# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppopy
# File copied from https://github.com/vwxyzjn/cleanrl
import os
import random
import time
from dataclasses import dataclass

import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import tyro
from torch.distributions.categorical import Categorical


@dataclass
class Args:
    exp_name: str = os.path.basename(__file__)[: -len(".py")]
    """the name of this experiment"""
    seed: int = 1
    """seed of the experiment"""
    torch_deterministic: bool = True
    """if toggled, `torch.backends.cudnn.deterministic=False`"""
    cuda: bool = True
    """if toggled, cuda will be enabled by default"""
    track: bool = True
    """if toggled, this experiment will be tracked with Weights and Biases"""
    wandb_project_name: str = "clean-rl-mlx"
    """the wandb's project name"""
    wandb_entity: str = None
    """the entity (team) of wandb's project"""
    capture_video: bool = False
    """whether to capture videos of the agent performances (check out `videos` folder)"""

    # Algorithm specific arguments
    env_id: str = "CartPole-v1"
    """the id of the environment"""
    total_timesteps: int = 500000
    """total timesteps of the experiments"""
    learning_rate: float = 2.5e-4
    """the learning rate of the optimizer"""
    num_envs: int = 4
    """the number of parallel game environments"""
    num_steps: int = 128
    """the number of steps to run in each environment per policy rollout"""
    anneal_lr: bool = True
    """Toggle learning rate annealing for policy and value networks"""
    gamma: float = 0.99
    """the discount factor gamma"""
    gae_lambda: float = 0.95
    """the lambda for the general advantage estimation"""
    num_minibatches: int = 4
    """the number of mini-batches"""
    update_epochs: int = 4
    """the K epochs to update the policy"""
    norm_adv: bool = True
    """Toggles advantages normalization"""
    clip_coef: float = 0.2
    """the surrogate clipping coefficient"""
    clip_vloss: bool = True
    """Toggles whether or not to use a clipped loss for the value function, as per the paper."""
    ent_coef: float = 0.01
    """coefficient of the entropy"""
    vf_coef: float = 0.5
    """coefficient of the value function"""
    max_grad_norm: float = 0.5
    """the maximum norm for the gradient clipping"""
    target_kl: float = None
    """the target KL divergence threshold"""

    # to be filled in runtime
    batch_size: int = 0
    """the batch size (computed in runtime)"""
    minibatch_size: int = 0
    """the mini-batch size (computed in runtime)"""
    num_iterations: int = 0
    """the number of iterations (computed in runtime)"""


def make_env(env_id, idx, capture_video, run_name):
    def thunk():
        if capture_video and idx == 0:
            env = gym.make(env_id, render_mode="rgb_array")
            env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
        else:
            env = gym.make(env_id)
        env = gym.wrappers.RecordEpisodeStatistics(env)
        return env

    return thunk


def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
    # torch.nn.init.orthogonal_(layer.weight, std)
    # torch.nn.init.constant_(layer.bias, bias_const)
    weight_shape = layer.weight.shape
    a = np.random.normal(0.0, std, weight_shape)
    u, _, v = np.linalg.svd(a, full_matrices=False)
    q = u if u.shape == weight_shape else v  # Pick the one with the correct shape
    q = q.transpose()
    weights = q.reshape(weight_shape)
    layer.weight = torch.nn.Parameter(torch.tensor(weights, dtype=torch.float))
    layer.bias = torch.nn.Parameter(torch.ones_like(layer.bias) * bias_const)
    return layer


class Agent(nn.Module):
    def __init__(self, envs):
        super().__init__()
        self.critic = nn.Sequential(
            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64, 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64, 1), std=1.0),
        )
        self.actor = nn.Sequential(
            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64, 64)),
            nn.Tanh(),
            layer_init(nn.Linear(64, envs.single_action_space.n), std=0.01),
        )

    def get_value(self, x):
        return self.critic(x)

    def get_action_and_value(self, x, action=None):
        logits = self.actor(x)
        # logits = torch.nn.functional.softmax(logits, dim=1)

        probs = Categorical(logits=logits)
        if action is None:
            action = probs.sample()
        return action, probs.log_prob(action), probs.entropy(), self.critic(x)


if __name__ == "__main__":
    args = tyro.cli(Args)
    args.batch_size = int(args.num_envs * args.num_steps)
    args.framework = "PyTorch"
    args.minibatch_size = int(args.batch_size // args.num_minibatches)
    args.num_iterations = args.total_timesteps // args.batch_size
    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
    if args.track:
        import wandb

        wandb.init(
            project=args.wandb_project_name,
            entity=args.wandb_entity,
            sync_tensorboard=True,
            config=vars(args),
            name=run_name,
            monitor_gym=True,
            save_code=True,
        )

    # TRY NOT TO MODIFY: seeding
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    torch.backends.cudnn.deterministic = args.torch_deterministic

    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")

    # env setup
    envs = gym.vector.SyncVectorEnv(
        [make_env(args.env_id, i, args.capture_video, run_name) for i in range(args.num_envs)],
    )
    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"

    agent = Agent(envs).to(device)
    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)

    # ALGO Logic: Storage setup
    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
    values = torch.zeros((args.num_steps, args.num_envs)).to(device)

    # TRY NOT TO MODIFY: start the game
    global_step = 0
    start_time = time.time()
    next_obs, _ = envs.reset(seed=args.seed)
    next_obs = torch.Tensor(next_obs).to(device)
    next_done = torch.zeros(args.num_envs).to(device)
    for iteration in range(1, args.num_iterations + 1):
        # Annealing the rate if instructed to do so.
        if args.anneal_lr:
            frac = 1.0 - (iteration - 1.0) / args.num_iterations
            lrnow = frac * args.learning_rate
            optimizer.param_groups[0]["lr"] = lrnow

        for step in range(0, args.num_steps):
            global_step += args.num_envs
            obs[step] = next_obs
            dones[step] = next_done

            # ALGO LOGIC: action logic
            s_t = time.time()
            with torch.no_grad():
                action, logprob, _, value = agent.get_action_and_value(next_obs)
                values[step] = value.flatten()
            action_time = time.time() - s_t
            wandb.log({"times/forward_pass_time": action_time})
            actions[step] = action
            logprobs[step] = logprob

            # TRY NOT TO MODIFY: execute the game and log data.
            s_t = time.time()
            next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy())
            step_time = time.time()-s_t
            wandb.log({"times/step_time": step_time})
            next_done = np.logical_or(terminations, truncations)
            rewards[step] = torch.tensor(reward).to(device).view(-1)
            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device)
            if "final_info" in infos:
                for info in infos["final_info"]:
                    if info and "episode" in info:
                        print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
                        wandb.log({"charts/episodic_return": info["episode"]["r"],
                                   "charts/episodic_length": info["episode"]["l"]})

        # bootstrap value if not done
        with torch.no_grad():
            next_value = agent.get_value(next_obs).reshape(1, -1)
            advantages = torch.zeros_like(rewards).to(device)
            lastgaelam = 0
            for t in reversed(range(args.num_steps)):
                if t == args.num_steps - 1:
                    nextnonterminal = 1.0 - next_done
                    nextvalues = next_value
                else:
                    nextnonterminal = 1.0 - dones[t + 1]
                    nextvalues = values[t + 1]
                delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
                advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
            returns = advantages + values

        # flatten the batch
        b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
        b_logprobs = logprobs.reshape(-1)
        b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
        b_advantages = advantages.reshape(-1)
        b_returns = returns.reshape(-1)
        b_values = values.reshape(-1)

        # Optimizing the policy and value network
        b_inds = np.arange(args.batch_size)
        clipfracs = []
        for epoch in range(args.update_epochs):
            np.random.shuffle(b_inds)
            mb_index = 0
            for start in range(0, args.batch_size, args.minibatch_size):
                step_index = global_step + epoch*args.minibatch_size + mb_index
                s_t = time.time()
                end = start + args.minibatch_size
                mb_inds = b_inds[start:end]
                s_t = time.time()
                _, newlogprob, entropy, newvalue = agent.get_action_and_value(b_obs[mb_inds], b_actions.long()[mb_inds])
                forward_pass_time = time.time()-s_t
                logratio = newlogprob - b_logprobs[mb_inds]
                ratio = logratio.exp()

                with torch.no_grad():
                    # calculate approx_kl http://joschu.net/blog/kl-approx.html
                    s_t = time.time()
                    old_approx_kl = (-logratio).mean()
                    approx_kl = ((ratio - 1) - logratio).mean()
                    kl_time = time.time() - s_t
                    clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]

                mb_advantages = b_advantages[mb_inds]
                if args.norm_adv:
                    mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)

                # Policy loss
                s_t = time.time()
                pg_loss1 = -mb_advantages * ratio
                pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef)
                pg_loss = torch.max(pg_loss1, pg_loss2).mean()
                pg_loss_time = time.time() - s_t

                s_t = time.time()
                # Value loss
                newvalue = newvalue.view(-1)
                if args.clip_vloss:
                    v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
                    v_clipped = b_values[mb_inds] + torch.clamp(
                        newvalue - b_values[mb_inds],
                        -args.clip_coef,
                        args.clip_coef,
                    )
                    v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
                    v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
                    v_loss = 0.5 * v_loss_max.mean()
                else:
                    v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()

                value_loss_time = time.time() - s_t
                entropy_loss = entropy.mean()
                loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
                loss_time = time.time()-s_t
                wandb.log({"times/loss_time": loss_time})
                s_t = time.time()
                optimizer.zero_grad()
                loss.backward()
                # nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
                optimizer.step()
                weight_time = time.time() - s_t
                wandb.log({"times/weight_update_time": weight_time})

                # TRY NOT TO MODIFY: record rewards for plotting purposes
                wandb.log({"charts/learning_rate": optimizer.param_groups[0]["lr"],
                           "losses/value_loss": v_loss.item(),
                           "losses/policy_loss": pg_loss.item(),
                           "losses/entropy": entropy_loss.item(),
                           "losses/old_approx_kl": old_approx_kl.item(),
                           "losses/approx_kl": approx_kl.item(),
                           "times/update_forward_pass_time": forward_pass_time,
                           "times/kl_time": kl_time,
                           "times/pg_loss_time": pg_loss_time,
                           "times/value_loss_time": value_loss_time
                           })

                mb_index += 1

            if args.target_kl is not None and approx_kl > args.target_kl:
                break

        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
        var_y = np.var(y_true)
        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y

        wandb.log({"losses/clipfrac": np.mean(clipfracs),
                   "losses/explained_variance": explained_var,
                   "steps_per_sec": int(global_step / (time.time() - start_time))})
    envs.close()
