#!/usr/bin/env python3
"""
ProgressLM demo-robustness — full batch (ORIGINAL ProgressLM-3B-RL).

Mirrors the finished Robometer prefix-robustness experiment, but for the
DEMO-based ProgressLM. ProgressLM has NO history prefix: it builds a labelled
visual demonstration (N reference frames tagged 0% .. 100%) and scores ONE
current frame (stage_to_estimate) against that demo. So the perturbation is the
DEMO ORGANISATION (how the reference frames are sampled/arranged), while the
target/current frame is held fixed.

For every episode and each of 5 demo-organisation modes, we score the 4
checkpoint frames (pool 1/4, 2/4, 3/4, end) against a self-demo built from the
SAME episode's pool. Result: 5 scores per checkpoint per episode.

  demo5_uniform (baseline) : 5 frames, anchors 0/25/50/75/100%   (total_steps=4)
  demo3_sparse             : 3 frames, anchors 0/50/100%          (total_steps=2)
  demo9_dense              : 9 frames, anchors every 12.5%        (total_steps=8)
  demo5_jitterA            : 5 frames, middle anchors jittered +/-5% (seed=0),
                             labels RECOMPUTED from the true jittered position
  demo5_jitterB            : same, seed=1

RED LINE (v4): demo labels stay honest — a demo frame's % label is its true
temporal position in the pool. Uniform/sparse/dense anchors sit at exact
uniform fractions, so ProgressLM's own uniform labels are honest. Jitter anchors
move, so their labels are recomputed to the true position (rounded to integer %).

Model call is REUSED from the parity-verified RMBench wrapper
(progresslm_src: core.model.Qwen2VLChat + prompts.visual_demo_prompt), pointed at
the original ProgressLM-3B-RL. We do not rewrite the model call; for the jitter
modes we only substitute an honest progress-shift label string into the exact
same prompt structure (the stock builder can only emit uniform labels).

Output (resume-safe: an existing <mode>.json is skipped):
  <out-dir>/episode_results/<chunk>_<episode>/<mode>.json
  <out-dir>/progresslm_refs/<chunk>_<episode>/f####.png   (demo + target frames)

Env (A6000 box): conda easyr1 (torch + transformers 4.57 + qwen-vl-utils + av + flash_attn)
  /home/vcj9002/miniconda3/envs/easyr1/bin/python run_batch.py --gpu 8
"""
from __future__ import annotations

import argparse
import importlib.util as ilu
import json
import os
import re
import sys
import time
import traceback
import zlib
from pathlib import Path


def parse_args():
    here = Path(__file__).resolve()
    p = argparse.ArgumentParser(description="ProgressLM demo-robustness batch")
    p.add_argument("--videos-root",
                   default="/home/vcj9002/jianshu/workspace/code_keliang/Videos",
                   help="Dir containing chunk-*_filtered/ with episode_tasks.json")
    p.add_argument("--progresslm-code",
                   default="/home/vcj9002/jianshu/workspace/code_keliang/autodl_upload/test/"
                           "RMBench/Vanilla_Baseline/ProgressLM/progresslm/progresslm_src",
                   help="Parity-verified ProgressLM runtime (core/ prompts/ datasets/)")
    p.add_argument("--model-path",
                   default="/home/vcj9002/jianshu/workspace/code_keliang/Current_Baseline/"
                           "ProgressLM/models/ProgressLM-3B-RL-qwen25vl",
                   help="ORIGINAL ProgressLM-3B-RL (7.6G). Points at a symlink whose "
                        "name carries 'qwen25' so the wrapper's own model-class fallback "
                        "(listinstr) selects Qwen2_5_VL under transformers>=4.57, which "
                        "reports model_type='qwen2_5_vl_text'. Same checkpoint, same "
                        "inference path — no wrapper edits.")
    p.add_argument("--out-dir", default=None,
                   help="Default: <this file>/../results_full")
    p.add_argument("--camera", default="wrist_image_left",
                   help="wrist_image_left = same as the Robometer experiment")
    p.add_argument("--fps", type=float, default=3.0,
                   help="Temporal downsample fps; 0 = keep native fps")
    p.add_argument("--max-frames", type=int, default=128,
                   help="Cap on pool size; 0 = no cap")
    p.add_argument("--gpu", default=None,
                   help="GPU id; default: auto-pick card with least used memory")
    p.add_argument("--limit", type=int, default=None,
                   help="Only process first N remaining episodes (smoke test)")
    return p.parse_args()


ARGS = parse_args()

# ── GPU choice must happen before torch import ─────────────────────────────
if "CUDA_VISIBLE_DEVICES" not in os.environ:
    if ARGS.gpu is not None:
        os.environ["CUDA_VISIBLE_DEVICES"] = str(ARGS.gpu)
    else:
        import subprocess
        try:
            out = subprocess.check_output(
                ["nvidia-smi", "--query-gpu=index,memory.used",
                 "--format=csv,noheader,nounits"], text=True)
            idx = min((l.split(",") for l in out.strip().splitlines()),
                      key=lambda x: int(x[1]))[0].strip()
        except Exception:
            idx = "0"
        os.environ["CUDA_VISIBLE_DEVICES"] = idx

import numpy as np  # noqa: E402
import av  # noqa: E402
from PIL import Image  # noqa: E402

# ── ProgressLM parity wrapper: REUSE model call + prompt builder ───────────
_CODE = str(Path(ARGS.progresslm_code).resolve())
sys.path.insert(0, _CODE)
from core.model import Qwen2VLChat  # noqa: E402  (healthy package)
# prompts/__init__ imports deleted modules and crashes -> load the file directly
_spec = ilu.spec_from_file_location("plm_vdp", os.path.join(_CODE, "prompts/visual_demo_prompt.py"))
_vdp = ilu.module_from_spec(_spec)
sys.modules["plm_vdp"] = _vdp
_spec.loader.exec_module(_vdp)
build_visual_demo_prompt_from_item = _vdp.build_visual_demo_prompt_from_item
VISUAL_DEMO_SYSTEM_PROMPT = _vdp.VISUAL_DEMO_SYSTEM_PROMPT

MODEL_PATH = ARGS.model_path
VIDEOS_ROOT = Path(ARGS.videos_root)
CAMERA_DIR = f"observation.images.{ARGS.camera}"

OUT_DIR = (Path(ARGS.out_dir) if ARGS.out_dir
           else Path(__file__).resolve().parent.parent / "results_full")
EP_DIR = OUT_DIR / "episode_results"
REFS_DIR = OUT_DIR / "progresslm_refs"
EP_DIR.mkdir(parents=True, exist_ok=True)
ERR_PATH = OUT_DIR / "errors.log"

MODES = ["demo5_uniform", "demo3_sparse", "demo9_dense", "demo5_jitterA", "demo5_jitterB"]
FRACS = ["1/4", "2/4", "3/4", "end"]


# ── pool sampling: COPIED VERBATIM from Robometer so pools are byte-identical ─
def load_all_video_frames(video_path: Path):
    all_frames = []
    with av.open(str(video_path)) as container:
        stream = container.streams.video[0]
        rate = stream.average_rate or stream.guessed_rate
        native_fps = float(rate) if rate is not None else 1.0
        for frame in container.decode(stream):
            all_frames.append(frame.to_ndarray(format="rgb24"))
    if not all_frames:
        raise RuntimeError(f"Could not extract frames from video: {video_path}")
    return all_frames, native_fps


def sample_video_frames_with_indices(all_frames, *, native_fps, fps, max_frames):
    """required_frames=[] branch of Robometer's sampler (identical pools)."""
    total_frames = len(all_frames)
    if fps <= 0:
        fps = native_fps
    if native_fps > 0:
        desired_frames = int(round(total_frames * (fps / native_fps)))
    else:
        desired_frames = total_frames
    desired_frames = max(1, min(desired_frames, total_frames, max_frames))
    if desired_frames == total_frames:
        sampled_indices = list(range(total_frames))
    else:
        base_indices = np.linspace(0, total_frames - 1, desired_frames, dtype=int).tolist()
        sampled_indices = sorted(set(base_indices))
        if len(sampled_indices) < desired_frames:
            for idx in base_indices:
                if idx not in sampled_indices:
                    sampled_indices.append(idx)
                    if len(sampled_indices) == desired_frames:
                        break
        if len(sampled_indices) < desired_frames:
            for idx in range(total_frames):
                if idx not in sampled_indices:
                    sampled_indices.append(idx)
                    if len(sampled_indices) == desired_frames:
                        break
        sampled_indices = sorted(sampled_indices[:desired_frames])
    sampled_frames = np.stack([all_frames[idx] for idx in sampled_indices], axis=0)
    return sampled_frames, sampled_indices


def load_pool(video_path: Path):
    """Returns (pool[N,H,W,3], total_raw_frames, native_fps). Same as Robometer."""
    all_frames, native_fps = load_all_video_frames(video_path)
    if ARGS.fps <= 0 and ARGS.max_frames <= 0:
        pool = np.stack(all_frames, axis=0)
        return pool, len(all_frames), float(native_fps)
    fps = ARGS.fps if ARGS.fps > 0 else 10_000.0
    max_frames = ARGS.max_frames if ARGS.max_frames > 0 else 10 ** 9
    pool, _idx = sample_video_frames_with_indices(
        all_frames, native_fps=native_fps, fps=fps, max_frames=max_frames)
    return pool, len(all_frames), float(native_fps)


# ── checkpoints (target frames) — identical to Robometer's render ──────────
def checkpoints_of(pool_n: int) -> list[int]:
    return [int((pool_n - 1) * k / 4) for k in (1, 2, 3, 4)]


# ── demo organisation ──────────────────────────────────────────────────────
def _ep_seed(ep_key: str, mode_seed: int) -> int:
    """Deterministic per (episode, mode) seed. Mirrors Robometer's
    `seed * 1_000_003 + <varying-unit>`; here the varying unit is the episode
    (each episode gets one demo per mode). Reproducible + resume-safe."""
    return mode_seed * 1_000_003 + int(zlib.crc32(ep_key.encode()))


def build_demo(mode: str, n: int, ep_key: str):
    """Return (demo_idx, labels, total_steps_or_None).

    total_steps is set for the uniform-spacing modes (uniform/sparse/dense): the
    stock ProgressLM builder emits its own uniform labels from total_steps, which
    are honest because the anchors sit at exact uniform fractions. For jitter,
    total_steps is None and `labels` are the honest recomputed integer percents.
    """
    if mode == "demo5_uniform":
        nd = 5
    elif mode == "demo3_sparse":
        nd = 3
    elif mode == "demo9_dense":
        nd = 9
    elif mode in ("demo5_jitterA", "demo5_jitterB"):
        seed = 0 if mode.endswith("A") else 1
        rng = np.random.default_rng(_ep_seed(ep_key, seed))
        base = [0.0, 0.25, 0.5, 0.75, 1.0]
        fracs = [base[0]]
        for f in base[1:-1]:                       # jitter middle anchors only
            fracs.append(min(1.0, max(0.0, f + float(rng.uniform(-0.05, 0.05)))))
        fracs.append(base[-1])                     # endpoints fixed at 0 / 100%
        demo_idx = [max(0, min(n - 1, int(round(f * (n - 1))))) for f in fracs]
        labels = [round(i / max(n - 1, 1) * 100) for i in demo_idx]  # honest
        return demo_idx, labels, None
    else:
        raise ValueError(f"unknown mode {mode}")
    # uniform-spacing modes: int(linspace) demo sampling == RMBench bundle parity
    demo_idx = [int(x) for x in np.linspace(0, n - 1, nd)]
    total_steps = nd - 1
    labels = [round(i / total_steps * 100) for i in range(nd)]
    return demo_idx, labels, total_steps


def build_prompt_custom_labels(task_goal, demo_paths, labels, target_path):
    """Replicate build_visual_demo_prompt EXACTLY, but with an explicit honest
    progress-shift label string (used only by the jitter modes)."""
    shifts = " ".join(f"<image> {lab}%" for lab in labels)
    msgs = [
        {"type": "text", "value": VISUAL_DEMO_SYSTEM_PROMPT},
        {"type": "text", "value": f"The overall task goal is {task_goal}"},
        {"type": "text", "value": _vdp.VISUAL_DEMO_INSTRUCTION_PART1},
    ]
    for dp in demo_paths:
        msgs.append({"type": "image", "value": dp})
    msgs.append({"type": "text",
                 "value": f"The progress shifts across all given visual demos is: {shifts}"})
    msgs.append({"type": "text", "value": _vdp.VISUAL_DEMO_INSTRUCTION_PART2})
    msgs.append({"type": "image", "value": target_path})
    msgs.append({"type": "text", "value": _vdp.VISUAL_DEMO_INSTRUCTION_PART3})
    return msgs


def parse_visual_demo_response(response: str):
    """Extract <score> as 0..1 (or 'n/a'/None). Mirrors ProgressLM's parser."""
    if not response:
        return {"score": None}
    m = re.search(r"<score>(.*?)</score>", response, re.DOTALL)
    if not m:
        return {"score": None}
    s = m.group(1).strip()
    if s.lower() in ("n/a", "na"):
        return {"score": "n/a"}
    try:
        v = float(s[:-1]) / 100.0 if s.endswith("%") else float(s)
        if v > 1.0:
            v = v / 100.0
        return {"score": max(0.0, min(1.0, v))}
    except ValueError:
        return {"score": None}


def save_frame(pool, idx: int, refs_dir: Path) -> str:
    refs_dir.mkdir(parents=True, exist_ok=True)
    p = refs_dir / f"f{int(idx):04d}.png"
    if not p.exists():
        Image.fromarray(pool[int(idx)]).save(p)
    return str(p)


# ── episode enumeration (identical to Robometer) ───────────────────────────
def list_episodes():
    eps = []
    for tasks_file in sorted(VIDEOS_ROOT.glob("chunk-*_filtered/episode_tasks.json")):
        meta = json.load(open(tasks_file))
        for e in meta["episodes"]:
            video = tasks_file.parent / CAMERA_DIR / e["episode"]
            if video.exists():
                eps.append({
                    "chunk": meta["chunk"],
                    "episode": e["episode"],
                    "task": " and ".join(e["tasks"]),
                    "video": video,
                })
    return eps


def episode_key(ep) -> str:
    return f"{ep['chunk']}_{ep['episode'].replace('.mp4', '')}"


def episode_dir(ep) -> Path:
    return EP_DIR / episode_key(ep)


def main():
    episodes = list_episodes()
    todo = [e for e in episodes
            if not all((episode_dir(e) / f"{m}.json").exists() for m in MODES)]
    if ARGS.limit:
        todo = todo[:ARGS.limit]
    print(f"GPU  : CUDA_VISIBLE_DEVICES={os.environ.get('CUDA_VISIBLE_DEVICES')}")
    print(f"Model: {MODEL_PATH}")
    print(f"Out  : {EP_DIR}")
    print(f"Sampling: fps={ARGS.fps or 'native'} max_frames={ARGS.max_frames or 'unlimited'} "
          f"camera={ARGS.camera}")
    print(f"Modes: {MODES}")
    print(f"Episodes: total={len(episodes)} todo={len(todo)}")
    if not todo:
        print("Nothing to do.")
        return

    model = Qwen2VLChat(model_path=str(MODEL_PATH), system_prompt=VISUAL_DEMO_SYSTEM_PROMPT)

    for i, ep in enumerate(todo, 1):
        ep_key = episode_key(ep)
        ep_out = episode_dir(ep)
        ep_out.mkdir(parents=True, exist_ok=True)
        refs_dir = REFS_DIR / ep_key
        print(f"[{i}/{len(todo)}] {ep['chunk']}/{ep['episode']}", flush=True)
        try:
            pool, total_raw, native_fps = load_pool(ep["video"])
            n = len(pool)
            target_idx = checkpoints_of(n)
            print(f"  pool={n} frames (raw={total_raw}, fps={native_fps:.2f}) "
                  f"targets={target_idx}", flush=True)
            # cache the 4 target frames once (shared across modes)
            target_paths = [save_frame(pool, t, refs_dir) for t in target_idx]

            for mode in MODES:
                mode_path = ep_out / f"{mode}.json"
                if mode_path.exists():
                    continue
                t0 = time.time()
                demo_idx, labels, total_steps = build_demo(mode, n, ep_key)
                demo_paths = [save_frame(pool, di, refs_dir) for di in demo_idx]

                scores_100, scores_raw, raw_responses = [], [], []
                for tp in target_paths:
                    if total_steps is not None:            # uniform/sparse/dense
                        item = {"task_goal": ep["task"], "visual_demo": demo_paths,
                                "total_steps": total_steps, "stage_to_estimate": tp}
                        msg = build_visual_demo_prompt_from_item(item)
                    else:                                  # jitter: honest labels
                        msg = build_prompt_custom_labels(ep["task"], demo_paths, labels, tp)
                    resp = model.generate(msg)
                    s = parse_visual_demo_response(resp).get("score")
                    raw_responses.append(resp)
                    if s in (None, "n/a"):
                        scores_raw.append(None)
                        scores_100.append(None)
                    else:
                        scores_raw.append(round(float(s), 6))
                        scores_100.append(round(float(s) * 100.0, 4))

                payload = {
                    "chunk": ep["chunk"], "episode": ep["episode"],
                    "task": ep["task"], "camera": ARGS.camera,
                    "native_fps": round(native_fps, 3),
                    "total_raw_frames": total_raw, "pool_n": n,
                    "fps_arg": ARGS.fps, "max_frames_arg": ARGS.max_frames,
                    "mode": mode,
                    "n_demo": len(demo_idx),
                    "total_steps": (total_steps if total_steps is not None
                                    else len(demo_idx) - 1),
                    "demo_frame_indices": demo_idx,
                    "demo_labels": labels,
                    "checkpoint_fracs": FRACS,
                    "target_frame_indices": target_idx,
                    "scores_raw": scores_raw,
                    "scores_100": scores_100,
                    "raw_responses": raw_responses,
                }
                tmp = mode_path.with_suffix(".json.tmp")
                tmp.write_text(json.dumps(payload))
                tmp.rename(mode_path)     # atomic: resume never sees half a file
                sv = ["n/a" if s is None else f"{s:.0f}" for s in scores_100]
                print(f"  {mode}: scores={sv} in {time.time()-t0:.1f}s", flush=True)
        except Exception:
            with open(ERR_PATH, "a") as ef:
                ef.write(f"=== {ep['chunk']}/{ep['episode']} ===\n")
                ef.write(traceback.format_exc() + "\n")
            print(f"  ERROR (logged to {ERR_PATH.name}), continuing", flush=True)

    print("Done:", EP_DIR)


if __name__ == "__main__":
    main()