ai-content-maker/.venv/Lib/site-packages/transformers/models/herbert/tokenization_herbert.py

# coding=utf-8
# Copyright 2020 The Google AI Language Team Authors, Allegro.pl, Facebook Inc. 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.
import json
import os
import re
import unicodedata
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


logger = logging.get_logger(__name__)

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


# Copied from transformers.models.xlm.tokenization_xlm.get_pairs
def get_pairs(word):
    """
    Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length
    strings)
    """
    pairs = set()
    prev_char = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char))
        prev_char = char
    return pairs


# Copied from transformers.models.xlm.tokenization_xlm.replace_unicode_punct
def replace_unicode_punct(text):
    """
    Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl
    """
    text = text.replace("，", ",")
    text = re.sub(r"。\s*", ". ", text)
    text = text.replace("、", ",")
    text = text.replace("”", '"')
    text = text.replace("“", '"')
    text = text.replace("∶", ":")
    text = text.replace("：", ":")
    text = text.replace("？", "?")
    text = text.replace("《", '"')
    text = text.replace("》", '"')
    text = text.replace("）", ")")
    text = text.replace("！", "!")
    text = text.replace("（", "(")
    text = text.replace("；", ";")
    text = text.replace("１", "1")
    text = text.replace("」", '"')
    text = text.replace("「", '"')
    text = text.replace("０", "0")
    text = text.replace("３", "3")
    text = text.replace("２", "2")
    text = text.replace("５", "5")
    text = text.replace("６", "6")
    text = text.replace("９", "9")
    text = text.replace("７", "7")
    text = text.replace("８", "8")
    text = text.replace("４", "4")
    text = re.sub(r"．\s*", ". ", text)
    text = text.replace("～", "~")
    text = text.replace("’", "'")
    text = text.replace("…", "...")
    text = text.replace("━", "-")
    text = text.replace("〈", "<")
    text = text.replace("〉", ">")
    text = text.replace("【", "[")
    text = text.replace("】", "]")
    text = text.replace("％", "%")
    return text


# Copied from transformers.models.xlm.tokenization_xlm.remove_non_printing_char
def remove_non_printing_char(text):
    """
    Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/remove-non-printing-char.perl
    """
    output = []
    for char in text:
        cat = unicodedata.category(char)
        if cat.startswith("C"):
            continue
        output.append(char)
    return "".join(output)


# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize
def whitespace_tokenize(text):
    """Runs basic whitespace cleaning and splitting on a piece of text."""
    text = text.strip()
    if not text:
        return []
    tokens = text.split()
    return tokens


# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
class BasicTokenizer(object):
    """
    Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).

    Args:
        do_lower_case (`bool`, *optional*, defaults to `True`):
            Whether or not to lowercase the input when tokenizing.
        never_split (`Iterable`, *optional*):
            Collection of tokens which will never be split during tokenization. Only has an effect when
            `do_basic_tokenize=True`
        tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
            Whether or not to tokenize Chinese characters.

            This should likely be deactivated for Japanese (see this
            [issue](https://github.com/huggingface/transformers/issues/328)).
        strip_accents (`bool`, *optional*):
            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
            value for `lowercase` (as in the original BERT).
        do_split_on_punc (`bool`, *optional*, defaults to `True`):
            In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
            the full context of the words, such as contractions.
    """

    def __init__(
        self,
        do_lower_case=True,
        never_split=None,
        tokenize_chinese_chars=True,
        strip_accents=None,
        do_split_on_punc=True,
    ):
        if never_split is None:
            never_split = []
        self.do_lower_case = do_lower_case
        self.never_split = set(never_split)
        self.tokenize_chinese_chars = tokenize_chinese_chars
        self.strip_accents = strip_accents
        self.do_split_on_punc = do_split_on_punc

    def tokenize(self, text, never_split=None):
        """
        Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.

        Args:
            never_split (`List[str]`, *optional*)
                Kept for backward compatibility purposes. Now implemented directly at the base class level (see
                [`PreTrainedTokenizer.tokenize`]) List of token not to split.
        """
        # union() returns a new set by concatenating the two sets.
        never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
        text = self._clean_text(text)

        # This was added on November 1st, 2018 for the multilingual and Chinese
        # models. This is also applied to the English models now, but it doesn't
        # matter since the English models were not trained on any Chinese data
        # and generally don't have any Chinese data in them (there are Chinese
        # characters in the vocabulary because Wikipedia does have some Chinese
        # words in the English Wikipedia.).
        if self.tokenize_chinese_chars:
            text = self._tokenize_chinese_chars(text)
        # prevents treating the same character with different unicode codepoints as different characters
        unicode_normalized_text = unicodedata.normalize("NFC", text)
        orig_tokens = whitespace_tokenize(unicode_normalized_text)
        split_tokens = []
        for token in orig_tokens:
            if token not in never_split:
                if self.do_lower_case:
                    token = token.lower()
                    if self.strip_accents is not False:
                        token = self._run_strip_accents(token)
                elif self.strip_accents:
                    token = self._run_strip_accents(token)
            split_tokens.extend(self._run_split_on_punc(token, never_split))

        output_tokens = whitespace_tokenize(" ".join(split_tokens))
        return output_tokens

    def _run_strip_accents(self, text):
        """Strips accents from a piece of text."""
        text = unicodedata.normalize("NFD", text)
        output = []
        for char in text:
            cat = unicodedata.category(char)
            if cat == "Mn":
                continue
            output.append(char)
        return "".join(output)

    def _run_split_on_punc(self, text, never_split=None):
        """Splits punctuation on a piece of text."""
        if not self.do_split_on_punc or (never_split is not None and text in never_split):
            return [text]
        chars = list(text)
        i = 0
        start_new_word = True
        output = []
        while i < len(chars):
            char = chars[i]
            if _is_punctuation(char):
                output.append([char])
                start_new_word = True
            else:
                if start_new_word:
                    output.append([])
                start_new_word = False
                output[-1].append(char)
            i += 1

        return ["".join(x) for x in output]

    def _tokenize_chinese_chars(self, text):
        """Adds whitespace around any CJK character."""
        output = []
        for char in text:
            cp = ord(char)
            if self._is_chinese_char(cp):
                output.append(" ")
                output.append(char)
                output.append(" ")
            else:
                output.append(char)
        return "".join(output)

    def _is_chinese_char(self, cp):
        """Checks whether CP is the codepoint of a CJK character."""
        # This defines a "chinese character" as anything in the CJK Unicode block:
        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
        #
        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
        # despite its name. The modern Korean Hangul alphabet is a different block,
        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
        # space-separated words, so they are not treated specially and handled
        # like the all of the other languages.
        if (
            (cp >= 0x4E00 and cp <= 0x9FFF)
            or (cp >= 0x3400 and cp <= 0x4DBF)  #
            or (cp >= 0x20000 and cp <= 0x2A6DF)  #
            or (cp >= 0x2A700 and cp <= 0x2B73F)  #
            or (cp >= 0x2B740 and cp <= 0x2B81F)  #
            or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
            or (cp >= 0xF900 and cp <= 0xFAFF)
            or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
        ):  #
            return True

        return False

    def _clean_text(self, text):
        """Performs invalid character removal and whitespace cleanup on text."""
        output = []
        for char in text:
            cp = ord(char)
            if cp == 0 or cp == 0xFFFD or _is_control(char):
                continue
            if _is_whitespace(char):
                output.append(" ")
            else:
                output.append(char)
        return "".join(output)


class HerbertTokenizer(PreTrainedTokenizer):
    """
    Construct a BPE tokenizer for HerBERT.

    Peculiarities:

    - uses BERT's pre-tokenizer: BaseTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of a
      punctuation character will be treated separately.

    - Such pretokenized input is BPE subtokenized

    This tokenizer inherits from [`XLMTokenizer`] which contains most of the methods. Users should refer to the
    superclass for more information regarding methods.
    """

    vocab_files_names = VOCAB_FILES_NAMES

    def __init__(
        self,
        vocab_file,
        merges_file,
        tokenizer_file=None,
        cls_token="<s>",
        unk_token="<unk>",
        pad_token="<pad>",
        mask_token="<mask>",
        sep_token="</s>",
        bos_token="<s>",
        do_lowercase_and_remove_accent=False,
        additional_special_tokens=[
            "<special0>",
            "<special1>",
            "<special2>",
            "<special3>",
            "<special4>",
            "<special5>",
            "<special6>",
            "<special7>",
            "<special8>",
            "<special9>",
        ],
        lang2id=None,
        id2lang=None,
        **kwargs,
    ):
        try:
            import sacremoses
        except ImportError:
            raise ImportError(
                "You need to install sacremoses to use HerbertTokenizer. "
                "See https://pypi.org/project/sacremoses/ for installation."
            )

        self.sm = sacremoses

        # cache of sm.MosesPunctNormalizer instance
        self.cache_moses_punct_normalizer = {}
        # cache of sm.MosesTokenizer instance
        self.cache_moses_tokenizer = {}
        self.lang_with_custom_tokenizer = {"zh", "th", "ja"}
        # True for current supported model (v1.2.0), False for XLM-17 & 100
        self.do_lowercase_and_remove_accent = do_lowercase_and_remove_accent
        self.lang2id = lang2id
        self.id2lang = id2lang
        if lang2id is not None and id2lang is not None:
            assert len(lang2id) == len(id2lang)

        self.ja_word_tokenizer = None
        self.zh_word_tokenizer = None

        with open(vocab_file, encoding="utf-8") as vocab_handle:
            self.encoder = json.load(vocab_handle)
        self.decoder = {v: k for k, v in self.encoder.items()}
        with open(merges_file, encoding="utf-8") as merges_handle:
            merges = merges_handle.read().split("\n")[:-1]
        merges = [tuple(merge.split()[:2]) for merge in merges]
        self.bpe_ranks = dict(zip(merges, range(len(merges))))
        self.cache = {}

        super().__init__(
            unk_token=unk_token,
            bos_token=bos_token,
            sep_token=sep_token,
            pad_token=pad_token,
            cls_token=cls_token,
            mask_token=mask_token,
            additional_special_tokens=additional_special_tokens,
            lang2id=lang2id,
            id2lang=id2lang,
            do_lowercase_and_remove_accent=do_lowercase_and_remove_accent,
            tokenizer_file=None,
            **kwargs,
        )

        self.bert_pre_tokenizer = BasicTokenizer(
            do_lower_case=False,
            never_split=self.all_special_tokens,
            tokenize_chinese_chars=False,
            strip_accents=False,
        )

    @property
    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.do_lower_case
    def do_lower_case(self):
        return self.do_lowercase_and_remove_accent

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_punct_norm
    def moses_punct_norm(self, text, lang):
        if lang not in self.cache_moses_punct_normalizer:
            punct_normalizer = self.sm.MosesPunctNormalizer(lang=lang)
            self.cache_moses_punct_normalizer[lang] = punct_normalizer
        else:
            punct_normalizer = self.cache_moses_punct_normalizer[lang]
        return punct_normalizer.normalize(text)

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_tokenize
    def moses_tokenize(self, text, lang):
        if lang not in self.cache_moses_tokenizer:
            moses_tokenizer = self.sm.MosesTokenizer(lang=lang)
            self.cache_moses_tokenizer[lang] = moses_tokenizer
        else:
            moses_tokenizer = self.cache_moses_tokenizer[lang]
        return moses_tokenizer.tokenize(text, return_str=False, escape=False)

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_pipeline
    def moses_pipeline(self, text, lang):
        text = replace_unicode_punct(text)
        text = self.moses_punct_norm(text, lang)
        text = remove_non_printing_char(text)
        return text

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.ja_tokenize
    def ja_tokenize(self, text):
        if self.ja_word_tokenizer is None:
            try:
                import Mykytea

                self.ja_word_tokenizer = Mykytea.Mykytea(
                    f"-model {os.path.expanduser('~')}/local/share/kytea/model.bin"
                )
            except (AttributeError, ImportError):
                logger.error(
                    "Make sure you install KyTea (https://github.com/neubig/kytea) and it's python wrapper"
                    " (https://github.com/chezou/Mykytea-python) with the following steps"
                )
                logger.error("1. git clone git@github.com:neubig/kytea.git && cd kytea")
                logger.error("2. autoreconf -i")
                logger.error("3. ./configure --prefix=$HOME/local")
                logger.error("4. make && make install")
                logger.error("5. pip install kytea")
                raise
        return list(self.ja_word_tokenizer.getWS(text))

    @property
    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.vocab_size
    def vocab_size(self):
        return len(self.encoder)

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.get_vocab
    def get_vocab(self):
        return dict(self.encoder, **self.added_tokens_encoder)

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.bpe
    def bpe(self, token):
        word = tuple(token[:-1]) + (token[-1] + "</w>",)
        if token in self.cache:
            return self.cache[token]
        pairs = get_pairs(word)

        if not pairs:
            return token + "</w>"

        while True:
            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
            if bigram not in self.bpe_ranks:
                break
            first, second = bigram
            new_word = []
            i = 0
            while i < len(word):
                try:
                    j = word.index(first, i)
                except ValueError:
                    new_word.extend(word[i:])
                    break
                else:
                    new_word.extend(word[i:j])
                    i = j

                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
                    new_word.append(first + second)
                    i += 2
                else:
                    new_word.append(word[i])
                    i += 1
            new_word = tuple(new_word)
            word = new_word
            if len(word) == 1:
                break
            else:
                pairs = get_pairs(word)
        word = " ".join(word)
        if word == "\n  </w>":
            word = "\n</w>"
        self.cache[token] = word
        return word

    def _tokenize(self, text):
        pre_tokens = self.bert_pre_tokenizer.tokenize(text)

        split_tokens = []
        for token in pre_tokens:
            if token:
                split_tokens.extend(list(self.bpe(token).split(" ")))

        return split_tokens

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer._convert_token_to_id
    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        return self.encoder.get(token, self.encoder.get(self.unk_token))

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer._convert_id_to_token
    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        return self.decoder.get(index, self.unk_token)

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.convert_tokens_to_string
    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        out_string = "".join(tokens).replace("</w>", " ").strip()
        return out_string

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.build_inputs_with_special_tokens
    def build_inputs_with_special_tokens(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
        adding special tokens. An XLM sequence has the following format:

        - single sequence: `<s> X </s>`
        - pair of sequences: `<s> A </s> B </s>`

        Args:
            token_ids_0 (`List[int]`):
                List of IDs to which the special tokens will be added.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.

        Returns:
            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.

        """
        bos = [self.bos_token_id]
        sep = [self.sep_token_id]

        if token_ids_1 is None:
            return bos + token_ids_0 + sep
        return bos + token_ids_0 + sep + token_ids_1 + sep

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.get_special_tokens_mask
    def get_special_tokens_mask(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
        """
        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
        special tokens using the tokenizer `prepare_for_model` method.

        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.
            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not the token list is already formatted with special tokens for the model.

        Returns:
            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """

        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
            )

        if token_ids_1 is not None:
            return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
        return [1] + ([0] * len(token_ids_0)) + [1]

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.create_token_type_ids_from_sequences
    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLM sequence
        pair mask has the following format:

        ```
        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
        | first sequence    | second sequence |
        ```

        If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).

        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.

        Returns:
            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
        """
        sep = [self.sep_token_id]
        cls = [self.cls_token_id]
        if token_ids_1 is None:
            return len(cls + token_ids_0 + sep) * [0]
        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.save_vocabulary
    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return
        vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )
        merge_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
        )

        with open(vocab_file, "w", encoding="utf-8") as f:
            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")

        index = 0
        with open(merge_file, "w", encoding="utf-8") as writer:
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
                if index != token_index:
                    logger.warning(
                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                        " Please check that the tokenizer is not corrupted!"
                    )
                    index = token_index
                writer.write(" ".join(bpe_tokens) + "\n")
                index += 1

        return vocab_file, merge_file

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.__getstate__
    def __getstate__(self):
        state = self.__dict__.copy()
        state["sm"] = None
        return state

    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.__setstate__
    def __setstate__(self, d):
        self.__dict__ = d

        try:
            import sacremoses
        except ImportError:
            raise ImportError(
                "You need to install sacremoses to use XLMTokenizer. "
                "See https://pypi.org/project/sacremoses/ for installation."
            )

        self.sm = sacremoses