from queue import Queue
from threading import Thread
from typing import Optional

import numpy as np
import torch
from flask import Flask, request, jsonify, send_file
from transformers import MusicgenForConditionalGeneration, MusicgenProcessor, set_seed
from transformers.generation.streamers import BaseStreamer
import io
import soundfile as sf

# Load the model and processor
model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
processor = MusicgenProcessor.from_pretrained("facebook/musicgen-small")

class MusicgenStreamer(BaseStreamer):
    def __init__(
        self,
        model: MusicgenForConditionalGeneration,
        device: Optional[str] = None,
        play_steps: Optional[int] = 10,
        stride: Optional[int] = None,
        timeout: Optional[float] = None,
    ):
        self.decoder = model.decoder
        self.audio_encoder = model.audio_encoder
        self.generation_config = model.generation_config
        self.device = device if device is not None else model.device

        self.play_steps = play_steps
        if stride is not None:
            self.stride = stride
        else:
            hop_length = np.prod(self.audio_encoder.config.upsampling_ratios)
            self.stride = hop_length * (play_steps - self.decoder.num_codebooks) // 6
        self.token_cache = None
        self.to_yield = 0

        self.audio_queue = Queue()
        self.stop_signal = None
        self.timeout = timeout

    def apply_delay_pattern_mask(self, input_ids):
        _, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask(
            input_ids[:, :1],
            pad_token_id=self.generation_config.decoder_start_token_id,
            max_length=input_ids.shape[-1],
        )
        input_ids = self.decoder.apply_delay_pattern_mask(input_ids, decoder_delay_pattern_mask)

        input_ids = input_ids[input_ids != self.generation_config.pad_token_id].reshape(
            1, self.decoder.num_codebooks, -1
        )

        input_ids = input_ids[None, ...]
        input_ids = input_ids.to(self.audio_encoder.device)

        output_values = self.audio_encoder.decode(
            input_ids,
            audio_scales=[None],
        )
        audio_values = output_values.audio_values[0, 0]
        return audio_values.cpu().float().numpy()

    def put(self, value):
        batch_size = value.shape[0] // self.decoder.num_codebooks
        if batch_size > 1:
            raise ValueError("MusicgenStreamer only supports batch size 1")

        if self.token_cache is None:
            self.token_cache = value
        else:
            self.token_cache = torch.concatenate([self.token_cache, value[:, None]], dim=-1)

        if self.token_cache.shape[-1] % self.play_steps == 0:
            audio_values = self.apply_delay_pattern_mask(self.token_cache)
            self.on_finalized_audio(audio_values[self.to_yield : -self.stride])
            self.to_yield += len(audio_values) - self.to_yield - self.stride

    def end(self):
        if self.token_cache is not None:
            audio_values = self.apply_delay_pattern_mask(self.token_cache)
        else:
            audio_values = np.zeros(self.to_yield)

        self.on_finalized_audio(audio_values[self.to_yield :], stream_end=True)

    def on_finalized_audio(self, audio: np.ndarray, stream_end: bool = False):
        self.audio_queue.put(audio, timeout=self.timeout)
        if stream_end:
            self.audio_queue.put(self.stop_signal, timeout=self.timeout)

    def __iter__(self):
        return self

    def __next__(self):
        value = self.audio_queue.get(timeout=self.timeout)
        if not isinstance(value, np.ndarray) and value == self.stop_signal:
            raise StopIteration()
        else:
            return value


sampling_rate = model.audio_encoder.config.sampling_rate
frame_rate = model.audio_encoder.config.frame_rate

app = Flask(__name__)

@app.route('/generate_audio', methods=['POST'])
def generate_audio():
    data = request.json
    text_prompt = data.get('text_prompt', '80s pop track with synth and instrumentals')
    audio_length_in_s = float(data.get('audio_length_in_s', 10.0))
    play_steps_in_s = float(data.get('play_steps_in_s', 2.0))
    seed = int(data.get('seed', 0))

    max_new_tokens = int(frame_rate * audio_length_in_s)
    play_steps = int(frame_rate * play_steps_in_s)

    device = "cuda:0" if torch.cuda.is_available() else "cpu"
    if device != model.device:
        model.to(device)
        if device == "cuda:0":
            model.half()

    inputs = processor(
        text=text_prompt,
        padding=True,
        return_tensors="pt",
    )

    streamer = MusicgenStreamer(model, device=device, play_steps=play_steps)

    generation_kwargs = dict(
        **inputs.to(device),
        streamer=streamer,
        max_new_tokens=max_new_tokens,
    )
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()

    set_seed(seed)
    generated_audio = []
    for new_audio in streamer:
        generated_audio.append(new_audio)

    # Concatenate the audio chunks
    final_audio = np.concatenate(generated_audio)

    # Save the audio to a buffer and send it as a response
    buffer = io.BytesIO()
    sf.write(buffer, final_audio, sampling_rate, format="wav")
    buffer.seek(0)

    return send_file(buffer, mimetype="audio/wav", as_attachment=True, download_name="generated_music.wav")

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860)