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111 lines
3.7 KiB
111 lines
3.7 KiB
# Copyright 2024 The HuggingFace Inc. team.
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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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from tokenizers import Tokenizer, decoders, normalizers
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from tokenizers.models import BPE
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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 GemmaTokenizer(TokenizersBackend):
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"""
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Construct a fast Gemma tokenizer (backed by HuggingFace's tokenizers library).
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This tokenizer uses a BPE model with byte fallback, no prefix space, and a normalizer that replaces
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spaces with "▁".
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Args:
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tokenizer_file (`str`, optional):
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A tokenizers JSON file containing the serialization of a tokenizer.
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unk_token (`str`, optional, defaults to "<unk>"):
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The unknown token.
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bos_token (`str`, optional, defaults to "<bos>"):
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The beginning of sequence token.
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eos_token (`str`, optional, defaults to "<eos>"):
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The end of sequence token.
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pad_token (`str`, optional, defaults to "<pad>"):
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The padding token.
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mask_token (`str`, optional, defaults to "<mask>"):
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The mask token.
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add_bos_token (`bool`, optional, defaults to True):
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Whether or not to add a `bos_token` at the start of sequences.
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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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vocab (`str` or `dict[str, int]`, optional):
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Custom vocabulary dict. If not provided, a minimal vocabulary is created using the special tokens.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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padding_side = "left"
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model_input_names = ["input_ids", "attention_mask"]
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model = BPE
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def __init__(
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self,
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vocab: str | dict[str, int] | None = None,
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merges: str | list[str] | None = None,
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unk_token: str = "<unk>",
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bos_token: str = "<bos>",
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eos_token: str = "<eos>",
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pad_token: str = "<pad>",
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mask_token: str = "<mask>",
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**kwargs,
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):
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if vocab is None:
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vocab = {
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str(pad_token): 0,
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str(eos_token): 1,
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str(bos_token): 2,
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str(unk_token): 3,
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str(mask_token): 4,
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}
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self._vocab = vocab
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self._merges = merges or []
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self._tokenizer = Tokenizer(
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BPE(
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vocab=self._vocab,
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merges=self._merges,
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fuse_unk=True,
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unk_token=str(unk_token),
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dropout=None,
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byte_fallback=True,
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)
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)
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self._tokenizer.decoder = decoders.Sequence(
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[decoders.Replace("▁", " "), decoders.ByteFallback(), decoders.Fuse()]
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)
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self._tokenizer.normalizer = normalizers.Replace(" ", "▁")
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super().__init__(
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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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mask_token=mask_token,
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**kwargs,
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)
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def _unk_id(self) -> int:
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# Align with historical Gemma convention: pad, eos, bos, unk
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return 3
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__all__ = ["GemmaTokenizer"]
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