





from typing import TYPE_CHECKING

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


_UpperCamelCase   :							List[Any]     =	{"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase   :							List[Any]     =	[
        """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """GPTJForCausalLM""",
        """GPTJForQuestionAnswering""",
        """GPTJForSequenceClassification""",
        """GPTJModel""",
        """GPTJPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase   :							Union[str, Any]     =	[
        """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   :							str     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








def 				__UpperCamelCase  (		snake_case = 1_0_0     )  ->						int:


   '''simple docstring'''







   __A				 =       [[0 for _ in range(snake_case     )] for _ in range(m + 1     )]
   for i in range(m + 1     ):
      __A				 =       1

   for n in range(m + 1     ):
      for k in range(1    ,	snake_case     ):
         memo[n][k] += memo[n][k - 1]
         if n > k:
            memo[n][k] += memo[n - k - 1][k]

   return memo[m][m - 1] - 1


if __name__ == "__main__":
    print(solution(int(input("""Enter a number: """).strip())))

