
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__(				_a ,						_a ,						_a ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Tuple         =	StableDiffusionInpaintPipeline
	a			:Tuple         =	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	a			:str         =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	a			:str         =	frozenset(
	    []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	a			:Union[str, Any]         =	frozenset([]							)
	def 						_lowercase  (       self  :			List[str]       )       ->     List[str]:
					torch.manual_seed(0       )
					lowercase_       =					UNetaDConditionModel(
					    block_out_channels=(3_2, 6_4)       ,							layers_per_block=2       ,							sample_size=3_2       ,							in_channels=9       ,							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=SCREAMING_SNAKE_CASE_       ,							)
					lowercase_       =					PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_       )
					torch.manual_seed(0       )
					lowercase_       =					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       )
					lowercase_       =					CLIPTextConfig(
					    bos_token_id=0       ,							eos_token_id=2       ,							hidden_size=3_2       ,							intermediate_size=3_7       ,							layer_norm_eps=1e-05       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							pad_token_id=1       ,							vocab_size=1_0_0_0       ,							hidden_act='''gelu'''       ,							projection_dim=5_1_2       ,							)
					lowercase_       =					CLIPTextModel(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

					lowercase_       =					{
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''vae''': vae,
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''safety_checker''': None,
					    '''feature_extractor''': None,
					}
					return components
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			str=0       )       ->     Optional[Any]:
					lowercase_       =					floats_tensor((1, 3, 3_2, 3_2)       ,							rng=random.Random(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					image.cpu().permute(0       ,							2       ,							3       ,							1       )[0]
					lowercase_       =					Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_       )       ).convert('''RGB'''       ).resize((6_4, 6_4)       )
					lowercase_       =					Image.fromarray(np.uinta(image + 4       )       ).convert('''RGB'''       ).resize((6_4, 6_4)       )
					if str(SCREAMING_SNAKE_CASE_       ).startswith('''mps'''       ):
									lowercase_       =					torch.manual_seed(SCREAMING_SNAKE_CASE_       )
					else:
									lowercase_       =					torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					{
					    '''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 						_lowercase  (       self  :			Dict       )       ->     Any:
					lowercase_       =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					lowercase_       =					self.get_dummy_components()
					lowercase_       =					StableDiffusionInpaintPipeline(**SCREAMING_SNAKE_CASE_       )
					lowercase_       =					sd_pipe.to(SCREAMING_SNAKE_CASE_       )
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					sd_pipe(**SCREAMING_SNAKE_CASE_       ).images
					lowercase_       =					image[0, -3:, -3:, -1]

					assert image.shape == (1, 6_4, 6_4, 3)
					lowercase_       =					np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]       )

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




	def 						_lowercase  (       self  :			Any       )       ->     List[Any]:
					super().test_inference_batch_single_identical(expected_max_diff=3e-3       )

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



	"""simple docstring"""




	def 						_lowercase  (       self  :			List[str]       )       ->     List[str]:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Dict:
					lowercase_       =					load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/sd2-inpaint/init_image.png'''       )
					lowercase_       =					load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''       )
					lowercase_       =					load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
					    '''/yellow_cat_sitting_on_a_park_bench.npy'''       )

					lowercase_       =					'''stabilityai/stable-diffusion-2-inpainting'''
					lowercase_       =					StableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_       ,							safety_checker=SCREAMING_SNAKE_CASE_       )
					pipe.to(SCREAMING_SNAKE_CASE_       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )
					pipe.enable_attention_slicing()

					lowercase_       =					'''Face of a yellow cat, high resolution, sitting on a park bench'''

					lowercase_       =					torch.manual_seed(0       )
					lowercase_       =					pipe(
					    prompt=SCREAMING_SNAKE_CASE_       ,							image=SCREAMING_SNAKE_CASE_       ,							mask_image=SCREAMING_SNAKE_CASE_       ,							generator=SCREAMING_SNAKE_CASE_       ,							output_type='''np'''       ,							)
					lowercase_       =					output.images[0]

					assert image.shape == (5_1_2, 5_1_2, 3)
					assert np.abs(expected_image - image       ).max() < 9e-3
	def 						_lowercase  (       self  :			Dict       )       ->     Optional[int]:
					lowercase_       =					load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/sd2-inpaint/init_image.png'''       )
					lowercase_       =					load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''       )
					lowercase_       =					load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
					    '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''       )

					lowercase_       =					'''stabilityai/stable-diffusion-2-inpainting'''
					lowercase_       =					StableDiffusionInpaintPipeline.from_pretrained(
					    SCREAMING_SNAKE_CASE_       ,							torch_dtype=torch.floataa       ,							safety_checker=SCREAMING_SNAKE_CASE_       ,							)
					pipe.to(SCREAMING_SNAKE_CASE_       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )
					pipe.enable_attention_slicing()

					lowercase_       =					'''Face of a yellow cat, high resolution, sitting on a park bench'''

					lowercase_       =					torch.manual_seed(0       )
					lowercase_       =					pipe(
					    prompt=SCREAMING_SNAKE_CASE_       ,							image=SCREAMING_SNAKE_CASE_       ,							mask_image=SCREAMING_SNAKE_CASE_       ,							generator=SCREAMING_SNAKE_CASE_       ,							output_type='''np'''       ,							)
					lowercase_       =					output.images[0]

					assert image.shape == (5_1_2, 5_1_2, 3)
					assert np.abs(expected_image - image       ).max() < 5e-1




	def 						_lowercase  (       self  :			str       )       ->     Any:
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()

					lowercase_       =					load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/sd2-inpaint/init_image.png'''       )
					lowercase_       =					load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''       )

					lowercase_       =					'''stabilityai/stable-diffusion-2-inpainting'''
					lowercase_       =					PNDMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_       ,							subfolder='''scheduler'''       )
					lowercase_       =					StableDiffusionInpaintPipeline.from_pretrained(
					    SCREAMING_SNAKE_CASE_       ,							safety_checker=SCREAMING_SNAKE_CASE_       ,							scheduler=SCREAMING_SNAKE_CASE_       ,							torch_dtype=torch.floataa       ,							)
					pipe.to(SCREAMING_SNAKE_CASE_       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )
					pipe.enable_attention_slicing(1       )
					pipe.enable_sequential_cpu_offload()

					lowercase_       =					'''Face of a yellow cat, high resolution, sitting on a park bench'''

					lowercase_       =					torch.manual_seed(0       )
					lowercase_       =					pipe(
					    prompt=SCREAMING_SNAKE_CASE_       ,							image=SCREAMING_SNAKE_CASE_       ,							mask_image=SCREAMING_SNAKE_CASE_       ,							generator=SCREAMING_SNAKE_CASE_       ,							num_inference_steps=2       ,							output_type='''np'''       ,							)

					lowercase_       =					torch.cuda.max_memory_allocated()
					# make sure that less than 2.65 GB is allocated
					assert mem_bytes < 2.65 * 1_0**9











import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.Resampling.BILINEAR,
	    '''bilinear''': PIL.Image.Resampling.BILINEAR,
	    '''bicubic''': PIL.Image.Resampling.BICUBIC,
	    '''lanczos''': PIL.Image.Resampling.LANCZOS,
	    '''nearest''': PIL.Image.Resampling.NEAREST,
	}
else:
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.LINEAR,
	    '''bilinear''': PIL.Image.BILINEAR,
	    '''bicubic''': PIL.Image.BICUBIC,
	    '''lanczos''': PIL.Image.LANCZOS,
	    '''nearest''': PIL.Image.NEAREST,
	}






def      lowerCamelCase_						(				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    Dict				=  (images / 2 + 0.5).clamp(0 ,				1 )
				snake_case_						:    Dict				=  images.cpu().permute(0 ,				2 ,				3 ,				1 ).float().numpy()
				snake_case_						:    int				=  numpy_to_pil(_UpperCamelCase )
				return images






def      lowerCamelCase_						(				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


				if images.ndim == 3:
								snake_case_						:    Optional[Any]				=  images[None, ...]
				snake_case_						:    Any				=  (images * 255).round().astype('''uint8''' )
				if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								snake_case_						:    str				=  [Image.fromarray(image.squeeze() ,				mode='''L''' ) for image in images]
				else:
								snake_case_						:    List[Any]				=  [Image.fromarray(_UpperCamelCase ) for image in images]

				return pil_images
