

a__												=						[
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]



def   __UpperCAmelCase					(      __a :     str      )     ->	int:

					"""simple docstring"""

					_a      :       Optional[int]   =						{'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1_000}
					_a      :       Union[str, Any]   =						0
					_a      :       List[Any]   =						0
					while place < len(__a      ):
										if (place + 1 < len(__a      )) and (vals[roman[place]] < vals[roman[place + 1]]):
															total += vals[roman[place + 1]] - vals[roman[place]]
															place += 2
										else:
															total += vals[roman[place]]
															place += 1
					return total



def   __UpperCAmelCase					(      __a :     int      )     ->	str:

					"""simple docstring"""

					_a      :       List[str]   =						[]
					for arabic, roman in ROMAN:
										((_a) ,   (_a))      :       Optional[Any]   =						divmod(__a						,__a      )
										result.append(roman * factor      )
										if number == 0:
															break
					return "".join(__a      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()




"""simple docstring"""


from typing import TYPE_CHECKING

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


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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """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

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