from typing import Any, Callable, Dict, List, Optional, Type

import gym
import torch as th
from torch import nn as nn

from stable_baselines3.common.policies import BasePolicy, ContinuousCritic, register_policy
from stable_baselines3.common.preprocessing import get_action_dim
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp


class Actor(BasePolicy):
    """
    Actor network (policy) for TD3.

    :param observation_space: (gym.spaces.Space) Obervation space
    :param action_space: (gym.spaces.Space) Action space
    :param net_arch: ([int]) Network architecture
    :param features_extractor: (nn.Module) Network to extract features
        (a CNN when using images, a nn.Flatten() layer otherwise)
    :param features_dim: (int) Number of features
    :param activation_fn: (Type[nn.Module]) Activation function
    :param normalize_images: (bool) Whether to normalize images or not,
         dividing by 255.0 (True by default)
    """

    def __init__(
        self,
        observation_space: gym.spaces.Space,
        action_space: gym.spaces.Space,
        net_arch: List[int],
        features_extractor: nn.Module,
        features_dim: int,
        activation_fn: Type[nn.Module] = nn.ReLU,
        normalize_images: bool = True,
    ):
        super(Actor, self).__init__(
            observation_space,
            action_space,
            features_extractor=features_extractor,
            normalize_images=normalize_images,
            squash_output=True,
        )

        self.features_extractor = features_extractor
        self.normalize_images = normalize_images
        self.net_arch = net_arch
        self.features_dim = features_dim
        self.activation_fn = activation_fn

        action_dim = get_action_dim(self.action_space)
        actor_net = create_mlp(features_dim, action_dim, net_arch, activation_fn, squash_output=True)
        # Deterministic action
        self.mu = nn.Sequential(*actor_net)

    def _get_data(self) -> Dict[str, Any]:
        data = super()._get_data()

        data.update(
            dict(
                net_arch=self.net_arch,
                features_dim=self.features_dim,
                activation_fn=self.activation_fn,
                features_extractor=self.features_extractor,
            )
        )
        return data

    def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
        # assert deterministic, 'The TD3 actor only outputs deterministic actions'
        features = self.extract_features(obs)
        return self.mu(features)

    def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
        return self.forward(observation, deterministic=deterministic)


class TD3Policy(BasePolicy):
    """
    Policy class (with both actor and critic) for TD3.

    :param observation_space: (gym.spaces.Space) Observation space
    :param action_space: (gym.spaces.Space) Action space
    :param lr_schedule: (Callable) Learning rate schedule (could be constant)
    :param net_arch: (Optional[List[int]]) The specification of the policy and value networks.
    :param activation_fn: (Type[nn.Module]) Activation function
    :param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
    :param features_extractor_kwargs: (Optional[Dict[str, Any]]) Keyword arguments
        to pass to the feature extractor.
    :param normalize_images: (bool) Whether to normalize images or not,
         dividing by 255.0 (True by default)
    :param optimizer_class: (Type[th.optim.Optimizer]) The optimizer to use,
        ``th.optim.Adam`` by default
    :param optimizer_kwargs: (Optional[Dict[str, Any]]) Additional keyword arguments,
        excluding the learning rate, to pass to the optimizer
    :param n_critics: (int) Number of critic networks to create.
    """

    def __init__(
        self,
        observation_space: gym.spaces.Space,
        action_space: gym.spaces.Space,
        lr_schedule: Callable,
        net_arch: Optional[List[int]] = None,
        activation_fn: Type[nn.Module] = nn.ReLU,
        features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
        normalize_images: bool = True,
        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
        optimizer_kwargs: Optional[Dict[str, Any]] = None,
        n_critics: int = 2,
    ):
        super(TD3Policy, self).__init__(
            observation_space,
            action_space,
            features_extractor_class,
            features_extractor_kwargs,
            optimizer_class=optimizer_class,
            optimizer_kwargs=optimizer_kwargs,
            squash_output=True,
        )

        # Default network architecture, from the original paper
        if net_arch is None:
            if features_extractor_class == FlattenExtractor:
                net_arch = [400, 300]
            else:
                net_arch = []

        self.features_extractor = features_extractor_class(self.observation_space, **self.features_extractor_kwargs)
        self.features_dim = self.features_extractor.features_dim

        self.net_arch = net_arch
        self.activation_fn = activation_fn
        self.net_args = {
            "observation_space": self.observation_space,
            "action_space": self.action_space,
            "features_extractor": self.features_extractor,
            "features_dim": self.features_dim,
            "net_arch": self.net_arch,
            "activation_fn": self.activation_fn,
            "normalize_images": normalize_images,
        }
        self.critic_kwargs = self.net_args.copy()
        self.critic_kwargs.update({"n_critics": n_critics})
        self.actor, self.actor_target = None, None
        self.critic, self.critic_target = None, None

        self._build(lr_schedule)

    def _build(self, lr_schedule: Callable) -> None:
        self.actor = self.make_actor()
        self.actor_target = self.make_actor()
        self.actor_target.load_state_dict(self.actor.state_dict())
        self.actor.optimizer = self.optimizer_class(self.actor.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
        self.critic = self.make_critic()
        self.critic_target = self.make_critic()
        self.critic_target.load_state_dict(self.critic.state_dict())
        self.critic.optimizer = self.optimizer_class(self.critic.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)

    def _get_data(self) -> Dict[str, Any]:
        data = super()._get_data()

        data.update(
            dict(
                net_arch=self.net_args["net_arch"],
                activation_fn=self.net_args["activation_fn"],
                n_critics=self.critic_kwargs["n_critics"],
                lr_schedule=self._dummy_schedule,  # dummy lr schedule, not needed for loading policy alone
                optimizer_class=self.optimizer_class,
                optimizer_kwargs=self.optimizer_kwargs,
                features_extractor_class=self.features_extractor_class,
                features_extractor_kwargs=self.features_extractor_kwargs,
            )
        )
        return data

    def make_actor(self) -> Actor:
        return Actor(**self.net_args).to(self.device)

    def make_critic(self) -> ContinuousCritic:
        return ContinuousCritic(**self.critic_kwargs).to(self.device)

    def forward(self, observation: th.Tensor, deterministic: bool = False):
        return self._predict(observation, deterministic=deterministic)

    def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
        return self.actor(observation, deterministic=deterministic)


MlpPolicy = TD3Policy


class CnnPolicy(TD3Policy):
    """
    Policy class (with both actor and critic) for TD3.

    :param observation_space: (gym.spaces.Space) Observation space
    :param action_space: (gym.spaces.Space) Action space
    :param lr_schedule: (Callable) Learning rate schedule (could be constant)
    :param net_arch: (Optional[List[int]]) The specification of the policy and value networks.
    :param activation_fn: (Type[nn.Module]) Activation function
    :param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
    :param features_extractor_kwargs: (Optional[Dict[str, Any]]) Keyword arguments
        to pass to the feature extractor.
    :param normalize_images: (bool) Whether to normalize images or not,
         dividing by 255.0 (True by default)
    :param optimizer_class: (Type[th.optim.Optimizer]) The optimizer to use,
        ``th.optim.Adam`` by default
    :param optimizer_kwargs: (Optional[Dict[str, Any]]) Additional keyword arguments,
        excluding the learning rate, to pass to the optimizer
    :param n_critics: (int) Number of critic networks to create.
    """

    def __init__(
        self,
        observation_space: gym.spaces.Space,
        action_space: gym.spaces.Space,
        lr_schedule: Callable,
        net_arch: Optional[List[int]] = None,
        activation_fn: Type[nn.Module] = nn.ReLU,
        features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,
        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
        normalize_images: bool = True,
        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
        optimizer_kwargs: Optional[Dict[str, Any]] = None,
        n_critics: int = 2,
    ):
        super(CnnPolicy, self).__init__(
            observation_space,
            action_space,
            lr_schedule,
            net_arch,
            activation_fn,
            features_extractor_class,
            features_extractor_kwargs,
            normalize_images,
            optimizer_class,
            optimizer_kwargs,
            n_critics,
        )


register_policy("MlpPolicy", MlpPolicy)
register_policy("CnnPolicy", CnnPolicy)