# Copyright 2025 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Joystick for OP3."""

from typing import Any, Dict, Optional, Union

import jax
import jax.numpy as jp
from ml_collections import config_dict
from mujoco import mjx
import numpy as np

from mujoco_playground._src import collision
from mujoco_playground._src import mjx_env
from mujoco_playground._src.locomotion.op3 import base as op3_base
from mujoco_playground._src.locomotion.op3 import op3_constants as consts


def default_config() -> config_dict.ConfigDict:
  return config_dict.create(
      ctrl_dt=0.02,
      sim_dt=0.004,
      episode_length=1000,
      Kp=21.1,
      Kd=1.084,
      early_termination=True,
      action_repeat=1,
      action_scale=0.3,
      obs_noise=0.05,
      obs_history_size=3,
      max_foot_height=0.07,
      lin_vel_x=[-0.6, 1.5],
      lin_vel_y=[-0.8, 0.8],
      ang_vel_yaw=[-0.7, 0.7],
      reward_config=config_dict.create(
          scales=config_dict.create(
              tracking_lin_vel=1.5,
              tracking_ang_vel=0.8,
              lin_vel_z=-2.0,
              ang_vel_xy=-0.05,
              orientation=-5.0,
              torques=-0.0002,
              action_rate=-0.01,
              zero_cmd=-0.5,
              termination=-1.0,
              feet_slip=-0.1,
              feet_clearance=0.0,
              energy=-0.0001,
          ),
          tracking_sigma=0.25,
      ),
      velocity_kick=[1.0, 5.0],
      kick_durations=[0.05, 0.2],
      kick_wait_times=[1.0, 3.0],
  )


class Joystick(op3_base.Op3Env):
  """Track a joystick command."""

  def __init__(
      self,
      config: config_dict.ConfigDict = default_config(),
      config_overrides: Optional[Dict[str, Union[str, int, list[Any]]]] = None,
  ):
    super().__init__(
        xml_path=str(consts.FEET_ONLY_XML),
        config=config,
        config_overrides=config_overrides,
    )
    self._post_init()

  def _post_init(self) -> None:
    self._init_q = jp.array(self._mj_model.keyframe("stand_bent_knees").qpos)
    self._default_pose = self._mj_model.keyframe("stand_bent_knees").qpos[7:]
    self._lowers = self._mj_model.actuator_ctrlrange[:, 0]
    self._uppers = self._mj_model.actuator_ctrlrange[:, 1]

    self._torso_body_id = self._mj_model.body(consts.ROOT_BODY).id
    self._torso_mass = self._mj_model.body_subtreemass[self._torso_body_id]

    self._feet_site_id = np.array(
        [self._mj_model.site(name).id for name in consts.FEET_SITES]
    )
    self._floor_geom_id = self._mj_model.geom("floor").id

    self._left_feet_geom_id = np.array(
        [self._mj_model.geom(name).id for name in consts.LEFT_FEET_GEOMS]
    )
    self._right_feet_geom_id = np.array(
        [self._mj_model.geom(name).id for name in consts.RIGHT_FEET_GEOMS]
    )

    foot_linvel_sensor_adr = []
    for site in consts.FEET_SITES:
      sensor_id = self._mj_model.sensor(f"{site}_global_linvel").id
      sensor_adr = self._mj_model.sensor_adr[sensor_id]
      sensor_dim = self._mj_model.sensor_dim[sensor_id]
      foot_linvel_sensor_adr.append(
          list(range(sensor_adr, sensor_adr + sensor_dim))
      )
    self._foot_linvel_sensor_adr = jp.array(foot_linvel_sensor_adr)

  def reset(self, rng: jax.Array) -> mjx_env.State:
    rng, cmd_rng, noise_rng, pert1_rng, pert2_rng, pert3_rng = jax.random.split(
        rng, 6
    )

    data = mjx_env.init(
        self.mjx_model, qpos=self._init_q, qvel=jp.zeros(self.mjx_model.nv)
    )

    time_until_next_pert = jax.random.uniform(
        pert1_rng,
        minval=self._config.kick_wait_times[0],
        maxval=self._config.kick_wait_times[1],
    )
    steps_until_next_pert = jp.round(time_until_next_pert / self.dt).astype(
        jp.int32
    )
    pert_duration_seconds = jax.random.uniform(
        pert2_rng,
        minval=self._config.kick_durations[0],
        maxval=self._config.kick_durations[1],
    )
    pert_duration_steps = jp.round(pert_duration_seconds / self.dt).astype(
        jp.int32
    )
    vel_kick = jax.random.uniform(
        pert3_rng,
        minval=self._config.velocity_kick[0],
        maxval=self._config.velocity_kick[1],
    )

    info = {
        "rng": rng,
        "last_act": jp.zeros(self.mjx_model.nu),
        "last_last_act": jp.zeros(self.mjx_model.nu),
        "last_vel": jp.zeros(self.mjx_model.nv - 6),
        "command": self.sample_command(cmd_rng),
        "step": 0,
        "steps_until_next_pert": steps_until_next_pert,
        "pert_duration_seconds": pert_duration_seconds,
        "pert_duration": pert_duration_steps,
        "steps_since_last_pert": 0,
        "pert_steps": 0,
        "direction": jp.zeros(3),
        "vel_kick": vel_kick,
        "motor_targets": jp.zeros(self.mjx_model.nu),
    }

    metrics = {}
    for k in self._config.reward_config.scales.keys():
      metrics[f"reward/{k}"] = jp.zeros(())

    obs_history = jp.zeros(self._config.obs_history_size * 49)
    obs = self._get_obs(data, info, obs_history, noise_rng)
    reward, done = jp.zeros(2)
    return mjx_env.State(data, obs, reward, done, metrics, info)

  def step(self, state: mjx_env.State, action: jax.Array) -> mjx_env.State:
    rng, cmd_rng, noise_rng = jax.random.split(state.info["rng"], 3)

    # state = self._maybe_apply_perturbation(state, pert_rng)

    motor_targets = self._default_pose + action * self._config.action_scale
    motor_targets = jp.clip(motor_targets, self._lowers, self._uppers)
    data = mjx_env.step(
        self.mjx_model, state.data, motor_targets, self.n_substeps
    )

    obs = self._get_obs(data, state.info, state.obs, noise_rng)
    done = self._get_termination(data)

    rewards = self._get_reward(data, action, state.info, state.metrics, done)
    rewards = {
        k: v * self._config.reward_config.scales[k] for k, v in rewards.items()
    }
    reward = jp.clip(sum(rewards.values()) * self.dt, 0.0, 10000.0)

    # Bookkeeping.
    state.info["motor_targets"] = motor_targets
    state.info["last_last_act"] = state.info["last_act"]
    state.info["last_act"] = action
    state.info["last_vel"] = data.qvel[6:]
    state.info["step"] += 1
    state.info["rng"] = rng
    state.info["command"] = jp.where(
        state.info["step"] > 500,
        self.sample_command(cmd_rng),
        state.info["command"],
    )
    state.info["step"] = jp.where(
        done | (state.info["step"] > 500),
        0,
        state.info["step"],
    )

    for k, v in rewards.items():
      state.metrics[f"reward/{k}"] = v

    done = jp.float32(done)
    state = state.replace(data=data, obs=obs, reward=reward, done=done)
    return state

  def _get_termination(self, data: mjx.Data) -> jax.Array:
    joint_angles = data.qpos[7:]
    torso_z = data.xpos[self._torso_body_id, -1]

    # Joint limits exceeded.
    joint_limit_exceed = jp.any(joint_angles < self._lowers)
    joint_limit_exceed |= jp.any(joint_angles > self._uppers)

    # Fall detection.
    fall_termination = self.get_gravity(data)[-1] < 0.85
    fall_termination |= torso_z < 0.21

    return jp.where(
        self._config.early_termination,
        joint_limit_exceed | fall_termination,
        joint_limit_exceed,
    )

  def _get_obs(
      self,
      data: mjx.Data,
      info: dict[str, Any],
      obs_history: jax.Array,
      rng: jax.Array,
  ) -> jax.Array:
    obs = jp.concatenate([
        self.get_gyro(data),  # 3
        self.get_gravity(data),  # 3
        info["command"],  # 3
        data.qpos[7:] - self._default_pose,  # 20
        info["last_act"],  # 20
    ])  # total = 49
    if self._config.obs_noise >= 0.0:
      noise = self._config.obs_noise * jax.random.uniform(
          rng, obs.shape, minval=-1.0, maxval=1.0
      )
      obs = jp.clip(obs, -100.0, 100.0) + noise
    obs = jp.roll(obs_history, obs.size).at[: obs.size].set(obs)
    return obs

  def _get_reward(
      self,
      data: mjx.Data,
      action: jax.Array,
      info: dict[str, Any],
      metrics: dict[str, Any],
      done: jax.Array,
  ) -> dict[str, jax.Array]:
    del metrics  # Unused.
    return {
        # Tracking rewards.
        "tracking_lin_vel": self._reward_tracking_lin_vel(
            info["command"], self.get_local_linvel(data)
        ),
        "tracking_ang_vel": self._reward_tracking_ang_vel(
            info["command"], self.get_gyro(data)
        ),
        # Regularization rewards.
        "lin_vel_z": self._cost_lin_vel_z(self.get_global_linvel(data)),
        "ang_vel_xy": self._cost_ang_vel_xy(self.get_global_angvel(data)),
        "orientation": self._cost_orientation(self.get_gravity(data)),
        "torques": self._cost_torques(data.actuator_force),
        "action_rate": self._cost_action_rate(
            action, info["last_act"], info["last_last_act"]
        ),
        "zero_cmd": self._cost_zero_cmd(
            info["command"],
            self.get_global_linvel(data),
            self.get_global_angvel(data),
            action,
        ),
        "termination": self._cost_termination(done, info["step"]),
        "feet_slip": self._cost_feet_slip(data),
        "feet_clearance": self._cost_feet_clearance(data),
        "energy": self._cost_energy(data.qvel[6:], data.actuator_force),
    }

  def _reward_tracking_lin_vel(
      self,
      commands: jax.Array,
      local_vel: jax.Array,
  ) -> jax.Array:
    # Tracking of linear velocity commands (xy axes).
    lin_vel_error = jp.sum(jp.square(commands[:2] - local_vel[:2]))
    reward = jp.exp(-lin_vel_error / self._config.reward_config.tracking_sigma)
    return reward

  def _reward_tracking_ang_vel(
      self,
      commands: jax.Array,
      ang_vel: jax.Array,
  ) -> jax.Array:
    # Tracking of angular velocity commands (yaw).
    ang_vel_error = jp.square(commands[2] - ang_vel[2])
    return jp.exp(-ang_vel_error / self._config.reward_config.tracking_sigma)

  def _cost_lin_vel_z(self, global_linvel) -> jax.Array:
    # Penalize z axis base linear velocity.
    return jp.square(global_linvel[2])

  def _cost_ang_vel_xy(self, global_angvel) -> jax.Array:
    # Penalize xy axes base angular velocity.
    return jp.sum(jp.square(global_angvel[:2]))

  def _cost_orientation(self, torso_zaxis: jax.Array) -> jax.Array:
    # Penalize non flat base orientation.
    return jp.sum(jp.square(torso_zaxis[:2]))

  def _cost_torques(self, torques: jax.Array) -> jax.Array:
    # Penalize torques.
    return jp.sqrt(jp.sum(jp.square(torques))) + jp.sum(jp.abs(torques))

  def _cost_energy(
      self, qvel: jax.Array, qfrc_actuator: jax.Array
  ) -> jax.Array:
    # Penalize energy consumption.
    return jp.sum(jp.abs(qvel) * jp.abs(qfrc_actuator))

  def _cost_action_rate(
      self, act: jax.Array, last_act: jax.Array, last_last_act: jax.Array
  ) -> jax.Array:
    # Penalize first and second derivative of actions.
    c1 = jp.sum(jp.square(act - last_act))
    c2 = jp.sum(jp.square(act - 2 * last_act + last_last_act))
    return c1 + c2

  def _cost_zero_cmd(
      self,
      commands: jax.Array,
      global_linvel: jax.Array,
      ang_vel: jax.Array,
      action: jax.Array,
  ) -> jax.Array:
    del global_linvel, ang_vel  # Unused.
    cmd_norm = jp.linalg.norm(commands)
    penalty = jp.sum(jp.square(action))
    return penalty * (cmd_norm < 0.1)
    # penalty = jp.sum(jp.square(global_linvel[:2]))
    # cmd_norm = jp.linalg.norm(commands[:2])
    # cost_linvel = penalty * (cmd_norm < 0.1)
    # penalty = jp.sum(jp.square(ang_vel[:2]))
    # cmd_norm = jp.linalg.norm(commands[2])
    # cost_angvel = penalty * (cmd_norm < 0.1)
    # return cost_linvel + cost_angvel

  def _cost_termination(self, done: jax.Array, step: jax.Array) -> jax.Array:
    return done & (step < 500)

  def _cost_feet_slip(self, data: mjx.Data) -> jax.Array:
    feet_vel = data.sensordata[self._foot_linvel_sensor_adr]
    vel_xy = feet_vel[..., :2]
    vel_xy_norm_sq = jp.sum(jp.square(vel_xy), axis=-1)

    left_feet_contact = jp.array([
        collision.geoms_colliding(data, geom_id, self._floor_geom_id)
        for geom_id in self._left_feet_geom_id
    ])
    right_feet_contact = jp.array([
        collision.geoms_colliding(data, geom_id, self._floor_geom_id)
        for geom_id in self._right_feet_geom_id
    ])
    feet_contact = jp.hstack(
        [jp.any(left_feet_contact), jp.any(right_feet_contact)]
    )
    return jp.sum(vel_xy_norm_sq * feet_contact)

  def _cost_feet_clearance(self, data: mjx.Data) -> jax.Array:
    feet_vel = data.sensordata[self._foot_linvel_sensor_adr]
    vel_xy = feet_vel[..., :2]
    vel_norm = jp.sqrt(jp.linalg.norm(vel_xy, axis=-1))
    # vel_norm = jp.linalg.norm(vel_xy, axis=-1)
    foot_pos = data.site_xpos[self._feet_site_id]
    foot_z = foot_pos[..., -1]
    delta = (foot_z - self._config.max_foot_height) ** 2
    return jp.sum(delta * vel_norm)

  # TODO(kevin): Rewrite this ugly function.
  def _maybe_apply_perturbation(
      self, state: mjx_env.State, rng: jax.Array
  ) -> mjx_env.State:
    def gen_dir(rng: jax.Array) -> jax.Array:
      angle = jax.random.uniform(rng, minval=0.0, maxval=jp.pi * 2)
      return jp.array([jp.cos(angle), jp.sin(angle), 0.0])

    def apply_pert(state: mjx_env.State) -> mjx_env.State:
      t = state.info["pert_steps"] * self.dt
      u_t = 0.5 * jp.sin(jp.pi * t / state.info["pert_duration_seconds"])
      force = (
          u_t
          * self._torso_mass
          * state.info["vel_kick"]
          / state.info["pert_duration_seconds"]
      )
      xfrc_applied = jp.zeros((self.mjx_model.nbody, 6))
      xfrc_applied = xfrc_applied.at[self._torso_body_id, :3].set(
          force * state.info["direction"]
      )
      data = state.data.replace(xfrc_applied=xfrc_applied)
      state = state.replace(data=data)
      state.info["steps_since_last_pert"] = jp.where(
          state.info["pert_steps"] >= state.info["pert_duration"],
          0,
          state.info["steps_since_last_pert"],
      )
      state.info["pert_steps"] += 1
      return state

    def wait(state: mjx_env.State) -> mjx_env.State:
      state.info["steps_since_last_pert"] += 1
      xfrc_applied = jp.zeros((self.mjx_model.nbody, 6))
      data = state.data.replace(xfrc_applied=xfrc_applied)
      state.info["pert_steps"] = jp.where(
          state.info["steps_since_last_pert"]
          >= state.info["steps_until_next_pert"],
          0,
          state.info["pert_steps"],
      )
      state.info["direction"] = jp.where(
          state.info["steps_since_last_pert"]
          >= state.info["steps_until_next_pert"],
          gen_dir(rng),
          state.info["direction"],
      )
      return state.replace(data=data)

    return jax.lax.cond(
        state.info["steps_since_last_pert"]
        >= state.info["steps_until_next_pert"],
        apply_pert,
        wait,
        state,
    )

  def sample_command(self, rng: jax.Array) -> jax.Array:
    """Samples a random command with a 10% chance of being zero."""
    _, rng1, rng2, rng3, rng4 = jax.random.split(rng, 5)

    lin_vel_x = jax.random.uniform(
        rng1, minval=self._config.lin_vel_x[0], maxval=self._config.lin_vel_x[1]
    )
    lin_vel_y = jax.random.uniform(
        rng2, minval=self._config.lin_vel_y[0], maxval=self._config.lin_vel_y[1]
    )
    ang_vel_yaw = jax.random.uniform(
        rng3,
        minval=self._config.ang_vel_yaw[0],
        maxval=self._config.ang_vel_yaw[1],
    )

    cmd = jp.hstack([lin_vel_x, lin_vel_y, ang_vel_yaw])
    return jp.where(jax.random.bernoulli(rng4, 0.1), jp.zeros(3), cmd)

  @property
  def observation_size(self) -> mjx_env.ObservationSize:
    return 147
