"""
Helios Benchmark Inference Script
- Runs T2V inference for a single model version on a single GPU
- Uses the first N prompts from a txt file
- Saves videos in two layouts:  by_prompt/<slug>/<version>.mp4
                                 by_version/<version>/<slug>.mp4
- Records per-video timing to timing_<version>.txt and computes summary stats
"""

import importlib
import os
import re
import shutil
import sys
import time

os.environ["HF_ENABLE_PARALLEL_LOADING"] = "yes"
os.environ["HF_PARALLEL_LOADING_WORKERS"] = "8"

import argparse
import subprocess
from pathlib import Path


SCRIPT_DIR = Path(__file__).resolve().parent
DEFAULT_PROMPT_FILE = SCRIPT_DIR / "demo_data" / "MovieGenVideoBench_extended.txt"
DEFAULT_MODEL_ROOT = SCRIPT_DIR / "checkpoints"
DEFAULT_OUTPUT_ROOT = SCRIPT_DIR / "output_helios" / "bench"


def pick_gpu_by_free_vram(min_free_mib=20000):
    """Pick physical GPU index with the most free memory (via nvidia-smi). No torch import."""
    try:
        out = subprocess.check_output(
            [
                "nvidia-smi",
                "--query-gpu=index,memory.free",
                "--format=csv,noheader,nounits",
            ],
            text=True,
            stderr=subprocess.DEVNULL,
        )
    except (subprocess.CalledProcessError, FileNotFoundError) as e:
        raise RuntimeError("nvidia-smi failed; specify --gpu explicitly") from e

    best_idx, best_free = None, -1
    for line in out.strip().splitlines():
        parts = [p.strip() for p in line.split(",")]
        if len(parts) < 2:
            continue
        idx, free = int(parts[0]), int(parts[1])
        if free > best_free:
            best_free, best_idx = free, idx
    if best_idx is None:
        raise RuntimeError("Could not parse nvidia-smi GPU list")
    if best_free < min_free_mib:
        print(
            f"[warn] Best GPU {best_idx} has only {best_free} MiB free "
            f"(<{min_free_mib} MiB); OOM risk — consider --enable_low_vram_mode",
            file=sys.stderr,
        )
    return best_idx, best_free


def _apply_cuda_visible_devices_before_torch():
    """CUDA_VISIBLE_DEVICES must be set before `import torch` (first CUDA init)."""
    pre = argparse.ArgumentParser(add_help=False)
    pre.add_argument("--gpu", type=str, default="auto")
    known, _ = pre.parse_known_args()
    g = known.gpu.strip().lower()
    if g == "auto":
        idx, free = pick_gpu_by_free_vram()
        os.environ["CUDA_VISIBLE_DEVICES"] = str(idx)
        os.environ["_BENCH_PHYSICAL_GPU"] = f"{idx} ({free} MiB free)"
    else:
        os.environ["CUDA_VISIBLE_DEVICES"] = known.gpu.strip()
        os.environ["_BENCH_PHYSICAL_GPU"] = known.gpu.strip()
    os.environ["_BENCH_GPU_ARG"] = known.gpu.strip()


_apply_cuda_visible_devices_before_torch()

import torch
from tqdm import tqdm

if importlib.util.find_spec("torch_npu") is not None:
    import torch_npu  # noqa: F401

from helios.diffusers_version.pipeline_helios_diffusers import HeliosPipeline
from helios.diffusers_version.scheduling_helios_diffusers import HeliosScheduler
from helios.diffusers_version.transformer_helios_diffusers import HeliosTransformer3DModel
from helios.modules.helios_kernels import (
    replace_all_norms_with_flash_norms,
    replace_rmsnorm_with_fp32,
    replace_rope_with_flash_rope,
)
from diffusers.models import AutoencoderKLWan
from diffusers.utils import export_to_video

# ── per-version inference presets (matching official scripts) ─────────────────

MODEL_PRESETS = {
    "base": dict(
        model_dir="Helios-Base",
        num_frames=99,
        num_inference_steps=50,
        guidance_scale=5.0,
        is_enable_stage2=False,
        pyramid_num_inference_steps_list=[20, 20, 20],
        is_amplify_first_chunk=False,
        use_zero_init=False,
        zero_steps=1,
    ),
    "mid": dict(
        model_dir="Helios-Mid",
        num_frames=99,
        num_inference_steps=50,
        guidance_scale=5.0,
        is_enable_stage2=True,
        pyramid_num_inference_steps_list=[20, 20, 20],
        is_amplify_first_chunk=False,
        use_zero_init=True,
        zero_steps=1,
    ),
    "distilled": dict(
        model_dir="Helios-Distilled",
        num_frames=240,
        num_inference_steps=50,
        guidance_scale=1.0,
        is_enable_stage2=True,
        pyramid_num_inference_steps_list=[2, 2, 2],
        is_amplify_first_chunk=True,
        use_zero_init=False,
        zero_steps=1,
    ),
}

NEGATIVE_PROMPT = (
    "Bright tones, overexposed, static, blurred details, subtitles, style, "
    "works, paintings, images, static, overall gray, worst quality, low quality, "
    "JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, "
    "poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, "
    "still picture, messy background, three legs, many people in the background, "
    "walking backwards"
)


def sanitize_filename(text, max_len=80):
    """Turn a prompt into a filesystem-safe slug."""
    text = text.strip().lower()
    text = re.sub(r"[^a-z0-9]+", "_", text)
    text = text.strip("_")
    return text[:max_len]


def load_prompt_indices(path):
    indices = []
    with open(path, "r", encoding="utf-8") as f:
        for line_no, raw_line in enumerate(f, start=1):
            line = raw_line.strip()
            if not line or line.startswith("#"):
                continue
            try:
                idx = int(line)
            except ValueError as exc:
                raise ValueError(
                    f"Invalid prompt index at {path}:{line_no}: {line!r}"
                ) from exc
            if idx < 0:
                raise ValueError(f"Prompt index must be >= 0 at {path}:{line_no}")
            indices.append(idx)
    return indices


def load_prompts(path, prompt_start=0, prompt_end=None, prompt_indices=None):
    with open(path, "r", encoding="utf-8") as f:
        lines = [line.strip() for line in f if line.strip()]
    if prompt_indices is not None:
        selected = []
        total = len(lines)
        for idx in prompt_indices:
            if idx >= total:
                raise ValueError(
                    f"Prompt index {idx} is out of range; prompt file has {total} prompts"
                )
            selected.append((idx, lines[idx]))
        return selected

    if prompt_start < 0:
        raise ValueError("prompt_start must be >= 0")
    if prompt_end is not None and prompt_end < prompt_start:
        raise ValueError("prompt_end must be >= prompt_start")

    selected = lines[prompt_start:prompt_end]
    return [(prompt_start + offset, prompt) for offset, prompt in enumerate(selected)]


def build_expected_outputs(prompts, version, by_version_dir):
    version_dir = os.path.join(by_version_dir, version)
    expected = []
    for idx, prompt in prompts:
        slug = sanitize_filename(prompt)
        vid_name = f"{idx:04d}_{slug}"
        expected.append((idx, slug, os.path.join(version_dir, f"{vid_name}.mp4")))
    return version_dir, expected


def output_exists(path):
    return os.path.isfile(path) and os.path.getsize(path) > 0


def find_missing_outputs(expected_outputs):
    return [item for item in expected_outputs if not output_exists(item[2])]


def make_timing_line(version, idx, elapsed, slug):
    return (
        f"  {version:10s}  #{idx:04d}  {elapsed:8.2f}s  "
        f"({elapsed / 60:5.2f}min)  {slug[:50]}"
    )


def load_existing_timing_records(timing_file, version):
    if not os.path.exists(timing_file):
        return {}

    pattern = re.compile(
        rf"^\s*{re.escape(version)}\s+#(\d+)\s+([0-9.]+)s\s+\([^)]+\)\s+(.*)$"
    )
    records = {}
    with open(timing_file, "r", encoding="utf-8") as f:
        for raw_line in f:
            line = raw_line.rstrip("\n")
            match = pattern.match(line)
            if not match:
                continue
            idx = int(match.group(1))
            elapsed = float(match.group(2))
            slug = match.group(3)
            records[idx] = (elapsed, slug)
    return records


def build_pipeline(
    model_path,
    device,
    weight_dtype,
    enable_low_vram=False,
    group_offloading_type="leaf_level",
    num_blocks_per_group=4,
):
    transformer = HeliosTransformer3DModel.from_pretrained(
        model_path, subfolder="transformer", torch_dtype=weight_dtype,
    )
    transformer = replace_rmsnorm_with_fp32(transformer)
    transformer = replace_all_norms_with_flash_norms(transformer)
    replace_rope_with_flash_rope()

    cuda_major = torch.cuda.get_device_capability()[0]
    if cuda_major >= 9:
        try:
            transformer.set_attention_backend("_flash_3_hub")
        except Exception:
            transformer.set_attention_backend("flash_hub")
    else:
        transformer.set_attention_backend("flash_hub")

    vae = AutoencoderKLWan.from_pretrained(
        model_path, subfolder="vae", torch_dtype=torch.float32,
    )
    scheduler = HeliosScheduler.from_pretrained(model_path, subfolder="scheduler")

    pipe = HeliosPipeline.from_pretrained(
        model_path,
        transformer=transformer,
        vae=vae,
        scheduler=scheduler,
        torch_dtype=weight_dtype,
    )
    if enable_low_vram:
        nbg = int(num_blocks_per_group) if group_offloading_type == "block_level" else None
        pipe.enable_group_offload(
            onload_device=torch.device("cuda"),
            offload_device=torch.device("cpu"),
            offload_type=group_offloading_type,
            num_blocks_per_group=nbg,
            use_stream=True,
            record_stream=True,
        )
    else:
        pipe = pipe.to(device)
    return pipe


def run_single(pipe, prompt, preset, height, width, seed):
    gen = torch.Generator(device="cuda").manual_seed(seed)

    t0 = time.time()
    with torch.no_grad():
        output = pipe(
            prompt=prompt,
            negative_prompt=NEGATIVE_PROMPT,
            height=height,
            width=width,
            num_frames=preset["num_frames"],
            num_inference_steps=preset["num_inference_steps"],
            guidance_scale=preset["guidance_scale"],
            generator=gen,
            history_sizes=[16, 2, 1],
            num_latent_frames_per_chunk=9,
            keep_first_frame=True,
            is_enable_stage2=preset["is_enable_stage2"],
            pyramid_num_inference_steps_list=preset["pyramid_num_inference_steps_list"],
            is_skip_first_chunk=False,
            is_amplify_first_chunk=preset["is_amplify_first_chunk"],
            use_zero_init=preset["use_zero_init"],
            zero_steps=preset["zero_steps"],
        ).frames[0]
    elapsed = time.time() - t0
    return output, elapsed


def _parse_gpu(s):
    if isinstance(s, str) and s.lower() == "auto":
        return "auto"
    return int(s)


def parse_args():
    p = argparse.ArgumentParser(description="Helios benchmark inference for one model version")
    p.add_argument("--prompt_file", type=str,
                    default=str(DEFAULT_PROMPT_FILE))
    p.add_argument("--prompt_start", type=int, default=0)
    p.add_argument("--prompt_end", type=int, default=100,
                    help="Exclusive end index for prompts, e.g. 50 means up to #49")
    p.add_argument("--prompt_indices_file", type=str, default=None,
                    help="Optional file containing exact prompt indices to run, one per line")
    p.add_argument("--model_root", type=str, default=str(DEFAULT_MODEL_ROOT),
                    help="Parent dir containing Helios-Base / Helios-Mid / Helios-Distilled")
    p.add_argument("--output_root", type=str, default=str(DEFAULT_OUTPUT_ROOT))
    p.add_argument("--version", type=str, choices=sorted(MODEL_PRESETS.keys()), required=True,
                    help="Which model version to run")
    p.add_argument("--timing_file", type=str, default=None,
                    help="Optional override for timing report path")
    p.add_argument("--height", type=int, default=384)
    p.add_argument("--width", type=int, default=640)
    p.add_argument("--num_frames", type=int, default=None,
                    help="Override preset frame count for all selected versions")
    p.add_argument("--seed", type=int, default=42)
    p.add_argument(
        "--gpu",
        type=_parse_gpu,
        default="auto",
        help='Physical GPU id or "auto" (pick most free VRAM via nvidia-smi)',
    )
    p.add_argument(
        "--enable_low_vram_mode",
        action="store_true",
        help="CPU group-offload (slower, less VRAM); use if GPU is shared or OOM",
    )
    p.add_argument(
        "--group_offloading_type",
        type=str,
        choices=["leaf_level", "block_level"],
        default="leaf_level",
    )
    p.add_argument("--num_blocks_per_group", type=int, default=4)
    return p.parse_args()


def main():
    args = parse_args()

    if not os.path.isfile(args.prompt_file):
        raise FileNotFoundError(f"Prompt file not found: {args.prompt_file}")
    if not os.path.isdir(args.model_root):
        raise FileNotFoundError(f"Model root not found: {args.model_root}")

    os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
    device = torch.device("cuda")
    weight_dtype = torch.bfloat16

    prompt_indices = None
    if args.prompt_indices_file:
        if not os.path.isfile(args.prompt_indices_file):
            raise FileNotFoundError(f"Prompt indices file not found: {args.prompt_indices_file}")
        prompt_indices = load_prompt_indices(args.prompt_indices_file)

    prompts = load_prompts(
        args.prompt_file,
        args.prompt_start,
        args.prompt_end,
        prompt_indices=prompt_indices,
    )
    prompt_map = dict(prompts)
    if args.prompt_indices_file:
        print(
            f"Loaded {len(prompts)} prompts from {args.prompt_file} "
            f"(indices: {args.prompt_indices_file})"
        )
    else:
        print(
            f"Loaded {len(prompts)} prompts from {args.prompt_file} "
            f"(range: {args.prompt_start}:{args.prompt_end})"
        )

    if args.num_frames is not None:
        MODEL_PRESETS[args.version]["num_frames"] = args.num_frames

    by_prompt_dir = os.path.join(args.output_root, "by_prompt")
    by_version_dir = os.path.join(args.output_root, "by_version")
    timing_file = args.timing_file or os.path.join(args.output_root, f"timing_{args.version}.txt")
    os.makedirs(args.output_root, exist_ok=True)

    preset = MODEL_PRESETS[args.version]
    model_path = os.path.join(args.model_root, preset["model_dir"])
    timing_records = load_existing_timing_records(timing_file, args.version)
    selected_indices = set(prompt_map)
    timing_records = {
        idx: record for idx, record in timing_records.items() if idx in selected_indices
    }
    ver_dir, expected_outputs = build_expected_outputs(prompts, args.version, by_version_dir)
    missing_outputs = find_missing_outputs(expected_outputs)
    if not os.path.isdir(model_path):
        raise FileNotFoundError(f"Model not found: {model_path}")

    peak_mem = None
    if not missing_outputs:
        print(
            f"[SKIP] All outputs already exist for version={args.version} under {ver_dir}"
        )
    else:
        header = (
            f"\n{'=' * 60}\n"
            f"  Version: {args.version}  |  Model: {preset['model_dir']}\n"
            f"  Frames: {preset['num_frames']}  |  guidance_scale: {preset['guidance_scale']}\n"
            f"  stage2: {preset['is_enable_stage2']}  |  pyramid_steps: {preset['pyramid_num_inference_steps_list']}\n"
            f"{'=' * 60}\n"
        )
        print(header)

        pipe = build_pipeline(
            model_path,
            device,
            weight_dtype,
            enable_low_vram=args.enable_low_vram_mode,
            group_offloading_type=args.group_offloading_type,
            num_blocks_per_group=args.num_blocks_per_group,
        )

        os.makedirs(ver_dir, exist_ok=True)

        print(
            f"[resume] version={args.version} existing={len(expected_outputs) - len(missing_outputs)} "
            f"missing={len(missing_outputs)} timed={len(timing_records)}"
        )
        for idx, slug, ver_out in tqdm(missing_outputs, desc=f"[{args.version}]"):
            if os.path.exists(ver_out):
                print(f"  [skip] {ver_out}")
                continue

            try:
                frames, elapsed = run_single(
                    pipe, prompt_map[idx], preset, args.height, args.width, args.seed,
                )
            except Exception as e:
                msg = f"  [FAIL] {args.version} #{idx:04d}: {e}"
                print(msg)
                continue

            export_to_video(frames, ver_out, fps=24)

            vid_name = os.path.splitext(os.path.basename(ver_out))[0]
            prompt_dir = os.path.join(by_prompt_dir, vid_name)
            os.makedirs(prompt_dir, exist_ok=True)
            shutil.copy2(ver_out, os.path.join(prompt_dir, f"{args.version}.mp4"))

            timing_records[idx] = (elapsed, slug)
            print(make_timing_line(args.version, idx, elapsed, slug))

        peak_mem = torch.cuda.max_memory_allocated() / 1024 ** 3
        print(f"  >> [{args.version}] peak GPU memory: {peak_mem:.2f} GB")

        del pipe
        torch.cuda.empty_cache()
        torch.cuda.reset_peak_memory_stats()

    sorted_records = [timing_records[idx] for idx in sorted(timing_records)]
    all_timings = [elapsed for elapsed, _ in sorted_records]

    with open(timing_file, "w", encoding="utf-8") as tf:
        tf.write(f"{'=' * 80}\n")
        tf.write(f"  Helios Benchmark Inference Timing Report\n")
        tf.write(f"  {time.strftime('%Y-%m-%d %H:%M:%S')}\n")
        tf.write(
            f"  Prompts: {len(prompts)}  |  Range: {args.prompt_start}:{args.prompt_end}  "
            f"|  Version: {args.version}\n"
        )
        if args.prompt_indices_file:
            tf.write(f"  Prompt indices file: {args.prompt_indices_file}\n")
        tf.write(
            f"  Resolution: {args.width}x{args.height}  |  Seed: {args.seed}  |  "
            f"GPU: {args.gpu}  |  low_vram: {args.enable_low_vram_mode}\n"
        )
        tf.write(f"{'=' * 80}\n\n")

        tf.write(
            f"\n{'=' * 60}\n"
            f"  Version: {args.version}  |  Model: {preset['model_dir']}\n"
            f"  Frames: {preset['num_frames']}  |  guidance_scale: {preset['guidance_scale']}\n"
            f"  stage2: {preset['is_enable_stage2']}  |  pyramid_steps: {preset['pyramid_num_inference_steps_list']}\n"
            f"{'=' * 60}\n"
        )
        tf.write(
            f"  Existing timing records: {len(timing_records)} / expected outputs: {len(expected_outputs)}\n"
        )

        for idx in sorted(timing_records):
            elapsed, slug = timing_records[idx]
            tf.write(make_timing_line(args.version, idx, elapsed, slug) + "\n")

        if all_timings:
            avg_t = sum(all_timings) / len(all_timings)
            total_t = sum(all_timings)
            summary = (
                f"\n  >> [{args.version}] completed {len(all_timings)} videos  |  "
                f"avg: {avg_t:.2f}s ({avg_t / 60:.2f}min)  |  "
                f"total: {total_t:.1f}s ({total_t / 60:.1f}min)\n"
            )
        else:
            summary = f"\n  >> [{args.version}] no timing records available\n"
        print(summary)
        tf.write(summary)

        if peak_mem is not None:
            mem_line = f"  >> [{args.version}] peak GPU memory: {peak_mem:.2f} GB\n"
            tf.write(mem_line)

        sep = f"\n{'=' * 80}\n"
        tf.write(sep)
        tf.write("  FINAL SUMMARY\n")
        tf.write(f"{'=' * 80}\n")
        print(sep)
        print("  FINAL SUMMARY")
        print(f"{'=' * 80}")

        fmt = "  {ver:12s} | videos: {n:3d} | avg: {avg:8.2f}s ({avgm:5.2f}min) | min: {mn:8.2f}s | max: {mx:8.2f}s | total: {tot:8.1f}s ({totm:5.1f}min)"
        if all_timings:
            line = fmt.format(
                ver=args.version, n=len(all_timings),
                avg=sum(all_timings) / len(all_timings), avgm=sum(all_timings) / len(all_timings) / 60,
                mn=min(all_timings), mx=max(all_timings),
                tot=sum(all_timings), totm=sum(all_timings) / 60,
            )
        else:
            line = f"  {args.version:12s} | N/A (no timing records)"
        print(line)
        tf.write(line + "\n")

        tf.write(f"{'=' * 80}\n")

    print(f"{'=' * 80}")
    print(f"\nTiming report: {timing_file}")
    print(f"Videos:        {by_prompt_dir}")
    print(f"               {by_version_dir}")


if __name__ == "__main__":
    main()