import numpy as np
from matrices import construir_matriz_Q, generar_k_transpuesta_y_v
from attention import encoder_layer, calcular_pesos_proyeccion, scaled_dot_product_attention_2D

def transform_word(word):
    significant_numbers = []
    for char in word:
        Q = construir_matriz_Q(char)
        K_transpose, V = generar_k_transpuesta_y_v(Q, char)
        output = scaled_dot_product_attention_2D(Q, K_transpose, V)
        Wq, Wk, Wv = calcular_pesos_proyeccion(output)
        encoder_output = encoder_layer(Q, K_transpose, V, Wq, Wk, Wv)
        significant_number = np.sum(np.max(encoder_output, axis=1))
        significant_numbers.append(significant_number)
    return significant_numbers