# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
import os
os.environ["PATH"] += os.pathsep + '/root/.mujoco/mujoco210/bin'
os.environ['MUJOCO_GL']='egl'
from pathlib import Path
from collections import deque
import hydra
import numpy as np
import torch
from dm_env import specs
import dmc
import utils.utils_drqv2 as utils
from utils.logger_drqv2 import Logger
from utils.replay_buffer_drqv2 import ReplayBufferStorage, make_replay_loader
from video import VideoRecorder, TrainVideoRecorder
# import meta2dmc
import metaworld_env as mw
torch.backends.cudnn.benchmark = True


def make_agent(obs_spec, action_spec, cfg):
    C, H, W = obs_spec.shape
    cfg.agent.obs_shape = (C*cfg.frame_stack, H, W)
    cfg.agent.action_shape = action_spec.shape
    return hydra.utils.instantiate(cfg.agent)

class Workspace:
    def __init__(self, cfg):
        self.work_dir = Path.cwd()
        print(f"workspace: {self.work_dir}")

        self.cfg = cfg
        utils.set_seed_everywhere(cfg.seed)
        self.device = torch.device(cfg.device)
        self.setup()
        
        self.agent = make_agent(
            self.train_env.observation_spec(),
            self.train_env.action_spec(),
            self.cfg,
        )
        self.timer = utils.Timer()
        self._global_step = 0
        self._global_episode = 0
        self.log_success = ('metaworld' in self.cfg.task_name)
        
    def setup(self):
        self.logger = Logger(self.work_dir, use_tb=self.cfg.use_tb, agent='drqv2')
        if 'metaworld' in self.cfg.task_name:
            # dummy
            self.dummy_env = dmc.make('walker_walk', 1, self.cfg.action_repeat, self.cfg.seed)
            self.dummy_env.reset()
            #
            
            self.train_env = mw.make(
                self.cfg.task_name,
                1,
                self.cfg.action_repeat,
                self.cfg.seed,
            )
            self.eval_env = mw.make(
                self.cfg.task_name,
                1,
                self.cfg.action_repeat,
                self.cfg.seed,
            )
            # del self.dummy_env
        else:
            self.train_env = dmc.make(
                self.cfg.task_name,
                1,
                self.cfg.action_repeat,
                self.cfg.seed,
            )
            self.eval_env = dmc.make(
                self.cfg.task_name,
                1,
                self.cfg.action_repeat,
                self.cfg.seed,
            )
        
        data_specs = (
            self.train_env.observation_spec(),
            self.train_env.action_spec(),
            specs.Array((1,), np.float32, "reward"),
            specs.Array((1,), np.float32, "discount"),
        )
        
        self.replay_storage = ReplayBufferStorage(data_specs, self.work_dir / "buffer")

        self.replay_loader = make_replay_loader(
            self.work_dir / "buffer",
            self.cfg.replay_buffer_size,
            self.cfg.batch_size,
            self.cfg.replay_buffer_num_workers,
            self.cfg.save_snapshot,
            self.cfg.frame_stack,
            self.cfg.nstep,
            self.cfg.discount,
        )
        self._replay_iter=None
        self.video_recorder = VideoRecorder(
            self.work_dir if self.cfg.save_video else None
        )
        self.train_video_recorder = TrainVideoRecorder(
            self.work_dir if self.cfg.save_train_video else None
        )

    @property
    def global_step(self):
        return self._global_step

    @property
    def global_episode(self):
        return self._global_episode

    @property
    def global_frame(self):
        return self.global_step * self.cfg.action_repeat

    @property
    def replay_iter(self):
        if self._replay_iter is None:
            self._replay_iter = iter(self.replay_loader)
        return self._replay_iter

    def eval(self):
        step, episode, total_reward = 0, 0, 0
        if self.log_success:
            success_rate = 0
        eval_until_episode = utils.Until(self.cfg.num_eval_episodes)

        while eval_until_episode(episode):
            if self.log_success:
                episode_success = 0
            time_step = self.eval_env.reset()
            # if 'metaworld' not in self.cfg.task_name:
            #     self.video_recorder.init(self.eval_env, enabled=(episode == 0))
            self.video_recorder.init(self.eval_env, enabled=(episode == 0), metaworld='metaworld' in self.cfg.task_name)
            observations = deque(maxlen=self.cfg.frame_stack)
            for _ in range(self.cfg.frame_stack):
                observations.append(time_step.observation)
            while not time_step.last():
                with torch.no_grad(), utils.eval_mode(self.agent):
                    action = self.agent.act(
                        np.concatenate(observations, 0),
                        self.global_step,
                        eval_mode=True,
                    )
                time_step = self.eval_env.step(action)
                # if 'metaworld' not in self.cfg.task_name:
                #     self.video_recorder.record(self.eval_env)
                self.video_recorder.record(self.eval_env, 'metaworld' in self.cfg.task_name)
                total_reward += time_step.reward
                step += 1
                observations.append(time_step.observation)
                if self.log_success:
                    episode_success = max(episode_success, time_step['success'])

            episode += 1
            if self.log_success:
                success_rate += episode_success
            # if 'metaworld' not in self.cfg.task_name:
            #     self.video_recorder.save(f"{self.global_frame}.mp4")
            self.video_recorder.save(f"{self.global_frame}.mp4")

        with self.logger.log_and_dump_ctx(self.global_frame, ty="eval") as log:
            log("episode_reward", total_reward / episode)
            if self.log_success:
                log("success_rate", success_rate * 100.0 / episode)
            log("episode_length", step * self.cfg.action_repeat / episode)
            log("episode", self.global_episode)
            log("step", self.global_step)

    def train(self):
        # predicates
        train_until_step = utils.Until(
            self.cfg.num_train_frames, self.cfg.action_repeat
        )
        seed_until_step = utils.Until(self.cfg.num_seed_frames, self.cfg.action_repeat)
        eval_every_step = utils.Every(
            self.cfg.eval_every_frames, self.cfg.action_repeat
        )

        episode_step, episode_reward = 0, 0
        if self.log_success:
            episode_success = 0
        time_step = self.train_env.reset()
        self.replay_storage.add(time_step)
        if 'metaworld' not in self.cfg.task_name:
            self.train_video_recorder.init(time_step.observation)
        metrics = None

        observations = deque(maxlen=self.cfg.frame_stack)
        for _ in range(self.cfg.frame_stack):
            observations.append(time_step.observation)

        while train_until_step(self.global_step):
            if time_step.last():
                self._global_episode += 1
                if 'metaworld' not in self.cfg.task_name:
                    self.train_video_recorder.save(f"{self.global_frame}.mp4")
                # wait until all the metrics schema is populated
                if metrics is not None:
                    # log stats
                    elapsed_time, total_time = self.timer.reset()
                    episode_frame = episode_step * self.cfg.action_repeat
                    with self.logger.log_and_dump_ctx(
                        self.global_frame, ty="train"
                    ) as log:
                        log("fps", episode_frame / elapsed_time)
                        log("total_time", total_time)
                        log("episode_reward", episode_reward)
                        if self.log_success:
                            log("episode_success", episode_success)
                        log("episode_length", episode_frame)
                        log("episode", self.global_episode)
                        log("buffer_size", len(self.replay_storage))
                        log("step", self.global_step)

                # reset env
                time_step = self.train_env.reset()
                self.replay_storage.add(time_step)
                if 'metaworld' not in self.cfg.task_name:
                    self.train_video_recorder.init(time_step.observation)
                # try to save snapshot
                # if self.cfg.save_snapshot:
                #     self.save_snapshot()
                self.save_snapshot()
                episode_step = 0
                episode_reward = 0
                if self.log_success:
                    episode_success = 0

                observations = deque(maxlen=self.cfg.frame_stack)
                for _ in range(self.cfg.frame_stack):
                    observations.append(time_step.observation)

            # try to evaluate
            if eval_every_step(self.global_step):
                self.logger.log(
                    "eval_total_time", self.timer.total_time(), self.global_frame
                )
                self.eval()

            # sample action
            with torch.no_grad(), utils.eval_mode(self.agent):
                action = self.agent.act(
                    np.concatenate(observations, 0), self.global_step, eval_mode=False
                )

            # try to update the agent
            if not seed_until_step(self.global_step):
                metrics = self.agent.update(self.replay_iter, self.global_step)
                self.logger.log_metrics(metrics, self.global_frame, ty="train")

            # take env step
            time_step = self.train_env.step(action)
            episode_reward += time_step.reward
            if self.log_success:
                episode_success = max(episode_success, time_step['success'])
            self.replay_storage.add(time_step)
            if 'metaworld' not in self.cfg.task_name:
                self.train_video_recorder.record(time_step.observation)
            episode_step += 1
            self._global_step += 1
            observations.append(time_step.observation)

    def save_snapshot(self):
        snapshot = self.work_dir / "snapshot.pt"
        keys_to_save = ["agent", "timer", "_global_step", "_global_episode"]
        payload = {k: self.__dict__[k] for k in keys_to_save}
        with snapshot.open("wb") as f:
            torch.save(payload, f)

    def load_snapshot(self):
        snapshot = self.work_dir / "snapshot.pt"
        with snapshot.open("rb") as f:
            payload = torch.load(f)
        for k, v in payload.items():
            self.__dict__[k] = v
            
@hydra.main(config_path='config', config_name='config', strict=True)
def main(cfg):
    root_dir = Path.cwd()
    workspace = Workspace(cfg)
    snapshot = root_dir / "snapshot.pt"
    if snapshot.exists():
        print(f"resuming: {snapshot}")
        workspace.load_snapshot()
    workspace.train()
    
if __name__ == '__main__':
    main()