





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__)













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)






