





'''simple docstring'''

from typing import TYPE_CHECKING

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


a        =						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a        =						[
        "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
        "GPTBigCodeForSequenceClassification",
        "GPTBigCodeForTokenClassification",
        "GPTBigCodeForCausalLM",
        "GPTBigCodeModel",
        "GPTBigCodePreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_bigcode import (
            GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTBigCodeForCausalLM,
            GPTBigCodeForSequenceClassification,
            GPTBigCodeForTokenClassification,
            GPTBigCodeModel,
            GPTBigCodePreTrainedModel,
        )


else:
    import sys

    a        =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''simple docstring'''

def 						__magic_name__       (							__UpperCAmelCase      )						->					bool:

   '''simple docstring'''







   if num < 0:
      return False

   __SCREAMING_SNAKE_CASE      							=       num
   __SCREAMING_SNAKE_CASE      							=       0
   while num > 0:
      __SCREAMING_SNAKE_CASE      							=       rev_num * 10 + (num % 10)
      num //= 10

   return num_copy == rev_num


if __name__ == "__main__":
    import doctest

    doctest.testmod()





