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Unverified Commit 36434220 authored by Anthony MOI's avatar Anthony MOI Committed by GitHub
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[HUGE] Refactoring tokenizers backend - padding - truncation - pre-tokenized... 


[HUGE] Refactoring tokenizers backend - padding - truncation - pre-tokenized pipeline - fast tokenizers - tests (#4510)

* Use tokenizers pre-tokenized pipeline

* failing pretrokenized test

* Fix is_pretokenized in python

* add pretokenized tests

* style and quality

* better tests for batched pretokenized inputs

* tokenizers clean up - new padding_strategy - split the files

* [HUGE] refactoring tokenizers - padding - truncation - tests

* style and quality

* bump up requied tokenizers version to 0.8.0-rc1

* switched padding/truncation API - simpler better backward compat

* updating tests for custom tokenizers

* style and quality - tests on pad

* fix QA pipeline

* fix backward compatibility for max_length only

* style and quality

* Various cleans up - add verbose

* fix tests

* update docstrings

* Fix tests

* Docs reformatted

* __call__ method documented
Co-authored-by: default avatarThomas Wolf <thomwolf@users.noreply.github.com>
Co-authored-by: default avatarLysandre <lysandre.debut@reseau.eseo.fr>
parent ebba39e4
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docs/source/main_classes/tokenizer.rst
View file @ 36434220
......@@ -17,12 +17,14 @@ The base classes ``PreTrainedTokenizer`` and ``PreTrainedTokenizerFast`` impleme
~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.PreTrainedTokenizer
:special-members: __call__
:members:
``PreTrainedTokenizerFast``
~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.PreTrainedTokenizerFast
:special-members: __call__
:members:
``BatchEncoding``
......
examples/summarization/utils.py
View file @ 36434220
......@@ -3,8 +3,6 @@ import os
import torch
from torch.utils.data import Dataset
from transformers.tokenization_utils import trim_batch
def encode_file(tokenizer, data_path, max_length, pad_to_max_length=True, return_tensors="pt"):
examples = []
......@@ -17,6 +15,17 @@ def encode_file(tokenizer, data_path, max_length, pad_to_max_length=True, return
return examples
def trim_batch(
input_ids, pad_token_id, attention_mask=None,
):
"""Remove columns that are populated exclusively by pad_token_id"""
keep_column_mask = input_ids.ne(pad_token_id).any(dim=0)
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class SummarizationDataset(Dataset):
def __init__(
self,
......
setup.py
View file @ 36434220
......@@ -108,7 +108,7 @@ setup(
packages=find_packages("src"),
install_requires=[
"numpy",
"tokenizers == 0.7.0",
"tokenizers == 0.8.0-rc1",
# dataclasses for Python versions that don't have it
"dataclasses;python_version<'3.7'",
# utilities from PyPA to e.g. compare versions
......
src/transformers/__init__.py
View file @ 36434220
......@@ -133,13 +133,16 @@ from .tokenization_reformer import ReformerTokenizer
from .tokenization_roberta import RobertaTokenizer, RobertaTokenizerFast
from .tokenization_t5 import T5Tokenizer
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer, TransfoXLTokenizerFast
from .tokenization_utils import (
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_utils_base import (
BatchEncoding,
PreTrainedTokenizer,
PreTrainedTokenizerFast,
CharSpan,
PreTrainedTokenizerBase,
SpecialTokensMixin,
TensorType,
TokenSpan,
)
from .tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_xlm import XLMTokenizer
from .tokenization_xlm_roberta import XLMRobertaTokenizer
from .tokenization_xlnet import SPIECE_UNDERLINE, XLNetTokenizer
......
src/transformers/modeling_tf_albert.py
View file @ 36434220
......@@ -1213,7 +1213,7 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
model = TFAlbertForMultipleChoice.from_pretrained('albert-base-v2')
choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
input_ids = tf.constant([tokenizer.encode(s, add_special_tokens=True) for s in choices])[None, :] # Batch size 1, 2 choices
input_ids = tokenizer(choices, add_special_tokens=True, return_tensors='tf', truncation=True, padding=True)[None, :] # Batch size 1, 2 choices
labels = tf.reshape(tf.constant(1), (-1, 1))
outputs = model(input_ids, labels=labels)
......
src/transformers/tokenization_bert.py
View file @ 36434220
......@@ -23,7 +23,8 @@ from typing import List, Optional
from tokenizers import BertWordPieceTokenizer
from .tokenization_utils import PreTrainedTokenizer, PreTrainedTokenizerFast
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_utils_fast import PreTrainedTokenizerFast
logger = logging.getLogger(__name__)
......
src/transformers/tokenization_gpt2.py
View file @ 36434220
......@@ -23,7 +23,9 @@ from functools import lru_cache
import regex as re
from tokenizers import ByteLevelBPETokenizer
from .tokenization_utils import PreTrainedTokenizer, PreTrainedTokenizerFast
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_utils_base import BatchEncoding
from .tokenization_utils_fast import PreTrainedTokenizerFast
logger = logging.getLogger(__name__)
......@@ -346,3 +348,24 @@ class GPT2TokenizerFast(PreTrainedTokenizerFast):
unk_token=unk_token,
**kwargs,
)
self.add_prefix_space = add_prefix_space
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_pretokenized = kwargs.get("is_pretokenized", False)
assert self.add_prefix_space or not is_pretokenized, (
"You need to instantiate GPT2TokenizerFast with add_prefix_space=False "
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*args, **kwargs)
def _encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_pretokenized = kwargs.get("is_pretokenized", False)
assert self.add_prefix_space or not is_pretokenized, (
"You need to instantiate GPT2TokenizerFast with add_prefix_space=False "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*args, **kwargs)
src/transformers/tokenization_openai.py
View file @ 36434220
......@@ -23,7 +23,8 @@ import re
from tokenizers import CharBPETokenizer
from .tokenization_bert import BasicTokenizer
from .tokenization_utils import PreTrainedTokenizer, PreTrainedTokenizerFast
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_utils_fast import PreTrainedTokenizerFast
logger = logging.getLogger(__name__)
......
src/transformers/tokenization_transfo_xl.py
View file @ 36434220
......@@ -35,7 +35,8 @@ from tokenizers.pre_tokenizers import CharDelimiterSplit, WhitespaceSplit
from tokenizers.processors import BertProcessing
from .file_utils import cached_path, is_torch_available
from .tokenization_utils import PreTrainedTokenizer, PreTrainedTokenizerFast
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_utils_fast import PreTrainedTokenizerFast
if is_torch_available():
......
src/transformers/tokenization_utils.py
View file @ 36434220
......@@ -12,756 +12,38 @@
# 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 for python and fast tokenizers. Fast tokenizers are provided by HuggingFace's tokenizers library."""
""" Tokenization classes for python tokenizers.
For fast tokenizers (provided by HuggingFace's tokenizers library) see tokenization_utils_fast.py
"""
import copy
import functools
import itertools
import json
import logging
import operator
import os
import re
import warnings
from collections import UserDict, defaultdict
from contextlib import contextmanager
from enum import Enum
from typing import Any, Dict, List, MutableMapping, NamedTuple, Optional, Sequence, Tuple, Union
import numpy as np
from tokenizers import AddedToken as AddedTokenFast
from tokenizers import Encoding as EncodingFast
from tokenizers.decoders import Decoder as DecoderFast
from tokenizers.implementations import BaseTokenizer as BaseTokenizerFast
from .file_utils import cached_path, hf_bucket_url, is_remote_url, is_tf_available, is_torch_available, torch_required
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
logger = logging.getLogger(__name__)
NO_PAD_TOKEN_FOR_BATCH_MSG = (
"No padding token is set for this model, therefore no batch can be made with uneven "
"sequences. Set a padding token or adjust the lengths of the sequences building the "
"batch so that every sequence is of the same length."
)
UNEVEN_SEQUENCES_FOR_BATCH_MSG = (
"The sequences building the batch are not of the same size, no tensor "
"can be built. Set `pad_to_max_length=True` to pad the smaller sequences"
"up to the larger sequence's length."
from typing import List, Optional, Tuple, Union
from .file_utils import add_end_docstrings
from .tokenization_utils_base import (
ENCODE_KWARGS_DOCSTRING,
ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING,
BatchEncoding,
EncodedInput,
EncodedInputPair,
PaddingStrategy,
PreTokenizedInput,
PreTokenizedInputPair,
PreTrainedTokenizerBase,
TensorType,
TextInput,
TextInputPair,
TruncationStrategy,
)
SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json"
ADDED_TOKENS_FILE = "added_tokens.json"
TOKENIZER_CONFIG_FILE = "tokenizer_config.json"
VERY_LARGE_INTEGER = int(1e30) # This is used to set the max input length for a model with infinite size input
LARGE_INTEGER = int(1e20) # This is used when we need something big but slightly smaller than VERY_LARGE_INTEGER
# Define type aliases and NamedTuples
TextInput = str
PreTokenizedInput = List[str]
EncodedInput = List[int]
TextInputPair = Tuple[str, str]
PreTokenizedInputPair = Tuple[List[str], List[str]]
EncodedInputPair = Tuple[List[int], List[int]]
class TensorType(Enum):
PYTORCH = "pt"
TENSORFLOW = "tf"
NUMPY = "np"
class CharSpan(NamedTuple):
""" Character span in the original string
Args:
start: index of the first character in the original string
end: index of the character following the last character in the original string
"""
start: int
end: int
class TokenSpan(NamedTuple):
""" Token span in an encoded string (list of tokens)
Args:
start: index of the first token in the span
end: index of the token following the last token in the span
"""
start: int
end: int
def flatten(x: Sequence):
"""
Flatten the provided (potentially nested) sequence
Args:
x (Sequence): Potentially nested sequence to flatten
Returns:
list: Flattened sequence
"""
return functools.reduce(operator.iconcat, x, [])
@contextmanager
def truncate_and_pad(
tokenizer: BaseTokenizerFast,
max_length: int,
stride: int,
strategy: str,
pad_to_max_length: bool,
padding_side: str,
pad_token_id: int,
pad_token_type_id: int,
pad_token: str,
):
""" This contextmanager is in charge of defining the truncation and the padding strategies for fast tokenizers
(provided by HuggingFace tokenizers library) and restore the tokenizer settings afterwards.
This contextmanager assumes the provider tokenizer has no padding / truncation strategy
before the managed section. If your tokenizer set a padding / truncation strategy before,
then it will be reset to no padding/truncation when exiting the managed section.
Args:
tokenizer (BaseTokenizerFast): The tokenizer which will be used
max_length (int): The maximum size of the sequence
stride (int): The stride to use when handling overflow
strategy (str): Overflowing logic to use
pad_to_max_length (bool): Boolean indicating if the output needs to be padded up to max_length
padding_side (str): "left" or "right" indicating the direction the output sequence will be padded
pad_token_id (int): The integer representation of the padding token to use
pad_token_type_id (int): The integer representation of the padding token type to use
pad_token (str): The string representation of the padding token to use
"""
# Handle all the truncation and padding stuff
if max_length is not None:
tokenizer.enable_truncation(max_length, stride=stride, strategy=strategy)
if pad_to_max_length and (pad_token and pad_token_id >= 0):
tokenizer.enable_padding(
max_length=max_length,
direction=padding_side,
pad_id=pad_token_id,
pad_type_id=pad_token_type_id,
pad_token=pad_token,
)
elif pad_to_max_length:
logger.warning(
"Disabled padding because no padding token set (pad_token: {}, pad_token_id: {}).\n"
"To remove this error, you can add a new pad token and then resize model embedding:\n"
"\ttokenizer.pad_token = '<PAD>'\n\tmodel.resize_token_embeddings(len(tokenizer))".format(
pad_token, pad_token_id
)
)
yield
# TODO(morgan, anthony): once we have a simple way to serialize tokenizers maybe store and restore the state afterward
# to avoid destructing the padding / truncation strategy as we do now.
if max_length is not None:
tokenizer.no_truncation()
if pad_to_max_length and (pad_token and pad_token_id >= 0):
tokenizer.no_padding()
def convert_to_tensors(
batch_outputs: MutableMapping, return_tensors: Union[str, TensorType], prepend_batch_axis: bool = False
) -> MutableMapping:
# Convert to TensorType
if not isinstance(return_tensors, TensorType):
return_tensors = TensorType(return_tensors)
# Get a function reference for the correct framework
if return_tensors == TensorType.TENSORFLOW and is_tf_available():
as_tensor = tf.constant
elif return_tensors == TensorType.PYTORCH and is_torch_available():
as_tensor = torch.tensor
elif return_tensors == TensorType.NUMPY:
as_tensor = np.asarray
else:
raise ImportError(
"Unable to convert output to tensors format {}, PyTorch or TensorFlow is not available.".format(
return_tensors
)
)
# Do the tensor conversion in batch
for key, value in batch_outputs.items():
try:
if prepend_batch_axis:
value = [value]
tensor = as_tensor(value)
# at-least2d
if tensor.ndim > 2:
tensor = tensor.squeeze(0)
elif tensor.ndim < 2:
tensor = tensor[None, :]
batch_outputs[key] = tensor
except ValueError:
if None in [item for sequence in value for item in sequence]:
raise ValueError(NO_PAD_TOKEN_FOR_BATCH_MSG)
else:
raise ValueError(UNEVEN_SEQUENCES_FOR_BATCH_MSG)
return batch_outputs
class BatchEncoding(UserDict):
""" BatchEncoding hold the output of the encode and batch_encode methods (tokens, attention_masks, etc).
This class is derived from a python Dictionary and can be used as a dictionnary.
In addition, this class expose utility methods to map from word/char space to token space.
Args:
data (:obj:`dict`): Dictionary of lists/arrays returned by the encode/batch_encode methods ('input_ids', 'attention_mask'...)
encoding (:obj:`EncodingFast`, :obj:`list(EncodingFast)`, `optional`, defaults to :obj:`None`):
If the tokenizer is a fast tokenizer which outputs additional informations like mapping from word/char space to token space
the `EncodingFast` instance or list of instance (for batches) hold these informations.
"""
def __init__(
self,
data: Optional[Dict[str, Any]] = None,
encoding: Optional[Union[EncodingFast, Sequence[EncodingFast]]] = None,
):
super().__init__(data)
if isinstance(encoding, EncodingFast):
encoding = [encoding]
self._encodings = encoding
def __getitem__(self, item: Union[int, str]) -> EncodingFast:
""" If the key is a string, get the value of the dict associated to `key` ('input_ids', 'attention_mask'...)
If the key is an integer, get the EncodingFast for batch item with index `key`
"""
if isinstance(item, str):
return self.data[item]
elif self._encodings is not None:
return self._encodings[item]
else:
raise KeyError(
"Indexing with integers (to access backend Encoding for a given batch index) "
"is not available when using Python based tokenizers"
)
def __getattr__(self, item: str):
try:
return self.data[item]
except KeyError:
raise AttributeError
def keys(self):
return self.data.keys()
def values(self):
return self.data.values()
def items(self):
return self.data.items()
# After this point:
# Extended properties and methods only available for fast (Rust-based) tokenizers
# provided by HuggingFace tokenizers library.
@property
def encodings(self) -> Optional[List[EncodingFast]]:
"""
Return the list all encoding from the tokenization process
Returns: List[EncodingFast] or None if input was tokenized through Python (i.e. not fast) tokenizer
"""
return self._encodings
def tokens(self, batch_index: int = 0) -> List[int]:
if not self._encodings:
raise ValueError("tokens() is not available when using Python based tokenizers")
return self._encodings[batch_index].tokens
def words(self, batch_index: int = 0) -> List[Optional[int]]:
if not self._encodings:
raise ValueError("words() is not available when using Python based tokenizers")
return self._encodings[batch_index].words
def token_to_word(self, batch_or_token_index: int, token_index: Optional[int] = None) -> int:
""" Get the index of the word corresponding (i.e. comprising) to an encoded token
in a sequence of the batch.
Can be called as:
- self.token_to_word(token_index) if batch size is 1
- self.token_to_word(batch_index, token_index) if batch size is greater than 1
This method is particularly suited when the input sequences are provided as
pre-tokenized sequences (i.e. words are defined by the user). In this case it allows
to easily associate encoded tokens with provided tokenized words.
Args:
batch_or_token_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence,
this can be the index of the token in the sequence
token_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index
of the token in the sequence.
Returns:
word_index (:obj:`int`):
index of the word in the input sequence.
"""
if not self._encodings:
raise ValueError("token_to_word() is not available when using Python based tokenizers")
if token_index is not None:
batch_index = batch_or_token_index
else:
batch_index = 0
token_index = batch_or_token_index
if batch_index < 0:
batch_index = self._batch_size + batch_index
if token_index < 0:
token_index = self._seq_len + token_index
return self._encodings[batch_index].token_to_word(token_index)
def word_to_tokens(self, batch_or_word_index: int, word_index: Optional[int] = None) -> TokenSpan:
""" Get the encoded token span corresponding to a word in the sequence of the batch.
Token spans are returned as a TokenSpan NamedTuple with:
start: index of the first token
end: index of the token following the last token
Can be called as:
- self.word_to_tokens(word_index) if batch size is 1
- self.word_to_tokens(batch_index, word_index) if batch size is greater or equal to 1
This method is particularly suited when the input sequences are provided as
pre-tokenized sequences (i.e. words are defined by the user). In this case it allows
to easily associate encoded tokens with provided tokenized words.
Args:
batch_or_word_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprises one sequence,
this can be the index of the word in the sequence
word_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index
of the word in the sequence.
Returns:
token_span (:obj:`TokenSpan`):
Span of tokens in the encoded sequence.
TokenSpan are NamedTuple with:
start: index of the first token
end: index of the token following the last token
"""
if not self._encodings:
raise ValueError("word_to_tokens() is not available when using Python based tokenizers")
if word_index is not None:
batch_index = batch_or_word_index
else:
batch_index = 0
word_index = batch_or_word_index
if batch_index < 0:
batch_index = self._batch_size + batch_index
if word_index < 0:
word_index = self._seq_len + word_index
return TokenSpan(*(self._encodings[batch_index].word_to_tokens(word_index)))
def token_to_chars(self, batch_or_token_index: int, token_index: Optional[int] = None) -> CharSpan:
""" Get the character span corresponding to an encoded token in a sequence of the batch.
Character spans are returned as a CharSpan NamedTuple with:
start: index of the first character in the original string associated to the token
end: index of the character following the last character in the original string associated to the token
Can be called as:
- self.token_to_chars(token_index) if batch size is 1
- self.token_to_chars(batch_index, token_index) if batch size is greater or equal to 1
Args:
batch_or_token_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence,
this can be the index of the token in the sequence
token_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index
of the token or tokens in the sequence.
Returns:
char_span (:obj:`CharSpan`):
Span of characters in the original string.
CharSpan are NamedTuple with:
start: index of the first character in the original string
end: index of the character following the last character in the original string
"""
if not self._encodings:
raise ValueError("token_to_chars() is not available when using Python based tokenizers")
if token_index is not None:
batch_index = batch_or_token_index
else:
batch_index = 0
token_index = batch_or_token_index
return CharSpan(*(self._encodings[batch_index].token_to_chars(token_index)))
def char_to_token(self, batch_or_char_index: int, char_index: Optional[int] = None) -> int:
""" Get the index of the token in the encoded output comprising a character
in the original string for a sequence of the batch.
Can be called as:
- self.char_to_token(char_index) if batch size is 1
- self.char_to_token(batch_index, char_index) if batch size is greater or equal to 1
This method is particularly suited when the input sequences are provided as
pre-tokenized sequences (i.e. words are defined by the user). In this case it allows
to easily associate encoded tokens with provided tokenized words.
Args:
batch_or_char_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence,
this can be the index of the word in the sequence
char_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index
of the word in the sequence.
Returns:
token_index (:obj:`int`):
Index of the token.
"""
if not self._encodings:
raise ValueError("char_to_token() is not available when using Python based tokenizers")
if char_index is not None:
batch_index = batch_or_char_index
else:
batch_index = 0
char_index = batch_or_char_index
return self._encodings[batch_index].char_to_token(char_index)
def word_to_chars(self, batch_or_word_index: int, word_index: Optional[int] = None) -> CharSpan:
""" Get the character span in the original string corresponding to given word in a sequence
of the batch.
Character spans are returned as a CharSpan NamedTuple with:
start: index of the first character in the original string
end: index of the character following the last character in the original string
Can be called as:
- self.word_to_chars(word_index) if batch size is 1
- self.word_to_chars(batch_index, word_index) if batch size is greater or equal to 1
Args:
batch_or_word_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence,
this can be the index of the word in the sequence
word_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index
of the word in the sequence.
Returns:
char_span (:obj:`CharSpan` or :obj:`List[CharSpan]`):
Span(s) of the associated character or characters in the string.
CharSpan are NamedTuple with:
start: index of the first character associated to the token in the original string
end: index of the character following the last character associated to the token in the original string
"""
if not self._encodings:
raise ValueError("word_to_chars() is not available when using Python based tokenizers")
if word_index is not None:
batch_index = batch_or_word_index
else:
batch_index = 0
word_index = batch_or_word_index
return CharSpan(*(self._encodings[batch_index].word_to_chars(word_index)))
def char_to_word(self, batch_or_char_index: int, char_index: Optional[int] = None) -> int:
""" Get the word in the original string corresponding to a character in the original string of
a sequence of the batch.
Can be called as:
- self.char_to_word(char_index) if batch size is 1
- self.char_to_word(batch_index, char_index) if batch size is greater than 1
This method is particularly suited when the input sequences are provided as
pre-tokenized sequences (i.e. words are defined by the user). In this case it allows
to easily associate encoded tokens with provided tokenized words.
Args:
batch_or_char_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence,
this can be the index of the character in the orginal string.
char_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index
of the character in the orginal string.
Returns:
token_index (:obj:`int` or :obj:`List[int]`):
Index or indices of the associated encoded token(s).
"""
if not self._encodings:
raise ValueError("char_to_word() is not available when using Python based tokenizers")
if char_index is not None:
batch_index = batch_or_char_index
else:
batch_index = 0
char_index = batch_or_char_index
return self._encodings[batch_index].char_to_word(char_index)
@torch_required
def to(self, device: str):
"""Send all values to device by calling v.to(device)"""
self.data = {k: v.to(device) for k, v in self.data.items()}
return self
class SpecialTokensMixin:
""" SpecialTokensMixin is derived by ``PreTrainedTokenizer`` and ``PreTrainedTokenizerFast`` and
handles specific behaviors related to special tokens. In particular, this class hold the
attributes which can be used to directly access to these special tokens in a
model-independant manner and allow to set and update the special tokens.
"""
SPECIAL_TOKENS_ATTRIBUTES = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
"additional_special_tokens",
]
def __init__(self, **kwargs):
self._bos_token = None
self._eos_token = None
self._unk_token = None
self._sep_token = None
self._pad_token = None
self._cls_token = None
self._mask_token = None
self._pad_token_type_id = 0
self._additional_special_tokens = []
for key, value in kwargs.items():
if key in self.SPECIAL_TOKENS_ATTRIBUTES:
if key == "additional_special_tokens":
assert isinstance(value, (list, tuple)) and all(isinstance(t, str) for t in value)
setattr(self, key, value)
elif isinstance(value, AddedTokenFast):
setattr(self, key, str(value))
elif isinstance(value, str):
setattr(self, key, value)
else:
raise TypeError(
"special token {} has to be either str or AddedTokenFast but got: {}".format(key, type(value))
)
@property
def bos_token(self):
""" Beginning of sentence token (string). Log an error if used while not having been set. """
if self._bos_token is None:
logger.error("Using bos_token, but it is not set yet.")
return self._bos_token
@property
def eos_token(self):
""" End of sentence token (string). Log an error if used while not having been set. """
if self._eos_token is None:
logger.error("Using eos_token, but it is not set yet.")
return self._eos_token
@property
def unk_token(self):
""" Unknown token (string). Log an error if used while not having been set. """
if self._unk_token is None:
logger.error("Using unk_token, but it is not set yet.")
return self._unk_token
@property
def sep_token(self):
""" Separation token (string). E.g. separate context and query in an input sequence. Log an error if used while not having been set. """
if self._sep_token is None:
logger.error("Using sep_token, but it is not set yet.")
return self._sep_token
@property
def pad_token(self):
""" Padding token (string). Log an error if used while not having been set. """
if self._pad_token is None:
logger.error("Using pad_token, but it is not set yet.")
return self._pad_token
@property
def cls_token(self):
""" Classification token (string). E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. """
if self._cls_token is None:
logger.error("Using cls_token, but it is not set yet.")
return self._cls_token
@property
def mask_token(self):
""" Mask token (string). E.g. when training a model with masked-language modeling. Log an error if used while not having been set. """
if self._mask_token is None:
logger.error("Using mask_token, but it is not set yet.")
return self._mask_token
@property
def additional_special_tokens(self):
""" All the additional special tokens you may want to use (list of strings). Log an error if used while not having been set. """
if self._additional_special_tokens is None:
logger.error("Using additional_special_tokens, but it is not set yet.")
return self._additional_special_tokens
def _maybe_update_backend(self, value):
""" To be overriden by derived class if a backend tokenizer has to be updated. """
pass
@bos_token.setter
def bos_token(self, value):
self._bos_token = value
self._maybe_update_backend([value])
@eos_token.setter
def eos_token(self, value):
self._eos_token = value
self._maybe_update_backend([value])
@unk_token.setter
def unk_token(self, value):
self._unk_token = value
self._maybe_update_backend([value])
@sep_token.setter
def sep_token(self, value):
self._sep_token = value
self._maybe_update_backend([value])
@pad_token.setter
def pad_token(self, value):
self._pad_token = value
self._maybe_update_backend([value])
@cls_token.setter
def cls_token(self, value):
self._cls_token = value
self._maybe_update_backend([value])
@mask_token.setter
def mask_token(self, value):
self._mask_token = value
self._maybe_update_backend([value])
@additional_special_tokens.setter
def additional_special_tokens(self, value):
self._additional_special_tokens = value
self._maybe_update_backend(value)
@property
def bos_token_id(self):
""" Id of the beginning of sentence token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.bos_token)
@property
def eos_token_id(self):
""" Id of the end of sentence token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.eos_token)
@property
def unk_token_id(self):
""" Id of the unknown token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.unk_token)
@property
def sep_token_id(self):
""" Id of the separation token in the vocabulary. E.g. separate context and query in an input sequence. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.sep_token)
@property
def pad_token_id(self):
""" Id of the padding token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.pad_token)
@property
def pad_token_type_id(self):
""" Id of the padding token type in the vocabulary."""
return self._pad_token_type_id
@property
def cls_token_id(self):
""" Id of the classification token in the vocabulary. E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.cls_token)
@property
def mask_token_id(self):
""" Id of the mask token in the vocabulary. E.g. when training a model with masked-language modeling. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.mask_token)
@property
def additional_special_tokens_ids(self):
""" Ids of all the additional special tokens in the vocabulary (list of integers). Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.additional_special_tokens)
@property
def special_tokens_map(self):
""" A dictionary mapping special token class attribute (cls_token, unk_token...) to their
values ('<unk>', '<cls>'...)
"""
set_attr = {}
for attr in self.SPECIAL_TOKENS_ATTRIBUTES:
attr_value = getattr(self, "_" + attr)
if attr_value:
set_attr[attr] = attr_value
return set_attr
@property
def all_special_tokens(self):
""" List all the special tokens ('<unk>', '<cls>'...) mapped to class attributes
(cls_token, unk_token...).
"""
all_toks = []
set_attr = self.special_tokens_map
for attr_value in set_attr.values():
all_toks = all_toks + (list(attr_value) if isinstance(attr_value, (list, tuple)) else [attr_value])
all_toks = list(set(all_toks))
return all_toks
@property
def all_special_ids(self):
""" List the vocabulary indices of the special tokens ('<unk>', '<cls>'...) mapped to
class attributes (cls_token, unk_token...).
"""
all_toks = self.all_special_tokens
all_ids = self.convert_tokens_to_ids(all_toks)
return all_ids
logger = logging.getLogger(__name__)
class PreTrainedTokenizer(SpecialTokensMixin):
""" Base class for all tokenizers.
class PreTrainedTokenizer(PreTrainedTokenizerBase):
""" Base class for all slow tokenizers.
Handle all the shared methods for tokenization and special tokens as well as methods
downloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.
......@@ -813,384 +95,35 @@ class PreTrainedTokenizer(SpecialTokensMixin):
- ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens.
Adding all special tokens here ensure they won't be split by the tokenization process.
Will be associated to ``self.additional_special_tokens`` and ``self.additional_special_tokens_ids``
"""
vocab_files_names: Dict[str, str] = {}
pretrained_vocab_files_map: Dict[str, Dict[str, str]] = {}
pretrained_init_configuration: Dict[str, Dict[str, Any]] = {}
max_model_input_sizes: Dict[str, int] = {}
model_input_names: List[str] = ["token_type_ids", "attention_mask"]
padding_side: str = "right"
.. automethod:: __call__
"""
@property
def vocab_size(self) -> int:
""" Size of the base vocabulary (without the added tokens) """
raise NotImplementedError
def __init__(self, **kwargs):
super().__init__(**kwargs)
# Added tokens
self.added_tokens_encoder = {}
self.unique_added_tokens_encoder = set()
self.added_tokens_decoder = {}
@property
def is_fast(self) -> bool:
return False
@property
def max_len(self) -> int:
""" Kept here for backward compatibility.
Now renamed to `model_max_length` to avoid ambiguity.
"""
return self.model_max_length
@property
def max_len_single_sentence(self) -> int:
return self.model_max_length - self.num_special_tokens_to_add(pair=False)
@property
def max_len_sentences_pair(self) -> int:
return self.model_max_length - self.num_special_tokens_to_add(pair=True)
@max_len_single_sentence.setter
def max_len_single_sentence(self, value) -> int:
""" For backward compatibility, allow to try to setup 'max_len_single_sentence' """
if value == self.model_max_length - self.num_special_tokens_to_add(pair=False):
logger.warning(
"Setting 'max_len_single_sentence' is now deprecated. " "This value is automatically set up."
)
else:
raise ValueError(
"Setting 'max_len_single_sentence' is now deprecated. " "This value is automatically set up."
)
@max_len_sentences_pair.setter
def max_len_sentences_pair(self, value) -> int:
""" For backward compatibility, allow to try to setup 'max_len_sentences_pair' """
if value == self.model_max_length - self.num_special_tokens_to_add(pair=True):
logger.warning(
"Setting 'max_len_sentences_pair' is now deprecated. " "This value is automatically set up."
)
else:
raise ValueError(
"Setting 'max_len_sentences_pair' is now deprecated. " "This value is automatically set up."
)
def vocab_size(self) -> int:
""" Size of the base vocabulary (without the added tokens) """
raise NotImplementedError
def get_vocab(self):
""" Returns the vocabulary as a dict of {token: index} pairs. `tokenizer.get_vocab()[token]` is equivalent to `tokenizer.convert_tokens_to_ids(token)` when `token` is in the vocab. """
raise NotImplementedError()
def __init__(self, model_max_length=None, **kwargs):
super().__init__(**kwargs)
# For backward compatibility we fallback to set model_max_length from max_len if provided
if "max_len" in kwargs:
warnings.warn(
"Parameter max_len is deprecated and will be removed in a future release. "
"Use model_max_length instead.",
category=FutureWarning,
)
model_max_length = kwargs.pop("max_len")
self.model_max_length = model_max_length if model_max_length is not None else VERY_LARGE_INTEGER
# Padding side is right by default and overridden in subclasses. If specified in the kwargs, it is changed.
self.padding_side = kwargs.pop("padding_side", self.padding_side)
assert self.padding_side in [
"right",
"left",
], f"Padding side should be selected between 'right' and 'left', current value: {self.padding_side}"
self.model_input_names = kwargs.pop("model_input_names", self.model_input_names)
# Added tokens
self.added_tokens_encoder = {}
self.unique_added_tokens_encoder = set()
self.added_tokens_decoder = {}
# inputs and kwargs for saving and re-loading (see ``from_pretrained`` and ``save_pretrained``)
self.init_inputs = ()
self.init_kwargs = {}
def __len__(self):
""" Size of the full vocabulary with the added tokens """
return self.vocab_size + len(self.added_tokens_encoder)
@classmethod
def from_pretrained(cls, *inputs, **kwargs):
r"""
Instantiate a :class:`~transformers.PreTrainedTokenizer` (or a derived class) from a predefined tokenizer.
Args:
pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``.
- a string with the `identifier name` of a predefined tokenizer that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.: ``./my_model_directory/``.
- (not applicable to all derived classes, deprecated) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False:
Force to (re-)download the vocabulary files and override the cached versions if they exists.
resume_download: (`optional`) boolean, default False:
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
inputs: (`optional`) positional arguments: will be passed to the Tokenizer ``__init__`` method.
kwargs: (`optional`) keyword arguments: will be passed to the Tokenizer ``__init__`` method. Can be used to set special tokens like ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``. See parameters in the doc string of :class:`~transformers.PreTrainedTokenizer` for details.
Examples::
# We can't instantiate directly the base class `PreTrainedTokenizer` so let's show our examples on a derived class: BertTokenizer
# Download vocabulary from S3 and cache.
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Download vocabulary from S3 (user-uploaded) and cache.
tokenizer = BertTokenizer.from_pretrained('dbmdz/bert-base-german-cased')
# If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)
tokenizer = BertTokenizer.from_pretrained('./test/saved_model/')
# If the tokenizer uses a single vocabulary file, you can point directly to this file
tokenizer = BertTokenizer.from_pretrained('./test/saved_model/my_vocab.txt')
# You can link tokens to special vocabulary when instantiating
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', unk_token='<unk>')
# You should be sure '<unk>' is in the vocabulary when doing that.
# Otherwise use tokenizer.add_special_tokens({'unk_token': '<unk>'}) instead)
assert tokenizer.unk_token == '<unk>'
"""
return cls._from_pretrained(*inputs, **kwargs)
@classmethod
def _from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs):
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", False)
s3_models = list(cls.max_model_input_sizes.keys())
vocab_files = {}
init_configuration = {}
if pretrained_model_name_or_path in s3_models:
# Get the vocabulary from AWS S3 bucket
for file_id, map_list in cls.pretrained_vocab_files_map.items():
vocab_files[file_id] = map_list[pretrained_model_name_or_path]
if (
cls.pretrained_init_configuration
and pretrained_model_name_or_path in cls.pretrained_init_configuration
):
init_configuration = cls.pretrained_init_configuration[pretrained_model_name_or_path].copy()
else:
# Get the vocabulary from local files
logger.info(
"Model name '{}' not found in model shortcut name list ({}). "
"Assuming '{}' is a path, a model identifier, or url to a directory containing tokenizer files.".format(
pretrained_model_name_or_path, ", ".join(s3_models), pretrained_model_name_or_path
)
)
if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
if len(cls.vocab_files_names) > 1:
raise ValueError(
f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is not supported."
"Use a model identifier or the path to a directory instead."
)
logger.warning(
f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is deprecated"
)
file_id = list(cls.vocab_files_names.keys())[0]
vocab_files[file_id] = pretrained_model_name_or_path
else:
# At this point pretrained_model_name_or_path is either a directory or a model identifier name
additional_files_names = {
"added_tokens_file": ADDED_TOKENS_FILE,
"special_tokens_map_file": SPECIAL_TOKENS_MAP_FILE,
"tokenizer_config_file": TOKENIZER_CONFIG_FILE,
}
# Look for the tokenizer main vocabulary files + the additional tokens files
for file_id, file_name in {**cls.vocab_files_names, **additional_files_names}.items():
if os.path.isdir(pretrained_model_name_or_path):
full_file_name = os.path.join(pretrained_model_name_or_path, file_name)
if not os.path.exists(full_file_name):
logger.info("Didn't find file {}. We won't load it.".format(full_file_name))
full_file_name = None
else:
full_file_name = hf_bucket_url(
pretrained_model_name_or_path, filename=file_name, use_cdn=False
)
vocab_files[file_id] = full_file_name
# Get files from url, cache, or disk depending on the case
try:
resolved_vocab_files = {}
for file_id, file_path in vocab_files.items():
if file_path is None:
resolved_vocab_files[file_id] = None
else:
resolved_vocab_files[file_id] = cached_path(
file_path,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
)
except EnvironmentError:
if pretrained_model_name_or_path in s3_models:
msg = "Couldn't reach server at '{}' to download vocabulary files."
else:
msg = (
"Model name '{}' was not found in tokenizers model name list ({}). "
"We assumed '{}' was a path or url to a directory containing vocabulary files "
"named {}, but couldn't find such vocabulary files at this path or url.".format(
pretrained_model_name_or_path,
", ".join(s3_models),
pretrained_model_name_or_path,
list(cls.vocab_files_names.values()),
)
)
raise EnvironmentError(msg)
if all(full_file_name is None for full_file_name in resolved_vocab_files.values()):
raise EnvironmentError(
"Model name '{}' was not found in tokenizers model name list ({}). "
"We assumed '{}' was a path, a model identifier, or url to a directory containing vocabulary files "
"named {} but couldn't find such vocabulary files at this path or url.".format(
pretrained_model_name_or_path,
", ".join(s3_models),
pretrained_model_name_or_path,
list(cls.vocab_files_names.values()),
)
)
for file_id, file_path in vocab_files.items():
if file_path == resolved_vocab_files[file_id]:
logger.info("loading file {}".format(file_path))
else:
logger.info("loading file {} from cache at {}".format(file_path, resolved_vocab_files[file_id]))
# Prepare tokenizer initialization kwargs
# Did we saved some inputs and kwargs to reload ?
tokenizer_config_file = resolved_vocab_files.pop("tokenizer_config_file", None)
if tokenizer_config_file is not None:
with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle:
init_kwargs = json.load(tokenizer_config_handle)
saved_init_inputs = init_kwargs.pop("init_inputs", ())
if not init_inputs:
init_inputs = saved_init_inputs
else:
init_kwargs = init_configuration
# Update with newly provided kwargs
init_kwargs.update(kwargs)
# Set max length if needed
if pretrained_model_name_or_path in cls.max_model_input_sizes:
# if we're using a pretrained model, ensure the tokenizer
# wont index sequences longer than the number of positional embeddings
model_max_length = cls.max_model_input_sizes[pretrained_model_name_or_path]
if model_max_length is not None and isinstance(model_max_length, (int, float)):
init_kwargs["model_max_length"] = min(init_kwargs.get("model_max_length", int(1e30)), model_max_length)
# Merge resolved_vocab_files arguments in init_kwargs.
added_tokens_file = resolved_vocab_files.pop("added_tokens_file", None)
special_tokens_map_file = resolved_vocab_files.pop("special_tokens_map_file", None)
for args_name, file_path in resolved_vocab_files.items():
if args_name not in init_kwargs:
init_kwargs[args_name] = file_path
if special_tokens_map_file is not None:
with open(special_tokens_map_file, encoding="utf-8") as special_tokens_map_handle:
special_tokens_map = json.load(special_tokens_map_handle)
for key, value in special_tokens_map.items():
if key not in init_kwargs:
init_kwargs[key] = value
# Instantiate tokenizer.
try:
tokenizer = cls(*init_inputs, **init_kwargs)
except OSError:
raise OSError(
"Unable to load vocabulary from file. "
"Please check that the provided vocabulary is accessible and not corrupted."
)
# Save inputs and kwargs for saving and re-loading with ``save_pretrained``
tokenizer.init_inputs = init_inputs
tokenizer.init_kwargs = init_kwargs
# update unique_added_tokens_encoder with special tokens for correct tokenization
tokenizer.unique_added_tokens_encoder.update(set(tokenizer.all_special_tokens))
# Add supplementary tokens.
if added_tokens_file is not None:
with open(added_tokens_file, encoding="utf-8") as added_tokens_handle:
added_tok_encoder = json.load(added_tokens_handle)
added_tok_decoder = {v: k for k, v in added_tok_encoder.items()}
tokenizer.added_tokens_encoder.update(added_tok_encoder)
tokenizer.added_tokens_decoder.update(added_tok_decoder)
tokenizer.unique_added_tokens_encoder.update(set(tokenizer.added_tokens_encoder.keys()))
return tokenizer
def save_pretrained(self, save_directory):
""" Save the tokenizer vocabulary files together with:
- added tokens,
- special-tokens-to-class-attributes-mapping,
- tokenizer instantiation positional and keywords inputs (e.g. do_lower_case for Bert).
Warning: This won't save modifications you may have applied to the tokenizer after the instantiation
(e.g. modifying tokenizer.do_lower_case after creation).
This method make sure the full tokenizer can then be re-loaded using the
:func:`~transformers.PreTrainedTokenizer.from_pretrained` class method.
"""
if not os.path.isdir(save_directory):
logger.error("Saving directory ({}) should be a directory".format(save_directory))
return
special_tokens_map_file = os.path.join(save_directory, SPECIAL_TOKENS_MAP_FILE)
added_tokens_file = os.path.join(save_directory, ADDED_TOKENS_FILE)
tokenizer_config_file = os.path.join(save_directory, TOKENIZER_CONFIG_FILE)
tokenizer_config = copy.deepcopy(self.init_kwargs)
if len(self.init_inputs) > 0:
tokenizer_config["init_inputs"] = copy.deepcopy(self.init_inputs)
for file_id in self.vocab_files_names.keys():
tokenizer_config.pop(file_id, None)
with open(tokenizer_config_file, "w", encoding="utf-8") as f:
f.write(json.dumps(tokenizer_config, ensure_ascii=False))
with open(special_tokens_map_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.special_tokens_map, ensure_ascii=False))
if len(self.added_tokens_encoder) > 0:
with open(added_tokens_file, "w", encoding="utf-8") as f:
out_str = json.dumps(self.added_tokens_encoder, ensure_ascii=False)
f.write(out_str)
vocab_files = self.save_vocabulary(save_directory)
return vocab_files + (special_tokens_map_file, added_tokens_file)
def save_vocabulary(self, save_directory) -> Tuple[str]:
""" Save the tokenizer vocabulary to a directory. This method does *NOT* save added tokens
and special token mappings.
Please use :func:`~transformers.PreTrainedTokenizer.save_pretrained` `()` to save the full
Tokenizer state if you want to reload it using the :func:`~transformers.PreTrainedTokenizer.from_pretrained`
class method.
"""
raise NotImplementedError
def add_tokens(self, new_tokens: Union[str, List[str]]) -> int:
"""
Add a list of new tokens to the tokenizer class. If the new tokens are not in the
......@@ -1230,6 +163,7 @@ class PreTrainedTokenizer(SpecialTokensMixin):
and token not in tokens_to_add
):
tokens_to_add.append(token)
if self.verbose:
logger.info("Adding %s to the vocabulary", token)
added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(tokens_to_add))
......@@ -1259,60 +193,6 @@ class PreTrainedTokenizer(SpecialTokensMixin):
token_ids_1 = []
return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None))
def add_special_tokens(self, special_tokens_dict):
"""
Add a dictionary of special tokens (eos, pad, cls...) to the encoder and link them
to class attributes. If special tokens are NOT in the vocabulary, they are added
to it (indexed starting from the last index of the current vocabulary).
Using `add_special_tokens` will ensure your special tokens can be used in several ways:
- special tokens are carefully handled by the tokenizer (they are never split)
- you can easily refer to special tokens using tokenizer class attributes like `tokenizer.cls_token`. This makes it easy to develop model-agnostic training and fine-tuning scripts.
When possible, special tokens are already registered for provided pretrained models (ex: BertTokenizer cls_token is already registered to be '[CLS]' and XLM's one is also registered to be '</s>')
Args:
special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes:
[``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``,
``additional_special_tokens``].
Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
Returns:
Number of tokens added to the vocabulary.
Examples::
# Let's see how to add a new classification token to GPT-2
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
special_tokens_dict = {'cls_token': '<CLS>'}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print('We have added', num_added_toks, 'tokens')
model.resize_token_embeddings(len(tokenizer)) # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
assert tokenizer.cls_token == '<CLS>'
"""
if not special_tokens_dict:
return 0
added_tokens = 0
for key, value in special_tokens_dict.items():
assert key in self.SPECIAL_TOKENS_ATTRIBUTES
if key == "additional_special_tokens":
assert isinstance(value, (list, tuple)) and all(isinstance(t, str) for t in value)
added_tokens += self.add_tokens(value)
else:
assert isinstance(value, str)
added_tokens += self.add_tokens([value])
logger.info("Assigning %s to the %s key of the tokenizer", value, key)
setattr(self, key, value)
return added_tokens
def tokenize(self, text: TextInput, **kwargs):
""" Converts a string in a sequence of tokens (string), using the tokenizer.
Split in words for word-based vocabulary or sub-words for sub-word-based
......@@ -1420,88 +300,15 @@ class PreTrainedTokenizer(SpecialTokensMixin):
def _convert_token_to_id(self, token):
raise NotImplementedError
def encode(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
add_special_tokens: bool = True,
max_length: Optional[int] = None,
stride: int = 0,
truncation_strategy: str = "longest_first",
pad_to_max_length: bool = False,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs
):
"""
Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary. Adds the model-specific
special tokens (such as beginning of sequence, end of sequence, sequence separator).
If specifying ``add_special_tokens=False``, same as doing ``self.convert_tokens_to_ids(self.tokenize(text))``.
Args:
text (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`):
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
method)
text_pair (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
string using the `tokenize` method) or a list of integers (tokenized string ids using the
`convert_tokens_to_ids` method)
add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to ``True``, the sequences will be encoded with the special tokens relative
to their model.
max_length (:obj:`int`, `optional`, defaults to :obj:`None`):
If set to a number, will limit the total sequence returned so that it has a maximum length.
If there are overflowing tokens, those will be added to the returned dictionary.
You can set it to the maximal input size of the model with `max_length = tokenizer.model_max_length`.
stride (:obj:`int`, `optional`, defaults to ``0``):
If set to a number along with max_length, the overflowing tokens returned will contain some tokens
from the main sequence returned. The value of this argument defines the number of additional tokens.
truncation_strategy (:obj:`str`, `optional`, defaults to `longest_first`):
String selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences)
- 'only_first': Only truncate the first sequence
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
pad_to_max_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
If set to True, the returned sequences will be padded according to the model's padding side and
padding index, up to their max length. If no max length is specified, the padding is done up to the
model's max length. The tokenizer padding sides are handled by the class attribute `padding_side`
which can be set to the following strings:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
Defaults to False: no padding.
return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):
Can be set to 'tf' or 'pt' to return respectively TensorFlow :obj:`tf.constant`
or PyTorch :obj:`torch.Tensor` instead of a list of python integers.
**kwargs: passed to the `self.tokenize()` method
"""
encoded_inputs = self.encode_plus(
text,
text_pair=text_pair,
max_length=max_length,
add_special_tokens=add_special_tokens,
stride=stride,
truncation_strategy=truncation_strategy,
pad_to_max_length=pad_to_max_length,
return_tensors=return_tensors,
**kwargs,
)
return encoded_inputs["input_ids"]
def encode_plus(
def _encode_plus(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
truncation_strategy: str = "longest_first",
pad_to_max_length: bool = False,
is_pretokenized: bool = False,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
......@@ -1509,108 +316,31 @@ class PreTrainedTokenizer(SpecialTokensMixin):
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
Returns a dictionary containing the encoded sequence or sequence pair and additional information:
the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.
Args:
text (:obj:`str`, :obj:`List[str]` or :obj:`List[int]` (the later only for not-fast tokenizers)):
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
method)
text_pair (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
string using the `tokenize` method) or a list of integers (tokenized string ids using the
`convert_tokens_to_ids` method)
add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to ``True``, the sequences will be encoded with the special tokens relative
to their model.
max_length (:obj:`int`, `optional`, defaults to :obj:`None`):
If set to a number, will limit the total sequence returned so that it has a maximum length.
If there are overflowing tokens, those will be added to the returned dictionary
You can set it to the maximal input size of the model with `max_length = tokenizer.model_max_length`.
stride (:obj:`int`, `optional`, defaults to ``0``):
If set to a number along with max_length, the overflowing tokens returned will contain some tokens
from the main sequence returned. The value of this argument defines the number of additional tokens.
truncation_strategy (:obj:`str`, `optional`, defaults to `longest_first`):
String selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences)
- 'only_first': Only truncate the first sequence
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
pad_to_max_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
If set to True, the returned sequences will be padded according to the model's padding side and
padding index, up to their max length. If no max length is specified, the padding is done up to the
model's max length. The tokenizer padding sides are handled by the class attribute `padding_side`
which can be set to the following strings:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
Defaults to False: no padding.
is_pretokenized (:obj:`bool`, defaults to :obj:`False`):
Set to True to indicate the input is already tokenized
return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):
Can be set to 'tf' or 'pt' to return respectively TensorFlow :obj:`tf.constant`
or PyTorch :obj:`torch.Tensor` instead of a list of python integers.
return_token_type_ids (:obj:`bool`, `optional`, defaults to :obj:`None`):
Whether to return token type IDs. If left to the default, will return the token type IDs according
to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are token type IDs? <../glossary.html#token-type-ids>`_
return_attention_mask (:obj:`bool`, `optional`, defaults to :obj:`none`):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are attention masks? <../glossary.html#attention-mask>`__
return_overflowing_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return overflowing token information (default False).
return_special_tokens_mask (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return special tokens mask information (default False).
return_offsets_mapping (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return (char_start, char_end) for each token (default False).
If using Python's tokenizer, this method will raise NotImplementedError.
This one is only available on fast tokenizers inheriting from PreTrainedTokenizerFast.
**kwargs: passed to the `self.tokenize()` method
Return:
A Dictionary of shape::
{
input_ids: list[int],
token_type_ids: list[int] if return_token_type_ids is True (default)
attention_mask: list[int] if return_attention_mask is True (default)
overflowing_tokens: list[int] if a ``max_length`` is specified and return_overflowing_tokens is True
num_truncated_tokens: int if a ``max_length`` is specified and return_overflowing_tokens is True
special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True``
and return_special_tokens_mask is True
}
With the fields:
- ``input_ids``: list of token ids to be fed to a model
- ``token_type_ids``: list of token type ids to be fed to a model
- ``attention_mask``: list of indices specifying which tokens should be attended to by the model
- ``overflowing_tokens``: list of overflowing tokens if a max length is specified.
- ``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
- ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
tokens and 1 specifying sequence tokens.
"""
def get_input_ids(text):
if isinstance(text, str):
tokens = self.tokenize(text, add_special_tokens=add_special_tokens, **kwargs)
return self.convert_tokens_to_ids(tokens)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
if is_pretokenized:
tokens = list(
itertools.chain(
*(
self.tokenize(t, add_special_tokens=False, add_prefix_space=True, **kwargs)
for t in text
)
)
)
return self.convert_tokens_to_ids(tokens)
else:
return self.convert_tokens_to_ids(text)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
return text
else:
raise ValueError(
"Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
f"Input {text} is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
)
if return_offsets_mapping:
......@@ -1622,34 +352,27 @@ class PreTrainedTokenizer(SpecialTokensMixin):
"https://github.com/huggingface/transformers/pull/2674"
)
# Throw an error if we can pad because there is no padding token
if pad_to_max_length and self.pad_token_id is None:
raise ValueError(
"Unable to set proper padding strategy as the tokenizer does not have a padding token. "
"In this case please set the `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` "
"or add a new pad token via the function add_special_tokens if you want to use a padding strategy"
)
first_ids = get_input_ids(text)
second_ids = get_input_ids(text_pair) if text_pair is not None else None
return self.prepare_for_model(
return self._prepare_for_model(
first_ids,
pair_ids=second_ids,
max_length=max_length,
pad_to_max_length=pad_to_max_length,
add_special_tokens=add_special_tokens,
stride=stride,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
return_tensors=return_tensors,
prepend_batch_axis=True,
return_attention_mask=return_attention_mask,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
prepend_batch_axis=return_tensors is not None,
verbose=verbose,
)
def batch_encode_plus(
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
......@@ -1660,10 +383,10 @@ class PreTrainedTokenizer(SpecialTokensMixin):
List[EncodedInputPair],
],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
truncation_strategy: str = "longest_first",
pad_to_max_length: bool = False,
is_pretokenized: bool = False,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
......@@ -1672,101 +395,25 @@ class PreTrainedTokenizer(SpecialTokensMixin):
return_special_tokens_masks: bool = False,
return_offsets_mapping: bool = False,
return_lengths: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
Returns a dictionary containing the encoded sequence or sequence pair and additional information:
the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.
Args:
batch_text_or_text_pairs (:obj:`List[str]`, :obj:`List[Tuple[str, str]]`,
:obj:`List[List[str]]`, :obj:`List[Tuple[List[str], List[str]]]`,
and for not-fast tokenizers, also:
:obj:`List[List[int]]`, :obj:`List[Tuple[List[int], List[int]]]`):
Batch of sequences or pair of sequences to be encoded.
This can be a list of string/string-sequences/int-sequences or a list of pair of
string/string-sequences/int-sequence (see details in encode_plus)
add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to ``True``, the sequences will be encoded with the special tokens relative
to their model.
max_length (:obj:`int`, `optional`, defaults to :obj:`None`):
If set to a number, will limit the total sequence returned so that it has a maximum length.
If there are overflowing tokens, those will be added to the returned dictionary
stride (:obj:`int`, `optional`, defaults to ``0``):
If set to a number along with max_length, the overflowing tokens returned will contain some tokens
from the main sequence returned. The value of this argument defines the number of additional tokens.
truncation_strategy (:obj:`str`, `optional`, defaults to `longest_first`):
String selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences)
- 'only_first': Only truncate the first sequence
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
pad_to_max_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
If set to True, the returned sequences will be padded according to the model's padding side and
padding index, up to their max length. If no max length is specified, the padding is done up to the
model's max length. The tokenizer padding sides are handled by the class attribute `padding_side`
which can be set to the following strings:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
Defaults to False: no padding.
is_pretokenized (:obj:`bool`, defaults to :obj:`False`):
Set to True to indicate the input is already tokenized
return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):
Can be set to 'tf' or 'pt' to return respectively TensorFlow :obj:`tf.constant`
or PyTorch :obj:`torch.Tensor` instead of a list of python integers.
return_token_type_ids (:obj:`bool`, `optional`, defaults to :obj:`None`):
Whether to return token type IDs. If left to the default, will return the token type IDs according
to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are token type IDs? <../glossary.html#token-type-ids>`_
return_attention_masks (:obj:`bool`, `optional`, defaults to :obj:`none`):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are attention masks? <../glossary.html#attention-mask>`__
return_overflowing_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return overflowing token information (default False).
return_special_tokens_masks (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return special tokens mask information (default False).
return_offsets_mapping (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return (char_start, char_end) for each token (default False).
If using Python's tokenizer, this method will raise NotImplementedError. This one is only available on
Rust-based tokenizers inheriting from PreTrainedTokenizerFast.
return_lengths (:obj:`bool`, `optional`, defaults to :obj:`False`):
If set the resulting dictionary will include the length of each encoded inputs
**kwargs: passed to the `self.tokenize()` method
Return:
A Dictionary of shape::
{
input_ids: list[List[int]],
token_type_ids: list[List[int]] if return_token_type_ids is True (default)
attention_mask: list[List[int]] if return_attention_mask is True (default)
overflowing_tokens: list[List[int]] if a ``max_length`` is specified and return_overflowing_tokens is True
num_truncated_tokens: List[int] if a ``max_length`` is specified and return_overflowing_tokens is True
special_tokens_mask: list[List[int]] if ``add_special_tokens`` if set to ``True`` and return_special_tokens_mask is True
}
With the fields:
- ``input_ids``: list of token ids to be fed to a model
- ``token_type_ids``: list of token type ids to be fed to a model
- ``attention_mask``: list of indices specifying which tokens should be attended to by the model
- ``overflowing_tokens``: list of overflowing tokens if a max length is specified.
- ``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
- ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
tokens and 1 specifying sequence tokens.
"""
def get_input_ids(text):
if isinstance(text, str):
tokens = self.tokenize(text, add_special_tokens=add_special_tokens, **kwargs)
return self.convert_tokens_to_ids(tokens)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
if is_pretokenized:
tokens = list(
itertools.chain(
*(
self.tokenize(t, add_special_tokens=False, add_prefix_space=True, **kwargs)
for t in text
)
)
)
return self.convert_tokens_to_ids(tokens)
else:
return self.convert_tokens_to_ids(text)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int):
return text
......@@ -1775,34 +422,70 @@ class PreTrainedTokenizer(SpecialTokensMixin):
"Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
)
# Throw an error if we can pad because there is no padding token
if pad_to_max_length and self.pad_token_id is None:
raise ValueError(
"Unable to set proper padding strategy as the tokenizer does not have a padding token. In this case please set the `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via the function add_special_tokens if you want to use a padding strategy"
)
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers."
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
"More information on available tokenizers at "
"https://github.com/huggingface/transformers/pull/2674"
)
input_ids = []
for ids_or_pair_ids in batch_text_or_text_pairs:
if isinstance(ids_or_pair_ids, (list, tuple)) and len(ids_or_pair_ids) == 2 and not is_pretokenized:
ids, pair_ids = ids_or_pair_ids
else:
if not isinstance(ids_or_pair_ids, (list, tuple)):
ids, pair_ids = ids_or_pair_ids, None
elif is_pretokenized and not isinstance(ids_or_pair_ids[0], (list, tuple)):
ids, pair_ids = ids_or_pair_ids, None
else:
ids, pair_ids = ids_or_pair_ids
first_ids = get_input_ids(ids)
second_ids = get_input_ids(pair_ids) if pair_ids is not None else None
input_ids.append((first_ids, second_ids))
if max_length is None and pad_to_max_length:
batch_outputs = self._batch_prepare_for_model(
input_ids,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
return_attention_masks=return_attention_masks,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_masks=return_special_tokens_masks,
return_lengths=return_lengths,
return_tensors=return_tensors,
verbose=verbose,
)
return BatchEncoding(batch_outputs)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def _batch_prepare_for_model(
self,
batch_ids_pairs: List[Union[PreTokenizedInputPair, Tuple[List[int], None]]],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_masks: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_masks: bool = False,
return_lengths: bool = False,
verbose: bool = True,
) -> BatchEncoding:
""" Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.
It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
manages a moving window (with user defined stride) for overflowing tokens
Args:
batch_ids_pairs: list of tokenized input ids or input ids pairs
"""
if padding_strategy == PaddingStrategy.LONGEST:
# For simplicity we keep the single sentnce path here
def total_sequence_length(input_pairs):
first_ids, second_ids = input_pairs
return len(first_ids) + (
......@@ -1811,27 +494,27 @@ class PreTrainedTokenizer(SpecialTokensMixin):
else (len(second_ids) + self.num_special_tokens_to_add(pair=True))
)
max_length = max([total_sequence_length(ids) for ids in input_ids])
max_length = max([total_sequence_length(input_pairs) for input_pairs in batch_ids_pairs])
padding_strategy = PaddingStrategy.MAX_LENGTH
batch_outputs = {}
for first_ids, second_ids in input_ids:
# Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by
# the model. It adds special tokens, truncates sequences if overflowing while taking into account
# the special tokens and manages a window stride for overflowing tokens
outputs = self.prepare_for_model(
for first_ids, second_ids in batch_ids_pairs:
outputs = self._prepare_for_model(
first_ids,
pair_ids=second_ids,
max_length=max_length,
pad_to_max_length=pad_to_max_length,
second_ids,
add_special_tokens=add_special_tokens,
stride=stride,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
return_attention_mask=return_attention_masks,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_masks,
return_lengths=return_lengths,
return_tensors=None, # We will convert the whole batch to tensors at the end
prepend_batch_axis=False,
verbose=verbose,
)
for key, value in outputs.items():
......@@ -1839,27 +522,28 @@ class PreTrainedTokenizer(SpecialTokensMixin):
batch_outputs[key] = []
batch_outputs[key].append(value)
if return_tensors is not None:
convert_to_tensors(batch_outputs, return_tensors)
batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
return BatchEncoding(batch_outputs)
return batch_outputs
def prepare_for_model(
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def _prepare_for_model(
self,
ids: List[int],
pair_ids: Optional[List[int]] = None,
max_length: Optional[int] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
truncation_strategy: str = "longest_first",
pad_to_max_length: bool = False,
return_tensors: Optional[Union[str, TensorType]] = None,
return_tensors: Optional[str] = None,
prepend_batch_axis: bool = False,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_lengths: bool = False,
prepend_batch_axis: bool = False,
verbose: bool = True,
) -> BatchEncoding:
""" Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.
It adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
......@@ -1870,56 +554,6 @@ class PreTrainedTokenizer(SpecialTokensMixin):
`tokenize` and `convert_tokens_to_ids` methods.
pair_ids: Optional second list of input ids. Can be obtained from a string by chaining the
`tokenize` and `convert_tokens_to_ids` methods.
max_length: maximum length of the returned list. Will truncate by taking into account the special tokens.
add_special_tokens: if set to ``True``, the sequences will be encoded with the special tokens relative
to their model.
stride: window stride for overflowing tokens. Can be useful to remove edge effect when using sequential
list of inputs. The overflowing token will contains a part of the previous window of tokens.
truncation_strategy: string selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences)
- 'only_first': Only truncate the first sequence
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
pad_to_max_length: if set to True, the returned sequences will be padded according to the model's padding side and
padding index, up to their max length. If no max length is specified, the padding is done up to the model's max length.
The tokenizer padding sides are handled by the following strings:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
Defaults to False: no padding.
return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
or PyTorch torch.Tensor instead of a list of python integers.
return_token_type_ids: (optional) Set to False to avoid returning token_type_ids (default: set to model specifics).
return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics)
return_overflowing_tokens: (optional) Set to True to return overflowing token information (default False).
return_special_tokens_mask: (optional) Set to True to return special tokens mask information (default False).
return_lengths (:obj:`bool`, `optional`, defaults to :obj:`False`):
If set the resulting dictionary will include the length of each encoded inputs
prepend_batch_axis (:obj:`bool`, `optional`, defaults to :obj:`False`):
If set the resulting object will feature an extra dim at position 0.
This can be seen as an unsqueezing operator.
Return:
A Dictionary of shape::
{
input_ids: list[int],
token_type_ids: list[int] if return_token_type_ids is True (default)
overflowing_tokens: list[int] if a ``max_length`` is specified and return_overflowing_tokens is True
num_truncated_tokens: int if a ``max_length`` is specified and return_overflowing_tokens is True
special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True`` and return_special_tokens_mask is True
length: int if return_lengths is True
}
With the fields:
- ``input_ids``: list of token ids to be fed to a model
- ``token_type_ids``: list of token type ids to be fed to a model
- ``overflowing_tokens``: list of overflowing tokens if a max length is specified.
- ``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
- ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
tokens and 1 specifying sequence tokens.
- ``length``: this is the length of ``input_ids``
"""
pair = bool(pair_ids is not None)
len_ids = len(ids)
......@@ -1935,7 +569,7 @@ class PreTrainedTokenizer(SpecialTokensMixin):
# Truncation: Handle max sequence length
total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
if max_length and total_len > max_length:
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
ids, pair_ids, overflowing_tokens = self.truncate_sequences(
ids,
pair_ids=pair_ids,
......@@ -1966,8 +600,7 @@ class PreTrainedTokenizer(SpecialTokensMixin):
encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
# Check lengths
assert max_length is None or len(encoded_inputs["input_ids"]) <= max_length
if max_length is None and len(encoded_inputs["input_ids"]) > self.model_max_length:
if max_length is None and len(encoded_inputs["input_ids"]) > self.model_max_length and verbose:
logger.warning(
"Token indices sequence length is longer than the specified maximum sequence length "
"for this model ({} > {}). Running this sequence through the model will result in "
......@@ -1975,57 +608,21 @@ class PreTrainedTokenizer(SpecialTokensMixin):
)
# Padding
needs_to_be_padded = pad_to_max_length and (
max_length
and len(encoded_inputs["input_ids"]) < max_length
or max_length is None
and len(encoded_inputs["input_ids"]) < self.model_max_length
and self.model_max_length <= LARGE_INTEGER
)
if pad_to_max_length and max_length is None and self.model_max_length > LARGE_INTEGER:
logger.warning(
"Sequence can't be padded as no maximum length is specified and the model maximum length is too high."
)
if needs_to_be_padded:
difference = (max_length if max_length is not None else self.model_max_length) - len(
encoded_inputs["input_ids"]
)
if self.padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"]) + [0] * difference
if return_token_type_ids:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
encoded_inputs = self.pad(
encoded_inputs,
max_length=max_length,
padding=padding_strategy.value,
return_attention_mask=return_attention_mask,
)
if return_special_tokens_mask:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference
elif self.padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + [1] * len(encoded_inputs["input_ids"])
if return_token_type_ids:
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
"token_type_ids"
]
if return_special_tokens_mask:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side))
else:
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
if return_lengths:
encoded_inputs["length"] = len(encoded_inputs["input_ids"])
# Prepare model inputs as tensors if asked
if return_tensors is not None:
convert_to_tensors(encoded_inputs, return_tensors, prepend_batch_axis)
batch_outputs = BatchEncoding(
encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
)
return BatchEncoding(encoded_inputs)
return batch_outputs
def prepare_for_tokenization(self, text: str, **kwargs) -> str:
""" Performs any necessary transformations before tokenization """
......@@ -2036,7 +633,7 @@ class PreTrainedTokenizer(SpecialTokensMixin):
ids: List[int],
pair_ids: Optional[List[int]] = None,
num_tokens_to_remove: int = 0,
truncation_strategy: str = "longest_first",
truncation_strategy: Union[str, TruncationStrategy] = "only_first",
stride: int = 0,
) -> Tuple[List[int], List[int], List[int]]:
""" Truncates a sequence pair in place to the maximum length.
......@@ -2048,13 +645,15 @@ class PreTrainedTokenizer(SpecialTokensMixin):
`tokenize` and `convert_tokens_to_ids` methods.
num_tokens_to_remove (:obj:`int`, `optional`, defaults to ``0``):
number of tokens to remove using the truncation strategy
truncation_strategy: string selected in the following options:
- 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
truncation_strategy (:obj:`string`, `optional`, defaults to "only_first"):
String selected in the following options:
- 'only_first' (default): Only truncate the first sequence. raise an error if the first sequence is shorter or equal to than num_tokens_to_remove.
- 'only_second': Only truncate the second sequence
- 'longest_first': Iteratively reduce the inputs sequence until the input is under max_length
starting from the longest one at each token (when there is a pair of input sequences).
Overflowing tokens only contains overflow from the first sequence.
- 'only_first': Only truncate the first sequence. raise an error if the first sequence is shorter or equal to than num_tokens_to_remove.
- 'only_second': Only truncate the second sequence
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
- 'do_not_truncate'
stride (:obj:`int`, `optional`, defaults to ``0``):
If set to a number along with max_length, the overflowing tokens returned will contain some tokens
from the main sequence returned. The value of this argument defines the number of additional tokens.
......@@ -2062,33 +661,27 @@ class PreTrainedTokenizer(SpecialTokensMixin):
if num_tokens_to_remove <= 0:
return ids, pair_ids, []
if truncation_strategy == "longest_first":
if not isinstance(truncation_strategy, TruncationStrategy):
truncation_strategy = TruncationStrategy(truncation_strategy)
overflowing_tokens = []
if truncation_strategy == TruncationStrategy.LONGEST_FIRST:
for _ in range(num_tokens_to_remove):
if pair_ids is None or len(ids) > len(pair_ids):
overflowing_tokens = [ids[-1]] + overflowing_tokens
ids = ids[:-1]
else:
pair_ids = pair_ids[:-1]
window_len = min(len(ids), stride)
if window_len > 0:
overflowing_tokens = ids[-window_len:] + overflowing_tokens
elif truncation_strategy == "only_first":
elif truncation_strategy == TruncationStrategy.ONLY_FIRST:
assert len(ids) > num_tokens_to_remove
window_len = min(len(ids), stride + num_tokens_to_remove)
overflowing_tokens = ids[-window_len:]
ids = ids[:-num_tokens_to_remove]
elif truncation_strategy == "only_second":
elif truncation_strategy == TruncationStrategy.ONLY_SECOND:
assert pair_ids is not None and len(pair_ids) > num_tokens_to_remove
window_len = min(len(pair_ids), stride + num_tokens_to_remove)
overflowing_tokens = pair_ids[-window_len:]
pair_ids = pair_ids[:-num_tokens_to_remove]
elif truncation_strategy == "do_not_truncate":
raise ValueError("Input sequence are too long for max_length. Please select a truncation strategy.")
else:
raise ValueError(
"Truncation_strategy should be selected in ['longest_first', 'only_first', 'only_second', 'do_not_truncate']"
)
return (ids, pair_ids, overflowing_tokens)
def create_token_type_ids_from_sequences(self, token_ids_0: List, token_ids_1: Optional[List] = None) -> List[int]:
......@@ -2199,493 +792,12 @@ class PreTrainedTokenizer(SpecialTokensMixin):
else:
return text
def batch_decode(self, sequences: List[List[int]], **kwargs) -> List[str]:
return [self.decode(seq, **kwargs) for seq in sequences]
@staticmethod
def clean_up_tokenization(out_string: str) -> str:
""" Clean up a list of simple English tokenization artifacts like spaces before punctuations and abreviated forms.
"""
out_string = (
out_string.replace(" .", ".")
.replace(" ?", "?")
.replace(" !", "!")
.replace(" ,", ",")
.replace(" ' ", "'")
.replace(" n't", "n't")
.replace(" 'm", "'m")
.replace(" 's", "'s")
.replace(" 've", "'ve")
.replace(" 're", "'re")
)
return out_string
class PreTrainedTokenizerFast(PreTrainedTokenizer):
""" Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).
Inherit from PreTrainedTokenizer.
Handle all the shared methods for tokenization and special tokens as well as methods
downloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.
This class also contain the added tokens in a unified way on top of all tokenizers so we don't
have to handle the specific vocabulary augmentation methods of the various underlying
dictionary structures (BPE, sentencepiece...).
Class attributes (overridden by derived classes):
- ``vocab_files_names``: a python ``dict`` with, as keys, the ``__init__`` keyword name of each vocabulary file
required by the model, and as associated values, the filename for saving the associated file (string).
- ``pretrained_vocab_files_map``: a python ``dict of dict`` the high-level keys
being the ``__init__`` keyword name of each vocabulary file required by the model, the low-level being the
`short-cut-names` (string) of the pretrained models with, as associated values, the `url` (string) to the
associated pretrained vocabulary file.
- ``max_model_input_sizes``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the pretrained
models, and as associated values, the maximum length of the sequence inputs of this model, or None if the
model has no maximum input size.
- ``pretrained_init_configuration``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the
pretrained models, and as associated values, a dictionnary of specific arguments to pass to the
``__init__``method of the tokenizer class for this pretrained model when loading the tokenizer with the
``from_pretrained()`` method.
Args:
- ``tokenizer`` (`BaseTokenizerFast`): A Fast tokenizer from the HuggingFace tokenizer library (in low level Rust language)
- ``model_max_length``: (`Optional`) int: the maximum length in number of tokens for the inputs to the transformer model.
When the tokenizer is loaded with `from_pretrained`, this will be set to the value stored for the associated
model in ``max_model_input_sizes`` (see above). If no value is provided, will default to VERY_LARGE_INTEGER (`int(1e30)`).
no associated max_length can be found in ``max_model_input_sizes``.
- ``padding_side``: (`Optional`) string: the side on which the model should have padding applied.
Should be selected between ['right', 'left']
- ``model_input_names``: (`Optional`) List[string]: the list of the forward pass inputs accepted by the
model ("token_type_ids", "attention_mask"...).
- ``bos_token``: (`Optional`) string: a beginning of sentence token.
Will be associated to ``self.bos_token`` and ``self.bos_token_id``
- ``eos_token``: (`Optional`) string: an end of sentence token.
Will be associated to ``self.eos_token`` and ``self.eos_token_id``
- ``unk_token``: (`Optional`) string: an unknown token.
Will be associated to ``self.unk_token`` and ``self.unk_token_id``
- ``sep_token``: (`Optional`) string: a separation token (e.g. to separate context and query in an input sequence).
Will be associated to ``self.sep_token`` and ``self.sep_token_id``
- ``pad_token``: (`Optional`) string: a padding token.
Will be associated to ``self.pad_token`` and ``self.pad_token_id``
- ``cls_token``: (`Optional`) string: a classification token (e.g. to extract a summary of an input sequence
leveraging self-attention along the full depth of the model).
Will be associated to ``self.cls_token`` and ``self.cls_token_id``
- ``mask_token``: (`Optional`) string: a masking token (e.g. when training a model with masked-language
modeling). Will be associated to ``self.mask_token`` and ``self.mask_token_id``
- ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens.
Adding all special tokens here ensure they won't be split by the tokenization process.
Will be associated to ``self.additional_special_tokens`` and ``self.additional_special_tokens_ids``
"""
def __init__(self, tokenizer: BaseTokenizerFast, **kwargs):
if not isinstance(tokenizer, BaseTokenizerFast):
raise ValueError(
"Tokenizer should be an instance of a Tokenizer " "provided by HuggingFace tokenizers library."
)
self._tokenizer: BaseTokenizerFast = tokenizer
# Initialize all the rest of the kwargs
super().__init__(**kwargs)
@property
def backend_tokenizer(self) -> BaseTokenizerFast:
return self._tokenizer
@property
def decoder(self) -> DecoderFast:
return self._tokenizer._tokenizer.decoder
@property
def is_fast(self) -> bool:
return True
@property
def vocab_size(self) -> int:
return self._tokenizer.get_vocab_size(with_added_tokens=False)
def __len__(self) -> int:
return self._tokenizer.get_vocab_size(with_added_tokens=True)
def _maybe_update_backend(self, value):
""" Update the backend fast tokenizer.
Override method from base class SpecialTokensMixin """
self._tokenizer.add_special_tokens(value)
def _convert_encoding(
self,
encoding: EncodingFast,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
) -> Dict[str, Any]:
""" Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict.
Overflowing tokens are converted to additional examples (like batches) so the output values of
the dict are lists (overflows) of lists (tokens).
If return_tensors is not None, these lists of lists are converted to 2-D tensors
for input_ids, token_type_ids and attention_mask.
Output shape: (overflows, sequence length)
"""
if return_token_type_ids is None:
return_token_type_ids = "token_type_ids" in self.model_input_names
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
if return_overflowing_tokens and encoding.overflowing is not None:
encodings = [encoding] + encoding.overflowing
else:
encodings = [encoding]
encoding_dict = defaultdict(list)
for e in encodings:
encoding_dict["input_ids"].append(e.ids)
if return_token_type_ids:
encoding_dict["token_type_ids"].append(e.type_ids)
if return_attention_mask:
encoding_dict["attention_mask"].append(e.attention_mask)
if return_special_tokens_mask:
encoding_dict["special_tokens_mask"].append(e.special_tokens_mask)
if return_offsets_mapping:
encoding_dict["offset_mapping"].append(e.offsets)
if return_tensors is not None:
encoding_dict = convert_to_tensors(encoding_dict, return_tensors)
return encoding_dict
def _convert_token_to_id_with_added_voc(self, token: int) -> str:
index = self._tokenizer.token_to_id(token)
if index is None:
return self.unk_token_id
return index
def _convert_id_to_token(self, index: int) -> Optional[str]:
return self._tokenizer.id_to_token(int(index))
def get_vocab(self):
return self._tokenizer.get_vocab(True)
def convert_tokens_to_string(self, tokens: List[int], skip_special_tokens: bool = False) -> str:
return self._tokenizer.decode(tokens, skip_special_tokens)
def add_tokens(self, new_tokens: List[Union[str, AddedTokenFast]]) -> int:
"""
Add a list of new tokens to the tokenizer class. If the new tokens are not in the
vocabulary, they are added to it with indices starting from length of the current vocabulary.
Args:
new_tokens: string or list of string or AddedTokenFast. Each string is a token to add.
Tokens are only added if they are not already in the vocabulary. AddedTokenFast wrap a string token to let you personnalize it's behavior (Whether this token should only match against single word, whether this token should strip all potential whitespaces on the left side, Whether this token should strip all potential whitespaces on the right side...).
See details for AddedToken in HuggingFace tokenizers library.
Returns:
Number of tokens added to the vocabulary.
Examples::
# Let's see how to increase the vocabulary of Bert model and tokenizer
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
def save_vocabulary(self, save_directory) -> Tuple[str]:
""" Save the tokenizer vocabulary to a directory. This method does *NOT* save added tokens
and special token mappings.
num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])
print('We have added', num_added_toks, 'tokens')
model.resize_token_embeddings(len(tokenizer)) # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
Please use :func:`~transformers.PreTrainedTokenizer.save_pretrained` `()` to save the full
Tokenizer state if you want to reload it using the :func:`~transformers.PreTrainedTokenizer.from_pretrained`
class method.
"""
if isinstance(new_tokens, str):
new_tokens = [new_tokens]
return self._tokenizer.add_tokens(new_tokens)
def add_special_tokens(self, special_tokens_dict: dict) -> int:
# Map special tokens to class attributes (self.pad_token...)
super().add_special_tokens(special_tokens_dict)
# If the backend tokenizer the only specificities of special tokens are that
# - they will never be processed by the model, and
# - they will be removed while decoding.
# But they are not mapped to special attributes in the backend so we can just
# send a list.
tokens = []
for token in special_tokens_dict.values():
if isinstance(token, list):
tokens += token
else:
tokens += [token]
num_added_tokens = self._tokenizer.add_special_tokens(tokens)
return num_added_tokens
def num_special_tokens_to_add(self, pair: bool = False) -> int:
return self._tokenizer.num_special_tokens_to_add(pair)
def tokenize(
self, text: TextInput, pair: Optional[TextInput] = None, add_special_tokens: bool = False
) -> List[str]:
return self._tokenizer.encode(text, pair, add_special_tokens).tokens
def batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput], List[TextInputPair], List[PreTokenizedInput], List[PreTokenizedInputPair]
],
add_special_tokens: bool = True,
max_length: Optional[int] = None,
stride: int = 0,
truncation_strategy: str = "longest_first",
pad_to_max_length: bool = False,
is_pretokenized: bool = False,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_lengths: bool = False,
**kwargs
) -> BatchEncoding:
if not isinstance(batch_text_or_text_pairs, list):
raise ValueError(
"batch_text_or_text_pairs has to be a list (got {})".format(type(batch_text_or_text_pairs))
)
# Needed if we have to return a tensor
pad_to_max_length = pad_to_max_length or (return_tensors is not None and len(batch_text_or_text_pairs) > 1)
# Throw an error if we can pad because there is no padding token
if pad_to_max_length and self.pad_token_id is None:
raise ValueError("Unable to set proper padding strategy as the tokenizer does not have a padding token")
# Set the truncation and padding strategy and restore the initial configuration
with truncate_and_pad(
tokenizer=self._tokenizer,
max_length=max_length,
stride=stride,
strategy=truncation_strategy,
pad_to_max_length=pad_to_max_length,
padding_side=self.padding_side,
pad_token_id=self.pad_token_id if self._pad_token is not None else None,
pad_token_type_id=self.pad_token_type_id,
pad_token=self._pad_token,
):
# Check for the pretokenized path
if is_pretokenized:
encodings = []
# Iterate over each sample (we don't know yet if they are pairs or simple input
for i, sample in enumerate(batch_text_or_text_pairs):
if not isinstance(sample, (list, tuple)):
raise TypeError(
"batch_encode_plus(..., is_pretokenized=True) requires batch_text_or_text_pairs "
"to be either List[List[str]] or List[Tuple[List[str], List[str]]] but sample at "
"index {} is of type {}".format(i, type(sample))
)
# Test if we have a pair of sentences by checking the depth of nesting
is_pair = bool(len(sample) > 0 and isinstance(sample[0], (list, tuple)))
# Take care of the first sequence - we multi-thread over the words
encodings_text = EncodingFast.merge(
self._tokenizer.encode_batch(sample[0] if is_pair else sample, add_special_tokens=False),
growing_offsets=True,
)
# Take care of the second sequence if we have a pair
if is_pair:
encodings_pair = EncodingFast.merge(
self._tokenizer.encode_batch([("", s) for s in sample[1]], add_special_tokens=False),
growing_offsets=True,
)
else:
encodings_pair = None
# Post-process - truncate/pad and add special tokens
encoding = self._tokenizer.post_process(encodings_text, encodings_pair, add_special_tokens)
encodings.append(encoding)
# Classical path with strings input
else:
# Avoid thread overhead if only one example.
if len(batch_text_or_text_pairs) == 1:
if isinstance(batch_text_or_text_pairs[0], (tuple, list)):
encodings = self._tokenizer.encode(
*batch_text_or_text_pairs[0], add_special_tokens=add_special_tokens
)
else:
encodings = self._tokenizer.encode(
batch_text_or_text_pairs[0], add_special_tokens=add_special_tokens
)
encodings = [encodings]
else:
encodings = self._tokenizer.encode_batch(
batch_text_or_text_pairs, add_special_tokens=add_special_tokens
)
# Convert encoding to dict
# `Tokens` has type: List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]]
# with nested dimensions corresponding to batch, overflows, sequence length
tokens = [
self._convert_encoding(
encoding=encoding,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
)
for encoding in encodings
]
# Sanitize the output to have dict[list] from list[dict]
sanitized = {}
for key in tokens[0].keys():
# To List[List[List[int]]] of shape (batch, overflows, sequence length)
stack = [e for item in tokens for e in item[key]]
if return_tensors == "tf":
stack = tf.stack(stack, axis=0)
elif return_tensors == "pt":
stack = torch.stack(stack, dim=0)
# elif not return_tensors and len(stack) == 1:
# stack = stack[0]
sanitized[key] = stack
# If returning overflowing tokens, we need to return a mapping
# from the batch idx to the original sample
if return_overflowing_tokens:
overflow_to_sample_mapping = flatten([[i] * len(enc["input_ids"]) for i, enc in enumerate(tokens)])
sanitized["overflow_to_sample_mapping"] = overflow_to_sample_mapping
return BatchEncoding(sanitized, encodings)
def encode_plus(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput]] = None,
add_special_tokens: bool = True,
max_length: Optional[int] = None,
pad_to_max_length: bool = False,
stride: int = 0,
truncation_strategy: str = "longest_first",
is_pretokenized: bool = False,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
**kwargs
) -> BatchEncoding:
# Check for pretokenized path (ie [token1, token2, ..., tokenN] -> [id1, id2, ..., idN]
if is_pretokenized:
if isinstance(text, list) and len(text) > 0:
# Encode through encode_batch with sequence of only one word which will be merged after hand
encoding = self._tokenizer.encode_batch(text, add_special_tokens=False)
encoding = EncodingFast.merge(encoding, growing_offsets=True)
# Let's do the same for pairs if provided
if isinstance(text_pair, list):
# We prepend empty string before each word so that encoding is aware content is a pair
encoding_pair = self._tokenizer.encode_batch(
[("", p) for p in text_pair], add_special_tokens=False
)
encoding_pair = EncodingFast.merge(encoding_pair, growing_offsets=True)
elif text_pair is None:
encoding_pair = None
else:
raise TypeError(
"encode_plus(..., is_pretokenized=True) requires text and text_pair to be List[str] "
"but got (text={}, text_pair={})".format(type(text), type(text_pair))
)
# Post process and if asked to do so, insert special tokens where needed
encoding = self._tokenizer.post_process(encoding, encoding_pair, add_special_tokens)
batched_output = BatchEncoding(
self._convert_encoding(
encoding,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
),
encoding,
)
else:
raise TypeError(
"encode_plus(..., is_pretokenized=True) requires text to be List[str] "
"but got (text={}, text_pair={})".format(type(text), type(text_pair))
)
else:
batched_input = [(text, text_pair)] if text_pair else [text]
batched_output = self.batch_encode_plus(
batched_input,
add_special_tokens=add_special_tokens,
max_length=max_length,
stride=stride,
truncation_strategy=truncation_strategy,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
pad_to_max_length=pad_to_max_length,
**kwargs,
)
# Return tensor is None, then we can remove the leading batch axis
if not return_tensors:
batched_output = BatchEncoding(
{
key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
for key, value in batched_output.items()
},
batched_output.encodings,
)
return batched_output
def decode(
self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True
) -> str:
text = self._tokenizer.decode(token_ids, skip_special_tokens)
if clean_up_tokenization_spaces:
clean_text = self.clean_up_tokenization(text)
return clean_text
else:
return text
def save_vocabulary(self, save_directory: str) -> Tuple[str]:
if os.path.isdir(save_directory):
files = self._tokenizer.save(save_directory)
else:
folder, file = os.path.split(os.path.abspath(save_directory))
files = self._tokenizer.save(folder, name=file)
return tuple(files)
def trim_batch(
input_ids, pad_token_id, attention_mask=None,
):
"""Remove columns that are populated exclusively by pad_token_id"""
keep_column_mask = input_ids.ne(pad_token_id).any(dim=0)
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
raise NotImplementedError
src/transformers/tokenization_utils_base.py 0 → 100644
View file @ 36434220
# coding=utf-8
# Copyright 2020 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.
""" Base classes common to both the slow and the fast tokenization classes:
PreTrainedTokenizerBase (host all the user fronting encoding methodes)
Special token mixing (host the special tokens logic) and
BatchEncoding (wrap the dictionnary of output with special method for the Fast tokenizers)
"""
import copy
import json
import logging
import os
import warnings
from collections import UserDict
from enum import Enum
from typing import Any, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union
import numpy as np
from tokenizers import AddedToken as AddedTokenFast
from tokenizers import Encoding as EncodingFast
from .file_utils import (
add_end_docstrings,
cached_path,
hf_bucket_url,
is_remote_url,
is_tf_available,
is_torch_available,
torch_required,
)
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
logger = logging.getLogger(__name__)
VERY_LARGE_INTEGER = int(1e30) # This is used to set the max input length for a model with infinite size input
LARGE_INTEGER = int(1e20) # This is used when we need something big but slightly smaller than VERY_LARGE_INTEGER
# Define type aliases and NamedTuples
TextInput = str
PreTokenizedInput = List[str]
EncodedInput = List[int]
TextInputPair = Tuple[str, str]
PreTokenizedInputPair = Tuple[List[str], List[str]]
EncodedInputPair = Tuple[List[int], List[int]]
SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json"
ADDED_TOKENS_FILE = "added_tokens.json"
TOKENIZER_CONFIG_FILE = "tokenizer_config.json"
FULL_TOKENIZER_FILE = "tokenizer.json"
class ExplicitEnum(Enum):
""" Enum with more explicit error message for missing values.
"""
@classmethod
def _missing_(cls, value):
raise ValueError(
"%r is not a valid %s, please select one of %s"
% (value, cls.__name__, str(list(cls._value2member_map_.keys())))
)
class TruncationStrategy(ExplicitEnum):
ONLY_FIRST = "only_first"
ONLY_SECOND = "only_second"
LONGEST_FIRST = "longest_first"
DO_NOT_TRUNCATE = "do_not_truncate"
class PaddingStrategy(ExplicitEnum):
LONGEST = "longest"
MAX_LENGTH = "max_length"
DO_NOT_PAD = "do_not_pad"
class TensorType(ExplicitEnum):
PYTORCH = "pt"
TENSORFLOW = "tf"
NUMPY = "np"
class CharSpan(NamedTuple):
""" Character span in the original string
Args:
start: index of the first character in the original string
end: index of the character following the last character in the original string
"""
start: int
end: int
class TokenSpan(NamedTuple):
""" Token span in an encoded string (list of tokens)
Args:
start: index of the first token in the span
end: index of the token following the last token in the span
"""
start: int
end: int
class BatchEncoding(UserDict):
""" BatchEncoding hold the output of the encode and batch_encode methods (tokens, attention_masks, etc).
This class is derived from a python Dictionary and can be used as a dictionnary.
In addition, this class expose utility methods to map from word/char space to token space.
Args:
data (:obj:`dict`): Dictionary of lists/arrays returned by the encode/batch_encode methods ('input_ids', 'attention_mask'...)
encoding (:obj:`EncodingFast`, :obj:`list(EncodingFast)`, `optional`, defaults to :obj:`None`):
If the tokenizer is a fast tokenizer which outputs additional informations like mapping from word/char space to token space
the `EncodingFast` instance or list of instance (for batches) hold these informations.
tensor_type (:obj:`Union[None, str, TensorType]`, `optional`, defaults to :obj:`None`):
You can give a tensor_type here to convert the lists of integers in PyTorch/TF/Numpy Tensors at initialization
prepend_batch_axis (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to add a batch axis when converting in Tensors (see :obj:`tensor_type` above)
"""
def __init__(
self,
data: Optional[Dict[str, Any]] = None,
encoding: Optional[Union[EncodingFast, Sequence[EncodingFast]]] = None,
tensor_type: Union[None, str, TensorType] = None,
prepend_batch_axis: bool = False,
):
super().__init__(data)
if isinstance(encoding, EncodingFast):
encoding = [encoding]
self._encodings = encoding
self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
def __getitem__(self, item: Union[int, str]) -> EncodingFast:
""" If the key is a string, get the value of the dict associated to `key` ('input_ids', 'attention_mask'...)
If the key is an integer, get the EncodingFast for batch item with index `key`
"""
if isinstance(item, str):
return self.data[item]
elif self._encodings is not None:
return self._encodings[item]
else:
raise KeyError(
"Indexing with integers (to access backend Encoding for a given batch index) "
"is not available when using Python based tokenizers"
)
def __getattr__(self, item: str):
try:
return self.data[item]
except KeyError:
raise AttributeError
def keys(self):
return self.data.keys()
def values(self):
return self.data.values()
def items(self):
return self.data.items()
# After this point:
# Extended properties and methods only available for fast (Rust-based) tokenizers
# provided by HuggingFace tokenizers library.
@property
def encodings(self) -> Optional[List[EncodingFast]]:
"""
Return the list all encoding from the tokenization process
Returns: List[EncodingFast] or None if input was tokenized through Python (i.e. not fast) tokenizer
"""
return self._encodings
def tokens(self, batch_index: int = 0) -> List[int]:
if not self._encodings:
raise ValueError("tokens() is not available when using Python based tokenizers")
return self._encodings[batch_index].tokens
def words(self, batch_index: int = 0) -> List[Optional[int]]:
if not self._encodings:
raise ValueError("words() is not available when using Python based tokenizers")
return self._encodings[batch_index].words
def token_to_word(self, batch_or_token_index: int, token_index: Optional[int] = None) -> int:
"""
Get the index of the word corresponding (i.e. comprising) to an encoded token
in a sequence of the batch.
Can be called as:
- ``self.token_to_word(token_index)`` if batch size is 1
- ``self.token_to_word(batch_index, token_index)`` if batch size is greater than 1
This method is particularly suited when the input sequences are provided as
pre-tokenized sequences (i.e. words are defined by the user). In this case it allows
to easily associate encoded tokens with provided tokenized words.
Args:
batch_or_token_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence,
this can be the index of the token in the sequence
token_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index
of the token in the sequence.
Returns:
:obj:`int`:
index of the word in the input sequence.
"""
if not self._encodings:
raise ValueError("token_to_word() is not available when using Python based tokenizers")
if token_index is not None:
batch_index = batch_or_token_index
else:
batch_index = 0
token_index = batch_or_token_index
if batch_index < 0:
batch_index = self._batch_size + batch_index
if token_index < 0:
token_index = self._seq_len + token_index
return self._encodings[batch_index].token_to_word(token_index)
def word_to_tokens(self, batch_or_word_index: int, word_index: Optional[int] = None) -> TokenSpan:
"""
Get the encoded token span corresponding to a word in the sequence of the batch.
Token spans are returned as a TokenSpan NamedTuple with:
- start: index of the first token
- end: index of the token following the last token
Can be called as:
- ``self.word_to_tokens(word_index)`` if batch size is 1
- ``self.word_to_tokens(batch_index, word_index)`` if batch size is greater or equal to 1
This method is particularly suited when the input sequences are provided as
pre-tokenized sequences (i.e. words are defined by the user). In this case it allows
to easily associate encoded tokens with provided tokenized words.
Args:
batch_or_word_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprises one sequence,
this can be the index of the word in the sequence
word_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index
of the word in the sequence.
Returns:
:obj:`TokenSpan`:
Span of tokens in the encoded sequence.
:obj:`TokenSpan` are NamedTuple with:
- start: index of the first token
- end: index of the token following the last token
"""
if not self._encodings:
raise ValueError("word_to_tokens() is not available when using Python based tokenizers")
if word_index is not None:
batch_index = batch_or_word_index
else:
batch_index = 0
word_index = batch_or_word_index
if batch_index < 0:
batch_index = self._batch_size + batch_index
if word_index < 0:
word_index = self._seq_len + word_index
return TokenSpan(*(self._encodings[batch_index].word_to_tokens(word_index)))
def token_to_chars(self, batch_or_token_index: int, token_index: Optional[int] = None) -> CharSpan:
"""
Get the character span corresponding to an encoded token in a sequence of the batch.
Character spans are returned as a CharSpan NamedTuple with:
- start: index of the first character in the original string associated to the token
- end: index of the character following the last character in the original string associated to the token
Can be called as:
- ``self.token_to_chars(token_index)`` if batch size is 1
- ``self.token_to_chars(batch_index, token_index)`` if batch size is greater or equal to 1
Args:
batch_or_token_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence,
this can be the index of the token in the sequence
token_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index
of the token or tokens in the sequence.
Returns:
:obj:`CharSpan`:
Span of characters in the original string.
:obj:`CharSpan` are NamedTuple with:
- start: index of the first character in the original string
- end: index of the character following the last character in the original string
"""
if not self._encodings:
raise ValueError("token_to_chars() is not available when using Python based tokenizers")
if token_index is not None:
batch_index = batch_or_token_index
else:
batch_index = 0
token_index = batch_or_token_index
return CharSpan(*(self._encodings[batch_index].token_to_chars(token_index)))
def char_to_token(self, batch_or_char_index: int, char_index: Optional[int] = None) -> int:
"""
Get the index of the token in the encoded output comprising a character
in the original string for a sequence of the batch.
Can be called as:
- ``self.char_to_token(char_index)`` if batch size is 1
- ``self.char_to_token(batch_index, char_index)`` if batch size is greater or equal to 1
This method is particularly suited when the input sequences are provided as
pre-tokenized sequences (i.e. words are defined by the user). In this case it allows
to easily associate encoded tokens with provided tokenized words.
Args:
batch_or_char_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence,
this can be the index of the word in the sequence
char_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index
of the word in the sequence.
Returns:
:obj:`int`: Index of the token.
"""
if not self._encodings:
raise ValueError("char_to_token() is not available when using Python based tokenizers")
if char_index is not None:
batch_index = batch_or_char_index
else:
batch_index = 0
char_index = batch_or_char_index
return self._encodings[batch_index].char_to_token(char_index)
def word_to_chars(self, batch_or_word_index: int, word_index: Optional[int] = None) -> CharSpan:
"""
Get the character span in the original string corresponding to given word in a sequence
of the batch.
Character spans are returned as a CharSpan NamedTuple with:
- start: index of the first character in the original string
- end: index of the character following the last character in the original string
Can be called as:
- ``self.word_to_chars(word_index)`` if batch size is 1
- ``self.word_to_chars(batch_index, word_index)`` if batch size is greater or equal to 1
Args:
batch_or_word_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence,
this can be the index of the word in the sequence
word_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index
of the word in the sequence.
Returns:
:obj:`CharSpan` or :obj:`List[CharSpan]`:
Span(s) of the associated character or characters in the string.
CharSpan are NamedTuple with:
- start: index of the first character associated to the token in the original string
- end: index of the character following the last character associated to the token in the original string
"""
if not self._encodings:
raise ValueError("word_to_chars() is not available when using Python based tokenizers")
if word_index is not None:
batch_index = batch_or_word_index
else:
batch_index = 0
word_index = batch_or_word_index
return CharSpan(*(self._encodings[batch_index].word_to_chars(word_index)))
def char_to_word(self, batch_or_char_index: int, char_index: Optional[int] = None) -> int:
"""
Get the word in the original string corresponding to a character in the original string of
a sequence of the batch.
Can be called as:
- ``self.char_to_word(char_index)`` if batch size is 1
- ``self.char_to_word(batch_index, char_index)`` if batch size is greater than 1
This method is particularly suited when the input sequences are provided as
pre-tokenized sequences (i.e. words are defined by the user). In this case it allows
to easily associate encoded tokens with provided tokenized words.
Args:
batch_or_char_index (:obj:`int`):
Index of the sequence in the batch. If the batch only comprise one sequence,
this can be the index of the character in the orginal string.
char_index (:obj:`int`, `optional`):
If a batch index is provided in `batch_or_token_index`, this can be the index
of the character in the orginal string.
Returns:
:obj:`int` or :obj:`List[int]`:
Index or indices of the associated encoded token(s).
"""
if not self._encodings:
raise ValueError("char_to_word() is not available when using Python based tokenizers")
if char_index is not None:
batch_index = batch_or_char_index
else:
batch_index = 0
char_index = batch_or_char_index
return self._encodings[batch_index].char_to_word(char_index)
def convert_to_tensors(self, tensor_type: Union[None, str, TensorType], prepend_batch_axis: bool = False):
if tensor_type is None:
return self
# Convert to TensorType
if not isinstance(tensor_type, TensorType):
tensor_type = TensorType(tensor_type)
# Get a function reference for the correct framework
if tensor_type == TensorType.TENSORFLOW and is_tf_available():
as_tensor = tf.constant
elif tensor_type == TensorType.PYTORCH and is_torch_available():
as_tensor = torch.tensor
elif tensor_type == TensorType.NUMPY:
as_tensor = np.asarray
else:
raise ImportError(
"Unable to convert output to tensors format {}, PyTorch or TensorFlow is not available.".format(
tensor_type
)
)
# Do the tensor conversion in batch
for key, value in self.items():
try:
if prepend_batch_axis:
value = [value]
tensor = as_tensor(value)
# at-least2d
if tensor.ndim > 2:
tensor = tensor.squeeze(0)
elif tensor.ndim < 2:
tensor = tensor[None, :]
self[key] = tensor
except: # noqa E722
raise ValueError(
"Unable to create tensor, you should probably activate truncation and/or padding "
"with 'padding=True' 'truncation=True' to have batched tensors with the same length."
)
return self
@torch_required
def to(self, device: str):
"""Send all values to device by calling v.to(device)"""
self.data = {k: v.to(device) for k, v in self.data.items()}
return self
class SpecialTokensMixin:
""" SpecialTokensMixin is derived by ``PreTrainedTokenizer`` and ``PreTrainedTokenizerFast`` and
handles specific behaviors related to special tokens. In particular, this class hold the
attributes which can be used to directly access to these special tokens in a
model-independant manner and allow to set and update the special tokens.
"""
SPECIAL_TOKENS_ATTRIBUTES = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
"additional_special_tokens",
]
def __init__(self, verbose=True, **kwargs):
self._bos_token = None
self._eos_token = None
self._unk_token = None
self._sep_token = None
self._pad_token = None
self._cls_token = None
self._mask_token = None
self._pad_token_type_id = 0
self._additional_special_tokens = []
self.verbose = verbose
for key, value in kwargs.items():
if key in self.SPECIAL_TOKENS_ATTRIBUTES:
if key == "additional_special_tokens":
assert isinstance(value, (list, tuple)) and all(isinstance(t, str) for t in value)
elif isinstance(value, AddedTokenFast):
setattr(self, key, str(value))
elif isinstance(value, str):
setattr(self, key, value)
else:
raise TypeError(
"special token {} has to be either str or AddedTokenFast but got: {}".format(key, type(value))
)
def add_special_tokens(self, special_tokens_dict):
"""
Add a dictionary of special tokens (eos, pad, cls...) to the encoder and link them
to class attributes. If special tokens are NOT in the vocabulary, they are added
to it (indexed starting from the last index of the current vocabulary).
Using `add_special_tokens` will ensure your special tokens can be used in several ways:
- special tokens are carefully handled by the tokenizer (they are never split)
- you can easily refer to special tokens using tokenizer class attributes like `tokenizer.cls_token`. This makes it easy to develop model-agnostic training and fine-tuning scripts.
When possible, special tokens are already registered for provided pretrained models (ex: BertTokenizer cls_token is already registered to be '[CLS]' and XLM's one is also registered to be '</s>')
Args:
special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes:
[``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``,
``additional_special_tokens``].
Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
Returns:
Number of tokens added to the vocabulary.
Examples::
# Let's see how to add a new classification token to GPT-2
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
special_tokens_dict = {'cls_token': '<CLS>'}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
print('We have added', num_added_toks, 'tokens')
model.resize_token_embeddings(len(tokenizer)) # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
assert tokenizer.cls_token == '<CLS>'
"""
if not special_tokens_dict:
return 0
added_tokens = 0
for key, value in special_tokens_dict.items():
assert key in self.SPECIAL_TOKENS_ATTRIBUTES
if key == "additional_special_tokens":
assert isinstance(value, (list, tuple)) and all(isinstance(t, str) for t in value)
added_tokens += self.add_tokens(value)
else:
assert isinstance(value, str)
added_tokens += self.add_tokens([value])
if self.verbose:
logger.info("Assigning %s to the %s key of the tokenizer", value, key)
setattr(self, key, value)
return added_tokens
def add_tokens(self, value):
""" To be overriden by derived class to add a token in the vocabulary. """
pass
def _maybe_update_backend(self, value):
""" To be overriden by derived class if a backend tokenizer has to be updated. """
pass
@property
def bos_token(self):
""" Beginning of sentence token (string). Log an error if used while not having been set. """
if self._bos_token is None and self.verbose:
logger.error("Using bos_token, but it is not set yet.")
return self._bos_token
@property
def eos_token(self):
""" End of sentence token (string). Log an error if used while not having been set. """
if self._eos_token is None and self.verbose:
logger.error("Using eos_token, but it is not set yet.")
return self._eos_token
@property
def unk_token(self):
""" Unknown token (string). Log an error if used while not having been set. """
if self._unk_token is None and self.verbose:
logger.error("Using unk_token, but it is not set yet.")
return self._unk_token
@property
def sep_token(self):
""" Separation token (string). E.g. separate context and query in an input sequence. Log an error if used while not having been set. """
if self._sep_token is None and self.verbose:
logger.error("Using sep_token, but it is not set yet.")
return self._sep_token
@property
def pad_token(self):
""" Padding token (string). Log an error if used while not having been set. """
if self._pad_token is None and self.verbose:
logger.error("Using pad_token, but it is not set yet.")
return self._pad_token
@property
def cls_token(self):
""" Classification token (string). E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. """
if self._cls_token is None and self.verbose:
logger.error("Using cls_token, but it is not set yet.")
return self._cls_token
@property
def mask_token(self):
""" Mask token (string). E.g. when training a model with masked-language modeling. Log an error if used while not having been set. """
if self._mask_token is None and self.verbose:
logger.error("Using mask_token, but it is not set yet.")
return self._mask_token
@property
def additional_special_tokens(self):
""" All the additional special tokens you may want to use (list of strings). Log an error if used while not having been set. """
if self._additional_special_tokens is None and self.verbose:
logger.error("Using additional_special_tokens, but it is not set yet.")
return self._additional_special_tokens
@bos_token.setter
def bos_token(self, value):
self._bos_token = value
self._maybe_update_backend([value])
@eos_token.setter
def eos_token(self, value):
self._eos_token = value
self._maybe_update_backend([value])
@unk_token.setter
def unk_token(self, value):
self._unk_token = value
self._maybe_update_backend([value])
@sep_token.setter
def sep_token(self, value):
self._sep_token = value
self._maybe_update_backend([value])
@pad_token.setter
def pad_token(self, value):
self._pad_token = value
self._maybe_update_backend([value])
@cls_token.setter
def cls_token(self, value):
self._cls_token = value
self._maybe_update_backend([value])
@mask_token.setter
def mask_token(self, value):
self._mask_token = value
self._maybe_update_backend([value])
@additional_special_tokens.setter
def additional_special_tokens(self, value):
self._additional_special_tokens = value
self._maybe_update_backend(value)
@property
def bos_token_id(self):
""" Id of the beginning of sentence token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.bos_token)
@property
def eos_token_id(self):
""" Id of the end of sentence token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.eos_token)
@property
def unk_token_id(self):
""" Id of the unknown token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.unk_token)
@property
def sep_token_id(self):
""" Id of the separation token in the vocabulary. E.g. separate context and query in an input sequence. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.sep_token)
@property
def pad_token_id(self):
""" Id of the padding token in the vocabulary. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.pad_token)
@property
def pad_token_type_id(self):
""" Id of the padding token type in the vocabulary."""
return self._pad_token_type_id
@property
def cls_token_id(self):
""" Id of the classification token in the vocabulary. E.g. to extract a summary of an input sequence leveraging self-attention along the full depth of the model. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.cls_token)
@property
def mask_token_id(self):
""" Id of the mask token in the vocabulary. E.g. when training a model with masked-language modeling. Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.mask_token)
@property
def additional_special_tokens_ids(self):
""" Ids of all the additional special tokens in the vocabulary (list of integers). Log an error if used while not having been set. """
return self.convert_tokens_to_ids(self.additional_special_tokens)
@property
def special_tokens_map(self):
""" A dictionary mapping special token class attribute (cls_token, unk_token...) to their
values ('<unk>', '<cls>'...)
"""
set_attr = {}
for attr in self.SPECIAL_TOKENS_ATTRIBUTES:
attr_value = getattr(self, "_" + attr)
if attr_value:
set_attr[attr] = attr_value
return set_attr
@property
def all_special_tokens(self):
""" List all the special tokens ('<unk>', '<cls>'...) mapped to class attributes
(cls_token, unk_token...).
"""
all_toks = []
set_attr = self.special_tokens_map
for attr_value in set_attr.values():
all_toks = all_toks + (list(attr_value) if isinstance(attr_value, (list, tuple)) else [attr_value])
all_toks = list(set(all_toks))
return all_toks
@property
def all_special_ids(self):
""" List the vocabulary indices of the special tokens ('<unk>', '<cls>'...) mapped to
class attributes (cls_token, unk_token...).
"""
all_toks = self.all_special_tokens
all_ids = self.convert_tokens_to_ids(all_toks)
return all_ids
ENCODE_KWARGS_DOCSTRING = r"""
add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to ``True``, the sequences will be encoded with the special tokens relative
to their model.
`padding` (:obj:`Union[bool, str]`, `optional`, defaults to :obj:`False`):
Activate and control padding. Accepts the following values:
* `True` or `'longest'`: pad to the longest sequence in the batch (or no padding if only a single sequence if provided),
* `'max_length'`: pad to a max length specified in `max_length` or to the max acceptable input length for the model if no length is provided (`max_length=None`)
* `False` or `'do_not_pad'` (default): No padding (i.e. can output batch with sequences of uneven lengths)
`truncation` (:obj:`Union[bool, str]`, `optional`, defaults to :obj:`False`):
Activate and control truncation. Accepts the following values:
* `True` or `'only_first'`: truncate to a max length specified in `max_length` or to the max acceptable input length for the model if no length is provided (`max_length=None`). This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided,
* `'only_second'`: truncate to a max length specified in `max_length` or to the max acceptable input length for the model if no length is provided (`max_length=None`). This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided,
* `'longest_first'`: truncate to a max length specified in `max_length` or to the max acceptable input length for the model if no length is provided (`max_length=None`). This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided,
* `False` or `'do_not_truncate'` (default): No truncation (i.e. can output batch with sequences length greater than the model max admissible input size)
`max_length` (:obj:`Union[int, None]`, `optional`, defaults to :obj:`None`):
Control the length for padding/truncation. Accepts the following values
* `None` (default): This will use the predefined model max length if required by one of the truncation/padding parameters. If the model has no specific max input length (e.g. XLNet) truncation/padding to max length is deactivated.
* `any integer value` (e.g. `42`): Use this specific maximum length value if required by one of the truncation/padding parameters.
stride (:obj:`int`, `optional`, defaults to ``0``):
If set to a number along with max_length, the overflowing tokens returned when `return_overflowing_tokens=True`
will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflow ing sequences.
The value of this argument defines the number of overlapping tokens.
is_pretokenized (:obj:`bool`, defaults to :obj:`False`):
Set to True to indicate the input is already tokenized
return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):
Can be set to 'tf', 'pt' or 'np' to return respectively TensorFlow :obj:`tf.constant`,
PyTorch :obj:`torch.Tensor` or Numpy :oj: `np.ndarray` instead of a list of python integers.
"""
ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
return_token_type_ids (:obj:`bool`, `optional`, defaults to :obj:`None`):
Whether to return token type IDs. If left to the default, will return the token type IDs according
to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are token type IDs? <../glossary.html#token-type-ids>`_
return_attention_mask (:obj:`bool`, `optional`, defaults to :obj:`none`):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are attention masks? <../glossary.html#attention-mask>`__
return_overflowing_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return overflowing token sequences (default False).
return_special_tokens_mask (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return special tokens mask information (default False).
return_offsets_mapping (:obj:`bool`, `optional`, defaults to :obj:`False`):
Set to True to return (char_start, char_end) for each token (default False).
If using Python's tokenizer, this method will raise NotImplementedError.
This one is only available on fast tokenizers inheriting from PreTrainedTokenizerFast.
**kwargs: passed to the `self.tokenize()` method
Return:
A Dictionary of shape::
{
input_ids: list[int],
token_type_ids: list[int] if return_token_type_ids is True (default)
attention_mask: list[int] if return_attention_mask is True (default)
overflowing_tokens: list[int] if the tokenizer is a slow tokenize, else a List[List[int]] if a ``max_length`` is specified and ``return_overflowing_tokens=True``
special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True``
and return_special_tokens_mask is True
}
With the fields:
- ``input_ids``: list of token ids to be fed to a model
- ``token_type_ids``: list of token type ids to be fed to a model
- ``attention_mask``: list of indices specifying which tokens should be attended to by the model
- ``overflowing_tokens``: list of overflowing tokens sequences if a max length is specified and ``return_overflowing_tokens=True``.
- ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
tokens and 1 specifying sequence tokens.
"""
class PreTrainedTokenizerBase(SpecialTokensMixin):
""" Base class for slow and fast tokenizers.
Handle shared (mostly boiler plate) methods for slow and fast tokenizers.
"""
vocab_files_names: Dict[str, str] = {}
pretrained_vocab_files_map: Dict[str, Dict[str, str]] = {}
pretrained_init_configuration: Dict[str, Dict[str, Any]] = {}
max_model_input_sizes: Dict[str, int] = {}
model_input_names: List[str] = ["token_type_ids", "attention_mask"]
padding_side: str = "right"
def __init__(self, model_max_length=None, **kwargs):
super().__init__(**kwargs)
# For backward compatibility we fallback to set model_max_length from max_len if provided
model_max_length = model_max_length if model_max_length is not None else kwargs.pop("max_len", None)
self.model_max_length = model_max_length if model_max_length is not None else VERY_LARGE_INTEGER
# Padding side is right by default and overridden in subclasses. If specified in the kwargs, it is changed.
self.padding_side = kwargs.pop("padding_side", self.padding_side)
assert self.padding_side in [
"right",
"left",
], f"Padding side should be selected between 'right' and 'left', current value: {self.padding_side}"
self.model_input_names = kwargs.pop("model_input_names", self.model_input_names)
# inputs and kwargs for saving and re-loading (see ``from_pretrained`` and ``save_pretrained``)
self.init_inputs = ()
self.init_kwargs = {}
@property
def max_len(self) -> int:
""" Kept here for backward compatibility.
Now renamed to `model_max_length` to avoid ambiguity.
"""
return self.model_max_length
@property
def max_len_single_sentence(self) -> int:
return self.model_max_length - self.num_special_tokens_to_add(pair=False)
@property
def max_len_sentences_pair(self) -> int:
return self.model_max_length - self.num_special_tokens_to_add(pair=True)
@max_len_single_sentence.setter
def max_len_single_sentence(self, value) -> int:
""" For backward compatibility, allow to try to setup 'max_len_single_sentence' """
if value == self.model_max_length - self.num_special_tokens_to_add(pair=False) and self.verbose:
logger.warning(
"Setting 'max_len_single_sentence' is now deprecated. " "This value is automatically set up."
)
else:
raise ValueError(
"Setting 'max_len_single_sentence' is now deprecated. " "This value is automatically set up."
)
@max_len_sentences_pair.setter
def max_len_sentences_pair(self, value) -> int:
""" For backward compatibility, allow to try to setup 'max_len_sentences_pair' """
if value == self.model_max_length - self.num_special_tokens_to_add(pair=True) and self.verbose:
logger.warning(
"Setting 'max_len_sentences_pair' is now deprecated. " "This value is automatically set up."
)
else:
raise ValueError(
"Setting 'max_len_sentences_pair' is now deprecated. " "This value is automatically set up."
)
@classmethod
def from_pretrained(cls, *inputs, **kwargs):
r"""
Instantiate a :class:`~transformers.PreTrainedTokenizer` (or a derived class) from a predefined tokenizer.
Args:
pretrained_model_name_or_path: either:
- a string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g.: ``bert-base-uncased``.
- a string with the `identifier name` of a predefined tokenizer that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g.: ``./my_model_directory/``.
- (not applicable to all derived classes, deprecated) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``.
cache_dir: (`optional`) string:
Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used.
force_download: (`optional`) boolean, default False:
Force to (re-)download the vocabulary files and override the cached versions if they exists.
resume_download: (`optional`) boolean, default False:
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
proxies: (`optional`) dict, default None:
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
The proxies are used on each request.
inputs: (`optional`) positional arguments: will be passed to the Tokenizer ``__init__`` method.
kwargs: (`optional`) keyword arguments: will be passed to the Tokenizer ``__init__`` method. Can be used to set special tokens like ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``. See parameters in the doc string of :class:`~transformers.PreTrainedTokenizer` for details.
Examples::
# We can't instantiate directly the base class `PreTrainedTokenizer` so let's show our examples on a derived class: BertTokenizer
# Download vocabulary from S3 and cache.
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Download vocabulary from S3 (user-uploaded) and cache.
tokenizer = BertTokenizer.from_pretrained('dbmdz/bert-base-german-cased')
# If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)
tokenizer = BertTokenizer.from_pretrained('./test/saved_model/')
# If the tokenizer uses a single vocabulary file, you can point directly to this file
tokenizer = BertTokenizer.from_pretrained('./test/saved_model/my_vocab.txt')
# You can link tokens to special vocabulary when instantiating
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', unk_token='<unk>')
# You should be sure '<unk>' is in the vocabulary when doing that.
# Otherwise use tokenizer.add_special_tokens({'unk_token': '<unk>'}) instead)
assert tokenizer.unk_token == '<unk>'
"""
return cls._from_pretrained(*inputs, **kwargs)
@classmethod
def _from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs):
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", False)
s3_models = list(cls.max_model_input_sizes.keys())
vocab_files = {}
init_configuration = {}
if pretrained_model_name_or_path in s3_models:
# Get the vocabulary from AWS S3 bucket
for file_id, map_list in cls.pretrained_vocab_files_map.items():
vocab_files[file_id] = map_list[pretrained_model_name_or_path]
if (
cls.pretrained_init_configuration
and pretrained_model_name_or_path in cls.pretrained_init_configuration
):
init_configuration = cls.pretrained_init_configuration[pretrained_model_name_or_path].copy()
else:
# Get the vocabulary from local files
logger.info(
"Model name '{}' not found in model shortcut name list ({}). "
"Assuming '{}' is a path, a model identifier, or url to a directory containing tokenizer files.".format(
pretrained_model_name_or_path, ", ".join(s3_models), pretrained_model_name_or_path
)
)
if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
if len(cls.vocab_files_names) > 1:
raise ValueError(
"Calling {}.from_pretrained() with the path to a single file or url is not supported."
"Use a model identifier or the path to a directory instead.".format(cls.__name__)
)
logger.warning(
"Calling {}.from_pretrained() with the path to a single file or url is deprecated".format(
cls.__name__
)
)
file_id = list(cls.vocab_files_names.keys())[0]
vocab_files[file_id] = pretrained_model_name_or_path
else:
# At this point pretrained_model_name_or_path is either a directory or a model identifier name
additional_files_names = {
"added_tokens_file": ADDED_TOKENS_FILE,
"special_tokens_map_file": SPECIAL_TOKENS_MAP_FILE,
"tokenizer_config_file": TOKENIZER_CONFIG_FILE,
}
# Look for the tokenizer main vocabulary files + the additional tokens files
for file_id, file_name in {**cls.vocab_files_names, **additional_files_names}.items():
if os.path.isdir(pretrained_model_name_or_path):
full_file_name = os.path.join(pretrained_model_name_or_path, file_name)
if not os.path.exists(full_file_name):
logger.info("Didn't find file {}. We won't load it.".format(full_file_name))
full_file_name = None
else:
full_file_name = hf_bucket_url(
pretrained_model_name_or_path, filename=file_name, use_cdn=False
)
vocab_files[file_id] = full_file_name
# Get files from url, cache, or disk depending on the case
try:
resolved_vocab_files = {}
for file_id, file_path in vocab_files.items():
if file_path is None:
resolved_vocab_files[file_id] = None
else:
resolved_vocab_files[file_id] = cached_path(
file_path,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
)
except EnvironmentError:
if pretrained_model_name_or_path in s3_models:
msg = "Couldn't reach server at '{}' to download vocabulary files."
else:
msg = (
"Model name '{}' was not found in tokenizers model name list ({}). "
"We assumed '{}' was a path or url to a directory containing vocabulary files "
"named {}, but couldn't find such vocabulary files at this path or url.".format(
pretrained_model_name_or_path,
", ".join(s3_models),
pretrained_model_name_or_path,
list(cls.vocab_files_names.values()),
)
)
raise EnvironmentError(msg)
if all(full_file_name is None for full_file_name in resolved_vocab_files.values()):
raise EnvironmentError(
"Model name '{}' was not found in tokenizers model name list ({}). "
"We assumed '{}' was a path, a model identifier, or url to a directory containing vocabulary files "
"named {} but couldn't find such vocabulary files at this path or url.".format(
pretrained_model_name_or_path,
", ".join(s3_models),
pretrained_model_name_or_path,
list(cls.vocab_files_names.values()),
)
)
for file_id, file_path in vocab_files.items():
if file_path == resolved_vocab_files[file_id]:
logger.info("loading file {}".format(file_path))
else:
logger.info("loading file {} from cache at {}".format(file_path, resolved_vocab_files[file_id]))
# Prepare tokenizer initialization kwargs
# Did we saved some inputs and kwargs to reload ?
tokenizer_config_file = resolved_vocab_files.pop("tokenizer_config_file", None)
if tokenizer_config_file is not None:
with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle:
init_kwargs = json.load(tokenizer_config_handle)
saved_init_inputs = init_kwargs.pop("init_inputs", ())
if not init_inputs:
init_inputs = saved_init_inputs
else:
init_kwargs = init_configuration
# Update with newly provided kwargs
init_kwargs.update(kwargs)
# Set max length if needed
if pretrained_model_name_or_path in cls.max_model_input_sizes:
# if we're using a pretrained model, ensure the tokenizer
# wont index sequences longer than the number of positional embeddings
model_max_length = cls.max_model_input_sizes[pretrained_model_name_or_path]
if model_max_length is not None and isinstance(model_max_length, (int, float)):
init_kwargs["model_max_length"] = min(init_kwargs.get("model_max_length", int(1e30)), model_max_length)
# Merge resolved_vocab_files arguments in init_kwargs.
added_tokens_file = resolved_vocab_files.pop("added_tokens_file", None)
special_tokens_map_file = resolved_vocab_files.pop("special_tokens_map_file", None)
for args_name, file_path in resolved_vocab_files.items():
if args_name not in init_kwargs:
init_kwargs[args_name] = file_path
if special_tokens_map_file is not None:
with open(special_tokens_map_file, encoding="utf-8") as special_tokens_map_handle:
special_tokens_map = json.load(special_tokens_map_handle)
for key, value in special_tokens_map.items():
if key not in init_kwargs:
init_kwargs[key] = value
# Instantiate tokenizer.
try:
tokenizer = cls(*init_inputs, **init_kwargs)
except OSError:
raise OSError(
"Unable to load vocabulary from file. "
"Please check that the provided vocabulary is accessible and not corrupted."
)
# Save inputs and kwargs for saving and re-loading with ``save_pretrained``
tokenizer.init_inputs = init_inputs
tokenizer.init_kwargs = init_kwargs
# update unique_added_tokens_encoder with special tokens for correct tokenization
if hasattr(tokenizer, "unique_added_tokens_encoder"):
tokenizer.unique_added_tokens_encoder.update(set(tokenizer.all_special_tokens))
# Add supplementary tokens.
if added_tokens_file is not None:
with open(added_tokens_file, encoding="utf-8") as added_tokens_handle:
added_tok_encoder = json.load(added_tokens_handle)
added_tok_decoder = {v: k for k, v in added_tok_encoder.items()}
tokenizer.added_tokens_encoder.update(added_tok_encoder)
tokenizer.added_tokens_decoder.update(added_tok_decoder)
tokenizer.unique_added_tokens_encoder.update(set(tokenizer.added_tokens_encoder.keys()))
return tokenizer
def save_pretrained(self, save_directory) -> Tuple[str]:
""" Save the tokenizer vocabulary files together with:
- added tokens,
- special-tokens-to-class-attributes-mapping,
- tokenizer instantiation positional and keywords inputs (e.g. do_lower_case for Bert).
Warning: This won't save modifications you may have applied to the tokenizer after the instantiation
(e.g. modifying tokenizer.do_lower_case after creation).
This method make sure the full tokenizer can then be re-loaded using the
:func:`~transformers.PreTrainedTokenizer.from_pretrained` class method.
"""
if not os.path.isdir(save_directory):
logger.error("Saving directory ({}) should be a directory".format(save_directory))
return
special_tokens_map_file = os.path.join(save_directory, SPECIAL_TOKENS_MAP_FILE)
added_tokens_file = os.path.join(save_directory, ADDED_TOKENS_FILE)
tokenizer_config_file = os.path.join(save_directory, TOKENIZER_CONFIG_FILE)
tokenizer_config = copy.deepcopy(self.init_kwargs)
if len(self.init_inputs) > 0:
tokenizer_config["init_inputs"] = copy.deepcopy(self.init_inputs)
for file_id in self.vocab_files_names.keys():
tokenizer_config.pop(file_id, None)
with open(tokenizer_config_file, "w", encoding="utf-8") as f:
f.write(json.dumps(tokenizer_config, ensure_ascii=False))
with open(special_tokens_map_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.special_tokens_map, ensure_ascii=False))
if hasattr(self, "added_tokens_encoder") and len(self.added_tokens_encoder) > 0:
with open(added_tokens_file, "w", encoding="utf-8") as f:
out_str = json.dumps(self.added_tokens_encoder, ensure_ascii=False)
f.write(out_str)
vocab_files = self.save_vocabulary(save_directory)
return vocab_files + (special_tokens_map_file, added_tokens_file)
@add_end_docstrings(
ENCODE_KWARGS_DOCSTRING,
"""
**kwargs: passed to the `self.tokenize()` method.
""",
)
def encode(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str] = False,
truncation: Union[bool, str] = False,
max_length: Optional[int] = None,
stride: int = 0,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs
):
"""
Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
Same as doing ``self.convert_tokens_to_ids(self.tokenize(text))``.
Args:
text (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`):
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
method)
text_pair (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
string using the `tokenize` method) or a list of integers (tokenized string ids using the
`convert_tokens_to_ids` method)
"""
encoded_inputs = self.encode_plus(
text,
text_pair=text_pair,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
return_tensors=return_tensors,
**kwargs,
)
return encoded_inputs["input_ids"]
def num_special_tokens_to_add(self, pair: bool = False) -> int:
raise NotImplementedError
def _get_padding_truncation_strategies(
self, padding=False, truncation=False, max_length=None, verbose=True, **kwargs
):
""" Find the correct padding/truncation strategy with backward compatibility
for old arguments (truncation_strategy and pad_to_max_length) and behaviors.
"""
old_truncation_strategy = kwargs.pop("truncation_strategy", "do_not_truncate")
old_pad_to_max_length = kwargs.pop("pad_to_max_length", False)
# Backward compatibility for previous behavior, maybe we should deprecate it:
# If you only set max_length, it activates truncation for max_length
if max_length is not None and padding is False and truncation is False:
if verbose:
logger.warning(
"Truncation was not explicitely activated but `max_length` is provided a specific value, "
"please use `truncation=True` to explicitely truncate examples to max length. "
"Defaulting to 'only_first' truncation strategy. "
"If you encode pairs of sequences (GLUE-style) with the tokenizer you may want to check this is the right behavior."
)
truncation = "only_first"
# Get padding strategy
if padding is False and old_pad_to_max_length:
if verbose:
warnings.warn(
"The `pad_to_max_length` argument is deprecated and will be removed in a future version, "
"use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or "
"use `padding='max_length'` to pad to a max length. In this case, you can give a specific "
"length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the "
"maximal input size of the model (e.g. 512 for Bert).",
DeprecationWarning,
)
if max_length is None:
padding_strategy = PaddingStrategy.LONGEST
else:
padding_strategy = PaddingStrategy.MAX_LENGTH
elif padding is not False:
if padding is True:
padding_strategy = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
else:
padding_strategy = PaddingStrategy(padding)
else:
padding_strategy = PaddingStrategy.DO_NOT_PAD
# Get truncation strategy
if truncation is False and old_truncation_strategy != "do_not_truncate":
if verbose:
warnings.warn(
"The `truncation_strategy` argument is deprecated and will be removed in a future version, "
"use `truncation=True` to truncate examples to a max length. You can give a specific "
"length with `max_length` (e.g. `max_length=45`) or leave max_length to None to truncate to the "
"maximal input size of the model (e.g. 512 for Bert). "
" If you have pairs of inputs, you can give a specific truncation strategy selected among "
"`truncation='only_first'` (will only truncate the first sentence in the pairs) "
"`truncation='only_second'` (will only truncate the second sentence in the pairs) "
"or `truncation='longest_first'` (will iteratively remove tokens from the longest sentence in the pairs).",
DeprecationWarning,
)
truncation_strategy = TruncationStrategy(old_truncation_strategy)
elif truncation is not False:
if truncation is True:
truncation_strategy = (
TruncationStrategy.ONLY_FIRST
) # Default to truncate the first sequences in pairs of inputs
else:
truncation_strategy = TruncationStrategy(truncation)
else:
truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
if self.model_max_length > LARGE_INTEGER:
if verbose:
logger.warning(
"Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. "
"Default to no padding."
)
padding_strategy = PaddingStrategy.DO_NOT_PAD
else:
max_length = self.model_max_length
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE:
if self.model_max_length > LARGE_INTEGER:
if verbose:
logger.warning(
"Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. "
"Default to no truncation."
)
truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE
else:
max_length = self.model_max_length
# Test if we have a padding token
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (not self.pad_token or self.pad_token_id < 0):
raise ValueError(
"Asking to pad but the tokenizer does not have a padding token. "
"Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` "
"or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`."
)
return padding_strategy, truncation_strategy, max_length, kwargs
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str] = False,
truncation: Union[bool, str] = False,
max_length: Optional[int] = None,
stride: int = 0,
is_pretokenized: bool = False,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_lengths: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
Returns a dictionary containing the encoded sequence or sequence pair and additional information:
the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.
Args:
text (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]``):
The sequence or batch of sequences to be encoded.
Each sequence can be a string or a list of strings (pre-tokenized string).
If the sequences are provided as list of strings (pretokenized), you must set `is_pretokenized=True`
(to lift the ambiguity with a batch of sequences)
text_pair (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]``):
The sequence or batch of sequences to be encoded.
Each sequence can be a string or a list of strings (pre-tokenized string).
If the sequences are provided as list of strings (pretokenized), you must set `is_pretokenized=True`
(to lift the ambiguity with a batch of sequences)
"""
is_batched = bool(
(not is_pretokenized and isinstance(text, (list, tuple)))
or (is_pretokenized and isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple)))
)
if is_batched:
batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
return self.batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
is_pretokenized=is_pretokenized,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_masks=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_masks=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_lengths=return_lengths,
verbose=verbose,
**kwargs,
)
else:
return self.encode_plus(
text=text,
text_pair=text_pair,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
is_pretokenized=is_pretokenized,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
verbose=verbose,
**kwargs,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def encode_plus(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str] = False,
truncation: Union[bool, str] = False,
max_length: Optional[int] = None,
stride: int = 0,
is_pretokenized: bool = False,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_lengths: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
Returns a dictionary containing the encoded sequence or sequence pair and additional information:
the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.
Args:
text (:obj:`str`, :obj:`List[str]` or :obj:`List[int]` (the later only for not-fast tokenizers)):
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
method)
text_pair (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`, `optional`, defaults to :obj:`None`):
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
string using the `tokenize` method) or a list of integers (tokenized string ids using the
`convert_tokens_to_ids` method)
"""
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding, truncation, max_length, verbose, **kwargs
)
return self._encode_plus(
text=text,
text_pair=text_pair,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
is_pretokenized=is_pretokenized,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_lengths=return_lengths,
verbose=verbose,
**kwargs,
)
def _encode_plus(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
is_pretokenized: bool = False,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
raise NotImplementedError
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
List[PreTokenizedInputPair],
List[EncodedInput],
List[EncodedInputPair],
],
add_special_tokens: bool = True,
padding: Union[bool, str] = False,
truncation: Union[bool, str] = False,
max_length: Optional[int] = None,
stride: int = 0,
is_pretokenized: bool = False,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_masks: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_masks: bool = False,
return_offsets_mapping: bool = False,
return_lengths: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
Returns a dictionary containing the encoded sequence or sequence pair and additional information:
the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.
Args:
batch_text_or_text_pairs (:obj:`List[str]`, :obj:`List[Tuple[str, str]]`,
:obj:`List[List[str]]`, :obj:`List[Tuple[List[str], List[str]]]`,
and for not-fast tokenizers, also:
:obj:`List[List[int]]`, :obj:`List[Tuple[List[int], List[int]]]`):
Batch of sequences or pair of sequences to be encoded.
This can be a list of string/string-sequences/int-sequences or a list of pair of
string/string-sequences/int-sequence (see details in encode_plus)
"""
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding, truncation, max_length, verbose, **kwargs
)
return self._batch_encode_plus(
batch_text_or_text_pairs=batch_text_or_text_pairs,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
is_pretokenized=is_pretokenized,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_masks=return_attention_masks,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_masks=return_special_tokens_masks,
return_offsets_mapping=return_offsets_mapping,
return_lengths=return_lengths,
verbose=verbose,
**kwargs,
)
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
List[PreTokenizedInputPair],
List[EncodedInput],
List[EncodedInputPair],
],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
is_pretokenized: bool = False,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_masks: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_masks: bool = False,
return_offsets_mapping: bool = False,
return_lengths: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
raise NotImplementedError
def pad(
self,
encoding_or_batch: Dict[str, Union[List[EncodedInput], EncodedInput]],
padding: Union[bool, str] = True,
max_length: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
verbose: bool = True,
) -> dict:
""" Pad encoded inputs (on left/right and up to predefined legnth or max length in the batch)
Args:
batch_ids: Dictionary of batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding: Boolean or specific strategy to use for padding.
Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among:
- 'longest' (or `True`) Pad to the longest sequence in the batch
- 'max_length': Pad to the max length (default)
- 'do_not_pad' (or `False`): Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
assert "input_ids" in encoding_or_batch, (
"You should supply an encoding to this method (a dict of lists/batch of int). "
"This is the output of encode/encode_plus/batch_encode_plus/__call__. "
)
if not encoding_or_batch["input_ids"]:
if return_attention_mask:
encoding_or_batch["attention_mask"] = []
return encoding_or_batch
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
padding=padding, max_length=max_length, verbose=verbose
)
if encoding_or_batch["input_ids"] and not isinstance(encoding_or_batch["input_ids"][0], (list, tuple)):
return self._pad(
encoding_or_batch,
max_length=max_length,
padding_strategy=padding_strategy,
return_attention_mask=return_attention_mask,
)
batch_size = len(encoding_or_batch["input_ids"])
assert all(
len(v) == batch_size for v in encoding_or_batch.values()
), "Some items in the output dictionnary have a different batch size than others."
if padding_strategy == PaddingStrategy.LONGEST:
max_length = max(len(inputs) for inputs in encoding_or_batch["input_ids"])
padding_strategy = PaddingStrategy.MAX_LENGTH
batch_outputs = {}
for i in range(batch_size):
inputs = dict((k, v[i]) for k, v in encoding_or_batch.items())
outputs = self._pad(
inputs,
max_length=max_length,
padding_strategy=padding_strategy,
return_attention_mask=return_attention_mask,
)
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
return batch_outputs
def _pad(
self,
encoded_inputs: Dict[str, EncodedInput],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
return_attention_mask: Optional[bool] = None,
) -> dict:
""" Pad encoded inputs (on left/right and up to predefined legnth or max length in the batch)
Args:
encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
# Load from model defaults
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
if padding_strategy == PaddingStrategy.LONGEST and max_length is None:
max_length = len(encoded_inputs["input_ids"])
needs_to_be_padded = (
padding_strategy != PaddingStrategy.DO_NOT_PAD and len(encoded_inputs["input_ids"]) != max_length
)
if needs_to_be_padded:
difference = max_length - len(encoded_inputs["input_ids"])
if self.padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"]) + [0] * difference
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = (
encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
)
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference
elif self.padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + [1] * len(encoded_inputs["input_ids"])
if "token_type_ids" in encoded_inputs:
encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
"token_type_ids"
]
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side))
else:
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
return encoded_inputs
def batch_decode(self, sequences: List[List[int]], **kwargs) -> List[str]:
return [self.decode(seq, **kwargs) for seq in sequences]
@staticmethod
def clean_up_tokenization(out_string: str) -> str:
""" Clean up a list of simple English tokenization artifacts like spaces before punctuations and abreviated forms.
"""
out_string = (
out_string.replace(" .", ".")
.replace(" ?", "?")
.replace(" !", "!")
.replace(" ,", ",")
.replace(" ' ", "'")
.replace(" n't", "n't")
.replace(" 'm", "'m")
.replace(" 's", "'s")
.replace(" 've", "'ve")
.replace(" 're", "'re")
)
return out_string
src/transformers/tokenization_utils_fast.py 0 → 100644
View file @ 36434220
# coding=utf-8
# Copyright 2020 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 for fast tokenizers (provided by HuggingFace's tokenizers library).
For slow (python) tokenizers see tokenization_utils.py
"""
import logging
import os
from collections import defaultdict
from typing import Any, Dict, List, Optional, Tuple, Union
from tokenizers import AddedToken as AddedTokenFast
from tokenizers import Encoding as EncodingFast
from tokenizers.decoders import Decoder as DecoderFast
from tokenizers.implementations import BaseTokenizer as BaseTokenizerFast
from .tokenization_utils_base import (
BatchEncoding,
PaddingStrategy,
PreTokenizedInput,
PreTokenizedInputPair,
PreTrainedTokenizerBase,
TextInput,
TextInputPair,
TruncationStrategy,
)
logger = logging.getLogger(__name__)
class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
""" Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).
Inherit from PreTrainedTokenizer.
Handle all the shared methods for tokenization and special tokens as well as methods
downloading/caching/loading pretrained tokenizers as well as adding tokens to the vocabulary.
This class also contain the added tokens in a unified way on top of all tokenizers so we don't
have to handle the specific vocabulary augmentation methods of the various underlying
dictionary structures (BPE, sentencepiece...).
Class attributes (overridden by derived classes):
- ``vocab_files_names``: a python ``dict`` with, as keys, the ``__init__`` keyword name of each vocabulary file
required by the model, and as associated values, the filename for saving the associated file (string).
- ``pretrained_vocab_files_map``: a python ``dict of dict`` the high-level keys
being the ``__init__`` keyword name of each vocabulary file required by the model, the low-level being the
`short-cut-names` (string) of the pretrained models with, as associated values, the `url` (string) to the
associated pretrained vocabulary file.
- ``max_model_input_sizes``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the pretrained
models, and as associated values, the maximum length of the sequence inputs of this model, or None if the
model has no maximum input size.
- ``pretrained_init_configuration``: a python ``dict`` with, as keys, the `short-cut-names` (string) of the
pretrained models, and as associated values, a dictionnary of specific arguments to pass to the
``__init__``method of the tokenizer class for this pretrained model when loading the tokenizer with the
``from_pretrained()`` method.
Args:
- ``tokenizer`` (`BaseTokenizerFast`): A Fast tokenizer from the HuggingFace tokenizer library (in low level Rust language)
- ``model_max_length``: (`Optional`) int: the maximum length in number of tokens for the inputs to the transformer model.
When the tokenizer is loaded with `from_pretrained`, this will be set to the value stored for the associated
model in ``max_model_input_sizes`` (see above). If no value is provided, will default to VERY_LARGE_INTEGER (`int(1e30)`).
no associated max_length can be found in ``max_model_input_sizes``.
- ``padding_side``: (`Optional`) string: the side on which the model should have padding applied.
Should be selected between ['right', 'left']
- ``model_input_names``: (`Optional`) List[string]: the list of the forward pass inputs accepted by the
model ("token_type_ids", "attention_mask"...).
- ``bos_token``: (`Optional`) string: a beginning of sentence token.
Will be associated to ``self.bos_token`` and ``self.bos_token_id``
- ``eos_token``: (`Optional`) string: an end of sentence token.
Will be associated to ``self.eos_token`` and ``self.eos_token_id``
- ``unk_token``: (`Optional`) string: an unknown token.
Will be associated to ``self.unk_token`` and ``self.unk_token_id``
- ``sep_token``: (`Optional`) string: a separation token (e.g. to separate context and query in an input sequence).
Will be associated to ``self.sep_token`` and ``self.sep_token_id``
- ``pad_token``: (`Optional`) string: a padding token.
Will be associated to ``self.pad_token`` and ``self.pad_token_id``
- ``cls_token``: (`Optional`) string: a classification token (e.g. to extract a summary of an input sequence
leveraging self-attention along the full depth of the model).
Will be associated to ``self.cls_token`` and ``self.cls_token_id``
- ``mask_token``: (`Optional`) string: a masking token (e.g. when training a model with masked-language
modeling). Will be associated to ``self.mask_token`` and ``self.mask_token_id``
- ``additional_special_tokens``: (`Optional`) list: a list of additional special tokens.
Adding all special tokens here ensure they won't be split by the tokenization process.
Will be associated to ``self.additional_special_tokens`` and ``self.additional_special_tokens_ids``
.. automethod:: __call__
"""
def __init__(self, tokenizer: BaseTokenizerFast, **kwargs):
if not isinstance(tokenizer, BaseTokenizerFast):
raise ValueError(
"Tokenizer should be an instance of a Tokenizer " "provided by HuggingFace tokenizers library."
)
self._tokenizer: BaseTokenizerFast = tokenizer
# We call this after having initialized the backend tokenizer because we update it.
super().__init__(**kwargs)
@property
def is_fast(self) -> bool:
return True
@property
def vocab_size(self) -> int:
return self._tokenizer.get_vocab_size(with_added_tokens=False)
def get_vocab(self) -> Dict[str, int]:
return self._tokenizer.get_vocab(with_added_tokens=True)
def __len__(self) -> int:
return self._tokenizer.get_vocab_size(with_added_tokens=True)
@property
def backend_tokenizer(self) -> BaseTokenizerFast:
return self._tokenizer
@property
def decoder(self) -> DecoderFast:
return self._tokenizer._tokenizer.decoder
def _maybe_update_backend(self, value):
""" Update the backend fast tokenizer.
Override method from base class SpecialTokensMixin """
self._tokenizer.add_special_tokens(value)
def _convert_encoding(
self,
encoding: EncodingFast,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
verbose: bool = True,
) -> Dict[str, Any]:
""" Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict.
Overflowing tokens are converted to additional examples (like batches) so the output values of
the dict are lists (overflows) of lists (tokens).
Output shape: (overflows, sequence length)
"""
if return_token_type_ids is None:
return_token_type_ids = "token_type_ids" in self.model_input_names
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
if return_overflowing_tokens and encoding.overflowing is not None:
encodings = [encoding] + encoding.overflowing
else:
encodings = [encoding]
encoding_dict = defaultdict(list)
for e in encodings:
encoding_dict["input_ids"].append(e.ids)
if return_token_type_ids:
encoding_dict["token_type_ids"].append(e.type_ids)
if return_attention_mask:
encoding_dict["attention_mask"].append(e.attention_mask)
if return_special_tokens_mask:
encoding_dict["special_tokens_mask"].append(e.special_tokens_mask)
if return_offsets_mapping:
encoding_dict["offset_mapping"].append(e.offsets)
return encoding_dict
def convert_tokens_to_ids(self, tokens):
""" Converts a token string (or a sequence of tokens) in a single integer id
(or a sequence of ids), using the vocabulary.
"""
if tokens is None:
return None
if isinstance(tokens, str):
return self._convert_token_to_id_with_added_voc(tokens)
ids = []
for token in tokens:
ids.append(self._convert_token_to_id_with_added_voc(token))
return ids
def _convert_token_to_id_with_added_voc(self, token: int) -> str:
index = self._tokenizer.token_to_id(token)
if index is None:
return self.unk_token_id
return index
def _convert_id_to_token(self, index: int) -> Optional[str]:
return self._tokenizer.id_to_token(int(index))
def convert_tokens_to_string(self, tokens: List[int], skip_special_tokens: bool = False) -> str:
return self._tokenizer.decode(tokens, skip_special_tokens=skip_special_tokens)
def add_tokens(self, new_tokens: List[Union[str, AddedTokenFast]]) -> int:
"""
Add a list of new tokens to the tokenizer class. If the new tokens are not in the
vocabulary, they are added to it with indices starting from length of the current vocabulary.
Args:
new_tokens: string or list of string or :class:`~transformers.AddedTokenFast`. Each string is a token to add.
Tokens are only added if they are not already in the vocabulary. AddedTokenFast wrap a string token to
let you personnalize it's behavior (Whether this token should only match against single word, whether
this token should strip all potential whitespaces on the left side, Whether this token should strip
all potential whitespaces on the right side...).
See details for :class:`~transformers.AddedToken` in HuggingFace tokenizers library.
Returns:
Number of tokens added to the vocabulary.
Examples::
# Let's see how to increase the vocabulary of Bert model and tokenizer
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
num_added_toks = tokenizer.add_tokens(['new_tok1', 'my_new-tok2'])
print('We have added', num_added_toks, 'tokens')
model.resize_token_embeddings(len(tokenizer)) # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
"""
if isinstance(new_tokens, str):
new_tokens = [new_tokens]
# TODO This should be done in tokenizers to be really clean.
# Removing for now
# tokens = []
# for token in new_tokens:
# if self.init_kwargs.get("do_lower_case", False) and token not in self.all_special_tokens:
# token = token.lower()
# if token not in tokens:
# tokens.append(token)
return self._tokenizer.add_tokens(new_tokens)
def num_special_tokens_to_add(self, pair: bool = False) -> int:
return self._tokenizer.num_special_tokens_to_add(pair)
def convert_ids_to_tokens(
self, ids: Union[int, List[int]], skip_special_tokens: bool = False
) -> Union[int, List[int]]:
""" Converts a single index or a sequence of indices (integers) in a token "
(resp.) a sequence of tokens (str), using the vocabulary and added tokens.
Args:
skip_special_tokens: Don't decode special tokens (self.all_special_tokens). Default: False
"""
if isinstance(ids, int):
return self._tokenizer.id_to_token(ids)
tokens = []
for index in ids:
index = int(index)
if skip_special_tokens and index in self.all_special_ids:
continue
tokens.append(self._tokenizer.id_to_token(index))
return tokens
def tokenize(
self, text: TextInput, pair: Optional[TextInput] = None, add_special_tokens: bool = False
) -> List[str]:
return self._tokenizer.encode(text, pair, add_special_tokens=add_special_tokens).tokens
def set_truncation_and_padding(
self, padding_strategy: PaddingStrategy, truncation_strategy: TruncationStrategy, max_length: int, stride: int,
):
""" This contextmanager is in charge of defining the truncation and the padding strategies for fast tokenizers
(provided by HuggingFace tokenizers library) and restore the tokenizer settings afterwards.
This contextmanager assumes the provider tokenizer has no padding / truncation strategy
before the managed section. If your tokenizer set a padding / truncation strategy before,
then it will be reset to no padding/truncation when exiting the managed section.
Args:
tokenizer (BaseTokenizerFast): The tokenizer which will be used
max_length (int): The maximum size of the sequence
stride (int): The stride to use when handling overflow
strategy (str): Overflowing logic to use
pad_to_max_length (bool): Boolean indicating if the output needs to be padded up to max_length
padding_side (str): "left" or "right" indicating the direction the output sequence will be padded
pad_token_id (int): The integer representation of the padding token to use
pad_token_type_id (int): The integer representation of the padding token type to use
pad_token (str): The string representation of the padding token to use
"""
# Set truncation and padding on the backend tokenizer
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE:
self._tokenizer.enable_truncation(max_length, stride=stride, strategy=truncation_strategy.value)
else:
self._tokenizer.no_truncation()
if padding_strategy != PaddingStrategy.DO_NOT_PAD:
self._tokenizer.enable_padding(
length=max_length if padding_strategy == PaddingStrategy.MAX_LENGTH else None,
direction=self.padding_side,
pad_id=self.pad_token_id,
pad_type_id=self.pad_token_type_id,
pad_token=self.pad_token,
)
else:
self._tokenizer.no_padding()
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput], List[TextInputPair], List[PreTokenizedInput], List[PreTokenizedInputPair]
],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
is_pretokenized: bool = False,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_lengths: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
if not isinstance(batch_text_or_text_pairs, list):
raise ValueError(
"batch_text_or_text_pairs has to be a list (got {})".format(type(batch_text_or_text_pairs))
)
# Set the truncation and padding strategy and restore the initial configuration
self.set_truncation_and_padding(
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
)
# Avoid thread overhead if only one example.
if len(batch_text_or_text_pairs) == 1:
if isinstance(batch_text_or_text_pairs[0], tuple):
# We got a Tuple with a pair of sequences
encodings = self._tokenizer.encode(
*batch_text_or_text_pairs[0],
add_special_tokens=add_special_tokens,
is_pretokenized=is_pretokenized,
)
else:
# We got a single sequence
encodings = self._tokenizer.encode(
batch_text_or_text_pairs[0],
add_special_tokens=add_special_tokens,
is_pretokenized=is_pretokenized,
)
encodings = [encodings]
else:
encodings = self._tokenizer.encode_batch(
batch_text_or_text_pairs, add_special_tokens=add_special_tokens, is_pretokenized=is_pretokenized
)
# Convert encoding to dict
# `Tokens` has type: List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]]
# with nested dimensions corresponding to batch, overflows, sequence length
tokens = [
self._convert_encoding(
encoding=encoding,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
verbose=verbose,
)
for encoding in encodings
]
# Convert the output to have dict[list] from list[dict]
sanitized = {}
for key in tokens[0].keys():
# To List[List[List[int]]] of shape (batch, overflows, sequence length)
stack = [e for item in tokens for e in item[key]]
sanitized[key] = stack
# If returning overflowing tokens, we need to return a mapping
# from the batch idx to the original sample
if return_overflowing_tokens:
overflow_to_sample_mapping = []
for i, enc in enumerate(tokens):
overflow_to_sample_mapping += [i] * len(enc["input_ids"])
sanitized["overflow_to_sample_mapping"] = overflow_to_sample_mapping
return BatchEncoding(sanitized, encodings, tensor_type=return_tensors)
def _encode_plus(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
is_pretokenized: bool = False,
return_tensors: Optional[bool] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
batched_input = [(text, text_pair)] if text_pair else [text]
batched_output = self._batch_encode_plus(
batched_input,
is_pretokenized=is_pretokenized,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
verbose=verbose,
**kwargs,
)
# Return tensor is None, then we can remove the leading batch axis
# Overfolwing tokens are returned as a batch of output so we keep them in this case
if return_tensors is None and not return_overflowing_tokens:
batched_output = BatchEncoding(
{
key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
for key, value in batched_output.items()
},
batched_output.encodings,
)
return batched_output
def decode(
self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True
) -> str:
text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
if clean_up_tokenization_spaces:
clean_text = self.clean_up_tokenization(text)
return clean_text
else:
return text
def save_vocabulary(self, save_directory: str) -> Tuple[str]:
if os.path.isdir(save_directory):
files = self._tokenizer.save_model(save_directory)
else:
folder, file = os.path.split(os.path.abspath(save_directory))
files = self._tokenizer.save_model(folder, name=file)
return tuple(files)
templates/adding_a_new_model/tests/test_tokenization_xxx.py
View file @ 36434220
......@@ -51,7 +51,7 @@ class XxxTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
def get_tokenizer(self, **kwargs):
return XxxTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
def get_input_output_texts(self, tokenizer):
input_text = "UNwant\u00E9d,running"
output_text = "unwanted, running"
return input_text, output_text
......
tests/test_tokenization_albert.py
View file @ 36434220
......@@ -36,7 +36,7 @@ class AlbertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer = AlbertTokenizer(SAMPLE_VOCAB)
tokenizer.save_pretrained(self.tmpdirname)
def get_input_output_texts(self):
def get_input_output_texts(self, tokenizer):
input_text = "this is a test"
output_text = "this is a test"
return input_text, output_text
......
tests/test_tokenization_bert.py
View file @ 36434220
......@@ -44,6 +44,8 @@ class BertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
......@@ -62,7 +64,7 @@ class BertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
def get_rust_tokenizer(self, **kwargs):
return BertTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
def get_input_output_texts(self, tokenizer):
input_text = "UNwant\u00E9d,running"
output_text = "unwanted, running"
return input_text, output_text
......@@ -72,7 +74,7 @@ class BertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokens = tokenizer.tokenize("UNwant\u00E9d,running")
self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9])
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [9, 6, 7, 12, 10, 11])
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
......@@ -96,6 +98,25 @@ class BertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
# With lower casing
tokenizer = self.get_tokenizer(do_lower_case=True)
rust_tokenizer = self.get_rust_tokenizer(do_lower_case=True)
sequence = "UNwant\u00E9d,running"
tokens = tokenizer.tokenize(sequence)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
rust_tokenizer = self.get_rust_tokenizer()
ids = tokenizer.encode(sequence)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
def test_chinese(self):
tokenizer = BasicTokenizer()
......
tests/test_tokenization_bert_japanese.py
View file @ 36434220
......@@ -60,11 +60,26 @@ class BertJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
def get_input_output_texts(self):
def get_input_output_texts(self, tokenizer):
input_text = "こんにちは、世界。 \nこんばんは、世界。"
output_text = "こんにちは 、 世界 。 こんばんは 、 世界 。"
return input_text, output_text
def get_clean_sequence(self, tokenizer):
input_text, output_text = self.get_input_output_texts(tokenizer)
ids = tokenizer.encode(output_text, add_special_tokens=False)
text = tokenizer.decode(ids, clean_up_tokenization_spaces=False)
return text, ids
def test_pretokenized_inputs(self):
pass # TODO add if relevant
def test_maximum_encoding_length_pair_input(self):
pass # TODO add if relevant
def test_maximum_encoding_length_single_input(self):
pass # TODO add if relevant
def test_full_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file)
......@@ -157,11 +172,20 @@ class BertJapaneseCharacterTokenizationTest(TokenizerTesterMixin, unittest.TestC
def get_tokenizer(self, **kwargs):
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname, subword_tokenizer_type="character", **kwargs)
def get_input_output_texts(self):
def get_input_output_texts(self, tokenizer):
input_text = "こんにちは、世界。 \nこんばんは、世界。"
output_text = "こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"
return input_text, output_text
def test_pretokenized_inputs(self):
pass # TODO add if relevant
def test_maximum_encoding_length_pair_input(self):
pass # TODO add if relevant
def test_maximum_encoding_length_single_input(self):
pass # TODO add if relevant
def test_full_tokenizer(self):
tokenizer = self.tokenizer_class(self.vocab_file, subword_tokenizer_type="character")
......
tests/test_tokenization_common.py
View file @ 36434220
......@@ -16,19 +16,19 @@
import os
import pickle
import re
import shutil
import tempfile
from collections import OrderedDict
from typing import TYPE_CHECKING, Dict, Tuple, Union
from tests.utils import require_tf, require_torch
from transformers import PreTrainedTokenizer
from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
if TYPE_CHECKING:
from transformers import (
PretrainedConfig,
PreTrainedTokenizerFast,
PreTrainedModel,
TFPreTrainedModel,
)
......@@ -67,19 +67,50 @@ class TokenizerTesterMixin:
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def get_input_output_texts(self, tokenizer):
input_txt = self.get_clean_sequence(tokenizer)[0]
return input_txt, input_txt
def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=None) -> Tuple[str, list]:
toks = [(i, tokenizer.decode([i], clean_up_tokenization_spaces=False)) for i in range(len(tokenizer))]
toks = list(filter(lambda t: re.match(r"^[ a-zA-Z]+$", t[1]), toks))
toks = list(filter(lambda t: [t[0]] == tokenizer.encode(t[1], add_special_tokens=False), toks))
if max_length is not None and len(toks) > max_length:
toks = toks[:max_length]
# toks_str = [t[1] for t in toks]
toks_ids = [t[0] for t in toks]
# Ensure consistency
output_txt = tokenizer.decode(toks_ids, clean_up_tokenization_spaces=False)
if " " not in output_txt and len(toks_ids) > 1:
output_txt = (
tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=False)
+ " "
+ tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=False)
)
if with_prefix_space:
output_txt = " " + output_txt
output_ids = tokenizer.encode(output_txt, add_special_tokens=False)
return output_txt, output_ids
def get_tokenizers(self, fast=True, **kwargs) -> PreTrainedTokenizer:
if fast and self.test_rust_tokenizer:
return [self.get_tokenizer(**kwargs), self.get_rust_tokenizer(**kwargs)]
return [self.get_tokenizer(**kwargs)]
def get_tokenizer(self, **kwargs) -> PreTrainedTokenizer:
return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
def get_rust_tokenizer(self, **kwargs):
raise NotImplementedError
def get_input_output_texts(self) -> Tuple[str, str]:
"""Feel free to overwrite"""
# TODO: @property
return (
"This is a test",
"This is a test",
)
# def get_input_output_texts(self) -> Tuple[str, str]:
# """Feel free to overwrite"""
# # TODO: @property
# return (
# "This is a test",
# "This is a test",
# )
@staticmethod
def convert_batch_encode_plus_format_to_encode_plus(batch_encode_plus_sequences):
......@@ -91,7 +122,9 @@ class TokenizerTesterMixin:
]
def test_tokenizers_common_properties(self):
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
attributes_list = [
"bos_token",
"eos_token",
......@@ -108,17 +141,30 @@ class TokenizerTesterMixin:
self.assertTrue(hasattr(tokenizer, "additional_special_tokens"))
self.assertTrue(hasattr(tokenizer, "additional_special_tokens_ids"))
attributes_list = ["max_len", "init_inputs", "init_kwargs", "added_tokens_encoder", "added_tokens_decoder"]
attributes_list = [
"model_max_length",
"init_inputs",
"init_kwargs",
]
if not isinstance(tokenizer, PreTrainedTokenizerFast):
attributes_list += [
"added_tokens_encoder",
"added_tokens_decoder",
]
for attr in attributes_list:
self.assertTrue(hasattr(tokenizer, attr))
def test_save_and_load_tokenizer(self):
# safety check on max_len default value so we are sure the test works
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(fast=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
self.assertNotEqual(tokenizer.max_len, 42)
# Now let's start the test
tokenizer = self.get_tokenizer(max_len=42)
tokenizers = self.get_tokenizers(fast=False, model_max_length=42)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sample_text = "He is very happy, UNwant\u00E9d,running"
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
......@@ -128,13 +174,15 @@ class TokenizerTesterMixin:
after_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
self.assertListEqual(before_tokens, after_tokens)
self.assertEqual(tokenizer.max_len, 42)
tokenizer = self.tokenizer_class.from_pretrained(self.tmpdirname, max_len=43)
self.assertEqual(tokenizer.max_len, 43)
self.assertEqual(tokenizer.model_max_length, 42)
tokenizer = self.tokenizer_class.from_pretrained(self.tmpdirname, model_max_length=43)
self.assertEqual(tokenizer.model_max_length, 43)
def test_pickle_tokenizer(self):
"""Google pickle __getstate__ __setstate__ if you are struggling with this."""
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
self.assertIsNotNone(tokenizer)
text = "Munich and Berlin are nice cities"
......@@ -152,8 +200,10 @@ class TokenizerTesterMixin:
self.assertListEqual(subwords, subwords_loaded)
def test_added_tokens_do_lower_case(self):
tokenizer = self.get_tokenizer(do_lower_case=True)
# TODO(thom) activate fast tokenizer tests once Rust tokenizers accepts white spaces in added tokens
tokenizers = self.get_tokenizers(fast=False, do_lower_case=True)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
special_token = tokenizer.all_special_tokens[0]
text = special_token + " aaaaa bbbbbb low cccccccccdddddddd l " + special_token
......@@ -169,8 +219,11 @@ class TokenizerTesterMixin:
toks2 = tokenizer.tokenize(text2)
self.assertEqual(len(toks), len(toks2))
self.assertNotEqual(len(toks), len(toks0)) # toks0 should be longer
self.assertListEqual(toks, toks2)
if not isinstance(tokenizer, PreTrainedTokenizerFast):
# Python tokenizers can have added tokens with spaces inside them
# cf https://github.com/huggingface/tokenizers/issues/302
self.assertNotEqual(len(toks), len(toks0)) # toks0 should be longer
# Check that none of the special tokens are lowercased
sequence_with_special_tokens = "A " + " yEs ".join(tokenizer.all_special_tokens) + " B"
......@@ -179,7 +232,17 @@ class TokenizerTesterMixin:
for special_token in tokenizer.all_special_tokens:
self.assertTrue(special_token in tokenized_sequence)
tokenizer = self.get_tokenizer(do_lower_case=False)
tokenizers = self.get_tokenizers(fast=False, do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
special_token = tokenizer.all_special_tokens[0]
text = special_token + " aaaaa bbbbbb low cccccccccdddddddd l " + special_token
text2 = special_token + " AAAAA BBBBBB low CCCCCCCCCDDDDDDDD l " + special_token
new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd", "AAAAA BBBBBB", "CCCCCCCCCDDDDDDDD"]
toks0 = tokenizer.tokenize(text) # toks before adding new_toks
added = tokenizer.add_tokens(new_toks)
self.assertEqual(added, 4)
......@@ -188,12 +251,16 @@ class TokenizerTesterMixin:
toks2 = tokenizer.tokenize(text2)
self.assertEqual(len(toks), len(toks2)) # Length should still be the same
self.assertNotEqual(len(toks), len(toks0))
self.assertNotEqual(toks[1], toks2[1]) # But at least the first non-special tokens should differ
if not isinstance(tokenizer, PreTrainedTokenizerFast):
# Python tokenizers can have added tokens with spaces inside them
# cf https://github.com/huggingface/tokenizers/issues/302
self.assertNotEqual(len(toks), len(toks0)) # toks0 should be longer
def test_add_tokens_tokenizer(self):
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
vocab_size = tokenizer.vocab_size
all_size = len(tokenizer)
......@@ -239,29 +306,32 @@ class TokenizerTesterMixin:
self.assertEqual(tokens[-2], tokenizer.pad_token_id)
def test_add_special_tokens(self):
tokenizer = self.get_tokenizer()
input_text, output_text = self.get_input_output_texts()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
input_text, ids = self.get_clean_sequence(tokenizer)
special_token = "[SPECIAL TOKEN]"
special_token = "[SPECIAL_TOKEN]"
tokenizer.add_special_tokens({"cls_token": special_token})
encoded_special_token = tokenizer.encode(special_token, add_special_tokens=False)
assert len(encoded_special_token) == 1
self.assertEqual(len(encoded_special_token), 1)
text = " ".join([input_text, special_token, output_text])
text = tokenizer.decode(ids + encoded_special_token, clean_up_tokenization_spaces=False)
encoded = tokenizer.encode(text, add_special_tokens=False)
input_encoded = tokenizer.encode(input_text, add_special_tokens=False)
output_encoded = tokenizer.encode(" " + output_text, add_special_tokens=False)
special_token_id = tokenizer.encode(special_token, add_special_tokens=False)
assert encoded == input_encoded + special_token_id + output_encoded
self.assertEqual(encoded, input_encoded + special_token_id)
decoded = tokenizer.decode(encoded, skip_special_tokens=True)
assert special_token not in decoded
self.assertTrue(special_token not in decoded)
def test_internal_consistency(self):
tokenizer = self.get_tokenizer()
input_text, output_text = self.get_input_output_texts()
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
input_text, output_text = self.get_input_output_texts(tokenizer)
tokens = tokenizer.tokenize(input_text)
ids = tokenizer.convert_tokens_to_ids(tokens)
......@@ -276,11 +346,13 @@ class TokenizerTesterMixin:
self.assertEqual(text_2, output_text)
def test_encode_decode_with_spaces(self):
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
new_toks = ["[ABC]", "[DEF]", "GHI IHG"]
new_toks = ["[ABC]", "[DEF]"] # TODO(thom) add this one back when Rust toks are ready: , "GHI IHG"]
tokenizer.add_tokens(new_toks)
input = "[ABC] [DEF] [ABC] GHI IHG [DEF]"
input = "[ABC] [DEF] [ABC] [DEF]" # TODO(thom) add back cf above: "[ABC] [DEF] [ABC] GHI IHG [DEF]"
encoded = tokenizer.encode(input, add_special_tokens=False)
decoded = tokenizer.decode(encoded)
self.assertEqual(decoded, input)
......@@ -295,7 +367,9 @@ class TokenizerTesterMixin:
self.assertListEqual(weights_list, weights_list_2)
def test_mask_output(self):
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(fast=False, do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if (
tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer"
......@@ -308,7 +382,9 @@ class TokenizerTesterMixin:
self.assertEqual(len(sequences), len(mask))
def test_number_of_added_tokens(self):
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
seq_0 = "Test this method."
seq_1 = "With these inputs."
......@@ -318,95 +394,217 @@ class TokenizerTesterMixin:
# Method is implemented (e.g. not GPT-2)
if len(attached_sequences) != 2:
self.assertEqual(tokenizer.num_special_tokens_to_add(pair=True), len(attached_sequences) - len(sequences))
self.assertEqual(
tokenizer.num_special_tokens_to_add(pair=True), len(attached_sequences) - len(sequences)
)
def test_maximum_encoding_length_single_input(self):
tokenizer = self.get_tokenizer()
seq_0 = "This is a sentence to be encoded."
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
seq_0, ids = self.get_clean_sequence(tokenizer)
stride = 2
sequence = tokenizer.encode(seq_0, add_special_tokens=False)
num_added_tokens = tokenizer.num_special_tokens_to_add()
total_length = len(sequence) + num_added_tokens
# self.assertEqual(sequence, ids)
total_length = len(sequence)
information = tokenizer.encode_plus(
seq_0,
max_length=total_length - 2,
add_special_tokens=True,
add_special_tokens=False,
stride=stride,
truncation="longest_first",
return_overflowing_tokens=True,
add_prefix_space=False,
)
truncated_sequence = information["input_ids"]
overflowing_tokens = information["overflowing_tokens"]
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, PreTrainedTokenizerFast):
truncated_sequence = information["input_ids"][0]
overflowing_tokens = information["input_ids"][1]
self.assertEqual(len(information["input_ids"]), 2)
self.assertEqual(len(truncated_sequence), total_length - 2)
self.assertEqual(truncated_sequence, sequence[:-2])
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, sequence[-(2 + stride) :])
else:
truncated_sequence = information["input_ids"]
overflowing_tokens = information["overflowing_tokens"]
self.assertEqual(len(truncated_sequence), total_length - 2)
self.assertEqual(truncated_sequence, tokenizer.build_inputs_with_special_tokens(sequence[:-2]))
self.assertEqual(truncated_sequence, sequence[:-2])
self.assertEqual(
len(overflowing_tokens), 0
) # No overflowing tokens when using 'longest' in python tokenizers
def test_maximum_encoding_length_pair_input(self):
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Build a sequence from our model's vocabulary
stride = 2
seq_0, ids = self.get_clean_sequence(tokenizer)
if len(ids) <= 2 + stride:
seq_0 = [s for s in seq_0 for _ in range(2 + stride)]
ids = [i for i in ids for _ in range(2 + stride)]
seq0_tokens = tokenizer.encode(seq_0, add_special_tokens=False)
assert len(seq0_tokens) > 2 + stride
seq_0 = "This is a sentence to be encoded."
seq_1 = "This is another sentence to be encoded."
stride = 2
seq1_tokens = tokenizer.encode(seq_1, add_special_tokens=False)
if len(seq0_tokens) == len(seq1_tokens):
seq1_tokens = seq1_tokens + seq1_tokens
seq_1 = tokenizer.decode(seq1_tokens, clean_up_tokenization_spaces=False)
seq1_tokens = tokenizer.encode(seq_1, add_special_tokens=False)
assert len(seq1_tokens) > 2 + stride
sequence_0_no_special_tokens = tokenizer.encode(seq_0, add_special_tokens=False)
sequence_1_no_special_tokens = tokenizer.encode(seq_1, add_special_tokens=False)
smallest = seq1_tokens if len(seq0_tokens) > len(seq1_tokens) else seq0_tokens
sequence = tokenizer.encode(seq_0, seq_1, add_special_tokens=True, add_prefix_space=False)
truncated_second_sequence = tokenizer.build_inputs_with_special_tokens(
tokenizer.encode(seq_0, add_special_tokens=False), tokenizer.encode(seq_1, add_special_tokens=False)[:-2],
# We are not using the special tokens - a bit too hard to test all the tokenizers with this
# TODO try this again later
sequence = tokenizer.encode(seq_0, seq_1, add_special_tokens=False, add_prefix_space=False)
truncated_first_sequence = tokenizer.encode(seq_0, add_special_tokens=False)[:-2] + tokenizer.encode(
seq_1, add_special_tokens=False
)
truncated_second_sequence = (
tokenizer.encode(seq_0, add_special_tokens=False)
+ tokenizer.encode(seq_1, add_special_tokens=False)[:-2]
)
truncated_longest_sequence = (
truncated_first_sequence if len(seq0_tokens) > len(seq1_tokens) else truncated_second_sequence
)
overflow_first_sequence = tokenizer.encode(seq_0, add_special_tokens=False)[
-(2 + stride) :
] + tokenizer.encode(seq_1, add_special_tokens=False)
overflow_second_sequence = (
tokenizer.encode(seq_0, add_special_tokens=False)
+ tokenizer.encode(seq_1, add_special_tokens=False)[-(2 + stride) :]
)
overflow_longest_sequence = (
overflow_first_sequence if len(seq0_tokens) > len(seq1_tokens) else overflow_second_sequence
)
information = tokenizer.encode_plus(
seq_0,
seq_1,
max_length=len(sequence) - 2,
add_special_tokens=True,
add_special_tokens=False,
stride=stride,
truncation_strategy="only_second",
truncation="longest_first",
return_overflowing_tokens=True,
add_prefix_space=False,
)
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, PreTrainedTokenizerFast):
truncated_sequence = information["input_ids"][0]
overflowing_tokens = information["input_ids"][1]
self.assertEqual(len(information["input_ids"]), 2)
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_longest_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest))
self.assertEqual(overflowing_tokens, overflow_longest_sequence)
else:
truncated_sequence = information["input_ids"]
overflowing_tokens = information["overflowing_tokens"]
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_longest_sequence)
self.assertEqual(
len(overflowing_tokens), 0
) # No overflowing tokens when using 'longest' in python tokenizers
information_first_truncated = tokenizer.encode_plus(
seq_0,
seq_1,
max_length=len(sequence) - 2,
add_special_tokens=True,
add_special_tokens=False,
stride=stride,
truncation_strategy="only_first",
truncation=True,
return_overflowing_tokens=True,
add_prefix_space=False,
)
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, PreTrainedTokenizerFast):
truncated_sequence = information_first_truncated["input_ids"][0]
overflowing_tokens = information_first_truncated["input_ids"][1]
self.assertEqual(len(information_first_truncated["input_ids"]), 2)
truncated_sequence = information["input_ids"]
overflowing_tokens = information["overflowing_tokens"]
overflowing_tokens_first_truncated = information_first_truncated["overflowing_tokens"]
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_first_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq1_tokens))
self.assertEqual(overflowing_tokens, overflow_first_sequence)
else:
truncated_sequence = information_first_truncated["input_ids"]
overflowing_tokens = information_first_truncated["overflowing_tokens"]
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_first_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, sequence_1_no_special_tokens[-(2 + stride) :])
self.assertEqual(overflowing_tokens_first_truncated, sequence_0_no_special_tokens[-(2 + stride) :])
self.assertEqual(overflowing_tokens, seq0_tokens[-(2 + stride) :])
information_second_truncated = tokenizer.encode_plus(
seq_0,
seq_1,
max_length=len(sequence) - 2,
add_special_tokens=False,
stride=stride,
truncation="only_second",
return_overflowing_tokens=True,
add_prefix_space=False,
)
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, PreTrainedTokenizerFast):
truncated_sequence = information_second_truncated["input_ids"][0]
overflowing_tokens = information_second_truncated["input_ids"][1]
self.assertEqual(len(information_second_truncated["input_ids"]), 2)
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_second_sequence)
def test_encode_input_type(self):
tokenizer = self.get_tokenizer()
self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq0_tokens))
self.assertEqual(overflowing_tokens, overflow_second_sequence)
else:
truncated_sequence = information_second_truncated["input_ids"]
overflowing_tokens = information_second_truncated["overflowing_tokens"]
sequence = "Let's encode this sequence"
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_second_sequence)
tokens = tokenizer.tokenize(sequence)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
formatted_input = tokenizer.encode(sequence, add_special_tokens=True, add_prefix_space=False)
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, seq1_tokens[-(2 + stride) :])
self.assertEqual(tokenizer.encode(tokens, add_special_tokens=True), formatted_input)
self.assertEqual(tokenizer.encode(input_ids, add_special_tokens=True), formatted_input)
# def test_encode_input_type(self):
# tokenizers = self.get_tokenizers(do_lower_case=False)
# for tokenizer in tokenizers:
# with self.subTest(f"{tokenizer.__class__.__name__}"):
# sequence = "Let's encode this sequence"
def test_swap_special_token(self):
tokenizer = self.get_tokenizer()
# tokens = sequence.split() # tokenizer.tokenize(sequence)
# # input_ids = tokenizer.convert_tokens_to_ids(tokens)
# formatted_input = tokenizer.encode(sequence, add_special_tokens=True, add_prefix_space=False)
# self.assertEqual(
# tokenizer.encode(tokens, is_pretokenized=True, add_special_tokens=True), formatted_input
# )
# # This is not supported with the Rust tokenizers
# # self.assertEqual(tokenizer.encode(input_ids, add_special_tokens=True), formatted_input)
def test_swap_special_token(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
mask = "<mask>"
sequence = "Encode this sequence"
sequence_masked_0 = "Encode <mask> sequence"
......@@ -433,7 +631,9 @@ class TokenizerTesterMixin:
self.assertEqual(encoded_masked, encoded)
def test_special_tokens_mask(self):
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequence_0 = "Encode this."
# Testing single inputs
encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
......@@ -448,13 +648,19 @@ class TokenizerTesterMixin:
self.assertEqual(encoded_sequence, filtered_sequence)
def test_special_tokens_mask_input_pairs(self):
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequence_0 = "Encode this."
sequence_1 = "This one too please."
encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
encoded_sequence += tokenizer.encode(sequence_1, add_special_tokens=False)
encoded_sequence_dict = tokenizer.encode_plus(
sequence_0, sequence_1, add_special_tokens=True, return_special_tokens_mask=True, add_prefix_space=False
sequence_0,
sequence_1,
add_special_tokens=True,
return_special_tokens_mask=True,
add_prefix_space=False,
)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
......@@ -467,24 +673,32 @@ class TokenizerTesterMixin:
self.assertEqual(encoded_sequence, filtered_sequence)
def test_special_tokens_mask_already_has_special_tokens(self):
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
if not hasattr(tokenizer, "get_special_tokens_mask") or tokenizer.get_special_tokens_mask(
[0, 1, 2, 3]
) == [0, 0, 0, 0]:
continue
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequence_0 = "Encode this."
if tokenizer.cls_token_id == tokenizer.unk_token_id and tokenizer.cls_token_id == tokenizer.unk_token_id:
if (
tokenizer.cls_token_id == tokenizer.unk_token_id
and tokenizer.cls_token_id == tokenizer.unk_token_id
):
tokenizer.add_special_tokens({"cls_token": "</s>", "sep_token": "<s>"})
encoded_sequence_dict = tokenizer.encode_plus(
sequence_0, add_special_tokens=True, return_special_tokens_mask=True
)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
# encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask_orig = encoded_sequence_dict["special_tokens_mask"]
special_tokens_mask = tokenizer.get_special_tokens_mask(
encoded_sequence_w_special, already_has_special_tokens=True
)
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
self.assertEqual(special_tokens_mask_orig, special_tokens_mask)
def test_padding_to_max_length(self):
tokenizer = self.get_tokenizer()
min_val = min(special_tokens_mask_orig)
max_val = max(special_tokens_mask_orig)
self.assertNotEqual(min_val, max_val)
def test_right_and_left_padding(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequence = "Sequence"
padding_size = 10
......@@ -497,7 +711,9 @@ class TokenizerTesterMixin:
tokenizer.padding_side = "right"
encoded_sequence = tokenizer.encode(sequence)
sequence_length = len(encoded_sequence)
padded_sequence = tokenizer.encode(sequence, max_length=sequence_length + padding_size, pad_to_max_length=True)
padded_sequence = tokenizer.encode(
sequence, max_length=sequence_length + padding_size, padding="max_length"
)
padded_sequence_length = len(padded_sequence)
assert sequence_length + padding_size == padded_sequence_length
assert encoded_sequence + [padding_idx] * padding_size == padded_sequence
......@@ -506,30 +722,80 @@ class TokenizerTesterMixin:
tokenizer.padding_side = "left"
encoded_sequence = tokenizer.encode(sequence)
sequence_length = len(encoded_sequence)
padded_sequence = tokenizer.encode(sequence, max_length=sequence_length + padding_size, pad_to_max_length=True)
padded_sequence = tokenizer.encode(
sequence, max_length=sequence_length + padding_size, padding="max_length"
)
padded_sequence_length = len(padded_sequence)
assert sequence_length + padding_size == padded_sequence_length
assert [padding_idx] * padding_size + encoded_sequence == padded_sequence
# RIGHT & LEFT PADDING - Check that nothing is done when a maximum length is not specified
# RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_padding'
encoded_sequence = tokenizer.encode(sequence)
sequence_length = len(encoded_sequence)
tokenizer.padding_side = "right"
padded_sequence_right = tokenizer.encode(sequence, pad_to_max_length=True)
padded_sequence_right = tokenizer.encode(sequence, padding=True)
padded_sequence_right_length = len(padded_sequence_right)
assert sequence_length == padded_sequence_right_length
assert encoded_sequence == padded_sequence_right
tokenizer.padding_side = "left"
padded_sequence_left = tokenizer.encode(sequence, pad_to_max_length=True)
padded_sequence_left = tokenizer.encode(sequence, padding="longest")
padded_sequence_left_length = len(padded_sequence_left)
assert sequence_length == padded_sequence_left_length
assert encoded_sequence == padded_sequence_left
def test_encode_plus_with_padding(self):
tokenizer = self.get_tokenizer()
tokenizer.padding_side = "right"
padded_sequence_right = tokenizer.encode(sequence)
padded_sequence_right_length = len(padded_sequence_right)
assert sequence_length == padded_sequence_right_length
assert encoded_sequence == padded_sequence_right
tokenizer.padding_side = "left"
padded_sequence_left = tokenizer.encode(sequence, padding=False)
padded_sequence_left_length = len(padded_sequence_left)
assert sequence_length == padded_sequence_left_length
assert encoded_sequence == padded_sequence_left
def test_padding_to_max_length(self):
""" We keep this test for backward compatibility but it should be remove when `pad_to_max_length` will e deprecated
"""
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequence = "Sequence"
padding_size = 10
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, sequence)
padding_idx = tokenizer.pad_token_id
# Check that it correctly pads when a maximum length is specified along with the padding flag set to True
tokenizer.padding_side = "right"
encoded_sequence = tokenizer.encode(sequence)
sequence_length = len(encoded_sequence)
padded_sequence = tokenizer.encode(
sequence, max_length=sequence_length + padding_size, pad_to_max_length=True
)
padded_sequence_length = len(padded_sequence)
assert sequence_length + padding_size == padded_sequence_length
assert encoded_sequence + [padding_idx] * padding_size == padded_sequence
# Check that nothing is done when a maximum length is not specified
encoded_sequence = tokenizer.encode(sequence)
sequence_length = len(encoded_sequence)
tokenizer.padding_side = "right"
padded_sequence_right = tokenizer.encode(sequence, pad_to_max_length=True)
padded_sequence_right_length = len(padded_sequence_right)
assert sequence_length == padded_sequence_right_length
assert encoded_sequence == padded_sequence_right
def test_encode_plus_with_padding(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequence = "Sequence"
# check correct behaviour if no pad_token_id exists and add it eventually
......@@ -544,13 +810,36 @@ class TokenizerTesterMixin:
special_tokens_mask = encoded_sequence["special_tokens_mask"]
sequence_length = len(input_ids)
# Test 'longest' and 'no_padding' don't do anything
tokenizer.padding_side = "right"
not_padded_sequence = tokenizer.encode_plus(sequence, padding=True, return_special_tokens_mask=True,)
not_padded_input_ids = not_padded_sequence["input_ids"]
not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"]
not_padded_sequence_length = len(not_padded_input_ids)
assert sequence_length == not_padded_sequence_length
assert input_ids == not_padded_input_ids
assert special_tokens_mask == not_padded_special_tokens_mask
not_padded_sequence = tokenizer.encode_plus(sequence, padding=False, return_special_tokens_mask=True,)
not_padded_input_ids = not_padded_sequence["input_ids"]
not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"]
not_padded_sequence_length = len(not_padded_input_ids)
assert sequence_length == not_padded_sequence_length
assert input_ids == not_padded_input_ids
assert special_tokens_mask == not_padded_special_tokens_mask
# Test right padding
tokenizer.padding_side = "right"
right_padded_sequence = tokenizer.encode_plus(
sequence,
max_length=sequence_length + padding_size,
pad_to_max_length=True,
padding="max_length",
return_special_tokens_mask=True,
)
right_padded_input_ids = right_padded_sequence["input_ids"]
......@@ -567,7 +856,7 @@ class TokenizerTesterMixin:
left_padded_sequence = tokenizer.encode_plus(
sequence,
max_length=sequence_length + padding_size,
pad_to_max_length=True,
padding="max_length",
return_special_tokens_mask=True,
)
left_padded_input_ids = left_padded_sequence["input_ids"]
......@@ -605,7 +894,9 @@ class TokenizerTesterMixin:
assert new_tokenizer.init_kwargs["random_argument"] is False
def test_get_vocab(self):
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
vocab = tokenizer.get_vocab()
self.assertIsInstance(vocab, dict)
......@@ -621,48 +912,107 @@ class TokenizerTesterMixin:
self.assertEqual(len(vocab), len(tokenizer))
def test_conversion_reversible(self):
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
vocab = tokenizer.get_vocab()
for word, ind in vocab.items():
self.assertEqual(tokenizer.convert_tokens_to_ids(word), ind)
self.assertEqual(tokenizer.convert_ids_to_tokens(ind), word)
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequences = [
"Testing batch encode plus",
"Testing batch encode plus with different sequence lengths",
"Testing batch encode plus with different sequence lengths correctly pads",
]
# Test not batched
encoded_sequences_1 = tokenizer.encode_plus(sequences[0])
encoded_sequences_2 = tokenizer(sequences[0])
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
# Test not batched pairs
encoded_sequences_1 = tokenizer.encode_plus(sequences[0], sequences[1])
encoded_sequences_2 = tokenizer(sequences[0], sequences[1])
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
# Test batched
encoded_sequences_1 = tokenizer.batch_encode_plus(sequences)
encoded_sequences_2 = tokenizer(sequences)
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
# Test batched pairs
encoded_sequences_1 = tokenizer.batch_encode_plus(list(zip(sequences, sequences)))
encoded_sequences_2 = tokenizer(sequences, sequences)
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
def test_batch_encode_plus_batch_sequence_length(self):
# Tests that all encoded values have the correct size
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequences = [
"Testing batch encode plus",
"Testing batch encode plus with different sequence lengths",
"Testing batch encode plus with different sequence lengths correctly pads",
]
encoded_sequences = [tokenizer.encode_plus(sequence, pad_to_max_length=False) for sequence in sequences]
encoded_sequences_batch = tokenizer.batch_encode_plus(sequences)
encoded_sequences = [tokenizer.encode_plus(sequence) for sequence in sequences]
encoded_sequences_batch = tokenizer.batch_encode_plus(sequences, padding=False)
self.assertListEqual(
encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
)
maximum_length = len(max([encoded_sequence["input_ids"] for encoded_sequence in encoded_sequences], key=len))
maximum_length = len(
max([encoded_sequence["input_ids"] for encoded_sequence in encoded_sequences], key=len)
)
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, sequences)
encoded_sequences_padded = [
tokenizer.encode_plus(sequence, pad_to_max_length=True, max_length=maximum_length)
tokenizer.encode_plus(sequence, max_length=maximum_length, padding="max_length")
for sequence in sequences
]
encoded_sequences_batch_padded = tokenizer.batch_encode_plus(sequences, pad_to_max_length=True)
encoded_sequences_batch_padded = tokenizer.batch_encode_plus(sequences, padding=True)
self.assertListEqual(
encoded_sequences_padded,
self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch_padded),
)
# check 'longest' is unsensitive to a max length
encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus(sequences, padding=True)
encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
sequences, max_length=maximum_length + 10, padding="longest"
)
for key in encoded_sequences_batch_padded_1.keys():
self.assertListEqual(
encoded_sequences_batch_padded_1[key], encoded_sequences_batch_padded_2[key],
)
# check 'no_padding' is unsensitive to a max length
encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus(sequences, padding=False)
encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
sequences, max_length=maximum_length + 10, padding=False
)
for key in encoded_sequences_batch_padded_1.keys():
self.assertListEqual(
encoded_sequences_batch_padded_1[key], encoded_sequences_batch_padded_2[key],
)
def test_batch_encode_plus_padding(self):
# Test that padded sequences are equivalent between batch_encode_plus and encode_plus
# Right padding tests
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequences = [
"Testing batch encode plus",
"Testing batch encode plus with different sequence lengths",
......@@ -675,16 +1025,20 @@ class TokenizerTesterMixin:
self._check_no_pad_token_padding(tokenizer, sequences)
encoded_sequences = [
tokenizer.encode_plus(sequence, pad_to_max_length=True, max_length=max_length) for sequence in sequences
tokenizer.encode_plus(sequence, max_length=max_length, padding="max_length")
for sequence in sequences
]
encoded_sequences_batch = tokenizer.batch_encode_plus(sequences, pad_to_max_length=True, max_length=max_length)
encoded_sequences_batch = tokenizer.batch_encode_plus(
sequences, max_length=max_length, padding="max_length"
)
self.assertListEqual(
encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
)
# Left padding tests
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
tokenizer.padding_side = "left"
sequences = [
"Testing batch encode plus",
......@@ -698,17 +1052,128 @@ class TokenizerTesterMixin:
self._check_no_pad_token_padding(tokenizer, sequences)
encoded_sequences = [
tokenizer.encode_plus(sequence, pad_to_max_length=True, max_length=max_length) for sequence in sequences
tokenizer.encode_plus(sequence, max_length=max_length, padding="max_length")
for sequence in sequences
]
encoded_sequences_batch = tokenizer.batch_encode_plus(sequences, pad_to_max_length=True, max_length=max_length)
encoded_sequences_batch = tokenizer.batch_encode_plus(
sequences, max_length=max_length, padding="max_length"
)
self.assertListEqual(
encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
)
def test_pretokenized_inputs(self):
# Test when inputs are pretokenized
tokenizers = self.get_tokenizers(do_lower_case=False, add_prefix_space=True)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Prepare a sequence from our tokenizer vocabulary
sequence, ids = self.get_clean_sequence(tokenizer, with_prefix_space=True, max_length=20)
# sequence = " " + sequence # To be sure the byte-level tokenizers are feeling good
token_sequence = sequence.split()
# sequence_no_prefix_space = sequence.strip()
# Test encode for pretokenized inputs
output = tokenizer.encode(token_sequence, is_pretokenized=True, add_special_tokens=False)
output_sequence = tokenizer.encode(sequence, add_special_tokens=False)
self.assertEqual(output, output_sequence)
output = tokenizer.encode(token_sequence, is_pretokenized=True, add_special_tokens=True)
output_sequence = tokenizer.encode(sequence, add_special_tokens=True)
self.assertEqual(output, output_sequence)
# Test encode_plus for pretokenized inputs
output = tokenizer.encode_plus(token_sequence, is_pretokenized=True, add_special_tokens=False)
output_sequence = tokenizer.encode_plus(sequence, add_special_tokens=False)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
output = tokenizer.encode_plus(token_sequence, is_pretokenized=True, add_special_tokens=True)
output_sequence = tokenizer.encode_plus(sequence, add_special_tokens=True)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
# Test batch_encode_plus for pretokenized inputs
sequence_batch = [sequence.strip()] * 2 + [sequence.strip() + " " + sequence.strip()]
token_sequence_batch = [s.split() for s in sequence_batch]
sequence_batch_cleaned_up_spaces = [" " + " ".join(s) for s in token_sequence_batch]
output = tokenizer.batch_encode_plus(
token_sequence_batch, is_pretokenized=True, add_special_tokens=False
)
output_sequence = tokenizer.batch_encode_plus(
sequence_batch_cleaned_up_spaces, add_special_tokens=False
)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
output = tokenizer.batch_encode_plus(
token_sequence_batch, is_pretokenized=True, add_special_tokens=True
)
output_sequence = tokenizer.batch_encode_plus(
sequence_batch_cleaned_up_spaces, add_special_tokens=True
)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
# Test encode for pretokenized inputs pairs
output = tokenizer.encode(
token_sequence, token_sequence, is_pretokenized=True, add_special_tokens=False
)
output_sequence = tokenizer.encode(sequence, sequence, add_special_tokens=False)
self.assertEqual(output, output_sequence)
output = tokenizer.encode(
token_sequence, token_sequence, is_pretokenized=True, add_special_tokens=True
)
output_sequence = tokenizer.encode(sequence, sequence, add_special_tokens=True)
self.assertEqual(output, output_sequence)
# Test encode_plus for pretokenized inputs pairs
output = tokenizer.encode_plus(
token_sequence, token_sequence, is_pretokenized=True, add_special_tokens=False
)
output_sequence = tokenizer.encode_plus(sequence, sequence, add_special_tokens=False)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
output = tokenizer.encode_plus(
token_sequence, token_sequence, is_pretokenized=True, add_special_tokens=True
)
output_sequence = tokenizer.encode_plus(sequence, sequence, add_special_tokens=True)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
# Test batch_encode_plus for pretokenized inputs pairs
sequence_pair_batch = [(sequence.strip(), sequence.strip())] * 2 + [
(sequence.strip() + " " + sequence.strip(), sequence.strip())
]
token_sequence_pair_batch = [tuple(s.split() for s in pair) for pair in sequence_pair_batch]
sequence_pair_batch_cleaned_up_spaces = [
tuple(" " + " ".join(s) for s in pair) for pair in token_sequence_pair_batch
]
output = tokenizer.batch_encode_plus(
token_sequence_pair_batch, is_pretokenized=True, add_special_tokens=False
)
output_sequence = tokenizer.batch_encode_plus(
sequence_pair_batch_cleaned_up_spaces, add_special_tokens=False
)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
output = tokenizer.batch_encode_plus(
token_sequence_pair_batch, is_pretokenized=True, add_special_tokens=True
)
output_sequence = tokenizer.batch_encode_plus(
sequence_pair_batch_cleaned_up_spaces, add_special_tokens=True
)
for key in output.keys():
self.assertEqual(output[key], output_sequence[key])
@require_torch
@require_tf
def test_batch_encode_plus_tensors(self):
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
sequences = [
"Testing batch encode plus",
"Testing batch encode plus with different sequence lengths",
......@@ -721,15 +1186,15 @@ class TokenizerTesterMixin:
if tokenizer.pad_token_id is None:
self.assertRaises(
ValueError, tokenizer.batch_encode_plus, sequences, pad_to_max_length=True, return_tensors="pt"
ValueError, tokenizer.batch_encode_plus, sequences, padding=True, return_tensors="pt",
)
self.assertRaises(
ValueError, tokenizer.batch_encode_plus, sequences, pad_to_max_length=True, return_tensors="tf"
ValueError, tokenizer.batch_encode_plus, sequences, padding="longest", return_tensors="tf",
)
else:
pytorch_tensor = tokenizer.batch_encode_plus(sequences, pad_to_max_length=True, return_tensors="pt")
tensorflow_tensor = tokenizer.batch_encode_plus(sequences, pad_to_max_length=True, return_tensors="tf")
encoded_sequences = tokenizer.batch_encode_plus(sequences, pad_to_max_length=True)
pytorch_tensor = tokenizer.batch_encode_plus(sequences, padding=True, return_tensors="pt")
tensorflow_tensor = tokenizer.batch_encode_plus(sequences, padding="longest", return_tensors="tf")
encoded_sequences = tokenizer.batch_encode_plus(sequences, padding=True)
for key in encoded_sequences.keys():
pytorch_value = pytorch_tensor[key].tolist()
......@@ -743,9 +1208,9 @@ class TokenizerTesterMixin:
if tokenizer.pad_token_id is None:
with self.assertRaises(ValueError):
if isinstance(sequences, list):
tokenizer.batch_encode_plus(sequences, pad_to_max_length=True)
tokenizer.batch_encode_plus(sequences, padding="longest")
else:
tokenizer.encode_plus(sequences, pad_to_max_length=True)
tokenizer.encode_plus(sequences, padding=True)
# add pad_token_id to pass subsequent tests
tokenizer.add_special_tokens({"pad_token": "<PAD>"})
......@@ -757,7 +1222,9 @@ class TokenizerTesterMixin:
MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING)
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
return
......@@ -772,7 +1239,11 @@ class TokenizerTesterMixin:
# Make sure the model contains at least the full vocabulary size in its embedding matrix
is_using_common_embeddings = hasattr(model.get_input_embeddings(), "weight")
assert (model.get_input_embeddings().weight.shape[0] >= len(tokenizer)) if is_using_common_embeddings else True
assert (
(model.get_input_embeddings().weight.shape[0] >= len(tokenizer))
if is_using_common_embeddings
else True
)
# Build sequence
first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
......@@ -785,13 +1256,13 @@ class TokenizerTesterMixin:
model(**encoded_sequence)
model(**batch_encoded_sequence)
if self.test_rust_tokenizer:
fast_tokenizer = self.get_rust_tokenizer()
encoded_sequence_fast = fast_tokenizer.encode_plus(sequence, return_tensors="pt")
batch_encoded_sequence_fast = fast_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
# This should not fail
model(**encoded_sequence_fast)
model(**batch_encoded_sequence_fast)
# if self.test_rust_tokenizer:
# fast_tokenizer = self.get_rust_tokenizer()
# encoded_sequence_fast = fast_tokenizer.encode_plus(sequence, return_tensors="pt")
# batch_encoded_sequence_fast = fast_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
# # This should not fail
# model(**encoded_sequence_fast)
# model(**batch_encoded_sequence_fast)
@require_tf
def test_tf_encode_plus_sent_to_model(self):
......@@ -799,8 +1270,9 @@ class TokenizerTesterMixin:
MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(TF_MODEL_MAPPING, TOKENIZER_MAPPING)
tokenizer = self.get_tokenizer()
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
return
......@@ -825,14 +1297,6 @@ class TokenizerTesterMixin:
model(encoded_sequence)
model(batch_encoded_sequence)
if self.test_rust_tokenizer:
fast_tokenizer = self.get_rust_tokenizer()
encoded_sequence_fast = fast_tokenizer.encode_plus(sequence, return_tensors="tf")
batch_encoded_sequence_fast = fast_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="tf")
# This should not fail
model(encoded_sequence_fast)
model(batch_encoded_sequence_fast)
# TODO: Check if require_torch is the best to test for numpy here ... Maybe move to require_flax when available
@require_torch
def test_np_encode_plus_sent_to_model(self):
......
tests/test_tokenization_ctrl.py
View file @ 36434220
......@@ -46,7 +46,7 @@ class CTRLTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
kwargs.update(self.special_tokens_map)
return CTRLTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
def get_input_output_texts(self, tokenizer):
input_text = "adapt react readapt apt"
output_text = "adapt react readapt apt"
return input_text, output_text
......
tests/test_tokenization_fast.py
View file @ 36434220
......@@ -27,7 +27,7 @@ logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
NON_ENGLISH_TAGS = ["chinese", "dutch", "french", "finnish", "german", "multilingual"]
Tokenizer = namedtuple("Tokenizer", ["name", "rust_cls", "python_cls", "vocab_key", "filter"])
Tokenizer = namedtuple("Tokenizer", ["name", "rust_cls", "python_cls", "vocab_key", "filter", "kwargs"])
def filter_non_english(_: Tokenizer, pretrained_name: str):
......@@ -60,10 +60,10 @@ class CommonFastTokenizerTest(unittest.TestCase):
tokenizer_r = tok_case.rust_cls.from_pretrained(pretrained_name)
tokenizer_p = tok_case.python_cls.from_pretrained(pretrained_name)
self.fast_align_python(tokenizer_r, tokenizer_p)
self.fast_align_python(tokenizer_r, tokenizer_p, tok_case, pretrained_name)
self.fast_only(tokenizer_r)
def fast_align_python(self, tokenizer_r, tokenizer_p):
def fast_align_python(self, tokenizer_r, tokenizer_p, tok_case, pretrained_name):
# Check is_fast is set correctly
self.assertFalse(tokenizer_p.is_fast)
self.assertTrue(tokenizer_r.is_fast)
......@@ -75,6 +75,7 @@ class CommonFastTokenizerTest(unittest.TestCase):
self.assert_special_tokens_map_equal(tokenizer_r, tokenizer_p)
self.assert_embeded_special_tokens(tokenizer_r, tokenizer_p)
self.assert_padding(tokenizer_r, tokenizer_p)
self.assert_pretokenized_inputs(tokenizer_r, tokenizer_p)
self.assert_create_token_type_ids(tokenizer_r, tokenizer_p)
# TODO: enable for v3.0.0
# self.assert_empty_output_no_special_tokens(tokenizer_r, tokenizer_p)
......@@ -90,6 +91,7 @@ class CommonFastTokenizerTest(unittest.TestCase):
self.assert_offsets_mapping(tokenizer_r)
self.assert_add_special_tokens(tokenizer_r)
self.assert_alignement_methods(tokenizer_r)
self.assert_batch_encode_dynamic_overflowing(tokenizer_r)
def assert_alignement_methods(self, tokenizer_r):
words = ["Wonderful", "no", "inspiration", "example", "with", "subtoken"]
......@@ -169,7 +171,7 @@ class CommonFastTokenizerTest(unittest.TestCase):
self.assertEqual(batch_encoding.word_to_chars(0, last_word_index).end, last_char_index + 1)
self.assertEqual(batch_encoding.word_to_chars(last_batch_index, last_word_index).end, last_char_index + 1)
def assert_tokenization_python_rust_equals(self, tokenizer_p, tokenizer_r):
def assert_tokenization_python_rust_equals(self, tokenizer_r, tokenizer_p):
# Ensure basic input match
input_p = tokenizer_p.encode_plus(self._data)
input_r = tokenizer_r.encode_plus(self._data)
......@@ -184,18 +186,22 @@ class CommonFastTokenizerTest(unittest.TestCase):
self.assertSequenceEqual(input_pairs_p[key], input_pairs_r[key])
# Ensure truncation match
input_p = tokenizer_p.encode_plus(self._data, max_length=512)
input_r = tokenizer_r.encode_plus(self._data, max_length=512)
input_p = tokenizer_p.encode_plus(self._data, max_length=512, truncation=True)
input_r = tokenizer_r.encode_plus(self._data, max_length=512, truncation=True)
for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
self.assertSequenceEqual(input_p[key], input_r[key])
# Ensure truncation with stride match
input_p = tokenizer_p.encode_plus(self._data, max_length=512, stride=3, return_overflowing_tokens=True)
input_r = tokenizer_r.encode_plus(self._data, max_length=512, stride=3, return_overflowing_tokens=True)
input_p = tokenizer_p.encode_plus(
self._data, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
)
input_r = tokenizer_r.encode_plus(
self._data, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
)
for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
self.assertSequenceEqual(input_p[key], input_r[key])
self.assertSequenceEqual(input_p[key], input_r[key][0])
def assert_num_special_tokens_to_add_equal(self, tokenizer_r, tokenizer_p):
# Check we have the same number of added_tokens for both pair and non-pair inputs.
......@@ -274,9 +280,14 @@ class CommonFastTokenizerTest(unittest.TestCase):
"""
returned_tensor = "pt" if is_torch_available() else "tf"
if not tokenizer.pad_token or tokenizer.pad_token_id < 0:
return
tokens = tokenizer.encode_plus(
"HuggingFace is solving NLP one commit at a time",
max_length=6,
padding=True,
truncation=True,
return_tensors=returned_tensor,
return_overflowing_tokens=True,
)
......@@ -288,7 +299,8 @@ class CommonFastTokenizerTest(unittest.TestCase):
tokens = tokenizer.batch_encode_plus(
["HuggingFace is solving NLP one commit at a time"],
max_length=6,
pad_to_max_len=True,
padding=True,
truncation="only_first",
return_tensors=returned_tensor,
return_overflowing_tokens=True,
)
......@@ -301,7 +313,8 @@ class CommonFastTokenizerTest(unittest.TestCase):
tokens = tokenizer.batch_encode_plus(
["HuggingFace is solving NLP one commit at a time", "Very tiny input"],
max_length=6,
pad_to_max_len=True,
padding=True,
truncation="only_first",
return_tensors=returned_tensor,
return_overflowing_tokens=True,
)
......@@ -310,6 +323,58 @@ class CommonFastTokenizerTest(unittest.TestCase):
self.assertEqual(len(tokens[key].shape), 2)
self.assertEqual(tokens[key].shape[-1], 6)
def assert_pretokenized_inputs(self, tokenizer_r, tokenizer_p):
# Input string
pretokenized_input_simple = "This is a sample input".split()
pretokenized_input_pair = "This is a sample pair".split()
# Test encode for pretokenized inputs
output_r = tokenizer_r.encode(pretokenized_input_simple, is_pretokenized=True)
output_p = tokenizer_p.encode(pretokenized_input_simple, is_pretokenized=True)
self.assertEqual(output_p, output_r)
kwargs = {
"is_pretokenized": True,
"return_token_type_ids": True,
"return_attention_mask": True,
"return_overflowing_tokens": False,
"return_special_tokens_mask": True,
"return_offsets_mapping": False, # Not implemented in python tokenizers
}
# Test encode_plus for pretokenized inputs
output_r = tokenizer_r.encode_plus(pretokenized_input_simple, **kwargs)
output_p = tokenizer_p.encode_plus(pretokenized_input_simple, **kwargs)
for key in output_p.keys():
self.assertEqual(output_p[key], output_r[key])
# Test batch_encode_plus for pretokenized inputs
input_batch = ([pretokenized_input_simple] * 2) + [pretokenized_input_simple + pretokenized_input_pair]
output_r = tokenizer_r.batch_encode_plus(input_batch, **kwargs)
output_p = tokenizer_p.batch_encode_plus(input_batch, **kwargs)
for key in output_p.keys():
self.assertEqual(output_p[key], output_r[key])
# Test encode for pretokenized inputs pairs
output_r = tokenizer_r.encode(pretokenized_input_simple, pretokenized_input_pair, is_pretokenized=True)
output_p = tokenizer_p.encode(pretokenized_input_simple, pretokenized_input_pair, is_pretokenized=True)
self.assertEqual(output_p, output_r)
# Test encode_plus for pretokenized inputs
output_r = tokenizer_r.encode_plus(pretokenized_input_simple, pretokenized_input_pair, **kwargs)
output_p = tokenizer_p.encode_plus(pretokenized_input_simple, pretokenized_input_pair, **kwargs)
for key in output_p.keys():
self.assertEqual(output_p[key], output_r[key])
# Test batch_encode_plus for pretokenized inputs
input_batch_pair = ([pretokenized_input_simple, pretokenized_input_pair] * 2) + [
pretokenized_input_simple + pretokenized_input_pair,
pretokenized_input_pair,
]
output_r = tokenizer_r.batch_encode_plus(input_batch_pair, **kwargs)
output_p = tokenizer_p.batch_encode_plus(input_batch_pair, **kwargs)
for key in output_p.keys():
self.assertEqual(output_p[key], output_r[key])
def assert_create_token_type_ids(self, tokenizer_r, tokenizer_p):
input_simple = [1, 2, 3]
input_pair = [1, 2, 3]
......@@ -357,17 +422,22 @@ class CommonFastTokenizerTest(unittest.TestCase):
def assert_padded_input_match(input_r: list, input_p: list, max_length: int):
# Ensure we match max_length
self.assertEqual(len(input_r), max_length), self.assertEqual(len(input_p), max_length)
self.assertEqual(len(input_r), max_length)
self.assertEqual(len(input_p), max_length)
# Ensure the number of padded tokens is the same
padded_tokens_r = list(takewhile(lambda i: i == tokenizer_r.pad_token_id, reversed(input_r)))
padded_tokens_p = list(takewhile(lambda i: i == tokenizer_p.pad_token_id, reversed(input_p)))
self.assertSequenceEqual(padded_tokens_r, padded_tokens_p)
def assert_batch_padded_input_match(input_r: dict, input_p: dict):
def assert_batch_padded_input_match(input_r: dict, input_p: dict, max_length: int):
for i_r in input_r.values():
self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), 15), self.assertEqual(len(i_r[1]), 15)
self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), 15), self.assertEqual(len(i_r[1]), 15)
self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
len(i_r[1]), max_length
)
self.assertEqual(len(i_r), 2), self.assertEqual(len(i_r[0]), max_length), self.assertEqual(
len(i_r[1]), max_length
)
for i_r, i_p in zip(input_r["input_ids"], input_p["input_ids"]):
assert_padded_input_match(i_r, i_p, max_length)
......@@ -375,12 +445,19 @@ class CommonFastTokenizerTest(unittest.TestCase):
for i_r, i_p in zip(input_r["attention_mask"], input_p["attention_mask"]):
self.assertSequenceEqual(i_r, i_p)
# Simple input
# Encode - Simple input
input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, pad_to_max_length=True)
assert_padded_input_match(input_r, input_p, max_length)
input_r = tokenizer_r.encode("This is a simple input", max_length=max_length, padding="max_length")
input_p = tokenizer_p.encode("This is a simple input", max_length=max_length, padding="max_length")
assert_padded_input_match(input_r, input_p, max_length)
# Pair input
input_r = tokenizer_r.encode("This is a simple input", padding="longest")
input_p = tokenizer_p.encode("This is a simple input", padding=True)
assert_padded_input_match(input_r, input_p, len(input_r))
# Encode - Pair input
input_r = tokenizer_r.encode(
"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
)
......@@ -388,14 +465,34 @@ class CommonFastTokenizerTest(unittest.TestCase):
"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
)
assert_padded_input_match(input_r, input_p, max_length)
input_r = tokenizer_r.encode(
"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
)
input_p = tokenizer_p.encode(
"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
)
assert_padded_input_match(input_r, input_p, max_length)
input_r = tokenizer_r.encode("This is a simple input", "This is a pair", padding=True)
input_p = tokenizer_p.encode("This is a simple input", "This is a pair", padding="longest")
assert_padded_input_match(input_r, input_p, len(input_r))
# Simple input
# Encode_plus - Simple input
input_r = tokenizer_r.encode_plus("This is a simple input", max_length=max_length, pad_to_max_length=True)
input_p = tokenizer_p.encode_plus("This is a simple input", max_length=max_length, pad_to_max_length=True)
assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
input_r = tokenizer_r.encode_plus("This is a simple input", max_length=max_length, padding="max_length")
input_p = tokenizer_p.encode_plus("This is a simple input", max_length=max_length, padding="max_length")
assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
# Pair input
input_r = tokenizer_r.encode_plus("This is a simple input", padding="longest")
input_p = tokenizer_p.encode_plus("This is a simple input", padding=True)
assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]))
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
# Encode_plus - Pair input
input_r = tokenizer_r.encode_plus(
"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
)
......@@ -404,34 +501,130 @@ class CommonFastTokenizerTest(unittest.TestCase):
)
assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
input_r = tokenizer_r.encode_plus(
"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
)
input_p = tokenizer_p.encode_plus(
"This is a simple input", "This is a pair", max_length=max_length, padding="max_length"
)
assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
input_r = tokenizer_r.encode_plus("This is a simple input", "This is a pair", padding="longest")
input_p = tokenizer_p.encode_plus("This is a simple input", "This is a pair", padding=True)
assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]))
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
# Simple input
# Batch_encode_plus - Simple input
input_r = tokenizer_r.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"], max_length=max_length, pad_to_max_length=True
)
input_p = tokenizer_p.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"], max_length=max_length, pad_to_max_length=True
)
assert_batch_padded_input_match(input_r, input_p)
assert_batch_padded_input_match(input_r, input_p, max_length)
input_r = tokenizer_r.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"], max_length=max_length, padding="max_length",
)
input_p = tokenizer_p.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"], max_length=max_length, padding="max_length",
)
assert_batch_padded_input_match(input_r, input_p, max_length)
input_r = tokenizer_r.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"], max_length=max_length, padding="longest",
)
input_p = tokenizer_p.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"], max_length=max_length, padding=True,
)
assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]))
input_r = tokenizer_r.batch_encode_plus(
["This is a simple input 1", "This is a simple input 2"], padding="longest"
)
input_p = tokenizer_p.batch_encode_plus(["This is a simple input 1", "This is a simple input 2"], padding=True)
assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]))
# Batch_encode_plus - Pair input
input_r = tokenizer_r.batch_encode_plus(
[
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
],
max_length=max_length,
truncation=True,
padding="max_length",
)
input_p = tokenizer_p.batch_encode_plus(
[
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
],
max_length=max_length,
truncation=True,
padding="max_length",
)
assert_batch_padded_input_match(input_r, input_p, max_length)
# Pair input
input_r = tokenizer_r.batch_encode_plus(
[
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
],
max_length=15,
pad_to_max_length=True,
padding=True,
)
input_p = tokenizer_p.batch_encode_plus(
[
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
],
max_length=15,
pad_to_max_length=True,
padding="longest",
)
assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]))
# Using pad on single examples after tokenization
input_r = tokenizer_r.encode_plus("This is a input 1")
input_r = tokenizer_r.pad(input_r)
input_p = tokenizer_r.encode_plus("This is a input 1")
input_p = tokenizer_r.pad(input_p)
assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]))
# Using pad on single examples after tokenization
input_r = tokenizer_r.encode_plus("This is a input 1")
input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
input_p = tokenizer_r.encode_plus("This is a input 1")
input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length)
# Using pad after tokenization
input_r = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
assert_batch_padded_input_match(input_r, input_p)
input_r = tokenizer_r.pad(input_r)
input_p = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
input_p = tokenizer_r.pad(input_p)
assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]))
# Using pad after tokenization
input_r = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
input_p = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
assert_batch_padded_input_match(input_r, input_p, max_length)
def assert_save_pretrained(self, tokenizer_r, tokenizer_p):
# Checks it save with the same files
......@@ -503,8 +696,10 @@ class WordPieceFastTokenizerTest(CommonFastTokenizerTest):
TOKENIZERS_CLASSES = frozenset(
[
Tokenizer("Bert", BertTokenizerFast, BertTokenizer, "vocab_file", filter_non_english),
Tokenizer("DistilBert", DistilBertTokenizerFast, DistilBertTokenizer, "vocab_file", filter_non_english),
Tokenizer("Bert", BertTokenizerFast, BertTokenizer, "vocab_file", filter_non_english, None),
Tokenizer(
"DistilBert", DistilBertTokenizerFast, DistilBertTokenizer, "vocab_file", filter_non_english, None
),
]
)
......@@ -552,7 +747,7 @@ class WordPieceFastTokenizerTest(CommonFastTokenizerTest):
class RobertaFastTokenizerTest(CommonFastTokenizerTest):
TOKENIZERS_CLASSES = frozenset(
[Tokenizer("Roberta", RobertaTokenizerFast, RobertaTokenizer, "vocab_file", filter_roberta_detectors)]
[Tokenizer("Roberta", RobertaTokenizerFast, RobertaTokenizer, "vocab_file", filter_roberta_detectors, None)]
)
def assert_embeded_special_tokens(self, tokenizer_r, tokenizer_p):
......@@ -580,10 +775,30 @@ class RobertaFastTokenizerTest(CommonFastTokenizerTest):
class NoPaddingTokenFastTokenizerMatchingTest(CommonFastTokenizerTest):
TOKENIZERS_CLASSES = [
Tokenizer("OpenAI GPT", OpenAIGPTTokenizerFast, OpenAIGPTTokenizer, "vocab_file", None),
Tokenizer("GPT2", GPT2TokenizerFast, GPT2Tokenizer, "vocab_file", None),
Tokenizer("OpenAI GPT", OpenAIGPTTokenizerFast, OpenAIGPTTokenizer, "vocab_file", None, None),
Tokenizer("GPT2", GPT2TokenizerFast, GPT2Tokenizer, "vocab_file", None, [("add_prefix_space", True)]),
]
def fast_align_python(self, tokenizer_r, tokenizer_p, tok_case, pretrained_name):
# Check is_fast is set correctly
self.assertFalse(tokenizer_p.is_fast)
self.assertTrue(tokenizer_r.is_fast)
# Check that Rust and Python align
self.assert_tokenization_python_rust_equals(tokenizer_r, tokenizer_p)
self.assert_num_special_tokens_to_add_equal(tokenizer_r, tokenizer_p)
self.assert_max_length_equal(tokenizer_r, tokenizer_p)
self.assert_special_tokens_map_equal(tokenizer_r, tokenizer_p)
self.assert_embeded_special_tokens(tokenizer_r, tokenizer_p)
self.assert_padding(tokenizer_r, tokenizer_p)
# Specific for
kwargs = {}
if tok_case.kwargs is not None:
kwargs = dict(tok_case.kwargs)
tokenizer_r = tok_case.rust_cls.from_pretrained(pretrained_name, **kwargs)
self.assert_pretokenized_inputs(tokenizer_r, tokenizer_p)
def assert_padding(self, tokenizer_r, tokenizer_p, max_length=15):
# Simple input
s = "This is a simple input"
......@@ -595,27 +810,31 @@ class NoPaddingTokenFastTokenizerMatchingTest(CommonFastTokenizerTest):
]
# Simple input tests
self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, pad_to_max_length=True)
self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, pad_to_max_length=True)
self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(ValueError, tokenizer_r.batch_encode_plus, s2, max_length=max_length, pad_to_max_length=True)
self.assertRaises(
ValueError, tokenizer_r.batch_encode_plus, s2, max_length=max_length, padding="max_length",
)
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, pad_to_max_length=True)
self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, pad_to_max_length=True)
self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(ValueError, tokenizer_r.batch_encode_plus, p2, max_length=max_length, pad_to_max_length=True)
self.assertRaises(
ValueError, tokenizer_r.batch_encode_plus, p2, max_length=max_length, padding="max_length",
)
class TransfoXLFastTokenizerTest(NoPaddingTokenFastTokenizerMatchingTest):
TOKENIZERS_CLASSES = frozenset(
[Tokenizer("TransfoXL", TransfoXLTokenizerFast, TransfoXLTokenizer, "pretrained_vocab_file", None)]
[Tokenizer("TransfoXL", TransfoXLTokenizerFast, TransfoXLTokenizer, "pretrained_vocab_file", None, None)]
)
@require_torch
......
tests/test_tokenization_gpt2.py
View file @ 36434220
......@@ -53,6 +53,7 @@ class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
......@@ -73,7 +74,7 @@ class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
kwargs.update(self.special_tokens_map)
return GPT2TokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
def get_input_output_texts(self, tokenizer):
input_text = "lower newer"
output_text = "lower newer"
return input_text, output_text
......@@ -118,3 +119,8 @@ class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
input_tokens = tokens + [rust_tokenizer.unk_token]
input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
def test_pretokenized_inputs(self, *args, **kwargs):
# It's very difficult to mix/test pretokenization with byte-level
# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
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