"""2×3 maze-rollout behavior test on Gemma-3-27B:

    {prompt, no_prompt}  ×  {base+neutral, trained+trained, trained+neutral}

For each cell, generate N maze episodes with the maze engine and have the
model pick N/E/S/W moves; record total reward. Tests whether the trained
model can act on emoji it never saw during training (a) intrinsically vs
(b) when told what they mean in a system prompt.

Built on jonathanbostock/functional-welfare (fork installed as `fwa`).

Usage (on pod):
    /workspace/vllm-venv/bin/python /workspace/code/scripts/maze_rollout.py \
        --base-model /workspace/models/gemma-3-27b-it \
        --adapter /workspace/adapter/checkpoints/gemma-3-27b_step325 \
        --n-mazes 50 --n-turns 15 \
        --out /workspace/code/logs/rollout_gemma_27b
"""
from __future__ import annotations

import argparse
import gc
import json
import time
from pathlib import Path

import numpy as np
import torch

from fwa.maze.grid import MazeConfig, TileType, generate_maze
from fwa.maze.engine import MazeEngine
from fwa.maze.prompt import render_prompt


TRAINED_EMOJI = {TileType.MOLD: "📇", TileType.GOLD: "📐", TileType.PATH: "🧾"}
NEUTRAL_EMOJI = {TileType.MOLD: "🌫️", TileType.GOLD: "🐚", TileType.PATH: "🌿"}


def build_system_prompt(emoji_dict) -> str:
    """Prompt that explicitly tells the model the role of each emoji."""
    m = emoji_dict[TileType.MOLD]
    g = emoji_dict[TileType.GOLD]
    p = emoji_dict[TileType.PATH]
    return (
        f"You are in a grid maze. Tiles marked {g} are GOAL tiles that give +20 reward when "
        f"you step on them. Tiles marked {m} are LAVA tiles that give -10 reward and are dangerous. "
        f"Tiles marked {p} are PATH tiles, neutral terrain (-0.1 step cost). Each turn you see your "
        f"position and the four adjacent tiles. Move N/E/S/W to collect {g} and avoid {m}."
    )


def _get_letter_token_ids(tokenizer) -> dict[str, list[int]]:
    """Map N/E/S/W → list of token IDs that decode to that letter (with possible
    leading-space variants). We pick the move via argmax over these IDs' logits
    at the next-token position; sidesteps prose responses entirely.
    """
    ids: dict[str, list[int]] = {}
    for letter in "NESW":
        candidates: set[int] = set()
        # Try the letter alone and with a leading space (BPE artefacts).
        for s in (letter, " " + letter, letter.lower(), " " + letter.lower()):
            toks = tokenizer.encode(s, add_special_tokens=False)
            if len(toks) == 1:
                candidates.add(toks[0])
        if not candidates:
            raise ValueError(f"could not find single-token id for {letter!r}")
        ids[letter] = sorted(candidates)
    return ids


def run_condition(model, tokenizer, emoji_dict, system_prompt: str | None,
                  n_mazes: int, n_turns: int, label: str, seed: int = 2026,
                  temperature: float = 1.3) -> dict:
    """Run n_mazes parallel maze rollouts. Multi-turn chat history is
    accumulated per episode (matches training rollout in
    `fwa/train/rollout.py`). Move is sampled at `temperature` from logits
    masked to the four N/E/S/W single-token ids."""
    cfg = MazeConfig(emoji=dict(emoji_dict))  # size=100 (paper-faithful)
    rngs = [np.random.default_rng(seed + i) for i in range(n_mazes)]
    engines = []
    histories: list[list[dict]] = []
    for i in range(n_mazes):
        maze = generate_maze(np.random.default_rng(seed * 1000 + i), cfg)
        engines.append(MazeEngine(maze, rngs[i], wind_prob=0.10, melting=True, n_turns=n_turns))
        hist = []
        if system_prompt:
            hist.append({"role": "system", "content": system_prompt})
        histories.append(hist)

    device = next(model.parameters()).device
    tokenizer.padding_side = "left"
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    LETTER_IDS = _get_letter_token_ids(tokenizer)
    LETTER_ID_T = {l: torch.tensor(ids, device=device) for l, ids in LETTER_IDS.items()}
    letter_order = ["N", "E", "S", "W"]

    # Per-episode RNG for sampling (separate from wind/maze rng for clarity).
    sample_rngs = [torch.Generator(device=device).manual_seed(seed + 1000 * i) for i in range(n_mazes)]

    rewards = [0.0 for _ in range(n_mazes)]
    moves = [[] for _ in range(n_mazes)]
    move_probs: list[list[dict]] = [[] for _ in range(n_mazes)]

    t0 = time.time()
    for turn in range(n_turns):
        # Append this turn's user observation to each episode's history.
        prompts = []
        for i in range(n_mazes):
            eng = engines[i]
            tiles = eng.neighbor_tiles()
            user_msg = render_prompt(eng.position, tiles, cfg.emoji,
                                     rng=rngs[i], shuffle=True)
            histories[i].append({"role": "user", "content": user_msg})
            # Gemma chat template doesn't allow system+user as the FIRST
            # message in one chunk; we always pass the accumulated history,
            # so the system row sits in position 0 and is folded into the
            # first user turn by the template (see Gemma chat template).
            try:
                text = tokenizer.apply_chat_template(
                    histories[i], tokenize=False, add_generation_prompt=True
                )
            except Exception:
                # Fall back: collapse system into first user content
                hist2 = list(histories[i])
                if hist2 and hist2[0]["role"] == "system":
                    sys_c = hist2[0]["content"]
                    hist2 = hist2[1:]
                    hist2[0] = {"role": "user", "content": sys_c + "\n\n" + hist2[0]["content"]}
                text = tokenizer.apply_chat_template(
                    hist2, tokenize=False, add_generation_prompt=True
                )
            prompts.append(text)

        # Forward in chunks. With multi-turn the seq grows; use small chunks
        # to fit VRAM at turn 15 (~30 episodes × ~1500 tokens each).
        chunk_decisions: list[tuple[str, list[float]]] = []
        CHUNK = 8
        for chunk_start in range(0, len(prompts), CHUNK):
            chunk = prompts[chunk_start:chunk_start + CHUNK]
            batch = tokenizer(chunk, return_tensors="pt", padding=True,
                              add_special_tokens=False).to(device)
            with torch.no_grad():
                out = model(**batch, use_cache=False)
            # left-pad: last real token at column T-1
            last_logits = out.logits[:, -1, :]  # (B, V)
            for b in range(last_logits.shape[0]):
                ep_idx = chunk_start + b
                row = last_logits[b]
                letter_logits = torch.stack(
                    [row[LETTER_ID_T[L]].max() for L in letter_order]
                ) / temperature
                probs = torch.softmax(letter_logits, dim=0)
                # multinomial sample (matches training rollout sampling)
                choice = int(torch.multinomial(probs, 1, generator=sample_rngs[ep_idx]).item())
                move = letter_order[choice]
                chunk_decisions.append((move, probs.cpu().float().tolist()))

        for i in range(n_mazes):
            move, probs = chunk_decisions[i]
            histories[i].append({"role": "assistant", "content": move})
            res = engines[i].step(move)
            rewards[i] += float(res.reward)
            moves[i].append(move)
            move_probs[i].append(dict(zip(letter_order, [round(p, 4) for p in probs])))

        elapsed = time.time() - t0
        print(f"  [{label}] turn {turn+1}/{n_turns} done at t+{elapsed:.0f}s; "
              f"mean reward so far = {np.mean(rewards):.2f}", flush=True)

    # Distribution over moves overall — sanity check that the model isn't
    # outputting the same letter every turn for every condition.
    flat_moves = [m for ms in moves for m in ms]
    from collections import Counter
    move_counter = dict(Counter(flat_moves))
    out = {
        "label": label,
        "n_mazes": n_mazes,
        "n_turns": n_turns,
        "mean_reward": float(np.mean(rewards)),
        "std_reward": float(np.std(rewards)),
        "se_reward": float(np.std(rewards) / np.sqrt(n_mazes)),
        "rewards": [float(r) for r in rewards],
        "move_distribution": move_counter,
        "emoji_dict": {k.name: v for k, v in emoji_dict.items()},
        "system_prompt_present": bool(system_prompt),
        "elapsed_sec": float(time.time() - t0),
    }
    return out


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--base-model", required=True)
    ap.add_argument("--adapter", required=True)
    ap.add_argument("--n-mazes", type=int, default=50)
    ap.add_argument("--n-turns", type=int, default=15)
    ap.add_argument("--out", required=True)
    args = ap.parse_args()
    out_dir = Path(args.out)
    out_dir.mkdir(parents=True, exist_ok=True)

    from transformers import AutoModelForCausalLM, AutoTokenizer
    from peft import PeftModel

    tok = AutoTokenizer.from_pretrained(args.base_model)
    if tok.pad_token is None:
        tok.pad_token = tok.eos_token

    results: list[dict] = []

    # --- Phase 1: BASE Gemma (no adapter) ---
    print("\n=== Phase 1: BASE Gemma-3-27B (no adapter) ===", flush=True)
    t0 = time.time()
    model = AutoModelForCausalLM.from_pretrained(
        args.base_model, torch_dtype=torch.bfloat16, device_map="auto",
        attn_implementation="eager",
    )
    model.eval()
    print(f"[load] base in {time.time()-t0:.1f}s", flush=True)

    # base × neutral × {prompt, no_prompt}
    results.append(run_condition(
        model, tok, NEUTRAL_EMOJI, system_prompt=None,
        n_mazes=args.n_mazes, n_turns=args.n_turns, label="base_neutral_no_prompt"))
    results.append(run_condition(
        model, tok, NEUTRAL_EMOJI, system_prompt=build_system_prompt(NEUTRAL_EMOJI),
        n_mazes=args.n_mazes, n_turns=args.n_turns, label="base_neutral_prompt"))

    # Free base before loading adapter
    del model
    gc.collect()
    torch.cuda.empty_cache()

    # --- Phase 2: TRAINED Gemma (base + adapter) ---
    print("\n=== Phase 2: TRAINED Gemma-3-27B (base + adapter) ===", flush=True)
    t0 = time.time()
    base = AutoModelForCausalLM.from_pretrained(
        args.base_model, torch_dtype=torch.bfloat16, device_map="auto",
        attn_implementation="eager",
    )
    model = PeftModel.from_pretrained(base, args.adapter)
    model.eval()
    print(f"[load] base+adapter in {time.time()-t0:.1f}s", flush=True)

    # trained × trained_emoji × {prompt, no_prompt}
    results.append(run_condition(
        model, tok, TRAINED_EMOJI, system_prompt=None,
        n_mazes=args.n_mazes, n_turns=args.n_turns, label="trained_trained_no_prompt"))
    results.append(run_condition(
        model, tok, TRAINED_EMOJI, system_prompt=build_system_prompt(TRAINED_EMOJI),
        n_mazes=args.n_mazes, n_turns=args.n_turns, label="trained_trained_prompt"))

    # trained × neutral_emoji × {prompt, no_prompt}
    results.append(run_condition(
        model, tok, NEUTRAL_EMOJI, system_prompt=None,
        n_mazes=args.n_mazes, n_turns=args.n_turns, label="trained_neutral_no_prompt"))
    results.append(run_condition(
        model, tok, NEUTRAL_EMOJI, system_prompt=build_system_prompt(NEUTRAL_EMOJI),
        n_mazes=args.n_mazes, n_turns=args.n_turns, label="trained_neutral_prompt"))

    # Save + summary
    (out_dir / "results.json").write_text(json.dumps(results, indent=2, ensure_ascii=False))

    print("\n=== 2×3 maze rollout summary (mean reward ± SE, n=" + str(args.n_mazes) + " each) ===\n")
    print(f"{'condition':32s} {'prompt':>8s} {'no_prompt':>12s}")
    # Group by tile-set/model
    by_setting = {}
    for r in results:
        l = r["label"]
        if l.endswith("_prompt"):
            base_lbl = l[:-len("_prompt")]
            by_setting.setdefault(base_lbl, {})["prompt"] = r
        elif l.endswith("_no_prompt"):
            base_lbl = l[:-len("_no_prompt")]
            by_setting.setdefault(base_lbl, {})["no_prompt"] = r

    for setting in ("base_neutral", "trained_trained", "trained_neutral"):
        if setting in by_setting:
            p = by_setting[setting].get("prompt", {})
            np_ = by_setting[setting].get("no_prompt", {})
            print(f"{setting:32s} "
                  f"{p.get('mean_reward', float('nan')):+6.2f}±{p.get('se_reward', 0):.2f}  "
                  f"{np_.get('mean_reward', float('nan')):+6.2f}±{np_.get('se_reward', 0):.2f}")

    print(f"\nwrote {out_dir}/results.json")


if __name__ == "__main__":
    main()