# Copyright (c) 2018-2023, NVIDIA Corporation
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import numpy as np
import os
import torch

from isaacgym import gymtorch
from isaacgym import gymapi
from isaacgym.gymtorch import *

from isaacgymenvs.utils.torch_jit_utils import *
from isaacgymenvs.tasks.base.vec_task import VecTask


class Ant(VecTask):

    def __init__(self, cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render):

        self.cfg = cfg

        self.max_episode_length = self.cfg["env"]["episodeLength"]

        self.randomization_params = self.cfg["task"]["randomization_params"]
        self.randomize = self.cfg["task"]["randomize"]
        self.dof_vel_scale = self.cfg["env"]["dofVelocityScale"]
        self.contact_force_scale = self.cfg["env"]["contactForceScale"]
        self.power_scale = self.cfg["env"]["powerScale"]
        self.heading_weight = self.cfg["env"]["headingWeight"]
        self.up_weight = self.cfg["env"]["upWeight"]
        self.actions_cost_scale = self.cfg["env"]["actionsCost"]
        self.energy_cost_scale = self.cfg["env"]["energyCost"]
        self.joints_at_limit_cost_scale = self.cfg["env"]["jointsAtLimitCost"]
        self.death_cost = self.cfg["env"]["deathCost"]
        self.termination_height = self.cfg["env"]["terminationHeight"]

        self.debug_viz = self.cfg["env"]["enableDebugVis"]
        self.plane_static_friction = self.cfg["env"]["plane"]["staticFriction"]
        self.plane_dynamic_friction = self.cfg["env"]["plane"]["dynamicFriction"]
        self.plane_restitution = self.cfg["env"]["plane"]["restitution"]

        self.cfg["env"]["numObservations"] = 60
        self.cfg["env"]["numActions"] = 8

        super().__init__(config=self.cfg, rl_device=rl_device, sim_device=sim_device, graphics_device_id=graphics_device_id, headless=headless, virtual_screen_capture=virtual_screen_capture, force_render=force_render)

        if self.viewer != None:
            cam_pos = gymapi.Vec3(50.0, 25.0, 2.4)
            cam_target = gymapi.Vec3(45.0, 25.0, 0.0)
            self.gym.viewer_camera_look_at(self.viewer, None, cam_pos, cam_target)

        # get gym GPU state tensors
        actor_root_state = self.gym.acquire_actor_root_state_tensor(self.sim)
        dof_state_tensor = self.gym.acquire_dof_state_tensor(self.sim)
        sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)

        sensors_per_env = 4
        self.vec_sensor_tensor = gymtorch.wrap_tensor(sensor_tensor).view(self.num_envs, sensors_per_env * 6)

        self.gym.refresh_dof_state_tensor(self.sim)
        self.gym.refresh_actor_root_state_tensor(self.sim)

        self.root_states = gymtorch.wrap_tensor(actor_root_state)
        self.initial_root_states = self.root_states.clone()
        self.initial_root_states[:, 7:13] = 0  # set lin_vel and ang_vel to 0

        # create some wrapper tensors for different slices
        self.dof_state = gymtorch.wrap_tensor(dof_state_tensor)
        self.dof_pos = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 0]
        self.dof_vel = self.dof_state.view(self.num_envs, self.num_dof, 2)[..., 1]
        self.initial_dof_pos = torch.zeros_like(self.dof_pos, device=self.device, dtype=torch.float)
        zero_tensor = torch.tensor([0.0], device=self.device)
        self.initial_dof_pos = torch.where(self.dof_limits_lower > zero_tensor, self.dof_limits_lower,
                                           torch.where(self.dof_limits_upper < zero_tensor, self.dof_limits_upper, self.initial_dof_pos))
        self.initial_dof_vel = torch.zeros_like(self.dof_vel, device=self.device, dtype=torch.float)

        # initialize some data used later on
        self.up_vec = to_torch(get_axis_params(1., self.up_axis_idx), device=self.device).repeat((self.num_envs, 1))
        self.heading_vec = to_torch([1, 0, 0], device=self.device).repeat((self.num_envs, 1))
        self.inv_start_rot = quat_conjugate(self.start_rotation).repeat((self.num_envs, 1))

        self.basis_vec0 = self.heading_vec.clone()
        self.basis_vec1 = self.up_vec.clone()

        self.targets = to_torch([1000, 0, 0], device=self.device).repeat((self.num_envs, 1))
        self.target_dirs = to_torch([1, 0, 0], device=self.device).repeat((self.num_envs, 1))
        self.dt = self.cfg["sim"]["dt"]
        self.potentials = to_torch([-1000./self.dt], device=self.device).repeat(self.num_envs)
        self.prev_potentials = self.potentials.clone()
        
        self.successes = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
        self.consecutive_successes = torch.zeros(1, dtype=torch.float, device=self.device)

    def create_sim(self):
        self.up_axis_idx = 2 # index of up axis: Y=1, Z=2
        self.sim = super().create_sim(self.device_id, self.graphics_device_id, self.physics_engine, self.sim_params)

        self._create_ground_plane()
        print(f'num envs {self.num_envs} env spacing {self.cfg["env"]["envSpacing"]}')
        self._create_envs(self.num_envs, self.cfg["env"]['envSpacing'], int(np.sqrt(self.num_envs)))

        # If randomizing, apply once immediately on startup before the fist sim step
        if self.randomize:
            self.apply_randomizations(self.randomization_params)

    def _create_ground_plane(self):
        plane_params = gymapi.PlaneParams()
        plane_params.normal = gymapi.Vec3(0.0, 0.0, 1.0)
        plane_params.static_friction = self.plane_static_friction
        plane_params.dynamic_friction = self.plane_dynamic_friction
        self.gym.add_ground(self.sim, plane_params)

    def _create_envs(self, num_envs, spacing, num_per_row):
        lower = gymapi.Vec3(-spacing, -spacing, 0.0)
        upper = gymapi.Vec3(spacing, spacing, spacing)

        asset_root = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../assets')
        asset_file = "mjcf/nv_ant.xml"

        if "asset" in self.cfg["env"]:
            asset_file = self.cfg["env"]["asset"].get("assetFileName", asset_file)

        asset_path = os.path.join(asset_root, asset_file)
        asset_root = os.path.dirname(asset_path)
        asset_file = os.path.basename(asset_path)

        asset_options = gymapi.AssetOptions()
        # Note - DOF mode is set in the MJCF file and loaded by Isaac Gym
        asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE
        asset_options.angular_damping = 0.0

        ant_asset = self.gym.load_asset(self.sim, asset_root, asset_file, asset_options)
        self.num_dof = self.gym.get_asset_dof_count(ant_asset)
        self.num_bodies = self.gym.get_asset_rigid_body_count(ant_asset)

        # Note - for this asset we are loading the actuator info from the MJCF
        actuator_props = self.gym.get_asset_actuator_properties(ant_asset)
        motor_efforts = [prop.motor_effort for prop in actuator_props]
        self.joint_gears = to_torch(motor_efforts, device=self.device)

        start_pose = gymapi.Transform()
        start_pose.p = gymapi.Vec3(*get_axis_params(0.44, self.up_axis_idx))

        self.start_rotation = torch.tensor([start_pose.r.x, start_pose.r.y, start_pose.r.z, start_pose.r.w], device=self.device)

        self.torso_index = 0
        self.num_bodies = self.gym.get_asset_rigid_body_count(ant_asset)
        body_names = [self.gym.get_asset_rigid_body_name(ant_asset, i) for i in range(self.num_bodies)]
        extremity_names = [s for s in body_names if "foot" in s]
        self.extremities_index = torch.zeros(len(extremity_names), dtype=torch.long, device=self.device)

        # create force sensors attached to the "feet"
        extremity_indices = [self.gym.find_asset_rigid_body_index(ant_asset, name) for name in extremity_names]
        sensor_pose = gymapi.Transform()
        for body_idx in extremity_indices:
            self.gym.create_asset_force_sensor(ant_asset, body_idx, sensor_pose)

        self.ant_handles = []
        self.envs = []
        self.dof_limits_lower = []
        self.dof_limits_upper = []

        for i in range(self.num_envs):
            # create env instance
            env_ptr = self.gym.create_env(
                self.sim, lower, upper, num_per_row
            )
            ant_handle = self.gym.create_actor(env_ptr, ant_asset, start_pose, "ant", i, 1, 0)

            for j in range(self.num_bodies):
                self.gym.set_rigid_body_color(
                    env_ptr, ant_handle, j, gymapi.MESH_VISUAL, gymapi.Vec3(0.97, 0.38, 0.06))

            self.envs.append(env_ptr)
            self.ant_handles.append(ant_handle)

        dof_prop = self.gym.get_actor_dof_properties(env_ptr, ant_handle)
        for j in range(self.num_dof):
            if dof_prop['lower'][j] > dof_prop['upper'][j]:
                self.dof_limits_lower.append(dof_prop['upper'][j])
                self.dof_limits_upper.append(dof_prop['lower'][j])
            else:
                self.dof_limits_lower.append(dof_prop['lower'][j])
                self.dof_limits_upper.append(dof_prop['upper'][j])

        self.dof_limits_lower = to_torch(self.dof_limits_lower, device=self.device)
        self.dof_limits_upper = to_torch(self.dof_limits_upper, device=self.device)

        for i in range(len(extremity_names)):
            self.extremities_index[i] = self.gym.find_actor_rigid_body_handle(self.envs[0], self.ant_handles[0], extremity_names[i])

    def compute_reward(self, actions):
        self.rew_buf[:], self.reset_buf[:], self.consecutive_successes[:], self.successes[:] = compute_ant_reward(
            self.obs_buf,
            self.reset_buf,
            self.consecutive_successes,
            self.progress_buf,
            self.actions,
            self.up_weight,
            self.heading_weight,
            self.potentials,
            self.prev_potentials,
            self.actions_cost_scale,
            self.energy_cost_scale,
            self.joints_at_limit_cost_scale,
            self.termination_height,
            self.death_cost,
            self.max_episode_length
        )

        self.binning_buf[:] = compute_ant_bins(self.obs_buf,
            self.reset_buf,
            self.consecutive_successes,
            self.progress_buf,
            self.actions,
            self.up_weight,
            self.heading_weight,
            self.potentials,
            self.prev_potentials,
            self.actions_cost_scale,
            self.energy_cost_scale,
            self.joints_at_limit_cost_scale,
            self.termination_height,
            self.death_cost,
            self.max_episode_length
        )
        self.extras['binning'] = self.binning_buf

        self.extras['successes'] = self.successes
        self.extras['consecutive_successes'] = self.consecutive_successes.mean()

    def compute_observations(self):
        self.gym.refresh_dof_state_tensor(self.sim)
        self.gym.refresh_actor_root_state_tensor(self.sim)
        self.gym.refresh_force_sensor_tensor(self.sim)
        #print("Feet forces and torques: ", self.vec_sensor_tensor[0, :])
        # print(self.vec_sensor_tensor.shape)

        self.obs_buf[:], self.potentials[:], self.prev_potentials[:], self.up_vec[:], self.heading_vec[:] = compute_ant_observations(
            self.obs_buf, self.root_states, self.targets, self.potentials,
            self.inv_start_rot, self.dof_pos, self.dof_vel,
            self.dof_limits_lower, self.dof_limits_upper, self.dof_vel_scale,
            self.vec_sensor_tensor, self.actions, self.dt, self.contact_force_scale,
            self.basis_vec0, self.basis_vec1, self.up_axis_idx)

    def reset_idx(self, env_ids):
        # Randomization can happen only at reset time, since it can reset actor positions on GPU
        if self.randomize:
            self.apply_randomizations(self.randomization_params)

        positions = torch_rand_float(-0.2, 0.2, (len(env_ids), self.num_dof), device=self.device)
        velocities = torch_rand_float(-0.1, 0.1, (len(env_ids), self.num_dof), device=self.device)

        self.dof_pos[env_ids] = tensor_clamp(self.initial_dof_pos[env_ids] + positions, self.dof_limits_lower, self.dof_limits_upper)
        self.dof_vel[env_ids] = velocities

        env_ids_int32 = env_ids.to(dtype=torch.int32)

        self.gym.set_actor_root_state_tensor_indexed(self.sim,
                                                     gymtorch.unwrap_tensor(self.initial_root_states),
                                                     gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))

        self.gym.set_dof_state_tensor_indexed(self.sim,
                                              gymtorch.unwrap_tensor(self.dof_state),
                                              gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))

        to_target = self.targets[env_ids] - self.initial_root_states[env_ids, 0:3]
        to_target[:, 2] = 0.0
        self.prev_potentials[env_ids] = -torch.norm(to_target, p=2, dim=-1) / self.dt
        self.potentials[env_ids] = self.prev_potentials[env_ids].clone()

        self.progress_buf[env_ids] = 0
        self.reset_buf[env_ids] = 0

    def pre_physics_step(self, actions):
        self.actions = actions.clone().to(self.device)
        forces = self.actions * self.joint_gears * self.power_scale
        force_tensor = gymtorch.unwrap_tensor(forces)
        self.gym.set_dof_actuation_force_tensor(self.sim, force_tensor)

    def post_physics_step(self):
        self.progress_buf += 1
        self.randomize_buf += 1

        env_ids = self.reset_buf.nonzero(as_tuple=False).flatten()
        if len(env_ids) > 0:
            self.reset_idx(env_ids)

        self.compute_observations()
        self.compute_reward(self.actions)

        # debug viz
        if self.viewer and self.debug_viz:
            self.gym.clear_lines(self.viewer)
            self.gym.refresh_actor_root_state_tensor(self.sim)

            points = []
            colors = []
            for i in range(self.num_envs):
                origin = self.gym.get_env_origin(self.envs[i])
                pose = self.root_states[:, 0:3][i].cpu().numpy()
                glob_pos = gymapi.Vec3(origin.x + pose[0], origin.y + pose[1], origin.z + pose[2])
                points.append([glob_pos.x, glob_pos.y, glob_pos.z, glob_pos.x + 4 * self.heading_vec[i, 0].cpu().numpy(),
                               glob_pos.y + 4 * self.heading_vec[i, 1].cpu().numpy(),
                               glob_pos.z + 4 * self.heading_vec[i, 2].cpu().numpy()])
                colors.append([0.97, 0.1, 0.06])
                points.append([glob_pos.x, glob_pos.y, glob_pos.z, glob_pos.x + 4 * self.up_vec[i, 0].cpu().numpy(), glob_pos.y + 4 * self.up_vec[i, 1].cpu().numpy(),
                               glob_pos.z + 4 * self.up_vec[i, 2].cpu().numpy()])
                colors.append([0.05, 0.99, 0.04])

            self.gym.add_lines(self.viewer, None, self.num_envs * 2, points, colors)

@torch.jit.script
def compute_ant_observations(obs_buf, root_states, targets, potentials,
                             inv_start_rot, dof_pos, dof_vel,
                             dof_limits_lower, dof_limits_upper, dof_vel_scale,
                             sensor_force_torques, actions, dt, contact_force_scale,
                             basis_vec0, basis_vec1, up_axis_idx):
    # type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, float, Tensor, Tensor, float, float, Tensor, Tensor, int) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]

    torso_position = root_states[:, 0:3]
    torso_rotation = root_states[:, 3:7]
    velocity = root_states[:, 7:10]
    ang_velocity = root_states[:, 10:13]

    to_target = targets - torso_position
    to_target[:, 2] = 0.0

    prev_potentials_new = potentials.clone()
    potentials = -torch.norm(to_target, p=2, dim=-1) / dt

    torso_quat, up_proj, heading_proj, up_vec, heading_vec = compute_heading_and_up(
        torso_rotation, inv_start_rot, to_target, basis_vec0, basis_vec1, 2)

    vel_loc, angvel_loc, roll, pitch, yaw, angle_to_target = compute_rot(
        torso_quat, velocity, ang_velocity, targets, torso_position)

    dof_pos_scaled = unscale(dof_pos, dof_limits_lower, dof_limits_upper)

    # obs_buf shapes: 1, 3, 3, 1, 1, 1, 1, 1, num_dofs(8), num_dofs(8), 24, num_dofs(8)
    obs = torch.cat((torso_position[:, up_axis_idx].view(-1, 1), vel_loc, angvel_loc,
                     yaw.unsqueeze(-1), roll.unsqueeze(-1), angle_to_target.unsqueeze(-1),
                     up_proj.unsqueeze(-1), heading_proj.unsqueeze(-1), dof_pos_scaled,
                     dof_vel * dof_vel_scale, sensor_force_torques.view(-1, 24) * contact_force_scale,
                     actions), dim=-1)

    return obs, potentials, prev_potentials_new, up_vec, heading_vec

#####################################################################
###=========================jit functions=========================###
#####################################################################


@torch.jit.script
def compute_ant_reward(
    obs_buf,
    reset_buf,
    consecutive_successes,
    progress_buf,
    actions,
    up_weight,
    heading_weight,
    potentials,
    prev_potentials,
    actions_cost_scale,
    energy_cost_scale,
    joints_at_limit_cost_scale,
    termination_height,
    death_cost,
    max_episode_length
):
    # type: (Tensor, Tensor, Tensor, Tensor, Tensor, float, float, Tensor, Tensor, float, float, float, float, float, float) -> Tuple[Tensor, Tensor, Tensor, Tensor]

    # reward from direction headed
    heading_weight_tensor = torch.ones_like(obs_buf[:, 11]) * heading_weight
    heading_reward = torch.where(obs_buf[:, 11] > 0.8, heading_weight_tensor, heading_weight * obs_buf[:, 11] / 0.8)

    # aligning up axis of ant and environment
    up_reward = torch.zeros_like(heading_reward)
    up_reward = torch.where(obs_buf[:, 10] > 0.93, up_reward + up_weight, up_reward)

    # energy penalty for movement
    actions_cost = torch.sum(actions ** 2, dim=-1)
    electricity_cost = torch.sum(torch.abs(actions * obs_buf[:, 20:28]), dim=-1)
    dof_at_limit_cost = torch.sum(obs_buf[:, 12:20] > 0.99, dim=-1)

    # reward for duration of staying alive
    alive_reward = torch.ones_like(potentials) * 0.5
    progress_reward = potentials - prev_potentials

    total_reward = progress_reward + alive_reward + up_reward + heading_reward - \
        actions_cost_scale * actions_cost - energy_cost_scale * electricity_cost - dof_at_limit_cost * joints_at_limit_cost_scale

    # adjust reward for fallen agents
    total_reward = torch.where(obs_buf[:, 0] < termination_height, torch.ones_like(total_reward) * death_cost, total_reward)

    # reset agents
    consecutive_successes = progress_reward.mean()
    reset = torch.where(obs_buf[:, 0] < termination_height, torch.ones_like(reset_buf), reset_buf)
    reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset)

    # total_reward = progress_reward
    return total_reward, reset, consecutive_successes, progress_reward

@torch.jit.script
def compute_ant_bins(
    obs_buf,
    reset_buf,
    consecutive_successes,
    progress_buf,
    actions,
    up_weight,
    heading_weight,
    potentials,
    prev_potentials,
    actions_cost_scale,
    energy_cost_scale,
    joints_at_limit_cost_scale,
    termination_height,
    death_cost,
    max_episode_length
):
    # type: (Tensor, Tensor, Tensor, Tensor, Tensor, float, float, Tensor, Tensor, float, float, float, float, float, float) -> Tensor

    # reward from direction headed
    heading_weight_tensor = torch.ones_like(obs_buf[:, 11]) * heading_weight
    heading_reward = torch.where(obs_buf[:, 11] > 0.8, heading_weight_tensor, heading_weight * obs_buf[:, 11] / 0.8)

    # aligning up axis of ant and environment
    up_reward = torch.zeros_like(heading_reward)
    up_reward = torch.where(obs_buf[:, 10] > 0.93, up_reward + up_weight, up_reward)

    # energy penalty for movement
    actions_cost = torch.sum(actions ** 2, dim=-1)
    electricity_cost = torch.sum(torch.abs(actions * obs_buf[:, 20:28]), dim=-1)
    dof_at_limit_cost = torch.sum(obs_buf[:, 12:20] > 0.99, dim=-1)

    # reward for duration of staying alive
    alive_reward = torch.ones_like(potentials) * 0.5
    progress_reward = potentials - prev_potentials

    print("potentials min max", torch.min(potentials), torch.max(potentials))

    total_reward = progress_reward + alive_reward + up_reward + heading_reward - \
        actions_cost_scale * actions_cost - energy_cost_scale * electricity_cost - dof_at_limit_cost * joints_at_limit_cost_scale

    # adjust reward for fallen agents
    total_reward = torch.where(obs_buf[:, 0] < termination_height, torch.ones_like(total_reward) * death_cost, total_reward)

    # reset agents
    consecutive_successes = progress_reward.mean()
    reset = torch.where(obs_buf[:, 0] < termination_height, torch.ones_like(reset_buf), reset_buf)
    reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset)

    # pass random bins for now
    bin_id = progress_buf

    # total_reward = progress_reward
    return bin_id