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transformers/tests/models/bert_generation/__init__.py Normal file
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transformers/tests/models/bert_generation/test_modeling_bert_generation.py Normal file
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# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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 tempfile
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder, DataCollatorWithFlattening
class BertGenerationEncoderTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=50,
initializer_range=0.02,
use_labels=True,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.use_labels = use_labels
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
if self.use_labels:
token_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.get_config()
return config, input_ids, input_mask, token_labels
def get_config(self):
return BertGenerationConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
is_decoder=False,
initializer_range=self.initializer_range,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
input_mask,
token_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self,
config,
input_ids,
input_mask,
token_labels,
**kwargs,
):
model = BertGenerationEncoder(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
**kwargs,
):
config.add_cross_attention = True
model = BertGenerationEncoder(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
**kwargs,
):
config.is_decoder = True
config.add_cross_attention = True
model = BertGenerationDecoder(config=config).to(torch_device).eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
input_mask,
token_labels,
*args,
):
model = BertGenerationDecoder(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def prepare_config_and_inputs_for_common(self):
config, input_ids, input_mask, token_labels = self.prepare_config_and_inputs()
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class BertGenerationEncoderTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
if is_torch_available()
else {}
)
# Overwriting to add `is_decoder` flag
def prepare_config_and_inputs_for_generate(self, batch_size=2):
config, inputs = super().prepare_config_and_inputs_for_generate(batch_size)
config.is_decoder = True
return config, inputs
def setUp(self):
self.model_tester = BertGenerationEncoderTester(self)
self.config_tester = ConfigTester(self, config_class=BertGenerationConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_as_bert(self):
config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs()
config.model_type = "bert"
self.model_tester.create_and_check_model(config, input_ids, input_mask, token_labels)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
(
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
model = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
self.assertIsNotNone(model)
def attention_mask_padding_matches_padding_free_with_position_ids(
self, attn_implementation: str, fa_kwargs: bool = False
):
"""
Overwritten to account for the embeddings that rely on position ids.
"""
if not self.has_attentions:
self.skipTest(reason="Model architecture does not support attentions")
max_new_tokens = 30
support_flag = {
"sdpa": "_supports_sdpa",
"flash_attention_2": "_supports_flash_attn",
"flash_attention_3": "_supports_flash_attn",
}
for model_class in self.all_generative_model_classes:
if attn_implementation != "eager" and not getattr(model_class, support_flag[attn_implementation]):
self.skipTest(f"{model_class.__name__} does not support {attn_implementation}")
# can't infer if new attn mask API is supported by assume that only model with attention backend support it
if not model_class._supports_attention_backend:
self.skipTest(f"{model_class.__name__} does not support new attention mask API")
if model_class._is_stateful: # non-transformer models most probably have no packing support
self.skipTest(f"{model_class.__name__} doesn't support packing!")
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if config.is_encoder_decoder:
self.skipTest("Model is an encoder-decoder")
if 0 not in inputs_dict.get("attention_mask", []) or "attention_mask" not in inputs_dict:
self.skipTest("Model dummy inputs should contain padding in their attention mask")
if "input_ids" not in inputs_dict or inputs_dict["input_ids"].ndim != 2:
self.skipTest("Model dummy inputs should contain text input ids")
# make sure that all models have enough positions for generation
dummy_input_ids = inputs_dict["input_ids"]
if hasattr(config, "max_position_embeddings"):
config.max_position_embeddings = max_new_tokens + dummy_input_ids.shape[1] + 1
model = model_class(config)
if "position_ids" not in inspect.signature(model.forward).parameters:
self.skipTest("Model does not support position_ids")
if (not fa_kwargs) and "position_ids" not in inspect.signature(model.forward).parameters:
continue # this model doesn't accept position ids as input
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
# Drop all keys except for the minimal set. Hard to manipulate with multimodals/head_mask/etc
inputs_dict = {k: v for k, v in inputs_dict.items() if k in ["input_ids", "attention_mask"]}
# Ensure left padding, to adapt for some models
if 0 in inputs_dict["attention_mask"][:, -1]:
inputs_dict["attention_mask"] = inputs_dict["attention_mask"].flip(1)
dummy_attention_mask = inputs_dict["attention_mask"]
dummy_input_ids[~dummy_attention_mask.bool()] = config.get_text_config().pad_token_id
# Main difference to other models, we need to prepare position ids according to the attention mask
# as we use it to extract embeddings that rely on the correct position - naively increasing sequences do
# not suffice anymore atp. The solution here calculates an increasing sequences for all 1s and puts 0s else.
inputs_dict["position_ids"] = ((inputs_dict["attention_mask"] == 1).long().cumsum(dim=1) - 1) * (
inputs_dict["attention_mask"] == 1
).long()
model = (
model_class.from_pretrained(
tmpdirname,
dtype=torch.bfloat16,
attn_implementation=attn_implementation,
)
.to(torch_device)
.eval()
)
if fa_kwargs:
# flatten
features = [
{"input_ids": i[a.bool()].tolist()} for i, a in zip(dummy_input_ids, dummy_attention_mask)
]
# add position_ids + fa_kwargs
data_collator = DataCollatorWithFlattening(return_tensors="pt", return_flash_attn_kwargs=True)
batch = data_collator(features)
padfree_inputs_dict = {
k: t.to(torch_device) if torch.is_tensor(t) else t for k, t in batch.items()
}
else:
# create packed position_ids
position_ids = (
torch.cat([torch.arange(length) for length in dummy_attention_mask.sum(1).tolist()])
.long()
.unsqueeze(0)
.to(torch_device)
)
padfree_inputs_dict = {
"input_ids": dummy_input_ids[dummy_attention_mask.bool()].unsqueeze(0),
"position_ids": position_ids,
}
# We need to do simple forward without cache in order to trigger packed SDPA/flex/eager attention path
res_padded = model(**inputs_dict, use_cache=False)
res_padfree = model(**padfree_inputs_dict, use_cache=False)
logits_padded = res_padded.logits[dummy_attention_mask.bool()]
logits_padfree = res_padfree.logits[0]
# acceptable numerical instability
tol = torch.finfo(torch.bfloat16).eps
torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol)
@require_torch
class BertGenerationEncoderIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head_absolute_embedding(self):
model = BertGenerationEncoder.from_pretrained(
"google/bert_for_seq_generation_L-24_bbc_encoder", attn_implementation="eager"
)
input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]])
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size([1, 8, 1024])
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]
)
torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
@require_torch
class BertGenerationDecoderIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head_absolute_embedding(self):
model = BertGenerationDecoder.from_pretrained(
"google/bert_for_seq_generation_L-24_bbc_encoder", attn_implementation="eager"
)
input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]])
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size([1, 8, 50358])
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]
)
torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)

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transformers/tests/models/bert_generation/test_tokenization_bert_generation.py Normal file
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# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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 unittest
from functools import cached_property
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
SPIECE_UNDERLINE = "▁"
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class BertGenerationTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
from_pretrained_id = "google/bert_for_seq_generation_L-24_bbc_encoder"
tokenizer_class = BertGenerationTokenizer
test_rust_tokenizer = False
test_sentencepiece = True
@classmethod
def setUpClass(cls):
super().setUpClass()
tokenizer = BertGenerationTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(cls.tmpdirname)
def test_convert_token_and_id(self):
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
token = "<s>"
token_id = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
def test_get_vocab(self):
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], "<unk>")
self.assertEqual(vocab_keys[1], "<s>")
self.assertEqual(vocab_keys[-1], "<pad>")
self.assertEqual(len(vocab_keys), 1_002)
def test_vocab_size(self):
self.assertEqual(self.get_tokenizer().vocab_size, 1_000)
def test_full_tokenizer(self):
tokenizer = BertGenerationTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[285, 46, 10, 170, 382],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
@cached_property
def big_tokenizer(self):
return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
@slow
def test_tokenization_base_easy_symbols(self):
symbols = "Hello World!"
original_tokenizer_encodings = [18536, 2260, 101]
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@slow
def test_tokenization_base_hard_symbols(self):
symbols = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
" add words that should not exist and be tokenized to <unk>, such as saoneuhaoesuth"
)
original_tokenizer_encodings = [
871,
419,
358,
946,
991,
2521,
452,
358,
1357,
387,
7751,
3536,
112,
985,
456,
126,
865,
938,
5400,
5734,
458,
1368,
467,
786,
2462,
5246,
1159,
633,
865,
4519,
457,
582,
852,
2557,
427,
916,
508,
405,
34324,
497,
391,
408,
11342,
1244,
385,
100,
938,
985,
456,
574,
362,
12597,
3200,
3129,
1172,
]
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@require_torch
@slow
def test_torch_encode_plus_sent_to_model(self):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
first_ten_tokens = list(self.big_tokenizer.get_vocab().keys())[:10]
sequence = " ".join(first_ten_tokens)
encoded_sequence = self.big_tokenizer.encode_plus(sequence, return_tensors="pt", return_token_type_ids=False)
batch_encoded_sequence = self.big_tokenizer.batch_encode_plus(
[sequence + " " + sequence], return_tensors="pt", return_token_type_ids=False
)
config = BertGenerationConfig()
model = BertGenerationEncoder(config)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**encoded_sequence)
model(**batch_encoded_sequence)
@slow
def test_tokenizer_integration(self):
expected_encoding = {'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip
self.tokenizer_integration_test_util(
expected_encoding=expected_encoding,
model_name="google/bert_for_seq_generation_L-24_bbc_encoder",
revision="c817d1fd1be2ffa69431227a1fe320544943d4db",
)