#!/usr/bin/env python3
"""Stage 1 for a decoder-only model on multiple-choice QA.

The classifier here is a pre-trained instruction-tuned decoder answering a
multiple-choice question zero-shot: the prediction is the answer letter with
the largest next-token logit at the last prompt position. That position plays
the role [CLS] plays for the encoders, so the residual stream there, at every
layer, is written in exactly the layout scripts/25 produces, and the rest of
the pipeline (scripts/26 onward) runs unchanged.

Writes, for cfg = run_name and split in {train, validation, test}:

    data/features/<cfg>/predictions_<split>.parquet     idx, split, label, pred,
                                                        correct, entropy, seq_len,
                                                        logit_i, prob_i, subject
    data/features/<cfg>/cls_{begin,mid,last}_<split>.npz   features, columns, idx
    data/layer_states/<cfg>/states_<split>.npy          (N, L+1, D) float16
    data/layer_states/<cfg>/meta_<split>.npz            idx, label, n_layers, dim
    extraction/outputs/<cfg>/predictions_<split>.parquet  (copy, as stage 1 does)

The three splits are a random, subject-stratified partition of the pooled
question set; the model is never trained on any of it. `data.split_seed`
picks the partition, so replicates differ in the partition rather than in
fine-tuning seed.

    python extraction/scripts/11_extract_decoder_states.py \
        --config extraction/configs/qwen15_mmlu.yaml
"""
from __future__ import annotations

import argparse
import shutil
import sys
import time
from pathlib import Path

import numpy as np
import pandas as pd
import torch

REPO = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(REPO / "extraction"))
from src.utils import load_config, seed_everything  # noqa: E402

LETTERS = "ABCDEFGH"


# ---------------------------------------------------------------- datasets --
def load_questions(dcfg: dict) -> pd.DataFrame:
    """One row per question: idx, subject, question, choices (list), label."""
    from datasets import load_dataset
    src = dcfg["source"]
    if src == "mmlu":
        parts = []
        for sp in dcfg.get("hf_splits", ["test", "validation"]):
            ds = load_dataset("cais/mmlu", "all", split=sp)
            df = ds.to_pandas()
            df["hf_split"] = sp
            parts.append(df)
        df = pd.concat(parts, ignore_index=True)
        df = df.rename(columns={"answer": "label"})
        df["choices"] = df["choices"].apply(list)
    elif src == "csqa":
        parts = []
        for sp in dcfg.get("hf_splits", ["train", "validation"]):
            ds = load_dataset("tau/commonsense_qa", split=sp)
            df = ds.to_pandas()
            df["hf_split"] = sp
            parts.append(df)
        df = pd.concat(parts, ignore_index=True)
        df["subject"] = df["question_concept"]
        df["choices"] = df["choices"].apply(lambda c: list(c["text"]))
        df["label"] = df["answerKey"].map(lambda k: LETTERS.index(k))
    elif src == "arc":
        # ARC-Easy and ARC-Challenge pooled; a few items use 1-4 keys and a
        # few have 3 or 5 choices; the 4-choice letter-keyed majority is kept
        # so the class set is fixed.
        parts = []
        for cfg_name in ("ARC-Easy", "ARC-Challenge"):
            for sp in dcfg.get("hf_splits", ["train", "validation", "test"]):
                ds = load_dataset("allenai/ai2_arc", cfg_name, split=sp)
                df = ds.to_pandas()
                df["hf_split"] = f"{cfg_name}/{sp}"
                parts.append(df)
        df = pd.concat(parts, ignore_index=True)
        df["choices"] = df["choices"].apply(lambda c: list(c["text"]))
        df["answerKey"] = df["answerKey"].replace({"1": "A", "2": "B", "3": "C", "4": "D"})
        df = df[df["choices"].apply(len) == 4].reset_index(drop=True)
        df["label"] = df["answerKey"].map(lambda k: LETTERS.index(k))
        df["subject"] = "science"
    elif src == "openbookqa":
        parts = []
        for sp in dcfg.get("hf_splits", ["train", "validation", "test"]):
            ds = load_dataset("allenai/openbookqa", "main", split=sp)
            df = ds.to_pandas()
            df["hf_split"] = sp
            parts.append(df)
        df = pd.concat(parts, ignore_index=True)
        df = df.rename(columns={"question_stem": "question"})
        df["choices"] = df["choices"].apply(lambda c: list(c["text"]))
        df["label"] = df["answerKey"].map(lambda k: LETTERS.index(k))
        df["subject"] = "elementary science"
    elif src == "sciq":
        parts = []
        for sp in dcfg.get("hf_splits", ["train", "validation", "test"]):
            ds = load_dataset("allenai/sciq", split=sp)
            df = ds.to_pandas()
            df["hf_split"] = sp
            parts.append(df)
        df = pd.concat(parts, ignore_index=True)
        # the correct answer is a separate field; shuffle it among the
        # distractors with a fixed seed so the label position is uniform
        rng = np.random.RandomState(int(dcfg.get("option_seed", 0)))
        ch, lab = [], []
        for _, r in df.iterrows():
            opts = [r["correct_answer"], r["distractor1"], r["distractor2"], r["distractor3"]]
            perm = rng.permutation(4)
            ch.append([opts[i] for i in perm]); lab.append(int(np.where(perm == 0)[0][0]))
        df["choices"], df["label"] = ch, lab
        df["subject"] = "science"
    elif src == "medmcqa":
        ds = load_dataset("openlifescienceai/medmcqa", split="train")
        df = ds.to_pandas()
        df["hf_split"] = "train"
        n = int(dcfg.get("n_questions", 16000))
        df = df.sample(n=min(n, len(df)), random_state=int(dcfg.get("sample_seed", 0)))
        df = df.reset_index(drop=True)
        df["choices"] = df.apply(lambda r: [r["opa"], r["opb"], r["opc"], r["opd"]], axis=1)
        df["label"] = df["cop"].astype(int)
        df["subject"] = df["subject_name"].fillna("medicine")
    elif src == "hellaswag":
        parts = []
        for sp in dcfg.get("hf_splits", ["validation", "train"]):
            ds = load_dataset("Rowan/hellaswag", split=sp)
            df = ds.to_pandas()
            df["hf_split"] = sp
            parts.append(df)
        df = pd.concat(parts, ignore_index=True)
        n = int(dcfg.get("n_questions", 16000))
        df = df.sample(n=min(n, len(df)), random_state=int(dcfg.get("sample_seed", 0)))
        df = df.reset_index(drop=True)
        df["question"] = df["ctx"]
        df["choices"] = df["endings"].apply(list)
        df["label"] = df["label"].astype(int)
        df["subject"] = df["activity_label"]
    else:
        raise ValueError(f"unknown data.source {src!r}")
    n_choice = dcfg.get("n_choices")
    if n_choice is not None:
        df = df[df["choices"].apply(len) == n_choice].reset_index(drop=True)
    df = df.reset_index(drop=True)
    df["idx"] = np.arange(len(df), dtype=np.int64)
    return df[["idx", "hf_split", "subject", "question", "choices", "label"]]


def partition(df: pd.DataFrame, fractions, seed: int) -> pd.Series:
    """Stratified (by subject) random assignment to train/validation/test."""
    rng = np.random.RandomState(seed)
    names = ["train", "validation", "test"]
    f = np.cumsum(fractions)
    assert abs(f[-1] - 1.0) < 1e-9, fractions
    out = pd.Series(index=df.index, dtype=object)
    for _, g in df.groupby("subject"):
        order = g.index.values[rng.permutation(len(g))]
        cut = (f * len(order)).round().astype(int)
        out[order[:cut[0]]] = names[0]
        out[order[cut[0]:cut[1]]] = names[1]
        out[order[cut[1]:]] = names[2]
    return out


# ------------------------------------------------------------------ prompt --
def build_prompt(tok, row, template: str, system: str | None) -> str:
    opts = "\n".join(f"{LETTERS[i]}. {c}" for i, c in enumerate(row["choices"]))
    subject = str(row["subject"]).replace("_", " ")
    user = template.format(subject=subject, question=row["question"].strip(), options=opts)
    msgs = ([{"role": "system", "content": system}] if system else []) + \
           [{"role": "user", "content": user}]
    return tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)


def letter_token_ids(tok, n: int) -> list[list[int]]:
    """For each letter, the single-token ids of its bare and space-prefixed forms."""
    ids = []
    for L in LETTERS[:n]:
        cands = set()
        for s in (L, " " + L):
            t = tok.encode(s, add_special_tokens=False)
            if len(t) == 1:
                cands.add(t[0])
        assert cands, f"letter {L!r} is not a single token"
        ids.append(sorted(cands))
    return ids


# --------------------------------------------------------------------- run --
@torch.no_grad()
def run_split(model, tok, df, letter_ids, bs, max_length, layers_keep):
    """Returns (pred_df, states (N, L+1, D) float16) in df row order."""
    n_cls = len(letter_ids)
    N = len(df)
    prompts = df["prompt"].tolist()
    lengths = np.array([len(tok.encode(p, add_special_tokens=False)) for p in prompts])
    order = np.argsort(-lengths)                # longest first: OOM shows up at once
    states = None
    logits_all = np.zeros((N, n_cls), np.float32)
    seq_len = np.zeros(N, np.int64)
    t0 = time.time()
    for b in range(0, N, bs):
        rows = order[b:b + bs]
        enc = tok([prompts[i] for i in rows], return_tensors="pt", padding=True,
                  truncation=True, max_length=max_length)
        enc = {k: v.to(model.device) for k, v in enc.items()}
        out = model(**enc, output_hidden_states=True)
        hs = torch.stack(out.hidden_states, dim=1)[:, :, -1, :]      # (B, L+1, D), left-padded
        if states is None:
            states = np.zeros((N, hs.shape[1], hs.shape[2]), np.float16)
        states[rows] = hs.to(torch.float16).cpu().numpy()
        last = out.logits[:, -1, :].float()                          # (B, V)
        lt = torch.stack([last[:, ids].max(1).values for ids in letter_ids], 1)
        logits_all[rows] = lt.cpu().numpy()
        seq_len[rows] = enc["attention_mask"].sum(1).cpu().numpy()
        if (b // bs) % 50 == 0:
            print(f"    {b + len(rows):6d}/{N}  maxlen={int(lengths[rows].max())}  "
                  f"{time.time() - t0:.0f}s", flush=True)
    probs = np.exp(logits_all - logits_all.max(1, keepdims=True))
    probs /= probs.sum(1, keepdims=True)
    pred = probs.argmax(1)
    ent = -(probs * np.log(np.clip(probs, 1e-12, None))).sum(1)
    label = df["label"].values.astype(np.int64)
    # idx is the row position within the split: every downstream reader
    # indexes the split's prediction frame positionally with it. The pooled
    # question id is kept alongside as qid.
    pdf = pd.DataFrame({"idx": np.arange(N, dtype=np.int64),
                        "qid": df["idx"].values.astype(np.int64),
                        "split": df["split"].values,
                        "label": label, "pred": pred.astype(np.int64),
                        "correct": (pred == label).astype(np.int64),
                        "entropy": ent.astype(np.float32),
                        "seq_len": seq_len})
    for c in range(n_cls):
        pdf[f"logit{c}"] = logits_all[:, c]
        pdf[f"prob{c}"] = probs[:, c].astype(np.float32)
    pdf["subject"] = df["subject"].values
    return pdf, states


def main():
    ap = argparse.ArgumentParser(description=__doc__.split("\n")[0])
    ap.add_argument("--config", required=True)
    ap.add_argument("--force", action="store_true")
    ap.add_argument("--limit", type=int, default=None, help="debug: rows per split")
    a = ap.parse_args()
    cfg = load_config(a.config)
    name = cfg["run_name"]
    feat_dir = REPO / "data" / "features" / name
    st_dir = REPO / "data" / "layer_states" / name
    out_dir = REPO / cfg["paths"]["output_dir"]
    for d in (feat_dir, st_dir, out_dir):
        d.mkdir(parents=True, exist_ok=True)
    if (st_dir / "states_test.npy").exists() and not a.force:
        print(f"[{name}] already extracted; use --force to redo"); return
    seed_everything(int(cfg.get("seed", 0)))

    dcfg = cfg["data"]
    df = load_questions(dcfg)
    df["split"] = partition(df, dcfg.get("fractions", [0.5, 0.2, 0.3]),
                            int(dcfg.get("split_seed", 0)))
    print(f"[{name}] {len(df)} questions, C={df['choices'].apply(len).max()}, "
          f"splits={df['split'].value_counts().to_dict()}")

    mcfg = cfg["model"]
    from transformers import AutoModelForCausalLM, AutoTokenizer
    tok = AutoTokenizer.from_pretrained(mcfg["pretrained_path"])
    tok.padding_side = "left"
    tok.truncation_side = "left"
    if tok.pad_token is None:
        tok.pad_token = tok.eos_token
    dtype = {"bfloat16": torch.bfloat16, "float16": torch.float16,
             "float32": torch.float32}[mcfg.get("dtype", "bfloat16")]
    model = AutoModelForCausalLM.from_pretrained(mcfg["pretrained_path"], torch_dtype=dtype)
    model = model.to(cfg["inference"].get("device", "cuda")).eval()
    n_cls = int(df["choices"].apply(len).max())
    letter_ids = letter_token_ids(tok, n_cls)
    print(f"  letter token ids: {letter_ids}")
    template = mcfg.get("prompt", "The following is a multiple choice question about "
                        "{subject}.\n\n{question}\n{options}\n\nAnswer with the letter only.")
    system = mcfg.get("system", None)
    df["prompt"] = [build_prompt(tok, r, template, system) for _, r in df.iterrows()]
    print("  example prompt:\n" + df["prompt"].iloc[0])

    for split in ("train", "validation", "test"):
        sub = df[df["split"] == split].reset_index(drop=True)
        if a.limit:
            sub = sub.iloc[:a.limit]
        t0 = time.time()
        pdf, states = run_split(model, tok, sub, letter_ids,
                                int(cfg["inference"].get("batch_size", 8)),
                                int(mcfg.get("max_length", 2048)), None)
        acc = pdf["correct"].mean()
        print(f"  [{split}] n={len(pdf)}  acc={acc:.4f}  states={states.shape}  "
              f"{time.time() - t0:.0f}s", flush=True)
        pdf.to_parquet(feat_dir / f"predictions_{split}.parquet", index=False)
        shutil.copy(feat_dir / f"predictions_{split}.parquet",
                    out_dir / f"predictions_{split}.parquet")
        np.save(st_dir / f"states_{split}.npy", states)
        L1, D = states.shape[1], states.shape[2]
        np.savez(st_dir / f"meta_{split}.npz", idx=pdf["idx"].values,
                 label=pdf["label"].values, n_layers=np.int64(L1), dim=np.int64(D))
        for tag, l in (("cls_begin", 0), ("cls_mid", (L1 - 1) // 2), ("cls_last", L1 - 1)):
            np.savez_compressed(
                feat_dir / f"{tag}_{split}.npz",
                features=states[:, l, :].astype(np.float32),
                columns=np.array([f"{tag}_{d}" for d in range(D)], dtype=object),
                idx=pdf["idx"].values)
    print(f"[{name}] DONE")


if __name__ == "__main__":
    main()