



import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

				if is_vision_available():
								from transformers import OneFormerImageProcessor
								from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
								from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
				from PIL import Image






def     a(      A       :    int ,     A       :    Optional[int]="shi-labs/oneformer_demo"       )		->       int:
					"""simple docstring"""





					with open(hf_hub_download(_lowercase ,     _lowercase ,     repo_type="dataset"       ) ,     "r"       ) as f:
										a					     =   json.load(_lowercase       )
					a					     =   {}
					a					     =   []
					a					     =   []
					for key, info in class_info.items():
										a					     =   info["""name"""]
										class_names.append(info["name"]       )
										if info["isthing"]:
															thing_ids.append(int(_lowercase       )       )
					a					     =   thing_ids
					a					     =   class_names
					return metadata



class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_=7		,       lowerCamelCase_=3		,       lowerCamelCase_=30		,       lowerCamelCase_=400		,       lowerCamelCase_=None		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=[0.5, 0.5, 0.5]		,       lowerCamelCase_=[0.5, 0.5, 0.5]		,       lowerCamelCase_=10		,       lowerCamelCase_=False		,       lowerCamelCase_=255		,       lowerCamelCase_="shi-labs/oneformer_demo"		,       lowerCamelCase_="ade20k_panoptic.json"		,       lowerCamelCase_=10		,       ):
						"""simple docstring"""

						a					     =   parent
						a					     =   batch_size
						a					     =   num_channels
						a					     =   min_resolution
						a					     =   max_resolution
						a					     =   do_resize
						a					     =   {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
						a					     =   do_normalize
						a					     =   image_mean
						a					     =   image_std
						a					     =   class_info_file
						a					     =   prepare_metadata(lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   num_text
						a					     =   repo_path

						# for the post_process_functions
						a					     =   2
						a					     =   10
						a					     =   10
						a					     =   3
						a					     =   4
						a					     =   num_labels
						a					     =   do_reduce_labels
						a					     =   ignore_index




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "num_labels": self.num_labels,
						    "do_reduce_labels": self.do_reduce_labels,
						    "ignore_index": self.ignore_index,
						    "class_info_file": self.class_info_file,
						    "metadata": self.metadata,
						    "num_text": self.num_text,
						}




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		):
						"""simple docstring"""

						if not batched:
											a					     =   image_inputs[0]
											if isinstance(lowerCamelCase_		,       Image.Image		):
																a					     =   image.size
											else:
																a					     =   image.shape[1], image.shape[2]
											if w < h:
																a					     =   int(self.size["shortest_edge"] * h / w		)
																a					     =   self.size["""shortest_edge"""]
											elif w > h:
																a					     =   self.size["""shortest_edge"""]
																a					     =   int(self.size["shortest_edge"] * w / h		)
											else:
																a					     =   self.size["""shortest_edge"""]
																a					     =   self.size["""shortest_edge"""]

						else:
											a					     =   []
											for image in image_inputs:
																a					     =   self.get_expected_values([image]		)
																expected_values.append((expected_height, expected_width)		)
											a					     =   max(lowerCamelCase_		,       key=lambda lowerCamelCase_		: item[0]		)[0]
											a					     =   max(lowerCamelCase_		,       key=lambda lowerCamelCase_		: item[1]		)[1]

						return expected_height, expected_width




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return OneFormerForUniversalSegmentationOutput(
						    # +1 for null class
						    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)		)		,       masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)		)		,       )




@require_torch
@require_vision
class 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
	# only for test_image_processing_common.test_image_proc_to_json_string
	__A							=							image_processing_class




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   OneFormerImageProcessorTester(self		)




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.image_processing_tester.prepare_image_processor_dict()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(lowerCamelCase_		,       "image_mean"		)		)
						self.assertTrue(hasattr(lowerCamelCase_		,       "image_std"		)		)
						self.assertTrue(hasattr(lowerCamelCase_		,       "do_normalize"		)		)
						self.assertTrue(hasattr(lowerCamelCase_		,       "do_resize"		)		)
						self.assertTrue(hasattr(lowerCamelCase_		,       "size"		)		)
						self.assertTrue(hasattr(lowerCamelCase_		,       "ignore_index"		)		)
						self.assertTrue(hasattr(lowerCamelCase_		,       "class_info_file"		)		)
						self.assertTrue(hasattr(lowerCamelCase_		,       "num_text"		)		)
						self.assertTrue(hasattr(lowerCamelCase_		,       "repo_path"		)		)
						self.assertTrue(hasattr(lowerCamelCase_		,       "metadata"		)		)
						self.assertTrue(hasattr(lowerCamelCase_		,       "do_reduce_labels"		)		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						a					     =   prepare_image_inputs(self.image_processing_tester		,       equal_resolution=lowerCamelCase_		)
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase_		,       Image.Image		)

						# Test not batched input
						a					     =   image_processor(image_inputs[0]		,       ["semantic"]		,       return_tensors="pt"		).pixel_values

						a					     =   self.image_processing_tester.get_expected_values(lowerCamelCase_		)

						self.assertEqual(
						    encoded_images.shape		,       (1, self.image_processing_tester.num_channels, expected_height, expected_width)		,       )

						# Test batched
						a					     =   self.image_processing_tester.get_expected_values(lowerCamelCase_		,       batched=lowerCamelCase_		)

						a					     =   image_processor(
						    lowerCamelCase_		,       ["semantic"] * len(lowerCamelCase_		)		,       return_tensors="pt"		).pixel_values
						self.assertEqual(
						    encoded_images.shape		,       (
						        self.image_processing_tester.batch_size,
						        self.image_processing_tester.num_channels,
						        expected_height,
						        expected_width,
						    )		,       )




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						a					     =   prepare_image_inputs(self.image_processing_tester		,       equal_resolution=lowerCamelCase_		,       numpify=lowerCamelCase_		)
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase_		,       np.ndarray		)

						# Test not batched input
						a					     =   image_processor(image_inputs[0]		,       ["semantic"]		,       return_tensors="pt"		).pixel_values

						a					     =   self.image_processing_tester.get_expected_values(lowerCamelCase_		)

						self.assertEqual(
						    encoded_images.shape		,       (1, self.image_processing_tester.num_channels, expected_height, expected_width)		,       )

						# Test batched
						a					     =   self.image_processing_tester.get_expected_values(lowerCamelCase_		,       batched=lowerCamelCase_		)

						a					     =   image_processor(
						    lowerCamelCase_		,       ["semantic"] * len(lowerCamelCase_		)		,       return_tensors="pt"		).pixel_values
						self.assertEqual(
						    encoded_images.shape		,       (
						        self.image_processing_tester.batch_size,
						        self.image_processing_tester.num_channels,
						        expected_height,
						        expected_width,
						    )		,       )




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						a					     =   prepare_image_inputs(self.image_processing_tester		,       equal_resolution=lowerCamelCase_		,       torchify=lowerCamelCase_		)
						for image in image_inputs:
											self.assertIsInstance(lowerCamelCase_		,       torch.Tensor		)

						# Test not batched input
						a					     =   image_processor(image_inputs[0]		,       ["semantic"]		,       return_tensors="pt"		).pixel_values

						a					     =   self.image_processing_tester.get_expected_values(lowerCamelCase_		)

						self.assertEqual(
						    encoded_images.shape		,       (1, self.image_processing_tester.num_channels, expected_height, expected_width)		,       )

						# Test batched
						a					     =   self.image_processing_tester.get_expected_values(lowerCamelCase_		,       batched=lowerCamelCase_		)

						a					     =   image_processor(
						    lowerCamelCase_		,       ["semantic"] * len(lowerCamelCase_		)		,       return_tensors="pt"		).pixel_values
						self.assertEqual(
						    encoded_images.shape		,       (
						        self.image_processing_tester.batch_size,
						        self.image_processing_tester.num_channels,
						        expected_height,
						        expected_width,
						    )		,       )




	def      UpperCamelCase_       (self		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_="np"		):
						"""simple docstring"""

						a					     =   self.image_processing_class(**self.image_processor_dict		)
						# prepare image and target
						a					     =   self.image_processing_tester.num_labels
						a					     =   None
						a					     =   None
						a					     =   prepare_image_inputs(self.image_processing_tester		,       equal_resolution=lowerCamelCase_		)
						if with_segmentation_maps:
											a					     =   num_labels
											if is_instance_map:
																a					     =   list(range(lowerCamelCase_		)		) * 2
																a					     =   dict(enumerate(lowerCamelCase_		)		)
											a					     =   [
											    np.random.randint(0		,       high * 2		,       (img.size[1], img.size[0])		).astype(np.uinta		) for img in image_inputs
											]
											if segmentation_type == "pil":
																a					     =   [Image.fromarray(lowerCamelCase_		) for annotation in annotations]

						a					     =   image_processor(
						    lowerCamelCase_		,       ["semantic"] * len(lowerCamelCase_		)		,       lowerCamelCase_		,       return_tensors="pt"		,       instance_id_to_semantic_id=lowerCamelCase_		,       pad_and_return_pixel_mask=lowerCamelCase_		,       )

						return inputs




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						def common(lowerCamelCase_=False		,       lowerCamelCase_=None		):
											a					     =   self.comm_get_image_processor_inputs(
											    with_segmentation_maps=lowerCamelCase_		,       is_instance_map=lowerCamelCase_		,       segmentation_type=lowerCamelCase_		)

											a					     =   inputs["""mask_labels"""]
											a					     =   inputs["""class_labels"""]
											a					     =   inputs["""pixel_values"""]
											a					     =   inputs["""text_inputs"""]

											# check the batch_size
											for mask_label, class_label, text_input in zip(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
																self.assertEqual(mask_label.shape[0]		,       class_label.shape[0]		)
																# this ensure padding has happened
																self.assertEqual(mask_label.shape[1:]		,       pixel_values.shape[2:]		)
																self.assertEqual(len(lowerCamelCase_		)		,       self.image_processing_tester.num_text		)

						common()
						common(is_instance_map=lowerCamelCase_		)
						common(is_instance_map=lowerCamelCase_		,       segmentation_type="pil"		)
						common(is_instance_map=lowerCamelCase_		,       segmentation_type="pil"		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   np.zeros((20, 50)		)
						a					     =   1
						a					     =   1
						a					     =   1

						a					     =   binary_mask_to_rle(lowerCamelCase_		)
						self.assertEqual(len(lowerCamelCase_		)		,       4		)
						self.assertEqual(rle[0]		,       21		)
						self.assertEqual(rle[1]		,       45		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.image_processing_class(
						    num_labels=self.image_processing_tester.num_classes		,       max_seq_length=77		,       task_seq_length=77		,       class_info_file="ade20k_panoptic.json"		,       num_text=self.image_processing_tester.num_text		,       repo_path="shi-labs/oneformer_demo"		,       )
						a					     =   self.image_processing_tester.get_fake_oneformer_outputs()

						a					     =   fature_extractor.post_process_semantic_segmentation(lowerCamelCase_		)

						self.assertEqual(len(lowerCamelCase_		)		,       self.image_processing_tester.batch_size		)
						self.assertEqual(
						    segmentation[0].shape		,       (
						        self.image_processing_tester.height,
						        self.image_processing_tester.width,
						    )		,       )

						a					     =   [(1, 4) for i in range(self.image_processing_tester.batch_size		)]
						a					     =   fature_extractor.post_process_semantic_segmentation(lowerCamelCase_		,       target_sizes=lowerCamelCase_		)

						self.assertEqual(segmentation[0].shape		,       target_sizes[0]		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.image_processing_class(
						    num_labels=self.image_processing_tester.num_classes		,       max_seq_length=77		,       task_seq_length=77		,       class_info_file="ade20k_panoptic.json"		,       num_text=self.image_processing_tester.num_text		,       repo_path="shi-labs/oneformer_demo"		,       )
						a					     =   self.image_processing_tester.get_fake_oneformer_outputs()
						a					     =   image_processor.post_process_instance_segmentation(lowerCamelCase_		,       threshold=0		)

						self.assertTrue(len(lowerCamelCase_		) == self.image_processing_tester.batch_size		)
						for el in segmentation:
											self.assertTrue("segmentation" in el		)
											self.assertTrue("segments_info" in el		)
											self.assertEqual(type(el["segments_info"]		)		,       lowerCamelCase_		)
											self.assertEqual(
											    el["segmentation"].shape		,       (self.image_processing_tester.height, self.image_processing_tester.width)		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.image_processing_class(
						    num_labels=self.image_processing_tester.num_classes		,       max_seq_length=77		,       task_seq_length=77		,       class_info_file="ade20k_panoptic.json"		,       num_text=self.image_processing_tester.num_text		,       repo_path="shi-labs/oneformer_demo"		,       )
						a					     =   self.image_processing_tester.get_fake_oneformer_outputs()
						a					     =   image_processor.post_process_panoptic_segmentation(lowerCamelCase_		,       threshold=0		)

						self.assertTrue(len(lowerCamelCase_		) == self.image_processing_tester.batch_size		)
						for el in segmentation:
											self.assertTrue("segmentation" in el		)
											self.assertTrue("segments_info" in el		)
											self.assertEqual(type(el["segments_info"]		)		,       lowerCamelCase_		)
											self.assertEqual(
											    el["segmentation"].shape		,       (self.image_processing_tester.height, self.image_processing_tester.width)		)





'''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

