bin/get_stats.py

import argparse
import csv
from collections import defaultdict
import json

from tqdm import tqdm
import smart_open

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Calculate instructions stats for a large JSONL file."
    )
    parser.add_argument("input_file", help="path to input JSONL file")
    parser.add_argument("output_file", help="path to output CSV file")

    args = parser.parse_args()

    stats = defaultdict(lambda: defaultdict(int))

    with smart_open.open(args.input_file, "rt", encoding="utf-8") as reader:
        for item in tqdm(map(json.loads, reader)):
            stats[item["language"]][f"{item['payload']['@type']} count"] += 1
            stats[item["language"]][f"{item['payload']['@type']} text length"] += item[
                "text_length"
            ]

            stats[item["language"]]["items count"] += 1
            stats[item["language"]]["text length"] += item["text_length"]

    with smart_open.open(args.output_file, "wt", encoding="utf-8") as writer:
        w = csv.DictWriter(
            writer,
            fieldnames=[
                "language",
                "FAQPage count",
                "FAQPage text length",
                "HowTo count",
                "HowTo text length",
                "items count",
                "text length",
            ],
        )
        w.writeheader()
        for language, language_stats in stats.items():
            w.writerow(
                {
                    "language": language,
                    "FAQPage count": language_stats["FAQPage count"],
                    "FAQPage text length": language_stats["FAQPage text length"],
                    "HowTo count": language_stats["HowTo count"],
                    "HowTo text length": language_stats["HowTo text length"],
                    "items count": language_stats["items count"],
                    "text length": language_stats["text length"],
                }
            )