webapp/seed/Lib/site-packages/transformers/models/mbart/tokenization_mbart.py

# Copyright 2020 The Facebook AI Research 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.


from tokenizers import Tokenizer, decoders, pre_tokenizers, processors
from tokenizers.models import Unigram

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


logger = logging.get_logger(__name__)


VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}


FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]  # fmt: skip


class MBartTokenizer(TokenizersBackend):
    """
    Construct an MBART tokenizer (backed by HuggingFace's *tokenizers* library). Based on
    [Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models).

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

    The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `<language code>
    <tokens> <eos>` for target language documents.

    Examples:

    ```python
    >>> from transformers import MBartTokenizer

    >>> tokenizer = MBartTokenizer.from_pretrained(
    ...     "facebook/mbart-large-en-ro", src_lang="en_XX", tgt_lang="ro_RO"
    ... )
    >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
    >>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
    >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_romanian, return_tensors="pt")
    ```"""

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

    prefix_tokens: list[int] = []
    suffix_tokens: list[int] = []

    def __init__(
        self,
        vocab: str | dict | list | None = None,
        bos_token="<s>",
        eos_token="</s>",
        sep_token="</s>",
        cls_token="<s>",
        unk_token="<unk>",
        pad_token="<pad>",
        mask_token="<mask>",
        src_lang=None,
        tgt_lang=None,
        additional_special_tokens=None,
        **kwargs,
    ):
        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token

        _additional_special_tokens = FAIRSEQ_LANGUAGE_CODES.copy()
        if additional_special_tokens is not None:
            _additional_special_tokens.extend(
                [t for t in additional_special_tokens if t not in _additional_special_tokens]
            )

        if vocab is None:
            vocab = [
                (str(bos_token), 0.0),
                (str(pad_token), 0.0),
                (str(eos_token), 0.0),
                (str(unk_token), 0.0),
            ]
            vocab += [("▁", -2.0)]
            for lang_code in FAIRSEQ_LANGUAGE_CODES:
                vocab.append((lang_code, 0.0))
            vocab.append((str(mask_token), 0.0))

        self._vocab = vocab
        self._tokenizer = Tokenizer(Unigram(self._vocab, unk_id=3, byte_fallback=False))

        self._tokenizer.normalizer = None

        self._tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
            [
                pre_tokenizers.WhitespaceSplit(),
                pre_tokenizers.Metaspace(replacement="▁", prepend_scheme="always", split=True),
            ]
        )

        self._tokenizer.decoder = decoders.Metaspace(replacement="▁", prepend_scheme="always", split=True)

        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            sep_token=sep_token,
            cls_token=cls_token,
            unk_token=unk_token,
            pad_token=pad_token,
            mask_token=mask_token,
            src_lang=src_lang,
            tgt_lang=tgt_lang,
            additional_special_tokens=_additional_special_tokens,
            **kwargs,
        )

        self.lang_code_to_id = {
            lang_code: self.convert_tokens_to_ids(lang_code) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }
        self.fairseq_offset = 1

        # Build fairseq token mappings for backward compatibility
        self.fairseq_tokens_to_ids = {
            "<s>": 0,
            "<pad>": 1,
            "</s>": 2,
            "<unk>": 3,
        }
        self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
        self.fairseq_tokens_to_ids["<mask>"] = self.convert_tokens_to_ids(str(mask_token))
        self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}

        self._src_lang = src_lang if src_lang is not None else "en_XX"
        self.cur_lang_code = self.convert_tokens_to_ids(self._src_lang)
        self.tgt_lang = tgt_lang
        self.set_src_lang_special_tokens(self._src_lang)

    @property
    def src_lang(self) -> str:
        return self._src_lang

    @src_lang.setter
    def src_lang(self, new_src_lang: str) -> None:
        self._src_lang = new_src_lang
        self.set_src_lang_special_tokens(self._src_lang)

    def _build_translation_inputs(
        self, raw_inputs, return_tensors: str, src_lang: str | None, tgt_lang: str | None, **extra_kwargs
    ):
        """Used by translation pipeline, to prepare inputs for the generate function"""
        if src_lang is None or tgt_lang is None:
            raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
        self.src_lang = src_lang
        inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
        tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
        inputs["forced_bos_token_id"] = tgt_lang_id
        return inputs

    def _switch_to_input_mode(self):
        return self.set_src_lang_special_tokens(self.src_lang)

    def _switch_to_target_mode(self):
        if self.tgt_lang is None:
            self.tgt_lang = self._src_lang
        return self.set_tgt_lang_special_tokens(self.tgt_lang)

    def set_src_lang_special_tokens(self, src_lang) -> None:
        """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""
        self.cur_lang_code = self.convert_tokens_to_ids(src_lang)
        self.prefix_tokens = []
        self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]

        prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
        suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)

        self._tokenizer.post_processor = processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
            pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
            special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
        )

    def set_tgt_lang_special_tokens(self, lang: str) -> None:
        """Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code]."""
        self.cur_lang_code = self.convert_tokens_to_ids(lang)
        self.prefix_tokens = []
        self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]

        prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
        suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)

        self._tokenizer.post_processor = processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
            pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
            special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
        )


__all__ = ["MBartTokenizer"]