"""Orchestrates the per-model, per-retrieval-mode, per-query-mode benchmark run."""

import statistics
import time

from . import config
from .data import book_to_text, load_books, load_queries
from .embedder import TimedEmbedder
from .fusion import reciprocal_rank_fusion
from .lexical import BM25Index
from .metrics import evaluate_ranking
from .vectorstore import BookVectorStore


def _mean(values):
    values = [v for v in values if v is not None]
    return statistics.mean(values) if values else None


def _retrieve(retrieval_mode, embedder, store, bm25_index, model_name, query_embedding, query_text, top_k, fusion_depth, rrf_k):
    """Returns (retrieved_book_ids, extra_retrieval_seconds) where extra excludes embedding time."""
    start = time.perf_counter()
    if retrieval_mode == "dense":
        retrieved = store.search(model_name, query_embedding, top_k)
    elif retrieval_mode == "hybrid":
        bm25_ranked = bm25_index.search(query_text, fusion_depth)
        dense_ranked = store.search(model_name, query_embedding, fusion_depth)
        fused = reciprocal_rank_fusion([bm25_ranked, dense_ranked], k=rrf_k)
        retrieved = fused[:top_k]
    else:
        raise ValueError(f"Unknown retrieval_mode: {retrieval_mode}")
    elapsed = time.perf_counter() - start
    return retrieved, elapsed


def _evaluate(embedder, store, bm25_index, model_name, queries, retrieval_mode, query_mode, top_k, fusion_depth, rrf_k):
    per_query = []
    embed_latencies_sec = []
    retrieval_latencies_sec = []

    for q in queries:
        text = q.text_for_mode(query_mode)
        embedding, embed_latency = embedder.encode_query(text)
        retrieved, extra_latency = _retrieve(
            retrieval_mode, embedder, store, bm25_index, model_name,
            embedding, text, top_k, fusion_depth, rrf_k,
        )
        total_latency = embed_latency + extra_latency

        embed_latencies_sec.append(embed_latency)
        retrieval_latencies_sec.append(total_latency)

        metrics = evaluate_ranking(retrieved, q.relevance)

        per_query.append(
            {
                "query": text,
                "categories": q.categories,
                "embedding_latency_ms": embed_latency * 1000,
                "retrieval_latency_ms": total_latency * 1000,
                **metrics,
            }
        )

    per_category = {}
    for entry in per_query:
        for cat in entry["categories"]:
            bucket = per_category.setdefault(
                cat, {"n": 0, "recall@10": [], "recall@50": [], "mrr": [], "ndcg@10": []}
            )
            bucket["n"] += 1
            for metric in ("recall@10", "recall@50", "mrr", "ndcg@10"):
                bucket[metric].append(entry[metric])

    per_category_summary = {
        cat: {
            "n": bucket["n"],
            "recall@10": _mean(bucket["recall@10"]),
            "recall@50": _mean(bucket["recall@50"]),
            "mrr": _mean(bucket["mrr"]),
            "ndcg@10": _mean(bucket["ndcg@10"]),
        }
        for cat, bucket in per_category.items()
    }

    mean_embed_latency_sec = _mean(embed_latencies_sec)
    mean_retrieval_latency_sec = _mean(retrieval_latencies_sec)

    return {
        "mean_embedding_latency_ms": mean_embed_latency_sec * 1000 if mean_embed_latency_sec else None,
        "mean_retrieval_latency_ms": mean_retrieval_latency_sec * 1000 if mean_retrieval_latency_sec else None,
        "queries_per_sec": (1.0 / mean_retrieval_latency_sec) if mean_retrieval_latency_sec else None,
        "recall@10": _mean(e["recall@10"] for e in per_query),
        "recall@50": _mean(e["recall@50"] for e in per_query),
        "mrr": _mean(e["mrr"] for e in per_query),
        "ndcg@10": _mean(e["ndcg@10"] for e in per_query),
        "per_query": per_query,
        "per_category": per_category_summary,
    }


def run_benchmark(
    models=None,
    query_modes=None,
    retrieval_modes=None,
    top_k=None,
    fusion_depth=None,
    rrf_k=None,
    books_path=None,
    queries_path=None,
    qdrant_path=None,
):
    models = models if models is not None else config.MODELS
    query_modes = query_modes if query_modes is not None else config.QUERY_MODES
    retrieval_modes = retrieval_modes if retrieval_modes is not None else config.RETRIEVAL_MODES
    top_k = top_k or config.TOP_K
    fusion_depth = fusion_depth or config.FUSION_DEPTH
    rrf_k = rrf_k if rrf_k is not None else config.RRF_K

    books = load_books(books_path or config.BOOKS_PATH)
    queries = load_queries(queries_path or config.QUERIES_PATH)
    store = BookVectorStore(qdrant_path or config.QDRANT_PATH)

    documents = [book_to_text(b) for b in books]
    book_ids = [b["book_id"] for b in books]
    num_docs = len(books)

    bm25_index = BM25Index(documents, book_ids) if "hybrid" in retrieval_modes else None

    results = []

    for model_cfg in models:
        model_name = model_cfg["name"]
        print(f"\n=== {model_name} ===")

        embedder = TimedEmbedder(
            model_name,
            query_prefix=model_cfg.get("query_prefix", ""),
            passage_prefix=model_cfg.get("passage_prefix", ""),
            trust_remote_code=model_cfg.get("trust_remote_code", False),
            query_encode_kwargs=model_cfg.get("query_encode_kwargs"),
            passage_encode_kwargs=model_cfg.get("passage_encode_kwargs"),
        )
        print(
            f"  loaded in {embedder.load_time_sec:.2f}s | "
            f"dim={embedder.embedding_dim} | size={embedder.model_size_mb:.1f} MB "
            f"| device={embedder.device}"
        )

        doc_embeddings, doc_embed_time = embedder.encode_passages(documents)
        doc_throughput = num_docs / doc_embed_time if doc_embed_time else None
        print(
            f"  embedded {num_docs} docs in {doc_embed_time:.2f}s "
            f"({doc_throughput:.1f} docs/sec)"
        )

        store.index(model_name, doc_embeddings, books)

        raw_vector_bytes = embedder.embedding_dim * 4 * num_docs
        qdrant_disk_mb = store.collection_disk_size_mb(model_name)
        peak_mem_mb = embedder.peak_encode_memory_mb(queries[0].query)

        model_result = {
            "model": model_name,
            "device": embedder.device,
            "embedding_dim": embedder.embedding_dim,
            "param_count": embedder.param_count,
            "model_size_mb": embedder.model_size_mb,
            "load_time_sec": embedder.load_time_sec,
            "doc_embed_time_sec": doc_embed_time,
            "doc_throughput_docs_per_sec": doc_throughput,
            "raw_vector_storage_mb": raw_vector_bytes / (1024 ** 2),
            "qdrant_collection_disk_mb": qdrant_disk_mb,
            "peak_query_encode_memory_mb": peak_mem_mb,
            "retrieval_modes": {},
        }

        for retrieval_mode in retrieval_modes:
            model_result["retrieval_modes"][retrieval_mode] = {}
            for query_mode in query_modes:
                print(f"  evaluating retrieval_mode={retrieval_mode} query_mode={query_mode} ...")
                mode_result = _evaluate(
                    embedder, store, bm25_index, model_name, queries,
                    retrieval_mode, query_mode, top_k, fusion_depth, rrf_k,
                )
                model_result["retrieval_modes"][retrieval_mode][query_mode] = mode_result
                print(
                    f"    recall@10={mode_result['recall@10']:.3f} "
                    f"recall@50={mode_result['recall@50']:.3f} "
                    f"mrr={mode_result['mrr']:.3f} "
                    f"ndcg@10={mode_result['ndcg@10']:.3f} "
                    f"retrieval_latency={mode_result['mean_retrieval_latency_ms']:.1f}ms"
                )

        results.append(model_result)
        embedder.unload()

    return results