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from unittest import TestCase
import torch
from torch.utils.data import DataLoader
from transformers import AutoConfig, AutoTokenizer
import autoprompt.utils as utils
class TestEncodeLabel(TestCase):
def setUp(self):
self._tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
def test_single_token(self):
output = utils.encode_label(self._tokenizer, 'the')
expected_output = torch.tensor([self._tokenizer.convert_tokens_to_ids(['the'])])
assert torch.equal(output, expected_output)
def test_multiple_tokens(self):
output = utils.encode_label(self._tokenizer, ['a', 'the'])
expected_output = torch.tensor([
self._tokenizer.convert_tokens_to_ids(['a', 'the'])
])
assert torch.equal(output, expected_output)
class TestTriggerTemplatizer(TestCase):
def setUp(self):
self.default_template = '[T] [T] {arbitrary} [T] {fields} [P]'
self.default_config = AutoConfig.from_pretrained('bert-base-cased')
self.default_tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
utils.add_task_specific_tokens(self.default_tokenizer)
self.default_instance = {
'arbitrary': 'does this',
'fields': 'work',
'label': 'and'
}
def test_bert(self):
templatizer = utils.TriggerTemplatizer(
self.default_template,
self.default_config,
self.default_tokenizer,
add_special_tokens=False
)
model_inputs, label = templatizer(self.default_instance)
# Label should be mapped to its token id
expected_label = torch.tensor([self.default_tokenizer.convert_tokens_to_ids([self.default_instance['label']])])
assert torch.equal(expected_label, label)
# For BERT ouput is expected to have the following keys
assert 'input_ids' in model_inputs
assert 'token_type_ids' in model_inputs
assert 'attention_mask' in model_inputs
# Test that the custom masks match our expectations
expected_trigger_mask = torch.tensor(
[[True, True, False, False, True, False, False]]
)
assert torch.equal(expected_trigger_mask, model_inputs['trigger_mask'])
expected_predict_mask = torch.tensor(
[[False, False, False, False, False, False, True]]
)
assert torch.equal(expected_predict_mask, model_inputs['predict_mask'])
# Lastly, ensure [P] is replaced by a [MASK] token
input_ids = model_inputs['input_ids']
predict_mask = model_inputs['predict_mask']
predict_token_id = input_ids[predict_mask].squeeze().item()
assert predict_token_id == self.default_tokenizer.mask_token_id
def test_roberta(self):
config = AutoConfig.from_pretrained('roberta-base')
tokenizer = AutoTokenizer.from_pretrained('roberta-base')
utils.add_task_specific_tokens(tokenizer)
templatizer = utils.TriggerTemplatizer(
self.default_template,
config,
tokenizer,
add_special_tokens=False
)
model_inputs, label = templatizer(self.default_instance)
# Label should be mapped to its token id
expected_label = torch.tensor([tokenizer.convert_tokens_to_ids([self.default_instance['label']])])
assert torch.equal(expected_label, label)
# For BERT ouput is expected to have the following keys
print(model_inputs)
assert 'input_ids' in model_inputs
assert 'attention_mask' in model_inputs
# Test that the custom masks match our expectations
expected_trigger_mask = torch.tensor(
[[True, True, False, False, True, False, False]]
)
assert torch.equal(expected_trigger_mask, model_inputs['trigger_mask'])
expected_predict_mask = torch.tensor(
[[False, False, False, False, False, False, True]]
)
assert torch.equal(expected_predict_mask, model_inputs['predict_mask'])
# Lastly, ensure [P] is replaced by a [MASK] token
input_ids = model_inputs['input_ids']
predict_mask = model_inputs['predict_mask']
predict_token_id = input_ids[predict_mask].squeeze().item()
assert predict_token_id == tokenizer.mask_token_id
class TestCollator(TestCase):
def test_collator(self):
template = '[T] [T] {arbitrary} [T] {fields} [P]'
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
config = AutoConfig.from_pretrained('bert-base-cased')
utils.add_task_specific_tokens(tokenizer)
templatizer = utils.TriggerTemplatizer(
template,
config,
tokenizer,
add_special_tokens=False
)
collator = utils.Collator(pad_token_id=tokenizer.pad_token_id)
instances = [
{'arbitrary': 'a', 'fields': 'the', 'label': 'hot'},
{'arbitrary': 'a a', 'fields': 'the the', 'label': 'cold'}
]
templatized_instances = [templatizer(x) for x in instances]
loader = DataLoader(
templatized_instances,
batch_size=2,
shuffle=False,
collate_fn=collator
)
model_inputs, labels = next(iter(loader))
# Check results match our expectations
expected_labels = torch.tensor([
tokenizer.encode('hot', add_special_tokens=False, add_prefix_space=True),
tokenizer.encode('cold', add_special_tokens=False, add_prefix_space=True),
])
assert torch.equal(expected_labels, labels)
expected_trigger_mask = torch.tensor([
[True, True, False, True, False, False, False, False],
[True, True, False, False, True, False, False, False],
])
assert torch.equal(expected_trigger_mask, model_inputs['trigger_mask'])
expected_predict_mask = torch.tensor([
[False, False, False, False, False, True, False, False],
[False, False, False, False, False, False, False, True],
])
assert torch.equal(expected_predict_mask, model_inputs['predict_mask'])
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