250 lines
7.9 KiB
Python
250 lines
7.9 KiB
Python
# coding=utf-8
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# Copyright 2018 Salesforce and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes for Salesforce CTRL."""
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import json
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import os
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from typing import Optional, Tuple
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import regex as re
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from ...tokenization_utils import PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.json",
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"merges_file": "merges.txt",
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}
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CONTROL_CODES = {
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"Pregnancy": 168629,
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"Christianity": 7675,
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"Explain": 106423,
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"Fitness": 63440,
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"Saving": 63163,
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"Ask": 27171,
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"Ass": 95985,
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"Joke": 163509,
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"Questions": 45622,
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"Thoughts": 49605,
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"Retail": 52342,
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"Feminism": 164338,
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"Writing": 11992,
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"Atheism": 192263,
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"Netflix": 48616,
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"Computing": 39639,
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"Opinion": 43213,
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"Alone": 44967,
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"Funny": 58917,
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"Gaming": 40358,
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"Human": 4088,
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"India": 1331,
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"Joker": 77138,
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"Diet": 36206,
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"Legal": 11859,
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"Norman": 4939,
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"Tip": 72689,
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"Weight": 52343,
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"Movies": 46273,
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"Running": 23425,
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"Science": 2090,
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"Horror": 37793,
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"Confession": 60572,
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"Finance": 12250,
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"Politics": 16360,
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"Scary": 191985,
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"Support": 12654,
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"Technologies": 32516,
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"Teenage": 66160,
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"Event": 32769,
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"Learned": 67460,
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"Notion": 182770,
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"Wikipedia": 37583,
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"Books": 6665,
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"Extract": 76050,
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"Confessions": 102701,
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"Conspiracy": 75932,
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"Links": 63674,
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"Narcissus": 150425,
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"Relationship": 54766,
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"Relationships": 134796,
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"Reviews": 41671,
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"News": 4256,
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"Translation": 26820,
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"multilingual": 128406,
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}
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def get_pairs(word):
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"""
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Return set of symbol pairs in a word.
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Word is represented as tuple of symbols (symbols being variable-length strings).
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"""
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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pairs = set(pairs)
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return pairs
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class CTRLTokenizer(PreTrainedTokenizer):
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"""
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Construct a CTRL tokenizer. Based on Byte-Pair-Encoding.
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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merges_file (`str`):
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Path to the merges file.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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control_codes = CONTROL_CODES
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def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs):
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with open(vocab_file, encoding="utf-8") as vocab_handle:
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self.encoder = json.load(vocab_handle)
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self.decoder = {v: k for k, v in self.encoder.items()}
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with open(merges_file, encoding="utf-8") as merges_handle:
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merges = merges_handle.read().split("\n")[1:-1]
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merges = [tuple(merge.split()) for merge in merges]
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self.bpe_ranks = dict(zip(merges, range(len(merges))))
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self.cache = {}
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super().__init__(unk_token=unk_token, **kwargs)
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@property
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def vocab_size(self):
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return len(self.encoder)
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def get_vocab(self):
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return dict(self.encoder, **self.added_tokens_encoder)
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def bpe(self, token):
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if token in self.cache:
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return self.cache[token]
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word = tuple(token)
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word = tuple(list(word[:-1]) + [word[-1] + "</w>"])
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pairs = get_pairs(word)
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if not pairs:
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return token
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while True:
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
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if bigram not in self.bpe_ranks:
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break
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first, second = bigram
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new_word = []
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i = 0
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while i < len(word):
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try:
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j = word.index(first, i)
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except ValueError:
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new_word.extend(word[i:])
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break
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else:
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new_word.extend(word[i:j])
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i = j
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if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
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new_word.append(first + second)
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i += 2
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else:
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new_word.append(word[i])
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i += 1
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
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word = "@@ ".join(word)
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word = word[:-4]
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self.cache[token] = word
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return word
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def _tokenize(self, text):
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"""Tokenize a string."""
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split_tokens = []
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words = re.findall(r"\S+\n?", text)
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for token in words:
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split_tokens.extend(list(self.bpe(token).split(" ")))
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return split_tokens
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.encoder.get(token, self.encoder.get(self.unk_token))
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.decoder.get(index, self.unk_token)
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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out_string = " ".join(tokens).replace("@@ ", "").strip()
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return out_string
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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if not os.path.isdir(save_directory):
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logger.error(f"Vocabulary path ({save_directory}) should be a directory")
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return
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vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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merge_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
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)
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with open(vocab_file, "w", encoding="utf-8") as f:
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f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
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index = 0
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with open(merge_file, "w", encoding="utf-8") as writer:
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writer.write("#version: 0.2\n")
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for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
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if index != token_index:
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logger.warning(
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f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
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" Please check that the tokenizer is not corrupted!"
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)
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index = token_index
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writer.write(" ".join(bpe_tokens) + "\n")
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index += 1
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return vocab_file, merge_file
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# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
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# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
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# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
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# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
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# return ''.join(tokens_generated_so_far)
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