tell me how to use this in self attention mechanism and generate the complete code for it : from gensim.models import KeyedVectors
from sklearn.metrics import mean_squared_error
from scipy.stats import pearsonr
import numpy as np

# Load GloVe embeddings (50D)
glove_file = '/content/glove.6B.50d.txt'
glove_model = KeyedVectors.load_word2vec_format(glove_file, binary=False, no_header=True)

# Example dataset with real words and their similarity scores
dataset = [
    (('king', 'queen'), 0.9),    # High similarity
    (('king', 'man'), 0.6),      # Medium similarity
    (('king', 'car'), 0.1),      # Low similarity
    (('queen', 'woman'), 0.8),   # High similarity
    (('car', 'bicycle'), 0.5)    # Medium similarity
]

# Quantum-inspired embedding function using loaded vectors
def create_superposition_embedding(word, model, n_states=3):
    """
    Create a superposition state for a word by combining multiple context vectors.
    """
    base_vector = model[word]  # Base vector from pre-trained embeddings
    state_vectors = [base_vector]

    # Generate additional states by adding slight variations to the base vector
    for _ in range(1, n_states):
        # Create small random noise to simulate different contexts
        random_noise = np.random.normal(scale=0.01, size=base_vector.shape)
        state_vectors.append(base_vector + random_noise)

    # Normalize each state to ensure they are valid quantum states
    state_vectors = [vec / np.linalg.norm(vec) for vec in state_vectors]

    # Combine them into a superposition (sum of basis states)
    superposition = np.sum(state_vectors, axis=0)
    return superposition / np.linalg.norm(superposition)

def entangle_embeddings(word1, word2, model, entanglement_factor=0.1):
    """
    Entangle the embeddings of two words by slightly modifying them based on their relationship.
    """
    vec1 = create_superposition_embedding(word1, model)
    vec2 = create_superposition_embedding(word2, model)

    # Apply entanglement: Modify each vector by incorporating a fraction of the other
    entangled_vec1 = vec1 + entanglement_factor * vec2
    entangled_vec2 = vec2 + entanglement_factor * vec1

    # Normalize the entangled vectors
    entangled_vec1 /= np.linalg.norm(entangled_vec1)
    entangled_vec2 /= np.linalg.norm(entangled_vec2)

    return entangled_vec1, entangled_vec2

# Update the embedding model with entangled embeddings
embedding_model = {}
for word_pair in dataset:
    w_i, w_k = word_pair[0]
    entangled_w_i, entangled_w_k = entangle_embeddings(w_i, w_k, glove_model)
    embedding_model[w_i] = entangled_w_i
    embedding_model[w_k] = entangled_w_k

def measure_contextual_similarity(embedding_model, word_pair):
    """
    Measure the similarity between two words in a specific context.
    """
    w_i, w_k = word_pair

    # Apply measurement by adding contextual influence
    contextual_influence = np.random.normal(scale=0.05, size=embedding_model[w_i].shape)
    measured_embedding_i = embedding_model[w_i] + contextual_influence
    measured_embedding_k = embedding_model[w_k] + contextual_influence

    # Normalize measured embeddings
    measured_embedding_i /= np.linalg.norm(measured_embedding_i)
    measured_embedding_k /= np.linalg.norm(measured_embedding_k)

    # Compute cosine similarity
    similarity = np.dot(measured_embedding_i, measured_embedding_k)
    return similarity

# Example evaluation on a word similarity dataset
def evaluate_embeddings(embedding_model, dataset):
    predictions = []
    ground_truth = []

    for word_pair, true_similarity in dataset:
        predicted_similarity = measure_contextual_similarity(embedding_model, word_pair)
        predictions.append(predicted_similarity)
        ground_truth.append(true_similarity)

    # Evaluate with suitable metrics
    mse = mean_squared_error(ground_truth, predictions)

    # Check if either array is constant
    if np.std(ground_truth) == 0 or np.std(predictions) == 0:
        pearson_corr = None  # Cannot compute Pearson correlation if inputs are constant
    else:
        pearson_corr, _ = pearsonr(ground_truth, predictions)

    return mse, pearson_corr

# Evaluate using the real dataset
mse, pearson_corr = evaluate_embeddings(embedding_model, dataset)
print("Mean Squared Error:", mse)
print("Pearson Correlation:", pearson_corr if pearson_corr is not None else "Not enough variance to compute correlation")