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96 lines
3.7 KiB
96 lines
3.7 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/siglip2/modular_siglip2.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_siglip2.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Copyright 2025 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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VOCAB_FILES_NAMES = {"tokenizer_file": "tokenizer.json"}
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class Siglip2Tokenizer(TokenizersBackend):
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"""
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Gemma tokenizer + SigLIP2 training default: lowercase normalization.
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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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# Persist for save/load + push_to_hub dynamic tokenizer test
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if hasattr(self, "init_kwargs") and isinstance(self.init_kwargs, dict):
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self.init_kwargs.setdefault("tokenizer_class", self.__class__.__name__)
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backend = getattr(self, "_tokenizer", None)
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if backend is not None and backend.normalizer is not None:
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backend.normalizer = normalizers.Sequence([normalizers.Lowercase(), backend.normalizer])
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__all__ = ["Siglip2Tokenizer"]
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