# cross_lingual implementation
import torch
import torch.nn as nn

class CROSS_LINGUAL:
    def __init__(self, multilingual_model):
        """
        Initialize cross-lingual learning.
        
        Args:
            multilingual_model: Model capable of handling multiple languages
        """
        self.multilingual_model = multilingual_model

    def translate(self, text_input, source_lang, target_lang):
        """
        Translate text from source language to target language.
        
        Args:
            text_input: Input text data
            source_lang: Source language code
            target_lang: Target language code
        """
        # Placeholder for translation logic
        translated_text = self.multilingual_model.translate(text_input, source_lang, target_lang)
        return translated_text

    def train(self, data_loader, criterion, optimizer, num_epochs):
        """
        Train the cross-lingual model.
        
        Args:
            data_loader: DataLoader for training data
            criterion: Loss function
            optimizer: Optimizer
            num_epochs: Number of training epochs
        """
        self.multilingual_model.train()

        for epoch in range(num_epochs):
            for text_input, labels in data_loader:
                optimizer.zero_grad()
                outputs = self.multilingual_model(text_input)
                loss = criterion(outputs, labels)
                loss.backward()
                optimizer.step()
            print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}")

    def evaluate(self, data_loader, criterion):
        """
        Evaluate the cross-lingual model.
        
        Args:
            data_loader: DataLoader for evaluation data
            criterion: Loss function
        """
        self.multilingual_model.eval()

        total_loss = 0
        with torch.no_grad():
            for text_input, labels in data_loader:
                outputs = self.multilingual_model(text_input)
                loss = criterion(outputs, labels)
                total_loss += loss.item()
        
        print(f"Evaluation Loss: {total_loss / len(data_loader):.4f}")
