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Grounded-Segment-Anything / transformers_4_35_0 /models /cpm /tokenization_cpm.py

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	# coding=utf-8
	# Copyright 2018 The Google AI Language Team Authors and 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."""
	import os
	import unicodedata
	from shutil import copyfile
	from typing import Any, Dict, List, Optional, Tuple

	import sentencepiece as spm

	from ...tokenization_utils import AddedToken, PreTrainedTokenizer
	from ...utils import SPIECE_UNDERLINE, logging


	logger = logging.get_logger(__name__)

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

	PRETRAINED_VOCAB_FILES_MAP = {
	"vocab_file": {
	"TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
	}
	}


	class CpmTokenizer(PreTrainedTokenizer):
	"""Runs pre-tokenization with Jieba segmentation tool. It is used in CPM models."""

	vocab_files_names = VOCAB_FILES_NAMES
	pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP

	def __init__(
	self,
	vocab_file,
	do_lower_case=False,
	remove_space=True,
	keep_accents=False,
	bos_token="<s>",
	eos_token="</s>",
	unk_token="<unk>",
	sep_token="<sep>",
	pad_token="<pad>",
	cls_token="<cls>",
	mask_token="<mask>",
	additional_special_tokens=["<eop>", "<eod>"],
	sp_model_kwargs: Optional[Dict[str, Any]] = None,
	**kwargs,
	) -> None:
	"""
	Construct a CPM tokenizer. Based on [Jieba](https://pypi.org/project/jieba/) and
	[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.
	do_lower_case (`bool`, optional, defaults to `True`):
	Whether to lowercase the input when tokenizing.
	remove_space (`bool`, optional, defaults to `True`):
	Whether to strip the text when tokenizing (removing excess spaces before and after the string).
	keep_accents (`bool`, optional, defaults to `False`):
	Whether to keep accents when tokenizing.
	bos_token (`str`, optional, defaults to `"<s>"`):
	The beginning of sequence token that was used during pretraining. Can be used a sequence classifier
	token.

	<Tip>

	When building a sequence using special tokens, this is not the token that is used for the beginning of
	sequence. The token used is the `cls_token`.

	</Tip>

	eos_token (`str`, optional, defaults to `"</s>"`):
	The end of sequence token.

	<Tip>

	When building a sequence using special tokens, this is not the token that is used for the end of
	sequence. The token used is the `sep_token`.

	</Tip>

	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.
	additional_special_tokens (`List[str]`, optional, defaults to `["<eop>", "<eod>"]`):
	Additional special tokens used by the tokenizer.

	Attributes:
	sp_model (`SentencePieceProcessor`):
	The SentencePiece processor that is used for every conversion (string, tokens and IDs).
	"""
	# Mask token behave like a normal word, i.e. include the space before it
	mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token

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

	self.do_lower_case = do_lower_case
	self.remove_space = remove_space
	self.keep_accents = keep_accents
	self.vocab_file = vocab_file

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

	try:
	import jieba
	except ModuleNotFoundError as error:
	raise error.__class__(
	"You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
	"See https://pypi.org/project/jieba/ for installation."
	)
	self.jieba = jieba
	self.translator = str.maketrans(" \n", "\u2582\u2583")

	super().__init__(
	do_lower_case=do_lower_case,
	remove_space=remove_space,
	keep_accents=keep_accents,
	bos_token=bos_token,
	eos_token=eos_token,
	unk_token=unk_token,
	sep_token=sep_token,
	pad_token=pad_token,
	cls_token=cls_token,
	mask_token=mask_token,
	additional_special_tokens=additional_special_tokens,
	sp_model_kwargs=self.sp_model_kwargs,
	**kwargs,
	)

	self._pad_token_type_id = 3

	@property
	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
	def vocab_size(self):
	return len(self.sp_model)

	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.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.xlnet.tokenization_xlnet.XLNetTokenizer.__getstate__
	def __getstate__(self):
	state = self.__dict__.copy()
	state["sp_model"] = None
	return state

	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.__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)

	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.preprocess_text
	def preprocess_text(self, inputs):
	if self.remove_space:
	outputs = " ".join(inputs.strip().split())
	else:
	outputs = inputs
	outputs = outputs.replace("``", '"').replace("''", '"')

	if not self.keep_accents:
	outputs = unicodedata.normalize("NFKD", outputs)
	outputs = "".join([c for c in outputs if not unicodedata.combining(c)])
	if self.do_lower_case:
	outputs = outputs.lower()

	return outputs

	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer._tokenize
	def _tokenize(self, text: str) -> List[str]:
	"""Tokenize a string."""
	text = self.preprocess_text(text)
	pieces = self.sp_model.encode(text, out_type=str)
	new_pieces = []
	for piece in pieces:
	if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
	cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, ""))
	if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
	if len(cur_pieces[0]) == 1:
	cur_pieces = cur_pieces[1:]
	else:
	cur_pieces[0] = cur_pieces[0][1:]
	cur_pieces.append(piece[-1])
	new_pieces.extend(cur_pieces)
	else:
	new_pieces.append(piece)

	return new_pieces

	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer._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.PieceToId(token)

	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer._convert_id_to_token
	def _convert_id_to_token(self, index):
	"""Converts an index (integer) in a token (str) using the vocab."""
	return self.sp_model.IdToPiece(index)

	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.convert_tokens_to_string
	def convert_tokens_to_string(self, tokens):
	"""Converts a sequence of tokens (strings for sub-words) in a single string."""
	out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
	return out_string

	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.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. An XLNet sequence has the following format:

	- single sequence: `X <sep> <cls>`
	- pair of sequences: `A <sep> B <sep> <cls>`

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

	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.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
	)

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

	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.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. An XLNet
	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_segment_id = [2]

	if token_ids_1 is None:
	return len(token_ids_0 + sep) * [0] + cls_segment_id
	return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + cls_segment_id

	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.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,)

	def _decode(self, args, *kwargs):
	text = super()._decode(args, *kwargs)
	text = text.replace(" ", "").replace("\u2582", " ").replace("\u2583", "\n")
	return text