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import os
import re
import sys
from tqdm import tqdm
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
langs_supported = [
"asm_Beng",
"ben_Beng",
"guj_Gujr",
"eng_Latn",
"hin_Deva",
"kas_Deva",
"kas_Arab",
"kan_Knda",
"mal_Mlym",
"mai_Deva",
"mar_Deva",
"mni_Beng",
"npi_Deva",
"ory_Orya",
"pan_Guru",
"san_Deva",
"snd_Arab",
"sat_Olck",
"tam_Taml",
"tel_Telu",
"urd_Arab",
]
def predict(batch, tokenizer, model, bos_token_id):
encoded_batch = tokenizer(batch, padding=True, return_tensors="pt").to(model.device)
generated_tokens = model.generate(
**encoded_batch,
num_beams=5,
max_length=256,
min_length=0,
forced_bos_token_id=bos_token_id,
)
hypothesis = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
return hypothesis
def main(devtest_data_dir, batch_size):
# load the pre-trained NLLB tokenizer and model
model_name = "facebook/nllb-moe-54b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()
# iterate over a list of language pairs from `devtest_data_dir`
for pair in sorted(os.listdir(devtest_data_dir)):
if "-" not in pair:
continue
src_lang, tgt_lang = pair.split("-")
# check if the source and target languages are supported
if (
src_lang not in langs_supported.keys()
or tgt_lang not in langs_supported.keys()
):
print(f"Skipping {src_lang}-{tgt_lang} ...")
continue
# -------------------------------------------------------------------
# source to target evaluation
# -------------------------------------------------------------------
print(f"Evaluating {src_lang}-{tgt_lang} ...")
infname = os.path.join(devtest_data_dir, pair, f"test.{src_lang}")
outfname = os.path.join(
devtest_data_dir, pair, f"test.{tgt_lang}.pred.nllb_moe"
)
with open(infname, "r") as f:
src_sents = f.read().split("\n")
add_new_line = False
if src_sents[-1] == "":
add_new_line = True
src_sents = src_sents[:-1]
# set the source language for tokenization
tokenizer.src_lang = src_lang
# process sentences in batches and generate predictions
hypothesis = []
for i in tqdm(range(0, len(src_sents), batch_size)):
start, end = i, int(min(len(src_sents), i + batch_size))
batch = src_sents[start:end]
if tgt_lang == "sat_Olck":
bos_token_id = tokenizer.lang_code_to_id["sat_Beng"]
else:
bos_token_id = tokenizer.lang_code_to_id[tgt_lang]
hypothesis += predict(batch, tokenizer, model, bos_token_id)
assert len(hypothesis) == len(src_sents)
hypothesis = [
re.sub("\s+", " ", x.replace("\n", " ").replace("\t", " ")).strip()
for x in hypothesis
]
if add_new_line:
hypothesis = hypothesis
with open(outfname, "w") as f:
f.write("\n".join(hypothesis))
# -------------------------------------------------------------------
# target to source evaluation
# -------------------------------------------------------------------
infname = os.path.join(devtest_data_dir, pair, f"test.{tgt_lang}")
outfname = os.path.join(
devtest_data_dir, pair, f"test.{src_lang}.pred.nllb_moe"
)
with open(infname, "r") as f:
src_sents = f.read().split("\n")
add_new_line = False
if src_sents[-1] == "":
add_new_line = True
src_sents = src_sents[:-1]
# set the source language for tokenization
tokenizer.src_lang = "sat_Beng" if tgt_lang == "sat_Olck" else tgt_lang
# process sentences in batches and generate predictions
hypothesis = []
for i in tqdm(range(0, len(src_sents), batch_size)):
start, end = i, int(min(len(src_sents), i + batch_size))
batch = src_sents[start:end]
bos_token_id = tokenizer.lang_code_to_id[langs_supported[src_lang]]
hypothesis += predict(batch, tokenizer, model, bos_token_id)
assert len(hypothesis) == len(src_sents)
hypothesis = [
re.sub("\s+", " ", x.replace("\n", " ").replace("\t", " ")).strip()
for x in hypothesis
]
if add_new_line:
hypothesis = hypothesis
with open(outfname, "w") as f:
f.write("\n".join(hypothesis))
if __name__ == "__main__":
# expects En-X subdirectories pairs within the devtest data directory
devtest_data_dir = sys.argv[1]
batch_size = int(sys.argv[2])
main(devtest_data_dir, batch_size)
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