#!/usr/bin/env python
"""
Mapped-embedding self-retrieval sanity check: index = spectrum→mapper embeddings of test set,
query = same embeddings. Expected Recall@1 ≈ 1.0.

Tests (1) spectrum→ChemBERTa-mapped (SpecBridge) and (2) spectrum→SMI-TED-mapped (DreamsToSmiTed or M_smi).
If either fails, the spectrum→mapped-embedding pipeline is broken.
"""
from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path

ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

import numpy as np
import torch

from spec_rag.embeddings import SpectrumEmbedder, l2_normalize
from spec_rag.faiss_index import build_hnsw_index, index_search


def _bin_peaks(mz, intensity, num_bins: int, max_mz: float):
    if not isinstance(mz, torch.Tensor):
        mz = torch.tensor(mz, dtype=torch.float32)
    if not isinstance(intensity, torch.Tensor):
        intensity = torch.tensor(intensity, dtype=torch.float32)
    bins = torch.zeros(num_bins, dtype=torch.float32)
    if mz.numel() == 0:
        return bins.numpy()
    idx = torch.clamp((mz / max_mz) * num_bins, min=0, max=num_bins - 1e-6).long()
    idx = torch.clamp(idx, max=num_bins - 1)
    bins.index_add_(0, idx, intensity)
    return bins.numpy()


def load_mgf_spectra(mgf_path: str, spec_bins: int = 2048, max_mz: float = 2000.0, max_peaks: int = 60):
    try:
        from pyteomics import mgf
    except ImportError:
        raise ImportError("pyteomics required")
    out = []
    with mgf.MGF(mgf_path) as reader:
        for spec in reader:
            params = spec.get("params", {})
            mz = spec.get("m/z array", [])
            inten = spec.get("intensity array", [])
            if len(mz) == 0 or len(inten) == 0:
                continue
            binned = _bin_peaks(mz, inten, num_bins=spec_bins, max_mz=max_mz)
            peaks = [[float(m), float(i)] for m, i in zip(mz, inten)]
            if max_peaks and peaks:
                peaks = sorted(peaks, key=lambda x: x[1], reverse=True)[:max_peaks]
            out.append({"binned": binned, "peaks": peaks})
    return out


def build_meta_peaks(records, max_peaks: int):
    if not records or "peaks" not in records[0]:
        return {}
    peaks_list = [r["peaks"] for r in records]
    max_len = min(max(len(p) for p in peaks_list), max_peaks) if max_peaks else max(len(p) for p in peaks_list)
    arr = np.zeros((len(peaks_list), max_len, 2), dtype=np.float32)
    for i, p in enumerate(peaks_list):
        for j, pair in enumerate(p[:max_len]):
            arr[i, j, 0] = pair[0]
            arr[i, j, 1] = pair[1]
    return {"peaks": torch.tensor(arr)}


def parse_args():
    p = argparse.ArgumentParser(
        description="Mapped self-retrieval: index = spectrum→mapper embeddings, query = same; expect Recall@1 ≈ 1.0"
    )
    p.add_argument("--mgf-path", required=True, help="Test MGF (e.g. MassSpecGym_test.mgf)")
    p.add_argument("--specbridge-ckpt", required=True)
    p.add_argument("--dreams-ckpt", default=None)
    p.add_argument("--smited-mapper-ckpt", default=None, help="De-SpecBridge SMI-TED mapper (e.g. mapper_best.pt)")
    p.add_argument("--despecbridge-path", default=None)
    p.add_argument("--mapper-dir", default=None, help="Spec-RAG mappers.pt dir (for SMI-TED when not using smited-mapper-ckpt)")
    p.add_argument("--spec-bins", type=int, default=2048)
    p.add_argument("--max-mz", type=float, default=2000.0)
    p.add_argument("--max-peaks", type=int, default=60)
    p.add_argument("--device", default="cuda")
    p.add_argument("--limit", type=int, default=None)
    p.add_argument("--report", default=None)
    return p.parse_args()


def main():
    args = parse_args()
    if args.device == "cuda" and not torch.cuda.is_available():
        args.device = "cpu"
    device = torch.device(args.device)

    records = load_mgf_spectra(
        args.mgf_path, spec_bins=args.spec_bins, max_mz=args.max_mz, max_peaks=args.max_peaks
    )
    if args.limit:
        records = records[: args.limit]
    n = len(records)
    if n == 0:
        raise SystemExit("No spectra in MGF")
    spectra_binned = np.stack([r["binned"] for r in records], axis=0).astype(np.float32)
    meta = build_meta_peaks(records, args.max_peaks)

    results = {"n": n, "mapped_chem": None, "mapped_smi": None}

    # --- Mapped ChemBERTa (spectrum → SpecBridge → q_chem) ---
    print("Mapped ChemBERTa: loading SpectrumEmbedder and encoding test spectra...")
    spec_embedder = SpectrumEmbedder(
        specbridge_ckpt=args.specbridge_ckpt,
        dreams_ckpt=args.dreams_ckpt,
        device=args.device,
        normalize=False,
        use_lightweight=False,
    )
    q_chem = spec_embedder.encode(spectra_binned, meta, batch_size=32)
    q_chem = l2_normalize(q_chem).astype(np.float32)
    index_chem = build_hnsw_index(q_chem, m=16, ef_construction=100, ef_search=64, metric="cosine")
    scores_chem, idx_chem = index_search(index_chem, q_chem, k=1)
    # Match if top-1 is self (or duplicate: inner product ≈ 1.0 for normalized vectors)
    r1_chem = sum(1 for i in range(n) if idx_chem[i, 0] == i or scores_chem[i, 0] >= 0.9999) / n
    results["mapped_chem"] = {"Recall@1": r1_chem}
    print(f"Mapped ChemBERTa self-retrieval Recall@1: {r1_chem:.4f} (expected ≈ 1.0)")
    spec_embedder_for_smi = spec_embedder  # reuse for Spec-RAG M_smi path if needed

    # --- Mapped SMI-TED ---
    use_pretrained_smited = args.smited_mapper_ckpt is not None
    q_smi = None

    if use_pretrained_smited:
        despec_root = Path(args.despecbridge_path or "").resolve()
        if not despec_root.exists():
            raise SystemExit("--despecbridge-path required when using --smited-mapper-ckpt")
        if str(despec_root) not in sys.path:
            sys.path.insert(0, str(despec_root))
        from despecbridge.models.dreams_to_smited import (
            build_dreams_adapter_for_smited,
            build_mapper,
            DreamsToSmiTed,
        )
        from despecbridge.models.smited_decoder import load_smited

        mapper_ckpt_path = Path(args.smited_mapper_ckpt)
        ckpt = torch.load(mapper_ckpt_path, map_location="cpu")
        ckpt_args = ckpt.get("args", {})
        if not ckpt_args:
            raise SystemExit(f"Mapper checkpoint missing 'args' dict.")
        cond_dim = int(ckpt_args.get("cond_dim", 512))
        spec_bins_ckpt = int(ckpt_args.get("spec_bins", 2048))
        dreams_ckpt = ckpt_args.get("dreams_ckpt", args.dreams_ckpt)
        spec_encoder = build_dreams_adapter_for_smited(
            dreams_ckpt=dreams_ckpt,
            cond_dim=cond_dim,
            spec_bins=spec_bins_ckpt,
        )
        if "spec_encoder" in ckpt:
            spec_encoder.load_state_dict(ckpt["spec_encoder"], strict=False)
        d_smited = int(ckpt_args.get("d_smited", 768))
        mapper = build_mapper(
            cond_dim,
            d_smited,
            n_blocks=int(ckpt_args.get("mapper_blocks", 2)),
            hidden=int(ckpt_args.get("mapper_hidden", 512)),
        )
        mapper_state = ckpt["mapper"]
        if mapper_state and list(mapper_state.keys())[0].startswith("module."):
            mapper_state = {k.replace("module.", ""): v for k, v in mapper_state.items()}
        mapper.load_state_dict(mapper_state, strict=True)
        smited_wrapper = load_smited(
            model_name=ckpt_args.get("smited_model", "ibm-research/materials.smi-ted"),
            device=device,
            use_original_weights=bool(ckpt_args.get("use_original_weights", False)),
        )
        smited_wrapper.eval()
        smited_mapper_model = DreamsToSmiTed(
            spec_encoder=spec_encoder,
            mapper=mapper,
            smited=smited_wrapper,
            freeze_spec=True,
            freeze_decoder=True,
        ).to(device)
        smited_mapper_model.eval()

        batch_size = 32
        all_latents = []
        total = spectra_binned.shape[0]
        with torch.no_grad():
            for start in range(0, total, batch_size):
                end = min(total, start + batch_size)
                spectra_t = torch.tensor(spectra_binned[start:end], dtype=torch.float32, device=device)
                meta_t = {}
                for k, v in meta.items():
                    if isinstance(v, torch.Tensor) and v.shape[0] == total:
                        meta_t[k] = v[start:end].to(device)
                    else:
                        meta_t[k] = v
                z = smited_mapper_model(spectra_t, meta_t)
                all_latents.append(z.detach().cpu().numpy().astype(np.float32))
        q_smi = np.concatenate(all_latents, axis=0)
    elif args.mapper_dir:
        mapper_dir = Path(args.mapper_dir)
        ckpt = torch.load(mapper_dir / "mappers.pt", map_location="cpu", weights_only=False)
        d_spec = ckpt["d_spec"]
        d_smi = ckpt["d_smi"]

        class MapperHead(torch.nn.Module):
            def __init__(self, d_in, d_out):
                super().__init__()
                self.proj = torch.nn.Linear(d_in, d_out)
            def forward(self, x):
                return self.proj(x)

        M_smi = MapperHead(d_spec, d_smi).to(device).eval()
        M_smi.load_state_dict(ckpt["M_smi"])
        x_spec = spec_embedder_for_smi.encode_spec_only(spectra_binned, meta, batch_size=32)
        with torch.no_grad():
            x = torch.tensor(x_spec, dtype=torch.float32, device=device)
            q_smi = M_smi(x).cpu().numpy().astype(np.float32)
    else:
        print("Mapped SMI-TED: skipped (provide --smited-mapper-ckpt + --despecbridge-path or --mapper-dir)")

    if q_smi is not None:
        q_smi = l2_normalize(q_smi).astype(np.float32)
        index_smi = build_hnsw_index(q_smi, m=16, ef_construction=100, ef_search=64, metric="cosine")
        scores_smi, idx_smi = index_search(index_smi, q_smi, k=1)
        r1_smi = sum(1 for i in range(n) if idx_smi[i, 0] == i or scores_smi[i, 0] >= 0.9999) / n
        results["mapped_smi"] = {"Recall@1": r1_smi}
        print(f"Mapped SMI-TED self-retrieval Recall@1: {r1_smi:.4f} (expected ≈ 1.0)")

    print("\nInterpretation:")
    if results["mapped_chem"] and results["mapped_chem"]["Recall@1"] < 0.99:
        print("  - Mapped ChemBERTa Recall@1 << 1.0 → spectrum→ChemBERTa pipeline may be broken.")
    elif results["mapped_chem"]:
        print("  - Mapped ChemBERTa passed (Recall@1 ≈ 1.0).")
    if results["mapped_smi"] is not None:
        if results["mapped_smi"]["Recall@1"] < 0.99:
            print("  - Mapped SMI-TED Recall@1 << 1.0 → spectrum→SMI-TED pipeline may be broken.")
        else:
            print("  - Mapped SMI-TED passed (Recall@1 ≈ 1.0).")
    elif not use_pretrained_smited and not args.mapper_dir:
        print("  - Mapped SMI-TED skipped (no mapper provided).")

    if args.report:
        with open(args.report, "w") as f:
            json.dump(results, f, indent=2)
        print(f"\nWrote {args.report}")


if __name__ == "__main__":
    main()