lowerCamelCase__					,negative_prompt=lowerCamelCase__					,inpaint_strength=0.7					,output_type='numpy'					,).images[0]

									UpperCAmelCase__    =							(
									    np.array(
									        load_image(
									            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									            '/diffedit/pears.png'    ).resize((768, 768)    )    )
									    / 255
									)
									assert np.abs((expected_image - image).max()    ) < 5e-1







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							torch.manual_seed(0    )

									UpperCAmelCase__    =							StableDiffusionDiffEditPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-1'					,safety_checker=lowerCamelCase__					,torch_dtype=torch.floataa    )
									UpperCAmelCase__    =							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
									UpperCAmelCase__    =							DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							'a bowl of fruit'
									UpperCAmelCase__    =							'a bowl of pears'

									UpperCAmelCase__    =							pipe.generate_mask(
									    image=self.raw_image					,source_prompt=lowerCamelCase__					,target_prompt=lowerCamelCase__					,generator=lowerCamelCase__					,)

									UpperCAmelCase__    =							pipe.invert(
									    prompt=lowerCamelCase__					,image=self.raw_image					,inpaint_strength=0.7					,generator=lowerCamelCase__					,num_inference_steps=25					,).latents

									UpperCAmelCase__    =							pipe(
									    prompt=lowerCamelCase__					,mask_image=lowerCamelCase__					,image_latents=lowerCamelCase__					,generator=lowerCamelCase__					,negative_prompt=lowerCamelCase__					,inpaint_strength=0.7					,num_inference_steps=25					,output_type='numpy'					,).images[0]

									UpperCAmelCase__    =							(
									    np.array(
									        load_image(
									            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									            '/diffedit/pears.png'    ).resize((768, 768)    )    )
									    / 255
									)
									assert np.abs((expected_image - image).max()    ) < 5e-1




"""simple docstring"""






import re



def 						a_		(      lowerCamelCase ):
				return [char.split() for char in re.split(r'[^ a-z A-Z 0-9 \s]'      ,    str_ )]



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							split_input(str_ )
				return "".join(
				    [''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				try:
								UpperCAmelCase__    =							split_input(lowerCamelCase )
								if upper:
												UpperCAmelCase__    =							''.join(
												    [
												        separator.join([char.upper() for char in sub_str] )
												        for sub_str in string_split
												    ] )
								else:
												UpperCAmelCase__    =							''.join(
												    [
												        separator.join([char.lower() for char in sub_str] )
												        for sub_str in string_split
												    ] )
								return res_str
				except IndexError:
								return "not valid string"



def 						a_		(      lowerCamelCase ):
				return to_simple_case(lowerCamelCase )



def 						a_		(      lowerCamelCase ):
				try:
								UpperCAmelCase__    =							to_simple_case(lowerCamelCase )
								return res_str[0].lower() + res_str[1:]
				except IndexError:
								return "not valid string"



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return to_complex_case(lowerCamelCase      ,    lowerCamelCase      ,    '_' )



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return to_complex_case(lowerCamelCase      ,    lowerCamelCase      ,    '-' )


if __name__ == "__main__":
						__import__('doctest').testmod()



