



'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a   :			str					  =    logging.get_logger(__name__)


if is_vision_available():
							import PIL

class 			UpperCamelCase_ (							__magic_name__			):
	lowercase          = ['pixel_values']





	def __init__(  self		,		A = True		,		A = None		,		A = PILImageResampling.BICUBIC		,		A = True		,		A = None		,		A = True		,		A = 1 / 255		,		A = True		,		A = None		,		A = None		,		A = True		,		**A		,		)			->			None:
								super().__init__(**A    )
								UpperCAmelCase     :		Optional[Any]							     =		size if size is not None else {"""shortest_edge""": 224}
								UpperCAmelCase     :		int							     =		get_size_dict(A		,		default_to_square=A    )
								UpperCAmelCase     :		int							     =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								UpperCAmelCase     :		Dict							     =		get_size_dict(A		,		default_to_square=A		,		param_name="""crop_size"""    )

								UpperCAmelCase     :		List[Any]							     =		do_resize
								UpperCAmelCase     :		Optional[Any]							     =		size
								UpperCAmelCase     :		Tuple							     =		resample
								UpperCAmelCase     :		List[Any]							     =		do_center_crop
								UpperCAmelCase     :		List[Any]							     =		crop_size
								UpperCAmelCase     :		Any							     =		do_rescale
								UpperCAmelCase     :		List[str]							     =		rescale_factor
								UpperCAmelCase     :		List[Any]							     =		do_normalize
								UpperCAmelCase     :		int							     =		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								UpperCAmelCase     :		Tuple							     =		image_std if image_std is not None else OPENAI_CLIP_STD
								UpperCAmelCase     :		Optional[Any]							     =		do_convert_rgb





	def  _lowercase(  self		,		A		,		A		,		A = PILImageResampling.BICUBIC		,		A = None		,		**A		,		)			->			np.ndarray:
								UpperCAmelCase     :		Dict							     =		get_size_dict(A		,		default_to_square=A    )
								if "shortest_edge" not in size:
															raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
								UpperCAmelCase     :		str							     =		get_resize_output_image_size(A		,		size=size["""shortest_edge"""]		,		default_to_square=A    )
								return resize(A		,		size=A		,		resample=A		,		data_format=A		,		**A    )





	def  _lowercase(  self		,		A		,		A		,		A = None		,		**A		,		)			->			np.ndarray:
								UpperCAmelCase     :		Tuple							     =		get_size_dict(A    )
								if "height" not in size or "width" not in size:
															raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''    )
								return center_crop(A		,		size=(size["""height"""], size["""width"""])		,		data_format=A		,		**A    )





	def  _lowercase(  self		,		A		,		A		,		A = None		,		**A		,		)			->			Dict:
								return rescale(A		,		scale=A		,		data_format=A		,		**A    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A = None		,		**A		,		)			->			np.ndarray:
								return normalize(A		,		mean=A		,		std=A		,		data_format=A		,		**A    )






	def  _lowercase(  self		,		A		,		A = None		,		A = None		,		A = None		,		A = None		,		A = None		,		A = None		,		A = None		,		A = None		,		A = None		,		A = None		,		A = None		,		A = None		,		A = ChannelDimension.FIRST		,		**A		,		)			->			PIL.Image.Image:
								UpperCAmelCase     :		str							     =		do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase     :		Optional[Any]							     =		size if size is not None else self.size
								UpperCAmelCase     :		Dict							     =		get_size_dict(A		,		param_name="""size"""		,		default_to_square=A    )
								UpperCAmelCase     :		Any							     =		resample if resample is not None else self.resample
								UpperCAmelCase     :		Any							     =		do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase     :		int							     =		crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase     :		int							     =		get_size_dict(A		,		param_name="""crop_size"""		,		default_to_square=A    )
								UpperCAmelCase     :		Optional[Any]							     =		do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase     :		int							     =		rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase     :		List[Any]							     =		do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase     :		str							     =		image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase     :		Dict							     =		image_std if image_std is not None else self.image_std
								UpperCAmelCase     :		Union[str, Any]							     =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								UpperCAmelCase     :		Optional[int]							     =		make_list_of_images(A    )

								if not valid_images(A    ):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

								if do_resize and size is None:
															raise ValueError("""Size must be specified if do_resize is True."""    )

								if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""    )

								if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

								if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("""Image mean and std must be specified if do_normalize is True."""    )

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
															UpperCAmelCase     :		str							     =		[convert_to_rgb(A    ) for image in images]

								# All transformations expect numpy arrays.
								UpperCAmelCase     :		Optional[int]							     =		[to_numpy_array(A    ) for image in images]

								if do_resize:
															UpperCAmelCase     :		str							     =		[self.resize(image=A		,		size=A		,		resample=A    ) for image in images]

								if do_center_crop:
															UpperCAmelCase     :		List[str]							     =		[self.center_crop(image=A		,		size=A    ) for image in images]

								if do_rescale:
															UpperCAmelCase     :		Any							     =		[self.rescale(image=A		,		scale=A    ) for image in images]

								if do_normalize:
															UpperCAmelCase     :		Any							     =		[self.normalize(image=A		,		mean=A		,		std=A    ) for image in images]

								UpperCAmelCase     :		Optional[Any]							     =		[to_channel_dimension_format(A		,		A    ) for image in images]

								UpperCAmelCase     :		Optional[Any]							     =		{"""pixel_values""": images}
								return BatchFeature(data=A		,		tensor_type=A    )






'''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 			UpperCamelCase_ (							__magic_name__      ,     __magic_name__      ,     unittest.TestCase			):
	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  _lowercase(  self    )			->			Optional[int]:
								torch.manual_seed(0    )
								UpperCAmelCase     :		Dict							     =		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=A		,		)
								UpperCAmelCase     :		int							     =		DDIMScheduler(
								    beta_start=0.0_0_0_8_5		,		beta_end=0.0_1_2		,		beta_schedule="""scaled_linear"""		,		clip_sample=A		,		set_alpha_to_one=A		,		)
								UpperCAmelCase     :		List[Any]							     =		DDIMInverseScheduler(
								    beta_start=0.0_0_0_8_5		,		beta_end=0.0_1_2		,		beta_schedule="""scaled_linear"""		,		clip_sample=A		,		set_alpha_to_zero=A		,		)
								torch.manual_seed(0    )
								UpperCAmelCase     :		int							     =		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     :		Optional[int]							     =		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=1000		,		hidden_act="""gelu"""		,		projection_dim=512		,		)
								UpperCAmelCase     :		Optional[Any]							     =		CLIPTextModel(A    )
								UpperCAmelCase     :		List[str]							     =		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  _lowercase(  self		,		A		,		A=0    )			->			Optional[Any]:
								UpperCAmelCase     :		Any							     =		floats_tensor((1, 16, 16)		,		rng=random.Random(A    )    ).to(A    )
								UpperCAmelCase     :		Any							     =		floats_tensor((1, 2, 4, 16, 16)		,		rng=random.Random(A    )    ).to(A    )
								if str(A    ).startswith("""mps"""    ):
															UpperCAmelCase     :		List[Any]							     =		torch.manual_seed(A    )
								else:
															UpperCAmelCase     :		List[Any]							     =		torch.Generator(device=A    ).manual_seed(A    )
								UpperCAmelCase     :		int							     =		{
								    """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  _lowercase(  self		,		A		,		A=0    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		floats_tensor((1, 3, 32, 32)		,		rng=random.Random(A    )    ).to(A    )
								UpperCAmelCase     :		Union[str, Any]							     =		image.cpu().permute(0		,		2		,		3		,		1    )[0]
								UpperCAmelCase     :		int							     =		Image.fromarray(np.uinta(A    )    ).convert("""RGB"""    )
								if str(A    ).startswith("""mps"""    ):
															UpperCAmelCase     :		Optional[int]							     =		torch.manual_seed(A    )
								else:
															UpperCAmelCase     :		Optional[Any]							     =		torch.Generator(device=A    ).manual_seed(A    )
								UpperCAmelCase     :		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  _lowercase(  self		,		A		,		A=0    )			->			str:
								UpperCAmelCase     :		int							     =		floats_tensor((1, 3, 32, 32)		,		rng=random.Random(A    )    ).to(A    )
								UpperCAmelCase     :		str							     =		image.cpu().permute(0		,		2		,		3		,		1    )[0]
								UpperCAmelCase     :		int							     =		Image.fromarray(np.uinta(A    )    ).convert("""RGB"""    )
								if str(A    ).startswith("""mps"""    ):
															UpperCAmelCase     :		Optional[int]							     =		torch.manual_seed(A    )
								else:
															UpperCAmelCase     :		Optional[int]							     =		torch.Generator(device=A    ).manual_seed(A    )
								UpperCAmelCase     :		str							     =		{
								    """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  _lowercase(  self    )			->			List[Any]:
								if not hasattr(self.pipeline_class		,		"""_optional_components"""    ):
															return

								UpperCAmelCase     :		Dict							     =		self.get_dummy_components()
								UpperCAmelCase     :		int							     =		self.pipeline_class(**A    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )

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

								UpperCAmelCase     :		Any							     =		self.get_dummy_inputs(A    )
								UpperCAmelCase     :		Optional[Any]							     =		pipe(**A    )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(A    )
															UpperCAmelCase     :		Dict							     =		self.pipeline_class.from_pretrained(A    )
															pipe_loaded.to(A    )
															pipe_loaded.set_progress_bar_config(disable=A    )

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

								UpperCAmelCase     :		Union[str, Any]							     =		self.get_dummy_inputs(A    )
								UpperCAmelCase     :		Tuple							     =		pipe_loaded(**A    )[0]

								UpperCAmelCase     :		Optional[Any]							     =		np.abs(output - output_loaded    ).max()
								self.assertLess(A		,		1e-4    )





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[int]							     =		"""cpu"""

								UpperCAmelCase     :		Optional[Any]							     =		self.get_dummy_components()
								UpperCAmelCase     :		List[str]							     =		self.pipeline_class(**A    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		Union[str, Any]							     =		self.get_dummy_mask_inputs(A    )
								UpperCAmelCase     :		List[Any]							     =		pipe.generate_mask(**A    )
								UpperCAmelCase     :		Dict							     =		mask[0, -3:, -3:]

								self.assertEqual(mask.shape		,		(1, 16, 16)    )
								UpperCAmelCase     :		Optional[int]							     =		np.array([0] * 9    )
								UpperCAmelCase     :		Union[str, Any]							     =		np.abs(mask_slice.flatten() - expected_slice    ).max()
								self.assertLessEqual(A		,		1e-3    )
								self.assertEqual(mask[0, -3, -4]		,		0    )





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[Any]							     =		"""cpu"""

								UpperCAmelCase     :		List[str]							     =		self.get_dummy_components()
								UpperCAmelCase     :		Optional[Any]							     =		self.pipeline_class(**A    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )

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

								self.assertEqual(image.shape		,		(2, 32, 32, 3)    )
								UpperCAmelCase     :		Dict							     =		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     :		Dict							     =		np.abs(image_slice.flatten() - expected_slice    ).max()
								self.assertLessEqual(A		,		1e-3    )





	def  _lowercase(  self    )			->			Dict:
								super().test_inference_batch_single_identical(expected_max_diff=5e-3    )






	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		List[Any]							     =		"""cpu"""

								UpperCAmelCase     :		int							     =		self.get_dummy_components()

								UpperCAmelCase     :		List[Any]							     =		{"""beta_start""": 0.0_0_0_8_5, """beta_end""": 0.0_1_2, """beta_schedule""": """scaled_linear"""}
								UpperCAmelCase     :		int							     =		DPMSolverMultistepScheduler(**A    )
								UpperCAmelCase     :		int							     =		DPMSolverMultistepInverseScheduler(**A    )

								UpperCAmelCase     :		List[str]							     =		self.pipeline_class(**A    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		Tuple							     =		self.get_dummy_inversion_inputs(A    )
								UpperCAmelCase     :		Any							     =		pipe.invert(**A    ).images
								UpperCAmelCase     :		Dict							     =		image[0, -1, -3:, -3:]

								self.assertEqual(image.shape		,		(2, 32, 32, 3)    )
								UpperCAmelCase     :		Any							     =		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     :		Optional[int]							     =		np.abs(image_slice.flatten() - expected_slice    ).max()
								self.assertLessEqual(A		,		1e-3    )




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





	def  _lowercase(  self    )			->			int:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





	@classmethod
	def  _lowercase(  cls    )			->			Dict:
								UpperCAmelCase     :		Tuple							     =		load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""    )

								UpperCAmelCase     :		Union[str, Any]							     =		raw_image.convert("""RGB"""    ).resize((768, 768)    )

								UpperCAmelCase     :		List[str]							     =		raw_image





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Dict							     =		torch.manual_seed(0    )

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

								UpperCAmelCase     :		Tuple							     =		"""a bowl of fruit"""
								UpperCAmelCase     :		List[Any]							     =		"""a bowl of pears"""

								UpperCAmelCase     :		str							     =		pipe.generate_mask(
								    image=self.raw_image		,		source_prompt=A		,		target_prompt=A		,		generator=A		,		)

								UpperCAmelCase     :		Tuple							     =		pipe.invert(
								    prompt=A		,		image=self.raw_image		,		inpaint_strength=0.7		,		generator=A    ).latents

								UpperCAmelCase     :		Any							     =		pipe(
								    prompt=A		,		mask_image=A		,		image_latents=A		,		generator=A		,		negative_prompt=A		,		inpaint_strength=0.7		,		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((768, 768)    )    )
								    / 255
								)
								assert np.abs((expected_image - image).max()    ) < 5e-1






	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[Any]							     =		torch.manual_seed(0    )

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

								UpperCAmelCase     :		int							     =		"""a bowl of fruit"""
								UpperCAmelCase     :		int							     =		"""a bowl of pears"""

								UpperCAmelCase     :		str							     =		pipe.generate_mask(
								    image=self.raw_image		,		source_prompt=A		,		target_prompt=A		,		generator=A		,		)

								UpperCAmelCase     :		Any							     =		pipe.invert(
								    prompt=A		,		image=self.raw_image		,		inpaint_strength=0.7		,		generator=A		,		num_inference_steps=25		,		).latents

								UpperCAmelCase     :		str							     =		pipe(
								    prompt=A		,		mask_image=A		,		image_latents=A		,		generator=A		,		negative_prompt=A		,		inpaint_strength=0.7		,		num_inference_steps=25		,		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((768, 768)    )    )
								    / 255
								)
								assert np.abs((expected_image - image).max()    ) < 5e-1

