code: 
  
  
  
  
  
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  snake_case   :   Optional[int]    		=		{"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Union[str, Any]    		=		[
       """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """GPTJForCausalLM""",
       """GPTJForQuestionAnswering""",
       """GPTJForSequenceClassification""",
       """GPTJModel""",
       """GPTJPreTrainedModel""",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Tuple    		=		[
       """TFGPTJForCausalLM""",
       """TFGPTJForQuestionAnswering""",
       """TFGPTJForSequenceClassification""",
       """TFGPTJModel""",
       """TFGPTJPreTrainedModel""",
   ]
  
  try:
   if not is_flax_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Union[str, Any]    		=		[
       """FlaxGPTJForCausalLM""",
       """FlaxGPTJModel""",
       """FlaxGPTJPreTrainedModel""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_gptj import (
        GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTJForCausalLM,
        GPTJForQuestionAnswering,
        GPTJForSequenceClassification,
        GPTJModel,
        GPTJPreTrainedModel,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_gptj import (
        TFGPTJForCausalLM,
        TFGPTJForQuestionAnswering,
        TFGPTJForSequenceClassification,
        TFGPTJModel,
        TFGPTJPreTrainedModel,
    )
  
   try:
    if not is_flax_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
  
  else:
   import sys
  
   snake_case   :   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
  
  
  snake_case   :   str    		=		{"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Union[str, Any]    		=		[
       """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SEWDForCTC""",
       """SEWDForSequenceClassification""",
       """SEWDModel""",
       """SEWDPreTrainedModel""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_sew_d import (
        SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWDForCTC,
        SEWDForSequenceClassification,
        SEWDModel,
        SEWDPreTrainedModel,
    )
  
  
  else:
   import sys
  
   snake_case   :   Union[str, Any]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)