"""Pick-place state machine and grasp-quaternion solver for Task E."""

import torch
from isaaclab.utils.math import matrix_from_quat, quat_from_matrix

from .config import (
    STEPS, STATE_ORDER, OBJ_STATE_STEP_OVERRIDES,
    CARRY_Z, PLACE_HEIGHT,
    RETRACT_POS_X, RETRACT_POS_Y,
    GRASP_Z_OFFSET, OBJ_GRASP_Z_OFFSETS, OBJ_CLOSE_Z_OFFSETS,
    OBJ_GRASP_YAW_OFFSETS,
    BASKET_CENTER_X, BASKET_CENTER_Y,
    DEFAULT_PLACE_QUAT_W,
    OBJ_GRASP_CENTER_OFFSETS,
    OBJ_CARRY_Z, OBJ_PLACE_HEIGHTS, OBJ_PLACE_XY_OFFSETS,
    OBJ_TRANSPORT_GRIPPER_CMDS, OBJ_KEEP_GRASP_QUAT_STATES,
    OBJ_TRANSPORT_PUSH_BIASES,
    OBJ_MANIPULATION_MODES, OBJ_PUSH_GRIPPER_CMDS,
    OBJ_PUSH_APPROACH_CLEARANCE,
    OBJ_PUSH_X_GAINS, OBJ_PUSH_MAX_X_CORRECTIONS,
    OBJ_PUSH_Y_GAINS, OBJ_PUSH_MAX_Y_CORRECTIONS,
    OBJ_PUSH_MIN_BEHIND,
    OBJ_SERVO_TO_BASKET_STATES, OBJ_SERVO_XY_GAINS, OBJ_SERVO_MAX_XY,
    OBJ_SERVO_HOLD_STATES, OBJ_SERVO_EXTRA_STEPS,
    BASKET_IN_X, BASKET_IN_Y,
)


def _build_grasp_matrix(long_axis: torch.Tensor, grip_z: torch.Tensor) -> torch.Tensor:
    """Build a right-handed gripper rotation matrix given the object's long axis.

    The Piper gripper jaw opens along its LOCAL Y axis, so local Y must be
    perpendicular to the object's long axis.

    Frame layout (columns of R_grip):
      col 0 (local X) = align_dir  ∥ long_axis
      col 1 (local Y) = jaw_dir    ⊥ long_axis  ← jaw opening direction
      col 2 (local Z) = grip_z     pointing down

    Right-hand check: col0 × col1 = align_dir × jaw_dir = grip_z ✓
    """
    jaw_dir   = torch.linalg.cross(long_axis, grip_z)   # ⊥ long_axis, in XY plane
    jaw_dir   = jaw_dir / jaw_dir.norm().clamp(min=1e-6)
    align_dir = torch.linalg.cross(jaw_dir, grip_z)     # ∥ long_axis
    align_dir = align_dir / align_dir.norm().clamp(min=1e-6)
    return torch.stack([align_dir, jaw_dir, grip_z], dim=1)   # (3, 3)


def compute_grasp_quat(obj_quat_w: torch.Tensor, device: str) -> torch.Tensor:
    """Compute a top-down grasp quaternion for the object.

    Finds the object axis most aligned with the world XY-plane (the long axis),
    builds a gripper frame where the jaw (local Y) is perpendicular to that axis,
    then picks the candidate orientation closest to the default top-down pose.

    Parameters
    ----------
    obj_quat_w : (4,) tensor, (w, x, y, z)
    device     : torch device string

    Returns
    -------
    grasp_quat : (4,) tensor, (w, x, y, z)
    """
    R_obj        = matrix_from_quat(obj_quat_w.unsqueeze(0)).squeeze(0)   # (3, 3)
    grip_z       = torch.tensor([0.0, 0.0, -1.0], device=device)
    default_quat = torch.tensor(DEFAULT_PLACE_QUAT_W, dtype=torch.float32, device=device)

    # Project each object column-axis onto XY plane; keep the most horizontal one(s)
    norms, axes_xy = [], []
    for col in range(3):
        ax = torch.tensor([R_obj[0, col].item(), R_obj[1, col].item(), 0.0], device=device)
        norms.append(ax.norm().item())
        axes_xy.append(ax)

    best_norm  = max(norms)
    candidates = [
        axes_xy[c] / max(norms[c], 1e-6)
        for c in range(3)
        if norms[c] >= best_norm - 1e-3
    ]

    # Among candidates, pick the one whose grasp frame is closest to the default orientation
    best_cos  = -2.0
    long_axis = candidates[0]
    for cand in candidates:
        q_cand  = quat_from_matrix(_build_grasp_matrix(cand, grip_z).unsqueeze(0)).squeeze(0)
        cos_sim = torch.abs((q_cand * default_quat).sum()).item()
        if cos_sim > best_cos:
            best_cos  = cos_sim
            long_axis = cand

    R_grip = _build_grasp_matrix(long_axis, grip_z)                  # (3, 3)
    return quat_from_matrix(R_grip.unsqueeze(0)).squeeze(0)           # (4,) w,x,y,z


def _quat_mul(q1: torch.Tensor, q2: torch.Tensor) -> torch.Tensor:
    w1, x1, y1, z1 = q1.unbind()
    w2, x2, y2, z2 = q2.unbind()
    return torch.stack([
        w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2,
        w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2,
        w1 * y2 - x1 * z2 + y1 * w2 + z1 * x2,
        w1 * z2 + x1 * y2 - y1 * x2 + z1 * w2,
    ])


class PickPlaceStateMachine:
    """Finite state machine that sequences pick-and-place for multiple objects.

    States (in order): INIT → PRE_GRASP → REACH → CLOSE → LIFT →
                       TRANSPORT → PLACE → OPEN → RETRACT → (next object or done)
    """

    def __init__(self, object_indices: list[int], device: str):
        self._obj_indices = object_indices
        self._device      = device
        self._grasp_quat_cache: dict[int, torch.Tensor] = {}
        self.reset()

    def reset(self) -> None:
        self._ptr             = 0
        self._state_idx       = 0
        self._count           = 0
        self.done             = False
        self._cached_obj_pos: torch.Tensor | None = None
        self._servo_extra_counts: dict[tuple[int, str], int] = {}
        self._grasp_quat_cache.clear()

    def set_grasp_quat(self, obj_idx: int, obj_quat_w: torch.Tensor) -> None:
        """Pre-compute and cache the grasp quaternion for one object."""
        grasp_quat = compute_grasp_quat(obj_quat_w, self._device)
        yaw = OBJ_GRASP_YAW_OFFSETS.get(obj_idx, 0.0)
        if abs(yaw) > 1e-6:
            half = torch.tensor(0.5 * yaw, dtype=torch.float32, device=self._device)
            yaw_quat = torch.stack([
                torch.cos(half),
                torch.tensor(0.0, dtype=torch.float32, device=self._device),
                torch.tensor(0.0, dtype=torch.float32, device=self._device),
                torch.sin(half),
            ])
            grasp_quat = _quat_mul(yaw_quat, grasp_quat)
            grasp_quat = grasp_quat / grasp_quat.norm().clamp(min=1e-6)
        self._grasp_quat_cache[obj_idx] = grasp_quat

    def tick(self, obj_pos: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, str]:
        """Advance the state machine by one step.

        Parameters
        ----------
        obj_pos : (3,) tensor — current object position in world frame

        Returns
        -------
        ee_pos_des  : (3,) target EE position
        ee_quat_des : (4,) target EE orientation (w,x,y,z)
        gripper_cmd : "open" | "close"
        """
        s = self.state
        d = self._device

        # Freeze object position at start of PRE_GRASP to avoid drift during descent
        if s == "PRE_GRASP" and self._count == 0:
            self._cached_obj_pos = obj_pos.clone()
        if s in ("REACH", "CLOSE", "LIFT") and self._cached_obj_pos is not None:
            obj_pos = self._cached_obj_pos

        ee_pos, gripper = self._get_target_pos_gripper(s, obj_pos, d)
        ee_quat         = self._get_target_quat(s, d)

        self._count += 1
        if self._count >= self._get_state_steps(s):
            if self._should_hold_servo_state(s, obj_pos):
                self._count = self._get_state_steps(s) - 1
                return ee_pos, ee_quat, gripper
            self._count = 0
            if s == "RETRACT":
                self._ptr          += 1
                self._cached_obj_pos = None
                if self._ptr >= len(self._obj_indices):
                    self.done = True
                    return ee_pos, ee_quat, gripper
                self._state_idx = STATE_ORDER.index("PRE_GRASP")
            else:
                self._state_idx += 1

        return ee_pos, ee_quat, gripper

    # ------------------------------------------------------------------ #
    # Properties
    # ------------------------------------------------------------------ #

    @property
    def state(self) -> str:
        return STATE_ORDER[self._state_idx]

    @property
    def current_object_key(self) -> str:
        return f"object_{self._obj_indices[self._ptr]}"

    # ------------------------------------------------------------------ #
    # Private helpers
    # ------------------------------------------------------------------ #

    def _get_target_pos_gripper(
        self, s: str, obj_pos: torch.Tensor, d: str
    ) -> tuple[torch.Tensor, str]:
        if self._is_push_mode():
            return self._get_push_target_pos_gripper(s, obj_pos, d)

        grasp_pos = self._get_grasp_pos(obj_pos, d)
        if s == "INIT":
            return torch.tensor([RETRACT_POS_X, RETRACT_POS_Y, CARRY_Z], device=d), "open"
        elif s == "PRE_GRASP":
            p = grasp_pos.clone(); p[2] = CARRY_Z
            return p, "open"
        elif s == "REACH":
            p = grasp_pos.clone(); p[2] += self._get_grasp_z_offset()
            return p, "open"
        elif s == "CLOSE":
            p = grasp_pos.clone(); p[2] += self._get_close_z_offset()
            return p, "close"
        elif s == "LIFT":
            p = grasp_pos.clone(); p[2] = self._get_carry_z()
            return p, self._get_transport_gripper_cmd()
        elif s == "TRANSPORT":
            servo = self._get_object_servo_target(s, obj_pos, grasp_pos, self._get_carry_z(), d)
            if servo is not None:
                return servo, self._get_transport_gripper_cmd()
            x, y = self._get_place_xy()
            return torch.tensor([x, y, self._get_carry_z()], device=d), self._get_transport_gripper_cmd()
        elif s == "PLACE":
            servo = self._get_object_servo_target(s, obj_pos, grasp_pos, self._get_place_height(), d)
            if servo is not None:
                return servo, self._get_transport_gripper_cmd()
            x, y = self._get_place_xy()
            return torch.tensor([x, y, self._get_place_height()], device=d), self._get_transport_gripper_cmd()
        elif s == "OPEN":
            servo = self._get_object_servo_target(s, obj_pos, grasp_pos, self._get_place_height(), d)
            if servo is not None:
                return servo, "open"
            x, y = self._get_place_xy()
            return torch.tensor([x, y, self._get_place_height()], device=d), "open"
        elif s == "LIFT_RETRACT":
            return torch.tensor([BASKET_CENTER_X, BASKET_CENTER_Y, CARRY_Z], device=d), "open"
        elif s == "RETRACT":
            return torch.tensor([RETRACT_POS_X, RETRACT_POS_Y, CARRY_Z], device=d), "open"
        else:
            raise ValueError(f"Unknown state: {s}")

    def _get_grasp_pos(self, obj_pos: torch.Tensor, d: str) -> torch.Tensor:
        cur_idx = self._obj_indices[min(self._ptr, len(self._obj_indices) - 1)]
        offset = OBJ_GRASP_CENTER_OFFSETS.get(cur_idx, (0.0, 0.0, 0.0))
        return obj_pos + torch.tensor(offset, dtype=torch.float32, device=d)

    def _get_current_obj_idx(self) -> int:
        return self._obj_indices[min(self._ptr, len(self._obj_indices) - 1)]

    def _is_push_mode(self) -> bool:
        return OBJ_MANIPULATION_MODES.get(self._get_current_obj_idx(), "pick") == "push"

    def _get_push_gripper_cmd(self) -> str:
        cur_idx = self._get_current_obj_idx()
        return OBJ_PUSH_GRIPPER_CMDS.get(cur_idx, self._get_transport_gripper_cmd())

    def _get_push_target_pos_gripper(
        self, s: str, obj_pos: torch.Tensor, d: str
    ) -> tuple[torch.Tensor, str]:
        """Low table-contact pushing primitive for objects that do not pinch reliably."""
        cur_idx = self._get_current_obj_idx()
        contact_obj_pos = self._cached_obj_pos if self._cached_obj_pos is not None else obj_pos
        start = self._get_grasp_pos(contact_obj_pos, d)
        z_low = self._get_place_height()

        if s == "INIT":
            return torch.tensor([RETRACT_POS_X, RETRACT_POS_Y, CARRY_Z], device=d), "open"
        if s == "PRE_GRASP":
            p = start.clone()
            p[2] = z_low + OBJ_PUSH_APPROACH_CLEARANCE.get(cur_idx, 0.14)
            return p, "open"
        if s in ("REACH", "CLOSE", "LIFT"):
            p = start.clone()
            p[2] = z_low
            return p, self._get_push_gripper_cmd()
        if s in ("TRANSPORT", "PLACE"):
            place_x, place_y = self._get_place_xy()
            target_xy = torch.tensor([place_x, place_y], dtype=torch.float32, device=d)
            start_xy = contact_obj_pos[:2]

            x_err = place_x - obj_pos[0]
            x_gain = OBJ_PUSH_X_GAINS.get(cur_idx, 0.55)
            x_max = OBJ_PUSH_MAX_X_CORRECTIONS.get(cur_idx, 0.08)
            x_corr = torch.clamp(x_err * x_gain, min=-x_max, max=x_max)

            progress = 1.0
            if s == "TRANSPORT":
                progress = min((self._count + 1) / max(self._get_state_steps(s), 1), 1.0)
                progress = min(progress * 1.35, 1.0)
            ref_xy = start_xy + (target_xy - start_xy) * progress
            p_live = torch.cat([
                ref_xy,
                torch.tensor([z_low], dtype=torch.float32, device=d),
            ])
            offset = torch.tensor(
                OBJ_GRASP_CENTER_OFFSETS.get(cur_idx, (0.0, 0.0, 0.0)),
                dtype=torch.float32,
                device=d,
            )
            p_live = p_live + offset
            p_live[0] = p_live[0] + x_corr

            # Keep the pusher on the rear side of the object.  If the reference
            # sweep gets ahead of a lagging object, contact is lost or the object
            # is knocked sideways instead of being driven into the basket.
            min_behind = OBJ_PUSH_MIN_BEHIND.get(cur_idx, 0.03)
            p_live[1] = torch.maximum(
                p_live[1],
                obj_pos[1] + torch.tensor(min_behind, dtype=torch.float32, device=d),
            )

            # During the hold phase, apply a bounded inward preload without
            # allowing the pusher centre to cross in front of the object.
            y_err = place_y - obj_pos[1]
            y_gain = OBJ_PUSH_Y_GAINS.get(cur_idx, 0.85)
            y_max = OBJ_PUSH_MAX_Y_CORRECTIONS.get(cur_idx, 0.22)
            inward = torch.clamp(y_err * y_gain, min=-y_max, max=0.0)
            p_live[1] = torch.maximum(p_live[1] + inward, obj_pos[1] + min_behind)
            p_live[2] = z_low

            if s == "TRANSPORT":
                p = start + (p_live - start) * min(progress * 3.0, 1.0)
            else:
                p = p_live
            return p, self._get_push_gripper_cmd()
        if s == "OPEN":
            p = self._get_grasp_pos(obj_pos, d)
            p[2] = z_low
            return p, "open"
        if s == "LIFT_RETRACT":
            x, y = self._get_place_xy()
            return torch.tensor([x, y, CARRY_Z], device=d), "open"
        if s == "RETRACT":
            return torch.tensor([RETRACT_POS_X, RETRACT_POS_Y, CARRY_Z], device=d), "open"
        raise ValueError(f"Unknown state: {s}")

    def _get_grasp_z_offset(self) -> float:
        cur_idx = self._get_current_obj_idx()
        return OBJ_GRASP_Z_OFFSETS.get(cur_idx, GRASP_Z_OFFSET)

    def _get_close_z_offset(self) -> float:
        cur_idx = self._get_current_obj_idx()
        return OBJ_CLOSE_Z_OFFSETS.get(cur_idx, self._get_grasp_z_offset())

    def _get_carry_z(self) -> float:
        cur_idx = self._get_current_obj_idx()
        return OBJ_CARRY_Z.get(cur_idx, CARRY_Z)

    def _get_place_height(self) -> float:
        cur_idx = self._get_current_obj_idx()
        return OBJ_PLACE_HEIGHTS.get(cur_idx, PLACE_HEIGHT)

    def _get_place_xy(self) -> tuple[float, float]:
        cur_idx = self._get_current_obj_idx()
        dx, dy = OBJ_PLACE_XY_OFFSETS.get(cur_idx, (0.0, 0.0))
        return BASKET_CENTER_X + dx, BASKET_CENTER_Y + dy

    def _get_transport_gripper_cmd(self) -> str:
        cur_idx = self._get_current_obj_idx()
        return OBJ_TRANSPORT_GRIPPER_CMDS.get(cur_idx, "close")

    def _get_state_steps(self, s: str) -> int:
        cur_idx = self._get_current_obj_idx()
        return OBJ_STATE_STEP_OVERRIDES.get(cur_idx, {}).get(s, STEPS[s])

    def _get_transport_push_bias(self) -> tuple[float, float, float] | None:
        cur_idx = self._get_current_obj_idx()
        return OBJ_TRANSPORT_PUSH_BIASES.get(cur_idx)

    def _get_object_servo_target(
        self,
        s: str,
        obj_pos: torch.Tensor,
        grasp_pos: torch.Tensor,
        z: float,
        d: str,
    ) -> torch.Tensor | None:
        cur_idx = self._get_current_obj_idx()
        if s not in OBJ_SERVO_TO_BASKET_STATES.get(cur_idx, ()):
            push_bias = self._get_transport_push_bias()
            if push_bias is None:
                return None
            p = grasp_pos + torch.tensor(push_bias, dtype=torch.float32, device=d)
            p[2] = z
            return p

        x, y = self._get_place_xy()
        target_xy = torch.tensor([x, y], dtype=torch.float32, device=d)
        xy_error = target_xy - obj_pos[:2]
        gain = OBJ_SERVO_XY_GAINS.get(cur_idx, 1.0)
        max_xy = OBJ_SERVO_MAX_XY.get(cur_idx, 0.25)
        correction = xy_error * gain
        norm = torch.linalg.norm(correction).clamp(min=1e-6)
        if norm.item() > max_xy:
            correction = correction / norm * max_xy
        if s == "TRANSPORT":
            progress = min((self._count + 1) / max(self._get_state_steps(s), 1), 1.0)
            correction = correction * max(0.15, progress)

        p = grasp_pos.clone()
        p[:2] = p[:2] + correction
        push_bias = self._get_transport_push_bias()
        if push_bias is not None:
            p = p + torch.tensor(push_bias, dtype=torch.float32, device=d)
        p[2] = z
        return p

    def _should_hold_servo_state(self, s: str, obj_pos: torch.Tensor) -> bool:
        cur_idx = self._get_current_obj_idx()
        if s not in OBJ_SERVO_HOLD_STATES.get(cur_idx, ()):
            return False
        dx = abs(float(obj_pos[0].item() - BASKET_CENTER_X))
        dy = abs(float(obj_pos[1].item() - BASKET_CENTER_Y))
        if dx <= BASKET_IN_X * 0.85 and dy <= BASKET_IN_Y * 0.85:
            return False
        key = (cur_idx, s)
        count = self._servo_extra_counts.get(key, 0)
        if count >= OBJ_SERVO_EXTRA_STEPS.get(cur_idx, 0):
            return False
        self._servo_extra_counts[key] = count + 1
        return True

    def _get_target_quat(self, s: str, d: str) -> torch.Tensor:
        default_quat = torch.tensor(DEFAULT_PLACE_QUAT_W, dtype=torch.float32, device=d)
        cur_idx = self._get_current_obj_idx()
        if self._is_push_mode() and s in ("REACH", "CLOSE", "LIFT", "TRANSPORT", "PLACE", "OPEN"):
            return self._grasp_quat_cache.get(cur_idx, default_quat)
        if s in OBJ_KEEP_GRASP_QUAT_STATES.get(cur_idx, ("REACH", "CLOSE", "LIFT")):
            return self._grasp_quat_cache.get(cur_idx, default_quat)
        return default_quat