#!/usr/bin/env python3
"""Audit chronological TRAIN vulnerable/fixed pairs for pairwise objectives.

This is a data-structure diagnostic only. It does not read VALID/TEST labels
or train a model. Similarity uses sets and is not a semantic equivalence test.
"""
from __future__ import annotations

import argparse
import hashlib
import json
import re
import statistics
from collections import Counter
from pathlib import Path

import pandas as pd


def sha256(path: Path) -> str:
    h = hashlib.sha256()
    with path.open("rb") as handle:
        for block in iter(lambda: handle.read(1 << 20), b""):
            h.update(block)
    return h.hexdigest()


def audit(corpus_path: Path, train_split: Path) -> dict:
    pair_ids = set(json.loads(train_split.read_text()))
    corpus = pd.read_parquet(corpus_path)
    train = corpus[corpus["pair_id"].isin(pair_ids)]
    group_sizes = train.groupby("pair_id").size()
    bad_pairs = []
    label_sets = Counter()
    line_jaccards, token_jaccards, line_deltas = [], [], []
    exact_equal = nonempty_both = 0
    token_re = re.compile(r"[A-Za-z_][A-Za-z0-9_]*|0[xX][0-9a-fA-F]+|\d+|[^\s]")

    for pair_id, group in train.groupby("pair_id", sort=False):
        labels = sorted(int(x) for x in group["target"])
        label_sets[tuple(labels)] += 1
        if len(group) != 2 or labels != [0, 1]:
            bad_pairs.append({"pair_id": pair_id, "n_rows": len(group), "labels": labels})
            continue

        row = group.iloc[0]
        vuln = str(row["func_vuln"] or "")
        fixed = str(row["func_patch"] or "")
        vuln_lines = {line.strip() for line in vuln.splitlines() if line.strip()}
        fixed_lines = {line.strip() for line in fixed.splitlines() if line.strip()}
        line_union = vuln_lines | fixed_lines
        line_jaccards.append(len(vuln_lines & fixed_lines) / len(line_union) if line_union else 1.0)
        line_deltas.append(len(vuln_lines ^ fixed_lines))
        vuln_tokens = set(token_re.findall(vuln[:200_000]))
        fixed_tokens = set(token_re.findall(fixed[:200_000]))
        token_union = vuln_tokens | fixed_tokens
        token_jaccards.append(len(vuln_tokens & fixed_tokens) / len(token_union)
                              if token_union else 1.0)
        exact_equal += int(vuln == fixed)
        nonempty_both += int(bool(vuln.strip() and fixed.strip()))

    n_pairs = len(pair_ids)
    if len(train) != 2 * n_pairs:
        # Report and fail: split integrity is a prerequisite for interpreting pairs.
        raise ValueError(f"expected {2*n_pairs} rows for {n_pairs} pair IDs, found {len(train)}")

    def summary(values: list[float]) -> dict:
        ordered = sorted(values)
        return {"mean": statistics.mean(values), "median": statistics.median(values),
                "p10": ordered[int(0.10 * len(ordered))],
                "p90": ordered[int(0.90 * len(ordered))]}

    return {
        "analysis": "chronological TRAIN pair structure for exploratory pairwise objective",
        "split": str(train_split),
        "test_touched": False,
        "train_pair_ids": n_pairs,
        "train_rows": len(train),
        "pair_group_sizes": {str(k): int(v) for k, v in group_sizes.value_counts().items()},
        "label_pair_counts": {"/".join(map(str, k)): v for k, v in label_sets.items()},
        "invalid_pair_count": len(bad_pairs),
        "invalid_pair_examples": bad_pairs[:10],
        "code_pair_similarity": {
            "exact_function_equal_count": exact_equal,
            "both_code_nonempty_fraction": nonempty_both / n_pairs,
            "normalized_line_set_jaccard": summary(line_jaccards),
            "normalized_token_set_jaccard": summary(token_jaccards),
            "line_set_symmetric_difference": {
                "median": statistics.median(line_deltas),
                "p90": sorted(line_deltas)[int(0.90 * len(line_deltas))],
                "fraction_at_most_two": sum(x <= 2 for x in line_deltas) / n_pairs,
            },
        },
        "method_caveat": (
            "Line/token set Jaccard ignores token order, repeated tokens, semantics, and patch correctness; "
            "it only establishes structural relatedness, not that pairwise supervision will help."
        ),
        "input_sha256": {str(corpus_path): sha256(corpus_path),
                         str(train_split): sha256(train_split)},
    }


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--corpus", type=Path, default=Path("data/processed/corpus.parquet"))
    parser.add_argument("--train-split", type=Path,
                        default=Path("data/splits/chronological/train.json"))
    parser.add_argument("--output", type=Path,
                        default=Path("results/phase6/pairwise_train_data_audit.json"))
    args = parser.parse_args()
    report = audit(args.corpus, args.train_split)
    args.output.parent.mkdir(parents=True, exist_ok=True)
    args.output.write_text(json.dumps(report, indent=2) + "\n")
    print(json.dumps(report, indent=2))


if __name__ == "__main__":
    main()