import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import gradio as gr

# Define the VAE model
class ConvVAE(nn.Module):
    def __init__(self, input_channels=3, latent_dim=32):
        super(ConvVAE, self).__init__()
        self.latent_dim = latent_dim
        # Encoder
        self.enc_conv1 = nn.Conv2d(input_channels, 64, kernel_size=3, stride=2, padding=1)
        self.enc_conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
        self.enc_conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.fc_mu = nn.Linear(256 * 4 * 10, latent_dim)
        self.fc_logvar = nn.Linear(256 * 4 * 10, latent_dim)
        # Decoder
        self.fc_decode = nn.Linear(latent_dim, 256 * 4 * 10)
        self.dec_conv1 = nn.ConvTranspose2d(256, 128, kernel_size=3, stride=1, padding=1)
        self.dec_conv2 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, output_padding=1)
        self.dec_conv3 = nn.ConvTranspose2d(64, 3, kernel_size=3, stride=2, padding=1, output_padding=(0,1))

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return mu + eps * std

    def forward(self, x):
        x = F.relu(self.enc_conv1(x))
        x = F.relu(self.enc_conv2(x))
        x = F.relu(self.enc_conv3(x))
        x = x.view(x.size(0), -1)
        mu = self.fc_mu(x)
        logvar = self.fc_logvar(x)
        z = self.reparameterize(mu, logvar)
        out = self.decode(z)
        return out, mu, logvar

    def decode(self, z):
        x = F.relu(self.fc_decode(z))
        x = x.view(x.size(0), 256, 4, 10)
        x = F.relu(self.dec_conv1(x))
        x = F.relu(self.dec_conv2(x))
        x = self.dec_conv3(x)
        return F.softmax(x, dim=1)

# Load trained model
model = ConvVAE()
model.load_state_dict(torch.load("vae_supertux.pth", map_location=torch.device("cpu")))
model.eval()

# Sampling
def sample_with_temperature(probs, temperature=1.2):
    logits = torch.log(probs + 1e-8) / temperature
    scaled_probs = torch.softmax(logits, dim=1)
    batch, channels, height, width = scaled_probs.shape
    scaled_probs = scaled_probs.permute(0, 2, 3, 1).contiguous().view(-1, channels)
    sampled = torch.multinomial(scaled_probs, num_samples=1)
    sampled = sampled.view(batch, height, width)
    return sampled
    
def generate_map(seed: int = 0):
    model.eval()
    if seed == 0:
        seed = torch.randint(10000, (1,)).item()
    torch.manual_seed(seed)
    z = torch.randn(1, model.latent_dim).to("cpu")
    with torch.no_grad():
        output = model.decode(z)
    output = sample_with_temperature(output, temperature=3)[0].cpu().numpy()
    grid = np.pad(output, ((5, 0), (0, 0)), mode='constant', constant_values=0)

    # Post-processing rule to collapse columns with inner air blocks
    for j in range(len(grid[0])):
        non_air_blocks = [grid[i, j] for i in range(len(grid)) if grid[i, j] != 0]
        k = len(non_air_blocks)
        if k > 0:
            grid[20 - k:20, j] = non_air_blocks
            grid[0:20 - k, j] = 0
            
    return ["".join(map(str, row)) for row in grid]  # Convert each row to a string

    

gr.Interface(
    fn=generate_map,
    inputs=gr.Number(label="Seed (set to 0 for random generation)"),
    outputs=gr.JSON(label="Generated Map Grid"),
    title="VAE Level Generator",
    description="Returns a 20x40 grid as a list of strings where 0=air, 1=ground, 2=lava"
).launch()