
"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_    					=						{
    '''configuration_clap''': [
        '''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ClapAudioConfig''',
        '''ClapConfig''',
        '''ClapTextConfig''',
    ],
    '''processing_clap''': ['''ClapProcessor'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_    					=						[
       '''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ClapModel''',
       '''ClapPreTrainedModel''',
       '''ClapTextModel''',
       '''ClapTextModelWithProjection''',
       '''ClapAudioModel''',
       '''ClapAudioModelWithProjection''',
   ]
   SCREAMING_SNAKE_CASE_    					=						['''ClapFeatureExtractor''']

if TYPE_CHECKING:
   from .configuration_clap import (
       CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
       ClapAudioConfig,
       ClapConfig,
       ClapTextConfig,
   )
   from .processing_clap import ClapProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_clap import ClapFeatureExtractor
      from .modeling_clap import (
          CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ClapAudioModel,
          ClapAudioModelWithProjection,
          ClapModel,
          ClapPreTrainedModel,
          ClapTextModel,
          ClapTextModelWithProjection,
      )


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]



