from __future__ import annotations

import argparse
import json
import math
from collections import Counter, defaultdict
from pathlib import Path
from statistics import median
from typing import Any, Iterable

from src.data.io_utils import read_jsonl, write_csv
from src.models.encoder_verifier import load_tokenizer

LABEL_ORDER = ["SUPPORTS", "REFUTES", "NEI", "CONFLICTING"]
DEFAULT_CLAIMS = [
    Path("data_processed/averitec/claims_train_inner.jsonl"),
    Path("data_processed/averitec/claims_dev_inner.jsonl"),
    Path("data_processed/averitec/claims_local_test.jsonl"),
]
DEFAULT_CANDIDATE_POOLS = [
    Path("outputs/retrieval/averitec/candidate_pool_train_inner.jsonl"),
    Path("outputs/retrieval/averitec/candidate_pool_dev_inner.jsonl"),
    Path("outputs/retrieval/averitec/candidate_pool_local_test.jsonl"),
]
DEFAULT_VERIFIER_INPUTS = [
    Path("outputs/verifier_inputs/averitec/train_inner_top10_qa.jsonl"),
    Path("outputs/verifier_inputs/averitec/dev_inner_top10_qa.jsonl"),
    Path("outputs/verifier_inputs/averitec/local_test_top10_qa.jsonl"),
]
DEFAULT_PREDICTIONS = Path(
    "outputs/baselines/averitec/encoder_verifier/"
    "answerdotai__ModernBERT-large_top10_qa_weighted_sampler/seed_13/predictions_test.jsonl"
)
DEFAULT_METRICS = Path(
    "outputs/baselines/averitec/encoder_verifier/"
    "answerdotai__ModernBERT-large_top10_qa_weighted_sampler/seed_13/metrics.json"
)


def existing(paths: Iterable[Path]) -> list[Path]:
    return [path for path in paths if path.exists()]


def infer_split(path: Path, row: dict[str, Any] | None = None) -> str:
    if row and row.get("split"):
        return str(row["split"])
    stem = path.stem
    if stem.startswith("claims_"):
        return stem.removeprefix("claims_")
    if stem.startswith("candidate_pool_"):
        return stem.removeprefix("candidate_pool_")
    for split in ["train_inner", "dev_inner", "local_test", "hidden_test", "train", "dev", "test"]:
        if split in stem:
            return split
    return stem


def pct(numerator: int | float, denominator: int | float) -> float:
    if not denominator:
        return 0.0
    return round(float(numerator) / float(denominator) * 100.0, 6)


def as_bool(value: Any) -> bool:
    return bool(value) if value is not None else False


def safe_float(value: Any) -> float:
    try:
        if value is None:
            return 0.0
        numeric = float(value)
        if math.isnan(numeric) or math.isinf(numeric):
            return 0.0
        return numeric
    except (TypeError, ValueError):
        return 0.0


def percentile(values: list[int], q: float) -> float:
    if not values:
        return 0.0
    ordered = sorted(values)
    if len(ordered) == 1:
        return float(ordered[0])
    position = (len(ordered) - 1) * q
    lower = math.floor(position)
    upper = math.ceil(position)
    if lower == upper:
        return float(ordered[int(position)])
    fraction = position - lower
    return float(ordered[lower] * (1 - fraction) + ordered[upper] * fraction)


def load_claims(paths: list[Path]) -> tuple[dict[str, dict[str, Any]], list[dict[str, Any]]]:
    claims_by_id: dict[str, dict[str, Any]] = {}
    rows: list[dict[str, Any]] = []
    for path in paths:
        for row in read_jsonl(path):
            claim_id = str(row.get("claim_id") or row.get("id") or "")
            if not claim_id:
                continue
            row = dict(row)
            row.setdefault("split", infer_split(path, row))
            claims_by_id[claim_id] = row
            rows.append(row)
    return claims_by_id, rows


def label_distribution_rows(claim_rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
    grouped: dict[str, list[dict[str, Any]]] = defaultdict(list)
    for row in claim_rows:
        grouped[str(row.get("split") or "unknown")].append(row)

    output: list[dict[str, Any]] = []
    for split in sorted(grouped):
        rows = grouped[split]
        counts = Counter(str(row.get("label") or "UNLABELED") for row in rows)
        labels = list(LABEL_ORDER)
        labels.extend(label for label in sorted(counts) if label not in labels)
        total = len(rows)
        out: dict[str, Any] = {"Split": split, "Total": total}
        for label in labels:
            out[label] = counts.get(label, 0)
        for label in labels:
            out[f"{label}_pct"] = pct(counts.get(label, 0), total)
        output.append(out)
    return output


def retrieval_by_label_rows(
    candidate_paths: list[Path],
    claims_by_id: dict[str, dict[str, Any]],
) -> list[dict[str, Any]]:
    grouped: dict[tuple[str, str], list[dict[str, Any]]] = defaultdict(list)
    for path in candidate_paths:
        fallback_split = infer_split(path)
        for row in read_jsonl(path):
            query_id = str(row.get("query_id") or row.get("id") or "")
            claim = claims_by_id.get(query_id, {})
            label = str(claim.get("label") or row.get("label") or "UNLABELED")
            split = str(row.get("split") or claim.get("split") or fallback_split)
            grouped[(split, label)].append(row)

    output: list[dict[str, Any]] = []
    for split, label in sorted(grouped, key=lambda key: (key[0], LABEL_ORDER.index(key[1]) if key[1] in LABEL_ORDER else 99, key[1])):
        rows = grouped[(split, label)]
        count = len(rows)
        r5 = sum(1 for row in rows if as_bool(row.get("metrics", {}).get("gold_at_5")))
        r10 = sum(1 for row in rows if as_bool(row.get("metrics", {}).get("gold_at_10")))
        r30 = sum(1 for row in rows if as_bool(row.get("metrics", {}).get("gold_at_30")))
        has_gold = sum(1 for row in rows if row.get("metrics", {}).get("has_gold") is not False)
        mrr_values = [safe_float(row.get("metrics", {}).get("mrr")) for row in rows]
        ndcg_values = [safe_float(row.get("metrics", {}).get("ndcg_at_10")) for row in rows]
        output.append(
            {
                "Split": split,
                "Label": label,
                "Count": count,
                "Has_gold_count": has_gold,
                "R@5": round(r5 / max(1, count), 6),
                "R@10": round(r10 / max(1, count), 6),
                "R@30": round(r30 / max(1, count), 6),
                "MRR": round(sum(mrr_values) / max(1, count), 6),
                "nDCG@10": round(sum(ndcg_values) / max(1, count), 6),
            }
        )
    return output


def parse_top_k(path: Path, rows: list[dict[str, Any]]) -> str:
    for row in rows:
        if row.get("top_k") is not None:
            return str(row["top_k"])
    stem = path.stem
    for part in stem.split("_"):
        if part.startswith("top") and part[3:].isdigit():
            return part[3:]
    return ""


def tokenize_lengths(tokenizer: Any, texts: list[str]) -> list[int]:
    lengths: list[int] = []
    for text in texts:
        lengths.append(len(tokenizer(str(text), add_special_tokens=True, truncation=False)["input_ids"]))
    return lengths


def truncation_report_rows(
    verifier_paths: list[Path],
    max_lengths: list[int],
    tokenizer_model: str,
) -> list[dict[str, Any]]:
    try:
        tokenizer = load_tokenizer(tokenizer_model)
        tokenizer_status = "ok"
    except Exception as exc:  # pragma: no cover - exercised only when local model cache is missing.
        tokenizer = None
        tokenizer_status = f"fallback_whitespace:{type(exc).__name__}"

    grouped: dict[tuple[str, str, str, str], list[int]] = defaultdict(list)
    for path in verifier_paths:
        rows = read_jsonl(path)
        fallback_split = infer_split(path)
        top_k = parse_top_k(path, rows)
        for row in rows:
            split = str(row.get("split") or fallback_split)
            label = str(row.get("label") or "UNLABELED")
            input_format = str(row.get("input_format") or ("qa" if "_qa" in path.stem else "flat"))
            text = str(row.get("input_text") or "")
            if tokenizer is None:
                length = len(text.split())
            else:
                length = tokenize_lengths(tokenizer, [text])[0]
            grouped[(split, label, input_format, top_k)].append(length)

    output: list[dict[str, Any]] = []
    for split, label, input_format, top_k in sorted(
        grouped,
        key=lambda key: (key[0], LABEL_ORDER.index(key[1]) if key[1] in LABEL_ORDER else 99, key[1], key[2], key[3]),
    ):
        values = grouped[(split, label, input_format, top_k)]
        for max_length in max_lengths:
            truncated = sum(1 for value in values if value > max_length)
            output.append(
                {
                    "Split": split,
                    "Label": label,
                    "Format": input_format,
                    "Top-k": top_k,
                    "Max length": max_length,
                    "Count": len(values),
                    "p50 tokens": round(float(median(values)), 3) if values else 0.0,
                    "p90": round(percentile(values, 0.90), 3),
                    "p95": round(percentile(values, 0.95), 3),
                    "max_tokens": max(values) if values else 0,
                    "% truncated": pct(truncated, len(values)),
                    "tokenizer": tokenizer_model,
                    "tokenizer_status": tokenizer_status,
                }
            )
    return output


def prediction_distribution_rows(prediction_path: Path) -> list[dict[str, Any]]:
    predictions = read_jsonl(prediction_path)
    matrix: Counter[tuple[str, str]] = Counter()
    gold_counts: Counter[str] = Counter()
    pred_counts: Counter[str] = Counter()
    correct_by_gold: Counter[str] = Counter()
    split_counts: Counter[str] = Counter()

    for row in predictions:
        split = str(row.get("split") or "unknown")
        gold = str(row.get("gold") or row.get("label") or "UNLABELED")
        pred = str(row.get("prediction") or "UNPREDICTED")
        key_gold = f"{split}:{gold}"
        key_pred = f"{split}:{pred}"
        matrix[(split, gold, pred)] += 1
        gold_counts[key_gold] += 1
        pred_counts[key_pred] += 1
        split_counts[split] += 1
        if gold == pred:
            correct_by_gold[key_gold] += 1

    output: list[dict[str, Any]] = []
    labels = list(LABEL_ORDER)
    seen_labels = sorted({gold for _, gold, _ in matrix} | {pred for _, _, pred in matrix})
    labels.extend(label for label in seen_labels if label not in labels)
    for split in sorted(split_counts):
        for gold in labels:
            total_gold = gold_counts.get(f"{split}:{gold}", 0)
            if not total_gold:
                continue
            for pred in labels:
                count = matrix.get((split, gold, pred), 0)
                output.append(
                    {
                        "Split": split,
                        "Gold": gold,
                        "Prediction": pred,
                        "Count": count,
                        "Pct_of_gold": pct(count, total_gold),
                        "Gold_count": total_gold,
                        "Predicted_label_count": pred_counts.get(f"{split}:{pred}", 0),
                        "Gold_recall_pct": pct(correct_by_gold.get(f"{split}:{gold}", 0), total_gold),
                    }
                )
    return output


def compact_text(value: Any, limit: int = 360) -> str:
    text = " ".join(str(value or "").split())
    if len(text) <= limit:
        return text
    return text[: limit - 3].rstrip() + "..."


def error_case_rows(
    prediction_path: Path,
    claims_by_id: dict[str, dict[str, Any]],
    verifier_paths: list[Path],
    candidate_paths: list[Path],
    max_cases: int,
) -> list[dict[str, Any]]:
    verifier_by_id: dict[str, dict[str, Any]] = {}
    for path in verifier_paths:
        for row in read_jsonl(path):
            verifier_by_id[str(row.get("id") or "")] = row

    candidates_by_id: dict[str, dict[str, Any]] = {}
    for path in candidate_paths:
        for row in read_jsonl(path):
            candidates_by_id[str(row.get("query_id") or "")] = row

    errors: list[dict[str, Any]] = []
    for row in read_jsonl(prediction_path):
        gold = str(row.get("gold") or row.get("label") or "")
        pred = str(row.get("prediction") or "")
        if gold == pred:
            continue
        claim_id = str(row.get("id") or "")
        claim = claims_by_id.get(claim_id, {})
        verifier = verifier_by_id.get(claim_id, {})
        candidate_row = candidates_by_id.get(claim_id, {})
        evidence = verifier.get("evidence") or []
        top_evidence = evidence[0] if evidence else {}
        metrics = candidate_row.get("metrics") if isinstance(candidate_row.get("metrics"), dict) else {}
        probabilities = row.get("probabilities") if isinstance(row.get("probabilities"), dict) else {}
        errors.append(
            {
                "id": claim_id,
                "split": row.get("split") or claim.get("split") or verifier.get("split"),
                "gold": gold,
                "prediction": pred,
                "confidence": row.get("confidence"),
                "gold_probability": probabilities.get(gold),
                "pred_probability": probabilities.get(pred),
                "claim": compact_text(row.get("claim") or claim.get("claim")),
                "gold_at_5": metrics.get("gold_at_5"),
                "gold_at_10": metrics.get("gold_at_10"),
                "gold_at_30": metrics.get("gold_at_30"),
                "mrr": metrics.get("mrr"),
                "ndcg_at_10": metrics.get("ndcg_at_10"),
                "top_evidence_id": top_evidence.get("candidate_id"),
                "top_evidence_is_gold": top_evidence.get("is_gold"),
                "top_question": compact_text(top_evidence.get("question"), limit=220),
                "top_answer": compact_text(top_evidence.get("answer"), limit=220),
                "top_evidence": compact_text(top_evidence.get("text"), limit=360),
                "evidence_ids": "|".join(str(item) for item in row.get("evidence_ids", [])),
            }
        )

    errors.sort(key=lambda item: (item.get("gold") != "CONFLICTING", item.get("gold") != "NEI", -safe_float(item.get("confidence"))))
    return errors[:max_cases]


def load_metrics(path: Path) -> dict[str, Any]:
    if not path.exists():
        return {}
    return json.loads(path.read_text(encoding="utf-8"))


def gold_qa_upper_bound_rows(metrics_path: Path) -> list[dict[str, Any]]:
    metrics = load_metrics(metrics_path)
    test = metrics.get("test") if isinstance(metrics.get("test"), dict) else {}
    per_class = test.get("per_class") if isinstance(test.get("per_class"), dict) else {}
    return [
        {
            "Method": "ModernBERT top10 retrieved QA",
            "Evidence input": "retrieved QA",
            "Status": "DONE",
            "Acc": test.get("accuracy", ""),
            "Macro-F1": test.get("macro_f1", ""),
            "NEI F1": per_class.get("NEI", {}).get("f1", ""),
            "CONFLICTING F1": per_class.get("CONFLICTING", {}).get("f1", ""),
            "Metrics file": str(metrics_path),
            "Next action": "",
        },
        {
            "Method": "ModernBERT gold QA upper-bound",
            "Evidence input": "gold QA",
            "Status": "PENDING_NEEDS_RUN",
            "Acc": "",
            "Macro-F1": "",
            "NEI F1": "",
            "CONFLICTING F1": "",
            "Metrics file": "",
            "Next action": "build_gold_qa_verifier_inputs_then_train_or_evaluate_as_diagnostic",
        },
    ]


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--claims", type=Path, nargs="*", default=existing(DEFAULT_CLAIMS))
    parser.add_argument("--candidate-pool", type=Path, nargs="*", default=existing(DEFAULT_CANDIDATE_POOLS))
    parser.add_argument("--predictions", type=Path, default=DEFAULT_PREDICTIONS)
    parser.add_argument("--verifier-input", type=Path, nargs="*", default=existing(DEFAULT_VERIFIER_INPUTS))
    parser.add_argument("--metrics", type=Path, default=DEFAULT_METRICS)
    parser.add_argument("--output-dir", type=Path, default=Path("outputs/analysis"))
    parser.add_argument("--tokenizer-model", default="answerdotai/ModernBERT-large")
    parser.add_argument("--max-lengths", type=int, nargs="*", default=[1024, 2048])
    parser.add_argument("--max-error-cases", type=int, default=200)
    args = parser.parse_args()

    claims_by_id, claim_rows = load_claims(args.claims)
    label_rows = label_distribution_rows(claim_rows)
    retrieval_rows = retrieval_by_label_rows(args.candidate_pool, claims_by_id)
    truncation_rows = truncation_report_rows(args.verifier_input, args.max_lengths, args.tokenizer_model)
    prediction_rows = prediction_distribution_rows(args.predictions)
    error_rows = error_case_rows(
        args.predictions,
        claims_by_id,
        args.verifier_input,
        args.candidate_pool,
        args.max_error_cases,
    )
    upper_bound_rows = gold_qa_upper_bound_rows(args.metrics)

    output_dir = args.output_dir
    outputs = {
        "label_distribution": output_dir / "averitec_label_distribution.csv",
        "retrieval_by_label": output_dir / "averitec_retrieval_by_label.csv",
        "input_truncation_report": output_dir / "averitec_input_truncation_report.csv",
        "prediction_distribution": output_dir / "averitec_prediction_distribution.csv",
        "error_cases": output_dir / "averitec_error_cases.csv",
        "gold_qa_upper_bound": output_dir / "averitec_gold_qa_upper_bound.csv",
    }
    write_csv(outputs["label_distribution"], label_rows)
    write_csv(outputs["retrieval_by_label"], retrieval_rows)
    write_csv(outputs["input_truncation_report"], truncation_rows)
    write_csv(outputs["prediction_distribution"], prediction_rows)
    write_csv(outputs["error_cases"], error_rows)
    write_csv(outputs["gold_qa_upper_bound"], upper_bound_rows)

    print("Wrote AVeriTeC diagnostic outputs:")
    for name, path in outputs.items():
        print(f"- {name}: {path}")


if __name__ == "__main__":
    main()