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Grounded-Segment-Anything / transformers_4_35_0 /models /convbert /tokenization_convbert.py

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	# coding=utf-8
	# Copyright 2018 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 classes for ConvBERT."""
	import collections
	import os
	import unicodedata
	from typing import List, Optional, Tuple

	from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
	from ...utils import logging


	logger = logging.get_logger(__name__)

	VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}

	PRETRAINED_VOCAB_FILES_MAP = {
	"vocab_file": {
	"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt",
	"YituTech/conv-bert-medium-small": (
	"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"
	),
	"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt",
	}
	}

	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
	"YituTech/conv-bert-base": 512,
	"YituTech/conv-bert-medium-small": 512,
	"YituTech/conv-bert-small": 512,
	}


	PRETRAINED_INIT_CONFIGURATION = {
	"YituTech/conv-bert-base": {"do_lower_case": True},
	"YituTech/conv-bert-medium-small": {"do_lower_case": True},
	"YituTech/conv-bert-small": {"do_lower_case": True},
	}


	# Copied from transformers.models.bert.tokenization_bert.load_vocab
	def load_vocab(vocab_file):
	"""Loads a vocabulary file into a dictionary."""
	vocab = collections.OrderedDict()
	with open(vocab_file, "r", encoding="utf-8") as reader:
	tokens = reader.readlines()
	for index, token in enumerate(tokens):
	token = token.rstrip("\n")
	vocab[token] = index
	return vocab


	# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize
	def whitespace_tokenize(text):
	"""Runs basic whitespace cleaning and splitting on a piece of text."""
	text = text.strip()
	if not text:
	return []
	tokens = text.split()
	return tokens


	# Copied from transformers.models.bert.tokenization_bert.BertTokenizer with bert-base-cased->YituTech/conv-bert-base, ConvBertTokenizer->BertTokenizer, BERT->ConvBERT
	class ConvBertTokenizer(PreTrainedTokenizer):
	r"""
	Construct a ConvBERT tokenizer. Based on WordPiece.

	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`):
	File containing the vocabulary.
	do_lower_case (`bool`, optional, defaults to `True`):
	Whether or not to lowercase the input when tokenizing.
	do_basic_tokenize (`bool`, optional, defaults to `True`):
	Whether or not to do basic tokenization before WordPiece.
	never_split (`Iterable`, optional):
	Collection of tokens which will never be split during tokenization. Only has an effect when
	`do_basic_tokenize=True`
	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.
	sep_token (`str`, optional, defaults to `"[SEP]"`):
	The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
	sequence classification or for a text and a question for question answering. It is also used as the last
	token of a sequence built with special tokens.
	pad_token (`str`, optional, defaults to `"[PAD]"`):
	The token used for padding, for example when batching sequences of different lengths.
	cls_token (`str`, optional, defaults to `"[CLS]"`):
	The classifier token which is used when doing sequence classification (classification of the whole sequence
	instead of per-token classification). It is the first token of the sequence when built with special tokens.
	mask_token (`str`, optional, defaults to `"[MASK]"`):
	The token used for masking values. This is the token used when training this model with masked language
	modeling. This is the token which the model will try to predict.
	tokenize_chinese_chars (`bool`, optional, defaults to `True`):
	Whether or not to tokenize Chinese characters.

	This should likely be deactivated for Japanese (see this
	[issue](https://github.com/huggingface/transformers/issues/328)).
	strip_accents (`bool`, optional):
	Whether or not to strip all accents. If this option is not specified, then it will be determined by the
	value for `lowercase` (as in the original ConvBERT).
	"""

	vocab_files_names = VOCAB_FILES_NAMES
	pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
	pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
	max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

	def __init__(
	self,
	vocab_file,
	do_lower_case=True,
	do_basic_tokenize=True,
	never_split=None,
	unk_token="[UNK]",
	sep_token="[SEP]",
	pad_token="[PAD]",
	cls_token="[CLS]",
	mask_token="[MASK]",
	tokenize_chinese_chars=True,
	strip_accents=None,
	**kwargs,
	):
	if not os.path.isfile(vocab_file):
	raise ValueError(
	f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
	" model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
	)
	self.vocab = load_vocab(vocab_file)
	self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
	self.do_basic_tokenize = do_basic_tokenize
	if do_basic_tokenize:
	self.basic_tokenizer = BasicTokenizer(
	do_lower_case=do_lower_case,
	never_split=never_split,
	tokenize_chinese_chars=tokenize_chinese_chars,
	strip_accents=strip_accents,
	)

	self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))

	super().__init__(
	do_lower_case=do_lower_case,
	do_basic_tokenize=do_basic_tokenize,
	never_split=never_split,
	unk_token=unk_token,
	sep_token=sep_token,
	pad_token=pad_token,
	cls_token=cls_token,
	mask_token=mask_token,
	tokenize_chinese_chars=tokenize_chinese_chars,
	strip_accents=strip_accents,
	**kwargs,
	)

	@property
	def do_lower_case(self):
	return self.basic_tokenizer.do_lower_case

	@property
	def vocab_size(self):
	return len(self.vocab)

	def get_vocab(self):
	return dict(self.vocab, **self.added_tokens_encoder)

	def _tokenize(self, text, split_special_tokens=False):
	split_tokens = []
	if self.do_basic_tokenize:
	for token in self.basic_tokenizer.tokenize(
	text, never_split=self.all_special_tokens if not split_special_tokens else None
	):
	# If the token is part of the never_split set
	if token in self.basic_tokenizer.never_split:
	split_tokens.append(token)
	else:
	split_tokens += self.wordpiece_tokenizer.tokenize(token)
	else:
	split_tokens = self.wordpiece_tokenizer.tokenize(text)
	return split_tokens

	def _convert_token_to_id(self, token):
	"""Converts a token (str) in an id using the vocab."""
	return self.vocab.get(token, self.vocab.get(self.unk_token))

	def _convert_id_to_token(self, index):
	"""Converts an index (integer) in a token (str) using the vocab."""
	return self.ids_to_tokens.get(index, self.unk_token)

	def convert_tokens_to_string(self, tokens):
	"""Converts a sequence of tokens (string) in a single string."""
	out_string = " ".join(tokens).replace(" ##", "").strip()
	return out_string

	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 ConvBERT sequence has the following format:

	- single sequence: `[CLS] X [SEP]`
	- pair of sequences: `[CLS] A [SEP] B [SEP]`

	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.
	"""
	if token_ids_1 is None:
	return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
	cls = [self.cls_token_id]
	sep = [self.sep_token_id]
	return cls + token_ids_0 + sep + token_ids_1 + sep

	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
	)

	if token_ids_1 is not None:
	return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
	return [1] + ([0] * len(token_ids_0)) + [1]

	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. A ConvBERT
	sequence pair mask has the following format:

	```
	0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
	\| first sequence \| second sequence \|
	```

	If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).

	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 [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
	"""
	sep = [self.sep_token_id]
	cls = [self.cls_token_id]
	if token_ids_1 is None:
	return len(cls + token_ids_0 + sep) * [0]
	return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]

	def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
	index = 0
	if os.path.isdir(save_directory):
	vocab_file = os.path.join(
	save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
	)
	else:
	vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
	with open(vocab_file, "w", encoding="utf-8") as writer:
	for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
	if index != token_index:
	logger.warning(
	f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
	" Please check that the vocabulary is not corrupted!"
	)
	index = token_index
	writer.write(token + "\n")
	index += 1
	return (vocab_file,)


	# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
	class BasicTokenizer(object):
	"""
	Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).

	Args:
	do_lower_case (`bool`, optional, defaults to `True`):
	Whether or not to lowercase the input when tokenizing.
	never_split (`Iterable`, optional):
	Collection of tokens which will never be split during tokenization. Only has an effect when
	`do_basic_tokenize=True`
	tokenize_chinese_chars (`bool`, optional, defaults to `True`):
	Whether or not to tokenize Chinese characters.

	This should likely be deactivated for Japanese (see this
	[issue](https://github.com/huggingface/transformers/issues/328)).
	strip_accents (`bool`, optional):
	Whether or not to strip all accents. If this option is not specified, then it will be determined by the
	value for `lowercase` (as in the original BERT).
	do_split_on_punc (`bool`, optional, defaults to `True`):
	In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
	the full context of the words, such as contractions.
	"""

	def __init__(
	self,
	do_lower_case=True,
	never_split=None,
	tokenize_chinese_chars=True,
	strip_accents=None,
	do_split_on_punc=True,
	):
	if never_split is None:
	never_split = []
	self.do_lower_case = do_lower_case
	self.never_split = set(never_split)
	self.tokenize_chinese_chars = tokenize_chinese_chars
	self.strip_accents = strip_accents
	self.do_split_on_punc = do_split_on_punc

	def tokenize(self, text, never_split=None):
	"""
	Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.

	Args:
	never_split (`List[str]`, optional)
	Kept for backward compatibility purposes. Now implemented directly at the base class level (see
	[`PreTrainedTokenizer.tokenize`]) List of token not to split.
	"""
	# union() returns a new set by concatenating the two sets.
	never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
	text = self._clean_text(text)

	# This was added on November 1st, 2018 for the multilingual and Chinese
	# models. This is also applied to the English models now, but it doesn't
	# matter since the English models were not trained on any Chinese data
	# and generally don't have any Chinese data in them (there are Chinese
	# characters in the vocabulary because Wikipedia does have some Chinese
	# words in the English Wikipedia.).
	if self.tokenize_chinese_chars:
	text = self._tokenize_chinese_chars(text)
	# prevents treating the same character with different unicode codepoints as different characters
	unicode_normalized_text = unicodedata.normalize("NFC", text)
	orig_tokens = whitespace_tokenize(unicode_normalized_text)
	split_tokens = []
	for token in orig_tokens:
	if token not in never_split:
	if self.do_lower_case:
	token = token.lower()
	if self.strip_accents is not False:
	token = self._run_strip_accents(token)
	elif self.strip_accents:
	token = self._run_strip_accents(token)
	split_tokens.extend(self._run_split_on_punc(token, never_split))

	output_tokens = whitespace_tokenize(" ".join(split_tokens))
	return output_tokens

	def _run_strip_accents(self, text):
	"""Strips accents from a piece of text."""
	text = unicodedata.normalize("NFD", text)
	output = []
	for char in text:
	cat = unicodedata.category(char)
	if cat == "Mn":
	continue
	output.append(char)
	return "".join(output)

	def _run_split_on_punc(self, text, never_split=None):
	"""Splits punctuation on a piece of text."""
	if not self.do_split_on_punc or (never_split is not None and text in never_split):
	return [text]
	chars = list(text)
	i = 0
	start_new_word = True
	output = []
	while i < len(chars):
	char = chars[i]
	if _is_punctuation(char):
	output.append([char])
	start_new_word = True
	else:
	if start_new_word:
	output.append([])
	start_new_word = False
	output[-1].append(char)
	i += 1

	return ["".join(x) for x in output]

	def _tokenize_chinese_chars(self, text):
	"""Adds whitespace around any CJK character."""
	output = []
	for char in text:
	cp = ord(char)
	if self._is_chinese_char(cp):
	output.append(" ")
	output.append(char)
	output.append(" ")
	else:
	output.append(char)
	return "".join(output)

	def _is_chinese_char(self, cp):
	"""Checks whether CP is the codepoint of a CJK character."""
	# This defines a "chinese character" as anything in the CJK Unicode block:
	# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
	#
	# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
	# despite its name. The modern Korean Hangul alphabet is a different block,
	# as is Japanese Hiragana and Katakana. Those alphabets are used to write
	# space-separated words, so they are not treated specially and handled
	# like the all of the other languages.
	if (
	(cp >= 0x4E00 and cp <= 0x9FFF)
	or (cp >= 0x3400 and cp <= 0x4DBF) #
	or (cp >= 0x20000 and cp <= 0x2A6DF) #
	or (cp >= 0x2A700 and cp <= 0x2B73F) #
	or (cp >= 0x2B740 and cp <= 0x2B81F) #
	or (cp >= 0x2B820 and cp <= 0x2CEAF) #
	or (cp >= 0xF900 and cp <= 0xFAFF)
	or (cp >= 0x2F800 and cp <= 0x2FA1F) #
	): #
	return True

	return False

	def _clean_text(self, text):
	"""Performs invalid character removal and whitespace cleanup on text."""
	output = []
	for char in text:
	cp = ord(char)
	if cp == 0 or cp == 0xFFFD or _is_control(char):
	continue
	if _is_whitespace(char):
	output.append(" ")
	else:
	output.append(char)
	return "".join(output)


	# Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
	class WordpieceTokenizer(object):
	"""Runs WordPiece tokenization."""

	def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
	self.vocab = vocab
	self.unk_token = unk_token
	self.max_input_chars_per_word = max_input_chars_per_word

	def tokenize(self, text):
	"""
	Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
	tokenization using the given vocabulary.

	For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`.

	Args:
	text: A single token or whitespace separated tokens. This should have
	already been passed through BasicTokenizer.

	Returns:
	A list of wordpiece tokens.
	"""

	output_tokens = []
	for token in whitespace_tokenize(text):
	chars = list(token)
	if len(chars) > self.max_input_chars_per_word:
	output_tokens.append(self.unk_token)
	continue

	is_bad = False
	start = 0
	sub_tokens = []
	while start < len(chars):
	end = len(chars)
	cur_substr = None
	while start < end:
	substr = "".join(chars[start:end])
	if start > 0:
	substr = "##" + substr
	if substr in self.vocab:
	cur_substr = substr
	break
	end -= 1
	if cur_substr is None:
	is_bad = True
	break
	sub_tokens.append(cur_substr)
	start = end

	if is_bad:
	output_tokens.append(self.unk_token)
	else:
	output_tokens.extend(sub_tokens)
	return output_tokens