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from utils import read_jsonl_file, write_jsonl_file, parse, read_line_labels
import os
import copy
label2nl = {"1": "First", "2": "Second"}
def preprocess_for_train_and_dev(args, file):
data_path = os.path.join(args.input_dir, f"{file}.jsonl")
data = read_jsonl_file(data_path)
label_path = os.path.join(args.input_dir, f"{file}-labels.lst")
labels = read_line_labels(label_path)
turns = []
for idx, example in enumerate(data):
turn = {
"turn": "multi",
"locale": "en",
"dialog": [
{"roles": ["First observation"], "utterance": example["obs1"]},
{
"roles": ["Second observation"],
"utterance": example["obs2"],
"roles_to_select": [f"hypothesis candidate {labels[idx]}"],
},
],
}
# turn["dialog"].append(
# {
# "roles": ["First hypothesis"],
# "utterance": example["hyp1"],
# }
# )
# turn["dialog"].append(
# {
# "roles": ["Second hypothesis"],
# "utterance": example["hyp2"],
# "roles_to_select": [label2nl[labels[idx]] + " hypothesis"],
# }
# )
turn["knowledge"] = {
"type": "text",
"value": {
"hypothesis candidate 1": example["hyp1"],
"hypothesis candidate 2": example["hyp2"],
},
}
# turn["roles_to_select"] = ["HYPOTHESIS " + labels[idx]]
turns.append(turn)
# if labels[idx] == "1":
# pos_hyp = example["hyp1"]
# neg_hyp = example["hyp2"]
# else:
# pos_hyp = example["hyp2"]
# neg_hyp = example["hyp1"]
# # possitive hypothesis
# pos_turn = copy.deepcopy(turn)
# pos_turn["dialog"].append({"roles": ["HYPOTHESIS"], "utterance": pos_hyp, "class_label": True})
# # negative hypothesis
# neg_turn = copy.deepcopy(turn)
# neg_turn["dialog"].append({"roles": ["HYPOTHESIS"], "utterance": neg_hyp, "class_label": False})
# turns.append(pos_turn)
# turns.append(neg_turn)
write_jsonl_file(turns, os.path.join(args.output_dir, f"{file}.jsonl"))
def preprocess(args):
preprocess_for_train_and_dev(args, "train")
preprocess_for_train_and_dev(args, "dev")
if __name__ == "__main__":
args = parse()
preprocess(args)
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