# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # 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. # ============================================================================== """Operations for working with string Tensors. See the [Strings](https://tensorflow.org/api_guides/python/string_ops) guide. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.python.compat import compat from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_string_ops from tensorflow.python.ops import math_ops # go/tf-wildcard-import # pylint: disable=wildcard-import # pylint: disable=g-bad-import-order from tensorflow.python.ops.gen_string_ops import * from tensorflow.python.util import compat as util_compat from tensorflow.python.util import deprecation from tensorflow.python.util.tf_export import tf_export # pylint: enable=g-bad-import-order # pylint: enable=wildcard-import # pylint: disable=redefined-builtin @tf_export("strings.regex_full_match") def regex_full_match(input, pattern, name=None): r"""Match elements of `input` with regex `pattern`. Args: input: string `Tensor`, the source strings to process. pattern: string or scalar string `Tensor`, regular expression to use, see more details at https://github.com/google/re2/wiki/Syntax name: Name of the op. Returns: bool `Tensor` of the same shape as `input` with match results. """ # TODO(b/112455102): Remove compat.forward_compatible once past the horizon. if not compat.forward_compatible(2018, 11, 10): return gen_string_ops.regex_full_match( input=input, pattern=pattern, name=name) if isinstance(pattern, util_compat.bytes_or_text_types): # When `pattern` is static through the life of the op we can # use a version which performs the expensive regex compilation once at # creation time. return gen_string_ops.static_regex_full_match( input=input, pattern=pattern, name=name) return gen_string_ops.regex_full_match( input=input, pattern=pattern, name=name) regex_full_match.__doc__ = gen_string_ops.regex_full_match.__doc__ @tf_export("strings.regex_replace", "regex_replace") @deprecation.deprecated_endpoints("regex_replace") def regex_replace(input, pattern, rewrite, replace_global=True, name=None): r"""Replace elements of `input` matching regex `pattern` with `rewrite`. Args: input: string `Tensor`, the source strings to process. pattern: string or scalar string `Tensor`, regular expression to use, see more details at https://github.com/google/re2/wiki/Syntax rewrite: string or scalar string `Tensor`, value to use in match replacement, supports backslash-escaped digits (\1 to \9) can be to insert text matching corresponding parenthesized group. replace_global: `bool`, if `True` replace all non-overlapping matches, else replace only the first match. name: A name for the operation (optional). Returns: string `Tensor` of the same shape as `input` with specified replacements. """ if (isinstance(pattern, util_compat.bytes_or_text_types) and isinstance(rewrite, util_compat.bytes_or_text_types)): # When `pattern` and `rewrite` are static through the life of the op we can # use a version which performs the expensive regex compilation once at # creation time. return gen_string_ops.static_regex_replace( input=input, pattern=pattern, rewrite=rewrite, replace_global=replace_global, name=name) return gen_string_ops.regex_replace( input=input, pattern=pattern, rewrite=rewrite, replace_global=replace_global, name=name) @tf_export("strings.format") def string_format(template, inputs, placeholder="{}", summarize=3, name=None): r"""Formats a string template using a list of tensors. Formats a string template using a list of tensors, abbreviating tensors by only printing the first and last `summarize` elements of each dimension (recursively). If formatting only one tensor into a template, the tensor does not have to be wrapped in a list. Example: Formatting a single-tensor template: ```python sess = tf.Session() with sess.as_default(): tensor = tf.range(10) formatted = tf.strings.format("tensor: {}, suffix", tensor) out = sess.run(formatted) expected = "tensor: [0 1 2 ... 7 8 9], suffix" assert(out.decode() == expected) ``` Formatting a multi-tensor template: ```python sess = tf.Session() with sess.as_default(): tensor_one = tf.reshape(tf.range(100), [10, 10]) tensor_two = tf.range(10) formatted = tf.strings.format("first: {}, second: {}, suffix", (tensor_one, tensor_two)) out = sess.run(formatted) expected = ("first: [[0 1 2 ... 7 8 9]\n" " [10 11 12 ... 17 18 19]\n" " [20 21 22 ... 27 28 29]\n" " ...\n" " [70 71 72 ... 77 78 79]\n" " [80 81 82 ... 87 88 89]\n" " [90 91 92 ... 97 98 99]], second: [0 1 2 ... 7 8 9], suffix") assert(out.decode() == expected) ``` Args: template: A string template to format tensor values into. inputs: A list of `Tensor` objects, or a single Tensor. The list of tensors to format into the template string. If a solitary tensor is passed in, the input tensor will automatically be wrapped as a list. placeholder: An optional `string`. Defaults to `{}`. At each placeholder occurring in the template, a subsequent tensor will be inserted. summarize: An optional `int`. Defaults to `3`. When formatting the tensors, show the first and last `summarize` entries of each tensor dimension (recursively). If set to -1, all elements of the tensor will be shown. name: A name for the operation (optional). Returns: A scalar `Tensor` of type `string`. Raises: ValueError: if the number of placeholders does not match the number of inputs. """ # If there is only one tensor to format, we will automatically wrap it in a # list to simplify the user experience if tensor_util.is_tensor(inputs): inputs = [inputs] if template.count(placeholder) != len(inputs): raise ValueError("%s placeholder(s) in template does not match %s tensor(s)" " provided as input" % (template.count(placeholder), len(inputs))) return gen_string_ops.string_format(inputs, template=template, placeholder=placeholder, summarize=summarize, name=name) @tf_export("string_split") def string_split(source, delimiter=" ", skip_empty=True): # pylint: disable=invalid-name """Split elements of `source` based on `delimiter` into a `SparseTensor`. Let N be the size of source (typically N will be the batch size). Split each element of `source` based on `delimiter` and return a `SparseTensor` containing the split tokens. Empty tokens are ignored. If `delimiter` is an empty string, each element of the `source` is split into individual strings, each containing one byte. (This includes splitting multibyte sequences of UTF-8.) If delimiter contains multiple bytes, it is treated as a set of delimiters with each considered a potential split point. For example: N = 2, source[0] is 'hello world' and source[1] is 'a b c', then the output will be st.indices = [0, 0; 0, 1; 1, 0; 1, 1; 1, 2] st.shape = [2, 3] st.values = ['hello', 'world', 'a', 'b', 'c'] Args: source: `1-D` string `Tensor`, the strings to split. delimiter: `0-D` string `Tensor`, the delimiter character, the string should be length 0 or 1. skip_empty: A `bool`. If `True`, skip the empty strings from the result. Raises: ValueError: If delimiter is not a string. Returns: A `SparseTensor` of rank `2`, the strings split according to the delimiter. The first column of the indices corresponds to the row in `source` and the second column corresponds to the index of the split component in this row. """ delimiter = ops.convert_to_tensor(delimiter, dtype=dtypes.string) source = ops.convert_to_tensor(source, dtype=dtypes.string) indices, values, shape = gen_string_ops.string_split( source, delimiter=delimiter, skip_empty=skip_empty) indices.set_shape([None, 2]) values.set_shape([None]) shape.set_shape([2]) return sparse_tensor.SparseTensor(indices, values, shape) @tf_export("strings.split") def string_split_v2(source, sep=None, maxsplit=-1): """Split elements of `source` based on `sep` into a `SparseTensor`. Let N be the size of source (typically N will be the batch size). Split each element of `source` based on `sep` and return a `SparseTensor` containing the split tokens. Empty tokens are ignored. For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c', then the output will be st.indices = [0, 0; 0, 1; 1, 0; 1, 1; 1, 2] st.shape = [2, 3] st.values = ['hello', 'world', 'a', 'b', 'c'] If `sep` is given, consecutive delimiters are not grouped together and are deemed to delimit empty strings. For example, source of `"1<>2<><>3"` and sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty string, consecutive whitespace are regarded as a single separator, and the result will contain no empty strings at the startor end if the string has leading or trailing whitespace. Note that the above mentioned behavior matches python's str.split. Args: source: `1-D` string `Tensor`, the strings to split. sep: `0-D` string `Tensor`, the delimiter character. maxsplit: An `int`. If `maxsplit > 0`, limit of the split of the result. Raises: ValueError: If sep is not a string. Returns: A `SparseTensor` of rank `2`, the strings split according to the delimiter. The first column of the indices corresponds to the row in `source` and the second column corresponds to the index of the split component in this row. """ if sep is None: sep = "" sep = ops.convert_to_tensor(sep, dtype=dtypes.string) source = ops.convert_to_tensor(source, dtype=dtypes.string) indices, values, shape = gen_string_ops.string_split_v2( source, sep=sep, maxsplit=maxsplit) indices.set_shape([None, 2]) values.set_shape([None]) shape.set_shape([2]) return sparse_tensor.SparseTensor(indices, values, shape) def _reduce_join_reduction_dims(x, axis, reduction_indices): """Returns range(rank(x) - 1, 0, -1) if reduction_indices is None.""" # TODO(aselle): Remove this after deprecation if reduction_indices is not None: if axis is not None: raise ValueError("Can't specify both 'axis' and 'reduction_indices'.") axis = reduction_indices if axis is not None: return axis else: # Fast path: avoid creating Rank and Range ops if ndims is known. if x.get_shape().ndims is not None: return constant_op.constant( np.arange(x.get_shape().ndims - 1, -1, -1), dtype=dtypes.int32) # Otherwise, we rely on Range and Rank to do the right thing at run-time. return math_ops.range(array_ops.rank(x) - 1, -1, -1) @tf_export("strings.reduce_join", "reduce_join") @deprecation.deprecated_endpoints("reduce_join") def reduce_join(inputs, axis=None, # pylint: disable=missing-docstring keep_dims=False, separator="", name=None, reduction_indices=None): inputs_t = ops.convert_to_tensor(inputs) reduction_indices = _reduce_join_reduction_dims( inputs_t, axis, reduction_indices) return gen_string_ops.reduce_join( inputs=inputs_t, reduction_indices=reduction_indices, keep_dims=keep_dims, separator=separator, name=name) reduce_join.__doc__ = deprecation.rewrite_argument_docstring( gen_string_ops.reduce_join.__doc__, "reduction_indices", "axis") reduce_join.__doc__ = reduce_join.__doc__.replace("tf.reduce_join(", "tf.strings.reduce_join(") # This wrapper provides backwards compatibility for code that predates the # unit argument and that passed 'name' as a positional argument. @tf_export("strings.length") def string_length(input, name=None, unit="BYTE"): return gen_string_ops.string_length(input, unit=unit, name=name) string_length.__doc__ = gen_string_ops.string_length.__doc__ @tf_export("substr") @deprecation.deprecated(None, "Use `tf.strings.substr` instead of `tf.substr`.") def substr_deprecated(input, pos, len, name=None, unit="BYTE"): return substr(input, pos, len, name=name, unit=unit) substr_deprecated.__doc__ = gen_string_ops.substr.__doc__ @tf_export("strings.substr") def substr(input, pos, len, name=None, unit="BYTE"): return gen_string_ops.substr(input, pos, len, unit=unit, name=name) substr.__doc__ = gen_string_ops.substr.__doc__ ops.NotDifferentiable("RegexReplace") ops.NotDifferentiable("StringToHashBucket") ops.NotDifferentiable("StringToHashBucketFast") ops.NotDifferentiable("StringToHashBucketStrong") ops.NotDifferentiable("ReduceJoin") ops.NotDifferentiable("StringJoin") ops.NotDifferentiable("StringSplit") ops.NotDifferentiable("AsString") ops.NotDifferentiable("EncodeBase64") ops.NotDifferentiable("DecodeBase64")