/* 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.

==============================================================================*/

#include "tensorflow/core/framework/common_shape_fns.h"

#include "tensorflow/core/framework/op.h"

#include "tensorflow/core/framework/shape_inference.h"

namespace tensorflow {

using shape_inference::Dimension;

using shape_inference::InferenceContext;

using shape_inference::Shape;

REGISTER_OP("StringToHashBucketFast")

.Input("input: string")

.Output("output: int64")

.Attr("num_buckets: int >= 1")

.SetShapeFn(shape_inference::UnchangedShape)

.Doc(R"doc(

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 and will never change. However, it is not suitable for cryptography.

This function may be used when CPU time is scarce and inputs are trusted or

unimportant. There is a risk of adversaries constructing inputs that all hash

to the same bucket. To prevent this problem, use a strong hash function with

`tf.string_to_hash_bucket_strong`.

input: The strings to assign a hash bucket.

num_buckets: The number of buckets.

output: A Tensor of the same shape as the input `string_tensor`.

)doc");

REGISTER_OP("StringToHashBucketStrong")

.Input("input: string")

.Output("output: int64")

.Attr("num_buckets: int >= 1")

.Attr("key: list(int)")

.SetShapeFn(shape_inference::UnchangedShape)

.Doc(R"doc(

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. The hash function is a keyed hash function, where attribute `key`

defines the key of the hash function. `key` is an array of 2 elements.

A strong hash is important when inputs may be malicious, e.g. URLs with

additional components. Adversaries could try to make their inputs hash to the

same bucket for a denial-of-service attack or to skew the results. A strong

hash prevents this by making it dificult, if not infeasible, to compute inputs

that hash to the same bucket. This comes at a cost of roughly 4x higher compute

time than tf.string_to_hash_bucket_fast.

input: The strings to assign a hash bucket.

num_buckets: The number of buckets.

key: The key for the keyed hash function passed as a list of two uint64

elements.

output: A Tensor of the same shape as the input `string_tensor`.

)doc");

REGISTER_OP("StringToHashBucket")

.Input("string_tensor: string")

.Output("output: int64")

.Attr("num_buckets: int >= 1")

.SetShapeFn(shape_inference::UnchangedShape)

.Doc(R"doc(

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.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`.

num_buckets: The number of buckets.

output: A Tensor of the same shape as the input `string_tensor`.

)doc");

REGISTER_OP("ReduceJoin")

.Input("inputs: string")

.Input("reduction_indices: int32")

.Attr("keep_dims: bool = false")

.Attr("separator: string = ''")

.Output("output: string")

.SetShapeFn([](InferenceContext* c) { return Status::OK(); })

.Doc(R"doc(

Joins a string Tensor across the given dimensions.

Computes the string join across dimensions in the given string Tensor of shape

`[d_0, d_1, ..., d_n-1]`. Returns a new Tensor created by joining the input

strings with the given separator (default: empty string). Negative indices are

counted backwards from the end, with `-1` being equivalent to `n - 1`. Passing

an empty `reduction_indices` joins all strings in linear index order and outputs

a scalar string.

For example:

```

# tensor `a` is [["a", "b"], ["c", "d"]]

tf.reduce_join(a, 0) ==> ["ac", "bd"]

tf.reduce_join(a, 1) ==> ["ab", "cd"]

tf.reduce_join(a, -2) = tf.reduce_join(a, 0) ==> ["ac", "bd"]

tf.reduce_join(a, -1) = tf.reduce_join(a, 1) ==> ["ab", "cd"]

tf.reduce_join(a, 0, keep_dims=True) ==> [["ac", "bd"]]

tf.reduce_join(a, 1, keep_dims=True) ==> [["ab"], ["cd"]]

tf.reduce_join(a, 0, separator=".") ==> ["a.c", "b.d"]

tf.reduce_join(a, [0, 1]) ==> ["acbd"]

tf.reduce_join(a, [1, 0]) ==> ["abcd"]

tf.reduce_join(a, []) ==> ["abcd"]

```

inputs: The input to be joined. All reduced indices must have non-zero size.

reduction_indices: The dimensions to reduce over. Dimensions are reduced in the

order specified. If `reduction_indices` has higher rank than `1`, it is

flattened. Omitting `reduction_indices` is equivalent to passing

`[n-1, n-2, ..., 0]`. Negative indices from `-n` to `-1` are supported.

keep_dims: If `True`, retain reduced dimensions with length `1`.

separator: The separator to use when joining.

output: Has shape equal to that of the input with reduced dimensions removed or

set to `1` depending on `keep_dims`.

)doc");

REGISTER_OP("AsString")

.Input("input: T")

.Output("output: string")

.Attr("T: {int32, int64, complex64, float, double, bool, int8}")

.Attr("precision: int = -1")

.Attr("scientific: bool = false")

.Attr("shortest: bool = false")

.Attr("width: int = -1")

.Attr("fill: string = ''")

.SetShapeFn(shape_inference::UnchangedShape)

.Doc(R"doc(

Converts each entry in the given tensor to strings. Supports many numeric

types and boolean.

precision: The post-decimal precision to use for floating point numbers.

Only used if precision > -1.

scientific: Use scientific notation for floating point numbers.

shortest: Use shortest representation (either scientific or standard) for

floating point numbers.

width: Pad pre-decimal numbers to this width.

Applies to both floating point and integer numbers.

Only used if width > -1.

fill: The value to pad if width > -1. If empty, pads with spaces.

Another typical value is '0'. String cannot be longer than 1 character.

)doc");

REGISTER_OP("StringJoin")

.Input("inputs: N * string")

.Attr("N: int")

.Attr("separator: string = ''")

.Output("output: string")

.SetShapeFn([](InferenceContext* c) {

// If all inputs are scalars, then return a scalar.

bool all_scalar = true;

for (int i = 0; i < c->num_inputs(); ++i) {

if (c->Rank(c->input(i)) != 0) all_scalar = false;

}

if (all_scalar) {

c->set_output(0, c->Scalar());

return Status::OK();

}

// At least one input is unknown or a scalar.

// Merge the non-scalars to find the output shape.

// Don't merge inputs with unknown rank, as they can actually be scalars

// or the output shape.

const Shape* out = c->UnknownShape();

for (int i = 0; i < c->num_inputs(); ++i) {

if (c->RankKnown(c->input(i)) && c->Rank(c->input(i)) != 0) {

TF_RETURN_IF_ERROR(c->Merge(out, c->input(i), &out));

}

}

c->set_output(0, out);

return Status::OK();

})

.Doc(R"doc(

Joins the strings in the given list of string tensors into one tensor;

with the given separator (default is an empty separator).

inputs: A list of string tensors. The tensors must all have the same shape,

or be scalars. Scalars may be mixed in; these will be broadcast to the shape

of non-scalar inputs.

separator: string, an optional join separator.

)doc");

} // namespace tensorflow