from typing import Union, Tuple, Dict, Any

import gym
import copy
import itertools

import torch
import torch.nn.functional as F

from ....memories.torch import Memory
from ....models.torch import Model

from .. import Agent


TD3_DEFAULT_CONFIG = {
    "gradient_steps": 1,            # gradient steps
    "batch_size": 64,               # training batch size
    
    "discount_factor": 0.99,        # discount factor (gamma)
    "polyak": 0.005,                # soft update hyperparameter (tau)
    
    "actor_learning_rate": 1e-3,    # actor learning rate
    "critic_learning_rate": 1e-3,   # critic learning rate
    "learning_rate_scheduler": None,        # learning rate scheduler class (see torch.optim.lr_scheduler)
    "learning_rate_scheduler_kwargs": {},   # learning rate scheduler's kwargs (e.g. {"step_size": 1e-3})

    "state_preprocessor": None,             # state preprocessor class (see skrl.resources.preprocessors)
    "state_preprocessor_kwargs": {},        # state preprocessor's kwargs (e.g. {"size": env.observation_space})

    "random_timesteps": 0,          # random exploration steps
    "learning_starts": 0,           # learning starts after this many steps

    "exploration": {
        "noise": None,              # exploration noise
        "initial_scale": 1.0,       # initial scale for noise
        "final_scale": 1e-3,        # final scale for noise
        "timesteps": None,          # timesteps for noise decay
    },

    "policy_delay": 2,                      # policy delay update with respect to critic update
    "smooth_regularization_noise": None,    # smooth noise for regularization
    "smooth_regularization_clip": 0.5,      # clip for smooth regularization

    "rewards_shaper": None,         # rewards shaping function: Callable(reward, timestep, timesteps) -> reward

    "experiment": {
        "directory": "",            # experiment's parent directory
        "experiment_name": "",      # experiment name
        "write_interval": 250,      # TensorBoard writing interval (timesteps)

        "checkpoint_interval": 1000,        # interval for checkpoints (timesteps)
        "store_separately": False,          # whether to store checkpoints separately
    }
}


class TD3(Agent):
    def __init__(self, 
                 models: Dict[str, Model], 
                 memory: Union[Memory, Tuple[Memory], None] = None, 
                 observation_space: Union[int, Tuple[int], gym.Space, None] = None, 
                 action_space: Union[int, Tuple[int], gym.Space, None] = None, 
                 device: Union[str, torch.device] = "cuda:0", 
                 cfg: dict = {}) -> None:
        """Twin Delayed DDPG (TD3)

        https://arxiv.org/abs/1802.09477
        
        :param models: Models used by the agent
        :type models: dictionary of skrl.models.torch.Model
        :param memory: Memory to storage the transitions.
                       If it is a tuple, the first element will be used for training and 
                       for the rest only the environment transitions will be added
        :type memory: skrl.memory.torch.Memory, list of skrl.memory.torch.Memory or None
        :param observation_space: Observation/state space or shape (default: None)
        :type observation_space: int, tuple or list of integers, gym.Space or None, optional
        :param action_space: Action space or shape (default: None)
        :type action_space: int, tuple or list of integers, gym.Space or None, optional
        :param device: Computing device (default: "cuda:0")
        :type device: str or torch.device, optional
        :param cfg: Configuration dictionary
        :type cfg: dict

        :raises KeyError: If the models dictionary is missing a required key
        """
        _cfg = copy.deepcopy(TD3_DEFAULT_CONFIG)
        _cfg.update(cfg)
        super().__init__(models=models, 
                         memory=memory, 
                         observation_space=observation_space, 
                         action_space=action_space, 
                         device=device, 
                         cfg=_cfg)

        # models
        self.policy = self.models.get("policy", None)
        self.target_policy = self.models.get("target_policy", None)
        self.critic_1 = self.models.get("critic_1", None)
        self.critic_2 = self.models.get("critic_2", None)
        self.target_critic_1 = self.models.get("target_critic_1", None)
        self.target_critic_2 = self.models.get("target_critic_2", None)
        
        # checkpoint models
        self.checkpoint_modules["policy"] = self.policy
        self.checkpoint_modules["target_policy"] = self.target_policy
        self.checkpoint_modules["critic_1"] = self.critic_1
        self.checkpoint_modules["critic_2"] = self.critic_2
        self.checkpoint_modules["target_critic_1"] = self.target_critic_1
        self.checkpoint_modules["target_critic_2"] = self.target_critic_2

        if self.target_policy is not None and self.target_critic_1 is not None and self.target_critic_2 is not None:
            # freeze target networks with respect to optimizers (update via .update_parameters())
            self.target_policy.freeze_parameters(True)
            self.target_critic_1.freeze_parameters(True)
            self.target_critic_2.freeze_parameters(True)

            # update target networks (hard update)
            self.target_policy.update_parameters(self.policy, polyak=1)
            self.target_critic_1.update_parameters(self.critic_1, polyak=1)
            self.target_critic_2.update_parameters(self.critic_2, polyak=1)

        # configuration
        self._gradient_steps = self.cfg["gradient_steps"]
        self._batch_size = self.cfg["batch_size"]

        self._discount_factor = self.cfg["discount_factor"]
        self._polyak = self.cfg["polyak"]
        
        self._actor_learning_rate = self.cfg["actor_learning_rate"]
        self._critic_learning_rate = self.cfg["critic_learning_rate"]
        self._learning_rate_scheduler = self.cfg["learning_rate_scheduler"]

        self._state_preprocessor = self.cfg["state_preprocessor"]
        
        self._random_timesteps = self.cfg["random_timesteps"]
        self._learning_starts = self.cfg["learning_starts"]

        self._exploration_noise = self.cfg["exploration"]["noise"]
        self._exploration_initial_scale = self.cfg["exploration"]["initial_scale"]
        self._exploration_final_scale = self.cfg["exploration"]["final_scale"]
        self._exploration_timesteps = self.cfg["exploration"]["timesteps"]

        self._policy_delay = self.cfg["policy_delay"]
        self._critic_update_counter = 0

        self._smooth_regularization_noise = self.cfg["smooth_regularization_noise"]
        self._smooth_regularization_clip = self.cfg["smooth_regularization_clip"]

        self._rewards_shaper = self.cfg["rewards_shaper"]

        # set up optimizers and learning rate schedulers
        if self.policy is not None and self.critic_1 is not None and self.critic_2 is not None:
            self.policy_optimizer = torch.optim.Adam(self.policy.parameters(), lr=self._actor_learning_rate)
            self.critic_optimizer = torch.optim.Adam(itertools.chain(self.critic_1.parameters(), self.critic_2.parameters()), 
                                                     lr=self._critic_learning_rate)
            if self._learning_rate_scheduler is not None:
                self.policy_scheduler = self._learning_rate_scheduler(self.policy_optimizer, **self.cfg["learning_rate_scheduler_kwargs"])
                self.critic_scheduler = self._learning_rate_scheduler(self.critic_optimizer, **self.cfg["learning_rate_scheduler_kwargs"])

            self.checkpoint_modules["policy_optimizer"] = self.policy_optimizer
            self.checkpoint_modules["critic_optimizer"] = self.critic_optimizer

        # set up preprocessors
        if self._state_preprocessor:
            self._state_preprocessor = self._state_preprocessor(**self.cfg["state_preprocessor_kwargs"])
            self.checkpoint_modules["state_preprocessor"] = self._state_preprocessor
        else:
            self._state_preprocessor = self._empty_preprocessor

    def init(self) -> None:
        """Initialize the agent
        """
        super().init()

        # create tensors in memory
        if self.memory is not None:
            self.memory.create_tensor(name="states", size=self.observation_space, dtype=torch.float32)
            self.memory.create_tensor(name="next_states", size=self.observation_space, dtype=torch.float32)
            self.memory.create_tensor(name="actions", size=self.action_space, dtype=torch.float32)
            self.memory.create_tensor(name="rewards", size=1, dtype=torch.float32)
            self.memory.create_tensor(name="dones", size=1, dtype=torch.bool)

        self.tensors_names = ["states", "actions", "rewards", "next_states", "dones"]

        # clip noise bounds
        self.clip_actions_min = torch.tensor(self.action_space.low, device=self.device)
        self.clip_actions_max = torch.tensor(self.action_space.high, device=self.device)

        # backward compatibility: torch < 1.9 clamp method does not support tensors
        self._backward_compatibility = tuple(map(int, (torch.__version__.split(".")[:2]))) < (1, 9)

    def act(self, states: torch.Tensor, timestep: int, timesteps: int) -> torch.Tensor:
        """Process the environment's states to make a decision (actions) using the main policy

        :param states: Environment's states
        :type states: torch.Tensor
        :param timestep: Current timestep
        :type timestep: int
        :param timesteps: Number of timesteps
        :type timesteps: int

        :return: Actions
        :rtype: torch.Tensor
        """
        states = self._state_preprocessor(states)

        # sample random actions
        if timestep < self._random_timesteps:
            return self.policy.random_act(states, taken_actions=None, role="policy")

        # sample deterministic actions
        actions = self.policy.act(states, taken_actions=None, role="policy")
        
        # add noise
        if self._exploration_noise is not None:
            # sample noises
            noises = self._exploration_noise.sample(actions[0].shape)
            
            # define exploration timesteps
            scale = self._exploration_final_scale
            if self._exploration_timesteps is None:
                self._exploration_timesteps = timesteps
            
            # apply exploration noise
            if timestep <= self._exploration_timesteps:
                scale = (1 - timestep / self._exploration_timesteps) \
                      * (self._exploration_initial_scale - self._exploration_final_scale) \
                      + self._exploration_final_scale
                noises.mul_(scale)

                # modify actions
                actions[0].add_(noises)

                if self._backward_compatibility:
                    actions = (torch.max(torch.min(actions[0], self.clip_actions_max), self.clip_actions_min), 
                               actions[1], 
                               actions[2])
                else:
                    actions[0].clamp_(min=self.clip_actions_min, max=self.clip_actions_max)

                # record noises
                self.track_data("Exploration / Exploration noise (max)", torch.max(noises).item())
                self.track_data("Exploration / Exploration noise (min)", torch.min(noises).item())
                self.track_data("Exploration / Exploration noise (mean)", torch.mean(noises).item())
            
            else:
                # record noises
                self.track_data("Exploration / Exploration noise (max)", 0)
                self.track_data("Exploration / Exploration noise (min)", 0)
                self.track_data("Exploration / Exploration noise (mean)", 0)

        return actions

    def record_transition(self, 
                          states: torch.Tensor, 
                          actions: torch.Tensor, 
                          rewards: torch.Tensor, 
                          next_states: torch.Tensor, 
                          dones: torch.Tensor, 
                          infos: Any, 
                          timestep: int, 
                          timesteps: int) -> None:
        """Record an environment transition in memory
        
        :param states: Observations/states of the environment used to make the decision
        :type states: torch.Tensor
        :param actions: Actions taken by the agent
        :type actions: torch.Tensor
        :param rewards: Instant rewards achieved by the current actions
        :type rewards: torch.Tensor
        :param next_states: Next observations/states of the environment
        :type next_states: torch.Tensor
        :param dones: Signals to indicate that episodes have ended
        :type dones: torch.Tensor
        :param infos: Additional information about the environment
        :type infos: Any type supported by the environment
        :param timestep: Current timestep
        :type timestep: int
        :param timesteps: Number of timesteps
        :type timesteps: int
        """
        super().record_transition(states, actions, rewards, next_states, dones, infos, timestep, timesteps)

        # reward shaping
        if self._rewards_shaper is not None:
            rewards = self._rewards_shaper(rewards, timestep, timesteps)
        
        if self.memory is not None:
            self.memory.add_samples(states=states, actions=actions, rewards=rewards, next_states=next_states, dones=dones)
            for memory in self.secondary_memories:
                memory.add_samples(states=states, actions=actions, rewards=rewards, next_states=next_states, dones=dones)

    def pre_interaction(self, timestep: int, timesteps: int) -> None:
        """Callback called before the interaction with the environment

        :param timestep: Current timestep
        :type timestep: int
        :param timesteps: Number of timesteps
        :type timesteps: int
        """
        pass

    def post_interaction(self, timestep: int, timesteps: int) -> None:
        """Callback called after the interaction with the environment

        :param timestep: Current timestep
        :type timestep: int
        :param timesteps: Number of timesteps
        :type timesteps: int
        """
        if timestep >= self._learning_starts:
            self._update(timestep, timesteps)
        
        # write tracking data and checkpoints
        super().post_interaction(timestep, timesteps)
    
    def _update(self, timestep: int, timesteps: int) -> None:
        """Algorithm's main update step

        :param timestep: Current timestep
        :type timestep: int
        :param timesteps: Number of timesteps
        :type timesteps: int
        """
        # sample a batch from memory
        sampled_states, sampled_actions, sampled_rewards, sampled_next_states, sampled_dones = \
            self.memory.sample(names=self.tensors_names, batch_size=self._batch_size)[0]

        # gradient steps
        for gradient_step in range(self._gradient_steps):

            sampled_states = self._state_preprocessor(sampled_states, train=not gradient_step)
            sampled_next_states = self._state_preprocessor(sampled_next_states)
            
            with torch.no_grad():
                # target policy smoothing
                next_actions, _, _ = self.target_policy.act(states=sampled_next_states, taken_actions=None, role="target_policy")
                noises = torch.clamp(self._smooth_regularization_noise.sample(next_actions.shape), 
                                     min=-self._smooth_regularization_clip, 
                                     max=self._smooth_regularization_clip)
                next_actions.add_(noises)

                if self._backward_compatibility:
                    next_actions = torch.max(torch.min(next_actions, self.clip_actions_max), self.clip_actions_min)
                else:
                    next_actions.clamp_(min=self.clip_actions_min, max=self.clip_actions_max)

                # compute target values
                target_q1_values, _, _ = self.target_critic_1.act(states=sampled_next_states, taken_actions=next_actions, role="target_critic_1")
                target_q2_values, _, _ = self.target_critic_2.act(states=sampled_next_states, taken_actions=next_actions, role="target_critic_2")
                target_q_values = torch.min(target_q1_values, target_q2_values)
                target_values = sampled_rewards + self._discount_factor * sampled_dones.logical_not() * target_q_values

            # compute critic loss
            critic_1_values, _, _ = self.critic_1.act(states=sampled_states, taken_actions=sampled_actions, role="critic_1")
            critic_2_values, _, _ = self.critic_2.act(states=sampled_states, taken_actions=sampled_actions, role="critic_2")
            
            critic_loss = F.mse_loss(critic_1_values, target_values) + F.mse_loss(critic_2_values, target_values)
            
            # optimization step (critic)
            self.critic_optimizer.zero_grad()
            critic_loss.backward()
            self.critic_optimizer.step()

            # delayed update
            self._critic_update_counter += 1
            if not self._critic_update_counter % self._policy_delay:

                # compute policy (actor) loss
                actions, _, _ = self.policy.act(states=sampled_states, taken_actions=None, role="policy")
                critic_values, _, _ = self.critic_1.act(states=sampled_states, taken_actions=actions, role="critic_1")

                policy_loss = -critic_values.mean()

                # optimization step (policy)
                self.policy_optimizer.zero_grad()
                policy_loss.backward()
                self.policy_optimizer.step()

                # update target networks
                self.target_critic_1.update_parameters(self.critic_1, polyak=self._polyak)
                self.target_critic_2.update_parameters(self.critic_2, polyak=self._polyak)
                self.target_policy.update_parameters(self.policy, polyak=self._polyak)

            # update learning rate
            if self._learning_rate_scheduler:
                self.policy_scheduler.step()
                self.critic_scheduler.step()

            # record data
            if not self._critic_update_counter % self._policy_delay:
                self.track_data("Loss / Policy loss", policy_loss.item())
            self.track_data("Loss / Critic loss", critic_loss.item())

            self.track_data("Q-network / Q1 (max)", torch.max(critic_1_values).item())
            self.track_data("Q-network / Q1 (min)", torch.min(critic_1_values).item())
            self.track_data("Q-network / Q1 (mean)", torch.mean(critic_1_values).item())

            self.track_data("Q-network / Q2 (max)", torch.max(critic_2_values).item())
            self.track_data("Q-network / Q2 (min)", torch.min(critic_2_values).item())
            self.track_data("Q-network / Q2 (mean)", torch.mean(critic_2_values).item())
            
            self.track_data("Target / Target (max)", torch.max(target_values).item())
            self.track_data("Target / Target (min)", torch.min(target_values).item())
            self.track_data("Target / Target (mean)", torch.mean(target_values).item())

            if self._learning_rate_scheduler:
                self.track_data("Learning / Policy learning rate", self.policy_scheduler.get_last_lr()[0])
                self.track_data("Learning / Critic learning rate", self.critic_scheduler.get_last_lr()[0])
