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143 lines
5.1 KiB
143 lines
5.1 KiB
# Copyright 2021 The Open AI Team Authors and 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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"""Tokenization classes for CLIP."""
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from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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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 = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
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class CLIPTokenizer(TokenizersBackend):
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"""
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Construct a CLIP tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
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Byte-Pair-Encoding.
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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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vocab (`str`, `dict` or `list`, *optional*):
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Vocabulary dict to use for the tokenizer.
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merges (`str` or `list`, *optional*):
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Merges list to use for the BPE 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.
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bos_token (`str`, *optional*, defaults to `"<|startoftext|>"`):
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The beginning of sequence token.
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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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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 = "<|endoftext|>",
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bos_token: str = "<|startoftext|>",
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eos_token: str = "<|endoftext|>",
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pad_token: str = "<|endoftext|>",
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**kwargs,
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):
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_vocab = (
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vocab
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if vocab is not None
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else {
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str(bos_token): 0,
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str(eos_token): 1,
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str(pad_token): 2,
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}
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)
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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=_vocab,
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merges=self._merges,
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dropout=None,
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continuing_subword_prefix="",
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end_of_word_suffix="</w>",
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fuse_unk=False,
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unk_token=str(unk_token),
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)
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)
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self._tokenizer.normalizer = normalizers.Sequence(
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[normalizers.NFC(), normalizers.Replace(Regex(r"\s+"), " "), normalizers.Lowercase()]
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)
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self._tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
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[
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pre_tokenizers.Split(
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Regex(
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r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+"""
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),
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behavior="removed",
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invert=True,
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),
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pre_tokenizers.ByteLevel(add_prefix_space=False),
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]
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)
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self._tokenizer.decoder = decoders.ByteLevel()
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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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**kwargs,
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)
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self._tokenizer.post_processor = processors.RobertaProcessing(
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sep=(str(eos_token), self.eos_token_id),
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cls=(str(bos_token), self.bos_token_id),
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add_prefix_space=False,
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trim_offsets=False,
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)
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# Very ugly hack to enable padding to have a correct decoding see https://github.com/huggingface/tokenizers/issues/872
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self._wrap_decode_method_backend_tokenizer()
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def _wrap_decode_method_backend_tokenizer(self):
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orig_decode_method = self.backend_tokenizer.decode
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## define this as a local variable to avoid circular reference
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## See: https://github.com/huggingface/transformers/issues/30930
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end_of_word_suffix = self.backend_tokenizer.model.end_of_word_suffix
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def new_decode_method(*args, **kwargs):
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text = orig_decode_method(*args, **kwargs)
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text = text.replace(end_of_word_suffix, " ").strip()
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return text
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self.backend_tokenizer.decode = new_decode_method
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__all__ = ["CLIPTokenizer"]
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