199 lines
7.8 KiB
Python
199 lines
7.8 KiB
Python
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# coding=utf-8
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# Copyright 2021 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 Perceiver."""
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from typing import Dict, List, Optional, Tuple
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class PerceiverTokenizer(PreTrainedTokenizer):
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"""
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Construct a Perceiver tokenizer. The Perceiver simply uses raw bytes utf-8 encoding.
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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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pad_token (`str`, *optional*, defaults to `"[PAD]"`):
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The token used for padding, for example when batching sequences of different lengths.
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bos_token (`str`, *optional*, defaults to `"[BOS]"`):
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The BOS token (reserved in the vocab, but not actually used).
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eos_token (`str`, *optional*, defaults to `"[EOS]"`):
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The end of sequence token (reserved in the vocab, but not actually used).
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the end of sequence.
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The token used is the `sep_token`.
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</Tip>
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mask_token (`str`, *optional*, defaults to `"[MASK]"`):
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The MASK token, useful for masked language modeling.
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cls_token (`str`, *optional*, defaults to `"[CLS]"`):
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The CLS token (reserved in the vocab, but not actually used).
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sep_token (`str`, *optional*, defaults to `"[SEP]"`):
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The separator token, which is used when building a sequence from two sequences.
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"""
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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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pad_token="[PAD]",
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bos_token="[BOS]",
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eos_token="[EOS]",
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mask_token="[MASK]",
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cls_token="[CLS]",
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sep_token="[SEP]",
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model_max_length=2048,
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**kwargs,
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) -> None:
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pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
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bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
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eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
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mask_token = AddedToken(mask_token, lstrip=False, rstrip=False) if isinstance(mask_token, str) else mask_token
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cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
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sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
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self._utf_vocab_size = 2**8 # utf is 8 bits
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# Since these tokens are not part of the vocabulary, we manually add them
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self._added_tokens_decoder: Dict[str, int] = {
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0: pad_token,
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1: bos_token,
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2: eos_token,
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3: mask_token,
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4: cls_token,
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5: sep_token,
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}
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self._num_special_tokens = len(self._added_tokens_decoder)
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super().__init__(
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pad_token=pad_token,
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bos_token=bos_token,
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eos_token=eos_token,
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mask_token=mask_token,
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cls_token=cls_token,
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sep_token=sep_token,
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model_max_length=model_max_length,
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**kwargs,
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)
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def get_vocab(self) -> Dict[str, int]:
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vocab = {}
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for i in range(self._utf_vocab_size):
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token = chr(i)
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vocab[token] = i + self._num_special_tokens
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vocab.update(self.added_tokens_encoder)
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return vocab
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@property
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def vocab_size(self):
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return self._utf_vocab_size
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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 [1] + [0] * len(token_ids_0) + [1]
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return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
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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. A sequence has the
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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 None:
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return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
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else:
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return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + token_ids_1 + [self.sep_token_id]
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def _tokenize(self, text: str) -> List[str]:
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"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
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tokens = [chr(i) for i in text.encode("utf-8")]
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return tokens
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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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if len(token) != 1:
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token_id = self.unk_token_id
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else:
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token_id = ord(token) + self._num_special_tokens
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return token_id
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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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token = chr(index - self._num_special_tokens)
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return token
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# TODO @ArthurZ refactor this as well....
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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bstring = b""
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for token in tokens:
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if token in self.added_tokens_encoder:
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tok_string = str(token).encode("utf-8")
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else:
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tok_string = bytes([ord(token)])
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bstring += tok_string
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string = bstring.decode("utf-8", errors="replace")
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return string
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# PerceiverTokenizer has no vocab file
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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return ()
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