from typing import Dict, List, Any
from scipy.io import wavfile
from transformers import AutoProcessor, MusicgenForConditionalGeneration
import torch
import io
import base64

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"):
        self.processor = AutoProcessor.from_pretrained(path)
        self.model = MusicgenForConditionalGeneration.from_pretrained(path)
        self.model.to('cuda:0') #type: ignore

    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"
        )

        params = create_params(params, self.model.config.audio_encoder.frame_rate) #type: ignore

        outputs = self.model.generate(**inputs.to('cuda:0'), **params) #type: ignore

        pred = outputs[0, 0].cpu().numpy()
        sr = self.model.config.audio_encoder.sampling_rate #type: ignore

        wav_buffer = io.BytesIO()
        wavfile.write(wav_buffer, rate=sr, data=pred)
        wav_data = wav_buffer.getvalue()

        base64_encoded_wav = base64.b64encode(wav_data).decode('utf-8')
        return  [{"audio": base64_encoded_wav, "sr": sr}]

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