

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_		(			__lowercase				,       __lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







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


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												torch.manual_seed(0       )
												_a	:    List[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=_a       ,							)
												_a	:    Optional[Any]         =		DDIMScheduler(
												    beta_start=0.0_0085       ,							beta_end=0.012       ,							beta_schedule='''scaled_linear'''       ,							clip_sample=_a       ,							set_alpha_to_one=_a       ,							)
												_a	:    Optional[Any]         =		DDIMInverseScheduler(
												    beta_start=0.0_0085       ,							beta_end=0.012       ,							beta_schedule='''scaled_linear'''       ,							clip_sample=_a       ,							set_alpha_to_zero=_a       ,							)
												torch.manual_seed(0       )
												_a	:    List[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       )
												_a	:    Optional[Any]         =		CLIPTextConfig(
												    bos_token_id=0       ,							eos_token_id=2       ,							hidden_size=3_2       ,							intermediate_size=3_7       ,							layer_norm_eps=1e-0_5       ,							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       ,							)
												_a	:    Dict         =		CLIPTextModel(_a       )
												_a	:    Optional[int]         =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

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

												_a	:    List[str]         =		self.get_dummy_components()
												_a	:    List[Any]         =		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}       )

												_a	:    int         =		self.get_dummy_inputs(_a       )
												_a	:    Optional[int]         =		pipe(**_a       )[0]

												with tempfile.TemporaryDirectory() as tmpdir:
																	pipe.save_pretrained(_a       )
																	_a	:    List[Any]         =		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."""       ,							)

												_a	:    List[Any]         =		self.get_dummy_inputs(_a       )
												_a	:    List[Any]         =		pipe_loaded(**_a       )[0]

												_a	:    Any         =		np.abs(output - output_loaded       ).max()
												self.assertLess(_a       ,							1e-4       )


							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Union[str, Any]         =		'''cpu'''

												_a	:    str         =		self.get_dummy_components()
												_a	:    Optional[int]         =		self.pipeline_class(**_a       )
												pipe.to(_a       )
												pipe.set_progress_bar_config(disable=_a       )

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

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


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    Dict         =		'''cpu'''

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

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

												self.assertEqual(image.shape       ,							(2, 3_2, 3_2, 3)       )
												_a	:    Any         =		np.array(
												    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]       ,							)
												_a	:    Optional[Any]         =		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:
												_a	:    Any         =		'''cpu'''

												_a	:    int         =		self.get_dummy_components()

												_a	:    str         =		{'''beta_start''': 0.0_0085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
												_a	:    Any         =		DPMSolverMultistepScheduler(**_a       )
												_a	:    int         =		DPMSolverMultistepInverseScheduler(**_a       )

												_a	:    Optional[int]         =		self.pipeline_class(**_a       )
												pipe.to(_a       )
												pipe.set_progress_bar_config(disable=_a       )

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

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



@require_torch_gpu
@slow
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







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


							@classmethod
							def 				__lowercase				(			cls       )    ->    Tuple:
												_a	:    Optional[int]         =		load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''       )

												_a	:    int         =		raw_image.convert('''RGB'''       ).resize((7_6_8, 7_6_8)       )

												_a	:    List[Any]         =		raw_image


							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    List[str]         =		torch.manual_seed(0       )

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

												_a	:    int         =		'''a bowl of fruit'''
												_a	:    Tuple         =		'''a bowl of pears'''

												_a	:    Optional[int]         =		pipe.generate_mask(
												    image=self.raw_image       ,							source_prompt=_a       ,							target_prompt=_a       ,							generator=_a       ,							)

												_a	:    Optional[Any]         =		pipe.invert(
												    prompt=_a       ,							image=self.raw_image       ,							inpaint_strength=0.7       ,							generator=_a       ).latents

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

												_a	:    Dict         =		(
												    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				(			self       )    ->    Tuple:
												_a	:    Dict         =		torch.manual_seed(0       )

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

												_a	:    str         =		'''a bowl of fruit'''
												_a	:    Tuple         =		'''a bowl of pears'''

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

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

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

												_a	:    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






from collections.abc import Callable

class 				UpperCAmelCase_		:
							"""simple docstring"""







							def __init__(			self       ,							_a = None       )    ->    None:
												# Stores actual heap items.
												_a	:    list         =		[]
												# Stores indexes of each item for supporting updates and deletion.
												_a	:    dict         =		{}
												# Stores current size of heap.
												_a	:    Tuple         =		0
												# Stores function used to evaluate the score of an item on which basis ordering
												# will be done.
												_a	:    Dict         =		key or (lambda _a       : x)


							def 				__lowercase				(			self       ,							_a       )    ->    int | None:
												return int((i - 1) / 2       ) if i > 0 else None


							def 				__lowercase				(			self       ,							_a       )    ->    int | None:
												_a	:    Optional[int]         =		int(2 * i + 1       )
												return left if 0 < left < self.size else None


							def 				__lowercase				(			self       ,							_a       )    ->    int | None:
												_a	:    int         =		int(2 * i + 2       )
												return right if 0 < right < self.size else None


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    None:
												_a      ,					_a	:    Union[str, Any]         =		(
												    self.pos_map[self.arr[j][0]],
												    self.pos_map[self.arr[i][0]],
												)
												# Then swap the items in the list.
												_a      ,					_a	:    List[Any]         =		self.arr[j], self.arr[i]


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    bool:
												return self.arr[i][1] < self.arr[j][1]


							def 				__lowercase				(			self       ,							_a       )    ->    int:
												_a	:    Dict         =		self._left(_a       )
												_a	:    str         =		self._right(_a       )
												_a	:    str         =		i

												if left is not None and not self._cmp(_a       ,							_a       ):
																	_a	:    Optional[Any]         =		left
												if right is not None and not self._cmp(_a       ,							_a       ):
																	_a	:    Any         =		right

												return valid_parent


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												_a	:    List[str]         =		self._parent(_a       )
												while parent is not None and not self._cmp(_a       ,							_a       ):
																	self._swap(_a       ,							_a       )
																	_a      ,					_a	:    Any         =		parent, self._parent(_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												_a	:    List[Any]         =		self._get_valid_parent(_a       )
												while valid_parent != index:
																	self._swap(_a       ,							_a       )
																	_a      ,					_a	:    int         =		valid_parent, self._get_valid_parent(_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    None:
												if item not in self.pos_map:
																	return
												_a	:    str         =		self.pos_map[item]
												_a	:    List[Any]         =		[item, self.key(_a       )]
												# Make sure heap is right in both up and down direction.
												# Ideally only one of them will make any change.
												self._heapify_up(_a       )
												self._heapify_down(_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												if item not in self.pos_map:
																	return
												_a	:    Tuple         =		self.pos_map[item]
												del self.pos_map[item]
												_a	:    Tuple         =		self.arr[self.size - 1]
												_a	:    str         =		index
												self.size -= 1
												# Make sure heap is right in both up and down direction. Ideally only one
												# of them will make any change- so no performance loss in calling both.
												if self.size > index:
																	self._heapify_up(_a       )
																	self._heapify_down(_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       )    ->    None:
												_a	:    Union[str, Any]         =		len(self.arr       )
												if arr_len == self.size:
																	self.arr.append([item, self.key(_a       )]       )
												else:
																	_a	:    Optional[int]         =		[item, self.key(_a       )]
												_a	:    Tuple         =		self.size
												self.size += 1
												self._heapify_up(self.size - 1       )


							def 				__lowercase				(			self       )    ->    tuple | None:
												return self.arr[0] if self.size else None




							def 				__lowercase				(			self       )    ->    tuple | None:
												_a	:    Tuple         =		self.get_top()
												if top_item_tuple:
																	self.delete_item(top_item_tuple[0]       )
												return top_item_tuple



def    __UpperCAmelCase (	)			->     None:

					"""simple docstring"""



if __name__ == "__main__":
			import doctest

			doctest.testmod()



