#!/usr/bin/env python3
"""Evaluate dashboard/dataset recall quality for BI search questions."""

from __future__ import annotations

import argparse
import csv
import json
import random
import sys
from collections import defaultdict
from dataclasses import dataclass
from pathlib import Path
from typing import Any

import requests

DEFAULT_RECALL_API_URL = "http://replace-with-your-recall-api"
DEFAULT_RECALL_TIMEOUT_SECONDS = 10
DEFAULT_FIRST_RECALL_SIZE = 500
DEFAULT_FIRST_RECALL_HIT_RATE = 0.8
DEFAULT_RANDOM_SEED = 20260311

RECALL_TYPE_DASHBOARD = "dashboard"
RECALL_TYPE_DATASET = "dataset"

REQUIRED_COLUMNS = {
    "card_id",
    "card_name",
    "dashboard_id",
    "dashboard_name",
    "dataset_id",
    "dataset_name",
    "branch_code",
}


@dataclass(frozen=True)
class QuestionCase:
    line_no: int
    card_id: str
    question: str


@dataclass
class CardMapping:
    card_name: str
    dashboard_ids: set[str]
    dashboard_names: set[str]
    dataset_ids: set[str]
    dataset_names: set[str]
    branch_codes: set[str]


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description="Evaluate three-route recall performance for BI dashboard/dataset search."
    )
    parser.add_argument(
        "--questions-txt",
        required=True,
        help="Question file. Recommended format: one line per case as 'card_id<TAB>question'.",
    )
    parser.add_argument(
        "--id-map-parquet",
        required=True,
        help="Parquet file containing card/dashboard/dataset mapping information.",
    )
    parser.add_argument(
        "--output-dir",
        default="output",
        help="Directory for summary JSON and per-question detail CSV.",
    )
    parser.add_argument(
        "--api-url",
        default=DEFAULT_RECALL_API_URL,
        help="Recall API URL. The script POSTs JSON: {'question': ..., 'recall_type': ...}.",
    )
    parser.add_argument(
        "--timeout",
        type=int,
        default=DEFAULT_RECALL_TIMEOUT_SECONDS,
        help="Recall API timeout in seconds.",
    )
    parser.add_argument(
        "--first-recall-size",
        type=int,
        default=DEFAULT_FIRST_RECALL_SIZE,
        help="Sample size for the simulated first recall route.",
    )
    parser.add_argument(
        "--first-recall-hit-rate",
        type=float,
        default=DEFAULT_FIRST_RECALL_HIT_RATE,
        help="Probability of forcing the gold dashboard into first-route results.",
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=DEFAULT_RANDOM_SEED,
        help="Random seed used for reproducible first-route simulation.",
    )
    return parser.parse_args()


def read_questions(path: Path) -> list[QuestionCase]:
    cases: list[QuestionCase] = []
    with path.open("r", encoding="utf-8") as handle:
        for line_no, raw_line in enumerate(handle, start=1):
            line = raw_line.strip()
            if not line or line.startswith("#"):
                continue
            card_id, question = split_question_line(line, line_no)
            cases.append(QuestionCase(line_no=line_no, card_id=card_id, question=question))
    if not cases:
        raise ValueError(f"No valid questions found in {path}")
    return cases


def split_question_line(line: str, line_no: int) -> tuple[str, str]:
    for separator in ("\t", ",", "|"):
        if separator in line:
            left, right = line.split(separator, 1)
            card_id = left.strip()
            question = right.strip()
            if card_id and question:
                return card_id, question
    parts = line.split(maxsplit=1)
    if len(parts) == 2 and parts[0].strip() and parts[1].strip():
        return parts[0].strip(), parts[1].strip()
    raise ValueError(
        f"Invalid question line {line_no}: '{line}'. Expected 'card_id<TAB>question'."
    )


def load_mapping(path: Path) -> tuple[dict[str, CardMapping], list[str]]:
    try:
        import pyarrow.parquet as pq
    except ModuleNotFoundError as exc:
        raise RuntimeError(
            "Missing dependency 'pyarrow'. Install it first, for example: pip install pyarrow requests"
        ) from exc

    table = pq.read_table(path)
    missing_columns = REQUIRED_COLUMNS.difference(table.column_names)
    if missing_columns:
        raise ValueError(f"{path} is missing required columns: {sorted(missing_columns)}")

    rows = table.to_pylist()
    if not rows:
        raise ValueError(f"{path} does not contain any rows")

    card_map: dict[str, CardMapping] = {}
    all_dashboard_ids: set[str] = set()
    for row in rows:
        card_id = normalize_id(row.get("card_id"))
        if card_id is None:
            continue
        mapping = card_map.setdefault(
            card_id,
            CardMapping(
                card_name=normalize_text(row.get("card_name")),
                dashboard_ids=set(),
                dashboard_names=set(),
                dataset_ids=set(),
                dataset_names=set(),
                branch_codes=set(),
            ),
        )
        dashboard_id = normalize_id(row.get("dashboard_id"))
        dataset_id = normalize_id(row.get("dataset_id"))
        dashboard_name = normalize_text(row.get("dashboard_name"))
        dataset_name = normalize_text(row.get("dataset_name"))
        branch_code = normalize_text(row.get("branch_code"))
        if dashboard_id is not None:
            mapping.dashboard_ids.add(dashboard_id)
            all_dashboard_ids.add(dashboard_id)
        if dataset_id is not None:
            mapping.dataset_ids.add(dataset_id)
        if dashboard_name:
            mapping.dashboard_names.add(dashboard_name)
        if dataset_name:
            mapping.dataset_names.add(dataset_name)
        if branch_code:
            mapping.branch_codes.add(branch_code)
        if not mapping.card_name:
            mapping.card_name = normalize_text(row.get("card_name"))
    return card_map, sorted(all_dashboard_ids)


def normalize_id(value: Any) -> str | None:
    if value is None:
        return None
    text = str(value).strip()
    return text or None


def normalize_text(value: Any) -> str:
    if value is None:
        return ""
    return str(value).strip()


def simulate_first_recall(
    all_dashboard_ids: list[str],
    gold_dashboard_ids: set[str],
    sample_size: int,
    force_hit_rate: float,
    rng: random.Random,
) -> set[str]:
    if not all_dashboard_ids:
        return set()

    capped_size = min(sample_size, len(all_dashboard_ids))
    gold_candidates = [
        dashboard_id for dashboard_id in gold_dashboard_ids if dashboard_id in all_dashboard_ids
    ]
    should_force_hit = bool(gold_candidates) and rng.random() < force_hit_rate

    if should_force_hit:
        forced_dashboard = rng.choice(gold_candidates)
        remaining = [
            dashboard_id for dashboard_id in all_dashboard_ids if dashboard_id != forced_dashboard
        ]
        picked = rng.sample(remaining, k=max(capped_size - 1, 0))
        selected = set(picked)
        selected.add(forced_dashboard)
        return selected

    non_gold_dashboards = [
        dashboard_id for dashboard_id in all_dashboard_ids if dashboard_id not in gold_dashboard_ids
    ]
    if len(non_gold_dashboards) >= capped_size:
        return set(rng.sample(non_gold_dashboards, k=capped_size))

    return set(rng.sample(all_dashboard_ids, k=capped_size))


def invoke_recall_api(
    api_url: str,
    question: str,
    recall_type: str,
    timeout_seconds: int,
) -> tuple[set[str], list[dict[str, Any]]]:
    if api_url == DEFAULT_RECALL_API_URL:
        raise ValueError(
            "Recall API URL is still the placeholder value. Pass --api-url with the actual endpoint."
        )

    response = requests.post(
        api_url,
        json={"question": question, "recall_type": recall_type},
        timeout=timeout_seconds,
    )
    response.raise_for_status()
    payload = response.json()
    records = extract_records(payload)
    ids = {record_id for record in records if (record_id := extract_record_id(record, recall_type))}
    return ids, records


def extract_records(payload: Any) -> list[dict[str, Any]]:
    if isinstance(payload, list):
        return [item for item in payload if isinstance(item, dict)]
    if not isinstance(payload, dict):
        return []
    for key in ("data", "items", "results", "records"):
        value = payload.get(key)
        if isinstance(value, list):
            return [item for item in value if isinstance(item, dict)]
    return [payload]


def extract_record_id(record: dict[str, Any], recall_type: str) -> str | None:
    preferred_keys = [f"{recall_type}_id", "id"]
    fallback_keys = ["dashboard_id", "dataset_id"]
    for key in preferred_keys + fallback_keys:
        value = normalize_id(record.get(key))
        if value is not None:
            return value
    return None


def safe_api_recall(
    api_url: str,
    question: str,
    recall_type: str,
    timeout_seconds: int,
) -> tuple[set[str], list[dict[str, Any]], str]:
    try:
        ids, records = invoke_recall_api(api_url, question, recall_type, timeout_seconds)
        return ids, records, ""
    except Exception as exc:  # noqa: BLE001
        return set(), [], str(exc)


def ensure_output_dir(path: Path) -> None:
    path.mkdir(parents=True, exist_ok=True)


def write_details_csv(path: Path, rows: list[dict[str, Any]]) -> None:
    fieldnames = [
        "line_no",
        "card_id",
        "card_name",
        "question",
        "gold_dashboard_ids",
        "gold_dataset_ids",
        "first_recall_ids",
        "first_recall_hit",
        "dashboard_recall_ids",
        "dashboard_recall_hit",
        "dashboard_recall_error",
        "dashboard_recall_records",
        "dataset_recall_ids",
        "dataset_recall_hit",
        "dataset_recall_error",
        "dataset_recall_records",
        "union_hit",
        "branch_codes",
    ]
    with path.open("w", encoding="utf-8", newline="") as handle:
        writer = csv.DictWriter(handle, fieldnames=fieldnames)
        writer.writeheader()
        for row in rows:
            writer.writerow(row)


def write_summary_json(path: Path, summary: dict[str, Any]) -> None:
    with path.open("w", encoding="utf-8") as handle:
        json.dump(summary, handle, ensure_ascii=False, indent=2)


def rate(hits: int, total: int) -> float:
    if total == 0:
        return 0.0
    return hits / total


def main() -> int:
    args = parse_args()
    rng = random.Random(args.seed)

    questions_path = Path(args.questions_txt)
    mapping_path = Path(args.id_map_parquet)
    output_dir = Path(args.output_dir)

    questions = read_questions(questions_path)
    card_map, all_dashboard_ids = load_mapping(mapping_path)
    ensure_output_dir(output_dir)

    first_hits = 0
    dashboard_hits = 0
    dataset_hits = 0
    union_hits = 0
    detail_rows: list[dict[str, Any]] = []
    missing_cards: list[dict[str, Any]] = []
    error_counter: dict[str, int] = defaultdict(int)

    for case in questions:
        mapping = card_map.get(case.card_id)
        if mapping is None:
            missing_cards.append(
                {"line_no": case.line_no, "card_id": case.card_id, "question": case.question}
            )
            detail_rows.append(
                {
                    "line_no": case.line_no,
                    "card_id": case.card_id,
                    "card_name": "",
                    "question": case.question,
                    "gold_dashboard_ids": "",
                    "gold_dataset_ids": "",
                    "first_recall_ids": "",
                    "first_recall_hit": False,
                    "dashboard_recall_ids": "",
                    "dashboard_recall_hit": False,
                    "dashboard_recall_error": "card_id not found in id_map.parquet",
                    "dashboard_recall_records": "",
                    "dataset_recall_ids": "",
                    "dataset_recall_hit": False,
                    "dataset_recall_error": "card_id not found in id_map.parquet",
                    "dataset_recall_records": "",
                    "union_hit": False,
                    "branch_codes": "",
                }
            )
            continue

        first_ids = simulate_first_recall(
            all_dashboard_ids=all_dashboard_ids,
            gold_dashboard_ids=mapping.dashboard_ids,
            sample_size=args.first_recall_size,
            force_hit_rate=args.first_recall_hit_rate,
            rng=rng,
        )
        dashboard_ids, dashboard_records, dashboard_error = safe_api_recall(
            api_url=args.api_url,
            question=case.question,
            recall_type=RECALL_TYPE_DASHBOARD,
            timeout_seconds=args.timeout,
        )
        dataset_ids, dataset_records, dataset_error = safe_api_recall(
            api_url=args.api_url,
            question=case.question,
            recall_type=RECALL_TYPE_DATASET,
            timeout_seconds=args.timeout,
        )

        first_hit = bool(first_ids.intersection(mapping.dashboard_ids))
        dashboard_hit = bool(dashboard_ids.intersection(mapping.dashboard_ids))
        dataset_hit = bool(dataset_ids.intersection(mapping.dataset_ids))
        union_hit = first_hit or dashboard_hit or dataset_hit

        first_hits += int(first_hit)
        dashboard_hits += int(dashboard_hit)
        dataset_hits += int(dataset_hit)
        union_hits += int(union_hit)

        if dashboard_error:
            error_counter["dashboard_api_errors"] += 1
        if dataset_error:
            error_counter["dataset_api_errors"] += 1

        detail_rows.append(
            {
                "line_no": case.line_no,
                "card_id": case.card_id,
                "card_name": mapping.card_name,
                "question": case.question,
                "gold_dashboard_ids": ",".join(sorted(mapping.dashboard_ids)),
                "gold_dataset_ids": ",".join(sorted(mapping.dataset_ids)),
                "first_recall_ids": ",".join(sorted(first_ids)),
                "first_recall_hit": first_hit,
                "dashboard_recall_ids": ",".join(sorted(dashboard_ids)),
                "dashboard_recall_hit": dashboard_hit,
                "dashboard_recall_error": dashboard_error,
                "dashboard_recall_records": json.dumps(
                    dashboard_records, ensure_ascii=False, separators=(",", ":")
                ),
                "dataset_recall_ids": ",".join(sorted(dataset_ids)),
                "dataset_recall_hit": dataset_hit,
                "dataset_recall_error": dataset_error,
                "dataset_recall_records": json.dumps(
                    dataset_records, ensure_ascii=False, separators=(",", ":")
                ),
                "union_hit": union_hit,
                "branch_codes": ",".join(sorted(mapping.branch_codes)),
            }
        )

        if dashboard_records:
            detail_rows[-1]["dashboard_recall_ids"] = ",".join(sorted(dashboard_ids))
        if dataset_records:
            detail_rows[-1]["dataset_recall_ids"] = ",".join(sorted(dataset_ids))

    total = len(questions)
    first_rate = rate(first_hits, total)
    dashboard_rate = rate(dashboard_hits, total)
    dataset_rate = rate(dataset_hits, total)
    union_rate = rate(union_hits, total)
    uplift_vs_first = union_rate - first_rate

    summary = {
        "input": {
            "questions_txt": str(questions_path),
            "id_map_parquet": str(mapping_path),
            "api_url": args.api_url,
            "first_recall_size": args.first_recall_size,
            "first_recall_hit_rate": args.first_recall_hit_rate,
            "seed": args.seed,
        },
        "counts": {
            "total_questions": total,
            "first_recall_hits": first_hits,
            "dashboard_recall_hits": dashboard_hits,
            "dataset_recall_hits": dataset_hits,
            "union_hits": union_hits,
            "missing_card_mappings": len(missing_cards),
        },
        "rates": {
            "first_recall_rate": first_rate,
            "dashboard_recall_rate": dashboard_rate,
            "dataset_recall_rate": dataset_rate,
            "union_recall_rate": union_rate,
            "uplift_vs_first_recall": uplift_vs_first,
        },
        "errors": dict(error_counter),
        "missing_cards": missing_cards,
        "outputs": {
            "detail_csv": str(output_dir / "recall_details.csv"),
            "summary_json": str(output_dir / "recall_summary.json"),
        },
    }

    write_details_csv(output_dir / "recall_details.csv", detail_rows)
    write_summary_json(output_dir / "recall_summary.json", summary)

    print(json.dumps(summary, ensure_ascii=False, indent=2))
    return 0


if __name__ == "__main__":
    try:
        raise SystemExit(main())
    except Exception as exc:  # noqa: BLE001
        print(f"[ERROR] {exc}", file=sys.stderr)
        raise SystemExit(1) from exc