import time
import json
from uuid import uuid4

import modal
from modal.cls import ClsMixin


from suno_utils.worker.modal_base import get_modal_base_image, MODAL_MOUNTS
from suno_utils.worker.utils import recursive_ls_dir
from suno_utils.worker.chirp_worker import ChirpV0Worker

aws_secret = modal.Secret.from_name("studio-aws")


def download_model_wrapper_1():
    ChirpV0Worker.download_models()


image = get_modal_base_image().run_function(download_model_wrapper_1, secret=aws_secret)
STUB_NAME = "chirp-v0"
stub = modal.Stub(STUB_NAME, image=image)


@stub.cls(
    cpu=2.0,
    # memory=16384,
    gpu=modal.gpu.A10G(count=1),
    secrets=[
        aws_secret,
        modal.Secret.from_dict({"SUNO_ASSETS_PATH": "/suno/models/assets"}),
    ],
    timeout=800,
    container_idle_timeout=400,
    mounts=MODAL_MOUNTS,
    retries=modal.Retries(
        max_retries=3,
        backoff_coefficient=2.0,
        initial_delay=10.0,
    ),
    keep_warm=1,
)
class ChirpV0Stub(ClsMixin):
    def __enter__(self):
        import torch

        num_gpus = torch.cuda.device_count()
        print(f"Found {num_gpus} GPUs.")
        recursive_ls_dir("/suno/models")

        self.worker = ChirpV0Worker(0, bg_image="/suno/models/assets/wave-bg-2.png")
        self.worker.preload()

    @modal.method()
    def generate(self, queue_item: str):
        import json

        from suno_utils.worker.schema import QueueItem

        print(queue_item)
        start_time = time.time()
        item = QueueItem(**json.loads(queue_item))
        item_id = item.id
        print(item.metadata)
        is_square = item.metadata["is_square"] if "is_square" in item.metadata.keys() else False
        ids = []

        f = modal.Function.lookup("stable-diffusion-cli", "StableDiffusion.generate_image")
        fn_call = f.spawn(queue_item)

        try:
            audios = self.worker.process_item(item)
            items = []
            for i, audio in enumerate(audios):
                new_id = f"{item_id}_{i}"
                ids.append(new_id)
                item.id = new_id
                items.append(item.json())
                self.worker._write_audio_only(
                    item,
                    audio,
                )

            image_url = fn_call.get(timeout=None)
            print("image located", image_url)

            video_time = time.time()
            f = modal.Function.lookup("dummy-v0", "DummyV0Stub.write_video")
            list(f.starmap([(item, image_url.split("/")[-1], is_square) for item in items]))

            print("Videos took", time.time() - video_time)

            ok = True
        except Exception:
            import traceback

            traceback.print_exc()
            ok = False

        finish_time = time.time()

        self.worker.notify_finish(
            item,
            {
                "id": item_id,
                "model": "chirp_v0",
                "n_audios": len(ids),
                "ok": 1 if ok else 0,
                "gen_duration": finish_time - start_time,
            },
            queue_name="results:q",
        )

        return item.id


@stub.local_entrypoint()
def main():
    uid = str(uuid4())
    inputs = [
        json.dumps(
            dict(
                id=uid,
                # prompt_audio="5d9025f4-2158-4219-b9d6-abfbcc178db2.mp3",
                prompt_text="""
Yo, let me tell you 'bout my favorite dish
It's the sizzling, spicy fajitas that I wish
I chop up peppers and onions with glee
And grill that chicken 'til it's just right, you see
""",
                metadata={},
            )
        )
    ]
    model = ChirpV0Stub()
    print(uid)
    for input in inputs:
        model.generate.call(
            input,
        )