






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       __SCREAMING_SNAKE_CASE   (				a_						,   a_						,   a_						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    int						=   StableDiffusionInpaintPipeline
						_SCREAMING_SNAKE_CASE		:    int						=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						_SCREAMING_SNAKE_CASE		:    Tuple						=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						_SCREAMING_SNAKE_CASE		:    int						=   frozenset(
						    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						_SCREAMING_SNAKE_CASE		:    List[str]						=   frozenset([])






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)
												lowerCAmelCase__        =  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=lowercase_       ,  )
												lowerCAmelCase__        =  PNDMScheduler(skip_prk_steps=lowercase_				)
												torch.manual_seed(0				)
												lowerCAmelCase__        =  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=1_28       ,  )
												torch.manual_seed(0				)
												lowerCAmelCase__        =  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=10_00       ,  hidden_act='gelu'       ,  projection_dim=5_12       ,  )
												lowerCAmelCase__        =  CLIPTextModel(lowercase_				)
												lowerCAmelCase__        =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

												lowerCAmelCase__        =  {
												    'unet': unet,
												    'scheduler': scheduler,
												    'vae': vae,
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'safety_checker': None,
												    'feature_extractor': None,
												}
												return components






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=0				):






												"""simple docstring"""
												lowerCAmelCase__        =  floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(lowercase_				)				).to(lowercase_				)
												lowerCAmelCase__        =  image.cpu().permute(0       ,  2       ,  3       ,  1				)[0]
												lowerCAmelCase__        =  Image.fromarray(np.uinta(lowercase_				)				).convert('RGB'				).resize((64, 64)				)
												lowerCAmelCase__        =  Image.fromarray(np.uinta(image + 4				)				).convert('RGB'				).resize((64, 64)				)
												if str(lowercase_				).startswith('mps'				):
																		lowerCAmelCase__        =  torch.manual_seed(lowercase_				)
												else:
																		lowerCAmelCase__        =  torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
												lowerCAmelCase__        =  {
												    '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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  'cpu'  # ensure determinism for the device-dependent torch.Generator
												lowerCAmelCase__        =  self.get_dummy_components()
												lowerCAmelCase__        =  StableDiffusionInpaintPipeline(**lowercase_				)
												lowerCAmelCase__        =  sd_pipe.to(lowercase_				)
												sd_pipe.set_progress_bar_config(disable=lowercase_				)

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

												assert image.shape == (1, 64, 64, 3)
												lowerCAmelCase__        =  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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-inpaint/init_image.png'				)
												lowerCAmelCase__        =  load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'				)
												lowerCAmelCase__        =  load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
												    '/yellow_cat_sitting_on_a_park_bench.npy'				)

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

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

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

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image				).max() < 9E-3






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-inpaint/init_image.png'				)
												lowerCAmelCase__        =  load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'				)
												lowerCAmelCase__        =  load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
												    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'				)

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

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

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

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image				).max() < 5E-1







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

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

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

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

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

												lowerCAmelCase__        =  torch.cuda.max_memory_allocated()
												# make sure that less than 2.65 GB is allocated
												assert mem_bytes < 2.65 * 10**9













import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    int						=   KandinskyVaaInpaintPipeline
						_SCREAMING_SNAKE_CASE		:    int						=   ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   [
						    '''image_embeds''',
						    '''negative_image_embeds''',
						    '''image''',
						    '''mask_image''',
						]
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   [
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''latents''',
						    '''guidance_scale''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   False






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 32






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 32






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.time_input_dim






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.time_input_dim * 4






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 1_00






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)

												lowerCAmelCase__        =  {
												    'in_channels': 9,
												    # Out channels is double in channels because predicts mean and variance
												    'out_channels': 8,
												    'addition_embed_type': 'image',
												    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
												    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
												    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
												    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
												    'layers_per_block': 1,
												    'encoder_hid_dim': self.text_embedder_hidden_size,
												    'encoder_hid_dim_type': 'image_proj',
												    'cross_attention_dim': self.cross_attention_dim,
												    'attention_head_dim': 4,
												    'resnet_time_scale_shift': 'scale_shift',
												    'class_embed_type': None,
												}

												lowerCAmelCase__        =  UNetaDConditionModel(**_UpperCamelCase				)
												return model






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return {
												    "block_out_channels": [32, 64],
												    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
												    "in_channels": 3,
												    "latent_channels": 4,
												    "layers_per_block": 1,
												    "norm_num_groups": 8,
												    "norm_type": "spatial",
												    "num_vq_embeddings": 12,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)
												lowerCAmelCase__        =  VQModel(**self.dummy_movq_kwargs				)
												return model






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.dummy_unet
												lowerCAmelCase__        =  self.dummy_movq

												lowerCAmelCase__        =  DDIMScheduler(
												    num_train_timesteps=10_00       ,  beta_schedule='linear'       ,  beta_start=0.0_00_85       ,  beta_end=0.0_12       ,  clip_sample=_UpperCamelCase       ,  set_alpha_to_one=_UpperCamelCase       ,  steps_offset=1       ,  prediction_type='epsilon'       ,  thresholding=_UpperCamelCase       ,  )

												lowerCAmelCase__        =  {
												    'unet': unet,
												    'scheduler': scheduler,
												    'movq': movq,
												}

												return components






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=0				):






												"""simple docstring"""
												lowerCAmelCase__        =  floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(_UpperCamelCase				)				).to(_UpperCamelCase				)
												lowerCAmelCase__        =  floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(seed + 1				)				).to(
												    _UpperCamelCase				)
												# create init_image
												lowerCAmelCase__        =  floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(_UpperCamelCase				)				).to(_UpperCamelCase				)
												lowerCAmelCase__        =  image.cpu().permute(0       ,  2       ,  3       ,  1				)[0]
												lowerCAmelCase__        =  Image.fromarray(np.uinta(_UpperCamelCase				)				).convert('RGB'				).resize((2_56, 2_56)				)
												# create mask
												lowerCAmelCase__        =  np.ones((64, 64)       ,  dtype=np.floataa				)
												lowerCAmelCase__        =  0

												if str(_UpperCamelCase				).startswith('mps'				):
																		lowerCAmelCase__        =  torch.manual_seed(_UpperCamelCase				)
												else:
																		lowerCAmelCase__        =  torch.Generator(device=_UpperCamelCase				).manual_seed(_UpperCamelCase				)
												lowerCAmelCase__        =  {
												    'image': init_image,
												    'mask_image': mask,
												    'image_embeds': image_embeds,
												    'negative_image_embeds': negative_image_embeds,
												    'generator': generator,
												    'height': 64,
												    'width': 64,
												    'num_inference_steps': 2,
												    'guidance_scale': 4.0,
												    'output_type': 'np',
												}
												return inputs






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  'cpu'

												lowerCAmelCase__        =  self.get_dummy_components()

												lowerCAmelCase__        =  self.pipeline_class(**_UpperCamelCase				)
												lowerCAmelCase__        =  pipe.to(_UpperCamelCase				)

												pipe.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  pipe(**self.get_dummy_inputs(_UpperCamelCase				)				)
												lowerCAmelCase__        =  output.images

												lowerCAmelCase__        =  pipe(
												    **self.get_dummy_inputs(_UpperCamelCase				)       ,  return_dict=_UpperCamelCase       ,  )[0]

												lowerCAmelCase__        =  image[0, -3:, -3:, -1]
												lowerCAmelCase__        =  image_from_tuple[0, -3:, -3:, -1]

												print(F"image.shape {image.shape}"				)

												assert image.shape == (1, 64, 64, 3)

												lowerCAmelCase__        =  np.array(
												    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]				)

												assert (
												    np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
												), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
												assert (
												    np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
												), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().test_inference_batch_single_identical(expected_max_diff=3E-3				)




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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'				)

												lowerCAmelCase__        =  load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
												lowerCAmelCase__        =  np.ones((7_68, 7_68)       ,  dtype=np.floataa				)
												lowerCAmelCase__        =  0

												lowerCAmelCase__        =  'a hat'

												lowerCAmelCase__        =  KandinskyVaaPriorPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-2-prior'       ,  torch_dtype=torch.floataa				)
												pipe_prior.to(_UpperCamelCase				)

												lowerCAmelCase__        =  KandinskyVaaInpaintPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-2-decoder-inpaint'       ,  torch_dtype=torch.floataa				)
												lowerCAmelCase__        =  pipeline.to(_UpperCamelCase				)
												pipeline.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  torch.Generator(device='cpu'				).manual_seed(0				)
												lowerCAmelCase__  ,    lowerCAmelCase__        =  pipe_prior(
												    _UpperCamelCase       ,  generator=_UpperCamelCase       ,  num_inference_steps=5       ,  negative_prompt=''       ,  ).to_tuple()

												lowerCAmelCase__        =  pipeline(
												    image=_UpperCamelCase       ,  mask_image=_UpperCamelCase       ,  image_embeds=_UpperCamelCase       ,  negative_image_embeds=_UpperCamelCase       ,  generator=_UpperCamelCase       ,  num_inference_steps=1_00       ,  height=7_68       ,  width=7_68       ,  output_type='np'       ,  )

												lowerCAmelCase__        =  output.images[0]

												assert image.shape == (7_68, 7_68, 3)

												assert_mean_pixel_difference(_UpperCamelCase       ,  _UpperCamelCase				)





