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127 lines
5.1 KiB
127 lines
5.1 KiB
# Copyright The HuggingFace Team 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 XGLM."""
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from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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from tokenizers.models import Unigram
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from ...tokenization_utils_tokenizers import TokenizersBackend
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"tokenizer_file": "tokenizer.json"}
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class XGLMTokenizer(TokenizersBackend):
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"""
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Construct a XGLM tokenizer (backed by HuggingFace's tokenizers library). Based on BPE.
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This tokenizer inherits from [`TokenizersBackend`] 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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tokenizer_file (`str`, *optional*):
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Path to a tokenizers JSON file containing the serialization of a tokenizer.
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bos_token (`str`, *optional*, defaults to `"<s>"`):
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The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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sep_token (`str`, *optional*, defaults to `"</s>"`):
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The separator token, which is used when building a sequence from multiple sequences.
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cls_token (`str`, *optional*, defaults to `"<s>"`):
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The classifier token which is used when doing sequence classification.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding.
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vocab (`str`, `dict` or `list`, *optional*):
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Custom vocabulary dictionary. If not provided, a minimal vocabulary is created.
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merges (`list[tuple[str, str]]`, *optional*):
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Custom merge rules for BPE. If not provided, merges are generated from the vocabulary.
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add_prefix_space (`bool`, *optional*, defaults to `True`):
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Whether to add a prefix space before encoding.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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model = Unigram
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def __init__(
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self,
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vocab: str | list[tuple[str, float]] | None = None,
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bos_token: str = "<s>",
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eos_token: str = "</s>",
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sep_token: str = "</s>",
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cls_token: str = "<s>",
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unk_token: str = "<unk>",
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pad_token: str = "<pad>",
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add_prefix_space: bool = True,
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**kwargs,
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):
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self.num_madeup_words = 7
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madeup_words = [f"<madeupword{i}>" for i in range(self.num_madeup_words)]
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kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", []) or []
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kwargs["additional_special_tokens"] += [
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word for word in madeup_words if word not in kwargs["additional_special_tokens"]
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]
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self.add_prefix_space = add_prefix_space
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if vocab is not None:
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self._vocab = vocab
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else:
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self._vocab = [
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(str(bos_token), 0.0),
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(str(pad_token), 0.0),
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(str(eos_token), 0.0),
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(str(unk_token), 0.0),
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]
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self._tokenizer = Tokenizer(Unigram(vocab=self._vocab, unk_id=3, byte_fallback=False))
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self._tokenizer.normalizer = normalizers.Sequence(
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[
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normalizers.Replace(Regex(r"[\n\r\t]"), " "),
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normalizers.NFKC(),
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normalizers.Replace(Regex(r" {2,}"), " "),
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]
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)
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prepend_scheme = "always" if add_prefix_space else "never"
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self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(replacement="▁", prepend_scheme=prepend_scheme)
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self._tokenizer.decoder = decoders.Metaspace(replacement="▁", prepend_scheme=prepend_scheme)
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super().__init__(
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bos_token=bos_token,
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eos_token=eos_token,
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sep_token=sep_token,
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cls_token=cls_token,
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unk_token=unk_token,
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pad_token=pad_token,
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add_prefix_space=add_prefix_space,
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**kwargs,
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)
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self._tokenizer.post_processor = processors.TemplateProcessing(
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single=f"{self.eos_token} $A {self.eos_token}",
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pair=f"{self.eos_token} $A {self.eos_token} {self.eos_token} $B {self.eos_token}",
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special_tokens=[
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(self.bos_token, self.bos_token_id),
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(self.eos_token, self.eos_token_id),
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],
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)
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__all__ = ["XGLMTokenizer"]
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