import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset


class CNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(1, 32, kernel_size=3),   # 28x28 -> 26x26
            nn.ReLU(),
            nn.MaxPool2d(2),                   # 26x26 -> 13x13
            nn.Conv2d(32, 64, kernel_size=3),  # 13x13 -> 11x11
            nn.ReLU(),
            nn.MaxPool2d(2),                   # 11x11 -> 5x5
        )
        self.fc = nn.Sequential(
            nn.Flatten(),
            nn.Linear(64 * 5 * 5, 128),
            nn.ReLU(),
            nn.Linear(128, 10),
        )

    def forward(self, x):
        x = x.view(-1, 1, 28, 28)
        return self.fc(self.conv(x))


class CNNClassifier:
    def __init__(self, epochs=5):
        self.epochs = epochs

    def fit(self, X, y):
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self._device = device

        X_tr = torch.tensor(X, dtype=torch.float32)
        y_tr = torch.tensor(y, dtype=torch.long)

        # Hold out 10% of the training data to track progress each epoch
        n_val = len(X_tr) // 10
        X_val, X_tr = X_tr[:n_val], X_tr[n_val:]
        y_val, y_tr = y_tr[:n_val], y_tr[n_val:]

        loader = DataLoader(TensorDataset(X_tr, y_tr), batch_size=64, shuffle=True)

        model = CNN().to(device)
        optimizer = optim.Adam(model.parameters(), lr=1e-3)
        loss_fn = nn.CrossEntropyLoss()

        print(f"\nTraining CNN (epochs={self.epochs})")
        for epoch in range(1, self.epochs + 1):
            model.train()
            total_loss = 0
            for xb, yb in loader:
                xb, yb = xb.to(device), yb.to(device)
                optimizer.zero_grad()
                loss = loss_fn(model(xb), yb)
                loss.backward()
                optimizer.step()
                total_loss += loss.item()

            model.eval()
            with torch.no_grad():
                val_pred = model(X_val.to(device)).argmax(dim=1).cpu()
                val_accuracy = (val_pred == y_val).float().mean().item()

            print(f"  epoch {epoch:2d}/{self.epochs}  loss={total_loss / len(loader):.3f}  val_accuracy={val_accuracy:.3f}")
        print()

        self._model = model
        return self

    def predict_proba(self, X):
        X_te = torch.tensor(X, dtype=torch.float32)
        self._model.eval()
        with torch.no_grad():
            logits = self._model(X_te.to(self._device))
            probabilities = torch.softmax(logits, dim=1).cpu().numpy()
        return probabilities

    def predict(self, X):
        return self.predict_proba(X).argmax(axis=1)