Source code for paddlenlp.transformers.ctrl.tokenizer

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 Salesforce 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
# 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.

import json
import os
import shutil

from paddle.utils import try_import
from .. import PretrainedTokenizer
from paddlenlp.utils.log import logger

__all__ = ["CTRLTokenizer"]

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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 CTRLTokenizer(PretrainedTokenizer): """ Constructs a CTRL tokenizer based on byte-level Byte-Pair-Encoding. This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer` which contains most of the main methods. For more information regarding those methods, please refer to this superclass. Args: vocab_file (str): Path to the vocab file. The vocab file contains a mapping from vocabulary strings to indices. merges_file (str): Path to the merge file. The merge file is used to split the input sentence into "subword" units. The vocab file is then used to encode those units as intices. max_len (int, optional): The maximum value of the input sequence length. Defaults to `None`. unk_token (str): A special token representing the *unknown (out-of-vocabulary)* token. An unknown token is set to be `unk_token` inorder to be converted to an ID. Defaults to "<unk>". """ resource_files_names = { "vocab_file": "vocab.json", "merges_file": "merges.txt", } pretrained_resource_files_map = { "vocab_file": { "ctrl": "", "sshleifer-tiny-ctrl": "", }, "merges_file": { "ctrl": "", "sshleifer-tiny-ctrl": "", }, } pretrained_init_configuration = {"ctrl": {}, "sshleifer-tiny-ctrl": {"max_len": 256}} CONTROL_CODES = CONTROL_CODES def __init__(self, vocab_file, merges_file, max_len=None, unk_token="<unk>", **kwargs): self._vocab_file = vocab_file self._merges_file = merges_file self.max_len = max_len if max_len is not None else int(1e12) 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): return len(self.encoder)
[docs] def get_vocab(self): return dict(self.encoder)
def __len__(self): return len(self.encoder) def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token) word = tuple(list(word[:-1]) + [word[-1] + "</w>"]) pairs = get_pairs(word) if not pairs: return token 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) word = word[:-4] self.cache[token] = word return word
[docs] def tokenize(self, text): """ Converts a string to a list of tokens. Args: text (str): The text to be tokenized. Returns: List[str]: A list of string representing converted tokens. Example: .. code-block:: from paddlenlp.transformers import CTRLTokenizer tokenizer = CTRLTokenizer.from_pretrained('ctrl') print(tokenizer.tokenize('Welcome to use PaddlePaddle and PaddleNLP')) # ['Welcome', 'to', 'use', 'Padd@@', 'le@@', 'Padd@@', 'le', 'and', 'Padd@@', 'le@@', 'N@@', 'LP'] """ return self._tokenize(text)
def _tokenize(self, text): """Tokenize a string.""" split_tokens = [] re = try_import("regex") 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): """Converts a token (str) to an id using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index): """Converts an index (integer) to a token (str) using the vocab.""" return self.decoder.get(index, self.unk_token)
[docs] def convert_tokens_to_string(self, tokens): """ Converts a sequence of tokens (list of string) to a single string. Args: tokens (List[str]): A sequence of tokens. Returns: str: Converted string. Example: .. code-block:: from paddlenlp.transformers import CTRLTokenizer tokenizer = CTRLTokenizer.from_pretrained('crtl') print(tokenizer.convert_tokens_to_string(['Welcome', 'to', 'use', 'Padd@@', 'le@@', 'Padd@@', 'le', 'and', 'Padd@@', 'le@@', 'N@@', 'LP'])) # 'Welcome to use PaddlePaddle and PaddleNLP' """ out_string = " ".join(tokens).replace("@@ ", "").strip() return out_string
[docs] def convert_tokens_to_ids(self, tokens): """ Converts a single token or a sequence of tokens to an index or a sequence of indices using the vocab. Args: tokens (str|List[str]|tuple(str)): A single token or a sequence of tokens. Returns: int|List[int]: The converted token id or token ids. Example: .. code-block:: from paddlenlp.transformers import CTRLTokenizer tokenizer = CTRLTokenizer.from_pretrained('crtl') print(tokenizer.convert_tokens_to_ids(['Welcome', 'to', 'use', 'Padd@@', 'le@@', 'Padd@@', 'le', 'and', 'Padd@@', 'le@@', 'N@@', 'LP'])) # [41116, 3, 191, 40324, 1162, 40324, 992, 2, 40324, 1162, 633, 11135] """ ids = [] if isinstance(tokens, str): return self._convert_token_to_id(tokens) for token in tokens: ids.append(self._convert_token_to_id(token)) if len(ids) > self.max_len: logger.warning( "Token indices sequence length is longer than the specified maximum " " sequence length for this CTRL model ({} > {}). Running this" " sequence through the model will result in indexing errors".format(len(ids), self.max_len) ) return ids
[docs] def convert_ids_to_tokens(self, ids, skip_special_tokens=False): """ Converts an index or a sequence indices to a single token or a sequence of tokens. Args: ids (int|List[int]): The token id (or token ids) to be converted to text. skip_special_tokens (bool, optional): Whether or not to skip the special tokens. Defaults to `False`, which means we don't skip the special tokens. Returns: str|List[str]: The converted token or the sequence of tokens. Example: .. code-block:: from paddlenlp.transformers import CTRLTokenizer tokenizer = CTRLTokenizer.from_pretrained('ctrl') print(tokenizer.convert_ids_to_tokens([41116, 3, 191, 40324, 1162, 40324, 992, 2, 40324, 1162, 633, 11135])) # ['Welcome', 'to', 'use', 'Padd@@', 'le@@', 'Padd@@', 'le', 'and', 'Padd@@', 'le@@', 'N@@', 'LP'] """ if isinstance(ids, int): return self._convert_id_to_token(ids) tokens = [] for index in ids: index = int(index) if skip_special_tokens and index in self.all_special_ids: continue tokens.append(self._convert_id_to_token(index)) return tokens
[docs] def save_resources(self, save_directory): """ Save tokenizer related resources to files under `save_directory`. Args: save_directory (str): Directory to save files into. """ for name, file_name in self.resource_files_names.items(): source_path = getattr(self, "_%s" % name) save_path = os.path.join(save_directory, file_name) if os.path.abspath(source_path) != os.path.abspath(save_path): shutil.copyfile(source_path, save_path)