from __future__ import annotations

import argparse
import difflib
from collections import defaultdict
from pathlib import Path
from typing import Any

import yaml

from src.data.io_utils import read_jsonl, write_csv
from src.data.normalize_text import normalize_whitespace


def normalize_text(text: str | None) -> str:
    return " ".join(normalize_whitespace(text).casefold().split())


def fuzzy_score(a: str, b: str) -> float:
    if not a or not b:
        return 0.0
    if a in b or b in a:
        return 1.0
    return difflib.SequenceMatcher(a=a, b=b).ratio()


def load_claim_chunks(context_chunks_path: Path) -> dict[str, list[dict[str, Any]]]:
    by_claim: dict[str, list[dict[str, Any]]] = defaultdict(list)
    for row in read_jsonl(context_chunks_path):
        claim_id = normalize_whitespace((row.get("metadata") or {}).get("claim_id"))
        if claim_id:
            by_claim[claim_id].append(row)
    return by_claim


def coverage_rows_for_split(
    claims_path: Path,
    context_chunks_path: Path,
    gold_path: Path,
    split_name: str | None,
    threshold: float,
) -> list[dict[str, Any]]:
    claim_ids = {normalize_whitespace(row.get("claim_id")) for row in read_jsonl(claims_path)}
    claim_chunks = load_claim_chunks(context_chunks_path)
    rows: list[dict[str, Any]] = []

    for gold in read_jsonl(gold_path):
        claim_id = normalize_whitespace(gold.get("claim_id"))
        if claim_id not in claim_ids:
            continue
        evidence_text = normalize_whitespace(gold.get("text"))
        evidence_norm = normalize_text(evidence_text)
        best_chunk_id = ""
        best_score = 0.0
        for chunk in claim_chunks.get(claim_id, []):
            chunk_text = normalize_whitespace(chunk.get("text"))
            score = fuzzy_score(evidence_norm, normalize_text(chunk_text))
            if score > best_score:
                best_score = score
                best_chunk_id = normalize_whitespace(chunk.get("chunk_id"))
        rows.append(
            {
                "claim_id": claim_id,
                "evidence_text": evidence_text,
                "best_context_chunk_id": best_chunk_id,
                "fuzzy_score": round(best_score, 6),
                "covered": best_score >= threshold,
                "coverage_threshold": threshold,
                "split": split_name or normalize_whitespace(gold.get("split")),
            }
        )
    return rows


def rows_from_config(config_path: Path, threshold: float) -> list[dict[str, Any]]:
    cfg = yaml.safe_load(config_path.read_text(encoding="utf-8"))
    dataset_cfg = cfg["datasets"]["vifactcheck"]
    gold_path = Path(dataset_cfg["gold"])
    context_chunks_path = Path(dataset_cfg["corpus"])

    rows: list[dict[str, Any]] = []
    for split, claims_path in dataset_cfg["queries"].items():
        rows.extend(
            coverage_rows_for_split(
                claims_path=Path(claims_path),
                context_chunks_path=context_chunks_path,
                gold_path=gold_path,
                split_name=split,
                threshold=threshold,
            )
        )
    return rows


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--config", type=Path, default=Path("configs/retrieval/candidate_pool.yaml"))
    parser.add_argument("--claims", type=Path, default=None)
    parser.add_argument("--context-chunks", type=Path, default=None)
    parser.add_argument("--gold", type=Path, default=None)
    parser.add_argument("--split-name", type=str, default=None)
    parser.add_argument("--threshold", type=float, default=0.75)
    parser.add_argument(
        "--output",
        type=Path,
        default=Path("outputs/stats/vifactcheck_context_gold_coverage_report.csv"),
    )
    args = parser.parse_args()

    if args.claims or args.context_chunks or args.gold:
        if not (args.claims and args.context_chunks and args.gold):
            parser.error("--claims, --context-chunks, and --gold must be provided together.")
        rows = coverage_rows_for_split(
            claims_path=args.claims,
            context_chunks_path=args.context_chunks,
            gold_path=args.gold,
            split_name=args.split_name,
            threshold=args.threshold,
        )
    else:
        rows = rows_from_config(args.config, threshold=args.threshold)

    write_csv(args.output, rows)
    print(f"Wrote {len(rows)} rows to {args.output}")


if __name__ == "__main__":
    main()