
"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_    					=						{
    '''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_    					=						[
       '''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''NezhaForNextSentencePrediction''',
       '''NezhaForMaskedLM''',
       '''NezhaForPreTraining''',
       '''NezhaForMultipleChoice''',
       '''NezhaForQuestionAnswering''',
       '''NezhaForSequenceClassification''',
       '''NezhaForTokenClassification''',
       '''NezhaModel''',
       '''NezhaPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_nezha import (
          NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
          NezhaForMaskedLM,
          NezhaForMultipleChoice,
          NezhaForNextSentencePrediction,
          NezhaForPreTraining,
          NezhaForQuestionAnswering,
          NezhaForSequenceClassification,
          NezhaForTokenClassification,
          NezhaModel,
          NezhaPreTrainedModel,
      )


else:
   import sys

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





"""simple docstring"""

from __future__ import annotations

import numpy as np




def     lowercase__						(					lowerCAmelCase				:					list[float] ) ->      Dict:






    """simple docstring"""




    return np.maximum(0 ,				lowerCAmelCase )


if __name__ == "__main__":
   print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



