Source code for transformers.models.flaubert.tokenization_flaubert

# coding=utf-8
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# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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"""Tokenization classes for Flaubert, based on XLM."""


import unicodedata

import six

from ...utils import logging
from ..xlm.tokenization_xlm import XLMTokenizer


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "flaubert/flaubert_small_cased": "https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/vocab.json",
        "flaubert/flaubert_base_uncased": "https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/vocab.json",
        "flaubert/flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/vocab.json",
        "flaubert/flaubert_large_cased": "https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/vocab.json",
    },
    "merges_file": {
        "flaubert/flaubert_small_cased": "https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/merges.txt",
        "flaubert/flaubert_base_uncased": "https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/merges.txt",
        "flaubert/flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/merges.txt",
        "flaubert/flaubert_large_cased": "https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/merges.txt",
    },
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "flaubert/flaubert_small_cased": 512,
    "flaubert/flaubert_base_uncased": 512,
    "flaubert/flaubert_base_cased": 512,
    "flaubert/flaubert_large_cased": 512,
}

PRETRAINED_INIT_CONFIGURATION = {
    "flaubert/flaubert_small_cased": {"do_lowercase": False},
    "flaubert/flaubert_base_uncased": {"do_lowercase": True},
    "flaubert/flaubert_base_cased": {"do_lowercase": False},
    "flaubert/flaubert_large_cased": {"do_lowercase": False},
}


def convert_to_unicode(text):
    """
    Converts `text` to Unicode (if it's not already), assuming UTF-8 input.
    """
    # six_ensure_text is copied from https://github.com/benjaminp/six
    def six_ensure_text(s, encoding="utf-8", errors="strict"):
        if isinstance(s, six.binary_type):
            return s.decode(encoding, errors)
        elif isinstance(s, six.text_type):
            return s
        else:
            raise TypeError(f"not expecting type '{type(s)}'")

    return six_ensure_text(text, encoding="utf-8", errors="ignore")


[docs]class FlaubertTokenizer(XLMTokenizer): """ Construct a Flaubert tokenizer. Based on Byte-Pair Encoding. The tokenization process is the following: - Moses preprocessing and tokenization. - Normalizing all inputs text. - The arguments ``special_tokens`` and the function ``set_special_tokens``, can be used to add additional symbols (like "__classify__") to a vocabulary. - The argument :obj:`do_lowercase` controls lower casing (automatically set for pretrained vocabularies). This tokenizer inherits from :class:`~transformers.XLMTokenizer`. Please check the superclass for usage examples and documentation regarding arguments. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__(self, do_lowercase=False, **kwargs): super().__init__(**kwargs) self.do_lowercase = do_lowercase self.do_lowercase_and_remove_accent = False def preprocess_text(self, text): text = text.replace("``", '"').replace("''", '"') text = convert_to_unicode(text) text = unicodedata.normalize("NFC", text) if self.do_lowercase: text = text.lower() return text def _tokenize(self, text, bypass_tokenizer=False): """ Tokenize a string given language code using Moses. Details of tokenization: - [sacremoses](https://github.com/alvations/sacremoses): port of Moses - Install with `pip install sacremoses` Args: - bypass_tokenizer: Allow users to preprocess and tokenize the sentences externally (default = False) (bool). If True, we only apply BPE. Returns: List of tokens. """ lang = "fr" if lang and self.lang2id and lang not in self.lang2id: logger.error( "Supplied language code not found in lang2id mapping. Please check that your language is supported by the loaded pretrained model." ) if bypass_tokenizer: text = text.split() else: text = self.preprocess_text(text) text = self.moses_pipeline(text, lang=lang) text = self.moses_tokenize(text, lang=lang) split_tokens = [] for token in text: if token: split_tokens.extend([t for t in self.bpe(token).split(" ")]) return split_tokens