import logging

import sys
import os
import numpy as np
import ctypes
from pydantic import Field
from typing_extensions import Literal
import glob
import cv2

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

logger = logging.getLogger(__name__)

DETECTOR_KEY = "rocm"

# XXX several detectors run yolov8, this should probably be common code in some utils module
def postprocess_yolov8(model_input_shape, tensor_output, box_count = 20):
    model_box_count = tensor_output.shape[2]
    model_class_count = tensor_output.shape[1] - 4
    probs = tensor_output[0, 4:, :]
    all_ids = np.argmax(probs, axis=0)
    all_confidences = np.take(probs.T, model_class_count*np.arange(0, model_box_count) + all_ids)
    all_boxes = tensor_output[0, 0:4, :].T
    mask = (all_confidences > 0.30)
    class_ids = all_ids[mask]
    confidences = all_confidences[mask]
    cx, cy, w, h = all_boxes[mask].T
    scale_y, scale_x = 1 / model_input_shape[2], 1 / model_input_shape[3]
    detections = np.stack((class_ids, confidences, scale_y * (cy - h / 2), scale_x * (cx - w / 2), scale_y * (cy + h / 2), scale_x * (cx + w / 2)), axis=1)
    if detections.shape[0] > box_count:
        detections = detections[np.argpartition(detections[:,1], -box_count)[-box_count:]]
    detections.resize((box_count, 6))
    return detections

class ROCmDetectorConfig(BaseDetectorConfig):
    type: Literal[DETECTOR_KEY]

class ROCmDetector(DetectionApi):
    type_key = DETECTOR_KEY

    def __init__(self, detector_config: ROCmDetectorConfig):
        try:
            sys.path.append("/opt/rocm/lib")
            import migraphx

            logger.info(f"AMD/ROCm: loaded migraphx module")
        except ValueError:
            logger.error(
                "AMD/ROCm: module loading failed, missing ROCm environment?"
            )
            raise

        assert detector_config.model.model_type == 'yolov8', "AMD/ROCm: detector_config.model.model_type: only yolov8 supported"
        assert detector_config.model.input_tensor == 'nhwc', "AMD/ROCm: detector_config.model.input_tensor: only nhwc supported"
        if detector_config.model.input_pixel_format != 'rgb':
            logger.warn("AMD/ROCm: detector_config.model.input_pixel_format: should be 'rgb' for yolov8, but '{detector_config.model.input_pixel_format}' specified!")

        assert detector_config.model.path is not None, "No model.path configured, please configure model.path and model.labelmap_path; some suggestions: " + ', '.join(glob.glob("/*.onnx")) + " and " + ', '.join(glob.glob("/*_labels.txt"))

        path = detector_config.model.path
        mxr_path = "/config/model_cache/rocm/" + os.path.basename(os.path.splitext(path)[0] + '.mxr')
        if path.endswith('.mxr'):
            logger.info(f"AMD/ROCm: loading parsed model from {mxr_path}")
            self.model = migraphx.load(mxr_path)
        elif os.path.exists(mxr_path):
            logger.info(f"AMD/ROCm: loading parsed model from {mxr_path}")
            self.model = migraphx.load(mxr_path)
        else:
            logger.info(f"AMD/ROCm: loading model from {path}")
            if path.endswith('.onnx'):
                self.model = migraphx.parse_onnx(path)
            elif path.endswith('.tf') or path.endswith('.tf2') or path.endswith('.tflite'):
                # untested
                self.model = migraphx.parse_tf(path)
            else:
                raise Exception(f"AMD/ROCm: unkown model format {path}")
            logger.info(f"AMD/ROCm: compiling the model")
            self.model.compile(migraphx.get_target('gpu'), offload_copy=True, fast_math=True)
            logger.info(f"AMD/ROCm: saving parsed model into {mxr_path}")
            os.makedirs("/config/model_cache/rocm", exist_ok=True)
            migraphx.save(self.model, mxr_path)
        logger.info(f"AMD/ROCm: model loaded")

    def detect_raw(self, tensor_input):
        model_input_name = self.model.get_parameter_names()[0];
        model_input_shape = tuple(self.model.get_parameter_shapes()[model_input_name].lens());

        tensor_input = cv2.dnn.blobFromImage(tensor_input[0], 1.0 / 255, (model_input_shape[3], model_input_shape[2]), None, swapRB=False)

        detector_result = self.model.run({model_input_name: tensor_input})[0]

        addr = ctypes.cast(detector_result.data_ptr(), ctypes.POINTER(ctypes.c_float))
        tensor_output = np.ctypeslib.as_array(addr, shape=detector_result.get_shape().lens())

        return postprocess_yolov8(model_input_shape, tensor_output)