import tensorflow as tf
from swarmauri.core.models.IModel import IModel
from typing import Any

class HierarchicalAttentionModel(IModel):
    def __init__(self, model_name: str):
        self._model_name = model_name
        self._model = None  # This will hold the TensorFlow model with attention

    @property
    def model_name(self) -> str:
        return self._model_name

    @model_name.setter
    def model_name(self, value: str) -> None:
        self._model_name = value

    def load_model(self) -> None:
        """
        Here, we define and compile the TensorFlow model described earlier.
        """
        # The following code is adapted from the attention model example provided earlier
        vocab_size = 10000  # Size of the vocabulary
        embedding_dim = 256  # Dimension of the embedding layer
        sentence_length = 100  # Max length of a sentence
        num_sentences = 10  # Number of sentences in a document
        units = 128  # Dimensionality of the output space of GRU
        
        # Word-level attention layer
        word_input = tf.keras.layers.Input(shape=(sentence_length,), dtype='int32')
        embedded_word = tf.keras.layers.Embedding(vocab_size, embedding_dim)(word_input)
        word_gru = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(units, return_sequences=True))(embedded_word)
        word_attention_layer = tf.keras.layers.Attention(use_scale=True, return_attention_scores=True)
        word_attention_output, word_attention_weights = word_attention_layer([word_gru, word_gru], return_attention_scores=True)
        word_encoder_with_attention = tf.keras.Model(inputs=word_input, outputs=[word_attention_output, word_attention_weights])
        
        # Sentence-level attention layer
        sentence_input = tf.keras.layers.Input(shape=(num_sentences, sentence_length), dtype='int32')
        sentence_encoder_with_attention = tf.keras.layers.TimeDistributed(word_encoder_with_attention)(sentence_input)
        sentence_gru = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(units, return_sequences=True))(sentence_encoder_with_attention[0])
        sentence_attention_layer = tf.keras.layers.Attention(use_scale=True, return_attention_scores=True)
        sentence_attention_output, sentence_attention_weights = sentence_attention_layer([sentence_gru, sentence_gru], return_attention_scores=True)
        doc_representation = tf.keras.layers.Dense(units, activation='tanh')(sentence_attention_output)
        
        # Classifier
        classifier = tf.keras.layers.Dense(1, activation='sigmoid')(doc_representation)
        
        # The model
        self._model = tf.keras.Model(inputs=sentence_input, outputs=[classifier, sentence_attention_weights])
        self._model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

    def predict(self, input_data: Any) -> Any:
        """
        Predict method to use the loaded model for making predictions.

        This example assumes `input_data` is preprocessed appropriately for the model's expected input.
        """
        if self._model is None:
            raise ValueError("Model is not loaded. Call `load_model` before prediction.")
            
        # Predicting with the model
        predictions, attention_weights = self._model.predict(input_data)
        
        # Additional logic to handle and package the predictions and attention weights could be added here
        
        return predictions, attention_weights