import type { Frame, FrameInternal } from './Frame';
import { Camera } from './Camera';
import { Worklets } from 'react-native-worklets/src';
import { assertJSIAvailable } from './JSIHelper';

assertJSIAvailable();

// Install VisionCamera Frame Processor JSI Bindings and Plugins
Camera.installFrameProcessorBindings();

type BasicParameterType = string | number | boolean | undefined;
type ParameterType = BasicParameterType | BasicParameterType[] | Record<string, BasicParameterType | undefined>;
type FrameProcessor = (frame: Frame, parameters?: Record<string, ParameterType | undefined>) => unknown;
type TFrameProcessorPlugins = Record<string, FrameProcessor>;

/**
 * All natively installed Frame Processor Plugins.
 */
// @ts-expect-error The global JSI Proxy object is not typed.
export const FrameProcessorPlugins = global.FrameProcessorPlugins as TFrameProcessorPlugins;

const lastFrameProcessorCall = Worklets.createSharedValue(performance.now());

/**
 * Runs the given function at the given target FPS rate.
 *
 * For example, if you want to run a heavy face detection algorithm
 * only once per second, you can use `runAtTargetFps(1, ...)` to
 * throttle it to 1 FPS.
 *
 * @param fps The target FPS rate at which the given function should be executed
 * @param func The function to execute.
 * @returns The result of the function if it was executed, or `undefined` otherwise.
 * @example
 *
 * ```ts
 * const frameProcessor = useFrameProcessor((frame) => {
 *   'worklet'
 *   console.log('New Frame')
 *   const face = runAtTargetFps(5, () => {
 *     'worklet'
 *     const faces = detectFaces(frame)
 *     return faces[0]
 *   })
 *   if (face != null) console.log(`Detected a new face: ${face}`)
 * })
 * ```
 */
export function runAtTargetFps<T>(fps: number, func: () => T): T | undefined {
  'worklet';
  const targetIntervalMs = 1000 / fps; // <-- 60 FPS => 16,6667ms interval
  const now = performance.now();
  const diffToLastCall = now - lastFrameProcessorCall.value;
  if (diffToLastCall >= targetIntervalMs) {
    lastFrameProcessorCall.value = now;
    // Last Frame Processor call is already so long ago that we want to make a new call
    return func();
  }
  return undefined;
}

const isAsyncContextBusy = Worklets.createSharedValue(false);
const asyncContext = Worklets.createContext('VisionCamera.async');
const runOnAsyncContext = Worklets.createRunInContextFn((frame: Frame, func: () => void) => {
  'worklet';
  try {
    // Call long-running function
    func();
  } finally {
    // Potentially delete Frame if we were the last ref
    (frame as FrameInternal).decrementRefCount();

    isAsyncContextBusy.value = false;
  }
}, asyncContext);

/**
 * Runs the given function asynchronously, while keeping a strong reference to the Frame.
 *
 * For example, if you want to run a heavy face detection algorithm
 * while still drawing to the screen at 60 FPS, you can use `runAsync(...)`
 * to offload the face detection algorithm to a separate thread.
 *
 * @param frame The current Frame of the Frame Processor.
 * @param func The function to execute.
 * @example
 *
 * ```ts
 * const frameProcessor = useFrameProcessor((frame) => {
 *   'worklet'
 *   console.log('New Frame')
 *   runAsync(frame, () => {
 *     'worklet'
 *     const faces = detectFaces(frame)
 *     const face = [faces0]
 *     console.log(`Detected a new face: ${face}`)
 *   })
 * })
 * ```
 */
export function runAsync(frame: Frame, func: () => void): void {
  'worklet';

  if (isAsyncContextBusy.value) {
    // async context is currently busy, we cannot schedule new work in time.
    // drop this frame/runAsync call.
    return;
  }

  // Increment ref count by one
  (frame as FrameInternal).incrementRefCount();

  isAsyncContextBusy.value = true;

  // Call in separate background context
  runOnAsyncContext(frame, func);
}