

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_a									=  {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a									=  [
      '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''GPTJForCausalLM''',
      '''GPTJForQuestionAnswering''',
      '''GPTJForSequenceClassification''',
      '''GPTJModel''',
      '''GPTJPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a									=  [
      '''TFGPTJForCausalLM''',
      '''TFGPTJForQuestionAnswering''',
      '''TFGPTJForSequenceClassification''',
      '''TFGPTJModel''',
      '''TFGPTJPreTrainedModel''',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a									=  [
      '''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

  _a									=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)













def __A       ( __lowerCAmelCase = 100					,						__lowerCAmelCase = 100      )->    int:





						"""simple docstring"""



						return max(
						    sum(int(__lowerCAmelCase      ) for x in str(base**power      )      )
						    for base in range(__lowerCAmelCase      )
						    for power in range(__lowerCAmelCase      )      )


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()
