

import numpy as np



def 				snake_case_(_UpperCamelCase					)							->     np.ndarray:



  """simple docstring"""






  return 1 / (1 + np.exp(-vector					))



def 				snake_case_(_UpperCamelCase					)							->     np.ndarray:



  """simple docstring"""






  return vector * sigmoid(_UpperCamelCase					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




from typing import TYPE_CHECKING

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


__A      		=     {
    '''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A      		=     [
     '''FALCON_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''FalconForCausalLM''',
     '''FalconModel''',
     '''FalconPreTrainedModel''',
     '''FalconForSequenceClassification''',
     '''FalconForTokenClassification''',
     '''FalconForQuestionAnswering''',
 ]


if TYPE_CHECKING:
 from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_falcon import (
      FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
      FalconForCausalLM,
      FalconForQuestionAnswering,
      FalconForSequenceClassification,
      FalconForTokenClassification,
      FalconModel,
      FalconPreTrainedModel,
  )


else:
 import sys

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

