def test_normalize_nfd(self):
txt = [u"\u1e9b\u0323"]
expected = [
u"\u017f\u0323\u0307".encode("utf-8"),
]
self.assertAllEqual(expected, normalize_ops.normalize_utf8(txt, "NFD"))
self.assertAllEqual(expected, normalize_ops.normalize_utf8(txt, "nfd"))
def tokenize_with_offsets(self, text_input):
"""Performs basic word tokenization for BERT.
Args:
text_input: A `Tensor` or `RaggedTensor` of untokenized UTF-8 strings.
Returns:
A `RaggedTensor` of tokenized strings from text_input.
"""
# lowercase and strip accents (if option is set)
if self._lower_case:
text_input = case_fold_utf8(text_input)
text_input = normalize_utf8(text_input, "NFD")
text_input = string_ops.regex_replace(text_input, r"\p{Mn}", "")
else:
# utf8 normalization
if self._normalization_form is not None:
text_input = normalize_utf8(text_input,
self._normalization_form)
# strip out control characters
text_input = string_ops.regex_replace(text_input, r"\p{Cc}|\p{Cf}",
" ")
return regex_split_ops.regex_split_with_offsets(
text_input, _DELIM_REGEX_PATTERN, self._keep_delim_regex_pattern,
"BertBasicTokenizer")
def test_normalize_nfkc_ragged(self):
txt = ragged_factory_ops.constant([[[u"\u1e9b\u0323 \ufb01"], []],
[[u"\u1e9b\u0323", u"\ufb01"]]])
expected = [[[u"ṩ fi".encode("utf-8")], []],
[[u"ṩ".encode("utf-8"), b"fi"]]]
self.assertRaggedEqual(expected, normalize_ops.normalize_utf8(txt, "NFKC"))
self.assertRaggedEqual(expected, normalize_ops.normalize_utf8(txt, "nfkc"))
def test_normalize_nfc(self):
txt = [
u"\u1e9b\u0323",
]
expected = [
u"\u1e9b\u0323".encode("utf-8"),
]
self.assertAllEqual(expected, normalize_ops.normalize_utf8(txt, "NFC"))
self.assertAllEqual(expected, normalize_ops.normalize_utf8(txt, "nfc"))
def test_normalize_nfkc_batch(self):
txt = [
u"\u1e9b\u0323",
u"\ufb01",
]
expected = [
b"\xe1\xb9\xa9",
b"fi",
]
self.assertAllEqual(expected, normalize_ops.normalize_utf8(txt, u"NFKC"))
self.assertAllEqual(expected, normalize_ops.normalize_utf8(txt, u"nfkc"))
def test_normalize_nfkc_batch(self):
txt = [
u"\u1e9b\u0323",
u"\ufb01",
]
expected = [
u"ṩ".encode("utf-8"),
"fi",
]
self.assertAllEqual(expected,
normalize_ops.normalize_utf8(txt, "NFKC"))
self.assertAllEqual(expected,
normalize_ops.normalize_utf8(txt, "nfkc"))
def tokenize(self, text_input):
"""Performs basic word tokenization for BERT.
Args:
text_input: A `Tensor` or `RaggedTensor` of untokenized UTF-8 strings.
Returns:
A `RaggedTensor` of tokenized strings from text_input.
"""
# lowercase and strip accents (if option is set)
if self._lower_case:
text_input = case_fold_utf8(text_input)
text_input = normalize_utf8(text_input, "NFD")
text_input = string_ops.regex_replace(text_input, r"\p{Mn}", "")
else:
# utf8 normalization
if self._normalization_form is not None:
text_input = normalize_utf8(text_input, self._normalization_form)
# strip out control characters
text_input = string_ops.regex_replace(text_input, r"\p{Cc}|\p{Cf}", " ")
# For chinese and emoji characters, tokenize by unicode codepoints
unicode_tokenizer = UnicodeScriptTokenizer(
keep_whitespace=self._keep_whitespace)
script_tokenized = unicode_tokenizer.tokenize(text_input)
split_cond = self._should_split(script_tokenized)
unicode_char_split = ragged_string_ops.unicode_split(
script_tokenized, "UTF-8")
unicode_split_tokens = array_ops.where(
array_ops.squeeze(split_cond),
y=array_ops.expand_dims(script_tokenized.values, axis=1),
x=unicode_char_split.values)
final_tokens = script_tokenized.with_flat_values(unicode_split_tokens)
return final_tokens.merge_dims(-2, -1)
def test_unknown_normalization_form(self):
with self.assertRaises(errors.InvalidArgumentError):
bomb = normalize_ops.normalize_utf8(
["cant readme", "wont read me"], "cantfindme")
self.evaluate(bomb)
def lower_case(self, text_input):
"""Lower-cases the `text_input'."""
text_input = case_fold_utf8(text_input)
text_input = normalize_utf8(text_input, "NFD")
text_input = string_ops.regex_replace(text_input, r"\p{Mn}", "")
return text_input
