

"""simple docstring"""


from typing import TYPE_CHECKING

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


__A		  =     {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A		  =     ["ConvBertTokenizerFast"]

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

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









'''simple docstring'''


from __future__ import annotations


def 						__a(SCREAMING_SNAKE_CASE_	:       list     ):



    '''simple docstring'''





    if not nums:
        raise ValueError("List is empty"     )
    return sum(SCREAMING_SNAKE_CASE_     ) / len(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


