import time
import wandb
import copy
import os
import numpy as np
import itertools
from itertools import chain
import torch
import imageio
import warnings
import functools
from bta.utils.util import update_linear_schedule, is_acyclic, pruning
from bta.runner.happo.base_runner import Runner
from pathlib import Path
from collections import defaultdict, deque
from tqdm import tqdm
from collections import defaultdict, deque
from typing import Dict
from icecream import ic
from scipy.stats import rankdata
from bta.algorithms.utils.util import check


def _t2n(x):
    return x.detach().cpu().numpy()

class FootballRunner(Runner):
    def __init__(self, config):
        super(FootballRunner, self).__init__(config)
        self.env_infos = defaultdict(list)
       
    def run(self):
        self.warmup()   

        start = time.time()
        episodes = int(self.num_env_steps) // self.episode_length // self.n_rollout_threads
        total_num_steps = 0

        for episode in range(episodes):
            if self.use_linear_lr_decay:
                for agent_id in range(self.num_agents):
                    self.trainer[agent_id].policy.lr_decay(episode, episodes)

            for step in range(self.episode_length):
                # Sample actions
                values, actions, action_log_probs, rnn_states, rnn_states_critic = self.collect(step)
                    
                # Obser reward and next obs
                obs, rewards, dones, infos = self.envs.step(np.squeeze(actions, axis=-1))
                share_obs = obs.copy()
                total_num_steps += (self.n_rollout_threads)
                data = obs, share_obs, rewards, dones, infos, \
                       values, actions, action_log_probs, \
                       rnn_states, rnn_states_critic
                
                # insert data into buffer
                self.insert(data)

            # compute return and update network
            self.compute()
            train_infos = self.train()
            
            # post process
            total_num_steps = (episode + 1) * self.episode_length * self.n_rollout_threads
            
            # save model
            if (episode % self.save_interval == 0 or episode == episodes - 1):
                self.save()

            # log information
            # log information
            if total_num_steps % self.log_interval == 0:
                end = time.time()
                print("\n Env {} Algo {} Exp {} updates {}/{} episodes, total num timesteps {}/{}, FPS {}.\n"
                        .format(self.env_name,
                                self.algorithm_name,
                                self.experiment_name,
                                episode,
                                episodes,
                                total_num_steps,
                                self.num_env_steps,
                                int(total_num_steps / (end - start))))
                
                total_mean = 0.0
                for a in range(self.num_agents):
                    train_infos[a]["average_episode_rewards_per_agent"] = np.mean(self.buffer[a].rewards) * self.episode_length
                    total_mean += train_infos[a]["average_episode_rewards_per_agent"]
                    # print("average episode rewards agent {} is {}".format(a, train_infos[a]["average_episode_rewards_per_agent"]))
                total_mean /= self.num_agents
                print("average episode rewards for team is {}".format(total_mean))
                for a in range(self.num_agents):
                    train_infos[a]["average_episode_rewards"] = total_mean
                if len(self.env_infos["win_rate"]) > 0:
                    print("Win rate: ", sum(self.env_infos["win_rate"]) / len(self.env_infos["win_rate"]))
                    
                self.log_train(train_infos, total_num_steps)
                self.log_env(self.env_infos, total_num_steps)
                self.env_infos = defaultdict(list)

            # eval
            if total_num_steps % self.eval_interval == 0 and self.use_eval:
                self.eval(total_num_steps)

    def warmup(self):
        # reset env
        obs = self.envs.reset()
        share_obs = obs.copy()

        if not self.use_centralized_V:  
            share_obs = obs      

        for agent_id in range(self.num_agents):
            self.buffer[agent_id].share_obs[0] = share_obs[:, agent_id].copy()
            self.buffer[agent_id].obs[0] = obs[:, agent_id].copy()

    @torch.no_grad()
    def collect(self, step):
        value_collector = []
        action_collector = []
        action_log_prob_collector = []
        rnn_state_collector = []
        rnn_state_critic_collector = []
        for agent_id in range(self.num_agents):
            self.trainer[agent_id].prep_rollout()
            value, action, action_log_prob, rnn_state, rnn_state_critic \
                = self.trainer[agent_id].policy.get_actions(self.buffer[agent_id].share_obs[step],
                                                            self.buffer[agent_id].obs[step],
                                                            self.buffer[agent_id].rnn_states[step],
                                                            self.buffer[agent_id].rnn_states_critic[step],
                                                            self.buffer[agent_id].masks[step])
            value_collector.append(_t2n(value))
            action_collector.append(_t2n(action))
            action_log_prob_collector.append(_t2n(action_log_prob))
            rnn_state_collector.append(_t2n(rnn_state))
            rnn_state_critic_collector.append(_t2n(rnn_state_critic))
        # [self.envs, agents, dim]
        values = np.array(value_collector).transpose(1, 0, 2)
        actions = np.array(action_collector).transpose(1, 0, 2)
        action_log_probs = np.array(action_log_prob_collector).transpose(1, 0, 2)
        rnn_states = np.array(rnn_state_collector).transpose(1, 0, 2, 3)
        rnn_states_critic = np.array(rnn_state_critic_collector).transpose(1, 0, 2, 3)

        return values, actions, action_log_probs, rnn_states, rnn_states_critic
    
    def insert(self, data):
        obs, share_obs, rewards, dones, infos, values, actions, action_log_probs, \
            rnn_states, rnn_states_critic = data
        
        dones_env = np.all(dones, axis=-1)
        if np.any(dones_env):
            for done, info in zip(dones_env, infos):
                if done:
                    self.env_infos["goal"].append(info["score_reward"])
                    if info["score_reward"] > 0:
                        self.env_infos["win_rate"].append(1)
                    else:
                        self.env_infos["win_rate"].append(0)
                    self.env_infos["steps"].append(info["max_steps"] - info["steps_left"])

        rnn_states[dones == True] = np.zeros(((dones == True).sum(), self.recurrent_N, self.hidden_size), dtype=np.float32)
        rnn_states_critic[dones == True] = np.zeros(((dones == True).sum(), self.recurrent_N, self.hidden_size), dtype=np.float32)
        masks = np.ones((self.n_rollout_threads, self.num_agents, 1), dtype=np.float32)
        masks[dones == True] = np.zeros(((dones == True).sum(), 1), dtype=np.float32)

        for agent_id in range(self.num_agents):
            if not self.use_centralized_V:
                share_obs = obs

            self.buffer[agent_id].insert(share_obs[:, agent_id],
                                        obs[:, agent_id],
                                        rnn_states[:, agent_id],
                                        rnn_states_critic[:, agent_id],
                                        actions[:, agent_id],
                                        action_log_probs[:, agent_id],
                                        values[:, agent_id],
                                        rewards[:, agent_id],
                                        masks[:, agent_id],
                                        )

    @torch.no_grad()
    def eval(self, total_num_steps):
        eval_env_infos = defaultdict(list)
        eval_episode_rewards = []
        eval_obs = self.eval_envs.reset()

        eval_rnn_states = np.zeros((self.n_eval_rollout_threads, self.num_agents, self.recurrent_N, self.hidden_size), dtype=np.float32)
        eval_masks = np.ones((self.n_eval_rollout_threads, self.num_agents, 1), dtype=np.float32)

        # init eval goals
        num_done = 0
        eval_goals = np.zeros(self.all_args.eval_episodes)
        eval_win_rates = np.zeros(self.all_args.eval_episodes)
        eval_steps = np.zeros(self.all_args.eval_episodes)
        step = 0
        quo = self.all_args.eval_episodes // self.n_eval_rollout_threads
        rem = self.all_args.eval_episodes % self.n_eval_rollout_threads
        done_episodes_per_thread = np.zeros(self.n_eval_rollout_threads, dtype=int)
        eval_episodes_per_thread = done_episodes_per_thread + quo
        eval_episodes_per_thread[:rem] += 1
        unfinished_thread = (done_episodes_per_thread != eval_episodes_per_thread)

        # loop until enough episodes
        while num_done < self.all_args.eval_episodes and step < self.episode_length:
            # get actions
            eval_actions = np.zeros((self.n_eval_rollout_threads, self.num_agents, 1), dtype=np.int32)
            for agent_id in range(self.num_agents):
                self.trainer[agent_id].prep_rollout()

                eval_action, eval_rnn_state = self.trainer[agent_id].policy.act(eval_obs[:, agent_id],
                                                                                eval_rnn_states[:, agent_id],
                                                                                eval_masks[:, agent_id],
                                                                                deterministic=True)
                eval_actions[:, agent_id] = _t2n(eval_action)

                eval_rnn_states[:, agent_id] = _t2n(eval_rnn_state)

            eval_actions_env = [eval_actions[idx, :, 0] for idx in range(self.n_eval_rollout_threads)]

            # step
            eval_obs, eval_rewards, eval_dones, eval_infos = self.eval_envs.step(eval_actions_env)

            # update goals if done
            eval_dones_env = np.all(eval_dones, axis=-1)
            eval_dones_unfinished_env = eval_dones_env[unfinished_thread]
            if np.any(eval_dones_unfinished_env):
                for idx_env in range(self.n_eval_rollout_threads):
                    if unfinished_thread[idx_env] and eval_dones_env[idx_env]:
                        eval_goals[num_done] = eval_infos[idx_env]["score_reward"]
                        eval_win_rates[num_done] = 1 if eval_infos[idx_env]["score_reward"] > 0 else 0
                        eval_steps[num_done] = eval_infos[idx_env]["max_steps"] - eval_infos[idx_env]["steps_left"]
                        # print("episode {:>2d} done by env {:>2d}: {}".format(num_done, idx_env, eval_infos[idx_env]["score_reward"]))
                        num_done += 1
                        done_episodes_per_thread[idx_env] += 1
            unfinished_thread = (done_episodes_per_thread != eval_episodes_per_thread)

            # reset rnn and masks for done envs
            eval_rnn_states[eval_dones_env == True] = np.zeros(((eval_dones_env == True).sum(), self.num_agents, self.recurrent_N, self.hidden_size), dtype=np.float32)
            eval_masks = np.ones((self.all_args.n_eval_rollout_threads, self.num_agents, 1), dtype=np.float32)
            eval_masks[eval_dones_env == True] = np.zeros(((eval_dones_env == True).sum(), self.num_agents, 1), dtype=np.float32)
            step += 1

        # get expected goal
        eval_goal = np.mean(eval_goals)
        eval_win_rate = np.mean(eval_win_rates)
        eval_step = np.mean(eval_steps)
    
        # log and print
        print("eval expected goal is {}.".format(eval_goal))
        print("eval win rate is {}.".format(eval_win_rate))
        if self.use_wandb:
            wandb.log({"eval_goal": eval_goal}, step=total_num_steps)
            wandb.log({"eval_win_rate": eval_win_rate}, step=total_num_steps)
            wandb.log({"eval_step": eval_step}, step=total_num_steps)
        else:
            self.writter.add_scalars("eval_goal", {"expected_goal": eval_goal}, total_num_steps)
            self.writter.add_scalars("eval_win_rate", {"eval_win_rate": eval_win_rate}, total_num_steps)
            self.writter.add_scalars("eval_step", {"expected_step": eval_step}, total_num_steps)
        
    @torch.no_grad()
    def render(self):
        envs = self.envs
        obs, share_obs, available_actions = envs.reset()
        obs = np.stack(obs)     

        for episode in range(self.all_args.render_episodes):
            episode_rewards = []
            trajectory = []

            rnn_states = np.zeros((self.n_rollout_threads, self.num_agents, self.recurrent_N, self.hidden_size), dtype=np.float32)
            masks = np.ones((self.n_rollout_threads, self.num_agents, 1), dtype=np.float32)

            for step in range(self.episode_length):
                calc_start = time.time()
                actions = []
                for agent_id in range(self.num_agents):
                    if not self.use_centralized_V:
                        share_obs = np.array(list(np.array(obs)[:, agent_id]))
                    self.trainer[agent_id].prep_rollout()
                    action, rnn_state = self.trainer[agent_id].policy.act(np.array(obs)[:, agent_id],
                                                                        rnn_states[:, agent_id],
                                                                        masks[:, agent_id],
                                                                        deterministic=True)

                    action = action.detach().cpu().numpy()
                    actions.append(action[0])
                    rnn_states[:, agent_id] = _t2n(rnn_state)

                # Obser reward and next obs
                print("action:",actions)
                obs, share_obs, rewards, dones, infos, available_actions = self.envs.step([actions])
                obs = np.stack(obs) 
                episode_rewards.append(rewards)

                rnn_states[dones == True] = np.zeros(((dones == True).sum(), self.recurrent_N, self.hidden_size), dtype=np.float32)
                masks = np.ones((self.n_rollout_threads, self.num_agents, 1), dtype=np.float32)
                masks[dones == True] = np.zeros(((dones == True).sum(), 1), dtype=np.float32)

            for info in infos:
                for a in range(self.num_agents):
                    ic(info['episode']['ep_sparse_r_by_agent'][a])
                    ic(info['episode']['ep_shaped_r_by_agent'][a])
                ic(info['episode']['ep_sparse_r'])
                ic(info['episode']['ep_shaped_r'])

            print("average episode rewards is: " + str(np.mean(np.sum(np.array(episode_rewards), axis=0))))
            #print("eval average episode rewards of agent%i: " % agent_id + str(average_episode_rewards))

    # def save(self, step):
    #     for agent_id in range(self.num_agents):
    #         if self.use_single_network:
    #             policy_model = self.trainer[agent_id].policy.model
    #             torch.save(policy_model.state_dict(), str(self.save_dir) + f"/model_agent{agent_id}_periodic_{step}.pt")
    #         else:
    #             policy_actor = self.trainer[agent_id].policy.actor
    #             torch.save(policy_actor.state_dict(), str(self.save_dir) + f"/actor_agent{agent_id}_periodic_{step}.pt")
    #             policy_critic = self.trainer[agent_id].policy.critic
    #             torch.save(policy_critic.state_dict(), str(self.save_dir) + f"/critic_agent{agent_id}_periodic_{step}.pt")