






import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 						lowercase_   (      lowercase					,    lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




      __snake_case	    =    AutoencoderKL
      __snake_case	    =    '''sample'''
      __snake_case	    =    1e-2







      @property
      def  __lowerCAmelCase   ( self :		List[str]    )       ->Tuple:




       """simple docstring"""


       a						    = 4
       a						    = 3
       a						    = (32, 32)

       a						    = floats_tensor((batch_size, num_channels) + sizes    ).to(__UpperCAmelCase    )

       return {"sample": image}







      @property
      def  __lowerCAmelCase   ( self :		Dict    )       ->Dict:




       """simple docstring"""


       return (3, 32, 32)







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->str:




       """simple docstring"""


       return (3, 32, 32)







      def  __lowerCAmelCase   ( self :		List[str]    )       ->str:




       """simple docstring"""


       a						    = {
           '''block_out_channels''': [32, 64],
           '''in_channels''': 3,
           '''out_channels''': 3,
           '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
           '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
           '''latent_channels''': 4,
       }
       a						    = self.dummy_input
       return init_dict, inputs_dict







      def  __lowerCAmelCase   ( self :		Any    )       ->Tuple:




       """simple docstring"""


       pass







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Optional[Any]:




       """simple docstring"""


       pass







      @unittest.skipIf(torch_device == '''mps'''					,   '''Gradient checkpointing skipped on MPS'''    )
      def  __lowerCAmelCase   ( self :		List[Any]    )       ->Union[str, Any]:




       """simple docstring"""


       a    ,	a						    = self.prepare_init_args_and_inputs_for_common()
       a						    = self.model_class(**__UpperCAmelCase    )
       model.to(__UpperCAmelCase    )

       assert not model.is_gradient_checkpointing and model.training

       a						    = model(**__UpperCAmelCase    ).sample
       # run the backwards pass on the model. For backwards pass, for simplicity purpose,
       # we won't calculate the loss and rather backprop on out.sum()
       model.zero_grad()

       a						    = torch.randn_like(__UpperCAmelCase    )
       a						    = (out - labels).mean()
       loss.backward()

       # re-instantiate the model now enabling gradient checkpointing
       a						    = self.model_class(**__UpperCAmelCase    )
       # clone model
       model_a.load_state_dict(model.state_dict()    )
       model_a.to(__UpperCAmelCase    )
       model_a.enable_gradient_checkpointing()

       assert model_a.is_gradient_checkpointing and model_a.training

       a						    = model_a(**__UpperCAmelCase    ).sample
       # run the backwards pass on the model. For backwards pass, for simplicity purpose,
       # we won't calculate the loss and rather backprop on out.sum()
       model_a.zero_grad()
       a						    = (out_a - labels).mean()
       loss_a.backward()

       # compare the output and parameters gradients
       self.assertTrue((loss - loss_a).abs() < 1e-5    )
       a						    = dict(model.named_parameters()    )
       a						    = dict(model_a.named_parameters()    )
       for name, param in named_params.items():
        self.assertTrue(torch_all_close(param.grad.data					,   named_params_a[name].grad.data					,   atol=5e-5    )    )







      def  __lowerCAmelCase   ( self :		Tuple    )       ->Tuple:




       """simple docstring"""


       a    ,	a						    = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''					,   output_loading_info=__UpperCAmelCase    )
       self.assertIsNotNone(__UpperCAmelCase    )
       self.assertEqual(len(loading_info['''missing_keys''']    )					,   0    )

       model.to(__UpperCAmelCase    )
       a						    = model(**self.dummy_input    )

       assert image is not None, "Make sure output is not None"







      def  __lowerCAmelCase   ( self :		Any    )       ->Dict:




       """simple docstring"""


       a						    = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''    )
       a						    = model.to(__UpperCAmelCase    )
       model.eval()

       if torch_device == "mps":
        a						    = torch.manual_seed(0    )
       else:
        a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(0    )

       a						    = torch.randn(
           1					,   model.config.in_channels					,   model.config.sample_size					,   model.config.sample_size					,   generator=torch.manual_seed(0    )					,   )
       a						    = image.to(__UpperCAmelCase    )
       with torch.no_grad():
        a						    = model(__UpperCAmelCase					,   sample_posterior=__UpperCAmelCase					,   generator=__UpperCAmelCase    ).sample

       a						    = output[0, -1, -3:, -3:].flatten().cpu()

       # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
       # the expected output slices are not the same for CPU and GPU.
       if torch_device == "mps":
        a						    = torch.tensor(
            [
                -4.0_0_7_8e-0_1,
                -3.8_3_2_3e-0_4,
                -1.2_6_8_1e-0_1,
                -1.1_4_6_2e-0_1,
                2.0_0_9_5e-0_1,
                1.0_8_9_3e-0_1,
                -8.8_2_4_7e-0_2,
                -3.0_3_6_1e-0_1,
                -9.8_6_4_4e-0_3,
            ]    )
       elif torch_device == "cpu":
        a						    = torch.tensor(
            [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]    )
       else:
        a						    = torch.tensor(
            [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]    )

       self.assertTrue(torch_all_close(__UpperCAmelCase					,   __UpperCAmelCase					,   rtol=1e-2    )    )






@slow
class 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		Union[str, Any]					,   __UpperCAmelCase :		List[Any]    )       ->Optional[Any]:




       """simple docstring"""


       return F"""gaussian_noise_s={seed}_shape={'_'.join([str(__UpperCAmelCase    ) for s in shape]    )}.npy"""







      def  __lowerCAmelCase   ( self :		Tuple    )       ->List[str]:




       """simple docstring"""


       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		List[str]=0					,   __UpperCAmelCase :		Optional[int]=(4, 3, 512, 512)					,   __UpperCAmelCase :		Any=False    )       ->List[str]:




       """simple docstring"""


       a						    = torch.floataa if fpaa else torch.floataa
       a						    = torch.from_numpy(load_hf_numpy(self.get_file_format(__UpperCAmelCase					,   __UpperCAmelCase    )    )    ).to(__UpperCAmelCase    ).to(__UpperCAmelCase    )
       return image







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		List[str]="CompVis/stable-diffusion-v1-4"					,   __UpperCAmelCase :		Tuple=False    )       ->Optional[Any]:




       """simple docstring"""


       a						    = '''fp16''' if fpaa else None
       a						    = torch.floataa if fpaa else torch.floataa

       a						    = AutoencoderKL.from_pretrained(
           __UpperCAmelCase					,   subfolder='''vae'''					,   torch_dtype=__UpperCAmelCase					,   revision=__UpperCAmelCase					,   )
       model.to(__UpperCAmelCase    ).eval()

       return model







      def  __lowerCAmelCase   ( self :		Optional[Any]					,   __UpperCAmelCase :		Tuple=0    )       ->List[str]:




       """simple docstring"""


       if torch_device == "mps":
        return torch.manual_seed(__UpperCAmelCase    )
       return torch.Generator(device=__UpperCAmelCase    ).manual_seed(__UpperCAmelCase    )







      @parameterized.expand(
          [
              # fmt: off
              [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
              [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
              # fmt: on
          ]    )
      def  __lowerCAmelCase   ( self :		List[str]					,   __UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		int    )       ->Tuple:




       """simple docstring"""


       a						    = self.get_sd_vae_model()
       a						    = self.get_sd_image(__UpperCAmelCase    )
       a						    = self.get_generator(__UpperCAmelCase    )

       with torch.no_grad():
        a						    = model(__UpperCAmelCase					,   generator=__UpperCAmelCase					,   sample_posterior=__UpperCAmelCase    ).sample

       assert sample.shape == image.shape

       a						    = sample[-1, -2:, -2:, :2].flatten().float().cpu()
       a						    = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice    )

       assert torch_all_close(__UpperCAmelCase					,   __UpperCAmelCase					,   atol=3e-3    )







      @parameterized.expand(
          [
              # fmt: off
              [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
              [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
              # fmt: on
          ]    )
      @require_torch_gpu
      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		List[Any]					,   __UpperCAmelCase :		Dict    )       ->Optional[Any]:




       """simple docstring"""


       a						    = self.get_sd_vae_model(fpaa=__UpperCAmelCase    )
       a						    = self.get_sd_image(__UpperCAmelCase					,   fpaa=__UpperCAmelCase    )
       a						    = self.get_generator(__UpperCAmelCase    )

       with torch.no_grad():
        a						    = model(__UpperCAmelCase					,   generator=__UpperCAmelCase					,   sample_posterior=__UpperCAmelCase    ).sample

       assert sample.shape == image.shape

       a						    = sample[-1, -2:, :2, -2:].flatten().float().cpu()
       a						    = torch.tensor(__UpperCAmelCase    )

       assert torch_all_close(__UpperCAmelCase					,   __UpperCAmelCase					,   atol=1e-2    )







      @parameterized.expand(
          [
              # fmt: off
              [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
              [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
              # fmt: on
          ]    )
      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		str    )       ->Optional[Any]:




       """simple docstring"""


       a						    = self.get_sd_vae_model()
       a						    = self.get_sd_image(__UpperCAmelCase    )

       with torch.no_grad():
        a						    = model(__UpperCAmelCase    ).sample

       assert sample.shape == image.shape

       a						    = sample[-1, -2:, -2:, :2].flatten().float().cpu()
       a						    = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice    )

       assert torch_all_close(__UpperCAmelCase					,   __UpperCAmelCase					,   atol=3e-3    )







      @parameterized.expand(
          [
              # fmt: off
              [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
              [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
              # fmt: on
          ]    )
      @require_torch_gpu
      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		Dict					,   __UpperCAmelCase :		Any    )       ->int:




       """simple docstring"""


       a						    = self.get_sd_vae_model()
       a						    = self.get_sd_image(__UpperCAmelCase					,   shape=(3, 4, 64, 64)    )

       with torch.no_grad():
        a						    = model.decode(__UpperCAmelCase    ).sample

       assert list(sample.shape    ) == [3, 3, 512, 512]

       a						    = sample[-1, -2:, :2, -2:].flatten().cpu()
       a						    = torch.tensor(__UpperCAmelCase    )

       assert torch_all_close(__UpperCAmelCase					,   __UpperCAmelCase					,   atol=1e-3    )







      @parameterized.expand(
          [
              # fmt: off
              [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
              [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
              # fmt: on
          ]    )
      @require_torch_gpu
      def  __lowerCAmelCase   ( self :		List[Any]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Union[str, Any]    )       ->Tuple:




       """simple docstring"""


       a						    = self.get_sd_vae_model(fpaa=__UpperCAmelCase    )
       a						    = self.get_sd_image(__UpperCAmelCase					,   shape=(3, 4, 64, 64)					,   fpaa=__UpperCAmelCase    )

       with torch.no_grad():
        a						    = model.decode(__UpperCAmelCase    ).sample

       assert list(sample.shape    ) == [3, 3, 512, 512]

       a						    = sample[-1, -2:, :2, -2:].flatten().float().cpu()
       a						    = torch.tensor(__UpperCAmelCase    )

       assert torch_all_close(__UpperCAmelCase					,   __UpperCAmelCase					,   atol=5e-3    )







      @parameterized.expand([(13,), (16,), (27,)]    )
      @require_torch_gpu
      @unittest.skipIf(not is_xformers_available()					,   reason='''xformers is not required when using PyTorch 2.0.'''    )
      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		Optional[int]    )       ->Optional[Any]:




       """simple docstring"""


       a						    = self.get_sd_vae_model(fpaa=__UpperCAmelCase    )
       a						    = self.get_sd_image(__UpperCAmelCase					,   shape=(3, 4, 64, 64)					,   fpaa=__UpperCAmelCase    )

       with torch.no_grad():
        a						    = model.decode(__UpperCAmelCase    ).sample

       model.enable_xformers_memory_efficient_attention()
       with torch.no_grad():
        a						    = model.decode(__UpperCAmelCase    ).sample

       assert list(sample.shape    ) == [3, 3, 512, 512]

       assert torch_all_close(__UpperCAmelCase					,   __UpperCAmelCase					,   atol=1e-1    )







      @parameterized.expand([(13,), (16,), (37,)]    )
      @require_torch_gpu
      @unittest.skipIf(not is_xformers_available()					,   reason='''xformers is not required when using PyTorch 2.0.'''    )
      def  __lowerCAmelCase   ( self :		Tuple					,   __UpperCAmelCase :		Dict    )       ->Union[str, Any]:




       """simple docstring"""


       a						    = self.get_sd_vae_model()
       a						    = self.get_sd_image(__UpperCAmelCase					,   shape=(3, 4, 64, 64)    )

       with torch.no_grad():
        a						    = model.decode(__UpperCAmelCase    ).sample

       model.enable_xformers_memory_efficient_attention()
       with torch.no_grad():
        a						    = model.decode(__UpperCAmelCase    ).sample

       assert list(sample.shape    ) == [3, 3, 512, 512]

       assert torch_all_close(__UpperCAmelCase					,   __UpperCAmelCase					,   atol=1e-2    )







      @parameterized.expand(
          [
              # fmt: off
              [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
              [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
              # fmt: on
          ]    )
      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		Tuple					,   __UpperCAmelCase :		Optional[int]    )       ->Union[str, Any]:




       """simple docstring"""


       a						    = self.get_sd_vae_model()
       a						    = self.get_sd_image(__UpperCAmelCase    )
       a						    = self.get_generator(__UpperCAmelCase    )

       with torch.no_grad():
        a						    = model.encode(__UpperCAmelCase    ).latent_dist
        a						    = dist.sample(generator=__UpperCAmelCase    )

       assert list(sample.shape    ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

       a						    = sample[0, -1, -3:, -3:].flatten().cpu()
       a						    = torch.tensor(__UpperCAmelCase    )

       a						    = 3e-3 if torch_device != '''mps''' else 1e-2
       assert torch_all_close(__UpperCAmelCase					,   __UpperCAmelCase					,   atol=__UpperCAmelCase    )










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 						lowercase_   (      lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




      __snake_case	    =    KandinskyVaaInpaintPipeline
      __snake_case	    =    ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      __snake_case	    =    [
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      __snake_case	    =    [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      __snake_case	    =    False







      @property
      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->Tuple:




       """simple docstring"""


       return 32







      @property
      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Dict:




       """simple docstring"""


       return 32







      @property
      def  __lowerCAmelCase   ( self :		List[Any]    )       ->Union[str, Any]:




       """simple docstring"""


       return self.time_input_dim







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->Optional[Any]:




       """simple docstring"""


       return self.time_input_dim * 4







      @property
      def  __lowerCAmelCase   ( self :		Any    )       ->Optional[int]:




       """simple docstring"""


       return 100







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->Optional[int]:




       """simple docstring"""


       torch.manual_seed(0    )

       a						    = {
           '''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						    = UNetaDConditionModel(**__UpperCAmelCase    )
       return model







      @property
      def  __lowerCAmelCase   ( self :		List[Any]    )       ->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  __lowerCAmelCase   ( self :		str    )       ->List[Any]:




       """simple docstring"""


       torch.manual_seed(0    )
       a						    = VQModel(**self.dummy_movq_kwargs    )
       return model







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Any:




       """simple docstring"""


       a						    = self.dummy_unet
       a						    = self.dummy_movq

       a						    = DDIMScheduler(
           num_train_timesteps=1_000					,   beta_schedule='''linear'''					,   beta_start=0.00085					,   beta_end=0.012					,   clip_sample=__UpperCAmelCase					,   set_alpha_to_one=__UpperCAmelCase					,   steps_offset=1					,   prediction_type='''epsilon'''					,   thresholding=__UpperCAmelCase					,   )

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

       return components







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		int=0    )       ->Optional[int]:




       """simple docstring"""


       a						    = floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
       a						    = floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(seed + 1    )    ).to(
           __UpperCAmelCase    )
       # create init_image
       a						    = floats_tensor((1, 3, 64, 64)					,   rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
       a						    = image.cpu().permute(0					,   2					,   3					,   1    )[0]
       a						    = Image.fromarray(np.uinta(__UpperCAmelCase    )    ).convert('''RGB'''    ).resize((256, 256)    )
       # create mask
       a						    = np.ones((64, 64)					,   dtype=np.floataa    )
       a						    = 0

       if str(__UpperCAmelCase    ).startswith('''mps'''    ):
        a						    = torch.manual_seed(__UpperCAmelCase    )
       else:
        a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(__UpperCAmelCase    )
       a						    = {
           '''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  __lowerCAmelCase   ( self :		Dict    )       ->int:




       """simple docstring"""


       a						    = '''cpu'''

       a						    = self.get_dummy_components()

       a						    = self.pipeline_class(**__UpperCAmelCase    )
       a						    = pipe.to(__UpperCAmelCase    )

       pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = pipe(**self.get_dummy_inputs(__UpperCAmelCase    )    )
       a						    = output.images

       a						    = pipe(
           **self.get_dummy_inputs(__UpperCAmelCase    )					,   return_dict=__UpperCAmelCase					,   )[0]

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

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

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

       a						    = np.array(
           [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]    )

       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  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->Optional[int]:




       """simple docstring"""


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






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






      '''simple docstring'''




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




       """simple docstring"""


       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







      def  __lowerCAmelCase   ( self :		List[str]    )       ->str:




       """simple docstring"""


       a						    = load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''    )

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

       a						    = '''a hat'''

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

       a						    = KandinskyVaaInpaintPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''					,   torch_dtype=torch.floataa    )
       a						    = pipeline.to(__UpperCAmelCase    )
       pipeline.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = torch.Generator(device='''cpu'''    ).manual_seed(0    )
       a    ,	a						    = pipe_prior(
           __UpperCAmelCase					,   generator=__UpperCAmelCase					,   num_inference_steps=5					,   negative_prompt=''''''					,   ).to_tuple()

       a						    = pipeline(
           image=__UpperCAmelCase					,   mask_image=__UpperCAmelCase					,   image_embeds=__UpperCAmelCase					,   negative_image_embeds=__UpperCAmelCase					,   generator=__UpperCAmelCase					,   num_inference_steps=100					,   height=768					,   width=768					,   output_type='''np'''					,   )

       a						    = output.images[0]

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

       assert_mean_pixel_difference(__UpperCAmelCase					,   __UpperCAmelCase    )


