def test_pipeline(self, texts):
for idx, text in enumerate(texts):
file_path = os.path.join(self.test_dir, f"{idx+1}.txt")
with open(file_path, 'w') as f:
f.write(text)
nlp = Pipeline()
reader_config = HParams(
{
"input_pack_name": "input",
"output_pack_name": "output"
}, MultiPackSentenceReader.default_hparams())
nlp.set_reader(reader=MultiPackSentenceReader(), config=reader_config)
translator_config = HParams(
{
"src_language": "de",
"target_language": "en",
"in_pack_name": "input",
"out_pack_name": "result"
}, None)
nlp.add_processor(MicrosoftBingTranslator(), config=translator_config)
nlp.initialize()
english_results = ["Hey good morning", "This is Forte. A tool for NLP"]
for idx, m_pack in enumerate(nlp.process_dataset(self.test_dir)):
self.assertEqual(set(m_pack._pack_names),
set(["input", "output", "result"]))
self.assertEqual(
m_pack.get_pack("result").text, english_results[idx] + "\n")
def test_pipeline(self, texts):
for idx, text in enumerate(texts):
file_path = os.path.join(self.test_dir, f"{idx+1}.txt")
with open(file_path, 'w') as f:
f.write(text)
nlp = Pipeline()
reader_config = HParams(
{
"input_pack_name": "query",
"output_pack_name": "output"
}, MultiPackSentenceReader.default_hparams())
nlp.set_reader(reader=MultiPackSentenceReader(), config=reader_config)
config = HParams(
{
"model": {
"name": "bert-base-uncased"
},
"tokenizer": {
"name": "bert-base-uncased"
},
"max_seq_length": 128,
"query_pack_name": "query"
}, None)
nlp.add_processor(BertBasedQueryCreator(), config=config)
nlp.initialize()
for idx, m_pack in enumerate(nlp.process_dataset(self.test_dir)):
query_pack = m_pack.get_pack("query")
self.assertEqual(len(query_pack.generics), 1)
self.assertIsInstance(query_pack.generics[0], Query)
query = query_pack.generics[0].value
self.assertEqual(query.shape, (1, 768))
def test_pipeline(self, texts):
for idx, text in enumerate(texts):
file_path = os.path.join(self.test_dir, f"{idx+1}.txt")
with open(file_path, 'w') as f:
f.write(text)
nlp = Pipeline()
reader_config = HParams({"input_pack_name": "input",
"output_pack_name": "output"},
MultiPackSentenceReader.default_hparams())
nlp.set_reader(reader=MultiPackSentenceReader(), config=reader_config)
nlp.initialize()
for idx, m_pack in enumerate(nlp.process_dataset(self.test_dir)):
self.assertEqual(m_pack._pack_names, ["input", "output"])
self.assertEqual(m_pack.get_pack("input").text, texts[idx] + "\n")
def test_parse_pack(self, text, annotation_length):
file_path = os.path.join(self.test_dir, 'test.txt')
with open(file_path, 'w') as f:
f.write(text)
multipack = list(MultiPackSentenceReader().parse_pack(file_path))[0]
input_pack = multipack.get_pack('input_src')
self.assertEqual(len(multipack.packs), 2)
self.assertEqual(multipack._pack_names, ['input_src', 'output_tgt'])
self.assertEqual(len(input_pack.annotations), annotation_length)
self.assertEqual(input_pack.text, text + "\n")
def test_parse_pack(self, text, annotation_length):
file_path = os.path.join(self.test_dir, 'test.txt')
with open(file_path, 'w') as f:
f.write(text)
pl = Pipeline()
pl.set_reader(MultiPackSentenceReader())
pl.initialize()
multipack: MultiPack = pl.process_one(self.test_dir)
input_pack = multipack.get_pack('input_src')
self.assertEqual(len(multipack.packs), 2)
self.assertEqual(multipack._pack_names, ['input_src', 'output_tgt'])
self.assertEqual(len(input_pack.annotations), annotation_length)
self.assertEqual(input_pack.text, text + "\n")
def test_pipeline(self, texts):
for idx, text in enumerate(texts):
file_path = os.path.join(self.test_dir, f"{idx + 1}.txt")
with open(file_path, 'w') as f:
f.write(text)
nlp = Pipeline[MultiPack]()
reader_config = {
"input_pack_name": "input",
"output_pack_name": "output"
}
nlp.set_reader(reader=MultiPackSentenceReader(), config=reader_config)
nlp.initialize()
m_pack: MultiPack
for m_pack in nlp.process_dataset(self.test_dir):
# Recover the test sentence order from the doc id.
docid = m_pack.get_pack("input").meta.doc_id
idx = int(os.path.basename(docid).rstrip('.txt')) - 1
self.assertEqual(m_pack._pack_names, ["input", "output"])
self.assertEqual(m_pack.get_pack("input").text, texts[idx] + "\n")
def test_replace_token(self, texts, expected_outputs, expected_tokens, expected_links):
for idx, text in enumerate(texts):
file_path = os.path.join(self.test_dir, f"{idx + 1}.txt")
with open(file_path, 'w') as f:
f.write(text)
nlp = Pipeline[MultiPack]()
reader_config = {
"input_pack_name": "input_src",
"output_pack_name": "output_tgt"
}
nlp.set_reader(reader=MultiPackSentenceReader(), config=reader_config)
nlp.add(component=NLTKWordTokenizer(), selector=AllPackSelector())
nlp.add(component=NLTKPOSTagger(), selector=AllPackSelector())
nlp.initialize()
processor_config = {
'augment_entry': "ft.onto.base_ontology.Token",
'other_entry_policy': {
"kwargs": {
"ft.onto.base_ontology.Sentence": "auto_align"
}
},
'type': 'data_augmentation_op',
'data_aug_op': 'tests.forte.processors.base.data_augment_replacement_processor_test.TmpReplacer',
"data_aug_op_config": {
'kwargs': {}
},
'augment_pack_names': {
'kwargs': {}
}
}
processor = ReplacementDataAugmentProcessor()
processor.initialize(resources=None, configs=processor_config)
for idx, m_pack in enumerate(nlp.process_dataset(self.test_dir)):
src_pack = m_pack.get_pack('input_src')
tgt_pack = m_pack.get_pack('output_tgt')
num_mpl_orig, num_mpg_orig = 0, 0
# Copy the source pack to target pack.
tgt_pack.set_text(src_pack.text)
src_pack.add_entry(Document(src_pack, 0, len(src_pack.text)))
for anno in src_pack.get(Annotation):
new_anno = type(anno)(
tgt_pack, anno.begin, anno.end
)
tgt_pack.add_entry(new_anno)
# Create MultiPackLink.
m_pack.add_entry(
MultiPackLink(
m_pack, anno, new_anno
)
)
# Create MultiPackGroup.
m_pack.add_entry(
MultiPackGroup(
m_pack, [anno, new_anno]
)
)
# Count the number of MultiPackLink/MultiPackGroup.
num_mpl_orig += 1
num_mpg_orig += 1
# Create Links in the source pack.
# The Links should be a tree:
#
# Link 3
# _________|_________
# | |
# Link 2 |
# _______|________ |
# | | |
# Link 1 | |
# ______|_____ | |
# | | | |
# token 1 token 2 token 3 token 4 ... ...
prev_entry = None
for i, token in enumerate(src_pack.get(Token)):
# Avoid overlapping with deleted tokens.
if i < 10:
continue
if prev_entry:
link = Link(src_pack, prev_entry, token)
src_pack.add_entry(
link
)
prev_entry = link
else:
prev_entry = token
# Create Groups in the target pack.
# The Groups should be a tree like the Links.
prev_entry = None
for i, token in enumerate(tgt_pack.get(Token)):
# Avoid overlapping with deleted tokens.
if i < 10:
continue
if prev_entry:
group = Group(tgt_pack, [prev_entry, token])
tgt_pack.add_entry(
group
)
prev_entry = group
else:
prev_entry = token
doc_src = list(src_pack.get(Document))[0]
doc_tgt = list(tgt_pack.get(Document))[0]
sent_src = list(src_pack.get(Sentence))[0]
sent_tgt = list(tgt_pack.get(Sentence))[0]
# Insert two extra Links in the src_pack.
# They should not be copied to new_src_pack, because the Document is not copied.
link_src_low = src_pack.add_entry(Link(src_pack, doc_src, sent_src))
src_pack.add_entry(Link(src_pack, link_src_low, sent_src))
# Insert two extra Groups in the tgt_pack.
# They should not be copied to new_tgt_pack, because the Document is not copied.
group_tgt_low = tgt_pack.add_entry(Group(tgt_pack, [doc_tgt, sent_tgt]))
tgt_pack.add_entry(Group(tgt_pack, [group_tgt_low, sent_tgt]))
# Call the augment function explicitly for duplicate replacement
# to test the False case of _replace function.
processor._augment(m_pack, ["input_src", "output_tgt"])
# Test the insertion and deletion
for pack in (src_pack, tgt_pack):
# Insert an "NLP" at the beginning
processor._insert(" NLP ", pack, 0)
processor._insert(" NLP ", pack, 18)
processor._insert(" NLP ", pack, len(pack.text) - 2)
processor._insert("NLP", pack, len(pack.text) - 1)
# Delete the second token "and"
processor._delete(list(pack.get(Token))[1])
# This duplicate insertion should be invalid.
processor._insert(" NLP ", pack, 0)
# This insertion overlaps with a replacement.
# It should be invalid.
processor._insert(" NLP ", pack, 2)
processor._process(m_pack)
new_src_pack = m_pack.get_pack('augmented_input_src')
new_tgt_pack = m_pack.get_pack('augmented_output_tgt')
self.assertEqual(new_src_pack.text, expected_outputs[idx] + "\n")
for j, token in enumerate(new_src_pack.get(Token)):
self.assertEqual(token.text, expected_tokens[idx][j])
for sent in new_src_pack.get(Sentence):
self.assertEqual(sent.text, expected_outputs[idx])
# Test the copied Links.
prev_link = None
for i, link in enumerate(new_src_pack.get(Link)):
if prev_link:
self.assertEqual(link.get_parent().tid, prev_link.tid)
self.assertEqual(link.get_child().text, expected_links[idx][i])
prev_link = link
# Test the copied Groups.
prev_group = None
for i, group in enumerate(new_tgt_pack.get(Group)):
members = group.get_members()
if isinstance(members[0], Token):
member_token = members[0]
member_group = members[1]
else:
member_token = members[1]
member_group = members[0]
if prev_group:
self.assertEqual(isinstance(member_token, Token), True)
self.assertEqual(isinstance(member_group, Group), True)
self.assertEqual(member_group.tid, prev_group.tid)
self.assertEqual(member_token.text, expected_links[idx][i])
prev_group = group
# The two extra Links should not be copied, because of missing Document.
self.assertEqual(len(list(src_pack.get(Link))) - 2, len(list(new_src_pack.get(Link))))
# The two extra Groups should not be copied, because of missing Document.
self.assertEqual(len(list(tgt_pack.get(Group))) - 2, len(list(new_tgt_pack.get(Group))))
# Test the MultiPackLink/MultiPackGroup
num_mpl_aug, num_mpg_aug = 0, 0
for mpl in m_pack.get(MultiPackLink):
parent = mpl.get_parent()
child = mpl.get_child()
num_mpl_aug += 1
self.assertEqual(parent.text, child.text)
self.assertNotEqual(parent.pack.meta.pack_id, child.pack.meta.pack_id)
for mpg in m_pack.get(MultiPackGroup):
members = mpg.get_members()
num_mpg_aug += 1
self.assertEqual(members[0].text, members[1].text)
self.assertNotEqual(members[0].pack.meta.pack_id, members[1].pack.meta.pack_id)
# Test the number of MultiPackLink/MultiPackGroup.
# Minus the aug and orig counters by 1, because the Document is not copied.
# So we ignore the MPL and MPG between Document.
# The number should be doubled, except for one deletion.
self.assertEqual(num_mpl_aug - 1, (num_mpl_orig - 1) * 2 - 1)
self.assertEqual(num_mpg_aug - 1, (num_mpg_orig - 1) * 2 - 1)
