import numpy as np
import os
import collections
import matplotlib.pyplot as plt
from dm_control import mujoco
from dm_control.rl import control
from dm_control.suite import base

from constants import DT, XML_DIR, START_ARM_POSE,B_2_N,DOF_ARM,DOF_CAP
from constants import PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN,PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN1
from constants import MASTER_GRIPPER_POSITION_NORMALIZE_FN
from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN
from constants import PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN
import math
import IPython
e = IPython.embed
import math
from ctypes import *
BOX_POSE = [None] # to be changed from outside



def quaternion_to_euler(q):
    """
    Convert a quaternion into euler angles (yaw, pitch, roll)
    q: A list of four elements representing a quaternion [q_w, q_x, q_y, q_z]
    """
    # Extract the values from the quaternion
    w, x, y, z = q

    # Yaw (Z-axis rotation)
    siny_cosp = 2 * (w * z + x * y)
    cosy_cosp = 1 - 2 * (y * y + z * z)
    yaw = math.atan2(siny_cosp, cosy_cosp)

    # Pitch (Y-axis rotation)
    sinp = 2 * (w * y - z * x)
    if abs(sinp) >= 1:
        pitch = math.copysign(math.pi / 2, sinp)  # Use 90 degrees if out of range
    else:
        pitch = math.asin(sinp)

    # Roll (X-axis rotation)
    sinr_cosp = 2 * (w * x + y * z)
    cosr_cosp = 1 - 2 * (x * x + y * y)
    roll = math.atan2(sinr_cosp, cosr_cosp)

    return pitch, roll, yaw 


def make_sim_env(task_name):
    """
    Environment for simulated robot bi-manual manipulation, with joint position control
    Action space:      [left_arm_qpos (6),             # absolute joint position
                        left_gripper_positions (1),    # normalized gripper position (0: close, 1: open)
                        right_arm_qpos (6),            # absolute joint position
                        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_transfer_cube.xml')
        physics = mujoco.Physics.from_xml_path(xml_path)
        task = TransferCubeTask(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_insertion.xml')
        physics = mujoco.Physics.from_xml_path(xml_path)
        task = InsertionTask(random=False)
        env = control.Environment(physics, task, time_limit=20, control_timestep=DT,
                                  n_sub_steps=None, flat_observation=False)
    elif 'human_test' == task_name:#---------------------------------------------new doghome
        xml_path = os.path.join(XML_DIR, f'human_test.xml')
        physics = mujoco.Physics.from_xml_path(xml_path)
        task = TransferCubeTask_Human(random=False)
        env = control.Environment(physics, task, time_limit=20, control_timestep=DT,
                                  n_sub_steps=None, flat_observation=False)  
    elif 'human_test1' == task_name:#---------------------------------------------new doghome
        xml_path = os.path.join(XML_DIR, f'human_test1.xml')
        physics = mujoco.Physics.from_xml_path(xml_path)
        task = TransferCubeTask_Human(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 BimanualViperXTask(base.Task):#基础类  被所有任务继承
    def __init__(self, random=None):
        super().__init__(random=random)

    def before_step(self, action, physics):#不同于ee这里直接送入了角度7+7+2
        left_arm_action = action[:7]#末端6个位姿
        right_arm_action = action[8:8+7]

        normalized_left_gripper_action = action[7]#末端行程
        normalized_right_gripper_action = action[7+8]

        left_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN1(normalized_left_gripper_action)
        right_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN1(normalized_right_gripper_action)

        full_left_gripper_action = [left_gripper_action, -left_gripper_action]
        full_right_gripper_action = [right_gripper_action, -right_gripper_action]
        #算出来14个关节角，Left: 0:7 +抓1+抓2; right：[9:15]+抓1+抓2  18个自由度
        new_qpos = np.concatenate([left_arm_action[:DOF_ARM],[0,0],right_arm_action[:DOF_ARM],[0,0]]) #18

        qpos_raw = physics.data.qpos.copy()[:(DOF_ARM+2)*2]

        tar_q_flt=np.zeros((DOF_ARM+2)*2)
        flt=1
        for i in range (0,len(qpos_raw)):
            tar_q_flt[i]=new_qpos[i]*flt+(1-flt)*qpos_raw[i]


        tar_q_flt[DOF_ARM]=left_gripper_action
        tar_q_flt[DOF_ARM+1]=-left_gripper_action#PUPPET_GRIPPER_POSITION_OPEN

        tar_q_flt[(DOF_ARM+1)*2]=right_gripper_action
        tar_q_flt[(DOF_ARM+1)*2+1]=-right_gripper_action 
        
        #env_action = np.concatenate([left_arm_action, full_left_gripper_action, right_arm_action, full_right_gripper_action])
        #env_action = np.copyto(physics.data.ctrl, np.concatenate([tar_q_flt,0.0*np.zeros(18)]))
        np.copyto(physics.data.ctrl, np.concatenate([tar_q_flt,np.zeros((DOF_ARM+2)*2)]))#输出位置控制器 由于没有DM DJ这里还是采用mojoco执行器或使用扭矩控制代替
        #print(physics.data.ctrl)
        #super().before_step(env_action, physics)#使用关节电机驱动
        return

    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[:(DOF_ARM+2)]
        right_qpos_raw = qpos_raw[(DOF_ARM+2):(DOF_ARM+2)*2]
        left_arm_qpos = left_qpos_raw[:DOF_ARM]
        right_arm_qpos = right_qpos_raw[:DOF_ARM]
        left_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(left_qpos_raw[DOF_ARM])]
        right_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(right_qpos_raw[DOF_ARM])]#从手夹爪比例需要修改
        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[:(DOF_ARM+2)]
        right_qvel_raw = qvel_raw[(DOF_ARM+2):(DOF_ARM+2)*2]
        left_arm_qvel = left_qvel_raw[:DOF_ARM]
        right_arm_qvel = right_qvel_raw[:DOF_ARM]
        left_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(left_qvel_raw[DOF_ARM])]
        right_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(right_qvel_raw[DOF_ARM])]
        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):
        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')#图像尺寸
        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')

        obs['images']['left_wrist'] = physics.render(height=480, width=640, camera_id='left_wrist')
        obs['images']['right_wrist'] = physics.render(height=480, width=640, camera_id='right_wrist')
        return obs

    def get_reward(self, physics):
        # return whether left gripper is holding the box
        raise NotImplementedError


class TransferCubeTask_Human(BimanualViperXTask):#--------------------------doghome
    def __init__(self, random=None):
        super().__init__(random=random)
        self.max_reward = 5#-------------------

    def initialize_episode(self, physics):
        """Sets the state of the environment at the start of each episode."""
        # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside
        # reset qpos, control and box position
        with physics.reset_context():
            box_start_idx = physics.model.name2id('red_box_joint', 'joint') 
            physics.named.data.qpos[:(DOF_ARM+2)*2] = START_ARM_POSE
            #np.copyto(physics.data.ctrl, START_ARM_POSE)
            np.copyto(physics.data.ctrl, np.concatenate([START_ARM_POSE,np.zeros((DOF_ARM+2)*2)])) #amao
            assert BOX_POSE[0] is not None
            #np.copyto(physics.named.data.qpos[box_start_idx : box_start_idx + 7], BOX_POSE[0])
            temp=BOX_POSE[0][:7*2]
            print(len(BOX_POSE[0]),len(temp))
            print(f"{BOX_POSE=}")
            np.copyto(physics.named.data.qpos[(DOF_ARM+2)*2:(DOF_ARM+2)*2+7*2], temp)
            #physics.named.data.qpos[(DOF_ARM+2)*2:(DOF_ARM+2)*2+7*2] = BOX_POSE[0] # two objects 方块的位姿态 将上一次的方块位置进行复位
            
        super().initialize_episode(physics)

    @staticmethod
    def get_env_state(physics):#---doghome
        env_state = physics.data.qpos.copy()[(DOF_ARM+2)*2:]
        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

        cube_red_place_good = ("red_box", "table") in all_contact_pairs
        cube_blue_place_good = ("blue_box", "table") in all_contact_pairs
        #print("Reward check",touch_left_gripper,touch_right_gripper,touch_table,cube_red_place_good)
        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
        if cube_red_place_good and cube_blue_place_good: # successful transfer
            reward = 5
        return reward


class TransferCubeTask(BimanualViperXTask):
    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."""
        # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside
        # reset qpos, control and box position
        with physics.reset_context():
            physics.named.data.qpos[:16] = START_ARM_POSE
            np.copyto(physics.data.ctrl, START_ARM_POSE)
            assert BOX_POSE[0] is not None
            physics.named.data.qpos[-7:] = BOX_POSE[0]
            # print(f"{BOX_POSE=}")
        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 InsertionTask(BimanualViperXTask):
    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."""
        # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside
        # reset qpos, control and box position
        with physics.reset_context():
            physics.named.data.qpos[:16] = START_ARM_POSE
            np.copyto(physics.data.ctrl, START_ARM_POSE)
            assert BOX_POSE[0] is not None
            physics.named.data.qpos[-7*2:] = BOX_POSE[0] # two objects
            # print(f"{BOX_POSE=}")
        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

#-------------------------------------------------------------------------------------
def get_action(master_bot_left, master_bot_right):#实物样机
    action = np.zeros(14)
    # arm action
    action[:6] = master_bot_left.dxl.joint_states.position[:6]
    action[7:7+6] = master_bot_right.dxl.joint_states.position[:6]
    # gripper action
    left_gripper_pos = master_bot_left.dxl.joint_states.position[7]
    right_gripper_pos = master_bot_right.dxl.joint_states.position[7]
    normalized_left_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(left_gripper_pos)
    normalized_right_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(right_gripper_pos)
    action[6] = normalized_left_pos
    action[7+6] = normalized_right_pos
    return action

def test_sim_teleop():#真机器人
    """ Testing teleoperation in sim with ALOHA. Requires hardware and ALOHA repo to work. """
    from interbotix_xs_modules.arm import InterbotixManipulatorXS

    BOX_POSE[0] = [0.2, 0.5, 0.05, 1, 0, 0, 0]

    # source of data
    master_bot_left = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper",
                                              robot_name=f'master_left', init_node=True)
    master_bot_right = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper",
                                              robot_name=f'master_right', init_node=False)

    # setup the environment
    env = make_sim_env('sim_transfer_cube')
    ts = env.reset()
    episode = [ts]
    # setup plotting
    ax = plt.subplot()
    plt_img = ax.imshow(ts.observation['images']['angle'])
    plt.ion()

    for t in range(1000):
        action = get_action(master_bot_left, master_bot_right)
        ts = env.step(action)
        episode.append(ts)

        plt_img.set_data(ts.observation['images']['angle'])
        plt.pause(0.02)


if __name__ == '__main__':
    test_sim_teleop()