# multi_modal implementation
import numpy as np

class MULTI_MODAL:
    def __init__(self, text_model, genre_model, audio_model=None):
        """
        Initialize multi-modal learning.

        Args:
            text_model: Vectorizer for processing text data (e.g., TfidfVectorizer)
            genre_model: Binarizer for processing genre data (e.g., MultiLabelBinarizer)
            audio_model: Model for processing audio data (optional)
        """
        self.text_model = text_model
        self.genre_model = genre_model
        self.audio_model = audio_model

    def forward(self, title_inputs, genre_inputs, audio_inputs=None):
        """
        Combine the features from different modalities.

        Args:
            title_inputs (list or array-like): List of movie titles.
            genre_inputs (list or array-like): List of movie genres.
            audio_inputs (list or array-like, optional): List of audio features.

        Returns:
            numpy.ndarray: Combined feature array.
        """
        # Transform text data
        title_features = self.text_model.transform(title_inputs).toarray()
        
        # Transform genre data
        genre_features = self.genre_model.transform(genre_inputs)
        
        if self.audio_model and audio_inputs is not None:
            # Example: If you have audio features, process them accordingly
            # For simplicity, let's assume audio_model is a transformer that returns numpy arrays
            audio_features = self.audio_model.transform(audio_inputs).toarray()
            combined_features = np.hstack((title_features, genre_features, audio_features))
        else:
            combined_features = np.hstack((title_features, genre_features))
        
        return combined_features

    def train(self, data_loader, criterion, optimizer, num_epochs):
        """
        Training method is not applicable for this implementation.
        """
        pass

    def evaluate(self, data_loader, criterion):
        """
        Evaluation method is not applicable for this implementation.
        """
        pass
