

'''simple docstring'''

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
    from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor





class 							_snake_case :
   def __init__( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      List[str]=13     ,SCREAMING_SNAKE_CASE__			:      Tuple=30     ,SCREAMING_SNAKE_CASE__			:      List[str]=2     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=3     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=True     ,SCREAMING_SNAKE_CASE__			:      List[str]=True     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=32     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=5     ,SCREAMING_SNAKE_CASE__			:      int=4     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=37     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]="gelu"     ,SCREAMING_SNAKE_CASE__			:      List[Any]=0.1     ,SCREAMING_SNAKE_CASE__			:      Tuple=0.1     ,SCREAMING_SNAKE_CASE__			:      List[str]=10     ,SCREAMING_SNAKE_CASE__			:      Any=0.02     ,SCREAMING_SNAKE_CASE__			:      List[str]=None     ,SCREAMING_SNAKE_CASE__			:      List[str]=2     ,):
     SCREAMING_SNAKE_CASE:int						      =					parent
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					batch_size
     SCREAMING_SNAKE_CASE:Dict						      =					image_size
     SCREAMING_SNAKE_CASE:int						      =					patch_size
     SCREAMING_SNAKE_CASE:Any						      =					num_channels
     SCREAMING_SNAKE_CASE:List[Any]						      =					is_training
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					use_labels
     SCREAMING_SNAKE_CASE:str						      =					hidden_size
     SCREAMING_SNAKE_CASE:int						      =					num_hidden_layers
     SCREAMING_SNAKE_CASE:Tuple						      =					num_attention_heads
     SCREAMING_SNAKE_CASE:Tuple						      =					intermediate_size
     SCREAMING_SNAKE_CASE:str						      =					hidden_act
     SCREAMING_SNAKE_CASE:Optional[int]						      =					hidden_dropout_prob
     SCREAMING_SNAKE_CASE:Tuple						      =					attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE:str						      =					type_sequence_label_size
     SCREAMING_SNAKE_CASE:Optional[int]						      =					initializer_range
     SCREAMING_SNAKE_CASE:Optional[int]						      =					scope
     SCREAMING_SNAKE_CASE:str						      =					encoder_stride

     # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     SCREAMING_SNAKE_CASE:List[Any]						      =					(image_size // patch_size) ** 2
     SCREAMING_SNAKE_CASE:Optional[int]						      =					num_patches + 1
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:str						      =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

     SCREAMING_SNAKE_CASE:str						      =					None
     if self.use_labels:
       SCREAMING_SNAKE_CASE:Dict						      =					ids_tensor([self.batch_size]     ,self.type_sequence_label_size							)

     SCREAMING_SNAKE_CASE:Tuple						      =					self.get_config()

     return config, pixel_values, labels
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     return ViTConfig(
         image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,hidden_size=self.hidden_size     ,num_hidden_layers=self.num_hidden_layers     ,num_attention_heads=self.num_attention_heads     ,intermediate_size=self.intermediate_size     ,hidden_act=self.hidden_act     ,hidden_dropout_prob=self.hidden_dropout_prob     ,attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,is_decoder=UpperCamelCase__     ,initializer_range=self.initializer_range     ,encoder_stride=self.encoder_stride     ,)
   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					ViTModel(config=UpperCamelCase__							)
     model.to(UpperCamelCase__							)
     model.eval()
     SCREAMING_SNAKE_CASE:int						      =					model(UpperCamelCase__							)
     self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)							)
   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Dict							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					ViTForMaskedImageModeling(config=UpperCamelCase__							)
     model.to(UpperCamelCase__							)
     model.eval()
     SCREAMING_SNAKE_CASE:Any						      =					model(UpperCamelCase__							)
     self.parent.assertEqual(
         result.reconstruction.shape     ,(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

     # test greyscale images
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					1
     SCREAMING_SNAKE_CASE:str						      =					ViTForMaskedImageModeling(UpperCamelCase__							)
     model.to(UpperCamelCase__							)
     model.eval()

     SCREAMING_SNAKE_CASE:Any						      =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
     SCREAMING_SNAKE_CASE:Dict						      =					model(UpperCamelCase__							)
     self.parent.assertEqual(result.reconstruction.shape     ,(self.batch_size, 1, self.image_size, self.image_size)							)
   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Dict							):
     SCREAMING_SNAKE_CASE:str						      =					self.type_sequence_label_size
     SCREAMING_SNAKE_CASE:str						      =					ViTForImageClassification(UpperCamelCase__							)
     model.to(UpperCamelCase__							)
     model.eval()
     SCREAMING_SNAKE_CASE:Any						      =					model(UpperCamelCase__     ,labels=UpperCamelCase__							)
     self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)							)

     # test greyscale images
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					1
     SCREAMING_SNAKE_CASE:int						      =					ViTForImageClassification(UpperCamelCase__							)
     model.to(UpperCamelCase__							)
     model.eval()

     SCREAMING_SNAKE_CASE:Optional[int]						      =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					model(UpperCamelCase__							)
     self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)							)







   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.prepare_config_and_inputs()
     (
         SCREAMING_SNAKE_CASE							
     ):Union[str, Any]						      =					config_and_inputs
     SCREAMING_SNAKE_CASE:Optional[int]						      =					{'''pixel_values''': pixel_values}
     return config, inputs_dict





@require_torch
class 							_snake_case (       SCREAMING_SNAKE_CASE_	,  SCREAMING_SNAKE_CASE_	,  unittest.TestCase						):
   _A       :     List[str]       =					(
       (
           ViTModel,
           ViTForImageClassification,
           ViTForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   _A       :     Tuple       =					(
       {'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
       if is_torch_available()
       else {}
   )
   _A       :     Optional[Any]       =					True

   _A       :     Dict       =					False
   _A       :     Union[str, Any]       =					False
   _A       :     Tuple       =					False
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					ViTModelTester(self							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					ConfigTester(self     ,config_class=UpperCamelCase__     ,has_text_modality=UpperCamelCase__     ,hidden_size=37							)
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     self.config_tester.run_common_tests()
   @unittest.skip(reason="ViT does not use inputs_embeds"							)
   def     __UpperCamelCase						( self			:      Dict							):
     pass
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Any						      =					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE:str						      =					model_class(UpperCamelCase__							)
       self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module)							)
       SCREAMING_SNAKE_CASE:int						      =					model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(UpperCamelCase__     ,nn.Linear							)							)
   def     __UpperCamelCase						( self			:      Any							):
     SCREAMING_SNAKE_CASE:int						      =					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					model_class(UpperCamelCase__							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					inspect.signature(model.forward							)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       SCREAMING_SNAKE_CASE:Any						      =					[*signature.parameters.keys()]

       SCREAMING_SNAKE_CASE:Optional[int]						      =					['''pixel_values''']
       self.assertListEqual(arg_names[:1]     ,UpperCamelCase__							)
   def     __UpperCamelCase						( self			:      Dict							):
     SCREAMING_SNAKE_CASE:int						      =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCamelCase__							)
   def     __UpperCamelCase						( self			:      List[str]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase__							)
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:Any						      =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__							)







   @slow
   def     __UpperCamelCase						( self			:      Any							):
     for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       SCREAMING_SNAKE_CASE:Tuple						      =					ViTModel.from_pretrained(UpperCamelCase__							)
       self.assertIsNotNone(UpperCamelCase__							)





def    A_  ( ):
  SCREAMING_SNAKE_CASE:Dict						      =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
  return image





@require_torch
@require_vision
class 							_snake_case (       unittest.TestCase						):
   @cached_property
   def     __UpperCamelCase						( self			:      Optional[int]							):
     return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"							) if is_vision_available() else None
   @slow
   def     __UpperCamelCase						( self			:      List[str]							):
     SCREAMING_SNAKE_CASE:Any						      =					ViTForImageClassification.from_pretrained("google/vit-base-patch16-224"							).to(UpperCamelCase__							)

     SCREAMING_SNAKE_CASE:List[str]						      =					self.default_image_processor
     SCREAMING_SNAKE_CASE:Dict						      =					prepare_img()
     SCREAMING_SNAKE_CASE:Dict						      =					image_processor(images=UpperCamelCase__     ,return_tensors="pt"							).to(UpperCamelCase__							)

     # forward pass
     with torch.no_grad():
       SCREAMING_SNAKE_CASE:Optional[int]						      =					model(**UpperCamelCase__							)

     # verify the logits
     SCREAMING_SNAKE_CASE:Any						      =					torch.Size((1, 1_000)							)
     self.assertEqual(outputs.logits.shape     ,UpperCamelCase__							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.tensor([-0.2_744, 0.8_215, -0.0_836]							).to(UpperCamelCase__							)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,UpperCamelCase__     ,atol=1e-4							)							)
   @slow
   def     __UpperCamelCase						( self			:      Any							):
     # ViT models have an `interpolate_pos_encoding` argument in their forward method,
     # allowing to interpolate the pre-trained position embeddings in order to use
     # the model on higher resolutions. The DINO model by Facebook AI leverages this
     # to visualize self-attention on higher resolution images.
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					ViTModel.from_pretrained("facebook/dino-vits8"							).to(UpperCamelCase__							)

     SCREAMING_SNAKE_CASE:List[str]						      =					ViTImageProcessor.from_pretrained("facebook/dino-vits8"     ,size=480							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					prepare_img()
     SCREAMING_SNAKE_CASE:int						      =					image_processor(images=UpperCamelCase__     ,return_tensors="pt"							)
     SCREAMING_SNAKE_CASE:str						      =					inputs.pixel_values.to(UpperCamelCase__							)

     # forward pass
     with torch.no_grad():
       SCREAMING_SNAKE_CASE:int						      =					model(UpperCamelCase__     ,interpolate_pos_encoding=UpperCamelCase__							)

     # verify the logits
     SCREAMING_SNAKE_CASE:Dict						      =					torch.Size((1, 3_601, 384)							)
     self.assertEqual(outputs.last_hidden_state.shape     ,UpperCamelCase__							)

     SCREAMING_SNAKE_CASE:int						      =					torch.tensor(
         [[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]]							).to(UpperCamelCase__							)

     self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,UpperCamelCase__     ,atol=1e-4							)							)







   @slow
   @require_accelerate
   @require_torch_gpu
   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Any						      =					ViTModel.from_pretrained("facebook/dino-vits8"     ,torch_dtype=torch.floataa     ,device_map="auto"							)
     SCREAMING_SNAKE_CASE:Any						      =					self.default_image_processor

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					prepare_img()
     SCREAMING_SNAKE_CASE:Tuple						      =					image_processor(images=UpperCamelCase__     ,return_tensors="pt"							)
     SCREAMING_SNAKE_CASE:Dict						      =					inputs.pixel_values.to(UpperCamelCase__							)

     # forward pass to make sure inference works in fp16
     with torch.no_grad():
       SCREAMING_SNAKE_CASE:List[Any]						      =					model(UpperCamelCase__							)









'''simple docstring'''

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 (       _a	,  unittest.TestCase						):
   _A       :     str       =					KandinskyVaaInpaintPipeline
   _A       :     Tuple       =					['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
   _A       :     List[str]       =					[
       '''image_embeds''',
       '''negative_image_embeds''',
       '''image''',
       '''mask_image''',
   ]
   _A       :     Optional[Any]       =					[
       '''generator''',
       '''height''',
       '''width''',
       '''latents''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   _A       :     int       =					False
   @property
   def     __UpperCamelCase						( self			:      Any							):
     return 32
   @property
   def     __UpperCamelCase						( self			:      Tuple							):
     return 32
   @property
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     return self.time_input_dim
   @property
   def     __UpperCamelCase						( self			:      Any							):
     return self.time_input_dim * 4
   @property
   def     __UpperCamelCase						( self			:      Any							):
     return 100
   @property
   def     __UpperCamelCase						( self			:      Any							):
     torch.manual_seed(0							)

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					{
         "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,
     }

     SCREAMING_SNAKE_CASE:Dict						      =					UNetaDConditionModel(**SCREAMING_SNAKE_CASE__							)
     return model
   @property
   def     __UpperCamelCase						( self			:      Dict							):
     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			:      int							):
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:str						      =					VQModel(**self.dummy_movq_kwargs							)
     return model
   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					self.dummy_unet
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.dummy_movq

     SCREAMING_SNAKE_CASE:Any						      =					DDIMScheduler(
         num_train_timesteps=1_000     ,beta_schedule="linear"     ,beta_start=0.00_085     ,beta_end=0.012     ,clip_sample=SCREAMING_SNAKE_CASE__     ,set_alpha_to_one=SCREAMING_SNAKE_CASE__     ,steps_offset=1     ,prediction_type="epsilon"     ,thresholding=SCREAMING_SNAKE_CASE__     ,)

     SCREAMING_SNAKE_CASE:Any						      =					{
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }

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

     if str(SCREAMING_SNAKE_CASE__							).startswith("mps"							):
       SCREAMING_SNAKE_CASE:Tuple						      =					torch.manual_seed(SCREAMING_SNAKE_CASE__							)
     else:
       SCREAMING_SNAKE_CASE:List[Any]						      =					torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE: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     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					"cpu"

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.get_dummy_components()

     SCREAMING_SNAKE_CASE:List[Any]						      =					self.pipeline_class(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					pipe.to(SCREAMING_SNAKE_CASE__							)

     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					output.images

     SCREAMING_SNAKE_CASE:int						      =					pipe(
         **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)     ,return_dict=SCREAMING_SNAKE_CASE__     ,)[0]

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_from_tuple[0, -3:, -3:, -1]

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

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

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					np.array(
         [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]							)

     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			:      Any							):
     super().test_inference_batch_single_identical(expected_max_diff=3e-3							)





@slow
@require_torch_gpu
class 							_snake_case (       unittest.TestCase						):
   def     __UpperCamelCase						( self			:      Any							):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







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

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

     SCREAMING_SNAKE_CASE:Optional[int]						      =					"a hat"

     SCREAMING_SNAKE_CASE:Optional[int]						      =					KandinskyVaaPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-prior"     ,torch_dtype=torch.floataa							)
     pipe_prior.to(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					KandinskyVaaInpaintPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-decoder-inpaint"     ,torch_dtype=torch.floataa							)
     SCREAMING_SNAKE_CASE:List[str]						      =					pipeline.to(SCREAMING_SNAKE_CASE__							)
     pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					torch.Generator(device="cpu"							).manual_seed(0							)
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					pipe_prior(
         SCREAMING_SNAKE_CASE__     ,generator=SCREAMING_SNAKE_CASE__     ,num_inference_steps=5     ,negative_prompt=""     ,).to_tuple()

     SCREAMING_SNAKE_CASE:Dict						      =					pipeline(
         image=SCREAMING_SNAKE_CASE__     ,mask_image=SCREAMING_SNAKE_CASE__     ,image_embeds=SCREAMING_SNAKE_CASE__     ,negative_image_embeds=SCREAMING_SNAKE_CASE__     ,generator=SCREAMING_SNAKE_CASE__     ,num_inference_steps=100     ,height=768     ,width=768     ,output_type="np"     ,)

     SCREAMING_SNAKE_CASE:List[Any]						      =					output.images[0]

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

     assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)






