code: 
  
  
  import unittest
  from pathlib import Path
  
  from transformers import is_vision_available, load_tool
  from transformers.testing_utils import get_tests_dir
  
  from .test_tools_common import ToolTesterMixin
  
  
  if is_vision_available():
  	from PIL import Image
  
  
  
  
  
  class    A					(     unittest.TestCase      ,       A_				):
  
  
  							def     _A (self      ):
  									__lowercase=    load_tool('image-segmentation'      )
  									self.tool.setup()
  									__lowercase=    load_tool('image-segmentation'   ,       remote=lowerCAmelCase      )
  
  
  							def     _A (self      ):
  									__lowercase=    Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
  									__lowercase=    self.tool(lowerCAmelCase   ,       'cat'      )
  									self.assertTrue(isinstance(lowerCAmelCase   ,       Image.Image      )      )
  
  
  							def     _A (self      ):
  									__lowercase=    Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
  									__lowercase=    self.remote_tool(lowerCAmelCase   ,       'cat'      )
  									self.assertTrue(isinstance(lowerCAmelCase   ,       Image.Image      )      )
  
  
  							def     _A (self      ):
  									__lowercase=    Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
  									__lowercase=    self.tool(image=lowerCAmelCase   ,       label='cat'      )
  									self.assertTrue(isinstance(lowerCAmelCase   ,       Image.Image      )      )
  
  
  
  
  
  
  
  							def     _A (self      ):
  									__lowercase=    Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
  									__lowercase=    self.remote_tool(image=lowerCAmelCase   ,       label='cat'      )
  									self.assertTrue(isinstance(lowerCAmelCase   ,       Image.Image      )      )
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  
  
  if is_transformers_available() and is_torch_available():
  	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
  	from .pipeline_alt_diffusion import AltDiffusionPipeline
  	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
  
  
  
  
  
  
  