






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







				def       A						(    self  ):
								"""simple docstring"""
								torch.manual_seed(0  )
								__magic_name__		:int   =   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=__lowerCAmelCase							,       )
								__magic_name__		:Dict   =   DDIMScheduler(
								    beta_start=0.00085							,       beta_end=0.012							,       beta_schedule='''scaled_linear'''							,       clip_sample=__lowerCAmelCase							,       set_alpha_to_one=__lowerCAmelCase							,       )
								__magic_name__		:Union[str, Any]   =   DDIMInverseScheduler(
								    beta_start=0.00085							,       beta_end=0.012							,       beta_schedule='''scaled_linear'''							,       clip_sample=__lowerCAmelCase							,       set_alpha_to_zero=__lowerCAmelCase							,       )
								torch.manual_seed(0  )
								__magic_name__		:Optional[int]   =   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  )
								__magic_name__		:Tuple   =   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							,       )
								__magic_name__		:Dict   =   CLIPTextModel(__lowerCAmelCase  )
								__magic_name__		:Any   =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

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

								__magic_name__		:Optional[int]   =   self.get_dummy_components()
								__magic_name__		:str   =   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}  )

								__magic_name__		:Tuple   =   self.get_dummy_inputs(__lowerCAmelCase  )
								__magic_name__		:List[Any]   =   pipe(**__lowerCAmelCase  )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
												pipe.save_pretrained(__lowerCAmelCase  )
												__magic_name__		:Any   =   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.'''							,       )

								__magic_name__		:int   =   self.get_dummy_inputs(__lowerCAmelCase  )
								__magic_name__		:str   =   pipe_loaded(**__lowerCAmelCase  )[0]

								__magic_name__		:int   =   np.abs(output - output_loaded  ).max()
								self.assertLess(__lowerCAmelCase							,       1E-4  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   '''cpu'''

								__magic_name__		:List[Any]   =   self.get_dummy_components()
								__magic_name__		:Tuple   =   self.pipeline_class(**__lowerCAmelCase  )
								pipe.to(__lowerCAmelCase  )
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:str   =   self.get_dummy_mask_inputs(__lowerCAmelCase  )
								__magic_name__		:Union[str, Any]   =   pipe.generate_mask(**__lowerCAmelCase  )
								__magic_name__		:str   =   mask[0, -3:, -3:]

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







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   '''cpu'''

								__magic_name__		:Tuple   =   self.get_dummy_components()
								__magic_name__		:int   =   self.pipeline_class(**__lowerCAmelCase  )
								pipe.to(__lowerCAmelCase  )
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

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

								self.assertEqual(image.shape							,       (2, 3_2, 3_2, 3)  )
								__magic_name__		:Any   =   np.array(
								    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]							,       )
								__magic_name__		:List[Any]   =   np.abs(image_slice.flatten() - expected_slice  ).max()
								self.assertLessEqual(__lowerCAmelCase							,       1E-3  )







				def       A						(    self  ):
								"""simple docstring"""
								super().test_inference_batch_single_identical(expected_max_diff=5E-3  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   '''cpu'''

								__magic_name__		:Optional[int]   =   self.get_dummy_components()

								__magic_name__		:int   =   {'''beta_start''': 0.00085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
								__magic_name__		:Dict   =   DPMSolverMultistepScheduler(**__lowerCAmelCase  )
								__magic_name__		:List[str]   =   DPMSolverMultistepInverseScheduler(**__lowerCAmelCase  )

								__magic_name__		:Union[str, Any]   =   self.pipeline_class(**__lowerCAmelCase  )
								pipe.to(__lowerCAmelCase  )
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:List[str]   =   self.get_dummy_inversion_inputs(__lowerCAmelCase  )
								__magic_name__		:Dict   =   pipe.invert(**__lowerCAmelCase  ).images
								__magic_name__		:List[str]   =   image[0, -1, -3:, -3:]

								self.assertEqual(image.shape							,       (2, 3_2, 3_2, 3)  )
								__magic_name__		:List[str]   =   np.array(
								    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]							,       )
								__magic_name__		:List[str]   =   np.abs(image_slice.flatten() - expected_slice  ).max()
								self.assertLessEqual(__lowerCAmelCase							,       1E-3  )







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







				def       A						(    self  ):
								"""simple docstring"""
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







				@classmethod
				def       A						(    cls  ):
								"""simple docstring"""
								__magic_name__		:Any   =   load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''  )

								__magic_name__		:Optional[Any]   =   raw_image.convert('''RGB'''  ).resize((7_6_8, 7_6_8)  )

								__magic_name__		:Optional[Any]   =   raw_image







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   torch.manual_seed(0  )

								__magic_name__		:Dict   =   StableDiffusionDiffEditPipeline.from_pretrained(
								    '''stabilityai/stable-diffusion-2-1'''							,       safety_checker=__lowerCAmelCase							,       torch_dtype=torch.floataa  )
								__magic_name__		:Tuple   =   DDIMScheduler.from_config(pipe.scheduler.config  )
								__magic_name__		:str   =   DDIMInverseScheduler.from_config(pipe.scheduler.config  )
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:Optional[Any]   =   '''a bowl of fruit'''
								__magic_name__		:Dict   =   '''a bowl of pears'''

								__magic_name__		:Dict   =   pipe.generate_mask(
								    image=self.raw_image							,       source_prompt=__lowerCAmelCase							,       target_prompt=__lowerCAmelCase							,       generator=__lowerCAmelCase							,       )

								__magic_name__		:Optional[int]   =   pipe.invert(
								    prompt=__lowerCAmelCase							,       image=self.raw_image							,       inpaint_strength=0.7							,       generator=__lowerCAmelCase  ).latents

								__magic_name__		:List[Any]   =   pipe(
								    prompt=__lowerCAmelCase							,       mask_image=__lowerCAmelCase							,       image_latents=__lowerCAmelCase							,       generator=__lowerCAmelCase							,       negative_prompt=__lowerCAmelCase							,       inpaint_strength=0.7							,       output_type='''numpy'''							,       ).images[0]

								__magic_name__		: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







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   torch.manual_seed(0  )

								__magic_name__		:Dict   =   StableDiffusionDiffEditPipeline.from_pretrained(
								    '''stabilityai/stable-diffusion-2-1'''							,       safety_checker=__lowerCAmelCase							,       torch_dtype=torch.floataa  )
								__magic_name__		:Optional[int]   =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
								__magic_name__		:Any   =   DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config  )
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=__lowerCAmelCase  )

								__magic_name__		:str   =   '''a bowl of fruit'''
								__magic_name__		:Any   =   '''a bowl of pears'''

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

								__magic_name__		:int   =   pipe.invert(
								    prompt=__lowerCAmelCase							,       image=self.raw_image							,       inpaint_strength=0.7							,       generator=__lowerCAmelCase							,       num_inference_steps=2_5							,       ).latents

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

								__magic_name__		: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














def   __lowercase       (			snake_case			):







				"""simple docstring"""






				if not isinstance(snake_case,	snake_case			):
								raise ValueError('''multiplicative_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''multiplicative_persistence() does not accept negative values'''			)

				__magic_name__		:str   =   0
				__magic_name__		:Dict   =   str(snake_case			)

				while len(snake_case			) != 1:
								__magic_name__		:Optional[Any]   =   [int(snake_case			) for i in num_string]

								__magic_name__		:Dict   =   1
								for i in range(0,	len(snake_case			)			):
												total *= numbers[i]

								__magic_name__		:int   =   str(snake_case			)

								steps += 1
				return steps

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				if not isinstance(snake_case,	snake_case			):
								raise ValueError('''additive_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''additive_persistence() does not accept negative values'''			)

				__magic_name__		:str   =   0
				__magic_name__		:Union[str, Any]   =   str(snake_case			)

				while len(snake_case			) != 1:
								__magic_name__		:str   =   [int(snake_case			) for i in num_string]

								__magic_name__		:Optional[int]   =   0
								for i in range(0,	len(snake_case			)			):
												total += numbers[i]

								__magic_name__		:int   =   str(snake_case			)

								steps += 1
				return steps


if __name__ == "__main__":
					import doctest

					doctest.testmod()






