"""Hugging Face Space entry point for InstructAV2AV."""

from __future__ import annotations

import logging
import os
import tempfile
from pathlib import Path
from types import SimpleNamespace
from typing import Any

import gradio as gr

try:
    import spaces
except ImportError:
    class _SpacesFallback:
        @staticmethod
        def GPU(*decorator_args: Any, **_decorator_kwargs: Any):
            if decorator_args and callable(decorator_args[0]):
                return decorator_args[0]
            return lambda function: function

    spaces = _SpacesFallback()

from scripts.demo import CSS, DEFAULT_CONFIG, DemoRuntime, build_demo
from space_model_store import HubModelStore


logging.basicConfig(
    level=logging.INFO,
    format="[%(asctime)s] %(levelname)s: %(message)s",
)


def default_hf_home() -> Path:
    persistent_storage = Path("/data")
    if persistent_storage.is_dir() and os.access(persistent_storage, os.W_OK):
        return persistent_storage / ".huggingface"
    return Path.home() / ".cache" / "huggingface"


HF_HOME = Path(
    os.getenv("HF_HOME", default_hf_home())
).expanduser()
MODEL_HOME = Path(
    os.getenv("INSTRUCTAV2AV_MODEL_HOME", HF_HOME / "instructav2av")
).expanduser()
OUTPUT_DIR = Path(
    os.getenv(
        "INSTRUCTAV2AV_OUTPUT_DIR",
        Path(tempfile.gettempdir()) / "instructav2av_outputs",
    )
).expanduser()
GPU_DURATION = int(os.getenv("INSTRUCTAV2AV_ZEROGPU_DURATION", "300"))


model_store = HubModelStore(MODEL_HOME, hub_cache_dir=HF_HOME / "hub")
if os.getenv("INSTRUCTAV2AV_EAGER_DOWNLOAD", "1").lower() not in {
    "0",
    "false",
    "no",
}:
    try:
        model_store.preload_default()
        logging.info("Shared weights and the General checkpoint are cached.")
    except Exception:
        logging.exception(
            "Startup model download failed; the first edit request will retry it."
        )

runtime_args = SimpleNamespace(
    config_file=str(DEFAULT_CONFIG),
    model_dir=str(model_store.ckpt_dir / "InstructAV2AV"),
    ckpt_dir=str(model_store.ckpt_dir),
    output_dir=str(OUTPUT_DIR),
    device=int(os.getenv("INSTRUCTAV2AV_CUDA_DEVICE", "0")),
    no_cpu_offload=os.getenv("INSTRUCTAV2AV_CPU_OFFLOAD", "0").lower()
    in {"0", "false", "no"},
)

runtime = DemoRuntime(
    runtime_args,
    checkpoint_resolver=model_store.resolve_checkpoint,
)


@spaces.GPU(duration=GPU_DURATION)
def zero_gpu_task(
    operation: str,
    video_value: Any,
    instruction: str,
    model_key: str,
    seed: float,
    sample_steps: float,
    video_guidance_scale: float,
    audio_guidance_scale: float,
    progress: gr.Progress = gr.Progress(),
) -> str:
    if operation == "warmup":
        return runtime.warmup("general", progress)
    if operation != "generate":
        raise gr.Error(f"Unsupported ZeroGPU operation: {operation}")
    return runtime.generate(
        video_value,
        instruction,
        model_key,
        seed,
        sample_steps,
        video_guidance_scale,
        audio_guidance_scale,
        progress,
    )


demo = build_demo(
    runtime,
    zero_gpu_fn=zero_gpu_task,
)
demo.queue(max_size=8, default_concurrency_limit=1)


if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=int(os.getenv("PORT", "7860")),
        allowed_paths=[str(runtime.output_dir)],
        show_error=True,
        theme=gr.themes.Default(),
        css=CSS,
        max_file_size=os.getenv("GRADIO_MAX_FILE_SIZE", "500mb"),
    )