import pathlib from typing import Collection from pandas import DataFrame from sacremoses import MosesTokenizer import fastai from fastai.basic_data import DataBunch from fastai.core import ListRules, PathOrStr, IntsOrStrs, is_listy from fastai.data_block import ItemLists from fastai.text import * class MosesPreprocessingFunc(): def __init__(self, lang: str): self.mt = MosesTokenizer(lang) def __call__(self, t: str) -> str: return self.mt.tokenize(t, return_str=True, escape=True) try: from fastai.text import SPProcessor except ImportError: def _join_texts(texts:Collection[str], mark_fields:bool=False, include_bos:bool=True, include_eos:bool=False): if not isinstance(texts, np.ndarray): texts = np.array(texts) if is1d(texts): texts = texts[:,None] df = pd.DataFrame({i:texts[:,i] for i in range(texts.shape[1])}) bos_tok = f'{BOS} ' if include_bos else '' text_col = f'{bos_tok}{FLD} {1} ' + df[0].astype(str) if mark_fields else f'{bos_tok}' + df[0].astype(str) for i in range(1,len(df.columns)): text_col += (f' {FLD} {i+1} ' if mark_fields else ' ') + df[i].astype(str) if include_eos: text_col = text_col + f' {EOS}' return text_col.values def apply_rules(text, pre_rules=None, post_rules=None): "Apply `pre_rules` and `post_rules` to `text`" text = text.strip(' ') for r in ifnone(pre_rules, defaults.text_pre_rules): text = r(text) toks = text.split() for r in ifnone(post_rules, defaults.text_post_rules): toks = r(toks) return ' '.join(toks) def get_default_size(texts, max_vocab_sz): "Either max_vocab_sz or one quarter of the number of unique words in `texts`" cnt = Counter() for t in texts: cnt.update(t.split()) if len(cnt)//4 > max_vocab_sz: return max_vocab_sz res = len(cnt)//4 while res%8 != 0: res+=1 return res full_char_coverage_langs = ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it","lt","lv","mt","nl","pl","pt","ro","sk","sl","sv"] # all European langs def train_sentencepiece(texts:Collection[str], path:PathOrStr, pre_rules: ListRules=None, post_rules:ListRules=None, vocab_sz:int=None, max_vocab_sz:int=30000, model_type:str='unigram', max_sentence_len:int=20480, lang='en', char_coverage=None, tmp_dir='tmp', enc='utf8'): "Train a sentencepiece tokenizer on `texts` and save it in `path/tmp_dir`" from sentencepiece import SentencePieceTrainer cache_dir = Path(path)/tmp_dir os.makedirs(cache_dir, exist_ok=True) if vocab_sz is None: vocab_sz=get_default_size(texts, max_vocab_sz) raw_text_path = cache_dir / 'all_text.out' with open(raw_text_path, 'w', encoding=enc) as f: f.write("\n".join(texts)) spec_tokens = ['\u2581'+s for s in defaults.text_spec_tok] SentencePieceTrainer.Train(" ".join([ f"--input={raw_text_path} --max_sentence_length={max_sentence_len}", f"--character_coverage={ifnone(char_coverage, 0.99999 if lang in full_char_coverage_langs else 0.9998)}", f"--unk_id={len(defaults.text_spec_tok)} --pad_id=-1 --bos_id=-1 --eos_id=-1", f"--user_defined_symbols={','.join(spec_tokens)}", f"--model_prefix={cache_dir/'spm'} --vocab_size={vocab_sz} --model_type={model_type}"])) raw_text_path.unlink() return cache_dir class SPProcessor(PreProcessor): "`PreProcessor` that tokenizes and numericalizes with `sentencepiece`" def __init__(self, ds:ItemList=None, pre_rules: ListRules=None, post_rules:ListRules=None, vocab_sz:int=None, max_vocab_sz:int=30000, model_type:str='unigram', max_sentence_len:int=20480, lang='en', char_coverage=None, tmp_dir='tmp', mark_fields:bool=False, include_bos:bool=True, include_eos:bool=False, sp_model=None, sp_vocab=None, n_cpus:int=None, enc='utf8'): try: from sentencepiece import SentencePieceTrainer,SentencePieceProcessor except ImportError: raise Exception('sentencepiece module is missing: run `pip install sentencepiece`') self.pre_rules,self.post_rules,self.enc = pre_rules,post_rules,enc self.mark_fields,self.include_bos,self.include_eos = mark_fields,include_bos,include_eos self.sp_model,self.sp_vocab,self.n_cpus = sp_model,sp_vocab,ifnone(n_cpus,defaults.cpus) self.train_func = partial(train_sentencepiece, pre_rules=pre_rules, post_rules=post_rules, vocab_sz=vocab_sz, max_vocab_sz=max_vocab_sz, model_type=model_type, max_sentence_len=max_sentence_len, lang=lang, char_coverage=char_coverage, tmp_dir=tmp_dir, enc=enc) def process_one(self, item, join=True): if join: text = _join_texts([item], self.mark_fields, self.include_bos, self.include_eos)[0] text = apply_rules(text, pre_rules=self.pre_rules, post_rules=self.post_rules) return self._encode_batch([text])[0] def process(self, ds): ds.items = _join_texts(ds.items, self.mark_fields, self.include_bos, self.include_eos) ds.items = [apply_rules(t, pre_rules=self.pre_rules, post_rules=self.post_rules) for t in progress_bar(ds.items, leave=False)] if self.sp_model is None or self.sp_vocab is None: cache_dir = self.train_func(ds.items, ds.path) self.sp_model,self.sp_vocab = cache_dir/'spm.model',cache_dir/'spm.vocab' if not getattr(self, 'vocab', False): with open(self.sp_vocab, 'r', encoding=self.enc) as f: self.vocab = Vocab([line.split('\t')[0] for line in f.readlines()]) if self.n_cpus <= 1: ds.items = self._encode_batch(ds.items) else: with ProcessPoolExecutor(self.n_cpus) as e: ds.items = np.array(sum(e.map(self._encode_batch, partition_by_cores(ds.items, self.n_cpus)), [])) ds.vocab = self.vocab def _encode_batch(self, texts): from sentencepiece import SentencePieceProcessor tok = SentencePieceProcessor() tok.Load(str(self.sp_model)) return [np.array(tok.EncodeAsIds(t)) for t in texts] @classmethod def load(cls, path:PathOrStr, tmp_dir:PathOrStr='tmp', name:str='spm'): cache_dir = Path(path)/tmp_dir return cls(sp_model=cache_dir/f'{name}.model', sp_vocab=cache_dir/f'{name}.vocab') class SPProcessor2(SPProcessor): def process(self, ds): super().process(ds) ds.vocab.sp_model = self.sp_model ds.vocab.sp_vocab = self.sp_vocab # temporary loading function as from_df does not support processors def make_data_bunch_from_df(cls, path: PathOrStr, train_df: DataFrame, valid_df: DataFrame, tokenizer: Tokenizer = None, vocab: Vocab = None, classes: Collection[str] = None, text_cols: IntsOrStrs = 1, label_cols: IntsOrStrs = 0, label_delim: str = None, chunksize: int = 10000, max_vocab: int = 60000, label_cls: Callable = None, min_freq: int = 2, mark_fields: bool = False, include_bos: bool = True, include_eos: bool = False, processor=None, **kwargs) -> DataBunch: "Create a `TextDataBunch` from DataFrames. `kwargs` are passed to the dataloader creation." assert processor is None or tokenizer is None, "Processor and tokenizer are mutually exclusive." if processor is None: processor = fastai.text.data._get_processor(tokenizer=tokenizer, vocab=vocab, chunksize=chunksize, max_vocab=max_vocab, min_freq=min_freq, mark_fields=mark_fields, include_bos=include_bos, include_eos=include_eos) if classes is None and is_listy(label_cols) and len(label_cols) > 1: classes = label_cols src = ItemLists(path, TextList.from_df(train_df, path, cols=text_cols, processor=processor, label_cls=label_cls), TextList.from_df(valid_df, path, cols=text_cols, processor=processor, label_cls=label_cls)) if cls == TextLMDataBunch: src = src.label_for_lm() else: if label_delim is not None: src = src.label_from_df(cols=label_cols, classes=classes, label_delim=label_delim) else: src = src.label_from_df(cols=label_cols, classes=classes) return src.databunch(**kwargs)