def setup_class(self):
self.processor = QnliProcessor()
# Note: these tests do not download automatically test datasets. Please download them manually and update your
# environment variables accordingly
self.examples = self.processor.get_train_examples(
os.environ["QNLI_PATH"])
self.test_dir = Path(tempfile.mkdtemp())
def setup_class(self):
self.processor = QnliProcessor()
self.test_dir = Path(tempfile.mkdtemp())
qnli_url = 'https://dl.fbaipublicfiles.com/glue/data/QNLIv2.zip'
contents = requests.get(qnli_url)
(self.test_dir / 'QNLI.zip').open('wb').write(contents.content)
with ZipFile(self.test_dir / 'QNLI.zip', 'r') as zipObj:
zipObj.extractall(self.test_dir)
self.examples = self.processor.get_train_examples(self.test_dir / 'QNLI')
sentence_piece_url = 'https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-spiece.model'
contents = requests.get(sentence_piece_url)
(self.test_dir / 'spiece.model').open('wb').write(contents.content)
def setup_class(self):
self.processor = QnliProcessor()
self.test_dir = Path(tempfile.mkdtemp())
qnli_url = 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601'
contents = requests.get(qnli_url)
(self.test_dir / 'QNLI.zip').open('wb').write(contents.content)
with ZipFile(self.test_dir / 'QNLI.zip', 'r') as zipObj:
zipObj.extractall(self.test_dir)
self.examples = self.processor.get_train_examples(self.test_dir / 'QNLI')
sentence_piece_url = 'https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-spiece.model'
contents = requests.get(sentence_piece_url)
(self.test_dir / 'spiece.model').open('wb').write(contents.content)
class TestTokenizationSST2:
def setup_class(self):
self.processor = QnliProcessor()
# Note: these tests do not download automatically test datasets. Please download them manually and update your
# environment variables accordingly
self.examples = self.processor.get_train_examples(
os.environ["QNLI_PATH"])
self.test_dir = Path(tempfile.mkdtemp())
def test_tokenization_bert(self):
# Given
self.base_tokenizer = BertTokenizer.from_pretrained(
'bert-base-uncased', do_lower_case=True, cache_dir=self.test_dir)
self.rust_tokenizer = PyBertTokenizer(
get_from_cache(
self.base_tokenizer.pretrained_vocab_files_map['vocab_file']
['bert-base-uncased']))
output_baseline = []
for example in self.examples:
output_baseline.append(
self.base_tokenizer.encode_plus(
example.text_a,
text_pair=example.text_b,
add_special_tokens=True,
return_overflowing_tokens=True,
return_special_tokens_mask=True,
max_length=128))
# When
output_rust = self.rust_tokenizer.encode_pair_list(
[(example.text_a, example.text_b) for example in self.examples],
max_len=128,
truncation_strategy='longest_first',
stride=0)
# Then
for rust, baseline in zip(output_rust, output_baseline):
assert (rust.token_ids == baseline['input_ids'])
assert (rust.segment_ids == baseline['token_type_ids'])
assert (
rust.special_tokens_mask == baseline['special_tokens_mask'])
def test_tokenization_distilbert(self):
# Given
self.base_tokenizer = DistilBertTokenizer.from_pretrained(
'distilbert-base-uncased',
do_lower_case=True,
cache_dir=self.test_dir)
self.rust_tokenizer = PyBertTokenizer(
get_from_cache(
self.base_tokenizer.pretrained_vocab_files_map['vocab_file']
['distilbert-base-uncased']))
output_baseline = []
for example in self.examples:
output_baseline.append(
self.base_tokenizer.encode_plus(
example.text_a,
text_pair=example.text_b,
add_special_tokens=True,
return_overflowing_tokens=True,
return_special_tokens_mask=True,
max_length=128))
# When
output_rust = self.rust_tokenizer.encode_pair_list(
[(example.text_a, example.text_b) for example in self.examples],
max_len=128,
truncation_strategy='longest_first',
stride=0)
# Then
for rust, baseline in zip(output_rust, output_baseline):
assert (rust.token_ids == baseline['input_ids'])
assert (rust.segment_ids == baseline['token_type_ids'])
assert (
rust.special_tokens_mask == baseline['special_tokens_mask'])
class TestTokenizationQNLI:
def setup_class(self):
self.processor = QnliProcessor()
self.test_dir = Path(tempfile.mkdtemp())
qnli_url = 'https://dl.fbaipublicfiles.com/glue/data/QNLIv2.zip'
contents = requests.get(qnli_url)
(self.test_dir / 'QNLI.zip').open('wb').write(contents.content)
with ZipFile(self.test_dir / 'QNLI.zip', 'r') as zipObj:
zipObj.extractall(self.test_dir)
self.examples = self.processor.get_train_examples(self.test_dir / 'QNLI')
sentence_piece_url = 'https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-spiece.model'
contents = requests.get(sentence_piece_url)
(self.test_dir / 'spiece.model').open('wb').write(contents.content)
def test_tokenization_bert(self):
# Given
self.base_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased',
do_lower_case=True,
cache_dir=self.test_dir)
self.rust_tokenizer = PyBertTokenizer(
get_from_cache(self.base_tokenizer.pretrained_vocab_files_map['vocab_file']['bert-base-uncased']),
do_lower_case=True,
strip_accents=True)
output_baseline = []
for example in self.examples:
output_baseline.append(self.base_tokenizer.encode_plus(example.text_a,
text_pair=example.text_b,
add_special_tokens=True,
return_overflowing_tokens=True,
return_special_tokens_mask=True,
max_length=128))
# When
output_rust = self.rust_tokenizer.encode_pair_list(
[(example.text_a, example.text_b) for example in self.examples],
max_len=128,
truncation_strategy='longest_first',
stride=0)
# Then
for idx, (rust, baseline) in enumerate(zip(output_rust, output_baseline)):
assert rust.token_ids == baseline[
'input_ids'], f'Difference in tokenization for {self.rust_tokenizer.__class__}: \n ' \
f'Sentence a: {self.examples[idx].text_a} \n' \
f'Sentence b: {self.examples[idx].text_b} \n' \
f'Token mismatch: {self.get_token_diff(rust.token_ids, baseline["input_ids"])} \n' \
f'Rust: {rust.token_ids} \n' \
f'Python {baseline["input_ids"]}'
assert (rust.segment_ids == baseline['token_type_ids'])
assert (rust.special_tokens_mask == baseline['special_tokens_mask'])
def test_tokenization_distilbert(self):
# Given
self.base_tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased',
do_lower_case=False,
cache_dir=self.test_dir)
self.rust_tokenizer = PyBertTokenizer(
get_from_cache(self.base_tokenizer.pretrained_vocab_files_map['vocab_file']['distilbert-base-cased']),
do_lower_case=False,
strip_accents=False)
output_baseline = []
for example in self.examples:
output_baseline.append(self.base_tokenizer.encode_plus(example.text_a,
text_pair=example.text_b,
add_special_tokens=True,
return_overflowing_tokens=True,
return_special_tokens_mask=True,
max_length=128))
# When
output_rust = self.rust_tokenizer.encode_pair_list(
[(example.text_a, example.text_b) for example in self.examples],
max_len=128,
truncation_strategy='longest_first',
stride=0)
# Then
for idx, (rust, baseline) in enumerate(zip(output_rust, output_baseline)):
assert rust.token_ids == baseline[
'input_ids'], f'Difference in tokenization for {self.rust_tokenizer.__class__}: \n ' \
f'Sentence a: {self.examples[idx].text_a} \n' \
f'Sentence b: {self.examples[idx].text_b} \n' \
f'Token mismatch: {self.get_token_diff(rust.token_ids, baseline["input_ids"])} \n' \
f'Rust: {rust.token_ids} \n' \
f'Python {baseline["input_ids"]}'
def test_tokenization_sentence_piece(self):
# Given
self.base_tokenizer = sentencepiece.SentencePieceProcessor()
self.base_tokenizer.Load(str(self.test_dir / 'spiece.model'))
self.rust_tokenizer = PySentencePieceTokenizer(str(self.test_dir / 'spiece.model'), do_lower_case=False)
output_baseline = []
for example in self.examples:
output_baseline.append(self.base_tokenizer.EncodeAsIds(example.text_a))
# When
# Note: the original sentence piece tokenizer strips trailing spaces and deletes consecutive spaces
output_rust = self.rust_tokenizer.encode_list(
[re.sub(' +', ' ', example.text_a.strip()) for example in self.examples],
max_len=256,
truncation_strategy='longest_first',
stride=0)
# Then
for idx, (rust, baseline) in enumerate(zip(output_rust, output_baseline)):
if rust.token_ids != baseline:
assert sum(self.base_tokenizer.get_score(baseline)) == \
sum(self.base_tokenizer.get_score(rust.token_ids)), \
f'Difference in tokenization for {self.rust_tokenizer.__class__}: \n ' \
f'Sentence a: {self.examples[idx].text_a} \n' \
f'Sentence b: {self.examples[idx].text_b} \n' \
f'Token mismatch: {self.get_token_diff_sentence_piece(rust.token_ids, baseline)} \n' \
f'Rust: {rust.token_ids} \n' \
f'Python {baseline}'
def get_token_diff(self, rust_tokens, python_tokens):
last_index = 1
first_index = 0
max_index = min(len(rust_tokens), len(python_tokens))
while rust_tokens[first_index] == python_tokens[first_index] and first_index < max_index - 1:
first_index += 1
first_index -= 1
while rust_tokens[-last_index] == python_tokens[-last_index] and first_index < max_index - 1:
last_index += 1
last_index += 1
python_last_index = len(python_tokens) + last_index
rust_last_index = len(rust_tokens) + last_index
rust_tokens_diff = rust_tokens[first_index:rust_last_index]
python_token_diff = python_tokens[first_index:python_last_index]
rust_decoded_tokens = self.base_tokenizer.convert_ids_to_tokens(rust_tokens_diff)
python_decoded_tokens = self.base_tokenizer.convert_ids_to_tokens(python_token_diff)
return rust_decoded_tokens, python_decoded_tokens
def get_token_diff_sentence_piece(self, rust_tokens, python_tokens):
last_index = 1
first_index = 0
max_index = min(len(rust_tokens), len(python_tokens))
while rust_tokens[first_index] == python_tokens[first_index] and first_index < max_index - 1:
first_index += 1
first_index -= 1
while rust_tokens[-last_index] == python_tokens[-last_index] and last_index < max_index - 1:
last_index += 1
last_index += 1
python_last_index = len(python_tokens) + last_index
rust_last_index = len(rust_tokens) + last_index
rust_tokens_diff = rust_tokens[first_index:rust_last_index]
python_token_diff = python_tokens[first_index:python_last_index]
rust_decoded_tokens = self.base_tokenizer.DecodeIds(rust_tokens_diff)
python_decoded_tokens = self.base_tokenizer.DecodeIds(python_token_diff)
return rust_decoded_tokens, python_decoded_tokens