import math
import os
from abc import ABC, abstractmethod
from typing import List

import cv2
import numpy as np
import onnxruntime as ort
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
from tqdm import tqdm

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


class BaseModel(ABC):
    def __init__(self, model_path: str, warmup: int):
        self.model_path = model_path
        self.runtime = ""

        if not os.path.exists(model_path):
            raise FileNotFoundError("File not found:", model_path)

        if model_path.endswith(".engine"):
            self.init_trt_engine(model_path)
            self.runtime = "trt"
        elif model_path.endswith(".onnx"):
            self.init_onnxruntime(model_path)
            self.runtime = "ort"
        else:
            raise ValueError("Unsupported model format:", model_path)

        if warmup > 0:
            print("Running warmup for '{}' ...".format(self.__class__.__name__))
            self.warmup(warmup // 2)
            self.warmup(warmup // 2)

    def init_onnxruntime(self, model_path):
        usetrt = True
        usegpu = 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(float32)":
                itype = np.float32
            elif input_type == "tensor(float16)":
                itype = np.float16
            elif input_type == "tensor(int32)":
                itype = np.int32
            elif input_type == "tensor(uint8)":
                itype = np.uint8
            else:
                raise ValueError("Undefined input type:", input_type)
            self.input_types.append(itype)

    def init_trt_engine(self, engine_path):
        # https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#python_topics
        # https://stackoverflow.com/a/79076885

        self.trt_logger = trt.Logger(trt.Logger.WARNING)
        with open(engine_path, "rb") as f:
            runtime = trt.Runtime(self.trt_logger)
            self.engine = runtime.deserialize_cuda_engine(f.read())
        self.context = self.engine.create_execution_context()
        self.stream = cuda.Stream()

        self.inputs, self.outputs, self.bindings = [], [], []
        self.input_names = []
        self.input_shapes = []
        self.input_types = []

        for i in range(self.engine.num_io_tensors):
            tensor_name = self.engine.get_tensor_name(i)
            shape = self.engine.get_tensor_shape(tensor_name)
            dtype = trt.nptype(self.engine.get_tensor_dtype(tensor_name))

            if -1 in shape:
                print("WARNING: Replacing dynamic shape with fixed for:", tensor_name)
                shape[list(shape).index(-1)] = 10

            # Allocate host and device buffers
            size = trt.volume(shape)
            host_mem = cuda.pagelocked_empty(size, dtype)
            device_mem = cuda.mem_alloc(host_mem.nbytes)
            self.bindings.append(int(device_mem))

            # Append to the appropriate input/output list
            if self.engine.get_tensor_mode(tensor_name) == trt.TensorIOMode.INPUT:
                self.inputs.append((host_mem, device_mem, shape))
                self.input_names.append(tensor_name)
                self.input_shapes.append(shape)
                self.input_types.append(dtype)
            else:
                self.outputs.append((host_mem, device_mem, shape))

            # Set tensor address
            self.context.set_tensor_address(
                self.engine.get_tensor_name(i), self.bindings[i]
            )

    @abstractmethod
    def preprocess(self, **kwargs):
        pass

    @abstractmethod
    def postprocess(self, **kwargs):
        pass

    def warmup(self, epoch: int):
        np.random.seed(42)

        for _ in tqdm(range(epoch)):
            inputs = {}
            for i in range(len(self.input_names)):
                iname = self.input_names[i]

                if "image" in iname:
                    ishape = list(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.call_model(list(inputs.values()))

    def call_model_ort(self, tensor):
        inputs = {}
        for i in range(len(self.input_names)):
            iname = self.input_names[i]
            inputs[iname] = tensor[i]
        result = self.session.run(None, inputs)
        return result

    def call_model_trt(self, tensor):
        # Transfer input data to device
        for i, input_data in enumerate(tensor):
            np.copyto(self.inputs[i][0], input_data.ravel())
            cuda.memcpy_htod_async(self.inputs[i][1], self.inputs[i][0], self.stream)

        # Empty the output buffers
        for i in range(len(self.outputs)):
            self.outputs[i][0].fill(0)
            cuda.memcpy_htod_async(self.outputs[i][1], self.outputs[i][0], self.stream)

        # Run inference
        self.context.execute_async_v3(stream_handle=self.stream.handle)

        # Transfer predictions back
        for i in range(len(self.outputs)):
            cuda.memcpy_dtoh_async(self.outputs[i][0], self.outputs[i][1], self.stream)

        # Synchronize the stream
        self.stream.synchronize()

        # Un-flatten the outputs
        outputs = []
        for i in range(len(self.outputs)):
            output = self.outputs[i][0].reshape(self.outputs[i][2])
            outputs.append(output)

        return outputs

    def call_model(self, tensor):
        if self.runtime == "trt":
            result = self.call_model_trt(tensor)
        elif self.runtime == "ort":
            result = self.call_model_ort(tensor)
        return result

    def __call__(self, **kwargs):
        tensor = self.preprocess(**kwargs)
        result = self.call_model(tensor)
        output = self.postprocess(result=result, **kwargs)
        return output


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


class LetterBox:
    def __init__(self, target_size, fill_value=0):
        self.target_size = target_size
        self.fill_value = fill_value

    def calc_params(self, ishape):
        img_h, img_w = ishape[:2]
        target_h, target_w = self.target_size

        scale = min(target_w / img_w, target_h / img_h)
        new_w = round(img_w * scale)
        new_h = round(img_h * scale)

        pad_w = target_w - new_w
        pad_h = target_h - new_h
        pad_left = pad_w // 2
        pad_top = pad_h // 2
        pad_right = pad_w - pad_left
        pad_bottom = pad_h - pad_top
        paddings = (pad_left, pad_right, pad_top, pad_bottom)

        return paddings, scale, (new_w, new_h)

    def resize_image(self, image):
        paddings, _, new_size = self.calc_params(image.shape)

        # Resize the image
        new_w, new_h = new_size
        resized_img = cv2.resize(
            image,
            (new_w, new_h),
            interpolation=cv2.INTER_NEAREST,
        )

        # Optionally pad the image
        pad_left, pad_right, pad_top, pad_bottom = paddings
        if pad_left == 0 and pad_right == 0 and pad_top == 0 and pad_bottom == 0:
            final_img = resized_img
        else:
            final_img = cv2.copyMakeBorder(
                resized_img,
                pad_top,
                pad_bottom,
                pad_left,
                pad_right,
                borderType=cv2.BORDER_CONSTANT,
                value=[self.fill_value, self.fill_value, self.fill_value],
            )

        return final_img


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


class BoxCrop:
    def __init__(self, target_size, padding_scale=1.0, fill_value=0):
        self.target_size = target_size
        self.padding_scale = padding_scale
        self.fill_value = fill_value

    def calc_params(self, ishape, bbox):
        start_x, start_y, end_x, end_y = bbox[0], bbox[1], bbox[2], bbox[3]
        target_h, target_w = self.target_size

        # Calculate original bounding box center
        center_x = (start_x + end_x) / 2.0
        center_y = (start_y + end_y) / 2.0

        # Scale the bounding box by the padding_scale
        bbox_w = end_x - start_x
        bbox_h = end_y - start_y
        scaled_w = bbox_w * self.padding_scale
        scaled_h = bbox_h * self.padding_scale

        # Calculate the aspect ratios
        bbox_aspect = scaled_w / scaled_h
        target_aspect = target_w / target_h

        # Adjust the scaled bounding box to match the target aspect ratio
        if bbox_aspect > target_aspect:
            adjusted_h = scaled_w / target_aspect
            adjusted_w = scaled_w
        else:
            adjusted_w = scaled_h * target_aspect
            adjusted_h = scaled_h

        # Calculate scaled bounding box coordinates
        bbox_w = adjusted_w
        bbox_h = adjusted_h
        new_start_x = center_x - bbox_w / 2.0
        new_start_y = center_y - bbox_h / 2.0
        new_end_x = center_x + bbox_w / 2.0
        new_end_y = center_y + bbox_h / 2.0

        # Round the box coordinates
        start_x = int(math.floor(new_start_x))
        start_y = int(math.floor(new_start_y))
        end_x = int(math.ceil(new_end_x))
        end_y = int(math.ceil(new_end_y))

        # Define the new box coordinates
        new_start_x = max(0, start_x)
        new_start_y = max(0, start_y)
        new_end_x = min(ishape[1] - 1, end_x)
        new_end_y = min(ishape[0] - 1, end_y)
        new_box = [new_start_x, new_start_y, new_end_x, new_end_y]

        # Calculate resized crop size
        bbox_w = new_box[2] - new_box[0]
        bbox_h = new_box[3] - new_box[1]
        scale = min(target_w / bbox_w, target_h / bbox_h)
        new_w = round(bbox_w * scale)
        new_h = round(bbox_h * scale)

        # Calculate paddings
        pad_w = target_w - new_w
        pad_h = target_h - new_h
        pad_left, pad_right, pad_top, pad_bottom = 0, 0, 0, 0
        if pad_w > 0:
            if start_x < 0:
                pad_left = pad_w
                pad_right = 0
            elif end_x > ishape[1]:
                pad_left = 0
                pad_right = pad_w
            else:
                # Can be caused by bbox rounding
                pad_left = pad_w // 2
                pad_right = pad_w - pad_left
        if pad_h > 0:
            if start_y < 0:
                pad_top = pad_h
                pad_bottom = 0
            elif end_y > ishape[0]:
                pad_top = 0
                pad_bottom = pad_h
            else:
                # Can be caused by bbox rounding
                pad_top = pad_h // 2
                pad_bottom = pad_h - pad_top
        paddings = (pad_left, pad_right, pad_top, pad_bottom)

        return paddings, scale, new_box, (new_w, new_h)

    def crop_resize_box(self, image, bbox):
        paddings, _, new_box, new_size = self.calc_params(image.shape, bbox)

        # Extract the bounding box
        cropped_img = image[new_box[1] : new_box[3], new_box[0] : new_box[2]]

        # Resize the image
        new_w, new_h = new_size
        resized_img = cv2.resize(
            cropped_img,
            (new_w, new_h),
            interpolation=cv2.INTER_NEAREST,
        )

        # Optionally pad the image
        pad_left, pad_right, pad_top, pad_bottom = paddings
        if pad_left == 0 and pad_right == 0 and pad_top == 0 and pad_bottom == 0:
            final_img = resized_img
        else:
            final_img = cv2.copyMakeBorder(
                resized_img,
                pad_top,
                pad_bottom,
                pad_left,
                pad_right,
                borderType=cv2.BORDER_CONSTANT,
                value=[self.fill_value, self.fill_value, self.fill_value],
            )

        return final_img


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


class RTMDet(BaseModel):
    def __init__(
        self,
        model_path: str,
        conf_threshold: float,
        min_area_fraction: float,
        warmup: int = 30,
    ):
        super(RTMDet, self).__init__(model_path, warmup)
        self.target_size = (320, 320)
        self.conf_threshold = conf_threshold
        self.letterbox = LetterBox(self.target_size, fill_value=114)

        img_area = self.target_size[0] * self.target_size[1]
        self.min_area = img_area * min_area_fraction

    def preprocess(self, image: np.ndarray):
        image = self.letterbox.resize_image(image)
        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, result: List[np.ndarray], image: np.ndarray):
        boxes = np.squeeze(result[0], axis=0)
        classes = np.squeeze(result[1], axis=0)

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

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

        if len(boxes) == 0:
            return np.array([])

        # Drop boxes with too small area
        boxes = boxes.astype(np.float32)
        areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
        keep = areas >= self.min_area
        boxes = boxes[keep]

        if len(boxes) == 0:
            return np.array([])

        paddings, scale, _ = self.letterbox.calc_params(image.shape)

        boxes[:, 0] -= paddings[0]
        boxes[:, 2] -= paddings[0]
        boxes[:, 1] -= paddings[2]
        boxes[:, 3] -= paddings[2]

        boxes = np.maximum(boxes, 0)
        th, tw = self.target_size
        pad_w = paddings[0] + paddings[1]
        pad_h = paddings[2] + paddings[3]
        max_w = tw - pad_w - 1
        max_h = th - pad_h - 1
        boxes[:, 0] = np.minimum(boxes[:, 0], max_w)
        boxes[:, 1] = np.minimum(boxes[:, 1], max_h)
        boxes[:, 2] = np.minimum(boxes[:, 2], max_w)
        boxes[:, 3] = np.minimum(boxes[:, 3], max_h)

        boxes[:, 0:4] /= scale
        return boxes


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


class RTMPose(BaseModel):
    def __init__(self, model_path: str, warmup: int = 30):
        super(RTMPose, self).__init__(model_path, warmup)
        self.target_size = (384, 288)
        self.boxcrop = BoxCrop(self.target_size, padding_scale=1.25, fill_value=0)

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

    def postprocess(
        self, result: List[np.ndarray], image: np.ndarray, bbox: np.ndarray
    ):
        scores = np.clip(result[1][0], 0, 1)
        kp = np.concatenate([result[0][0], np.expand_dims(scores, axis=-1)], axis=-1)

        paddings, scale, bbox, _ = self.boxcrop.calc_params(image.shape, bbox)
        kp[:, 0] -= paddings[0]
        kp[:, 1] -= paddings[2]
        kp[:, 0:2] /= scale
        kp[:, 0] += bbox[0]
        kp[:, 1] += bbox[1]
        kp[:, 0:2] = np.maximum(kp[:, 0:2], 0)
        max_w = image.shape[1] - 1
        max_h = image.shape[0] - 1
        kp[:, 0] = np.minimum(kp[:, 0], max_w)
        kp[:, 1] = np.minimum(kp[:, 1], max_h)

        return kp


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


class TopDown:
    def __init__(
        self,
        det_model_path: str,
        pose_model_path: str,
        box_conf_threshold: float,
        box_min_area: float,
        warmup: int = 30,
    ):
        self.det_model = RTMDet(
            det_model_path, box_conf_threshold, box_min_area, warmup
        )
        self.pose_model = RTMPose(pose_model_path, warmup)

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


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


def load_model(min_bbox_score=0.3, min_bbox_area=0.1 * 0.1):
    print("Loading 2D model ...")

    model = TopDown(
        # "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_320x320_fp16_extra-steps.onnx",
        # "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_384x288_fp16_extra-steps.onnx",
        "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmdet-nano_1x320x320x3_fp16_extra-steps.engine",
        "/RapidPoseTriangulation/extras/mmdeploy/exports/rtmpose-m_1x384x288x3_fp16_extra-steps.engine",
        box_conf_threshold=min_bbox_score,
        box_min_area=min_bbox_area,
        warmup=30,
    )

    print("Loaded 2D 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