import logging
import os.path
import urllib.request
from typing import Literal

import numpy as np

try:
    from hide_warnings import hide_warnings
except:  # noqa: E722

    def hide_warnings(func):
        pass


from pydantic import Field

from frigate.detectors.detection_api import DetectionApi
from frigate.detectors.detector_config import BaseDetectorConfig

logger = logging.getLogger(__name__)

DETECTOR_KEY = "rknn"

yolov8_rknn_models = {
    "default-yolov8n": "n",
    "default-yolov8s": "s",
    "default-yolov8m": "m",
    "default-yolov8l": "l",
    "default-yolov8x": "x",
}


class RknnDetectorConfig(BaseDetectorConfig):
    type: Literal[DETECTOR_KEY]
    core_mask: int = Field(default=0, ge=0, le=7, title="Core mask for NPU.")


class Rknn(DetectionApi):
    type_key = DETECTOR_KEY

    def __init__(self, config: RknnDetectorConfig):
        self.model_path = config.model.path or "default-yolov8n"
        self.core_mask = config.core_mask
        self.height = config.model.height
        self.width = config.model.width

        if self.model_path in yolov8_rknn_models:
            if self.model_path == "default-yolov8n":
                self.model_path = "/models/yolov8n-320x320.rknn"
            else:
                model_suffix = yolov8_rknn_models[self.model_path]
                self.model_path = (
                    "/config/model_cache/rknn/yolov8{}-320x320.rknn".format(
                        model_suffix
                    )
                )

                os.makedirs("/config/model_cache/rknn", exist_ok=True)
                if not os.path.isfile(self.model_path):
                    logger.info("Downloading yolov8{} model.".format(model_suffix))
                    urllib.request.urlretrieve(
                        "https://github.com/MarcA711/rknn-models/releases/download/latest/yolov8{}-320x320.rknn".format(
                            model_suffix
                        ),
                        self.model_path,
                    )

            if (config.model.width != 320) or (config.model.height != 320):
                logger.error(
                    "Make sure to set the model width and heigth to 320 in your config.yml."
                )
                raise Exception(
                    "Make sure to set the model width and heigth to 320 in your config.yml."
                )

            if config.model.input_pixel_format != "bgr":
                logger.error(
                    'Make sure to set the model input_pixel_format to "bgr" in your config.yml.'
                )
                raise Exception(
                    'Make sure to set the model input_pixel_format to "bgr" in your config.yml.'
                )

            if config.model.input_tensor != "nhwc":
                logger.error(
                    'Make sure to set the model input_tensor to "nhwc" in your config.yml.'
                )
                raise Exception(
                    'Make sure to set the model input_tensor to "nhwc" in your config.yml.'
                )

        from rknnlite.api import RKNNLite

        self.rknn = RKNNLite(verbose=False)
        if self.rknn.load_rknn(self.model_path) != 0:
            logger.error("Error initializing rknn model.")
        if self.rknn.init_runtime(core_mask=self.core_mask) != 0:
            logger.error(
                "Error initializing rknn runtime. Do you run docker in privileged mode?"
            )

    def __del__(self):
        self.rknn.release()

    def postprocess(self, results):
        """
        Processes yolov8 output.

        Args:
        results: array with shape: (1, 84, n, 1) where n depends on yolov8 model size (for 320x320 model n=2100)

        Returns:
        detections: array with shape (20, 6) with 20 rows of (class, confidence, y_min, x_min, y_max, x_max)
        """

        results = np.transpose(results[0, :, :, 0])  # array shape (2100, 84)
        scores = np.max(
            results[:, 4:], axis=1
        )  # array shape (2100,); max confidence of each row

        # remove lines with score scores < 0.4
        filtered_arg = np.argwhere(scores > 0.4)
        results = results[filtered_arg[:, 0]]
        scores = scores[filtered_arg[:, 0]]

        num_detections = len(scores)

        if num_detections == 0:
            return np.zeros((20, 6), np.float32)

        if num_detections > 20:
            top_arg = np.argpartition(scores, -20)[-20:]
            results = results[top_arg]
            scores = scores[top_arg]
            num_detections = 20

        classes = np.argmax(results[:, 4:], axis=1)

        boxes = np.transpose(
            np.vstack(
                (
                    results[:, 1] - 0.5 * results[:, 3],
                    results[:, 0] - 0.5 * results[:, 2],
                    results[:, 3] + 0.5 * results[:, 3],
                    results[:, 2] + 0.5 * results[:, 2],
                )
            )
        )

        detections = np.zeros((20, 6), np.float32)
        detections[:num_detections, 0] = classes
        detections[:num_detections, 1] = scores
        detections[:num_detections, 2:] = boxes

        return detections

    @hide_warnings
    def inference(self, tensor_input):
        return self.rknn.inference(inputs=tensor_input)

    def detect_raw(self, tensor_input):
        output = self.inference(
            [
                tensor_input,
            ]
        )
        return self.postprocess(output[0])