wehub-resource-sync
132 lines
5.1 KiB
Python
132 lines
5.1 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2024 The Qwen team, Alibaba Group and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes for Qwen2."""
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from typing import Optional, Tuple
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from ..tokenizer_utils import AddedToken
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from ..tokenizer_utils_fast import PretrainedTokenizerFast
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from .tokenizer import Qwen2Tokenizer
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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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MAX_MODEL_INPUT_SIZES = {"qwen/qwen-tokenizer": 32768}
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class Qwen2TokenizerFast(PretrainedTokenizerFast):
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"""
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Construct a "fast" Qwen2 tokenizer (backed by PaddleNLP'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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"""
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vocab_files_names = VOCAB_FILES_NAMES
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resource_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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slow_tokenizer_class = Qwen2Tokenizer
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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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**kwargs,
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):
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# We need to at least pass vocab_file and merges_file to base class
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# in case a slow tokenizer needs to be initialized; other can be
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# configured through files.
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# following GPT2TokenizerFast, also adding unk_token, bos_token, and eos_token
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bos_token = (
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AddedToken(bos_token, lstrip=False, rstrip=False, special=True, normalized=False)
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if isinstance(bos_token, str)
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else bos_token
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)
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eos_token = (
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AddedToken(eos_token, lstrip=False, rstrip=False, special=True, normalized=False)
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if isinstance(eos_token, str)
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else eos_token
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)
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unk_token = (
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AddedToken(unk_token, lstrip=False, rstrip=False, special=True, normalized=False)
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if isinstance(unk_token, str)
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else unk_token
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)
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pad_token = (
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AddedToken(pad_token, lstrip=False, rstrip=False, special=True, normalized=False)
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if isinstance(pad_token, str)
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else pad_token
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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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# Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast.save_vocabulary
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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files = self._tokenizer.model.save(save_directory, name=filename_prefix)
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return tuple(files)
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