






from typing import TYPE_CHECKING

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


__a      :       List[str]				  =   {
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a      :       Union[str, Any]				  =   [
		    "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MegaForCausalLM",
		    "MegaForMaskedLM",
		    "MegaForMultipleChoice",
		    "MegaForQuestionAnswering",
		    "MegaForSequenceClassification",
		    "MegaForTokenClassification",
		    "MegaModel",
		    "MegaPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mega import (
				    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MegaForCausalLM,
				    MegaForMaskedLM,
				    MegaForMultipleChoice,
				    MegaForQuestionAnswering,
				    MegaForSequenceClassification,
				    MegaForTokenClassification,
				    MegaModel,
				    MegaPreTrainedModel,
				)

else:
		import sys

		__a      :       List[str]				  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












from collections.abc import Sequence



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Sequence[float]			,				__lowercase   :			float    )						->      float:





    """simple docstring"""

    return sum(c * (x**i) for i, c in enumerate(__lowercase    )    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Sequence[float]			,				__lowercase   :			float    )						->      float:





    """simple docstring"""

    __A         =	0.0
    for coeff in reversed(__lowercase    ):
        __A         =	result * x + coeff
    return result


if __name__ == "__main__":
  __a      :       Dict				  =   (0.0, 0.0, 5.0, 9.3, 7.0)
  __a      :       Optional[Any]				  =   1_0.0
  print(evaluate_poly(poly, x))
  print(horner(poly, x))




