siglip-so400m-14-980-flash-attn2-navit / tokenization_siglip.py

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	# coding=utf-8
	# Copyright 2024 The HuggingFace Inc. team.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" Tokenization class for SigLIP model."""

	import os
	import re
	import string
	import warnings
	from shutil import copyfile
	from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

	import sentencepiece as spm

	from transformers.convert_slow_tokenizer import import_protobuf
	from transformers.tokenization_utils import PreTrainedTokenizer
	from transformers.tokenization_utils_base import AddedToken


	if TYPE_CHECKING:
	from transformers.tokenization_utils_base import TextInput
	from transformers.utils import logging, requires_backends


	logger = logging.get_logger(__name__)

	VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}

	PRETRAINED_VOCAB_FILES_MAP = {
	"vocab_file": {
	"google/siglip-base-patch16-224": "https://huggingface.co/google/siglip-base-patch16-224/resolve/main/spiece.model",
	}
	}

	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
	"google/siglip-base-patch16-224": 256,
	}

	SPIECE_UNDERLINE = "▁"


	class SiglipTokenizer(PreTrainedTokenizer):
	"""
	Construct a Siglip tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).

	This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
	this superclass for more information regarding those methods.

	Args:
	vocab_file (`str`):
	[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
	contains the vocabulary necessary to instantiate a tokenizer.
	eos_token (`str`, optional, defaults to `"</s>"`):
	The end of sequence token.
	unk_token (`str`, optional, defaults to `"<unk>"`):
	The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
	token instead.
	pad_token (`str`, optional, defaults to `"</s>"`):
	The token used for padding, for example when batching sequences of different lengths.
	additional_special_tokens (`List[str]`, optional):
	Additional special tokens used by the tokenizer.
	sp_model_kwargs (`dict`, optional):
	Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
	SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
	to set:

	- `enable_sampling`: Enable subword regularization.
	- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.

	- `nbest_size = {0,1}`: No sampling is performed.
	- `nbest_size > 1`: samples from the nbest_size results.
	- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
	using forward-filtering-and-backward-sampling algorithm.

	- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
	BPE-dropout.
	model_max_length (`int`, optional, defaults to 64):
	The maximum length (in number of tokens) for model inputs.
	do_lower_case (`bool`, optional, defaults to `True`):
	Whether or not to lowercase the input when tokenizing.
	"""

	vocab_files_names = VOCAB_FILES_NAMES
	pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
	max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	model_input_names = ["input_ids", "attention_mask"]

	def __init__(
	self,
	vocab_file,
	eos_token="</s>",
	unk_token="<unk>",
	pad_token="</s>",
	additional_special_tokens=None,
	sp_model_kwargs: Optional[Dict[str, Any]] = None,
	model_max_length=64,
	do_lower_case=True,
	**kwargs,
	) -> None:
	requires_backends(self, "protobuf")

	pad_token = (
	AddedToken(pad_token, rstrip=True, lstrip=True, normalized=False, special=True)
	if isinstance(pad_token, str)
	else pad_token
	)
	unk_token = (
	AddedToken(unk_token, rstrip=True, lstrip=True, normalized=False, special=True)
	if isinstance(unk_token, str)
	else unk_token
	)
	eos_token = (
	AddedToken(eos_token, rstrip=True, lstrip=True, normalized=False, special=True)
	if isinstance(eos_token, str)
	else eos_token
	)

	self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs

	self.do_lower_case = do_lower_case
	self.vocab_file = vocab_file

	self.sp_model = self.get_spm_processor()
	self.vocab_file = vocab_file

	super().__init__(
	eos_token=eos_token,
	unk_token=unk_token,
	pad_token=pad_token,
	additional_special_tokens=additional_special_tokens,
	sp_model_kwargs=self.sp_model_kwargs,
	model_max_length=model_max_length,
	do_lower_case=do_lower_case,
	**kwargs,
	)

	def get_spm_processor(self):
	tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
	with open(self.vocab_file, "rb") as f:
	sp_model = f.read()
	model_pb2 = import_protobuf()
	model = model_pb2.ModelProto.FromString(sp_model)
	normalizer_spec = model_pb2.NormalizerSpec()
	normalizer_spec.add_dummy_prefix = False
	model.normalizer_spec.MergeFrom(normalizer_spec)
	sp_model = model.SerializeToString()
	tokenizer.LoadFromSerializedProto(sp_model)
	return tokenizer

	@property
	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.vocab_size
	def vocab_size(self):
	return self.sp_model.get_piece_size()

	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_vocab
	def get_vocab(self):
	vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
	vocab.update(self.added_tokens_encoder)
	return vocab

	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_special_tokens_mask
	def get_special_tokens_mask(
	self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
	) -> List[int]:
	"""
	Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
	special tokens using the tokenizer `prepare_for_model` method.

	Args:
	token_ids_0 (`List[int]`):
	List of IDs.
	token_ids_1 (`List[int]`, optional):
	Optional second list of IDs for sequence pairs.
	already_has_special_tokens (`bool`, optional, defaults to `False`):
	Whether or not the token list is already formatted with special tokens for the model.

	Returns:
	`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
	"""
	if already_has_special_tokens:
	return super().get_special_tokens_mask(
	token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
	)

	# normal case: some special tokens
	if token_ids_1 is None:
	return ([0] * len(token_ids_0)) + [1]
	return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]

	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._add_eos_if_not_present
	def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
	"""Do not add eos again if user already added it."""
	if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
	warnings.warn(
	f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
	" eos tokens being added."
	)
	return token_ids
	else:
	return token_ids + [self.eos_token_id]

	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.create_token_type_ids_from_sequences
	def create_token_type_ids_from_sequences(
	self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
	) -> List[int]:
	"""
	Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
	use of token type ids, therefore a list of zeros is returned.

	Args:
	token_ids_0 (`List[int]`):
	List of IDs.
	token_ids_1 (`List[int]`, optional):
	Optional second list of IDs for sequence pairs.

	Returns:
	`List[int]`: List of zeros.
	"""
	eos = [self.eos_token_id]

	if token_ids_1 is None:
	return len(token_ids_0 + eos) * [0]
	return len(token_ids_0 + eos + token_ids_1 + eos) * [0]

	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.build_inputs_with_special_tokens
	def build_inputs_with_special_tokens(
	self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
	) -> List[int]:
	"""
	Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
	adding special tokens. A sequence has the following format:

	- single sequence: `X </s>`
	- pair of sequences: `A </s> B </s>`

	Args:
	token_ids_0 (`List[int]`):
	List of IDs to which the special tokens will be added.
	token_ids_1 (`List[int]`, optional):
	Optional second list of IDs for sequence pairs.

	Returns:
	`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
	"""
	token_ids_0 = self._add_eos_if_not_present(token_ids_0)
	if token_ids_1 is None:
	return token_ids_0
	else:
	token_ids_1 = self._add_eos_if_not_present(token_ids_1)
	return token_ids_0 + token_ids_1

	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.__getstate__
	def __getstate__(self):
	state = self.__dict__.copy()
	state["sp_model"] = None
	return state

	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.__setstate__
	def __setstate__(self, d):
	self.__dict__ = d

	# for backward compatibility
	if not hasattr(self, "sp_model_kwargs"):
	self.sp_model_kwargs = {}

	self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
	self.sp_model.Load(self.vocab_file)

	def remove_punctuation(self, text: str) -> str:
	return text.translate(str.maketrans("", "", string.punctuation))

	# source: https://github.com/google-research/big_vision/blob/3b8e5ab6ad4f96e32b32826f9e1b8fd277914f9c/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94
	def canonicalize_text(self, text, *, keep_punctuation_exact_string=None):
	"""Returns canonicalized `text` (puncuation removed).

	Args:
	text (`str`):
	String to be canonicalized.
	keep_punctuation_exact_string (`str`, optional):
	If provided, then this exact string is kept. For example providing '{}' will keep any occurrences of '{}'
	(but will still remove '{' and '}' that appear separately).
	"""
	if keep_punctuation_exact_string:
	text = keep_punctuation_exact_string.join(
	self.remove_punctuation(part) for part in text.split(keep_punctuation_exact_string)
	)
	else:
	text = self.remove_punctuation(text)
	text = re.sub(r"\s+", " ", text)
	text = text.strip()

	return text

	def tokenize(self, text: "TextInput", add_special_tokens=False, **kwargs) -> List[str]:
	"""
	Converts a string to a list of tokens.
	"""
	tokens = super().tokenize(SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " "), **kwargs)

	if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
	tokens = tokens[1:]
	return tokens

	@property
	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.unk_token_length
	def unk_token_length(self):
	return len(self.sp_model.encode(str(self.unk_token)))

	def _tokenize(self, text, **kwargs):
	"""
	Returns a tokenized string.

	We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
	SPIECE_UNDERLINE.

	For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give `['H', 'e', 'y']` instead of `['▁He', 'y']`.

	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`.
	`self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
	"""
	text = self.canonicalize_text(text, keep_punctuation_exact_string=None)
	tokens = self.sp_model.encode(text, out_type=str)

	# 1. Encode string + prefix ex: "<unk> Hey"
	tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
	# 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
	return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens

	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._convert_token_to_id
	def _convert_token_to_id(self, token):
	"""Converts a token (str) in an id using the vocab."""
	return self.sp_model.piece_to_id(token)

	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._convert_id_to_token
	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

	# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.convert_tokens_to_string
	def convert_tokens_to_string(self, tokens):
	"""Converts a sequence of tokens (string) in a single string."""
	current_sub_tokens = []
	# since we manually add the prefix space, we have to remove it
	tokens[0] = tokens[0].lstrip(SPIECE_UNDERLINE)
	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,)