




import numpy as np

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			int:
       lowerCamelCase       :				Optional[int]      			= np.exp(_SCREAMING_SNAKE_CASE   )

       # Add up the all the exponentials
       lowerCamelCase       :				Any      			= np.sum(_SCREAMING_SNAKE_CASE   )

       # Divide every exponent by the sum of all exponents
       lowerCamelCase       :				int      			= exponent_vector / sum_of_exponents

       return softmax_vector


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












# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/






