import torch
import torch.nn as nn


LATENT_DIM = 100
NUM_CLASSES = 10


class Generator(nn.Module):
    def __init__(self, latent_dim=LATENT_DIM, num_classes=NUM_CLASSES):
        super().__init__()
        self.latent_dim = latent_dim
        self.label_embedding = nn.Embedding(num_classes, 32)
        self.project = nn.Sequential(
            nn.Linear(latent_dim + 32, 128 * 7 * 7),
            nn.BatchNorm1d(128 * 7 * 7),
            nn.ReLU(inplace=True),
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(64, 32, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(32, 1, kernel_size=4, stride=2, padding=1),
            nn.Tanh(),
        )

    def forward(self, noise, labels):
        if noise.ndim > 2:
            noise = noise.flatten(start_dim=1)
        conditioned = torch.cat([noise, self.label_embedding(labels)], dim=1)
        features = self.project(conditioned).view(-1, 128, 7, 7)
        return self.decoder(features)