def test_load_result(
ws_tknzr: WsTknzr,
exp_name: str,
file_path: str,
):
r"""Ensure configuration consistency between save and load."""
# Test case: Type mismatched.
wrong_typed_inputs = [
False, True, 0, 1, 0.0, 0.1, 1.0, (), [], {}, set(), None, ...,
NotImplemented,
]
for bad_exp_name in wrong_typed_inputs:
with pytest.raises(TypeError) as excinfo:
WsTknzr.load(exp_name=bad_exp_name)
assert (
'`exp_name` must be an instance of `str`' in str(excinfo.value)
)
# Test case: Valid input.
ws_tknzr.save(exp_name)
load_tknzr = WsTknzr.load(exp_name)
assert ws_tknzr.__dict__ == load_tknzr.__dict__
def test_tknz(parameters, test_input: str, expected: List[str]):
r"""Tokenize text based on whitespaces."""
tknzr = WsTknzr(
is_uncased=parameters['is_uncased'],
max_vocab=parameters['max_vocab'],
min_count=parameters['min_count'],
)
assert tknzr.tknz(test_input) == expected
def test_dtknz(parameters, test_input: List[str], expected: str):
r"""Detokenize characters back to text."""
tknzr = WsTknzr(
is_uncased=parameters['is_uncased'],
max_vocab=parameters['max_vocab'],
min_count=parameters['min_count'],
)
assert tknzr.dtknz(test_input) == expected
def test_config_file_exist(
ws_tknzr: WsTknzr,
exp_name: str,
file_path: str,
):
r"""Save configuration as file."""
ws_tknzr.save(exp_name)
assert os.path.exists(file_path)
def test_dec(parameters, test_input: List[int], expected: str):
r"""Decode token ids to text."""
tknzr = WsTknzr(
is_uncased=parameters['is_uncased'],
max_vocab=parameters['max_vocab'],
min_count=parameters['min_count'],
tk2id=parameters['tk2id'],
)
assert (tknzr.dec(test_input,
rm_sp_tks=parameters['rm_sp_tks']) == expected)
def test_config_file_format(
ws_tknzr: WsTknzr,
exp_name: str,
file_path: str,
):
r"""Saved configuration must be JSON format."""
ws_tknzr.save(exp_name)
with open(file_path, 'r', encoding='utf-8') as input_file:
# Raise error if file is invalid JSON.
assert json.load(input_file)
def test_min_count():
r"""``min_count`` must be an integer larger than ``0``."""
# Test case: Type mismatched.
wrong_typed_inputs = [
-1.0,
0.0,
1.0,
'',
(),
[],
{},
set(),
None,
...,
NotImplemented,
]
for bad_min_count in wrong_typed_inputs:
with pytest.raises(TypeError) as excinfo:
WsTknzr(
is_uncased=True,
max_vocab=-1,
min_count=bad_min_count,
tk2id=None,
)
assert '`min_count` must be an instance of `int`' in str(excinfo.value)
# Test case: Invalid value.
wrong_value_inputs = [-1, 0]
for bad_min_count in wrong_value_inputs:
with pytest.raises(ValueError) as excinfo:
WsTknzr(
is_uncased=True,
max_vocab=-1,
min_count=bad_min_count,
tk2id=None,
)
assert '`min_count` must be larger than `0`' in str(excinfo.value)
# Test case: Correct input.
for good_min_count in range(1, 10):
tknzr = WsTknzr(
is_uncased=True,
max_vocab=-1,
min_count=good_min_count,
tk2id=None,
)
assert tknzr.min_count == good_min_count
def test_max_vocab():
r"""``max_vocab`` must be an integer larger than or equal to ``-1``."""
# Test case: Type mismatched.
wrong_typed_inputs = [
-1.0,
0.0,
1.0,
'',
(),
[],
{},
set(),
None,
...,
NotImplemented,
]
for bad_max_vocab in wrong_typed_inputs:
with pytest.raises(TypeError) as excinfo:
WsTknzr(
is_uncased=True,
max_vocab=bad_max_vocab,
min_count=1,
tk2id=None,
)
assert '`max_vocab` must be an instance of `int`' in str(excinfo.value)
# Test case: Invalid value.
with pytest.raises(ValueError) as excinfo:
WsTknzr(
is_uncased=True,
max_vocab=-2,
min_count=1,
tk2id=None,
)
assert ('`max_vocab` must be larger than or equal to `-1`'
in str(excinfo.value))
# Test case: Correct input.
for good_max_vocab in range(-1, 10, 1):
tknzr = WsTknzr(
is_uncased=True,
max_vocab=good_max_vocab,
min_count=1,
tk2id=None,
)
assert tknzr.max_vocab == good_max_vocab
def test_lower_case(is_uncased: bool, cased_txt: Dict[str, str]):
r"""Convert output text to lowercase when ``is_uncased == True``."""
tknzr = WsTknzr(
is_uncased=is_uncased,
max_vocab=-1,
min_count=1,
tk2id=None,
)
if tknzr.is_uncased:
assert tknzr.norm(cased_txt['input']) == cased_txt['output']
else:
assert tknzr.norm(cased_txt['input']) == cased_txt['input']
def test_slow_tensor_dset(max_seq_len: int) -> None:
"""Load dataset and convert to tensor on the fly."""
tknzr = WsTknzr(is_uncased=True, max_vocab=-1, min_count=10)
tknzr.build_vocab(batch_txt=['a', 'b', 'c'])
wiki_dset = WikiText2Dset(ver='valid')
dset = lmp.util.dset.SlowTensorDset(dset=wiki_dset, max_seq_len=max_seq_len, tknzr=tknzr)
assert isinstance(dset, lmp.util.dset.SlowTensorDset)
assert len(dset) == len(wiki_dset)
for idx, tkids in enumerate(dset):
assert isinstance(tkids, torch.Tensor), 'Each sample in the tensor dataset must be tensor.'
assert tkids.size() == torch.Size([max_seq_len]), 'Each sample in the tensor dataset must have same length.'
assert torch.all(dset[idx] == tkids), 'Support ``__getitem__`` and ``__iter__``.'
def test_enc(parameters, test_input: str, expected: List[int]):
r"""Encode text to token ids."""
tknzr = WsTknzr(
is_uncased=parameters['is_uncased'],
max_vocab=parameters['max_vocab'],
min_count=parameters['min_count'],
tk2id=parameters['tk2id'],
)
out = tknzr.enc(test_input, max_seq_len=parameters['max_seq_len'])
assert out == expected
if parameters['max_seq_len'] != -1:
assert len(out) == parameters['max_seq_len']
def test_build_vocab(
parameters,
test_input: Sequence[str],
expected: Dict[str, int],
):
r"""Correctly build vocabulary under the constraint of given parameters."""
tknzr = WsTknzr(
is_uncased=parameters['is_uncased'],
max_vocab=parameters['max_vocab'],
min_count=parameters['min_count'],
tk2id=parameters['tk2id'],
)
tknzr.build_vocab(test_input)
assert tknzr.tk2id == expected
def test_batch_enc(
parameters,
test_input: List[str],
expected: List[List[int]],
):
r"""Encode batch of text to batch of token ids."""
tknzr = WsTknzr(
is_uncased=parameters['is_uncased'],
max_vocab=parameters['max_vocab'],
min_count=parameters['min_count'],
tk2id=parameters['tk2id'],
)
outs = tknzr.batch_enc(test_input, max_seq_len=parameters['max_seq_len'])
assert outs == expected
if parameters['max_seq_len'] != -1:
for out in outs:
assert len(out) == parameters['max_seq_len']
def test_is_uncased():
r"""``is_uncased`` must be an instance of `bool`."""
# Test case: Type mismatched.
wrong_typed_inputs = [
0,
1,
-1,
0.1,
'',
(),
[],
{},
set(),
None,
...,
NotImplemented,
]
for bad_is_uncased in wrong_typed_inputs:
with pytest.raises(TypeError) as excinfo:
WsTknzr(
is_uncased=bad_is_uncased,
max_vocab=-1,
min_count=1,
tk2id=None,
)
assert ('`is_uncased` must be an instance of `bool`'
in str(excinfo.value))
# Test case: Correct input.
for good_is_uncased in [False, True]:
tknzr = WsTknzr(
is_uncased=good_is_uncased,
max_vocab=-1,
min_count=1,
tk2id=None,
)
assert tknzr.is_uncased == good_is_uncased
def test_ws_tknzr(capsys, ws_tknzr: WsTknzr, exp_name: str, seed: int) -> None:
"""Ensure tokenize script output consistency when using :py:class:`lmp.tknzr.WsTknzr`."""
txt = 'a b c'
lmp.script.tknz_txt.main(argv=[
'--exp_name',
exp_name,
'--seed',
str(seed),
'--txt',
txt,
])
captured = capsys.readouterr()
assert str(ws_tknzr.tknz(txt=txt)) in captured.out
def ws_tknzr() -> WsTknzr:
r"""Common setup of whitespace tokenizer."""
return WsTknzr(
is_uncased=True,
max_vocab=-1,
min_count=1,
tk2id={
WsTknzr.bos_tk: WsTknzr.bos_tkid,
WsTknzr.eos_tk: WsTknzr.eos_tkid,
WsTknzr.pad_tk: WsTknzr.pad_tkid,
WsTknzr.unk_tk: WsTknzr.unk_tkid,
'a': 4,
'b': 5,
'c': 6,
},
)
def test_ws_tknzr( exp_name: str, is_uncased: bool, max_vocab: int, min_count: int, tknzr_file_path: str, ) -> None: """Ensure consistency between save and load.""" tknzr = WsTknzr(is_uncased=is_uncased, max_vocab=max_vocab, min_count=min_count) tknzr.build_vocab(batch_txt=['a', 'b', 'c']) lmp.util.tknzr.save(exp_name=exp_name, tknzr=tknzr) assert os.path.exists(tknzr_file_path) load_tknzr = lmp.util.tknzr.load(exp_name=exp_name) assert isinstance(load_tknzr, WsTknzr) assert load_tknzr.is_uncased == tknzr.is_uncased assert load_tknzr.max_vocab == tknzr.max_vocab assert load_tknzr.min_count == tknzr.min_count assert load_tknzr.tk2id == tknzr.tk2id assert load_tknzr.id2tk == tknzr.id2tk
def test_vocab_size(
parameters,
expected: int,
):
r"""``WsTknzr.vocab_size`` is an instance property
Value of ``WsTknzr.vocab_size`` is the number of tokens included in the
vocabulary, thus must be a postive integer.
"""
tknzr = WsTknzr(
is_uncased=parameters['is_uncased'],
max_vocab=parameters['max_vocab'],
min_count=parameters['min_count'],
tk2id=parameters['tk2id'],
)
# Check the type of `vocab_size`.
assert isinstance(tknzr.vocab_size, int)
# Check the value of `vocab_size`.
assert tknzr.vocab_size == expected
def test_tk2id():
r"""``tk2id`` must be an dictionary which maps `str` to `int`."""
# Test case: Type mismatched.
wrong_typed_inputs = [
False,
True,
-1,
0,
1,
-1.0,
0.1,
'',
(),
[],
set(),
...,
NotImplemented,
]
for bad_tk2id in wrong_typed_inputs:
with pytest.raises(TypeError) as excinfo:
WsTknzr(
is_uncased=True,
max_vocab=-1,
min_count=1,
tk2id=bad_tk2id,
)
assert '`tk2id` must be an instance of `dict`' in str(excinfo.value)
with pytest.raises(TypeError) as excinfo:
WsTknzr(
is_uncased=True,
max_vocab=-1,
min_count=1,
tk2id={1: 1},
)
assert ('All keys in `tk2id` must be instances of `str`'
in str(excinfo.value))
with pytest.raises(TypeError) as excinfo:
WsTknzr(
is_uncased=True,
max_vocab=-1,
min_count=1,
tk2id={'a': 'a'},
)
assert ('All values in `tk2id` must be instances of `int`'
in str(excinfo.value))
# Test case: Invalid value.
with pytest.raises(ValueError) as excinfo:
WsTknzr(
is_uncased=True,
max_vocab=-1,
min_count=1,
tk2id={'a': -1},
)
assert ('All values in `tk2id` must be non-negative integers'
in str(excinfo.value))
# Test case: Correct input.
good_tk2id = {
'a': 1,
'b': 2,
}
tknzr = WsTknzr(
is_uncased=True,
max_vocab=-1,
min_count=1,
tk2id=good_tk2id,
)
assert tknzr.tk2id == good_tk2id
# Test case: Default value.
tknzr = WsTknzr(
is_uncased=True,
max_vocab=-1,
min_count=1,
tk2id=None,
)
assert tknzr.tk2id == {
WsTknzr.bos_tk: WsTknzr.bos_tkid,
WsTknzr.eos_tk: WsTknzr.eos_tkid,
WsTknzr.pad_tk: WsTknzr.pad_tkid,
WsTknzr.unk_tk: WsTknzr.unk_tkid,
}
def test_nfkc(ws_tknzr: WsTknzr, non_nfkc_txt: Dict[str, str]):
r"""Normalize output text with NFKC."""
assert ws_tknzr.norm(non_nfkc_txt['input']) == non_nfkc_txt['output']
def ws_tknzr(exp_name: str, request, tknzr_file_path: None) -> WsTknzr:
"""Whitespace tokenizer example."""
tknzr = WsTknzr(is_uncased=True, max_vocab=-1, min_count=0)
tknzr.build_vocab(batch_txt=['a', 'b', 'c'])
lmp.util.tknzr.save(exp_name=exp_name, tknzr=tknzr)
return tknzr
def test_collapse_whitespace(ws_tknzr: WsTknzr, cws_txt: Dict[str, str]):
r"""Collapse whitespaces in output text."""
assert ws_tknzr.norm(cws_txt['input']) == cws_txt['output']
def test_strip_whitespace(ws_tknzr: WsTknzr, htws_txt: Dict[str, str]):
r"""Strip output text."""
assert ws_tknzr.norm(htws_txt['input']) == htws_txt['output']