import torch
import numpy as np
from torch import nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self, layer_num, state_shape, action_shape=0, 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)]
        if action_shape:
            self.model += [nn.Linear(128, np.prod(action_shape))]
        self.model = nn.Sequential(*self.model)

    def forward(self, s, state=None, info={}):
        if not isinstance(s, torch.Tensor):
            s = torch.tensor(s, device=self.device, dtype=torch.float)
        batch = s.shape[0]
        s = s.view(batch, -1)
        logits = self.model(s)
        return logits, state


class Actor(nn.Module):
    def __init__(self, preprocess_net, action_shape):
        super().__init__()
        self.preprocess = preprocess_net
        self.last = nn.Linear(128, np.prod(action_shape))

    def forward(self, s, state=None, info={}):
        logits, h = self.preprocess(s, state)
        logits = F.softmax(self.last(logits), dim=-1)
        return logits, h


class Critic(nn.Module):
    def __init__(self, preprocess_net):
        super().__init__()
        self.preprocess = preprocess_net
        self.last = nn.Linear(128, 1)

    def forward(self, s):
        logits, h = self.preprocess(s, None)
        logits = self.last(logits)
        return logits