Source code for transformers.models.blenderbot_small.tokenization_blenderbot_small

# coding=utf-8
# Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved.
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization class for BlenderbotSmall."""

import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging

logger = logging.get_logger(__name__)

    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",

    "vocab_file": {
        "facebook/blenderbot_small-90M": ""
    "merges_file": {
        "facebook/blenderbot_small-90M": ""
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": ""

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/blenderbot_small-90M": 512}

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

    pairs = set(pairs)
    return pairs

[docs]class BlenderbotSmallTokenizer(PreTrainedTokenizer): """ Constructs a Blenderbot-90M tokenizer based on BPE (Byte-Pair-Encoding) This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods. Users should refer to the superclass for more information regarding methods. Args: vocab_file (:obj:`str`): File containing the vocabulary. merges_file (:obj:`str`): Path to the merges file. bos_token (:obj:`str`, `optional`, defaults to :obj:`"__start__"`): The beginning of sentence token. eos_token (:obj:`str`, `optional`, defaults to :obj:`"__end__"`): The end of sentence token. 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. pad_token (:obj:`str`, `optional`, defaults to :obj:`"__pad__"`): The token used for padding, for example when batching sequences of different lengths. **kwargs Additional keyword arguments passed along to :class:`~transformers.PreTrainedTokenizer` """ 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 = ["input_ids", "attention_mask"] def __init__( self, vocab_file, merges_file, bos_token="__start__", eos_token="__end__", unk_token="__unk__", pad_token="__null__", **kwargs ): super().__init__(unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, **kwargs) 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 ="\n")[1:-1] merges = [tuple(merge.split()) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} @property def vocab_size(self) -> int: return len(self.encoder) def get_vocab(self) -> Dict: return dict(self.encoder, **self.added_tokens_encoder) def bpe(self, token: str) -> str: if token in self.cache: return self.cache[token] token = re.sub("([.,!?()])", r" \1", token) token = re.sub("(')", r" \1 ", token) token = re.sub(r"\s{2,}", " ", token) if "\n" in token: token = token.replace("\n", " __newln__") tokens = token.split(" ") words = [] for token in tokens: if not len(token): continue token = token.lower() word = tuple(token) word = tuple(list(word[:-1]) + [word[-1] + "</w>"]) pairs = get_pairs(word) if not pairs: words.append(token) continue 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) new_word.extend(word[i:j]) i = j except ValueError: new_word.extend(word[i:]) break 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) word = word[:-4] self.cache[token] = word words.append(word) return " ".join(words) def _tokenize(self, text: str) -> List[str]: """Split a string into tokens using BPE.""" split_tokens = [] words = re.findall(r"\S+\n?", text) for token in words: split_tokens.extend([t for t in self.bpe(token).split(" ")]) return split_tokens def _convert_token_to_id(self, token: str) -> int: """Converts a token to an id using the vocab.""" token = token.lower() return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" return self.decoder.get(index, self.unk_token) def convert_tokens_to_string(self, tokens: List[str]) -> str: """Converts a sequence of tokens in a single string.""" out_string = " ".join(tokens).replace("@@ ", "").strip() return out_string
[docs] 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, ensure_ascii=False)) index = 0 with open(merge_file, "w", encoding="utf-8") as writer: writer.write("#version: 0.2\n") 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