Lightstar/venv/Lib/site-packages/transformers/models/gpt2/tokenization_gpt2_fast.py

# coding=utf-8
# Copyright 2018 The Open AI Team Authors and 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""Tokenization classes for OpenAI GPT."""

import json
from typing import Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpt2 import GPT2Tokenizer


logger = logging.get_logger(__name__)

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


class GPT2TokenizerFast(PreTrainedTokenizerFast):
    """
    Construct a "fast" GPT-2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
    Byte-Pair-Encoding.

    This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
    be encoded differently whether it is at the beginning of the sentence (without space) or not:

    ```python
    >>> from transformers import GPT2TokenizerFast

    >>> tokenizer = GPT2TokenizerFast.from_pretrained("openai-community/gpt2")
    >>> tokenizer("Hello world")["input_ids"]
    [15496, 995]

    >>> tokenizer(" Hello world")["input_ids"]
    [18435, 995]
    ```

    You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since
    the model was not pretrained this way, it might yield a decrease in performance.

    <Tip>

    When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.

    </Tip>

    This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
    refer to this superclass for more information regarding those methods.

    Args:
        vocab_file (`str`, *optional*):
            Path to the vocabulary file.
        merges_file (`str`, *optional*):
            Path to the merges file.
        tokenizer_file (`str`, *optional*):
            Path to [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
            contains everything needed to load the tokenizer.
        unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
            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.
        bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
            The beginning of sequence token.
        eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
            The end of sequence token.
        add_prefix_space (`bool`, *optional*, defaults to `False`):
            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
            other word. (GPT2 tokenizer detect beginning of words by the preceding space).
    """

    vocab_files_names = VOCAB_FILES_NAMES
    model_input_names = ["input_ids", "attention_mask"]
    slow_tokenizer_class = GPT2Tokenizer

    def __init__(
        self,
        vocab_file=None,
        merges_file=None,
        tokenizer_file=None,
        unk_token="<|endoftext|>",
        bos_token="<|endoftext|>",
        eos_token="<|endoftext|>",
        add_prefix_space=False,
        **kwargs,
    ):
        super().__init__(
            vocab_file=vocab_file,
            merges_file=merges_file,
            tokenizer_file=tokenizer_file,
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            add_prefix_space=add_prefix_space,
            **kwargs,
        )

        self.add_bos_token = kwargs.pop("add_bos_token", False)

        pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space:
            pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type"))
            pre_tok_state["add_prefix_space"] = add_prefix_space
            self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state)

        self.add_prefix_space = add_prefix_space

    def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
        is_split_into_words = kwargs.get("is_split_into_words", False)
        assert self.add_prefix_space or not is_split_into_words, (
            f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*args, **kwargs)

    def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
        is_split_into_words = kwargs.get("is_split_into_words", False)

        assert self.add_prefix_space or not is_split_into_words, (
            f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*args, **kwargs)

    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)