wehub-resource-sync
358 lines
12 KiB
Python
Executable File
358 lines
12 KiB
Python
Executable File
# 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
|
|
#
|
|
# 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 shutil
|
|
|
|
from paddle.utils import try_import
|
|
from .. import PretrainedTokenizer
|
|
from paddlenlp.utils.log import logger
|
|
|
|
__all__ = ["CTRLTokenizer"]
|
|
|
|
CONTROL_CODES = {
|
|
"Pregnancy": 168629,
|
|
"Christianity": 7675,
|
|
"Explain": 106423,
|
|
"Fitness": 63440,
|
|
"Saving": 63163,
|
|
"Ask": 27171,
|
|
"Ass": 95985,
|
|
"Joke": 163509,
|
|
"Questions": 45622,
|
|
"Thoughts": 49605,
|
|
"Retail": 52342,
|
|
"Feminism": 164338,
|
|
"Writing": 11992,
|
|
"Atheism": 192263,
|
|
"Netflix": 48616,
|
|
"Computing": 39639,
|
|
"Opinion": 43213,
|
|
"Alone": 44967,
|
|
"Funny": 58917,
|
|
"Gaming": 40358,
|
|
"Human": 4088,
|
|
"India": 1331,
|
|
"Joker": 77138,
|
|
"Diet": 36206,
|
|
"Legal": 11859,
|
|
"Norman": 4939,
|
|
"Tip": 72689,
|
|
"Weight": 52343,
|
|
"Movies": 46273,
|
|
"Running": 23425,
|
|
"Science": 2090,
|
|
"Horror": 37793,
|
|
"Confession": 60572,
|
|
"Finance": 12250,
|
|
"Politics": 16360,
|
|
"Scary": 191985,
|
|
"Support": 12654,
|
|
"Technologies": 32516,
|
|
"Teenage": 66160,
|
|
"Event": 32769,
|
|
"Learned": 67460,
|
|
"Notion": 182770,
|
|
"Wikipedia": 37583,
|
|
"Books": 6665,
|
|
"Extract": 76050,
|
|
"Confessions": 102701,
|
|
"Conspiracy": 75932,
|
|
"Links": 63674,
|
|
"Narcissus": 150425,
|
|
"Relationship": 54766,
|
|
"Relationships": 134796,
|
|
"Reviews": 41671,
|
|
"News": 4256,
|
|
"Translation": 26820,
|
|
"multilingual": 128406,
|
|
}
|
|
|
|
|
|
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
|
|
|
|
|
|
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": "http://bj.bcebos.com/paddlenlp/models/transformers/ctrl/vocab.json",
|
|
"sshleifer-tiny-ctrl": "http://bj.bcebos.com/paddlenlp/models/transformers/sshleifer-tiny-ctrl/vocab.json",
|
|
},
|
|
"merges_file": {
|
|
"ctrl": "http://bj.bcebos.com/paddlenlp/models/transformers/ctrl/merges.txt",
|
|
"sshleifer-tiny-ctrl": "http://bj.bcebos.com/paddlenlp/models/transformers/sshleifer-tiny-ctrl/merges.txt",
|
|
},
|
|
}
|
|
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 = merges_handle.read().split("\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)
|
|
|
|
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
|
|
|
|
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)
|
|
|
|
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
|
|
|
|
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
|
|
|
|
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
|
|
|
|
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)
|