346 lines
12 KiB
Python
346 lines
12 KiB
Python
"""Tokenization classes for ChatGLM."""
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import sys
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import unicodedata
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from typing import List, Optional, Union
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from functools import lru_cache
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import os
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import collections
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import re
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from transformers.tokenization_utils import PreTrainedTokenizer
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from icetk.text_tokenizer import TextTokenizer
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from icetk.utils import auto_create
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import icetk.sentencepiece_model_pb2 as sp_model
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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"THUDM/chatglm-6b": 2048,
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}
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class SPTokenizer:
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def __init__(
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self,
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vocab_file,
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max_blank_length=80,
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byte_fallback=True,
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):
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assert vocab_file is not None
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self.vocab_file = vocab_file
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self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"]
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self.max_blank_length = max_blank_length
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self.byte_fallback = byte_fallback
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self.text_tokenizer = self._build_text_tokenizer(encode_special_tokens=False)
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self.special_text_tokenizer = self._build_text_tokenizer(encode_special_tokens=True)
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@staticmethod
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def _configure_tokenizer(
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text_tokenizer: TextTokenizer,
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special_tokens: List[str],
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max_blank_length: int,
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byte_fallback: bool,
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encode_special_tokens=False,
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):
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# special token
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special_token_type = 4 if encode_special_tokens else 3 # 3 - CONTROL, 4 - USER_DEFINE
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for token in special_tokens:
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text_tokenizer.proto.pieces.append(
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sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=special_token_type)
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)
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# whitespaces
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for token in [SPTokenizer.get_tab_token()] + [
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SPTokenizer.get_blank_token(i) for i in range(2, max_blank_length + 1)
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]:
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text_tokenizer.proto.pieces.append(sp_model.ModelProto.SentencePiece(piece=token, score=0.0, type=4))
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# byte fallback
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if byte_fallback:
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text_tokenizer.proto.trainer_spec.byte_fallback = True
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for i in range(256):
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text_tokenizer.proto.pieces.append(
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sp_model.ModelProto.SentencePiece(piece="<0x{:02X}>".format(i), score=0.0, type=6)
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)
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text_tokenizer.refresh()
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def _build_text_tokenizer(self, encode_special_tokens=False):
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tokenizer = TextTokenizer(self.vocab_file)
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self._configure_tokenizer(
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tokenizer, self.special_tokens, self.max_blank_length, self.byte_fallback, encode_special_tokens
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)
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return tokenizer
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def _get_text_tokenizer(self, encode_special_tokens=False):
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if encode_special_tokens:
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return self.special_text_tokenizer
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else:
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return self.text_tokenizer
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@staticmethod
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def get_blank_token(length: int):
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assert length >= 2
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return f"<|blank_{length}|>"
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@staticmethod
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def get_tab_token():
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return f"<|tab|>"
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@property
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def num_image_tokens(self):
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return 20000
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@property
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def num_text_tokens(self):
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return self.text_tokenizer.num_tokens
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@property
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def num_tokens(self):
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return self.num_image_tokens + self.num_text_tokens
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@staticmethod
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def _encode_whitespaces(text: str, max_len: int = 80):
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text = text.replace("\t", SPTokenizer.get_tab_token())
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for i in range(max_len, 1, -1):
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text = text.replace(" " * i, SPTokenizer.get_blank_token(i))
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return text
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def _preprocess(self, text: str, linebreak=True, whitespaces=True):
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if linebreak:
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text = text.replace("\n", "<n>")
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if whitespaces:
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text = self._encode_whitespaces(text, max_len=self.max_blank_length)
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return text
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def encode(
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self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True
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) -> List[int]:
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"""
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@param text: Text to encode.
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@param linebreak: Whether to encode newline (\n) in text.
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@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
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@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
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@param add_dummy_prefix: Whether to add dummy blank space in the beginning.
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"""
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text = self._preprocess(text, linebreak, whitespaces)
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if not add_dummy_prefix:
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text = "<n>" + text
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tmp = self._get_text_tokenizer(encode_special_tokens=special_tokens).encode(text)
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tokens = [x + self.num_image_tokens for x in tmp]
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return tokens if add_dummy_prefix else tokens[2:]
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def decode(self, text_ids: List[int], special_tokens=False) -> str:
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ids = [int(_id) - self.num_image_tokens for _id in text_ids]
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text = self._get_text_tokenizer(encode_special_tokens=special_tokens).decode(ids)
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text = text.replace("<n>", "\n")
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text = text.replace(SPTokenizer.get_tab_token(), "\t")
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for i in range(2, self.max_blank_length + 1):
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text = text.replace(self.get_blank_token(i), " " * i)
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return text
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def tokenize(
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self, text: str, linebreak=True, whitespaces=True, special_tokens=False, add_dummy_prefix=True
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) -> List[str]:
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"""
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@param text: Text to encode.
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@param linebreak: Whether to encode newline (\n) in text.
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@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
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@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
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@param add_dummy_prefix: Whether to add dummy blank space in the beginning.
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"""
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text = self._preprocess(text, linebreak, whitespaces)
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if not add_dummy_prefix:
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text = "<n>" + text
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tokens = self._get_text_tokenizer(encode_special_tokens=special_tokens).tokenize(text)
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return tokens if add_dummy_prefix else tokens[2:]
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def __getitem__(self, x: Union[int, str]):
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if isinstance(x, int):
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if x < self.num_image_tokens:
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return "<image_{}>".format(x)
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else:
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return self.text_tokenizer.convert_id_to_token(x - self.num_image_tokens)
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elif isinstance(x, str):
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if x.startswith("<image_") and x.endswith(">") and x[7:-1].isdigit():
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return int(x[7:-1])
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else:
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return self.text_tokenizer.convert_token_to_id(x) + self.num_image_tokens
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else:
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raise ValueError("The key should be str or int.")
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class ChatGLMTokenizer(PreTrainedTokenizer):
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"""
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Construct a ChatGLM tokenizer. Based on byte-level Byte-Pair-Encoding.
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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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"""
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vocab_files_names = {"vocab_file": "ice_text.model"}
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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model_input_names = ["input_ids"]
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def __init__(
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self,
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vocab_file,
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do_lower_case=False,
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remove_space=False,
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bos_token='sop',
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eos_token='eos',
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eop_token='eop',
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mask_token='[MASK]',
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gmask_token='[gMASK]',
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padding_side="left",
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**kwargs
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) -> None:
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super().__init__(
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do_lower_case=do_lower_case,
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remove_space=remove_space,
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padding_side=padding_side,
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**kwargs
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)
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self.do_lower_case = do_lower_case
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self.remove_space = remove_space
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self.vocab_file = vocab_file
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self.bos_token = bos_token
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self.eos_token = eos_token
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self.eop_token = eop_token
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self.mask_token = mask_token
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self.gMASK_token = gmask_token
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self.sp_tokenizer = SPTokenizer(vocab_file)
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""" Initialisation """
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@property
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def eop_token_id(self) -> Optional[int]:
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"""
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`Optional[int]`: Id of the end of sentence token in the vocabulary. Returns `None` if the token has not been
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set.
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"""
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if self.eop_token is None:
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return None
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return self.convert_tokens_to_ids(self.eop_token)
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@property
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def vocab_size(self):
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""" Returns vocab size """
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return self.sp_tokenizer.num_tokens
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def get_vocab(self):
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""" Returns vocab as a dict """
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vocab = {self._convert_id_to_token(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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def preprocess_text(self, inputs):
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if self.remove_space:
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outputs = " ".join(inputs.strip().split())
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else:
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outputs = inputs
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if self.do_lower_case:
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outputs = outputs.lower()
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return outputs
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def _tokenize(self, text, **kwargs):
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""" Returns a tokenized string. """
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text = self.preprocess_text(text)
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seq = self.sp_tokenizer.tokenize(text)
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return seq
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def decode(
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self,
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token_ids: Union[List[int], List[List[int]]],
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skip_special_tokens: bool = False,
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clean_up_tokenization_spaces: bool = True,
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spaces_between_special_tokens: bool = True,
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**kwargs
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) -> str:
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if isinstance(token_ids[0], list):
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tokens = []
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for single_token_ids in token_ids:
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if self.pad_token_id in single_token_ids: # remove pad
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single_token_ids = list(filter((self.pad_token_id).__ne__, single_token_ids))
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tokens.append(self.sp_tokenizer.decode(single_token_ids))
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return (tokens)
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else:
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if self.pad_token_id in token_ids: # remove pad
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token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
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return self.sp_tokenizer.decode(token_ids)
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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_tokenizer[token]
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.sp_tokenizer[index]
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def save_vocabulary(self, save_directory, filename_prefix=None):
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"""
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Save the vocabulary and special tokens file to a directory.
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Args:
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save_directory (`str`):
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The directory in which to save the vocabulary.
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filename_prefix (`str`, *optional*):
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An optional prefix to add to the named of the saved files.
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Returns:
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`Tuple(str)`: Paths to the files saved.
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"""
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if os.path.isdir(save_directory):
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vocab_file = os.path.join(
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save_directory, VOCAB_FILES_NAMES["vocab_file"]
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)
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else:
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vocab_file = save_directory
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with open(self.vocab_file, 'rb') as fin:
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proto_str = fin.read()
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with open(vocab_file, "wb") as writer:
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writer.write(proto_str)
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return (vocab_file,)
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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 BERT sequence has the following format:
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- single sequence: `[CLS] X [SEP]`
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- pair of sequences: `[CLS] A [SEP] B [SEP]`
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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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if token_ids_1 is not None:
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token_ids_0 += token_ids_1
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mask_ids = self.sp_tokenizer[self.mask_token]
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gmask_ids = self.sp_tokenizer[self.gMASK_token]
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if mask_ids not in token_ids_0 and gmask_ids not in token_ids_0:
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token_ids_0 += [gmask_ids]
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if token_ids_0[-1] != mask_ids and token_ids_0[-1] != gmask_ids:
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token_ids_0 += [self.sp_tokenizer[self.eos_token]]
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token_ids_0 += [self.sp_tokenizer[self.bos_token]]
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return token_ids_0
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