"""Kalman-only MyTrack variant without any motion-model code."""

from __future__ import annotations

from collections import deque
from pathlib import Path
from typing import Optional

import numpy as np
import torch

from boxmot.appearance.reid.auto_backend import ReidAutoBackend
from boxmot.motion.cmc import get_cmc_method
from boxmot.motion.kalman_filters.aabb.xysr_kf import KalmanFilterXYSR
from boxmot.trackers.basetracker import BaseTracker
from boxmot.utils import logger as LOGGER
from boxmot.utils.association import _associate, associate, linear_assignment
from boxmot.utils.ops import xyxy2xysr


def k_previous_obs(observations, cur_age, k):
    """Fetch the closest valid observation within the requested time window."""
    if len(observations) == 0:
        return [-1, -1, -1, -1, -1]
    for i in range(k):
        dt = k - i
        if cur_age - dt in observations:
            return observations[cur_age - dt]
    max_age = max(observations.keys())
    return observations[max_age]


def convert_x_to_bbox(x, score=None):
    """Convert [x, y, s, r] state back to an xyxy bounding box."""
    w = np.sqrt(x[2] * x[3])
    h = x[2] / w
    if score is None:
        return np.array([x[0] - w / 2.0, x[1] - h / 2.0,
                         x[0] + w / 2.0, x[1] + h / 2.0]).reshape((1, 4))
    return np.array([x[0] - w / 2.0, x[1] - h / 2.0,
                     x[0] + w / 2.0, x[1] + h / 2.0, score]
        ).reshape((1, 5))


def speed_direction(bbox1, bbox2):
    """Estimate the normalized center displacement from bbox1 to bbox2."""
    cx1, cy1 = (bbox1[0] + bbox1[2]) / 2.0, (bbox1[1] + bbox1[3]) / 2.0
    cx2, cy2 = (bbox2[0] + bbox2[2]) / 2.0, (bbox2[1] + bbox2[3]) / 2.0
    speed = np.array([cy2 - cy1, cx2 - cx1])
    norm = np.sqrt((cy2 - cy1) ** 2 + (cx2 - cx1) ** 2) + 1e-6
    return speed / norm


class KalmanBoxTracker:
    """
    This class represents the internal state of individual tracked objects observed as bbox.
    """

    count = 0

    def __init__(
        self,
        det,
        delta_t=3,
        emb=None,
        alpha=0,
        max_obs=50,
        Q_xy_scaling=0.01,
        Q_s_scaling=0.0001,
    ):
        """
        Initialises a tracker using initial bounding box.

        """
        # define constant velocity model
        self.max_obs = max_obs
        bbox = det[0:5]
        self.conf = det[4]
        self.cls = det[5]
        self.det_ind = det[6]

        self.Q_xy_scaling = Q_xy_scaling
        self.Q_s_scaling = Q_s_scaling

        self.kf = KalmanFilterXYSR(dim_x=7, dim_z=4)
        self.kf.F = np.array(
            [
                # x  y  s  r  x' y' s'
                [1, 0, 0, 0, 1, 0, 0],
                [0, 1, 0, 0, 0, 1, 0],
                [0, 0, 1, 0, 0, 0, 1],
                [0, 0, 0, 1, 0, 0, 0],
                [0, 0, 0, 0, 1, 0, 0],
                [0, 0, 0, 0, 0, 1, 0],
                [0, 0, 0, 0, 0, 0, 1],
            ]
        )
        self.kf.H = np.array(
            [
                [1, 0, 0, 0, 0, 0, 0],
                [0, 1, 0, 0, 0, 0, 0],
                [0, 0, 1, 0, 0, 0, 0],
                [0, 0, 0, 1, 0, 0, 0],
            ]
        )
        self.kf.R[2:, 2:] *= 10.0
        self.kf.P[
            4:, 4:
        ] *= 1000.0  # give high uncertainty to the unobservable initial velocities
        self.kf.P *= 10.0
        self.kf.Q[4:6, 4:6] *= self.Q_xy_scaling
        self.kf.Q[-1, -1] *= self.Q_s_scaling

        self.bbox_to_z_func = xyxy2xysr
        self.x_to_bbox_func = convert_x_to_bbox

        self.kf.x[:4] = self.bbox_to_z_func(bbox)

        self.time_since_update = 0
        self.id = KalmanBoxTracker.count
        KalmanBoxTracker.count += 1
        self.history = deque([], maxlen=self.max_obs)
        self.hits = 0
        self.hit_streak = 0
        self.age = 0
        """
        NOTE: [-1,-1,-1,-1,-1] is a compromising placeholder for non-observation status, the same for the return of
        function k_previous_obs. It is ugly and I do not like it. But to support generate observation array in a
        fast and unified way, which you would see below k_observations = np.array([k_previous_obs(...]]),
        let's bear it for now.
        """
        # Used for OCR
        self.last_observation = np.array([-1, -1, -1, -1, -1])  # placeholder
        # Used to output track after min_hits reached
        self.features = deque([], maxlen=self.max_obs)
        # Used for velocity
        self.observations = dict()
        self.velocity = None
        self.delta_t = delta_t
        self.history_observations = deque([], maxlen=self.max_obs)

        self.emb = emb

        self.frozen = False

    def update(self, det):
        """
        Updates the state vector with observed bbox.
        """

        if det is not None:
            bbox = det[0:5]
            self.conf = det[4]
            self.cls = det[5]
            self.det_ind = det[6]
            self.frozen = False

            if self.last_observation.sum() >= 0:  # no previous observation
                previous_box = None
                for dt in range(self.delta_t, 0, -1):
                    if self.age - dt in self.observations:
                        previous_box = self.observations[self.age - dt]
                        break
                if previous_box is None:
                    previous_box = self.last_observation
                # Estimate the track speed direction with observations Δt steps away
                self.velocity = speed_direction(previous_box, bbox)
            """
              Insert new observations. This is a ugly way to maintain both self.observations
              and self.history_observations. Bear it for the moment.
            """
            self.last_observation = bbox
            self.observations[self.age] = bbox
            self.history_observations.append(bbox)

            self.time_since_update = 0
            self.hits += 1
            self.hit_streak += 1

            self.kf.update(self.bbox_to_z_func(bbox))
        else:
            self.kf.update(det)
            self.frozen = True

    def update_emb(self, emb, alpha=0.9):
        self.emb = alpha * self.emb + (1 - alpha) * emb
        self.emb /= np.linalg.norm(self.emb)

    def get_emb(self):
        return self.emb

    def apply_affine_correction(self, affine):
        m = affine[:, :2]
        t = affine[:, 2].reshape(2, 1)
        # For OCR
        if self.last_observation.sum() > 0:
            ps = self.last_observation[:4].reshape(2, 2).T
            ps = m @ ps + t
            self.last_observation[:4] = ps.T.reshape(-1)

        # Apply to each box in the range of velocity computation
        for dt in range(self.delta_t, -1, -1):
            if self.age - dt in self.observations:
                ps = self.observations[self.age - dt][:4].reshape(2, 2).T
                ps = m @ ps + t
                self.observations[self.age - dt][:4] = ps.T.reshape(-1)

        # Also need to change kf state, but might be frozen
        self.kf.apply_affine_correction(m, t)

    def predict(self):
        """
        Advances the state vector and returns the predicted bounding box estimate.
        """
        # Don't allow negative bounding boxes
        if (self.kf.x[6] + self.kf.x[2]) <= 0:
            self.kf.x[6] *= 0.0
        Q = None

        self.kf.predict(Q=Q)
        self.age += 1
        if self.time_since_update > 0:
            self.hit_streak = 0
        self.time_since_update += 1
        self.history.append(self.x_to_bbox_func(self.kf.x))
        return self.history[-1]

    def get_state(self):
        """
        Returns the current bounding box estimate.
        """
        return self.x_to_bbox_func(self.kf.x)

    def mahalanobis(self, bbox):
        """Should be run after a predict() call for accuracy."""
        return self.kf.md_for_measurement(self.bbox_to_z_func(bbox))


class MyTrack(BaseTracker):
    """DeepOcSort variant.
    Initialize the DeepOcSort tracker with various parameters.

    Parameters:
    - reid_weights (Path): Path to the re-identification model weights.
    - device (torch.device): Device to run the model on (e.g., 'cpu', 'cuda').
    - half (bool): Whether to use half-precision (fp16) for faster inference.
    - det_thresh (float): Detection threshold for considering detections.
    - max_age (int): Maximum age (in frames) of a track before it is considered lost.
    - max_obs (int): Maximum number of historical observations stored for each track. Always greater than max_age by minimum 5.
    - min_hits (int): Minimum number of detection hits before a track is considered confirmed.
    - iou_threshold (float): IOU threshold for determining match between detection and tracks.
    - per_class (bool): Enables class-separated tracking.
    - nr_classes (int): Total number of object classes that the tracker will handle (for per_class=True).
    - asso_func (str): Algorithm name used for data association between detections and tracks.
    - is_obb (bool): Work with Oriented Bounding Boxes (OBB) instead of standard axis-aligned bounding boxes.
    
    DeepOcSort-specific parameters:
    - delta_t (int): Time window size for motion estimation.
    - inertia (float): Motion model weight, higher values favor motion consistency.
    - w_association_emb (float): Weight for embedding association in the matching cost.
    - alpha_fixed_emb (float): Fixed update rate for embeddings.
    - aw_param (float): Adaptive weight parameter for cost function.
    - embedding_off (bool): Whether to disable appearance embedding for tracking.
    - cmc_off (bool): Whether to disable camera motion compensation.
    - aw_off (bool): Whether to disable adaptive weights for appearance/motion balance.
    - Q_xy_scaling (float): Scaling factor for process noise in position coordinates.
    - Q_s_scaling (float): Scaling factor for process noise in scale coordinates.
    
    Attributes:
    - frame_count (int): Counter for the frames processed.
    - active_tracks (list): List to hold active tracks.
    - model: ReID model for appearance feature extraction.
    - cmc: Camera motion compensation object.
    - kalman_filter: Kalman filter for motion estimation.
    """

    def __init__(
        self,
        reid_weights: Path,
        device: torch.device,
        half: bool,
        # BaseTracker parameters
        det_thresh: float = 0.3,
        max_age: int = 30,
        max_obs: int = 50,
        min_hits: int = 3,
        iou_threshold: float = 0.3,
        per_class: bool = False,
        nr_classes: int = 80,
        asso_func: str = "iou",
        is_obb: bool = False,
        # DeepOcSort-specific parameters
        delta_t: int = 3,
        inertia: float = 0.2,
        w_association_emb: float = 0.5,
        alpha_fixed_emb: float = 0.95,
        aw_param: float = 0.5,
        embedding_off: bool = False,
        cmc_off: bool = False,
        aw_off: bool = False,
        Q_xy_scaling: float = 0.01,
        Q_s_scaling: float = 0.0001,
        aarm_open: bool = False,
        **kwargs  # Additional BaseTracker parameters
    ):
        # Forward all BaseTracker parameters explicitly
        super().__init__(
            det_thresh=det_thresh,
            max_age=max_age,
            max_obs=max_obs,
            min_hits=min_hits,
            iou_threshold=iou_threshold,
            per_class=per_class,
            nr_classes=nr_classes,
            asso_func=asso_func,
            is_obb=is_obb,
            **kwargs
        )
        
        """
        Sets key parameters for SORT
        """
        self.max_age = max_age
        self.min_hits = min_hits
        self.iou_threshold = iou_threshold
        self.det_thresh = det_thresh
        self.delta_t = delta_t
        self.asso_func = asso_func
        self.inertia = inertia
        self.w_association_emb = w_association_emb
        self.alpha_fixed_emb = alpha_fixed_emb
        self.aw_param = aw_param
        self.per_class = per_class
        self.Q_xy_scaling = Q_xy_scaling
        self.Q_s_scaling = Q_s_scaling
        self.aarm_open = bool(aarm_open)
        KalmanBoxTracker.count = 1

        self.embedding_off = embedding_off
        if self.embedding_off:
            self.model = None
        else:
            self.model = ReidAutoBackend(
                weights=reid_weights, device=device, half=half
            ).model
        # "similarity transforms using feature point extraction, optical flow, and RANSAC"
        self.cmc = get_cmc_method("sof")()
        self.cmc_off = cmc_off
        self.aw_off = aw_off

        LOGGER.success(
            f"Initialized MyTrack | asso={self.asso_func} | aarm_open={self.aarm_open} |"
        )

    def _associate_tracks(
        self,
        dets: np.ndarray,
        trks: np.ndarray,
        dets_embs: np.ndarray,
        trk_embs: np.ndarray,
        velocities: np.ndarray,
        k_observations: np.ndarray,
        stage1_emb_cost: Optional[np.ndarray],
    ):
        """Run the configured association path."""
        if len(trks) == 0:
            return (
                np.empty((0, 2), dtype=int),
                np.arange(len(dets), dtype=int),
                np.empty((0,), dtype=int),
            )
        if len(dets) == 0:
            return (
                np.empty((0, 2), dtype=int),
                np.empty((0,), dtype=int),
                np.arange(len(trks), dtype=int),
            )

        use_attention_assoc = self.aarm_open
        valid_embs = (
            dets_embs is not None
            and trk_embs is not None
            and dets_embs.ndim == 2
            and trk_embs.ndim == 2
            and dets_embs.shape[0] == dets.shape[0]
            and trk_embs.shape[0] == trks.shape[0]
            and dets_embs.shape[1] > 0
            and trk_embs.shape[1] > 0
            and dets_embs.shape[1] == trk_embs.shape[1]
        )

        if use_attention_assoc and valid_embs:
            return _associate(
                dets[:, 0:5],
                trks,
                self.asso_func,
                dets_embs,
                trk_embs,
                self.iou_threshold,
                velocities,
                k_observations,
                self.inertia,
                self.aarm_open,
                stage1_emb_cost,
                self.w_association_emb,
                self.aw_off,
                self.aw_param,
            )
        return associate(
            dets[:, 0:5],
            trks,
            self.asso_func,
            self.iou_threshold,
            velocities,
            k_observations,
            self.inertia,
            self.width,
            self.height,
            stage1_emb_cost,
            self.w_association_emb,
            self.aw_off,
            self.aw_param,
        )

    @BaseTracker.setup_decorator
    @BaseTracker.per_class_decorator
    def update(
        self, dets: np.ndarray, img: np.ndarray, embs: np.ndarray = None
    ) -> np.ndarray:
        """
        Params:
          dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
        Requires: this method must be called once for each frame even with empty detections
        (use np.empty((0, 5)) for frames without detections).
        Returns the a similar array, where the last column is the object ID.
        NOTE: The number of objects returned may differ from the number of detections provided.
        """
        # dets, s, c = dets.data
        # print(dets, s, c)
        self.check_inputs(dets, img, embs)

        self.frame_count += 1
        self.height, self.width = img.shape[:2]

        scores = dets[:, 4]
        dets = np.hstack([dets, np.arange(len(dets)).reshape(-1, 1)])
        assert dets.shape[1] == 7
        remain_inds = scores > self.det_thresh
        dets = dets[remain_inds]

        # appearance descriptor extraction
        if self.embedding_off or dets.shape[0] == 0:
            dets_embs = np.ones((dets.shape[0], 1))
        elif embs is not None:
            dets_embs = embs[remain_inds]
        else:
            # (Ndets x X) [512, 1024, 2048]
            dets_embs = self.model.get_features(dets[:, 0:4], img)

        # CMC
        if not self.cmc_off:
            transform = self.cmc.apply(img, dets[:, :4])
            for trk in self.active_tracks:
                trk.apply_affine_correction(transform)

        trust = (dets[:, 4] - self.det_thresh) / (1 - self.det_thresh)
        af = self.alpha_fixed_emb
        # From [self.alpha_fixed_emb, 1], goes to 1 as detector is less confident
        dets_alpha = af + (1 - af) * (1 - trust)

        # get predicted locations from existing trackers.
        trks = np.zeros((len(self.active_tracks), 5))
        trk_embs = []
        to_del = []
        ret = []
        for t, trk in enumerate(trks):
            pos = self.active_tracks[t].predict()[0]
            trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
            if np.any(np.isnan(pos)):
                to_del.append(t)
            else:
                trk_embs.append(self.active_tracks[t].get_emb())
        trks = np.ma.compress_rows(np.ma.masked_invalid(trks))

        if len(trk_embs) > 0:
            trk_embs = np.vstack(trk_embs)
        else:
            trk_embs = np.array(trk_embs)

        for t in reversed(to_del):
            self.active_tracks.pop(t)

        velocities = np.array(
            [trk.velocity if trk.velocity is not None else np.array((0, 0)) for trk in self.active_tracks])
        last_boxes = np.array([trk.last_observation for trk in self.active_tracks])
        k_observations = np.array(
            [k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.active_tracks])
        
        """
            First round of association
        """
        # (M detections X N tracks, final score)
        if self.embedding_off or dets.shape[0] == 0 or trk_embs.shape[0] == 0:
            stage1_emb_cost = None
        else:
            stage1_emb_cost = dets_embs @ trk_embs.T
        matched, unmatched_dets, unmatched_trks = self._associate_tracks(
            dets[:, 0:5],
            trks,
            dets_embs,
            trk_embs,
            velocities,
            k_observations,
            stage1_emb_cost,
        )

        for m in matched:
            self.active_tracks[m[1]].update(dets[m[0], :])
            self.active_tracks[m[1]].update_emb(dets_embs[m[0]], alpha=dets_alpha[m[0]])

        """
            Second round of associaton by OCR
        """
        if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:
            left_dets = dets[unmatched_dets]
            left_dets_embs = dets_embs[unmatched_dets]
            left_trks = last_boxes[unmatched_trks]
            left_trks_embs = trk_embs[unmatched_trks]

            iou_left = self.asso_func(left_dets, left_trks)
            # TODO: is better without this
            emb_cost_left = left_dets_embs @ left_trks_embs.T
            if self.embedding_off:
                emb_cost_left = np.zeros_like(emb_cost_left)
            iou_left = np.array(iou_left)
            if iou_left.max() > self.iou_threshold:
                """
                NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may
                get a higher performance especially on MOT17/MOT20 datasets. But we keep it
                uniform here for simplicity
                """
                rematched_indices = linear_assignment(-iou_left)
                to_remove_det_indices = []
                to_remove_trk_indices = []
                for m in rematched_indices:
                    det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]
                    if iou_left[m[0], m[1]] < self.iou_threshold:
                        continue
                    self.active_tracks[trk_ind].update(dets[det_ind, :])
                    self.active_tracks[trk_ind].update_emb(
                        dets_embs[det_ind], alpha=dets_alpha[det_ind]
                    )
                    to_remove_det_indices.append(det_ind)
                    to_remove_trk_indices.append(trk_ind)
                unmatched_dets = np.setdiff1d(unmatched_dets, np.array(to_remove_det_indices))
                unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))

        for m in unmatched_trks:
            self.active_tracks[m].update(None)

        # create and initialise new trackers for unmatched detections
        for i in unmatched_dets:
            trk = KalmanBoxTracker(
                dets[i],
                delta_t=self.delta_t,
                emb=dets_embs[i],
                alpha=dets_alpha[i],
                Q_xy_scaling=self.Q_xy_scaling,
                Q_s_scaling=self.Q_s_scaling,
                max_obs=self.max_obs,
            )
            self.active_tracks.append(trk)

        i = len(self.active_tracks)
        for trk in reversed(self.active_tracks):
            if trk.last_observation.sum() < 0:
                d = trk.get_state()[0]
            else:
                """
                this is optional to use the recent observation or the kalman filter prediction,
                we didn't notice significant difference here
                """
                d = trk.last_observation[:4]
            if (trk.time_since_update < 1) and (
                trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits
            ):
                # +1 as MOT benchmark requires positive
                ret.append(
                    np.concatenate(
                        (d, [trk.id], [trk.conf], [trk.cls], [trk.det_ind])
                    ).reshape(1, -1)
                )
            i -= 1
            # remove dead tracklet
            if trk.time_since_update > self.max_age:
                self.active_tracks.pop(i)

        if len(ret) > 0:
            return np.concatenate(ret)
        return np.array([])
