fi = open("multinli_1.0_train.txt", "r")
output_file = open("multinli_sub.txt", "w")
output_index_list = []
count_entailment = 0
count_neutral = 0
count_contradiction = 0

for line_index, line in enumerate(fi):
    parts = line.strip().split("\t")

    premise = parts[5]
    hypothesis = parts[6]
    label = parts[0]

    prem_words = []
    hyp_words = []

    for word in premise.split():
        if word not in [".", "?", "!"]:
            prem_words.append(word.lower())

    for word in hypothesis.split():
        if word not in [".", "?", "!"]:
            hyp_words.append(word.lower())

    prem_filtered = " ".join(prem_words)
    hyp_filtered = " ".join(hyp_words)

    if hyp_filtered in prem_filtered:
        #print(premise, hypothesis, label, parts[1])
        #print(label)
        if label == "entailment":
            count_entailment += 1
        if label == "neutral":
            count_neutral += 1
            print(premise, hypothesis, label)
        if label == "contradiction":
            count_contradiction += 1
            print(premise, hypothesis, label)
        output_index_list.append(line_index)
        # output_file.write(line)

    #print(premise, hypothesis, label)

# open json file
with open('multinli_1.0_train.jsonl', 'r') as f:
    for line_index, line in enumerate(f):
        if line_index in output_index_list:
            output_file.write(line)
    # load json file
    # data = json.load(f)

print("Entailment:", count_entailment)
print("Contradiction:", count_contradiction)
print("Neutral:", count_neutral)