import numpy as np
import torch
from torch import nn as nn

from rlkit.policies.base import ExplorationPolicy, Policy
from rlkit.torch.core import eval_np
from rlkit.torch.distributions import TanhNormal
from rlkit.torch.networks import Mlp
from rlkit.torch.pytorch_util import from_numpy

LOG_SIG_MAX = 2
LOG_SIG_MIN = -5
MEAN_MIN = -9.0
MEAN_MAX = 9.0

def atanh(x):
    one_plus_x = (1 + x).clamp(min=1e-6)
    one_minus_x = (1 - x).clamp(min=1e-6)
    return 0.5 * torch.log(one_plus_x / one_minus_x)


class TanhGaussianPolicy(Mlp, ExplorationPolicy):
    """
    Usage:

    ```
    policy = TanhGaussianPolicy(...)
    action, mean, log_std, _ = policy(obs)
    action, mean, log_std, _ = policy(obs, deterministic=True)
    action, mean, log_std, log_prob = policy(obs, return_log_prob=True)
    ```

    Here, mean and log_std are the mean and log_std of the Gaussian that is
    sampled from.

    If deterministic is True, action = tanh(mean).
    If return_log_prob is False (default), log_prob = None
        This is done because computing the log_prob can be a bit expensive.
    """
    def __init__(
            self,
            hidden_sizes,
            obs_dim,
            action_dim,
            std=None,
            init_w=1e-3,
            **kwargs
    ):
        super().__init__(
            hidden_sizes,
            input_size=obs_dim,
            output_size=action_dim,
            init_w=init_w,
            **kwargs
        )
        self.log_std = None
        self.std = std
        if std is None:
            last_hidden_size = obs_dim
            if len(hidden_sizes) > 0:
                last_hidden_size = hidden_sizes[-1]
            self.last_fc_log_std = nn.Linear(last_hidden_size, action_dim)
            self.last_fc_log_std.weight.data.uniform_(-init_w, init_w)
            self.last_fc_log_std.bias.data.uniform_(-init_w, init_w)
        else:
            self.log_std = np.log(std)
            assert LOG_SIG_MIN <= self.log_std <= LOG_SIG_MAX

    def set_param_with_d4rl_dataset(self, d4rl_dataset):

        with torch.no_grad():

            assert tuple(self.fcs[0].weight.size()) == d4rl_dataset['metadata/policy/fc0/weight'].shape
            self.fcs[0].weight = nn.Parameter(from_numpy(d4rl_dataset['metadata/policy/fc0/weight']))
            self.fcs[0].bias = nn.Parameter(from_numpy(d4rl_dataset['metadata/policy/fc0/bias']))

            assert tuple(self.fcs[1].weight.size()) == d4rl_dataset['metadata/policy/fc1/weight'].shape
            self.fcs[1].weight = nn.Parameter(from_numpy(d4rl_dataset['metadata/policy/fc1/weight']))
            self.fcs[1].bias = nn.Parameter(from_numpy(d4rl_dataset['metadata/policy/fc1/bias']))

            assert tuple(self.last_fc.weight.size()) == d4rl_dataset['metadata/policy/last_fc/weight'].shape
            self.last_fc.weight = nn.Parameter(from_numpy(d4rl_dataset['metadata/policy/last_fc/weight']))
            self.last_fc.bias = nn.Parameter(from_numpy(d4rl_dataset['metadata/policy/last_fc/bias']))

            assert tuple(self.last_fc_log_std.weight.size()) == d4rl_dataset['metadata/policy/last_fc_log_std/weight'].shape
            self.last_fc_log_std.weight = nn.Parameter(
                from_numpy(d4rl_dataset['metadata/policy/last_fc_log_std/weight']))
            self.last_fc_log_std.bias = nn.Parameter(
                from_numpy(d4rl_dataset['metadata/policy/last_fc_log_std/bias']))

    def get_action(self, obs_np, deterministic=False):
        actions = self.get_actions(obs_np[None], deterministic=deterministic)
        return actions[0, :], {}

    def get_actions(self, obs_np, deterministic=False):
        return eval_np(self, obs_np, deterministic=deterministic)[0]

    def log_prob(self, obs, actions):
        raw_actions = atanh(actions)
        h = obs
        for i, fc in enumerate(self.fcs):
            h = self.hidden_activation(fc(h))
        mean = self.last_fc(h)
        mean = torch.clamp(mean, MEAN_MIN, MEAN_MAX)
        if self.std is None:
            log_std = self.last_fc_log_std(h)
            log_std = torch.clamp(log_std, LOG_SIG_MIN, LOG_SIG_MAX)
            std = torch.exp(log_std)
        else:
            std = self.std
            log_std = self.log_std

        tanh_normal = TanhNormal(mean, std)
        log_prob = tanh_normal.log_prob(value=actions, pre_tanh_value=raw_actions)
        return log_prob.sum(-1)

    def forward(
            self,
            obs,
            reparameterize=True,
            deterministic=False,
            return_log_prob=False,
            chosen_actions=None,
    ):
        """
        :param obs: Observation
        :param deterministic: If True, do not sample
        :param return_log_prob: If True, return a sample and its log probability
        """
        h = obs
        for i, fc in enumerate(self.fcs):
            h = self.hidden_activation(fc(h))
        mean = self.last_fc(h)
        if self.std is None:
            log_std = self.last_fc_log_std(h)
            log_std = torch.clamp(log_std, LOG_SIG_MIN, LOG_SIG_MAX)
            std = torch.exp(log_std)
        else:
            std = self.std
            log_std = self.log_std

        log_prob = None
        entropy = None
        mean_action_log_prob = None
        pre_tanh_value = None
        if chosen_actions is None:
            if deterministic:
                action = torch.tanh(mean)
            else:
                tanh_normal = TanhNormal(mean, std)
                if return_log_prob:
                    if reparameterize is True:
                        action, pre_tanh_value = tanh_normal.rsample(
                            return_pretanh_value=True
                        )
                    else:
                        action, pre_tanh_value = tanh_normal.sample(
                            return_pretanh_value=True
                        )
                    log_prob = tanh_normal.log_prob(
                        action,
                        pre_tanh_value=pre_tanh_value
                    )
                    log_prob = log_prob.sum(dim=1, keepdim=True)
                else:
                    if reparameterize is True:
                        action = tanh_normal.rsample()
                    else:
                        action = tanh_normal.sample()

            return (
                action, mean, log_std, log_prob, entropy, std,
                mean_action_log_prob, pre_tanh_value,
            )
        else:
            tanh_normal = TanhNormal(mean, std)
            log_prob = tanh_normal.log_prob(chosen_actions)
            log_prob = log_prob.sum(dim=1, keepdim=True)
            return log_prob

class MakeDeterministic(nn.Module, Policy):
    def __init__(self, stochastic_policy):
        super().__init__()
        self.stochastic_policy = stochastic_policy

    def get_action(self, observation):
        return self.stochastic_policy.get_action(observation,
                                                 deterministic=True)
