code: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  UpperCAmelCase			:			Tuple	  = {"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCAmelCase			:			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:
       UpperCAmelCase			:			int	  = [
           """TFGPTJForCausalLM""",
           """TFGPTJForQuestionAnswering""",
           """TFGPTJForSequenceClassification""",
           """TFGPTJModel""",
           """TFGPTJPreTrainedModel""",
       ]
  
  try:
       if not is_flax_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCAmelCase			:			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
  
       UpperCAmelCase			:			Optional[Any]	  = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def   _A (	SCREAMING_SNAKE_CASE  :     int		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		) or number < 0:
      raise ValueError("Input must be a non-negative integer"		)
  
    a__			:      List[str]            =number & ~(number - 1)
    a__			:      Optional[int]            =0
    while intermediate:
      intermediate >>= 1
      index += 1
    return index - 1
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod(verbose=True)
  
  
  
  
  
  
  