import ray
from ray.util.queue import Queue
from ray.actor import ActorHandle
import torch
import numpy as np


@ray.remote
class WebRtcAVQueueActor:
    def __init__(self):
        self.in_audio_queue = Queue(maxsize=100)  # Adjust the size as needed
        self.in_video_queue = Queue(maxsize=100)  # Adjust the size as needed
        self.out_audio_queue = Queue(maxsize=100)  # Adjust the size as needed


    def enqueue_in_video_frame(self, shared_tensor_ref):
        if self.in_video_queue.full():
            evicted_item = self.in_video_queue.get()
            del evicted_item
        self.in_video_queue.put(shared_tensor_ref)

    def enqueue_in_audio_frame(self, shared_buffer_ref):
        if self.in_audio_queue.full():
            evicted_item = self.in_audio_queue.get()
            del evicted_item
        self.in_audio_queue.put(shared_buffer_ref)


    def get_in_audio_frames(self):
        audio_frames = []
        if self.in_audio_queue.empty():
            return audio_frames
        while not self.in_audio_queue.empty():
            shared_tensor_ref = self.in_audio_queue.get()
            audio_frames.append(shared_tensor_ref)
        return audio_frames

    def get_in_video_frames(self):
        video_frames = []
        if self.in_video_queue.empty():
            return video_frames
        while not self.in_video_queue.empty():
            shared_tensor_ref = self.in_video_queue.get()
            video_frames.append(shared_tensor_ref)
        return video_frames
    
    def get_out_audio_queue(self):
        return self.out_audio_queue
    
    async def get_out_audio_frame(self):
        if self.out_audio_queue.empty():
            return None
        audio_frame = await self.out_audio_queue.get_async()
        return audio_frame