# This code is from leafqycc/rknn-multi-threaded
# Following Apache License 2.0

import os
from concurrent.futures import ThreadPoolExecutor
from queue import Queue
import numpy as np
from numpy.typing import NDArray
from app.config import log

supported_socs = ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"]
coremask_supported_socs = ["rk3576","rk3588"]

try:
    from rknnlite.api import RKNNLite

    with open("/proc/device-tree/compatible") as f:
        device_compatible_str = f.read()
        for soc in supported_socs:
            if soc in device_compatible_str:
                is_available = True
                soc_name = soc
                break
            else:
                is_available = False
                soc_name = None
    is_available = is_available and os.path.exists("/sys/kernel/debug/rknpu/load")
except (FileNotFoundError, ImportError):
    log.debug("RKNN is not available")
    is_available = False
    soc_name = None


def initRKNN(rknnModel, id) -> RKNNLite:
    if not is_available:
        raise RuntimeError("rknn is not available!")
    rknn_lite = RKNNLite()
    ret = rknn_lite.load_rknn(rknnModel)
    if ret != 0:
        raise RuntimeError("Load RKNN rknnModel failed")
    
    if soc_name in coremask_supported_socs:
        if id == 0:
            ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
        elif id == 1:
            ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_1)
        elif id == 2:
            ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_2)
        elif id == -1:
            ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0_1_2)
        else:
            ret = rknn_lite.init_runtime()
    else:
        ret = rknn_lite.init_runtime() # Please do not set this parameter on other platforms.

    if ret != 0:
        raise RuntimeError("Init runtime environment failed")
    print(rknnModel, "\t\tdone")
    return rknn_lite


def initRKNNs(rknnModel, tpes) -> list[RKNNLite]:
    rknn_list = []
    for i in range(tpes):
        rknn_list.append(initRKNN(rknnModel, i % 3))
    return rknn_list


class RknnPoolExecutor:
    def __init__(self, rknnModel: str, tpes: int, func):
        self.tpes = tpes
        self.queue = Queue()
        self.rknn_pool = initRKNNs(rknnModel, tpes)
        self.pool = ThreadPoolExecutor(max_workers=tpes)
        self.func = func
        self.num = 0

    def put(self, inputs) -> None:
        self.queue.put(self.pool.submit(self.func, self.rknn_pool[self.num % self.tpes], inputs))
        self.num += 1

    def get(self) -> list[list[NDArray[np.float32]], bool]:
        if self.queue.empty():
            return None
        fut = self.queue.get()
        return fut.result()

    def release(self) -> None:
        self.pool.shutdown()
        for rknn_lite in self.rknn_pool:
            rknn_lite.release()