code: 
  
  
  import pytest
  
  from datasets.utils.version import Version
  
  
  
  
  
  
  
  @pytest.mark.parametrize(
      'other, expected_equality'     ,				[
          (Version('1.0.0'			), True),
          ('1.0.0', True),
          (Version('2.0.0'			), False),
          ('2.0.0', False),
          ('1', False),
          ('a', False),
          (1, False),
          (None, False),
      ]     ,				)
  def  _lowerCamelCase(			lowercase__     ,				lowercase__			)		->			Any:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    Version('1.0.0'			)
  		assert (version == other) is expected_equality
  		assert (version != other) is not expected_equality
  		assert (hash(lowercase__			) == hash(lowercase__			)) is expected_equality
  
  
  
  
  
  
  
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
  
  
  
  
  
  
  