image-match/python/py/Lib/site-packages/transformers/models/gpt2/tokenization_gpt2.py

# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for OpenAI GPT."""

from tokenizers import Tokenizer, decoders, pre_tokenizers
from tokenizers.models import BPE

from ...tokenization_utils_tokenizers import AddedToken, TokenizersBackend
from ...utils import logging


logger = logging.get_logger(__name__)

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


class GPT2Tokenizer(TokenizersBackend):
    """
    Construct a GPT-2 tokenizer. 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 GPT2Tokenizer

    >>> tokenizer = GPT2Tokenizer.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 or when you
    call it on some text, 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 will add a space before each word (even the first one).

    </Tip>

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

    Args:
        vocab_file (`str`):
            Path to the vocabulary file.
        merges_file (`str`):
            Path to the merges file.
        errors (`str`, *optional*, defaults to `"replace"`):
            Paradigm to follow when decoding bytes to UTF-8. See
            [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
        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.
        pad_token (`str`, *optional*):
            The token used for padding, for example when batching sequences of different lengths.
        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).
        add_bos_token (`bool`, *optional*, defaults to `False`):
            Whether or not to add an initial beginning of sentence token to the input. This allows to treat the leading
            word just as any other word.
        vocab (`str` or `dict[str, int]`, *optional*):
            Custom vocabulary dictionary. If not provided, vocabulary is loaded from `vocab_file`.
        merges (`str` or `list[str]`, *optional*):
            Custom merges list. If not provided, merges are loaded from `merges_file`.
    """

    vocab_files_names = VOCAB_FILES_NAMES
    model_input_names = ["input_ids", "attention_mask"]
    model = BPE

    def __init__(
        self,
        vocab: str | dict[str, int] | None = None,
        merges: str | list[str] | None = None,
        errors: str = "replace",
        unk_token: AddedToken | str = "<|endoftext|>",
        bos_token: AddedToken | str = "<|endoftext|>",
        eos_token: AddedToken | str = "<|endoftext|>",
        pad_token: AddedToken | str | None = None,
        add_prefix_space=False,
        **kwargs,
    ):
        self.add_prefix_space = add_prefix_space
        self._vocab = vocab if vocab is not None else {}
        self._merges = merges or []
        self._tokenizer = Tokenizer(
            BPE(
                vocab=self._vocab,
                merges=self._merges,
                dropout=None,
                continuing_subword_prefix="",
                end_of_word_suffix="",
                fuse_unk=False,
            )
        )
        self._tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=add_prefix_space)
        self._tokenizer.decoder = decoders.ByteLevel()
        super().__init__(
            errors=errors,
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            add_prefix_space=add_prefix_space,
            **kwargs,
        )


__all__ = ["GPT2Tokenizer"]