import torch
import numpy as np
from torch import nn


class Actor(nn.Module):
    def __init__(self, layer_num, state_shape, action_shape,
                 max_action, device='cpu'):
        super().__init__()
        self.device = device
        self.model = [
            nn.Linear(np.prod(state_shape), 128),
            nn.ReLU(inplace=True)]
        for i in range(layer_num):
            self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
        self.model += [nn.Linear(128, np.prod(action_shape))]
        self.model = nn.Sequential(*self.model)
        self._max = max_action

    def forward(self, s, **kwargs):
        s = torch.tensor(s, device=self.device, dtype=torch.float)
        batch = s.shape[0]
        s = s.view(batch, -1)
        logits = self.model(s)
        logits = self._max * torch.tanh(logits)
        return logits, None


class Critic(nn.Module):
    def __init__(self, layer_num, state_shape, action_shape, device='cpu'):
        super().__init__()
        self.device = device
        self.model = [
            nn.Linear(np.prod(state_shape) + np.prod(action_shape), 128),
            nn.ReLU(inplace=True)]
        for i in range(layer_num):
            self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
        self.model += [nn.Linear(128, 1)]
        self.model = nn.Sequential(*self.model)

    def forward(self, s, a):
        s = torch.tensor(s, device=self.device, dtype=torch.float)
        if isinstance(a, np.ndarray):
            a = torch.tensor(a, device=self.device, dtype=torch.float)
        batch = s.shape[0]
        s = s.view(batch, -1)
        a = a.view(batch, -1)
        logits = self.model(torch.cat([s, a], dim=1))
        return logits