





'''simple docstring'''


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  lowerCAmelCase_		(		__magic_name__    ,__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Optional[Any]								=       AutoencoderKL
 __lowerCamelCase    :				List[Any]								=       "sample"
 __lowerCamelCase    :				Tuple								=       1e-2




 @property
 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   4
     _lowerCAmelCase						      =   3
     _lowerCAmelCase						      =   (32, 32)

     _lowerCAmelCase						      =   floats_tensor((batch_size, num_channels) + sizes		).to(_lowerCAmelCase		)

     return {"sample": image}




 @property
 def 				_snake_case (						self		)     ->				Any:
     return (3, 32, 32)




 @property
 def 				_snake_case (						self		)     ->				List[Any]:
     return (3, 32, 32)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   {
         "block_out_channels": [32, 64],
         "in_channels": 3,
         "out_channels": 3,
         "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
         "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
         "latent_channels": 4,
     }
     _lowerCAmelCase						      =   self.dummy_input
     return init_dict, inputs_dict




 def 				_snake_case (						self		)     ->				Optional[int]:
     pass




 def 				_snake_case (						self		)     ->				Any:
     pass




 @unittest.skipIf(torch_device == "mps"      ,  "Gradient checkpointing skipped on MPS"		)
 def 				_snake_case (						self		)     ->				str:
     # enable deterministic behavior for gradient checkpointing
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.prepare_init_args_and_inputs_for_common()
     _lowerCAmelCase						      =   self.model_class(**_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)

     assert not model.is_gradient_checkpointing and model.training

     _lowerCAmelCase						      =   model(**_lowerCAmelCase		).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()

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

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

     assert model_a.is_gradient_checkpointing and model_a.training

     _lowerCAmelCase						      =   model_a(**_lowerCAmelCase		).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()
     _lowerCAmelCase						      =   (out_a - labels).mean()
     loss_a.backward()

     # compare the output and parameters gradients
     self.assertTrue((loss - loss_a).abs() < 1E-5		)
     _lowerCAmelCase						      =   dict(model.named_parameters()		)
     _lowerCAmelCase						      =   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 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"      ,  output_loading_info=_lowerCAmelCase		)
     self.assertIsNotNone(_lowerCAmelCase		)
     self.assertEqual(len(loading_info["missing_keys"]		)      ,  0		)

     model.to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model(**self.dummy_input		)

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





 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"		)
     _lowerCAmelCase						      =   model.to(_lowerCAmelCase		)
     model.eval()

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

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

     _lowerCAmelCase						      =   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":
         _lowerCAmelCase						      =   torch.tensor(
             [
                 -4.0078E-01,
                 -3.8323E-04,
                 -1.2681E-01,
                 -1.1462E-01,
                 2.0095E-01,
                 1.0893E-01,
                 -8.8247E-02,
                 -3.0361E-01,
                 -9.8644E-03,
             ]		)
     elif torch_device == "cpu":
         _lowerCAmelCase						      =   torch.tensor(
             [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]		)
     else:
         _lowerCAmelCase						      =   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(_lowerCAmelCase      ,  _lowerCAmelCase      ,  rtol=1E-2		)		)







@slow
class  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Union[str, Any]:
     return f'''gaussian_noise_s={seed}_shape={'_'.join([str(_lowerCAmelCase		) for s in shape]		)}.npy'''




 def 				_snake_case (						self		)     ->				List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=(4, 3, 512, 512)      ,  _lowerCAmelCase=False		)     ->				Tuple:
     _lowerCAmelCase						      =   torch.floataa if fpaa else torch.floataa
     _lowerCAmelCase						      =   torch.from_numpy(load_hf_numpy(self.get_file_format(_lowerCAmelCase      ,  _lowerCAmelCase		)		)		).to(_lowerCAmelCase		).to(_lowerCAmelCase		)
     return image




 def 				_snake_case (						self      ,  _lowerCAmelCase="CompVis/stable-diffusion-v1-4"      ,  _lowerCAmelCase=False		)     ->				Tuple:
     _lowerCAmelCase						      =   "fp16" if fpaa else None
     _lowerCAmelCase						      =   torch.floataa if fpaa else torch.floataa

     _lowerCAmelCase						      =   AutoencoderKL.from_pretrained(
         _lowerCAmelCase      ,  subfolder="vae"      ,  torch_dtype=_lowerCAmelCase      ,  revision=_lowerCAmelCase      ,  )
     model.to(_lowerCAmelCase		).eval()

     return model




 def 				_snake_case (						self      ,  _lowerCAmelCase=0		)     ->				str:
     if torch_device == "mps":
         return torch.manual_seed(_lowerCAmelCase		)
     return torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)




 @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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Dict:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_generator(_lowerCAmelCase		)

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

     assert sample.shape == image.shape

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

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.get_sd_vae_model(fpaa=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  fpaa=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_generator(_lowerCAmelCase		)

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

     assert sample.shape == image.shape

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

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Tuple:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase		).sample

     assert sample.shape == image.shape

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

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  shape=(3, 4, 64, 64)		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

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

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

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   self.get_sd_vae_model(fpaa=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  shape=(3, 4, 64, 64)      ,  fpaa=_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

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

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

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  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 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				List[str]:
     _lowerCAmelCase						      =   self.get_sd_vae_model(fpaa=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  shape=(3, 4, 64, 64)      ,  fpaa=_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

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

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

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  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 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  shape=(3, 4, 64, 64)		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

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

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

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				int:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_generator(_lowerCAmelCase		)

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

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

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

     _lowerCAmelCase						      =   3E-3 if torch_device != "mps" else 1E-2
     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=_lowerCAmelCase		)





"""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       lowerCamelCase	(       lowerCAmelCase__					,					unittest.TestCase   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  KandinskyInpaintPipeline
  SCREAMING_SNAKE_CASE					      =  ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
  SCREAMING_SNAKE_CASE					      =  [
      'prompt',
      'negative_prompt',
      'image_embeds',
      'negative_image_embeds',
      'image',
      'mask_image',
  ]
  SCREAMING_SNAKE_CASE					      =  [
      'generator',
      'height',
      'width',
      'latents',
      'guidance_scale',
      'negative_prompt',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  SCREAMING_SNAKE_CASE					      =  False



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return 32



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim



  @property
  def  _a      (self	):


        """simple docstring"""

        return self.time_input_dim * 4



  @property
  def  _a      (self	):


        """simple docstring"""

        return 100



  @property
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
        return tokenizer



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Optional[int]							=    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							,       )

        UpperCAmelCase__			:							List[Any]							=    MultilingualCLIP(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    text_encoder.eval()

        return text_encoder



  @property
  def  _a      (self	):


        """simple docstring"""

        torch.manual_seed(0	)

        UpperCAmelCase__			:							Dict							=    {
            """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,
        }

        UpperCAmelCase__			:							List[Any]							=    UNetaDConditionModel(**_lowerCamelCase	)
        return model



  @property
  def  _a      (self	):


        """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	):


        """simple docstring"""

        torch.manual_seed(0	)
        UpperCAmelCase__			:							Any							=    VQModel(**self.dummy_movq_kwargs	)
        return model



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    self.dummy_text_encoder
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_tokenizer
        UpperCAmelCase__			:							Optional[Any]							=    self.dummy_unet
        UpperCAmelCase__			:							List[Any]							=    self.dummy_movq

        UpperCAmelCase__			:							Union[str, Any]							=    DDIMScheduler(
            num_train_timesteps=1000							,       beta_schedule="""linear"""							,       beta_start=0.00_085							,       beta_end=0.012							,       clip_sample=_lowerCamelCase							,       set_alpha_to_one=_lowerCamelCase							,       steps_offset=1							,       prediction_type="""epsilon"""							,       thresholding=_lowerCamelCase							,       )

        UpperCAmelCase__			:							Any							=    {
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase=0	):


        """simple docstring"""

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

        if str(_lowerCamelCase	).startswith("""mps"""	):
              UpperCAmelCase__			:							Tuple							=    torch.manual_seed(_lowerCamelCase	)
        else:
              UpperCAmelCase__			:							Optional[int]							=    torch.Generator(device=_lowerCamelCase	).manual_seed(_lowerCamelCase	)
        UpperCAmelCase__			:							Any							=    {
            """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	):


        """simple docstring"""

        UpperCAmelCase__			:							Union[str, Any]							=    """cpu"""

        UpperCAmelCase__			:							Optional[Any]							=    self.get_dummy_components()

        UpperCAmelCase__			:							Optional[Any]							=    self.pipeline_class(**_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    pipe.to(_lowerCamelCase	)

        pipe.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							List[str]							=    pipe(**self.get_dummy_inputs(_lowerCamelCase	)	)
        UpperCAmelCase__			:							Optional[int]							=    output.images

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

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

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

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

        UpperCAmelCase__			:							Any							=    np.array(
            [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]	)

        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	):


        """simple docstring"""

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




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


  '''simple docstring'''







  def  _a      (self	):


        """simple docstring"""

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




  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""	)

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

        UpperCAmelCase__			:							int							=    """a hat"""

        UpperCAmelCase__			:							List[Any]							=    KandinskyPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-prior"""							,       torch_dtype=torch.floataa	)
        pipe_prior.to(_lowerCamelCase	)

        UpperCAmelCase__			:							str							=    KandinskyInpaintPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-1-inpaint"""							,       torch_dtype=torch.floataa	)
        UpperCAmelCase__			:							int							=    pipeline.to(_lowerCamelCase	)
        pipeline.set_progress_bar_config(disable=_lowerCamelCase	)

        UpperCAmelCase__			:							int							=    torch.Generator(device="""cpu"""	).manual_seed(0	)
        UpperCAmelCase__					,		UpperCAmelCase__			:							List[str]							=    pipe_prior(
            _lowerCamelCase							,       generator=_lowerCamelCase							,       num_inference_steps=5							,       negative_prompt=""""""							,       ).to_tuple()

        UpperCAmelCase__			:							Union[str, Any]							=    pipeline(
            _lowerCamelCase							,       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"""							,       )

        UpperCAmelCase__			:							Any							=    output.images[0]

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

        assert_mean_pixel_difference(_lowerCamelCase							,       _lowerCamelCase	)

