import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from nltk.tokenize import word_tokenize
import gensim
from torch.utils.data import DataLoader, Dataset
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt

# NLTK downloads
import nltk
nltk.download('punkt_tab')
nltk.download('punkt')

# Check if GPU is available (CUDA for NVIDIA or MPS for Apple devices)
if torch.cuda.is_available():
    DEVICE = torch.device('cuda')  # Use CUDA if available
elif torch.backends.mps.is_available():
    DEVICE = torch.device('mps')  # Use MPS if available
else:
    DEVICE = torch.device('cpu')  # Default to CPU if no GPU support is available

print('Using device:', DEVICE)

# Maximum sequence length
MAX_LEN = 100


# Data processing helpers
def get_embedding(model, word):
    if word in model.wv:
        return model.wv.key_to_index[word]
    else:
        return unk_index

def encode_tokens(tokens):
    return [get_embedding(model_embedding, token) for token in tokens]

def pad_sequences(sequences, MAX_LEN):
    return np.array([
        np.pad(seq, (0, MAX_LEN - len(seq)), mode='constant', constant_values=unk_index)
        if len(seq) < MAX_LEN else seq[:MAX_LEN]
        for seq in sequences
    ])
# Dataset class
class HumorDataset(Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels

    def __getitem__(self, idx):
        item = {'input_ids': torch.tensor(self.encodings[idx], dtype=torch.long)}
        item['labels'] = torch.tensor(self.labels[idx], dtype=torch.float)
        return item

    def __len__(self):
        return len(self.labels)

# CNN Model
class CNNBinaryClassifier(nn.Module):
    def __init__(self, vocab_size, embed_dim, num_filters, kernel_sizes, hidden_dim, dropout=0.1):
        super(CNNBinaryClassifier, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.conv_layers = nn.ModuleList([
            nn.Conv1d(in_channels=embed_dim, out_channels=num_filters, kernel_size=k)
            for k in kernel_sizes
        ])
        self.fc = nn.Linear(num_filters * len(kernel_sizes), hidden_dim)
        self.out = nn.Linear(hidden_dim, 1)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(dropout)
        self.sigmoid = nn.Sigmoid()

    def forward(self, input_ids):
        embedded = self.embedding(input_ids).permute(0, 2, 1)
        conv_outs = [self.relu(conv(embedded)) for conv in self.conv_layers]
        pooled_outs = [torch.max(out, dim=2)[0] for out in conv_outs]
        concatenated = torch.cat(pooled_outs, dim=1)
        fc_out = self.relu(self.fc(self.dropout(concatenated)))
        logits = self.out(fc_out)
        return self.sigmoid(logits)

# Main
if __name__ == "__main__":
    # Load and process data
    df = pd.read_csv('ANLP_WS24_CA2/data/hack.csv')  # Ensure this file exists
    print(f"Loaded dataset: {df.shape}")

    X = df['text']
    y = df['is_humor']

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Tokenize the datapp
    train_tokens = [word_tokenize(text.lower()) for text in X_train]
    test_tokens = [word_tokenize(text.lower()) for text in X_test]

    # Train Word2Vec model
    model_embedding = gensim.models.Word2Vec(train_tokens, window=5, min_count=5, workers=4)

    # Add unknown token
    model_embedding.wv.add_vector('<UNK>', np.zeros(model_embedding.vector_size))
    unk_index = model_embedding.wv.key_to_index['<UNK>']

    # Encode tokens
    train_encodings = [encode_tokens(tokens) for tokens in train_tokens]
    test_encodings = [encode_tokens(tokens) for tokens in test_tokens]

    # Pad sequences
    train_encodings = pad_sequences(train_encodings, MAX_LEN)
    test_encodings = pad_sequences(test_encodings, MAX_LEN)

    # Create datasets
    train_dataset = HumorDataset(train_encodings, y_train.reset_index(drop=True))
    test_dataset = HumorDataset(test_encodings, y_test.reset_index(drop=True))

    # Model parameters
    vocab_size = len(model_embedding.wv.key_to_index)
    embed_dim = model_embedding.vector_size
    num_filters = 200
    kernel_sizes = [3, 4, 5]
    hidden_dim = 128
    dropout = 0.5

    model = CNNBinaryClassifier(vocab_size, embed_dim, num_filters, kernel_sizes, hidden_dim, dropout)

    # Training parameters
    epochs = 10
    batch_size = 8
    learning_rate = 2e-5

    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    criterion = nn.BCELoss()

    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

    # Move model to device
    model.to(DEVICE)

    # Training loop with loss visualization
    print("Starting training...")
    train_losses = []
    val_losses = []

    for epoch in range(epochs):
        epoch_loss = 0
        model.train()
        for batch in train_loader:
            optimizer.zero_grad()
            input_ids = batch['input_ids'].to(DEVICE)
            labels = batch['labels'].unsqueeze(1).to(DEVICE)
            outputs = model(input_ids)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            epoch_loss += loss.item()

        train_loss = epoch_loss / len(train_loader)
        train_losses.append(train_loss)

        # Evaluation during training
        model.eval()
        val_loss = 0
        with torch.no_grad():
            for batch in test_loader:
                input_ids = batch['input_ids'].to(DEVICE)
                labels = batch['labels'].unsqueeze(1).to(DEVICE)
                outputs = model(input_ids)
                loss = criterion(outputs, labels)
                val_loss += loss.item()

        val_loss /= len(test_loader)
        val_losses.append(val_loss)

        print(f"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}")

    # Final evaluation
    print("Starting evaluation...")
    model.eval()
    predictions, true_labels = [], []
    with torch.no_grad():
        for batch in test_loader:
            input_ids = batch['input_ids'].to(DEVICE)
            labels = batch['labels'].unsqueeze(1).to(DEVICE)
            outputs = model(input_ids)
            preds = (outputs > 0.5).float()
            predictions.extend(preds.cpu().numpy())
            true_labels.extend(labels.cpu().numpy())

    accuracy = accuracy_score(true_labels, predictions)
    print(f"Final Accuracy: {accuracy:.4f}")

    # Visualize Losses
    plt.plot(train_losses, label="Train Loss")
    plt.plot(val_losses, label="Validation Loss")
    plt.xlabel("Epochs")
    plt.ylabel("Loss")
    plt.title("Loss Curve")
    plt.legend()
    plt.show()