utils.py

import heapq
import pickle


def save_data(outputs, src_tokens, tgt_tokens, attn_scores):
    data = {'outputs': outputs, 'src_tokens': src_tokens, 'tgt_tokens': tgt_tokens, 'attn_scores': attn_scores}

    # Save to file
    with open("data.pkl", "wb") as f:
        pickle.dump(data, f)


def get_attn_list(attentions, layer_index):
    avg_attn_list = []

    for i in range(len(attentions)):
        token_index = i  # pick a token index from the output (1 to 18)
        attn_tensor = attentions[token_index][layer_index]  # shape: [1, 8, 1, 24]
        avg_attn_list.append(attn_tensor.squeeze(0).squeeze(1).mean(0))  # shape: [24], mean across heads

    return avg_attn_list

def get_top_attns(avg_attn_list):
    avg_attn_top = []

    for i in range(len(avg_attn_list)):
        # Get top 3 (index, value) pairs
        top_3 = heapq.nlargest(3, enumerate(avg_attn_list[i]), key=lambda x: x[1])

        # get the indices and values of the source tokens
        top_values = [round(val.item(), 2) for idx, val in top_3]
        top_index = [idx for idx, val in top_3]

        avg_attn_top.append({
            "top_values": top_values,
            "top_index": top_index
        })

    return avg_attn_top



# def get_encoder_attn_list(decoder_attentions, layer_index):
#     avg_attn_list = []
#
#     for i in range(len(decoder_attentions)):
#         token_index = i  # pick a token index from the output (1 to 18)
#         attn_tensor = decoder_attentions[token_index][layer_index]  # shape: [1, 8, 1, 24]
#         avg_attn_list.append(attn_tensor.squeeze(0).squeeze(1).mean(0))  # shape: [24], mean across heads
#
#     return avg_attn_list