Source code for transformers.models.openai.tokenization_openai_fast

# coding=utf-8
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Fast Tokenization classes for OpenAI GPT."""


from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


logger = logging.get_logger(__name__)

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

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
    "tokenizer_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"},
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "openai-gpt": 512,
}


[docs]class OpenAIGPTTokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" GPT Tokenizer (backed by HuggingFace's `tokenizers` library). Based on Byte-Pair-Encoding with the following peculiarities: - lower case all inputs - uses BERT's BasicTokenizer for pre-BPE tokenization This tokenizer inherits from :class:`~transformers.PreTrainedTokenizerFast` which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (:obj:`str`): Path to the vocabulary file. merges_file (:obj:`str`): Path to the merges file. unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["attention_mask"] slow_tokenizer_class = OpenAIGPTTokenizer def __init__(self, vocab_file, merges_file, tokenizer_file=None, unk_token="<unk>", **kwargs): super().__init__(vocab_file, merges_file, tokenizer_file=tokenizer_file, unk_token=unk_token, **kwargs) @property def do_lower_case(self): return True
[docs] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files)