import torch
import torch.nn as nn
import torch.nn.functional as f
from torch.distributions import Categorical

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

class ICM(nn.Module):
    def __init__(self, channels, encoded_state_size, action_size):
        super(ICM, self).__init__()
        self.channels = channels
        self.encoded_state_size = encoded_state_size
        self.action_size = action_size

        self.feature_encoder = nn.Sequential(
            nn.Conv2d(in_channels=self.channels, out_channels=32, kernel_size=3, stride=2),
            nn.LeakyReLU(),
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=2),
            nn.LeakyReLU(),
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=2),
            nn.LeakyReLU(),
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=2),
            nn.LeakyReLU(),
            nn.Flatten(),
            nn.Linear(in_features=32*4*4, out_features=self.encoded_state_size),  
        ).to(device)

        self.inverse_model = nn.Sequential(
            nn.Linear(in_features=self.encoded_state_size*2, out_features=256),
            nn.LeakyReLU(),
            nn.Linear(in_features=256, out_features=self.action_size),
            nn.Softmax(dim=-1)
        ).to(device)

        self.forward_model = nn.Sequential(
            nn.Linear(in_features=self.encoded_state_size+self.action_size, out_features=256),
            nn.LeakyReLU(),
            nn.Linear(in_features=256, out_features=self.encoded_state_size),
        ).to(device)

    def forward(self, state, next_state, action):
        if state.dim() == 3:
            state = state.unsqueeze(0)
            next_state = next_state.unsqueeze(0)
        
        encoded_state = self.feature_encoder(state)
        next_encoded_state = self.feature_encoder(next_state)
        action_pred = self.inverse_model(torch.cat((encoded_state, next_encoded_state), dim=-1))
        next_encoded_state_pred = self.forward_model(torch.cat((encoded_state, action), dim=-1))
        return encoded_state, next_encoded_state, action_pred, next_encoded_state_pred

    def _init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.xavier_uniform_(m.weight)
                nn.init.zeros_(m.bias)

class ActorCritic(nn.Module):
    def __init__(self,encoded_state_size, action_size, state_size=4):
        super(ActorCritic, self).__init__()
        self.channels = state_size
        self.encoded_state_size = encoded_state_size
        self.action_size = action_size

        self.feature_encoder = nn.Sequential(
            nn.Conv2d(in_channels=self.channels, out_channels=32, kernel_size=3, stride=2),
            nn.LeakyReLU(),
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=2),
            nn.LeakyReLU(),
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=2),
            nn.LeakyReLU(),
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=2),
            nn.LeakyReLU(),
            nn.Flatten(),
            nn.Linear(in_features=32*4*4, out_features=self.encoded_state_size),  
        ).to(device)

    def actor(self,state):
        policy = nn.Sequential(
            nn.Linear(in_features=self.encoded_state_size , out_features=256),
            nn.LeakyReLU(),
            nn.Linear(in_features=256, out_features=self.action_size),
            nn.Softmax(dim=-1)
        ).to(device)
        return policy(state)
    
    def critic(self,state):
        value = nn.Sequential(
            nn.Linear(in_features=self.encoded_state_size , out_features=256),
            nn.LeakyReLU(),
            nn.Linear(in_features=256, out_features=1),
        ).to(device)
        return value(state)

    def forward(self, state):
        if state.dim() == 3:
            state = state.unsqueeze(0)

        state = self.feature_encoder(state)
        value = self.critic(state)
        policy = self.actor(state)
        actions = Categorical(policy)
        return actions, value

    def _init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.xavier_uniform_(m.weight)
                nn.init.zeros_(m.bias)

#vec = torch.randn(4,84,84).to(device)
#ac = ActorCritic(256,12).to(device)
#a,v = ac(vec)
#print(a,v)