code: 
  
  
  
  
  
  from typing import List
  
  import datasets
  from datasets.tasks import ImageClassification
  
  from ..folder_based_builder import folder_based_builder
  
  
  A_    :					List[Any]             =datasets.utils.logging.get_logger(__name__)
  
  
  
  
  
  class        lowercase_				( folder_based_builder.FolderBasedBuilderConfig):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		None
  							snake_case_        =		None
  
  
  
  
  
  class        lowercase_				( folder_based_builder.FolderBasedBuilder):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		datasets.Image()
  							snake_case_        =		'''image'''
  							snake_case_        =		ImageFolderConfig
  							snake_case_        =		42  # definition at the bottom of the script
  							snake_case_        =		ImageClassification(image_column='''image'''			,label_column='''label''')
  
  
  
  
  A_    :					Tuple             =[
      """.blp""",
      """.bmp""",
      """.dib""",
      """.bufr""",
      """.cur""",
      """.pcx""",
      """.dcx""",
      """.dds""",
      """.ps""",
      """.eps""",
      """.fit""",
      """.fits""",
      """.fli""",
      """.flc""",
      """.ftc""",
      """.ftu""",
      """.gbr""",
      """.gif""",
      """.grib""",
      """.h5""",
      """.hdf""",
      """.png""",
      """.apng""",
      """.jp2""",
      """.j2k""",
      """.jpc""",
      """.jpf""",
      """.jpx""",
      """.j2c""",
      """.icns""",
      """.ico""",
      """.im""",
      """.iim""",
      """.tif""",
      """.tiff""",
      """.jfif""",
      """.jpe""",
      """.jpg""",
      """.jpeg""",
      """.mpg""",
      """.mpeg""",
      """.msp""",
      """.pcd""",
      """.pxr""",
      """.pbm""",
      """.pgm""",
      """.ppm""",
      """.pnm""",
      """.psd""",
      """.bw""",
      """.rgb""",
      """.rgba""",
      """.sgi""",
      """.ras""",
      """.tga""",
      """.icb""",
      """.vda""",
      """.vst""",
      """.webp""",
      """.wmf""",
      """.emf""",
      """.xbm""",
      """.xpm""",
  ]
  A_    :					Optional[int]             =IMAGE_EXTENSIONS
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  A_    :					Optional[Any]             ={
      """configuration_wav2vec2_conformer""": [
          """WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """Wav2Vec2ConformerConfig""",
      ],
  }
  
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	A_    :					Union[str, Any]             =[
  	    """WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """Wav2Vec2ConformerForAudioFrameClassification""",
  	    """Wav2Vec2ConformerForCTC""",
  	    """Wav2Vec2ConformerForPreTraining""",
  	    """Wav2Vec2ConformerForSequenceClassification""",
  	    """Wav2Vec2ConformerForXVector""",
  	    """Wav2Vec2ConformerModel""",
  	    """Wav2Vec2ConformerPreTrainedModel""",
  	]
  
  if TYPE_CHECKING:
  	from .configuration_wavaveca_conformer import (
  	    WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
  	    WavaVecaConformerConfig,
  	)
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_wavaveca_conformer import (
  		    WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    WavaVecaConformerForAudioFrameClassification,
  		    WavaVecaConformerForCTC,
  		    WavaVecaConformerForPreTraining,
  		    WavaVecaConformerForSequenceClassification,
  		    WavaVecaConformerForXVector,
  		    WavaVecaConformerModel,
  		    WavaVecaConformerPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	A_    :					Union[str, Any]             =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)