376 lines
16 KiB
Python
376 lines
16 KiB
Python
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# coding=utf-8
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# 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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""" Tokenization class for SigLIP model."""
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import os
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import re
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import string
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import warnings
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from shutil import copyfile
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
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import sentencepiece as spm
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from ...convert_slow_tokenizer import import_protobuf
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from ...tokenization_utils import PreTrainedTokenizer
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from ...tokenization_utils_base import AddedToken
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if TYPE_CHECKING:
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from ...tokenization_utils_base import TextInput
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from ...utils import logging, requires_backends
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
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SPIECE_UNDERLINE = "▁"
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class SiglipTokenizer(PreTrainedTokenizer):
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"""
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Construct a Siglip tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
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contains the vocabulary necessary to instantiate a tokenizer.
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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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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pad_token (`str`, *optional*, defaults to `"</s>"`):
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The token used for padding, for example when batching sequences of different lengths.
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additional_special_tokens (`List[str]`, *optional*):
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Additional special tokens used by the tokenizer.
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sp_model_kwargs (`dict`, *optional*):
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Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
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SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
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to set:
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- `enable_sampling`: Enable subword regularization.
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- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
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- `nbest_size = {0,1}`: No sampling is performed.
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- `nbest_size > 1`: samples from the nbest_size results.
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- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
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using forward-filtering-and-backward-sampling algorithm.
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- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
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BPE-dropout.
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model_max_length (`int`, *optional*, defaults to 64):
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The maximum length (in number of tokens) for model inputs.
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do_lower_case (`bool`, *optional*, defaults to `True`):
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Whether or not to lowercase the input when tokenizing.
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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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def __init__(
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self,
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vocab_file,
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eos_token="</s>",
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unk_token="<unk>",
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pad_token="</s>",
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additional_special_tokens=None,
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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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model_max_length=64,
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do_lower_case=True,
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**kwargs,
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) -> None:
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requires_backends(self, "protobuf")
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pad_token = (
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AddedToken(pad_token, rstrip=True, lstrip=True, normalized=False, special=True)
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if isinstance(pad_token, str)
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else pad_token
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)
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unk_token = (
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AddedToken(unk_token, rstrip=True, lstrip=True, normalized=False, special=True)
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if isinstance(unk_token, str)
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else unk_token
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)
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eos_token = (
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AddedToken(eos_token, rstrip=True, lstrip=True, normalized=False, special=True)
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if isinstance(eos_token, str)
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else eos_token
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)
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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self.do_lower_case = do_lower_case
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self.vocab_file = vocab_file
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self.sp_model = self.get_spm_processor()
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self.vocab_file = vocab_file
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super().__init__(
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eos_token=eos_token,
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unk_token=unk_token,
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pad_token=pad_token,
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additional_special_tokens=additional_special_tokens,
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sp_model_kwargs=self.sp_model_kwargs,
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model_max_length=model_max_length,
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do_lower_case=do_lower_case,
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**kwargs,
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)
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def get_spm_processor(self):
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tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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with open(self.vocab_file, "rb") as f:
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sp_model = f.read()
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model_pb2 = import_protobuf()
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model = model_pb2.ModelProto.FromString(sp_model)
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normalizer_spec = model_pb2.NormalizerSpec()
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normalizer_spec.add_dummy_prefix = False
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model.normalizer_spec.MergeFrom(normalizer_spec)
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sp_model = model.SerializeToString()
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tokenizer.LoadFromSerializedProto(sp_model)
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return tokenizer
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@property
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# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.vocab_size
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def vocab_size(self):
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return self.sp_model.get_piece_size()
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# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_vocab
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def get_vocab(self):
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_special_tokens_mask
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def get_special_tokens_mask(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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"""
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer `prepare_for_model` method.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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# normal case: some special tokens
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if token_ids_1 is None:
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return ([0] * len(token_ids_0)) + [1]
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return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
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# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._add_eos_if_not_present
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def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
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"""Do not add eos again if user already added it."""
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if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
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warnings.warn(
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f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
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" eos tokens being added."
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)
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return token_ids
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else:
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return token_ids + [self.eos_token_id]
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# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.create_token_type_ids_from_sequences
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def create_token_type_ids_from_sequences(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
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use of token type ids, therefore a list of zeros is returned.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of zeros.
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"""
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eos = [self.eos_token_id]
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if token_ids_1 is None:
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return len(token_ids_0 + eos) * [0]
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return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
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# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.build_inputs_with_special_tokens
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def build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. A sequence has the following format:
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- single sequence: `X </s>`
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- pair of sequences: `A </s> B </s>`
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Args:
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token_ids_0 (`List[int]`):
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List of IDs to which the special tokens will be added.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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token_ids_0 = self._add_eos_if_not_present(token_ids_0)
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if token_ids_1 is None:
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return token_ids_0
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else:
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token_ids_1 = self._add_eos_if_not_present(token_ids_1)
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return token_ids_0 + token_ids_1
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# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.__getstate__
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def __getstate__(self):
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state = self.__dict__.copy()
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state["sp_model"] = None
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return state
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# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.__setstate__
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def __setstate__(self, d):
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self.__dict__ = d
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# for backward compatibility
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if not hasattr(self, "sp_model_kwargs"):
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self.sp_model_kwargs = {}
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(self.vocab_file)
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def remove_punctuation(self, text: str) -> str:
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return text.translate(str.maketrans("", "", string.punctuation))
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# source: https://github.com/google-research/big_vision/blob/3b8e5ab6ad4f96e32b32826f9e1b8fd277914f9c/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94
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def canonicalize_text(self, text, *, keep_punctuation_exact_string=None):
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"""Returns canonicalized `text` (puncuation removed).
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Args:
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text (`str`):
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String to be canonicalized.
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keep_punctuation_exact_string (`str`, *optional*):
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If provided, then this exact string is kept. For example providing '{}' will keep any occurrences of '{}'
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(but will still remove '{' and '}' that appear separately).
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"""
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if keep_punctuation_exact_string:
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text = keep_punctuation_exact_string.join(
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self.remove_punctuation(part) for part in text.split(keep_punctuation_exact_string)
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)
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else:
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text = self.remove_punctuation(text)
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text = re.sub(r"\s+", " ", text)
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text = text.strip()
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return text
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def tokenize(self, text: "TextInput", add_special_tokens=False, **kwargs) -> List[str]:
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"""
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Converts a string to a list of tokens.
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"""
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tokens = super().tokenize(SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " "), **kwargs)
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if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
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tokens = tokens[1:]
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return tokens
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@property
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# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.unk_token_length
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def unk_token_length(self):
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return len(self.sp_model.encode(str(self.unk_token)))
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def _tokenize(self, text, **kwargs):
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"""
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Returns a tokenized string.
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We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
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SPIECE_UNDERLINE.
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For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give `['H', 'e', 'y']` instead of `['▁He', 'y']`.
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Thus we always encode `f"{unk_token}text"` and strip the `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
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`self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
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"""
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text = self.canonicalize_text(text, keep_punctuation_exact_string=None)
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tokens = self.sp_model.encode(text, out_type=str)
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# 1. Encode string + prefix ex: "<unk> Hey"
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tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
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# 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
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return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
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# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._convert_token_to_id
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.sp_model.piece_to_id(token)
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# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._convert_id_to_token
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def _convert_id_to_token(self, index):
|
||
|
|
"""Converts an index (integer) in a token (str) using the vocab."""
|
||
|
|
token = self.sp_model.IdToPiece(index)
|
||
|
|
return token
|
||
|
|
|
||
|
|
def convert_tokens_to_string(self, tokens):
|
||
|
|
"""Converts a sequence of tokens (string) in a single string."""
|
||
|
|
current_sub_tokens = []
|
||
|
|
out_string = ""
|
||
|
|
prev_is_special = False
|
||
|
|
for token in tokens:
|
||
|
|
# make sure that special tokens are not decoded using sentencepiece model
|
||
|
|
if token in self.all_special_tokens:
|
||
|
|
if not prev_is_special:
|
||
|
|
out_string += " "
|
||
|
|
out_string += self.sp_model.decode(current_sub_tokens) + token
|
||
|
|
prev_is_special = True
|
||
|
|
current_sub_tokens = []
|
||
|
|
else:
|
||
|
|
current_sub_tokens.append(token)
|
||
|
|
prev_is_special = False
|
||
|
|
out_string += self.sp_model.decode(current_sub_tokens)
|
||
|
|
return out_string.strip()
|
||
|
|
|
||
|
|
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.save_vocabulary
|
||
|
|
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||
|
|
if not os.path.isdir(save_directory):
|
||
|
|
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
||
|
|
return
|
||
|
|
out_vocab_file = os.path.join(
|
||
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
||
|
|
)
|
||
|
|
|
||
|
|
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
||
|
|
copyfile(self.vocab_file, out_vocab_file)
|
||
|
|
elif not os.path.isfile(self.vocab_file):
|
||
|
|
with open(out_vocab_file, "wb") as fi:
|
||
|
|
content_spiece_model = self.sp_model.serialized_model_proto()
|
||
|
|
fi.write(content_spiece_model)
|
||
|
|
|
||
|
|
return (out_vocab_file,)
|