
from typing import TYPE_CHECKING

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


_lowerCamelCase       :				int       =			{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase       :				Optional[Any]       =			['''ConvBertTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase       :				List[Any]       =			[
			    '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ConvBertForMaskedLM''',
			    '''ConvBertForMultipleChoice''',
			    '''ConvBertForQuestionAnswering''',
			    '''ConvBertForSequenceClassification''',
			    '''ConvBertForTokenClassification''',
			    '''ConvBertLayer''',
			    '''ConvBertModel''',
			    '''ConvBertPreTrainedModel''',
			    '''load_tf_weights_in_convbert''',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase       :				str       =			[
			    '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFConvBertForMaskedLM''',
			    '''TFConvBertForMultipleChoice''',
			    '''TFConvBertForQuestionAnswering''',
			    '''TFConvBertForSequenceClassification''',
			    '''TFConvBertForTokenClassification''',
			    '''TFConvBertLayer''',
			    '''TFConvBertModel''',
			    '''TFConvBertPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
			from .tokenization_convbert import ConvBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_convbert_fast import ConvBertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_convbert import (
						    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ConvBertForMaskedLM,
						    ConvBertForMultipleChoice,
						    ConvBertForQuestionAnswering,
						    ConvBertForSequenceClassification,
						    ConvBertForTokenClassification,
						    ConvBertLayer,
						    ConvBertModel,
						    ConvBertPreTrainedModel,
						    load_tf_weights_in_convbert,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_convbert import (
						    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFConvBertForMaskedLM,
						    TFConvBertForMultipleChoice,
						    TFConvBertForQuestionAnswering,
						    TFConvBertForSequenceClassification,
						    TFConvBertForTokenClassification,
						    TFConvBertLayer,
						    TFConvBertModel,
						    TFConvBertPreTrainedModel,
						)


else:
			import sys

			_lowerCamelCase       :				Optional[Any]       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

from collections.abc import Sequence






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	float ) ->  float:
			return sum(c * (x**i) for i, c in enumerate(__lowercase ) )






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	float ) ->  float:
			_snake_case		=			0.0
			for coeff in reversed(__lowercase ):
						_snake_case		=			result * x + coeff
			return result


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