"""simple docstring"""






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 PIL import Image

       from transformers import DetrImageProcessor


class        UpperCAmelCase		(unittest.TestCase    ):



    """simple docstring"""

    def __init__(  self       ,     _UpperCAmelCase       ,     _UpperCAmelCase=7       ,     _UpperCAmelCase=3       ,     _UpperCAmelCase=30       ,     _UpperCAmelCase=400       ,     _UpperCAmelCase=True       ,     _UpperCAmelCase=None       ,     _UpperCAmelCase=True       ,     _UpperCAmelCase=1 / 255       ,     _UpperCAmelCase=True       ,     _UpperCAmelCase=[0.5, 0.5, 0.5]       ,     _UpperCAmelCase=[0.5, 0.5, 0.5]       ,     _UpperCAmelCase=True       ,     ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       lowercase__:  Optional[int]   = size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
       lowercase__:  List[str]   = parent
       lowercase__:  str   = batch_size
       lowercase__:  List[Any]   = num_channels
       lowercase__:  str   = min_resolution
       lowercase__:  Optional[int]   = max_resolution
       lowercase__:  Optional[int]   = do_resize
       lowercase__:  Tuple   = size
       lowercase__:  List[Any]   = do_rescale
       lowercase__:  str   = rescale_factor
       lowercase__:  Tuple   = do_normalize
       lowercase__:  int   = image_mean
       lowercase__:  Union[str, Any]   = image_std
       lowercase__:  List[str]   = do_pad






    def _snake_case     (  self				):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_pad": self.do_pad,
       }






    def _snake_case     (  self       ,     _UpperCAmelCase       ,     _UpperCAmelCase=False				):
       if not batched:
          lowercase__:  str   = image_inputs[0]
          if isinstance(_lowerCamelCase       ,     Image.Image				):
             lowercase__:  Optional[Any]   = image.size
          else:
             lowercase__:  Optional[int]   = image.shape[1], image.shape[2]
          if w < h:
             lowercase__:  Any   = int(self.size['''shortest_edge'''] * h / w				)
             lowercase__:  List[Any]   = self.size['''shortest_edge''']
          elif w > h:
             lowercase__:  int   = self.size['''shortest_edge''']
             lowercase__:  List[str]   = int(self.size['''shortest_edge'''] * w / h				)
          else:
             lowercase__:  int   = self.size['''shortest_edge''']
             lowercase__:  Any   = self.size['''shortest_edge''']

       else:
          lowercase__:  int   = []
          for image in image_inputs:
             lowercase__:  Optional[Any]   = self.get_expected_values([image]				)
             expected_values.append((expected_height, expected_width)				)
          lowercase__:  List[Any]   = max(_lowerCamelCase       ,     key=lambda _UpperCAmelCase				: item[0]				)[0]
          lowercase__:  Dict   = max(_lowerCamelCase       ,     key=lambda _UpperCAmelCase				: item[1]				)[1]

       return expected_height, expected_width



@require_torch
@require_vision
class        UpperCAmelCase		(_snake_case       ,unittest.TestCase    ):



    """simple docstring"""

    _UpperCAmelCase      :int		       =					DetrImageProcessor if is_vision_available() else None






    def _snake_case     (  self				):
       lowercase__:  Optional[int]   = DetrImageProcessingTester(self				)






    @property
    def _snake_case     (  self				):
       return self.image_processor_tester.prepare_image_processor_dict()






    def _snake_case     (  self				):
       lowercase__:  str   = 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_rescale'''				)				)
       self.assertTrue(hasattr(_lowerCamelCase       ,     '''rescale_factor'''				)				)
       self.assertTrue(hasattr(_lowerCamelCase       ,     '''do_resize'''				)				)
       self.assertTrue(hasattr(_lowerCamelCase       ,     '''size'''				)				)
       self.assertTrue(hasattr(_lowerCamelCase       ,     '''do_pad'''				)				)






    def _snake_case     (  self				):
       lowercase__:  List[Any]   = self.image_processing_class.from_dict(self.image_processor_dict				)
       self.assertEqual(image_processor.size       ,     {'''shortest_edge''': 18, '''longest_edge''': 1333}				)
       self.assertEqual(image_processor.do_pad       ,     _lowerCamelCase				)

       lowercase__:  Dict   = self.image_processing_class.from_dict(
           self.image_processor_dict       ,     size=42       ,     max_size=84       ,     pad_and_return_pixel_mask=_lowerCamelCase				)
       self.assertEqual(image_processor.size       ,     {'''shortest_edge''': 42, '''longest_edge''': 84}				)
       self.assertEqual(image_processor.do_pad       ,     _lowerCamelCase				)






    def _snake_case     (  self				):
       pass






    def _snake_case     (  self				):
       # Initialize image_processing
       lowercase__:  List[str]   = self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       lowercase__:  int   = prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=_lowerCamelCase				)
       for image in image_inputs:
          self.assertIsInstance(_lowerCamelCase       ,     Image.Image				)

       # Test not batched input
       lowercase__:  Any   = image_processing(image_inputs[0]       ,     return_tensors='''pt'''				).pixel_values

       lowercase__:  int   = self.image_processor_tester.get_expected_values(_lowerCamelCase				)

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

       # Test batched
       lowercase__:  Dict   = self.image_processor_tester.get_expected_values(_lowerCamelCase       ,     batched=_lowerCamelCase				)

       lowercase__:  Optional[Any]   = image_processing(_lowerCamelCase       ,     return_tensors='''pt'''				).pixel_values
       self.assertEqual(
           encoded_images.shape       ,     (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )       ,     )






    def _snake_case     (  self				):
       # Initialize image_processing
       lowercase__:  Tuple   = self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       lowercase__:  int   = prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=_lowerCamelCase       ,     numpify=_lowerCamelCase				)
       for image in image_inputs:
          self.assertIsInstance(_lowerCamelCase       ,     np.ndarray				)

       # Test not batched input
       lowercase__:  Optional[Any]   = image_processing(image_inputs[0]       ,     return_tensors='''pt'''				).pixel_values

       lowercase__:  List[Any]   = self.image_processor_tester.get_expected_values(_lowerCamelCase				)

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

       # Test batched
       lowercase__:  Union[str, Any]   = image_processing(_lowerCamelCase       ,     return_tensors='''pt'''				).pixel_values

       lowercase__:  Optional[int]   = self.image_processor_tester.get_expected_values(_lowerCamelCase       ,     batched=_lowerCamelCase				)

       self.assertEqual(
           encoded_images.shape       ,     (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )       ,     )






    def _snake_case     (  self				):
       # Initialize image_processing
       lowercase__:  Any   = self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       lowercase__:  int   = prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=_lowerCamelCase       ,     torchify=_lowerCamelCase				)
       for image in image_inputs:
          self.assertIsInstance(_lowerCamelCase       ,     torch.Tensor				)

       # Test not batched input
       lowercase__:  int   = image_processing(image_inputs[0]       ,     return_tensors='''pt'''				).pixel_values

       lowercase__:  Any   = self.image_processor_tester.get_expected_values(_lowerCamelCase				)

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

       # Test batched
       lowercase__:  Tuple   = image_processing(_lowerCamelCase       ,     return_tensors='''pt'''				).pixel_values

       lowercase__:  int   = self.image_processor_tester.get_expected_values(_lowerCamelCase       ,     batched=_lowerCamelCase				)

       self.assertEqual(
           encoded_images.shape       ,     (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )       ,     )






    @slow
    def _snake_case     (  self				):
       # prepare image and target
       lowercase__:  int   = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''       ,     '''r'''				) as f:
          lowercase__:  Dict   = json.loads(f.read()				)

       lowercase__:  Tuple   = {'''image_id''': 39769, '''annotations''': target}

       # encode them
       lowercase__:  List[Any]   = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''				)
       lowercase__:  Optional[int]   = image_processing(images=_lowerCamelCase       ,     annotations=_lowerCamelCase       ,     return_tensors='''pt'''				)

       # verify pixel values
       lowercase__:  Union[str, Any]   = torch.Size([1, 3, 800, 1066]				)
       self.assertEqual(encoding['''pixel_values'''].shape       ,     _lowerCamelCase				)

       lowercase__:  Union[str, Any]   = torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,     _lowerCamelCase       ,     atol=1e-4				)				)

       # verify area
       lowercase__:  List[Any]   = torch.tensor([5_887.9_600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,     _lowerCamelCase				)				)
       # verify boxes
       lowercase__:  List[Any]   = torch.Size([6, 4]				)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,     _lowerCamelCase				)
       lowercase__:  Dict   = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,     _lowerCamelCase       ,     atol=1e-3				)				)
       # verify image_id
       lowercase__:  List[str]   = torch.tensor([39769]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,     _lowerCamelCase				)				)
       # verify is_crowd
       lowercase__:  Tuple   = torch.tensor([0, 0, 0, 0, 0, 0]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,     _lowerCamelCase				)				)
       # verify class_labels
       lowercase__:  Dict   = torch.tensor([75, 75, 63, 65, 17, 17]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,     _lowerCamelCase				)				)
       # verify orig_size
       lowercase__:  List[Any]   = torch.tensor([480, 640]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,     _lowerCamelCase				)				)
       # verify size
       lowercase__:  str   = torch.tensor([800, 1066]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,     _lowerCamelCase				)				)






    @slow
    def _snake_case     (  self				):
       # prepare image, target and masks_path
       lowercase__:  int   = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''       ,     '''r'''				) as f:
          lowercase__:  Union[str, Any]   = json.loads(f.read()				)

       lowercase__:  int   = {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

       lowercase__:  Optional[Any]   = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''				)

       # encode them
       lowercase__:  Any   = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''				)
       lowercase__:  List[Any]   = image_processing(images=_lowerCamelCase       ,     annotations=_lowerCamelCase       ,     masks_path=_lowerCamelCase       ,     return_tensors='''pt'''				)

       # verify pixel values
       lowercase__:  Dict   = torch.Size([1, 3, 800, 1066]				)
       self.assertEqual(encoding['''pixel_values'''].shape       ,     _lowerCamelCase				)

       lowercase__:  Optional[Any]   = torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,     _lowerCamelCase       ,     atol=1e-4				)				)

       # verify area
       lowercase__:  List[str]   = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5_879.6_562, 7_634.1_147]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,     _lowerCamelCase				)				)
       # verify boxes
       lowercase__:  Tuple   = torch.Size([6, 4]				)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,     _lowerCamelCase				)
       lowercase__:  Dict   = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,     _lowerCamelCase       ,     atol=1e-3				)				)
       # verify image_id
       lowercase__:  Optional[Any]   = torch.tensor([39769]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,     _lowerCamelCase				)				)
       # verify is_crowd
       lowercase__:  Tuple   = torch.tensor([0, 0, 0, 0, 0, 0]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,     _lowerCamelCase				)				)
       # verify class_labels
       lowercase__:  List[Any]   = torch.tensor([17, 17, 63, 75, 75, 93]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,     _lowerCamelCase				)				)
       # verify masks
       lowercase__:  Any   = 822873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,     _lowerCamelCase				)
       # verify orig_size
       lowercase__:  Optional[Any]   = torch.tensor([480, 640]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,     _lowerCamelCase				)				)
       # verify size
       lowercase__:  Dict   = torch.tensor([800, 1066]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,     _lowerCamelCase				)				)




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       )





