import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch




class  __magic_name__  (		unittest.TestCase    ):





  """simple docstring"""




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



  @require_torch
  def   lowerCAmelCase						(	self					:  Union[str, Any]							):



   """simple docstring"""



   import torch

   _UpperCamelCase:     str							= JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics'''							)
   _UpperCamelCase:     Union[str, Any]							= tokenizer(**self.metas							)['input_ids']
   # fmt: off
   _UpperCamelCase:     Optional[Any]							= [
       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   lowerCAmelCase						(	self					:  Any							):



   """simple docstring"""



   import torch

   _UpperCamelCase:     Optional[int]							= JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics'''							)
   _UpperCamelCase:     Any							= tokenizer(**self.metas							)['input_ids']
   # fmt: off
   _UpperCamelCase:     str							= [
       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]							)							)
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class  __magic_name__  (		__a      ,	__a      ,	__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[int]  			=   StableDiffusionInpaintPipeline
   lowerCAmelCase      :    Optional[Any]  			=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   lowerCAmelCase      :    str  			=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCAmelCase      :    str  			=   frozenset(
       []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   lowerCAmelCase      :    Tuple  			=   frozenset([]    )



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    torch.manual_seed(0							)
    _UpperCamelCase:     List[str]							= UNetaDConditionModel(
        block_out_channels=(32, 64)		,  layers_per_block=2		,  sample_size=32		,  in_channels=9		,  out_channels=4		,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,  cross_attention_dim=32		,  attention_head_dim=(2, 4)		,  use_linear_projection=_lowercase		,  )
    _UpperCamelCase:     List[str]							= PNDMScheduler(skip_prk_steps=_lowercase							)
    torch.manual_seed(0							)
    _UpperCamelCase:     List[str]							= AutoencoderKL(
        block_out_channels=[32, 64]		,  in_channels=3		,  out_channels=3		,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,  latent_channels=4		,  sample_size=128		,  )
    torch.manual_seed(0							)
    _UpperCamelCase:     Optional[int]							= CLIPTextConfig(
        bos_token_id=0		,  eos_token_id=2		,  hidden_size=32		,  intermediate_size=37		,  layer_norm_eps=1E-05		,  num_attention_heads=4		,  num_hidden_layers=5		,  pad_token_id=1		,  vocab_size=1_000		,  hidden_act='''gelu'''		,  projection_dim=512		,  )
    _UpperCamelCase:     Optional[int]							= CLIPTextModel(_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

    _UpperCamelCase:     Optional[Any]							= {
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vae''': vae,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''safety_checker''': None,
        '''feature_extractor''': None,
    }
    return components



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  Any		,  _lowercase					:  int=0							):



    """simple docstring"""



    _UpperCamelCase:     Any							= floats_tensor((1, 3, 32, 32)		,  rng=random.Random(_lowercase							)							).to(_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= image.cpu().permute(0		,  2		,  3		,  1							)[0]
    _UpperCamelCase:     Optional[int]							= Image.fromarray(np.uinta(_lowercase							)							).convert('''RGB'''							).resize((64, 64)							)
    _UpperCamelCase:     Optional[Any]							= Image.fromarray(np.uinta(image + 4							)							).convert('''RGB'''							).resize((64, 64)							)
    if str(_lowercase							).startswith('''mps'''							):
     _UpperCamelCase:     Dict							= torch.manual_seed(_lowercase							)
    else:
     _UpperCamelCase:     Union[str, Any]							= torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
    _UpperCamelCase:     List[str]							= {
        '''prompt''': '''A painting of a squirrel eating a burger''',
        '''image''': init_image,
        '''mask_image''': mask_image,
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
    }
    return inputs



   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    _UpperCamelCase:     str							= self.get_dummy_components()
    _UpperCamelCase:     Any							= StableDiffusionInpaintPipeline(**_lowercase							)
    _UpperCamelCase:     Any							= sd_pipe.to(_lowercase							)
    sd_pipe.set_progress_bar_config(disable=_lowercase							)

    _UpperCamelCase:     Tuple							= self.get_dummy_inputs(_lowercase							)
    _UpperCamelCase:     List[Any]							= sd_pipe(**_lowercase							).images
    _UpperCamelCase:     Optional[Any]							= image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    _UpperCamelCase:     List[str]							= np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]							)

    assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



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




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





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Tuple							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)
    _UpperCamelCase:     Dict							= load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
        '''/yellow_cat_sitting_on_a_park_bench.npy'''							)

    _UpperCamelCase:     Optional[Any]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     Optional[Any]							= StableDiffusionInpaintPipeline.from_pretrained(_lowercase		,  safety_checker=_lowercase							)
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing()

    _UpperCamelCase:     List[str]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     List[Any]							= torch.manual_seed(0							)
    _UpperCamelCase:     str							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  output_type='''np'''		,  )
    _UpperCamelCase:     Optional[Any]							= output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image							).max() < 9E-3



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Union[str, Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)
    _UpperCamelCase:     str							= load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
        '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''							)

    _UpperCamelCase:     Optional[Any]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     int							= StableDiffusionInpaintPipeline.from_pretrained(
        _lowercase		,  torch_dtype=torch.floataa		,  safety_checker=_lowercase		,  )
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing()

    _UpperCamelCase:     Optional[Any]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     Dict							= torch.manual_seed(0							)
    _UpperCamelCase:     int							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  output_type='''np'''		,  )
    _UpperCamelCase:     Optional[Any]							= output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image							).max() < 5E-1



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    _UpperCamelCase:     Union[str, Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/sd2-inpaint/init_image.png'''							)
    _UpperCamelCase:     Optional[Any]							= load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''							)

    _UpperCamelCase:     Optional[int]							= '''stabilityai/stable-diffusion-2-inpainting'''
    _UpperCamelCase:     Union[str, Any]							= PNDMScheduler.from_pretrained(_lowercase		,  subfolder='''scheduler'''							)
    _UpperCamelCase:     Optional[int]							= StableDiffusionInpaintPipeline.from_pretrained(
        _lowercase		,  safety_checker=_lowercase		,  scheduler=_lowercase		,  torch_dtype=torch.floataa		,  )
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)
    pipe.enable_attention_slicing(1							)
    pipe.enable_sequential_cpu_offload()

    _UpperCamelCase:     Optional[int]							= '''Face of a yellow cat, high resolution, sitting on a park bench'''

    _UpperCamelCase:     Union[str, Any]							= torch.manual_seed(0							)
    _UpperCamelCase:     Union[str, Any]							= pipe(
        prompt=_lowercase		,  image=_lowercase		,  mask_image=_lowercase		,  generator=_lowercase		,  num_inference_steps=2		,  output_type='''np'''		,  )

    _UpperCamelCase:     Dict							= torch.cuda.max_memory_allocated()
    # make sure that less than 2.65 GB is allocated
    assert mem_bytes < 2.65 * 10**9