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from isaacgym import gymapi, gymtorch  # type: ignore[misc]

import torch

import numpy as np

from phys_anim.envs.masked_mimic.tasks.base_task.isaacgym import (
    MaskedMimicTaskHumanoid,
)
from phys_anim.envs.masked_mimic.tasks.reach.common import BaseMaskedMimicReach


class MaskedMimicReachHumanoid(BaseMaskedMimicReach, MaskedMimicTaskHumanoid):
    def __init__(self, config, device: torch.device):
        super().__init__(config=config, device=device)

        reach_body_name = self.config.reach_params.reach_body_name
        self.reach_body_id = self.build_body_ids_tensor([reach_body_name]).item()

        if not self.headless:
            self._build_marker_state_tensors()

    ###############################################################
    # Set up IsaacGym environment
    ###############################################################
    def create_envs(self, num_envs, spacing, num_per_row):
        if not self.headless:
            self._marker_handles = []
            self._load_marker_asset()

        super().create_envs(num_envs, spacing, num_per_row)

    def _load_marker_asset(self):
        asset_root = "phys_anim/data/assets/urdf/"

        asset_file = "location_marker.urdf"

        asset_options = gymapi.AssetOptions()
        asset_options.angular_damping = 0.01
        asset_options.linear_damping = 0.01
        asset_options.max_angular_velocity = 100.0
        asset_options.density = 1.0
        asset_options.fix_base_link = True
        asset_options.default_dof_drive_mode = gymapi.DOF_MODE_NONE

        self._marker_asset = self.gym.load_asset(
            self.sim, asset_root, asset_file, asset_options
        )

    def build_env(self, env_id, env_ptr, humanoid_asset):
        super().build_env(env_id, env_ptr, humanoid_asset)

        if not self.headless:
            self._build_marker(env_id, env_ptr)

    def _build_marker(self, env_id, env_ptr):
        default_pose = gymapi.Transform()

        marker_handle = self.gym.create_actor(
            env_ptr,
            self._marker_asset,
            default_pose,
            "marker",
            self.num_envs + 10,
            1,
            0,
        )
        self.gym.set_rigid_body_color(
            env_ptr, marker_handle, 0, gymapi.MESH_VISUAL, gymapi.Vec3(0.8, 0.0, 0.0)
        )
        self._marker_handles.append(marker_handle)

    def _build_marker_state_tensors(self):
        num_actors = self.get_num_actors_per_env()
        if self.total_num_objects > 0:
            self._marker_states = self.root_states[: -self.total_num_objects].view(
                self.num_envs, num_actors, self.root_states.shape[-1]
            )[..., 1, :]
        else:
            self._marker_states = self.root_states.view(
                self.num_envs, num_actors, self.root_states.shape[-1]
            )[..., 1, :]
        self._marker_pos = self._marker_states[..., :3]

        self._marker_actor_ids = self.humanoid_actor_ids + 1

    ###############################################################
    # Helpers
    ###############################################################
    def _update_marker(self):
        marker_pos = self._tar_pos.clone()

        self._marker_pos[:] = marker_pos
        self.gym.set_actor_root_state_tensor_indexed(
            self.sim,
            gymtorch.unwrap_tensor(self.root_states),
            gymtorch.unwrap_tensor(self._marker_actor_ids),
            len(self._marker_actor_ids),
        )

        should_reach = (self._tar_reach_steps - self.progress_buf) <= 0
        for env_idx in range(self.num_envs):
            if should_reach[env_idx]:
                self.gym.set_rigid_body_color(
                    self.envs[env_idx],
                    self._marker_handles[env_idx],
                    0,
                    gymapi.MESH_VISUAL,
                    gymapi.Vec3(0.0, 0.8, 0.0),
                )
            else:
                self.gym.set_rigid_body_color(
                    self.envs[env_idx],
                    self._marker_handles[env_idx],
                    0,
                    gymapi.MESH_VISUAL,
                    gymapi.Vec3(0.8, 0.0, 0.0),
                )

    def draw_task(self):
        self._update_marker()

        cols = np.array([[0.0, 1.0, 0.0]], dtype=np.float32)

        self.gym.clear_lines(self.viewer)

        current_state = self.get_bodies_state()
        body_pos = current_state.body_pos
        starts = body_pos[:, self.reach_body_id, :]

        ends = self._tar_pos.clone()
        verts = torch.cat([starts, ends], dim=-1).cpu().numpy()

        for i, env_ptr in enumerate(self.envs):
            curr_verts = verts[i]
            curr_verts = curr_verts.reshape([1, 6])
            self.gym.add_lines(
                self.viewer, env_ptr, curr_verts.shape[0], curr_verts, cols
            )
