import torch
import torch.nn as nn

# The Generator model
class Generator(nn.Module):
    def __init__(self, channels, noise_dim=100, embed_dim=1024, embed_out_dim=128):
        super(Generator, self).__init__()
        self.channels = channels
        self.noise_dim = noise_dim
        self.embed_dim = embed_dim
        self.embed_out_dim = embed_out_dim

        # Text embedding layers
        self.text_embedding = nn.Sequential(
            nn.Linear(self.embed_dim, self.embed_out_dim),
            nn.BatchNorm1d(1),
            nn.LeakyReLU(0.2, inplace=True)
        )

        # Generator architecture
        model = []
        model += self._create_layer(self.noise_dim + self.embed_out_dim, 512, 4, stride=1, padding=0)
        model += self._create_layer(512, 256, 4, stride=2, padding=1)
        model += self._create_layer(256, 128, 4, stride=2, padding=1)
        model += self._create_layer(128, 64, 4, stride=2, padding=1)
        model += self._create_layer(64, 32, 4, stride=2, padding=1)
        model += self._create_layer(32, self.channels, 4, stride=2, padding=1, output=True)

        self.model = nn.Sequential(*model)

    def _create_layer(self, size_in, size_out, kernel_size=4, stride=2, padding=1, output=False):
        layers = [nn.ConvTranspose2d(size_in, size_out, kernel_size, stride=stride, padding=padding, bias=False)]
        if output:
            layers.append(nn.Tanh())  # Tanh activation for the output layer
        else:
            layers += [nn.BatchNorm2d(size_out), nn.ReLU(True)]  # Batch normalization and ReLU for other layers
        return layers

    def forward(self, noise, text):
        # Apply text embedding to the input text
        text = self.text_embedding(text)
        text = text.view(text.shape[0], text.shape[2], 1, 1)  # Reshape to match the generator input size
        z = torch.cat([text, noise], 1)  # Concatenate text embedding with noise
        return self.model(z)