from __future__ import annotations
import argparse
import ast
import csv
import json
from pathlib import Path
from sklearn.metrics import f1_score, precision_score, recall_score


def read_rows(path):
    with open(path, encoding="utf-8", newline="") as f:
        return list(csv.DictReader(f))


def parse_boundary_prediction(value, word):
    value = value.strip()
    if value.startswith("["):
        parsed = json.loads(value)
        return [int(x) for x in parsed]
    if "-" in value:
        syllables = value.split("-")
        if "".join(syllables) != word:
            raise ValueError(f"Segmentation does not reconstruct word {word!r}: {value!r}")
        labels = [0] * (len(word) - 1)
        cursor = 0
        for syllable in syllables[:-1]:
            cursor += len(syllable)
            labels[cursor - 1] = 1
        return labels
    try:
        parsed = ast.literal_eval(value)
        return [int(x) for x in parsed]
    except Exception as exc:
        raise ValueError(f"Invalid boundary prediction for {word!r}: {value!r}") from exc


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--gold", required=True)
    parser.add_argument("--predictions", required=True)
    parser.add_argument("--output")
    args = parser.parse_args()
    gold_rows = read_rows(args.gold)
    pred_rows = read_rows(args.predictions)
    if not pred_rows or not {"id", "prediction"}.issubset(pred_rows[0]):
        raise ValueError("Prediction CSV must contain columns: id,prediction")
    preds = {row["id"]: row["prediction"] for row in pred_rows}
    y_true, y_pred = [], []
    exact = 0
    for row in gold_rows:
        if row["id"] not in preds:
            raise ValueError(f"Missing prediction for {row['id']}")
        gold = [int(x) for x in json.loads(row["boundary_labels"])]
        pred = parse_boundary_prediction(preds[row["id"]], row["word"])
        if len(pred) != len(gold):
            raise ValueError(f"Boundary length mismatch for {row['id']}: {len(pred)} != {len(gold)}")
        y_true.extend(gold)
        y_pred.extend(pred)
        exact += int(gold == pred)
    metrics = {
        "n_examples": len(gold_rows),
        "boundary_precision": precision_score(y_true, y_pred, zero_division=0),
        "boundary_recall": recall_score(y_true, y_pred, zero_division=0),
        "boundary_f1": f1_score(y_true, y_pred, zero_division=0),
        "exact_match": exact / len(gold_rows),
    }
    text = json.dumps(metrics, ensure_ascii=False, indent=2)
    print(text)
    if args.output:
        Path(args.output).write_text(text + "\n", encoding="utf-8")


if __name__ == "__main__":
    main()