# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) 2020 PaddlePaddle Authors. 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
#
# 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.
from __future__ import annotations
import bisect
import io
import itertools
import json
import os
import re
import unicodedata
from collections import OrderedDict
from dataclasses import asdict, dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy
import numpy as np
import paddle
import six
from jinja2.exceptions import TemplateError
from jinja2.sandbox import ImmutableSandboxedEnvironment
from paddle.utils import try_import
from paddlenlp.utils.env import CHAT_TEMPLATE_CONFIG_NAME
from paddlenlp.utils.log import logger
try:
from functools import lru_cache
except ImportError:
from backports.functools_lru_cache import lru_cache
from ..data.vocab import Vocab
from .tokenizer_utils_base import (
AddedToken,
BatchEncoding,
EncodedInput,
EncodedInputPair,
PaddingStrategy,
PreTokenizedInput,
PreTokenizedInputPair,
PretrainedTokenizerBase,
TensorType,
TextInput,
TextInputPair,
TruncationStrategy,
)
from .utils import InitTrackerMeta, fn_args_to_dict
__all__ = [
"PretrainedTokenizer",
"BPETokenizer",
"tokenize_chinese_chars",
"is_chinese_char",
"normalize_chars",
"tokenize_special_chars",
"convert_to_unicode",
]
[文档]def convert_to_unicode(text):
"""
Converts `text` to Unicode (if it's not already), assuming utf-8 input.
Args:
text (str|bytes): Text to be converted to unicode.
Returns:
str: converted text.
"""
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
def whitespace_tokenize(text):
"""
Runs basic whitespace cleaning and splitting on a peice of text.
Args:
text (str): Text to be tokenized.
Returns:
list(str): Token list.
"""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
def _is_whitespace(char):
"""
Checks whether `chars` is a whitespace character.
"""
# \t, \n, and \r are technically contorl characters but we treat them
# as whitespace since they are generally considered as such.
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False
def _is_control(char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat.startswith("C"):
return True
return False
def _is_punctuation(char):
"""Checks whether `chars` is a punctuation character."""
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
cat = unicodedata.category(char)
if cat.startswith("P"):
return True
return False
def _is_end_of_word(text):
"""Checks whether the last character in text is one of a punctuation, control or whitespace character."""
last_char = text[-1]
return bool(_is_control(last_char) | _is_punctuation(last_char) | _is_whitespace(last_char))
def _is_start_of_word(text):
"""Checks whether the first character in text is one of a punctuation, control or whitespace character."""
first_char = text[0]
return bool(_is_control(first_char) | _is_punctuation(first_char) | _is_whitespace(first_char))
def _insert_one_token_to_ordered_list(token_list: List[str], new_token: str):
"""
Inserts one token to an ordered list if it does not already exist. Note: token_list must be sorted.
"""
insertion_idx = bisect.bisect_left(token_list, new_token)
# Checks if new_token is already in the ordered token_list
if insertion_idx < len(token_list) and token_list[insertion_idx] == new_token:
# new_token is in token_list, don't add
return
else:
token_list.insert(insertion_idx, new_token)
[文档]def is_chinese_char(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 _is_nonnormalized_char(char):
"""Check whther `chars` is a non-normalized character."""
cp = ord(char)
if (
(0xFF00 <= cp <= 0xFFEF)
or (0xFE50 <= cp <= 0xFE6B) # Halfwidth and Fullwidth Forms
or (0x3358 <= cp <= 0x33FF) # Small Form Variants
or (0x249C <= cp <= 0x24E9) # CJK Compatibility
or (0x3200 <= cp <= 0x32FF) # Enclosed Alphanumerics: Ⓛ ⒰
): # Enclosed CJK Letters and Months
return True
return False
def _is_nonnormalized_numeric(char):
"""Check whether `chars` is a non-normalized numeric character."""
cp = ord(char)
if (
(0x2460 <= cp <= 0x249B)
or (0x24EA <= cp <= 0x24FF) #
or (0x2776 <= cp <= 0x2793) #
or (0x2160 <= cp <= 0x217F) # Enclosed Alphanumerics
): # Number Forms
return True
return False
[文档]def normalize_chars(text):
"""
Normalize the text for multiligual and chinese models. Unicode range:
https://www.ling.upenn.edu/courses/Spring_2003/ling538/UnicodeRanges.html
"""
output = []
for char in text:
if _is_nonnormalized_char(char):
for c in unicodedata.normalize("NFKC", char):
output.append(c)
elif _is_nonnormalized_numeric(char):
output.append(" ")
for c in str(int(unicodedata.numeric(char))):
output.append(c)
output.append(" ")
elif ord(char) == 0xF979: # https://www.zhihu.com/question/20697984
output.append("凉")
else:
output.append(char)
return "".join(output)
def _is_symbol(char):
"""Check whether CP is the codepoint of a Symbol character."""
cp = ord(char)
if unicodedata.category(char).startswith("S") or (
cp in [0x00AD, 0x00B2, 0x00BA, 0x3007, 0x00B5, 0x00D8, 0x014B, 0x01B1]
):
return True
return False
[文档]def tokenize_special_chars(text):
"""Adds whitespace around any special character."""
output = []
for char in text:
cp = ord(char)
if (
(0x3040 <= cp <= 0x30FF)
or (0x0370 <= cp <= 0x04FF) # Japanese
or (0x0250 <= cp <= 0x02AF) # Greek/Coptic & Cyrillic
or _is_symbol(char) # IPA
):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
class Trie:
"""
Trie in Python. Creates a Trie out of a list of words. The trie is used to split on `added_tokens` in one pass
Loose reference https://en.wikipedia.org/wiki/Trie
"""
def __init__(self):
self.data = {}
def add(self, word: str):
"""
Passes over every char (utf-8 char) on word and recursively adds it to the internal `data` trie representation.
The special key `""` is used to represent termination.
This function is idempotent, adding twice the same word will leave the trie unchanged
Example:
```python
>>> trie = Trie()
>>> trie.add("Hello 友達")
>>> trie.data
{"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}}
>>> trie.add("Hello")
>>> trie.data
{"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}}
```
"""
if not word:
# Prevent empty string
return
ref = self.data
for char in word:
ref[char] = char in ref and ref[char] or {}
ref = ref[char]
ref[""] = 1
def split(self, text: str) -> List[str]:
"""
Will look for the words added to the trie within `text`. Output is the original string splitted along the
boundaries of the words found.
This trie will match the longest possible word first !
Example:
```python
>>> trie = Trie()
>>> trie.split("[CLS] This is a extra_id_100")
["[CLS] This is a extra_id_100"]
>>> trie.add("[CLS]")
>>> trie.add("extra_id_1")
>>> trie.add("extra_id_100")
>>> trie.split("[CLS] This is a extra_id_100")
["[CLS]", " This is a ", "extra_id_100"]
```
"""
# indexes are counted left of the chars index.
# "hello", index 0, is left of h, index 1 is between h and e.
# index 5 is right of the "o".
# States are going to capture every possible start (indexes as above)
# as keys, and have as values, a pointer to the position in the trie
# where we're at. This is a partial match for now.
# This enables to keep track of multiple matches while we're iterating
# the string
# If the trie contains, "blowing", and "lower" and we encounter the
# string "blower", we need to split into ["b", "lower"].
# This is where we need to keep track of multiple possible starts.
states = OrderedDict()
# This will contain every indices where we need
# to cut.
# We force to cut at offset 0 and len(text) (added later)
offsets = [0]
# This is used by the lookahead which needs to skip over
# some text where the full match exceeded the place in the initial
# for loop
skip = 0
# Main loop, Giving this algorithm O(n) complexity
for current, current_char in enumerate(text):
if skip and current < skip:
# Prevents the lookahead for matching twice
# like extra_id_100 and id_100
continue
# This will track every state
# that stop matching, we need to stop tracking them.
# If we look at "lowball", we're going to match "l" (add it to states), "o", "w", then
# fail on "b", we need to remove 0 from the valid states.
to_remove = set()
# Whenever we found a match, we need to drop everything
# this is a greedy algorithm, it will match on the first found token
reset = False
# In this case, we already have partial matches (But unfinished)
for start, trie_pointer in states.items():
if "" in trie_pointer:
# This is a final match, we need to reset and
# store the results in `offsets`.
# Lookahead to match longest first
# Important in case of extra_id_1 vs extra_id_100
# Here we are also actively looking for other earlier partial
# matches
# "[CLS]", "L", we need to match CLS even if L is special
for lookstart, looktrie_pointer in states.items():
if lookstart > start:
# This partial match is later, we can stop looking
break
elif lookstart < start:
# This partial match is earlier, the trie pointer
# was already updated, so index is + 1
lookahead_index = current + 1
end = current + 1
else:
# Here lookstart == start and
# looktrie_pointer == trie_pointer
# It wasn't updated yet so indices are current ones
lookahead_index = current
end = current
next_char = text[lookahead_index] if lookahead_index < len(text) else None
if "" in looktrie_pointer:
start = lookstart
end = lookahead_index
skip = lookahead_index
while next_char in looktrie_pointer:
looktrie_pointer = looktrie_pointer[next_char]
lookahead_index += 1
if "" in looktrie_pointer:
start = lookstart
end = lookahead_index
skip = lookahead_index
if lookahead_index == len(text):
# End of string
break
next_char = text[lookahead_index]
# End lookahead
# Storing and resetting
offsets.append(start)
offsets.append(end)
reset = True
break
elif current_char in trie_pointer:
# The current character being looked at has a match within the trie
# update the pointer (it will be stored back into states later).
trie_pointer = trie_pointer[current_char]
# Storing back the new pointer into the states.
# Partial matches got longer by one.
states[start] = trie_pointer
else:
# The new character has not match in the trie, we need
# to stop keeping track of this partial match.
# We can't do it directly within the loop because of how
# python iteration works
to_remove.add(start)
# Either clearing the full start (we found a real match)
# Or clearing only the partial matches that didn't work.
if reset:
states = {}
else:
for start in to_remove:
del states[start]
# If this character is a starting character within the trie
# start keeping track of this partial match.
if current >= skip and current_char in self.data:
states[current] = self.data[current_char]
# We have a cut at the end with states.
for start, trie_pointer in states.items():
if "" in trie_pointer:
# This is a final match, we need to reset and
# store the results in `offsets`.
end = len(text)
offsets.append(start)
offsets.append(end)
# Longest cut is always the one with lower start so the first
# item so we need to break.
break
return self.cut_text(text, offsets)
def cut_text(self, text, offsets):
# We have all the offsets now, we just need to do the actual splitting.
# We need to eventually add the first part of the string and the eventual
# last part.
offsets.append(len(text))
tokens = []
start = 0
for end in offsets:
if start > end:
logger.error(
"There was a bug in Trie algorithm in tokenization. Attempting to recover. Please report it anyway."
)
continue
elif start == end:
# This might happen if there's a match at index 0
# we're also preventing zero-width cuts in case of two
# consecutive matches
continue
tokens.append(text[start:end])
start = end
return tokens
[文档]def tokenize_chinese_chars(text):
"""Adds whitespace around any CJK character."""
output = []
buff = ""
for char in text:
cp = ord(char)
if is_chinese_char(cp):
if buff != "":
output.append(buff)
buff = ""
output.append(char)
else:
buff += char
if buff != "":
output.append(buff)
return output
@dataclass
class ChatTemplate:
conversation: list[str] | None = None
system: str | None = None
query: str = None
@staticmethod
@lru_cache()
def _compile_jinja_template(chat_template):
def raise_exception(message):
raise TemplateError(message)
jinja_env = ImmutableSandboxedEnvironment(trim_blocks=True, lstrip_blocks=True, keep_trailing_newline=True)
jinja_env.globals["raise_exception"] = raise_exception
return jinja_env.from_string(chat_template)
def render_conversation(
self, conversation_data: list[str] | dict[str, str], index: int = 0, context_data: Dict[str, Any] = {}
) -> list[str]:
"""
Args:
conversation_data (list[str]): the conversation data which must be two parts
index (int): the index of current conversation
Returns:
list[str]: the rendered conversation data
"""
if self.conversation is None:
raise ValueError(
"The template for multi-turns is invalid, please check `conversation` filed in your chat-template."
)
if isinstance(conversation_data, (list, tuple)):
assert (
len(conversation_data) == 2
), "Each round/turn of conversation must be two participants, eg: [user-query, bot-query]"
conversation_data = {"user": conversation_data[0], "bot": conversation_data[1], "index": index}
conversation_data.update(context_data)
one_turn_conversation = []
for conversation in self.conversation:
template = self._compile_jinja_template(conversation)
result = template.render(conversation_data)
one_turn_conversation.append(result)
return one_turn_conversation
def render_query(self, query: str, index: int = 0, context_data: Dict[str, Union[int, str]] = {}):
if self.query is None:
return query
template = self._compile_jinja_template(self.query)
return template.render(query=query, index=index, **context_data)
def _init_context_data(self, context_data: Dict[str, Union[int, str]] = {}) -> Dict[str, Union[int, str]]:
"""init the context data for chat-template"""
context_data["is_training"] = context_data.get("is_training", False)
return context_data
def render_system(self, context_data: Dict[str, Union[int, str]] = {}) -> str:
if self.system is None:
return ""
template = self._compile_jinja_template(self.system)
return template.render(**context_data)
def __call__(self, conversations: list[list[str]] | str, context_data: Dict[str, Union[int, str]] = {}) -> str:
"""render the conversations by chat-template
Args:
conversations (list[list[str]]): the conversations of use and bot
Returns:
str: the result of conversation
"""
if isinstance(conversations, str):
conversations = [[conversations]]
# [1 ... n-1] conversation
final_query = self.render_system(context_data=context_data)
context_data["length"] = len(conversations)
for index, conversation in enumerate(conversations[:-1]):
context_data["is_first"] = index == 0
context_data["is_last"] = False
final_query += "".join(self.render_conversation(conversation, index=index, context_data=context_data))
if not isinstance(conversations[-1], list) and not len(conversations[-1]) != 1:
raise ValueError(
"The length of last conversation must be one, eg: [[user-query, bot-answer], [user-query, bot-answer], ..., [user-query]]"
)
if len(conversations[-1]) > 1:
logger.warning(
f"The last conversation is not a single-round, chat-template will skip the conversation: {conversations[-1][1:]}"
)
final_query += self.render_query(conversations[-1][0], index=len(conversations) - 1, context_data=context_data)
return final_query
@classmethod
def from_dict(cls, config: dict):
return cls(**config)
@classmethod
def from_file(cls, file: str):
with open(file, "r", encoding="utf-8") as f:
config = json.load(f)
return cls.from_dict(config)
class ChatTemplateMixin:
chat_template: Optional[ChatTemplate] = None
def apply_chat_template(
self,
conversation: List[List[str, str]] | str,
tokenize: bool = True,
context_data: Dict[str, Any] = {},
**tokenizer_kwargs
) -> str | dict[str, numpy.ndarray | paddle.Tensor]:
"""apply chat_template rules to conversation which should not be batched data
Args:
conversation (List[List[str, str]] | str): the conversation messages between user and bot
context_data (Dict[str, Any]): the context data for chat_template.json
tokenize (bool, optional): whether do tokenization. Defaults to True.
Returns:
str | dict[str, numpy.ndarray | paddle.Tensor]: return the result of applied data
"""
context_data = self.chat_template._init_context_data(context_data)
if isinstance(conversation, str):
conversation = [[conversation]]
elif isinstance(conversation, list) and isinstance(conversation[0], str):
raise ValueError(
"apply_chat_template do not support appling batch conversations, "
"so you should apply the conversation one by one."
)
query = self.chat_template(conversation, context_data=context_data)
if not tokenize:
return query
# chat_template should not add special tokens
tokenizer_kwargs["add_special_tokens"] = False
return self(query, **tokenizer_kwargs)
def encode_chat_inputs(self, conversations: List[List[str, str]], context_data: Dict[str, Any] = {}):
"""Encodes conversation to pairs of token ids.
Turn 0: bos + system + sep + user bot + eos
Turn t: sep + bot + query bot + eos
Args:
conversation (List[List[str, str]]): the conversation of data
context_data (Dict[str, Any]): the context data of conversation
Returns:
List[list[int], list[int]]: the pair of input_ids and target_ids
"""
context_data = self.chat_template._init_context_data(context_data)
# encode system
result = {}
if self.chat_template.system:
system = self.chat_template.render_system(context_data)
result["system"] = self.encode(system, add_special_tokens=False)["input_ids"]
# encode conversation
conversation_ids = []
for index, conversation in enumerate(conversations):
# give more control to chat_template
context_data["is_first"] = index == 0
context_data["is_last"] = index == len(conversations) - 1
user_input, bot_output = self.chat_template.render_conversation(
conversation, index=index, context_data=context_data
)
user_ids = self.encode(user_input, add_special_tokens=False)["input_ids"]
bot_ids = self.encode(bot_output, add_special_tokens=False)["input_ids"]
conversation_ids.append([user_ids, bot_ids])
result["conversations"] = conversation_ids
return result
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
cache_dir = kwargs.pop("cache_dir", None)
from_hf_hub = kwargs.pop("from_hf_hub", False)
from_aistudio = kwargs.pop("from_aistudio", False)
subfolder = kwargs.pop("subfolder", "")
if subfolder is None:
subfolder = ""
kwargs["subfolder"] = subfolder
kwargs["cache_dir"] = cache_dir
kwargs["from_hf_hub"] = from_hf_hub
kwargs["from_aistudio"] = from_aistudio
kwargs["return_tokenizer_file_dir"] = True
tokenizer, tokenizer_config_file_dir = super().from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
# load chat-template
chat_template_file = os.path.join(tokenizer_config_file_dir, CHAT_TEMPLATE_CONFIG_NAME)
if not os.path.exists(chat_template_file):
return tokenizer
tokenizer.init_chat_template(chat_template_file)
return tokenizer
def init_chat_template(self, chat_template: str | dict):
"""init chat_tempalte by file_path or template dict data
Args:
chat_template (str | dict): file_path or template dict data
"""
if isinstance(chat_template, str):
if not os.path.exists(chat_template):
raise FileNotFoundError("The chat-template file does not exist: {}".format(chat_template))
self.chat_template = ChatTemplate.from_file(chat_template)
elif isinstance(chat_template, dict):
self.chat_template = ChatTemplate.from_dict(chat_template)
elif isinstance(chat_template, ChatTemplate):
self.chat_template = chat_template
else:
raise ValueError("Receive error chat_template data: ", chat_template)
def save_resources(self, save_directory):
super().save_resources(save_directory)
if self.chat_template is not None:
chat_template_file = os.path.join(save_directory, CHAT_TEMPLATE_CONFIG_NAME)
with open(chat_template_file, "w", encoding="utf-8") as f:
json.dump(asdict(self.chat_template), f, ensure_ascii=False, indent=4)
logger.info("Chat-template config file saved in " + chat_template_file)
[文档]@six.add_metaclass(InitTrackerMeta)
class PretrainedTokenizer(ChatTemplateMixin, PretrainedTokenizerBase):
"""
Base class for all tokenizers.
Inherits from [`~tokenizer_utils_base.PretrainedTokenizerBase`].
Handle all the shared methods for tokenization and special tokens as well as methods downloading/caching/loading
pretrained tokenizers as well as adding tokens to the vocabulary.
This class also contain the added tokens in a unified way on top of all tokenizers so we don't have to handle the
specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...).
- **resource_files_names** (`Dict[str, str]`) -- A dictionary with, as keys, the `__init__` keyword name of each
vocabulary file required by the model, and as associated values, the filename for saving the associated file
(string).
- **pretrained_resource_files_map** (`Dict[str, Dict[str, str]]`) -- A dictionary of dictionaries, with the
high-level keys being the `__init__` keyword name of each vocabulary file required by the model, the
low-level being the `short-cut-names` of the pretrained models with, as associated values, the `url` to the
associated pretrained vocabulary file.
- **max_model_input_sizes** (`Dict[str, Optional[int]]`) -- A dictionary with, as keys, the `short-cut-names`
of the pretrained models, and as associated values, the maximum length of the sequence inputs of this model,
or `None` if the model has no maximum input size.
- **pretrained_init_configuration** (`Dict[str, Dict[str, Any]]`) -- A dictionary with, as keys, the
`short-cut-names` of the pretrained models, and as associated values, a dictionary of specific arguments to
pass to the `__init__` method of the tokenizer class for this pretrained model when loading the tokenizer
with the [`~tokenizer_utils_base.PretrainedTokenizerBase.from_pretrained`] method.
- **model_input_names** (`List[str]`) -- A list of inputs expected in the forward pass of the model.
- **padding_side** (`str`) -- The default value for the side on which the model should have padding applied.
Should be `'right'` or `'left'`.
- **truncation_side** (`str`) -- The default value for the side on which the model should have truncation
applied. Should be `'right'` or `'left'`.
Moreover, methods common to tokenizers for tokenization, token/id conversion
and encoding as model inputs are also provided here.
Besides, metaclass `InitTrackerMeta` is used to create `PretrainedTokenizer`,
by which subclasses can track arguments for initialization automatically
and expose special tokens initialization used as attributes.
"""
added_tokens_encoder: Dict[str, int] = {}
added_tokens_decoder: Dict[int, str] = {}
unique_no_split_tokens: List[str] = []
tokens_trie = Trie()
_decode_use_source_tokenizer = False
def _pre_init(self, original_init, *args, **kwargs):
"""
It would be hooked before `__init__` to add specials tokens (arguments of
`__init__` whose name ends with `_token`) as attributes of the tokenizer
instance.
"""
init_dict = fn_args_to_dict(original_init, *((self,) + args), **kwargs)
init_dict.pop("self", None)
super(PretrainedTokenizer, self).__init__(**init_dict)
self.added_tokens_encoder: Dict[str, int] = {}
self.added_tokens_decoder: Dict[int, str] = {}
self.unique_no_split_tokens: List[str] = []
self.tokens_trie = Trie()
self._decode_use_source_tokenizer = False
def _build_special_tokens_map_extended(self, **kwargs):
for key, value in kwargs.items():
if value is None:
continue
if key in self.SPECIAL_TOKENS_ATTRIBUTES:
if key == "additional_special_tokens":
assert isinstance(value, (list, tuple)), f"Value {value} is not a list or tuple"
assert all(
isinstance(t, (str, AddedToken)) for t in value
), "One of the tokens is not a string or an AddedToken"
setattr(self, key, value)
elif isinstance(value, (str, AddedToken)):
setattr(self, key, value)
else:
raise TypeError(f"special token {key} has to be either str or AddedToken but got: {type(value)}")
@property
def vocab_size(self) -> int:
"""
`int`: Size of the base vocabulary (without the added tokens).
"""
raise NotImplementedError
@property
def is_fast(self) -> bool:
return False
[文档] def get_added_vocab(self) -> Dict[str, int]:
"""
Returns the added tokens in the vocabulary as a dictionary of token to index.
Returns:
`Dict[str, int]`: The added tokens.
"""
return self.added_tokens_encoder
def __len__(self):
"""
Size of the full vocabulary with the added tokens.
"""
return self.vocab_size + len(self.added_tokens_encoder)
def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
"""
Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to
it with indices starting from length of the current vocabulary.
Args:
new_tokens (`List[str]`or `List[AddedToken]`):
Token(s) to add in vocabulary. A token is only added if it's not already in the vocabulary (tested by
checking if the tokenizer assign the index of the `unk_token` to them).
special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the tokens should be added as special tokens.
Returns:
`int`: The number of tokens actually added to the vocabulary.
Examples:
```python
# Let's see how to increase the vocabulary of Bert model and tokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = BertModel.from_pretrained("bert-base-uncased")
num_added_toks = tokenizer.add_tokens(["new_tok1", "my_new-tok2"])
print("We have added", num_added_toks, "tokens")
```"""
new_tokens = [str(tok) for tok in new_tokens]
tokens_to_add = []
for token in new_tokens:
if not isinstance(token, str):
raise TypeError(f"Token {token} is not a string but a {type(token)}.")
if not special_tokens and hasattr(self, "do_lower_case") and self.do_lower_case:
token = token.lower()
if (
token != self.unk_token
and self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token)
and token not in tokens_to_add
):
tokens_to_add.append(token)
if self.verbose:
logger.info(f"Adding {token} to the vocabulary")
added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(tokens_to_add))
added_tok_decoder = {v: k for k, v in added_tok_encoder.items()}
self.added_tokens_encoder.update(added_tok_encoder)
self.added_tokens_decoder.update(added_tok_decoder)
# Make sure we don't split on any special tokens (even they were already in the vocab before e.g. for Albert)
if special_tokens:
if len(new_tokens) == 1:
_insert_one_token_to_ordered_list(self.unique_no_split_tokens, new_tokens[0])
else:
self.unique_no_split_tokens = sorted(set(self.unique_no_split_tokens).union(set(new_tokens)))
else:
# Or on the newly added tokens
if len(tokens_to_add) == 1:
_insert_one_token_to_ordered_list(self.unique_no_split_tokens, tokens_to_add[0])
else:
self.unique_no_split_tokens = sorted(set(self.unique_no_split_tokens).union(set(tokens_to_add)))
self._create_trie(self.unique_no_split_tokens)
return len(tokens_to_add)
def _create_trie(self, unique_no_split_tokens):
trie = Trie()
for token in unique_no_split_tokens:
if hasattr(self, "do_lower_case") and self.do_lower_case and token not in self.all_special_tokens:
trie.add(token.lower())
else:
trie.add(token)
self.tokens_trie = trie
[文档] def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
"""
Performs any necessary transformations before tokenization.
This method should pop the arguments from kwargs and return the remaining `kwargs` as well. We test the
`kwargs` at the end of the encoding process to be sure all the arguments have been used.
Args:
text (`str`):
The text to prepare.
is_split_into_words (`bool`, *optional*, defaults to `False`):
Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.
kwargs:
Keyword arguments to use for the tokenization.
Returns:
`Tuple[str, Dict[str, Any]]`: The prepared text and the unused kwargs.
"""
return (text, kwargs)
[文档] def tokenize(self, text: TextInput, **kwargs) -> List[str]:
"""
Converts a string in a sequence of tokens, using the tokenizer.
Split in words for word-based vocabulary or sub-words for sub-word-based vocabularies
(BPE/SentencePieces/WordPieces). Takes care of added tokens.
Args:
text (`str`):
The sequence to be encoded.
**kwargs (additional keyword arguments):
Passed along to the model-specific `prepare_for_tokenization` preprocessing method.
Returns:
`List[str]`: The list of tokens.
"""
# Simple mapping string => AddedToken for special tokens with specific tokenization behaviors
all_special_tokens_extended = dict(
(str(t), t) for t in self.all_special_tokens_extended if isinstance(t, AddedToken)
)
text, kwargs = self.prepare_for_tokenization(text, **kwargs)
# TODO: should this be in the base class?
if hasattr(self, "do_lower_case") and self.do_lower_case:
# convert non-special tokens to lowercase
escaped_special_toks = [
re.escape(s_tok) for s_tok in (self.unique_no_split_tokens + self.all_special_tokens)
]
pattern = r"(" + r"|".join(escaped_special_toks) + r")|" + r"(.+?)"
text = re.sub(pattern, lambda m: m.groups()[0] or m.groups()[1].lower(), text)
no_split_token = set(self.unique_no_split_tokens)
tokens = self.tokens_trie.split(text)
# ["This is something", "<special_token_1>", " else"]
for i, token in enumerate(tokens):
if token in no_split_token:
tok_extended = all_special_tokens_extended.get(token, None)
left = tokens[i - 1] if i > 0 else None
right = tokens[i + 1] if i < len(tokens) - 1 else None
if isinstance(tok_extended, AddedToken):
if tok_extended.rstrip and right:
# A bit counter-intuitive but we strip the left of the string
# since tok_extended.rstrip means the special token is eating all white spaces on its right
tokens[i + 1] = right.lstrip()
# Strip white spaces on the left
if tok_extended.lstrip and left:
tokens[i - 1] = left.rstrip() # Opposite here
else:
# We strip left and right by default
if right:
tokens[i + 1] = right.lstrip()
if left:
tokens[i - 1] = left.rstrip()
# ["This is something", "<special_token_1>", "else"]
tokenized_text = []
for token in tokens:
# Need to skip eventual empty (fully stripped) tokens
if not token:
continue
if token in no_split_token:
tokenized_text.append(token)
else:
tokenized_text.extend(self._tokenize(token))
# ["This", " is", " something", "<special_token_1>", "else"]
return tokenized_text
def _tokenize(self, text, **kwargs):
"""
Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
Do NOT take care of added tokens.
"""
raise NotImplementedError
def convert_tokens_to_ids(self, tokens):
if tokens is None:
return None
if isinstance(tokens, str):
return self._convert_token_to_id_with_added_voc(tokens)
ids = []
for token in tokens:
ids.append(self._convert_token_to_id_with_added_voc(token))
return ids
def _convert_token_to_id_with_added_voc(self, token):
if token is None:
return None
if token in self.added_tokens_encoder:
return self.added_tokens_encoder[token]
return self._convert_token_to_id(token)
def _convert_token_to_id(self, token):
return self.vocab.to_indices(token)
[文档] def convert_tokens_to_string(self, tokens):
"""
Converts a sequence of tokens (list of string) to a single string by
using ``' '.join(tokens)`` .
Args:
tokens (list[str]): A sequence of tokens.
Returns:
str: Converted string.
"""
return " ".join(tokens)
def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
if isinstance(ids, int):
if ids in self.added_tokens_decoder:
return self.added_tokens_decoder[ids]
else:
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
if index in self.added_tokens_decoder:
tokens.append(self.added_tokens_decoder[index])
else:
tokens.append(self._convert_id_to_token(index))
return tokens
def _convert_id_to_token(self, index):
return self.vocab.to_tokens(index)
[文档] @staticmethod
def load_vocabulary(filepath, unk_token=None, pad_token=None, bos_token=None, eos_token=None, **kwargs):
"""
Instantiate an instance of `Vocab` from a file reserving all tokens
by using `Vocab.from_dict`. The file contains a token per line, and the
line number would be the index of corresponding token.
Args:
filepath (str): path of file to construct vocabulary.
unk_token (str): special token for unknown token. If no need, it also
could be `None`. Defaults to `None`.
pad_token (str): special token for padding token. If no need, it also
could be `None`. Defaults to `None`.
bos_token (str): special token for bos token. If no need, it also
could be `None`. Defaults to `None`.
eos_token (str): special token for eos token. If no need, it also
could be `None`. Defaults to `None`.
**kwargs (dict): keyword arguments for `Vocab.from_dict`.
Returns:
Vocab: An instance of `Vocab`.
"""
token_to_idx = {}
with io.open(filepath, "r", encoding="utf-8") as f:
for index, line in enumerate(f):
token = line.rstrip("\n")
token_to_idx[token] = int(index)
vocab = Vocab.from_dict(
token_to_idx, unk_token=unk_token, pad_token=pad_token, bos_token=bos_token, eos_token=eos_token, **kwargs
)
return vocab
[文档] @staticmethod
def save_vocabulary(filepath, vocab):
"""
Save all tokens to a vocabulary file. The file contains a token per line,
and the line number would be the index of corresponding token.
Args:
filepath (str): File path to be saved to.
vocab (Vocab|dict): The `Vocab` or `dict` instance to be saved.
"""
if isinstance(vocab, Vocab):
tokens = vocab.idx_to_token
else:
tokens = sorted(vocab.keys(), key=lambda token: vocab[token])
with io.open(filepath, "w", encoding="utf-8") as f:
for token in tokens:
f.write(token + "\n")
[文档] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``encode`` methods.
Args:
token_ids_0 (List[int]): List of ids of the first sequence.
token_ids_1 (List[int], optional): List of ids of the second sequence.
already_has_special_tokens (bool, optional): Whether or not the token list is already
formatted with special tokens for the model. Defaults to None.
Returns:
results (List[int]): The list of integers in the range [0, 1]:
1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
return [0] * ((len(token_ids_1) if token_ids_1 else 0) + len(token_ids_0))
[文档] def num_special_tokens_to_add(self, pair):
"""
Returns the number of added tokens when encoding a sequence with special tokens.
Args:
pair (bool, optional):
Whether the number of added tokens should be computed in the case of a sequence pair or a single
sequence. Defaults to `False`.
Returns:
int: Number of special tokens added to sequences.
"""
token_ids_0 = []
token_ids_1 = []
return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None))
def _encode_plus(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_position_ids: Optional[bool] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
def get_input_ids(text):
if isinstance(text, str):
tokens = self.tokenize(text, **kwargs)
return self.convert_tokens_to_ids(tokens)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
if is_split_into_words:
tokens = list(
itertools.chain(*(self.tokenize(t, is_split_into_words=True, **kwargs) for t in text))
)
return self.convert_tokens_to_ids(tokens)
else:
return self.convert_tokens_to_ids(text)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
return text
else:
if is_split_into_words:
raise ValueError(
f"Input {text} is not valid. Should be a string or a list/tuple of strings when `is_split_into_words=True`."
)
else:
raise ValueError(
f"Input {text} is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
)
first_ids = get_input_ids(text)
second_ids = get_input_ids(text_pair) if text_pair is not None else None
if return_offsets_mapping:
kwargs["text"] = text
kwargs["text_pair"] = text_pair
return self.prepare_for_model(
first_ids,
pair_ids=second_ids,
add_special_tokens=add_special_tokens,
padding=padding_strategy.value,
truncation=truncation_strategy.value,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
prepend_batch_axis=True,
return_position_ids=return_position_ids,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
List[PreTokenizedInputPair],
List[EncodedInput],
List[EncodedInputPair],
],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_position_ids: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_dict: bool = True,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
def get_input_ids(text):
if isinstance(text, str):
tokens = self.tokenize(text, **kwargs)
return self.convert_tokens_to_ids(tokens)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
if is_split_into_words:
tokens = list(
itertools.chain(*(self.tokenize(t, is_split_into_words=True, **kwargs) for t in text))
)
return self.convert_tokens_to_ids(tokens)
else:
return self.convert_tokens_to_ids(text)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
return text
else:
raise ValueError(
"Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
)
input_ids = []
for ids_or_pair_ids in batch_text_or_text_pairs:
if not isinstance(ids_or_pair_ids, (list, tuple)):
ids, pair_ids = ids_or_pair_ids, None
elif is_split_into_words and not isinstance(ids_or_pair_ids[0], (list, tuple)):
ids, pair_ids = ids_or_pair_ids, None
else:
ids, pair_ids = ids_or_pair_ids
first_ids = get_input_ids(ids)
second_ids = get_input_ids(pair_ids) if pair_ids is not None else None
input_ids.append((first_ids, second_ids))
if stride > 0 and second_ids is not None:
kwargs["batch_text_or_text_pairs"] = batch_text_or_text_pairs
else:
if return_offsets_mapping:
has_pair = False
if len(batch_text_or_text_pairs) > 0:
if isinstance(batch_text_or_text_pairs[0], (list, tuple)):
has_pair = True
kwargs["texts"] = None
kwargs["text_pairs"] = None
if has_pair:
kwargs["texts"] = [text[0] for text in batch_text_or_text_pairs]
kwargs["text_pairs"] = [text[1] for text in batch_text_or_text_pairs]
else:
kwargs["texts"] = [text for text in batch_text_or_text_pairs]
batch_outputs = self._batch_prepare_for_model(
input_ids,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_position_ids=return_position_ids,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_dict=return_dict,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
return_tensors=return_tensors,
verbose=verbose,
**kwargs,
)
return batch_outputs
def _batch_prepare_for_model(
self,
batch_ids_pairs: List[Union[PreTokenizedInputPair, Tuple[List[int], None]]],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_position_ids: Optional[bool] = None,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_dict: bool = True,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
manages a moving window (with user defined stride) for overflowing tokens
Args:
batch_ids_pairs: list of tokenized input ids or input ids pairs
"""
if return_token_type_ids and not add_special_tokens:
raise ValueError(
"Asking to return token_type_ids while setting add_special_tokens to False "
"results in an undefined behavior. Please set add_special_tokens to True or "
"set return_token_type_ids to None."
)
batch_outputs = {}
batch_outputs_list = []
for example_id, (first_ids, second_ids) in enumerate(batch_ids_pairs):
if stride > 0 and second_ids is not None:
if return_token_type_ids is None:
return_token_type_ids = "token_type_ids" in self.model_input_names
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
max_len_for_pair = (
max_length
- len(first_ids)
- (self.num_special_tokens_to_add(pair=True) if add_special_tokens else 0)
)
text, text_pair = kwargs["batch_text_or_text_pairs"][example_id]
token_offset_mapping = self.get_offset_mapping(text)
token_pair_offset_mapping = self.get_offset_mapping(text_pair)
offset = 0
while offset < len(second_ids):
encoded_inputs = {}
length = len(second_ids) - offset
if length > max_len_for_pair:
length = max_len_for_pair
ids = first_ids
pair_ids = second_ids[offset : offset + length]
pair = bool(pair_ids is not None)
mapping = token_offset_mapping
pair_mapping = token_pair_offset_mapping[offset : offset + length]
if add_special_tokens:
offset_mapping = self.build_offset_mapping_with_special_tokens(mapping, pair_mapping)
sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
else:
offset_mapping = mapping + pair_mapping
sequence = ids + pair_ids if pair else ids
token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
encoded_inputs["offset_mapping"] = offset_mapping
# Build output dictionnary
encoded_inputs["input_ids"] = sequence
if return_token_type_ids:
encoded_inputs["token_type_ids"] = token_type_ids
if return_special_tokens_mask:
if add_special_tokens:
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
else:
encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
# Check lengths
self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
if return_position_ids:
encoded_inputs["position_ids"] = list(range(len(encoded_inputs["input_ids"])))
if return_length:
encoded_inputs["length"] = len(encoded_inputs["input_ids"])
encoded_inputs["seq_len"] = encoded_inputs["length"]
encoded_inputs["overflow_to_sample"] = example_id
for key, value in encoded_inputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
if offset + length == len(second_ids):
break
offset += min(length, stride)
else:
if return_offsets_mapping:
kwargs["text"] = kwargs["texts"][example_id]
kwargs["text_pair"] = None
if kwargs["text_pairs"] is not None:
kwargs["text_pair"] = kwargs["text_pairs"][example_id]
encoded_inputs = self.prepare_for_model(
first_ids,
second_ids,
add_special_tokens=add_special_tokens,
padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
truncation=truncation_strategy.value,
max_length=max_length,
stride=stride,
pad_to_multiple_of=None, # we pad in batch afterward
return_position_ids=return_position_ids, # we pad in batch afterward
return_attention_mask=False, # we pad in batch afterward
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
return_tensors=None, # We convert the whole batch to tensors at the end
prepend_batch_axis=False,
verbose=verbose,
**kwargs,
)
for key, value in encoded_inputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
batch_outputs = self.pad(
batch_outputs,
padding=padding_strategy.value,
max_length=max_length,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_mask=return_attention_mask,
)
if return_dict:
batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
return batch_outputs
else:
for k, v in batch_outputs.items():
for i in range(len(v)):
if i >= len(batch_outputs_list):
batch_outputs_list.append({k: v[i]})
else:
batch_outputs_list[i][k] = v[i]
return batch_outputs_list
def _get_bert_like_offset_mapping(self, text: str):
"""
Returns the map of tokens and the start and end index of their start and end character.
Modified from https://github.com/bojone/bert4keras/blob/master/bert4keras/tokenizers.py#L372
Args:
text (str):
Input text.
Returns:
list: The offset map of input text.
"""
if text is None:
return None
split_tokens = self.tokenize(text)
normalized_text, char_mapping = "", []
for i, ch in enumerate(text):
if hasattr(self, "do_lower_case") and self.do_lower_case:
ch = ch.lower()
if self.basic_tokenizer.strip_accents is not False:
ch = unicodedata.normalize("NFD", ch)
ch = "".join([c for c in ch if unicodedata.category(c) != "Mn"])
elif self.basic_tokenizer.strip_accents:
ch = unicodedata.normalize("NFD", ch)
ch = "".join([c for c in ch if unicodedata.category(c) != "Mn"])
ch = "".join([c for c in ch if not (ord(c) == 0 or ord(c) == 0xFFFD or _is_control(c))])
normalized_text += ch
char_mapping.extend([i] * len(ch))
text, token_mapping, offset = normalized_text, [], 0
char_mapping_indexes = []
for index, token in enumerate(split_tokens):
if token[:2] == "##":
token = token[2:]
if token in self.all_special_tokens:
token = token.lower() if hasattr(self, "do_lower_case") and self.do_lower_case else token
# The greek letter "sigma" has 2 forms of lowercase, σ and ς respectively.
# When used as a final letter of a word, the final form (ς) is used. Otherwise, the form (σ) is used.
# https://latin.stackexchange.com/questions/6168/how-and-when-did-we-get-two-forms-of-sigma
if "σ" in token or "ς" in token:
start = text[offset:].replace("ς", "σ").index(token.replace("ς", "σ")) + offset
else:
# try to fix: https://github.com/PaddlePaddle/PaddleNLP/issues/3985
if token not in text[offset:]:
# check whether there are consecutive UNK tokens, eg: ['好', '[UNK]', '[UNK]', 'good']
if index < len(split_tokens) - 1 and split_tokens[index + 1] in self.all_special_tokens:
start = offset
token = " " # only contains one char
else:
start = -1
else:
start = text[offset:].index(token) + offset
end = start + len(token)
char_mapping_indexes.append([start, end])
if start != -1:
offset = end
token_mapping = []
for index, (start, end) in enumerate(char_mapping_indexes):
if start == -1:
# init start
if index == 0:
start = 0
else:
start = char_mapping_indexes[index - 1][1]
# init end
if index == len(char_mapping_indexes) - 1:
end = len(char_mapping)
else:
# next start
end = char_mapping_indexes[index + 1][0]
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1))
return token_mapping
[文档] def get_offset_mapping(self, text: str, split_tokens: Optional[List[str]] = None):
"""
Returns the map of tokens and the start and end index of their start and end character.
Modified from https://github.com/bojone/bert4keras/blob/master/bert4keras/tokenizers.py#L372
Args:
text (str):
Input text.
split_tokens (Optional[List[str]]):
the tokens which has been split which can accelerate the operation.
Returns:
list: The offset map of input text.
"""
if text is None:
return None
split_tokens = self.tokenize(text)
# bert-like tokenizer use the old-school code block
if hasattr(self, "basic_tokenizer") or hasattr(self, "wordpiece_tokenizer"):
return self._get_bert_like_offset_mapping(text)
if not split_tokens:
split_tokens = self.tokenize(text)
normalized_text, char_mapping = "", []
for i, ch in enumerate(text):
normalized_text += normalize_chars(ch)
char_mapping.extend([i] * len(ch))
text, token_mapping, offset = normalized_text, [], 0
do_lower_case = getattr(self, "do_lower_case", False)
# lower the text if the token is lower-cased
# keep align with token
if do_lower_case:
text = text.lower()
char_mapping_indexes = []
for token in split_tokens:
# convert tokens into original string
token: str = self.convert_tokens_to_string(token).strip()
if token in self.all_special_tokens:
if do_lower_case:
token = token.lower()
# The greek letter "sigma" has 2 forms of lowercase, σ and ς respectively.
# When used as a final letter of a word, the final form (ς) is used. Otherwise, the form (σ) is used.
# https://latin.stackexchange.com/questions/6168/how-and-when-did-we-get-two-forms-of-sigma
if "σ" in token or "ς" in token:
start = text[offset:].replace("ς", "σ").index(token.replace("ς", "σ")) + offset
else:
# try to fix: https://github.com/PaddlePaddle/PaddleNLP/issues/3985
if token not in text[offset:]:
start = -1
else:
start = text[offset:].index(token) + offset
end = start + len(token)
char_mapping_indexes.append([start, end])
if start != -1:
offset = end
token_mapping = []
for index, (start, end) in enumerate(char_mapping_indexes):
if start == -1:
# init start
if index == 0:
start = 0
else:
start = char_mapping_indexes[index - 1][1]
# init end
if index == len(char_mapping_indexes) - 1:
end = len(char_mapping)
else:
# next start
end = char_mapping_indexes[index + 1][0]
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1))
return token_mapping
def _decode(
self,
token_ids: List[int],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = True,
spaces_between_special_tokens: bool = True,
**kwargs
) -> str:
if isinstance(token_ids, np.ndarray):
token_ids = token_ids.tolist()
self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)
filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
sub_texts = []
current_sub_text = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
current_sub_text = []
sub_texts.append(token)
else:
current_sub_text.append(token)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text))
if spaces_between_special_tokens:
text = " ".join(sub_texts)
else:
text = "".join(sub_texts)
if clean_up_tokenization_spaces:
clean_text = self.clean_up_tokenization(text)
return clean_text
else:
return text
[文档] def decode_token(
self,
all_input_ids: List[int],
prefix_offset: int = 0,
read_offset: int = 0,
) -> Tuple[str, int, int]:
"""tokenizer decoding for the streaming generation use case. This method can be overrided for tokenizer that doesn't follow this API"""
# The prefix text is necessary only to defeat cleanup algorithms in the decode
# which decide to add a space or not depending on the surrounding ids.
prefix_text = self.decode(all_input_ids[prefix_offset:read_offset], skip_special_tokens=False)
new_text = self.decode(all_input_ids[prefix_offset:], skip_special_tokens=False)
if len(new_text) > len(prefix_text) and not new_text.endswith("�"):
# utf-8 char at the end means it's a potential unfinished byte sequence
# from byte fallback tokenization.
# If it's in the middle, it's probably a real invalid id generated
# by the model
prefix_index = new_text.index(prefix_text)
new_text = new_text[prefix_index + len(prefix_text) :]
return new_text, read_offset, len(all_input_ids)
else:
return "", prefix_offset, read_offset
[文档]class BPETokenizer(PretrainedTokenizer):
"""
The base class for all bpe tokenizers. It mainly provides common tokenize
methods for bpe type tokenizer.
Args:
vocab_file (str):
file path of the vocabulary.
encoder_json_path (str, optional):
file path of the id to vocab.
vocab_bpe_path (str, optional):
file path of word merge text.
unk_token (str, optional):
The special token for unknown words.
Defaults to "[UNK]".
sep_token (str, optional):
The special token for separator token.
Defaults to "[SEP]".
pad_token (str, optional):
The special token for padding.
Defaults to "[PAD]".
cls_token (str, optional):
The special token for cls.
Defaults to "[CLS]".
mask_token (str, optional):
The special token for mask.
Defaults to "[MASK]".
"""
class Encoder(object):
def __init__(self, encoder, bpe_merges, errors="replace", special_tokens=["[SEP]", "[p]", "[q]", "[/q]"]):
self.encoder = encoder
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = self._bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
self.re = try_import("regex")
self.special_tokens = special_tokens
# Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = self.re.compile(
r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
)
@lru_cache()
def _bytes_to_unicode(self):
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a signficant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = (
list(range(ord("!"), ord("~") + 1))
+ list(range(ord("¡"), ord("¬") + 1))
+ list(range(ord("®"), ord("ÿ") + 1))
)
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
def _get_pairs(self, 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
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = self._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)
new_word.extend(word[i:j])
i = j
except: # noqa: E722
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 = self._get_pairs(word)
word = " ".join(word)
self.cache[token] = word
return word
def tokenize(self, text):
tokens = text.split(" ")
sub_tokens = []
for token_i, token in enumerate(tokens):
if self.is_special_token(token):
if token_i == 0:
sub_tokens.extend([token])
else:
sub_tokens.extend([" " + token])
else:
if token_i == 0:
sub_tokens.extend(self.re.findall(self.pat, token))
else:
sub_tokens.extend(self.re.findall(self.pat, " " + token))
return sub_tokens
def tokenize_old(self, text):
return self.re.findall(self.pat, text)
def is_special_token(self, tok):
if isinstance(tok, int):
return False
res = False
for t in self.special_tokens:
# if tok.find(t) != -1:
if tok.strip() == t:
res = True
break
return res
def tokenize_bpe(self, token):
if self.is_special_token(token):
return [token.strip()] # remove space for convert_to_ids
else:
token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
return [self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")]
def encode(self, text):
bpe_tokens = []
for token in self.tokenize(text):
bpe_tokens.extend(self.tokenize_bpe(token))
return bpe_tokens
def decode(self, tokens):
pre_token_i = 0
texts = []
for token_i, token in enumerate(tokens):
if self.is_special_token(token):
# proprecess tokens before token_i
if token_i - pre_token_i > 0:
text = "".join([self.decoder[int(tok)] for tok in tokens[pre_token_i:token_i]])
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
texts.append(text)
# texts.append(token)
if token_i == 0:
texts.append(token) # in the beginning, there is no space before special tokens
else:
texts.extend([" ", token]) # in middle sentence, there must be a space before special tokens
pre_token_i = token_i + 1
if pre_token_i < len(tokens):
text = "".join([self.decoder[int(tok)] for tok in tokens[pre_token_i:]])
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
texts.append(text)
return "".join(texts)
def __init__(
self,
vocab_file,
encoder_json_path="./configs/encoder.json",
vocab_bpe_path="./configs/vocab.bpe",
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
):
self.vocab = self.load_vocabulary(
vocab_file, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, mask_token=mask_token
)
self.encoder_json_path = encoder_json_path
self.vocab_bpe_path = vocab_bpe_path
self.encoder = self._get_encoder(encoder_json_path, vocab_bpe_path)
self.nltk = try_import("nltk")
def _tokenize(self, text, is_sentencepiece=True):
text = convert_to_unicode(text)
text = " ".join(text.split()) # remove duplicate whitespace
if is_sentencepiece:
sents = self.nltk.tokenize.sent_tokenize(text)
bpe_ids = sum([self.encoder.encode(sent) for sent in sents], [])
else:
bpe_ids = self.encoder.encode(text)
tokens = [str(bpe_id) for bpe_id in bpe_ids]
return tokens
def _get_encoder(self, encoder_json_path, vocab_bpe_path):
with open(encoder_json_path, "r") as f:
encoder = json.load(f)
with open(vocab_bpe_path, "r", encoding="utf-8") as f:
bpe_data = f.read()
bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]]
return self.Encoder(
encoder=encoder,
bpe_merges=bpe_merges,
)
