def test_padding(is_uncased: bool, max_vocab: int, min_count: int) -> None:
"""Pad to specified length."""
assert CharTknzr(
is_uncased=is_uncased,
max_vocab=max_vocab,
min_count=min_count,
).pad_to_max(max_seq_len=2, tkids=[]) == [
PAD_TKID,
PAD_TKID,
]
assert CharTknzr(
is_uncased=is_uncased,
max_vocab=max_vocab,
min_count=min_count,
).pad_to_max(max_seq_len=5, tkids=[
BOS_TKID,
UNK_TKID,
EOS_TKID,
]) == [
BOS_TKID,
UNK_TKID,
EOS_TKID,
PAD_TKID,
PAD_TKID,
]
def test_no_limit_build() -> None:
"""Include all tokens when ``max_vocab == -1``."""
tknzr = CharTknzr(is_uncased=False, max_vocab=-1, min_count=0)
CJK_txt = [chr(i) for i in range(ord('\u4e00'), ord('\u9fff') + 1)]
norm_CJK_txt = [tknzr.norm(t) for t in CJK_txt]
tknzr.build_vocab(CJK_txt)
assert tknzr.vocab_size == len(set(norm_CJK_txt)) + 4
assert all(map(lambda tk: tk in tknzr.tk2id, norm_CJK_txt))
def test_case_insensitive() -> None:
"""Must be case-insensitive when ``is_uncased = True``."""
tknzr = CharTknzr(is_uncased=True, max_vocab=-1, min_count=0)
tknzr.build_vocab(['a', 'A'])
assert tknzr.tk2id == {
BOS_TK: BOS_TKID,
EOS_TK: EOS_TKID,
PAD_TK: PAD_TKID,
UNK_TK: UNK_TKID,
'a': max(BOS_TKID, EOS_TKID, PAD_TKID, UNK_TKID) + 1,
}
def test_empty_build() -> None:
"""Build nothing when given empty list."""
tknzr = CharTknzr(is_uncased=False, max_vocab=-1, min_count=0)
tknzr.build_vocab([])
assert tknzr.vocab_size == 4
assert tknzr.tk2id == {
BOS_TK: BOS_TKID,
EOS_TK: EOS_TKID,
PAD_TK: PAD_TKID,
UNK_TK: UNK_TKID
}
def test_minimum_occurrence_counts() -> None:
"""Must satisfy minumum occurrence counts to include tokens in vocabulary."""
tknzr = CharTknzr(is_uncased=False, max_vocab=-1, min_count=2)
tknzr.build_vocab(['c', 'bc', 'abc'])
assert tknzr.tk2id == {
BOS_TK: BOS_TKID,
EOS_TK: EOS_TKID,
PAD_TK: PAD_TKID,
UNK_TK: UNK_TKID,
'c': max(BOS_TKID, EOS_TKID, PAD_TKID, UNK_TKID) + 1,
'b': max(BOS_TKID, EOS_TKID, PAD_TKID, UNK_TKID) + 2,
}
def test_sort_by_occurrence_counts() -> None:
"""Sort vocabulary by occurrence counts."""
tknzr = CharTknzr(is_uncased=False, max_vocab=-1, min_count=0)
tknzr.build_vocab(['c', 'bc', 'abc'])
assert tknzr.tk2id == {
BOS_TK: BOS_TKID,
EOS_TK: EOS_TKID,
PAD_TK: PAD_TKID,
UNK_TK: UNK_TKID,
'c': max(BOS_TKID, EOS_TKID, PAD_TKID, UNK_TKID) + 1,
'b': max(BOS_TKID, EOS_TKID, PAD_TKID, UNK_TKID) + 2,
'a': max(BOS_TKID, EOS_TKID, PAD_TKID, UNK_TKID) + 3,
}
def test_tknz(tknzr: CharTknzr) -> None:
"""Tokenize text into characters."""
# Return empty list when input empty string.
assert tknzr.tknz('') == []
# Normalize with NFKC.
assert tknzr.tknz('０é') == [
unicodedata.normalize('NFKC', '０'),
unicodedata.normalize('NFKC', 'é')
]
# Case-sensitive and case-insensitive.
assert (tknzr.is_uncased and tknzr.tknz('ABc') == ['a', 'b', 'c']) or \
(not tknzr.is_uncased and tknzr.tknz('ABc') == ['A', 'B', 'c'])
# Collapse consecutive whitespaces.
assert tknzr.tknz('a b c') == ['a', ' ', 'b', ' ', 'c']
# Strip whitespaces.
assert tknzr.tknz(' abc ') == ['a', 'b', 'c']
# Avoid tokenizing special tokens.
assert tknzr.tknz(f'{BOS_TK}a{UNK_TK}bc{EOS_TK}{PAD_TK}{PAD_TK}') == [
BOS_TK,
'a',
UNK_TK,
'b',
'c',
EOS_TK,
PAD_TK,
PAD_TK,
]
def test_normalization() -> None:
"""Must normalize text first."""
tknzr = CharTknzr(is_uncased=False, max_vocab=-1, min_count=0)
tknzr.build_vocab(['０', '０é'])
assert tknzr.tk2id == {
BOS_TK:
BOS_TKID,
EOS_TK:
EOS_TKID,
PAD_TK:
PAD_TKID,
UNK_TK:
UNK_TKID,
unicodedata.normalize('NFKC', '０'):
max(BOS_TKID, EOS_TKID, PAD_TKID, UNK_TKID) + 1,
unicodedata.normalize('NFKC', 'é'):
max(BOS_TKID, EOS_TKID, PAD_TKID, UNK_TKID) + 2,
}
def test_arguments(is_uncased: bool, max_vocab: int, min_count: int) -> None:
"""Must have correct arguments."""
parser = argparse.ArgumentParser()
CharTknzr.add_CLI_args(parser=parser)
argv = [
'--max_vocab',
str(max_vocab),
'--min_count',
str(min_count),
]
if is_uncased:
argv.append('--is_uncased')
args = parser.parse_args(argv)
assert args.is_uncased == is_uncased
assert args.max_vocab == max_vocab
assert args.min_count == min_count
def test_truncation(is_uncased: bool, max_vocab: int, min_count: int) -> None:
"""Truncate to specified length."""
assert CharTknzr(
is_uncased=is_uncased,
max_vocab=max_vocab,
min_count=min_count,
).trunc_to_max(max_seq_len=5, tkids=[]) == []
assert CharTknzr(
is_uncased=is_uncased,
max_vocab=max_vocab,
min_count=min_count,
).trunc_to_max(max_seq_len=2,
tkids=[
BOS_TKID,
UNK_TKID,
EOS_TKID,
PAD_TKID,
PAD_TKID,
]) == [
BOS_TKID,
UNK_TKID,
]
def test_dec() -> None:
"""Decode token ids to text."""
tknzr = CharTknzr(is_uncased=False, max_vocab=-1, min_count=0)
tknzr.build_vocab(batch_txt=['A', 'a'])
# Return empty string when given empty list.
assert tknzr.dec(tkids=[]) == ''
# Decoding format.
assert tknzr.dec(
tkids=[
BOS_TKID,
tknzr.tk2id['a'],
UNK_TKID,
tknzr.tk2id['A'],
EOS_TKID,
PAD_TKID,
],
rm_sp_tks=False,
) == f'{BOS_TK}a{UNK_TK}A{EOS_TK}{PAD_TK}'
# Remove special tokens but not unknown tokens.
assert tknzr.dec(
tkids=[
BOS_TKID,
tknzr.tk2id['a'],
UNK_TKID,
tknzr.tk2id['A'],
UNK_TKID,
EOS_TKID,
PAD_TKID,
],
rm_sp_tks=True,
) == f'a{UNK_TK}A{UNK_TK}'
# Convert unknown id to unknown tokens.
assert tknzr.dec(
tkids=[
BOS_TKID,
max(tknzr.tk2id.values()) + 1,
max(tknzr.tk2id.values()) + 2,
EOS_TKID,
PAD_TKID,
],
rm_sp_tks=False,
) == f'{BOS_TK}{UNK_TK}{UNK_TK}{EOS_TK}{PAD_TK}'
def test_good_values(is_uncased: bool, max_vocab: int, min_count: int) -> None:
"""Must correctly construct tokenizer."""
tknzr = CharTknzr(is_uncased=is_uncased,
max_vocab=max_vocab,
min_count=min_count)
assert tknzr.is_uncased == is_uncased
assert tknzr.max_vocab == max_vocab
assert tknzr.min_count == min_count
assert tknzr.tk2id == {
BOS_TK: BOS_TKID,
EOS_TK: EOS_TKID,
PAD_TK: PAD_TKID,
UNK_TK: UNK_TKID
}
assert tknzr.id2tk == {
BOS_TKID: BOS_TK,
EOS_TKID: EOS_TK,
PAD_TKID: PAD_TK,
UNK_TKID: UNK_TK
}
def test_continue_build() -> None:
"""Build vocabulary based on existed vocabulary."""
tknzr = CharTknzr(is_uncased=True, max_vocab=-1, min_count=0)
tknzr.build_vocab(['a'])
tknzr.build_vocab(['b'])
tknzr.build_vocab(['c'])
assert tknzr.tk2id == {
BOS_TK: BOS_TKID,
EOS_TK: EOS_TKID,
PAD_TK: PAD_TKID,
UNK_TK: UNK_TKID,
'a': max(BOS_TKID, EOS_TKID, PAD_TKID, UNK_TKID) + 1,
'b': max(BOS_TKID, EOS_TKID, PAD_TKID, UNK_TKID) + 2,
'c': max(BOS_TKID, EOS_TKID, PAD_TKID, UNK_TKID) + 3,
}
def test_dtknz(tknzr: CharTknzr) -> None:
"""Detokenize characters back to text."""
# Return empty string when input empty list.
assert tknzr.dtknz([]) == ''
# Normalize with NFKC.
assert tknzr.dtknz(['０', 'é']) == unicodedata.normalize('NFKC', '０é')
# Case-sensitive and case-insensitive.
assert (tknzr.is_uncased and tknzr.dtknz(['A', 'B', 'c']) == 'abc') or \
(not tknzr.is_uncased and tknzr.dtknz(['A', 'B', 'c']) == 'ABc')
# Collapse consecutive whitespaces.
assert tknzr.dtknz(['a', ' ', ' ', 'b', ' ', ' ', ' ', 'c']) == 'a b c'
# Strip whitespaces.
assert tknzr.dtknz([' ', 'a', 'b', 'c', ' ']) == 'abc'
# Correct joint special tokens.
assert tknzr.dtknz([
BOS_TK,
'a',
UNK_TK,
'b',
'c',
EOS_TK,
PAD_TK,
PAD_TK,
]) == f'{BOS_TK}a{UNK_TK}bc{EOS_TK}{PAD_TK}{PAD_TK}'
def test_uncased_enc() -> None:
"""Encode text to token ids (case-insensitive)."""
tknzr = CharTknzr(is_uncased=True, max_vocab=-1, min_count=0)
tknzr.build_vocab(batch_txt=['a'])
# Return `[bos] [eos]` when given empty input.
assert tknzr.enc(max_seq_len=2, txt='') == [BOS_TKID, EOS_TKID]
# Encoding format.
assert tknzr.enc(max_seq_len=4, txt='aA') == [
BOS_TKID, tknzr.tk2id['a'], tknzr.tk2id['a'], EOS_TKID
]
# Padding.
assert tknzr.enc(max_seq_len=5, txt='aA') == [
BOS_TKID, tknzr.tk2id['a'], tknzr.tk2id['a'], EOS_TKID, PAD_TKID
]
# Truncate.
assert tknzr.enc(max_seq_len=3, txt='aA') == [
BOS_TKID, tknzr.tk2id['a'], tknzr.tk2id['a']
]
# Unknown tokens.
assert tknzr.enc(max_seq_len=4,
txt='bB') == [BOS_TKID, UNK_TKID, UNK_TKID, EOS_TKID]
# Unknown tokens with padding.
assert tknzr.enc(max_seq_len=5, txt='bB') == [
BOS_TKID, UNK_TKID, UNK_TKID, EOS_TKID, PAD_TKID
]
# Unknown tokens with truncation.
assert tknzr.enc(max_seq_len=2, txt='bB') == [BOS_TKID, UNK_TKID]
def tknzr(is_uncased: bool, max_vocab: int, min_count: int) -> CharTknzr: """Character tokenizer shared in this module.""" return CharTknzr(is_uncased=is_uncased, max_vocab=max_vocab, min_count=min_count)
def test_uncased_batch_enc() -> None:
"""Encode batch of text to batch of token ids (case-insensitive)."""
tknzr = CharTknzr(is_uncased=True, max_vocab=-1, min_count=0)
tknzr.build_vocab(batch_txt=['a'])
# Return empty list when given empty list.
assert tknzr.batch_enc(batch_txt=[], max_seq_len=2) == []
# Batch encoding format.
assert tknzr.batch_enc(batch_txt=['aA', 'Aa'], max_seq_len=4) == [
[
BOS_TKID,
tknzr.tk2id['a'],
tknzr.tk2id['a'],
EOS_TKID,
],
[
BOS_TKID,
tknzr.tk2id['a'],
tknzr.tk2id['a'],
EOS_TKID,
],
]
# Truncate and pad to specified length.
assert tknzr.batch_enc(batch_txt=['a', 'aA', 'aAA'], max_seq_len=4) == [
[
BOS_TKID,
tknzr.tk2id['a'],
EOS_TKID,
PAD_TKID,
],
[
BOS_TKID,
tknzr.tk2id['a'],
tknzr.tk2id['a'],
EOS_TKID,
],
[
BOS_TKID,
tknzr.tk2id['a'],
tknzr.tk2id['a'],
tknzr.tk2id['a'],
],
]
# Unknown tokens.
assert tknzr.batch_enc(batch_txt=['a', 'ab', 'abc'], max_seq_len=4) == [
[
BOS_TKID,
tknzr.tk2id['a'],
EOS_TKID,
PAD_TKID,
],
[
BOS_TKID,
tknzr.tk2id['a'],
UNK_TKID,
EOS_TKID,
],
[
BOS_TKID,
tknzr.tk2id['a'],
UNK_TKID,
UNK_TKID,
],
]
def test_nfkc(nfkc_txt: Dict[str, str], tknzr: CharTknzr) -> None: """Normalize text with NFKC.""" assert tknzr.norm(nfkc_txt['input']) == nfkc_txt['output']
def test_collapse_whitespace(tknzr: CharTknzr, ws_collapse_txt: Dict[str, str]) -> None: """Collapse consecutive whitespaces.""" assert tknzr.norm(ws_collapse_txt['input']) == ws_collapse_txt['output']
def test_strip_whitespace(tknzr: CharTknzr, ws_strip_txt: Dict[str, str]) -> None: """Strip text.""" assert tknzr.norm(ws_strip_txt['input']) == ws_strip_txt['output']
def test_limit_build() -> None:
"""Must have correct vocabulary size."""
tknzr = CharTknzr(is_uncased=False, max_vocab=10, min_count=0)
tknzr.build_vocab([chr(i) for i in range(65536)])
assert tknzr.vocab_size == 10
def test_uncased(tknzr: CharTknzr, uncased_txt: Dict[str, str]) -> None:
"""Convert output text to lower cases when ``is_uncased == True``."""
assert (tknzr.is_uncased and tknzr.norm(uncased_txt['input']) == uncased_txt['output']) or \
(not tknzr.is_uncased and tknzr.norm(uncased_txt['input']) == uncased_txt['input'])
