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import os
import pickle
import numpy as np
from tqdm import tqdm
from prettytable import PrettyTable
from pyarabic.araby import tokenize, strip_tashkeel
import diac_utils as du
class DatasetUtils:
def __init__(self, config):
self.base_path = config["paths"]["base"]
self.special_tokens = ['<pad>', '<unk>', '<num>', '<punc>']
self.delimeters = config["sentence-break"]["delimeters"]
self.load_constants(config["paths"]["constants"])
self.debug = config["debug"]
self.stride = config["sentence-break"]["stride"]
self.window = config["sentence-break"]["window"]
self.val_stride = config["sentence-break"].get("val-stride", self.stride)
self.test_stride = config["predictor"]["stride"]
self.test_window = config["predictor"]["window"]
self.max_word_len = config["train"]["max-word-len"]
self.max_sent_len = config["train"]["max-sent-len"]
self.max_token_count = config["train"]["max-token-count"]
self.pad_target_val = -100
self.pad_char_id = du.DIAC_PAD_IDX #LETTER_LIST.index('<pad>')
self.markov_signal = config['train'].get('markov-signal', False)
self.batch_first = config['train'].get('batch-first', True)
self.gt_prob = config["predictor"]["gt-signal-prob"]
if self.gt_prob > 0:
self.s_idx = config["predictor"]["seed-idx"]
subpath = f"test_gt_mask_{self.gt_prob}_{self.s_idx}.txt"
mask_path = os.path.join(self.base_path, "test", subpath)
with open(mask_path, 'r') as fin:
self.gt_mask = fin.readlines()
if "word-embs" in config["paths"] and config["paths"]["word-embs"].strip() != "":
self.pad_val = self.special_tokens.index("<pad>")
self.embeddings, self.vocab = self.load_embeddings(config["paths"]["word-embs"], config["loader"]["wembs-limit"])
self.embeddings = self.normalize(self.embeddings, ["unit", "centeremb", "unit"])
self.w2idx = {word: i for i, word in enumerate(self.vocab)}
def load_file(self, path):
with open(path, 'rb') as f:
return list(pickle.load(f))
def normalize(self, matrix, actions, mean=None):
def length_normalize(matrix):
norms = np.sqrt(np.sum(matrix**2, axis=1))
norms[norms == 0] = 1
matrix = matrix / norms[:, np.newaxis]
return matrix
def mean_center(matrix):
return matrix - mean
def length_normalize_dimensionwise(matrix):
norms = np.sqrt(np.sum(matrix**2, axis=0))
norms[norms == 0] = 1
matrix = matrix / norms
return matrix
def mean_center_embeddingwise(matrix):
avg = np.mean(matrix, axis=1)
matrix = matrix - avg[:, np.newaxis]
return matrix
for action in actions:
if action == 'unit':
matrix = length_normalize(matrix)
elif action == 'center':
matrix = mean_center(matrix)
elif action == 'unitdim':
matrix = length_normalize_dimensionwise(matrix)
elif action == 'centeremb':
matrix = mean_center_embeddingwise(matrix)
return matrix
def load_constants(self, path):
# self.numbers = [c for c in "0123456789"]
# self.letter_list = self.special_tokens + self.load_file(os.path.join(path, 'ARABIC_LETTERS_LIST.pickle'))
# self.diacritic_list = [' '] + self.load_file(os.path.join(path, 'DIACRITICS_LIST.pickle'))
self.numbers = du.NUMBERS
self.letter_list = du.LETTER_LIST
self.diacritic_list = du.DIACRITICS_SHORT
def split_word_on_characters_with_diacritics(self, word: str):
return du.split_word_on_characters_with_diacritics(word)
def load_mapping_v3(self, dtype, file_ext=None):
mapping = {}
if file_ext is None:
file_ext = f"-{self.test_stride}-{self.test_window}.map"
f_name = os.path.join(self.base_path, dtype, dtype + file_ext)
with open(f_name, 'r') as fin:
for line in fin:
sent_idx, seg_idx, t_idx, c_idx = map(int, line.split(','))
if sent_idx not in mapping:
mapping[sent_idx] = {}
if seg_idx not in mapping[sent_idx]:
mapping[sent_idx][seg_idx] = {}
if t_idx not in mapping[sent_idx][seg_idx]:
mapping[sent_idx][seg_idx][t_idx] = []
mapping[sent_idx][seg_idx][t_idx] += [c_idx]
return mapping
def load_mapping_v3_from_list(self, mapping_list):
mapping = {}
for line in mapping_list:
sent_idx, seg_idx, t_idx, c_idx = map(int, line.split(','))
if sent_idx not in mapping:
mapping[sent_idx] = {}
if seg_idx not in mapping[sent_idx]:
mapping[sent_idx][seg_idx] = {}
if t_idx not in mapping[sent_idx][seg_idx]:
mapping[sent_idx][seg_idx][t_idx] = []
mapping[sent_idx][seg_idx][t_idx] += [c_idx]
return mapping
def load_embeddings(self, embs_path, limit=-1):
if self.debug:
return np.zeros((200+len(self.special_tokens),300)), self.special_tokens + ["c"] * 200
words = [self.special_tokens[0]]
print(f"[INFO] Reading Embeddings from {embs_path}")
with open(embs_path, encoding='utf-8', mode='r') as fin:
n, d = map(int, fin.readline().split())
limit = n if limit <= 0 else limit
embeddings = np.zeros((limit+1, d))
for i, line in tqdm(enumerate(fin), total=limit):
if i >= limit: break
tokens = line.rstrip().split()
words += [tokens[0]]
embeddings[i+1] = list(map(float, tokens[1:]))
return embeddings, words
def load_file_clean(self, dtype, strip=False):
f_name = os.path.join(self.base_path, dtype, dtype + ".txt")
with open(f_name, 'r', encoding="utf-8", newline='\n') as fin:
if strip:
original_lines = [strip_tashkeel(self.preprocess(line)) for line in fin.readlines()]
else:
original_lines = [self.preprocess(line) for line in fin.readlines()]
return original_lines
def preprocess(self, line):
return ' '.join(tokenize(line))
def pad_and_truncate_sequence(self, tokens, max_len, pad=None):
if pad is None:
pad = self.special_tokens.index("<pad>")
if len(tokens) < max_len:
offset = max_len - len(tokens)
return tokens + [pad] * offset
else:
return tokens[:max_len]
def stats(self, freq, percentile=90, name="stats"):
table = PrettyTable(["Dataset", "Mean", "Std", "Min", "Max", f"{percentile}th Percentile"])
freq = np.array(sorted(freq))
table.add_row([name, freq.mean(), freq.std(), freq.min(), freq.max(), np.percentile(freq, percentile)])
print(table)
def create_gt_mask(self, lines, prob, idx, seed=1111):
np.random.seed(seed)
gt_masks = []
for line in lines:
tokens = tokenize(line.strip())
gt_mask_token = ""
for t_idx, token in enumerate(tokens):
gt_mask_token += ''.join(map(str, np.random.binomial(1, prob, len(token))))
if t_idx+1 < len(tokens):
gt_mask_token += " "
gt_masks += [gt_mask_token]
subpath = f"test_gt_mask_{prob}_{idx}.txt"
mask_path = os.path.join(self.base_path, "test", subpath)
with open(mask_path, 'w') as fout:
fout.write('\n'.join(gt_masks))
def create_gt_labels(self, lines):
gt_labels = []
for line in lines:
gt_labels_line = []
tokens = tokenize(line.strip())
for w_idx, word in enumerate(tokens):
split_word = self.split_word_on_characters_with_diacritics(word)
_, cy_flat, _ = du.create_label_for_word(split_word)
gt_labels_line.extend(cy_flat)
if w_idx+1 < len(tokens):
gt_labels_line += [0]
gt_labels += [gt_labels_line]
return gt_labels
def get_ce(self, diac_word_y, e_idx=None, return_idx=False):
#^ diac_word_y: [Tw 3]
if e_idx is None: e_idx = len(diac_word_y)
for c_idx in reversed(range(e_idx)):
if diac_word_y[c_idx] != [0,0,0]:
return diac_word_y[c_idx] if not return_idx else c_idx
return diac_word_y[e_idx-1] if not return_idx else e_idx-1
def create_decoder_input(self, diac_code_y, prob=0):
#^ diac_code_y: [Ts Tw 3]
diac_code_x = np.zeros((*np.array(diac_code_y).shape[:-1], 8))
if not self.markov_signal:
return list(diac_code_x)
prev_ce = list(np.eye(6)[-1]) + [0,0] # bos tag
for w_idx, word in enumerate(diac_code_y):
diac_code_x[w_idx, 0, :] = prev_ce
for c_idx, char in enumerate(word[:-1]):
# if np.random.rand() < prob:
# continue
if char[0] == self.pad_target_val:
break
haraka = list(np.eye(6)[char[0]])
diac_code_x[w_idx, c_idx+1, :] = haraka + char[1:]
ce = self.get_ce(diac_code_y[w_idx], c_idx)
prev_ce = list(np.eye(6)[ce[0]]) + ce[1:]
return list(diac_code_x) |