
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a									=				{'configuration_qdqbert': ['QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'QDQBertConfig']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a									=				[
        'QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'QDQBertForMaskedLM',
        'QDQBertForMultipleChoice',
        'QDQBertForNextSentencePrediction',
        'QDQBertForQuestionAnswering',
        'QDQBertForSequenceClassification',
        'QDQBertForTokenClassification',
        'QDQBertLayer',
        'QDQBertLMHeadModel',
        'QDQBertModel',
        'QDQBertPreTrainedModel',
        'load_tf_weights_in_qdqbert',
    ]


if TYPE_CHECKING:
    from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_qdqbert import (
            QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            QDQBertForMaskedLM,
            QDQBertForMultipleChoice,
            QDQBertForNextSentencePrediction,
            QDQBertForQuestionAnswering,
            QDQBertForSequenceClassification,
            QDQBertForTokenClassification,
            QDQBertLayer,
            QDQBertLMHeadModel,
            QDQBertModel,
            QDQBertPreTrainedModel,
            load_tf_weights_in_qdqbert,
        )


else:
    import sys

    _a									=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





import numpy as np







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=np.exp(lowerCAmelCase_     )

     # Add up the all the exponentials
     SCREAMING_SNAKE_CASE 		=np.sum(lowerCAmelCase_     )

     # Divide every exponent by the sum of all exponents
     SCREAMING_SNAKE_CASE 		=exponent_vector / sum_of_exponents

     return softmax_vector


if __name__ == "__main__":
 print(softmax((0,)))

