from typing import Any, Dict, Optional, Sequence

from ..argument_utility import (
    ActionScalerArg,
    EncoderArg,
    QFuncArg,
    RewardScalerArg,
    ScalerArg,
    UseGPUArg,
    check_encoder,
    check_q_func,
    check_use_gpu,
)
from ..constants import IMPL_NOT_INITIALIZED_ERROR, ActionSpace
from ..dataset import TransitionMiniBatch
from ..gpu import Device
from ..models.encoders import EncoderFactory
from ..models.optimizers import AdamFactory, OptimizerFactory
from ..models.q_functions import QFunctionFactory
from .base import AlgoBase
from .dqn import DoubleDQN
from .torch.cql_impl import CQLImpl, DiscreteCQLImpl

import ipdb

class CQL(AlgoBase):
    r"""Conservative Q-Learning algorithm.

    CQL is a SAC-based data-driven deep reinforcement learning algorithm, which
    achieves state-of-the-art performance in offline RL problems.

    CQL mitigates overestimation error by minimizing action-values under the
    current policy and maximizing values under data distribution for
    underestimation issue.

    .. math::

        L(\theta_i) = \alpha\, \mathbb{E}_{s_t \sim D}
            \left[\log{\sum_a \exp{Q_{\theta_i}(s_t, a)}}
             - \mathbb{E}_{a \sim D} \big[Q_{\theta_i}(s_t, a)\big] - \tau\right]
            + L_\mathrm{SAC}(\theta_i)

    where :math:`\alpha` is an automatically adjustable value via Lagrangian
    dual gradient descent and :math:`\tau` is a threshold value.
    If the action-value difference is smaller than :math:`\tau`, the
    :math:`\alpha` will become smaller.
    Otherwise, the :math:`\alpha` will become larger to aggressively penalize
    action-values.

    In continuous control, :math:`\log{\sum_a \exp{Q(s, a)}}` is computed as
    follows.

    .. math::

        \log{\sum_a \exp{Q(s, a)}} \approx \log{\left(
            \frac{1}{2N} \sum_{a_i \sim \text{Unif}(a)}^N
                \left[\frac{\exp{Q(s, a_i)}}{\text{Unif}(a)}\right]
            + \frac{1}{2N} \sum_{a_i \sim \pi_\phi(a|s)}^N
                \left[\frac{\exp{Q(s, a_i)}}{\pi_\phi(a_i|s)}\right]\right)}

    where :math:`N` is the number of sampled actions.

    The rest of optimization is exactly same as :class:`d3rlpy.algos.SAC`.

    References:
        * `Kumar et al., Conservative Q-Learning for Offline Reinforcement
          Learning. <https://arxiv.org/abs/2006.04779>`_

    Args:
        actor_learning_rate (float): learning rate for policy function.
        critic_learning_rate (float): learning rate for Q functions.
        temp_learning_rate (float):
            learning rate for temperature parameter of SAC.
        alpha_learning_rate (float): learning rate for :math:`\alpha`.
        actor_optim_factory (d3rlpy.models.optimizers.OptimizerFactory):
            optimizer factory for the actor.
        critic_optim_factory (d3rlpy.models.optimizers.OptimizerFactory):
            optimizer factory for the critic.
        temp_optim_factory (d3rlpy.models.optimizers.OptimizerFactory):
            optimizer factory for the temperature.
        alpha_optim_factory (d3rlpy.models.optimizers.OptimizerFactory):
            optimizer factory for :math:`\alpha`.
        actor_encoder_factory (d3rlpy.models.encoders.EncoderFactory or str):
            encoder factory for the actor.
        critic_encoder_factory (d3rlpy.models.encoders.EncoderFactory or str):
            encoder factory for the critic.
        q_func_factory (d3rlpy.models.q_functions.QFunctionFactory or str):
            Q function factory.
        batch_size (int): mini-batch size.
        n_frames (int): the number of frames to stack for image observation.
        n_steps (int): N-step TD calculation.
        gamma (float): discount factor.
        tau (float): target network synchronization coefficiency.
        n_critics (int): the number of Q functions for ensemble.
        initial_temperature (float): initial temperature value.
        initial_alpha (float): initial :math:`\alpha` value.
        alpha_threshold (float): threshold value described as :math:`\tau`.
        conservative_weight (float): constant weight to scale conservative loss.
        n_action_samples (int): the number of sampled actions to compute
            :math:`\log{\sum_a \exp{Q(s, a)}}`.
        soft_q_backup (bool): flag to use SAC-style backup.
        use_gpu (bool, int or d3rlpy.gpu.Device):
            flag to use GPU, device ID or device.
        scaler (d3rlpy.preprocessing.Scaler or str): preprocessor.
            The available options are `['pixel', 'min_max', 'standard']`.
        action_scaler (d3rlpy.preprocessing.ActionScaler or str):
            action preprocessor. The available options are ``['min_max']``.
        reward_scaler (d3rlpy.preprocessing.RewardScaler or str):
            reward preprocessor. The available options are
            ``['clip', 'min_max', 'standard']``.
        impl (d3rlpy.algos.torch.cql_impl.CQLImpl): algorithm implementation.

    """

    _actor_learning_rate: float
    _critic_learning_rate: float
    _temp_learning_rate: float
    _alpha_learning_rate: float
    _actor_optim_factory: OptimizerFactory
    _critic_optim_factory: OptimizerFactory
    _temp_optim_factory: OptimizerFactory
    _alpha_optim_factory: OptimizerFactory
    _actor_encoder_factory: EncoderFactory
    _critic_encoder_factory: EncoderFactory
    _q_func_factory: QFunctionFactory
    _tau: float
    _n_critics: int
    _initial_temperature: float
    _initial_alpha: float
    _alpha_threshold: float
    _conservative_weight: float
    _n_action_samples: int
    _soft_q_backup: bool
    _use_gpu: Optional[Device]
    _impl: Optional[CQLImpl]

    def __init__(
        self,
        *,
        actor_learning_rate: float = 1e-4,
        critic_learning_rate: float = 3e-4,
        temp_learning_rate: float = 1e-4,
        alpha_learning_rate: float = 1e-4,
        actor_optim_factory: OptimizerFactory = AdamFactory(),
        critic_optim_factory: OptimizerFactory = AdamFactory(),
        temp_optim_factory: OptimizerFactory = AdamFactory(),
        alpha_optim_factory: OptimizerFactory = AdamFactory(),
        actor_encoder_factory: EncoderArg = "default",
        critic_encoder_factory: EncoderArg = "default",
        q_func_factory: QFuncArg = "mean",
        batch_size: int = 256,
        n_frames: int = 1,
        n_steps: int = 1,
        gamma: float = 0.99,
        tau: float = 0.005,
        n_critics: int = 2,
        initial_temperature: float = 1.0,
        initial_alpha: float = 1.0,
        alpha_threshold: float = 10.0,
        conservative_weight: float = 5.0,
        n_action_samples: int = 10,
        soft_q_backup: bool = False,
        use_gpu: UseGPUArg = False,
        scaler: ScalerArg = None,
        action_scaler: ActionScalerArg = None,
        reward_scaler: RewardScalerArg = None,
        impl: Optional[CQLImpl] = None,
        **kwargs: Any,
    ):
        super().__init__(
            batch_size=batch_size,
            n_frames=n_frames,
            n_steps=n_steps,
            gamma=gamma,
            scaler=scaler,
            action_scaler=action_scaler,
            reward_scaler=reward_scaler,
            kwargs=kwargs,
        )
        self._actor_learning_rate = actor_learning_rate
        self._critic_learning_rate = critic_learning_rate
        self._temp_learning_rate = temp_learning_rate
        self._alpha_learning_rate = alpha_learning_rate
        self._actor_optim_factory = actor_optim_factory
        self._critic_optim_factory = critic_optim_factory
        self._temp_optim_factory = temp_optim_factory
        self._alpha_optim_factory = alpha_optim_factory
        self._actor_encoder_factory = check_encoder(actor_encoder_factory)
        self._critic_encoder_factory = check_encoder(critic_encoder_factory)
        self._q_func_factory = check_q_func(q_func_factory)
        self._tau = tau
        self._n_critics = n_critics
        self._initial_temperature = initial_temperature
        self._initial_alpha = initial_alpha
        self._alpha_threshold = alpha_threshold
        self._conservative_weight = conservative_weight
        self._n_action_samples = n_action_samples
        self._soft_q_backup = soft_q_backup
        self._use_gpu = check_use_gpu(use_gpu)
        self._impl = impl

    def _create_impl(
        self, observation_shape: Sequence[int], action_size: int
    ) -> None:
        self._impl = CQLImpl(
            observation_shape=observation_shape,
            action_size=action_size,
            actor_learning_rate=self._actor_learning_rate,
            critic_learning_rate=self._critic_learning_rate,
            temp_learning_rate=self._temp_learning_rate,
            alpha_learning_rate=self._alpha_learning_rate,
            actor_optim_factory=self._actor_optim_factory,
            critic_optim_factory=self._critic_optim_factory,
            temp_optim_factory=self._temp_optim_factory,
            alpha_optim_factory=self._alpha_optim_factory,
            actor_encoder_factory=self._actor_encoder_factory,
            critic_encoder_factory=self._critic_encoder_factory,
            q_func_factory=self._q_func_factory,
            gamma=self._gamma,
            tau=self._tau,
            n_critics=self._n_critics,
            initial_temperature=self._initial_temperature,
            initial_alpha=self._initial_alpha,
            alpha_threshold=self._alpha_threshold,
            conservative_weight=self._conservative_weight,
            n_action_samples=self._n_action_samples,
            soft_q_backup=self._soft_q_backup,
            use_gpu=self._use_gpu,
            scaler=self._scaler,
            action_scaler=self._action_scaler,
            reward_scaler=self._reward_scaler,
        )
        self._impl.build()

    def _update(self, batch: TransitionMiniBatch) -> Dict[str, float]:
        assert self._impl is not None, IMPL_NOT_INITIALIZED_ERROR

        metrics = {}
        # lagrangian parameter update for SAC temperature
        if self._temp_learning_rate > 0:
            temp_loss, temp = self._impl.update_temp(batch)
            metrics.update({"temp_loss": temp_loss, "temp": temp})

        # lagrangian parameter update for conservative loss weight
        if self._alpha_learning_rate > 0:
            alpha_loss, alpha = self._impl.update_alpha(batch)
            metrics.update({"alpha_loss": alpha_loss, "alpha": alpha})

        critic_loss = self._impl.update_critic(batch)
        metrics.update({"critic_loss": critic_loss})

        actor_loss = self._impl.update_actor(batch)
        metrics.update({"actor_loss": actor_loss})

        self._impl.update_critic_target()
        self._impl.update_actor_target()

        return metrics

    def get_action_type(self) -> ActionSpace:
        return ActionSpace.CONTINUOUS


class DiscreteCQL(DoubleDQN):
    r"""Discrete version of Conservative Q-Learning algorithm.

    Discrete version of CQL is a DoubleDQN-based data-driven deep reinforcement
    learning algorithm (the original paper uses DQN), which achieves
    state-of-the-art performance in offline RL problems.

    CQL mitigates overestimation error by minimizing action-values under the
    current policy and maximizing values under data distribution for
    underestimation issue.

    .. math::

        L(\theta) = \alpha \mathbb{E}_{s_t \sim D}
            [\log{\sum_a \exp{Q_{\theta}(s_t, a)}}
             - \mathbb{E}_{a \sim D} [Q_{\theta}(s, a)]]
            + L_{DoubleDQN}(\theta)

    References:
        * `Kumar et al., Conservative Q-Learning for Offline Reinforcement
          Learning. <https://arxiv.org/abs/2006.04779>`_

    Args:
        learning_rate (float): learning rate.
        optim_factory (d3rlpy.models.optimizers.OptimizerFactory):
            optimizer factory.
        encoder_factory (d3rlpy.models.encoders.EncoderFactory or str):
            encoder factory.
        q_func_factory (d3rlpy.models.q_functions.QFunctionFactory or str):
            Q function factory.
        batch_size (int): mini-batch size.
        n_frames (int): the number of frames to stack for image observation.
        n_steps (int): N-step TD calculation.
        gamma (float): discount factor.
        n_critics (int): the number of Q functions for ensemble.
        target_update_interval (int): interval to synchronize the target
            network.
        alpha (float): the :math:`\alpha` value above.
        use_gpu (bool, int or d3rlpy.gpu.Device):
            flag to use GPU, device ID or device.
        scaler (d3rlpy.preprocessing.Scaler or str): preprocessor.
            The available options are `['pixel', 'min_max', 'standard']`
        reward_scaler (d3rlpy.preprocessing.RewardScaler or str):
            reward preprocessor. The available options are
            ``['clip', 'min_max', 'standard']``.
        impl (d3rlpy.algos.torch.cql_impl.DiscreteCQLImpl):
            algorithm implementation.

    """

    _alpha: float
    _impl: Optional[DiscreteCQLImpl]
    _sampler_impl: Optional[DiscreteCQLImpl]

    def __init__(
        self,
        *,
        learning_rate: float = 6.25e-5,
        optim_factory: OptimizerFactory = AdamFactory(),
        encoder_factory: EncoderArg = "default",
        q_func_factory: QFuncArg = "mean",
        batch_size: int = 32,
        n_frames: int = 1,
        n_steps: int = 1,
        gamma: float = 0.99,
        n_critics: int = 1,
        target_update_interval: int = 8000,
        alpha: float = 1.0,
        use_gpu: UseGPUArg = False,
        scaler: ScalerArg = None,
        reward_scaler: RewardScalerArg = None,
        impl: Optional[DiscreteCQLImpl] = None,
        **kwargs: Any,
    ):
        super().__init__(
            learning_rate=learning_rate,
            optim_factory=optim_factory,
            encoder_factory=encoder_factory,
            q_func_factory=q_func_factory,
            batch_size=batch_size,
            n_frames=n_frames,
            n_steps=n_steps,
            gamma=gamma,
            n_critics=n_critics,
            target_update_interval=target_update_interval,
            use_gpu=use_gpu,
            scaler=scaler,
            reward_scaler=reward_scaler,
            impl=impl,
            **kwargs,
        )
        self._alpha = alpha
        self._sampler_impl = None

    def _create_impl(
        self, observation_shape: Sequence[int], action_size: int
    ) -> None:
        self._impl = DiscreteCQLImpl(
            observation_shape=observation_shape,
            action_size=action_size,
            learning_rate=self._learning_rate,
            optim_factory=self._optim_factory,
            encoder_factory=self._encoder_factory,
            q_func_factory=self._q_func_factory,
            gamma=self._gamma,
            n_critics=self._n_critics,
            alpha=self._alpha,
            use_gpu=self._use_gpu,
            scaler=self._scaler,
            reward_scaler=self._reward_scaler,
        )
        self._impl.build()

    def _create_sampler_impl(
        self, observation_shape: Sequence[int], action_size: int
    ) -> None:
        self._sampler_impl = DiscreteCQLImpl(
            observation_shape=observation_shape,
            action_size=action_size,
            learning_rate=self._learning_rate,
            optim_factory=self._optim_factory,
            encoder_factory=self._encoder_factory,
            q_func_factory=self._q_func_factory,
            gamma=self._gamma,
            n_critics=5, # self._n_critics,     hardcoded for now
            alpha=self._alpha,
            use_gpu=self._use_gpu,
            scaler=self._scaler,
            reward_scaler=self._reward_scaler,
        )
        self._sampler_impl.build()

    def create_sampler_impl(
        self, observation_shape: Sequence[int], action_size: int
    ) -> None:
        """Instantiate implementation objects with the dataset shapes.
        This method will be used internally when `fit` method is called.
        Args:
            observation_shape: observation shape.
            action_size: dimension of action-space.
        """
        if self._sampler_impl:
            LOG.warn("Parameters will be reinitialized.")
        self._create_sampler_impl(observation_shape, action_size)

    def sampler_update(self, batch: TransitionMiniBatch) -> Dict[str, float]:
        """Update parameters with mini-batch of data.

        Args:
            batch: mini-batch data.

        Returns:
            dictionary of metrics.

        """
        loss = self._sampler_update(batch)
        # self._grad_step += 1
        return loss

    def _sampler_update(self, batch: TransitionMiniBatch) -> Dict[str, float]:
        assert self._sampler_impl is not None, IMPL_NOT_INITIALIZED_ERROR
        loss = self._sampler_impl.update(batch)
        if self._grad_step % self._target_update_interval == 0:
            # print('updating target')
            self._sampler_impl.update_target()
        return {"loss": loss}
