

'''simple docstring'''

import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch





class 							_snake_case (       unittest.TestCase						):
   _A       :     List[str]       =					JukeboxTokenizer
   _A       :     Tuple       =					{
       '''artist''': '''Zac Brown Band''',
       '''genres''': '''Country''',
       '''lyrics''': '''I met a traveller from an antique land,\n        Who said "Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ''',
   }
   @require_torch
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     import torch

     SCREAMING_SNAKE_CASE:Tuple						      =					JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics"							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					tokenizer(**self.metas							)["input_ids"]
     # fmt: off
     SCREAMING_SNAKE_CASE:List[str]						      =					[
         torch.tensor([[
             0, 0, 0, 7_169, 507, 9, 76, 39, 31, 46, 76, 27,
             76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
             44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
             47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
             76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
             30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
             27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
             45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
             41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
             19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
             76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
             76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
             64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
             30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
             27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
             34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
             27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
             41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
             76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
             44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
             76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
             32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
             40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
             20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
             45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
             31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
             45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
             76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
             34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
             31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
             40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
             38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
             78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
             76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
             41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
             27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
             46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
             76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
             41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
             46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
             41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
             78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
             40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
             27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
             76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
             76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
             41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
             76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
             27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
             78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
             34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
             44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
             40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
             78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
             46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
             38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
             40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
             27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
             20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
             76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
             76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
             76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
             76, 76]]							),
         torch.tensor([[0, 0, 0, 1_069, 11]]							),
         torch.tensor([[0, 0, 0, 1_069, 11]]							),
     ]
     # fmt: on
     self.assertTrue(torch.allclose(tokens[0]     ,EXPECTED_OUTPUT[0]							)							)
     self.assertTrue(torch.allclose(tokens[1]     ,EXPECTED_OUTPUT[1]							)							)
     self.assertTrue(torch.allclose(tokens[2]     ,EXPECTED_OUTPUT[2]							)							)







   @require_torch
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     import torch

     SCREAMING_SNAKE_CASE:Tuple						      =					JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics"							)
     SCREAMING_SNAKE_CASE:List[str]						      =					tokenizer(**self.metas							)["input_ids"]
     # fmt: off
     SCREAMING_SNAKE_CASE:int						      =					[
         torch.tensor([[
             0, 0, 0, 1_069, 11, -1, -1, -1, -1, 9, 77, 39,
             31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
             31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
             40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
             79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
             77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
             27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
             37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
             32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
             77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
             77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
             77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
             46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
             77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
             77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
             77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
             77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
             64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
             40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
             40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
             38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
             31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
             41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
             77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
             46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
             41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
             31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
             31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
             23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
             44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
             31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
             38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
             40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
             77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
             27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
             31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
             34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
             31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
             1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
             31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
             45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
             31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
             77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
             15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
             11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
             45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
             41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
             44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
             46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
             27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
             77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
             35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
             77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
             31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
             77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
             41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
             77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
             40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
             77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
             77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
             27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
             77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
             77, 77, 77, 77, 77, 77]]							),
         torch.tensor([[0, 0, 0, 1_069, 11, -1, -1, -1, -1]]							),
         torch.tensor([[0, 0, 0, 1_069, 11, -1, -1, -1, -1]]							),
     ]
     # fmt: on
     self.assertTrue(torch.allclose(tokens[0]     ,EXPECTED_OUTPUT[0]							)							)
     self.assertTrue(torch.allclose(tokens[1]     ,EXPECTED_OUTPUT[1]							)							)
     self.assertTrue(torch.allclose(tokens[2]     ,EXPECTED_OUTPUT[2]							)							)









'''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__							)






