"""Gradio ZeroGPU Space for LongCat-Video-Avatar 1.5 (single-person AI2V).

Follows the same pattern as multimodalart/LongCat-Video: download weights to
local container disk, eagerly construct the pipeline at module level (CPU dtype),
then `pipe.to(device)` once. Inside @spaces.GPU, spaces transparently materializes
weights on the real GPU.
"""

# spaces must be imported before torch
import spaces  # noqa: F401

import json
import hashlib
import math
import librosa
import os
import shutil
import sys
import subprocess
import tempfile
import time
import uuid
from collections import OrderedDict
from pathlib import Path

os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")
os.environ.setdefault("HF_MODULES_CACHE", "/tmp/hf_modules")
os.environ.setdefault("MPLCONFIGDIR", "/tmp/matplotlib")
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")

WEIGHTS_DIR = Path(os.environ.get("WEIGHTS_DIR", "weights"))
WEIGHTS_DIR.mkdir(parents=True, exist_ok=True)
BASE_DIR = WEIGHTS_DIR / "LongCat-Video"
AVATAR_DIR = WEIGHTS_DIR / "LongCat-Video-Avatar-1.5"
print(f"[boot] WEIGHTS_DIR={WEIGHTS_DIR.resolve()}", flush=True)

sys.path.insert(0, str(Path(__file__).parent.resolve()))

import numpy as np
import torch
import torch.nn.functional as F
import gradio as gr
from huggingface_hub import snapshot_download
import imageio
from PIL import Image

if torch.cuda.is_available():
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.allow_tf32 = True
try:
    torch.set_float32_matmul_precision("high")
except Exception:
    pass


# ---------------------------------------------------------------------------
# 0) xformers → SDPA shim (the published xformers wheel is incompatible with
#    the torch version on zero-a10g; SDPA matches the call sites).
# ---------------------------------------------------------------------------

def _install_sdpa_shim():
    import xformers.ops

    class _BDShim:
        def __init__(self, q_seqlen, kv_seqlen):
            self.q_seqlen = list(q_seqlen)
            self.kv_seqlen = list(kv_seqlen)

        @classmethod
        def from_seqlens(cls, q_seqlen, kv_seqlen):
            return cls(q_seqlen, kv_seqlen)

    xformers.ops.fmha.attn_bias.BlockDiagonalMask = _BDShim

    def _meff(q, k, v, attn_bias=None, op=None, **_):
        if attn_bias is None:
            q_ = q.transpose(1, 2).contiguous()
            k_ = k.transpose(1, 2).contiguous()
            v_ = v.transpose(1, 2).contiguous()
            return F.scaled_dot_product_attention(q_, k_, v_).transpose(1, 2)
        if isinstance(attn_bias, _BDShim):
            outs, q_off, k_off = [], 0, 0
            for q_len, k_len in zip(attn_bias.q_seqlen, attn_bias.kv_seqlen):
                q_b = q[:, q_off:q_off + q_len].transpose(1, 2).contiguous()
                k_b = k[:, k_off:k_off + k_len].transpose(1, 2).contiguous()
                v_b = v[:, k_off:k_off + k_len].transpose(1, 2).contiguous()
                outs.append(F.scaled_dot_product_attention(q_b, k_b, v_b).transpose(1, 2))
                q_off += q_len
                k_off += k_len
            return torch.cat(outs, dim=1)
        raise NotImplementedError(f"Unsupported attn_bias in SDPA shim: {type(attn_bias)}")

    xformers.ops.memory_efficient_attention = _meff
    print("[boot] installed xformers→SDPA shim", flush=True)


_install_sdpa_shim()


# ---------------------------------------------------------------------------
# 1) Download weights (one-time per container) — local disk, no bucket
# ---------------------------------------------------------------------------

token = os.environ.get("HF_TOKEN")

if not (BASE_DIR / "vae" / "config.json").exists():
    print("[boot] downloading LongCat-Video (vae/text_encoder/tokenizer)…", flush=True)
    snapshot_download(
        "meituan-longcat/LongCat-Video",
        local_dir=str(BASE_DIR),
        token=token,
        allow_patterns=[
            "tokenizer/*",
            "text_encoder/*.safetensors",
            "text_encoder/*.json",
            "vae/*.safetensors",
            "vae/*.json",
        ],
        ignore_patterns=[
            "text_encoder/*.fp32*",
            "text_encoder/*.bin",
            "text_encoder/flax_model*",
            "text_encoder/tf_model*",
            "vae/flax_model*",
            "vae/tf_model*",
        ],
    )

if not (AVATAR_DIR / "base_model_int8" / "config.json").exists():
    print("[boot] downloading LongCat-Video-Avatar-1.5 (INT8 + lora + whisper + vocal_separator)…", flush=True)
    snapshot_download(
        "meituan-longcat/LongCat-Video-Avatar-1.5",
        local_dir=str(AVATAR_DIR),
        token=token,
        allow_patterns=[
            "base_model_int8/*",
            "lora/*",
            "scheduler/*",
            "vocal_separator/*",
            "whisper-large-v3/model.safetensors",
            "whisper-large-v3/*.json",
            "whisper-large-v3/*.txt",
        ],
        ignore_patterns=[
            "whisper-large-v3/model.fp32*",
            "whisper-large-v3/flax_model*",
            "whisper-large-v3/tf_model*",
            "whisper-large-v3/pytorch_model*",
        ],
    )
print("[boot] weights ready", flush=True)


# ---------------------------------------------------------------------------
# 2) Patch DiT config so it uses xformers (== our SDPA shim) instead of flash.
# ---------------------------------------------------------------------------

_cfg_path = AVATAR_DIR / "base_model_int8" / "config.json"
if _cfg_path.exists():
    _cfg = json.loads(_cfg_path.read_text())
    _changed = False
    for k in ("enable_flashattn2", "enable_flashattn3", "enable_bsa"):
        if _cfg.get(k):
            _cfg[k] = False
            _changed = True
    if not _cfg.get("enable_xformers"):
        _cfg["enable_xformers"] = True
        _changed = True
    if _changed:
        _cfg_path.write_text(json.dumps(_cfg, indent=2))
        print("[boot] patched DiT config -> SDPA backend", flush=True)


# ---------------------------------------------------------------------------
# 3) Eager model load at module level (multimodalart/LongCat-Video pattern)
# ---------------------------------------------------------------------------

from transformers import AutoTokenizer, UMT5EncoderModel  # noqa: E402

from longcat_video.pipeline_longcat_video_avatar import LongCatVideoAvatarPipeline  # noqa: E402
from longcat_video.modules.scheduling_flow_match_euler_discrete import (  # noqa: E402
    FlowMatchEulerDiscreteScheduler,
)
from longcat_video.modules.autoencoder_kl_wan import AutoencoderKLWan  # noqa: E402
from longcat_video.modules.quantization import load_quantized_dit  # noqa: E402
from longcat_video.audio_process import (  # noqa: E402
    get_audio_encoder,
    get_audio_feature_extractor,
)

device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.bfloat16 if device == "cuda" else torch.float32
CP_SPLIT_HW = [1, 1]

print(f"[boot] device={device} dtype={torch_dtype}", flush=True)
print("[boot] tokenizer + text_encoder…", flush=True); _t = time.time()
tokenizer = AutoTokenizer.from_pretrained(str(BASE_DIR), subfolder="tokenizer", torch_dtype=torch_dtype)
text_encoder = UMT5EncoderModel.from_pretrained(str(BASE_DIR), subfolder="text_encoder", torch_dtype=torch_dtype)
print(f"[boot] text_encoder loaded in {time.time()-_t:.1f}s", flush=True)

print("[boot] VAE + scheduler…", flush=True); _t = time.time()
vae = AutoencoderKLWan.from_pretrained(str(BASE_DIR), subfolder="vae", torch_dtype=torch_dtype)
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(str(AVATAR_DIR), subfolder="scheduler", torch_dtype=torch_dtype)
print(f"[boot] VAE+scheduler loaded in {time.time()-_t:.1f}s", flush=True)

print("[boot] INT8 DiT + DMD2 LoRA…", flush=True); _t = time.time()
dit = load_quantized_dit(str(AVATAR_DIR), subfolder="base_model_int8", cp_split_hw=CP_SPLIT_HW)
_lora_path = AVATAR_DIR / "lora" / "dmd_lora.safetensors"
if _lora_path.exists():
    dit.load_lora(str(_lora_path), "dmd", multiplier=1.0, lora_network_dim=128, lora_network_alpha=64)
    dit.enable_loras(["dmd"])
    print("[boot] DMD2 8-step LoRA enabled", flush=True)
print(f"[boot] DiT loaded in {time.time()-_t:.1f}s", flush=True)

print("[boot] Whisper-Large-v3…", flush=True); _t = time.time()
audio_encoder = get_audio_encoder(str(AVATAR_DIR / "whisper-large-v3"), "avatar-v1.5")
audio_feature_extractor = get_audio_feature_extractor(str(AVATAR_DIR / "whisper-large-v3"), "avatar-v1.5")
print(f"[boot] Whisper loaded in {time.time()-_t:.1f}s", flush=True)

print("[boot] vocal separator (Kim_Vocal_2)…", flush=True)
from audio_separator.separator import Separator  # noqa: E402

VOCAL_TMP = Path("/tmp/vocal_out")
(VOCAL_TMP / "vocals").mkdir(parents=True, exist_ok=True)
vocal_separator = Separator(
    output_dir=str(VOCAL_TMP / "vocals"),
    output_single_stem="vocals",
    model_file_dir=str(AVATAR_DIR / "vocal_separator"),
)
vocal_separator.load_model("Kim_Vocal_2.onnx")

print("[boot] assembling pipeline…", flush=True)
pipe = LongCatVideoAvatarPipeline(
    tokenizer=tokenizer,
    text_encoder=text_encoder,
    vae=vae,
    scheduler=scheduler,
    dit=dit,
    audio_encoder=audio_encoder,
    audio_feature_extractor=audio_feature_extractor,
    model_type="avatar-v1.5",
)
pipe.to(device)
# pipe.to() doesn't move the audio_encoder (Whisper); do it explicitly.
audio_encoder.to(device, dtype=torch_dtype)
print("[boot] ready.", flush=True)


# ---------------------------------------------------------------------------
# 4) Inference
# ---------------------------------------------------------------------------

NEGATIVE_PROMPT = (
    "Close-up, 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"
)

VOCAL_MODE_FAST = "Clean speech (fast)"
VOCAL_MODE_QUALITY = "Isolate vocals (quality)"
ACCEL_MODE_EXACT = "Exact 8-step"
ACCEL_MODE_DBCACHE = "DBCache fast"
ACCEL_MODE_DBCACHE_FASTER = "DBCache faster"
SAVE_FPS = 25

# LongCat/Wan-style video frame counts need to follow 4n + 1.
# The original demo used 125 frames = 5.00 seconds = 4*31 + 1.
# For production, derive frame count from uploaded audio and cap around 15 seconds.
MIN_NUM_FRAMES = 125
MAX_VIDEO_SECONDS = 15
MAX_NUM_FRAMES = 377  # 15.08 sec at 25fps and valid 4n + 1

def _ceil_to_valid_4n_plus_1(frame_count: int) -> int:
    frame_count = max(int(frame_count), MIN_NUM_FRAMES)
    remainder = frame_count % 4
    if remainder == 1:
        return frame_count
    return frame_count + ((1 - remainder) % 4)

def get_safe_num_frames(audio_path: str) -> int:
    duration = float(librosa.get_duration(path=audio_path))
    if duration <= 0:
        return MIN_NUM_FRAMES

    if duration > MAX_VIDEO_SECONDS:
        raise ValueError(
            f"Audio is {duration:.2f}s, but this Space is capped at {MAX_VIDEO_SECONDS}s. "
            "Please upload audio that is 15 seconds or shorter."
        )

    requested_frames = math.ceil(duration * SAVE_FPS)
    safe_frames = _ceil_to_valid_4n_plus_1(requested_frames)
    return min(max(safe_frames, MIN_NUM_FRAMES), MAX_NUM_FRAMES)

VIDEO_SECONDS = MAX_VIDEO_SECONDS
_AUDIO_EMB_CACHE = OrderedDict()
_VOCAL_CACHE = OrderedDict()
_CACHE_LIMIT = 8
_DISK_CACHE_DIR = Path(tempfile.gettempdir()) / "longcat_cache"
_AUDIO_CACHE_DIR = _DISK_CACHE_DIR / "audio_emb"
_AUDIO_CACHE_DIR.mkdir(parents=True, exist_ok=True)

CUSTOM_CSS = """
main,
.gradio-container,
.fillable:not(.fill_width) {
  width: min(100%, 1320px) !important;
  max-width: 1320px !important;
  margin-left: auto !important;
  margin-right: auto !important;
}

.prose blockquote {
  margin-top: 0 !important;
}
"""


def _file_sha256(path: str) -> str:
    h = hashlib.sha256()
    with open(path, "rb") as f:
        for chunk in iter(lambda: f.read(1024 * 1024), b""):
            h.update(chunk)
    return h.hexdigest()


def _cache_get(cache: OrderedDict, key):
    value = cache.get(key)
    if value is not None:
        cache.move_to_end(key)
    return value


def _cache_put(cache: OrderedDict, key, value):
    cache[key] = value
    cache.move_to_end(key)
    while len(cache) > _CACHE_LIMIT:
        cache.popitem(last=False)


def _cache_file(namespace: Path, key) -> Path:
    key_json = json.dumps(key, sort_keys=True, separators=(",", ":"))
    return namespace / f"{hashlib.sha256(key_json.encode('utf-8')).hexdigest()}.pt"


def _load_audio_16k(path: str):
    try:
        import soundfile as sf
        from scipy.signal import resample_poly

        speech, sr = sf.read(path, dtype="float32", always_2d=False)
        if speech.ndim > 1:
            speech = speech.mean(axis=1)
        if sr != 16000:
            gcd = math.gcd(int(sr), 16000)
            speech = resample_poly(speech, 16000 // gcd, int(sr) // gcd).astype(np.float32)
            sr = 16000
        return np.ascontiguousarray(speech, dtype=np.float32), sr
    except Exception as e:
        print(f"[audio] soundfile load failed, falling back to librosa: {e}", flush=True)
        import librosa

        speech, sr = librosa.load(path, sr=16000)
        return np.ascontiguousarray(speech, dtype=np.float32), sr


def _extract_vocal(src: str, audio_hash: str) -> str:
    cached_path = _cache_get(_VOCAL_CACHE, audio_hash)
    if cached_path and Path(cached_path).exists():
        print(f"[cache] vocal hit {audio_hash[:10]}", flush=True)
        return cached_path

    stable_path = VOCAL_TMP / "vocals" / f"{audio_hash[:16]}_vocals.wav"
    if stable_path.exists():
        _cache_put(_VOCAL_CACHE, audio_hash, str(stable_path))
        return str(stable_path)

    try:
        outputs = vocal_separator.separate(src)
        if outputs:
            separated = (VOCAL_TMP / "vocals" / outputs[0]).resolve()
            shutil.copyfile(separated, stable_path)
            _cache_put(_VOCAL_CACHE, audio_hash, str(stable_path))
            return str(stable_path)
    except Exception as e:
        print(f"[vocal] separation failed, using raw audio: {e}", flush=True)
    return src


def _check_duration(*args, **kwargs):
    return 600


def _prepare_audio_embedding(audio_path: str, vocal_mode: str, num_frames: int, save_fps: int, audio_stride: int, progress):
    audio_hash = _file_sha256(audio_path)
    cache_key = (audio_hash, vocal_mode, num_frames, save_fps, audio_stride)
    cached = _cache_get(_AUDIO_EMB_CACHE, cache_key)
    if cached is not None:
        progress(0.20, desc="Using cached audio conditioning…")
        print(f"[cache] audio embedding hit {audio_hash[:10]}", flush=True)
        return cached.to(device, non_blocking=True)
    cache_path = _cache_file(_AUDIO_CACHE_DIR, cache_key)
    if cache_path.exists():
        try:
            cached = torch.load(cache_path, map_location="cpu")
            _cache_put(_AUDIO_EMB_CACHE, cache_key, cached)
            progress(0.20, desc="Using cached audio conditioning…")
            print(f"[cache] audio embedding disk hit {audio_hash[:10]}", flush=True)
            return cached.to(device, non_blocking=True)
        except Exception as e:
            print(f"[cache] audio disk cache read failed: {e}", flush=True)

    t0 = time.perf_counter()
    if vocal_mode == VOCAL_MODE_QUALITY:
        progress(0.05, desc="Isolating vocals…")
        vocal_path = _extract_vocal(audio_path, audio_hash)
    else:
        progress(0.05, desc="Using clean speech directly…")
        vocal_path = audio_path
    print(f"[timing] audio_input_ready={time.perf_counter() - t0:.2f}s mode={vocal_mode} hash={audio_hash[:10]}", flush=True)

    t0 = time.perf_counter()
    speech, sr = _load_audio_16k(vocal_path)
    pad = math.ceil((num_frames / save_fps - len(speech) / sr) * sr)
    if pad > 0:
        speech = np.concatenate([speech, np.zeros(pad, dtype=speech.dtype)])
    print(f"[timing] audio_load={time.perf_counter() - t0:.2f}s sr={sr} samples={len(speech)}", flush=True)

    progress(0.15, desc="Encoding audio (Whisper-Large-v3)…")
    t0 = time.perf_counter()
    full_audio_emb = pipe.get_audio_embedding(
        speech, fps=save_fps * audio_stride, device=device, sample_rate=sr, model_type="avatar-v1.5"
    )
    if torch.isnan(full_audio_emb).any():
        raise gr.Error("Audio embedding contains NaN — try a different audio clip.")

    indices = torch.arange(2 * 2 + 1, device=full_audio_emb.device) - 2
    center = torch.arange(0, audio_stride * num_frames, audio_stride, device=full_audio_emb.device).unsqueeze(1) + indices.unsqueeze(0)
    center = torch.clamp(center, min=0, max=full_audio_emb.shape[0] - 1)
    audio_emb = full_audio_emb[center][None, ...].to(device)
    print(f"[timing] audio_encode={time.perf_counter() - t0:.2f}s shape={tuple(audio_emb.shape)}", flush=True)

    audio_emb_cpu = audio_emb.detach().cpu()
    _cache_put(_AUDIO_EMB_CACHE, cache_key, audio_emb_cpu)
    try:
        torch.save(audio_emb_cpu, cache_path)
    except Exception as e:
        print(f"[cache] audio disk cache write failed: {e}", flush=True)
    return audio_emb


def _save_video_ffmpeg_fast(frames: np.ndarray, out_base: Path, audio_path: str, fps: int, quality: int = 5) -> str:
    out_base = str(out_base)
    temp_video = out_base + "-video.mp4"
    out_path = out_base + ".mp4"

    writer = imageio.get_writer(temp_video, fps=fps, codec="libx264", quality=quality)
    try:
        for frame in frames:
            writer.append_data(np.asarray(frame))
    finally:
        writer.close()

    duration = len(frames) / fps
    cmd = [
        "ffmpeg",
        "-y",
        "-loglevel",
        "error",
        "-i",
        temp_video,
        "-i",
        audio_path,
        "-t",
        f"{duration:.3f}",
        "-map",
        "0:v:0",
        "-map",
        "1:a:0",
        "-c:v",
        "copy",
        "-c:a",
        "aac",
        "-b:a",
        "96k",
        "-shortest",
        out_path,
    ]
    subprocess.run(cmd, check=True)
    try:
        os.remove(temp_video)
    except OSError:
        pass
    return out_path


def _configure_dit_acceleration(acceleration: str):
    if acceleration in (ACCEL_MODE_DBCACHE, ACCEL_MODE_DBCACHE_FASTER):
        faster = acceleration == ACCEL_MODE_DBCACHE_FASTER
        pipe.dit.configure_dbcache(
            enabled=True,
            fn=1,
            bn=0,
            warmup_steps=1,
            max_cached_steps=3 if faster else 2,
            max_continuous_cached_steps=1,
            # The distilled 8-step schedule has larger residual deltas than
            # upstream 50-step DBCache examples, so cache only bounded steps.
            residual_diff_threshold=0.35,
            downsample_factor=4,
        )
        return "DMD2 8-step + DBCache" + (" faster" if faster else "")

    pipe.dit.configure_dbcache(enabled=False)
    return "DMD2 8-step"


@spaces.GPU(duration=_check_duration, size="xlarge")
def generate(
    image_path: str,
    audio_path: str,
    prompt: str,
    resolution: str,
    seed: int,
    vocal_mode: str = VOCAL_MODE_FAST,
    acceleration: str = ACCEL_MODE_DBCACHE_FASTER,
    progress=gr.Progress(track_tqdm=True),
):
    if not image_path:
        raise gr.Error("Please upload a reference image.")
    if not audio_path:
        raise gr.Error("Please upload an audio clip.")
    prompt = (prompt or "A person is talking naturally.").strip()

    save_fps = SAVE_FPS
    audio_stride = 1
    num_frames = get_safe_num_frames(audio_path)
    t_total = time.perf_counter()

    audio_emb = _prepare_audio_embedding(audio_path, vocal_mode, num_frames, save_fps, audio_stride, progress)

    generation_mode = _configure_dit_acceleration(acceleration)
    progress(0.30, desc=f"Generating video ({generation_mode})…")
    image = Image.open(image_path).convert("RGB")
    generator = torch.Generator(device=device).manual_seed(int(seed))

    t0 = time.perf_counter()
    with torch.inference_mode():
        output = pipe.generate_ai2v(
            image=image,
            prompt=prompt,
            negative_prompt=NEGATIVE_PROMPT,
            resolution=resolution,
            num_frames=num_frames,
            num_inference_steps=8,
            text_guidance_scale=1.0,
            audio_guidance_scale=1.0,
            output_type="np",
            generator=generator,
            audio_emb=audio_emb,
            use_distill=True,
        )
    print(f"[timing] video_generate={time.perf_counter() - t0:.2f}s mode={acceleration}", flush=True)
    if acceleration in (ACCEL_MODE_DBCACHE, ACCEL_MODE_DBCACHE_FASTER):
        print(f"[dbcache] {pipe.dit.get_dbcache_stats()}", flush=True)

    progress(0.92, desc="Muxing audio + video…")
    t0 = time.perf_counter()
    frames = (output[0] * 255).astype(np.uint8)
    out_base = Path(tempfile.gettempdir()) / f"longcat_{uuid.uuid4().hex[:8]}"
    out_path = _save_video_ffmpeg_fast(frames, out_base, audio_path, fps=save_fps, quality=5)
    print(f"[timing] mux={time.perf_counter() - t0:.2f}s total={time.perf_counter() - t_total:.2f}s", flush=True)
    print(f"[gen] wrote {out_path}", flush=True)
    return out_path


# ---------------------------------------------------------------------------
# 5) Gradio UI
# ---------------------------------------------------------------------------

ASSET_DIR = Path(__file__).parent / "assets" / "avatar"
EXAMPLE_CACHE_VERSION = "elevenlabs-example-voices-v4"
EXAMPLES = []


def _reset_example_cache_if_needed():
    cache_root = Path(".gradio") / "cached_examples"
    marker = cache_root / ".longcat_example_version"
    try:
        current = marker.read_text().strip() if marker.exists() else ""
        if current != EXAMPLE_CACHE_VERSION:
            shutil.rmtree(cache_root, ignore_errors=True)
            cache_root.mkdir(parents=True, exist_ok=True)
            marker.write_text(EXAMPLE_CACHE_VERSION)
    except Exception as e:
        print(f"[cache] example cache reset skipped: {e}", flush=True)


def _add_example(image_path: Path, audio_path: Path, prompt_text: str, seed: int):
    if image_path.exists() and audio_path.exists():
        EXAMPLES.append([
            str(image_path),
            str(audio_path),
            prompt_text,
            "480p",
            seed,
            VOCAL_MODE_FAST,
            ACCEL_MODE_DBCACHE_FASTER,
        ])


_add_example(
    ASSET_DIR / "single" / "character.png",
    ASSET_DIR / "examples" / "audio" / "character_voice.wav",
    "A friendly cartoon character with short brown hair speaking to the camera "
    "against a soft blue background, simple flat illustration style.",
    42,
)
_add_example(
    ASSET_DIR / "examples" / "orc_warrior.png",
    ASSET_DIR / "examples" / "audio" / "orc_warrior_voice.wav",
    "A fantasy orc warrior portrait speaking directly to the camera, cinematic "
    "studio lighting, detailed armor, expressive face, smoky neutral backdrop.",
    77,
)
_add_example(
    ASSET_DIR / "examples" / "photoreal_person.png",
    ASSET_DIR / "examples" / "audio" / "photoreal_person_voice.wav",
    "A photorealistic person speaking naturally to the camera in a clean studio "
    "portrait, soft neutral background, realistic facial expression.",
    123,
)

_reset_example_cache_if_needed()

with gr.Blocks(title="LongCat-Video-Avatar 1.5", css=CUSTOM_CSS) as demo:
    gr.Markdown(
        """
        # 🎤 LongCat-Video-Avatar 1.5: Audio-Image-to-Video

        Upload a reference image + audio clip + a short text prompt.
        Generates a up to ~1up to 15-second lip-synced video using Meituan's
        LongCat-Video-Avatar 1.5 (INT8 DiT + DMD2 8-step distilled).

        > 🤖 Built autonomously by an agent across real Spaces sessions. See [Building ZeroGPU Spaces Autonomously](https://huggingface.co/blog/victor/building-zerogpu-spaces-autonomously) for the story behind it.
        """
    )
    with gr.Row():
        with gr.Column(scale=1):
            image_in = gr.Image(label="Reference image", type="filepath")
            audio_in = gr.Audio(label="Driving audio", type="filepath")
            prompt = gr.Textbox(
                label="Prompt",
                value="A person is speaking expressively, looking at the camera.",
                lines=3,
            )
            with gr.Row():
                resolution = gr.Radio(["480p", "720p"], value="480p", label="Resolution")
                seed = gr.Number(value=42, precision=0, label="Seed")
            vocal_mode = gr.Radio(
                [VOCAL_MODE_FAST, VOCAL_MODE_QUALITY],
                value=VOCAL_MODE_FAST,
                label="Audio preprocessing",
            )
            acceleration = gr.Radio(
                [ACCEL_MODE_EXACT, ACCEL_MODE_DBCACHE, ACCEL_MODE_DBCACHE_FASTER],
                value=ACCEL_MODE_DBCACHE_FASTER,
                label="Acceleration",
            )
            go = gr.Button("Generate", variant="primary")
        with gr.Column(scale=1):
            video_out = gr.Video(label="Output", autoplay=True, height=420)
            if EXAMPLES:
                gr.Examples(
                    examples=EXAMPLES,
                    inputs=[image_in, audio_in, prompt, resolution, seed, vocal_mode, acceleration],
                    outputs=video_out,
                    fn=generate,
                    cache_examples=True,
                    cache_mode="lazy",
                    examples_per_page=3,
                )

    go.click(
        generate,
        inputs=[image_in, audio_in, prompt, resolution, seed, vocal_mode, acceleration],
        outputs=video_out,
    )

if __name__ == "__main__":
    demo.queue(max_size=8).launch(show_error=True)