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37ed3ab719
transformers/tests/test_tokenization_common.py
Philip May 37ed3ab719
Enable option for subword regularization in more tokenizers. (#11417) 
* improve slow class tok usage at xlm rob

* add subword regularization for barthez

* improve barthez tok. test

* fix tokenizer tests

* add subword regularization for camembert

* add subword regularization for deberta v2 tokenizer

* add more doc to deberta v2 tokenizer

* add subword regularization for speech to text tok.

* fix sp_model_kwargs type in speech 2 text tok.

* add subword regularization for M2M100 tok.

* add more concrete type hints

* fix tests for m2m100 and s2t tok.

* add missing Any import

* fix syntax error in m2m100 tok.

* fix unpickle of m2m100 and s2t tok.

* fix test of m2m100 and s2t tok.

* improve unpickle of deberta v2 tok.

* add test for pickle of barthez & camembert

* fix pickle of barthez & camembert

* add test for deberta v2 tok. pickle

* fix m2m100 tok. pickle

* fix s2t tok. pickle

* add subword regularization to albert tok.

* refactor subword reg. test into TokenizerTesterMixin

improve albert tok. test

remove sample argument form albert tok.

check subword reg. using TokenizerTesterMixin

improve tok. tests

improve xlm roberta tok. tests

improve xlm roberta tok. tests

* add subword regularization for big bird t.

* improve xlm roberta tok. test

* add subword regularization for mbart50 tok.

* add subword regularization for pegasus tok.

* add subword regularization for reformer tok.

* add subword regularization for T5 tok.

* fix t5 tok. test formatting

* add subword regularization for xlm_proph. tok.

* add subword regularization for xlnet tok.

* add subword regularization for gert_gen tok.

* add typing to tokenizers

* add typing to xlm rob. tok

* add subword regularization for marian tok.

* add reverse tok. test

* fix marian tok test

* fix marian tok test

* fix casing in tok. tests

* fix style of tok. common test

* fix deberta v2 tok test

* add type annotations to tok. tests

* add type annotations to tok. __init__

* add typing to kokenizer

* add type annotations to tok. __init__

* don't specify the default when it's None

* fix barthez tok. doc

* move sentencepiece tok. tests to TokenizerTesterMixin

* fix unused imports

* fix albert tok. test

* add comment to sentencepiece test options

* fix Any import at big bird tok.

* fix Any import at xlm prophetnet tok.

* empty commit to trigger CI
2021-05-13 02:44:55 -04:00

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# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# 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.
import inspect
import itertools
import os
import pickle
import re
import shutil
import tempfile
import unittest
from collections import OrderedDict
from itertools import takewhile
from typing import TYPE_CHECKING, Dict, List, Tuple, Union
from huggingface_hub import HfApi
from requests.exceptions import HTTPError
from transformers import (
BertTokenizer,
PreTrainedTokenizer,
PreTrainedTokenizerBase,
PreTrainedTokenizerFast,
is_tf_available,
is_torch_available,
)
from transformers.testing_utils import (
ENDPOINT_STAGING,
PASS,
USER,
get_tests_dir,
is_pt_tf_cross_test,
is_staging_test,
require_tf,
require_tokenizers,
require_torch,
slow,
)
from transformers.tokenization_utils import AddedToken
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedModel, TFPreTrainedModel
NON_ENGLISH_TAGS = ["chinese", "dutch", "french", "finnish", "german", "multilingual"]
def filter_non_english(_, pretrained_name: str):
"""Filter all the model for non-english language"""
return not any([lang in pretrained_name for lang in NON_ENGLISH_TAGS])
def filter_roberta_detectors(_, pretrained_name: str):
return "detector" not in pretrained_name
def merge_model_tokenizer_mappings(
model_mapping: Dict["PretrainedConfig", Union["PreTrainedModel", "TFPreTrainedModel"]],
tokenizer_mapping: Dict["PretrainedConfig", Tuple["PreTrainedTokenizer", "PreTrainedTokenizerFast"]],
) -> Dict[
Union["PreTrainedTokenizer", "PreTrainedTokenizerFast"],
Tuple["PretrainedConfig", Union["PreTrainedModel", "TFPreTrainedModel"]],
]:
configurations = list(model_mapping.keys())
model_tokenizer_mapping = OrderedDict([])
for configuration in configurations:
if configuration in model_mapping and configuration in tokenizer_mapping:
model = model_mapping[configuration]
tokenizer = tokenizer_mapping[configuration][0]
tokenizer_fast = tokenizer_mapping[configuration][1]
model_tokenizer_mapping.update({tokenizer: (configuration, model)})
if tokenizer_fast is not None:
model_tokenizer_mapping.update({tokenizer_fast: (configuration, model)})
return model_tokenizer_mapping
class TokenizerTesterMixin:
tokenizer_class = None
rust_tokenizer_class = None
test_rust_tokenizer = False
space_between_special_tokens = False
from_pretrained_kwargs = None
from_pretrained_filter = None
from_pretrained_vocab_key = "vocab_file"
test_seq2seq = True
# set to True to test a sentencepiece tokenizer
test_sentencepiece = False
# set to True to ignore casing when testing a sentencepiece tokenizer
# test_sentencepiece must also be set to True
test_sentencepiece_ignore_case = False
def setUp(self) -> None:
# Tokenizer.filter makes it possible to filter which Tokenizer to case based on all the
# information available in Tokenizer (name, rust class, python class, vocab key name)
if self.test_rust_tokenizer:
tokenizers_list = [
(
self.rust_tokenizer_class,
pretrained_name,
self.from_pretrained_kwargs if self.from_pretrained_kwargs is not None else {},
)
for pretrained_name in self.rust_tokenizer_class.pretrained_vocab_files_map[
self.from_pretrained_vocab_key
].keys()
if self.from_pretrained_filter is None
or (self.from_pretrained_filter is not None and self.from_pretrained_filter(pretrained_name))
]
self.tokenizers_list = tokenizers_list[:1] # Let's just test the first pretrained vocab for speed
else:
self.tokenizers_list = []
with open(f"{get_tests_dir()}/fixtures/sample_text.txt", encoding="utf-8") as f_data:
self._data = f_data.read().replace("\n\n", "\n").strip()
self.tmpdirname = tempfile.mkdtemp()
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=20, min_length=5) -> 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]
if min_length is not None and len(toks) < min_length and len(toks) > 0:
while len(toks) < min_length:
toks = toks + toks
# 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) -> List[PreTrainedTokenizerBase]:
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) -> PreTrainedTokenizerFast:
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
# 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 assert_padded_input_match(self, input_r: list, input_p: list, max_length: int, pad_token_id: int):
# Ensure we match 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 == pad_token_id, reversed(input_r)))
padded_tokens_p = list(takewhile(lambda i: i == pad_token_id, reversed(input_p)))
self.assertSequenceEqual(padded_tokens_r, padded_tokens_p)
def assert_batch_padded_input_match(
self,
input_r: dict,
input_p: dict,
max_length: int,
pad_token_id: int,
model_main_input_name: str = "input_ids",
):
for i_r in input_r.values():
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[model_main_input_name], input_p[model_main_input_name]):
self.assert_padded_input_match(i_r, i_p, max_length, pad_token_id)
for i_r, i_p in zip(input_r["attention_mask"], input_p["attention_mask"]):
self.assertSequenceEqual(i_r, i_p)
@staticmethod
def convert_batch_encode_plus_format_to_encode_plus(batch_encode_plus_sequences):
# Switch from batch_encode_plus format: {'input_ids': [[...], [...]], ...}
# to the list of examples/ encode_plus format: [{'input_ids': [...], ...}, {'input_ids': [...], ...}]
return [
{value: batch_encode_plus_sequences[value][i] for value in batch_encode_plus_sequences.keys()}
for i in range(len(batch_encode_plus_sequences["input_ids"]))
]
def test_subword_regularization_tokenizer(self) -> None:
if not self.test_sentencepiece:
return
# Subword regularization is only available for the slow tokenizer.
sp_model_kwargs = {"enable_sampling": True, "alpha": 0.1, "nbest_size": -1}
tokenizer = self.get_tokenizer(sp_model_kwargs=sp_model_kwargs)
self.assertTrue(hasattr(tokenizer, "sp_model_kwargs"))
self.assertIsNotNone(tokenizer.sp_model_kwargs)
self.assertTrue(isinstance(tokenizer.sp_model_kwargs, dict))
self.assertEqual(tokenizer.sp_model_kwargs, sp_model_kwargs)
self.check_subword_sampling(tokenizer)
def test_pickle_subword_regularization_tokenizer(self) -> None:
if not self.test_sentencepiece:
return
"""Google pickle __getstate__ __setstate__ if you are struggling with this."""
# Subword regularization is only available for the slow tokenizer.
sp_model_kwargs = {"enable_sampling": True, "alpha": 0.1, "nbest_size": -1}
tokenizer = self.get_tokenizer(sp_model_kwargs=sp_model_kwargs)
tokenizer_bin = pickle.dumps(tokenizer)
del tokenizer
tokenizer_new = pickle.loads(tokenizer_bin)
self.assertTrue(hasattr(tokenizer_new, "sp_model_kwargs"))
self.assertIsNotNone(tokenizer_new.sp_model_kwargs)
self.assertTrue(isinstance(tokenizer_new.sp_model_kwargs, dict))
self.assertEqual(tokenizer_new.sp_model_kwargs, sp_model_kwargs)
self.check_subword_sampling(tokenizer_new)
def test_model_input_names_signature(self):
accepted_model_main_input_names = [
"input_ids", # nlp models
"input_values", # speech models
]
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
# first name of model_input_names has to correspond to main model input name
# to make sure `tokenizer.pad(...)` works correctly
self.assertTrue(tokenizer.model_input_names[0] in accepted_model_main_input_names)
def test_rust_tokenizer_signature(self):
if not self.test_rust_tokenizer:
return
signature = inspect.signature(self.rust_tokenizer_class.__init__)
self.assertIn("tokenizer_file", signature.parameters)
self.assertIsNone(signature.parameters["tokenizer_file"].default)
def test_tokenizer_slow_store_full_signature(self):
signature = inspect.signature(self.tokenizer_class.__init__)
tokenizer = self.get_tokenizer()
for parameter_name, parameter in signature.parameters.items():
if parameter.default != inspect.Parameter.empty:
self.assertIn(parameter_name, tokenizer.init_kwargs)
def test_tokenizer_fast_store_full_signature(self):
if not self.test_rust_tokenizer:
return
signature = inspect.signature(self.rust_tokenizer_class.__init__)
tokenizer = self.get_rust_tokenizer()
for parameter_name, parameter in signature.parameters.items():
if parameter.default != inspect.Parameter.empty and parameter_name != "tokenizer_file":
self.assertIn(parameter_name, tokenizer.init_kwargs)
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
return
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
sequence, _ = self.get_input_output_texts(tokenizer)
# We don't have an exact equivalence on `tokenize()` between Rust and Slow
# Slow tokenizer only split tokens, Rust tokenizers will replace with <unk>
# 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)
ids = tokenizer.encode(sequence, add_special_tokens=True)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=True)
self.assertListEqual(ids, rust_ids)
def test_tokenizers_common_properties(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
attributes_list = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
]
for attr in attributes_list:
self.assertTrue(hasattr(tokenizer, attr))
self.assertTrue(hasattr(tokenizer, attr + "_id"))
self.assertTrue(hasattr(tokenizer, "additional_special_tokens"))
self.assertTrue(hasattr(tokenizer, "additional_special_tokens_ids"))
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
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
self.assertNotEqual(tokenizer.model_max_length, 42)
# Now let's start the test
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00E9d,running"
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
before_vocab = tokenizer.get_vocab()
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
after_vocab = after_tokenizer.get_vocab()
self.assertListEqual(before_tokens, after_tokens)
self.assertDictEqual(before_vocab, after_vocab)
shutil.rmtree(tmpdirname)
tokenizers = self.get_tokenizers(model_max_length=42)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00E9d,running"
tokenizer.add_tokens(["bim", "bambam"])
additional_special_tokens = tokenizer.additional_special_tokens
additional_special_tokens.append("new_additional_special_token")
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
before_vocab = tokenizer.get_vocab()
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
after_vocab = after_tokenizer.get_vocab()
self.assertListEqual(before_tokens, after_tokens)
self.assertDictEqual(before_vocab, after_vocab)
self.assertIn("bim", after_vocab)
self.assertIn("bambam", after_vocab)
self.assertIn("new_additional_special_token", after_tokenizer.additional_special_tokens)
self.assertEqual(after_tokenizer.model_max_length, 42)
tokenizer = tokenizer.__class__.from_pretrained(tmpdirname, model_max_length=43)
self.assertEqual(tokenizer.model_max_length, 43)
shutil.rmtree(tmpdirname)
# Test that we can also use the non-legacy saving format for fast tokenizers
tokenizers = self.get_tokenizers(model_max_length=42)
for tokenizer in tokenizers:
if not tokenizer.is_fast:
continue
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00E9d,running"
tokenizer.add_tokens(["bim", "bambam"])
additional_special_tokens = tokenizer.additional_special_tokens
additional_special_tokens.append("new_additional_special_token")
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
before_vocab = tokenizer.get_vocab()
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
after_vocab = after_tokenizer.get_vocab()
self.assertListEqual(before_tokens, after_tokens)
self.assertDictEqual(before_vocab, after_vocab)
self.assertIn("bim", after_vocab)
self.assertIn("bambam", after_vocab)
self.assertIn("new_additional_special_token", after_tokenizer.additional_special_tokens)
self.assertEqual(after_tokenizer.model_max_length, 42)
tokenizer = tokenizer.__class__.from_pretrained(tmpdirname, model_max_length=43)
self.assertEqual(tokenizer.model_max_length, 43)
shutil.rmtree(tmpdirname)
def test_pickle_tokenizer(self):
"""Google pickle __getstate__ __setstate__ if you are struggling with this."""
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"
subwords = tokenizer.tokenize(text)
filename = os.path.join(self.tmpdirname, "tokenizer.bin")
with open(filename, "wb") as handle:
pickle.dump(tokenizer, handle)
with open(filename, "rb") as handle:
tokenizer_new = pickle.load(handle)
subwords_loaded = tokenizer_new.tokenize(text)
self.assertListEqual(subwords, subwords_loaded)
@require_tokenizers
def test_pickle_added_tokens(self):
tok1 = AddedToken("<s>", rstrip=True, lstrip=True, normalized=False, single_word=True)
tok2 = pickle.loads(pickle.dumps(tok1))
self.assertEqual(tok1.__getstate__(), tok2.__getstate__())
def test_added_tokens_do_lower_case(self):
# 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__}"):
if not hasattr(tokenizer, "do_lower_case") or not tokenizer.do_lower_case:
continue
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
toks0 = tokenizer.tokenize(text) # toks before adding new_toks
new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd", "AAAAA BBBBBB", "CCCCCCCCCDDDDDDDD"]
added = tokenizer.add_tokens(new_toks)
self.assertEqual(added, 2)
toks = tokenizer.tokenize(text)
toks2 = tokenizer.tokenize(text2)
self.assertEqual(len(toks), len(toks2))
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"
tokenized_sequence = tokenizer.tokenize(sequence_with_special_tokens)
for special_token in tokenizer.all_special_tokens:
self.assertTrue(special_token in tokenized_sequence)
tokenizers = self.get_tokenizers(fast=False, do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if hasattr(tokenizer, "do_lower_case") and tokenizer.do_lower_case:
continue
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.assertIn(added, [2, 4])
toks = tokenizer.tokenize(text)
toks2 = tokenizer.tokenize(text2)
self.assertEqual(len(toks), len(toks2)) # Length should still be the same
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):
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)
self.assertNotEqual(vocab_size, 0)
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
added_toks = tokenizer.add_tokens(new_toks)
vocab_size_2 = tokenizer.vocab_size
all_size_2 = len(tokenizer)
self.assertNotEqual(vocab_size_2, 0)
self.assertEqual(vocab_size, vocab_size_2)
self.assertEqual(added_toks, len(new_toks))
self.assertEqual(all_size_2, all_size + len(new_toks))
tokens = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l", add_special_tokens=False)
self.assertGreaterEqual(len(tokens), 4)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
vocab_size_3 = tokenizer.vocab_size
all_size_3 = len(tokenizer)
self.assertNotEqual(vocab_size_3, 0)
self.assertEqual(vocab_size, vocab_size_3)
self.assertEqual(added_toks_2, len(new_toks_2))
self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
tokens = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l", add_special_tokens=False
)
self.assertGreaterEqual(len(tokens), 6)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
self.assertGreater(tokens[0], tokens[1])
self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
self.assertGreater(tokens[-2], tokens[-3])
self.assertEqual(tokens[0], tokenizer.eos_token_id)
self.assertEqual(tokens[-2], tokenizer.pad_token_id)
def test_add_special_tokens(self):
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]"
tokenizer.add_special_tokens({"cls_token": special_token})
encoded_special_token = tokenizer.encode(special_token, add_special_tokens=False)
self.assertEqual(len(encoded_special_token), 1)
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)
special_token_id = tokenizer.encode(special_token, add_special_tokens=False)
self.assertEqual(encoded, input_encoded + special_token_id)
decoded = tokenizer.decode(encoded, skip_special_tokens=True)
self.assertTrue(special_token not in decoded)
def test_internal_consistency(self):
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)
ids_2 = tokenizer.encode(input_text, add_special_tokens=False)
self.assertListEqual(ids, ids_2)
tokens_2 = tokenizer.convert_ids_to_tokens(ids)
self.assertNotEqual(len(tokens_2), 0)
text_2 = tokenizer.decode(ids)
self.assertIsInstance(text_2, str)
self.assertEqual(text_2, output_text)
@require_tokenizers
def test_encode_decode_with_spaces(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# new_toks = ["[ABC]", "[DEF]"] # TODO(thom) add this one back when Rust toks are ready: , "GHI IHG"]
new_toks = [AddedToken("[ABC]", normalized=False), AddedToken("[DEF]", normalized=False)]
tokenizer.add_tokens(new_toks)
input = "[ABC][DEF][ABC][DEF]" # TODO(thom) add back cf above: "[ABC] [DEF] [ABC] GHI IHG [DEF]"
if self.space_between_special_tokens:
output = "[ABC] [DEF] [ABC] [DEF]"
else:
output = input
encoded = tokenizer.encode(input, add_special_tokens=False)
decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens)
self.assertIn(decoded, [output, output.lower()])
def test_pretrained_model_lists(self):
# We should have at least one default checkpoint for each tokenizer
# We should specify the max input length as well (used in some part to list the pretrained checkpoints)
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map), 1)
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]), 1)
self.assertEqual(
len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]),
len(self.tokenizer_class.max_model_input_sizes),
)
weights_list = list(self.tokenizer_class.max_model_input_sizes.keys())
weights_lists_2 = []
for file_id, map_list in self.tokenizer_class.pretrained_vocab_files_map.items():
weights_lists_2.append(list(map_list.keys()))
for weights_list_2 in weights_lists_2:
self.assertListEqual(weights_list, weights_list_2)
def test_mask_output(self):
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"
and "token_type_ids" in tokenizer.model_input_names
):
seq_0 = "Test this method."
seq_1 = "With these inputs."
information = tokenizer.encode_plus(seq_0, seq_1, add_special_tokens=True)
sequences, mask = information["input_ids"], information["token_type_ids"]
self.assertEqual(len(sequences), len(mask))
def test_token_type_ids(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
seq_0 = "Test this method."
# We want to have sequence 0 and sequence 1 are tagged
# respectively with 0 and 1 token_ids
# (regardless of whether the model use token type ids)
# We use this assumption in the QA pipeline among other place
output = tokenizer(seq_0, return_token_type_ids=True)
self.assertIn(0, output["token_type_ids"])
def test_sequence_ids(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
if not tokenizer.is_fast:
continue
with self.subTest(f"{tokenizer.__class__.__name__}"):
seq_0 = "Test this method."
seq_1 = "With these inputs."
# We want to have sequence 0 and sequence 1 are tagged
# respectively with 0 and 1 token_ids
# (regardless of whether the model use token type ids)
# We use this assumption in the QA pipeline among other place
output = tokenizer(seq_0)
self.assertIn(0, output.sequence_ids())
output = tokenizer(seq_0, seq_1)
self.assertIn(0, output.sequence_ids())
self.assertIn(1, output.sequence_ids())
if tokenizer.num_special_tokens_to_add(pair=True):
self.assertIn(None, output.sequence_ids())
def test_number_of_added_tokens(self):
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."
sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=False)
attached_sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=True)
# 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)
)
def test_maximum_encoding_length_single_input(self):
tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
seq_0, ids = self.get_clean_sequence(tokenizer, max_length=20)
sequence = tokenizer.encode(seq_0, add_special_tokens=False)
total_length = len(sequence)
assert total_length > 4, "Issue with the testing sequence, please update it it's too short"
# Test with max model input length
model_max_length = tokenizer.model_max_length
self.assertEqual(model_max_length, 100)
seq_1 = seq_0 * model_max_length
sequence1 = tokenizer(seq_1, add_special_tokens=False)
total_length1 = len(sequence1["input_ids"])
assert (
total_length1 > model_max_length
), "Issue with the testing sequence, please update it it's too short"
# Simple
padding_strategies = (
[False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False]
)
for padding_state in padding_strategies:
with self.subTest(f"Padding: {padding_state}"):
for truncation_state in [True, "longest_first", "only_first"]:
with self.subTest(f"Truncation: {truncation_state}"):
output = tokenizer(seq_1, padding=padding_state, truncation=truncation_state)
self.assertEqual(len(output["input_ids"]), model_max_length)
output = tokenizer([seq_1], padding=padding_state, truncation=truncation_state)
self.assertEqual(len(output["input_ids"][0]), model_max_length)
# Simple with no truncation
# Reset warnings
tokenizer.deprecation_warnings = {}
with self.assertLogs("transformers", level="WARNING") as cm:
output = tokenizer(seq_1, padding=padding_state, truncation=False)
self.assertNotEqual(len(output["input_ids"]), model_max_length)
self.assertEqual(len(cm.records), 1)
self.assertTrue(
cm.records[0].message.startswith(
"Token indices sequence length is longer than the specified maximum sequence length for this model"
)
)
tokenizer.deprecation_warnings = {}
with self.assertLogs("transformers", level="WARNING") as cm:
output = tokenizer([seq_1], padding=padding_state, truncation=False)
self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
self.assertEqual(len(cm.records), 1)
self.assertTrue(
cm.records[0].message.startswith(
"Token indices sequence length is longer than the specified maximum sequence length for this model"
)
)
# Overflowing tokens
stride = 2
information = tokenizer(
seq_0,
max_length=total_length - 2,
add_special_tokens=False,
stride=stride,
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), 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, sequence[:-2])
self.assertEqual(len(overflowing_tokens), 2 + stride)
def test_maximum_encoding_length_pair_input(self):
tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
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, max_length=20)
if len(ids) <= 2 + stride:
seq_0 = (seq_0 + " ") * (2 + stride)
ids = None
seq0_tokens = tokenizer.encode(seq_0, add_special_tokens=False)
assert len(seq0_tokens) > 2 + stride
seq_1 = "This is another sentence to be encoded."
seq1_tokens = tokenizer.encode(seq_1, add_special_tokens=False)
if abs(len(seq0_tokens) - len(seq1_tokens)) <= 2:
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
smallest = seq1_tokens if len(seq0_tokens) > len(seq1_tokens) else seq0_tokens
# 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)
# Test with max model input length
model_max_length = tokenizer.model_max_length
self.assertEqual(model_max_length, 100)
seq_2 = seq_0 * model_max_length
assert len(seq_2) > model_max_length
sequence1 = tokenizer(seq_1, add_special_tokens=False)
total_length1 = len(sequence1["input_ids"])
sequence2 = tokenizer(seq_2, seq_1, add_special_tokens=False)
total_length2 = len(sequence2["input_ids"])
assert total_length1 < model_max_length - 10, "Issue with the testing sequence, please update it."
assert total_length2 > model_max_length, "Issue with the testing sequence, please update it."
# Simple
padding_strategies = (
[False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False]
)
for padding_state in padding_strategies:
with self.subTest(f"{tokenizer.__class__.__name__} Padding: {padding_state}"):
for truncation_state in [True, "longest_first", "only_first"]:
with self.subTest(f"{tokenizer.__class__.__name__} Truncation: {truncation_state}"):
output = tokenizer(seq_2, seq_1, padding=padding_state, truncation=truncation_state)
self.assertEqual(len(output["input_ids"]), model_max_length)
output = tokenizer(
[seq_2], [seq_1], padding=padding_state, truncation=truncation_state
)
self.assertEqual(len(output["input_ids"][0]), model_max_length)
# Simple
output = tokenizer(seq_1, seq_2, padding=padding_state, truncation="only_second")
self.assertEqual(len(output["input_ids"]), model_max_length)
output = tokenizer([seq_1], [seq_2], padding=padding_state, truncation="only_second")
self.assertEqual(len(output["input_ids"][0]), model_max_length)
# Simple with no truncation
# Reset warnings
tokenizer.deprecation_warnings = {}
with self.assertLogs("transformers", level="WARNING") as cm:
output = tokenizer(seq_1, seq_2, padding=padding_state, truncation=False)
self.assertNotEqual(len(output["input_ids"]), model_max_length)
self.assertEqual(len(cm.records), 1)
self.assertTrue(
cm.records[0].message.startswith(
"Token indices sequence length is longer than the specified maximum sequence length for this model"
)
)
tokenizer.deprecation_warnings = {}
with self.assertLogs("transformers", level="WARNING") as cm:
output = tokenizer([seq_1], [seq_2], padding=padding_state, truncation=False)
self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
self.assertEqual(len(cm.records), 1)
self.assertTrue(
cm.records[0].message.startswith(
"Token indices sequence length is longer than the specified maximum sequence length for this model"
)
)
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=False,
stride=stride,
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), 2 + stride
) # No overflowing tokens when using 'longest' in python tokenizers
information = tokenizer.encode_plus(
seq_0,
seq_1,
max_length=len(sequence) - 2,
add_special_tokens=False,
stride=stride,
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["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), 2 + stride
) # 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=False,
stride=stride,
truncation="only_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_first_truncated["input_ids"][0]
overflowing_tokens = information_first_truncated["input_ids"][1]
self.assertEqual(len(information_first_truncated["input_ids"]), 2)
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, 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)
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"]
self.assertEqual(len(truncated_sequence), len(sequence) - 2)
self.assertEqual(truncated_sequence, truncated_second_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, seq1_tokens[-(2 + stride) :])
# 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"
# 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_split_into_words=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__}"):
# # Our mask token
# mask = "<mask>"
# # We take a single word in the middle of the vocabulary
# all_tokens = sorted(tokenizer.get_vocab().keys())
# word = tokenizer.decode(tokenizer.encode(all_tokens[len(all_tokens)//2], add_special_tokens=False)[:1])
# sequence_0 = "Encode " + word + " sequence"
# sequence_masked_0 = "Encode " + mask + " sequence"
# sequence_1 = word + " this sequence"
# sequence_masked_1 = mask + " this sequence"
# # Add tokens so that masked token isn't split
# # tokens = [AddedToken(t, lstrip=True, normalized=False) for t in sequence.split()]
# # tokenizer.add_tokens(tokens)
# tokenizer.add_special_tokens(
# {"mask_token": AddedToken(mask, normalized=False)}
# ) # Eat left space on Byte-level BPE tokenizers
# mask_ind = tokenizer.convert_tokens_to_ids(mask)
# # Test first masked sequence
# encoded_0 = tokenizer.encode(sequence_0, add_special_tokens=False)
# encoded_masked = tokenizer.encode(sequence_masked_0, add_special_tokens=False)
# assert len(encoded_masked) == len(encoded_0)
# mask_loc = encoded_masked.index(mask_ind)
# encoded_masked[mask_loc] = encoded_0[mask_loc]
# self.assertEqual(encoded_masked, encoded_0)
# # Test second masked sequence
# encoded_1 = tokenizer.encode(sequence_1, add_special_tokens=False)
# encoded_masked = tokenizer.encode(sequence_masked_1, add_special_tokens=False)
# assert len(encoded_masked) == len(encoded_1)
# mask_loc = encoded_masked.index(mask_ind)
# encoded_masked[mask_loc] = encoded_1[mask_loc]
# self.assertEqual(encoded_masked, encoded_1)
def test_special_tokens_mask(self):
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)
encoded_sequence_dict = tokenizer.encode_plus(
sequence_0, 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"]
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
filtered_sequence = [x for i, x in enumerate(encoded_sequence_w_special) if not special_tokens_mask[i]]
self.assertEqual(encoded_sequence, filtered_sequence)
def test_special_tokens_mask_input_pairs(self):
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,
)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
filtered_sequence = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special)
]
filtered_sequence = [x for x in filtered_sequence if x is not None]
self.assertEqual(encoded_sequence, filtered_sequence)
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
# 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
# RIGHT PADDING - 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, 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
# LEFT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
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, 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 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, 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, padding="longest")
padded_sequence_left_length = len(padded_sequence_left)
assert sequence_length == padded_sequence_left_length
assert encoded_sequence == padded_sequence_left
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)
# FIXME: the next line should be padding(max_length) to avoid warning
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_padding_to_multiple_of(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if tokenizer.pad_token is None:
self.skipTest("No padding token.")
else:
empty_tokens = tokenizer("", padding=True, pad_to_multiple_of=8)
normal_tokens = tokenizer("This is a sample input", padding=True, pad_to_multiple_of=8)
for key, value in empty_tokens.items():
self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
for key, value in normal_tokens.items():
self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
normal_tokens = tokenizer("This", pad_to_multiple_of=8)
for key, value in normal_tokens.items():
self.assertNotEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
# Should also work with truncation
normal_tokens = tokenizer("This", padding=True, truncation=True, pad_to_multiple_of=8)
for key, value in normal_tokens.items():
self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
# truncation to something which is not a multiple of pad_to_multiple_of raises an error
self.assertRaises(
ValueError,
tokenizer.__call__,
"This",
padding=True,
truncation=True,
max_length=12,
pad_to_multiple_of=8,
)
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
self._check_no_pad_token_padding(tokenizer, sequence)
padding_size = 10
padding_idx = tokenizer.pad_token_id
token_type_padding_idx = tokenizer.pad_token_type_id
encoded_sequence = tokenizer.encode_plus(sequence, return_special_tokens_mask=True)
input_ids = encoded_sequence["input_ids"]
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,
padding="max_length",
return_special_tokens_mask=True,
)
right_padded_input_ids = right_padded_sequence["input_ids"]
right_padded_special_tokens_mask = right_padded_sequence["special_tokens_mask"]
right_padded_sequence_length = len(right_padded_input_ids)
assert sequence_length + padding_size == right_padded_sequence_length
assert input_ids + [padding_idx] * padding_size == right_padded_input_ids
assert special_tokens_mask + [1] * padding_size == right_padded_special_tokens_mask
# Test left padding
tokenizer.padding_side = "left"
left_padded_sequence = tokenizer.encode_plus(
sequence,
max_length=sequence_length + padding_size,
padding="max_length",
return_special_tokens_mask=True,
)
left_padded_input_ids = left_padded_sequence["input_ids"]
left_padded_special_tokens_mask = left_padded_sequence["special_tokens_mask"]
left_padded_sequence_length = len(left_padded_input_ids)
assert sequence_length + padding_size == left_padded_sequence_length
assert [padding_idx] * padding_size + input_ids == left_padded_input_ids
assert [1] * padding_size + special_tokens_mask == left_padded_special_tokens_mask
if "token_type_ids" in tokenizer.model_input_names:
token_type_ids = encoded_sequence["token_type_ids"]
left_padded_token_type_ids = left_padded_sequence["token_type_ids"]
right_padded_token_type_ids = right_padded_sequence["token_type_ids"]
assert token_type_ids + [token_type_padding_idx] * padding_size == right_padded_token_type_ids
assert [token_type_padding_idx] * padding_size + token_type_ids == left_padded_token_type_ids
if "attention_mask" in tokenizer.model_input_names:
attention_mask = encoded_sequence["attention_mask"]
right_padded_attention_mask = right_padded_sequence["attention_mask"]
left_padded_attention_mask = left_padded_sequence["attention_mask"]
assert attention_mask + [0] * padding_size == right_padded_attention_mask
assert [0] * padding_size + attention_mask == left_padded_attention_mask
def test_separate_tokenizers(self):
# This tests that tokenizers don't impact others. Unfortunately the case where it fails is when
# we're loading an S3 configuration from a pre-trained identifier, and we have no way of testing those today.
tokenizer = self.get_tokenizer(random_argument=True)
assert tokenizer.init_kwargs["random_argument"] is True
new_tokenizer = self.get_tokenizer(random_argument=False)
assert tokenizer.init_kwargs["random_argument"] is True
assert new_tokenizer.init_kwargs["random_argument"] is False
def test_get_vocab(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
vocab_dict = tokenizer.get_vocab()
self.assertIsInstance(vocab_dict, dict)
self.assertGreaterEqual(len(tokenizer), len(vocab_dict))
vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
self.assertEqual(len(vocab), len(tokenizer))
tokenizer.add_tokens(["asdfasdfasdfasdf"])
vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
self.assertEqual(len(vocab), len(tokenizer))
def test_conversion_reversible(self):
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():
if word == tokenizer.unk_token:
continue
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
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) 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)
)
# 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, max_length=maximum_length, padding="max_length")
for sequence in sequences
]
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],
)
@require_tokenizers
def test_added_token_serializable(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
new_token = AddedToken("new_token", lstrip=True)
tokenizer.add_special_tokens({"additional_special_tokens": [new_token]})
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(tmp_dir_name)
tokenizer.from_pretrained(tmp_dir_name)
def test_batch_encode_plus_padding(self):
# Test that padded sequences are equivalent between batch_encode_plus and encode_plus
# Right padding tests
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",
]
max_length = 100
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, sequences)
encoded_sequences = [
tokenizer.encode_plus(sequence, max_length=max_length, padding="max_length")
for sequence in sequences
]
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
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",
"Testing batch encode plus with different sequence lengths",
"Testing batch encode plus with different sequence lengths correctly pads",
]
max_length = 100
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, sequences)
encoded_sequences = [
tokenizer.encode_plus(sequence, max_length=max_length, padding="max_length")
for sequence in sequences
]
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__}"):
if hasattr(tokenizer, "add_prefix_space") and not tokenizer.add_prefix_space:
continue
# 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_split_into_words=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_split_into_words=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_split_into_words=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_split_into_words=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_split_into_words=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_split_into_words=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_split_into_words=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_split_into_words=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_split_into_words=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_split_into_words=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_split_into_words=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_split_into_words=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])
def test_prepare_for_model(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
string_sequence = "Testing the prepare_for_model method."
ids = tokenizer.encode(string_sequence, add_special_tokens=False)
prepared_input_dict = tokenizer.prepare_for_model(ids, add_special_tokens=True)
input_dict = tokenizer.encode_plus(string_sequence, add_special_tokens=True)
self.assertEqual(input_dict, prepared_input_dict)
def test_batch_encode_plus_overflowing_tokens(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
string_sequences = ["Testing the prepare_for_model method.", "Test"]
if tokenizer.pad_token is None:
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
tokenizer.batch_encode_plus(
string_sequences, return_overflowing_tokens=True, truncation=True, padding=True, max_length=3
)
@is_pt_tf_cross_test
def test_batch_encode_plus_tensors(self):
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",
]
# A Tensor cannot be build by sequences which are not the same size
self.assertRaises(ValueError, tokenizer.batch_encode_plus, sequences, return_tensors="pt")
self.assertRaises(ValueError, tokenizer.batch_encode_plus, sequences, return_tensors="tf")
if tokenizer.pad_token_id is None:
self.assertRaises(
ValueError,
tokenizer.batch_encode_plus,
sequences,
padding=True,
return_tensors="pt",
)
self.assertRaises(
ValueError,
tokenizer.batch_encode_plus,
sequences,
padding="longest",
return_tensors="tf",
)
else:
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()
tensorflow_value = tensorflow_tensor[key].numpy().tolist()
encoded_value = encoded_sequences[key]
self.assertEqual(pytorch_value, tensorflow_value, encoded_value)
def _check_no_pad_token_padding(self, tokenizer, sequences):
# if tokenizer does not have pad_token_id, an error should be thrown
if tokenizer.pad_token_id is None:
with self.assertRaises(ValueError):
if isinstance(sequences, list):
tokenizer.batch_encode_plus(sequences, padding="longest")
else:
tokenizer.encode_plus(sequences, padding=True)
# add pad_token_id to pass subsequent tests
tokenizer.add_special_tokens({"pad_token": "<PAD>"})
def check_subword_sampling(
self,
tokenizer: PreTrainedTokenizer,
text: str = None,
) -> None:
"""
Check if the tokenizer generates different results when subword regularization is enabled.
Subword regularization augments training data with subword sampling.
This has a random component.
Args:
tokenizer: The tokenizer to check.
text: The text to use for the checks.
"""
text = "This is a test for subword regularization." if text is None else text
if self.test_sentencepiece_ignore_case:
text = text.lower()
tokens_list = []
for _ in range(5):
tokens_list.append(tokenizer.tokenize(text))
# the list of different pairs of tokens_list
combinations = itertools.combinations(tokens_list, 2)
# check of sampling is done
subword_sampling_found = False
for combination in combinations:
if combination[0] != combination[1]:
subword_sampling_found = True
self.assertTrue(subword_sampling_found)
# check if converting back to original text works
for tokens in tokens_list:
if self.test_sentencepiece_ignore_case:
self.assertEqual(text, tokenizer.convert_tokens_to_string(tokens).lower())
else:
self.assertEqual(text, tokenizer.convert_tokens_to_string(tokens))
@require_torch
@slow
def test_torch_encode_plus_sent_to_model(self):
import torch
from transformers import MODEL_MAPPING, TOKENIZER_MAPPING
MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING)
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
config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
config = config_class()
if config.is_encoder_decoder or config.pad_token_id is None:
return
model = model_class(config)
# 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
)
# Build sequence
first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
sequence = " ".join(first_ten_tokens)
encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="pt")
# Ensure that the BatchEncoding.to() method works.
encoded_sequence.to(model.device)
batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
# This should not fail
with torch.no_grad(): # saves some time
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)
@require_tf
@slow
def test_tf_encode_plus_sent_to_model(self):
from transformers import TF_MODEL_MAPPING, TOKENIZER_MAPPING
MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(TF_MODEL_MAPPING, TOKENIZER_MAPPING)
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
config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
config = config_class()
if config.is_encoder_decoder or config.pad_token_id is None:
return
model = model_class(config)
# Make sure the model contains at least the full vocabulary size in its embedding matrix
assert model.config.vocab_size >= len(tokenizer)
# Build sequence
first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
sequence = " ".join(first_ten_tokens)
encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="tf")
batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="tf")
# This should not fail
model(encoded_sequence)
model(batch_encoded_sequence)
# TODO: Check if require_torch is the best to test for numpy here ... Maybe move to require_flax when available
@require_torch
@slow
def test_np_encode_plus_sent_to_model(self):
from transformers import MODEL_MAPPING, TOKENIZER_MAPPING
MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING)
tokenizer = self.get_tokenizer()
if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
return
config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
config = config_class()
if config.is_encoder_decoder or config.pad_token_id is None:
return
# Build sequence
first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
sequence = " ".join(first_ten_tokens)
encoded_sequence = tokenizer.encode_plus(sequence, return_tensors="np")
batch_encoded_sequence = tokenizer.batch_encode_plus([sequence, sequence], return_tensors="np")
# TODO: add forward through JAX/Flax when PR is merged
# This is currently here to make flake8 happy !
if encoded_sequence is None:
raise ValueError("Cannot convert list to numpy tensor on encode_plus()")
if batch_encoded_sequence is None:
raise ValueError("Cannot convert list to numpy tensor on batch_encode_plus()")
if self.test_rust_tokenizer:
fast_tokenizer = self.get_rust_tokenizer()
encoded_sequence_fast = fast_tokenizer.encode_plus(sequence, return_tensors="np")
batch_encoded_sequence_fast = fast_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="np")
# TODO: add forward through JAX/Flax when PR is merged
# This is currently here to make flake8 happy !
if encoded_sequence_fast is None:
raise ValueError("Cannot convert list to numpy tensor on encode_plus() (fast)")
if batch_encoded_sequence_fast is None:
raise ValueError("Cannot convert list to numpy tensor on batch_encode_plus() (fast)")
@require_torch
def test_prepare_seq2seq_batch(self):
if not self.test_seq2seq:
return
tokenizer = self.get_tokenizer()
# Longer text that will definitely require truncation.
src_text = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
]
tgt_text = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei "
'pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu '
"vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
try:
batch = tokenizer.prepare_seq2seq_batch(
src_texts=src_text,
tgt_texts=tgt_text,
max_length=3,
max_target_length=10,
return_tensors="pt",
src_lang="en_XX", # this should be ignored (for all but mbart) but not cause an error
)
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.labels.shape[1], 10)
# max_target_length will default to max_length if not specified
batch = tokenizer.prepare_seq2seq_batch(src_text, tgt_texts=tgt_text, max_length=3, return_tensors="pt")
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.labels.shape[1], 3)
batch_encoder_only = tokenizer.prepare_seq2seq_batch(
src_texts=src_text, max_length=3, max_target_length=10, return_tensors="pt"
)
self.assertEqual(batch_encoder_only.input_ids.shape[1], 3)
self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3)
self.assertNotIn("decoder_input_ids", batch_encoder_only)
def test_is_fast(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Check is_fast is set correctly
self.assertFalse(tokenizer_p.is_fast)
self.assertTrue(tokenizer_r.is_fast)
def test_fast_only_inputs(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Ensure None raise an error
self.assertRaises(TypeError, tokenizer_r.tokenize, None)
self.assertRaises(TypeError, tokenizer_r.encode, None)
self.assertRaises(TypeError, tokenizer_r.encode_plus, None)
self.assertRaises(TypeError, tokenizer_r.batch_encode_plus, None)
def test_alignement_methods(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
words = ["Wonderful", "no", "inspiration", "example", "with", "subtoken"]
text = " ".join(words)
batch_size = 3
encoding = tokenizer_r.encode_plus(text, add_special_tokens=False)
batch_encoding = tokenizer_r.batch_encode_plus([text] * batch_size, add_special_tokens=False)
num_tokens = len(encoding["input_ids"])
last_word_index = len(words) - 1
last_token_index = num_tokens - 1
last_batch_index = batch_size - 1
last_char_index = len(text) - 1
# words, tokens
self.assertEqual(len(encoding.words(0)), num_tokens)
self.assertEqual(max(encoding.words(0)), last_word_index)
self.assertEqual(min(encoding.words(0)), 0)
self.assertEqual(len(batch_encoding.words(last_batch_index)), num_tokens)
self.assertEqual(max(batch_encoding.words(last_batch_index)), last_word_index)
self.assertEqual(min(batch_encoding.words(last_batch_index)), 0)
self.assertEqual(len(encoding.tokens(0)), num_tokens)
# Assert token_to_word
self.assertEqual(encoding.token_to_word(0), 0)
self.assertEqual(encoding.token_to_word(0, 0), 0)
self.assertEqual(encoding.token_to_word(last_token_index), last_word_index)
self.assertEqual(encoding.token_to_word(0, last_token_index), last_word_index)
self.assertEqual(batch_encoding.token_to_word(1, 0), 0)
self.assertEqual(batch_encoding.token_to_word(0, last_token_index), last_word_index)
self.assertEqual(batch_encoding.token_to_word(last_batch_index, last_token_index), last_word_index)
# Assert word_to_tokens
self.assertEqual(encoding.word_to_tokens(0).start, 0)
self.assertEqual(encoding.word_to_tokens(0, 0).start, 0)
self.assertEqual(encoding.word_to_tokens(last_word_index).end, last_token_index + 1)
self.assertEqual(encoding.word_to_tokens(0, last_word_index).end, last_token_index + 1)
self.assertEqual(batch_encoding.word_to_tokens(1, 0).start, 0)
self.assertEqual(batch_encoding.word_to_tokens(0, last_word_index).end, last_token_index + 1)
self.assertEqual(
batch_encoding.word_to_tokens(last_batch_index, last_word_index).end, last_token_index + 1
)
# Assert token_to_chars
self.assertEqual(encoding.token_to_chars(0).start, 0)
self.assertEqual(encoding.token_to_chars(0, 0).start, 0)
self.assertEqual(encoding.token_to_chars(last_token_index).end, last_char_index + 1)
self.assertEqual(encoding.token_to_chars(0, last_token_index).end, last_char_index + 1)
self.assertEqual(batch_encoding.token_to_chars(1, 0).start, 0)
self.assertEqual(batch_encoding.token_to_chars(0, last_token_index).end, last_char_index + 1)
self.assertEqual(
batch_encoding.token_to_chars(last_batch_index, last_token_index).end, last_char_index + 1
)
# Assert char_to_token
self.assertEqual(encoding.char_to_token(0), 0)
self.assertEqual(encoding.char_to_token(0, 0), 0)
self.assertEqual(encoding.char_to_token(last_char_index), last_token_index)
self.assertEqual(encoding.char_to_token(0, last_char_index), last_token_index)
self.assertEqual(batch_encoding.char_to_token(1, 0), 0)
self.assertEqual(batch_encoding.char_to_token(0, last_char_index), last_token_index)
self.assertEqual(batch_encoding.char_to_token(last_batch_index, last_char_index), last_token_index)
# Assert char_to_word
self.assertEqual(encoding.char_to_word(0), 0)
self.assertEqual(encoding.char_to_word(0, 0), 0)
self.assertEqual(encoding.char_to_word(last_char_index), last_word_index)
self.assertEqual(encoding.char_to_word(0, last_char_index), last_word_index)
self.assertEqual(batch_encoding.char_to_word(1, 0), 0)
self.assertEqual(batch_encoding.char_to_word(0, last_char_index), last_word_index)
self.assertEqual(batch_encoding.char_to_word(last_batch_index, last_char_index), last_word_index)
# Assert word_to_chars
self.assertEqual(encoding.word_to_chars(0).start, 0)
self.assertEqual(encoding.word_to_chars(0, 0).start, 0)
self.assertEqual(encoding.word_to_chars(last_word_index).end, last_char_index + 1)
self.assertEqual(encoding.word_to_chars(0, last_word_index).end, last_char_index + 1)
self.assertEqual(batch_encoding.word_to_chars(1, 0).start, 0)
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
)
# Assert token_to_sequence
self.assertEqual(encoding.token_to_sequence(num_tokens // 2), 0)
self.assertEqual(encoding.token_to_sequence(0, num_tokens // 2), 0)
self.assertEqual(batch_encoding.token_to_sequence(1, num_tokens // 2), 0)
self.assertEqual(batch_encoding.token_to_sequence(0, num_tokens // 2), 0)
self.assertEqual(batch_encoding.token_to_sequence(last_batch_index, num_tokens // 2), 0)
# Pair of input sequences
words = ["Wonderful", "no", "inspiration", "example", "with", "subtoken"]
text = " ".join(words)
pair_words = ["Amazing", "example", "full", "of", "inspiration"]
pair_text = " ".join(pair_words)
batch_size = 3
index_word_in_first_seq = words.index("inspiration")
index_word_in_pair_seq = pair_words.index("inspiration")
index_char_in_first_seq = text.find("inspiration")
index_char_in_pair_seq = pair_text.find("inspiration")
pair_encoding = tokenizer_r.encode_plus(text, pair_text, add_special_tokens=False)
pair_batch_encoding = tokenizer_r.batch_encode_plus(
[(text, pair_text)] * batch_size, add_special_tokens=False
)
num_tokens = len(encoding["input_ids"])
last_word_index = len(words) - 1
last_token_index = num_tokens - 1
last_batch_index = batch_size - 1
last_char_index = len(text) - 1
# Assert word_to_tokens
self.assertNotEqual(
pair_encoding.word_to_tokens(index_word_in_first_seq, sequence_index=0).start,
pair_encoding.word_to_tokens(index_word_in_pair_seq, sequence_index=1).start,
)
self.assertEqual(
pair_encoding["input_ids"][
pair_encoding.word_to_tokens(index_word_in_first_seq, sequence_index=0).start
],
pair_encoding["input_ids"][
pair_encoding.word_to_tokens(index_word_in_pair_seq, sequence_index=1).start
],
)
self.assertNotEqual(
pair_batch_encoding.word_to_tokens(1, index_word_in_first_seq, sequence_index=0).start,
pair_batch_encoding.word_to_tokens(1, index_word_in_pair_seq, sequence_index=1).start,
)
self.assertEqual(
pair_batch_encoding["input_ids"][1][
pair_batch_encoding.word_to_tokens(1, index_word_in_first_seq, sequence_index=0).start
],
pair_batch_encoding["input_ids"][1][
pair_batch_encoding.word_to_tokens(1, index_word_in_pair_seq, sequence_index=1).start
],
)
# Assert char_to_token
self.assertNotEqual(
pair_encoding.char_to_token(index_char_in_first_seq, sequence_index=0),
pair_encoding.char_to_token(index_char_in_pair_seq, sequence_index=1),
)
self.assertEqual(
pair_encoding["input_ids"][pair_encoding.char_to_token(index_char_in_first_seq, sequence_index=0)],
pair_encoding["input_ids"][pair_encoding.char_to_token(index_char_in_pair_seq, sequence_index=1)],
)
self.assertNotEqual(
pair_batch_encoding.char_to_token(1, index_char_in_first_seq, sequence_index=0),
pair_batch_encoding.char_to_token(1, index_char_in_pair_seq, sequence_index=1),
)
self.assertEqual(
pair_batch_encoding["input_ids"][1][
pair_batch_encoding.char_to_token(1, index_char_in_first_seq, sequence_index=0)
],
pair_batch_encoding["input_ids"][1][
pair_batch_encoding.char_to_token(1, index_char_in_pair_seq, sequence_index=1)
],
)
# Assert char_to_word
self.assertNotEqual(
pair_encoding.char_to_word(index_char_in_first_seq, sequence_index=0),
pair_encoding.char_to_word(index_char_in_pair_seq, sequence_index=1),
)
self.assertEqual(
words[pair_encoding.char_to_word(index_char_in_first_seq, sequence_index=0)],
pair_words[pair_encoding.char_to_word(index_char_in_pair_seq, sequence_index=1)],
)
self.assertNotEqual(
pair_batch_encoding.char_to_word(1, index_char_in_first_seq, sequence_index=0),
pair_batch_encoding.char_to_word(1, index_char_in_pair_seq, sequence_index=1),
)
self.assertEqual(
words[pair_batch_encoding.char_to_word(1, index_char_in_first_seq, sequence_index=0)],
pair_words[pair_batch_encoding.char_to_word(1, index_char_in_pair_seq, sequence_index=1)],
)
# Assert word_to_chars
self.assertNotEqual(
pair_encoding.word_to_chars(index_word_in_first_seq, sequence_index=0).start,
pair_encoding.word_to_chars(index_word_in_pair_seq, sequence_index=1).start,
)
self.assertEqual(
text[pair_encoding.word_to_chars(index_word_in_first_seq, sequence_index=0).start],
pair_text[pair_encoding.word_to_chars(index_word_in_pair_seq, sequence_index=1).start],
)
self.assertNotEqual(
pair_batch_encoding.word_to_chars(1, index_word_in_first_seq, sequence_index=0).start,
pair_batch_encoding.word_to_chars(1, index_word_in_pair_seq, sequence_index=1).start,
)
self.assertEqual(
text[pair_batch_encoding.word_to_chars(1, index_word_in_first_seq, sequence_index=0).start],
pair_text[pair_batch_encoding.word_to_chars(1, index_word_in_pair_seq, sequence_index=1).start],
)
# Assert token_to_sequence
pair_encoding = tokenizer_r.encode_plus(text, pair_text, add_special_tokens=True)
pair_sequence_ids = [
pair_encoding.token_to_sequence(i) for i in range(len(pair_encoding["input_ids"]))
]
self.assertIn(0, pair_sequence_ids)
self.assertIn(1, pair_sequence_ids)
if tokenizer_r.num_special_tokens_to_add(pair=True):
self.assertIn(None, pair_sequence_ids)
pair_batch_encoding = tokenizer_r.batch_encode_plus(
[(text, pair_text)] * batch_size, add_special_tokens=True
)
pair_batch_sequence_ids = [
pair_batch_encoding.token_to_sequence(1, i)
for i in range(len(pair_batch_encoding["input_ids"][0]))
]
self.assertIn(0, pair_batch_sequence_ids)
self.assertIn(1, pair_batch_sequence_ids)
if tokenizer_r.num_special_tokens_to_add(pair=True):
self.assertIn(None, pair_batch_sequence_ids)
def test_tokenization_python_rust_equals(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Ensure basic input match
input_p = tokenizer_p.encode_plus(self._data)
input_r = tokenizer_r.encode_plus(self._data)
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])
input_pairs_p = tokenizer_p.encode_plus(self._data, self._data)
input_pairs_r = tokenizer_r.encode_plus(self._data, self._data)
for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
self.assertSequenceEqual(input_pairs_p[key], input_pairs_r[key])
# Ensure truncation match
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, 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][0])
def test_num_special_tokens_to_add_equal(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Check we have the same number of added_tokens for both pair and non-pair inputs.
self.assertEqual(
tokenizer_r.num_special_tokens_to_add(False), tokenizer_p.num_special_tokens_to_add(False)
)
self.assertEqual(
tokenizer_r.num_special_tokens_to_add(True), tokenizer_p.num_special_tokens_to_add(True)
)
def test_max_length_equal(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Check we have the correct max_length for both pair and non-pair inputs.
self.assertEqual(tokenizer_r.max_len_single_sentence, tokenizer_p.max_len_single_sentence)
self.assertEqual(tokenizer_r.max_len_sentences_pair, tokenizer_p.max_len_sentences_pair)
def test_special_tokens_map_equal(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Assert the set of special tokens match.
self.assertSequenceEqual(
tokenizer_p.special_tokens_map.items(),
tokenizer_r.special_tokens_map.items(),
)
def test_add_tokens(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
vocab_size = len(tokenizer_r)
self.assertEqual(tokenizer_r.add_tokens(""), 0)
self.assertEqual(tokenizer_r.add_tokens("testoken"), 1)
self.assertEqual(tokenizer_r.add_tokens(["testoken1", "testtoken2"]), 2)
self.assertEqual(len(tokenizer_r), vocab_size + 3)
self.assertEqual(tokenizer_r.add_special_tokens({}), 0)
self.assertEqual(tokenizer_r.add_special_tokens({"bos_token": "[BOS]", "eos_token": "[EOS]"}), 2)
self.assertRaises(
AssertionError, tokenizer_r.add_special_tokens, {"additional_special_tokens": "<testtoken1>"}
)
self.assertEqual(tokenizer_r.add_special_tokens({"additional_special_tokens": ["<testtoken2>"]}), 1)
self.assertEqual(
tokenizer_r.add_special_tokens({"additional_special_tokens": ["<testtoken3>", "<testtoken4>"]}), 2
)
self.assertEqual(len(tokenizer_r), vocab_size + 8)
def test_offsets_mapping(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
text = "Wonderful no inspiration example with subtoken"
pair = "Along with an awesome pair"
# No pair
tokens_with_offsets = tokenizer_r.encode_plus(
text, return_special_tokens_mask=True, return_offsets_mapping=True, add_special_tokens=True
)
added_tokens = tokenizer_r.num_special_tokens_to_add(False)
offsets = tokens_with_offsets["offset_mapping"]
# Assert there is the same number of tokens and offsets
self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"]))
# Assert there is online added_tokens special_tokens
self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens)
# Pairs
tokens_with_offsets = tokenizer_r.encode_plus(
text, pair, return_special_tokens_mask=True, return_offsets_mapping=True, add_special_tokens=True
)
added_tokens = tokenizer_r.num_special_tokens_to_add(True)
offsets = tokens_with_offsets["offset_mapping"]
# Assert there is the same number of tokens and offsets
self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"]))
# Assert there is online added_tokens special_tokens
self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens)
def test_batch_encode_dynamic_overflowing(self):
"""
When calling batch_encode with multiple sequence it can returns different number of
overflowing encoding for each sequence:
[
Sequence 1: [Encoding 1, Encoding 2],
Sequence 2: [Encoding 1],
Sequence 3: [Encoding 1, Encoding 2, ... Encoding N]
]
This needs to be padded so that it can represented as a tensor
"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})"):
if is_torch_available():
returned_tensor = "pt"
elif is_tf_available():
returned_tensor = "tf"
else:
returned_tensor = "jax"
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,
)
for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
self.assertEqual(len(tokens[key].shape), 2)
# Mono sample
tokens = tokenizer.batch_encode_plus(
["HuggingFace is solving NLP one commit at a time"],
max_length=6,
padding=True,
truncation="only_first",
return_tensors=returned_tensor,
return_overflowing_tokens=True,
)
for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
self.assertEqual(len(tokens[key].shape), 2)
self.assertEqual(tokens[key].shape[-1], 6)
# Multi sample
tokens = tokenizer.batch_encode_plus(
["HuggingFace is solving NLP one commit at a time", "Very tiny input"],
max_length=6,
padding=True,
truncation="only_first",
return_tensors=returned_tensor,
return_overflowing_tokens=True,
)
for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
self.assertEqual(len(tokens[key].shape), 2)
self.assertEqual(tokens[key].shape[-1], 6)
def test_compare_pretokenized_inputs(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
if hasattr(tokenizer_p, "add_prefix_space") and not tokenizer_p.add_prefix_space:
continue # Too hard to test for now
# 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_split_into_words=True, add_special_tokens=False
)
output_p = tokenizer_p.encode(
pretokenized_input_simple, is_split_into_words=True, add_special_tokens=False
)
self.assertEqual(output_p, output_r)
kwargs = {
"is_split_into_words": True,
# "return_token_type_ids": True, # Use the defaults for each tokenizers
# "return_attention_mask": True, # Use the defaults for each tokenizers
"return_overflowing_tokens": False,
"return_special_tokens_mask": True,
"return_offsets_mapping": False, # Not implemented in python tokenizers
# "add_special_tokens": False,
}
batch_kwargs = {
"is_split_into_words": True,
# "return_token_type_ids": True, # Use the defaults for each tokenizers
# "return_attention_mask": True, # Use the defaults for each tokenizers
"return_overflowing_tokens": False,
"return_special_tokens_mask": True,
"return_offsets_mapping": False, # Not implemented in python tokenizers
# "add_special_tokens": False,
}
# 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, **batch_kwargs)
output_p = tokenizer_p.batch_encode_plus(input_batch, **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_split_into_words=True
)
output_p = tokenizer_p.encode(
pretokenized_input_simple, pretokenized_input_pair, is_split_into_words=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, **batch_kwargs)
output_p = tokenizer_p.batch_encode_plus(input_batch_pair, **batch_kwargs)
for key in output_p.keys():
self.assertEqual(output_p[key], output_r[key])
def test_create_token_type_ids(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
input_simple = [1, 2, 3]
input_pair = [1, 2, 3]
# Generate output
output_r = tokenizer_r.create_token_type_ids_from_sequences(input_simple)
output_p = tokenizer_p.create_token_type_ids_from_sequences(input_simple)
self.assertEqual(output_p, output_r)
# Generate pair output
output_r = tokenizer_r.create_token_type_ids_from_sequences(input_simple, input_pair)
output_p = tokenizer_p.create_token_type_ids_from_sequences(input_simple, input_pair)
self.assertEqual(output_p, output_r)
def test_build_inputs_with_special_tokens(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# # Input string
# input_simple = tokenizer_p.tokenize("This is a sample input", add_special_tokens=False)
# input_pair = tokenizer_p.tokenize("This is a sample pair", add_special_tokens=False)
# # Generate output
# output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple)
# output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple)
# self.assertEqual(output_p, output_r)
# # Generate pair output
# output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple, input_pair)
# output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple, input_pair)
# self.assertEqual(output_p, output_r)
# Input tokens id
input_simple = tokenizer_p.encode("This is a sample input", add_special_tokens=False)
input_pair = tokenizer_p.encode("This is a sample pair", add_special_tokens=False)
# Generate output
output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple)
output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple)
self.assertEqual(output_p, output_r)
# Generate pair output
output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple, input_pair)
output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple, input_pair)
self.assertEqual(output_p, output_r)
def test_padding(self, max_length=50):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
pad_token_id = tokenizer_p.pad_token_id
# 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)
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
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")
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.encode("This is a simple input", padding="longest")
input_p = tokenizer_p.encode("This is a simple input", padding=True)
self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
# 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
)
input_p = tokenizer_p.encode(
"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
)
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
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"
)
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
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")
self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
# 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
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
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"
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
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)
self.assert_padded_input_match(
input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
)
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
)
input_p = tokenizer_p.encode_plus(
"This is a simple input", "This is a pair", max_length=max_length, pad_to_max_length=True
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
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"
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
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)
self.assert_padded_input_match(
input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
# 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,
)
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
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",
)
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
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,
)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
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
)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
# 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",
)
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
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"),
],
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"),
],
padding="longest",
)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
# 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)
self.assert_padded_input_match(
input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
)
# 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")
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
# 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)
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)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
# 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")
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
def test_padding_different_model_input_name(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
pad_token_id = tokenizer_p.pad_token_id
input_r = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
input_p = tokenizer_r.batch_encode_plus(
["This is a input 1", "This is a much longer input whilch should be padded"]
)
# rename encoded batch to "inputs"
input_r["inputs"] = input_r[tokenizer_r.model_input_names[0]]
del input_r[tokenizer_r.model_input_names[0]]
input_p["inputs"] = input_p[tokenizer_p.model_input_names[0]]
del input_p[tokenizer_p.model_input_names[0]]
# Renaming `input_ids` to `inputs`
tokenizer_r.model_input_names = ["inputs"] + tokenizer_r.model_input_names[1:]
tokenizer_p.model_input_names = ["inputs"] + tokenizer_p.model_input_names[1:]
input_r = tokenizer_r.pad(input_r, padding="longest")
input_p = tokenizer_r.pad(input_p, padding="longest")
max_length = len(input_p["inputs"][0])
self.assert_batch_padded_input_match(
input_r, input_p, max_length, pad_token_id, model_main_input_name="inputs"
)
def test_save_pretrained(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=True
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=False
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
def test_embeded_special_tokens(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
sentence = "A, <mask> AllenNLP sentence."
tokens_r = tokenizer_r.encode_plus(
sentence,
add_special_tokens=True,
)
tokens_p = tokenizer_p.encode_plus(
sentence,
add_special_tokens=True,
)
for key in tokens_p.keys():
self.assertEqual(tokens_r[key], tokens_p[key])
if "token_type_ids" in tokens_r:
self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))
tokens_r = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
tokens_p = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
self.assertSequenceEqual(tokens_r, tokens_p)
def test_compare_add_special_tokens(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
simple_num_special_tokens_to_add = tokenizer_r.num_special_tokens_to_add(pair=False)
# pair_num_special_tokens_to_add = tokenizer_r.num_special_tokens_to_add(pair=True)
for text in ["", " "]:
# tokenize()
no_special_tokens = tokenizer_r.tokenize(text, add_special_tokens=False)
with_special_tokens = tokenizer_r.tokenize(text, add_special_tokens=True)
self.assertEqual(
len(no_special_tokens), len(with_special_tokens) - simple_num_special_tokens_to_add
)
# encode()
no_special_tokens = tokenizer_r.encode(text, add_special_tokens=False)
with_special_tokens = tokenizer_r.encode(text, add_special_tokens=True)
self.assertEqual(
len(no_special_tokens), len(with_special_tokens) - simple_num_special_tokens_to_add
)
# encode_plus()
no_special_tokens = tokenizer_r.encode_plus(text, add_special_tokens=False)
with_special_tokens = tokenizer_r.encode_plus(text, add_special_tokens=True)
for key in no_special_tokens.keys():
self.assertEqual(
len(no_special_tokens[key]),
len(with_special_tokens[key]) - simple_num_special_tokens_to_add,
)
# # batch_encode_plus
no_special_tokens = tokenizer_r.batch_encode_plus([text, text], add_special_tokens=False)
with_special_tokens = tokenizer_r.batch_encode_plus([text, text], add_special_tokens=True)
for key in no_special_tokens.keys():
for i_no, i_with in zip(no_special_tokens[key], with_special_tokens[key]):
self.assertEqual(len(i_no), len(i_with) - simple_num_special_tokens_to_add)
def test_compare_prepare_for_model(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
string_sequence = "Asserting that both tokenizers are equal"
python_output = tokenizer_p.prepare_for_model(
tokenizer_p.encode(string_sequence, add_special_tokens=False)
)
rust_output = tokenizer_r.prepare_for_model(
tokenizer_r.encode(string_sequence, add_special_tokens=False)
)
for key in python_output:
self.assertEqual(python_output[key], rust_output[key])
def test_special_tokens_initialization(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
added_tokens = [AddedToken("<special>", lstrip=True)]
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True
)
tokenizer_p = self.tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
p_output = tokenizer_p.encode("Hey this is a <special> token")
r_output = tokenizer_r.encode("Hey this is a <special> token")
cr_output = tokenizer_cr.encode("Hey this is a <special> token")
special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
self.assertEqual(p_output, r_output)
self.assertEqual(cr_output, r_output)
self.assertTrue(special_token_id in p_output)
self.assertTrue(special_token_id in r_output)
self.assertTrue(special_token_id in cr_output)
@is_staging_test
class TokenizerPushToHubTester(unittest.TestCase):
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
@classmethod
def setUpClass(cls):
cls._api = HfApi(endpoint=ENDPOINT_STAGING)
cls._token = cls._api.login(username=USER, password=PASS)
@classmethod
def tearDownClass(cls):
try:
cls._api.delete_repo(token=cls._token, name="test-tokenizer")
except HTTPError:
pass
try:
cls._api.delete_repo(token=cls._token, name="test-tokenizer-org", organization="valid_org")
except HTTPError:
pass
def test_push_to_hub(self):
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
tokenizer = BertTokenizer(vocab_file)
tokenizer.save_pretrained(
tmp_dir, push_to_hub=True, repo_name="test-tokenizer", use_auth_token=self._token
)
new_tokenizer = BertTokenizer.from_pretrained(f"{USER}/test-tokenizer")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
def test_push_to_hub_in_organization(self):
with tempfile.TemporaryDirectory() as tmp_dir:
vocab_file = os.path.join(tmp_dir, "vocab.txt")
with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
tokenizer = BertTokenizer(vocab_file)
tokenizer.save_pretrained(
tmp_dir,
push_to_hub=True,
repo_name="test-tokenizer-org",
use_auth_token=self._token,
organization="valid_org",
)
new_tokenizer = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org")
self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
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