

from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
						from transformers import (
						    AutoConfig,
						    BertConfig,
						    GPTaConfig,
						    TaConfig,
						    TFAutoModel,
						    TFAutoModelForCausalLM,
						    TFAutoModelForMaskedLM,
						    TFAutoModelForPreTraining,
						    TFAutoModelForQuestionAnswering,
						    TFAutoModelForSeqaSeqLM,
						    TFAutoModelForSequenceClassification,
						    TFAutoModelWithLMHead,
						    TFBertForMaskedLM,
						    TFBertForPreTraining,
						    TFBertForQuestionAnswering,
						    TFBertForSequenceClassification,
						    TFBertModel,
						    TFGPTaLMHeadModel,
						    TFRobertaForMaskedLM,
						    TFTaForConditionalGeneration,
						)
						from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
						from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
						from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
						from transformers import (
						    AutoModel,
						    AutoModelForCausalLM,
						    AutoModelForMaskedLM,
						    AutoModelForPreTraining,
						    AutoModelForQuestionAnswering,
						    AutoModelForSeqaSeqLM,
						    AutoModelForSequenceClassification,
						    AutoModelWithLMHead,
						    BertForMaskedLM,
						    BertForPreTraining,
						    BertForQuestionAnswering,
						    BertForSequenceClassification,
						    BertModel,
						    GPTaLMHeadModel,
						    RobertaForMaskedLM,
						    TaForConditionalGeneration,
						)



@is_pt_tf_cross_test
class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									for model_name in ["bert-base-uncased"]:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoConfig.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			int  		=	TFAutoModel.from_pretrained(lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			int  		=	AutoModel.from_pretrained(lowerCAmelCase__       ,						from_tf=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									for model_name in ["bert-base-uncased"]:
															SCREAMING_SNAKE_CASE_:			int  		=	AutoConfig.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			List[Any]  		=	TFAutoModelForPreTraining.from_pretrained(lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoModelForPreTraining.from_pretrained(lowerCAmelCase__       ,						from_tf=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															SCREAMING_SNAKE_CASE_:			Dict  		=	AutoConfig.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	TFAutoModelForCausalLM.from_pretrained(
															    lowerCAmelCase__       ,						output_loading_info=lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			Dict  		=	AutoModelForCausalLM.from_pretrained(lowerCAmelCase__       ,						from_tf=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	AutoModelForCausalLM.from_pretrained(
															    lowerCAmelCase__       ,						output_loading_info=lowerCAmelCase__       ,						from_tf=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AutoConfig.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			int  		=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase__       ,						from_tf=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															SCREAMING_SNAKE_CASE_:			str  		=	AutoConfig.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			Any  		=	TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[Any]  		=	TFAutoModelForMaskedLM.from_pretrained(
															    lowerCAmelCase__       ,						output_loading_info=lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			Tuple  		=	AutoModelForMaskedLM.from_pretrained(lowerCAmelCase__       ,						from_tf=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			str  		=	AutoModelForMaskedLM.from_pretrained(
															    lowerCAmelCase__       ,						output_loading_info=lowerCAmelCase__       ,						from_tf=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoConfig.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			str  		=	TFAutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	TFAutoModelForSeqaSeqLM.from_pretrained(
															    lowerCAmelCase__       ,						output_loading_info=lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			Dict  		=	AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__       ,						from_tf=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	AutoModelForSeqaSeqLM.from_pretrained(
															    lowerCAmelCase__       ,						output_loading_info=lowerCAmelCase__       ,						from_tf=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									for model_name in ["bert-base-uncased"]:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoConfig.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			List[Any]  		=	TFAutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			Dict  		=	AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__       ,						from_tf=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									for model_name in ["bert-base-uncased"]:
															SCREAMING_SNAKE_CASE_:			Dict  		=	AutoConfig.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			List[str]  		=	TFAutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_:			Any  		=	AutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase__       ,						from_tf=lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)
															self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			int  		=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)
									self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)
									self.assertEqual(model.num_parameters()       ,						1_4410)
									self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__)       ,						1_4410)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase__       ,						from_tf=lowerCAmelCase__)
									self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)
									self.assertEqual(model.num_parameters()       ,						1_4410)
									self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__)       ,						1_4410)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__       ,						from_pt=lowerCAmelCase__)
									self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)
									self.assertEqual(model.num_parameters()       ,						1_4410)
									self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__)       ,						1_4410)

									SCREAMING_SNAKE_CASE_:			int  		=	AutoModelWithLMHead.from_pretrained(lowerCAmelCase__       ,						from_tf=lowerCAmelCase__)
									self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)
									self.assertEqual(model.num_parameters()       ,						1_4410)
									self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__)       ,						1_4410)









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Dict      							=					StableDiffusionInpaintPipeline
			_UpperCAmelCase       :							Tuple      							=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			_UpperCAmelCase       :							List[str]      							=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			_UpperCAmelCase       :							Tuple      							=					frozenset(
			    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			_UpperCAmelCase       :							Optional[int]      							=					frozenset([]     )



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	UNetaDConditionModel(
									    block_out_channels=(32, 64)       ,						layers_per_block=2       ,						sample_size=32       ,						in_channels=9       ,						out_channels=4       ,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,						cross_attention_dim=32       ,						attention_head_dim=(2, 4)       ,						use_linear_projection=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	PNDMScheduler(skip_prk_steps=lowerCAmelCase__)
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	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)
									SCREAMING_SNAKE_CASE_:			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       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CLIPTextModel(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	{
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "safety_checker": None,
									    "feature_extractor": None,
									}
									return components



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Optional[int]=0):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									SCREAMING_SNAKE_CASE_:			Dict  		=	floats_tensor((1, 3, 32, 32)       ,						rng=random.Random(lowerCAmelCase__)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	image.cpu().permute(0       ,						2       ,						3       ,						1)[0]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	Image.fromarray(np.uinta(lowerCAmelCase__)).convert("RGB").resize((64, 64))
									SCREAMING_SNAKE_CASE_:			List[str]  		=	Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			Any  		=	torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	{
									    "prompt": "A painting of a squirrel eating a burger",
									    "image": init_image,
									    "mask_image": mask_image,
									    "generator": generator,
									    "num_inference_steps": 2,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}
									return inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	"cpu"  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE_:			int  		=	self.get_dummy_components()
									SCREAMING_SNAKE_CASE_:			int  		=	StableDiffusionInpaintPipeline(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	sd_pipe.to(lowerCAmelCase__)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Any  		=	self.get_dummy_inputs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	sd_pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			Dict  		=	image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])

									assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3)







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




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			Any  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			int  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench.npy")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Any  		=	StableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase__       ,						safety_checker=lowerCAmelCase__)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE_:			str  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						output_type="np"       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image).max() < 9E-3



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
									SCREAMING_SNAKE_CASE_:			Any  		=	load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
									    "/yellow_cat_sitting_on_a_park_bench_fp16.npy")

									SCREAMING_SNAKE_CASE_:			str  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Dict  		=	StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCAmelCase__       ,						torch_dtype=torch.floataa       ,						safety_checker=lowerCAmelCase__       ,						)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing()

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Dict  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						output_type="np"       ,						)
									SCREAMING_SNAKE_CASE_:			Any  		=	output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image).max() < 5E-1




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									SCREAMING_SNAKE_CASE_:			Tuple  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/sd2-inpaint/init_image.png")
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	"stabilityai/stable-diffusion-2-inpainting"
									SCREAMING_SNAKE_CASE_:			Tuple  		=	PNDMScheduler.from_pretrained(lowerCAmelCase__       ,						subfolder="scheduler")
									SCREAMING_SNAKE_CASE_:			Any  		=	StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCAmelCase__       ,						safety_checker=lowerCAmelCase__       ,						scheduler=lowerCAmelCase__       ,						torch_dtype=torch.floataa       ,						)
									pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)
									pipe.enable_attention_slicing(1)
									pipe.enable_sequential_cpu_offload()

									SCREAMING_SNAKE_CASE_:			Any  		=	"Face of a yellow cat, high resolution, sitting on a park bench"

									SCREAMING_SNAKE_CASE_:			Any  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						mask_image=lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						num_inference_steps=2       ,						output_type="np"       ,						)

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9






