





import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, 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 transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
     from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor



class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  def 							lowercase						(							self					:						Any	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      self.config_class(**self.inputs_dict	)
     self.parent.assertTrue(hasattr(lowerCAmelCase_   ,    'hidden_sizes'	)	)
     self.parent.assertTrue(hasattr(lowerCAmelCase_   ,    'neck_hidden_sizes'	)	)
     self.parent.assertTrue(hasattr(lowerCAmelCase_   ,    'num_attention_heads'	)	)





class 							_UpperCAmelCase       :

  """simple docstring"""


  def __init__(							self					:						Any   ,    lowerCAmelCase_					:						Any   ,    lowerCAmelCase_					:						Union[str, Any]=1_3   ,    lowerCAmelCase_					:						List[str]=3_2   ,    lowerCAmelCase_					:						int=2   ,    lowerCAmelCase_					:						List[str]=3   ,    lowerCAmelCase_					:						Optional[int]=6_4_0   ,    lowerCAmelCase_					:						List[str]=4   ,    lowerCAmelCase_					:						Optional[int]="silu"   ,    lowerCAmelCase_					:						Optional[int]=3   ,    lowerCAmelCase_					:						Any=3_2   ,    lowerCAmelCase_					:						Optional[Any]=0.1   ,    lowerCAmelCase_					:						Tuple=0.1   ,    lowerCAmelCase_					:						Any=0.1   ,    lowerCAmelCase_					:						Tuple=0.02   ,    lowerCAmelCase_					:						List[str]=True   ,    lowerCAmelCase_					:						Optional[Any]=True   ,    lowerCAmelCase_					:						Optional[int]=1_0   ,    lowerCAmelCase_					:						int=None   ,    )				->		int:
     __lowerCAmelCase				      =      parent
     __lowerCAmelCase				      =      batch_size
     __lowerCAmelCase				      =      image_size
     __lowerCAmelCase				      =      patch_size
     __lowerCAmelCase				      =      num_channels
     __lowerCAmelCase				      =      last_hidden_size
     __lowerCAmelCase				      =      num_attention_heads
     __lowerCAmelCase				      =      hidden_act
     __lowerCAmelCase				      =      conv_kernel_size
     __lowerCAmelCase				      =      output_stride
     __lowerCAmelCase				      =      hidden_dropout_prob
     __lowerCAmelCase				      =      attention_probs_dropout_prob
     __lowerCAmelCase				      =      classifier_dropout_prob
     __lowerCAmelCase				      =      use_labels
     __lowerCAmelCase				      =      is_training
     __lowerCAmelCase				      =      num_labels
     __lowerCAmelCase				      =      initializer_range
     __lowerCAmelCase				      =      scope






  def 							lowercase						(							self					:						Optional[int]	)				->		Dict:
     __lowerCAmelCase				      =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

     __lowerCAmelCase				      =      None
     __lowerCAmelCase				      =      None
     if self.use_labels:
        __lowerCAmelCase				      =      ids_tensor([self.batch_size]   ,    self.num_labels	)
        __lowerCAmelCase				      =      ids_tensor([self.batch_size, self.image_size, self.image_size]   ,    self.num_labels	)

     __lowerCAmelCase				      =      self.get_config()

     return config, pixel_values, labels, pixel_labels






  def 							lowercase						(							self					:						Dict	)				->		Optional[Any]:
     return MobileViTConfig(
         image_size=self.image_size   ,    patch_size=self.patch_size   ,    num_channels=self.num_channels   ,    num_attention_heads=self.num_attention_heads   ,    hidden_act=self.hidden_act   ,    conv_kernel_size=self.conv_kernel_size   ,    output_stride=self.output_stride   ,    hidden_dropout_prob=self.hidden_dropout_prob   ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,    classifier_dropout_prob=self.classifier_dropout_prob   ,    initializer_range=self.initializer_range   ,    )






  def 							lowercase						(							self					:						Any   ,    lowerCAmelCase_					:						Tuple   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						List[Any]	)				->		Any:
     __lowerCAmelCase				      =      MobileViTModel(config=lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     __lowerCAmelCase				      =      model(lowerCAmelCase_	)
     self.parent.assertEqual(
         result.last_hidden_state.shape   ,    (
             self.batch_size,
             self.last_hidden_size,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )   ,    )






  def 							lowercase						(							self					:						Any   ,    lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						Tuple   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						str	)				->		int:
     __lowerCAmelCase				      =      self.num_labels
     __lowerCAmelCase				      =      MobileViTForImageClassification(lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     __lowerCAmelCase				      =      model(lowerCAmelCase_   ,    labels=lowerCAmelCase_	)
     self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)	)






  def 							lowercase						(							self					:						Optional[Any]   ,    lowerCAmelCase_					:						Dict   ,    lowerCAmelCase_					:						Union[str, Any]   ,    lowerCAmelCase_					:						Dict   ,    lowerCAmelCase_					:						List[Any]	)				->		Dict:
     __lowerCAmelCase				      =      self.num_labels
     __lowerCAmelCase				      =      MobileViTForSemanticSegmentation(lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     __lowerCAmelCase				      =      model(lowerCAmelCase_	)
     self.parent.assertEqual(
         result.logits.shape   ,    (
             self.batch_size,
             self.num_labels,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )   ,    )
     __lowerCAmelCase				      =      model(lowerCAmelCase_   ,    labels=lowerCAmelCase_	)
     self.parent.assertEqual(
         result.logits.shape   ,    (
             self.batch_size,
             self.num_labels,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )   ,    )






  def 							lowercase						(							self					:						Dict	)				->		int:
     __lowerCAmelCase				      =      self.prepare_config_and_inputs()
     __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      config_and_inputs
     __lowerCAmelCase				      =      {'pixel_values': pixel_values}
     return config, inputs_dict





@require_torch
class 							_UpperCAmelCase       (       _UpperCamelCase						,       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  a_              =					(
      (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  a_              =					(
      {
          """feature-extraction""": MobileViTModel,
          """image-classification""": MobileViTForImageClassification,
          """image-segmentation""": MobileViTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  a_              =					False
  a_              =					False
  a_              =					False
  a_              =					False






  def 							lowercase						(							self					:						List[str]	)				->		Any:
     __lowerCAmelCase				      =      MobileViTModelTester(self	)
     __lowerCAmelCase				      =      MobileViTConfigTester(self   ,    config_class=lowerCAmelCase_   ,    has_text_modality=lowerCAmelCase_	)






  def 							lowercase						(							self					:						int	)				->		int:
     self.config_tester.run_common_tests()






  @unittest.skip(reason='MobileViT does not use inputs_embeds'	)
  def 							lowercase						(							self					:						List[str]	)				->		Dict:
     pass






  @unittest.skip(reason='MobileViT does not support input and output embeddings'	)
  def 							lowercase						(							self					:						Union[str, Any]	)				->		Optional[int]:
     pass






  @unittest.skip(reason='MobileViT does not output attentions'	)
  def 							lowercase						(							self					:						List[str]	)				->		Tuple:
     pass






  def 							lowercase						(							self					:						str	)				->		Optional[Any]:
     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase				      =      model_class(lowerCAmelCase_	)
        __lowerCAmelCase				      =      inspect.signature(model.forward	)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __lowerCAmelCase				      =      [*signature.parameters.keys()]

        __lowerCAmelCase				      =      ['pixel_values']
        self.assertListEqual(arg_names[:1]   ,    lowerCAmelCase_	)






  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
  def 							lowercase						(							self					:						Optional[Any]	)				->		int:
     pass






  def 							lowercase						(							self					:						Optional[Any]	)				->		int:
     __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase_	)






  def 							lowercase						(							self					:						str	)				->		Optional[Any]:
     def check_hidden_states_output(lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						int   ,    lowerCAmelCase_					:						int	):
        __lowerCAmelCase				      =      model_class(lowerCAmelCase_	)
        model.to(lowerCAmelCase_	)
        model.eval()

        with torch.no_grad():
           __lowerCAmelCase				      =      model(**self._prepare_for_class(lowerCAmelCase_   ,    lowerCAmelCase_	)	)

        __lowerCAmelCase				      =      outputs.hidden_states

        __lowerCAmelCase				      =      5
        self.assertEqual(len(lowerCAmelCase_	)   ,    lowerCAmelCase_	)

        # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
        # with the width and height being successively divided by 2.
        __lowerCAmelCase				      =      2
        for i in range(len(lowerCAmelCase_	)	):
           self.assertListEqual(
               list(hidden_states[i].shape[-2:]	)   ,    [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]   ,    )
           divisor *= 2

        self.assertEqual(self.model_tester.output_stride   ,    divisor // 2	)

     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase				      =      True
        check_hidden_states_output(lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_	)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        __lowerCAmelCase				      =      True

        check_hidden_states_output(lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_	)






  def 							lowercase						(							self					:						Any	)				->		Optional[Any]:
     __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_	)






  def 							lowercase						(							self					:						Dict	)				->		Tuple:
     __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_	)






  @slow
  def 							lowercase						(							self					:						int	)				->		Optional[Any]:
     for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCAmelCase				      =      MobileViTModel.from_pretrained(lowerCAmelCase_	)
        self.assertIsNotNone(lowerCAmelCase_	)





def     a_       (					):
   __lowerCAmelCase				      =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
   return image




@require_torch
@require_vision
class 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


  @cached_property
  def 							lowercase						(							self					:						Optional[int]	)				->		List[Any]:
     return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'	) if is_vision_available() else None






  @slow
  def 							lowercase						(							self					:						Dict	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'	).to(lowerCAmelCase_	)

     __lowerCAmelCase				      =      self.default_image_processor
     __lowerCAmelCase				      =      prepare_img()
     __lowerCAmelCase				      =      image_processor(images=lowerCAmelCase_   ,    return_tensors='pt'	).to(lowerCAmelCase_	)

     # forward pass
     with torch.no_grad():
        __lowerCAmelCase				      =      model(**lowerCAmelCase_	)

     # verify the logits
     __lowerCAmelCase				      =      torch.Size((1, 1_0_0_0)	)
     self.assertEqual(outputs.logits.shape   ,    lowerCAmelCase_	)

     __lowerCAmelCase				      =      torch.tensor([-1.93_64, -1.23_27, -0.46_53]	).to(lowerCAmelCase_	)

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






  @slow
  def 							lowercase						(							self					:						Dict	)				->		Dict:
     __lowerCAmelCase				      =      MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'	)
     __lowerCAmelCase				      =      model.to(lowerCAmelCase_	)

     __lowerCAmelCase				      =      MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'	)

     __lowerCAmelCase				      =      prepare_img()
     __lowerCAmelCase				      =      image_processor(images=lowerCAmelCase_   ,    return_tensors='pt'	).to(lowerCAmelCase_	)

     # forward pass
     with torch.no_grad():
        __lowerCAmelCase				      =      model(**lowerCAmelCase_	)
     __lowerCAmelCase				      =      outputs.logits

     # verify the logits
     __lowerCAmelCase				      =      torch.Size((1, 2_1, 3_2, 3_2)	)
     self.assertEqual(logits.shape   ,    lowerCAmelCase_	)

     __lowerCAmelCase				      =      torch.tensor(
         [
             [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
             [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
             [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
         ]   ,    device=lowerCAmelCase_   ,    )

     self.assertTrue(torch.allclose(logits[0, :3, :3, :3]   ,    lowerCAmelCase_   ,    atol=1e-4	)	)






  @slow
  def 							lowercase						(							self					:						List[Any]	)				->		List[str]:
     __lowerCAmelCase				      =      MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'	)
     __lowerCAmelCase				      =      model.to(lowerCAmelCase_	)

     __lowerCAmelCase				      =      MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'	)

     __lowerCAmelCase				      =      prepare_img()
     __lowerCAmelCase				      =      image_processor(images=lowerCAmelCase_   ,    return_tensors='pt'	).to(lowerCAmelCase_	)

     # forward pass
     with torch.no_grad():
        __lowerCAmelCase				      =      model(**lowerCAmelCase_	)

     __lowerCAmelCase				      =      outputs.logits.detach().cpu()

     __lowerCAmelCase				      =      image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_   ,    target_sizes=[(5_0, 6_0)]	)
     __lowerCAmelCase				      =      torch.Size((5_0, 6_0)	)
     self.assertEqual(segmentation[0].shape   ,    lowerCAmelCase_	)

     __lowerCAmelCase				      =      image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_	)
     __lowerCAmelCase				      =      torch.Size((3_2, 3_2)	)
     self.assertEqual(segmentation[0].shape   ,    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       )





