code: 
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  a__						:			Optional[int]										=			{
      'configuration_led': ['LED_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LEDConfig'],
      'tokenization_led': ['LEDTokenizer'],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a__						:			Optional[Any]										=			['LEDTokenizerFast']
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a__						:			Optional[int]										=			[
       'LED_PRETRAINED_MODEL_ARCHIVE_LIST',
       'LEDForConditionalGeneration',
       'LEDForQuestionAnswering',
       'LEDForSequenceClassification',
       'LEDModel',
       'LEDPreTrainedModel',
   ]
  
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a__						:			Any										=			['TFLEDForConditionalGeneration', 'TFLEDModel', 'TFLEDPreTrainedModel']
  
  
  if TYPE_CHECKING:
   from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
   from .tokenization_led import LEDTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_led_fast import LEDTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_led import (
        LED_PRETRAINED_MODEL_ARCHIVE_LIST,
        LEDForConditionalGeneration,
        LEDForQuestionAnswering,
        LEDForSequenceClassification,
        LEDModel,
        LEDPreTrainedModel,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel
  
  else:
   import sys
  
   a__						:			List[Any]										=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  a__						:			Optional[int]										=			{
      'configuration_bark': [
          'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'BarkCoarseConfig',
          'BarkConfig',
          'BarkFineConfig',
          'BarkSemanticConfig',
      ],
      'processing_bark': ['BarkProcessor'],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a__						:			Any										=			[
       'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BarkFineModel',
       'BarkSemanticModel',
       'BarkCoarseModel',
       'BarkModel',
       'BarkPreTrainedModel',
       'BarkCausalModel',
   ]
  
  if TYPE_CHECKING:
   from .configuration_bark import (
       BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BarkCoarseConfig,
       BarkConfig,
       BarkFineConfig,
       BarkSemanticConfig,
   )
   from .processing_bark import BarkProcessor
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_bark import (
        BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
        BarkCausalModel,
        BarkCoarseModel,
        BarkFineModel,
        BarkModel,
        BarkPreTrainedModel,
        BarkSemanticModel,
    )
  
  else:
   import sys
  
   a__						:			Tuple										=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  