code: 
  
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
  from diffusers.utils.testing_utils import (
      enable_full_determinism,
      load_image,
      load_numpy,
      nightly,
      require_torch_gpu,
      skip_mps,
      torch_device,
  )
  
  from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  class        UpperCAmelCase__  (    A__							,			unittest.TestCase):
   __SCREAMING_SNAKE_CASE					      =				RePaintPipeline
   __SCREAMING_SNAKE_CASE					      =				IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
   __SCREAMING_SNAKE_CASE					      =				PipelineTesterMixin.required_optional_params - {
       '''latents''',
       '''num_images_per_prompt''',
       '''callback''',
       '''callback_steps''',
   }
   __SCREAMING_SNAKE_CASE					      =				IMAGE_INPAINTING_BATCH_PARAMS
   __SCREAMING_SNAKE_CASE					      =				False
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
    torch.manual_seed(0			)
    torch.manual_seed(0			)
    __UpperCamelCase				      =  UNetaDModel(
        block_out_channels=(3_2, 6_4)		,	layers_per_block=2		,	sample_size=3_2		,	in_channels=3		,	out_channels=3		,	down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")		,	up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")		,	)
    __UpperCamelCase				      =  RePaintScheduler()
    __UpperCamelCase				      =  {"unet": unet, "scheduler": scheduler}
    return components
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase=0			)  ->							Tuple:
  
  
  
  
  
  
    if str(lowercase			).startswith("""mps"""			):
     __UpperCamelCase				      =  torch.manual_seed(lowercase			)
    else:
     __UpperCamelCase				      =  torch.Generator(device=lowercase			).manual_seed(lowercase			)
    __UpperCamelCase				      =  np.random.RandomState(lowercase			).standard_normal((1, 3, 3_2, 3_2)			)
    __UpperCamelCase				      =  torch.from_numpy(lowercase			).to(device=lowercase		,	dtype=torch.floataa			)
    __UpperCamelCase				      =  (image > 0).to(device=lowercase		,	dtype=torch.floataa			)
    __UpperCamelCase				      =  {
        "image": image,
        "mask_image": mask,
        "generator": generator,
        "num_inference_steps": 5,
        "eta": 0.0,
        "jump_length": 2,
        "jump_n_sample": 2,
        "output_type": "numpy",
    }
    return inputs
  
  
  
   def    __lowerCamelCase				(  self			)  ->							int:
    __UpperCamelCase				      =  "cpu"  # ensure determinism for the device-dependent torch.Generator
    __UpperCamelCase				      =  self.get_dummy_components()
    __UpperCamelCase				      =  RePaintPipeline(**lowercase			)
    __UpperCamelCase				      =  sd_pipe.to(lowercase			)
    sd_pipe.set_progress_bar_config(disable=lowercase			)
  
    __UpperCamelCase				      =  self.get_dummy_inputs(lowercase			)
    __UpperCamelCase				      =  sd_pipe(**lowercase			).images
    __UpperCamelCase				      =  image[0, -3:, -3:, -1]
  
    assert image.shape == (1, 3_2, 3_2, 3)
    __UpperCamelCase				      =  np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]			)
  
    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3
  
  
  
   @skip_mps
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    return super().test_save_load_local()
  
  
  
   @unittest.skip("""non-deterministic pipeline"""			)
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    return super().test_inference_batch_single_identical()
  
  
  
   @skip_mps
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    return super().test_dict_tuple_outputs_equivalent()
  
  
  
   @skip_mps
   def    __lowerCamelCase				(  self			)  ->							Dict:
    return super().test_save_load_optional_components()
  
  
  
  
   @skip_mps
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    return super().test_attention_slicing_forward_pass()
  
  
  @nightly
  @require_torch_gpu
  class        UpperCAmelCase__  (    unittest.TestCase):
  
  
  
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()
  
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Dict:
    __UpperCamelCase				      =  load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
        """repaint/celeba_hq_256.png"""			)
    __UpperCamelCase				      =  load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""			)
    __UpperCamelCase				      =  load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
        """repaint/celeba_hq_256_result.npy"""			)
  
    __UpperCamelCase				      =  "google/ddpm-ema-celebahq-256"
    __UpperCamelCase				      =  UNetaDModel.from_pretrained(lowercase			)
    __UpperCamelCase				      =  RePaintScheduler.from_pretrained(lowercase			)
  
    __UpperCamelCase				      =  RePaintPipeline(unet=lowercase		,	scheduler=lowercase			).to(lowercase			)
    repaint.set_progress_bar_config(disable=lowercase			)
    repaint.enable_attention_slicing()
  
    __UpperCamelCase				      =  torch.manual_seed(0			)
    __UpperCamelCase				      =  repaint(
        lowercase		,	lowercase		,	num_inference_steps=2_5_0		,	eta=0.0		,	jump_length=1_0		,	jump_n_sample=1_0		,	generator=lowercase		,	output_type="""np"""		,	)
    __UpperCamelCase				      =  output.images[0]
  
    assert image.shape == (2_5_6, 2_5_6, 3)
    assert np.abs(expected_image - image			).mean() < 1E-2
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import textwrap
  
  import pyarrow as pa
  import pytest
  
  from datasets import Features, Image
  from datasets.packaged_modules.text.text import Text
  
  from ..utils import require_pil
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text.txt"""
   __UpperCamelCase				      =  textwrap.dedent(
       """\
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
  
          Second paragraph:
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
          """       )
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text_with_image.txt"""
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""keep_linebreaks"""							,[True, False]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines(keepends=__A       )
   __UpperCamelCase				      =  Text(keep_linebreaks=__A							,encoding="""utf-8"""       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  
  
  @require_pil
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines()[0]
   __UpperCamelCase				      =  Text(encoding="""utf-8"""							,features=Features({"""image""": Image()}       )       )
   __UpperCamelCase				      =  text._generate_tables([[text_file_with_image]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       )
   assert pa_table.schema.field("""image"""       ).type == Image()()
   __UpperCamelCase				      =  pa_table.to_pydict()["""image"""]
   assert generated_content == [{"path": image_file, "bytes": None}]
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""sample_by"""							,["""line""", """paragraph""", """document"""]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read()
   if sample_by == "line":
    __UpperCamelCase				      =  expected_content.splitlines()
   elif sample_by == "paragraph":
    __UpperCamelCase				      =  expected_content.split("""\n\n"""       )
   elif sample_by == "document":
    __UpperCamelCase				      =  [expected_content]
   __UpperCamelCase				      =  Text(sample_by=__A							,encoding="""utf-8"""							,chunksize=100       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  