"""Retrieval: BM25 + Dense (FAISS) + RRF fusion + cross-encoder reranking."""
from __future__ import annotations

import os
import pickle
import re
from typing import Optional

import numpy as np
import pandas as pd

from src.citations import Citation
from src.config import (
    BM25_FILE, CHUNKS_FILE, EMBED_MODEL, FAISS_FILE,
    RRF_K, RERANK_MODEL, TOP_K_BM25, TOP_K_DENSE, TOP_K_FUSED, TOP_N_FINAL,
)

# ── tokeniser ────────────────────────────────────────────────────────────────

_TOKEN_RE  = re.compile(r"[A-Za-z_][A-Za-z0-9_.:]*|\d+")
_CAMEL_RE  = re.compile(r"(?<!^)(?=[A-Z])")
_STOP      = {"the","a","an","of","to","in","is","are","and","or","this","that","it","be"}


def _tokenize(text: str) -> list[str]:
    tokens = _TOKEN_RE.findall(text)
    out: list[str] = []
    for t in tokens:
        tl = t.lower()
        if tl in _STOP:
            continue
        out.append(tl)
        parts = _CAMEL_RE.split(t)
        if len(parts) > 1:
            out.extend(p.lower() for p in parts if p and p.lower() not in _STOP)
        for sub in re.split(r"[._:]+", t):
            if sub and sub.lower() not in _STOP and sub.lower() != tl:
                out.append(sub.lower())
    return out


# ── lazy singletons ───────────────────────────────────────────────────────────

_chunks_df: Optional[pd.DataFrame]    = None
_bm25_index                           = None
_faiss_index                          = None
_embed_model                          = None
_rerank_model                         = None


def _load_chunks() -> pd.DataFrame:
    global _chunks_df
    if _chunks_df is None:
        if not os.path.exists(CHUNKS_FILE):
            raise FileNotFoundError(
                f"{CHUNKS_FILE} not found. Run `python build_index.py` first."
            )
        _chunks_df = pd.read_parquet(CHUNKS_FILE)
    return _chunks_df


def _load_bm25():
    global _bm25_index
    if _bm25_index is None:
        if not os.path.exists(BM25_FILE):
            raise FileNotFoundError(f"{BM25_FILE} not found.")
        with open(BM25_FILE, "rb") as f:
            _bm25_index = pickle.load(f)
    return _bm25_index


def _load_faiss():
    global _faiss_index
    if _faiss_index is None:
        import faiss  # noqa: PLC0415
        if not os.path.exists(FAISS_FILE):
            raise FileNotFoundError(f"{FAISS_FILE} not found.")
        _faiss_index = faiss.read_index(FAISS_FILE)
    return _faiss_index


def _load_embed():
    global _embed_model
    if _embed_model is None:
        from sentence_transformers import SentenceTransformer  # noqa: PLC0415
        _embed_model = SentenceTransformer(EMBED_MODEL)
    return _embed_model


def _load_reranker():
    global _rerank_model
    if _rerank_model is None:
        from sentence_transformers import CrossEncoder  # noqa: PLC0415
        _rerank_model = CrossEncoder(RERANK_MODEL)
    return _rerank_model


def indices_ready() -> bool:
    return all(os.path.exists(p) for p in (CHUNKS_FILE, BM25_FILE, FAISS_FILE))


# ── retrieval methods ─────────────────────────────────────────────────────────

def _bm25_search(query: str, top_k: int) -> list[tuple[int, float]]:
    """Returns [(chunk_id, score), ...]."""
    import bm25s  # noqa: PLC0415
    bm25 = _load_bm25()
    query_tokens_arr = bm25s.tokenize([" ".join(_tokenize(query))])
    results, scores = bm25.retrieve(query_tokens_arr, k=top_k)
    return list(zip(results[0].tolist(), scores[0].tolist()))


def _dense_search(query: str, top_k: int) -> list[tuple[int, float]]:
    """Returns [(chunk_id, score), ...]."""
    model  = _load_embed()
    index  = _load_faiss()
    # BGE models expect a query prefix
    vec = model.encode(f"Represent this sentence for searching relevant passages: {query}",
                       normalize_embeddings=True).reshape(1, -1).astype("float32")
    scores, ids = index.search(vec, top_k)
    return [(int(i), float(s)) for i, s in zip(ids[0], scores[0]) if i >= 0]


def _rrf_fuse(
    bm25_hits: list[tuple[int, float]],
    dense_hits: list[tuple[int, float]],
    k: int = RRF_K,
    top_n: int = TOP_K_FUSED,
) -> list[tuple[int, float]]:
    scores: dict[int, float] = {}
    for rank, (cid, _) in enumerate(bm25_hits):
        scores[cid] = scores.get(cid, 0.0) + 1.0 / (k + rank + 1)
    for rank, (cid, _) in enumerate(dense_hits):
        scores[cid] = scores.get(cid, 0.0) + 1.0 / (k + rank + 1)
    ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
    return ranked[:top_n]


def _rerank(query: str, hits: list[tuple[int, float]], top_n: int, df: pd.DataFrame) -> list[tuple[int, float]]:
    reranker = _load_reranker()
    pairs = [(query, df.loc[cid, "text"]) for cid, _ in hits]
    scores = reranker.predict(pairs)
    ranked = sorted(zip([cid for cid, _ in hits], scores), key=lambda x: x[1], reverse=True)
    return [(int(cid), float(s)) for cid, s in ranked[:top_n]]


# ── public API ────────────────────────────────────────────────────────────────

class HybridRetriever:
    def __init__(
        self,
        use_bm25:   bool = True,
        use_dense:  bool = True,
        use_rerank: bool = True,
        top_n:      int  = TOP_N_FINAL,
    ):
        self.use_bm25   = use_bm25
        self.use_dense  = use_dense
        self.use_rerank = use_rerank
        self.top_n      = top_n

    def retrieve(self, query: str) -> list[Citation]:
        df = _load_chunks()

        bm25_hits:  list[tuple[int, float]] = []
        dense_hits: list[tuple[int, float]] = []

        if self.use_bm25:
            bm25_hits = _bm25_search(query, TOP_K_BM25)
        if self.use_dense:
            dense_hits = _dense_search(query, TOP_K_DENSE)

        if self.use_bm25 and self.use_dense:
            fused = _rrf_fuse(bm25_hits, dense_hits)
        elif self.use_bm25:
            fused = bm25_hits[:TOP_K_FUSED]
        elif self.use_dense:
            fused = dense_hits[:TOP_K_FUSED]
        else:
            return []

        if self.use_rerank and len(fused) > 0:
            final = _rerank(query, fused, self.top_n, df)
        else:
            final = fused[:self.top_n]

        citations: list[Citation] = []
        for rank, (cid, score) in enumerate(final, start=1):
            row = df.loc[cid]
            citations.append(Citation(
                id=rank,
                chunk_id=int(cid),
                source_url=str(row["source_url"]),
                page_title=str(row["page_title"]),
                section=str(row.get("section", "")),
                snippet=str(row["text"])[:600],
                score=float(score),
            ))
        return citations