from abc import ABC, abstractmethod
from typing import List

import numpy as np
import onnxruntime as ort
from tqdm import tqdm

# ==================================================================================================


class BaseModel(ABC):
    def __init__(
        self, model_path: str, warmup: int, usetrt: bool = True, usegpu: bool = True
    ):
        self.opt = ort.SessionOptions()
        providers = ort.get_available_providers()
        # ort.set_default_logger_severity(1)

        self.providers = []
        if usetrt and "TensorrtExecutionProvider" in providers:
            self.providers.append("TensorrtExecutionProvider")
        if usegpu and "CUDAExecutionProvider" in providers:
            self.providers.append("CUDAExecutionProvider")
        self.providers.append("CPUExecutionProvider")
        print("Using providers:", self.providers)

        self.session = ort.InferenceSession(
            model_path, providers=self.providers, sess_options=self.opt
        )

        self.input_names = [input.name for input in self.session.get_inputs()]
        self.input_shapes = [input.shape for input in self.session.get_inputs()]

        input_types = [input.type for input in self.session.get_inputs()]
        self.input_types = []
        for i in range(len(input_types)):
            input_type = input_types[i]
            if input_type == "tensor(float16)":
                itype = np.float16
            elif input_type == "tensor(uint8)":
                itype = np.uint8
            elif input_type == "tensor(int32)":
                itype = np.int32
            else:
                itype = np.float32
            self.input_types.append(itype)

        if warmup > 0:
            self.warmup(warmup)

    @abstractmethod
    def preprocess(self, image: np.ndarray, *args, **kwargs):
        pass

    @abstractmethod
    def postprocess(self, tensor: List[np.ndarray], *args, **kwargs):
        pass

    def warmup(self, epoch: int):
        print("Running warmup for '{}' ...".format(self.__class__.__name__))
        for _ in tqdm(range(epoch)):
            inputs = {}
            for i in range(len(self.input_names)):
                iname = self.input_names[i]

                if "image" in iname:
                    ishape = self.input_shapes[i]
                    if "batch_size" in ishape:
                        if "TensorrtExecutionProvider" in self.providers:
                            # Using different images sizes for TensorRT warmup takes too long
                            ishape = [1, 1000, 1000, 3]
                        else:
                            ishape = [
                                1,
                                np.random.randint(300, 1000),
                                np.random.randint(300, 1000),
                                3,
                            ]
                    tensor = np.random.random(ishape)
                    tensor = tensor * 255
                elif "bbox" in iname:
                    tensor = np.array(
                        [
                            [
                                np.random.randint(30, 100),
                                np.random.randint(30, 100),
                                np.random.randint(200, 300),
                                np.random.randint(200, 300),
                            ]
                        ]
                    )
                else:
                    raise ValueError("Undefined input type:", iname)

                tensor = tensor.astype(self.input_types[i])
                inputs[iname] = tensor

            self.session.run(None, inputs)

    def __call__(self, image: np.ndarray, *args, **kwargs):
        tensor = self.preprocess(image, *args, **kwargs)
        inputs = {}
        for i in range(len(self.input_names)):
            iname = self.input_names[i]
            inputs[iname] = tensor[i]
        result = self.session.run(None, inputs)
        output = self.postprocess(result, *args, **kwargs)
        return output


# ==================================================================================================


class RTMDet(BaseModel):
    def __init__(
        self,
        model_path: str,
        conf_threshold: float,
        warmup: int = 30,
    ):
        super(RTMDet, self).__init__(model_path, warmup)
        self.conf_threshold = conf_threshold

    def preprocess(self, image: np.ndarray):
        tensor = np.asarray(image).astype(self.input_types[0], copy=False)
        tensor = np.expand_dims(tensor, axis=0)
        tensor = [tensor]
        return tensor

    def postprocess(self, tensor: List[np.ndarray]):
        boxes = np.squeeze(tensor[1], axis=0)
        classes = np.squeeze(tensor[0], axis=0)

        human_class = classes[:] == 0
        boxes = boxes[human_class]

        keep = boxes[:, 4] > self.conf_threshold
        boxes = boxes[keep]

        return boxes


# ==================================================================================================


class RTMPose(BaseModel):
    def __init__(self, model_path: str, warmup: int = 30):
        super(RTMPose, self).__init__(model_path, warmup)
        self.bbox = None

    def preprocess(self, image: np.ndarray, bbox: np.ndarray):
        tensor = np.asarray(image).astype(self.input_types[0], copy=False)
        tensor = np.expand_dims(tensor, axis=0)
        bbox = np.asarray(bbox)[0:4]
        bbox += np.array([-0.5, -0.5, 0.5 - 1e-8, 0.5 - 1e-8])
        bbox = bbox.round().astype(np.int32)
        bbox = np.expand_dims(bbox, axis=0)
        tensor = [tensor, bbox]
        return tensor

    def postprocess(self, tensor: List[np.ndarray], **kwargs):
        scores = np.clip(tensor[0][0], 0, 1)
        kp = np.concatenate([tensor[1][0], np.expand_dims(scores, axis=-1)], axis=-1)
        return kp


# ==================================================================================================


class TopDown:
    def __init__(
        self,
        det_model_path,
        pose_model_path,
        box_conf_threshold=0.6,
        warmup=30,
    ):
        if (not det_model_path.endswith(".onnx")) or (
            not pose_model_path.endswith(".onnx")
        ):
            raise ValueError("Only ONNX models are supported.")

        self.det_model = RTMDet(det_model_path, box_conf_threshold, warmup)
        self.pose_model = RTMPose(pose_model_path, warmup)

    def predict(self, image):
        boxes = self.det_model(image)
        results = []
        for i in range(boxes.shape[0]):
            kp = self.pose_model(image, bbox=boxes[i])
            results.append(kp)
        return results


# ==================================================================================================


def load_model():
    print("Loading onnx model ...")

    model = TopDown(
        # "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_320x320_extra-steps.onnx",
        "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_320x320_fp16_extra-steps.onnx",
        # "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_384x288_extra-steps.onnx",
        "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_384x288_fp16_extra-steps.onnx",
        box_conf_threshold=0.3,
        warmup=30,
    )

    print("Loaded onnx model")
    return model


def load_wb_model():
    print("Loading mmpose whole body model ...")

    model = None

    print("Loaded mmpose model")
    return model


# ==================================================================================================


def get_2d_pose(model, imgs, num_joints=17):

    new_poses = []
    for i in range(len(imgs)):
        img = imgs[i]

        poses = []
        dets = model.predict(img)
        for pose in dets:
            pose = np.asarray(pose)
            poses.append(pose)

        if len(poses) == 0:
            poses.append(np.zeros([num_joints, 3]))

        poses = np.array(poses)
        new_poses.append(poses)

    return new_poses