import numpy as np
import collections
import os

from constants import DT, XML_DIR, START_ARM_POSE
from constants import PUPPET_GRIPPER_POSITION_CLOSE
from constants import PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN
from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN
from constants import PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN

from utils import sample_box_pose, sample_insertion_pose
from dm_control import mujoco
from dm_control.rl import control
from dm_control.suite import base

import IPython

e = IPython.embed


def make_ee_sim_env(task_name):
    """
    Environment for simulated robot bi-manual manipulation, with end-effector control.
    Action space:      [left_arm_pose (7),             # position and quaternion for end effector
                        left_gripper_positions (1),    # normalized gripper position (0: close, 1: open)
                        right_arm_pose (7),            # position and quaternion for end effector
                        right_gripper_positions (1),]  # normalized gripper position (0: close, 1: open)

    Observation space: {"qpos": Concat[ left_arm_qpos (6),         # absolute joint position
                                        left_gripper_position (1),  # normalized gripper position (0: close, 1: open)
                                        right_arm_qpos (6),         # absolute joint position
                                        right_gripper_qpos (1)]     # normalized gripper position (0: close, 1: open)
                        "qvel": Concat[ left_arm_qvel (6),         # absolute joint velocity (rad)
                                        left_gripper_velocity (1),  # normalized gripper velocity (pos: opening, neg: closing)
                                        right_arm_qvel (6),         # absolute joint velocity (rad)
                                        right_gripper_qvel (1)]     # normalized gripper velocity (pos: opening, neg: closing)
                        "images": {"main": (480x640x3)}        # h, w, c, dtype='uint8'
    """
    if "sim_transfer_cube" in task_name:
        xml_path = os.path.join(XML_DIR, f"bimanual_viperx_ee_transfer_cube.xml")
        physics = mujoco.Physics.from_xml_path(xml_path)
        task = TransferCubeEETask(random=False)
        env = control.Environment(
            physics,
            task,
            time_limit=20,
            control_timestep=DT,
            n_sub_steps=None,
            flat_observation=False,
        )
    elif "sim_insertion" in task_name:
        xml_path = os.path.join(XML_DIR, f"bimanual_viperx_ee_insertion.xml")
        physics = mujoco.Physics.from_xml_path(xml_path)
        task = InsertionEETask(random=False)
        env = control.Environment(
            physics,
            task,
            time_limit=20,
            control_timestep=DT,
            n_sub_steps=None,
            flat_observation=False,
        )
    else:
        raise NotImplementedError
    return env


class BimanualViperXEETask(base.Task):
    def __init__(self, random=None):
        super().__init__(random=random)

    def before_step(self, action, physics):
        a_len = len(action) // 2
        action_left = action[:a_len]
        action_right = action[a_len:]

        # set mocap position and quat
        # left
        np.copyto(physics.data.mocap_pos[0], action_left[:3])
        np.copyto(physics.data.mocap_quat[0], action_left[3:7])
        # right
        np.copyto(physics.data.mocap_pos[1], action_right[:3])
        np.copyto(physics.data.mocap_quat[1], action_right[3:7])

        # set gripper
        g_left_ctrl = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(action_left[7])
        g_right_ctrl = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(action_right[7])
        np.copyto(
            physics.data.ctrl,
            np.array([g_left_ctrl, -g_left_ctrl, g_right_ctrl, -g_right_ctrl]),
        )

    def initialize_robots(self, physics):
        # reset joint position
        physics.named.data.qpos[:16] = START_ARM_POSE

        # reset mocap to align with end effector
        # to obtain these numbers:
        # (1) make an ee_sim env and reset to the same start_pose
        # (2) get env._physics.named.data.xpos['vx300s_left/gripper_link']
        #     get env._physics.named.data.xquat['vx300s_left/gripper_link']
        #     repeat the same for right side
        np.copyto(physics.data.mocap_pos[0], [-0.31718881, 0.5, 0.29525084])
        np.copyto(physics.data.mocap_quat[0], [1, 0, 0, 0])
        # right
        np.copyto(
            physics.data.mocap_pos[1], np.array([0.31718881, 0.49999888, 0.29525084])
        )
        np.copyto(physics.data.mocap_quat[1], [1, 0, 0, 0])

        # reset gripper control
        close_gripper_control = np.array(
            [
                PUPPET_GRIPPER_POSITION_CLOSE,
                -PUPPET_GRIPPER_POSITION_CLOSE,
                PUPPET_GRIPPER_POSITION_CLOSE,
                -PUPPET_GRIPPER_POSITION_CLOSE,
            ]
        )
        np.copyto(physics.data.ctrl, close_gripper_control)

    def initialize_episode(self, physics):
        """Sets the state of the environment at the start of each episode."""
        super().initialize_episode(physics)

    @staticmethod
    def get_qpos(physics):
        qpos_raw = physics.data.qpos.copy()
        left_qpos_raw = qpos_raw[:8]
        right_qpos_raw = qpos_raw[8:16]
        left_arm_qpos = left_qpos_raw[:6]
        right_arm_qpos = right_qpos_raw[:6]
        left_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(left_qpos_raw[6])]
        right_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(right_qpos_raw[6])]
        return np.concatenate(
            [left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos]
        )

    @staticmethod
    def get_qvel(physics):
        qvel_raw = physics.data.qvel.copy()
        left_qvel_raw = qvel_raw[:8]
        right_qvel_raw = qvel_raw[8:16]
        left_arm_qvel = left_qvel_raw[:6]
        right_arm_qvel = right_qvel_raw[:6]
        left_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(left_qvel_raw[6])]
        right_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(right_qvel_raw[6])]
        return np.concatenate(
            [left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel]
        )

    @staticmethod
    def get_env_state(physics):
        raise NotImplementedError

    def get_observation(self, physics):
        # note: it is important to do .copy()
        obs = collections.OrderedDict()
        obs["qpos"] = self.get_qpos(physics)
        obs["qvel"] = self.get_qvel(physics)
        obs["env_state"] = self.get_env_state(physics)
        obs["images"] = dict()
        obs["images"]["top"] = physics.render(height=480, width=640, camera_id="top")
        obs["images"]["angle"] = physics.render(
            height=480, width=640, camera_id="angle"
        )
        obs["images"]["vis"] = physics.render(
            height=480, width=640, camera_id="front_close"
        )
        # used in scripted policy to obtain starting pose
        obs["mocap_pose_left"] = np.concatenate(
            [physics.data.mocap_pos[0], physics.data.mocap_quat[0]]
        ).copy()
        obs["mocap_pose_right"] = np.concatenate(
            [physics.data.mocap_pos[1], physics.data.mocap_quat[1]]
        ).copy()

        # used when replaying joint trajectory
        obs["gripper_ctrl"] = physics.data.ctrl.copy()
        return obs

    def get_reward(self, physics):
        raise NotImplementedError


class TransferCubeEETask(BimanualViperXEETask):
    def __init__(self, random=None):
        super().__init__(random=random)
        self.max_reward = 4

    def initialize_episode(self, physics):
        """Sets the state of the environment at the start of each episode."""
        self.initialize_robots(physics)
        # randomize box position
        cube_pose = sample_box_pose()
        box_start_idx = physics.model.name2id("red_box_joint", "joint")
        np.copyto(physics.data.qpos[box_start_idx : box_start_idx + 7], cube_pose)
        # print(f"randomized cube position to {cube_position}")

        super().initialize_episode(physics)

    @staticmethod
    def get_env_state(physics):
        env_state = physics.data.qpos.copy()[16:]
        return env_state

    def get_reward(self, physics):
        # return whether left gripper is holding the box
        all_contact_pairs = []
        for i_contact in range(physics.data.ncon):
            id_geom_1 = physics.data.contact[i_contact].geom1
            id_geom_2 = physics.data.contact[i_contact].geom2
            name_geom_1 = physics.model.id2name(id_geom_1, "geom")
            name_geom_2 = physics.model.id2name(id_geom_2, "geom")
            contact_pair = (name_geom_1, name_geom_2)
            all_contact_pairs.append(contact_pair)

        touch_left_gripper = (
            "red_box",
            "vx300s_left/10_left_gripper_finger",
        ) in all_contact_pairs
        touch_right_gripper = (
            "red_box",
            "vx300s_right/10_right_gripper_finger",
        ) in all_contact_pairs
        touch_table = ("red_box", "table") in all_contact_pairs

        reward = 0
        if touch_right_gripper:
            reward = 1
        if touch_right_gripper and not touch_table:  # lifted
            reward = 2
        if touch_left_gripper:  # attempted transfer
            reward = 3
        if touch_left_gripper and not touch_table:  # successful transfer
            reward = 4
        return reward


class InsertionEETask(BimanualViperXEETask):
    def __init__(self, random=None):
        super().__init__(random=random)
        self.max_reward = 4

    def initialize_episode(self, physics):
        """Sets the state of the environment at the start of each episode."""
        self.initialize_robots(physics)
        # randomize peg and socket position
        peg_pose, socket_pose = sample_insertion_pose()
        id2index = (
            lambda j_id: 16 + (j_id - 16) * 7
        )  # first 16 is robot qpos, 7 is pose dim # hacky

        peg_start_id = physics.model.name2id("red_peg_joint", "joint")
        peg_start_idx = id2index(peg_start_id)
        np.copyto(physics.data.qpos[peg_start_idx : peg_start_idx + 7], peg_pose)
        # print(f"randomized cube position to {cube_position}")

        socket_start_id = physics.model.name2id("blue_socket_joint", "joint")
        socket_start_idx = id2index(socket_start_id)
        np.copyto(
            physics.data.qpos[socket_start_idx : socket_start_idx + 7], socket_pose
        )
        # print(f"randomized cube position to {cube_position}")

        super().initialize_episode(physics)

    @staticmethod
    def get_env_state(physics):
        env_state = physics.data.qpos.copy()[16:]
        return env_state

    def get_reward(self, physics):
        # return whether peg touches the pin
        all_contact_pairs = []
        for i_contact in range(physics.data.ncon):
            id_geom_1 = physics.data.contact[i_contact].geom1
            id_geom_2 = physics.data.contact[i_contact].geom2
            name_geom_1 = physics.model.id2name(id_geom_1, "geom")
            name_geom_2 = physics.model.id2name(id_geom_2, "geom")
            contact_pair = (name_geom_1, name_geom_2)
            all_contact_pairs.append(contact_pair)

        touch_right_gripper = (
            "red_peg",
            "vx300s_right/10_right_gripper_finger",
        ) in all_contact_pairs
        touch_left_gripper = (
            ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
            or ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
            or ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
            or ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs
        )

        peg_touch_table = ("red_peg", "table") in all_contact_pairs
        socket_touch_table = (
            ("socket-1", "table") in all_contact_pairs
            or ("socket-2", "table") in all_contact_pairs
            or ("socket-3", "table") in all_contact_pairs
            or ("socket-4", "table") in all_contact_pairs
        )
        peg_touch_socket = (
            ("red_peg", "socket-1") in all_contact_pairs
            or ("red_peg", "socket-2") in all_contact_pairs
            or ("red_peg", "socket-3") in all_contact_pairs
            or ("red_peg", "socket-4") in all_contact_pairs
        )
        pin_touched = ("red_peg", "pin") in all_contact_pairs

        reward = 0
        if touch_left_gripper and touch_right_gripper:  # touch both
            reward = 1
        if (
            touch_left_gripper
            and touch_right_gripper
            and (not peg_touch_table)
            and (not socket_touch_table)
        ):  # grasp both
            reward = 2
        if (
            peg_touch_socket and (not peg_touch_table) and (not socket_touch_table)
        ):  # peg and socket touching
            reward = 3
        if pin_touched:  # successful insertion
            reward = 4
        return reward
