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