tensorflow/python/ops/string_ops.py - platform/external/tensorflow - Git at Google

 # -*- coding: utf-8 -*-
 # 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."""

 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

 import numpy as np

 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_parsing_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 import dispatch
 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")
 @dispatch.add_dispatch_support
 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.
   """
   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", v1=["strings.regex_replace", "regex_replace"])
 @deprecation.deprecated_endpoints("regex_replace")
 @dispatch.add_dispatch_support
 def regex_replace(input, pattern, rewrite, replace_global=True, name=None):
   r"""Replace elements of `input` matching regex `pattern` with `rewrite`.

   Examples:
   >>> tf.strings.regex_replace(["py", "py lib"], "py", "TF").numpy()
   array([b'TF', b'TF lib'], dtype=object)

   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.compat.v1.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.compat.v1.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)


 # Note: tf.strings.split is exported in ragged/ragged_string_ops.py, which
 # defines a wrapper for this function.
 def string_split(source, sep=None, skip_empty=True, delimiter=None):  # 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 `sep` 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.
     sep: `0-D` string `Tensor`, the delimiter character, the string should
       be length 0 or 1. Default is ' '.
     skip_empty: A `bool`. If `True`, skip the empty strings from the result.
     delimiter: deprecated alias for `sep`.

   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 = deprecation.deprecated_argument_lookup(
       "sep", sep, "delimiter", delimiter)

   if delimiter is None:
     delimiter = " "
   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)


 # Note: tf.strings.split is exported in ragged/ragged_string_ops.py, which
 # defines a wrapper for this function.
 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 start or 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):
   """Returns range(rank(x) - 1, 0, -1) if axis is None; or axis otherwise."""
   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(v1=["strings.reduce_join", "reduce_join"])
 @deprecation.deprecated_args(None,
                              "keep_dims is deprecated, use keepdims instead",
                              "keep_dims")
 @deprecation.deprecated_endpoints("reduce_join")
 def reduce_join(inputs, axis=None,  # pylint: disable=missing-docstring
                 keep_dims=None,
                 separator="",
                 name=None,
                 reduction_indices=None,
                 keepdims=None):
   keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims,
                                                     "keep_dims", keep_dims)
   if keep_dims is None:
     keep_dims = False
   axis = deprecation.deprecated_argument_lookup("axis", axis,
                                                 "reduction_indices",
                                                 reduction_indices)
   return reduce_join_v2(
       inputs=inputs,
       axis=axis,
       keepdims=keepdims,
       separator=separator,
       name=name)


 @tf_export("strings.reduce_join", v1=[])
 @dispatch.add_dispatch_support
 def reduce_join_v2(  # pylint: disable=missing-docstring
     inputs,
     axis=None,
     keepdims=False,
     separator="",
     name=None):
   """Joins all strings into a single string, or joins along an axis.

   >>> tf.strings.reduce_join([['abc','123'],
   ...                         ['def','456']]).numpy()
   b'abc123def456'
   >>> tf.strings.reduce_join([['abc','123'],
   ...                         ['def','456']], axis=-1).numpy()
   array([b'abc123', b'def456'], dtype=object)
   >>> tf.strings.reduce_join([['abc','123'],
   ...                         ['def','456']],
   ...                        axis=-1,
   ...                        separator=" ").numpy()
   array([b'abc 123', b'def 456'], dtype=object)

   Args:
     inputs: A `tf.string` tensor.
     axis: Which axis to join along. The default behavior is to join all
       elements, producing a scalar.
     keepdims: If true, retains reduced dimensions with length 1.
     separator: a string added between each string being joined.
     name: A name for the operation (optional).

   Returns:
     A `tf.string` tensor.
   """
   with ops.name_scope(None, "ReduceJoin", [inputs, axis]):
     inputs_t = ops.convert_to_tensor(inputs)
     axis = _reduce_join_reduction_dims(inputs_t, axis)
     return gen_string_ops.reduce_join(
         inputs=inputs_t,
         reduction_indices=axis,
         keep_dims=keepdims,
         separator=separator,
         name=name)

 reduce_join.__doc__ = reduce_join_v2.__doc__


 # This wrapper provides backwards compatibility for code that predates the
 # unit argument and that passed 'name' as a positional argument.
 @tf_export(v1=["strings.length"])
 @dispatch.add_dispatch_support
 def string_length(input, name=None, unit="BYTE"):
   """Computes the length of each string given in the input tensor.

   >>> strings = tf.constant(['Hello','TensorFlow', '🙂'])
   >>> tf.strings.length(strings).numpy() # default counts bytes
   array([ 5, 10, 4], dtype=int32)
   >>> tf.strings.length(strings, unit="UTF8_CHAR").numpy()
   array([ 5, 10, 1], dtype=int32)

   Args:
     input: A `Tensor` of type `string`. The strings for which to compute the
       length for each element.
     name: A name for the operation (optional).
     unit: An optional `string` from: `"BYTE", "UTF8_CHAR"`. Defaults to
       `"BYTE"`. The unit that is counted to compute string length.  One of:
         `"BYTE"` (for the number of bytes in each string) or `"UTF8_CHAR"` (for
         the number of UTF-8 encoded Unicode code points in each string). Results
         are undefined if `unit=UTF8_CHAR` and the `input` strings do not contain
         structurally valid UTF-8.

   Returns:
     A `Tensor` of type `int32`, containing the length of the input string in
     the same element of the input tensor.
   """
   return gen_string_ops.string_length(input, unit=unit, name=name)


 @tf_export("strings.length", v1=[])
 @dispatch.add_dispatch_support
 def string_length_v2(input, unit="BYTE", name=None):
   return gen_string_ops.string_length(input, unit=unit, name=name)


 string_length_v2.__doc__ = gen_string_ops.string_length.__doc__


 @tf_export(v1=["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(v1=["strings.substr"])
 @dispatch.add_dispatch_support
 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__


 @tf_export("strings.substr", v1=[])
 @dispatch.add_dispatch_support
 def substr_v2(input, pos, len, unit="BYTE", name=None):
   return gen_string_ops.substr(input, pos, len, unit=unit, name=name)

 substr_v2.__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")


 @tf_export("strings.to_number", v1=[])
 @dispatch.add_dispatch_support
 def string_to_number(input, out_type=dtypes.float32, name=None):
   r"""Converts each string in the input Tensor to the specified numeric type.

   (Note that int32 overflow results in an error while float overflow
   results in a rounded value.)

   Examples:
   >>> tf.strings.to_number("1.55")
   <tf.Tensor: id=345, shape=(), dtype=float32, numpy=1.55>
   >>> tf.strings.to_number("3", tf.int32)
   <tf.Tensor: id=347, shape=(), dtype=int32, numpy=3>

   Args:
     input: A `Tensor` of type `string`.
     out_type: An optional `tf.DType` from: `tf.float32, tf.float64, tf.int32,
       tf.int64`. Defaults to `tf.float32`.
       The numeric type to interpret each string in `string_tensor` as.
     name: A name for the operation (optional).

   Returns:
     A `Tensor` of type `out_type`.
   """
   return gen_parsing_ops.string_to_number(input, out_type, name)


 @tf_export(v1=["strings.to_number", "string_to_number"])
 def string_to_number_v1(
     string_tensor=None,
     out_type=dtypes.float32,
     name=None,
     input=None):
   string_tensor = deprecation.deprecated_argument_lookup(
       "input", input, "string_tensor", string_tensor)
   return gen_parsing_ops.string_to_number(string_tensor, out_type, name)

 string_to_number_v1.__doc__ = gen_parsing_ops.string_to_number.__doc__


 @tf_export("strings.to_hash_bucket", v1=[])
 @dispatch.add_dispatch_support
 def string_to_hash_bucket(input, num_buckets, name=None):
   # pylint: disable=line-too-long
   r"""Converts each string in the input Tensor to its hash mod by a number of buckets.

   The hash function is deterministic on the content of the string within the
   process.

   Note that the hash function may change from time to time.
   This functionality will be deprecated and it's recommended to use
   `tf.strings.to_hash_bucket_fast()` or `tf.strings.to_hash_bucket_strong()`.

   Examples:
   >>> tf.strings.to_hash_bucket(["Hello", "TensorFlow", "2.x"], 3)
   <tf.Tensor: id=332, shape=(3,), dtype=int64, numpy=array([2, 0, 1], dtype=int64)>

   Args:
     input: A `Tensor` of type `string`.
     num_buckets: An `int` that is `>= 1`. The number of buckets.
     name: A name for the operation (optional).

   Returns:
     A `Tensor` of type `int64`.
   """
   # pylint: enable=line-too-long
   return gen_string_ops.string_to_hash_bucket(input, num_buckets, name)


 @tf_export(v1=["strings.to_hash_bucket", "string_to_hash_bucket"])
 def string_to_hash_bucket_v1(
     string_tensor=None,
     num_buckets=None,
     name=None,
     input=None):
   string_tensor = deprecation.deprecated_argument_lookup(
       "input", input, "string_tensor", string_tensor)
   return gen_string_ops.string_to_hash_bucket(string_tensor, num_buckets, name)

 string_to_hash_bucket_v1.__doc__ = gen_string_ops.string_to_hash_bucket.__doc__
	# -- coding: utf-8 --
	# 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."""

	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import numpy as np

	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_parsing_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 import dispatch
	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")
	@dispatch.add_dispatch_support
	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.
	"""
	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", v1=["strings.regex_replace", "regex_replace"])
	@deprecation.deprecated_endpoints("regex_replace")
	@dispatch.add_dispatch_support
	def regex_replace(input, pattern, rewrite, replace_global=True, name=None):
	r"""Replace elements of `input` matching regex `pattern` with `rewrite`.

	Examples:
	>>> tf.strings.regex_replace(["py", "py lib"], "py", "TF").numpy()
	array([b'TF', b'TF lib'], dtype=object)

	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.compat.v1.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.compat.v1.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)


	# Note: tf.strings.split is exported in ragged/ragged_string_ops.py, which
	# defines a wrapper for this function.
	def string_split(source, sep=None, skip_empty=True, delimiter=None): # 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 `sep` 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.
	sep: `0-D` string `Tensor`, the delimiter character, the string should
	be length 0 or 1. Default is ' '.
	skip_empty: A `bool`. If `True`, skip the empty strings from the result.
	delimiter: deprecated alias for `sep`.

	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 = deprecation.deprecated_argument_lookup(
	"sep", sep, "delimiter", delimiter)

	if delimiter is None:
	delimiter = " "
	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)


	# Note: tf.strings.split is exported in ragged/ragged_string_ops.py, which
	# defines a wrapper for this function.
	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 start or 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):
	"""Returns range(rank(x) - 1, 0, -1) if axis is None; or axis otherwise."""
	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(v1=["strings.reduce_join", "reduce_join"])
	@deprecation.deprecated_args(None,
	"keep_dims is deprecated, use keepdims instead",
	"keep_dims")
	@deprecation.deprecated_endpoints("reduce_join")
	def reduce_join(inputs, axis=None, # pylint: disable=missing-docstring
	keep_dims=None,
	separator="",
	name=None,
	reduction_indices=None,
	keepdims=None):
	keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims,
	"keep_dims", keep_dims)
	if keep_dims is None:
	keep_dims = False
	axis = deprecation.deprecated_argument_lookup("axis", axis,
	"reduction_indices",
	reduction_indices)
	return reduce_join_v2(
	inputs=inputs,
	axis=axis,
	keepdims=keepdims,
	separator=separator,
	name=name)


	@tf_export("strings.reduce_join", v1=[])
	@dispatch.add_dispatch_support
	def reduce_join_v2( # pylint: disable=missing-docstring
	inputs,
	axis=None,
	keepdims=False,
	separator="",
	name=None):
	"""Joins all strings into a single string, or joins along an axis.

	>>> tf.strings.reduce_join([['abc','123'],
	... ['def','456']]).numpy()
	b'abc123def456'
	>>> tf.strings.reduce_join([['abc','123'],
	... ['def','456']], axis=-1).numpy()
	array([b'abc123', b'def456'], dtype=object)
	>>> tf.strings.reduce_join([['abc','123'],
	... ['def','456']],
	... axis=-1,
	... separator=" ").numpy()
	array([b'abc 123', b'def 456'], dtype=object)

	Args:
	inputs: A `tf.string` tensor.
	axis: Which axis to join along. The default behavior is to join all
	elements, producing a scalar.
	keepdims: If true, retains reduced dimensions with length 1.
	separator: a string added between each string being joined.
	name: A name for the operation (optional).

	Returns:
	A `tf.string` tensor.
	"""
	with ops.name_scope(None, "ReduceJoin", [inputs, axis]):
	inputs_t = ops.convert_to_tensor(inputs)
	axis = _reduce_join_reduction_dims(inputs_t, axis)
	return gen_string_ops.reduce_join(
	inputs=inputs_t,
	reduction_indices=axis,
	keep_dims=keepdims,
	separator=separator,
	name=name)

	reduce_join.__doc__ = reduce_join_v2.__doc__


	# This wrapper provides backwards compatibility for code that predates the
	# unit argument and that passed 'name' as a positional argument.
	@tf_export(v1=["strings.length"])
	@dispatch.add_dispatch_support
	def string_length(input, name=None, unit="BYTE"):
	"""Computes the length of each string given in the input tensor.

	>>> strings = tf.constant(['Hello','TensorFlow', '🙂'])
	>>> tf.strings.length(strings).numpy() # default counts bytes
	array([ 5, 10, 4], dtype=int32)
	>>> tf.strings.length(strings, unit="UTF8_CHAR").numpy()
	array([ 5, 10, 1], dtype=int32)

	Args:
	input: A `Tensor` of type `string`. The strings for which to compute the
	length for each element.
	name: A name for the operation (optional).
	unit: An optional `string` from: `"BYTE", "UTF8_CHAR"`. Defaults to
	`"BYTE"`. The unit that is counted to compute string length. One of:
	`"BYTE"` (for the number of bytes in each string) or `"UTF8_CHAR"` (for
	the number of UTF-8 encoded Unicode code points in each string). Results
	are undefined if `unit=UTF8_CHAR` and the `input` strings do not contain
	structurally valid UTF-8.

	Returns:
	A `Tensor` of type `int32`, containing the length of the input string in
	the same element of the input tensor.
	"""
	return gen_string_ops.string_length(input, unit=unit, name=name)


	@tf_export("strings.length", v1=[])
	@dispatch.add_dispatch_support
	def string_length_v2(input, unit="BYTE", name=None):
	return gen_string_ops.string_length(input, unit=unit, name=name)


	string_length_v2.__doc__ = gen_string_ops.string_length.__doc__


	@tf_export(v1=["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(v1=["strings.substr"])
	@dispatch.add_dispatch_support
	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__


	@tf_export("strings.substr", v1=[])
	@dispatch.add_dispatch_support
	def substr_v2(input, pos, len, unit="BYTE", name=None):
	return gen_string_ops.substr(input, pos, len, unit=unit, name=name)

	substr_v2.__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")


	@tf_export("strings.to_number", v1=[])
	@dispatch.add_dispatch_support
	def string_to_number(input, out_type=dtypes.float32, name=None):
	r"""Converts each string in the input Tensor to the specified numeric type.

	(Note that int32 overflow results in an error while float overflow
	results in a rounded value.)

	Examples:
	>>> tf.strings.to_number("1.55")
	<tf.Tensor: id=345, shape=(), dtype=float32, numpy=1.55>
	>>> tf.strings.to_number("3", tf.int32)
	<tf.Tensor: id=347, shape=(), dtype=int32, numpy=3>

	Args:
	input: A `Tensor` of type `string`.
	out_type: An optional `tf.DType` from: `tf.float32, tf.float64, tf.int32,
	tf.int64`. Defaults to `tf.float32`.
	The numeric type to interpret each string in `string_tensor` as.
	name: A name for the operation (optional).

	Returns:
	A `Tensor` of type `out_type`.
	"""
	return gen_parsing_ops.string_to_number(input, out_type, name)


	@tf_export(v1=["strings.to_number", "string_to_number"])
	def string_to_number_v1(
	string_tensor=None,
	out_type=dtypes.float32,
	name=None,
	input=None):
	string_tensor = deprecation.deprecated_argument_lookup(
	"input", input, "string_tensor", string_tensor)
	return gen_parsing_ops.string_to_number(string_tensor, out_type, name)

	string_to_number_v1.__doc__ = gen_parsing_ops.string_to_number.__doc__


	@tf_export("strings.to_hash_bucket", v1=[])
	@dispatch.add_dispatch_support
	def string_to_hash_bucket(input, num_buckets, name=None):
	# pylint: disable=line-too-long
	r"""Converts each string in the input Tensor to its hash mod by a number of buckets.

	The hash function is deterministic on the content of the string within the
	process.

	Note that the hash function may change from time to time.
	This functionality will be deprecated and it's recommended to use
	`tf.strings.to_hash_bucket_fast()` or `tf.strings.to_hash_bucket_strong()`.

	Examples:
	>>> tf.strings.to_hash_bucket(["Hello", "TensorFlow", "2.x"], 3)
	<tf.Tensor: id=332, shape=(3,), dtype=int64, numpy=array([2, 0, 1], dtype=int64)>

	Args:
	input: A `Tensor` of type `string`.
	num_buckets: An `int` that is `>= 1`. The number of buckets.
	name: A name for the operation (optional).

	Returns:
	A `Tensor` of type `int64`.
	"""
	# pylint: enable=line-too-long
	return gen_string_ops.string_to_hash_bucket(input, num_buckets, name)


	@tf_export(v1=["strings.to_hash_bucket", "string_to_hash_bucket"])
	def string_to_hash_bucket_v1(
	string_tensor=None,
	num_buckets=None,
	name=None,
	input=None):
	string_tensor = deprecation.deprecated_argument_lookup(
	"input", input, "string_tensor", string_tensor)
	return gen_string_ops.string_to_hash_bucket(string_tensor, num_buckets, name)

	string_to_hash_bucket_v1.__doc__ = gen_string_ops.string_to_hash_bucket.__doc__