from typing import Dict, List, Any
from transformers import AutoProcessor, MusicgenForConditionalGeneration
import torch

def create_params(params, fr):
    # default
    out = { "do_sample": True,
          "guidance_scale": 3, 
          "max_new_tokens": 256
          }

    has_tokens = False

    if params is None:
       return out

    if 'duration' in params:
        out['max_new_tokens'] =  params['duration'] * fr
        has_tokens = True

    for k, p in params.items():
        if k in out: 
          if has_tokens and k == 'max_new_tokens':
            continue

          out[k] = p

    return out


class EndpointHandler:
    def __init__(self, path="pbotsaris/musicgen-small"):
        # load model and processor
        self.processor = AutoProcessor.from_pretrained(path)
        self.model = MusicgenForConditionalGeneration.from_pretrained(path, torch_dtype=torch.float16)
        self.model.to('cuda')

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Args:
            data (:dict:):
                The payload with the text prompt and generation parameters.
        """

        inputs = data.pop("inputs", data)
        params = data.pop("parameters", None)

        inputs = self.processor(
            text=[inputs],
            padding=True,
            return_tensors="pt"
        ).to('cuda')

        params = create_params(params, self.model.config.audio_encoder.frame_rate)

        with torch.cuda.amp.autocast():
            outputs = self.model.generate(**inputs, **params)

        pred = outputs[0].cpu().numpy().tolist()
        sr = 32000

        try:
             sr = self.model.config.audio_encoder.sampling_rate

        except:
             sr = 32000

        return [{"audio": pred, "sr":sr}]


if __name__ == "__main__":
    handler = EndpointHandler()