419 lines
14 KiB
Python
419 lines
14 KiB
Python
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from typing import Dict, List, Optional, Tuple, Union
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from tokenizers import AddedToken, EncodeInput, Encoding, InputSequence, Tokenizer
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from tokenizers.decoders import Decoder
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from tokenizers.models import Model
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from tokenizers.normalizers import Normalizer
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from tokenizers.pre_tokenizers import PreTokenizer
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from tokenizers.processors import PostProcessor
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Offsets = Tuple[int, int]
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class BaseTokenizer:
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def __init__(self, tokenizer: Tokenizer, parameters=None):
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self._tokenizer = tokenizer
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self._parameters = parameters if parameters is not None else {}
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def __repr__(self):
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return "Tokenizer(vocabulary_size={}, {})".format(
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self._tokenizer.get_vocab_size(),
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", ".join(k + "=" + str(v) for k, v in self._parameters.items()),
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)
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def num_special_tokens_to_add(self, is_pair: bool) -> int:
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"""
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Return the number of special tokens that would be added for single/pair sentences.
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:param is_pair: Boolean indicating if the input would be a single sentence or a pair
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:return:
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"""
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return self._tokenizer.num_special_tokens_to_add(is_pair)
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def get_vocab(self, with_added_tokens: bool = True) -> Dict[str, int]:
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"""Returns the vocabulary
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Args:
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with_added_tokens: boolean:
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Whether to include the added tokens in the vocabulary
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Returns:
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The vocabulary
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"""
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return self._tokenizer.get_vocab(with_added_tokens=with_added_tokens)
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def get_added_tokens_decoder(self) -> Dict[int, AddedToken]:
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"""Returns the added reverse vocabulary
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Returns:
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The added vocabulary mapping ints to AddedTokens
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"""
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return self._tokenizer.get_added_tokens_decoder()
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def get_vocab_size(self, with_added_tokens: bool = True) -> int:
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"""Return the size of vocabulary, with or without added tokens.
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Args:
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with_added_tokens: (`optional`) bool:
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Whether to count in added special tokens or not
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Returns:
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Size of vocabulary
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"""
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return self._tokenizer.get_vocab_size(with_added_tokens=with_added_tokens)
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def enable_padding(
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self,
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direction: Optional[str] = "right",
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pad_to_multiple_of: Optional[int] = None,
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pad_id: Optional[int] = 0,
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pad_type_id: Optional[int] = 0,
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pad_token: Optional[str] = "[PAD]",
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length: Optional[int] = None,
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):
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"""Change the padding strategy
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Args:
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direction: (`optional`) str:
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Can be one of: `right` or `left`
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pad_to_multiple_of: (`optional`) unsigned int:
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If specified, the padding length should always snap to the next multiple of
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the given value. For example if we were going to pad with a length of 250 but
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`pad_to_multiple_of=8` then we will pad to 256.
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pad_id: (`optional`) unsigned int:
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The indice to be used when padding
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pad_type_id: (`optional`) unsigned int:
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The type indice to be used when padding
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pad_token: (`optional`) str:
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The pad token to be used when padding
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length: (`optional`) unsigned int:
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If specified, the length at which to pad. If not specified
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we pad using the size of the longest sequence in a batch
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"""
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return self._tokenizer.enable_padding(
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direction=direction,
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pad_to_multiple_of=pad_to_multiple_of,
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pad_id=pad_id,
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pad_type_id=pad_type_id,
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pad_token=pad_token,
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length=length,
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)
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def no_padding(self):
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"""Disable padding"""
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return self._tokenizer.no_padding()
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@property
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def padding(self) -> Optional[dict]:
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"""Get the current padding parameters
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Returns:
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None if padding is disabled, a dict with the currently set parameters
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if the padding is enabled.
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"""
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return self._tokenizer.padding
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def enable_truncation(self, max_length: int, stride: Optional[int] = 0, strategy: Optional[str] = "longest_first"):
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"""Change the truncation options
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Args:
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max_length: unsigned int:
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The maximum length at which to truncate
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stride: (`optional`) unsigned int:
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The length of the previous first sequence to be included
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in the overflowing sequence
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strategy: (`optional`) str:
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Can be one of `longest_first`, `only_first` or `only_second`
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"""
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return self._tokenizer.enable_truncation(max_length, stride=stride, strategy=strategy)
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def no_truncation(self):
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"""Disable truncation"""
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return self._tokenizer.no_truncation()
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@property
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def truncation(self) -> Optional[dict]:
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"""Get the current truncation parameters
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Returns:
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None if truncation is disabled, a dict with the current truncation parameters if
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truncation is enabled
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"""
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return self._tokenizer.truncation
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def add_tokens(self, tokens: List[Union[str, AddedToken]]) -> int:
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"""Add the given tokens to the vocabulary
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Args:
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tokens: List[Union[str, AddedToken]]:
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A list of tokens to add to the vocabulary. Each token can either be
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a string, or an instance of AddedToken
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Returns:
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The number of tokens that were added to the vocabulary
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"""
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return self._tokenizer.add_tokens(tokens)
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def add_special_tokens(self, special_tokens: List[Union[str, AddedToken]]) -> int:
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"""Add the given special tokens to the vocabulary, and treat them as special tokens.
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The special tokens will never be processed by the model, and will be
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removed while decoding.
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Args:
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tokens: List[Union[str, AddedToken]]:
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A list of special tokens to add to the vocabulary. Each token can either be
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a string, or an instance of AddedToken
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Returns:
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The number of tokens that were added to the vocabulary
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"""
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return self._tokenizer.add_special_tokens(special_tokens)
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def normalize(self, sequence: str) -> str:
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"""Normalize the given sequence
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Args:
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sequence: str:
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The sequence to normalize
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Returns:
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The normalized string
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"""
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return self._tokenizer.normalize(sequence)
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def encode(
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self,
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sequence: InputSequence,
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pair: Optional[InputSequence] = None,
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is_pretokenized: bool = False,
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add_special_tokens: bool = True,
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) -> Encoding:
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"""Encode the given sequence and pair. This method can process raw text sequences as well
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as already pre-tokenized sequences.
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Args:
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sequence: InputSequence:
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The sequence we want to encode. This sequence can be either raw text or
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pre-tokenized, according to the `is_pretokenized` argument:
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- If `is_pretokenized=False`: `InputSequence` is expected to be `str`
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- If `is_pretokenized=True`: `InputSequence` is expected to be
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`Union[List[str], Tuple[str]]`
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is_pretokenized: bool:
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Whether the input is already pre-tokenized.
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add_special_tokens: bool:
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Whether to add the special tokens while encoding.
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Returns:
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An Encoding
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"""
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if sequence is None:
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raise ValueError("encode: `sequence` can't be `None`")
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return self._tokenizer.encode(sequence, pair, is_pretokenized, add_special_tokens)
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def encode_batch(
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self,
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inputs: List[EncodeInput],
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is_pretokenized: bool = False,
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add_special_tokens: bool = True,
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) -> List[Encoding]:
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"""Encode the given inputs. This method accept both raw text sequences as well as already
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pre-tokenized sequences.
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Args:
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inputs: List[EncodeInput]:
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A list of single sequences or pair sequences to encode. Each `EncodeInput` is
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expected to be of the following form:
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`Union[InputSequence, Tuple[InputSequence, InputSequence]]`
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Each `InputSequence` can either be raw text or pre-tokenized,
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according to the `is_pretokenized` argument:
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- If `is_pretokenized=False`: `InputSequence` is expected to be `str`
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- If `is_pretokenized=True`: `InputSequence` is expected to be
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`Union[List[str], Tuple[str]]`
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is_pretokenized: bool:
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Whether the input is already pre-tokenized.
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add_special_tokens: bool:
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Whether to add the special tokens while encoding.
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Returns:
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A list of Encoding
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"""
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if inputs is None:
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raise ValueError("encode_batch: `inputs` can't be `None`")
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return self._tokenizer.encode_batch(inputs, is_pretokenized, add_special_tokens)
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def decode(self, ids: List[int], skip_special_tokens: Optional[bool] = True) -> str:
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"""Decode the given list of ids to a string sequence
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Args:
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ids: List[unsigned int]:
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A list of ids to be decoded
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skip_special_tokens: (`optional`) boolean:
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Whether to remove all the special tokens from the output string
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Returns:
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The decoded string
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"""
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if ids is None:
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raise ValueError("None input is not valid. Should be a list of integers.")
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return self._tokenizer.decode(ids, skip_special_tokens=skip_special_tokens)
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def decode_batch(self, sequences: List[List[int]], skip_special_tokens: Optional[bool] = True) -> str:
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"""Decode the list of sequences to a list of string sequences
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Args:
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sequences: List[List[unsigned int]]:
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A list of sequence of ids to be decoded
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skip_special_tokens: (`optional`) boolean:
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Whether to remove all the special tokens from the output strings
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Returns:
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A list of decoded strings
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"""
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if sequences is None:
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raise ValueError("None input is not valid. Should be list of list of integers.")
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return self._tokenizer.decode_batch(sequences, skip_special_tokens=skip_special_tokens)
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def token_to_id(self, token: str) -> Optional[int]:
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"""Convert the given token to its corresponding id
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Args:
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token: str:
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The token to convert
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Returns:
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The corresponding id if it exists, None otherwise
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"""
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return self._tokenizer.token_to_id(token)
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def id_to_token(self, id: int) -> Optional[str]:
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"""Convert the given token id to its corresponding string
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Args:
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token: id:
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The token id to convert
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Returns:
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The corresponding string if it exists, None otherwise
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"""
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return self._tokenizer.id_to_token(id)
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def save_model(self, directory: str, prefix: Optional[str] = None):
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"""Save the current model to the given directory
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Args:
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directory: str:
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A path to the destination directory
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prefix: (Optional) str:
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An optional prefix, used to prefix each file name
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"""
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return self._tokenizer.model.save(directory, prefix=prefix)
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def save(self, path: str, pretty: bool = True):
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"""Save the current Tokenizer at the given path
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Args:
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path: str:
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A path to the destination Tokenizer file
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"""
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return self._tokenizer.save(path, pretty)
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def to_str(self, pretty: bool = False):
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"""Get a serialized JSON version of the Tokenizer as a str
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Args:
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pretty: bool:
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Whether the JSON string should be prettified
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Returns:
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str
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"""
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return self._tokenizer.to_str(pretty)
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def post_process(
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self, encoding: Encoding, pair: Optional[Encoding] = None, add_special_tokens: bool = True
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) -> Encoding:
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"""Apply all the post-processing steps to the given encodings.
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The various steps are:
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1. Truncate according to global params (provided to `enable_truncation`)
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2. Apply the PostProcessor
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3. Pad according to global params. (provided to `enable_padding`)
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Args:
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encoding: Encoding:
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The main Encoding to post process
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pair: Optional[Encoding]:
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An optional pair Encoding
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add_special_tokens: bool:
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Whether to add special tokens
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Returns:
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The resulting Encoding
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"""
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return self._tokenizer.post_process(encoding, pair, add_special_tokens)
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@property
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def model(self) -> Model:
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return self._tokenizer.model
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@model.setter
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def model(self, model: Model):
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self._tokenizer.model = model
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@property
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def normalizer(self) -> Normalizer:
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return self._tokenizer.normalizer
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@normalizer.setter
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def normalizer(self, normalizer: Normalizer):
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self._tokenizer.normalizer = normalizer
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@property
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def pre_tokenizer(self) -> PreTokenizer:
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return self._tokenizer.pre_tokenizer
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@pre_tokenizer.setter
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|
def pre_tokenizer(self, pre_tokenizer: PreTokenizer):
|
||
|
|
self._tokenizer.pre_tokenizer = pre_tokenizer
|
||
|
|
|
||
|
|
@property
|
||
|
|
def post_processor(self) -> PostProcessor:
|
||
|
|
return self._tokenizer.post_processor
|
||
|
|
|
||
|
|
@post_processor.setter
|
||
|
|
def post_processor(self, post_processor: PostProcessor):
|
||
|
|
self._tokenizer.post_processor = post_processor
|
||
|
|
|
||
|
|
@property
|
||
|
|
def decoder(self) -> Decoder:
|
||
|
|
return self._tokenizer.decoder
|
||
|
|
|
||
|
|
@decoder.setter
|
||
|
|
def decoder(self, decoder: Decoder):
|
||
|
|
self._tokenizer.decoder = decoder
|