

from typing import TYPE_CHECKING

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


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

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

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

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

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








def  _snake_case		(snake_case_					:     int = 100    ,       snake_case_					:     int = 100  )							->		int:
  return max(
      sum(int(snake_case_  ) for x in str(base**power  )  )
      for base in range(snake_case_  )
      for power in range(snake_case_  )  )


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

      doctest.testmod()





