"""wizardcoder Tokenizer"""
import json
from functools import lru_cache
from typing import List, Optional
import os
import regex as re
from mindformers.tools.register import MindFormerRegister, MindFormerModuleType
from mindformers.models.base_tokenizer import Tokenizer
__all__ = ['WizardCoderTokenizer']
@lru_cache()
def bytes_to_unicode():
"""
bytes to unicode
"""
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(i) for i in cs]
return dict(zip(bs, cs))
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
return pairs
@MindFormerRegister.register(MindFormerModuleType.TOKENIZER)
class WizardCoderTokenizer(Tokenizer):
r"""
Tokenize the input string and convert them into the ids. The tokenizer use the sentence piece internally.
Args:
vocab_file(str): The vocabulary file path.
merge_file(str): The merge file path.
unk_token(str): The token that represents the unknown. Default "<|endoftext|>".
bos_token(str): The token that represents the begin-of-sentence. Default "<|endoftext|>".
eos_token(str): The token that represents the end-of-sentence. Default "<|endoftext|>".
add_prefix_space(bool): whether to add a whitespace in the front of text. Default "False"
**kwargs: Other kwargs that will be passed into the base class of the `Tokenizer`.
Examples:
>>> from research.wizardcoder.wizardcoder_tokenizer import WizardCoderTokenizer
>>> tokenizer = WizardCoderTokenizer("vocab.json", "merges.txt")
>>> res = tokenizer("Hello world")
>>> print(res)
{'input_ids': [8279, 5788], 'token_type_ids': [0, 0], 'attention_mask': [1, 1]}
Outputs:
A dict contains the processed ids, attention_mask that specific by the member `MODEL_INPUT_NAME`
of the subclass.
"""
VOCAB_FILES = {'merge_file': 'merges.txt', 'vocab_file': 'vocab.json'}
FILE_LIST = ['tokenizer_config.json']
def __init__(
self,
vocab_file,
merge_file,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
pad_token="[PAD]",
add_prefix_space=False,
add_bos_token=False,
add_eos_token=False,
**kwargs
):
self.add_bos_token = add_bos_token
self.add_eos_token = add_eos_token
with open(vocab_file, 'r', 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(merge_file, 'r', encoding="utf-8") as merge_handle:
bpe_merges = merge_handle.read().split('\n')[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
self.the_unk_token = unk_token
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
self.add_prefix_space = add_prefix_space
self.cache = {}
super(WizardCoderTokenizer, self).__init__(
unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, **kwargs
)
self.add_tokens([self.pad_token, unk_token, bos_token, eos_token], special_tokens=True)
def bpe(self, token):
""" bpe encode """
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(token)
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 + 1 < len(word) 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)
self.cache[token] = word
return word
def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None):
"""
Build model inputs from a sequence or a pair of sequence by concatenating and adding special tokens.
A WizardCoder sequence has the following format:
- single sequence: ``<bos> X <eos>``
- pair of sequences: ``<bos> A <eos> B <eos>``
Args:
token_ids_0 (List[int]): List of IDs to which the special tokens will be added
token_ids_1 (List[int], `optional`, defaults to `None`): Optional second list of IDs for sequence pairs.
"""
bos = [self.bos_token_id] if self.add_bos_token else []
eos = [self.eos_token_id] if self.add_eos_token else []
if token_ids_1 is None:
return bos + token_ids_0 + eos
return bos + token_ids_0 + eos + token_ids_1 + eos
def _tokenize(self, text):
""" Tokenize a string using bpe encode. """
text, _ = self.prepare_for_tokenization(text, is_pretokenized=False)
bpe_tokens = []
for token in re.findall(self.pat, text):
token = "".join(
self.byte_encoder[b] for b in token.encode("utf-8")
)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
return bpe_tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.the_unk_token))
def _convert_tokens_to_ids(self, tokens):
""" the index of the tokens in the vocabulary. """
if isinstance(tokens, str):
return self.encoder.get(tokens, self.encoder.get(self.the_unk_token))
output = []
for token in tokens:
output.append(self.encoder.get(token, self.encoder.get(self.the_unk_token)))
return output
def _convert_ids_to_tokens(self, ids):
""" return the origin bpe tokens according to ids """
if isinstance(ids, int):
return self.decoder.get(ids)
if isinstance(ids, list):
output = []
for item in ids:
output.append(self.decoder.get(item))
return output
raise TypeError(f"The type of ids should be int or list, but found {type(ids)}.")
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index)
def _convert_tokens_to_string(self, tokens):
""" return a string according to the list of tokens"""
text = "".join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors='ignore')
return text
def convert_tokens_to_string(self, tokens):
"""Convert the tokens to the string"""
return self._convert_tokens_to_string(tokens)
def prepare_for_tokenization(self, text, **kwargs):
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
is_split_into_words = kwargs.pop("is_split_into_words", False)
if is_split_into_words or add_prefix_space:
text = " " + text
return (text, kwargs)
def save_vocabulary(self, save_directory, filename_prefix):
"""write the word to the files"""
output_file_path = os.path.join(save_directory, filename_prefix)
with open(output_file_path, 'w') as fp:
for k in self.vocab_dict.keys():
fp.write(k + '\n')
return output_file_path
@property
def vocab_size(self):
"""Get the vocab size of the """
return len(self.decoder)
def get_vocab(self):
"""Returns vocab as a dict"""
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
