


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 						_snake_case    ( _snake_case		,       _snake_case		,       _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			StableDiffusionInpaintPipeline
 SCREAMING_SNAKE_CASE__ =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 SCREAMING_SNAKE_CASE__ =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 SCREAMING_SNAKE_CASE__ =			frozenset(
     []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 SCREAMING_SNAKE_CASE__ =			frozenset([]				)




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:str									=			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   ,    )
       a		:Optional[int]									=			PNDMScheduler(skip_prk_steps=_lowerCamelCase       )
       torch.manual_seed(0       )
       a		:Optional[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       )
       a		:str									=			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   ,    )
       a		:List[Any]									=			CLIPTextModel(_lowerCamelCase       )
       a		:Optional[int]									=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

       a		: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   ,    _lowerCamelCase   ,    _lowerCamelCase=0       ):
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       a		:List[Any]									=			floats_tensor((1, 3, 32, 32)   ,    rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )
       a		:Optional[int]									=			image.cpu().permute(0   ,    2   ,    3   ,    1       )[0]
       a		:Any									=			Image.fromarray(np.uinta(_lowerCamelCase       )       ).convert('''RGB'''       ).resize((64, 64)       )
       a		:Union[str, Any]									=			Image.fromarray(np.uinta(image + 4       )       ).convert('''RGB'''       ).resize((64, 64)       )
       if str(_lowerCamelCase       ).startswith('''mps'''       ):
             a		:Any									=			torch.manual_seed(_lowerCamelCase       )
       else:
             a		:Optional[Any]									=			torch.Generator(device=_lowerCamelCase       ).manual_seed(_lowerCamelCase       )
       a		:Optional[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       ):
       a		:int									=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       a		:Optional[Any]									=			self.get_dummy_components()
       a		:Optional[Any]									=			StableDiffusionInpaintPipeline(**_lowerCamelCase       )
       a		:List[str]									=			sd_pipe.to(_lowerCamelCase       )
       sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Any									=			self.get_dummy_inputs(_lowerCamelCase       )
       a		:Any									=			sd_pipe(**_lowerCamelCase       ).images
       a		:str									=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       a		:Union[str, Any]									=			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       ):
       super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







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




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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png'''       )
       a		:List[Any]									=			load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''       )
       a		:str									=			load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench.npy'''       )

       a		:Dict									=			'''stabilityai/stable-diffusion-2-inpainting'''
       a		:List[Any]									=			StableDiffusionInpaintPipeline.from_pretrained(_lowerCamelCase   ,    safety_checker=_lowerCamelCase       )
       pipe.to(_lowerCamelCase       )
       pipe.set_progress_bar_config(disable=_lowerCamelCase       )
       pipe.enable_attention_slicing()

       a		:Optional[int]									=			'''Face of a yellow cat, high resolution, sitting on a park bench'''

       a		:Optional[int]									=			torch.manual_seed(0       )
       a		:List[Any]									=			pipe(
           prompt=_lowerCamelCase   ,    image=_lowerCamelCase   ,    mask_image=_lowerCamelCase   ,    generator=_lowerCamelCase   ,    output_type='''np'''   ,    )
       a		:Optional[int]									=			output.images[0]

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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:str									=			load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png'''       )
       a		:Optional[int]									=			load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''       )
       a		:Optional[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'''       )

       a		:List[Any]									=			'''stabilityai/stable-diffusion-2-inpainting'''
       a		:List[str]									=			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()

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

       a		:Tuple									=			torch.manual_seed(0       )
       a		:int									=			pipe(
           prompt=_lowerCamelCase   ,    image=_lowerCamelCase   ,    mask_image=_lowerCamelCase   ,    generator=_lowerCamelCase   ,    output_type='''np'''   ,    )
       a		:Union[str, Any]									=			output.images[0]

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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

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

       a		:int									=			'''stabilityai/stable-diffusion-2-inpainting'''
       a		:Optional[int]									=			PNDMScheduler.from_pretrained(_lowerCamelCase   ,    subfolder='''scheduler'''       )
       a		:Union[str, 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()

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

       a		:int									=			torch.manual_seed(0       )
       a		:Union[str, Any]									=			pipe(
           prompt=_lowerCamelCase   ,    image=_lowerCamelCase   ,    mask_image=_lowerCamelCase   ,    generator=_lowerCamelCase   ,    num_inference_steps=2   ,    output_type='''np'''   ,    )

       a		:Any									=			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 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			KandinskyVaaInpaintPipeline
 SCREAMING_SNAKE_CASE__ =			['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
 SCREAMING_SNAKE_CASE__ =			[
     'image_embeds',
     'negative_image_embeds',
     'image',
     'mask_image',
 ]
 SCREAMING_SNAKE_CASE__ =			[
     'generator',
     'height',
     'width',
     'latents',
     'guidance_scale',
     'num_inference_steps',
     'return_dict',
     'guidance_scale',
     'num_images_per_prompt',
     'output_type',
     'return_dict',
 ]
 SCREAMING_SNAKE_CASE__ =			False




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return 32




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return 32




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return self.time_input_dim




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return self.time_input_dim * 4




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return 100




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )

       a		:Tuple									=			{
           '''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,
       }

       a		:int									=			UNetaDConditionModel(**_lowerCamelCase       )
       return model




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       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 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:Any									=			VQModel(**self.dummy_movq_kwargs       )
       return model




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.dummy_unet
       a		:Any									=			self.dummy_movq

       a		:Dict									=			DDIMScheduler(
           num_train_timesteps=1000   ,    beta_schedule='''linear'''   ,    beta_start=0.0_0085   ,    beta_end=0.012   ,    clip_sample=_lowerCamelCase   ,    set_alpha_to_one=_lowerCamelCase   ,    steps_offset=1   ,    prediction_type='''epsilon'''   ,    thresholding=_lowerCamelCase   ,    )

       a		:Tuple									=			{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=0       ):
       a		:Tuple									=			floats_tensor((1, self.text_embedder_hidden_size)   ,    rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )
       a		:int									=			floats_tensor((1, self.text_embedder_hidden_size)   ,    rng=random.Random(seed + 1       )       ).to(
           _lowerCamelCase       )
       # create init_image
       a		:int									=			floats_tensor((1, 3, 64, 64)   ,    rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )
       a		:Union[str, Any]									=			image.cpu().permute(0   ,    2   ,    3   ,    1       )[0]
       a		:Any									=			Image.fromarray(np.uinta(_lowerCamelCase       )       ).convert('''RGB'''       ).resize((256, 256)       )
       # create mask
       a		:Dict									=			np.ones((64, 64)   ,    dtype=np.floataa       )
       a		:Tuple									=			0

       if str(_lowerCamelCase       ).startswith('''mps'''       ):
             a		:Dict									=			torch.manual_seed(_lowerCamelCase       )
       else:
             a		:Union[str, Any]									=			torch.Generator(device=_lowerCamelCase       ).manual_seed(_lowerCamelCase       )
       a		:Tuple									=			{
           '''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 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[Any]									=			'''cpu'''

       a		:List[Any]									=			self.get_dummy_components()

       a		:int									=			self.pipeline_class(**_lowerCamelCase       )
       a		:Tuple									=			pipe.to(_lowerCamelCase       )

       pipe.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:str									=			pipe(**self.get_dummy_inputs(_lowerCamelCase       )       )
       a		:Optional[int]									=			output.images

       a		:List[str]									=			pipe(
           **self.get_dummy_inputs(_lowerCamelCase       )   ,    return_dict=_lowerCamelCase   ,    )[0]

       a		:Optional[int]									=			image[0, -3:, -3:, -1]
       a		:Tuple									=			image_from_tuple[0, -3:, -3:, -1]

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

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

       a		:str									=			np.array(
           [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]       )

       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 			SCREAMING_SNAKE_CASE__							(						self       ):
       super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







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




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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''       )

       a		:Tuple									=			load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''       )
       a		:List[Any]									=			np.ones((768, 768)   ,    dtype=np.floataa       )
       a		:Optional[Any]									=			0

       a		:List[Any]									=			'''a hat'''

       a		:int									=			KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''   ,    torch_dtype=torch.floataa       )
       pipe_prior.to(_lowerCamelCase       )

       a		:List[Any]									=			KandinskyVaaInpaintPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''   ,    torch_dtype=torch.floataa       )
       a		:str									=			pipeline.to(_lowerCamelCase       )
       pipeline.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Union[str, Any]									=			torch.Generator(device='''cpu'''       ).manual_seed(0       )
       a				,							a		:Optional[Any]									=			pipe_prior(
           _lowerCamelCase   ,    generator=_lowerCamelCase   ,    num_inference_steps=5   ,    negative_prompt=''''''   ,    ).to_tuple()

       a		:List[str]									=			pipeline(
           image=_lowerCamelCase   ,    mask_image=_lowerCamelCase   ,    image_embeds=_lowerCamelCase   ,    negative_image_embeds=_lowerCamelCase   ,    generator=_lowerCamelCase   ,    num_inference_steps=100   ,    height=768   ,    width=768   ,    output_type='''np'''   ,    )

       a		:Any									=			output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(_lowerCamelCase   ,    _lowerCamelCase       )





