from __future__ import annotations
import collections
import json
import os
import string
from collections.abc import Iterable
from .WordTokenizer import ENGLISH_STOP_WORDS, WordTokenizer
class WhitespaceTokenizer(WordTokenizer):
"""
Simple and fast white-space tokenizer. Splits sentence based on white spaces.
Punctuation are stripped from tokens.
"""
def __init__(
self, vocab: Iterable[str] = [], stop_words: Iterable[str] = ENGLISH_STOP_WORDS, do_lower_case: bool = False
):
self.stop_words = set(stop_words)
self.do_lower_case = do_lower_case
self.set_vocab(vocab)
def get_vocab(self):
return self.vocab
def set_vocab(self, vocab: Iterable[str]):
self.vocab = vocab
self.word2idx = collections.OrderedDict([(word, idx) for idx, word in enumerate(vocab)])
def tokenize(self, text: str, **kwargs) -> list[int]:
if self.do_lower_case:
text = text.lower()
tokens = text.split()
tokens_filtered = []
for token in tokens:
if token in self.stop_words:
continue
elif token in self.word2idx:
tokens_filtered.append(self.word2idx[token])
continue
token = token.strip(string.punctuation)
if token in self.stop_words:
continue
elif len(token) > 0 and token in self.word2idx:
tokens_filtered.append(self.word2idx[token])
continue
token = token.lower()
if token in self.stop_words:
continue
elif token in self.word2idx:
tokens_filtered.append(self.word2idx[token])
continue
return tokens_filtered
def save(self, output_path: str):
with open(os.path.join(output_path, "whitespacetokenizer_config.json"), "w") as fOut:
json.dump(
{
"vocab": list(self.word2idx.keys()),
"stop_words": list(self.stop_words),
"do_lower_case": self.do_lower_case,
},
fOut,
)
@staticmethod
def load(input_path: str):
with open(os.path.join(input_path, "whitespacetokenizer_config.json")) as fIn:
config = json.load(fIn)
return WhitespaceTokenizer(**config)