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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from typing import Any, Dict, List, Optional
from transformers import T5Config, T5TokenizerFast
class EncT5Config(T5Config):
model_type = "enc-t5"
def __init__(self, **kwargs: Any) -> None:
super().__init__(**kwargs)
class EncT5Tokenizer(T5TokenizerFast):
def __init__(
self,
vocab_file,
bos_token="<s>",
eos_token="</s>",
unk_token="<unk>",
pad_token="<pad>",
extra_ids=100,
additional_special_tokens=None,
sp_model_kwargs: Optional[Dict[str, Any]] = None,
**kwargs,
) -> None:
sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
vocab_file=vocab_file,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
extra_ids=extra_ids,
additional_special_tokens=additional_special_tokens,
sp_model_kwargs=sp_model_kwargs,
**kwargs,
)
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,
)
# normal case: some special tokens
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [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. T5 does not make
use of token type ids, therefore a list of zeros is returned.
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 zeros.
"""
bos = [self.bos_token_id]
eos = [self.eos_token_id]
if token_ids_1 is None:
return len(bos + token_ids_0 + eos) * [0]
return len(bos + token_ids_0 + eos + token_ids_1 + eos) * [0]
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 sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s> B </s>`
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.bos_token_id] + token_ids_0 + [self.eos_token_id]
else:
return (
[self.bos_token_id]
+ token_ids_0
+ [self.eos_token_id]
+ token_ids_1
+ [self.eos_token_id]
)
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