tokenization_auto.py

# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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""" Auto Model class. """

from __future__ import absolute_import, division, print_function, unicode_literals

import logging

from .tokenization_bert import BertTokenizer
from .tokenization_openai import OpenAIGPTTokenizer
from .tokenization_gpt2 import GPT2Tokenizer
from .tokenization_transfo_xl import TransfoXLTokenizer
from .tokenization_xlnet import XLNetTokenizer
from .tokenization_xlm import XLMTokenizer
from .tokenization_roberta import RobertaTokenizer

logger = logging.getLogger(__name__)

class AutoTokenizer(object):
    r""":class:`~pytorch_transformers.AutoTokenizer` is a generic tokenizer class
        that will be instantiated as one of the tokenizer classes of the library
        when created with the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)`
        class method.

        The `from_pretrained()` method take care of returning the correct tokenizer class instance
        using pattern matching on the `pretrained_model_name_or_path` string.

        The tokenizer class to instantiate is selected as the first pattern matching
        in the `pretrained_model_name_or_path` string (in the following order):
            - contains `bert`: BertTokenizer (Bert model)
            - contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
            - contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
            - contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
            - contains `xlnet`: XLNetTokenizer (XLNet model)
            - contains `xlm`: XLMTokenizer (XLM model)
            - contains `roberta`: RobertaTokenizer (RoBERTa model)

        This class cannot be instantiated using `__init__()` (throw an error).
    """
    def __init__(self):
        raise EnvironmentError("AutoTokenizer is designed to be instantiated "
            "using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method.")

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
        r""" Instantiate a one of the tokenizer classes of the library
        from a pre-trained model vocabulary.

        The tokenizer class to instantiate is selected as the first pattern matching
        in the `pretrained_model_name_or_path` string (in the following order):
            - contains `bert`: BertTokenizer (Bert model)
            - contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
            - contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
            - contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
            - contains `xlnet`: XLNetTokenizer (XLNet model)
            - contains `xlm`: XLMTokenizer (XLM model)
            - contains `roberta`: RobertaTokenizer (XLM model)

        Params:
            **pretrained_model_name_or_path**: either:
                - a string with the `shortcut name` of a pre-trained model configuration to load from cache
                    or download and cache if not already stored in cache (e.g. 'bert-base-uncased').
                - a path to a `directory` containing a configuration file saved
                    using the `save_pretrained(save_directory)` method.
                - a path or url to a saved configuration `file`.
            **cache_dir**: (`optional`) string:
                Path to a directory in which a downloaded pre-trained model
                configuration should be cached if the standard cache should not be used.

        Examples::

            config = AutoTokenizer.from_pretrained('bert-base-uncased')    # Download vocabulary from S3 and cache.
            config = AutoTokenizer.from_pretrained('./test/bert_saved_model/')  # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`

        """
        if 'roberta' in pretrained_model_name_or_path:
            return RobertaTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
        elif 'bert' in pretrained_model_name_or_path:
            return BertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
        elif 'openai-gpt' in pretrained_model_name_or_path:
            return OpenAIGPTTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
        elif 'gpt2' in pretrained_model_name_or_path:
            return GPT2Tokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
        elif 'transfo-xl' in pretrained_model_name_or_path:
            return TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
        elif 'xlnet' in pretrained_model_name_or_path:
            return XLNetTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
        elif 'xlm' in pretrained_model_name_or_path:
            return XLMTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)

        raise ValueError("Unrecognized model identifier in {}. Should contains one of "
                         "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
                         "'xlm', 'roberta'".format(pretrained_model_name_or_path))