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    _snake_case      (  unittest.TestCase       ,		snake_case       ):
  
  
  
  
  
  
  
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							__magic_name__	:	Union[str, Any]			     =  load_tool("image-segmentation"	)
  							self.tool.setup()
  							__magic_name__	:	Optional[int]			     =  load_tool("image-segmentation"		,     remote=_a	)
  
  
  
  
  
  
  
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							__magic_name__	:	Union[str, Any]			     =  Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
  							__magic_name__	:	Optional[int]			     =  self.tool(_a		,     "cat"	)
  							self.assertTrue(isinstance(_a		,     Image.Image	)	)
  
  
  
  
  
  
  
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							__magic_name__	:	int			     =  Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
  							__magic_name__	:	List[str]			     =  self.remote_tool(_a		,     "cat"	)
  							self.assertTrue(isinstance(_a		,     Image.Image	)	)
  
  
  
  
  
  
  
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							__magic_name__	:	List[str]			     =  Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
  							__magic_name__	:	Any			     =  self.tool(image=_a		,     label="cat"	)
  							self.assertTrue(isinstance(_a		,     Image.Image	)	)
  
  
  
  
  
  
  
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							__magic_name__	:	List[Any]			     =  Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
  							__magic_name__	:	Optional[Any]			     =  self.remote_tool(image=_a		,     label="cat"	)
  							self.assertTrue(isinstance(_a		,     Image.Image	)	)
  
style_context: 
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  snake_case				:	Optional[int]							   =						{
      "configuration_mobilevitv2": [
          "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "MobileViTV2Config",
          "MobileViTV2OnnxConfig",
      ],
  }
  
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				snake_case				:	Optional[int]							   =						[
  				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "MobileViTV2ForImageClassification",
  				    "MobileViTV2ForSemanticSegmentation",
  				    "MobileViTV2Model",
  				    "MobileViTV2PreTrainedModel",
  				]
  
  if TYPE_CHECKING:
  				from .configuration_mobilevitva import (
  				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  				    MobileViTVaConfig,
  				    MobileViTVaOnnxConfig,
  				)
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_mobilevitva import (
  								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    MobileViTVaForImageClassification,
  								    MobileViTVaForSemanticSegmentation,
  								    MobileViTVaModel,
  								    MobileViTVaPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				snake_case				:	Tuple							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  