



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.27.0')):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import (
	    AudioLDMPipeline,
	)
else:
	from .pipeline_audioldm import AudioLDMPipeline











import numpy as np







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[Any]     ):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[int]       			=    np.exp(lowerCAmelCase__     )

			# Add up the all the exponentials
			UpperCAmelCase_:		Tuple       			=    np.sum(lowerCAmelCase__     )

			# Divide every exponent by the sum of all exponents
			UpperCAmelCase_:		str       			=    exponent_vector / sum_of_exponents

			return softmax_vector


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






