"""Plug-in interface for NEW, simulation-only controllers (NEW CODE).

A plug-in sees exactly what the released node sees and is subjected to exactly
the released post-processing:

* state: the released ControlNode.state_callback / vel_callback are applied to
  the plug-in's state object (same theta formula, same planar speed);
* error state: e = ControlNode.error_state(1.0), e_track = error_state(0.0)
  (the released functions, called on the plug-in state object);
* schedule: the same 10 Hz tick / latency model as the released controllers;
* post-processing: the released 0.25-per-tick steering rate limit and
  clipping (path_follower.py:230-242) -- copied verbatim below and verified
  bit-for-bit against the released pub_callback (parity test).

Plug-in contract:
    class MyController(PluginBase):
        name = 'my_controller'
        def reset(self, ref_traj, params): ...
        def compute(self, e, e_track, st) -> (throttle, steering)
`st` exposes x, y, theta, v (as set by the released callbacks), ref_traj,
and the previous throttle/steering. Return raw (unclipped) commands.

The simulation-only controllers below (pure pursuit, Stanley, MPC-N5 and the wrapped released laws) have fixed
defaults; pure pursuit and Stanley gains can be set per episode. They are not used in the paper (except the wrapped
NN-HD) and have no physical evaluation.
"""
import time

import numpy as np


class PluginBase:
    name = 'base'

    def __init__(self, params=None):
        self.params = dict(params or {})

    def reset(self, ref_traj, params):
        pass

    def compute(self, e, e_track, st):
        raise NotImplementedError


class _State:
    pass


class PluginController:
    """Adapter giving a plug-in the released node's interface (observe/tick)."""

    kind = 'plugin'

    def __init__(self, plugin, module):
        self.plugin = plugin
        self.name = 'plugin:' + plugin.name
        self.module = module
        self.st = None

    def reset(self, reference):
        st = _State()
        # same initial values as ControlNode.__init__ (path_follower.py:84-98)
        st.steering = 0.0
        st.throttle = 0.0
        st.braking = 0.0
        st.x = 0.0
        st.y = 0.0
        st.theta = 0.0
        st.v = 0.0
        st.go = False
        st.ref_traj = np.array(reference.array, dtype=float, copy=True)
        self.st = st
        self.plugin.reset(st.ref_traj, self.plugin.params)
        return st

    def observe(self, pose_msg, twist_msg):
        self.module.ControlNode.state_callback(self.st, pose_msg)
        self.module.ControlNode.vel_callback(self.st, twist_msg)

    def tick(self):
        from chrono_ros_interfaces.msg import DriverInputs as VehicleInput
        st = self.st
        out = {'published': False, 'n_published': 0, 'exception': None, 'e': None, 'e_track': None,
               'raw_throttle': None, 'raw_steering': None, 'rate_limited': None,
               'obs_x': float(st.x), 'obs_y': float(st.y), 'obs_theta': float(st.theta), 'obs_v': float(st.v)}
        t0 = time.perf_counter()
        try:
            if not st.go:
                out['ratelimited_steering'] = float(st.steering)
                out['compute_wall_s'] = time.perf_counter() - t0
                return out
            e = self.module.ControlNode.error_state(st, 1.0)
            e_tracking = self.module.ControlNode.error_state(st, 0.0)
            throttle, steering = self.plugin.compute(e, e_tracking, st)
            st.throttle = throttle
            # ---- verbatim copy of path_follower.py:230-242 ----
            delta_steering = steering - st.steering
            if abs(delta_steering) > 0.25:
                st.steering = st.steering + 0.25 * delta_steering / abs(delta_steering)
            else:
                st.steering = steering
            msg = VehicleInput()
            msg.steering = np.clip(st.steering, -1.0, 1.0)
            msg.throttle = np.clip(st.throttle, 0, 1)
            msg.braking = np.clip(st.braking, 0, 1)
            # ----------------------------------------------------
            out.update({'published': True, 'n_published': 1, 'e': [float(v) for v in e],
                        'e_track': [float(v) for v in e_tracking], 'raw_throttle': float(throttle),
                        'raw_steering': float(steering), 'rate_limited': abs(float(delta_steering)) > 0.25,
                        'pub_steering': float(msg.steering), 'pub_throttle': float(msg.throttle),
                        'pub_braking': float(msg.braking)})
        except Exception as exc:
            out['exception'] = exc
        out['ratelimited_steering'] = float(st.steering)
        out['compute_wall_s'] = time.perf_counter() - t0
        return out


# ---------------------------------------------------------------------------
# Simulation-only controllers
# ---------------------------------------------------------------------------
# Every plug-in lists its parameters and defaults in config.PLUGIN_PARAM_SPECS; config.normalize rejects unknown
# plug-ins and unknown / non-finite parameters before a batch starts. The defaults are not tuned.
from .config import PLUGIN_PARAM_SPECS, plugin_effective_params  # noqa: E402

STEER_ANGLE_PER_COMMAND = 0.6  # rad per unit command, as assumed by mpc_wpts.py (delta = u*0.6; cmd = angle*1.6667)


def _wrap(a):
    return float(np.arctan2(np.sin(a), np.cos(a)))


def p_speed_throttle(e, throttle_ff, k_v):
    """P speed loop of the new controllers: throttle = clip(throttle_ff + k_v * (v_ref - v), 0, 1).
    v_ref - v = e[3] of the released error_state(1.0), the same speed error the released controllers receive (every
    released reference and the TR straight line have v_ref = 1.0 m/s at every waypoint)."""
    return float(np.clip(float(throttle_ff) + float(k_v) * float(e[3]), 0.0, 1.0))


class ReplicaPID(PluginBase):
    """Exact replica of the released PID law (path_follower.py:226-227), used to
    verify the plug-in path end-to-end against the released node."""
    name = 'replica_pid'

    def compute(self, e, e_track, st):
        throttle = sum([x * y for x, y in zip(e, [0.37013526, 0.00507144, 0.15476554, 1.0235402])])
        steering = sum([x * y for x, y in zip(e, [0.02176878, 0.72672704, 0.78409284, -0.0105355])])
        return throttle, steering


class PurePursuit(PluginBase):
    """Geometric pure pursuit (simulation-only). Definition:
    position = observed COG (st.x, st.y), heading = st.theta (released state_callback);
    i0 = nearest waypoint (first argmin of the Euclidean distance, as the released zero-lookahead error state);
    target j = first waypoint at or after i0 whose distance from the vehicle is >= lookahead_m (last waypoint if none;
    if the vehicle is farther than lookahead_m from the path, the target is the nearest waypoint);
    L_d = distance to the target, alpha = wrap(atan2(target - position) - heading);
    steering angle delta = atan2(2 * wheelbase_m * sin(alpha), L_d) (= atan(2 L sin(alpha) / L_d));
    steering command = delta / 0.6 (released MPC mapping); throttle = P speed loop (p_speed_throttle).
    Defaults: lookahead_m = 1.0 (the released controller lookahead), wheelbase_m = 0.5 (l_car of the released MPC
    model), k_v = 1.0, throttle_ff = 0.0 (untuned)."""
    name = 'pure_pursuit'

    def reset(self, ref_traj, params):
        self.p = plugin_effective_params(self.name, self.params)

    def compute(self, e, e_track, st):
        p = self.p
        ref = st.ref_traj
        d = np.hypot(ref[:, 0] - st.x, ref[:, 1] - st.y)
        i0 = int(d.argmin())
        ahead = np.flatnonzero(d[i0:] >= p['lookahead_m'])
        j = i0 + int(ahead[0]) if len(ahead) else len(ref) - 1
        dx, dy = float(ref[j, 0] - st.x), float(ref[j, 1] - st.y)
        ld = float(np.hypot(dx, dy))
        alpha = _wrap(np.arctan2(dy, dx) - st.theta)
        delta = float(np.arctan2(2.0 * p['wheelbase_m'] * np.sin(alpha), ld))
        return p_speed_throttle(e, p['throttle_ff'], p['k_v']), delta / STEER_ANGLE_PER_COMMAND


class Stanley(PluginBase):
    """Stanley lateral law (simulation-only), referenced to the observed COG (no front-axle projection).
    Definition: delta = e_track[2] + atan2(k * e_track[1], softening + max(v, 0)), with e_track = the
    released error_state(0.0) (e_track[1] > 0 when the path lies to the vehicle's left, e_track[2] = yaw_ref - yaw;
    positive steering = CCW); steering command = delta / 0.6; throttle = P speed loop (p_speed_throttle).
    Defaults: k = 1.0, softening = 0.5 m/s, k_v = 1.0, throttle_ff = 0.0 (untuned)."""
    name = 'stanley'

    def reset(self, ref_traj, params):
        self.p = plugin_effective_params(self.name, self.params)

    def compute(self, e, e_track, st):
        p = self.p
        delta = float(e_track[2]) + float(np.arctan2(p['k'] * float(e_track[1]), p['softening'] + max(float(st.v), 0.0)))
        return p_speed_throttle(e, p['throttle_ff'], p['k_v']), delta / STEER_ANGLE_PER_COMMAND


class WrappedReleased(PluginBase):
    """Wraps a RELEASED controller law (mpc / nnmpc / pid / nnhd), optionally
    (a) scaling its steering (e.g. NN-HD {base: nnhd, steering_scale: 1.75 or 1.5}; MPC-sluggish
        {base: mpc, steering_scale: 0.5}, PID-aggressive {base: pid, steering_scale: 2.0}) and/or
    (b) replacing its throttle with a shared longitudinal law
        throttle = throttle_bias + kp_speed * (v_ref - v)  (shared-speed option; not used in the paper).
    The released law is called directly (mpc_wpts_solver / model.predict / PID gains) on the released error state,
    inside _call_released_law (the frame the outcome classification treats as the released branch)."""
    name = 'wrapped_released'

    def __init__(self, params=None, module=None):
        super().__init__(params)
        self.module = module

    def reset(self, ref_traj, params):
        self.p = plugin_effective_params(self.name, self.params)
        self.base = self.p['base']
        if self.base not in ('mpc', 'nnmpc', 'pid', 'nnhd'):
            raise ValueError('base must be a released controller')

    def _call_released_law(self, e, st):
        m = self.module
        if self.base == 'mpc':
            thr, steer = m.mpc_wpts_solver(e, [st.throttle, st.steering], st.v, 1)
            steer = steer * 1.6667
        elif self.base in ('nnmpc', 'nnhd'):
            path = ('/sbel/Desktop/ros_ws/src/nn_models_lib/single_speed_MPC_IL_NN.keras' if self.base == 'nnmpc'
                    else '/sbel/Desktop/ros_ws/src/nn_models_lib/single_speed_MC_IL_NN.keras')
            out = m._tr_cached_load_model(path).predict(np.array(e).reshape(1, -1))
            thr, steer = out[0][0], out[0][1]
        else:
            thr = sum([x * y for x, y in zip(e, [0.37013526, 0.00507144, 0.15476554, 1.0235402])])
            steer = sum([x * y for x, y in zip(e, [0.02176878, 0.72672704, 0.78409284, -0.0105355])])
        return thr, steer

    def compute(self, e, e_track, st):
        thr, steer = self._call_released_law(e, st)
        steer = steer * float(self.params.get('steering_scale', 1.0))
        if self.params.get('shared_speed', False):
            thr = float(self.params.get('throttle_bias', 0.0)) + float(self.params.get('kp_speed', 1.0)) * e[3]
        return thr, steer


_MPC_N5 = {'module': None}


def load_mpc_n5_module():
    """Import runner/controllers/mpc_wpts_n5.py once per process after two checks: (1) its SHA256 equals the pinned
    value; (2) its bytes equal the image's released mpc_wpts.py (hash-checked) with the single line
    '        N = 10' replaced by '        N = 5' (so the variant differs from the released MPC ONLY in the horizon)."""
    if _MPC_N5['module'] is not None:
        return _MPC_N5['module']
    import importlib.util
    import os
    import sys
    from .released import MPC_N5_FILE, RELEASED_DIR, RELEASED_SHA256, sha256_file
    got = sha256_file(MPC_N5_FILE)
    if got != MPC_N5_SHA256:
        raise RuntimeError(f'mpc_wpts_n5.py hash mismatch: {got}')
    rel = os.path.join(RELEASED_DIR, 'mpc_wpts.py')
    if sha256_file(rel) != RELEASED_SHA256['mpc_wpts.py']:
        raise RuntimeError('released mpc_wpts.py hash mismatch')
    with open(rel, 'rb') as f:
        released_bytes = f.read()
    with open(MPC_N5_FILE, 'rb') as f:
        copy_bytes = f.read()
    old, new = b'\n        N = 10\n', b'\n        N = 5\n'
    if released_bytes.count(old) != 1 or released_bytes.replace(old, new) != copy_bytes:
        raise RuntimeError('mpc_wpts_n5.py is not the released mpc_wpts.py with only N = 10 -> N = 5')
    spec = importlib.util.spec_from_file_location('mpc_wpts_n5_runner_copy', MPC_N5_FILE)
    mod = importlib.util.module_from_spec(spec)
    sys.modules['mpc_wpts_n5_runner_copy'] = mod
    spec.loader.exec_module(mod)
    _MPC_N5['module'] = mod
    return mod


MPC_N5_SHA256 = 'd6a9530c9e6feed836d1e3a147c91b1a20e07a2227a8972b078197ce19d94b8f'


class MpcN5(PluginBase):
    """MPC-N5 (variant): the released MPC branch of pub_callback (u_prev = [throttle, steering],
    v, v_ref = 1, steering x 1.6667) with the solver from runner/controllers/mpc_wpts_n5.py, i.e. the released
    mpc_wpts.py with prediction horizon N = 5 instead of 10 and no other change. OSQP is pinned and diagnosed exactly
    as for the released MPC (same osqp.OSQP class). No parameters."""
    name = 'mpc_n5'

    def __init__(self, params=None, module=None):
        super().__init__(params)
        self.module = module

    def reset(self, ref_traj, params):
        self.p = plugin_effective_params(self.name, self.params)
        self.solver = load_mpc_n5_module().mpc_wpts_solver

    def _call_mpc_n5_law(self, e, st):
        thr, steer = self.solver(e, [st.throttle, st.steering], st.v, 1)
        return thr, steer * 1.6667

    def compute(self, e, e_track, st):
        return self._call_mpc_n5_law(e, st)


PLUGINS = {cls.name: cls for cls in (ReplicaPID, PurePursuit, Stanley, WrappedReleased, MpcN5)}
_NEEDS_MODULE = (WrappedReleased, MpcN5)


def make_plugin(name, params, module):
    cls = PLUGINS[name]
    if cls in _NEEDS_MODULE:
        return cls(params, module=module)
    return cls(params)