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from typing import List, Optional
from transformers.models.qwen2.tokenization_qwen2 import Qwen2Tokenizer as OriginalQwen2Tokenizer
from transformers.models.qwen2.tokenization_qwen2_fast import Qwen2TokenizerFast as OriginalQwen2TokenizerFast
from tokenizers import processors
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_file": "tokenizer.json",
}
class Qwen2Tokenizer(OriginalQwen2Tokenizer):
"""
Construct a Qwen2 tokenizer. Based on byte-level Byte-Pair-Encoding.
Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import Qwen2Tokenizer
>>> tokenizer = Qwen2Tokenizer.from_pretrained("Qwen/Qwen-tokenizer")
>>> tokenizer("Hello world")["input_ids"]
[9707, 1879]
>>> tokenizer(" Hello world")["input_ids"]
[21927, 1879]
```
This is expected.
You should not use GPT2Tokenizer instead, because of the different pretokenization rules.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
bos_token (`str`, *optional*):
The beginning of sequence token. Not applicable for this tokenizer.
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The end of sequence token.
pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The token used for padding, for example when batching sequences of different lengths.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not the model should cleanup the spaces that were added when splitting the input text during the
tokenization process. Not applicable to this tokenizer, since tokenization does not add spaces.
split_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the special tokens should be split during the tokenization process. The default behavior is
to not split special tokens. This means that if `<|endoftext|>` is the `eos_token`, then `tokenizer.tokenize("<|endoftext|>") =
['<|endoftext|>`]. Otherwise, if `split_special_tokens=True`, then `tokenizer.tokenize("<|endoftext|>")` will be give `['<',
'|', 'endo', 'ft', 'ext', '|', '>']`. This argument is only supported for `slow` tokenizers for the moment.
add_eos_token (`bool`, *optional*, defaults to `False`):
Whether or not to add an `eos_token` at the end of sequences.
"""
def __init__(
self,
vocab_file,
merges_file,
errors="replace",
unk_token="<|endoftext|>",
bos_token=None,
eos_token="<|endoftext|>",
pad_token="<|endoftext|>",
clean_up_tokenization_spaces=False,
split_special_tokens=False,
add_eos_token=False,
**kwargs,
):
# The add_eos_token code was inspired by the LlamaTokenizer
self.add_eos_token = add_eos_token
super().__init__(
vocab_file=vocab_file,
merges_file=merges_file,
errors=errors,
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
split_special_tokens=split_special_tokens,
add_eos_token=add_eos_token,
**kwargs,
)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
output = token_ids_0 + eos_token_id
if token_ids_1 is not None:
output = output + token_ids_1 + eos_token_id
return output
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
eos_token_id = [1] if self.add_eos_token else []
if token_ids_1 is None:
return ([0] * len(token_ids_0)) + eos_token_id
return (
([0] * len(token_ids_0))
+ eos_token_id
+ ([0] * len(token_ids_1))
+ eos_token_id
)
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
sequence pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
if token_ids_1 is None, only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
output = [0] * len(token_ids_0 + eos_token_id)
if token_ids_1 is not None:
output += [1] * len(token_ids_1 + eos_token_id)
return output
class Qwen2TokenizerFast(OriginalQwen2TokenizerFast):
"""
Construct a "fast" Qwen2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import Qwen2TokenizerFast
>>> tokenizer = Qwen2TokenizerFast.from_pretrained("Qwen/Qwen-tokenizer")
>>> tokenizer("Hello world")["input_ids"]
[9707, 1879]
>>> tokenizer(" Hello world")["input_ids"]
[21927, 1879]
```
This is expected.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`, *optional*):
Path to the vocabulary file.
merges_file (`str`, *optional*):
Path to the merges file.
tokenizer_file (`str`, *optional*):
Path to [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
contains everything needed to load the tokenizer.
unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead. Not applicable to this tokenizer.
bos_token (`str`, *optional*):
The beginning of sequence token. Not applicable for this tokenizer.
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The end of sequence token.
pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The token used for padding, for example when batching sequences of different lengths.
add_eos_token (`bool`, *optional*, defaults to `False`):
Whether or not to add an `eos_token` at the end of sequences.
"""
slow_tokenizer_class = Qwen2Tokenizer
padding_side = "left"
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
unk_token="<|endoftext|>",
bos_token=None,
eos_token="<|endoftext|>",
pad_token="<|endoftext|>",
add_eos_token=False,
**kwargs,
):
super().__init__(
vocab_file=vocab_file,
merges_file=merges_file,
tokenizer_file=tokenizer_file,
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
**kwargs,
)
self._add_eos_token = add_eos_token
self.update_post_processor()
def update_post_processor(self):
"""
Updates the underlying post processor with the current `eos_token`.
"""
eos = self.eos_token
eos_token_id = self.eos_token_id
if eos is None and self.add_eos_token:
raise ValueError("add_eos_token = True but eos_token = None")
single = f"$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
pair = f"{single} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
special_tokens = []
if self.add_eos_token:
special_tokens.append((eos, eos_token_id))
self._tokenizer.post_processor = processors.TemplateProcessing(
single=single, pair=pair, special_tokens=special_tokens
)
@property
def add_eos_token(self):
return self._add_eos_token |