


'''simple docstring'''






def A_					(				__SCREAMING_SNAKE_CASE :					list[int]       )     ->  int:
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = len(__SCREAMING_SNAKE_CASE       )
					if n in (0, 1):
										return 0
					return sum(query * (n - i - 1) for i, query in enumerate(sorted(__SCREAMING_SNAKE_CASE       )       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()






'''simple docstring'''






# 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/


