macleginn

import re
from typing import Optional, Tuple, List, Any

class TreeNode:
    """Represents a node in the parse tree."""
    
    def __init__(self, rule_name: str, content: Any = None, children: List['TreeNode'] = None):
        self.rule_name = rule_name
        self.content = content  # For terminal nodes
        self.children = children or []

macleginn

from copy import copy
from collections import defaultdict, Counter
from functools import cache
from glob import glob
import pandas as pd
import json
from tqdm import tqdm


@cache

macleginn

import torch
import torch.nn as nn
from collections import defaultdict

# --- Hook Implementation ---

# Dictionary to store attention weights during the forward pass
# Structure: {layer_index: attention_weights_tensor}
# The tensor shape will be (batch_size, num_heads, seq_len, seq_len)

macleginn

import urllib.request
from collections import defaultdict

raw_bytes = urllib.request.urlopen(
    'http://www.sls.hawaii.edu/bley-vroman/brown.txt')
brown_corpus = raw_bytes.read().decode('utf8').replace('\r\n', '\n')
B = brown_corpus[:250]

def find_pairs(text):
    pairs = defaultdict(int)

macleginn

import pickle
import numpy as np
import pandas as pd
import torch
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm.auto import tqdm

with open('sts_attributions/shelf_approx_attr_l-9_N-100.pkl', 'rb') as inp:
    shelf_approx = pickle.load(inp)

macleginn

import os
import sys
import pickle
import requests
import torch
from sentence_transformers import SentenceTransformer
from sentence_transformers.models import Pooling
from sentence_transformers import util
from xsbert import models

macleginn

import json
from http.server import BaseHTTPRequestHandler, HTTPServer
import pandas as pd

hostName = "localhost"
serverPort = 20000
# A global variable to store the queue elements
queue = []

macleginn

from collections import defaultdict
from itertools import combinations
import pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer, util


def compute_kernel_bias(vecs, k=None):
    """
    Code taken from: https://github.com/bojone/BERT-whitening

macleginn

import os
import sys
import shutil


def copy_tree(src, dst):
    '''
    Copy a directory tree from src to dst ignoring dangling
    symlinks, retrieving files symlinks point to, and
    breaking the cycles, i.e. never copying the same

macleginn

import json
from math import ceil
from random import shuffle

import torch
import torch.nn as nn

from transformers import AutoTokenizer, AutoModel
from transformers import AdamW, get_scheduler