





'''simple docstring'''

import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
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 BeitImageProcessor
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       def __init__(				self  :						Optional[int]			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						int=7			,					lowerCAmelCase__  :						Optional[Any]=3			,					lowerCAmelCase__  :						Optional[Any]=18			,					lowerCAmelCase__  :						int=30			,					lowerCAmelCase__  :						Any=400			,					lowerCAmelCase__  :						Tuple=True			,					lowerCAmelCase__  :						Union[str, Any]=None			,					lowerCAmelCase__  :						int=True			,					lowerCAmelCase__  :						Union[str, Any]=None			,					lowerCAmelCase__  :						Union[str, Any]=True			,					lowerCAmelCase__  :						Union[str, Any]=[0.5, 0.5, 0.5]			,					lowerCAmelCase__  :						Dict=[0.5, 0.5, 0.5]			,					lowerCAmelCase__  :						Dict=False			,					)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			size if size is not None else {'''height''': 20, '''width''': 20}
            _UpperCamelCase							       =			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
            _UpperCamelCase							       =			parent
            _UpperCamelCase							       =			batch_size
            _UpperCamelCase							       =			num_channels
            _UpperCamelCase							       =			image_size
            _UpperCamelCase							       =			min_resolution
            _UpperCamelCase							       =			max_resolution
            _UpperCamelCase							       =			do_resize
            _UpperCamelCase							       =			size
            _UpperCamelCase							       =			do_center_crop
            _UpperCamelCase							       =			crop_size
            _UpperCamelCase							       =			do_normalize
            _UpperCamelCase							       =			image_mean
            _UpperCamelCase							       =			image_std
            _UpperCamelCase							       =			do_reduce_labels







       def snake_case__		(				self  :						Optional[Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_reduce_labels": self.do_reduce_labels,
            }







def    a__							(							)						->						Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase							       =			load_dataset('''hf-internal-testing/fixtures_ade20k''',   split='''test''' )

     _UpperCamelCase							       =			Image.open(dataset[0]['''file'''] )
     _UpperCamelCase							       =			Image.open(dataset[1]['''file'''] )

     return image, map







def    a__							(							)						->						Tuple:





     """simple docstring"""

     _UpperCamelCase							       =			load_dataset('''hf-internal-testing/fixtures_ade20k''',   split='''test''' )

     _UpperCamelCase							       =			Image.open(ds[0]['''file'''] )
     _UpperCamelCase							       =			Image.open(ds[1]['''file'''] )
     _UpperCamelCase							       =			Image.open(ds[2]['''file'''] )
     _UpperCamelCase							       =			Image.open(ds[3]['''file'''] )

     return [imagea, imagea], [mapa, mapa]




@require_torch
@require_vision
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	str									=       BeitImageProcessor if is_vision_available() else None





       def snake_case__		(				self  :						List[Any]	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			BeitImageProcessingTester(self	)





       @property
       def snake_case__		(				self  :						str	)       ->	Dict:

            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()





       def snake_case__		(				self  :						Optional[int]	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.image_processing_class(**self.image_processor_dict	)
            self.assertTrue(hasattr(lowerCAmelCase__			,					'''do_resize'''	)	)
            self.assertTrue(hasattr(lowerCAmelCase__			,					'''size'''	)	)
            self.assertTrue(hasattr(lowerCAmelCase__			,					'''do_center_crop'''	)	)
            self.assertTrue(hasattr(lowerCAmelCase__			,					'''center_crop'''	)	)
            self.assertTrue(hasattr(lowerCAmelCase__			,					'''do_normalize'''	)	)
            self.assertTrue(hasattr(lowerCAmelCase__			,					'''image_mean'''	)	)
            self.assertTrue(hasattr(lowerCAmelCase__			,					'''image_std'''	)	)





       def snake_case__		(				self  :						List[Any]	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.image_processing_class.from_dict(self.image_processor_dict	)
            self.assertEqual(image_processor.size			,					{'''height''': 20, '''width''': 20}	)
            self.assertEqual(image_processor.crop_size			,					{'''height''': 18, '''width''': 18}	)
            self.assertEqual(image_processor.do_reduce_labels			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			self.image_processing_class.from_dict(
                self.image_processor_dict			,					size=42			,					crop_size=84			,					reduce_labels=lowerCAmelCase__	)
            self.assertEqual(image_processor.size			,					{'''height''': 42, '''width''': 42}	)
            self.assertEqual(image_processor.crop_size			,					{'''height''': 84, '''width''': 84}	)
            self.assertEqual(image_processor.do_reduce_labels			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						List[str]	)       ->	Optional[Any]:

            '''simple docstring'''




            pass





       def snake_case__		(				self  :						Any	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            _UpperCamelCase							       =			prepare_image_inputs(self.image_processor_tester			,					equal_resolution=lowerCAmelCase__	)
            for image in image_inputs:
                 self.assertIsInstance(lowerCAmelCase__			,					Image.Image	)

            # Test not batched input
            _UpperCamelCase							       =			image_processing(image_inputs[0]			,					return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape			,					(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)

            # Test batched
            _UpperCamelCase							       =			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,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)





       def snake_case__		(				self  :						Tuple	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            _UpperCamelCase							       =			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
            _UpperCamelCase							       =			image_processing(image_inputs[0]			,					return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape			,					(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)

            # Test batched
            _UpperCamelCase							       =			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,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)





       def snake_case__		(				self  :						List[str]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            _UpperCamelCase							       =			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
            _UpperCamelCase							       =			image_processing(image_inputs[0]			,					return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape			,					(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)

            # Test batched
            _UpperCamelCase							       =			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,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)





       def snake_case__		(				self  :						Union[str, Any]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            _UpperCamelCase							       =			prepare_image_inputs(self.image_processor_tester			,					equal_resolution=lowerCAmelCase__			,					torchify=lowerCAmelCase__	)
            _UpperCamelCase							       =			[]
            for image in image_inputs:
                 self.assertIsInstance(lowerCAmelCase__			,					torch.Tensor	)
                 maps.append(torch.zeros(image.shape[-2:]	).long()	)

            # Test not batched input
            _UpperCamelCase							       =			image_processing(image_inputs[0]			,					maps[0]			,					return_tensors='''pt'''	)
            self.assertEqual(
                encoding['''pixel_values'''].shape			,					(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)
            self.assertEqual(
                encoding['''labels'''].shape			,					(
                    1,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)
            self.assertEqual(encoding['''labels'''].dtype			,					torch.long	)
            self.assertTrue(encoding['''labels'''].min().item() >= 0	)
            self.assertTrue(encoding['''labels'''].max().item() <= 255	)

            # Test batched
            _UpperCamelCase							       =			image_processing(lowerCAmelCase__			,					lowerCAmelCase__			,					return_tensors='''pt'''	)
            self.assertEqual(
                encoding['''pixel_values'''].shape			,					(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)
            self.assertEqual(
                encoding['''labels'''].shape			,					(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)
            self.assertEqual(encoding['''labels'''].dtype			,					torch.long	)
            self.assertTrue(encoding['''labels'''].min().item() >= 0	)
            self.assertTrue(encoding['''labels'''].max().item() <= 255	)

            # Test not batched input (PIL images)
            _UpperCamelCase       ,							_UpperCamelCase							       =			prepare_semantic_single_inputs()

            _UpperCamelCase							       =			image_processing(lowerCAmelCase__			,					lowerCAmelCase__			,					return_tensors='''pt'''	)
            self.assertEqual(
                encoding['''pixel_values'''].shape			,					(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)
            self.assertEqual(
                encoding['''labels'''].shape			,					(
                    1,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)
            self.assertEqual(encoding['''labels'''].dtype			,					torch.long	)
            self.assertTrue(encoding['''labels'''].min().item() >= 0	)
            self.assertTrue(encoding['''labels'''].max().item() <= 255	)

            # Test batched input (PIL images)
            _UpperCamelCase       ,							_UpperCamelCase							       =			prepare_semantic_batch_inputs()

            _UpperCamelCase							       =			image_processing(lowerCAmelCase__			,					lowerCAmelCase__			,					return_tensors='''pt'''	)
            self.assertEqual(
                encoding['''pixel_values'''].shape			,					(
                    2,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)
            self.assertEqual(
                encoding['''labels'''].shape			,					(
                    2,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )			,					)
            self.assertEqual(encoding['''labels'''].dtype			,					torch.long	)
            self.assertTrue(encoding['''labels'''].min().item() >= 0	)
            self.assertTrue(encoding['''labels'''].max().item() <= 255	)







       def snake_case__		(				self  :						Any	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.image_processing_class(**self.image_processor_dict	)

            # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
            _UpperCamelCase       ,							_UpperCamelCase							       =			prepare_semantic_single_inputs()
            _UpperCamelCase							       =			image_processing(lowerCAmelCase__			,					lowerCAmelCase__			,					return_tensors='''pt'''	)
            self.assertTrue(encoding['''labels'''].min().item() >= 0	)
            self.assertTrue(encoding['''labels'''].max().item() <= 150	)

            _UpperCamelCase							       =			True
            _UpperCamelCase							       =			image_processing(lowerCAmelCase__			,					lowerCAmelCase__			,					return_tensors='''pt'''	)
            self.assertTrue(encoding['''labels'''].min().item() >= 0	)
            self.assertTrue(encoding['''labels'''].max().item() <= 255	)








'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= KandinskyInpaintPipeline
 A__				= ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
 A__				= [
     '''prompt''',
     '''negative_prompt''',
     '''image_embeds''',
     '''negative_image_embeds''',
     '''image''',
     '''mask_image''',
 ]
 A__				= [
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''negative_prompt''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 A__				= False
 @property
 def A_   (   self						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       return self.time_input_dim
 @property
 def A_   (   self						:							Dict  )      ->   Any:







       '''simple docstring'''
       return self.time_input_dim * 4
 @property
 def A_   (   self						:							List[Any]  )      ->   List[str]:







       '''simple docstring'''
       return 100
 @property
 def A_   (   self						:							int  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   List[str]													=    XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
       return tokenizer
 @property
 def A_   (   self						:							Tuple  )      ->   str:







       '''simple docstring'''
       torch.manual_seed(0  )
       __snake_case :   str													=    MCLIPConfig(
           numDims=self.cross_attention_dim      ,     transformerDimensions=self.text_embedder_hidden_size      ,     hidden_size=self.text_embedder_hidden_size      ,     intermediate_size=37      ,     num_attention_heads=4      ,     num_hidden_layers=5      ,     vocab_size=1005      ,     )

       __snake_case :   List[Any]													=    MultilingualCLIP(__a  )
       __snake_case :   List[str]													=    text_encoder.eval()

       return text_encoder
 @property
 def A_   (   self						:							Dict  )      ->   List[str]:







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

       __snake_case :   Tuple													=    {
           'in_channels': 9,
           # Out channels is double in channels because predicts mean and variance
           'out_channels': 8,
           'addition_embed_type': 'text_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': 'text_image_proj',
           'cross_attention_dim': self.cross_attention_dim,
           'attention_head_dim': 4,
           'resnet_time_scale_shift': 'scale_shift',
           'class_embed_type': None,
       }

       __snake_case :   Any													=    UNetaDConditionModel(**__a  )
       return model
 @property
 def A_   (   self						:							List[str]  )      ->   List[Any]:







       '''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 A_   (   self						:							int  )      ->   List[str]:







       '''simple docstring'''
       torch.manual_seed(0  )
       __snake_case :   Optional[int]													=    VQModel(**self.dummy_movq_kwargs  )
       return model
 def A_   (   self						:							Dict  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Tuple													=    self.dummy_text_encoder
       __snake_case :   str													=    self.dummy_tokenizer
       __snake_case :   Any													=    self.dummy_unet
       __snake_case :   Optional[Any]													=    self.dummy_movq

       __snake_case :   Optional[Any]													=    DDIMScheduler(
           num_train_timesteps=1000      ,     beta_schedule='linear'      ,     beta_start=0.0_0_0_8_5      ,     beta_end=0.0_1_2      ,     clip_sample=__a      ,     set_alpha_to_one=__a      ,     steps_offset=1      ,     prediction_type='epsilon'      ,     thresholding=__a      ,     )

       __snake_case :   List[str]													=    {
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'unet': unet,
           'scheduler': scheduler,
           'movq': movq,
       }

       return components
 def A_   (   self						:							List[str]      ,     __a						:							Dict      ,     __a						:							int=0  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   int													=    floats_tensor((1, self.cross_attention_dim)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   str													=    floats_tensor((1, self.cross_attention_dim)      ,     rng=random.Random(seed + 1  )  ).to(__a  )
       # create init_image
       __snake_case :   Any													=    floats_tensor((1, 3, 64, 64)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   Optional[int]													=    image.cpu().permute(0      ,     2      ,     3      ,     1  )[0]
       __snake_case :   List[str]													=    Image.fromarray(np.uinta(__a  )  ).convert('RGB'  ).resize((256, 256)  )
       # create mask
       __snake_case :   Optional[int]													=    np.ones((64, 64)      ,     dtype=np.floataa  )
       __snake_case :   List[Any]													=    0

       if str(__a  ).startswith('mps'  ):
             __snake_case :   Any													=    torch.manual_seed(__a  )
       else:
             __snake_case :   Optional[int]													=    torch.Generator(device=__a  ).manual_seed(__a  )
       __snake_case :   int													=    {
           'prompt': 'horse',
           '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 A_   (   self						:							Any  )      ->   int:







       '''simple docstring'''
       __snake_case :   List[str]													=    'cpu'

       __snake_case :   Tuple													=    self.get_dummy_components()

       __snake_case :   Optional[int]													=    self.pipeline_class(**__a  )
       __snake_case :   Union[str, Any]													=    pipe.to(__a  )

       pipe.set_progress_bar_config(disable=__a  )

       __snake_case :   Optional[Any]													=    pipe(**self.get_dummy_inputs(__a  )  )
       __snake_case :   Dict													=    output.images

       __snake_case :   Union[str, Any]													=    pipe(
           **self.get_dummy_inputs(__a  )      ,     return_dict=__a      ,     )[0]

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

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

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

       __snake_case :   List[Any]													=    np.array(
           [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]  )

       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 A_   (   self						:							List[Any]  )      ->   List[str]:







       '''simple docstring'''
       super().test_inference_batch_single_identical(expected_max_diff=3e-3  )



@slow
@require_torch_gpu
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							Dict  )      ->   List[str]:







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





 def A_   (   self						:							Optional[int]  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'  )

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

       __snake_case :   Tuple													=    'a hat'

       __snake_case :   Optional[Any]													=    KandinskyPriorPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-1-prior'      ,     torch_dtype=torch.floataa  )
       pipe_prior.to(__a  )

       __snake_case :   int													=    KandinskyInpaintPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-1-inpaint'      ,     torch_dtype=torch.floataa  )
       __snake_case :   Optional[Any]													=    pipeline.to(__a  )
       pipeline.set_progress_bar_config(disable=__a  )

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

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

       __snake_case :   int													=    output.images[0]

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

       assert_mean_pixel_difference(__a      ,     __a  )



