import time
import torch
import numpy as np

import queue
from queue import Queue
import multiprocessing as mp

from wav2lip import audio

class LipASR:
    def __init__(self, opt):
        self.opt = opt

        self.fps = opt.fps # 20 ms per frame
        self.sample_rate = 16000
        self.chunk = self.sample_rate // self.fps # 320 samples per chunk (20ms * 16000 / 1000)
        self.queue = Queue()
        # self.input_stream = BytesIO()
        self.output_queue = mp.Queue()

        #self.audio_processor = audio_processor
        self.batch_size = opt.batch_size

        self.frames = []
        self.stride_left_size = opt.l
        self.stride_right_size = opt.r
        #self.context_size = 10
        self.feat_queue = mp.Queue(5)

        self.warm_up()

    def put_audio_frame(self,audio_chunk): #16khz 20ms pcm
        self.queue.put(audio_chunk)

    def __get_audio_frame(self):        
        try:
            frame = self.queue.get(block=True,timeout=0.01)
            type = 0
            #print(f'[INFO] get frame {frame.shape}')
        except queue.Empty:
            frame = np.zeros(self.chunk, dtype=np.float32)
            type = 1

        return frame,type 

    def get_audio_out(self):  #get origin audio pcm to nerf
        return self.output_queue.get()
    
    def warm_up(self):
        for _ in range(self.stride_left_size + self.stride_right_size):
            audio_frame,type=self.__get_audio_frame()
            self.frames.append(audio_frame)
            self.output_queue.put((audio_frame,type))
        for _ in range(self.stride_left_size):
            self.output_queue.get()

    def run_step(self):
        ############################################## extract audio feature ##############################################
        # get a frame of audio
        for _ in range(self.batch_size*2):
            frame,type = self.__get_audio_frame()
            self.frames.append(frame)
            # put to output
            self.output_queue.put((frame,type))
        # context not enough, do not run network.
        if len(self.frames) <= self.stride_left_size + self.stride_right_size:
            return
        
        inputs = np.concatenate(self.frames) # [N * chunk]
        mel = audio.melspectrogram(inputs)
        #print(mel.shape[0],mel.shape,len(mel[0]),len(self.frames))
        # cut off stride
        left = max(0, self.stride_left_size*80/50)
        right = min(len(mel[0]), len(mel[0]) - self.stride_right_size*80/50)
        mel_idx_multiplier = 80.*2/self.fps 
        mel_step_size = 16
        i = 0
        mel_chunks = []
        while i < (len(self.frames)-self.stride_left_size-self.stride_right_size)/2:
            start_idx = int(left + i * mel_idx_multiplier)
            #print(start_idx)
            if start_idx + mel_step_size > len(mel[0]):
                mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])
            else:
                mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
            i += 1
        self.feat_queue.put(mel_chunks)
        
        # discard the old part to save memory
        self.frames = self.frames[-(self.stride_left_size + self.stride_right_size):]


    def get_next_feat(self,block,timeout):        
        return self.feat_queue.get(block,timeout)