from __future__ import annotations

import argparse
import json
import math
import re
import time
from collections import Counter, defaultdict
from pathlib import Path
from typing import Callable

import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

ROOT = Path(__file__).resolve().parent
DATA_PATH = ROOT / "benchmark-data.json"
CROSS_DATA_PATH = DATA_PATH
DEFAULT_OUTPUT = ROOT / "my-results-monolingual.json"

SEMANTIC_MODELS = {
    "multilingual-e5-small": {
        "repo_id": "intfloat/multilingual-e5-small",
        "query_prefix": "query: ",
        "passage_prefix": "passage: ",
        "languages": 94,
        "license": "MIT",
        "role": "efficient multilingual retrieval model",
    },
    "multilingual-minilm": {
        "repo_id": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
        "query_prefix": "",
        "passage_prefix": "",
        "languages": 50,
        "license": "Apache-2.0",
        "role": "compact multilingual sentence similarity baseline",
    },
    "labse": {
        "repo_id": "sentence-transformers/LaBSE",
        "query_prefix": "",
        "passage_prefix": "",
        "languages": 110,
        "license": "Apache-2.0",
        "role": "high-coverage multilingual embedding reference",
    },
}


def load_rows(path: Path = DATA_PATH) -> list[dict]:
    payload = json.loads(path.read_text(encoding="utf-8"))
    split = "cross-language" if getattr(load_rows, "use_cross", False) else "monolingual"
    return payload["splits"][split]["rows"]

def language_of(row: dict) -> str:
    return row.get("language") or row.get("query_language") or "unknown"

def pair_of(row: dict) -> str | None:
    q = row.get("query_language")
    p = row.get("passage_language")
    return f"{q}->{p}" if q and p else None


def candidate_passages(row: dict) -> list[str]:
    return [row["positive_passage"], row["hard_negative"], row["easy_negative"]]


def _result_record(row: dict, scores: list[float], elapsed_ms: float) -> dict:
    order = np.argsort(np.asarray(scores))[::-1].tolist()
    rank = order.index(0) + 1
    return {
        "id": row["id"],
        "language": language_of(row),
        "language_pair": pair_of(row),
        "domain": row["domain"],
        "positive_rank": rank,
        "scores": [float(x) for x in scores],
        "latency_ms": float(elapsed_ms),
    }


def evaluate_scorer(rows: list[dict], scorer: Callable[[str, list[str]], list[float]]) -> list[dict]:
    out = []
    for row in rows:
        passages = candidate_passages(row)
        t0 = time.perf_counter()
        scores = scorer(row["query"], passages)
        elapsed_ms = (time.perf_counter() - t0) * 1000
        out.append(_result_record(row, scores, elapsed_ms))
    return out


def char_tfidf_scorer(query: str, passages: list[str]) -> list[float]:
    vec = TfidfVectorizer(analyzer="char_wb", ngram_range=(3, 5), sublinear_tf=True)
    matrix = vec.fit_transform(passages + [query])
    return cosine_similarity(matrix[-1], matrix[:-1]).ravel().tolist()


def word_tfidf_scorer(query: str, passages: list[str]) -> list[float]:
    vec = TfidfVectorizer(analyzer="word", ngram_range=(1, 2), sublinear_tf=True, lowercase=True)
    matrix = vec.fit_transform(passages + [query])
    return cosine_similarity(matrix[-1], matrix[:-1]).ravel().tolist()


def hybrid_tfidf_scorer(query: str, passages: list[str]) -> list[float]:
    char_scores = np.asarray(char_tfidf_scorer(query, passages))
    word_scores = np.asarray(word_tfidf_scorer(query, passages))
    return (0.6 * char_scores + 0.4 * word_scores).tolist()


def tokenize(text: str) -> list[str]:
    return re.findall(r"\b\w+\b", text.lower(), flags=re.UNICODE)


def bm25_scorer(query: str, passages: list[str], k1: float = 1.5, b: float = 0.75) -> list[float]:
    docs = [tokenize(p) for p in passages]
    q = tokenize(query)
    n = len(docs)
    avgdl = sum(len(d) for d in docs) / max(n, 1)
    df = Counter()
    for d in docs:
        for term in set(d):
            df[term] += 1
    scores = []
    for doc in docs:
        tf = Counter(doc)
        dl = len(doc)
        score = 0.0
        for term in q:
            if term not in tf:
                continue
            idf = math.log(1 + (n - df[term] + 0.5) / (df[term] + 0.5))
            denom = tf[term] + k1 * (1 - b + b * dl / max(avgdl, 1e-9))
            score += idf * (tf[term] * (k1 + 1)) / denom
        scores.append(score)
    return scores


def summarize(name: str, kind: str, results: list[dict], *, metadata: dict | None = None, load_seconds: float | None = None) -> dict:
    n = len(results)
    by_language = {}
    by_domain = {}
    by_pair = {}
    for field, target in [("language", by_language), ("domain", by_domain), ("language_pair", by_pair)]:
        for value in sorted({r[field] for r in results}):
            part = [r for r in results if r[field] == value]
            target[value] = {
                "n": len(part),
                "accuracy_at_1": sum(r["positive_rank"] == 1 for r in part) / len(part),
                "mrr": sum(1 / r["positive_rank"] for r in part) / len(part),
            }
    return {
        "name": name,
        "kind": kind,
        "status": "executed",
        "n": n,
        "accuracy_at_1": sum(r["positive_rank"] == 1 for r in results) / n,
        "mrr": sum(1 / r["positive_rank"] for r in results) / n,
        "avg_latency_ms": sum(r["latency_ms"] for r in results) / n,
        "load_seconds": load_seconds,
        "by_language": by_language,
        "by_domain": by_domain,
        "by_language_pair": {k:v for k,v in by_pair.items() if k is not None},
        "metadata": metadata or {},
    }


def run_lexical(rows: list[dict]) -> list[dict]:
    scorers = {
        "char-tfidf": char_tfidf_scorer,
        "word-tfidf": word_tfidf_scorer,
        "hybrid-tfidf": hybrid_tfidf_scorer,
        "bm25": bm25_scorer,
    }
    return [summarize(name, "lexical", evaluate_scorer(rows, scorer)) for name, scorer in scorers.items()]


def run_semantic(rows: list[dict], key: str) -> dict:
    spec = SEMANTIC_MODELS[key]
    try:
        from sentence_transformers import SentenceTransformer
    except ImportError as exc:
        raise RuntimeError("sentence-transformers is required for semantic benchmarks") from exc

    t0 = time.perf_counter()
    model = SentenceTransformer(spec["repo_id"])
    load_seconds = time.perf_counter() - t0
    results = []
    for row in rows:
        passages = candidate_passages(row)
        q = spec["query_prefix"] + row["query"]
        docs = [spec["passage_prefix"] + p for p in passages]
        t1 = time.perf_counter()
        q_emb = model.encode([q], normalize_embeddings=True)
        p_emb = model.encode(docs, normalize_embeddings=True)
        scores = (q_emb @ p_emb.T)[0].tolist()
        elapsed_ms = (time.perf_counter() - t1) * 1000
        results.append(_result_record(row, scores, elapsed_ms))
    return summarize(key, "semantic", results, metadata=spec, load_seconds=load_seconds)


def configured_semantic_placeholders() -> list[dict]:
    return [
        {
            "name": key,
            "kind": "semantic",
            "status": "configured-not-executed",
            "n": 0,
            "accuracy_at_1": None,
            "mrr": None,
            "avg_latency_ms": None,
            "load_seconds": None,
            "by_language": {},
            "by_domain": {},
            "metadata": spec,
        }
        for key, spec in SEMANTIC_MODELS.items()
    ]


def main() -> None:
    parser = argparse.ArgumentParser(description="Benchmark multilingual retrieval methods on Research Retrieval Eval Mini.")
    parser.add_argument("--models", nargs="*", default=["lexical"], help="lexical, all, or semantic keys")
    parser.add_argument("--split", choices=["monolingual", "cross-language"], default="monolingual")
    parser.add_argument("--output", type=Path, default=None)
    args = parser.parse_args()

    data_path = DATA_PATH if args.split == "monolingual" else CROSS_DATA_PATH
    load_rows.use_cross = args.split == "cross-language"
    rows = load_rows(data_path)
    output = args.output or (DEFAULT_OUTPUT if args.split == "monolingual" else ROOT / "my-results-cross-language.json")
    requested = set(args.models)
    models = run_lexical(rows) if ("lexical" in requested or "all" in requested) else []
    semantic_keys = list(SEMANTIC_MODELS) if "all" in requested else [x for x in requested if x in SEMANTIC_MODELS]
    if not semantic_keys and ("lexical" in requested):
        models.extend(configured_semantic_placeholders())
    else:
        for key in semantic_keys:
            try:
                models.append(run_semantic(rows, key))
            except Exception as exc:
                spec = SEMANTIC_MODELS[key]
                models.append({
                    "name": key,
                    "kind": "semantic",
                    "status": "error",
                    "error": str(exc),
                    "n": 0,
                    "accuracy_at_1": None,
                    "mrr": None,
                    "avg_latency_ms": None,
                    "load_seconds": None,
                    "by_language": {},
                    "by_domain": {},
                    "metadata": spec,
                })

    payload = {
        "benchmark": "Research Retrieval Eval Mini",
        "split": args.split,
        "dataset_rows": len(rows),
        "languages": sorted({language_of(r) for r in rows}),
        "language_pairs": sorted({pair_of(r) for r in rows if pair_of(r)}),
        "domains": sorted({r["domain"] for r in rows}),
        "metrics": ["accuracy_at_1", "mrr", "avg_latency_ms"],
        "models": models,
        "interpretation_note": "This is a small diagnostic benchmark. Results should not be generalized to broader retrieval quality without larger external evaluation.",
    }
    output.parent.mkdir(parents=True, exist_ok=True)
    output.write_text(json.dumps(payload, indent=2, ensure_ascii=False), encoding="utf-8")
    print(json.dumps(payload, indent=2, ensure_ascii=False))


if __name__ == "__main__":
    main()