code: 
  
  
  
  
  from dataclasses import dataclass
  from typing import Callable, Optional
  
  import datasets
  
  
  @dataclass
  class 							_lowerCAmelCase		(     datasets.BuilderConfig	):
  				lowerCamelCase__									=    None
  				lowerCamelCase__									=    None
  				lowerCamelCase__									=    None
  
  
  
  
  				def     __a		( self							)    ->      Dict:
  											assert self.generator is not None, "generator must be specified"
  
  											if self.gen_kwargs is None:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     ={}
  
  
  
  
  
  
  
  class 							_lowerCAmelCase		(     datasets.GeneratorBasedBuilder	):
  				lowerCamelCase__									=    GeneratorConfig
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											return datasets.DatasetInfo(features=self.config.features							)
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      Any:
  											return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,		gen_kwargs=self.config.gen_kwargs							)]
  
  
  
  
  
  
  
  				def     __a		( self		,		**snake_case_							)    ->      Any:
  											for idx, ex in enumerate(self.config.generator(**snake_case_							)							):
  																		yield idx, ex
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}
  
  try:
  							if not is_torch_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """RegNetForImageClassification""",
  							    """RegNetModel""",
  							    """RegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_tf_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """TFRegNetForImageClassification""",
  							    """TFRegNetModel""",
  							    """TFRegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_flax_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """FlaxRegNetForImageClassification""",
  							    """FlaxRegNetModel""",
  							    """FlaxRegNetPreTrainedModel""",
  							]
  
  
  if TYPE_CHECKING:
  							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
  
  							try:
  														if not is_torch_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_regnet import (
  														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    RegNetForImageClassification,
  														    RegNetModel,
  														    RegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_tf_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_tf_regnet import (
  														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    TFRegNetForImageClassification,
  														    TFRegNetModel,
  														    TFRegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_flax_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_flax_regnet import (
  														    FlaxRegNetForImageClassification,
  														    FlaxRegNetModel,
  														    FlaxRegNetPreTrainedModel,
  														)
  
  
  else:
  							import sys
  
  							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)
  
  
  
  
  
  
  