/
normalize_ops.py
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/
normalize_ops.py
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# coding=utf-8
# Copyright 2024 TF.Text Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# coding=utf-8
"""Tensorflow lowercasing operation for UTF8 strings."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops.ragged import ragged_conversion_ops
from tensorflow.python.ops.ragged import ragged_tensor
from tensorflow.python.framework import load_library
from tensorflow.python.platform import resource_loader
gen_normalize_ops = load_library.load_op_library(resource_loader.get_path_to_datafile('_normalize_ops.so'))
# pylint: disable=redefined-builtin
def case_fold_utf8(input, name=None):
"""Applies case folding to every UTF-8 string in the input.
The input is a `Tensor` or `RaggedTensor` of any shape, and the resulting
output has the same shape as the input. Note that NFKC normalization is
implicitly applied to the strings.
#### Examples:
>>> # input: <string>[num_strings]
>>> case_fold_utf8(['The Quick-Brown',
... 'CAT jumped over',
... 'the lazy dog !! '])
>>> # output: <string>[num_strings]
<tf.Tensor: shape=(3,), dtype=string, numpy=
array([b'the quick-brown', b'cat jumped over', b'the lazy dog !! '],
dtype=object)>
Args:
input: A `Tensor` or `RaggedTensor` of UTF-8 encoded strings.
name: The name for this op (optional).
Returns:
A `Tensor` or `RaggedTensor` of type string, with case-folded contents.
"""
with ops.name_scope(name, "CaseFoldUTF8", [input]):
input_tensor = ragged_tensor.convert_to_tensor_or_ragged_tensor(
input, dtype=dtypes.string)
if ragged_tensor.is_ragged(input_tensor):
result = gen_normalize_ops.case_fold_utf8(input_tensor.flat_values)
return input_tensor.with_flat_values(result)
else:
return gen_normalize_ops.case_fold_utf8(input_tensor)
# pylint: disable=redefined-builtin)
def normalize_utf8(input, normalization_form="NFKC", name=None):
r"""Normalizes each UTF-8 string in the input tensor using the specified rule.
See http://unicode.org/reports/tr15/
#### Examples:
>>> # input: <string>[num_strings]
>>> normalize_utf8(["株式会社", "ＫＡＤＯＫＡＷＡ"])
>>> # output: <string>[num_strings]
<tf.Tensor: shape=(2,), dtype=string, numpy=
array([b'\xe6\xa0\xaa\xe5\xbc\x8f\xe4\xbc\x9a\xe7\xa4\xbe', b'KADOKAWA'],
dtype=object)>
Args:
input: A `Tensor` or `RaggedTensor` of type string. (Must be UTF-8.)
normalization_form: One of the following string values ('NFC', 'NFKC',
'NFD', 'NFKD'). Default is 'NFKC'.
name: The name for this op (optional).
Returns:
A `Tensor` or `RaggedTensor` of type string, with normalized contents.
"""
with ops.name_scope(name, "NormalizeUTF8", [input]):
input_tensor = ragged_tensor.convert_to_tensor_or_ragged_tensor(
input, dtype=dtypes.string)
if ragged_tensor.is_ragged(input_tensor):
result = gen_normalize_ops.normalize_utf8(input_tensor.flat_values,
normalization_form)
return input_tensor.with_flat_values(result)
else:
return gen_normalize_ops.normalize_utf8(input_tensor, normalization_form)
# pylint: disable=redefined-builtin)
def normalize_utf8_with_offsets_map(input,
normalization_form="NFKC",
name=None):
r"""Normalizes each UTF-8 string in the input tensor using the specified rule.
Returns normalized strings and an offset map used by another operation to map
post-normalized string offsets to pre-normalized string offsets.
See http://unicode.org/reports/tr15/
#### Examples:
>>> # input: <string>[num_strings]
>>> normalize_utf8_with_offsets_map(["株式会社", "ＫＡＤＯＫＡＷＡ"])
>>> # output: <string>[num_strings], <variant>[num_strings]
NormalizeUTF8WithOffsetsMap(output=<tf.Tensor: shape=(2,), dtype=string,
numpy=
array([b'\xe6\xa0\xaa\xe5\xbc\x8f\xe4\xbc\x9a\xe7\xa4\xbe', b'KADOKAWA'],
dtype=object)>, offsets_map=<tf.Tensor: shape=(2,), dtype=variant,
numpy=<unprintable>>)
Args:
input: A `Tensor` or `RaggedTensor` of type string. (Must be UTF-8.)
normalization_form: One of the following string values ('NFC', 'NFKC',
'NFD', 'NFKD'). Default is 'NFKC'. NOTE: `NFD` and `NFKD` for
`normalize_utf8_with_offsets_map` will not be available until the
tf.text release w/ ICU 69 (scheduled after 4/2021).
name: The name for this op (optional).
Returns:
A tuple of (results, offsets_map) where:
results: A `Tensor` or `RaggedTensor` of type string, with normalized
contents.
offsets_map: A `Tensor` or `RaggedTensor` of type `variant`, used to map
the post-normalized string offsets to pre-normalized string offsets. It
has the same shape as the results tensor. offsets_map is an input to
`find_source_offsets` op.
"""
with ops.name_scope(name, "NormalizeUTF8WithOffsets", [input]):
input_tensor = ragged_tensor.convert_to_tensor_or_ragged_tensor(
input, dtype=dtypes.string)
if ragged_tensor.is_ragged(input_tensor):
result, offsets_map = gen_normalize_ops.normalize_utf8_with_offsets_map(
input_tensor.flat_values, normalization_form)
return input_tensor.with_flat_values(
result), input_tensor.with_flat_values(offsets_map)
else:
return gen_normalize_ops.normalize_utf8_with_offsets_map(
input_tensor, normalization_form)
# pylint: disable=redefined-builtin)
def find_source_offsets(offsets_map, input_offsets, name=None):
"""Maps the input post-normalized string offsets to pre-normalized offsets.
Returns the source (i.e. pre-normalized) string offsets mapped from the input
post-normalized string offsets using the input offsets_map, which is an output
from the `normalize_utf8_with_offsets_map` op. offsets_map can be indexed or
sliced along with the input_offsets.
#### Examples:
>>> # input: <string>[num_strings]
>>> post_normalized_str, offsets_map = normalize_utf8_with_offsets_map(
... ["株式会社", "ＫＡＤＯＫＡＷＡ"])
>>> # input: <variant>[num_strings], <int64>[num_strings, num_offsets]
>>> find_source_offsets(offsets_map, [[0, 1, 2], [0, 1, 2]])
>>> # output: <int64>[num_strings, num_offsets]
<tf.Tensor: shape=(2, 3), dtype=int64, numpy=array([[0, 1, 2], [0, 3, 6]])>
>>> # Offsets map can be indexed.
>>> find_source_offsets(offsets_map[1], [[0, 1, 2]])
<tf.Tensor: shape=(1, 3), dtype=int64, numpy=array([[0, 3, 6]])>
Args:
offsets_map: A `Tensor` or `RaggedTensor` of type `variant`, used to map the
post-normalized string offsets to pre-normalized string offsets.
offsets_map is an output from `normalize_utf8_with_offsets_map` function.
input_offsets: A `Tensor` or `RaggedTensor` of type int64 representing the
the post-normalized string offsets,
name: The name for this op (optional).
Returns:
results: A `Tensor` or `RaggedTensor` of type int64, with pre-normalized
string offsets.
"""
with ops.name_scope(name, "FindSourceOffsets", [offsets_map, input_offsets]):
offsets_map_tensor = ragged_tensor.convert_to_tensor_or_ragged_tensor(
offsets_map, dtype=dtypes.variant)
input_offsets_tensor = ragged_tensor.convert_to_tensor_or_ragged_tensor(
input_offsets, dtype=dtypes.int64)
if ragged_tensor.is_ragged(input_offsets_tensor):
if ragged_tensor.is_ragged(offsets_map_tensor):
offsets_map_values = offsets_map_tensor.flat_values
else:
offsets_map_values = array_ops.reshape(offsets_map_tensor, [-1])
output_values = gen_normalize_ops.find_source_offsets(
offsets_map=offsets_map_values,
input_offsets_values=input_offsets_tensor.flat_values,
input_offsets_splits=input_offsets_tensor.nested_row_splits[-1])
return input_offsets_tensor.with_flat_values(output_values)
else:
if input_offsets_tensor.shape.ndims > 1:
output_offsets = find_source_offsets(
offsets_map,
ragged_conversion_ops.from_tensor(
input_offsets_tensor,
ragged_rank=input_offsets_tensor.shape.ndims - 1))
return ragged_conversion_ops.to_tensor(output_offsets)
elif input_offsets_tensor.shape.ndims == 0:
output_offsets = find_source_offsets(
offsets_map, array_ops.expand_dims(input_offsets_tensor, 0))
return output_offsets[0]
else:
output_offsets = find_source_offsets(
offsets_map, array_ops.expand_dims(input_offsets_tensor, 0))
return array_ops.squeeze(output_offsets, [0])